From 801800511d0eaedc99f7d4935aa1c3bff5c3da54 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 7 Aug 2026 19:02:08 +0530
Subject: [PATCH 01/55] feat: added call-imports metadata

---
 .../components/callImports/AuditMetaChips.tsx |  175 +++
 .../pages/callImports/CallImportDetail.tsx    |   30 +-
 .../CallImportEvaluationDetail.tsx            |   23 +-
 .../src/pages/callImports/CallImports.tsx     | 1128 +++++++++--------
 4 files changed, 773 insertions(+), 583 deletions(-)
 create mode 100644 frontend/src/components/callImports/AuditMetaChips.tsx

diff --git a/frontend/src/components/callImports/AuditMetaChips.tsx b/frontend/src/components/callImports/AuditMetaChips.tsx
new file mode 100644
index 00000000..a7bae2e4
--- /dev/null
+++ b/frontend/src/components/callImports/AuditMetaChips.tsx
@@ -0,0 +1,175 @@
+import type { ReactNode } from 'react'
+
+/** Bordered metadata chips for call-import audit fields. */
+
+const CHIP_CLASS =
+  'inline-flex flex-col min-w-0 rounded-md border border-slate-200 bg-white px-2 py-1 text-[11px] leading-tight shadow-sm'
+
+export function formatMetaDateTime(iso: string | null | undefined): string {
+  if (!iso) return '—'
+  const parsed = new Date(iso)
+  if (Number.isNaN(parsed.getTime())) return '—'
+  return parsed.toLocaleString()
+}
+
+type AuditMetaChipProps = {
+  label: string
+  value: string | null | undefined
+  wide?: boolean
+  /** Tighter chip for horizontal list rows */
+  dense?: boolean
+  /** Fill a grid cell (2×2 activity column on list page) */
+  stacked?: boolean
+  className?: string
+}
+
+export function AuditMetaChip({
+  label,
+  value,
+  wide,
+  dense,
+  stacked,
+  className = '',
+}: AuditMetaChipProps) {
+  const display = value?.trim() || '—'
+  const sizeClass = dense
+    ? stacked
+      ? 'w-full min-w-0 px-1.5 py-0.5 text-[10px]'
+      : 'shrink-0 px-1.5 py-0.5 text-[10px] min-w-[4.75rem] max-w-[8.5rem]'
+    : wide
+      ? 'min-w-[7.5rem] max-w-[12rem]'
+      : 'min-w-[6.5rem] max-w-[10rem]'
+  return (
+    <div
+      className={`${CHIP_CLASS} ${sizeClass} ${className}`.trim()}
+      title={display === '—' ? `${label}: unknown` : `${label}: ${display}`}
+    >
+      <span className="text-gray-500 font-medium">{label}</span>
+      <span className="text-gray-800 truncate font-medium">{display}</span>
+    </div>
+  )
+}
+
+type AuditMetaChipRowProps = {
+  className?: string
+  wide?: boolean
+  compact?: boolean
+  children: ReactNode
+}
+
+function AuditMetaChipRow({
+  className = '',
+  wide,
+  compact,
+  children,
+}: AuditMetaChipRowProps) {
+  return (
+    <div
+      className={
+        compact
+          ? `flex flex-nowrap items-stretch gap-1.5 overflow-x-auto max-w-full scrollbar-thin ${className}`.trim()
+          : `flex flex-wrap items-stretch gap-1.5 ${className}`.trim()
+      }
+      data-wide={wide ? 'true' : undefined}
+    >
+      {children}
+    </div>
+  )
+}
+
+type CallImportAuditMetaProps = {
+  createdAt: string | null | undefined
+  updatedAt: string | null | undefined
+  createdByEmail?: string | null
+  lastUpdatedByEmail?: string | null
+  className?: string
+  wide?: boolean
+  compact?: boolean
+  /** 2×2 grid for table cells (no horizontal scroll) */
+  stacked?: boolean
+}
+
+/** Created / updated timestamps + actor emails for a call-import batch. */
+export function CallImportAuditMeta({
+  createdAt,
+  updatedAt,
+  createdByEmail,
+  lastUpdatedByEmail,
+  className,
+  wide = true,
+  compact,
+  stacked,
+}: CallImportAuditMetaProps) {
+  const chipCommon = { wide, dense: compact, stacked }
+  if (stacked) {
+    return (
+      <div
+        className={`grid grid-cols-2 gap-1.5 w-full min-w-[12rem] max-w-[22rem] ${className ?? ''}`.trim()}
+      >
+        <AuditMetaChip label="Created" value={formatMetaDateTime(createdAt)} {...chipCommon} />
+        <AuditMetaChip label="Updated" value={formatMetaDateTime(updatedAt)} {...chipCommon} />
+        <AuditMetaChip label="Created by" value={createdByEmail} {...chipCommon} />
+        <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} {...chipCommon} />
+      </div>
+    )
+  }
+  return (
+    <AuditMetaChipRow className={className} wide={wide} compact={compact}>
+      <AuditMetaChip label="Created" value={formatMetaDateTime(createdAt)} wide={wide} dense={compact} />
+      <AuditMetaChip label="Updated" value={formatMetaDateTime(updatedAt)} wide={wide} dense={compact} />
+      <AuditMetaChip label="Created by" value={createdByEmail} wide={wide} dense={compact} />
+      <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} wide={wide} dense={compact} />
+    </AuditMetaChipRow>
+  )
+}
+
+type EvaluationAuditMetaProps = {
+  createdAt: string | null | undefined
+  updatedAt?: string | null | undefined
+  startedAt?: string | null
+  finishedAt?: string | null
+  /** User who started this evaluation run (API: created_by_email). */
+  runByEmail?: string | null
+  /** @deprecated Use runByEmail */
+  createdByEmail?: string | null
+  lastUpdatedByEmail?: string | null
+  formatDate?: (iso: string | null | undefined) => string
+  className?: string
+  wide?: boolean
+  compact?: boolean
+  showRunTimes?: boolean
+}
+
+/** Evaluation run metadata (detail header or list card). */
+export function EvaluationAuditMeta({
+  createdAt,
+  updatedAt,
+  startedAt,
+  finishedAt,
+  runByEmail,
+  createdByEmail,
+  lastUpdatedByEmail,
+  formatDate = formatMetaDateTime,
+  className,
+  wide = true,
+  compact,
+  showRunTimes = true,
+}: EvaluationAuditMetaProps) {
+  const runner = runByEmail ?? createdByEmail
+  return (
+    <AuditMetaChipRow className={className} wide={wide} compact={compact}>
+      <AuditMetaChip label="Run by" value={runner} wide={wide} dense={compact} />
+      <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} wide={wide} dense={compact} />
+      <AuditMetaChip label="Created" value={formatDate(createdAt)} wide={wide} dense={compact} />
+      {updatedAt ? (
+        <AuditMetaChip label="Updated" value={formatDate(updatedAt)} wide={wide} dense={compact} />
+      ) : null}
+      {showRunTimes && startedAt ? (
+        <AuditMetaChip label="Started" value={formatDate(startedAt)} wide={wide} dense={compact} />
+      ) : null}
+      {showRunTimes && finishedAt ? (
+        <AuditMetaChip label="Finished" value={formatDate(finishedAt)} wide={wide} dense={compact} />
+      ) : null}
+    </AuditMetaChipRow>
+  )
+}
diff --git a/frontend/src/pages/callImports/CallImportDetail.tsx b/frontend/src/pages/callImports/CallImportDetail.tsx
index bc8687b8..9270e14e 100644
--- a/frontend/src/pages/callImports/CallImportDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportDetail.tsx
@@ -57,6 +57,10 @@ import Button from '../../components/Button'
 import ConfirmModal from '../../components/ConfirmModal'
 import Pagination from '../../components/Pagination'
 import StatusBadge from '../../components/shared/StatusBadge'
+import {
+  CallImportAuditMeta,
+  EvaluationAuditMeta,
+} from '../../components/callImports/AuditMetaChips'
 import DiariseStatusPill from '../../components/callImports/DiariseStatusPill'
 import ProviderModelPicker, {
   type ProviderModelValue,
@@ -1680,12 +1684,12 @@ export default function CallImportDetail() {
                   )}
                 </span>
               </span>
-              <span className="text-sm text-gray-600">
-                Created: {new Date(data.created_at).toLocaleString()}
-              </span>
-              <span className="text-sm text-gray-600">
-                Updated: {new Date(data.updated_at).toLocaleString()}
-              </span>
+              <CallImportAuditMeta
+                createdAt={data.created_at}
+                updatedAt={data.updated_at}
+                createdByEmail={data.created_by_email}
+                lastUpdatedByEmail={data.last_updated_by_email}
+              />
             </div>
           </div>
 
@@ -3183,13 +3187,19 @@ export default function CallImportDetail() {
                     to={`/call-imports/${id}/evaluations/${evaluation.id}`}
                     className="flex-1 min-w-0 flex items-center justify-between gap-3"
                   >
-                    <div className="min-w-0">
+                    <div className="min-w-0 flex-1">
                       <p className="text-sm font-medium text-gray-900 truncate">
                         {headerLabel}
                       </p>
-                      <p className="text-[11px] text-gray-400 mt-0.5">
-                        Created {new Date(evaluation.created_at).toLocaleString()}
-                      </p>
+                      <EvaluationAuditMeta
+                        className="mt-1.5"
+                        compact
+                        showRunTimes={false}
+                        createdAt={evaluation.created_at}
+                        updatedAt={evaluation.updated_at}
+                        runByEmail={evaluation.created_by_email}
+                        lastUpdatedByEmail={evaluation.last_updated_by_email}
+                      />
                     </div>
                     <div className="text-right flex-shrink-0">
                       <StatusBadge status={evaluation.status} size="sm" />
diff --git a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
index 542fdd6c..0c42f91a 100644
--- a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
@@ -92,6 +92,7 @@ import ProviderModelPicker, {
 } from '../../components/providers/ProviderModelPicker'
 import { getActiveWorkspaceId, useWorkspaceStore } from '../../store/workspaceStore'
 import StatusBadge from '../../components/shared/StatusBadge'
+import { EvaluationAuditMeta } from '../../components/callImports/AuditMetaChips'
 import DiariseStatusPill from '../../components/callImports/DiariseStatusPill'
 import CallImportProgressBar from './components/CallImportProgressBar'
 import MetricPromptImprovementsPanel from './components/MetricPromptImprovementsPanel'
@@ -2499,19 +2500,15 @@ export default function CallImportEvaluationDetail() {
                   ? 'Evaluated on Diarised transcript'
                   : 'Evaluated on Production transcript'}
               </span>
-              <span className="text-sm text-gray-600">
-                Created: {formatDateTime(evaluation.created_at)}
-              </span>
-              {evaluation.started_at && (
-                <span className="text-sm text-gray-600">
-                  Started: {formatDateTime(evaluation.started_at)}
-                </span>
-              )}
-              {evaluation.finished_at && (
-                <span className="text-sm text-gray-600">
-                  Finished: {formatDateTime(evaluation.finished_at)}
-                </span>
-              )}
+              <EvaluationAuditMeta
+                createdAt={evaluation.created_at}
+                updatedAt={evaluation.updated_at}
+                startedAt={evaluation.started_at}
+                finishedAt={evaluation.finished_at}
+                runByEmail={evaluation.created_by_email}
+                lastUpdatedByEmail={evaluation.last_updated_by_email}
+                formatDate={formatDateTime}
+              />
             </div>
           </div>
 
diff --git a/frontend/src/pages/callImports/CallImports.tsx b/frontend/src/pages/callImports/CallImports.tsx
index c9ccaf26..b3d6295a 100644
--- a/frontend/src/pages/callImports/CallImports.tsx
+++ b/frontend/src/pages/callImports/CallImports.tsx
@@ -1,560 +1,568 @@
-import { useMemo, useState } from 'react'
-import { Link, useNavigate } from 'react-router-dom'
-import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
-import {
-  ChevronLeft,
-  ChevronRight,
-  FileAudio,
-  FileSpreadsheet,
-  Layers,
-  Phone,
-  RefreshCw,
-  Trash2,
-  Upload,
-} from 'lucide-react'
-import { Tag as TagIcon } from 'lucide-react'
-import { apiClient } from '../../lib/api'
-import { getApiErrorMessage } from '../../lib/apiErrors'
-import { useToast } from '../../hooks/useToast'
-import { useWorkspaceStore } from '../../store/workspaceStore'
-import type { CallImport, CallImportStatus, CallImportTag } from '../../types/api'
-import Button from '../../components/Button'
-import ConfirmModal from '../../components/ConfirmModal'
-import StatusBadge from '../../components/shared/StatusBadge'
-import CallImportProgressBar from './components/CallImportProgressBar'
-import UploadAudioModal from './components/UploadAudioModal'
-import UploadCsvModal from './components/UploadCsvModal'
-
-const PAGE_SIZE = 20
-
-const STATUS_OPTIONS: Array<{ label: string; value: '' | CallImportStatus }> = [
-  { label: 'All statuses', value: '' },
-  { label: 'Uploaded', value: 'uploaded' },
-  { label: 'Mapped', value: 'mapped' },
-  { label: 'Pending', value: 'pending' },
-  { label: 'Processing', value: 'processing' },
-  { label: 'Completed', value: 'completed' },
-  { label: 'Partial', value: 'partial' },
-  { label: 'Failed', value: 'failed' },
-  { label: 'Deleting', value: 'deleting' },
-]
-
-type UploadTab = 'datasets' | 'audio'
-
-export default function CallImports() {
-  const navigate = useNavigate()
-  const queryClient = useQueryClient()
-  const { showToast, ToastContainer } = useToast()
-  // Active workspace is part of every workspace-scoped queryKey so a
-  // workspace switch produces a clean cache miss instead of leaking
-  // rows from the previously-active workspace.
-  const activeWorkspaceId = useWorkspaceStore((s) => s.activeWorkspaceId)
-  const [page, setPage] = useState(1)
-  const [statusFilter, setStatusFilter] = useState<'' | CallImportStatus>('')
-  const [datasetFilter, setDatasetFilter] = useState<string>('')
-  const [tagFilter, setTagFilter] = useState<string[]>([])
-  const [activeTab, setActiveTab] = useState<UploadTab>('datasets')
-  const [showUpload, setShowUpload] = useState(false)
-  const [showAudioUpload, setShowAudioUpload] = useState(false)
-  const [pendingDelete, setPendingDelete] = useState<CallImport | null>(null)
-  const [deleteError, setDeleteError] = useState<string | null>(null)
-
-  const { data: datasets = [] } = useQuery({
-    queryKey: ['call-import-datasets', activeWorkspaceId],
-    queryFn: () => apiClient.listCallImportDatasets(),
-  })
-
-  const { data: allTags = [] } = useQuery({
-    queryKey: ['call-import-tags', activeWorkspaceId],
-    queryFn: () => apiClient.listCallImportTags(),
-  })
-
-  const deleteMutation = useMutation({
-    mutationFn: (id: string) => apiClient.deleteCallImport(id),
-    onSuccess: (result) => {
-      queryClient.invalidateQueries({ queryKey: ['call-imports'] })
-      setPendingDelete(null)
-      setDeleteError(null)
-      if (result.status === 'accepted') {
-        showToast(
-          'Deletion started — large imports may take a minute.',
-          'success',
-        )
-      }
-    },
-    onError: (err: unknown) => {
-      const message = getApiErrorMessage(err, 'Failed to delete import.')
-      setDeleteError(message)
-      showToast(message, 'error')
-    },
-  })
-
-  const queryParams = useMemo(
-    () => ({
-      page,
-      page_size: PAGE_SIZE,
-      ...(statusFilter ? { status: statusFilter } : {}),
-      ...(datasetFilter ? { dataset: datasetFilter } : {}),
-      ...(tagFilter.length > 0 ? { tag_id: tagFilter } : {}),
-      source_format: activeTab === 'audio' ? 'audio' : '__non_audio__',
-    }),
-    [page, statusFilter, datasetFilter, tagFilter, activeTab],
-  )
-
-  const { data, isLoading, isFetching, refetch } = useQuery({
-    queryKey: ['call-imports', activeWorkspaceId, queryParams],
-    queryFn: () => apiClient.listCallImports(queryParams),
-    refetchInterval: (query) => {
-      const items = query.state.data?.items ?? []
-      const hasActive = items.some(
-        (i: CallImport) => i.status === 'pending' || i.status === 'processing',
-      )
-      const hasDeleting = items.some(
-        (i: CallImport) => i.status === 'deleting',
-      )
-      if (hasDeleting) return 3000
-      return hasActive ? 5000 : false
-    },
-  })
-
-  const items = data?.items ?? []
-  const total = data?.total ?? 0
-  const totalPages = Math.max(1, Math.ceil(total / PAGE_SIZE))
-
-  return (
-    <div className="space-y-6">
-      <ToastContainer />
-      <div className="flex justify-between items-center">
-        <div>
-          <h1 className="text-3xl font-bold text-gray-900">Call Imports</h1>
-          <p className="mt-2 text-sm text-gray-600">
-            Upload datasets from CSV / Excel, or add manual call recordings
-            directly and use the same diarisation and evaluation tools.
-          </p>
-        </div>
-        <div className="flex gap-3">
-          <Link to="/call-imports/schemas">
-            <Button
-              variant="ghost"
-              leftIcon={<Layers className="h-5 w-5" />}
-            >
-              Manage Schemas
-            </Button>
-          </Link>
-          <Link to="/call-imports/tags">
-            <Button
-              variant="ghost"
-              leftIcon={<TagIcon className="h-5 w-5" />}
-            >
-              Manage Tags
-            </Button>
-          </Link>
-          <Button
-            variant="primary"
-            onClick={() =>
-              activeTab === 'audio' ? setShowAudioUpload(true) : setShowUpload(true)
-            }
-            leftIcon={<Upload className="h-5 w-5" />}
-          >
-            {activeTab === 'audio' ? 'Upload Audio' : 'Upload CSV'}
-          </Button>
-          <Button
-            variant="secondary"
-            onClick={() => refetch()}
-            isLoading={isFetching && !isLoading}
-            leftIcon={!(isFetching && !isLoading) ? <RefreshCw className="h-5 w-5" /> : undefined}
-          >
-            Refresh
-          </Button>
-        </div>
-      </div>
-
-      <div className="bg-white shadow rounded-lg p-1 inline-flex gap-1">
-        <button
-          type="button"
-          onClick={() => {
-            setActiveTab('datasets')
-            setPage(1)
-          }}
-          className={`flex items-center gap-2 px-4 py-2 rounded-md text-sm font-medium transition-colors ${
-            activeTab === 'datasets'
-              ? 'bg-primary-50 text-primary-700'
-              : 'text-gray-600 hover:bg-gray-50 hover:text-gray-900'
-          }`}
-        >
-          <FileSpreadsheet className="h-4 w-4" />
-          CSV / Dataset Uploads
-        </button>
-        <button
-          type="button"
-          onClick={() => {
-            setActiveTab('audio')
-            setPage(1)
-          }}
-          className={`flex items-center gap-2 px-4 py-2 rounded-md text-sm font-medium transition-colors ${
-            activeTab === 'audio'
-              ? 'bg-primary-50 text-primary-700'
-              : 'text-gray-600 hover:bg-gray-50 hover:text-gray-900'
-          }`}
-        >
-          <FileAudio className="h-4 w-4" />
-          Call Imports / Uploads
-        </button>
-      </div>
-
-      {/*
-        High-level dataset segregation lives at the top of the page so users can
-        scope all filtering/searching that follows to a specific dataset. We
-        intentionally render this above the main card to make it visually
-        distinct from the in-card status/tag filters.
-      */}
-      <div className="bg-white shadow rounded-lg p-4 flex items-center gap-3 flex-wrap">
-        <label htmlFor="dataset-filter" className="text-sm font-medium text-gray-700">
-          Dataset:
-        </label>
-        <select
-          id="dataset-filter"
-          value={datasetFilter}
-          onChange={(e) => {
-            setDatasetFilter(e.target.value)
-            setPage(1)
-          }}
-          className="px-3 py-1.5 text-sm border border-gray-300 rounded-md focus:ring-2 focus:ring-primary-500 focus:border-transparent min-w-[12rem]"
-        >
-          <option value="">All datasets</option>
-          {datasets.map((d) => (
-            <option key={d} value={d}>
-              {d}
-            </option>
-          ))}
-        </select>
-        {datasetFilter && (
-          <span className="text-xs text-gray-500">
-            Showing imports tagged with dataset “{datasetFilter}”.
-          </span>
-        )}
-      </div>
-
-      <div className="bg-white shadow rounded-lg p-6">
-        <div className="flex items-center justify-between mb-4 gap-4 flex-wrap">
-          <div className="flex items-center gap-4 flex-wrap">
-            <div className="flex items-center gap-2">
-              <label htmlFor="status-filter" className="text-sm text-gray-600">
-                Status:
-              </label>
-              <select
-                id="status-filter"
-                value={statusFilter}
-                onChange={(e) => {
-                  setStatusFilter(e.target.value as '' | CallImportStatus)
-                  setPage(1)
-                }}
-                className="px-3 py-1.5 text-sm border border-gray-300 rounded-md focus:ring-2 focus:ring-primary-500 focus:border-transparent"
-              >
-                {STATUS_OPTIONS.map((opt) => (
-                  <option key={opt.value || 'all'} value={opt.value}>
-                    {opt.label}
-                  </option>
-                ))}
-              </select>
-            </div>
-            {allTags.length > 0 && (
-              <div className="flex items-center gap-2 flex-wrap">
-                <span className="text-sm text-gray-600">Tags:</span>
-                {allTags.map((tag: CallImportTag) => {
-                  const active = tagFilter.includes(tag.id)
-                  return (
-                    <button
-                      key={tag.id}
-                      type="button"
-                      onClick={() => {
-                        setTagFilter((prev) =>
-                          prev.includes(tag.id)
-                            ? prev.filter((t) => t !== tag.id)
-                            : [...prev, tag.id],
-                        )
-                        setPage(1)
-                      }}
-                      className={`px-2.5 py-1 text-xs rounded-full border transition-colors ${
-                        active
-                          ? 'bg-primary-600 border-primary-600 text-white'
-                          : 'bg-white border-gray-300 text-gray-700 hover:bg-gray-50'
-                      }`}
-                      style={
-                        !active && tag.color
-                          ? { borderColor: tag.color, color: tag.color }
-                          : undefined
-                      }
-                    >
-                      {tag.name}
-                    </button>
-                  )
-                })}
-                {tagFilter.length > 0 && (
-                  <button
-                    type="button"
-                    onClick={() => {
-                      setTagFilter([])
-                      setPage(1)
-                    }}
-                    className="text-xs text-gray-500 hover:text-gray-700 underline"
-                  >
-                    Clear
-                  </button>
-                )}
-              </div>
-            )}
-          </div>
-          <p className="text-sm text-gray-500">
-            {total} {activeTab === 'audio' ? 'manual upload' : 'dataset import'}
-            {total === 1 ? '' : 's'}
-          </p>
-        </div>
-
-        {isLoading ? (
-          <div className="text-center py-12 text-gray-500">
-            <RefreshCw className="h-8 w-8 mx-auto mb-2 animate-spin" />
-            <p>Loading imports...</p>
-          </div>
-        ) : items.length === 0 ? (
-          <div className="text-center py-12">
-            <Phone className="h-12 w-12 mx-auto mb-3 text-gray-300" />
-            <p className="text-gray-500 mb-3">
-              {statusFilter
-                ? 'No imports match this filter.'
-                : activeTab === 'audio'
-                  ? 'No manual audio uploads yet.'
-                  : 'No dataset uploads yet.'}
-            </p>
-            {!statusFilter && (
-              <Button
-                variant="ghost"
-                size="sm"
-                onClick={() =>
-                  activeTab === 'audio'
-                    ? setShowAudioUpload(true)
-                    : setShowUpload(true)
-                }
-                leftIcon={<Upload className="h-4 w-4" />}
-              >
-                {activeTab === 'audio'
-                  ? 'Upload your first recording'
-                  : 'Upload your first CSV'}
-              </Button>
-            )}
-          </div>
-        ) : (
-          <div className="overflow-x-auto">
-            <table className="min-w-full divide-y divide-gray-200">
-              <thead className="bg-gray-50">
-                <tr>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                    Filename
-                  </th>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                    Provider
-                  </th>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                    Dataset / Tags
-                  </th>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-64">
-                    Progress
-                  </th>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                    Status
-                  </th>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                    Created
-                  </th>
-                  <th className="px-6 py-3 text-right text-xs font-medium text-gray-500 uppercase tracking-wider">
-                    Actions
-                  </th>
-                </tr>
-              </thead>
-              <tbody className="bg-white divide-y divide-gray-200">
-                {items.map((item: CallImport) => {
-                  const isDeleting = item.status === 'deleting'
-                  return (
-                  <tr
-                    key={item.id}
-                    className={`hover:bg-gray-50 ${isDeleting ? 'opacity-60' : 'cursor-pointer'}`}
-                    onClick={() => {
-                      if (isDeleting) return
-                      navigate(`/call-imports/${item.id}`)
-                    }}
-                  >
-                    <td className="px-6 py-4 whitespace-nowrap">
-                      <div className="text-sm font-medium text-gray-900 truncate max-w-xs">
-                        {item.original_filename || '(unnamed)'}
-                      </div>
-                      <div className="text-xs text-gray-400 font-mono mt-0.5">
-                        {item.id.slice(0, 8)}
-                      </div>
-                    </td>
-                    <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 capitalize">
-                      {item.source_format === 'audio' ? (
-                        <span className="inline-flex items-center gap-1 normal-case text-gray-700">
-                          <FileAudio className="h-3.5 w-3.5" />
-                          Manual upload
-                        </span>
-                      ) : item.provider || (
-                        <span className="text-gray-400 italic normal-case">
-                          —
-                        </span>
-                      )}
-                    </td>
-                    <td className="px-6 py-4 align-top">
-                      <div className="flex flex-col gap-1">
-                        {item.dataset ? (
-                          <span className="inline-flex items-center text-xs font-medium text-gray-800 bg-gray-100 rounded px-2 py-0.5 self-start">
-                            {item.dataset}
-                          </span>
-                        ) : (
-                          <span className="text-xs text-gray-400 italic">
-                            no dataset
-                          </span>
-                        )}
-                        {item.tags.length > 0 && (
-                          <div className="flex flex-wrap gap-1">
-                            {item.tags.map((tag) => (
-                              <span
-                                key={tag.id}
-                                className="inline-flex items-center text-[10px] uppercase tracking-wide rounded-full px-2 py-0.5 border"
-                                style={{
-                                  borderColor: tag.color || '#d1d5db',
-                                  color: tag.color || '#4b5563',
-                                }}
-                              >
-                                {tag.name}
-                              </span>
-                            ))}
-                          </div>
-                        )}
-                      </div>
-                    </td>
-                    <td className="px-6 py-4 whitespace-nowrap">
-                      <CallImportProgressBar
-                        total={item.total_rows}
-                        completed={item.completed_rows}
-                        failed={item.failed_rows}
-                        deleting={isDeleting}
-                      />
-                    </td>
-                    <td className="px-6 py-4 whitespace-nowrap">
-                      <StatusBadge status={item.status} />
-                    </td>
-                    <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-500">
-                      {new Date(item.created_at).toLocaleString()}
-                    </td>
-                    <td
-                      className="px-6 py-4 whitespace-nowrap text-right text-sm"
-                      onClick={(e) => e.stopPropagation()}
-                    >
-                      <div className="flex justify-end items-center gap-3">
-                        <Link
-                          to={`/call-imports/${item.id}`}
-                          className={`font-medium ${isDeleting ? 'pointer-events-none text-gray-400' : 'text-primary-600 hover:text-primary-700'}`}
-                          aria-disabled={isDeleting}
-                          tabIndex={isDeleting ? -1 : undefined}
-                        >
-                          View
-                        </Link>
-                        <button
-                          type="button"
-                          aria-label={`Delete ${item.original_filename || 'import'}`}
-                          title="Delete import"
-                          className="text-gray-400 hover:text-red-600 transition-colors disabled:opacity-40"
-                          onClick={() => {
-                            setDeleteError(null)
-                            setPendingDelete(item)
-                          }}
-                          disabled={
-                            isDeleting ||
-                            (deleteMutation.isPending && pendingDelete?.id === item.id)
-                          }
-                        >
-                          <Trash2 className="h-4 w-4" />
-                        </button>
-                      </div>
-                    </td>
-                  </tr>
-                  )
-                })}
-              </tbody>
-            </table>
-
-            {totalPages > 1 && (
-              <div className="px-6 py-3 bg-gray-50 border-t border-gray-200 flex items-center justify-between">
-                <p className="text-sm text-gray-600">
-                  Page {page} of {totalPages}
-                </p>
-                <div className="flex gap-2">
-                  <Button
-                    variant="ghost"
-                    size="sm"
-                    onClick={() => setPage((p) => Math.max(1, p - 1))}
-                    disabled={page <= 1}
-                    leftIcon={<ChevronLeft className="h-4 w-4" />}
-                  >
-                    Prev
-                  </Button>
-                  <Button
-                    variant="ghost"
-                    size="sm"
-                    onClick={() => setPage((p) => Math.min(totalPages, p + 1))}
-                    disabled={page >= totalPages}
-                    rightIcon={<ChevronRight className="h-4 w-4" />}
-                  >
-                    Next
-                  </Button>
-                </div>
-              </div>
-            )}
-          </div>
-        )}
-      </div>
-
-      <UploadCsvModal open={showUpload} onClose={() => setShowUpload(false)} />
-      <UploadAudioModal
-        open={showAudioUpload}
-        onClose={() => setShowAudioUpload(false)}
-      />
-
-      <ConfirmModal
-        isOpen={pendingDelete !== null}
-        title="Delete call import?"
-        description={(() => {
-          if (!pendingDelete) return ''
-          const name = pendingDelete.original_filename || '(unnamed)'
-          const total = pendingDelete.total_rows
-          const completed = pendingDelete.completed_rows
-          const inFlight =
-            pendingDelete.status === 'pending' ||
-            pendingDelete.status === 'processing' ||
-            pendingDelete.status === 'deleting'
-          const lines = [
-            `“${name}” will be permanently deleted, along with all ${total} row record${total === 1 ? '' : 's'} and ${completed} stored recording${completed === 1 ? '' : 's'} in S3.`,
-            inFlight
-              ? 'This batch is still processing — pending tasks will be revoked before deletion.'
-              : '',
-            'This cannot be undone.',
-            deleteError ? `Error: ${deleteError}` : '',
-          ]
-          return lines.filter(Boolean).join('\n\n')
-        })()}
-        confirmLabel="Delete"
-        cancelLabel="Cancel"
-        variant="danger"
-        isLoading={deleteMutation.isPending}
-        onConfirm={() => {
-          if (pendingDelete) deleteMutation.mutate(pendingDelete.id)
-        }}
-        onCancel={() => {
-          if (deleteMutation.isPending) return
-          setPendingDelete(null)
-          setDeleteError(null)
-        }}
-      />
-    </div>
-  )
-}
+import { useMemo, useState } from 'react'
+import { Link, useNavigate } from 'react-router-dom'
+import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
+import {
+  ChevronLeft,
+  ChevronRight,
+  FileAudio,
+  FileSpreadsheet,
+  Layers,
+  Phone,
+  RefreshCw,
+  Trash2,
+  Upload,
+} from 'lucide-react'
+import { Tag as TagIcon } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { getApiErrorMessage } from '../../lib/apiErrors'
+import { useToast } from '../../hooks/useToast'
+import { useWorkspaceStore } from '../../store/workspaceStore'
+import type { CallImport, CallImportStatus, CallImportTag } from '../../types/api'
+import Button from '../../components/Button'
+import ConfirmModal from '../../components/ConfirmModal'
+import StatusBadge from '../../components/shared/StatusBadge'
+import { CallImportAuditMeta } from '../../components/callImports/AuditMetaChips'
+import CallImportProgressBar from './components/CallImportProgressBar'
+import UploadAudioModal from './components/UploadAudioModal'
+import UploadCsvModal from './components/UploadCsvModal'
+
+const PAGE_SIZE = 20
+
+const STATUS_OPTIONS: Array<{ label: string; value: '' | CallImportStatus }> = [
+  { label: 'All statuses', value: '' },
+  { label: 'Uploaded', value: 'uploaded' },
+  { label: 'Mapped', value: 'mapped' },
+  { label: 'Pending', value: 'pending' },
+  { label: 'Processing', value: 'processing' },
+  { label: 'Completed', value: 'completed' },
+  { label: 'Partial', value: 'partial' },
+  { label: 'Failed', value: 'failed' },
+  { label: 'Deleting', value: 'deleting' },
+]
+
+type UploadTab = 'datasets' | 'audio'
+
+export default function CallImports() {
+  const navigate = useNavigate()
+  const queryClient = useQueryClient()
+  const { showToast, ToastContainer } = useToast()
+  // Active workspace is part of every workspace-scoped queryKey so a
+  // workspace switch produces a clean cache miss instead of leaking
+  // rows from the previously-active workspace.
+  const activeWorkspaceId = useWorkspaceStore((s) => s.activeWorkspaceId)
+  const [page, setPage] = useState(1)
+  const [statusFilter, setStatusFilter] = useState<'' | CallImportStatus>('')
+  const [datasetFilter, setDatasetFilter] = useState<string>('')
+  const [tagFilter, setTagFilter] = useState<string[]>([])
+  const [activeTab, setActiveTab] = useState<UploadTab>('datasets')
+  const [showUpload, setShowUpload] = useState(false)
+  const [showAudioUpload, setShowAudioUpload] = useState(false)
+  const [pendingDelete, setPendingDelete] = useState<CallImport | null>(null)
+  const [deleteError, setDeleteError] = useState<string | null>(null)
+
+  const { data: datasets = [] } = useQuery({
+    queryKey: ['call-import-datasets', activeWorkspaceId],
+    queryFn: () => apiClient.listCallImportDatasets(),
+  })
+
+  const { data: allTags = [] } = useQuery({
+    queryKey: ['call-import-tags', activeWorkspaceId],
+    queryFn: () => apiClient.listCallImportTags(),
+  })
+
+  const deleteMutation = useMutation({
+    mutationFn: (id: string) => apiClient.deleteCallImport(id),
+    onSuccess: (result) => {
+      queryClient.invalidateQueries({ queryKey: ['call-imports'] })
+      setPendingDelete(null)
+      setDeleteError(null)
+      if (result.status === 'accepted') {
+        showToast(
+          'Deletion started — large imports may take a minute.',
+          'success',
+        )
+      }
+    },
+    onError: (err: unknown) => {
+      const message = getApiErrorMessage(err, 'Failed to delete import.')
+      setDeleteError(message)
+      showToast(message, 'error')
+    },
+  })
+
+  const queryParams = useMemo(
+    () => ({
+      page,
+      page_size: PAGE_SIZE,
+      ...(statusFilter ? { status: statusFilter } : {}),
+      ...(datasetFilter ? { dataset: datasetFilter } : {}),
+      ...(tagFilter.length > 0 ? { tag_id: tagFilter } : {}),
+      source_format: activeTab === 'audio' ? 'audio' : '__non_audio__',
+    }),
+    [page, statusFilter, datasetFilter, tagFilter, activeTab],
+  )
+
+  const { data, isLoading, isFetching, refetch } = useQuery({
+    queryKey: ['call-imports', activeWorkspaceId, queryParams],
+    queryFn: () => apiClient.listCallImports(queryParams),
+    refetchInterval: (query) => {
+      const items = query.state.data?.items ?? []
+      const hasActive = items.some(
+        (i: CallImport) => i.status === 'pending' || i.status === 'processing',
+      )
+      const hasDeleting = items.some(
+        (i: CallImport) => i.status === 'deleting',
+      )
+      if (hasDeleting) return 3000
+      return hasActive ? 5000 : false
+    },
+  })
+
+  const items = data?.items ?? []
+  const total = data?.total ?? 0
+  const totalPages = Math.max(1, Math.ceil(total / PAGE_SIZE))
+
+  return (
+    <div className="space-y-6">
+      <ToastContainer />
+      <div className="flex justify-between items-center">
+        <div>
+          <h1 className="text-3xl font-bold text-gray-900">Call Imports</h1>
+          <p className="mt-2 text-sm text-gray-600">
+            Upload datasets from CSV / Excel, or add manual call recordings
+            directly and use the same diarisation and evaluation tools.
+          </p>
+        </div>
+        <div className="flex gap-3">
+          <Link to="/call-imports/schemas">
+            <Button
+              variant="ghost"
+              leftIcon={<Layers className="h-5 w-5" />}
+            >
+              Manage Schemas
+            </Button>
+          </Link>
+          <Link to="/call-imports/tags">
+            <Button
+              variant="ghost"
+              leftIcon={<TagIcon className="h-5 w-5" />}
+            >
+              Manage Tags
+            </Button>
+          </Link>
+          <Button
+            variant="primary"
+            onClick={() =>
+              activeTab === 'audio' ? setShowAudioUpload(true) : setShowUpload(true)
+            }
+            leftIcon={<Upload className="h-5 w-5" />}
+          >
+            {activeTab === 'audio' ? 'Upload Audio' : 'Upload CSV'}
+          </Button>
+          <Button
+            variant="secondary"
+            onClick={() => refetch()}
+            isLoading={isFetching && !isLoading}
+            leftIcon={!(isFetching && !isLoading) ? <RefreshCw className="h-5 w-5" /> : undefined}
+          >
+            Refresh
+          </Button>
+        </div>
+      </div>
+
+      <div className="bg-white shadow rounded-lg p-1 inline-flex gap-1">
+        <button
+          type="button"
+          onClick={() => {
+            setActiveTab('datasets')
+            setPage(1)
+          }}
+          className={`flex items-center gap-2 px-4 py-2 rounded-md text-sm font-medium transition-colors ${
+            activeTab === 'datasets'
+              ? 'bg-primary-50 text-primary-700'
+              : 'text-gray-600 hover:bg-gray-50 hover:text-gray-900'
+          }`}
+        >
+          <FileSpreadsheet className="h-4 w-4" />
+          CSV / Dataset Uploads
+        </button>
+        <button
+          type="button"
+          onClick={() => {
+            setActiveTab('audio')
+            setPage(1)
+          }}
+          className={`flex items-center gap-2 px-4 py-2 rounded-md text-sm font-medium transition-colors ${
+            activeTab === 'audio'
+              ? 'bg-primary-50 text-primary-700'
+              : 'text-gray-600 hover:bg-gray-50 hover:text-gray-900'
+          }`}
+        >
+          <FileAudio className="h-4 w-4" />
+          Call Imports / Uploads
+        </button>
+      </div>
+
+      {/*
+        High-level dataset segregation lives at the top of the page so users can
+        scope all filtering/searching that follows to a specific dataset. We
+        intentionally render this above the main card to make it visually
+        distinct from the in-card status/tag filters.
+      */}
+      <div className="bg-white shadow rounded-lg p-4 flex items-center gap-3 flex-wrap">
+        <label htmlFor="dataset-filter" className="text-sm font-medium text-gray-700">
+          Dataset:
+        </label>
+        <select
+          id="dataset-filter"
+          value={datasetFilter}
+          onChange={(e) => {
+            setDatasetFilter(e.target.value)
+            setPage(1)
+          }}
+          className="px-3 py-1.5 text-sm border border-gray-300 rounded-md focus:ring-2 focus:ring-primary-500 focus:border-transparent min-w-[12rem]"
+        >
+          <option value="">All datasets</option>
+          {datasets.map((d) => (
+            <option key={d} value={d}>
+              {d}
+            </option>
+          ))}
+        </select>
+        {datasetFilter && (
+          <span className="text-xs text-gray-500">
+            Showing imports tagged with dataset “{datasetFilter}”.
+          </span>
+        )}
+      </div>
+
+      <div className="bg-white shadow rounded-lg p-6">
+        <div className="flex items-center justify-between mb-4 gap-4 flex-wrap">
+          <div className="flex items-center gap-4 flex-wrap">
+            <div className="flex items-center gap-2">
+              <label htmlFor="status-filter" className="text-sm text-gray-600">
+                Status:
+              </label>
+              <select
+                id="status-filter"
+                value={statusFilter}
+                onChange={(e) => {
+                  setStatusFilter(e.target.value as '' | CallImportStatus)
+                  setPage(1)
+                }}
+                className="px-3 py-1.5 text-sm border border-gray-300 rounded-md focus:ring-2 focus:ring-primary-500 focus:border-transparent"
+              >
+                {STATUS_OPTIONS.map((opt) => (
+                  <option key={opt.value || 'all'} value={opt.value}>
+                    {opt.label}
+                  </option>
+                ))}
+              </select>
+            </div>
+            {allTags.length > 0 && (
+              <div className="flex items-center gap-2 flex-wrap">
+                <span className="text-sm text-gray-600">Tags:</span>
+                {allTags.map((tag: CallImportTag) => {
+                  const active = tagFilter.includes(tag.id)
+                  return (
+                    <button
+                      key={tag.id}
+                      type="button"
+                      onClick={() => {
+                        setTagFilter((prev) =>
+                          prev.includes(tag.id)
+                            ? prev.filter((t) => t !== tag.id)
+                            : [...prev, tag.id],
+                        )
+                        setPage(1)
+                      }}
+                      className={`px-2.5 py-1 text-xs rounded-full border transition-colors ${
+                        active
+                          ? 'bg-primary-600 border-primary-600 text-white'
+                          : 'bg-white border-gray-300 text-gray-700 hover:bg-gray-50'
+                      }`}
+                      style={
+                        !active && tag.color
+                          ? { borderColor: tag.color, color: tag.color }
+                          : undefined
+                      }
+                    >
+                      {tag.name}
+                    </button>
+                  )
+                })}
+                {tagFilter.length > 0 && (
+                  <button
+                    type="button"
+                    onClick={() => {
+                      setTagFilter([])
+                      setPage(1)
+                    }}
+                    className="text-xs text-gray-500 hover:text-gray-700 underline"
+                  >
+                    Clear
+                  </button>
+                )}
+              </div>
+            )}
+          </div>
+          <p className="text-sm text-gray-500">
+            {total} {activeTab === 'audio' ? 'manual upload' : 'dataset import'}
+            {total === 1 ? '' : 's'}
+          </p>
+        </div>
+
+        {isLoading ? (
+          <div className="text-center py-12 text-gray-500">
+            <RefreshCw className="h-8 w-8 mx-auto mb-2 animate-spin" />
+            <p>Loading imports...</p>
+          </div>
+        ) : items.length === 0 ? (
+          <div className="text-center py-12">
+            <Phone className="h-12 w-12 mx-auto mb-3 text-gray-300" />
+            <p className="text-gray-500 mb-3">
+              {statusFilter
+                ? 'No imports match this filter.'
+                : activeTab === 'audio'
+                  ? 'No manual audio uploads yet.'
+                  : 'No dataset uploads yet.'}
+            </p>
+            {!statusFilter && (
+              <Button
+                variant="ghost"
+                size="sm"
+                onClick={() =>
+                  activeTab === 'audio'
+                    ? setShowAudioUpload(true)
+                    : setShowUpload(true)
+                }
+                leftIcon={<Upload className="h-4 w-4" />}
+              >
+                {activeTab === 'audio'
+                  ? 'Upload your first recording'
+                  : 'Upload your first CSV'}
+              </Button>
+            )}
+          </div>
+        ) : (
+          <div className="overflow-x-auto">
+            <table className="min-w-full divide-y divide-gray-200">
+              <thead className="bg-gray-50">
+                <tr>
+                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Filename
+                  </th>
+                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Provider
+                  </th>
+                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Dataset / Tags
+                  </th>
+                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-64">
+                    Progress
+                  </th>
+                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Status
+                  </th>
+                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider min-w-[17rem]">
+                    Activity
+                  </th>
+                  <th className="px-6 py-3 text-right text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Actions
+                  </th>
+                </tr>
+              </thead>
+              <tbody className="bg-white divide-y divide-gray-200">
+                {items.map((item: CallImport) => {
+                  const isDeleting = item.status === 'deleting'
+                  return (
+                  <tr
+                    key={item.id}
+                    className={`hover:bg-gray-50 ${isDeleting ? 'opacity-60' : 'cursor-pointer'}`}
+                    onClick={() => {
+                      if (isDeleting) return
+                      navigate(`/call-imports/${item.id}`)
+                    }}
+                  >
+                    <td className="px-6 py-4 whitespace-nowrap">
+                      <div className="text-sm font-medium text-gray-900 truncate max-w-xs">
+                        {item.original_filename || '(unnamed)'}
+                      </div>
+                      <div className="text-xs text-gray-400 font-mono mt-0.5">
+                        {item.id.slice(0, 8)}
+                      </div>
+                    </td>
+                    <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 capitalize">
+                      {item.source_format === 'audio' ? (
+                        <span className="inline-flex items-center gap-1 normal-case text-gray-700">
+                          <FileAudio className="h-3.5 w-3.5" />
+                          Manual upload
+                        </span>
+                      ) : item.provider || (
+                        <span className="text-gray-400 italic normal-case">
+                          —
+                        </span>
+                      )}
+                    </td>
+                    <td className="px-6 py-4 align-top">
+                      <div className="flex flex-col gap-1">
+                        {item.dataset ? (
+                          <span className="inline-flex items-center text-xs font-medium text-gray-800 bg-gray-100 rounded px-2 py-0.5 self-start">
+                            {item.dataset}
+                          </span>
+                        ) : (
+                          <span className="text-xs text-gray-400 italic">
+                            no dataset
+                          </span>
+                        )}
+                        {item.tags.length > 0 && (
+                          <div className="flex flex-wrap gap-1">
+                            {item.tags.map((tag) => (
+                              <span
+                                key={tag.id}
+                                className="inline-flex items-center text-[10px] uppercase tracking-wide rounded-full px-2 py-0.5 border"
+                                style={{
+                                  borderColor: tag.color || '#d1d5db',
+                                  color: tag.color || '#4b5563',
+                                }}
+                              >
+                                {tag.name}
+                              </span>
+                            ))}
+                          </div>
+                        )}
+                      </div>
+                    </td>
+                    <td className="px-6 py-4 whitespace-nowrap">
+                      <CallImportProgressBar
+                        total={item.total_rows}
+                        completed={item.completed_rows}
+                        failed={item.failed_rows}
+                        deleting={isDeleting}
+                      />
+                    </td>
+                    <td className="px-6 py-4 whitespace-nowrap">
+                      <StatusBadge status={item.status} />
+                    </td>
+                    <td className="px-6 py-4 align-top">
+                      <CallImportAuditMeta
+                        compact
+                        stacked
+                        createdAt={item.created_at}
+                        updatedAt={item.updated_at}
+                        createdByEmail={item.created_by_email}
+                        lastUpdatedByEmail={item.last_updated_by_email}
+                      />
+                    </td>
+                    <td
+                      className="px-6 py-4 whitespace-nowrap text-right text-sm"
+                      onClick={(e) => e.stopPropagation()}
+                    >
+                      <div className="flex justify-end items-center gap-3">
+                        <Link
+                          to={`/call-imports/${item.id}`}
+                          className={`font-medium ${isDeleting ? 'pointer-events-none text-gray-400' : 'text-primary-600 hover:text-primary-700'}`}
+                          aria-disabled={isDeleting}
+                          tabIndex={isDeleting ? -1 : undefined}
+                        >
+                          View
+                        </Link>
+                        <button
+                          type="button"
+                          aria-label={`Delete ${item.original_filename || 'import'}`}
+                          title="Delete import"
+                          className="text-gray-400 hover:text-red-600 transition-colors disabled:opacity-40"
+                          onClick={() => {
+                            setDeleteError(null)
+                            setPendingDelete(item)
+                          }}
+                          disabled={
+                            isDeleting ||
+                            (deleteMutation.isPending && pendingDelete?.id === item.id)
+                          }
+                        >
+                          <Trash2 className="h-4 w-4" />
+                        </button>
+                      </div>
+                    </td>
+                  </tr>
+                  )
+                })}
+              </tbody>
+            </table>
+
+            {totalPages > 1 && (
+              <div className="px-6 py-3 bg-gray-50 border-t border-gray-200 flex items-center justify-between">
+                <p className="text-sm text-gray-600">
+                  Page {page} of {totalPages}
+                </p>
+                <div className="flex gap-2">
+                  <Button
+                    variant="ghost"
+                    size="sm"
+                    onClick={() => setPage((p) => Math.max(1, p - 1))}
+                    disabled={page <= 1}
+                    leftIcon={<ChevronLeft className="h-4 w-4" />}
+                  >
+                    Prev
+                  </Button>
+                  <Button
+                    variant="ghost"
+                    size="sm"
+                    onClick={() => setPage((p) => Math.min(totalPages, p + 1))}
+                    disabled={page >= totalPages}
+                    rightIcon={<ChevronRight className="h-4 w-4" />}
+                  >
+                    Next
+                  </Button>
+                </div>
+              </div>
+            )}
+          </div>
+        )}
+      </div>
+
+      <UploadCsvModal open={showUpload} onClose={() => setShowUpload(false)} />
+      <UploadAudioModal
+        open={showAudioUpload}
+        onClose={() => setShowAudioUpload(false)}
+      />
+
+      <ConfirmModal
+        isOpen={pendingDelete !== null}
+        title="Delete call import?"
+        description={(() => {
+          if (!pendingDelete) return ''
+          const name = pendingDelete.original_filename || '(unnamed)'
+          const total = pendingDelete.total_rows
+          const completed = pendingDelete.completed_rows
+          const inFlight =
+            pendingDelete.status === 'pending' ||
+            pendingDelete.status === 'processing' ||
+            pendingDelete.status === 'deleting'
+          const lines = [
+            `“${name}” will be permanently deleted, along with all ${total} row record${total === 1 ? '' : 's'} and ${completed} stored recording${completed === 1 ? '' : 's'} in S3.`,
+            inFlight
+              ? 'This batch is still processing — pending tasks will be revoked before deletion.'
+              : '',
+            'This cannot be undone.',
+            deleteError ? `Error: ${deleteError}` : '',
+          ]
+          return lines.filter(Boolean).join('\n\n')
+        })()}
+        confirmLabel="Delete"
+        cancelLabel="Cancel"
+        variant="danger"
+        isLoading={deleteMutation.isPending}
+        onConfirm={() => {
+          if (pendingDelete) deleteMutation.mutate(pendingDelete.id)
+        }}
+        onCancel={() => {
+          if (deleteMutation.isPending) return
+          setPendingDelete(null)
+          setDeleteError(null)
+        }}
+      />
+    </div>
+  )
+}

From 6d46894fd54222537ff8314df962a91223ba6705 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 7 Aug 2026 19:14:52 +0530
Subject: [PATCH 02/55] feat: added import-calls user metadata

---
 app/api/v1/routes/call_import_evaluations.py  | 17804 ++++++++--------
 app/api/v1/routes/call_imports.py             |  7963 +++----
 app/config.py                                 |     2 +-
 app/migrations/059_call_import_audit_users.py |    69 +
 app/models/database.py                        |  5464 ++---
 app/models/schemas.py                         |     4 +
 app/services/call_imports/audit.py            |    99 +
 env.example                                   |     2 +-
 frontend/src/types/api.ts                     |  4268 ++--
 tests/test_api/test_call_import_audit.py      |    78 +
 .../test_api/test_call_import_evaluations.py  |    40 +
 11 files changed, 18106 insertions(+), 17687 deletions(-)
 create mode 100644 app/migrations/059_call_import_audit_users.py
 create mode 100644 app/services/call_imports/audit.py
 create mode 100644 tests/test_api/test_call_import_audit.py

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index f73744ad..95222d6d 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -1,8877 +1,8927 @@
-"""Evaluation routes scoped to a Call Import batch."""
-
-from __future__ import annotations
-
-import asyncio
-import csv
-import base64
-import io
-import json
-import math
-import re
-import statistics
-from typing import Any, Dict, Iterator, List, Literal, Optional, Set, Tuple
-from uuid import UUID
-
-from datetime import date, datetime, timedelta, timezone
-
-from fastapi import APIRouter, BackgroundTasks, Body, Depends, HTTPException, Query, Response, status
-from fastapi.responses import StreamingResponse
-from loguru import logger
-from pydantic import BaseModel, Field, field_validator
-from sqlalchemy import desc, func, or_, text
-from sqlalchemy.orm import Session
-from sqlalchemy.orm.attributes import flag_modified
-
-from app.core.auth import Principal, get_principal
-from app.core.auth.capabilities import REPORTS_GENERATE, capability_denied_message
-from app.database import get_db
-from app.dependencies import (
-    get_api_key,
-    get_organization_id,
-    get_workspace_id,
-    require_enterprise_feature,
-)
-from app.services.workspace_rbac import resolve_workspace_capabilities
-from app.models.database import (
-    AIProvider,
-    CallImport,
-    CallImportEvaluation,
-    CallImportEvaluationReportSnapshot,
-    CallImportEvaluationRow,
-    CallImportRow,
-    Metric,
-    PromptPartial,
-    Workspace,
-)
-from app.models.enums import CallImportRowStatus, ModelProvider
-from app.models.schemas import (
-    CallImportEvaluationAggregateResponse,
-    CallImportEvaluationBulkDelete,
-    CallImportEvaluationBulkActionResponse,
-    CallImportEvaluationCreate,
-    CallImportEvaluationListResponse,
-    CallImportEvaluationResponse,
-    CallImportEvaluationRetryRequest,
-    CallImportEvaluationRetryResponse,
-    CallImportEvaluationRetrySkippedItem,
-    CallImportEvaluationRowListResponse,
-    CallImportEvaluationRowResponse,
-    CallImportEvaluationUpdate,
-    CallImportMetricAggregate,
-    CallImportMetricHistogramBucket,
-    CallImportMetricLabelPair,
-    CallImportMetricSummary,
-    CallImportMetricValueCount,
-    DiscoveredLabelDeleteRequest,
-    DiscoveredLabelItem,
-    DiscoveredLabelMergeRequest,
-    DiscoveredLabelsResponse,
-    DiscoveredMetricDeleteRequest,
-    DiscoveredMetricItem,
-    DiscoveredMetricMergeRequest,
-    DiscoveredMetricsResponse,
-    EvaluationInsightsRequest,
-    EvaluationTldrSummary,
-    EvaluationMetricClustersRequest,
-    EvaluationMetricClustersState,
-    EvaluationPromptImprovementsRequest,
-    EvaluationPromptImprovementsState,
-    MetricFailurePoliciesResponse,
-    MetricFailurePoliciesSaveRequest,
-    MetricFailurePolicy,
-    MetricClusterEligibleRow,
-    MetricClusterEligibleRowsResponse,
-    EvaluationUserInsightsRequest,
-    EvaluationUserInsightsState,
-    MetricFlowEdge,
-    MetricPeriodDelta,
-    MetricFlowNode,
-    MetricFlowResponse,
-)
-from app.services.reporting.call_import_evaluation_pdf_report import (
-    call_import_evaluation_pdf_report_service,
-)
-from app.services.call_import_metric_clusters import (
-    METRIC_CLUSTERS_CANCELLED_BY_USER_ERROR,
-    estimate_metric_clusters_llm_calls,
-    filter_completed_row_pairs,
-    list_eligible_cluster_rows,
-    metric_clusters_raw_is_cancelled,
-    metric_clusters_state_from_raw,
-    metric_clusters_state_to_db,
-)
-from app.services.metric_failure_policy import (
-    aggregate_primary_percent,
-    build_failure_policy_previews,
-    effective_policies,
-    failure_rate_percent_from_rows,
-    failure_policies_to_db,
-    has_clusterable_metrics,
-    merge_clustering_policies,
-    merge_failure_policies_into_raw,
-    policies_from_evaluation_raw,
-    validate_failure_policies_for_metrics,
-)
-from app.services.call_import_user_insights import (
-    normalize_max_llm_calls,
-    total_llm_calls_for_rows,
-    user_insights_state_from_raw,
-)
-
-router = APIRouter(
-    prefix="/call-imports/{call_import_id}/evaluations",
-    tags=["Call Import Evaluations"],
-    dependencies=[Depends(require_enterprise_feature("call_imports"))],
-)
-
-
-class CallImportEvaluationPdfReportRequest(BaseModel):
-    vendor_name: str = Field(..., min_length=1, max_length=120)
-    report_type: Literal["external", "internal"] = "external"
-    include_weekly_delta: bool = False
-    include_period_delta: bool = False
-    baseline_evaluation_id: Optional[str] = None
-    period_label: Optional[str] = Field(default=None, max_length=64)
-    use_case: Optional[str] = Field(default=None, max_length=120)
-    internal_brand_image_id: Optional[str] = None
-    external_brand_image_id: Optional[str] = None
-    report_config: Dict[str, Any] = Field(default_factory=dict)
-    platform_base_url: Optional[str] = Field(
-        default=None,
-        max_length=512,
-        description="Frontend origin for deep links to example calls in internal PDFs.",
-    )
-
-    @field_validator("vendor_name")
-    @classmethod
-    def _clean_vendor_name(cls, value: str) -> str:
-        cleaned = value.strip()
-        if not cleaned:
-            raise ValueError("Vendor name is required.")
-        return cleaned
-
-
-class CallImportEvaluationBaselineCandidate(BaseModel):
-    evaluation_id: str
-    name: str
-    dataset: str
-    period_label: Optional[str] = None
-    period_start: Optional[date] = None
-    period_end: Optional[date] = None
-    period_display: str
-    completed_rows: int
-    created_at: datetime
-    is_default: bool = False
-
-
-class CallImportEvaluationBaselineCandidatesResponse(BaseModel):
-    items: List[CallImportEvaluationBaselineCandidate]
-    default_evaluation_id: Optional[str] = None
-
-
-def _require_import(
-    db: Session,
-    call_import_id: UUID,
-    organization_id: UUID,
-) -> CallImport:
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(status_code=404, detail="Call import not found")
-    return call_import
-
-
-def require_call_import_capability(capability: str):
-    """Ensure the caller has *capability* in the call import's workspace (not just the header)."""
-
-    def _dep(
-        call_import_id: UUID,
-        principal: Principal = Depends(get_principal),
-        organization_id: UUID = Depends(get_organization_id),
-        db: Session = Depends(get_db),
-    ) -> CallImport:
-        call_import = _require_import(db, call_import_id, organization_id)
-        caps, _, role = resolve_workspace_capabilities(
-            db,
-            principal=principal,
-            workspace_id=call_import.workspace_id,
-            organization_id=organization_id,
-        )
-        if capability not in caps:
-            raise HTTPException(
-                status_code=403,
-                detail=capability_denied_message(
-                    capability,
-                    role_name=role.name if role else None,
-                    workspace_label="the active workspace",
-                ),
-            )
-        return call_import
-
-    return _dep
-
-
-def _flatten_transcript(text: Optional[str]) -> str:
-    """Collapse a multi-line transcript onto a single line for spreadsheet export.
-
-    The diarised transcript is stored as ``<speaker>: <text>`` lines joined
-    by ``\\n`` because the in-app ``TranscriptView`` parses those line
-    breaks to render chat bubbles. In Excel / Google Sheets that same
-    newline-per-turn formatting causes each cell to balloon vertically,
-    which the user reads as "lots of empty space on top of the cell".
-    Flattening at export time keeps the DB shape intact while giving the
-    spreadsheet a single-line cell per row.
-    """
-    if not text:
-        return ""
-    parts = [
-        segment.strip()
-        for segment in text.replace("\r\n", "\n").replace("\r", "\n").split("\n")
-    ]
-    return " ".join(p for p in parts if p)
-
-
-def _evaluated_transcript_source_label(
-    evaluation: CallImportEvaluation,
-    source_row: CallImportRow,
-) -> str:
-    """Label which transcript source this row was scored against."""
-    source = (evaluation.transcript_source or "diarised").strip().lower()
-    if source == "production":
-        if not (source_row.transcript or "").strip():
-            return ""
-        return "Production"
-    if not (source_row.diarised_transcript or "").strip():
-        return ""
-    return "Diarised"
-
-
-def _pick_evaluation_row_transcript(
-    source_row: Optional[CallImportRow],
-    evaluation: Optional[CallImportEvaluation] = None,
-) -> Optional[str]:
-    """Transcript shown in evaluation row detail for the run's source."""
-    if source_row is None:
-        return None
-    source = (
-        (evaluation.transcript_source or "diarised").strip().lower()
-        if evaluation is not None
-        else "diarised"
-    )
-    if source == "production":
-        raw = (source_row.transcript or "").strip()
-        return raw or None
-    diarised = (source_row.diarised_transcript or "").strip()
-    if diarised:
-        return diarised
-    raw = (source_row.transcript or "").strip()
-    return raw or None
-
-
-def _to_evaluation_row_response(
-    eval_row_obj: CallImportEvaluationRow,
-    source_row: Optional[CallImportRow],
-    evaluation: Optional[CallImportEvaluation] = None,
-) -> CallImportEvaluationRowResponse:
-    """Serialize one evaluation row plus joined source-row metadata."""
-    return CallImportEvaluationRowResponse(
-        id=eval_row_obj.id,
-        evaluation_id=eval_row_obj.evaluation_id,
-        call_import_row_id=eval_row_obj.call_import_row_id,
-        row_index=source_row.row_index if source_row else None,
-        conversation_id=source_row.conversation_id if source_row else None,
-        transcript=_pick_evaluation_row_transcript(source_row, evaluation),
-        raw_columns=source_row.raw_columns if source_row else None,
-        recording_url=source_row.recording_url if source_row else None,
-        recording_date=source_row.recording_date if source_row else None,
-        recording_s3_key=source_row.recording_s3_key if source_row else None,
-        diarised_transcript_status=(
-            source_row.diarised_transcript_status if source_row else None
-        ),
-        diarised_transcript_error=(
-            source_row.diarised_transcript_error if source_row else None
-        ),
-        status=eval_row_obj.status,
-        metric_scores=eval_row_obj.metric_scores or {},
-        error_message=eval_row_obj.error_message,
-        started_at=eval_row_obj.started_at,
-        finished_at=eval_row_obj.finished_at,
-        created_at=eval_row_obj.created_at,
-        updated_at=eval_row_obj.updated_at,
-    )
-
-
-def _serialize_selected_metric_ids(value) -> List[UUID]:
-    result: List[UUID] = []
-    if not isinstance(value, list):
-        return result
-    for item in value:
-        try:
-            result.append(UUID(str(item)))
-        except (TypeError, ValueError):
-            continue
-    return result
-
-
-def _metrics_for_ids(db: Session, org_id: UUID, ids: List[UUID]) -> List[Metric]:
-    if not ids:
-        return []
-    rows = (
-        db.query(Metric)
-        .filter(
-            Metric.organization_id == org_id,
-            Metric.id.in_(ids),
-        )
-        .all()
-    )
-    by_id = {row.id: row for row in rows}
-    return [by_id[mid] for mid in ids if mid in by_id]
-
-
-def _expand_metric_selection(
-    db: Session,
-    org_id: UUID,
-    selected_ids: List[UUID],
-) -> Tuple[List[Metric], Dict[UUID, List[Metric]]]:
-    """Resolve user-supplied metric ids into actual leaves + parent grouping.
-
-    Rules:
-      * If a parent id is in ``selected_ids`` and no specific children of
-        that parent are also listed, include EVERY enabled child of that
-        parent.
-      * If a parent id AND some of its children are listed, include only
-        the listed children (treat the parent selection as the
-        "container" so users can deselect labels).
-      * Standalone metrics (no parent, no children) pass through
-        unchanged.
-      * Disabled metrics are filtered out at this layer so the caller
-        doesn't have to repeat the check.
-
-    Returns:
-        (effective_metrics, parent_to_children)
-
-        ``effective_metrics`` is the deduplicated list of metrics the
-        worker will actually score (children + standalone). Order is
-        preserved from ``selected_ids`` for display stability.
-
-        ``parent_to_children`` maps each parent metric id (UUID) to the
-        list of its selected children. Useful for grouping in the LLM
-        prompt builder.
-    """
-    if not selected_ids:
-        return [], {}
-
-    requested = list(selected_ids)
-    initial_rows = (
-        db.query(Metric)
-        .filter(
-            Metric.organization_id == org_id,
-            Metric.id.in_(requested),
-        )
-        .all()
-    )
-    initial_by_id = {row.id: row for row in initial_rows}
-
-    parent_ids_requested = {
-        m.id for m in initial_rows if m.selection_mode and not m.parent_metric_id
-    }
-    # Map parent id -> children explicitly requested by the user.
-    explicit_children_by_parent: Dict[UUID, List[Metric]] = {}
-    for m in initial_rows:
-        if m.parent_metric_id and m.parent_metric_id in parent_ids_requested:
-            explicit_children_by_parent.setdefault(
-                m.parent_metric_id, []
-            ).append(m)
-
-    # For parents without explicit children, hydrate every enabled child.
-    parents_needing_full_expansion = [
-        pid
-        for pid in parent_ids_requested
-        if pid not in explicit_children_by_parent
-    ]
-    auto_expanded_children: Dict[UUID, List[Metric]] = {}
-    if parents_needing_full_expansion:
-        for pid in parents_needing_full_expansion:
-            child_rows = (
-                db.query(Metric)
-                .filter(
-                    Metric.organization_id == org_id,
-                    Metric.parent_metric_id == pid,
-                    Metric.enabled.is_(True),
-                )
-                .order_by(Metric.created_at.asc())
-                .all()
-            )
-            auto_expanded_children[pid] = child_rows
-
-    parent_to_children: Dict[UUID, List[Metric]] = {}
-    for pid in parent_ids_requested:
-        children = explicit_children_by_parent.get(
-            pid
-        ) or auto_expanded_children.get(pid, [])
-        # Drop disabled children so the worker doesn't waste a slot on
-        # them. Empty parents (no enabled children) are still tracked
-        # because the UI may want to show "0 of 0" rather than swallow
-        # them silently.
-        parent_to_children[pid] = [c for c in children if c.enabled]
-
-    effective: List[Metric] = []
-    seen: set[UUID] = set()
-    for mid in requested:
-        m = initial_by_id.get(mid)
-        if m is None:
-            continue
-        if m.selection_mode and not m.parent_metric_id:
-            # Parent row itself is not scored — only its children.
-            for child in parent_to_children.get(m.id, []):
-                if child.id in seen or not child.enabled:
-                    continue
-                seen.add(child.id)
-                effective.append(child)
-            continue
-        if m.parent_metric_id and m.parent_metric_id in parent_ids_requested:
-            # Already accounted for via the parent expansion above.
-            continue
-        if not m.enabled:
-            continue
-        if m.id in seen:
-            continue
-        seen.add(m.id)
-        effective.append(m)
-
-    return effective, parent_to_children
-
-
-def _evaluation_bulk_operation_for_response(
-    evaluation_id: UUID,
-) -> Optional[str]:
-    from app.services.call_imports.evaluation_bulk_op import (
-        get_evaluation_bulk_operation,
-    )
-
-    return get_evaluation_bulk_operation(evaluation_id)
-
-
-def _serialize_eval(
-    db: Session,
-    row: CallImportEvaluation,
-    *,
-    sibling_evaluation_ids: Optional[List[UUID]] = None,
-) -> CallImportEvaluationResponse:
-    selected_ids = _serialize_selected_metric_ids(row.selected_metric_ids)
-
-    # Pull every metric referenced anywhere in the run's grouping (leaves,
-    # standalone, AND parents from selected_metric_groups) so the UI can
-    # render parent labels even when only children were materialized into
-    # selected_metric_ids.
-    groups_raw: Dict[str, List[str]] = {}
-    if isinstance(row.selected_metric_groups, dict):
-        for parent_str, children in row.selected_metric_groups.items():
-            if not isinstance(children, list):
-                continue
-            cleaned: List[str] = []
-            for c in children:
-                try:
-                    UUID(str(c))
-                    cleaned.append(str(c))
-                except (TypeError, ValueError):
-                    continue
-            try:
-                UUID(parent_str)
-                groups_raw[parent_str] = cleaned
-            except (TypeError, ValueError):
-                continue
-
-    metric_ids_for_lookup: List[UUID] = list(selected_ids)
-    for parent_str in groups_raw.keys():
-        try:
-            pid = UUID(parent_str)
-            if pid not in metric_ids_for_lookup:
-                metric_ids_for_lookup.append(pid)
-        except (TypeError, ValueError):
-            continue
-
-    metrics = _metrics_for_ids(
-        db, row.organization_id, metric_ids_for_lookup
-    )
-
-    from app.services.call_imports.progress_counters import merge_eval_counters_for_ui
-
-    ui_completed_raw, ui_failed_raw = merge_eval_counters_for_ui(row)
-    total = int(row.total_rows or 0)
-    ui_completed = (
-        min(ui_completed_raw, total) if total else ui_completed_raw
-    )
-    ui_failed = min(ui_failed_raw, total) if total else ui_failed_raw
-
-    return CallImportEvaluationResponse(
-        id=row.id,
-        call_import_id=row.call_import_id,
-        organization_id=row.organization_id,
-        name=row.name,
-        selected_metric_ids=selected_ids,
-        selected_metric_groups=groups_raw or None,
-        metrics=[
-            CallImportMetricSummary(
-                id=metric.id,
-                name=metric.name,
-                metric_type=metric.metric_type,
-                description=metric.description,
-                parent_metric_id=metric.parent_metric_id,
-                selection_mode=metric.selection_mode,
-                # Required by the Flow tab to know whether a parent
-                # opted into discovery; without it the
-                # DiscoveredLabelsPanel stays hidden even when the
-                # worker is actively producing discovered_labels.
-                allow_discovery=bool(
-                    getattr(metric, "allow_discovery", False)
-                ),
-            )
-            for metric in metrics
-        ],
-        status=row.status,
-        total_rows=row.total_rows,
-        completed_rows=ui_completed,
-        failed_rows=ui_failed,
-        error_message=row.error_message,
-        llm_provider=row.llm_provider,
-        llm_model=row.llm_model,
-        llm_credential_id=row.llm_credential_id,
-        llm_config=(
-            row.llm_config if isinstance(getattr(row, "llm_config", None), dict) else None
-        ),
-        metric_llm_overrides=(
-            row.metric_llm_overrides
-            if isinstance(row.metric_llm_overrides, dict)
-            else None
-        ),
-        stt_provider=row.stt_provider,
-        stt_model=row.stt_model,
-        stt_credential_id=row.stt_credential_id,
-        diarisation_llm_provider=getattr(row, "diarisation_llm_provider", None),
-        diarisation_llm_model=getattr(row, "diarisation_llm_model", None),
-        diarisation_llm_credential_id=getattr(
-            row, "diarisation_llm_credential_id", None
-        ),
-        diarisation_prompt=getattr(row, "diarisation_prompt", None),
-        transcribe_mode=(
-            (getattr(row, "transcribe_mode", None) or "stt_llm")
-        ),
-        transcript_source=(row.transcript_source or "diarised"),
-        sibling_evaluation_ids=list(sibling_evaluation_ids or []),
-        started_at=row.started_at,
-        finished_at=row.finished_at,
-        created_at=row.created_at,
-        updated_at=row.updated_at,
-        tldr_summary=_tldr_summary_payload(row),
-        user_insights=_user_insights_payload(row),
-        metric_clusters=_metric_clusters_payload(row),
-        discover_new_metrics=bool(
-            getattr(row, "discover_new_metrics", False)
-        ),
-        bulk_operation=_evaluation_bulk_operation_for_response(row.id),
-    )
-
-
-def _normalize_name(value: Optional[str]) -> Optional[str]:
-    """Trim user-supplied name; empty string becomes ``NULL``."""
-    if value is None:
-        return None
-    trimmed = value.strip()
-    return trimmed or None
-
-
-def _rollup_evaluation_status(evaluation: CallImportEvaluation, db: Session) -> None:
-    """Recompute counters + terminal status after rows are added/removed.
-
-    Uses a single aggregate query instead of loading every row status.
-    """
-    from app.workers.tasks.evaluate_call_import_row_core import (
-        _apply_parent_status_from_counters,
-        reconcile_evaluation_counters,
-    )
-
-    reconcile_evaluation_counters(db, evaluation)
-    _apply_parent_status_from_counters(evaluation)
-    db.flush()
-
-    if evaluation.status in {"completed", "failed", "partial"}:
-        from app.models.database import CallImport
-        from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
-
-        call_import = (
-            db.query(CallImport)
-            .filter(CallImport.id == evaluation.call_import_id)
-            .first()
-        )
-        if call_import is not None:
-            rollup_call_import_batch_status(db, call_import)
-
-
-@router.post(
-    "",
-    response_model=CallImportEvaluationResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="createCallImportEvaluation",
-)
-async def create_call_import_evaluation(
-    call_import_id: UUID,
-    payload: CallImportEvaluationCreate,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationResponse:
-    del api_key
-    call_import = _require_import(db, call_import_id, organization_id)
-
-    metric_ids = payload.metric_ids
-    if not metric_ids:
-        raise HTTPException(
-            status_code=400,
-            detail="Select at least one metric to run the evaluation against.",
-        )
-
-    org_metrics = (
-        db.query(Metric)
-        .filter(
-            Metric.organization_id == organization_id,
-            Metric.id.in_(metric_ids),
-        )
-        .all()
-    )
-    by_id = {metric.id: metric for metric in org_metrics}
-    unknown_ids = [mid for mid in metric_ids if mid not in by_id]
-    if unknown_ids:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "These metric ids do not exist in your organization: "
-                f"{', '.join(str(mid) for mid in unknown_ids)}. "
-                "Refresh the metrics list and try again."
-            ),
-        )
-    # Parents themselves are containers, not scored rows, so a disabled
-    # parent shouldn't block the run as long as it has enabled children.
-    # We only reject disabled rows that the worker will actually try to
-    # evaluate (children + standalone leaves).
-    disabled_leaves = [
-        metric
-        for metric in org_metrics
-        if not metric.enabled
-        and not (metric.selection_mode and not metric.parent_metric_id)
-    ]
-    if disabled_leaves:
-        names = ", ".join(metric.name for metric in disabled_leaves)
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                f"These metrics are disabled and cannot be evaluated: {names}. "
-                "Enable them on the Metrics page (or pick different ones) and "
-                "try again."
-            ),
-        )
-
-    # Expand hierarchical selection: parents auto-include their enabled
-    # children, mixed parent+child selections respect the user's subset.
-    effective_metrics, parent_to_children = _expand_metric_selection(
-        db, organization_id, metric_ids
-    )
-    if not effective_metrics:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "None of the selected metrics yielded an enabled leaf to "
-                "evaluate. Check that parent categories have enabled "
-                "children, then try again."
-            ),
-        )
-
-    # The effective list (children + standalone leaves) is what gets
-    # persisted to ``selected_metric_ids`` and scored by the worker.
-    # The original parents are preserved in ``selected_metric_groups``
-    # so the UI can rebuild the tree later.
-    leaf_metric_ids: List[UUID] = [m.id for m in effective_metrics]
-    selected_metric_groups: Dict[str, List[str]] = {
-        str(pid): [str(c.id) for c in children]
-        for pid, children in parent_to_children.items()
-    }
-    metric_rows = effective_metrics
-    valid_metric_id_strs = {str(m.id) for m in metric_rows}
-
-    # ----- Validate run-level + per-metric LLM config -----
-    llm_provider_norm: Optional[str] = None
-    llm_model_norm: Optional[str] = None
-    if payload.llm_provider or payload.llm_model:
-        if not (payload.llm_provider and payload.llm_model):
-            raise HTTPException(
-                status_code=400,
-                detail="Both llm_provider and llm_model are required when overriding the run LLM.",
-            )
-        try:
-            llm_provider_norm = ModelProvider(
-                payload.llm_provider.lower()
-            ).value
-        except ValueError:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    f"Unknown LLM provider '{payload.llm_provider}'. "
-                    "Valid keys are documented in ModelProvider."
-                ),
-            )
-        llm_model_norm = payload.llm_model.strip() or None
-        if not llm_model_norm:
-            raise HTTPException(
-                status_code=400, detail="llm_model cannot be empty."
-            )
-
-    if payload.llm_credential_id is not None:
-        cred = (
-            db.query(AIProvider)
-            .filter(
-                AIProvider.id == payload.llm_credential_id,
-                AIProvider.organization_id == organization_id,
-            )
-            .first()
-        )
-        if not cred:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "The provided llm_credential_id does not exist in this "
-                    "organization."
-                ),
-            )
-
-    # Per-metric overrides: keys can be either a leaf metric id (applies
-    # to that metric only) or a parent metric id (applies to every
-    # child of that parent). Parent keys are expanded to their
-    # children so the worker only sees concrete leaf ids.
-    metric_overrides_payload: Optional[Dict[str, Dict[str, Any]]] = None
-    if payload.metric_llm_overrides:
-        metric_overrides_payload = {}
-        for metric_id, override in payload.metric_llm_overrides.items():
-            target_leaf_ids: List[str] = []
-            if metric_id in valid_metric_id_strs:
-                target_leaf_ids = [metric_id]
-            else:
-                # Maybe it's a parent id — expand to the children that
-                # are part of THIS run.
-                try:
-                    parent_uuid = UUID(metric_id)
-                except (TypeError, ValueError):
-                    raise HTTPException(
-                        status_code=400,
-                        detail=(
-                            "metric_llm_overrides references metric "
-                            f"{metric_id} which is not a valid UUID."
-                        ),
-                    )
-                children_for_parent = parent_to_children.get(parent_uuid)
-                if not children_for_parent:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=(
-                            "metric_llm_overrides references metric "
-                            f"{metric_id} which is not in metric_ids."
-                        ),
-                    )
-                target_leaf_ids = [str(c.id) for c in children_for_parent]
-
-            override_dict: Dict[str, Any] = {}
-            if override.provider is not None:
-                if not override.model:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=(
-                            f"Override for metric {metric_id} has a provider "
-                            "but no model."
-                        ),
-                    )
-                try:
-                    override_dict["provider"] = ModelProvider(
-                        override.provider.lower()
-                    ).value
-                except ValueError:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=(
-                            f"Override for metric {metric_id} uses unknown "
-                            f"provider '{override.provider}'."
-                        ),
-                    )
-                override_dict["model"] = override.model.strip()
-            elif override.model:
-                # Model without provider doesn't make sense — treat as 400
-                # so the UI can fix it instead of silently falling back.
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Override for metric {metric_id} has a model but "
-                        "no provider."
-                    ),
-                )
-            if override.credential_id is not None:
-                override_dict["credential_id"] = str(override.credential_id)
-            if override.llm_config is not None:
-                override_dict["llm_config"] = override.llm_config
-            if override_dict:
-                for leaf_id in target_leaf_ids:
-                    metric_overrides_payload[leaf_id] = override_dict
-
-    # ----- Validate auto-transcribe settings -----
-    # Diarised runs auto-diarise rows missing a diarised transcript and
-    # require STT + diariser LLM config. Production runs score the CSV
-    # transcript directly and skip diarisation entirely.
-    use_diarised = payload.transcript_sources[0] == "diarised"
-    auto_transcribe = use_diarised
-
-    transcribe_mode_norm: Optional[str] = None
-    stt_provider_norm: Optional[str] = None
-    stt_model_norm: Optional[str] = None
-    diarisation_llm_provider_norm: Optional[str] = None
-    diarisation_llm_model_norm: Optional[str] = None
-    diarisation_prompt_norm: Optional[str] = None
-
-    if use_diarised:
-        transcribe_mode_norm = (payload.transcribe_mode or "stt_llm").strip().lower()
-        if transcribe_mode_norm not in {"stt_llm", "llm_only"}:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    f"Unknown transcribe_mode '{payload.transcribe_mode}'. "
-                    "Expected 'stt_llm' or 'llm_only'."
-                ),
-            )
-
-        if transcribe_mode_norm == "stt_llm":
-            if not payload.stt_provider:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        "stt_provider is required when "
-                        "transcribe_mode='stt_llm': every evaluation run "
-                        "auto-diarises rows that are missing a diarised "
-                        "transcript."
-                    ),
-                )
-            if not payload.stt_model:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        "stt_model is required when transcribe_mode='stt_llm'."
-                    ),
-                )
-            try:
-                stt_provider_norm = ModelProvider(
-                    payload.stt_provider.lower()
-                ).value
-            except ValueError:
-                raise HTTPException(
-                    status_code=400,
-                    detail=f"Unknown STT provider '{payload.stt_provider}'.",
-                )
-            stt_model_norm = payload.stt_model.strip() or None
-            if not stt_model_norm:
-                raise HTTPException(
-                    status_code=400, detail="stt_model cannot be empty."
-                )
-        else:
-            # llm_only — explicitly reject lingering STT inputs so the
-            # contract is unambiguous (the worker would ignore them but
-            # silent acceptance hides accidental misconfiguration).
-            if (payload.stt_provider or "").strip() or (
-                payload.stt_model or ""
-            ).strip():
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        "stt_provider / stt_model must be omitted when "
-                        "transcribe_mode='llm_only'; the LLM consumes the "
-                        "audio directly."
-                    ),
-                )
-
-        # --- Validate LLM diariser settings -----
-        if not payload.diarization_llm_provider:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "diarization_llm_provider is required: every evaluation "
-                    "run diarises STT output with an LLM."
-                ),
-            )
-        if not payload.diarization_llm_model:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "diarization_llm_model is required: every evaluation "
-                    "run diarises STT output with an LLM."
-                ),
-            )
-        try:
-            diarisation_llm_provider_norm = ModelProvider(
-                payload.diarization_llm_provider.lower()
-            ).value
-        except ValueError:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    f"Unknown diarisation LLM provider "
-                    f"'{payload.diarization_llm_provider}'."
-                ),
-            )
-        diarisation_llm_model_norm = (
-            payload.diarization_llm_model.strip() or None
-        )
-        if not diarisation_llm_model_norm:
-            raise HTTPException(
-                status_code=400,
-                detail="diarization_llm_model cannot be empty.",
-            )
-        diarisation_prompt_norm = (
-            payload.diarization_prompt.strip()
-            if isinstance(payload.diarization_prompt, str)
-            else None
-        ) or None
-
-    from app.models.enums import CallImportParameterType, CallImportStatus
-    from app.services.call_imports.bulk_ops import (
-        count_all_source_rows,
-        count_completed_source_rows,
-        count_source_rows_with_production_transcript,
-    )
-
-    starting_from_mapped = False
-    if call_import.status == CallImportStatus.MAPPED:
-        if not call_import.source_s3_key or not call_import.source_format:
-            raise HTTPException(
-                status_code=status.HTTP_409_CONFLICT,
-                detail=(
-                    "This batch has no staged source file. Upload and map "
-                    "a CSV/Excel file before running evaluation."
-                ),
-            )
-        if not call_import.schema_id:
-            raise HTTPException(
-                status_code=status.HTTP_409_CONFLICT,
-                detail="Cannot run evaluation without a mapped schema.",
-            )
-        from app.api.v1.routes.call_imports import (
-            _ensure_blob_storage_enabled,
-            _resolve_schema,
-            _resolve_telephony_integration,
-            _validate_direct_url_import_ready,
-        )
-
-        workspace_id = call_import.workspace_id
-        schema = _resolve_schema(
-            db, organization_id, workspace_id, call_import.schema_id
-        )
-        parameters = list(schema.parameters)
-        if not use_diarised:
-            transcript_mapped = any(
-                param.type == CallImportParameterType.TRANSCRIPT
-                and (call_import.parameter_mapping or {}).get(param.name)
-                for param in parameters
-            )
-            if not transcript_mapped:
-                raise HTTPException(
-                    status_code=status.HTTP_409_CONFLICT,
-                    detail=(
-                        "No transcript column is mapped in this batch. "
-                        "Map a schema transcript parameter to a CSV column, "
-                        "or choose 'Diarize then evaluate'."
-                    ),
-                )
-        if payload.telephony_integration_id is not None:
-            integration = _resolve_telephony_integration(
-                db,
-                organization_id,
-                payload.telephony_integration_id,
-                payload.provider or "",
-            )
-        else:
-            _validate_direct_url_import_ready(
-                parameters, dict(call_import.parameter_mapping or {})
-            )
-            integration = None
-
-        _ensure_blob_storage_enabled()
-
-        if integration is not None:
-            call_import.provider = integration.provider
-            call_import.telephony_integration_id = integration.id
-        else:
-            call_import.provider = None
-            call_import.telephony_integration_id = None
-
-        call_import.total_rows = 0
-        call_import.completed_rows = 0
-        call_import.failed_rows = 0
-        call_import.error_message = None
-        call_import.status = CallImportStatus.PROCESSING
-        db.commit()
-        db.refresh(call_import)
-        starting_from_mapped = True
-
-    if use_diarised:
-        total_row_count = count_completed_source_rows(db, call_import.id)
-    else:
-        # Production runs score CSV text — rows need not wait for
-        # recording fetch to finish before they are evaluable.
-        total_row_count = count_source_rows_with_production_transcript(
-            db, call_import.id
-        )
-
-    requested_sources: List[str] = list(payload.transcript_sources)
-
-    if (
-        not use_diarised
-        and not starting_from_mapped
-        and count_all_source_rows(db, call_import.id) > 0
-        and total_row_count == 0
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=(
-                "No rows have a production transcript. "
-                "Choose 'Diarize then evaluate' or import rows with "
-                "a transcript column."
-            ),
-        )
-
-    base_name = _normalize_name(payload.name)
-
-    def _name_for_source(source: str) -> Optional[str]:
-        # Single-source runs preserve the user's chosen name verbatim.
-        del source
-        return base_name
-
-    created_evaluations: List[CallImportEvaluation] = []
-
-    for source in requested_sources:
-        evaluation = CallImportEvaluation(
-            call_import_id=call_import.id,
-            organization_id=organization_id,
-            # Mirror the parent CallImport's workspace so listings can
-            # filter on workspace_id directly without joining.
-            workspace_id=call_import.workspace_id,
-            name=_name_for_source(source),
-            selected_metric_ids=[
-                str(metric_id) for metric_id in leaf_metric_ids
-            ],
-            selected_metric_groups=selected_metric_groups or None,
-            status="pending",
-            total_rows=total_row_count,
-            completed_rows=0,
-            failed_rows=0,
-            llm_provider=llm_provider_norm,
-            llm_model=llm_model_norm,
-            llm_credential_id=payload.llm_credential_id,
-            llm_config=payload.llm_config,
-            metric_llm_overrides=metric_overrides_payload,
-            stt_provider=stt_provider_norm,
-            stt_model=stt_model_norm,
-            stt_credential_id=(
-                payload.stt_credential_id if auto_transcribe else None
-            ),
-            diarisation_llm_provider=diarisation_llm_provider_norm,
-            diarisation_llm_model=diarisation_llm_model_norm,
-            diarisation_llm_credential_id=(
-                payload.diarization_llm_credential_id if auto_transcribe else None
-            ),
-            diarisation_prompt=diarisation_prompt_norm,
-            transcribe_mode=transcribe_mode_norm,
-            transcript_source=source,
-            discover_new_metrics=bool(
-                getattr(payload, "discover_new_metrics", False)
-            ),
-        )
-        db.add(evaluation)
-        db.flush()
-        created_evaluations.append(evaluation)
-
-    db.commit()
-    for evaluation in created_evaluations:
-        db.refresh(evaluation)
-
-    primary_evaluation = created_evaluations[0]
-    sibling_ids = [e.id for e in created_evaluations[1:]]
-
-    if not total_row_count and not starting_from_mapped:
-        for evaluation in created_evaluations:
-            evaluation.status = "completed"
-        db.commit()
-        for evaluation in created_evaluations:
-            db.refresh(evaluation)
-        return _serialize_eval(
-            db, primary_evaluation, sibling_evaluation_ids=sibling_ids
-        )
-
-    if starting_from_mapped:
-        from app.workers.tasks.call_import_bulk_ops import (
-            materialize_mapped_call_import_evaluation_task,
-        )
-
-        for evaluation in created_evaluations:
-            materialize_mapped_call_import_evaluation_task.delay(
-                str(call_import.id),
-                str(organization_id),
-                str(call_import.workspace_id),
-                str(evaluation.id),
-                transcribe_overwrite=payload.transcribe_overwrite,
-            )
-    else:
-        from app.workers.tasks.call_import_bulk_ops import (
-            materialize_call_import_evaluation_task,
-        )
-
-        for evaluation in created_evaluations:
-            materialize_call_import_evaluation_task.delay(
-                str(evaluation.id),
-                transcribe_overwrite=payload.transcribe_overwrite,
-            )
-
-    for evaluation in created_evaluations:
-        db.refresh(evaluation)
-
-    return _serialize_eval(
-        db, primary_evaluation, sibling_evaluation_ids=sibling_ids
-    )
-
-
-@router.get(
-    "",
-    response_model=CallImportEvaluationListResponse,
-    operation_id="listCallImportEvaluations",
-)
-async def list_call_import_evaluations(
-    call_import_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationListResponse:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-    rows = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .order_by(desc(CallImportEvaluation.created_at))
-        .all()
-    )
-    return CallImportEvaluationListResponse(
-        items=[_serialize_eval(db, row) for row in rows],
-        total=len(rows),
-    )
-
-
-@router.get(
-    "/{eval_id}",
-    response_model=CallImportEvaluationResponse,
-    operation_id="getCallImportEvaluation",
-)
-async def get_call_import_evaluation(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationResponse:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-    row = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not row:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-    return _serialize_eval(db, row)
-
-
-@router.get(
-    "/{eval_id}/rows",
-    response_model=CallImportEvaluationRowListResponse,
-    operation_id="listCallImportEvaluationRows",
-)
-async def list_call_import_evaluation_rows(
-    call_import_id: UUID,
-    eval_id: UUID,
-    page: int = Query(1, ge=1),
-    page_size: int = Query(100, ge=1, le=500),
-    q: Optional[str] = Query(
-        None,
-        description=(
-            "Free-text search across conversation_id and transcript "
-            "(case-insensitive substring match)."
-        ),
-    ),
-    metric_id: Optional[UUID] = Query(
-        None,
-        description=(
-            "If set, only return rows whose ``metric_scores[metric_id].value`` "
-            "exactly matches ``metric_value`` (string-compared). "
-            "Use together with ``metric_value``."
-        ),
-    ),
-    metric_value: Optional[str] = Query(
-        None,
-        description="Value to match against metric_id (string compare).",
-    ),
-    status_filter: Optional[str] = Query(
-        None,
-        alias="status",
-        description="Restrict to rows with this evaluation row status.",
-    ),
-    flow_parent_id: Optional[UUID] = Query(
-        None,
-        description=(
-            "Parent (category) metric whose ``sequence`` array should be "
-            "checked against ``flow_node`` and ``flow_edge_target``. Used "
-            "to drill into the calls behind a flow-chart node or edge."
-        ),
-    ),
-    flow_node: Optional[str] = Query(
-        None,
-        description=(
-            "If set together with ``flow_parent_id``, only return rows "
-            "whose sequence under that parent contains this step. Accepts "
-            "either a child metric UUID (resolved to slug(name)), a "
-            "``disc:<slug>`` discovered-label id, or a raw slug."
-        ),
-    ),
-    flow_edge_target: Optional[str] = Query(
-        None,
-        description=(
-            "Optional companion to ``flow_node``: when set, restrict to "
-            "rows whose sequence contains the directed transition "
-            "``flow_node -> flow_edge_target`` (immediately adjacent). "
-            "Same id format as ``flow_node``."
-        ),
-    ),
-    discovered_parent_id: Optional[UUID] = Query(
-        None,
-        description=(
-            "Parent (category) metric that defines the discovery scope "
-            "for ``discovered_label_key`` / ``has_discovered``."
-        ),
-    ),
-    discovered_label_key: Optional[str] = Query(
-        None,
-        description=(
-            "If set together with ``discovered_parent_id``, only return "
-            "rows whose ``metric_scores[parent].discovered_labels`` "
-            "list contains an entry with this slug (after applying "
-            "evaluation-level merge aliases)."
-        ),
-    ),
-    has_discovered: Optional[bool] = Query(
-        None,
-        description=(
-            "If true together with ``discovered_parent_id``, only return "
-            "rows that have at least one LLM-discovered label for the "
-            "parent. Useful to triage which calls produced novel labels."
-        ),
-    ),
-    sort_by: Optional[str] = Query(
-        None,
-        description=(
-            "Column to sort by. Accepted values: ``row_index`` (default "
-            "when omitted), ``conversation_id``, ``status`` (the "
-            "evaluation-row status), or ``metric:<metric_uuid>`` to sort "
-            "by ``metric_scores[<uuid>].value``. Metric sorts compare "
-            "the extracted JSON text — adequate for booleans, enum "
-            "labels, and 0-1 ratings; large integer values may sort "
-            "lexicographically (10 before 2)."
-        ),
-    ),
-    sort_dir: Optional[str] = Query(
-        "asc",
-        description="Sort direction: ``asc`` (default) or ``desc``.",
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationRowListResponse:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    eval_row = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not eval_row:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    query = (
-        db.query(CallImportEvaluationRow, CallImportRow)
-        .join(CallImportRow, CallImportRow.id == CallImportEvaluationRow.call_import_row_id)
-        .filter(CallImportEvaluationRow.evaluation_id == eval_id)
-    )
-
-    # --- Filters ----------------------------------------------------------
-    if q and q.strip():
-        needle = f"%{q.strip()}%"
-        # Search across both transcript columns so a hit in either the
-        # production or the diarised version surfaces the row,
-        # independent of which source the evaluation actually scored.
-        query = query.filter(
-            or_(
-                CallImportRow.conversation_id.ilike(needle),
-                CallImportRow.transcript.ilike(needle),
-                CallImportRow.diarised_transcript.ilike(needle),
-            )
-        )
-
-    if status_filter:
-        # The CallImportEvaluationRow.status column is a string in PG so a
-        # plain == filter works; we lowercase to match the stored values.
-        query = query.filter(
-            CallImportEvaluationRow.status == status_filter.strip().lower()
-        )
-
-    if metric_id is not None and metric_value is not None:
-        # ``metric_scores`` is a JSONB column shaped like
-        # ``{"<metric_id>": {"value": <X>, "type": "boolean", ...}}``. We
-        # extract the nested ``value`` as text and compare to the user
-        # input as a string — that handles bool/int/enum without needing
-        # per-type casts. ``metric_value`` is matched case-insensitively
-        # so chart clicks on labels like "True" survive any casing drift
-        # between worker output and the chart label.
-        path_value = func.json_extract_path_text(
-            CallImportEvaluationRow.metric_scores,
-            str(metric_id),
-            "value",
-        )
-        query = query.filter(func.lower(path_value) == metric_value.strip().lower())
-
-    # --- Flow chart drilldown filter -------------------------------------
-    # Translates a clicked node (or edge) on the flow chart into a
-    # SQL filter against ``metric_scores[<parent>].sequence``. The
-    # frontend sends either a child UUID, a ``disc:<slug>`` discovered
-    # node id, or a raw slug — we normalize all three to the slug that
-    # actually appears in stored ``sequence`` arrays.
-    if flow_parent_id is not None and flow_node and flow_node.strip():
-        parent_id_str_local = str(flow_parent_id)
-        alias_map_flow = _alias_map_for_parent(eval_row, flow_parent_id)
-
-        def _flow_node_to_slug(raw: str) -> Optional[str]:
-            raw_clean = raw.strip()
-            if not raw_clean:
-                return None
-            if raw_clean == _FLOW_START_NODE_ID:
-                # The synthetic START node isn't a real sequence entry;
-                # filtering on it is meaningless so we skip silently.
-                return None
-            if raw_clean.startswith(_DISCOVERED_NODE_PREFIX):
-                return _resolve_alias(
-                    alias_map_flow,
-                    _slug_label(raw_clean[len(_DISCOVERED_NODE_PREFIX) :]),
-                )
-            # Try to interpret as a child metric UUID first; fall back
-            # to treating it as a slug.
-            try:
-                child_uuid = UUID(raw_clean)
-            except (TypeError, ValueError):
-                return _resolve_alias(alias_map_flow, _slug_label(raw_clean))
-            child = (
-                db.query(Metric.name)
-                .filter(
-                    Metric.id == child_uuid,
-                    Metric.organization_id == organization_id,
-                )
-                .first()
-            )
-            if child and child[0]:
-                return _resolve_alias(alias_map_flow, _slug_label(child[0]))
-            return _resolve_alias(alias_map_flow, _slug_label(raw_clean))
-
-        from_slug = _flow_node_to_slug(flow_node)
-        target_slug: Optional[str] = None
-        if flow_edge_target and flow_edge_target.strip():
-            target_slug = _flow_node_to_slug(flow_edge_target)
-
-        if from_slug:
-            # The ``metric_scores`` column is declared as ``Column(JSON)``
-            # in the model so on databases where the table was created
-            # from the model (rather than the migration) the physical
-            # type is ``json``, not ``jsonb``. The JSONB-only operators
-            # below (``jsonb_exists``, ``jsonb_array_elements_text``,
-            # ``@>``) require a JSONB input — we cast once up front so
-            # the same SQL works regardless of which path created the
-            # table.
-            scores_jsonb = (
-                "(call_import_evaluation_rows.metric_scores)::jsonb"
-            )
-            if target_slug:
-                # Edge filter: rows whose sequence under this parent
-                # contains ``from_slug`` immediately followed by
-                # ``target_slug``. Implemented as a correlated EXISTS
-                # over ``jsonb_array_elements_text`` with ORDINALITY,
-                # which is the portable way to express "next array
-                # index" against a JSONB array in Postgres.
-                edge_filter_sql = text(
-                    f"""
-                    EXISTS (
-                        SELECT 1
-                        FROM jsonb_array_elements_text(
-                            COALESCE(
-                                {scores_jsonb} -> :p_id -> 'sequence',
-                                '[]'::jsonb
-                            )
-                        ) WITH ORDINALITY AS s1(elem, ord)
-                        JOIN jsonb_array_elements_text(
-                            COALESCE(
-                                {scores_jsonb} -> :p_id -> 'sequence',
-                                '[]'::jsonb
-                            )
-                        ) WITH ORDINALITY AS s2(elem, ord)
-                          ON s2.ord = s1.ord + 1
-                        WHERE s1.elem = :from_slug
-                          AND s2.elem = :to_slug
-                    )
-                    """
-                ).bindparams(
-                    p_id=parent_id_str_local,
-                    from_slug=from_slug,
-                    to_slug=target_slug,
-                )
-                query = query.filter(edge_filter_sql)
-            else:
-                # Node filter: rows whose ``metric_scores -> parent ->
-                # 'sequence'`` array contains ``from_slug``. We use the
-                # function form ``jsonb_exists`` rather than the ``?``
-                # operator to avoid psycopg2 mistaking the question
-                # mark for a parameter placeholder.
-                node_filter_sql = text(
-                    f"""
-                    jsonb_exists(
-                        COALESCE(
-                            {scores_jsonb} -> :p_id -> 'sequence',
-                            '[]'::jsonb
-                        ),
-                        :slug
-                    )
-                    """
-                ).bindparams(p_id=parent_id_str_local, slug=from_slug)
-                query = query.filter(node_filter_sql)
-
-    # --- Discovered label filters ---------------------------------------
-    # Surfaces "which calls produced THIS LLM-discovered label" and the
-    # broader "which calls produced ANY LLM-discovered label". Both
-    # operate on ``metric_scores[<parent>].discovered_labels`` (a list
-    # of dicts) plus the same ``sequence`` array — covering both legacy
-    # rows where the slug only made it into ``sequence`` and newer
-    # rows where it landed in both.
-    if discovered_parent_id is not None and (
-        discovered_label_key or has_discovered
-    ):
-        d_parent_str = str(discovered_parent_id)
-        alias_map_disc = _alias_map_for_parent(eval_row, discovered_parent_id)
-        # See note above: cast once so the JSONB operators don't reject
-        # the column when it's typed as ``json`` in the database.
-        scores_jsonb = "(call_import_evaluation_rows.metric_scores)::jsonb"
-        if discovered_label_key and discovered_label_key.strip():
-            target = _resolve_alias(
-                alias_map_disc, _slug_label(discovered_label_key)
-            )
-            if target:
-                # Match rows whose discovered_labels list has an entry
-                # ``{"key": <target>}`` OR whose sequence array still
-                # contains the slug. The latter covers older rows that
-                # were rewritten by a merge in the discovered_labels
-                # blob but whose sequence may have lagged.
-                contains_json = json.dumps(
-                    {d_parent_str: {"discovered_labels": [{"key": target}]}}
-                )
-                disc_filter_sql = text(
-                    f"""
-                    (
-                        {scores_jsonb} @> CAST(:contains AS JSONB)
-                        OR
-                        jsonb_exists(
-                            COALESCE(
-                                {scores_jsonb} -> :p_id -> 'sequence',
-                                '[]'::jsonb
-                            ),
-                            :slug
-                        )
-                    )
-                    """
-                ).bindparams(
-                    contains=contains_json,
-                    p_id=d_parent_str,
-                    slug=target,
-                )
-                query = query.filter(disc_filter_sql)
-        elif has_discovered:
-            # No specific slug — just rows that surfaced any candidate
-            # under this parent. We coalesce missing paths to ``[]`` so
-            # ``jsonb_array_length`` always sees an array (it raises on
-            # non-array inputs, but our shape guarantees a list when
-            # the key is present).
-            has_disc_sql = text(
-                f"""
-                jsonb_array_length(
-                    COALESCE(
-                        {scores_jsonb} -> :p_id -> 'discovered_labels',
-                        '[]'::jsonb
-                    )
-                ) > 0
-                """
-            ).bindparams(p_id=d_parent_str)
-            query = query.filter(has_disc_sql)
-
-    # --- Sorting ----------------------------------------------------------
-    # Column-click sorting from the UI. Falls back to ``row_index`` so
-    # paging stays stable when the user clears the sort. We always add a
-    # secondary ``row_index`` tiebreaker so duplicate sort keys (e.g.
-    # many rows with ``status = 'completed'``) keep a deterministic
-    # order across page boundaries — without this, pagination can
-    # double-show or skip rows when Postgres picks a different physical
-    # order on each query.
-    direction_desc = (sort_dir or "asc").strip().lower() == "desc"
-
-    def _apply_direction(column_expr):
-        return column_expr.desc() if direction_desc else column_expr.asc()
-
-    # Whether the caller's ``sort_by`` resolved to a known column. We
-    # use this flag to decide whether ``sort_dir`` is honoured on the
-    # fallback path: unrecognized columns (typos, stale UI state) fall
-    # back to the implicit ``row_index ASC`` default and intentionally
-    # ignore ``sort_dir`` so users don't get a surprise reverse order
-    # from a typo'd column name.
-    sort_recognized = False
-    sort_by_clean = (sort_by or "").strip()
-    primary_sort = None
-    metric_uuid: Optional[UUID] = None
-    if sort_by_clean == "row_index":
-        sort_recognized = True
-        # Falls through to the default ``order_by`` below with
-        # ``primary_sort`` still None — but ``sort_recognized=True``
-        # tells the fallback branch to apply the requested direction.
-    elif sort_by_clean == "conversation_id":
-        sort_recognized = True
-        primary_sort = _apply_direction(CallImportRow.conversation_id)
-    elif sort_by_clean == "status":
-        sort_recognized = True
-        primary_sort = _apply_direction(CallImportEvaluationRow.status)
-    elif sort_by_clean.startswith("metric:"):
-        raw_metric_id = sort_by_clean.split(":", 1)[1].strip()
-        try:
-            metric_uuid = UUID(raw_metric_id)
-        except (TypeError, ValueError):
-            metric_uuid = None
-        if metric_uuid is not None:
-            sort_recognized = True
-            # ``metric_scores`` is JSON-typed but the helper functions
-            # for path extraction differ between Postgres (production)
-            # and SQLite (default test backend). Branch on the active
-            # dialect so we can use the right primitive:
-            #   * Postgres → ``json_extract_path_text(col, key, "value")``
-            #     which returns the value as TEXT for both ``json`` and
-            #     ``jsonb`` columns.
-            #   * SQLite   → ``json_extract(col, '$."<uuid>".value')``
-            #     using JSONPath syntax. ``metric_uuid`` is already
-            #     validated above (``UUID(raw_metric_id)``), so the
-            #     interpolated path is safe from injection.
-            # NULL values (rows where the metric wasn't scored) sort
-            # to the END regardless of direction so un-scored rows
-            # don't crowd the top of an ascending sort.
-            dialect_name = (
-                db.bind.dialect.name if db.bind is not None else "postgresql"
-            )
-            if dialect_name == "sqlite":
-                json_path = f'$."{metric_uuid}".value'
-                path_value = func.json_extract(
-                    CallImportEvaluationRow.metric_scores,
-                    json_path,
-                )
-            else:
-                path_value = func.json_extract_path_text(
-                    CallImportEvaluationRow.metric_scores,
-                    str(metric_uuid),
-                    "value",
-                )
-            primary_sort = (
-                path_value.desc().nullslast()
-                if direction_desc
-                else path_value.asc().nullslast()
-            )
-
-    if primary_sort is not None:
-        query = query.order_by(primary_sort, CallImportRow.row_index.asc())
-    elif sort_recognized:
-        # Explicit ``sort_by=row_index`` request — honour direction.
-        query = query.order_by(_apply_direction(CallImportRow.row_index))
-    else:
-        # No sort requested OR unrecognized column — safe default of
-        # ``row_index ASC``. We deliberately ignore ``sort_dir`` here
-        # so a typo'd / stale ``sort_by`` doesn't quietly invert the
-        # default order.
-        query = query.order_by(CallImportRow.row_index.asc())
-    from app.db_sharding.eval_rows import fetch_evaluation_row_pairs_page
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    def _pair_row_index(
-        pair: Tuple[CallImportEvaluationRow, CallImportRow],
-    ) -> int:
-        return int(pair[1].row_index or 0)
-
-    def _directed_string(value: Optional[str], desc: bool) -> Tuple[int, ...]:
-        text = value or ""
-        if not desc:
-            return (0, *text.encode("utf-8"))
-        return (1, *(-byte for byte in text.encode("utf-8")))
-
-    if sort_by_clean == "conversation_id":
-        def _pair_sort_key(
-            pair: Tuple[CallImportEvaluationRow, CallImportRow],
-        ) -> Tuple[Any, ...]:
-            return (
-                _directed_string(pair[1].conversation_id, direction_desc),
-                _pair_row_index(pair),
-            )
-    elif sort_by_clean == "status":
-        def _pair_sort_key(
-            pair: Tuple[CallImportEvaluationRow, CallImportRow],
-        ) -> Tuple[Any, ...]:
-            return (
-                _directed_string(pair[0].status, direction_desc),
-                _pair_row_index(pair),
-            )
-    elif sort_by_clean.startswith("metric:") and metric_uuid is not None:
-        metric_id_str = str(metric_uuid)
-
-        def _pair_sort_key(
-            pair: Tuple[CallImportEvaluationRow, CallImportRow],
-        ) -> Tuple[Any, ...]:
-            scores = pair[0].metric_scores or {}
-            entry = scores.get(metric_id_str, {})
-            raw_value = entry.get("value") if isinstance(entry, dict) else None
-            null_rank = 1 if raw_value is None else 0
-            return (
-                null_rank,
-                _directed_string(
-                    str(raw_value) if raw_value is not None else None,
-                    direction_desc,
-                ),
-                _pair_row_index(pair),
-            )
-    elif sort_recognized and sort_by_clean == "row_index":
-        def _pair_sort_key(
-            pair: Tuple[CallImportEvaluationRow, CallImportRow],
-        ) -> Tuple[int, ...]:
-            idx = _pair_row_index(pair)
-            return (-idx,) if direction_desc else (idx,)
-    else:
-        def _pair_sort_key(
-            pair: Tuple[CallImportEvaluationRow, CallImportRow],
-        ) -> Tuple[int, ...]:
-            return (_pair_row_index(pair),)
-
-    if is_sharding_enabled():
-        def _build_query(session: Session):
-            return query.with_session(session)
-
-        total, rows = fetch_evaluation_row_pairs_page(
-            db,
-            _build_query,
-            page=page,
-            page_size=page_size,
-            sort_key=_pair_sort_key,
-            bounded_shard_fetch=(
-                not sort_recognized or sort_by_clean == "row_index"
-            ),
-        )
-    else:
-        total = query.count()
-        rows = query.offset((page - 1) * page_size).limit(page_size).all()
-
-    # Row detail shows the transcript for this run's chosen source.
-    items: List[CallImportEvaluationRowResponse] = [
-        _to_evaluation_row_response(eval_row_obj, source_row, eval_row)
-        for eval_row_obj, source_row in rows
-    ]
-
-    return CallImportEvaluationRowListResponse(
-        items=items,
-        total=total,
-        page=page,
-        page_size=page_size,
-    )
-
-
-@router.get(
-    "/{eval_id}/export",
-    operation_id="exportCallImportEvaluationCsv",
-)
-async def export_call_import_evaluation_csv(
-    call_import_id: UUID,
-    eval_id: UUID,
-    format: Literal["csv", "xlsx"] = Query(
-        "csv",
-        description=(
-            "Output format. ``csv`` returns a UTF-8 BOM CSV; ``xlsx`` "
-            "returns a native Excel workbook (single sheet)."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> StreamingResponse:
-    del api_key
-    call_import = _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    selected_metric_ids = _serialize_selected_metric_ids(evaluation.selected_metric_ids)
-    # Include parent metric ids referenced in selected_metric_groups so
-    # the export shows a parent "Chosen Label" column next to its
-    # children's true/false columns.
-    lookup_ids: List[UUID] = list(selected_metric_ids)
-    groups_raw = (
-        evaluation.selected_metric_groups
-        if isinstance(evaluation.selected_metric_groups, dict)
-        else {}
-    )
-    for parent_str in groups_raw.keys():
-        try:
-            pid = UUID(parent_str)
-            if pid not in lookup_ids:
-                lookup_ids.append(pid)
-        except (TypeError, ValueError):
-            continue
-    metrics = _metrics_for_ids(db, organization_id, lookup_ids)
-    metric_names = {str(metric.id): metric.name for metric in metrics}
-    metrics_by_id = {str(metric.id): metric for metric in metrics}
-
-    # Two export-time modes depending on how the batch was uploaded:
-    #
-    #   * Schema-driven (new): ``call_imports.schema_id`` is set,
-    #     ``parameter_mapping`` records which CSV header fed each
-    #     parameter, and ``raw_columns`` on each row is keyed by
-    #     parameter NAME. Export headers are the parameter names.
-    #   * Legacy (pre-schema): ``column_mapping`` / ``extra_columns`` /
-    #     ``custom_column_mapping`` drive the columns and
-    #     ``raw_columns`` is keyed by the original CSV header.
-    #
-    # We bucket entries into ``standard_export_headers`` (raw_columns
-    # key == export header) and ``custom_export`` (export header
-    # differs from the raw_columns key) so the row-projection loop
-    # below stays mode-agnostic.
-    standard_export_headers: List[str] = []
-    custom_export: List[tuple[str, str]] = []  # [(export_header, raw_columns_key)]
-
-    if call_import.schema_id is not None:
-        # Use the live schema parameter list for column ordering. Falls
-        # back to whatever's in ``parameter_mapping`` if the schema was
-        # deleted (defensive - the FK is ON DELETE RESTRICT, but tests
-        # / future cascades may still hit this branch).
-        from app.models.database import CallImportSchema as _ImportSchema
-
-        schema_obj = (
-            db.query(_ImportSchema)
-            .filter(_ImportSchema.id == call_import.schema_id)
-            .first()
-        )
-        if schema_obj is not None:
-            params_sorted = sorted(
-                schema_obj.parameters, key=lambda p: p.ordering or 0
-            )
-            for param in params_sorted:
-                if param.name and param.name not in standard_export_headers:
-                    standard_export_headers.append(param.name)
-        else:
-            for param_name in (call_import.parameter_mapping or {}).keys():
-                if param_name and param_name not in standard_export_headers:
-                    standard_export_headers.append(param_name)
-    else:
-        mapping = call_import.column_mapping or {}
-        mapped_headers = [
-            mapping.get("external_call_id"),
-            mapping.get("transcript"),
-            mapping.get("recording_url"),
-        ]
-        for header in [*mapped_headers, *(call_import.extra_columns or [])]:
-            if (
-                isinstance(header, str)
-                and header
-                and header not in standard_export_headers
-            ):
-                standard_export_headers.append(header)
-
-        custom_mapping = call_import.custom_column_mapping or {}
-        if isinstance(custom_mapping, dict):
-            for name, csv_header in custom_mapping.items():
-                if not isinstance(name, str) or not isinstance(csv_header, str):
-                    continue
-                if not name or not csv_header:
-                    continue
-                if name in standard_export_headers:
-                    continue  # would clobber a real column
-                custom_export.append((name, csv_header))
-
-    if (
-        call_import.source_format == "audio"
-        and "conversation_id" not in standard_export_headers
-    ):
-        standard_export_headers.insert(0, "conversation_id")
-
-    # Build the metric columns: each parent (if any) gets a value column
-    # and (when capture_rationale=true) a "<Parent> - LLM Rationale"
-    # column. The per-child boolean columns are intentionally suppressed
-    # — categorization metrics now collapse to exactly two columns in
-    # the export, mirroring the in-app table.
-    child_ids_in_groups: set[str] = set()
-    for parent_str, child_strs in groups_raw.items():
-        for child_str in child_strs:
-            if isinstance(child_str, str):
-                child_ids_in_groups.add(child_str)
-
-    metric_headers: List[str] = []
-    rationale_headers: Dict[str, str] = {}  # metric_id_str -> rationale column name
-    seen_metric_ids: set[str] = set()
-
-    def _add_metric_column(metric: Metric) -> None:
-        mid_str = str(metric.id)
-        if mid_str in seen_metric_ids:
-            return
-        # Skip any child whose parent is part of this run — the parent
-        # column above already shows the chosen child name as its
-        # value.
-        if mid_str in child_ids_in_groups:
-            return
-        seen_metric_ids.add(mid_str)
-        header = metric_names[mid_str]
-        metric_headers.append(header)
-        if bool(getattr(metric, "capture_rationale", False)):
-            rationale_header = f"{header} - LLM Rationale"
-            metric_headers.append(rationale_header)
-            rationale_headers[mid_str] = rationale_header
-
-    for parent_str in groups_raw.keys():
-        parent = metrics_by_id.get(parent_str)
-        if parent:
-            _add_metric_column(parent)
-        # Children of an in-run parent are deliberately not emitted —
-        # the ``child_ids_in_groups`` guard inside ``_add_metric_column``
-        # is what enforces this. We still iterate the keys above (not
-        # ``.items()``) so the parent-only emission is explicit.
-    # Append anything left over (standalone metrics not in any group, or
-    # legacy runs without ``selected_metric_groups``).
-    for metric in metrics:
-        if metric.selection_mode and not metric.parent_metric_id:
-            continue  # already handled above
-        if str(metric.id) in seen_metric_ids:
-            continue
-        _add_metric_column(metric)
-
-    # Three new fixed columns surface the two transcript fields and the
-    # evaluation's transcript_source as live values pulled from the
-    # ``CallImportRow`` (not from the frozen ``raw_columns`` snapshot).
-    # The user can now compare "what was in the CSV" vs "what the
-    # diarisation worker produced" without round-tripping through the
-    # UI, and downstream tools can verify which transcript the metrics
-    # were computed against.
-    PRODUCTION_TRANSCRIPT_HEADER = "Production Transcript"
-    DIARISED_TRANSCRIPT_HEADER = "Diarised Transcript"
-    EVAL_SOURCE_HEADER = "Evaluated Transcript Source"
-
-    fieldnames = [
-        *standard_export_headers,
-        *[h for h, _ in custom_export],
-        PRODUCTION_TRANSCRIPT_HEADER,
-        DIARISED_TRANSCRIPT_HEADER,
-        EVAL_SOURCE_HEADER,
-        *metric_headers,
-    ]
-
-    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    if is_sharding_enabled():
-        rows = sorted(
-            load_evaluation_row_pairs(db, eval_id),
-            key=lambda pair: int(pair[1].row_index or 0),
-        )
-    else:
-        rows = (
-            db.query(CallImportEvaluationRow, CallImportRow)
-            .join(
-                CallImportRow,
-                CallImportRow.id == CallImportEvaluationRow.call_import_row_id,
-            )
-            .filter(CallImportEvaluationRow.evaluation_id == eval_id)
-            .order_by(CallImportRow.row_index.asc())
-            .all()
-        )
-
-    def _project_rows() -> Iterator[Dict[str, str]]:
-        for eval_row, source_row in rows:
-            row_out: Dict[str, str] = {}
-            raw = (
-                source_row.raw_columns
-                if isinstance(source_row.raw_columns, dict)
-                else {}
-            )
-            for header in standard_export_headers:
-                value = raw.get(header)
-                if value is None and header == "conversation_id":
-                    value = source_row.conversation_id
-                row_out[header] = "" if value is None else str(value)
-            for export_header, csv_header in custom_export:
-                value = raw.get(csv_header)
-                row_out[export_header] = "" if value is None else str(value)
-
-            # Live transcripts pulled from the row, NOT from raw_columns,
-            # so re-diarised values are always reflected in the export.
-            # Both transcript columns are flattened to a single line so the
-            # spreadsheet cell doesn't balloon vertically — the in-app
-            # ``TranscriptView`` still has the DB copy with line breaks
-            # intact for chat-bubble rendering.
-            row_out[PRODUCTION_TRANSCRIPT_HEADER] = _flatten_transcript(
-                source_row.transcript
-            )
-            row_out[DIARISED_TRANSCRIPT_HEADER] = _flatten_transcript(
-                source_row.diarised_transcript
-            )
-            row_out[EVAL_SOURCE_HEADER] = _evaluated_transcript_source_label(
-                evaluation,
-                source_row,
-            )
-
-            scores = (
-                eval_row.metric_scores
-                if isinstance(eval_row.metric_scores, dict)
-                else {}
-            )
-            for metric in metrics:
-                metric_score = (
-                    scores.get(str(metric.id))
-                    if isinstance(scores, dict)
-                    else None
-                )
-                value = (
-                    metric_score.get("value")
-                    if isinstance(metric_score, dict)
-                    else None
-                )
-                # Parent metrics (selection_mode set) render the chosen
-                # child name for single_choice or the ";"-joined list of
-                # true child names for multi_label.
-                if (
-                    metric.selection_mode
-                    and not metric.parent_metric_id
-                    and isinstance(metric_score, dict)
-                ):
-                    if metric.selection_mode == "multi_label":
-                        selected = metric_score.get("selected_child_names")
-                        if isinstance(selected, list):
-                            value = ";".join(str(s) for s in selected)
-                    else:
-                        value = (
-                            metric_score.get("chosen_child_name")
-                            or metric_score.get("value")
-                        )
-                row_out[metric.name] = "" if value is None else str(value)
-                rationale_header = rationale_headers.get(str(metric.id))
-                if rationale_header is not None:
-                    rationale = (
-                        metric_score.get("rationale")
-                        if isinstance(metric_score, dict)
-                        else None
-                    )
-                    row_out[rationale_header] = (
-                        "" if rationale is None else str(rationale)
-                    )
-            yield row_out
-
-    base_filename = f"call-import-{call_import_id}-evaluation-{eval_id}"
-
-    if format == "xlsx":
-        # xlsx is unicode-native (Hindi/Devanagari, emoji, etc.) so the
-        # UTF-8-BOM dance isn't needed here. ``write_only`` mode keeps
-        # peak memory bounded for large evaluations because openpyxl
-        # only buffers the current row.
-        try:
-            from openpyxl import Workbook  # type: ignore
-            from openpyxl.cell import WriteOnlyCell  # type: ignore
-            from openpyxl.styles import Font  # type: ignore
-        except ImportError as exc:  # pragma: no cover - exercised by pyproject lock
-            raise HTTPException(
-                status_code=500,
-                detail=(
-                    "Excel export requires the 'openpyxl' package which is "
-                    "not installed."
-                ),
-            ) from exc
-
-        workbook = Workbook(write_only=True)
-        worksheet = workbook.create_sheet(title="Evaluation")
-
-        bold_font = Font(bold=True)
-        header_cells = []
-        for header in fieldnames:
-            cell = WriteOnlyCell(worksheet, value=header)
-            cell.font = bold_font
-            header_cells.append(cell)
-        worksheet.append(header_cells)
-
-        for row_dict in _project_rows():
-            worksheet.append([row_dict.get(h, "") for h in fieldnames])
-
-        buffer = io.BytesIO()
-        workbook.save(buffer)
-        xlsx_bytes = buffer.getvalue()
-        filename = f"{base_filename}.xlsx"
-        return StreamingResponse(
-            iter([xlsx_bytes]),
-            media_type=(
-                "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
-            ),
-            headers={"Content-Disposition": f'attachment; filename="{filename}"'},
-        )
-
-    output = io.StringIO()
-    writer = csv.DictWriter(output, fieldnames=fieldnames, extrasaction="ignore")
-    writer.writeheader()
-    for row_dict in _project_rows():
-        writer.writerow(row_dict)
-
-    # Excel on Windows defaults to the system ANSI codepage (Windows-1252)
-    # when a CSV has no encoding marker, which turns UTF-8 Hindi/Devanagari
-    # / any non-ASCII text into mojibake (e.g. ``ठीक`` → ``ठीक``).
-    # A UTF-8 BOM tells Excel to switch to UTF-8 decoding and is silently
-    # skipped by every other UTF-8-aware reader (pandas, LibreOffice,
-    # Google Sheets, etc.), so the data round-trips correctly everywhere.
-    csv_text = output.getvalue()
-    # ``utf-8-sig`` adds the UTF-8 BOM so Excel on Windows decodes the file
-    # as UTF-8 instead of the system codepage. We also declare the same
-    # codec in the Content-Type header so well-behaved HTTP clients (incl.
-    # ``httpx`` / ``requests`` in our tests) strip the BOM during decode.
-    csv_bytes = csv_text.encode("utf-8-sig")
-    filename = f"{base_filename}.csv"
-    return StreamingResponse(
-        iter([csv_bytes]),
-        media_type="text/csv; charset=utf-8-sig",
-        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
-    )
-
-
-def _report_filename_slug(value: str) -> str:
-    slug = re.sub(r"[^a-zA-Z0-9]+", "-", value.strip().lower()).strip("-")
-    return slug or "client"
-
-
-def _report_branding_for_import_workspace(
-    db: Session,
-    organization_id: UUID,
-    workspace_id: UUID,
-    *,
-    internal_brand_image_id: Optional[str] = None,
-    external_brand_image_id: Optional[str] = None,
-) -> tuple[dict[str, str] | list[str], Optional[str]]:
-    workspace = (
-        db.query(Workspace)
-        .filter(
-            Workspace.id == workspace_id,
-            Workspace.organization_id == organization_id,
-        )
-        .first()
-    )
-    raw = workspace.report_branding if workspace and isinstance(workspace.report_branding, dict) else {}
-    images = raw.get("images") if isinstance(raw.get("images"), list) else []
-    loaded_images: list[dict[str, str]] = []
-    for item in images:
-        if not isinstance(item, dict) or not item.get("s3_key"):
-            continue
-        content_type = str(item.get("content_type") or "image/png")
-        try:
-            from app.services.storage.s3_service import s3_service
-
-            image_bytes = s3_service.download_file_by_key(str(item["s3_key"]))
-        except Exception as exc:  # noqa: BLE001
-            logger.warning(
-                "Unable to load report branding image for workspace {}: {}",
-                workspace_id,
-                exc,
-            )
-            continue
-        encoded = base64.b64encode(image_bytes).decode("ascii")
-        role = str(item.get("role") or "generic")
-        if role not in {"internal", "external", "generic"}:
-            role = "generic"
-        loaded_images.append(
-            {
-                "id": str(item.get("id") or ""),
-                "role": role,
-                "data_uri": f"data:{content_type};base64,{encoded}",
-            }
-        )
-
-    def _pick(role: str, selected_id: Optional[str]) -> Optional[str]:
-        if selected_id:
-            for loaded in loaded_images:
-                if loaded["id"] == selected_id:
-                    return loaded["data_uri"]
-        for loaded in loaded_images:
-            if loaded["role"] == role:
-                return loaded["data_uri"]
-        return None
-
-    logo_data_uris: dict[str, str] = {}
-    internal_uri = _pick("internal", internal_brand_image_id)
-    external_uri = _pick("external", external_brand_image_id)
-    if internal_uri:
-        logo_data_uris["internal"] = internal_uri
-    if external_uri:
-        logo_data_uris["external"] = external_uri
-    if (
-        not logo_data_uris
-        and not internal_brand_image_id
-        and not external_brand_image_id
-    ):
-        # Backward compatibility for workspaces that only had a generic logo
-        # library before the two-slot report header existed.
-        generic_uris = [
-            loaded["data_uri"]
-            for loaded in loaded_images
-            if loaded.get("data_uri")
-        ]
-        if generic_uris:
-            heading = raw.get("heading") if isinstance(raw.get("heading"), str) else None
-            return generic_uris[:4], heading
-    heading = raw.get("heading") if isinstance(raw.get("heading"), str) else None
-    return logo_data_uris, heading
-
-
-def _display_metrics_for_pdf_report(
-    db: Session,
-    organization_id: UUID,
-    evaluation: CallImportEvaluation,
-) -> list[Metric]:
-    selected_metric_ids = _serialize_selected_metric_ids(evaluation.selected_metric_ids)
-    lookup_ids: List[UUID] = list(selected_metric_ids)
-    groups_raw = (
-        evaluation.selected_metric_groups
-        if isinstance(evaluation.selected_metric_groups, dict)
-        else {}
-    )
-    for parent_str in groups_raw.keys():
-        try:
-            parent_id = UUID(parent_str)
-        except (TypeError, ValueError):
-            continue
-        if parent_id not in lookup_ids:
-            lookup_ids.append(parent_id)
-
-    metrics = _metrics_for_ids(db, organization_id, lookup_ids)
-    child_ids_in_groups: set[str] = set()
-    for child_strs in groups_raw.values():
-        if not isinstance(child_strs, list):
-            continue
-        child_ids_in_groups.update(str(child_id) for child_id in child_strs)
-
-    metrics_by_id = {str(metric.id): metric for metric in metrics}
-    display: list[Metric] = []
-    seen: set[str] = set()
-
-    for parent_str in groups_raw.keys():
-        parent = metrics_by_id.get(str(parent_str))
-        if parent and str(parent.id) not in seen:
-            display.append(parent)
-            seen.add(str(parent.id))
-
-    for metric in metrics:
-        metric_id = str(metric.id)
-        if metric_id in seen or metric_id in child_ids_in_groups:
-            continue
-        if metric.selection_mode and not metric.parent_metric_id:
-            continue
-        display.append(metric)
-        seen.add(metric_id)
-
-    return display
-
-
-def _metrics_for_clustering(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    eval_rows: List[CallImportEvaluationRow],
-) -> List[Metric]:
-    """All enabled quality metrics scored in this run, normalized for clustering.
-
-    Hierarchical children are collapsed to their parent metric so cluster
-    groups render at the category level (e.g. ``AI reveal``) instead of the
-    child label level (e.g. ``Yes`` / ``No``).
-    """
-    aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
-    aggregate_metric_ids: List[UUID] = []
-    for agg in aggregates:
-        if (agg.metric_category or "quality") == "user_insight":
-            continue
-        try:
-            aggregate_metric_ids.append(UUID(agg.metric_id))
-        except (TypeError, ValueError):
-            continue
-    if not aggregate_metric_ids:
-        return []
-
-    aggregate_metrics = _metrics_for_ids(
-        db, evaluation.organization_id, aggregate_metric_ids
-    )
-    by_id = {metric.id: metric for metric in aggregate_metrics}
-
-    normalized_ids: List[UUID] = []
-    seen: set[UUID] = set()
-    for metric_id in aggregate_metric_ids:
-        metric = by_id.get(metric_id)
-        target_id = (
-            metric.parent_metric_id
-            if metric is not None and metric.parent_metric_id
-            else metric_id
-        )
-        if target_id in seen:
-            continue
-        seen.add(target_id)
-        normalized_ids.append(target_id)
-
-    metrics = _metrics_for_ids(db, evaluation.organization_id, normalized_ids)
-    return [
-        metric
-        for metric in metrics
-        if getattr(metric, "enabled", True) and not _metric_is_user_insight(metric)
-    ]
-
-
-def _metric_is_user_insight(metric: Metric) -> bool:
-    if (getattr(metric, "metric_category", "quality") or "quality") == "user_insight":
-        return True
-    text_value = " ".join(
-        str(part or "").lower()
-        for part in (getattr(metric, "name", ""), getattr(metric, "description", ""))
-    )
-    normalized = text_value.replace("-", " ").replace("_", " ")
-    phrases = (
-        "call context",
-        "caller context",
-        "product identification",
-        "out of scope",
-        "identity match",
-        "user identity",
-        "caller identity",
-        "frustration trigger",
-        "video call offer",
-        "video call reception",
-    )
-    return any(phrase in normalized for phrase in phrases)
-
-
-def _evaluation_rows_for_period(
-    db: Session,
-    evaluation_id: UUID,
-) -> list[tuple[CallImportEvaluationRow, CallImportRow]]:
-    return (
-        db.query(CallImportEvaluationRow, CallImportRow)
-        .join(CallImportRow, CallImportRow.id == CallImportEvaluationRow.call_import_row_id)
-        .filter(CallImportEvaluationRow.evaluation_id == evaluation_id)
-        .order_by(CallImportRow.row_index.asc())
-        .all()
-    )
-
-
-def _baseline_candidate_evaluations(
-    db: Session,
-    organization_id: UUID,
-    workspace_id: UUID,
-    current_evaluation: CallImportEvaluation,
-    current_period_start: Optional[date],
-    *,
-    limit: int = 20,
-) -> list[dict[str, Any]]:
-    candidates = (
-        db.query(CallImportEvaluation, CallImport)
-        .join(CallImport, CallImport.id == CallImportEvaluation.call_import_id)
-        .filter(
-            CallImportEvaluation.organization_id == organization_id,
-            CallImport.workspace_id == workspace_id,
-            CallImportEvaluation.id != current_evaluation.id,
-            CallImportEvaluation.status == "completed",
-            CallImportEvaluation.completed_rows > 0,
-        )
-        .order_by(desc(CallImportEvaluation.created_at))
-        .limit(limit * 3)
-        .all()
-    )
-    items: list[dict[str, Any]] = []
-    for candidate_eval, candidate_import in candidates:
-        rows = _evaluation_rows_for_period(db, candidate_eval.id)
-        period_start, period_end, period_label, period_display = _report_period_from_rows(rows)
-        if current_period_start and period_start and period_start >= current_period_start:
-            continue
-        dataset = (
-            (candidate_import.dataset or "").strip()
-            or (candidate_import.original_filename or candidate_import.filename or "").strip()
-            or "Unknown dataset"
-        )
-        evaluation_name = (
-            (candidate_eval.name or "").strip()
-            or str(candidate_eval.id)[:8]
-        )
-        items.append(
-            {
-                "evaluation_id": str(candidate_eval.id),
-                "name": evaluation_name,
-                "dataset": dataset,
-                "period_label": period_label,
-                "period_start": period_start,
-                "period_end": period_end,
-                "period_display": period_display,
-                "completed_rows": int(candidate_eval.completed_rows or 0),
-                "created_at": candidate_eval.created_at,
-                "is_default": False,
-            }
-        )
-        if len(items) >= limit:
-            break
-    items.sort(
-        key=lambda item: (
-            item["period_start"] or date.min,
-            item["created_at"] or datetime.min.replace(tzinfo=timezone.utc),
-        ),
-        reverse=True,
-    )
-    if items:
-        items[0]["is_default"] = True
-    return items
-
-
-def _resolve_baseline_evaluation(
-    db: Session,
-    organization_id: UUID,
-    workspace_id: UUID,
-    current_evaluation: CallImportEvaluation,
-    current_period_start: Optional[date],
-    baseline_evaluation_id: Optional[str],
-) -> Optional[CallImportEvaluation]:
-    candidates = _baseline_candidate_evaluations(
-        db,
-        organization_id,
-        workspace_id,
-        current_evaluation,
-        current_period_start,
-    )
-    allowed_ids = {item["evaluation_id"] for item in candidates}
-    if baseline_evaluation_id:
-        baseline_id = str(baseline_evaluation_id).strip()
-        if baseline_id not in allowed_ids:
-            raise HTTPException(
-                status_code=400,
-                detail="Selected baseline evaluation is not a valid prior run for this report.",
-            )
-        return (
-            db.query(CallImportEvaluation)
-            .filter(
-                CallImportEvaluation.id == UUID(baseline_id),
-                CallImportEvaluation.organization_id == organization_id,
-            )
-            .first()
-        )
-    if not candidates:
-        return None
-    default_id = candidates[0]["evaluation_id"]
-    return (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == UUID(default_id),
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-
-
-def _benchmark_context_for_evaluation(
-    db: Session,
-    baseline_evaluation: Optional[CallImportEvaluation],
-) -> Optional[dict[str, str]]:
-    if baseline_evaluation is None:
-        return None
-    baseline_import = (
-        db.query(CallImport)
-        .filter(CallImport.id == baseline_evaluation.call_import_id)
-        .first()
-    )
-    rows = _evaluation_rows_for_period(db, baseline_evaluation.id)
-    period_start, _period_end, period_label, _period_display = _report_period_from_rows(rows)
-    dataset = (
-        (baseline_import.dataset or "").strip()
-        if baseline_import and baseline_import.dataset
-        else None
-    )
-    filename = (
-        (baseline_import.original_filename or baseline_import.filename or "").strip()
-        if baseline_import
-        else None
-    )
-    evaluation_label = (
-        (baseline_evaluation.name or "").strip()
-        if baseline_evaluation.name
-        else str(baseline_evaluation.id)[:8]
-    )
-    period = period_label or (
-        period_start.isoformat() if period_start else "previous report"
-    )
-    return {
-        "dataset": dataset or filename or "Unknown dataset",
-        "evaluation": evaluation_label,
-        "evaluation_id": str(baseline_evaluation.id),
-        "period": period,
-    }
-
-
-def _period_deltas_from_evaluation(
-    db: Session,
-    baseline_evaluation: CallImportEvaluation,
-    current_metric_aggregates: list[dict[str, Any]],
-    current_evaluation: CallImportEvaluation,
-    current_eval_rows: List[CallImportEvaluationRow],
-) -> dict[str, dict[str, str]]:
-    baseline_rows = _evaluation_rows_for_period(db, baseline_evaluation.id)
-    baseline_eval_rows = [eval_row for eval_row, _source_row in baseline_rows]
-    baseline_aggregate_models = _compute_metric_aggregates(
-        db,
-        baseline_evaluation,
-        baseline_eval_rows,
-    )
-    baseline_metric_aggregates = [
-        _aggregate_to_dict(aggregate) for aggregate in baseline_aggregate_models
-    ]
-    _metrics, _aggs, policies, _source, _child_map = _clustering_context(
-        db, current_evaluation, current_eval_rows
-    )
-    metric_by_id = {str(m.id): m for m in _metrics}
-    current_by_id = {
-        str(item.get("metric_id")): item for item in current_metric_aggregates
-    }
-    previous_by_id = {
-        str(item.get("metric_id")): item
-        for item in baseline_metric_aggregates
-        if isinstance(item, dict)
-    }
-    deltas: dict[str, dict[str, str]] = {}
-    for metric_id, current in current_by_id.items():
-        metric = metric_by_id.get(metric_id)
-        policy = policies.get(metric_id)
-        previous_raw = previous_by_id.get(metric_id)
-        if metric is None or policy is None:
-            deltas[metric_id] = {
-                "label": "No previous-week baseline",
-                "detail": "No comparable prior report snapshot was found.",
-            }
-            continue
-        current_pct = failure_rate_percent_from_rows(
-            current_eval_rows, metric, policy
-        )
-        previous_pct = failure_rate_percent_from_rows(
-            baseline_eval_rows, metric, policy
-        )
-        if current_pct is None or previous_pct is None:
-            current_pct = current_pct or _aggregate_primary_percent(current, policy)
-            previous_pct = (
-                previous_pct or _aggregate_primary_percent(previous_raw, policy)
-                if previous_raw
-                else None
-            )
-        if current_pct is None or previous_pct is None:
-            deltas[metric_id] = {
-                "label": "No previous-week baseline",
-                "detail": "No comparable prior report snapshot was found.",
-            }
-            continue
-        delta = current_pct - previous_pct
-        sign = "+" if delta >= 0 else ""
-        deltas[metric_id] = {
-            "label": f"{sign}{delta:.1f} pp",
-            "detail": (
-                f"Current report {current_pct:.1f}% vs previous report "
-                f"{previous_pct:.1f}%"
-            ),
-        }
-    return deltas
-
-
-_DELTA_EXPLANATION_SYSTEM_PROMPT = (
-    "You are a senior conversation-analytics reviewer. You will receive "
-    "week-over-week metric failure-rate deltas plus reconciled failure "
-    "cluster context per metric.\n\n"
-    "Return STRICT JSON only:\n"
-    "{\n"
-    '  "explanations": {"<metric_id>": "<1-2 sentence explanation of why the delta likely occurred>"}\n'
-    "}\n\n"
-    "Constraints:\n"
-    "- Only include metrics supplied in the prompt.\n"
-    "- Cluster labels are generated independently each run and are NOT stable "
-    "IDs. Never compare an unmatched current label to 0% baseline.\n"
-    "- Use matched_theme_shifts for label-aligned comparisons, "
-    "gap_label_shifts for structural shifts, and new_themes_current_period "
-    "for themes that emerged without a baseline match.\n"
-    "- If reconciliation is uncertain, explain using the numeric delta and "
-    "gap_label_shifts only.\n"
-    "- Keep each explanation to 1-2 short sentences (~220 chars).\n"
-    "- Vendor-safe, factual language; no markdown."
-)
-
-
-def _period_delta_explanation_cache_key(
-    baseline_evaluation_id: UUID,
-    *,
-    completed_rows: int,
-    baseline_completed_rows: int,
-) -> str:
-    return (
-        f"{baseline_evaluation_id}:{completed_rows}:"
-        f"{baseline_completed_rows}:reconciled-v2"
-    )
-
-
-def _normalize_cluster_label(label: str) -> str:
-    return re.sub(r"[^a-z0-9]+", " ", (label or "").lower()).strip()
-
-
-_CLUSTER_LABEL_STOPWORDS = frozenset(
-    {
-        "a",
-        "an",
-        "the",
-        "and",
-        "or",
-        "during",
-        "while",
-        "with",
-        "for",
-        "from",
-        "into",
-        "general",
-        "user",
-        "bot",
-        "agent",
-    }
-)
-
-
-def _cluster_label_tokens(label: str) -> set[str]:
-    return {
-        token
-        for token in _normalize_cluster_label(label).split()
-        if token and token not in _CLUSTER_LABEL_STOPWORDS and len(token) > 2
-    }
-
-
-def _cluster_label_similarity(left: str, right: str) -> float:
-    tokens_left = _cluster_label_tokens(left)
-    tokens_right = _cluster_label_tokens(right)
-    if not tokens_left or not tokens_right:
-        return 0.0
-    intersection = tokens_left & tokens_right
-    if not intersection:
-        return 0.0
-    union = tokens_left | tokens_right
-    jaccard = len(intersection) / len(union)
-    smaller = tokens_left if len(tokens_left) <= len(tokens_right) else tokens_right
-    overlap_ratio = len(intersection) / len(smaller)
-    return max(jaccard, overlap_ratio * 0.85)
-
-
-def _group_clusters_by_gap_label(
-    clusters: list[dict[str, Any]],
-) -> dict[str, list[dict[str, Any]]]:
-    grouped: dict[str, list[dict[str, Any]]] = {}
-    for cluster in clusters:
-        gap_label = str(cluster.get("gap_label") or "UNKNOWN")
-        grouped.setdefault(gap_label, []).append(cluster)
-    return grouped
-
-
-def _append_matched_cluster_pair(
-    matched: list[dict[str, Any]],
-    current: dict[str, Any],
-    baseline: dict[str, Any],
-    *,
-    match_confidence: float,
-    match_method: str,
-) -> None:
-    matched.append(
-        {
-            "current_label": current.get("label"),
-            "baseline_label": baseline.get("label"),
-            "gap_label": current.get("gap_label") or baseline.get("gap_label"),
-            "current_share_pct": current.get("share_pct"),
-            "baseline_share_pct": baseline.get("share_pct"),
-            "share_delta_pp": round(
-                float(current.get("share_pct") or 0.0)
-                - float(baseline.get("share_pct") or 0.0),
-                1,
-            ),
-            "match_confidence": round(match_confidence, 2),
-            "match_method": match_method,
-        }
-    )
-
-
-def _aggregate_share_by_gap_label(
-    clusters: list[dict[str, Any]],
-) -> dict[str, float]:
-    totals: dict[str, float] = {}
-    for cluster in clusters:
-        gap_label = str(cluster.get("gap_label") or "UNKNOWN")
-        totals[gap_label] = totals.get(gap_label, 0.0) + float(
-            cluster.get("share_pct") or 0.0
-        )
-    return {gap: round(share, 1) for gap, share in totals.items()}
-
-
-def _reconcile_cluster_periods(
-    current_clusters: list[dict[str, Any]],
-    baseline_clusters: list[dict[str, Any]],
-    *,
-    similarity_threshold: float = 0.35,
-) -> dict[str, Any]:
-    """Align independently-generated cluster labels before delta explanation."""
-    matched: list[dict[str, Any]] = []
-    current_unmatched = list(current_clusters)
-    remaining_baseline = list(baseline_clusters)
-
-    current_by_gap = _group_clusters_by_gap_label(current_unmatched)
-    baseline_by_gap = _group_clusters_by_gap_label(remaining_baseline)
-    for gap_label in list(current_by_gap):
-        current_group = current_by_gap.get(gap_label) or []
-        baseline_group = baseline_by_gap.get(gap_label) or []
-        if len(current_group) != 1 or len(baseline_group) != 1:
-            continue
-        current = current_group[0]
-        baseline = baseline_group[0]
-        _append_matched_cluster_pair(
-            matched,
-            current,
-            baseline,
-            match_confidence=0.75,
-            match_method="single_cluster_per_gap_label",
-        )
-        current_unmatched.remove(current)
-        remaining_baseline.remove(baseline)
-        current_by_gap[gap_label] = []
-        baseline_by_gap[gap_label] = []
-
-    for current in list(current_unmatched):
-        best_idx: Optional[int] = None
-        best_score = 0.0
-        for idx, baseline in enumerate(remaining_baseline):
-            score = _cluster_label_similarity(
-                str(current.get("label") or ""),
-                str(baseline.get("label") or ""),
-            )
-            if current.get("gap_label") == baseline.get("gap_label"):
-                score += 0.1
-            if score > best_score:
-                best_score = score
-                best_idx = idx
-
-        if best_idx is not None and best_score >= similarity_threshold:
-            baseline = remaining_baseline.pop(best_idx)
-            _append_matched_cluster_pair(
-                matched,
-                current,
-                baseline,
-                match_confidence=best_score,
-                match_method="label_similarity",
-            )
-
-    matched_current_labels = {
-        str(item.get("current_label") or "") for item in matched
-    }
-    matched_baseline_labels = {
-        str(item.get("baseline_label") or "") for item in matched
-    }
-    current_unmatched = [
-        cluster
-        for cluster in current_clusters
-        if str(cluster.get("label") or "") not in matched_current_labels
-    ]
-    remaining_baseline = [
-        cluster
-        for cluster in baseline_clusters
-        if str(cluster.get("label") or "") not in matched_baseline_labels
-    ]
-
-    new_themes = [
-        {
-            "label": cluster.get("label"),
-            "gap_label": cluster.get("gap_label"),
-            "share_pct": cluster.get("share_pct"),
-            "note": "New theme in current period (no close baseline match).",
-        }
-        for cluster in current_unmatched
-    ]
-
-    retired_themes = [
-        {
-            "label": baseline.get("label"),
-            "gap_label": baseline.get("gap_label"),
-            "share_pct": baseline.get("share_pct"),
-            "note": "Theme present in baseline only (retired or renamed).",
-        }
-        for baseline in remaining_baseline
-    ]
-
-    current_gap = _aggregate_share_by_gap_label(current_clusters)
-    baseline_gap = _aggregate_share_by_gap_label(baseline_clusters)
-    gap_label_shifts: dict[str, dict[str, float]] = {}
-    for gap_label in set(current_gap) | set(baseline_gap):
-        current_share = current_gap.get(gap_label, 0.0)
-        baseline_share = baseline_gap.get(gap_label, 0.0)
-        if abs(current_share - baseline_share) >= 0.5:
-            gap_label_shifts[gap_label] = {
-                "current_share_pct": current_share,
-                "baseline_share_pct": baseline_share,
-                "share_delta_pp": round(current_share - baseline_share, 1),
-            }
-
-    return {
-        "matched_theme_shifts": matched,
-        "new_themes_current_period": new_themes,
-        "retired_themes_baseline_period": retired_themes,
-        "gap_label_shifts": gap_label_shifts,
-        "reconciliation_note": (
-            "Cluster labels are generated independently each run and may "
-            "rename the same failure mode. Do not treat unmatched current "
-            "labels as 0% in the baseline period."
-        ),
-    }
-
-
-def _load_period_delta_explanations_cache(
-    evaluation: CallImportEvaluation,
-    cache_key: str,
-) -> Optional[dict[str, str]]:
-    raw = getattr(evaluation, "period_delta_explanations", None)
-    if not isinstance(raw, dict):
-        return None
-    entry = raw.get(cache_key)
-    if not isinstance(entry, dict):
-        return None
-    explanations_raw = entry.get("explanations")
-    if not isinstance(explanations_raw, dict):
-        return None
-    return {
-        str(metric_id): str(why).strip()
-        for metric_id, why in explanations_raw.items()
-        if str(metric_id).strip() and isinstance(why, str) and why.strip()
-    }
-
-
-def _save_period_delta_explanations_cache(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    cache_key: str,
-    explanations: dict[str, str],
-) -> None:
-    raw = evaluation.period_delta_explanations
-    if not isinstance(raw, dict):
-        raw = {}
-    updated = dict(raw)
-    updated[cache_key] = {
-        "explanations": explanations,
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-    }
-    evaluation.period_delta_explanations = updated
-    flag_modified(evaluation, "period_delta_explanations")
-    db.commit()
-
-
-def _cluster_summary_for_metric(
-    state: Optional[EvaluationMetricClustersState],
-    metric_id: str,
-) -> list[dict[str, Any]]:
-    if state is None or state.status != "completed":
-        return []
-    for group in state.groups:
-        if str(group.metric_id) != metric_id:
-            continue
-        return [
-            {
-                "label": cluster.label,
-                "gap_label": cluster.gap_label,
-                "share_pct": round(cluster.share_pct, 1),
-                "count": cluster.count,
-            }
-            for cluster in group.clusters[:5]
-        ]
-    return []
-
-
-def _merge_delta_why(
-    raw_deltas: dict[str, dict[str, str]],
-    explanations: dict[str, str],
-) -> dict[str, dict[str, str]]:
-    if not explanations:
-        return raw_deltas
-    merged: dict[str, dict[str, str]] = {}
-    for metric_id, delta in raw_deltas.items():
-        updated = dict(delta)
-        why = explanations.get(metric_id)
-        if why:
-            updated["why"] = why
-        merged[metric_id] = updated
-    return merged
-
-
-def _explain_period_deltas(
-    db: Session,
-    organization_id: UUID,
-    evaluation: CallImportEvaluation,
-    baseline_evaluation: CallImportEvaluation,
-    raw_deltas: dict[str, dict[str, str]],
-    *,
-    min_delta_pp: float = 0.5,
-) -> dict[str, dict[str, str]]:
-    """Attach ``why`` explanations to period deltas using cached LLM output."""
-    if not raw_deltas:
-        return raw_deltas
-
-    cache_key = _period_delta_explanation_cache_key(
-        baseline_evaluation.id,
-        completed_rows=evaluation.completed_rows,
-        baseline_completed_rows=baseline_evaluation.completed_rows,
-    )
-    cached = _load_period_delta_explanations_cache(evaluation, cache_key)
-    if cached is not None:
-        return _merge_delta_why(raw_deltas, cached)
-
-    current_clusters = _metric_clusters_payload(evaluation)
-    baseline_clusters = _metric_clusters_payload(baseline_evaluation)
-    metrics_for_prompt: list[dict[str, Any]] = []
-    for metric_id, delta in raw_deltas.items():
-        label = delta.get("label") or ""
-        if "No previous-week baseline" in label:
-            continue
-        match = re.search(r"([+-]?\d+(?:\.\d+)?)\s*pp", label)
-        if match and abs(float(match.group(1))) < min_delta_pp:
-            continue
-        current_summary = _cluster_summary_for_metric(current_clusters, metric_id)
-        baseline_summary = _cluster_summary_for_metric(baseline_clusters, metric_id)
-        if not current_summary and not baseline_summary:
-            continue
-        cluster_reconciliation = _reconcile_cluster_periods(
-            current_summary,
-            baseline_summary,
-        )
-        metrics_for_prompt.append(
-            {
-                "metric_id": metric_id,
-                "delta_label": label,
-                "delta_detail": delta.get("detail") or "",
-                "cluster_reconciliation": cluster_reconciliation,
-            }
-        )
-
-    if not metrics_for_prompt:
-        return raw_deltas
-
-    provider_hint: Optional[str] = None
-    model_hint: Optional[str] = None
-    tldr_raw = evaluation.tldr_summary
-    if isinstance(tldr_raw, dict):
-        if isinstance(tldr_raw.get("provider"), str):
-            provider_hint = tldr_raw["provider"]
-        if isinstance(tldr_raw.get("model"), str):
-            model_hint = tldr_raw["model"]
-
-    from app.services.ai.llm_resolver import get_llm_provider_and_model
-    from app.services.call_import_user_insights import _call_llm, _parse_json_object
-
-    provider_enum, model_str = get_llm_provider_and_model(
-        organization_id, db, provider_hint, model_hint
-    )
-    try:
-        text = _call_llm(
-            db,
-            organization_id,
-            provider_enum,
-            model_str,
-            [
-                {"role": "system", "content": _DELTA_EXPLANATION_SYSTEM_PROMPT},
-                {
-                    "role": "user",
-                    "content": json.dumps(
-                        {"metrics": metrics_for_prompt},
-                        ensure_ascii=False,
-                        default=str,
-                    ),
-                },
-            ],
-            temperature=0.3,
-            max_tokens=900,
-        )
-    except Exception as exc:
-        logger.warning("[PeriodDeltaExplain] LLM call failed: {}", exc)
-        return raw_deltas
-
-    parsed = _parse_json_object(text)
-    explanations_raw = parsed.get("explanations")
-    explanations: dict[str, str] = {}
-    if isinstance(explanations_raw, dict):
-        for metric_id, why in explanations_raw.items():
-            if isinstance(why, str) and why.strip():
-                explanations[str(metric_id)] = why.strip()
-
-    if explanations:
-        _save_period_delta_explanations_cache(
-            db, evaluation, cache_key, explanations
-        )
-    return _merge_delta_why(raw_deltas, explanations)
-
-
-def _period_deltas_with_explanations(
-    db: Session,
-    organization_id: UUID,
-    evaluation: CallImportEvaluation,
-    baseline_evaluation: CallImportEvaluation,
-    raw_deltas: dict[str, dict[str, str]],
-) -> dict[str, dict[str, str]]:
-    return _explain_period_deltas(
-        db,
-        organization_id,
-        evaluation,
-        baseline_evaluation,
-        raw_deltas,
-    )
-
-
-def _benchmark_context_for_snapshot(
-    db: Session,
-    previous_snapshot: Optional[CallImportEvaluationReportSnapshot],
-) -> Optional[dict[str, str]]:
-    if previous_snapshot is None:
-        return None
-    previous_import = (
-        db.query(CallImport)
-        .filter(CallImport.id == previous_snapshot.call_import_id)
-        .first()
-    )
-    previous_eval = (
-        db.query(CallImportEvaluation)
-        .filter(CallImportEvaluation.id == previous_snapshot.evaluation_id)
-        .first()
-    )
-    dataset = (
-        (previous_import.dataset or "").strip()
-        if previous_import and previous_import.dataset
-        else None
-    )
-    filename = (
-        (previous_import.original_filename or previous_import.filename or "").strip()
-        if previous_import
-        else None
-    )
-    evaluation_label = (
-        (previous_eval.name or "").strip()
-        if previous_eval and previous_eval.name
-        else str(previous_snapshot.evaluation_id)[:8]
-    )
-    period = previous_snapshot.period_label or (
-        previous_snapshot.period_start.isoformat()
-        if previous_snapshot.period_start
-        else "previous report"
-    )
-    return {
-        "dataset": dataset or filename or "Unknown dataset",
-        "evaluation": evaluation_label,
-        "evaluation_id": str(previous_snapshot.evaluation_id),
-        "period": period,
-    }
-
-
-def _clamp_prose_to_sentences(
-    text: str,
-    *,
-    max_sentences: int = 3,
-    max_chars: int = 300,
-) -> str:
-    """Keep concise audit/TLDR prose within sentence and character limits."""
-    cleaned = (text or "").strip()
-    if not cleaned:
-        return cleaned
-    cleaned = re.sub(r"\s*\n+\s*", " ", cleaned).strip()
-    sentences = [
-        sentence.strip()
-        for sentence in re.split(r"(?<=[.!?])\s+", cleaned)
-        if sentence.strip()
-    ]
-    if sentences:
-        result = " ".join(sentences[:max_sentences]).strip()
-    else:
-        result = cleaned
-    if len(result) > max_chars:
-        trimmed = result[: max_chars - 3].rsplit(" ", 1)[0].rstrip(".,;:")
-        result = f"{trimmed}..." if trimmed else result[:max_chars]
-    return result
-
-
-def _audit_summary_text_from_tldr(
-    summary: Optional[EvaluationTldrSummary],
-) -> Optional[str]:
-    if summary is None:
-        return None
-    narrative = _clamp_prose_to_sentences(summary.narrative.strip())
-    return narrative or None
-
-
-def _metric_insights_from_tldr(
-    summary: Optional[EvaluationTldrSummary],
-) -> dict[str, str]:
-    if summary is None:
-        return {}
-    return {
-        str(metric_id): insight.strip()
-        for metric_id, insight in summary.metric_insights.items()
-        if str(metric_id).strip() and insight.strip()
-    }
-
-
-def _report_period_from_rows(
-    rows: list[tuple[CallImportEvaluationRow, CallImportRow]],
-) -> tuple[Optional[date], Optional[date], Optional[str], str]:
-    dates = [
-        source_row.recording_date
-        for eval_row, source_row in rows
-        if eval_row.status == "completed" and source_row.recording_date
-    ]
-    if not dates:
-        return None, None, None, "Not specified"
-    start = min(dates)
-    end = max(dates)
-    week_anchor = max(dates)
-    week_start = week_anchor - timedelta(days=week_anchor.weekday())
-    week_end = week_start + timedelta(days=6)
-    iso_year, iso_week, _ = week_anchor.isocalendar()
-    label = f"{iso_year}-W{iso_week:02d}"
-    if week_start.year == week_end.year:
-        week_range = f"{week_start.strftime('%b %d')}–{week_end.strftime('%b %d, %Y')}"
-    else:
-        week_range = (
-            f"{week_start.strftime('%b %d, %Y')}–{week_end.strftime('%b %d, %Y')}"
-        )
-    display = f"W{iso_week:02d} · {week_range}"
-    return start, end, label, display
-
-
-def _aggregate_to_dict(aggregate: CallImportMetricAggregate) -> dict[str, Any]:
-    if hasattr(aggregate, "model_dump"):
-        return aggregate.model_dump(mode="json")
-    return aggregate.dict()
-
-
-def _aggregate_primary_percent(
-    raw: dict[str, Any],
-    policy: Optional[MetricFailurePolicy] = None,
-) -> Optional[float]:
-    return aggregate_primary_percent(raw, policy)
-
-
-def _child_names_by_parent(
-    db: Session,
-    organization_id: UUID,
-    parent_metric_ids: Sequence[UUID],
-) -> Dict[str, List[str]]:
-    if not parent_metric_ids:
-        return {}
-    children = (
-        db.query(Metric)
-        .filter(
-            Metric.organization_id == organization_id,
-            Metric.parent_metric_id.in_(list(parent_metric_ids)),
-        )
-        .all()
-    )
-    out: Dict[str, List[str]] = {}
-    for child in children:
-        pid = str(child.parent_metric_id)
-        out.setdefault(pid, []).append(child.name)
-    return out
-
-
-def _clustering_context(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    eval_rows: List[CallImportEvaluationRow],
-) -> Tuple[
-    List[Metric],
-    List[CallImportMetricAggregate],
-    Dict[str, MetricFailurePolicy],
-    Literal["inferred", "user"],
-    Dict[str, List[str]],
-]:
-    metrics = _metrics_for_clustering(db, evaluation, eval_rows)
-    aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
-    parent_ids = [
-        m.id
-        for m in metrics
-        if getattr(m, "selection_mode", None) and not getattr(m, "parent_metric_id", None)
-    ]
-    child_names_by_parent = _child_names_by_parent(
-        db, evaluation.organization_id, parent_ids
-    )
-    policies, source = effective_policies(
-        evaluation,
-        metrics,
-        aggregates,
-        child_names_by_parent=child_names_by_parent,
-    )
-    return metrics, aggregates, policies, source, child_names_by_parent
-
-
-def _period_deltas_from_aggregates(
-    previous_metric_aggregates: list[dict[str, Any]],
-    current_metric_aggregates: list[dict[str, Any]],
-    policies: Optional[Dict[str, MetricFailurePolicy]] = None,
-) -> dict[str, dict[str, str]]:
-    current_by_id = {str(item.get("metric_id")): item for item in current_metric_aggregates}
-    previous_by_id = {
-        str(item.get("metric_id")): item
-        for item in previous_metric_aggregates
-        if isinstance(item, dict)
-    }
-    deltas: dict[str, dict[str, str]] = {}
-    for metric_id, current in current_by_id.items():
-        previous_raw = previous_by_id.get(metric_id)
-        policy = (policies or {}).get(metric_id)
-        current_pct = _aggregate_primary_percent(current, policy)
-        previous_pct = (
-            _aggregate_primary_percent(previous_raw, policy)
-            if previous_raw
-            else None
-        )
-        if current_pct is None or previous_pct is None:
-            deltas[metric_id] = {
-                "label": "No previous-week baseline",
-                "detail": "No comparable prior report snapshot was found.",
-            }
-            continue
-        delta = current_pct - previous_pct
-        sign = "+" if delta >= 0 else ""
-        deltas[metric_id] = {
-            "label": f"{sign}{delta:.1f} pp",
-            "detail": f"Current report {current_pct:.1f}% vs previous report {previous_pct:.1f}%",
-        }
-    return deltas
-
-
-def _period_deltas_from_snapshot(
-    previous: Optional[CallImportEvaluationReportSnapshot],
-    current_metric_aggregates: list[dict[str, Any]],
-) -> dict[str, dict[str, str]]:
-    previous_items = (
-        previous.metric_aggregates
-        if previous and isinstance(previous.metric_aggregates, list)
-        else []
-    )
-    return _period_deltas_from_aggregates(previous_items, current_metric_aggregates)
-
-
-def _sample_evidence_for_metrics(
-    rows: list[tuple[CallImportEvaluationRow, CallImportRow]],
-    metric_ids: set[str],
-) -> dict[str, list[dict[str, str]]]:
-    samples: dict[str, list[dict[str, str]]] = {metric_id: [] for metric_id in metric_ids}
-    for eval_row, source_row in rows:
-        scores = eval_row.metric_scores if isinstance(eval_row.metric_scores, dict) else {}
-        for metric_id in metric_ids:
-            if len(samples.get(metric_id, [])) >= 4:
-                continue
-            score = scores.get(metric_id)
-            if not isinstance(score, dict):
-                continue
-            rationale = score.get("rationale")
-            transcript = source_row.diarised_transcript or source_row.transcript or ""
-            quote = rationale if isinstance(rationale, str) and rationale.strip() else transcript[:350]
-            if quote:
-                samples.setdefault(metric_id, []).append(
-                    {
-                        "conversation_id": source_row.conversation_id,
-                        "quote": str(quote).strip()[:500],
-                    }
-                )
-    return samples
-
-
-def _fallback_report_narrative(
-    insight_aggregates: list[dict[str, Any]],
-    evidence_samples: dict[str, list[dict[str, str]]],
-) -> dict[str, Any]:
-    observations: dict[str, str] = {}
-    evidence: dict[str, dict[str, str]] = {}
-    design_notes: list[str] = []
-    for aggregate in insight_aggregates:
-        metric_id = str(aggregate.get("metric_id") or "")
-        name = str(aggregate.get("metric_name") or "Insight")
-        counts = aggregate.get("value_counts") if isinstance(aggregate.get("value_counts"), list) else []
-        if counts:
-            top = counts[0]
-            total = int(aggregate.get("count") or 0) or sum(
-                int(item.get("count") or 0) for item in counts if isinstance(item, dict)
-            )
-            pct = (int(top.get("count") or 0) / total) * 100 if total else 0
-            observations[metric_id] = (
-                f"{top.get('label')} is the dominant {name.lower()} category at {pct:.1f}% of classified calls."
-            )
-            design_notes.append(
-                f"{name}: {top.get('label')} is the largest segment and should be reviewed for workflow or prompt improvements."
-            )
-        sample = (evidence_samples.get(metric_id) or [{}])[0]
-        if sample:
-            evidence[metric_id] = sample
-    return {
-        "observations": observations,
-        "evidence": evidence,
-        "design_notes": design_notes[:7],
-        "audit_summary": None,
-    }
-
-
-def _generate_report_narrative(
-    db: Session,
-    organization_id: UUID,
-    *,
-    metric_aggregates: list[dict[str, Any]],
-    insight_aggregates: list[dict[str, Any]],
-    period_delta_by_metric: dict[str, dict[str, str]],
-    evidence_samples: dict[str, list[dict[str, str]]],
-    report_config: dict[str, Any],
-) -> dict[str, Any]:
-    if not insight_aggregates:
-        return {"observations": {}, "evidence": {}, "design_notes": [], "audit_summary": None}
-    try:
-        from app.services.ai.llm_resolver import get_llm_provider_and_model
-        from app.services.ai.llm_service import llm_service
-
-        provider_enum, model_str = get_llm_provider_and_model(organization_id, db, None, None)
-        prompt = (
-            "You are writing a vendor-safe external call quality audit report. "
-            "Return strict JSON with keys observations (object keyed by metric_id), "
-            "evidence (object keyed by metric_id with conversation_id and quote), "
-            "design_notes (array of concise numbered-note strings), and audit_summary (string). "
-            "Use only the supplied aggregates and evidence samples.\n\n"
-            + json.dumps(
-                {
-                    "metric_aggregates": metric_aggregates[:30],
-                    "insight_aggregates": insight_aggregates,
-                    "period_deltas": period_delta_by_metric,
-                    "evidence_samples": evidence_samples,
-                    "report_config": report_config,
-                },
-                default=str,
-            )
-        )
-        llm_result = llm_service.generate_response(
-            messages=[
-                {"role": "system", "content": "Return JSON only. No markdown."},
-                {"role": "user", "content": prompt},
-            ],
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            temperature=0.2,
-            max_tokens=1200,
-        )
-        parsed = json.loads(str(llm_result.content or "{}"))
-        if isinstance(parsed, dict):
-            fallback = _fallback_report_narrative(insight_aggregates, evidence_samples)
-            return {
-                "observations": parsed.get("observations") or fallback["observations"],
-                "evidence": parsed.get("evidence") or fallback["evidence"],
-                "design_notes": parsed.get("design_notes") or fallback["design_notes"],
-                "audit_summary": parsed.get("audit_summary") or fallback["audit_summary"],
-            }
-    except Exception as exc:  # noqa: BLE001
-        logger.warning("Report narrative LLM generation fell back to deterministic text: {}", exc)
-    return _fallback_report_narrative(insight_aggregates, evidence_samples)
-
-
-@router.get(
-    "/{eval_id}/baseline-candidates",
-    response_model=CallImportEvaluationBaselineCandidatesResponse,
-    operation_id="listCallImportEvaluationBaselineCandidates",
-)
-async def list_call_import_evaluation_baseline_candidates(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationBaselineCandidatesResponse:
-    del api_key
-    call_import = _require_import(db, call_import_id, organization_id)
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    rows = _evaluation_rows_for_period(db, evaluation.id)
-    period_start, _period_end, _derived_period_label, _period_display = _report_period_from_rows(
-        rows
-    )
-    candidates = _baseline_candidate_evaluations(
-        db,
-        organization_id,
-        call_import.workspace_id,
-        evaluation,
-        period_start,
-    )
-    default_evaluation_id = next(
-        (item["evaluation_id"] for item in candidates if item.get("is_default")),
-        None,
-    )
-    return CallImportEvaluationBaselineCandidatesResponse(
-        items=[CallImportEvaluationBaselineCandidate(**item) for item in candidates],
-        default_evaluation_id=default_evaluation_id,
-    )
-
-
-@router.post(
-    "/{eval_id}/pdf-report",
-    operation_id="generateCallImportEvaluationPdfReport",
-    dependencies=[Depends(require_call_import_capability(REPORTS_GENERATE))],
-)
-async def generate_call_import_evaluation_pdf_report(
-    call_import_id: UUID,
-    eval_id: UUID,
-    payload: CallImportEvaluationPdfReportRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> StreamingResponse:
-    del api_key
-    call_import = _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    is_internal = payload.report_type == "internal"
-    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    if is_sharding_enabled():
-        rows = sorted(
-            load_evaluation_row_pairs(db, eval_id),
-            key=lambda pair: int(pair[1].row_index or 0),
-        )
-    else:
-        rows = (
-            db.query(CallImportEvaluationRow, CallImportRow)
-            .join(
-                CallImportRow,
-                CallImportRow.id == CallImportEvaluationRow.call_import_row_id,
-            )
-            .filter(CallImportEvaluationRow.evaluation_id == eval_id)
-            .order_by(CallImportRow.row_index.asc())
-            .all()
-        )
-    report_config = payload.report_config if isinstance(payload.report_config, dict) else {}
-    metrics = _display_metrics_for_pdf_report(db, organization_id, evaluation)
-    configured_quality_ids = {
-        str(item)
-        for item in report_config.get("quality_metric_ids", [])
-        if item
-    }
-    configured_insight_ids = {
-        str(item.get("metric_id") or item)
-        for item in report_config.get("insights", [])
-        if item
-    }
-    if configured_quality_ids or configured_insight_ids:
-        allowed_ids = configured_quality_ids | configured_insight_ids
-        metrics = [metric for metric in metrics if str(metric.id) in allowed_ids]
-
-    eval_rows = [eval_row for eval_row, _source_row in rows]
-    aggregate_models = _compute_metric_aggregates(db, evaluation, eval_rows)
-    selected_report_metric_ids = {str(metric.id) for metric in metrics}
-    aggregate_dicts = [
-        _aggregate_to_dict(aggregate)
-        for aggregate in aggregate_models
-        if aggregate.metric_id in selected_report_metric_ids
-    ]
-    insight_metric_ids = {
-        str(metric.id)
-        for metric in metrics
-        if _metric_is_user_insight(metric)
-    }
-    metric_aggregates = [
-        item for item in aggregate_dicts if str(item.get("metric_id")) not in insight_metric_ids
-    ]
-    insight_aggregates = [
-        item for item in aggregate_dicts if str(item.get("metric_id")) in insight_metric_ids
-    ]
-    period_start, period_end, derived_period_label, period_display = _report_period_from_rows(rows)
-    period_label = (payload.period_label or derived_period_label or "").strip() or None
-    include_period_delta = (
-        payload.include_period_delta or payload.include_weekly_delta
-    )
-    previous_snapshot = None
-    period_delta_by_metric: dict[str, dict[str, str]] = {}
-    baseline_evaluation: Optional[CallImportEvaluation] = None
-    if include_period_delta and period_start:
-        baseline_evaluation = _resolve_baseline_evaluation(
-            db,
-            organization_id,
-            call_import.workspace_id,
-            evaluation,
-            period_start,
-            payload.baseline_evaluation_id,
-        )
-        if baseline_evaluation:
-            period_delta_by_metric = _period_deltas_from_evaluation(
-                db,
-                baseline_evaluation,
-                metric_aggregates,
-                evaluation,
-                [eval_row for eval_row, _ in rows],
-            )
-            period_delta_by_metric = _period_deltas_with_explanations(
-                db,
-                organization_id,
-                evaluation,
-                baseline_evaluation,
-                period_delta_by_metric,
-            )
-    benchmark_context = _benchmark_context_for_evaluation(db, baseline_evaluation)
-    evidence_samples = _sample_evidence_for_metrics(rows, insight_metric_ids)
-    cached_tldr_summary = _tldr_summary_payload(evaluation)
-    cached_user_insights = _user_insights_payload(evaluation)
-    cached_metric_clusters = _metric_clusters_payload(evaluation)
-    cached_prompt_improvements = _prompt_improvements_payload(evaluation)
-    generated_insights_for_pdf = _selected_generated_user_insights(
-        cached_user_insights,
-        report_config,
-    )
-    metric_clusters_for_pdf = _selected_metric_clusters_for_pdf(
-        cached_metric_clusters,
-        report_config,
-    )
-    prompt_improvements_for_pdf = _selected_prompt_improvements_for_pdf(
-        cached_prompt_improvements,
-        report_config,
-    )
-    narrative = _generate_report_narrative(
-        db,
-        organization_id,
-        metric_aggregates=metric_aggregates,
-        insight_aggregates=insight_aggregates if is_internal else [],
-        period_delta_by_metric=period_delta_by_metric,
-        evidence_samples=evidence_samples if is_internal else {},
-        report_config=report_config,
-    )
-
-    generated_at = datetime.now(timezone.utc)
-    branding_images, custom_heading = _report_branding_for_import_workspace(
-        db,
-        organization_id,
-        call_import.workspace_id,
-        internal_brand_image_id=payload.internal_brand_image_id,
-        external_brand_image_id=payload.external_brand_image_id,
-    )
-    eval_row_list = [eval_row for eval_row, _ in rows]
-    pdf_aggregates = _compute_metric_aggregates(db, evaluation, eval_row_list)
-    pdf_parent_ids = [
-        m.id
-        for m in metrics
-        if getattr(m, "selection_mode", None)
-        and not getattr(m, "parent_metric_id", None)
-    ]
-    pdf_child_map = _child_names_by_parent(
-        db, evaluation.organization_id, pdf_parent_ids
-    )
-    failure_policies_for_pdf, _fp_source = effective_policies(
-        evaluation,
-        metrics,
-        pdf_aggregates,
-        child_names_by_parent=pdf_child_map,
-    )
-    try:
-        pdf_started = datetime.now(timezone.utc)
-        pdf_bytes = await asyncio.to_thread(
-            call_import_evaluation_pdf_report_service.render_pdf,
-            vendor_name=payload.vendor_name,
-            call_import=call_import,
-            evaluation=evaluation,
-            metrics=metrics,
-            rows=rows,
-            failure_policies=failure_policies_for_pdf,
-            generated_at=generated_at,
-            internal=is_internal,
-            logo_data_uris=branding_images,
-            custom_heading=custom_heading,
-            include_weekly_delta=include_period_delta,
-            period_delta_by_metric=period_delta_by_metric,
-            use_case=payload.use_case,
-            period_display=period_display,
-            total_metric_count=db.query(Metric)
-            .filter(Metric.organization_id == organization_id, Metric.enabled.is_(True))
-            .count(),
-            report_config=report_config,
-            narrative=narrative,
-            audit_summary=_audit_summary_text_from_tldr(cached_tldr_summary),
-            metric_insights=_metric_insights_from_tldr(cached_tldr_summary),
-            benchmark_context=benchmark_context,
-            generated_user_insights=generated_insights_for_pdf,
-            user_insights_overview=(
-                cached_user_insights.overview if cached_user_insights else None
-            ),
-            metric_clusters=metric_clusters_for_pdf,
-            metric_clusters_overview=(
-                cached_metric_clusters.overview if cached_metric_clusters else None
-            ),
-            prompt_improvements=prompt_improvements_for_pdf,
-            platform_base_url=payload.platform_base_url,
-        )
-        logger.info(
-            "PDF report render finished in {:.1f}s for evaluation {}",
-            (datetime.now(timezone.utc) - pdf_started).total_seconds(),
-            eval_id,
-        )
-    except Exception as exc:  # noqa: BLE001
-        logger.exception(
-            "Failed to generate PDF report for call import {} evaluation {}",
-            call_import_id,
-            eval_id,
-        )
-        raise HTTPException(
-            status_code=500,
-            detail=f"Failed to generate PDF report: {exc}",
-        ) from exc
-
-    snapshot = CallImportEvaluationReportSnapshot(
-        evaluation_id=evaluation.id,
-        call_import_id=call_import.id,
-        organization_id=organization_id,
-        workspace_id=call_import.workspace_id,
-        period_label=period_label,
-        period_start=period_start,
-        period_end=period_end,
-        report_config=report_config,
-        selected_metric_ids=[str(metric.id) for metric in metrics],
-        metric_aggregates=metric_aggregates,
-        insight_aggregates=insight_aggregates,
-        narrative=narrative,
-        total_calls=evaluation.total_rows,
-        selected_metric_count=len(metrics),
-        total_metric_count=db.query(Metric)
-        .filter(Metric.organization_id == organization_id, Metric.enabled.is_(True))
-        .count(),
-    )
-    db.add(snapshot)
-    db.commit()
-
-    filename = (
-        f"{_report_filename_slug(payload.vendor_name)}-"
-        f"{payload.report_type}-quality-metric-audit-{eval_id}.pdf"
-    )
-    return StreamingResponse(
-        iter([pdf_bytes]),
-        media_type="application/pdf",
-        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
-    )
-
-
-@router.patch(
-    "/{eval_id}",
-    response_model=CallImportEvaluationResponse,
-    operation_id="updateCallImportEvaluation",
-)
-async def update_call_import_evaluation(
-    call_import_id: UUID,
-    eval_id: UUID,
-    payload: CallImportEvaluationUpdate,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationResponse:
-    """Edit metadata on an existing evaluation run (currently just ``name``)."""
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    row = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not row:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    # Treat unset vs explicit ``None`` differently: unset = leave alone,
-    # explicit ``None`` or empty string = clear the name.
-    payload_data = payload.model_dump(exclude_unset=True)
-    if "name" in payload_data:
-        row.name = _normalize_name(payload_data["name"])
-
-    db.commit()
-    db.refresh(row)
-    return _serialize_eval(db, row)
-
-
-def _revoke_pending_tasks(evaluation: CallImportEvaluation) -> None:
-    """Best-effort cancel of any in-flight Celery tasks for an evaluation."""
-
-    if not evaluation.celery_group_id and not any(
-        r.celery_task_id for r in evaluation.row_results
-    ):
-        return
-    try:
-        from app.workers.celery_app import celery_app
-
-        pending_task_ids = [
-            eval_row.celery_task_id
-            for eval_row in evaluation.row_results
-            if eval_row.celery_task_id
-            and eval_row.status in {"pending", "running"}
-        ]
-        if pending_task_ids:
-            celery_app.control.revoke(pending_task_ids, terminate=False)
-    except Exception:
-        # Best effort — DB delete remains the source of truth.
-        pass
-
-
-# ---------------------------------------------------------------------------
-# User-initiated cancel for in-flight evaluation rows
-# ---------------------------------------------------------------------------
-#
-# Evaluation rows can sit in ``running`` for many minutes when the underlying
-# LLM / audio metric call is slow or wedged (the worker carries an 8 min
-# soft / 10 min hard time limit). Without a cancel affordance the operator's
-# only recourse is to wait for Celery's time limit to fire — or to manually
-# mutate the DB. These helpers + the two endpoints below give the UI a
-# first-class "Abort" button mirroring the diarisation cancel pattern at
-# ``app.api.v1.routes.call_imports`` (``_apply_diarisation_cancel`` etc.).
-#
-# Why ``terminate=True``: the legacy ``_revoke_pending_tasks`` above uses
-# ``terminate=False`` because it's called from delete-flow paths where the
-# task may simply not get to run (a worker pulls it off the queue and drops
-# it). For a user-initiated cancel we want SIGTERM to interrupt the worker
-# mid-LLM/audio call so the in-flight HTTP request actually aborts.
-# ``terminate=True`` routes the signal to the executing process; we spell
-# ``signal="SIGTERM"`` out for clarity even though it's the default.
-
-# Sentinel error message stamped on cancelled rows. Read by the eval worker's
-# ``_was_cancelled_externally`` guard (see
-# :mod:`app.workers.tasks.evaluate_call_import_row`) so a worker that's already
-# past its slowest operation can't overwrite the cancelled state with its own
-# terminal status. Touching either copy means touching both.
-EVAL_CANCELLED_BY_USER_ERROR: str = "Evaluation cancelled by user"
-
-
-def _cancellable_eval_states() -> Tuple[str, ...]:
-    """States that an evaluation row can be cancelled from.
-
-    Kept as a tiny helper so adding a future ``"queued"`` / ``"retrying"``
-    state only needs one edit.
-    """
-    return ("pending", "running")
-
-
-def _revoke_eval_task(eval_row: CallImportEvaluationRow) -> None:
-    """Best-effort revoke of a single eval row's Celery task.
-
-    Always swallows control-plane exceptions — Celery's control bus is
-    inherently best-effort and a missed revoke is not catastrophic
-    because the DB row is already flipped to ``failed`` by the caller
-    before this runs (so the UI immediately reflects the cancel; if
-    the task happens to finish anyway, the worker's finaliser skips
-    over the row via :data:`EVAL_CANCELLED_BY_USER_ERROR`).
-    """
-    task_id = (eval_row.celery_task_id or "").strip()
-    if not task_id:
-        return
-    try:
-        from app.workers.celery_app import celery_app
-
-        celery_app.control.revoke(
-            task_id, terminate=True, signal="SIGTERM"
-        )
-        logger.info(
-            "Revoked evaluation task {} for eval row {}",
-            task_id,
-            eval_row.id,
-        )
-    except Exception as exc:  # noqa: BLE001 — revoke is best-effort
-        logger.warning(
-            "Failed to revoke evaluation task {} for eval row {}: {}",
-            task_id,
-            eval_row.id,
-            exc,
-        )
-
-
-def _apply_evaluation_cancel(
-    eval_rows: List[CallImportEvaluationRow],
-) -> Tuple[int, int]:
-    """Cancel every cancellable row in ``eval_rows``.
-
-    Returns ``(cancelled, skipped)`` so the caller can build a typed
-    response without re-querying the DB. The caller is responsible for
-    ``db.commit()`` after this returns — we deliberately don't commit
-    here so a batch endpoint can flush all rows in one transaction.
-    """
-    cancellable_states = _cancellable_eval_states()
-    cancelled = 0
-    skipped = 0
-    now = datetime.now(timezone.utc)
-    for eval_row in eval_rows:
-        if (eval_row.status or "").lower() not in cancellable_states:
-            skipped += 1
-            continue
-        # Flip the row state BEFORE we revoke so the UI's next poll
-        # already shows the cancel, even if Celery's control plane is
-        # slow to ack.
-        eval_row.status = "failed"
-        eval_row.error_message = EVAL_CANCELLED_BY_USER_ERROR
-        eval_row.finished_at = now
-        _revoke_eval_task(eval_row)
-        # Drop the task id so a follow-up retry (or a stale poll) can't
-        # accidentally re-revoke or get confused.
-        eval_row.celery_task_id = None
-        cancelled += 1
-    return cancelled, skipped
-
-
-def _claim_evaluation_bulk_operation(
-    evaluation_id: UUID,
-    operation: str,
-) -> None:
-    """Reserve the run for a single bulk worker pass; 409 if one is active."""
-    from app.services.call_imports.evaluation_bulk_op import (
-        get_evaluation_bulk_operation,
-        try_set_evaluation_bulk_operation,
-    )
-
-    if try_set_evaluation_bulk_operation(evaluation_id, operation):  # type: ignore[arg-type]
-        return
-    existing = get_evaluation_bulk_operation(evaluation_id) or operation
-    raise HTTPException(
-        status_code=409,
-        detail=(
-            f"A bulk {existing.replace('_', ' ')} operation is already in "
-            "progress for this evaluation. Wait for it to finish before "
-            "starting another action."
-        ),
-    )
-
-
-def _require_no_evaluation_bulk_operation(evaluation_id: UUID) -> None:
-    from app.services.call_imports.evaluation_bulk_op import (
-        get_evaluation_bulk_operation,
-    )
-
-    existing = get_evaluation_bulk_operation(evaluation_id)
-    if existing:
-        raise HTTPException(
-            status_code=409,
-            detail=(
-                f"A bulk {existing.replace('_', ' ')} operation is already in "
-                "progress for this evaluation. Wait for it to finish before "
-                "starting another action."
-            ),
-        )
-
-
-@router.post(
-    "/{eval_id}/cancel",
-    response_model=CallImportEvaluationBulkActionResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="cancelCallImportEvaluation",
-)
-async def cancel_call_import_evaluation(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationBulkActionResponse:
-    """Abort all in-flight (or queued) rows in a single evaluation run.
-
-    Idempotent: calling on a run whose rows are already terminal returns
-    ``target_count=0`` with 202 so the UI can fire this from an
-    "Abort" button without having to pre-check the state.
-
-    Heavy row resets and Celery revokes run in a background worker so
-    large batches do not block the API thread.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    from app.services.call_imports.bulk_ops import count_evaluation_cancel_targets
-
-    target_count = count_evaluation_cancel_targets(db, eval_id, mode="abort")
-    if target_count == 0:
-        return CallImportEvaluationBulkActionResponse(
-            accepted=True,
-            target_count=0,
-            evaluation_id=eval_id,
-        )
-
-    _claim_evaluation_bulk_operation(eval_id, "abort")
-    evaluation.status = "cancelled"
-    db.commit()
-
-    from app.workers.tasks.call_import_bulk_ops import (
-        cancel_call_import_evaluation_task,
-    )
-
-    cancel_call_import_evaluation_task.delay(str(eval_id), mode="abort")
-    return CallImportEvaluationBulkActionResponse(
-        accepted=True,
-        target_count=target_count,
-        evaluation_id=eval_id,
-    )
-
-
-@router.post(
-    "/{eval_id}/force-fail-pending",
-    response_model=CallImportEvaluationBulkActionResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="forceFailCallImportEvaluationPending",
-)
-async def force_fail_pending_call_import_evaluation_rows(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationBulkActionResponse:
-    """Force-fail only rows currently in ``pending`` for a single run.
-
-    This is narrower than :func:`cancel_call_import_evaluation`: it leaves
-    ``running`` rows untouched so operators can clear permanently queued rows
-    without interrupting in-flight evaluations.
-
-    Row updates run in a background worker so large batches do not block
-    the API thread.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    from app.services.call_imports.bulk_ops import count_evaluation_cancel_targets
-
-    target_count = count_evaluation_cancel_targets(
-        db, eval_id, mode="force_fail_pending"
-    )
-    if target_count == 0:
-        return CallImportEvaluationBulkActionResponse(
-            accepted=True,
-            target_count=0,
-            evaluation_id=eval_id,
-        )
-
-    _claim_evaluation_bulk_operation(eval_id, "force_fail_pending")
-
-    from app.workers.tasks.call_import_bulk_ops import (
-        cancel_call_import_evaluation_task,
-    )
-
-    cancel_call_import_evaluation_task.delay(
-        str(eval_id), mode="force_fail_pending"
-    )
-    return CallImportEvaluationBulkActionResponse(
-        accepted=True,
-        target_count=target_count,
-        evaluation_id=eval_id,
-    )
-
-
-@router.post(
-    "/{eval_id}/rows/{eval_row_id}/cancel",
-    response_model=CallImportEvaluationRowResponse,
-    status_code=status.HTTP_200_OK,
-    operation_id="cancelCallImportEvaluationRow",
-)
-async def cancel_call_import_evaluation_row(
-    call_import_id: UUID,
-    eval_id: UUID,
-    eval_row_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationRowResponse:
-    """Abort an in-flight (or queued) evaluation for a single row.
-
-    Idempotent: calling on a row that's already terminal (``completed``
-    / ``failed``) returns the row unchanged with a 200 so the UI can
-    wire this to a "Stop" button without having to pre-check the
-    state. Updates the parent run's rollup so its counters reflect
-    the cancel immediately.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    _require_no_evaluation_bulk_operation(eval_id)
-
-    from app.db_sharding.eval_rows import evaluation_row_session
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    if is_sharding_enabled():
-        try:
-            with evaluation_row_session(eval_row_id) as (
-                row_db,
-                _catalog_db,
-                eval_row,
-                source_row,
-                _shard_id,
-            ):
-                if eval_row.evaluation_id != eval_id:
-                    raise HTTPException(
-                        status_code=404,
-                        detail="Evaluation row not found in this run",
-                    )
-                _apply_evaluation_cancel([eval_row])
-                row_db.commit()
-                _rollup_evaluation_status(evaluation, db)
-                db.commit()
-                row_db.refresh(eval_row)
-                return _to_evaluation_row_response(eval_row, source_row, evaluation)
-        except LookupError as exc:
-            raise HTTPException(
-                status_code=404, detail="Evaluation row not found in this run"
-            ) from exc
-
-    eval_row = (
-        db.query(CallImportEvaluationRow)
-        .filter(
-            CallImportEvaluationRow.id == eval_row_id,
-            CallImportEvaluationRow.evaluation_id == eval_id,
-        )
-        .first()
-    )
-    if not eval_row:
-        raise HTTPException(
-            status_code=404, detail="Evaluation row not found in this run"
-        )
-
-    _apply_evaluation_cancel([eval_row])
-    db.flush()
-    _rollup_evaluation_status(evaluation, db)
-    db.commit()
-    db.refresh(eval_row)
-
-    source_row = (
-        db.query(CallImportRow)
-        .filter(CallImportRow.id == eval_row.call_import_row_id)
-        .first()
-    )
-
-    return _to_evaluation_row_response(eval_row, source_row, evaluation)
-
-
-@router.delete(
-    "/{eval_id}",
-    status_code=status.HTTP_204_NO_CONTENT,
-    operation_id="deleteCallImportEvaluation",
-)
-async def delete_call_import_evaluation(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Response:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    row = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not row:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    _revoke_pending_tasks(row)
-
-    db.delete(row)
-    db.commit()
-    return Response(status_code=status.HTTP_204_NO_CONTENT)
-
-
-@router.post(
-    "/bulk-delete",
-    status_code=status.HTTP_200_OK,
-    operation_id="bulkDeleteCallImportEvaluations",
-)
-async def bulk_delete_call_import_evaluations(
-    call_import_id: UUID,
-    payload: CallImportEvaluationBulkDelete,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Dict[str, int]:
-    """Delete multiple evaluation runs scoped to one call import.
-
-    Mirrors :func:`delete_call_import_evaluation` but in bulk so the UI
-    can clear out a multi-select. Unknown ids (already deleted, or
-    belonging to a different org/import) are silently skipped — the
-    response just reports how many actually went away.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    if not payload.evaluation_ids:
-        return {"deleted": 0}
-
-    rows = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id.in_(payload.evaluation_ids),
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .all()
-    )
-    deleted = 0
-    for row in rows:
-        _revoke_pending_tasks(row)
-        db.delete(row)
-        deleted += 1
-    db.commit()
-    return {"deleted": deleted}
-
-
-# ---------------------------------------------------------------------------
-# Aggregation: turns per-row metric scores into histograms / value counts.
-#
-# Designed to be cheap enough to call on every page load: we read each
-# evaluation row once, bucket numeric values into a fixed 10-bin
-# histogram, and tally the top categorical values. Scaling concerns
-# (millions of rows) are deferred — at that point we'd push this into a
-# Postgres aggregate query, but for typical CSV imports (<10k rows) the
-# Python pass is fast enough and dramatically simpler.
-# ---------------------------------------------------------------------------
-
-
-_HISTOGRAM_BUCKETS = 10
-_TOP_VALUE_COUNTS = 10
-
-
-def _coerce_numeric(value: Any) -> Optional[float]:
-    """Return ``value`` as ``float`` when it's numeric; ``None`` otherwise."""
-    if isinstance(value, bool):
-        # Booleans are ints in Python; treat them as categorical so
-        # pass/fail metrics show up in value_counts instead of becoming
-        # a degenerate {0,1} histogram.
-        return None
-    if isinstance(value, (int, float)) and math.isfinite(value):
-        return float(value)
-    if isinstance(value, str):
-        try:
-            f = float(value)
-            if math.isfinite(f):
-                return f
-        except ValueError:
-            return None
-    return None
-
-
-def _coerce_category(value: Any) -> Optional[str]:
-    """Render ``value`` as a label suitable for a value_counts bucket."""
-    if value is None:
-        return None
-    if isinstance(value, bool):
-        return "true" if value else "false"
-    if isinstance(value, (int, float)):
-        return str(value)
-    if isinstance(value, str):
-        text = value.strip()
-        return text or None
-    # Lists / dicts: stringify so they still group sensibly without
-    # exploding the cardinality (worst case: everything is "[…]" once).
-    return str(value)
-
-
-def _build_histogram(
-    values: List[float],
-) -> List[CallImportMetricHistogramBucket]:
-    """Fixed-bin histogram over ``values``; returns [] for <2 values."""
-    if len(values) < 2:
-        return []
-    lo = min(values)
-    hi = max(values)
-    if lo == hi:
-        # All values identical — render a single bucket so the UI shows a
-        # spike rather than empty space.
-        return [
-            CallImportMetricHistogramBucket(x0=lo, x1=hi, count=len(values))
-        ]
-    width = (hi - lo) / _HISTOGRAM_BUCKETS
-    buckets: List[List[float]] = [[] for _ in range(_HISTOGRAM_BUCKETS)]
-    for v in values:
-        # Right-edge inclusive on the last bucket so ``hi`` doesn't fall
-        # off into a non-existent bucket index.
-        idx = int((v - lo) / width)
-        if idx >= _HISTOGRAM_BUCKETS:
-            idx = _HISTOGRAM_BUCKETS - 1
-        buckets[idx].append(v)
-    return [
-        CallImportMetricHistogramBucket(
-            x0=lo + i * width,
-            x1=lo + (i + 1) * width,
-            count=len(bucket),
-        )
-        for i, bucket in enumerate(buckets)
-    ]
-
-
-def _percentile(values: List[float], pct: float) -> Optional[float]:
-    """Linear-interpolated percentile compatible with NumPy default."""
-    if not values:
-        return None
-    sorted_vals = sorted(values)
-    if len(sorted_vals) == 1:
-        return sorted_vals[0]
-    rank = (pct / 100.0) * (len(sorted_vals) - 1)
-    lo = int(math.floor(rank))
-    hi = int(math.ceil(rank))
-    if lo == hi:
-        return sorted_vals[lo]
-    frac = rank - lo
-    return sorted_vals[lo] + (sorted_vals[hi] - sorted_vals[lo]) * frac
-
-
-def _compute_metric_aggregates(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    eval_rows: List[CallImportEvaluationRow],
-) -> List[CallImportMetricAggregate]:
-    """Collapse per-row ``metric_scores`` into one aggregate per metric.
-
-    Selected metrics are read fresh from the DB so the response always
-    surfaces the current ``metric.name`` / ``metric_type`` even when a
-    metric was renamed after the run finished.
-    """
-
-    selected_ids = _serialize_selected_metric_ids(evaluation.selected_metric_ids)
-    # Include parent metrics from selected_metric_groups so they appear
-    # alongside their children in the aggregate response. Use ``getattr``
-    # with a default so the helper still works for callers that pass
-    # lightweight objects (tests, in-memory shims) that don't carry the
-    # attribute at all.
-    groups_raw_candidate = getattr(evaluation, "selected_metric_groups", None)
-    groups_raw = (
-        groups_raw_candidate if isinstance(groups_raw_candidate, dict) else {}
-    )
-    for parent_str in groups_raw.keys():
-        try:
-            pid = UUID(parent_str)
-            if pid not in selected_ids:
-                selected_ids.append(pid)
-        except (TypeError, ValueError):
-            continue
-
-    metrics = _metrics_for_ids(db, evaluation.organization_id, selected_ids)
-    metric_meta: Dict[str, Metric] = {str(m.id): m for m in metrics}
-
-    # Default to selected metrics, but also include any metric ids that
-    # surface in row scores even if missing from the metric registry —
-    # otherwise renaming/deleting a metric mid-run would silently drop
-    # results from the chart.
-    discovered_ids: List[str] = list(metric_meta.keys())
-    for row in eval_rows:
-        scores = row.metric_scores if isinstance(row.metric_scores, dict) else {}
-        for metric_id_str in scores.keys():
-            if metric_id_str not in metric_meta and metric_id_str not in discovered_ids:
-                discovered_ids.append(metric_id_str)
-
-    results: List[CallImportMetricAggregate] = []
-
-    for metric_id_str in discovered_ids:
-        meta = metric_meta.get(metric_id_str)
-        numeric_values: List[float] = []
-        category_counts: Dict[str, int] = {}
-        # For multi-label parents we still need to know how many rows
-        # were scored (each row votes for >=1 label) so the n-badge in
-        # the UI shows "n=50" instead of the misleading "n=208" sum.
-        multi_label_rows_scored = 0
-        # Unordered pair tally for the co-occurrence heatmap. Keys are
-        # ``(label_a, label_b)`` with ``a < b`` so we never double-count
-        # the same unordered pair. Only populated for multi-label
-        # parents — every other metric leaves this empty.
-        multi_label_pair_counts: Dict[Tuple[str, str], int] = {}
-        skipped = 0
-        errored = 0
-        observed_metric_type: Optional[str] = None
-        observed_name: Optional[str] = None
-
-        # ``meta`` is a real ``Metric`` row in production, but tests
-        # frequently pass a lightweight stub. Pull the two attributes
-        # we need via ``getattr`` so a stub that only sets ``id`` /
-        # ``name`` / ``metric_type`` doesn't blow up here.
-        is_multi_label_parent = bool(
-            meta
-            and getattr(meta, "selection_mode", None) == "multi_label"
-            and not getattr(meta, "parent_metric_id", None)
-        )
-
-        for row in eval_rows:
-            scores = (
-                row.metric_scores
-                if isinstance(row.metric_scores, dict)
-                else {}
-            )
-            entry = scores.get(metric_id_str)
-            if not isinstance(entry, dict):
-                continue
-            if entry.get("metric_name"):
-                observed_name = entry.get("metric_name")
-            if entry.get("type"):
-                observed_metric_type = entry.get("type")
-            if entry.get("skipped"):
-                skipped += 1
-                continue
-            if entry.get("error"):
-                errored += 1
-                continue
-
-            # Multi-label parents store a comma-joined value that
-            # isn't useful as a single category; instead tally each
-            # selected child individually so the chart shows per-label
-            # counts that mirror the children's own boolean histograms.
-            if is_multi_label_parent:
-                selected = entry.get("selected_child_names")
-                if isinstance(selected, list) and selected:
-                    multi_label_rows_scored += 1
-                    cleaned: List[str] = []
-                    for label in selected:
-                        text_label = str(label).strip() or None
-                        if text_label:
-                            cleaned.append(text_label)
-                            category_counts[text_label] = (
-                                category_counts.get(text_label, 0) + 1
-                            )
-                    # Emit one increment per unordered pair of distinct
-                    # labels that fired together on this row. ``cleaned``
-                    # is deduplicated first because the LLM occasionally
-                    # repeats a label inside ``selected_child_names``.
-                    distinct = sorted(set(cleaned))
-                    for i in range(len(distinct)):
-                        for j in range(i + 1, len(distinct)):
-                            pair = (distinct[i], distinct[j])
-                            multi_label_pair_counts[pair] = (
-                                multi_label_pair_counts.get(pair, 0) + 1
-                            )
-                continue
-
-            value = entry.get("value")
-            numeric = _coerce_numeric(value)
-            if numeric is not None:
-                numeric_values.append(numeric)
-                continue
-            category = _coerce_category(value)
-            if category is not None:
-                category_counts[category] = category_counts.get(category, 0) + 1
-
-        # ``count`` is "rows scored". For numeric / single-choice
-        # metrics that's the same as ``len(numeric) + sum(categories)``
-        # because each scored row contributes exactly one observation.
-        # Multi-label parents however contribute one observation per
-        # selected child, so summing ``category_counts`` over-counts —
-        # we tracked rows-scored separately above and use it here.
-        rows_scored = (
-            multi_label_rows_scored
-            if is_multi_label_parent
-            else len(numeric_values) + sum(category_counts.values())
-        )
-
-        # Build numeric stats first, then categorical (both can coexist).
-        agg = CallImportMetricAggregate(
-            metric_id=metric_id_str,
-            metric_name=(
-                (meta.name if meta else observed_name) or "Unknown metric"
-            ),
-            metric_type=(
-                meta.metric_type if meta else observed_metric_type
-            ),
-            metric_category=(
-                "user_insight"
-                if meta is not None and _metric_is_user_insight(meta)
-                else "quality"
-            )
-            or "quality",
-            is_multi_label_parent=is_multi_label_parent,
-            count=rows_scored,
-            skipped_count=skipped,
-            error_count=errored,
-        )
-        if numeric_values:
-            agg.mean = float(statistics.fmean(numeric_values))
-            agg.median = float(statistics.median(numeric_values))
-            agg.min = min(numeric_values)
-            agg.max = max(numeric_values)
-            agg.stddev = (
-                float(statistics.pstdev(numeric_values))
-                if len(numeric_values) > 1
-                else 0.0
-            )
-            agg.p25 = _percentile(numeric_values, 25)
-            agg.p75 = _percentile(numeric_values, 75)
-            agg.p95 = _percentile(numeric_values, 95)
-            agg.histogram_buckets = _build_histogram(numeric_values)
-        if category_counts:
-            sorted_counts = sorted(
-                category_counts.items(), key=lambda kv: kv[1], reverse=True
-            )
-            agg.value_counts = [
-                CallImportMetricValueCount(label=label, count=count)
-                for label, count in sorted_counts[:_TOP_VALUE_COUNTS]
-            ]
-            # Restrict the heatmap to pairs of labels we actually
-            # rendered above so the frontend never has to match
-            # against truncated/missing rows. Sorted desc by pair
-            # count to keep the most informative cells in the
-            # response when ``_TOP_VALUE_COUNTS`` clipped the matrix.
-            if is_multi_label_parent and multi_label_pair_counts:
-                kept_labels = {
-                    label for label, _ in sorted_counts[:_TOP_VALUE_COUNTS]
-                }
-                pair_items = [
-                    (a, b, count)
-                    for (a, b), count in multi_label_pair_counts.items()
-                    if a in kept_labels and b in kept_labels
-                ]
-                pair_items.sort(key=lambda t: t[2], reverse=True)
-                agg.co_occurrence = [
-                    CallImportMetricLabelPair(a=a, b=b, count=count)
-                    for a, b, count in pair_items
-                ]
-
-        results.append(agg)
-
-    # Sort so each parent metric immediately precedes its children.
-    # The Visualizations grid renders metrics top-to-bottom in this
-    # order, so multi-label parents (the "summary" chart) sit above
-    # the per-child boolean histograms that drill into them. Metrics
-    # whose ``meta`` row was deleted mid-run (``meta is None``) sink
-    # to the bottom but keep their relative order.
-    enumerated = list(enumerate(results))
-
-    def _sort_key(item: Tuple[int, CallImportMetricAggregate]):
-        original_idx, agg = item
-        meta = metric_meta.get(agg.metric_id)
-        if meta is None:
-            return (1, "", 1, "", original_idx)
-        parent_id = getattr(meta, "parent_metric_id", None)
-        # Group key: a child shares its parent's UUID; a parent
-        # uses its own UUID. Within a group, depth=0 (parent) sorts
-        # before depth=1 (child); ties break alphabetically by name
-        # so children render in a stable order regardless of which
-        # row scored which label first.
-        if parent_id is None:
-            group_key = str(meta.id)
-            depth = 0
-        else:
-            group_key = str(parent_id)
-            depth = 1
-        return (
-            0,
-            group_key,
-            depth,
-            (getattr(meta, "name", "") or "").lower(),
-            original_idx,
-        )
-
-    enumerated.sort(key=_sort_key)
-    return [agg for _idx, agg in enumerated]
-
-
-@router.get(
-    "/{eval_id}/aggregate",
-    response_model=CallImportEvaluationAggregateResponse,
-    operation_id="getCallImportEvaluationAggregate",
-)
-async def get_call_import_evaluation_aggregate(
-    call_import_id: UUID,
-    eval_id: UUID,
-    baseline_evaluation_id: Optional[UUID] = Query(None),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationAggregateResponse:
-    """Return per-metric distributions for the Visualizations tab.
-
-    The shape is intentionally chart-friendly: histograms for numeric
-    metrics, top-N value counts for categorical/text metrics, plus
-    summary stats (mean/p50/p95) so the UI can render summary cards
-    without recomputing on the client.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    eval_rows = _load_eval_rows(db, eval_id)
-
-    metrics = _compute_metric_aggregates(db, evaluation, eval_rows)
-
-    period_deltas: dict[str, MetricPeriodDelta] = {}
-    resolved_baseline_id: Optional[UUID] = None
-    if baseline_evaluation_id is not None:
-        call_import = _require_import(db, call_import_id, organization_id)
-        from app.db_sharding.scatter_gather import load_evaluation_row_pairs
-
-        rows = load_evaluation_row_pairs(db, eval_id)
-        period_start, _, _, _ = _report_period_from_rows(rows)
-        baseline_evaluation = _resolve_baseline_evaluation(
-            db,
-            organization_id,
-            call_import.workspace_id,
-            evaluation,
-            period_start,
-            str(baseline_evaluation_id),
-        )
-        if baseline_evaluation:
-            resolved_baseline_id = baseline_evaluation.id
-            metric_aggregates_dicts = [
-                _aggregate_to_dict(agg) for agg in metrics
-            ]
-            raw_deltas = _period_deltas_from_evaluation(
-                db,
-                baseline_evaluation,
-                metric_aggregates_dicts,
-                evaluation,
-                eval_rows,
-            )
-            raw_deltas = _period_deltas_with_explanations(
-                db,
-                organization_id,
-                evaluation,
-                baseline_evaluation,
-                raw_deltas,
-            )
-            period_deltas = {
-                metric_id: MetricPeriodDelta(
-                    label=delta.get("label") or "",
-                    detail=delta.get("detail") or "",
-                    why=(delta.get("why") or "").strip() or None,
-                )
-                for metric_id, delta in raw_deltas.items()
-            }
-
-    _fp_stored, failure_policies_source = policies_from_evaluation_raw(
-        evaluation.metric_clusters
-    )
-    return CallImportEvaluationAggregateResponse(
-        evaluation_id=eval_id,
-        total_rows=evaluation.total_rows,
-        completed_rows=evaluation.completed_rows,
-        failed_rows=evaluation.failed_rows,
-        metrics=metrics,
-        period_deltas=period_deltas,
-        baseline_evaluation_id=resolved_baseline_id,
-        failure_policies_source=failure_policies_source,
-    )
-
-
-# ---------------------------------------------------------------------------
-# TLDR insights: LLM-generated narrative + bullet patterns rendered above
-# the Visualizations charts. Cached on ``CallImportEvaluation.tldr_summary``
-# so the page never auto-burns LLM tokens; the user explicitly clicks
-# "Generate summary" or "Regenerate" from the empty-state CTA.
-# ---------------------------------------------------------------------------
-
-
-_INSIGHTS_SYSTEM_PROMPT = (
-    "You are a senior conversation-analytics reviewer. You will be "
-    "given aggregated metric statistics + a sample of rationales for "
-    "the rows of a single call-import evaluation. Identify the most "
-    "useful PATTERNS that hold ACROSS the calls -- not just per-metric "
-    "numbers. Look for combinations (e.g. `when X happens, Y also "
-    "tends to happen`), notable outliers, frequent failure modes, and "
-    "any signal that would change how a reviewer triages the run.\n\n"
-    "Return STRICT JSON only, with this shape and no extra keys:\n"
-    "{\n"
-    '  "narrative": "<max 3 short sentences, <=300 chars total>",\n'
-    '  "patterns": ["<bullet 1>", "<bullet 2>", ...],\n'
-    '  "metric_insights": {"<metric_id>": "<2-3 line business meaning>"}\n'
-    "}\n\n"
-    "Constraints:\n"
-    "- narrative is the ONLY text shown in the external audit summary and "
-    "Visualizations TLDR; keep it to at most 3 short sentences (~300 chars).\n"
-    "- patterns are optional supporting notes and are NOT rendered in the "
-    "audit summary; keep 0 to 3 bullets if supplied, each <= 120 characters.\n"
-    "- metric_insights must include one entry for each top-level metric id supplied.\n"
-    "- Each metric insight should explain what the metric means for the business and what the current distribution suggests, not restate the metric rubric.\n"
-    "- Avoid restating raw counts unless they reveal a pattern.\n"
-    "- Use neutral, factual language ('frustration appeared in...') "
-    "rather than judgemental ('the agents failed to...')."
-)
-
-
-def _tldr_summary_payload(
-    evaluation: CallImportEvaluation,
-) -> Optional[EvaluationTldrSummary]:
-    """Return the cached TLDR (with ``is_stale`` set) or ``None``.
-
-    ``CallImportEvaluation.tldr_summary`` is a ``JSON`` column so we
-    have to validate shape defensively -- a half-written or hand-edited
-    blob should not break the aggregate response. Returns ``None`` when
-    no cached summary exists.
-    """
-    raw = evaluation.tldr_summary
-    if not isinstance(raw, dict):
-        return None
-    narrative = raw.get("narrative")
-    if not isinstance(narrative, str) or not narrative.strip():
-        return None
-    patterns_raw = raw.get("patterns")
-    patterns = (
-        [str(p) for p in patterns_raw if isinstance(p, str) and p.strip()]
-        if isinstance(patterns_raw, list)
-        else []
-    )
-    metric_insights_raw = raw.get("metric_insights")
-    metric_insights = (
-        {
-            str(metric_id): str(insight).strip()
-            for metric_id, insight in metric_insights_raw.items()
-            if str(metric_id).strip()
-            and isinstance(insight, str)
-            and insight.strip()
-        }
-        if isinstance(metric_insights_raw, dict)
-        else {}
-    )
-    generated_at_raw = raw.get("generated_at")
-    try:
-        generated_at = (
-            datetime.fromisoformat(generated_at_raw)
-            if isinstance(generated_at_raw, str)
-            else evaluation.updated_at or datetime.now(timezone.utc)
-        )
-    except ValueError:
-        generated_at = evaluation.updated_at or datetime.now(timezone.utc)
-    snapshot = raw.get("generated_at_completed_rows")
-    snapshot_int = int(snapshot) if isinstance(snapshot, (int, float)) else 0
-    return EvaluationTldrSummary(
-        narrative=_clamp_prose_to_sentences(narrative.strip()),
-        patterns=patterns,
-        metric_insights=metric_insights,
-        generated_at=generated_at,
-        generated_at_completed_rows=snapshot_int,
-        provider=raw.get("provider") if isinstance(raw.get("provider"), str) else None,
-        model=raw.get("model") if isinstance(raw.get("model"), str) else None,
-        is_stale=evaluation.completed_rows > snapshot_int,
-    )
-
-
-def _sample_rationales_per_metric(
-    eval_rows: List[CallImportEvaluationRow],
-    *,
-    per_metric_cap: int = 3,
-    rationale_char_cap: int = 600,
-) -> Dict[str, List[str]]:
-    """Collect up to ``per_metric_cap`` distinct rationales per metric.
-
-    Distinctness is case- and whitespace-insensitive. We truncate each
-    rationale to ``rationale_char_cap`` so a few unusually verbose rows
-    can't dominate the prompt budget. Empty / non-string rationales are
-    skipped.
-    """
-    out: Dict[str, List[str]] = {}
-    seen: Dict[str, set[str]] = {}
-    for row in eval_rows:
-        scores = row.metric_scores if isinstance(row.metric_scores, dict) else {}
-        for metric_id, entry in scores.items():
-            if not isinstance(entry, dict):
-                continue
-            rationale = entry.get("rationale")
-            if not isinstance(rationale, str):
-                continue
-            text = rationale.strip()
-            if not text:
-                continue
-            bucket = out.setdefault(metric_id, [])
-            if len(bucket) >= per_metric_cap:
-                continue
-            key = " ".join(text.lower().split())
-            seen_set = seen.setdefault(metric_id, set())
-            if key in seen_set:
-                continue
-            seen_set.add(key)
-            bucket.append(text[:rationale_char_cap])
-    return out
-
-
-def _build_insights_messages(
-    evaluation: CallImportEvaluation,
-    aggregate: List[CallImportMetricAggregate],
-    rationale_samples: Dict[str, List[str]],
-    metric_meta: Dict[str, Metric],
-) -> List[Dict[str, str]]:
-    """Render the user prompt fed to the LLM.
-
-    The shape is plain markdown-ish text instead of JSON so the LLM can
-    skim it without us spending tokens on verbose schema delimiters.
-    Parent metrics surface their child metrics nested underneath so the
-    model sees the hierarchy and can talk about "X often co-occurred
-    with Y" rather than treating sub-labels as standalone metrics.
-    """
-    name = evaluation.name or f"Run {str(evaluation.id)[:8]}"
-    lines: List[str] = [
-        f"Evaluation: {name}",
-        (
-            f"Rows: total={evaluation.total_rows} "
-            f"completed={evaluation.completed_rows} "
-            f"failed={evaluation.failed_rows}"
-        ),
-        "",
-        "## Per-metric aggregate",
-    ]
-
-    # Group metrics by parent so the prompt mirrors the hierarchy. Any
-    # aggregate row whose ``metric_id`` is missing from ``metric_meta``
-    # is rendered as a leaf at the top-level list (handles renamed /
-    # deleted parents).
-    children_by_parent: Dict[str, List[CallImportMetricAggregate]] = {}
-    top_level: List[CallImportMetricAggregate] = []
-    for agg in aggregate:
-        meta = metric_meta.get(agg.metric_id)
-        parent_id = (
-            str(meta.parent_metric_id)
-            if meta is not None and getattr(meta, "parent_metric_id", None)
-            else None
-        )
-        if parent_id:
-            children_by_parent.setdefault(parent_id, []).append(agg)
-        else:
-            top_level.append(agg)
-
-    def _format_metric_block(agg: CallImportMetricAggregate, indent: int) -> List[str]:
-        prefix = "  " * indent + "- "
-        bits: List[str] = [f"{prefix}{agg.metric_name} [id={agg.metric_id}] (n={agg.count}"]
-        if agg.skipped_count:
-            bits.append(f", skipped={agg.skipped_count}")
-        if agg.error_count:
-            bits.append(f", errors={agg.error_count}")
-        bits.append(")")
-        meta = metric_meta.get(agg.metric_id)
-        description = (meta.description or "").strip() if meta else ""
-        if description:
-            bits.append(f" | definition={description[:500]}")
-        if agg.mean is not None:
-            mean_s = f"{agg.mean:.2f}"
-            stddev_s = f"{agg.stddev:.2f}" if agg.stddev is not None else "-"
-            bits.append(f" | mean={mean_s} stddev={stddev_s}")
-            if agg.min is not None and agg.max is not None:
-                bits.append(f" range=[{agg.min:.2f}, {agg.max:.2f}]")
-        if agg.value_counts:
-            total = sum(v.count for v in agg.value_counts) or 1
-            top = agg.value_counts[:3]
-            shares = ", ".join(
-                f'"{v.label}"={v.count}/{total}' for v in top
-            )
-            bits.append(f" | top={shares}")
-        result = ["".join(bits)]
-        rationales = rationale_samples.get(agg.metric_id, [])
-        for r in rationales:
-            result.append("  " * (indent + 1) + f"- rationale: {r}")
-        return result
-
-    for agg in top_level:
-        lines.extend(_format_metric_block(agg, indent=0))
-        meta = metric_meta.get(agg.metric_id)
-        children = children_by_parent.get(str(meta.id), []) if meta else []
-        for child in children:
-            lines.extend(_format_metric_block(child, indent=1))
-
-    lines.append("")
-    top_level_ids = [agg.metric_id for agg in top_level]
-    if top_level_ids:
-        lines.append(
-            "metric_insights keys must exactly use these top-level metric ids: "
-            + ", ".join(top_level_ids)
-        )
-        lines.append("")
-    lines.append(
-        "Write the JSON object as instructed. Do not include "
-        "preamble, code fences, or trailing commentary."
-    )
-
-    return [
-        {"role": "system", "content": _INSIGHTS_SYSTEM_PROMPT},
-        {"role": "user", "content": "\n".join(lines)},
-    ]
-
-
-def _parse_insights_response(text: str) -> EvaluationTldrSummary:
-    """Coerce the LLM response into ``narrative`` + ``patterns``.
-
-    Matches the JSON-with-fallback pattern used by
-    ``app.api.v1.routes.metrics._parse_metric_generation_response``: try
-    ``json.loads`` first, then fall back to regex extraction of the
-    first ``{...}`` block. Raises ``HTTPException`` with a 502 when the
-    response can't be parsed at all.
-    """
-    cleaned = (text or "").strip()
-    if not cleaned:
-        raise HTTPException(
-            status_code=502, detail="LLM returned an empty insights response"
-        )
-    try:
-        parsed = json.loads(cleaned)
-    except json.JSONDecodeError:
-        import re
-
-        match = re.search(r"\{.*\}", cleaned, re.DOTALL)
-        if not match:
-            raise HTTPException(
-                status_code=502,
-                detail="Could not parse LLM insights response as JSON",
-            )
-        try:
-            parsed = json.loads(match.group(0))
-        except json.JSONDecodeError as e:
-            raise HTTPException(
-                status_code=502,
-                detail=f"Could not parse LLM insights response: {e}",
-            )
-
-    if not isinstance(parsed, dict):
-        raise HTTPException(
-            status_code=502, detail="LLM insights JSON was not an object"
-        )
-
-    narrative = parsed.get("narrative")
-    if not isinstance(narrative, str) or not narrative.strip():
-        raise HTTPException(
-            status_code=502,
-            detail="LLM insights JSON missing 'narrative' string",
-        )
-
-    patterns_raw = parsed.get("patterns")
-    if patterns_raw is None:
-        patterns: List[str] = []
-    elif isinstance(patterns_raw, list):
-        patterns = [
-            str(p).strip()
-            for p in patterns_raw
-            if isinstance(p, str) and p.strip()
-        ]
-    else:
-        raise HTTPException(
-            status_code=502,
-            detail="LLM insights JSON 'patterns' must be a list of strings",
-        )
-    metric_insights_raw = parsed.get("metric_insights")
-    if metric_insights_raw is None:
-        metric_insights: Dict[str, str] = {}
-    elif isinstance(metric_insights_raw, dict):
-        metric_insights = {
-            str(metric_id): str(insight).strip()
-            for metric_id, insight in metric_insights_raw.items()
-            if str(metric_id).strip()
-            and isinstance(insight, str)
-            and insight.strip()
-        }
-    else:
-        raise HTTPException(
-            status_code=502,
-            detail="LLM insights JSON 'metric_insights' must be an object",
-        )
-
-    return EvaluationTldrSummary(
-        narrative=_clamp_prose_to_sentences(narrative.strip()),
-        patterns=patterns,
-        metric_insights=metric_insights,
-        generated_at=datetime.now(timezone.utc),
-        generated_at_completed_rows=0,  # filled in by caller
-        is_stale=False,
-    )
-
-
-def _generate_and_persist_tldr_summary(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    *,
-    organization_id: UUID,
-    provider: Optional[str] = None,
-    model: Optional[str] = None,
-) -> EvaluationTldrSummary:
-    """LLM TLDR generation used by the imports-queue Celery worker."""
-    eval_id = evaluation.id
-    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
-
-    pairs = load_evaluation_row_pairs(db, eval_id)
-    eval_rows = [eval_row for eval_row, _ in pairs]
-    aggregate = _compute_metric_aggregates(db, evaluation, eval_rows)
-    if not aggregate:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "No metric data yet. Wait for at least one row to "
-                "finish scoring before generating a summary."
-            ),
-        )
-
-    metric_ids: List[UUID] = []
-    for agg in aggregate:
-        try:
-            metric_ids.append(UUID(agg.metric_id))
-        except (TypeError, ValueError):
-            continue
-    metrics = _metrics_for_ids(db, organization_id, metric_ids)
-    metric_meta: Dict[str, Metric] = {str(m.id): m for m in metrics}
-
-    rationale_samples = _sample_rationales_per_metric(eval_rows)
-    messages = _build_insights_messages(
-        evaluation, aggregate, rationale_samples, metric_meta
-    )
-
-    from app.services.ai.llm_resolver import get_llm_provider_and_model
-    from app.services.ai.llm_service import llm_service
-
-    provider_enum, model_str = get_llm_provider_and_model(
-        organization_id, db, provider, model
-    )
-
-    try:
-        llm_result = llm_service.generate_response(
-            messages=messages,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            temperature=0.4,
-            max_tokens=1400,
-        )
-    except Exception as e:
-        logger.error(f"[CallImportInsights] LLM call failed: {e}")
-        raise HTTPException(
-            status_code=502, detail=f"LLM call failed: {e}"
-        ) from e
-
-    summary = _parse_insights_response(llm_result.get("text", ""))
-    total = int(evaluation.total_rows or 0)
-    ui_completed = min(int(evaluation.completed_rows or 0), total) if total else int(
-        evaluation.completed_rows or 0
-    )
-    summary.generated_at_completed_rows = ui_completed
-    summary.provider = provider_enum.value
-    summary.model = model_str
-    summary.is_stale = False
-
-    evaluation.tldr_summary = {
-        "narrative": summary.narrative,
-        "patterns": summary.patterns,
-        "metric_insights": summary.metric_insights,
-        "generated_at": summary.generated_at.isoformat(),
-        "generated_at_completed_rows": summary.generated_at_completed_rows,
-        "provider": summary.provider,
-        "model": summary.model,
-    }
-    flag_modified(evaluation, "tldr_summary")
-    db.commit()
-    db.refresh(evaluation)
-    return summary
-
-
-@router.get(
-    "/{eval_id}/insights",
-    response_model=Optional[EvaluationTldrSummary],
-    operation_id="getCallImportEvaluationInsights",
-)
-async def get_call_import_evaluation_insights(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Optional[EvaluationTldrSummary]:
-    """Return the cached TLDR (or ``null``) without contacting the LLM.
-
-    Used by the Visualizations tab on first paint so the empty-state
-    CTA can show up before the user opts into generation.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-    return _tldr_summary_payload(evaluation)
-
-
-@router.post(
-    "/{eval_id}/insights",
-    response_model=EvaluationTldrSummary,
-    operation_id="generateCallImportEvaluationInsights",
-)
-async def generate_call_import_evaluation_insights(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: EvaluationInsightsRequest = Body(default_factory=EvaluationInsightsRequest),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> EvaluationTldrSummary:
-    """Generate (or return-cached) the LLM TLDR for an evaluation run.
-
-    Behavior:
-
-    * ``body.regenerate=False`` and a cached summary at the current
-      ``completed_rows`` watermark exists -> return it as-is.
-    * ``body.regenerate=False`` and a stale cached summary exists
-      (``generated_at_completed_rows < completed_rows``) -> return it
-      with ``is_stale=True``; the UI prompts the user to regenerate.
-    * Otherwise -> resolve provider+model (auto-detect when omitted),
-      call the LLM, persist the new summary, return it.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    if not body.regenerate:
-        cached = _tldr_summary_payload(evaluation)
-        if cached is not None:
-            return cached
-
-    # Run the TLDR LLM on the imports worker (not the default worker or API).
-    from app.workers.tasks.generate_evaluation_tldr_insights import (
-        generate_evaluation_tldr_insights_task,
-    )
-
-    try:
-        task_result = generate_evaluation_tldr_insights_task.apply_async(
-            kwargs={
-                "evaluation_id": str(eval_id),
-                "call_import_id": str(call_import_id),
-                "organization_id": str(organization_id),
-                "provider": body.provider,
-                "model": body.model,
-            },
-        ).get(timeout=25 * 60)
-    except Exception as exc:
-        logger.error(
-            "[CallImportInsights] TLDR task failed for evaluation {}: {}",
-            eval_id,
-            exc,
-        )
-        raise HTTPException(
-            status_code=502,
-            detail=f"Summary generation failed: {exc}",
-        ) from exc
-
-    if isinstance(task_result, dict) and task_result.get("error"):
-        status_code = int(task_result.get("status_code") or 502)
-        raise HTTPException(
-            status_code=status_code,
-            detail=str(task_result["error"]),
-        )
-
-    summary = EvaluationTldrSummary.model_validate(task_result)
-    db.refresh(evaluation)
-
-    from app.services.ai.llm_resolver import get_llm_provider_and_model
-
-    provider_enum, model_str = get_llm_provider_and_model(
-        organization_id, db, body.provider, body.model, body.credential_id
-    )
-
-    _enqueue_user_insights_job(
-        evaluation,
-        provider=summary.provider or provider_enum.value,
-        model=summary.model or model_str,
-        force=body.regenerate,
-        max_llm_calls=body.max_llm_calls,
-        db=db,
-    )
-
-    return summary
-
-
-def _user_insights_payload(
-    evaluation: CallImportEvaluation,
-) -> Optional[EvaluationUserInsightsState]:
-    raw = getattr(evaluation, "user_insights", None)
-    if raw is None:
-        return None
-    return user_insights_state_from_raw(
-        raw,
-        completed_rows=evaluation.completed_rows,
-    )
-
-
-def _selected_generated_user_insights(
-    state: Optional[EvaluationUserInsightsState],
-    report_config: dict[str, Any],
-) -> list[dict[str, Any]]:
-    """Filter and order generated insights for PDF section 03."""
-    if state is None or state.status != "completed" or not state.insights:
-        return []
-
-    selected_ids = report_config.get("user_insight_ids")
-    if isinstance(selected_ids, list) and selected_ids:
-        allowed = {str(item) for item in selected_ids if item}
-        items = [item for item in state.insights if item.id in allowed]
-    else:
-        items = list(state.insights)
-
-    order_raw = report_config.get("order")
-    order_ids: list[str] = []
-    if isinstance(order_raw, dict):
-        user_order = order_raw.get("user_insights")
-        if isinstance(user_order, list):
-            order_ids = [str(item) for item in user_order if item]
-
-    if order_ids:
-        by_id = {item.id: item for item in items}
-        ordered = [by_id[iid] for iid in order_ids if iid in by_id]
-        seen = set(order_ids)
-        ordered.extend(item for item in items if item.id not in seen)
-        items = ordered
-
-    return [item.model_dump(mode="json") for item in items]
-
-
-def _enqueue_user_insights_job(
-    evaluation: CallImportEvaluation,
-    *,
-    provider: Optional[str] = None,
-    model: Optional[str] = None,
-    force: bool = False,
-    max_llm_calls: Optional[int] = None,
-    db: Optional[Session] = None,
-) -> None:
-    """Enqueue background user-insights generation unless already running."""
-    current = _user_insights_payload(evaluation)
-    if current is not None and current.status == "running" and not force:
-        return
-
-    llm_budget = normalize_max_llm_calls(max_llm_calls)
-
-    completed_count = (
-        _count_completed_eval_rows(db, evaluation.id)
-        if db is not None
-        else evaluation.completed_rows
-    )
-    total_calls = total_llm_calls_for_rows(completed_count, max_llm_calls=llm_budget)
-    evaluation.user_insights = {
-        "status": "running",
-        "insights": (
-            (evaluation.user_insights or {}).get("insights", [])
-            if isinstance(evaluation.user_insights, dict)
-            else []
-        ),
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "generated_at_completed_rows": evaluation.completed_rows,
-        "progress": {"completed_llm_calls": 0, "total_llm_calls": total_calls},
-        "provider": provider,
-        "model": model,
-        "max_llm_calls": llm_budget,
-        "llm_calls_used": 0,
-        "error_message": None,
-    }
-    if db is not None:
-        flag_modified(evaluation, "user_insights")
-        db.commit()
-
-    from app.workers.tasks.generate_evaluation_user_insights import (
-        generate_evaluation_user_insights_task,
-    )
-
-    generate_evaluation_user_insights_task.delay(
-        str(evaluation.id),
-        provider=provider,
-        model=model,
-        max_llm_calls=llm_budget,
-    )
-
-
-@router.get(
-    "/{eval_id}/user-insights",
-    response_model=Optional[EvaluationUserInsightsState],
-    operation_id="getCallImportEvaluationUserInsights",
-)
-async def get_call_import_evaluation_user_insights(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Optional[EvaluationUserInsightsState]:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-    return _user_insights_payload(evaluation)
-
-
-@router.post(
-    "/{eval_id}/user-insights",
-    response_model=EvaluationUserInsightsState,
-    operation_id="generateCallImportEvaluationUserInsights",
-)
-async def generate_call_import_evaluation_user_insights(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: EvaluationUserInsightsRequest = Body(
-        default_factory=EvaluationUserInsightsRequest
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> EvaluationUserInsightsState:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    if not body.regenerate and not body.force:
-        cached = _user_insights_payload(evaluation)
-        if cached is not None and cached.status in {"running", "completed"}:
-            return cached
-
-    eval_rows = _load_eval_rows(db, eval_id)
-    if not any(row.status == "completed" for row in eval_rows):
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "No completed rows yet. Wait for at least one row to "
-                "finish scoring before generating user insights."
-            ),
-        )
-
-    from app.services.ai.llm_resolver import get_llm_provider_and_model
-
-    provider_enum, model_str = get_llm_provider_and_model(
-        organization_id, db, body.provider, body.model, body.credential_id
-    )
-
-    _enqueue_user_insights_job(
-        evaluation,
-        provider=provider_enum.value,
-        model=model_str,
-        force=body.force or body.regenerate,
-        max_llm_calls=body.max_llm_calls,
-        db=db,
-    )
-
-    db.refresh(evaluation)
-    return _user_insights_payload(evaluation) or EvaluationUserInsightsState(
-        status="running"
-    )
-
-
-def _metric_clusters_payload(
-    evaluation: CallImportEvaluation,
-) -> Optional[EvaluationMetricClustersState]:
-    raw = getattr(evaluation, "metric_clusters", None)
-    if raw is None:
-        return None
-    return metric_clusters_state_from_raw(
-        raw,
-        completed_rows=evaluation.completed_rows,
-    )
-
-
-def _selected_metric_clusters_for_pdf(
-    state: Optional[EvaluationMetricClustersState],
-    report_config: dict[str, Any],
-) -> dict[str, Any]:
-    if state is None or state.status != "completed":
-        return {}
-    sections = report_config.get("sections")
-    if isinstance(sections, dict) and sections.get("failure_diagnostics") is False:
-        return {}
-    payload: dict[str, Any] = {
-        "groups": [g.model_dump(mode="json") for g in state.groups],
-        "discovered_problems": [
-            d.model_dump(mode="json") for d in state.discovered_problems
-        ],
-    }
-    if state.rca_summary is not None:
-        payload["rca_summary"] = state.rca_summary.model_dump(mode="json")
-    return payload
-
-
-def _prompt_improvements_payload(
-    evaluation: CallImportEvaluation,
-) -> Optional[EvaluationPromptImprovementsState]:
-    from app.services.call_import_prompt_improvements import (
-        prompt_improvements_state_from_raw,
-    )
-
-    raw = getattr(evaluation, "prompt_improvements", None)
-    if raw is None:
-        return None
-    return prompt_improvements_state_from_raw(
-        raw,
-        completed_rows=evaluation.completed_rows,
-    )
-
-
-def _selected_prompt_improvements_for_pdf(
-    state: Optional[EvaluationPromptImprovementsState],
-    report_config: dict[str, Any],
-) -> dict[str, Any]:
-    if state is None or state.status != "completed":
-        return {}
-    sections = report_config.get("sections")
-    if isinstance(sections, dict) and sections.get("prompt_improvements") is False:
-        return {}
-    return {
-        "imported_agent_id": state.imported_agent_id,
-        "imported_agent_name": state.imported_agent_name,
-        "overview": state.overview,
-        "suggestions": [s.model_dump(mode="json") for s in state.suggestions],
-    }
-
-
-def _enqueue_prompt_improvements_job(
-    evaluation: CallImportEvaluation,
-    *,
-    imported_agent_id: UUID,
-    imported_agent_name: str,
-    provider: Optional[str] = None,
-    model: Optional[str] = None,
-    credential_id: Optional[UUID] = None,
-    force: bool = False,
-    db: Optional[Session] = None,
-) -> None:
-    current = _prompt_improvements_payload(evaluation)
-    if current is not None and current.status == "running" and not force:
-        return
-
-    evaluation.prompt_improvements = {
-        "status": "running",
-        "imported_agent_id": str(imported_agent_id),
-        "imported_agent_name": imported_agent_name,
-        "suggestions": [],
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "generated_at_completed_rows": evaluation.completed_rows,
-        "provider": provider,
-        "model": model,
-        "error_message": None,
-    }
-    if db is not None:
-        flag_modified(evaluation, "prompt_improvements")
-        db.commit()
-
-    from app.workers.tasks.generate_evaluation_prompt_improvements import (
-        generate_evaluation_prompt_improvements_task,
-    )
-
-    async_result = generate_evaluation_prompt_improvements_task.apply_async(
-        kwargs={
-            "evaluation_id": str(evaluation.id),
-            "imported_agent_id": str(imported_agent_id),
-            "provider": provider,
-            "model": model,
-            "credential_id": str(credential_id) if credential_id else None,
-        },
-        queue="imports",
-    )
-    if db is not None and isinstance(evaluation.prompt_improvements, dict):
-        evaluation.prompt_improvements["celery_task_id"] = async_result.id
-        flag_modified(evaluation, "prompt_improvements")
-        db.commit()
-
-
-def _load_eval_rows(db: Session, evaluation_id: UUID) -> List[CallImportEvaluationRow]:
-    from app.db_sharding.eval_rows import load_evaluation_rows_for_run
-
-    return load_evaluation_rows_for_run(db, evaluation_id)
-
-
-def _count_completed_eval_rows(db: Session, evaluation_id: UUID) -> int:
-    from app.db_sharding.eval_rows import count_evaluation_rows_for_run
-
-    return count_evaluation_rows_for_run(
-        db, evaluation_id, statuses=["completed"]
-    )
-
-
-def _completed_row_pairs_for_evaluation(
-    db: Session,
-    evaluation_id: UUID,
-) -> List[Tuple[CallImportEvaluationRow, CallImportRow]]:
-    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
-
-    row_pairs = load_evaluation_row_pairs(db, evaluation_id)
-    return [
-        (eval_row, source_row)
-        for eval_row, source_row in row_pairs
-        if eval_row.status == "completed"
-    ]
-
-
-def _resolve_metric_cluster_row_selection(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    eval_rows: List[CallImportEvaluationRow],
-    evaluation_row_ids: Optional[List[UUID]],
-    *,
-    row_limit: Optional[int] = None,
-    policies: Optional[Dict[str, MetricFailurePolicy]] = None,
-) -> Tuple[List[Tuple[CallImportEvaluationRow, CallImportRow]], List[str]]:
-    """Return filtered completed row pairs and the selected row id strings."""
-    completed_pairs = _completed_row_pairs_for_evaluation(db, evaluation.id)
-    metrics = _metrics_for_clustering(db, evaluation, eval_rows)
-    if policies is None:
-        aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
-        parent_ids = [
-            m.id
-            for m in metrics
-            if getattr(m, "selection_mode", None)
-            and not getattr(m, "parent_metric_id", None)
-        ]
-        child_names_by_parent = _child_names_by_parent(
-            db, evaluation.organization_id, parent_ids
-        )
-        policies, _ = effective_policies(
-            evaluation,
-            metrics,
-            aggregates,
-            child_names_by_parent=child_names_by_parent,
-        )
-    eligible = list_eligible_cluster_rows(
-        evaluation, completed_pairs, metrics, policies
-    )
-    eligible_ordered_ids = [str(item["evaluation_row_id"]) for item in eligible]
-    eligible_id_set = set(eligible_ordered_ids)
-
-    if evaluation_row_ids is None and row_limit is not None:
-        selected_ids = eligible_ordered_ids[:row_limit]
-        filtered = filter_completed_row_pairs(
-            completed_pairs,
-            [UUID(rid) for rid in selected_ids],
-        )
-        return filtered, selected_ids
-
-    if evaluation_row_ids is None:
-        selected_ids = eligible_ordered_ids
-        filtered = filter_completed_row_pairs(
-            completed_pairs,
-            [UUID(rid) for rid in selected_ids],
-        )
-        return filtered, selected_ids
-
-    requested = {str(rid) for rid in evaluation_row_ids}
-    completed_id_set = {str(eval_row.id) for eval_row, _ in completed_pairs}
-    unknown = sorted(requested - completed_id_set)
-    if unknown:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "One or more evaluation_row_ids are missing or not completed: "
-                + ", ".join(unknown[:5])
-                + ("…" if len(unknown) > 5 else "")
-            ),
-        )
-    not_eligible = sorted(requested - eligible_id_set)
-    if not_eligible:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "Each selected row must have at least one flagged quality metric. "
-                "Ineligible row(s): "
-                + ", ".join(not_eligible[:5])
-                + ("…" if len(not_eligible) > 5 else "")
-            ),
-        )
-    selected_ids = sorted(requested)
-    filtered = filter_completed_row_pairs(completed_pairs, evaluation_row_ids)
-    return filtered, selected_ids
-
-
-def _enqueue_metric_clusters_job(
-    evaluation: CallImportEvaluation,
-    *,
-    provider: Optional[str] = None,
-    model: Optional[str] = None,
-    credential_id: Optional[UUID] = None,
-    force: bool = False,
-    max_llm_calls: Optional[int] = None,
-    evaluation_row_ids: Optional[List[UUID]] = None,
-    selected_evaluation_row_ids: Optional[List[str]] = None,
-    failure_policies: Optional[Dict[str, MetricFailurePolicy]] = None,
-    db: Optional[Session] = None,
-) -> None:
-    current = _metric_clusters_payload(evaluation)
-    if current is not None and current.status == "running" and not force:
-        return
-
-    llm_budget = normalize_max_llm_calls(max_llm_calls)
-    total_calls = 1
-    row_ids_for_task: Optional[List[str]] = None
-    if db is not None:
-        eval_rows = _load_eval_rows(db, evaluation.id)
-        if selected_evaluation_row_ids is None:
-            _, selected_evaluation_row_ids = _resolve_metric_cluster_row_selection(
-                db,
-                evaluation,
-                eval_rows,
-                evaluation_row_ids,
-            )
-        completed_pairs = filter_completed_row_pairs(
-            _completed_row_pairs_for_evaluation(db, evaluation.id),
-            [UUID(rid) for rid in selected_evaluation_row_ids],
-        )
-        metrics = _metrics_for_clustering(db, evaluation, eval_rows)
-        policies_for_estimate = failure_policies
-        if policies_for_estimate is None:
-            aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
-            parent_ids = [
-                m.id
-                for m in metrics
-                if getattr(m, "selection_mode", None)
-                and not getattr(m, "parent_metric_id", None)
-            ]
-            child_names_by_parent = _child_names_by_parent(
-                db, evaluation.organization_id, parent_ids
-            )
-            policies_for_estimate, _ = effective_policies(
-                evaluation,
-                metrics,
-                aggregates,
-                child_names_by_parent=child_names_by_parent,
-            )
-        _, total_calls = estimate_metric_clusters_llm_calls(
-            evaluation,
-            metrics,
-            completed_pairs,
-            policies_for_estimate,
-            max_llm_calls=llm_budget,
-        )
-        row_ids_for_task = list(selected_evaluation_row_ids)
-
-    prior_raw = (
-        evaluation.metric_clusters
-        if isinstance(evaluation.metric_clusters, dict)
-        else {}
-    )
-    policy_blob: Dict[str, Any] = {}
-    if failure_policies:
-        policy_blob = failure_policies_to_db(failure_policies, source="user")
-
-    evaluation.metric_clusters = {
-        "status": "running",
-        "groups": prior_raw.get("groups", []) if isinstance(prior_raw, dict) else [],
-        "discovered_problems": (
-            prior_raw.get("discovered_problems", [])
-            if isinstance(prior_raw, dict)
-            else []
-        ),
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "generated_at_completed_rows": evaluation.completed_rows,
-        "progress": {"completed_llm_calls": 0, "total_llm_calls": total_calls},
-        "provider": provider,
-        "model": model,
-        "max_llm_calls": llm_budget,
-        "llm_calls_used": 0,
-        "error_message": None,
-        "selected_evaluation_row_ids": selected_evaluation_row_ids or [],
-        **policy_blob,
-    }
-    if db is not None:
-        flag_modified(evaluation, "metric_clusters")
-        db.commit()
-
-    from app.workers.tasks.generate_evaluation_metric_clusters import (
-        generate_evaluation_metric_clusters_task,
-    )
-
-    async_result = generate_evaluation_metric_clusters_task.apply_async(
-        kwargs={
-            "evaluation_id": str(evaluation.id),
-            "provider": provider,
-            "model": model,
-            "credential_id": str(credential_id) if credential_id else None,
-            "max_llm_calls": llm_budget,
-            "evaluation_row_ids": row_ids_for_task,
-        },
-        queue="imports",
-    )
-    if db is not None and isinstance(evaluation.metric_clusters, dict):
-        evaluation.metric_clusters["celery_task_id"] = async_result.id
-        flag_modified(evaluation, "metric_clusters")
-        db.commit()
-
-
-def _revoke_metric_clusters_task(evaluation: CallImportEvaluation) -> None:
-    """Best-effort SIGTERM revoke of the in-flight clustering Celery task."""
-    raw = evaluation.metric_clusters
-    if not isinstance(raw, dict):
-        return
-    task_id = str(raw.get("celery_task_id") or "").strip()
-    if not task_id:
-        return
-    try:
-        from app.workers.celery_app import celery_app
-
-        celery_app.control.revoke(task_id, terminate=True, signal="SIGTERM")
-        logger.info(
-            "Revoked metric-clusters task {} for evaluation {}",
-            task_id,
-            evaluation.id,
-        )
-    except Exception as exc:  # noqa: BLE001
-        logger.warning(
-            "Failed to revoke metric-clusters task {} for evaluation {}: {}",
-            task_id,
-            evaluation.id,
-            exc,
-        )
-
-
-def _apply_metric_clusters_cancel(evaluation: CallImportEvaluation) -> bool:
-    """Mark clustering as cancelled and revoke the worker task.
-
-    Returns True if a running job was cancelled, False if already terminal.
-    """
-    raw = evaluation.metric_clusters
-    if not isinstance(raw, dict):
-        return False
-    if (raw.get("status") or "").lower() != "running":
-        return False
-
-    _revoke_metric_clusters_task(evaluation)
-    progress = raw.get("progress") if isinstance(raw.get("progress"), dict) else {}
-    evaluation.metric_clusters = {
-        **raw,
-        "status": "cancelled",
-        "error_message": METRIC_CLUSTERS_CANCELLED_BY_USER_ERROR,
-        "progress": progress,
-        "celery_task_id": None,
-    }
-    return True
-
-
-@router.get(
-    "/{eval_id}/metric-clusters/failure-policies",
-    response_model=MetricFailurePoliciesResponse,
-    operation_id="getCallImportEvaluationMetricClusterFailurePolicies",
-)
-async def get_call_import_evaluation_metric_cluster_failure_policies(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> MetricFailurePoliciesResponse:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    eval_rows = _load_eval_rows(db, eval_id)
-    metrics, aggregates, policies, source, child_names_by_parent = _clustering_context(
-        db, evaluation, eval_rows
-    )
-    previews = build_failure_policy_previews(
-        metrics,
-        aggregates,
-        child_names_by_parent=child_names_by_parent,
-        effective=policies,
-    )
-    updated_at = None
-    raw_mc = evaluation.metric_clusters
-    if isinstance(raw_mc, dict) and raw_mc.get("failure_policies_updated_at"):
-        try:
-            updated_at = datetime.fromisoformat(
-                str(raw_mc["failure_policies_updated_at"])
-            )
-        except ValueError:
-            updated_at = None
-    return MetricFailurePoliciesResponse(
-        previews=previews,
-        policies=policies,
-        source=source,
-        updated_at=updated_at,
-    )
-
-
-@router.put(
-    "/{eval_id}/metric-clusters/failure-policies",
-    response_model=MetricFailurePoliciesResponse,
-    operation_id="saveCallImportEvaluationMetricClusterFailurePolicies",
-)
-async def save_call_import_evaluation_metric_cluster_failure_policies(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: MetricFailurePoliciesSaveRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> MetricFailurePoliciesResponse:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    eval_rows = _load_eval_rows(db, eval_id)
-    metrics, aggregates, _existing, _source, child_names_by_parent = _clustering_context(
-        db, evaluation, eval_rows
-    )
-    try:
-        validate_failure_policies_for_metrics(body.policies, metrics)
-    except ValueError as exc:
-        raise HTTPException(status_code=400, detail=str(exc)) from exc
-
-    prior = (
-        evaluation.metric_clusters
-        if isinstance(evaluation.metric_clusters, dict)
-        else {}
-    )
-    evaluation.metric_clusters = merge_failure_policies_into_raw(
-        prior,
-        body.policies,
-        source="user",
-    )
-    flag_modified(evaluation, "metric_clusters")
-    db.commit()
-    db.refresh(evaluation)
-
-    policies, source = policies_from_evaluation_raw(evaluation.metric_clusters)
-    if source != "user":
-        source = "user"
-    previews = build_failure_policy_previews(
-        metrics,
-        aggregates,
-        child_names_by_parent=child_names_by_parent,
-        effective=policies,
-    )
-    updated_at = None
-    raw_mc = evaluation.metric_clusters
-    if isinstance(raw_mc, dict) and raw_mc.get("failure_policies_updated_at"):
-        try:
-            updated_at = datetime.fromisoformat(
-                str(raw_mc["failure_policies_updated_at"])
-            )
-        except ValueError:
-            updated_at = None
-    return MetricFailurePoliciesResponse(
-        previews=previews,
-        policies=policies,
-        source="user",
-        updated_at=updated_at,
-    )
-
-
-@router.get(
-    "/{eval_id}/metric-clusters/eligible-rows",
-    response_model=MetricClusterEligibleRowsResponse,
-    operation_id="listCallImportEvaluationMetricClusterEligibleRows",
-)
-async def list_call_import_evaluation_metric_cluster_eligible_rows(
-    call_import_id: UUID,
-    eval_id: UUID,
-    limit: Optional[int] = Query(default=None, ge=1),
-    count_only: bool = Query(default=False),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> MetricClusterEligibleRowsResponse:
-    """Completed rows that have at least one flagged quality metric."""
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    eval_rows = _load_eval_rows(db, eval_id)
-    completed_pairs = _completed_row_pairs_for_evaluation(db, eval_id)
-    metrics, _aggregates, policies, _source, _child_map = _clustering_context(
-        db, evaluation, eval_rows
-    )
-    all_eligible = list_eligible_cluster_rows(
-        evaluation, completed_pairs, metrics, policies
-    )
-    total = len(all_eligible)
-    if count_only:
-        return MetricClusterEligibleRowsResponse(items=[], total=total)
-    raw_items = all_eligible if limit is None else all_eligible[:limit]
-    items = [MetricClusterEligibleRow.model_validate(item) for item in raw_items]
-    return MetricClusterEligibleRowsResponse(items=items, total=total)
-
-
-@router.get(
-    "/{eval_id}/metric-clusters",
-    response_model=Optional[EvaluationMetricClustersState],
-    operation_id="getCallImportEvaluationMetricClusters",
-)
-async def get_call_import_evaluation_metric_clusters(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Optional[EvaluationMetricClustersState]:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-    return _metric_clusters_payload(evaluation)
-
-
-@router.post(
-    "/{eval_id}/metric-clusters",
-    response_model=EvaluationMetricClustersState,
-    operation_id="generateCallImportEvaluationMetricClusters",
-)
-async def generate_call_import_evaluation_metric_clusters(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: EvaluationMetricClustersRequest = Body(
-        default_factory=EvaluationMetricClustersRequest
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> EvaluationMetricClustersState:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    if not body.regenerate and not body.force:
-        cached = _metric_clusters_payload(evaluation)
-        if cached is not None and cached.status in {"running", "completed"}:
-            return cached
-
-    eval_rows = _load_eval_rows(db, eval_id)
-    if not any(row.status == "completed" for row in eval_rows):
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "No completed rows yet. Wait for at least one row to "
-                "finish scoring before generating metric clusters."
-            ),
-        )
-
-    if body.evaluation_row_ids and body.row_limit is not None:
-        raise HTTPException(
-            status_code=400,
-            detail="Specify either evaluation_row_ids or row_limit, not both.",
-        )
-
-    if body.evaluation_row_ids:
-        completed_pairs = _completed_row_pairs_for_evaluation(db, evaluation.id)
-        completed_id_set = {str(eval_row.id) for eval_row, _ in completed_pairs}
-        requested = {str(rid) for rid in body.evaluation_row_ids}
-        unknown = sorted(requested - completed_id_set)
-        if unknown:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "One or more evaluation_row_ids are missing or not completed: "
-                    + ", ".join(unknown[:5])
-                    + ("…" if len(unknown) > 5 else "")
-                ),
-            )
-
-    from app.services.ai.llm_resolver import get_llm_provider_and_model
-
-    provider_enum, model_str = get_llm_provider_and_model(
-        organization_id, db, body.provider, body.model, body.credential_id
-    )
-
-    metrics, aggregates, _inferred, _source, child_names_by_parent = _clustering_context(
-        db, evaluation, eval_rows
-    )
-    merged_policies = merge_clustering_policies(
-        body.failure_policies,
-        evaluation,
-        metrics,
-        aggregates,
-        child_names_by_parent=child_names_by_parent,
-    )
-    try:
-        validate_failure_policies_for_metrics(
-            body.failure_policies or merged_policies, metrics
-        )
-    except ValueError as exc:
-        raise HTTPException(status_code=400, detail=str(exc)) from exc
-
-    if not has_clusterable_metrics(metrics, merged_policies, eval_rows):
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "No calls match any failure policy. Select failure values on "
-                "metrics that have matching rows, or leave metrics with no "
-                "failures unchecked — they are skipped automatically."
-            ),
-        )
-
-    filtered_pairs, selected_row_ids = _resolve_metric_cluster_row_selection(
-        db,
-        evaluation,
-        eval_rows,
-        body.evaluation_row_ids,
-        row_limit=body.row_limit,
-        policies=merged_policies,
-    )
-    if not selected_row_ids:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "No eligible rows to cluster. Select completed calls that match "
-                "at least one configured failure policy."
-            ),
-        )
-    if not filtered_pairs:
-        raise HTTPException(
-            status_code=400,
-            detail="No completed rows match the selected evaluation_row_ids.",
-        )
-
-    _enqueue_metric_clusters_job(
-        evaluation,
-        provider=provider_enum.value,
-        model=model_str,
-        credential_id=body.credential_id,
-        force=body.force or body.regenerate,
-        max_llm_calls=body.max_llm_calls,
-        evaluation_row_ids=body.evaluation_row_ids,
-        selected_evaluation_row_ids=selected_row_ids,
-        failure_policies=merged_policies,
-        db=db,
-    )
-
-    db.refresh(evaluation)
-    return _metric_clusters_payload(evaluation) or EvaluationMetricClustersState(
-        status="running"
-    )
-
-
-@router.post(
-    "/{eval_id}/metric-clusters/cancel",
-    response_model=EvaluationMetricClustersState,
-    operation_id="cancelCallImportEvaluationMetricClusters",
-)
-async def cancel_call_import_evaluation_metric_clusters(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> EvaluationMetricClustersState:
-    """Abort in-flight failure-diagnostics clustering.
-
-    Idempotent: if clustering is not ``running``, returns the current state
-    unchanged.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    _apply_metric_clusters_cancel(evaluation)
-    flag_modified(evaluation, "metric_clusters")
-    db.commit()
-    db.refresh(evaluation)
-
-    return _metric_clusters_payload(evaluation) or EvaluationMetricClustersState(
-        status="idle"
-    )
-
-
-@router.get(
-    "/{eval_id}/prompt-improvements",
-    response_model=Optional[EvaluationPromptImprovementsState],
-    operation_id="getCallImportEvaluationPromptImprovements",
-)
-async def get_call_import_evaluation_prompt_improvements(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Optional[EvaluationPromptImprovementsState]:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-    return _prompt_improvements_payload(evaluation)
-
-
-@router.post(
-    "/{eval_id}/prompt-improvements",
-    response_model=EvaluationPromptImprovementsState,
-    operation_id="generateCallImportEvaluationPromptImprovements",
-)
-async def generate_call_import_evaluation_prompt_improvements(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: EvaluationPromptImprovementsRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> EvaluationPromptImprovementsState:
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    clusters = _metric_clusters_payload(evaluation)
-    if clusters is None or clusters.status != "completed":
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "Metric clusters must be completed before generating prompt "
-                "improvements. Run failure diagnostics first."
-            ),
-        )
-
-    from app.services.call_import_prompt_improvements import is_imported_agent
-    from app.services.ai.llm_resolver import get_llm_provider_and_model
-
-    imported_agent = (
-        db.query(PromptPartial)
-        .filter(
-            PromptPartial.id == body.imported_agent_id,
-            PromptPartial.organization_id == organization_id,
-            PromptPartial.workspace_id == workspace_id,
-        )
-        .first()
-    )
-    if imported_agent is None or not is_imported_agent(imported_agent):
-        raise HTTPException(
-            status_code=404,
-            detail="Imported agent not found in the active workspace",
-        )
-
-    if not body.regenerate and not body.force:
-        cached = _prompt_improvements_payload(evaluation)
-        if (
-            cached is not None
-            and cached.status in {"running", "completed"}
-            and cached.imported_agent_id == str(body.imported_agent_id)
-        ):
-            return cached
-
-    provider_enum, model_str = get_llm_provider_and_model(
-        organization_id, db, body.provider, body.model, body.credential_id
-    )
-
-    _enqueue_prompt_improvements_job(
-        evaluation,
-        imported_agent_id=body.imported_agent_id,
-        imported_agent_name=imported_agent.name,
-        provider=provider_enum.value,
-        model=model_str,
-        credential_id=body.credential_id,
-        force=body.force or body.regenerate,
-        db=db,
-    )
-
-    db.refresh(evaluation)
-    return _prompt_improvements_payload(evaluation) or EvaluationPromptImprovementsState(
-        status="running",
-        imported_agent_id=str(body.imported_agent_id),
-        imported_agent_name=imported_agent.name,
-    )
-
-
-# ---------------------------------------------------------------------------
-# Flow chart: turns per-row LLM-inferred ``sequence`` arrays into a
-# directed graph of (label -> label) transitions across the whole run.
-# Powers the aggregate Sankey-style React Flow chart on the evaluation
-# overview; per-call flow charts are built client-side from the same
-# ``sequence`` field on a single row's metric_scores entry.
-# ---------------------------------------------------------------------------
-
-
-_FLOW_TERMINAL_THRESHOLD = 0.2  # Mark as terminal when >=20% of sequences end here.
-_FLOW_START_NODE_ID = "__START__"
-_DISCOVERED_NODE_PREFIX = "disc:"
-
-
-def _slug_label(value: Any) -> str:
-    """Lowercase + whitespace-collapse + underscore-join.
-
-    Used everywhere we need a stable key for a metric/label name —
-    matching the same convention the worker uses when emitting
-    ``sequence`` entries and discovered keys.
-    """
-    if value is None:
-        return ""
-    return "_".join(str(value).strip().lower().split())
-
-
-def _resolve_alias(alias_map: Dict[str, str], key: str) -> str:
-    """Walk the alias map until we hit a slug that doesn't redirect.
-
-    The merge endpoint stores ``from_slug -> to_slug`` pairs. The delete
-    endpoint stores ``from_slug -> ""`` (empty string sentinel) to mark
-    a slug as tombstoned. Chains can accumulate when the user merges
-    A→B and later merges B→C; this helper collapses them so callers
-    always land on the final canonical slug.
-
-    Returns:
-      * the canonical slug if it still resolves to a real label,
-      * an empty string if the slug has been tombstoned (callers MUST
-        treat an empty result as "drop this entry entirely"),
-      * the input ``key`` if it isn't aliased.
-
-    Cycles are guarded by a hard step limit since the alias map is
-    user-driven.
-    """
-    if not key:
-        return ""
-    if not alias_map:
-        return key
-    current = key
-    seen: set[str] = set()
-    for _ in range(16):
-        if current in seen:
-            return current
-        seen.add(current)
-        if current not in alias_map:
-            return current
-        nxt = alias_map[current]
-        if nxt == current:
-            return current
-        if nxt == "":
-            # Deletion sentinel — the user has explicitly retired this
-            # slug. Propagate the empty string up so callers drop it.
-            return ""
-        current = nxt
-    return current
-
-
-# Reserved JSON key under which the worker stores top-level metric
-# discoveries on each row's ``metric_scores`` dict. Mirrors the constant
-# in ``app/workers/tasks/helpers/llm_evaluation.py`` — kept local here to
-# avoid a worker import cycle from the routes module.
-DISCOVERED_METRICS_KEY = "__discovered_metrics__"
-
-# Allowed values for an LLM-suggested top-level metric type. Kept in
-# sync with ``DiscoveredMetricSuggestedType`` in
-# ``app/models/schemas.py``.
-_DISCOVERED_METRIC_TYPES = ("boolean", "rating", "category")
-
-
-def normalize_scores_with_aliases(
-    metric_scores: Dict[str, Any],
-    evaluation: CallImportEvaluation,
-    db: Session,
-    organization_id: UUID,
-) -> Dict[str, Any]:
-    """Rewrite per-row ``metric_scores`` to honor merges + promotions.
-
-    Called by the worker right after ``evaluate_with_llm`` returns so
-    every row that finishes AFTER a user has merged or promoted a
-    discovered label persists data already reflecting that decision.
-    Without this hook, a worker holding a stale prompt could re-emit a
-    ``from_key`` slug long after the user merged it away.
-
-    For every parent entry (``selection_mode != null`` and a
-    ``discovered_labels`` / ``sequence`` field) we:
-
-      * resolve discovered slugs through the evaluation's
-        ``discovered_label_aliases`` map (transitively),
-      * drop any discovered_labels entry whose canonical slug now
-        matches a real promoted child of the parent (merging them out
-        of the panel for free), and
-      * collapse adjacent duplicate sequence entries that result.
-
-    Returns ``metric_scores`` (mutated in place) for chaining.
-    """
-    if not isinstance(metric_scores, dict):
-        return metric_scores
-
-    aliases_top = (
-        evaluation.discovered_label_aliases
-        if isinstance(evaluation.discovered_label_aliases, dict)
-        else {}
-    )
-
-    # Identify the parent entries inside metric_scores. They're the
-    # dicts that carry a ``selection_mode`` key (set by the LLM
-    # hierarchy parser) and either a ``sequence`` or a
-    # ``discovered_labels`` list.
-    for key, entry in list(metric_scores.items()):
-        if not isinstance(entry, dict):
-            continue
-        if entry.get("type") != "category" and not entry.get("selection_mode"):
-            continue
-        try:
-            parent_uuid = UUID(str(key))
-        except (TypeError, ValueError):
-            continue
-
-        alias_map = {}
-        sub = aliases_top.get(str(parent_uuid))
-        if isinstance(sub, dict):
-            alias_map = {
-                str(k): str(v)
-                for k, v in sub.items()
-                if isinstance(k, str) and isinstance(v, str)
-            }
-        promoted = _promoted_child_slugs(db, parent_uuid, organization_id)
-
-        # Rewrite discovered_labels: alias-resolve keys, drop duplicates
-        # post-resolution, and drop entries that have been promoted.
-        discovered = entry.get("discovered_labels")
-        if isinstance(discovered, list):
-            kept_disc: List[Dict[str, Any]] = []
-            seen: set[str] = set()
-            for d in discovered:
-                if not isinstance(d, dict):
-                    continue
-                slug = _slug_label(d.get("key") or d.get("name"))
-                slug = _resolve_alias(alias_map, slug)
-                if not slug or slug in promoted or slug in seen:
-                    continue
-                seen.add(slug)
-                new_entry = dict(d)
-                new_entry["key"] = slug
-                kept_disc.append(new_entry)
-            entry["discovered_labels"] = kept_disc
-
-        # Rewrite sequence: alias-resolve every entry; collapse adjacent
-        # duplicates that result. We DON'T drop slugs that match
-        # promoted children — the promoted child slug is still a valid
-        # sequence entry; the flow chart will resolve it to the real
-        # child node.
-        seq = entry.get("sequence")
-        if isinstance(seq, list):
-            new_seq: List[str] = []
-            last: Optional[str] = None
-            for item in seq:
-                if not isinstance(item, str):
-                    continue
-                slug = _resolve_alias(alias_map, _slug_label(item))
-                if not slug or slug == last:
-                    continue
-                new_seq.append(slug)
-                last = slug
-            entry["sequence"] = new_seq
-
-    # Top-level metric discoveries live alongside the parent entries
-    # under the reserved ``DISCOVERED_METRICS_KEY`` slot. Apply the
-    # flat evaluation-level alias/tombstone map + suppress slugs that
-    # already correspond to a real top-level Metric so workers that
-    # finish AFTER the user has merged / deleted / promoted can't
-    # resurrect a retired candidate.
-    discovered_metrics_payload = metric_scores.get(DISCOVERED_METRICS_KEY)
-    if isinstance(discovered_metrics_payload, list):
-        flat_alias_map = (
-            evaluation.discovered_metric_aliases
-            if isinstance(evaluation.discovered_metric_aliases, dict)
-            else {}
-        )
-        promoted_metric_slugs = _promoted_top_level_metric_slugs(
-            db, organization_id
-        )
-        kept_metrics: List[Dict[str, Any]] = []
-        seen_metrics: set[str] = set()
-        for d in discovered_metrics_payload:
-            if not isinstance(d, dict):
-                continue
-            slug = _slug_label(d.get("key") or d.get("name"))
-            slug = _resolve_alias(flat_alias_map, slug)
-            if (
-                not slug
-                or slug in promoted_metric_slugs
-                or slug in seen_metrics
-            ):
-                continue
-            seen_metrics.add(slug)
-            new_entry = dict(d)
-            new_entry["key"] = slug
-            kept_metrics.append(new_entry)
-        if kept_metrics:
-            metric_scores[DISCOVERED_METRICS_KEY] = kept_metrics
-        else:
-            # No survivors — drop the empty array so empty-discovery rows
-            # keep their pre-feature payload shape.
-            metric_scores.pop(DISCOVERED_METRICS_KEY, None)
-
-    return metric_scores
-
-
-def _alias_map_for_parent(
-    evaluation: CallImportEvaluation, parent_metric_id: UUID
-) -> Dict[str, str]:
-    """Pull ``{from_slug: to_slug}`` for one parent out of the eval's blob.
-
-    Stored shape on the evaluation row is
-    ``{parent_id_str: {from_slug: to_slug, ...}}``. Returns an empty
-    dict for parents that have never had a merge applied.
-    """
-    raw = getattr(evaluation, "discovered_label_aliases", None)
-    if not isinstance(raw, dict):
-        return {}
-    submap = raw.get(str(parent_metric_id))
-    if not isinstance(submap, dict):
-        return {}
-    return {
-        str(k): str(v)
-        for k, v in submap.items()
-        if isinstance(k, str) and isinstance(v, str)
-    }
-
-
-def _promoted_child_slugs(
-    db: Session, parent_metric_id: UUID, organization_id: UUID
-) -> set[str]:
-    """Slugs of every real child currently sitting under the parent.
-
-    The Discovered Labels panel hides any candidate whose slug already
-    matches a real child — that covers both freshly-promoted candidates
-    and legacy children the LLM happened to re-discover. We pull from
-    the live ``metrics`` table rather than the eval's
-    ``selected_metric_groups`` snapshot so newly-promoted children take
-    effect immediately, even on evaluations that ran before the
-    promotion.
-    """
-    children = (
-        db.query(Metric.name)
-        .filter(
-            Metric.parent_metric_id == parent_metric_id,
-            Metric.organization_id == organization_id,
-        )
-        .all()
-    )
-    out: set[str] = set()
-    for (name,) in children:
-        slug = _slug_label(name)
-        if slug:
-            out.add(slug)
-    return out
-
-
-def _promoted_top_level_metric_slugs(
-    db: Session, organization_id: UUID
-) -> set[str]:
-    """Slugs of every top-level (non-child) Metric in the organization.
-
-    Used to suppress discovered-metric candidates whose slug already
-    matches a real standalone metric. We intentionally include both
-    standalone metrics AND parent category metrics — a top-level
-    discovery that collides with either name is a duplicate by
-    definition.
-    """
-    rows = (
-        db.query(Metric.name)
-        .filter(
-            Metric.organization_id == organization_id,
-            Metric.parent_metric_id.is_(None),
-        )
-        .all()
-    )
-    out: set[str] = set()
-    for (name,) in rows:
-        slug = _slug_label(name)
-        if slug:
-            out.add(slug)
-    return out
-
-
-def _get_running_discovered_labels(
-    db: Session,
-    eval_id: UUID,
-    parent_metric_id: UUID,
-    organization_id: Optional[UUID] = None,
-    alias_map: Optional[Dict[str, str]] = None,
-) -> List[Dict[str, Any]]:
-    """Slug-deduped view of every discovered label seen in this eval so far.
-
-    Walks each ``call_import_evaluation_rows`` row's
-    ``metric_scores[parent_id]["discovered_labels"]`` and folds entries
-    that share the same slug. Returns a list ordered by descending
-    count and stable on label key, shaped like::
-
-        [{"key": "customer_on_hold", "name": "Customer put on hold",
-          "description": "...", "sample_rationale": "...", "count": 12}]
-
-    Powers two callers:
-      * The worker prompt builder ("REUSE the existing key if it fits")
-        — invoked just before each row's LLM call to feed the model the
-        running list of previously-discovered labels in this evaluation.
-      * The ``/discovered-labels`` API surface used by the frontend
-        Discovered Labels panel to render candidates with counts +
-        sample rationales.
-
-    Non-completed rows are skipped: an in-flight row's discoveries are
-    not yet reliable (the row could fail and never produce final
-    metric_scores). We accept the tradeoff that rows running
-    concurrently won't see each other's labels — slug-collision dedup
-    catches identical re-inventions, and near-paraphrases surface in
-    the UI panel where the user can manually merge.
-    """
-
-    parent_id_str = str(parent_metric_id)
-    from app.db_sharding.eval_rows import load_evaluation_rows_for_run
-
-    eval_rows = load_evaluation_rows_for_run(db, eval_id)
-    rows = [
-        (row.metric_scores,)
-        for row in eval_rows
-        if row.status == CallImportRowStatus.COMPLETED.value
-    ]
-
-    # Suppress slugs that have either:
-    #  * been promoted to a real child of the parent (so the panel doesn't
-    #    keep nagging the user about a candidate they've already
-    #    accepted), or
-    #  * been merged INTO another slug (the "from" side of a merge) —
-    #    those occurrences fold into the canonical target instead.
-    promoted_slugs: set[str] = set()
-    if organization_id is not None:
-        promoted_slugs = _promoted_child_slugs(
-            db, parent_metric_id, organization_id
-        )
-    aliases = alias_map or {}
-
-    by_key: Dict[str, Dict[str, Any]] = {}
-    for (scores,) in rows:
-        if not isinstance(scores, dict):
-            continue
-        parent_entry = scores.get(parent_id_str)
-        if not isinstance(parent_entry, dict):
-            continue
-        discovered = parent_entry.get("discovered_labels")
-        if not isinstance(discovered, list):
-            continue
-        for entry in discovered:
-            if not isinstance(entry, dict):
-                continue
-            raw_key = entry.get("key") or entry.get("name")
-            key = _slug_label(raw_key)
-            if not key:
-                continue
-            # Apply user merges + deletions first, THEN drop anything
-            # that ended up on a real child slug. Order matters: a
-            # candidate that was merged into a slug which has since
-            # been promoted should disappear, not show up at the
-            # canonical slug. An empty resolved key means the slug was
-            # tombstoned via the delete endpoint.
-            key = _resolve_alias(aliases, key)
-            if not key or key in promoted_slugs:
-                continue
-            name = (entry.get("name") or "").strip() or key.replace("_", " ")
-            description = (entry.get("description") or "").strip() or None
-            sample = (entry.get("rationale") or "").strip() or None
-
-            existing = by_key.get(key)
-            if existing is None:
-                # Track up to N=3 distinct rationales per candidate so
-                # the Promote-to-child flow can pre-fill the new
-                # sub-metric's rubric with concrete LLM examples
-                # without the user copy-pasting from the row table.
-                # ``sample_rationale`` is preserved for back-compat
-                # with older clients; ``examples`` is the new field.
-                examples = [sample] if sample else []
-                by_key[key] = {
-                    "key": key,
-                    "name": name,
-                    "description": description,
-                    "sample_rationale": sample,
-                    "examples": examples,
-                    "count": 1,
-                }
-                continue
-
-            existing["count"] += 1
-            if not existing["description"] and description:
-                existing["description"] = description
-            if not existing["sample_rationale"] and sample:
-                existing["sample_rationale"] = sample
-            # Append distinct rationales (case-insensitive trim) up
-            # to a small cap. Headroom is intentionally one above
-            # what the UI surfaces (2) so we have a backup when the
-            # first rationale is unhelpful.
-            if sample:
-                ex_list: List[str] = existing.setdefault("examples", [])
-                if len(ex_list) < 3 and not any(
-                    s.strip().lower() == sample.strip().lower() for s in ex_list
-                ):
-                    ex_list.append(sample)
-
-    return sorted(
-        by_key.values(),
-        key=lambda item: (-item["count"], item["key"]),
-    )
-
-
-def _get_running_discovered_metrics(
-    db: Session,
-    eval_id: UUID,
-    organization_id: Optional[UUID] = None,
-    alias_map: Optional[Dict[str, str]] = None,
-) -> List[Dict[str, Any]]:
-    """Slug-deduped view of every discovered top-level metric in this eval.
-
-    Mirrors :func:`_get_running_discovered_labels` but is keyed at the
-    evaluation level (no ``parent_metric_id``). Walks each completed
-    row's ``metric_scores[DISCOVERED_METRICS_KEY]`` list, folds entries
-    that share the same slug (post-alias resolution), and suppresses
-    slugs that already correspond to a real top-level :class:`Metric`
-    in the organization.
-
-    Each returned entry is shaped::
-
-        {"key": "customer_satisfaction",
-         "name": "Customer Satisfaction",
-         "description": "...",
-         "suggested_type": "boolean" | "rating" | "category",
-         "sample_rationale": "...",
-         "examples": ["..."],
-         "count": 12}
-    """
-
-    from app.db_sharding.eval_rows import load_evaluation_rows_for_run
-
-    eval_rows = load_evaluation_rows_for_run(db, eval_id)
-    rows = [
-        (row.metric_scores,)
-        for row in eval_rows
-        if row.status == CallImportRowStatus.COMPLETED.value
-    ]
-
-    promoted_slugs: set[str] = set()
-    if organization_id is not None:
-        promoted_slugs = _promoted_top_level_metric_slugs(
-            db, organization_id
-        )
-    aliases = alias_map or {}
-
-    by_key: Dict[str, Dict[str, Any]] = {}
-    for (scores,) in rows:
-        if not isinstance(scores, dict):
-            continue
-        discovered = scores.get(DISCOVERED_METRICS_KEY)
-        if not isinstance(discovered, list):
-            continue
-        for entry in discovered:
-            if not isinstance(entry, dict):
-                continue
-            raw_key = entry.get("key") or entry.get("name")
-            key = _slug_label(raw_key)
-            if not key:
-                continue
-            # Apply user merges + deletions first, THEN drop anything
-            # that ended up on an already-existing top-level metric
-            # slug. Empty resolved key = tombstoned.
-            key = _resolve_alias(aliases, key)
-            if not key or key in promoted_slugs:
-                continue
-            name = (entry.get("name") or "").strip() or key.replace(
-                "_", " "
-            )
-            description = (entry.get("description") or "").strip() or None
-            sample = (entry.get("rationale") or "").strip() or None
-            raw_type = str(entry.get("suggested_type") or "").strip().lower()
-            if raw_type not in _DISCOVERED_METRIC_TYPES:
-                raw_type = "boolean"
-
-            existing = by_key.get(key)
-            if existing is None:
-                examples = [sample] if sample else []
-                by_key[key] = {
-                    "key": key,
-                    "name": name,
-                    "description": description,
-                    "suggested_type": raw_type,
-                    "sample_rationale": sample,
-                    "examples": examples,
-                    "count": 1,
-                }
-                continue
-
-            existing["count"] += 1
-            if not existing["description"] and description:
-                existing["description"] = description
-            if not existing["sample_rationale"] and sample:
-                existing["sample_rationale"] = sample
-            # Keep the most-frequently-suggested type. We don't track
-            # per-type frequency yet; defer to the first non-default
-            # type encountered when the existing entry has the default.
-            if existing.get("suggested_type") == "boolean" and raw_type != "boolean":
-                existing["suggested_type"] = raw_type
-            if sample:
-                ex_list: List[str] = existing.setdefault("examples", [])
-                if len(ex_list) < 3 and not any(
-                    s.strip().lower() == sample.strip().lower() for s in ex_list
-                ):
-                    ex_list.append(sample)
-
-    return sorted(
-        by_key.values(),
-        key=lambda item: (-item["count"], item["key"]),
-    )
-
-
-def _build_flow_graph(
-    eval_rows: List[CallImportEvaluationRow],
-    parent_metric: Metric,
-    children: List[Metric],
-    alias_map: Optional[Dict[str, str]] = None,
-    extra_children: Optional[List[Metric]] = None,
-) -> MetricFlowResponse:
-    """Walk per-row ``sequence`` arrays and produce aggregate nodes/edges.
-
-    A synthetic ``START`` node is prepended to every sequence so the
-    diagram has a single origin. Children that never appear in any
-    sequence are still emitted as nodes (count=0) so the UI can render
-    them in the legend.
-
-    ``alias_map`` lets callers fold merged-out discovered slugs into
-    their canonical target before building the graph; ``extra_children``
-    are children of the parent that aren't in the legend list (e.g.
-    children promoted *after* the evaluation was created and therefore
-    missing from ``selected_metric_groups``) but should still resolve in
-    sequences so the slug doesn't get redrawn as a discovered candidate.
-    """
-    parent_id_str = str(parent_metric.id)
-    aliases = alias_map or {}
-    # Build a fast lookup keyed by both the lower_snake child key (what the
-    # LLM emits in ``sequence``) and the child UUID (what some clients may
-    # store) so legacy / drifted payloads still resolve.
-    child_lookup: Dict[str, Metric] = {}
-    for child in children:
-        slug = _slug_label(child.name)
-        child_lookup[slug] = child
-        child_lookup[str(child.id)] = child
-    # ``extra_children`` are resolved-only — they shouldn't add legend
-    # nodes (those come from the explicit ``children`` argument), but
-    # they need to be in ``child_lookup`` so a sequence step that
-    # matches a freshly-promoted child resolves to the real child UUID
-    # instead of falling through to ``discovered_lookup`` and rendering
-    # as a "discovered" node.
-    if extra_children:
-        for child in extra_children:
-            slug = _slug_label(child.name)
-            if slug and slug not in child_lookup:
-                child_lookup[slug] = child
-            cid = str(child.id)
-            child_lookup.setdefault(cid, child)
-
-    # Discovered labels: walk every row's discovered_labels first so we
-    # know which discovered slugs are valid before resolving sequences.
-    # Discovered nodes get a ``disc:`` prefixed id so they can't collide
-    # with real child UUIDs in the node/edge graph. We apply
-    # ``alias_map`` first so merged-out source slugs fold into their
-    # canonical target — preserving the user's "merge" intent on still-
-    # in-flight rows whose JSON wasn't rewritten by the merge endpoint.
-    discovered_lookup: Dict[str, Dict[str, Any]] = {}
-    for row in eval_rows:
-        scores = (
-            row.metric_scores if isinstance(row.metric_scores, dict) else {}
-        )
-        parent_entry = scores.get(parent_id_str)
-        if not isinstance(parent_entry, dict):
-            continue
-        raw_discovered = parent_entry.get("discovered_labels")
-        if not isinstance(raw_discovered, list):
-            continue
-        for entry in raw_discovered:
-            if not isinstance(entry, dict):
-                continue
-            slug = _slug_label(entry.get("key") or entry.get("name"))
-            slug = _resolve_alias(aliases, slug)
-            if not slug or slug in child_lookup:
-                continue
-            name = (entry.get("name") or "").strip() or slug.replace("_", " ")
-            existing = discovered_lookup.get(slug)
-            if existing is None:
-                discovered_lookup[slug] = {
-                    "id": f"{_DISCOVERED_NODE_PREFIX}{slug}",
-                    "name": name,
-                }
-
-    node_counts: Dict[str, int] = {}
-    edge_counts: Dict[tuple[str, str], int] = {}
-    terminal_counts: Dict[str, int] = {}
-
-    total_rows = len(eval_rows)
-    rows_with_sequence = 0
-
-    for row in eval_rows:
-        scores = (
-            row.metric_scores if isinstance(row.metric_scores, dict) else {}
-        )
-        parent_entry = scores.get(parent_id_str)
-        if not isinstance(parent_entry, dict):
-            continue
-        raw_sequence = parent_entry.get("sequence")
-        if not isinstance(raw_sequence, list):
-            continue
-
-        resolved_ids: List[str] = []
-        last_resolved: Optional[str] = None
-        for item in raw_sequence:
-            if not isinstance(item, str):
-                continue
-            normalized = _resolve_alias(aliases, _slug_label(item))
-            child = child_lookup.get(normalized) or child_lookup.get(item)
-            if child is not None:
-                cid = str(child.id)
-                # Adjacent dedupe AFTER alias resolution so two
-                # different raw slugs that fold to the same target
-                # don't draw a self-edge through the chart.
-                if cid == last_resolved:
-                    continue
-                resolved_ids.append(cid)
-                last_resolved = cid
-                continue
-            disc = discovered_lookup.get(normalized)
-            if disc is not None:
-                if disc["id"] == last_resolved:
-                    continue
-                resolved_ids.append(disc["id"])
-                last_resolved = disc["id"]
-
-        if not resolved_ids:
-            continue
-
-        rows_with_sequence += 1
-        for nid in resolved_ids:
-            node_counts[nid] = node_counts.get(nid, 0) + 1
-
-        edge_counts[(_FLOW_START_NODE_ID, resolved_ids[0])] = (
-            edge_counts.get((_FLOW_START_NODE_ID, resolved_ids[0]), 0) + 1
-        )
-        for src, tgt in zip(resolved_ids, resolved_ids[1:]):
-            if src == tgt:
-                continue
-            edge_counts[(src, tgt)] = edge_counts.get((src, tgt), 0) + 1
-
-        terminal_id = resolved_ids[-1]
-        terminal_counts[terminal_id] = terminal_counts.get(terminal_id, 0) + 1
-
-    nodes: List[MetricFlowNode] = []
-    # Always include a START node so the UI has a stable entry point.
-    nodes.append(
-        MetricFlowNode(
-            id=_FLOW_START_NODE_ID,
-            label="Start",
-            count=rows_with_sequence,
-            is_terminal=False,
-        )
-    )
-
-    def _emit_child_node(child: Metric) -> None:
-        cid = str(child.id)
-        count = node_counts.get(cid, 0)
-        terminal_count = terminal_counts.get(cid, 0)
-        is_terminal = False
-        if rows_with_sequence > 0:
-            is_terminal = (
-                terminal_count / rows_with_sequence
-            ) >= _FLOW_TERMINAL_THRESHOLD
-        nodes.append(
-            MetricFlowNode(
-                id=cid,
-                label=child.name,
-                count=count,
-                is_terminal=is_terminal,
-            )
-        )
-
-    emitted_child_ids: set[str] = set()
-    for child in children:
-        cid = str(child.id)
-        if cid in emitted_child_ids:
-            continue
-        emitted_child_ids.add(cid)
-        _emit_child_node(child)
-    # Extra children (promoted after the eval was created) only get
-    # legend nodes if they actually appear in the data — otherwise we'd
-    # pollute the diagram with every standalone promotion the user has
-    # ever made under this parent.
-    if extra_children:
-        for child in extra_children:
-            cid = str(child.id)
-            if cid in emitted_child_ids:
-                continue
-            if node_counts.get(cid, 0) == 0:
-                continue
-            emitted_child_ids.add(cid)
-            _emit_child_node(child)
-    # Append discovered nodes after the real children so legend ordering
-    # keeps user-defined labels first.
-    for slug, info in discovered_lookup.items():
-        nid = info["id"]
-        count = node_counts.get(nid, 0)
-        terminal_count = terminal_counts.get(nid, 0)
-        is_terminal = False
-        if rows_with_sequence > 0:
-            is_terminal = (
-                terminal_count / rows_with_sequence
-            ) >= _FLOW_TERMINAL_THRESHOLD
-        nodes.append(
-            MetricFlowNode(
-                id=nid,
-                label=info["name"],
-                count=count,
-                is_terminal=is_terminal,
-                is_discovered=True,
-            )
-        )
-
-    edges: List[MetricFlowEdge] = [
-        MetricFlowEdge(source=src, target=tgt, count=count)
-        for (src, tgt), count in sorted(
-            edge_counts.items(), key=lambda kv: kv[1], reverse=True
-        )
-    ]
-
-    return MetricFlowResponse(
-        parent_metric_id=parent_id_str,
-        parent_metric_name=parent_metric.name,
-        selection_mode=parent_metric.selection_mode,
-        nodes=nodes,
-        edges=edges,
-        total_rows=total_rows,
-        rows_with_sequence=rows_with_sequence,
-    )
-
-
-@router.get(
-    "/{eval_id}/flow",
-    response_model=MetricFlowResponse,
-    operation_id="getCallImportEvaluationFlow",
-)
-async def get_call_import_evaluation_flow(
-    call_import_id: UUID,
-    eval_id: UUID,
-    parent_metric_id: UUID = Query(
-        ...,
-        description=(
-            "Parent (category) metric whose children's sequences should be "
-            "aggregated into a flow graph."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> MetricFlowResponse:
-    """Aggregate the LLM-inferred per-row sequences into one flow graph.
-
-    Returns ``nodes`` (one per child of the parent metric, plus a
-    synthetic ``START`` node) and ``edges`` (counts of consecutive
-    label transitions across every row that produced a sequence). The
-    frontend feeds this directly into a React Flow / xyflow canvas;
-    edge thickness should scale with ``count / total_rows`` and
-    ``is_terminal`` nodes should be styled as outcomes.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    parent = (
-        db.query(Metric)
-        .filter(
-            Metric.id == parent_metric_id,
-            Metric.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not parent:
-        raise HTTPException(
-            status_code=404,
-            detail="Parent metric not found in this organization.",
-        )
-    if not parent.selection_mode:
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                "Flow charts are only meaningful for parent metrics "
-                "(selection_mode set). This metric is standalone."
-            ),
-        )
-
-    # Children are taken from selected_metric_groups when present so the
-    # flow chart reflects exactly the subset that ran in this
-    # evaluation; otherwise fall back to every enabled child of the
-    # parent.
-    groups_raw = (
-        evaluation.selected_metric_groups
-        if isinstance(evaluation.selected_metric_groups, dict)
-        else {}
-    )
-    parent_id_str = str(parent.id)
-    children: List[Metric] = []
-    if parent_id_str in groups_raw and isinstance(
-        groups_raw[parent_id_str], list
-    ):
-        child_ids: List[UUID] = []
-        for c in groups_raw[parent_id_str]:
-            try:
-                child_ids.append(UUID(str(c)))
-            except (TypeError, ValueError):
-                continue
-        if child_ids:
-            children = (
-                db.query(Metric)
-                .filter(
-                    Metric.organization_id == organization_id,
-                    Metric.id.in_(child_ids),
-                )
-                .order_by(Metric.created_at.asc())
-                .all()
-            )
-    if not children:
-        children = (
-            db.query(Metric)
-            .filter(
-                Metric.organization_id == organization_id,
-                Metric.parent_metric_id == parent.id,
-            )
-            .order_by(Metric.created_at.asc())
-            .all()
-        )
-
-    # Children promoted AFTER this evaluation was created aren't in
-    # ``selected_metric_groups`` but their slugs still appear in already-
-    # scored rows' sequences. Pass them as ``extra_children`` so those
-    # sequence entries resolve against the real (now promoted) child
-    # instead of being redrawn as discovered candidates.
-    extra_children: List[Metric] = []
-    if children:
-        existing_ids = {child.id for child in children}
-        all_children = (
-            db.query(Metric)
-            .filter(
-                Metric.organization_id == organization_id,
-                Metric.parent_metric_id == parent.id,
-            )
-            .all()
-        )
-        extra_children = [c for c in all_children if c.id not in existing_ids]
-
-    eval_rows = _load_eval_rows(db, eval_id)
-
-    alias_map = _alias_map_for_parent(evaluation, parent.id)
-    return _build_flow_graph(
-        eval_rows,
-        parent,
-        children,
-        alias_map=alias_map,
-        extra_children=extra_children,
-    )
-
-
-@router.get(
-    "/{eval_id}/discovered-labels",
-    response_model=DiscoveredLabelsResponse,
-    operation_id="getCallImportEvaluationDiscoveredLabels",
-)
-async def get_call_import_evaluation_discovered_labels(
-    call_import_id: UUID,
-    eval_id: UUID,
-    parent_metric_id: UUID = Query(
-        ...,
-        description=(
-            "Parent (category) metric whose LLM-discovered candidate "
-            "sub-labels should be aggregated across rows."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> DiscoveredLabelsResponse:
-    """Aggregate candidate sub-labels the LLM discovered during this eval.
-
-    Only meaningful for parents with ``allow_discovery=true``; for other
-    parents we just return an empty ``items`` list rather than 400-ing
-    so the frontend can call the endpoint unconditionally for every
-    parent on the Flow tab without branching.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    parent = (
-        db.query(Metric)
-        .filter(
-            Metric.id == parent_metric_id,
-            Metric.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not parent:
-        raise HTTPException(
-            status_code=404,
-            detail="Parent metric not found in this organization.",
-        )
-
-    alias_map = _alias_map_for_parent(evaluation, parent_metric_id)
-    items_raw = _get_running_discovered_labels(
-        db,
-        eval_id,
-        parent_metric_id,
-        organization_id=organization_id,
-        alias_map=alias_map,
-    )
-    items = [DiscoveredLabelItem(**item) for item in items_raw]
-    return DiscoveredLabelsResponse(
-        parent_metric_id=str(parent.id), items=items
-    )
-
-
-@router.post(
-    "/{eval_id}/discovered-labels/merge",
-    response_model=DiscoveredLabelsResponse,
-    operation_id="mergeCallImportEvaluationDiscoveredLabels",
-)
-async def merge_call_import_evaluation_discovered_labels(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: DiscoveredLabelMergeRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> DiscoveredLabelsResponse:
-    """Rewrite every row's ``discovered_labels`` entry from from_key -> to_key.
-
-    Idempotent — re-merging the same pair is a no-op. Discovered slugs
-    inside per-row ``sequence`` arrays are also rewritten so the flow
-    chart stays consistent with the panel. When a row already has
-    ``to_key`` and we're merging ``from_key`` into it, we drop the
-    ``from_key`` entry instead of producing two entries with the same
-    slug.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    parent = (
-        db.query(Metric)
-        .filter(
-            Metric.id == body.parent_metric_id,
-            Metric.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not parent:
-        raise HTTPException(
-            status_code=404,
-            detail="Parent metric not found in this organization.",
-        )
-
-    from_key = _slug_label(body.from_key)
-    to_key = _slug_label(body.to_key)
-    if not from_key or not to_key:
-        raise HTTPException(
-            status_code=400,
-            detail="from_key and to_key must be non-empty slugs.",
-        )
-    if from_key == to_key:
-        # No-op; just return the current aggregate so the client can
-        # refresh its view.
-        alias_map_existing = _alias_map_for_parent(evaluation, parent.id)
-        items_raw = _get_running_discovered_labels(
-            db,
-            eval_id,
-            body.parent_metric_id,
-            organization_id=organization_id,
-            alias_map=alias_map_existing,
-        )
-        return DiscoveredLabelsResponse(
-            parent_metric_id=str(parent.id),
-            items=[DiscoveredLabelItem(**item) for item in items_raw],
-        )
-
-    parent_id_str = str(parent.id)
-    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
-
-    def _merge_discovered_label_row(row: CallImportEvaluationRow) -> bool:
-        scores = (
-            row.metric_scores
-            if isinstance(row.metric_scores, dict)
-            else None
-        )
-        if not scores:
-            return False
-        parent_entry = scores.get(parent_id_str)
-        if not isinstance(parent_entry, dict):
-            return False
-
-        mutated = False
-        discovered = parent_entry.get("discovered_labels")
-        if isinstance(discovered, list):
-            kept: List[Dict[str, Any]] = []
-            existing_to = next(
-                (
-                    e
-                    for e in discovered
-                    if isinstance(e, dict)
-                    and _slug_label(e.get("key") or e.get("name")) == to_key
-                ),
-                None,
-            )
-            for entry in discovered:
-                if not isinstance(entry, dict):
-                    kept.append(entry)
-                    continue
-                key = _slug_label(entry.get("key") or entry.get("name"))
-                if key == from_key:
-                    if existing_to is not None:
-                        mutated = True
-                        continue
-                    new_entry = dict(entry)
-                    new_entry["key"] = to_key
-                    kept.append(new_entry)
-                    mutated = True
-                else:
-                    kept.append(entry)
-            if mutated:
-                parent_entry["discovered_labels"] = kept
-
-        seq = parent_entry.get("sequence")
-        if isinstance(seq, list):
-            new_seq: List[str] = []
-            seq_changed = False
-            last_added: Optional[str] = None
-            for item in seq:
-                if isinstance(item, str) and _slug_label(item) == from_key:
-                    seq_changed = True
-                    if last_added == to_key:
-                        continue
-                    new_seq.append(to_key)
-                    last_added = to_key
-                else:
-                    new_seq.append(item)
-                    last_added = (
-                        _slug_label(item) if isinstance(item, str) else None
-                    )
-            if seq_changed:
-                parent_entry["sequence"] = new_seq
-                mutated = True
-
-        if mutated:
-            row.metric_scores = dict(scores)
-        return mutated
-
-    foreach_evaluation_row_mutating(db, eval_id, _merge_discovered_label_row)
-
-    # Persist the merge at the evaluation level too. This is what makes
-    # the merge survive future scoring: rows that finish AFTER this
-    # call (e.g. retries, in-flight workers) will go through the
-    # alias map in the API surface even if the per-row JSON they
-    # write still mentions ``from_key``. We chain through any existing
-    # alias so merging A→B and then B→C resolves A→C in the panel.
-    raw_aliases = (
-        evaluation.discovered_label_aliases
-        if isinstance(evaluation.discovered_label_aliases, dict)
-        else {}
-    )
-    aliases_top = dict(raw_aliases)
-    parent_aliases = dict(aliases_top.get(parent_id_str) or {})
-    # Resolve transitively: if to_key itself was previously merged into
-    # something else, point from_key at the canonical end-of-chain.
-    canonical_to = _resolve_alias(parent_aliases, to_key)
-    parent_aliases[from_key] = canonical_to
-    # Re-target any earlier aliases that pointed AT from_key — without
-    # this, A→B and then B→C would leave A still pointing to B (now a
-    # broken pointer because B is gone). Rewriting them keeps the
-    # alias map self-consistent.
-    for k, v in list(parent_aliases.items()):
-        if v == from_key:
-            parent_aliases[k] = canonical_to
-    aliases_top[parent_id_str] = parent_aliases
-    evaluation.discovered_label_aliases = aliases_top
-
-    db.commit()
-
-    alias_map_after = _alias_map_for_parent(evaluation, parent.id)
-    items_raw = _get_running_discovered_labels(
-        db,
-        eval_id,
-        body.parent_metric_id,
-        organization_id=organization_id,
-        alias_map=alias_map_after,
-    )
-    return DiscoveredLabelsResponse(
-        parent_metric_id=str(parent.id),
-        items=[DiscoveredLabelItem(**item) for item in items_raw],
-    )
-
-
-@router.post(
-    "/{eval_id}/discovered-labels/delete",
-    response_model=DiscoveredLabelsResponse,
-    operation_id="deleteCallImportEvaluationDiscoveredLabel",
-)
-async def delete_call_import_evaluation_discovered_label(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: DiscoveredLabelDeleteRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> DiscoveredLabelsResponse:
-    """Tombstone a single LLM-discovered candidate for this evaluation.
-
-    Symmetric with the merge endpoint, but instead of redirecting the
-    slug at another candidate we mark it as deleted. After this call:
-
-      * the slug is stripped from every row's
-        ``metric_scores[parent].discovered_labels`` list, and from
-        every row's ``sequence`` array (so the flow chart no longer
-        draws a node for it);
-      * the slug is recorded in
-        ``evaluation.discovered_label_aliases[parent][slug] = ""``
-        so any worker that finishes a row AFTER this call (e.g. a row
-        still in flight when the user clicked Delete) silently drops
-        the slug instead of resurrecting it.
-
-    Idempotent: deleting an already-deleted slug is a no-op.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    parent = (
-        db.query(Metric)
-        .filter(
-            Metric.id == body.parent_metric_id,
-            Metric.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not parent:
-        raise HTTPException(
-            status_code=404,
-            detail="Parent metric not found in this organization.",
-        )
-
-    target_key = _slug_label(body.key)
-    if not target_key:
-        raise HTTPException(
-            status_code=400,
-            detail="key must be a non-empty slug.",
-        )
-
-    parent_id_str = str(parent.id)
-    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
-
-    def _delete_discovered_label_row(row: CallImportEvaluationRow) -> bool:
-        scores = (
-            row.metric_scores
-            if isinstance(row.metric_scores, dict)
-            else None
-        )
-        if not scores:
-            return False
-        parent_entry = scores.get(parent_id_str)
-        if not isinstance(parent_entry, dict):
-            return False
-
-        mutated = False
-        discovered = parent_entry.get("discovered_labels")
-        if isinstance(discovered, list):
-            kept = [
-                e
-                for e in discovered
-                if not (
-                    isinstance(e, dict)
-                    and _slug_label(e.get("key") or e.get("name"))
-                    == target_key
-                )
-            ]
-            if len(kept) != len(discovered):
-                parent_entry["discovered_labels"] = kept
-                mutated = True
-
-        seq = parent_entry.get("sequence")
-        if isinstance(seq, list):
-            new_seq: List[str] = []
-            seq_changed = False
-            last_added: Optional[str] = None
-            for item in seq:
-                if isinstance(item, str) and _slug_label(item) == target_key:
-                    seq_changed = True
-                    continue
-                if isinstance(item, str):
-                    norm = _slug_label(item)
-                    if norm == last_added:
-                        seq_changed = True
-                        continue
-                    last_added = norm
-                new_seq.append(item)
-            if seq_changed:
-                parent_entry["sequence"] = new_seq
-                mutated = True
-
-        if mutated:
-            row.metric_scores = dict(scores)
-        return mutated
-
-    foreach_evaluation_row_mutating(db, eval_id, _delete_discovered_label_row)
-
-    # 3. Persist the tombstone on the evaluation so workers that finish
-    # later don't re-surface the deleted slug. We also retarget any
-    # existing aliases whose ``to_key`` was the deleted slug — without
-    # this, a previous merge that pointed at this slug would leave a
-    # dangling pointer.
-    raw_aliases = (
-        evaluation.discovered_label_aliases
-        if isinstance(evaluation.discovered_label_aliases, dict)
-        else {}
-    )
-    aliases_top = dict(raw_aliases)
-    parent_aliases = dict(aliases_top.get(parent_id_str) or {})
-    parent_aliases[target_key] = ""  # deletion sentinel
-    for k, v in list(parent_aliases.items()):
-        if v == target_key:
-            parent_aliases[k] = ""
-    aliases_top[parent_id_str] = parent_aliases
-    evaluation.discovered_label_aliases = aliases_top
-
-    db.commit()
-
-    alias_map_after = _alias_map_for_parent(evaluation, parent.id)
-    items_raw = _get_running_discovered_labels(
-        db,
-        eval_id,
-        body.parent_metric_id,
-        organization_id=organization_id,
-        alias_map=alias_map_after,
-    )
-    return DiscoveredLabelsResponse(
-        parent_metric_id=str(parent.id),
-        items=[DiscoveredLabelItem(**item) for item in items_raw],
-    )
-
-
-# ---------------------------------------------------------------------------
-# Discovered TOP-LEVEL METRICS (per-evaluation discovery)
-#
-# These endpoints are the parallel of the discovered-labels trio above but
-# scoped to the evaluation as a whole instead of to a parent metric. They
-# all live under ``/{eval_id}/discovered-metrics`` and operate on the
-# reserved ``DISCOVERED_METRICS_KEY`` slot of each per-row
-# ``metric_scores`` plus the flat ``CallImportEvaluation.discovered_metric_aliases``
-# map (no parent-id nesting).
-# ---------------------------------------------------------------------------
-
-
-def _flat_metric_aliases(
-    evaluation: CallImportEvaluation,
-) -> Dict[str, str]:
-    """Pull the flat ``{from_slug: to_slug}`` map for an evaluation."""
-    raw = getattr(evaluation, "discovered_metric_aliases", None)
-    if not isinstance(raw, dict):
-        return {}
-    return {
-        str(k): str(v)
-        for k, v in raw.items()
-        if isinstance(k, str) and isinstance(v, str)
-    }
-
-
-@router.get(
-    "/{eval_id}/discovered-metrics",
-    response_model=DiscoveredMetricsResponse,
-    operation_id="getCallImportEvaluationDiscoveredMetrics",
-)
-async def get_call_import_evaluation_discovered_metrics(
-    call_import_id: UUID,
-    eval_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> DiscoveredMetricsResponse:
-    """Aggregate top-level metric candidates the LLM discovered during this eval.
-
-    Returns an empty ``items`` list when the evaluation did not opt
-    into top-level metric discovery; this keeps the frontend able to
-    call the endpoint unconditionally without branching on the
-    evaluation's ``discover_new_metrics`` flag.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    if not bool(getattr(evaluation, "discover_new_metrics", False)):
-        return DiscoveredMetricsResponse(evaluation_id=evaluation.id, items=[])
-
-    items_raw = _get_running_discovered_metrics(
-        db,
-        eval_id,
-        organization_id=organization_id,
-        alias_map=_flat_metric_aliases(evaluation),
-    )
-    return DiscoveredMetricsResponse(
-        evaluation_id=evaluation.id,
-        items=[DiscoveredMetricItem(**item) for item in items_raw],
-    )
-
-
-@router.post(
-    "/{eval_id}/discovered-metrics/merge",
-    response_model=DiscoveredMetricsResponse,
-    operation_id="mergeCallImportEvaluationDiscoveredMetrics",
-)
-async def merge_call_import_evaluation_discovered_metrics(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: DiscoveredMetricMergeRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> DiscoveredMetricsResponse:
-    """Rewrite every row's ``__discovered_metrics__`` entry from→to.
-
-    Mirrors the discovered-labels merge endpoint but operates on the
-    flat top-level metric list. Idempotent — re-merging is a no-op.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    from_key = _slug_label(body.from_key)
-    to_key = _slug_label(body.to_key)
-    if not from_key or not to_key:
-        raise HTTPException(
-            status_code=400,
-            detail="from_key and to_key must be non-empty slugs.",
-        )
-    if from_key == to_key:
-        items_raw = _get_running_discovered_metrics(
-            db,
-            eval_id,
-            organization_id=organization_id,
-            alias_map=_flat_metric_aliases(evaluation),
-        )
-        return DiscoveredMetricsResponse(
-            evaluation_id=evaluation.id,
-            items=[DiscoveredMetricItem(**item) for item in items_raw],
-        )
-
-    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
-
-    def _merge_discovered_metric_row(row: CallImportEvaluationRow) -> bool:
-        scores = (
-            row.metric_scores
-            if isinstance(row.metric_scores, dict)
-            else None
-        )
-        if not scores:
-            return False
-        discovered = scores.get(DISCOVERED_METRICS_KEY)
-        if not isinstance(discovered, list):
-            return False
-
-        kept: List[Dict[str, Any]] = []
-        mutated = False
-        existing_to = next(
-            (
-                e
-                for e in discovered
-                if isinstance(e, dict)
-                and _slug_label(e.get("key") or e.get("name")) == to_key
-            ),
-            None,
-        )
-        for entry in discovered:
-            if not isinstance(entry, dict):
-                kept.append(entry)
-                continue
-            key = _slug_label(entry.get("key") or entry.get("name"))
-            if key == from_key:
-                if existing_to is not None:
-                    mutated = True
-                    continue
-                new_entry = dict(entry)
-                new_entry["key"] = to_key
-                kept.append(new_entry)
-                mutated = True
-            else:
-                kept.append(entry)
-        if mutated:
-            scores[DISCOVERED_METRICS_KEY] = kept
-            row.metric_scores = dict(scores)
-        return mutated
-
-    foreach_evaluation_row_mutating(db, eval_id, _merge_discovered_metric_row)
-
-    raw_aliases = (
-        evaluation.discovered_metric_aliases
-        if isinstance(evaluation.discovered_metric_aliases, dict)
-        else {}
-    )
-    aliases = dict(raw_aliases)
-    canonical_to = _resolve_alias(aliases, to_key)
-    aliases[from_key] = canonical_to
-    for k, v in list(aliases.items()):
-        if v == from_key:
-            aliases[k] = canonical_to
-    evaluation.discovered_metric_aliases = aliases
-
-    db.commit()
-
-    items_raw = _get_running_discovered_metrics(
-        db,
-        eval_id,
-        organization_id=organization_id,
-        alias_map=_flat_metric_aliases(evaluation),
-    )
-    return DiscoveredMetricsResponse(
-        evaluation_id=evaluation.id,
-        items=[DiscoveredMetricItem(**item) for item in items_raw],
-    )
-
-
-@router.post(
-    "/{eval_id}/discovered-metrics/delete",
-    response_model=DiscoveredMetricsResponse,
-    operation_id="deleteCallImportEvaluationDiscoveredMetric",
-)
-async def delete_call_import_evaluation_discovered_metric(
-    call_import_id: UUID,
-    eval_id: UUID,
-    body: DiscoveredMetricDeleteRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> DiscoveredMetricsResponse:
-    """Tombstone a single LLM-discovered top-level metric candidate."""
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    target_key = _slug_label(body.key)
-    if not target_key:
-        raise HTTPException(
-            status_code=400,
-            detail="key must be a non-empty slug.",
-        )
-
-    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
-
-    def _delete_discovered_metric_row(row: CallImportEvaluationRow) -> bool:
-        scores = (
-            row.metric_scores
-            if isinstance(row.metric_scores, dict)
-            else None
-        )
-        if not scores:
-            return False
-        discovered = scores.get(DISCOVERED_METRICS_KEY)
-        if not isinstance(discovered, list):
-            return False
-        kept = [
-            e
-            for e in discovered
-            if not (
-                isinstance(e, dict)
-                and _slug_label(e.get("key") or e.get("name"))
-                == target_key
-            )
-        ]
-        if len(kept) == len(discovered):
-            return False
-        if kept:
-            scores[DISCOVERED_METRICS_KEY] = kept
-        else:
-            scores.pop(DISCOVERED_METRICS_KEY, None)
-        row.metric_scores = dict(scores)
-        return True
-
-    foreach_evaluation_row_mutating(db, eval_id, _delete_discovered_metric_row)
-
-    raw_aliases = (
-        evaluation.discovered_metric_aliases
-        if isinstance(evaluation.discovered_metric_aliases, dict)
-        else {}
-    )
-    aliases = dict(raw_aliases)
-    aliases[target_key] = ""  # tombstone
-    for k, v in list(aliases.items()):
-        if v == target_key:
-            aliases[k] = ""
-    evaluation.discovered_metric_aliases = aliases
-
-    db.commit()
-
-    items_raw = _get_running_discovered_metrics(
-        db,
-        eval_id,
-        organization_id=organization_id,
-        alias_map=_flat_metric_aliases(evaluation),
-    )
-    return DiscoveredMetricsResponse(
-        evaluation_id=evaluation.id,
-        items=[DiscoveredMetricItem(**item) for item in items_raw],
-    )
-
-
-@router.delete(
-    "/{eval_id}/rows/{eval_row_id}",
-    status_code=status.HTTP_204_NO_CONTENT,
-    operation_id="deleteCallImportEvaluationRow",
-)
-async def delete_call_import_evaluation_row(
-    call_import_id: UUID,
-    eval_id: UUID,
-    eval_row_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Response:
-    """Delete a single per-row scoring entry within an evaluation run.
-
-    Useful when the user wants to drop a noisy row before re-exporting
-    the CSV — e.g. a row whose audio was corrupt and skewed the
-    aggregate. Counters on the parent are recomputed so the rolled-up
-    status stays accurate.
-    """
-
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(status_code=404, detail="Call import evaluation not found")
-
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    if is_sharding_enabled():
-        from app.db_sharding.eval_rows import delete_evaluation_row_on_shards
-
-        if not delete_evaluation_row_on_shards(eval_row_id, eval_id):
-            raise HTTPException(
-                status_code=404, detail="Evaluation row not found in this run"
-            )
-        _rollup_evaluation_status(evaluation, db)
-        db.commit()
-        return Response(status_code=status.HTTP_204_NO_CONTENT)
-
-    eval_row = (
-        db.query(CallImportEvaluationRow)
-        .filter(
-            CallImportEvaluationRow.id == eval_row_id,
-            CallImportEvaluationRow.evaluation_id == eval_id,
-        )
-        .first()
-    )
-    if not eval_row:
-        raise HTTPException(
-            status_code=404, detail="Evaluation row not found in this run"
-        )
-
-    # If the row was still in flight, best-effort revoke the worker task
-    # so it doesn't try to write into a deleted DB row mid-execution.
-    if eval_row.celery_task_id and eval_row.status in {"pending", "running"}:
-        try:
-            from app.workers.celery_app import celery_app
-
-            celery_app.control.revoke(eval_row.celery_task_id, terminate=False)
-        except Exception:
-            pass
-
-    db.delete(eval_row)
-    db.flush()
-    _rollup_evaluation_status(evaluation, db)
-    db.commit()
-    return Response(status_code=status.HTTP_204_NO_CONTENT)
-
-
-# ---------------------------------------------------------------------------
-# Retry endpoints
-# ---------------------------------------------------------------------------
-#
-# The create endpoint enqueues every row of a fresh run; these endpoints
-# let the user re-enqueue a *subset* of rows in an existing run — most
-# commonly the ones that failed. We keep the worker contract identical
-# (``evaluate_call_import_row_task(eval_row_id)``), so the retry path
-# only has to reset row state and re-fan-out. When a row is missing its
-# diarised transcript and the run was configured for diarised
-# transcripts, we chain through ``transcribe_call_import_row_task`` the
-# same way the create endpoint does — that's what makes "retry" feel
-# like "just fix it" instead of "fail again immediately".
-
-
-def _prepare_source_row_for_retry(
-    source_row: CallImportRow,
-    *,
-    transcribe_overwrite: bool,
-) -> None:
-    """Clear stale diarisation markers so retry dispatch can re-run the pipeline."""
-    source_row.celery_task_id = None
-
-    # Re-fetch recordings when a prior import failed or stalled without S3 audio.
-    # Mirrors retry_failed_call_import_rows so eval retry can re-enqueue imports.
-    if (
-        source_row.status
-        in (CallImportRowStatus.FAILED, CallImportRowStatus.PROCESSING)
-        and not (source_row.recording_s3_key or "").strip()
-    ):
-        source_row.status = CallImportRowStatus.PENDING
-        source_row.error_message = None
-
-    if transcribe_overwrite and (source_row.diarised_transcript or "").strip():
-        source_row.diarised_transcript = None
-
-    has_dia = bool((source_row.diarised_transcript or "").strip())
-    dia_status = (source_row.diarised_transcript_status or "").strip().lower()
-
-    if has_dia and not transcribe_overwrite:
-        source_row.diarised_transcript_status = "completed"
-        source_row.diarised_transcript_error = None
-        return
-
-    if dia_status in {"failed", "pending", "running", "idle"}:
-        source_row.diarised_transcript_status = "idle"
-        source_row.diarised_transcript_error = None
-
-
-def _reset_eval_row_for_retry(
-    eval_row: CallImportEvaluationRow,
-    *,
-    metric_ids: Optional[List[UUID]] = None,
-    skip_revoke: bool = False,
-) -> None:
-    """Wipe per-row state so the worker can re-run it cleanly.
-
-    Mirrors the initial state used by ``create_call_import_evaluation``
-    when it first inserts a row, with the addition of revoking any
-    lingering Celery task id.
-
-    When ``metric_ids`` is provided, this is a **metric-subset retry**:
-    only the scores for those metrics are removed from
-    ``metric_scores`` (other metrics' previously-computed values are
-    preserved so the worker's partial-merge write keeps them intact).
-    Otherwise the entire ``metric_scores`` dict is reset, matching the
-    legacy behaviour.
-    """
-    if (
-        not skip_revoke
-        and eval_row.celery_task_id
-        and eval_row.status in {"pending", "running"}
-    ):
-        try:
-            from app.workers.celery_app import celery_app
-
-            celery_app.control.revoke(eval_row.celery_task_id, terminate=False)
-        except Exception:  # noqa: BLE001 — revoke is best-effort
-            pass
-    eval_row.status = "pending"
-    eval_row.error_message = None
-    if metric_ids:
-        # Strip ONLY the targeted metric keys. Both string and UUID
-        # forms can appear in ``metric_scores`` depending on which
-        # code path wrote the dict, so we normalise to lower-case
-        # strings for the comparison.
-        existing = (
-            eval_row.metric_scores if isinstance(eval_row.metric_scores, dict) else {}
-        )
-        target_keys = {str(mid).lower() for mid in metric_ids}
-        eval_row.metric_scores = {
-            key: value
-            for key, value in existing.items()
-            if str(key).lower() not in target_keys
-        }
-    else:
-        eval_row.metric_scores = {}
-    eval_row.started_at = None
-    eval_row.finished_at = None
-    eval_row.celery_task_id = None
-
-
-def _enqueue_eval_rows_with_optional_transcribe(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    eval_rows_with_source: List[
-        Tuple[CallImportEvaluationRow, CallImportRow]
-    ],
-    *,
-    transcribe_overwrite: bool = False,
-    restricted_metric_ids: Optional[List[UUID]] = None,
-) -> Tuple[int, int]:
-    """Schedule throttled evaluation dispatch for pending eval rows.
-
-    Returns ``(evaluate_only_count, transcribe_then_evaluate_count)`` for
-    logging/UI compatibility. Actual Celery fan-out is handled by
-    :func:`dispatch_evaluation_rows_task` under Redis fair-share limits.
-    """
-    from app.workers.concurrency.eval_dispatch import _needs_transcribe_for_eval
-    from app.workers.concurrency.fair_dispatch import (
-        schedule_fair_dispatch,
-        store_evaluation_transcribe_overwrite,
-        store_row_restricted_metrics,
-    )
-
-    eval_only_count = 0
-    transcribe_count = 0
-    if eval_rows_with_source:
-        for eval_row, source_row in eval_rows_with_source:
-            if _needs_transcribe_for_eval(
-                evaluation,
-                source_row,
-                transcribe_overwrite=transcribe_overwrite,
-            ):
-                transcribe_count += 1
-            else:
-                eval_only_count += 1
-
-        restricted_metric_ids_str: Optional[List[str]] = (
-            [str(mid) for mid in restricted_metric_ids]
-            if restricted_metric_ids
-            else None
-        )
-        if restricted_metric_ids_str:
-            for eval_row, _ in eval_rows_with_source:
-                store_row_restricted_metrics(eval_row.id, restricted_metric_ids_str)
-    else:
-        restricted_metric_ids_str = (
-            [str(mid) for mid in restricted_metric_ids] if restricted_metric_ids else None
-        )
-    store_evaluation_transcribe_overwrite(
-        evaluation.id,
-        overwrite=transcribe_overwrite,
-    )
-    schedule_fair_dispatch(max_workspace_turns=999)
-    return eval_only_count, transcribe_count
-
-
-def _apply_telephony_retry_overrides(
-    db: Session,
-    *,
-    call_import: CallImport,
-    organization_id: UUID,
-    payload: CallImportEvaluationRetryRequest,
-) -> None:
-    """Pin or clear telephony credentials on the batch for this retry pass."""
-    fields_set = payload.model_fields_set
-    if (
-        "provider" not in fields_set
-        and "telephony_integration_id" not in fields_set
-    ):
-        return
-
-    from app.api.v1.routes.call_imports import _resolve_telephony_integration
-
-    if payload.telephony_integration_id is not None:
-        integration = _resolve_telephony_integration(
-            db,
-            organization_id,
-            payload.telephony_integration_id,
-            payload.provider or "",
-        )
-        call_import.provider = integration.provider
-        call_import.telephony_integration_id = integration.id
-    else:
-        call_import.provider = None
-        call_import.telephony_integration_id = None
-    db.flush()
-
-
-def _apply_retry_overrides(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    organization_id: UUID,
-    payload: CallImportEvaluationRetryRequest,
-) -> None:
-    """Validate + persist the LLM/STT override fields on the run.
-
-    Mirrors the validation in ``create_call_import_evaluation`` but
-    only touches the fields the caller actually sent — leaving any
-    field ``None`` preserves the run's existing value. Raises
-    ``HTTPException(400)`` on bad input so the route handler can let
-    FastAPI turn it into a clean 400 response.
-    """
-    # --- LLM provider + model (must be sent together) ---
-    if payload.llm_provider is not None or payload.llm_model is not None:
-        if not (payload.llm_provider and payload.llm_model):
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "Both llm_provider and llm_model are required when "
-                    "overriding the run LLM on retry."
-                ),
-            )
-        try:
-            evaluation.llm_provider = ModelProvider(
-                payload.llm_provider.lower()
-            ).value
-        except ValueError:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    f"Unknown LLM provider '{payload.llm_provider}'. "
-                    "Valid keys are documented in ModelProvider."
-                ),
-            )
-        new_model = payload.llm_model.strip() or None
-        if not new_model:
-            raise HTTPException(
-                status_code=400, detail="llm_model cannot be empty."
-            )
-        evaluation.llm_model = new_model
-
-    # --- LLM credential pin ---
-    if payload.llm_credential_id is not None:
-        cred = (
-            db.query(AIProvider)
-            .filter(
-                AIProvider.id == payload.llm_credential_id,
-                AIProvider.organization_id == organization_id,
-            )
-            .first()
-        )
-        if not cred:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "The provided llm_credential_id does not exist in "
-                    "this organization."
-                ),
-            )
-        evaluation.llm_credential_id = payload.llm_credential_id
-
-    if payload.llm_config is not None:
-        evaluation.llm_config = payload.llm_config
-
-    # --- Per-metric LLM overrides ---
-    # We accept the same dict shape as the create endpoint but
-    # constrain keys to leaf metrics that are actually in this run.
-    # Passing an empty dict explicitly clears existing overrides.
-    if payload.metric_llm_overrides is not None:
-        valid_leaf_ids = {
-            str(mid) for mid in (evaluation.selected_metric_ids or [])
-        }
-        overrides_payload: Dict[str, Dict[str, Any]] = {}
-        for metric_id, override in payload.metric_llm_overrides.items():
-            if metric_id not in valid_leaf_ids:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        "metric_llm_overrides references metric "
-                        f"{metric_id} which is not a leaf metric in "
-                        "this run."
-                    ),
-                )
-            override_dict: Dict[str, Any] = {}
-            if override.provider is not None:
-                if not override.model:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=(
-                            f"Override for metric {metric_id} has a "
-                            "provider but no model."
-                        ),
-                    )
-                try:
-                    override_dict["provider"] = ModelProvider(
-                        override.provider.lower()
-                    ).value
-                except ValueError:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=(
-                            f"Override for metric {metric_id} uses "
-                            f"unknown provider '{override.provider}'."
-                        ),
-                    )
-                override_dict["model"] = override.model.strip()
-            elif override.model:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Override for metric {metric_id} has a model "
-                        "but no provider."
-                    ),
-                )
-            if override.credential_id is not None:
-                override_dict["credential_id"] = str(override.credential_id)
-            if override.llm_config is not None:
-                override_dict["llm_config"] = override.llm_config
-            if override_dict:
-                overrides_payload[metric_id] = override_dict
-        evaluation.metric_llm_overrides = overrides_payload or None
-
-    # --- STT provider + model (must be sent together) ---
-    if payload.stt_provider is not None or payload.stt_model is not None:
-        if not (payload.stt_provider and payload.stt_model):
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "Both stt_provider and stt_model are required "
-                    "when overriding the run STT on retry."
-                ),
-            )
-        try:
-            evaluation.stt_provider = ModelProvider(
-                payload.stt_provider.lower()
-            ).value
-        except ValueError:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Unknown STT provider '{payload.stt_provider}'.",
-            )
-        new_stt_model = payload.stt_model.strip() or None
-        if not new_stt_model:
-            raise HTTPException(
-                status_code=400, detail="stt_model cannot be empty."
-            )
-        evaluation.stt_model = new_stt_model
-
-    # --- STT credential pin ---
-    if payload.stt_credential_id is not None:
-        evaluation.stt_credential_id = payload.stt_credential_id
-
-    # --- LLM diariser provider + model (must be sent together) ---
-    if (
-        payload.diarization_llm_provider is not None
-        or payload.diarization_llm_model is not None
-    ):
-        if not (
-            payload.diarization_llm_provider
-            and payload.diarization_llm_model
-        ):
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "Both diarization_llm_provider and "
-                    "diarization_llm_model are required when overriding "
-                    "the run diariser on retry."
-                ),
-            )
-        try:
-            evaluation.diarisation_llm_provider = ModelProvider(
-                payload.diarization_llm_provider.lower()
-            ).value
-        except ValueError:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "Unknown diarisation LLM provider "
-                    f"'{payload.diarization_llm_provider}'."
-                ),
-            )
-        new_diariser_model = (
-            payload.diarization_llm_model.strip() or None
-        )
-        if not new_diariser_model:
-            raise HTTPException(
-                status_code=400,
-                detail="diarization_llm_model cannot be empty.",
-            )
-        evaluation.diarisation_llm_model = new_diariser_model
-
-    if payload.diarization_llm_credential_id is not None:
-        evaluation.diarisation_llm_credential_id = (
-            payload.diarization_llm_credential_id
-        )
-
-    # ``diarization_prompt`` semantics: None = leave untouched;
-    # empty string = clear (fall back to the canonical default at
-    # worker time); anything else = persist verbatim.
-    if payload.diarization_prompt is not None:
-        cleaned = payload.diarization_prompt.strip()
-        evaluation.diarisation_prompt = cleaned or None
-
-    if payload.transcribe_mode is not None:
-        mode = payload.transcribe_mode.strip().lower()
-        if mode not in {"stt_llm", "llm_only"}:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    f"Unknown transcribe_mode '{payload.transcribe_mode}'. "
-                    "Valid values are 'stt_llm' and 'llm_only'."
-                ),
-            )
-        evaluation.transcribe_mode = mode
-
-
-def _gather_retry_targets(
-    db: Session,
-    evaluation: CallImportEvaluation,
-    requested_ids: Optional[List[UUID]],
-    *,
-    include_completed: bool = False,
-) -> Tuple[
-    List[Tuple[CallImportEvaluationRow, CallImportRow]],
-    List[CallImportEvaluationRetrySkippedItem],
-]:
-    """Resolve which rows to retry + reasons for any we refuse.
-
-    When ``requested_ids`` is None we retry every row whose status is
-    ``failed`` (or every row when ``include_completed`` is also set —
-    used by the metric-subset retry path which legitimately wants to
-    recompute a metric on already-successful rows). When the caller
-    passes ids explicitly we still filter out rows that are currently
-    in flight; ``include_completed`` controls whether previously-
-    successful rows are eligible.
-    """
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    if is_sharding_enabled():
-        from app.db_sharding.eval_rows import gather_retry_targets_sharded
-
-        return gather_retry_targets_sharded(
-            db,
-            evaluation,
-            requested_ids,
-            include_completed=include_completed,
-        )
-
-    eval_rows_query = db.query(CallImportEvaluationRow).filter(
-        CallImportEvaluationRow.evaluation_id == evaluation.id
-    )
-
-    targets: List[Tuple[CallImportEvaluationRow, CallImportRow]] = []
-    skipped: List[CallImportEvaluationRetrySkippedItem] = []
-
-    if requested_ids is None:
-        if include_completed:
-            # "Retry everything" path used by the metric-subset re-run
-            # UI. Still skip in-flight rows below so we don't trample
-            # work the worker is actively doing.
-            candidate_rows = eval_rows_query.filter(
-                CallImportEvaluationRow.status.in_(["failed", "completed"])
-            ).all()
-        else:
-            candidate_rows = eval_rows_query.filter(
-                CallImportEvaluationRow.status == "failed"
-            ).all()
-    else:
-        requested_set = set(requested_ids)
-        candidate_rows = eval_rows_query.filter(
-            CallImportEvaluationRow.id.in_(requested_set)
-        ).all()
-        found_ids = {row.id for row in candidate_rows}
-        for missing in requested_set - found_ids:
-            skipped.append(
-                CallImportEvaluationRetrySkippedItem(
-                    eval_row_id=missing,
-                    reason="unknown",
-                )
-            )
-
-    if not candidate_rows:
-        return targets, skipped
-
-    source_row_ids = [row.call_import_row_id for row in candidate_rows]
-    source_rows = (
-        db.query(CallImportRow)
-        .filter(CallImportRow.id.in_(source_row_ids))
-        .all()
-    )
-    source_by_id = {row.id: row for row in source_rows}
-
-    for eval_row in candidate_rows:
-        if eval_row.status in {"pending", "running"}:
-            skipped.append(
-                CallImportEvaluationRetrySkippedItem(
-                    eval_row_id=eval_row.id,
-                    reason="in_progress",
-                )
-            )
-            continue
-        if eval_row.status == "completed" and not include_completed:
-            skipped.append(
-                CallImportEvaluationRetrySkippedItem(
-                    eval_row_id=eval_row.id,
-                    reason="completed",
-                )
-            )
-            continue
-        source_row = source_by_id.get(eval_row.call_import_row_id)
-        if source_row is None:
-            skipped.append(
-                CallImportEvaluationRetrySkippedItem(
-                    eval_row_id=eval_row.id,
-                    reason="source_row_missing",
-                )
-            )
-            continue
-        targets.append((eval_row, source_row))
-
-    return targets, skipped
-
-
-@router.post(
-    "/{eval_id}/retry",
-    response_model=CallImportEvaluationRetryResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="retryCallImportEvaluation",
-)
-async def retry_call_import_evaluation(
-    call_import_id: UUID,
-    eval_id: UUID,
-    payload: Optional[CallImportEvaluationRetryRequest] = Body(default=None),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationRetryResponse:
-    """Re-enqueue failed rows in an evaluation run.
-
-    Default behavior (no body) is "retry every row that failed". Pass
-    ``eval_row_ids`` to scope the retry to a specific subset (e.g. the
-    single row a user clicked in the UI). Rows that are still
-    in-flight or already completed are returned in ``skipped`` rather
-    than re-enqueued, so this endpoint is always safe to call.
-
-    When ``metric_ids`` is set in the payload, this is a **metric-
-    subset retry**: only the listed metrics are recomputed (and merged
-    into the row's existing ``metric_scores`` — other metrics' values
-    are preserved). The route auto-flips ``include_completed=True`` in
-    that case so previously-successful rows are eligible for re-
-    scoring; without it the call would no-op because every row would
-    be skipped as ``completed``.
-
-    The worker contract is the same as the create endpoint:
-    ``evaluate_call_import_row_task(eval_row_id, [restricted_metric_ids])``.
-    When the run is configured for diarised transcripts and the row's
-    diarised transcript is missing, we chain through
-    ``transcribe_call_import_row_task`` first — matching the
-    auto-transcribe behavior of POST ``/evaluations``.
-    """
-    del api_key
-    call_import = _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    requested_ids = payload.eval_row_ids if payload else None
-    # Metric-subset retry: validate that every metric is something this
-    # run actually scored. Empty list is rejected too — callers that
-    # want a full re-run should omit the field entirely.
-    #
-    # ``selected_metric_ids`` holds the LEAVES only (children for
-    # hierarchical / category metrics, standalone metrics otherwise) —
-    # see ``leaf_metric_ids`` in :func:`create_call_import_evaluation`.
-    # Parent IDs for hierarchical metrics live separately in
-    # ``selected_metric_groups`` (``{parent_id: [child_ids]}``) so the
-    # UI can reconstruct the tree without round-tripping through the
-    # metric table.
-    #
-    # The Re-run-metrics modal surfaces PARENTS for hierarchical
-    # metrics (it suppresses individual children via
-    # ``childrenInGroups`` in ``CallImportEvaluationDetail.tsx``), so a
-    # naive ``metric_ids ⊆ selected_metric_ids`` check rejects every
-    # parent-ID request with a misleading "unknown ids" 400. We accept
-    # both shapes here and then EXPAND any parent IDs into
-    # ``{parent_id, *child_ids}`` so the downstream helpers see the
-    # full set of keys that need clearing + the full set of leaves
-    # that need re-scoring.
-    metric_ids: Optional[List[UUID]] = (
-        payload.metric_ids if payload else None
-    )
-    if metric_ids is not None:
-        if not metric_ids:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "metric_ids must be a non-empty list. Omit the "
-                    "field to re-run all metrics."
-                ),
-            )
-
-        leaf_set: Set[str] = {
-            str(item).lower()
-            for item in (evaluation.selected_metric_ids or [])
-        }
-        # ``selected_metric_groups`` is a dict ``{parent_id_str:
-        # [child_id_str, ...]}`` (see line ~487 in
-        # ``create_call_import_evaluation``). We tolerate stale data
-        # (string / UUID / non-dict) without crashing the retry path —
-        # if it's malformed we just treat it as "no parents" and fall
-        # back to the leaf-only check.
-        groups_raw = (
-            evaluation.selected_metric_groups
-            if isinstance(evaluation.selected_metric_groups, dict)
-            else {}
-        )
-        parent_to_children_str: Dict[str, List[str]] = {}
-        for parent_key, children_raw in groups_raw.items():
-            if not isinstance(children_raw, (list, tuple)):
-                continue
-            children_norm = [
-                str(c).lower() for c in children_raw if c is not None
-            ]
-            parent_to_children_str[str(parent_key).lower()] = children_norm
-        parent_set = set(parent_to_children_str.keys())
-
-        unknown = [
-            mid for mid in metric_ids
-            if str(mid).lower() not in leaf_set
-            and str(mid).lower() not in parent_set
-        ]
-        if unknown:
-            raise HTTPException(
-                status_code=400,
-                detail=(
-                    "metric_ids must be a subset of this evaluation's "
-                    f"selected metrics; unknown ids: {[str(u) for u in unknown]}."
-                ),
-            )
-
-        # Expand parent IDs into ``{parent, *children}`` so:
-        #   * ``_reset_eval_row_for_retry`` strips BOTH the parent
-        #     entry (with ``chosen_child_id`` / rationale) AND every
-        #     per-child boolean entry that the LLM evaluator wrote
-        #     under each child's ID (see
-        #     ``app/workers/tasks/helpers/llm_evaluation.py`` lines
-        #     1584 and 1649).
-        #   * ``_enqueue_eval_rows_with_optional_transcribe`` →
-        #     ``evaluate_call_import_row_task`` filters the work-list
-        #     off ``selected_metric_ids`` (leaves), so we MUST hand it
-        #     the child IDs for the parent to actually get re-scored.
-        # Leaves pass through unchanged.
-        expanded: List[UUID] = []
-        seen: Set[str] = set()
-        for mid in metric_ids:
-            mid_norm = str(mid).lower()
-            children_str = parent_to_children_str.get(mid_norm)
-            if children_str is not None:
-                # Parent: include the parent ID itself (so the parent
-                # entry in ``metric_scores`` is also cleared) and all
-                # of its children.
-                candidates = [mid_norm, *children_str]
-            else:
-                candidates = [mid_norm]
-            for candidate in candidates:
-                if candidate in seen:
-                    continue
-                try:
-                    expanded.append(UUID(candidate))
-                except (TypeError, ValueError):
-                    # Defensive: skip junk values rather than 500.
-                    continue
-                seen.add(candidate)
-        metric_ids = expanded
-
-    # ``include_completed`` is auto-enabled when the caller asked for a
-    # metric subset (otherwise the metric-subset retry would always
-    # no-op on a green run, which is the whole reason this feature
-    # exists). The explicit payload flag wins for full-row retries.
-    include_completed = bool(
-        (payload.include_completed if payload else False)
-        or (metric_ids is not None)
-    )
-
-    transcribe_overwrite = bool(
-        payload.transcribe_overwrite if payload else False
-    )
-
-    skipped: List[CallImportEvaluationRetrySkippedItem] = []
-    if requested_ids is None:
-        from app.db_sharding.eval_rows import count_evaluation_rows_for_run
-        from app.db_sharding.sessions import is_sharding_enabled
-
-        if is_sharding_enabled():
-            statuses = (
-                ["failed", "completed"] if include_completed else ["failed"]
-            )
-            target_count = count_evaluation_rows_for_run(
-                db, eval_id, statuses=statuses
-            )
-        else:
-            from sqlalchemy import func
-
-            count_query = db.query(func.count(CallImportEvaluationRow.id)).filter(
-                CallImportEvaluationRow.evaluation_id == eval_id
-            )
-            if include_completed:
-                count_query = count_query.filter(
-                    CallImportEvaluationRow.status.in_(["failed", "completed"])
-                )
-            else:
-                count_query = count_query.filter(
-                    CallImportEvaluationRow.status == "failed"
-                )
-            target_count = int(count_query.scalar() or 0)
-        if target_count == 0:
-            return CallImportEvaluationRetryResponse(
-                requeued=0,
-                transcribe_requeued=0,
-                skipped=skipped,
-            )
-    else:
-        targets, skipped = _gather_retry_targets(
-            db,
-            evaluation,
-            requested_ids,
-            include_completed=include_completed,
-        )
-        if not targets:
-            return CallImportEvaluationRetryResponse(
-                requeued=0,
-                transcribe_requeued=0,
-                skipped=skipped,
-            )
-        target_count = len(targets)
-
-    # Apply LLM / STT overrides BEFORE enqueueing so the persisted run
-    # config is correct by the time the worker reads it.
-    if payload is not None:
-        _apply_retry_overrides(db, evaluation, organization_id, payload)
-        _apply_telephony_retry_overrides(
-            db,
-            call_import=call_import,
-            organization_id=organization_id,
-            payload=payload,
-        )
-
-    evaluation.error_message = None
-    evaluation.finished_at = None
-    evaluation.status = "running"
-    if not evaluation.started_at:
-        from datetime import datetime, timezone
-
-        evaluation.started_at = datetime.now(timezone.utc)
-
-    _claim_evaluation_bulk_operation(eval_id, "retry")
-    db.commit()
-
-    from app.workers.tasks.call_import_bulk_ops import (
-        retry_call_import_evaluation_task,
-    )
-
-    retry_call_import_evaluation_task.delay(
-        str(eval_id),
-        {
-            "eval_row_ids": [str(rid) for rid in requested_ids]
-            if requested_ids
-            else None,
-            "metric_ids": [str(mid) for mid in metric_ids] if metric_ids else None,
-            "include_completed": include_completed,
-            "transcribe_overwrite": transcribe_overwrite,
-        },
-    )
-
-    return CallImportEvaluationRetryResponse(
-        requeued=target_count,
-        transcribe_requeued=0,
-        skipped=skipped,
-    )
-
-
-@router.post(
-    "/{eval_id}/rows/{eval_row_id}/retry",
-    response_model=CallImportEvaluationRowResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="retryCallImportEvaluationRow",
-)
-async def retry_call_import_evaluation_row(
-    call_import_id: UUID,
-    eval_id: UUID,
-    eval_row_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportEvaluationRowResponse:
-    """Re-enqueue a single failed evaluation row.
-
-    Convenience wrapper around ``retry_call_import_evaluation`` for the
-    "Retry this row" affordance in the row table. Returns the
-    refreshed row so the UI can update its badge immediately, without
-    waiting for the next polling tick.
-    """
-    del api_key
-    _require_import(db, call_import_id, organization_id)
-
-    evaluation = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.id == eval_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not evaluation:
-        raise HTTPException(
-            status_code=404, detail="Call import evaluation not found"
-        )
-
-    _require_no_evaluation_bulk_operation(eval_id)
-
-    from app.db_sharding.eval_rows import (
-        evaluation_row_session,
-        find_evaluation_row_in_run,
-    )
-    from app.db_sharding.sessions import is_sharding_enabled
-
-    eval_row, _source_stub = find_evaluation_row_in_run(db, eval_id, eval_row_id)
-    if eval_row is None:
-        raise HTTPException(
-            status_code=404, detail="Evaluation row not found in this run"
-        )
-
-    if eval_row.status in {"pending", "running"}:
-        raise HTTPException(
-            status_code=409,
-            detail=(
-                "This row is still in progress — wait for it to finish "
-                "before retrying."
-            ),
-        )
-
-    targets, _ = _gather_retry_targets(db, evaluation, [eval_row.id])
-    if not targets:
-        raise HTTPException(
-            status_code=409,
-            detail=(
-                "This row cannot be retried in its current state "
-                f"(status={eval_row.status})."
-            ),
-        )
-
-    if is_sharding_enabled():
-        with evaluation_row_session(eval_row_id) as (
-            row_db,
-            _catalog_db,
-            eval_row,
-            source_row,
-            _shard_id,
-        ):
-            _prepare_source_row_for_retry(source_row, transcribe_overwrite=False)
-            _reset_eval_row_for_retry(eval_row)
-            row_db.commit()
-            targets = [(eval_row, source_row)]
-    else:
-        for er, source_row in targets:
-            _prepare_source_row_for_retry(source_row, transcribe_overwrite=False)
-            _reset_eval_row_for_retry(er)
-
-    evaluation.error_message = None
-    evaluation.finished_at = None
-    evaluation.status = "running"
-    if not evaluation.started_at:
-        from datetime import datetime, timezone
-
-        evaluation.started_at = datetime.now(timezone.utc)
-    db.flush()
-    _rollup_evaluation_status(evaluation, db)
-    db.commit()
-
-    try:
-        _enqueue_eval_rows_with_optional_transcribe(db, evaluation, targets)
-    except Exception as exc:  # noqa: BLE001
-        logger.exception(
-            "Failed to re-enqueue retry for evaluation row {}", eval_row_id
-        )
-        if is_sharding_enabled():
-            with evaluation_row_session(eval_row_id) as (
-                row_db,
-                _catalog_db,
-                eval_row,
-                _source_row,
-                _shard_id,
-            ):
-                eval_row.status = "failed"
-                eval_row.error_message = f"Failed to re-enqueue retry: {exc}"
-                row_db.commit()
-        else:
-            eval_row.status = "failed"
-            eval_row.error_message = f"Failed to re-enqueue retry: {exc}"
-        _rollup_evaluation_status(evaluation, db)
-        db.commit()
-        raise HTTPException(
-            status_code=500,
-            detail=f"Failed to re-enqueue retry: {exc}",
-        )
-
-    if is_sharding_enabled():
-        with evaluation_row_session(eval_row_id) as (
-            _row_db,
-            _catalog_db,
-            eval_row,
-            source_row,
-            _shard_id,
-        ):
-            return _to_evaluation_row_response(eval_row, source_row, evaluation)
-
-    db.refresh(eval_row)
-    source_row = targets[0][1]
-    return _to_evaluation_row_response(eval_row, source_row, evaluation)
-
-
-from app.core.auth.capabilities import EVALS_RUN, EVALS_VIEW, REPORTS_GENERATE
-from app.core.auth.workspace_route_capabilities import apply_workspace_route_capabilities
-
-apply_workspace_route_capabilities(
-    router,
-    view_capability=EVALS_VIEW,
-    manage_capability=EVALS_RUN,
-    run_capability=EVALS_RUN,
-    report_capability=REPORTS_GENERATE,
-)
+"""Evaluation routes scoped to a Call Import batch."""
+
+from __future__ import annotations
+
+import asyncio
+import csv
+import base64
+import io
+import json
+import math
+import re
+import statistics
+from typing import Any, Dict, Iterator, List, Literal, Optional, Set, Tuple
+from uuid import UUID
+
+from datetime import date, datetime, timedelta, timezone
+
+from fastapi import APIRouter, BackgroundTasks, Body, Depends, HTTPException, Query, Response, status
+from fastapi.responses import StreamingResponse
+from loguru import logger
+from pydantic import BaseModel, Field, field_validator
+from sqlalchemy import desc, func, or_, text
+from sqlalchemy.orm import Session
+from sqlalchemy.orm.attributes import flag_modified
+
+from app.core.auth import Principal, get_principal
+from app.core.auth.capabilities import REPORTS_GENERATE, capability_denied_message
+from app.database import get_db
+from app.dependencies import (
+    get_api_key,
+    get_organization_id,
+    get_workspace_id,
+    require_enterprise_feature,
+)
+from app.services.call_imports.audit import (
+    actor_emails_for_evaluation,
+    emails_for_user_ids,
+    stamp_evaluation_actor,
+    user_ids_from_evaluations,
+)
+from app.services.workspace_rbac import resolve_workspace_capabilities
+from app.models.database import (
+    AIProvider,
+    CallImport,
+    CallImportEvaluation,
+    CallImportEvaluationReportSnapshot,
+    CallImportEvaluationRow,
+    CallImportRow,
+    Metric,
+    PromptPartial,
+    Workspace,
+)
+from app.models.enums import CallImportRowStatus, ModelProvider
+from app.models.schemas import (
+    CallImportEvaluationAggregateResponse,
+    CallImportEvaluationBulkDelete,
+    CallImportEvaluationBulkActionResponse,
+    CallImportEvaluationCreate,
+    CallImportEvaluationListResponse,
+    CallImportEvaluationResponse,
+    CallImportEvaluationRetryRequest,
+    CallImportEvaluationRetryResponse,
+    CallImportEvaluationRetrySkippedItem,
+    CallImportEvaluationRowListResponse,
+    CallImportEvaluationRowResponse,
+    CallImportEvaluationUpdate,
+    CallImportMetricAggregate,
+    CallImportMetricHistogramBucket,
+    CallImportMetricLabelPair,
+    CallImportMetricSummary,
+    CallImportMetricValueCount,
+    DiscoveredLabelDeleteRequest,
+    DiscoveredLabelItem,
+    DiscoveredLabelMergeRequest,
+    DiscoveredLabelsResponse,
+    DiscoveredMetricDeleteRequest,
+    DiscoveredMetricItem,
+    DiscoveredMetricMergeRequest,
+    DiscoveredMetricsResponse,
+    EvaluationInsightsRequest,
+    EvaluationTldrSummary,
+    EvaluationMetricClustersRequest,
+    EvaluationMetricClustersState,
+    EvaluationPromptImprovementsRequest,
+    EvaluationPromptImprovementsState,
+    MetricFailurePoliciesResponse,
+    MetricFailurePoliciesSaveRequest,
+    MetricFailurePolicy,
+    MetricClusterEligibleRow,
+    MetricClusterEligibleRowsResponse,
+    EvaluationUserInsightsRequest,
+    EvaluationUserInsightsState,
+    MetricFlowEdge,
+    MetricPeriodDelta,
+    MetricFlowNode,
+    MetricFlowResponse,
+)
+from app.services.reporting.call_import_evaluation_pdf_report import (
+    call_import_evaluation_pdf_report_service,
+)
+from app.services.call_import_metric_clusters import (
+    METRIC_CLUSTERS_CANCELLED_BY_USER_ERROR,
+    estimate_metric_clusters_llm_calls,
+    filter_completed_row_pairs,
+    list_eligible_cluster_rows,
+    metric_clusters_raw_is_cancelled,
+    metric_clusters_state_from_raw,
+    metric_clusters_state_to_db,
+)
+from app.services.metric_failure_policy import (
+    aggregate_primary_percent,
+    build_failure_policy_previews,
+    effective_policies,
+    failure_rate_percent_from_rows,
+    failure_policies_to_db,
+    has_clusterable_metrics,
+    merge_clustering_policies,
+    merge_failure_policies_into_raw,
+    policies_from_evaluation_raw,
+    validate_failure_policies_for_metrics,
+)
+from app.services.call_import_user_insights import (
+    normalize_max_llm_calls,
+    total_llm_calls_for_rows,
+    user_insights_state_from_raw,
+)
+
+router = APIRouter(
+    prefix="/call-imports/{call_import_id}/evaluations",
+    tags=["Call Import Evaluations"],
+    dependencies=[Depends(require_enterprise_feature("call_imports"))],
+)
+
+
+class CallImportEvaluationPdfReportRequest(BaseModel):
+    vendor_name: str = Field(..., min_length=1, max_length=120)
+    report_type: Literal["external", "internal"] = "external"
+    include_weekly_delta: bool = False
+    include_period_delta: bool = False
+    baseline_evaluation_id: Optional[str] = None
+    period_label: Optional[str] = Field(default=None, max_length=64)
+    use_case: Optional[str] = Field(default=None, max_length=120)
+    internal_brand_image_id: Optional[str] = None
+    external_brand_image_id: Optional[str] = None
+    report_config: Dict[str, Any] = Field(default_factory=dict)
+    platform_base_url: Optional[str] = Field(
+        default=None,
+        max_length=512,
+        description="Frontend origin for deep links to example calls in internal PDFs.",
+    )
+
+    @field_validator("vendor_name")
+    @classmethod
+    def _clean_vendor_name(cls, value: str) -> str:
+        cleaned = value.strip()
+        if not cleaned:
+            raise ValueError("Vendor name is required.")
+        return cleaned
+
+
+class CallImportEvaluationBaselineCandidate(BaseModel):
+    evaluation_id: str
+    name: str
+    dataset: str
+    period_label: Optional[str] = None
+    period_start: Optional[date] = None
+    period_end: Optional[date] = None
+    period_display: str
+    completed_rows: int
+    created_at: datetime
+    is_default: bool = False
+
+
+class CallImportEvaluationBaselineCandidatesResponse(BaseModel):
+    items: List[CallImportEvaluationBaselineCandidate]
+    default_evaluation_id: Optional[str] = None
+
+
+def _require_import(
+    db: Session,
+    call_import_id: UUID,
+    organization_id: UUID,
+) -> CallImport:
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(status_code=404, detail="Call import not found")
+    return call_import
+
+
+def require_call_import_capability(capability: str):
+    """Ensure the caller has *capability* in the call import's workspace (not just the header)."""
+
+    def _dep(
+        call_import_id: UUID,
+        principal: Principal = Depends(get_principal),
+        organization_id: UUID = Depends(get_organization_id),
+        db: Session = Depends(get_db),
+    ) -> CallImport:
+        call_import = _require_import(db, call_import_id, organization_id)
+        caps, _, role = resolve_workspace_capabilities(
+            db,
+            principal=principal,
+            workspace_id=call_import.workspace_id,
+            organization_id=organization_id,
+        )
+        if capability not in caps:
+            raise HTTPException(
+                status_code=403,
+                detail=capability_denied_message(
+                    capability,
+                    role_name=role.name if role else None,
+                    workspace_label="the active workspace",
+                ),
+            )
+        return call_import
+
+    return _dep
+
+
+def _flatten_transcript(text: Optional[str]) -> str:
+    """Collapse a multi-line transcript onto a single line for spreadsheet export.
+
+    The diarised transcript is stored as ``<speaker>: <text>`` lines joined
+    by ``\\n`` because the in-app ``TranscriptView`` parses those line
+    breaks to render chat bubbles. In Excel / Google Sheets that same
+    newline-per-turn formatting causes each cell to balloon vertically,
+    which the user reads as "lots of empty space on top of the cell".
+    Flattening at export time keeps the DB shape intact while giving the
+    spreadsheet a single-line cell per row.
+    """
+    if not text:
+        return ""
+    parts = [
+        segment.strip()
+        for segment in text.replace("\r\n", "\n").replace("\r", "\n").split("\n")
+    ]
+    return " ".join(p for p in parts if p)
+
+
+def _evaluated_transcript_source_label(
+    evaluation: CallImportEvaluation,
+    source_row: CallImportRow,
+) -> str:
+    """Label which transcript source this row was scored against."""
+    source = (evaluation.transcript_source or "diarised").strip().lower()
+    if source == "production":
+        if not (source_row.transcript or "").strip():
+            return ""
+        return "Production"
+    if not (source_row.diarised_transcript or "").strip():
+        return ""
+    return "Diarised"
+
+
+def _pick_evaluation_row_transcript(
+    source_row: Optional[CallImportRow],
+    evaluation: Optional[CallImportEvaluation] = None,
+) -> Optional[str]:
+    """Transcript shown in evaluation row detail for the run's source."""
+    if source_row is None:
+        return None
+    source = (
+        (evaluation.transcript_source or "diarised").strip().lower()
+        if evaluation is not None
+        else "diarised"
+    )
+    if source == "production":
+        raw = (source_row.transcript or "").strip()
+        return raw or None
+    diarised = (source_row.diarised_transcript or "").strip()
+    if diarised:
+        return diarised
+    raw = (source_row.transcript or "").strip()
+    return raw or None
+
+
+def _to_evaluation_row_response(
+    eval_row_obj: CallImportEvaluationRow,
+    source_row: Optional[CallImportRow],
+    evaluation: Optional[CallImportEvaluation] = None,
+) -> CallImportEvaluationRowResponse:
+    """Serialize one evaluation row plus joined source-row metadata."""
+    return CallImportEvaluationRowResponse(
+        id=eval_row_obj.id,
+        evaluation_id=eval_row_obj.evaluation_id,
+        call_import_row_id=eval_row_obj.call_import_row_id,
+        row_index=source_row.row_index if source_row else None,
+        conversation_id=source_row.conversation_id if source_row else None,
+        transcript=_pick_evaluation_row_transcript(source_row, evaluation),
+        raw_columns=source_row.raw_columns if source_row else None,
+        recording_url=source_row.recording_url if source_row else None,
+        recording_date=source_row.recording_date if source_row else None,
+        recording_s3_key=source_row.recording_s3_key if source_row else None,
+        diarised_transcript_status=(
+            source_row.diarised_transcript_status if source_row else None
+        ),
+        diarised_transcript_error=(
+            source_row.diarised_transcript_error if source_row else None
+        ),
+        status=eval_row_obj.status,
+        metric_scores=eval_row_obj.metric_scores or {},
+        error_message=eval_row_obj.error_message,
+        started_at=eval_row_obj.started_at,
+        finished_at=eval_row_obj.finished_at,
+        created_at=eval_row_obj.created_at,
+        updated_at=eval_row_obj.updated_at,
+    )
+
+
+def _serialize_selected_metric_ids(value) -> List[UUID]:
+    result: List[UUID] = []
+    if not isinstance(value, list):
+        return result
+    for item in value:
+        try:
+            result.append(UUID(str(item)))
+        except (TypeError, ValueError):
+            continue
+    return result
+
+
+def _metrics_for_ids(db: Session, org_id: UUID, ids: List[UUID]) -> List[Metric]:
+    if not ids:
+        return []
+    rows = (
+        db.query(Metric)
+        .filter(
+            Metric.organization_id == org_id,
+            Metric.id.in_(ids),
+        )
+        .all()
+    )
+    by_id = {row.id: row for row in rows}
+    return [by_id[mid] for mid in ids if mid in by_id]
+
+
+def _expand_metric_selection(
+    db: Session,
+    org_id: UUID,
+    selected_ids: List[UUID],
+) -> Tuple[List[Metric], Dict[UUID, List[Metric]]]:
+    """Resolve user-supplied metric ids into actual leaves + parent grouping.
+
+    Rules:
+      * If a parent id is in ``selected_ids`` and no specific children of
+        that parent are also listed, include EVERY enabled child of that
+        parent.
+      * If a parent id AND some of its children are listed, include only
+        the listed children (treat the parent selection as the
+        "container" so users can deselect labels).
+      * Standalone metrics (no parent, no children) pass through
+        unchanged.
+      * Disabled metrics are filtered out at this layer so the caller
+        doesn't have to repeat the check.
+
+    Returns:
+        (effective_metrics, parent_to_children)
+
+        ``effective_metrics`` is the deduplicated list of metrics the
+        worker will actually score (children + standalone). Order is
+        preserved from ``selected_ids`` for display stability.
+
+        ``parent_to_children`` maps each parent metric id (UUID) to the
+        list of its selected children. Useful for grouping in the LLM
+        prompt builder.
+    """
+    if not selected_ids:
+        return [], {}
+
+    requested = list(selected_ids)
+    initial_rows = (
+        db.query(Metric)
+        .filter(
+            Metric.organization_id == org_id,
+            Metric.id.in_(requested),
+        )
+        .all()
+    )
+    initial_by_id = {row.id: row for row in initial_rows}
+
+    parent_ids_requested = {
+        m.id for m in initial_rows if m.selection_mode and not m.parent_metric_id
+    }
+    # Map parent id -> children explicitly requested by the user.
+    explicit_children_by_parent: Dict[UUID, List[Metric]] = {}
+    for m in initial_rows:
+        if m.parent_metric_id and m.parent_metric_id in parent_ids_requested:
+            explicit_children_by_parent.setdefault(
+                m.parent_metric_id, []
+            ).append(m)
+
+    # For parents without explicit children, hydrate every enabled child.
+    parents_needing_full_expansion = [
+        pid
+        for pid in parent_ids_requested
+        if pid not in explicit_children_by_parent
+    ]
+    auto_expanded_children: Dict[UUID, List[Metric]] = {}
+    if parents_needing_full_expansion:
+        for pid in parents_needing_full_expansion:
+            child_rows = (
+                db.query(Metric)
+                .filter(
+                    Metric.organization_id == org_id,
+                    Metric.parent_metric_id == pid,
+                    Metric.enabled.is_(True),
+                )
+                .order_by(Metric.created_at.asc())
+                .all()
+            )
+            auto_expanded_children[pid] = child_rows
+
+    parent_to_children: Dict[UUID, List[Metric]] = {}
+    for pid in parent_ids_requested:
+        children = explicit_children_by_parent.get(
+            pid
+        ) or auto_expanded_children.get(pid, [])
+        # Drop disabled children so the worker doesn't waste a slot on
+        # them. Empty parents (no enabled children) are still tracked
+        # because the UI may want to show "0 of 0" rather than swallow
+        # them silently.
+        parent_to_children[pid] = [c for c in children if c.enabled]
+
+    effective: List[Metric] = []
+    seen: set[UUID] = set()
+    for mid in requested:
+        m = initial_by_id.get(mid)
+        if m is None:
+            continue
+        if m.selection_mode and not m.parent_metric_id:
+            # Parent row itself is not scored — only its children.
+            for child in parent_to_children.get(m.id, []):
+                if child.id in seen or not child.enabled:
+                    continue
+                seen.add(child.id)
+                effective.append(child)
+            continue
+        if m.parent_metric_id and m.parent_metric_id in parent_ids_requested:
+            # Already accounted for via the parent expansion above.
+            continue
+        if not m.enabled:
+            continue
+        if m.id in seen:
+            continue
+        seen.add(m.id)
+        effective.append(m)
+
+    return effective, parent_to_children
+
+
+def _evaluation_bulk_operation_for_response(
+    evaluation_id: UUID,
+) -> Optional[str]:
+    from app.services.call_imports.evaluation_bulk_op import (
+        get_evaluation_bulk_operation,
+    )
+
+    return get_evaluation_bulk_operation(evaluation_id)
+
+
+def _serialize_eval(
+    db: Session,
+    row: CallImportEvaluation,
+    *,
+    sibling_evaluation_ids: Optional[List[UUID]] = None,
+    user_emails: Optional[Dict[UUID, str]] = None,
+) -> CallImportEvaluationResponse:
+    selected_ids = _serialize_selected_metric_ids(row.selected_metric_ids)
+
+    # Pull every metric referenced anywhere in the run's grouping (leaves,
+    # standalone, AND parents from selected_metric_groups) so the UI can
+    # render parent labels even when only children were materialized into
+    # selected_metric_ids.
+    groups_raw: Dict[str, List[str]] = {}
+    if isinstance(row.selected_metric_groups, dict):
+        for parent_str, children in row.selected_metric_groups.items():
+            if not isinstance(children, list):
+                continue
+            cleaned: List[str] = []
+            for c in children:
+                try:
+                    UUID(str(c))
+                    cleaned.append(str(c))
+                except (TypeError, ValueError):
+                    continue
+            try:
+                UUID(parent_str)
+                groups_raw[parent_str] = cleaned
+            except (TypeError, ValueError):
+                continue
+
+    metric_ids_for_lookup: List[UUID] = list(selected_ids)
+    for parent_str in groups_raw.keys():
+        try:
+            pid = UUID(parent_str)
+            if pid not in metric_ids_for_lookup:
+                metric_ids_for_lookup.append(pid)
+        except (TypeError, ValueError):
+            continue
+
+    metrics = _metrics_for_ids(
+        db, row.organization_id, metric_ids_for_lookup
+    )
+
+    from app.services.call_imports.progress_counters import merge_eval_counters_for_ui
+
+    ui_completed_raw, ui_failed_raw = merge_eval_counters_for_ui(row)
+    total = int(row.total_rows or 0)
+    ui_completed = (
+        min(ui_completed_raw, total) if total else ui_completed_raw
+    )
+    ui_failed = min(ui_failed_raw, total) if total else ui_failed_raw
+
+    if user_emails is None:
+        user_emails = emails_for_user_ids(db, user_ids_from_evaluations([row]))
+    created_email, updated_email = actor_emails_for_evaluation(row, user_emails)
+
+    return CallImportEvaluationResponse(
+        id=row.id,
+        call_import_id=row.call_import_id,
+        organization_id=row.organization_id,
+        name=row.name,
+        selected_metric_ids=selected_ids,
+        selected_metric_groups=groups_raw or None,
+        metrics=[
+            CallImportMetricSummary(
+                id=metric.id,
+                name=metric.name,
+                metric_type=metric.metric_type,
+                description=metric.description,
+                parent_metric_id=metric.parent_metric_id,
+                selection_mode=metric.selection_mode,
+                # Required by the Flow tab to know whether a parent
+                # opted into discovery; without it the
+                # DiscoveredLabelsPanel stays hidden even when the
+                # worker is actively producing discovered_labels.
+                allow_discovery=bool(
+                    getattr(metric, "allow_discovery", False)
+                ),
+            )
+            for metric in metrics
+        ],
+        status=row.status,
+        total_rows=row.total_rows,
+        completed_rows=ui_completed,
+        failed_rows=ui_failed,
+        error_message=row.error_message,
+        llm_provider=row.llm_provider,
+        llm_model=row.llm_model,
+        llm_credential_id=row.llm_credential_id,
+        llm_config=(
+            row.llm_config if isinstance(getattr(row, "llm_config", None), dict) else None
+        ),
+        metric_llm_overrides=(
+            row.metric_llm_overrides
+            if isinstance(row.metric_llm_overrides, dict)
+            else None
+        ),
+        stt_provider=row.stt_provider,
+        stt_model=row.stt_model,
+        stt_credential_id=row.stt_credential_id,
+        diarisation_llm_provider=getattr(row, "diarisation_llm_provider", None),
+        diarisation_llm_model=getattr(row, "diarisation_llm_model", None),
+        diarisation_llm_credential_id=getattr(
+            row, "diarisation_llm_credential_id", None
+        ),
+        diarisation_prompt=getattr(row, "diarisation_prompt", None),
+        transcribe_mode=(
+            (getattr(row, "transcribe_mode", None) or "stt_llm")
+        ),
+        transcript_source=(row.transcript_source or "diarised"),
+        sibling_evaluation_ids=list(sibling_evaluation_ids or []),
+        started_at=row.started_at,
+        finished_at=row.finished_at,
+        created_at=row.created_at,
+        updated_at=row.updated_at,
+        created_by_email=created_email,
+        last_updated_by_email=updated_email,
+        tldr_summary=_tldr_summary_payload(row),
+        user_insights=_user_insights_payload(row),
+        metric_clusters=_metric_clusters_payload(row),
+        discover_new_metrics=bool(
+            getattr(row, "discover_new_metrics", False)
+        ),
+        bulk_operation=_evaluation_bulk_operation_for_response(row.id),
+    )
+
+
+def _normalize_name(value: Optional[str]) -> Optional[str]:
+    """Trim user-supplied name; empty string becomes ``NULL``."""
+    if value is None:
+        return None
+    trimmed = value.strip()
+    return trimmed or None
+
+
+def _rollup_evaluation_status(evaluation: CallImportEvaluation, db: Session) -> None:
+    """Recompute counters + terminal status after rows are added/removed.
+
+    Uses a single aggregate query instead of loading every row status.
+    """
+    from app.workers.tasks.evaluate_call_import_row_core import (
+        _apply_parent_status_from_counters,
+        reconcile_evaluation_counters,
+    )
+
+    reconcile_evaluation_counters(db, evaluation)
+    _apply_parent_status_from_counters(evaluation)
+    db.flush()
+
+    if evaluation.status in {"completed", "failed", "partial"}:
+        from app.models.database import CallImport
+        from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
+
+        call_import = (
+            db.query(CallImport)
+            .filter(CallImport.id == evaluation.call_import_id)
+            .first()
+        )
+        if call_import is not None:
+            rollup_call_import_batch_status(db, call_import)
+
+
+@router.post(
+    "",
+    response_model=CallImportEvaluationResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="createCallImportEvaluation",
+)
+async def create_call_import_evaluation(
+    call_import_id: UUID,
+    payload: CallImportEvaluationCreate,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationResponse:
+    del api_key
+    call_import = _require_import(db, call_import_id, organization_id)
+
+    metric_ids = payload.metric_ids
+    if not metric_ids:
+        raise HTTPException(
+            status_code=400,
+            detail="Select at least one metric to run the evaluation against.",
+        )
+
+    org_metrics = (
+        db.query(Metric)
+        .filter(
+            Metric.organization_id == organization_id,
+            Metric.id.in_(metric_ids),
+        )
+        .all()
+    )
+    by_id = {metric.id: metric for metric in org_metrics}
+    unknown_ids = [mid for mid in metric_ids if mid not in by_id]
+    if unknown_ids:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "These metric ids do not exist in your organization: "
+                f"{', '.join(str(mid) for mid in unknown_ids)}. "
+                "Refresh the metrics list and try again."
+            ),
+        )
+    # Parents themselves are containers, not scored rows, so a disabled
+    # parent shouldn't block the run as long as it has enabled children.
+    # We only reject disabled rows that the worker will actually try to
+    # evaluate (children + standalone leaves).
+    disabled_leaves = [
+        metric
+        for metric in org_metrics
+        if not metric.enabled
+        and not (metric.selection_mode and not metric.parent_metric_id)
+    ]
+    if disabled_leaves:
+        names = ", ".join(metric.name for metric in disabled_leaves)
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                f"These metrics are disabled and cannot be evaluated: {names}. "
+                "Enable them on the Metrics page (or pick different ones) and "
+                "try again."
+            ),
+        )
+
+    # Expand hierarchical selection: parents auto-include their enabled
+    # children, mixed parent+child selections respect the user's subset.
+    effective_metrics, parent_to_children = _expand_metric_selection(
+        db, organization_id, metric_ids
+    )
+    if not effective_metrics:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "None of the selected metrics yielded an enabled leaf to "
+                "evaluate. Check that parent categories have enabled "
+                "children, then try again."
+            ),
+        )
+
+    # The effective list (children + standalone leaves) is what gets
+    # persisted to ``selected_metric_ids`` and scored by the worker.
+    # The original parents are preserved in ``selected_metric_groups``
+    # so the UI can rebuild the tree later.
+    leaf_metric_ids: List[UUID] = [m.id for m in effective_metrics]
+    selected_metric_groups: Dict[str, List[str]] = {
+        str(pid): [str(c.id) for c in children]
+        for pid, children in parent_to_children.items()
+    }
+    metric_rows = effective_metrics
+    valid_metric_id_strs = {str(m.id) for m in metric_rows}
+
+    # ----- Validate run-level + per-metric LLM config -----
+    llm_provider_norm: Optional[str] = None
+    llm_model_norm: Optional[str] = None
+    if payload.llm_provider or payload.llm_model:
+        if not (payload.llm_provider and payload.llm_model):
+            raise HTTPException(
+                status_code=400,
+                detail="Both llm_provider and llm_model are required when overriding the run LLM.",
+            )
+        try:
+            llm_provider_norm = ModelProvider(
+                payload.llm_provider.lower()
+            ).value
+        except ValueError:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    f"Unknown LLM provider '{payload.llm_provider}'. "
+                    "Valid keys are documented in ModelProvider."
+                ),
+            )
+        llm_model_norm = payload.llm_model.strip() or None
+        if not llm_model_norm:
+            raise HTTPException(
+                status_code=400, detail="llm_model cannot be empty."
+            )
+
+    if payload.llm_credential_id is not None:
+        cred = (
+            db.query(AIProvider)
+            .filter(
+                AIProvider.id == payload.llm_credential_id,
+                AIProvider.organization_id == organization_id,
+            )
+            .first()
+        )
+        if not cred:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "The provided llm_credential_id does not exist in this "
+                    "organization."
+                ),
+            )
+
+    # Per-metric overrides: keys can be either a leaf metric id (applies
+    # to that metric only) or a parent metric id (applies to every
+    # child of that parent). Parent keys are expanded to their
+    # children so the worker only sees concrete leaf ids.
+    metric_overrides_payload: Optional[Dict[str, Dict[str, Any]]] = None
+    if payload.metric_llm_overrides:
+        metric_overrides_payload = {}
+        for metric_id, override in payload.metric_llm_overrides.items():
+            target_leaf_ids: List[str] = []
+            if metric_id in valid_metric_id_strs:
+                target_leaf_ids = [metric_id]
+            else:
+                # Maybe it's a parent id — expand to the children that
+                # are part of THIS run.
+                try:
+                    parent_uuid = UUID(metric_id)
+                except (TypeError, ValueError):
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            "metric_llm_overrides references metric "
+                            f"{metric_id} which is not a valid UUID."
+                        ),
+                    )
+                children_for_parent = parent_to_children.get(parent_uuid)
+                if not children_for_parent:
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            "metric_llm_overrides references metric "
+                            f"{metric_id} which is not in metric_ids."
+                        ),
+                    )
+                target_leaf_ids = [str(c.id) for c in children_for_parent]
+
+            override_dict: Dict[str, Any] = {}
+            if override.provider is not None:
+                if not override.model:
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            f"Override for metric {metric_id} has a provider "
+                            "but no model."
+                        ),
+                    )
+                try:
+                    override_dict["provider"] = ModelProvider(
+                        override.provider.lower()
+                    ).value
+                except ValueError:
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            f"Override for metric {metric_id} uses unknown "
+                            f"provider '{override.provider}'."
+                        ),
+                    )
+                override_dict["model"] = override.model.strip()
+            elif override.model:
+                # Model without provider doesn't make sense — treat as 400
+                # so the UI can fix it instead of silently falling back.
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        f"Override for metric {metric_id} has a model but "
+                        "no provider."
+                    ),
+                )
+            if override.credential_id is not None:
+                override_dict["credential_id"] = str(override.credential_id)
+            if override.llm_config is not None:
+                override_dict["llm_config"] = override.llm_config
+            if override_dict:
+                for leaf_id in target_leaf_ids:
+                    metric_overrides_payload[leaf_id] = override_dict
+
+    # ----- Validate auto-transcribe settings -----
+    # Diarised runs auto-diarise rows missing a diarised transcript and
+    # require STT + diariser LLM config. Production runs score the CSV
+    # transcript directly and skip diarisation entirely.
+    use_diarised = payload.transcript_sources[0] == "diarised"
+    auto_transcribe = use_diarised
+
+    transcribe_mode_norm: Optional[str] = None
+    stt_provider_norm: Optional[str] = None
+    stt_model_norm: Optional[str] = None
+    diarisation_llm_provider_norm: Optional[str] = None
+    diarisation_llm_model_norm: Optional[str] = None
+    diarisation_prompt_norm: Optional[str] = None
+
+    if use_diarised:
+        transcribe_mode_norm = (payload.transcribe_mode or "stt_llm").strip().lower()
+        if transcribe_mode_norm not in {"stt_llm", "llm_only"}:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    f"Unknown transcribe_mode '{payload.transcribe_mode}'. "
+                    "Expected 'stt_llm' or 'llm_only'."
+                ),
+            )
+
+        if transcribe_mode_norm == "stt_llm":
+            if not payload.stt_provider:
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        "stt_provider is required when "
+                        "transcribe_mode='stt_llm': every evaluation run "
+                        "auto-diarises rows that are missing a diarised "
+                        "transcript."
+                    ),
+                )
+            if not payload.stt_model:
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        "stt_model is required when transcribe_mode='stt_llm'."
+                    ),
+                )
+            try:
+                stt_provider_norm = ModelProvider(
+                    payload.stt_provider.lower()
+                ).value
+            except ValueError:
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Unknown STT provider '{payload.stt_provider}'.",
+                )
+            stt_model_norm = payload.stt_model.strip() or None
+            if not stt_model_norm:
+                raise HTTPException(
+                    status_code=400, detail="stt_model cannot be empty."
+                )
+        else:
+            # llm_only — explicitly reject lingering STT inputs so the
+            # contract is unambiguous (the worker would ignore them but
+            # silent acceptance hides accidental misconfiguration).
+            if (payload.stt_provider or "").strip() or (
+                payload.stt_model or ""
+            ).strip():
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        "stt_provider / stt_model must be omitted when "
+                        "transcribe_mode='llm_only'; the LLM consumes the "
+                        "audio directly."
+                    ),
+                )
+
+        # --- Validate LLM diariser settings -----
+        if not payload.diarization_llm_provider:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "diarization_llm_provider is required: every evaluation "
+                    "run diarises STT output with an LLM."
+                ),
+            )
+        if not payload.diarization_llm_model:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "diarization_llm_model is required: every evaluation "
+                    "run diarises STT output with an LLM."
+                ),
+            )
+        try:
+            diarisation_llm_provider_norm = ModelProvider(
+                payload.diarization_llm_provider.lower()
+            ).value
+        except ValueError:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    f"Unknown diarisation LLM provider "
+                    f"'{payload.diarization_llm_provider}'."
+                ),
+            )
+        diarisation_llm_model_norm = (
+            payload.diarization_llm_model.strip() or None
+        )
+        if not diarisation_llm_model_norm:
+            raise HTTPException(
+                status_code=400,
+                detail="diarization_llm_model cannot be empty.",
+            )
+        diarisation_prompt_norm = (
+            payload.diarization_prompt.strip()
+            if isinstance(payload.diarization_prompt, str)
+            else None
+        ) or None
+
+    from app.models.enums import CallImportParameterType, CallImportStatus
+    from app.services.call_imports.bulk_ops import (
+        count_all_source_rows,
+        count_completed_source_rows,
+        count_source_rows_with_production_transcript,
+    )
+
+    starting_from_mapped = False
+    if call_import.status == CallImportStatus.MAPPED:
+        if not call_import.source_s3_key or not call_import.source_format:
+            raise HTTPException(
+                status_code=status.HTTP_409_CONFLICT,
+                detail=(
+                    "This batch has no staged source file. Upload and map "
+                    "a CSV/Excel file before running evaluation."
+                ),
+            )
+        if not call_import.schema_id:
+            raise HTTPException(
+                status_code=status.HTTP_409_CONFLICT,
+                detail="Cannot run evaluation without a mapped schema.",
+            )
+        from app.api.v1.routes.call_imports import (
+            _ensure_blob_storage_enabled,
+            _resolve_schema,
+            _resolve_telephony_integration,
+            _validate_direct_url_import_ready,
+        )
+
+        workspace_id = call_import.workspace_id
+        schema = _resolve_schema(
+            db, organization_id, workspace_id, call_import.schema_id
+        )
+        parameters = list(schema.parameters)
+        if not use_diarised:
+            transcript_mapped = any(
+                param.type == CallImportParameterType.TRANSCRIPT
+                and (call_import.parameter_mapping or {}).get(param.name)
+                for param in parameters
+            )
+            if not transcript_mapped:
+                raise HTTPException(
+                    status_code=status.HTTP_409_CONFLICT,
+                    detail=(
+                        "No transcript column is mapped in this batch. "
+                        "Map a schema transcript parameter to a CSV column, "
+                        "or choose 'Diarize then evaluate'."
+                    ),
+                )
+        if payload.telephony_integration_id is not None:
+            integration = _resolve_telephony_integration(
+                db,
+                organization_id,
+                payload.telephony_integration_id,
+                payload.provider or "",
+            )
+        else:
+            _validate_direct_url_import_ready(
+                parameters, dict(call_import.parameter_mapping or {})
+            )
+            integration = None
+
+        _ensure_blob_storage_enabled()
+
+        if integration is not None:
+            call_import.provider = integration.provider
+            call_import.telephony_integration_id = integration.id
+        else:
+            call_import.provider = None
+            call_import.telephony_integration_id = None
+
+        call_import.total_rows = 0
+        call_import.completed_rows = 0
+        call_import.failed_rows = 0
+        call_import.error_message = None
+        call_import.status = CallImportStatus.PROCESSING
+        db.commit()
+        db.refresh(call_import)
+        starting_from_mapped = True
+
+    if use_diarised:
+        total_row_count = count_completed_source_rows(db, call_import.id)
+    else:
+        # Production runs score CSV text — rows need not wait for
+        # recording fetch to finish before they are evaluable.
+        total_row_count = count_source_rows_with_production_transcript(
+            db, call_import.id
+        )
+
+    requested_sources: List[str] = list(payload.transcript_sources)
+
+    if (
+        not use_diarised
+        and not starting_from_mapped
+        and count_all_source_rows(db, call_import.id) > 0
+        and total_row_count == 0
+    ):
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=(
+                "No rows have a production transcript. "
+                "Choose 'Diarize then evaluate' or import rows with "
+                "a transcript column."
+            ),
+        )
+
+    base_name = _normalize_name(payload.name)
+
+    def _name_for_source(source: str) -> Optional[str]:
+        # Single-source runs preserve the user's chosen name verbatim.
+        del source
+        return base_name
+
+    created_evaluations: List[CallImportEvaluation] = []
+
+    for source in requested_sources:
+        evaluation = CallImportEvaluation(
+            call_import_id=call_import.id,
+            organization_id=organization_id,
+            # Mirror the parent CallImport's workspace so listings can
+            # filter on workspace_id directly without joining.
+            workspace_id=call_import.workspace_id,
+            name=_name_for_source(source),
+            selected_metric_ids=[
+                str(metric_id) for metric_id in leaf_metric_ids
+            ],
+            selected_metric_groups=selected_metric_groups or None,
+            status="pending",
+            total_rows=total_row_count,
+            completed_rows=0,
+            failed_rows=0,
+            llm_provider=llm_provider_norm,
+            llm_model=llm_model_norm,
+            llm_credential_id=payload.llm_credential_id,
+            llm_config=payload.llm_config,
+            metric_llm_overrides=metric_overrides_payload,
+            stt_provider=stt_provider_norm,
+            stt_model=stt_model_norm,
+            stt_credential_id=(
+                payload.stt_credential_id if auto_transcribe else None
+            ),
+            diarisation_llm_provider=diarisation_llm_provider_norm,
+            diarisation_llm_model=diarisation_llm_model_norm,
+            diarisation_llm_credential_id=(
+                payload.diarization_llm_credential_id if auto_transcribe else None
+            ),
+            diarisation_prompt=diarisation_prompt_norm,
+            transcribe_mode=transcribe_mode_norm,
+            transcript_source=source,
+            discover_new_metrics=bool(
+                getattr(payload, "discover_new_metrics", False)
+            ),
+        )
+        stamp_evaluation_actor(evaluation, principal, creating=True)
+        db.add(evaluation)
+        db.flush()
+        created_evaluations.append(evaluation)
+
+    db.commit()
+    for evaluation in created_evaluations:
+        db.refresh(evaluation)
+
+    primary_evaluation = created_evaluations[0]
+    sibling_ids = [e.id for e in created_evaluations[1:]]
+
+    if not total_row_count and not starting_from_mapped:
+        for evaluation in created_evaluations:
+            evaluation.status = "completed"
+        db.commit()
+        for evaluation in created_evaluations:
+            db.refresh(evaluation)
+        return _serialize_eval(
+            db, primary_evaluation, sibling_evaluation_ids=sibling_ids
+        )
+
+    if starting_from_mapped:
+        from app.workers.tasks.call_import_bulk_ops import (
+            materialize_mapped_call_import_evaluation_task,
+        )
+
+        for evaluation in created_evaluations:
+            materialize_mapped_call_import_evaluation_task.delay(
+                str(call_import.id),
+                str(organization_id),
+                str(call_import.workspace_id),
+                str(evaluation.id),
+                transcribe_overwrite=payload.transcribe_overwrite,
+            )
+    else:
+        from app.workers.tasks.call_import_bulk_ops import (
+            materialize_call_import_evaluation_task,
+        )
+
+        for evaluation in created_evaluations:
+            materialize_call_import_evaluation_task.delay(
+                str(evaluation.id),
+                transcribe_overwrite=payload.transcribe_overwrite,
+            )
+
+    for evaluation in created_evaluations:
+        db.refresh(evaluation)
+
+    return _serialize_eval(
+        db, primary_evaluation, sibling_evaluation_ids=sibling_ids
+    )
+
+
+@router.get(
+    "",
+    response_model=CallImportEvaluationListResponse,
+    operation_id="listCallImportEvaluations",
+)
+async def list_call_import_evaluations(
+    call_import_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationListResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+    rows = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .order_by(desc(CallImportEvaluation.created_at))
+        .all()
+    )
+    email_map = emails_for_user_ids(db, user_ids_from_evaluations(rows))
+    return CallImportEvaluationListResponse(
+        items=[_serialize_eval(db, row, user_emails=email_map) for row in rows],
+        total=len(rows),
+    )
+
+
+@router.get(
+    "/{eval_id}",
+    response_model=CallImportEvaluationResponse,
+    operation_id="getCallImportEvaluation",
+)
+async def get_call_import_evaluation(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+    row = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not row:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+    return _serialize_eval(db, row)
+
+
+@router.get(
+    "/{eval_id}/rows",
+    response_model=CallImportEvaluationRowListResponse,
+    operation_id="listCallImportEvaluationRows",
+)
+async def list_call_import_evaluation_rows(
+    call_import_id: UUID,
+    eval_id: UUID,
+    page: int = Query(1, ge=1),
+    page_size: int = Query(100, ge=1, le=500),
+    q: Optional[str] = Query(
+        None,
+        description=(
+            "Free-text search across conversation_id and transcript "
+            "(case-insensitive substring match)."
+        ),
+    ),
+    metric_id: Optional[UUID] = Query(
+        None,
+        description=(
+            "If set, only return rows whose ``metric_scores[metric_id].value`` "
+            "exactly matches ``metric_value`` (string-compared). "
+            "Use together with ``metric_value``."
+        ),
+    ),
+    metric_value: Optional[str] = Query(
+        None,
+        description="Value to match against metric_id (string compare).",
+    ),
+    status_filter: Optional[str] = Query(
+        None,
+        alias="status",
+        description="Restrict to rows with this evaluation row status.",
+    ),
+    flow_parent_id: Optional[UUID] = Query(
+        None,
+        description=(
+            "Parent (category) metric whose ``sequence`` array should be "
+            "checked against ``flow_node`` and ``flow_edge_target``. Used "
+            "to drill into the calls behind a flow-chart node or edge."
+        ),
+    ),
+    flow_node: Optional[str] = Query(
+        None,
+        description=(
+            "If set together with ``flow_parent_id``, only return rows "
+            "whose sequence under that parent contains this step. Accepts "
+            "either a child metric UUID (resolved to slug(name)), a "
+            "``disc:<slug>`` discovered-label id, or a raw slug."
+        ),
+    ),
+    flow_edge_target: Optional[str] = Query(
+        None,
+        description=(
+            "Optional companion to ``flow_node``: when set, restrict to "
+            "rows whose sequence contains the directed transition "
+            "``flow_node -> flow_edge_target`` (immediately adjacent). "
+            "Same id format as ``flow_node``."
+        ),
+    ),
+    discovered_parent_id: Optional[UUID] = Query(
+        None,
+        description=(
+            "Parent (category) metric that defines the discovery scope "
+            "for ``discovered_label_key`` / ``has_discovered``."
+        ),
+    ),
+    discovered_label_key: Optional[str] = Query(
+        None,
+        description=(
+            "If set together with ``discovered_parent_id``, only return "
+            "rows whose ``metric_scores[parent].discovered_labels`` "
+            "list contains an entry with this slug (after applying "
+            "evaluation-level merge aliases)."
+        ),
+    ),
+    has_discovered: Optional[bool] = Query(
+        None,
+        description=(
+            "If true together with ``discovered_parent_id``, only return "
+            "rows that have at least one LLM-discovered label for the "
+            "parent. Useful to triage which calls produced novel labels."
+        ),
+    ),
+    sort_by: Optional[str] = Query(
+        None,
+        description=(
+            "Column to sort by. Accepted values: ``row_index`` (default "
+            "when omitted), ``conversation_id``, ``status`` (the "
+            "evaluation-row status), or ``metric:<metric_uuid>`` to sort "
+            "by ``metric_scores[<uuid>].value``. Metric sorts compare "
+            "the extracted JSON text — adequate for booleans, enum "
+            "labels, and 0-1 ratings; large integer values may sort "
+            "lexicographically (10 before 2)."
+        ),
+    ),
+    sort_dir: Optional[str] = Query(
+        "asc",
+        description="Sort direction: ``asc`` (default) or ``desc``.",
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationRowListResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    eval_row = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not eval_row:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    query = (
+        db.query(CallImportEvaluationRow, CallImportRow)
+        .join(CallImportRow, CallImportRow.id == CallImportEvaluationRow.call_import_row_id)
+        .filter(CallImportEvaluationRow.evaluation_id == eval_id)
+    )
+
+    # --- Filters ----------------------------------------------------------
+    if q and q.strip():
+        needle = f"%{q.strip()}%"
+        # Search across both transcript columns so a hit in either the
+        # production or the diarised version surfaces the row,
+        # independent of which source the evaluation actually scored.
+        query = query.filter(
+            or_(
+                CallImportRow.conversation_id.ilike(needle),
+                CallImportRow.transcript.ilike(needle),
+                CallImportRow.diarised_transcript.ilike(needle),
+            )
+        )
+
+    if status_filter:
+        # The CallImportEvaluationRow.status column is a string in PG so a
+        # plain == filter works; we lowercase to match the stored values.
+        query = query.filter(
+            CallImportEvaluationRow.status == status_filter.strip().lower()
+        )
+
+    if metric_id is not None and metric_value is not None:
+        # ``metric_scores`` is a JSONB column shaped like
+        # ``{"<metric_id>": {"value": <X>, "type": "boolean", ...}}``. We
+        # extract the nested ``value`` as text and compare to the user
+        # input as a string — that handles bool/int/enum without needing
+        # per-type casts. ``metric_value`` is matched case-insensitively
+        # so chart clicks on labels like "True" survive any casing drift
+        # between worker output and the chart label.
+        path_value = func.json_extract_path_text(
+            CallImportEvaluationRow.metric_scores,
+            str(metric_id),
+            "value",
+        )
+        query = query.filter(func.lower(path_value) == metric_value.strip().lower())
+
+    # --- Flow chart drilldown filter -------------------------------------
+    # Translates a clicked node (or edge) on the flow chart into a
+    # SQL filter against ``metric_scores[<parent>].sequence``. The
+    # frontend sends either a child UUID, a ``disc:<slug>`` discovered
+    # node id, or a raw slug — we normalize all three to the slug that
+    # actually appears in stored ``sequence`` arrays.
+    if flow_parent_id is not None and flow_node and flow_node.strip():
+        parent_id_str_local = str(flow_parent_id)
+        alias_map_flow = _alias_map_for_parent(eval_row, flow_parent_id)
+
+        def _flow_node_to_slug(raw: str) -> Optional[str]:
+            raw_clean = raw.strip()
+            if not raw_clean:
+                return None
+            if raw_clean == _FLOW_START_NODE_ID:
+                # The synthetic START node isn't a real sequence entry;
+                # filtering on it is meaningless so we skip silently.
+                return None
+            if raw_clean.startswith(_DISCOVERED_NODE_PREFIX):
+                return _resolve_alias(
+                    alias_map_flow,
+                    _slug_label(raw_clean[len(_DISCOVERED_NODE_PREFIX) :]),
+                )
+            # Try to interpret as a child metric UUID first; fall back
+            # to treating it as a slug.
+            try:
+                child_uuid = UUID(raw_clean)
+            except (TypeError, ValueError):
+                return _resolve_alias(alias_map_flow, _slug_label(raw_clean))
+            child = (
+                db.query(Metric.name)
+                .filter(
+                    Metric.id == child_uuid,
+                    Metric.organization_id == organization_id,
+                )
+                .first()
+            )
+            if child and child[0]:
+                return _resolve_alias(alias_map_flow, _slug_label(child[0]))
+            return _resolve_alias(alias_map_flow, _slug_label(raw_clean))
+
+        from_slug = _flow_node_to_slug(flow_node)
+        target_slug: Optional[str] = None
+        if flow_edge_target and flow_edge_target.strip():
+            target_slug = _flow_node_to_slug(flow_edge_target)
+
+        if from_slug:
+            # The ``metric_scores`` column is declared as ``Column(JSON)``
+            # in the model so on databases where the table was created
+            # from the model (rather than the migration) the physical
+            # type is ``json``, not ``jsonb``. The JSONB-only operators
+            # below (``jsonb_exists``, ``jsonb_array_elements_text``,
+            # ``@>``) require a JSONB input — we cast once up front so
+            # the same SQL works regardless of which path created the
+            # table.
+            scores_jsonb = (
+                "(call_import_evaluation_rows.metric_scores)::jsonb"
+            )
+            if target_slug:
+                # Edge filter: rows whose sequence under this parent
+                # contains ``from_slug`` immediately followed by
+                # ``target_slug``. Implemented as a correlated EXISTS
+                # over ``jsonb_array_elements_text`` with ORDINALITY,
+                # which is the portable way to express "next array
+                # index" against a JSONB array in Postgres.
+                edge_filter_sql = text(
+                    f"""
+                    EXISTS (
+                        SELECT 1
+                        FROM jsonb_array_elements_text(
+                            COALESCE(
+                                {scores_jsonb} -> :p_id -> 'sequence',
+                                '[]'::jsonb
+                            )
+                        ) WITH ORDINALITY AS s1(elem, ord)
+                        JOIN jsonb_array_elements_text(
+                            COALESCE(
+                                {scores_jsonb} -> :p_id -> 'sequence',
+                                '[]'::jsonb
+                            )
+                        ) WITH ORDINALITY AS s2(elem, ord)
+                          ON s2.ord = s1.ord + 1
+                        WHERE s1.elem = :from_slug
+                          AND s2.elem = :to_slug
+                    )
+                    """
+                ).bindparams(
+                    p_id=parent_id_str_local,
+                    from_slug=from_slug,
+                    to_slug=target_slug,
+                )
+                query = query.filter(edge_filter_sql)
+            else:
+                # Node filter: rows whose ``metric_scores -> parent ->
+                # 'sequence'`` array contains ``from_slug``. We use the
+                # function form ``jsonb_exists`` rather than the ``?``
+                # operator to avoid psycopg2 mistaking the question
+                # mark for a parameter placeholder.
+                node_filter_sql = text(
+                    f"""
+                    jsonb_exists(
+                        COALESCE(
+                            {scores_jsonb} -> :p_id -> 'sequence',
+                            '[]'::jsonb
+                        ),
+                        :slug
+                    )
+                    """
+                ).bindparams(p_id=parent_id_str_local, slug=from_slug)
+                query = query.filter(node_filter_sql)
+
+    # --- Discovered label filters ---------------------------------------
+    # Surfaces "which calls produced THIS LLM-discovered label" and the
+    # broader "which calls produced ANY LLM-discovered label". Both
+    # operate on ``metric_scores[<parent>].discovered_labels`` (a list
+    # of dicts) plus the same ``sequence`` array — covering both legacy
+    # rows where the slug only made it into ``sequence`` and newer
+    # rows where it landed in both.
+    if discovered_parent_id is not None and (
+        discovered_label_key or has_discovered
+    ):
+        d_parent_str = str(discovered_parent_id)
+        alias_map_disc = _alias_map_for_parent(eval_row, discovered_parent_id)
+        # See note above: cast once so the JSONB operators don't reject
+        # the column when it's typed as ``json`` in the database.
+        scores_jsonb = "(call_import_evaluation_rows.metric_scores)::jsonb"
+        if discovered_label_key and discovered_label_key.strip():
+            target = _resolve_alias(
+                alias_map_disc, _slug_label(discovered_label_key)
+            )
+            if target:
+                # Match rows whose discovered_labels list has an entry
+                # ``{"key": <target>}`` OR whose sequence array still
+                # contains the slug. The latter covers older rows that
+                # were rewritten by a merge in the discovered_labels
+                # blob but whose sequence may have lagged.
+                contains_json = json.dumps(
+                    {d_parent_str: {"discovered_labels": [{"key": target}]}}
+                )
+                disc_filter_sql = text(
+                    f"""
+                    (
+                        {scores_jsonb} @> CAST(:contains AS JSONB)
+                        OR
+                        jsonb_exists(
+                            COALESCE(
+                                {scores_jsonb} -> :p_id -> 'sequence',
+                                '[]'::jsonb
+                            ),
+                            :slug
+                        )
+                    )
+                    """
+                ).bindparams(
+                    contains=contains_json,
+                    p_id=d_parent_str,
+                    slug=target,
+                )
+                query = query.filter(disc_filter_sql)
+        elif has_discovered:
+            # No specific slug — just rows that surfaced any candidate
+            # under this parent. We coalesce missing paths to ``[]`` so
+            # ``jsonb_array_length`` always sees an array (it raises on
+            # non-array inputs, but our shape guarantees a list when
+            # the key is present).
+            has_disc_sql = text(
+                f"""
+                jsonb_array_length(
+                    COALESCE(
+                        {scores_jsonb} -> :p_id -> 'discovered_labels',
+                        '[]'::jsonb
+                    )
+                ) > 0
+                """
+            ).bindparams(p_id=d_parent_str)
+            query = query.filter(has_disc_sql)
+
+    # --- Sorting ----------------------------------------------------------
+    # Column-click sorting from the UI. Falls back to ``row_index`` so
+    # paging stays stable when the user clears the sort. We always add a
+    # secondary ``row_index`` tiebreaker so duplicate sort keys (e.g.
+    # many rows with ``status = 'completed'``) keep a deterministic
+    # order across page boundaries — without this, pagination can
+    # double-show or skip rows when Postgres picks a different physical
+    # order on each query.
+    direction_desc = (sort_dir or "asc").strip().lower() == "desc"
+
+    def _apply_direction(column_expr):
+        return column_expr.desc() if direction_desc else column_expr.asc()
+
+    # Whether the caller's ``sort_by`` resolved to a known column. We
+    # use this flag to decide whether ``sort_dir`` is honoured on the
+    # fallback path: unrecognized columns (typos, stale UI state) fall
+    # back to the implicit ``row_index ASC`` default and intentionally
+    # ignore ``sort_dir`` so users don't get a surprise reverse order
+    # from a typo'd column name.
+    sort_recognized = False
+    sort_by_clean = (sort_by or "").strip()
+    primary_sort = None
+    metric_uuid: Optional[UUID] = None
+    if sort_by_clean == "row_index":
+        sort_recognized = True
+        # Falls through to the default ``order_by`` below with
+        # ``primary_sort`` still None — but ``sort_recognized=True``
+        # tells the fallback branch to apply the requested direction.
+    elif sort_by_clean == "conversation_id":
+        sort_recognized = True
+        primary_sort = _apply_direction(CallImportRow.conversation_id)
+    elif sort_by_clean == "status":
+        sort_recognized = True
+        primary_sort = _apply_direction(CallImportEvaluationRow.status)
+    elif sort_by_clean.startswith("metric:"):
+        raw_metric_id = sort_by_clean.split(":", 1)[1].strip()
+        try:
+            metric_uuid = UUID(raw_metric_id)
+        except (TypeError, ValueError):
+            metric_uuid = None
+        if metric_uuid is not None:
+            sort_recognized = True
+            # ``metric_scores`` is JSON-typed but the helper functions
+            # for path extraction differ between Postgres (production)
+            # and SQLite (default test backend). Branch on the active
+            # dialect so we can use the right primitive:
+            #   * Postgres → ``json_extract_path_text(col, key, "value")``
+            #     which returns the value as TEXT for both ``json`` and
+            #     ``jsonb`` columns.
+            #   * SQLite   → ``json_extract(col, '$."<uuid>".value')``
+            #     using JSONPath syntax. ``metric_uuid`` is already
+            #     validated above (``UUID(raw_metric_id)``), so the
+            #     interpolated path is safe from injection.
+            # NULL values (rows where the metric wasn't scored) sort
+            # to the END regardless of direction so un-scored rows
+            # don't crowd the top of an ascending sort.
+            dialect_name = (
+                db.bind.dialect.name if db.bind is not None else "postgresql"
+            )
+            if dialect_name == "sqlite":
+                json_path = f'$."{metric_uuid}".value'
+                path_value = func.json_extract(
+                    CallImportEvaluationRow.metric_scores,
+                    json_path,
+                )
+            else:
+                path_value = func.json_extract_path_text(
+                    CallImportEvaluationRow.metric_scores,
+                    str(metric_uuid),
+                    "value",
+                )
+            primary_sort = (
+                path_value.desc().nullslast()
+                if direction_desc
+                else path_value.asc().nullslast()
+            )
+
+    if primary_sort is not None:
+        query = query.order_by(primary_sort, CallImportRow.row_index.asc())
+    elif sort_recognized:
+        # Explicit ``sort_by=row_index`` request — honour direction.
+        query = query.order_by(_apply_direction(CallImportRow.row_index))
+    else:
+        # No sort requested OR unrecognized column — safe default of
+        # ``row_index ASC``. We deliberately ignore ``sort_dir`` here
+        # so a typo'd / stale ``sort_by`` doesn't quietly invert the
+        # default order.
+        query = query.order_by(CallImportRow.row_index.asc())
+    from app.db_sharding.eval_rows import fetch_evaluation_row_pairs_page
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    def _pair_row_index(
+        pair: Tuple[CallImportEvaluationRow, CallImportRow],
+    ) -> int:
+        return int(pair[1].row_index or 0)
+
+    def _directed_string(value: Optional[str], desc: bool) -> Tuple[int, ...]:
+        text = value or ""
+        if not desc:
+            return (0, *text.encode("utf-8"))
+        return (1, *(-byte for byte in text.encode("utf-8")))
+
+    if sort_by_clean == "conversation_id":
+        def _pair_sort_key(
+            pair: Tuple[CallImportEvaluationRow, CallImportRow],
+        ) -> Tuple[Any, ...]:
+            return (
+                _directed_string(pair[1].conversation_id, direction_desc),
+                _pair_row_index(pair),
+            )
+    elif sort_by_clean == "status":
+        def _pair_sort_key(
+            pair: Tuple[CallImportEvaluationRow, CallImportRow],
+        ) -> Tuple[Any, ...]:
+            return (
+                _directed_string(pair[0].status, direction_desc),
+                _pair_row_index(pair),
+            )
+    elif sort_by_clean.startswith("metric:") and metric_uuid is not None:
+        metric_id_str = str(metric_uuid)
+
+        def _pair_sort_key(
+            pair: Tuple[CallImportEvaluationRow, CallImportRow],
+        ) -> Tuple[Any, ...]:
+            scores = pair[0].metric_scores or {}
+            entry = scores.get(metric_id_str, {})
+            raw_value = entry.get("value") if isinstance(entry, dict) else None
+            null_rank = 1 if raw_value is None else 0
+            return (
+                null_rank,
+                _directed_string(
+                    str(raw_value) if raw_value is not None else None,
+                    direction_desc,
+                ),
+                _pair_row_index(pair),
+            )
+    elif sort_recognized and sort_by_clean == "row_index":
+        def _pair_sort_key(
+            pair: Tuple[CallImportEvaluationRow, CallImportRow],
+        ) -> Tuple[int, ...]:
+            idx = _pair_row_index(pair)
+            return (-idx,) if direction_desc else (idx,)
+    else:
+        def _pair_sort_key(
+            pair: Tuple[CallImportEvaluationRow, CallImportRow],
+        ) -> Tuple[int, ...]:
+            return (_pair_row_index(pair),)
+
+    if is_sharding_enabled():
+        def _build_query(session: Session):
+            return query.with_session(session)
+
+        total, rows = fetch_evaluation_row_pairs_page(
+            db,
+            _build_query,
+            page=page,
+            page_size=page_size,
+            sort_key=_pair_sort_key,
+            bounded_shard_fetch=(
+                not sort_recognized or sort_by_clean == "row_index"
+            ),
+        )
+    else:
+        total = query.count()
+        rows = query.offset((page - 1) * page_size).limit(page_size).all()
+
+    # Row detail shows the transcript for this run's chosen source.
+    items: List[CallImportEvaluationRowResponse] = [
+        _to_evaluation_row_response(eval_row_obj, source_row, eval_row)
+        for eval_row_obj, source_row in rows
+    ]
+
+    return CallImportEvaluationRowListResponse(
+        items=items,
+        total=total,
+        page=page,
+        page_size=page_size,
+    )
+
+
+@router.get(
+    "/{eval_id}/export",
+    operation_id="exportCallImportEvaluationCsv",
+)
+async def export_call_import_evaluation_csv(
+    call_import_id: UUID,
+    eval_id: UUID,
+    format: Literal["csv", "xlsx"] = Query(
+        "csv",
+        description=(
+            "Output format. ``csv`` returns a UTF-8 BOM CSV; ``xlsx`` "
+            "returns a native Excel workbook (single sheet)."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> StreamingResponse:
+    del api_key
+    call_import = _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    selected_metric_ids = _serialize_selected_metric_ids(evaluation.selected_metric_ids)
+    # Include parent metric ids referenced in selected_metric_groups so
+    # the export shows a parent "Chosen Label" column next to its
+    # children's true/false columns.
+    lookup_ids: List[UUID] = list(selected_metric_ids)
+    groups_raw = (
+        evaluation.selected_metric_groups
+        if isinstance(evaluation.selected_metric_groups, dict)
+        else {}
+    )
+    for parent_str in groups_raw.keys():
+        try:
+            pid = UUID(parent_str)
+            if pid not in lookup_ids:
+                lookup_ids.append(pid)
+        except (TypeError, ValueError):
+            continue
+    metrics = _metrics_for_ids(db, organization_id, lookup_ids)
+    metric_names = {str(metric.id): metric.name for metric in metrics}
+    metrics_by_id = {str(metric.id): metric for metric in metrics}
+
+    # Two export-time modes depending on how the batch was uploaded:
+    #
+    #   * Schema-driven (new): ``call_imports.schema_id`` is set,
+    #     ``parameter_mapping`` records which CSV header fed each
+    #     parameter, and ``raw_columns`` on each row is keyed by
+    #     parameter NAME. Export headers are the parameter names.
+    #   * Legacy (pre-schema): ``column_mapping`` / ``extra_columns`` /
+    #     ``custom_column_mapping`` drive the columns and
+    #     ``raw_columns`` is keyed by the original CSV header.
+    #
+    # We bucket entries into ``standard_export_headers`` (raw_columns
+    # key == export header) and ``custom_export`` (export header
+    # differs from the raw_columns key) so the row-projection loop
+    # below stays mode-agnostic.
+    standard_export_headers: List[str] = []
+    custom_export: List[tuple[str, str]] = []  # [(export_header, raw_columns_key)]
+
+    if call_import.schema_id is not None:
+        # Use the live schema parameter list for column ordering. Falls
+        # back to whatever's in ``parameter_mapping`` if the schema was
+        # deleted (defensive - the FK is ON DELETE RESTRICT, but tests
+        # / future cascades may still hit this branch).
+        from app.models.database import CallImportSchema as _ImportSchema
+
+        schema_obj = (
+            db.query(_ImportSchema)
+            .filter(_ImportSchema.id == call_import.schema_id)
+            .first()
+        )
+        if schema_obj is not None:
+            params_sorted = sorted(
+                schema_obj.parameters, key=lambda p: p.ordering or 0
+            )
+            for param in params_sorted:
+                if param.name and param.name not in standard_export_headers:
+                    standard_export_headers.append(param.name)
+        else:
+            for param_name in (call_import.parameter_mapping or {}).keys():
+                if param_name and param_name not in standard_export_headers:
+                    standard_export_headers.append(param_name)
+    else:
+        mapping = call_import.column_mapping or {}
+        mapped_headers = [
+            mapping.get("external_call_id"),
+            mapping.get("transcript"),
+            mapping.get("recording_url"),
+        ]
+        for header in [*mapped_headers, *(call_import.extra_columns or [])]:
+            if (
+                isinstance(header, str)
+                and header
+                and header not in standard_export_headers
+            ):
+                standard_export_headers.append(header)
+
+        custom_mapping = call_import.custom_column_mapping or {}
+        if isinstance(custom_mapping, dict):
+            for name, csv_header in custom_mapping.items():
+                if not isinstance(name, str) or not isinstance(csv_header, str):
+                    continue
+                if not name or not csv_header:
+                    continue
+                if name in standard_export_headers:
+                    continue  # would clobber a real column
+                custom_export.append((name, csv_header))
+
+    if (
+        call_import.source_format == "audio"
+        and "conversation_id" not in standard_export_headers
+    ):
+        standard_export_headers.insert(0, "conversation_id")
+
+    # Build the metric columns: each parent (if any) gets a value column
+    # and (when capture_rationale=true) a "<Parent> - LLM Rationale"
+    # column. The per-child boolean columns are intentionally suppressed
+    # — categorization metrics now collapse to exactly two columns in
+    # the export, mirroring the in-app table.
+    child_ids_in_groups: set[str] = set()
+    for parent_str, child_strs in groups_raw.items():
+        for child_str in child_strs:
+            if isinstance(child_str, str):
+                child_ids_in_groups.add(child_str)
+
+    metric_headers: List[str] = []
+    rationale_headers: Dict[str, str] = {}  # metric_id_str -> rationale column name
+    seen_metric_ids: set[str] = set()
+
+    def _add_metric_column(metric: Metric) -> None:
+        mid_str = str(metric.id)
+        if mid_str in seen_metric_ids:
+            return
+        # Skip any child whose parent is part of this run — the parent
+        # column above already shows the chosen child name as its
+        # value.
+        if mid_str in child_ids_in_groups:
+            return
+        seen_metric_ids.add(mid_str)
+        header = metric_names[mid_str]
+        metric_headers.append(header)
+        if bool(getattr(metric, "capture_rationale", False)):
+            rationale_header = f"{header} - LLM Rationale"
+            metric_headers.append(rationale_header)
+            rationale_headers[mid_str] = rationale_header
+
+    for parent_str in groups_raw.keys():
+        parent = metrics_by_id.get(parent_str)
+        if parent:
+            _add_metric_column(parent)
+        # Children of an in-run parent are deliberately not emitted —
+        # the ``child_ids_in_groups`` guard inside ``_add_metric_column``
+        # is what enforces this. We still iterate the keys above (not
+        # ``.items()``) so the parent-only emission is explicit.
+    # Append anything left over (standalone metrics not in any group, or
+    # legacy runs without ``selected_metric_groups``).
+    for metric in metrics:
+        if metric.selection_mode and not metric.parent_metric_id:
+            continue  # already handled above
+        if str(metric.id) in seen_metric_ids:
+            continue
+        _add_metric_column(metric)
+
+    # Three new fixed columns surface the two transcript fields and the
+    # evaluation's transcript_source as live values pulled from the
+    # ``CallImportRow`` (not from the frozen ``raw_columns`` snapshot).
+    # The user can now compare "what was in the CSV" vs "what the
+    # diarisation worker produced" without round-tripping through the
+    # UI, and downstream tools can verify which transcript the metrics
+    # were computed against.
+    PRODUCTION_TRANSCRIPT_HEADER = "Production Transcript"
+    DIARISED_TRANSCRIPT_HEADER = "Diarised Transcript"
+    EVAL_SOURCE_HEADER = "Evaluated Transcript Source"
+
+    fieldnames = [
+        *standard_export_headers,
+        *[h for h, _ in custom_export],
+        PRODUCTION_TRANSCRIPT_HEADER,
+        DIARISED_TRANSCRIPT_HEADER,
+        EVAL_SOURCE_HEADER,
+        *metric_headers,
+    ]
+
+    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    if is_sharding_enabled():
+        rows = sorted(
+            load_evaluation_row_pairs(db, eval_id),
+            key=lambda pair: int(pair[1].row_index or 0),
+        )
+    else:
+        rows = (
+            db.query(CallImportEvaluationRow, CallImportRow)
+            .join(
+                CallImportRow,
+                CallImportRow.id == CallImportEvaluationRow.call_import_row_id,
+            )
+            .filter(CallImportEvaluationRow.evaluation_id == eval_id)
+            .order_by(CallImportRow.row_index.asc())
+            .all()
+        )
+
+    def _project_rows() -> Iterator[Dict[str, str]]:
+        for eval_row, source_row in rows:
+            row_out: Dict[str, str] = {}
+            raw = (
+                source_row.raw_columns
+                if isinstance(source_row.raw_columns, dict)
+                else {}
+            )
+            for header in standard_export_headers:
+                value = raw.get(header)
+                if value is None and header == "conversation_id":
+                    value = source_row.conversation_id
+                row_out[header] = "" if value is None else str(value)
+            for export_header, csv_header in custom_export:
+                value = raw.get(csv_header)
+                row_out[export_header] = "" if value is None else str(value)
+
+            # Live transcripts pulled from the row, NOT from raw_columns,
+            # so re-diarised values are always reflected in the export.
+            # Both transcript columns are flattened to a single line so the
+            # spreadsheet cell doesn't balloon vertically — the in-app
+            # ``TranscriptView`` still has the DB copy with line breaks
+            # intact for chat-bubble rendering.
+            row_out[PRODUCTION_TRANSCRIPT_HEADER] = _flatten_transcript(
+                source_row.transcript
+            )
+            row_out[DIARISED_TRANSCRIPT_HEADER] = _flatten_transcript(
+                source_row.diarised_transcript
+            )
+            row_out[EVAL_SOURCE_HEADER] = _evaluated_transcript_source_label(
+                evaluation,
+                source_row,
+            )
+
+            scores = (
+                eval_row.metric_scores
+                if isinstance(eval_row.metric_scores, dict)
+                else {}
+            )
+            for metric in metrics:
+                metric_score = (
+                    scores.get(str(metric.id))
+                    if isinstance(scores, dict)
+                    else None
+                )
+                value = (
+                    metric_score.get("value")
+                    if isinstance(metric_score, dict)
+                    else None
+                )
+                # Parent metrics (selection_mode set) render the chosen
+                # child name for single_choice or the ";"-joined list of
+                # true child names for multi_label.
+                if (
+                    metric.selection_mode
+                    and not metric.parent_metric_id
+                    and isinstance(metric_score, dict)
+                ):
+                    if metric.selection_mode == "multi_label":
+                        selected = metric_score.get("selected_child_names")
+                        if isinstance(selected, list):
+                            value = ";".join(str(s) for s in selected)
+                    else:
+                        value = (
+                            metric_score.get("chosen_child_name")
+                            or metric_score.get("value")
+                        )
+                row_out[metric.name] = "" if value is None else str(value)
+                rationale_header = rationale_headers.get(str(metric.id))
+                if rationale_header is not None:
+                    rationale = (
+                        metric_score.get("rationale")
+                        if isinstance(metric_score, dict)
+                        else None
+                    )
+                    row_out[rationale_header] = (
+                        "" if rationale is None else str(rationale)
+                    )
+            yield row_out
+
+    base_filename = f"call-import-{call_import_id}-evaluation-{eval_id}"
+
+    if format == "xlsx":
+        # xlsx is unicode-native (Hindi/Devanagari, emoji, etc.) so the
+        # UTF-8-BOM dance isn't needed here. ``write_only`` mode keeps
+        # peak memory bounded for large evaluations because openpyxl
+        # only buffers the current row.
+        try:
+            from openpyxl import Workbook  # type: ignore
+            from openpyxl.cell import WriteOnlyCell  # type: ignore
+            from openpyxl.styles import Font  # type: ignore
+        except ImportError as exc:  # pragma: no cover - exercised by pyproject lock
+            raise HTTPException(
+                status_code=500,
+                detail=(
+                    "Excel export requires the 'openpyxl' package which is "
+                    "not installed."
+                ),
+            ) from exc
+
+        workbook = Workbook(write_only=True)
+        worksheet = workbook.create_sheet(title="Evaluation")
+
+        bold_font = Font(bold=True)
+        header_cells = []
+        for header in fieldnames:
+            cell = WriteOnlyCell(worksheet, value=header)
+            cell.font = bold_font
+            header_cells.append(cell)
+        worksheet.append(header_cells)
+
+        for row_dict in _project_rows():
+            worksheet.append([row_dict.get(h, "") for h in fieldnames])
+
+        buffer = io.BytesIO()
+        workbook.save(buffer)
+        xlsx_bytes = buffer.getvalue()
+        filename = f"{base_filename}.xlsx"
+        return StreamingResponse(
+            iter([xlsx_bytes]),
+            media_type=(
+                "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            ),
+            headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+        )
+
+    output = io.StringIO()
+    writer = csv.DictWriter(output, fieldnames=fieldnames, extrasaction="ignore")
+    writer.writeheader()
+    for row_dict in _project_rows():
+        writer.writerow(row_dict)
+
+    # Excel on Windows defaults to the system ANSI codepage (Windows-1252)
+    # when a CSV has no encoding marker, which turns UTF-8 Hindi/Devanagari
+    # / any non-ASCII text into mojibake (e.g. ``ठीक`` → ``ठीक``).
+    # A UTF-8 BOM tells Excel to switch to UTF-8 decoding and is silently
+    # skipped by every other UTF-8-aware reader (pandas, LibreOffice,
+    # Google Sheets, etc.), so the data round-trips correctly everywhere.
+    csv_text = output.getvalue()
+    # ``utf-8-sig`` adds the UTF-8 BOM so Excel on Windows decodes the file
+    # as UTF-8 instead of the system codepage. We also declare the same
+    # codec in the Content-Type header so well-behaved HTTP clients (incl.
+    # ``httpx`` / ``requests`` in our tests) strip the BOM during decode.
+    csv_bytes = csv_text.encode("utf-8-sig")
+    filename = f"{base_filename}.csv"
+    return StreamingResponse(
+        iter([csv_bytes]),
+        media_type="text/csv; charset=utf-8-sig",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
+def _report_filename_slug(value: str) -> str:
+    slug = re.sub(r"[^a-zA-Z0-9]+", "-", value.strip().lower()).strip("-")
+    return slug or "client"
+
+
+def _report_branding_for_import_workspace(
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    *,
+    internal_brand_image_id: Optional[str] = None,
+    external_brand_image_id: Optional[str] = None,
+) -> tuple[dict[str, str] | list[str], Optional[str]]:
+    workspace = (
+        db.query(Workspace)
+        .filter(
+            Workspace.id == workspace_id,
+            Workspace.organization_id == organization_id,
+        )
+        .first()
+    )
+    raw = workspace.report_branding if workspace and isinstance(workspace.report_branding, dict) else {}
+    images = raw.get("images") if isinstance(raw.get("images"), list) else []
+    loaded_images: list[dict[str, str]] = []
+    for item in images:
+        if not isinstance(item, dict) or not item.get("s3_key"):
+            continue
+        content_type = str(item.get("content_type") or "image/png")
+        try:
+            from app.services.storage.s3_service import s3_service
+
+            image_bytes = s3_service.download_file_by_key(str(item["s3_key"]))
+        except Exception as exc:  # noqa: BLE001
+            logger.warning(
+                "Unable to load report branding image for workspace {}: {}",
+                workspace_id,
+                exc,
+            )
+            continue
+        encoded = base64.b64encode(image_bytes).decode("ascii")
+        role = str(item.get("role") or "generic")
+        if role not in {"internal", "external", "generic"}:
+            role = "generic"
+        loaded_images.append(
+            {
+                "id": str(item.get("id") or ""),
+                "role": role,
+                "data_uri": f"data:{content_type};base64,{encoded}",
+            }
+        )
+
+    def _pick(role: str, selected_id: Optional[str]) -> Optional[str]:
+        if selected_id:
+            for loaded in loaded_images:
+                if loaded["id"] == selected_id:
+                    return loaded["data_uri"]
+        for loaded in loaded_images:
+            if loaded["role"] == role:
+                return loaded["data_uri"]
+        return None
+
+    logo_data_uris: dict[str, str] = {}
+    internal_uri = _pick("internal", internal_brand_image_id)
+    external_uri = _pick("external", external_brand_image_id)
+    if internal_uri:
+        logo_data_uris["internal"] = internal_uri
+    if external_uri:
+        logo_data_uris["external"] = external_uri
+    if (
+        not logo_data_uris
+        and not internal_brand_image_id
+        and not external_brand_image_id
+    ):
+        # Backward compatibility for workspaces that only had a generic logo
+        # library before the two-slot report header existed.
+        generic_uris = [
+            loaded["data_uri"]
+            for loaded in loaded_images
+            if loaded.get("data_uri")
+        ]
+        if generic_uris:
+            heading = raw.get("heading") if isinstance(raw.get("heading"), str) else None
+            return generic_uris[:4], heading
+    heading = raw.get("heading") if isinstance(raw.get("heading"), str) else None
+    return logo_data_uris, heading
+
+
+def _display_metrics_for_pdf_report(
+    db: Session,
+    organization_id: UUID,
+    evaluation: CallImportEvaluation,
+) -> list[Metric]:
+    selected_metric_ids = _serialize_selected_metric_ids(evaluation.selected_metric_ids)
+    lookup_ids: List[UUID] = list(selected_metric_ids)
+    groups_raw = (
+        evaluation.selected_metric_groups
+        if isinstance(evaluation.selected_metric_groups, dict)
+        else {}
+    )
+    for parent_str in groups_raw.keys():
+        try:
+            parent_id = UUID(parent_str)
+        except (TypeError, ValueError):
+            continue
+        if parent_id not in lookup_ids:
+            lookup_ids.append(parent_id)
+
+    metrics = _metrics_for_ids(db, organization_id, lookup_ids)
+    child_ids_in_groups: set[str] = set()
+    for child_strs in groups_raw.values():
+        if not isinstance(child_strs, list):
+            continue
+        child_ids_in_groups.update(str(child_id) for child_id in child_strs)
+
+    metrics_by_id = {str(metric.id): metric for metric in metrics}
+    display: list[Metric] = []
+    seen: set[str] = set()
+
+    for parent_str in groups_raw.keys():
+        parent = metrics_by_id.get(str(parent_str))
+        if parent and str(parent.id) not in seen:
+            display.append(parent)
+            seen.add(str(parent.id))
+
+    for metric in metrics:
+        metric_id = str(metric.id)
+        if metric_id in seen or metric_id in child_ids_in_groups:
+            continue
+        if metric.selection_mode and not metric.parent_metric_id:
+            continue
+        display.append(metric)
+        seen.add(metric_id)
+
+    return display
+
+
+def _metrics_for_clustering(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    eval_rows: List[CallImportEvaluationRow],
+) -> List[Metric]:
+    """All enabled quality metrics scored in this run, normalized for clustering.
+
+    Hierarchical children are collapsed to their parent metric so cluster
+    groups render at the category level (e.g. ``AI reveal``) instead of the
+    child label level (e.g. ``Yes`` / ``No``).
+    """
+    aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
+    aggregate_metric_ids: List[UUID] = []
+    for agg in aggregates:
+        if (agg.metric_category or "quality") == "user_insight":
+            continue
+        try:
+            aggregate_metric_ids.append(UUID(agg.metric_id))
+        except (TypeError, ValueError):
+            continue
+    if not aggregate_metric_ids:
+        return []
+
+    aggregate_metrics = _metrics_for_ids(
+        db, evaluation.organization_id, aggregate_metric_ids
+    )
+    by_id = {metric.id: metric for metric in aggregate_metrics}
+
+    normalized_ids: List[UUID] = []
+    seen: set[UUID] = set()
+    for metric_id in aggregate_metric_ids:
+        metric = by_id.get(metric_id)
+        target_id = (
+            metric.parent_metric_id
+            if metric is not None and metric.parent_metric_id
+            else metric_id
+        )
+        if target_id in seen:
+            continue
+        seen.add(target_id)
+        normalized_ids.append(target_id)
+
+    metrics = _metrics_for_ids(db, evaluation.organization_id, normalized_ids)
+    return [
+        metric
+        for metric in metrics
+        if getattr(metric, "enabled", True) and not _metric_is_user_insight(metric)
+    ]
+
+
+def _metric_is_user_insight(metric: Metric) -> bool:
+    if (getattr(metric, "metric_category", "quality") or "quality") == "user_insight":
+        return True
+    text_value = " ".join(
+        str(part or "").lower()
+        for part in (getattr(metric, "name", ""), getattr(metric, "description", ""))
+    )
+    normalized = text_value.replace("-", " ").replace("_", " ")
+    phrases = (
+        "call context",
+        "caller context",
+        "product identification",
+        "out of scope",
+        "identity match",
+        "user identity",
+        "caller identity",
+        "frustration trigger",
+        "video call offer",
+        "video call reception",
+    )
+    return any(phrase in normalized for phrase in phrases)
+
+
+def _evaluation_rows_for_period(
+    db: Session,
+    evaluation_id: UUID,
+) -> list[tuple[CallImportEvaluationRow, CallImportRow]]:
+    return (
+        db.query(CallImportEvaluationRow, CallImportRow)
+        .join(CallImportRow, CallImportRow.id == CallImportEvaluationRow.call_import_row_id)
+        .filter(CallImportEvaluationRow.evaluation_id == evaluation_id)
+        .order_by(CallImportRow.row_index.asc())
+        .all()
+    )
+
+
+def _baseline_candidate_evaluations(
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    current_evaluation: CallImportEvaluation,
+    current_period_start: Optional[date],
+    *,
+    limit: int = 20,
+) -> list[dict[str, Any]]:
+    candidates = (
+        db.query(CallImportEvaluation, CallImport)
+        .join(CallImport, CallImport.id == CallImportEvaluation.call_import_id)
+        .filter(
+            CallImportEvaluation.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+            CallImportEvaluation.id != current_evaluation.id,
+            CallImportEvaluation.status == "completed",
+            CallImportEvaluation.completed_rows > 0,
+        )
+        .order_by(desc(CallImportEvaluation.created_at))
+        .limit(limit * 3)
+        .all()
+    )
+    items: list[dict[str, Any]] = []
+    for candidate_eval, candidate_import in candidates:
+        rows = _evaluation_rows_for_period(db, candidate_eval.id)
+        period_start, period_end, period_label, period_display = _report_period_from_rows(rows)
+        if current_period_start and period_start and period_start >= current_period_start:
+            continue
+        dataset = (
+            (candidate_import.dataset or "").strip()
+            or (candidate_import.original_filename or candidate_import.filename or "").strip()
+            or "Unknown dataset"
+        )
+        evaluation_name = (
+            (candidate_eval.name or "").strip()
+            or str(candidate_eval.id)[:8]
+        )
+        items.append(
+            {
+                "evaluation_id": str(candidate_eval.id),
+                "name": evaluation_name,
+                "dataset": dataset,
+                "period_label": period_label,
+                "period_start": period_start,
+                "period_end": period_end,
+                "period_display": period_display,
+                "completed_rows": int(candidate_eval.completed_rows or 0),
+                "created_at": candidate_eval.created_at,
+                "is_default": False,
+            }
+        )
+        if len(items) >= limit:
+            break
+    items.sort(
+        key=lambda item: (
+            item["period_start"] or date.min,
+            item["created_at"] or datetime.min.replace(tzinfo=timezone.utc),
+        ),
+        reverse=True,
+    )
+    if items:
+        items[0]["is_default"] = True
+    return items
+
+
+def _resolve_baseline_evaluation(
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    current_evaluation: CallImportEvaluation,
+    current_period_start: Optional[date],
+    baseline_evaluation_id: Optional[str],
+) -> Optional[CallImportEvaluation]:
+    candidates = _baseline_candidate_evaluations(
+        db,
+        organization_id,
+        workspace_id,
+        current_evaluation,
+        current_period_start,
+    )
+    allowed_ids = {item["evaluation_id"] for item in candidates}
+    if baseline_evaluation_id:
+        baseline_id = str(baseline_evaluation_id).strip()
+        if baseline_id not in allowed_ids:
+            raise HTTPException(
+                status_code=400,
+                detail="Selected baseline evaluation is not a valid prior run for this report.",
+            )
+        return (
+            db.query(CallImportEvaluation)
+            .filter(
+                CallImportEvaluation.id == UUID(baseline_id),
+                CallImportEvaluation.organization_id == organization_id,
+            )
+            .first()
+        )
+    if not candidates:
+        return None
+    default_id = candidates[0]["evaluation_id"]
+    return (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == UUID(default_id),
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+
+
+def _benchmark_context_for_evaluation(
+    db: Session,
+    baseline_evaluation: Optional[CallImportEvaluation],
+) -> Optional[dict[str, str]]:
+    if baseline_evaluation is None:
+        return None
+    baseline_import = (
+        db.query(CallImport)
+        .filter(CallImport.id == baseline_evaluation.call_import_id)
+        .first()
+    )
+    rows = _evaluation_rows_for_period(db, baseline_evaluation.id)
+    period_start, _period_end, period_label, _period_display = _report_period_from_rows(rows)
+    dataset = (
+        (baseline_import.dataset or "").strip()
+        if baseline_import and baseline_import.dataset
+        else None
+    )
+    filename = (
+        (baseline_import.original_filename or baseline_import.filename or "").strip()
+        if baseline_import
+        else None
+    )
+    evaluation_label = (
+        (baseline_evaluation.name or "").strip()
+        if baseline_evaluation.name
+        else str(baseline_evaluation.id)[:8]
+    )
+    period = period_label or (
+        period_start.isoformat() if period_start else "previous report"
+    )
+    return {
+        "dataset": dataset or filename or "Unknown dataset",
+        "evaluation": evaluation_label,
+        "evaluation_id": str(baseline_evaluation.id),
+        "period": period,
+    }
+
+
+def _period_deltas_from_evaluation(
+    db: Session,
+    baseline_evaluation: CallImportEvaluation,
+    current_metric_aggregates: list[dict[str, Any]],
+    current_evaluation: CallImportEvaluation,
+    current_eval_rows: List[CallImportEvaluationRow],
+) -> dict[str, dict[str, str]]:
+    baseline_rows = _evaluation_rows_for_period(db, baseline_evaluation.id)
+    baseline_eval_rows = [eval_row for eval_row, _source_row in baseline_rows]
+    baseline_aggregate_models = _compute_metric_aggregates(
+        db,
+        baseline_evaluation,
+        baseline_eval_rows,
+    )
+    baseline_metric_aggregates = [
+        _aggregate_to_dict(aggregate) for aggregate in baseline_aggregate_models
+    ]
+    _metrics, _aggs, policies, _source, _child_map = _clustering_context(
+        db, current_evaluation, current_eval_rows
+    )
+    metric_by_id = {str(m.id): m for m in _metrics}
+    current_by_id = {
+        str(item.get("metric_id")): item for item in current_metric_aggregates
+    }
+    previous_by_id = {
+        str(item.get("metric_id")): item
+        for item in baseline_metric_aggregates
+        if isinstance(item, dict)
+    }
+    deltas: dict[str, dict[str, str]] = {}
+    for metric_id, current in current_by_id.items():
+        metric = metric_by_id.get(metric_id)
+        policy = policies.get(metric_id)
+        previous_raw = previous_by_id.get(metric_id)
+        if metric is None or policy is None:
+            deltas[metric_id] = {
+                "label": "No previous-week baseline",
+                "detail": "No comparable prior report snapshot was found.",
+            }
+            continue
+        current_pct = failure_rate_percent_from_rows(
+            current_eval_rows, metric, policy
+        )
+        previous_pct = failure_rate_percent_from_rows(
+            baseline_eval_rows, metric, policy
+        )
+        if current_pct is None or previous_pct is None:
+            current_pct = current_pct or _aggregate_primary_percent(current, policy)
+            previous_pct = (
+                previous_pct or _aggregate_primary_percent(previous_raw, policy)
+                if previous_raw
+                else None
+            )
+        if current_pct is None or previous_pct is None:
+            deltas[metric_id] = {
+                "label": "No previous-week baseline",
+                "detail": "No comparable prior report snapshot was found.",
+            }
+            continue
+        delta = current_pct - previous_pct
+        sign = "+" if delta >= 0 else ""
+        deltas[metric_id] = {
+            "label": f"{sign}{delta:.1f} pp",
+            "detail": (
+                f"Current report {current_pct:.1f}% vs previous report "
+                f"{previous_pct:.1f}%"
+            ),
+        }
+    return deltas
+
+
+_DELTA_EXPLANATION_SYSTEM_PROMPT = (
+    "You are a senior conversation-analytics reviewer. You will receive "
+    "week-over-week metric failure-rate deltas plus reconciled failure "
+    "cluster context per metric.\n\n"
+    "Return STRICT JSON only:\n"
+    "{\n"
+    '  "explanations": {"<metric_id>": "<1-2 sentence explanation of why the delta likely occurred>"}\n'
+    "}\n\n"
+    "Constraints:\n"
+    "- Only include metrics supplied in the prompt.\n"
+    "- Cluster labels are generated independently each run and are NOT stable "
+    "IDs. Never compare an unmatched current label to 0% baseline.\n"
+    "- Use matched_theme_shifts for label-aligned comparisons, "
+    "gap_label_shifts for structural shifts, and new_themes_current_period "
+    "for themes that emerged without a baseline match.\n"
+    "- If reconciliation is uncertain, explain using the numeric delta and "
+    "gap_label_shifts only.\n"
+    "- Keep each explanation to 1-2 short sentences (~220 chars).\n"
+    "- Vendor-safe, factual language; no markdown."
+)
+
+
+def _period_delta_explanation_cache_key(
+    baseline_evaluation_id: UUID,
+    *,
+    completed_rows: int,
+    baseline_completed_rows: int,
+) -> str:
+    return (
+        f"{baseline_evaluation_id}:{completed_rows}:"
+        f"{baseline_completed_rows}:reconciled-v2"
+    )
+
+
+def _normalize_cluster_label(label: str) -> str:
+    return re.sub(r"[^a-z0-9]+", " ", (label or "").lower()).strip()
+
+
+_CLUSTER_LABEL_STOPWORDS = frozenset(
+    {
+        "a",
+        "an",
+        "the",
+        "and",
+        "or",
+        "during",
+        "while",
+        "with",
+        "for",
+        "from",
+        "into",
+        "general",
+        "user",
+        "bot",
+        "agent",
+    }
+)
+
+
+def _cluster_label_tokens(label: str) -> set[str]:
+    return {
+        token
+        for token in _normalize_cluster_label(label).split()
+        if token and token not in _CLUSTER_LABEL_STOPWORDS and len(token) > 2
+    }
+
+
+def _cluster_label_similarity(left: str, right: str) -> float:
+    tokens_left = _cluster_label_tokens(left)
+    tokens_right = _cluster_label_tokens(right)
+    if not tokens_left or not tokens_right:
+        return 0.0
+    intersection = tokens_left & tokens_right
+    if not intersection:
+        return 0.0
+    union = tokens_left | tokens_right
+    jaccard = len(intersection) / len(union)
+    smaller = tokens_left if len(tokens_left) <= len(tokens_right) else tokens_right
+    overlap_ratio = len(intersection) / len(smaller)
+    return max(jaccard, overlap_ratio * 0.85)
+
+
+def _group_clusters_by_gap_label(
+    clusters: list[dict[str, Any]],
+) -> dict[str, list[dict[str, Any]]]:
+    grouped: dict[str, list[dict[str, Any]]] = {}
+    for cluster in clusters:
+        gap_label = str(cluster.get("gap_label") or "UNKNOWN")
+        grouped.setdefault(gap_label, []).append(cluster)
+    return grouped
+
+
+def _append_matched_cluster_pair(
+    matched: list[dict[str, Any]],
+    current: dict[str, Any],
+    baseline: dict[str, Any],
+    *,
+    match_confidence: float,
+    match_method: str,
+) -> None:
+    matched.append(
+        {
+            "current_label": current.get("label"),
+            "baseline_label": baseline.get("label"),
+            "gap_label": current.get("gap_label") or baseline.get("gap_label"),
+            "current_share_pct": current.get("share_pct"),
+            "baseline_share_pct": baseline.get("share_pct"),
+            "share_delta_pp": round(
+                float(current.get("share_pct") or 0.0)
+                - float(baseline.get("share_pct") or 0.0),
+                1,
+            ),
+            "match_confidence": round(match_confidence, 2),
+            "match_method": match_method,
+        }
+    )
+
+
+def _aggregate_share_by_gap_label(
+    clusters: list[dict[str, Any]],
+) -> dict[str, float]:
+    totals: dict[str, float] = {}
+    for cluster in clusters:
+        gap_label = str(cluster.get("gap_label") or "UNKNOWN")
+        totals[gap_label] = totals.get(gap_label, 0.0) + float(
+            cluster.get("share_pct") or 0.0
+        )
+    return {gap: round(share, 1) for gap, share in totals.items()}
+
+
+def _reconcile_cluster_periods(
+    current_clusters: list[dict[str, Any]],
+    baseline_clusters: list[dict[str, Any]],
+    *,
+    similarity_threshold: float = 0.35,
+) -> dict[str, Any]:
+    """Align independently-generated cluster labels before delta explanation."""
+    matched: list[dict[str, Any]] = []
+    current_unmatched = list(current_clusters)
+    remaining_baseline = list(baseline_clusters)
+
+    current_by_gap = _group_clusters_by_gap_label(current_unmatched)
+    baseline_by_gap = _group_clusters_by_gap_label(remaining_baseline)
+    for gap_label in list(current_by_gap):
+        current_group = current_by_gap.get(gap_label) or []
+        baseline_group = baseline_by_gap.get(gap_label) or []
+        if len(current_group) != 1 or len(baseline_group) != 1:
+            continue
+        current = current_group[0]
+        baseline = baseline_group[0]
+        _append_matched_cluster_pair(
+            matched,
+            current,
+            baseline,
+            match_confidence=0.75,
+            match_method="single_cluster_per_gap_label",
+        )
+        current_unmatched.remove(current)
+        remaining_baseline.remove(baseline)
+        current_by_gap[gap_label] = []
+        baseline_by_gap[gap_label] = []
+
+    for current in list(current_unmatched):
+        best_idx: Optional[int] = None
+        best_score = 0.0
+        for idx, baseline in enumerate(remaining_baseline):
+            score = _cluster_label_similarity(
+                str(current.get("label") or ""),
+                str(baseline.get("label") or ""),
+            )
+            if current.get("gap_label") == baseline.get("gap_label"):
+                score += 0.1
+            if score > best_score:
+                best_score = score
+                best_idx = idx
+
+        if best_idx is not None and best_score >= similarity_threshold:
+            baseline = remaining_baseline.pop(best_idx)
+            _append_matched_cluster_pair(
+                matched,
+                current,
+                baseline,
+                match_confidence=best_score,
+                match_method="label_similarity",
+            )
+
+    matched_current_labels = {
+        str(item.get("current_label") or "") for item in matched
+    }
+    matched_baseline_labels = {
+        str(item.get("baseline_label") or "") for item in matched
+    }
+    current_unmatched = [
+        cluster
+        for cluster in current_clusters
+        if str(cluster.get("label") or "") not in matched_current_labels
+    ]
+    remaining_baseline = [
+        cluster
+        for cluster in baseline_clusters
+        if str(cluster.get("label") or "") not in matched_baseline_labels
+    ]
+
+    new_themes = [
+        {
+            "label": cluster.get("label"),
+            "gap_label": cluster.get("gap_label"),
+            "share_pct": cluster.get("share_pct"),
+            "note": "New theme in current period (no close baseline match).",
+        }
+        for cluster in current_unmatched
+    ]
+
+    retired_themes = [
+        {
+            "label": baseline.get("label"),
+            "gap_label": baseline.get("gap_label"),
+            "share_pct": baseline.get("share_pct"),
+            "note": "Theme present in baseline only (retired or renamed).",
+        }
+        for baseline in remaining_baseline
+    ]
+
+    current_gap = _aggregate_share_by_gap_label(current_clusters)
+    baseline_gap = _aggregate_share_by_gap_label(baseline_clusters)
+    gap_label_shifts: dict[str, dict[str, float]] = {}
+    for gap_label in set(current_gap) | set(baseline_gap):
+        current_share = current_gap.get(gap_label, 0.0)
+        baseline_share = baseline_gap.get(gap_label, 0.0)
+        if abs(current_share - baseline_share) >= 0.5:
+            gap_label_shifts[gap_label] = {
+                "current_share_pct": current_share,
+                "baseline_share_pct": baseline_share,
+                "share_delta_pp": round(current_share - baseline_share, 1),
+            }
+
+    return {
+        "matched_theme_shifts": matched,
+        "new_themes_current_period": new_themes,
+        "retired_themes_baseline_period": retired_themes,
+        "gap_label_shifts": gap_label_shifts,
+        "reconciliation_note": (
+            "Cluster labels are generated independently each run and may "
+            "rename the same failure mode. Do not treat unmatched current "
+            "labels as 0% in the baseline period."
+        ),
+    }
+
+
+def _load_period_delta_explanations_cache(
+    evaluation: CallImportEvaluation,
+    cache_key: str,
+) -> Optional[dict[str, str]]:
+    raw = getattr(evaluation, "period_delta_explanations", None)
+    if not isinstance(raw, dict):
+        return None
+    entry = raw.get(cache_key)
+    if not isinstance(entry, dict):
+        return None
+    explanations_raw = entry.get("explanations")
+    if not isinstance(explanations_raw, dict):
+        return None
+    return {
+        str(metric_id): str(why).strip()
+        for metric_id, why in explanations_raw.items()
+        if str(metric_id).strip() and isinstance(why, str) and why.strip()
+    }
+
+
+def _save_period_delta_explanations_cache(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    cache_key: str,
+    explanations: dict[str, str],
+) -> None:
+    raw = evaluation.period_delta_explanations
+    if not isinstance(raw, dict):
+        raw = {}
+    updated = dict(raw)
+    updated[cache_key] = {
+        "explanations": explanations,
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+    }
+    evaluation.period_delta_explanations = updated
+    flag_modified(evaluation, "period_delta_explanations")
+    db.commit()
+
+
+def _cluster_summary_for_metric(
+    state: Optional[EvaluationMetricClustersState],
+    metric_id: str,
+) -> list[dict[str, Any]]:
+    if state is None or state.status != "completed":
+        return []
+    for group in state.groups:
+        if str(group.metric_id) != metric_id:
+            continue
+        return [
+            {
+                "label": cluster.label,
+                "gap_label": cluster.gap_label,
+                "share_pct": round(cluster.share_pct, 1),
+                "count": cluster.count,
+            }
+            for cluster in group.clusters[:5]
+        ]
+    return []
+
+
+def _merge_delta_why(
+    raw_deltas: dict[str, dict[str, str]],
+    explanations: dict[str, str],
+) -> dict[str, dict[str, str]]:
+    if not explanations:
+        return raw_deltas
+    merged: dict[str, dict[str, str]] = {}
+    for metric_id, delta in raw_deltas.items():
+        updated = dict(delta)
+        why = explanations.get(metric_id)
+        if why:
+            updated["why"] = why
+        merged[metric_id] = updated
+    return merged
+
+
+def _explain_period_deltas(
+    db: Session,
+    organization_id: UUID,
+    evaluation: CallImportEvaluation,
+    baseline_evaluation: CallImportEvaluation,
+    raw_deltas: dict[str, dict[str, str]],
+    *,
+    min_delta_pp: float = 0.5,
+) -> dict[str, dict[str, str]]:
+    """Attach ``why`` explanations to period deltas using cached LLM output."""
+    if not raw_deltas:
+        return raw_deltas
+
+    cache_key = _period_delta_explanation_cache_key(
+        baseline_evaluation.id,
+        completed_rows=evaluation.completed_rows,
+        baseline_completed_rows=baseline_evaluation.completed_rows,
+    )
+    cached = _load_period_delta_explanations_cache(evaluation, cache_key)
+    if cached is not None:
+        return _merge_delta_why(raw_deltas, cached)
+
+    current_clusters = _metric_clusters_payload(evaluation)
+    baseline_clusters = _metric_clusters_payload(baseline_evaluation)
+    metrics_for_prompt: list[dict[str, Any]] = []
+    for metric_id, delta in raw_deltas.items():
+        label = delta.get("label") or ""
+        if "No previous-week baseline" in label:
+            continue
+        match = re.search(r"([+-]?\d+(?:\.\d+)?)\s*pp", label)
+        if match and abs(float(match.group(1))) < min_delta_pp:
+            continue
+        current_summary = _cluster_summary_for_metric(current_clusters, metric_id)
+        baseline_summary = _cluster_summary_for_metric(baseline_clusters, metric_id)
+        if not current_summary and not baseline_summary:
+            continue
+        cluster_reconciliation = _reconcile_cluster_periods(
+            current_summary,
+            baseline_summary,
+        )
+        metrics_for_prompt.append(
+            {
+                "metric_id": metric_id,
+                "delta_label": label,
+                "delta_detail": delta.get("detail") or "",
+                "cluster_reconciliation": cluster_reconciliation,
+            }
+        )
+
+    if not metrics_for_prompt:
+        return raw_deltas
+
+    provider_hint: Optional[str] = None
+    model_hint: Optional[str] = None
+    tldr_raw = evaluation.tldr_summary
+    if isinstance(tldr_raw, dict):
+        if isinstance(tldr_raw.get("provider"), str):
+            provider_hint = tldr_raw["provider"]
+        if isinstance(tldr_raw.get("model"), str):
+            model_hint = tldr_raw["model"]
+
+    from app.services.ai.llm_resolver import get_llm_provider_and_model
+    from app.services.call_import_user_insights import _call_llm, _parse_json_object
+
+    provider_enum, model_str = get_llm_provider_and_model(
+        organization_id, db, provider_hint, model_hint
+    )
+    try:
+        text = _call_llm(
+            db,
+            organization_id,
+            provider_enum,
+            model_str,
+            [
+                {"role": "system", "content": _DELTA_EXPLANATION_SYSTEM_PROMPT},
+                {
+                    "role": "user",
+                    "content": json.dumps(
+                        {"metrics": metrics_for_prompt},
+                        ensure_ascii=False,
+                        default=str,
+                    ),
+                },
+            ],
+            temperature=0.3,
+            max_tokens=900,
+        )
+    except Exception as exc:
+        logger.warning("[PeriodDeltaExplain] LLM call failed: {}", exc)
+        return raw_deltas
+
+    parsed = _parse_json_object(text)
+    explanations_raw = parsed.get("explanations")
+    explanations: dict[str, str] = {}
+    if isinstance(explanations_raw, dict):
+        for metric_id, why in explanations_raw.items():
+            if isinstance(why, str) and why.strip():
+                explanations[str(metric_id)] = why.strip()
+
+    if explanations:
+        _save_period_delta_explanations_cache(
+            db, evaluation, cache_key, explanations
+        )
+    return _merge_delta_why(raw_deltas, explanations)
+
+
+def _period_deltas_with_explanations(
+    db: Session,
+    organization_id: UUID,
+    evaluation: CallImportEvaluation,
+    baseline_evaluation: CallImportEvaluation,
+    raw_deltas: dict[str, dict[str, str]],
+) -> dict[str, dict[str, str]]:
+    return _explain_period_deltas(
+        db,
+        organization_id,
+        evaluation,
+        baseline_evaluation,
+        raw_deltas,
+    )
+
+
+def _benchmark_context_for_snapshot(
+    db: Session,
+    previous_snapshot: Optional[CallImportEvaluationReportSnapshot],
+) -> Optional[dict[str, str]]:
+    if previous_snapshot is None:
+        return None
+    previous_import = (
+        db.query(CallImport)
+        .filter(CallImport.id == previous_snapshot.call_import_id)
+        .first()
+    )
+    previous_eval = (
+        db.query(CallImportEvaluation)
+        .filter(CallImportEvaluation.id == previous_snapshot.evaluation_id)
+        .first()
+    )
+    dataset = (
+        (previous_import.dataset or "").strip()
+        if previous_import and previous_import.dataset
+        else None
+    )
+    filename = (
+        (previous_import.original_filename or previous_import.filename or "").strip()
+        if previous_import
+        else None
+    )
+    evaluation_label = (
+        (previous_eval.name or "").strip()
+        if previous_eval and previous_eval.name
+        else str(previous_snapshot.evaluation_id)[:8]
+    )
+    period = previous_snapshot.period_label or (
+        previous_snapshot.period_start.isoformat()
+        if previous_snapshot.period_start
+        else "previous report"
+    )
+    return {
+        "dataset": dataset or filename or "Unknown dataset",
+        "evaluation": evaluation_label,
+        "evaluation_id": str(previous_snapshot.evaluation_id),
+        "period": period,
+    }
+
+
+def _clamp_prose_to_sentences(
+    text: str,
+    *,
+    max_sentences: int = 3,
+    max_chars: int = 300,
+) -> str:
+    """Keep concise audit/TLDR prose within sentence and character limits."""
+    cleaned = (text or "").strip()
+    if not cleaned:
+        return cleaned
+    cleaned = re.sub(r"\s*\n+\s*", " ", cleaned).strip()
+    sentences = [
+        sentence.strip()
+        for sentence in re.split(r"(?<=[.!?])\s+", cleaned)
+        if sentence.strip()
+    ]
+    if sentences:
+        result = " ".join(sentences[:max_sentences]).strip()
+    else:
+        result = cleaned
+    if len(result) > max_chars:
+        trimmed = result[: max_chars - 3].rsplit(" ", 1)[0].rstrip(".,;:")
+        result = f"{trimmed}..." if trimmed else result[:max_chars]
+    return result
+
+
+def _audit_summary_text_from_tldr(
+    summary: Optional[EvaluationTldrSummary],
+) -> Optional[str]:
+    if summary is None:
+        return None
+    narrative = _clamp_prose_to_sentences(summary.narrative.strip())
+    return narrative or None
+
+
+def _metric_insights_from_tldr(
+    summary: Optional[EvaluationTldrSummary],
+) -> dict[str, str]:
+    if summary is None:
+        return {}
+    return {
+        str(metric_id): insight.strip()
+        for metric_id, insight in summary.metric_insights.items()
+        if str(metric_id).strip() and insight.strip()
+    }
+
+
+def _report_period_from_rows(
+    rows: list[tuple[CallImportEvaluationRow, CallImportRow]],
+) -> tuple[Optional[date], Optional[date], Optional[str], str]:
+    dates = [
+        source_row.recording_date
+        for eval_row, source_row in rows
+        if eval_row.status == "completed" and source_row.recording_date
+    ]
+    if not dates:
+        return None, None, None, "Not specified"
+    start = min(dates)
+    end = max(dates)
+    week_anchor = max(dates)
+    week_start = week_anchor - timedelta(days=week_anchor.weekday())
+    week_end = week_start + timedelta(days=6)
+    iso_year, iso_week, _ = week_anchor.isocalendar()
+    label = f"{iso_year}-W{iso_week:02d}"
+    if week_start.year == week_end.year:
+        week_range = f"{week_start.strftime('%b %d')}–{week_end.strftime('%b %d, %Y')}"
+    else:
+        week_range = (
+            f"{week_start.strftime('%b %d, %Y')}–{week_end.strftime('%b %d, %Y')}"
+        )
+    display = f"W{iso_week:02d} · {week_range}"
+    return start, end, label, display
+
+
+def _aggregate_to_dict(aggregate: CallImportMetricAggregate) -> dict[str, Any]:
+    if hasattr(aggregate, "model_dump"):
+        return aggregate.model_dump(mode="json")
+    return aggregate.dict()
+
+
+def _aggregate_primary_percent(
+    raw: dict[str, Any],
+    policy: Optional[MetricFailurePolicy] = None,
+) -> Optional[float]:
+    return aggregate_primary_percent(raw, policy)
+
+
+def _child_names_by_parent(
+    db: Session,
+    organization_id: UUID,
+    parent_metric_ids: Sequence[UUID],
+) -> Dict[str, List[str]]:
+    if not parent_metric_ids:
+        return {}
+    children = (
+        db.query(Metric)
+        .filter(
+            Metric.organization_id == organization_id,
+            Metric.parent_metric_id.in_(list(parent_metric_ids)),
+        )
+        .all()
+    )
+    out: Dict[str, List[str]] = {}
+    for child in children:
+        pid = str(child.parent_metric_id)
+        out.setdefault(pid, []).append(child.name)
+    return out
+
+
+def _clustering_context(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    eval_rows: List[CallImportEvaluationRow],
+) -> Tuple[
+    List[Metric],
+    List[CallImportMetricAggregate],
+    Dict[str, MetricFailurePolicy],
+    Literal["inferred", "user"],
+    Dict[str, List[str]],
+]:
+    metrics = _metrics_for_clustering(db, evaluation, eval_rows)
+    aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
+    parent_ids = [
+        m.id
+        for m in metrics
+        if getattr(m, "selection_mode", None) and not getattr(m, "parent_metric_id", None)
+    ]
+    child_names_by_parent = _child_names_by_parent(
+        db, evaluation.organization_id, parent_ids
+    )
+    policies, source = effective_policies(
+        evaluation,
+        metrics,
+        aggregates,
+        child_names_by_parent=child_names_by_parent,
+    )
+    return metrics, aggregates, policies, source, child_names_by_parent
+
+
+def _period_deltas_from_aggregates(
+    previous_metric_aggregates: list[dict[str, Any]],
+    current_metric_aggregates: list[dict[str, Any]],
+    policies: Optional[Dict[str, MetricFailurePolicy]] = None,
+) -> dict[str, dict[str, str]]:
+    current_by_id = {str(item.get("metric_id")): item for item in current_metric_aggregates}
+    previous_by_id = {
+        str(item.get("metric_id")): item
+        for item in previous_metric_aggregates
+        if isinstance(item, dict)
+    }
+    deltas: dict[str, dict[str, str]] = {}
+    for metric_id, current in current_by_id.items():
+        previous_raw = previous_by_id.get(metric_id)
+        policy = (policies or {}).get(metric_id)
+        current_pct = _aggregate_primary_percent(current, policy)
+        previous_pct = (
+            _aggregate_primary_percent(previous_raw, policy)
+            if previous_raw
+            else None
+        )
+        if current_pct is None or previous_pct is None:
+            deltas[metric_id] = {
+                "label": "No previous-week baseline",
+                "detail": "No comparable prior report snapshot was found.",
+            }
+            continue
+        delta = current_pct - previous_pct
+        sign = "+" if delta >= 0 else ""
+        deltas[metric_id] = {
+            "label": f"{sign}{delta:.1f} pp",
+            "detail": f"Current report {current_pct:.1f}% vs previous report {previous_pct:.1f}%",
+        }
+    return deltas
+
+
+def _period_deltas_from_snapshot(
+    previous: Optional[CallImportEvaluationReportSnapshot],
+    current_metric_aggregates: list[dict[str, Any]],
+) -> dict[str, dict[str, str]]:
+    previous_items = (
+        previous.metric_aggregates
+        if previous and isinstance(previous.metric_aggregates, list)
+        else []
+    )
+    return _period_deltas_from_aggregates(previous_items, current_metric_aggregates)
+
+
+def _sample_evidence_for_metrics(
+    rows: list[tuple[CallImportEvaluationRow, CallImportRow]],
+    metric_ids: set[str],
+) -> dict[str, list[dict[str, str]]]:
+    samples: dict[str, list[dict[str, str]]] = {metric_id: [] for metric_id in metric_ids}
+    for eval_row, source_row in rows:
+        scores = eval_row.metric_scores if isinstance(eval_row.metric_scores, dict) else {}
+        for metric_id in metric_ids:
+            if len(samples.get(metric_id, [])) >= 4:
+                continue
+            score = scores.get(metric_id)
+            if not isinstance(score, dict):
+                continue
+            rationale = score.get("rationale")
+            transcript = source_row.diarised_transcript or source_row.transcript or ""
+            quote = rationale if isinstance(rationale, str) and rationale.strip() else transcript[:350]
+            if quote:
+                samples.setdefault(metric_id, []).append(
+                    {
+                        "conversation_id": source_row.conversation_id,
+                        "quote": str(quote).strip()[:500],
+                    }
+                )
+    return samples
+
+
+def _fallback_report_narrative(
+    insight_aggregates: list[dict[str, Any]],
+    evidence_samples: dict[str, list[dict[str, str]]],
+) -> dict[str, Any]:
+    observations: dict[str, str] = {}
+    evidence: dict[str, dict[str, str]] = {}
+    design_notes: list[str] = []
+    for aggregate in insight_aggregates:
+        metric_id = str(aggregate.get("metric_id") or "")
+        name = str(aggregate.get("metric_name") or "Insight")
+        counts = aggregate.get("value_counts") if isinstance(aggregate.get("value_counts"), list) else []
+        if counts:
+            top = counts[0]
+            total = int(aggregate.get("count") or 0) or sum(
+                int(item.get("count") or 0) for item in counts if isinstance(item, dict)
+            )
+            pct = (int(top.get("count") or 0) / total) * 100 if total else 0
+            observations[metric_id] = (
+                f"{top.get('label')} is the dominant {name.lower()} category at {pct:.1f}% of classified calls."
+            )
+            design_notes.append(
+                f"{name}: {top.get('label')} is the largest segment and should be reviewed for workflow or prompt improvements."
+            )
+        sample = (evidence_samples.get(metric_id) or [{}])[0]
+        if sample:
+            evidence[metric_id] = sample
+    return {
+        "observations": observations,
+        "evidence": evidence,
+        "design_notes": design_notes[:7],
+        "audit_summary": None,
+    }
+
+
+def _generate_report_narrative(
+    db: Session,
+    organization_id: UUID,
+    *,
+    metric_aggregates: list[dict[str, Any]],
+    insight_aggregates: list[dict[str, Any]],
+    period_delta_by_metric: dict[str, dict[str, str]],
+    evidence_samples: dict[str, list[dict[str, str]]],
+    report_config: dict[str, Any],
+) -> dict[str, Any]:
+    if not insight_aggregates:
+        return {"observations": {}, "evidence": {}, "design_notes": [], "audit_summary": None}
+    try:
+        from app.services.ai.llm_resolver import get_llm_provider_and_model
+        from app.services.ai.llm_service import llm_service
+
+        provider_enum, model_str = get_llm_provider_and_model(organization_id, db, None, None)
+        prompt = (
+            "You are writing a vendor-safe external call quality audit report. "
+            "Return strict JSON with keys observations (object keyed by metric_id), "
+            "evidence (object keyed by metric_id with conversation_id and quote), "
+            "design_notes (array of concise numbered-note strings), and audit_summary (string). "
+            "Use only the supplied aggregates and evidence samples.\n\n"
+            + json.dumps(
+                {
+                    "metric_aggregates": metric_aggregates[:30],
+                    "insight_aggregates": insight_aggregates,
+                    "period_deltas": period_delta_by_metric,
+                    "evidence_samples": evidence_samples,
+                    "report_config": report_config,
+                },
+                default=str,
+            )
+        )
+        llm_result = llm_service.generate_response(
+            messages=[
+                {"role": "system", "content": "Return JSON only. No markdown."},
+                {"role": "user", "content": prompt},
+            ],
+            llm_provider=provider_enum,
+            llm_model=model_str,
+            organization_id=organization_id,
+            db=db,
+            temperature=0.2,
+            max_tokens=1200,
+        )
+        parsed = json.loads(str(llm_result.content or "{}"))
+        if isinstance(parsed, dict):
+            fallback = _fallback_report_narrative(insight_aggregates, evidence_samples)
+            return {
+                "observations": parsed.get("observations") or fallback["observations"],
+                "evidence": parsed.get("evidence") or fallback["evidence"],
+                "design_notes": parsed.get("design_notes") or fallback["design_notes"],
+                "audit_summary": parsed.get("audit_summary") or fallback["audit_summary"],
+            }
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("Report narrative LLM generation fell back to deterministic text: {}", exc)
+    return _fallback_report_narrative(insight_aggregates, evidence_samples)
+
+
+@router.get(
+    "/{eval_id}/baseline-candidates",
+    response_model=CallImportEvaluationBaselineCandidatesResponse,
+    operation_id="listCallImportEvaluationBaselineCandidates",
+)
+async def list_call_import_evaluation_baseline_candidates(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationBaselineCandidatesResponse:
+    del api_key
+    call_import = _require_import(db, call_import_id, organization_id)
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    rows = _evaluation_rows_for_period(db, evaluation.id)
+    period_start, _period_end, _derived_period_label, _period_display = _report_period_from_rows(
+        rows
+    )
+    candidates = _baseline_candidate_evaluations(
+        db,
+        organization_id,
+        call_import.workspace_id,
+        evaluation,
+        period_start,
+    )
+    default_evaluation_id = next(
+        (item["evaluation_id"] for item in candidates if item.get("is_default")),
+        None,
+    )
+    return CallImportEvaluationBaselineCandidatesResponse(
+        items=[CallImportEvaluationBaselineCandidate(**item) for item in candidates],
+        default_evaluation_id=default_evaluation_id,
+    )
+
+
+@router.post(
+    "/{eval_id}/pdf-report",
+    operation_id="generateCallImportEvaluationPdfReport",
+    dependencies=[Depends(require_call_import_capability(REPORTS_GENERATE))],
+)
+async def generate_call_import_evaluation_pdf_report(
+    call_import_id: UUID,
+    eval_id: UUID,
+    payload: CallImportEvaluationPdfReportRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> StreamingResponse:
+    del api_key
+    call_import = _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    is_internal = payload.report_type == "internal"
+    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    if is_sharding_enabled():
+        rows = sorted(
+            load_evaluation_row_pairs(db, eval_id),
+            key=lambda pair: int(pair[1].row_index or 0),
+        )
+    else:
+        rows = (
+            db.query(CallImportEvaluationRow, CallImportRow)
+            .join(
+                CallImportRow,
+                CallImportRow.id == CallImportEvaluationRow.call_import_row_id,
+            )
+            .filter(CallImportEvaluationRow.evaluation_id == eval_id)
+            .order_by(CallImportRow.row_index.asc())
+            .all()
+        )
+    report_config = payload.report_config if isinstance(payload.report_config, dict) else {}
+    metrics = _display_metrics_for_pdf_report(db, organization_id, evaluation)
+    configured_quality_ids = {
+        str(item)
+        for item in report_config.get("quality_metric_ids", [])
+        if item
+    }
+    configured_insight_ids = {
+        str(item.get("metric_id") or item)
+        for item in report_config.get("insights", [])
+        if item
+    }
+    if configured_quality_ids or configured_insight_ids:
+        allowed_ids = configured_quality_ids | configured_insight_ids
+        metrics = [metric for metric in metrics if str(metric.id) in allowed_ids]
+
+    eval_rows = [eval_row for eval_row, _source_row in rows]
+    aggregate_models = _compute_metric_aggregates(db, evaluation, eval_rows)
+    selected_report_metric_ids = {str(metric.id) for metric in metrics}
+    aggregate_dicts = [
+        _aggregate_to_dict(aggregate)
+        for aggregate in aggregate_models
+        if aggregate.metric_id in selected_report_metric_ids
+    ]
+    insight_metric_ids = {
+        str(metric.id)
+        for metric in metrics
+        if _metric_is_user_insight(metric)
+    }
+    metric_aggregates = [
+        item for item in aggregate_dicts if str(item.get("metric_id")) not in insight_metric_ids
+    ]
+    insight_aggregates = [
+        item for item in aggregate_dicts if str(item.get("metric_id")) in insight_metric_ids
+    ]
+    period_start, period_end, derived_period_label, period_display = _report_period_from_rows(rows)
+    period_label = (payload.period_label or derived_period_label or "").strip() or None
+    include_period_delta = (
+        payload.include_period_delta or payload.include_weekly_delta
+    )
+    previous_snapshot = None
+    period_delta_by_metric: dict[str, dict[str, str]] = {}
+    baseline_evaluation: Optional[CallImportEvaluation] = None
+    if include_period_delta and period_start:
+        baseline_evaluation = _resolve_baseline_evaluation(
+            db,
+            organization_id,
+            call_import.workspace_id,
+            evaluation,
+            period_start,
+            payload.baseline_evaluation_id,
+        )
+        if baseline_evaluation:
+            period_delta_by_metric = _period_deltas_from_evaluation(
+                db,
+                baseline_evaluation,
+                metric_aggregates,
+                evaluation,
+                [eval_row for eval_row, _ in rows],
+            )
+            period_delta_by_metric = _period_deltas_with_explanations(
+                db,
+                organization_id,
+                evaluation,
+                baseline_evaluation,
+                period_delta_by_metric,
+            )
+    benchmark_context = _benchmark_context_for_evaluation(db, baseline_evaluation)
+    evidence_samples = _sample_evidence_for_metrics(rows, insight_metric_ids)
+    cached_tldr_summary = _tldr_summary_payload(evaluation)
+    cached_user_insights = _user_insights_payload(evaluation)
+    cached_metric_clusters = _metric_clusters_payload(evaluation)
+    cached_prompt_improvements = _prompt_improvements_payload(evaluation)
+    generated_insights_for_pdf = _selected_generated_user_insights(
+        cached_user_insights,
+        report_config,
+    )
+    metric_clusters_for_pdf = _selected_metric_clusters_for_pdf(
+        cached_metric_clusters,
+        report_config,
+    )
+    prompt_improvements_for_pdf = _selected_prompt_improvements_for_pdf(
+        cached_prompt_improvements,
+        report_config,
+    )
+    narrative = _generate_report_narrative(
+        db,
+        organization_id,
+        metric_aggregates=metric_aggregates,
+        insight_aggregates=insight_aggregates if is_internal else [],
+        period_delta_by_metric=period_delta_by_metric,
+        evidence_samples=evidence_samples if is_internal else {},
+        report_config=report_config,
+    )
+
+    generated_at = datetime.now(timezone.utc)
+    branding_images, custom_heading = _report_branding_for_import_workspace(
+        db,
+        organization_id,
+        call_import.workspace_id,
+        internal_brand_image_id=payload.internal_brand_image_id,
+        external_brand_image_id=payload.external_brand_image_id,
+    )
+    eval_row_list = [eval_row for eval_row, _ in rows]
+    pdf_aggregates = _compute_metric_aggregates(db, evaluation, eval_row_list)
+    pdf_parent_ids = [
+        m.id
+        for m in metrics
+        if getattr(m, "selection_mode", None)
+        and not getattr(m, "parent_metric_id", None)
+    ]
+    pdf_child_map = _child_names_by_parent(
+        db, evaluation.organization_id, pdf_parent_ids
+    )
+    failure_policies_for_pdf, _fp_source = effective_policies(
+        evaluation,
+        metrics,
+        pdf_aggregates,
+        child_names_by_parent=pdf_child_map,
+    )
+    try:
+        pdf_started = datetime.now(timezone.utc)
+        pdf_bytes = await asyncio.to_thread(
+            call_import_evaluation_pdf_report_service.render_pdf,
+            vendor_name=payload.vendor_name,
+            call_import=call_import,
+            evaluation=evaluation,
+            metrics=metrics,
+            rows=rows,
+            failure_policies=failure_policies_for_pdf,
+            generated_at=generated_at,
+            internal=is_internal,
+            logo_data_uris=branding_images,
+            custom_heading=custom_heading,
+            include_weekly_delta=include_period_delta,
+            period_delta_by_metric=period_delta_by_metric,
+            use_case=payload.use_case,
+            period_display=period_display,
+            total_metric_count=db.query(Metric)
+            .filter(Metric.organization_id == organization_id, Metric.enabled.is_(True))
+            .count(),
+            report_config=report_config,
+            narrative=narrative,
+            audit_summary=_audit_summary_text_from_tldr(cached_tldr_summary),
+            metric_insights=_metric_insights_from_tldr(cached_tldr_summary),
+            benchmark_context=benchmark_context,
+            generated_user_insights=generated_insights_for_pdf,
+            user_insights_overview=(
+                cached_user_insights.overview if cached_user_insights else None
+            ),
+            metric_clusters=metric_clusters_for_pdf,
+            metric_clusters_overview=(
+                cached_metric_clusters.overview if cached_metric_clusters else None
+            ),
+            prompt_improvements=prompt_improvements_for_pdf,
+            platform_base_url=payload.platform_base_url,
+        )
+        logger.info(
+            "PDF report render finished in {:.1f}s for evaluation {}",
+            (datetime.now(timezone.utc) - pdf_started).total_seconds(),
+            eval_id,
+        )
+    except Exception as exc:  # noqa: BLE001
+        logger.exception(
+            "Failed to generate PDF report for call import {} evaluation {}",
+            call_import_id,
+            eval_id,
+        )
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to generate PDF report: {exc}",
+        ) from exc
+
+    snapshot = CallImportEvaluationReportSnapshot(
+        evaluation_id=evaluation.id,
+        call_import_id=call_import.id,
+        organization_id=organization_id,
+        workspace_id=call_import.workspace_id,
+        period_label=period_label,
+        period_start=period_start,
+        period_end=period_end,
+        report_config=report_config,
+        selected_metric_ids=[str(metric.id) for metric in metrics],
+        metric_aggregates=metric_aggregates,
+        insight_aggregates=insight_aggregates,
+        narrative=narrative,
+        total_calls=evaluation.total_rows,
+        selected_metric_count=len(metrics),
+        total_metric_count=db.query(Metric)
+        .filter(Metric.organization_id == organization_id, Metric.enabled.is_(True))
+        .count(),
+    )
+    db.add(snapshot)
+    db.commit()
+
+    filename = (
+        f"{_report_filename_slug(payload.vendor_name)}-"
+        f"{payload.report_type}-quality-metric-audit-{eval_id}.pdf"
+    )
+    return StreamingResponse(
+        iter([pdf_bytes]),
+        media_type="application/pdf",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
+@router.patch(
+    "/{eval_id}",
+    response_model=CallImportEvaluationResponse,
+    operation_id="updateCallImportEvaluation",
+)
+async def update_call_import_evaluation(
+    call_import_id: UUID,
+    eval_id: UUID,
+    payload: CallImportEvaluationUpdate,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationResponse:
+    """Edit metadata on an existing evaluation run (currently just ``name``)."""
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    row = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not row:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    # Treat unset vs explicit ``None`` differently: unset = leave alone,
+    # explicit ``None`` or empty string = clear the name.
+    payload_data = payload.model_dump(exclude_unset=True)
+    if "name" in payload_data:
+        row.name = _normalize_name(payload_data["name"])
+
+    stamp_evaluation_actor(row, principal)
+    db.commit()
+    db.refresh(row)
+    return _serialize_eval(db, row)
+
+
+def _revoke_pending_tasks(evaluation: CallImportEvaluation) -> None:
+    """Best-effort cancel of any in-flight Celery tasks for an evaluation."""
+
+    if not evaluation.celery_group_id and not any(
+        r.celery_task_id for r in evaluation.row_results
+    ):
+        return
+    try:
+        from app.workers.celery_app import celery_app
+
+        pending_task_ids = [
+            eval_row.celery_task_id
+            for eval_row in evaluation.row_results
+            if eval_row.celery_task_id
+            and eval_row.status in {"pending", "running"}
+        ]
+        if pending_task_ids:
+            celery_app.control.revoke(pending_task_ids, terminate=False)
+    except Exception:
+        # Best effort — DB delete remains the source of truth.
+        pass
+
+
+# ---------------------------------------------------------------------------
+# User-initiated cancel for in-flight evaluation rows
+# ---------------------------------------------------------------------------
+#
+# Evaluation rows can sit in ``running`` for many minutes when the underlying
+# LLM / audio metric call is slow or wedged (the worker carries an 8 min
+# soft / 10 min hard time limit). Without a cancel affordance the operator's
+# only recourse is to wait for Celery's time limit to fire — or to manually
+# mutate the DB. These helpers + the two endpoints below give the UI a
+# first-class "Abort" button mirroring the diarisation cancel pattern at
+# ``app.api.v1.routes.call_imports`` (``_apply_diarisation_cancel`` etc.).
+#
+# Why ``terminate=True``: the legacy ``_revoke_pending_tasks`` above uses
+# ``terminate=False`` because it's called from delete-flow paths where the
+# task may simply not get to run (a worker pulls it off the queue and drops
+# it). For a user-initiated cancel we want SIGTERM to interrupt the worker
+# mid-LLM/audio call so the in-flight HTTP request actually aborts.
+# ``terminate=True`` routes the signal to the executing process; we spell
+# ``signal="SIGTERM"`` out for clarity even though it's the default.
+
+# Sentinel error message stamped on cancelled rows. Read by the eval worker's
+# ``_was_cancelled_externally`` guard (see
+# :mod:`app.workers.tasks.evaluate_call_import_row`) so a worker that's already
+# past its slowest operation can't overwrite the cancelled state with its own
+# terminal status. Touching either copy means touching both.
+EVAL_CANCELLED_BY_USER_ERROR: str = "Evaluation cancelled by user"
+
+
+def _cancellable_eval_states() -> Tuple[str, ...]:
+    """States that an evaluation row can be cancelled from.
+
+    Kept as a tiny helper so adding a future ``"queued"`` / ``"retrying"``
+    state only needs one edit.
+    """
+    return ("pending", "running")
+
+
+def _revoke_eval_task(eval_row: CallImportEvaluationRow) -> None:
+    """Best-effort revoke of a single eval row's Celery task.
+
+    Always swallows control-plane exceptions — Celery's control bus is
+    inherently best-effort and a missed revoke is not catastrophic
+    because the DB row is already flipped to ``failed`` by the caller
+    before this runs (so the UI immediately reflects the cancel; if
+    the task happens to finish anyway, the worker's finaliser skips
+    over the row via :data:`EVAL_CANCELLED_BY_USER_ERROR`).
+    """
+    task_id = (eval_row.celery_task_id or "").strip()
+    if not task_id:
+        return
+    try:
+        from app.workers.celery_app import celery_app
+
+        celery_app.control.revoke(
+            task_id, terminate=True, signal="SIGTERM"
+        )
+        logger.info(
+            "Revoked evaluation task {} for eval row {}",
+            task_id,
+            eval_row.id,
+        )
+    except Exception as exc:  # noqa: BLE001 — revoke is best-effort
+        logger.warning(
+            "Failed to revoke evaluation task {} for eval row {}: {}",
+            task_id,
+            eval_row.id,
+            exc,
+        )
+
+
+def _apply_evaluation_cancel(
+    eval_rows: List[CallImportEvaluationRow],
+) -> Tuple[int, int]:
+    """Cancel every cancellable row in ``eval_rows``.
+
+    Returns ``(cancelled, skipped)`` so the caller can build a typed
+    response without re-querying the DB. The caller is responsible for
+    ``db.commit()`` after this returns — we deliberately don't commit
+    here so a batch endpoint can flush all rows in one transaction.
+    """
+    cancellable_states = _cancellable_eval_states()
+    cancelled = 0
+    skipped = 0
+    now = datetime.now(timezone.utc)
+    for eval_row in eval_rows:
+        if (eval_row.status or "").lower() not in cancellable_states:
+            skipped += 1
+            continue
+        # Flip the row state BEFORE we revoke so the UI's next poll
+        # already shows the cancel, even if Celery's control plane is
+        # slow to ack.
+        eval_row.status = "failed"
+        eval_row.error_message = EVAL_CANCELLED_BY_USER_ERROR
+        eval_row.finished_at = now
+        _revoke_eval_task(eval_row)
+        # Drop the task id so a follow-up retry (or a stale poll) can't
+        # accidentally re-revoke or get confused.
+        eval_row.celery_task_id = None
+        cancelled += 1
+    return cancelled, skipped
+
+
+def _claim_evaluation_bulk_operation(
+    evaluation_id: UUID,
+    operation: str,
+) -> None:
+    """Reserve the run for a single bulk worker pass; 409 if one is active."""
+    from app.services.call_imports.evaluation_bulk_op import (
+        get_evaluation_bulk_operation,
+        try_set_evaluation_bulk_operation,
+    )
+
+    if try_set_evaluation_bulk_operation(evaluation_id, operation):  # type: ignore[arg-type]
+        return
+    existing = get_evaluation_bulk_operation(evaluation_id) or operation
+    raise HTTPException(
+        status_code=409,
+        detail=(
+            f"A bulk {existing.replace('_', ' ')} operation is already in "
+            "progress for this evaluation. Wait for it to finish before "
+            "starting another action."
+        ),
+    )
+
+
+def _require_no_evaluation_bulk_operation(evaluation_id: UUID) -> None:
+    from app.services.call_imports.evaluation_bulk_op import (
+        get_evaluation_bulk_operation,
+    )
+
+    existing = get_evaluation_bulk_operation(evaluation_id)
+    if existing:
+        raise HTTPException(
+            status_code=409,
+            detail=(
+                f"A bulk {existing.replace('_', ' ')} operation is already in "
+                "progress for this evaluation. Wait for it to finish before "
+                "starting another action."
+            ),
+        )
+
+
+@router.post(
+    "/{eval_id}/cancel",
+    response_model=CallImportEvaluationBulkActionResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="cancelCallImportEvaluation",
+)
+async def cancel_call_import_evaluation(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationBulkActionResponse:
+    """Abort all in-flight (or queued) rows in a single evaluation run.
+
+    Idempotent: calling on a run whose rows are already terminal returns
+    ``target_count=0`` with 202 so the UI can fire this from an
+    "Abort" button without having to pre-check the state.
+
+    Heavy row resets and Celery revokes run in a background worker so
+    large batches do not block the API thread.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    from app.services.call_imports.bulk_ops import count_evaluation_cancel_targets
+
+    target_count = count_evaluation_cancel_targets(db, eval_id, mode="abort")
+    if target_count == 0:
+        return CallImportEvaluationBulkActionResponse(
+            accepted=True,
+            target_count=0,
+            evaluation_id=eval_id,
+        )
+
+    _claim_evaluation_bulk_operation(eval_id, "abort")
+    evaluation.status = "cancelled"
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    from app.workers.tasks.call_import_bulk_ops import (
+        cancel_call_import_evaluation_task,
+    )
+
+    cancel_call_import_evaluation_task.delay(str(eval_id), mode="abort")
+    return CallImportEvaluationBulkActionResponse(
+        accepted=True,
+        target_count=target_count,
+        evaluation_id=eval_id,
+    )
+
+
+@router.post(
+    "/{eval_id}/force-fail-pending",
+    response_model=CallImportEvaluationBulkActionResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="forceFailCallImportEvaluationPending",
+)
+async def force_fail_pending_call_import_evaluation_rows(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationBulkActionResponse:
+    """Force-fail only rows currently in ``pending`` for a single run.
+
+    This is narrower than :func:`cancel_call_import_evaluation`: it leaves
+    ``running`` rows untouched so operators can clear permanently queued rows
+    without interrupting in-flight evaluations.
+
+    Row updates run in a background worker so large batches do not block
+    the API thread.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    from app.services.call_imports.bulk_ops import count_evaluation_cancel_targets
+
+    target_count = count_evaluation_cancel_targets(
+        db, eval_id, mode="force_fail_pending"
+    )
+    if target_count == 0:
+        return CallImportEvaluationBulkActionResponse(
+            accepted=True,
+            target_count=0,
+            evaluation_id=eval_id,
+        )
+
+    _claim_evaluation_bulk_operation(eval_id, "force_fail_pending")
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    from app.workers.tasks.call_import_bulk_ops import (
+        cancel_call_import_evaluation_task,
+    )
+
+    cancel_call_import_evaluation_task.delay(
+        str(eval_id), mode="force_fail_pending"
+    )
+    return CallImportEvaluationBulkActionResponse(
+        accepted=True,
+        target_count=target_count,
+        evaluation_id=eval_id,
+    )
+
+
+@router.post(
+    "/{eval_id}/rows/{eval_row_id}/cancel",
+    response_model=CallImportEvaluationRowResponse,
+    status_code=status.HTTP_200_OK,
+    operation_id="cancelCallImportEvaluationRow",
+)
+async def cancel_call_import_evaluation_row(
+    call_import_id: UUID,
+    eval_id: UUID,
+    eval_row_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationRowResponse:
+    """Abort an in-flight (or queued) evaluation for a single row.
+
+    Idempotent: calling on a row that's already terminal (``completed``
+    / ``failed``) returns the row unchanged with a 200 so the UI can
+    wire this to a "Stop" button without having to pre-check the
+    state. Updates the parent run's rollup so its counters reflect
+    the cancel immediately.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    _require_no_evaluation_bulk_operation(eval_id)
+
+    from app.db_sharding.eval_rows import evaluation_row_session
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    if is_sharding_enabled():
+        try:
+            with evaluation_row_session(eval_row_id) as (
+                row_db,
+                _catalog_db,
+                eval_row,
+                source_row,
+                _shard_id,
+            ):
+                if eval_row.evaluation_id != eval_id:
+                    raise HTTPException(
+                        status_code=404,
+                        detail="Evaluation row not found in this run",
+                    )
+                _apply_evaluation_cancel([eval_row])
+                row_db.commit()
+                _rollup_evaluation_status(evaluation, db)
+                stamp_evaluation_actor(evaluation, principal)
+                db.commit()
+                row_db.refresh(eval_row)
+                return _to_evaluation_row_response(eval_row, source_row, evaluation)
+        except LookupError as exc:
+            raise HTTPException(
+                status_code=404, detail="Evaluation row not found in this run"
+            ) from exc
+
+    eval_row = (
+        db.query(CallImportEvaluationRow)
+        .filter(
+            CallImportEvaluationRow.id == eval_row_id,
+            CallImportEvaluationRow.evaluation_id == eval_id,
+        )
+        .first()
+    )
+    if not eval_row:
+        raise HTTPException(
+            status_code=404, detail="Evaluation row not found in this run"
+        )
+
+    _apply_evaluation_cancel([eval_row])
+    db.flush()
+    _rollup_evaluation_status(evaluation, db)
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+    db.refresh(eval_row)
+
+    source_row = (
+        db.query(CallImportRow)
+        .filter(CallImportRow.id == eval_row.call_import_row_id)
+        .first()
+    )
+
+    return _to_evaluation_row_response(eval_row, source_row, evaluation)
+
+
+@router.delete(
+    "/{eval_id}",
+    status_code=status.HTTP_204_NO_CONTENT,
+    operation_id="deleteCallImportEvaluation",
+)
+async def delete_call_import_evaluation(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> Response:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    row = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not row:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    _revoke_pending_tasks(row)
+
+    db.delete(row)
+    db.commit()
+    return Response(status_code=status.HTTP_204_NO_CONTENT)
+
+
+@router.post(
+    "/bulk-delete",
+    status_code=status.HTTP_200_OK,
+    operation_id="bulkDeleteCallImportEvaluations",
+)
+async def bulk_delete_call_import_evaluations(
+    call_import_id: UUID,
+    payload: CallImportEvaluationBulkDelete,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> Dict[str, int]:
+    """Delete multiple evaluation runs scoped to one call import.
+
+    Mirrors :func:`delete_call_import_evaluation` but in bulk so the UI
+    can clear out a multi-select. Unknown ids (already deleted, or
+    belonging to a different org/import) are silently skipped — the
+    response just reports how many actually went away.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    if not payload.evaluation_ids:
+        return {"deleted": 0}
+
+    rows = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id.in_(payload.evaluation_ids),
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .all()
+    )
+    deleted = 0
+    for row in rows:
+        _revoke_pending_tasks(row)
+        db.delete(row)
+        deleted += 1
+    db.commit()
+    return {"deleted": deleted}
+
+
+# ---------------------------------------------------------------------------
+# Aggregation: turns per-row metric scores into histograms / value counts.
+#
+# Designed to be cheap enough to call on every page load: we read each
+# evaluation row once, bucket numeric values into a fixed 10-bin
+# histogram, and tally the top categorical values. Scaling concerns
+# (millions of rows) are deferred — at that point we'd push this into a
+# Postgres aggregate query, but for typical CSV imports (<10k rows) the
+# Python pass is fast enough and dramatically simpler.
+# ---------------------------------------------------------------------------
+
+
+_HISTOGRAM_BUCKETS = 10
+_TOP_VALUE_COUNTS = 10
+
+
+def _coerce_numeric(value: Any) -> Optional[float]:
+    """Return ``value`` as ``float`` when it's numeric; ``None`` otherwise."""
+    if isinstance(value, bool):
+        # Booleans are ints in Python; treat them as categorical so
+        # pass/fail metrics show up in value_counts instead of becoming
+        # a degenerate {0,1} histogram.
+        return None
+    if isinstance(value, (int, float)) and math.isfinite(value):
+        return float(value)
+    if isinstance(value, str):
+        try:
+            f = float(value)
+            if math.isfinite(f):
+                return f
+        except ValueError:
+            return None
+    return None
+
+
+def _coerce_category(value: Any) -> Optional[str]:
+    """Render ``value`` as a label suitable for a value_counts bucket."""
+    if value is None:
+        return None
+    if isinstance(value, bool):
+        return "true" if value else "false"
+    if isinstance(value, (int, float)):
+        return str(value)
+    if isinstance(value, str):
+        text = value.strip()
+        return text or None
+    # Lists / dicts: stringify so they still group sensibly without
+    # exploding the cardinality (worst case: everything is "[…]" once).
+    return str(value)
+
+
+def _build_histogram(
+    values: List[float],
+) -> List[CallImportMetricHistogramBucket]:
+    """Fixed-bin histogram over ``values``; returns [] for <2 values."""
+    if len(values) < 2:
+        return []
+    lo = min(values)
+    hi = max(values)
+    if lo == hi:
+        # All values identical — render a single bucket so the UI shows a
+        # spike rather than empty space.
+        return [
+            CallImportMetricHistogramBucket(x0=lo, x1=hi, count=len(values))
+        ]
+    width = (hi - lo) / _HISTOGRAM_BUCKETS
+    buckets: List[List[float]] = [[] for _ in range(_HISTOGRAM_BUCKETS)]
+    for v in values:
+        # Right-edge inclusive on the last bucket so ``hi`` doesn't fall
+        # off into a non-existent bucket index.
+        idx = int((v - lo) / width)
+        if idx >= _HISTOGRAM_BUCKETS:
+            idx = _HISTOGRAM_BUCKETS - 1
+        buckets[idx].append(v)
+    return [
+        CallImportMetricHistogramBucket(
+            x0=lo + i * width,
+            x1=lo + (i + 1) * width,
+            count=len(bucket),
+        )
+        for i, bucket in enumerate(buckets)
+    ]
+
+
+def _percentile(values: List[float], pct: float) -> Optional[float]:
+    """Linear-interpolated percentile compatible with NumPy default."""
+    if not values:
+        return None
+    sorted_vals = sorted(values)
+    if len(sorted_vals) == 1:
+        return sorted_vals[0]
+    rank = (pct / 100.0) * (len(sorted_vals) - 1)
+    lo = int(math.floor(rank))
+    hi = int(math.ceil(rank))
+    if lo == hi:
+        return sorted_vals[lo]
+    frac = rank - lo
+    return sorted_vals[lo] + (sorted_vals[hi] - sorted_vals[lo]) * frac
+
+
+def _compute_metric_aggregates(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    eval_rows: List[CallImportEvaluationRow],
+) -> List[CallImportMetricAggregate]:
+    """Collapse per-row ``metric_scores`` into one aggregate per metric.
+
+    Selected metrics are read fresh from the DB so the response always
+    surfaces the current ``metric.name`` / ``metric_type`` even when a
+    metric was renamed after the run finished.
+    """
+
+    selected_ids = _serialize_selected_metric_ids(evaluation.selected_metric_ids)
+    # Include parent metrics from selected_metric_groups so they appear
+    # alongside their children in the aggregate response. Use ``getattr``
+    # with a default so the helper still works for callers that pass
+    # lightweight objects (tests, in-memory shims) that don't carry the
+    # attribute at all.
+    groups_raw_candidate = getattr(evaluation, "selected_metric_groups", None)
+    groups_raw = (
+        groups_raw_candidate if isinstance(groups_raw_candidate, dict) else {}
+    )
+    for parent_str in groups_raw.keys():
+        try:
+            pid = UUID(parent_str)
+            if pid not in selected_ids:
+                selected_ids.append(pid)
+        except (TypeError, ValueError):
+            continue
+
+    metrics = _metrics_for_ids(db, evaluation.organization_id, selected_ids)
+    metric_meta: Dict[str, Metric] = {str(m.id): m for m in metrics}
+
+    # Default to selected metrics, but also include any metric ids that
+    # surface in row scores even if missing from the metric registry —
+    # otherwise renaming/deleting a metric mid-run would silently drop
+    # results from the chart.
+    discovered_ids: List[str] = list(metric_meta.keys())
+    for row in eval_rows:
+        scores = row.metric_scores if isinstance(row.metric_scores, dict) else {}
+        for metric_id_str in scores.keys():
+            if metric_id_str not in metric_meta and metric_id_str not in discovered_ids:
+                discovered_ids.append(metric_id_str)
+
+    results: List[CallImportMetricAggregate] = []
+
+    for metric_id_str in discovered_ids:
+        meta = metric_meta.get(metric_id_str)
+        numeric_values: List[float] = []
+        category_counts: Dict[str, int] = {}
+        # For multi-label parents we still need to know how many rows
+        # were scored (each row votes for >=1 label) so the n-badge in
+        # the UI shows "n=50" instead of the misleading "n=208" sum.
+        multi_label_rows_scored = 0
+        # Unordered pair tally for the co-occurrence heatmap. Keys are
+        # ``(label_a, label_b)`` with ``a < b`` so we never double-count
+        # the same unordered pair. Only populated for multi-label
+        # parents — every other metric leaves this empty.
+        multi_label_pair_counts: Dict[Tuple[str, str], int] = {}
+        skipped = 0
+        errored = 0
+        observed_metric_type: Optional[str] = None
+        observed_name: Optional[str] = None
+
+        # ``meta`` is a real ``Metric`` row in production, but tests
+        # frequently pass a lightweight stub. Pull the two attributes
+        # we need via ``getattr`` so a stub that only sets ``id`` /
+        # ``name`` / ``metric_type`` doesn't blow up here.
+        is_multi_label_parent = bool(
+            meta
+            and getattr(meta, "selection_mode", None) == "multi_label"
+            and not getattr(meta, "parent_metric_id", None)
+        )
+
+        for row in eval_rows:
+            scores = (
+                row.metric_scores
+                if isinstance(row.metric_scores, dict)
+                else {}
+            )
+            entry = scores.get(metric_id_str)
+            if not isinstance(entry, dict):
+                continue
+            if entry.get("metric_name"):
+                observed_name = entry.get("metric_name")
+            if entry.get("type"):
+                observed_metric_type = entry.get("type")
+            if entry.get("skipped"):
+                skipped += 1
+                continue
+            if entry.get("error"):
+                errored += 1
+                continue
+
+            # Multi-label parents store a comma-joined value that
+            # isn't useful as a single category; instead tally each
+            # selected child individually so the chart shows per-label
+            # counts that mirror the children's own boolean histograms.
+            if is_multi_label_parent:
+                selected = entry.get("selected_child_names")
+                if isinstance(selected, list) and selected:
+                    multi_label_rows_scored += 1
+                    cleaned: List[str] = []
+                    for label in selected:
+                        text_label = str(label).strip() or None
+                        if text_label:
+                            cleaned.append(text_label)
+                            category_counts[text_label] = (
+                                category_counts.get(text_label, 0) + 1
+                            )
+                    # Emit one increment per unordered pair of distinct
+                    # labels that fired together on this row. ``cleaned``
+                    # is deduplicated first because the LLM occasionally
+                    # repeats a label inside ``selected_child_names``.
+                    distinct = sorted(set(cleaned))
+                    for i in range(len(distinct)):
+                        for j in range(i + 1, len(distinct)):
+                            pair = (distinct[i], distinct[j])
+                            multi_label_pair_counts[pair] = (
+                                multi_label_pair_counts.get(pair, 0) + 1
+                            )
+                continue
+
+            value = entry.get("value")
+            numeric = _coerce_numeric(value)
+            if numeric is not None:
+                numeric_values.append(numeric)
+                continue
+            category = _coerce_category(value)
+            if category is not None:
+                category_counts[category] = category_counts.get(category, 0) + 1
+
+        # ``count`` is "rows scored". For numeric / single-choice
+        # metrics that's the same as ``len(numeric) + sum(categories)``
+        # because each scored row contributes exactly one observation.
+        # Multi-label parents however contribute one observation per
+        # selected child, so summing ``category_counts`` over-counts —
+        # we tracked rows-scored separately above and use it here.
+        rows_scored = (
+            multi_label_rows_scored
+            if is_multi_label_parent
+            else len(numeric_values) + sum(category_counts.values())
+        )
+
+        # Build numeric stats first, then categorical (both can coexist).
+        agg = CallImportMetricAggregate(
+            metric_id=metric_id_str,
+            metric_name=(
+                (meta.name if meta else observed_name) or "Unknown metric"
+            ),
+            metric_type=(
+                meta.metric_type if meta else observed_metric_type
+            ),
+            metric_category=(
+                "user_insight"
+                if meta is not None and _metric_is_user_insight(meta)
+                else "quality"
+            )
+            or "quality",
+            is_multi_label_parent=is_multi_label_parent,
+            count=rows_scored,
+            skipped_count=skipped,
+            error_count=errored,
+        )
+        if numeric_values:
+            agg.mean = float(statistics.fmean(numeric_values))
+            agg.median = float(statistics.median(numeric_values))
+            agg.min = min(numeric_values)
+            agg.max = max(numeric_values)
+            agg.stddev = (
+                float(statistics.pstdev(numeric_values))
+                if len(numeric_values) > 1
+                else 0.0
+            )
+            agg.p25 = _percentile(numeric_values, 25)
+            agg.p75 = _percentile(numeric_values, 75)
+            agg.p95 = _percentile(numeric_values, 95)
+            agg.histogram_buckets = _build_histogram(numeric_values)
+        if category_counts:
+            sorted_counts = sorted(
+                category_counts.items(), key=lambda kv: kv[1], reverse=True
+            )
+            agg.value_counts = [
+                CallImportMetricValueCount(label=label, count=count)
+                for label, count in sorted_counts[:_TOP_VALUE_COUNTS]
+            ]
+            # Restrict the heatmap to pairs of labels we actually
+            # rendered above so the frontend never has to match
+            # against truncated/missing rows. Sorted desc by pair
+            # count to keep the most informative cells in the
+            # response when ``_TOP_VALUE_COUNTS`` clipped the matrix.
+            if is_multi_label_parent and multi_label_pair_counts:
+                kept_labels = {
+                    label for label, _ in sorted_counts[:_TOP_VALUE_COUNTS]
+                }
+                pair_items = [
+                    (a, b, count)
+                    for (a, b), count in multi_label_pair_counts.items()
+                    if a in kept_labels and b in kept_labels
+                ]
+                pair_items.sort(key=lambda t: t[2], reverse=True)
+                agg.co_occurrence = [
+                    CallImportMetricLabelPair(a=a, b=b, count=count)
+                    for a, b, count in pair_items
+                ]
+
+        results.append(agg)
+
+    # Sort so each parent metric immediately precedes its children.
+    # The Visualizations grid renders metrics top-to-bottom in this
+    # order, so multi-label parents (the "summary" chart) sit above
+    # the per-child boolean histograms that drill into them. Metrics
+    # whose ``meta`` row was deleted mid-run (``meta is None``) sink
+    # to the bottom but keep their relative order.
+    enumerated = list(enumerate(results))
+
+    def _sort_key(item: Tuple[int, CallImportMetricAggregate]):
+        original_idx, agg = item
+        meta = metric_meta.get(agg.metric_id)
+        if meta is None:
+            return (1, "", 1, "", original_idx)
+        parent_id = getattr(meta, "parent_metric_id", None)
+        # Group key: a child shares its parent's UUID; a parent
+        # uses its own UUID. Within a group, depth=0 (parent) sorts
+        # before depth=1 (child); ties break alphabetically by name
+        # so children render in a stable order regardless of which
+        # row scored which label first.
+        if parent_id is None:
+            group_key = str(meta.id)
+            depth = 0
+        else:
+            group_key = str(parent_id)
+            depth = 1
+        return (
+            0,
+            group_key,
+            depth,
+            (getattr(meta, "name", "") or "").lower(),
+            original_idx,
+        )
+
+    enumerated.sort(key=_sort_key)
+    return [agg for _idx, agg in enumerated]
+
+
+@router.get(
+    "/{eval_id}/aggregate",
+    response_model=CallImportEvaluationAggregateResponse,
+    operation_id="getCallImportEvaluationAggregate",
+)
+async def get_call_import_evaluation_aggregate(
+    call_import_id: UUID,
+    eval_id: UUID,
+    baseline_evaluation_id: Optional[UUID] = Query(None),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationAggregateResponse:
+    """Return per-metric distributions for the Visualizations tab.
+
+    The shape is intentionally chart-friendly: histograms for numeric
+    metrics, top-N value counts for categorical/text metrics, plus
+    summary stats (mean/p50/p95) so the UI can render summary cards
+    without recomputing on the client.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    eval_rows = _load_eval_rows(db, eval_id)
+
+    metrics = _compute_metric_aggregates(db, evaluation, eval_rows)
+
+    period_deltas: dict[str, MetricPeriodDelta] = {}
+    resolved_baseline_id: Optional[UUID] = None
+    if baseline_evaluation_id is not None:
+        call_import = _require_import(db, call_import_id, organization_id)
+        from app.db_sharding.scatter_gather import load_evaluation_row_pairs
+
+        rows = load_evaluation_row_pairs(db, eval_id)
+        period_start, _, _, _ = _report_period_from_rows(rows)
+        baseline_evaluation = _resolve_baseline_evaluation(
+            db,
+            organization_id,
+            call_import.workspace_id,
+            evaluation,
+            period_start,
+            str(baseline_evaluation_id),
+        )
+        if baseline_evaluation:
+            resolved_baseline_id = baseline_evaluation.id
+            metric_aggregates_dicts = [
+                _aggregate_to_dict(agg) for agg in metrics
+            ]
+            raw_deltas = _period_deltas_from_evaluation(
+                db,
+                baseline_evaluation,
+                metric_aggregates_dicts,
+                evaluation,
+                eval_rows,
+            )
+            raw_deltas = _period_deltas_with_explanations(
+                db,
+                organization_id,
+                evaluation,
+                baseline_evaluation,
+                raw_deltas,
+            )
+            period_deltas = {
+                metric_id: MetricPeriodDelta(
+                    label=delta.get("label") or "",
+                    detail=delta.get("detail") or "",
+                    why=(delta.get("why") or "").strip() or None,
+                )
+                for metric_id, delta in raw_deltas.items()
+            }
+
+    _fp_stored, failure_policies_source = policies_from_evaluation_raw(
+        evaluation.metric_clusters
+    )
+    return CallImportEvaluationAggregateResponse(
+        evaluation_id=eval_id,
+        total_rows=evaluation.total_rows,
+        completed_rows=evaluation.completed_rows,
+        failed_rows=evaluation.failed_rows,
+        metrics=metrics,
+        period_deltas=period_deltas,
+        baseline_evaluation_id=resolved_baseline_id,
+        failure_policies_source=failure_policies_source,
+    )
+
+
+# ---------------------------------------------------------------------------
+# TLDR insights: LLM-generated narrative + bullet patterns rendered above
+# the Visualizations charts. Cached on ``CallImportEvaluation.tldr_summary``
+# so the page never auto-burns LLM tokens; the user explicitly clicks
+# "Generate summary" or "Regenerate" from the empty-state CTA.
+# ---------------------------------------------------------------------------
+
+
+_INSIGHTS_SYSTEM_PROMPT = (
+    "You are a senior conversation-analytics reviewer. You will be "
+    "given aggregated metric statistics + a sample of rationales for "
+    "the rows of a single call-import evaluation. Identify the most "
+    "useful PATTERNS that hold ACROSS the calls -- not just per-metric "
+    "numbers. Look for combinations (e.g. `when X happens, Y also "
+    "tends to happen`), notable outliers, frequent failure modes, and "
+    "any signal that would change how a reviewer triages the run.\n\n"
+    "Return STRICT JSON only, with this shape and no extra keys:\n"
+    "{\n"
+    '  "narrative": "<max 3 short sentences, <=300 chars total>",\n'
+    '  "patterns": ["<bullet 1>", "<bullet 2>", ...],\n'
+    '  "metric_insights": {"<metric_id>": "<2-3 line business meaning>"}\n'
+    "}\n\n"
+    "Constraints:\n"
+    "- narrative is the ONLY text shown in the external audit summary and "
+    "Visualizations TLDR; keep it to at most 3 short sentences (~300 chars).\n"
+    "- patterns are optional supporting notes and are NOT rendered in the "
+    "audit summary; keep 0 to 3 bullets if supplied, each <= 120 characters.\n"
+    "- metric_insights must include one entry for each top-level metric id supplied.\n"
+    "- Each metric insight should explain what the metric means for the business and what the current distribution suggests, not restate the metric rubric.\n"
+    "- Avoid restating raw counts unless they reveal a pattern.\n"
+    "- Use neutral, factual language ('frustration appeared in...') "
+    "rather than judgemental ('the agents failed to...')."
+)
+
+
+def _tldr_summary_payload(
+    evaluation: CallImportEvaluation,
+) -> Optional[EvaluationTldrSummary]:
+    """Return the cached TLDR (with ``is_stale`` set) or ``None``.
+
+    ``CallImportEvaluation.tldr_summary`` is a ``JSON`` column so we
+    have to validate shape defensively -- a half-written or hand-edited
+    blob should not break the aggregate response. Returns ``None`` when
+    no cached summary exists.
+    """
+    raw = evaluation.tldr_summary
+    if not isinstance(raw, dict):
+        return None
+    narrative = raw.get("narrative")
+    if not isinstance(narrative, str) or not narrative.strip():
+        return None
+    patterns_raw = raw.get("patterns")
+    patterns = (
+        [str(p) for p in patterns_raw if isinstance(p, str) and p.strip()]
+        if isinstance(patterns_raw, list)
+        else []
+    )
+    metric_insights_raw = raw.get("metric_insights")
+    metric_insights = (
+        {
+            str(metric_id): str(insight).strip()
+            for metric_id, insight in metric_insights_raw.items()
+            if str(metric_id).strip()
+            and isinstance(insight, str)
+            and insight.strip()
+        }
+        if isinstance(metric_insights_raw, dict)
+        else {}
+    )
+    generated_at_raw = raw.get("generated_at")
+    try:
+        generated_at = (
+            datetime.fromisoformat(generated_at_raw)
+            if isinstance(generated_at_raw, str)
+            else evaluation.updated_at or datetime.now(timezone.utc)
+        )
+    except ValueError:
+        generated_at = evaluation.updated_at or datetime.now(timezone.utc)
+    snapshot = raw.get("generated_at_completed_rows")
+    snapshot_int = int(snapshot) if isinstance(snapshot, (int, float)) else 0
+    return EvaluationTldrSummary(
+        narrative=_clamp_prose_to_sentences(narrative.strip()),
+        patterns=patterns,
+        metric_insights=metric_insights,
+        generated_at=generated_at,
+        generated_at_completed_rows=snapshot_int,
+        provider=raw.get("provider") if isinstance(raw.get("provider"), str) else None,
+        model=raw.get("model") if isinstance(raw.get("model"), str) else None,
+        is_stale=evaluation.completed_rows > snapshot_int,
+    )
+
+
+def _sample_rationales_per_metric(
+    eval_rows: List[CallImportEvaluationRow],
+    *,
+    per_metric_cap: int = 3,
+    rationale_char_cap: int = 600,
+) -> Dict[str, List[str]]:
+    """Collect up to ``per_metric_cap`` distinct rationales per metric.
+
+    Distinctness is case- and whitespace-insensitive. We truncate each
+    rationale to ``rationale_char_cap`` so a few unusually verbose rows
+    can't dominate the prompt budget. Empty / non-string rationales are
+    skipped.
+    """
+    out: Dict[str, List[str]] = {}
+    seen: Dict[str, set[str]] = {}
+    for row in eval_rows:
+        scores = row.metric_scores if isinstance(row.metric_scores, dict) else {}
+        for metric_id, entry in scores.items():
+            if not isinstance(entry, dict):
+                continue
+            rationale = entry.get("rationale")
+            if not isinstance(rationale, str):
+                continue
+            text = rationale.strip()
+            if not text:
+                continue
+            bucket = out.setdefault(metric_id, [])
+            if len(bucket) >= per_metric_cap:
+                continue
+            key = " ".join(text.lower().split())
+            seen_set = seen.setdefault(metric_id, set())
+            if key in seen_set:
+                continue
+            seen_set.add(key)
+            bucket.append(text[:rationale_char_cap])
+    return out
+
+
+def _build_insights_messages(
+    evaluation: CallImportEvaluation,
+    aggregate: List[CallImportMetricAggregate],
+    rationale_samples: Dict[str, List[str]],
+    metric_meta: Dict[str, Metric],
+) -> List[Dict[str, str]]:
+    """Render the user prompt fed to the LLM.
+
+    The shape is plain markdown-ish text instead of JSON so the LLM can
+    skim it without us spending tokens on verbose schema delimiters.
+    Parent metrics surface their child metrics nested underneath so the
+    model sees the hierarchy and can talk about "X often co-occurred
+    with Y" rather than treating sub-labels as standalone metrics.
+    """
+    name = evaluation.name or f"Run {str(evaluation.id)[:8]}"
+    lines: List[str] = [
+        f"Evaluation: {name}",
+        (
+            f"Rows: total={evaluation.total_rows} "
+            f"completed={evaluation.completed_rows} "
+            f"failed={evaluation.failed_rows}"
+        ),
+        "",
+        "## Per-metric aggregate",
+    ]
+
+    # Group metrics by parent so the prompt mirrors the hierarchy. Any
+    # aggregate row whose ``metric_id`` is missing from ``metric_meta``
+    # is rendered as a leaf at the top-level list (handles renamed /
+    # deleted parents).
+    children_by_parent: Dict[str, List[CallImportMetricAggregate]] = {}
+    top_level: List[CallImportMetricAggregate] = []
+    for agg in aggregate:
+        meta = metric_meta.get(agg.metric_id)
+        parent_id = (
+            str(meta.parent_metric_id)
+            if meta is not None and getattr(meta, "parent_metric_id", None)
+            else None
+        )
+        if parent_id:
+            children_by_parent.setdefault(parent_id, []).append(agg)
+        else:
+            top_level.append(agg)
+
+    def _format_metric_block(agg: CallImportMetricAggregate, indent: int) -> List[str]:
+        prefix = "  " * indent + "- "
+        bits: List[str] = [f"{prefix}{agg.metric_name} [id={agg.metric_id}] (n={agg.count}"]
+        if agg.skipped_count:
+            bits.append(f", skipped={agg.skipped_count}")
+        if agg.error_count:
+            bits.append(f", errors={agg.error_count}")
+        bits.append(")")
+        meta = metric_meta.get(agg.metric_id)
+        description = (meta.description or "").strip() if meta else ""
+        if description:
+            bits.append(f" | definition={description[:500]}")
+        if agg.mean is not None:
+            mean_s = f"{agg.mean:.2f}"
+            stddev_s = f"{agg.stddev:.2f}" if agg.stddev is not None else "-"
+            bits.append(f" | mean={mean_s} stddev={stddev_s}")
+            if agg.min is not None and agg.max is not None:
+                bits.append(f" range=[{agg.min:.2f}, {agg.max:.2f}]")
+        if agg.value_counts:
+            total = sum(v.count for v in agg.value_counts) or 1
+            top = agg.value_counts[:3]
+            shares = ", ".join(
+                f'"{v.label}"={v.count}/{total}' for v in top
+            )
+            bits.append(f" | top={shares}")
+        result = ["".join(bits)]
+        rationales = rationale_samples.get(agg.metric_id, [])
+        for r in rationales:
+            result.append("  " * (indent + 1) + f"- rationale: {r}")
+        return result
+
+    for agg in top_level:
+        lines.extend(_format_metric_block(agg, indent=0))
+        meta = metric_meta.get(agg.metric_id)
+        children = children_by_parent.get(str(meta.id), []) if meta else []
+        for child in children:
+            lines.extend(_format_metric_block(child, indent=1))
+
+    lines.append("")
+    top_level_ids = [agg.metric_id for agg in top_level]
+    if top_level_ids:
+        lines.append(
+            "metric_insights keys must exactly use these top-level metric ids: "
+            + ", ".join(top_level_ids)
+        )
+        lines.append("")
+    lines.append(
+        "Write the JSON object as instructed. Do not include "
+        "preamble, code fences, or trailing commentary."
+    )
+
+    return [
+        {"role": "system", "content": _INSIGHTS_SYSTEM_PROMPT},
+        {"role": "user", "content": "\n".join(lines)},
+    ]
+
+
+def _parse_insights_response(text: str) -> EvaluationTldrSummary:
+    """Coerce the LLM response into ``narrative`` + ``patterns``.
+
+    Matches the JSON-with-fallback pattern used by
+    ``app.api.v1.routes.metrics._parse_metric_generation_response``: try
+    ``json.loads`` first, then fall back to regex extraction of the
+    first ``{...}`` block. Raises ``HTTPException`` with a 502 when the
+    response can't be parsed at all.
+    """
+    cleaned = (text or "").strip()
+    if not cleaned:
+        raise HTTPException(
+            status_code=502, detail="LLM returned an empty insights response"
+        )
+    try:
+        parsed = json.loads(cleaned)
+    except json.JSONDecodeError:
+        import re
+
+        match = re.search(r"\{.*\}", cleaned, re.DOTALL)
+        if not match:
+            raise HTTPException(
+                status_code=502,
+                detail="Could not parse LLM insights response as JSON",
+            )
+        try:
+            parsed = json.loads(match.group(0))
+        except json.JSONDecodeError as e:
+            raise HTTPException(
+                status_code=502,
+                detail=f"Could not parse LLM insights response: {e}",
+            )
+
+    if not isinstance(parsed, dict):
+        raise HTTPException(
+            status_code=502, detail="LLM insights JSON was not an object"
+        )
+
+    narrative = parsed.get("narrative")
+    if not isinstance(narrative, str) or not narrative.strip():
+        raise HTTPException(
+            status_code=502,
+            detail="LLM insights JSON missing 'narrative' string",
+        )
+
+    patterns_raw = parsed.get("patterns")
+    if patterns_raw is None:
+        patterns: List[str] = []
+    elif isinstance(patterns_raw, list):
+        patterns = [
+            str(p).strip()
+            for p in patterns_raw
+            if isinstance(p, str) and p.strip()
+        ]
+    else:
+        raise HTTPException(
+            status_code=502,
+            detail="LLM insights JSON 'patterns' must be a list of strings",
+        )
+    metric_insights_raw = parsed.get("metric_insights")
+    if metric_insights_raw is None:
+        metric_insights: Dict[str, str] = {}
+    elif isinstance(metric_insights_raw, dict):
+        metric_insights = {
+            str(metric_id): str(insight).strip()
+            for metric_id, insight in metric_insights_raw.items()
+            if str(metric_id).strip()
+            and isinstance(insight, str)
+            and insight.strip()
+        }
+    else:
+        raise HTTPException(
+            status_code=502,
+            detail="LLM insights JSON 'metric_insights' must be an object",
+        )
+
+    return EvaluationTldrSummary(
+        narrative=_clamp_prose_to_sentences(narrative.strip()),
+        patterns=patterns,
+        metric_insights=metric_insights,
+        generated_at=datetime.now(timezone.utc),
+        generated_at_completed_rows=0,  # filled in by caller
+        is_stale=False,
+    )
+
+
+def _generate_and_persist_tldr_summary(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    *,
+    organization_id: UUID,
+    provider: Optional[str] = None,
+    model: Optional[str] = None,
+) -> EvaluationTldrSummary:
+    """LLM TLDR generation used by the imports-queue Celery worker."""
+    eval_id = evaluation.id
+    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
+
+    pairs = load_evaluation_row_pairs(db, eval_id)
+    eval_rows = [eval_row for eval_row, _ in pairs]
+    aggregate = _compute_metric_aggregates(db, evaluation, eval_rows)
+    if not aggregate:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "No metric data yet. Wait for at least one row to "
+                "finish scoring before generating a summary."
+            ),
+        )
+
+    metric_ids: List[UUID] = []
+    for agg in aggregate:
+        try:
+            metric_ids.append(UUID(agg.metric_id))
+        except (TypeError, ValueError):
+            continue
+    metrics = _metrics_for_ids(db, organization_id, metric_ids)
+    metric_meta: Dict[str, Metric] = {str(m.id): m for m in metrics}
+
+    rationale_samples = _sample_rationales_per_metric(eval_rows)
+    messages = _build_insights_messages(
+        evaluation, aggregate, rationale_samples, metric_meta
+    )
+
+    from app.services.ai.llm_resolver import get_llm_provider_and_model
+    from app.services.ai.llm_service import llm_service
+
+    provider_enum, model_str = get_llm_provider_and_model(
+        organization_id, db, provider, model
+    )
+
+    try:
+        llm_result = llm_service.generate_response(
+            messages=messages,
+            llm_provider=provider_enum,
+            llm_model=model_str,
+            organization_id=organization_id,
+            db=db,
+            temperature=0.4,
+            max_tokens=1400,
+        )
+    except Exception as e:
+        logger.error(f"[CallImportInsights] LLM call failed: {e}")
+        raise HTTPException(
+            status_code=502, detail=f"LLM call failed: {e}"
+        ) from e
+
+    summary = _parse_insights_response(llm_result.get("text", ""))
+    total = int(evaluation.total_rows or 0)
+    ui_completed = min(int(evaluation.completed_rows or 0), total) if total else int(
+        evaluation.completed_rows or 0
+    )
+    summary.generated_at_completed_rows = ui_completed
+    summary.provider = provider_enum.value
+    summary.model = model_str
+    summary.is_stale = False
+
+    evaluation.tldr_summary = {
+        "narrative": summary.narrative,
+        "patterns": summary.patterns,
+        "metric_insights": summary.metric_insights,
+        "generated_at": summary.generated_at.isoformat(),
+        "generated_at_completed_rows": summary.generated_at_completed_rows,
+        "provider": summary.provider,
+        "model": summary.model,
+    }
+    flag_modified(evaluation, "tldr_summary")
+    db.commit()
+    db.refresh(evaluation)
+    return summary
+
+
+@router.get(
+    "/{eval_id}/insights",
+    response_model=Optional[EvaluationTldrSummary],
+    operation_id="getCallImportEvaluationInsights",
+)
+async def get_call_import_evaluation_insights(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> Optional[EvaluationTldrSummary]:
+    """Return the cached TLDR (or ``null``) without contacting the LLM.
+
+    Used by the Visualizations tab on first paint so the empty-state
+    CTA can show up before the user opts into generation.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+    return _tldr_summary_payload(evaluation)
+
+
+@router.post(
+    "/{eval_id}/insights",
+    response_model=EvaluationTldrSummary,
+    operation_id="generateCallImportEvaluationInsights",
+)
+async def generate_call_import_evaluation_insights(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: EvaluationInsightsRequest = Body(default_factory=EvaluationInsightsRequest),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> EvaluationTldrSummary:
+    """Generate (or return-cached) the LLM TLDR for an evaluation run.
+
+    Behavior:
+
+    * ``body.regenerate=False`` and a cached summary at the current
+      ``completed_rows`` watermark exists -> return it as-is.
+    * ``body.regenerate=False`` and a stale cached summary exists
+      (``generated_at_completed_rows < completed_rows``) -> return it
+      with ``is_stale=True``; the UI prompts the user to regenerate.
+    * Otherwise -> resolve provider+model (auto-detect when omitted),
+      call the LLM, persist the new summary, return it.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    if not body.regenerate:
+        cached = _tldr_summary_payload(evaluation)
+        if cached is not None:
+            return cached
+
+    # Run the TLDR LLM on the imports worker (not the default worker or API).
+    from app.workers.tasks.generate_evaluation_tldr_insights import (
+        generate_evaluation_tldr_insights_task,
+    )
+
+    try:
+        task_result = generate_evaluation_tldr_insights_task.apply_async(
+            kwargs={
+                "evaluation_id": str(eval_id),
+                "call_import_id": str(call_import_id),
+                "organization_id": str(organization_id),
+                "provider": body.provider,
+                "model": body.model,
+            },
+        ).get(timeout=25 * 60)
+    except Exception as exc:
+        logger.error(
+            "[CallImportInsights] TLDR task failed for evaluation {}: {}",
+            eval_id,
+            exc,
+        )
+        raise HTTPException(
+            status_code=502,
+            detail=f"Summary generation failed: {exc}",
+        ) from exc
+
+    if isinstance(task_result, dict) and task_result.get("error"):
+        status_code = int(task_result.get("status_code") or 502)
+        raise HTTPException(
+            status_code=status_code,
+            detail=str(task_result["error"]),
+        )
+
+    summary = EvaluationTldrSummary.model_validate(task_result)
+    db.refresh(evaluation)
+
+    from app.services.ai.llm_resolver import get_llm_provider_and_model
+
+    provider_enum, model_str = get_llm_provider_and_model(
+        organization_id, db, body.provider, body.model, body.credential_id
+    )
+
+    _enqueue_user_insights_job(
+        evaluation,
+        provider=summary.provider or provider_enum.value,
+        model=summary.model or model_str,
+        force=body.regenerate,
+        max_llm_calls=body.max_llm_calls,
+        db=db,
+    )
+
+    return summary
+
+
+def _user_insights_payload(
+    evaluation: CallImportEvaluation,
+) -> Optional[EvaluationUserInsightsState]:
+    raw = getattr(evaluation, "user_insights", None)
+    if raw is None:
+        return None
+    return user_insights_state_from_raw(
+        raw,
+        completed_rows=evaluation.completed_rows,
+    )
+
+
+def _selected_generated_user_insights(
+    state: Optional[EvaluationUserInsightsState],
+    report_config: dict[str, Any],
+) -> list[dict[str, Any]]:
+    """Filter and order generated insights for PDF section 03."""
+    if state is None or state.status != "completed" or not state.insights:
+        return []
+
+    selected_ids = report_config.get("user_insight_ids")
+    if isinstance(selected_ids, list) and selected_ids:
+        allowed = {str(item) for item in selected_ids if item}
+        items = [item for item in state.insights if item.id in allowed]
+    else:
+        items = list(state.insights)
+
+    order_raw = report_config.get("order")
+    order_ids: list[str] = []
+    if isinstance(order_raw, dict):
+        user_order = order_raw.get("user_insights")
+        if isinstance(user_order, list):
+            order_ids = [str(item) for item in user_order if item]
+
+    if order_ids:
+        by_id = {item.id: item for item in items}
+        ordered = [by_id[iid] for iid in order_ids if iid in by_id]
+        seen = set(order_ids)
+        ordered.extend(item for item in items if item.id not in seen)
+        items = ordered
+
+    return [item.model_dump(mode="json") for item in items]
+
+
+def _enqueue_user_insights_job(
+    evaluation: CallImportEvaluation,
+    *,
+    provider: Optional[str] = None,
+    model: Optional[str] = None,
+    force: bool = False,
+    max_llm_calls: Optional[int] = None,
+    db: Optional[Session] = None,
+) -> None:
+    """Enqueue background user-insights generation unless already running."""
+    current = _user_insights_payload(evaluation)
+    if current is not None and current.status == "running" and not force:
+        return
+
+    llm_budget = normalize_max_llm_calls(max_llm_calls)
+
+    completed_count = (
+        _count_completed_eval_rows(db, evaluation.id)
+        if db is not None
+        else evaluation.completed_rows
+    )
+    total_calls = total_llm_calls_for_rows(completed_count, max_llm_calls=llm_budget)
+    evaluation.user_insights = {
+        "status": "running",
+        "insights": (
+            (evaluation.user_insights or {}).get("insights", [])
+            if isinstance(evaluation.user_insights, dict)
+            else []
+        ),
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "generated_at_completed_rows": evaluation.completed_rows,
+        "progress": {"completed_llm_calls": 0, "total_llm_calls": total_calls},
+        "provider": provider,
+        "model": model,
+        "max_llm_calls": llm_budget,
+        "llm_calls_used": 0,
+        "error_message": None,
+    }
+    if db is not None:
+        flag_modified(evaluation, "user_insights")
+        db.commit()
+
+    from app.workers.tasks.generate_evaluation_user_insights import (
+        generate_evaluation_user_insights_task,
+    )
+
+    generate_evaluation_user_insights_task.delay(
+        str(evaluation.id),
+        provider=provider,
+        model=model,
+        max_llm_calls=llm_budget,
+    )
+
+
+@router.get(
+    "/{eval_id}/user-insights",
+    response_model=Optional[EvaluationUserInsightsState],
+    operation_id="getCallImportEvaluationUserInsights",
+)
+async def get_call_import_evaluation_user_insights(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> Optional[EvaluationUserInsightsState]:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+    return _user_insights_payload(evaluation)
+
+
+@router.post(
+    "/{eval_id}/user-insights",
+    response_model=EvaluationUserInsightsState,
+    operation_id="generateCallImportEvaluationUserInsights",
+)
+async def generate_call_import_evaluation_user_insights(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: EvaluationUserInsightsRequest = Body(
+        default_factory=EvaluationUserInsightsRequest
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> EvaluationUserInsightsState:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    if not body.regenerate and not body.force:
+        cached = _user_insights_payload(evaluation)
+        if cached is not None and cached.status in {"running", "completed"}:
+            return cached
+
+    eval_rows = _load_eval_rows(db, eval_id)
+    if not any(row.status == "completed" for row in eval_rows):
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "No completed rows yet. Wait for at least one row to "
+                "finish scoring before generating user insights."
+            ),
+        )
+
+    from app.services.ai.llm_resolver import get_llm_provider_and_model
+
+    provider_enum, model_str = get_llm_provider_and_model(
+        organization_id, db, body.provider, body.model, body.credential_id
+    )
+
+    _enqueue_user_insights_job(
+        evaluation,
+        provider=provider_enum.value,
+        model=model_str,
+        force=body.force or body.regenerate,
+        max_llm_calls=body.max_llm_calls,
+        db=db,
+    )
+
+    db.refresh(evaluation)
+    return _user_insights_payload(evaluation) or EvaluationUserInsightsState(
+        status="running"
+    )
+
+
+def _metric_clusters_payload(
+    evaluation: CallImportEvaluation,
+) -> Optional[EvaluationMetricClustersState]:
+    raw = getattr(evaluation, "metric_clusters", None)
+    if raw is None:
+        return None
+    return metric_clusters_state_from_raw(
+        raw,
+        completed_rows=evaluation.completed_rows,
+    )
+
+
+def _selected_metric_clusters_for_pdf(
+    state: Optional[EvaluationMetricClustersState],
+    report_config: dict[str, Any],
+) -> dict[str, Any]:
+    if state is None or state.status != "completed":
+        return {}
+    sections = report_config.get("sections")
+    if isinstance(sections, dict) and sections.get("failure_diagnostics") is False:
+        return {}
+    payload: dict[str, Any] = {
+        "groups": [g.model_dump(mode="json") for g in state.groups],
+        "discovered_problems": [
+            d.model_dump(mode="json") for d in state.discovered_problems
+        ],
+    }
+    if state.rca_summary is not None:
+        payload["rca_summary"] = state.rca_summary.model_dump(mode="json")
+    return payload
+
+
+def _prompt_improvements_payload(
+    evaluation: CallImportEvaluation,
+) -> Optional[EvaluationPromptImprovementsState]:
+    from app.services.call_import_prompt_improvements import (
+        prompt_improvements_state_from_raw,
+    )
+
+    raw = getattr(evaluation, "prompt_improvements", None)
+    if raw is None:
+        return None
+    return prompt_improvements_state_from_raw(
+        raw,
+        completed_rows=evaluation.completed_rows,
+    )
+
+
+def _selected_prompt_improvements_for_pdf(
+    state: Optional[EvaluationPromptImprovementsState],
+    report_config: dict[str, Any],
+) -> dict[str, Any]:
+    if state is None or state.status != "completed":
+        return {}
+    sections = report_config.get("sections")
+    if isinstance(sections, dict) and sections.get("prompt_improvements") is False:
+        return {}
+    return {
+        "imported_agent_id": state.imported_agent_id,
+        "imported_agent_name": state.imported_agent_name,
+        "overview": state.overview,
+        "suggestions": [s.model_dump(mode="json") for s in state.suggestions],
+    }
+
+
+def _enqueue_prompt_improvements_job(
+    evaluation: CallImportEvaluation,
+    *,
+    imported_agent_id: UUID,
+    imported_agent_name: str,
+    provider: Optional[str] = None,
+    model: Optional[str] = None,
+    credential_id: Optional[UUID] = None,
+    force: bool = False,
+    db: Optional[Session] = None,
+) -> None:
+    current = _prompt_improvements_payload(evaluation)
+    if current is not None and current.status == "running" and not force:
+        return
+
+    evaluation.prompt_improvements = {
+        "status": "running",
+        "imported_agent_id": str(imported_agent_id),
+        "imported_agent_name": imported_agent_name,
+        "suggestions": [],
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "generated_at_completed_rows": evaluation.completed_rows,
+        "provider": provider,
+        "model": model,
+        "error_message": None,
+    }
+    if db is not None:
+        flag_modified(evaluation, "prompt_improvements")
+        db.commit()
+
+    from app.workers.tasks.generate_evaluation_prompt_improvements import (
+        generate_evaluation_prompt_improvements_task,
+    )
+
+    async_result = generate_evaluation_prompt_improvements_task.apply_async(
+        kwargs={
+            "evaluation_id": str(evaluation.id),
+            "imported_agent_id": str(imported_agent_id),
+            "provider": provider,
+            "model": model,
+            "credential_id": str(credential_id) if credential_id else None,
+        },
+        queue="imports",
+    )
+    if db is not None and isinstance(evaluation.prompt_improvements, dict):
+        evaluation.prompt_improvements["celery_task_id"] = async_result.id
+        flag_modified(evaluation, "prompt_improvements")
+        db.commit()
+
+
+def _load_eval_rows(db: Session, evaluation_id: UUID) -> List[CallImportEvaluationRow]:
+    from app.db_sharding.eval_rows import load_evaluation_rows_for_run
+
+    return load_evaluation_rows_for_run(db, evaluation_id)
+
+
+def _count_completed_eval_rows(db: Session, evaluation_id: UUID) -> int:
+    from app.db_sharding.eval_rows import count_evaluation_rows_for_run
+
+    return count_evaluation_rows_for_run(
+        db, evaluation_id, statuses=["completed"]
+    )
+
+
+def _completed_row_pairs_for_evaluation(
+    db: Session,
+    evaluation_id: UUID,
+) -> List[Tuple[CallImportEvaluationRow, CallImportRow]]:
+    from app.db_sharding.scatter_gather import load_evaluation_row_pairs
+
+    row_pairs = load_evaluation_row_pairs(db, evaluation_id)
+    return [
+        (eval_row, source_row)
+        for eval_row, source_row in row_pairs
+        if eval_row.status == "completed"
+    ]
+
+
+def _resolve_metric_cluster_row_selection(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    eval_rows: List[CallImportEvaluationRow],
+    evaluation_row_ids: Optional[List[UUID]],
+    *,
+    row_limit: Optional[int] = None,
+    policies: Optional[Dict[str, MetricFailurePolicy]] = None,
+) -> Tuple[List[Tuple[CallImportEvaluationRow, CallImportRow]], List[str]]:
+    """Return filtered completed row pairs and the selected row id strings."""
+    completed_pairs = _completed_row_pairs_for_evaluation(db, evaluation.id)
+    metrics = _metrics_for_clustering(db, evaluation, eval_rows)
+    if policies is None:
+        aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
+        parent_ids = [
+            m.id
+            for m in metrics
+            if getattr(m, "selection_mode", None)
+            and not getattr(m, "parent_metric_id", None)
+        ]
+        child_names_by_parent = _child_names_by_parent(
+            db, evaluation.organization_id, parent_ids
+        )
+        policies, _ = effective_policies(
+            evaluation,
+            metrics,
+            aggregates,
+            child_names_by_parent=child_names_by_parent,
+        )
+    eligible = list_eligible_cluster_rows(
+        evaluation, completed_pairs, metrics, policies
+    )
+    eligible_ordered_ids = [str(item["evaluation_row_id"]) for item in eligible]
+    eligible_id_set = set(eligible_ordered_ids)
+
+    if evaluation_row_ids is None and row_limit is not None:
+        selected_ids = eligible_ordered_ids[:row_limit]
+        filtered = filter_completed_row_pairs(
+            completed_pairs,
+            [UUID(rid) for rid in selected_ids],
+        )
+        return filtered, selected_ids
+
+    if evaluation_row_ids is None:
+        selected_ids = eligible_ordered_ids
+        filtered = filter_completed_row_pairs(
+            completed_pairs,
+            [UUID(rid) for rid in selected_ids],
+        )
+        return filtered, selected_ids
+
+    requested = {str(rid) for rid in evaluation_row_ids}
+    completed_id_set = {str(eval_row.id) for eval_row, _ in completed_pairs}
+    unknown = sorted(requested - completed_id_set)
+    if unknown:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "One or more evaluation_row_ids are missing or not completed: "
+                + ", ".join(unknown[:5])
+                + ("…" if len(unknown) > 5 else "")
+            ),
+        )
+    not_eligible = sorted(requested - eligible_id_set)
+    if not_eligible:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "Each selected row must have at least one flagged quality metric. "
+                "Ineligible row(s): "
+                + ", ".join(not_eligible[:5])
+                + ("…" if len(not_eligible) > 5 else "")
+            ),
+        )
+    selected_ids = sorted(requested)
+    filtered = filter_completed_row_pairs(completed_pairs, evaluation_row_ids)
+    return filtered, selected_ids
+
+
+def _enqueue_metric_clusters_job(
+    evaluation: CallImportEvaluation,
+    *,
+    provider: Optional[str] = None,
+    model: Optional[str] = None,
+    credential_id: Optional[UUID] = None,
+    force: bool = False,
+    max_llm_calls: Optional[int] = None,
+    evaluation_row_ids: Optional[List[UUID]] = None,
+    selected_evaluation_row_ids: Optional[List[str]] = None,
+    failure_policies: Optional[Dict[str, MetricFailurePolicy]] = None,
+    db: Optional[Session] = None,
+) -> None:
+    current = _metric_clusters_payload(evaluation)
+    if current is not None and current.status == "running" and not force:
+        return
+
+    llm_budget = normalize_max_llm_calls(max_llm_calls)
+    total_calls = 1
+    row_ids_for_task: Optional[List[str]] = None
+    if db is not None:
+        eval_rows = _load_eval_rows(db, evaluation.id)
+        if selected_evaluation_row_ids is None:
+            _, selected_evaluation_row_ids = _resolve_metric_cluster_row_selection(
+                db,
+                evaluation,
+                eval_rows,
+                evaluation_row_ids,
+            )
+        completed_pairs = filter_completed_row_pairs(
+            _completed_row_pairs_for_evaluation(db, evaluation.id),
+            [UUID(rid) for rid in selected_evaluation_row_ids],
+        )
+        metrics = _metrics_for_clustering(db, evaluation, eval_rows)
+        policies_for_estimate = failure_policies
+        if policies_for_estimate is None:
+            aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
+            parent_ids = [
+                m.id
+                for m in metrics
+                if getattr(m, "selection_mode", None)
+                and not getattr(m, "parent_metric_id", None)
+            ]
+            child_names_by_parent = _child_names_by_parent(
+                db, evaluation.organization_id, parent_ids
+            )
+            policies_for_estimate, _ = effective_policies(
+                evaluation,
+                metrics,
+                aggregates,
+                child_names_by_parent=child_names_by_parent,
+            )
+        _, total_calls = estimate_metric_clusters_llm_calls(
+            evaluation,
+            metrics,
+            completed_pairs,
+            policies_for_estimate,
+            max_llm_calls=llm_budget,
+        )
+        row_ids_for_task = list(selected_evaluation_row_ids)
+
+    prior_raw = (
+        evaluation.metric_clusters
+        if isinstance(evaluation.metric_clusters, dict)
+        else {}
+    )
+    policy_blob: Dict[str, Any] = {}
+    if failure_policies:
+        policy_blob = failure_policies_to_db(failure_policies, source="user")
+
+    evaluation.metric_clusters = {
+        "status": "running",
+        "groups": prior_raw.get("groups", []) if isinstance(prior_raw, dict) else [],
+        "discovered_problems": (
+            prior_raw.get("discovered_problems", [])
+            if isinstance(prior_raw, dict)
+            else []
+        ),
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "generated_at_completed_rows": evaluation.completed_rows,
+        "progress": {"completed_llm_calls": 0, "total_llm_calls": total_calls},
+        "provider": provider,
+        "model": model,
+        "max_llm_calls": llm_budget,
+        "llm_calls_used": 0,
+        "error_message": None,
+        "selected_evaluation_row_ids": selected_evaluation_row_ids or [],
+        **policy_blob,
+    }
+    if db is not None:
+        flag_modified(evaluation, "metric_clusters")
+        db.commit()
+
+    from app.workers.tasks.generate_evaluation_metric_clusters import (
+        generate_evaluation_metric_clusters_task,
+    )
+
+    async_result = generate_evaluation_metric_clusters_task.apply_async(
+        kwargs={
+            "evaluation_id": str(evaluation.id),
+            "provider": provider,
+            "model": model,
+            "credential_id": str(credential_id) if credential_id else None,
+            "max_llm_calls": llm_budget,
+            "evaluation_row_ids": row_ids_for_task,
+        },
+        queue="imports",
+    )
+    if db is not None and isinstance(evaluation.metric_clusters, dict):
+        evaluation.metric_clusters["celery_task_id"] = async_result.id
+        flag_modified(evaluation, "metric_clusters")
+        db.commit()
+
+
+def _revoke_metric_clusters_task(evaluation: CallImportEvaluation) -> None:
+    """Best-effort SIGTERM revoke of the in-flight clustering Celery task."""
+    raw = evaluation.metric_clusters
+    if not isinstance(raw, dict):
+        return
+    task_id = str(raw.get("celery_task_id") or "").strip()
+    if not task_id:
+        return
+    try:
+        from app.workers.celery_app import celery_app
+
+        celery_app.control.revoke(task_id, terminate=True, signal="SIGTERM")
+        logger.info(
+            "Revoked metric-clusters task {} for evaluation {}",
+            task_id,
+            evaluation.id,
+        )
+    except Exception as exc:  # noqa: BLE001
+        logger.warning(
+            "Failed to revoke metric-clusters task {} for evaluation {}: {}",
+            task_id,
+            evaluation.id,
+            exc,
+        )
+
+
+def _apply_metric_clusters_cancel(evaluation: CallImportEvaluation) -> bool:
+    """Mark clustering as cancelled and revoke the worker task.
+
+    Returns True if a running job was cancelled, False if already terminal.
+    """
+    raw = evaluation.metric_clusters
+    if not isinstance(raw, dict):
+        return False
+    if (raw.get("status") or "").lower() != "running":
+        return False
+
+    _revoke_metric_clusters_task(evaluation)
+    progress = raw.get("progress") if isinstance(raw.get("progress"), dict) else {}
+    evaluation.metric_clusters = {
+        **raw,
+        "status": "cancelled",
+        "error_message": METRIC_CLUSTERS_CANCELLED_BY_USER_ERROR,
+        "progress": progress,
+        "celery_task_id": None,
+    }
+    return True
+
+
+@router.get(
+    "/{eval_id}/metric-clusters/failure-policies",
+    response_model=MetricFailurePoliciesResponse,
+    operation_id="getCallImportEvaluationMetricClusterFailurePolicies",
+)
+async def get_call_import_evaluation_metric_cluster_failure_policies(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> MetricFailurePoliciesResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    eval_rows = _load_eval_rows(db, eval_id)
+    metrics, aggregates, policies, source, child_names_by_parent = _clustering_context(
+        db, evaluation, eval_rows
+    )
+    previews = build_failure_policy_previews(
+        metrics,
+        aggregates,
+        child_names_by_parent=child_names_by_parent,
+        effective=policies,
+    )
+    updated_at = None
+    raw_mc = evaluation.metric_clusters
+    if isinstance(raw_mc, dict) and raw_mc.get("failure_policies_updated_at"):
+        try:
+            updated_at = datetime.fromisoformat(
+                str(raw_mc["failure_policies_updated_at"])
+            )
+        except ValueError:
+            updated_at = None
+    return MetricFailurePoliciesResponse(
+        previews=previews,
+        policies=policies,
+        source=source,
+        updated_at=updated_at,
+    )
+
+
+@router.put(
+    "/{eval_id}/metric-clusters/failure-policies",
+    response_model=MetricFailurePoliciesResponse,
+    operation_id="saveCallImportEvaluationMetricClusterFailurePolicies",
+)
+async def save_call_import_evaluation_metric_cluster_failure_policies(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: MetricFailurePoliciesSaveRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> MetricFailurePoliciesResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    eval_rows = _load_eval_rows(db, eval_id)
+    metrics, aggregates, _existing, _source, child_names_by_parent = _clustering_context(
+        db, evaluation, eval_rows
+    )
+    try:
+        validate_failure_policies_for_metrics(body.policies, metrics)
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    prior = (
+        evaluation.metric_clusters
+        if isinstance(evaluation.metric_clusters, dict)
+        else {}
+    )
+    evaluation.metric_clusters = merge_failure_policies_into_raw(
+        prior,
+        body.policies,
+        source="user",
+    )
+    flag_modified(evaluation, "metric_clusters")
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+    db.refresh(evaluation)
+
+    policies, source = policies_from_evaluation_raw(evaluation.metric_clusters)
+    if source != "user":
+        source = "user"
+    previews = build_failure_policy_previews(
+        metrics,
+        aggregates,
+        child_names_by_parent=child_names_by_parent,
+        effective=policies,
+    )
+    updated_at = None
+    raw_mc = evaluation.metric_clusters
+    if isinstance(raw_mc, dict) and raw_mc.get("failure_policies_updated_at"):
+        try:
+            updated_at = datetime.fromisoformat(
+                str(raw_mc["failure_policies_updated_at"])
+            )
+        except ValueError:
+            updated_at = None
+    return MetricFailurePoliciesResponse(
+        previews=previews,
+        policies=policies,
+        source="user",
+        updated_at=updated_at,
+    )
+
+
+@router.get(
+    "/{eval_id}/metric-clusters/eligible-rows",
+    response_model=MetricClusterEligibleRowsResponse,
+    operation_id="listCallImportEvaluationMetricClusterEligibleRows",
+)
+async def list_call_import_evaluation_metric_cluster_eligible_rows(
+    call_import_id: UUID,
+    eval_id: UUID,
+    limit: Optional[int] = Query(default=None, ge=1),
+    count_only: bool = Query(default=False),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> MetricClusterEligibleRowsResponse:
+    """Completed rows that have at least one flagged quality metric."""
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    eval_rows = _load_eval_rows(db, eval_id)
+    completed_pairs = _completed_row_pairs_for_evaluation(db, eval_id)
+    metrics, _aggregates, policies, _source, _child_map = _clustering_context(
+        db, evaluation, eval_rows
+    )
+    all_eligible = list_eligible_cluster_rows(
+        evaluation, completed_pairs, metrics, policies
+    )
+    total = len(all_eligible)
+    if count_only:
+        return MetricClusterEligibleRowsResponse(items=[], total=total)
+    raw_items = all_eligible if limit is None else all_eligible[:limit]
+    items = [MetricClusterEligibleRow.model_validate(item) for item in raw_items]
+    return MetricClusterEligibleRowsResponse(items=items, total=total)
+
+
+@router.get(
+    "/{eval_id}/metric-clusters",
+    response_model=Optional[EvaluationMetricClustersState],
+    operation_id="getCallImportEvaluationMetricClusters",
+)
+async def get_call_import_evaluation_metric_clusters(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> Optional[EvaluationMetricClustersState]:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+    return _metric_clusters_payload(evaluation)
+
+
+@router.post(
+    "/{eval_id}/metric-clusters",
+    response_model=EvaluationMetricClustersState,
+    operation_id="generateCallImportEvaluationMetricClusters",
+)
+async def generate_call_import_evaluation_metric_clusters(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: EvaluationMetricClustersRequest = Body(
+        default_factory=EvaluationMetricClustersRequest
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> EvaluationMetricClustersState:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    if not body.regenerate and not body.force:
+        cached = _metric_clusters_payload(evaluation)
+        if cached is not None and cached.status in {"running", "completed"}:
+            return cached
+
+    eval_rows = _load_eval_rows(db, eval_id)
+    if not any(row.status == "completed" for row in eval_rows):
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "No completed rows yet. Wait for at least one row to "
+                "finish scoring before generating metric clusters."
+            ),
+        )
+
+    if body.evaluation_row_ids and body.row_limit is not None:
+        raise HTTPException(
+            status_code=400,
+            detail="Specify either evaluation_row_ids or row_limit, not both.",
+        )
+
+    if body.evaluation_row_ids:
+        completed_pairs = _completed_row_pairs_for_evaluation(db, evaluation.id)
+        completed_id_set = {str(eval_row.id) for eval_row, _ in completed_pairs}
+        requested = {str(rid) for rid in body.evaluation_row_ids}
+        unknown = sorted(requested - completed_id_set)
+        if unknown:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "One or more evaluation_row_ids are missing or not completed: "
+                    + ", ".join(unknown[:5])
+                    + ("…" if len(unknown) > 5 else "")
+                ),
+            )
+
+    from app.services.ai.llm_resolver import get_llm_provider_and_model
+
+    provider_enum, model_str = get_llm_provider_and_model(
+        organization_id, db, body.provider, body.model, body.credential_id
+    )
+
+    metrics, aggregates, _inferred, _source, child_names_by_parent = _clustering_context(
+        db, evaluation, eval_rows
+    )
+    merged_policies = merge_clustering_policies(
+        body.failure_policies,
+        evaluation,
+        metrics,
+        aggregates,
+        child_names_by_parent=child_names_by_parent,
+    )
+    try:
+        validate_failure_policies_for_metrics(
+            body.failure_policies or merged_policies, metrics
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    if not has_clusterable_metrics(metrics, merged_policies, eval_rows):
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "No calls match any failure policy. Select failure values on "
+                "metrics that have matching rows, or leave metrics with no "
+                "failures unchecked — they are skipped automatically."
+            ),
+        )
+
+    filtered_pairs, selected_row_ids = _resolve_metric_cluster_row_selection(
+        db,
+        evaluation,
+        eval_rows,
+        body.evaluation_row_ids,
+        row_limit=body.row_limit,
+        policies=merged_policies,
+    )
+    if not selected_row_ids:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "No eligible rows to cluster. Select completed calls that match "
+                "at least one configured failure policy."
+            ),
+        )
+    if not filtered_pairs:
+        raise HTTPException(
+            status_code=400,
+            detail="No completed rows match the selected evaluation_row_ids.",
+        )
+
+    _enqueue_metric_clusters_job(
+        evaluation,
+        provider=provider_enum.value,
+        model=model_str,
+        credential_id=body.credential_id,
+        force=body.force or body.regenerate,
+        max_llm_calls=body.max_llm_calls,
+        evaluation_row_ids=body.evaluation_row_ids,
+        selected_evaluation_row_ids=selected_row_ids,
+        failure_policies=merged_policies,
+        db=db,
+    )
+
+    db.refresh(evaluation)
+    return _metric_clusters_payload(evaluation) or EvaluationMetricClustersState(
+        status="running"
+    )
+
+
+@router.post(
+    "/{eval_id}/metric-clusters/cancel",
+    response_model=EvaluationMetricClustersState,
+    operation_id="cancelCallImportEvaluationMetricClusters",
+)
+async def cancel_call_import_evaluation_metric_clusters(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> EvaluationMetricClustersState:
+    """Abort in-flight failure-diagnostics clustering.
+
+    Idempotent: if clustering is not ``running``, returns the current state
+    unchanged.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    _apply_metric_clusters_cancel(evaluation)
+    flag_modified(evaluation, "metric_clusters")
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+    db.refresh(evaluation)
+
+    return _metric_clusters_payload(evaluation) or EvaluationMetricClustersState(
+        status="idle"
+    )
+
+
+@router.get(
+    "/{eval_id}/prompt-improvements",
+    response_model=Optional[EvaluationPromptImprovementsState],
+    operation_id="getCallImportEvaluationPromptImprovements",
+)
+async def get_call_import_evaluation_prompt_improvements(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> Optional[EvaluationPromptImprovementsState]:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+    return _prompt_improvements_payload(evaluation)
+
+
+@router.post(
+    "/{eval_id}/prompt-improvements",
+    response_model=EvaluationPromptImprovementsState,
+    operation_id="generateCallImportEvaluationPromptImprovements",
+)
+async def generate_call_import_evaluation_prompt_improvements(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: EvaluationPromptImprovementsRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> EvaluationPromptImprovementsState:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    clusters = _metric_clusters_payload(evaluation)
+    if clusters is None or clusters.status != "completed":
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "Metric clusters must be completed before generating prompt "
+                "improvements. Run failure diagnostics first."
+            ),
+        )
+
+    from app.services.call_import_prompt_improvements import is_imported_agent
+    from app.services.ai.llm_resolver import get_llm_provider_and_model
+
+    imported_agent = (
+        db.query(PromptPartial)
+        .filter(
+            PromptPartial.id == body.imported_agent_id,
+            PromptPartial.organization_id == organization_id,
+            PromptPartial.workspace_id == workspace_id,
+        )
+        .first()
+    )
+    if imported_agent is None or not is_imported_agent(imported_agent):
+        raise HTTPException(
+            status_code=404,
+            detail="Imported agent not found in the active workspace",
+        )
+
+    if not body.regenerate and not body.force:
+        cached = _prompt_improvements_payload(evaluation)
+        if (
+            cached is not None
+            and cached.status in {"running", "completed"}
+            and cached.imported_agent_id == str(body.imported_agent_id)
+        ):
+            return cached
+
+    provider_enum, model_str = get_llm_provider_and_model(
+        organization_id, db, body.provider, body.model, body.credential_id
+    )
+
+    _enqueue_prompt_improvements_job(
+        evaluation,
+        imported_agent_id=body.imported_agent_id,
+        imported_agent_name=imported_agent.name,
+        provider=provider_enum.value,
+        model=model_str,
+        credential_id=body.credential_id,
+        force=body.force or body.regenerate,
+        db=db,
+    )
+
+    db.refresh(evaluation)
+    return _prompt_improvements_payload(evaluation) or EvaluationPromptImprovementsState(
+        status="running",
+        imported_agent_id=str(body.imported_agent_id),
+        imported_agent_name=imported_agent.name,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Flow chart: turns per-row LLM-inferred ``sequence`` arrays into a
+# directed graph of (label -> label) transitions across the whole run.
+# Powers the aggregate Sankey-style React Flow chart on the evaluation
+# overview; per-call flow charts are built client-side from the same
+# ``sequence`` field on a single row's metric_scores entry.
+# ---------------------------------------------------------------------------
+
+
+_FLOW_TERMINAL_THRESHOLD = 0.2  # Mark as terminal when >=20% of sequences end here.
+_FLOW_START_NODE_ID = "__START__"
+_DISCOVERED_NODE_PREFIX = "disc:"
+
+
+def _slug_label(value: Any) -> str:
+    """Lowercase + whitespace-collapse + underscore-join.
+
+    Used everywhere we need a stable key for a metric/label name —
+    matching the same convention the worker uses when emitting
+    ``sequence`` entries and discovered keys.
+    """
+    if value is None:
+        return ""
+    return "_".join(str(value).strip().lower().split())
+
+
+def _resolve_alias(alias_map: Dict[str, str], key: str) -> str:
+    """Walk the alias map until we hit a slug that doesn't redirect.
+
+    The merge endpoint stores ``from_slug -> to_slug`` pairs. The delete
+    endpoint stores ``from_slug -> ""`` (empty string sentinel) to mark
+    a slug as tombstoned. Chains can accumulate when the user merges
+    A→B and later merges B→C; this helper collapses them so callers
+    always land on the final canonical slug.
+
+    Returns:
+      * the canonical slug if it still resolves to a real label,
+      * an empty string if the slug has been tombstoned (callers MUST
+        treat an empty result as "drop this entry entirely"),
+      * the input ``key`` if it isn't aliased.
+
+    Cycles are guarded by a hard step limit since the alias map is
+    user-driven.
+    """
+    if not key:
+        return ""
+    if not alias_map:
+        return key
+    current = key
+    seen: set[str] = set()
+    for _ in range(16):
+        if current in seen:
+            return current
+        seen.add(current)
+        if current not in alias_map:
+            return current
+        nxt = alias_map[current]
+        if nxt == current:
+            return current
+        if nxt == "":
+            # Deletion sentinel — the user has explicitly retired this
+            # slug. Propagate the empty string up so callers drop it.
+            return ""
+        current = nxt
+    return current
+
+
+# Reserved JSON key under which the worker stores top-level metric
+# discoveries on each row's ``metric_scores`` dict. Mirrors the constant
+# in ``app/workers/tasks/helpers/llm_evaluation.py`` — kept local here to
+# avoid a worker import cycle from the routes module.
+DISCOVERED_METRICS_KEY = "__discovered_metrics__"
+
+# Allowed values for an LLM-suggested top-level metric type. Kept in
+# sync with ``DiscoveredMetricSuggestedType`` in
+# ``app/models/schemas.py``.
+_DISCOVERED_METRIC_TYPES = ("boolean", "rating", "category")
+
+
+def normalize_scores_with_aliases(
+    metric_scores: Dict[str, Any],
+    evaluation: CallImportEvaluation,
+    db: Session,
+    organization_id: UUID,
+) -> Dict[str, Any]:
+    """Rewrite per-row ``metric_scores`` to honor merges + promotions.
+
+    Called by the worker right after ``evaluate_with_llm`` returns so
+    every row that finishes AFTER a user has merged or promoted a
+    discovered label persists data already reflecting that decision.
+    Without this hook, a worker holding a stale prompt could re-emit a
+    ``from_key`` slug long after the user merged it away.
+
+    For every parent entry (``selection_mode != null`` and a
+    ``discovered_labels`` / ``sequence`` field) we:
+
+      * resolve discovered slugs through the evaluation's
+        ``discovered_label_aliases`` map (transitively),
+      * drop any discovered_labels entry whose canonical slug now
+        matches a real promoted child of the parent (merging them out
+        of the panel for free), and
+      * collapse adjacent duplicate sequence entries that result.
+
+    Returns ``metric_scores`` (mutated in place) for chaining.
+    """
+    if not isinstance(metric_scores, dict):
+        return metric_scores
+
+    aliases_top = (
+        evaluation.discovered_label_aliases
+        if isinstance(evaluation.discovered_label_aliases, dict)
+        else {}
+    )
+
+    # Identify the parent entries inside metric_scores. They're the
+    # dicts that carry a ``selection_mode`` key (set by the LLM
+    # hierarchy parser) and either a ``sequence`` or a
+    # ``discovered_labels`` list.
+    for key, entry in list(metric_scores.items()):
+        if not isinstance(entry, dict):
+            continue
+        if entry.get("type") != "category" and not entry.get("selection_mode"):
+            continue
+        try:
+            parent_uuid = UUID(str(key))
+        except (TypeError, ValueError):
+            continue
+
+        alias_map = {}
+        sub = aliases_top.get(str(parent_uuid))
+        if isinstance(sub, dict):
+            alias_map = {
+                str(k): str(v)
+                for k, v in sub.items()
+                if isinstance(k, str) and isinstance(v, str)
+            }
+        promoted = _promoted_child_slugs(db, parent_uuid, organization_id)
+
+        # Rewrite discovered_labels: alias-resolve keys, drop duplicates
+        # post-resolution, and drop entries that have been promoted.
+        discovered = entry.get("discovered_labels")
+        if isinstance(discovered, list):
+            kept_disc: List[Dict[str, Any]] = []
+            seen: set[str] = set()
+            for d in discovered:
+                if not isinstance(d, dict):
+                    continue
+                slug = _slug_label(d.get("key") or d.get("name"))
+                slug = _resolve_alias(alias_map, slug)
+                if not slug or slug in promoted or slug in seen:
+                    continue
+                seen.add(slug)
+                new_entry = dict(d)
+                new_entry["key"] = slug
+                kept_disc.append(new_entry)
+            entry["discovered_labels"] = kept_disc
+
+        # Rewrite sequence: alias-resolve every entry; collapse adjacent
+        # duplicates that result. We DON'T drop slugs that match
+        # promoted children — the promoted child slug is still a valid
+        # sequence entry; the flow chart will resolve it to the real
+        # child node.
+        seq = entry.get("sequence")
+        if isinstance(seq, list):
+            new_seq: List[str] = []
+            last: Optional[str] = None
+            for item in seq:
+                if not isinstance(item, str):
+                    continue
+                slug = _resolve_alias(alias_map, _slug_label(item))
+                if not slug or slug == last:
+                    continue
+                new_seq.append(slug)
+                last = slug
+            entry["sequence"] = new_seq
+
+    # Top-level metric discoveries live alongside the parent entries
+    # under the reserved ``DISCOVERED_METRICS_KEY`` slot. Apply the
+    # flat evaluation-level alias/tombstone map + suppress slugs that
+    # already correspond to a real top-level Metric so workers that
+    # finish AFTER the user has merged / deleted / promoted can't
+    # resurrect a retired candidate.
+    discovered_metrics_payload = metric_scores.get(DISCOVERED_METRICS_KEY)
+    if isinstance(discovered_metrics_payload, list):
+        flat_alias_map = (
+            evaluation.discovered_metric_aliases
+            if isinstance(evaluation.discovered_metric_aliases, dict)
+            else {}
+        )
+        promoted_metric_slugs = _promoted_top_level_metric_slugs(
+            db, organization_id
+        )
+        kept_metrics: List[Dict[str, Any]] = []
+        seen_metrics: set[str] = set()
+        for d in discovered_metrics_payload:
+            if not isinstance(d, dict):
+                continue
+            slug = _slug_label(d.get("key") or d.get("name"))
+            slug = _resolve_alias(flat_alias_map, slug)
+            if (
+                not slug
+                or slug in promoted_metric_slugs
+                or slug in seen_metrics
+            ):
+                continue
+            seen_metrics.add(slug)
+            new_entry = dict(d)
+            new_entry["key"] = slug
+            kept_metrics.append(new_entry)
+        if kept_metrics:
+            metric_scores[DISCOVERED_METRICS_KEY] = kept_metrics
+        else:
+            # No survivors — drop the empty array so empty-discovery rows
+            # keep their pre-feature payload shape.
+            metric_scores.pop(DISCOVERED_METRICS_KEY, None)
+
+    return metric_scores
+
+
+def _alias_map_for_parent(
+    evaluation: CallImportEvaluation, parent_metric_id: UUID
+) -> Dict[str, str]:
+    """Pull ``{from_slug: to_slug}`` for one parent out of the eval's blob.
+
+    Stored shape on the evaluation row is
+    ``{parent_id_str: {from_slug: to_slug, ...}}``. Returns an empty
+    dict for parents that have never had a merge applied.
+    """
+    raw = getattr(evaluation, "discovered_label_aliases", None)
+    if not isinstance(raw, dict):
+        return {}
+    submap = raw.get(str(parent_metric_id))
+    if not isinstance(submap, dict):
+        return {}
+    return {
+        str(k): str(v)
+        for k, v in submap.items()
+        if isinstance(k, str) and isinstance(v, str)
+    }
+
+
+def _promoted_child_slugs(
+    db: Session, parent_metric_id: UUID, organization_id: UUID
+) -> set[str]:
+    """Slugs of every real child currently sitting under the parent.
+
+    The Discovered Labels panel hides any candidate whose slug already
+    matches a real child — that covers both freshly-promoted candidates
+    and legacy children the LLM happened to re-discover. We pull from
+    the live ``metrics`` table rather than the eval's
+    ``selected_metric_groups`` snapshot so newly-promoted children take
+    effect immediately, even on evaluations that ran before the
+    promotion.
+    """
+    children = (
+        db.query(Metric.name)
+        .filter(
+            Metric.parent_metric_id == parent_metric_id,
+            Metric.organization_id == organization_id,
+        )
+        .all()
+    )
+    out: set[str] = set()
+    for (name,) in children:
+        slug = _slug_label(name)
+        if slug:
+            out.add(slug)
+    return out
+
+
+def _promoted_top_level_metric_slugs(
+    db: Session, organization_id: UUID
+) -> set[str]:
+    """Slugs of every top-level (non-child) Metric in the organization.
+
+    Used to suppress discovered-metric candidates whose slug already
+    matches a real standalone metric. We intentionally include both
+    standalone metrics AND parent category metrics — a top-level
+    discovery that collides with either name is a duplicate by
+    definition.
+    """
+    rows = (
+        db.query(Metric.name)
+        .filter(
+            Metric.organization_id == organization_id,
+            Metric.parent_metric_id.is_(None),
+        )
+        .all()
+    )
+    out: set[str] = set()
+    for (name,) in rows:
+        slug = _slug_label(name)
+        if slug:
+            out.add(slug)
+    return out
+
+
+def _get_running_discovered_labels(
+    db: Session,
+    eval_id: UUID,
+    parent_metric_id: UUID,
+    organization_id: Optional[UUID] = None,
+    alias_map: Optional[Dict[str, str]] = None,
+) -> List[Dict[str, Any]]:
+    """Slug-deduped view of every discovered label seen in this eval so far.
+
+    Walks each ``call_import_evaluation_rows`` row's
+    ``metric_scores[parent_id]["discovered_labels"]`` and folds entries
+    that share the same slug. Returns a list ordered by descending
+    count and stable on label key, shaped like::
+
+        [{"key": "customer_on_hold", "name": "Customer put on hold",
+          "description": "...", "sample_rationale": "...", "count": 12}]
+
+    Powers two callers:
+      * The worker prompt builder ("REUSE the existing key if it fits")
+        — invoked just before each row's LLM call to feed the model the
+        running list of previously-discovered labels in this evaluation.
+      * The ``/discovered-labels`` API surface used by the frontend
+        Discovered Labels panel to render candidates with counts +
+        sample rationales.
+
+    Non-completed rows are skipped: an in-flight row's discoveries are
+    not yet reliable (the row could fail and never produce final
+    metric_scores). We accept the tradeoff that rows running
+    concurrently won't see each other's labels — slug-collision dedup
+    catches identical re-inventions, and near-paraphrases surface in
+    the UI panel where the user can manually merge.
+    """
+
+    parent_id_str = str(parent_metric_id)
+    from app.db_sharding.eval_rows import load_evaluation_rows_for_run
+
+    eval_rows = load_evaluation_rows_for_run(db, eval_id)
+    rows = [
+        (row.metric_scores,)
+        for row in eval_rows
+        if row.status == CallImportRowStatus.COMPLETED.value
+    ]
+
+    # Suppress slugs that have either:
+    #  * been promoted to a real child of the parent (so the panel doesn't
+    #    keep nagging the user about a candidate they've already
+    #    accepted), or
+    #  * been merged INTO another slug (the "from" side of a merge) —
+    #    those occurrences fold into the canonical target instead.
+    promoted_slugs: set[str] = set()
+    if organization_id is not None:
+        promoted_slugs = _promoted_child_slugs(
+            db, parent_metric_id, organization_id
+        )
+    aliases = alias_map or {}
+
+    by_key: Dict[str, Dict[str, Any]] = {}
+    for (scores,) in rows:
+        if not isinstance(scores, dict):
+            continue
+        parent_entry = scores.get(parent_id_str)
+        if not isinstance(parent_entry, dict):
+            continue
+        discovered = parent_entry.get("discovered_labels")
+        if not isinstance(discovered, list):
+            continue
+        for entry in discovered:
+            if not isinstance(entry, dict):
+                continue
+            raw_key = entry.get("key") or entry.get("name")
+            key = _slug_label(raw_key)
+            if not key:
+                continue
+            # Apply user merges + deletions first, THEN drop anything
+            # that ended up on a real child slug. Order matters: a
+            # candidate that was merged into a slug which has since
+            # been promoted should disappear, not show up at the
+            # canonical slug. An empty resolved key means the slug was
+            # tombstoned via the delete endpoint.
+            key = _resolve_alias(aliases, key)
+            if not key or key in promoted_slugs:
+                continue
+            name = (entry.get("name") or "").strip() or key.replace("_", " ")
+            description = (entry.get("description") or "").strip() or None
+            sample = (entry.get("rationale") or "").strip() or None
+
+            existing = by_key.get(key)
+            if existing is None:
+                # Track up to N=3 distinct rationales per candidate so
+                # the Promote-to-child flow can pre-fill the new
+                # sub-metric's rubric with concrete LLM examples
+                # without the user copy-pasting from the row table.
+                # ``sample_rationale`` is preserved for back-compat
+                # with older clients; ``examples`` is the new field.
+                examples = [sample] if sample else []
+                by_key[key] = {
+                    "key": key,
+                    "name": name,
+                    "description": description,
+                    "sample_rationale": sample,
+                    "examples": examples,
+                    "count": 1,
+                }
+                continue
+
+            existing["count"] += 1
+            if not existing["description"] and description:
+                existing["description"] = description
+            if not existing["sample_rationale"] and sample:
+                existing["sample_rationale"] = sample
+            # Append distinct rationales (case-insensitive trim) up
+            # to a small cap. Headroom is intentionally one above
+            # what the UI surfaces (2) so we have a backup when the
+            # first rationale is unhelpful.
+            if sample:
+                ex_list: List[str] = existing.setdefault("examples", [])
+                if len(ex_list) < 3 and not any(
+                    s.strip().lower() == sample.strip().lower() for s in ex_list
+                ):
+                    ex_list.append(sample)
+
+    return sorted(
+        by_key.values(),
+        key=lambda item: (-item["count"], item["key"]),
+    )
+
+
+def _get_running_discovered_metrics(
+    db: Session,
+    eval_id: UUID,
+    organization_id: Optional[UUID] = None,
+    alias_map: Optional[Dict[str, str]] = None,
+) -> List[Dict[str, Any]]:
+    """Slug-deduped view of every discovered top-level metric in this eval.
+
+    Mirrors :func:`_get_running_discovered_labels` but is keyed at the
+    evaluation level (no ``parent_metric_id``). Walks each completed
+    row's ``metric_scores[DISCOVERED_METRICS_KEY]`` list, folds entries
+    that share the same slug (post-alias resolution), and suppresses
+    slugs that already correspond to a real top-level :class:`Metric`
+    in the organization.
+
+    Each returned entry is shaped::
+
+        {"key": "customer_satisfaction",
+         "name": "Customer Satisfaction",
+         "description": "...",
+         "suggested_type": "boolean" | "rating" | "category",
+         "sample_rationale": "...",
+         "examples": ["..."],
+         "count": 12}
+    """
+
+    from app.db_sharding.eval_rows import load_evaluation_rows_for_run
+
+    eval_rows = load_evaluation_rows_for_run(db, eval_id)
+    rows = [
+        (row.metric_scores,)
+        for row in eval_rows
+        if row.status == CallImportRowStatus.COMPLETED.value
+    ]
+
+    promoted_slugs: set[str] = set()
+    if organization_id is not None:
+        promoted_slugs = _promoted_top_level_metric_slugs(
+            db, organization_id
+        )
+    aliases = alias_map or {}
+
+    by_key: Dict[str, Dict[str, Any]] = {}
+    for (scores,) in rows:
+        if not isinstance(scores, dict):
+            continue
+        discovered = scores.get(DISCOVERED_METRICS_KEY)
+        if not isinstance(discovered, list):
+            continue
+        for entry in discovered:
+            if not isinstance(entry, dict):
+                continue
+            raw_key = entry.get("key") or entry.get("name")
+            key = _slug_label(raw_key)
+            if not key:
+                continue
+            # Apply user merges + deletions first, THEN drop anything
+            # that ended up on an already-existing top-level metric
+            # slug. Empty resolved key = tombstoned.
+            key = _resolve_alias(aliases, key)
+            if not key or key in promoted_slugs:
+                continue
+            name = (entry.get("name") or "").strip() or key.replace(
+                "_", " "
+            )
+            description = (entry.get("description") or "").strip() or None
+            sample = (entry.get("rationale") or "").strip() or None
+            raw_type = str(entry.get("suggested_type") or "").strip().lower()
+            if raw_type not in _DISCOVERED_METRIC_TYPES:
+                raw_type = "boolean"
+
+            existing = by_key.get(key)
+            if existing is None:
+                examples = [sample] if sample else []
+                by_key[key] = {
+                    "key": key,
+                    "name": name,
+                    "description": description,
+                    "suggested_type": raw_type,
+                    "sample_rationale": sample,
+                    "examples": examples,
+                    "count": 1,
+                }
+                continue
+
+            existing["count"] += 1
+            if not existing["description"] and description:
+                existing["description"] = description
+            if not existing["sample_rationale"] and sample:
+                existing["sample_rationale"] = sample
+            # Keep the most-frequently-suggested type. We don't track
+            # per-type frequency yet; defer to the first non-default
+            # type encountered when the existing entry has the default.
+            if existing.get("suggested_type") == "boolean" and raw_type != "boolean":
+                existing["suggested_type"] = raw_type
+            if sample:
+                ex_list: List[str] = existing.setdefault("examples", [])
+                if len(ex_list) < 3 and not any(
+                    s.strip().lower() == sample.strip().lower() for s in ex_list
+                ):
+                    ex_list.append(sample)
+
+    return sorted(
+        by_key.values(),
+        key=lambda item: (-item["count"], item["key"]),
+    )
+
+
+def _build_flow_graph(
+    eval_rows: List[CallImportEvaluationRow],
+    parent_metric: Metric,
+    children: List[Metric],
+    alias_map: Optional[Dict[str, str]] = None,
+    extra_children: Optional[List[Metric]] = None,
+) -> MetricFlowResponse:
+    """Walk per-row ``sequence`` arrays and produce aggregate nodes/edges.
+
+    A synthetic ``START`` node is prepended to every sequence so the
+    diagram has a single origin. Children that never appear in any
+    sequence are still emitted as nodes (count=0) so the UI can render
+    them in the legend.
+
+    ``alias_map`` lets callers fold merged-out discovered slugs into
+    their canonical target before building the graph; ``extra_children``
+    are children of the parent that aren't in the legend list (e.g.
+    children promoted *after* the evaluation was created and therefore
+    missing from ``selected_metric_groups``) but should still resolve in
+    sequences so the slug doesn't get redrawn as a discovered candidate.
+    """
+    parent_id_str = str(parent_metric.id)
+    aliases = alias_map or {}
+    # Build a fast lookup keyed by both the lower_snake child key (what the
+    # LLM emits in ``sequence``) and the child UUID (what some clients may
+    # store) so legacy / drifted payloads still resolve.
+    child_lookup: Dict[str, Metric] = {}
+    for child in children:
+        slug = _slug_label(child.name)
+        child_lookup[slug] = child
+        child_lookup[str(child.id)] = child
+    # ``extra_children`` are resolved-only — they shouldn't add legend
+    # nodes (those come from the explicit ``children`` argument), but
+    # they need to be in ``child_lookup`` so a sequence step that
+    # matches a freshly-promoted child resolves to the real child UUID
+    # instead of falling through to ``discovered_lookup`` and rendering
+    # as a "discovered" node.
+    if extra_children:
+        for child in extra_children:
+            slug = _slug_label(child.name)
+            if slug and slug not in child_lookup:
+                child_lookup[slug] = child
+            cid = str(child.id)
+            child_lookup.setdefault(cid, child)
+
+    # Discovered labels: walk every row's discovered_labels first so we
+    # know which discovered slugs are valid before resolving sequences.
+    # Discovered nodes get a ``disc:`` prefixed id so they can't collide
+    # with real child UUIDs in the node/edge graph. We apply
+    # ``alias_map`` first so merged-out source slugs fold into their
+    # canonical target — preserving the user's "merge" intent on still-
+    # in-flight rows whose JSON wasn't rewritten by the merge endpoint.
+    discovered_lookup: Dict[str, Dict[str, Any]] = {}
+    for row in eval_rows:
+        scores = (
+            row.metric_scores if isinstance(row.metric_scores, dict) else {}
+        )
+        parent_entry = scores.get(parent_id_str)
+        if not isinstance(parent_entry, dict):
+            continue
+        raw_discovered = parent_entry.get("discovered_labels")
+        if not isinstance(raw_discovered, list):
+            continue
+        for entry in raw_discovered:
+            if not isinstance(entry, dict):
+                continue
+            slug = _slug_label(entry.get("key") or entry.get("name"))
+            slug = _resolve_alias(aliases, slug)
+            if not slug or slug in child_lookup:
+                continue
+            name = (entry.get("name") or "").strip() or slug.replace("_", " ")
+            existing = discovered_lookup.get(slug)
+            if existing is None:
+                discovered_lookup[slug] = {
+                    "id": f"{_DISCOVERED_NODE_PREFIX}{slug}",
+                    "name": name,
+                }
+
+    node_counts: Dict[str, int] = {}
+    edge_counts: Dict[tuple[str, str], int] = {}
+    terminal_counts: Dict[str, int] = {}
+
+    total_rows = len(eval_rows)
+    rows_with_sequence = 0
+
+    for row in eval_rows:
+        scores = (
+            row.metric_scores if isinstance(row.metric_scores, dict) else {}
+        )
+        parent_entry = scores.get(parent_id_str)
+        if not isinstance(parent_entry, dict):
+            continue
+        raw_sequence = parent_entry.get("sequence")
+        if not isinstance(raw_sequence, list):
+            continue
+
+        resolved_ids: List[str] = []
+        last_resolved: Optional[str] = None
+        for item in raw_sequence:
+            if not isinstance(item, str):
+                continue
+            normalized = _resolve_alias(aliases, _slug_label(item))
+            child = child_lookup.get(normalized) or child_lookup.get(item)
+            if child is not None:
+                cid = str(child.id)
+                # Adjacent dedupe AFTER alias resolution so two
+                # different raw slugs that fold to the same target
+                # don't draw a self-edge through the chart.
+                if cid == last_resolved:
+                    continue
+                resolved_ids.append(cid)
+                last_resolved = cid
+                continue
+            disc = discovered_lookup.get(normalized)
+            if disc is not None:
+                if disc["id"] == last_resolved:
+                    continue
+                resolved_ids.append(disc["id"])
+                last_resolved = disc["id"]
+
+        if not resolved_ids:
+            continue
+
+        rows_with_sequence += 1
+        for nid in resolved_ids:
+            node_counts[nid] = node_counts.get(nid, 0) + 1
+
+        edge_counts[(_FLOW_START_NODE_ID, resolved_ids[0])] = (
+            edge_counts.get((_FLOW_START_NODE_ID, resolved_ids[0]), 0) + 1
+        )
+        for src, tgt in zip(resolved_ids, resolved_ids[1:]):
+            if src == tgt:
+                continue
+            edge_counts[(src, tgt)] = edge_counts.get((src, tgt), 0) + 1
+
+        terminal_id = resolved_ids[-1]
+        terminal_counts[terminal_id] = terminal_counts.get(terminal_id, 0) + 1
+
+    nodes: List[MetricFlowNode] = []
+    # Always include a START node so the UI has a stable entry point.
+    nodes.append(
+        MetricFlowNode(
+            id=_FLOW_START_NODE_ID,
+            label="Start",
+            count=rows_with_sequence,
+            is_terminal=False,
+        )
+    )
+
+    def _emit_child_node(child: Metric) -> None:
+        cid = str(child.id)
+        count = node_counts.get(cid, 0)
+        terminal_count = terminal_counts.get(cid, 0)
+        is_terminal = False
+        if rows_with_sequence > 0:
+            is_terminal = (
+                terminal_count / rows_with_sequence
+            ) >= _FLOW_TERMINAL_THRESHOLD
+        nodes.append(
+            MetricFlowNode(
+                id=cid,
+                label=child.name,
+                count=count,
+                is_terminal=is_terminal,
+            )
+        )
+
+    emitted_child_ids: set[str] = set()
+    for child in children:
+        cid = str(child.id)
+        if cid in emitted_child_ids:
+            continue
+        emitted_child_ids.add(cid)
+        _emit_child_node(child)
+    # Extra children (promoted after the eval was created) only get
+    # legend nodes if they actually appear in the data — otherwise we'd
+    # pollute the diagram with every standalone promotion the user has
+    # ever made under this parent.
+    if extra_children:
+        for child in extra_children:
+            cid = str(child.id)
+            if cid in emitted_child_ids:
+                continue
+            if node_counts.get(cid, 0) == 0:
+                continue
+            emitted_child_ids.add(cid)
+            _emit_child_node(child)
+    # Append discovered nodes after the real children so legend ordering
+    # keeps user-defined labels first.
+    for slug, info in discovered_lookup.items():
+        nid = info["id"]
+        count = node_counts.get(nid, 0)
+        terminal_count = terminal_counts.get(nid, 0)
+        is_terminal = False
+        if rows_with_sequence > 0:
+            is_terminal = (
+                terminal_count / rows_with_sequence
+            ) >= _FLOW_TERMINAL_THRESHOLD
+        nodes.append(
+            MetricFlowNode(
+                id=nid,
+                label=info["name"],
+                count=count,
+                is_terminal=is_terminal,
+                is_discovered=True,
+            )
+        )
+
+    edges: List[MetricFlowEdge] = [
+        MetricFlowEdge(source=src, target=tgt, count=count)
+        for (src, tgt), count in sorted(
+            edge_counts.items(), key=lambda kv: kv[1], reverse=True
+        )
+    ]
+
+    return MetricFlowResponse(
+        parent_metric_id=parent_id_str,
+        parent_metric_name=parent_metric.name,
+        selection_mode=parent_metric.selection_mode,
+        nodes=nodes,
+        edges=edges,
+        total_rows=total_rows,
+        rows_with_sequence=rows_with_sequence,
+    )
+
+
+@router.get(
+    "/{eval_id}/flow",
+    response_model=MetricFlowResponse,
+    operation_id="getCallImportEvaluationFlow",
+)
+async def get_call_import_evaluation_flow(
+    call_import_id: UUID,
+    eval_id: UUID,
+    parent_metric_id: UUID = Query(
+        ...,
+        description=(
+            "Parent (category) metric whose children's sequences should be "
+            "aggregated into a flow graph."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> MetricFlowResponse:
+    """Aggregate the LLM-inferred per-row sequences into one flow graph.
+
+    Returns ``nodes`` (one per child of the parent metric, plus a
+    synthetic ``START`` node) and ``edges`` (counts of consecutive
+    label transitions across every row that produced a sequence). The
+    frontend feeds this directly into a React Flow / xyflow canvas;
+    edge thickness should scale with ``count / total_rows`` and
+    ``is_terminal`` nodes should be styled as outcomes.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    parent = (
+        db.query(Metric)
+        .filter(
+            Metric.id == parent_metric_id,
+            Metric.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not parent:
+        raise HTTPException(
+            status_code=404,
+            detail="Parent metric not found in this organization.",
+        )
+    if not parent.selection_mode:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                "Flow charts are only meaningful for parent metrics "
+                "(selection_mode set). This metric is standalone."
+            ),
+        )
+
+    # Children are taken from selected_metric_groups when present so the
+    # flow chart reflects exactly the subset that ran in this
+    # evaluation; otherwise fall back to every enabled child of the
+    # parent.
+    groups_raw = (
+        evaluation.selected_metric_groups
+        if isinstance(evaluation.selected_metric_groups, dict)
+        else {}
+    )
+    parent_id_str = str(parent.id)
+    children: List[Metric] = []
+    if parent_id_str in groups_raw and isinstance(
+        groups_raw[parent_id_str], list
+    ):
+        child_ids: List[UUID] = []
+        for c in groups_raw[parent_id_str]:
+            try:
+                child_ids.append(UUID(str(c)))
+            except (TypeError, ValueError):
+                continue
+        if child_ids:
+            children = (
+                db.query(Metric)
+                .filter(
+                    Metric.organization_id == organization_id,
+                    Metric.id.in_(child_ids),
+                )
+                .order_by(Metric.created_at.asc())
+                .all()
+            )
+    if not children:
+        children = (
+            db.query(Metric)
+            .filter(
+                Metric.organization_id == organization_id,
+                Metric.parent_metric_id == parent.id,
+            )
+            .order_by(Metric.created_at.asc())
+            .all()
+        )
+
+    # Children promoted AFTER this evaluation was created aren't in
+    # ``selected_metric_groups`` but their slugs still appear in already-
+    # scored rows' sequences. Pass them as ``extra_children`` so those
+    # sequence entries resolve against the real (now promoted) child
+    # instead of being redrawn as discovered candidates.
+    extra_children: List[Metric] = []
+    if children:
+        existing_ids = {child.id for child in children}
+        all_children = (
+            db.query(Metric)
+            .filter(
+                Metric.organization_id == organization_id,
+                Metric.parent_metric_id == parent.id,
+            )
+            .all()
+        )
+        extra_children = [c for c in all_children if c.id not in existing_ids]
+
+    eval_rows = _load_eval_rows(db, eval_id)
+
+    alias_map = _alias_map_for_parent(evaluation, parent.id)
+    return _build_flow_graph(
+        eval_rows,
+        parent,
+        children,
+        alias_map=alias_map,
+        extra_children=extra_children,
+    )
+
+
+@router.get(
+    "/{eval_id}/discovered-labels",
+    response_model=DiscoveredLabelsResponse,
+    operation_id="getCallImportEvaluationDiscoveredLabels",
+)
+async def get_call_import_evaluation_discovered_labels(
+    call_import_id: UUID,
+    eval_id: UUID,
+    parent_metric_id: UUID = Query(
+        ...,
+        description=(
+            "Parent (category) metric whose LLM-discovered candidate "
+            "sub-labels should be aggregated across rows."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> DiscoveredLabelsResponse:
+    """Aggregate candidate sub-labels the LLM discovered during this eval.
+
+    Only meaningful for parents with ``allow_discovery=true``; for other
+    parents we just return an empty ``items`` list rather than 400-ing
+    so the frontend can call the endpoint unconditionally for every
+    parent on the Flow tab without branching.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    parent = (
+        db.query(Metric)
+        .filter(
+            Metric.id == parent_metric_id,
+            Metric.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not parent:
+        raise HTTPException(
+            status_code=404,
+            detail="Parent metric not found in this organization.",
+        )
+
+    alias_map = _alias_map_for_parent(evaluation, parent_metric_id)
+    items_raw = _get_running_discovered_labels(
+        db,
+        eval_id,
+        parent_metric_id,
+        organization_id=organization_id,
+        alias_map=alias_map,
+    )
+    items = [DiscoveredLabelItem(**item) for item in items_raw]
+    return DiscoveredLabelsResponse(
+        parent_metric_id=str(parent.id), items=items
+    )
+
+
+@router.post(
+    "/{eval_id}/discovered-labels/merge",
+    response_model=DiscoveredLabelsResponse,
+    operation_id="mergeCallImportEvaluationDiscoveredLabels",
+)
+async def merge_call_import_evaluation_discovered_labels(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: DiscoveredLabelMergeRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> DiscoveredLabelsResponse:
+    """Rewrite every row's ``discovered_labels`` entry from from_key -> to_key.
+
+    Idempotent — re-merging the same pair is a no-op. Discovered slugs
+    inside per-row ``sequence`` arrays are also rewritten so the flow
+    chart stays consistent with the panel. When a row already has
+    ``to_key`` and we're merging ``from_key`` into it, we drop the
+    ``from_key`` entry instead of producing two entries with the same
+    slug.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    parent = (
+        db.query(Metric)
+        .filter(
+            Metric.id == body.parent_metric_id,
+            Metric.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not parent:
+        raise HTTPException(
+            status_code=404,
+            detail="Parent metric not found in this organization.",
+        )
+
+    from_key = _slug_label(body.from_key)
+    to_key = _slug_label(body.to_key)
+    if not from_key or not to_key:
+        raise HTTPException(
+            status_code=400,
+            detail="from_key and to_key must be non-empty slugs.",
+        )
+    if from_key == to_key:
+        # No-op; just return the current aggregate so the client can
+        # refresh its view.
+        alias_map_existing = _alias_map_for_parent(evaluation, parent.id)
+        items_raw = _get_running_discovered_labels(
+            db,
+            eval_id,
+            body.parent_metric_id,
+            organization_id=organization_id,
+            alias_map=alias_map_existing,
+        )
+        return DiscoveredLabelsResponse(
+            parent_metric_id=str(parent.id),
+            items=[DiscoveredLabelItem(**item) for item in items_raw],
+        )
+
+    parent_id_str = str(parent.id)
+    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
+
+    def _merge_discovered_label_row(row: CallImportEvaluationRow) -> bool:
+        scores = (
+            row.metric_scores
+            if isinstance(row.metric_scores, dict)
+            else None
+        )
+        if not scores:
+            return False
+        parent_entry = scores.get(parent_id_str)
+        if not isinstance(parent_entry, dict):
+            return False
+
+        mutated = False
+        discovered = parent_entry.get("discovered_labels")
+        if isinstance(discovered, list):
+            kept: List[Dict[str, Any]] = []
+            existing_to = next(
+                (
+                    e
+                    for e in discovered
+                    if isinstance(e, dict)
+                    and _slug_label(e.get("key") or e.get("name")) == to_key
+                ),
+                None,
+            )
+            for entry in discovered:
+                if not isinstance(entry, dict):
+                    kept.append(entry)
+                    continue
+                key = _slug_label(entry.get("key") or entry.get("name"))
+                if key == from_key:
+                    if existing_to is not None:
+                        mutated = True
+                        continue
+                    new_entry = dict(entry)
+                    new_entry["key"] = to_key
+                    kept.append(new_entry)
+                    mutated = True
+                else:
+                    kept.append(entry)
+            if mutated:
+                parent_entry["discovered_labels"] = kept
+
+        seq = parent_entry.get("sequence")
+        if isinstance(seq, list):
+            new_seq: List[str] = []
+            seq_changed = False
+            last_added: Optional[str] = None
+            for item in seq:
+                if isinstance(item, str) and _slug_label(item) == from_key:
+                    seq_changed = True
+                    if last_added == to_key:
+                        continue
+                    new_seq.append(to_key)
+                    last_added = to_key
+                else:
+                    new_seq.append(item)
+                    last_added = (
+                        _slug_label(item) if isinstance(item, str) else None
+                    )
+            if seq_changed:
+                parent_entry["sequence"] = new_seq
+                mutated = True
+
+        if mutated:
+            row.metric_scores = dict(scores)
+        return mutated
+
+    foreach_evaluation_row_mutating(db, eval_id, _merge_discovered_label_row)
+
+    # Persist the merge at the evaluation level too. This is what makes
+    # the merge survive future scoring: rows that finish AFTER this
+    # call (e.g. retries, in-flight workers) will go through the
+    # alias map in the API surface even if the per-row JSON they
+    # write still mentions ``from_key``. We chain through any existing
+    # alias so merging A→B and then B→C resolves A→C in the panel.
+    raw_aliases = (
+        evaluation.discovered_label_aliases
+        if isinstance(evaluation.discovered_label_aliases, dict)
+        else {}
+    )
+    aliases_top = dict(raw_aliases)
+    parent_aliases = dict(aliases_top.get(parent_id_str) or {})
+    # Resolve transitively: if to_key itself was previously merged into
+    # something else, point from_key at the canonical end-of-chain.
+    canonical_to = _resolve_alias(parent_aliases, to_key)
+    parent_aliases[from_key] = canonical_to
+    # Re-target any earlier aliases that pointed AT from_key — without
+    # this, A→B and then B→C would leave A still pointing to B (now a
+    # broken pointer because B is gone). Rewriting them keeps the
+    # alias map self-consistent.
+    for k, v in list(parent_aliases.items()):
+        if v == from_key:
+            parent_aliases[k] = canonical_to
+    aliases_top[parent_id_str] = parent_aliases
+    evaluation.discovered_label_aliases = aliases_top
+
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    alias_map_after = _alias_map_for_parent(evaluation, parent.id)
+    items_raw = _get_running_discovered_labels(
+        db,
+        eval_id,
+        body.parent_metric_id,
+        organization_id=organization_id,
+        alias_map=alias_map_after,
+    )
+    return DiscoveredLabelsResponse(
+        parent_metric_id=str(parent.id),
+        items=[DiscoveredLabelItem(**item) for item in items_raw],
+    )
+
+
+@router.post(
+    "/{eval_id}/discovered-labels/delete",
+    response_model=DiscoveredLabelsResponse,
+    operation_id="deleteCallImportEvaluationDiscoveredLabel",
+)
+async def delete_call_import_evaluation_discovered_label(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: DiscoveredLabelDeleteRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> DiscoveredLabelsResponse:
+    """Tombstone a single LLM-discovered candidate for this evaluation.
+
+    Symmetric with the merge endpoint, but instead of redirecting the
+    slug at another candidate we mark it as deleted. After this call:
+
+      * the slug is stripped from every row's
+        ``metric_scores[parent].discovered_labels`` list, and from
+        every row's ``sequence`` array (so the flow chart no longer
+        draws a node for it);
+      * the slug is recorded in
+        ``evaluation.discovered_label_aliases[parent][slug] = ""``
+        so any worker that finishes a row AFTER this call (e.g. a row
+        still in flight when the user clicked Delete) silently drops
+        the slug instead of resurrecting it.
+
+    Idempotent: deleting an already-deleted slug is a no-op.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    parent = (
+        db.query(Metric)
+        .filter(
+            Metric.id == body.parent_metric_id,
+            Metric.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not parent:
+        raise HTTPException(
+            status_code=404,
+            detail="Parent metric not found in this organization.",
+        )
+
+    target_key = _slug_label(body.key)
+    if not target_key:
+        raise HTTPException(
+            status_code=400,
+            detail="key must be a non-empty slug.",
+        )
+
+    parent_id_str = str(parent.id)
+    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
+
+    def _delete_discovered_label_row(row: CallImportEvaluationRow) -> bool:
+        scores = (
+            row.metric_scores
+            if isinstance(row.metric_scores, dict)
+            else None
+        )
+        if not scores:
+            return False
+        parent_entry = scores.get(parent_id_str)
+        if not isinstance(parent_entry, dict):
+            return False
+
+        mutated = False
+        discovered = parent_entry.get("discovered_labels")
+        if isinstance(discovered, list):
+            kept = [
+                e
+                for e in discovered
+                if not (
+                    isinstance(e, dict)
+                    and _slug_label(e.get("key") or e.get("name"))
+                    == target_key
+                )
+            ]
+            if len(kept) != len(discovered):
+                parent_entry["discovered_labels"] = kept
+                mutated = True
+
+        seq = parent_entry.get("sequence")
+        if isinstance(seq, list):
+            new_seq: List[str] = []
+            seq_changed = False
+            last_added: Optional[str] = None
+            for item in seq:
+                if isinstance(item, str) and _slug_label(item) == target_key:
+                    seq_changed = True
+                    continue
+                if isinstance(item, str):
+                    norm = _slug_label(item)
+                    if norm == last_added:
+                        seq_changed = True
+                        continue
+                    last_added = norm
+                new_seq.append(item)
+            if seq_changed:
+                parent_entry["sequence"] = new_seq
+                mutated = True
+
+        if mutated:
+            row.metric_scores = dict(scores)
+        return mutated
+
+    foreach_evaluation_row_mutating(db, eval_id, _delete_discovered_label_row)
+
+    # 3. Persist the tombstone on the evaluation so workers that finish
+    # later don't re-surface the deleted slug. We also retarget any
+    # existing aliases whose ``to_key`` was the deleted slug — without
+    # this, a previous merge that pointed at this slug would leave a
+    # dangling pointer.
+    raw_aliases = (
+        evaluation.discovered_label_aliases
+        if isinstance(evaluation.discovered_label_aliases, dict)
+        else {}
+    )
+    aliases_top = dict(raw_aliases)
+    parent_aliases = dict(aliases_top.get(parent_id_str) or {})
+    parent_aliases[target_key] = ""  # deletion sentinel
+    for k, v in list(parent_aliases.items()):
+        if v == target_key:
+            parent_aliases[k] = ""
+    aliases_top[parent_id_str] = parent_aliases
+    evaluation.discovered_label_aliases = aliases_top
+
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    alias_map_after = _alias_map_for_parent(evaluation, parent.id)
+    items_raw = _get_running_discovered_labels(
+        db,
+        eval_id,
+        body.parent_metric_id,
+        organization_id=organization_id,
+        alias_map=alias_map_after,
+    )
+    return DiscoveredLabelsResponse(
+        parent_metric_id=str(parent.id),
+        items=[DiscoveredLabelItem(**item) for item in items_raw],
+    )
+
+
+# ---------------------------------------------------------------------------
+# Discovered TOP-LEVEL METRICS (per-evaluation discovery)
+#
+# These endpoints are the parallel of the discovered-labels trio above but
+# scoped to the evaluation as a whole instead of to a parent metric. They
+# all live under ``/{eval_id}/discovered-metrics`` and operate on the
+# reserved ``DISCOVERED_METRICS_KEY`` slot of each per-row
+# ``metric_scores`` plus the flat ``CallImportEvaluation.discovered_metric_aliases``
+# map (no parent-id nesting).
+# ---------------------------------------------------------------------------
+
+
+def _flat_metric_aliases(
+    evaluation: CallImportEvaluation,
+) -> Dict[str, str]:
+    """Pull the flat ``{from_slug: to_slug}`` map for an evaluation."""
+    raw = getattr(evaluation, "discovered_metric_aliases", None)
+    if not isinstance(raw, dict):
+        return {}
+    return {
+        str(k): str(v)
+        for k, v in raw.items()
+        if isinstance(k, str) and isinstance(v, str)
+    }
+
+
+@router.get(
+    "/{eval_id}/discovered-metrics",
+    response_model=DiscoveredMetricsResponse,
+    operation_id="getCallImportEvaluationDiscoveredMetrics",
+)
+async def get_call_import_evaluation_discovered_metrics(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> DiscoveredMetricsResponse:
+    """Aggregate top-level metric candidates the LLM discovered during this eval.
+
+    Returns an empty ``items`` list when the evaluation did not opt
+    into top-level metric discovery; this keeps the frontend able to
+    call the endpoint unconditionally without branching on the
+    evaluation's ``discover_new_metrics`` flag.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    if not bool(getattr(evaluation, "discover_new_metrics", False)):
+        return DiscoveredMetricsResponse(evaluation_id=evaluation.id, items=[])
+
+    items_raw = _get_running_discovered_metrics(
+        db,
+        eval_id,
+        organization_id=organization_id,
+        alias_map=_flat_metric_aliases(evaluation),
+    )
+    return DiscoveredMetricsResponse(
+        evaluation_id=evaluation.id,
+        items=[DiscoveredMetricItem(**item) for item in items_raw],
+    )
+
+
+@router.post(
+    "/{eval_id}/discovered-metrics/merge",
+    response_model=DiscoveredMetricsResponse,
+    operation_id="mergeCallImportEvaluationDiscoveredMetrics",
+)
+async def merge_call_import_evaluation_discovered_metrics(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: DiscoveredMetricMergeRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> DiscoveredMetricsResponse:
+    """Rewrite every row's ``__discovered_metrics__`` entry from→to.
+
+    Mirrors the discovered-labels merge endpoint but operates on the
+    flat top-level metric list. Idempotent — re-merging is a no-op.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    from_key = _slug_label(body.from_key)
+    to_key = _slug_label(body.to_key)
+    if not from_key or not to_key:
+        raise HTTPException(
+            status_code=400,
+            detail="from_key and to_key must be non-empty slugs.",
+        )
+    if from_key == to_key:
+        items_raw = _get_running_discovered_metrics(
+            db,
+            eval_id,
+            organization_id=organization_id,
+            alias_map=_flat_metric_aliases(evaluation),
+        )
+        return DiscoveredMetricsResponse(
+            evaluation_id=evaluation.id,
+            items=[DiscoveredMetricItem(**item) for item in items_raw],
+        )
+
+    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
+
+    def _merge_discovered_metric_row(row: CallImportEvaluationRow) -> bool:
+        scores = (
+            row.metric_scores
+            if isinstance(row.metric_scores, dict)
+            else None
+        )
+        if not scores:
+            return False
+        discovered = scores.get(DISCOVERED_METRICS_KEY)
+        if not isinstance(discovered, list):
+            return False
+
+        kept: List[Dict[str, Any]] = []
+        mutated = False
+        existing_to = next(
+            (
+                e
+                for e in discovered
+                if isinstance(e, dict)
+                and _slug_label(e.get("key") or e.get("name")) == to_key
+            ),
+            None,
+        )
+        for entry in discovered:
+            if not isinstance(entry, dict):
+                kept.append(entry)
+                continue
+            key = _slug_label(entry.get("key") or entry.get("name"))
+            if key == from_key:
+                if existing_to is not None:
+                    mutated = True
+                    continue
+                new_entry = dict(entry)
+                new_entry["key"] = to_key
+                kept.append(new_entry)
+                mutated = True
+            else:
+                kept.append(entry)
+        if mutated:
+            scores[DISCOVERED_METRICS_KEY] = kept
+            row.metric_scores = dict(scores)
+        return mutated
+
+    foreach_evaluation_row_mutating(db, eval_id, _merge_discovered_metric_row)
+
+    raw_aliases = (
+        evaluation.discovered_metric_aliases
+        if isinstance(evaluation.discovered_metric_aliases, dict)
+        else {}
+    )
+    aliases = dict(raw_aliases)
+    canonical_to = _resolve_alias(aliases, to_key)
+    aliases[from_key] = canonical_to
+    for k, v in list(aliases.items()):
+        if v == from_key:
+            aliases[k] = canonical_to
+    evaluation.discovered_metric_aliases = aliases
+
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    items_raw = _get_running_discovered_metrics(
+        db,
+        eval_id,
+        organization_id=organization_id,
+        alias_map=_flat_metric_aliases(evaluation),
+    )
+    return DiscoveredMetricsResponse(
+        evaluation_id=evaluation.id,
+        items=[DiscoveredMetricItem(**item) for item in items_raw],
+    )
+
+
+@router.post(
+    "/{eval_id}/discovered-metrics/delete",
+    response_model=DiscoveredMetricsResponse,
+    operation_id="deleteCallImportEvaluationDiscoveredMetric",
+)
+async def delete_call_import_evaluation_discovered_metric(
+    call_import_id: UUID,
+    eval_id: UUID,
+    body: DiscoveredMetricDeleteRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> DiscoveredMetricsResponse:
+    """Tombstone a single LLM-discovered top-level metric candidate."""
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    target_key = _slug_label(body.key)
+    if not target_key:
+        raise HTTPException(
+            status_code=400,
+            detail="key must be a non-empty slug.",
+        )
+
+    from app.db_sharding.eval_rows import foreach_evaluation_row_mutating
+
+    def _delete_discovered_metric_row(row: CallImportEvaluationRow) -> bool:
+        scores = (
+            row.metric_scores
+            if isinstance(row.metric_scores, dict)
+            else None
+        )
+        if not scores:
+            return False
+        discovered = scores.get(DISCOVERED_METRICS_KEY)
+        if not isinstance(discovered, list):
+            return False
+        kept = [
+            e
+            for e in discovered
+            if not (
+                isinstance(e, dict)
+                and _slug_label(e.get("key") or e.get("name"))
+                == target_key
+            )
+        ]
+        if len(kept) == len(discovered):
+            return False
+        if kept:
+            scores[DISCOVERED_METRICS_KEY] = kept
+        else:
+            scores.pop(DISCOVERED_METRICS_KEY, None)
+        row.metric_scores = dict(scores)
+        return True
+
+    foreach_evaluation_row_mutating(db, eval_id, _delete_discovered_metric_row)
+
+    raw_aliases = (
+        evaluation.discovered_metric_aliases
+        if isinstance(evaluation.discovered_metric_aliases, dict)
+        else {}
+    )
+    aliases = dict(raw_aliases)
+    aliases[target_key] = ""  # tombstone
+    for k, v in list(aliases.items()):
+        if v == target_key:
+            aliases[k] = ""
+    evaluation.discovered_metric_aliases = aliases
+
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    items_raw = _get_running_discovered_metrics(
+        db,
+        eval_id,
+        organization_id=organization_id,
+        alias_map=_flat_metric_aliases(evaluation),
+    )
+    return DiscoveredMetricsResponse(
+        evaluation_id=evaluation.id,
+        items=[DiscoveredMetricItem(**item) for item in items_raw],
+    )
+
+
+@router.delete(
+    "/{eval_id}/rows/{eval_row_id}",
+    status_code=status.HTTP_204_NO_CONTENT,
+    operation_id="deleteCallImportEvaluationRow",
+)
+async def delete_call_import_evaluation_row(
+    call_import_id: UUID,
+    eval_id: UUID,
+    eval_row_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> Response:
+    """Delete a single per-row scoring entry within an evaluation run.
+
+    Useful when the user wants to drop a noisy row before re-exporting
+    the CSV — e.g. a row whose audio was corrupt and skewed the
+    aggregate. Counters on the parent are recomputed so the rolled-up
+    status stays accurate.
+    """
+
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    if is_sharding_enabled():
+        from app.db_sharding.eval_rows import delete_evaluation_row_on_shards
+
+        if not delete_evaluation_row_on_shards(eval_row_id, eval_id):
+            raise HTTPException(
+                status_code=404, detail="Evaluation row not found in this run"
+            )
+        _rollup_evaluation_status(evaluation, db)
+        db.commit()
+        return Response(status_code=status.HTTP_204_NO_CONTENT)
+
+    eval_row = (
+        db.query(CallImportEvaluationRow)
+        .filter(
+            CallImportEvaluationRow.id == eval_row_id,
+            CallImportEvaluationRow.evaluation_id == eval_id,
+        )
+        .first()
+    )
+    if not eval_row:
+        raise HTTPException(
+            status_code=404, detail="Evaluation row not found in this run"
+        )
+
+    # If the row was still in flight, best-effort revoke the worker task
+    # so it doesn't try to write into a deleted DB row mid-execution.
+    if eval_row.celery_task_id and eval_row.status in {"pending", "running"}:
+        try:
+            from app.workers.celery_app import celery_app
+
+            celery_app.control.revoke(eval_row.celery_task_id, terminate=False)
+        except Exception:
+            pass
+
+    db.delete(eval_row)
+    db.flush()
+    _rollup_evaluation_status(evaluation, db)
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+    return Response(status_code=status.HTTP_204_NO_CONTENT)
+
+
+# ---------------------------------------------------------------------------
+# Retry endpoints
+# ---------------------------------------------------------------------------
+#
+# The create endpoint enqueues every row of a fresh run; these endpoints
+# let the user re-enqueue a *subset* of rows in an existing run — most
+# commonly the ones that failed. We keep the worker contract identical
+# (``evaluate_call_import_row_task(eval_row_id)``), so the retry path
+# only has to reset row state and re-fan-out. When a row is missing its
+# diarised transcript and the run was configured for diarised
+# transcripts, we chain through ``transcribe_call_import_row_task`` the
+# same way the create endpoint does — that's what makes "retry" feel
+# like "just fix it" instead of "fail again immediately".
+
+
+def _prepare_source_row_for_retry(
+    source_row: CallImportRow,
+    *,
+    transcribe_overwrite: bool,
+) -> None:
+    """Clear stale diarisation markers so retry dispatch can re-run the pipeline."""
+    source_row.celery_task_id = None
+
+    # Re-fetch recordings when a prior import failed or stalled without S3 audio.
+    # Mirrors retry_failed_call_import_rows so eval retry can re-enqueue imports.
+    if (
+        source_row.status
+        in (CallImportRowStatus.FAILED, CallImportRowStatus.PROCESSING)
+        and not (source_row.recording_s3_key or "").strip()
+    ):
+        source_row.status = CallImportRowStatus.PENDING
+        source_row.error_message = None
+
+    if transcribe_overwrite and (source_row.diarised_transcript or "").strip():
+        source_row.diarised_transcript = None
+
+    has_dia = bool((source_row.diarised_transcript or "").strip())
+    dia_status = (source_row.diarised_transcript_status or "").strip().lower()
+
+    if has_dia and not transcribe_overwrite:
+        source_row.diarised_transcript_status = "completed"
+        source_row.diarised_transcript_error = None
+        return
+
+    if dia_status in {"failed", "pending", "running", "idle"}:
+        source_row.diarised_transcript_status = "idle"
+        source_row.diarised_transcript_error = None
+
+
+def _reset_eval_row_for_retry(
+    eval_row: CallImportEvaluationRow,
+    *,
+    metric_ids: Optional[List[UUID]] = None,
+    skip_revoke: bool = False,
+) -> None:
+    """Wipe per-row state so the worker can re-run it cleanly.
+
+    Mirrors the initial state used by ``create_call_import_evaluation``
+    when it first inserts a row, with the addition of revoking any
+    lingering Celery task id.
+
+    When ``metric_ids`` is provided, this is a **metric-subset retry**:
+    only the scores for those metrics are removed from
+    ``metric_scores`` (other metrics' previously-computed values are
+    preserved so the worker's partial-merge write keeps them intact).
+    Otherwise the entire ``metric_scores`` dict is reset, matching the
+    legacy behaviour.
+    """
+    if (
+        not skip_revoke
+        and eval_row.celery_task_id
+        and eval_row.status in {"pending", "running"}
+    ):
+        try:
+            from app.workers.celery_app import celery_app
+
+            celery_app.control.revoke(eval_row.celery_task_id, terminate=False)
+        except Exception:  # noqa: BLE001 — revoke is best-effort
+            pass
+    eval_row.status = "pending"
+    eval_row.error_message = None
+    if metric_ids:
+        # Strip ONLY the targeted metric keys. Both string and UUID
+        # forms can appear in ``metric_scores`` depending on which
+        # code path wrote the dict, so we normalise to lower-case
+        # strings for the comparison.
+        existing = (
+            eval_row.metric_scores if isinstance(eval_row.metric_scores, dict) else {}
+        )
+        target_keys = {str(mid).lower() for mid in metric_ids}
+        eval_row.metric_scores = {
+            key: value
+            for key, value in existing.items()
+            if str(key).lower() not in target_keys
+        }
+    else:
+        eval_row.metric_scores = {}
+    eval_row.started_at = None
+    eval_row.finished_at = None
+    eval_row.celery_task_id = None
+
+
+def _enqueue_eval_rows_with_optional_transcribe(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    eval_rows_with_source: List[
+        Tuple[CallImportEvaluationRow, CallImportRow]
+    ],
+    *,
+    transcribe_overwrite: bool = False,
+    restricted_metric_ids: Optional[List[UUID]] = None,
+) -> Tuple[int, int]:
+    """Schedule throttled evaluation dispatch for pending eval rows.
+
+    Returns ``(evaluate_only_count, transcribe_then_evaluate_count)`` for
+    logging/UI compatibility. Actual Celery fan-out is handled by
+    :func:`dispatch_evaluation_rows_task` under Redis fair-share limits.
+    """
+    from app.workers.concurrency.eval_dispatch import _needs_transcribe_for_eval
+    from app.workers.concurrency.fair_dispatch import (
+        schedule_fair_dispatch,
+        store_evaluation_transcribe_overwrite,
+        store_row_restricted_metrics,
+    )
+
+    eval_only_count = 0
+    transcribe_count = 0
+    if eval_rows_with_source:
+        for eval_row, source_row in eval_rows_with_source:
+            if _needs_transcribe_for_eval(
+                evaluation,
+                source_row,
+                transcribe_overwrite=transcribe_overwrite,
+            ):
+                transcribe_count += 1
+            else:
+                eval_only_count += 1
+
+        restricted_metric_ids_str: Optional[List[str]] = (
+            [str(mid) for mid in restricted_metric_ids]
+            if restricted_metric_ids
+            else None
+        )
+        if restricted_metric_ids_str:
+            for eval_row, _ in eval_rows_with_source:
+                store_row_restricted_metrics(eval_row.id, restricted_metric_ids_str)
+    else:
+        restricted_metric_ids_str = (
+            [str(mid) for mid in restricted_metric_ids] if restricted_metric_ids else None
+        )
+    store_evaluation_transcribe_overwrite(
+        evaluation.id,
+        overwrite=transcribe_overwrite,
+    )
+    schedule_fair_dispatch(max_workspace_turns=999)
+    return eval_only_count, transcribe_count
+
+
+def _apply_telephony_retry_overrides(
+    db: Session,
+    *,
+    call_import: CallImport,
+    organization_id: UUID,
+    payload: CallImportEvaluationRetryRequest,
+) -> None:
+    """Pin or clear telephony credentials on the batch for this retry pass."""
+    fields_set = payload.model_fields_set
+    if (
+        "provider" not in fields_set
+        and "telephony_integration_id" not in fields_set
+    ):
+        return
+
+    from app.api.v1.routes.call_imports import _resolve_telephony_integration
+
+    if payload.telephony_integration_id is not None:
+        integration = _resolve_telephony_integration(
+            db,
+            organization_id,
+            payload.telephony_integration_id,
+            payload.provider or "",
+        )
+        call_import.provider = integration.provider
+        call_import.telephony_integration_id = integration.id
+    else:
+        call_import.provider = None
+        call_import.telephony_integration_id = None
+    db.flush()
+
+
+def _apply_retry_overrides(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    organization_id: UUID,
+    payload: CallImportEvaluationRetryRequest,
+) -> None:
+    """Validate + persist the LLM/STT override fields on the run.
+
+    Mirrors the validation in ``create_call_import_evaluation`` but
+    only touches the fields the caller actually sent — leaving any
+    field ``None`` preserves the run's existing value. Raises
+    ``HTTPException(400)`` on bad input so the route handler can let
+    FastAPI turn it into a clean 400 response.
+    """
+    # --- LLM provider + model (must be sent together) ---
+    if payload.llm_provider is not None or payload.llm_model is not None:
+        if not (payload.llm_provider and payload.llm_model):
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "Both llm_provider and llm_model are required when "
+                    "overriding the run LLM on retry."
+                ),
+            )
+        try:
+            evaluation.llm_provider = ModelProvider(
+                payload.llm_provider.lower()
+            ).value
+        except ValueError:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    f"Unknown LLM provider '{payload.llm_provider}'. "
+                    "Valid keys are documented in ModelProvider."
+                ),
+            )
+        new_model = payload.llm_model.strip() or None
+        if not new_model:
+            raise HTTPException(
+                status_code=400, detail="llm_model cannot be empty."
+            )
+        evaluation.llm_model = new_model
+
+    # --- LLM credential pin ---
+    if payload.llm_credential_id is not None:
+        cred = (
+            db.query(AIProvider)
+            .filter(
+                AIProvider.id == payload.llm_credential_id,
+                AIProvider.organization_id == organization_id,
+            )
+            .first()
+        )
+        if not cred:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "The provided llm_credential_id does not exist in "
+                    "this organization."
+                ),
+            )
+        evaluation.llm_credential_id = payload.llm_credential_id
+
+    if payload.llm_config is not None:
+        evaluation.llm_config = payload.llm_config
+
+    # --- Per-metric LLM overrides ---
+    # We accept the same dict shape as the create endpoint but
+    # constrain keys to leaf metrics that are actually in this run.
+    # Passing an empty dict explicitly clears existing overrides.
+    if payload.metric_llm_overrides is not None:
+        valid_leaf_ids = {
+            str(mid) for mid in (evaluation.selected_metric_ids or [])
+        }
+        overrides_payload: Dict[str, Dict[str, Any]] = {}
+        for metric_id, override in payload.metric_llm_overrides.items():
+            if metric_id not in valid_leaf_ids:
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        "metric_llm_overrides references metric "
+                        f"{metric_id} which is not a leaf metric in "
+                        "this run."
+                    ),
+                )
+            override_dict: Dict[str, Any] = {}
+            if override.provider is not None:
+                if not override.model:
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            f"Override for metric {metric_id} has a "
+                            "provider but no model."
+                        ),
+                    )
+                try:
+                    override_dict["provider"] = ModelProvider(
+                        override.provider.lower()
+                    ).value
+                except ValueError:
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            f"Override for metric {metric_id} uses "
+                            f"unknown provider '{override.provider}'."
+                        ),
+                    )
+                override_dict["model"] = override.model.strip()
+            elif override.model:
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        f"Override for metric {metric_id} has a model "
+                        "but no provider."
+                    ),
+                )
+            if override.credential_id is not None:
+                override_dict["credential_id"] = str(override.credential_id)
+            if override.llm_config is not None:
+                override_dict["llm_config"] = override.llm_config
+            if override_dict:
+                overrides_payload[metric_id] = override_dict
+        evaluation.metric_llm_overrides = overrides_payload or None
+
+    # --- STT provider + model (must be sent together) ---
+    if payload.stt_provider is not None or payload.stt_model is not None:
+        if not (payload.stt_provider and payload.stt_model):
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "Both stt_provider and stt_model are required "
+                    "when overriding the run STT on retry."
+                ),
+            )
+        try:
+            evaluation.stt_provider = ModelProvider(
+                payload.stt_provider.lower()
+            ).value
+        except ValueError:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Unknown STT provider '{payload.stt_provider}'.",
+            )
+        new_stt_model = payload.stt_model.strip() or None
+        if not new_stt_model:
+            raise HTTPException(
+                status_code=400, detail="stt_model cannot be empty."
+            )
+        evaluation.stt_model = new_stt_model
+
+    # --- STT credential pin ---
+    if payload.stt_credential_id is not None:
+        evaluation.stt_credential_id = payload.stt_credential_id
+
+    # --- LLM diariser provider + model (must be sent together) ---
+    if (
+        payload.diarization_llm_provider is not None
+        or payload.diarization_llm_model is not None
+    ):
+        if not (
+            payload.diarization_llm_provider
+            and payload.diarization_llm_model
+        ):
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "Both diarization_llm_provider and "
+                    "diarization_llm_model are required when overriding "
+                    "the run diariser on retry."
+                ),
+            )
+        try:
+            evaluation.diarisation_llm_provider = ModelProvider(
+                payload.diarization_llm_provider.lower()
+            ).value
+        except ValueError:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "Unknown diarisation LLM provider "
+                    f"'{payload.diarization_llm_provider}'."
+                ),
+            )
+        new_diariser_model = (
+            payload.diarization_llm_model.strip() or None
+        )
+        if not new_diariser_model:
+            raise HTTPException(
+                status_code=400,
+                detail="diarization_llm_model cannot be empty.",
+            )
+        evaluation.diarisation_llm_model = new_diariser_model
+
+    if payload.diarization_llm_credential_id is not None:
+        evaluation.diarisation_llm_credential_id = (
+            payload.diarization_llm_credential_id
+        )
+
+    # ``diarization_prompt`` semantics: None = leave untouched;
+    # empty string = clear (fall back to the canonical default at
+    # worker time); anything else = persist verbatim.
+    if payload.diarization_prompt is not None:
+        cleaned = payload.diarization_prompt.strip()
+        evaluation.diarisation_prompt = cleaned or None
+
+    if payload.transcribe_mode is not None:
+        mode = payload.transcribe_mode.strip().lower()
+        if mode not in {"stt_llm", "llm_only"}:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    f"Unknown transcribe_mode '{payload.transcribe_mode}'. "
+                    "Valid values are 'stt_llm' and 'llm_only'."
+                ),
+            )
+        evaluation.transcribe_mode = mode
+
+
+def _gather_retry_targets(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    requested_ids: Optional[List[UUID]],
+    *,
+    include_completed: bool = False,
+) -> Tuple[
+    List[Tuple[CallImportEvaluationRow, CallImportRow]],
+    List[CallImportEvaluationRetrySkippedItem],
+]:
+    """Resolve which rows to retry + reasons for any we refuse.
+
+    When ``requested_ids`` is None we retry every row whose status is
+    ``failed`` (or every row when ``include_completed`` is also set —
+    used by the metric-subset retry path which legitimately wants to
+    recompute a metric on already-successful rows). When the caller
+    passes ids explicitly we still filter out rows that are currently
+    in flight; ``include_completed`` controls whether previously-
+    successful rows are eligible.
+    """
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    if is_sharding_enabled():
+        from app.db_sharding.eval_rows import gather_retry_targets_sharded
+
+        return gather_retry_targets_sharded(
+            db,
+            evaluation,
+            requested_ids,
+            include_completed=include_completed,
+        )
+
+    eval_rows_query = db.query(CallImportEvaluationRow).filter(
+        CallImportEvaluationRow.evaluation_id == evaluation.id
+    )
+
+    targets: List[Tuple[CallImportEvaluationRow, CallImportRow]] = []
+    skipped: List[CallImportEvaluationRetrySkippedItem] = []
+
+    if requested_ids is None:
+        if include_completed:
+            # "Retry everything" path used by the metric-subset re-run
+            # UI. Still skip in-flight rows below so we don't trample
+            # work the worker is actively doing.
+            candidate_rows = eval_rows_query.filter(
+                CallImportEvaluationRow.status.in_(["failed", "completed"])
+            ).all()
+        else:
+            candidate_rows = eval_rows_query.filter(
+                CallImportEvaluationRow.status == "failed"
+            ).all()
+    else:
+        requested_set = set(requested_ids)
+        candidate_rows = eval_rows_query.filter(
+            CallImportEvaluationRow.id.in_(requested_set)
+        ).all()
+        found_ids = {row.id for row in candidate_rows}
+        for missing in requested_set - found_ids:
+            skipped.append(
+                CallImportEvaluationRetrySkippedItem(
+                    eval_row_id=missing,
+                    reason="unknown",
+                )
+            )
+
+    if not candidate_rows:
+        return targets, skipped
+
+    source_row_ids = [row.call_import_row_id for row in candidate_rows]
+    source_rows = (
+        db.query(CallImportRow)
+        .filter(CallImportRow.id.in_(source_row_ids))
+        .all()
+    )
+    source_by_id = {row.id: row for row in source_rows}
+
+    for eval_row in candidate_rows:
+        if eval_row.status in {"pending", "running"}:
+            skipped.append(
+                CallImportEvaluationRetrySkippedItem(
+                    eval_row_id=eval_row.id,
+                    reason="in_progress",
+                )
+            )
+            continue
+        if eval_row.status == "completed" and not include_completed:
+            skipped.append(
+                CallImportEvaluationRetrySkippedItem(
+                    eval_row_id=eval_row.id,
+                    reason="completed",
+                )
+            )
+            continue
+        source_row = source_by_id.get(eval_row.call_import_row_id)
+        if source_row is None:
+            skipped.append(
+                CallImportEvaluationRetrySkippedItem(
+                    eval_row_id=eval_row.id,
+                    reason="source_row_missing",
+                )
+            )
+            continue
+        targets.append((eval_row, source_row))
+
+    return targets, skipped
+
+
+@router.post(
+    "/{eval_id}/retry",
+    response_model=CallImportEvaluationRetryResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="retryCallImportEvaluation",
+)
+async def retry_call_import_evaluation(
+    call_import_id: UUID,
+    eval_id: UUID,
+    payload: Optional[CallImportEvaluationRetryRequest] = Body(default=None),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationRetryResponse:
+    """Re-enqueue failed rows in an evaluation run.
+
+    Default behavior (no body) is "retry every row that failed". Pass
+    ``eval_row_ids`` to scope the retry to a specific subset (e.g. the
+    single row a user clicked in the UI). Rows that are still
+    in-flight or already completed are returned in ``skipped`` rather
+    than re-enqueued, so this endpoint is always safe to call.
+
+    When ``metric_ids`` is set in the payload, this is a **metric-
+    subset retry**: only the listed metrics are recomputed (and merged
+    into the row's existing ``metric_scores`` — other metrics' values
+    are preserved). The route auto-flips ``include_completed=True`` in
+    that case so previously-successful rows are eligible for re-
+    scoring; without it the call would no-op because every row would
+    be skipped as ``completed``.
+
+    The worker contract is the same as the create endpoint:
+    ``evaluate_call_import_row_task(eval_row_id, [restricted_metric_ids])``.
+    When the run is configured for diarised transcripts and the row's
+    diarised transcript is missing, we chain through
+    ``transcribe_call_import_row_task`` first — matching the
+    auto-transcribe behavior of POST ``/evaluations``.
+    """
+    del api_key
+    call_import = _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    requested_ids = payload.eval_row_ids if payload else None
+    # Metric-subset retry: validate that every metric is something this
+    # run actually scored. Empty list is rejected too — callers that
+    # want a full re-run should omit the field entirely.
+    #
+    # ``selected_metric_ids`` holds the LEAVES only (children for
+    # hierarchical / category metrics, standalone metrics otherwise) —
+    # see ``leaf_metric_ids`` in :func:`create_call_import_evaluation`.
+    # Parent IDs for hierarchical metrics live separately in
+    # ``selected_metric_groups`` (``{parent_id: [child_ids]}``) so the
+    # UI can reconstruct the tree without round-tripping through the
+    # metric table.
+    #
+    # The Re-run-metrics modal surfaces PARENTS for hierarchical
+    # metrics (it suppresses individual children via
+    # ``childrenInGroups`` in ``CallImportEvaluationDetail.tsx``), so a
+    # naive ``metric_ids ⊆ selected_metric_ids`` check rejects every
+    # parent-ID request with a misleading "unknown ids" 400. We accept
+    # both shapes here and then EXPAND any parent IDs into
+    # ``{parent_id, *child_ids}`` so the downstream helpers see the
+    # full set of keys that need clearing + the full set of leaves
+    # that need re-scoring.
+    metric_ids: Optional[List[UUID]] = (
+        payload.metric_ids if payload else None
+    )
+    if metric_ids is not None:
+        if not metric_ids:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "metric_ids must be a non-empty list. Omit the "
+                    "field to re-run all metrics."
+                ),
+            )
+
+        leaf_set: Set[str] = {
+            str(item).lower()
+            for item in (evaluation.selected_metric_ids or [])
+        }
+        # ``selected_metric_groups`` is a dict ``{parent_id_str:
+        # [child_id_str, ...]}`` (see line ~487 in
+        # ``create_call_import_evaluation``). We tolerate stale data
+        # (string / UUID / non-dict) without crashing the retry path —
+        # if it's malformed we just treat it as "no parents" and fall
+        # back to the leaf-only check.
+        groups_raw = (
+            evaluation.selected_metric_groups
+            if isinstance(evaluation.selected_metric_groups, dict)
+            else {}
+        )
+        parent_to_children_str: Dict[str, List[str]] = {}
+        for parent_key, children_raw in groups_raw.items():
+            if not isinstance(children_raw, (list, tuple)):
+                continue
+            children_norm = [
+                str(c).lower() for c in children_raw if c is not None
+            ]
+            parent_to_children_str[str(parent_key).lower()] = children_norm
+        parent_set = set(parent_to_children_str.keys())
+
+        unknown = [
+            mid for mid in metric_ids
+            if str(mid).lower() not in leaf_set
+            and str(mid).lower() not in parent_set
+        ]
+        if unknown:
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "metric_ids must be a subset of this evaluation's "
+                    f"selected metrics; unknown ids: {[str(u) for u in unknown]}."
+                ),
+            )
+
+        # Expand parent IDs into ``{parent, *children}`` so:
+        #   * ``_reset_eval_row_for_retry`` strips BOTH the parent
+        #     entry (with ``chosen_child_id`` / rationale) AND every
+        #     per-child boolean entry that the LLM evaluator wrote
+        #     under each child's ID (see
+        #     ``app/workers/tasks/helpers/llm_evaluation.py`` lines
+        #     1584 and 1649).
+        #   * ``_enqueue_eval_rows_with_optional_transcribe`` →
+        #     ``evaluate_call_import_row_task`` filters the work-list
+        #     off ``selected_metric_ids`` (leaves), so we MUST hand it
+        #     the child IDs for the parent to actually get re-scored.
+        # Leaves pass through unchanged.
+        expanded: List[UUID] = []
+        seen: Set[str] = set()
+        for mid in metric_ids:
+            mid_norm = str(mid).lower()
+            children_str = parent_to_children_str.get(mid_norm)
+            if children_str is not None:
+                # Parent: include the parent ID itself (so the parent
+                # entry in ``metric_scores`` is also cleared) and all
+                # of its children.
+                candidates = [mid_norm, *children_str]
+            else:
+                candidates = [mid_norm]
+            for candidate in candidates:
+                if candidate in seen:
+                    continue
+                try:
+                    expanded.append(UUID(candidate))
+                except (TypeError, ValueError):
+                    # Defensive: skip junk values rather than 500.
+                    continue
+                seen.add(candidate)
+        metric_ids = expanded
+
+    # ``include_completed`` is auto-enabled when the caller asked for a
+    # metric subset (otherwise the metric-subset retry would always
+    # no-op on a green run, which is the whole reason this feature
+    # exists). The explicit payload flag wins for full-row retries.
+    include_completed = bool(
+        (payload.include_completed if payload else False)
+        or (metric_ids is not None)
+    )
+
+    transcribe_overwrite = bool(
+        payload.transcribe_overwrite if payload else False
+    )
+
+    skipped: List[CallImportEvaluationRetrySkippedItem] = []
+    if requested_ids is None:
+        from app.db_sharding.eval_rows import count_evaluation_rows_for_run
+        from app.db_sharding.sessions import is_sharding_enabled
+
+        if is_sharding_enabled():
+            statuses = (
+                ["failed", "completed"] if include_completed else ["failed"]
+            )
+            target_count = count_evaluation_rows_for_run(
+                db, eval_id, statuses=statuses
+            )
+        else:
+            from sqlalchemy import func
+
+            count_query = db.query(func.count(CallImportEvaluationRow.id)).filter(
+                CallImportEvaluationRow.evaluation_id == eval_id
+            )
+            if include_completed:
+                count_query = count_query.filter(
+                    CallImportEvaluationRow.status.in_(["failed", "completed"])
+                )
+            else:
+                count_query = count_query.filter(
+                    CallImportEvaluationRow.status == "failed"
+                )
+            target_count = int(count_query.scalar() or 0)
+        if target_count == 0:
+            return CallImportEvaluationRetryResponse(
+                requeued=0,
+                transcribe_requeued=0,
+                skipped=skipped,
+            )
+    else:
+        targets, skipped = _gather_retry_targets(
+            db,
+            evaluation,
+            requested_ids,
+            include_completed=include_completed,
+        )
+        if not targets:
+            return CallImportEvaluationRetryResponse(
+                requeued=0,
+                transcribe_requeued=0,
+                skipped=skipped,
+            )
+        target_count = len(targets)
+
+    # Apply LLM / STT overrides BEFORE enqueueing so the persisted run
+    # config is correct by the time the worker reads it.
+    if payload is not None:
+        _apply_retry_overrides(db, evaluation, organization_id, payload)
+        _apply_telephony_retry_overrides(
+            db,
+            call_import=call_import,
+            organization_id=organization_id,
+            payload=payload,
+        )
+
+    evaluation.error_message = None
+    evaluation.finished_at = None
+    evaluation.status = "running"
+    if not evaluation.started_at:
+        from datetime import datetime, timezone
+
+        evaluation.started_at = datetime.now(timezone.utc)
+
+    _claim_evaluation_bulk_operation(eval_id, "retry")
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    from app.workers.tasks.call_import_bulk_ops import (
+        retry_call_import_evaluation_task,
+    )
+
+    retry_call_import_evaluation_task.delay(
+        str(eval_id),
+        {
+            "eval_row_ids": [str(rid) for rid in requested_ids]
+            if requested_ids
+            else None,
+            "metric_ids": [str(mid) for mid in metric_ids] if metric_ids else None,
+            "include_completed": include_completed,
+            "transcribe_overwrite": transcribe_overwrite,
+        },
+    )
+
+    return CallImportEvaluationRetryResponse(
+        requeued=target_count,
+        transcribe_requeued=0,
+        skipped=skipped,
+    )
+
+
+@router.post(
+    "/{eval_id}/rows/{eval_row_id}/retry",
+    response_model=CallImportEvaluationRowResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="retryCallImportEvaluationRow",
+)
+async def retry_call_import_evaluation_row(
+    call_import_id: UUID,
+    eval_id: UUID,
+    eval_row_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationRowResponse:
+    """Re-enqueue a single failed evaluation row.
+
+    Convenience wrapper around ``retry_call_import_evaluation`` for the
+    "Retry this row" affordance in the row table. Returns the
+    refreshed row so the UI can update its badge immediately, without
+    waiting for the next polling tick.
+    """
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(
+            status_code=404, detail="Call import evaluation not found"
+        )
+
+    _require_no_evaluation_bulk_operation(eval_id)
+
+    from app.db_sharding.eval_rows import (
+        evaluation_row_session,
+        find_evaluation_row_in_run,
+    )
+    from app.db_sharding.sessions import is_sharding_enabled
+
+    eval_row, _source_stub = find_evaluation_row_in_run(db, eval_id, eval_row_id)
+    if eval_row is None:
+        raise HTTPException(
+            status_code=404, detail="Evaluation row not found in this run"
+        )
+
+    if eval_row.status in {"pending", "running"}:
+        raise HTTPException(
+            status_code=409,
+            detail=(
+                "This row is still in progress — wait for it to finish "
+                "before retrying."
+            ),
+        )
+
+    targets, _ = _gather_retry_targets(db, evaluation, [eval_row.id])
+    if not targets:
+        raise HTTPException(
+            status_code=409,
+            detail=(
+                "This row cannot be retried in its current state "
+                f"(status={eval_row.status})."
+            ),
+        )
+
+    if is_sharding_enabled():
+        with evaluation_row_session(eval_row_id) as (
+            row_db,
+            _catalog_db,
+            eval_row,
+            source_row,
+            _shard_id,
+        ):
+            _prepare_source_row_for_retry(source_row, transcribe_overwrite=False)
+            _reset_eval_row_for_retry(eval_row)
+            row_db.commit()
+            targets = [(eval_row, source_row)]
+    else:
+        for er, source_row in targets:
+            _prepare_source_row_for_retry(source_row, transcribe_overwrite=False)
+            _reset_eval_row_for_retry(er)
+
+    evaluation.error_message = None
+    evaluation.finished_at = None
+    evaluation.status = "running"
+    if not evaluation.started_at:
+        from datetime import datetime, timezone
+
+        evaluation.started_at = datetime.now(timezone.utc)
+    db.flush()
+    _rollup_evaluation_status(evaluation, db)
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+
+    try:
+        _enqueue_eval_rows_with_optional_transcribe(db, evaluation, targets)
+    except Exception as exc:  # noqa: BLE001
+        logger.exception(
+            "Failed to re-enqueue retry for evaluation row {}", eval_row_id
+        )
+        if is_sharding_enabled():
+            with evaluation_row_session(eval_row_id) as (
+                row_db,
+                _catalog_db,
+                eval_row,
+                _source_row,
+                _shard_id,
+            ):
+                eval_row.status = "failed"
+                eval_row.error_message = f"Failed to re-enqueue retry: {exc}"
+                row_db.commit()
+        else:
+            eval_row.status = "failed"
+            eval_row.error_message = f"Failed to re-enqueue retry: {exc}"
+        _rollup_evaluation_status(evaluation, db)
+        db.commit()
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to re-enqueue retry: {exc}",
+        )
+
+    if is_sharding_enabled():
+        with evaluation_row_session(eval_row_id) as (
+            _row_db,
+            _catalog_db,
+            eval_row,
+            source_row,
+            _shard_id,
+        ):
+            return _to_evaluation_row_response(eval_row, source_row, evaluation)
+
+    db.refresh(eval_row)
+    source_row = targets[0][1]
+    return _to_evaluation_row_response(eval_row, source_row, evaluation)
+
+
+from app.core.auth.capabilities import EVALS_RUN, EVALS_VIEW, REPORTS_GENERATE
+from app.core.auth.workspace_route_capabilities import apply_workspace_route_capabilities
+
+apply_workspace_route_capabilities(
+    router,
+    view_capability=EVALS_VIEW,
+    manage_capability=EVALS_RUN,
+    run_capability=EVALS_RUN,
+    report_capability=REPORTS_GENERATE,
+)
diff --git a/app/api/v1/routes/call_imports.py b/app/api/v1/routes/call_imports.py
index e7461504..3b7bab55 100644
--- a/app/api/v1/routes/call_imports.py
+++ b/app/api/v1/routes/call_imports.py
@@ -1,3947 +1,4016 @@
-"""CSV-driven call import routes.
-
-Users upload a CSV plus a per-batch column mapping (CSV header -> system
-field). The backend persists a CallImport batch + one CallImportRow per
-line, then fans the rows out to the Celery ``imports`` queue where each
-row is downloaded using the telephony credential pinned on the batch.
-Exotel credentialed imports require a ``recording_url`` on every row;
-direct-URL imports (no credential) also require a mapped recording URL.
-"""
-
-from __future__ import annotations
-
-import csv
-import io
-import json
-import re
-from dataclasses import dataclass, field
-from datetime import date, datetime, time, timedelta
-from typing import Any, Dict, Iterable, List, Optional, Tuple
-from uuid import UUID, uuid4
-
-from fastapi import APIRouter, Body, BackgroundTasks, Depends, File, Form, HTTPException, Query, Response, UploadFile, status
-from loguru import logger
-from sqlalchemy import desc, func, or_
-from sqlalchemy.orm import Session
-
-from app.config import settings
-from app.core.auth.rbac import require_admin
-from app.database import get_db
-from app.db_sharding.sessions import is_sharding_enabled
-from app.dependencies import (
-    get_api_key,
-    get_organization_id,
-    get_workspace_id,
-    require_enterprise_feature,
-)
-from app.services.billing.flexprice_service import record_call_import_batch_created
-from app.services.call_imports.dispatch_diagnostics import (
-    build_call_import_dispatch_diagnostics,
-)
-from app.models.database import (
-    CallImport,
-    CallImportRow,
-    CallImportSchema,
-    CallImportSchemaParameter,
-    CallImportTag,
-    TelephonyIntegration,
-)
-from app.models.enums import (
-    CallImportParameterType,
-    CallImportRowStatus,
-    CallImportStatus,
-)
-from app.models.schemas import (
-    CallImportCancelDiarisationRequest,
-    CallImportCancelDiarisationResponse,
-    CallImportDetailResponse,
-    CallImportDeleteResponse,
-    CallImportDiarisationPromptDefaultResponse,
-    CallImportDispatchDiagnosticsResponse,
-    CallImportInsightsMetric,
-    CallImportInsightsResponse,
-    CallImportInsightsRunPoint,
-    CallImportListResponse,
-    CallImportMappingUpdate,
-    CallImportMetricAggregate,
-    CallImportPreviewResponse,
-    CallImportPreviewSheet,
-    CallImportRetryFailedRowsRequest,
-    CallImportRetryFailedRowsResponse,
-    CallImportResponse,
-    CallImportRowIdsResponse,
-    CallImportRowBulkDelete,
-    CallImportRowBulkDeleteResponse,
-    CallImportRowResponse,
-    CallImportStartRequest,
-    CallImportTranscribeRequest,
-    CallImportTranscribeResponse,
-    CallImportUpdate,
-    CallImportUploadResponse,
-)
-
-
-router = APIRouter(
-    prefix="/call-imports",
-    tags=["Call Imports"],
-    dependencies=[Depends(require_enterprise_feature("call_imports"))],
-)
-
-
-@dataclass(frozen=True)
-class CallImportParseSkip:
-    """One source row excluded during CSV/Excel parse (identity / recording URL)."""
-
-    source_row: int
-    reason: str
-    message: str
-
-
-@dataclass
-class CallImportParseResult:
-    rows: List[Dict[str, Any]] = field(default_factory=list)
-    skipped: List[CallImportParseSkip] = field(default_factory=list)
-
-
-def parse_skips_to_json(skips: List[CallImportParseSkip]) -> List[Dict[str, Any]]:
-    """Persistable JSON shape for ``CallImport.source_row_skips``."""
-    return [
-        {
-            "source_row": item.source_row,
-            "reason": item.reason,
-            "message": item.message,
-        }
-        for item in skips
-    ]
-
-
-def _normalize_dataset(raw: Optional[str]) -> Optional[str]:
-    """Trim and treat empty strings as 'no dataset' (NULL)."""
-    if raw is None:
-        return None
-    cleaned = raw.strip()
-    return cleaned or None
-
-
-def _serialize_call_import(db: Session, call_import: CallImport) -> CallImportResponse:
-    """Catalog parent fields; counters come from SQL rollup (not Redis merge)."""
-    from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
-    from app.services.call_imports.progress_counters import (
-        clear_import_progress_redis,
-        read_import_progress,
-    )
-
-    redis_completed, redis_failed = read_import_progress(call_import.id)
-    if (
-        redis_completed
-        or redis_failed
-        or int(call_import.completed_rows or 0) > int(call_import.total_rows or 0)
-        or int(call_import.failed_rows or 0) > int(call_import.total_rows or 0)
-    ):
-        rollup_call_import_batch_status(db, call_import)
-        db.flush()
-
-    clear_import_progress_redis(call_import.id)
-    db.refresh(call_import)
-    total = int(call_import.total_rows or 0)
-    completed = min(int(call_import.completed_rows or 0), total) if total else int(
-        call_import.completed_rows or 0
-    )
-    failed = min(int(call_import.failed_rows or 0), total) if total else int(
-        call_import.failed_rows or 0
-    )
-    base = CallImportResponse.model_validate(call_import)
-    return base.model_copy(update={"completed_rows": completed, "failed_rows": failed})
-
-
-def _resolve_tags(
-    db: Session, organization_id: UUID, tag_ids: Optional[List[UUID]]
-) -> List[CallImportTag]:
-    """Look up tag rows by id, scoped to the organization.
-
-    Raises HTTPException(400) if any id is unknown for the org.
-    """
-    if not tag_ids:
-        return []
-    rows = (
-        db.query(CallImportTag)
-        .filter(
-            CallImportTag.organization_id == organization_id,
-            CallImportTag.id.in_(tag_ids),
-        )
-        .all()
-    )
-    found_ids = {row.id for row in rows}
-    missing = [str(tag_id) for tag_id in tag_ids if tag_id not in found_ids]
-    if missing:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Unknown call_import_tag id(s): {missing}",
-        )
-    return rows
-
-
-MAX_UPLOAD_BYTES = 15 * 1024 * 1024  # 15 MB upload cap (CSV or Excel)
-
-# File extensions accepted by the upload + preview endpoints. Keep in
-# lockstep with the frontend ``accept`` attribute on the file picker.
-CSV_EXTENSIONS = (".csv",)
-XLSX_EXTENSIONS = (".xlsx", ".xlsm")
-ALLOWED_EXTENSIONS = CSV_EXTENSIONS + XLSX_EXTENSIONS
-
-AUDIO_CONTENT_TYPES = {
-    "wav": "audio/wav",
-    "mp3": "audio/mpeg",
-    "flac": "audio/flac",
-    "m4a": "audio/mp4",
-}
-
-
-def _file_format(filename: Optional[str]) -> Optional[str]:
-    """Classify ``filename`` as ``'csv'`` / ``'xlsx'`` or ``None`` if unsupported."""
-    if not filename:
-        return None
-    name = filename.lower()
-    if name.endswith(CSV_EXTENSIONS):
-        return "csv"
-    if name.endswith(XLSX_EXTENSIONS):
-        return "xlsx"
-    return None
-
-
-def _audio_extension(filename: Optional[str]) -> Optional[str]:
-    """Return the validated lower-case extension for a manual recording."""
-    if not filename or "." not in filename:
-        return None
-    ext = filename.rsplit(".", 1)[-1].lower().strip()
-    allowed = {fmt.lower().lstrip(".") for fmt in settings.ALLOWED_AUDIO_FORMATS}
-    return ext if ext in allowed else None
-
-
-def _audio_content_type(ext: str, upload_content_type: Optional[str]) -> str:
-    """Prefer the browser-supplied audio content type, with a safe fallback."""
-    supplied = (upload_content_type or "").strip()
-    if supplied and supplied != "application/octet-stream":
-        return supplied
-    return AUDIO_CONTENT_TYPES.get(ext.lower(), "application/octet-stream")
-
-
-def _audio_s3_key(
-    organization_id: UUID, call_import_id: UUID, row_id: UUID, ext: str
-) -> str:
-    """Build the canonical S3 key for a manually uploaded recording."""
-    from app.services.storage.s3_service import s3_service
-
-    return (
-        f"{s3_service.prefix}organizations/{organization_id}/call_imports/"
-        f"{call_import_id}/{row_id}.{ext}"
-    )
-
-
-def _filename_stem(filename: Optional[str]) -> str:
-    """Extract a cross-platform filename stem from an UploadFile name."""
-    raw = (filename or "").strip()
-    basename = re.split(r"[\\/]", raw)[-1] if raw else ""
-    if "." in basename:
-        basename = basename.rsplit(".", 1)[0]
-    return basename.strip()
-
-
-def _sanitize_conversation_id(raw: str) -> str:
-    """Turn a filename stem into a stable conversation_id."""
-    cleaned = re.sub(r"[^A-Za-z0-9._-]+", "_", raw.strip())
-    cleaned = re.sub(r"_+", "_", cleaned).strip("._-")
-    return (cleaned or "recording")[:255]
-
-
-def _dedupe_conversation_id(
-    base: str, counts: Dict[str, int]
-) -> str:
-    """Make conversation ids unique within one manual upload batch."""
-    count = counts.get(base, 0) + 1
-    counts[base] = count
-    if count == 1:
-        return base
-    suffix = f"-{count}"
-    return f"{base[: 255 - len(suffix)]}{suffix}"
-
-
-def _normalize_header(name: str) -> str:
-    return (name or "").strip().lower()
-
-
-def _header_lookup(fieldnames: List[str]) -> Dict[str, str]:
-    """Map normalized header -> original header for case-insensitive lookup."""
-    return {_normalize_header(h): h for h in fieldnames or []}
-
-
-def _resolve_mapped_header(
-    mapping_value: Optional[str], header_lookup: Dict[str, str]
-) -> Optional[str]:
-    """Translate a user-supplied CSV header into the actual column key.
-
-    The frontend sends headers exactly as they appear in the source file,
-    but we still normalize on the server so trailing whitespace / casing
-    doesn't break matching. Returns the canonical fieldname or ``None``
-    if not present in the file.
-    """
-    if not mapping_value:
-        return None
-    return header_lookup.get(_normalize_header(mapping_value))
-
-
-def _xlsx_cell_to_str(value: Any) -> str:
-    """Coerce an openpyxl cell value to the string the rest of the
-    pipeline expects.
-
-    openpyxl returns native Python types (int, float, datetime, bool,
-    None). The CSV path always works with strings, so we mirror that:
-    integers stringify cleanly (no ``.0`` suffix on whole-number floats),
-    datetimes use ISO-8601, booleans use SQL-style ``TRUE`` / ``FALSE``.
-    """
-    if value is None:
-        return ""
-    if isinstance(value, bool):
-        return "TRUE" if value else "FALSE"
-    if isinstance(value, int):
-        return str(value)
-    if isinstance(value, float):
-        if value.is_integer():
-            return str(int(value))
-        return str(value)
-    if isinstance(value, datetime):
-        return value.isoformat()
-    if isinstance(value, date):
-        return value.isoformat()
-    if isinstance(value, time):
-        return value.isoformat()
-    if isinstance(value, timedelta):
-        return str(value)
-    return str(value)
-
-
-def _parse_recording_date_cell(cell: str) -> date:
-    """Parse day-first dates with one/two digit day-month parts."""
-    match = re.fullmatch(r"\s*(\d{1,2})[/-](\d{1,2})[/-](\d{4})\s*", cell)
-    if match:
-        day, month, year = (int(part) for part in match.groups())
-        return date(year, month, day)
-
-    # Native Excel date cells arrive from ``_xlsx_cell_to_str`` as ISO
-    # datetimes (e.g. ``2026-01-04T00:00:00``). Accept that resolved date,
-    # while keeping plain ISO dates rejected for hand-entered text/CSV cells.
-    if "T" in cell:
-        return datetime.fromisoformat(cell.replace("Z", "+00:00")).date()
-
-    raise ValueError("expected D/M/YYYY or D-M-YYYY")
-
-
-def _coerce_parameter_value(
-    raw: str,
-    param_type: CallImportParameterType,
-    *,
-    row_idx: int,
-    param_name: str,
-) -> Any:
-    """Validate + coerce a single CSV cell against its declared type.
-
-    Returns the typed Python value to surface in ``raw_columns``. Empty
-    strings are returned as ``None`` regardless of the parameter type so
-    optional cells stay null end-to-end. Coercion failures raise a
-    400 with a row-anchored message.
-    """
-    cell = (raw or "").strip()
-    if not cell:
-        return None
-
-    if param_type == CallImportParameterType.CONVERSATION_ID:
-        return cell
-    if param_type == CallImportParameterType.RECORDING_URL:
-        # Recording URLs are exercised by the worker (which downloads
-        # them); we only do a light "starts with http" check here so a
-        # paste-error surfaces immediately at upload time.
-        lower = cell.lower()
-        if not (lower.startswith("http://") or lower.startswith("https://")):
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
-                    "valid recording URL (must start with http:// or https://)."
-                ),
-            )
-        return cell
-    if param_type == CallImportParameterType.RECORDING_DATE:
-        try:
-            parsed_date = _parse_recording_date_cell(cell)
-        except ValueError:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
-                    f"valid recording date ({cell!r}); expected day-first "
-                    "D/M/YYYY or D-M-YYYY."
-                ),
-            )
-        return parsed_date.strftime("%d/%m/%Y")
-    if param_type == CallImportParameterType.TRANSCRIPT:
-        return cell
-    if param_type == CallImportParameterType.TEXT:
-        return cell
-    if param_type == CallImportParameterType.NUMBER:
-        try:
-            value = float(cell)
-        except ValueError:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
-                    f"valid number ({cell!r})."
-                ),
-            )
-        if value.is_integer():
-            return int(value)
-        return value
-    if param_type == CallImportParameterType.BOOLEAN:
-        truthy = {"true", "yes", "y", "1", "t"}
-        falsy = {"false", "no", "n", "0", "f"}
-        norm = cell.lower()
-        if norm in truthy:
-            return True
-        if norm in falsy:
-            return False
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"Row {row_idx + 1}: value for '{param_name}' is not a "
-                f"valid boolean ({cell!r})."
-            ),
-        )
-    if param_type == CallImportParameterType.DATETIME:
-        try:
-            parsed = datetime.fromisoformat(cell.replace("Z", "+00:00"))
-        except ValueError:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
-                    f"valid ISO-8601 date/time ({cell!r})."
-                ),
-            )
-        return parsed.isoformat()
-    if param_type == CallImportParameterType.URL:
-        lower = cell.lower()
-        if not (lower.startswith("http://") or lower.startswith("https://")):
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
-                    "valid URL (must start with http:// or https://)."
-                ),
-            )
-        return cell
-    # Unknown types: store as text and let the next migration catch up.
-    return cell
-
-
-def _recording_url_cell_is_valid_http(raw: str) -> bool:
-    cell = (raw or "").strip()
-    if not cell:
-        return False
-    lower = cell.lower()
-    return lower.startswith("http://") or lower.startswith("https://")
-
-
-def _parameter_is_required(param: CallImportSchemaParameter) -> bool:
-    """Return whether a schema parameter must be mapped on every upload."""
-    if param.is_required:
-        return True
-    try:
-        param_type = CallImportParameterType(param.type)
-    except ValueError:
-        return False
-    return param_type in (
-        CallImportParameterType.CONVERSATION_ID,
-        CallImportParameterType.RECORDING_URL,
-    )
-
-
-def _apply_schema_mapping(
-    fieldnames: List[str],
-    rows_iter: Iterable[Dict[str, str]],
-    parameters: List[CallImportSchemaParameter],
-    parameter_mapping: Dict[str, str],
-    skipped_columns: List[str],
-    *,
-    source_label: str = "CSV",
-    validate_only: bool = False,
-) -> CallImportParseResult:
-    """Schema-driven row projection: parameter -> CSV header -> typed value.
-
-    Validates that every required schema parameter is mapped to a CSV
-    header that actually exists in the file, and that every CSV header
-    is either mapped to a parameter or explicitly listed in
-    ``skipped_columns``. Returns one dict per non-empty data row with:
-
-      * ``conversation_id`` (str, mandatory)
-      * ``recording_date`` (Optional[str], DD/MM/YYYY date)
-      * ``recording_url`` (Optional[str])
-      * ``transcript`` (Optional[str])
-      * ``parameter_values`` (Dict[str, Any]) of typed values keyed by
-        parameter name (drives ``raw_columns`` so the export can
-        reproduce the source).
-
-    ``validate_only=True`` runs the header / mapping / skipped-column
-    checks (every check that doesn't need to read row data) and then
-    returns an empty list — used by the MAP stage to validate a
-    mapping payload against the cached sheet snapshot without
-    re-fetching the source bytes from S3.
-    """
-    header_lookup = _header_lookup(list(fieldnames))
-
-    # 1. Look up the conversation_id parameter so we can address it
-    #    directly while building each row.
-    conv_param = next(
-        (p for p in parameters if p.type == CallImportParameterType.CONVERSATION_ID),
-        None,
-    )
-    if conv_param is None:
-        # The schema invariant should have caught this on create/update,
-        # but a defensive 400 here keeps us safe against hand-rolled
-        # API callers that bypassed validation.
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Selected schema is missing the mandatory conversation_id parameter.",
-        )
-    # 2. Resolve every mapped parameter to a canonical fieldname.
-    #    Required parameters MUST resolve; optional ones may resolve to
-    #    None if the user left them blank (no mapping).
-    canonical_by_param: Dict[str, Optional[str]] = {}
-    recording_date_param_name: Optional[str] = None
-    rec_url_param_name: Optional[str] = None
-    transcript_param_name: Optional[str] = None
-    for param in parameters:
-        mapped_header = parameter_mapping.get(param.name)
-        canonical = (
-            _resolve_mapped_header(mapped_header, header_lookup)
-            if mapped_header
-            else None
-        )
-        if _parameter_is_required(param) and canonical is None:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"{source_label} does not contain the column "
-                    f"'{mapped_header or ''}' mapped to required parameter "
-                    f"'{param.name}'."
-                ),
-            )
-        canonical_by_param[param.name] = canonical
-        if param.type == CallImportParameterType.RECORDING_DATE:
-            recording_date_param_name = param.name
-        elif param.type == CallImportParameterType.RECORDING_URL:
-            rec_url_param_name = param.name
-        elif param.type == CallImportParameterType.TRANSCRIPT:
-            transcript_param_name = param.name
-
-    # 3. Every CSV column must either be mapped to a parameter or
-    #    explicitly skipped. Catches "I forgot to skip the email
-    #    column" gracefully instead of dropping data silently.
-    mapped_canonicals = {c for c in canonical_by_param.values() if c}
-    skipped_canonicals = {
-        _resolve_mapped_header(h, header_lookup)
-        for h in skipped_columns
-    }
-    skipped_canonicals.discard(None)
-    unhandled = [
-        h
-        for h in fieldnames
-        if h not in mapped_canonicals and h not in skipped_canonicals
-    ]
-    if unhandled:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"{source_label} columns must either be mapped to a schema "
-                f"parameter or explicitly skipped. Unhandled: {unhandled}."
-            ),
-        )
-
-    conv_canonical = canonical_by_param[conv_param.name]
-    rec_canonical = (
-        canonical_by_param.get(rec_url_param_name)
-        if rec_url_param_name
-        else None
-    )
-    recording_date_canonical = (
-        canonical_by_param.get(recording_date_param_name)
-        if recording_date_param_name
-        else None
-    )
-    transcript_canonical = (
-        canonical_by_param.get(transcript_param_name)
-        if transcript_param_name
-        else None
-    )
-
-    if validate_only:
-        # MAP-stage validation: every header check above has already
-        # run; the row loop only matters at IMPORT time. Skip it (and
-        # the "no data rows" guard at the bottom of the function) so
-        # the caller gets a clean pass when the mapping is shaped right.
-        return CallImportParseResult()
-
-    parsed: List[Dict[str, Any]] = []
-    skipped: List[CallImportParseSkip] = []
-    for idx, row in enumerate(rows_iter):
-        # Drop fully-blank lines - matches the legacy parser behavior so
-        # trailing-newline edge cases don't fail an otherwise-good upload.
-        non_blank = any(
-            (row.get(c) or "").strip()
-            for c in mapped_canonicals
-            if c
-        )
-        if not non_blank:
-            continue
-
-        source_row = idx + 1
-        conv_value = (row.get(conv_canonical) or "").strip() if conv_canonical else ""
-        if not conv_value:
-            skipped.append(
-                CallImportParseSkip(
-                    source_row=source_row,
-                    reason="missing_conversation_id",
-                    message=(
-                        f"Row {source_row} is missing the '{conv_param.name}' "
-                        "(conversation_id) value."
-                    ),
-                )
-            )
-            continue
-
-        if rec_canonical and rec_url_param_name:
-            rec_param = next(
-                (p for p in parameters if p.name == rec_url_param_name),
-                None,
-            )
-            if rec_param is not None and _parameter_is_required(rec_param):
-                rec_raw = (row.get(rec_canonical) or "").strip()
-                if not rec_raw:
-                    skipped.append(
-                        CallImportParseSkip(
-                            source_row=source_row,
-                            reason="missing_recording_url",
-                            message=(
-                                f"Row {source_row} is missing the required "
-                                f"'{rec_url_param_name}' value."
-                            ),
-                        )
-                    )
-                    continue
-                if not _recording_url_cell_is_valid_http(rec_raw):
-                    skipped.append(
-                        CallImportParseSkip(
-                            source_row=source_row,
-                            reason="invalid_recording_url",
-                            message=(
-                                f"Row {source_row}: value for "
-                                f"'{rec_url_param_name}' is not a valid recording "
-                                "URL (must start with http:// or https://)."
-                            ),
-                        )
-                    )
-                    continue
-
-        # Materialize every mapped parameter into the per-row snapshot,
-        # running per-type coercion so a bad cell aborts the upload
-        # rather than silently storing garbage.
-        parameter_values: Dict[str, Any] = {}
-        row_skipped = False
-        for param in parameters:
-            canonical = canonical_by_param[param.name]
-            if canonical is None:
-                continue
-            try:
-                param_type = CallImportParameterType(param.type)
-            except ValueError:
-                param_type = CallImportParameterType.TEXT
-            coerced = _coerce_parameter_value(
-                row.get(canonical) or "",
-                param_type,
-                row_idx=idx,
-                param_name=param.name,
-            )
-            if _parameter_is_required(param) and coerced is None:
-                if param_type == CallImportParameterType.RECORDING_URL:
-                    skipped.append(
-                        CallImportParseSkip(
-                            source_row=source_row,
-                            reason="missing_recording_url",
-                            message=(
-                                f"Row {source_row} is missing the required "
-                                f"'{param.name}' value."
-                            ),
-                        )
-                    )
-                    row_skipped = True
-                    break
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    detail=(
-                        f"Row {source_row} is missing the required "
-                        f"'{param.name}' value."
-                    ),
-                )
-            parameter_values[param.name] = coerced
-        if row_skipped:
-            continue
-
-        rec_value = (
-            (row.get(rec_canonical) or "").strip() if rec_canonical else ""
-        )
-        transcript_value = (
-            (row.get(transcript_canonical) or "").strip()
-            if transcript_canonical
-            else ""
-        )
-        recording_date_value = (
-            parameter_values.get(recording_date_param_name)
-            if recording_date_param_name
-            else None
-        )
-
-        parsed.append(
-            {
-                "conversation_id": conv_value,
-                "recording_date": recording_date_value,
-                "recording_url": rec_value or None,
-                "transcript": transcript_value or None,
-                "parameter_values": parameter_values,
-            }
-        )
-
-    if not parsed and not skipped:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"{source_label} did not contain any data rows.",
-        )
-
-    return CallImportParseResult(rows=parsed, skipped=skipped)
-
-
-def _raise_if_no_importable_rows(
-    result: CallImportParseResult, *, source_label: str = "CSV"
-) -> None:
-    """Sync upload / API callers fail fast when every data row was skipped."""
-    if result.rows:
-        return
-    if result.skipped:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"No importable rows. {len(result.skipped)} row(s) skipped due "
-                "to missing or invalid conversation ID or recording URL."
-            ),
-        )
-    raise HTTPException(
-        status_code=status.HTTP_400_BAD_REQUEST,
-        detail=f"{source_label} did not contain any data rows.",
-    )
-
-
-def _parse_csv(
-    file_bytes: bytes,
-    parameters: List[CallImportSchemaParameter],
-    parameter_mapping: Dict[str, str],
-    skipped_columns: List[str],
-) -> CallImportParseResult:
-    """Parse a CSV file using the resolved schema parameters."""
-    if not file_bytes:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Uploaded CSV is empty.",
-        )
-
-    try:
-        text_stream = io.StringIO(file_bytes.decode("utf-8-sig"))
-    except UnicodeDecodeError:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="CSV must be UTF-8 encoded.",
-        )
-
-    reader = csv.DictReader(text_stream)
-    if not reader.fieldnames:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="CSV is missing a header row.",
-        )
-
-    return _apply_schema_mapping(
-        list(reader.fieldnames),
-        reader,
-        parameters,
-        parameter_mapping,
-        skipped_columns,
-        source_label="CSV",
-    )
-
-
-def _open_xlsx_workbook(file_bytes: bytes):
-    """Open an xlsx/xlsm workbook from in-memory bytes (read-only stream).
-
-    Imports openpyxl lazily so the module loads even in environments that
-    haven't installed the optional dep yet (e.g. lightweight tooling
-    images). Surfaces a clean 400 if openpyxl is missing or the file is
-    not a valid Office Open XML workbook.
-    """
-    if not file_bytes:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Uploaded Excel file is empty.",
-        )
-    try:
-        from openpyxl import load_workbook  # type: ignore
-        from openpyxl.utils.exceptions import InvalidFileException  # type: ignore
-    except ImportError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=(
-                "Excel uploads require the 'openpyxl' package which is "
-                "not installed in this environment."
-            ),
-        ) from exc
-
-    try:
-        return load_workbook(
-            io.BytesIO(file_bytes),
-            read_only=True,
-            data_only=True,
-        )
-    except InvalidFileException as exc:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"File is not a valid .xlsx workbook: {exc}",
-        ) from exc
-    except Exception as exc:  # zipfile.BadZipFile etc.
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Could not open Excel workbook: {exc}",
-        ) from exc
-
-
-def _xlsx_sheet_headers_and_rows(
-    worksheet,
-) -> Tuple[List[str], List[Dict[str, str]]]:
-    """Read row 1 as headers and the rest as dicts of stringified cells.
-
-    Empty trailing header cells are dropped. Duplicate headers preserve
-    the first occurrence (matches ``csv.DictReader`` behavior, which
-    silently drops duplicates).
-    """
-    iterator = worksheet.iter_rows(values_only=True)
-    try:
-        header_row = next(iterator)
-    except StopIteration:
-        return [], []
-
-    headers: List[str] = []
-    seen: set[str] = set()
-    for cell in header_row:
-        name = _xlsx_cell_to_str(cell).strip()
-        if not name:
-            # Stop at the first blank header — treats trailing empty
-            # columns as not part of the table (matches typical Excel
-            # workbook conventions).
-            break
-        norm = name.lower()
-        if norm in seen:
-            continue
-        seen.add(norm)
-        headers.append(name)
-
-    rows: List[Dict[str, str]] = []
-    for row in iterator:
-        if row is None:
-            continue
-        # Pad / truncate to the header length so dict construction is
-        # stable even when a row has fewer / extra cells than the header.
-        cells = list(row[: len(headers)])
-        if len(cells) < len(headers):
-            cells.extend([None] * (len(headers) - len(cells)))
-        if not any(_xlsx_cell_to_str(c).strip() for c in cells):
-            # Skip fully-blank rows (openpyxl read_only routinely yields
-            # trailing empties when the worksheet's used range exceeds
-            # the actual data).
-            continue
-        rows.append(
-            {
-                header: _xlsx_cell_to_str(value)
-                for header, value in zip(headers, cells)
-            }
-        )
-
-    return headers, rows
-
-
-def _parse_xlsx(
-    file_bytes: bytes,
-    sheet_name: Optional[str],
-    parameters: List[CallImportSchemaParameter],
-    parameter_mapping: Dict[str, str],
-    skipped_columns: List[str],
-) -> CallImportParseResult:
-    """Parse a single worksheet from an xlsx/xlsm workbook.
-
-    ``sheet_name`` must match one of the workbook's sheets (case
-    insensitive whitespace-trimmed match). Returns the same shape as
-    :func:`_parse_csv` so the upload handler can persist either format
-    through the same code path.
-    """
-    if not sheet_name or not sheet_name.strip():
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="sheet_name is required when uploading an Excel workbook.",
-        )
-
-    workbook = _open_xlsx_workbook(file_bytes)
-    try:
-        sheet_names = list(workbook.sheetnames)
-        target_norm = sheet_name.strip().lower()
-        match = next(
-            (s for s in sheet_names if s.strip().lower() == target_norm),
-            None,
-        )
-        if match is None:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"Sheet '{sheet_name}' not found in workbook. "
-                    f"Available sheets: {sheet_names}"
-                ),
-            )
-        worksheet = workbook[match]
-        headers, rows = _xlsx_sheet_headers_and_rows(worksheet)
-    finally:
-        workbook.close()
-
-    if not headers:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Sheet '{sheet_name}' is missing a header row.",
-        )
-
-    return _apply_schema_mapping(
-        headers,
-        rows,
-        parameters,
-        parameter_mapping,
-        skipped_columns,
-        source_label=f"Sheet '{sheet_name}'",
-    )
-
-
-def _csv_preview_sheets(
-    file_bytes: bytes, filename: Optional[str]
-) -> List[CallImportPreviewSheet]:
-    """Build the synthetic single-sheet preview entry for a CSV upload."""
-    if not file_bytes:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Uploaded CSV is empty.",
-        )
-    try:
-        text_stream = io.StringIO(file_bytes.decode("utf-8-sig"))
-    except UnicodeDecodeError:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="CSV must be UTF-8 encoded.",
-        )
-    reader = csv.DictReader(text_stream)
-    if not reader.fieldnames:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="CSV is missing a header row.",
-        )
-    headers = list(reader.fieldnames)
-    row_count = 0
-    for row in reader:
-        # Match the parse-time skip: ignore fully blank rows so the
-        # count the user sees lines up with what /upload will ingest.
-        if any((v or "").strip() for v in row.values()):
-            row_count += 1
-
-    sheet_label = (filename or "sheet1").rsplit("/", 1)[-1] or "sheet1"
-    return [
-        CallImportPreviewSheet(
-            name=sheet_label,
-            headers=headers,
-            row_count=row_count,
-        )
-    ]
-
-
-def _xlsx_preview_sheets(file_bytes: bytes) -> List[CallImportPreviewSheet]:
-    """List every worksheet in the workbook with its headers and row count."""
-    workbook = _open_xlsx_workbook(file_bytes)
-    sheets: List[CallImportPreviewSheet] = []
-    try:
-        for name in workbook.sheetnames:
-            worksheet = workbook[name]
-            headers, rows = _xlsx_sheet_headers_and_rows(worksheet)
-            sheets.append(
-                CallImportPreviewSheet(
-                    name=name,
-                    headers=headers,
-                    row_count=len(rows),
-                )
-            )
-    finally:
-        workbook.close()
-    return sheets
-
-
-def _parse_json_form_field(name: str, raw: Optional[str], default):
-    """Decode a JSON-encoded form field with a friendly 400 on bad JSON."""
-    if raw is None or raw == "":
-        return default
-    try:
-        return json.loads(raw)
-    except json.JSONDecodeError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"{name} must be valid JSON: {exc}",
-        )
-
-
-# ---------------------------------------------------------------------------
-# Shared helpers used by the staged endpoints (UPLOAD / MAP / IMPORT) and the
-# legacy one-shot ``POST /upload`` shim. Extracted here so each stage and the
-# back-compat path operate on the exact same validation + persistence code.
-# ---------------------------------------------------------------------------
-
-
-def _source_content_type(fmt: str) -> str:
-    """Return the canonical ``Content-Type`` for a parsed file format."""
-    if fmt == "csv":
-        return "text/csv"
-    if fmt == "xlsx":
-        return (
-            "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
-        )
-    return "application/octet-stream"
-
-
-def _source_s3_key(
-    organization_id: UUID, call_import_id: UUID, fmt: str
-) -> str:
-    """Build the canonical S3 key for an upload's source file.
-
-    Mirrors the per-row recording key convention used by
-    ``process_call_import_row`` so a single prefix sweep on delete still
-    cleans up both the source artefact and every fetched recording.
-    """
-    from app.services.storage.s3_service import s3_service
-
-    ext = "xlsx" if fmt == "xlsx" else "csv"
-    return (
-        f"{s3_service.prefix}organizations/{organization_id}/call_imports/"
-        f"{call_import_id}/source.{ext}"
-    )
-
-
-def _build_available_sheets(
-    file_bytes: bytes, fmt: str, filename: Optional[str]
-) -> List[CallImportPreviewSheet]:
-    """Snapshot of sheets + headers cached on the batch at UPLOAD time."""
-    if fmt == "csv":
-        return _csv_preview_sheets(file_bytes, filename)
-    return _xlsx_preview_sheets(file_bytes)
-
-
-def _resolve_schema(
-    db: Session,
-    organization_id: UUID,
-    workspace_id: UUID,
-    schema_id: UUID,
-) -> CallImportSchema:
-    """Fetch + validate a schema row in the active workspace.
-
-    Eager-loads ``parameters`` so callers can iterate without re-querying.
-    """
-    from sqlalchemy.orm import selectinload as _selectinload
-
-    schema = (
-        db.query(CallImportSchema)
-        .options(_selectinload(CallImportSchema.parameters))
-        .filter(
-            CallImportSchema.id == schema_id,
-            CallImportSchema.organization_id == organization_id,
-            CallImportSchema.workspace_id == workspace_id,
-        )
-        .first()
-    )
-    if not schema:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Call import schema not found in the active workspace.",
-        )
-    if not list(schema.parameters):
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Selected schema has no parameters defined.",
-        )
-    return schema
-
-
-def _validate_direct_url_import_ready(
-    parameters: List[CallImportSchemaParameter],
-    parameter_mapping: Dict[str, Any],
-) -> None:
-    """Ensure direct-URL import has a mapped recording_url column."""
-    rec_url_param = next(
-        (
-            p
-            for p in parameters
-            if p.type == CallImportParameterType.RECORDING_URL.value
-        ),
-        None,
-    )
-    if rec_url_param is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Direct URL import requires a schema parameter of type "
-                "'recording_url'."
-            ),
-        )
-    mapped_header = (parameter_mapping or {}).get(rec_url_param.name)
-    if not (mapped_header or "").strip():
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Direct URL import requires the 'recording_url' parameter to "
-                "be mapped to a source column."
-            ),
-        )
-
-
-def _validate_exotel_import_ready(
-    parameters: List[CallImportSchemaParameter],
-    parameter_mapping: Dict[str, Any],
-) -> None:
-    """Ensure Exotel credentialed import has a mapped recording_url column."""
-    rec_url_param = next(
-        (
-            p
-            for p in parameters
-            if p.type == CallImportParameterType.RECORDING_URL.value
-        ),
-        None,
-    )
-    if rec_url_param is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Exotel import requires a schema parameter of type "
-                "'recording_url'."
-            ),
-        )
-    mapped_header = (parameter_mapping or {}).get(rec_url_param.name)
-    if not (mapped_header or "").strip():
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Exotel import requires the 'recording_url' parameter to "
-                "be mapped to a source column."
-            ),
-        )
-
-
-def _resolve_telephony_integration(
-    db: Session,
-    organization_id: UUID,
-    telephony_integration_id: UUID,
-    provider: str,
-) -> TelephonyIntegration:
-    """Fetch + validate a telephony credential against the requested provider."""
-    integration = (
-        db.query(TelephonyIntegration)
-        .filter(
-            TelephonyIntegration.id == telephony_integration_id,
-            TelephonyIntegration.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not integration:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Telephony credential not found for this organization.",
-        )
-    if (integration.provider or "").lower() != provider.lower():
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"Selected credential is for provider '{integration.provider}', "
-                f"but request specified '{provider}'."
-            ),
-        )
-    if not integration.is_active:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Selected telephony credential is inactive.",
-        )
-    return integration
-
-
-def _clean_parameter_mapping(
-    mapping_payload: Any,
-    parameters: List[CallImportSchemaParameter],
-    schema_name: str,
-) -> Dict[str, str]:
-    """Trim values and drop empties; reject unknown parameter names.
-
-    Accepts an already-decoded value (dict-shaped) so the same helper
-    works for the JSON-form upload path and the JSON-body PATCH path.
-    """
-    if not isinstance(mapping_payload, dict) or not all(
-        isinstance(k, str) and (v is None or isinstance(v, str))
-        for k, v in mapping_payload.items()
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "parameter_mapping must be an object of "
-                "{parameter_name: csv_header}."
-            ),
-        )
-
-    valid_param_names = {p.name for p in parameters}
-    cleaned: Dict[str, str] = {}
-    for raw_name, raw_header in mapping_payload.items():
-        if raw_name not in valid_param_names:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    f"parameter_mapping references unknown parameter "
-                    f"'{raw_name}' on schema '{schema_name}'."
-                ),
-            )
-        header = (raw_header or "").strip()
-        if header:
-            cleaned[raw_name] = header
-    return cleaned
-
-
-def _clean_skipped_columns(skipped_payload: Any) -> List[str]:
-    """Dedupe (case-insensitively) and drop blanks; preserve original casing."""
-    if not isinstance(skipped_payload, list) or not all(
-        isinstance(item, str) for item in skipped_payload
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="skipped_columns must be a list of header strings.",
-        )
-    cleaned: List[str] = []
-    seen: set[str] = set()
-    for item in skipped_payload:
-        norm = _normalize_header(item)
-        if not norm or norm in seen:
-            continue
-        seen.add(norm)
-        cleaned.append(item)
-    return cleaned
-
-
-def _parse_source_file(
-    file_bytes: bytes,
-    fmt: str,
-    sheet_name: Optional[str],
-    parameters: List[CallImportSchemaParameter],
-    cleaned_mapping: Dict[str, str],
-    cleaned_skipped: List[str],
-) -> CallImportParseResult:
-    """Run the format-appropriate parser against a buffer of file bytes."""
-    if fmt == "csv":
-        return _parse_csv(file_bytes, parameters, cleaned_mapping, cleaned_skipped)
-    return _parse_xlsx(
-        file_bytes, sheet_name, parameters, cleaned_mapping, cleaned_skipped
-    )
-
-
-def _materialize_rows(
-    db: Session,
-    call_import: CallImport,
-    parsed_rows: List[Dict[str, Any]],
-    organization_id: UUID,
-) -> List[CallImportRow]:
-    """Insert one ``CallImportRow`` per parsed row, returning the new models."""
-    row_models: List[CallImportRow] = []
-    for idx, row in enumerate(parsed_rows):
-        # Stamp ``transcript_source='csv'`` when the upload actually
-        # provided a transcript so the UI badge ("From CSV") works from
-        # day one. Blank cells stay NULL so the row reads as "no
-        # production transcript yet".
-        csv_transcript = row["transcript"]
-        row_model = CallImportRow(
-            call_import_id=call_import.id,
-            organization_id=organization_id,
-            workspace_id=call_import.workspace_id,
-            row_index=idx,
-            conversation_id=row["conversation_id"],
-            recording_date=(
-                _parse_recording_date_cell(row["recording_date"])
-                if row.get("recording_date")
-                else None
-            ),
-            recording_url=row["recording_url"],
-            transcript=csv_transcript,
-            transcript_source=(
-                "csv" if csv_transcript and csv_transcript.strip() else None
-            ),
-            raw_columns=row["parameter_values"] or None,
-            status=CallImportRowStatus.PENDING,
-        )
-        db.add(row_model)
-        row_models.append(row_model)
-    return row_models
-
-
-def _enqueue_row_tasks(
-    db: Session,
-    call_import: CallImport,
-    row_models: List[CallImportRow],
-) -> None:
-    """Schedule fair round-robin dispatch for pending import rows."""
-    del db, call_import, row_models
-    from app.workers.concurrency.fair_import_dispatch import (
-        schedule_fair_import_dispatch,
-    )
-
-    schedule_fair_import_dispatch(max_workspace_turns=999)
-
-
-def _ensure_blob_storage_enabled() -> None:
-    """Hard-fail UPLOAD if cloud blob storage isn't configured (no local fallback)."""
-    from app.services.storage.s3_service import s3_service
-
-    if not s3_service.is_enabled():
-        err = (
-            s3_service.get_status_message()
-            or "Cloud blob storage is not enabled or not configured"
-        )
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail=(
-                "Call uploads require cloud blob storage so the file can be "
-                f"persisted between stages: {err}"
-            ),
-        )
-
-
-def _validate_sheet_choice(
-    fmt: str,
-    sheet_name: Optional[str],
-    available_sheets: Optional[List[Dict[str, Any]]],
-) -> Optional[str]:
-    """Normalize / validate ``sheet_name`` against the persisted snapshot.
-
-    Returns the canonical sheet name (matching the workbook's casing)
-    so downstream parsing addresses the right worksheet.
-    """
-    if fmt == "csv":
-        if sheet_name and sheet_name.strip():
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail="sheet_name is not applicable to CSV uploads.",
-            )
-        return None
-
-    cleaned = (sheet_name or "").strip() or None
-    if cleaned is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="sheet_name is required when the source is an Excel workbook.",
-        )
-
-    if not available_sheets:
-        # Nothing to validate against (e.g. legacy batch without snapshot);
-        # let downstream parsing error out instead of silently importing.
-        return cleaned
-
-    target = cleaned.strip().lower()
-    for entry in available_sheets:
-        name = entry.get("name") if isinstance(entry, dict) else None
-        if isinstance(name, str) and name.strip().lower() == target:
-            return name
-    sheet_names = [
-        entry.get("name") for entry in available_sheets if isinstance(entry, dict)
-    ]
-    raise HTTPException(
-        status_code=status.HTTP_400_BAD_REQUEST,
-        detail=(
-            f"Sheet '{cleaned}' not found in the staged file. "
-            f"Available sheets: {sheet_names}"
-        ),
-    )
-
-
-def _tag_response_payload(tags: Optional[List[CallImportTag]]) -> List[Dict[str, Any]]:
-    """Shape a CallImport's tag relationship for the upload response."""
-    return [
-        {
-            "id": tag.id,
-            "name": tag.name,
-            "color": tag.color,
-            "created_at": tag.created_at,
-            "updated_at": tag.updated_at,
-        }
-        for tag in (tags or [])
-    ]
-
-
-@router.post(
-    "/preview",
-    response_model=CallImportPreviewResponse,
-    operation_id="previewCallImportFile",
-)
-async def preview_call_import_file(
-    file: UploadFile = File(...),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportPreviewResponse:
-    """Inspect an uploaded CSV / Excel file and return its sheets + headers.
-
-    Drives the column-mapping UI without forcing the frontend to parse
-    CSV / xlsx itself — keeps client and server in lockstep on quoted
-    fields, encodings, and Excel cell coercion. CSVs return a single
-    synthetic sheet named after the filename; Excel workbooks return one
-    entry per worksheet (in workbook order).
-    """
-    del api_key, organization_id, workspace_id, db  # auth only
-
-    fmt = _file_format(file.filename)
-    if fmt is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Unsupported file format. Allowed extensions: "
-                f"{', '.join(ALLOWED_EXTENSIONS)}."
-            ),
-        )
-
-    file_bytes = await file.read()
-    if len(file_bytes) > MAX_UPLOAD_BYTES:
-        raise HTTPException(
-            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
-            detail=f"File exceeds {MAX_UPLOAD_BYTES} bytes",
-        )
-
-    if fmt == "csv":
-        sheets = _csv_preview_sheets(file_bytes, file.filename)
-    else:
-        sheets = _xlsx_preview_sheets(file_bytes)
-
-    return CallImportPreviewResponse(format=fmt, sheets=sheets)
-
-
-@router.post(
-    "",
-    response_model=CallImportResponse,
-    status_code=status.HTTP_201_CREATED,
-    operation_id="createCallImport",
-)
-async def create_call_import(
-    file: UploadFile = File(
-        ...,
-        description="CSV / Excel file to stage. Persisted to S3 between stages.",
-    ),
-    dataset: str = Form(
-        ...,
-        description=(
-            "Required free-text dataset label. Collected up-front so the "
-            "batch is filterable from the moment it lands."
-        ),
-    ),
-    tag_ids: Optional[List[UUID]] = Form(
-        None,
-        description="Optional list of CallImportTag ids to attach to the new batch.",
-    ),
-    schema_id: Optional[UUID] = Form(
-        None,
-        description=(
-            "Optional schema pre-pick. The user can still change it during "
-            "the MAP stage; provided here only so the detail page can pre-"
-            "select the schema dropdown."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportResponse:
-    """UPLOAD stage of the staged call-import flow.
-
-    Persists the source file to S3 and creates a ``CallImport`` row with
-    ``status='uploaded'``. No mapping, no provider, no rows yet — the
-    user moves through MAP and IMPORT as separate idempotent steps.
-
-    Dataset is collected here (rather than at IMPORT) so the batch is
-    filterable from the moment it appears in the list view.
-    """
-    del api_key
-
-    fmt = _file_format(file.filename)
-    if fmt is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Unsupported file format. Allowed extensions: "
-                f"{', '.join(ALLOWED_EXTENSIONS)}."
-            ),
-        )
-
-    normalized_dataset = _normalize_dataset(dataset)
-    if not normalized_dataset:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="dataset is required and must be a non-empty string.",
-        )
-
-    file_bytes = await file.read()
-    if len(file_bytes) > MAX_UPLOAD_BYTES:
-        raise HTTPException(
-            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
-            detail=f"File exceeds {MAX_UPLOAD_BYTES} bytes",
-        )
-
-    # Parse-now so we (a) reject garbage uploads up-front instead of
-    # later in the MAP step, and (b) capture the sheets snapshot the
-    # MAP UI needs without having to re-fetch the file from S3.
-    sheets = _build_available_sheets(file_bytes, fmt, file.filename)
-
-    # Optional schema pre-pick: validated only if supplied (the user is
-    # allowed to set it for the first time during MAP).
-    if schema_id is not None:
-        _resolve_schema(db, organization_id, workspace_id, schema_id)
-
-    tag_rows = _resolve_tags(db, organization_id, tag_ids)
-
-    _ensure_blob_storage_enabled()
-
-    # Pre-generate the id so we can compute a deterministic S3 key
-    # before the row is persisted, keeping ``source_s3_key`` consistent
-    # with the prefix sweep used at delete-time.
-    import uuid as _uuid
-
-    call_import_id = _uuid.uuid4()
-    s3_key = _source_s3_key(organization_id, call_import_id, fmt)
-    content_type = _source_content_type(fmt)
-
-    from app.services.storage.s3_service import s3_service, StorageError
-
-    try:
-        s3_service.upload_file_by_key(file_bytes, s3_key, content_type=content_type)
-    except StorageError as exc:
-        logger.exception(
-            "Failed to upload source file to S3 for new call import {}",
-            call_import_id,
-        )
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail=f"Failed to persist upload to S3: {exc}",
-        )
-
-    call_import = CallImport(
-        id=call_import_id,
-        organization_id=organization_id,
-        workspace_id=workspace_id,
-        # Provider + credential aren't known until the IMPORT stage; leave
-        # them NULL so the staged-vs-legacy distinction is visible at a
-        # glance from the DB.
-        provider=None,
-        telephony_integration_id=None,
-        original_filename=file.filename,
-        sheet_name=None,
-        dataset=normalized_dataset,
-        schema_id=schema_id,
-        parameter_mapping={},
-        skipped_columns=[],
-        column_mapping={},
-        extra_columns=[],
-        custom_column_mapping={},
-        source_s3_key=s3_key,
-        source_format=fmt,
-        source_size_bytes=len(file_bytes),
-        source_content_type=content_type,
-        available_sheets=[sheet.model_dump() for sheet in sheets],
-        total_rows=0,
-        completed_rows=0,
-        failed_rows=0,
-        status=CallImportStatus.UPLOADED,
-    )
-    if tag_rows:
-        call_import.tags = tag_rows
-
-    db.add(call_import)
-    try:
-        db.commit()
-    except Exception:
-        db.rollback()
-        # Best-effort cleanup of the uploaded S3 object so a failed
-        # commit doesn't leak storage.
-        try:
-            s3_service.delete_file_by_key(s3_key)
-        except Exception as cleanup_exc:  # noqa: BLE001
-            logger.warning(
-                "Failed to clean up orphaned S3 object {} after DB rollback: {}",
-                s3_key,
-                cleanup_exc,
-            )
-        raise
-
-    db.refresh(call_import)
-    return _serialize_call_import(db, call_import)
-
-
-@router.patch(
-    "/{call_import_id}/mapping",
-    response_model=CallImportResponse,
-    operation_id="updateCallImportMapping",
-)
-async def update_call_import_mapping(
-    call_import_id: UUID,
-    payload: CallImportMappingUpdate,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportResponse:
-    """MAP stage of the staged call-import flow.
-
-    Validates ``parameter_mapping`` + ``skipped_columns`` against the
-    sheet headers captured at UPLOAD time and persists them on the
-    batch. Idempotent: callers may submit this multiple times while
-    the batch is in ``uploaded`` or ``mapped`` state.
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-            CallImport.workspace_id == workspace_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    if call_import.status not in (
-        CallImportStatus.UPLOADED,
-        CallImportStatus.MAPPED,
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=(
-                f"Cannot edit mapping on a batch in status "
-                f"'{call_import.status.value}'. Mapping can only be edited "
-                "before the IMPORT stage."
-            ),
-        )
-
-    if not call_import.source_s3_key or not call_import.source_format:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=(
-                "This batch was not uploaded through the staged flow and "
-                "cannot have its mapping edited."
-            ),
-        )
-
-    schema = _resolve_schema(
-        db, organization_id, workspace_id, payload.schema_id
-    )
-    parameters = list(schema.parameters)
-
-    canonical_sheet = _validate_sheet_choice(
-        call_import.source_format,
-        payload.sheet_name,
-        call_import.available_sheets,
-    )
-
-    # Pull the headers for the selected sheet straight out of the
-    # snapshot so we don't have to re-download the file from S3 just to
-    # validate the mapping.
-    headers: List[str] = []
-    if call_import.available_sheets:
-        if canonical_sheet is None:
-            # CSV: single synthetic sheet.
-            entry = call_import.available_sheets[0]
-            headers = list(entry.get("headers") or [])
-        else:
-            for entry in call_import.available_sheets:
-                if not isinstance(entry, dict):
-                    continue
-                name = entry.get("name")
-                if isinstance(name, str) and name == canonical_sheet:
-                    headers = list(entry.get("headers") or [])
-                    break
-
-    cleaned_mapping = _clean_parameter_mapping(
-        payload.parameter_mapping, parameters, schema.name
-    )
-    cleaned_skipped = _clean_skipped_columns(payload.skipped_columns)
-
-    # Run the same per-column validation as the parse path so the user
-    # gets an immediate 400 if a required parameter is left unmapped or
-    # a header is neither mapped nor skipped — without needing to read
-    # the file. ``validate_only`` skips the row loop (and the empty-rows
-    # guard) since the row data lives in S3, not in this request.
-    if headers:
-        _apply_schema_mapping(
-            headers,
-            iter(()),
-            parameters,
-            cleaned_mapping,
-            cleaned_skipped,
-            source_label=(
-                f"Sheet '{canonical_sheet}'"
-                if canonical_sheet is not None
-                else "CSV"
-            ),
-            validate_only=True,
-        )
-
-    call_import.schema_id = schema.id
-    call_import.parameter_mapping = dict(cleaned_mapping)
-    call_import.skipped_columns = list(cleaned_skipped)
-    call_import.sheet_name = canonical_sheet
-    call_import.status = CallImportStatus.MAPPED
-    db.commit()
-    db.refresh(call_import)
-    return _serialize_call_import(db, call_import)
-
-
-@router.post(
-    "/{call_import_id}/import",
-    response_model=CallImportUploadResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="startCallImport",
-)
-async def start_call_import(
-    call_import_id: UUID,
-    payload: CallImportStartRequest,
-    background_tasks: BackgroundTasks,
-    legacy: bool = Query(
-        False,
-        description=(
-            "Deprecated escape hatch for import-only processing. "
-            "New batches should use Run Evaluation instead."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportUploadResponse:
-    """Deprecated IMPORT stage — use Run Evaluation for new batches.
-
-    Recording fetch is part of the unified evaluation pipeline. This
-    endpoint remains available only with ``?legacy=true`` for backward
-    compatibility.
-    """
-    del api_key, background_tasks
-
-    if not legacy:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=(
-                "Standalone import is deprecated. Use Run Evaluation — "
-                "recording fetch is part of the evaluation pipeline. "
-                "Append ?legacy=true to use the import-only path."
-            ),
-        )
-
-    from sqlalchemy.orm import selectinload as _selectinload
-
-    call_import = (
-        db.query(CallImport)
-        .options(_selectinload(CallImport.tags))
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-            CallImport.workspace_id == workspace_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    if call_import.status != CallImportStatus.MAPPED:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=(
-                f"Cannot start import for a batch in status "
-                f"'{call_import.status.value}'. Map the columns first."
-            ),
-        )
-
-    if not call_import.source_s3_key or not call_import.source_format:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=(
-                "This batch has no staged source file and cannot be imported "
-                "through the staged flow."
-            ),
-        )
-
-    if not call_import.schema_id:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail="Cannot start import without a mapped schema.",
-        )
-
-    schema = _resolve_schema(
-        db, organization_id, workspace_id, call_import.schema_id
-    )
-    parameters = list(schema.parameters)
-
-    if payload.telephony_integration_id is not None:
-        integration = _resolve_telephony_integration(
-            db,
-            organization_id,
-            payload.telephony_integration_id,
-            payload.provider or "",
-        )
-        if (integration.provider or "").lower() == "exotel":
-            _validate_exotel_import_ready(
-                parameters, dict(call_import.parameter_mapping or {})
-            )
-    else:
-        _validate_direct_url_import_ready(
-            parameters, dict(call_import.parameter_mapping or {})
-        )
-        integration = None
-
-    _ensure_blob_storage_enabled()
-
-    if integration is not None:
-        call_import.provider = integration.provider
-        call_import.telephony_integration_id = integration.id
-    else:
-        call_import.provider = None
-        call_import.telephony_integration_id = None
-
-    call_import.total_rows = 0
-    call_import.completed_rows = 0
-    call_import.failed_rows = 0
-    call_import.error_message = None
-    call_import.status = CallImportStatus.PROCESSING
-    db.commit()
-    db.refresh(call_import)
-
-    from app.workers.tasks.call_import_bulk_ops import (
-        materialize_call_import_rows_task,
-    )
-
-    materialize_call_import_rows_task.delay(
-        str(call_import_id),
-        str(organization_id),
-        str(workspace_id),
-        schedule_import_dispatch=True,
-    )
-
-    return CallImportUploadResponse(
-        id=call_import.id,
-        total_rows=0,
-        status=call_import.status,
-        dataset=call_import.dataset,
-        tags=_tag_response_payload(call_import.tags),
-        message=(
-            "Import accepted. Rows are being materialized in the background; "
-            "recordings will be fetched asynchronously."
-        ),
-    )
-
-
-@router.post(
-    "/upload",
-    response_model=CallImportUploadResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="uploadCallImportCsv",
-    deprecated=True,
-)
-async def upload_call_import_csv(
-    background_tasks: BackgroundTasks,
-    file: UploadFile = File(...),
-    provider: Optional[str] = Form(
-        None,
-        description=(
-            "Telephony provider key (e.g. 'exotel', 'plivo'). Must match the "
-            "selected telephony_integration_id's provider. Omit together "
-            "with telephony_integration_id for direct-URL import."
-        ),
-    ),
-    telephony_integration_id: Optional[UUID] = Form(
-        None,
-        description=(
-            "Specific TelephonyIntegration credential row to use when "
-            "downloading recordings for this batch. Omit together with "
-            "provider for direct-URL import."
-        ),
-    ),
-    schema_id: UUID = Form(
-        ...,
-        description=(
-            "Reusable Input Parameter schema this upload is mapped against. "
-            "Must belong to the active workspace."
-        ),
-    ),
-    parameter_mapping: str = Form(
-        ...,
-        description=(
-            "JSON-encoded ``{schema_parameter_name: source_header}`` map "
-            "covering every required schema parameter. Optional parameters "
-            "may be omitted or set to an empty string."
-        ),
-    ),
-    skipped_columns: Optional[str] = Form(
-        None,
-        description=(
-            "JSON-encoded list of source header strings the uploader has "
-            "explicitly skipped. Every header in the file must either be "
-            "mapped or appear here; otherwise the upload is rejected so a "
-            "forgotten column never silently drops."
-        ),
-    ),
-    dataset: Optional[str] = Form(
-        None,
-        description=(
-            "Optional free-text dataset label for high-level segregation. "
-            "Empty strings are stored as NULL."
-        ),
-    ),
-    tag_ids: Optional[List[UUID]] = Form(
-        None,
-        description="Optional list of CallImportTag ids to attach to the new batch.",
-    ),
-    sheet_name: Optional[str] = Form(
-        None,
-        description=(
-            "Worksheet to import when the file is an Excel workbook "
-            "(.xlsx / .xlsm). REQUIRED for Excel uploads. Ignored for CSV "
-            "uploads (rejected with 400 if non-empty so typos surface "
-            "instead of silently importing the wrong source)."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportUploadResponse:
-    """Legacy one-shot upload kept for backward compatibility.
-
-    DEPRECATED: prefer the staged flow
-    (``POST /`` → ``PATCH /{id}/mapping`` → ``POST /{id}/import``) so
-    each step is idempotent and resumable. This endpoint runs all three
-    stages inline in a single transaction so existing scripts /
-    integrations keep working unchanged.
-    """
-    del api_key
-
-    fmt = _file_format(file.filename)
-    if fmt is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "Unsupported file format. Allowed extensions: "
-                f"{', '.join(ALLOWED_EXTENSIONS)}."
-            ),
-        )
-
-    sheet_name_clean = (sheet_name or "").strip() or None
-    if fmt == "csv" and sheet_name_clean is not None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="sheet_name is not applicable to CSV uploads.",
-        )
-    if fmt == "xlsx" and sheet_name_clean is None:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="sheet_name is required when uploading an Excel workbook.",
-        )
-
-    file_bytes = await file.read()
-    if len(file_bytes) > MAX_UPLOAD_BYTES:
-        raise HTTPException(
-            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
-            detail=f"File exceeds {MAX_UPLOAD_BYTES} bytes",
-        )
-
-    schema = _resolve_schema(db, organization_id, workspace_id, schema_id)
-    parameters = list(schema.parameters)
-
-    mapping_payload = _parse_json_form_field(
-        "parameter_mapping", parameter_mapping, {}
-    )
-    cleaned_mapping = _clean_parameter_mapping(
-        mapping_payload, parameters, schema.name
-    )
-
-    skipped_payload = _parse_json_form_field("skipped_columns", skipped_columns, [])
-    cleaned_skipped = _clean_skipped_columns(skipped_payload)
-
-    has_provider = bool((provider or "").strip())
-    has_integration = telephony_integration_id is not None
-    if has_provider != has_integration:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "provider and telephony_integration_id must both be provided "
-                "or both omitted for direct-URL import."
-            ),
-        )
-
-    if telephony_integration_id is not None:
-        integration = _resolve_telephony_integration(
-            db, organization_id, telephony_integration_id, provider or ""
-        )
-        if (integration.provider or "").lower() == "exotel":
-            _validate_exotel_import_ready(parameters, cleaned_mapping)
-    else:
-        _validate_direct_url_import_ready(parameters, cleaned_mapping)
-        integration = None
-
-    parsed_rows = _parse_source_file(
-        file_bytes, fmt, sheet_name_clean, parameters, cleaned_mapping, cleaned_skipped
-    )
-    _raise_if_no_importable_rows(parsed_rows, source_label=fmt)
-
-    tag_rows = _resolve_tags(db, organization_id, tag_ids)
-
-    call_import = CallImport(
-        organization_id=organization_id,
-        workspace_id=workspace_id,
-        provider=integration.provider if integration is not None else None,
-        telephony_integration_id=integration.id if integration is not None else None,
-        original_filename=file.filename,
-        sheet_name=sheet_name_clean,
-        dataset=_normalize_dataset(dataset),
-        schema_id=schema.id,
-        parameter_mapping=dict(cleaned_mapping),
-        skipped_columns=list(cleaned_skipped),
-        # Legacy columns are left empty on new uploads; the detail page
-        # falls back to ``parameter_mapping`` when ``schema_id`` is set.
-        column_mapping={},
-        extra_columns=[],
-        custom_column_mapping={},
-        total_rows=len(parsed_rows.rows),
-        completed_rows=0,
-        failed_rows=0,
-        status=CallImportStatus.PENDING,
-        source_row_skips=parse_skips_to_json(parsed_rows.skipped),
-    )
-    if tag_rows:
-        call_import.tags = tag_rows
-    db.add(call_import)
-    db.flush()  # populate call_import.id
-    if integration is None:
-        # The model's historical Python default is "exotel"; direct-URL
-        # imports intentionally have no telephony provider.
-        call_import.provider = None
-
-    row_models = _materialize_rows(
-        db, call_import, parsed_rows.rows, organization_id
-    )
-
-    call_import.status = CallImportStatus.PROCESSING
-    db.commit()
-    db.refresh(call_import)
-
-    background_tasks.add_task(
-        record_call_import_batch_created,
-        organization_id,
-        call_import.id,
-        workspace_id=workspace_id,
-        total_rows=call_import.total_rows,
-        source="csv",
-        provider=call_import.provider,
-    )
-
-    _enqueue_row_tasks(db, call_import, row_models)
-
-    return CallImportUploadResponse(
-        id=call_import.id,
-        total_rows=call_import.total_rows,
-        status=call_import.status,
-        dataset=call_import.dataset,
-        tags=_tag_response_payload(call_import.tags),
-        message=(
-            f"Accepted {call_import.total_rows} rows for import. "
-            "Recordings will be fetched asynchronously."
-        ),
-    )
-
-
-@router.post(
-    "/audio-upload",
-    response_model=CallImportUploadResponse,
-    status_code=status.HTTP_201_CREATED,
-    operation_id="uploadCallImportAudio",
-)
-async def upload_call_import_audio(
-    background_tasks: BackgroundTasks,
-    files: List[UploadFile] = File(
-        ...,
-        description="One or more manual call recording audio files.",
-    ),
-    dataset: str = Form(
-        ...,
-        description="Required free-text dataset label for the manual upload batch.",
-    ),
-    tag_ids: Optional[List[UUID]] = Form(
-        None,
-        description="Optional list of CallImportTag ids to attach to the new batch.",
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportUploadResponse:
-    """Persist manually uploaded recordings as completed CallImport rows.
-
-    The rows skip the provider-download worker entirely because the audio
-    bytes are already in hand. From this point onward they behave exactly
-    like completed CSV-import rows: playback reads ``recording_s3_key`` and
-    the existing diarisation/evaluation endpoints can operate on them.
-    """
-
-    normalized_dataset = _normalize_dataset(dataset)
-    if not normalized_dataset:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="dataset is required and must be a non-empty string.",
-        )
-    if not files:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="At least one audio file is required.",
-        )
-
-    _ensure_blob_storage_enabled()
-    tag_rows = _resolve_tags(db, organization_id, tag_ids)
-
-    max_bytes = int(settings.MAX_FILE_SIZE_MB) * 1024 * 1024
-    prepared: List[Dict[str, Any]] = []
-    conversation_counts: Dict[str, int] = {}
-
-    for idx, upload in enumerate(files):
-        filename = upload.filename or f"recording-{idx + 1}"
-        ext = _audio_extension(filename)
-        if not ext:
-            allowed = ", ".join(settings.ALLOWED_AUDIO_FORMATS)
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Unsupported audio file '{filename}'. Allowed formats: {allowed}.",
-            )
-
-        contents = await upload.read()
-        if not contents:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Audio file '{filename}' is empty.",
-            )
-        if len(contents) > max_bytes:
-            raise HTTPException(
-                status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
-                detail=(
-                    f"Audio file '{filename}' exceeds "
-                    f"{settings.MAX_FILE_SIZE_MB} MB."
-                ),
-            )
-
-        base_conversation_id = _sanitize_conversation_id(_filename_stem(filename))
-        conversation_id = _dedupe_conversation_id(
-            base_conversation_id,
-            conversation_counts,
-        )
-        prepared.append(
-            {
-                "filename": filename,
-                "extension": ext,
-                "content_type": _audio_content_type(ext, upload.content_type),
-                "contents": contents,
-                "conversation_id": conversation_id,
-            }
-        )
-
-    original_filename = (
-        prepared[0]["filename"]
-        if len(prepared) == 1
-        else f"{len(prepared)} manual recordings"
-    )
-    total_size = sum(len(item["contents"]) for item in prepared)
-    uploaded_keys: List[str] = []
-
-    from app.services.storage.s3_service import s3_service
-
-    call_import = CallImport(
-        organization_id=organization_id,
-        workspace_id=workspace_id,
-        provider=None,
-        telephony_integration_id=None,
-        original_filename=original_filename,
-        source_format="audio",
-        source_size_bytes=total_size,
-        source_content_type="audio/*",
-        dataset=normalized_dataset,
-        total_rows=len(prepared),
-        completed_rows=len(prepared),
-        failed_rows=0,
-        status=CallImportStatus.COMPLETED,
-    )
-    if tag_rows:
-        call_import.tags = tag_rows
-
-    try:
-        db.add(call_import)
-        db.flush()
-        # The model's historical Python default is "exotel"; manual uploads
-        # intentionally have no telephony provider.
-        call_import.provider = None
-
-        row_mappings: List[Dict[str, Any]] = []
-        for idx, item in enumerate(prepared):
-            row_id = uuid4()
-            key = _audio_s3_key(
-                organization_id,
-                call_import.id,
-                row_id,
-                item["extension"],
-            )
-            s3_service.upload_file_by_key(
-                item["contents"],
-                key,
-                content_type=item["content_type"],
-            )
-            uploaded_keys.append(key)
-
-            row_mappings.append(
-                {
-                    "id": row_id,
-                    "call_import_id": call_import.id,
-                    "organization_id": organization_id,
-                    "workspace_id": workspace_id,
-                    "row_index": idx,
-                    "conversation_id": item["conversation_id"],
-                    "recording_url": None,
-                    "transcript": None,
-                    "transcript_source": None,
-                    "raw_columns": {"conversation_id": item["conversation_id"]},
-                    "status": CallImportRowStatus.COMPLETED,
-                    "recording_s3_key": key,
-                    "recording_content_type": item["content_type"],
-                    "recording_size_bytes": len(item["contents"]),
-                }
-            )
-
-        if is_sharding_enabled():
-            from app.db_sharding.row_ops import (
-                bulk_insert_mappings_on_shards,
-                register_shard_slices,
-            )
-
-            bulk_insert_mappings_on_shards(db, call_import.id, row_mappings)
-            register_shard_slices(db, call_import.id, len(row_mappings))
-        else:
-            for mapping in row_mappings:
-                db.add(CallImportRow(**mapping))
-
-        db.commit()
-    except Exception as exc:
-        db.rollback()
-        if uploaded_keys and s3_service.is_enabled():
-            try:
-                s3_service.delete_keys(uploaded_keys)
-            except Exception:
-                logger.exception(
-                    "Failed to clean up manual audio upload keys after error"
-                )
-        logger.exception("Failed to persist manual call recording upload")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to upload manual recordings: {exc}",
-        ) from exc
-
-    db.refresh(call_import)
-    background_tasks.add_task(
-        record_call_import_batch_created,
-        organization_id,
-        call_import.id,
-        workspace_id=workspace_id,
-        total_rows=call_import.total_rows,
-        source="audio",
-        provider=None,
-    )
-    return CallImportUploadResponse(
-        id=call_import.id,
-        total_rows=call_import.total_rows,
-        status=call_import.status,
-        dataset=call_import.dataset,
-        tags=_tag_response_payload(call_import.tags),
-        message=(
-            f"Uploaded {call_import.total_rows} manual recording"
-            f"{'' if call_import.total_rows == 1 else 's'}."
-        ),
-    )
-
-
-@router.get(
-    "",
-    response_model=CallImportListResponse,
-    operation_id="listCallImports",
-)
-async def list_call_imports(
-    page: int = Query(1, ge=1),
-    page_size: int = Query(20, ge=1, le=100),
-    status_filter: Optional[CallImportStatus] = Query(None, alias="status"),
-    dataset: Optional[str] = Query(
-        None,
-        description=(
-            "Filter by exact dataset string (case-insensitive). Pass the "
-            "literal value '__none__' to filter to imports with no dataset."
-        ),
-    ),
-    tag_id: Optional[List[UUID]] = Query(
-        None,
-        description="Filter to imports tagged with ALL of the given tag ids.",
-    ),
-    source_format: Optional[str] = Query(
-        None,
-        description=(
-            "Filter by source format. Use 'audio' for manual recordings or "
-            "'__non_audio__' for CSV/Excel/legacy imports."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportListResponse:
-    """List call-import batches for the active workspace, newest first.
-
-    Scoped to (organization_id, workspace_id) so users only see imports
-    for the workspace they're currently in. Supports a high-level
-    ``dataset`` filter (powers the segregation dropdown at the top of
-    the imports page) plus an AND-style multi-tag filter via repeated
-    ``tag_id`` parameters.
-    """
-
-    query = (
-        db.query(CallImport)
-        .filter(
-            CallImport.organization_id == organization_id,
-            CallImport.workspace_id == workspace_id,
-        )
-    )
-    if status_filter is not None:
-        query = query.filter(CallImport.status == status_filter)
-
-    source_filter = (source_format or "").strip().lower()
-    if source_filter == "__non_audio__":
-        query = query.filter(
-            or_(CallImport.source_format.is_(None), CallImport.source_format != "audio")
-        )
-    elif source_filter:
-        query = query.filter(func.lower(CallImport.source_format) == source_filter)
-
-    if dataset is not None:
-        if dataset == "__none__":
-            query = query.filter(CallImport.dataset.is_(None))
-        elif dataset.strip():
-            query = query.filter(
-                func.lower(CallImport.dataset) == dataset.strip().lower()
-            )
-
-    if tag_id:
-        from app.models.database import CallImportTagAssignment
-
-        for single_tag_id in tag_id:
-            sub = (
-                db.query(CallImportTagAssignment.call_import_id)
-                .filter(CallImportTagAssignment.tag_id == single_tag_id)
-                .subquery()
-            )
-            query = query.filter(CallImport.id.in_(sub))
-
-    total = query.count()
-    items = (
-        query.order_by(desc(CallImport.created_at))
-        .offset((page - 1) * page_size)
-        .limit(page_size)
-        .all()
-    )
-
-    return CallImportListResponse(
-        items=[_serialize_call_import(db, item) for item in items],
-        total=total,
-        page=page,
-        page_size=page_size,
-    )
-
-
-@router.get(
-    "/dispatch-diagnostics",
-    response_model=CallImportDispatchDiagnosticsResponse,
-    operation_id="getCallImportDispatchDiagnostics",
-    dependencies=[Depends(require_admin)],
-)
-async def get_call_import_dispatch_diagnostics(
-    workspace_id: Optional[UUID] = Query(
-        None,
-        description=(
-            "Optional workspace filter. When omitted, returns every workspace "
-            "in the organization with active eval dispatch state."
-        ),
-    ),
-    include_idle_workspaces: bool = Query(
-        False,
-        description=(
-            "When true, include org workspaces with zero pending rows and "
-            "zero in-flight slots."
-        ),
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportDispatchDiagnosticsResponse:
-    """Live eval slot usage and fair-dispatch state for operators.
-
-    Org admins use this to diagnose cross-workspace starvation (e.g. one
-    workspace's 10k run blocking another's pending eval rows) by inspecting
-    Redis in-flight counters, pending dispatch rows, and scheduler cursors.
-    """
-    del api_key
-    payload = build_call_import_dispatch_diagnostics(
-        db,
-        organization_id,
-        workspace_id=workspace_id,
-        include_idle_workspaces=include_idle_workspaces,
-    )
-    return CallImportDispatchDiagnosticsResponse.model_validate(payload)
-
-
-@router.get(
-    "/datasets",
-    response_model=List[str],
-    operation_id="listCallImportDatasets",
-)
-async def list_call_import_datasets(
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> List[str]:
-    """Return the distinct, non-null dataset labels in use for the active
-    workspace.
-
-    Scoped per-workspace so each workspace's Dataset dropdown only shows
-    its own segregation labels.
-    """
-    rows = (
-        db.query(CallImport.dataset)
-        .filter(
-            CallImport.organization_id == organization_id,
-            CallImport.workspace_id == workspace_id,
-            CallImport.dataset.isnot(None),
-            CallImport.dataset != "",
-        )
-        .distinct()
-        .order_by(CallImport.dataset.asc())
-        .all()
-    )
-    return [row[0] for row in rows if row[0]]
-
-
-@router.get(
-    "/diarisation-prompt-default",
-    response_model=CallImportDiarisationPromptDefaultResponse,
-    operation_id="getCallImportDiarisationPromptDefault",
-)
-async def get_call_import_diarisation_prompt_default(
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-) -> CallImportDiarisationPromptDefaultResponse:
-    """Return the canonical LLM diariser prompt.
-
-    The Transcribe / Run Evaluation modals call this on open so they
-    can pre-fill the prompt textarea. Returning the constant from the
-    backend (rather than hard-coding it in the frontend) keeps the
-    fallback used by the worker and the placeholder shown in the UI
-    in lock-step — operators always see the *actual* default they'd
-    get if they leave the field blank.
-
-    Registered before ``GET /{call_import_id}`` so the static path is
-    not mistaken for a UUID import id (which would 422).
-    """
-    del api_key, organization_id
-    from app.workers.tasks.helpers.llm_diarisation import (
-        DEFAULT_DIARIZATION_PROMPT,
-    )
-
-    return CallImportDiarisationPromptDefaultResponse(
-        prompt=DEFAULT_DIARIZATION_PROMPT
-    )
-
-
-@router.patch(
-    "/{call_import_id}",
-    response_model=CallImportResponse,
-    operation_id="updateCallImport",
-)
-async def update_call_import(
-    call_import_id: UUID,
-    payload: CallImportUpdate,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    db: Session = Depends(get_db),
-) -> CallImportResponse:
-    """Edit dataset / tag assignments (and schema, pre-import) on a batch.
-
-    ``dataset = ""`` clears the label; ``tag_ids = []`` removes all tag
-    assignments. Fields omitted from the body are left untouched.
-
-    ``schema_id`` is only honoured while the batch is in
-    ``uploaded`` / ``mapped`` state — once rows have been materialised
-    the schema is locked. Changing the schema resets any persisted
-    mapping (the user must re-MAP) and rewinds status to ``uploaded``.
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    body = payload.model_dump(exclude_unset=True)
-    if "dataset" in body:
-        call_import.dataset = _normalize_dataset(body["dataset"])
-
-    if "tag_ids" in body:
-        tag_ids = body["tag_ids"] or []
-        call_import.tags = _resolve_tags(db, organization_id, tag_ids)
-
-    if "schema_id" in body and body["schema_id"] is not None:
-        if call_import.status not in (
-            CallImportStatus.UPLOADED,
-            CallImportStatus.MAPPED,
-        ):
-            raise HTTPException(
-                status_code=status.HTTP_409_CONFLICT,
-                detail=(
-                    f"Cannot reassign schema on a batch in status "
-                    f"'{call_import.status.value}'."
-                ),
-            )
-        new_schema = _resolve_schema(
-            db, organization_id, workspace_id, body["schema_id"]
-        )
-        if call_import.schema_id != new_schema.id:
-            # Switching schemas invalidates the persisted mapping —
-            # parameter names won't line up with the new schema, so
-            # reset to UPLOADED and force a fresh MAP.
-            call_import.schema_id = new_schema.id
-            call_import.parameter_mapping = {}
-            call_import.skipped_columns = []
-            call_import.sheet_name = None
-            call_import.status = CallImportStatus.UPLOADED
-
-    db.commit()
-    db.refresh(call_import)
-    return _serialize_call_import(db, call_import)
-
-
-@router.get(
-    "/{call_import_id}",
-    response_model=CallImportDetailResponse,
-    operation_id="getCallImportDetail",
-)
-async def get_call_import_detail(
-    call_import_id: UUID,
-    row_limit: int = Query(500, ge=0, le=5000),
-    row_offset: int = Query(0, ge=0),
-    q: Optional[str] = Query(
-        None,
-        description=(
-            "Optional case-insensitive substring filter on "
-            "``conversation_id``. When set, ``filtered_total_rows`` in "
-            "the response reflects the post-filter row count so the UI "
-            "can paginate against the filtered slice."
-        ),
-    ),
-    diarised_status: Optional[str] = Query(
-        None,
-        description=(
-            "Optional filter on ``CallImportRow.diarised_transcript_status``. "
-            "Accepts one of ``pending``, ``running``, ``completed``, "
-            "``failed``. When set, ``filtered_total_rows`` reflects the "
-            "post-filter row count (combined with the ``q`` filter when "
-            "both are supplied) so the UI can paginate against the same "
-            "slice it's displaying."
-        ),
-        pattern="^(pending|running|completed|failed)$",
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportDetailResponse:
-    """Fetch a single import batch with a slice of its rows.
-
-    ``row_limit=0`` is intentionally allowed so callers that only need the
-    batch metadata (e.g. the evaluation-detail page rendering the parent's
-    column mapping) can skip the rows payload entirely.
-    """
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    if call_import.status == CallImportStatus.PROCESSING:
-        from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
-
-        prior_status = call_import.status
-        rollup_call_import_batch_status(db, call_import)
-        if call_import.status != prior_status:
-            db.commit()
-            db.refresh(call_import)
-
-    search_term = (q or "").strip()
-    diarised_status_filter = (diarised_status or "").strip() or None
-    filtered_total_rows: Optional[int] = None
-    has_row_filters = bool(search_term or diarised_status_filter)
-
-    if has_row_filters:
-        if is_sharding_enabled():
-            from app.db_sharding.scatter_gather import count_call_import_rows_filtered
-
-            filtered_total_rows = count_call_import_rows_filtered(
-                db,
-                call_import.id,
-                search_term=search_term,
-                diarised_status_filter=diarised_status_filter,
-            )
-        else:
-            rows_query = db.query(CallImportRow).filter(
-                CallImportRow.call_import_id == call_import.id
-            )
-            if search_term:
-                rows_query = rows_query.filter(
-                    CallImportRow.conversation_id.ilike(f"%{search_term}%")
-                )
-            if diarised_status_filter:
-                rows_query = rows_query.filter(
-                    CallImportRow.diarised_transcript_status == diarised_status_filter
-                )
-            filtered_total_rows = rows_query.count()
-
-    if row_limit == 0:
-        rows: List[CallImportRow] = []
-    elif is_sharding_enabled():
-        from app.db_sharding.scatter_gather import (
-            fetch_call_import_rows_filtered_page,
-            fetch_call_import_rows_page,
-        )
-
-        if has_row_filters:
-            rows = fetch_call_import_rows_filtered_page(
-                db,
-                call_import.id,
-                search_term=search_term,
-                diarised_status_filter=diarised_status_filter,
-                offset=row_offset,
-                limit=row_limit,
-            )
-        else:
-            rows = fetch_call_import_rows_page(
-                db,
-                call_import.id,
-                offset=row_offset,
-                limit=row_limit,
-            )
-    else:
-        rows_query = db.query(CallImportRow).filter(
-            CallImportRow.call_import_id == call_import.id
-        )
-        if search_term:
-            rows_query = rows_query.filter(
-                CallImportRow.conversation_id.ilike(f"%{search_term}%")
-            )
-        if diarised_status_filter:
-            rows_query = rows_query.filter(
-                CallImportRow.diarised_transcript_status == diarised_status_filter
-            )
-        rows = (
-            rows_query.order_by(CallImportRow.row_index)
-            .offset(row_offset)
-            .limit(row_limit)
-            .all()
-        )
-
-    # Batch-wide diarisation status aggregate. One ``GROUP BY`` query
-    # across the whole batch — much cheaper than paging through every
-    # row to recount on the client and lets the UI render a
-    # transcribe/diarise progress bar without a separate roundtrip.
-    if is_sharding_enabled():
-        from app.db_sharding.scatter_gather import aggregate_diarised_transcript_counts
-
-        diarised_status_counts = aggregate_diarised_transcript_counts(
-            db, call_import.id
-        )
-    else:
-        diarised_status_counts: Dict[str, int] = {}
-        for status_value, count in (
-            db.query(CallImportRow.diarised_transcript_status, func.count())
-            .filter(CallImportRow.call_import_id == call_import.id)
-            .group_by(CallImportRow.diarised_transcript_status)
-            .all()
-        ):
-            if isinstance(status_value, str):
-                diarised_status_counts[status_value] = int(count or 0)
-
-    detail = CallImportDetailResponse.model_validate(
-        _serialize_call_import(db, call_import).model_dump()
-    )
-    detail.rows = [CallImportRowResponse.model_validate(r) for r in rows]
-    detail.filtered_total_rows = filtered_total_rows
-    detail.diarised_pending_rows = diarised_status_counts.get("pending", 0)
-    detail.diarised_running_rows = diarised_status_counts.get("running", 0)
-    detail.diarised_completed_rows = diarised_status_counts.get("completed", 0)
-    detail.diarised_failed_rows = diarised_status_counts.get("failed", 0)
-    return detail
-
-
-@router.get(
-    "/{call_import_id}/row-ids",
-    response_model=CallImportRowIdsResponse,
-    operation_id="listCallImportRowIds",
-)
-async def list_call_import_row_ids(
-    call_import_id: UUID,
-    q: Optional[str] = Query(
-        None,
-        description=(
-            "Optional case-insensitive substring filter on "
-            "``conversation_id``. Same semantics as the detail endpoint."
-        ),
-    ),
-    diarised_status: Optional[str] = Query(
-        None,
-        description=(
-            "Optional filter on ``CallImportRow.diarised_transcript_status``. "
-            "Accepts ``pending`` / ``running`` / ``completed`` / ``failed``."
-        ),
-        pattern="^(pending|running|completed|failed)$",
-    ),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportRowIdsResponse:
-    """Return every matching ``CallImportRow.id`` for cross-page bulk select.
-
-    Lightweight companion to ``GET /{call_import_id}`` — the detail
-    endpoint caps ``row_limit`` at 5000 and ships the entire row body
-    on each page, so harvesting ids that way is wasteful when the
-    user just wants to bulk-delete or bulk-transcribe everything that
-    matches the current filters. This endpoint applies the same ``q``
-    and ``diarised_status`` filters and returns only the ids.
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    search_term = (q or "").strip()
-    status_filter = (diarised_status or "").strip() or None
-
-    if is_sharding_enabled():
-        from app.db_sharding.scatter_gather import list_call_import_row_ids_filtered
-
-        ids = list_call_import_row_ids_filtered(
-            db,
-            call_import.id,
-            search_term=search_term,
-            diarised_status_filter=status_filter,
-        )
-        return CallImportRowIdsResponse(ids=ids, total=len(ids))
-
-    rows_query = db.query(CallImportRow.id).filter(
-        CallImportRow.call_import_id == call_import.id
-    )
-    if search_term:
-        rows_query = rows_query.filter(
-            CallImportRow.conversation_id.ilike(f"%{search_term}%")
-        )
-    if status_filter:
-        rows_query = rows_query.filter(
-            CallImportRow.diarised_transcript_status == status_filter
-        )
-
-    ids = [
-        row_id
-        for (row_id,) in rows_query.order_by(CallImportRow.row_index).all()
-    ]
-    return CallImportRowIdsResponse(ids=ids, total=len(ids))
-
-
-def _revoke_pending_tasks(rows: List[CallImportRow]) -> None:
-    """Best-effort revoke of in-flight Celery tasks for the given rows.
-
-    Failures are logged and swallowed — Celery's control plane is async and
-    best-effort by design, and we always do an idempotent S3 cleanup
-    afterwards so a missed revoke can't leak storage.
-    """
-    task_ids = [
-        r.celery_task_id
-        for r in rows
-        if r.celery_task_id
-        and r.status in (CallImportRowStatus.PENDING, CallImportRowStatus.PROCESSING)
-    ]
-    if not task_ids:
-        return
-
-    try:
-        from app.workers.celery_app import celery_app
-
-        celery_app.control.revoke(task_ids, terminate=False)
-        logger.info("Revoked {} pending call-import tasks", len(task_ids))
-    except Exception as exc:  # noqa: BLE001
-        logger.warning("Failed to revoke pending call-import tasks: {}", exc)
-
-
-def _delete_s3_objects(
-    organization_id: UUID,
-    call_import_id: UUID,
-    rows: List[CallImportRow],
-) -> tuple[int, int]:
-    """Delete every recording associated with ``rows`` plus a prefix sweep.
-
-    The prefix sweep also cleans up the staged source file written at
-    UPLOAD time (``…/call_imports/{id}/source.{csv,xlsx}``) — both the
-    per-row recording keys and the source artefact share the same
-    organization-scoped prefix, so a single sweep covers them all.
-
-    Returns ``(deleted_count, error_count)``. Never raises — callers proceed
-    with the DB delete regardless; orphans, if any, can be cleaned up by
-    re-running the same delete (it's idempotent).
-    """
-    from app.services.storage.s3_service import s3_service
-
-    if not s3_service.is_enabled():
-        return 0, 0
-
-    keys = [r.recording_s3_key for r in rows if r.recording_s3_key]
-    deleted = 0
-    errors = 0
-
-    if keys:
-        try:
-            d, errs = s3_service.delete_keys(keys)
-            deleted += d
-            errors += len(errs)
-            if errs:
-                logger.warning(
-                    "S3 bulk-delete reported {} errors for call_import {}",
-                    len(errs),
-                    call_import_id,
-                )
-        except Exception as exc:  # noqa: BLE001
-            logger.exception(
-                "Bulk S3 delete failed for call_import {}: {}", call_import_id, exc
-            )
-            errors += len(keys)
-
-    # Belt-and-braces sweep: catch anything that landed under the import's
-    # prefix but never made it into a row's recording_s3_key (narrow
-    # window where the S3 upload succeeded but the DB commit didn't).
-    sweep_prefix = (
-        f"{s3_service.prefix}organizations/{organization_id}/"
-        f"call_imports/{call_import_id}/"
-    )
-    try:
-        d, errs = s3_service.delete_keys_by_prefix(sweep_prefix)
-        deleted += d
-        errors += len(errs)
-    except Exception as exc:  # noqa: BLE001
-        logger.exception(
-            "S3 prefix sweep failed for {}: {}", sweep_prefix, exc
-        )
-
-    return deleted, errors
-
-
-@router.delete(
-    "/{call_import_id}",
-    response_model=CallImportDeleteResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="deleteCallImport",
-)
-async def delete_call_import(
-    call_import_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportDeleteResponse:
-    """Delete a call-import batch asynchronously.
-
-    Flips the batch to ``deleting`` and enqueues background teardown so
-    large imports (thousands of rows + S3 objects) do not block the API.
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        return CallImportDeleteResponse(
-            id=call_import_id,
-            status="completed",
-        )
-
-    if call_import.status == CallImportStatus.DELETING:
-        return CallImportDeleteResponse(
-            id=call_import.id,
-            status="accepted",
-        )
-
-    call_import.status = CallImportStatus.DELETING
-    call_import.error_message = None
-    db.commit()
-
-    from app.workers.tasks.call_import_bulk_ops import delete_call_import_task
-
-    delete_call_import_task.delay(
-        str(call_import_id),
-        str(organization_id),
-    )
-
-    return CallImportDeleteResponse(
-        id=call_import.id,
-        status="accepted",
-    )
-
-
-def _locate_call_import_row_or_404(
-    catalog_db: Session,
-    *,
-    call_import_id: UUID,
-    row_id: UUID,
-    organization_id: UUID,
-) -> Tuple[Session, CallImportRow, Optional[Session]]:
-    """Find a call import row on the correct DB session for mutation.
-
-    When sharding is enabled rows live on shard databases; ``get_db`` only
-    opens the catalog. Returns ``(row_db, row, extra_catalog_to_close)``
-    where ``extra_catalog_to_close`` is the catalog session opened by
-    :func:`locate_call_import_row` (distinct from the route's catalog
-    session) and must be closed via :func:`close_row_sessions`.
-    """
-    from app.db_sharding.row_ops import close_row_sessions, locate_call_import_row
-
-    try:
-        row_db, located_catalog, row, _shard_id = locate_call_import_row(row_id)
-    except LookupError:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import row not found",
-        ) from None
-    if (
-        row.call_import_id != call_import_id
-        or row.organization_id != organization_id
-    ):
-        close_row_sessions(
-            row_db,
-            located_catalog if located_catalog is not row_db else None,
-        )
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import row not found",
-        )
-    extra_catalog = located_catalog if located_catalog is not row_db else None
-    return row_db, row, extra_catalog
-
-
-@router.delete(
-    "/{call_import_id}/rows/{row_id}",
-    status_code=status.HTTP_204_NO_CONTENT,
-    operation_id="deleteCallImportRow",
-)
-async def delete_call_import_row(
-    call_import_id: UUID,
-    row_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> Response:
-    """Delete a single CallImportRow and its S3 recording.
-
-    The parent ``CallImport`` is left in place. After deletion we recompute
-    its ``total_rows`` / ``completed_rows`` / ``failed_rows`` / ``status``
-    so the UI's progress bar stays consistent with reality.
-    """
-    from app.services.storage.s3_service import s3_service
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    from app.db_sharding.row_ops import close_row_sessions
-
-    row_db, row, extra_catalog = _locate_call_import_row_or_404(
-        db,
-        call_import_id=call_import.id,
-        row_id=row_id,
-        organization_id=organization_id,
-    )
-    try:
-        _revoke_pending_tasks([row])
-
-        if row.recording_s3_key and s3_service.is_enabled():
-            try:
-                s3_service.delete_file_by_key(row.recording_s3_key)
-            except Exception as exc:  # noqa: BLE001 — best-effort, DB is source of truth
-                logger.warning(
-                    "Failed to delete S3 object {} for row {}: {}",
-                    row.recording_s3_key,
-                    row.id,
-                    exc,
-                )
-
-        row_db.delete(row)
-        row_db.commit()
-
-        _recompute_call_import_counters(db, call_import)
-        db.commit()
-    finally:
-        close_row_sessions(row_db, extra_catalog)
-
-    logger.info(
-        "Deleted call_import_row {} (call_import={}, org={})",
-        row_id,
-        call_import.id,
-        organization_id,
-    )
-
-    return Response(status_code=status.HTTP_204_NO_CONTENT)
-
-
-def _recompute_call_import_counters(
-    db: Session, call_import: CallImport
-) -> None:
-    """Resync ``total/completed/failed_rows`` + status on the parent batch.
-
-    Called after row-level mutations (single delete, bulk delete) so the
-    UI's progress bar stays consistent with the actual row set. The
-    rules mirror :func:`delete_call_import_row` so behavior doesn't
-    diverge between the per-row and bulk paths.
-    """
-
-    from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
-
-    rollup_call_import_batch_status(db, call_import)
-
-
-@router.post(
-    "/{call_import_id}/retry-failed",
-    response_model=CallImportRetryFailedRowsResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="retryFailedCallImportRows",
-)
-async def retry_failed_call_import_rows(
-    call_import_id: UUID,
-    payload: Optional[CallImportRetryFailedRowsRequest] = Body(None),
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportRetryFailedRowsResponse:
-    """Re-enqueue every failed import row in this batch.
-
-    Useful when transient provider issues are resolved and the operator wants
-    a one-click "try failed downloads again" pass without re-uploading the CSV.
-
-    Pass ``provider`` + ``telephony_integration_id`` (or both omitted for
-    direct-URL retry) to change how recordings are fetched on this pass.
-    When the body is omitted entirely, the batch keeps its existing pinned
-    credentials.
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    if payload is not None:
-        if payload.telephony_integration_id is not None:
-            integration = _resolve_telephony_integration(
-                db,
-                organization_id,
-                payload.telephony_integration_id,
-                payload.provider or "",
-            )
-            call_import.provider = integration.provider
-            call_import.telephony_integration_id = integration.id
-            if (integration.provider or "").lower() == "exotel":
-                schema = _resolve_schema(
-                    db,
-                    organization_id,
-                    call_import.workspace_id,
-                    call_import.schema_id,
-                )
-                _validate_exotel_import_ready(
-                    list(schema.parameters),
-                    dict(call_import.parameter_mapping or {}),
-                )
-        else:
-            call_import.provider = None
-            call_import.telephony_integration_id = None
-        db.flush()
-
-    failed_rows = (
-        db.query(CallImportRow)
-        .filter(
-            CallImportRow.call_import_id == call_import.id,
-            CallImportRow.status == CallImportRowStatus.FAILED,
-        )
-        .order_by(CallImportRow.row_index.asc())
-        .all()
-    )
-    if not failed_rows:
-        return CallImportRetryFailedRowsResponse(
-            requeued=0,
-            enqueue_failed=0,
-            skipped=0,
-        )
-
-    from app.workers.concurrency.fair_import_dispatch import (
-        schedule_fair_import_dispatch,
-    )
-
-    # Reset rows to pending BEFORE enqueue so the UI reflects "retry in
-    # progress" immediately even if the worker queue is backlogged.
-    for row in failed_rows:
-        row.status = CallImportRowStatus.PENDING
-        row.error_message = None
-        row.celery_task_id = None
-
-    db.flush()
-    _recompute_call_import_counters(db, call_import)
-    db.commit()
-
-    try:
-        schedule_fair_import_dispatch(max_workspace_turns=999)
-        requeued = len(failed_rows)
-        enqueue_failed = 0
-        skipped = 0
-    except Exception as exc:  # noqa: BLE001
-        logger.exception(
-            "Failed to schedule fair import dispatch for import {}",
-            call_import.id,
-        )
-        requeued = 0
-        enqueue_failed = len(failed_rows)
-        skipped = 0
-        for row in failed_rows:
-            db.refresh(row)
-            if row.status != CallImportRowStatus.PENDING:
-                skipped += 1
-                enqueue_failed -= 1
-                continue
-            row.status = CallImportRowStatus.FAILED
-            row.error_message = f"Failed to enqueue retry: {exc}"
-        db.flush()
-        _recompute_call_import_counters(db, call_import)
-        db.commit()
-
-    return CallImportRetryFailedRowsResponse(
-        requeued=requeued,
-        enqueue_failed=enqueue_failed,
-        skipped=skipped,
-    )
-
-
-@router.post(
-    "/{call_import_id}/rows/bulk-delete",
-    response_model=CallImportRowBulkDeleteResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="bulkDeleteCallImportRows",
-)
-async def bulk_delete_call_import_rows(
-    call_import_id: UUID,
-    payload: CallImportRowBulkDelete,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportRowBulkDeleteResponse:
-    """Delete multiple ``CallImportRow`` rows in one request.
-
-    Unknown / cross-tenant row ids are silently skipped — the response
-    reports how many actually went away so a UI that holds onto stale
-    ids (e.g. after another tab already deleted a row) doesn't 404
-    the entire bulk action.
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    if not payload.row_ids:
-        return CallImportRowBulkDeleteResponse(deleted=0, status="completed")
-
-    from app.workers.tasks.call_import_bulk_ops import bulk_delete_call_import_rows_task
-
-    row_id_strs = [str(rid) for rid in payload.row_ids]
-
-    bulk_delete_call_import_rows_task.delay(
-        str(call_import_id),
-        str(organization_id),
-        row_id_strs,
-    )
-
-    return CallImportRowBulkDeleteResponse(deleted=0, status="accepted")
-
-
-# ---------------------------------------------------------------------------
-# Diarization / transcription endpoints
-# ---------------------------------------------------------------------------
-
-
-def _select_rows_for_transcription(
-    db: Session,
-    call_import: CallImport,
-    payload: CallImportTranscribeRequest,
-    requested_row_ids: Optional[List[UUID]] = None,
-) -> tuple[List[CallImportRow], Dict[str, int]]:
-    """Pick which rows to enqueue for diarisation (delegates to bulk_ops)."""
-    from app.services.call_imports.bulk_ops import select_rows_for_transcription
-
-    try:
-        return select_rows_for_transcription(
-            db, call_import, payload, requested_row_ids=requested_row_ids
-        )
-    except ValueError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=str(exc),
-        ) from exc
-
-
-@router.post(
-    "/{call_import_id}/transcribe",
-    response_model=CallImportTranscribeResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="transcribeCallImport",
-)
-async def transcribe_call_import(
-    call_import_id: UUID,
-    payload: CallImportTranscribeRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportTranscribeResponse:
-    """Fan out diarization tasks for many rows in a single call.
-
-    Returns a summary with how many rows were queued and how many were
-    skipped (broken down by reason) so the UI can show a meaningful
-    toast even when nothing actually got enqueued (e.g. "All 12 rows
-    already have transcripts").
-    """
-
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    from app.workers.tasks.call_import_bulk_ops import bulk_diarize_call_import_task
-
-    bulk_diarize_call_import_task.delay(
-        str(call_import_id),
-        str(organization_id),
-        payload.model_dump(mode="json"),
-        [str(rid) for rid in payload.row_ids] if payload.row_ids else None,
-    )
-
-    return CallImportTranscribeResponse(
-        queued=0,
-        skipped_rows=0,
-        skipped_reason_counts={},
-        accepted=True,
-    )
-
-
-@router.post(
-    "/{call_import_id}/rows/{row_id}/transcribe",
-    response_model=CallImportTranscribeResponse,
-    status_code=status.HTTP_202_ACCEPTED,
-    operation_id="transcribeCallImportRow",
-)
-async def transcribe_call_import_row(
-    call_import_id: UUID,
-    row_id: UUID,
-    payload: CallImportTranscribeRequest,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportTranscribeResponse:
-    """Diarize / transcribe a single row.
-
-    Thin wrapper over the batch endpoint that hard-codes a single
-    ``row_ids`` filter. Skip counts still surface so the UI can render
-    "Skipped — transcript present" diagnostics consistently.
-    """
-
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    from app.services.call_imports.bulk_ops import execute_bulk_diarization
-
-    try:
-        result = execute_bulk_diarization(
-            db,
-            call_import,
-            payload,
-            requested_row_ids=[row_id],
-        )
-    except ValueError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=str(exc),
-        ) from exc
-
-    return CallImportTranscribeResponse(
-        queued=result.queued,
-        skipped_rows=result.skipped_rows,
-        skipped_reason_counts=result.skipped_reason_counts,
-    )
-
-
-# ---------------------------------------------------------------------------
-# Cancel-in-flight diarisation
-# ---------------------------------------------------------------------------
-#
-# Long-running multimodal LLM diarisation calls (especially LLM-only mode on
-# slow audio) can sit in ``pending`` / ``running`` for tens of minutes when an
-# upstream provider stalls. Without an abort affordance the operator's only
-# recourse is to wait for Celery's ``time_limit`` to fire — which can be
-# several minutes — or to manually mutate the DB. These helpers + the two
-# endpoints below give the UI a first-class "Stop diarisation" button.
-#
-# Why ``terminate=True``: the legacy ``_revoke_pending_tasks`` helper uses
-# ``terminate=False`` because it's called from delete-flow paths where the
-# task may simply not get to run (a worker pulls it off the queue and drops
-# it). For a user-initiated cancel we want SIGTERM to interrupt the worker
-# mid-LLM call so the audio HTTP request actually aborts. ``terminate=True``
-# routes SIGTERM to the executing process; ``signal="SIGTERM"`` is the
-# default but we spell it out so the intent is obvious to reviewers.
-
-# Sentinel error message stamped on cancelled rows. Read by the transcribe
-# worker's finaliser (see ``app/workers/tasks/transcribe_call_import_row.py``)
-# to detect a row that was cancelled mid-flight and AVOID overwriting it
-# with whatever partial result the worker had managed to compute before the
-# SIGTERM landed.
-CANCELLED_BY_USER_ERROR: str = "Diarisation cancelled by user"
-
-
-def _cancellable_diarisation_states() -> Tuple[str, ...]:
-    """States that a diarisation row can be cancelled from.
-
-    Kept as a tiny helper so adding a future ``"queued"`` / ``"retrying"``
-    state only needs one edit.
-    """
-    return ("pending", "running")
-
-
-def _revoke_diarisation_task(row: CallImportRow) -> None:
-    """Best-effort revoke of a single row's diarisation Celery task.
-
-    Always swallows control-plane exceptions — Celery's control bus is
-    inherently best-effort and a missed revoke is not catastrophic
-    because the DB row is already flipped to ``failed`` by the caller
-    before this runs (so the UI immediately reflects the cancel; if
-    the task happens to finish anyway, the worker's finaliser skips
-    over the row via :data:`CANCELLED_BY_USER_ERROR`).
-    """
-    task_id = (row.celery_task_id or "").strip()
-    if not task_id:
-        return
-    try:
-        from app.workers.celery_app import celery_app
-
-        celery_app.control.revoke(
-            task_id, terminate=True, signal="SIGTERM"
-        )
-        logger.info(
-            "Revoked diarisation task {} for call-import row {}",
-            task_id,
-            row.id,
-        )
-    except Exception as exc:  # noqa: BLE001 — revoke is best-effort
-        logger.warning(
-            "Failed to revoke diarisation task {} for row {}: {}",
-            task_id,
-            row.id,
-            exc,
-        )
-
-
-def _apply_diarisation_cancel(rows: List[CallImportRow]) -> Tuple[int, int]:
-    """Cancel diarisation on every cancellable row in ``rows``.
-
-    Returns ``(cancelled, skipped)`` so the caller can build a typed
-    response without re-querying the DB. The caller is responsible for
-    ``db.commit()`` after this returns — we deliberately don't commit
-    here so a batch endpoint can flush all rows in one transaction.
-    """
-    cancellable_states = _cancellable_diarisation_states()
-    cancelled = 0
-    skipped = 0
-    for row in rows:
-        if (row.diarised_transcript_status or "").lower() not in cancellable_states:
-            skipped += 1
-            continue
-        # Flip the row state BEFORE we revoke so the UI's next poll
-        # already shows the cancel, even if Celery's control plane is
-        # slow to ack.
-        row.diarised_transcript_status = "failed"
-        row.diarised_transcript_error = CANCELLED_BY_USER_ERROR
-        _revoke_diarisation_task(row)
-        # Drop the task id so a follow-up retry (or a stale poll) can't
-        # accidentally re-revoke or get confused.
-        row.celery_task_id = None
-        cancelled += 1
-    return cancelled, skipped
-
-
-@router.post(
-    "/{call_import_id}/rows/{row_id}/cancel-diarisation",
-    response_model=CallImportRowResponse,
-    status_code=status.HTTP_200_OK,
-    operation_id="cancelCallImportRowDiarisation",
-)
-async def cancel_call_import_row_diarisation(
-    call_import_id: UUID,
-    row_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportRowResponse:
-    """Abort an in-flight (or queued) diarisation for a single row.
-
-    Idempotent: calling on a row that's already terminal (``completed``
-    / ``failed`` / ``idle``) returns the row unchanged with a 200, so
-    the UI can fire this from a "Stop" button without having to
-    pre-check the state.
-
-    Race notes:
-
-    * The row's ``diarised_transcript_status`` is flipped to ``failed``
-      with :data:`CANCELLED_BY_USER_ERROR` BEFORE the Celery revoke,
-      so the polling UI sees the cancel immediately.
-    * If the worker happens to finish between our DB flip and the
-      SIGTERM landing, its finaliser will detect the cancelled
-      sentinel on the row and skip its own status / score writes
-      (see :mod:`app.workers.tasks.transcribe_call_import_row`).
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    from app.db_sharding.row_ops import close_row_sessions
-
-    row_db, row, extra_catalog = _locate_call_import_row_or_404(
-        db,
-        call_import_id=call_import_id,
-        row_id=row_id,
-        organization_id=organization_id,
-    )
-    try:
-        _apply_diarisation_cancel([row])
-        row_db.commit()
-        row_db.refresh(row)
-        return CallImportRowResponse.model_validate(row)
-    finally:
-        close_row_sessions(row_db, extra_catalog)
-
-
-@router.post(
-    "/{call_import_id}/cancel-diarisation",
-    response_model=CallImportCancelDiarisationResponse,
-    status_code=status.HTTP_200_OK,
-    operation_id="cancelCallImportDiarisation",
-)
-async def cancel_call_import_diarisation(
-    call_import_id: UUID,
-    payload: Optional[CallImportCancelDiarisationRequest] = None,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportCancelDiarisationResponse:
-    """Abort in-flight diarisation for many rows in a single call.
-
-    Default body (no ``row_ids``) cancels every row in this import
-    whose ``diarised_transcript_status`` is ``pending`` or
-    ``running`` — the "stop everything" button. Pass ``row_ids`` to
-    scope the cancel to the rows the operator has selected.
-
-    Returns ``(cancelled, skipped)`` so the UI can render a tight
-    toast ("Cancelled 3 rows · 1 skipped (already completed)").
-    """
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    base_query = db.query(CallImportRow).filter(
-        CallImportRow.call_import_id == call_import_id
-    )
-
-    requested_ids = (
-        payload.row_ids if payload and payload.row_ids is not None else None
-    )
-    if requested_ids is not None:
-        if not requested_ids:
-            # Empty list is "no rows requested" — treat as a no-op
-            # 200 rather than 400 so the UI can pass through an empty
-            # selection without a special-case.
-            return CallImportCancelDiarisationResponse(cancelled=0, skipped=0)
-        rows = base_query.filter(CallImportRow.id.in_(requested_ids)).all()
-        found_ids = {r.id for r in rows}
-        # Treat requested-but-not-found ids as ``skipped`` so the UI's
-        # numbers reconcile (a stale selection that includes deleted
-        # rows shouldn't 404 the whole call).
-        missing = [rid for rid in requested_ids if rid not in found_ids]
-        skipped_missing = len(missing)
-    else:
-        # Implicit "cancel every cancellable row in this import" path.
-        rows = base_query.filter(
-            CallImportRow.diarised_transcript_status.in_(
-                list(_cancellable_diarisation_states())
-            )
-        ).all()
-        skipped_missing = 0
-
-    cancelled, skipped = _apply_diarisation_cancel(rows)
-    db.commit()
-    return CallImportCancelDiarisationResponse(
-        cancelled=cancelled,
-        skipped=skipped + skipped_missing,
-    )
-
-
-def _render_diarised_segments_text(
-    segments: Optional[List[Dict[str, Any]]],
-    *,
-    swap: bool = False,
-) -> str:
-    """Render ``CallImportRow.diarised_segments`` as ``<speaker>: <text>`` lines.
-
-    Mirrors the worker's ``_render_turns_as_text`` (kept duplicated so
-    the route doesn't need to import a Celery task module just to
-    rebuild the rendered transcript). Only ``agent`` and ``user`` are
-    swapped — multi-party calls keep their ``speaker_N`` labels through
-    a swap so we don't silently collapse a third speaker into the user
-    side.
-    """
-    if not segments:
-        return ""
-    out: List[str] = []
-    for turn in segments:
-        if not isinstance(turn, dict):
-            continue
-        speaker = (turn.get("speaker") or "").strip()
-        text = (turn.get("text") or "").strip()
-        if not speaker or not text:
-            continue
-        if swap:
-            if speaker == "agent":
-                speaker = "user"
-            elif speaker == "user":
-                speaker = "agent"
-        out.append(f"{speaker}: {text}")
-    return "\n".join(out)
-
-
-@router.post(
-    "/{call_import_id}/rows/{row_id}/diarised-speaker-swap",
-    response_model=CallImportRowResponse,
-    operation_id="toggleCallImportRowSpeakerSwap",
-)
-async def toggle_call_import_row_speaker_swap(
-    call_import_id: UUID,
-    row_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportRowResponse:
-    """Flip the user <-> agent mapping on a diarised row.
-
-    The worker's "first speaker is the agent" heuristic is right most of
-    the time but does fail on inbound recordings where the customer
-    greets first, on recordings where the agent stays silent for the
-    intro, etc. Rather than rerun the (expensive) STT + pyannote
-    pipeline for those cases, we let reviewers flip the mapping in
-    place: the structured ``diarised_segments`` are the source of truth
-    and we re-render the plain-text ``diarised_transcript`` from them
-    with the swap applied. The next CSV export will then show the
-    corrected labels.
-
-    Returns the updated row so the frontend can refresh without an
-    extra round-trip.
-    """
-
-    del api_key
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    from app.db_sharding.row_ops import close_row_sessions
-
-    row_db, row, extra_catalog = _locate_call_import_row_or_404(
-        db,
-        call_import_id=call_import_id,
-        row_id=row_id,
-        organization_id=organization_id,
-    )
-    try:
-        segments = (
-            row.diarised_segments if isinstance(row.diarised_segments, list) else None
-        )
-        if not segments:
-            # Without structured turns the swap toggle would have nothing to
-            # re-render — surface a clear error rather than silently
-            # flipping a flag the UI never read.
-            raise HTTPException(
-                status_code=status.HTTP_409_CONFLICT,
-                detail=(
-                    "This row has no structured diarised segments to swap. "
-                    "Re-run diarisation to generate per-speaker turns first."
-                ),
-            )
-
-        new_swap = not bool(row.diarised_speaker_swap)
-        row.diarised_speaker_swap = new_swap
-        row.diarised_transcript = (
-            _render_diarised_segments_text(segments, swap=new_swap) or None
-        )
-        row_db.commit()
-        row_db.refresh(row)
-        return CallImportRowResponse.model_validate(row)
-    finally:
-        close_row_sessions(row_db, extra_catalog)
-
-
-# ---------------------------------------------------------------------------
-# Cross-run insights for the import detail page
-# ---------------------------------------------------------------------------
-
-
-@router.get(
-    "/{call_import_id}/insights",
-    response_model=CallImportInsightsResponse,
-    operation_id="getCallImportInsights",
-)
-async def get_call_import_insights(
-    call_import_id: UUID,
-    api_key: str = Depends(get_api_key),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-) -> CallImportInsightsResponse:
-    """Aggregate signals across every evaluation run on this import.
-
-    Powers the Insights tab on the call-import detail page: returns
-    per-metric "latest run" summaries plus a trend series of mean values
-    across runs so the UI can render a small line chart per metric. Also
-    bundles transcript coverage stats since those are the cheapest
-    pre-eval health-check (e.g. "30 of 50 rows still missing
-    transcripts").
-    """
-
-    del api_key
-
-    from app.models.database import (
-        CallImportEvaluation,
-        CallImportEvaluationRow,
-        Metric,
-    )
-
-    call_import = (
-        db.query(CallImport)
-        .filter(
-            CallImport.id == call_import_id,
-            CallImport.organization_id == organization_id,
-        )
-        .first()
-    )
-    if not call_import:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call import not found",
-        )
-
-    rows = (
-        db.query(CallImportRow)
-        .filter(CallImportRow.call_import_id == call_import_id)
-        .all()
-    )
-    # A row "has a transcript" if EITHER the production (CSV) or the
-    # diarised (worker) column is populated — the insights tile reports
-    # the union so users see total coverage regardless of which source
-    # produced the value.
-    rows_with_transcript = sum(
-        1
-        for r in rows
-        if (r.transcript or "").strip()
-        or (r.diarised_transcript or "").strip()
-    )
-    rows_without_transcript = len(rows) - rows_with_transcript
-    source_counts: Dict[str, int] = {}
-    for r in rows:
-        has_production = bool((r.transcript or "").strip())
-        has_diarised = bool((r.diarised_transcript or "").strip())
-        if has_production:
-            key = r.transcript_source or "csv"
-            source_counts[key] = source_counts.get(key, 0) + 1
-        if has_diarised:
-            source_counts["diarised"] = source_counts.get("diarised", 0) + 1
-
-    evaluations = (
-        db.query(CallImportEvaluation)
-        .filter(
-            CallImportEvaluation.call_import_id == call_import_id,
-            CallImportEvaluation.organization_id == organization_id,
-        )
-        .order_by(CallImportEvaluation.created_at.asc())
-        .all()
-    )
-
-    # Defer heavy lifting to the aggregation helper so this endpoint and
-    # the per-run aggregate endpoint share the exact same metric
-    # bucketing math (no chance of "trend" disagreeing with "latest" on
-    # the same data set).
-    from app.api.v1.routes.call_import_evaluations import (
-        _compute_metric_aggregates,
-    )
-
-    metric_history: Dict[str, List[CallImportInsightsRunPoint]] = {}
-    metric_meta: Dict[str, Metric] = {}
-    metric_latest: Dict[str, CallImportMetricAggregate] = {}
-
-    for evaluation in evaluations:
-        eval_rows = (
-            db.query(CallImportEvaluationRow)
-            .filter(CallImportEvaluationRow.evaluation_id == evaluation.id)
-            .all()
-        )
-        aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
-        for agg in aggregates:
-            if agg.metric_id not in metric_meta:
-                # ``agg.metric_id`` is normally a UUID string, but the
-                # aggregator also emits ids that surface in row scores
-                # without a matching ``Metric`` row (e.g. a metric the
-                # user deleted mid-run, or LLM-discovered slugs). Those
-                # are not valid UUIDs, so coerce defensively and skip
-                # the metric registry lookup when the cast fails — the
-                # ``meta is None`` branch below already handles the
-                # display via the values stored on ``agg`` itself.
-                try:
-                    metric_uuid = UUID(agg.metric_id)
-                except (ValueError, AttributeError, TypeError):
-                    metric_uuid = None
-                if metric_uuid is not None:
-                    metric_obj = (
-                        db.query(Metric)
-                        .filter(
-                            Metric.id == metric_uuid,
-                            Metric.organization_id == organization_id,
-                        )
-                        .first()
-                    )
-                    if metric_obj is not None:
-                        metric_meta[agg.metric_id] = metric_obj
-            history = metric_history.setdefault(agg.metric_id, [])
-            history.append(
-                CallImportInsightsRunPoint(
-                    evaluation_id=evaluation.id,
-                    name=evaluation.name,
-                    created_at=evaluation.created_at,
-                    mean=agg.mean,
-                    completed_rows=agg.count,
-                )
-            )
-            metric_latest[agg.metric_id] = agg
-
-    metrics_payload: List[CallImportInsightsMetric] = []
-    for metric_id, latest in metric_latest.items():
-        meta = metric_meta.get(metric_id)
-        metrics_payload.append(
-            CallImportInsightsMetric(
-                metric_id=metric_id,
-                metric_name=(meta.name if meta else latest.metric_name),
-                metric_type=(meta.metric_type if meta else latest.metric_type),
-                latest=latest,
-                trend=metric_history.get(metric_id, []),
-            )
-        )
-
-    return CallImportInsightsResponse(
-        call_import_id=call_import_id,
-        total_rows=len(rows),
-        rows_with_transcript=rows_with_transcript,
-        rows_without_transcript=rows_without_transcript,
-        transcript_source_counts=source_counts,
-        evaluation_count=len(evaluations),
-        metrics=metrics_payload,
-    )
-
-
-from app.core.auth.capabilities import CALLS_DELETE, CALLS_IMPORT, CALLS_VIEW
-from app.core.auth.workspace_route_capabilities import apply_workspace_route_capabilities
-
-apply_workspace_route_capabilities(
-    router,
-    view_capability=CALLS_VIEW,
-    manage_capability=CALLS_IMPORT,
-    delete_capability=CALLS_DELETE,
-)
+"""CSV-driven call import routes.
+
+Users upload a CSV plus a per-batch column mapping (CSV header -> system
+field). The backend persists a CallImport batch + one CallImportRow per
+line, then fans the rows out to the Celery ``imports`` queue where each
+row is downloaded using the telephony credential pinned on the batch.
+Exotel credentialed imports require a ``recording_url`` on every row;
+direct-URL imports (no credential) also require a mapped recording URL.
+"""
+
+from __future__ import annotations
+
+import csv
+import io
+import json
+import re
+from dataclasses import dataclass, field
+from datetime import date, datetime, time, timedelta
+from typing import Any, Dict, Iterable, List, Optional, Tuple
+from uuid import UUID, uuid4
+
+from fastapi import APIRouter, Body, BackgroundTasks, Depends, File, Form, HTTPException, Query, Response, UploadFile, status
+from loguru import logger
+from sqlalchemy import desc, func, or_
+from sqlalchemy.orm import Session
+
+from app.config import settings
+from app.core.auth import Principal, get_principal
+from app.core.auth.rbac import require_admin
+from app.database import get_db
+from app.db_sharding.sessions import is_sharding_enabled
+from app.dependencies import (
+    get_api_key,
+    get_organization_id,
+    get_workspace_id,
+    require_enterprise_feature,
+)
+from app.services.billing.flexprice_service import record_call_import_batch_created
+from app.services.call_imports.audit import (
+    actor_emails_for_call_import,
+    emails_for_user_ids,
+    stamp_call_import_actor,
+    user_ids_from_call_imports,
+)
+from app.services.call_imports.dispatch_diagnostics import (
+    build_call_import_dispatch_diagnostics,
+)
+from app.models.database import (
+    CallImport,
+    CallImportRow,
+    CallImportSchema,
+    CallImportSchemaParameter,
+    CallImportTag,
+    TelephonyIntegration,
+)
+from app.models.enums import (
+    CallImportParameterType,
+    CallImportRowStatus,
+    CallImportStatus,
+)
+from app.models.schemas import (
+    CallImportCancelDiarisationRequest,
+    CallImportCancelDiarisationResponse,
+    CallImportDetailResponse,
+    CallImportDeleteResponse,
+    CallImportDiarisationPromptDefaultResponse,
+    CallImportDispatchDiagnosticsResponse,
+    CallImportInsightsMetric,
+    CallImportInsightsResponse,
+    CallImportInsightsRunPoint,
+    CallImportListResponse,
+    CallImportMappingUpdate,
+    CallImportMetricAggregate,
+    CallImportPreviewResponse,
+    CallImportPreviewSheet,
+    CallImportRetryFailedRowsRequest,
+    CallImportRetryFailedRowsResponse,
+    CallImportResponse,
+    CallImportRowIdsResponse,
+    CallImportRowBulkDelete,
+    CallImportRowBulkDeleteResponse,
+    CallImportRowResponse,
+    CallImportStartRequest,
+    CallImportTranscribeRequest,
+    CallImportTranscribeResponse,
+    CallImportUpdate,
+    CallImportUploadResponse,
+)
+
+
+router = APIRouter(
+    prefix="/call-imports",
+    tags=["Call Imports"],
+    dependencies=[Depends(require_enterprise_feature("call_imports"))],
+)
+
+
+@dataclass(frozen=True)
+class CallImportParseSkip:
+    """One source row excluded during CSV/Excel parse (identity / recording URL)."""
+
+    source_row: int
+    reason: str
+    message: str
+
+
+@dataclass
+class CallImportParseResult:
+    rows: List[Dict[str, Any]] = field(default_factory=list)
+    skipped: List[CallImportParseSkip] = field(default_factory=list)
+
+
+def parse_skips_to_json(skips: List[CallImportParseSkip]) -> List[Dict[str, Any]]:
+    """Persistable JSON shape for ``CallImport.source_row_skips``."""
+    return [
+        {
+            "source_row": item.source_row,
+            "reason": item.reason,
+            "message": item.message,
+        }
+        for item in skips
+    ]
+
+
+def _normalize_dataset(raw: Optional[str]) -> Optional[str]:
+    """Trim and treat empty strings as 'no dataset' (NULL)."""
+    if raw is None:
+        return None
+    cleaned = raw.strip()
+    return cleaned or None
+
+
+def _serialize_call_import(
+    db: Session,
+    call_import: CallImport,
+    *,
+    user_emails: Optional[Dict[UUID, str]] = None,
+) -> CallImportResponse:
+    """Catalog parent fields; counters come from SQL rollup (not Redis merge)."""
+    from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
+    from app.services.call_imports.progress_counters import (
+        clear_import_progress_redis,
+        read_import_progress,
+    )
+
+    redis_completed, redis_failed = read_import_progress(call_import.id)
+    if (
+        redis_completed
+        or redis_failed
+        or int(call_import.completed_rows or 0) > int(call_import.total_rows or 0)
+        or int(call_import.failed_rows or 0) > int(call_import.total_rows or 0)
+    ):
+        rollup_call_import_batch_status(db, call_import)
+        db.flush()
+
+    clear_import_progress_redis(call_import.id)
+    db.refresh(call_import)
+    total = int(call_import.total_rows or 0)
+    completed = min(int(call_import.completed_rows or 0), total) if total else int(
+        call_import.completed_rows or 0
+    )
+    failed = min(int(call_import.failed_rows or 0), total) if total else int(
+        call_import.failed_rows or 0
+    )
+    if user_emails is None:
+        user_emails = emails_for_user_ids(
+            db, user_ids_from_call_imports([call_import])
+        )
+    created_email, updated_email = actor_emails_for_call_import(
+        call_import, user_emails
+    )
+    base = CallImportResponse.model_validate(call_import)
+    return base.model_copy(
+        update={
+            "completed_rows": completed,
+            "failed_rows": failed,
+            "created_by_email": created_email,
+            "last_updated_by_email": updated_email,
+        }
+    )
+
+
+def _resolve_tags(
+    db: Session, organization_id: UUID, tag_ids: Optional[List[UUID]]
+) -> List[CallImportTag]:
+    """Look up tag rows by id, scoped to the organization.
+
+    Raises HTTPException(400) if any id is unknown for the org.
+    """
+    if not tag_ids:
+        return []
+    rows = (
+        db.query(CallImportTag)
+        .filter(
+            CallImportTag.organization_id == organization_id,
+            CallImportTag.id.in_(tag_ids),
+        )
+        .all()
+    )
+    found_ids = {row.id for row in rows}
+    missing = [str(tag_id) for tag_id in tag_ids if tag_id not in found_ids]
+    if missing:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Unknown call_import_tag id(s): {missing}",
+        )
+    return rows
+
+
+MAX_UPLOAD_BYTES = 15 * 1024 * 1024  # 15 MB upload cap (CSV or Excel)
+
+# File extensions accepted by the upload + preview endpoints. Keep in
+# lockstep with the frontend ``accept`` attribute on the file picker.
+CSV_EXTENSIONS = (".csv",)
+XLSX_EXTENSIONS = (".xlsx", ".xlsm")
+ALLOWED_EXTENSIONS = CSV_EXTENSIONS + XLSX_EXTENSIONS
+
+AUDIO_CONTENT_TYPES = {
+    "wav": "audio/wav",
+    "mp3": "audio/mpeg",
+    "flac": "audio/flac",
+    "m4a": "audio/mp4",
+}
+
+
+def _file_format(filename: Optional[str]) -> Optional[str]:
+    """Classify ``filename`` as ``'csv'`` / ``'xlsx'`` or ``None`` if unsupported."""
+    if not filename:
+        return None
+    name = filename.lower()
+    if name.endswith(CSV_EXTENSIONS):
+        return "csv"
+    if name.endswith(XLSX_EXTENSIONS):
+        return "xlsx"
+    return None
+
+
+def _audio_extension(filename: Optional[str]) -> Optional[str]:
+    """Return the validated lower-case extension for a manual recording."""
+    if not filename or "." not in filename:
+        return None
+    ext = filename.rsplit(".", 1)[-1].lower().strip()
+    allowed = {fmt.lower().lstrip(".") for fmt in settings.ALLOWED_AUDIO_FORMATS}
+    return ext if ext in allowed else None
+
+
+def _audio_content_type(ext: str, upload_content_type: Optional[str]) -> str:
+    """Prefer the browser-supplied audio content type, with a safe fallback."""
+    supplied = (upload_content_type or "").strip()
+    if supplied and supplied != "application/octet-stream":
+        return supplied
+    return AUDIO_CONTENT_TYPES.get(ext.lower(), "application/octet-stream")
+
+
+def _audio_s3_key(
+    organization_id: UUID, call_import_id: UUID, row_id: UUID, ext: str
+) -> str:
+    """Build the canonical S3 key for a manually uploaded recording."""
+    from app.services.storage.s3_service import s3_service
+
+    return (
+        f"{s3_service.prefix}organizations/{organization_id}/call_imports/"
+        f"{call_import_id}/{row_id}.{ext}"
+    )
+
+
+def _filename_stem(filename: Optional[str]) -> str:
+    """Extract a cross-platform filename stem from an UploadFile name."""
+    raw = (filename or "").strip()
+    basename = re.split(r"[\\/]", raw)[-1] if raw else ""
+    if "." in basename:
+        basename = basename.rsplit(".", 1)[0]
+    return basename.strip()
+
+
+def _sanitize_conversation_id(raw: str) -> str:
+    """Turn a filename stem into a stable conversation_id."""
+    cleaned = re.sub(r"[^A-Za-z0-9._-]+", "_", raw.strip())
+    cleaned = re.sub(r"_+", "_", cleaned).strip("._-")
+    return (cleaned or "recording")[:255]
+
+
+def _dedupe_conversation_id(
+    base: str, counts: Dict[str, int]
+) -> str:
+    """Make conversation ids unique within one manual upload batch."""
+    count = counts.get(base, 0) + 1
+    counts[base] = count
+    if count == 1:
+        return base
+    suffix = f"-{count}"
+    return f"{base[: 255 - len(suffix)]}{suffix}"
+
+
+def _normalize_header(name: str) -> str:
+    return (name or "").strip().lower()
+
+
+def _header_lookup(fieldnames: List[str]) -> Dict[str, str]:
+    """Map normalized header -> original header for case-insensitive lookup."""
+    return {_normalize_header(h): h for h in fieldnames or []}
+
+
+def _resolve_mapped_header(
+    mapping_value: Optional[str], header_lookup: Dict[str, str]
+) -> Optional[str]:
+    """Translate a user-supplied CSV header into the actual column key.
+
+    The frontend sends headers exactly as they appear in the source file,
+    but we still normalize on the server so trailing whitespace / casing
+    doesn't break matching. Returns the canonical fieldname or ``None``
+    if not present in the file.
+    """
+    if not mapping_value:
+        return None
+    return header_lookup.get(_normalize_header(mapping_value))
+
+
+def _xlsx_cell_to_str(value: Any) -> str:
+    """Coerce an openpyxl cell value to the string the rest of the
+    pipeline expects.
+
+    openpyxl returns native Python types (int, float, datetime, bool,
+    None). The CSV path always works with strings, so we mirror that:
+    integers stringify cleanly (no ``.0`` suffix on whole-number floats),
+    datetimes use ISO-8601, booleans use SQL-style ``TRUE`` / ``FALSE``.
+    """
+    if value is None:
+        return ""
+    if isinstance(value, bool):
+        return "TRUE" if value else "FALSE"
+    if isinstance(value, int):
+        return str(value)
+    if isinstance(value, float):
+        if value.is_integer():
+            return str(int(value))
+        return str(value)
+    if isinstance(value, datetime):
+        return value.isoformat()
+    if isinstance(value, date):
+        return value.isoformat()
+    if isinstance(value, time):
+        return value.isoformat()
+    if isinstance(value, timedelta):
+        return str(value)
+    return str(value)
+
+
+def _parse_recording_date_cell(cell: str) -> date:
+    """Parse day-first dates with one/two digit day-month parts."""
+    match = re.fullmatch(r"\s*(\d{1,2})[/-](\d{1,2})[/-](\d{4})\s*", cell)
+    if match:
+        day, month, year = (int(part) for part in match.groups())
+        return date(year, month, day)
+
+    # Native Excel date cells arrive from ``_xlsx_cell_to_str`` as ISO
+    # datetimes (e.g. ``2026-01-04T00:00:00``). Accept that resolved date,
+    # while keeping plain ISO dates rejected for hand-entered text/CSV cells.
+    if "T" in cell:
+        return datetime.fromisoformat(cell.replace("Z", "+00:00")).date()
+
+    raise ValueError("expected D/M/YYYY or D-M-YYYY")
+
+
+def _coerce_parameter_value(
+    raw: str,
+    param_type: CallImportParameterType,
+    *,
+    row_idx: int,
+    param_name: str,
+) -> Any:
+    """Validate + coerce a single CSV cell against its declared type.
+
+    Returns the typed Python value to surface in ``raw_columns``. Empty
+    strings are returned as ``None`` regardless of the parameter type so
+    optional cells stay null end-to-end. Coercion failures raise a
+    400 with a row-anchored message.
+    """
+    cell = (raw or "").strip()
+    if not cell:
+        return None
+
+    if param_type == CallImportParameterType.CONVERSATION_ID:
+        return cell
+    if param_type == CallImportParameterType.RECORDING_URL:
+        # Recording URLs are exercised by the worker (which downloads
+        # them); we only do a light "starts with http" check here so a
+        # paste-error surfaces immediately at upload time.
+        lower = cell.lower()
+        if not (lower.startswith("http://") or lower.startswith("https://")):
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
+                    "valid recording URL (must start with http:// or https://)."
+                ),
+            )
+        return cell
+    if param_type == CallImportParameterType.RECORDING_DATE:
+        try:
+            parsed_date = _parse_recording_date_cell(cell)
+        except ValueError:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
+                    f"valid recording date ({cell!r}); expected day-first "
+                    "D/M/YYYY or D-M-YYYY."
+                ),
+            )
+        return parsed_date.strftime("%d/%m/%Y")
+    if param_type == CallImportParameterType.TRANSCRIPT:
+        return cell
+    if param_type == CallImportParameterType.TEXT:
+        return cell
+    if param_type == CallImportParameterType.NUMBER:
+        try:
+            value = float(cell)
+        except ValueError:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
+                    f"valid number ({cell!r})."
+                ),
+            )
+        if value.is_integer():
+            return int(value)
+        return value
+    if param_type == CallImportParameterType.BOOLEAN:
+        truthy = {"true", "yes", "y", "1", "t"}
+        falsy = {"false", "no", "n", "0", "f"}
+        norm = cell.lower()
+        if norm in truthy:
+            return True
+        if norm in falsy:
+            return False
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                f"Row {row_idx + 1}: value for '{param_name}' is not a "
+                f"valid boolean ({cell!r})."
+            ),
+        )
+    if param_type == CallImportParameterType.DATETIME:
+        try:
+            parsed = datetime.fromisoformat(cell.replace("Z", "+00:00"))
+        except ValueError:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
+                    f"valid ISO-8601 date/time ({cell!r})."
+                ),
+            )
+        return parsed.isoformat()
+    if param_type == CallImportParameterType.URL:
+        lower = cell.lower()
+        if not (lower.startswith("http://") or lower.startswith("https://")):
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Row {row_idx + 1}: value for '{param_name}' is not a "
+                    "valid URL (must start with http:// or https://)."
+                ),
+            )
+        return cell
+    # Unknown types: store as text and let the next migration catch up.
+    return cell
+
+
+def _recording_url_cell_is_valid_http(raw: str) -> bool:
+    cell = (raw or "").strip()
+    if not cell:
+        return False
+    lower = cell.lower()
+    return lower.startswith("http://") or lower.startswith("https://")
+
+
+def _parameter_is_required(param: CallImportSchemaParameter) -> bool:
+    """Return whether a schema parameter must be mapped on every upload."""
+    if param.is_required:
+        return True
+    try:
+        param_type = CallImportParameterType(param.type)
+    except ValueError:
+        return False
+    return param_type in (
+        CallImportParameterType.CONVERSATION_ID,
+        CallImportParameterType.RECORDING_URL,
+    )
+
+
+def _apply_schema_mapping(
+    fieldnames: List[str],
+    rows_iter: Iterable[Dict[str, str]],
+    parameters: List[CallImportSchemaParameter],
+    parameter_mapping: Dict[str, str],
+    skipped_columns: List[str],
+    *,
+    source_label: str = "CSV",
+    validate_only: bool = False,
+) -> CallImportParseResult:
+    """Schema-driven row projection: parameter -> CSV header -> typed value.
+
+    Validates that every required schema parameter is mapped to a CSV
+    header that actually exists in the file, and that every CSV header
+    is either mapped to a parameter or explicitly listed in
+    ``skipped_columns``. Returns one dict per non-empty data row with:
+
+      * ``conversation_id`` (str, mandatory)
+      * ``recording_date`` (Optional[str], DD/MM/YYYY date)
+      * ``recording_url`` (Optional[str])
+      * ``transcript`` (Optional[str])
+      * ``parameter_values`` (Dict[str, Any]) of typed values keyed by
+        parameter name (drives ``raw_columns`` so the export can
+        reproduce the source).
+
+    ``validate_only=True`` runs the header / mapping / skipped-column
+    checks (every check that doesn't need to read row data) and then
+    returns an empty list — used by the MAP stage to validate a
+    mapping payload against the cached sheet snapshot without
+    re-fetching the source bytes from S3.
+    """
+    header_lookup = _header_lookup(list(fieldnames))
+
+    # 1. Look up the conversation_id parameter so we can address it
+    #    directly while building each row.
+    conv_param = next(
+        (p for p in parameters if p.type == CallImportParameterType.CONVERSATION_ID),
+        None,
+    )
+    if conv_param is None:
+        # The schema invariant should have caught this on create/update,
+        # but a defensive 400 here keeps us safe against hand-rolled
+        # API callers that bypassed validation.
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Selected schema is missing the mandatory conversation_id parameter.",
+        )
+    # 2. Resolve every mapped parameter to a canonical fieldname.
+    #    Required parameters MUST resolve; optional ones may resolve to
+    #    None if the user left them blank (no mapping).
+    canonical_by_param: Dict[str, Optional[str]] = {}
+    recording_date_param_name: Optional[str] = None
+    rec_url_param_name: Optional[str] = None
+    transcript_param_name: Optional[str] = None
+    for param in parameters:
+        mapped_header = parameter_mapping.get(param.name)
+        canonical = (
+            _resolve_mapped_header(mapped_header, header_lookup)
+            if mapped_header
+            else None
+        )
+        if _parameter_is_required(param) and canonical is None:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"{source_label} does not contain the column "
+                    f"'{mapped_header or ''}' mapped to required parameter "
+                    f"'{param.name}'."
+                ),
+            )
+        canonical_by_param[param.name] = canonical
+        if param.type == CallImportParameterType.RECORDING_DATE:
+            recording_date_param_name = param.name
+        elif param.type == CallImportParameterType.RECORDING_URL:
+            rec_url_param_name = param.name
+        elif param.type == CallImportParameterType.TRANSCRIPT:
+            transcript_param_name = param.name
+
+    # 3. Every CSV column must either be mapped to a parameter or
+    #    explicitly skipped. Catches "I forgot to skip the email
+    #    column" gracefully instead of dropping data silently.
+    mapped_canonicals = {c for c in canonical_by_param.values() if c}
+    skipped_canonicals = {
+        _resolve_mapped_header(h, header_lookup)
+        for h in skipped_columns
+    }
+    skipped_canonicals.discard(None)
+    unhandled = [
+        h
+        for h in fieldnames
+        if h not in mapped_canonicals and h not in skipped_canonicals
+    ]
+    if unhandled:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                f"{source_label} columns must either be mapped to a schema "
+                f"parameter or explicitly skipped. Unhandled: {unhandled}."
+            ),
+        )
+
+    conv_canonical = canonical_by_param[conv_param.name]
+    rec_canonical = (
+        canonical_by_param.get(rec_url_param_name)
+        if rec_url_param_name
+        else None
+    )
+    recording_date_canonical = (
+        canonical_by_param.get(recording_date_param_name)
+        if recording_date_param_name
+        else None
+    )
+    transcript_canonical = (
+        canonical_by_param.get(transcript_param_name)
+        if transcript_param_name
+        else None
+    )
+
+    if validate_only:
+        # MAP-stage validation: every header check above has already
+        # run; the row loop only matters at IMPORT time. Skip it (and
+        # the "no data rows" guard at the bottom of the function) so
+        # the caller gets a clean pass when the mapping is shaped right.
+        return CallImportParseResult()
+
+    parsed: List[Dict[str, Any]] = []
+    skipped: List[CallImportParseSkip] = []
+    for idx, row in enumerate(rows_iter):
+        # Drop fully-blank lines - matches the legacy parser behavior so
+        # trailing-newline edge cases don't fail an otherwise-good upload.
+        non_blank = any(
+            (row.get(c) or "").strip()
+            for c in mapped_canonicals
+            if c
+        )
+        if not non_blank:
+            continue
+
+        source_row = idx + 1
+        conv_value = (row.get(conv_canonical) or "").strip() if conv_canonical else ""
+        if not conv_value:
+            skipped.append(
+                CallImportParseSkip(
+                    source_row=source_row,
+                    reason="missing_conversation_id",
+                    message=(
+                        f"Row {source_row} is missing the '{conv_param.name}' "
+                        "(conversation_id) value."
+                    ),
+                )
+            )
+            continue
+
+        if rec_canonical and rec_url_param_name:
+            rec_param = next(
+                (p for p in parameters if p.name == rec_url_param_name),
+                None,
+            )
+            if rec_param is not None and _parameter_is_required(rec_param):
+                rec_raw = (row.get(rec_canonical) or "").strip()
+                if not rec_raw:
+                    skipped.append(
+                        CallImportParseSkip(
+                            source_row=source_row,
+                            reason="missing_recording_url",
+                            message=(
+                                f"Row {source_row} is missing the required "
+                                f"'{rec_url_param_name}' value."
+                            ),
+                        )
+                    )
+                    continue
+                if not _recording_url_cell_is_valid_http(rec_raw):
+                    skipped.append(
+                        CallImportParseSkip(
+                            source_row=source_row,
+                            reason="invalid_recording_url",
+                            message=(
+                                f"Row {source_row}: value for "
+                                f"'{rec_url_param_name}' is not a valid recording "
+                                "URL (must start with http:// or https://)."
+                            ),
+                        )
+                    )
+                    continue
+
+        # Materialize every mapped parameter into the per-row snapshot,
+        # running per-type coercion so a bad cell aborts the upload
+        # rather than silently storing garbage.
+        parameter_values: Dict[str, Any] = {}
+        row_skipped = False
+        for param in parameters:
+            canonical = canonical_by_param[param.name]
+            if canonical is None:
+                continue
+            try:
+                param_type = CallImportParameterType(param.type)
+            except ValueError:
+                param_type = CallImportParameterType.TEXT
+            coerced = _coerce_parameter_value(
+                row.get(canonical) or "",
+                param_type,
+                row_idx=idx,
+                param_name=param.name,
+            )
+            if _parameter_is_required(param) and coerced is None:
+                if param_type == CallImportParameterType.RECORDING_URL:
+                    skipped.append(
+                        CallImportParseSkip(
+                            source_row=source_row,
+                            reason="missing_recording_url",
+                            message=(
+                                f"Row {source_row} is missing the required "
+                                f"'{param.name}' value."
+                            ),
+                        )
+                    )
+                    row_skipped = True
+                    break
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail=(
+                        f"Row {source_row} is missing the required "
+                        f"'{param.name}' value."
+                    ),
+                )
+            parameter_values[param.name] = coerced
+        if row_skipped:
+            continue
+
+        rec_value = (
+            (row.get(rec_canonical) or "").strip() if rec_canonical else ""
+        )
+        transcript_value = (
+            (row.get(transcript_canonical) or "").strip()
+            if transcript_canonical
+            else ""
+        )
+        recording_date_value = (
+            parameter_values.get(recording_date_param_name)
+            if recording_date_param_name
+            else None
+        )
+
+        parsed.append(
+            {
+                "conversation_id": conv_value,
+                "recording_date": recording_date_value,
+                "recording_url": rec_value or None,
+                "transcript": transcript_value or None,
+                "parameter_values": parameter_values,
+            }
+        )
+
+    if not parsed and not skipped:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"{source_label} did not contain any data rows.",
+        )
+
+    return CallImportParseResult(rows=parsed, skipped=skipped)
+
+
+def _raise_if_no_importable_rows(
+    result: CallImportParseResult, *, source_label: str = "CSV"
+) -> None:
+    """Sync upload / API callers fail fast when every data row was skipped."""
+    if result.rows:
+        return
+    if result.skipped:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                f"No importable rows. {len(result.skipped)} row(s) skipped due "
+                "to missing or invalid conversation ID or recording URL."
+            ),
+        )
+    raise HTTPException(
+        status_code=status.HTTP_400_BAD_REQUEST,
+        detail=f"{source_label} did not contain any data rows.",
+    )
+
+
+def _parse_csv(
+    file_bytes: bytes,
+    parameters: List[CallImportSchemaParameter],
+    parameter_mapping: Dict[str, str],
+    skipped_columns: List[str],
+) -> CallImportParseResult:
+    """Parse a CSV file using the resolved schema parameters."""
+    if not file_bytes:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Uploaded CSV is empty.",
+        )
+
+    try:
+        text_stream = io.StringIO(file_bytes.decode("utf-8-sig"))
+    except UnicodeDecodeError:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="CSV must be UTF-8 encoded.",
+        )
+
+    reader = csv.DictReader(text_stream)
+    if not reader.fieldnames:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="CSV is missing a header row.",
+        )
+
+    return _apply_schema_mapping(
+        list(reader.fieldnames),
+        reader,
+        parameters,
+        parameter_mapping,
+        skipped_columns,
+        source_label="CSV",
+    )
+
+
+def _open_xlsx_workbook(file_bytes: bytes):
+    """Open an xlsx/xlsm workbook from in-memory bytes (read-only stream).
+
+    Imports openpyxl lazily so the module loads even in environments that
+    haven't installed the optional dep yet (e.g. lightweight tooling
+    images). Surfaces a clean 400 if openpyxl is missing or the file is
+    not a valid Office Open XML workbook.
+    """
+    if not file_bytes:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Uploaded Excel file is empty.",
+        )
+    try:
+        from openpyxl import load_workbook  # type: ignore
+        from openpyxl.utils.exceptions import InvalidFileException  # type: ignore
+    except ImportError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=(
+                "Excel uploads require the 'openpyxl' package which is "
+                "not installed in this environment."
+            ),
+        ) from exc
+
+    try:
+        return load_workbook(
+            io.BytesIO(file_bytes),
+            read_only=True,
+            data_only=True,
+        )
+    except InvalidFileException as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"File is not a valid .xlsx workbook: {exc}",
+        ) from exc
+    except Exception as exc:  # zipfile.BadZipFile etc.
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Could not open Excel workbook: {exc}",
+        ) from exc
+
+
+def _xlsx_sheet_headers_and_rows(
+    worksheet,
+) -> Tuple[List[str], List[Dict[str, str]]]:
+    """Read row 1 as headers and the rest as dicts of stringified cells.
+
+    Empty trailing header cells are dropped. Duplicate headers preserve
+    the first occurrence (matches ``csv.DictReader`` behavior, which
+    silently drops duplicates).
+    """
+    iterator = worksheet.iter_rows(values_only=True)
+    try:
+        header_row = next(iterator)
+    except StopIteration:
+        return [], []
+
+    headers: List[str] = []
+    seen: set[str] = set()
+    for cell in header_row:
+        name = _xlsx_cell_to_str(cell).strip()
+        if not name:
+            # Stop at the first blank header — treats trailing empty
+            # columns as not part of the table (matches typical Excel
+            # workbook conventions).
+            break
+        norm = name.lower()
+        if norm in seen:
+            continue
+        seen.add(norm)
+        headers.append(name)
+
+    rows: List[Dict[str, str]] = []
+    for row in iterator:
+        if row is None:
+            continue
+        # Pad / truncate to the header length so dict construction is
+        # stable even when a row has fewer / extra cells than the header.
+        cells = list(row[: len(headers)])
+        if len(cells) < len(headers):
+            cells.extend([None] * (len(headers) - len(cells)))
+        if not any(_xlsx_cell_to_str(c).strip() for c in cells):
+            # Skip fully-blank rows (openpyxl read_only routinely yields
+            # trailing empties when the worksheet's used range exceeds
+            # the actual data).
+            continue
+        rows.append(
+            {
+                header: _xlsx_cell_to_str(value)
+                for header, value in zip(headers, cells)
+            }
+        )
+
+    return headers, rows
+
+
+def _parse_xlsx(
+    file_bytes: bytes,
+    sheet_name: Optional[str],
+    parameters: List[CallImportSchemaParameter],
+    parameter_mapping: Dict[str, str],
+    skipped_columns: List[str],
+) -> CallImportParseResult:
+    """Parse a single worksheet from an xlsx/xlsm workbook.
+
+    ``sheet_name`` must match one of the workbook's sheets (case
+    insensitive whitespace-trimmed match). Returns the same shape as
+    :func:`_parse_csv` so the upload handler can persist either format
+    through the same code path.
+    """
+    if not sheet_name or not sheet_name.strip():
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="sheet_name is required when uploading an Excel workbook.",
+        )
+
+    workbook = _open_xlsx_workbook(file_bytes)
+    try:
+        sheet_names = list(workbook.sheetnames)
+        target_norm = sheet_name.strip().lower()
+        match = next(
+            (s for s in sheet_names if s.strip().lower() == target_norm),
+            None,
+        )
+        if match is None:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Sheet '{sheet_name}' not found in workbook. "
+                    f"Available sheets: {sheet_names}"
+                ),
+            )
+        worksheet = workbook[match]
+        headers, rows = _xlsx_sheet_headers_and_rows(worksheet)
+    finally:
+        workbook.close()
+
+    if not headers:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Sheet '{sheet_name}' is missing a header row.",
+        )
+
+    return _apply_schema_mapping(
+        headers,
+        rows,
+        parameters,
+        parameter_mapping,
+        skipped_columns,
+        source_label=f"Sheet '{sheet_name}'",
+    )
+
+
+def _csv_preview_sheets(
+    file_bytes: bytes, filename: Optional[str]
+) -> List[CallImportPreviewSheet]:
+    """Build the synthetic single-sheet preview entry for a CSV upload."""
+    if not file_bytes:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Uploaded CSV is empty.",
+        )
+    try:
+        text_stream = io.StringIO(file_bytes.decode("utf-8-sig"))
+    except UnicodeDecodeError:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="CSV must be UTF-8 encoded.",
+        )
+    reader = csv.DictReader(text_stream)
+    if not reader.fieldnames:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="CSV is missing a header row.",
+        )
+    headers = list(reader.fieldnames)
+    row_count = 0
+    for row in reader:
+        # Match the parse-time skip: ignore fully blank rows so the
+        # count the user sees lines up with what /upload will ingest.
+        if any((v or "").strip() for v in row.values()):
+            row_count += 1
+
+    sheet_label = (filename or "sheet1").rsplit("/", 1)[-1] or "sheet1"
+    return [
+        CallImportPreviewSheet(
+            name=sheet_label,
+            headers=headers,
+            row_count=row_count,
+        )
+    ]
+
+
+def _xlsx_preview_sheets(file_bytes: bytes) -> List[CallImportPreviewSheet]:
+    """List every worksheet in the workbook with its headers and row count."""
+    workbook = _open_xlsx_workbook(file_bytes)
+    sheets: List[CallImportPreviewSheet] = []
+    try:
+        for name in workbook.sheetnames:
+            worksheet = workbook[name]
+            headers, rows = _xlsx_sheet_headers_and_rows(worksheet)
+            sheets.append(
+                CallImportPreviewSheet(
+                    name=name,
+                    headers=headers,
+                    row_count=len(rows),
+                )
+            )
+    finally:
+        workbook.close()
+    return sheets
+
+
+def _parse_json_form_field(name: str, raw: Optional[str], default):
+    """Decode a JSON-encoded form field with a friendly 400 on bad JSON."""
+    if raw is None or raw == "":
+        return default
+    try:
+        return json.loads(raw)
+    except json.JSONDecodeError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"{name} must be valid JSON: {exc}",
+        )
+
+
+# ---------------------------------------------------------------------------
+# Shared helpers used by the staged endpoints (UPLOAD / MAP / IMPORT) and the
+# legacy one-shot ``POST /upload`` shim. Extracted here so each stage and the
+# back-compat path operate on the exact same validation + persistence code.
+# ---------------------------------------------------------------------------
+
+
+def _source_content_type(fmt: str) -> str:
+    """Return the canonical ``Content-Type`` for a parsed file format."""
+    if fmt == "csv":
+        return "text/csv"
+    if fmt == "xlsx":
+        return (
+            "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )
+    return "application/octet-stream"
+
+
+def _source_s3_key(
+    organization_id: UUID, call_import_id: UUID, fmt: str
+) -> str:
+    """Build the canonical S3 key for an upload's source file.
+
+    Mirrors the per-row recording key convention used by
+    ``process_call_import_row`` so a single prefix sweep on delete still
+    cleans up both the source artefact and every fetched recording.
+    """
+    from app.services.storage.s3_service import s3_service
+
+    ext = "xlsx" if fmt == "xlsx" else "csv"
+    return (
+        f"{s3_service.prefix}organizations/{organization_id}/call_imports/"
+        f"{call_import_id}/source.{ext}"
+    )
+
+
+def _build_available_sheets(
+    file_bytes: bytes, fmt: str, filename: Optional[str]
+) -> List[CallImportPreviewSheet]:
+    """Snapshot of sheets + headers cached on the batch at UPLOAD time."""
+    if fmt == "csv":
+        return _csv_preview_sheets(file_bytes, filename)
+    return _xlsx_preview_sheets(file_bytes)
+
+
+def _resolve_schema(
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    schema_id: UUID,
+) -> CallImportSchema:
+    """Fetch + validate a schema row in the active workspace.
+
+    Eager-loads ``parameters`` so callers can iterate without re-querying.
+    """
+    from sqlalchemy.orm import selectinload as _selectinload
+
+    schema = (
+        db.query(CallImportSchema)
+        .options(_selectinload(CallImportSchema.parameters))
+        .filter(
+            CallImportSchema.id == schema_id,
+            CallImportSchema.organization_id == organization_id,
+            CallImportSchema.workspace_id == workspace_id,
+        )
+        .first()
+    )
+    if not schema:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Call import schema not found in the active workspace.",
+        )
+    if not list(schema.parameters):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Selected schema has no parameters defined.",
+        )
+    return schema
+
+
+def _validate_direct_url_import_ready(
+    parameters: List[CallImportSchemaParameter],
+    parameter_mapping: Dict[str, Any],
+) -> None:
+    """Ensure direct-URL import has a mapped recording_url column."""
+    rec_url_param = next(
+        (
+            p
+            for p in parameters
+            if p.type == CallImportParameterType.RECORDING_URL.value
+        ),
+        None,
+    )
+    if rec_url_param is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Direct URL import requires a schema parameter of type "
+                "'recording_url'."
+            ),
+        )
+    mapped_header = (parameter_mapping or {}).get(rec_url_param.name)
+    if not (mapped_header or "").strip():
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Direct URL import requires the 'recording_url' parameter to "
+                "be mapped to a source column."
+            ),
+        )
+
+
+def _validate_exotel_import_ready(
+    parameters: List[CallImportSchemaParameter],
+    parameter_mapping: Dict[str, Any],
+) -> None:
+    """Ensure Exotel credentialed import has a mapped recording_url column."""
+    rec_url_param = next(
+        (
+            p
+            for p in parameters
+            if p.type == CallImportParameterType.RECORDING_URL.value
+        ),
+        None,
+    )
+    if rec_url_param is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Exotel import requires a schema parameter of type "
+                "'recording_url'."
+            ),
+        )
+    mapped_header = (parameter_mapping or {}).get(rec_url_param.name)
+    if not (mapped_header or "").strip():
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Exotel import requires the 'recording_url' parameter to "
+                "be mapped to a source column."
+            ),
+        )
+
+
+def _resolve_telephony_integration(
+    db: Session,
+    organization_id: UUID,
+    telephony_integration_id: UUID,
+    provider: str,
+) -> TelephonyIntegration:
+    """Fetch + validate a telephony credential against the requested provider."""
+    integration = (
+        db.query(TelephonyIntegration)
+        .filter(
+            TelephonyIntegration.id == telephony_integration_id,
+            TelephonyIntegration.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not integration:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Telephony credential not found for this organization.",
+        )
+    if (integration.provider or "").lower() != provider.lower():
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                f"Selected credential is for provider '{integration.provider}', "
+                f"but request specified '{provider}'."
+            ),
+        )
+    if not integration.is_active:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Selected telephony credential is inactive.",
+        )
+    return integration
+
+
+def _clean_parameter_mapping(
+    mapping_payload: Any,
+    parameters: List[CallImportSchemaParameter],
+    schema_name: str,
+) -> Dict[str, str]:
+    """Trim values and drop empties; reject unknown parameter names.
+
+    Accepts an already-decoded value (dict-shaped) so the same helper
+    works for the JSON-form upload path and the JSON-body PATCH path.
+    """
+    if not isinstance(mapping_payload, dict) or not all(
+        isinstance(k, str) and (v is None or isinstance(v, str))
+        for k, v in mapping_payload.items()
+    ):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "parameter_mapping must be an object of "
+                "{parameter_name: csv_header}."
+            ),
+        )
+
+    valid_param_names = {p.name for p in parameters}
+    cleaned: Dict[str, str] = {}
+    for raw_name, raw_header in mapping_payload.items():
+        if raw_name not in valid_param_names:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"parameter_mapping references unknown parameter "
+                    f"'{raw_name}' on schema '{schema_name}'."
+                ),
+            )
+        header = (raw_header or "").strip()
+        if header:
+            cleaned[raw_name] = header
+    return cleaned
+
+
+def _clean_skipped_columns(skipped_payload: Any) -> List[str]:
+    """Dedupe (case-insensitively) and drop blanks; preserve original casing."""
+    if not isinstance(skipped_payload, list) or not all(
+        isinstance(item, str) for item in skipped_payload
+    ):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="skipped_columns must be a list of header strings.",
+        )
+    cleaned: List[str] = []
+    seen: set[str] = set()
+    for item in skipped_payload:
+        norm = _normalize_header(item)
+        if not norm or norm in seen:
+            continue
+        seen.add(norm)
+        cleaned.append(item)
+    return cleaned
+
+
+def _parse_source_file(
+    file_bytes: bytes,
+    fmt: str,
+    sheet_name: Optional[str],
+    parameters: List[CallImportSchemaParameter],
+    cleaned_mapping: Dict[str, str],
+    cleaned_skipped: List[str],
+) -> CallImportParseResult:
+    """Run the format-appropriate parser against a buffer of file bytes."""
+    if fmt == "csv":
+        return _parse_csv(file_bytes, parameters, cleaned_mapping, cleaned_skipped)
+    return _parse_xlsx(
+        file_bytes, sheet_name, parameters, cleaned_mapping, cleaned_skipped
+    )
+
+
+def _materialize_rows(
+    db: Session,
+    call_import: CallImport,
+    parsed_rows: List[Dict[str, Any]],
+    organization_id: UUID,
+) -> List[CallImportRow]:
+    """Insert one ``CallImportRow`` per parsed row, returning the new models."""
+    row_models: List[CallImportRow] = []
+    for idx, row in enumerate(parsed_rows):
+        # Stamp ``transcript_source='csv'`` when the upload actually
+        # provided a transcript so the UI badge ("From CSV") works from
+        # day one. Blank cells stay NULL so the row reads as "no
+        # production transcript yet".
+        csv_transcript = row["transcript"]
+        row_model = CallImportRow(
+            call_import_id=call_import.id,
+            organization_id=organization_id,
+            workspace_id=call_import.workspace_id,
+            row_index=idx,
+            conversation_id=row["conversation_id"],
+            recording_date=(
+                _parse_recording_date_cell(row["recording_date"])
+                if row.get("recording_date")
+                else None
+            ),
+            recording_url=row["recording_url"],
+            transcript=csv_transcript,
+            transcript_source=(
+                "csv" if csv_transcript and csv_transcript.strip() else None
+            ),
+            raw_columns=row["parameter_values"] or None,
+            status=CallImportRowStatus.PENDING,
+        )
+        db.add(row_model)
+        row_models.append(row_model)
+    return row_models
+
+
+def _enqueue_row_tasks(
+    db: Session,
+    call_import: CallImport,
+    row_models: List[CallImportRow],
+) -> None:
+    """Schedule fair round-robin dispatch for pending import rows."""
+    del db, call_import, row_models
+    from app.workers.concurrency.fair_import_dispatch import (
+        schedule_fair_import_dispatch,
+    )
+
+    schedule_fair_import_dispatch(max_workspace_turns=999)
+
+
+def _ensure_blob_storage_enabled() -> None:
+    """Hard-fail UPLOAD if cloud blob storage isn't configured (no local fallback)."""
+    from app.services.storage.s3_service import s3_service
+
+    if not s3_service.is_enabled():
+        err = (
+            s3_service.get_status_message()
+            or "Cloud blob storage is not enabled or not configured"
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=(
+                "Call uploads require cloud blob storage so the file can be "
+                f"persisted between stages: {err}"
+            ),
+        )
+
+
+def _validate_sheet_choice(
+    fmt: str,
+    sheet_name: Optional[str],
+    available_sheets: Optional[List[Dict[str, Any]]],
+) -> Optional[str]:
+    """Normalize / validate ``sheet_name`` against the persisted snapshot.
+
+    Returns the canonical sheet name (matching the workbook's casing)
+    so downstream parsing addresses the right worksheet.
+    """
+    if fmt == "csv":
+        if sheet_name and sheet_name.strip():
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="sheet_name is not applicable to CSV uploads.",
+            )
+        return None
+
+    cleaned = (sheet_name or "").strip() or None
+    if cleaned is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="sheet_name is required when the source is an Excel workbook.",
+        )
+
+    if not available_sheets:
+        # Nothing to validate against (e.g. legacy batch without snapshot);
+        # let downstream parsing error out instead of silently importing.
+        return cleaned
+
+    target = cleaned.strip().lower()
+    for entry in available_sheets:
+        name = entry.get("name") if isinstance(entry, dict) else None
+        if isinstance(name, str) and name.strip().lower() == target:
+            return name
+    sheet_names = [
+        entry.get("name") for entry in available_sheets if isinstance(entry, dict)
+    ]
+    raise HTTPException(
+        status_code=status.HTTP_400_BAD_REQUEST,
+        detail=(
+            f"Sheet '{cleaned}' not found in the staged file. "
+            f"Available sheets: {sheet_names}"
+        ),
+    )
+
+
+def _tag_response_payload(tags: Optional[List[CallImportTag]]) -> List[Dict[str, Any]]:
+    """Shape a CallImport's tag relationship for the upload response."""
+    return [
+        {
+            "id": tag.id,
+            "name": tag.name,
+            "color": tag.color,
+            "created_at": tag.created_at,
+            "updated_at": tag.updated_at,
+        }
+        for tag in (tags or [])
+    ]
+
+
+@router.post(
+    "/preview",
+    response_model=CallImportPreviewResponse,
+    operation_id="previewCallImportFile",
+)
+async def preview_call_import_file(
+    file: UploadFile = File(...),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+) -> CallImportPreviewResponse:
+    """Inspect an uploaded CSV / Excel file and return its sheets + headers.
+
+    Drives the column-mapping UI without forcing the frontend to parse
+    CSV / xlsx itself — keeps client and server in lockstep on quoted
+    fields, encodings, and Excel cell coercion. CSVs return a single
+    synthetic sheet named after the filename; Excel workbooks return one
+    entry per worksheet (in workbook order).
+    """
+    del api_key, organization_id, workspace_id, db  # auth only
+
+    fmt = _file_format(file.filename)
+    if fmt is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Unsupported file format. Allowed extensions: "
+                f"{', '.join(ALLOWED_EXTENSIONS)}."
+            ),
+        )
+
+    file_bytes = await file.read()
+    if len(file_bytes) > MAX_UPLOAD_BYTES:
+        raise HTTPException(
+            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+            detail=f"File exceeds {MAX_UPLOAD_BYTES} bytes",
+        )
+
+    if fmt == "csv":
+        sheets = _csv_preview_sheets(file_bytes, file.filename)
+    else:
+        sheets = _xlsx_preview_sheets(file_bytes)
+
+    return CallImportPreviewResponse(format=fmt, sheets=sheets)
+
+
+@router.post(
+    "",
+    response_model=CallImportResponse,
+    status_code=status.HTTP_201_CREATED,
+    operation_id="createCallImport",
+)
+async def create_call_import(
+    file: UploadFile = File(
+        ...,
+        description="CSV / Excel file to stage. Persisted to S3 between stages.",
+    ),
+    dataset: str = Form(
+        ...,
+        description=(
+            "Required free-text dataset label. Collected up-front so the "
+            "batch is filterable from the moment it lands."
+        ),
+    ),
+    tag_ids: Optional[List[UUID]] = Form(
+        None,
+        description="Optional list of CallImportTag ids to attach to the new batch.",
+    ),
+    schema_id: Optional[UUID] = Form(
+        None,
+        description=(
+            "Optional schema pre-pick. The user can still change it during "
+            "the MAP stage; provided here only so the detail page can pre-"
+            "select the schema dropdown."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportResponse:
+    """UPLOAD stage of the staged call-import flow.
+
+    Persists the source file to S3 and creates a ``CallImport`` row with
+    ``status='uploaded'``. No mapping, no provider, no rows yet — the
+    user moves through MAP and IMPORT as separate idempotent steps.
+
+    Dataset is collected here (rather than at IMPORT) so the batch is
+    filterable from the moment it appears in the list view.
+    """
+    del api_key
+
+    fmt = _file_format(file.filename)
+    if fmt is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Unsupported file format. Allowed extensions: "
+                f"{', '.join(ALLOWED_EXTENSIONS)}."
+            ),
+        )
+
+    normalized_dataset = _normalize_dataset(dataset)
+    if not normalized_dataset:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="dataset is required and must be a non-empty string.",
+        )
+
+    file_bytes = await file.read()
+    if len(file_bytes) > MAX_UPLOAD_BYTES:
+        raise HTTPException(
+            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+            detail=f"File exceeds {MAX_UPLOAD_BYTES} bytes",
+        )
+
+    # Parse-now so we (a) reject garbage uploads up-front instead of
+    # later in the MAP step, and (b) capture the sheets snapshot the
+    # MAP UI needs without having to re-fetch the file from S3.
+    sheets = _build_available_sheets(file_bytes, fmt, file.filename)
+
+    # Optional schema pre-pick: validated only if supplied (the user is
+    # allowed to set it for the first time during MAP).
+    if schema_id is not None:
+        _resolve_schema(db, organization_id, workspace_id, schema_id)
+
+    tag_rows = _resolve_tags(db, organization_id, tag_ids)
+
+    _ensure_blob_storage_enabled()
+
+    # Pre-generate the id so we can compute a deterministic S3 key
+    # before the row is persisted, keeping ``source_s3_key`` consistent
+    # with the prefix sweep used at delete-time.
+    import uuid as _uuid
+
+    call_import_id = _uuid.uuid4()
+    s3_key = _source_s3_key(organization_id, call_import_id, fmt)
+    content_type = _source_content_type(fmt)
+
+    from app.services.storage.s3_service import s3_service, StorageError
+
+    try:
+        s3_service.upload_file_by_key(file_bytes, s3_key, content_type=content_type)
+    except StorageError as exc:
+        logger.exception(
+            "Failed to upload source file to S3 for new call import {}",
+            call_import_id,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Failed to persist upload to S3: {exc}",
+        )
+
+    call_import = CallImport(
+        id=call_import_id,
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        # Provider + credential aren't known until the IMPORT stage; leave
+        # them NULL so the staged-vs-legacy distinction is visible at a
+        # glance from the DB.
+        provider=None,
+        telephony_integration_id=None,
+        original_filename=file.filename,
+        sheet_name=None,
+        dataset=normalized_dataset,
+        schema_id=schema_id,
+        parameter_mapping={},
+        skipped_columns=[],
+        column_mapping={},
+        extra_columns=[],
+        custom_column_mapping={},
+        source_s3_key=s3_key,
+        source_format=fmt,
+        source_size_bytes=len(file_bytes),
+        source_content_type=content_type,
+        available_sheets=[sheet.model_dump() for sheet in sheets],
+        total_rows=0,
+        completed_rows=0,
+        failed_rows=0,
+        status=CallImportStatus.UPLOADED,
+    )
+    if tag_rows:
+        call_import.tags = tag_rows
+
+    stamp_call_import_actor(call_import, principal, creating=True)
+    db.add(call_import)
+    try:
+        db.commit()
+    except Exception:
+        db.rollback()
+        # Best-effort cleanup of the uploaded S3 object so a failed
+        # commit doesn't leak storage.
+        try:
+            s3_service.delete_file_by_key(s3_key)
+        except Exception as cleanup_exc:  # noqa: BLE001
+            logger.warning(
+                "Failed to clean up orphaned S3 object {} after DB rollback: {}",
+                s3_key,
+                cleanup_exc,
+            )
+        raise
+
+    db.refresh(call_import)
+    return _serialize_call_import(db, call_import)
+
+
+@router.patch(
+    "/{call_import_id}/mapping",
+    response_model=CallImportResponse,
+    operation_id="updateCallImportMapping",
+)
+async def update_call_import_mapping(
+    call_import_id: UUID,
+    payload: CallImportMappingUpdate,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportResponse:
+    """MAP stage of the staged call-import flow.
+
+    Validates ``parameter_mapping`` + ``skipped_columns`` against the
+    sheet headers captured at UPLOAD time and persists them on the
+    batch. Idempotent: callers may submit this multiple times while
+    the batch is in ``uploaded`` or ``mapped`` state.
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    if call_import.status not in (
+        CallImportStatus.UPLOADED,
+        CallImportStatus.MAPPED,
+    ):
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=(
+                f"Cannot edit mapping on a batch in status "
+                f"'{call_import.status.value}'. Mapping can only be edited "
+                "before the IMPORT stage."
+            ),
+        )
+
+    if not call_import.source_s3_key or not call_import.source_format:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=(
+                "This batch was not uploaded through the staged flow and "
+                "cannot have its mapping edited."
+            ),
+        )
+
+    schema = _resolve_schema(
+        db, organization_id, workspace_id, payload.schema_id
+    )
+    parameters = list(schema.parameters)
+
+    canonical_sheet = _validate_sheet_choice(
+        call_import.source_format,
+        payload.sheet_name,
+        call_import.available_sheets,
+    )
+
+    # Pull the headers for the selected sheet straight out of the
+    # snapshot so we don't have to re-download the file from S3 just to
+    # validate the mapping.
+    headers: List[str] = []
+    if call_import.available_sheets:
+        if canonical_sheet is None:
+            # CSV: single synthetic sheet.
+            entry = call_import.available_sheets[0]
+            headers = list(entry.get("headers") or [])
+        else:
+            for entry in call_import.available_sheets:
+                if not isinstance(entry, dict):
+                    continue
+                name = entry.get("name")
+                if isinstance(name, str) and name == canonical_sheet:
+                    headers = list(entry.get("headers") or [])
+                    break
+
+    cleaned_mapping = _clean_parameter_mapping(
+        payload.parameter_mapping, parameters, schema.name
+    )
+    cleaned_skipped = _clean_skipped_columns(payload.skipped_columns)
+
+    # Run the same per-column validation as the parse path so the user
+    # gets an immediate 400 if a required parameter is left unmapped or
+    # a header is neither mapped nor skipped — without needing to read
+    # the file. ``validate_only`` skips the row loop (and the empty-rows
+    # guard) since the row data lives in S3, not in this request.
+    if headers:
+        _apply_schema_mapping(
+            headers,
+            iter(()),
+            parameters,
+            cleaned_mapping,
+            cleaned_skipped,
+            source_label=(
+                f"Sheet '{canonical_sheet}'"
+                if canonical_sheet is not None
+                else "CSV"
+            ),
+            validate_only=True,
+        )
+
+    call_import.schema_id = schema.id
+    call_import.parameter_mapping = dict(cleaned_mapping)
+    call_import.skipped_columns = list(cleaned_skipped)
+    call_import.sheet_name = canonical_sheet
+    call_import.status = CallImportStatus.MAPPED
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+    db.refresh(call_import)
+    return _serialize_call_import(db, call_import)
+
+
+@router.post(
+    "/{call_import_id}/import",
+    response_model=CallImportUploadResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="startCallImport",
+)
+async def start_call_import(
+    call_import_id: UUID,
+    payload: CallImportStartRequest,
+    background_tasks: BackgroundTasks,
+    legacy: bool = Query(
+        False,
+        description=(
+            "Deprecated escape hatch for import-only processing. "
+            "New batches should use Run Evaluation instead."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportUploadResponse:
+    """Deprecated IMPORT stage — use Run Evaluation for new batches.
+
+    Recording fetch is part of the unified evaluation pipeline. This
+    endpoint remains available only with ``?legacy=true`` for backward
+    compatibility.
+    """
+    del api_key, background_tasks
+
+    if not legacy:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=(
+                "Standalone import is deprecated. Use Run Evaluation — "
+                "recording fetch is part of the evaluation pipeline. "
+                "Append ?legacy=true to use the import-only path."
+            ),
+        )
+
+    from sqlalchemy.orm import selectinload as _selectinload
+
+    call_import = (
+        db.query(CallImport)
+        .options(_selectinload(CallImport.tags))
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    if call_import.status != CallImportStatus.MAPPED:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=(
+                f"Cannot start import for a batch in status "
+                f"'{call_import.status.value}'. Map the columns first."
+            ),
+        )
+
+    if not call_import.source_s3_key or not call_import.source_format:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=(
+                "This batch has no staged source file and cannot be imported "
+                "through the staged flow."
+            ),
+        )
+
+    if not call_import.schema_id:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="Cannot start import without a mapped schema.",
+        )
+
+    schema = _resolve_schema(
+        db, organization_id, workspace_id, call_import.schema_id
+    )
+    parameters = list(schema.parameters)
+
+    if payload.telephony_integration_id is not None:
+        integration = _resolve_telephony_integration(
+            db,
+            organization_id,
+            payload.telephony_integration_id,
+            payload.provider or "",
+        )
+        if (integration.provider or "").lower() == "exotel":
+            _validate_exotel_import_ready(
+                parameters, dict(call_import.parameter_mapping or {})
+            )
+    else:
+        _validate_direct_url_import_ready(
+            parameters, dict(call_import.parameter_mapping or {})
+        )
+        integration = None
+
+    _ensure_blob_storage_enabled()
+
+    if integration is not None:
+        call_import.provider = integration.provider
+        call_import.telephony_integration_id = integration.id
+    else:
+        call_import.provider = None
+        call_import.telephony_integration_id = None
+
+    call_import.total_rows = 0
+    call_import.completed_rows = 0
+    call_import.failed_rows = 0
+    call_import.error_message = None
+    call_import.status = CallImportStatus.PROCESSING
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+    db.refresh(call_import)
+
+    from app.workers.tasks.call_import_bulk_ops import (
+        materialize_call_import_rows_task,
+    )
+
+    materialize_call_import_rows_task.delay(
+        str(call_import_id),
+        str(organization_id),
+        str(workspace_id),
+        schedule_import_dispatch=True,
+    )
+
+    return CallImportUploadResponse(
+        id=call_import.id,
+        total_rows=0,
+        status=call_import.status,
+        dataset=call_import.dataset,
+        tags=_tag_response_payload(call_import.tags),
+        message=(
+            "Import accepted. Rows are being materialized in the background; "
+            "recordings will be fetched asynchronously."
+        ),
+    )
+
+
+@router.post(
+    "/upload",
+    response_model=CallImportUploadResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="uploadCallImportCsv",
+    deprecated=True,
+)
+async def upload_call_import_csv(
+    background_tasks: BackgroundTasks,
+    file: UploadFile = File(...),
+    provider: Optional[str] = Form(
+        None,
+        description=(
+            "Telephony provider key (e.g. 'exotel', 'plivo'). Must match the "
+            "selected telephony_integration_id's provider. Omit together "
+            "with telephony_integration_id for direct-URL import."
+        ),
+    ),
+    telephony_integration_id: Optional[UUID] = Form(
+        None,
+        description=(
+            "Specific TelephonyIntegration credential row to use when "
+            "downloading recordings for this batch. Omit together with "
+            "provider for direct-URL import."
+        ),
+    ),
+    schema_id: UUID = Form(
+        ...,
+        description=(
+            "Reusable Input Parameter schema this upload is mapped against. "
+            "Must belong to the active workspace."
+        ),
+    ),
+    parameter_mapping: str = Form(
+        ...,
+        description=(
+            "JSON-encoded ``{schema_parameter_name: source_header}`` map "
+            "covering every required schema parameter. Optional parameters "
+            "may be omitted or set to an empty string."
+        ),
+    ),
+    skipped_columns: Optional[str] = Form(
+        None,
+        description=(
+            "JSON-encoded list of source header strings the uploader has "
+            "explicitly skipped. Every header in the file must either be "
+            "mapped or appear here; otherwise the upload is rejected so a "
+            "forgotten column never silently drops."
+        ),
+    ),
+    dataset: Optional[str] = Form(
+        None,
+        description=(
+            "Optional free-text dataset label for high-level segregation. "
+            "Empty strings are stored as NULL."
+        ),
+    ),
+    tag_ids: Optional[List[UUID]] = Form(
+        None,
+        description="Optional list of CallImportTag ids to attach to the new batch.",
+    ),
+    sheet_name: Optional[str] = Form(
+        None,
+        description=(
+            "Worksheet to import when the file is an Excel workbook "
+            "(.xlsx / .xlsm). REQUIRED for Excel uploads. Ignored for CSV "
+            "uploads (rejected with 400 if non-empty so typos surface "
+            "instead of silently importing the wrong source)."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportUploadResponse:
+    """Legacy one-shot upload kept for backward compatibility.
+
+    DEPRECATED: prefer the staged flow
+    (``POST /`` → ``PATCH /{id}/mapping`` → ``POST /{id}/import``) so
+    each step is idempotent and resumable. This endpoint runs all three
+    stages inline in a single transaction so existing scripts /
+    integrations keep working unchanged.
+    """
+    del api_key
+
+    fmt = _file_format(file.filename)
+    if fmt is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "Unsupported file format. Allowed extensions: "
+                f"{', '.join(ALLOWED_EXTENSIONS)}."
+            ),
+        )
+
+    sheet_name_clean = (sheet_name or "").strip() or None
+    if fmt == "csv" and sheet_name_clean is not None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="sheet_name is not applicable to CSV uploads.",
+        )
+    if fmt == "xlsx" and sheet_name_clean is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="sheet_name is required when uploading an Excel workbook.",
+        )
+
+    file_bytes = await file.read()
+    if len(file_bytes) > MAX_UPLOAD_BYTES:
+        raise HTTPException(
+            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+            detail=f"File exceeds {MAX_UPLOAD_BYTES} bytes",
+        )
+
+    schema = _resolve_schema(db, organization_id, workspace_id, schema_id)
+    parameters = list(schema.parameters)
+
+    mapping_payload = _parse_json_form_field(
+        "parameter_mapping", parameter_mapping, {}
+    )
+    cleaned_mapping = _clean_parameter_mapping(
+        mapping_payload, parameters, schema.name
+    )
+
+    skipped_payload = _parse_json_form_field("skipped_columns", skipped_columns, [])
+    cleaned_skipped = _clean_skipped_columns(skipped_payload)
+
+    has_provider = bool((provider or "").strip())
+    has_integration = telephony_integration_id is not None
+    if has_provider != has_integration:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                "provider and telephony_integration_id must both be provided "
+                "or both omitted for direct-URL import."
+            ),
+        )
+
+    if telephony_integration_id is not None:
+        integration = _resolve_telephony_integration(
+            db, organization_id, telephony_integration_id, provider or ""
+        )
+        if (integration.provider or "").lower() == "exotel":
+            _validate_exotel_import_ready(parameters, cleaned_mapping)
+    else:
+        _validate_direct_url_import_ready(parameters, cleaned_mapping)
+        integration = None
+
+    parsed_rows = _parse_source_file(
+        file_bytes, fmt, sheet_name_clean, parameters, cleaned_mapping, cleaned_skipped
+    )
+    _raise_if_no_importable_rows(parsed_rows, source_label=fmt)
+
+    tag_rows = _resolve_tags(db, organization_id, tag_ids)
+
+    call_import = CallImport(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        provider=integration.provider if integration is not None else None,
+        telephony_integration_id=integration.id if integration is not None else None,
+        original_filename=file.filename,
+        sheet_name=sheet_name_clean,
+        dataset=_normalize_dataset(dataset),
+        schema_id=schema.id,
+        parameter_mapping=dict(cleaned_mapping),
+        skipped_columns=list(cleaned_skipped),
+        # Legacy columns are left empty on new uploads; the detail page
+        # falls back to ``parameter_mapping`` when ``schema_id`` is set.
+        column_mapping={},
+        extra_columns=[],
+        custom_column_mapping={},
+        total_rows=len(parsed_rows.rows),
+        completed_rows=0,
+        failed_rows=0,
+        status=CallImportStatus.PENDING,
+        source_row_skips=parse_skips_to_json(parsed_rows.skipped),
+    )
+    if tag_rows:
+        call_import.tags = tag_rows
+    stamp_call_import_actor(call_import, principal, creating=True)
+    db.add(call_import)
+    db.flush()  # populate call_import.id
+    if integration is None:
+        # The model's historical Python default is "exotel"; direct-URL
+        # imports intentionally have no telephony provider.
+        call_import.provider = None
+
+    row_models = _materialize_rows(
+        db, call_import, parsed_rows.rows, organization_id
+    )
+
+    call_import.status = CallImportStatus.PROCESSING
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+    db.refresh(call_import)
+
+    background_tasks.add_task(
+        record_call_import_batch_created,
+        organization_id,
+        call_import.id,
+        workspace_id=workspace_id,
+        total_rows=call_import.total_rows,
+        source="csv",
+        provider=call_import.provider,
+    )
+
+    _enqueue_row_tasks(db, call_import, row_models)
+
+    return CallImportUploadResponse(
+        id=call_import.id,
+        total_rows=call_import.total_rows,
+        status=call_import.status,
+        dataset=call_import.dataset,
+        tags=_tag_response_payload(call_import.tags),
+        message=(
+            f"Accepted {call_import.total_rows} rows for import. "
+            "Recordings will be fetched asynchronously."
+        ),
+    )
+
+
+@router.post(
+    "/audio-upload",
+    response_model=CallImportUploadResponse,
+    status_code=status.HTTP_201_CREATED,
+    operation_id="uploadCallImportAudio",
+)
+async def upload_call_import_audio(
+    background_tasks: BackgroundTasks,
+    files: List[UploadFile] = File(
+        ...,
+        description="One or more manual call recording audio files.",
+    ),
+    dataset: str = Form(
+        ...,
+        description="Required free-text dataset label for the manual upload batch.",
+    ),
+    tag_ids: Optional[List[UUID]] = Form(
+        None,
+        description="Optional list of CallImportTag ids to attach to the new batch.",
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportUploadResponse:
+    """Persist manually uploaded recordings as completed CallImport rows.
+
+    The rows skip the provider-download worker entirely because the audio
+    bytes are already in hand. From this point onward they behave exactly
+    like completed CSV-import rows: playback reads ``recording_s3_key`` and
+    the existing diarisation/evaluation endpoints can operate on them.
+    """
+
+    normalized_dataset = _normalize_dataset(dataset)
+    if not normalized_dataset:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="dataset is required and must be a non-empty string.",
+        )
+    if not files:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="At least one audio file is required.",
+        )
+
+    _ensure_blob_storage_enabled()
+    tag_rows = _resolve_tags(db, organization_id, tag_ids)
+
+    max_bytes = int(settings.MAX_FILE_SIZE_MB) * 1024 * 1024
+    prepared: List[Dict[str, Any]] = []
+    conversation_counts: Dict[str, int] = {}
+
+    for idx, upload in enumerate(files):
+        filename = upload.filename or f"recording-{idx + 1}"
+        ext = _audio_extension(filename)
+        if not ext:
+            allowed = ", ".join(settings.ALLOWED_AUDIO_FORMATS)
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Unsupported audio file '{filename}'. Allowed formats: {allowed}.",
+            )
+
+        contents = await upload.read()
+        if not contents:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Audio file '{filename}' is empty.",
+            )
+        if len(contents) > max_bytes:
+            raise HTTPException(
+                status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+                detail=(
+                    f"Audio file '{filename}' exceeds "
+                    f"{settings.MAX_FILE_SIZE_MB} MB."
+                ),
+            )
+
+        base_conversation_id = _sanitize_conversation_id(_filename_stem(filename))
+        conversation_id = _dedupe_conversation_id(
+            base_conversation_id,
+            conversation_counts,
+        )
+        prepared.append(
+            {
+                "filename": filename,
+                "extension": ext,
+                "content_type": _audio_content_type(ext, upload.content_type),
+                "contents": contents,
+                "conversation_id": conversation_id,
+            }
+        )
+
+    original_filename = (
+        prepared[0]["filename"]
+        if len(prepared) == 1
+        else f"{len(prepared)} manual recordings"
+    )
+    total_size = sum(len(item["contents"]) for item in prepared)
+    uploaded_keys: List[str] = []
+
+    from app.services.storage.s3_service import s3_service
+
+    call_import = CallImport(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        provider=None,
+        telephony_integration_id=None,
+        original_filename=original_filename,
+        source_format="audio",
+        source_size_bytes=total_size,
+        source_content_type="audio/*",
+        dataset=normalized_dataset,
+        total_rows=len(prepared),
+        completed_rows=len(prepared),
+        failed_rows=0,
+        status=CallImportStatus.COMPLETED,
+    )
+    if tag_rows:
+        call_import.tags = tag_rows
+
+    stamp_call_import_actor(call_import, principal, creating=True)
+    try:
+        db.add(call_import)
+        db.flush()
+        # The model's historical Python default is "exotel"; manual uploads
+        # intentionally have no telephony provider.
+        call_import.provider = None
+
+        row_mappings: List[Dict[str, Any]] = []
+        for idx, item in enumerate(prepared):
+            row_id = uuid4()
+            key = _audio_s3_key(
+                organization_id,
+                call_import.id,
+                row_id,
+                item["extension"],
+            )
+            s3_service.upload_file_by_key(
+                item["contents"],
+                key,
+                content_type=item["content_type"],
+            )
+            uploaded_keys.append(key)
+
+            row_mappings.append(
+                {
+                    "id": row_id,
+                    "call_import_id": call_import.id,
+                    "organization_id": organization_id,
+                    "workspace_id": workspace_id,
+                    "row_index": idx,
+                    "conversation_id": item["conversation_id"],
+                    "recording_url": None,
+                    "transcript": None,
+                    "transcript_source": None,
+                    "raw_columns": {"conversation_id": item["conversation_id"]},
+                    "status": CallImportRowStatus.COMPLETED,
+                    "recording_s3_key": key,
+                    "recording_content_type": item["content_type"],
+                    "recording_size_bytes": len(item["contents"]),
+                }
+            )
+
+        if is_sharding_enabled():
+            from app.db_sharding.row_ops import (
+                bulk_insert_mappings_on_shards,
+                register_shard_slices,
+            )
+
+            bulk_insert_mappings_on_shards(db, call_import.id, row_mappings)
+            register_shard_slices(db, call_import.id, len(row_mappings))
+        else:
+            for mapping in row_mappings:
+                db.add(CallImportRow(**mapping))
+
+        db.commit()
+    except Exception as exc:
+        db.rollback()
+        if uploaded_keys and s3_service.is_enabled():
+            try:
+                s3_service.delete_keys(uploaded_keys)
+            except Exception:
+                logger.exception(
+                    "Failed to clean up manual audio upload keys after error"
+                )
+        logger.exception("Failed to persist manual call recording upload")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to upload manual recordings: {exc}",
+        ) from exc
+
+    db.refresh(call_import)
+    background_tasks.add_task(
+        record_call_import_batch_created,
+        organization_id,
+        call_import.id,
+        workspace_id=workspace_id,
+        total_rows=call_import.total_rows,
+        source="audio",
+        provider=None,
+    )
+    return CallImportUploadResponse(
+        id=call_import.id,
+        total_rows=call_import.total_rows,
+        status=call_import.status,
+        dataset=call_import.dataset,
+        tags=_tag_response_payload(call_import.tags),
+        message=(
+            f"Uploaded {call_import.total_rows} manual recording"
+            f"{'' if call_import.total_rows == 1 else 's'}."
+        ),
+    )
+
+
+@router.get(
+    "",
+    response_model=CallImportListResponse,
+    operation_id="listCallImports",
+)
+async def list_call_imports(
+    page: int = Query(1, ge=1),
+    page_size: int = Query(20, ge=1, le=100),
+    status_filter: Optional[CallImportStatus] = Query(None, alias="status"),
+    dataset: Optional[str] = Query(
+        None,
+        description=(
+            "Filter by exact dataset string (case-insensitive). Pass the "
+            "literal value '__none__' to filter to imports with no dataset."
+        ),
+    ),
+    tag_id: Optional[List[UUID]] = Query(
+        None,
+        description="Filter to imports tagged with ALL of the given tag ids.",
+    ),
+    source_format: Optional[str] = Query(
+        None,
+        description=(
+            "Filter by source format. Use 'audio' for manual recordings or "
+            "'__non_audio__' for CSV/Excel/legacy imports."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+) -> CallImportListResponse:
+    """List call-import batches for the active workspace, newest first.
+
+    Scoped to (organization_id, workspace_id) so users only see imports
+    for the workspace they're currently in. Supports a high-level
+    ``dataset`` filter (powers the segregation dropdown at the top of
+    the imports page) plus an AND-style multi-tag filter via repeated
+    ``tag_id`` parameters.
+    """
+
+    query = (
+        db.query(CallImport)
+        .filter(
+            CallImport.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+        )
+    )
+    if status_filter is not None:
+        query = query.filter(CallImport.status == status_filter)
+
+    source_filter = (source_format or "").strip().lower()
+    if source_filter == "__non_audio__":
+        query = query.filter(
+            or_(CallImport.source_format.is_(None), CallImport.source_format != "audio")
+        )
+    elif source_filter:
+        query = query.filter(func.lower(CallImport.source_format) == source_filter)
+
+    if dataset is not None:
+        if dataset == "__none__":
+            query = query.filter(CallImport.dataset.is_(None))
+        elif dataset.strip():
+            query = query.filter(
+                func.lower(CallImport.dataset) == dataset.strip().lower()
+            )
+
+    if tag_id:
+        from app.models.database import CallImportTagAssignment
+
+        for single_tag_id in tag_id:
+            sub = (
+                db.query(CallImportTagAssignment.call_import_id)
+                .filter(CallImportTagAssignment.tag_id == single_tag_id)
+                .subquery()
+            )
+            query = query.filter(CallImport.id.in_(sub))
+
+    total = query.count()
+    items = (
+        query.order_by(desc(CallImport.created_at))
+        .offset((page - 1) * page_size)
+        .limit(page_size)
+        .all()
+    )
+
+    email_map = emails_for_user_ids(db, user_ids_from_call_imports(items))
+    return CallImportListResponse(
+        items=[
+            _serialize_call_import(db, item, user_emails=email_map)
+            for item in items
+        ],
+        total=total,
+        page=page,
+        page_size=page_size,
+    )
+
+
+@router.get(
+    "/dispatch-diagnostics",
+    response_model=CallImportDispatchDiagnosticsResponse,
+    operation_id="getCallImportDispatchDiagnostics",
+    dependencies=[Depends(require_admin)],
+)
+async def get_call_import_dispatch_diagnostics(
+    workspace_id: Optional[UUID] = Query(
+        None,
+        description=(
+            "Optional workspace filter. When omitted, returns every workspace "
+            "in the organization with active eval dispatch state."
+        ),
+    ),
+    include_idle_workspaces: bool = Query(
+        False,
+        description=(
+            "When true, include org workspaces with zero pending rows and "
+            "zero in-flight slots."
+        ),
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportDispatchDiagnosticsResponse:
+    """Live eval slot usage and fair-dispatch state for operators.
+
+    Org admins use this to diagnose cross-workspace starvation (e.g. one
+    workspace's 10k run blocking another's pending eval rows) by inspecting
+    Redis in-flight counters, pending dispatch rows, and scheduler cursors.
+    """
+    del api_key
+    payload = build_call_import_dispatch_diagnostics(
+        db,
+        organization_id,
+        workspace_id=workspace_id,
+        include_idle_workspaces=include_idle_workspaces,
+    )
+    return CallImportDispatchDiagnosticsResponse.model_validate(payload)
+
+
+@router.get(
+    "/datasets",
+    response_model=List[str],
+    operation_id="listCallImportDatasets",
+)
+async def list_call_import_datasets(
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+) -> List[str]:
+    """Return the distinct, non-null dataset labels in use for the active
+    workspace.
+
+    Scoped per-workspace so each workspace's Dataset dropdown only shows
+    its own segregation labels.
+    """
+    rows = (
+        db.query(CallImport.dataset)
+        .filter(
+            CallImport.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+            CallImport.dataset.isnot(None),
+            CallImport.dataset != "",
+        )
+        .distinct()
+        .order_by(CallImport.dataset.asc())
+        .all()
+    )
+    return [row[0] for row in rows if row[0]]
+
+
+@router.get(
+    "/diarisation-prompt-default",
+    response_model=CallImportDiarisationPromptDefaultResponse,
+    operation_id="getCallImportDiarisationPromptDefault",
+)
+async def get_call_import_diarisation_prompt_default(
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+) -> CallImportDiarisationPromptDefaultResponse:
+    """Return the canonical LLM diariser prompt.
+
+    The Transcribe / Run Evaluation modals call this on open so they
+    can pre-fill the prompt textarea. Returning the constant from the
+    backend (rather than hard-coding it in the frontend) keeps the
+    fallback used by the worker and the placeholder shown in the UI
+    in lock-step — operators always see the *actual* default they'd
+    get if they leave the field blank.
+
+    Registered before ``GET /{call_import_id}`` so the static path is
+    not mistaken for a UUID import id (which would 422).
+    """
+    del api_key, organization_id
+    from app.workers.tasks.helpers.llm_diarisation import (
+        DEFAULT_DIARIZATION_PROMPT,
+    )
+
+    return CallImportDiarisationPromptDefaultResponse(
+        prompt=DEFAULT_DIARIZATION_PROMPT
+    )
+
+
+@router.patch(
+    "/{call_import_id}",
+    response_model=CallImportResponse,
+    operation_id="updateCallImport",
+)
+async def update_call_import(
+    call_import_id: UUID,
+    payload: CallImportUpdate,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportResponse:
+    """Edit dataset / tag assignments (and schema, pre-import) on a batch.
+
+    ``dataset = ""`` clears the label; ``tag_ids = []`` removes all tag
+    assignments. Fields omitted from the body are left untouched.
+
+    ``schema_id`` is only honoured while the batch is in
+    ``uploaded`` / ``mapped`` state — once rows have been materialised
+    the schema is locked. Changing the schema resets any persisted
+    mapping (the user must re-MAP) and rewinds status to ``uploaded``.
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    body = payload.model_dump(exclude_unset=True)
+    if "dataset" in body:
+        call_import.dataset = _normalize_dataset(body["dataset"])
+
+    if "tag_ids" in body:
+        tag_ids = body["tag_ids"] or []
+        call_import.tags = _resolve_tags(db, organization_id, tag_ids)
+
+    if "schema_id" in body and body["schema_id"] is not None:
+        if call_import.status not in (
+            CallImportStatus.UPLOADED,
+            CallImportStatus.MAPPED,
+        ):
+            raise HTTPException(
+                status_code=status.HTTP_409_CONFLICT,
+                detail=(
+                    f"Cannot reassign schema on a batch in status "
+                    f"'{call_import.status.value}'."
+                ),
+            )
+        new_schema = _resolve_schema(
+            db, organization_id, workspace_id, body["schema_id"]
+        )
+        if call_import.schema_id != new_schema.id:
+            # Switching schemas invalidates the persisted mapping —
+            # parameter names won't line up with the new schema, so
+            # reset to UPLOADED and force a fresh MAP.
+            call_import.schema_id = new_schema.id
+            call_import.parameter_mapping = {}
+            call_import.skipped_columns = []
+            call_import.sheet_name = None
+            call_import.status = CallImportStatus.UPLOADED
+
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+    db.refresh(call_import)
+    return _serialize_call_import(db, call_import)
+
+
+@router.get(
+    "/{call_import_id}",
+    response_model=CallImportDetailResponse,
+    operation_id="getCallImportDetail",
+)
+async def get_call_import_detail(
+    call_import_id: UUID,
+    row_limit: int = Query(500, ge=0, le=5000),
+    row_offset: int = Query(0, ge=0),
+    q: Optional[str] = Query(
+        None,
+        description=(
+            "Optional case-insensitive substring filter on "
+            "``conversation_id``. When set, ``filtered_total_rows`` in "
+            "the response reflects the post-filter row count so the UI "
+            "can paginate against the filtered slice."
+        ),
+    ),
+    diarised_status: Optional[str] = Query(
+        None,
+        description=(
+            "Optional filter on ``CallImportRow.diarised_transcript_status``. "
+            "Accepts one of ``pending``, ``running``, ``completed``, "
+            "``failed``. When set, ``filtered_total_rows`` reflects the "
+            "post-filter row count (combined with the ``q`` filter when "
+            "both are supplied) so the UI can paginate against the same "
+            "slice it's displaying."
+        ),
+        pattern="^(pending|running|completed|failed)$",
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportDetailResponse:
+    """Fetch a single import batch with a slice of its rows.
+
+    ``row_limit=0`` is intentionally allowed so callers that only need the
+    batch metadata (e.g. the evaluation-detail page rendering the parent's
+    column mapping) can skip the rows payload entirely.
+    """
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    if call_import.status == CallImportStatus.PROCESSING:
+        from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
+
+        prior_status = call_import.status
+        rollup_call_import_batch_status(db, call_import)
+        if call_import.status != prior_status:
+            db.commit()
+            db.refresh(call_import)
+
+    search_term = (q or "").strip()
+    diarised_status_filter = (diarised_status or "").strip() or None
+    filtered_total_rows: Optional[int] = None
+    has_row_filters = bool(search_term or diarised_status_filter)
+
+    if has_row_filters:
+        if is_sharding_enabled():
+            from app.db_sharding.scatter_gather import count_call_import_rows_filtered
+
+            filtered_total_rows = count_call_import_rows_filtered(
+                db,
+                call_import.id,
+                search_term=search_term,
+                diarised_status_filter=diarised_status_filter,
+            )
+        else:
+            rows_query = db.query(CallImportRow).filter(
+                CallImportRow.call_import_id == call_import.id
+            )
+            if search_term:
+                rows_query = rows_query.filter(
+                    CallImportRow.conversation_id.ilike(f"%{search_term}%")
+                )
+            if diarised_status_filter:
+                rows_query = rows_query.filter(
+                    CallImportRow.diarised_transcript_status == diarised_status_filter
+                )
+            filtered_total_rows = rows_query.count()
+
+    if row_limit == 0:
+        rows: List[CallImportRow] = []
+    elif is_sharding_enabled():
+        from app.db_sharding.scatter_gather import (
+            fetch_call_import_rows_filtered_page,
+            fetch_call_import_rows_page,
+        )
+
+        if has_row_filters:
+            rows = fetch_call_import_rows_filtered_page(
+                db,
+                call_import.id,
+                search_term=search_term,
+                diarised_status_filter=diarised_status_filter,
+                offset=row_offset,
+                limit=row_limit,
+            )
+        else:
+            rows = fetch_call_import_rows_page(
+                db,
+                call_import.id,
+                offset=row_offset,
+                limit=row_limit,
+            )
+    else:
+        rows_query = db.query(CallImportRow).filter(
+            CallImportRow.call_import_id == call_import.id
+        )
+        if search_term:
+            rows_query = rows_query.filter(
+                CallImportRow.conversation_id.ilike(f"%{search_term}%")
+            )
+        if diarised_status_filter:
+            rows_query = rows_query.filter(
+                CallImportRow.diarised_transcript_status == diarised_status_filter
+            )
+        rows = (
+            rows_query.order_by(CallImportRow.row_index)
+            .offset(row_offset)
+            .limit(row_limit)
+            .all()
+        )
+
+    # Batch-wide diarisation status aggregate. One ``GROUP BY`` query
+    # across the whole batch — much cheaper than paging through every
+    # row to recount on the client and lets the UI render a
+    # transcribe/diarise progress bar without a separate roundtrip.
+    if is_sharding_enabled():
+        from app.db_sharding.scatter_gather import aggregate_diarised_transcript_counts
+
+        diarised_status_counts = aggregate_diarised_transcript_counts(
+            db, call_import.id
+        )
+    else:
+        diarised_status_counts: Dict[str, int] = {}
+        for status_value, count in (
+            db.query(CallImportRow.diarised_transcript_status, func.count())
+            .filter(CallImportRow.call_import_id == call_import.id)
+            .group_by(CallImportRow.diarised_transcript_status)
+            .all()
+        ):
+            if isinstance(status_value, str):
+                diarised_status_counts[status_value] = int(count or 0)
+
+    detail = CallImportDetailResponse.model_validate(
+        _serialize_call_import(db, call_import).model_dump()
+    )
+    detail.rows = [CallImportRowResponse.model_validate(r) for r in rows]
+    detail.filtered_total_rows = filtered_total_rows
+    detail.diarised_pending_rows = diarised_status_counts.get("pending", 0)
+    detail.diarised_running_rows = diarised_status_counts.get("running", 0)
+    detail.diarised_completed_rows = diarised_status_counts.get("completed", 0)
+    detail.diarised_failed_rows = diarised_status_counts.get("failed", 0)
+    return detail
+
+
+@router.get(
+    "/{call_import_id}/row-ids",
+    response_model=CallImportRowIdsResponse,
+    operation_id="listCallImportRowIds",
+)
+async def list_call_import_row_ids(
+    call_import_id: UUID,
+    q: Optional[str] = Query(
+        None,
+        description=(
+            "Optional case-insensitive substring filter on "
+            "``conversation_id``. Same semantics as the detail endpoint."
+        ),
+    ),
+    diarised_status: Optional[str] = Query(
+        None,
+        description=(
+            "Optional filter on ``CallImportRow.diarised_transcript_status``. "
+            "Accepts ``pending`` / ``running`` / ``completed`` / ``failed``."
+        ),
+        pattern="^(pending|running|completed|failed)$",
+    ),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportRowIdsResponse:
+    """Return every matching ``CallImportRow.id`` for cross-page bulk select.
+
+    Lightweight companion to ``GET /{call_import_id}`` — the detail
+    endpoint caps ``row_limit`` at 5000 and ships the entire row body
+    on each page, so harvesting ids that way is wasteful when the
+    user just wants to bulk-delete or bulk-transcribe everything that
+    matches the current filters. This endpoint applies the same ``q``
+    and ``diarised_status`` filters and returns only the ids.
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    search_term = (q or "").strip()
+    status_filter = (diarised_status or "").strip() or None
+
+    if is_sharding_enabled():
+        from app.db_sharding.scatter_gather import list_call_import_row_ids_filtered
+
+        ids = list_call_import_row_ids_filtered(
+            db,
+            call_import.id,
+            search_term=search_term,
+            diarised_status_filter=status_filter,
+        )
+        return CallImportRowIdsResponse(ids=ids, total=len(ids))
+
+    rows_query = db.query(CallImportRow.id).filter(
+        CallImportRow.call_import_id == call_import.id
+    )
+    if search_term:
+        rows_query = rows_query.filter(
+            CallImportRow.conversation_id.ilike(f"%{search_term}%")
+        )
+    if status_filter:
+        rows_query = rows_query.filter(
+            CallImportRow.diarised_transcript_status == status_filter
+        )
+
+    ids = [
+        row_id
+        for (row_id,) in rows_query.order_by(CallImportRow.row_index).all()
+    ]
+    return CallImportRowIdsResponse(ids=ids, total=len(ids))
+
+
+def _revoke_pending_tasks(rows: List[CallImportRow]) -> None:
+    """Best-effort revoke of in-flight Celery tasks for the given rows.
+
+    Failures are logged and swallowed — Celery's control plane is async and
+    best-effort by design, and we always do an idempotent S3 cleanup
+    afterwards so a missed revoke can't leak storage.
+    """
+    task_ids = [
+        r.celery_task_id
+        for r in rows
+        if r.celery_task_id
+        and r.status in (CallImportRowStatus.PENDING, CallImportRowStatus.PROCESSING)
+    ]
+    if not task_ids:
+        return
+
+    try:
+        from app.workers.celery_app import celery_app
+
+        celery_app.control.revoke(task_ids, terminate=False)
+        logger.info("Revoked {} pending call-import tasks", len(task_ids))
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("Failed to revoke pending call-import tasks: {}", exc)
+
+
+def _delete_s3_objects(
+    organization_id: UUID,
+    call_import_id: UUID,
+    rows: List[CallImportRow],
+) -> tuple[int, int]:
+    """Delete every recording associated with ``rows`` plus a prefix sweep.
+
+    The prefix sweep also cleans up the staged source file written at
+    UPLOAD time (``…/call_imports/{id}/source.{csv,xlsx}``) — both the
+    per-row recording keys and the source artefact share the same
+    organization-scoped prefix, so a single sweep covers them all.
+
+    Returns ``(deleted_count, error_count)``. Never raises — callers proceed
+    with the DB delete regardless; orphans, if any, can be cleaned up by
+    re-running the same delete (it's idempotent).
+    """
+    from app.services.storage.s3_service import s3_service
+
+    if not s3_service.is_enabled():
+        return 0, 0
+
+    keys = [r.recording_s3_key for r in rows if r.recording_s3_key]
+    deleted = 0
+    errors = 0
+
+    if keys:
+        try:
+            d, errs = s3_service.delete_keys(keys)
+            deleted += d
+            errors += len(errs)
+            if errs:
+                logger.warning(
+                    "S3 bulk-delete reported {} errors for call_import {}",
+                    len(errs),
+                    call_import_id,
+                )
+        except Exception as exc:  # noqa: BLE001
+            logger.exception(
+                "Bulk S3 delete failed for call_import {}: {}", call_import_id, exc
+            )
+            errors += len(keys)
+
+    # Belt-and-braces sweep: catch anything that landed under the import's
+    # prefix but never made it into a row's recording_s3_key (narrow
+    # window where the S3 upload succeeded but the DB commit didn't).
+    sweep_prefix = (
+        f"{s3_service.prefix}organizations/{organization_id}/"
+        f"call_imports/{call_import_id}/"
+    )
+    try:
+        d, errs = s3_service.delete_keys_by_prefix(sweep_prefix)
+        deleted += d
+        errors += len(errs)
+    except Exception as exc:  # noqa: BLE001
+        logger.exception(
+            "S3 prefix sweep failed for {}: {}", sweep_prefix, exc
+        )
+
+    return deleted, errors
+
+
+@router.delete(
+    "/{call_import_id}",
+    response_model=CallImportDeleteResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="deleteCallImport",
+)
+async def delete_call_import(
+    call_import_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportDeleteResponse:
+    """Delete a call-import batch asynchronously.
+
+    Flips the batch to ``deleting`` and enqueues background teardown so
+    large imports (thousands of rows + S3 objects) do not block the API.
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        return CallImportDeleteResponse(
+            id=call_import_id,
+            status="completed",
+        )
+
+    if call_import.status == CallImportStatus.DELETING:
+        return CallImportDeleteResponse(
+            id=call_import.id,
+            status="accepted",
+        )
+
+    call_import.status = CallImportStatus.DELETING
+    call_import.error_message = None
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+
+    from app.workers.tasks.call_import_bulk_ops import delete_call_import_task
+
+    delete_call_import_task.delay(
+        str(call_import_id),
+        str(organization_id),
+    )
+
+    return CallImportDeleteResponse(
+        id=call_import.id,
+        status="accepted",
+    )
+
+
+def _locate_call_import_row_or_404(
+    catalog_db: Session,
+    *,
+    call_import_id: UUID,
+    row_id: UUID,
+    organization_id: UUID,
+) -> Tuple[Session, CallImportRow, Optional[Session]]:
+    """Find a call import row on the correct DB session for mutation.
+
+    When sharding is enabled rows live on shard databases; ``get_db`` only
+    opens the catalog. Returns ``(row_db, row, extra_catalog_to_close)``
+    where ``extra_catalog_to_close`` is the catalog session opened by
+    :func:`locate_call_import_row` (distinct from the route's catalog
+    session) and must be closed via :func:`close_row_sessions`.
+    """
+    from app.db_sharding.row_ops import close_row_sessions, locate_call_import_row
+
+    try:
+        row_db, located_catalog, row, _shard_id = locate_call_import_row(row_id)
+    except LookupError:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import row not found",
+        ) from None
+    if (
+        row.call_import_id != call_import_id
+        or row.organization_id != organization_id
+    ):
+        close_row_sessions(
+            row_db,
+            located_catalog if located_catalog is not row_db else None,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import row not found",
+        )
+    extra_catalog = located_catalog if located_catalog is not row_db else None
+    return row_db, row, extra_catalog
+
+
+@router.delete(
+    "/{call_import_id}/rows/{row_id}",
+    status_code=status.HTTP_204_NO_CONTENT,
+    operation_id="deleteCallImportRow",
+)
+async def delete_call_import_row(
+    call_import_id: UUID,
+    row_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> Response:
+    """Delete a single CallImportRow and its S3 recording.
+
+    The parent ``CallImport`` is left in place. After deletion we recompute
+    its ``total_rows`` / ``completed_rows`` / ``failed_rows`` / ``status``
+    so the UI's progress bar stays consistent with reality.
+    """
+    from app.services.storage.s3_service import s3_service
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    from app.db_sharding.row_ops import close_row_sessions
+
+    row_db, row, extra_catalog = _locate_call_import_row_or_404(
+        db,
+        call_import_id=call_import.id,
+        row_id=row_id,
+        organization_id=organization_id,
+    )
+    try:
+        _revoke_pending_tasks([row])
+
+        if row.recording_s3_key and s3_service.is_enabled():
+            try:
+                s3_service.delete_file_by_key(row.recording_s3_key)
+            except Exception as exc:  # noqa: BLE001 — best-effort, DB is source of truth
+                logger.warning(
+                    "Failed to delete S3 object {} for row {}: {}",
+                    row.recording_s3_key,
+                    row.id,
+                    exc,
+                )
+
+        row_db.delete(row)
+        row_db.commit()
+
+        _recompute_call_import_counters(db, call_import)
+        stamp_call_import_actor(call_import, principal)
+        db.commit()
+    finally:
+        close_row_sessions(row_db, extra_catalog)
+
+    logger.info(
+        "Deleted call_import_row {} (call_import={}, org={})",
+        row_id,
+        call_import.id,
+        organization_id,
+    )
+
+    return Response(status_code=status.HTTP_204_NO_CONTENT)
+
+
+def _recompute_call_import_counters(
+    db: Session, call_import: CallImport
+) -> None:
+    """Resync ``total/completed/failed_rows`` + status on the parent batch.
+
+    Called after row-level mutations (single delete, bulk delete) so the
+    UI's progress bar stays consistent with the actual row set. The
+    rules mirror :func:`delete_call_import_row` so behavior doesn't
+    diverge between the per-row and bulk paths.
+    """
+
+    from app.services.call_imports.bulk_ops import rollup_call_import_batch_status
+
+    rollup_call_import_batch_status(db, call_import)
+
+
+@router.post(
+    "/{call_import_id}/retry-failed",
+    response_model=CallImportRetryFailedRowsResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="retryFailedCallImportRows",
+)
+async def retry_failed_call_import_rows(
+    call_import_id: UUID,
+    payload: Optional[CallImportRetryFailedRowsRequest] = Body(None),
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportRetryFailedRowsResponse:
+    """Re-enqueue every failed import row in this batch.
+
+    Useful when transient provider issues are resolved and the operator wants
+    a one-click "try failed downloads again" pass without re-uploading the CSV.
+
+    Pass ``provider`` + ``telephony_integration_id`` (or both omitted for
+    direct-URL retry) to change how recordings are fetched on this pass.
+    When the body is omitted entirely, the batch keeps its existing pinned
+    credentials.
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    if payload is not None:
+        if payload.telephony_integration_id is not None:
+            integration = _resolve_telephony_integration(
+                db,
+                organization_id,
+                payload.telephony_integration_id,
+                payload.provider or "",
+            )
+            call_import.provider = integration.provider
+            call_import.telephony_integration_id = integration.id
+            if (integration.provider or "").lower() == "exotel":
+                schema = _resolve_schema(
+                    db,
+                    organization_id,
+                    call_import.workspace_id,
+                    call_import.schema_id,
+                )
+                _validate_exotel_import_ready(
+                    list(schema.parameters),
+                    dict(call_import.parameter_mapping or {}),
+                )
+        else:
+            call_import.provider = None
+            call_import.telephony_integration_id = None
+        db.flush()
+
+    failed_rows = (
+        db.query(CallImportRow)
+        .filter(
+            CallImportRow.call_import_id == call_import.id,
+            CallImportRow.status == CallImportRowStatus.FAILED,
+        )
+        .order_by(CallImportRow.row_index.asc())
+        .all()
+    )
+    if not failed_rows:
+        return CallImportRetryFailedRowsResponse(
+            requeued=0,
+            enqueue_failed=0,
+            skipped=0,
+        )
+
+    from app.workers.concurrency.fair_import_dispatch import (
+        schedule_fair_import_dispatch,
+    )
+
+    # Reset rows to pending BEFORE enqueue so the UI reflects "retry in
+    # progress" immediately even if the worker queue is backlogged.
+    for row in failed_rows:
+        row.status = CallImportRowStatus.PENDING
+        row.error_message = None
+        row.celery_task_id = None
+
+    db.flush()
+    _recompute_call_import_counters(db, call_import)
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+
+    try:
+        schedule_fair_import_dispatch(max_workspace_turns=999)
+        requeued = len(failed_rows)
+        enqueue_failed = 0
+        skipped = 0
+    except Exception as exc:  # noqa: BLE001
+        logger.exception(
+            "Failed to schedule fair import dispatch for import {}",
+            call_import.id,
+        )
+        requeued = 0
+        enqueue_failed = len(failed_rows)
+        skipped = 0
+        for row in failed_rows:
+            db.refresh(row)
+            if row.status != CallImportRowStatus.PENDING:
+                skipped += 1
+                enqueue_failed -= 1
+                continue
+            row.status = CallImportRowStatus.FAILED
+            row.error_message = f"Failed to enqueue retry: {exc}"
+        db.flush()
+        _recompute_call_import_counters(db, call_import)
+        db.commit()
+
+    return CallImportRetryFailedRowsResponse(
+        requeued=requeued,
+        enqueue_failed=enqueue_failed,
+        skipped=skipped,
+    )
+
+
+@router.post(
+    "/{call_import_id}/rows/bulk-delete",
+    response_model=CallImportRowBulkDeleteResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="bulkDeleteCallImportRows",
+)
+async def bulk_delete_call_import_rows(
+    call_import_id: UUID,
+    payload: CallImportRowBulkDelete,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportRowBulkDeleteResponse:
+    """Delete multiple ``CallImportRow`` rows in one request.
+
+    Unknown / cross-tenant row ids are silently skipped — the response
+    reports how many actually went away so a UI that holds onto stale
+    ids (e.g. after another tab already deleted a row) doesn't 404
+    the entire bulk action.
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    if not payload.row_ids:
+        return CallImportRowBulkDeleteResponse(deleted=0, status="completed")
+
+    from app.workers.tasks.call_import_bulk_ops import bulk_delete_call_import_rows_task
+
+    row_id_strs = [str(rid) for rid in payload.row_ids]
+
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+
+    bulk_delete_call_import_rows_task.delay(
+        str(call_import_id),
+        str(organization_id),
+        row_id_strs,
+    )
+
+    return CallImportRowBulkDeleteResponse(deleted=0, status="accepted")
+
+
+# ---------------------------------------------------------------------------
+# Diarization / transcription endpoints
+# ---------------------------------------------------------------------------
+
+
+def _select_rows_for_transcription(
+    db: Session,
+    call_import: CallImport,
+    payload: CallImportTranscribeRequest,
+    requested_row_ids: Optional[List[UUID]] = None,
+) -> tuple[List[CallImportRow], Dict[str, int]]:
+    """Pick which rows to enqueue for diarisation (delegates to bulk_ops)."""
+    from app.services.call_imports.bulk_ops import select_rows_for_transcription
+
+    try:
+        return select_rows_for_transcription(
+            db, call_import, payload, requested_row_ids=requested_row_ids
+        )
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=str(exc),
+        ) from exc
+
+
+@router.post(
+    "/{call_import_id}/transcribe",
+    response_model=CallImportTranscribeResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="transcribeCallImport",
+)
+async def transcribe_call_import(
+    call_import_id: UUID,
+    payload: CallImportTranscribeRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportTranscribeResponse:
+    """Fan out diarization tasks for many rows in a single call.
+
+    Returns a summary with how many rows were queued and how many were
+    skipped (broken down by reason) so the UI can show a meaningful
+    toast even when nothing actually got enqueued (e.g. "All 12 rows
+    already have transcripts").
+    """
+
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    from app.workers.tasks.call_import_bulk_ops import bulk_diarize_call_import_task
+
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+
+    bulk_diarize_call_import_task.delay(
+        str(call_import_id),
+        str(organization_id),
+        payload.model_dump(mode="json"),
+        [str(rid) for rid in payload.row_ids] if payload.row_ids else None,
+    )
+
+    return CallImportTranscribeResponse(
+        queued=0,
+        skipped_rows=0,
+        skipped_reason_counts={},
+        accepted=True,
+    )
+
+
+@router.post(
+    "/{call_import_id}/rows/{row_id}/transcribe",
+    response_model=CallImportTranscribeResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+    operation_id="transcribeCallImportRow",
+)
+async def transcribe_call_import_row(
+    call_import_id: UUID,
+    row_id: UUID,
+    payload: CallImportTranscribeRequest,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportTranscribeResponse:
+    """Diarize / transcribe a single row.
+
+    Thin wrapper over the batch endpoint that hard-codes a single
+    ``row_ids`` filter. Skip counts still surface so the UI can render
+    "Skipped — transcript present" diagnostics consistently.
+    """
+
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    from app.services.call_imports.bulk_ops import execute_bulk_diarization
+
+    try:
+        result = execute_bulk_diarization(
+            db,
+            call_import,
+            payload,
+            requested_row_ids=[row_id],
+        )
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=str(exc),
+        ) from exc
+
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+
+    return CallImportTranscribeResponse(
+        queued=result.queued,
+        skipped_rows=result.skipped_rows,
+        skipped_reason_counts=result.skipped_reason_counts,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Cancel-in-flight diarisation
+# ---------------------------------------------------------------------------
+#
+# Long-running multimodal LLM diarisation calls (especially LLM-only mode on
+# slow audio) can sit in ``pending`` / ``running`` for tens of minutes when an
+# upstream provider stalls. Without an abort affordance the operator's only
+# recourse is to wait for Celery's ``time_limit`` to fire — which can be
+# several minutes — or to manually mutate the DB. These helpers + the two
+# endpoints below give the UI a first-class "Stop diarisation" button.
+#
+# Why ``terminate=True``: the legacy ``_revoke_pending_tasks`` helper uses
+# ``terminate=False`` because it's called from delete-flow paths where the
+# task may simply not get to run (a worker pulls it off the queue and drops
+# it). For a user-initiated cancel we want SIGTERM to interrupt the worker
+# mid-LLM call so the audio HTTP request actually aborts. ``terminate=True``
+# routes SIGTERM to the executing process; ``signal="SIGTERM"`` is the
+# default but we spell it out so the intent is obvious to reviewers.
+
+# Sentinel error message stamped on cancelled rows. Read by the transcribe
+# worker's finaliser (see ``app/workers/tasks/transcribe_call_import_row.py``)
+# to detect a row that was cancelled mid-flight and AVOID overwriting it
+# with whatever partial result the worker had managed to compute before the
+# SIGTERM landed.
+CANCELLED_BY_USER_ERROR: str = "Diarisation cancelled by user"
+
+
+def _cancellable_diarisation_states() -> Tuple[str, ...]:
+    """States that a diarisation row can be cancelled from.
+
+    Kept as a tiny helper so adding a future ``"queued"`` / ``"retrying"``
+    state only needs one edit.
+    """
+    return ("pending", "running")
+
+
+def _revoke_diarisation_task(row: CallImportRow) -> None:
+    """Best-effort revoke of a single row's diarisation Celery task.
+
+    Always swallows control-plane exceptions — Celery's control bus is
+    inherently best-effort and a missed revoke is not catastrophic
+    because the DB row is already flipped to ``failed`` by the caller
+    before this runs (so the UI immediately reflects the cancel; if
+    the task happens to finish anyway, the worker's finaliser skips
+    over the row via :data:`CANCELLED_BY_USER_ERROR`).
+    """
+    task_id = (row.celery_task_id or "").strip()
+    if not task_id:
+        return
+    try:
+        from app.workers.celery_app import celery_app
+
+        celery_app.control.revoke(
+            task_id, terminate=True, signal="SIGTERM"
+        )
+        logger.info(
+            "Revoked diarisation task {} for call-import row {}",
+            task_id,
+            row.id,
+        )
+    except Exception as exc:  # noqa: BLE001 — revoke is best-effort
+        logger.warning(
+            "Failed to revoke diarisation task {} for row {}: {}",
+            task_id,
+            row.id,
+            exc,
+        )
+
+
+def _apply_diarisation_cancel(rows: List[CallImportRow]) -> Tuple[int, int]:
+    """Cancel diarisation on every cancellable row in ``rows``.
+
+    Returns ``(cancelled, skipped)`` so the caller can build a typed
+    response without re-querying the DB. The caller is responsible for
+    ``db.commit()`` after this returns — we deliberately don't commit
+    here so a batch endpoint can flush all rows in one transaction.
+    """
+    cancellable_states = _cancellable_diarisation_states()
+    cancelled = 0
+    skipped = 0
+    for row in rows:
+        if (row.diarised_transcript_status or "").lower() not in cancellable_states:
+            skipped += 1
+            continue
+        # Flip the row state BEFORE we revoke so the UI's next poll
+        # already shows the cancel, even if Celery's control plane is
+        # slow to ack.
+        row.diarised_transcript_status = "failed"
+        row.diarised_transcript_error = CANCELLED_BY_USER_ERROR
+        _revoke_diarisation_task(row)
+        # Drop the task id so a follow-up retry (or a stale poll) can't
+        # accidentally re-revoke or get confused.
+        row.celery_task_id = None
+        cancelled += 1
+    return cancelled, skipped
+
+
+@router.post(
+    "/{call_import_id}/rows/{row_id}/cancel-diarisation",
+    response_model=CallImportRowResponse,
+    status_code=status.HTTP_200_OK,
+    operation_id="cancelCallImportRowDiarisation",
+)
+async def cancel_call_import_row_diarisation(
+    call_import_id: UUID,
+    row_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportRowResponse:
+    """Abort an in-flight (or queued) diarisation for a single row.
+
+    Idempotent: calling on a row that's already terminal (``completed``
+    / ``failed`` / ``idle``) returns the row unchanged with a 200, so
+    the UI can fire this from a "Stop" button without having to
+    pre-check the state.
+
+    Race notes:
+
+    * The row's ``diarised_transcript_status`` is flipped to ``failed``
+      with :data:`CANCELLED_BY_USER_ERROR` BEFORE the Celery revoke,
+      so the polling UI sees the cancel immediately.
+    * If the worker happens to finish between our DB flip and the
+      SIGTERM landing, its finaliser will detect the cancelled
+      sentinel on the row and skip its own status / score writes
+      (see :mod:`app.workers.tasks.transcribe_call_import_row`).
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    from app.db_sharding.row_ops import close_row_sessions
+
+    row_db, row, extra_catalog = _locate_call_import_row_or_404(
+        db,
+        call_import_id=call_import_id,
+        row_id=row_id,
+        organization_id=organization_id,
+    )
+    try:
+        _apply_diarisation_cancel([row])
+        row_db.commit()
+        stamp_call_import_actor(call_import, principal)
+        db.commit()
+        row_db.refresh(row)
+        return CallImportRowResponse.model_validate(row)
+    finally:
+        close_row_sessions(row_db, extra_catalog)
+
+
+@router.post(
+    "/{call_import_id}/cancel-diarisation",
+    response_model=CallImportCancelDiarisationResponse,
+    status_code=status.HTTP_200_OK,
+    operation_id="cancelCallImportDiarisation",
+)
+async def cancel_call_import_diarisation(
+    call_import_id: UUID,
+    payload: Optional[CallImportCancelDiarisationRequest] = None,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportCancelDiarisationResponse:
+    """Abort in-flight diarisation for many rows in a single call.
+
+    Default body (no ``row_ids``) cancels every row in this import
+    whose ``diarised_transcript_status`` is ``pending`` or
+    ``running`` — the "stop everything" button. Pass ``row_ids`` to
+    scope the cancel to the rows the operator has selected.
+
+    Returns ``(cancelled, skipped)`` so the UI can render a tight
+    toast ("Cancelled 3 rows · 1 skipped (already completed)").
+    """
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    base_query = db.query(CallImportRow).filter(
+        CallImportRow.call_import_id == call_import_id
+    )
+
+    requested_ids = (
+        payload.row_ids if payload and payload.row_ids is not None else None
+    )
+    if requested_ids is not None:
+        if not requested_ids:
+            # Empty list is "no rows requested" — treat as a no-op
+            # 200 rather than 400 so the UI can pass through an empty
+            # selection without a special-case.
+            return CallImportCancelDiarisationResponse(cancelled=0, skipped=0)
+        rows = base_query.filter(CallImportRow.id.in_(requested_ids)).all()
+        found_ids = {r.id for r in rows}
+        # Treat requested-but-not-found ids as ``skipped`` so the UI's
+        # numbers reconcile (a stale selection that includes deleted
+        # rows shouldn't 404 the whole call).
+        missing = [rid for rid in requested_ids if rid not in found_ids]
+        skipped_missing = len(missing)
+    else:
+        # Implicit "cancel every cancellable row in this import" path.
+        rows = base_query.filter(
+            CallImportRow.diarised_transcript_status.in_(
+                list(_cancellable_diarisation_states())
+            )
+        ).all()
+        skipped_missing = 0
+
+    cancelled, skipped = _apply_diarisation_cancel(rows)
+    stamp_call_import_actor(call_import, principal)
+    db.commit()
+    return CallImportCancelDiarisationResponse(
+        cancelled=cancelled,
+        skipped=skipped + skipped_missing,
+    )
+
+
+def _render_diarised_segments_text(
+    segments: Optional[List[Dict[str, Any]]],
+    *,
+    swap: bool = False,
+) -> str:
+    """Render ``CallImportRow.diarised_segments`` as ``<speaker>: <text>`` lines.
+
+    Mirrors the worker's ``_render_turns_as_text`` (kept duplicated so
+    the route doesn't need to import a Celery task module just to
+    rebuild the rendered transcript). Only ``agent`` and ``user`` are
+    swapped — multi-party calls keep their ``speaker_N`` labels through
+    a swap so we don't silently collapse a third speaker into the user
+    side.
+    """
+    if not segments:
+        return ""
+    out: List[str] = []
+    for turn in segments:
+        if not isinstance(turn, dict):
+            continue
+        speaker = (turn.get("speaker") or "").strip()
+        text = (turn.get("text") or "").strip()
+        if not speaker or not text:
+            continue
+        if swap:
+            if speaker == "agent":
+                speaker = "user"
+            elif speaker == "user":
+                speaker = "agent"
+        out.append(f"{speaker}: {text}")
+    return "\n".join(out)
+
+
+@router.post(
+    "/{call_import_id}/rows/{row_id}/diarised-speaker-swap",
+    response_model=CallImportRowResponse,
+    operation_id="toggleCallImportRowSpeakerSwap",
+)
+async def toggle_call_import_row_speaker_swap(
+    call_import_id: UUID,
+    row_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
+    db: Session = Depends(get_db),
+) -> CallImportRowResponse:
+    """Flip the user <-> agent mapping on a diarised row.
+
+    The worker's "first speaker is the agent" heuristic is right most of
+    the time but does fail on inbound recordings where the customer
+    greets first, on recordings where the agent stays silent for the
+    intro, etc. Rather than rerun the (expensive) STT + pyannote
+    pipeline for those cases, we let reviewers flip the mapping in
+    place: the structured ``diarised_segments`` are the source of truth
+    and we re-render the plain-text ``diarised_transcript`` from them
+    with the swap applied. The next CSV export will then show the
+    corrected labels.
+
+    Returns the updated row so the frontend can refresh without an
+    extra round-trip.
+    """
+
+    del api_key
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    from app.db_sharding.row_ops import close_row_sessions
+
+    row_db, row, extra_catalog = _locate_call_import_row_or_404(
+        db,
+        call_import_id=call_import_id,
+        row_id=row_id,
+        organization_id=organization_id,
+    )
+    try:
+        segments = (
+            row.diarised_segments if isinstance(row.diarised_segments, list) else None
+        )
+        if not segments:
+            # Without structured turns the swap toggle would have nothing to
+            # re-render — surface a clear error rather than silently
+            # flipping a flag the UI never read.
+            raise HTTPException(
+                status_code=status.HTTP_409_CONFLICT,
+                detail=(
+                    "This row has no structured diarised segments to swap. "
+                    "Re-run diarisation to generate per-speaker turns first."
+                ),
+            )
+
+        new_swap = not bool(row.diarised_speaker_swap)
+        row.diarised_speaker_swap = new_swap
+        row.diarised_transcript = (
+            _render_diarised_segments_text(segments, swap=new_swap) or None
+        )
+        row_db.commit()
+        stamp_call_import_actor(call_import, principal)
+        db.commit()
+        row_db.refresh(row)
+        return CallImportRowResponse.model_validate(row)
+    finally:
+        close_row_sessions(row_db, extra_catalog)
+
+
+# ---------------------------------------------------------------------------
+# Cross-run insights for the import detail page
+# ---------------------------------------------------------------------------
+
+
+@router.get(
+    "/{call_import_id}/insights",
+    response_model=CallImportInsightsResponse,
+    operation_id="getCallImportInsights",
+)
+async def get_call_import_insights(
+    call_import_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportInsightsResponse:
+    """Aggregate signals across every evaluation run on this import.
+
+    Powers the Insights tab on the call-import detail page: returns
+    per-metric "latest run" summaries plus a trend series of mean values
+    across runs so the UI can render a small line chart per metric. Also
+    bundles transcript coverage stats since those are the cheapest
+    pre-eval health-check (e.g. "30 of 50 rows still missing
+    transcripts").
+    """
+
+    del api_key
+
+    from app.models.database import (
+        CallImportEvaluation,
+        CallImportEvaluationRow,
+        Metric,
+    )
+
+    call_import = (
+        db.query(CallImport)
+        .filter(
+            CallImport.id == call_import_id,
+            CallImport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not call_import:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call import not found",
+        )
+
+    rows = (
+        db.query(CallImportRow)
+        .filter(CallImportRow.call_import_id == call_import_id)
+        .all()
+    )
+    # A row "has a transcript" if EITHER the production (CSV) or the
+    # diarised (worker) column is populated — the insights tile reports
+    # the union so users see total coverage regardless of which source
+    # produced the value.
+    rows_with_transcript = sum(
+        1
+        for r in rows
+        if (r.transcript or "").strip()
+        or (r.diarised_transcript or "").strip()
+    )
+    rows_without_transcript = len(rows) - rows_with_transcript
+    source_counts: Dict[str, int] = {}
+    for r in rows:
+        has_production = bool((r.transcript or "").strip())
+        has_diarised = bool((r.diarised_transcript or "").strip())
+        if has_production:
+            key = r.transcript_source or "csv"
+            source_counts[key] = source_counts.get(key, 0) + 1
+        if has_diarised:
+            source_counts["diarised"] = source_counts.get("diarised", 0) + 1
+
+    evaluations = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .order_by(CallImportEvaluation.created_at.asc())
+        .all()
+    )
+
+    # Defer heavy lifting to the aggregation helper so this endpoint and
+    # the per-run aggregate endpoint share the exact same metric
+    # bucketing math (no chance of "trend" disagreeing with "latest" on
+    # the same data set).
+    from app.api.v1.routes.call_import_evaluations import (
+        _compute_metric_aggregates,
+    )
+
+    metric_history: Dict[str, List[CallImportInsightsRunPoint]] = {}
+    metric_meta: Dict[str, Metric] = {}
+    metric_latest: Dict[str, CallImportMetricAggregate] = {}
+
+    for evaluation in evaluations:
+        eval_rows = (
+            db.query(CallImportEvaluationRow)
+            .filter(CallImportEvaluationRow.evaluation_id == evaluation.id)
+            .all()
+        )
+        aggregates = _compute_metric_aggregates(db, evaluation, eval_rows)
+        for agg in aggregates:
+            if agg.metric_id not in metric_meta:
+                # ``agg.metric_id`` is normally a UUID string, but the
+                # aggregator also emits ids that surface in row scores
+                # without a matching ``Metric`` row (e.g. a metric the
+                # user deleted mid-run, or LLM-discovered slugs). Those
+                # are not valid UUIDs, so coerce defensively and skip
+                # the metric registry lookup when the cast fails — the
+                # ``meta is None`` branch below already handles the
+                # display via the values stored on ``agg`` itself.
+                try:
+                    metric_uuid = UUID(agg.metric_id)
+                except (ValueError, AttributeError, TypeError):
+                    metric_uuid = None
+                if metric_uuid is not None:
+                    metric_obj = (
+                        db.query(Metric)
+                        .filter(
+                            Metric.id == metric_uuid,
+                            Metric.organization_id == organization_id,
+                        )
+                        .first()
+                    )
+                    if metric_obj is not None:
+                        metric_meta[agg.metric_id] = metric_obj
+            history = metric_history.setdefault(agg.metric_id, [])
+            history.append(
+                CallImportInsightsRunPoint(
+                    evaluation_id=evaluation.id,
+                    name=evaluation.name,
+                    created_at=evaluation.created_at,
+                    mean=agg.mean,
+                    completed_rows=agg.count,
+                )
+            )
+            metric_latest[agg.metric_id] = agg
+
+    metrics_payload: List[CallImportInsightsMetric] = []
+    for metric_id, latest in metric_latest.items():
+        meta = metric_meta.get(metric_id)
+        metrics_payload.append(
+            CallImportInsightsMetric(
+                metric_id=metric_id,
+                metric_name=(meta.name if meta else latest.metric_name),
+                metric_type=(meta.metric_type if meta else latest.metric_type),
+                latest=latest,
+                trend=metric_history.get(metric_id, []),
+            )
+        )
+
+    return CallImportInsightsResponse(
+        call_import_id=call_import_id,
+        total_rows=len(rows),
+        rows_with_transcript=rows_with_transcript,
+        rows_without_transcript=rows_without_transcript,
+        transcript_source_counts=source_counts,
+        evaluation_count=len(evaluations),
+        metrics=metrics_payload,
+    )
+
+
+from app.core.auth.capabilities import CALLS_DELETE, CALLS_IMPORT, CALLS_VIEW
+from app.core.auth.workspace_route_capabilities import apply_workspace_route_capabilities
+
+apply_workspace_route_capabilities(
+    router,
+    view_capability=CALLS_VIEW,
+    manage_capability=CALLS_IMPORT,
+    delete_capability=CALLS_DELETE,
+)
diff --git a/app/config.py b/app/config.py
index 91ecdbb3..94b51f18 100644
--- a/app/config.py
+++ b/app/config.py
@@ -107,7 +107,7 @@ class Settings(BaseSettings):
     RATE_LIMIT_PER_MINUTE: int = 60
 
     # Authentication
-    AUTH_PROVIDERS: List[str] = ["api_key"]
+    AUTH_PROVIDERS: Annotated[List[str], NoDecode] = ["api_key"]
     AUTH_LOCAL_ALLOW_SIGNUP: bool = True
     AUTH_LOCAL_TOKEN_TTL_MINUTES: int = 15
     AUTH_REFRESH_TOKEN_TTL_DAYS: int = 7
diff --git a/app/migrations/059_call_import_audit_users.py b/app/migrations/059_call_import_audit_users.py
new file mode 100644
index 00000000..41ec06db
--- /dev/null
+++ b/app/migrations/059_call_import_audit_users.py
@@ -0,0 +1,69 @@
+"""
+Migration: last_updated_by_user_id on call imports and evaluations.
+
+Supports surfacing who created / last modified a batch or evaluation run
+via FK to users (email resolved at read time).
+"""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Add last_updated_by_user_id to call_imports and call_import_evaluations"
+)
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    row = db.execute(
+        text(
+            """
+            SELECT 1
+            FROM information_schema.columns
+            WHERE table_name = :table_name AND column_name = :column_name
+            """
+        ),
+        {"table_name": table, "column_name": column},
+    ).first()
+    return row is not None
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    row = db.execute(
+        text(
+            "SELECT 1 FROM information_schema.tables WHERE table_name = :t"
+        ),
+        {"t": table},
+    ).first()
+    return row is not None
+
+
+def upgrade(db: Session):
+    for table in ("call_imports", "call_import_evaluations"):
+        if not _table_exists(db, table):
+            print(f"{table} does not exist, skipping...")
+            continue
+        if _column_exists(db, table, "last_updated_by_user_id"):
+            print(f"{table}.last_updated_by_user_id already exists, skipping...")
+            continue
+        db.execute(
+            text(
+                f"""
+                ALTER TABLE {table}
+                ADD COLUMN last_updated_by_user_id UUID NULL
+                    REFERENCES users(id) ON DELETE SET NULL
+                """
+            )
+        )
+        print(f"Added {table}.last_updated_by_user_id")
+
+
+def downgrade(db: Session):
+    for table in ("call_import_evaluations", "call_imports"):
+        if not _table_exists(db, table):
+            continue
+        if not _column_exists(db, table, "last_updated_by_user_id"):
+            continue
+        db.execute(
+            text(f"ALTER TABLE {table} DROP COLUMN last_updated_by_user_id")
+        )
+        print(f"Dropped {table}.last_updated_by_user_id")
diff --git a/app/models/database.py b/app/models/database.py
index a21a4819..3ce548de 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -1,2729 +1,2735 @@
-"""SQLAlchemy database models."""
-
-from sqlalchemy import (
-    BigInteger,
-    Boolean,
-    Column,
-    Date,
-    DateTime,
-    DDL,
-    Enum,
-    event,
-    Float,
-    ForeignKey,
-    Integer,
-    JSON,
-    String,
-    Text,
-    UniqueConstraint,
-    select,
-    text,
-)
-from sqlalchemy.dialects.postgresql import UUID
-from sqlalchemy.orm import relationship
-from sqlalchemy.sql import func
-import uuid
-import enum
-from app.models.enums import (
-    EvaluationType, EvaluationStatus, EvaluatorResultStatus, RoleEnum, InvitationStatus,
-    LanguageEnum, CallTypeEnum, CallMediumEnum, GenderEnum, AccentEnum, BackgroundNoiseEnum,
-    IntegrationPlatform, ModelProvider, VoiceBundleType, TestAgentConversationStatus,
-    MetricType, MetricCategory, MetricTrigger, CallRecordingStatus, AlertMetricType, AlertAggregation,
-    AlertOperator, AlertNotifyFrequency, AlertStatus, AlertHistoryStatus, CronJobStatus,
-    PromptOptimizationStatus, CallImportStatus, CallImportRowStatus,
-)
-
-def get_enum_values(enum_class):
-    """Helper to get values from enum class for SQLAlchemy."""
-    return [e.value for e in enum_class]
-
-from app.database import Base
-
-
-# Enums moved to enums.py
-
-
-class Organization(Base):
-    """Organization model for multi-tenancy."""
-
-    __tablename__ = "organizations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    name = Column(String(255), nullable=False)
-    voice_playground_threshold_overrides = Column(JSON, nullable=True)
-    # AlignEval-style judge alignment thresholds.
-    # Shape: {"min_labels_to_evaluate": int, "min_labels_to_optimize": int}
-    # Falls back to system defaults (20 / 50) when null.
-    judge_alignment_settings = Column(JSON, nullable=True)
-    # Per-org LLM gateway overrides (enabled, gateway_type, base_url, keys).
-    llm_gateway_settings = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    # Relationships
-    api_keys = relationship("APIKey", back_populates="organization")
-    members = relationship("OrganizationMember", back_populates="organization", cascade="all, delete-orphan")
-    invitations = relationship("Invitation", back_populates="organization", cascade="all, delete-orphan")
-    workspaces = relationship(
-        "Workspace",
-        back_populates="organization",
-        cascade="all, delete-orphan",
-    )
-    workspace_roles = relationship(
-        "WorkspaceRole",
-        back_populates="organization",
-        cascade="all, delete-orphan",
-    )
-
-
-class Workspace(Base):
-    """Workspace - in-org isolation boundary for call imports and metrics.
-
-    Every organization has at least one workspace (``is_default = True``,
-    seeded by migration 033). Users pick an "active workspace" in the UI;
-    list endpoints filter by it so users only see calls/metrics from the
-    project they're currently working in. Access is governed by
-    ``workspace_members`` and org-scoped ``workspace_roles`` (capability
-    bundles); org admins implicitly access all workspaces.
-    """
-
-    __tablename__ = "workspaces"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "slug", name="uq_workspaces_org_slug"),
-    )
-
-    # ``server_default`` is required so that raw-SQL INSERTs (e.g. the
-    # per-org Default seed in migration 033) can omit ``id`` and let the
-    # database fill it in. Without it, ``create_all`` produces a column
-    # with NOT NULL but no DEFAULT, and the migration crashes with
-    # ``null value in column "id"``.
-    id = Column(
-        UUID(as_uuid=True),
-        primary_key=True,
-        default=uuid.uuid4,
-        server_default=text("gen_random_uuid()"),
-    )
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    slug = Column(String(255), nullable=False)
-    # At most one default per org. Enforced on Postgres by the partial
-    # unique index attached via the after_create event below; on
-    # SQLite (test runs) we rely on the route-level _check_slug_unique
-    # check + the Default-workspace conftest fixture instead, because
-    # SQLite doesn't support partial indexes the same way.
-    is_default = Column(Boolean, nullable=False, default=False, server_default="false")
-    # Reusable PDF/report branding metadata scoped to this workspace. Images
-    # live in S3. Shape: {"heading": str|null, "images": [{id, s3_key,
-    # content_type, filename, size_bytes, updated_at}, ...]}.
-    report_branding = Column(JSON, nullable=True)
-    created_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
-    )
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    organization = relationship("Organization", back_populates="workspaces")
-    members = relationship(
-        "WorkspaceMember",
-        back_populates="workspace",
-        cascade="all, delete-orphan",
-    )
-
-
-class WorkspaceRole(Base):
-    """Org-scoped workspace role (system or custom) as a capability bundle."""
-
-    __tablename__ = "workspace_roles"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "name", name="uq_workspace_roles_org_name"),
-    )
-
-    id = Column(
-        UUID(as_uuid=True),
-        primary_key=True,
-        default=uuid.uuid4,
-        server_default=text("gen_random_uuid()"),
-    )
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-    capabilities = Column(JSON, nullable=False, default=list)
-    is_system = Column(Boolean, nullable=False, default=False, server_default="false")
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    organization = relationship("Organization", back_populates="workspace_roles")
-    members = relationship("WorkspaceMember", back_populates="role")
-
-
-class WorkspaceMember(Base):
-    """User membership in a workspace with an assigned workspace role."""
-
-    __tablename__ = "workspace_members"
-    __table_args__ = (
-        UniqueConstraint("workspace_id", "user_id", name="uq_workspace_members_ws_user"),
-    )
-
-    id = Column(
-        UUID(as_uuid=True),
-        primary_key=True,
-        default=uuid.uuid4,
-        server_default=text("gen_random_uuid()"),
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    user_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("users.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    role_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspace_roles.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    added_by_user_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("users.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    workspace = relationship("Workspace", back_populates="members")
-    user = relationship("User", foreign_keys=[user_id])
-    role = relationship("WorkspaceRole", back_populates="members")
-    added_by = relationship("User", foreign_keys=[added_by_user_id])
-
-
-# Partial unique index: "at most one default workspace per org". This
-# is attached as an after_create event (rather than declared in
-# ``__table_args__``) because SQLAlchemy's ``Index(...,
-# postgresql_where=...)`` silently degrades to a *full* unique index on
-# SQLite - which then forbids any second workspace per org and breaks
-# the test suite. ``execute_if(dialect="postgresql")`` makes this DDL
-# a no-op on SQLite while still emitting it on Postgres (prod, CI).
-event.listen(
-    Workspace.__table__,
-    "after_create",
-    DDL(
-        "CREATE UNIQUE INDEX IF NOT EXISTS uq_workspaces_org_default "
-        "ON workspaces (organization_id) WHERE is_default"
-    ).execute_if(dialect="postgresql"),
-)
-
-
-class User(Base):
-    """User model for authentication and profile management."""
-
-    __tablename__ = "users"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    email = Column(String(255), unique=True, nullable=False, index=True)
-    name = Column(String(255), nullable=True)
-    first_name = Column(String(255), nullable=True)
-    last_name = Column(String(255), nullable=True)
-    password_hash = Column(String(255), nullable=True)  # Nullable for users created via invitation
-    external_id = Column(String(255), unique=True, nullable=True, index=True)
-    auth_provider = Column(String(50), nullable=True)
-    mfa_enabled = Column(Boolean, default=False, nullable=False)
-    last_login_at = Column(DateTime(timezone=True), nullable=True)
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    # Relationships
-    organization_memberships = relationship("OrganizationMember", back_populates="user", cascade="all, delete-orphan")
-    api_keys = relationship("APIKey", back_populates="user")
-    invitations = relationship("Invitation", back_populates="invited_user", foreign_keys="Invitation.invited_user_id")
-    refresh_tokens = relationship("RefreshToken", back_populates="user", cascade="all, delete-orphan")
-
-
-class RefreshToken(Base):
-    """Opaque refresh token for extending local-password sessions."""
-
-    __tablename__ = "refresh_tokens"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id", ondelete="CASCADE"), nullable=False, index=True)
-    token_hash = Column(String(64), unique=True, nullable=False, index=True)
-    expires_at = Column(DateTime(timezone=True), nullable=False)
-    revoked_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    user = relationship("User", back_populates="refresh_tokens")
-
-
-class OrganizationMember(Base):
-    """Organization membership with role."""
-
-    __tablename__ = "organization_members"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False, index=True)
-    role = Column(String, nullable=False, default=RoleEnum.READER.value)
-    
-    # User preferences for this organization
-    default_agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
-
-    joined_at = Column(DateTime(timezone=True), server_default=func.now())
-    
-    # Unique constraint: one membership per user per organization
-    __table_args__ = (
-        UniqueConstraint('organization_id', 'user_id', name='uq_org_user'),
-    )
-
-    # Relationships
-    organization = relationship("Organization", back_populates="members")
-    user = relationship("User", back_populates="organization_memberships")
-    default_agent = relationship("Agent", foreign_keys=[default_agent_id])
-
-
-class Invitation(Base):
-    """Invitation model for inviting users to organizations."""
-
-    __tablename__ = "invitations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    invited_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Null if user doesn't exist yet
-    invited_by_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False)
-    email = Column(String(255), nullable=False)  # Email of invited user
-    role = Column(String, nullable=False, default=RoleEnum.READER.value)
-    status = Column(String, nullable=False, default=InvitationStatus.PENDING.value)
-
-
-
-    token = Column(String(255), unique=True, nullable=False, index=True)  # Invitation token
-    expires_at = Column(DateTime(timezone=True), nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    accepted_at = Column(DateTime(timezone=True), nullable=True)
-
-    # Relationships
-    organization = relationship("Organization", back_populates="invitations")
-    invited_user = relationship("User", foreign_keys=[invited_user_id], back_populates="invitations")
-    invited_by = relationship("User", foreign_keys=[invited_by_id])
-
-
-class APIKey(Base):
-    """API Key model for authentication."""
-
-    __tablename__ = "api_keys"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    key = Column(String(255), unique=True, nullable=False, index=True)
-    name = Column(String(255), nullable=True)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Optional: link to user
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    last_used = Column(DateTime(timezone=True), nullable=True)
-
-    # Relationships
-    organization = relationship("Organization", back_populates="api_keys")
-    user = relationship("User", back_populates="api_keys")
-
-
-class AudioFile(Base):
-    """Audio file model."""
-
-    __tablename__ = "audio_files"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    filename = Column(String(255), nullable=False)
-    file_path = Column(String(512), nullable=False)
-    file_size = Column(Integer, nullable=False)  # Size in bytes
-    duration = Column(Float, nullable=True)  # Duration in seconds
-    sample_rate = Column(Integer, nullable=True)
-    channels = Column(Integer, nullable=True)
-    format = Column(String(10), nullable=False)  # wav, mp3, flac, etc.
-    uploaded_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    # Relationships
-    evaluations = relationship("Evaluation", back_populates="audio_file")
-
-
-class Evaluation(Base):
-    """Evaluation job model."""
-
-    __tablename__ = "evaluations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every legacy audio evaluation belongs to a
-    # workspace within its org. Stamped from the X-Workspace-Id header
-    # (falling back to the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    audio_id = Column(UUID(as_uuid=True), ForeignKey("audio_files.id"), nullable=False)
-    reference_text = Column(String, nullable=True)  # For WER calculation
-    evaluation_type = Column(String, nullable=False)
-    model_name = Column(String(100), nullable=True)
-    status = Column(String, default=EvaluationStatus.PENDING.value, nullable=False)
-
-
-
-    metrics_requested = Column(JSON, nullable=True)  # List of requested metrics
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    completed_at = Column(DateTime(timezone=True), nullable=True)
-    error_message = Column(String, nullable=True)
-
-    # Relationships
-    audio_file = relationship("AudioFile", back_populates="evaluations")
-    result = relationship("EvaluationResult", back_populates="evaluation", uselist=False)
-
-
-class EvaluationResult(Base):
-    """Evaluation result model."""
-
-    __tablename__ = "evaluation_results"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(UUID(as_uuid=True), ForeignKey("evaluations.id"), nullable=False, unique=True)
-    # Workspace isolation: mirrors the parent Evaluation's workspace.
-    # Denormalized for fast filter-by-workspace listings without a join.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    transcript = Column(String, nullable=True)
-    metrics = Column(JSON, nullable=False)  # {"wer": 0.05, "latency_ms": 1250, ...}
-    raw_output = Column(JSON, nullable=True)  # Full model output
-    processing_time = Column(Float, nullable=True)  # Processing time in seconds
-    model_used = Column(String(100), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    # Relationships
-    evaluation = relationship("Evaluation", back_populates="result")
-
-
-# ============================================
-# VAIOPS MODELS - Voice AI Ops
-# ============================================
-
-# Enums moved to enums.py
-
-
-class Agent(Base):
-    """Test Agent - The voice AI agent being evaluated"""
-    __tablename__ = "agents"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    agent_id = Column(String(6), unique=True, nullable=True, index=True)  # 6-digit ID
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every agent belongs to a workspace within its
-    # org. Stamped from the X-Workspace-Id header (falling back to the
-    # org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String, nullable=False)
-    phone_number = Column(String, nullable=True)  # Optional, required only for phone_call
-    language = Column(String, nullable=False, default=LanguageEnum.ENGLISH.value)
-    description = Column(String)
-    provider_prompt = Column(Text, nullable=True)
-    provider_prompt_synced_at = Column(DateTime(timezone=True), nullable=True)
-    call_type = Column(String, nullable=False, default=CallTypeEnum.OUTBOUND.value)
-    call_medium = Column(String, nullable=False, default=CallMediumEnum.PHONE_CALL.value)
-    telephony_phone_number_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("telephony_phone_numbers.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-
-
-
-    
-    # Voice configuration - either voice_bundle_id OR ai_provider_id OR voice_ai_integration_id (mutually exclusive)
-    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True, index=True)
-    ai_provider_id = Column(UUID(as_uuid=True), ForeignKey("aiproviders.id"), nullable=True, index=True)
-    
-    # Voice AI agent integration (Retell, Vapi, etc.)
-    voice_ai_integration_id = Column(UUID(as_uuid=True), ForeignKey("integrations.id"), nullable=True, index=True)
-    voice_ai_agent_id = Column(String, nullable=True)  # Agent ID from the external provider (Retell/Vapi)
-    prompt_variables = Column(JSON, nullable=True)
-    silence_hangup_secs = Column(Integer, nullable=False, server_default="15")
-    
-    created_at = Column(DateTime, server_default=func.now())
-    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
-    created_by = Column(String)
-
-
-class Persona(Base):
-    """Persona - TTS provider-tied voice identity for testing"""
-    __tablename__ = "personas"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every persona belongs to a workspace within
-    # its org. Stamped from the X-Workspace-Id header (falling back to
-    # the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String, nullable=False)
-    gender = Column(String, nullable=False, default=GenderEnum.NEUTRAL.value)
-    tts_provider = Column(String(100), nullable=True)
-    tts_voice_id = Column(String(255), nullable=True)
-    tts_voice_name = Column(String(255), nullable=True)
-    is_custom = Column(Boolean, default=False)
-    description = Column(Text, nullable=True)
-    tts_config = Column(JSON, nullable=True)
-    llm_temperature = Column(Float, nullable=True)
-    llm_max_tokens = Column(Integer, nullable=True)
-    response_delay_ms = Column(Integer, nullable=True)
-    max_turns = Column(Integer, nullable=True)
-    allow_interruptions = Column(Boolean, nullable=True)
-
-    created_at = Column(DateTime, server_default=func.now())
-    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
-    created_by = Column(String)
-
-
-class Scenario(Base):
-    """Scenario - The conversation scenario/test case"""
-    __tablename__ = "scenarios"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every scenario belongs to a workspace within
-    # its org. Stamped from the X-Workspace-Id header (falling back to
-    # the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
-    name = Column(String, nullable=False)
-    description = Column(String)
-    required_info = Column(JSON)
-    
-    created_at = Column(DateTime, server_default=func.now())
-    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
-    created_by = Column(String)
-
-
-# Enums moved to enums.py
-
-
-class Integration(Base):
-    """Integration model for connecting with external voice AI platforms."""
-    __tablename__ = "integrations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    platform = Column(String, nullable=False)
-
-
-
-    name = Column(String, nullable=True)  # Optional friendly name
-    api_key = Column(String, nullable=False)  # Encrypted Private API key for the platform
-    public_key = Column(String, nullable=True)  # Optional Public API key (e.g. for Vapi)
-    is_active = Column(Boolean, default=True, nullable=False)
-    # Multiple credentials per (org, platform) are allowed. is_default marks
-    # the row used when a caller does not explicitly select a credential.
-    # A partial unique index in migration 028 enforces at most one default
-    # per (org, platform) at the DB level.
-    is_default = Column(Boolean, default=False, nullable=False)
-    # inherit | gateway | direct — per-credential LLM routing override
-    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
-
-
-class ManualTranscription(Base):
-    """Manual transcription model for storing transcriptions from S3 audio files."""
-
-    __tablename__ = "manual_transcriptions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    name = Column(String(255), nullable=True)  # User-friendly name for the transcription
-    audio_file_key = Column(String(512), nullable=False)  # S3 key or file path
-    transcript = Column(String, nullable=False)  # Full transcript text
-    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
-    stt_model = Column(String(100), nullable=True)  # STT model used (e.g., "whisper-1", "google-speech-v2")
-    stt_provider = Column(String, nullable=True)  # Provider used
-
-
-
-    language = Column(String(10), nullable=True)  # Detected or specified language
-    processing_time = Column(Float, nullable=True)  # Processing time in seconds
-    raw_output = Column(JSON, nullable=True)  # Full model output for reference
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class ConversationEvaluation(Base):
-    """Conversation evaluation model for evaluating manual transcriptions against agent objectives."""
-    
-    __tablename__ = "conversation_evaluations"
-    
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    transcription_id = Column(UUID(as_uuid=True), ForeignKey("manual_transcriptions.id"), nullable=False, index=True)
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
-    
-    # Evaluation results
-    objective_achieved = Column(Boolean, nullable=False)  # Binary: was the conversation objective achieved?
-    objective_achieved_reason = Column(String, nullable=True)  # Explanation for the binary result
-    additional_metrics = Column(JSON, nullable=True)  # Additional evaluation metrics (e.g., professionalism, clarity, etc.)
-    overall_score = Column(Float, nullable=True)  # Overall score (0.0 to 1.0)
-    
-    # LLM metadata
-    llm_provider = Column(Enum(ModelProvider, native_enum=False), nullable=True)
-
-    llm_model = Column(String(100), nullable=True)
-    llm_response = Column(JSON, nullable=True)  # Full LLM response for reference
-    
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class AIProvider(Base):
-    """AI Provider - Stores API keys for different AI platforms."""
-    __tablename__ = "aiproviders"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider = Column(String, nullable=False)
-
-
-
-    api_key = Column(String, nullable=False)  # Encrypted API key
-    name = Column(String, nullable=True)  # Optional friendly name
-    # Azure OpenAI resource endpoint (e.g. https://my-resource.openai.azure.com).
-    # Only used when provider is azure; other providers ignore this column.
-    endpoint_url = Column(String, nullable=True)
-    is_active = Column(Boolean, default=True, nullable=False)
-    # Multiple AIProvider rows per (org, provider) are allowed. is_default
-    # marks the row resolved when no explicit credential id is selected.
-    # A partial unique index in migration 028 enforces at most one default.
-    is_default = Column(Boolean, default=False, nullable=False)
-    # inherit | gateway | direct — per-credential LLM routing override
-    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
-    # Bifrost custom model ID used when routing via gateway
-    gateway_model = Column(String(255), nullable=True)
-    # inherit | litellm_shim | native_openai — Bifrost API surface override
-    gateway_interface = Column(String(20), nullable=False, default="inherit", server_default="inherit")
-    # Optional per-credential Bifrost/gateway base URL override
-    gateway_base_url = Column(String(512), nullable=True)
-    # Optional auth header for Bifrost (e.g. x-bf-vk, Authorization, x-api-key)
-    gateway_auth_header = Column(String(64), nullable=True)
-    # Env var name whose value is sent as the gateway auth secret
-    gateway_auth_secret_env = Column(String(128), nullable=True)
-    # Encrypted inline gateway auth secret (alternative to env var)
-    gateway_auth_secret = Column(String, nullable=True)
-    # Arbitrary HTTP headers sent with gateway-routed LiteLLM calls
-    gateway_extra_headers = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
-
-
-# Enums moved to enums.py
-
-
-class VoiceBundle(Base):
-    """VoiceBundle - Composable unit combining STT, LLM, and TTS for voice AI testing, or S2S models."""
-    __tablename__ = "voicebundles"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    name = Column(String, nullable=False)
-    description = Column(String, nullable=True)
-    
-    # Bundle type: either STT+LLM+TTS or S2S
-    # Using String instead of Enum to avoid SQLAlchemy enum conversion issues
-    # The enum conversion is handled in the Pydantic schemas
-    bundle_type = Column(String(50), nullable=False, default=VoiceBundleType.STT_LLM_TTS.value)
-    
-    # STT Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
-    stt_provider = Column(String, nullable=True)
-    # Optional explicit credential row (aiproviders.id or integrations.id).
-    # When NULL the credential resolver picks the default row for the
-    # provider. No FK is set because the target table varies by provider.
-    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    stt_model = Column(String, nullable=True)  # e.g., "whisper-1", "google-speech-v2"
-    
-    # LLM Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
-    llm_provider = Column(String, nullable=True)
-    llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    llm_model = Column(String, nullable=True)  # e.g., "gpt-4", "claude-3-opus"
-    llm_temperature = Column(Float, nullable=True, default=0.7)
-    llm_max_tokens = Column(Integer, nullable=True)
-    llm_config = Column(JSON, nullable=True)  # Additional LLM configuration (extensible)
-    
-    # TTS Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
-    tts_provider = Column(String, nullable=True)
-    tts_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    tts_model = Column(String, nullable=True)  # e.g., "tts-1", "neural-voice"
-    tts_voice = Column(String, nullable=True)  # Voice selection if applicable
-    tts_config = Column(JSON, nullable=True)  # Additional TTS configuration (extensible)
-    
-    # S2S Configuration - required for S2S type, optional for STT_LLM_TTS
-    s2s_provider = Column(String, nullable=True)
-    s2s_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-
-
-    s2s_model = Column(String, nullable=True)  # e.g., "gpt-4o-transcribe", speech-to-speech model
-    s2s_config = Column(JSON, nullable=True)  # Additional S2S configuration (extensible)
-    
-    # Additional configuration for extensibility
-    extra_metadata = Column(JSON, nullable=True)  # For future extensions (renamed from 'metadata' to avoid SQLAlchemy conflict)
-    
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-# Enums moved to enums.py
-
-
-class TestAgentConversation(Base):
-    """Test Agent Conversation - Records conversations between test AI agent and voice AI agent."""
-    __tablename__ = "test_agent_conversations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every playground conversation belongs to a
-    # workspace within its org. Stamped from the X-Workspace-Id header
-    # (falling back to the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    
-    # Configuration
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
-    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=False)
-    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
-    
-    # Conversation data
-    status = Column(String, nullable=False, default=TestAgentConversationStatus.INITIALIZING.value)
-
-
-
-    live_transcription = Column(JSON, nullable=True)  # Array of conversation turns with timestamps
-    conversation_audio_key = Column(String, nullable=True)  # S3 key for recorded conversation audio
-    full_transcript = Column(String, nullable=True)  # Full conversation transcript
-    
-    # Metadata
-    started_at = Column(DateTime(timezone=True), server_default=func.now())
-    ended_at = Column(DateTime(timezone=True), nullable=True)
-    duration_seconds = Column(Float, nullable=True)
-    
-    # Additional metadata
-    conversation_metadata = Column(JSON, nullable=True)  # Additional conversation metadata
-    
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class EvaluatorSuite(Base):
-    """Evaluator suite — one agent + one persona + N scenario combinations."""
-
-    __tablename__ = "evaluator_suites"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    name = Column(String, nullable=True)
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
-    metric_ids = Column(JSON, nullable=True)
-    llm_provider = Column(String, nullable=True)
-    llm_model = Column(String, nullable=True)
-    llm_config = Column(JSON, nullable=True)
-    tags = Column(JSON, nullable=True)
-    default_runs_per_combination = Column(Integer, nullable=False, default=1)
-    round_robin_index = Column(Integer, nullable=False, default=0)
-    is_active = Column(Boolean, nullable=False, default=False)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class Evaluator(Base):
-    """Evaluator - Configuration for testing agents with specific persona and scenario combinations, or custom prompt evaluators."""
-    __tablename__ = "evaluators"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluator_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every evaluator belongs to a workspace within
-    # its org. Stamped from the X-Workspace-Id header (falling back to
-    # the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    
-    # Display name (required for custom evaluators, optional for standard)
-    name = Column(String, nullable=True)
-    
-    # Parent suite (nullable for legacy/custom evaluators)
-    suite_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("evaluator_suites.id", ondelete="CASCADE"),
-        nullable=True,
-        index=True,
-    )
-
-    # Standard evaluator configuration (nullable for custom evaluators)
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)
-    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)
-    
-    # Custom evaluator prompt (used instead of agent/persona/scenario)
-    custom_prompt = Column(Text, nullable=True)
-
-    # Custom evaluator metric selection. When set, the worker filters the
-    # enabled-org metrics down to only these IDs (list of metric UUID strings).
-    # Standard evaluators leave this NULL and use all enabled agent metrics.
-    metric_ids = Column(JSON, nullable=True)
-
-    # LLM configuration for evaluation (overrides hardcoded defaults)
-    llm_provider = Column(String, nullable=True)  # e.g. "openai", "anthropic", "google"
-    llm_model = Column(String, nullable=True)  # e.g. "gpt-4.1", "claude-sonnet-4-20250514"
-    llm_config = Column(JSON, nullable=True)
-    
-    # Tags for categorization
-    tags = Column(JSON, nullable=True)  # Array of tag strings
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-# Enums moved to enums.py
-
-
-class Metric(Base):
-    """Metric - Configuration for evaluation metrics.
-
-    Supports a 2-level hierarchy via ``parent_metric_id``: a "category"
-    parent metric (e.g. "Call Outcome") owns N child sub-metric labels
-    (e.g. "happy_completion", "angry_hangup"). ``selection_mode`` is set
-    only on parents and controls how the LLM scores children together
-    (``single_choice`` = pick exactly one; ``multi_label`` = independent
-    yes/no with logical consistency). Children are always boolean.
-    """
-    __tablename__ = "metrics"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: two-shape column.
-    #
-    #   * ``workspace_id = <uuid>`` — workspace-scoped metric. Only
-    #     visible inside that workspace (the default behavior; existing
-    #     rows all look like this).
-    #   * ``workspace_id IS NULL`` — org-shared metric. Surfaces in
-    #     every workspace's listing under this org so users don't have
-    #     to recreate the same metric per workspace.
-    #
-    # Children always inherit their parent's ``workspace_id`` (including
-    # NULL) so a category metric's whole subtree shares one scope; the
-    # add-child / promote-discovered endpoints enforce this.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=True,
-        index=True,
-    )
-
-    # Basic information
-    name = Column(String, nullable=False)
-    description = Column(String, nullable=True)
-    # Free-form illustrative example used to sharpen the LLM judge's
-    # rubric. Today this is consumed by child sub-labels of a
-    # categorization parent metric so each label can carry "what does
-    # this look like in a transcript?" text alongside the rubric in
-    # ``description``. The column lives on every Metric row for
-    # forward-compat: a standalone metric could later surface its own
-    # example without another migration.
-    example = Column(Text, nullable=True)
-
-    # Configuration
-    metric_type = Column(String, nullable=False, default=MetricType.RATING.value)
-    metric_category = Column(
-        String(30),
-        nullable=False,
-        default=MetricCategory.QUALITY.value,
-        server_default=MetricCategory.QUALITY.value,
-    )
-    trigger = Column(String, nullable=False, default=MetricTrigger.ALWAYS.value)
-    metric_origin = Column(String(30), nullable=False, default="default")
-    supported_surfaces = Column(JSON, nullable=False, default=list)  # ["agent", "voice_playground", "blind_test"]
-    enabled_surfaces = Column(JSON, nullable=False, default=list)  # subset of supported_surfaces
-    custom_data_type = Column(String(30), nullable=True)  # "boolean" | "enum" | "number_range"
-    custom_config = Column(JSON, nullable=True)  # enum options / number range config
-    tags = Column(JSON, nullable=True)  # ["tone", "latency", ...]
-
-    # Hierarchy: NULL = standalone or parent. When set, this row is a
-    # child sub-metric of the referenced parent. ON DELETE CASCADE so
-    # deleting a category removes its children atomically.
-    parent_metric_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("metrics.id", ondelete="CASCADE"),
-        nullable=True,
-        index=True,
-    )
-    # Set only on parent rows (``parent_metric_id IS NULL``). Either
-    # ``single_choice`` or ``multi_label``. NULL = legacy / non-hierarchical
-    # metric (no children).
-    selection_mode = Column(String(20), nullable=True)
-
-    # When true on a parent metric (any selection_mode), the LLM is
-    # invited during call-import evaluation to emit additional
-    # candidate sub-labels beyond the user-defined children. The
-    # candidates surface in a "Discovered labels" panel where the user
-    # manually promotes them into real child Metric rows. For
-    # ``single_choice`` parents the discovered entries are
-    # supplemental — the chosen child is still picked from the
-    # predefined children so the exactly-one-true invariant holds.
-    # The validator rejects this flag on standalone / child metrics.
-    allow_discovery = Column(
-        Boolean, nullable=False, default=False, server_default="false"
-    )
-
-    # When True, this metric is a "transcript-compare judge": the
-    # call-import evaluator feeds BOTH the production transcript
-    # (``call_import_rows.transcript``, CSV-supplied) and the diarised
-    # transcript (``call_import_rows.diarised_transcript``, worker-
-    # produced by the STT/diarisation pipeline) to the LLM as a
-    # labeled pair instead of feeding one transcript. The parent
-    # evaluation's ``CallImportEvaluation.transcript_source`` is
-    # ignored for these metrics — they always read both columns.
-    # Rows where either transcript is missing are skipped per-metric
-    # with ``skipped="comparison_missing_transcript"`` so the rest of
-    # the row's metrics still produce scores. The Pydantic validator
-    # rejects ``compare_transcripts`` combined with ``parent_metric_id``
-    # or ``selection_mode`` (i.e. it can't simultaneously be part of
-    # a parent/child hierarchy). The call-import worker also
-    # auto-promotes a metric to comparison mode when its description
-    # references the production / diarised transcripts in well-known
-    # phrases (see ``_metric_text_references_production`` in
-    # ``app.workers.tasks.evaluate_call_import_row``).
-    compare_transcripts = Column(
-        Boolean, nullable=False, default=False, server_default="false"
-    )
-
-    parent = relationship(
-        "Metric",
-        remote_side=[id],
-        backref="children",
-    )
-
-    # When true, the LLM-judge is asked to also return a short free-form
-    # rationale alongside the value (stored under ``metric_scores[id].rationale``).
-    # Adds a second "<Name> - LLM Rationale" column in the call-import CSV export.
-    capture_rationale = Column(Boolean, nullable=False, default=False)
-
-    enabled = Column(Boolean, nullable=False, default=True)
-    
-    # Metadata
-    is_default = Column(Boolean, nullable=False, default=False)  # Pre-defined metrics
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class EvaluatorResult(Base):
-    """EvaluatorResult - Results from running an evaluator with transcription and metric evaluations."""
-    __tablename__ = "evaluator_results"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    result_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every evaluator result belongs to a workspace
-    # within its org. Stamped from the active workspace at creation time
-    # (either the X-Workspace-Id header or the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    
-    # References
-    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True, index=True)  # Optional - can be None for test calls without persona/scenario
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Nullable for custom evaluators
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)  # Optional - can be None for test calls
-    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)  # Optional - can be None for test calls
-    
-    # Result data
-    name = Column(String, nullable=True)  # Scenario name or test call name (optional)
-    timestamp = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
-    duration_seconds = Column(Float, nullable=True)  # Call duration
-    status = Column(String(20), nullable=False, default=EvaluatorResultStatus.QUEUED.value)
-    
-    # Audio and transcription
-    audio_s3_key = Column(String, nullable=True)  # S3 key for audio file
-    transcription = Column(String, nullable=True)  # Full transcription
-    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
-    
-    # Metric scores - JSON object with metric_id as key and score as value
-    # Format: {"metric_id_1": {"value": 85, "type": "rating"}, "metric_id_2": {"value": true, "type": "boolean"}}
-    metric_scores = Column(JSON, nullable=True)
-    
-    # Celery task tracking
-    celery_task_id = Column(String, nullable=True, index=True)  # Celery task ID for tracking
-    
-    # Error information
-    error_message = Column(String, nullable=True)
-    
-    # Call event tracking (similar to CallRecording)
-    call_event = Column(String, nullable=True, index=True)  # Latest call event (e.g., call_started, call_ended)
-    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
-    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
-    call_data = Column(JSON, nullable=True)  # Full call details from provider (like CallRecording)
-    
-    # Data-plane shard routing (payload rows on shard DBs when sharding enabled)
-    shard_id = Column(String(64), nullable=True, index=True)
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-# Enums moved to enums.py
-
-
-class CallRecordingSource(str, enum.Enum):
-    """Source of the call recording data."""
-    
-    PLAYGROUND = "playground"
-    WEBHOOK = "webhook"
-
-
-class CallRecording(Base):
-    """Call Recording model for tracking voice provider calls."""
-    __tablename__ = "call_recordings"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every recording belongs to a workspace within
-    # its org. For playground-origin rows this is stamped from the active
-    # workspace at creation time; for webhook-origin rows the worker
-    # looks up the recording's agent and inherits its workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    call_short_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
-    status = Column(Enum(CallRecordingStatus), nullable=False, default=CallRecordingStatus.PENDING, index=True)
-    call_event = Column(String, nullable=True, index=True)  # Latest webhook event (e.g., call_started, call_ended)
-    source = Column(Enum(CallRecordingSource), nullable=False, default=CallRecordingSource.PLAYGROUND, index=True)
-    call_data = Column(JSON, nullable=True)  # JSON blob for provider response
-    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
-    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Reference to our agent
-    
-    # Link to EvaluatorResult for metric evaluations
-    evaluator_result_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("evaluator_results.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-    
-    shard_id = Column(String(64), nullable=True, index=True)
-    
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class EvaluatorResultPayload(Base):
-    """Heavy evaluator result fields stored on data shards when sharding is enabled."""
-
-    __tablename__ = "evaluator_result_payloads"
-
-    evaluator_result_id = Column(UUID(as_uuid=True), primary_key=True)
-    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
-    audio_s3_key = Column(String, nullable=True)
-    transcription = Column(String, nullable=True)
-    speaker_segments = Column(JSON, nullable=True)
-    metric_scores = Column(JSON, nullable=True)
-    call_data = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class CallRecordingPayload(Base):
-    """Heavy call recording fields stored on data shards when sharding is enabled."""
-
-    __tablename__ = "call_recording_payloads"
-
-    call_recording_id = Column(UUID(as_uuid=True), primary_key=True)
-    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
-    call_data = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class Alert(Base):
-    """Alert model for configuring monitoring alerts."""
-    __tablename__ = "alerts"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    
-    # Basic information
-    name = Column(String(255), nullable=False)
-    description = Column(String, nullable=True)
-    
-    # Metric condition configuration
-    metric_type = Column(String, nullable=False, default=AlertMetricType.NUMBER_OF_CALLS.value)
-    aggregation = Column(String, nullable=False, default=AlertAggregation.SUM.value)
-    operator = Column(String, nullable=False, default=AlertOperator.GREATER_THAN.value)
-    threshold_value = Column(Float, nullable=False)
-    time_window_minutes = Column(Integer, nullable=False, default=60)  # Time window for aggregation
-    
-    # Agent selection (JSON array of agent UUIDs, null means all agents)
-    agent_ids = Column(JSON, nullable=True)
-    
-    # Notification configuration
-    notify_frequency = Column(String, nullable=False, default=AlertNotifyFrequency.IMMEDIATE.value)
-    notify_emails = Column(JSON, nullable=True)  # Array of email addresses
-    notify_webhooks = Column(JSON, nullable=True)  # Array of webhook URLs (Slack, etc.)
-    
-    # Status
-    status = Column(String, nullable=False, default=AlertStatus.ACTIVE.value)
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-    
-    # Relationships
-    alert_history = relationship("AlertHistory", back_populates="alert", cascade="all, delete-orphan")
-
-
-class AlertHistory(Base):
-    """Alert history model for tracking triggered alerts."""
-    __tablename__ = "alert_history"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    alert_id = Column(UUID(as_uuid=True), ForeignKey("alerts.id"), nullable=False, index=True)
-    
-    # Trigger information
-    triggered_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
-    triggered_value = Column(Float, nullable=False)  # The actual value that triggered the alert
-    threshold_value = Column(Float, nullable=False)  # The threshold at time of trigger
-    
-    # Status tracking
-    status = Column(String, nullable=False, default=AlertHistoryStatus.TRIGGERED.value)
-    
-    # Notification tracking
-    notified_at = Column(DateTime(timezone=True), nullable=True)
-    notification_details = Column(JSON, nullable=True)  # Details of sent notifications
-    
-    # Resolution
-    acknowledged_at = Column(DateTime(timezone=True), nullable=True)
-    acknowledged_by = Column(String, nullable=True)
-    resolved_at = Column(DateTime(timezone=True), nullable=True)
-    resolved_by = Column(String, nullable=True)
-    resolution_notes = Column(String, nullable=True)
-    
-    # Additional context
-    context_data = Column(JSON, nullable=True)  # Additional data about the trigger
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    
-    # Relationships
-    alert = relationship("Alert", back_populates="alert_history")
-
-
-class CronJob(Base):
-    """Cron job model for scheduling automated evaluator runs."""
-    __tablename__ = "cron_jobs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    
-    # Basic information
-    name = Column(String(255), nullable=False)
-    cron_expression = Column(String(100), nullable=False)  # e.g., "0 9 * * 1-5"
-    timezone = Column(String(100), nullable=False, default="UTC")
-    
-    # Run configuration
-    max_runs = Column(Integer, nullable=False, default=10)
-    current_runs = Column(Integer, nullable=False, default=0)
-    
-    # Evaluators to trigger (JSON array of evaluator UUIDs)
-    evaluator_ids = Column(JSON, nullable=False)
-    
-    # Status
-    status = Column(String, nullable=False, default=CronJobStatus.ACTIVE.value)
-    
-    # Run tracking
-    next_run_at = Column(DateTime(timezone=True), nullable=True)
-    last_run_at = Column(DateTime(timezone=True), nullable=True)
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class TTSComparisonStatus(str, enum.Enum):
-    PENDING = "pending"
-    GENERATING = "generating"
-    EVALUATING = "evaluating"
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-
-class TTSSampleStatus(str, enum.Enum):
-    PENDING = "pending"
-    GENERATING = "generating"
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-
-class TTSReportJobStatus(str, enum.Enum):
-    PENDING = "pending"
-    PROCESSING = "processing"
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-
-class TTSComparison(Base):
-    """TTS Comparison session for A/B testing voice providers."""
-    __tablename__ = "tts_comparisons"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every voice playground comparison belongs to
-    # a workspace within its org. Children (samples, report jobs, blind
-    # test shares) inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    simulation_id = Column(String(6), unique=True, index=True, nullable=True)
-
-    name = Column(String(255), nullable=True)
-    status = Column(String(50), nullable=False, default=TTSComparisonStatus.PENDING.value)
-
-    # 'benchmark' = traditional TTS A/B benchmark (provider-generated audio).
-    # 'blind_test_only' = standalone blind test built from existing recordings
-    # / uploads / past TTS samples; no TTS generation happens.
-    mode = Column(String(32), nullable=False, default="benchmark")
-
-    provider_a = Column(String(100), nullable=True)
-    model_a = Column(String(100), nullable=True)
-    voices_a = Column(JSON, nullable=True)
-
-    provider_b = Column(String(100), nullable=True)
-    model_b = Column(String(100), nullable=True)
-    voices_b = Column(JSON, nullable=True)
-
-    sample_texts = Column(JSON, nullable=False)
-    num_runs = Column(Integer, nullable=False, default=1)
-
-    blind_test_results = Column(JSON, nullable=True)
-    evaluation_summary = Column(JSON, nullable=True)
-
-    eval_stt_provider = Column(String(100), nullable=True)
-    eval_stt_model = Column(String(100), nullable=True)
-
-    celery_task_id = Column(String, nullable=True, index=True)
-    error_message = Column(String, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    samples = relationship("TTSSample", back_populates="comparison", cascade="all, delete-orphan")
-
-
-class TTSSample(Base):
-    """Individual TTS audio sample within a comparison."""
-    __tablename__ = "tts_samples"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent TTSComparison's workspace.
-    # Denormalized for fast filter-by-workspace listings without a join.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    provider = Column(String(100), nullable=True)
-    model = Column(String(100), nullable=True)
-    voice_id = Column(String(255), nullable=True)
-    voice_name = Column(String(255), nullable=True)
-    side = Column(String(1), nullable=True)  # "A" or "B"
-    sample_index = Column(Integer, nullable=False)
-    run_index = Column(Integer, nullable=False, default=0)
-
-    # 'tts' (default, audio is synthesized by a provider), 'recording' (audio
-    # is reused from a CallImportRow recording), or 'upload' (audio was
-    # uploaded by the user). Non-tts samples are marked completed up-front
-    # by the API and skipped by the generation worker.
-    source_type = Column(String(32), nullable=False, default="tts")
-    # When source_type == 'recording', references CallImportRow.id (no FK
-    # constraint to keep cascading deletes simple if a call import is later
-    # removed; the audio_s3_key is what's actually used).
-    source_ref_id = Column(UUID(as_uuid=True), nullable=True)
-
-    text = Column(String, nullable=False)
-    audio_s3_key = Column(String(512), nullable=True)
-    duration_seconds = Column(Float, nullable=True)
-    latency_ms = Column(Float, nullable=True)
-    ttfb_ms = Column(Float, nullable=True)
-
-    evaluation_metrics = Column(JSON, nullable=True)
-    status = Column(String(50), nullable=False, default=TTSSampleStatus.PENDING.value)
-    error_message = Column(String, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    comparison = relationship("TTSComparison", back_populates="samples")
-
-
-class TTSReportJob(Base):
-    """Asynchronous PDF report generation jobs for Voice Playground."""
-    __tablename__ = "tts_report_jobs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent TTSComparison's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
-
-    status = Column(String(50), nullable=False, default=TTSReportJobStatus.PENDING.value)
-    format = Column(String(20), nullable=False, default="pdf")
-    filename = Column(String(255), nullable=True)
-    s3_key = Column(String(512), nullable=True)
-    error_message = Column(String, nullable=True)
-    celery_task_id = Column(String, nullable=True, index=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    comparison = relationship("TTSComparison")
-
-
-class TTSBlindTestShareStatus(str, enum.Enum):
-    OPEN = "open"
-    CLOSED = "closed"
-
-
-class TTSBlindTestShare(Base):
-    """A publicly sharable blind test for a TTSComparison.
-
-    The share_token is the capability: anyone holding it can open the public
-    form and submit a response. Each comparison has at most one share row.
-    """
-    __tablename__ = "tts_blind_test_shares"
-    __table_args__ = (
-        UniqueConstraint("comparison_id", name="uq_blind_test_shares_comparison"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    comparison_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("tts_comparisons.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent TTSComparison's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    share_token = Column(String(64), unique=True, nullable=False, index=True)
-
-    title = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-
-    # Internal notes visible only to the share creator (e.g. which voice
-    # corresponds to which side, source notes for standalone blind tests).
-    # Never exposed via the public blind test payload.
-    creator_notes = Column(Text, nullable=True)
-
-    # JSON list: [{ "key": str, "label": str, "type": "rating"|"comment", "scale": int? }]
-    custom_metrics = Column(JSON, nullable=False)
-
-    status = Column(String(20), nullable=False, default=TTSBlindTestShareStatus.OPEN.value)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    closed_at = Column(DateTime(timezone=True), nullable=True)
-    created_by = Column(String, nullable=True)
-
-    comparison = relationship("TTSComparison")
-    responses = relationship(
-        "TTSBlindTestResponse",
-        back_populates="share",
-        cascade="all, delete-orphan",
-    )
-
-
-class TTSBlindTestResponse(Base):
-    """A single rater's submission against a TTSBlindTestShare."""
-    __tablename__ = "tts_blind_test_responses"
-    __table_args__ = (
-        UniqueConstraint("share_id", "rater_email", name="uq_blind_test_response_share_email"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    share_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("tts_blind_test_shares.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    # Workspace isolation: mirrors the parent TTSBlindTestShare's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    rater_name = Column(String(255), nullable=False)
-    rater_email = Column(String(320), nullable=False, index=True)
-
-    # JSON list keyed by sample_index. Server stores in TRUE A/B orientation
-    # (already de-flipped from whatever the rater's UI showed):
-    # [{
-    #   "sample_index": int,
-    #   "preferred": "A" | "B",
-    #   "ratings_a": { metric_key: number },
-    #   "ratings_b": { metric_key: number },
-    #   "comment": str?
-    # }]
-    responses = Column(JSON, nullable=False)
-
-    ip = Column(String(64), nullable=True)
-    user_agent = Column(String(512), nullable=True)
-
-    submitted_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    share = relationship("TTSBlindTestShare", back_populates="responses")
-
-
-class PromptPartial(Base):
-    """Prompt Partial - Reusable prompt templates with version history."""
-    __tablename__ = "prompt_partials"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every prompt partial belongs to a workspace
-    # within its org. Versions inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    description = Column(String, nullable=True)
-    content = Column(Text, nullable=False)
-    tags = Column(JSON, nullable=True)
-    current_version = Column(Integer, nullable=False, default=1)
-    # Cached LLM-generated flowchart for imported production agent prompts.
-    # Shape: AgentFlowGraph JSON (nodes[], edges[]).
-    agent_flowchart = Column(JSON, nullable=True)
-    agent_flowchart_status = Column(String(20), nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    versions = relationship("PromptPartialVersion", back_populates="prompt_partial", cascade="all, delete-orphan", order_by="PromptPartialVersion.version.desc()")
-
-
-class PromptPartialVersion(Base):
-    """Version history for a prompt partial."""
-    __tablename__ = "prompt_partial_versions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    prompt_partial_id = Column(UUID(as_uuid=True), ForeignKey("prompt_partials.id", ondelete="CASCADE"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent PromptPartial's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    version = Column(Integer, nullable=False)
-    content = Column(Text, nullable=False)
-    change_summary = Column(String, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    created_by = Column(String, nullable=True)
-
-    prompt_partial = relationship("PromptPartial", back_populates="versions")
-
-    __table_args__ = (
-        UniqueConstraint('prompt_partial_id', 'version', name='uq_prompt_partial_version'),
-    )
-
-
-class CustomTTSVoice(Base):
-    """Organization-scoped custom TTS voice metadata."""
-    __tablename__ = "custom_tts_voices"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "provider", "voice_id", name="uq_custom_tts_voice_org_provider_voice_id"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider = Column(String(100), nullable=False, index=True)
-    voice_id = Column(String(255), nullable=False)
-    name = Column(String(255), nullable=False)
-    gender = Column(String(50), nullable=True)
-    accent = Column(String(100), nullable=True)
-    description = Column(Text, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-class PromptOptimizationRun(Base):
-    """A single GEPA prompt optimization run for an agent."""
-    __tablename__ = "prompt_optimization_runs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every optimization run belongs to a workspace
-    # within its org. Candidates inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
-    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True)
-    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
-
-    seed_prompt = Column(Text, nullable=False)
-    best_prompt = Column(Text, nullable=True)
-    best_score = Column(Float, nullable=True)
-
-    status = Column(String(20), nullable=False, default=PromptOptimizationStatus.PENDING.value)
-    config = Column(JSON, nullable=True)
-    reflection_trace = Column(JSON, nullable=True)
-    metric_history = Column(JSON, nullable=True)
-
-    num_iterations = Column(Integer, nullable=True)
-    num_metric_calls = Column(Integer, nullable=True)
-
-    celery_task_id = Column(String, nullable=True, index=True)
-    error_message = Column(Text, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    candidates = relationship("PromptOptimizationCandidate", back_populates="optimization_run", cascade="all, delete-orphan")
-
-
-class PromptOptimizationCandidate(Base):
-    """A candidate prompt generated during an optimization run."""
-    __tablename__ = "prompt_optimization_candidates"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    optimization_run_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_runs.id", ondelete="CASCADE"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent PromptOptimizationRun's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    prompt_text = Column(Text, nullable=False)
-    score = Column(Float, nullable=True)
-    metric_breakdown = Column(JSON, nullable=True)
-    reflection_summary = Column(Text, nullable=True)
-
-    parent_candidate_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_candidates.id"), nullable=True)
-
-    is_accepted = Column(Boolean, nullable=False, default=False)
-    pushed_to_provider_at = Column(DateTime(timezone=True), nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    optimization_run = relationship("PromptOptimizationRun", back_populates="candidates")
-
-
-class TelephonyIntegration(Base):
-    """Per-organization telephony provider credentials and configuration.
-
-    Multiple rows per (organization_id, provider) are allowed so that an
-    organization can keep several Plivo / Exotel accounts side-by-side.
-    A partial unique index in migration 028 enforces at most one row with
-    is_default = TRUE per (org, provider); resolution falls back to that
-    default row when the caller does not pin a specific credential.
-    """
-
-    __tablename__ = "telephony_integrations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider = Column(String(50), nullable=False, default="plivo")
-    name = Column(String(255), nullable=True)  # Optional friendly name to disambiguate multiple credentials
-
-    auth_id = Column(String(255), nullable=False)
-    auth_token = Column(String(512), nullable=False)
-
-    verify_app_uuid = Column(String(255), nullable=True)
-    voice_app_id = Column(String(255), nullable=True)
-    sip_domain = Column(String(255), nullable=True)
-    masking_config = Column(JSON, nullable=True)
-
-    is_active = Column(Boolean, default=True, nullable=False)
-    is_default = Column(Boolean, default=False, nullable=False)
-    last_tested_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyPhoneNumber(Base):
-    """Inventory of telephony phone numbers owned by an organization."""
-
-    __tablename__ = "telephony_phone_numbers"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_number_org_phone"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    telephony_integration_id = Column(
-        UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=True, index=True
-    )
-
-    phone_number = Column(String(20), nullable=False, index=True)
-    country_iso2 = Column(String(2), nullable=True)
-    region = Column(String(100), nullable=True)
-    number_type = Column(String(20), nullable=True)
-    capabilities = Column(JSON, nullable=True)
-    provider_app_id = Column(String(255), nullable=True)
-
-    is_masking_pool = Column(Boolean, default=False, nullable=False)
-    inbound_enabled = Column(Boolean, default=True, nullable=False)
-    outbound_enabled = Column(Boolean, default=True, nullable=False)
-    source = Column(String(20), nullable=False, default="imported")
-    agent_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey(
-            "agents.id",
-            ondelete="SET NULL",
-            use_alter=True,
-            name="fk_telephony_phone_numbers_agent_id",
-        ),
-        nullable=True,
-        index=True,
-    )
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyDialTarget(Base):
-    """Org-scoped saved destination numbers for outbound test calls."""
-
-    __tablename__ = "telephony_dial_targets"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_dial_target_org_phone"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    phone_number = Column(String(20), nullable=False, index=True)
-    label = Column(String(255), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyVerifySession(Base):
-    """Tracks voice OTP verification sessions via telephony provider."""
-
-    __tablename__ = "telephony_verify_sessions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider_session_uuid = Column(String(255), nullable=False, unique=True, index=True)
-    recipient_number = Column(String(20), nullable=False)
-    channel = Column(String(10), nullable=False, default="voice")
-    status = Column(String(20), nullable=False, default="pending")
-    initiated_by = Column(String(255), nullable=True)
-    verify_app_uuid = Column(String(255), nullable=True)
-    verified_at = Column(DateTime(timezone=True), nullable=True)
-    expires_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyMaskedSession(Base):
-    """Number-masking session between two parties through a middle number."""
-
-    __tablename__ = "telephony_masked_sessions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    telephony_integration_id = Column(UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=False)
-    masked_number_id = Column(
-        UUID(as_uuid=True), ForeignKey("telephony_phone_numbers.id"), nullable=False, index=True
-    )
-    masked_number = Column(String(20), nullable=False)
-    party_a_number = Column(String(20), nullable=False)
-    party_b_number = Column(String(20), nullable=False)
-    status = Column(String(20), nullable=False, default="active")
-    expires_at = Column(DateTime(timezone=True), nullable=True)
-    ended_at = Column(DateTime(timezone=True), nullable=True)
-    session_metadata = Column("metadata", JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class CallImportSchema(Base):
-    """Reusable Input Parameter schema for the call-uploads flow.
-
-    A schema is workspace-scoped: users define a named bundle of typed
-    Input Parameters once (e.g. "Standard Voice QA" with conversation_id +
-    recording_url + transcript + agent_name) and then map those parameters
-    to CSV/Excel headers each time they upload a new batch.
-
-    Every schema MUST contain exactly one parameter with
-    ``type='conversation_id'`` and ``is_required=True`` - that's the
-    mandatory identity field every imported row needs. The invariant is
-    enforced in app code on create/update (no DB-level CHECK because the
-    parent + children are written across two tables in one transaction).
-    """
-
-    __tablename__ = "call_import_schemas"
-    __table_args__ = (
-        # Case-insensitive uniqueness is enforced via the matching partial
-        # index on ``LOWER(name)`` in the migration; this constraint here
-        # would be case-sensitive and is intentionally omitted to avoid
-        # confusing the user.
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-    created_by_user_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("users.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    parameters = relationship(
-        "CallImportSchemaParameter",
-        back_populates="schema",
-        cascade="all, delete-orphan",
-        order_by="CallImportSchemaParameter.ordering",
-    )
-
-
-class CallImportSchemaParameter(Base):
-    """A single typed parameter inside a :class:`CallImportSchema`.
-
-    ``type`` is one of the strings tracked by
-    :data:`app.models.enums.CallImportParameterType`. ``conversation_id``
-    is reserved for the mandatory identity parameter every schema must
-    contain.
-    """
-
-    __tablename__ = "call_import_schema_parameters"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    schema_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_schemas.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    type = Column(String(32), nullable=False)
-    description = Column(Text, nullable=True)
-    is_required = Column(Boolean, nullable=False, default=False)
-    # Stable ordering so the UI renders parameters in the order the
-    # schema author defined them (matters when conversation_id is pinned
-    # first and the user re-orders the rest).
-    ordering = Column(Integer, nullable=False, default=0)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    schema = relationship("CallImportSchema", back_populates="parameters")
-
-
-class CallImport(Base):
-    """Batch record for a CSV-driven call import job."""
-
-    __tablename__ = "call_imports"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every imported batch belongs to a workspace
-    # within its org. The /upload endpoint stamps it from the active
-    # workspace header (or the org's Default if absent).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    created_by_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True)
-
-    # Telephony provider key (e.g. ``'exotel'``, ``'plivo'``). In the
-    # legacy one-shot ``POST /upload`` endpoint this is supplied with the
-    # file; in the three-stage flow (UPLOAD -> MAP -> IMPORT) the value
-    # isn't known until the IMPORT stage, so the column is nullable for
-    # ``uploaded`` / ``mapped`` batches.
-    provider = Column(String(50), nullable=True, default="exotel")
-    # Pin a specific telephony credential for this batch so the worker
-    # downloads recordings using *that* row instead of the org default.
-    # NULL preserves legacy behavior (resolve by provider + default).
-    telephony_integration_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("telephony_integrations.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-    original_filename = Column(String(512), nullable=True)
-    # When the source file was a multi-sheet Excel workbook, this records
-    # the worksheet the rows came from (one batch per sheet). NULL for CSV
-    # uploads since CSV has no sheet concept.
-    sheet_name = Column(String(255), nullable=True)
-
-    # --- Source-file staging (UPLOAD stage) ---------------------------
-    # The raw CSV / Excel file is stored in S3 between stages so the
-    # user can come back later to MAP and IMPORT without re-uploading.
-    # ``source_s3_key`` is NULL on legacy batches that were imported via
-    # the one-shot endpoint (those batches stay read-only post-import).
-    source_s3_key = Column(Text, nullable=True)
-    source_format = Column(String(16), nullable=True)
-    source_size_bytes = Column(BigInteger, nullable=True)
-    source_content_type = Column(String(255), nullable=True)
-
-    # Snapshot of the file's sheets + headers captured at UPLOAD time
-    # so the MAP UI doesn't need to re-fetch the source bytes from S3.
-    # Shape: ``[{"name": str, "headers": [str, ...], "row_count": int}, ...]``.
-    available_sheets = Column(JSON, nullable=True)
-
-    # User's explicit "drop these columns" decision captured at MAP
-    # time. Was validation-only and ephemeral in the legacy flow; now
-    # persisted so the IMPORT stage can re-parse the file with the same
-    # mapping/skip intent.
-    skipped_columns = Column(JSON, nullable=False, default=list)
-    # Rows skipped at parse time (missing/invalid conversation_id or URL).
-    # Shape: ``[{"source_row": int, "reason": str, "message": str}, ...]``.
-    source_row_skips = Column(JSON, nullable=False, default=list)
-
-    # Free-text high-level segregation label. Powers the "Dataset" filter
-    # at the top of the imports page; multiple imports can share a value.
-    dataset = Column(String(255), nullable=True, index=True)
-
-    # Reusable Input Parameter schema this batch was uploaded against.
-    # NULL on legacy batches uploaded before the schema-driven flow
-    # shipped; those still render via ``column_mapping`` + ``extra_columns``
-    # + ``custom_column_mapping`` below.
-    schema_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_schemas.id", ondelete="RESTRICT"),
-        nullable=True,
-        index=True,
-    )
-    # New schema-driven mapping: ``{schema_parameter_name: csv_header}``.
-    # Populated for new uploads; empty dict on legacy batches.
-    parameter_mapping = Column(JSON, nullable=False, default=dict)
-
-    # Legacy free-form mapping (pre-schema-flow). Kept on the model so
-    # batches that were uploaded before the schema feature shipped still
-    # render correctly on the detail page; new uploads stop writing here.
-    # Keys: external_call_id (required), transcript, recording_url.
-    # (DB column ``external_call_id`` is now ``conversation_id``; this
-    # JSON key stays as-is for historical batches.)
-    # Values: original CSV header strings (preserve user casing for export).
-    column_mapping = Column(JSON, nullable=False, default=dict)
-    # Ordered list of additional CSV header strings the uploader wants
-    # preserved verbatim into the evaluation export CSV.
-    extra_columns = Column(JSON, nullable=False, default=list)
-    # User-defined ``{custom_field_name: csv_header}`` mappings on top of
-    # the three system fields above. Cells from the mapped CSV columns are
-    # preserved per row (keyed by the CSV header in ``raw_columns``) and
-    # surface in the evaluation export under the uploader-chosen name.
-    custom_column_mapping = Column(JSON, nullable=False, default=dict)
-
-    total_rows = Column(Integer, nullable=False, default=0)
-    completed_rows = Column(Integer, nullable=False, default=0)
-    failed_rows = Column(Integer, nullable=False, default=0)
-
-    status = Column(
-        Enum(CallImportStatus, values_callable=get_enum_values),
-        nullable=False,
-        default=CallImportStatus.PENDING,
-        index=True,
-    )
-    error_message = Column(Text, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    rows = relationship(
-        "CallImportRow",
-        back_populates="call_import",
-        cascade="all, delete-orphan",
-        order_by="CallImportRow.row_index",
-    )
-    tags = relationship(
-        "CallImportTag",
-        secondary="call_import_tag_assignments",
-        backref="call_imports",
-        lazy="selectin",
-    )
-    evaluations = relationship(
-        "CallImportEvaluation",
-        back_populates="call_import",
-        cascade="all, delete-orphan",
-    )
-
-
-class CallImportShardSlice(Base):
-    """Registry row: which shard stores a slice of rows for an import."""
-
-    __tablename__ = "call_import_shard_slices"
-
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        primary_key=True,
-    )
-    slice_id = Column(Integer, primary_key=True)
-    shard_id = Column(String(64), nullable=False, index=True)
-    row_index_min = Column(Integer, nullable=False)
-    row_index_max = Column(Integer, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-
-class CallImportRow(Base):
-    """A single row within a CallImport batch (one CSV line / one external call)."""
-
-    __tablename__ = "call_import_rows"
-    __table_args__ = (
-        UniqueConstraint("call_import_id", "row_index", name="uq_call_import_row_index"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    row_index = Column(Integer, nullable=False)
-    # Was historically named ``external_call_id``; renamed to
-    # ``conversation_id`` so the new schema-driven upload flow can refer
-    # to it by a single canonical name across the schema definition,
-    # exports, and downstream evaluation tables.
-    conversation_id = Column(String(255), nullable=False, index=True)
-    # Supplied via CSV for Exotel credentialed imports (required per row).
-    # Nullable in the schema for legacy rows imported before recording_url
-    # was mandatory on every Exotel upload.
-    recording_url = Column(Text, nullable=True)
-    # Date-only call recording date supplied by the import schema. Used
-    # for historical report comparisons without timezone/time ambiguity.
-    recording_date = Column(Date, nullable=True, index=True)
-    # The "production" transcript: the value supplied via the CSV
-    # upload mapping. Never overwritten by the diarisation worker —
-    # the worker writes its output into ``diarised_transcript`` so
-    # the user keeps both versions side by side.
-    transcript = Column(Text, nullable=True)
-    # Snapshot of the original CSV row keyed by the user's headers so the
-    # evaluation export can reproduce every column the uploader supplied
-    # (mapped + extra). NULL on legacy rows imported before this column.
-    raw_columns = Column(JSON, nullable=True)
-
-    # Where the value in ``transcript`` came from. ``csv`` = supplied via
-    # the upload mapping, ``edited`` = manually changed in the UI. NULL
-    # on rows that have never had a production transcript.
-    # (Worker-produced transcripts now live in ``diarised_transcript``
-    # and are tracked via ``diarised_transcript_*`` metadata below.)
-    transcript_source = Column(String(20), nullable=True)
-    # Provider/model recorded by the (legacy) post-hoc transcription
-    # worker. New worker runs leave these NULL and write into the
-    # ``diarised_transcript_*`` columns instead; kept on the model for
-    # backwards compatibility with pre-split rows that still carry the
-    # original transcription metadata here.
-    transcript_provider = Column(String(50), nullable=True)
-    transcript_model = Column(String(100), nullable=True)
-    # Lifecycle status for the legacy transcription workflow itself,
-    # independent of the row's recording-fetch ``status``. ``idle`` =
-    # no transcribe task has touched this column. New diarisation runs
-    # update ``diarised_transcript_status`` instead.
-    transcript_status = Column(
-        String(20),
-        nullable=False,
-        default="idle",
-    )
-    transcript_error = Column(Text, nullable=True)
-    transcribed_at = Column(DateTime(timezone=True), nullable=True)
-
-    # The "diarised" transcript: produced by the post-hoc
-    # transcription/diarisation worker. Stored separately so a manual
-    # diarisation run never clobbers the production transcript above.
-    # Evaluations can be configured to score against either column
-    # (see ``CallImportEvaluation.transcript_source``).
-    diarised_transcript = Column(Text, nullable=True)
-    # Provider/model the diarisation worker used. Surfaced in the UI
-    # as "Diarised via deepgram/nova-2" next to the diarised
-    # transcript section.
-    diarised_transcript_provider = Column(String(50), nullable=True)
-    diarised_transcript_model = Column(String(100), nullable=True)
-    # Lifecycle status for the diarisation workflow.
-    # ``idle`` = no diarisation task has run; ``pending``/``running`` =
-    # a Celery task is queued or in flight; ``completed``/``failed`` =
-    # terminal. Independent of ``transcript_status`` so the two
-    # transcripts can be in different lifecycle states.
-    diarised_transcript_status = Column(
-        String(20),
-        nullable=False,
-        default="idle",
-        server_default="idle",
-    )
-    diarised_transcript_error = Column(Text, nullable=True)
-    diarised_at = Column(DateTime(timezone=True), nullable=True)
-
-    # Structured speaker turns produced by the diarisation worker —
-    # ``[{ "speaker": "agent"|"user"|"speaker_3", "text": "...",
-    #      "start": float, "end": float, "raw_speaker": "Speaker 1" }, ...]``
-    # The plain-text ``diarised_transcript`` above is a rendered view
-    # of this list (``<speaker>: <text>`` per line). When the worker
-    # cannot recover structured turns (no pyannote token / single-
-    # speaker recording / provider that doesn't surface segments) this
-    # column stays NULL and the plain-text path is still populated.
-    diarised_segments = Column(JSON, nullable=True)
-    # When True the ``agent`` <-> ``user`` mapping inside
-    # ``diarised_segments`` is inverted at render / export time. The
-    # worker writes the canonical mapping using the "first speaker is
-    # the agent" heuristic; reviewers can flip the toggle from the row
-    # detail panel without re-running diarisation.
-    diarised_speaker_swap = Column(
-        Boolean,
-        nullable=False,
-        default=False,
-        server_default="false",
-    )
-    # LLM that turned the STT plain-text output into structured
-    # ``diarised_segments``. The legacy diarisation worker used
-    # pyannote and left these NULL; the current path always runs an
-    # LLM with the operator-supplied (or default) ``diarised_prompt``
-    # below, and records exactly which model + prompt produced each
-    # row so reviewers can reproduce a specific run.
-    diarised_llm_provider = Column(String(50), nullable=True)
-    diarised_llm_model = Column(String(100), nullable=True)
-    diarised_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
-    diarised_prompt = Column(Text, nullable=True)
-    # Which diarisation pipeline produced this row's turns.
-    #   * ``"stt_llm"`` (default) — two-stage: STT then LLM diariser.
-    #     ``diarised_transcript_provider``/``_model`` describe the STT
-    #     side; ``diarised_llm_provider``/``_model`` the LLM side.
-    #   * ``"llm_only"`` — single-stage: audio fed straight to a
-    #     multimodal LLM. ``diarised_transcript_provider`` is stamped
-    #     with the sentinel ``"llm_only"``; the real model is on
-    #     ``diarised_llm_*``.
-    # Persisting it on the row (not just the run) lets the row detail
-    # panel render the right "Diarised via …" label even for ad-hoc
-    # standalone transcribes (no parent evaluation).
-    transcribe_mode = Column(
-        String(20),
-        nullable=False,
-        default="stt_llm",
-        server_default="stt_llm",
-    )
-
-    status = Column(
-        Enum(CallImportRowStatus, values_callable=get_enum_values),
-        nullable=False,
-        default=CallImportRowStatus.PENDING,
-        index=True,
-    )
-
-    recording_s3_key = Column(String(1024), nullable=True)
-    recording_content_type = Column(String(128), nullable=True)
-    recording_size_bytes = Column(Integer, nullable=True)
-
-    error_message = Column(Text, nullable=True)
-    attempts = Column(Integer, nullable=False, default=0)
-    celery_task_id = Column(String(255), nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    call_import = relationship("CallImport", back_populates="rows")
-
-
-@event.listens_for(CallImportRow, "before_insert")
-def _call_import_row_fill_workspace_id(_mapper, connection, target):
-    """Denormalize workspace_id from the parent import when omitted."""
-    if target.workspace_id is not None or target.call_import_id is None:
-        return
-    workspace_id = connection.execute(
-        select(CallImport.workspace_id).where(
-            CallImport.id == target.call_import_id
-        )
-    ).scalar_one_or_none()
-    if workspace_id is not None:
-        target.workspace_id = workspace_id
-
-
-class CallImportTag(Base):
-    """User-defined tag that can be attached to one or more call imports.
-
-    Tags coexist with the free-text ``CallImport.dataset`` column: dataset
-    is the primary high-level segregation, tags are an optional secondary
-    classification (an import can have many tags).
-    """
-
-    __tablename__ = "call_import_tags"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "name", name="uq_call_import_tag_org_name"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    name = Column(String(255), nullable=False)
-    color = Column(String(32), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-
-class CallImportTagAssignment(Base):
-    """Many-to-many join table between CallImport and CallImportTag."""
-
-    __tablename__ = "call_import_tag_assignments"
-
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        primary_key=True,
-    )
-    tag_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_tags.id", ondelete="CASCADE"),
-        primary_key=True,
-        index=True,
-    )
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-
-class CallImportEvaluation(Base):
-    """Parent record for an evaluation run over a CallImport batch.
-
-    A user picks a subset of org ``Metric`` rows and triggers an evaluation;
-    we fan out one ``CallImportEvaluationRow`` per source row and roll up
-    counters as workers finish. Status mirrors ``CallImportStatus`` plus a
-    ``RUNNING`` value so the UI can distinguish "queued" from "in flight".
-    """
-
-    __tablename__ = "call_import_evaluations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id"),
-        nullable=False,
-        index=True,
-    )
-    # Workspace isolation: mirrors the parent CallImport's workspace.
-    # Denormalized for fast filter-by-workspace listings without a join.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    created_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id"), nullable=True
-    )
-
-    # Optional user-supplied label for this run. Lets the UI surface
-    # something more meaningful than the UUID prefix (e.g. "March QA pass").
-    name = Column(String(255), nullable=True)
-
-    # JSON list of Metric UUID strings selected for this run. Stored as text
-    # in JSON so we don't have to deal with PG arrays of UUIDs / cascade
-    # delete policies when metrics are removed; the loader filters for
-    # still-existing org metrics at run time.
-    selected_metric_ids = Column(JSON, nullable=False, default=list)
-    # Hierarchy grouping snapshot: ``{parent_id_str: [child_id_str, ...]}``.
-    # Captures which children belong to which parent for THIS run so the UI
-    # / aggregator can reconstruct the tree even when the user selected
-    # only a subset of children, or after metrics are deleted / renamed.
-    # NULL on legacy rows means "no hierarchy" → fall back to flat
-    # ``selected_metric_ids`` semantics.
-    selected_metric_groups = Column(JSON, nullable=True)
-    # User-driven merges of LLM-discovered candidate sub-labels for
-    # ``allow_discovery`` parents. Shape:
-    # ``{"<parent_metric_id>": {"<from_slug>": "<to_slug>", ...}}``.
-    # Populated via ``POST .../discovered-labels/merge``; consulted by
-    # the discovered-labels aggregator, the flow graph builder, and the
-    # worker so that rows finishing AFTER a merge cannot reintroduce
-    # the merged-away slug. Empty dict on fresh rows.
-    discovered_label_aliases = Column(
-        JSON, nullable=False, default=dict, server_default="{}"
-    )
-
-    # Per-run opt-in for top-level metric discovery. When True, the LLM
-    # is asked to propose brand-new top-level metrics (boolean / rating /
-    # category) observed in the transcripts in addition to scoring the
-    # ``selected_metric_ids`` for the row. Candidates surface in a
-    # "Discovered metrics" panel on the evaluation's Flow tab and can
-    # be promoted into real standalone ``Metric`` rows via
-    # ``POST /metrics/from-discovered``. Defaults to False so existing
-    # evaluation creation payloads keep their previous behaviour.
-    discover_new_metrics = Column(
-        Boolean, nullable=False, default=False, server_default="false"
-    )
-    # Flat slug-to-slug redirect map for user merges + tombstones of
-    # discovered top-level metric candidates. Mirrors
-    # ``discovered_label_aliases`` but is NOT nested per parent —
-    # top-level metric discovery is not scoped to any parent. Shape::
-    #
-    #     {"<from_slug>": "<to_slug>", ...}
-    #
-    # An empty-string value tombstones the slug so workers finishing
-    # later can't re-introduce it.
-    discovered_metric_aliases = Column(
-        JSON, nullable=False, default=dict, server_default="{}"
-    )
-
-    # Run-level LLM config picked from the Run Evaluation modal. NULL on
-    # legacy rows means "use the historical OpenAI/gpt-4o default" — the
-    # worker checks for this and falls back accordingly. ``llm_credential_id``
-    # pins a specific AIProvider row when the org has multiple credentials
-    # for the same provider.
-    llm_provider = Column(String(50), nullable=True)
-    llm_model = Column(String(100), nullable=True)
-    llm_credential_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("aiproviders.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-    llm_config = Column(JSON, nullable=True)
-    # Optional per-metric LLM override:
-    # ``{"<metric_id>": {"provider": "...", "model": "...", "credential_id": "..."}}``.
-    # Each entry overrides the run-level default for that metric only;
-    # missing keys = use run-level default. Stored as JSON so the UI can
-    # round-trip arbitrary {provider, model} pairs without migrations.
-    metric_llm_overrides = Column(JSON, nullable=True)
-
-    # When ``auto_transcribe`` was set on the create payload, record the
-    # STT provider/model used so the UI can show "Auto-transcribed via
-    # deepgram/nova-2" on the evaluation header. ``stt_credential_id`` is
-    # untyped (no FK) because STT keys may live in either ``aiproviders``
-    # (OpenAI) or ``integrations`` (Deepgram, ElevenLabs) — the
-    # transcription service handles the lookup.
-    stt_provider = Column(String(50), nullable=True)
-    stt_model = Column(String(100), nullable=True)
-    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    # Run-level LLM diariser config. Used when the create-run /
-    # retry-run paths chain a ``transcribe_call_import_row_task``
-    # because the row is missing a diarised transcript. Persisted on
-    # the run so a retry uses the same diariser the original create
-    # call picked (unless the retry payload explicitly overrides).
-    diarisation_llm_provider = Column(String(50), nullable=True)
-    diarisation_llm_model = Column(String(100), nullable=True)
-    diarisation_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
-    diarisation_prompt = Column(Text, nullable=True)
-    # Mode the run was *created* with for its auto-transcribe step.
-    # Retry chains read this to decide whether to enqueue an STT+LLM
-    # transcribe or a single-stage multimodal LLM transcribe — without
-    # it we'd have to infer the mode from "stt_provider is NULL", which
-    # would silently break legacy rows that simply never configured
-    # auto-transcribe. See migration 041 for the column DDL.
-    transcribe_mode = Column(
-        String(20),
-        nullable=False,
-        default="stt_llm",
-        server_default="stt_llm",
-    )
-
-    # Which of the two transcripts on each ``CallImportRow`` this run
-    # scored against. ``'production'`` reads ``CallImportRow.transcript``
-    # (the CSV-supplied value); ``'diarised'`` reads
-    # ``CallImportRow.diarised_transcript`` (the worker output). When
-    # the user ticks both checkboxes in the Run Evaluation modal we
-    # create two ``CallImportEvaluation`` rows — one per source — so
-    # the two scorings can be compared side-by-side. Defaults to
-    # ``'production'`` so legacy runs (which always read the single
-    # historical ``transcript`` column) keep their semantics.
-    transcript_source = Column(
-        String(20),
-        nullable=False,
-        default="production",
-        server_default="production",
-    )
-
-    # Cached LLM-generated TLDR rendered above the Visualizations charts.
-    # Populated lazily by ``POST /evaluations/{eval_id}/insights`` so we
-    # never auto-burn LLM tokens on page load. Shape::
-    #   {"narrative": str, "patterns": [str, ...],
-    #    "generated_at": iso8601, "generated_at_completed_rows": int,
-    #    "provider": str, "model": str}
-    # NULL on rows that have never been summarised.
-    tldr_summary = Column(JSON, nullable=True)
-
-    # Cached LLM-generated user insights for External Audit PDF section 03.
-    # Populated by a background Celery job triggered alongside TLDR generation.
-    # Shape: EvaluationUserInsightsState JSON (status, insights[], progress, …).
-    user_insights = Column(JSON, nullable=True)
-
-    # Cached per-metric failure clustering for internal diagnostics PDF/UI.
-    # Shape: EvaluationMetricClustersState JSON (status, groups[], …).
-    metric_clusters = Column(JSON, nullable=True)
-
-    # Cached LLM-generated prompt improvement suggestions keyed to an
-    # imported agent (PromptPartial tagged __imported_agent__).
-    # Shape: EvaluationPromptImprovementsState JSON.
-    prompt_improvements = Column(JSON, nullable=True)
-
-    # Cached LLM explanations for week-over-week metric deltas keyed by
-    # baseline evaluation id + completed row counts.
-    period_delta_explanations = Column(JSON, nullable=True)
-
-    status = Column(String(20), nullable=False, default="pending", index=True)
-
-    total_rows = Column(Integer, nullable=False, default=0)
-    completed_rows = Column(Integer, nullable=False, default=0)
-    failed_rows = Column(Integer, nullable=False, default=0)
-    # Flexprice pass-level delta billing watermark: rows already emitted
-    # on ``call_import.evaluation_completed`` for this evaluation run.
-    billed_completed_rows = Column(
-        Integer, nullable=False, default=0, server_default="0"
-    )
-    error_message = Column(Text, nullable=True)
-    celery_group_id = Column(String(255), nullable=True)
-
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    finished_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    call_import = relationship("CallImport", back_populates="evaluations")
-    row_results = relationship(
-        "CallImportEvaluationRow",
-        back_populates="evaluation",
-        cascade="all, delete-orphan",
-    )
-
-
-class CallImportEvaluationRow(Base):
-    """Per-source-row scoring output for a CallImportEvaluation parent."""
-
-    __tablename__ = "call_import_evaluation_rows"
-    __table_args__ = (
-        UniqueConstraint(
-            "evaluation_id", "call_import_row_id", name="uq_call_import_evaluation_row"
-        ),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    call_import_row_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_rows.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    status = Column(String(20), nullable=False, default="pending", index=True)
-    # Same shape as EvaluatorResult.metric_scores: {metric_id_str: {value, type, metric_name, ...}}
-    metric_scores = Column(JSON, nullable=False, default=dict)
-    error_message = Column(Text, nullable=True)
-    celery_task_id = Column(String(255), nullable=True)
-
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    finished_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    evaluation = relationship("CallImportEvaluation", back_populates="row_results")
-    source_row = relationship("CallImportRow")
-
-
-@event.listens_for(CallImportEvaluationRow, "before_insert")
-def _call_import_evaluation_row_fill_workspace_id(_mapper, connection, target):
-    """Denormalize workspace_id from the parent evaluation when omitted."""
-    if target.workspace_id is not None or target.evaluation_id is None:
-        return
-    workspace_id = connection.execute(
-        select(CallImportEvaluation.workspace_id).where(
-            CallImportEvaluation.id == target.evaluation_id
-        )
-    ).scalar_one_or_none()
-    if workspace_id is not None:
-        target.workspace_id = workspace_id
-
-
-class CallImportEvaluationReportSnapshot(Base):
-    """Persisted PDF-report aggregate used for period-over-period deltas."""
-
-    __tablename__ = "call_import_evaluation_report_snapshots"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    period_label = Column(String(64), nullable=True, index=True)
-    period_start = Column(Date, nullable=True, index=True)
-    period_end = Column(Date, nullable=True, index=True)
-    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
-    selected_metric_ids = Column(JSON, nullable=False, default=list, server_default="[]")
-    metric_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
-    insight_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
-    narrative = Column(JSON, nullable=True)
-    total_calls = Column(Integer, nullable=False, default=0)
-    selected_metric_count = Column(Integer, nullable=False, default=0)
-    total_metric_count = Column(Integer, nullable=False, default=0)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-
-# ---------------------------------------------------------------------------
-# Judge Alignment (AlignEval-style hybrid integration)
-#
-# Three tables back the "Judge Alignment" surface:
-#   - JudgeDataset:     a labeled dataset materialised from one of three sources
-#                       (voice transcripts, existing Metric/Evaluator outputs,
-#                       or a generic CSV upload). Holds the dataset's source
-#                       config + which fields play the role of input/output.
-#   - JudgeSample:      one row in a dataset (input/output pair plus an
-#                       optional binary pass/fail human label).
-#   - JudgeRun:         a single run of an LLM-judge (existing Evaluator) over
-#                       a subset of samples, with computed alignment metrics
-#                       (precision/recall/F1/Cohen's kappa) and per-sample
-#                       predictions. Optionally links to a GEPA optimization
-#                       run when the user kicks off prompt tuning from a
-#                       dataset.
-# ---------------------------------------------------------------------------
-
-
-class JudgeDataset(Base):
-    """Container for binary-labeled samples used to calibrate an LLM-judge."""
-
-    __tablename__ = "judge_datasets"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    # Workspace isolation: every judge dataset belongs to a workspace
-    # within its org. Samples and runs inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    name = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-
-    # One of: "transcript", "metric_output", "csv"
-    source_type = Column(String(32), nullable=False, index=True)
-    # Source-specific config. Examples:
-    #   transcript:     {"transcription_ids": [...]} or {"agent_id": "..."}
-    #   metric_output:  {"metric_id": "...", "evaluator_id": "..."}
-    #   csv:            {"s3_key": "...", "filename": "..."}
-    source_config = Column(JSON, nullable=False, default=dict)
-
-    # Field roles - which textual content is "input" vs "output" for the judge.
-    # For voice transcripts both default to the transcript text but can be
-    # tightened (e.g. agent-only turns vs full conversation).
-    input_field = Column(String(64), nullable=False, default="input")
-    output_field = Column(String(64), nullable=False, default="output")
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    samples = relationship(
-        "JudgeSample",
-        back_populates="dataset",
-        cascade="all, delete-orphan",
-        order_by="JudgeSample.created_at",
-    )
-    runs = relationship(
-        "JudgeRun",
-        back_populates="dataset",
-        cascade="all, delete-orphan",
-        order_by="JudgeRun.created_at.desc()",
-    )
-
-
-class JudgeSample(Base):
-    """One labelable input/output pair within a JudgeDataset."""
-
-    __tablename__ = "judge_samples"
-    __table_args__ = (
-        UniqueConstraint("dataset_id", "external_id", name="uq_judge_samples_dataset_external"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    dataset_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    # Stable identifier within the source (e.g. transcription UUID, CSV row id).
-    # Used to dedupe re-imports and link back to the originating record.
-    external_id = Column(String(128), nullable=True, index=True)
-
-    input_text = Column(Text, nullable=False)
-    output_text = Column(Text, nullable=False)
-
-    # Binary human label: "pass" | "fail" | null (unlabeled).
-    # Stored as string (rather than enum) so it stays trivially extendable.
-    label = Column(String(16), nullable=True, index=True)
-    labeled_by = Column(String(255), nullable=True)
-    labeled_at = Column(DateTime(timezone=True), nullable=True)
-
-    # Source-specific context (e.g. agent_id, original metric value, csv row).
-    extra = Column(JSON, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    dataset = relationship("JudgeDataset", back_populates="samples")
-
-
-class JudgeRun(Base):
-    """One execution of an LLM-judge against a JudgeDataset, with alignment metrics."""
-
-    __tablename__ = "judge_runs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    dataset_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    # Reuses the existing Evaluator row (its custom_prompt + llm_provider + llm_model
-    # define the judge under test). Nullable so a run may target an inline prompt
-    # in the future without inflating the Evaluator table.
-    evaluator_id = Column(
-        UUID(as_uuid=True), ForeignKey("evaluators.id", ondelete="SET NULL"), nullable=True, index=True
-    )
-
-    # Which subset was scored: "all" | "dev" | "test"
-    split = Column(String(16), nullable=False, default="all")
-
-    # Snapshot of the model used (so a later Evaluator edit doesn't rewrite history).
-    llm_provider = Column(String(64), nullable=True)
-    llm_model = Column(String(128), nullable=True)
-
-    # Computed alignment metrics:
-    #   {"precision": float, "recall": float, "f1": float, "kappa": float,
-    #    "tp": int, "fp": int, "tn": int, "fn": int, "n": int}
-    metrics = Column(JSON, nullable=True)
-
-    # Per-sample predictions, keyed by sample_id (UUID string):
-    #   {sample_id: {"prediction": "pass"|"fail", "explanation": str, "raw": str}}
-    predictions = Column(JSON, nullable=True)
-
-    # Run lifecycle.
-    status = Column(String(20), nullable=False, default="pending", index=True)
-    error_message = Column(Text, nullable=True)
-    celery_task_id = Column(String, nullable=True, index=True)
-
-    # Optional link to a GEPA optimization run kicked off from this dataset.
-    gepa_optimization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("prompt_optimization_runs.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    dataset = relationship("JudgeDataset", back_populates="runs")
+"""SQLAlchemy database models."""
+
+from sqlalchemy import (
+    BigInteger,
+    Boolean,
+    Column,
+    Date,
+    DateTime,
+    DDL,
+    Enum,
+    event,
+    Float,
+    ForeignKey,
+    Integer,
+    JSON,
+    String,
+    Text,
+    UniqueConstraint,
+    select,
+    text,
+)
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import relationship
+from sqlalchemy.sql import func
+import uuid
+import enum
+from app.models.enums import (
+    EvaluationType, EvaluationStatus, EvaluatorResultStatus, RoleEnum, InvitationStatus,
+    LanguageEnum, CallTypeEnum, CallMediumEnum, GenderEnum, AccentEnum, BackgroundNoiseEnum,
+    IntegrationPlatform, ModelProvider, VoiceBundleType, TestAgentConversationStatus,
+    MetricType, MetricCategory, MetricTrigger, CallRecordingStatus, AlertMetricType, AlertAggregation,
+    AlertOperator, AlertNotifyFrequency, AlertStatus, AlertHistoryStatus, CronJobStatus,
+    PromptOptimizationStatus, CallImportStatus, CallImportRowStatus,
+)
+
+def get_enum_values(enum_class):
+    """Helper to get values from enum class for SQLAlchemy."""
+    return [e.value for e in enum_class]
+
+from app.database import Base
+
+
+# Enums moved to enums.py
+
+
+class Organization(Base):
+    """Organization model for multi-tenancy."""
+
+    __tablename__ = "organizations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    name = Column(String(255), nullable=False)
+    voice_playground_threshold_overrides = Column(JSON, nullable=True)
+    # AlignEval-style judge alignment thresholds.
+    # Shape: {"min_labels_to_evaluate": int, "min_labels_to_optimize": int}
+    # Falls back to system defaults (20 / 50) when null.
+    judge_alignment_settings = Column(JSON, nullable=True)
+    # Per-org LLM gateway overrides (enabled, gateway_type, base_url, keys).
+    llm_gateway_settings = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    # Relationships
+    api_keys = relationship("APIKey", back_populates="organization")
+    members = relationship("OrganizationMember", back_populates="organization", cascade="all, delete-orphan")
+    invitations = relationship("Invitation", back_populates="organization", cascade="all, delete-orphan")
+    workspaces = relationship(
+        "Workspace",
+        back_populates="organization",
+        cascade="all, delete-orphan",
+    )
+    workspace_roles = relationship(
+        "WorkspaceRole",
+        back_populates="organization",
+        cascade="all, delete-orphan",
+    )
+
+
+class Workspace(Base):
+    """Workspace - in-org isolation boundary for call imports and metrics.
+
+    Every organization has at least one workspace (``is_default = True``,
+    seeded by migration 033). Users pick an "active workspace" in the UI;
+    list endpoints filter by it so users only see calls/metrics from the
+    project they're currently working in. Access is governed by
+    ``workspace_members`` and org-scoped ``workspace_roles`` (capability
+    bundles); org admins implicitly access all workspaces.
+    """
+
+    __tablename__ = "workspaces"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "slug", name="uq_workspaces_org_slug"),
+    )
+
+    # ``server_default`` is required so that raw-SQL INSERTs (e.g. the
+    # per-org Default seed in migration 033) can omit ``id`` and let the
+    # database fill it in. Without it, ``create_all`` produces a column
+    # with NOT NULL but no DEFAULT, and the migration crashes with
+    # ``null value in column "id"``.
+    id = Column(
+        UUID(as_uuid=True),
+        primary_key=True,
+        default=uuid.uuid4,
+        server_default=text("gen_random_uuid()"),
+    )
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    slug = Column(String(255), nullable=False)
+    # At most one default per org. Enforced on Postgres by the partial
+    # unique index attached via the after_create event below; on
+    # SQLite (test runs) we rely on the route-level _check_slug_unique
+    # check + the Default-workspace conftest fixture instead, because
+    # SQLite doesn't support partial indexes the same way.
+    is_default = Column(Boolean, nullable=False, default=False, server_default="false")
+    # Reusable PDF/report branding metadata scoped to this workspace. Images
+    # live in S3. Shape: {"heading": str|null, "images": [{id, s3_key,
+    # content_type, filename, size_bytes, updated_at}, ...]}.
+    report_branding = Column(JSON, nullable=True)
+    created_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    organization = relationship("Organization", back_populates="workspaces")
+    members = relationship(
+        "WorkspaceMember",
+        back_populates="workspace",
+        cascade="all, delete-orphan",
+    )
+
+
+class WorkspaceRole(Base):
+    """Org-scoped workspace role (system or custom) as a capability bundle."""
+
+    __tablename__ = "workspace_roles"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "name", name="uq_workspace_roles_org_name"),
+    )
+
+    id = Column(
+        UUID(as_uuid=True),
+        primary_key=True,
+        default=uuid.uuid4,
+        server_default=text("gen_random_uuid()"),
+    )
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+    capabilities = Column(JSON, nullable=False, default=list)
+    is_system = Column(Boolean, nullable=False, default=False, server_default="false")
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    organization = relationship("Organization", back_populates="workspace_roles")
+    members = relationship("WorkspaceMember", back_populates="role")
+
+
+class WorkspaceMember(Base):
+    """User membership in a workspace with an assigned workspace role."""
+
+    __tablename__ = "workspace_members"
+    __table_args__ = (
+        UniqueConstraint("workspace_id", "user_id", name="uq_workspace_members_ws_user"),
+    )
+
+    id = Column(
+        UUID(as_uuid=True),
+        primary_key=True,
+        default=uuid.uuid4,
+        server_default=text("gen_random_uuid()"),
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    user_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("users.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    role_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspace_roles.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    added_by_user_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("users.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    workspace = relationship("Workspace", back_populates="members")
+    user = relationship("User", foreign_keys=[user_id])
+    role = relationship("WorkspaceRole", back_populates="members")
+    added_by = relationship("User", foreign_keys=[added_by_user_id])
+
+
+# Partial unique index: "at most one default workspace per org". This
+# is attached as an after_create event (rather than declared in
+# ``__table_args__``) because SQLAlchemy's ``Index(...,
+# postgresql_where=...)`` silently degrades to a *full* unique index on
+# SQLite - which then forbids any second workspace per org and breaks
+# the test suite. ``execute_if(dialect="postgresql")`` makes this DDL
+# a no-op on SQLite while still emitting it on Postgres (prod, CI).
+event.listen(
+    Workspace.__table__,
+    "after_create",
+    DDL(
+        "CREATE UNIQUE INDEX IF NOT EXISTS uq_workspaces_org_default "
+        "ON workspaces (organization_id) WHERE is_default"
+    ).execute_if(dialect="postgresql"),
+)
+
+
+class User(Base):
+    """User model for authentication and profile management."""
+
+    __tablename__ = "users"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    email = Column(String(255), unique=True, nullable=False, index=True)
+    name = Column(String(255), nullable=True)
+    first_name = Column(String(255), nullable=True)
+    last_name = Column(String(255), nullable=True)
+    password_hash = Column(String(255), nullable=True)  # Nullable for users created via invitation
+    external_id = Column(String(255), unique=True, nullable=True, index=True)
+    auth_provider = Column(String(50), nullable=True)
+    mfa_enabled = Column(Boolean, default=False, nullable=False)
+    last_login_at = Column(DateTime(timezone=True), nullable=True)
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    # Relationships
+    organization_memberships = relationship("OrganizationMember", back_populates="user", cascade="all, delete-orphan")
+    api_keys = relationship("APIKey", back_populates="user")
+    invitations = relationship("Invitation", back_populates="invited_user", foreign_keys="Invitation.invited_user_id")
+    refresh_tokens = relationship("RefreshToken", back_populates="user", cascade="all, delete-orphan")
+
+
+class RefreshToken(Base):
+    """Opaque refresh token for extending local-password sessions."""
+
+    __tablename__ = "refresh_tokens"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id", ondelete="CASCADE"), nullable=False, index=True)
+    token_hash = Column(String(64), unique=True, nullable=False, index=True)
+    expires_at = Column(DateTime(timezone=True), nullable=False)
+    revoked_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    user = relationship("User", back_populates="refresh_tokens")
+
+
+class OrganizationMember(Base):
+    """Organization membership with role."""
+
+    __tablename__ = "organization_members"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False, index=True)
+    role = Column(String, nullable=False, default=RoleEnum.READER.value)
+    
+    # User preferences for this organization
+    default_agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
+
+    joined_at = Column(DateTime(timezone=True), server_default=func.now())
+    
+    # Unique constraint: one membership per user per organization
+    __table_args__ = (
+        UniqueConstraint('organization_id', 'user_id', name='uq_org_user'),
+    )
+
+    # Relationships
+    organization = relationship("Organization", back_populates="members")
+    user = relationship("User", back_populates="organization_memberships")
+    default_agent = relationship("Agent", foreign_keys=[default_agent_id])
+
+
+class Invitation(Base):
+    """Invitation model for inviting users to organizations."""
+
+    __tablename__ = "invitations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    invited_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Null if user doesn't exist yet
+    invited_by_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False)
+    email = Column(String(255), nullable=False)  # Email of invited user
+    role = Column(String, nullable=False, default=RoleEnum.READER.value)
+    status = Column(String, nullable=False, default=InvitationStatus.PENDING.value)
+
+
+
+    token = Column(String(255), unique=True, nullable=False, index=True)  # Invitation token
+    expires_at = Column(DateTime(timezone=True), nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    accepted_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Relationships
+    organization = relationship("Organization", back_populates="invitations")
+    invited_user = relationship("User", foreign_keys=[invited_user_id], back_populates="invitations")
+    invited_by = relationship("User", foreign_keys=[invited_by_id])
+
+
+class APIKey(Base):
+    """API Key model for authentication."""
+
+    __tablename__ = "api_keys"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    key = Column(String(255), unique=True, nullable=False, index=True)
+    name = Column(String(255), nullable=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Optional: link to user
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    last_used = Column(DateTime(timezone=True), nullable=True)
+
+    # Relationships
+    organization = relationship("Organization", back_populates="api_keys")
+    user = relationship("User", back_populates="api_keys")
+
+
+class AudioFile(Base):
+    """Audio file model."""
+
+    __tablename__ = "audio_files"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    filename = Column(String(255), nullable=False)
+    file_path = Column(String(512), nullable=False)
+    file_size = Column(Integer, nullable=False)  # Size in bytes
+    duration = Column(Float, nullable=True)  # Duration in seconds
+    sample_rate = Column(Integer, nullable=True)
+    channels = Column(Integer, nullable=True)
+    format = Column(String(10), nullable=False)  # wav, mp3, flac, etc.
+    uploaded_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    # Relationships
+    evaluations = relationship("Evaluation", back_populates="audio_file")
+
+
+class Evaluation(Base):
+    """Evaluation job model."""
+
+    __tablename__ = "evaluations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every legacy audio evaluation belongs to a
+    # workspace within its org. Stamped from the X-Workspace-Id header
+    # (falling back to the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    audio_id = Column(UUID(as_uuid=True), ForeignKey("audio_files.id"), nullable=False)
+    reference_text = Column(String, nullable=True)  # For WER calculation
+    evaluation_type = Column(String, nullable=False)
+    model_name = Column(String(100), nullable=True)
+    status = Column(String, default=EvaluationStatus.PENDING.value, nullable=False)
+
+
+
+    metrics_requested = Column(JSON, nullable=True)  # List of requested metrics
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    completed_at = Column(DateTime(timezone=True), nullable=True)
+    error_message = Column(String, nullable=True)
+
+    # Relationships
+    audio_file = relationship("AudioFile", back_populates="evaluations")
+    result = relationship("EvaluationResult", back_populates="evaluation", uselist=False)
+
+
+class EvaluationResult(Base):
+    """Evaluation result model."""
+
+    __tablename__ = "evaluation_results"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(UUID(as_uuid=True), ForeignKey("evaluations.id"), nullable=False, unique=True)
+    # Workspace isolation: mirrors the parent Evaluation's workspace.
+    # Denormalized for fast filter-by-workspace listings without a join.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    transcript = Column(String, nullable=True)
+    metrics = Column(JSON, nullable=False)  # {"wer": 0.05, "latency_ms": 1250, ...}
+    raw_output = Column(JSON, nullable=True)  # Full model output
+    processing_time = Column(Float, nullable=True)  # Processing time in seconds
+    model_used = Column(String(100), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    # Relationships
+    evaluation = relationship("Evaluation", back_populates="result")
+
+
+# ============================================
+# VAIOPS MODELS - Voice AI Ops
+# ============================================
+
+# Enums moved to enums.py
+
+
+class Agent(Base):
+    """Test Agent - The voice AI agent being evaluated"""
+    __tablename__ = "agents"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    agent_id = Column(String(6), unique=True, nullable=True, index=True)  # 6-digit ID
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every agent belongs to a workspace within its
+    # org. Stamped from the X-Workspace-Id header (falling back to the
+    # org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String, nullable=False)
+    phone_number = Column(String, nullable=True)  # Optional, required only for phone_call
+    language = Column(String, nullable=False, default=LanguageEnum.ENGLISH.value)
+    description = Column(String)
+    provider_prompt = Column(Text, nullable=True)
+    provider_prompt_synced_at = Column(DateTime(timezone=True), nullable=True)
+    call_type = Column(String, nullable=False, default=CallTypeEnum.OUTBOUND.value)
+    call_medium = Column(String, nullable=False, default=CallMediumEnum.PHONE_CALL.value)
+    telephony_phone_number_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("telephony_phone_numbers.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+
+
+    
+    # Voice configuration - either voice_bundle_id OR ai_provider_id OR voice_ai_integration_id (mutually exclusive)
+    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True, index=True)
+    ai_provider_id = Column(UUID(as_uuid=True), ForeignKey("aiproviders.id"), nullable=True, index=True)
+    
+    # Voice AI agent integration (Retell, Vapi, etc.)
+    voice_ai_integration_id = Column(UUID(as_uuid=True), ForeignKey("integrations.id"), nullable=True, index=True)
+    voice_ai_agent_id = Column(String, nullable=True)  # Agent ID from the external provider (Retell/Vapi)
+    prompt_variables = Column(JSON, nullable=True)
+    silence_hangup_secs = Column(Integer, nullable=False, server_default="15")
+    
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+    created_by = Column(String)
+
+
+class Persona(Base):
+    """Persona - TTS provider-tied voice identity for testing"""
+    __tablename__ = "personas"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every persona belongs to a workspace within
+    # its org. Stamped from the X-Workspace-Id header (falling back to
+    # the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String, nullable=False)
+    gender = Column(String, nullable=False, default=GenderEnum.NEUTRAL.value)
+    tts_provider = Column(String(100), nullable=True)
+    tts_voice_id = Column(String(255), nullable=True)
+    tts_voice_name = Column(String(255), nullable=True)
+    is_custom = Column(Boolean, default=False)
+    description = Column(Text, nullable=True)
+    tts_config = Column(JSON, nullable=True)
+    llm_temperature = Column(Float, nullable=True)
+    llm_max_tokens = Column(Integer, nullable=True)
+    response_delay_ms = Column(Integer, nullable=True)
+    max_turns = Column(Integer, nullable=True)
+    allow_interruptions = Column(Boolean, nullable=True)
+
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+    created_by = Column(String)
+
+
+class Scenario(Base):
+    """Scenario - The conversation scenario/test case"""
+    __tablename__ = "scenarios"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every scenario belongs to a workspace within
+    # its org. Stamped from the X-Workspace-Id header (falling back to
+    # the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
+    name = Column(String, nullable=False)
+    description = Column(String)
+    required_info = Column(JSON)
+    
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+    created_by = Column(String)
+
+
+# Enums moved to enums.py
+
+
+class Integration(Base):
+    """Integration model for connecting with external voice AI platforms."""
+    __tablename__ = "integrations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    platform = Column(String, nullable=False)
+
+
+
+    name = Column(String, nullable=True)  # Optional friendly name
+    api_key = Column(String, nullable=False)  # Encrypted Private API key for the platform
+    public_key = Column(String, nullable=True)  # Optional Public API key (e.g. for Vapi)
+    is_active = Column(Boolean, default=True, nullable=False)
+    # Multiple credentials per (org, platform) are allowed. is_default marks
+    # the row used when a caller does not explicitly select a credential.
+    # A partial unique index in migration 028 enforces at most one default
+    # per (org, platform) at the DB level.
+    is_default = Column(Boolean, default=False, nullable=False)
+    # inherit | gateway | direct — per-credential LLM routing override
+    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
+
+
+class ManualTranscription(Base):
+    """Manual transcription model for storing transcriptions from S3 audio files."""
+
+    __tablename__ = "manual_transcriptions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    name = Column(String(255), nullable=True)  # User-friendly name for the transcription
+    audio_file_key = Column(String(512), nullable=False)  # S3 key or file path
+    transcript = Column(String, nullable=False)  # Full transcript text
+    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
+    stt_model = Column(String(100), nullable=True)  # STT model used (e.g., "whisper-1", "google-speech-v2")
+    stt_provider = Column(String, nullable=True)  # Provider used
+
+
+
+    language = Column(String(10), nullable=True)  # Detected or specified language
+    processing_time = Column(Float, nullable=True)  # Processing time in seconds
+    raw_output = Column(JSON, nullable=True)  # Full model output for reference
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class ConversationEvaluation(Base):
+    """Conversation evaluation model for evaluating manual transcriptions against agent objectives."""
+    
+    __tablename__ = "conversation_evaluations"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    transcription_id = Column(UUID(as_uuid=True), ForeignKey("manual_transcriptions.id"), nullable=False, index=True)
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
+    
+    # Evaluation results
+    objective_achieved = Column(Boolean, nullable=False)  # Binary: was the conversation objective achieved?
+    objective_achieved_reason = Column(String, nullable=True)  # Explanation for the binary result
+    additional_metrics = Column(JSON, nullable=True)  # Additional evaluation metrics (e.g., professionalism, clarity, etc.)
+    overall_score = Column(Float, nullable=True)  # Overall score (0.0 to 1.0)
+    
+    # LLM metadata
+    llm_provider = Column(Enum(ModelProvider, native_enum=False), nullable=True)
+
+    llm_model = Column(String(100), nullable=True)
+    llm_response = Column(JSON, nullable=True)  # Full LLM response for reference
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class AIProvider(Base):
+    """AI Provider - Stores API keys for different AI platforms."""
+    __tablename__ = "aiproviders"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider = Column(String, nullable=False)
+
+
+
+    api_key = Column(String, nullable=False)  # Encrypted API key
+    name = Column(String, nullable=True)  # Optional friendly name
+    # Azure OpenAI resource endpoint (e.g. https://my-resource.openai.azure.com).
+    # Only used when provider is azure; other providers ignore this column.
+    endpoint_url = Column(String, nullable=True)
+    is_active = Column(Boolean, default=True, nullable=False)
+    # Multiple AIProvider rows per (org, provider) are allowed. is_default
+    # marks the row resolved when no explicit credential id is selected.
+    # A partial unique index in migration 028 enforces at most one default.
+    is_default = Column(Boolean, default=False, nullable=False)
+    # inherit | gateway | direct — per-credential LLM routing override
+    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
+    # Bifrost custom model ID used when routing via gateway
+    gateway_model = Column(String(255), nullable=True)
+    # inherit | litellm_shim | native_openai — Bifrost API surface override
+    gateway_interface = Column(String(20), nullable=False, default="inherit", server_default="inherit")
+    # Optional per-credential Bifrost/gateway base URL override
+    gateway_base_url = Column(String(512), nullable=True)
+    # Optional auth header for Bifrost (e.g. x-bf-vk, Authorization, x-api-key)
+    gateway_auth_header = Column(String(64), nullable=True)
+    # Env var name whose value is sent as the gateway auth secret
+    gateway_auth_secret_env = Column(String(128), nullable=True)
+    # Encrypted inline gateway auth secret (alternative to env var)
+    gateway_auth_secret = Column(String, nullable=True)
+    # Arbitrary HTTP headers sent with gateway-routed LiteLLM calls
+    gateway_extra_headers = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
+
+
+# Enums moved to enums.py
+
+
+class VoiceBundle(Base):
+    """VoiceBundle - Composable unit combining STT, LLM, and TTS for voice AI testing, or S2S models."""
+    __tablename__ = "voicebundles"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    name = Column(String, nullable=False)
+    description = Column(String, nullable=True)
+    
+    # Bundle type: either STT+LLM+TTS or S2S
+    # Using String instead of Enum to avoid SQLAlchemy enum conversion issues
+    # The enum conversion is handled in the Pydantic schemas
+    bundle_type = Column(String(50), nullable=False, default=VoiceBundleType.STT_LLM_TTS.value)
+    
+    # STT Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
+    stt_provider = Column(String, nullable=True)
+    # Optional explicit credential row (aiproviders.id or integrations.id).
+    # When NULL the credential resolver picks the default row for the
+    # provider. No FK is set because the target table varies by provider.
+    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    stt_model = Column(String, nullable=True)  # e.g., "whisper-1", "google-speech-v2"
+    
+    # LLM Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
+    llm_provider = Column(String, nullable=True)
+    llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    llm_model = Column(String, nullable=True)  # e.g., "gpt-4", "claude-3-opus"
+    llm_temperature = Column(Float, nullable=True, default=0.7)
+    llm_max_tokens = Column(Integer, nullable=True)
+    llm_config = Column(JSON, nullable=True)  # Additional LLM configuration (extensible)
+    
+    # TTS Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
+    tts_provider = Column(String, nullable=True)
+    tts_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    tts_model = Column(String, nullable=True)  # e.g., "tts-1", "neural-voice"
+    tts_voice = Column(String, nullable=True)  # Voice selection if applicable
+    tts_config = Column(JSON, nullable=True)  # Additional TTS configuration (extensible)
+    
+    # S2S Configuration - required for S2S type, optional for STT_LLM_TTS
+    s2s_provider = Column(String, nullable=True)
+    s2s_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+
+
+    s2s_model = Column(String, nullable=True)  # e.g., "gpt-4o-transcribe", speech-to-speech model
+    s2s_config = Column(JSON, nullable=True)  # Additional S2S configuration (extensible)
+    
+    # Additional configuration for extensibility
+    extra_metadata = Column(JSON, nullable=True)  # For future extensions (renamed from 'metadata' to avoid SQLAlchemy conflict)
+    
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+# Enums moved to enums.py
+
+
+class TestAgentConversation(Base):
+    """Test Agent Conversation - Records conversations between test AI agent and voice AI agent."""
+    __tablename__ = "test_agent_conversations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every playground conversation belongs to a
+    # workspace within its org. Stamped from the X-Workspace-Id header
+    # (falling back to the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    
+    # Configuration
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=False)
+    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
+    
+    # Conversation data
+    status = Column(String, nullable=False, default=TestAgentConversationStatus.INITIALIZING.value)
+
+
+
+    live_transcription = Column(JSON, nullable=True)  # Array of conversation turns with timestamps
+    conversation_audio_key = Column(String, nullable=True)  # S3 key for recorded conversation audio
+    full_transcript = Column(String, nullable=True)  # Full conversation transcript
+    
+    # Metadata
+    started_at = Column(DateTime(timezone=True), server_default=func.now())
+    ended_at = Column(DateTime(timezone=True), nullable=True)
+    duration_seconds = Column(Float, nullable=True)
+    
+    # Additional metadata
+    conversation_metadata = Column(JSON, nullable=True)  # Additional conversation metadata
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class EvaluatorSuite(Base):
+    """Evaluator suite — one agent + one persona + N scenario combinations."""
+
+    __tablename__ = "evaluator_suites"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    name = Column(String, nullable=True)
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
+    metric_ids = Column(JSON, nullable=True)
+    llm_provider = Column(String, nullable=True)
+    llm_model = Column(String, nullable=True)
+    llm_config = Column(JSON, nullable=True)
+    tags = Column(JSON, nullable=True)
+    default_runs_per_combination = Column(Integer, nullable=False, default=1)
+    round_robin_index = Column(Integer, nullable=False, default=0)
+    is_active = Column(Boolean, nullable=False, default=False)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class Evaluator(Base):
+    """Evaluator - Configuration for testing agents with specific persona and scenario combinations, or custom prompt evaluators."""
+    __tablename__ = "evaluators"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluator_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every evaluator belongs to a workspace within
+    # its org. Stamped from the X-Workspace-Id header (falling back to
+    # the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    
+    # Display name (required for custom evaluators, optional for standard)
+    name = Column(String, nullable=True)
+    
+    # Parent suite (nullable for legacy/custom evaluators)
+    suite_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("evaluator_suites.id", ondelete="CASCADE"),
+        nullable=True,
+        index=True,
+    )
+
+    # Standard evaluator configuration (nullable for custom evaluators)
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)
+    
+    # Custom evaluator prompt (used instead of agent/persona/scenario)
+    custom_prompt = Column(Text, nullable=True)
+
+    # Custom evaluator metric selection. When set, the worker filters the
+    # enabled-org metrics down to only these IDs (list of metric UUID strings).
+    # Standard evaluators leave this NULL and use all enabled agent metrics.
+    metric_ids = Column(JSON, nullable=True)
+
+    # LLM configuration for evaluation (overrides hardcoded defaults)
+    llm_provider = Column(String, nullable=True)  # e.g. "openai", "anthropic", "google"
+    llm_model = Column(String, nullable=True)  # e.g. "gpt-4.1", "claude-sonnet-4-20250514"
+    llm_config = Column(JSON, nullable=True)
+    
+    # Tags for categorization
+    tags = Column(JSON, nullable=True)  # Array of tag strings
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+# Enums moved to enums.py
+
+
+class Metric(Base):
+    """Metric - Configuration for evaluation metrics.
+
+    Supports a 2-level hierarchy via ``parent_metric_id``: a "category"
+    parent metric (e.g. "Call Outcome") owns N child sub-metric labels
+    (e.g. "happy_completion", "angry_hangup"). ``selection_mode`` is set
+    only on parents and controls how the LLM scores children together
+    (``single_choice`` = pick exactly one; ``multi_label`` = independent
+    yes/no with logical consistency). Children are always boolean.
+    """
+    __tablename__ = "metrics"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: two-shape column.
+    #
+    #   * ``workspace_id = <uuid>`` — workspace-scoped metric. Only
+    #     visible inside that workspace (the default behavior; existing
+    #     rows all look like this).
+    #   * ``workspace_id IS NULL`` — org-shared metric. Surfaces in
+    #     every workspace's listing under this org so users don't have
+    #     to recreate the same metric per workspace.
+    #
+    # Children always inherit their parent's ``workspace_id`` (including
+    # NULL) so a category metric's whole subtree shares one scope; the
+    # add-child / promote-discovered endpoints enforce this.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=True,
+        index=True,
+    )
+
+    # Basic information
+    name = Column(String, nullable=False)
+    description = Column(String, nullable=True)
+    # Free-form illustrative example used to sharpen the LLM judge's
+    # rubric. Today this is consumed by child sub-labels of a
+    # categorization parent metric so each label can carry "what does
+    # this look like in a transcript?" text alongside the rubric in
+    # ``description``. The column lives on every Metric row for
+    # forward-compat: a standalone metric could later surface its own
+    # example without another migration.
+    example = Column(Text, nullable=True)
+
+    # Configuration
+    metric_type = Column(String, nullable=False, default=MetricType.RATING.value)
+    metric_category = Column(
+        String(30),
+        nullable=False,
+        default=MetricCategory.QUALITY.value,
+        server_default=MetricCategory.QUALITY.value,
+    )
+    trigger = Column(String, nullable=False, default=MetricTrigger.ALWAYS.value)
+    metric_origin = Column(String(30), nullable=False, default="default")
+    supported_surfaces = Column(JSON, nullable=False, default=list)  # ["agent", "voice_playground", "blind_test"]
+    enabled_surfaces = Column(JSON, nullable=False, default=list)  # subset of supported_surfaces
+    custom_data_type = Column(String(30), nullable=True)  # "boolean" | "enum" | "number_range"
+    custom_config = Column(JSON, nullable=True)  # enum options / number range config
+    tags = Column(JSON, nullable=True)  # ["tone", "latency", ...]
+
+    # Hierarchy: NULL = standalone or parent. When set, this row is a
+    # child sub-metric of the referenced parent. ON DELETE CASCADE so
+    # deleting a category removes its children atomically.
+    parent_metric_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("metrics.id", ondelete="CASCADE"),
+        nullable=True,
+        index=True,
+    )
+    # Set only on parent rows (``parent_metric_id IS NULL``). Either
+    # ``single_choice`` or ``multi_label``. NULL = legacy / non-hierarchical
+    # metric (no children).
+    selection_mode = Column(String(20), nullable=True)
+
+    # When true on a parent metric (any selection_mode), the LLM is
+    # invited during call-import evaluation to emit additional
+    # candidate sub-labels beyond the user-defined children. The
+    # candidates surface in a "Discovered labels" panel where the user
+    # manually promotes them into real child Metric rows. For
+    # ``single_choice`` parents the discovered entries are
+    # supplemental — the chosen child is still picked from the
+    # predefined children so the exactly-one-true invariant holds.
+    # The validator rejects this flag on standalone / child metrics.
+    allow_discovery = Column(
+        Boolean, nullable=False, default=False, server_default="false"
+    )
+
+    # When True, this metric is a "transcript-compare judge": the
+    # call-import evaluator feeds BOTH the production transcript
+    # (``call_import_rows.transcript``, CSV-supplied) and the diarised
+    # transcript (``call_import_rows.diarised_transcript``, worker-
+    # produced by the STT/diarisation pipeline) to the LLM as a
+    # labeled pair instead of feeding one transcript. The parent
+    # evaluation's ``CallImportEvaluation.transcript_source`` is
+    # ignored for these metrics — they always read both columns.
+    # Rows where either transcript is missing are skipped per-metric
+    # with ``skipped="comparison_missing_transcript"`` so the rest of
+    # the row's metrics still produce scores. The Pydantic validator
+    # rejects ``compare_transcripts`` combined with ``parent_metric_id``
+    # or ``selection_mode`` (i.e. it can't simultaneously be part of
+    # a parent/child hierarchy). The call-import worker also
+    # auto-promotes a metric to comparison mode when its description
+    # references the production / diarised transcripts in well-known
+    # phrases (see ``_metric_text_references_production`` in
+    # ``app.workers.tasks.evaluate_call_import_row``).
+    compare_transcripts = Column(
+        Boolean, nullable=False, default=False, server_default="false"
+    )
+
+    parent = relationship(
+        "Metric",
+        remote_side=[id],
+        backref="children",
+    )
+
+    # When true, the LLM-judge is asked to also return a short free-form
+    # rationale alongside the value (stored under ``metric_scores[id].rationale``).
+    # Adds a second "<Name> - LLM Rationale" column in the call-import CSV export.
+    capture_rationale = Column(Boolean, nullable=False, default=False)
+
+    enabled = Column(Boolean, nullable=False, default=True)
+    
+    # Metadata
+    is_default = Column(Boolean, nullable=False, default=False)  # Pre-defined metrics
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class EvaluatorResult(Base):
+    """EvaluatorResult - Results from running an evaluator with transcription and metric evaluations."""
+    __tablename__ = "evaluator_results"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    result_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every evaluator result belongs to a workspace
+    # within its org. Stamped from the active workspace at creation time
+    # (either the X-Workspace-Id header or the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    
+    # References
+    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True, index=True)  # Optional - can be None for test calls without persona/scenario
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Nullable for custom evaluators
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)  # Optional - can be None for test calls
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)  # Optional - can be None for test calls
+    
+    # Result data
+    name = Column(String, nullable=True)  # Scenario name or test call name (optional)
+    timestamp = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
+    duration_seconds = Column(Float, nullable=True)  # Call duration
+    status = Column(String(20), nullable=False, default=EvaluatorResultStatus.QUEUED.value)
+    
+    # Audio and transcription
+    audio_s3_key = Column(String, nullable=True)  # S3 key for audio file
+    transcription = Column(String, nullable=True)  # Full transcription
+    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
+    
+    # Metric scores - JSON object with metric_id as key and score as value
+    # Format: {"metric_id_1": {"value": 85, "type": "rating"}, "metric_id_2": {"value": true, "type": "boolean"}}
+    metric_scores = Column(JSON, nullable=True)
+    
+    # Celery task tracking
+    celery_task_id = Column(String, nullable=True, index=True)  # Celery task ID for tracking
+    
+    # Error information
+    error_message = Column(String, nullable=True)
+    
+    # Call event tracking (similar to CallRecording)
+    call_event = Column(String, nullable=True, index=True)  # Latest call event (e.g., call_started, call_ended)
+    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
+    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
+    call_data = Column(JSON, nullable=True)  # Full call details from provider (like CallRecording)
+    
+    # Data-plane shard routing (payload rows on shard DBs when sharding enabled)
+    shard_id = Column(String(64), nullable=True, index=True)
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+# Enums moved to enums.py
+
+
+class CallRecordingSource(str, enum.Enum):
+    """Source of the call recording data."""
+    
+    PLAYGROUND = "playground"
+    WEBHOOK = "webhook"
+
+
+class CallRecording(Base):
+    """Call Recording model for tracking voice provider calls."""
+    __tablename__ = "call_recordings"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every recording belongs to a workspace within
+    # its org. For playground-origin rows this is stamped from the active
+    # workspace at creation time; for webhook-origin rows the worker
+    # looks up the recording's agent and inherits its workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    call_short_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
+    status = Column(Enum(CallRecordingStatus), nullable=False, default=CallRecordingStatus.PENDING, index=True)
+    call_event = Column(String, nullable=True, index=True)  # Latest webhook event (e.g., call_started, call_ended)
+    source = Column(Enum(CallRecordingSource), nullable=False, default=CallRecordingSource.PLAYGROUND, index=True)
+    call_data = Column(JSON, nullable=True)  # JSON blob for provider response
+    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
+    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Reference to our agent
+    
+    # Link to EvaluatorResult for metric evaluations
+    evaluator_result_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("evaluator_results.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    
+    shard_id = Column(String(64), nullable=True, index=True)
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class EvaluatorResultPayload(Base):
+    """Heavy evaluator result fields stored on data shards when sharding is enabled."""
+
+    __tablename__ = "evaluator_result_payloads"
+
+    evaluator_result_id = Column(UUID(as_uuid=True), primary_key=True)
+    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    audio_s3_key = Column(String, nullable=True)
+    transcription = Column(String, nullable=True)
+    speaker_segments = Column(JSON, nullable=True)
+    metric_scores = Column(JSON, nullable=True)
+    call_data = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class CallRecordingPayload(Base):
+    """Heavy call recording fields stored on data shards when sharding is enabled."""
+
+    __tablename__ = "call_recording_payloads"
+
+    call_recording_id = Column(UUID(as_uuid=True), primary_key=True)
+    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    call_data = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class Alert(Base):
+    """Alert model for configuring monitoring alerts."""
+    __tablename__ = "alerts"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    
+    # Basic information
+    name = Column(String(255), nullable=False)
+    description = Column(String, nullable=True)
+    
+    # Metric condition configuration
+    metric_type = Column(String, nullable=False, default=AlertMetricType.NUMBER_OF_CALLS.value)
+    aggregation = Column(String, nullable=False, default=AlertAggregation.SUM.value)
+    operator = Column(String, nullable=False, default=AlertOperator.GREATER_THAN.value)
+    threshold_value = Column(Float, nullable=False)
+    time_window_minutes = Column(Integer, nullable=False, default=60)  # Time window for aggregation
+    
+    # Agent selection (JSON array of agent UUIDs, null means all agents)
+    agent_ids = Column(JSON, nullable=True)
+    
+    # Notification configuration
+    notify_frequency = Column(String, nullable=False, default=AlertNotifyFrequency.IMMEDIATE.value)
+    notify_emails = Column(JSON, nullable=True)  # Array of email addresses
+    notify_webhooks = Column(JSON, nullable=True)  # Array of webhook URLs (Slack, etc.)
+    
+    # Status
+    status = Column(String, nullable=False, default=AlertStatus.ACTIVE.value)
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+    
+    # Relationships
+    alert_history = relationship("AlertHistory", back_populates="alert", cascade="all, delete-orphan")
+
+
+class AlertHistory(Base):
+    """Alert history model for tracking triggered alerts."""
+    __tablename__ = "alert_history"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    alert_id = Column(UUID(as_uuid=True), ForeignKey("alerts.id"), nullable=False, index=True)
+    
+    # Trigger information
+    triggered_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
+    triggered_value = Column(Float, nullable=False)  # The actual value that triggered the alert
+    threshold_value = Column(Float, nullable=False)  # The threshold at time of trigger
+    
+    # Status tracking
+    status = Column(String, nullable=False, default=AlertHistoryStatus.TRIGGERED.value)
+    
+    # Notification tracking
+    notified_at = Column(DateTime(timezone=True), nullable=True)
+    notification_details = Column(JSON, nullable=True)  # Details of sent notifications
+    
+    # Resolution
+    acknowledged_at = Column(DateTime(timezone=True), nullable=True)
+    acknowledged_by = Column(String, nullable=True)
+    resolved_at = Column(DateTime(timezone=True), nullable=True)
+    resolved_by = Column(String, nullable=True)
+    resolution_notes = Column(String, nullable=True)
+    
+    # Additional context
+    context_data = Column(JSON, nullable=True)  # Additional data about the trigger
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    
+    # Relationships
+    alert = relationship("Alert", back_populates="alert_history")
+
+
+class CronJob(Base):
+    """Cron job model for scheduling automated evaluator runs."""
+    __tablename__ = "cron_jobs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    
+    # Basic information
+    name = Column(String(255), nullable=False)
+    cron_expression = Column(String(100), nullable=False)  # e.g., "0 9 * * 1-5"
+    timezone = Column(String(100), nullable=False, default="UTC")
+    
+    # Run configuration
+    max_runs = Column(Integer, nullable=False, default=10)
+    current_runs = Column(Integer, nullable=False, default=0)
+    
+    # Evaluators to trigger (JSON array of evaluator UUIDs)
+    evaluator_ids = Column(JSON, nullable=False)
+    
+    # Status
+    status = Column(String, nullable=False, default=CronJobStatus.ACTIVE.value)
+    
+    # Run tracking
+    next_run_at = Column(DateTime(timezone=True), nullable=True)
+    last_run_at = Column(DateTime(timezone=True), nullable=True)
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class TTSComparisonStatus(str, enum.Enum):
+    PENDING = "pending"
+    GENERATING = "generating"
+    EVALUATING = "evaluating"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TTSSampleStatus(str, enum.Enum):
+    PENDING = "pending"
+    GENERATING = "generating"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TTSReportJobStatus(str, enum.Enum):
+    PENDING = "pending"
+    PROCESSING = "processing"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TTSComparison(Base):
+    """TTS Comparison session for A/B testing voice providers."""
+    __tablename__ = "tts_comparisons"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every voice playground comparison belongs to
+    # a workspace within its org. Children (samples, report jobs, blind
+    # test shares) inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    simulation_id = Column(String(6), unique=True, index=True, nullable=True)
+
+    name = Column(String(255), nullable=True)
+    status = Column(String(50), nullable=False, default=TTSComparisonStatus.PENDING.value)
+
+    # 'benchmark' = traditional TTS A/B benchmark (provider-generated audio).
+    # 'blind_test_only' = standalone blind test built from existing recordings
+    # / uploads / past TTS samples; no TTS generation happens.
+    mode = Column(String(32), nullable=False, default="benchmark")
+
+    provider_a = Column(String(100), nullable=True)
+    model_a = Column(String(100), nullable=True)
+    voices_a = Column(JSON, nullable=True)
+
+    provider_b = Column(String(100), nullable=True)
+    model_b = Column(String(100), nullable=True)
+    voices_b = Column(JSON, nullable=True)
+
+    sample_texts = Column(JSON, nullable=False)
+    num_runs = Column(Integer, nullable=False, default=1)
+
+    blind_test_results = Column(JSON, nullable=True)
+    evaluation_summary = Column(JSON, nullable=True)
+
+    eval_stt_provider = Column(String(100), nullable=True)
+    eval_stt_model = Column(String(100), nullable=True)
+
+    celery_task_id = Column(String, nullable=True, index=True)
+    error_message = Column(String, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    samples = relationship("TTSSample", back_populates="comparison", cascade="all, delete-orphan")
+
+
+class TTSSample(Base):
+    """Individual TTS audio sample within a comparison."""
+    __tablename__ = "tts_samples"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent TTSComparison's workspace.
+    # Denormalized for fast filter-by-workspace listings without a join.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    provider = Column(String(100), nullable=True)
+    model = Column(String(100), nullable=True)
+    voice_id = Column(String(255), nullable=True)
+    voice_name = Column(String(255), nullable=True)
+    side = Column(String(1), nullable=True)  # "A" or "B"
+    sample_index = Column(Integer, nullable=False)
+    run_index = Column(Integer, nullable=False, default=0)
+
+    # 'tts' (default, audio is synthesized by a provider), 'recording' (audio
+    # is reused from a CallImportRow recording), or 'upload' (audio was
+    # uploaded by the user). Non-tts samples are marked completed up-front
+    # by the API and skipped by the generation worker.
+    source_type = Column(String(32), nullable=False, default="tts")
+    # When source_type == 'recording', references CallImportRow.id (no FK
+    # constraint to keep cascading deletes simple if a call import is later
+    # removed; the audio_s3_key is what's actually used).
+    source_ref_id = Column(UUID(as_uuid=True), nullable=True)
+
+    text = Column(String, nullable=False)
+    audio_s3_key = Column(String(512), nullable=True)
+    duration_seconds = Column(Float, nullable=True)
+    latency_ms = Column(Float, nullable=True)
+    ttfb_ms = Column(Float, nullable=True)
+
+    evaluation_metrics = Column(JSON, nullable=True)
+    status = Column(String(50), nullable=False, default=TTSSampleStatus.PENDING.value)
+    error_message = Column(String, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    comparison = relationship("TTSComparison", back_populates="samples")
+
+
+class TTSReportJob(Base):
+    """Asynchronous PDF report generation jobs for Voice Playground."""
+    __tablename__ = "tts_report_jobs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent TTSComparison's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
+
+    status = Column(String(50), nullable=False, default=TTSReportJobStatus.PENDING.value)
+    format = Column(String(20), nullable=False, default="pdf")
+    filename = Column(String(255), nullable=True)
+    s3_key = Column(String(512), nullable=True)
+    error_message = Column(String, nullable=True)
+    celery_task_id = Column(String, nullable=True, index=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    comparison = relationship("TTSComparison")
+
+
+class TTSBlindTestShareStatus(str, enum.Enum):
+    OPEN = "open"
+    CLOSED = "closed"
+
+
+class TTSBlindTestShare(Base):
+    """A publicly sharable blind test for a TTSComparison.
+
+    The share_token is the capability: anyone holding it can open the public
+    form and submit a response. Each comparison has at most one share row.
+    """
+    __tablename__ = "tts_blind_test_shares"
+    __table_args__ = (
+        UniqueConstraint("comparison_id", name="uq_blind_test_shares_comparison"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    comparison_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("tts_comparisons.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent TTSComparison's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    share_token = Column(String(64), unique=True, nullable=False, index=True)
+
+    title = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+
+    # Internal notes visible only to the share creator (e.g. which voice
+    # corresponds to which side, source notes for standalone blind tests).
+    # Never exposed via the public blind test payload.
+    creator_notes = Column(Text, nullable=True)
+
+    # JSON list: [{ "key": str, "label": str, "type": "rating"|"comment", "scale": int? }]
+    custom_metrics = Column(JSON, nullable=False)
+
+    status = Column(String(20), nullable=False, default=TTSBlindTestShareStatus.OPEN.value)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    closed_at = Column(DateTime(timezone=True), nullable=True)
+    created_by = Column(String, nullable=True)
+
+    comparison = relationship("TTSComparison")
+    responses = relationship(
+        "TTSBlindTestResponse",
+        back_populates="share",
+        cascade="all, delete-orphan",
+    )
+
+
+class TTSBlindTestResponse(Base):
+    """A single rater's submission against a TTSBlindTestShare."""
+    __tablename__ = "tts_blind_test_responses"
+    __table_args__ = (
+        UniqueConstraint("share_id", "rater_email", name="uq_blind_test_response_share_email"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    share_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("tts_blind_test_shares.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    # Workspace isolation: mirrors the parent TTSBlindTestShare's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    rater_name = Column(String(255), nullable=False)
+    rater_email = Column(String(320), nullable=False, index=True)
+
+    # JSON list keyed by sample_index. Server stores in TRUE A/B orientation
+    # (already de-flipped from whatever the rater's UI showed):
+    # [{
+    #   "sample_index": int,
+    #   "preferred": "A" | "B",
+    #   "ratings_a": { metric_key: number },
+    #   "ratings_b": { metric_key: number },
+    #   "comment": str?
+    # }]
+    responses = Column(JSON, nullable=False)
+
+    ip = Column(String(64), nullable=True)
+    user_agent = Column(String(512), nullable=True)
+
+    submitted_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    share = relationship("TTSBlindTestShare", back_populates="responses")
+
+
+class PromptPartial(Base):
+    """Prompt Partial - Reusable prompt templates with version history."""
+    __tablename__ = "prompt_partials"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every prompt partial belongs to a workspace
+    # within its org. Versions inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    description = Column(String, nullable=True)
+    content = Column(Text, nullable=False)
+    tags = Column(JSON, nullable=True)
+    current_version = Column(Integer, nullable=False, default=1)
+    # Cached LLM-generated flowchart for imported production agent prompts.
+    # Shape: AgentFlowGraph JSON (nodes[], edges[]).
+    agent_flowchart = Column(JSON, nullable=True)
+    agent_flowchart_status = Column(String(20), nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    versions = relationship("PromptPartialVersion", back_populates="prompt_partial", cascade="all, delete-orphan", order_by="PromptPartialVersion.version.desc()")
+
+
+class PromptPartialVersion(Base):
+    """Version history for a prompt partial."""
+    __tablename__ = "prompt_partial_versions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    prompt_partial_id = Column(UUID(as_uuid=True), ForeignKey("prompt_partials.id", ondelete="CASCADE"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent PromptPartial's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    version = Column(Integer, nullable=False)
+    content = Column(Text, nullable=False)
+    change_summary = Column(String, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    created_by = Column(String, nullable=True)
+
+    prompt_partial = relationship("PromptPartial", back_populates="versions")
+
+    __table_args__ = (
+        UniqueConstraint('prompt_partial_id', 'version', name='uq_prompt_partial_version'),
+    )
+
+
+class CustomTTSVoice(Base):
+    """Organization-scoped custom TTS voice metadata."""
+    __tablename__ = "custom_tts_voices"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "provider", "voice_id", name="uq_custom_tts_voice_org_provider_voice_id"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider = Column(String(100), nullable=False, index=True)
+    voice_id = Column(String(255), nullable=False)
+    name = Column(String(255), nullable=False)
+    gender = Column(String(50), nullable=True)
+    accent = Column(String(100), nullable=True)
+    description = Column(Text, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+class PromptOptimizationRun(Base):
+    """A single GEPA prompt optimization run for an agent."""
+    __tablename__ = "prompt_optimization_runs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every optimization run belongs to a workspace
+    # within its org. Candidates inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
+    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True)
+    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
+
+    seed_prompt = Column(Text, nullable=False)
+    best_prompt = Column(Text, nullable=True)
+    best_score = Column(Float, nullable=True)
+
+    status = Column(String(20), nullable=False, default=PromptOptimizationStatus.PENDING.value)
+    config = Column(JSON, nullable=True)
+    reflection_trace = Column(JSON, nullable=True)
+    metric_history = Column(JSON, nullable=True)
+
+    num_iterations = Column(Integer, nullable=True)
+    num_metric_calls = Column(Integer, nullable=True)
+
+    celery_task_id = Column(String, nullable=True, index=True)
+    error_message = Column(Text, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    candidates = relationship("PromptOptimizationCandidate", back_populates="optimization_run", cascade="all, delete-orphan")
+
+
+class PromptOptimizationCandidate(Base):
+    """A candidate prompt generated during an optimization run."""
+    __tablename__ = "prompt_optimization_candidates"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    optimization_run_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_runs.id", ondelete="CASCADE"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent PromptOptimizationRun's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    prompt_text = Column(Text, nullable=False)
+    score = Column(Float, nullable=True)
+    metric_breakdown = Column(JSON, nullable=True)
+    reflection_summary = Column(Text, nullable=True)
+
+    parent_candidate_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_candidates.id"), nullable=True)
+
+    is_accepted = Column(Boolean, nullable=False, default=False)
+    pushed_to_provider_at = Column(DateTime(timezone=True), nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    optimization_run = relationship("PromptOptimizationRun", back_populates="candidates")
+
+
+class TelephonyIntegration(Base):
+    """Per-organization telephony provider credentials and configuration.
+
+    Multiple rows per (organization_id, provider) are allowed so that an
+    organization can keep several Plivo / Exotel accounts side-by-side.
+    A partial unique index in migration 028 enforces at most one row with
+    is_default = TRUE per (org, provider); resolution falls back to that
+    default row when the caller does not pin a specific credential.
+    """
+
+    __tablename__ = "telephony_integrations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider = Column(String(50), nullable=False, default="plivo")
+    name = Column(String(255), nullable=True)  # Optional friendly name to disambiguate multiple credentials
+
+    auth_id = Column(String(255), nullable=False)
+    auth_token = Column(String(512), nullable=False)
+
+    verify_app_uuid = Column(String(255), nullable=True)
+    voice_app_id = Column(String(255), nullable=True)
+    sip_domain = Column(String(255), nullable=True)
+    masking_config = Column(JSON, nullable=True)
+
+    is_active = Column(Boolean, default=True, nullable=False)
+    is_default = Column(Boolean, default=False, nullable=False)
+    last_tested_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyPhoneNumber(Base):
+    """Inventory of telephony phone numbers owned by an organization."""
+
+    __tablename__ = "telephony_phone_numbers"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_number_org_phone"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    telephony_integration_id = Column(
+        UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=True, index=True
+    )
+
+    phone_number = Column(String(20), nullable=False, index=True)
+    country_iso2 = Column(String(2), nullable=True)
+    region = Column(String(100), nullable=True)
+    number_type = Column(String(20), nullable=True)
+    capabilities = Column(JSON, nullable=True)
+    provider_app_id = Column(String(255), nullable=True)
+
+    is_masking_pool = Column(Boolean, default=False, nullable=False)
+    inbound_enabled = Column(Boolean, default=True, nullable=False)
+    outbound_enabled = Column(Boolean, default=True, nullable=False)
+    source = Column(String(20), nullable=False, default="imported")
+    agent_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey(
+            "agents.id",
+            ondelete="SET NULL",
+            use_alter=True,
+            name="fk_telephony_phone_numbers_agent_id",
+        ),
+        nullable=True,
+        index=True,
+    )
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyDialTarget(Base):
+    """Org-scoped saved destination numbers for outbound test calls."""
+
+    __tablename__ = "telephony_dial_targets"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_dial_target_org_phone"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    phone_number = Column(String(20), nullable=False, index=True)
+    label = Column(String(255), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyVerifySession(Base):
+    """Tracks voice OTP verification sessions via telephony provider."""
+
+    __tablename__ = "telephony_verify_sessions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider_session_uuid = Column(String(255), nullable=False, unique=True, index=True)
+    recipient_number = Column(String(20), nullable=False)
+    channel = Column(String(10), nullable=False, default="voice")
+    status = Column(String(20), nullable=False, default="pending")
+    initiated_by = Column(String(255), nullable=True)
+    verify_app_uuid = Column(String(255), nullable=True)
+    verified_at = Column(DateTime(timezone=True), nullable=True)
+    expires_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyMaskedSession(Base):
+    """Number-masking session between two parties through a middle number."""
+
+    __tablename__ = "telephony_masked_sessions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    telephony_integration_id = Column(UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=False)
+    masked_number_id = Column(
+        UUID(as_uuid=True), ForeignKey("telephony_phone_numbers.id"), nullable=False, index=True
+    )
+    masked_number = Column(String(20), nullable=False)
+    party_a_number = Column(String(20), nullable=False)
+    party_b_number = Column(String(20), nullable=False)
+    status = Column(String(20), nullable=False, default="active")
+    expires_at = Column(DateTime(timezone=True), nullable=True)
+    ended_at = Column(DateTime(timezone=True), nullable=True)
+    session_metadata = Column("metadata", JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class CallImportSchema(Base):
+    """Reusable Input Parameter schema for the call-uploads flow.
+
+    A schema is workspace-scoped: users define a named bundle of typed
+    Input Parameters once (e.g. "Standard Voice QA" with conversation_id +
+    recording_url + transcript + agent_name) and then map those parameters
+    to CSV/Excel headers each time they upload a new batch.
+
+    Every schema MUST contain exactly one parameter with
+    ``type='conversation_id'`` and ``is_required=True`` - that's the
+    mandatory identity field every imported row needs. The invariant is
+    enforced in app code on create/update (no DB-level CHECK because the
+    parent + children are written across two tables in one transaction).
+    """
+
+    __tablename__ = "call_import_schemas"
+    __table_args__ = (
+        # Case-insensitive uniqueness is enforced via the matching partial
+        # index on ``LOWER(name)`` in the migration; this constraint here
+        # would be case-sensitive and is intentionally omitted to avoid
+        # confusing the user.
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+    created_by_user_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("users.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    parameters = relationship(
+        "CallImportSchemaParameter",
+        back_populates="schema",
+        cascade="all, delete-orphan",
+        order_by="CallImportSchemaParameter.ordering",
+    )
+
+
+class CallImportSchemaParameter(Base):
+    """A single typed parameter inside a :class:`CallImportSchema`.
+
+    ``type`` is one of the strings tracked by
+    :data:`app.models.enums.CallImportParameterType`. ``conversation_id``
+    is reserved for the mandatory identity parameter every schema must
+    contain.
+    """
+
+    __tablename__ = "call_import_schema_parameters"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    schema_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_schemas.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    type = Column(String(32), nullable=False)
+    description = Column(Text, nullable=True)
+    is_required = Column(Boolean, nullable=False, default=False)
+    # Stable ordering so the UI renders parameters in the order the
+    # schema author defined them (matters when conversation_id is pinned
+    # first and the user re-orders the rest).
+    ordering = Column(Integer, nullable=False, default=0)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    schema = relationship("CallImportSchema", back_populates="parameters")
+
+
+class CallImport(Base):
+    """Batch record for a CSV-driven call import job."""
+
+    __tablename__ = "call_imports"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every imported batch belongs to a workspace
+    # within its org. The /upload endpoint stamps it from the active
+    # workspace header (or the org's Default if absent).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    created_by_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True)
+    last_updated_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+
+    # Telephony provider key (e.g. ``'exotel'``, ``'plivo'``). In the
+    # legacy one-shot ``POST /upload`` endpoint this is supplied with the
+    # file; in the three-stage flow (UPLOAD -> MAP -> IMPORT) the value
+    # isn't known until the IMPORT stage, so the column is nullable for
+    # ``uploaded`` / ``mapped`` batches.
+    provider = Column(String(50), nullable=True, default="exotel")
+    # Pin a specific telephony credential for this batch so the worker
+    # downloads recordings using *that* row instead of the org default.
+    # NULL preserves legacy behavior (resolve by provider + default).
+    telephony_integration_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("telephony_integrations.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    original_filename = Column(String(512), nullable=True)
+    # When the source file was a multi-sheet Excel workbook, this records
+    # the worksheet the rows came from (one batch per sheet). NULL for CSV
+    # uploads since CSV has no sheet concept.
+    sheet_name = Column(String(255), nullable=True)
+
+    # --- Source-file staging (UPLOAD stage) ---------------------------
+    # The raw CSV / Excel file is stored in S3 between stages so the
+    # user can come back later to MAP and IMPORT without re-uploading.
+    # ``source_s3_key`` is NULL on legacy batches that were imported via
+    # the one-shot endpoint (those batches stay read-only post-import).
+    source_s3_key = Column(Text, nullable=True)
+    source_format = Column(String(16), nullable=True)
+    source_size_bytes = Column(BigInteger, nullable=True)
+    source_content_type = Column(String(255), nullable=True)
+
+    # Snapshot of the file's sheets + headers captured at UPLOAD time
+    # so the MAP UI doesn't need to re-fetch the source bytes from S3.
+    # Shape: ``[{"name": str, "headers": [str, ...], "row_count": int}, ...]``.
+    available_sheets = Column(JSON, nullable=True)
+
+    # User's explicit "drop these columns" decision captured at MAP
+    # time. Was validation-only and ephemeral in the legacy flow; now
+    # persisted so the IMPORT stage can re-parse the file with the same
+    # mapping/skip intent.
+    skipped_columns = Column(JSON, nullable=False, default=list)
+    # Rows skipped at parse time (missing/invalid conversation_id or URL).
+    # Shape: ``[{"source_row": int, "reason": str, "message": str}, ...]``.
+    source_row_skips = Column(JSON, nullable=False, default=list)
+
+    # Free-text high-level segregation label. Powers the "Dataset" filter
+    # at the top of the imports page; multiple imports can share a value.
+    dataset = Column(String(255), nullable=True, index=True)
+
+    # Reusable Input Parameter schema this batch was uploaded against.
+    # NULL on legacy batches uploaded before the schema-driven flow
+    # shipped; those still render via ``column_mapping`` + ``extra_columns``
+    # + ``custom_column_mapping`` below.
+    schema_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_schemas.id", ondelete="RESTRICT"),
+        nullable=True,
+        index=True,
+    )
+    # New schema-driven mapping: ``{schema_parameter_name: csv_header}``.
+    # Populated for new uploads; empty dict on legacy batches.
+    parameter_mapping = Column(JSON, nullable=False, default=dict)
+
+    # Legacy free-form mapping (pre-schema-flow). Kept on the model so
+    # batches that were uploaded before the schema feature shipped still
+    # render correctly on the detail page; new uploads stop writing here.
+    # Keys: external_call_id (required), transcript, recording_url.
+    # (DB column ``external_call_id`` is now ``conversation_id``; this
+    # JSON key stays as-is for historical batches.)
+    # Values: original CSV header strings (preserve user casing for export).
+    column_mapping = Column(JSON, nullable=False, default=dict)
+    # Ordered list of additional CSV header strings the uploader wants
+    # preserved verbatim into the evaluation export CSV.
+    extra_columns = Column(JSON, nullable=False, default=list)
+    # User-defined ``{custom_field_name: csv_header}`` mappings on top of
+    # the three system fields above. Cells from the mapped CSV columns are
+    # preserved per row (keyed by the CSV header in ``raw_columns``) and
+    # surface in the evaluation export under the uploader-chosen name.
+    custom_column_mapping = Column(JSON, nullable=False, default=dict)
+
+    total_rows = Column(Integer, nullable=False, default=0)
+    completed_rows = Column(Integer, nullable=False, default=0)
+    failed_rows = Column(Integer, nullable=False, default=0)
+
+    status = Column(
+        Enum(CallImportStatus, values_callable=get_enum_values),
+        nullable=False,
+        default=CallImportStatus.PENDING,
+        index=True,
+    )
+    error_message = Column(Text, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    rows = relationship(
+        "CallImportRow",
+        back_populates="call_import",
+        cascade="all, delete-orphan",
+        order_by="CallImportRow.row_index",
+    )
+    tags = relationship(
+        "CallImportTag",
+        secondary="call_import_tag_assignments",
+        backref="call_imports",
+        lazy="selectin",
+    )
+    evaluations = relationship(
+        "CallImportEvaluation",
+        back_populates="call_import",
+        cascade="all, delete-orphan",
+    )
+
+
+class CallImportShardSlice(Base):
+    """Registry row: which shard stores a slice of rows for an import."""
+
+    __tablename__ = "call_import_shard_slices"
+
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    slice_id = Column(Integer, primary_key=True)
+    shard_id = Column(String(64), nullable=False, index=True)
+    row_index_min = Column(Integer, nullable=False)
+    row_index_max = Column(Integer, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+class CallImportRow(Base):
+    """A single row within a CallImport batch (one CSV line / one external call)."""
+
+    __tablename__ = "call_import_rows"
+    __table_args__ = (
+        UniqueConstraint("call_import_id", "row_index", name="uq_call_import_row_index"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    row_index = Column(Integer, nullable=False)
+    # Was historically named ``external_call_id``; renamed to
+    # ``conversation_id`` so the new schema-driven upload flow can refer
+    # to it by a single canonical name across the schema definition,
+    # exports, and downstream evaluation tables.
+    conversation_id = Column(String(255), nullable=False, index=True)
+    # Supplied via CSV for Exotel credentialed imports (required per row).
+    # Nullable in the schema for legacy rows imported before recording_url
+    # was mandatory on every Exotel upload.
+    recording_url = Column(Text, nullable=True)
+    # Date-only call recording date supplied by the import schema. Used
+    # for historical report comparisons without timezone/time ambiguity.
+    recording_date = Column(Date, nullable=True, index=True)
+    # The "production" transcript: the value supplied via the CSV
+    # upload mapping. Never overwritten by the diarisation worker —
+    # the worker writes its output into ``diarised_transcript`` so
+    # the user keeps both versions side by side.
+    transcript = Column(Text, nullable=True)
+    # Snapshot of the original CSV row keyed by the user's headers so the
+    # evaluation export can reproduce every column the uploader supplied
+    # (mapped + extra). NULL on legacy rows imported before this column.
+    raw_columns = Column(JSON, nullable=True)
+
+    # Where the value in ``transcript`` came from. ``csv`` = supplied via
+    # the upload mapping, ``edited`` = manually changed in the UI. NULL
+    # on rows that have never had a production transcript.
+    # (Worker-produced transcripts now live in ``diarised_transcript``
+    # and are tracked via ``diarised_transcript_*`` metadata below.)
+    transcript_source = Column(String(20), nullable=True)
+    # Provider/model recorded by the (legacy) post-hoc transcription
+    # worker. New worker runs leave these NULL and write into the
+    # ``diarised_transcript_*`` columns instead; kept on the model for
+    # backwards compatibility with pre-split rows that still carry the
+    # original transcription metadata here.
+    transcript_provider = Column(String(50), nullable=True)
+    transcript_model = Column(String(100), nullable=True)
+    # Lifecycle status for the legacy transcription workflow itself,
+    # independent of the row's recording-fetch ``status``. ``idle`` =
+    # no transcribe task has touched this column. New diarisation runs
+    # update ``diarised_transcript_status`` instead.
+    transcript_status = Column(
+        String(20),
+        nullable=False,
+        default="idle",
+    )
+    transcript_error = Column(Text, nullable=True)
+    transcribed_at = Column(DateTime(timezone=True), nullable=True)
+
+    # The "diarised" transcript: produced by the post-hoc
+    # transcription/diarisation worker. Stored separately so a manual
+    # diarisation run never clobbers the production transcript above.
+    # Evaluations can be configured to score against either column
+    # (see ``CallImportEvaluation.transcript_source``).
+    diarised_transcript = Column(Text, nullable=True)
+    # Provider/model the diarisation worker used. Surfaced in the UI
+    # as "Diarised via deepgram/nova-2" next to the diarised
+    # transcript section.
+    diarised_transcript_provider = Column(String(50), nullable=True)
+    diarised_transcript_model = Column(String(100), nullable=True)
+    # Lifecycle status for the diarisation workflow.
+    # ``idle`` = no diarisation task has run; ``pending``/``running`` =
+    # a Celery task is queued or in flight; ``completed``/``failed`` =
+    # terminal. Independent of ``transcript_status`` so the two
+    # transcripts can be in different lifecycle states.
+    diarised_transcript_status = Column(
+        String(20),
+        nullable=False,
+        default="idle",
+        server_default="idle",
+    )
+    diarised_transcript_error = Column(Text, nullable=True)
+    diarised_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Structured speaker turns produced by the diarisation worker —
+    # ``[{ "speaker": "agent"|"user"|"speaker_3", "text": "...",
+    #      "start": float, "end": float, "raw_speaker": "Speaker 1" }, ...]``
+    # The plain-text ``diarised_transcript`` above is a rendered view
+    # of this list (``<speaker>: <text>`` per line). When the worker
+    # cannot recover structured turns (no pyannote token / single-
+    # speaker recording / provider that doesn't surface segments) this
+    # column stays NULL and the plain-text path is still populated.
+    diarised_segments = Column(JSON, nullable=True)
+    # When True the ``agent`` <-> ``user`` mapping inside
+    # ``diarised_segments`` is inverted at render / export time. The
+    # worker writes the canonical mapping using the "first speaker is
+    # the agent" heuristic; reviewers can flip the toggle from the row
+    # detail panel without re-running diarisation.
+    diarised_speaker_swap = Column(
+        Boolean,
+        nullable=False,
+        default=False,
+        server_default="false",
+    )
+    # LLM that turned the STT plain-text output into structured
+    # ``diarised_segments``. The legacy diarisation worker used
+    # pyannote and left these NULL; the current path always runs an
+    # LLM with the operator-supplied (or default) ``diarised_prompt``
+    # below, and records exactly which model + prompt produced each
+    # row so reviewers can reproduce a specific run.
+    diarised_llm_provider = Column(String(50), nullable=True)
+    diarised_llm_model = Column(String(100), nullable=True)
+    diarised_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
+    diarised_prompt = Column(Text, nullable=True)
+    # Which diarisation pipeline produced this row's turns.
+    #   * ``"stt_llm"`` (default) — two-stage: STT then LLM diariser.
+    #     ``diarised_transcript_provider``/``_model`` describe the STT
+    #     side; ``diarised_llm_provider``/``_model`` the LLM side.
+    #   * ``"llm_only"`` — single-stage: audio fed straight to a
+    #     multimodal LLM. ``diarised_transcript_provider`` is stamped
+    #     with the sentinel ``"llm_only"``; the real model is on
+    #     ``diarised_llm_*``.
+    # Persisting it on the row (not just the run) lets the row detail
+    # panel render the right "Diarised via …" label even for ad-hoc
+    # standalone transcribes (no parent evaluation).
+    transcribe_mode = Column(
+        String(20),
+        nullable=False,
+        default="stt_llm",
+        server_default="stt_llm",
+    )
+
+    status = Column(
+        Enum(CallImportRowStatus, values_callable=get_enum_values),
+        nullable=False,
+        default=CallImportRowStatus.PENDING,
+        index=True,
+    )
+
+    recording_s3_key = Column(String(1024), nullable=True)
+    recording_content_type = Column(String(128), nullable=True)
+    recording_size_bytes = Column(Integer, nullable=True)
+
+    error_message = Column(Text, nullable=True)
+    attempts = Column(Integer, nullable=False, default=0)
+    celery_task_id = Column(String(255), nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    call_import = relationship("CallImport", back_populates="rows")
+
+
+@event.listens_for(CallImportRow, "before_insert")
+def _call_import_row_fill_workspace_id(_mapper, connection, target):
+    """Denormalize workspace_id from the parent import when omitted."""
+    if target.workspace_id is not None or target.call_import_id is None:
+        return
+    workspace_id = connection.execute(
+        select(CallImport.workspace_id).where(
+            CallImport.id == target.call_import_id
+        )
+    ).scalar_one_or_none()
+    if workspace_id is not None:
+        target.workspace_id = workspace_id
+
+
+class CallImportTag(Base):
+    """User-defined tag that can be attached to one or more call imports.
+
+    Tags coexist with the free-text ``CallImport.dataset`` column: dataset
+    is the primary high-level segregation, tags are an optional secondary
+    classification (an import can have many tags).
+    """
+
+    __tablename__ = "call_import_tags"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "name", name="uq_call_import_tag_org_name"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    name = Column(String(255), nullable=False)
+    color = Column(String(32), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+
+class CallImportTagAssignment(Base):
+    """Many-to-many join table between CallImport and CallImportTag."""
+
+    __tablename__ = "call_import_tag_assignments"
+
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    tag_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_tags.id", ondelete="CASCADE"),
+        primary_key=True,
+        index=True,
+    )
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+class CallImportEvaluation(Base):
+    """Parent record for an evaluation run over a CallImport batch.
+
+    A user picks a subset of org ``Metric`` rows and triggers an evaluation;
+    we fan out one ``CallImportEvaluationRow`` per source row and roll up
+    counters as workers finish. Status mirrors ``CallImportStatus`` plus a
+    ``RUNNING`` value so the UI can distinguish "queued" from "in flight".
+    """
+
+    __tablename__ = "call_import_evaluations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id"),
+        nullable=False,
+        index=True,
+    )
+    # Workspace isolation: mirrors the parent CallImport's workspace.
+    # Denormalized for fast filter-by-workspace listings without a join.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    created_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id"), nullable=True
+    )
+    last_updated_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+
+    # Optional user-supplied label for this run. Lets the UI surface
+    # something more meaningful than the UUID prefix (e.g. "March QA pass").
+    name = Column(String(255), nullable=True)
+
+    # JSON list of Metric UUID strings selected for this run. Stored as text
+    # in JSON so we don't have to deal with PG arrays of UUIDs / cascade
+    # delete policies when metrics are removed; the loader filters for
+    # still-existing org metrics at run time.
+    selected_metric_ids = Column(JSON, nullable=False, default=list)
+    # Hierarchy grouping snapshot: ``{parent_id_str: [child_id_str, ...]}``.
+    # Captures which children belong to which parent for THIS run so the UI
+    # / aggregator can reconstruct the tree even when the user selected
+    # only a subset of children, or after metrics are deleted / renamed.
+    # NULL on legacy rows means "no hierarchy" → fall back to flat
+    # ``selected_metric_ids`` semantics.
+    selected_metric_groups = Column(JSON, nullable=True)
+    # User-driven merges of LLM-discovered candidate sub-labels for
+    # ``allow_discovery`` parents. Shape:
+    # ``{"<parent_metric_id>": {"<from_slug>": "<to_slug>", ...}}``.
+    # Populated via ``POST .../discovered-labels/merge``; consulted by
+    # the discovered-labels aggregator, the flow graph builder, and the
+    # worker so that rows finishing AFTER a merge cannot reintroduce
+    # the merged-away slug. Empty dict on fresh rows.
+    discovered_label_aliases = Column(
+        JSON, nullable=False, default=dict, server_default="{}"
+    )
+
+    # Per-run opt-in for top-level metric discovery. When True, the LLM
+    # is asked to propose brand-new top-level metrics (boolean / rating /
+    # category) observed in the transcripts in addition to scoring the
+    # ``selected_metric_ids`` for the row. Candidates surface in a
+    # "Discovered metrics" panel on the evaluation's Flow tab and can
+    # be promoted into real standalone ``Metric`` rows via
+    # ``POST /metrics/from-discovered``. Defaults to False so existing
+    # evaluation creation payloads keep their previous behaviour.
+    discover_new_metrics = Column(
+        Boolean, nullable=False, default=False, server_default="false"
+    )
+    # Flat slug-to-slug redirect map for user merges + tombstones of
+    # discovered top-level metric candidates. Mirrors
+    # ``discovered_label_aliases`` but is NOT nested per parent —
+    # top-level metric discovery is not scoped to any parent. Shape::
+    #
+    #     {"<from_slug>": "<to_slug>", ...}
+    #
+    # An empty-string value tombstones the slug so workers finishing
+    # later can't re-introduce it.
+    discovered_metric_aliases = Column(
+        JSON, nullable=False, default=dict, server_default="{}"
+    )
+
+    # Run-level LLM config picked from the Run Evaluation modal. NULL on
+    # legacy rows means "use the historical OpenAI/gpt-4o default" — the
+    # worker checks for this and falls back accordingly. ``llm_credential_id``
+    # pins a specific AIProvider row when the org has multiple credentials
+    # for the same provider.
+    llm_provider = Column(String(50), nullable=True)
+    llm_model = Column(String(100), nullable=True)
+    llm_credential_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("aiproviders.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    llm_config = Column(JSON, nullable=True)
+    # Optional per-metric LLM override:
+    # ``{"<metric_id>": {"provider": "...", "model": "...", "credential_id": "..."}}``.
+    # Each entry overrides the run-level default for that metric only;
+    # missing keys = use run-level default. Stored as JSON so the UI can
+    # round-trip arbitrary {provider, model} pairs without migrations.
+    metric_llm_overrides = Column(JSON, nullable=True)
+
+    # When ``auto_transcribe`` was set on the create payload, record the
+    # STT provider/model used so the UI can show "Auto-transcribed via
+    # deepgram/nova-2" on the evaluation header. ``stt_credential_id`` is
+    # untyped (no FK) because STT keys may live in either ``aiproviders``
+    # (OpenAI) or ``integrations`` (Deepgram, ElevenLabs) — the
+    # transcription service handles the lookup.
+    stt_provider = Column(String(50), nullable=True)
+    stt_model = Column(String(100), nullable=True)
+    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    # Run-level LLM diariser config. Used when the create-run /
+    # retry-run paths chain a ``transcribe_call_import_row_task``
+    # because the row is missing a diarised transcript. Persisted on
+    # the run so a retry uses the same diariser the original create
+    # call picked (unless the retry payload explicitly overrides).
+    diarisation_llm_provider = Column(String(50), nullable=True)
+    diarisation_llm_model = Column(String(100), nullable=True)
+    diarisation_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
+    diarisation_prompt = Column(Text, nullable=True)
+    # Mode the run was *created* with for its auto-transcribe step.
+    # Retry chains read this to decide whether to enqueue an STT+LLM
+    # transcribe or a single-stage multimodal LLM transcribe — without
+    # it we'd have to infer the mode from "stt_provider is NULL", which
+    # would silently break legacy rows that simply never configured
+    # auto-transcribe. See migration 041 for the column DDL.
+    transcribe_mode = Column(
+        String(20),
+        nullable=False,
+        default="stt_llm",
+        server_default="stt_llm",
+    )
+
+    # Which of the two transcripts on each ``CallImportRow`` this run
+    # scored against. ``'production'`` reads ``CallImportRow.transcript``
+    # (the CSV-supplied value); ``'diarised'`` reads
+    # ``CallImportRow.diarised_transcript`` (the worker output). When
+    # the user ticks both checkboxes in the Run Evaluation modal we
+    # create two ``CallImportEvaluation`` rows — one per source — so
+    # the two scorings can be compared side-by-side. Defaults to
+    # ``'production'`` so legacy runs (which always read the single
+    # historical ``transcript`` column) keep their semantics.
+    transcript_source = Column(
+        String(20),
+        nullable=False,
+        default="production",
+        server_default="production",
+    )
+
+    # Cached LLM-generated TLDR rendered above the Visualizations charts.
+    # Populated lazily by ``POST /evaluations/{eval_id}/insights`` so we
+    # never auto-burn LLM tokens on page load. Shape::
+    #   {"narrative": str, "patterns": [str, ...],
+    #    "generated_at": iso8601, "generated_at_completed_rows": int,
+    #    "provider": str, "model": str}
+    # NULL on rows that have never been summarised.
+    tldr_summary = Column(JSON, nullable=True)
+
+    # Cached LLM-generated user insights for External Audit PDF section 03.
+    # Populated by a background Celery job triggered alongside TLDR generation.
+    # Shape: EvaluationUserInsightsState JSON (status, insights[], progress, …).
+    user_insights = Column(JSON, nullable=True)
+
+    # Cached per-metric failure clustering for internal diagnostics PDF/UI.
+    # Shape: EvaluationMetricClustersState JSON (status, groups[], …).
+    metric_clusters = Column(JSON, nullable=True)
+
+    # Cached LLM-generated prompt improvement suggestions keyed to an
+    # imported agent (PromptPartial tagged __imported_agent__).
+    # Shape: EvaluationPromptImprovementsState JSON.
+    prompt_improvements = Column(JSON, nullable=True)
+
+    # Cached LLM explanations for week-over-week metric deltas keyed by
+    # baseline evaluation id + completed row counts.
+    period_delta_explanations = Column(JSON, nullable=True)
+
+    status = Column(String(20), nullable=False, default="pending", index=True)
+
+    total_rows = Column(Integer, nullable=False, default=0)
+    completed_rows = Column(Integer, nullable=False, default=0)
+    failed_rows = Column(Integer, nullable=False, default=0)
+    # Flexprice pass-level delta billing watermark: rows already emitted
+    # on ``call_import.evaluation_completed`` for this evaluation run.
+    billed_completed_rows = Column(
+        Integer, nullable=False, default=0, server_default="0"
+    )
+    error_message = Column(Text, nullable=True)
+    celery_group_id = Column(String(255), nullable=True)
+
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    finished_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    call_import = relationship("CallImport", back_populates="evaluations")
+    row_results = relationship(
+        "CallImportEvaluationRow",
+        back_populates="evaluation",
+        cascade="all, delete-orphan",
+    )
+
+
+class CallImportEvaluationRow(Base):
+    """Per-source-row scoring output for a CallImportEvaluation parent."""
+
+    __tablename__ = "call_import_evaluation_rows"
+    __table_args__ = (
+        UniqueConstraint(
+            "evaluation_id", "call_import_row_id", name="uq_call_import_evaluation_row"
+        ),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    call_import_row_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_rows.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    status = Column(String(20), nullable=False, default="pending", index=True)
+    # Same shape as EvaluatorResult.metric_scores: {metric_id_str: {value, type, metric_name, ...}}
+    metric_scores = Column(JSON, nullable=False, default=dict)
+    error_message = Column(Text, nullable=True)
+    celery_task_id = Column(String(255), nullable=True)
+
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    finished_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    evaluation = relationship("CallImportEvaluation", back_populates="row_results")
+    source_row = relationship("CallImportRow")
+
+
+@event.listens_for(CallImportEvaluationRow, "before_insert")
+def _call_import_evaluation_row_fill_workspace_id(_mapper, connection, target):
+    """Denormalize workspace_id from the parent evaluation when omitted."""
+    if target.workspace_id is not None or target.evaluation_id is None:
+        return
+    workspace_id = connection.execute(
+        select(CallImportEvaluation.workspace_id).where(
+            CallImportEvaluation.id == target.evaluation_id
+        )
+    ).scalar_one_or_none()
+    if workspace_id is not None:
+        target.workspace_id = workspace_id
+
+
+class CallImportEvaluationReportSnapshot(Base):
+    """Persisted PDF-report aggregate used for period-over-period deltas."""
+
+    __tablename__ = "call_import_evaluation_report_snapshots"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    period_label = Column(String(64), nullable=True, index=True)
+    period_start = Column(Date, nullable=True, index=True)
+    period_end = Column(Date, nullable=True, index=True)
+    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
+    selected_metric_ids = Column(JSON, nullable=False, default=list, server_default="[]")
+    metric_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
+    insight_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
+    narrative = Column(JSON, nullable=True)
+    total_calls = Column(Integer, nullable=False, default=0)
+    selected_metric_count = Column(Integer, nullable=False, default=0)
+    total_metric_count = Column(Integer, nullable=False, default=0)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+
+# ---------------------------------------------------------------------------
+# Judge Alignment (AlignEval-style hybrid integration)
+#
+# Three tables back the "Judge Alignment" surface:
+#   - JudgeDataset:     a labeled dataset materialised from one of three sources
+#                       (voice transcripts, existing Metric/Evaluator outputs,
+#                       or a generic CSV upload). Holds the dataset's source
+#                       config + which fields play the role of input/output.
+#   - JudgeSample:      one row in a dataset (input/output pair plus an
+#                       optional binary pass/fail human label).
+#   - JudgeRun:         a single run of an LLM-judge (existing Evaluator) over
+#                       a subset of samples, with computed alignment metrics
+#                       (precision/recall/F1/Cohen's kappa) and per-sample
+#                       predictions. Optionally links to a GEPA optimization
+#                       run when the user kicks off prompt tuning from a
+#                       dataset.
+# ---------------------------------------------------------------------------
+
+
+class JudgeDataset(Base):
+    """Container for binary-labeled samples used to calibrate an LLM-judge."""
+
+    __tablename__ = "judge_datasets"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    # Workspace isolation: every judge dataset belongs to a workspace
+    # within its org. Samples and runs inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    name = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+
+    # One of: "transcript", "metric_output", "csv"
+    source_type = Column(String(32), nullable=False, index=True)
+    # Source-specific config. Examples:
+    #   transcript:     {"transcription_ids": [...]} or {"agent_id": "..."}
+    #   metric_output:  {"metric_id": "...", "evaluator_id": "..."}
+    #   csv:            {"s3_key": "...", "filename": "..."}
+    source_config = Column(JSON, nullable=False, default=dict)
+
+    # Field roles - which textual content is "input" vs "output" for the judge.
+    # For voice transcripts both default to the transcript text but can be
+    # tightened (e.g. agent-only turns vs full conversation).
+    input_field = Column(String(64), nullable=False, default="input")
+    output_field = Column(String(64), nullable=False, default="output")
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    samples = relationship(
+        "JudgeSample",
+        back_populates="dataset",
+        cascade="all, delete-orphan",
+        order_by="JudgeSample.created_at",
+    )
+    runs = relationship(
+        "JudgeRun",
+        back_populates="dataset",
+        cascade="all, delete-orphan",
+        order_by="JudgeRun.created_at.desc()",
+    )
+
+
+class JudgeSample(Base):
+    """One labelable input/output pair within a JudgeDataset."""
+
+    __tablename__ = "judge_samples"
+    __table_args__ = (
+        UniqueConstraint("dataset_id", "external_id", name="uq_judge_samples_dataset_external"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    dataset_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    # Stable identifier within the source (e.g. transcription UUID, CSV row id).
+    # Used to dedupe re-imports and link back to the originating record.
+    external_id = Column(String(128), nullable=True, index=True)
+
+    input_text = Column(Text, nullable=False)
+    output_text = Column(Text, nullable=False)
+
+    # Binary human label: "pass" | "fail" | null (unlabeled).
+    # Stored as string (rather than enum) so it stays trivially extendable.
+    label = Column(String(16), nullable=True, index=True)
+    labeled_by = Column(String(255), nullable=True)
+    labeled_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Source-specific context (e.g. agent_id, original metric value, csv row).
+    extra = Column(JSON, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    dataset = relationship("JudgeDataset", back_populates="samples")
+
+
+class JudgeRun(Base):
+    """One execution of an LLM-judge against a JudgeDataset, with alignment metrics."""
+
+    __tablename__ = "judge_runs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    dataset_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    # Reuses the existing Evaluator row (its custom_prompt + llm_provider + llm_model
+    # define the judge under test). Nullable so a run may target an inline prompt
+    # in the future without inflating the Evaluator table.
+    evaluator_id = Column(
+        UUID(as_uuid=True), ForeignKey("evaluators.id", ondelete="SET NULL"), nullable=True, index=True
+    )
+
+    # Which subset was scored: "all" | "dev" | "test"
+    split = Column(String(16), nullable=False, default="all")
+
+    # Snapshot of the model used (so a later Evaluator edit doesn't rewrite history).
+    llm_provider = Column(String(64), nullable=True)
+    llm_model = Column(String(128), nullable=True)
+
+    # Computed alignment metrics:
+    #   {"precision": float, "recall": float, "f1": float, "kappa": float,
+    #    "tp": int, "fp": int, "tn": int, "fn": int, "n": int}
+    metrics = Column(JSON, nullable=True)
+
+    # Per-sample predictions, keyed by sample_id (UUID string):
+    #   {sample_id: {"prediction": "pass"|"fail", "explanation": str, "raw": str}}
+    predictions = Column(JSON, nullable=True)
+
+    # Run lifecycle.
+    status = Column(String(20), nullable=False, default="pending", index=True)
+    error_message = Column(Text, nullable=True)
+    celery_task_id = Column(String, nullable=True, index=True)
+
+    # Optional link to a GEPA optimization run kicked off from this dataset.
+    gepa_optimization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("prompt_optimization_runs.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    dataset = relationship("JudgeDataset", back_populates="runs")
diff --git a/app/models/schemas.py b/app/models/schemas.py
index 78dd14f9..de5c203e 100644
--- a/app/models/schemas.py
+++ b/app/models/schemas.py
@@ -3168,6 +3168,8 @@ class CallImportResponse(BaseModel):
     error_message: Optional[str] = None
     created_at: datetime
     updated_at: datetime
+    created_by_email: Optional[str] = None
+    last_updated_by_email: Optional[str] = None
 
     model_config = ConfigDict(from_attributes=True)
 
@@ -3981,6 +3983,8 @@ class CallImportEvaluationResponse(BaseModel):
     finished_at: Optional[datetime] = None
     created_at: datetime
     updated_at: datetime
+    created_by_email: Optional[str] = None
+    last_updated_by_email: Optional[str] = None
     # Cached LLM-generated TLDR for the Visualizations tab. Lazily
     # populated by ``POST /evaluations/{eval_id}/insights``; ``None``
     # for runs the user has not summarised yet. ``is_stale`` on the
diff --git a/app/services/call_imports/audit.py b/app/services/call_imports/audit.py
new file mode 100644
index 00000000..6479ef18
--- /dev/null
+++ b/app/services/call_imports/audit.py
@@ -0,0 +1,99 @@
+"""Actor stamping and email resolution for call import audit fields."""
+
+from __future__ import annotations
+
+from typing import Dict, Iterable, Optional, Set, Tuple
+from uuid import UUID
+
+from sqlalchemy.orm import Session
+
+from app.core.auth.principal import Principal
+from app.models.database import CallImport, CallImportEvaluation, User
+
+
+def stamp_call_import_actor(
+    call_import: CallImport,
+    principal: Principal,
+    *,
+    creating: bool = False,
+) -> None:
+    if creating and principal.user_id is not None:
+        call_import.created_by_user_id = principal.user_id
+    if principal.user_id is not None:
+        call_import.last_updated_by_user_id = principal.user_id
+
+
+def stamp_evaluation_actor(
+    evaluation: CallImportEvaluation,
+    principal: Principal,
+    *,
+    creating: bool = False,
+) -> None:
+    if creating and principal.user_id is not None:
+        evaluation.created_by_user_id = principal.user_id
+    if principal.user_id is not None:
+        evaluation.last_updated_by_user_id = principal.user_id
+
+
+def user_ids_from_call_imports(imports: Iterable[CallImport]) -> Set[UUID]:
+    ids: Set[UUID] = set()
+    for row in imports:
+        if row.created_by_user_id is not None:
+            ids.add(row.created_by_user_id)
+        if row.last_updated_by_user_id is not None:
+            ids.add(row.last_updated_by_user_id)
+    return ids
+
+
+def user_ids_from_evaluations(
+    evaluations: Iterable[CallImportEvaluation],
+) -> Set[UUID]:
+    ids: Set[UUID] = set()
+    for row in evaluations:
+        if row.created_by_user_id is not None:
+            ids.add(row.created_by_user_id)
+        if row.last_updated_by_user_id is not None:
+            ids.add(row.last_updated_by_user_id)
+    return ids
+
+
+def emails_for_user_ids(db: Session, user_ids: Iterable[UUID]) -> Dict[UUID, str]:
+    unique = {uid for uid in user_ids if uid is not None}
+    if not unique:
+        return {}
+    rows = db.query(User.id, User.email).filter(User.id.in_(unique)).all()
+    return {row.id: row.email for row in rows if row.email}
+
+
+def actor_emails_for_call_import(
+    call_import: CallImport,
+    email_by_id: Dict[UUID, str],
+) -> Tuple[Optional[str], Optional[str]]:
+    created = (
+        email_by_id.get(call_import.created_by_user_id)
+        if call_import.created_by_user_id
+        else None
+    )
+    updated = (
+        email_by_id.get(call_import.last_updated_by_user_id)
+        if call_import.last_updated_by_user_id
+        else None
+    )
+    return created, updated
+
+
+def actor_emails_for_evaluation(
+    evaluation: CallImportEvaluation,
+    email_by_id: Dict[UUID, str],
+) -> Tuple[Optional[str], Optional[str]]:
+    created = (
+        email_by_id.get(evaluation.created_by_user_id)
+        if evaluation.created_by_user_id
+        else None
+    )
+    updated = (
+        email_by_id.get(evaluation.last_updated_by_user_id)
+        if evaluation.last_updated_by_user_id
+        else None
+    )
+    return created, updated
diff --git a/env.example b/env.example
index ff2f226a..c434c850 100644
--- a/env.example
+++ b/env.example
@@ -41,7 +41,7 @@ RATE_LIMIT_PER_MINUTE=60
 # Comma-separated or JSON list. See config.yml.example for full docs.
 # OSS default:        api_key,local_password
 # Enterprise SSO:     api_key,external_oidc   (Okta / Azure AD / Google / Cognito / Auth0 / ...)
-AUTH_PROVIDERS=api_key,local_password
+AUTH_PROVIDERS=["api_key","local_password"]
 
 # Local password (HS256 Bearer tokens signed with SECRET_KEY)
 AUTH_LOCAL_TOKEN_TTL_MINUTES=15
diff --git a/frontend/src/types/api.ts b/frontend/src/types/api.ts
index 91fbc9c5..39c0fed7 100644
--- a/frontend/src/types/api.ts
+++ b/frontend/src/types/api.ts
@@ -1,2132 +1,2136 @@
-// API Types matching the backend schemas
-
-export type { LLMGenerationConfig } from '../config/llmGenerationParams'
-import type { LLMGenerationConfig } from '../config/llmGenerationParams'
-
-export enum EvaluationType {
-  ASR = 'asr',
-  TTS = 'tts',
-}
-
-export enum EvaluationStatus {
-  PENDING = 'pending',
-  PROCESSING = 'processing',
-  COMPLETED = 'completed',
-  FAILED = 'failed',
-  CANCELLED = 'cancelled',
-}
-
-export interface AudioFile {
-  id: string
-  filename: string
-  format: string
-  file_size: number
-  duration?: number | null
-  sample_rate?: number | null
-  channels?: number | null
-  uploaded_at: string
-}
-
-export interface Evaluation {
-  id: string
-  audio_id: string
-  reference_text?: string | null
-  evaluation_type: EvaluationType
-  model_name?: string | null
-  status: EvaluationStatus
-  metrics_requested?: string[] | null
-  created_at: string
-  started_at?: string | null
-  completed_at?: string | null
-  error_message?: string | null
-}
-
-export interface DashboardSummary {
-  evaluations: {
-    total: number
-    completed: number
-    pending: number
-    failed: number
-  }
-  resources: {
-    agents: number
-    personas: number
-    scenarios: number
-    integrations: number
-    voice_bundles: number
-  }
-  setup_progress: {
-    has_integration: boolean
-    has_voice_bundle: boolean
-    has_agent: boolean
-    has_evaluation: boolean
-  }
-  metrics: {
-    total: number
-    enabled: number
-  }
-  call_imports: {
-    total: number
-  }
-  call_import_evaluations: {
-    total: number
-    completed: number
-    running: number
-    failed: number
-  }
-  recent_evaluations: Evaluation[]
-}
-
-export interface ModelConfigEntry {
-  provider: string
-  model_type: string
-  description?: string
-  featured?: boolean
-  featured_rank?: number
-  highlights?: string[]
-}
-
-export interface EvaluationCreate {
-  audio_id: string
-  reference_text?: string | null
-  evaluation_type: EvaluationType
-  model_name?: string | null
-  metrics?: string[]
-}
-
-export interface EvaluationResult {
-  evaluation_id: string
-  status: EvaluationStatus
-  transcript?: string | null
-  metrics: Record<string, any>
-  processing_time?: number | null
-  model_used?: string | null
-  created_at: string
-}
-
-export interface BatchEvaluationResult {
-  processed_files: number
-  failed_files: number
-  aggregated_metrics?: Record<string, any> | null
-  individual_results: EvaluationResult[]
-}
-
-/** Voice agent evaluator run (evaluator_results table). */
-export type EvaluatorResultStatus =
-  | 'queued'
-  | 'call_initiating'
-  | 'call_connecting'
-  | 'call_in_progress'
-  | 'call_ended'
-  | 'transcribing'
-  | 'evaluating'
-  | 'fetching_details'
-  | 'completed'
-  | 'failed'
-
-export interface EvaluatorResultMetricScore {
-  value: unknown
-  type: string
-  metric_name: string
-  parent_metric_id?: string | null
-}
-
-export interface EvaluatorResultRow {
-  id: string
-  result_id: string
-  name: string | null
-  evaluator_id: string | null
-  agent_id?: string | null
-  persona_id?: string | null
-  scenario_id?: string | null
-  suite_id?: string | null
-  timestamp: string
-  duration_seconds: number | null
-  status: EvaluatorResultStatus
-  metric_scores: Record<string, EvaluatorResultMetricScore> | null
-  error_message: string | null
-  agent?: { id: string; name: string } | null
-  scenario?: { id: string; name: string } | null
-}
-
-export interface EvaluatorResultListResponse {
-  items: EvaluatorResultRow[]
-  total: number
-}
-
-export interface EvaluatorResultCounts {
-  total: number
-  completed: number
-  failed: number
-  in_progress: number
-  last_run_at?: string | null
-}
-
-export interface EvaluatorResultsScenarioSummary {
-  scenario_id: string
-  scenario_name: string
-  counts: EvaluatorResultCounts
-}
-
-export interface EvaluatorResultsSuiteSummary {
-  suite_id: string
-  suite_name?: string | null
-  agent_id: string
-  persona_id?: string | null
-  counts: EvaluatorResultCounts
-  scenarios?: EvaluatorResultsScenarioSummary[] | null
-}
-
-export interface EvaluatorResultsAgentSummary {
-  agent_id: string
-  agent_name: string
-  counts: EvaluatorResultCounts
-  suites?: EvaluatorResultsSuiteSummary[] | null
-}
-
-export interface EvaluatorResultsOverviewResponse {
-  workspace_counts: EvaluatorResultCounts
-  agents: EvaluatorResultsAgentSummary[]
-  unassigned: {
-    counts: EvaluatorResultCounts
-    recent_result_ids: string[]
-  }
-}
-
-export interface ListEvaluatorResultsParams {
-  skip?: number
-  limit?: number
-  evaluatorId?: string
-  agentId?: string
-  suiteId?: string
-  scenarioId?: string
-  status?: 'completed' | 'failed' | 'in_progress'
-  unassignedOnly?: boolean
-  playground?: boolean
-  testAgentsOnly?: boolean
-}
-
-export interface APIKey {
-  id: string
-  key: string
-  name?: string | null
-  is_active: boolean
-  created_at: string
-  last_used?: string | null
-  message?: string
-}
-
-export interface MessageResponse {
-  message: string
-}
-
-// IAM & User Types
-export enum Role {
-  READER = 'reader',
-  WRITER = 'writer',
-  ADMIN = 'admin',
-}
-
-export enum InvitationStatus {
-  PENDING = 'pending',
-  ACCEPTED = 'accepted',
-  DECLINED = 'declined',
-  EXPIRED = 'expired',
-}
-
-export interface User {
-  id: string
-  email: string
-  name?: string | null
-  is_active: boolean
-  created_at: string
-}
-
-export interface OrganizationMember {
-  id: string
-  user_id: string
-  organization_id: string
-  role: Role
-  joined_at: string
-  user: User
-}
-
-export interface Invitation {
-  id: string
-  organization_id: string
-  email: string
-  role: Role
-  status: InvitationStatus
-  expires_at: string
-  created_at: string
-  organization_name?: string | null
-}
-
-export interface InvitationCreate {
-  email: string
-  role: Role
-}
-
-export interface RoleUpdate {
-  role: Role
-}
-
-export interface Profile {
-  id: string
-  email: string
-  name?: string | null
-  first_name?: string | null
-  last_name?: string | null
-  created_at: string
-  organizations: Array<{
-    id: string
-    name: string
-    role: string
-    joined_at: string
-  }>
-}
-
-export interface UserUpdate {
-  name?: string | null
-  first_name?: string | null
-  last_name?: string | null
-  email?: string | null
-}
-
-export interface UserPreferences {
-  theme?: string
-  notifications_enabled?: boolean
-  email_notifications?: boolean
-  default_language?: string
-  [key: string]: any
-}
-
-export interface UserPreferencesUpdate {
-  theme?: string
-  notifications_enabled?: boolean
-  email_notifications?: boolean
-  default_language?: string
-  [key: string]: any
-}
-
-// Integration Types
-export enum IntegrationPlatform {
-  RETELL = 'retell',
-  VAPI = 'vapi',
-  CARTESIA = 'cartesia',
-  ELEVENLABS = 'elevenlabs',
-  DEEPGRAM = 'deepgram',
-  MURF = 'murf',
-  SARVAM = 'sarvam',
-  VOICEMAKER = 'voicemaker',
-  SMALLEST = 'smallest',
-}
-
-export enum TelephonyProvider {
-  PLIVO = 'plivo',
-  EXOTEL = 'exotel',
-  VOBIZ = 'vobiz',
-}
-
-export type CredentialRoutingMode = 'inherit' | 'gateway' | 'direct'
-export type GatewayInterfaceMode = 'inherit' | 'litellm_shim' | 'native_openai'
-
-export type EffectiveCredentialRouting =
-  | 'inherit'
-  | 'direct'
-  | 'gateway'
-  | 'bifrost'
-  | 'litellm_proxy'
-
-export interface Integration {
-  id: string
-  organization_id: string
-  platform: IntegrationPlatform
-  name?: string | null
-  public_key?: string | null
-  is_active: boolean
-  /** True if this row is the default credential for (org, platform). */
-  is_default?: boolean
-  routing_mode?: CredentialRoutingMode
-  effective_routing?: EffectiveCredentialRouting
-  created_at: string
-  updated_at: string
-  last_tested_at?: string | null
-}
-
-export interface IntegrationCreate {
-  platform: IntegrationPlatform
-  api_key: string
-  public_key?: string
-  name?: string | null
-  routing_mode?: CredentialRoutingMode
-  /** Mark the new credential as the default for (org, platform). */
-  is_default?: boolean
-}
-
-// VoiceBundle Types
-export enum ModelProvider {
-  OPENAI = 'openai',
-  ANTHROPIC = 'anthropic',
-  GOOGLE = 'google',
-  XAI = 'xai',
-  FIREWORKS = 'fireworks',
-  COHERE = 'cohere',
-  MISTRAL = 'mistral',
-  META = 'meta',
-  TOGETHER = 'together',
-  PERPLEXITY = 'perplexity',
-  AZURE = 'azure',
-  AWS = 'aws',
-  DEEPGRAM = 'deepgram',
-  CARTESIA = 'cartesia',
-  ELEVENLABS = 'elevenlabs',
-  MURF = 'murf',
-  CUSTOM = 'custom',
-  SARVAM = 'sarvam',
-  VOICEMAKER = 'voicemaker',
-  SMALLEST = 'smallest',
-}
-
-// AI Provider Types
-export interface AIProvider {
-  id: string
-  provider: ModelProvider
-  api_key?: string | null
-  name?: string | null
-  endpoint_url?: string | null
-  is_active: boolean
-  /** True if this row is the default credential for (org, provider). */
-  is_default?: boolean
-  routing_mode?: CredentialRoutingMode
-  gateway_model?: string | null
-  gateway_interface?: GatewayInterfaceMode
-  gateway_base_url?: string | null
-  gateway_auth_header?: string | null
-  gateway_auth_secret_env?: string | null
-  has_gateway_auth_secret?: boolean
-  gateway_extra_headers?: Record<string, string> | null
-  /** True when provider secrets are resolved by the Bifrost gateway. */
-  gateway_managed?: boolean
-  effective_routing?: EffectiveCredentialRouting
-  effective_gateway_interface?: 'litellm_shim' | 'native_openai'
-  created_at: string
-  updated_at: string
-  last_tested_at?: string | null
-}
-
-export interface AIProviderCreate {
-  provider: ModelProvider
-  api_key?: string | null
-  name?: string | null
-  endpoint_url?: string | null
-  routing_mode?: CredentialRoutingMode
-  gateway_model?: string | null
-  gateway_interface?: GatewayInterfaceMode
-  gateway_base_url?: string | null
-  gateway_auth_header?: string | null
-  gateway_auth_secret_env?: string | null
-  gateway_auth_secret?: string | null
-  gateway_extra_headers?: Record<string, string> | null
-  /** Mark the new credential as the default for (org, provider). */
-  is_default?: boolean
-}
-
-export interface AIProviderUpdate {
-  api_key?: string | null
-  name?: string | null
-  endpoint_url?: string | null
-  is_active?: boolean
-  routing_mode?: CredentialRoutingMode
-  gateway_model?: string | null
-  gateway_interface?: GatewayInterfaceMode
-  gateway_base_url?: string | null
-  gateway_auth_header?: string | null
-  gateway_auth_secret_env?: string | null
-  gateway_auth_secret?: string | null
-  clear_gateway_auth_secret?: boolean
-  gateway_extra_headers?: Record<string, string> | null
-}
-
-export enum VoiceBundleType {
-  STT_LLM_TTS = 'stt_llm_tts',
-  S2S = 's2s',
-}
-
-export interface VoiceBundle {
-  id: string
-  name: string
-  description?: string | null
-  bundle_type: VoiceBundleType
-  stt_provider?: ModelProvider | null
-  stt_model?: string | null
-  /**
-   * Optional explicit AIProvider/Integration row id for STT. When null the
-   * runtime resolver picks the default credential for stt_provider.
-   */
-  stt_credential_id?: string | null
-  llm_provider?: ModelProvider | null
-  llm_model?: string | null
-  llm_temperature?: number | null
-  llm_max_tokens?: number | null
-  llm_config?: Record<string, any> | null
-  llm_credential_id?: string | null
-  tts_provider?: ModelProvider | null
-  tts_model?: string | null
-  tts_voice?: string | null
-  tts_config?: Record<string, any> | null
-  tts_credential_id?: string | null
-  s2s_provider?: ModelProvider | null
-  s2s_model?: string | null
-  s2s_config?: Record<string, any> | null
-  s2s_credential_id?: string | null
-  extra_metadata?: Record<string, any> | null
-  is_active: boolean
-  created_at: string
-  updated_at: string
-  created_by?: string | null
-}
-
-export interface VoiceBundleCreate {
-  name: string
-  description?: string | null
-  bundle_type?: VoiceBundleType
-  stt_provider?: ModelProvider | null
-  stt_model?: string | null
-  stt_credential_id?: string | null
-  llm_provider?: ModelProvider | null
-  llm_model?: string | null
-  llm_temperature?: number | null
-  llm_max_tokens?: number | null
-  llm_config?: Record<string, any> | null
-  llm_credential_id?: string | null
-  tts_provider?: ModelProvider | null
-  tts_model?: string | null
-  tts_voice?: string | null
-  tts_config?: Record<string, any> | null
-  tts_credential_id?: string | null
-  s2s_provider?: ModelProvider | null
-  s2s_model?: string | null
-  s2s_config?: Record<string, any> | null
-  s2s_credential_id?: string | null
-  extra_metadata?: Record<string, any> | null
-}
-
-// Test Agent Types
-export interface AgentPhoneAssignmentConflict {
-  agent_id: string
-  agent_name: string
-  phone_number: string
-}
-
-export interface AgentPhoneAssignmentCheckResponse {
-  available: boolean
-  phone_number?: string | null
-  conflict?: AgentPhoneAssignmentConflict | null
-}
-
-export interface TestAgent {
-  id: string
-  agent_id?: string | null
-  name: string
-  phone_number?: string | null
-  telephony_phone_number_id?: string | null
-  language: string
-  description: string | null
-  prompt_variables?: Record<string, string> | null
-  silence_hangup_secs?: number
-  call_type: string
-  call_medium: string
-  voice_bundle_id?: string | null
-  voice_ai_integration_id?: string | null
-  voice_ai_agent_id?: string | null
-  provider_prompt?: string | null
-  provider_prompt_synced_at?: string | null
-  created_at: string
-  updated_at: string
-}
-
-// Test Agent Conversation Types
-export interface TestAgentConversation {
-  id: string
-  organization_id: string
-  agent_id: string
-  persona_id: string
-  scenario_id: string
-  voice_bundle_id: string
-  status: string
-  live_transcription?: Array<{
-    speaker: string
-    text: string
-    timestamp: number
-    audio_segment_key?: string
-  }> | null
-  conversation_audio_key?: string | null
-  full_transcript?: string | null
-  started_at: string
-  ended_at?: string | null
-  duration_seconds?: number | null
-  conversation_metadata?: Record<string, any> | null
-  created_at: string
-  updated_at: string
-  created_by?: string | null
-}
-
-export interface TestAgentConversationCreate {
-  agent_id: string
-  persona_id: string
-  scenario_id: string
-  voice_bundle_id: string
-  conversation_metadata?: Record<string, any> | null
-}
-
-export interface TestAgentConversationUpdate {
-  status?: string | null
-  live_transcription?: Array<Record<string, any>> | null
-  full_transcript?: string | null
-  conversation_metadata?: Record<string, any> | null
-}
-
-export interface VoiceBundleUpdate {
-  name?: string
-  description?: string | null
-  stt_provider?: ModelProvider
-  stt_model?: string
-  stt_credential_id?: string | null
-  llm_provider?: ModelProvider
-  llm_model?: string
-  llm_temperature?: number | null
-  llm_max_tokens?: number | null
-  llm_config?: Record<string, any> | null
-  llm_credential_id?: string | null
-  tts_provider?: ModelProvider
-  tts_model?: string
-  tts_voice?: string | null
-  tts_config?: Record<string, any> | null
-  tts_credential_id?: string | null
-  s2s_provider?: ModelProvider | null
-  s2s_model?: string | null
-  s2s_config?: Record<string, any> | null
-  s2s_credential_id?: string | null
-  extra_metadata?: Record<string, any> | null
-  is_active?: boolean
-}
-
-// Data Sources Types
-export interface S3ConnectionTest {
-  bucket_name: string
-  region?: string
-  access_key_id: string
-  secret_access_key: string
-  endpoint_url?: string | null
-}
-
-export interface S3ConnectionTestResponse {
-  success: boolean
-  message: string
-  bucket_name?: string | null
-}
-
-export interface S3FileInfo {
-  key: string
-  filename: string
-  size: number
-  last_modified: string
-}
-
-export interface S3FolderInfo {
-  name: string
-  path: string
-}
-
-export interface S3ListFilesResponse {
-  files: S3FileInfo[]
-  total: number
-  prefix?: string | null
-}
-
-export interface S3BrowseResponse {
-  folders: S3FolderInfo[]
-  files: S3FileInfo[]
-  current_path: string
-  organization_id: string
-}
-
-export interface S3Status {
-  enabled: boolean
-  provider?: 's3' | 'gcs' | string
-  error?: string | null
-}
-
-// Alert Types
-export enum AlertMetricType {
-  NUMBER_OF_CALLS = 'number_of_calls',
-  CALL_DURATION = 'call_duration',
-  ERROR_RATE = 'error_rate',
-  SUCCESS_RATE = 'success_rate',
-  LATENCY = 'latency',
-  CUSTOM = 'custom',
-}
-
-export enum AlertAggregation {
-  SUM = 'sum',
-  AVG = 'avg',
-  COUNT = 'count',
-  MIN = 'min',
-  MAX = 'max',
-}
-
-export enum AlertOperator {
-  GREATER_THAN = '>',
-  LESS_THAN = '<',
-  GREATER_THAN_OR_EQUAL = '>=',
-  LESS_THAN_OR_EQUAL = '<=',
-  EQUAL = '=',
-  NOT_EQUAL = '!=',
-}
-
-export enum AlertNotifyFrequency {
-  IMMEDIATE = 'immediate',
-  HOURLY = 'hourly',
-  DAILY = 'daily',
-  WEEKLY = 'weekly',
-}
-
-export enum AlertStatus {
-  ACTIVE = 'active',
-  PAUSED = 'paused',
-  DISABLED = 'disabled',
-}
-
-export enum AlertHistoryStatus {
-  TRIGGERED = 'triggered',
-  NOTIFIED = 'notified',
-  ACKNOWLEDGED = 'acknowledged',
-  RESOLVED = 'resolved',
-}
-
-export interface Alert {
-  id: string
-  organization_id: string
-  name: string
-  description?: string | null
-  metric_type: AlertMetricType
-  aggregation: AlertAggregation
-  operator: AlertOperator
-  threshold_value: number
-  time_window_minutes: number
-  agent_ids?: string[] | null
-  notify_frequency: AlertNotifyFrequency
-  notify_emails?: string[] | null
-  notify_webhooks?: string[] | null
-  status: AlertStatus
-  created_at: string
-  updated_at: string
-  created_by?: string | null
-}
-
-export interface AlertCreate {
-  name: string
-  description?: string | null
-  metric_type?: AlertMetricType
-  aggregation?: AlertAggregation
-  operator?: AlertOperator
-  threshold_value: number
-  time_window_minutes?: number
-  agent_ids?: string[] | null
-  notify_frequency?: AlertNotifyFrequency
-  notify_emails?: string[]
-  notify_webhooks?: string[]
-}
-
-export interface AlertUpdate {
-  name?: string
-  description?: string | null
-  metric_type?: AlertMetricType
-  aggregation?: AlertAggregation
-  operator?: AlertOperator
-  threshold_value?: number
-  time_window_minutes?: number
-  agent_ids?: string[] | null
-  notify_frequency?: AlertNotifyFrequency
-  notify_emails?: string[]
-  notify_webhooks?: string[]
-  status?: AlertStatus
-}
-
-export interface AlertHistoryItem {
-  id: string
-  organization_id: string
-  alert_id: string
-  triggered_at: string
-  triggered_value: number
-  threshold_value: number
-  status: AlertHistoryStatus
-  notified_at?: string | null
-  notification_details?: Record<string, any> | null
-  acknowledged_at?: string | null
-  acknowledged_by?: string | null
-  resolved_at?: string | null
-  resolved_by?: string | null
-  resolution_notes?: string | null
-  context_data?: Record<string, any> | null
-  created_at: string
-  updated_at: string
-  alert?: Alert
-}
-
-
-// Cron Job Types
-export enum CronJobStatus {
-  ACTIVE = 'active',
-  PAUSED = 'paused',
-  COMPLETED = 'completed',
-}
-
-export interface CronJob {
-  id: string
-  organization_id: string
-  name: string
-  cron_expression: string
-  timezone: string
-  max_runs: number
-  current_runs: number
-  evaluator_ids: string[]
-  status: CronJobStatus
-  next_run_at?: string | null
-  last_run_at?: string | null
-  created_at: string
-  updated_at: string
-  created_by?: string | null
-}
-
-export interface CronJobCreate {
-  name: string
-  cron_expression: string
-  timezone: string
-  max_runs: number
-  evaluator_ids: string[]
-}
-
-export interface CronJobUpdate {
-  name?: string
-  cron_expression?: string
-  timezone?: string
-  max_runs?: number
-  evaluator_ids?: string[]
-  status?: CronJobStatus
-}
-
-// --- Call Imports ---
-
-/**
- * Lifecycle for a call-import batch.
- *
- *  - ``uploaded``   : file landed in S3, no mapping yet.
- *  - ``mapped``     : user picked a schema + sheet + column mapping; no
- *                     rows materialised yet, no worker enqueued.
- *  - ``processing`` : rows materialised + workers enqueued.
- *  - ``pending``    : transient state used by the legacy one-shot
- *                     ``POST /upload`` endpoint just before transitioning
- *                     to ``processing``.
- */
-export type CallImportStatus =
-  | 'pending'
-  | 'uploaded'
-  | 'mapped'
-  | 'processing'
-  | 'completed'
-  | 'partial'
-  | 'failed'
-  | 'deleting'
-
-export type CallImportRowStatus =
-  | 'pending'
-  | 'processing'
-  | 'completed'
-  | 'failed'
-
-/** Where the value in `transcript` came from. */
-export type CallImportTranscriptSource =
-  | 'csv'
-  | 'transcribed'
-  | 'edited'
-  | null
-/** Lifecycle status for the post-hoc transcription workflow itself. */
-export type CallImportTranscriptStatus =
-  | 'idle'
-  | 'pending'
-  | 'running'
-  | 'completed'
-  | 'failed'
-  | null
-
-/**
- * Which transcript an evaluation run scored against.
- *  - `production`: the CSV-supplied value on `CallImportRow.transcript`.
- *  - `diarised`: the worker-produced value on `CallImportRow.diarised_transcript`.
- */
-export type CallImportEvaluationTranscriptSource = 'production' | 'diarised'
-
-/**
- * One contiguous turn inside ``CallImportRow.diarised_segments``.
- *
- * The diarisation worker rewrites each pyannote ``Speaker N`` label
- * into ``agent`` / ``user`` (first speaker = agent heuristic). Anything
- * beyond two distinct speakers keeps a generic ``speaker_N`` label so
- * multi-party recordings still render every voice.
- */
-export interface CallImportDiarisedSegment {
-  speaker: string
-  text: string
-  start: number
-  end: number
-  /** Original pyannote label (``Speaker 1`` / ``Speaker 2`` / ...). */
-  raw_speaker: string
-}
-
-export interface CallImportRow {
-  id: string
-  row_index: number
-  /** Mandatory identifier per row. Renamed from ``external_call_id``. */
-  conversation_id: string
-  recording_url: string | null
-  recording_date: string | null
-  /** Production transcript — the value supplied via the CSV upload. */
-  transcript: string | null
-  /** Provenance of the stored production transcript (csv = CSV upload, edited = manual edit). */
-  transcript_source: CallImportTranscriptSource
-  /** Legacy: provider recorded by the original transcription worker before the split. */
-  transcript_provider: string | null
-  transcript_model: string | null
-  transcript_status: CallImportTranscriptStatus
-  transcript_error: string | null
-  transcribed_at: string | null
-  /** Diarised transcript — produced by the post-hoc diarisation worker. */
-  diarised_transcript: string | null
-  /** Provider used by the diarisation worker (e.g. "deepgram"). */
-  diarised_transcript_provider: string | null
-  diarised_transcript_model: string | null
-  diarised_transcript_status: CallImportTranscriptStatus
-  diarised_transcript_error: string | null
-  diarised_at: string | null
-  /**
-   * Structured speaker turns produced by the diarisation worker. Each
-   * entry is a single contiguous turn shaped as
-   * `{ speaker: 'agent' | 'user' | 'speaker_N', text, start, end,
-   *   raw_speaker }`. ``diarised_transcript`` is a rendered
-   * `<speaker>: <text>` view of this list with
-   * ``diarised_speaker_swap`` applied. ``null`` on legacy rows that
-   * were diarised before structured turns were persisted (or when the
-   * STT provider didn't surface segments).
-   */
-  diarised_segments: CallImportDiarisedSegment[] | null
-  /**
-   * When ``true`` the ``agent`` <-> ``user`` mapping inside
-   * ``diarised_segments`` is inverted in the rendered transcript /
-   * CSV export. The worker writes the canonical mapping using a
-   * "first speaker is the agent" heuristic; reviewers can flip the
-   * toggle from the row detail panel without re-running diarisation.
-   */
-  diarised_speaker_swap: boolean
-  /**
-   * LLM that turned the STT plain-text output into structured
-   * ``diarised_segments``. NULL on legacy rows (pre-LLM-diariser).
-   */
-  diarised_llm_provider: string | null
-  diarised_llm_model: string | null
-  /**
-   * Exact prompt the LLM diariser ran with. Useful for the modal to
-   * pre-fill its textarea when the operator wants to iterate on a
-   * previously-diarised row.
-   */
-  diarised_prompt: string | null
-  /**
-   * Diarisation pipeline that produced this row's turns.
-   * - `stt_llm` (default) — two-stage STT then LLM diariser.
-   * - `llm_only` — single-stage multimodal LLM (audio in).
-   * Read-only; written by the worker on each diarisation.
-   */
-  transcribe_mode?: 'stt_llm' | 'llm_only'
-  /**
-   * Per-row preservation of the mapped source cells. Values land here
-   * as whatever type the schema parameter coerced them to —
-   * strings (text / url / conversation_id / recording_url /
-   * recording_date / transcript / datetime), numbers, booleans, or
-   * ``null`` for blanks. Always
-   * coerce with ``String(value)`` before string operations.
-   */
-  raw_columns: Record<string, string | number | boolean | null> | null
-  status: CallImportRowStatus
-  recording_s3_key: string | null
-  recording_content_type: string | null
-  recording_size_bytes: number | null
-  error_message: string | null
-  attempts: number
-  created_at: string
-  updated_at: string
-}
-
-export interface CallImportTag {
-  id: string
-  name: string
-  color: string | null
-  created_at: string
-  updated_at: string
-}
-
-/**
- * Parameter type tag on a Call Import schema parameter.
- *
- *  - ``conversation_id``: mandatory identifier (one per schema).
- *  - ``recording_url``: feeds ``CallImportRow.recording_url``.
- *  - ``recording_date``: date-only call recording date used for reports.
- *  - ``transcript``: feeds ``CallImportRow.transcript``.
- *  - ``text`` / ``number`` / ``boolean`` / ``datetime`` / ``url``:
- *    generic typed fields preserved per row in ``raw_columns`` and
- *    surfaced in the evaluation export under the parameter's name.
- */
-export type CallImportSchemaParameterType =
-  | 'conversation_id'
-  | 'recording_url'
-  | 'recording_date'
-  | 'transcript'
-  | 'text'
-  | 'number'
-  | 'boolean'
-  | 'datetime'
-  | 'url'
-
-export interface CallImportSchemaParameter {
-  id?: string
-  name: string
-  type: CallImportSchemaParameterType
-  description: string | null
-  is_required: boolean
-  ordering?: number
-}
-
-export interface CallImportSchema {
-  id: string
-  organization_id: string
-  workspace_id: string
-  name: string
-  description: string | null
-  parameters: CallImportSchemaParameter[]
-  /** How many CallImport batches reference this schema. */
-  usage_count: number
-  created_at: string
-  updated_at: string
-}
-
-export interface CallImportSchemaListResponse {
-  items: CallImportSchema[]
-  total: number
-}
-
-export interface CallImportSchemaCreate {
-  name: string
-  description?: string | null
-  parameters: Array<Omit<CallImportSchemaParameter, 'id' | 'ordering'>>
-}
-
-export interface CallImportSchemaUpdate {
-  name?: string
-  description?: string | null
-  parameters?: Array<Omit<CallImportSchemaParameter, 'id' | 'ordering'>>
-}
-
-/**
- * In-org Workspace - the active workspace scopes call imports and
- * metrics in the UI. The org's Default workspace is auto-seeded by
- * migration 033 and cannot be deleted.
- */
-export interface Workspace {
-  id: string
-  organization_id: string
-  name: string
-  slug: string
-  is_default: boolean
-  created_at: string
-  updated_at: string
-  role_id?: string | null
-  role_name?: string | null
-  capabilities?: string[]
-}
-
-export interface WorkspaceRole {
-  id: string
-  organization_id: string
-  name: string
-  description?: string | null
-  capabilities: string[]
-  is_system: boolean
-  created_at: string
-  updated_at: string
-}
-
-export interface WorkspaceMember {
-  id: string
-  workspace_id: string
-  user_id: string
-  role_id: string
-  role_name: string
-  user_email: string
-  user_name?: string | null
-  added_by_user_id?: string | null
-  created_at: string
-}
-
-export interface CapabilityInfo {
-  key: string
-  label: string
-}
-
-export interface CapabilityDomain {
-  key: string
-  label: string
-  capabilities: CapabilityInfo[]
-}
-
-export interface WorkspaceRoleCreate {
-  name: string
-  description?: string | null
-  capabilities: string[]
-}
-
-export interface WorkspaceRoleUpdate {
-  name?: string
-  description?: string | null
-  capabilities?: string[]
-}
-
-export interface CallImportSourceRowSkip {
-  source_row: number
-  reason: string
-  message: string
-}
-
-export interface CallImport {
-  id: string
-  organization_id: string
-  /** Workspace this import belongs to. */
-  workspace_id: string
-  /**
-   * Telephony provider key. ``null`` until the IMPORT stage in the
-   * staged flow (which is the first step that knows the provider).
-   * Always populated on post-import batches and on legacy one-shot
-   * uploads.
-   */
-  provider: string | null
-  telephony_integration_id: string | null
-  original_filename: string | null
-  /**
-   * For Excel uploads, which worksheet this batch came from. ``null``
-   * for CSV uploads (CSV files have no sheet concept) and for any
-   * imports created before multi-sheet support landed.
-   */
-  sheet_name: string | null
-  /** Optional free-text dataset label (high-level segregation filter). */
-  dataset: string | null
-  /** Tags currently attached to this import. Empty array if untagged. */
-  tags: CallImportTag[]
-  /**
-   * Reusable Input Parameter schema the batch was uploaded against.
-   * NULL on legacy batches uploaded before the schema-driven flow shipped.
-   */
-  schema_id: string | null
-  /**
-   * Schema-driven mapping: ``{parameter_name: csv_header}``. Empty on
-   * legacy batches; check ``column_mapping`` / ``extra_columns`` /
-   * ``custom_column_mapping`` instead for those.
-   */
-  parameter_mapping: Record<string, string>
-  /** Legacy free-form mapping kept for batches uploaded before schemas. */
-  column_mapping: Record<string, string | null>
-  /** Legacy extra-column list kept for backwards-compat. */
-  extra_columns: string[]
-  /** Legacy uploader-named columns kept for backwards-compat. */
-  custom_column_mapping: Record<string, string>
-  /**
-   * Source headers the uploader explicitly skipped, captured at the
-   * MAP stage. Empty for legacy one-shot uploads where the value was
-   * ephemeral.
-   */
-  skipped_columns: string[]
-  /**
-   * Source rows skipped at parse time (missing/invalid conversation ID or URL).
-   */
-  source_row_skips?: CallImportSourceRowSkip[]
-  /** S3 key for the staged source file. ``null`` on legacy batches. */
-  source_s3_key: string | null
-  /** ``'csv'`` / ``'xlsx'`` for staged files, or ``'audio'`` for manual uploads. */
-  source_format: string | null
-  source_size_bytes: number | null
-  source_content_type: string | null
-  /**
-   * Snapshot of the file's sheets + headers captured at UPLOAD time so
-   * the MAP UI can render without re-fetching the source from S3.
-   * ``null`` on legacy batches.
-   */
-  available_sheets: CallImportPreviewSheet[] | null
-  total_rows: number
-  completed_rows: number
-  failed_rows: number
-  status: CallImportStatus
-  error_message: string | null
-  created_at: string
-  updated_at: string
-}
-
-export interface CallImportDetail extends CallImport {
-  rows: CallImportRow[]
-  /**
-   * Total row count *after* applying the optional ``q`` search filter.
-   * ``null`` when no filter is active — paginate against ``total_rows``
-   * in that case.
-   */
-  filtered_total_rows: number | null
-  /**
-   * Batch-wide aggregates of ``CallImportRow.diarised_transcript_status``.
-   * The ``idle`` bucket (rows never touched by the transcribe/diarise
-   * worker) is implicit: ``total_rows - (pending + running + completed
-   * + failed)``. Lets the UI render a transcribe-and-diarise progress
-   * bar without paginating through every row.
-   */
-  diarised_pending_rows: number
-  diarised_running_rows: number
-  diarised_completed_rows: number
-  diarised_failed_rows: number
-}
-
-export interface CallImportListResponse {
-  items: CallImport[]
-  total: number
-  page: number
-  page_size: number
-}
-
-export interface CallImportUploadResponse {
-  id: string
-  total_rows: number
-  status: CallImportStatus
-  dataset: string | null
-  tags: CallImportTag[]
-  message: string
-}
-
-/** One worksheet (or one CSV file synthesized as a single sheet). */
-export interface CallImportPreviewSheet {
-  /** Sheet name for xlsx; filename for csv. */
-  name: string
-  /** Column headers from the first non-empty row. */
-  headers: string[]
-  /** Approximate count of data rows (excludes the header row). */
-  row_count: number
-}
-
-/**
- * Sheets / headers extracted server-side from an uploaded CSV or Excel
- * workbook. Drives the modal's column-mapping UI without forcing the
- * frontend to parse the file itself.
- */
-export interface CallImportPreviewResponse {
-  /** ``'csv'`` or ``'xlsx'``. */
-  format: 'csv' | 'xlsx'
-  sheets: CallImportPreviewSheet[]
-}
-
-export type MetricSelectionMode = 'single_choice' | 'multi_label'
-
-export interface CallImportMetricSummary {
-  id: string
-  name: string
-  metric_type: string | null
-  description: string | null
-  parent_metric_id?: string | null
-  selection_mode?: MetricSelectionMode | null
-  /** Only meaningful on multi_label parents; gates the Discovered
-   *  Labels panel on the Flow tab. Defaults to false. */
-  allow_discovery?: boolean
-}
-
-/** Per-metric LLM override (provider+model+optional credential + generation params). */
-export interface CallImportEvaluationLLMOverride {
-  provider?: string | null
-  model?: string | null
-  credential_id?: string | null
-  llm_config?: LLMGenerationConfig | null
-}
-
-export interface CallImportEvaluation {
-  id: string
-  call_import_id: string
-  organization_id: string
-  /** User-supplied label for the run; null when not named. */
-  name: string | null
-  selected_metric_ids: string[]
-  /** parent_id -> [child_id, ...] snapshot captured at run time. */
-  selected_metric_groups?: Record<string, string[]> | null
-  metrics: CallImportMetricSummary[]
-  status: 'pending' | 'running' | 'completed' | 'partial' | 'failed'
-  total_rows: number
-  completed_rows: number
-  failed_rows: number
-  error_message: string | null
-  /** Run-level LLM provider chosen by the user (null = legacy default). */
-  llm_provider: string | null
-  llm_model: string | null
-  llm_credential_id: string | null
-  llm_config?: LLMGenerationConfig | null
-  metric_llm_overrides: Record<string, CallImportEvaluationLLMOverride> | null
-  stt_provider: string | null
-  stt_model: string | null
-  stt_credential_id: string | null
-  /**
-   * Run-level LLM diariser config used when the worker auto-diarises
-   * rows that are missing a diarised transcript.
-   */
-  diarisation_llm_provider?: string | null
-  diarisation_llm_model?: string | null
-  diarisation_llm_credential_id?: string | null
-  diarisation_prompt?: string | null
-  /**
-   * Diarisation pipeline shape this run was created with.
-   * - `stt_llm` (default) — STT then an LLM diariser over the text.
-   * - `llm_only` — audio fed directly to a multimodal diariser LLM.
-   * Surfaced so the retry / re-run UI can preselect the right mode.
-   */
-  transcribe_mode?: 'stt_llm' | 'llm_only'
-  /**
-   * Which transcript column this run scored against.
-   * Defaults to `production` on legacy runs.
-   */
-  transcript_source: CallImportEvaluationTranscriptSource
-  /**
-   * Other evaluation ids created in the same Run Evaluation request.
-   * Populated only on the POST response when the user ticked both
-   * Production and Diarised. Empty array on all other reads.
-   */
-  sibling_evaluation_ids: string[]
-  started_at: string | null
-  finished_at: string | null
-  created_at: string
-  updated_at: string
-  /**
-   * Cached LLM-generated TLDR rendered above the Visualizations tab.
-   * Populated lazily via ``POST /evaluations/{id}/insights``; null on
-   * runs the user has not summarised yet.
-   */
-  tldr_summary?: EvaluationTldrSummary | null
-  user_insights?: EvaluationUserInsightsState | null
-  metric_clusters?: EvaluationMetricClustersState | null
-  /**
-   * True when the user opted into top-level metric discovery on the
-   * Run Evaluation modal. Gates the Discovered metrics panel on the
-   * evaluation detail Flow tab.
-   */
-  discover_new_metrics?: boolean
-  /**
-   * Set while a bulk background operation (abort, force-fail, retry) is
-   * still running. The UI disables other mutating actions until cleared.
-   */
-  bulk_operation?: 'abort' | 'force_fail_pending' | 'retry' | null
-}
-
-/**
- * LLM-generated narrative + bullet patterns for a single evaluation
- * run. Cached on the evaluation row so re-opening the Visualizations
- * tab doesn't auto-burn LLM tokens. ``is_stale`` is computed by the
- * backend at read time when ``completed_rows`` has grown since the
- * summary was generated.
- */
-export interface EvaluationTldrSummary {
-  narrative: string
-  patterns: string[]
-  metric_insights?: Record<string, string>
-  generated_at: string
-  generated_at_completed_rows: number
-  provider?: string | null
-  model?: string | null
-  is_stale: boolean
-}
-
-export interface UserInsightCategory {
-  label: string
-  count: number
-  share_pct: number
-}
-
-export interface UserInsightEvidenceTurn {
-  speaker: string
-  text: string
-}
-
-export interface UserInsightEvidence {
-  conversation_id?: string | null
-  quote: string
-  turns?: UserInsightEvidenceTurn[]
-}
-
-export interface EvaluationUserInsightItem {
-  id: string
-  title: string
-  categories: UserInsightCategory[]
-  observation: string
-  evidence: UserInsightEvidence
-}
-
-export interface EvaluationUserInsightsState {
-  status: 'idle' | 'running' | 'completed' | 'failed'
-  insights: EvaluationUserInsightItem[]
-  overview?: string | null
-  generated_at?: string | null
-  generated_at_completed_rows: number
-  progress?: { completed_llm_calls: number; total_llm_calls: number } | null
-  provider?: string | null
-  model?: string | null
-  llm_calls_used: number
-  max_llm_calls?: number | null
-  error_message?: string | null
-  is_stale: boolean
-}
-
-export type MetricClusterGapLabel =
-  | 'LOGIC_GAP'
-  | 'UNDERSPEC'
-  | 'EXISTS_NO_TRIGGER'
-  | 'MISSING'
-
-export interface MetricSubCluster {
-  label: string
-  count: number
-  share_pct: number
-}
-
-export interface MetricClusterEvidenceTurn {
-  speaker: string
-  text: string
-}
-
-export interface MetricClusterEvidence {
-  conversation_id?: string | null
-  evaluation_row_id?: string | null
-  quote: string
-  turns?: MetricClusterEvidenceTurn[]
-}
-
-export interface MetricCluster {
-  id: string
-  label: string
-  gap_label: MetricClusterGapLabel
-  level: number
-  count: number
-  share_pct: number
-  sub_clusters: MetricSubCluster[]
-  observation: string
-  failure_reason?: string
-  evidence: MetricClusterEvidence
-  is_discovered: boolean
-}
-
-export interface MetricClusterGroup {
-  metric_id: string
-  metric_name: string
-  flagged_count: number
-  failure_reason?: string
-  clusters: MetricCluster[]
-}
-
-export interface DiscoveredProblemCluster {
-  id: string
-  label: string
-  gap_label: MetricClusterGapLabel
-  count: number
-  share_pct: number
-  observation: string
-  failure_reason?: string
-  evidence: MetricClusterEvidence
-}
-
-export interface RcaRepeatedPatternRow {
-  metric_id: string
-  metric_name: string
-  top_rca_patterns: string
-  evidence_share_pct: number
-  evidence_calls: number
-  evidence_cluster_count?: number
-  failure_reason: string
-}
-
-export interface RcaMetricHotspotRow {
-  metric_id: string
-  metric_name: string
-  description: string
-  metric_rate_pct: number
-  flagged_calls: number
-}
-
-export interface RcaPromptAreaRow {
-  label: string
-  share_pct: number
-  gap_label: MetricClusterGapLabel
-}
-
-export interface MetricClustersRcaSummary {
-  total_clusters: number
-  total_clustered_instances: number
-  total_flagged_instances?: number
-  analysed_calls: number
-  repeated_patterns: RcaRepeatedPatternRow[]
-  metric_hotspots: RcaMetricHotspotRow[]
-  prompt_areas: RcaPromptAreaRow[]
-}
-
-export interface MetricFailurePolicy {
-  metric_id: string
-  failure_values: string[]
-  failure_child_names?: string[]
-  numeric_rule?: { op: 'lt' | 'lte' | 'gt' | 'gte'; threshold: number } | null
-}
-
-export interface MetricFailurePolicyValueCount {
-  label: string
-  count: number
-}
-
-export interface MetricFailurePolicyMetricPreview {
-  metric_id: string
-  metric_name: string
-  metric_type?: string | null
-  selection_mode?: string | null
-  is_multi_label_parent: boolean
-  value_counts: MetricFailurePolicyValueCount[]
-  child_names: string[]
-  row_count_by_value: Record<string, number>
-  suggested_policy: MetricFailurePolicy
-  effective_policy: MetricFailurePolicy
-}
-
-export interface MetricFailurePoliciesResponse {
-  previews: MetricFailurePolicyMetricPreview[]
-  policies: Record<string, MetricFailurePolicy>
-  source: 'inferred' | 'user'
-  updated_at?: string | null
-}
-
-export interface MetricClusterEligibleRow {
-  evaluation_row_id: string
-  conversation_id?: string | null
-  row_index?: number | null
-  flagged_metric_names: string[]
-}
-
-export interface MetricClusterEligibleRowsResponse {
-  items: MetricClusterEligibleRow[]
-  total: number
-}
-
-export interface EvaluationMetricClustersState {
-  status: 'idle' | 'running' | 'completed' | 'failed' | 'cancelled'
-  groups: MetricClusterGroup[]
-  discovered_problems: DiscoveredProblemCluster[]
-  overview?: string | null
-  generated_at?: string | null
-  generated_at_completed_rows: number
-  progress?: { completed_llm_calls: number; total_llm_calls: number } | null
-  provider?: string | null
-  model?: string | null
-  llm_calls_used: number
-  max_llm_calls?: number | null
-  error_message?: string | null
-  is_stale: boolean
-  selected_evaluation_row_ids?: string[]
-  failure_policies?: Record<string, MetricFailurePolicy>
-  failure_policies_source?: 'inferred' | 'user'
-  failure_policies_updated_at?: string | null
-  rca_summary?: MetricClustersRcaSummary | null
-}
-
-export interface AgentFlowNode {
-  id: string
-  label: string
-  node_type: 'start' | 'decision' | 'action' | 'terminal'
-  position_x?: number | null
-  position_y?: number | null
-  prompt_excerpt?: string | null
-  start_offset?: number | null
-  end_offset?: number | null
-}
-
-export interface AgentFlowEdge {
-  source: string
-  target: string
-  condition?: string | null
-}
-
-export interface AgentFlowGraph {
-  nodes: AgentFlowNode[]
-  edges: AgentFlowEdge[]
-  generated_at?: string | null
-  provider?: string | null
-  model?: string | null
-  layout_saved_at?: string | null
-  prompt_content_hash?: string | null
-  mapping_error?: string | null
-  generation_error?: string | null
-}
-
-export interface ImportedAgent {
-  id: string
-  organization_id: string
-  name: string
-  description: string | null
-  content: string
-  tags: string[] | null
-  current_version: number
-  agent_flowchart?: AgentFlowGraph | null
-  agent_flowchart_status?: string | null
-  created_at: string
-  updated_at: string
-  created_by: string | null
-}
-
-export interface ImportedAgentDetail extends ImportedAgent {
-  versions: PromptPartialVersion[]
-}
-
-export interface MetricPartialChild {
-  name: string
-  description: string
-  example: string
-}
-
-export interface MetricPartialContent {
-  schema_version: 1
-  metric_kind: 'single' | 'category'
-  description: string
-  children?: MetricPartialChild[]
-}
-
-export interface MetricPartial {
-  id: string
-  organization_id: string
-  name: string
-  description: string | null
-  content: string
-  tags: string[] | null
-  current_version: number
-  created_at: string
-  updated_at: string
-  created_by: string | null
-}
-
-export interface MetricPartialDetail extends MetricPartial {
-  versions: PromptPartialVersion[]
-}
-
-export interface PromptPartialVersion {
-  id: string
-  prompt_partial_id: string
-  version: number
-  content: string
-  change_summary: string | null
-  created_at: string
-  created_by: string | null
-}
-
-export interface PromptImprovementSuggestion {
-  id: string
-  metric_id: string
-  metric_name: string
-  cluster_id: string
-  cluster_label: string
-  gap_label: MetricClusterGapLabel
-  share_pct: number
-  priority: 'high' | 'medium' | 'low'
-  change_type?: 'edit' | 'add'
-  target_section: string
-  anchor_excerpt?: string
-  current_gap: string
-  suggested_text: string
-  rationale: string
-  flow_node_id?: string
-  flow_node_label?: string
-}
-
-export interface EvaluationPromptImprovementsState {
-  status: 'idle' | 'running' | 'completed' | 'failed'
-  imported_agent_id?: string | null
-  imported_agent_name?: string | null
-  suggestions: PromptImprovementSuggestion[]
-  overview?: string | null
-  generated_at?: string | null
-  generated_at_completed_rows: number
-  provider?: string | null
-  model?: string | null
-  error_message?: string | null
-  is_stale: boolean
-}
-
-export interface MetricPeriodDelta {
-  label: string
-  detail: string
-  why?: string | null
-}
-
-export interface CallImportEvaluationListResponse {
-  items: CallImportEvaluation[]
-  total: number
-}
-
-export interface CallImportEvaluationBaselineCandidate {
-  evaluation_id: string
-  name: string
-  dataset: string
-  period_label: string | null
-  period_start: string | null
-  period_end: string | null
-  period_display: string
-  completed_rows: number
-  created_at: string
-  is_default: boolean
-}
-
-export interface CallImportEvaluationBaselineCandidatesResponse {
-  items: CallImportEvaluationBaselineCandidate[]
-  default_evaluation_id: string | null
-}
-
-export interface CallImportEvaluationRow {
-  id: string
-  evaluation_id: string
-  call_import_row_id: string
-  row_index: number | null
-  /** Mandatory identifier from the source batch (renamed from ``external_call_id``). */
-  conversation_id: string | null
-  transcript: string | null
-  raw_columns: Record<string, any> | null
-  recording_url: string | null
-  recording_date: string | null
-  /**
-   * S3 object key for the downloaded recording. Prefer this over
-   * ``recording_url`` for playback — we resolve it to a presigned URL
-   * so audio plays from our storage instead of the (often expired)
-   * provider URL.
-   */
-  recording_s3_key: string | null
-  diarised_transcript_status?: string | null
-  diarised_transcript_error?: string | null
-  status: 'pending' | 'running' | 'completed' | 'failed' | 'skipped'
-  metric_scores: Record<string, any>
-  error_message: string | null
-  started_at: string | null
-  finished_at: string | null
-  created_at: string
-  updated_at: string
-}
-
-export interface CallImportEvaluationRowListResponse {
-  items: CallImportEvaluationRow[]
-  total: number
-  page: number
-  page_size: number
-}
-
-// --- Retry (re-enqueue failed rows on an existing evaluation run) ---
-
-export interface CallImportEvaluationRetryRequest {
-  /**
-   * Restrict the retry to a specific subset of evaluation rows.
-   * When omitted, every row with status='failed' in this run is
-   * re-enqueued.
-   */
-  eval_row_ids?: string[]
-
-  /**
-   * Optional LLM overrides. When provided, persisted onto the run so
-   * future retries default to the new config. ``llm_provider`` and
-   * ``llm_model`` must be sent together — the backend 400s on
-   * half-configured input.
-   */
-  llm_provider?: string
-  llm_model?: string
-  llm_credential_id?: string | null
-
-  /**
-   * Optional STT overrides (only meaningful when the run scores the
-   * diarised transcript). Same paired-field rule as LLM.
-   */
-  stt_provider?: string
-  stt_model?: string
-  stt_credential_id?: string | null
-
-  /**
-   * When true, wipe the diarised transcript on every retried row so
-   * the (possibly new) STT runs from scratch. Only takes effect for
-   * diarised runs that have STT config.
-   */
-  transcribe_overwrite?: boolean
-}
-
-export interface CallImportEvaluationRetrySkippedItem {
-  eval_row_id: string
-  /**
-   * Why this row was not re-enqueued. Known values:
-   * - 'unknown' (id not in this run)
-   * - 'in_progress' (status is pending/running)
-   * - 'completed' (already successful)
-   * - 'source_row_missing'
-   */
-  reason: 'unknown' | 'in_progress' | 'completed' | 'source_row_missing'
-}
-
-export interface CallImportEvaluationRetryResponse {
-  requeued: number
-  /**
-   * Of those, how many were chained through a diarisation task first
-   * because the diarised transcript was missing.
-   */
-  transcribe_requeued: number
-  skipped: CallImportEvaluationRetrySkippedItem[]
-}
-
-export interface CallImportEvaluationBulkActionResponse {
-  accepted: boolean
-  target_count: number
-  evaluation_id: string
-}
-
-// --- Diarization / transcription ---
-
-export interface CallImportTranscribeRequest {
-  /**
-   * Diarisation pipeline shape.
-   * - `stt_llm` (default) — STT produces plain text, then an LLM
-   *   diariser splits it into agent/user turns. STT fields required.
-   * - `llm_only` — skip STT entirely and feed the audio bytes
-   *   directly to a multimodal `diarization_llm_*` model along with
-   *   `diarization_prompt`. STT fields MUST be omitted in this mode.
-   */
-  mode?: 'stt_llm' | 'llm_only'
-  /** Required when `mode === 'stt_llm'`; must be null/omitted in `llm_only`. */
-  stt_provider?: string | null
-  /** Required when `mode === 'stt_llm'`; must be null/omitted in `llm_only`. */
-  stt_model?: string | null
-  credential_id?: string | null
-  language?: string | null
-  only_missing?: boolean
-  overwrite_existing?: boolean
-  row_ids?: string[]
-  /**
-   * LLM diariser. In `stt_llm` mode it splits the STT plain-text into
-   * agent/user turns; in `llm_only` mode it directly receives the
-   * audio along with `diarization_prompt`. Always required.
-   */
-  diarization_llm_provider: string
-  diarization_llm_model: string
-  diarization_llm_credential_id?: string | null
-  /**
-   * Operator-supplied system prompt for the diariser LLM. NULL/empty
-   * means "fall back to the canonical default" (see
-   * ``getDiarisationDefaultPrompt``).
-   */
-  diarization_prompt?: string | null
-}
-
-export interface CallImportTranscribeResponse {
-  queued: number
-  skipped_rows: number
-  skipped_reason_counts: Record<string, number>
-  accepted?: boolean
-}
-
-export interface CallImportRowBulkDeleteResponse {
-  deleted: number
-  status?: 'completed' | 'accepted'
-}
-
-export interface CallImportRetryFailedRowsResponse {
-  requeued: number
-  enqueue_failed: number
-  skipped: number
-}
-
-export interface CallImportDiarisationPromptDefaultResponse {
-  prompt: string
-}
-
-// --- Aggregation / visualization payloads ---
-
-export interface CallImportMetricHistogramBucket {
-  x0: number
-  x1: number
-  count: number
-}
-
-export interface CallImportMetricValueCount {
-  label: string
-  count: number
-}
-
-/**
- * One unordered pair-count cell from a multi-label parent's
- * co-occurrence matrix. ``a`` and ``b`` are child label names with
- * ``a < b`` lexicographically; ``count`` is the number of rows on
- * which both labels fired together.
- */
-export interface CallImportMetricLabelPair {
-  a: string
-  b: string
-  count: number
-}
-
-export interface CallImportMetricAggregate {
-  metric_id: string
-  metric_name: string
-  metric_type: string | null
-  metric_category?: 'quality' | 'user_insight' | string
-  /**
-   * True when the metric is a multi-label classifier parent.
-   * ``value_counts`` then lists per-child label tallies and one row
-   * may contribute to several labels, so the chart layout has to
-   * ignore the pie toggle (slices wouldn't sum to 100%) and the
-   * n-badge represents rows scored, not label occurrences.
-   */
-  is_multi_label_parent?: boolean
-  count: number
-  skipped_count: number
-  error_count: number
-  mean: number | null
-  median: number | null
-  p25: number | null
-  p75: number | null
-  p95: number | null
-  min: number | null
-  max: number | null
-  stddev: number | null
-  histogram_buckets: CallImportMetricHistogramBucket[]
-  value_counts: CallImportMetricValueCount[]
-  /**
-   * Pairwise label intersections for multi-label parent metrics.
-   * Empty for everything else. The Visualizations tab reconstructs
-   * a square symmetric matrix from these unordered pairs to render
-   * the co-occurrence heatmap chart type.
-   */
-  co_occurrence?: CallImportMetricLabelPair[]
-}
-
-export interface CallImportEvaluationAggregateResponse {
-  evaluation_id: string
-  total_rows: number
-  completed_rows: number
-  failed_rows: number
-  metrics: CallImportMetricAggregate[]
-  period_deltas?: Record<string, MetricPeriodDelta>
-  baseline_evaluation_id?: string | null
-  failure_policies_source?: 'inferred' | 'user' | null
-}
-
-export interface EvaluatorResultsAggregateResponse {
-  scope: string
-  suite_id?: string | null
-  agent_id?: string | null
-  scenario_id?: string | null
-  total_rows: number
-  completed_rows: number
-  failed_rows: number
-  metrics: CallImportMetricAggregate[]
-}
-
-export interface CallImportInsightsRunPoint {
-  evaluation_id: string
-  name: string | null
-  created_at: string
-  mean: number | null
-  completed_rows: number
-}
-
-export interface CallImportInsightsMetric {
-  metric_id: string
-  metric_name: string
-  metric_type: string | null
-  latest: CallImportMetricAggregate | null
-  trend: CallImportInsightsRunPoint[]
-}
-
-export interface CallImportInsightsResponse {
-  call_import_id: string
-  total_rows: number
-  rows_with_transcript: number
-  rows_without_transcript: number
-  transcript_source_counts: Record<string, number>
-  evaluation_count: number
-  metrics: CallImportInsightsMetric[]
-}
-
-// --- Metrics hierarchy + flow visualization ---
-
-export interface MetricSummary {
-  id: string
-  organization_id: string
-  name: string
-  description: string | null
-  metric_type: string
-  metric_category?: 'quality' | 'user_insight' | string
-  trigger: string
-  enabled: boolean
-  is_default: boolean
-  metric_origin: string
-  supported_surfaces: string[]
-  enabled_surfaces: string[]
-  custom_data_type: string | null
-  custom_config: Record<string, any> | null
-  tags: string[] | null
-  capture_rationale: boolean
-  parent_metric_id: string | null
-  selection_mode: MetricSelectionMode | null
-  allow_discovery?: boolean
-  /**
-   * When true, this metric is a "transcript-compare judge": at
-   * call-import evaluation time the worker feeds BOTH the production
-   * transcript and the diarised transcript to the LLM as a labeled
-   * pair, and the run's transcript_source toggle is ignored for this
-   * metric. Mutually exclusive with parent_metric_id and selection_mode
-   * — comparison metrics stay standalone.
-   */
-  compare_transcripts?: boolean
-  children?: MetricSummary[]
-  created_at: string
-  updated_at: string
-  created_by: string | null
-}
-
-export interface MetricChildDraft {
-  name: string
-  description?: string | null
-  enabled?: boolean
-  capture_rationale?: boolean | null
-  tags?: string[] | null
-}
-
-export interface MetricCreateWithChildrenPayload {
-  name: string
-  description?: string | null
-  selection_mode: MetricSelectionMode
-  enabled?: boolean
-  supported_surfaces?: string[]
-  enabled_surfaces?: string[]
-  tags?: string[] | null
-  allow_discovery?: boolean
-  children: MetricChildDraft[]
-}
-
-export interface MetricFlowNode {
-  id: string
-  label: string
-  count: number
-  is_terminal: boolean
-  is_discovered?: boolean
-}
-
-export interface MetricFlowEdge {
-  source: string
-  target: string
-  count: number
-}
-
-export interface MetricFlowResponse {
-  parent_metric_id: string
-  parent_metric_name: string
-  selection_mode: MetricSelectionMode | null
-  nodes: MetricFlowNode[]
-  edges: MetricFlowEdge[]
-  total_rows: number
-  rows_with_sequence: number
-}
-
-export interface DiscoveredLabel {
-  key: string
-  name: string
-  description?: string | null
-  sample_rationale?: string | null
-  /**
-   * Up to 3 distinct LLM rationales captured for this candidate
-   * across rows. The Discovered Labels promote flow surfaces the
-   * first 2 as an ``Examples:`` block on the new sub-metric's
-   * rubric so the user starts with concrete cases in the prompt.
-   */
-  examples?: string[]
-  count: number
-}
-
-export interface DiscoveredLabelsResponse {
-  parent_metric_id: string
-  items: DiscoveredLabel[]
-}
-
-/**
- * One LLM-discovered candidate TOP-LEVEL metric aggregated across all
- * rows of an evaluation. Mirrors :class:`DiscoveredLabel` but adds a
- * ``suggested_type`` field — the LLM's guess at the best shape for
- * the new metric — that the promote modal can pre-fill the type radio
- * with.
- */
-export interface DiscoveredMetric {
-  key: string
-  name: string
-  description?: string | null
-  suggested_type: 'boolean' | 'rating' | 'category'
-  sample_rationale?: string | null
-  examples?: string[]
-  count: number
-}
-
-export interface DiscoveredMetricsResponse {
-  evaluation_id: string
-  items: DiscoveredMetric[]
-}
-
-export interface ObservabilityCallAgent {
-  id: string
-  agent_id?: string | null
-  name: string
-}
-
-export interface ObservabilityCallData {
-  startedAt?: string
-  started_at?: string
-  endedAt?: string
-  ended_at?: string
-  from_phone_number?: string
-  to_phone_number?: string
-  endedReason?: string
-  recording_s3_key?: string
-  recording_url?: string
-  duration_seconds?: number
-  agent_name?: string
-  _agent_ref?: string | number
-  direction?: string
-  messages?: Array<{ role: string; content: string; start_time?: number; end_time?: number }>
-  live_transcript?: Array<{ role: string; content: string; timestamp?: string; start_time?: number }>
-  metadata?: Record<string, unknown>
-  call_short_id?: string
-}
-
-export interface ObservabilityCall {
-  id: string
-  call_short_id: string
-  status?: string | null
-  call_event?: string | null
-  is_live?: boolean
-  direction?: string | null
-  source?: string | null
-  provider_platform?: string | null
-  provider_call_id?: string | null
-  agent_id?: string | null
-  agent?: ObservabilityCallAgent | null
-  created_at?: string | null
-  updated_at?: string | null
-  call_data?: ObservabilityCallData | null
-  live_transcript?: Array<{ role: string; content: string; timestamp?: string }>
-}
+// API Types matching the backend schemas
+
+export type { LLMGenerationConfig } from '../config/llmGenerationParams'
+import type { LLMGenerationConfig } from '../config/llmGenerationParams'
+
+export enum EvaluationType {
+  ASR = 'asr',
+  TTS = 'tts',
+}
+
+export enum EvaluationStatus {
+  PENDING = 'pending',
+  PROCESSING = 'processing',
+  COMPLETED = 'completed',
+  FAILED = 'failed',
+  CANCELLED = 'cancelled',
+}
+
+export interface AudioFile {
+  id: string
+  filename: string
+  format: string
+  file_size: number
+  duration?: number | null
+  sample_rate?: number | null
+  channels?: number | null
+  uploaded_at: string
+}
+
+export interface Evaluation {
+  id: string
+  audio_id: string
+  reference_text?: string | null
+  evaluation_type: EvaluationType
+  model_name?: string | null
+  status: EvaluationStatus
+  metrics_requested?: string[] | null
+  created_at: string
+  started_at?: string | null
+  completed_at?: string | null
+  error_message?: string | null
+}
+
+export interface DashboardSummary {
+  evaluations: {
+    total: number
+    completed: number
+    pending: number
+    failed: number
+  }
+  resources: {
+    agents: number
+    personas: number
+    scenarios: number
+    integrations: number
+    voice_bundles: number
+  }
+  setup_progress: {
+    has_integration: boolean
+    has_voice_bundle: boolean
+    has_agent: boolean
+    has_evaluation: boolean
+  }
+  metrics: {
+    total: number
+    enabled: number
+  }
+  call_imports: {
+    total: number
+  }
+  call_import_evaluations: {
+    total: number
+    completed: number
+    running: number
+    failed: number
+  }
+  recent_evaluations: Evaluation[]
+}
+
+export interface ModelConfigEntry {
+  provider: string
+  model_type: string
+  description?: string
+  featured?: boolean
+  featured_rank?: number
+  highlights?: string[]
+}
+
+export interface EvaluationCreate {
+  audio_id: string
+  reference_text?: string | null
+  evaluation_type: EvaluationType
+  model_name?: string | null
+  metrics?: string[]
+}
+
+export interface EvaluationResult {
+  evaluation_id: string
+  status: EvaluationStatus
+  transcript?: string | null
+  metrics: Record<string, any>
+  processing_time?: number | null
+  model_used?: string | null
+  created_at: string
+}
+
+export interface BatchEvaluationResult {
+  processed_files: number
+  failed_files: number
+  aggregated_metrics?: Record<string, any> | null
+  individual_results: EvaluationResult[]
+}
+
+/** Voice agent evaluator run (evaluator_results table). */
+export type EvaluatorResultStatus =
+  | 'queued'
+  | 'call_initiating'
+  | 'call_connecting'
+  | 'call_in_progress'
+  | 'call_ended'
+  | 'transcribing'
+  | 'evaluating'
+  | 'fetching_details'
+  | 'completed'
+  | 'failed'
+
+export interface EvaluatorResultMetricScore {
+  value: unknown
+  type: string
+  metric_name: string
+  parent_metric_id?: string | null
+}
+
+export interface EvaluatorResultRow {
+  id: string
+  result_id: string
+  name: string | null
+  evaluator_id: string | null
+  agent_id?: string | null
+  persona_id?: string | null
+  scenario_id?: string | null
+  suite_id?: string | null
+  timestamp: string
+  duration_seconds: number | null
+  status: EvaluatorResultStatus
+  metric_scores: Record<string, EvaluatorResultMetricScore> | null
+  error_message: string | null
+  agent?: { id: string; name: string } | null
+  scenario?: { id: string; name: string } | null
+}
+
+export interface EvaluatorResultListResponse {
+  items: EvaluatorResultRow[]
+  total: number
+}
+
+export interface EvaluatorResultCounts {
+  total: number
+  completed: number
+  failed: number
+  in_progress: number
+  last_run_at?: string | null
+}
+
+export interface EvaluatorResultsScenarioSummary {
+  scenario_id: string
+  scenario_name: string
+  counts: EvaluatorResultCounts
+}
+
+export interface EvaluatorResultsSuiteSummary {
+  suite_id: string
+  suite_name?: string | null
+  agent_id: string
+  persona_id?: string | null
+  counts: EvaluatorResultCounts
+  scenarios?: EvaluatorResultsScenarioSummary[] | null
+}
+
+export interface EvaluatorResultsAgentSummary {
+  agent_id: string
+  agent_name: string
+  counts: EvaluatorResultCounts
+  suites?: EvaluatorResultsSuiteSummary[] | null
+}
+
+export interface EvaluatorResultsOverviewResponse {
+  workspace_counts: EvaluatorResultCounts
+  agents: EvaluatorResultsAgentSummary[]
+  unassigned: {
+    counts: EvaluatorResultCounts
+    recent_result_ids: string[]
+  }
+}
+
+export interface ListEvaluatorResultsParams {
+  skip?: number
+  limit?: number
+  evaluatorId?: string
+  agentId?: string
+  suiteId?: string
+  scenarioId?: string
+  status?: 'completed' | 'failed' | 'in_progress'
+  unassignedOnly?: boolean
+  playground?: boolean
+  testAgentsOnly?: boolean
+}
+
+export interface APIKey {
+  id: string
+  key: string
+  name?: string | null
+  is_active: boolean
+  created_at: string
+  last_used?: string | null
+  message?: string
+}
+
+export interface MessageResponse {
+  message: string
+}
+
+// IAM & User Types
+export enum Role {
+  READER = 'reader',
+  WRITER = 'writer',
+  ADMIN = 'admin',
+}
+
+export enum InvitationStatus {
+  PENDING = 'pending',
+  ACCEPTED = 'accepted',
+  DECLINED = 'declined',
+  EXPIRED = 'expired',
+}
+
+export interface User {
+  id: string
+  email: string
+  name?: string | null
+  is_active: boolean
+  created_at: string
+}
+
+export interface OrganizationMember {
+  id: string
+  user_id: string
+  organization_id: string
+  role: Role
+  joined_at: string
+  user: User
+}
+
+export interface Invitation {
+  id: string
+  organization_id: string
+  email: string
+  role: Role
+  status: InvitationStatus
+  expires_at: string
+  created_at: string
+  organization_name?: string | null
+}
+
+export interface InvitationCreate {
+  email: string
+  role: Role
+}
+
+export interface RoleUpdate {
+  role: Role
+}
+
+export interface Profile {
+  id: string
+  email: string
+  name?: string | null
+  first_name?: string | null
+  last_name?: string | null
+  created_at: string
+  organizations: Array<{
+    id: string
+    name: string
+    role: string
+    joined_at: string
+  }>
+}
+
+export interface UserUpdate {
+  name?: string | null
+  first_name?: string | null
+  last_name?: string | null
+  email?: string | null
+}
+
+export interface UserPreferences {
+  theme?: string
+  notifications_enabled?: boolean
+  email_notifications?: boolean
+  default_language?: string
+  [key: string]: any
+}
+
+export interface UserPreferencesUpdate {
+  theme?: string
+  notifications_enabled?: boolean
+  email_notifications?: boolean
+  default_language?: string
+  [key: string]: any
+}
+
+// Integration Types
+export enum IntegrationPlatform {
+  RETELL = 'retell',
+  VAPI = 'vapi',
+  CARTESIA = 'cartesia',
+  ELEVENLABS = 'elevenlabs',
+  DEEPGRAM = 'deepgram',
+  MURF = 'murf',
+  SARVAM = 'sarvam',
+  VOICEMAKER = 'voicemaker',
+  SMALLEST = 'smallest',
+}
+
+export enum TelephonyProvider {
+  PLIVO = 'plivo',
+  EXOTEL = 'exotel',
+  VOBIZ = 'vobiz',
+}
+
+export type CredentialRoutingMode = 'inherit' | 'gateway' | 'direct'
+export type GatewayInterfaceMode = 'inherit' | 'litellm_shim' | 'native_openai'
+
+export type EffectiveCredentialRouting =
+  | 'inherit'
+  | 'direct'
+  | 'gateway'
+  | 'bifrost'
+  | 'litellm_proxy'
+
+export interface Integration {
+  id: string
+  organization_id: string
+  platform: IntegrationPlatform
+  name?: string | null
+  public_key?: string | null
+  is_active: boolean
+  /** True if this row is the default credential for (org, platform). */
+  is_default?: boolean
+  routing_mode?: CredentialRoutingMode
+  effective_routing?: EffectiveCredentialRouting
+  created_at: string
+  updated_at: string
+  last_tested_at?: string | null
+}
+
+export interface IntegrationCreate {
+  platform: IntegrationPlatform
+  api_key: string
+  public_key?: string
+  name?: string | null
+  routing_mode?: CredentialRoutingMode
+  /** Mark the new credential as the default for (org, platform). */
+  is_default?: boolean
+}
+
+// VoiceBundle Types
+export enum ModelProvider {
+  OPENAI = 'openai',
+  ANTHROPIC = 'anthropic',
+  GOOGLE = 'google',
+  XAI = 'xai',
+  FIREWORKS = 'fireworks',
+  COHERE = 'cohere',
+  MISTRAL = 'mistral',
+  META = 'meta',
+  TOGETHER = 'together',
+  PERPLEXITY = 'perplexity',
+  AZURE = 'azure',
+  AWS = 'aws',
+  DEEPGRAM = 'deepgram',
+  CARTESIA = 'cartesia',
+  ELEVENLABS = 'elevenlabs',
+  MURF = 'murf',
+  CUSTOM = 'custom',
+  SARVAM = 'sarvam',
+  VOICEMAKER = 'voicemaker',
+  SMALLEST = 'smallest',
+}
+
+// AI Provider Types
+export interface AIProvider {
+  id: string
+  provider: ModelProvider
+  api_key?: string | null
+  name?: string | null
+  endpoint_url?: string | null
+  is_active: boolean
+  /** True if this row is the default credential for (org, provider). */
+  is_default?: boolean
+  routing_mode?: CredentialRoutingMode
+  gateway_model?: string | null
+  gateway_interface?: GatewayInterfaceMode
+  gateway_base_url?: string | null
+  gateway_auth_header?: string | null
+  gateway_auth_secret_env?: string | null
+  has_gateway_auth_secret?: boolean
+  gateway_extra_headers?: Record<string, string> | null
+  /** True when provider secrets are resolved by the Bifrost gateway. */
+  gateway_managed?: boolean
+  effective_routing?: EffectiveCredentialRouting
+  effective_gateway_interface?: 'litellm_shim' | 'native_openai'
+  created_at: string
+  updated_at: string
+  last_tested_at?: string | null
+}
+
+export interface AIProviderCreate {
+  provider: ModelProvider
+  api_key?: string | null
+  name?: string | null
+  endpoint_url?: string | null
+  routing_mode?: CredentialRoutingMode
+  gateway_model?: string | null
+  gateway_interface?: GatewayInterfaceMode
+  gateway_base_url?: string | null
+  gateway_auth_header?: string | null
+  gateway_auth_secret_env?: string | null
+  gateway_auth_secret?: string | null
+  gateway_extra_headers?: Record<string, string> | null
+  /** Mark the new credential as the default for (org, provider). */
+  is_default?: boolean
+}
+
+export interface AIProviderUpdate {
+  api_key?: string | null
+  name?: string | null
+  endpoint_url?: string | null
+  is_active?: boolean
+  routing_mode?: CredentialRoutingMode
+  gateway_model?: string | null
+  gateway_interface?: GatewayInterfaceMode
+  gateway_base_url?: string | null
+  gateway_auth_header?: string | null
+  gateway_auth_secret_env?: string | null
+  gateway_auth_secret?: string | null
+  clear_gateway_auth_secret?: boolean
+  gateway_extra_headers?: Record<string, string> | null
+}
+
+export enum VoiceBundleType {
+  STT_LLM_TTS = 'stt_llm_tts',
+  S2S = 's2s',
+}
+
+export interface VoiceBundle {
+  id: string
+  name: string
+  description?: string | null
+  bundle_type: VoiceBundleType
+  stt_provider?: ModelProvider | null
+  stt_model?: string | null
+  /**
+   * Optional explicit AIProvider/Integration row id for STT. When null the
+   * runtime resolver picks the default credential for stt_provider.
+   */
+  stt_credential_id?: string | null
+  llm_provider?: ModelProvider | null
+  llm_model?: string | null
+  llm_temperature?: number | null
+  llm_max_tokens?: number | null
+  llm_config?: Record<string, any> | null
+  llm_credential_id?: string | null
+  tts_provider?: ModelProvider | null
+  tts_model?: string | null
+  tts_voice?: string | null
+  tts_config?: Record<string, any> | null
+  tts_credential_id?: string | null
+  s2s_provider?: ModelProvider | null
+  s2s_model?: string | null
+  s2s_config?: Record<string, any> | null
+  s2s_credential_id?: string | null
+  extra_metadata?: Record<string, any> | null
+  is_active: boolean
+  created_at: string
+  updated_at: string
+  created_by?: string | null
+}
+
+export interface VoiceBundleCreate {
+  name: string
+  description?: string | null
+  bundle_type?: VoiceBundleType
+  stt_provider?: ModelProvider | null
+  stt_model?: string | null
+  stt_credential_id?: string | null
+  llm_provider?: ModelProvider | null
+  llm_model?: string | null
+  llm_temperature?: number | null
+  llm_max_tokens?: number | null
+  llm_config?: Record<string, any> | null
+  llm_credential_id?: string | null
+  tts_provider?: ModelProvider | null
+  tts_model?: string | null
+  tts_voice?: string | null
+  tts_config?: Record<string, any> | null
+  tts_credential_id?: string | null
+  s2s_provider?: ModelProvider | null
+  s2s_model?: string | null
+  s2s_config?: Record<string, any> | null
+  s2s_credential_id?: string | null
+  extra_metadata?: Record<string, any> | null
+}
+
+// Test Agent Types
+export interface AgentPhoneAssignmentConflict {
+  agent_id: string
+  agent_name: string
+  phone_number: string
+}
+
+export interface AgentPhoneAssignmentCheckResponse {
+  available: boolean
+  phone_number?: string | null
+  conflict?: AgentPhoneAssignmentConflict | null
+}
+
+export interface TestAgent {
+  id: string
+  agent_id?: string | null
+  name: string
+  phone_number?: string | null
+  telephony_phone_number_id?: string | null
+  language: string
+  description: string | null
+  prompt_variables?: Record<string, string> | null
+  silence_hangup_secs?: number
+  call_type: string
+  call_medium: string
+  voice_bundle_id?: string | null
+  voice_ai_integration_id?: string | null
+  voice_ai_agent_id?: string | null
+  provider_prompt?: string | null
+  provider_prompt_synced_at?: string | null
+  created_at: string
+  updated_at: string
+}
+
+// Test Agent Conversation Types
+export interface TestAgentConversation {
+  id: string
+  organization_id: string
+  agent_id: string
+  persona_id: string
+  scenario_id: string
+  voice_bundle_id: string
+  status: string
+  live_transcription?: Array<{
+    speaker: string
+    text: string
+    timestamp: number
+    audio_segment_key?: string
+  }> | null
+  conversation_audio_key?: string | null
+  full_transcript?: string | null
+  started_at: string
+  ended_at?: string | null
+  duration_seconds?: number | null
+  conversation_metadata?: Record<string, any> | null
+  created_at: string
+  updated_at: string
+  created_by?: string | null
+}
+
+export interface TestAgentConversationCreate {
+  agent_id: string
+  persona_id: string
+  scenario_id: string
+  voice_bundle_id: string
+  conversation_metadata?: Record<string, any> | null
+}
+
+export interface TestAgentConversationUpdate {
+  status?: string | null
+  live_transcription?: Array<Record<string, any>> | null
+  full_transcript?: string | null
+  conversation_metadata?: Record<string, any> | null
+}
+
+export interface VoiceBundleUpdate {
+  name?: string
+  description?: string | null
+  stt_provider?: ModelProvider
+  stt_model?: string
+  stt_credential_id?: string | null
+  llm_provider?: ModelProvider
+  llm_model?: string
+  llm_temperature?: number | null
+  llm_max_tokens?: number | null
+  llm_config?: Record<string, any> | null
+  llm_credential_id?: string | null
+  tts_provider?: ModelProvider
+  tts_model?: string
+  tts_voice?: string | null
+  tts_config?: Record<string, any> | null
+  tts_credential_id?: string | null
+  s2s_provider?: ModelProvider | null
+  s2s_model?: string | null
+  s2s_config?: Record<string, any> | null
+  s2s_credential_id?: string | null
+  extra_metadata?: Record<string, any> | null
+  is_active?: boolean
+}
+
+// Data Sources Types
+export interface S3ConnectionTest {
+  bucket_name: string
+  region?: string
+  access_key_id: string
+  secret_access_key: string
+  endpoint_url?: string | null
+}
+
+export interface S3ConnectionTestResponse {
+  success: boolean
+  message: string
+  bucket_name?: string | null
+}
+
+export interface S3FileInfo {
+  key: string
+  filename: string
+  size: number
+  last_modified: string
+}
+
+export interface S3FolderInfo {
+  name: string
+  path: string
+}
+
+export interface S3ListFilesResponse {
+  files: S3FileInfo[]
+  total: number
+  prefix?: string | null
+}
+
+export interface S3BrowseResponse {
+  folders: S3FolderInfo[]
+  files: S3FileInfo[]
+  current_path: string
+  organization_id: string
+}
+
+export interface S3Status {
+  enabled: boolean
+  provider?: 's3' | 'gcs' | string
+  error?: string | null
+}
+
+// Alert Types
+export enum AlertMetricType {
+  NUMBER_OF_CALLS = 'number_of_calls',
+  CALL_DURATION = 'call_duration',
+  ERROR_RATE = 'error_rate',
+  SUCCESS_RATE = 'success_rate',
+  LATENCY = 'latency',
+  CUSTOM = 'custom',
+}
+
+export enum AlertAggregation {
+  SUM = 'sum',
+  AVG = 'avg',
+  COUNT = 'count',
+  MIN = 'min',
+  MAX = 'max',
+}
+
+export enum AlertOperator {
+  GREATER_THAN = '>',
+  LESS_THAN = '<',
+  GREATER_THAN_OR_EQUAL = '>=',
+  LESS_THAN_OR_EQUAL = '<=',
+  EQUAL = '=',
+  NOT_EQUAL = '!=',
+}
+
+export enum AlertNotifyFrequency {
+  IMMEDIATE = 'immediate',
+  HOURLY = 'hourly',
+  DAILY = 'daily',
+  WEEKLY = 'weekly',
+}
+
+export enum AlertStatus {
+  ACTIVE = 'active',
+  PAUSED = 'paused',
+  DISABLED = 'disabled',
+}
+
+export enum AlertHistoryStatus {
+  TRIGGERED = 'triggered',
+  NOTIFIED = 'notified',
+  ACKNOWLEDGED = 'acknowledged',
+  RESOLVED = 'resolved',
+}
+
+export interface Alert {
+  id: string
+  organization_id: string
+  name: string
+  description?: string | null
+  metric_type: AlertMetricType
+  aggregation: AlertAggregation
+  operator: AlertOperator
+  threshold_value: number
+  time_window_minutes: number
+  agent_ids?: string[] | null
+  notify_frequency: AlertNotifyFrequency
+  notify_emails?: string[] | null
+  notify_webhooks?: string[] | null
+  status: AlertStatus
+  created_at: string
+  updated_at: string
+  created_by?: string | null
+}
+
+export interface AlertCreate {
+  name: string
+  description?: string | null
+  metric_type?: AlertMetricType
+  aggregation?: AlertAggregation
+  operator?: AlertOperator
+  threshold_value: number
+  time_window_minutes?: number
+  agent_ids?: string[] | null
+  notify_frequency?: AlertNotifyFrequency
+  notify_emails?: string[]
+  notify_webhooks?: string[]
+}
+
+export interface AlertUpdate {
+  name?: string
+  description?: string | null
+  metric_type?: AlertMetricType
+  aggregation?: AlertAggregation
+  operator?: AlertOperator
+  threshold_value?: number
+  time_window_minutes?: number
+  agent_ids?: string[] | null
+  notify_frequency?: AlertNotifyFrequency
+  notify_emails?: string[]
+  notify_webhooks?: string[]
+  status?: AlertStatus
+}
+
+export interface AlertHistoryItem {
+  id: string
+  organization_id: string
+  alert_id: string
+  triggered_at: string
+  triggered_value: number
+  threshold_value: number
+  status: AlertHistoryStatus
+  notified_at?: string | null
+  notification_details?: Record<string, any> | null
+  acknowledged_at?: string | null
+  acknowledged_by?: string | null
+  resolved_at?: string | null
+  resolved_by?: string | null
+  resolution_notes?: string | null
+  context_data?: Record<string, any> | null
+  created_at: string
+  updated_at: string
+  alert?: Alert
+}
+
+
+// Cron Job Types
+export enum CronJobStatus {
+  ACTIVE = 'active',
+  PAUSED = 'paused',
+  COMPLETED = 'completed',
+}
+
+export interface CronJob {
+  id: string
+  organization_id: string
+  name: string
+  cron_expression: string
+  timezone: string
+  max_runs: number
+  current_runs: number
+  evaluator_ids: string[]
+  status: CronJobStatus
+  next_run_at?: string | null
+  last_run_at?: string | null
+  created_at: string
+  updated_at: string
+  created_by?: string | null
+}
+
+export interface CronJobCreate {
+  name: string
+  cron_expression: string
+  timezone: string
+  max_runs: number
+  evaluator_ids: string[]
+}
+
+export interface CronJobUpdate {
+  name?: string
+  cron_expression?: string
+  timezone?: string
+  max_runs?: number
+  evaluator_ids?: string[]
+  status?: CronJobStatus
+}
+
+// --- Call Imports ---
+
+/**
+ * Lifecycle for a call-import batch.
+ *
+ *  - ``uploaded``   : file landed in S3, no mapping yet.
+ *  - ``mapped``     : user picked a schema + sheet + column mapping; no
+ *                     rows materialised yet, no worker enqueued.
+ *  - ``processing`` : rows materialised + workers enqueued.
+ *  - ``pending``    : transient state used by the legacy one-shot
+ *                     ``POST /upload`` endpoint just before transitioning
+ *                     to ``processing``.
+ */
+export type CallImportStatus =
+  | 'pending'
+  | 'uploaded'
+  | 'mapped'
+  | 'processing'
+  | 'completed'
+  | 'partial'
+  | 'failed'
+  | 'deleting'
+
+export type CallImportRowStatus =
+  | 'pending'
+  | 'processing'
+  | 'completed'
+  | 'failed'
+
+/** Where the value in `transcript` came from. */
+export type CallImportTranscriptSource =
+  | 'csv'
+  | 'transcribed'
+  | 'edited'
+  | null
+/** Lifecycle status for the post-hoc transcription workflow itself. */
+export type CallImportTranscriptStatus =
+  | 'idle'
+  | 'pending'
+  | 'running'
+  | 'completed'
+  | 'failed'
+  | null
+
+/**
+ * Which transcript an evaluation run scored against.
+ *  - `production`: the CSV-supplied value on `CallImportRow.transcript`.
+ *  - `diarised`: the worker-produced value on `CallImportRow.diarised_transcript`.
+ */
+export type CallImportEvaluationTranscriptSource = 'production' | 'diarised'
+
+/**
+ * One contiguous turn inside ``CallImportRow.diarised_segments``.
+ *
+ * The diarisation worker rewrites each pyannote ``Speaker N`` label
+ * into ``agent`` / ``user`` (first speaker = agent heuristic). Anything
+ * beyond two distinct speakers keeps a generic ``speaker_N`` label so
+ * multi-party recordings still render every voice.
+ */
+export interface CallImportDiarisedSegment {
+  speaker: string
+  text: string
+  start: number
+  end: number
+  /** Original pyannote label (``Speaker 1`` / ``Speaker 2`` / ...). */
+  raw_speaker: string
+}
+
+export interface CallImportRow {
+  id: string
+  row_index: number
+  /** Mandatory identifier per row. Renamed from ``external_call_id``. */
+  conversation_id: string
+  recording_url: string | null
+  recording_date: string | null
+  /** Production transcript — the value supplied via the CSV upload. */
+  transcript: string | null
+  /** Provenance of the stored production transcript (csv = CSV upload, edited = manual edit). */
+  transcript_source: CallImportTranscriptSource
+  /** Legacy: provider recorded by the original transcription worker before the split. */
+  transcript_provider: string | null
+  transcript_model: string | null
+  transcript_status: CallImportTranscriptStatus
+  transcript_error: string | null
+  transcribed_at: string | null
+  /** Diarised transcript — produced by the post-hoc diarisation worker. */
+  diarised_transcript: string | null
+  /** Provider used by the diarisation worker (e.g. "deepgram"). */
+  diarised_transcript_provider: string | null
+  diarised_transcript_model: string | null
+  diarised_transcript_status: CallImportTranscriptStatus
+  diarised_transcript_error: string | null
+  diarised_at: string | null
+  /**
+   * Structured speaker turns produced by the diarisation worker. Each
+   * entry is a single contiguous turn shaped as
+   * `{ speaker: 'agent' | 'user' | 'speaker_N', text, start, end,
+   *   raw_speaker }`. ``diarised_transcript`` is a rendered
+   * `<speaker>: <text>` view of this list with
+   * ``diarised_speaker_swap`` applied. ``null`` on legacy rows that
+   * were diarised before structured turns were persisted (or when the
+   * STT provider didn't surface segments).
+   */
+  diarised_segments: CallImportDiarisedSegment[] | null
+  /**
+   * When ``true`` the ``agent`` <-> ``user`` mapping inside
+   * ``diarised_segments`` is inverted in the rendered transcript /
+   * CSV export. The worker writes the canonical mapping using a
+   * "first speaker is the agent" heuristic; reviewers can flip the
+   * toggle from the row detail panel without re-running diarisation.
+   */
+  diarised_speaker_swap: boolean
+  /**
+   * LLM that turned the STT plain-text output into structured
+   * ``diarised_segments``. NULL on legacy rows (pre-LLM-diariser).
+   */
+  diarised_llm_provider: string | null
+  diarised_llm_model: string | null
+  /**
+   * Exact prompt the LLM diariser ran with. Useful for the modal to
+   * pre-fill its textarea when the operator wants to iterate on a
+   * previously-diarised row.
+   */
+  diarised_prompt: string | null
+  /**
+   * Diarisation pipeline that produced this row's turns.
+   * - `stt_llm` (default) — two-stage STT then LLM diariser.
+   * - `llm_only` — single-stage multimodal LLM (audio in).
+   * Read-only; written by the worker on each diarisation.
+   */
+  transcribe_mode?: 'stt_llm' | 'llm_only'
+  /**
+   * Per-row preservation of the mapped source cells. Values land here
+   * as whatever type the schema parameter coerced them to —
+   * strings (text / url / conversation_id / recording_url /
+   * recording_date / transcript / datetime), numbers, booleans, or
+   * ``null`` for blanks. Always
+   * coerce with ``String(value)`` before string operations.
+   */
+  raw_columns: Record<string, string | number | boolean | null> | null
+  status: CallImportRowStatus
+  recording_s3_key: string | null
+  recording_content_type: string | null
+  recording_size_bytes: number | null
+  error_message: string | null
+  attempts: number
+  created_at: string
+  updated_at: string
+}
+
+export interface CallImportTag {
+  id: string
+  name: string
+  color: string | null
+  created_at: string
+  updated_at: string
+}
+
+/**
+ * Parameter type tag on a Call Import schema parameter.
+ *
+ *  - ``conversation_id``: mandatory identifier (one per schema).
+ *  - ``recording_url``: feeds ``CallImportRow.recording_url``.
+ *  - ``recording_date``: date-only call recording date used for reports.
+ *  - ``transcript``: feeds ``CallImportRow.transcript``.
+ *  - ``text`` / ``number`` / ``boolean`` / ``datetime`` / ``url``:
+ *    generic typed fields preserved per row in ``raw_columns`` and
+ *    surfaced in the evaluation export under the parameter's name.
+ */
+export type CallImportSchemaParameterType =
+  | 'conversation_id'
+  | 'recording_url'
+  | 'recording_date'
+  | 'transcript'
+  | 'text'
+  | 'number'
+  | 'boolean'
+  | 'datetime'
+  | 'url'
+
+export interface CallImportSchemaParameter {
+  id?: string
+  name: string
+  type: CallImportSchemaParameterType
+  description: string | null
+  is_required: boolean
+  ordering?: number
+}
+
+export interface CallImportSchema {
+  id: string
+  organization_id: string
+  workspace_id: string
+  name: string
+  description: string | null
+  parameters: CallImportSchemaParameter[]
+  /** How many CallImport batches reference this schema. */
+  usage_count: number
+  created_at: string
+  updated_at: string
+}
+
+export interface CallImportSchemaListResponse {
+  items: CallImportSchema[]
+  total: number
+}
+
+export interface CallImportSchemaCreate {
+  name: string
+  description?: string | null
+  parameters: Array<Omit<CallImportSchemaParameter, 'id' | 'ordering'>>
+}
+
+export interface CallImportSchemaUpdate {
+  name?: string
+  description?: string | null
+  parameters?: Array<Omit<CallImportSchemaParameter, 'id' | 'ordering'>>
+}
+
+/**
+ * In-org Workspace - the active workspace scopes call imports and
+ * metrics in the UI. The org's Default workspace is auto-seeded by
+ * migration 033 and cannot be deleted.
+ */
+export interface Workspace {
+  id: string
+  organization_id: string
+  name: string
+  slug: string
+  is_default: boolean
+  created_at: string
+  updated_at: string
+  role_id?: string | null
+  role_name?: string | null
+  capabilities?: string[]
+}
+
+export interface WorkspaceRole {
+  id: string
+  organization_id: string
+  name: string
+  description?: string | null
+  capabilities: string[]
+  is_system: boolean
+  created_at: string
+  updated_at: string
+}
+
+export interface WorkspaceMember {
+  id: string
+  workspace_id: string
+  user_id: string
+  role_id: string
+  role_name: string
+  user_email: string
+  user_name?: string | null
+  added_by_user_id?: string | null
+  created_at: string
+}
+
+export interface CapabilityInfo {
+  key: string
+  label: string
+}
+
+export interface CapabilityDomain {
+  key: string
+  label: string
+  capabilities: CapabilityInfo[]
+}
+
+export interface WorkspaceRoleCreate {
+  name: string
+  description?: string | null
+  capabilities: string[]
+}
+
+export interface WorkspaceRoleUpdate {
+  name?: string
+  description?: string | null
+  capabilities?: string[]
+}
+
+export interface CallImportSourceRowSkip {
+  source_row: number
+  reason: string
+  message: string
+}
+
+export interface CallImport {
+  id: string
+  organization_id: string
+  /** Workspace this import belongs to. */
+  workspace_id: string
+  /**
+   * Telephony provider key. ``null`` until the IMPORT stage in the
+   * staged flow (which is the first step that knows the provider).
+   * Always populated on post-import batches and on legacy one-shot
+   * uploads.
+   */
+  provider: string | null
+  telephony_integration_id: string | null
+  original_filename: string | null
+  /**
+   * For Excel uploads, which worksheet this batch came from. ``null``
+   * for CSV uploads (CSV files have no sheet concept) and for any
+   * imports created before multi-sheet support landed.
+   */
+  sheet_name: string | null
+  /** Optional free-text dataset label (high-level segregation filter). */
+  dataset: string | null
+  /** Tags currently attached to this import. Empty array if untagged. */
+  tags: CallImportTag[]
+  /**
+   * Reusable Input Parameter schema the batch was uploaded against.
+   * NULL on legacy batches uploaded before the schema-driven flow shipped.
+   */
+  schema_id: string | null
+  /**
+   * Schema-driven mapping: ``{parameter_name: csv_header}``. Empty on
+   * legacy batches; check ``column_mapping`` / ``extra_columns`` /
+   * ``custom_column_mapping`` instead for those.
+   */
+  parameter_mapping: Record<string, string>
+  /** Legacy free-form mapping kept for batches uploaded before schemas. */
+  column_mapping: Record<string, string | null>
+  /** Legacy extra-column list kept for backwards-compat. */
+  extra_columns: string[]
+  /** Legacy uploader-named columns kept for backwards-compat. */
+  custom_column_mapping: Record<string, string>
+  /**
+   * Source headers the uploader explicitly skipped, captured at the
+   * MAP stage. Empty for legacy one-shot uploads where the value was
+   * ephemeral.
+   */
+  skipped_columns: string[]
+  /**
+   * Source rows skipped at parse time (missing/invalid conversation ID or URL).
+   */
+  source_row_skips?: CallImportSourceRowSkip[]
+  /** S3 key for the staged source file. ``null`` on legacy batches. */
+  source_s3_key: string | null
+  /** ``'csv'`` / ``'xlsx'`` for staged files, or ``'audio'`` for manual uploads. */
+  source_format: string | null
+  source_size_bytes: number | null
+  source_content_type: string | null
+  /**
+   * Snapshot of the file's sheets + headers captured at UPLOAD time so
+   * the MAP UI can render without re-fetching the source from S3.
+   * ``null`` on legacy batches.
+   */
+  available_sheets: CallImportPreviewSheet[] | null
+  total_rows: number
+  completed_rows: number
+  failed_rows: number
+  status: CallImportStatus
+  error_message: string | null
+  created_at: string
+  updated_at: string
+  created_by_email?: string | null
+  last_updated_by_email?: string | null
+}
+
+export interface CallImportDetail extends CallImport {
+  rows: CallImportRow[]
+  /**
+   * Total row count *after* applying the optional ``q`` search filter.
+   * ``null`` when no filter is active — paginate against ``total_rows``
+   * in that case.
+   */
+  filtered_total_rows: number | null
+  /**
+   * Batch-wide aggregates of ``CallImportRow.diarised_transcript_status``.
+   * The ``idle`` bucket (rows never touched by the transcribe/diarise
+   * worker) is implicit: ``total_rows - (pending + running + completed
+   * + failed)``. Lets the UI render a transcribe-and-diarise progress
+   * bar without paginating through every row.
+   */
+  diarised_pending_rows: number
+  diarised_running_rows: number
+  diarised_completed_rows: number
+  diarised_failed_rows: number
+}
+
+export interface CallImportListResponse {
+  items: CallImport[]
+  total: number
+  page: number
+  page_size: number
+}
+
+export interface CallImportUploadResponse {
+  id: string
+  total_rows: number
+  status: CallImportStatus
+  dataset: string | null
+  tags: CallImportTag[]
+  message: string
+}
+
+/** One worksheet (or one CSV file synthesized as a single sheet). */
+export interface CallImportPreviewSheet {
+  /** Sheet name for xlsx; filename for csv. */
+  name: string
+  /** Column headers from the first non-empty row. */
+  headers: string[]
+  /** Approximate count of data rows (excludes the header row). */
+  row_count: number
+}
+
+/**
+ * Sheets / headers extracted server-side from an uploaded CSV or Excel
+ * workbook. Drives the modal's column-mapping UI without forcing the
+ * frontend to parse the file itself.
+ */
+export interface CallImportPreviewResponse {
+  /** ``'csv'`` or ``'xlsx'``. */
+  format: 'csv' | 'xlsx'
+  sheets: CallImportPreviewSheet[]
+}
+
+export type MetricSelectionMode = 'single_choice' | 'multi_label'
+
+export interface CallImportMetricSummary {
+  id: string
+  name: string
+  metric_type: string | null
+  description: string | null
+  parent_metric_id?: string | null
+  selection_mode?: MetricSelectionMode | null
+  /** Only meaningful on multi_label parents; gates the Discovered
+   *  Labels panel on the Flow tab. Defaults to false. */
+  allow_discovery?: boolean
+}
+
+/** Per-metric LLM override (provider+model+optional credential + generation params). */
+export interface CallImportEvaluationLLMOverride {
+  provider?: string | null
+  model?: string | null
+  credential_id?: string | null
+  llm_config?: LLMGenerationConfig | null
+}
+
+export interface CallImportEvaluation {
+  id: string
+  call_import_id: string
+  organization_id: string
+  /** User-supplied label for the run; null when not named. */
+  name: string | null
+  selected_metric_ids: string[]
+  /** parent_id -> [child_id, ...] snapshot captured at run time. */
+  selected_metric_groups?: Record<string, string[]> | null
+  metrics: CallImportMetricSummary[]
+  status: 'pending' | 'running' | 'completed' | 'partial' | 'failed'
+  total_rows: number
+  completed_rows: number
+  failed_rows: number
+  error_message: string | null
+  /** Run-level LLM provider chosen by the user (null = legacy default). */
+  llm_provider: string | null
+  llm_model: string | null
+  llm_credential_id: string | null
+  llm_config?: LLMGenerationConfig | null
+  metric_llm_overrides: Record<string, CallImportEvaluationLLMOverride> | null
+  stt_provider: string | null
+  stt_model: string | null
+  stt_credential_id: string | null
+  /**
+   * Run-level LLM diariser config used when the worker auto-diarises
+   * rows that are missing a diarised transcript.
+   */
+  diarisation_llm_provider?: string | null
+  diarisation_llm_model?: string | null
+  diarisation_llm_credential_id?: string | null
+  diarisation_prompt?: string | null
+  /**
+   * Diarisation pipeline shape this run was created with.
+   * - `stt_llm` (default) — STT then an LLM diariser over the text.
+   * - `llm_only` — audio fed directly to a multimodal diariser LLM.
+   * Surfaced so the retry / re-run UI can preselect the right mode.
+   */
+  transcribe_mode?: 'stt_llm' | 'llm_only'
+  /**
+   * Which transcript column this run scored against.
+   * Defaults to `production` on legacy runs.
+   */
+  transcript_source: CallImportEvaluationTranscriptSource
+  /**
+   * Other evaluation ids created in the same Run Evaluation request.
+   * Populated only on the POST response when the user ticked both
+   * Production and Diarised. Empty array on all other reads.
+   */
+  sibling_evaluation_ids: string[]
+  started_at: string | null
+  finished_at: string | null
+  created_at: string
+  updated_at: string
+  created_by_email?: string | null
+  last_updated_by_email?: string | null
+  /**
+   * Cached LLM-generated TLDR rendered above the Visualizations tab.
+   * Populated lazily via ``POST /evaluations/{id}/insights``; null on
+   * runs the user has not summarised yet.
+   */
+  tldr_summary?: EvaluationTldrSummary | null
+  user_insights?: EvaluationUserInsightsState | null
+  metric_clusters?: EvaluationMetricClustersState | null
+  /**
+   * True when the user opted into top-level metric discovery on the
+   * Run Evaluation modal. Gates the Discovered metrics panel on the
+   * evaluation detail Flow tab.
+   */
+  discover_new_metrics?: boolean
+  /**
+   * Set while a bulk background operation (abort, force-fail, retry) is
+   * still running. The UI disables other mutating actions until cleared.
+   */
+  bulk_operation?: 'abort' | 'force_fail_pending' | 'retry' | null
+}
+
+/**
+ * LLM-generated narrative + bullet patterns for a single evaluation
+ * run. Cached on the evaluation row so re-opening the Visualizations
+ * tab doesn't auto-burn LLM tokens. ``is_stale`` is computed by the
+ * backend at read time when ``completed_rows`` has grown since the
+ * summary was generated.
+ */
+export interface EvaluationTldrSummary {
+  narrative: string
+  patterns: string[]
+  metric_insights?: Record<string, string>
+  generated_at: string
+  generated_at_completed_rows: number
+  provider?: string | null
+  model?: string | null
+  is_stale: boolean
+}
+
+export interface UserInsightCategory {
+  label: string
+  count: number
+  share_pct: number
+}
+
+export interface UserInsightEvidenceTurn {
+  speaker: string
+  text: string
+}
+
+export interface UserInsightEvidence {
+  conversation_id?: string | null
+  quote: string
+  turns?: UserInsightEvidenceTurn[]
+}
+
+export interface EvaluationUserInsightItem {
+  id: string
+  title: string
+  categories: UserInsightCategory[]
+  observation: string
+  evidence: UserInsightEvidence
+}
+
+export interface EvaluationUserInsightsState {
+  status: 'idle' | 'running' | 'completed' | 'failed'
+  insights: EvaluationUserInsightItem[]
+  overview?: string | null
+  generated_at?: string | null
+  generated_at_completed_rows: number
+  progress?: { completed_llm_calls: number; total_llm_calls: number } | null
+  provider?: string | null
+  model?: string | null
+  llm_calls_used: number
+  max_llm_calls?: number | null
+  error_message?: string | null
+  is_stale: boolean
+}
+
+export type MetricClusterGapLabel =
+  | 'LOGIC_GAP'
+  | 'UNDERSPEC'
+  | 'EXISTS_NO_TRIGGER'
+  | 'MISSING'
+
+export interface MetricSubCluster {
+  label: string
+  count: number
+  share_pct: number
+}
+
+export interface MetricClusterEvidenceTurn {
+  speaker: string
+  text: string
+}
+
+export interface MetricClusterEvidence {
+  conversation_id?: string | null
+  evaluation_row_id?: string | null
+  quote: string
+  turns?: MetricClusterEvidenceTurn[]
+}
+
+export interface MetricCluster {
+  id: string
+  label: string
+  gap_label: MetricClusterGapLabel
+  level: number
+  count: number
+  share_pct: number
+  sub_clusters: MetricSubCluster[]
+  observation: string
+  failure_reason?: string
+  evidence: MetricClusterEvidence
+  is_discovered: boolean
+}
+
+export interface MetricClusterGroup {
+  metric_id: string
+  metric_name: string
+  flagged_count: number
+  failure_reason?: string
+  clusters: MetricCluster[]
+}
+
+export interface DiscoveredProblemCluster {
+  id: string
+  label: string
+  gap_label: MetricClusterGapLabel
+  count: number
+  share_pct: number
+  observation: string
+  failure_reason?: string
+  evidence: MetricClusterEvidence
+}
+
+export interface RcaRepeatedPatternRow {
+  metric_id: string
+  metric_name: string
+  top_rca_patterns: string
+  evidence_share_pct: number
+  evidence_calls: number
+  evidence_cluster_count?: number
+  failure_reason: string
+}
+
+export interface RcaMetricHotspotRow {
+  metric_id: string
+  metric_name: string
+  description: string
+  metric_rate_pct: number
+  flagged_calls: number
+}
+
+export interface RcaPromptAreaRow {
+  label: string
+  share_pct: number
+  gap_label: MetricClusterGapLabel
+}
+
+export interface MetricClustersRcaSummary {
+  total_clusters: number
+  total_clustered_instances: number
+  total_flagged_instances?: number
+  analysed_calls: number
+  repeated_patterns: RcaRepeatedPatternRow[]
+  metric_hotspots: RcaMetricHotspotRow[]
+  prompt_areas: RcaPromptAreaRow[]
+}
+
+export interface MetricFailurePolicy {
+  metric_id: string
+  failure_values: string[]
+  failure_child_names?: string[]
+  numeric_rule?: { op: 'lt' | 'lte' | 'gt' | 'gte'; threshold: number } | null
+}
+
+export interface MetricFailurePolicyValueCount {
+  label: string
+  count: number
+}
+
+export interface MetricFailurePolicyMetricPreview {
+  metric_id: string
+  metric_name: string
+  metric_type?: string | null
+  selection_mode?: string | null
+  is_multi_label_parent: boolean
+  value_counts: MetricFailurePolicyValueCount[]
+  child_names: string[]
+  row_count_by_value: Record<string, number>
+  suggested_policy: MetricFailurePolicy
+  effective_policy: MetricFailurePolicy
+}
+
+export interface MetricFailurePoliciesResponse {
+  previews: MetricFailurePolicyMetricPreview[]
+  policies: Record<string, MetricFailurePolicy>
+  source: 'inferred' | 'user'
+  updated_at?: string | null
+}
+
+export interface MetricClusterEligibleRow {
+  evaluation_row_id: string
+  conversation_id?: string | null
+  row_index?: number | null
+  flagged_metric_names: string[]
+}
+
+export interface MetricClusterEligibleRowsResponse {
+  items: MetricClusterEligibleRow[]
+  total: number
+}
+
+export interface EvaluationMetricClustersState {
+  status: 'idle' | 'running' | 'completed' | 'failed' | 'cancelled'
+  groups: MetricClusterGroup[]
+  discovered_problems: DiscoveredProblemCluster[]
+  overview?: string | null
+  generated_at?: string | null
+  generated_at_completed_rows: number
+  progress?: { completed_llm_calls: number; total_llm_calls: number } | null
+  provider?: string | null
+  model?: string | null
+  llm_calls_used: number
+  max_llm_calls?: number | null
+  error_message?: string | null
+  is_stale: boolean
+  selected_evaluation_row_ids?: string[]
+  failure_policies?: Record<string, MetricFailurePolicy>
+  failure_policies_source?: 'inferred' | 'user'
+  failure_policies_updated_at?: string | null
+  rca_summary?: MetricClustersRcaSummary | null
+}
+
+export interface AgentFlowNode {
+  id: string
+  label: string
+  node_type: 'start' | 'decision' | 'action' | 'terminal'
+  position_x?: number | null
+  position_y?: number | null
+  prompt_excerpt?: string | null
+  start_offset?: number | null
+  end_offset?: number | null
+}
+
+export interface AgentFlowEdge {
+  source: string
+  target: string
+  condition?: string | null
+}
+
+export interface AgentFlowGraph {
+  nodes: AgentFlowNode[]
+  edges: AgentFlowEdge[]
+  generated_at?: string | null
+  provider?: string | null
+  model?: string | null
+  layout_saved_at?: string | null
+  prompt_content_hash?: string | null
+  mapping_error?: string | null
+  generation_error?: string | null
+}
+
+export interface ImportedAgent {
+  id: string
+  organization_id: string
+  name: string
+  description: string | null
+  content: string
+  tags: string[] | null
+  current_version: number
+  agent_flowchart?: AgentFlowGraph | null
+  agent_flowchart_status?: string | null
+  created_at: string
+  updated_at: string
+  created_by: string | null
+}
+
+export interface ImportedAgentDetail extends ImportedAgent {
+  versions: PromptPartialVersion[]
+}
+
+export interface MetricPartialChild {
+  name: string
+  description: string
+  example: string
+}
+
+export interface MetricPartialContent {
+  schema_version: 1
+  metric_kind: 'single' | 'category'
+  description: string
+  children?: MetricPartialChild[]
+}
+
+export interface MetricPartial {
+  id: string
+  organization_id: string
+  name: string
+  description: string | null
+  content: string
+  tags: string[] | null
+  current_version: number
+  created_at: string
+  updated_at: string
+  created_by: string | null
+}
+
+export interface MetricPartialDetail extends MetricPartial {
+  versions: PromptPartialVersion[]
+}
+
+export interface PromptPartialVersion {
+  id: string
+  prompt_partial_id: string
+  version: number
+  content: string
+  change_summary: string | null
+  created_at: string
+  created_by: string | null
+}
+
+export interface PromptImprovementSuggestion {
+  id: string
+  metric_id: string
+  metric_name: string
+  cluster_id: string
+  cluster_label: string
+  gap_label: MetricClusterGapLabel
+  share_pct: number
+  priority: 'high' | 'medium' | 'low'
+  change_type?: 'edit' | 'add'
+  target_section: string
+  anchor_excerpt?: string
+  current_gap: string
+  suggested_text: string
+  rationale: string
+  flow_node_id?: string
+  flow_node_label?: string
+}
+
+export interface EvaluationPromptImprovementsState {
+  status: 'idle' | 'running' | 'completed' | 'failed'
+  imported_agent_id?: string | null
+  imported_agent_name?: string | null
+  suggestions: PromptImprovementSuggestion[]
+  overview?: string | null
+  generated_at?: string | null
+  generated_at_completed_rows: number
+  provider?: string | null
+  model?: string | null
+  error_message?: string | null
+  is_stale: boolean
+}
+
+export interface MetricPeriodDelta {
+  label: string
+  detail: string
+  why?: string | null
+}
+
+export interface CallImportEvaluationListResponse {
+  items: CallImportEvaluation[]
+  total: number
+}
+
+export interface CallImportEvaluationBaselineCandidate {
+  evaluation_id: string
+  name: string
+  dataset: string
+  period_label: string | null
+  period_start: string | null
+  period_end: string | null
+  period_display: string
+  completed_rows: number
+  created_at: string
+  is_default: boolean
+}
+
+export interface CallImportEvaluationBaselineCandidatesResponse {
+  items: CallImportEvaluationBaselineCandidate[]
+  default_evaluation_id: string | null
+}
+
+export interface CallImportEvaluationRow {
+  id: string
+  evaluation_id: string
+  call_import_row_id: string
+  row_index: number | null
+  /** Mandatory identifier from the source batch (renamed from ``external_call_id``). */
+  conversation_id: string | null
+  transcript: string | null
+  raw_columns: Record<string, any> | null
+  recording_url: string | null
+  recording_date: string | null
+  /**
+   * S3 object key for the downloaded recording. Prefer this over
+   * ``recording_url`` for playback — we resolve it to a presigned URL
+   * so audio plays from our storage instead of the (often expired)
+   * provider URL.
+   */
+  recording_s3_key: string | null
+  diarised_transcript_status?: string | null
+  diarised_transcript_error?: string | null
+  status: 'pending' | 'running' | 'completed' | 'failed' | 'skipped'
+  metric_scores: Record<string, any>
+  error_message: string | null
+  started_at: string | null
+  finished_at: string | null
+  created_at: string
+  updated_at: string
+}
+
+export interface CallImportEvaluationRowListResponse {
+  items: CallImportEvaluationRow[]
+  total: number
+  page: number
+  page_size: number
+}
+
+// --- Retry (re-enqueue failed rows on an existing evaluation run) ---
+
+export interface CallImportEvaluationRetryRequest {
+  /**
+   * Restrict the retry to a specific subset of evaluation rows.
+   * When omitted, every row with status='failed' in this run is
+   * re-enqueued.
+   */
+  eval_row_ids?: string[]
+
+  /**
+   * Optional LLM overrides. When provided, persisted onto the run so
+   * future retries default to the new config. ``llm_provider`` and
+   * ``llm_model`` must be sent together — the backend 400s on
+   * half-configured input.
+   */
+  llm_provider?: string
+  llm_model?: string
+  llm_credential_id?: string | null
+
+  /**
+   * Optional STT overrides (only meaningful when the run scores the
+   * diarised transcript). Same paired-field rule as LLM.
+   */
+  stt_provider?: string
+  stt_model?: string
+  stt_credential_id?: string | null
+
+  /**
+   * When true, wipe the diarised transcript on every retried row so
+   * the (possibly new) STT runs from scratch. Only takes effect for
+   * diarised runs that have STT config.
+   */
+  transcribe_overwrite?: boolean
+}
+
+export interface CallImportEvaluationRetrySkippedItem {
+  eval_row_id: string
+  /**
+   * Why this row was not re-enqueued. Known values:
+   * - 'unknown' (id not in this run)
+   * - 'in_progress' (status is pending/running)
+   * - 'completed' (already successful)
+   * - 'source_row_missing'
+   */
+  reason: 'unknown' | 'in_progress' | 'completed' | 'source_row_missing'
+}
+
+export interface CallImportEvaluationRetryResponse {
+  requeued: number
+  /**
+   * Of those, how many were chained through a diarisation task first
+   * because the diarised transcript was missing.
+   */
+  transcribe_requeued: number
+  skipped: CallImportEvaluationRetrySkippedItem[]
+}
+
+export interface CallImportEvaluationBulkActionResponse {
+  accepted: boolean
+  target_count: number
+  evaluation_id: string
+}
+
+// --- Diarization / transcription ---
+
+export interface CallImportTranscribeRequest {
+  /**
+   * Diarisation pipeline shape.
+   * - `stt_llm` (default) — STT produces plain text, then an LLM
+   *   diariser splits it into agent/user turns. STT fields required.
+   * - `llm_only` — skip STT entirely and feed the audio bytes
+   *   directly to a multimodal `diarization_llm_*` model along with
+   *   `diarization_prompt`. STT fields MUST be omitted in this mode.
+   */
+  mode?: 'stt_llm' | 'llm_only'
+  /** Required when `mode === 'stt_llm'`; must be null/omitted in `llm_only`. */
+  stt_provider?: string | null
+  /** Required when `mode === 'stt_llm'`; must be null/omitted in `llm_only`. */
+  stt_model?: string | null
+  credential_id?: string | null
+  language?: string | null
+  only_missing?: boolean
+  overwrite_existing?: boolean
+  row_ids?: string[]
+  /**
+   * LLM diariser. In `stt_llm` mode it splits the STT plain-text into
+   * agent/user turns; in `llm_only` mode it directly receives the
+   * audio along with `diarization_prompt`. Always required.
+   */
+  diarization_llm_provider: string
+  diarization_llm_model: string
+  diarization_llm_credential_id?: string | null
+  /**
+   * Operator-supplied system prompt for the diariser LLM. NULL/empty
+   * means "fall back to the canonical default" (see
+   * ``getDiarisationDefaultPrompt``).
+   */
+  diarization_prompt?: string | null
+}
+
+export interface CallImportTranscribeResponse {
+  queued: number
+  skipped_rows: number
+  skipped_reason_counts: Record<string, number>
+  accepted?: boolean
+}
+
+export interface CallImportRowBulkDeleteResponse {
+  deleted: number
+  status?: 'completed' | 'accepted'
+}
+
+export interface CallImportRetryFailedRowsResponse {
+  requeued: number
+  enqueue_failed: number
+  skipped: number
+}
+
+export interface CallImportDiarisationPromptDefaultResponse {
+  prompt: string
+}
+
+// --- Aggregation / visualization payloads ---
+
+export interface CallImportMetricHistogramBucket {
+  x0: number
+  x1: number
+  count: number
+}
+
+export interface CallImportMetricValueCount {
+  label: string
+  count: number
+}
+
+/**
+ * One unordered pair-count cell from a multi-label parent's
+ * co-occurrence matrix. ``a`` and ``b`` are child label names with
+ * ``a < b`` lexicographically; ``count`` is the number of rows on
+ * which both labels fired together.
+ */
+export interface CallImportMetricLabelPair {
+  a: string
+  b: string
+  count: number
+}
+
+export interface CallImportMetricAggregate {
+  metric_id: string
+  metric_name: string
+  metric_type: string | null
+  metric_category?: 'quality' | 'user_insight' | string
+  /**
+   * True when the metric is a multi-label classifier parent.
+   * ``value_counts`` then lists per-child label tallies and one row
+   * may contribute to several labels, so the chart layout has to
+   * ignore the pie toggle (slices wouldn't sum to 100%) and the
+   * n-badge represents rows scored, not label occurrences.
+   */
+  is_multi_label_parent?: boolean
+  count: number
+  skipped_count: number
+  error_count: number
+  mean: number | null
+  median: number | null
+  p25: number | null
+  p75: number | null
+  p95: number | null
+  min: number | null
+  max: number | null
+  stddev: number | null
+  histogram_buckets: CallImportMetricHistogramBucket[]
+  value_counts: CallImportMetricValueCount[]
+  /**
+   * Pairwise label intersections for multi-label parent metrics.
+   * Empty for everything else. The Visualizations tab reconstructs
+   * a square symmetric matrix from these unordered pairs to render
+   * the co-occurrence heatmap chart type.
+   */
+  co_occurrence?: CallImportMetricLabelPair[]
+}
+
+export interface CallImportEvaluationAggregateResponse {
+  evaluation_id: string
+  total_rows: number
+  completed_rows: number
+  failed_rows: number
+  metrics: CallImportMetricAggregate[]
+  period_deltas?: Record<string, MetricPeriodDelta>
+  baseline_evaluation_id?: string | null
+  failure_policies_source?: 'inferred' | 'user' | null
+}
+
+export interface EvaluatorResultsAggregateResponse {
+  scope: string
+  suite_id?: string | null
+  agent_id?: string | null
+  scenario_id?: string | null
+  total_rows: number
+  completed_rows: number
+  failed_rows: number
+  metrics: CallImportMetricAggregate[]
+}
+
+export interface CallImportInsightsRunPoint {
+  evaluation_id: string
+  name: string | null
+  created_at: string
+  mean: number | null
+  completed_rows: number
+}
+
+export interface CallImportInsightsMetric {
+  metric_id: string
+  metric_name: string
+  metric_type: string | null
+  latest: CallImportMetricAggregate | null
+  trend: CallImportInsightsRunPoint[]
+}
+
+export interface CallImportInsightsResponse {
+  call_import_id: string
+  total_rows: number
+  rows_with_transcript: number
+  rows_without_transcript: number
+  transcript_source_counts: Record<string, number>
+  evaluation_count: number
+  metrics: CallImportInsightsMetric[]
+}
+
+// --- Metrics hierarchy + flow visualization ---
+
+export interface MetricSummary {
+  id: string
+  organization_id: string
+  name: string
+  description: string | null
+  metric_type: string
+  metric_category?: 'quality' | 'user_insight' | string
+  trigger: string
+  enabled: boolean
+  is_default: boolean
+  metric_origin: string
+  supported_surfaces: string[]
+  enabled_surfaces: string[]
+  custom_data_type: string | null
+  custom_config: Record<string, any> | null
+  tags: string[] | null
+  capture_rationale: boolean
+  parent_metric_id: string | null
+  selection_mode: MetricSelectionMode | null
+  allow_discovery?: boolean
+  /**
+   * When true, this metric is a "transcript-compare judge": at
+   * call-import evaluation time the worker feeds BOTH the production
+   * transcript and the diarised transcript to the LLM as a labeled
+   * pair, and the run's transcript_source toggle is ignored for this
+   * metric. Mutually exclusive with parent_metric_id and selection_mode
+   * — comparison metrics stay standalone.
+   */
+  compare_transcripts?: boolean
+  children?: MetricSummary[]
+  created_at: string
+  updated_at: string
+  created_by: string | null
+}
+
+export interface MetricChildDraft {
+  name: string
+  description?: string | null
+  enabled?: boolean
+  capture_rationale?: boolean | null
+  tags?: string[] | null
+}
+
+export interface MetricCreateWithChildrenPayload {
+  name: string
+  description?: string | null
+  selection_mode: MetricSelectionMode
+  enabled?: boolean
+  supported_surfaces?: string[]
+  enabled_surfaces?: string[]
+  tags?: string[] | null
+  allow_discovery?: boolean
+  children: MetricChildDraft[]
+}
+
+export interface MetricFlowNode {
+  id: string
+  label: string
+  count: number
+  is_terminal: boolean
+  is_discovered?: boolean
+}
+
+export interface MetricFlowEdge {
+  source: string
+  target: string
+  count: number
+}
+
+export interface MetricFlowResponse {
+  parent_metric_id: string
+  parent_metric_name: string
+  selection_mode: MetricSelectionMode | null
+  nodes: MetricFlowNode[]
+  edges: MetricFlowEdge[]
+  total_rows: number
+  rows_with_sequence: number
+}
+
+export interface DiscoveredLabel {
+  key: string
+  name: string
+  description?: string | null
+  sample_rationale?: string | null
+  /**
+   * Up to 3 distinct LLM rationales captured for this candidate
+   * across rows. The Discovered Labels promote flow surfaces the
+   * first 2 as an ``Examples:`` block on the new sub-metric's
+   * rubric so the user starts with concrete cases in the prompt.
+   */
+  examples?: string[]
+  count: number
+}
+
+export interface DiscoveredLabelsResponse {
+  parent_metric_id: string
+  items: DiscoveredLabel[]
+}
+
+/**
+ * One LLM-discovered candidate TOP-LEVEL metric aggregated across all
+ * rows of an evaluation. Mirrors :class:`DiscoveredLabel` but adds a
+ * ``suggested_type`` field — the LLM's guess at the best shape for
+ * the new metric — that the promote modal can pre-fill the type radio
+ * with.
+ */
+export interface DiscoveredMetric {
+  key: string
+  name: string
+  description?: string | null
+  suggested_type: 'boolean' | 'rating' | 'category'
+  sample_rationale?: string | null
+  examples?: string[]
+  count: number
+}
+
+export interface DiscoveredMetricsResponse {
+  evaluation_id: string
+  items: DiscoveredMetric[]
+}
+
+export interface ObservabilityCallAgent {
+  id: string
+  agent_id?: string | null
+  name: string
+}
+
+export interface ObservabilityCallData {
+  startedAt?: string
+  started_at?: string
+  endedAt?: string
+  ended_at?: string
+  from_phone_number?: string
+  to_phone_number?: string
+  endedReason?: string
+  recording_s3_key?: string
+  recording_url?: string
+  duration_seconds?: number
+  agent_name?: string
+  _agent_ref?: string | number
+  direction?: string
+  messages?: Array<{ role: string; content: string; start_time?: number; end_time?: number }>
+  live_transcript?: Array<{ role: string; content: string; timestamp?: string; start_time?: number }>
+  metadata?: Record<string, unknown>
+  call_short_id?: string
+}
+
+export interface ObservabilityCall {
+  id: string
+  call_short_id: string
+  status?: string | null
+  call_event?: string | null
+  is_live?: boolean
+  direction?: string | null
+  source?: string | null
+  provider_platform?: string | null
+  provider_call_id?: string | null
+  agent_id?: string | null
+  agent?: ObservabilityCallAgent | null
+  created_at?: string | null
+  updated_at?: string | null
+  call_data?: ObservabilityCallData | null
+  live_transcript?: Array<{ role: string; content: string; timestamp?: string }>
+}
diff --git a/tests/test_api/test_call_import_audit.py b/tests/test_api/test_call_import_audit.py
new file mode 100644
index 00000000..60592ff0
--- /dev/null
+++ b/tests/test_api/test_call_import_audit.py
@@ -0,0 +1,78 @@
+"""Audit fields (created_by / last_updated_by email) on call imports."""
+
+from uuid import uuid4
+
+from app.models.database import CallImport, Workspace
+from app.models.enums import CallImportStatus
+
+
+def _ensure_default_workspace(db_session, org_id):
+    ws = (
+        db_session.query(Workspace)
+        .filter(Workspace.organization_id == org_id, Workspace.is_default.is_(True))
+        .first()
+    )
+    if ws is None:
+        ws = Workspace(
+            organization_id=org_id, name="Default", slug="default", is_default=True
+        )
+        db_session.add(ws)
+        db_session.commit()
+    return ws
+
+
+def test_update_call_import_metadata_stamps_actor_emails(
+    authenticated_client, db_session, org_id, seed_org
+):
+    workspace = _ensure_default_workspace(db_session, org_id)
+    call_import = CallImport(
+        id=uuid4(),
+        organization_id=org_id,
+        workspace_id=workspace.id,
+        provider="exotel",
+        original_filename="batch.csv",
+        total_rows=0,
+        completed_rows=0,
+        failed_rows=0,
+        status=CallImportStatus.COMPLETED,
+        dataset="before",
+    )
+    db_session.add(call_import)
+    db_session.commit()
+
+    response = authenticated_client.patch(
+        f"/api/v1/call-imports/{call_import.id}",
+        json={"dataset": "after"},
+    )
+    assert response.status_code == 200, response.text
+    body = response.json()
+    assert body["dataset"] == "after"
+    assert body["created_by_email"] is None
+    assert body["last_updated_by_email"] == "owner@example.com"
+
+
+def test_list_call_imports_includes_actor_emails(
+    authenticated_client, db_session, org_id, seed_org
+):
+    workspace = _ensure_default_workspace(db_session, org_id)
+    call_import = CallImport(
+        id=uuid4(),
+        organization_id=org_id,
+        workspace_id=workspace.id,
+        provider=None,
+        original_filename="listed.csv",
+        total_rows=0,
+        completed_rows=0,
+        failed_rows=0,
+        status=CallImportStatus.UPLOADED,
+    )
+    db_session.add(call_import)
+    db_session.commit()
+
+    listing = authenticated_client.get("/api/v1/call-imports")
+    assert listing.status_code == 200, listing.text
+    items = listing.json()["items"]
+    match = [item for item in items if item["id"] == str(call_import.id)]
+    assert len(match) == 1
+    assert match[0]["created_by_email"] is None
+    assert match[0]["last_updated_by_email"] is None
diff --git a/tests/test_api/test_call_import_evaluations.py b/tests/test_api/test_call_import_evaluations.py
index faf3ff9d..43d9230b 100644
--- a/tests/test_api/test_call_import_evaluations.py
+++ b/tests/test_api/test_call_import_evaluations.py
@@ -1152,3 +1152,43 @@ def test_evaluation_retry_can_override_telephony_credentials(
     db_session.refresh(call_import)
     assert call_import.telephony_integration_id == right_integration.id
     assert call_import.provider == "exotel"
+
+
+def test_create_evaluation_sets_actor_emails(
+    authenticated_client, db_session, org_id, seed_org
+):
+    metric = _make_metric(db_session, org_id)
+    call_import, _rows = _make_call_import(db_session, org_id, rows=2)
+
+    response = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations",
+        json=_eval_body([metric.id]),
+    )
+    assert response.status_code == 202, response.text
+    body = response.json()
+    assert body["created_by_email"] == "owner@example.com"
+    assert body["last_updated_by_email"] == "owner@example.com"
+
+
+def test_update_evaluation_name_stamps_last_updated_by_email(
+    authenticated_client, db_session, org_id, seed_org
+):
+    metric = _make_metric(db_session, org_id)
+    call_import, _rows = _make_call_import(db_session, org_id, rows=1)
+
+    created = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations",
+        json=_eval_body([metric.id]),
+    )
+    assert created.status_code == 202, created.text
+    eval_id = created.json()["id"]
+
+    patched = authenticated_client.patch(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{eval_id}",
+        json={"name": "Renamed run"},
+    )
+    assert patched.status_code == 200, patched.text
+    body = patched.json()
+    assert body["name"] == "Renamed run"
+    assert body["created_by_email"] == "owner@example.com"
+    assert body["last_updated_by_email"] == "owner@example.com"

From 3115b13f8653173ae810970f52410efcfe583ac3 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 7 Aug 2026 19:46:46 +0530
Subject: [PATCH 03/55] feat: implement actor stamping for call import
 evaluations and user insights

---
 app/api/v1/routes/call_import_evaluations.py  | 16 +++++++
 ...st_call_import_evaluation_user_insights.py | 26 +++++++++++
 ...st_call_import_evaluations_mapped_async.py | 45 +++++++++++++++++++
 .../test_call_import_metric_clusters_rows.py  | 40 +++++++++++++++++
 4 files changed, 127 insertions(+)

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 95222d6d..12069420 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -35,6 +35,7 @@
 from app.services.call_imports.audit import (
     actor_emails_for_evaluation,
     emails_for_user_ids,
+    stamp_call_import_actor,
     stamp_evaluation_actor,
     user_ids_from_evaluations,
 )
@@ -1032,6 +1033,7 @@ async def create_call_import_evaluation(
         call_import.failed_rows = 0
         call_import.error_message = None
         call_import.status = CallImportStatus.PROCESSING
+        stamp_call_import_actor(call_import, principal)
         db.commit()
         db.refresh(call_import)
         starting_from_mapped = True
@@ -5139,6 +5141,9 @@ async def generate_call_import_evaluation_insights(
 
     summary = EvaluationTldrSummary.model_validate(task_result)
     db.refresh(evaluation)
+    stamp_evaluation_actor(evaluation, principal)
+    db.commit()
+    db.refresh(evaluation)
 
     from app.services.ai.llm_resolver import get_llm_provider_and_model
 
@@ -5153,6 +5158,7 @@ async def generate_call_import_evaluation_insights(
         force=body.regenerate,
         max_llm_calls=body.max_llm_calls,
         db=db,
+        principal=principal,
     )
 
     return summary
@@ -5210,6 +5216,7 @@ def _enqueue_user_insights_job(
     force: bool = False,
     max_llm_calls: Optional[int] = None,
     db: Optional[Session] = None,
+    principal: Optional[Principal] = None,
 ) -> None:
     """Enqueue background user-insights generation unless already running."""
     current = _user_insights_payload(evaluation)
@@ -5241,6 +5248,8 @@ def _enqueue_user_insights_job(
         "error_message": None,
     }
     if db is not None:
+        if principal is not None:
+            stamp_evaluation_actor(evaluation, principal)
         flag_modified(evaluation, "user_insights")
         db.commit()
 
@@ -5348,6 +5357,7 @@ async def generate_call_import_evaluation_user_insights(
         force=body.force or body.regenerate,
         max_llm_calls=body.max_llm_calls,
         db=db,
+        principal=principal,
     )
 
     db.refresh(evaluation)
@@ -5590,6 +5600,7 @@ def _enqueue_metric_clusters_job(
     selected_evaluation_row_ids: Optional[List[str]] = None,
     failure_policies: Optional[Dict[str, MetricFailurePolicy]] = None,
     db: Optional[Session] = None,
+    principal: Optional[Principal] = None,
 ) -> None:
     current = _metric_clusters_payload(evaluation)
     if current is not None and current.status == "running" and not force:
@@ -5668,6 +5679,8 @@ def _enqueue_metric_clusters_job(
         **policy_blob,
     }
     if db is not None:
+        if principal is not None:
+            stamp_evaluation_actor(evaluation, principal)
         flag_modified(evaluation, "metric_clusters")
         db.commit()
 
@@ -5689,6 +5702,8 @@ def _enqueue_metric_clusters_job(
     if db is not None and isinstance(evaluation.metric_clusters, dict):
         evaluation.metric_clusters["celery_task_id"] = async_result.id
         flag_modified(evaluation, "metric_clusters")
+        if principal is not None:
+            stamp_evaluation_actor(evaluation, principal)
         db.commit()
 
 
@@ -6092,6 +6107,7 @@ async def generate_call_import_evaluation_metric_clusters(
         selected_evaluation_row_ids=selected_row_ids,
         failure_policies=merged_policies,
         db=db,
+        principal=principal,
     )
 
     db.refresh(evaluation)
diff --git a/tests/test_api/test_call_import_evaluation_user_insights.py b/tests/test_api/test_call_import_evaluation_user_insights.py
index 1fd76744..4d9a1336 100644
--- a/tests/test_api/test_call_import_evaluation_user_insights.py
+++ b/tests/test_api/test_call_import_evaluation_user_insights.py
@@ -191,6 +191,32 @@ def test_post_user_insights_enqueues_task(
     assert response.json()["max_llm_calls"] == 100
 
 
+def test_post_user_insights_stamps_last_updated_by_email(
+    authenticated_client,
+    db_session,
+    org_id,
+    seed_org,
+    make_ai_provider,
+    stub_user_insights_worker,
+):
+    make_ai_provider(provider="openai", is_active=True)
+    call_import, evaluation = _seed_evaluation(db_session, org_id)
+    evaluation.last_updated_by_user_id = None
+    db_session.commit()
+
+    response = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}/user-insights",
+        json={"regenerate": True, "force": True},
+    )
+    assert response.status_code == 200, response.text
+
+    detail = authenticated_client.get(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}"
+    )
+    assert detail.status_code == 200, detail.text
+    assert detail.json()["last_updated_by_email"] == "owner@example.com"
+
+
 def test_post_user_insights_requires_completed_rows(
     authenticated_client, db_session, org_id, seed_org
 ):
diff --git a/tests/test_api/test_call_import_evaluations_mapped_async.py b/tests/test_api/test_call_import_evaluations_mapped_async.py
index ac3035f9..f072dd68 100644
--- a/tests/test_api/test_call_import_evaluations_mapped_async.py
+++ b/tests/test_api/test_call_import_evaluations_mapped_async.py
@@ -117,3 +117,48 @@ def test_create_evaluation_from_mapped_enqueues_async_materialization(
         .first()
     )
     assert refreshed_import.status == CallImportStatus.PROCESSING
+
+
+def test_create_evaluation_from_mapped_stamps_parent_last_updated_by(
+    authenticated_client,
+    db_session,
+    org_id,
+    seed_org,
+    monkeypatch,
+):
+    from tests.test_api.test_call_import_evaluations import (
+        _eval_body,
+        _make_metric,
+    )
+
+    monkeypatch.setattr(
+        "app.api.v1.routes.call_imports._ensure_blob_storage_enabled",
+        lambda: None,
+    )
+
+    metric = _make_metric(db_session, org_id)
+    workspace = metric.workspace_id
+    call_import = _make_mapped_call_import(db_session, org_id, workspace)
+    call_import.last_updated_by_user_id = None
+    db_session.commit()
+
+    delay_mock = MagicMock(return_value=MagicMock(id="async-task"))
+    fake_bulk_ops = types.ModuleType("app.workers.tasks.call_import_bulk_ops")
+    fake_bulk_ops.materialize_mapped_call_import_evaluation_task = MagicMock(
+        delay=delay_mock,
+    )
+    monkeypatch.setitem(
+        sys.modules,
+        "app.workers.tasks.call_import_bulk_ops",
+        fake_bulk_ops,
+    )
+
+    response = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations",
+        json=_eval_body([metric.id]),
+    )
+    assert response.status_code == 202, response.text
+
+    detail = authenticated_client.get(f"/api/v1/call-imports/{call_import.id}")
+    assert detail.status_code == 200, detail.text
+    assert detail.json()["last_updated_by_email"] == "owner@example.com"
diff --git a/tests/test_api/test_call_import_metric_clusters_rows.py b/tests/test_api/test_call_import_metric_clusters_rows.py
index a1d1bd31..6595b5ab 100644
--- a/tests/test_api/test_call_import_metric_clusters_rows.py
+++ b/tests/test_api/test_call_import_metric_clusters_rows.py
@@ -147,6 +147,46 @@ def fake_apply_async(*, kwargs=None, **_kw):
     assert len(captured["evaluation_row_ids"]) == 2
 
 
+def test_generate_metric_clusters_stamps_last_updated_by_email(
+    authenticated_client,
+    db_session,
+    org_id,
+    seed_org,
+    make_ai_provider,
+    monkeypatch,
+):
+    make_ai_provider(provider="openai", is_active=True)
+    call_import, evaluation, _ = _seed_eval_with_rows(
+        db_session,
+        org_id,
+        rows=[
+            {"conversation_id": "c0", "status": "completed", "score_value": 0.2},
+        ],
+    )
+    evaluation.last_updated_by_user_id = None
+    db_session.commit()
+
+    def fake_apply_async(*, kwargs=None, **_kw):
+        return types.SimpleNamespace(id="cluster-task-1")
+
+    monkeypatch.setattr(
+        "app.workers.tasks.generate_evaluation_metric_clusters.generate_evaluation_metric_clusters_task.apply_async",
+        fake_apply_async,
+    )
+
+    response = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}/metric-clusters",
+        json={"row_limit": 1},
+    )
+    assert response.status_code == 200, response.text
+
+    detail = authenticated_client.get(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}"
+    )
+    assert detail.status_code == 200, detail.text
+    assert detail.json()["last_updated_by_email"] == "owner@example.com"
+
+
 def test_cancel_preserves_selected_row_ids_in_state(
     authenticated_client, db_session, org_id, seed_org, make_ai_provider, monkeypatch
 ):

From 681fe3719682e6169deddefe17fc26f16ed4c722 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 7 Aug 2026 20:09:48 +0530
Subject: [PATCH 04/55] feat: enhance user insights with improved call import
 evaluations

---
 .gitignore         |   1 -
 app/config.py      |  31 ++++++++--
 config.docker.yml  | 137 ---------------------------------------------
 docker-compose.yml |   8 +++
 env.example        |   4 ++
 5 files changed, 39 insertions(+), 142 deletions(-)
 delete mode 100644 config.docker.yml

diff --git a/.gitignore b/.gitignore
index 3fb88623..106532ab 100644
--- a/.gitignore
+++ b/.gitignore
@@ -34,7 +34,6 @@ env/
 ENV/
 .venv
 config.yml
-config.docker.yml
 # uv
 .python-version
 uv.lock
diff --git a/app/config.py b/app/config.py
index 94b51f18..0eec6b2a 100644
--- a/app/config.py
+++ b/app/config.py
@@ -1,6 +1,8 @@
 """Configuration management using Pydantic settings."""
 
 import json
+import os
+import re
 import yaml
 from pathlib import Path
 from typing import Annotated, Any, Dict, List, Optional, Union
@@ -412,6 +414,19 @@ def apply_service_mode(mode: str) -> None:
     settings.SERVICE_MODE = normalized
 
 
+_ENV_REF_PATTERN = re.compile(r"^\$\{([A-Za-z_][A-Za-z0-9_]*)\}$")
+
+
+def _expand_env_ref(value: Any) -> Any:
+    """Replace ``${VAR}`` with ``os.environ[VAR]`` when loading YAML secrets."""
+    if not isinstance(value, str):
+        return value
+    match = _ENV_REF_PATTERN.match(value.strip())
+    if not match:
+        return value
+    return os.environ.get(match.group(1), "")
+
+
 def load_config_from_file(config_path: str) -> None:
     """Load configuration from a YAML file and update global settings."""
     import yaml
@@ -577,9 +592,13 @@ def load_config_from_file(config_path: str) -> None:
         if "region" in s3_config:
             settings.S3_REGION = s3_config["region"]
         if "access_key_id" in s3_config:
-            settings.S3_ACCESS_KEY_ID = s3_config["access_key_id"]
+            resolved = _expand_env_ref(s3_config["access_key_id"])
+            if resolved:
+                settings.S3_ACCESS_KEY_ID = resolved
         if "secret_access_key" in s3_config:
-            settings.S3_SECRET_ACCESS_KEY = s3_config["secret_access_key"]
+            resolved = _expand_env_ref(s3_config["secret_access_key"])
+            if resolved:
+                settings.S3_SECRET_ACCESS_KEY = resolved
         if "endpoint_url" in s3_config:
             settings.S3_ENDPOINT_URL = s3_config["endpoint_url"]
         if "prefix" in s3_config:
@@ -730,9 +749,13 @@ def load_config_from_file(config_path: str) -> None:
                 if "region" in loki_s3:
                     settings.LOKI_S3_REGION = loki_s3["region"]
                 if "access_key_id" in loki_s3:
-                    settings.LOKI_S3_ACCESS_KEY_ID = loki_s3["access_key_id"]
+                    resolved = _expand_env_ref(loki_s3["access_key_id"])
+                    if resolved:
+                        settings.LOKI_S3_ACCESS_KEY_ID = resolved
                 if "secret_access_key" in loki_s3:
-                    settings.LOKI_S3_SECRET_ACCESS_KEY = loki_s3["secret_access_key"]
+                    resolved = _expand_env_ref(loki_s3["secret_access_key"])
+                    if resolved:
+                        settings.LOKI_S3_SECRET_ACCESS_KEY = resolved
                 if "prefix" in loki_s3:
                     settings.LOKI_S3_PREFIX = loki_s3["prefix"]
     if "plivo" in config_data:
diff --git a/config.docker.yml b/config.docker.yml
deleted file mode 100644
index 6b84d505..00000000
--- a/config.docker.yml
+++ /dev/null
@@ -1,137 +0,0 @@
-# EfficientAI Docker Configuration File
-# This config uses Docker service names for networking
-
-# Application Settings
-app:
-  name: "EfficientAI Voice AI Evaluation Platform"
-  version: "0.1.0"
-  debug: true
-  secret_key: "your-secret-key-here-change-in-production"
-
-# Server Settings
-server:
-  host: "0.0.0.0"
-  port: 8000
-
-# Operational endpoints (/metrics). /health is always open for load balancers.
-# Add VPC CIDRs here if Prometheus scrapes /metrics from inside the VPC.
-operational:
-  public: false
-  trusted_ips:
-    - "10.0.0.0/8"
-
-# Database Configuration (Docker service name)
-database:
-  url: "postgresql://efficientai:password@db:5432/efficientai"
-
-# Redis Configuration (Docker service name)
-redis:
-  url: "redis://redis:6379/0"
-
-# Celery Configuration (Docker service names)
-celery:
-  broker_url: "redis://redis:6379/0"
-  result_backend: "redis://redis:6379/0"
-
-# File Storage
-storage:
-  upload_dir: "/app/uploads"
-  max_file_size_mb: 500
-  allowed_audio_formats:
-    - "wav"
-    - "mp3"
-    - "flac"
-    - "m4a"
-
-# S3 Configuration (for data sources integration)
-s3:
-  enabled: true
-  bucket_name: "voiceai-evals-test"
-  region: "us-east-1"
-  access_key_id: "${S3_ACCESS_KEY_ID}"
-  secret_access_key: "${S3_SECRET_ACCESS_KEY}"
-  endpoint_url: null
-  prefix: "audio/"
-
-# Speaker Diarization (pyannote.audio)
-# Requires accepting model terms at https://huggingface.co/pyannote/speaker-diarization-3.1
-diarization:
-  huggingface_token:   # Set your HuggingFace token here (e.g., "hf_xxxxx")
-  num_speakers: 2  # Force exact speaker count (2 = agent + customer). Set to null for auto-detect.
-
-# CORS Settings
-cors:
-  origins:
-    - "http://localhost:3000"
-    - "http://localhost:8000"
-
-# API Settings
-api:
-  prefix: "/api/v1"
-  key_header: "X-API-Key"
-  rate_limit_per_minute: 60
-
-# Observability (Loki log aggregation)
-# storage: "filesystem" (default, local Docker volume) or "s3" (durable, for production)
-# multi_tenant: false (default, single tenant) or true (per-org log isolation)
-observability:
-  enabled: false
-  loki:
-    enabled: false
-    url: "http://loki:3100"
-    storage: filesystem
-    multi_tenant: false
-    platform_tenant: "platform"
-    # s3:
-    #   bucket_name: "my-logs-bucket"
-    #   region: "us-east-1"
-    #   access_key_id: ""
-    #   secret_access_key: ""
-    #   prefix: "logs/"
-
-auth:
-  providers:
-    - api_key
-    - local_password
-    # - external_oidc     # requires EFFICIENTAI_LICENSE feature: oidc_sso
-
-  local_password:
-    # Lifetime of the Bearer tokens minted at POST /auth/login (in minutes).
-    # Keep this short; clients re-authenticate silently.
-    token_ttl_minutes: 720  # 12 hours
-    # Turn this off in Cloud SaaS to block self-serve signup.
-    allow_signup: true
-
-  # External OIDC (enterprise): bring your own IdP. Works with any
-  # OIDC-compliant provider. See README.md > Authentication & Deployment
-  # Recipes for copy-paste recipes for Okta, Azure AD, Google Workspace
-  # and AWS Cognito.
-  #
-  # oidc:
-  #   issuer: "https://example.okta.com"                    # REQUIRED
-  #   audience: "efficientai"                               # expected `aud` claim
-  #   client_id: "0oa..."                                   # SPA client id
-  #   jwks_uri: "https://example.okta.com/oauth2/v1/keys"   # optional, derived from issuer
-  #   default_org_name: "Example Inc"                       # used when no org claim is present
-  #   org_claim_path: ["https://efficientai.com/org"]       # dotted path into the JWT to find the org name
-
-# Judge Alignment (AlignEval-style hybrid integration).
-# Per-org thresholds and judge model selection are UI-driven; only the
-# operator kill switch + CSV upload limit live here.
-judge_alignment:
-  enabled: true
-  csv_max_rows: 5000
-
-# LLM gateway (optional). Per-org overrides in Integrations UI.
-# llm_gateway:
-#   enabled: false
-#   type: bifrost              # bifrost | litellm_proxy
-#   base_url: "http://localhost:8080/litellm"
-#   virtual_key: null
-#   master_key: null
-#   passthrough_provider_keys: true
-
-# Enterprise License (JWT signed with RS256). Unlocks gated features like
-# oidc_sso, mfa_enforce, audit_export, voice_playground, gepa_optimization.
-# license:
-#   key: "eyJhbGciOi..."
diff --git a/docker-compose.yml b/docker-compose.yml
index 066b7832..552884f3 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -47,6 +47,8 @@ services:
       context: .
       dockerfile: docker/Dockerfile.api
     container_name: efficientai_api
+    env_file:
+      - .env
     environment:
       DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
       REDIS_URL: redis://redis:6379/0
@@ -95,6 +97,8 @@ services:
       args:
         INSTALL_EXTRAS: "qualitative-voice"
     container_name: efficientai_media
+    env_file:
+      - .env
     environment:
       DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
       REDIS_URL: redis://redis:6379/0
@@ -126,6 +130,8 @@ services:
       args:
         INSTALL_EXTRAS: "qualitative-voice"
     container_name: efficientai_worker
+    env_file:
+      - .env
     environment:
       # Worker uses Docker network, so it reaches DB/Redis via service names
       DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
@@ -162,6 +168,8 @@ services:
       args:
         INSTALL_EXTRAS: ""
     container_name: efficientai_worker_imports
+    env_file:
+      - .env
     environment:
       DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
       REDIS_URL: redis://redis:6379/0
diff --git a/env.example b/env.example
index c434c850..0de4f151 100644
--- a/env.example
+++ b/env.example
@@ -24,6 +24,10 @@ UPLOAD_DIR=/app/uploads
 MAX_FILE_SIZE_MB=500
 ALLOWED_AUDIO_FORMATS=wav,mp3,flac,m4a
 
+# S3 (optional — used when config.docker.yml references ${S3_ACCESS_KEY_ID})
+# S3_ACCESS_KEY_ID=
+# S3_SECRET_ACCESS_KEY=
+
 # Celery Configuration
 CELERY_BROKER_URL=redis://redis:6379/0
 CELERY_RESULT_BACKEND=redis://redis:6379/0

From 8786a43520d9968fb7109fc45ab3bb8c03e35660 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 7 Aug 2026 20:17:54 +0530
Subject: [PATCH 05/55] chore: add config.docker.yml to .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 106532ab..3fb88623 100644
--- a/.gitignore
+++ b/.gitignore
@@ -34,6 +34,7 @@ env/
 ENV/
 .venv
 config.yml
+config.docker.yml
 # uv
 .python-version
 uv.lock

From 55b3bfd05dcd23be019233ba48b950d6167b7775 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 7 Aug 2026 22:52:50 +0530
Subject: [PATCH 06/55] feat: Cache evaluation PDF reports by content
 fingerprint and reuse S3 artifacts on preview

---
 app/api/v1/routes/call_import_evaluations.py  | 338 +++++++++++++++++-
 .../060_call_import_evaluation_pdf_reports.py |  76 ++++
 ...call_import_eval_pdf_report_cache_index.py | 137 +++++++
 app/models/database.py                        |  44 +++
 .../call_import_pdf_report_storage.py         | 207 +++++++++++
 app/services/storage/azure_blob_service.py    |  27 +-
 app/services/storage/blob_storage_service.py  |  14 +-
 app/services/storage/gcs_service.py           |  24 +-
 app/services/storage/s3_service.py            |  13 +-
 env.example                                   |   2 +
 frontend/src/lib/api.ts                       |  32 +-
 .../CallImportEvaluationDetail.tsx            | 229 ++++++++++--
 frontend/src/types/api.ts                     |  29 ++
 .../test_call_import_evaluation_pdf_report.py | 131 +++++++
 14 files changed, 1233 insertions(+), 70 deletions(-)
 create mode 100644 app/migrations/060_call_import_evaluation_pdf_reports.py
 create mode 100644 app/migrations/061_call_import_eval_pdf_report_cache_index.py
 create mode 100644 app/services/reporting/call_import_pdf_report_storage.py

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 12069420..94c371d0 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -11,7 +11,7 @@
 import re
 import statistics
 from typing import Any, Dict, Iterator, List, Literal, Optional, Set, Tuple
-from uuid import UUID
+from uuid import UUID, uuid4
 
 from datetime import date, datetime, timedelta, timezone
 
@@ -20,6 +20,7 @@
 from loguru import logger
 from pydantic import BaseModel, Field, field_validator
 from sqlalchemy import desc, func, or_, text
+from sqlalchemy.exc import IntegrityError
 from sqlalchemy.orm import Session
 from sqlalchemy.orm.attributes import flag_modified
 
@@ -45,6 +46,7 @@
     CallImport,
     CallImportEvaluation,
     CallImportEvaluationReportSnapshot,
+    CallImportEvaluationPdfReport,
     CallImportEvaluationRow,
     CallImportRow,
     Metric,
@@ -99,6 +101,15 @@
 from app.services.reporting.call_import_evaluation_pdf_report import (
     call_import_evaluation_pdf_report_service,
 )
+from app.services.reporting.call_import_pdf_report_storage import (
+    build_pdf_report_s3_key,
+    compute_pdf_report_cache_fingerprint,
+    compute_pdf_report_config_fingerprint,
+    compute_pdf_report_content_fingerprint,
+    config_summary_from_report_config,
+    find_cached_pdf_report,
+    presigned_urls_for_pdf_report,
+)
 from app.services.call_import_metric_clusters import (
     METRIC_CLUSTERS_CANCELLED_BY_USER_ERROR,
     estimate_metric_clusters_llm_calls,
@@ -159,6 +170,35 @@ def _clean_vendor_name(cls, value: str) -> str:
         return cleaned
 
 
+class CallImportEvaluationPdfReportResponse(BaseModel):
+    id: str
+    filename: str
+    preview_url: Optional[str] = None
+    download_url: Optional[str] = None
+    created_at: datetime
+    created_by: Optional[str] = None
+    report_type: str
+    vendor_name: str
+    config_summary: Optional[str] = None
+    storage_available: bool = True
+    cache_hit: bool = False
+
+
+class CallImportEvaluationPdfReportListItem(BaseModel):
+    id: str
+    filename: Optional[str] = None
+    vendor_name: str
+    report_type: str
+    created_by: Optional[str] = None
+    created_at: datetime
+    config_summary: Optional[str] = None
+    cache_fingerprint: Optional[str] = None
+
+
+class CallImportEvaluationPdfReportListResponse(BaseModel):
+    items: List[CallImportEvaluationPdfReportListItem]
+
+
 class CallImportEvaluationBaselineCandidate(BaseModel):
     evaluation_id: str
     name: str
@@ -2112,6 +2152,57 @@ def _report_filename_slug(value: str) -> str:
     return slug or "client"
 
 
+def _pdf_report_actor(principal: Principal) -> tuple[Optional[str], Optional[UUID]]:
+    created_by = principal.email
+    if not created_by and principal.user_id:
+        created_by = str(principal.user_id)
+    return created_by, principal.user_id
+
+
+def _pdf_report_response_from_row(
+    row: CallImportEvaluationPdfReport,
+    *,
+    cache_hit: bool = False,
+) -> CallImportEvaluationPdfReportResponse:
+    filename = row.filename or "report.pdf"
+    preview_url, download_url = presigned_urls_for_pdf_report(
+        row.s3_key or "",
+        filename,
+    )
+    return CallImportEvaluationPdfReportResponse(
+        id=str(row.id),
+        filename=filename,
+        preview_url=preview_url,
+        download_url=download_url,
+        created_at=row.created_at or datetime.now(timezone.utc),
+        created_by=row.created_by,
+        report_type=row.report_type,
+        vendor_name=row.vendor_name,
+        config_summary=config_summary_from_report_config(
+            row.report_config if isinstance(row.report_config, dict) else {}
+        ),
+        storage_available=bool(row.s3_key),
+        cache_hit=cache_hit,
+    )
+
+
+def _pdf_report_list_item_from_row(
+    row: CallImportEvaluationPdfReport,
+) -> CallImportEvaluationPdfReportListItem:
+    return CallImportEvaluationPdfReportListItem(
+        id=str(row.id),
+        filename=row.filename,
+        vendor_name=row.vendor_name,
+        report_type=row.report_type,
+        created_by=row.created_by,
+        created_at=row.created_at or datetime.now(timezone.utc),
+        config_summary=config_summary_from_report_config(
+            row.report_config if isinstance(row.report_config, dict) else {}
+        ),
+        cache_fingerprint=row.cache_fingerprint,
+    )
+
+
 def _report_branding_for_import_workspace(
     db: Session,
     organization_id: UUID,
@@ -3405,8 +3496,9 @@ async def generate_call_import_evaluation_pdf_report(
     payload: CallImportEvaluationPdfReportRequest,
     api_key: str = Depends(get_api_key),
     organization_id: UUID = Depends(get_organization_id),
+    principal: Principal = Depends(get_principal),
     db: Session = Depends(get_db),
-) -> StreamingResponse:
+):
     del api_key
     call_import = _require_import(db, call_import_id, organization_id)
 
@@ -3527,17 +3619,6 @@ async def generate_call_import_evaluation_pdf_report(
         cached_prompt_improvements,
         report_config,
     )
-    narrative = _generate_report_narrative(
-        db,
-        organization_id,
-        metric_aggregates=metric_aggregates,
-        insight_aggregates=insight_aggregates if is_internal else [],
-        period_delta_by_metric=period_delta_by_metric,
-        evidence_samples=evidence_samples if is_internal else {},
-        report_config=report_config,
-    )
-
-    generated_at = datetime.now(timezone.utc)
     branding_images, custom_heading = _report_branding_for_import_workspace(
         db,
         organization_id,
@@ -3562,6 +3643,76 @@ async def generate_call_import_evaluation_pdf_report(
         pdf_aggregates,
         child_names_by_parent=pdf_child_map,
     )
+
+    from app.services.storage.s3_service import s3_service
+
+    config_fingerprint = compute_pdf_report_config_fingerprint(
+        report_type=payload.report_type,
+        include_period_delta=bool(payload.include_period_delta),
+        include_weekly_delta=bool(payload.include_weekly_delta),
+        baseline_evaluation_id=payload.baseline_evaluation_id,
+        internal_brand_image_id=payload.internal_brand_image_id,
+        external_brand_image_id=payload.external_brand_image_id,
+        use_case=payload.use_case,
+        report_config=report_config,
+        report_heading=custom_heading,
+        vendor_name=payload.vendor_name,
+        platform_base_url=payload.platform_base_url,
+        period_label=period_label,
+    )
+    content_fingerprint = compute_pdf_report_content_fingerprint(
+        evaluation_status=evaluation.status,
+        completed_rows=int(evaluation.completed_rows or 0),
+        total_rows=int(evaluation.total_rows or 0),
+        failed_rows=int(evaluation.failed_rows or 0),
+        metric_aggregates=metric_aggregates,
+        insight_aggregates=insight_aggregates,
+        period_delta_by_metric=period_delta_by_metric,
+        benchmark_context=benchmark_context,
+        metric_metadata=[
+            {
+                "id": str(metric.id),
+                "name": metric.name,
+                "description": metric.description,
+            }
+            for metric in metrics
+        ],
+        failure_policies=failure_policies_for_pdf,
+        tldr_summary=cached_tldr_summary,
+        user_insights_for_pdf=generated_insights_for_pdf,
+        metric_clusters_for_pdf=metric_clusters_for_pdf,
+        prompt_improvements_for_pdf=prompt_improvements_for_pdf,
+    )
+    cache_fingerprint = compute_pdf_report_cache_fingerprint(
+        config_fingerprint=config_fingerprint,
+        content_fingerprint=content_fingerprint,
+    )
+    if s3_service.is_enabled():
+        cached_pdf_report = find_cached_pdf_report(
+            db,
+            evaluation_id=evaluation.id,
+            organization_id=organization_id,
+            cache_fingerprint=cache_fingerprint,
+        )
+        if cached_pdf_report is not None:
+            logger.info(
+                "Reusing stored PDF report {} for evaluation {} (cache fingerprint match)",
+                cached_pdf_report.id,
+                eval_id,
+            )
+            return _pdf_report_response_from_row(cached_pdf_report, cache_hit=True)
+
+    narrative = _generate_report_narrative(
+        db,
+        organization_id,
+        metric_aggregates=metric_aggregates,
+        insight_aggregates=insight_aggregates if is_internal else [],
+        period_delta_by_metric=period_delta_by_metric,
+        evidence_samples=evidence_samples if is_internal else {},
+        report_config=report_config,
+    )
+
+    generated_at = datetime.now(timezone.utc)
     try:
         pdf_started = datetime.now(timezone.utc)
         pdf_bytes = await asyncio.to_thread(
@@ -3635,17 +3786,168 @@ async def generate_call_import_evaluation_pdf_report(
         .count(),
     )
     db.add(snapshot)
-    db.commit()
+    db.flush()
 
     filename = (
         f"{_report_filename_slug(payload.vendor_name)}-"
         f"{payload.report_type}-quality-metric-audit-{eval_id}.pdf"
     )
-    return StreamingResponse(
-        iter([pdf_bytes]),
-        media_type="application/pdf",
-        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+
+    if not s3_service.is_enabled():
+        db.commit()
+        return StreamingResponse(
+            iter([pdf_bytes]),
+            media_type="application/pdf",
+            headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+        )
+
+    report_id = uuid4()
+    s3_key = build_pdf_report_s3_key(
+        organization_id=organization_id,
+        call_import_id=call_import.id,
+        evaluation_id=evaluation.id,
+        report_id=report_id,
     )
+    try:
+        s3_service.upload_file_by_key(
+            file_content=pdf_bytes,
+            key=s3_key,
+            content_type="application/pdf",
+        )
+    except Exception as exc:  # noqa: BLE001
+        logger.exception(
+            "Failed to upload PDF report for evaluation {} to object storage",
+            eval_id,
+        )
+        db.rollback()
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to store PDF report: {exc}",
+        ) from exc
+
+    created_by, created_by_user_id = _pdf_report_actor(principal)
+    pdf_report = CallImportEvaluationPdfReport(
+        id=report_id,
+        evaluation_id=evaluation.id,
+        call_import_id=call_import.id,
+        organization_id=organization_id,
+        workspace_id=call_import.workspace_id,
+        snapshot_id=snapshot.id,
+        vendor_name=payload.vendor_name,
+        report_type=payload.report_type,
+        filename=filename,
+        s3_key=s3_key,
+        report_config=report_config,
+        cache_fingerprint=cache_fingerprint,
+        created_by=created_by,
+        created_by_user_id=created_by_user_id,
+    )
+    db.add(pdf_report)
+    try:
+        db.commit()
+    except IntegrityError:
+        db.rollback()
+        try:
+            s3_service.delete_file_by_key(s3_key)
+        except Exception:  # noqa: BLE001
+            logger.warning(
+                "Failed to delete orphan PDF after cache race for evaluation {}",
+                eval_id,
+            )
+        raced_winner = find_cached_pdf_report(
+            db,
+            evaluation_id=evaluation.id,
+            organization_id=organization_id,
+            cache_fingerprint=cache_fingerprint,
+        )
+        if raced_winner is not None:
+            logger.info(
+                "PDF report cache race resolved for evaluation {} (winner {})",
+                eval_id,
+                raced_winner.id,
+            )
+            return _pdf_report_response_from_row(raced_winner, cache_hit=True)
+        raise HTTPException(
+            status_code=500,
+            detail="Failed to store PDF report due to a concurrent duplicate request.",
+        ) from None
+    db.refresh(pdf_report)
+    return _pdf_report_response_from_row(pdf_report)
+
+
+@router.get(
+    "/{eval_id}/pdf-reports",
+    response_model=CallImportEvaluationPdfReportListResponse,
+    operation_id="listCallImportEvaluationPdfReports",
+)
+async def list_call_import_evaluation_pdf_reports(
+    call_import_id: UUID,
+    eval_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationPdfReportListResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+    evaluation = (
+        db.query(CallImportEvaluation)
+        .filter(
+            CallImportEvaluation.id == eval_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+            CallImportEvaluation.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not evaluation:
+        raise HTTPException(status_code=404, detail="Call import evaluation not found")
+
+    rows = (
+        db.query(CallImportEvaluationPdfReport)
+        .filter(
+            CallImportEvaluationPdfReport.evaluation_id == eval_id,
+            CallImportEvaluationPdfReport.organization_id == organization_id,
+        )
+        .order_by(desc(CallImportEvaluationPdfReport.created_at))
+        .all()
+    )
+    return CallImportEvaluationPdfReportListResponse(
+        items=[_pdf_report_list_item_from_row(row) for row in rows],
+    )
+
+
+@router.get(
+    "/{eval_id}/pdf-reports/{report_id}",
+    response_model=CallImportEvaluationPdfReportResponse,
+    operation_id="getCallImportEvaluationPdfReport",
+)
+async def get_call_import_evaluation_pdf_report(
+    call_import_id: UUID,
+    eval_id: UUID,
+    report_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+) -> CallImportEvaluationPdfReportResponse:
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+    row = (
+        db.query(CallImportEvaluationPdfReport)
+        .filter(
+            CallImportEvaluationPdfReport.id == report_id,
+            CallImportEvaluationPdfReport.evaluation_id == eval_id,
+            CallImportEvaluationPdfReport.call_import_id == call_import_id,
+            CallImportEvaluationPdfReport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not row:
+        raise HTTPException(status_code=404, detail="PDF report not found")
+    if not row.s3_key:
+        raise HTTPException(
+            status_code=404,
+            detail="PDF report file is not available in object storage",
+        )
+    return _pdf_report_response_from_row(row)
 
 
 @router.patch(
diff --git a/app/migrations/060_call_import_evaluation_pdf_reports.py b/app/migrations/060_call_import_evaluation_pdf_reports.py
new file mode 100644
index 00000000..8f2c6012
--- /dev/null
+++ b/app/migrations/060_call_import_evaluation_pdf_reports.py
@@ -0,0 +1,76 @@
+"""Migration: stored PDF reports for call import evaluations."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add call_import_evaluation_pdf_reports for S3-stored evaluation PDFs"
+
+
+def _table_exists(db: Session, table_name: str) -> bool:
+    row = db.execute(
+        text(
+            """
+            SELECT 1
+            FROM information_schema.tables
+            WHERE table_name = :table_name
+            """
+        ),
+        {"table_name": table_name},
+    ).first()
+    return row is not None
+
+
+def upgrade(db: Session):
+    if _table_exists(db, "call_import_evaluation_pdf_reports"):
+        print("call_import_evaluation_pdf_reports already exists, skipping")
+        db.commit()
+        return
+
+    db.execute(
+        text(
+            """
+            CREATE TABLE call_import_evaluation_pdf_reports (
+                id UUID PRIMARY KEY,
+                evaluation_id UUID NOT NULL REFERENCES call_import_evaluations(id) ON DELETE CASCADE,
+                call_import_id UUID NOT NULL REFERENCES call_imports(id) ON DELETE CASCADE,
+                organization_id UUID NOT NULL REFERENCES organizations(id),
+                workspace_id UUID NOT NULL REFERENCES workspaces(id),
+                snapshot_id UUID REFERENCES call_import_evaluation_report_snapshots(id) ON DELETE SET NULL,
+                vendor_name VARCHAR(120) NOT NULL,
+                report_type VARCHAR(20) NOT NULL DEFAULT 'external',
+                filename VARCHAR(255),
+                s3_key VARCHAR(512),
+                report_config JSONB NOT NULL DEFAULT '{}'::jsonb,
+                cache_fingerprint VARCHAR(64),
+                created_by TEXT,
+                created_by_user_id UUID,
+                created_at TIMESTAMPTZ NOT NULL DEFAULT now()
+            )
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_call_import_eval_pdf_reports_eval
+            ON call_import_evaluation_pdf_reports (evaluation_id, created_at DESC)
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE UNIQUE INDEX IF NOT EXISTS
+            uq_call_import_eval_pdf_reports_eval_cache_fp
+            ON call_import_evaluation_pdf_reports (evaluation_id, cache_fingerprint)
+            WHERE cache_fingerprint IS NOT NULL
+            """
+        )
+    )
+    print("Created call_import_evaluation_pdf_reports")
+    db.commit()
+
+
+def downgrade(db: Session):
+    db.execute(text("DROP TABLE IF EXISTS call_import_evaluation_pdf_reports"))
+    db.commit()
diff --git a/app/migrations/061_call_import_eval_pdf_report_cache_index.py b/app/migrations/061_call_import_eval_pdf_report_cache_index.py
new file mode 100644
index 00000000..baa579b0
--- /dev/null
+++ b/app/migrations/061_call_import_eval_pdf_report_cache_index.py
@@ -0,0 +1,137 @@
+"""Migration: PDF report cache fingerprint column rename and lookup index."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Rename config_fingerprint to cache_fingerprint and add unique "
+    "(evaluation_id, cache_fingerprint) for scale-safe cache lookups"
+)
+
+
+def _table_exists(db: Session, table_name: str) -> bool:
+    row = db.execute(
+        text(
+            """
+            SELECT 1
+            FROM information_schema.tables
+            WHERE table_name = :table_name
+            """
+        ),
+        {"table_name": table_name},
+    ).first()
+    return row is not None
+
+
+def _column_exists(db: Session, column_name: str) -> bool:
+    row = db.execute(
+        text(
+            """
+            SELECT 1
+            FROM information_schema.columns
+            WHERE table_name = 'call_import_evaluation_pdf_reports'
+              AND column_name = :column_name
+            """
+        ),
+        {"column_name": column_name},
+    ).first()
+    return row is not None
+
+
+def upgrade(db: Session):
+    if not _table_exists(db, "call_import_evaluation_pdf_reports"):
+        print("call_import_evaluation_pdf_reports missing, skipping 061")
+        db.commit()
+        return
+
+    if _column_exists(db, "config_fingerprint") and not _column_exists(
+        db, "cache_fingerprint"
+    ):
+        db.execute(
+            text(
+                """
+                ALTER TABLE call_import_evaluation_pdf_reports
+                RENAME COLUMN config_fingerprint TO cache_fingerprint
+                """
+            )
+        )
+        print("Renamed config_fingerprint -> cache_fingerprint")
+
+    if not _column_exists(db, "cache_fingerprint"):
+        print("cache_fingerprint column missing, skipping index work")
+        db.commit()
+        return
+
+    db.execute(
+        text(
+            """
+            DELETE FROM call_import_evaluation_pdf_reports stale
+            USING call_import_evaluation_pdf_reports keep
+            WHERE stale.evaluation_id = keep.evaluation_id
+              AND stale.cache_fingerprint = keep.cache_fingerprint
+              AND stale.cache_fingerprint IS NOT NULL
+              AND stale.id <> keep.id
+              AND (
+                stale.created_at < keep.created_at
+                OR (
+                    stale.created_at = keep.created_at
+                    AND stale.id::text < keep.id::text
+                )
+              )
+            """
+        )
+    )
+
+    db.execute(
+        text(
+            """
+            DROP INDEX IF EXISTS ix_call_import_eval_pdf_reports_fingerprint
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE UNIQUE INDEX IF NOT EXISTS
+            uq_call_import_eval_pdf_reports_eval_cache_fp
+            ON call_import_evaluation_pdf_reports (evaluation_id, cache_fingerprint)
+            WHERE cache_fingerprint IS NOT NULL
+            """
+        )
+    )
+    print("Ensured unique (evaluation_id, cache_fingerprint) index")
+    db.commit()
+
+
+def downgrade(db: Session):
+    if not _table_exists(db, "call_import_evaluation_pdf_reports"):
+        db.commit()
+        return
+
+    db.execute(
+        text(
+            """
+            DROP INDEX IF EXISTS uq_call_import_eval_pdf_reports_eval_cache_fp
+            """
+        )
+    )
+    if _column_exists(db, "cache_fingerprint") and not _column_exists(
+        db, "config_fingerprint"
+    ):
+        db.execute(
+            text(
+                """
+                ALTER TABLE call_import_evaluation_pdf_reports
+                RENAME COLUMN cache_fingerprint TO config_fingerprint
+                """
+            )
+        )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_call_import_eval_pdf_reports_fingerprint
+            ON call_import_evaluation_pdf_reports (config_fingerprint)
+            """
+        )
+    )
+    db.commit()
diff --git a/app/models/database.py b/app/models/database.py
index 3ce548de..68bcfebc 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -2552,6 +2552,50 @@ class CallImportEvaluationReportSnapshot(Base):
     )
 
 
+class CallImportEvaluationPdfReport(Base):
+    """Stored PDF artifact for a call import evaluation report generation."""
+
+    __tablename__ = "call_import_evaluation_pdf_reports"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    snapshot_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluation_report_snapshots.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    vendor_name = Column(String(120), nullable=False)
+    report_type = Column(String(20), nullable=False, default="external")
+    filename = Column(String(255), nullable=True)
+    s3_key = Column(String(512), nullable=True)
+    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
+    cache_fingerprint = Column(String(64), nullable=True)
+    created_by = Column(String, nullable=True)
+    created_by_user_id = Column(UUID(as_uuid=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
 # ---------------------------------------------------------------------------
 # Judge Alignment (AlignEval-style hybrid integration)
 #
diff --git a/app/services/reporting/call_import_pdf_report_storage.py b/app/services/reporting/call_import_pdf_report_storage.py
new file mode 100644
index 00000000..17992d05
--- /dev/null
+++ b/app/services/reporting/call_import_pdf_report_storage.py
@@ -0,0 +1,207 @@
+"""Helpers for call import evaluation PDF report storage and config fingerprinting."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from typing import TYPE_CHECKING, Any
+from uuid import UUID
+
+from app.services.storage.s3_service import s3_service
+
+if TYPE_CHECKING:
+    from sqlalchemy.orm import Session
+
+    from app.models.database import CallImportEvaluationPdfReport
+
+
+def build_pdf_report_s3_key(
+    *,
+    organization_id: UUID,
+    call_import_id: UUID,
+    evaluation_id: UUID,
+    report_id: UUID,
+) -> str:
+    prefix = s3_service.prefix or ""
+    return (
+        f"{prefix}organizations/{organization_id}/call_imports/{call_import_id}/"
+        f"evaluations/{evaluation_id}/reports/{report_id}.pdf"
+    )
+
+
+def _canonicalize_report_config(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {str(k): _canonicalize_report_config(v) for k, v in sorted(value.items())}
+    if isinstance(value, list):
+        normalized = [_canonicalize_report_config(item) for item in value]
+        try:
+            return sorted(
+                normalized,
+                key=lambda item: json.dumps(item, sort_keys=True, default=str),
+            )
+        except TypeError:
+            return normalized
+    return value
+
+
+def _fingerprint_digest(payload: dict[str, Any]) -> str:
+    canonical = json.dumps(payload, sort_keys=True, separators=(",", ":"), default=str)
+    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()
+
+
+def _json_fingerprint_value(value: Any) -> Any:
+    if value is None:
+        return None
+    if hasattr(value, "model_dump"):
+        return value.model_dump(mode="json")
+    if isinstance(value, dict):
+        return {str(k): _json_fingerprint_value(v) for k, v in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [_json_fingerprint_value(item) for item in value]
+    return value
+
+
+def compute_pdf_report_config_fingerprint(
+    *,
+    report_type: str,
+    include_period_delta: bool,
+    include_weekly_delta: bool,
+    baseline_evaluation_id: str | None,
+    internal_brand_image_id: str | None,
+    external_brand_image_id: str | None,
+    use_case: str | None,
+    report_config: dict[str, Any],
+    report_heading: str | None,
+    vendor_name: str | None = None,
+    platform_base_url: str | None = None,
+    period_label: str | None = None,
+) -> str:
+    payload = {
+        "report_type": report_type,
+        "include_period_delta": include_period_delta,
+        "include_weekly_delta": include_weekly_delta,
+        "baseline_evaluation_id": baseline_evaluation_id,
+        "internal_brand_image_id": internal_brand_image_id,
+        "external_brand_image_id": external_brand_image_id,
+        "use_case": use_case,
+        "report_config": _canonicalize_report_config(report_config or {}),
+        "report_heading": (report_heading or "").strip(),
+        "vendor_name": (vendor_name or "").strip(),
+        "platform_base_url": (platform_base_url or "").strip(),
+        "period_label": (period_label or "").strip(),
+    }
+    return _fingerprint_digest(payload)
+
+
+def compute_pdf_report_content_fingerprint(
+    *,
+    evaluation_status: str,
+    completed_rows: int,
+    total_rows: int,
+    failed_rows: int,
+    metric_aggregates: list[dict[str, Any]],
+    insight_aggregates: list[dict[str, Any]],
+    period_delta_by_metric: dict[str, Any],
+    benchmark_context: Any,
+    metric_metadata: list[dict[str, Any]],
+    failure_policies: dict[str, Any],
+    tldr_summary: Any = None,
+    user_insights_for_pdf: Any = None,
+    metric_clusters_for_pdf: Any = None,
+    prompt_improvements_for_pdf: Any = None,
+) -> str:
+    payload = {
+        "evaluation_status": evaluation_status,
+        "completed_rows": completed_rows,
+        "total_rows": total_rows,
+        "failed_rows": failed_rows,
+        "metric_aggregates": _canonicalize_report_config(metric_aggregates or []),
+        "insight_aggregates": _canonicalize_report_config(insight_aggregates or []),
+        "period_delta_by_metric": _canonicalize_report_config(period_delta_by_metric or {}),
+        "benchmark_context": _json_fingerprint_value(benchmark_context),
+        "metric_metadata": _canonicalize_report_config(metric_metadata or []),
+        "failure_policies": _json_fingerprint_value(failure_policies or {}),
+        "tldr_summary": _json_fingerprint_value(tldr_summary),
+        "user_insights_for_pdf": _json_fingerprint_value(user_insights_for_pdf),
+        "metric_clusters_for_pdf": _json_fingerprint_value(metric_clusters_for_pdf),
+        "prompt_improvements_for_pdf": _json_fingerprint_value(prompt_improvements_for_pdf),
+    }
+    return _fingerprint_digest(payload)
+
+
+def compute_pdf_report_cache_fingerprint(
+    *,
+    config_fingerprint: str,
+    content_fingerprint: str,
+) -> str:
+    combined = f"{config_fingerprint}:{content_fingerprint}"
+    return hashlib.sha256(combined.encode("utf-8")).hexdigest()
+
+
+def find_cached_pdf_report(
+    db: "Session",
+    *,
+    evaluation_id: UUID,
+    organization_id: UUID,
+    cache_fingerprint: str,
+) -> "CallImportEvaluationPdfReport | None":
+    from sqlalchemy import desc
+
+    from app.models.database import CallImportEvaluationPdfReport
+
+    if not cache_fingerprint:
+        return None
+    return (
+        db.query(CallImportEvaluationPdfReport)
+        .filter(
+            CallImportEvaluationPdfReport.evaluation_id == evaluation_id,
+            CallImportEvaluationPdfReport.organization_id == organization_id,
+            CallImportEvaluationPdfReport.cache_fingerprint == cache_fingerprint,
+            CallImportEvaluationPdfReport.s3_key.isnot(None),
+        )
+        .order_by(desc(CallImportEvaluationPdfReport.created_at))
+        .first()
+    )
+
+
+def config_summary_from_report_config(report_config: dict[str, Any] | None) -> str:
+    cfg = report_config if isinstance(report_config, dict) else {}
+    quality_ids = cfg.get("quality_metric_ids") or []
+    insight_ids = cfg.get("insights") or []
+    user_insight_ids = cfg.get("user_insight_ids") or []
+    metric_count = len(quality_ids) if isinstance(quality_ids, list) else 0
+    insight_count = len(insight_ids) if isinstance(insight_ids, list) else 0
+    user_count = len(user_insight_ids) if isinstance(user_insight_ids, list) else 0
+    parts: list[str] = []
+    if metric_count:
+        parts.append(f"{metric_count} quality metric{'s' if metric_count != 1 else ''}")
+    if insight_count:
+        parts.append(f"{insight_count} insight{'s' if insight_count != 1 else ''}")
+    if user_count:
+        parts.append(f"{user_count} user insight{'s' if user_count != 1 else ''}")
+    return ", ".join(parts) if parts else "default sections"
+
+
+def presigned_urls_for_pdf_report(
+    s3_key: str,
+    filename: str,
+    *,
+    expiration: int = 3600,
+) -> tuple[str | None, str | None]:
+    if not s3_key or not s3_service.is_enabled():
+        return None, None
+    safe_name = filename.replace('"', "'")
+    try:
+        preview_url = s3_service.generate_presigned_url_by_key(
+            s3_key,
+            expiration=expiration,
+            response_content_disposition="inline",
+        )
+        download_url = s3_service.generate_presigned_url_by_key(
+            s3_key,
+            expiration=expiration,
+            response_content_disposition=f'attachment; filename="{safe_name}"',
+        )
+        return preview_url, download_url
+    except Exception:
+        return None, None
diff --git a/app/services/storage/azure_blob_service.py b/app/services/storage/azure_blob_service.py
index 3b1cb19e..9e6e8425 100644
--- a/app/services/storage/azure_blob_service.py
+++ b/app/services/storage/azure_blob_service.py
@@ -553,7 +553,13 @@ def generate_presigned_url(
         key = self._get_key(file_id, file_format)
         return self.generate_presigned_url_by_key(key, expiration=expiration)
 
-    def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str:
+    def generate_presigned_url_by_key(
+        self,
+        key: str,
+        expiration: int = 3600,
+        *,
+        response_content_disposition: str | None = None,
+    ) -> str:
         """Generate a SAS URL for temporary file access by key."""
         self._ensure_initialized()
         if not self.is_enabled():
@@ -574,14 +580,17 @@ def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str
             )
 
         try:
-            sas_token = generate_blob_sas(
-                account_name=account_name,
-                container_name=self.bucket_name,
-                blob_name=key,
-                account_key=account_key,
-                permission=BlobSasPermissions(read=True),
-                expiry=datetime.now(UTC) + timedelta(seconds=expiration),
-            )
+            sas_kwargs: dict = {
+                "account_name": account_name,
+                "container_name": self.bucket_name,
+                "blob_name": key,
+                "account_key": account_key,
+                "permission": BlobSasPermissions(read=True),
+                "expiry": datetime.now(UTC) + timedelta(seconds=expiration),
+            }
+            if response_content_disposition:
+                sas_kwargs["content_disposition"] = response_content_disposition
+            sas_token = generate_blob_sas(**sas_kwargs)
             blob_client = self.container_client.get_blob_client(key)
             return f"{blob_client.url}?{sas_token}"
         except Exception as e:
diff --git a/app/services/storage/blob_storage_service.py b/app/services/storage/blob_storage_service.py
index 5f4b3bc4..03374464 100644
--- a/app/services/storage/blob_storage_service.py
+++ b/app/services/storage/blob_storage_service.py
@@ -128,8 +128,18 @@ def generate_presigned_url(
     ) -> str:
         return self._backend().generate_presigned_url(file_id, file_format, expiration)
 
-    def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str:
-        return self._backend().generate_presigned_url_by_key(key, expiration)
+    def generate_presigned_url_by_key(
+        self,
+        key: str,
+        expiration: int = 3600,
+        *,
+        response_content_disposition: str | None = None,
+    ) -> str:
+        return self._backend().generate_presigned_url_by_key(
+            key,
+            expiration,
+            response_content_disposition=response_content_disposition,
+        )
 
 
 blob_storage_service = BlobStorageService()
diff --git a/app/services/storage/gcs_service.py b/app/services/storage/gcs_service.py
index 06a53550..07213e62 100644
--- a/app/services/storage/gcs_service.py
+++ b/app/services/storage/gcs_service.py
@@ -553,7 +553,13 @@ def generate_presigned_url(
         key = self._get_key(file_id, file_format)
         return self.generate_presigned_url_by_key(key, expiration=expiration)
 
-    def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str:
+    def generate_presigned_url_by_key(
+        self,
+        key: str,
+        expiration: int = 3600,
+        *,
+        response_content_disposition: str | None = None,
+    ) -> str:
         """Generate a signed URL for temporary file access by key."""
         self._ensure_initialized()
         if not self.is_enabled():
@@ -567,23 +573,27 @@ def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str
         if credentials is None and iam_params is None:
             raise StorageError(_GCS_SIGNING_UNAVAILABLE_MSG)
 
+        signed_url_kwargs: dict = {
+            "version": "v4",
+            "expiration": timedelta(seconds=expiration),
+            "method": "GET",
+        }
+        if response_content_disposition:
+            signed_url_kwargs["response_disposition"] = response_content_disposition
+
         try:
             blob = self.bucket.blob(key)
             if credentials is not None:
                 url = blob.generate_signed_url(
-                    version="v4",
-                    expiration=timedelta(seconds=expiration),
-                    method="GET",
                     credentials=credentials,
+                    **signed_url_kwargs,
                 )
             else:
                 sa_email, access_token = iam_params
                 url = blob.generate_signed_url(
-                    version="v4",
-                    expiration=timedelta(seconds=expiration),
-                    method="GET",
                     service_account_email=sa_email,
                     access_token=access_token,
+                    **signed_url_kwargs,
                 )
             return url
         except GoogleCloudError as e:
diff --git a/app/services/storage/s3_service.py b/app/services/storage/s3_service.py
index 1c22d168..b8376a85 100644
--- a/app/services/storage/s3_service.py
+++ b/app/services/storage/s3_service.py
@@ -478,7 +478,13 @@ def generate_presigned_url(self, file_id: uuid.UUID, file_format: str, expiratio
         except Exception as e:
             raise StorageError(f"Unexpected error generating presigned URL: {str(e)}")
 
-    def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str:
+    def generate_presigned_url_by_key(
+        self,
+        key: str,
+        expiration: int = 3600,
+        *,
+        response_content_disposition: str | None = None,
+    ) -> str:
         """Generate a presigned URL for temporary file access by key."""
         self._ensure_initialized()
         if not self.is_enabled():
@@ -486,9 +492,12 @@ def generate_presigned_url_by_key(self, key: str, expiration: int = 3600) -> str
             raise StorageError(error_msg)
 
         try:
+            params: dict[str, str] = {"Bucket": self.bucket_name, "Key": key}
+            if response_content_disposition:
+                params["ResponseContentDisposition"] = response_content_disposition
             url = self.s3_client.generate_presigned_url(
                 "get_object",
-                Params={"Bucket": self.bucket_name, "Key": key},
+                Params=params,
                 ExpiresIn=expiration,
             )
             return url
diff --git a/env.example b/env.example
index 0de4f151..cd34c25a 100644
--- a/env.example
+++ b/env.example
@@ -25,6 +25,8 @@ MAX_FILE_SIZE_MB=500
 ALLOWED_AUDIO_FORMATS=wav,mp3,flac,m4a
 
 # S3 (optional — used when config.docker.yml references ${S3_ACCESS_KEY_ID})
+# Required for call-import PDF report history (stored artifacts); without blob storage,
+# PDF generation still streams inline but is not versioned.
 # S3_ACCESS_KEY_ID=
 # S3_SECRET_ACCESS_KEY=
 
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 04e1b025..15724db3 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -43,6 +43,8 @@ import type {
   CallImportPreviewResponse,
   CallImportEvaluation,
   CallImportEvaluationBaselineCandidatesResponse,
+  CallImportEvaluationPdfReport,
+  CallImportEvaluationPdfReportListResponse,
   CallImportEvaluationLLMOverride,
   CallImportEvaluationListResponse,
   CallImportEvaluationRow,
@@ -2882,7 +2884,7 @@ class ApiClient {
       reportConfig?: Record<string, any>
       platformBaseUrl?: string | null
     },
-  ): Promise<Blob> {
+  ): Promise<CallImportEvaluationPdfReport | Blob> {
     const response = await this.client.post(
       `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-report`,
       {
@@ -2897,7 +2899,33 @@ class ApiClient {
         report_config: options?.reportConfig || {},
         platform_base_url: options?.platformBaseUrl || null,
       },
-      { responseType: 'blob' },
+      { responseType: 'arraybuffer' },
+    )
+    const contentType = String(response.headers['content-type'] || '')
+    if (contentType.includes('application/pdf')) {
+      return new Blob([response.data], { type: 'application/pdf' })
+    }
+    const text = new TextDecoder().decode(response.data)
+    return JSON.parse(text) as CallImportEvaluationPdfReport
+  }
+
+  async listCallImportEvaluationPdfReports(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<CallImportEvaluationPdfReportListResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports`,
+    )
+    return response.data
+  }
+
+  async getCallImportEvaluationPdfReport(
+    callImportId: string,
+    evaluationId: string,
+    reportId: string,
+  ): Promise<CallImportEvaluationPdfReport> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports/${reportId}`,
     )
     return response.data
   }
diff --git a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
index 0c42f91a..7e68664c 100644
--- a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
@@ -435,6 +435,8 @@ export default function CallImportEvaluationDetail() {
   const [forceFailPendingOpen, setForceFailPendingOpen] = useState(false)
   const [downloadMenuOpen, setDownloadMenuOpen] = useState(false)
   const downloadMenuRef = useRef<HTMLDivElement>(null)
+  const [pdfReportMenuOpen, setPdfReportMenuOpen] = useState(false)
+  const pdfReportMenuRef = useRef<HTMLDivElement>(null)
   const [pdfReportOpen, setPdfReportOpen] = useState(false)
   const [pdfWizardStep, setPdfWizardStep] = useState(1)
   const [pdfUserInsightsTriggering, setPdfUserInsightsTriggering] =
@@ -890,6 +892,12 @@ export default function CallImportEvaluationDetail() {
     },
   })
 
+  const pdfReportsQuery = useQuery({
+    queryKey: ['call-import-evaluation-pdf-reports', activeWorkspaceId, id, evalId],
+    queryFn: () => apiClient.listCallImportEvaluationPdfReports(id!, evalId!),
+    enabled: pdfReportMenuOpen && !!id && !!evalId,
+  })
+
   useEffect(() => {
     if (!deepLinkConversationId && !deepLinkRowId) return
     if (deepLinkConversationId && searchQuery !== deepLinkConversationId) {
@@ -1817,6 +1825,33 @@ export default function CallImportEvaluationDetail() {
     )
   }
 
+  const openStoredPdfPreview = async (reportId: string) => {
+    if (!id || !evalId) return
+    try {
+      const report = await apiClient.getCallImportEvaluationPdfReport(
+        id,
+        evalId,
+        reportId,
+      )
+      if (!report.preview_url) {
+        throw new Error('Preview URL is not available.')
+      }
+      if (pdfPreviewUrl?.startsWith('blob:')) {
+        window.URL.revokeObjectURL(pdfPreviewUrl)
+      }
+      setPdfPreviewUrl(report.preview_url)
+      setPdfPreviewFilename(report.filename || 'report.pdf')
+      setPdfPreviewOpen(true)
+      setPdfReportMenuOpen(false)
+    } catch (e: unknown) {
+      console.error('Failed to open stored PDF report', e)
+      showToast(
+        getApiErrorMessage(e, 'Failed to open PDF preview.'),
+        'error',
+      )
+    }
+  }
+
   const handlePdfReportSubmit = async () => {
     if (!id || !evalId || pdfReportLoading) return
     const vendorName = pdfVendorName.trim()
@@ -1827,20 +1862,45 @@ export default function CallImportEvaluationDetail() {
     setPdfReportLoadingAction('download')
     setPdfReportError(null)
     try {
-      const blob = await generatePdfReportBlob(vendorName)
+      const result = await generatePdfReportBlob(vendorName)
       const vendorSlug =
         vendorName
           .toLowerCase()
           .replace(/[^a-z0-9]+/g, '-')
           .replace(/^-+|-+$/g, '') || 'client'
-      const url = window.URL.createObjectURL(blob)
-      const link = document.createElement('a')
-      link.href = url
-      link.download = `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`
-      document.body.appendChild(link)
-      link.click()
-      link.remove()
-      window.URL.revokeObjectURL(url)
+      const defaultFilename = `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`
+
+      if (result instanceof Blob) {
+        const url = window.URL.createObjectURL(result)
+        const link = document.createElement('a')
+        link.href = url
+        link.download = defaultFilename
+        document.body.appendChild(link)
+        link.click()
+        link.remove()
+        window.URL.revokeObjectURL(url)
+      } else {
+        const downloadUrl = result.download_url
+        if (!downloadUrl) {
+          throw new Error('Download URL is not available.')
+        }
+        const link = document.createElement('a')
+        link.href = downloadUrl
+        link.download = result.filename || defaultFilename
+        link.target = '_blank'
+        link.rel = 'noopener noreferrer'
+        document.body.appendChild(link)
+        link.click()
+        link.remove()
+        await queryClient.invalidateQueries({
+          queryKey: [
+            'call-import-evaluation-pdf-reports',
+            activeWorkspaceId,
+            id,
+            evalId,
+          ],
+        })
+      }
       setPdfReportOpen(false)
       setPdfWizardStep(1)
       setPdfVendorName('')
@@ -1866,18 +1926,43 @@ export default function CallImportEvaluationDetail() {
     setPdfReportLoadingAction('preview')
     setPdfReportError(null)
     try {
-      const blob = await generatePdfReportBlob(vendorName)
+      const result = await generatePdfReportBlob(vendorName)
       const vendorSlug =
         vendorName
           .toLowerCase()
           .replace(/[^a-z0-9]+/g, '-')
           .replace(/^-+|-+$/g, '') || 'client'
-      if (pdfPreviewUrl) window.URL.revokeObjectURL(pdfPreviewUrl)
-      const url = window.URL.createObjectURL(blob)
-      setPdfPreviewUrl(url)
-      setPdfPreviewFilename(
-        `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`,
-      )
+
+      if (result instanceof Blob) {
+        if (pdfPreviewUrl?.startsWith('blob:')) {
+          window.URL.revokeObjectURL(pdfPreviewUrl)
+        }
+        const url = window.URL.createObjectURL(result)
+        setPdfPreviewUrl(url)
+        setPdfPreviewFilename(
+          `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`,
+        )
+      } else {
+        if (!result.preview_url) {
+          throw new Error('Preview URL is not available.')
+        }
+        if (pdfPreviewUrl?.startsWith('blob:')) {
+          window.URL.revokeObjectURL(pdfPreviewUrl)
+        }
+        setPdfPreviewUrl(result.preview_url)
+        setPdfPreviewFilename(
+          result.filename ||
+            `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`,
+        )
+        await queryClient.invalidateQueries({
+          queryKey: [
+            'call-import-evaluation-pdf-reports',
+            activeWorkspaceId,
+            id,
+            evalId,
+          ],
+        })
+      }
       setPdfPreviewOpen(true)
     } catch (e: unknown) {
       console.error('Failed to preview PDF report', e)
@@ -2165,6 +2250,20 @@ export default function CallImportEvaluationDetail() {
     return () => document.removeEventListener('mousedown', handleClickOutside)
   }, [downloadMenuOpen])
 
+  useEffect(() => {
+    if (!pdfReportMenuOpen) return
+    const handleClickOutside = (event: MouseEvent) => {
+      if (
+        pdfReportMenuRef.current &&
+        !pdfReportMenuRef.current.contains(event.target as Node)
+      ) {
+        setPdfReportMenuOpen(false)
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside)
+    return () => document.removeEventListener('mousedown', handleClickOutside)
+  }, [pdfReportMenuOpen])
+
   if (!id || !evalId) {
     return <div className="text-sm text-red-600">Missing identifiers.</div>
   }
@@ -2347,20 +2446,90 @@ export default function CallImportEvaluationDetail() {
               Re-run metrics
             </Button>
           )}
-          <Button
-            variant="outline"
-            size="sm"
-            leftIcon={<FileText className="h-4 w-4" />}
-            onClick={() => {
-              setPdfReportError(null)
-              setPdfGenerationError(null)
-              setPdfWizardStep(1)
-              setPdfReportOpen(true)
-            }}
-            disabled={!rowsQuery.data?.items?.length}
-          >
-            Generate PDF Report
-          </Button>
+          <div className="relative" ref={pdfReportMenuRef}>
+            <Button
+              variant="outline"
+              size="sm"
+              leftIcon={<FileText className="h-4 w-4" />}
+              rightIcon={
+                <ChevronDown
+                  className={`h-4 w-4 transition-transform ${
+                    pdfReportMenuOpen ? 'rotate-180' : ''
+                  }`}
+                />
+              }
+              onClick={() => setPdfReportMenuOpen((prev) => !prev)}
+              disabled={(evaluation?.total_rows ?? 0) < 1}
+            >
+              PDF report
+            </Button>
+            {pdfReportMenuOpen && (
+              <div
+                className="absolute right-0 z-20 mt-1 w-80 flex flex-col max-h-[min(28rem,70vh)] bg-white border border-gray-200 rounded-lg shadow-lg overflow-hidden"
+                role="menu"
+              >
+                <div className="p-2 border-b border-gray-100 shrink-0">
+                  <button
+                    type="button"
+                    role="menuitem"
+                    className="w-full flex items-center justify-center gap-1.5 rounded-md border border-amber-500 bg-white px-2.5 py-1.5 text-xs font-semibold text-amber-900 hover:border-amber-600 hover:bg-amber-50/50 transition-colors"
+                    onClick={() => {
+                      setPdfReportMenuOpen(false)
+                      setPdfReportError(null)
+                      setPdfGenerationError(null)
+                      setPdfWizardStep(1)
+                      setPdfReportOpen(true)
+                    }}
+                  >
+                    <FileText className="h-3.5 w-3.5 shrink-0" aria-hidden />
+                    Generate new PDF
+                  </button>
+                </div>
+                <div className="px-3 py-2 text-xs font-semibold text-gray-500 uppercase tracking-wide border-b border-gray-100 shrink-0 bg-white">
+                  History
+                </div>
+                <div
+                  className="overflow-y-auto overscroll-y-contain min-h-0 flex-1 [scrollbar-gutter:stable]"
+                  role="group"
+                  aria-label="Stored PDF reports"
+                >
+                  {pdfReportsQuery.isLoading ? (
+                    <p className="px-3 py-3 text-sm text-gray-500">Loading…</p>
+                  ) : (pdfReportsQuery.data?.items?.length ?? 0) === 0 ? (
+                    <p className="px-3 py-3 text-sm text-gray-500">
+                      No stored reports yet.
+                    </p>
+                  ) : (
+                    <ul className="py-1">
+                      {pdfReportsQuery.data!.items.map((item) => (
+                        <li key={item.id}>
+                          <button
+                            type="button"
+                            className="w-full px-3 py-2.5 text-left text-sm text-gray-700 hover:bg-gray-50 focus:outline-none focus-visible:bg-gray-50 border-b border-gray-50 last:border-b-0"
+                            role="menuitem"
+                            onClick={() => openStoredPdfPreview(item.id)}
+                          >
+                            <span className="font-medium text-gray-900 block truncate">
+                              {item.vendor_name} · {item.report_type}
+                            </span>
+                            <span className="text-xs text-gray-500 block truncate mt-0.5">
+                              {new Date(item.created_at).toLocaleString()}
+                              {item.created_by ? ` · ${item.created_by}` : ''}
+                            </span>
+                            {item.config_summary ? (
+                              <span className="text-xs text-gray-400 block truncate mt-0.5">
+                                {item.config_summary}
+                              </span>
+                            ) : null}
+                          </button>
+                        </li>
+                      ))}
+                    </ul>
+                  )}
+                </div>
+              </div>
+            )}
+          </div>
           <div className="relative" ref={downloadMenuRef}>
             <Button
               variant="outline"
diff --git a/frontend/src/types/api.ts b/frontend/src/types/api.ts
index 39c0fed7..eb4a9baa 100644
--- a/frontend/src/types/api.ts
+++ b/frontend/src/types/api.ts
@@ -1701,6 +1701,35 @@ export interface CallImportEvaluationBaselineCandidatesResponse {
   default_evaluation_id: string | null
 }
 
+export interface CallImportEvaluationPdfReport {
+  id: string
+  filename: string
+  preview_url?: string | null
+  download_url?: string | null
+  created_at: string
+  created_by?: string | null
+  report_type: string
+  vendor_name: string
+  config_summary?: string | null
+  storage_available?: boolean
+  cache_hit?: boolean
+}
+
+export interface CallImportEvaluationPdfReportListItem {
+  id: string
+  filename?: string | null
+  vendor_name: string
+  report_type: string
+  created_by?: string | null
+  created_at: string
+  config_summary?: string | null
+  cache_fingerprint?: string | null
+}
+
+export interface CallImportEvaluationPdfReportListResponse {
+  items: CallImportEvaluationPdfReportListItem[]
+}
+
 export interface CallImportEvaluationRow {
   id: string
   evaluation_id: string
diff --git a/tests/test_api/test_call_import_evaluation_pdf_report.py b/tests/test_api/test_call_import_evaluation_pdf_report.py
index 9b2f316b..717cb29b 100644
--- a/tests/test_api/test_call_import_evaluation_pdf_report.py
+++ b/tests/test_api/test_call_import_evaluation_pdf_report.py
@@ -1496,3 +1496,134 @@ def _override_db():
     assert response.status_code == 403
     assert "Editor role" in response.json()["detail"]
     assert "Viewer" in response.json()["detail"]
+
+
+def _enable_mock_blob_storage(monkeypatch):
+    from app.services.storage.blob_storage_service import blob_storage_service
+
+    stored: dict[str, bytes] = {}
+
+    def upload_file_by_key(file_content, key, content_type="audio/mpeg"):
+        stored[key] = file_content
+        return key
+
+    monkeypatch.setattr(blob_storage_service._s3, "is_enabled", lambda: True)
+    monkeypatch.setattr(blob_storage_service, "upload_file_by_key", upload_file_by_key)
+    monkeypatch.setattr(
+        blob_storage_service,
+        "generate_presigned_url_by_key",
+        lambda key, expiration=3600, **kwargs: f"https://storage.example/{key}",
+    )
+    return stored
+
+
+def test_pdf_report_returns_json_and_stores_when_storage_enabled(
+    authenticated_client, db_session, org_id, seed_org, monkeypatch
+):
+    from app.models.database import CallImportEvaluationPdfReport
+
+    monkeypatch.setattr(
+        call_import_evaluation_pdf_report_service,
+        "_render_weasyprint",
+        lambda _html, **_kwargs: None,
+    )
+    stored = _enable_mock_blob_storage(monkeypatch)
+    call_import, evaluation = _seed_completed_evaluation(db_session, org_id)
+
+    response = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}/pdf-report",
+        json={"vendor_name": "Acme Vendor", "report_type": "external"},
+    )
+
+    assert response.status_code == 200, response.text
+    body = response.json()
+    assert body["vendor_name"] == "Acme Vendor"
+    assert body["report_type"] == "external"
+    assert body["preview_url"].startswith("https://storage.example/")
+    assert body["download_url"].startswith("https://storage.example/")
+
+    row = (
+        db_session.query(CallImportEvaluationPdfReport)
+        .filter(CallImportEvaluationPdfReport.evaluation_id == evaluation.id)
+        .one()
+    )
+    assert row.s3_key
+    assert row.s3_key in stored
+    assert stored[row.s3_key].startswith(b"%PDF")
+
+
+def test_pdf_report_reuses_cached_report_when_unchanged(
+    authenticated_client, db_session, org_id, seed_org, monkeypatch
+):
+    render_calls = {"count": 0}
+
+    def counting_render(*_args, **_kwargs):
+        render_calls["count"] += 1
+        return None
+
+    monkeypatch.setattr(
+        call_import_evaluation_pdf_report_service,
+        "_render_weasyprint",
+        counting_render,
+    )
+    stored = _enable_mock_blob_storage(monkeypatch)
+    call_import, evaluation = _seed_completed_evaluation(db_session, org_id)
+    base = f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}"
+    payload = {"vendor_name": "Acme Vendor", "report_type": "external"}
+
+    first = authenticated_client.post(f"{base}/pdf-report", json=payload)
+    second = authenticated_client.post(f"{base}/pdf-report", json=payload)
+
+    assert first.status_code == 200, first.text
+    assert second.status_code == 200, second.text
+    assert first.json()["id"] == second.json()["id"]
+    assert second.json().get("cache_hit") is True
+    assert render_calls["count"] == 1
+    assert len(stored) == 1
+
+
+def test_pdf_report_list_and_get_stored_versions(
+    authenticated_client, db_session, org_id, seed_org, monkeypatch
+):
+    monkeypatch.setattr(
+        call_import_evaluation_pdf_report_service,
+        "_render_weasyprint",
+        lambda _html, **_kwargs: None,
+    )
+    _enable_mock_blob_storage(monkeypatch)
+    call_import, evaluation = _seed_completed_evaluation(db_session, org_id)
+    base = f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}"
+
+    first = authenticated_client.post(
+        f"{base}/pdf-report",
+        json={
+            "vendor_name": "Acme Vendor",
+            "report_type": "external",
+            "report_config": {"quality_metric_ids": ["a"]},
+        },
+    )
+    second = authenticated_client.post(
+        f"{base}/pdf-report",
+        json={
+            "vendor_name": "Acme Vendor",
+            "report_type": "internal",
+            "report_config": {"quality_metric_ids": ["b"]},
+        },
+    )
+    assert first.status_code == 200
+    assert second.status_code == 200
+    first_id = first.json()["id"]
+    second_id = second.json()["id"]
+    assert first_id != second_id
+
+    listed = authenticated_client.get(f"{base}/pdf-reports")
+    assert listed.status_code == 200
+    items = listed.json()["items"]
+    assert len(items) == 2
+    listed_ids = {item["id"] for item in items}
+    assert listed_ids == {first_id, second_id}
+
+    detail = authenticated_client.get(f"{base}/pdf-reports/{first_id}")
+    assert detail.status_code == 200
+    assert detail.json()["id"] == first_id
+    assert detail.json()["preview_url"].startswith("https://storage.example/")

From 7579e28a54766162fd784284df5375c0897f6529 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 10 Aug 2026 11:58:15 +0530
Subject: [PATCH 07/55] refactor:  update tests for new principal handling

---
 .gitignore                                    |   2 +-
 app/services/call_imports/audit.py            |  20 ++-
 config.docker.yml                             | 137 ++++++++++++++++++
 ...ll_import_evaluation_serialize_progress.py |   2 +
 .../test_call_import_sharded_row_mutations.py |  12 ++
 .../test_call_import_unified_dispatch.py      |   9 +-
 .../test_process_call_import_row.py           |   3 +
 7 files changed, 174 insertions(+), 11 deletions(-)
 create mode 100644 config.docker.yml

diff --git a/.gitignore b/.gitignore
index 3fb88623..b4453aff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,6 +26,7 @@ MANIFEST
 secrets/
 *.json
 !package.json
+.cursorrules
 
 # Virtual environments
 venv/
@@ -34,7 +35,6 @@ env/
 ENV/
 .venv
 config.yml
-config.docker.yml
 # uv
 .python-version
 uv.lock
diff --git a/app/services/call_imports/audit.py b/app/services/call_imports/audit.py
index 6479ef18..cc940562 100644
--- a/app/services/call_imports/audit.py
+++ b/app/services/call_imports/audit.py
@@ -38,10 +38,12 @@ def stamp_evaluation_actor(
 def user_ids_from_call_imports(imports: Iterable[CallImport]) -> Set[UUID]:
     ids: Set[UUID] = set()
     for row in imports:
-        if row.created_by_user_id is not None:
-            ids.add(row.created_by_user_id)
-        if row.last_updated_by_user_id is not None:
-            ids.add(row.last_updated_by_user_id)
+        created_by = getattr(row, "created_by_user_id", None)
+        updated_by = getattr(row, "last_updated_by_user_id", None)
+        if created_by is not None:
+            ids.add(created_by)
+        if updated_by is not None:
+            ids.add(updated_by)
     return ids
 
 
@@ -50,10 +52,12 @@ def user_ids_from_evaluations(
 ) -> Set[UUID]:
     ids: Set[UUID] = set()
     for row in evaluations:
-        if row.created_by_user_id is not None:
-            ids.add(row.created_by_user_id)
-        if row.last_updated_by_user_id is not None:
-            ids.add(row.last_updated_by_user_id)
+        created_by = getattr(row, "created_by_user_id", None)
+        updated_by = getattr(row, "last_updated_by_user_id", None)
+        if created_by is not None:
+            ids.add(created_by)
+        if updated_by is not None:
+            ids.add(updated_by)
     return ids
 
 
diff --git a/config.docker.yml b/config.docker.yml
new file mode 100644
index 00000000..490f43d3
--- /dev/null
+++ b/config.docker.yml
@@ -0,0 +1,137 @@
+# EfficientAI Docker Configuration File
+# This config uses Docker service names for networking
+
+# Application Settings
+app:
+  name: "EfficientAI Voice AI Evaluation Platform"
+  version: "0.1.0"
+  debug: true
+  secret_key: "your-secret-key-here-change-in-production"
+
+# Server Settings
+server:
+  host: "0.0.0.0"
+  port: 8000
+
+# Operational endpoints (/metrics). /health is always open for load balancers.
+# Add VPC CIDRs here if Prometheus scrapes /metrics from inside the VPC.
+operational:
+  public: false
+  trusted_ips:
+    - "10.0.0.0/8"
+
+# Database Configuration (Docker service name)
+database:
+  url: "postgresql://efficientai:password@db:5432/efficientai"
+
+# Redis Configuration (Docker service name)
+redis:
+  url: "redis://redis:6379/0"
+
+# Celery Configuration (Docker service names)
+celery:
+  broker_url: "redis://redis:6379/0"
+  result_backend: "redis://redis:6379/0"
+
+# File Storage
+storage:
+  upload_dir: "/app/uploads"
+  max_file_size_mb: 500
+  allowed_audio_formats:
+    - "wav"
+    - "mp3"
+    - "flac"
+    - "m4a"
+
+# S3 Configuration (for data sources integration)
+s3:
+  enabled: true
+  bucket_name: "voiceai-evals-test"
+  region: "us-east-1"
+  access_key_id: "${S3_ACCESS_KEY_ID}"
+  secret_access_key: "${S3_SECRET_ACCESS_KEY}"
+  endpoint_url: null
+  prefix: "audio/"
+
+# Speaker Diarization (pyannote.audio)
+# Requires accepting model terms at https://huggingface.co/pyannote/speaker-diarization-3.1
+diarization:
+  huggingface_token:   # Set your HuggingFace token here (e.g., "hf_xxxxx")
+  num_speakers: 2  # Force exact speaker count (2 = agent + customer). Set to null for auto-detect.
+
+# CORS Settings
+cors:
+  origins:
+    - "http://localhost:3000"
+    - "http://localhost:8000"
+
+# API Settings
+api:
+  prefix: "/api/v1"
+  key_header: "X-API-Key"
+  rate_limit_per_minute: 60
+
+# Observability (Loki log aggregation)
+# storage: "filesystem" (default, local Docker volume) or "s3" (durable, for production)
+# multi_tenant: false (default, single tenant) or true (per-org log isolation)
+observability:
+  enabled: false
+  loki:
+    enabled: false
+    url: "http://loki:3100"
+    storage: filesystem
+    multi_tenant: false
+    platform_tenant: "platform"
+    # s3:
+    #   bucket_name: "my-logs-bucket"
+    #   region: "us-east-1"
+    #   access_key_id: ""
+    #   secret_access_key: ""
+    #   prefix: "logs/"
+
+auth:
+  providers:
+    - api_key
+    - local_password
+    # - external_oidc     # requires EFFICIENTAI_LICENSE feature: oidc_sso
+
+  local_password:
+    # Lifetime of the Bearer tokens minted at POST /auth/login (in minutes).
+    # Keep this short; clients re-authenticate silently.
+    token_ttl_minutes: 720  # 12 hours
+    # Turn this off in Cloud SaaS to block self-serve signup.
+    allow_signup: true
+
+  # External OIDC (enterprise): bring your own IdP. Works with any
+  # OIDC-compliant provider. See README.md > Authentication & Deployment
+  # Recipes for copy-paste recipes for Okta, Azure AD, Google Workspace
+  # and AWS Cognito.
+  #
+  # oidc:
+  #   issuer: "https://example.okta.com"                    # REQUIRED
+  #   audience: "efficientai"                               # expected `aud` claim
+  #   client_id: "0oa..."                                   # SPA client id
+  #   jwks_uri: "https://example.okta.com/oauth2/v1/keys"   # optional, derived from issuer
+  #   default_org_name: "Example Inc"                       # used when no org claim is present
+  #   org_claim_path: ["https://efficientai.com/org"]       # dotted path into the JWT to find the org name
+
+# Judge Alignment (AlignEval-style hybrid integration).
+# Per-org thresholds and judge model selection are UI-driven; only the
+# operator kill switch + CSV upload limit live here.
+judge_alignment:
+  enabled: true
+  csv_max_rows: 5000
+
+# LLM gateway (optional). Per-org overrides in Integrations UI.
+# llm_gateway:
+#   enabled: false
+#   type: bifrost              # bifrost | litellm_proxy
+#   base_url: "http://localhost:8080/litellm"
+#   virtual_key: null
+#   master_key: null
+#   passthrough_provider_keys: true
+
+# Enterprise License (JWT signed with RS256). Unlocks gated features like
+# oidc_sso, mfa_enforce, audit_export, voice_playground, gepa_optimization.
+# license:
+#   key: "eyJhbGciOi..."
\ No newline at end of file
diff --git a/tests/test_api/test_call_import_evaluation_serialize_progress.py b/tests/test_api/test_call_import_evaluation_serialize_progress.py
index f6c42aad..430dd244 100644
--- a/tests/test_api/test_call_import_evaluation_serialize_progress.py
+++ b/tests/test_api/test_call_import_evaluation_serialize_progress.py
@@ -40,6 +40,8 @@ def test_serialize_eval_merges_redis_deltas_without_clearing(db_session, org_id,
             "finished_at": None,
             "created_at": now,
             "updated_at": now,
+            "created_by_user_id": None,
+            "last_updated_by_user_id": None,
         },
     )()
 
diff --git a/tests/test_api/test_call_import_sharded_row_mutations.py b/tests/test_api/test_call_import_sharded_row_mutations.py
index 6bc8b1ad..dde88d01 100644
--- a/tests/test_api/test_call_import_sharded_row_mutations.py
+++ b/tests/test_api/test_call_import_sharded_row_mutations.py
@@ -15,6 +15,7 @@
 import pytest
 from fastapi import HTTPException
 
+from app.core.auth.principal import AuthMethod, Principal
 from app.api.v1.routes.call_imports import (
     cancel_call_import_row_diarisation,
     toggle_call_import_row_speaker_swap,
@@ -32,6 +33,14 @@ def _fake_catalog_db(call_import):
     return catalog_db
 
 
+def _test_principal(organization_id):
+    return Principal(
+        organization_id=organization_id,
+        auth_method=AuthMethod.LOCAL_PASSWORD,
+        user_id=uuid4(),
+    )
+
+
 def _fake_shard_row(
     *,
     call_import_id,
@@ -97,6 +106,7 @@ def fake_locate(_row_id):
         row_id=row_id,
         api_key="",
         organization_id=organization_id,
+        principal=_test_principal(organization_id),
         db=_fake_catalog_db(_fake_call_import(call_import_id, organization_id)),
     )
 
@@ -128,6 +138,7 @@ def fake_locate(_row_id):
             row_id=row_id,
             api_key="",
             organization_id=organization_id,
+            principal=_test_principal(organization_id),
             db=_fake_catalog_db(_fake_call_import(call_import_id, organization_id)),
         )
 
@@ -175,6 +186,7 @@ async def test_cancel_diarisation_commits_on_shard_session(monkeypatch):
         row_id=row_id,
         api_key="",
         organization_id=organization_id,
+        principal=_test_principal(organization_id),
         db=_fake_catalog_db(_fake_call_import(call_import_id, organization_id)),
     )
 
diff --git a/tests/test_workers/test_call_import_unified_dispatch.py b/tests/test_workers/test_call_import_unified_dispatch.py
index d923ee4a..3097581e 100644
--- a/tests/test_workers/test_call_import_unified_dispatch.py
+++ b/tests/test_workers/test_call_import_unified_dispatch.py
@@ -1,6 +1,7 @@
 """Unit tests for the unified call-import eval dispatch pipeline."""
 
 from types import SimpleNamespace
+from unittest.mock import MagicMock
 from uuid import uuid4
 
 import pytest
@@ -121,9 +122,13 @@ def test_try_dispatch_enqueues_import_for_pending_row(monkeypatch):
     class _AsyncResult:
         id = "import-task-123"
 
+    import_task = MagicMock()
+    import_task.apply_async = lambda *a, **kw: captured.update(
+        {"apply_async_kwargs": kw}
+    ) or _AsyncResult()
     monkeypatch.setattr(
-        "app.workers.tasks.process_call_import_row.process_call_import_row_task.apply_async",
-        lambda *a, **kw: captured.update({"apply_async_kwargs": kw}) or _AsyncResult(),
+        "app.workers.tasks.process_call_import_row.process_call_import_row_task",
+        import_task,
     )
 
     def fake_reserve(**kwargs):
diff --git a/tests/test_workers/test_process_call_import_row.py b/tests/test_workers/test_process_call_import_row.py
index e9f56f09..0dcdbc22 100644
--- a/tests/test_workers/test_process_call_import_row.py
+++ b/tests/test_workers/test_process_call_import_row.py
@@ -226,6 +226,9 @@ def run(self, row_id, *args, **kwargs):
             def retry(self, exc=None, countdown=None):
                 raise RetryCalled((exc, countdown))
 
+            def apply_async(self, *args, **kwargs):
+                return types.SimpleNamespace(id="test-task-id")
+
         return _FakeBindTask()
 
     class _CeleryDelegate:

From b3f56098d8248be211733b149d3bb9df2ba8e01c Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 10 Aug 2026 17:59:48 +0530
Subject: [PATCH 08/55] test: add is_enabled method to fake S3 for PDF report
 branding logo test

---
 tests/test_api/test_call_import_evaluation_pdf_report.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_api/test_call_import_evaluation_pdf_report.py b/tests/test_api/test_call_import_evaluation_pdf_report.py
index 717cb29b..d24bd739 100644
--- a/tests/test_api/test_call_import_evaluation_pdf_report.py
+++ b/tests/test_api/test_call_import_evaluation_pdf_report.py
@@ -516,6 +516,7 @@ def test_pdf_report_uses_saved_report_branding_logo(
 
     fake_s3 = SimpleNamespace(
         download_file_by_key=lambda key: b"custom-logo",
+        is_enabled=lambda: False,
     )
     monkeypatch.setattr(s3_module, "s3_service", fake_s3)
     captured: dict[str, object] = {}

From ebad2cbff510a5d7c6962565e8987e8533c4a673 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 10 Aug 2026 18:17:39 +0530
Subject: [PATCH 09/55] feat: add actor stamping to call import evaluation
 deletion process

---
 app/api/v1/routes/call_import_evaluations.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 94c371d0..7973b752 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -8270,6 +8270,7 @@ async def delete_call_import_evaluation_row(
                 status_code=404, detail="Evaluation row not found in this run"
             )
         _rollup_evaluation_status(evaluation, db)
+        stamp_evaluation_actor(evaluation, principal)
         db.commit()
         return Response(status_code=status.HTTP_204_NO_CONTENT)
 

From e1281b42972322227715bf18fa6b15c79b90db77 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 10 Aug 2026 22:08:12 +0530
Subject: [PATCH 10/55] feat: enhance call import evaluation components with
 inline metadata and actor stamping

---
 app/api/v1/routes/call_import_evaluations.py  |   4 +
 .../components/callImports/AuditMetaChips.tsx | 129 +++-----
 .../pages/callImports/CallImportDetail.tsx    | 281 ++++++++++--------
 .../CallImportEvaluationDetail.tsx            |   8 +-
 .../src/pages/callImports/CallImports.tsx     |  14 -
 ...l_import_evaluation_prompt_improvements.py |  66 ++++
 6 files changed, 276 insertions(+), 226 deletions(-)
 create mode 100644 tests/test_api/test_call_import_evaluation_prompt_improvements.py

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 7973b752..1b7c91ce 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -5743,6 +5743,7 @@ def _enqueue_prompt_improvements_job(
     credential_id: Optional[UUID] = None,
     force: bool = False,
     db: Optional[Session] = None,
+    principal: Optional[Principal] = None,
 ) -> None:
     current = _prompt_improvements_payload(evaluation)
     if current is not None and current.status == "running" and not force:
@@ -5760,6 +5761,8 @@ def _enqueue_prompt_improvements_job(
         "error_message": None,
     }
     if db is not None:
+        if principal is not None:
+            stamp_evaluation_actor(evaluation, principal)
         flag_modified(evaluation, "prompt_improvements")
         db.commit()
 
@@ -6577,6 +6580,7 @@ async def generate_call_import_evaluation_prompt_improvements(
         credential_id=body.credential_id,
         force=body.force or body.regenerate,
         db=db,
+        principal=principal,
     )
 
     db.refresh(evaluation)
diff --git a/frontend/src/components/callImports/AuditMetaChips.tsx b/frontend/src/components/callImports/AuditMetaChips.tsx
index a7bae2e4..fbe0d8c6 100644
--- a/frontend/src/components/callImports/AuditMetaChips.tsx
+++ b/frontend/src/components/callImports/AuditMetaChips.tsx
@@ -1,9 +1,6 @@
 import type { ReactNode } from 'react'
 
-/** Bordered metadata chips for call-import audit fields. */
-
-const CHIP_CLASS =
-  'inline-flex flex-col min-w-0 rounded-md border border-slate-200 bg-white px-2 py-1 text-[11px] leading-tight shadow-sm'
+/** Inline audit metadata for call imports and evaluations. */
 
 export function formatMetaDateTime(iso: string | null | undefined): string {
   if (!iso) return '—'
@@ -12,65 +9,49 @@ export function formatMetaDateTime(iso: string | null | undefined): string {
   return parsed.toLocaleString()
 }
 
-type AuditMetaChipProps = {
+type AuditMetaInlineItemProps = {
   label: string
   value: string | null | undefined
-  wide?: boolean
-  /** Tighter chip for horizontal list rows */
+  /** `text-xs` for dense rows (evaluation list); default `text-sm` */
   dense?: boolean
-  /** Fill a grid cell (2×2 activity column on list page) */
-  stacked?: boolean
   className?: string
 }
 
 export function AuditMetaChip({
   label,
   value,
-  wide,
   dense,
-  stacked,
   className = '',
-}: AuditMetaChipProps) {
+}: AuditMetaInlineItemProps) {
   const display = value?.trim() || '—'
-  const sizeClass = dense
-    ? stacked
-      ? 'w-full min-w-0 px-1.5 py-0.5 text-[10px]'
-      : 'shrink-0 px-1.5 py-0.5 text-[10px] min-w-[4.75rem] max-w-[8.5rem]'
-    : wide
-      ? 'min-w-[7.5rem] max-w-[12rem]'
-      : 'min-w-[6.5rem] max-w-[10rem]'
+  const sizeClass = dense ? 'text-xs' : 'text-sm'
   return (
-    <div
-      className={`${CHIP_CLASS} ${sizeClass} ${className}`.trim()}
+    <span
+      className={`inline-flex max-w-full min-w-0 flex-wrap items-baseline gap-x-1 ${sizeClass} ${className}`.trim()}
       title={display === '—' ? `${label}: unknown` : `${label}: ${display}`}
     >
-      <span className="text-gray-500 font-medium">{label}</span>
-      <span className="text-gray-800 truncate font-medium">{display}</span>
-    </div>
+      <span className="text-gray-500 shrink-0">{label}:</span>
+      <span className="font-medium text-gray-800 break-all">{display}</span>
+    </span>
   )
 }
 
-type AuditMetaChipRowProps = {
+type AuditMetaRowProps = {
   className?: string
-  wide?: boolean
-  compact?: boolean
+  dense?: boolean
+  /** Join parent flex row (chips become siblings of status/provider). */
+  inline?: boolean
   children: ReactNode
 }
 
-function AuditMetaChipRow({
-  className = '',
-  wide,
-  compact,
-  children,
-}: AuditMetaChipRowProps) {
+function AuditMetaRow({ className = '', dense, inline, children }: AuditMetaRowProps) {
+  const textClass = dense ? 'text-xs' : 'text-sm'
+  if (inline) {
+    return <div className={`contents ${className}`.trim()}>{children}</div>
+  }
   return (
     <div
-      className={
-        compact
-          ? `flex flex-nowrap items-stretch gap-1.5 overflow-x-auto max-w-full scrollbar-thin ${className}`.trim()
-          : `flex flex-wrap items-stretch gap-1.5 ${className}`.trim()
-      }
-      data-wide={wide ? 'true' : undefined}
+      className={`flex flex-wrap items-baseline gap-x-3 gap-y-1 min-w-0 max-w-full text-gray-600 ${textClass} ${className}`.trim()}
     >
       {children}
     </div>
@@ -83,10 +64,8 @@ type CallImportAuditMetaProps = {
   createdByEmail?: string | null
   lastUpdatedByEmail?: string | null
   className?: string
-  wide?: boolean
-  compact?: boolean
-  /** 2×2 grid for table cells (no horizontal scroll) */
-  stacked?: boolean
+  dense?: boolean
+  inline?: boolean
 }
 
 /** Created / updated timestamps + actor emails for a call-import batch. */
@@ -96,48 +75,33 @@ export function CallImportAuditMeta({
   createdByEmail,
   lastUpdatedByEmail,
   className,
-  wide = true,
-  compact,
-  stacked,
+  dense,
+  inline,
 }: CallImportAuditMetaProps) {
-  const chipCommon = { wide, dense: compact, stacked }
-  if (stacked) {
-    return (
-      <div
-        className={`grid grid-cols-2 gap-1.5 w-full min-w-[12rem] max-w-[22rem] ${className ?? ''}`.trim()}
-      >
-        <AuditMetaChip label="Created" value={formatMetaDateTime(createdAt)} {...chipCommon} />
-        <AuditMetaChip label="Updated" value={formatMetaDateTime(updatedAt)} {...chipCommon} />
-        <AuditMetaChip label="Created by" value={createdByEmail} {...chipCommon} />
-        <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} {...chipCommon} />
-      </div>
-    )
-  }
   return (
-    <AuditMetaChipRow className={className} wide={wide} compact={compact}>
-      <AuditMetaChip label="Created" value={formatMetaDateTime(createdAt)} wide={wide} dense={compact} />
-      <AuditMetaChip label="Updated" value={formatMetaDateTime(updatedAt)} wide={wide} dense={compact} />
-      <AuditMetaChip label="Created by" value={createdByEmail} wide={wide} dense={compact} />
-      <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} wide={wide} dense={compact} />
-    </AuditMetaChipRow>
+    <AuditMetaRow className={className} dense={dense} inline={inline}>
+      <AuditMetaChip label="Created" value={formatMetaDateTime(createdAt)} dense={dense} />
+      <AuditMetaChip label="Updated" value={formatMetaDateTime(updatedAt)} dense={dense} />
+      <AuditMetaChip label="Created by" value={createdByEmail} dense={dense} />
+      <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} dense={dense} />
+    </AuditMetaRow>
   )
 }
 
 type EvaluationAuditMetaProps = {
-  createdAt: string | null | undefined
+  createdAt?: string | null | undefined
   updatedAt?: string | null | undefined
   startedAt?: string | null
   finishedAt?: string | null
-  /** User who started this evaluation run (API: created_by_email). */
   runByEmail?: string | null
-  /** @deprecated Use runByEmail */
   createdByEmail?: string | null
   lastUpdatedByEmail?: string | null
   formatDate?: (iso: string | null | undefined) => string
   className?: string
-  wide?: boolean
-  compact?: boolean
+  dense?: boolean
+  inline?: boolean
   showRunTimes?: boolean
+  showTimestamps?: boolean
 }
 
 /** Evaluation run metadata (detail header or list card). */
@@ -151,25 +115,28 @@ export function EvaluationAuditMeta({
   lastUpdatedByEmail,
   formatDate = formatMetaDateTime,
   className,
-  wide = true,
-  compact,
+  dense,
+  inline,
   showRunTimes = true,
+  showTimestamps = false,
 }: EvaluationAuditMetaProps) {
   const runner = runByEmail ?? createdByEmail
   return (
-    <AuditMetaChipRow className={className} wide={wide} compact={compact}>
-      <AuditMetaChip label="Run by" value={runner} wide={wide} dense={compact} />
-      <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} wide={wide} dense={compact} />
-      <AuditMetaChip label="Created" value={formatDate(createdAt)} wide={wide} dense={compact} />
-      {updatedAt ? (
-        <AuditMetaChip label="Updated" value={formatDate(updatedAt)} wide={wide} dense={compact} />
+    <AuditMetaRow className={className} dense={dense} inline={inline}>
+      <AuditMetaChip label="Run by" value={runner} dense={dense} />
+      <AuditMetaChip label="Last updated by" value={lastUpdatedByEmail} dense={dense} />
+      {showTimestamps && createdAt ? (
+        <AuditMetaChip label="Created" value={formatDate(createdAt)} dense={dense} />
+      ) : null}
+      {showTimestamps && updatedAt ? (
+        <AuditMetaChip label="Updated" value={formatDate(updatedAt)} dense={dense} />
       ) : null}
       {showRunTimes && startedAt ? (
-        <AuditMetaChip label="Started" value={formatDate(startedAt)} wide={wide} dense={compact} />
+        <AuditMetaChip label="Started" value={formatDate(startedAt)} dense={dense} />
       ) : null}
       {showRunTimes && finishedAt ? (
-        <AuditMetaChip label="Finished" value={formatDate(finishedAt)} wide={wide} dense={compact} />
+        <AuditMetaChip label="Finished" value={formatDate(finishedAt)} dense={dense} />
       ) : null}
-    </AuditMetaChipRow>
+    </AuditMetaRow>
   )
 }
diff --git a/frontend/src/pages/callImports/CallImportDetail.tsx b/frontend/src/pages/callImports/CallImportDetail.tsx
index 9270e14e..98a41f3a 100644
--- a/frontend/src/pages/callImports/CallImportDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportDetail.tsx
@@ -59,7 +59,6 @@ import Pagination from '../../components/Pagination'
 import StatusBadge from '../../components/shared/StatusBadge'
 import {
   CallImportAuditMeta,
-  EvaluationAuditMeta,
 } from '../../components/callImports/AuditMetaChips'
 import DiariseStatusPill from '../../components/callImports/DiariseStatusPill'
 import ProviderModelPicker, {
@@ -1238,9 +1237,6 @@ export default function CallImportDetail() {
     },
   })
 
-  // Tracks which evaluation run is currently being aborted so we can show
-  // a tiny spinner inline on its Abort button without blocking the rest
-  // of the evaluations list. Cleared on success or error.
   const [cancellingEvalId, setCancellingEvalId] = useState<string | null>(null)
 
   const cancelEvaluationMutation = useMutation({
@@ -1255,12 +1251,7 @@ export default function CallImportDetail() {
       }
       queryClient.invalidateQueries({ queryKey: evaluationsQueryKey })
     },
-    onError: (err: any) => {
-      // Cancel is idempotent on the server — the most likely failure
-      // is a 404 because the run was just deleted, in which case the
-      // refetch above will reconcile state. Surface a console.error
-      // so dev-tools shows it without breaking the layout.
-      // eslint-disable-next-line no-console
+    onError: (err: unknown) => {
       console.error('cancelEvaluationMutation failed:', err)
     },
     onSettled: () => {
@@ -1268,12 +1259,7 @@ export default function CallImportDetail() {
     },
   })
 
-  // Bulk-abort companion to ``cancelEvaluationMutation`` — fires the
-  // single-run cancel endpoint per selected run in parallel. We
-  // deliberately keep this client-side fan-out (rather than a new
-  // bulk endpoint) so the UI surface stays small; cancel is cheap
-  // server-side because each call only revokes the rows that are
-  // still in-flight.
+  // Bulk-abort — fires the single-run cancel endpoint per selected run in parallel.
   const bulkCancelEvalsMutation = useMutation({
     mutationFn: async (ids: string[]) => {
       const results = await Promise.allSettled(
@@ -1672,19 +1658,20 @@ export default function CallImportDetail() {
               {data.original_filename || '(unnamed import)'}
             </h1>
             <div className="mt-1 text-xs text-gray-500 font-mono">{data.id}</div>
-            <div className="mt-3 flex items-center gap-3 flex-wrap">
+            <div className="mt-3 flex flex-wrap items-baseline gap-x-3 gap-y-1 min-w-0 max-w-full text-sm text-gray-600">
               <StatusBadge status={data.status} />
-              <span className="text-sm text-gray-600 capitalize">
+              <span className="capitalize shrink-0">
                 Provider:{' '}
-                <span className="font-medium">
+                <span className="font-medium text-gray-800">
                   {data.provider || (
-                    <span className="text-gray-400 italic normal-case">
+                    <span className="text-gray-400 italic normal-case font-normal">
                       not selected yet
                     </span>
                   )}
                 </span>
               </span>
               <CallImportAuditMeta
+                inline
                 createdAt={data.created_at}
                 updatedAt={data.updated_at}
                 createdByEmail={data.created_by_email}
@@ -3127,32 +3114,51 @@ export default function CallImportDetail() {
             No evaluations have been run for this dataset yet.
           </p>
         ) : (
-          <div className="space-y-2">
-            {(() => {
-              const items = evaluationsData?.items ?? []
-              const allSelected =
-                items.length > 0 &&
-                items.every((row) => selectedEvalIds.has(row.id))
-              return (
-                <div className="flex items-center gap-2 px-3 py-2 border border-gray-200 rounded-lg bg-gray-50">
-                  <input
-                    type="checkbox"
-                    aria-label="Select all evaluations"
-                    checked={allSelected}
-                    onChange={(e) => {
-                      if (e.target.checked) {
-                        setSelectedEvalIds(new Set(items.map((row) => row.id)))
-                      } else {
-                        setSelectedEvalIds(new Set())
-                      }
-                    }}
-                  />
-                  <span className="text-xs text-gray-600">
-                    Select all ({items.length})
-                  </span>
-                </div>
-              )
-            })()}
+          <div className="overflow-x-auto border border-gray-200 rounded-lg">
+            <table className="min-w-full divide-y divide-gray-200">
+              <thead className="bg-gray-50">
+                <tr>
+                  <th className="w-10 px-3 py-3">
+                    {(() => {
+                      const items = evaluationsData?.items ?? []
+                      const allSelected =
+                        items.length > 0 &&
+                        items.every((row) => selectedEvalIds.has(row.id))
+                      return (
+                        <input
+                          type="checkbox"
+                          aria-label="Select all evaluations"
+                          checked={allSelected}
+                          onChange={(e) => {
+                            if (e.target.checked) {
+                              setSelectedEvalIds(new Set(items.map((row) => row.id)))
+                            } else {
+                              setSelectedEvalIds(new Set())
+                            }
+                          }}
+                        />
+                      )
+                    })()}
+                  </th>
+                  <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Evaluation
+                  </th>
+                  <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Run by
+                  </th>
+                  <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Last updated by
+                  </th>
+                  <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Progress
+                  </th>
+                  <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                    Status
+                  </th>
+                  <th className="w-0 px-2 py-3" aria-hidden />
+                </tr>
+              </thead>
+              <tbody className="bg-white divide-y divide-gray-200">
             {evaluationsData?.items.map((evaluation: CallImportEvaluation) => {
               const isSelected = selectedEvalIds.has(evaluation.id)
               const bulkOperationActive = evaluationHasActiveBulkOperation(
@@ -3161,97 +3167,118 @@ export default function CallImportDetail() {
               const headerLabel = evaluation.name?.trim()
                 ? evaluation.name
                 : `Evaluation ${evaluation.id.slice(0, 8)}`
+              const runBy = evaluation.created_by_email?.trim() || '—'
+              const lastUpdatedBy = evaluation.last_updated_by_email?.trim() || '—'
               return (
-                <div
+                <tr
                   key={evaluation.id}
-                  className={`flex items-center gap-3 border rounded-lg px-3 py-2.5 bg-white transition ${
-                    isSelected
-                      ? 'border-primary-400 bg-primary-50/40'
-                      : 'border-gray-200 hover:border-gray-300'
+                  role="link"
+                  tabIndex={0}
+                  onClick={() =>
+                    navigate(
+                      `/call-imports/${id}/evaluations/${evaluation.id}`,
+                    )
+                  }
+                  onKeyDown={(e) => {
+                    if (e.key === 'Enter' || e.key === ' ') {
+                      e.preventDefault()
+                      navigate(
+                        `/call-imports/${id}/evaluations/${evaluation.id}`,
+                      )
+                    }
+                  }}
+                  className={`cursor-pointer group hover:bg-gray-50 ${
+                    isSelected ? 'bg-primary-50/40' : ''
                   }`}
                 >
-                  <input
-                    type="checkbox"
-                    aria-label={`Select ${headerLabel}`}
-                    checked={isSelected}
-                    onChange={(e) => {
-                      setSelectedEvalIds((prev) => {
-                        const next = new Set(prev)
-                        if (e.target.checked) next.add(evaluation.id)
-                        else next.delete(evaluation.id)
-                        return next
-                      })
-                    }}
-                  />
-                  <Link
-                    to={`/call-imports/${id}/evaluations/${evaluation.id}`}
-                    className="flex-1 min-w-0 flex items-center justify-between gap-3"
+                  <td
+                    className="px-3 py-4 align-middle"
+                    onClick={(e) => e.stopPropagation()}
                   >
-                    <div className="min-w-0 flex-1">
-                      <p className="text-sm font-medium text-gray-900 truncate">
+                    <input
+                      type="checkbox"
+                      aria-label={`Select ${headerLabel}`}
+                      checked={isSelected}
+                      onChange={(e) => {
+                        setSelectedEvalIds((prev) => {
+                          const next = new Set(prev)
+                          if (e.target.checked) next.add(evaluation.id)
+                          else next.delete(evaluation.id)
+                          return next
+                        })
+                      }}
+                      onClick={(e) => e.stopPropagation()}
+                    />
+                  </td>
+                  <td className="px-4 py-4 align-top min-w-[10rem]">
+                    <div className="min-w-0">
+                      <div className="text-sm font-medium text-gray-900 truncate group-hover:text-primary-700">
                         {headerLabel}
-                      </p>
-                      <EvaluationAuditMeta
-                        className="mt-1.5"
-                        compact
-                        showRunTimes={false}
-                        createdAt={evaluation.created_at}
-                        updatedAt={evaluation.updated_at}
-                        runByEmail={evaluation.created_by_email}
-                        lastUpdatedByEmail={evaluation.last_updated_by_email}
-                      />
+                      </div>
+                      <div className="text-xs text-gray-400 font-mono mt-0.5">
+                        {evaluation.id.slice(0, 8)}
+                      </div>
                     </div>
-                    <div className="text-right flex-shrink-0">
-                      <StatusBadge status={evaluation.status} size="sm" />
-                      {evaluation.bulk_operation && (
-                        <p className="text-[11px] text-amber-700 mt-1 flex items-center justify-end gap-1">
-                          <Loader2 className="h-3 w-3 animate-spin shrink-0" />
-                          {evaluationBulkOperationLabel(evaluation.bulk_operation)}
-                        </p>
-                      )}
-                      <p className="text-xs text-gray-500 mt-1">
-                        {evaluation.completed_rows}/{evaluation.total_rows} rows
+                  </td>
+                  <td className="px-4 py-4 align-top text-sm text-gray-700 max-w-[12rem]">
+                    <span className="break-all">{runBy}</span>
+                  </td>
+                  <td className="px-4 py-4 align-top text-sm text-gray-700 max-w-[12rem]">
+                    <span className="break-all">{lastUpdatedBy}</span>
+                  </td>
+                  <td className="px-4 py-4 align-top whitespace-nowrap text-sm text-gray-600">
+                    {evaluation.completed_rows}/{evaluation.total_rows} rows
+                  </td>
+                  <td className="px-4 py-4 align-top whitespace-nowrap">
+                    <StatusBadge status={evaluation.status} size="sm" />
+                    {evaluation.bulk_operation && (
+                      <p className="text-[11px] text-amber-700 mt-1 flex items-center gap-1">
+                        <Loader2 className="h-3 w-3 animate-spin shrink-0" />
+                        {evaluationBulkOperationLabel(evaluation.bulk_operation)}
                       </p>
-                    </div>
-                  </Link>
-                  {(evaluation.status === 'pending' ||
-                    evaluation.status === 'running') && (
-                    <Button
-                      variant="ghost"
-                      size="sm"
-                      leftIcon={<XCircle className="h-4 w-4" />}
-                      isLoading={
-                        bulkOperationActive ||
-                        (cancellingEvalId === evaluation.id &&
-                          cancelEvaluationMutation.isPending)
-                      }
-                      disabled={
-                        bulkOperationActive ||
-                        (cancellingEvalId === evaluation.id &&
-                          cancelEvaluationMutation.isPending)
-                      }
-                      onClick={(e) => {
-                        // Stop the click from bubbling into the parent
-                        // Link (which would navigate to the evaluation
-                        // detail page mid-cancel).
-                        e.preventDefault()
-                        e.stopPropagation()
-                        if (bulkOperationActive) return
-                        cancelEvaluationMutation.mutate(evaluation.id)
-                      }}
-                      className="flex-shrink-0 text-amber-700 hover:text-amber-800 hover:bg-amber-50"
-                      title={
-                        bulkOperationActive
-                          ? 'Wait for the current bulk operation to finish'
-                          : 'Abort this evaluation run'
-                      }
-                    >
-                      Abort
-                    </Button>
-                  )}
-                </div>
+                    )}
+                  </td>
+                  <td
+                    className="px-2 py-4 align-top text-right whitespace-nowrap"
+                    onClick={(e) => e.stopPropagation()}
+                  >
+                    {(evaluation.status === 'pending' ||
+                      evaluation.status === 'running') && (
+                      <Button
+                        variant="ghost"
+                        size="sm"
+                        leftIcon={<XCircle className="h-4 w-4" />}
+                        isLoading={
+                          bulkOperationActive ||
+                          (cancellingEvalId === evaluation.id &&
+                            cancelEvaluationMutation.isPending)
+                        }
+                        disabled={
+                          bulkOperationActive ||
+                          (cancellingEvalId === evaluation.id &&
+                            cancelEvaluationMutation.isPending)
+                        }
+                        onClick={(e) => {
+                          e.stopPropagation()
+                          if (bulkOperationActive) return
+                          cancelEvaluationMutation.mutate(evaluation.id)
+                        }}
+                        className="text-amber-700 hover:text-amber-800 hover:bg-amber-50"
+                        title={
+                          bulkOperationActive
+                            ? 'Wait for the current bulk operation to finish'
+                            : 'Abort this evaluation run'
+                        }
+                      >
+                        Abort
+                      </Button>
+                    )}
+                  </td>
+                </tr>
               )
             })}
+              </tbody>
+            </table>
           </div>
         )}
       </div>
diff --git a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
index 7e68664c..9cfbaa22 100644
--- a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
@@ -2650,11 +2650,11 @@ export default function CallImportEvaluationDetail() {
             <div className="mt-1 text-xs text-gray-500 font-mono">
               {evaluation.id}
             </div>
-            <div className="mt-3 flex items-center gap-3 flex-wrap">
+            <div className="mt-3 flex flex-wrap items-baseline gap-x-3 gap-y-1 min-w-0 max-w-full text-sm text-gray-600">
               <StatusBadge status={evaluation.status} />
               <span
                 className={
-                  'inline-flex items-center gap-1 rounded-full px-2 py-0.5 text-xs font-medium ' +
+                  'inline-flex shrink-0 items-center gap-1 rounded-full px-2 py-0.5 text-xs font-medium ' +
                   (evaluation.transcript_source === 'diarised'
                     ? 'bg-purple-50 text-purple-700'
                     : 'bg-gray-100 text-gray-700')
@@ -2670,8 +2670,8 @@ export default function CallImportEvaluationDetail() {
                   : 'Evaluated on Production transcript'}
               </span>
               <EvaluationAuditMeta
-                createdAt={evaluation.created_at}
-                updatedAt={evaluation.updated_at}
+                inline
+                showTimestamps={false}
                 startedAt={evaluation.started_at}
                 finishedAt={evaluation.finished_at}
                 runByEmail={evaluation.created_by_email}
diff --git a/frontend/src/pages/callImports/CallImports.tsx b/frontend/src/pages/callImports/CallImports.tsx
index b3d6295a..9dbc680b 100644
--- a/frontend/src/pages/callImports/CallImports.tsx
+++ b/frontend/src/pages/callImports/CallImports.tsx
@@ -21,7 +21,6 @@ import type { CallImport, CallImportStatus, CallImportTag } from '../../types/ap
 import Button from '../../components/Button'
 import ConfirmModal from '../../components/ConfirmModal'
 import StatusBadge from '../../components/shared/StatusBadge'
-import { CallImportAuditMeta } from '../../components/callImports/AuditMetaChips'
 import CallImportProgressBar from './components/CallImportProgressBar'
 import UploadAudioModal from './components/UploadAudioModal'
 import UploadCsvModal from './components/UploadCsvModal'
@@ -364,9 +363,6 @@ export default function CallImports() {
                   <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
                     Status
                   </th>
-                  <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider min-w-[17rem]">
-                    Activity
-                  </th>
                   <th className="px-6 py-3 text-right text-xs font-medium text-gray-500 uppercase tracking-wider">
                     Actions
                   </th>
@@ -444,16 +440,6 @@ export default function CallImports() {
                     <td className="px-6 py-4 whitespace-nowrap">
                       <StatusBadge status={item.status} />
                     </td>
-                    <td className="px-6 py-4 align-top">
-                      <CallImportAuditMeta
-                        compact
-                        stacked
-                        createdAt={item.created_at}
-                        updatedAt={item.updated_at}
-                        createdByEmail={item.created_by_email}
-                        lastUpdatedByEmail={item.last_updated_by_email}
-                      />
-                    </td>
                     <td
                       className="px-6 py-4 whitespace-nowrap text-right text-sm"
                       onClick={(e) => e.stopPropagation()}
diff --git a/tests/test_api/test_call_import_evaluation_prompt_improvements.py b/tests/test_api/test_call_import_evaluation_prompt_improvements.py
new file mode 100644
index 00000000..ecce8a00
--- /dev/null
+++ b/tests/test_api/test_call_import_evaluation_prompt_improvements.py
@@ -0,0 +1,66 @@
+"""API tests for evaluation prompt-improvements enqueue and audit stamping."""
+
+from __future__ import annotations
+
+import types
+
+from app.models.database import PromptPartial
+from app.services.imported_agent_constants import IMPORTED_AGENT_TAG
+from tests.test_api.test_call_import_evaluation_insights import _seed_eval_with_data
+
+
+def test_post_prompt_improvements_stamps_last_updated_by_email(
+    authenticated_client,
+    db_session,
+    org_id,
+    seed_org,
+    make_ai_provider,
+    monkeypatch,
+):
+    make_ai_provider(provider="openai", is_active=True)
+    call_import, evaluation, _ = _seed_eval_with_data(db_session, org_id)
+
+    evaluation.metric_clusters = {
+        "status": "completed",
+        "groups": [],
+        "overview": "done",
+        "generated_at": "2026-01-01T00:00:00+00:00",
+        "generated_at_completed_rows": 1,
+    }
+    evaluation.last_updated_by_user_id = None
+    db_session.flush()
+
+    agent = PromptPartial(
+        organization_id=org_id,
+        workspace_id=call_import.workspace_id,
+        name="Imported agent",
+        content="You are a helpful agent.",
+        tags=[IMPORTED_AGENT_TAG],
+    )
+    db_session.add(agent)
+    db_session.commit()
+
+    def fake_apply_async(*, kwargs=None, **_kw):
+        return types.SimpleNamespace(id="prompt-improvements-task-1")
+
+    monkeypatch.setattr(
+        "app.workers.tasks.generate_evaluation_prompt_improvements.generate_evaluation_prompt_improvements_task.apply_async",
+        fake_apply_async,
+    )
+
+    response = authenticated_client.post(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}/prompt-improvements",
+        json={
+            "imported_agent_id": str(agent.id),
+            "regenerate": True,
+            "force": True,
+        },
+    )
+    assert response.status_code == 200, response.text
+    assert response.json()["status"] == "running"
+
+    detail = authenticated_client.get(
+        f"/api/v1/call-imports/{call_import.id}/evaluations/{evaluation.id}"
+    )
+    assert detail.status_code == 200, detail.text
+    assert detail.json()["last_updated_by_email"] == "owner@example.com"

From 8cf5ec77e4595972c3efd365d77078a8b070475a Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 10 Aug 2026 22:31:55 +0530
Subject: [PATCH 11/55] fix: update token revocation logic to use correct Redis
 set parameters

---
 app/core/auth/token_revocation.py             |  2 +-
 .../test_call_import_unified_dispatch.py      | 30 +++++++++----------
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/app/core/auth/token_revocation.py b/app/core/auth/token_revocation.py
index b1e2f0ac..379c20be 100644
--- a/app/core/auth/token_revocation.py
+++ b/app/core/auth/token_revocation.py
@@ -34,7 +34,7 @@ def revoke_access_jti(jti: str, ttl_seconds: int) -> None:
     """Blacklist an access token until its natural expiry."""
     ttl = max(int(ttl_seconds), 1)
     try:
-        _get_redis().setex(f"revoked:jti:{jti}", ttl, "1")
+        _get_redis().set(f"revoked:jti:{jti}", "1", ex=ttl)
     except redis.RedisError as exc:
         logger.warning("Redis unavailable for token revocation; using in-memory fallback: %s", exc)
         _in_memory_revoked[jti] = time.time() + ttl
diff --git a/tests/test_workers/test_call_import_unified_dispatch.py b/tests/test_workers/test_call_import_unified_dispatch.py
index 3097581e..6a74c80f 100644
--- a/tests/test_workers/test_call_import_unified_dispatch.py
+++ b/tests/test_workers/test_call_import_unified_dispatch.py
@@ -117,27 +117,25 @@ def test_try_dispatch_enqueues_import_for_pending_row(monkeypatch):
     source_row = _source_row(
         call_import_id=evaluation.call_import_id,
     )
-    captured = {}
+    captured: dict = {}
 
-    class _AsyncResult:
-        id = "import-task-123"
+    def _fake_apply_async(*args, **kwargs):
+        captured["apply_async_kwargs"] = kwargs
+        result = MagicMock()
+        result.id = kwargs.get("task_id", "import-task-123")
+        return result
 
-    import_task = MagicMock()
-    import_task.apply_async = lambda *a, **kw: captured.update(
-        {"apply_async_kwargs": kw}
-    ) or _AsyncResult()
     monkeypatch.setattr(
-        "app.workers.tasks.process_call_import_row.process_call_import_row_task",
-        import_task,
+        "app.workers.concurrency.eval_dispatch.acquire_eval_slot",
+        lambda **kwargs: True,
+    )
+    monkeypatch.setattr(
+        "app.workers.tasks.process_call_import_row.process_call_import_row_task.apply_async",
+        _fake_apply_async,
     )
-
-    def fake_reserve(**kwargs):
-        kwargs["enqueue_fn"]("reserved-id")
-        return True
-
     monkeypatch.setattr(
-        "app.workers.concurrency.eval_dispatch._reserve_slot_and_enqueue",
-        fake_reserve,
+        "app.workers.concurrency.import_dispatch._peek_authenticated_import_credit",
+        lambda **kwargs: None,
     )
 
     result = _try_dispatch_single_row(

From 78a664cec1c9e36dfd93e2dd130e60943868844b Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 10 Aug 2026 22:46:58 +0530
Subject: [PATCH 12/55] feat: implement build_eval_chain_import_apply_async
 function for improved task dispatching

---
 app/workers/concurrency/eval_dispatch.py      | 38 ++++++---
 .../test_call_import_unified_dispatch.py      | 79 +++++++++++++------
 2 files changed, 82 insertions(+), 35 deletions(-)

diff --git a/app/workers/concurrency/eval_dispatch.py b/app/workers/concurrency/eval_dispatch.py
index 89debff8..f2dd59d7 100644
--- a/app/workers/concurrency/eval_dispatch.py
+++ b/app/workers/concurrency/eval_dispatch.py
@@ -163,6 +163,28 @@ def recover_eval_row_for_eval_chain(eval_row: CallImportEvaluationRow) -> None:
     eval_row.finished_at = None
 
 
+def build_eval_chain_import_apply_async(
+    *,
+    source_row: CallImportRow,
+    eval_row: CallImportEvaluationRow,
+    reserved_task_id: str,
+):
+    """Build a Celery ``apply_async`` for eval-chain recording import."""
+    from app.workers.tasks.process_call_import_row import (
+        process_call_import_row_task,
+    )
+
+    return process_call_import_row_task.apply_async(
+        args=(str(source_row.id),),
+        kwargs={
+            "_eval_slot_task_id": reserved_task_id,
+            "run_eval_row_id": str(eval_row.id),
+        },
+        queue=IMPORTS_QUEUE,
+        task_id=reserved_task_id,
+    )
+
+
 def build_eval_chain_transcribe_apply_async(
     *,
     evaluation: CallImportEvaluation,
@@ -376,10 +398,6 @@ def _try_dispatch_single_row(
     from app.workers.tasks.evaluate_call_import_row_core import (
         row_needs_audio_phase,
     )
-    from app.workers.tasks.process_call_import_row import (
-        process_call_import_row_task,
-    )
-
     attached = _attach_sharded_eval_dispatch_rows(
         db,
         evaluation,
@@ -442,14 +460,10 @@ def _try_dispatch_single_row(
             def _enqueue_import(reserved_task_id: str):
                 source_row.celery_task_id = reserved_task_id
                 mutate_db.flush()
-                return process_call_import_row_task.apply_async(
-                    args=(str(source_row.id),),
-                    kwargs={
-                        "_eval_slot_task_id": reserved_task_id,
-                        "run_eval_row_id": str(eval_row.id),
-                    },
-                    queue=IMPORTS_QUEUE,
-                    task_id=reserved_task_id,
+                return build_eval_chain_import_apply_async(
+                    source_row=source_row,
+                    eval_row=eval_row,
+                    reserved_task_id=reserved_task_id,
                 )
 
             if _reserve_slot_and_enqueue(
diff --git a/tests/test_workers/test_call_import_unified_dispatch.py b/tests/test_workers/test_call_import_unified_dispatch.py
index 6a74c80f..32c5ab22 100644
--- a/tests/test_workers/test_call_import_unified_dispatch.py
+++ b/tests/test_workers/test_call_import_unified_dispatch.py
@@ -1,17 +1,16 @@
 """Unit tests for the unified call-import eval dispatch pipeline."""
 
 from types import SimpleNamespace
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 from uuid import uuid4
 
-import pytest
-
 from app.models.enums import CallImportRowStatus
 from app.workers.concurrency.eval_dispatch import (
     EvalDispatchOutcome,
     _needs_import_for_eval,
     _needs_transcribe_for_eval,
     _try_dispatch_single_row,
+    build_eval_chain_import_apply_async,
 )
 
 
@@ -100,11 +99,51 @@ def test_needs_transcribe_after_recording_ready():
     )
 
 
-def test_try_dispatch_enqueues_import_for_pending_row(monkeypatch):
+@patch("app.workers.tasks.process_call_import_row.process_call_import_row_task")
+def test_build_eval_chain_import_apply_async_passes_run_eval_row_id(mock_task):
+    source_row = _source_row()
+    eval_row = SimpleNamespace(id=uuid4())
+    reserved_task_id = "reserved-import-task"
+
+    async_result = MagicMock()
+    async_result.id = reserved_task_id
+    mock_task.apply_async.return_value = async_result
+
+    result = build_eval_chain_import_apply_async(
+        source_row=source_row,
+        eval_row=eval_row,
+        reserved_task_id=reserved_task_id,
+    )
+
+    assert result is async_result
+    mock_task.apply_async.assert_called_once_with(
+        args=(str(source_row.id),),
+        kwargs={
+            "_eval_slot_task_id": reserved_task_id,
+            "run_eval_row_id": str(eval_row.id),
+        },
+        queue="imports",
+        task_id=reserved_task_id,
+    )
+
+
+@patch("app.workers.concurrency.eval_dispatch.build_eval_chain_import_apply_async")
+def test_try_dispatch_enqueues_import_for_pending_row(
+    mock_build_import_apply_async, monkeypatch
+):
     monkeypatch.setattr(
         "app.db_sharding.sessions.is_sharding_enabled",
         lambda: False,
     )
+    monkeypatch.setattr(
+        "app.services.call_imports.evaluation_bulk_op.get_evaluation_bulk_operation",
+        lambda _evaluation_id: None,
+    )
+    monkeypatch.setattr(
+        "app.workers.concurrency.import_dispatch._peek_authenticated_import_credit",
+        lambda **kwargs: None,
+    )
+
     evaluation = _evaluation()
     eval_row = SimpleNamespace(id=uuid4(), celery_task_id=None, status="pending")
     call_import = SimpleNamespace(
@@ -117,25 +156,18 @@ def test_try_dispatch_enqueues_import_for_pending_row(monkeypatch):
     source_row = _source_row(
         call_import_id=evaluation.call_import_id,
     )
-    captured: dict = {}
 
-    def _fake_apply_async(*args, **kwargs):
-        captured["apply_async_kwargs"] = kwargs
-        result = MagicMock()
-        result.id = kwargs.get("task_id", "import-task-123")
-        return result
+    async_result = MagicMock()
+    async_result.id = "import-task-123"
+    mock_build_import_apply_async.return_value = async_result
+
+    def fake_reserve(**kwargs):
+        kwargs["enqueue_fn"]("reserved-id")
+        return True
 
     monkeypatch.setattr(
-        "app.workers.concurrency.eval_dispatch.acquire_eval_slot",
-        lambda **kwargs: True,
-    )
-    monkeypatch.setattr(
-        "app.workers.tasks.process_call_import_row.process_call_import_row_task.apply_async",
-        _fake_apply_async,
-    )
-    monkeypatch.setattr(
-        "app.workers.concurrency.import_dispatch._peek_authenticated_import_credit",
-        lambda **kwargs: None,
+        "app.workers.concurrency.eval_dispatch._reserve_slot_and_enqueue",
+        fake_reserve,
     )
 
     result = _try_dispatch_single_row(
@@ -147,7 +179,8 @@ def _fake_apply_async(*args, **kwargs):
     )
 
     assert result == EvalDispatchOutcome("dispatched")
-    assert captured["apply_async_kwargs"]["kwargs"]["run_eval_row_id"] == str(
-        eval_row.id
+    mock_build_import_apply_async.assert_called_once_with(
+        source_row=source_row,
+        eval_row=eval_row,
+        reserved_task_id="reserved-id",
     )
-    assert captured["apply_async_kwargs"]["queue"] == "imports"

From e10fec4a3dcc97d1cc76e8ed99f311c178cfd977 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Tue, 11 Aug 2026 16:14:21 +0530
Subject: [PATCH 13/55] chore: placeholder

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index b4453aff..df987a07 100644
--- a/.gitignore
+++ b/.gitignore
@@ -109,3 +109,5 @@ frontend/dist/
 
 # Enterprise license private keys
 enterprise/keys/*.pem
+
+#
\ No newline at end of file

From 0ce2d6a1d1451795356e8239d2c206eba4d8f0d3 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Tue, 11 Aug 2026 20:19:51 +0530
Subject: [PATCH 14/55] fix: downloading call import evaluation PDF reports and
 UI improvments

---
 app/api/v1/routes/call_import_evaluations.py  |  59 +++++++++
 frontend/src/components/Layout.tsx            |   5 +-
 frontend/src/lib/api.ts                       |  12 ++
 frontend/src/lib/download.ts                  |  10 ++
 .../pages/callImports/CallImportDetail.tsx    |  34 ++++-
 .../CallImportEvaluationDetail.tsx            | 106 ++++++++++-----
 .../callImports/components/MappingPanel.tsx   | 124 +++++++++++++++---
 .../components/RunEvaluationStep.tsx          |  10 +-
 8 files changed, 306 insertions(+), 54 deletions(-)
 create mode 100644 frontend/src/lib/download.ts

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 1b7c91ce..109004a3 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -3950,6 +3950,65 @@ async def get_call_import_evaluation_pdf_report(
     return _pdf_report_response_from_row(row)
 
 
+@router.get(
+    "/{eval_id}/pdf-reports/{report_id}/download",
+    operation_id="downloadCallImportEvaluationPdfReport",
+)
+async def download_call_import_evaluation_pdf_report(
+    call_import_id: UUID,
+    eval_id: UUID,
+    report_id: UUID,
+    api_key: str = Depends(get_api_key),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    del api_key
+    _require_import(db, call_import_id, organization_id)
+    row = (
+        db.query(CallImportEvaluationPdfReport)
+        .filter(
+            CallImportEvaluationPdfReport.id == report_id,
+            CallImportEvaluationPdfReport.evaluation_id == eval_id,
+            CallImportEvaluationPdfReport.call_import_id == call_import_id,
+            CallImportEvaluationPdfReport.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not row:
+        raise HTTPException(status_code=404, detail="PDF report not found")
+    if not row.s3_key:
+        raise HTTPException(
+            status_code=404,
+            detail="PDF report file is not available in object storage",
+        )
+    from app.services.storage.s3_service import s3_service
+
+    if not s3_service.is_enabled():
+        raise HTTPException(
+            status_code=503,
+            detail="Object storage is not enabled or not configured.",
+        )
+    try:
+        file_bytes = s3_service.download_file_by_key(row.s3_key)
+    except Exception as exc:  # noqa: BLE001
+        logger.exception(
+            "Failed to download PDF report {} for evaluation {}",
+            report_id,
+            eval_id,
+        )
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to download PDF report: {exc}",
+        ) from exc
+    filename = row.filename or "report.pdf"
+    safe_name = filename.replace('"', "'")
+    return StreamingResponse(
+        iter([file_bytes]),
+        media_type="application/pdf",
+        headers={"Content-Disposition": f'attachment; filename="{safe_name}"'},
+    )
+
+
 @router.patch(
     "/{eval_id}",
     response_model=CallImportEvaluationResponse,
diff --git a/frontend/src/components/Layout.tsx b/frontend/src/components/Layout.tsx
index bfafc1b5..c621a485 100644
--- a/frontend/src/components/Layout.tsx
+++ b/frontend/src/components/Layout.tsx
@@ -436,9 +436,6 @@ function SidebarContent({
   )
 
   const toggleSection = (title: string) => {
-    if (title === 'Playground') {
-      return
-    }
     const newExpanded = new Set(expandedSections)
     if (newExpanded.has(title)) {
       newExpanded.delete(title)
@@ -552,7 +549,7 @@ function SidebarContent({
           {/* Navigation Sections */}
           <div className="mt-4 space-y-1">
             {navigationSections.map((section) => {
-              const isExpanded = section.title === 'Playground' || expandedSections.has(section.title)
+              const isExpanded = expandedSections.has(section.title)
               const isActive = isSectionActive(section)
               const SectionIcon = section.icon
 
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 15724db3..1ac0029d 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -2930,6 +2930,18 @@ class ApiClient {
     return response.data
   }
 
+  async downloadCallImportEvaluationPdfReport(
+    callImportId: string,
+    evaluationId: string,
+    reportId: string,
+  ): Promise<Blob> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports/${reportId}/download`,
+      { responseType: 'blob' },
+    )
+    return response.data
+  }
+
   async listCallImportEvaluationBaselineCandidates(
     callImportId: string,
     evaluationId: string,
diff --git a/frontend/src/lib/download.ts b/frontend/src/lib/download.ts
new file mode 100644
index 00000000..3351ff15
--- /dev/null
+++ b/frontend/src/lib/download.ts
@@ -0,0 +1,10 @@
+export function downloadBlob(blob: Blob, filename: string): void {
+  const url = window.URL.createObjectURL(blob)
+  const link = document.createElement('a')
+  link.href = url
+  link.download = filename
+  document.body.appendChild(link)
+  link.click()
+  link.remove()
+  window.URL.revokeObjectURL(url)
+}
diff --git a/frontend/src/pages/callImports/CallImportDetail.tsx b/frontend/src/pages/callImports/CallImportDetail.tsx
index 98a41f3a..4b262eaf 100644
--- a/frontend/src/pages/callImports/CallImportDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportDetail.tsx
@@ -592,6 +592,17 @@ export default function CallImportDetail() {
     queryClient,
   ])
 
+  const runEvaluationStepRef = useRef<HTMLDivElement>(null)
+  const [highlightRunEvaluation, setHighlightRunEvaluation] = useState(false)
+  const [pendingScrollToEvaluation, setPendingScrollToEvaluation] = useState(false)
+
+  const handleMappingSaved = useCallback(() => {
+    showToast('Mapping saved. Continue with Run Evaluation below.', 'success')
+    setHighlightRunEvaluation(true)
+    setPendingScrollToEvaluation(true)
+    window.setTimeout(() => setHighlightRunEvaluation(false), 4000)
+  }, [showToast])
+
   const { data: aiProviders = [] } = useQuery({
     queryKey: ['ai-providers'],
     queryFn: () => apiClient.listAIProviders(),
@@ -944,6 +955,17 @@ export default function CallImportDetail() {
     staleTime: 0,
   })
 
+  useEffect(() => {
+    if (!pendingScrollToEvaluation || data?.status !== 'mapped') return
+    setPendingScrollToEvaluation(false)
+    window.setTimeout(() => {
+      runEvaluationStepRef.current?.scrollIntoView({
+        behavior: 'smooth',
+        block: 'center',
+      })
+    }, 100)
+  }, [pendingScrollToEvaluation, data?.status])
+
   const { data: metrics = [] } = useQuery({
     queryKey: ['metrics', activeWorkspaceId, 'agent'],
     queryFn: () => apiClient.listMetrics('agent'),
@@ -1954,10 +1976,18 @@ export default function CallImportDetail() {
       {needsMapping && data.source_s3_key && (
         <>
           {(data.status === 'uploaded' || data.status === 'mapped') && (
-            <MappingPanel callImport={data} />
+            <MappingPanel
+              callImport={data}
+              onMappingSaved={handleMappingSaved}
+            />
           )}
           {data.status === 'mapped' && !isDeleting && (
-            <RunEvaluationStep onRunEvaluation={openRunEvaluationModal} />
+            <div ref={runEvaluationStepRef}>
+              <RunEvaluationStep
+                onRunEvaluation={openRunEvaluationModal}
+                highlighted={highlightRunEvaluation}
+              />
+            </div>
           )}
         </>
       )}
diff --git a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
index 9cfbaa22..35c4f703 100644
--- a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
@@ -59,6 +59,7 @@ import {
 } from 'recharts'
 import { apiClient, type ReportBranding } from '../../lib/api'
 import { getApiErrorMessage } from '../../lib/apiErrors'
+import { downloadBlob } from '../../lib/download'
 import {
   isLLMSelectionComplete,
   resolveLLMModelForSubmit,
@@ -474,6 +475,10 @@ export default function CallImportEvaluationDetail() {
   const [pdfPreviewOpen, setPdfPreviewOpen] = useState(false)
   const [pdfPreviewUrl, setPdfPreviewUrl] = useState<string | null>(null)
   const [pdfPreviewFilename, setPdfPreviewFilename] = useState('')
+  const [pdfPreviewReportId, setPdfPreviewReportId] = useState<string | null>(
+    null,
+  )
+  const [pdfPreviewDownloading, setPdfPreviewDownloading] = useState(false)
   const [vizBaselineEvaluationId, setVizBaselineEvaluationId] = useState<
     string | null
   >(null)
@@ -1841,6 +1846,7 @@ export default function CallImportEvaluationDetail() {
       }
       setPdfPreviewUrl(report.preview_url)
       setPdfPreviewFilename(report.filename || 'report.pdf')
+      setPdfPreviewReportId(reportId)
       setPdfPreviewOpen(true)
       setPdfReportMenuOpen(false)
     } catch (e: unknown) {
@@ -1871,27 +1877,29 @@ export default function CallImportEvaluationDetail() {
       const defaultFilename = `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`
 
       if (result instanceof Blob) {
-        const url = window.URL.createObjectURL(result)
-        const link = document.createElement('a')
-        link.href = url
-        link.download = defaultFilename
-        document.body.appendChild(link)
-        link.click()
-        link.remove()
-        window.URL.revokeObjectURL(url)
+        downloadBlob(result, defaultFilename)
       } else {
-        const downloadUrl = result.download_url
-        if (!downloadUrl) {
-          throw new Error('Download URL is not available.')
+        if (result.id && id && evalId) {
+          const blob = await apiClient.downloadCallImportEvaluationPdfReport(
+            id,
+            evalId,
+            result.id,
+          )
+          downloadBlob(blob, result.filename || defaultFilename)
+        } else {
+          const downloadUrl = result.download_url
+          if (!downloadUrl) {
+            throw new Error('Download URL is not available.')
+          }
+          const response = await fetch(downloadUrl)
+          if (!response.ok) {
+            throw new Error('Failed to download PDF report.')
+          }
+          downloadBlob(
+            await response.blob(),
+            result.filename || defaultFilename,
+          )
         }
-        const link = document.createElement('a')
-        link.href = downloadUrl
-        link.download = result.filename || defaultFilename
-        link.target = '_blank'
-        link.rel = 'noopener noreferrer'
-        document.body.appendChild(link)
-        link.click()
-        link.remove()
         await queryClient.invalidateQueries({
           queryKey: [
             'call-import-evaluation-pdf-reports',
@@ -1942,6 +1950,7 @@ export default function CallImportEvaluationDetail() {
         setPdfPreviewFilename(
           `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`,
         )
+        setPdfPreviewReportId(null)
       } else {
         if (!result.preview_url) {
           throw new Error('Preview URL is not available.')
@@ -1954,6 +1963,7 @@ export default function CallImportEvaluationDetail() {
           result.filename ||
             `${vendorSlug}-${pdfReportType}-quality-metric-audit-${evalId}.pdf`,
         )
+        setPdfPreviewReportId(result.id)
         await queryClient.invalidateQueries({
           queryKey: [
             'call-import-evaluation-pdf-reports',
@@ -1973,6 +1983,43 @@ export default function CallImportEvaluationDetail() {
     }
   }
 
+  const handlePdfPreviewDownload = async () => {
+    if (!pdfPreviewFilename) return
+    setPdfPreviewDownloading(true)
+    try {
+      if (pdfPreviewReportId && id && evalId) {
+        const blob = await apiClient.downloadCallImportEvaluationPdfReport(
+          id,
+          evalId,
+          pdfPreviewReportId,
+        )
+        downloadBlob(blob, pdfPreviewFilename)
+        return
+      }
+      if (pdfPreviewUrl?.startsWith('blob:')) {
+        downloadBlob(
+          await fetch(pdfPreviewUrl).then((r) => r.blob()),
+          pdfPreviewFilename,
+        )
+        return
+      }
+      if (!pdfPreviewUrl) return
+      const response = await fetch(pdfPreviewUrl)
+      if (!response.ok) {
+        throw new Error('Failed to download PDF report.')
+      }
+      downloadBlob(await response.blob(), pdfPreviewFilename)
+    } catch (e: unknown) {
+      console.error('Failed to download PDF report', e)
+      showToast(
+        getApiErrorMessage(e, 'Failed to download PDF report.'),
+        'error',
+      )
+    } finally {
+      setPdfPreviewDownloading(false)
+    }
+  }
+
   const PDF_WIZARD_STEPS = [
     { id: 1, label: 'Type & comparison' },
     { id: 2, label: 'Details & branding' },
@@ -2105,7 +2152,9 @@ export default function CallImportEvaluationDetail() {
 
   useEffect(() => {
     return () => {
-      if (pdfPreviewUrl) window.URL.revokeObjectURL(pdfPreviewUrl)
+      if (pdfPreviewUrl?.startsWith('blob:')) {
+        window.URL.revokeObjectURL(pdfPreviewUrl)
+      }
     }
   }, [pdfPreviewUrl])
 
@@ -5434,23 +5483,20 @@ export default function CallImportEvaluationDetail() {
               <div className="flex items-center gap-2 shrink-0">
                 <Button
                   variant="outline"
-                  onClick={() => {
-                    if (!pdfPreviewUrl) return
-                    const link = document.createElement('a')
-                    link.href = pdfPreviewUrl
-                    link.download = pdfPreviewFilename
-                    document.body.appendChild(link)
-                    link.click()
-                    link.remove()
-                  }}
+                  onClick={handlePdfPreviewDownload}
+                  isLoading={pdfPreviewDownloading}
+                  disabled={pdfPreviewDownloading}
                 >
                   Download
                 </Button>
                 <Button
                   variant="primary"
                   onClick={() => {
-                    if (pdfPreviewUrl) window.URL.revokeObjectURL(pdfPreviewUrl)
+                    if (pdfPreviewUrl?.startsWith('blob:')) {
+                      window.URL.revokeObjectURL(pdfPreviewUrl)
+                    }
                     setPdfPreviewUrl(null)
+                    setPdfPreviewReportId(null)
                     setPdfPreviewOpen(false)
                   }}
                 >
diff --git a/frontend/src/pages/callImports/components/MappingPanel.tsx b/frontend/src/pages/callImports/components/MappingPanel.tsx
index bab832c6..6de28ec9 100644
--- a/frontend/src/pages/callImports/components/MappingPanel.tsx
+++ b/frontend/src/pages/callImports/components/MappingPanel.tsx
@@ -1,6 +1,6 @@
 import { useEffect, useMemo, useState } from 'react'
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
-import { AlertCircle, Layers, Save } from 'lucide-react'
+import { AlertCircle, CheckCircle2, ChevronDown, ChevronRight, Layers, Save } from 'lucide-react'
 import { Link } from 'react-router-dom'
 import { apiClient } from '../../../lib/api'
 import { useWorkspaceStore } from '../../../store/workspaceStore'
@@ -20,6 +20,7 @@ import ParameterMappingTable, {
 
 interface MappingPanelProps {
   callImport: CallImport
+  onMappingSaved?: () => void
 }
 
 /** True when the UI's schema/sheet selection matches what the server persisted. */
@@ -46,9 +47,15 @@ function persistedMappingContextMatches(
  * Idempotent: the user can come back later and re-submit to edit the
  * mapping as long as the batch hasn't been imported yet.
  */
-export default function MappingPanel({ callImport }: MappingPanelProps) {
+export default function MappingPanel({
+  callImport,
+  onMappingSaved,
+}: MappingPanelProps) {
   const queryClient = useQueryClient()
   const activeWorkspaceId = useWorkspaceStore((s) => s.activeWorkspaceId)
+  const isMapped = callImport.status === 'mapped'
+  const [collapsed, setCollapsed] = useState(isMapped)
+  const [justSaved, setJustSaved] = useState(false)
 
   const isXlsx = callImport.source_format === 'xlsx'
   const sheets: CallImportPreviewSheet[] = callImport.available_sheets ?? []
@@ -70,6 +77,15 @@ export default function MappingPanel({ callImport }: MappingPanelProps) {
   })
   const [submitError, setSubmitError] = useState<string | null>(null)
 
+  useEffect(() => {
+    if (isMapped) {
+      setCollapsed(true)
+    } else {
+      setCollapsed(false)
+      setJustSaved(false)
+    }
+  }, [isMapped])
+
   const { data: schemasResponse } = useQuery({
     queryKey: ['call-import-schemas', activeWorkspaceId],
     queryFn: () => apiClient.listCallImportSchemas(),
@@ -170,14 +186,19 @@ export default function MappingPanel({ callImport }: MappingPanelProps) {
     },
     onSuccess: (updated) => {
       setSubmitError(null)
+      setCollapsed(true)
+      setJustSaved(true)
       const cacheKey = ['call-import', activeWorkspaceId, callImport.id]
       queryClient.setQueriesData(
         { queryKey: cacheKey },
         (old: CallImportDetail | undefined) =>
-          old ? { ...old, ...updated } : old,
+          old
+            ? { ...old, ...updated, status: updated.status ?? 'mapped' }
+            : old,
       )
       queryClient.invalidateQueries({ queryKey: cacheKey })
       queryClient.invalidateQueries({ queryKey: ['call-imports'] })
+      onMappingSaved?.()
     },
     onError: (err: any) => {
       setSubmitError(
@@ -188,26 +209,93 @@ export default function MappingPanel({ callImport }: MappingPanelProps) {
     },
   })
 
-  const isAlreadyMapped = callImport.status === 'mapped'
+  const mappedParamCount = Object.values(mappingState.parameterMapping).filter(
+    (header) => header && header.trim(),
+  ).length
+  const skippedCount = Object.values(mappingState.skipped).filter(Boolean).length
   const canSubmit =
     !!selectedSchema &&
     !!selectedSheet &&
     !!validation?.isValid &&
     !mappingMutation.isPending
 
+  const mappingSummary = selectedSchema
+    ? `${selectedSchema.name} · ${mappedParamCount} mapped · ${skippedCount} skipped`
+    : 'Mapping saved'
+
   return (
-    <div className="bg-white shadow rounded-lg p-6 space-y-5">
-      <div>
-        <h2 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
-          <Layers className="h-5 w-5 text-primary-600" />
-          {isAlreadyMapped ? 'Edit mapping' : 'Configure mapping'}
-        </h2>
-        <p className="text-sm text-gray-600 mt-1">
-          Pick the Input Parameter schema this batch maps against, then
-          assign each schema parameter to a source column. Columns you
-          don't want imported must be explicitly skipped.
-        </p>
-      </div>
+    <div
+      className={`bg-white shadow rounded-lg overflow-hidden ${
+        isMapped ? 'border border-green-200' : ''
+      }`}
+    >
+      <button
+        type="button"
+        onClick={() => isMapped && setCollapsed((prev) => !prev)}
+        className={`w-full flex items-start justify-between gap-3 p-6 text-left ${
+          isMapped ? 'hover:bg-gray-50 cursor-pointer' : 'cursor-default'
+        }`}
+        aria-expanded={!collapsed}
+      >
+        <div className="flex items-start gap-3 min-w-0">
+          <div
+            className={`h-10 w-10 rounded-full flex items-center justify-center flex-shrink-0 ${
+              isMapped ? 'bg-green-100 text-green-700' : 'bg-primary-50 text-primary-700'
+            }`}
+          >
+            {isMapped ? (
+              <CheckCircle2 className="h-5 w-5" />
+            ) : (
+              <Layers className="h-5 w-5" />
+            )}
+          </div>
+          <div className="min-w-0">
+            <h2 className="text-lg font-semibold text-gray-900">
+              2. {isMapped ? 'Mapping saved' : 'Configure mapping'}
+            </h2>
+            {collapsed && isMapped ? (
+              <p
+                className={`text-sm mt-1 truncate ${
+                  justSaved ? 'text-green-700 font-medium' : 'text-gray-600'
+                }`}
+              >
+                {justSaved
+                  ? 'Saved — continue to Run Evaluation below.'
+                  : mappingSummary}
+              </p>
+            ) : (
+              <p className="text-sm text-gray-600 mt-1">
+                Pick the Input Parameter schema this batch maps against, then
+                assign each schema parameter to a source column. Columns you
+                don't want imported must be explicitly skipped.
+              </p>
+            )}
+          </div>
+        </div>
+        {isMapped && (
+          <span className="text-gray-400 flex-shrink-0 mt-1">
+            {collapsed ? (
+              <ChevronRight className="h-5 w-5" />
+            ) : (
+              <ChevronDown className="h-5 w-5" />
+            )}
+          </span>
+        )}
+      </button>
+
+      {!collapsed && (
+        <div className="px-6 pb-6 space-y-5 border-t border-gray-100 pt-5">
+      {justSaved && (
+        <div className="rounded-md bg-green-50 border border-green-200 p-3">
+          <div className="flex items-start gap-2">
+            <CheckCircle2 className="h-4 w-4 text-green-600 mt-0.5 flex-shrink-0" />
+            <p className="text-sm text-green-800">
+              Mapping saved. Continue to <strong>Run Evaluation</strong> below to
+              fetch recordings, diarize transcripts, and score each row.
+            </p>
+          </div>
+        </div>
+      )}
 
       <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
         <div>
@@ -300,9 +388,11 @@ export default function MappingPanel({ callImport }: MappingPanelProps) {
           isLoading={mappingMutation.isPending}
           disabled={!canSubmit}
         >
-          {isAlreadyMapped ? 'Save mapping' : 'Save and continue'}
+          {isMapped ? 'Save mapping' : 'Save and continue'}
         </Button>
       </div>
+        </div>
+      )}
     </div>
   )
 }
diff --git a/frontend/src/pages/callImports/components/RunEvaluationStep.tsx b/frontend/src/pages/callImports/components/RunEvaluationStep.tsx
index 7fef9cf2..1a78da35 100644
--- a/frontend/src/pages/callImports/components/RunEvaluationStep.tsx
+++ b/frontend/src/pages/callImports/components/RunEvaluationStep.tsx
@@ -4,6 +4,7 @@ import Button from '../../../components/Button'
 interface RunEvaluationStepProps {
   onRunEvaluation: () => void
   disabled?: boolean
+  highlighted?: boolean
 }
 
 /**
@@ -14,9 +15,16 @@ interface RunEvaluationStepProps {
 export default function RunEvaluationStep({
   onRunEvaluation,
   disabled = false,
+  highlighted = false,
 }: RunEvaluationStepProps) {
   return (
-    <div className="bg-white shadow rounded-lg p-6 border-2 border-primary-100">
+    <div
+      className={`bg-white shadow rounded-lg p-6 border-2 transition-all duration-500 ${
+        highlighted
+          ? 'border-primary-500 ring-2 ring-primary-200 shadow-md'
+          : 'border-primary-100'
+      }`}
+    >
       <div className="flex items-start gap-4">
         <div className="h-10 w-10 rounded-full bg-primary-100 text-primary-700 flex items-center justify-center flex-shrink-0">
           <PlayCircle className="h-5 w-5" />

From 7e09155b1cb4b37d57c9d69266b4f24eee7d8af8 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 12 Aug 2026 00:40:30 +0530
Subject: [PATCH 15/55] feat: integrate LLM usage tracking and reporting across
 various components

---
 .gitignore                                    |   5 +
 app/api/v1/api.py                             |   2 +
 app/api/v1/routes/call_import_evaluations.py  |  64 ++-
 app/api/v1/routes/chat.py                     |  38 +-
 app/api/v1/routes/org_usage.py                | 426 +++++++++++++++
 app/app_factory.py                            |   2 +
 app/cli.py                                    |  16 +-
 app/core/usage_context_middleware.py          |  43 ++
 app/dependencies.py                           |  16 +
 app/migrations/062_llm_usage_daily.py         |  92 ++++
 app/models/database.py                        |  37 ++
 app/services/ai/llm_gateway.py                |  26 +-
 app/services/ai/llm_service.py                |  24 +
 app/services/usage/__init__.py                |  32 ++
 app/services/usage/context.py                 | 177 +++++++
 app/services/usage/llm_usage.py               | 491 ++++++++++++++++++
 app/services/usage/normalize.py               |  99 ++++
 app/services/usage/voice_usage_processor.py   |  80 +++
 app/services/voice_agent/bot_fast_api.py      |  35 +-
 app/workers/config.py                         |   9 +
 app/workers/tasks/__init__.py                 |   2 +
 app/workers/tasks/evaluate_call_import_row.py |  21 +
 app/workers/tasks/flush_usage_counters.py     |  18 +
 .../generate_evaluation_tldr_insights.py      |  29 +-
 app/workers/tasks/process_evaluator_result.py |  22 +
 .../tasks/transcribe_call_import_row.py       |  49 ++
 frontend/src/App.tsx                          |   4 +
 frontend/src/components/Layout.tsx            |   8 +
 frontend/src/lib/api.ts                       |  82 +++
 frontend/src/pages/usage/Usage.tsx            | 294 +++++++++++
 tests/test_services/test_usage/__init__.py    |   0
 .../test_usage/test_llm_usage.py              | 387 ++++++++++++++
 32 files changed, 2584 insertions(+), 46 deletions(-)
 create mode 100644 app/api/v1/routes/org_usage.py
 create mode 100644 app/core/usage_context_middleware.py
 create mode 100644 app/migrations/062_llm_usage_daily.py
 create mode 100644 app/services/usage/__init__.py
 create mode 100644 app/services/usage/context.py
 create mode 100644 app/services/usage/llm_usage.py
 create mode 100644 app/services/usage/normalize.py
 create mode 100644 app/services/usage/voice_usage_processor.py
 create mode 100644 app/workers/tasks/flush_usage_counters.py
 create mode 100644 frontend/src/pages/usage/Usage.tsx
 create mode 100644 tests/test_services/test_usage/__init__.py
 create mode 100644 tests/test_services/test_usage/test_llm_usage.py

diff --git a/.gitignore b/.gitignore
index df987a07..8a24e3a7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -110,4 +110,9 @@ frontend/dist/
 # Enterprise license private keys
 enterprise/keys/*.pem
 
+# Celery Beat persistent schedule (SQLite + WAL sidecars)
+celerybeat-schedule
+celerybeat-schedule-*
+celerybeat-schedule.*
+
 #
\ No newline at end of file
diff --git a/app/api/v1/api.py b/app/api/v1/api.py
index d6dc6d9d..51d81945 100644
--- a/app/api/v1/api.py
+++ b/app/api/v1/api.py
@@ -45,6 +45,7 @@
     workspace_iam,
     dashboard,
     llm_gateway,
+    org_usage,
 )
 
 api_router = APIRouter()
@@ -93,3 +94,4 @@
 api_router.include_router(workspace_iam.router)
 api_router.include_router(dashboard.router)
 api_router.include_router(llm_gateway.router)
+api_router.include_router(org_usage.router)
diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 109004a3..030c98ef 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -3702,16 +3702,32 @@ async def generate_call_import_evaluation_pdf_report(
             )
             return _pdf_report_response_from_row(cached_pdf_report, cache_hit=True)
 
-    narrative = _generate_report_narrative(
-        db,
-        organization_id,
-        metric_aggregates=metric_aggregates,
-        insight_aggregates=insight_aggregates if is_internal else [],
-        period_delta_by_metric=period_delta_by_metric,
-        evidence_samples=evidence_samples if is_internal else {},
-        report_config=report_config,
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
     )
 
+    with llm_usage_context(
+        LLMUsageContext(
+            organization_id=organization_id,
+            workspace_id=getattr(call_import, "workspace_id", None)
+            or evaluation.workspace_id,
+            product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
+            resource_id=evaluation.id,
+            resource_type="call_import_evaluation",
+        )
+    ):
+        narrative = _generate_report_narrative(
+            db,
+            organization_id,
+            metric_aggregates=metric_aggregates,
+            insight_aggregates=insight_aggregates if is_internal else [],
+            period_delta_by_metric=period_delta_by_metric,
+            evidence_samples=evidence_samples if is_internal else {},
+            report_config=report_config,
+        )
+
     generated_at = datetime.now(timezone.utc)
     try:
         pdf_started = datetime.now(timezone.utc)
@@ -5337,21 +5353,35 @@ def _generate_and_persist_tldr_summary(
 
     from app.services.ai.llm_resolver import get_llm_provider_and_model
     from app.services.ai.llm_service import llm_service
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
+    )
 
     provider_enum, model_str = get_llm_provider_and_model(
         organization_id, db, provider, model
     )
 
     try:
-        llm_result = llm_service.generate_response(
-            messages=messages,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            temperature=0.4,
-            max_tokens=1400,
-        )
+        with llm_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=evaluation.workspace_id,
+                product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
+                resource_id=evaluation.id,
+                resource_type="call_import_evaluation",
+            )
+        ):
+            llm_result = llm_service.generate_response(
+                messages=messages,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                temperature=0.4,
+                max_tokens=1400,
+            )
     except Exception as e:
         logger.error(f"[CallImportInsights] LLM call failed: {e}")
         raise HTTPException(
diff --git a/app/api/v1/routes/chat.py b/app/api/v1/routes/chat.py
index a8317844..6fc026f8 100644
--- a/app/api/v1/routes/chat.py
+++ b/app/api/v1/routes/chat.py
@@ -47,20 +47,32 @@ async def chat_completion(
 ):
     """Generate a chat completion using the specified AI provider and model."""
     try:
-        # Convert ChatMessage to dict format expected by LLM service
-        messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
-        
-        result = llm_service.generate_response(
-            messages=messages,
-            llm_provider=request.provider,
-            llm_model=request.model,
-            organization_id=organization_id,
-            db=db,
-            llm_config=request.llm_config,
-            temperature=request.temperature,
-            max_tokens=request.max_tokens,
-            task_defaults={"temperature": 0.7},
+        from app.services.usage.context import (
+            LLMUsageContext,
+            LLMUsageProductSection,
+            llm_usage_context,
         )
+
+        messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
+
+        with llm_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section=LLMUsageProductSection.CHAT,
+            )
+        ):
+            result = llm_service.generate_response(
+                messages=messages,
+                llm_provider=request.provider,
+                llm_model=request.model,
+                organization_id=organization_id,
+                db=db,
+                llm_config=request.llm_config,
+                temperature=request.temperature,
+                max_tokens=request.max_tokens,
+                task_defaults={"temperature": 0.7},
+            )
         
         background_tasks.add_task(
             record_chat_completion,
diff --git a/app/api/v1/routes/org_usage.py b/app/api/v1/routes/org_usage.py
new file mode 100644
index 00000000..05f984a0
--- /dev/null
+++ b/app/api/v1/routes/org_usage.py
@@ -0,0 +1,426 @@
+"""Org-scoped LLM Usage API (tokens + call counts)."""
+
+from __future__ import annotations
+
+from datetime import date, datetime, timedelta, timezone
+from typing import List, Literal, Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+from pydantic import BaseModel, Field
+from sqlalchemy import func
+from sqlalchemy.orm import Session
+
+from app.database import get_db
+from app.dependencies import get_organization_id
+from app.models.database import CallImportEvaluation, LLMUsageDaily, Workspace
+from app.services.usage.llm_usage import flush_usage_to_catalog, merge_usage_totals
+
+router = APIRouter(prefix="/organizations/usage", tags=["Usage"])
+
+GroupBy = Literal["workspace", "product_section", "model", "resource"]
+
+SECTION_LABELS = {
+    "call_import_evaluations": "Call Import Evaluations",
+    "call_imports": "Call Imports",
+    "playground": "Playground",
+    "voice_playground": "Voice Playground",
+    "evaluators": "Evaluators",
+    "metrics": "Metrics",
+    "chat": "Chat",
+    "judge_alignment": "Judge Alignment",
+    "prompt_optimization": "Prompt Optimization",
+    "personas": "Personas",
+    "agents": "Agents",
+    "prompt_partials": "Prompt Partials",
+    "conversation_evaluations": "Conversation Evaluations",
+    "telephony": "Telephony",
+    "test_agent": "Test Agent",
+    "other": "Other",
+}
+
+
+class UsageTotals(BaseModel):
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+    cache_read_tokens: int = 0
+    cache_creation_tokens: int = 0
+    reasoning_tokens: int = 0
+    call_count: int = 0
+
+
+class UsageSummaryResponse(BaseModel):
+    start: date
+    end: date
+    totals: UsageTotals
+    last_updated_at: Optional[datetime] = None
+
+
+class UsageBreakdownRow(BaseModel):
+    workspace_id: Optional[UUID] = None
+    workspace_name: Optional[str] = None
+    product_section: Optional[str] = None
+    product_section_label: Optional[str] = None
+    model: Optional[str] = None
+    resource_id: Optional[UUID] = None
+    resource_type: Optional[str] = None
+    resource_label: Optional[str] = None
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+    cache_read_tokens: int = 0
+    cache_creation_tokens: int = 0
+    reasoning_tokens: int = 0
+    call_count: int = 0
+
+
+class UsageBreakdownResponse(BaseModel):
+    start: date
+    end: date
+    group_by: GroupBy
+    rows: List[UsageBreakdownRow]
+    total_count: int
+    last_updated_at: Optional[datetime] = None
+
+
+class UsageFiltersResponse(BaseModel):
+    workspaces: List[dict] = Field(default_factory=list)
+    product_sections: List[dict] = Field(default_factory=list)
+    models: List[str] = Field(default_factory=list)
+    resources: List[dict] = Field(default_factory=list)
+
+
+def _default_range() -> tuple[date, date]:
+    end = datetime.now(timezone.utc).date()
+    start = end - timedelta(days=29)
+    return start, end
+
+
+def _apply_filters(
+    query,
+    *,
+    organization_id: UUID,
+    start: date,
+    end: date,
+    workspace_id: Optional[UUID],
+    product_section: Optional[str],
+    model: Optional[str],
+    resource_id: Optional[UUID],
+):
+    query = query.filter(
+        LLMUsageDaily.organization_id == organization_id,
+        LLMUsageDaily.usage_date >= start,
+        LLMUsageDaily.usage_date <= end,
+    )
+    if workspace_id is not None:
+        query = query.filter(LLMUsageDaily.workspace_id == workspace_id)
+    if product_section:
+        query = query.filter(LLMUsageDaily.product_section == product_section)
+    if model:
+        query = query.filter(LLMUsageDaily.model == model)
+    if resource_id is not None:
+        query = query.filter(LLMUsageDaily.resource_id == resource_id)
+    return query
+
+
+def _last_updated(db: Session, organization_id: UUID) -> Optional[datetime]:
+    return (
+        db.query(func.max(LLMUsageDaily.updated_at))
+        .filter(LLMUsageDaily.organization_id == organization_id)
+        .scalar()
+    )
+
+
+@router.get("/summary", response_model=UsageSummaryResponse)
+def get_usage_summary(
+    start: Optional[date] = Query(None),
+    end: Optional[date] = Query(None),
+    workspace_id: Optional[UUID] = Query(None),
+    product_section: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    resource_id: Optional[UUID] = Query(None),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    if start is None or end is None:
+        start, end = _default_range()
+    if end < start:
+        raise HTTPException(status_code=400, detail="end must be >= start")
+
+    flush_usage_to_catalog(db, organization_id)
+
+    query = _apply_filters(
+        db.query(LLMUsageDaily),
+        organization_id=organization_id,
+        start=start,
+        end=end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+    )
+    rows = query.all()
+    totals = merge_usage_totals(rows)
+    return UsageSummaryResponse(
+        start=start,
+        end=end,
+        totals=UsageTotals(**totals),
+        last_updated_at=_last_updated(db, organization_id),
+    )
+
+
+@router.get("/breakdown", response_model=UsageBreakdownResponse)
+def get_usage_breakdown(
+    start: Optional[date] = Query(None),
+    end: Optional[date] = Query(None),
+    group_by: GroupBy = Query("workspace"),
+    workspace_id: Optional[UUID] = Query(None),
+    product_section: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    resource_id: Optional[UUID] = Query(None),
+    limit: int = Query(100, ge=1, le=500),
+    offset: int = Query(0, ge=0),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    if start is None or end is None:
+        start, end = _default_range()
+    if end < start:
+        raise HTTPException(status_code=400, detail="end must be >= start")
+
+    flush_usage_to_catalog(db, organization_id)
+
+    dim = {
+        "workspace": LLMUsageDaily.workspace_id,
+        "product_section": LLMUsageDaily.product_section,
+        "model": LLMUsageDaily.model,
+        "resource": LLMUsageDaily.resource_id,
+    }[group_by]
+
+    aggregates = [
+        func.coalesce(func.sum(LLMUsageDaily.prompt_tokens), 0).label("prompt_tokens"),
+        func.coalesce(func.sum(LLMUsageDaily.completion_tokens), 0).label(
+            "completion_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_read_tokens), 0).label(
+            "cache_read_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_creation_tokens), 0).label(
+            "cache_creation_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
+            "reasoning_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
+    ]
+
+    select_cols = [dim]
+    group_cols = [dim]
+    if group_by == "resource":
+        select_cols.append(LLMUsageDaily.resource_type)
+        group_cols.append(LLMUsageDaily.resource_type)
+
+    query = _apply_filters(
+        db.query(*select_cols, *aggregates),
+        organization_id=organization_id,
+        start=start,
+        end=end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+    ).group_by(*group_cols)
+
+    total_count = query.count()
+    results = (
+        query.order_by(func.sum(LLMUsageDaily.call_count).desc())
+        .offset(offset)
+        .limit(limit)
+        .all()
+    )
+
+    workspace_names = {
+        w.id: w.name
+        for w in db.query(Workspace)
+        .filter(Workspace.organization_id == organization_id)
+        .all()
+    }
+    resource_labels: dict = {}
+    if group_by == "resource":
+        resource_ids = [r[0] for r in results if r[0] is not None]
+        if resource_ids:
+            evals = (
+                db.query(CallImportEvaluation)
+                .filter(
+                    CallImportEvaluation.organization_id == organization_id,
+                    CallImportEvaluation.id.in_(resource_ids),
+                )
+                .all()
+            )
+            for evaluation in evals:
+                label = (evaluation.name or "").strip() or str(evaluation.id)[:8]
+                resource_labels[evaluation.id] = label
+
+    rows: List[UsageBreakdownRow] = []
+    for result in results:
+        if group_by == "workspace":
+            ws_id = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    workspace_id=ws_id,
+                    workspace_name=workspace_names.get(ws_id) if ws_id else "Unknown",
+                    prompt_tokens=int(metrics[0]),
+                    completion_tokens=int(metrics[1]),
+                    total_tokens=int(metrics[0]) + int(metrics[1]),
+                    cache_read_tokens=int(metrics[2]),
+                    cache_creation_tokens=int(metrics[3]),
+                    reasoning_tokens=int(metrics[4]),
+                    call_count=int(metrics[5]),
+                )
+            )
+        elif group_by == "product_section":
+            section = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    product_section=section,
+                    product_section_label=SECTION_LABELS.get(section or "", section),
+                    prompt_tokens=int(metrics[0]),
+                    completion_tokens=int(metrics[1]),
+                    total_tokens=int(metrics[0]) + int(metrics[1]),
+                    cache_read_tokens=int(metrics[2]),
+                    cache_creation_tokens=int(metrics[3]),
+                    reasoning_tokens=int(metrics[4]),
+                    call_count=int(metrics[5]),
+                )
+            )
+        elif group_by == "model":
+            model_name = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    model=model_name,
+                    prompt_tokens=int(metrics[0]),
+                    completion_tokens=int(metrics[1]),
+                    total_tokens=int(metrics[0]) + int(metrics[1]),
+                    cache_read_tokens=int(metrics[2]),
+                    cache_creation_tokens=int(metrics[3]),
+                    reasoning_tokens=int(metrics[4]),
+                    call_count=int(metrics[5]),
+                )
+            )
+        else:
+            res_id, res_type = result[0], result[1]
+            metrics = result[2:]
+            rows.append(
+                UsageBreakdownRow(
+                    resource_id=res_id,
+                    resource_type=res_type,
+                    resource_label=resource_labels.get(res_id)
+                    if res_id
+                    else "Unscoped",
+                    prompt_tokens=int(metrics[0]),
+                    completion_tokens=int(metrics[1]),
+                    total_tokens=int(metrics[0]) + int(metrics[1]),
+                    cache_read_tokens=int(metrics[2]),
+                    cache_creation_tokens=int(metrics[3]),
+                    reasoning_tokens=int(metrics[4]),
+                    call_count=int(metrics[5]),
+                )
+            )
+
+    return UsageBreakdownResponse(
+        start=start,
+        end=end,
+        group_by=group_by,
+        rows=rows,
+        total_count=total_count,
+        last_updated_at=_last_updated(db, organization_id),
+    )
+
+
+@router.get("/filters", response_model=UsageFiltersResponse)
+def get_usage_filters(
+    start: Optional[date] = Query(None),
+    end: Optional[date] = Query(None),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    if start is None or end is None:
+        start, end = _default_range()
+
+    flush_usage_to_catalog(db, organization_id)
+
+    base = db.query(LLMUsageDaily).filter(
+        LLMUsageDaily.organization_id == organization_id,
+        LLMUsageDaily.usage_date >= start,
+        LLMUsageDaily.usage_date <= end,
+    )
+
+    workspace_ids = {
+        row[0]
+        for row in base.with_entities(LLMUsageDaily.workspace_id).distinct().all()
+        if row[0] is not None
+    }
+    workspaces = [
+        {"id": str(w.id), "name": w.name}
+        for w in db.query(Workspace)
+        .filter(Workspace.id.in_(workspace_ids) if workspace_ids else False)
+        .order_by(Workspace.name)
+        .all()
+    ]
+
+    sections = sorted(
+        {
+            row[0]
+            for row in base.with_entities(LLMUsageDaily.product_section).distinct().all()
+            if row[0]
+        }
+    )
+    models = sorted(
+        {
+            row[0]
+            for row in base.with_entities(LLMUsageDaily.model).distinct().all()
+            if row[0]
+        }
+    )
+
+    resource_rows = (
+        base.with_entities(LLMUsageDaily.resource_id, LLMUsageDaily.resource_type)
+        .filter(LLMUsageDaily.resource_id.isnot(None))
+        .distinct()
+        .limit(100)
+        .all()
+    )
+    resource_ids = [r[0] for r in resource_rows]
+    eval_names = {}
+    if resource_ids:
+        for evaluation in (
+            db.query(CallImportEvaluation)
+            .filter(CallImportEvaluation.id.in_(resource_ids))
+            .all()
+        ):
+            eval_names[evaluation.id] = (evaluation.name or "").strip() or str(
+                evaluation.id
+            )[:8]
+
+    resources = [
+        {
+            "id": str(rid),
+            "type": rtype,
+            "label": eval_names.get(rid, str(rid)[:8]),
+        }
+        for rid, rtype in resource_rows
+        if rid is not None
+    ]
+
+    return UsageFiltersResponse(
+        workspaces=workspaces,
+        product_sections=[
+            {"id": s, "label": SECTION_LABELS.get(s, s)} for s in sections
+        ],
+        models=models,
+        resources=resources,
+    )
diff --git a/app/app_factory.py b/app/app_factory.py
index 2b6b8d61..c2af3e09 100644
--- a/app/app_factory.py
+++ b/app/app_factory.py
@@ -19,6 +19,7 @@
 from app.core.operational_access_middleware import OperationalAccessMiddleware
 from app.core.rbac_middleware import ReaderReadOnlyMiddleware
 from app.core.security_headers_middleware import SecurityHeadersMiddleware
+from app.core.usage_context_middleware import LLMUsageContextMiddleware
 from app.database import init_db
 
 logger = logging.getLogger(__name__)
@@ -98,6 +99,7 @@ def _add_common_middleware(app: FastAPI) -> None:
     if _includes_http_routes():
         app.add_middleware(MigrationCheckMiddleware)
         app.add_middleware(ReaderReadOnlyMiddleware)
+        app.add_middleware(LLMUsageContextMiddleware)
 
     if settings.OBSERVABILITY_ENABLED and settings.LOKI_ENABLED and settings.LOKI_MULTI_TENANT:
         from app.core.observability_middleware import OrgLoggingMiddleware
diff --git a/app/cli.py b/app/cli.py
index 3078344f..453b2a2d 100644
--- a/app/cli.py
+++ b/app/cli.py
@@ -692,6 +692,7 @@ def start_all(
     # Store worker processes for cleanup.
     worker_process = None
     worker_imports_process = None
+    beat_process = None
     telephony_process = None
 
     def _terminate(proc, label: str):
@@ -711,8 +712,9 @@ def _terminate(proc, label: str):
 
     def cleanup_processes():
         """Clean up spawned processes."""
-        nonlocal worker_process, worker_imports_process, telephony_process
+        nonlocal worker_process, worker_imports_process, beat_process, telephony_process
         _terminate(telephony_process, "Telephony media server")
+        _terminate(beat_process, "Celery beat")
         _terminate(worker_process, "Celery worker (default)")
         _terminate(worker_imports_process, "Celery worker (imports)")
     
@@ -867,6 +869,18 @@ def _stream_telephony():
                 ),
                 prefix="[WORKER-IMPORTS]",
             )
+
+        beat_process = _spawn_worker(
+            [
+                "celery",
+                "-A",
+                "app.workers.celery_app",
+                "beat",
+                f"--loglevel={worker_loglevel}",
+            ],
+            label="Celery beat (periodic flush_usage_counters)",
+            prefix="[BEAT]",
+        )
     except FileNotFoundError:
         click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
         sys.exit(1)
diff --git a/app/core/usage_context_middleware.py b/app/core/usage_context_middleware.py
new file mode 100644
index 00000000..c9531c98
--- /dev/null
+++ b/app/core/usage_context_middleware.py
@@ -0,0 +1,43 @@
+"""Clear LLM usage ContextVar at request boundaries to avoid thread reuse leaks."""
+
+from __future__ import annotations
+
+from uuid import UUID
+
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.requests import Request
+
+from app.services.usage.context import (
+    infer_product_section_from_path,
+    reset_usage_context,
+    reset_usage_hints,
+    set_usage_context,
+    set_usage_hints,
+)
+
+
+class LLMUsageContextMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        section = infer_product_section_from_path(request.url.path)
+        request.state.usage_product_section = section
+
+        workspace_hint = None
+        raw_ws = request.headers.get("x-workspace-id") or request.query_params.get(
+            "workspace_id"
+        )
+        if raw_ws:
+            try:
+                workspace_hint = UUID(raw_ws)
+            except (TypeError, ValueError):
+                workspace_hint = None
+
+        ctx_token = set_usage_context(None)
+        hint_tokens = set_usage_hints(
+            workspace_id=workspace_hint,
+            product_section=section,
+        )
+        try:
+            return await call_next(request)
+        finally:
+            reset_usage_hints(hint_tokens)
+            reset_usage_context(ctx_token)
diff --git a/app/dependencies.py b/app/dependencies.py
index 6277777a..c3597d75 100644
--- a/app/dependencies.py
+++ b/app/dependencies.py
@@ -193,6 +193,22 @@ def get_workspace_context(
         is_org_admin=org_role == RoleEnum.ADMIN,
     )
     request.state.workspace_context = ctx
+
+    from app.services.usage.context import (
+        LLMUsageProductSection,
+        ensure_usage_context,
+    )
+
+    section = getattr(
+        request.state, "usage_product_section", LLMUsageProductSection.OTHER
+    )
+    ensure_usage_context(
+        organization_id,
+        workspace_id=workspace.id,
+        product_section=section
+        if isinstance(section, LLMUsageProductSection)
+        else LLMUsageProductSection.OTHER,
+    )
     return ctx
 
 
diff --git a/app/migrations/062_llm_usage_daily.py b/app/migrations/062_llm_usage_daily.py
new file mode 100644
index 00000000..e160c955
--- /dev/null
+++ b/app/migrations/062_llm_usage_daily.py
@@ -0,0 +1,92 @@
+"""Migration: LLM usage daily rollups for org-scoped Usage reporting."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add llm_usage_daily table for LLM token/call rollups"
+
+
+def upgrade(db: Session):
+    exists = db.execute(
+        text(
+            """
+            SELECT 1 FROM information_schema.tables
+            WHERE table_name = 'llm_usage_daily'
+            """
+        )
+    ).first()
+    if exists:
+        print("llm_usage_daily already exists, skipping 062")
+        db.commit()
+        return
+
+    db.execute(
+        text(
+            """
+            CREATE TABLE llm_usage_daily (
+                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                organization_id UUID NOT NULL REFERENCES organizations(id) ON DELETE CASCADE,
+                workspace_id UUID REFERENCES workspaces(id) ON DELETE SET NULL,
+                product_section VARCHAR(64) NOT NULL,
+                model VARCHAR(255) NOT NULL,
+                resource_id UUID,
+                resource_type VARCHAR(64),
+                usage_date DATE NOT NULL,
+                prompt_tokens BIGINT NOT NULL DEFAULT 0,
+                completion_tokens BIGINT NOT NULL DEFAULT 0,
+                cache_read_tokens BIGINT NOT NULL DEFAULT 0,
+                cache_creation_tokens BIGINT NOT NULL DEFAULT 0,
+                reasoning_tokens BIGINT NOT NULL DEFAULT 0,
+                call_count BIGINT NOT NULL DEFAULT 0,
+                created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                updated_at TIMESTAMPTZ NOT NULL DEFAULT now()
+            )
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX ix_llm_usage_daily_org_date
+            ON llm_usage_daily (organization_id, usage_date)
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX ix_llm_usage_daily_org_workspace_date
+            ON llm_usage_daily (organization_id, workspace_id, usage_date)
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX ix_llm_usage_daily_org_resource_date
+            ON llm_usage_daily (organization_id, resource_id, usage_date)
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE UNIQUE INDEX uq_llm_usage_daily_bucket
+            ON llm_usage_daily (
+                organization_id,
+                COALESCE(workspace_id, '00000000-0000-0000-0000-000000000000'::uuid),
+                product_section,
+                model,
+                COALESCE(resource_id, '00000000-0000-0000-0000-000000000000'::uuid),
+                usage_date
+            )
+            """
+        )
+    )
+    db.commit()
+    print("Created llm_usage_daily table")
+
+
+def downgrade(db: Session):
+    db.execute(text("DROP TABLE IF EXISTS llm_usage_daily"))
+    db.commit()
diff --git a/app/models/database.py b/app/models/database.py
index 68bcfebc..9f32f212 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -2777,3 +2777,40 @@ class JudgeRun(Base):
     created_by = Column(String, nullable=True)
 
     dataset = relationship("JudgeDataset", back_populates="runs")
+
+
+class LLMUsageDaily(Base):
+    """Daily LLM usage rollups for org-scoped Usage reporting."""
+
+    __tablename__ = "llm_usage_daily"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    product_section = Column(String(64), nullable=False, index=True)
+    model = Column(String(255), nullable=False, index=True)
+    resource_id = Column(UUID(as_uuid=True), nullable=True, index=True)
+    resource_type = Column(String(64), nullable=True)
+    usage_date = Column(Date, nullable=False, index=True)
+    prompt_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    completion_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    cache_read_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    cache_creation_tokens = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    reasoning_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    call_count = Column(BigInteger, nullable=False, default=0, server_default="0")
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
diff --git a/app/services/ai/llm_gateway.py b/app/services/ai/llm_gateway.py
index b0731840..750b1641 100644
--- a/app/services/ai/llm_gateway.py
+++ b/app/services/ai/llm_gateway.py
@@ -705,4 +705,28 @@ def litellm_completion(
         db=db,
         credential=credential,
     )
-    return litellm.completion(**kwargs)
+    response = litellm.completion(**kwargs)
+    try:
+        from app.services.usage.context import (
+            LLMUsageProductSection,
+            ensure_usage_context,
+            reset_usage_context,
+        )
+        from app.services.usage.llm_usage import record_llm_usage
+        from app.services.usage.normalize import normalize_llm_usage
+
+        usage_token = ensure_usage_context(
+            organization_id,
+            product_section=LLMUsageProductSection.OTHER,
+        )
+        try:
+            model_name = str(kwargs.get("model") or "unknown")
+            if "/" in model_name:
+                model_name = model_name.rsplit("/", 1)[-1]
+            record_llm_usage(model_name, normalize_llm_usage(raw_response=response))
+        finally:
+            if usage_token is not None:
+                reset_usage_context(usage_token)
+    except Exception as exc:
+        logger.debug("litellm_completion usage record skipped: {}", exc)
+    return response
diff --git a/app/services/ai/llm_service.py b/app/services/ai/llm_service.py
index fb22285d..a0b2090a 100644
--- a/app/services/ai/llm_service.py
+++ b/app/services/ai/llm_service.py
@@ -549,6 +549,30 @@ def generate_response(
             "raw_response": response,
             "processing_time": time.time() - start_time,
         }
+        try:
+            from app.services.usage.context import (
+                LLMUsageProductSection,
+                ensure_usage_context,
+                reset_usage_context,
+            )
+            from app.services.usage.normalize import normalize_llm_usage
+            from app.services.usage.llm_usage import record_llm_usage
+
+            usage_token = ensure_usage_context(
+                organization_id,
+                product_section=LLMUsageProductSection.OTHER,
+            )
+            try:
+                snapshot = normalize_llm_usage(raw_response=response)
+                result["usage"]["cache_read_tokens"] = snapshot.cache_read_tokens
+                result["usage"]["cache_creation_tokens"] = snapshot.cache_creation_tokens
+                result["usage"]["reasoning_tokens"] = snapshot.reasoning_tokens
+                record_llm_usage(llm_model, snapshot)
+            finally:
+                if usage_token is not None:
+                    reset_usage_context(usage_token)
+        except Exception as exc:
+            logger.debug("llm usage record skipped: {}", exc)
         return result
 
 
diff --git a/app/services/usage/__init__.py b/app/services/usage/__init__.py
new file mode 100644
index 00000000..646e9adb
--- /dev/null
+++ b/app/services/usage/__init__.py
@@ -0,0 +1,32 @@
+"""LLM usage tracking (tokens, calls) with Redis buffer and catalog rollups."""
+
+from app.services.usage.context import (
+    LLMUsageContext,
+    LLMUsageProductSection,
+    ensure_usage_context,
+    infer_product_section_from_path,
+    llm_usage_context,
+    reset_usage_context,
+    reset_usage_hints,
+    set_usage_context,
+    set_usage_hints,
+)
+from app.services.usage.llm_usage import flush_all_usage_to_catalog, flush_usage_to_catalog, record_llm_usage
+from app.services.usage.normalize import UsageSnapshot, normalize_llm_usage
+
+__all__ = [
+    "LLMUsageContext",
+    "LLMUsageProductSection",
+    "UsageSnapshot",
+    "ensure_usage_context",
+    "flush_all_usage_to_catalog",
+    "flush_usage_to_catalog",
+    "infer_product_section_from_path",
+    "llm_usage_context",
+    "normalize_llm_usage",
+    "record_llm_usage",
+    "reset_usage_context",
+    "reset_usage_hints",
+    "set_usage_context",
+    "set_usage_hints",
+]
diff --git a/app/services/usage/context.py b/app/services/usage/context.py
new file mode 100644
index 00000000..80e84fac
--- /dev/null
+++ b/app/services/usage/context.py
@@ -0,0 +1,177 @@
+"""Usage attribution context (org, workspace, product section, resource)."""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from contextvars import ContextVar, Token
+from dataclasses import dataclass
+from enum import Enum
+from typing import Iterator, Optional
+from uuid import UUID
+
+
+class LLMUsageProductSection(str, Enum):
+    CALL_IMPORT_EVALUATIONS = "call_import_evaluations"
+    CALL_IMPORTS = "call_imports"
+    PLAYGROUND = "playground"
+    VOICE_PLAYGROUND = "voice_playground"
+    EVALUATORS = "evaluators"
+    METRICS = "metrics"
+    CHAT = "chat"
+    JUDGE_ALIGNMENT = "judge_alignment"
+    PROMPT_OPTIMIZATION = "prompt_optimization"
+    PERSONAS = "personas"
+    AGENTS = "agents"
+    PROMPT_PARTIALS = "prompt_partials"
+    CONVERSATION_EVALUATIONS = "conversation_evaluations"
+    TELEPHONY = "telephony"
+    TEST_AGENT = "test_agent"
+    OTHER = "other"
+
+
+@dataclass(frozen=True)
+class LLMUsageContext:
+    organization_id: UUID
+    workspace_id: Optional[UUID] = None
+    product_section: LLMUsageProductSection = LLMUsageProductSection.OTHER
+    resource_id: Optional[UUID] = None
+    resource_type: Optional[str] = None
+
+
+_usage_context_var: ContextVar[Optional[LLMUsageContext]] = ContextVar(
+    "llm_usage_context", default=None
+)
+_usage_workspace_hint_var: ContextVar[Optional[UUID]] = ContextVar(
+    "llm_usage_workspace_hint", default=None
+)
+_usage_section_hint_var: ContextVar[LLMUsageProductSection] = ContextVar(
+    "llm_usage_section_hint", default=LLMUsageProductSection.OTHER
+)
+
+# Path fragments under /api/v1 → product section (longest match wins).
+_PATH_SECTION_RULES: tuple[tuple[str, LLMUsageProductSection], ...] = (
+    ("call-import-evaluations", LLMUsageProductSection.CALL_IMPORT_EVALUATIONS),
+    ("call-imports", LLMUsageProductSection.CALL_IMPORTS),
+    ("voice-playground", LLMUsageProductSection.VOICE_PLAYGROUND),
+    ("playground", LLMUsageProductSection.PLAYGROUND),
+    ("evaluators", LLMUsageProductSection.EVALUATORS),
+    ("metrics", LLMUsageProductSection.METRICS),
+    ("chat", LLMUsageProductSection.CHAT),
+    ("judge-alignment", LLMUsageProductSection.JUDGE_ALIGNMENT),
+    ("prompt-optimization", LLMUsageProductSection.PROMPT_OPTIMIZATION),
+    ("personas", LLMUsageProductSection.PERSONAS),
+    ("agents", LLMUsageProductSection.AGENTS),
+    ("prompt-partials", LLMUsageProductSection.PROMPT_PARTIALS),
+    ("conversation-evaluations", LLMUsageProductSection.CONVERSATION_EVALUATIONS),
+    ("telephony", LLMUsageProductSection.TELEPHONY),
+    ("test-agent", LLMUsageProductSection.TEST_AGENT),
+)
+
+
+def get_usage_context() -> Optional[LLMUsageContext]:
+    return _usage_context_var.get()
+
+
+def set_usage_context(ctx: Optional[LLMUsageContext]) -> Token:
+    return _usage_context_var.set(ctx)
+
+
+def reset_usage_context(token: Token) -> None:
+    _usage_context_var.reset(token)
+
+
+def set_usage_hints(
+    *,
+    workspace_id: Optional[UUID] = None,
+    product_section: Optional[LLMUsageProductSection] = None,
+) -> tuple[Token, Token]:
+    ws_token = _usage_workspace_hint_var.set(workspace_id)
+    section = product_section or LLMUsageProductSection.OTHER
+    section_token = _usage_section_hint_var.set(section)
+    return ws_token, section_token
+
+
+def reset_usage_hints(tokens: tuple[Token, Token]) -> None:
+    _usage_workspace_hint_var.reset(tokens[0])
+    _usage_section_hint_var.reset(tokens[1])
+
+
+def get_usage_workspace_hint() -> Optional[UUID]:
+    return _usage_workspace_hint_var.get()
+
+
+def get_usage_section_hint() -> LLMUsageProductSection:
+    return _usage_section_hint_var.get()
+
+
+@contextmanager
+def llm_usage_context(ctx: LLMUsageContext) -> Iterator[None]:
+    token = set_usage_context(ctx)
+    try:
+        yield
+    finally:
+        reset_usage_context(token)
+
+
+def infer_product_section_from_path(path: str) -> LLMUsageProductSection:
+    normalized = (path or "").lower()
+    for fragment, section in _PATH_SECTION_RULES:
+        if f"/{fragment}" in normalized or normalized.endswith(fragment):
+            return section
+    return LLMUsageProductSection.OTHER
+
+
+def ensure_usage_context(
+    organization_id: UUID,
+    *,
+    workspace_id: Optional[UUID] = None,
+    product_section: LLMUsageProductSection = LLMUsageProductSection.OTHER,
+    resource_id: Optional[UUID] = None,
+    resource_type: Optional[str] = None,
+) -> Token | None:
+    """Set or enrich usage context. Returns token to reset, or None if unchanged."""
+    resolved_workspace = workspace_id or get_usage_workspace_hint()
+    resolved_section = product_section
+    if resolved_section == LLMUsageProductSection.OTHER:
+        hint = get_usage_section_hint()
+        if hint != LLMUsageProductSection.OTHER:
+            resolved_section = hint
+
+    current = get_usage_context()
+    if current is None:
+        return set_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=resolved_workspace,
+                product_section=resolved_section,
+                resource_id=resource_id,
+                resource_type=resource_type,
+            )
+        )
+
+    upgraded_workspace = current.workspace_id or resolved_workspace
+    upgraded_section = (
+        resolved_section
+        if current.product_section == LLMUsageProductSection.OTHER
+        and resolved_section != LLMUsageProductSection.OTHER
+        else current.product_section
+    )
+    upgraded_resource_id = current.resource_id or resource_id
+    upgraded_resource_type = current.resource_type or resource_type
+    if (
+        upgraded_workspace == current.workspace_id
+        and upgraded_section == current.product_section
+        and upgraded_resource_id == current.resource_id
+        and upgraded_resource_type == current.resource_type
+    ):
+        return None
+
+    return set_usage_context(
+        LLMUsageContext(
+            organization_id=current.organization_id,
+            workspace_id=upgraded_workspace,
+            product_section=upgraded_section,
+            resource_id=upgraded_resource_id,
+            resource_type=upgraded_resource_type,
+        )
+    )
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
new file mode 100644
index 00000000..4c1fcecb
--- /dev/null
+++ b/app/services/usage/llm_usage.py
@@ -0,0 +1,491 @@
+"""Redis-buffered LLM usage counters with catalog rollup flush."""
+
+from __future__ import annotations
+
+import time
+import uuid
+from datetime import date, datetime, timezone
+from typing import Any, Dict, Iterable, List, Optional, Tuple
+from uuid import UUID
+
+import redis
+from loguru import logger
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.config import settings
+from app.services.usage.context import (
+    LLMUsageContext,
+    get_usage_context,
+)
+from app.services.usage.normalize import UsageSnapshot
+
+_redis: redis.Redis | None = None
+
+_NONE = "__none__"
+_PENDING_TTL_SECONDS = 14 * 24 * 60 * 60
+_FLUSH_LOCK_TTL_SECONDS = 45
+_FLUSH_LOCK_WAIT_SECONDS = 3.0
+_METRIC_FIELDS = (
+    "prompt_tokens",
+    "completion_tokens",
+    "cache_read_tokens",
+    "cache_creation_tokens",
+    "reasoning_tokens",
+    "call_count",
+)
+
+# Atomically move pending hash → claim key so only one flusher owns the deltas.
+_CLAIM_LUA = """
+if redis.call('EXISTS', KEYS[1]) == 0 then
+  return 0
+end
+redis.call('RENAME', KEYS[1], KEYS[2])
+return 1
+"""
+
+
+def _client() -> redis.Redis:
+    global _redis
+    if _redis is None:
+        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+    return _redis
+
+
+def _token(value: Optional[UUID]) -> str:
+    return str(value) if value else _NONE
+
+
+def _pending_hash_key(organization_id: UUID) -> str:
+    return f"usage:pending:{organization_id}"
+
+
+def _flush_lock_key(organization_id: UUID) -> str:
+    return f"usage:flush:lock:{organization_id}"
+
+
+def _claim_hash_key(organization_id: UUID, claim_id: str) -> str:
+    return f"usage:flushing:{organization_id}:{claim_id}"
+
+
+def _bucket_prefix(
+    *,
+    workspace_id: Optional[UUID],
+    product_section: str,
+    model: str,
+    resource_id: Optional[UUID],
+    resource_type: Optional[str],
+    usage_date: date,
+) -> str:
+    return "|".join(
+        [
+            _token(workspace_id),
+            product_section,
+            model,
+            _token(resource_id),
+            resource_type or _NONE,
+            usage_date.isoformat(),
+        ]
+    )
+
+
+def _parse_bucket_prefix(prefix: str) -> Optional[Dict[str, Any]]:
+    parts = prefix.split("|")
+    if len(parts) != 6:
+        return None
+    ws_token, section, model, resource_token, resource_type, day_str = parts
+    try:
+        usage_date = date.fromisoformat(day_str)
+    except ValueError:
+        return None
+    workspace_id = None if ws_token == _NONE else UUID(ws_token)
+    resource_id = None if resource_token == _NONE else UUID(resource_token)
+    resolved_resource_type = None if resource_type == _NONE else resource_type
+    return {
+        "workspace_id": workspace_id,
+        "product_section": section,
+        "model": model,
+        "resource_id": resource_id,
+        "resource_type": resolved_resource_type,
+        "usage_date": usage_date,
+    }
+
+
+def _resolve_context(ctx: Optional[LLMUsageContext]) -> LLMUsageContext:
+    if ctx is not None:
+        return ctx
+    current = get_usage_context()
+    if current is not None:
+        return current
+    raise ValueError("LLM usage context is not set")
+
+
+def _deltas_from_usage(usage: UsageSnapshot) -> Dict[str, int]:
+    return {
+        "prompt_tokens": usage.prompt_tokens,
+        "completion_tokens": usage.completion_tokens,
+        "cache_read_tokens": usage.cache_read_tokens,
+        "cache_creation_tokens": usage.cache_creation_tokens,
+        "reasoning_tokens": usage.reasoning_tokens,
+        "call_count": 1,
+    }
+
+
+def record_llm_usage(
+    model: str,
+    usage: UsageSnapshot,
+    *,
+    ctx: Optional[LLMUsageContext] = None,
+    usage_date: Optional[date] = None,
+) -> None:
+    """Increment Redis counters for one LLM call (best-effort, never raises)."""
+    if not model:
+        model = "unknown"
+    try:
+        context = _resolve_context(ctx)
+    except ValueError:
+        logger.debug("llm usage record skipped: missing context")
+        return
+
+    deltas = _deltas_from_usage(usage)
+    if not any(deltas.values()):
+        return
+
+    day = usage_date or datetime.now(timezone.utc).date()
+    prefix = _bucket_prefix(
+        workspace_id=context.workspace_id,
+        product_section=context.product_section.value,
+        model=model,
+        resource_id=context.resource_id,
+        resource_type=context.resource_type,
+        usage_date=day,
+    )
+    hash_key = _pending_hash_key(context.organization_id)
+
+    try:
+        client = _client()
+        pipe = client.pipeline()
+        for metric, delta in deltas.items():
+            if delta:
+                pipe.hincrby(hash_key, f"{prefix}|{metric}", int(delta))
+        pipe.sadd("usage:pending:orgs", str(context.organization_id))
+        pipe.expire(hash_key, _PENDING_TTL_SECONDS)
+        pipe.execute()
+    except redis.RedisError as exc:
+        logger.warning("llm usage counter skipped: {}", exc)
+
+
+def _parse_hash_to_buckets(raw: Dict[str, str]) -> Dict[str, Dict[str, int]]:
+    buckets: Dict[str, Dict[str, int]] = {}
+    for field, value in raw.items():
+        if "|" not in field:
+            continue
+        prefix, metric = field.rsplit("|", 1)
+        if metric not in _METRIC_FIELDS:
+            continue
+        amount = int(value or 0)
+        if not amount:
+            continue
+        bucket = buckets.setdefault(prefix, {})
+        bucket[metric] = bucket.get(metric, 0) + amount
+    return buckets
+
+
+def _read_hash_buckets(hash_key: str) -> Dict[str, Dict[str, int]]:
+    try:
+        client = _client()
+        raw = client.hgetall(hash_key)
+    except redis.RedisError as exc:
+        logger.warning("llm usage read hash failed: {}", exc)
+        return {}
+    return _parse_hash_to_buckets(raw)
+
+
+def _restore_buckets_to_pending(
+    organization_id: UUID, buckets: Dict[str, Dict[str, int]]
+) -> None:
+    try:
+        client = _client()
+        pipe = client.pipeline()
+        hash_key = _pending_hash_key(organization_id)
+        for prefix, metrics in buckets.items():
+            for metric, amount in metrics.items():
+                if amount:
+                    pipe.hincrby(hash_key, f"{prefix}|{metric}", amount)
+        pipe.sadd("usage:pending:orgs", str(organization_id))
+        pipe.expire(hash_key, _PENDING_TTL_SECONDS)
+        pipe.execute()
+    except redis.RedisError as exc:
+        logger.warning("llm usage redis restore failed: {}", exc)
+
+
+def _acquire_flush_lock(organization_id: UUID) -> bool:
+    try:
+        client = _client()
+        lock_key = _flush_lock_key(organization_id)
+        if client.set(lock_key, "1", nx=True, ex=_FLUSH_LOCK_TTL_SECONDS):
+            return True
+        deadline = time.monotonic() + _FLUSH_LOCK_WAIT_SECONDS
+        while time.monotonic() < deadline:
+            time.sleep(0.05)
+            if client.set(lock_key, "1", nx=True, ex=_FLUSH_LOCK_TTL_SECONDS):
+                return True
+            if client.get(lock_key) is None:
+                continue
+        return False
+    except redis.RedisError as exc:
+        logger.warning("llm usage flush lock failed: {}", exc)
+        return False
+
+
+def _release_flush_lock(organization_id: UUID) -> None:
+    try:
+        _client().delete(_flush_lock_key(organization_id))
+    except redis.RedisError:
+        pass
+
+
+def _claim_pending(
+    organization_id: UUID,
+) -> Tuple[Optional[str], Dict[str, Dict[str, int]]]:
+    """Rename pending → claim key. Returns (claim_key, buckets) or (None, {})."""
+    claim_id = str(uuid.uuid4())
+    pending_key = _pending_hash_key(organization_id)
+    claim_key = _claim_hash_key(organization_id, claim_id)
+    try:
+        client = _client()
+        claimed = int(client.eval(_CLAIM_LUA, 2, pending_key, claim_key) or 0)
+        if not claimed:
+            return None, {}
+        buckets = _read_hash_buckets(claim_key)
+        if not buckets:
+            client.delete(claim_key)
+            return None, {}
+        return claim_key, buckets
+    except redis.RedisError as exc:
+        logger.warning("llm usage claim failed: {}", exc)
+        return None, {}
+
+
+def _ack_claim(claim_key: str, organization_id: UUID) -> None:
+    try:
+        client = _client()
+        client.delete(claim_key)
+        pending_key = _pending_hash_key(organization_id)
+        if not client.exists(pending_key):
+            client.srem("usage:pending:orgs", str(organization_id))
+    except redis.RedisError:
+        pass
+
+
+def _upsert_bucket(
+    db: Session,
+    organization_id: UUID,
+    bucket: Dict[str, Any],
+    deltas: Dict[str, int],
+    *,
+    resource_type: Optional[str],
+) -> None:
+    params = {
+        "organization_id": str(organization_id),
+        "workspace_id": str(bucket["workspace_id"]) if bucket["workspace_id"] else None,
+        "product_section": bucket["product_section"],
+        "model": bucket["model"],
+        "resource_id": str(bucket["resource_id"]) if bucket["resource_id"] else None,
+        "resource_type": resource_type,
+        "usage_date": bucket["usage_date"].isoformat(),
+        "prompt_tokens": int(deltas.get("prompt_tokens", 0)),
+        "completion_tokens": int(deltas.get("completion_tokens", 0)),
+        "cache_read_tokens": int(deltas.get("cache_read_tokens", 0)),
+        "cache_creation_tokens": int(deltas.get("cache_creation_tokens", 0)),
+        "reasoning_tokens": int(deltas.get("reasoning_tokens", 0)),
+        "call_count": int(deltas.get("call_count", 0)),
+    }
+    result = db.execute(
+        text(
+            """
+            UPDATE llm_usage_daily SET
+                prompt_tokens = prompt_tokens + :prompt_tokens,
+                completion_tokens = completion_tokens + :completion_tokens,
+                cache_read_tokens = cache_read_tokens + :cache_read_tokens,
+                cache_creation_tokens = cache_creation_tokens + :cache_creation_tokens,
+                reasoning_tokens = reasoning_tokens + :reasoning_tokens,
+                call_count = call_count + :call_count,
+                resource_type = COALESCE(resource_type, :resource_type),
+                updated_at = now()
+            WHERE organization_id = CAST(:organization_id AS uuid)
+              AND product_section = :product_section
+              AND model = :model
+              AND usage_date = CAST(:usage_date AS date)
+              AND workspace_id IS NOT DISTINCT FROM CAST(:workspace_id AS uuid)
+              AND resource_id IS NOT DISTINCT FROM CAST(:resource_id AS uuid)
+            """
+        ),
+        params,
+    )
+    if result.rowcount:
+        return
+
+    db.execute(
+        text(
+            """
+            INSERT INTO llm_usage_daily (
+                id, organization_id, workspace_id, product_section, model,
+                resource_id, resource_type, usage_date,
+                prompt_tokens, completion_tokens, cache_read_tokens,
+                cache_creation_tokens, reasoning_tokens, call_count,
+                created_at, updated_at
+            ) VALUES (
+                gen_random_uuid(), CAST(:organization_id AS uuid),
+                CAST(:workspace_id AS uuid), :product_section, :model,
+                CAST(:resource_id AS uuid), :resource_type, CAST(:usage_date AS date),
+                :prompt_tokens, :completion_tokens, :cache_read_tokens,
+                :cache_creation_tokens, :reasoning_tokens, :call_count,
+                now(), now()
+            )
+            """
+        ),
+        params,
+    )
+
+
+def _is_missing_organization_fk(exc: BaseException) -> bool:
+    """True when insert failed because organization_id is not in organizations."""
+    text_blob = " ".join(
+        str(part)
+        for part in (exc, getattr(exc, "orig", None), getattr(exc, "args", None))
+        if part is not None
+    ).lower()
+    return "llm_usage_daily_organization_id_fkey" in text_blob
+
+
+def flush_usage_to_catalog(db: Session, organization_id: UUID) -> int:
+    """Claim Redis deltas, commit to llm_usage_daily, then ack the claim."""
+    if not _acquire_flush_lock(organization_id):
+        return 0
+
+    claim_key = None
+    buckets: Dict[str, Dict[str, int]] = {}
+    try:
+        claim_key, buckets = _claim_pending(organization_id)
+        if not claim_key or not buckets:
+            return 0
+
+        flushed = 0
+        try:
+            for prefix, deltas in buckets.items():
+                parsed = _parse_bucket_prefix(prefix)
+                if not parsed:
+                    continue
+                _upsert_bucket(
+                    db,
+                    organization_id,
+                    parsed,
+                    deltas,
+                    resource_type=parsed.get("resource_type"),
+                )
+                flushed += 1
+            db.commit()
+        except Exception as exc:
+            db.rollback()
+            if _is_missing_organization_fk(exc):
+                # Stale/test org ids must not be restored — that loops forever on beat.
+                logger.warning(
+                    "llm usage flush dropped for unknown organization {}: {}",
+                    organization_id,
+                    exc,
+                )
+                if claim_key:
+                    _ack_claim(claim_key, organization_id)
+                return 0
+            logger.warning("llm usage catalog flush failed, restoring redis: {}", exc)
+            _restore_buckets_to_pending(organization_id, buckets)
+            if claim_key:
+                try:
+                    _client().delete(claim_key)
+                except redis.RedisError:
+                    pass
+            return 0
+
+        _ack_claim(claim_key, organization_id)
+        return flushed
+    finally:
+        _release_flush_lock(organization_id)
+
+
+def _recover_orphaned_claims() -> None:
+    """Re-queue claim hashes left behind by a crashed flusher."""
+    try:
+        client = _client()
+        for claim_key in client.scan_iter(match="usage:flushing:*", count=100):
+            parts = claim_key.split(":")
+            if len(parts) < 4:
+                continue
+            try:
+                org_id = UUID(parts[2])
+            except ValueError:
+                continue
+            if client.exists(_flush_lock_key(org_id)):
+                continue
+            buckets = _read_hash_buckets(claim_key)
+            if buckets:
+                _restore_buckets_to_pending(org_id, buckets)
+            client.delete(claim_key)
+    except redis.RedisError as exc:
+        logger.warning("llm usage orphan claim recovery failed: {}", exc)
+
+
+def list_pending_organization_ids() -> List[UUID]:
+    try:
+        client = _client()
+        raw_ids = client.smembers("usage:pending:orgs")
+        result: List[UUID] = []
+        stale: List[str] = []
+        for value in raw_ids:
+            try:
+                org_id = UUID(value)
+            except ValueError:
+                stale.append(value)
+                continue
+            if client.exists(_pending_hash_key(org_id)):
+                result.append(org_id)
+            else:
+                stale.append(value)
+        if stale:
+            client.srem("usage:pending:orgs", *stale)
+        return result
+    except (redis.RedisError, ValueError):
+        return []
+
+
+def flush_all_usage_to_catalog(db_factory) -> int:
+    """Flush all orgs with pending usage (Celery beat)."""
+    _recover_orphaned_claims()
+    total = 0
+    for org_id in list_pending_organization_ids():
+        db = db_factory()
+        try:
+            total += flush_usage_to_catalog(db, org_id)
+        except Exception as exc:
+            db.rollback()
+            logger.warning("flush_all usage failed for {}: {}", org_id, exc)
+        finally:
+            db.close()
+    return total
+
+
+def merge_usage_totals(
+    rows: Iterable[Any],
+) -> Dict[str, int]:
+    totals = {field: 0 for field in _METRIC_FIELDS}
+    for row in rows:
+        totals["prompt_tokens"] += int(getattr(row, "prompt_tokens", 0) or 0)
+        totals["completion_tokens"] += int(getattr(row, "completion_tokens", 0) or 0)
+        totals["cache_read_tokens"] += int(getattr(row, "cache_read_tokens", 0) or 0)
+        totals["cache_creation_tokens"] += int(
+            getattr(row, "cache_creation_tokens", 0) or 0
+        )
+        totals["reasoning_tokens"] += int(getattr(row, "reasoning_tokens", 0) or 0)
+        totals["call_count"] += int(getattr(row, "call_count", 0) or 0)
+    totals["total_tokens"] = totals["prompt_tokens"] + totals["completion_tokens"]
+    return totals
diff --git a/app/services/usage/normalize.py b/app/services/usage/normalize.py
new file mode 100644
index 00000000..bfdcf5f6
--- /dev/null
+++ b/app/services/usage/normalize.py
@@ -0,0 +1,99 @@
+"""Normalize LiteLLM / provider usage objects into UsageSnapshot."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Mapping, Optional
+
+
+@dataclass(frozen=True)
+class UsageSnapshot:
+    prompt_tokens: int
+    completion_tokens: int
+    cache_read_tokens: int = 0
+    cache_creation_tokens: int = 0
+    reasoning_tokens: int = 0
+
+    @property
+    def total_tokens(self) -> int:
+        return self.prompt_tokens + self.completion_tokens
+
+
+def _as_int(value: Any) -> int:
+    try:
+        return int(value or 0)
+    except (TypeError, ValueError):
+        return 0
+
+
+def _usage_mapping(raw: Any) -> Mapping[str, Any]:
+    if raw is None:
+        return {}
+    if isinstance(raw, Mapping):
+        return raw
+    if hasattr(raw, "model_dump"):
+        try:
+            return raw.model_dump()
+        except Exception:
+            pass
+    if hasattr(raw, "__dict__"):
+        return {
+            key: value
+            for key, value in vars(raw).items()
+            if not key.startswith("_")
+        }
+    return {}
+
+
+def _details_dict(usage: Mapping[str, Any], key: str) -> Mapping[str, Any]:
+    details = usage.get(key)
+    if details is None:
+        return {}
+    if isinstance(details, Mapping):
+        return details
+    if hasattr(details, "model_dump"):
+        try:
+            return details.model_dump()
+        except Exception:
+            pass
+    if hasattr(details, "__dict__"):
+        return {
+            k: v for k, v in vars(details).items() if not k.startswith("_")
+        }
+    return {}
+
+
+def normalize_llm_usage(raw_response: Any = None, *, usage: Any = None) -> UsageSnapshot:
+    """Extract token buckets from a LiteLLM response or raw usage object."""
+    usage_obj = usage
+    if usage_obj is None and raw_response is not None:
+        usage_obj = getattr(raw_response, "usage", None)
+
+    data = _usage_mapping(usage_obj)
+    prompt_tokens = _as_int(data.get("prompt_tokens") or data.get("input_tokens"))
+    completion_tokens = _as_int(
+        data.get("completion_tokens") or data.get("output_tokens")
+    )
+
+    cache_read = _as_int(data.get("cache_read_input_tokens"))
+    cache_creation = _as_int(data.get("cache_creation_input_tokens"))
+
+    prompt_details = _details_dict(data, "prompt_tokens_details")
+    if not cache_read:
+        cache_read = _as_int(prompt_details.get("cached_tokens"))
+    if not cache_creation:
+        cache_creation = _as_int(
+            prompt_details.get("cache_write_tokens")
+            or prompt_details.get("cache_creation_tokens")
+        )
+
+    completion_details = _details_dict(data, "completion_tokens_details")
+    reasoning_tokens = _as_int(completion_details.get("reasoning_tokens"))
+
+    return UsageSnapshot(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        cache_read_tokens=cache_read,
+        cache_creation_tokens=cache_creation,
+        reasoning_tokens=reasoning_tokens,
+    )
diff --git a/app/services/usage/voice_usage_processor.py b/app/services/usage/voice_usage_processor.py
new file mode 100644
index 00000000..56d65f57
--- /dev/null
+++ b/app/services/usage/voice_usage_processor.py
@@ -0,0 +1,80 @@
+"""Voice pipeline processor that records LLM token usage from MetricsFrames."""
+
+from __future__ import annotations
+
+from typing import Optional
+from uuid import UUID
+
+from loguru import logger
+
+
+def create_llm_usage_recorder(
+    *,
+    organization_id: UUID | str | None,
+    workspace_id: UUID | str | None = None,
+    product_section: str = "playground",
+    resource_id: UUID | str | None = None,
+    resource_type: Optional[str] = None,
+):
+    """Build a FrameProcessor that records LLM usage from MetricsFrames.
+
+    Returns None when organization_id is missing or efficientai is unavailable.
+    """
+    if not organization_id:
+        return None
+
+    try:
+        from efficientai.frames.frames import Frame, MetricsFrame
+        from efficientai.metrics.metrics import LLMUsageMetricsData
+        from efficientai.processors.frame_processor import FrameDirection, FrameProcessor
+
+        from app.services.usage.context import (
+            LLMUsageContext,
+            LLMUsageProductSection,
+            set_usage_context,
+        )
+        from app.services.usage.llm_usage import record_llm_usage
+        from app.services.usage.normalize import UsageSnapshot
+    except Exception as exc:
+        logger.debug("voice usage recorder unavailable: {}", exc)
+        return None
+
+    try:
+        section = LLMUsageProductSection(product_section)
+    except ValueError:
+        section = LLMUsageProductSection.OTHER
+
+    org_uuid = UUID(str(organization_id))
+    ws_uuid = UUID(str(workspace_id)) if workspace_id else None
+    res_uuid = UUID(str(resource_id)) if resource_id else None
+
+    set_usage_context(
+        LLMUsageContext(
+            organization_id=org_uuid,
+            workspace_id=ws_uuid,
+            product_section=section,
+            resource_id=res_uuid,
+            resource_type=resource_type,
+        )
+    )
+
+    class LLMUsageRecorderProcessor(FrameProcessor):
+        async def process_frame(self, frame: Frame, direction: FrameDirection):
+            await super().process_frame(frame, direction)
+            if isinstance(frame, MetricsFrame):
+                for item in frame.data or []:
+                    if isinstance(item, LLMUsageMetricsData) and item.value is not None:
+                        tokens = item.value
+                        snapshot = UsageSnapshot(
+                            prompt_tokens=int(tokens.prompt_tokens or 0),
+                            completion_tokens=int(tokens.completion_tokens or 0),
+                            cache_read_tokens=int(tokens.cache_read_input_tokens or 0),
+                            cache_creation_tokens=int(
+                                tokens.cache_creation_input_tokens or 0
+                            ),
+                            reasoning_tokens=int(tokens.reasoning_tokens or 0),
+                        )
+                        record_llm_usage(item.model or "unknown", snapshot)
+            await self.push_frame(frame, direction)
+
+    return LLMUsageRecorderProcessor()
diff --git a/app/services/voice_agent/bot_fast_api.py b/app/services/voice_agent/bot_fast_api.py
index 38fa2733..3e2d25e4 100644
--- a/app/services/voice_agent/bot_fast_api.py
+++ b/app/services/voice_agent/bot_fast_api.py
@@ -98,7 +98,7 @@ def _get_imports():
 """
 
 
-async def run_bot(websocket_client, google_api_key: str, system_instruction: str = None, organization_id: str = None, agent_id: str = None, persona_id: str = None, scenario_id: str = None, evaluator_id: str = None, result_id: str = None, model_name: str = None, serializer=None, telephony_mode: bool = False, call_short_id: str = None, silence_hangup_secs: float | None = None):
+async def run_bot(websocket_client, google_api_key: str, system_instruction: str = None, organization_id: str = None, agent_id: str = None, persona_id: str = None, scenario_id: str = None, evaluator_id: str = None, result_id: str = None, model_name: str = None, serializer=None, telephony_mode: bool = False, call_short_id: str = None, silence_hangup_secs: float | None = None, workspace_id: str = None):
     """
     Run the voice agent bot with the provided Google API key.
     
@@ -246,6 +246,15 @@ async def on_silence_hangup():
             if user_transcript_processor:
                 pipeline_processors.append(user_transcript_processor)
             pipeline_processors.append(llm)
+            from app.services.usage.voice_usage_processor import create_llm_usage_recorder
+
+            usage_recorder = create_llm_usage_recorder(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section="telephony" if telephony_mode else "playground",
+            )
+            if usage_recorder:
+                pipeline_processors.append(usage_recorder)
             if agent_transcript_processor:
                 pipeline_processors.append(agent_transcript_processor)
             pipeline_processors.extend([
@@ -278,18 +287,30 @@ async def on_client_disconnected(transport, client):
             # RTVI events for efficientai client UI
             rtvi = imports["RTVIProcessor"](config=imports["RTVIConfig"](config=[]))
 
-            pipeline = imports["Pipeline"](
+            from app.services.usage.voice_usage_processor import create_llm_usage_recorder
+
+            usage_recorder = create_llm_usage_recorder(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section="playground",
+            )
+            pipeline_steps = [
+                ws_transport.input(),
+                user_recorder,
+                context_aggregator.user(),
+                rtvi,
+                llm,
+            ]
+            if usage_recorder:
+                pipeline_steps.append(usage_recorder)
+            pipeline_steps.extend(
                 [
-                    ws_transport.input(),
-                    user_recorder,
-                    context_aggregator.user(),
-                    rtvi,
-                    llm,
                     bot_recorder,
                     ws_transport.output(),
                     context_aggregator.assistant(),
                 ]
             )
+            pipeline = imports["Pipeline"](pipeline_steps)
 
             task = imports["PipelineTask"](
                 pipeline,
diff --git a/app/workers/config.py b/app/workers/config.py
index dc1cb711..3448429d 100644
--- a/app/workers/config.py
+++ b/app/workers/config.py
@@ -137,4 +137,13 @@
     "generate_evaluation_prompt_improvements": {"queue": "evaluations"},
     "generate_agent_flowchart": {"queue": "celery"},
     "map_agent_flowchart_prompt_sections": {"queue": "celery"},
+    "flush_usage_counters": {"queue": "celery"},
+}
+
+# Periodic flush of Redis LLM usage counters into catalog rollups.
+celery_app.conf.beat_schedule = {
+    "flush-llm-usage-counters": {
+        "task": "flush_usage_counters",
+        "schedule": 120.0,
+    },
 }
diff --git a/app/workers/tasks/__init__.py b/app/workers/tasks/__init__.py
index e4862d8e..04eb0380 100644
--- a/app/workers/tasks/__init__.py
+++ b/app/workers/tasks/__init__.py
@@ -22,6 +22,7 @@
 from . import initiate_vobiz_outbound
 from . import finalize_telephony_recording
 from . import call_import_bulk_ops
+from . import flush_usage_counters
 from app.workers.concurrency import eval_dispatch
 from app.workers.concurrency import fair_dispatch
 from app.workers.concurrency import fair_diarization_dispatch
@@ -115,3 +116,4 @@
 materialize_call_import_evaluation_task = (
     call_import_bulk_ops.materialize_call_import_evaluation_task
 )
+flush_usage_counters_task = flush_usage_counters.flush_usage_counters_task
diff --git a/app/workers/tasks/evaluate_call_import_row.py b/app/workers/tasks/evaluate_call_import_row.py
index 29247d24..e87b3285 100644
--- a/app/workers/tasks/evaluate_call_import_row.py
+++ b/app/workers/tasks/evaluate_call_import_row.py
@@ -332,6 +332,7 @@ def evaluate_call_import_row_task(
     slot_task_id = _eval_slot_task_id or self.request.id
     scoring_inputs: dict[str, Any] | None = None
     restricted_metric_uuids: list[UUID] | None = None
+    usage_ctx_token = None
     try:
         from app.db_sharding.row_ops import (
             close_row_sessions,
@@ -363,6 +364,22 @@ def evaluate_call_import_row_task(
                 row_db.commit()
                 return {"status": "failed", "reason": "evaluation_missing"}
 
+            from app.services.usage.context import (
+                LLMUsageContext,
+                LLMUsageProductSection,
+                set_usage_context,
+            )
+
+            usage_ctx_token = set_usage_context(
+                LLMUsageContext(
+                    organization_id=evaluation.organization_id,
+                    workspace_id=evaluation.workspace_id,
+                    product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
+                    resource_id=evaluation.id,
+                    resource_type="call_import_evaluation",
+                )
+            )
+
             previous_row_status = eval_row.status
 
             eval_row.status = "running"
@@ -761,6 +778,10 @@ def evaluate_call_import_row_task(
             if row_db is not None:
                 close_row_sessions(row_db, catalog_db)
     finally:
+        if usage_ctx_token is not None:
+            from app.services.usage.context import reset_usage_context
+
+            reset_usage_context(usage_ctx_token)
         from app.workers.concurrency.fair_dispatch import (
             finish_eval_work_and_redispatch,
         )
diff --git a/app/workers/tasks/flush_usage_counters.py b/app/workers/tasks/flush_usage_counters.py
new file mode 100644
index 00000000..3dc158d0
--- /dev/null
+++ b/app/workers/tasks/flush_usage_counters.py
@@ -0,0 +1,18 @@
+"""Celery task: flush Redis LLM usage counters into catalog rollups."""
+
+from __future__ import annotations
+
+from loguru import logger
+
+from app.database import SessionLocal
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="flush_usage_counters")
+def flush_usage_counters_task() -> dict:
+    from app.services.usage.llm_usage import flush_all_usage_to_catalog
+
+    flushed = flush_all_usage_to_catalog(SessionLocal)
+    if flushed:
+        logger.info("Flushed {} LLM usage rollup buckets", flushed)
+    return {"flushed_buckets": flushed}
diff --git a/app/workers/tasks/generate_evaluation_tldr_insights.py b/app/workers/tasks/generate_evaluation_tldr_insights.py
index c1e5de73..b236d0f7 100644
--- a/app/workers/tasks/generate_evaluation_tldr_insights.py
+++ b/app/workers/tasks/generate_evaluation_tldr_insights.py
@@ -40,14 +40,29 @@ def generate_evaluation_tldr_insights_task(
         if evaluation is None:
             return {"error": "evaluation_not_found", "status_code": 404}
 
+        from app.services.usage.context import (
+            LLMUsageContext,
+            LLMUsageProductSection,
+            llm_usage_context,
+        )
+
         try:
-            summary = _generate_and_persist_tldr_summary(
-                db,
-                evaluation,
-                organization_id=UUID(organization_id),
-                provider=provider,
-                model=model,
-            )
+            with llm_usage_context(
+                LLMUsageContext(
+                    organization_id=evaluation.organization_id,
+                    workspace_id=evaluation.workspace_id,
+                    product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
+                    resource_id=evaluation.id,
+                    resource_type="call_import_evaluation",
+                )
+            ):
+                summary = _generate_and_persist_tldr_summary(
+                    db,
+                    evaluation,
+                    organization_id=UUID(organization_id),
+                    provider=provider,
+                    model=model,
+                )
         except HTTPException as exc:
             return {"error": exc.detail, "status_code": exc.status_code}
 
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index d338f294..dc0e1b64 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -488,6 +488,23 @@ def process_evaluator_result_task(self, result_id: str):
             logger.error(f"[EvaluatorResult {result_id}] Job not found in database")
             return {"error": "Evaluator result not found"}
 
+        from app.services.usage.context import (
+            LLMUsageContext,
+            LLMUsageProductSection,
+            set_usage_context,
+            reset_usage_context,
+        )
+
+        usage_token = set_usage_context(
+            LLMUsageContext(
+                organization_id=result.organization_id,
+                workspace_id=result.workspace_id,
+                product_section=LLMUsageProductSection.EVALUATORS,
+                resource_id=result.id,
+                resource_type="evaluator_result",
+            )
+        )
+
         logger.info(f"[EvaluatorResult {result.result_id}] Starting processing task")
 
         result.celery_task_id = self.request.id
@@ -746,4 +763,9 @@ def process_evaluator_result_task(self, result_id: str):
     except Exception as exc:
         raise self.retry(exc=exc, countdown=60)
     finally:
+        try:
+            if "usage_token" in locals() and usage_token is not None:
+                reset_usage_context(usage_token)
+        except Exception:
+            pass
         db.close()
diff --git a/app/workers/tasks/transcribe_call_import_row.py b/app/workers/tasks/transcribe_call_import_row.py
index c6e89198..0f342ab3 100644
--- a/app/workers/tasks/transcribe_call_import_row.py
+++ b/app/workers/tasks/transcribe_call_import_row.py
@@ -391,6 +391,11 @@ def _persist_diarization_failure(
 def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
     """STT / S3 / LLM diarisation without a long-lived DB session."""
     from app.models.enums import ModelProvider
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
+    )
     from app.workers.tasks.helpers.llm_diarisation import (
         LLMDiarisationError,
         diarize_audio_with_llm,
@@ -406,6 +411,48 @@ def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
     llm_credential_uuid = ctx["llm_credential_uuid"]
     effective_prompt = ctx["effective_prompt"]
 
+    with llm_usage_context(
+        LLMUsageContext(
+            organization_id=organization_id,
+            workspace_id=ctx.get("workspace_id"),
+            product_section=LLMUsageProductSection.CALL_IMPORTS,
+            resource_id=ctx.get("call_import_id"),
+            resource_type="call_import",
+        )
+    ):
+        return _run_diarization_pipeline_inner(
+            ctx,
+            row_id=row_id,
+            normalised_mode=normalised_mode,
+            recording_key=recording_key,
+            organization_id=organization_id,
+            llm_provider_value=llm_provider_value,
+            llm_model_value=llm_model_value,
+            llm_credential_uuid=llm_credential_uuid,
+            effective_prompt=effective_prompt,
+            ModelProvider=ModelProvider,
+            LLMDiarisationError=LLMDiarisationError,
+            diarize_audio_with_llm=diarize_audio_with_llm,
+            diarize_transcript_with_llm=diarize_transcript_with_llm,
+        )
+
+
+def _run_diarization_pipeline_inner(
+    ctx: dict[str, Any],
+    *,
+    row_id,
+    normalised_mode,
+    recording_key,
+    organization_id,
+    llm_provider_value,
+    llm_model_value,
+    llm_credential_uuid,
+    effective_prompt,
+    ModelProvider,
+    LLMDiarisationError,
+    diarize_audio_with_llm,
+    diarize_transcript_with_llm,
+) -> dict[str, Any]:
     plain_text: Optional[str] = None
     raw_turns: Optional[List[Dict[str, Any]]] = None
 
@@ -911,6 +958,8 @@ def transcribe_call_import_row_task(
                 "normalised_mode": normalised_mode,
                 "recording_key": recording_key,
                 "organization_id": row.organization_id,
+                "workspace_id": getattr(row, "workspace_id", None),
+                "call_import_id": row.call_import_id,
                 "stt_provider": provider_enum.value if provider_enum else None,
                 "stt_model": stt_model,
                 "credential_uuid": credential_uuid,
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 2d745a02..d18118b0 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -11,6 +11,9 @@ import SelectOrganization from './pages/auth/SelectOrganization'
 // Dashboard
 import Dashboard from './pages/dashboard/Dashboard'
 
+// Usage
+import Usage from './pages/usage/Usage'
+
 // Prompt Partials
 import PromptPartials from './pages/promptPartials/PromptPartials'
 
@@ -182,6 +185,7 @@ function App() {
           <Route path="observability/calls" element={<ObservabilityCalls />} />
           <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
           <Route path="iam" element={<IAM />} />
+          <Route path="usage" element={<Usage />} />
           <Route
             path="workspace-members"
             element={<Navigate to="/iam?tab=workspace-members" replace />}
diff --git a/frontend/src/components/Layout.tsx b/frontend/src/components/Layout.tsx
index c621a485..bfe7c0ad 100644
--- a/frontend/src/components/Layout.tsx
+++ b/frontend/src/components/Layout.tsx
@@ -25,6 +25,7 @@ import {
   Mic,
   Bot,
   Activity,
+  PieChart,
   Bell,
   History,
   Key,
@@ -99,6 +100,13 @@ const navigationSections: NavSection[] = [
       { name: 'Calls', href: '/observability/calls', icon: Phone },
     ],
   },
+  {
+    title: 'Usage',
+    icon: PieChart,
+    items: [
+      { name: 'Overview', href: '/usage', icon: PieChart },
+    ],
+  },
   {
     title: 'Alerting',
     icon: Bell,
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 1ac0029d..f8701723 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -2991,6 +2991,88 @@ class ApiClient {
     return response.data
   }
 
+  async getOrgUsageSummary(params: {
+    start?: string
+    end?: string
+    workspace_id?: string
+    product_section?: string
+    model?: string
+    resource_id?: string
+  }): Promise<{
+    start: string
+    end: string
+    totals: {
+      prompt_tokens: number
+      completion_tokens: number
+      total_tokens: number
+      cache_read_tokens: number
+      cache_creation_tokens: number
+      reasoning_tokens: number
+      call_count: number
+    }
+    last_updated_at?: string | null
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/summary', {
+      params,
+    })
+    return response.data
+  }
+
+  async getOrgUsageBreakdown(params: {
+    start?: string
+    end?: string
+    group_by?: 'workspace' | 'product_section' | 'model' | 'resource'
+    workspace_id?: string
+    product_section?: string
+    model?: string
+    resource_id?: string
+    limit?: number
+    offset?: number
+  }): Promise<{
+    start: string
+    end: string
+    group_by: string
+    rows: Array<{
+      workspace_id?: string | null
+      workspace_name?: string | null
+      product_section?: string | null
+      product_section_label?: string | null
+      model?: string | null
+      resource_id?: string | null
+      resource_type?: string | null
+      resource_label?: string | null
+      prompt_tokens: number
+      completion_tokens: number
+      total_tokens: number
+      cache_read_tokens: number
+      cache_creation_tokens: number
+      reasoning_tokens: number
+      call_count: number
+    }>
+    total_count: number
+    last_updated_at?: string | null
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/breakdown', {
+      params,
+    })
+    return response.data
+  }
+
+  async getOrgUsageFilters(params?: {
+    start?: string
+    end?: string
+  }): Promise<{
+    workspaces: Array<{ id: string; name: string }>
+    product_sections: Array<{ id: string; label: string }>
+    models: string[]
+    resources: Array<{ id: string; type?: string; label: string }>
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/filters', {
+      params,
+    })
+    return response.data
+  }
+
   async getAllModels(): Promise<Record<string, ModelConfigEntry>> {
     const response = await this.client.get('/api/v1/model-config/models')
     return response.data
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
new file mode 100644
index 00000000..0f7cdcb9
--- /dev/null
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -0,0 +1,294 @@
+import { useMemo } from 'react'
+import { useQuery } from '@tanstack/react-query'
+import { useSearchParams } from 'react-router-dom'
+import { Card, CardBody, Spinner } from '@heroui/react'
+import { Activity } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+
+type GroupBy = 'workspace' | 'product_section' | 'model' | 'resource'
+
+function formatNumber(value: number): string {
+  return new Intl.NumberFormat().format(value || 0)
+}
+
+function toDateInput(d: Date): string {
+  return d.toISOString().slice(0, 10)
+}
+
+function FilterField({
+  label,
+  children,
+}: {
+  label: string
+  children: React.ReactNode
+}) {
+  return (
+    <label className="flex flex-col gap-1 text-xs font-medium text-gray-600">
+      {label}
+      {children}
+    </label>
+  )
+}
+
+const fieldClassName =
+  'h-9 rounded-lg border border-gray-200 bg-white px-3 text-sm text-gray-900 outline-none focus:border-indigo-400 focus:ring-1 focus:ring-indigo-200'
+
+export default function Usage() {
+  const [searchParams, setSearchParams] = useSearchParams()
+  const today = useMemo(() => new Date(), [])
+  const defaultStart = useMemo(() => {
+    const d = new Date()
+    d.setDate(d.getDate() - 29)
+    return toDateInput(d)
+  }, [])
+
+  const start = searchParams.get('start') || defaultStart
+  const end = searchParams.get('end') || toDateInput(today)
+  const groupBy = (searchParams.get('group_by') as GroupBy) || 'workspace'
+  const workspaceId = searchParams.get('workspace_id') || ''
+  const productSection = searchParams.get('product_section') || ''
+  const model = searchParams.get('model') || ''
+  const resourceId = searchParams.get('resource_id') || ''
+
+  const setParam = (key: string, value: string) => {
+    const next = new URLSearchParams(searchParams)
+    if (!value) next.delete(key)
+    else next.set(key, value)
+    setSearchParams(next)
+  }
+
+  const filterParams = {
+    start,
+    end,
+    workspace_id: workspaceId || undefined,
+    product_section: productSection || undefined,
+    model: model || undefined,
+    resource_id: resourceId || undefined,
+  }
+
+  const { data: summary, isLoading: summaryLoading } = useQuery({
+    queryKey: ['org-usage', 'summary', filterParams],
+    queryFn: () => apiClient.getOrgUsageSummary(filterParams),
+  })
+
+  const { data: breakdown, isLoading: breakdownLoading } = useQuery({
+    queryKey: ['org-usage', 'breakdown', groupBy, filterParams],
+    queryFn: () =>
+      apiClient.getOrgUsageBreakdown({
+        ...filterParams,
+        group_by: groupBy,
+        limit: 100,
+      }),
+  })
+
+  const { data: filters } = useQuery({
+    queryKey: ['org-usage', 'filters', start, end],
+    queryFn: () => apiClient.getOrgUsageFilters({ start, end }),
+  })
+
+  const totals = summary?.totals
+  const rows = breakdown?.rows || []
+
+  const dimensionLabel = (row: (typeof rows)[number]): string => {
+    if (groupBy === 'workspace') return row.workspace_name || 'Unknown'
+    if (groupBy === 'product_section')
+      return row.product_section_label || row.product_section || '—'
+    if (groupBy === 'model') return row.model || '—'
+    return row.resource_label || row.resource_id || 'Unscoped'
+  }
+
+  return (
+    <div className="space-y-6">
+      <div className="flex items-start justify-between gap-4">
+        <div>
+          <h1 className="text-2xl font-bold text-gray-900 flex items-center gap-2">
+            <Activity className="h-6 w-6 text-indigo-600" />
+            Usage
+          </h1>
+          <p className="text-sm text-gray-500 mt-1">
+            Organization-wide LLM tokens and calls. Filter by workspace, product
+            section, model, or evaluation.
+          </p>
+        </div>
+        {summary?.last_updated_at && (
+          <p className="text-xs text-gray-400 whitespace-nowrap">
+            Updated {new Date(summary.last_updated_at).toLocaleString()}
+          </p>
+        )}
+      </div>
+
+      <div className="grid grid-cols-2 md:grid-cols-4 gap-3">
+        <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={summaryLoading} />
+        <StatCard label="Output tokens" value={totals?.completion_tokens} loading={summaryLoading} />
+        <StatCard label="Total tokens" value={totals?.total_tokens} loading={summaryLoading} />
+        <StatCard label="LLM calls" value={totals?.call_count} loading={summaryLoading} />
+      </div>
+
+      {(totals?.cache_read_tokens || totals?.cache_creation_tokens || totals?.reasoning_tokens) ? (
+        <div className="grid grid-cols-1 md:grid-cols-3 gap-3">
+          <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={summaryLoading} />
+          <StatCard label="Cache write" value={totals?.cache_creation_tokens} loading={summaryLoading} />
+          <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={summaryLoading} />
+        </div>
+      ) : null}
+
+      <Card shadow="sm">
+        <CardBody className="gap-4 p-4">
+          <div className="grid grid-cols-1 md:grid-cols-3 lg:grid-cols-6 gap-3">
+            <FilterField label="Start">
+              <input
+                type="date"
+                className={fieldClassName}
+                value={start}
+                onChange={(e) => setParam('start', e.target.value)}
+              />
+            </FilterField>
+            <FilterField label="End">
+              <input
+                type="date"
+                className={fieldClassName}
+                value={end}
+                onChange={(e) => setParam('end', e.target.value)}
+              />
+            </FilterField>
+            <FilterField label="Workspace">
+              <select
+                className={fieldClassName}
+                value={workspaceId}
+                onChange={(e) => setParam('workspace_id', e.target.value)}
+              >
+                <option value="">All workspaces</option>
+                {(filters?.workspaces || []).map((w) => (
+                  <option key={w.id} value={w.id}>
+                    {w.name}
+                  </option>
+                ))}
+              </select>
+            </FilterField>
+            <FilterField label="Product section">
+              <select
+                className={fieldClassName}
+                value={productSection}
+                onChange={(e) => setParam('product_section', e.target.value)}
+              >
+                <option value="">All sections</option>
+                {(filters?.product_sections || []).map((s) => (
+                  <option key={s.id} value={s.id}>
+                    {s.label}
+                  </option>
+                ))}
+              </select>
+            </FilterField>
+            <FilterField label="Model">
+              <select
+                className={fieldClassName}
+                value={model}
+                onChange={(e) => setParam('model', e.target.value)}
+              >
+                <option value="">All models</option>
+                {(filters?.models || []).map((m) => (
+                  <option key={m} value={m}>
+                    {m}
+                  </option>
+                ))}
+              </select>
+            </FilterField>
+            <FilterField label="Group by">
+              <select
+                className={fieldClassName}
+                value={groupBy}
+                onChange={(e) => setParam('group_by', e.target.value)}
+              >
+                <option value="workspace">Workspace</option>
+                <option value="product_section">Product section</option>
+                <option value="model">Model</option>
+                <option value="resource">Evaluation / resource</option>
+              </select>
+            </FilterField>
+          </div>
+        </CardBody>
+      </Card>
+
+      <Card shadow="sm">
+        <CardBody className="p-0 overflow-x-auto">
+          {breakdownLoading ? (
+            <div className="flex justify-center py-12">
+              <Spinner />
+            </div>
+          ) : rows.length === 0 ? (
+            <div className="py-12 text-center text-sm text-gray-500">
+              No usage in this period. Run evaluations or playground calls, then
+              refresh in a minute.
+            </div>
+          ) : (
+            <table className="min-w-full text-sm">
+              <thead className="bg-gray-50 text-left text-xs uppercase text-gray-500">
+                <tr>
+                  <th className="px-4 py-3 font-semibold">
+                    {groupBy === 'workspace'
+                      ? 'Workspace'
+                      : groupBy === 'product_section'
+                        ? 'Section'
+                        : groupBy === 'model'
+                          ? 'Model'
+                          : 'Resource'}
+                  </th>
+                  <th className="px-4 py-3 font-semibold text-right">Calls</th>
+                  <th className="px-4 py-3 font-semibold text-right">Input</th>
+                  <th className="px-4 py-3 font-semibold text-right">Output</th>
+                  <th className="px-4 py-3 font-semibold text-right">Total</th>
+                  <th className="px-4 py-3 font-semibold text-right">Cache read</th>
+                </tr>
+              </thead>
+              <tbody className="divide-y divide-gray-100">
+                {rows.map((row, idx) => (
+                  <tr key={idx} className="hover:bg-gray-50">
+                    <td className="px-4 py-3 text-gray-900">{dimensionLabel(row)}</td>
+                    <td className="px-4 py-3 text-right tabular-nums">
+                      {formatNumber(row.call_count)}
+                    </td>
+                    <td className="px-4 py-3 text-right tabular-nums">
+                      {formatNumber(row.prompt_tokens)}
+                    </td>
+                    <td className="px-4 py-3 text-right tabular-nums">
+                      {formatNumber(row.completion_tokens)}
+                    </td>
+                    <td className="px-4 py-3 text-right tabular-nums font-medium">
+                      {formatNumber(row.total_tokens)}
+                    </td>
+                    <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                      {formatNumber(row.cache_read_tokens)}
+                    </td>
+                  </tr>
+                ))}
+              </tbody>
+            </table>
+          )}
+        </CardBody>
+      </Card>
+    </div>
+  )
+}
+
+function StatCard({
+  label,
+  value,
+  loading,
+}: {
+  label: string
+  value?: number
+  loading?: boolean
+}) {
+  return (
+    <Card shadow="sm">
+      <CardBody className="p-4">
+        <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">
+          {label}
+        </p>
+        <p className="mt-1 text-2xl font-semibold text-gray-900 tabular-nums">
+          {loading ? '—' : formatNumber(value || 0)}
+        </p>
+      </CardBody>
+    </Card>
+  )
+}
diff --git a/tests/test_services/test_usage/__init__.py b/tests/test_services/test_usage/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
new file mode 100644
index 00000000..0fdd77bf
--- /dev/null
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -0,0 +1,387 @@
+"""Tests for Redis-buffered LLM usage counters and flush durability."""
+
+from __future__ import annotations
+
+from datetime import date
+from typing import Any, Dict, List, Optional
+from unittest.mock import MagicMock
+from uuid import uuid4
+
+import pytest
+
+from app.services.usage import llm_usage as usage_mod
+from app.services.usage.context import (
+    LLMUsageContext,
+    LLMUsageProductSection,
+    ensure_usage_context,
+    get_usage_context,
+    infer_product_section_from_path,
+    llm_usage_context,
+    reset_usage_context,
+    set_usage_context,
+)
+from app.services.usage.normalize import UsageSnapshot
+
+
+class _FakePipeline:
+    def __init__(self, client: "_FakeRedis"):
+        self._client = client
+        self._ops: List[tuple] = []
+
+    def hincrby(self, key: str, field: str, amount: int):
+        self._ops.append(("hincrby", key, field, amount))
+        return self
+
+    def sadd(self, key: str, *members: str):
+        self._ops.append(("sadd", key, members))
+        return self
+
+    def expire(self, key: str, ttl: int):
+        self._ops.append(("expire", key, ttl))
+        return self
+
+    def execute(self):
+        results = []
+        for op in self._ops:
+            kind = op[0]
+            if kind == "hincrby":
+                results.append(self._client.hincrby(op[1], op[2], op[3]))
+            elif kind == "sadd":
+                results.append(self._client.sadd(op[1], *op[2]))
+            elif kind == "expire":
+                results.append(self._client.expire(op[1], op[2]))
+        self._ops.clear()
+        return results
+
+
+class _FakeRedis:
+    """Minimal Redis stand-in for usage counter tests."""
+
+    def __init__(self):
+        self.hashes: Dict[str, Dict[str, int]] = {}
+        self.sets: Dict[str, set] = {}
+        self.kv: Dict[str, str] = {}
+
+    def pipeline(self):
+        return _FakePipeline(self)
+
+    def hincrby(self, key: str, field: str, amount: int) -> int:
+        bucket = self.hashes.setdefault(key, {})
+        bucket[field] = int(bucket.get(field, 0)) + int(amount)
+        return bucket[field]
+
+    def hgetall(self, key: str) -> Dict[str, str]:
+        return {k: str(v) for k, v in self.hashes.get(key, {}).items()}
+
+    def hdel(self, key: str, *fields: str) -> int:
+        bucket = self.hashes.get(key, {})
+        deleted = 0
+        for field in fields:
+            if field in bucket:
+                del bucket[field]
+                deleted += 1
+        if key in self.hashes and not self.hashes[key]:
+            del self.hashes[key]
+        return deleted
+
+    def hlen(self, key: str) -> int:
+        return len(self.hashes.get(key, {}))
+
+    def sadd(self, key: str, *members: str) -> int:
+        s = self.sets.setdefault(key, set())
+        before = len(s)
+        s.update(members)
+        return len(s) - before
+
+    def srem(self, key: str, *members: str) -> int:
+        s = self.sets.get(key, set())
+        removed = 0
+        for member in members:
+            if member in s:
+                s.remove(member)
+                removed += 1
+        return removed
+
+    def smembers(self, key: str) -> set:
+        return set(self.sets.get(key, set()))
+
+    def exists(self, key: str) -> int:
+        return int(key in self.hashes or key in self.sets or key in self.kv)
+
+    def delete(self, *keys: str) -> int:
+        deleted = 0
+        for key in keys:
+            if key in self.hashes:
+                del self.hashes[key]
+                deleted += 1
+            if key in self.sets:
+                del self.sets[key]
+                deleted += 1
+            if key in self.kv:
+                del self.kv[key]
+                deleted += 1
+        return deleted
+
+    def expire(self, key: str, ttl: int) -> bool:
+        return self.exists(key) == 1
+
+    def set(self, key: str, value: str, nx: bool = False, ex: Optional[int] = None) -> Optional[bool]:
+        if nx and key in self.kv:
+            return None
+        self.kv[key] = value
+        return True
+
+    def get(self, key: str) -> Optional[str]:
+        return self.kv.get(key)
+
+    def rename(self, src: str, dst: str) -> bool:
+        if src not in self.hashes:
+            raise KeyError(src)
+        self.hashes[dst] = self.hashes.pop(src)
+        return True
+
+    def eval(self, script: str, numkeys: int, *keys_and_args: Any) -> int:
+        pending, claim = keys_and_args[0], keys_and_args[1]
+        if pending not in self.hashes:
+            return 0
+        self.rename(pending, claim)
+        return 1
+
+    def scan_iter(self, match: str = "*", count: int = 100):
+        prefix = match.rstrip("*")
+        for key in list(self.hashes.keys()):
+            if key.startswith(prefix):
+                yield key
+
+
+@pytest.fixture(autouse=True)
+def fake_redis(monkeypatch):
+    """Always use in-memory Redis for this module — never touch real REDIS_URL."""
+    client = _FakeRedis()
+    usage_mod._redis = client
+
+    def _forbid_real_redis(*_args, **_kwargs):
+        raise AssertionError(
+            "usage tests must not open real Redis; fake_redis fixture failed to isolate"
+        )
+
+    monkeypatch.setattr(usage_mod.redis, "from_url", _forbid_real_redis)
+    yield client
+    usage_mod._redis = None
+
+
+@pytest.fixture
+def org_ctx():
+    org_id = uuid4()
+    workspace_id = uuid4()
+    ctx = LLMUsageContext(
+        organization_id=org_id,
+        workspace_id=workspace_id,
+        product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
+        resource_id=uuid4(),
+        resource_type="call_import_evaluation",
+    )
+    return org_id, workspace_id, ctx
+
+
+def test_record_increments_pending_and_counts_zero_token_calls(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=10, completion_tokens=5),
+            usage_date=date(2026, 8, 11),
+        )
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=0, completion_tokens=0),
+            usage_date=date(2026, 8, 11),
+        )
+
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    assert str(org_id) in fake_redis.smembers("usage:pending:orgs")
+
+    prompt = sum(int(v) for k, v in fields.items() if k.endswith("|prompt_tokens"))
+    completion = sum(
+        int(v) for k, v in fields.items() if k.endswith("|completion_tokens")
+    )
+    calls = sum(int(v) for k, v in fields.items() if k.endswith("|call_count"))
+    assert prompt == 10
+    assert completion == 5
+    assert calls == 2
+
+
+def test_record_skipped_without_context(fake_redis):
+    usage_mod.record_llm_usage(
+        "gpt-test",
+        UsageSnapshot(prompt_tokens=10, completion_tokens=5),
+    )
+    assert fake_redis.hashes == {}
+    assert fake_redis.sets == {}
+
+
+def test_flush_commits_and_acks_claim(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=4, completion_tokens=2),
+            usage_date=date(2026, 8, 11),
+        )
+
+    db = MagicMock()
+    db.execute.return_value = MagicMock(rowcount=1)
+
+    flushed = usage_mod.flush_usage_to_catalog(db, org_id)
+    assert flushed == 1
+    db.commit.assert_called_once()
+    assert usage_mod._pending_hash_key(org_id) not in fake_redis.hashes
+    assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
+    assert str(org_id) not in fake_redis.smembers("usage:pending:orgs")
+    assert fake_redis.get(usage_mod._flush_lock_key(org_id)) is None
+
+
+def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=7, completion_tokens=3),
+            usage_date=date(2026, 8, 11),
+        )
+
+    db = MagicMock()
+    db.execute.side_effect = RuntimeError("db down")
+
+    flushed = usage_mod.flush_usage_to_catalog(db, org_id)
+    assert flushed == 0
+    db.rollback.assert_called()
+    db.commit.assert_not_called()
+
+    pending = fake_redis.hgetall(usage_mod._pending_hash_key(org_id))
+    prompt = sum(int(v) for k, v in pending.items() if k.endswith("|prompt_tokens"))
+    completion = sum(
+        int(v) for k, v in pending.items() if k.endswith("|completion_tokens")
+    )
+    assert prompt == 7
+    assert completion == 3
+    assert str(org_id) in fake_redis.smembers("usage:pending:orgs")
+    assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
+
+
+def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx):
+    """Unknown org FK must not restore Redis (avoids infinite beat retries)."""
+    from sqlalchemy.exc import IntegrityError
+
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=3, completion_tokens=1),
+            usage_date=date(2026, 8, 11),
+        )
+
+    db = MagicMock()
+    db.execute.side_effect = IntegrityError(
+        "INSERT",
+        {},
+        Exception(
+            'insert or update on table "llm_usage_daily" violates foreign key '
+            'constraint "llm_usage_daily_organization_id_fkey"'
+        ),
+    )
+
+    flushed = usage_mod.flush_usage_to_catalog(db, org_id)
+    assert flushed == 0
+    db.rollback.assert_called()
+    assert usage_mod._pending_hash_key(org_id) not in fake_redis.hashes
+    assert str(org_id) not in fake_redis.smembers("usage:pending:orgs")
+    assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
+
+
+def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=9, completion_tokens=1),
+            usage_date=date(2026, 8, 11),
+        )
+
+    db_a = MagicMock()
+    db_a.execute.return_value = MagicMock(rowcount=0)  # force INSERT path
+    db_b = MagicMock()
+    db_b.execute.return_value = MagicMock(rowcount=1)
+
+    first = usage_mod.flush_usage_to_catalog(db_a, org_id)
+    second = usage_mod.flush_usage_to_catalog(db_b, org_id)
+
+    assert first == 1
+    assert second == 0
+    assert db_a.commit.call_count == 1
+    assert db_b.commit.call_count == 0
+    # INSERT once for the claimed bucket; second flusher never upserts.
+    assert db_a.execute.call_count == 2  # UPDATE miss + INSERT
+    assert db_b.execute.call_count == 0
+
+
+def test_ensure_usage_context_and_path_inference():
+    assert (
+        infer_product_section_from_path("/api/v1/call-imports/abc")
+        == LLMUsageProductSection.CALL_IMPORTS
+    )
+    assert (
+        infer_product_section_from_path("/api/v1/chat/completion")
+        == LLMUsageProductSection.CHAT
+    )
+    assert (
+        infer_product_section_from_path("/api/v1/unknown")
+        == LLMUsageProductSection.OTHER
+    )
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    token = set_usage_context(None)
+    try:
+        assert get_usage_context() is None
+        created = ensure_usage_context(
+            org_id,
+            product_section=LLMUsageProductSection.PLAYGROUND,
+        )
+        assert created is not None
+        assert get_usage_context().organization_id == org_id
+        assert get_usage_context().product_section == LLMUsageProductSection.PLAYGROUND
+
+        # Enrich missing workspace / upgrade OTHER section.
+        upgraded = ensure_usage_context(
+            org_id,
+            workspace_id=workspace_id,
+            product_section=LLMUsageProductSection.CHAT,
+        )
+        assert upgraded is not None
+        assert get_usage_context().workspace_id == workspace_id
+        # Existing non-OTHER section is preserved.
+        assert get_usage_context().product_section == LLMUsageProductSection.PLAYGROUND
+    finally:
+        reset_usage_context(token)
+
+
+def test_ensure_uses_workspace_and_section_hints():
+    from app.services.usage.context import reset_usage_hints, set_usage_hints
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    ctx_token = set_usage_context(None)
+    hint_tokens = set_usage_hints(
+        workspace_id=workspace_id,
+        product_section=LLMUsageProductSection.METRICS,
+    )
+    try:
+        created = ensure_usage_context(org_id)
+        assert created is not None
+        assert get_usage_context().workspace_id == workspace_id
+        assert get_usage_context().product_section == LLMUsageProductSection.METRICS
+    finally:
+        reset_usage_hints(hint_tokens)
+        reset_usage_context(ctx_token)

From f0ebac9382e17d504a46b6f277d526676f391cc4 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 13 Aug 2026 03:17:12 +0530
Subject: [PATCH 16/55] feat: enhance LLM/STT usage tracking with additional
 context and metrics across services

---
 app/api/v1/routes/agents.py                   |   34 +-
 app/api/v1/routes/call_import_evaluations.py  |   30 +-
 app/api/v1/routes/org_usage.py                | 1242 ++++++++++++++--
 app/api/v1/routes/playground.py               |  291 +---
 app/api/v1/routes/vobiz_telephony.py          |    1 +
 app/api/v1/routes/voice_agent.py              |    1 +
 .../063_usage_kind_stt_and_buffer.py          |  233 +++
 .../064_usage_kind_tts_characters.py          |   74 +
 app/migrations/065_usage_context_jsonb.py     |  216 +++
 ...ll_llm_usage_workspace_from_call_import.py |  279 ++++
 .../067_fix_llm_usage_bucket_unique_index.py  |  264 ++++
 app/models/database.py                        |   10 +-
 app/services/ai/llm_gateway.py                |   56 +-
 app/services/ai/llm_service.py                |    4 +-
 app/services/ai/stt_clients/google.py         |   24 +
 app/services/ai/transcription_service.py      |   45 +
 app/services/ai/tts_service.py                |   37 +
 .../evaluators/call_data_transcript.py        |  195 +++
 app/services/judge_alignment/gepa_bridge.py   |   51 +-
 app/services/optimization/gepa_service.py     |   26 +-
 .../testing/test_agent_bridge_service.py      |    2 +
 app/services/usage/__init__.py                |   22 +-
 app/services/usage/bucket_context.py          |  114 ++
 app/services/usage/call_import_context.py     |  200 +++
 app/services/usage/context.py                 |  111 +-
 app/services/usage/dates.py                   |   38 +
 app/services/usage/llm_usage.py               |  779 ++++++++--
 app/services/usage/usage_labels.py            |  424 ++++++
 app/services/usage/voice_usage_processor.py   |   56 +-
 app/services/voice_agent/bot_fast_api.py      |    8 +-
 app/services/voice_agent/voice_bundle.py      |   32 +
 .../webrtc_bridge/test_agent_processor.py     |   88 +-
 app/workers/tasks/agent_flowchart_jobs.py     |   40 +-
 app/workers/tasks/evaluate_call_import_row.py |   16 +-
 .../generate_evaluation_metric_clusters.py    |   30 +-
 ...generate_evaluation_prompt_improvements.py |   42 +-
 .../generate_evaluation_tldr_insights.py      |   14 +-
 .../generate_evaluation_user_insights.py      |   34 +-
 app/workers/tasks/process_evaluator_result.py |  671 +++++----
 app/workers/tasks/run_judge_alignment.py      |    8 +-
 app/workers/tasks/run_prompt_optimization.py  |   27 +-
 .../tasks/transcribe_call_import_row.py       |   32 +-
 app/workers/tasks/tts_comparison.py           |  200 +--
 frontend/src/lib/api.ts                       |   42 +-
 .../agents/components/AgentTalkSidebar.tsx    |   29 +-
 frontend/src/pages/usage/SearchableSelect.tsx |  125 ++
 frontend/src/pages/usage/Usage.tsx            | 1305 ++++++++++++++---
 .../src/pages/usage/UsageDateRangePicker.tsx  |  252 ++++
 frontend/src/pages/usage/UsageDrillPath.tsx   |   40 +
 frontend/src/pages/usage/UsageFiltersBar.tsx  |  353 +++++
 frontend/src/pages/usage/usageProductHints.ts |   47 +
 frontend/src/pages/usage/usageTheme.ts        |   18 +
 frontend/src/pages/usage/usageTimezone.ts     |   11 +
 .../test_call_data_transcript.py              |   34 +
 .../test_usage/test_bucket_context.py         |   40 +
 .../test_usage/test_call_import_context.py    |  120 ++
 .../test_usage/test_llm_usage.py              |  210 ++-
 .../test_usage/test_usage_dates.py            |   32 +
 .../test_usage/test_usage_labels.py           |  287 ++++
 .../test_process_evaluator_result_helpers.py  |   41 +-
 60 files changed, 7935 insertions(+), 1152 deletions(-)
 create mode 100644 app/migrations/063_usage_kind_stt_and_buffer.py
 create mode 100644 app/migrations/064_usage_kind_tts_characters.py
 create mode 100644 app/migrations/065_usage_context_jsonb.py
 create mode 100644 app/migrations/066_backfill_llm_usage_workspace_from_call_import.py
 create mode 100644 app/migrations/067_fix_llm_usage_bucket_unique_index.py
 create mode 100644 app/services/evaluators/call_data_transcript.py
 create mode 100644 app/services/usage/bucket_context.py
 create mode 100644 app/services/usage/call_import_context.py
 create mode 100644 app/services/usage/dates.py
 create mode 100644 app/services/usage/usage_labels.py
 create mode 100644 frontend/src/pages/usage/SearchableSelect.tsx
 create mode 100644 frontend/src/pages/usage/UsageDateRangePicker.tsx
 create mode 100644 frontend/src/pages/usage/UsageDrillPath.tsx
 create mode 100644 frontend/src/pages/usage/UsageFiltersBar.tsx
 create mode 100644 frontend/src/pages/usage/usageProductHints.ts
 create mode 100644 frontend/src/pages/usage/usageTheme.ts
 create mode 100644 frontend/src/pages/usage/usageTimezone.ts
 create mode 100644 tests/test_services/test_evaluators/test_call_data_transcript.py
 create mode 100644 tests/test_services/test_usage/test_bucket_context.py
 create mode 100644 tests/test_services/test_usage/test_call_import_context.py
 create mode 100644 tests/test_services/test_usage/test_usage_dates.py
 create mode 100644 tests/test_services/test_usage/test_usage_labels.py

diff --git a/app/api/v1/routes/agents.py b/app/api/v1/routes/agents.py
index 9d1d77b0..4c4b295e 100644
--- a/app/api/v1/routes/agents.py
+++ b/app/api/v1/routes/agents.py
@@ -149,7 +149,10 @@ async def generate_agent_description(
     db: Session = Depends(get_db),
 ):
     """Generate an agent description using AI from a brief description."""
+    from contextlib import nullcontext
+
     from app.services.ai.llm_service import llm_service
+    from app.services.usage.context import llm_usage_context, usage_context_for_agent
     from app.services.testing.test_agent_simulation_prompt import (
         format_scenarios_for_generation_context,
         format_scenarios_reference_appendix,
@@ -202,15 +205,28 @@ async def generate_agent_description(
     ]
 
     try:
-        result = llm_service.generate_response(
-            messages=messages,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            temperature=0.7,
-            max_tokens=4000,
-        )
+        usage_ctx = nullcontext()
+        if data.agent_id:
+            agent_for_usage = db.query(Agent).filter(
+                Agent.id == data.agent_id,
+                Agent.organization_id == organization_id,
+                Agent.workspace_id == workspace_id,
+            ).first()
+            if agent_for_usage:
+                usage_ctx = llm_usage_context(
+                    usage_context_for_agent(agent_for_usage, workspace_id=workspace_id)
+                )
+
+        with usage_ctx:
+            result = llm_service.generate_response(
+                messages=messages,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                temperature=0.7,
+                max_tokens=4000,
+            )
         content = result["text"]
         if data.append_scenarios_to_output and linked_scenarios:
             appendix = format_scenarios_reference_appendix(linked_scenarios)
diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 030c98ef..880393c7 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -3702,20 +3702,18 @@ async def generate_call_import_evaluation_pdf_report(
             )
             return _pdf_report_response_from_row(cached_pdf_report, cache_hit=True)
 
-    from app.services.usage.context import (
-        LLMUsageContext,
-        LLMUsageProductSection,
-        llm_usage_context,
+    from app.services.usage.call_import_context import (
+        call_import_evaluation_usage_context,
     )
+    from app.services.usage.context import llm_usage_context
 
     with llm_usage_context(
-        LLMUsageContext(
+        call_import_evaluation_usage_context(
             organization_id=organization_id,
             workspace_id=getattr(call_import, "workspace_id", None)
             or evaluation.workspace_id,
-            product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
-            resource_id=evaluation.id,
-            resource_type="call_import_evaluation",
+            evaluation_id=evaluation.id,
+            call_import_id=evaluation.call_import_id,
         )
     ):
         narrative = _generate_report_narrative(
@@ -5353,11 +5351,10 @@ def _generate_and_persist_tldr_summary(
 
     from app.services.ai.llm_resolver import get_llm_provider_and_model
     from app.services.ai.llm_service import llm_service
-    from app.services.usage.context import (
-        LLMUsageContext,
-        LLMUsageProductSection,
-        llm_usage_context,
+    from app.services.usage.call_import_context import (
+        call_import_evaluation_usage_context,
     )
+    from app.services.usage.context import llm_usage_context
 
     provider_enum, model_str = get_llm_provider_and_model(
         organization_id, db, provider, model
@@ -5365,12 +5362,11 @@ def _generate_and_persist_tldr_summary(
 
     try:
         with llm_usage_context(
-            LLMUsageContext(
+            call_import_evaluation_usage_context(
                 organization_id=organization_id,
                 workspace_id=evaluation.workspace_id,
-                product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
-                resource_id=evaluation.id,
-                resource_type="call_import_evaluation",
+                evaluation_id=evaluation.id,
+                call_import_id=evaluation.call_import_id,
             )
         ):
             llm_result = llm_service.generate_response(
@@ -5388,8 +5384,8 @@ def _generate_and_persist_tldr_summary(
             status_code=502, detail=f"LLM call failed: {e}"
         ) from e
 
-    summary = _parse_insights_response(llm_result.get("text", ""))
     total = int(evaluation.total_rows or 0)
+    summary = _parse_insights_response(llm_result.get("text", ""))
     ui_completed = min(int(evaluation.completed_rows or 0), total) if total else int(
         evaluation.completed_rows or 0
     )
diff --git a/app/api/v1/routes/org_usage.py b/app/api/v1/routes/org_usage.py
index 05f984a0..4c702138 100644
--- a/app/api/v1/routes/org_usage.py
+++ b/app/api/v1/routes/org_usage.py
@@ -2,23 +2,49 @@
 
 from __future__ import annotations
 
-from datetime import date, datetime, timedelta, timezone
+from datetime import date, datetime, timezone
+from app.services.usage.dates import usage_date_filter_bounds, usage_local_today
 from typing import List, Literal, Optional
 from uuid import UUID
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel, Field
-from sqlalchemy import func
+from sqlalchemy import and_, case, cast, func, or_, select, String
+from sqlalchemy.dialects.postgresql import UUID as PG_UUID
 from sqlalchemy.orm import Session
 
 from app.database import get_db
 from app.dependencies import get_organization_id
-from app.models.database import CallImportEvaluation, LLMUsageDaily, Workspace
-from app.services.usage.llm_usage import flush_usage_to_catalog, merge_usage_totals
+from app.models.database import (
+    CallImport,
+    CallImportEvaluation,
+    CallImportEvaluationRow,
+    CallImportRow,
+    CallImportTag,
+    CallImportTagAssignment,
+    Agent,
+    LLMUsageDaily,
+    Workspace,
+)
+from app.services.usage.llm_usage import flush_usage_to_catalog
+from app.services.usage.usage_labels import (
+    labels_for_call_import_ids,
+    labels_for_resource_buckets,
+    usage_kind_label,
+    UsageNameResolver,
+    parse_uuid,
+)
 
 router = APIRouter(prefix="/organizations/usage", tags=["Usage"])
 
-GroupBy = Literal["workspace", "product_section", "model", "resource"]
+GroupBy = Literal[
+    "workspace",
+    "product_section",
+    "model",
+    "resource",
+    "usage_kind",
+    "call_import",
+]
 
 SECTION_LABELS = {
     "call_import_evaluations": "Call Import Evaluations",
@@ -39,6 +65,27 @@
     "other": "Other",
 }
 
+_LABEL_ROW_LIMIT = 5000
+
+
+def _usage_row_weight():
+    return (
+        LLMUsageDaily.prompt_tokens
+        + LLMUsageDaily.completion_tokens
+        + LLMUsageDaily.cache_read_tokens
+        + LLMUsageDaily.cache_creation_tokens
+        + LLMUsageDaily.reasoning_tokens
+        + LLMUsageDaily.audio_seconds
+        + LLMUsageDaily.tts_characters
+    )
+
+
+def _label_row_order():
+    return (
+        _usage_row_weight().desc(),
+        LLMUsageDaily.call_count.desc(),
+    )
+
 
 class UsageTotals(BaseModel):
     prompt_tokens: int = 0
@@ -47,6 +94,8 @@ class UsageTotals(BaseModel):
     cache_read_tokens: int = 0
     cache_creation_tokens: int = 0
     reasoning_tokens: int = 0
+    audio_seconds: int = 0
+    tts_characters: int = 0
     call_count: int = 0
 
 
@@ -66,12 +115,17 @@ class UsageBreakdownRow(BaseModel):
     resource_id: Optional[UUID] = None
     resource_type: Optional[str] = None
     resource_label: Optional[str] = None
+    call_import_id: Optional[UUID] = None
+    call_import_label: Optional[str] = None
+    usage_kind: Optional[str] = None
     prompt_tokens: int = 0
     completion_tokens: int = 0
     total_tokens: int = 0
     cache_read_tokens: int = 0
     cache_creation_tokens: int = 0
     reasoning_tokens: int = 0
+    audio_seconds: int = 0
+    tts_characters: int = 0
     call_count: int = 0
 
 
@@ -81,20 +135,249 @@ class UsageBreakdownResponse(BaseModel):
     group_by: GroupBy
     rows: List[UsageBreakdownRow]
     total_count: int
+    truncated_at_limit: bool = False
     last_updated_at: Optional[datetime] = None
 
 
 class UsageFiltersResponse(BaseModel):
     workspaces: List[dict] = Field(default_factory=list)
     product_sections: List[dict] = Field(default_factory=list)
+    call_imports: List[dict] = Field(default_factory=list)
+    evaluations: List[dict] = Field(default_factory=list)
     models: List[str] = Field(default_factory=list)
     resources: List[dict] = Field(default_factory=list)
+    usage_kinds: List[dict] = Field(default_factory=list)
+    datasets: List[str] = Field(default_factory=list)
+    tags: List[dict] = Field(default_factory=list)
 
 
-def _default_range() -> tuple[date, date]:
-    end = datetime.now(timezone.utc).date()
-    start = end - timedelta(days=29)
-    return start, end
+def _parse_usage_range(
+    start: Optional[date],
+    end: Optional[date],
+    tz: Optional[str],
+) -> tuple[date, date, date, date]:
+    """Return display_start, display_end, filter_start, filter_end."""
+    today = usage_local_today(tz)
+    display_start = start or today
+    display_end = end or today
+    filter_start, filter_end = usage_date_filter_bounds(
+        display_start, display_end, tz
+    )
+    return display_start, display_end, filter_start, filter_end
+
+
+def _evaluation_id_expr():
+    return func.coalesce(
+        LLMUsageDaily.context["evaluation_id"].astext,
+        case(
+            (
+                LLMUsageDaily.context["resource_type"].astext
+                == "call_import_evaluation",
+                LLMUsageDaily.context["resource_id"].astext,
+            ),
+            else_=None,
+        ),
+    )
+
+
+def _resource_id_expr():
+    """Resource rollup key: explicit resource_id or agent_id fallback."""
+    return func.coalesce(
+        LLMUsageDaily.context["resource_id"].astext,
+        LLMUsageDaily.context["agent_id"].astext,
+    )
+
+
+def _resource_type_expr():
+    rid_expr = _resource_id_expr()
+    return func.coalesce(
+        LLMUsageDaily.context["resource_type"].astext,
+        case(
+            (LLMUsageDaily.context["agent_id"].astext.isnot(None), "agent"),
+            (
+                and_(
+                    LLMUsageDaily.product_section == "agents",
+                    rid_expr.isnot(None),
+                ),
+                "agent",
+            ),
+            else_=None,
+        ),
+    )
+
+
+def _call_import_group_expr():
+    """Resolve call import id from context, resource row, evaluation, or import row."""
+    eval_call_import = (
+        select(CallImportEvaluation.call_import_id)
+        .where(
+            CallImportEvaluation.id == cast(_evaluation_id_expr(), PG_UUID)
+        )
+        .correlate(LLMUsageDaily)
+        .scalar_subquery()
+    )
+    row_call_import = (
+        select(CallImportRow.call_import_id)
+        .where(
+            CallImportRow.id
+            == cast(LLMUsageDaily.context["call_import_row_id"].astext, PG_UUID)
+        )
+        .correlate(LLMUsageDaily)
+        .scalar_subquery()
+    )
+    eval_row_call_import = (
+        select(CallImportEvaluation.call_import_id)
+        .select_from(CallImportEvaluationRow)
+        .join(
+            CallImportEvaluation,
+            CallImportEvaluation.id == CallImportEvaluationRow.evaluation_id,
+        )
+        .where(
+            CallImportEvaluationRow.id
+            == cast(LLMUsageDaily.context["evaluation_row_id"].astext, PG_UUID)
+        )
+        .correlate(LLMUsageDaily)
+        .scalar_subquery()
+    )
+    return cast(
+        func.coalesce(
+            LLMUsageDaily.context["call_import_id"].astext,
+            case(
+                (
+                    LLMUsageDaily.context["resource_type"].astext == "call_import",
+                    LLMUsageDaily.context["resource_id"].astext,
+                ),
+                else_=None,
+            ),
+            cast(eval_call_import, String),
+            cast(row_call_import, String),
+            cast(eval_row_call_import, String),
+        ),
+        String,
+    )
+
+
+def _resource_scope_filter(resource_id: UUID):
+    """Match usage attributed to a product resource (agent, simulation, etc.)."""
+    rid = str(resource_id)
+    return or_(
+        LLMUsageDaily.context["resource_id"].astext == rid,
+        LLMUsageDaily.context["agent_id"].astext == rid,
+    )
+
+
+def _evaluation_scope_filter(
+    evaluation_id: UUID,
+    organization_id: UUID,
+    db: Session,
+):
+    eid = str(evaluation_id)
+    row_ids = [
+        str(row[0])
+        for row in db.query(CallImportEvaluationRow.id)
+        .filter(
+            CallImportEvaluationRow.evaluation_id == evaluation_id,
+        )
+        .all()
+    ]
+    clauses = [
+        LLMUsageDaily.context["evaluation_id"].astext == eid,
+        and_(
+            LLMUsageDaily.context["resource_id"].astext == eid,
+            LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
+        ),
+    ]
+    if row_ids:
+        clauses.append(LLMUsageDaily.context["evaluation_row_id"].astext.in_(row_ids))
+    return or_(*clauses)
+
+
+def _call_import_scope_filter(
+    call_import_id: UUID,
+    organization_id: UUID,
+    db: Session,
+):
+    """Match usage tied to a call import (direct context, resource row, or eval runs)."""
+    cid = str(call_import_id)
+    eval_ids = [
+        str(row[0])
+        for row in db.query(CallImportEvaluation.id)
+        .filter(
+            CallImportEvaluation.organization_id == organization_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+        )
+        .all()
+    ]
+    clauses = [
+        LLMUsageDaily.context["call_import_id"].astext == cid,
+        and_(
+            LLMUsageDaily.context["resource_id"].astext == cid,
+            LLMUsageDaily.context["resource_type"].astext == "call_import",
+        ),
+    ]
+    if eval_ids:
+        clauses.append(LLMUsageDaily.context["evaluation_id"].astext.in_(eval_ids))
+        clauses.append(
+            and_(
+                LLMUsageDaily.context["resource_id"].astext.in_(eval_ids),
+                LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
+            )
+        )
+    return or_(*clauses)
+
+
+def _call_import_ids_for_filters(
+    db: Session,
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+) -> List[UUID]:
+    query = db.query(CallImport.id).filter(
+        CallImport.organization_id == organization_id,
+    )
+    if workspace_id is not None:
+        query = query.filter(CallImport.workspace_id == workspace_id)
+    if dataset:
+        query = query.filter(CallImport.dataset == dataset)
+    if tag_id is not None:
+        query = query.filter(
+            CallImport.id.in_(
+                db.query(CallImportTagAssignment.call_import_id).filter(
+                    CallImportTagAssignment.tag_id == tag_id,
+                )
+            )
+        )
+    return [row[0] for row in query.all()]
+
+
+def _call_import_ids_scope_filter(
+    allowed_import_ids: List[UUID],
+):
+    if not allowed_import_ids:
+        return LLMUsageDaily.id.is_(None)
+    allowed = [str(uid) for uid in allowed_import_ids]
+    return cast(_call_import_group_expr(), String).in_(allowed)
+
+
+def _workspace_scope_filter(
+    workspace_id: UUID,
+    organization_id: UUID,
+):
+    """Match workspace-scoped rows plus legacy call-import usage with null workspace_id."""
+    ws_call_import_ids = (
+        select(cast(CallImport.id, String))
+        .where(
+            CallImport.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+        )
+    )
+    legacy_call_import = and_(
+        LLMUsageDaily.workspace_id.is_(None),
+        _call_import_group_expr().in_(ws_call_import_ids),
+    )
+    return or_(LLMUsageDaily.workspace_id == workspace_id, legacy_call_import)
 
 
 def _apply_filters(
@@ -107,6 +390,13 @@ def _apply_filters(
     product_section: Optional[str],
     model: Optional[str],
     resource_id: Optional[UUID],
+    usage_kind: Optional[str] = None,
+    call_import_id: Optional[UUID] = None,
+    evaluation_id: Optional[UUID] = None,
+    evaluation_row_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+    db: Optional[Session] = None,
 ):
     query = query.filter(
         LLMUsageDaily.organization_id == organization_id,
@@ -114,16 +404,449 @@ def _apply_filters(
         LLMUsageDaily.usage_date <= end,
     )
     if workspace_id is not None:
-        query = query.filter(LLMUsageDaily.workspace_id == workspace_id)
+        query = query.filter(
+            _workspace_scope_filter(workspace_id, organization_id)
+        )
     if product_section:
         query = query.filter(LLMUsageDaily.product_section == product_section)
     if model:
         query = query.filter(LLMUsageDaily.model == model)
     if resource_id is not None:
-        query = query.filter(LLMUsageDaily.resource_id == resource_id)
+        rid_filter = _resource_scope_filter(resource_id)
+        if db is not None:
+            query = query.filter(
+                or_(
+                    _evaluation_scope_filter(resource_id, organization_id, db),
+                    rid_filter,
+                )
+            )
+        else:
+            query = query.filter(
+                or_(
+                    LLMUsageDaily.context["resource_id"].astext == str(resource_id),
+                    LLMUsageDaily.context["agent_id"].astext == str(resource_id),
+                    LLMUsageDaily.context["evaluation_id"].astext == str(resource_id),
+                    LLMUsageDaily.context["evaluation_row_id"].astext == str(resource_id),
+                )
+            )
+    if call_import_id is not None:
+        if db is not None:
+            query = query.filter(
+                _call_import_scope_filter(call_import_id, organization_id, db)
+            )
+        else:
+            query = query.filter(
+                LLMUsageDaily.context["call_import_id"].astext == str(call_import_id)
+            )
+    if evaluation_id is not None:
+        if db is not None:
+            query = query.filter(
+                _evaluation_scope_filter(evaluation_id, organization_id, db)
+            )
+        else:
+            query = query.filter(
+                LLMUsageDaily.context["evaluation_id"].astext == str(evaluation_id)
+            )
+    if evaluation_row_id is not None:
+        query = query.filter(
+            LLMUsageDaily.context["evaluation_row_id"].astext
+            == str(evaluation_row_id)
+        )
+    if usage_kind:
+        query = query.filter(LLMUsageDaily.usage_kind == usage_kind)
+    if dataset or tag_id is not None:
+        if db is None:
+            raise ValueError("db required for dataset/tag filters")
+        allowed = _call_import_ids_for_filters(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            dataset=dataset,
+            tag_id=tag_id,
+        )
+        query = query.filter(_call_import_ids_scope_filter(allowed))
     return query
 
 
+def _filtered_query(
+    db: Session,
+    *,
+    organization_id: UUID,
+    start: date,
+    end: date,
+    workspace_id: Optional[UUID] = None,
+    product_section: Optional[str] = None,
+    model: Optional[str] = None,
+    resource_id: Optional[UUID] = None,
+    usage_kind: Optional[str] = None,
+    call_import_id: Optional[UUID] = None,
+    evaluation_id: Optional[UUID] = None,
+    evaluation_row_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+):
+    return _apply_filters(
+        db.query(LLMUsageDaily),
+        organization_id=organization_id,
+        start=start,
+        end=end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        db=db,
+    )
+
+
+def _infer_agent_types_for_label_buckets(
+    db: Session,
+    organization_id: UUID,
+    grouped: dict[str, tuple[Optional[str], list]],
+) -> None:
+    """When context JSON omits resource_type, infer agent rows from Agent.id."""
+    candidate_ids: list[UUID] = []
+    for rid, (rtype, _) in grouped.items():
+        if rtype:
+            continue
+        uid = parse_uuid(rid)
+        if uid:
+            candidate_ids.append(uid)
+    if not candidate_ids:
+        return
+    known_agent_ids = {
+        row.id
+        for row in db.query(Agent.id).filter(
+            Agent.organization_id == organization_id,
+            Agent.id.in_(candidate_ids),
+        ).all()
+    }
+    for rid in grouped:
+        uid = parse_uuid(rid)
+        if uid and uid in known_agent_ids:
+            rtype, contexts = grouped[rid]
+            if not rtype:
+                grouped[rid] = ("agent", contexts)
+
+
+def _resource_label_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, str]:
+    """Build resource_id -> hierarchical label from usage rows in query."""
+    rows = (
+        query.with_entities(
+            _resource_id_expr(),
+            _resource_type_expr(),
+            LLMUsageDaily.context,
+        )
+        .filter(
+            or_(
+                LLMUsageDaily.context["resource_id"].astext.isnot(None),
+                LLMUsageDaily.context["agent_id"].astext.isnot(None),
+            )
+        )
+        .order_by(*_label_row_order())
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    grouped: dict[str, tuple[Optional[str], list]] = {}
+    for raw_id, rtype, ctx in rows:
+        if not raw_id:
+            continue
+        key = str(raw_id)
+        if key not in grouped:
+            grouped[key] = (rtype, [])
+        grouped[key][1].append(ctx)
+
+    _infer_agent_types_for_label_buckets(db, organization_id, grouped)
+
+    buckets = [(rid, rtype, contexts) for rid, (rtype, contexts) in grouped.items()]
+    resolver = UsageNameResolver(db, organization_id)
+    contexts_for_preload = []
+    for rid, rtype, contexts in buckets:
+        for ctx in contexts:
+            merged = dict(ctx or {})
+            if rid:
+                merged.setdefault("resource_id", rid)
+            if rtype:
+                merged.setdefault("resource_type", rtype)
+            contexts_for_preload.append(merged)
+    resolver.preload(contexts_for_preload)
+    return labels_for_resource_buckets(buckets, resolver)
+
+
+def _resource_filter_meta_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, dict[str, Optional[str]]]:
+    """Map resource id -> {type, product_section} for filter dropdowns."""
+    rows = (
+        query.with_entities(
+            _resource_id_expr(),
+            _resource_type_expr(),
+            LLMUsageDaily.product_section,
+        )
+        .filter(
+            or_(
+                LLMUsageDaily.context["resource_id"].astext.isnot(None),
+                LLMUsageDaily.context["agent_id"].astext.isnot(None),
+            )
+        )
+        .distinct()
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    meta: dict[str, dict[str, Optional[str]]] = {}
+    for raw_id, rtype, section in rows:
+        if not raw_id:
+            continue
+        key = str(raw_id)
+        entry = meta.setdefault(key, {"type": None, "product_section": None})
+        if rtype:
+            entry["type"] = str(rtype)
+        if section:
+            entry["product_section"] = str(section)
+        if not entry["type"] and section == "agents":
+            entry["type"] = "agent"
+    return meta
+
+
+def _breakdown_resource_label(
+    raw_res_id: Optional[str],
+    res_type: Optional[str],
+    section: Optional[str],
+    resource_labels: dict[str, str],
+    db: Session,
+    organization_id: UUID,
+) -> str:
+    if not raw_res_id:
+        return "Unscoped"
+    key = str(raw_res_id)
+    label = resource_labels.get(key)
+    if label and label != "Unscoped":
+        return label
+    effective_type = res_type or ("agent" if section == "agents" else None)
+    if effective_type == "agent":
+        resolver = UsageNameResolver(db, organization_id)
+        resolver.preload([{"resource_id": key, "resource_type": "agent"}])
+        return resolver.agent_name(key)
+    return label or "Unscoped"
+
+
+def _collect_call_import_ids_from_usage_rows(
+    db: Session,
+    organization_id: UUID,
+    rows: list,
+) -> set[UUID]:
+    """Extract call import ids referenced in usage row context tuples."""
+    import_ids: set[UUID] = set()
+    eval_ids: set[UUID] = set()
+    row_ids: set[UUID] = set()
+    eval_row_ids: set[UUID] = set()
+
+    for row in rows:
+        cid_raw = row[0] if len(row) > 0 else None
+        res_raw = row[1] if len(row) > 1 else None
+        rtype = row[2] if len(row) > 2 else None
+        eval_raw = row[3] if len(row) > 3 else None
+        row_id_raw = row[4] if len(row) > 4 else None
+        eval_row_raw = row[5] if len(row) > 5 else None
+
+        if cid_raw:
+            uid = parse_uuid(cid_raw)
+            if uid:
+                import_ids.add(uid)
+        if res_raw and rtype == "call_import":
+            uid = parse_uuid(res_raw)
+            if uid:
+                import_ids.add(uid)
+        if eval_raw:
+            uid = parse_uuid(eval_raw)
+            if uid:
+                eval_ids.add(uid)
+        if res_raw and rtype == "call_import_evaluation":
+            uid = parse_uuid(res_raw)
+            if uid:
+                eval_ids.add(uid)
+        if row_id_raw:
+            uid = parse_uuid(row_id_raw)
+            if uid:
+                row_ids.add(uid)
+        if eval_row_raw:
+            uid = parse_uuid(eval_row_raw)
+            if uid:
+                eval_row_ids.add(uid)
+
+    if eval_ids:
+        for ev in (
+            db.query(CallImportEvaluation)
+            .filter(
+                CallImportEvaluation.organization_id == organization_id,
+                CallImportEvaluation.id.in_(eval_ids),
+            )
+            .all()
+        ):
+            import_ids.add(ev.call_import_id)
+
+    if row_ids:
+        for cid in (
+            db.query(CallImportRow.call_import_id)
+            .filter(CallImportRow.id.in_(row_ids))
+            .distinct()
+            .all()
+        ):
+            import_ids.add(cid[0])
+
+    if eval_row_ids:
+        for cid in (
+            db.query(CallImportEvaluation.call_import_id)
+            .join(
+                CallImportEvaluationRow,
+                CallImportEvaluationRow.evaluation_id == CallImportEvaluation.id,
+            )
+            .filter(CallImportEvaluationRow.id.in_(eval_row_ids))
+            .distinct()
+            .all()
+        ):
+            import_ids.add(cid[0])
+
+    return import_ids
+
+
+def _call_import_label_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, str]:
+    rows = (
+        query.with_entities(
+            LLMUsageDaily.context["call_import_id"].astext,
+            LLMUsageDaily.context["resource_id"].astext,
+            LLMUsageDaily.context["resource_type"].astext,
+            LLMUsageDaily.context["evaluation_id"].astext,
+            LLMUsageDaily.context["call_import_row_id"].astext,
+            LLMUsageDaily.context["evaluation_row_id"].astext,
+        )
+        .order_by(*_label_row_order())
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    import_ids = _collect_call_import_ids_from_usage_rows(
+        db, organization_id, rows
+    )
+
+    if not import_ids:
+        return {}
+    resolver = UsageNameResolver(db, organization_id)
+    resolver.preload(
+        [{"call_import_id": str(uid)} for uid in import_ids]
+    )
+    return labels_for_call_import_ids(list(import_ids), resolver)
+
+
+def _call_import_filter_labels(
+    db: Session,
+    organization_id: UUID,
+    query,
+    workspace_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+) -> dict[str, str]:
+    """Call imports for filters: usage in range plus workspace imports when scoped."""
+    import_ids = _collect_call_import_ids_from_usage_rows(
+        db,
+        organization_id,
+        query.with_entities(
+            LLMUsageDaily.context["call_import_id"].astext,
+            LLMUsageDaily.context["resource_id"].astext,
+            LLMUsageDaily.context["resource_type"].astext,
+            LLMUsageDaily.context["evaluation_id"].astext,
+            LLMUsageDaily.context["call_import_row_id"].astext,
+            LLMUsageDaily.context["evaluation_row_id"].astext,
+        ).order_by(*_label_row_order()).limit(_LABEL_ROW_LIMIT).all(),
+    )
+
+    if workspace_id is not None:
+        scoped_ids = _call_import_ids_for_filters(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            dataset=dataset,
+            tag_id=tag_id,
+        )
+        import_ids.update(scoped_ids)
+
+    if not import_ids:
+        return {}
+    resolver = UsageNameResolver(db, organization_id)
+    resolver.preload([{"call_import_id": str(uid)} for uid in import_ids])
+    return labels_for_call_import_ids(list(import_ids), resolver)
+
+
+def _evaluation_label_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, str]:
+    """Evaluation id -> short label (name + id suffix) for filter dropdowns."""
+    rows = (
+        query.with_entities(
+            LLMUsageDaily.context["evaluation_id"].astext,
+            LLMUsageDaily.context["resource_id"].astext,
+            LLMUsageDaily.context["resource_type"].astext,
+            LLMUsageDaily.context,
+        )
+        .filter(
+            or_(
+                LLMUsageDaily.context["evaluation_id"].astext.isnot(None),
+                LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
+            )
+        )
+        .order_by(*_label_row_order())
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    grouped: dict[str, tuple[Optional[str], list]] = {}
+    for eval_raw, res_raw, rtype, ctx in rows:
+        key_raw = eval_raw
+        if not key_raw and rtype == "call_import_evaluation" and res_raw:
+            key_raw = res_raw
+        if not key_raw:
+            continue
+        key = str(key_raw)
+        if key not in grouped:
+            grouped[key] = (rtype, [])
+        grouped[key][1].append(ctx)
+
+    buckets = [(rid, rtype, contexts) for rid, (rtype, contexts) in grouped.items()]
+    resolver = UsageNameResolver(db, organization_id)
+    contexts_for_preload = []
+    for _, rtype, contexts in buckets:
+        for ctx in contexts:
+            merged = dict(ctx or {})
+            if rtype and "resource_type" not in merged:
+                merged["resource_type"] = rtype
+            contexts_for_preload.append(merged)
+    resolver.preload(contexts_for_preload)
+
+    labels: dict[str, str] = {}
+    for raw_id, rtype, contexts in buckets:
+        ctx = max([dict(c or {}) for c in contexts], key=len, default={})
+        if rtype and "resource_type" not in ctx:
+            ctx["resource_type"] = rtype
+        eval_key = ctx.get("evaluation_id") or raw_id
+        labels[str(raw_id)] = resolver.evaluation_name(str(eval_key))
+    return labels
+
+
 def _last_updated(db: Session, organization_id: UUID) -> Optional[datetime]:
     return (
         db.query(func.max(LLMUsageDaily.updated_at))
@@ -132,39 +855,120 @@ def _last_updated(db: Session, organization_id: UUID) -> Optional[datetime]:
     )
 
 
+def _summary_aggregate_query(
+    db: Session,
+    *,
+    organization_id: UUID,
+    start: date,
+    end: date,
+    workspace_id: Optional[UUID],
+    product_section: Optional[str],
+    model: Optional[str],
+    resource_id: Optional[UUID],
+    usage_kind: Optional[str],
+    call_import_id: Optional[UUID],
+    evaluation_id: Optional[UUID],
+    evaluation_row_id: Optional[UUID],
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+):
+    return _apply_filters(
+        db.query(
+            func.coalesce(func.sum(LLMUsageDaily.prompt_tokens), 0).label("prompt_tokens"),
+            func.coalesce(func.sum(LLMUsageDaily.completion_tokens), 0).label(
+                "completion_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.cache_read_tokens), 0).label(
+                "cache_read_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.cache_creation_tokens), 0).label(
+                "cache_creation_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
+                "reasoning_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
+            func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
+            func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
+        ),
+        organization_id=organization_id,
+        start=start,
+        end=end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        db=db,
+    )
+
+
 @router.get("/summary", response_model=UsageSummaryResponse)
 def get_usage_summary(
     start: Optional[date] = Query(None),
     end: Optional[date] = Query(None),
+    tz: Optional[str] = Query(
+        None,
+        description="IANA timezone for interpreting start/end calendar dates",
+    ),
     workspace_id: Optional[UUID] = Query(None),
     product_section: Optional[str] = Query(None),
     model: Optional[str] = Query(None),
     resource_id: Optional[UUID] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    call_import_id: Optional[UUID] = Query(None),
+    evaluation_id: Optional[UUID] = Query(None),
+    evaluation_row_id: Optional[UUID] = Query(None),
+    dataset: Optional[str] = Query(None),
+    tag_id: Optional[UUID] = Query(None),
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
-    if start is None or end is None:
-        start, end = _default_range()
-    if end < start:
+    display_start, display_end, filter_start, filter_end = _parse_usage_range(
+        start, end, tz
+    )
+    if display_end < display_start:
         raise HTTPException(status_code=400, detail="end must be >= start")
 
     flush_usage_to_catalog(db, organization_id)
 
-    query = _apply_filters(
-        db.query(LLMUsageDaily),
+    row = _summary_aggregate_query(
+        db,
         organization_id=organization_id,
-        start=start,
-        end=end,
+        start=filter_start,
+        end=filter_end,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
         resource_id=resource_id,
-    )
-    rows = query.all()
-    totals = merge_usage_totals(rows)
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    ).one()
+    prompt = int(row.prompt_tokens)
+    completion = int(row.completion_tokens)
+    totals = {
+        "prompt_tokens": prompt,
+        "completion_tokens": completion,
+        "total_tokens": prompt + completion,
+        "cache_read_tokens": int(row.cache_read_tokens),
+        "cache_creation_tokens": int(row.cache_creation_tokens),
+        "reasoning_tokens": int(row.reasoning_tokens),
+        "audio_seconds": int(row.audio_seconds),
+        "tts_characters": int(row.tts_characters),
+        "call_count": int(row.call_count),
+    }
     return UsageSummaryResponse(
-        start=start,
-        end=end,
+        start=display_start,
+        end=display_end,
         totals=UsageTotals(**totals),
         last_updated_at=_last_updated(db, organization_id),
     )
@@ -174,19 +978,30 @@ def get_usage_summary(
 def get_usage_breakdown(
     start: Optional[date] = Query(None),
     end: Optional[date] = Query(None),
+    tz: Optional[str] = Query(
+        None,
+        description="IANA timezone for interpreting start/end calendar dates",
+    ),
     group_by: GroupBy = Query("workspace"),
     workspace_id: Optional[UUID] = Query(None),
     product_section: Optional[str] = Query(None),
     model: Optional[str] = Query(None),
     resource_id: Optional[UUID] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    call_import_id: Optional[UUID] = Query(None),
+    evaluation_id: Optional[UUID] = Query(None),
+    evaluation_row_id: Optional[UUID] = Query(None),
+    dataset: Optional[str] = Query(None),
+    tag_id: Optional[UUID] = Query(None),
     limit: int = Query(100, ge=1, le=500),
     offset: int = Query(0, ge=0),
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
-    if start is None or end is None:
-        start, end = _default_range()
-    if end < start:
+    display_start, display_end, filter_start, filter_end = _parse_usage_range(
+        start, end, tz
+    )
+    if display_end < display_start:
         raise HTTPException(status_code=400, detail="end must be >= start")
 
     flush_usage_to_catalog(db, organization_id)
@@ -195,7 +1010,9 @@ def get_usage_breakdown(
         "workspace": LLMUsageDaily.workspace_id,
         "product_section": LLMUsageDaily.product_section,
         "model": LLMUsageDaily.model,
-        "resource": LLMUsageDaily.resource_id,
+        "resource": cast(_resource_id_expr(), String),
+        "usage_kind": LLMUsageDaily.usage_kind,
+        "call_import": _call_import_group_expr(),
     }[group_by]
 
     aggregates = [
@@ -212,27 +1029,37 @@ def get_usage_breakdown(
         func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
             "reasoning_tokens"
         ),
+        func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
+        func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
         func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
     ]
 
     select_cols = [dim]
     group_cols = [dim]
     if group_by == "resource":
-        select_cols.append(LLMUsageDaily.resource_type)
-        group_cols.append(LLMUsageDaily.resource_type)
+        select_cols.append(cast(_resource_type_expr(), String))
+        group_cols.append(cast(_resource_type_expr(), String))
+        select_cols.append(LLMUsageDaily.product_section)
+        group_cols.append(LLMUsageDaily.product_section)
 
     query = _apply_filters(
         db.query(*select_cols, *aggregates),
         organization_id=organization_id,
-        start=start,
-        end=end,
+        start=filter_start,
+        end=filter_end,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
         resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        db=db,
     ).group_by(*group_cols)
 
-    total_count = query.count()
     results = (
         query.order_by(func.sum(LLMUsageDaily.call_count).desc())
         .offset(offset)
@@ -240,27 +1067,53 @@ def get_usage_breakdown(
         .all()
     )
 
-    workspace_names = {
-        w.id: w.name
-        for w in db.query(Workspace)
-        .filter(Workspace.organization_id == organization_id)
-        .all()
-    }
-    resource_labels: dict = {}
-    if group_by == "resource":
-        resource_ids = [r[0] for r in results if r[0] is not None]
-        if resource_ids:
-            evals = (
-                db.query(CallImportEvaluation)
+    workspace_names: dict = {}
+    if group_by == "workspace":
+        ws_ids = {r[0] for r in results if r[0] is not None}
+        if ws_ids:
+            workspace_names = {
+                w.id: w.name
+                for w in db.query(Workspace)
                 .filter(
-                    CallImportEvaluation.organization_id == organization_id,
-                    CallImportEvaluation.id.in_(resource_ids),
+                    Workspace.organization_id == organization_id,
+                    Workspace.id.in_(ws_ids),
                 )
                 .all()
-            )
-            for evaluation in evals:
-                label = (evaluation.name or "").strip() or str(evaluation.id)[:8]
-                resource_labels[evaluation.id] = label
+            }
+    resource_labels: dict[str, str] = {}
+    call_import_labels: dict[str, str] = {}
+    if group_by == "resource":
+        label_query = _filtered_query(
+            db,
+            organization_id=organization_id,
+            start=filter_start,
+            end=filter_end,
+            workspace_id=workspace_id,
+            product_section=product_section,
+            model=model,
+            resource_id=resource_id,
+            usage_kind=usage_kind,
+            call_import_id=call_import_id,
+            evaluation_id=evaluation_id,
+            evaluation_row_id=evaluation_row_id,
+        )
+        resource_labels = _resource_label_map(db, organization_id, label_query)
+    elif group_by == "call_import":
+        label_query = _filtered_query(
+            db,
+            organization_id=organization_id,
+            start=filter_start,
+            end=filter_end,
+            workspace_id=workspace_id,
+            product_section=product_section,
+            model=model,
+            resource_id=resource_id,
+            usage_kind=usage_kind,
+            call_import_id=call_import_id,
+            evaluation_id=evaluation_id,
+            evaluation_row_id=evaluation_row_id,
+        )
+        call_import_labels = _call_import_label_map(db, organization_id, label_query)
 
     rows: List[UsageBreakdownRow] = []
     for result in results:
@@ -277,7 +1130,9 @@ def get_usage_breakdown(
                     cache_read_tokens=int(metrics[2]),
                     cache_creation_tokens=int(metrics[3]),
                     reasoning_tokens=int(metrics[4]),
-                    call_count=int(metrics[5]),
+                    audio_seconds=int(metrics[5]),
+                    tts_characters=int(metrics[6]),
+                    call_count=int(metrics[7]),
                 )
             )
         elif group_by == "product_section":
@@ -293,7 +1148,9 @@ def get_usage_breakdown(
                     cache_read_tokens=int(metrics[2]),
                     cache_creation_tokens=int(metrics[3]),
                     reasoning_tokens=int(metrics[4]),
-                    call_count=int(metrics[5]),
+                    audio_seconds=int(metrics[5]),
+                    tts_characters=int(metrics[6]),
+                    call_count=int(metrics[7]),
                 )
             )
         elif group_by == "model":
@@ -308,35 +1165,101 @@ def get_usage_breakdown(
                     cache_read_tokens=int(metrics[2]),
                     cache_creation_tokens=int(metrics[3]),
                     reasoning_tokens=int(metrics[4]),
-                    call_count=int(metrics[5]),
+                    audio_seconds=int(metrics[5]),
+                    tts_characters=int(metrics[6]),
+                    call_count=int(metrics[7]),
+                )
+            )
+        elif group_by == "usage_kind":
+            kind = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    usage_kind=kind,
+                    prompt_tokens=int(metrics[0]),
+                    completion_tokens=int(metrics[1]),
+                    total_tokens=int(metrics[0]) + int(metrics[1]),
+                    cache_read_tokens=int(metrics[2]),
+                    cache_creation_tokens=int(metrics[3]),
+                    reasoning_tokens=int(metrics[4]),
+                    audio_seconds=int(metrics[5]),
+                    tts_characters=int(metrics[6]),
+                    call_count=int(metrics[7]),
+                )
+            )
+        elif group_by == "call_import":
+            raw_cid = result[0]
+            metrics = result[1:]
+            cid = None
+            if raw_cid:
+                try:
+                    cid = UUID(str(raw_cid))
+                except (ValueError, TypeError):
+                    cid = None
+            label = (
+                call_import_labels.get(str(raw_cid), "Unscoped")
+                if raw_cid
+                else "Unscoped"
+            )
+            rows.append(
+                UsageBreakdownRow(
+                    call_import_id=cid,
+                    call_import_label=label,
+                    prompt_tokens=int(metrics[0]),
+                    completion_tokens=int(metrics[1]),
+                    total_tokens=int(metrics[0]) + int(metrics[1]),
+                    cache_read_tokens=int(metrics[2]),
+                    cache_creation_tokens=int(metrics[3]),
+                    reasoning_tokens=int(metrics[4]),
+                    audio_seconds=int(metrics[5]),
+                    tts_characters=int(metrics[6]),
+                    call_count=int(metrics[7]),
                 )
             )
         else:
-            res_id, res_type = result[0], result[1]
-            metrics = result[2:]
+            raw_res_id, res_type, section = result[0], result[1], result[2]
+            metrics = result[3:]
+            res_id = None
+            if raw_res_id:
+                try:
+                    res_id = UUID(str(raw_res_id))
+                except (ValueError, TypeError):
+                    res_id = None
             rows.append(
                 UsageBreakdownRow(
                     resource_id=res_id,
-                    resource_type=res_type,
-                    resource_label=resource_labels.get(res_id)
-                    if res_id
-                    else "Unscoped",
+                    resource_type=res_type or (
+                        "agent" if section == "agents" and raw_res_id else None
+                    ),
+                    resource_label=_breakdown_resource_label(
+                        raw_res_id,
+                        res_type,
+                        section,
+                        resource_labels,
+                        db,
+                        organization_id,
+                    ),
+                    product_section=section,
+                    product_section_label=SECTION_LABELS.get(section or "", section),
                     prompt_tokens=int(metrics[0]),
                     completion_tokens=int(metrics[1]),
                     total_tokens=int(metrics[0]) + int(metrics[1]),
                     cache_read_tokens=int(metrics[2]),
                     cache_creation_tokens=int(metrics[3]),
                     reasoning_tokens=int(metrics[4]),
-                    call_count=int(metrics[5]),
+                    audio_seconds=int(metrics[5]),
+                    tts_characters=int(metrics[6]),
+                    call_count=int(metrics[7]),
                 )
             )
 
     return UsageBreakdownResponse(
-        start=start,
-        end=end,
+        start=display_start,
+        end=display_end,
         group_by=group_by,
         rows=rows,
-        total_count=total_count,
+        total_count=len(rows),
+        truncated_at_limit=len(rows) >= limit,
         last_updated_at=_last_updated(db, organization_id),
     )
 
@@ -345,23 +1268,93 @@ def get_usage_breakdown(
 def get_usage_filters(
     start: Optional[date] = Query(None),
     end: Optional[date] = Query(None),
+    tz: Optional[str] = Query(
+        None,
+        description="IANA timezone for interpreting start/end calendar dates",
+    ),
+    workspace_id: Optional[UUID] = Query(None),
+    product_section: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    resource_id: Optional[UUID] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    call_import_id: Optional[UUID] = Query(None),
+    dataset: Optional[str] = Query(None),
+    tag_id: Optional[UUID] = Query(None),
+    q: Optional[str] = Query(None, description="Optional resource label search"),
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
-    if start is None or end is None:
-        start, end = _default_range()
+    _, _, filter_start, filter_end = _parse_usage_range(start, end, tz)
 
     flush_usage_to_catalog(db, organization_id)
 
-    base = db.query(LLMUsageDaily).filter(
-        LLMUsageDaily.organization_id == organization_id,
-        LLMUsageDaily.usage_date >= start,
-        LLMUsageDaily.usage_date <= end,
+    workspace_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    section_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        workspace_id=workspace_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    kind_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        call_import_id=call_import_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    model_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    call_import_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    resource_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        dataset=dataset,
+        tag_id=tag_id,
     )
 
     workspace_ids = {
         row[0]
-        for row in base.with_entities(LLMUsageDaily.workspace_id).distinct().all()
+        for row in workspace_base.with_entities(LLMUsageDaily.workspace_id).distinct().all()
         if row[0] is not None
     }
     workspaces = [
@@ -375,45 +1368,94 @@ def get_usage_filters(
     sections = sorted(
         {
             row[0]
-            for row in base.with_entities(LLMUsageDaily.product_section).distinct().all()
+            for row in section_base.with_entities(LLMUsageDaily.product_section).distinct().all()
             if row[0]
         }
     )
     models = sorted(
         {
             row[0]
-            for row in base.with_entities(LLMUsageDaily.model).distinct().all()
+            for row in model_base.with_entities(LLMUsageDaily.model).distinct().all()
+            if row[0]
+        }
+    )
+    kinds = sorted(
+        {
+            row[0]
+            for row in kind_base.with_entities(LLMUsageDaily.usage_kind).distinct().all()
             if row[0]
         }
     )
 
-    resource_rows = (
-        base.with_entities(LLMUsageDaily.resource_id, LLMUsageDaily.resource_type)
-        .filter(LLMUsageDaily.resource_id.isnot(None))
-        .distinct()
-        .limit(100)
-        .all()
+    call_import_labels = _call_import_filter_labels(
+        db,
+        organization_id,
+        call_import_base,
+        workspace_id=workspace_id,
+        dataset=dataset,
+        tag_id=tag_id,
     )
-    resource_ids = [r[0] for r in resource_rows]
-    eval_names = {}
-    if resource_ids:
-        for evaluation in (
-            db.query(CallImportEvaluation)
-            .filter(CallImportEvaluation.id.in_(resource_ids))
-            .all()
-        ):
-            eval_names[evaluation.id] = (evaluation.name or "").strip() or str(
-                evaluation.id
-            )[:8]
+    call_imports = [
+        {"id": cid, "label": label}
+        for cid, label in sorted(call_import_labels.items(), key=lambda x: x[1].lower())
+    ]
 
-    resources = [
-        {
-            "id": str(rid),
-            "type": rtype,
-            "label": eval_names.get(rid, str(rid)[:8]),
-        }
-        for rid, rtype in resource_rows
-        if rid is not None
+    evaluation_labels = _evaluation_label_map(db, organization_id, resource_base)
+    needle = (q or "").strip().lower()
+    evaluations = []
+    for rid, label in sorted(evaluation_labels.items(), key=lambda x: x[1].lower()):
+        if needle and needle not in label.lower():
+            continue
+        evaluations.append({"id": rid, "label": label})
+
+    resource_labels = _resource_label_map(db, organization_id, resource_base)
+    resource_meta = _resource_filter_meta_map(db, organization_id, resource_base)
+    resources = []
+    for rid, label in sorted(resource_labels.items(), key=lambda x: x[1].lower()):
+        if needle and needle not in label.lower():
+            continue
+        info = resource_meta.get(rid, {})
+        rtype = info.get("type")
+        if rtype == "call_import_evaluation":
+            continue
+        resources.append(
+            {
+                "id": rid,
+                "label": label,
+                "type": rtype,
+                "product_section": info.get("product_section"),
+            }
+        )
+
+    dataset_query = db.query(CallImport.dataset).filter(
+        CallImport.organization_id == organization_id,
+        CallImport.dataset.isnot(None),
+        CallImport.dataset != "",
+    )
+    if workspace_id is not None:
+        dataset_query = dataset_query.filter(CallImport.workspace_id == workspace_id)
+    if dataset or tag_id is not None:
+        scoped_import_ids = _call_import_ids_for_filters(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            dataset=dataset,
+            tag_id=tag_id,
+        )
+        if scoped_import_ids:
+            dataset_query = dataset_query.filter(CallImport.id.in_(scoped_import_ids))
+        else:
+            dataset_query = dataset_query.filter(CallImport.id.is_(None))
+    datasets = sorted({row[0] for row in dataset_query.distinct().all() if row[0]})
+
+    tags = [
+        {"id": str(tag.id), "label": tag.name}
+        for tag in (
+            db.query(CallImportTag)
+            .filter(CallImportTag.organization_id == organization_id)
+            .order_by(CallImportTag.name)
+            .all()
+        )
     ]
 
     return UsageFiltersResponse(
@@ -421,6 +1463,12 @@ def get_usage_filters(
         product_sections=[
             {"id": s, "label": SECTION_LABELS.get(s, s)} for s in sections
         ],
+        call_imports=call_imports,
+        evaluations=evaluations,
         models=models,
         resources=resources,
+        usage_kinds=[{"id": k, "label": usage_kind_label(k)} for k in kinds],
+        datasets=datasets,
+        tags=tags,
     )
+
diff --git a/app/api/v1/routes/playground.py b/app/api/v1/routes/playground.py
index 19e97a65..4c4d93dd 100644
--- a/app/api/v1/routes/playground.py
+++ b/app/api/v1/routes/playground.py
@@ -38,215 +38,9 @@
 from app.services.evaluators.evaluator_result_call_data import slim_call_data_for_evaluator_result
 from app.utils.call_recordings import generate_unique_call_short_id
 
-router = APIRouter(prefix="/playground", tags=["playground"])
-
-
-def extract_transcript_from_call_data(call_data: Dict[str, Any], provider_platform: str) -> tuple:
-    """
-    Extract transcript and speaker segments from provider call_data.
-    
-    Args:
-        call_data: Full call data from voice provider
-        provider_platform: The provider platform ("vapi", "retell", "elevenlabs", "smallest")
-        
-    Returns:
-        Tuple of (transcript_text, speaker_segments)
-        - transcript_text: Plain text transcript
-        - speaker_segments: List of segments with speaker labels
-    """
-    transcript_text = ""
-    speaker_segments = []
-    
-    if not call_data:
-        return transcript_text, speaker_segments
-    
-    provider_platform_lower = provider_platform.lower() if provider_platform else ""
-    
-    if provider_platform_lower == "vapi":
-        # Vapi: keep provider payload raw and derive transcript from transcript/messages.
-        transcript_text = call_data.get("transcript", "")
-        
-        # Get structured messages for speaker segments
-        transcript_object = call_data.get("transcript_object", [])
-        if not transcript_object:
-            # Try messages array
-            artifact = call_data.get("artifact", {}) if isinstance(call_data, dict) else {}
-            messages = call_data.get("messages", []) or artifact.get("messages", [])
-            for msg in messages:
-                role = msg.get("role", "unknown")
-                content = msg.get("message", "") or msg.get("content", "")
-                
-                if not content or role == "system":
-                    continue
-                
-                # Map roles
-                if role in ["bot", "assistant"]:
-                    normalized_role = "agent"
-                elif role == "user":
-                    normalized_role = "user"
-                else:
-                    continue
-                
-                speaker_segments.append({
-                    "speaker": "Agent" if normalized_role == "agent" else "User",
-                    "text": content,
-                    "start": msg.get("secondsFromStart", 0),
-                    "end": msg.get("secondsFromStart", 0) + (msg.get("duration", 0) / 1000),
-                })
-        else:
-            for entry in transcript_object:
-                role = entry.get("role", "unknown")
-                content = entry.get("content", "")
-                
-                if not content:
-                    continue
-                
-                speaker_segments.append({
-                    "speaker": "Agent" if role == "agent" else "User",
-                    "text": content,
-                    "start": entry.get("seconds_from_start", 0),
-                    "end": entry.get("seconds_from_start", 0) + (entry.get("duration_ms", 0) / 1000),
-                })
-        
-        # Build transcript text from segments if not available
-        if not transcript_text and speaker_segments:
-            transcript_text = "\n".join([
-                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
-            ])
-            
-    elif provider_platform_lower == "elevenlabs":
-        raw_transcript = call_data.get("transcript")
-        transcript_obj = call_data.get("transcript_object", [])
-
-        # retrieve_call_metrics already processes the transcript into a
-        # formatted string + speaker_segments list, so handle both the
-        # pre-processed shape and the raw ElevenLabs API shape.
-        if isinstance(raw_transcript, str) and raw_transcript:
-            transcript_text = raw_transcript
-            if isinstance(transcript_obj, list):
-                for seg in transcript_obj:
-                    speaker_segments.append({
-                        "speaker": seg.get("speaker", "Unknown"),
-                        "text": seg.get("text", ""),
-                        "start": seg.get("start", 0),
-                        "end": seg.get("end", 0),
-                    })
-        elif isinstance(raw_transcript, list):
-            for entry in raw_transcript:
-                role = entry.get("role", "unknown")
-                content = entry.get("message", "") or entry.get("text", "")
-                if not content:
-                    continue
-                speaker = "Agent" if role in ("agent", "assistant", "ai") else "User"
-                speaker_segments.append({
-                    "speaker": speaker,
-                    "text": content,
-                    "start": entry.get("time_in_call_secs", 0) or entry.get("start", 0),
-                    "end": entry.get("time_in_call_secs", 0) or entry.get("end", 0),
-                })
-            transcript_text = "\n".join(
-                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
-            )
-
-    elif provider_platform_lower == "smallest":
-        transcript_raw = call_data.get("transcript")
-        transcript_object = call_data.get("transcript_object", [])
-        if isinstance(transcript_object, list) and transcript_object:
-            for entry in transcript_object:
-                if not isinstance(entry, dict):
-                    continue
-                text = entry.get("text", "")
-                if not text:
-                    continue
-                speaker = entry.get("speaker", "Unknown")
-                speaker_segments.append(
-                    {
-                        "speaker": speaker,
-                        "text": text,
-                        "start": entry.get("start", 0),
-                        "end": entry.get("end", entry.get("start", 0)),
-                    }
-                )
-            if not transcript_text:
-                transcript_text = "\n".join(
-                    f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
-                )
-        elif isinstance(transcript_raw, list):
-            for entry in transcript_raw:
-                if not isinstance(entry, dict):
-                    continue
-                role = str(entry.get("speaker") or entry.get("role") or "").lower()
-                speaker = "Agent" if role in ("agent", "assistant", "ai", "bot") else "User"
-                text = entry.get("text", "") or entry.get("message", "") or entry.get("content", "")
-                if not text:
-                    continue
-                ts = entry.get("timeInCallSecs", 0) or entry.get("start", 0) or entry.get("timestamp", 0)
-                speaker_segments.append(
-                    {
-                        "speaker": speaker,
-                        "text": text,
-                        "start": ts,
-                        "end": entry.get("end", ts),
-                    }
-                )
-            transcript_text = "\n".join(
-                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
-            )
-        elif isinstance(transcript_raw, str):
-            transcript_text = transcript_raw
-
-    elif provider_platform_lower == "retell":
-        # Retell: transcript can be a string or list of objects
-        transcript_raw = call_data.get("transcript", "")
-        
-        if isinstance(transcript_raw, str):
-            transcript_text = transcript_raw
-            # Parse transcript text into speaker segments if it has pattern like "Agent: text\nUser: text"
-            lines = transcript_raw.split("\n") if transcript_raw else []
-            for line in lines:
-                line = line.strip()
-                if not line:
-                    continue
-                if line.startswith("Agent:") or line.startswith("agent:"):
-                    speaker_segments.append({
-                        "speaker": "Agent",
-                        "text": line.split(":", 1)[1].strip() if ":" in line else line,
-                        "start": 0,
-                        "end": 0,
-                    })
-                elif line.startswith("User:") or line.startswith("user:"):
-                    speaker_segments.append({
-                        "speaker": "User",
-                        "text": line.split(":", 1)[1].strip() if ":" in line else line,
-                        "start": 0,
-                        "end": 0,
-                    })
-        elif isinstance(transcript_raw, list):
-            # Retell sometimes returns transcript as array of objects
-            for item in transcript_raw:
-                if isinstance(item, dict):
-                    role = item.get("role", "")
-                    content = item.get("content", "") or item.get("text", "")
-                    
-                    if not content:
-                        continue
-                    
-                    speaker = "Agent" if role in ["agent", "assistant", "bot"] else "User"
-                    speaker_segments.append({
-                        "speaker": speaker,
-                        "text": content,
-                        "start": item.get("start_time", 0) or item.get("timestamp", 0),
-                        "end": item.get("end_time", 0),
-                    })
-            
-            # Build transcript text from segments
-            transcript_text = "\n".join([
-                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
-            ])
-    
-    return transcript_text, speaker_segments
-
+from app.services.evaluators.call_data_transcript import extract_transcript_from_call_data
 
+router = APIRouter(prefix="/playground", tags=["playground"])
 def generate_unique_result_id(db: Session) -> str:
     """Generate a unique 6-digit result ID for EvaluatorResult."""
     max_attempts = 100
@@ -411,7 +205,8 @@ def poll_call_metrics(
                             or recording_urls.get("stereo_url")
                         )
                         if audio_url:
-                            resp = _http.get(audio_url, timeout=120)
+                            vapi_headers = {"Authorization": f"Bearer {integration_api_key}"}
+                            resp = _http.get(audio_url, headers=vapi_headers, timeout=120)
                             if resp.status_code == 200:
                                 audio_bytes = resp.content
 
@@ -656,13 +451,32 @@ async def create_web_call(
             # For now, we'll skip it for Retell. Other providers can handle it in their implementation.
             if integration.platform != "retell" and web_call_data.custom_sip_headers:
                 call_params["custom_sip_headers"] = web_call_data.custom_sip_headers
-            
-            web_call_response = provider.create_web_call(**call_params)
-            
-            # Store call recording in database
+
+            platform_value = (
+                integration.platform.value
+                if hasattr(integration.platform, "value")
+                else integration.platform
+            )
+            plat_lower = str(platform_value).lower()
+
+            # Vapi Web SDK creates the call in the browser; server-side /call/web
+            # would spawn a second call that never receives the user's microphone.
+            if plat_lower == "vapi":
+                from app.services.voice_providers.vapi import VAPI_SAMPLE_RATE
+
+                web_call_response = {
+                    "call_type": "web_call",
+                    "agent_id": agent.voice_ai_agent_id,
+                    "metadata": web_call_data.metadata or {},
+                    "sample_rate": VAPI_SAMPLE_RATE,
+                    "client_sdk_creates_call": True,
+                }
+                provider_call_id = None
+            else:
+                web_call_response = provider.create_web_call(**call_params)
+                provider_call_id = web_call_response.get("call_id")
+
             call_short_id = generate_unique_call_short_id(db)
-            provider_call_id = web_call_response.get("call_id")
-            
             call_recording = CallRecording(
                 organization_id=organization_id,
                 workspace_id=workspace_id,
@@ -702,17 +516,13 @@ async def create_web_call(
             # Add call_short_id to response for frontend
             response = web_call_response.copy()
             response["call_short_id"] = call_short_id
-            
-            platform_value = integration.platform.value if hasattr(integration.platform, 'value') else integration.platform
-            
-            # For Vapi, include the public key in the response (needed for frontend SDK)
-            if platform_value.lower() == "vapi" and integration.public_key:
+
+            if plat_lower == "vapi" and integration.public_key:
                 response["public_key"] = integration.public_key
-            
-            # For ElevenLabs, pass through the signed_url (frontend SDK connects directly)
-            if platform_value.lower() == "elevenlabs":
+
+            if plat_lower == "elevenlabs":
                 response["signed_url"] = web_call_response.get("signed_url")
-            
+
             return response
         except Exception as e:
             raise HTTPException(
@@ -1850,7 +1660,10 @@ async def summarize_transcript(
       1. The voice bundle of ``agent_id`` (or the agent on ``call_short_id``).
       2. Any configured AIProvider matching the fallback preference list.
     """
+    from contextlib import nullcontext
+
     from app.services.ai.llm_service import llm_service
+    from app.services.usage.context import llm_usage_context, usage_context_for_agent
 
     transcript_text = (payload.transcript or "").strip()
     if not transcript_text and payload.entries:
@@ -1947,15 +1760,27 @@ async def summarize_transcript(
     ]
 
     try:
-        result = llm_service.generate_response(
-            messages=messages,
-            llm_provider=llm_provider,
-            llm_model=llm_model,
-            organization_id=organization_id,
-            db=db,
-            temperature=0.3,
-            max_tokens=400,
-        )
+        usage_ctx = nullcontext()
+        if agent_uuid:
+            agent_row = db.query(Agent).filter(
+                Agent.id == agent_uuid,
+                Agent.organization_id == organization_id,
+            ).first()
+            if agent_row:
+                usage_ctx = llm_usage_context(
+                    usage_context_for_agent(agent_row, workspace_id=workspace_id)
+                )
+
+        with usage_ctx:
+            result = llm_service.generate_response(
+                messages=messages,
+                llm_provider=llm_provider,
+                llm_model=llm_model,
+                organization_id=organization_id,
+                db=db,
+                temperature=0.3,
+                max_tokens=400,
+            )
     except Exception as e:
         logger.error(f"[summarize-transcript] LLM call failed: {e}")
         raise HTTPException(
diff --git a/app/api/v1/routes/vobiz_telephony.py b/app/api/v1/routes/vobiz_telephony.py
index 084d03b7..052d03b9 100644
--- a/app/api/v1/routes/vobiz_telephony.py
+++ b/app/api/v1/routes/vobiz_telephony.py
@@ -698,6 +698,7 @@ async def vobiz_media_websocket(websocket: WebSocket):
                     websocket,
                     context.system_instruction,
                     str(context.organization_id),
+                    str(context.workspace_id) if context.workspace_id else None,
                     agent_id,
                     persona_id,
                     scenario_id,
diff --git a/app/api/v1/routes/voice_agent.py b/app/api/v1/routes/voice_agent.py
index 47f7350e..e6d553b5 100644
--- a/app/api/v1/routes/voice_agent.py
+++ b/app/api/v1/routes/voice_agent.py
@@ -517,6 +517,7 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
                     websocket,
                     system_instruction,
                     str(organization_id),
+                    str(workspace_id) if workspace_id else None,
                     agent_id,
                     persona_id,
                     scenario_id,
diff --git a/app/migrations/063_usage_kind_stt_and_buffer.py b/app/migrations/063_usage_kind_stt_and_buffer.py
new file mode 100644
index 00000000..1cbb51d9
--- /dev/null
+++ b/app/migrations/063_usage_kind_stt_and_buffer.py
@@ -0,0 +1,233 @@
+"""Migration: STT usage_kind/audio_seconds + Redis-fallback pending buffer."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Add usage_kind/audio_seconds to llm_usage_daily and usage_pending_buffer "
+    "for durable STT + LLM usage when Redis is unavailable"
+)
+
+_ZERO_UUID = "00000000-0000-0000-0000-000000000000"
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def _dedupe_llm_usage_daily(db: Session) -> int:
+    """Merge duplicate bucket rows so the unique index can be created.
+
+    Keeps the oldest row (MIN id), sums metrics into it, deletes extras.
+    """
+    db.execute(
+        text(
+            f"""
+            WITH dupes AS (
+                SELECT
+                    organization_id,
+                    COALESCE(
+                        workspace_id,
+                        '{_ZERO_UUID}'::uuid
+                    ) AS ws_key,
+                    product_section,
+                    model,
+                    COALESCE(
+                        resource_id,
+                        '{_ZERO_UUID}'::uuid
+                    ) AS rid_key,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm') AS kind_key,
+                    MIN(id::text)::uuid AS keep_id,
+                    SUM(prompt_tokens)::bigint AS prompt_tokens,
+                    SUM(completion_tokens)::bigint AS completion_tokens,
+                    SUM(cache_read_tokens)::bigint AS cache_read_tokens,
+                    SUM(cache_creation_tokens)::bigint AS cache_creation_tokens,
+                    SUM(reasoning_tokens)::bigint AS reasoning_tokens,
+                    SUM(COALESCE(audio_seconds, 0))::bigint AS audio_seconds,
+                    SUM(call_count)::bigint AS call_count
+                FROM llm_usage_daily
+                GROUP BY 1, 2, 3, 4, 5, 6, 7
+                HAVING COUNT(*) > 1
+            )
+            UPDATE llm_usage_daily AS u SET
+                prompt_tokens = d.prompt_tokens,
+                completion_tokens = d.completion_tokens,
+                cache_read_tokens = d.cache_read_tokens,
+                cache_creation_tokens = d.cache_creation_tokens,
+                reasoning_tokens = d.reasoning_tokens,
+                audio_seconds = d.audio_seconds,
+                call_count = d.call_count,
+                updated_at = now()
+            FROM dupes AS d
+            WHERE u.id = d.keep_id
+            """
+        )
+    )
+
+    result = db.execute(
+        text(
+            f"""
+            WITH keepers AS (
+                SELECT MIN(id::text)::uuid AS keep_id
+                FROM llm_usage_daily
+                GROUP BY
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                    product_section,
+                    model,
+                    COALESCE(resource_id, '{_ZERO_UUID}'::uuid),
+                    usage_date,
+                    COALESCE(usage_kind, 'llm')
+                HAVING COUNT(*) > 1
+            )
+            DELETE FROM llm_usage_daily AS u
+            USING keepers AS k,
+                  llm_usage_daily AS peer
+            WHERE peer.id = k.keep_id
+              AND u.id <> k.keep_id
+              AND u.organization_id = peer.organization_id
+              AND u.product_section = peer.product_section
+              AND u.model = peer.model
+              AND u.usage_date = peer.usage_date
+              AND COALESCE(u.usage_kind, 'llm') = COALESCE(peer.usage_kind, 'llm')
+              AND u.workspace_id IS NOT DISTINCT FROM peer.workspace_id
+              AND u.resource_id IS NOT DISTINCT FROM peer.resource_id
+            """
+        )
+    )
+    return int(result.rowcount or 0)
+
+
+def _ensure_unique_bucket_index(db: Session) -> None:
+    """Drop legacy/broken unique index and recreate with usage_kind."""
+    db.execute(text("DROP INDEX IF EXISTS uq_llm_usage_daily_bucket"))
+    db.execute(
+        text(
+            f"""
+            CREATE UNIQUE INDEX uq_llm_usage_daily_bucket
+            ON llm_usage_daily (
+                organization_id,
+                COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                product_section,
+                model,
+                COALESCE(resource_id, '{_ZERO_UUID}'::uuid),
+                usage_date,
+                usage_kind
+            )
+            """
+        )
+    )
+
+
+def upgrade(db: Session):
+    if not _table_exists(db, "llm_usage_daily"):
+        print("llm_usage_daily missing; run 062 first — skipping 063")
+        db.commit()
+        return
+
+    if not _column_exists(db, "llm_usage_daily", "usage_kind"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE llm_usage_daily
+                ADD COLUMN usage_kind VARCHAR(16) NOT NULL DEFAULT 'llm'
+                """
+            )
+        )
+
+    if not _column_exists(db, "llm_usage_daily", "audio_seconds"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE llm_usage_daily
+                ADD COLUMN audio_seconds BIGINT NOT NULL DEFAULT 0
+                """
+            )
+        )
+
+    removed = _dedupe_llm_usage_daily(db)
+    if removed:
+        print(f"Merged/removed {removed} duplicate llm_usage_daily row(s)")
+
+    _ensure_unique_bucket_index(db)
+
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_llm_usage_daily_org_kind_date
+            ON llm_usage_daily (organization_id, usage_kind, usage_date)
+            """
+        )
+    )
+    print("Ensured usage_kind + audio_seconds + unique bucket index")
+
+    if not _table_exists(db, "usage_pending_buffer"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE usage_pending_buffer (
+                    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                    organization_id UUID NOT NULL,
+                    workspace_id UUID,
+                    product_section VARCHAR(64) NOT NULL,
+                    model VARCHAR(255) NOT NULL,
+                    context JSONB NOT NULL DEFAULT '{}'::jsonb,
+                    usage_date DATE NOT NULL,
+                    usage_kind VARCHAR(16) NOT NULL DEFAULT 'llm',
+                    prompt_tokens BIGINT NOT NULL DEFAULT 0,
+                    completion_tokens BIGINT NOT NULL DEFAULT 0,
+                    cache_read_tokens BIGINT NOT NULL DEFAULT 0,
+                    cache_creation_tokens BIGINT NOT NULL DEFAULT 0,
+                    reasoning_tokens BIGINT NOT NULL DEFAULT 0,
+                    audio_seconds BIGINT NOT NULL DEFAULT 0,
+                    call_count BIGINT NOT NULL DEFAULT 0,
+                    created_at TIMESTAMPTZ NOT NULL DEFAULT now()
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                CREATE INDEX IF NOT EXISTS ix_usage_pending_buffer_org_created
+                ON usage_pending_buffer (organization_id, created_at)
+                """
+            )
+        )
+        print("Created usage_pending_buffer")
+
+    db.commit()
+
+
+def downgrade(db: Session):
+    db.execute(text("DROP TABLE IF EXISTS usage_pending_buffer"))
+    db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_org_kind_date"))
+    # Keep usage_kind/audio_seconds columns on downgrade to avoid data loss.
+    db.commit()
diff --git a/app/migrations/064_usage_kind_tts_characters.py b/app/migrations/064_usage_kind_tts_characters.py
new file mode 100644
index 00000000..57d42da0
--- /dev/null
+++ b/app/migrations/064_usage_kind_tts_characters.py
@@ -0,0 +1,74 @@
+"""Migration: TTS usage_kind + tts_characters metric."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add tts_characters to llm_usage_daily and usage_pending_buffer for TTS usage"
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def upgrade(db: Session):
+    if _table_exists(db, "llm_usage_daily") and not _column_exists(
+        db, "llm_usage_daily", "tts_characters"
+    ):
+        db.execute(
+            text(
+                """
+                ALTER TABLE llm_usage_daily
+                ADD COLUMN tts_characters BIGINT NOT NULL DEFAULT 0
+                """
+            )
+        )
+        print("Added tts_characters to llm_usage_daily")
+
+    if _table_exists(db, "usage_pending_buffer") and not _column_exists(
+        db, "usage_pending_buffer", "tts_characters"
+    ):
+        db.execute(
+            text(
+                """
+                ALTER TABLE usage_pending_buffer
+                ADD COLUMN tts_characters BIGINT NOT NULL DEFAULT 0
+                """
+            )
+        )
+        print("Added tts_characters to usage_pending_buffer")
+
+    db.commit()
+
+
+def downgrade(db: Session):
+    if _column_exists(db, "llm_usage_daily", "tts_characters"):
+        db.execute(text("ALTER TABLE llm_usage_daily DROP COLUMN tts_characters"))
+    if _column_exists(db, "usage_pending_buffer", "tts_characters"):
+        db.execute(text("ALTER TABLE usage_pending_buffer DROP COLUMN tts_characters"))
+    db.commit()
diff --git a/app/migrations/065_usage_context_jsonb.py b/app/migrations/065_usage_context_jsonb.py
new file mode 100644
index 00000000..4dd62378
--- /dev/null
+++ b/app/migrations/065_usage_context_jsonb.py
@@ -0,0 +1,216 @@
+"""Migration: JSONB context column for usage attribution metadata."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Move resource_id/resource_type into llm_usage_daily.context JSONB and "
+    "recreate bucket uniqueness on context keys"
+)
+
+_ZERO_UUID = "00000000-0000-0000-0000-000000000000"
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def _ensure_unique_bucket_index(db: Session) -> None:
+    db.execute(text("DROP INDEX IF EXISTS uq_llm_usage_daily_bucket"))
+    db.execute(
+        text(
+            f"""
+            CREATE UNIQUE INDEX uq_llm_usage_daily_bucket
+            ON llm_usage_daily (
+                organization_id,
+                COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                product_section,
+                model,
+                usage_date,
+                usage_kind,
+                context
+            )
+            """
+        )
+    )
+
+
+def _migrate_table_context(db: Session, table: str) -> None:
+    if not _table_exists(db, table):
+        return
+    if not _column_exists(db, table, "context"):
+        db.execute(
+            text(
+                f"""
+                ALTER TABLE {table}
+                ADD COLUMN context JSONB NOT NULL DEFAULT '{{}}'::jsonb
+                """
+            )
+        )
+
+    if _column_exists(db, table, "resource_id") or _column_exists(db, table, "resource_type"):
+        db.execute(
+            text(
+                f"""
+                UPDATE {table}
+                SET context = COALESCE(context, '{{}}'::jsonb)
+                    || CASE
+                        WHEN resource_id IS NOT NULL THEN
+                            jsonb_build_object('resource_id', resource_id::text)
+                        ELSE '{{}}'::jsonb
+                    END
+                    || CASE
+                        WHEN resource_type IS NOT NULL AND resource_type <> '' THEN
+                            jsonb_build_object('resource_type', resource_type)
+                        ELSE '{{}}'::jsonb
+                    END
+                WHERE resource_id IS NOT NULL
+                   OR (resource_type IS NOT NULL AND resource_type <> '')
+                """
+            )
+        )
+        if _column_exists(db, table, "resource_id"):
+            db.execute(text(f"DROP INDEX IF EXISTS ix_llm_usage_daily_org_resource_date"))
+            db.execute(text(f"ALTER TABLE {table} DROP COLUMN resource_id"))
+        if _column_exists(db, table, "resource_type"):
+            db.execute(text(f"ALTER TABLE {table} DROP COLUMN resource_type"))
+
+
+def upgrade(db: Session):
+    if not _table_exists(db, "llm_usage_daily"):
+        print("llm_usage_daily missing; run 062 first — skipping 065")
+        db.commit()
+        return
+
+    _migrate_table_context(db, "llm_usage_daily")
+    _migrate_table_context(db, "usage_pending_buffer")
+
+    _ensure_unique_bucket_index(db)
+
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_llm_usage_daily_context_gin
+            ON llm_usage_daily USING gin (context)
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_llm_usage_daily_context_resource_id
+            ON llm_usage_daily ((context->>'resource_id'))
+            WHERE context ? 'resource_id'
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_llm_usage_daily_context_call_import_id
+            ON llm_usage_daily ((context->>'call_import_id'))
+            WHERE context ? 'call_import_id'
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_llm_usage_daily_context_evaluation_id
+            ON llm_usage_daily ((context->>'evaluation_id'))
+            WHERE context ? 'evaluation_id'
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_llm_usage_daily_context_evaluation_row_id
+            ON llm_usage_daily ((context->>'evaluation_row_id'))
+            WHERE context ? 'evaluation_row_id'
+            """
+        )
+    )
+    print("Added context JSONB + migrated resource attribution")
+    db.commit()
+
+
+def downgrade(db: Session):
+    if not _table_exists(db, "llm_usage_daily"):
+        db.commit()
+        return
+
+    if not _column_exists(db, "llm_usage_daily", "resource_id"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE llm_usage_daily
+                ADD COLUMN resource_id UUID,
+                ADD COLUMN resource_type VARCHAR(64)
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                UPDATE llm_usage_daily
+                SET resource_id = NULLIF(context->>'resource_id', '')::uuid,
+                    resource_type = NULLIF(context->>'resource_type', '')
+                WHERE context IS NOT NULL
+                """
+            )
+        )
+
+    db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_context_evaluation_row_id"))
+    db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_context_evaluation_id"))
+    db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_context_call_import_id"))
+    db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_context_resource_id"))
+    db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_context_gin"))
+    db.execute(text("DROP INDEX IF EXISTS uq_llm_usage_daily_bucket"))
+
+    if _column_exists(db, "llm_usage_daily", "context"):
+        db.execute(text("ALTER TABLE llm_usage_daily DROP COLUMN context"))
+
+    db.execute(
+        text(
+            f"""
+            CREATE UNIQUE INDEX uq_llm_usage_daily_bucket
+            ON llm_usage_daily (
+                organization_id,
+                COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                product_section,
+                model,
+                COALESCE(resource_id, '{_ZERO_UUID}'::uuid),
+                usage_date,
+                usage_kind
+            )
+            """
+        )
+    )
+    db.commit()
diff --git a/app/migrations/066_backfill_llm_usage_workspace_from_call_import.py b/app/migrations/066_backfill_llm_usage_workspace_from_call_import.py
new file mode 100644
index 00000000..478afd4f
--- /dev/null
+++ b/app/migrations/066_backfill_llm_usage_workspace_from_call_import.py
@@ -0,0 +1,279 @@
+"""Migration: Backfill llm_usage_daily.workspace_id from call-import attribution."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Backfill workspace_id on llm_usage_daily and usage_pending_buffer rows "
+    "that have call-import context but were recorded with NULL workspace_id"
+)
+
+_ZERO_UUID = "00000000-0000-0000-0000-000000000000"
+_UUID_RE = "^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$"
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def _dedupe_llm_usage_daily(db: Session) -> int:
+    """Merge duplicate buckets after workspace backfill."""
+    tts_col = (
+        "SUM(COALESCE(tts_characters, 0))::bigint AS tts_characters,"
+        if _column_exists(db, "llm_usage_daily", "tts_characters")
+        else ""
+    )
+    tts_set = (
+        "tts_characters = d.tts_characters,"
+        if _column_exists(db, "llm_usage_daily", "tts_characters")
+        else ""
+    )
+
+    db.execute(
+        text(
+            f"""
+            WITH dupes AS (
+                SELECT
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid) AS ws_key,
+                    product_section,
+                    model,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm') AS kind_key,
+                    context,
+                    MIN(id::text)::uuid AS keep_id,
+                    SUM(prompt_tokens)::bigint AS prompt_tokens,
+                    SUM(completion_tokens)::bigint AS completion_tokens,
+                    SUM(cache_read_tokens)::bigint AS cache_read_tokens,
+                    SUM(cache_creation_tokens)::bigint AS cache_creation_tokens,
+                    SUM(reasoning_tokens)::bigint AS reasoning_tokens,
+                    SUM(COALESCE(audio_seconds, 0))::bigint AS audio_seconds,
+                    {tts_col}
+                    SUM(call_count)::bigint AS call_count
+                FROM llm_usage_daily
+                GROUP BY 1, 2, 3, 4, 5, 6, 7
+                HAVING COUNT(*) > 1
+            )
+            UPDATE llm_usage_daily AS u SET
+                prompt_tokens = d.prompt_tokens,
+                completion_tokens = d.completion_tokens,
+                cache_read_tokens = d.cache_read_tokens,
+                cache_creation_tokens = d.cache_creation_tokens,
+                reasoning_tokens = d.reasoning_tokens,
+                audio_seconds = d.audio_seconds,
+                {tts_set}
+                call_count = d.call_count,
+                updated_at = now()
+            FROM dupes AS d
+            WHERE u.id = d.keep_id
+            """
+        )
+    )
+
+    result = db.execute(
+        text(
+            f"""
+            WITH keepers AS (
+                SELECT MIN(id::text)::uuid AS keep_id
+                FROM llm_usage_daily
+                GROUP BY
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                    product_section,
+                    model,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm'),
+                    context
+                HAVING COUNT(*) > 1
+            )
+            DELETE FROM llm_usage_daily AS u
+            USING keepers AS k,
+                  llm_usage_daily AS peer
+            WHERE peer.id = k.keep_id
+              AND u.id <> k.keep_id
+              AND u.organization_id = peer.organization_id
+              AND u.product_section = peer.product_section
+              AND u.model = peer.model
+              AND u.usage_date = peer.usage_date
+              AND COALESCE(u.usage_kind, 'llm') = COALESCE(peer.usage_kind, 'llm')
+              AND u.workspace_id IS NOT DISTINCT FROM peer.workspace_id
+              AND u.context IS NOT DISTINCT FROM peer.context
+            """
+        )
+    )
+    return int(result.rowcount or 0)
+
+
+def _backfill_table_workspace(db: Session, table: str) -> int:
+    if not _table_exists(db, table):
+        return 0
+    if not _column_exists(db, table, "workspace_id"):
+        return 0
+    if not _column_exists(db, table, "context"):
+        return 0
+
+    total = 0
+
+    # context.call_import_id
+    result = db.execute(
+        text(
+            f"""
+            UPDATE {table} AS u
+            SET workspace_id = ci.workspace_id
+            FROM call_imports AS ci
+            WHERE u.workspace_id IS NULL
+              AND u.context ? 'call_import_id'
+              AND u.context->>'call_import_id' ~ :uuid_re
+              AND ci.id = (u.context->>'call_import_id')::uuid
+              AND ci.organization_id = u.organization_id
+            """
+        ),
+        {"uuid_re": _UUID_RE},
+    )
+    total += int(result.rowcount or 0)
+
+    # resource_type = call_import
+    result = db.execute(
+        text(
+            f"""
+            UPDATE {table} AS u
+            SET workspace_id = ci.workspace_id
+            FROM call_imports AS ci
+            WHERE u.workspace_id IS NULL
+              AND u.context->>'resource_type' = 'call_import'
+              AND u.context->>'resource_id' ~ :uuid_re
+              AND ci.id = (u.context->>'resource_id')::uuid
+              AND ci.organization_id = u.organization_id
+            """
+        ),
+        {"uuid_re": _UUID_RE},
+    )
+    total += int(result.rowcount or 0)
+
+    # context.evaluation_id
+    result = db.execute(
+        text(
+            f"""
+            UPDATE {table} AS u
+            SET workspace_id = e.workspace_id
+            FROM call_import_evaluations AS e
+            WHERE u.workspace_id IS NULL
+              AND u.context ? 'evaluation_id'
+              AND u.context->>'evaluation_id' ~ :uuid_re
+              AND e.id = (u.context->>'evaluation_id')::uuid
+              AND e.organization_id = u.organization_id
+            """
+        ),
+        {"uuid_re": _UUID_RE},
+    )
+    total += int(result.rowcount or 0)
+
+    # resource_type = call_import_evaluation
+    result = db.execute(
+        text(
+            f"""
+            UPDATE {table} AS u
+            SET workspace_id = e.workspace_id
+            FROM call_import_evaluations AS e
+            WHERE u.workspace_id IS NULL
+              AND u.context->>'resource_type' = 'call_import_evaluation'
+              AND u.context->>'resource_id' ~ :uuid_re
+              AND e.id = (u.context->>'resource_id')::uuid
+              AND e.organization_id = u.organization_id
+            """
+        ),
+        {"uuid_re": _UUID_RE},
+    )
+    total += int(result.rowcount or 0)
+
+    # context.call_import_row_id
+    result = db.execute(
+        text(
+            f"""
+            UPDATE {table} AS u
+            SET workspace_id = cir.workspace_id
+            FROM call_import_rows AS cir
+            WHERE u.workspace_id IS NULL
+              AND u.context ? 'call_import_row_id'
+              AND u.context->>'call_import_row_id' ~ :uuid_re
+              AND cir.id = (u.context->>'call_import_row_id')::uuid
+              AND cir.organization_id = u.organization_id
+            """
+        ),
+        {"uuid_re": _UUID_RE},
+    )
+    total += int(result.rowcount or 0)
+
+    # context.evaluation_row_id
+    result = db.execute(
+        text(
+            f"""
+            UPDATE {table} AS u
+            SET workspace_id = e.workspace_id
+            FROM call_import_evaluation_rows AS er
+            JOIN call_import_evaluations AS e ON e.id = er.evaluation_id
+            WHERE u.workspace_id IS NULL
+              AND u.context ? 'evaluation_row_id'
+              AND u.context->>'evaluation_row_id' ~ :uuid_re
+              AND er.id = (u.context->>'evaluation_row_id')::uuid
+              AND e.organization_id = u.organization_id
+            """
+        ),
+        {"uuid_re": _UUID_RE},
+    )
+    total += int(result.rowcount or 0)
+
+    return total
+
+
+def upgrade(db: Session):
+    if not _table_exists(db, "llm_usage_daily"):
+        print("llm_usage_daily missing; skipping 066")
+        db.commit()
+        return
+
+    daily_updated = _backfill_table_workspace(db, "llm_usage_daily")
+    print(f"Backfilled workspace_id on {daily_updated} llm_usage_daily row(s)")
+
+    removed = _dedupe_llm_usage_daily(db)
+    if removed:
+        print(f"Removed {removed} duplicate llm_usage_daily row(s) after merge")
+
+    buffer_updated = _backfill_table_workspace(db, "usage_pending_buffer")
+    if buffer_updated:
+        print(
+            f"Backfilled workspace_id on {buffer_updated} usage_pending_buffer row(s)"
+        )
+
+    db.commit()
+
+
+def downgrade(db: Session):
+    # No-op: cannot distinguish backfilled workspace_id from originally recorded values.
+    db.commit()
diff --git a/app/migrations/067_fix_llm_usage_bucket_unique_index.py b/app/migrations/067_fix_llm_usage_bucket_unique_index.py
new file mode 100644
index 00000000..98a78e5d
--- /dev/null
+++ b/app/migrations/067_fix_llm_usage_bucket_unique_index.py
@@ -0,0 +1,264 @@
+"""Migration: Reconcile llm_usage_daily bucket unique index with full JSONB context."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Merge legacy resource-scoped usage buckets and recreate uq_llm_usage_daily_bucket "
+    "on full context JSONB (per-row attribution)"
+)
+
+_ZERO_UUID = "00000000-0000-0000-0000-000000000000"
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _dedupe_by_context_resource_keys(db: Session) -> int:
+    """Merge rows that share context resource_id/resource_type (legacy unique index)."""
+    tts_col = (
+        "SUM(COALESCE(tts_characters, 0))::bigint AS tts_characters,"
+        if _column_exists(db, "llm_usage_daily", "tts_characters")
+        else ""
+    )
+    tts_set = (
+        "tts_characters = d.tts_characters,"
+        if _column_exists(db, "llm_usage_daily", "tts_characters")
+        else ""
+    )
+    db.execute(
+        text(
+            f"""
+            WITH dupes AS (
+                SELECT
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid) AS ws_key,
+                    product_section,
+                    model,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm') AS kind_key,
+                    COALESCE(context->>'resource_id', '') AS res_id_key,
+                    COALESCE(context->>'resource_type', '') AS res_type_key,
+                    MIN(id::text)::uuid AS keep_id,
+                    SUM(prompt_tokens)::bigint AS prompt_tokens,
+                    SUM(completion_tokens)::bigint AS completion_tokens,
+                    SUM(cache_read_tokens)::bigint AS cache_read_tokens,
+                    SUM(cache_creation_tokens)::bigint AS cache_creation_tokens,
+                    SUM(reasoning_tokens)::bigint AS reasoning_tokens,
+                    SUM(COALESCE(audio_seconds, 0))::bigint AS audio_seconds,
+                    {tts_col}
+                    SUM(call_count)::bigint AS call_count
+                FROM llm_usage_daily
+                GROUP BY 1, 2, 3, 4, 5, 6, 7, 8
+                HAVING COUNT(*) > 1
+            )
+            UPDATE llm_usage_daily AS u SET
+                prompt_tokens = d.prompt_tokens,
+                completion_tokens = d.completion_tokens,
+                cache_read_tokens = d.cache_read_tokens,
+                cache_creation_tokens = d.cache_creation_tokens,
+                reasoning_tokens = d.reasoning_tokens,
+                audio_seconds = d.audio_seconds,
+                {tts_set}
+                call_count = d.call_count,
+                updated_at = now()
+            FROM dupes AS d
+            WHERE u.id = d.keep_id
+            """
+        )
+    )
+    result = db.execute(
+        text(
+            f"""
+            WITH keepers AS (
+                SELECT MIN(id::text)::uuid AS keep_id
+                FROM llm_usage_daily
+                GROUP BY
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                    product_section,
+                    model,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm'),
+                    COALESCE(context->>'resource_id', ''),
+                    COALESCE(context->>'resource_type', '')
+                HAVING COUNT(*) > 1
+            )
+            DELETE FROM llm_usage_daily AS u
+            USING keepers AS k,
+                  llm_usage_daily AS peer
+            WHERE peer.id = k.keep_id
+              AND u.id <> k.keep_id
+              AND u.organization_id = peer.organization_id
+              AND u.product_section = peer.product_section
+              AND u.model = peer.model
+              AND u.usage_date = peer.usage_date
+              AND COALESCE(u.usage_kind, 'llm') = COALESCE(peer.usage_kind, 'llm')
+              AND u.workspace_id IS NOT DISTINCT FROM peer.workspace_id
+              AND COALESCE(u.context->>'resource_id', '') =
+                  COALESCE(peer.context->>'resource_id', '')
+              AND COALESCE(u.context->>'resource_type', '') =
+                  COALESCE(peer.context->>'resource_type', '')
+            """
+        )
+    )
+    return int(result.rowcount or 0)
+
+
+def _dedupe_by_full_context(db: Session) -> int:
+    tts_col = (
+        "SUM(COALESCE(tts_characters, 0))::bigint AS tts_characters,"
+        if _column_exists(db, "llm_usage_daily", "tts_characters")
+        else ""
+    )
+    tts_set = (
+        "tts_characters = d.tts_characters,"
+        if _column_exists(db, "llm_usage_daily", "tts_characters")
+        else ""
+    )
+    db.execute(
+        text(
+            f"""
+            WITH dupes AS (
+                SELECT
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid) AS ws_key,
+                    product_section,
+                    model,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm') AS kind_key,
+                    context,
+                    MIN(id::text)::uuid AS keep_id,
+                    SUM(prompt_tokens)::bigint AS prompt_tokens,
+                    SUM(completion_tokens)::bigint AS completion_tokens,
+                    SUM(cache_read_tokens)::bigint AS cache_read_tokens,
+                    SUM(cache_creation_tokens)::bigint AS cache_creation_tokens,
+                    SUM(reasoning_tokens)::bigint AS reasoning_tokens,
+                    SUM(COALESCE(audio_seconds, 0))::bigint AS audio_seconds,
+                    {tts_col}
+                    SUM(call_count)::bigint AS call_count
+                FROM llm_usage_daily
+                GROUP BY 1, 2, 3, 4, 5, 6, 7
+                HAVING COUNT(*) > 1
+            )
+            UPDATE llm_usage_daily AS u SET
+                prompt_tokens = d.prompt_tokens,
+                completion_tokens = d.completion_tokens,
+                cache_read_tokens = d.cache_read_tokens,
+                cache_creation_tokens = d.cache_creation_tokens,
+                reasoning_tokens = d.reasoning_tokens,
+                audio_seconds = d.audio_seconds,
+                {tts_set}
+                call_count = d.call_count,
+                updated_at = now()
+            FROM dupes AS d
+            WHERE u.id = d.keep_id
+            """
+        )
+    )
+    result = db.execute(
+        text(
+            f"""
+            WITH keepers AS (
+                SELECT MIN(id::text)::uuid AS keep_id
+                FROM llm_usage_daily
+                GROUP BY
+                    organization_id,
+                    COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                    product_section,
+                    model,
+                    usage_date,
+                    COALESCE(usage_kind, 'llm'),
+                    context
+                HAVING COUNT(*) > 1
+            )
+            DELETE FROM llm_usage_daily AS u
+            USING keepers AS k,
+                  llm_usage_daily AS peer
+            WHERE peer.id = k.keep_id
+              AND u.id <> k.keep_id
+              AND u.organization_id = peer.organization_id
+              AND u.product_section = peer.product_section
+              AND u.model = peer.model
+              AND u.usage_date = peer.usage_date
+              AND COALESCE(u.usage_kind, 'llm') = COALESCE(peer.usage_kind, 'llm')
+              AND u.workspace_id IS NOT DISTINCT FROM peer.workspace_id
+              AND u.context IS NOT DISTINCT FROM peer.context
+            """
+        )
+    )
+    return int(result.rowcount or 0)
+
+
+def _ensure_context_bucket_index(db: Session) -> None:
+    db.execute(text("DROP INDEX IF EXISTS uq_llm_usage_daily_bucket"))
+    db.execute(
+        text(
+            f"""
+            CREATE UNIQUE INDEX uq_llm_usage_daily_bucket
+            ON llm_usage_daily (
+                organization_id,
+                COALESCE(workspace_id, '{_ZERO_UUID}'::uuid),
+                product_section,
+                model,
+                usage_date,
+                usage_kind,
+                context
+            )
+            """
+        )
+    )
+
+
+def upgrade(db: Session):
+    if not _table_exists(db, "llm_usage_daily"):
+        print("llm_usage_daily missing; skipping 067")
+        db.commit()
+        return
+    if not _column_exists(db, "llm_usage_daily", "context"):
+        print("llm_usage_daily.context missing; run 065 first — skipping 067")
+        db.commit()
+        return
+
+    legacy_removed = _dedupe_by_context_resource_keys(db)
+    if legacy_removed:
+        print(f"Merged {legacy_removed} legacy resource-scoped duplicate row(s)")
+
+    context_removed = _dedupe_by_full_context(db)
+    if context_removed:
+        print(f"Merged {context_removed} duplicate full-context row(s)")
+
+    _ensure_context_bucket_index(db)
+    print("Recreated uq_llm_usage_daily_bucket on full context JSONB")
+    db.commit()
+
+
+def downgrade(db: Session):
+    db.commit()
diff --git a/app/models/database.py b/app/models/database.py
index 9f32f212..00b99fdb 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -19,7 +19,7 @@
     select,
     text,
 )
-from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.dialects.postgresql import JSONB, UUID
 from sqlalchemy.orm import relationship
 from sqlalchemy.sql import func
 import uuid
@@ -2780,7 +2780,7 @@ class JudgeRun(Base):
 
 
 class LLMUsageDaily(Base):
-    """Daily LLM usage rollups for org-scoped Usage reporting."""
+    """Daily LLM/STT usage rollups for org-scoped Usage reporting."""
 
     __tablename__ = "llm_usage_daily"
 
@@ -2799,9 +2799,9 @@ class LLMUsageDaily(Base):
     )
     product_section = Column(String(64), nullable=False, index=True)
     model = Column(String(255), nullable=False, index=True)
-    resource_id = Column(UUID(as_uuid=True), nullable=True, index=True)
-    resource_type = Column(String(64), nullable=True)
+    context = Column(JSONB, nullable=False, server_default="{}", default=dict)
     usage_date = Column(Date, nullable=False, index=True)
+    usage_kind = Column(String(16), nullable=False, default="llm", server_default="llm")
     prompt_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
     completion_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
     cache_read_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
@@ -2809,6 +2809,8 @@ class LLMUsageDaily(Base):
         BigInteger, nullable=False, default=0, server_default="0"
     )
     reasoning_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    audio_seconds = Column(BigInteger, nullable=False, default=0, server_default="0")
+    tts_characters = Column(BigInteger, nullable=False, default=0, server_default="0")
     call_count = Column(BigInteger, nullable=False, default=0, server_default="0")
     created_at = Column(DateTime(timezone=True), server_default=func.now())
     updated_at = Column(
diff --git a/app/services/ai/llm_gateway.py b/app/services/ai/llm_gateway.py
index 750b1641..4b1a08dc 100644
--- a/app/services/ai/llm_gateway.py
+++ b/app/services/ai/llm_gateway.py
@@ -10,6 +10,7 @@
 from __future__ import annotations
 
 import os
+from contextlib import contextmanager
 from dataclasses import dataclass
 from typing import Any, Dict, Literal, Optional, Tuple
 from urllib.parse import urlparse
@@ -723,10 +724,63 @@ def litellm_completion(
             model_name = str(kwargs.get("model") or "unknown")
             if "/" in model_name:
                 model_name = model_name.rsplit("/", 1)[-1]
-            record_llm_usage(model_name, normalize_llm_usage(raw_response=response))
+            record_llm_usage(
+                model_name,
+                normalize_llm_usage(raw_response=response),
+                organization_id=organization_id,
+            )
         finally:
             if usage_token is not None:
                 reset_usage_context(usage_token)
     except Exception as exc:
         logger.debug("litellm_completion usage record skipped: {}", exc)
     return response
+
+
+@contextmanager
+def litellm_batch_completion_recording(
+    *,
+    organization_id: UUID,
+    db: Session,
+    model: Optional[str] = None,
+    credential: Optional[CredentialRoutingContext] = None,
+):
+    """Temporarily wrap litellm.batch_completion to record usage for each response."""
+    import litellm
+
+    from app.services.usage.llm_usage import record_llm_usage
+    from app.services.usage.normalize import normalize_llm_usage
+
+    original = litellm.batch_completion
+
+    def _recording_batch(**kwargs: Any):
+        kwargs = apply_llm_gateway(
+            kwargs,
+            organization_id=organization_id,
+            db=db,
+            model=model or kwargs.get("model"),
+            credential=credential,
+        )
+        responses = original(**kwargs)
+        model_name = str(kwargs.get("model") or model or "unknown")
+        if "/" in model_name:
+            model_name = model_name.rsplit("/", 1)[-1]
+        items = responses if isinstance(responses, list) else [responses]
+        for resp in items:
+            if resp is None:
+                continue
+            try:
+                record_llm_usage(
+                    model_name,
+                    normalize_llm_usage(raw_response=resp),
+                    organization_id=organization_id,
+                )
+            except Exception as exc:
+                logger.debug("litellm batch usage record skipped: {}", exc)
+        return responses
+
+    litellm.batch_completion = _recording_batch
+    try:
+        yield
+    finally:
+        litellm.batch_completion = original
diff --git a/app/services/ai/llm_service.py b/app/services/ai/llm_service.py
index a0b2090a..4bf5b61f 100644
--- a/app/services/ai/llm_service.py
+++ b/app/services/ai/llm_service.py
@@ -567,7 +567,9 @@ def generate_response(
                 result["usage"]["cache_read_tokens"] = snapshot.cache_read_tokens
                 result["usage"]["cache_creation_tokens"] = snapshot.cache_creation_tokens
                 result["usage"]["reasoning_tokens"] = snapshot.reasoning_tokens
-                record_llm_usage(llm_model, snapshot)
+                record_llm_usage(
+                    llm_model, snapshot, organization_id=organization_id
+                )
             finally:
                 if usage_token is not None:
                     reset_usage_context(usage_token)
diff --git a/app/services/ai/stt_clients/google.py b/app/services/ai/stt_clients/google.py
index 6d86ec30..2bc100a1 100644
--- a/app/services/ai/stt_clients/google.py
+++ b/app/services/ai/stt_clients/google.py
@@ -152,6 +152,30 @@ def transcribe_google(
             f"Gemini transcription failed for {litellm_model}: {e}"
         )
 
+    if organization_id is not None:
+        try:
+            from app.services.usage.llm_usage import record_llm_usage
+            from app.services.usage.normalize import normalize_llm_usage
+
+            gemini_model = _strip_stt_suffix(model)
+            record_llm_usage(
+                gemini_model,
+                normalize_llm_usage(raw_response=response),
+                organization_id=organization_id,
+            )
+            from app.services.usage.llm_usage import probe_audio_seconds, record_stt_usage
+
+            audio_seconds = probe_audio_seconds(audio_file_path)
+            if audio_seconds > 0:
+                record_stt_usage(
+                    gemini_model,
+                    audio_seconds=audio_seconds,
+                    organization_id=organization_id,
+                    count_call=False,
+                )
+        except Exception as exc:
+            logger.debug("[transcribe_google] llm usage record skipped: %s", exc)
+
     text = ""
     try:
         text = (response.choices[0].message.content or "").strip()
diff --git a/app/services/ai/transcription_service.py b/app/services/ai/transcription_service.py
index fbe87c7c..9620e1ca 100644
--- a/app/services/ai/transcription_service.py
+++ b/app/services/ai/transcription_service.py
@@ -579,6 +579,24 @@ def transcribe_text_only(
                 return None
 
             text = (result.get("text") or "").strip()
+            try:
+                from app.services.usage.llm_usage import (
+                    probe_audio_seconds,
+                    record_stt_usage,
+                )
+
+                audio_seconds = probe_audio_seconds(audio_file_path)
+                if stt_provider != ModelProvider.GOOGLE:
+                    record_stt_usage(
+                        stt_model or "unknown",
+                        audio_seconds=audio_seconds,
+                        organization_id=organization_id,
+                    )
+            except Exception as exc:
+                logger.debug(
+                    "[TranscriptionService] text-only stt usage record skipped: %s",
+                    exc,
+                )
             return text or None
         except Exception as e:
             logger.error(f"[TranscriptionService] text-only transcription failed ({stt_provider}/{stt_model}): {e}")
@@ -661,6 +679,33 @@ def transcribe(
             else:
                 result = self._transcribe_with_whisper_local(temp_file_path, "base")
 
+            try:
+                from app.services.usage.llm_usage import (
+                    probe_audio_seconds,
+                    record_stt_usage,
+                )
+
+                audio_seconds = 0
+                if isinstance(result, dict):
+                    raw_duration = result.get("duration")
+                    if raw_duration is not None:
+                        try:
+                            audio_seconds = int(float(raw_duration))
+                        except (TypeError, ValueError):
+                            audio_seconds = 0
+                if audio_seconds <= 0 and temp_file_path:
+                    audio_seconds = probe_audio_seconds(temp_file_path)
+                if stt_provider != ModelProvider.GOOGLE:
+                    record_stt_usage(
+                        stt_model or "unknown",
+                        audio_seconds=audio_seconds,
+                        organization_id=organization_id,
+                    )
+            except Exception as exc:
+                logger.debug(
+                    "[TranscriptionService] stt usage record skipped: %s", exc
+                )
+
             # Apply speaker diarization if enabled
             speaker_segments = None
             if enable_speaker_diarization:
diff --git a/app/services/ai/tts_service.py b/app/services/ai/tts_service.py
index 640c682c..4d1162f4 100644
--- a/app/services/ai/tts_service.py
+++ b/app/services/ai/tts_service.py
@@ -262,6 +262,11 @@ def synthesize(
         api_key = self._get_api_key_for_provider(tts_provider, db, organization_id)
         handler = self._get_tts_handler(tts_provider)
         audio_bytes, _ttfb_ms = handler(text, tts_model, api_key, voice, config)
+        self._record_tts_usage(
+            text=text,
+            tts_model=tts_model,
+            organization_id=organization_id,
+        )
         return audio_bytes
 
     def synthesize_timed(
@@ -280,8 +285,40 @@ def synthesize_timed(
         start = time.time()
         audio_bytes, ttfb_ms = handler(text, tts_model, api_key, voice, config)
         total_latency_ms = (time.time() - start) * 1000
+        self._record_tts_usage(
+            text=text,
+            tts_model=tts_model,
+            organization_id=organization_id,
+        )
         return audio_bytes, total_latency_ms, ttfb_ms
 
+    def _record_tts_usage(
+        self,
+        *,
+        text: str,
+        tts_model: str,
+        organization_id: UUID,
+    ) -> None:
+        try:
+            from app.services.usage.context import (
+                ensure_usage_context,
+                reset_usage_context,
+            )
+            from app.services.usage.llm_usage import record_tts_usage
+
+            usage_token = ensure_usage_context(organization_id)
+            try:
+                record_tts_usage(
+                    tts_model,
+                    characters=len(text or ""),
+                    organization_id=organization_id,
+                )
+            finally:
+                if usage_token is not None:
+                    reset_usage_context(usage_token)
+        except Exception:
+            pass
+
     def synthesize_and_upload(
         self,
         text: str,
diff --git a/app/services/evaluators/call_data_transcript.py b/app/services/evaluators/call_data_transcript.py
new file mode 100644
index 00000000..4c7afe2e
--- /dev/null
+++ b/app/services/evaluators/call_data_transcript.py
@@ -0,0 +1,195 @@
+"""Extract transcript text from voice-provider call payloads."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Tuple
+
+
+def extract_transcript_from_call_data(
+    call_data: Dict[str, Any],
+    provider_platform: str,
+) -> Tuple[str, List[dict]]:
+    """Return plain-text transcript and speaker segments from provider call_data."""
+    transcript_text = ""
+    speaker_segments: List[dict] = []
+
+    if not call_data:
+        return transcript_text, speaker_segments
+
+    provider_platform_lower = provider_platform.lower() if provider_platform else ""
+
+    if provider_platform_lower == "vapi":
+        transcript_text = call_data.get("transcript", "") or ""
+        transcript_object = call_data.get("transcript_object", [])
+        if not transcript_object:
+            artifact = call_data.get("artifact", {}) if isinstance(call_data, dict) else {}
+            messages = call_data.get("messages", []) or artifact.get("messages", [])
+            for msg in messages:
+                role = msg.get("role", "unknown")
+                content = msg.get("message", "") or msg.get("content", "")
+                if not content or role == "system":
+                    continue
+                if role in ("bot", "assistant"):
+                    normalized_role = "agent"
+                elif role == "user":
+                    normalized_role = "user"
+                else:
+                    continue
+                speaker_segments.append(
+                    {
+                        "speaker": "Agent" if normalized_role == "agent" else "User",
+                        "text": content,
+                        "start": msg.get("secondsFromStart", 0),
+                        "end": msg.get("secondsFromStart", 0)
+                        + (msg.get("duration", 0) / 1000),
+                    }
+                )
+        else:
+            for entry in transcript_object:
+                role = entry.get("role", "unknown")
+                content = entry.get("content", "")
+                if not content:
+                    continue
+                speaker_segments.append(
+                    {
+                        "speaker": "Agent" if role == "agent" else "User",
+                        "text": content,
+                        "start": entry.get("seconds_from_start", 0),
+                        "end": entry.get("seconds_from_start", 0)
+                        + (entry.get("duration_ms", 0) / 1000),
+                    }
+                )
+        if not transcript_text and speaker_segments:
+            transcript_text = "\n".join(
+                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
+            )
+
+    elif provider_platform_lower == "elevenlabs":
+        raw_transcript = call_data.get("transcript")
+        transcript_obj = call_data.get("transcript_object", [])
+        if isinstance(raw_transcript, str) and raw_transcript:
+            transcript_text = raw_transcript
+            if isinstance(transcript_obj, list):
+                for seg in transcript_obj:
+                    speaker_segments.append(
+                        {
+                            "speaker": seg.get("speaker", "Unknown"),
+                            "text": seg.get("text", ""),
+                            "start": seg.get("start", 0),
+                            "end": seg.get("end", 0),
+                        }
+                    )
+        elif isinstance(raw_transcript, list):
+            for entry in raw_transcript:
+                role = entry.get("role", "unknown")
+                content = entry.get("message", "") or entry.get("text", "")
+                if not content:
+                    continue
+                speaker = "Agent" if role in ("agent", "assistant", "ai") else "User"
+                speaker_segments.append(
+                    {
+                        "speaker": speaker,
+                        "text": content,
+                        "start": entry.get("time_in_call_secs", 0) or entry.get("start", 0),
+                        "end": entry.get("time_in_call_secs", 0) or entry.get("end", 0),
+                    }
+                )
+            transcript_text = "\n".join(
+                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
+            )
+
+    elif provider_platform_lower == "smallest":
+        transcript_raw = call_data.get("transcript")
+        transcript_object = call_data.get("transcript_object", [])
+        if isinstance(transcript_object, list) and transcript_object:
+            for entry in transcript_object:
+                if not isinstance(entry, dict):
+                    continue
+                text = entry.get("text", "")
+                if not text:
+                    continue
+                speaker_segments.append(
+                    {
+                        "speaker": entry.get("speaker", "Unknown"),
+                        "text": text,
+                        "start": entry.get("start", 0),
+                        "end": entry.get("end", entry.get("start", 0)),
+                    }
+                )
+            if not transcript_text:
+                transcript_text = "\n".join(
+                    f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
+                )
+        elif isinstance(transcript_raw, list):
+            for entry in transcript_raw:
+                if not isinstance(entry, dict):
+                    continue
+                role = str(entry.get("speaker") or entry.get("role") or "").lower()
+                speaker = "Agent" if role in ("agent", "assistant", "ai", "bot") else "User"
+                text = entry.get("text", "") or entry.get("message", "") or entry.get("content", "")
+                if not text:
+                    continue
+                ts = entry.get("timeInCallSecs", 0) or entry.get("start", 0) or entry.get("timestamp", 0)
+                speaker_segments.append(
+                    {
+                        "speaker": speaker,
+                        "text": text,
+                        "start": ts,
+                        "end": entry.get("end", ts),
+                    }
+                )
+            transcript_text = "\n".join(
+                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
+            )
+        elif isinstance(transcript_raw, str):
+            transcript_text = transcript_raw
+
+    elif provider_platform_lower == "retell":
+        transcript_raw = call_data.get("transcript", "")
+        if isinstance(transcript_raw, str):
+            transcript_text = transcript_raw
+            lines = transcript_raw.split("\n") if transcript_raw else []
+            for line in lines:
+                line = line.strip()
+                if not line:
+                    continue
+                if line.startswith("Agent:") or line.startswith("agent:"):
+                    speaker_segments.append(
+                        {
+                            "speaker": "Agent",
+                            "text": line.split(":", 1)[1].strip() if ":" in line else line,
+                            "start": 0,
+                            "end": 0,
+                        }
+                    )
+                elif line.startswith("User:") or line.startswith("user:"):
+                    speaker_segments.append(
+                        {
+                            "speaker": "User",
+                            "text": line.split(":", 1)[1].strip() if ":" in line else line,
+                            "start": 0,
+                            "end": 0,
+                        }
+                    )
+        elif isinstance(transcript_raw, list):
+            for item in transcript_raw:
+                if not isinstance(item, dict):
+                    continue
+                role = item.get("role", "")
+                content = item.get("content", "") or item.get("text", "")
+                if not content:
+                    continue
+                speaker = "Agent" if role in ["agent", "assistant", "bot"] else "User"
+                speaker_segments.append(
+                    {
+                        "speaker": speaker,
+                        "text": content,
+                        "start": item.get("start_time", 0) or item.get("timestamp", 0),
+                        "end": item.get("end_time", 0),
+                    }
+                )
+            transcript_text = "\n".join(
+                f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
+            )
+
+    return transcript_text, speaker_segments
diff --git a/app/services/judge_alignment/gepa_bridge.py b/app/services/judge_alignment/gepa_bridge.py
index 37926fa1..6a12506b 100644
--- a/app/services/judge_alignment/gepa_bridge.py
+++ b/app/services/judge_alignment/gepa_bridge.py
@@ -219,6 +219,31 @@ def execute_judge_gepa(run_id: str, db: Session) -> Dict[str, Any]:
     if not evaluator:
         raise RuntimeError("Evaluator vanished between dispatch and execution")
 
+    from app.services.usage.context import (
+        reset_usage_context,
+        set_usage_context,
+        usage_context_for_prompt_optimization_run,
+    )
+
+    usage_token = set_usage_context(usage_context_for_prompt_optimization_run(run))
+    try:
+        return _execute_judge_gepa_with_context(
+            run=run,
+            cfg=cfg,
+            evaluator=evaluator,
+            db=db,
+        )
+    finally:
+        reset_usage_context(usage_token)
+
+
+def _execute_judge_gepa_with_context(
+    *,
+    run: PromptOptimizationRun,
+    cfg: Dict[str, Any],
+    evaluator: Evaluator,
+    db: Session,
+) -> Dict[str, Any]:
     dev_ids: List[str] = cfg.get("dev_sample_ids", [])
     if not dev_ids:
         raise RuntimeError("Optimisation run has no dev_sample_ids in config")
@@ -370,19 +395,27 @@ def reflection_lm(prompt: str) -> str:
         resp = litellm_completion(**reflection_kwargs, credential=credential_ctx)
         return resp.choices[0].message.content
 
+    from app.services.ai.llm_gateway import litellm_batch_completion_recording
+
     run.status = PromptOptimizationStatus.RUNNING.value
     db.commit()
 
     try:
-        result = gepa_optimize(
-            seed_candidate={"system_prompt": evaluator.custom_prompt},
-            trainset=trainset,
-            adapter=adapter,
-            reflection_lm=reflection_lm,
-            max_metric_calls=int(cfg.get("max_metric_calls", 20)),
-            reflection_minibatch_size=1,
-            candidate_selection_strategy="pareto",
-        )
+        with litellm_batch_completion_recording(
+            organization_id=run.organization_id,
+            db=db,
+            model=lm_identifier,
+            credential=credential_ctx,
+        ):
+            result = gepa_optimize(
+                seed_candidate={"system_prompt": evaluator.custom_prompt},
+                trainset=trainset,
+                adapter=adapter,
+                reflection_lm=reflection_lm,
+                max_metric_calls=int(cfg.get("max_metric_calls", 20)),
+                reflection_minibatch_size=1,
+                candidate_selection_strategy="pareto",
+            )
     except Exception as exc:
         run.status = PromptOptimizationStatus.FAILED.value
         run.error_message = str(exc)
diff --git a/app/services/optimization/gepa_service.py b/app/services/optimization/gepa_service.py
index 5328dba9..d92895ac 100644
--- a/app/services/optimization/gepa_service.py
+++ b/app/services/optimization/gepa_service.py
@@ -149,15 +149,23 @@ def reflection_lm(prompt: str) -> str:
         resp = litellm_completion(**reflection_kwargs, credential=credential_ctx)
         return resp.choices[0].message.content
 
-    result = gepa_optimize(
-        seed_candidate={"system_prompt": seed_prompt},
-        trainset=trainset,
-        adapter=adapter,
-        reflection_lm=reflection_lm,
-        max_metric_calls=max_metric_calls,
-        reflection_minibatch_size=min(minibatch_size, len(trainset)),
-        candidate_selection_strategy="pareto",
-    )
+    from app.services.ai.llm_gateway import litellm_batch_completion_recording
+
+    with litellm_batch_completion_recording(
+        organization_id=organization_id,
+        db=db,
+        model=lm_identifier,
+        credential=credential_ctx,
+    ):
+        result = gepa_optimize(
+            seed_candidate={"system_prompt": seed_prompt},
+            trainset=trainset,
+            adapter=adapter,
+            reflection_lm=reflection_lm,
+            max_metric_calls=max_metric_calls,
+            reflection_minibatch_size=min(minibatch_size, len(trainset)),
+            candidate_selection_strategy="pareto",
+        )
 
     return _format_result(result, seed_prompt)
 
diff --git a/app/services/testing/test_agent_bridge_service.py b/app/services/testing/test_agent_bridge_service.py
index f8e8d41a..4a05cdb0 100644
--- a/app/services/testing/test_agent_bridge_service.py
+++ b/app/services/testing/test_agent_bridge_service.py
@@ -613,6 +613,8 @@ def resolve_api_key_for_provider(
                 )
 
                 test_agent_config = TestAgentConfig(
+                    organization_id=organization_id,
+                    workspace_id=getattr(agent, "workspace_id", None),
                     agent_name=agent.name or "Voice AI Agent",
                     agent_description=agent.description or "A voice AI assistant",
                     test_agent_simulation_prompt=simulation_prompt,
diff --git a/app/services/usage/__init__.py b/app/services/usage/__init__.py
index 646e9adb..4ebc75ca 100644
--- a/app/services/usage/__init__.py
+++ b/app/services/usage/__init__.py
@@ -1,4 +1,4 @@
-"""LLM usage tracking (tokens, calls) with Redis buffer and catalog rollups."""
+"""LLM/STT/TTS usage tracking with Redis buffer, PG fallback, and catalog rollups."""
 
 from app.services.usage.context import (
     LLMUsageContext,
@@ -10,13 +10,27 @@
     reset_usage_hints,
     set_usage_context,
     set_usage_hints,
+    usage_context_for_judge_run,
+    usage_context_for_prompt_optimization_run,
+    usage_context_for_prompt_partial,
+)
+from app.services.usage.llm_usage import (
+    flush_all_usage_to_catalog,
+    flush_usage_to_catalog,
+    probe_audio_seconds,
+    record_call_usage,
+    record_llm_usage,
+    record_stt_usage,
+    record_tts_usage,
 )
-from app.services.usage.llm_usage import flush_all_usage_to_catalog, flush_usage_to_catalog, record_llm_usage
 from app.services.usage.normalize import UsageSnapshot, normalize_llm_usage
 
 __all__ = [
     "LLMUsageContext",
     "LLMUsageProductSection",
+    "usage_context_for_judge_run",
+    "usage_context_for_prompt_optimization_run",
+    "usage_context_for_prompt_partial",
     "UsageSnapshot",
     "ensure_usage_context",
     "flush_all_usage_to_catalog",
@@ -24,7 +38,11 @@
     "infer_product_section_from_path",
     "llm_usage_context",
     "normalize_llm_usage",
+    "probe_audio_seconds",
+    "record_call_usage",
     "record_llm_usage",
+    "record_stt_usage",
+    "record_tts_usage",
     "reset_usage_context",
     "reset_usage_hints",
     "set_usage_context",
diff --git a/app/services/usage/bucket_context.py b/app/services/usage/bucket_context.py
new file mode 100644
index 00000000..ad6202d9
--- /dev/null
+++ b/app/services/usage/bucket_context.py
@@ -0,0 +1,114 @@
+"""Canonical usage bucket context (JSONB) helpers."""
+
+from __future__ import annotations
+
+import json
+from typing import Any, Dict, Optional
+from uuid import UUID
+
+_NONE = "__none__"
+
+# Stable keys stored in llm_usage_daily.context (extend without new columns).
+KNOWN_CONTEXT_KEYS = frozenset(
+    {
+        "resource_id",
+        "resource_type",
+        "call_import_id",
+        "evaluation_id",
+        "evaluation_row_id",
+        "call_import_row_id",
+        "credential_id",
+        "agent_id",
+        "job_id",
+        "user_id",
+        "trace_id",
+    }
+)
+
+# Never store roll-up counters in JSONB — they stay as BIGINT columns for SUM().
+_FORBIDDEN_CONTEXT_KEYS = frozenset(
+    {
+        "prompt_tokens",
+        "completion_tokens",
+        "cache_read_tokens",
+        "cache_creation_tokens",
+        "reasoning_tokens",
+        "audio_seconds",
+        "tts_characters",
+        "call_count",
+        "total_tokens",
+    }
+)
+
+
+def build_bucket_context(
+    *,
+    resource_id: Optional[UUID] = None,
+    resource_type: Optional[str] = None,
+    extra: Optional[Dict[str, Any]] = None,
+) -> Dict[str, str]:
+    """Build a normalized string-keyed context dict for rollup buckets."""
+    ctx: Dict[str, str] = {}
+    if resource_id is not None:
+        ctx["resource_id"] = str(resource_id)
+    if resource_type:
+        ctx["resource_type"] = resource_type
+    if extra:
+        for key, value in extra.items():
+            if value is None or key in ctx:
+                continue
+            if key in _FORBIDDEN_CONTEXT_KEYS:
+                continue
+            ctx[key] = str(value)
+    return ctx
+
+
+def context_bucket_token(context: Optional[Dict[str, Any]]) -> str:
+    """Stable Redis bucket token for a context dict."""
+    if not context:
+        return _NONE
+    normalized = {k: str(v) for k, v in sorted(context.items()) if v is not None}
+    if not normalized:
+        return _NONE
+    return json.dumps(normalized, sort_keys=True, separators=(",", ":"))
+
+
+def parse_context_bucket_token(token: str) -> Dict[str, str]:
+    if not token or token == _NONE:
+        return {}
+    try:
+        parsed = json.loads(token)
+    except json.JSONDecodeError:
+        return {}
+    if not isinstance(parsed, dict):
+        return {}
+    return {str(k): str(v) for k, v in parsed.items() if v is not None}
+
+
+def legacy_resource_context(
+    resource_id: Optional[UUID],
+    resource_type: Optional[str],
+) -> Dict[str, str]:
+    return build_bucket_context(
+        resource_id=resource_id,
+        resource_type=resource_type,
+    )
+
+
+def resource_id_from_context(context: Optional[Dict[str, Any]]) -> Optional[UUID]:
+    if not context:
+        return None
+    raw = context.get("resource_id")
+    if not raw:
+        return None
+    try:
+        return UUID(str(raw))
+    except (ValueError, TypeError):
+        return None
+
+
+def resource_type_from_context(context: Optional[Dict[str, Any]]) -> Optional[str]:
+    if not context:
+        return None
+    raw = context.get("resource_type")
+    return str(raw) if raw else None
diff --git a/app/services/usage/call_import_context.py b/app/services/usage/call_import_context.py
new file mode 100644
index 00000000..e23f46cf
--- /dev/null
+++ b/app/services/usage/call_import_context.py
@@ -0,0 +1,200 @@
+"""Usage context builders for call-import evaluation pipelines."""
+
+from __future__ import annotations
+
+from typing import Any, Optional
+from uuid import UUID
+
+from app.services.usage.context import LLMUsageContext, LLMUsageProductSection
+
+
+def _parse_uuid(raw: Any) -> Optional[UUID]:
+    try:
+        return UUID(str(raw))
+    except (TypeError, ValueError):
+        return None
+
+
+def call_import_ids_from_usage_context(ctx: LLMUsageContext) -> dict[str, Optional[UUID]]:
+    """Extract call-import linkage ids from a usage context (no DB)."""
+    extra = ctx.extra or {}
+    ids: dict[str, Optional[UUID]] = {
+        "call_import_id": _parse_uuid(extra.get("call_import_id")),
+        "evaluation_id": _parse_uuid(extra.get("evaluation_id")),
+        "call_import_row_id": _parse_uuid(extra.get("call_import_row_id")),
+        "evaluation_row_id": _parse_uuid(extra.get("evaluation_row_id")),
+    }
+    if ctx.resource_type == "call_import" and ctx.resource_id:
+        ids["call_import_id"] = ids["call_import_id"] or ctx.resource_id
+    if ctx.resource_type == "call_import_evaluation" and ctx.resource_id:
+        ids["evaluation_id"] = ids["evaluation_id"] or ctx.resource_id
+    return ids
+
+
+def resolve_workspace_id_for_usage_context(ctx: LLMUsageContext) -> Optional[UUID]:
+    """Resolve workspace from call-import entities when context omitted workspace_id."""
+    if ctx.workspace_id is not None:
+        return ctx.workspace_id
+
+    ids = call_import_ids_from_usage_context(ctx)
+    if not any(ids.values()):
+        return None
+
+    from app.database import SessionLocal
+    from app.models.database import (
+        CallImport,
+        CallImportEvaluation,
+        CallImportEvaluationRow,
+        CallImportRow,
+    )
+
+    org_id = ctx.organization_id
+    db = SessionLocal()
+    try:
+        if ids["call_import_id"]:
+            ws = (
+                db.query(CallImport.workspace_id)
+                .filter(
+                    CallImport.id == ids["call_import_id"],
+                    CallImport.organization_id == org_id,
+                )
+                .scalar()
+            )
+            if ws:
+                return ws
+
+        if ids["evaluation_id"]:
+            ws = (
+                db.query(CallImportEvaluation.workspace_id)
+                .filter(
+                    CallImportEvaluation.id == ids["evaluation_id"],
+                    CallImportEvaluation.organization_id == org_id,
+                )
+                .scalar()
+            )
+            if ws:
+                return ws
+
+        if ids["call_import_row_id"]:
+            ws = (
+                db.query(CallImportRow.workspace_id)
+                .filter(
+                    CallImportRow.id == ids["call_import_row_id"],
+                    CallImportRow.organization_id == org_id,
+                )
+                .scalar()
+            )
+            if ws:
+                return ws
+
+        if ids["evaluation_row_id"]:
+            ws = (
+                db.query(CallImportEvaluation.workspace_id)
+                .join(
+                    CallImportEvaluationRow,
+                    CallImportEvaluationRow.evaluation_id == CallImportEvaluation.id,
+                )
+                .filter(
+                    CallImportEvaluationRow.id == ids["evaluation_row_id"],
+                    CallImportEvaluation.organization_id == org_id,
+                )
+                .scalar()
+            )
+            if ws:
+                return ws
+
+        return None
+    finally:
+        db.close()
+
+
+def enrich_usage_context_workspace(ctx: LLMUsageContext) -> LLMUsageContext:
+    """Fill workspace_id from call-import linkage when missing at record time."""
+    if ctx.workspace_id is not None:
+        return ctx
+    try:
+        resolved = resolve_workspace_id_for_usage_context(ctx)
+    except Exception:
+        return ctx
+    if resolved is None:
+        return ctx
+    return LLMUsageContext(
+        organization_id=ctx.organization_id,
+        workspace_id=resolved,
+        product_section=ctx.product_section,
+        resource_id=ctx.resource_id,
+        resource_type=ctx.resource_type,
+        extra=ctx.extra,
+    )
+
+
+def call_import_evaluation_usage_context(
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID],
+    evaluation_id: UUID,
+    call_import_id: UUID,
+    evaluation_row_id: Optional[UUID] = None,
+    call_import_row_id: Optional[UUID] = None,
+) -> LLMUsageContext:
+    """Full attribution for eval-run LLM/STT/TTS inside a call import evaluation."""
+    extra: dict[str, str] = {
+        "call_import_id": str(call_import_id),
+        "evaluation_id": str(evaluation_id),
+    }
+    if evaluation_row_id is not None:
+        extra["evaluation_row_id"] = str(evaluation_row_id)
+    if call_import_row_id is not None:
+        extra["call_import_row_id"] = str(call_import_row_id)
+    return LLMUsageContext(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
+        resource_id=evaluation_id,
+        resource_type="call_import_evaluation",
+        extra=extra,
+    )
+
+
+def call_import_row_usage_context(
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID],
+    call_import_id: UUID,
+    call_import_row_id: UUID,
+    evaluation_id: Optional[UUID] = None,
+    evaluation_row_id: Optional[UUID] = None,
+) -> LLMUsageContext:
+    """Attribution for diarisation / STT on a single call-import row."""
+    if evaluation_id is not None:
+        return call_import_evaluation_usage_context(
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            evaluation_id=evaluation_id,
+            call_import_id=call_import_id,
+            evaluation_row_id=evaluation_row_id,
+            call_import_row_id=call_import_row_id,
+        )
+    return LLMUsageContext(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        product_section=LLMUsageProductSection.CALL_IMPORTS,
+        resource_id=call_import_id,
+        resource_type="call_import",
+        extra={
+            "call_import_id": str(call_import_id),
+            "call_import_row_id": str(call_import_row_id),
+        },
+    )
+
+
+def usage_context_for_evaluation(
+    evaluation: Any,
+) -> LLMUsageContext:
+    """Usage context from a loaded CallImportEvaluation row."""
+    return call_import_evaluation_usage_context(
+        organization_id=evaluation.organization_id,
+        workspace_id=evaluation.workspace_id,
+        evaluation_id=evaluation.id,
+        call_import_id=evaluation.call_import_id,
+    )
diff --git a/app/services/usage/context.py b/app/services/usage/context.py
index 80e84fac..8bd895e1 100644
--- a/app/services/usage/context.py
+++ b/app/services/usage/context.py
@@ -6,7 +6,7 @@
 from contextvars import ContextVar, Token
 from dataclasses import dataclass
 from enum import Enum
-from typing import Iterator, Optional
+from typing import Iterator, Optional, Any
 from uuid import UUID
 
 
@@ -36,6 +36,7 @@ class LLMUsageContext:
     product_section: LLMUsageProductSection = LLMUsageProductSection.OTHER
     resource_id: Optional[UUID] = None
     resource_type: Optional[str] = None
+    extra: Optional[dict[str, str]] = None
 
 
 _usage_context_var: ContextVar[Optional[LLMUsageContext]] = ContextVar(
@@ -128,6 +129,7 @@ def ensure_usage_context(
     product_section: LLMUsageProductSection = LLMUsageProductSection.OTHER,
     resource_id: Optional[UUID] = None,
     resource_type: Optional[str] = None,
+    extra: Optional[dict[str, str]] = None,
 ) -> Token | None:
     """Set or enrich usage context. Returns token to reset, or None if unchanged."""
     resolved_workspace = workspace_id or get_usage_workspace_hint()
@@ -146,6 +148,7 @@ def ensure_usage_context(
                 product_section=resolved_section,
                 resource_id=resource_id,
                 resource_type=resource_type,
+                extra=extra,
             )
         )
 
@@ -158,11 +161,13 @@ def ensure_usage_context(
     )
     upgraded_resource_id = current.resource_id or resource_id
     upgraded_resource_type = current.resource_type or resource_type
+    upgraded_extra = {**(current.extra or {}), **(extra or {})} or None
     if (
         upgraded_workspace == current.workspace_id
         and upgraded_section == current.product_section
         and upgraded_resource_id == current.resource_id
         and upgraded_resource_type == current.resource_type
+        and upgraded_extra == current.extra
     ):
         return None
 
@@ -173,5 +178,109 @@ def ensure_usage_context(
             product_section=upgraded_section,
             resource_id=upgraded_resource_id,
             resource_type=upgraded_resource_type,
+            extra=upgraded_extra,
         )
     )
+
+
+def usage_context_for_agent(
+    agent: Any,
+    *,
+    workspace_id: Optional[UUID] = None,
+    extra: Optional[dict[str, str]] = None,
+) -> LLMUsageContext:
+    """Usage context for agent-scoped LLM work (simulations, setup, summaries)."""
+    merged: dict[str, str] = dict(extra or {})
+    merged.setdefault("agent_id", str(agent.id))
+    short = getattr(agent, "agent_id", None)
+    if short:
+        merged.setdefault("agent_short_id", str(short))
+    return LLMUsageContext(
+        organization_id=agent.organization_id,
+        workspace_id=workspace_id or agent.workspace_id,
+        product_section=LLMUsageProductSection.AGENTS,
+        resource_id=agent.id,
+        resource_type="agent",
+        extra=merged,
+    )
+
+
+def usage_context_for_evaluator_result(result: Any) -> LLMUsageContext:
+    """Usage context for processing an evaluator result (Vapi / playground runs)."""
+    if result.agent_id:
+        return LLMUsageContext(
+            organization_id=result.organization_id,
+            workspace_id=result.workspace_id,
+            product_section=LLMUsageProductSection.AGENTS,
+            resource_id=result.agent_id,
+            resource_type="agent",
+            extra={"agent_id": str(result.agent_id)},
+        )
+    extra: dict[str, str] = {"evaluator_result_id": str(result.id)}
+    if getattr(result, "result_id", None):
+        extra["result_short_id"] = str(result.result_id)
+    if result.evaluator_id:
+        extra["evaluator_id"] = str(result.evaluator_id)
+    return LLMUsageContext(
+        organization_id=result.organization_id,
+        workspace_id=result.workspace_id,
+        product_section=LLMUsageProductSection.EVALUATORS,
+        resource_id=result.id,
+        resource_type="evaluator_result",
+        extra=extra,
+    )
+
+
+def usage_context_for_prompt_optimization_run(run: Any) -> LLMUsageContext:
+    """Usage context for a GEPA prompt optimization run."""
+    cfg = run.config if isinstance(run.config, dict) else {}
+    is_judge = cfg.get("source") == "judge_alignment"
+    extra: dict[str, str] = {
+        "optimization_run_id": str(run.id),
+        "agent_id": str(run.agent_id),
+    }
+    if run.evaluator_id:
+        extra["evaluator_id"] = str(run.evaluator_id)
+    if is_judge:
+        extra["source"] = "judge_alignment"
+        if cfg.get("judge_dataset_id"):
+            extra["judge_dataset_id"] = str(cfg["judge_dataset_id"])
+    return LLMUsageContext(
+        organization_id=run.organization_id,
+        workspace_id=run.workspace_id,
+        product_section=(
+            LLMUsageProductSection.JUDGE_ALIGNMENT
+            if is_judge
+            else LLMUsageProductSection.PROMPT_OPTIMIZATION
+        ),
+        resource_id=run.agent_id,
+        resource_type="agent",
+        extra=extra,
+    )
+
+
+def usage_context_for_judge_run(run: Any) -> LLMUsageContext:
+    """Usage context for a judge alignment scoring run."""
+    return LLMUsageContext(
+        organization_id=run.organization_id,
+        workspace_id=run.workspace_id,
+        product_section=LLMUsageProductSection.JUDGE_ALIGNMENT,
+        resource_id=run.evaluator_id,
+        resource_type="evaluator",
+        extra={
+            "judge_run_id": str(run.id),
+            "judge_dataset_id": str(run.dataset_id),
+        },
+    )
+
+
+def usage_context_for_prompt_partial(partial: Any) -> LLMUsageContext:
+    """Usage context for prompt partial / agent flowchart LLM work."""
+    return LLMUsageContext(
+        organization_id=partial.organization_id,
+        workspace_id=partial.workspace_id,
+        product_section=LLMUsageProductSection.PROMPT_PARTIALS,
+        resource_id=partial.id,
+        resource_type="prompt_partial",
+        extra={"prompt_partial_id": str(partial.id)},
+    )
diff --git a/app/services/usage/dates.py b/app/services/usage/dates.py
new file mode 100644
index 00000000..340ba9eb
--- /dev/null
+++ b/app/services/usage/dates.py
@@ -0,0 +1,38 @@
+"""Local calendar dates vs UTC usage_date bucket bounds."""
+
+from __future__ import annotations
+
+from datetime import date, datetime, time, timedelta, timezone
+from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
+
+
+def resolve_usage_timezone(tz: str | None) -> ZoneInfo:
+    if not tz:
+        return ZoneInfo("UTC")
+    try:
+        return ZoneInfo(tz)
+    except ZoneInfoNotFoundError:
+        return ZoneInfo("UTC")
+
+
+def usage_local_today(tz: str | None) -> date:
+    return datetime.now(resolve_usage_timezone(tz)).date()
+
+
+def usage_date_filter_bounds(
+    start: date,
+    end: date,
+    tz: str | None,
+) -> tuple[date, date]:
+    """Map inclusive local calendar days to usage_date (UTC-day) filter bounds."""
+    if not tz:
+        return start, end
+
+    zone = resolve_usage_timezone(tz)
+    start_local = datetime.combine(start, time.min, tzinfo=zone)
+    end_exclusive = datetime.combine(end + timedelta(days=1), time.min, tzinfo=zone)
+    filter_start = start_local.astimezone(timezone.utc).date()
+    filter_end = (
+        end_exclusive - timedelta(seconds=1)
+    ).astimezone(timezone.utc).date()
+    return filter_start, filter_end
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index 4c1fcecb..33fda254 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -1,9 +1,11 @@
-"""Redis-buffered LLM usage counters with catalog rollup flush."""
+"""Redis-buffered LLM/STT usage counters with catalog rollup flush."""
 
 from __future__ import annotations
 
+import math
 import time
 import uuid
+import json
 from datetime import date, datetime, timezone
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 from uuid import UUID
@@ -11,11 +13,19 @@
 import redis
 from loguru import logger
 from sqlalchemy import text
+from sqlalchemy.exc import IntegrityError
 from sqlalchemy.orm import Session
 
 from app.config import settings
+from app.services.usage.bucket_context import (
+    build_bucket_context,
+    context_bucket_token,
+    legacy_resource_context,
+    parse_context_bucket_token,
+)
 from app.services.usage.context import (
     LLMUsageContext,
+    LLMUsageProductSection,
     get_usage_context,
 )
 from app.services.usage.normalize import UsageSnapshot
@@ -26,16 +36,20 @@
 _PENDING_TTL_SECONDS = 14 * 24 * 60 * 60
 _FLUSH_LOCK_TTL_SECONDS = 45
 _FLUSH_LOCK_WAIT_SECONDS = 3.0
+USAGE_KIND_LLM = "llm"
+USAGE_KIND_STT = "stt"
+USAGE_KIND_TTS = "tts"
 _METRIC_FIELDS = (
     "prompt_tokens",
     "completion_tokens",
     "cache_read_tokens",
     "cache_creation_tokens",
     "reasoning_tokens",
+    "audio_seconds",
+    "tts_characters",
     "call_count",
 )
 
-# Atomically move pending hash → claim key so only one flusher owns the deltas.
 _CLAIM_LUA = """
 if redis.call('EXISTS', KEYS[1]) == 0 then
   return 0
@@ -73,51 +87,106 @@ def _bucket_prefix(
     workspace_id: Optional[UUID],
     product_section: str,
     model: str,
-    resource_id: Optional[UUID],
-    resource_type: Optional[str],
+    context: Optional[Dict[str, Any]],
     usage_date: date,
+    usage_kind: str,
 ) -> str:
     return "|".join(
         [
             _token(workspace_id),
             product_section,
             model,
-            _token(resource_id),
-            resource_type or _NONE,
+            context_bucket_token(context),
             usage_date.isoformat(),
+            usage_kind or USAGE_KIND_LLM,
         ]
     )
 
 
 def _parse_bucket_prefix(prefix: str) -> Optional[Dict[str, Any]]:
     parts = prefix.split("|")
-    if len(parts) != 6:
+    # New: 6 parts with JSON context token + usage_kind.
+    if len(parts) == 6:
+        ws_token, section, model, context_token, day_str, usage_kind = parts
+        context = parse_context_bucket_token(context_token)
+    elif len(parts) == 7:
+        # Legacy Redis keys: resource_id + resource_type before date/kind.
+        (
+            ws_token,
+            section,
+            model,
+            resource_token,
+            resource_type,
+            day_str,
+            usage_kind,
+        ) = parts
+        resource_id = None if resource_token == _NONE else UUID(resource_token)
+        resolved_resource_type = None if resource_type == _NONE else resource_type
+        context = legacy_resource_context(resource_id, resolved_resource_type)
+    elif len(parts) == 5:
+        ws_token, section, model, context_token, day_str = parts
+        usage_kind = USAGE_KIND_LLM
+        context = parse_context_bucket_token(context_token)
+    else:
         return None
-    ws_token, section, model, resource_token, resource_type, day_str = parts
     try:
         usage_date = date.fromisoformat(day_str)
     except ValueError:
         return None
     workspace_id = None if ws_token == _NONE else UUID(ws_token)
-    resource_id = None if resource_token == _NONE else UUID(resource_token)
-    resolved_resource_type = None if resource_type == _NONE else resource_type
     return {
         "workspace_id": workspace_id,
         "product_section": section,
         "model": model,
-        "resource_id": resource_id,
-        "resource_type": resolved_resource_type,
+        "context": context,
         "usage_date": usage_date,
+        "usage_kind": usage_kind or USAGE_KIND_LLM,
     }
 
 
-def _resolve_context(ctx: Optional[LLMUsageContext]) -> LLMUsageContext:
+def _bucket_from_context(
+    context: LLMUsageContext,
+    *,
+    model: str,
+    usage_date: date,
+    usage_kind: str,
+) -> Dict[str, Any]:
+    return {
+        "workspace_id": context.workspace_id,
+        "product_section": context.product_section.value,
+        "model": model,
+        "context": build_bucket_context(
+            resource_id=context.resource_id,
+            resource_type=context.resource_type,
+            extra=context.extra,
+        ),
+        "usage_date": usage_date,
+        "usage_kind": usage_kind,
+    }
+
+
+def _resolve_context(
+    *,
+    organization_id: Optional[UUID] = None,
+    ctx: Optional[LLMUsageContext] = None,
+) -> Optional[LLMUsageContext]:
     if ctx is not None:
         return ctx
     current = get_usage_context()
     if current is not None:
         return current
-    raise ValueError("LLM usage context is not set")
+    if organization_id is not None:
+        from app.services.usage.context import (
+            get_usage_section_hint,
+            get_usage_workspace_hint,
+        )
+
+        return LLMUsageContext(
+            organization_id=organization_id,
+            workspace_id=get_usage_workspace_hint(),
+            product_section=get_usage_section_hint(),
+        )
+    return None
 
 
 def _deltas_from_usage(usage: UsageSnapshot) -> Dict[str, int]:
@@ -127,24 +196,147 @@ def _deltas_from_usage(usage: UsageSnapshot) -> Dict[str, int]:
         "cache_read_tokens": usage.cache_read_tokens,
         "cache_creation_tokens": usage.cache_creation_tokens,
         "reasoning_tokens": usage.reasoning_tokens,
+        "audio_seconds": 0,
+        "tts_characters": 0,
+        "call_count": 1,
+    }
+
+
+def _deltas_from_stt(audio_seconds: int, *, count_call: bool = True) -> Dict[str, int]:
+    return {
+        "prompt_tokens": 0,
+        "completion_tokens": 0,
+        "cache_read_tokens": 0,
+        "cache_creation_tokens": 0,
+        "reasoning_tokens": 0,
+        "audio_seconds": max(0, int(audio_seconds)),
+        "tts_characters": 0,
+        "call_count": 1 if count_call else 0,
+    }
+
+
+def _deltas_from_tts(characters: int) -> Dict[str, int]:
+    return {
+        "prompt_tokens": 0,
+        "completion_tokens": 0,
+        "cache_read_tokens": 0,
+        "cache_creation_tokens": 0,
+        "reasoning_tokens": 0,
+        "audio_seconds": 0,
+        "tts_characters": max(0, int(characters)),
         "call_count": 1,
     }
 
 
+def _buffer_to_postgres(
+    organization_id: UUID,
+    bucket: Dict[str, Any],
+    deltas: Dict[str, int],
+) -> None:
+    """Durable fallback when Redis is unavailable."""
+    try:
+        from app.database import SessionLocal
+    except Exception as exc:
+        logger.warning("usage postgres fallback unavailable: {}", exc)
+        return
+
+    params = {
+        "organization_id": str(organization_id),
+        "workspace_id": str(bucket["workspace_id"]) if bucket.get("workspace_id") else None,
+        "product_section": bucket["product_section"],
+        "model": bucket["model"],
+        "context": json.dumps(bucket.get("context") or {}),
+        "usage_date": bucket["usage_date"].isoformat(),
+        "usage_kind": bucket.get("usage_kind") or USAGE_KIND_LLM,
+        "prompt_tokens": int(deltas.get("prompt_tokens", 0)),
+        "completion_tokens": int(deltas.get("completion_tokens", 0)),
+        "cache_read_tokens": int(deltas.get("cache_read_tokens", 0)),
+        "cache_creation_tokens": int(deltas.get("cache_creation_tokens", 0)),
+        "reasoning_tokens": int(deltas.get("reasoning_tokens", 0)),
+        "audio_seconds": int(deltas.get("audio_seconds", 0)),
+        "tts_characters": int(deltas.get("tts_characters", 0)),
+        "call_count": int(deltas.get("call_count", 0)),
+    }
+    db = SessionLocal()
+    try:
+        db.execute(
+            text(
+                """
+                INSERT INTO usage_pending_buffer (
+                    id, organization_id, workspace_id, product_section, model,
+                    context, usage_date, usage_kind,
+                    prompt_tokens, completion_tokens, cache_read_tokens,
+                    cache_creation_tokens, reasoning_tokens, audio_seconds,
+                    tts_characters, call_count, created_at
+                ) VALUES (
+                    gen_random_uuid(), CAST(:organization_id AS uuid),
+                    CAST(:workspace_id AS uuid), :product_section, :model,
+                    CAST(:context AS jsonb),
+                    CAST(:usage_date AS date), :usage_kind,
+                    :prompt_tokens, :completion_tokens, :cache_read_tokens,
+                    :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
+                    :tts_characters, :call_count, now()
+                )
+                """
+            ),
+            params,
+        )
+        db.commit()
+    except Exception as exc:
+        db.rollback()
+        logger.warning("usage postgres fallback insert failed: {}", exc)
+    finally:
+        db.close()
+
+
+def _incr_pending(
+    organization_id: UUID,
+    prefix: str,
+    deltas: Dict[str, int],
+    bucket: Dict[str, Any],
+) -> None:
+    hash_key = _pending_hash_key(organization_id)
+    try:
+        client = _client()
+        pipe = client.pipeline()
+        for metric, delta in deltas.items():
+            if delta:
+                pipe.hincrby(hash_key, f"{prefix}|{metric}", int(delta))
+        pipe.sadd("usage:pending:orgs", str(organization_id))
+        pipe.expire(hash_key, _PENDING_TTL_SECONDS)
+        pipe.execute()
+    except redis.RedisError as exc:
+        logger.warning("usage redis counter failed, buffering to postgres: {}", exc)
+        _buffer_to_postgres(organization_id, bucket, deltas)
+
+
+def _context_for_record(
+    *,
+    organization_id: Optional[UUID] = None,
+    ctx: Optional[LLMUsageContext] = None,
+) -> Optional[LLMUsageContext]:
+    context = _resolve_context(organization_id=organization_id, ctx=ctx)
+    if context is None:
+        return None
+    from app.services.usage.call_import_context import enrich_usage_context_workspace
+
+    return enrich_usage_context_workspace(context)
+
+
 def record_llm_usage(
     model: str,
     usage: UsageSnapshot,
     *,
+    organization_id: Optional[UUID] = None,
     ctx: Optional[LLMUsageContext] = None,
     usage_date: Optional[date] = None,
 ) -> None:
-    """Increment Redis counters for one LLM call (best-effort, never raises)."""
+    """Increment counters for one LLM call (best-effort, never raises)."""
     if not model:
         model = "unknown"
-    try:
-        context = _resolve_context(ctx)
-    except ValueError:
-        logger.debug("llm usage record skipped: missing context")
+    context = _context_for_record(organization_id=organization_id, ctx=ctx)
+    if context is None:
+        logger.warning("llm usage record skipped: missing organization_id")
         return
 
     deltas = _deltas_from_usage(usage)
@@ -152,27 +344,133 @@ def record_llm_usage(
         return
 
     day = usage_date or datetime.now(timezone.utc).date()
-    prefix = _bucket_prefix(
-        workspace_id=context.workspace_id,
-        product_section=context.product_section.value,
+    bucket = _bucket_from_context(
+        context,
         model=model,
-        resource_id=context.resource_id,
-        resource_type=context.resource_type,
         usage_date=day,
+        usage_kind=USAGE_KIND_LLM,
     )
-    hash_key = _pending_hash_key(context.organization_id)
+    prefix = _bucket_prefix(**bucket)
+    _incr_pending(context.organization_id, prefix, deltas, bucket)
+
+
+def record_stt_usage(
+    model: str,
+    *,
+    audio_seconds: float | int = 0,
+    organization_id: Optional[UUID] = None,
+    ctx: Optional[LLMUsageContext] = None,
+    usage_date: Optional[date] = None,
+    count_call: bool = True,
+) -> None:
+    """Increment counters for one STT call (audio seconds + optional call_count)."""
+    if not model:
+        model = "unknown"
+    context = _context_for_record(organization_id=organization_id, ctx=ctx)
+    if context is None:
+        logger.warning("stt usage record skipped: missing organization_id")
+        return
 
+    seconds = int(max(0, math.ceil(float(audio_seconds or 0))))
+    deltas = _deltas_from_stt(seconds, count_call=count_call)
+    day = usage_date or datetime.now(timezone.utc).date()
+    bucket = _bucket_from_context(
+        context,
+        model=model,
+        usage_date=day,
+        usage_kind=USAGE_KIND_STT,
+    )
+    prefix = _bucket_prefix(**bucket)
+    _incr_pending(context.organization_id, prefix, deltas, bucket)
+
+
+def record_call_usage(
+    model: str = "voice-call",
+    *,
+    organization_id: Optional[UUID] = None,
+    ctx: Optional[LLMUsageContext] = None,
+    usage_date: Optional[date] = None,
+    audio_seconds: int = 0,
+) -> None:
+    """Record one completed call session (call_count + optional duration).
+
+    Best-effort, never raises. Uses the same Redis-buffered path as other
+    usage counters (one pipelined HINCRBY batch per call).
+    """
+    if not model:
+        model = "voice-call"
+    context = _context_for_record(organization_id=organization_id, ctx=ctx)
+    if context is None:
+        logger.warning("call usage record skipped: missing organization_id")
+        return
+
+    seconds = max(0, int(audio_seconds or 0))
+    deltas = {
+        "prompt_tokens": 0,
+        "completion_tokens": 0,
+        "cache_read_tokens": 0,
+        "cache_creation_tokens": 0,
+        "reasoning_tokens": 0,
+        "audio_seconds": seconds,
+        "tts_characters": 0,
+        "call_count": 1,
+    }
+    day = usage_date or datetime.now(timezone.utc).date()
+    bucket = _bucket_from_context(
+        context,
+        model=model,
+        usage_date=day,
+        usage_kind=USAGE_KIND_LLM,
+    )
+    prefix = _bucket_prefix(**bucket)
+    _incr_pending(context.organization_id, prefix, deltas, bucket)
+
+
+def record_tts_usage(
+    model: str,
+    *,
+    characters: int = 0,
+    organization_id: Optional[UUID] = None,
+    ctx: Optional[LLMUsageContext] = None,
+    usage_date: Optional[date] = None,
+) -> None:
+    """Increment counters for one TTS call (characters + call_count)."""
+    if not model:
+        model = "unknown"
+    context = _context_for_record(organization_id=organization_id, ctx=ctx)
+    if context is None:
+        logger.warning("tts usage record skipped: missing organization_id")
+        return
+
+    chars = max(0, int(characters or 0))
+    deltas = _deltas_from_tts(chars)
+    day = usage_date or datetime.now(timezone.utc).date()
+    bucket = _bucket_from_context(
+        context,
+        model=model,
+        usage_date=day,
+        usage_kind=USAGE_KIND_TTS,
+    )
+    prefix = _bucket_prefix(**bucket)
+    _incr_pending(context.organization_id, prefix, deltas, bucket)
+
+
+def probe_audio_seconds(audio_file_path: str) -> int:
+    """Best-effort audio duration in whole seconds."""
+    if not audio_file_path:
+        return 0
     try:
-        client = _client()
-        pipe = client.pipeline()
-        for metric, delta in deltas.items():
-            if delta:
-                pipe.hincrby(hash_key, f"{prefix}|{metric}", int(delta))
-        pipe.sadd("usage:pending:orgs", str(context.organization_id))
-        pipe.expire(hash_key, _PENDING_TTL_SECONDS)
-        pipe.execute()
-    except redis.RedisError as exc:
-        logger.warning("llm usage counter skipped: {}", exc)
+        from pydub import AudioSegment
+
+        return max(0, int(math.ceil(AudioSegment.from_file(audio_file_path).duration_seconds)))
+    except Exception:
+        pass
+    try:
+        import librosa
+
+        return max(0, int(math.ceil(librosa.get_duration(path=audio_file_path))))
+    except Exception:
+        return 0
 
 
 def _parse_hash_to_buckets(raw: Dict[str, str]) -> Dict[str, Dict[str, int]]:
@@ -216,7 +514,42 @@ def _restore_buckets_to_pending(
         pipe.expire(hash_key, _PENDING_TTL_SECONDS)
         pipe.execute()
     except redis.RedisError as exc:
-        logger.warning("llm usage redis restore failed: {}", exc)
+        logger.warning("llm usage redis restore failed, buffering: {}", exc)
+        for prefix, metrics in buckets.items():
+            parsed = _parse_bucket_prefix(prefix)
+            if parsed:
+                _buffer_to_postgres(organization_id, parsed, metrics)
+
+
+_CLAIM_COMMITTED_TTL_SECONDS = 24 * 60 * 60
+
+
+def _claim_committed_key(claim_key: str) -> str:
+    return f"usage:claim_done:{claim_key}"
+
+
+def _mark_claim_committed(claim_key: str) -> None:
+    try:
+        _client().set(_claim_committed_key(claim_key), "1", ex=_CLAIM_COMMITTED_TTL_SECONDS)
+    except redis.RedisError:
+        pass
+
+
+def _is_claim_committed(claim_key: str) -> bool:
+    try:
+        return bool(_client().exists(_claim_committed_key(claim_key)))
+    except redis.RedisError:
+        return False
+
+
+def _has_pending_usage(organization_id: UUID) -> bool:
+    try:
+        client = _client()
+        if client.exists(_pending_hash_key(organization_id)):
+            return True
+        return bool(client.sismember("usage:pending:orgs", str(organization_id)))
+    except redis.RedisError:
+        return False
 
 
 def _acquire_flush_lock(organization_id: UUID) -> bool:
@@ -248,7 +581,6 @@ def _release_flush_lock(organization_id: UUID) -> None:
 def _claim_pending(
     organization_id: UUID,
 ) -> Tuple[Optional[str], Dict[str, Dict[str, int]]]:
-    """Rename pending → claim key. Returns (claim_key, buckets) or (None, {})."""
     claim_id = str(uuid.uuid4())
     pending_key = _pending_hash_key(organization_id)
     claim_key = _claim_hash_key(organization_id, claim_id)
@@ -283,74 +615,137 @@ def _upsert_bucket(
     organization_id: UUID,
     bucket: Dict[str, Any],
     deltas: Dict[str, int],
-    *,
-    resource_type: Optional[str],
 ) -> None:
+    context = bucket.get("context") or {}
     params = {
         "organization_id": str(organization_id),
         "workspace_id": str(bucket["workspace_id"]) if bucket["workspace_id"] else None,
         "product_section": bucket["product_section"],
         "model": bucket["model"],
-        "resource_id": str(bucket["resource_id"]) if bucket["resource_id"] else None,
-        "resource_type": resource_type,
+        "context": json.dumps(context),
+        "context_resource_id": str(context.get("resource_id") or ""),
+        "context_resource_type": str(context.get("resource_type") or ""),
         "usage_date": bucket["usage_date"].isoformat(),
+        "usage_kind": bucket.get("usage_kind") or USAGE_KIND_LLM,
         "prompt_tokens": int(deltas.get("prompt_tokens", 0)),
         "completion_tokens": int(deltas.get("completion_tokens", 0)),
         "cache_read_tokens": int(deltas.get("cache_read_tokens", 0)),
         "cache_creation_tokens": int(deltas.get("cache_creation_tokens", 0)),
         "reasoning_tokens": int(deltas.get("reasoning_tokens", 0)),
+        "audio_seconds": int(deltas.get("audio_seconds", 0)),
+        "tts_characters": int(deltas.get("tts_characters", 0)),
         "call_count": int(deltas.get("call_count", 0)),
     }
-    result = db.execute(
-        text(
-            """
-            UPDATE llm_usage_daily SET
+    update_set = """
                 prompt_tokens = prompt_tokens + :prompt_tokens,
                 completion_tokens = completion_tokens + :completion_tokens,
                 cache_read_tokens = cache_read_tokens + :cache_read_tokens,
                 cache_creation_tokens = cache_creation_tokens + :cache_creation_tokens,
                 reasoning_tokens = reasoning_tokens + :reasoning_tokens,
+                audio_seconds = audio_seconds + :audio_seconds,
+                tts_characters = tts_characters + :tts_characters,
                 call_count = call_count + :call_count,
-                resource_type = COALESCE(resource_type, :resource_type),
+                context = CASE
+                    WHEN context = '{}'::jsonb THEN CAST(:context AS jsonb)
+                    ELSE context || CAST(:context AS jsonb)
+                END,
                 updated_at = now()
+    """
+    where_base = """
             WHERE organization_id = CAST(:organization_id AS uuid)
               AND product_section = :product_section
               AND model = :model
               AND usage_date = CAST(:usage_date AS date)
+              AND usage_kind = :usage_kind
               AND workspace_id IS NOT DISTINCT FROM CAST(:workspace_id AS uuid)
-              AND resource_id IS NOT DISTINCT FROM CAST(:resource_id AS uuid)
-            """
-        ),
+    """
+    exact_context_where = where_base + " AND context = CAST(:context AS jsonb)"
+    legacy_context_where = (
+        where_base
+        + """
+              AND COALESCE(context->>'resource_id', '') = :context_resource_id
+              AND COALESCE(context->>'resource_type', '') = :context_resource_type
+    """
+    )
+
+    result = db.execute(
+        text(f"UPDATE llm_usage_daily SET {update_set} {exact_context_where}"),
         params,
     )
     if result.rowcount:
         return
 
-    db.execute(
-        text(
-            """
+    result = db.execute(
+        text(f"UPDATE llm_usage_daily SET {update_set} {legacy_context_where}"),
+        params,
+    )
+    if result.rowcount:
+        return
+
+    db.execute(text("SAVEPOINT llm_usage_bucket_insert"))
+    try:
+        db.execute(
+            text(
+                """
             INSERT INTO llm_usage_daily (
                 id, organization_id, workspace_id, product_section, model,
-                resource_id, resource_type, usage_date,
+                context, usage_date, usage_kind,
                 prompt_tokens, completion_tokens, cache_read_tokens,
-                cache_creation_tokens, reasoning_tokens, call_count,
+                cache_creation_tokens, reasoning_tokens, audio_seconds,
+                tts_characters, call_count,
                 created_at, updated_at
             ) VALUES (
                 gen_random_uuid(), CAST(:organization_id AS uuid),
                 CAST(:workspace_id AS uuid), :product_section, :model,
-                CAST(:resource_id AS uuid), :resource_type, CAST(:usage_date AS date),
+                CAST(:context AS jsonb), CAST(:usage_date AS date),
+                :usage_kind,
                 :prompt_tokens, :completion_tokens, :cache_read_tokens,
-                :cache_creation_tokens, :reasoning_tokens, :call_count,
+                :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
+                :tts_characters, :call_count,
                 now(), now()
             )
             """
-        ),
-        params,
-    )
+            ),
+            params,
+        )
+        db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
+    except IntegrityError as exc:
+        if not _is_unique_violation(exc):
+            db.execute(text("ROLLBACK TO SAVEPOINT llm_usage_bucket_insert"))
+            db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
+            raise
+        db.execute(text("ROLLBACK TO SAVEPOINT llm_usage_bucket_insert"))
+        result = db.execute(
+            text(f"UPDATE llm_usage_daily SET {update_set} {legacy_context_where}"),
+            params,
+        )
+        if not result.rowcount:
+            result = db.execute(
+                text(f"UPDATE llm_usage_daily SET {update_set} {exact_context_where}"),
+                params,
+            )
+        db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
+        if not result.rowcount:
+            logger.warning(
+                "llm usage upsert unique conflict but no matching bucket for org {}",
+                organization_id,
+            )
+
+
+def _is_unique_violation(exc: BaseException) -> bool:
+    text_blob = " ".join(
+        str(part)
+        for part in (
+            exc,
+            getattr(exc, "orig", None),
+            getattr(getattr(exc, "orig", None), "pgcode", None),
+        )
+        if part is not None
+    ).lower()
+    return "uniqueviolation" in text_blob or "duplicate key" in text_blob
 
 
 def _is_missing_organization_fk(exc: BaseException) -> bool:
-    """True when insert failed because organization_id is not in organizations."""
     text_blob = " ".join(
         str(part)
         for part in (exc, getattr(exc, "orig", None), getattr(exc, "args", None))
@@ -359,62 +754,193 @@ def _is_missing_organization_fk(exc: BaseException) -> bool:
     return "llm_usage_daily_organization_id_fkey" in text_blob
 
 
-def flush_usage_to_catalog(db: Session, organization_id: UUID) -> int:
-    """Claim Redis deltas, commit to llm_usage_daily, then ack the claim."""
-    if not _acquire_flush_lock(organization_id):
+def _flush_pending_buffer(db: Session, organization_id: UUID) -> int:
+    """Drain Postgres write-ahead rows into llm_usage_daily."""
+    try:
+        rows = db.execute(
+            text(
+                """
+                SELECT id, workspace_id, product_section, model, context,
+                       usage_date, usage_kind,
+                       prompt_tokens, completion_tokens, cache_read_tokens,
+                       cache_creation_tokens, reasoning_tokens, audio_seconds,
+                       tts_characters, call_count
+                FROM usage_pending_buffer
+                WHERE organization_id = CAST(:organization_id AS uuid)
+                ORDER BY created_at ASC
+                LIMIT 2000
+                """
+            ),
+            {"organization_id": str(organization_id)},
+        ).mappings().all()
+    except Exception as exc:
+        db.rollback()
+        logger.debug("usage buffer read skipped: {}", exc)
+        return 0
+    if not rows:
         return 0
 
-    claim_key = None
-    buckets: Dict[str, Dict[str, int]] = {}
+    flushed = 0
+    ids: List[str] = []
     try:
-        claim_key, buckets = _claim_pending(organization_id)
-        if not claim_key or not buckets:
-            return 0
+        for row in rows:
+            row_context = row["context"] or {}
+            if isinstance(row_context, str):
+                row_context = json.loads(row_context)
+            bucket = {
+                "workspace_id": row["workspace_id"],
+                "product_section": row["product_section"],
+                "model": row["model"],
+                "context": row_context,
+                "usage_date": row["usage_date"],
+                "usage_kind": row["usage_kind"] or USAGE_KIND_LLM,
+            }
+            deltas = {
+                "prompt_tokens": int(row["prompt_tokens"] or 0),
+                "completion_tokens": int(row["completion_tokens"] or 0),
+                "cache_read_tokens": int(row["cache_read_tokens"] or 0),
+                "cache_creation_tokens": int(row["cache_creation_tokens"] or 0),
+                "reasoning_tokens": int(row["reasoning_tokens"] or 0),
+                "audio_seconds": int(row["audio_seconds"] or 0),
+                "tts_characters": int(row.get("tts_characters") or 0),
+                "call_count": int(row["call_count"] or 0),
+            }
+            _upsert_bucket(
+                db,
+                organization_id,
+                bucket,
+                deltas,
+            )
+            ids.append(str(row["id"]))
+            flushed += 1
+        if ids:
+            from sqlalchemy import bindparam
 
-        flushed = 0
-        try:
-            for prefix, deltas in buckets.items():
-                parsed = _parse_bucket_prefix(prefix)
-                if not parsed:
-                    continue
-                _upsert_bucket(
-                    db,
-                    organization_id,
-                    parsed,
-                    deltas,
-                    resource_type=parsed.get("resource_type"),
-                )
-                flushed += 1
-            db.commit()
-        except Exception as exc:
-            db.rollback()
-            if _is_missing_organization_fk(exc):
-                # Stale/test org ids must not be restored — that loops forever on beat.
-                logger.warning(
-                    "llm usage flush dropped for unknown organization {}: {}",
-                    organization_id,
-                    exc,
-                )
-                if claim_key:
-                    _ack_claim(claim_key, organization_id)
-                return 0
-            logger.warning("llm usage catalog flush failed, restoring redis: {}", exc)
-            _restore_buckets_to_pending(organization_id, buckets)
-            if claim_key:
+            db.execute(
+                text(
+                    "DELETE FROM usage_pending_buffer WHERE id IN :ids"
+                ).bindparams(bindparam("ids", expanding=True)),
+                {"ids": ids},
+            )
+        db.commit()
+        return flushed
+    except Exception as exc:
+        db.rollback()
+        if _is_missing_organization_fk(exc):
+            logger.warning(
+                "usage buffer dropped for unknown organization {}: {}",
+                organization_id,
+                exc,
+            )
+            if ids:
                 try:
-                    _client().delete(claim_key)
-                except redis.RedisError:
-                    pass
+                    db.execute(
+                        text(
+                            """
+                            DELETE FROM usage_pending_buffer
+                            WHERE organization_id = CAST(:organization_id AS uuid)
+                            """
+                        ),
+                        {"organization_id": str(organization_id)},
+                    )
+                    db.commit()
+                except Exception:
+                    db.rollback()
             return 0
+        logger.warning("usage buffer flush failed: {}", exc)
+        return 0
 
-        _ack_claim(claim_key, organization_id)
-        return flushed
-    finally:
-        _release_flush_lock(organization_id)
+
+_CATALOG_FLUSH_COOLDOWN_SEC = 20
+
+
+def _catalog_flush_recently(organization_id: UUID) -> bool:
+    """Skip flush if another request flushed this org within the cooldown window."""
+    try:
+        client = _client()
+        key = f"usage:catalog_flush:{organization_id}"
+        return not client.set(key, "1", nx=True, ex=_CATALOG_FLUSH_COOLDOWN_SEC)
+    except redis.RedisError:
+        return False
+
+
+def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool = False) -> int:
+    """Claim Redis deltas + drain PG buffer into llm_usage_daily."""
+    _recover_orphaned_claims()
+    skip_redis_flush = not force and _catalog_flush_recently(organization_id)
+    if skip_redis_flush and _has_pending_usage(organization_id):
+        skip_redis_flush = False
+    flushed = 0
+    if not skip_redis_flush:
+        redis_locked = _acquire_flush_lock(organization_id)
+        claim_key = None
+        buckets: Dict[str, Dict[str, int]] = {}
+        try:
+            if redis_locked:
+                claim_key, buckets = _claim_pending(organization_id)
+                if claim_key and buckets:
+                    skipped: Dict[str, Dict[str, int]] = {}
+                    try:
+                        for prefix, deltas in buckets.items():
+                            parsed = _parse_bucket_prefix(prefix)
+                            if not parsed:
+                                skipped[prefix] = deltas
+                                logger.warning(
+                                    "llm usage skipped unparseable bucket prefix for org {}",
+                                    organization_id,
+                                )
+                                continue
+                            _upsert_bucket(
+                                db,
+                                organization_id,
+                                parsed,
+                                deltas,
+                            )
+                            flushed += 1
+                        db.commit()
+                    except Exception as exc:
+                        db.rollback()
+                        if _is_missing_organization_fk(exc):
+                            logger.warning(
+                                "llm usage flush dropped for unknown organization {}: {}",
+                                organization_id,
+                                exc,
+                            )
+                            if claim_key:
+                                _mark_claim_committed(claim_key)
+                                _ack_claim(claim_key, organization_id)
+                            claim_key = None
+                            buckets = {}
+                        else:
+                            logger.warning(
+                                "llm usage catalog flush failed, restoring redis: {}", exc
+                            )
+                            restore_buckets = dict(buckets)
+                            if skipped:
+                                restore_buckets.update(skipped)
+                            _restore_buckets_to_pending(organization_id, restore_buckets)
+                            if claim_key:
+                                try:
+                                    _client().delete(claim_key)
+                                except redis.RedisError:
+                                    pass
+                            claim_key = None
+                            return flushed + _flush_pending_buffer(db, organization_id)
+                    if skipped:
+                        _restore_buckets_to_pending(organization_id, skipped)
+                    if claim_key:
+                        _mark_claim_committed(claim_key)
+                        _ack_claim(claim_key, organization_id)
+                        claim_key = None
+        finally:
+            if redis_locked:
+                _release_flush_lock(organization_id)
+
+    flushed += _flush_pending_buffer(db, organization_id)
+    return flushed
 
 
 def _recover_orphaned_claims() -> None:
-    """Re-queue claim hashes left behind by a crashed flusher."""
     try:
         client = _client()
         for claim_key in client.scan_iter(match="usage:flushing:*", count=100):
@@ -427,6 +953,9 @@ def _recover_orphaned_claims() -> None:
                 continue
             if client.exists(_flush_lock_key(org_id)):
                 continue
+            if _is_claim_committed(claim_key):
+                client.delete(claim_key)
+                continue
             buckets = _read_hash_buckets(claim_key)
             if buckets:
                 _restore_buckets_to_pending(org_id, buckets)
@@ -436,10 +965,11 @@ def _recover_orphaned_claims() -> None:
 
 
 def list_pending_organization_ids() -> List[UUID]:
+    result: List[UUID] = []
+    seen: set[UUID] = set()
     try:
         client = _client()
         raw_ids = client.smembers("usage:pending:orgs")
-        result: List[UUID] = []
         stale: List[str] = []
         for value in raw_ids:
             try:
@@ -449,17 +979,44 @@ def list_pending_organization_ids() -> List[UUID]:
                 continue
             if client.exists(_pending_hash_key(org_id)):
                 result.append(org_id)
+                seen.add(org_id)
             else:
                 stale.append(value)
         if stale:
             client.srem("usage:pending:orgs", *stale)
-        return result
     except (redis.RedisError, ValueError):
-        return []
+        pass
+
+    try:
+        from app.database import SessionLocal
+
+        db = SessionLocal()
+        try:
+            rows = db.execute(
+                text(
+                    """
+                    SELECT DISTINCT organization_id
+                    FROM usage_pending_buffer
+                    LIMIT 5000
+                    """
+                )
+            ).all()
+            for row in rows:
+                org_id = row[0] if not isinstance(row, dict) else row["organization_id"]
+                if isinstance(org_id, str):
+                    org_id = UUID(org_id)
+                if org_id not in seen:
+                    result.append(org_id)
+                    seen.add(org_id)
+        finally:
+            db.close()
+    except Exception as exc:
+        logger.debug("usage buffer org scan skipped: {}", exc)
+
+    return result
 
 
 def flush_all_usage_to_catalog(db_factory) -> int:
-    """Flush all orgs with pending usage (Celery beat)."""
     _recover_orphaned_claims()
     total = 0
     for org_id in list_pending_organization_ids():
@@ -486,6 +1043,8 @@ def merge_usage_totals(
             getattr(row, "cache_creation_tokens", 0) or 0
         )
         totals["reasoning_tokens"] += int(getattr(row, "reasoning_tokens", 0) or 0)
+        totals["audio_seconds"] += int(getattr(row, "audio_seconds", 0) or 0)
+        totals["tts_characters"] += int(getattr(row, "tts_characters", 0) or 0)
         totals["call_count"] += int(getattr(row, "call_count", 0) or 0)
     totals["total_tokens"] = totals["prompt_tokens"] + totals["completion_tokens"]
     return totals
diff --git a/app/services/usage/usage_labels.py b/app/services/usage/usage_labels.py
new file mode 100644
index 00000000..4cee5167
--- /dev/null
+++ b/app/services/usage/usage_labels.py
@@ -0,0 +1,424 @@
+"""Human-readable labels for usage attribution context (no raw UUIDs in UI)."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional, Set
+from uuid import UUID
+
+from sqlalchemy.orm import Session
+
+from app.models.database import (
+    Agent,
+    CallImport,
+    CallImportEvaluation,
+    CallImportRow,
+    CallImportTag,
+    CallImportTagAssignment,
+    EvaluatorResult,
+    TTSComparison,
+)
+
+RESOURCE_TYPE_LABELS = {
+    "call_import_evaluation": "Evaluation",
+    "call_import": "Import",
+    "tts_comparison": "Simulation",
+    "evaluator_result": "Evaluator result",
+    "agent": "Agent",
+    "metric": "Metric",
+}
+
+_USAGE_KIND_LABELS = {
+    "llm": "LLM",
+    "stt": "STT",
+    "tts": "TTS",
+}
+
+
+def usage_kind_label(kind: Optional[str]) -> str:
+    if not kind:
+        return "—"
+    return _USAGE_KIND_LABELS.get(kind, kind)
+
+
+def short_entity_id(uid: UUID) -> str:
+    return str(uid)[:8]
+
+
+def format_entity_label(
+    custom_name: Optional[str],
+    uid: UUID,
+    default_prefix: str,
+) -> str:
+    """Human label: name-shortId (e.g. unauthenticated sheet.xlsx-3111d376)."""
+    short = short_entity_id(uid)
+    text = (custom_name or "").strip()
+    if text:
+        return f"{text}-{short}"
+    return f"{default_prefix}-{short}"
+
+
+class UsageNameResolver:
+    """Batch-resolve entity names for usage context JSONB keys."""
+
+    def __init__(self, db: Session, organization_id: UUID) -> None:
+        self._db = db
+        self._organization_id = organization_id
+        self._evaluations: Dict[UUID, str] = {}
+        self._call_imports: Dict[UUID, str] = {}
+        self._call_import_rows: Dict[UUID, str] = {}
+        self._tts_comparisons: Dict[UUID, str] = {}
+        self._agents: Dict[UUID, str] = {}
+
+    def preload(self, contexts: list[Dict[str, Any]]) -> None:
+        eval_ids: Set[UUID] = set()
+        import_ids: Set[UUID] = set()
+        row_ids: Set[UUID] = set()
+        comparison_ids: Set[UUID] = set()
+        agent_ids: Set[UUID] = set()
+        evaluator_result_ids: Set[UUID] = set()
+
+        for ctx in contexts:
+            if not ctx:
+                continue
+            norm = _normalize_context(ctx)
+            rtype = norm.get("resource_type")
+            resource_id = norm.get("resource_id")
+            if resource_id:
+                uid = parse_uuid(resource_id)
+                if uid:
+                    if rtype == "call_import":
+                        import_ids.add(uid)
+                    elif rtype == "call_import_evaluation":
+                        eval_ids.add(uid)
+                    elif rtype == "tts_comparison":
+                        comparison_ids.add(uid)
+                    elif rtype == "agent":
+                        agent_ids.add(uid)
+                    elif rtype == "evaluator_result":
+                        eval_ids.add(uid)
+                    else:
+                        eval_ids.add(uid)
+                        import_ids.add(uid)
+            for key, bucket in (
+                ("evaluation_id", eval_ids),
+                ("call_import_id", import_ids),
+                ("call_import_row_id", row_ids),
+                ("agent_id", agent_ids),
+                ("evaluator_result_id", evaluator_result_ids),
+            ):
+                raw = norm.get(key)
+                if not raw:
+                    continue
+                uid = parse_uuid(raw)
+                if uid:
+                    bucket.add(uid)
+
+        if eval_ids:
+            for row in (
+                self._db.query(CallImportEvaluation)
+                .filter(
+                    CallImportEvaluation.organization_id == self._organization_id,
+                    CallImportEvaluation.id.in_(eval_ids),
+                )
+                .all()
+            ):
+                self._evaluations[row.id] = format_entity_label(
+                    row.name, row.id, "Evaluation"
+                )
+
+        tag_names_by_import: Dict[UUID, list[str]] = {}
+        if import_ids:
+            for cid, tag_name in (
+                self._db.query(
+                    CallImportTagAssignment.call_import_id,
+                    CallImportTag.name,
+                )
+                .join(
+                    CallImportTag,
+                    CallImportTag.id == CallImportTagAssignment.tag_id,
+                )
+                .filter(CallImportTagAssignment.call_import_id.in_(import_ids))
+                .all()
+            ):
+                tag_names_by_import.setdefault(cid, []).append(tag_name)
+
+        if import_ids:
+            for row in (
+                self._db.query(CallImport)
+                .filter(
+                    CallImport.organization_id == self._organization_id,
+                    CallImport.id.in_(import_ids),
+                )
+                .all()
+            ):
+                display_name, prefix = _call_import_label_parts(
+                    row,
+                    sorted(tag_names_by_import.get(row.id, [])),
+                )
+                self._call_imports[row.id] = format_entity_label(
+                    display_name, row.id, prefix
+                )
+
+        if comparison_ids:
+            for row in (
+                self._db.query(TTSComparison)
+                .filter(
+                    TTSComparison.organization_id == self._organization_id,
+                    TTSComparison.id.in_(comparison_ids),
+                )
+                .all()
+            ):
+                self._tts_comparisons[row.id] = _tts_comparison_display_name(row)
+
+        if evaluator_result_ids:
+            for row in (
+                self._db.query(EvaluatorResult)
+                .filter(
+                    EvaluatorResult.organization_id == self._organization_id,
+                    EvaluatorResult.id.in_(evaluator_result_ids),
+                )
+                .all()
+            ):
+                if row.agent_id:
+                    agent_ids.add(row.agent_id)
+
+        missing_agent_ids = [uid for uid in agent_ids if uid not in self._agents]
+        if missing_agent_ids:
+            for row in (
+                self._db.query(Agent)
+                .filter(
+                    Agent.organization_id == self._organization_id,
+                    Agent.id.in_(missing_agent_ids),
+                )
+                .all()
+            ):
+                self._agents[row.id] = _agent_display_name(row)
+
+        if row_ids:
+            for row in (
+                self._db.query(CallImportRow)
+                .filter(
+                    CallImportRow.organization_id == self._organization_id,
+                    CallImportRow.id.in_(row_ids),
+                )
+                .all()
+            ):
+                self._call_import_rows[row.id] = _clean_name(
+                    row.conversation_id, "Conversation"
+                )
+
+    def evaluation_name(self, raw_id: str) -> str:
+        uid = parse_uuid(raw_id)
+        if uid and uid in self._evaluations:
+            return self._evaluations[uid]
+        if uid:
+            return format_entity_label(None, uid, "Evaluation")
+        return "Evaluation"
+
+    def call_import_name(self, raw_id: str) -> str:
+        uid = parse_uuid(raw_id)
+        if uid and uid in self._call_imports:
+            return self._call_imports[uid]
+        if uid:
+            return format_entity_label(None, uid, "Import")
+        return "Import"
+
+    def tts_comparison_name(self, raw_id: str) -> str:
+        uid = parse_uuid(raw_id)
+        if uid and uid in self._tts_comparisons:
+            return self._tts_comparisons[uid]
+        if uid:
+            return format_entity_label(None, uid, "Simulation")
+        return "Simulation"
+
+    def agent_name(self, raw_id: str) -> str:
+        uid = parse_uuid(raw_id)
+        if uid and uid in self._agents:
+            return self._agents[uid]
+        if uid:
+            return format_entity_label(None, uid, "Agent")
+        return "Agent"
+
+    def resource_name(self, raw_id: str, resource_type: Optional[str]) -> str:
+        if resource_type == "call_import_evaluation":
+            return self.evaluation_name(raw_id)
+        if resource_type == "call_import":
+            return self.call_import_name(raw_id)
+        if resource_type == "tts_comparison":
+            return self.tts_comparison_name(raw_id)
+        if resource_type == "agent":
+            return self.agent_name(raw_id)
+        uid = parse_uuid(raw_id)
+        if uid:
+            prefix = RESOURCE_TYPE_LABELS.get(resource_type or "", "Resource")
+            return format_entity_label(None, uid, prefix)
+        return RESOURCE_TYPE_LABELS.get(resource_type or "", "Unscoped")
+
+    def call_import_row_name(self, raw_id: str) -> str:
+        uid = parse_uuid(raw_id)
+        if uid and uid in self._call_import_rows:
+            return self._call_import_rows[uid]
+        return "Conversation"
+
+
+def parse_uuid(raw: Any) -> Optional[UUID]:
+    try:
+        return UUID(str(raw))
+    except (ValueError, TypeError):
+        return None
+
+
+def _clean_name(value: Optional[str], fallback: str) -> str:
+    text = (value or "").strip()
+    return text or fallback
+
+
+def _call_import_title(row: CallImport) -> Optional[str]:
+    filename = (row.original_filename or "").strip()
+    if filename:
+        return filename
+    dataset = (row.dataset or "").strip()
+    if dataset:
+        return dataset
+    return None
+
+
+def _call_import_meta_suffix(row: CallImport, tag_names: list[str]) -> str:
+    parts: list[str] = []
+    dataset = (row.dataset or "").strip()
+    filename = (row.original_filename or "").strip()
+    if dataset and dataset != filename:
+        parts.append(dataset)
+    for name in tag_names:
+        if name and name not in parts:
+            parts.append(name)
+    if not parts:
+        return ""
+    return f" ({' · '.join(parts)})"
+
+
+def _call_import_label_parts(
+    row: CallImport,
+    tag_names: list[str],
+) -> tuple[Optional[str], str]:
+    base = _call_import_title(row)
+    suffix = _call_import_meta_suffix(row, tag_names)
+    if base:
+        return f"{base}{suffix}", "Import"
+    if suffix:
+        return suffix.strip(" ()"), "Import"
+    return None, "Import"
+
+
+def _tts_comparison_display_name(row: TTSComparison) -> str:
+    name = (row.name or "").strip() or "Simulation"
+    sim = (row.simulation_id or "").strip()
+    if sim:
+        return f"{name} #{sim}"
+    return format_entity_label(name, row.id, "Simulation")
+
+
+def _agent_display_name(row: Agent) -> str:
+    name = (row.name or "").strip() or "Agent"
+    short = (row.agent_id or "").strip()
+    if short:
+        return f"{name} #{short}"
+    return format_entity_label(name, row.id, "Agent")
+
+
+def _normalize_context(raw: Any) -> Dict[str, str]:
+    if not raw or not isinstance(raw, dict):
+        return {}
+    return {str(k): str(v) for k, v in raw.items() if v is not None}
+
+
+def _richest_context(contexts: list[Dict[str, str]]) -> Dict[str, str]:
+    if not contexts:
+        return {}
+    return max(contexts, key=lambda c: len(c))
+
+
+def build_usage_resource_label(
+    context: Optional[Dict[str, Any]],
+    resource_type: Optional[str],
+    resolver: UsageNameResolver,
+) -> str:
+    """Hierarchical label: call import · evaluation · conversation."""
+    ctx = _normalize_context(context)
+    parts: list[str] = []
+
+    call_import_id = ctx.get("call_import_id")
+    if call_import_id:
+        parts.append(resolver.call_import_name(call_import_id))
+    elif resource_type == "call_import" and ctx.get("resource_id"):
+        parts.append(resolver.call_import_name(ctx["resource_id"]))
+
+    evaluation_id = ctx.get("evaluation_id")
+    agent_id = ctx.get("agent_id")
+    resource_id = ctx.get("resource_id")
+    if evaluation_id:
+        parts.append(resolver.evaluation_name(evaluation_id))
+    elif resource_type == "call_import_evaluation" and resource_id:
+        parts.append(resolver.evaluation_name(resource_id))
+    elif resource_type == "tts_comparison" and resource_id:
+        parts.append(resolver.tts_comparison_name(resource_id))
+    elif resource_type == "agent" and resource_id:
+        parts.append(resolver.agent_name(resource_id))
+    elif agent_id:
+        parts.append(resolver.agent_name(agent_id))
+    elif resource_type == "evaluator_result" and resource_id:
+        parts.append(resolver.resource_name(resource_id, resource_type))
+    elif resource_type and resource_id:
+        parts.append(resolver.resource_name(resource_id, resource_type))
+
+    row_id = ctx.get("call_import_row_id")
+    if row_id:
+        parts.append(resolver.call_import_row_name(row_id))
+
+    if not parts:
+        rid = resource_id or agent_id
+        if rid and (resource_type == "agent" or agent_id):
+            return resolver.agent_name(rid)
+        if resource_type:
+            prefix = RESOURCE_TYPE_LABELS.get(resource_type, resource_type)
+            uid = parse_uuid(rid)
+            if uid:
+                return format_entity_label(None, uid, prefix)
+            return prefix
+        return "Unscoped"
+
+    return " / ".join(parts)
+
+
+def labels_for_resource_buckets(
+    buckets: list[tuple[Optional[str], Optional[str], list[Dict[str, Any]]]],
+    resolver: UsageNameResolver,
+) -> Dict[str, str]:
+    """Map resource_id string -> label; buckets are (resource_id, resource_type, contexts)."""
+    labels: Dict[str, str] = {}
+    for raw_id, resource_type, contexts in buckets:
+        if not raw_id:
+            continue
+        ctx = _richest_context([_normalize_context(c) for c in contexts])
+        merged = dict(ctx)
+        merged.setdefault("resource_id", raw_id)
+        if resource_type:
+            merged.setdefault("resource_type", resource_type)
+        labels[str(raw_id)] = build_usage_resource_label(
+            merged, resource_type, resolver
+        )
+    return labels
+
+
+def labels_for_call_import_ids(
+    import_ids: list[UUID],
+    resolver: UsageNameResolver,
+) -> Dict[str, str]:
+    labels: Dict[str, str] = {}
+    for uid in import_ids:
+        labels[str(uid)] = resolver.call_import_name(str(uid))
+    return labels
+
+
+def collect_contexts_from_rows(rows: list[Any]) -> list[Dict[str, Any]]:
+    return [_normalize_context(r[0]) for r in rows if r and r[0]]
diff --git a/app/services/usage/voice_usage_processor.py b/app/services/usage/voice_usage_processor.py
index 56d65f57..d1529507 100644
--- a/app/services/usage/voice_usage_processor.py
+++ b/app/services/usage/voice_usage_processor.py
@@ -1,7 +1,8 @@
-"""Voice pipeline processor that records LLM token usage from MetricsFrames."""
+"""Voice pipeline processor that records LLM/TTS usage from MetricsFrames."""
 
 from __future__ import annotations
 
+import math
 from typing import Optional
 from uuid import UUID
 
@@ -16,7 +17,7 @@ def create_llm_usage_recorder(
     resource_id: UUID | str | None = None,
     resource_type: Optional[str] = None,
 ):
-    """Build a FrameProcessor that records LLM usage from MetricsFrames.
+    """Build a FrameProcessor that records LLM/TTS usage from MetricsFrames.
 
     Returns None when organization_id is missing or efficientai is unavailable.
     """
@@ -25,15 +26,22 @@ def create_llm_usage_recorder(
 
     try:
         from efficientai.frames.frames import Frame, MetricsFrame
-        from efficientai.metrics.metrics import LLMUsageMetricsData
+        from efficientai.metrics.metrics import (
+            LLMUsageMetricsData,
+            ProcessingMetricsData,
+            TTSUsageMetricsData,
+        )
         from efficientai.processors.frame_processor import FrameDirection, FrameProcessor
 
         from app.services.usage.context import (
             LLMUsageContext,
             LLMUsageProductSection,
-            set_usage_context,
         )
-        from app.services.usage.llm_usage import record_llm_usage
+        from app.services.usage.llm_usage import (
+            record_llm_usage,
+            record_stt_usage,
+            record_tts_usage,
+        )
         from app.services.usage.normalize import UsageSnapshot
     except Exception as exc:
         logger.debug("voice usage recorder unavailable: {}", exc)
@@ -48,14 +56,12 @@ def create_llm_usage_recorder(
     ws_uuid = UUID(str(workspace_id)) if workspace_id else None
     res_uuid = UUID(str(resource_id)) if resource_id else None
 
-    set_usage_context(
-        LLMUsageContext(
-            organization_id=org_uuid,
-            workspace_id=ws_uuid,
-            product_section=section,
-            resource_id=res_uuid,
-            resource_type=resource_type,
-        )
+    usage_ctx = LLMUsageContext(
+        organization_id=org_uuid,
+        workspace_id=ws_uuid,
+        product_section=section,
+        resource_id=res_uuid,
+        resource_type=resource_type,
     )
 
     class LLMUsageRecorderProcessor(FrameProcessor):
@@ -74,7 +80,29 @@ async def process_frame(self, frame: Frame, direction: FrameDirection):
                             ),
                             reasoning_tokens=int(tokens.reasoning_tokens or 0),
                         )
-                        record_llm_usage(item.model or "unknown", snapshot)
+                        record_llm_usage(
+                            item.model or "unknown",
+                            snapshot,
+                            organization_id=org_uuid,
+                            ctx=usage_ctx,
+                        )
+                    elif isinstance(item, TTSUsageMetricsData):
+                        record_tts_usage(
+                            item.model or "unknown",
+                            characters=int(item.value or 0),
+                            organization_id=org_uuid,
+                            ctx=usage_ctx,
+                        )
+                    elif isinstance(item, ProcessingMetricsData):
+                        processor_name = (item.processor or "").lower()
+                        if "stt" in processor_name and item.value is not None:
+                            seconds = max(1, int(math.ceil(float(item.value))))
+                            record_stt_usage(
+                                item.model or "unknown",
+                                audio_seconds=seconds,
+                                organization_id=org_uuid,
+                                ctx=usage_ctx,
+                            )
             await self.push_frame(frame, direction)
 
     return LLMUsageRecorderProcessor()
diff --git a/app/services/voice_agent/bot_fast_api.py b/app/services/voice_agent/bot_fast_api.py
index 3e2d25e4..3bcd92a7 100644
--- a/app/services/voice_agent/bot_fast_api.py
+++ b/app/services/voice_agent/bot_fast_api.py
@@ -251,7 +251,9 @@ async def on_silence_hangup():
             usage_recorder = create_llm_usage_recorder(
                 organization_id=organization_id,
                 workspace_id=workspace_id,
-                product_section="telephony" if telephony_mode else "playground",
+                product_section="agents" if agent_id else ("telephony" if telephony_mode else "playground"),
+                resource_id=agent_id,
+                resource_type="agent" if agent_id else None,
             )
             if usage_recorder:
                 pipeline_processors.append(usage_recorder)
@@ -292,7 +294,9 @@ async def on_client_disconnected(transport, client):
             usage_recorder = create_llm_usage_recorder(
                 organization_id=organization_id,
                 workspace_id=workspace_id,
-                product_section="playground",
+                product_section="agents" if agent_id else "playground",
+                resource_id=agent_id,
+                resource_type="agent" if agent_id else None,
             )
             pipeline_steps = [
                 ws_transport.input(),
diff --git a/app/services/voice_agent/voice_bundle.py b/app/services/voice_agent/voice_bundle.py
index 296bf032..edebc4d0 100644
--- a/app/services/voice_agent/voice_bundle.py
+++ b/app/services/voice_agent/voice_bundle.py
@@ -510,6 +510,7 @@ async def run_voice_bundle_fastapi(
     websocket_client,
     system_instruction: str | None = None,
     organization_id: str | None = None,
+    workspace_id: str | None = None,
     agent_id: str | None = None,
     persona_id: str | None = None,
     scenario_id: str | None = None,
@@ -835,8 +836,27 @@ async def on_silence_hangup():
         if telephony_mode and call_short_id and agent_transcript_processor:
             pipeline_processors.append(agent_transcript_processor)
 
+        from app.services.usage.voice_usage_processor import create_llm_usage_recorder
+
+        agent_usage_section = (
+            "agents"
+            if agent_id
+            else ("telephony" if telephony_mode else "voice_playground")
+        )
+        usage_recorder = create_llm_usage_recorder(
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            product_section=agent_usage_section,
+            resource_id=agent_id,
+            resource_type="agent" if agent_id else None,
+        )
+
         pipeline_processors.extend([
             tts,
+        ])
+        if usage_recorder:
+            pipeline_processors.append(usage_recorder)
+        pipeline_processors.extend([
             bot_recorder if use_aligned_recorders else audio_buffer_output,
             ws_transport.output(),
             context_aggregator.assistant(),
@@ -865,6 +885,7 @@ async def on_client_disconnected(transport, client):
                 await task.cancel()
         else:
             rtvi = imports["RTVIProcessor"](config=imports["RTVIConfig"](config=[]))
+
             rtvi_processors = [ws_transport.input()]
             if silence_hangup_processor:
                 rtvi_processors.append(silence_hangup_processor)
@@ -875,6 +896,17 @@ async def on_client_disconnected(transport, client):
                 rtvi,
                 llm,
                 tts,
+            ])
+            usage_recorder = create_llm_usage_recorder(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section="agents" if agent_id else "voice_playground",
+                resource_id=agent_id,
+                resource_type="agent" if agent_id else None,
+            )
+            if usage_recorder:
+                rtvi_processors.append(usage_recorder)
+            rtvi_processors.extend([
                 audio_buffer_output,
                 ws_transport.output(),
                 context_aggregator.assistant(),
diff --git a/app/services/webrtc_bridge/test_agent_processor.py b/app/services/webrtc_bridge/test_agent_processor.py
index 6d5ea658..990fa6bd 100644
--- a/app/services/webrtc_bridge/test_agent_processor.py
+++ b/app/services/webrtc_bridge/test_agent_processor.py
@@ -9,8 +9,9 @@
 import asyncio
 import io
 import os
-from typing import Optional, Callable, Awaitable, List, Dict, Any
+from typing import Optional, Callable, Awaitable, List, Dict, Any, Union
 from dataclasses import dataclass, field
+from uuid import UUID
 from loguru import logger
 
 # TTS service imports
@@ -90,6 +91,9 @@ class TestAgentConfig:
     response_delay_ms: int = 500  # Delay before responding (more natural)
     allow_interruptions: bool = False
 
+    organization_id: Optional[Union[UUID, str]] = None
+    workspace_id: Optional[Union[UUID, str]] = None
+
 
 class TestAgentProcessor:
     """
@@ -366,7 +370,9 @@ async def _generate_llm_response(self) -> Optional[str]:
                 max_tokens=self.config.llm_max_tokens if self.config.llm_max_tokens is not None else 150,
                 temperature=self.config.llm_temperature if self.config.llm_temperature is not None else 0.7,
             )
-            
+
+            self._record_llm_usage(response=response)
+
             return response.choices[0].message.content.strip()
             
         except Exception as e:
@@ -392,6 +398,8 @@ async def _text_to_speech(self, text: str) -> Optional[bytes]:
             else:
                 audio = await self._tts_cartesia(text)
 
+            if audio:
+                self._record_tts_usage(text=text)
             return audio
         except Exception as e:
             logger.error(f"[TestAgent] TTS ({provider}) error: {e}")
@@ -400,6 +408,82 @@ async def _text_to_speech(self, text: str) -> Optional[bytes]:
     def _tts_settings(self) -> Dict[str, Any]:
         return dict(self.config.tts_config or {})
 
+    def _record_tts_usage(self, *, text: str) -> None:
+        if not self.config.organization_id:
+            return
+        try:
+            from app.services.usage.context import (
+                LLMUsageContext,
+                LLMUsageProductSection,
+                llm_usage_context,
+            )
+            from app.services.usage.llm_usage import record_tts_usage
+
+            model = self.config.tts_model or TTS_DEFAULT_MODELS.get(
+                self.config.tts_provider.lower(), "unknown"
+            )
+            org_id = UUID(str(self.config.organization_id))
+            ws_id = (
+                UUID(str(self.config.workspace_id))
+                if self.config.workspace_id
+                else None
+            )
+            with llm_usage_context(
+                LLMUsageContext(
+                    organization_id=org_id,
+                    workspace_id=ws_id,
+                    product_section=LLMUsageProductSection.TEST_AGENT,
+                )
+            ):
+                record_tts_usage(
+                    model,
+                    characters=len(text or ""),
+                    organization_id=org_id,
+                )
+        except Exception as exc:
+            logger.debug("test agent tts usage record skipped: {}", exc)
+
+    def _record_llm_usage(self, *, response: Any) -> None:
+        if not self.config.organization_id:
+            return
+        try:
+            from app.services.usage.context import (
+                LLMUsageContext,
+                LLMUsageProductSection,
+                llm_usage_context,
+            )
+            from app.services.usage.llm_usage import record_llm_usage
+            from app.services.usage.normalize import UsageSnapshot
+
+            usage = getattr(response, "usage", None)
+            if usage is None:
+                return
+            org_id = UUID(str(self.config.organization_id))
+            ws_id = (
+                UUID(str(self.config.workspace_id))
+                if self.config.workspace_id
+                else None
+            )
+            model = self.config.llm_model or "unknown"
+            snapshot = UsageSnapshot(
+                prompt_tokens=int(getattr(usage, "prompt_tokens", 0) or 0),
+                completion_tokens=int(getattr(usage, "completion_tokens", 0) or 0),
+            )
+            with llm_usage_context(
+                LLMUsageContext(
+                    organization_id=org_id,
+                    workspace_id=ws_id,
+                    product_section=LLMUsageProductSection.TEST_AGENT,
+                )
+            ):
+                record_llm_usage(
+                    model,
+                    snapshot,
+                    organization_id=org_id,
+                )
+        except Exception as exc:
+            logger.debug("test agent llm usage record skipped: {}", exc)
+
     async def _tts_cartesia(self, text: str) -> Optional[bytes]:
         """Synthesize speech via Cartesia."""
         import httpx
diff --git a/app/workers/tasks/agent_flowchart_jobs.py b/app/workers/tasks/agent_flowchart_jobs.py
index 8704fd6c..02431794 100644
--- a/app/workers/tasks/agent_flowchart_jobs.py
+++ b/app/workers/tasks/agent_flowchart_jobs.py
@@ -52,13 +52,19 @@ def generate_agent_flowchart_task(
             )
             return
 
-        graph, provider_enum, model_str = generate_agent_flowchart(
-            prompt_text=partial.content,
-            organization_id=partial.organization_id,
-            db=db,
-            provider=provider,
-            model=model,
+        from app.services.usage.context import (
+            llm_usage_context,
+            usage_context_for_prompt_partial,
         )
+
+        with llm_usage_context(usage_context_for_prompt_partial(partial)):
+            graph, provider_enum, model_str = generate_agent_flowchart(
+                prompt_text=partial.content,
+                organization_id=partial.organization_id,
+                db=db,
+                provider=provider,
+                model=model,
+            )
         partial.agent_flowchart = graph.model_dump(mode="json")
         if isinstance(partial.agent_flowchart, dict):
             generated_at = graph.generated_at
@@ -130,18 +136,24 @@ def map_agent_flowchart_prompt_sections_task(
         ):
             raise ValueError("Generate a flowchart before mapping prompt sections")
 
+        from app.services.usage.context import (
+            llm_usage_context,
+            usage_context_for_prompt_partial,
+        )
+
         graph = apply_prompt_hash_staleness(
             AgentFlowGraph.model_validate(partial.agent_flowchart),
             partial.content,
         )
-        mapped_graph = map_all_flow_nodes_to_prompt(
-            prompt_text=partial.content,
-            graph=graph,
-            organization_id=partial.organization_id,
-            db=db,
-            provider=provider,
-            model=model,
-        )
+        with llm_usage_context(usage_context_for_prompt_partial(partial)):
+            mapped_graph = map_all_flow_nodes_to_prompt(
+                prompt_text=partial.content,
+                graph=graph,
+                organization_id=partial.organization_id,
+                db=db,
+                provider=provider,
+                model=model,
+            )
         partial.agent_flowchart = mapped_graph.model_dump(mode="json")
         if isinstance(partial.agent_flowchart, dict):
             if mapped_graph.generated_at is not None:
diff --git a/app/workers/tasks/evaluate_call_import_row.py b/app/workers/tasks/evaluate_call_import_row.py
index e87b3285..dafba330 100644
--- a/app/workers/tasks/evaluate_call_import_row.py
+++ b/app/workers/tasks/evaluate_call_import_row.py
@@ -364,19 +364,19 @@ def evaluate_call_import_row_task(
                 row_db.commit()
                 return {"status": "failed", "reason": "evaluation_missing"}
 
-            from app.services.usage.context import (
-                LLMUsageContext,
-                LLMUsageProductSection,
-                set_usage_context,
+            from app.services.usage.call_import_context import (
+                call_import_evaluation_usage_context,
             )
+            from app.services.usage.context import set_usage_context
 
             usage_ctx_token = set_usage_context(
-                LLMUsageContext(
+                call_import_evaluation_usage_context(
                     organization_id=evaluation.organization_id,
                     workspace_id=evaluation.workspace_id,
-                    product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
-                    resource_id=evaluation.id,
-                    resource_type="call_import_evaluation",
+                    evaluation_id=evaluation.id,
+                    call_import_id=evaluation.call_import_id,
+                    evaluation_row_id=eval_row.id,
+                    call_import_row_id=source_row.id,
                 )
             )
 
diff --git a/app/workers/tasks/generate_evaluation_metric_clusters.py b/app/workers/tasks/generate_evaluation_metric_clusters.py
index e91dc108..8cb59775 100644
--- a/app/workers/tasks/generate_evaluation_metric_clusters.py
+++ b/app/workers/tasks/generate_evaluation_metric_clusters.py
@@ -120,19 +120,23 @@ def on_progress(completed: int, total: int) -> None:
             flag_modified(evaluation, "metric_clusters")
             db.commit()
 
-        state = generate_metric_clusters(
-            db,
-            evaluation,
-            evaluation.organization_id,
-            provider_enum,
-            model_str,
-            completed_row_pairs=completed_pairs,
-            metrics=metrics,
-            policies=policies,
-            on_progress=on_progress,
-            max_llm_calls=max_llm_calls,
-            is_cancelled=_reload_cancelled,
-        )
+        from app.services.usage.call_import_context import usage_context_for_evaluation
+        from app.services.usage.context import llm_usage_context
+
+        with llm_usage_context(usage_context_for_evaluation(evaluation)):
+            state = generate_metric_clusters(
+                db,
+                evaluation,
+                evaluation.organization_id,
+                provider_enum,
+                model_str,
+                completed_row_pairs=completed_pairs,
+                metrics=metrics,
+                policies=policies,
+                on_progress=on_progress,
+                max_llm_calls=max_llm_calls,
+                is_cancelled=_reload_cancelled,
+            )
 
         if _reload_cancelled():
             return
diff --git a/app/workers/tasks/generate_evaluation_prompt_improvements.py b/app/workers/tasks/generate_evaluation_prompt_improvements.py
index 0ba282a2..fd22b1fb 100644
--- a/app/workers/tasks/generate_evaluation_prompt_improvements.py
+++ b/app/workers/tasks/generate_evaluation_prompt_improvements.py
@@ -107,25 +107,29 @@ def generate_evaluation_prompt_improvements_task(
                     eval_rows,
                 )
 
-        state = generate_prompt_improvements(
-            evaluation=evaluation,
-            imported_agent=imported_agent,
-            clusters_state=clusters_state,
-            organization_id=evaluation.organization_id,
-            db=db,
-            provider=provider,
-            model=model,
-            credential_id=UUID(credential_id) if credential_id else None,
-            period_deltas=period_deltas,
-        )
-        evaluation.prompt_improvements = prompt_improvements_state_to_db(state)
-        flag_modified(evaluation, "prompt_improvements")
-        db.commit()
-        logger.info(
-            "Prompt improvements completed for evaluation {} ({} suggestions)",
-            evaluation_id,
-            len(state.suggestions),
-        )
+        from app.services.usage.call_import_context import usage_context_for_evaluation
+        from app.services.usage.context import llm_usage_context
+
+        with llm_usage_context(usage_context_for_evaluation(evaluation)):
+            state = generate_prompt_improvements(
+                evaluation=evaluation,
+                imported_agent=imported_agent,
+                clusters_state=clusters_state,
+                organization_id=evaluation.organization_id,
+                db=db,
+                provider=provider,
+                model=model,
+                credential_id=UUID(credential_id) if credential_id else None,
+                period_deltas=period_deltas,
+            )
+            evaluation.prompt_improvements = prompt_improvements_state_to_db(state)
+            flag_modified(evaluation, "prompt_improvements")
+            db.commit()
+            logger.info(
+                "Prompt improvements completed for evaluation {} ({} suggestions)",
+                evaluation_id,
+                len(state.suggestions),
+            )
     except Exception as exc:
         logger.exception(
             "Prompt improvements failed for evaluation {}: {}",
diff --git a/app/workers/tasks/generate_evaluation_tldr_insights.py b/app/workers/tasks/generate_evaluation_tldr_insights.py
index b236d0f7..6f9e96a1 100644
--- a/app/workers/tasks/generate_evaluation_tldr_insights.py
+++ b/app/workers/tasks/generate_evaluation_tldr_insights.py
@@ -40,20 +40,18 @@ def generate_evaluation_tldr_insights_task(
         if evaluation is None:
             return {"error": "evaluation_not_found", "status_code": 404}
 
-        from app.services.usage.context import (
-            LLMUsageContext,
-            LLMUsageProductSection,
-            llm_usage_context,
+        from app.services.usage.call_import_context import (
+            call_import_evaluation_usage_context,
         )
+        from app.services.usage.context import llm_usage_context
 
         try:
             with llm_usage_context(
-                LLMUsageContext(
+                call_import_evaluation_usage_context(
                     organization_id=evaluation.organization_id,
                     workspace_id=evaluation.workspace_id,
-                    product_section=LLMUsageProductSection.CALL_IMPORT_EVALUATIONS,
-                    resource_id=evaluation.id,
-                    resource_type="call_import_evaluation",
+                    evaluation_id=evaluation.id,
+                    call_import_id=evaluation.call_import_id,
                 )
             ):
                 summary = _generate_and_persist_tldr_summary(
diff --git a/app/workers/tasks/generate_evaluation_user_insights.py b/app/workers/tasks/generate_evaluation_user_insights.py
index 6029ce4a..71f666df 100644
--- a/app/workers/tasks/generate_evaluation_user_insights.py
+++ b/app/workers/tasks/generate_evaluation_user_insights.py
@@ -81,21 +81,25 @@ def on_progress(completed: int, total: int) -> None:
             flag_modified(evaluation, "user_insights")
             db.commit()
 
-        state = generate_user_insights(
-            db,
-            evaluation,
-            evaluation.organization_id,
-            provider_enum,
-            model_str,
-            completed_row_pairs=completed_pairs,
-            metrics=metrics,
-            aggregate=aggregate,
-            on_progress=on_progress,
-            max_llm_calls=max_llm_calls,
-        )
-        evaluation.user_insights = user_insights_state_to_db(state)
-        flag_modified(evaluation, "user_insights")
-        db.commit()
+        from app.services.usage.call_import_context import usage_context_for_evaluation
+        from app.services.usage.context import llm_usage_context
+
+        with llm_usage_context(usage_context_for_evaluation(evaluation)):
+            state = generate_user_insights(
+                db,
+                evaluation,
+                evaluation.organization_id,
+                provider_enum,
+                model_str,
+                completed_row_pairs=completed_pairs,
+                metrics=metrics,
+                aggregate=aggregate,
+                on_progress=on_progress,
+                max_llm_calls=max_llm_calls,
+            )
+            evaluation.user_insights = user_insights_state_to_db(state)
+            flag_modified(evaluation, "user_insights")
+            db.commit()
     except Exception as exc:  # noqa: BLE001
         logger.exception(
             "User insights generation failed for evaluation {}: {}",
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index dc0e1b64..a86a4503 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -1,5 +1,6 @@
 """Celery task: process evaluator result (transcribe and evaluate metrics)."""
 
+import math
 import time
 import uuid as _uuid
 from uuid import UUID
@@ -24,6 +25,11 @@
     handle_llm_evaluation_error,
 )
 from app.services.evaluators.evaluator_result_call_data import slim_call_data_for_evaluator_result
+from app.services.evaluators.call_data_transcript import extract_transcript_from_call_data
+
+
+class EvaluatorInputUnavailableError(ValueError):
+    """Permanent failure: no audio or transcript available for evaluation."""
 
 
 def _commit_evaluator_result(db, result) -> None:
@@ -288,6 +294,98 @@ def _run_bucket(bucket, parent_metric=None):
     return metric_scores, evaluation_time
 
 
+_PERMANENT_VAPI_ENDED_REASONS = frozenset(
+    {
+        "call.in-progress.error-assistant-did-not-receive-customer-audio",
+        "call.in-progress.error-assistant-did-not-receive-customer-media",
+        "customer-did-not-give-microphone-permission",
+        "customer-did-not-answer",
+    }
+)
+
+
+def _call_data_for_transcript_extraction(result, db) -> tuple[dict, str]:
+    """Resolve provider payload and platform for transcript extraction."""
+    platform = _normalize_platform(result.provider_platform)
+    recording = _playground_call_recording(db, result) if db is not None else None
+    if recording:
+        if not result.provider_call_id and recording.provider_call_id:
+            result.provider_call_id = recording.provider_call_id
+        if not result.provider_platform and recording.provider_platform:
+            result.provider_platform = recording.provider_platform
+        platform = _normalize_platform(result.provider_platform or recording.provider_platform)
+
+    result_data = result.call_data if isinstance(result.call_data, dict) else {}
+    recording_data = (
+        recording.call_data
+        if recording and isinstance(recording.call_data, dict)
+        else {}
+    )
+
+    if not result_data and not recording_data:
+        return {}, platform
+
+    merged = dict(recording_data)
+    merged.update(result_data)
+    for key in ("endedReason", "messages", "transcript", "transcript_object"):
+        if not merged.get(key) and recording_data.get(key):
+            merged[key] = recording_data[key]
+    return merged, platform
+
+
+def _hydrate_transcript_from_call_data(result, db) -> bool:
+    """Copy transcript from provider call_data onto the result row when missing."""
+    if (result.transcription or "").strip():
+        return True
+
+    call_data, platform = _call_data_for_transcript_extraction(result, db)
+    if not call_data or not platform:
+        return False
+
+    transcript_text, speaker_segments = extract_transcript_from_call_data(
+        call_data, platform
+    )
+    if not (transcript_text or "").strip():
+        return False
+
+    result.transcription = transcript_text.strip()
+    if speaker_segments:
+        result.speaker_segments = speaker_segments
+
+    if isinstance(result.call_data, dict) and result.call_data:
+        result.call_data = slim_call_data_for_evaluator_result(result.call_data)
+
+    _commit_evaluator_result(db, result)
+    logger.info(
+        f"[EvaluatorResult {result.result_id}] Hydrated transcript from provider call_data"
+    )
+    return True
+
+
+def _permanent_input_failure_message(result, db) -> str:
+    """Return a user-facing error when retrying cannot succeed."""
+    call_data, platform = _call_data_for_transcript_extraction(result, db)
+    if not isinstance(call_data, dict):
+        call_data = {}
+
+    ended_reason = str(
+        call_data.get("endedReason") or call_data.get("ended_reason") or ""
+    ).strip()
+    if not platform and ended_reason.startswith("call."):
+        platform = "vapi"
+
+    if platform == "vapi":
+        if ended_reason in _PERMANENT_VAPI_ENDED_REASONS:
+            return (
+                "Call ended before customer audio was available "
+                f"({ended_reason}). Check microphone permissions and try again."
+            )
+        if ended_reason:
+            return f"Call ended without usable audio or transcript ({ended_reason})."
+
+    return "No audio or transcript available for evaluation."
+
+
 def _normalize_platform(platform: object) -> str:
     """Normalize provider platform enum/string into lowercase string."""
     if not platform:
@@ -394,7 +492,11 @@ def _recover_missing_audio_for_result(result, db, refresh_call_data: bool = True
         )
         return False
 
-    headers = {"xi-api-key": decrypted_key} if platform == "elevenlabs" and decrypted_key else None
+    headers = None
+    if platform == "elevenlabs" and decrypted_key:
+        headers = {"xi-api-key": decrypted_key}
+    elif platform == "vapi" and decrypted_key:
+        headers = {"Authorization": f"Bearer {decrypted_key}"}
     try:
         response = _http.get(audio_url, headers=headers, timeout=120)
     except Exception as download_err:
@@ -458,6 +560,71 @@ def _playground_call_recording(db, result):
     )
 
 
+_EXTERNAL_VOICE_PROVIDER_PLATFORMS = frozenset(
+    {"vapi", "retell", "elevenlabs", "smallest"}
+)
+
+
+def _should_record_external_agent_call_usage(result) -> bool:
+    """Only bill completed calls that ran on an external voice provider.
+
+    Internal voice-bundle / WebSocket calls already emit LLM/STT/TTS usage
+    from the live pipeline; recording again here would double-count.
+    """
+    if not result.agent_id:
+        return False
+    platform = (getattr(result, "provider_platform", None) or "").strip().lower()
+    return platform in _EXTERNAL_VOICE_PROVIDER_PLATFORMS
+
+
+def _resolve_call_duration_seconds(result) -> int:
+    if result.duration_seconds:
+        try:
+            return max(0, int(math.ceil(float(result.duration_seconds))))
+        except (TypeError, ValueError):
+            pass
+    call_data = result.call_data if isinstance(result.call_data, dict) else {}
+    for key in ("duration_seconds", "duration"):
+        raw = call_data.get(key)
+        if raw is not None:
+            try:
+                return max(0, int(math.ceil(float(raw))))
+            except (TypeError, ValueError):
+                continue
+    started_at = call_data.get("startedAt") or call_data.get("start_timestamp")
+    ended_at = call_data.get("endedAt") or call_data.get("end_timestamp")
+    if started_at and ended_at:
+        try:
+            from datetime import datetime
+
+            start = datetime.fromisoformat(str(started_at).replace("Z", "+00:00"))
+            end = datetime.fromisoformat(str(ended_at).replace("Z", "+00:00"))
+            return max(0, int(math.ceil((end - start).total_seconds())))
+        except Exception:
+            return 0
+    return 0
+
+
+def _record_agent_call_usage(result, *, usage_ctx) -> None:
+    if not _should_record_external_agent_call_usage(result):
+        return
+    try:
+        from app.services.usage.llm_usage import record_call_usage
+
+        record_call_usage(
+            "voice-agent-call",
+            organization_id=result.organization_id,
+            ctx=usage_ctx,
+            audio_seconds=_resolve_call_duration_seconds(result),
+        )
+    except Exception as exc:
+        logger.debug(
+            "[EvaluatorResult {}] agent call usage record skipped: {}",
+            result.result_id,
+            exc,
+        )
+
+
 @celery_app.task(name="process_evaluator_result", bind=True, max_retries=3)
 def process_evaluator_result_task(self, result_id: str):
     """
@@ -489,283 +656,295 @@ def process_evaluator_result_task(self, result_id: str):
             return {"error": "Evaluator result not found"}
 
         from app.services.usage.context import (
-            LLMUsageContext,
-            LLMUsageProductSection,
-            set_usage_context,
-            reset_usage_context,
-        )
-
-        usage_token = set_usage_context(
-            LLMUsageContext(
-                organization_id=result.organization_id,
-                workspace_id=result.workspace_id,
-                product_section=LLMUsageProductSection.EVALUATORS,
-                resource_id=result.id,
-                resource_type="evaluator_result",
-            )
+            llm_usage_context,
+            usage_context_for_evaluator_result,
         )
 
-        logger.info(f"[EvaluatorResult {result.result_id}] Starting processing task")
+        usage_ctx = usage_context_for_evaluator_result(result)
+        with llm_usage_context(usage_ctx):
+            logger.info(f"[EvaluatorResult {result.result_id}] Starting processing task")
 
-        result.celery_task_id = self.request.id
-        db.commit()
+            result.celery_task_id = self.request.id
+            db.commit()
 
-        try:
-            if not result.audio_s3_key:
-                _recover_missing_audio_for_result(result, db, refresh_call_data=True)
-
-            has_existing_transcript = bool(result.transcription)
-            if not result.audio_s3_key and not has_existing_transcript:
-                raise ValueError("No audio S3 key or existing transcript found")
+            try:
+                if not result.audio_s3_key:
+                    _recover_missing_audio_for_result(result, db, refresh_call_data=True)
 
-            evaluator, agent, persona, scenario = _load_related_entities(db, result)
-            is_custom_evaluator = evaluator and (
-                bool(evaluator.custom_prompt)
-                or evaluator.agent_id is None
-            )
+                _hydrate_transcript_from_call_data(result, db)
 
-            if not is_custom_evaluator and not agent:
-                raise ValueError("Agent not found and no custom prompt available")
+                has_existing_transcript = bool((result.transcription or "").strip())
+                if not result.audio_s3_key and not has_existing_transcript:
+                    raise EvaluatorInputUnavailableError(
+                        _permanent_input_failure_message(result, db)
+                    )
 
-            ai_providers = db.query(AIProvider).filter(
-                AIProvider.organization_id == result.organization_id,
-                AIProvider.is_active == True,
-            ).all()
+                evaluator, agent, persona, scenario = _load_related_entities(db, result)
+                is_custom_evaluator = evaluator and (
+                    bool(evaluator.custom_prompt)
+                    or evaluator.agent_id is None
+                )
 
-            # Step 1: Transcription
-            if has_existing_transcript:
-                transcription = result.transcription
-                speaker_segments = result.speaker_segments or []
-                transcription_time = 0.0
-            else:
-                result.status = EvaluatorResultStatus.TRANSCRIBING.value
-                db.commit()
+                if not is_custom_evaluator and not agent:
+                    raise ValueError("Agent not found and no custom prompt available")
+
+                ai_providers = db.query(AIProvider).filter(
+                    AIProvider.organization_id == result.organization_id,
+                    AIProvider.is_active == True,
+                ).all()
+
+                # Step 1: Transcription
+                if has_existing_transcript:
+                    transcription = result.transcription
+                    speaker_segments = result.speaker_segments or []
+                    transcription_time = 0.0
+                else:
+                    result.status = EvaluatorResultStatus.TRANSCRIBING.value
+                    db.commit()
 
-                transcription, speaker_segments, transcription_time = _transcribe_audio(
-                    result, ai_providers, db
-                )
-                result.transcription = transcription
-                # Avoid duplicating transcript structure when provider call_data already carries it.
-                if not result.call_data:
-                    result.speaker_segments = speaker_segments if speaker_segments else None
-                db.commit()
+                    transcription, speaker_segments, transcription_time = _transcribe_audio(
+                        result, ai_providers, db
+                    )
+                    result.transcription = transcription
+                    # Avoid duplicating transcript structure when provider call_data already carries it.
+                    if not result.call_data:
+                        result.speaker_segments = speaker_segments if speaker_segments else None
+                    db.commit()
 
-            # Step 2: Load and categorize metrics
-            # Include metrics that have "agent" in their enabled_surfaces so users can
-            # restrict metrics to specific surfaces from the metrics page. Legacy rows
-            # (created before the surfaces column existed, or via fixtures that don't
-            # set the field) keep the original behavior: an enabled=True metric with
-            # an empty enabled_surfaces list is treated as agent-enabled.
-            enabled_metrics = db.query(Metric).filter(
-                Metric.organization_id == result.organization_id,
-                Metric.enabled == True,
-            ).all()
-            enabled_metrics = [
-                m for m in enabled_metrics
-                if (m.name or "").strip().lower() not in REMOVED_EVALUATION_METRIC_NAMES
-                and (
-                    "agent" in (m.enabled_surfaces or [])
-                    or not (m.enabled_surfaces or [])  # legacy/unset → default to agent
-                )
-            ]
-
-            # Explicit metric selection on the evaluator/suite. Categorization
-            # parents are stored as a single ID and expanded to child labels here.
-            from app.services.evaluators.evaluator_helpers import expand_metric_ids_for_evaluation
-
-            selected_metric_ids = {
-                str(mid) for mid in (getattr(evaluator, "metric_ids", None) or [])
-            }
-            if selected_metric_ids:
-                expanded_ids = expand_metric_ids_for_evaluation(
-                    db,
-                    result.organization_id,
-                    list(selected_metric_ids),
-                ) or set()
+                # Step 2: Load and categorize metrics
+                # Include metrics that have "agent" in their enabled_surfaces so users can
+                # restrict metrics to specific surfaces from the metrics page. Legacy rows
+                # (created before the surfaces column existed, or via fixtures that don't
+                # set the field) keep the original behavior: an enabled=True metric with
+                # an empty enabled_surfaces list is treated as agent-enabled.
+                enabled_metrics = db.query(Metric).filter(
+                    Metric.organization_id == result.organization_id,
+                    Metric.enabled == True,
+                ).all()
                 enabled_metrics = [
-                    m for m in enabled_metrics if str(m.id) in expanded_ids
+                    m for m in enabled_metrics
+                    if (m.name or "").strip().lower() not in REMOVED_EVALUATION_METRIC_NAMES
+                    and (
+                        "agent" in (m.enabled_surfaces or [])
+                        or not (m.enabled_surfaces or [])  # legacy/unset → default to agent
+                    )
                 ]
 
-            has_audio = bool(result.audio_s3_key)
-            llm_metrics, audio_metrics, metric_scores = _categorize_metrics(enabled_metrics, has_audio)
-            selected_metric_count = len(llm_metrics) + len(audio_metrics)
+                # Explicit metric selection on the evaluator/suite. Categorization
+                # parents are stored as a single ID and expanded to child labels here.
+                from app.services.evaluators.evaluator_helpers import expand_metric_ids_for_evaluation
 
-            call_recording = _playground_call_recording(db, result)
-            if call_recording:
-                from app.services.billing.flexprice_service import (
-                    record_playground_call_evaluated,
-                )
+                selected_metric_ids = {
+                    str(mid) for mid in (getattr(evaluator, "metric_ids", None) or [])
+                }
+                if selected_metric_ids:
+                    expanded_ids = expand_metric_ids_for_evaluation(
+                        db,
+                        result.organization_id,
+                        list(selected_metric_ids),
+                    ) or set()
+                    enabled_metrics = [
+                        m for m in enabled_metrics if str(m.id) in expanded_ids
+                    ]
+
+                has_audio = bool(result.audio_s3_key)
+                llm_metrics, audio_metrics, metric_scores = _categorize_metrics(enabled_metrics, has_audio)
+                selected_metric_count = len(llm_metrics) + len(audio_metrics)
+
+                call_recording = _playground_call_recording(db, result)
+                if call_recording:
+                    from app.services.billing.flexprice_service import (
+                        record_playground_call_evaluated,
+                    )
 
-                evaluation_attempt_id = f"{result.id}:{self.request.id}"
-                record_playground_call_evaluated(
-                    result.organization_id,
-                    evaluation_attempt_id,
-                    evaluator_result_id=result.id,
-                    workspace_id=result.workspace_id,
-                    call_short_id=call_recording.call_short_id,
-                    metric_count=selected_metric_count,
-                )
+                    evaluation_attempt_id = f"{result.id}:{self.request.id}"
+                    record_playground_call_evaluated(
+                        result.organization_id,
+                        evaluation_attempt_id,
+                        evaluator_result_id=result.id,
+                        workspace_id=result.workspace_id,
+                        call_short_id=call_recording.call_short_id,
+                        metric_count=selected_metric_count,
+                    )
 
-            evaluation_time = None
+                evaluation_time = None
 
-            # Step 3: Audio metrics evaluation
-            if audio_metrics and has_audio:
-                try:
-                    audio_scores = evaluate_audio_metrics(
-                        audio_s3_key=result.audio_s3_key,
-                        audio_metrics=audio_metrics,
-                        result_id=result.result_id,
-                    )
+                # Step 3: Audio metrics evaluation
+                if audio_metrics and has_audio:
+                    try:
+                        audio_scores = evaluate_audio_metrics(
+                            audio_s3_key=result.audio_s3_key,
+                            audio_metrics=audio_metrics,
+                            result_id=result.result_id,
+                        )
 
-                    if _all_audio_scores_download_failed(audio_scores):
+                        if _all_audio_scores_download_failed(audio_scores):
+                            logger.warning(
+                                f"[EvaluatorResult {result.result_id}] Existing S3 audio unavailable; "
+                                "attempting provider audio recovery"
+                            )
+                            recovered = _recover_missing_audio_for_result(result, db, refresh_call_data=True)
+                            if recovered and result.audio_s3_key:
+                                audio_scores = evaluate_audio_metrics(
+                                    audio_s3_key=result.audio_s3_key,
+                                    audio_metrics=audio_metrics,
+                                    result_id=result.result_id,
+                                )
+
+                        metric_scores.update(audio_scores)
+                    except Exception as audio_err:
+                        logger.error(
+                            f"[EvaluatorResult {result.result_id}] Audio analysis failed: {audio_err}",
+                            exc_info=True,
+                        )
+                        metric_scores.update(handle_audio_evaluation_error(audio_metrics, audio_err))
+
+                # Step 4: LLM metrics evaluation
+                if llm_metrics and transcription:
+                    result.status = EvaluatorResultStatus.EVALUATING.value
+                    db.commit()
+
+                    try:
+                        llm_scores, evaluation_time = _evaluate_llm_metrics_grouped(
+                            transcription=transcription,
+                            llm_metrics=llm_metrics,
+                            ai_providers=ai_providers,
+                            organization_id=result.organization_id,
+                            result_id=result.result_id,
+                            db=db,
+                            evaluator=evaluator,
+                            agent=agent,
+                            persona=persona,
+                            scenario=scenario,
+                        )
+                        metric_scores.update(llm_scores)
+                    except Exception as llm_err:
+                        error_msg = str(llm_err).replace("{", "{{").replace("}", "}}")
+                        logger.error(
+                            f"[EvaluatorResult {result.result_id}] ✗ LLM evaluation failed: {error_msg}",
+                            exc_info=True,
+                        )
+                        metric_scores.update(handle_llm_evaluation_error(llm_metrics, llm_err))
+                else:
+                    if not llm_metrics:
                         logger.warning(
-                            f"[EvaluatorResult {result.result_id}] Existing S3 audio unavailable; "
-                            "attempting provider audio recovery"
+                            f"[EvaluatorResult {result.result_id}] No LLM-evaluable metrics found "
+                            "(audio-only metrics were skipped), skipping evaluation"
+                        )
+                    if not transcription:
+                        logger.warning(
+                            f"[EvaluatorResult {result.result_id}] No transcription available, "
+                            "skipping evaluation"
                         )
-                        recovered = _recover_missing_audio_for_result(result, db, refresh_call_data=True)
-                        if recovered and result.audio_s3_key:
-                            audio_scores = evaluate_audio_metrics(
-                                audio_s3_key=result.audio_s3_key,
-                                audio_metrics=audio_metrics,
-                                result_id=result.result_id,
-                            )
 
-                    metric_scores.update(audio_scores)
-                except Exception as audio_err:
-                    logger.error(
-                        f"[EvaluatorResult {result.result_id}] Audio analysis failed: {audio_err}",
-                        exc_info=True,
-                    )
-                    metric_scores.update(handle_audio_evaluation_error(audio_metrics, audio_err))
+                # Step 5: Call Analysis
+                if transcription and not (result.call_data and result.call_data.get("call_analysis")):
+                    try:
+                        call_analysis = _generate_call_analysis(
+                            transcription=transcription,
+                            ai_providers=ai_providers,
+                            organization_id=result.organization_id,
+                            result_id=result.result_id,
+                            db=db,
+                            agent=agent,
+                            scenario=scenario,
+                        )
+                        if call_analysis:
+                            existing_call_data = dict(result.call_data) if isinstance(result.call_data, dict) else {}
+                            existing_call_data["call_analysis"] = call_analysis
+                            result.call_data = slim_call_data_for_evaluator_result(existing_call_data)
+                    except Exception as analysis_err:
+                        logger.warning(
+                            f"[EvaluatorResult {result.result_id}] Call analysis failed (non-fatal): {analysis_err}"
+                        )
 
-            # Step 4: LLM metrics evaluation
-            if llm_metrics and transcription:
-                result.status = EvaluatorResultStatus.EVALUATING.value
-                db.commit()
+                # Step 6: Complete
+                from sqlalchemy.orm.attributes import flag_modified
+
+                result.metric_scores = _make_json_serializable(metric_scores)
+                flag_modified(result, "metric_scores")
+                if isinstance(result.call_data, dict):
+                    result.call_data = slim_call_data_for_evaluator_result(result.call_data)
+                if isinstance(result.call_data, (dict, list)):
+                    result.call_data = _make_json_serializable(result.call_data)
+                    flag_modified(result, "call_data")
+                result.status = EvaluatorResultStatus.COMPLETED.value
+                result.error_message = None
+                _record_agent_call_usage(result, usage_ctx=usage_ctx)
+                _commit_evaluator_result(db, result)
 
-                try:
-                    llm_scores, evaluation_time = _evaluate_llm_metrics_grouped(
-                        transcription=transcription,
-                        llm_metrics=llm_metrics,
-                        ai_providers=ai_providers,
-                        organization_id=result.organization_id,
-                        result_id=result.result_id,
-                        db=db,
-                        evaluator=evaluator,
-                        agent=agent,
-                        persona=persona,
-                        scenario=scenario,
-                    )
-                    metric_scores.update(llm_scores)
-                except Exception as llm_err:
-                    error_msg = str(llm_err).replace("{", "{{").replace("}", "}}")
-                    logger.error(
-                        f"[EvaluatorResult {result.result_id}] ✗ LLM evaluation failed: {error_msg}",
-                        exc_info=True,
-                    )
-                    metric_scores.update(handle_llm_evaluation_error(llm_metrics, llm_err))
-            else:
-                if not llm_metrics:
-                    logger.warning(
-                        f"[EvaluatorResult {result.result_id}] No LLM-evaluable metrics found "
-                        "(audio-only metrics were skipped), skipping evaluation"
-                    )
-                if not transcription:
-                    logger.warning(
-                        f"[EvaluatorResult {result.result_id}] No transcription available, "
-                        "skipping evaluation"
-                    )
+                from app.services.billing.flexprice_service import (
+                    record_playground_evaluation_completed,
+                )
 
-            # Step 5: Call Analysis
-            if transcription and not (result.call_data and result.call_data.get("call_analysis")):
-                try:
-                    call_analysis = _generate_call_analysis(
-                        transcription=transcription,
-                        ai_providers=ai_providers,
-                        organization_id=result.organization_id,
-                        result_id=result.result_id,
-                        db=db,
-                        agent=agent,
-                        scenario=scenario,
-                    )
-                    if call_analysis:
-                        existing_call_data = dict(result.call_data) if isinstance(result.call_data, dict) else {}
-                        existing_call_data["call_analysis"] = call_analysis
-                        result.call_data = slim_call_data_for_evaluator_result(existing_call_data)
-                except Exception as analysis_err:
-                    logger.warning(
-                        f"[EvaluatorResult {result.result_id}] Call analysis failed (non-fatal): {analysis_err}"
+                call_recording = _playground_call_recording(db, result)
+                if call_recording:
+                    record_playground_evaluation_completed(
+                        result.organization_id,
+                        f"{result.id}:{self.request.id}",
+                        evaluator_result_id=result.id,
+                        workspace_id=result.workspace_id,
+                        call_short_id=call_recording.call_short_id,
+                        duration_seconds=result.duration_seconds,
+                        metric_count=len(metric_scores) or selected_metric_count,
                     )
 
-            # Step 6: Complete
-            from sqlalchemy.orm.attributes import flag_modified
-
-            result.metric_scores = _make_json_serializable(metric_scores)
-            flag_modified(result, "metric_scores")
-            if isinstance(result.call_data, dict):
-                result.call_data = slim_call_data_for_evaluator_result(result.call_data)
-            if isinstance(result.call_data, (dict, list)):
-                result.call_data = _make_json_serializable(result.call_data)
-                flag_modified(result, "call_data")
-            result.status = EvaluatorResultStatus.COMPLETED.value
-            result.error_message = None
-            _commit_evaluator_result(db, result)
-
-            from app.services.billing.flexprice_service import (
-                record_playground_evaluation_completed,
-            )
-
-            call_recording = _playground_call_recording(db, result)
-            if call_recording:
-                record_playground_evaluation_completed(
-                    result.organization_id,
-                    f"{result.id}:{self.request.id}",
-                    evaluator_result_id=result.id,
-                    workspace_id=result.workspace_id,
-                    call_short_id=call_recording.call_short_id,
-                    duration_seconds=result.duration_seconds,
-                    metric_count=len(metric_scores) or selected_metric_count,
+                total_time = time.time() - task_start_time
+                logger.info(
+                    f"[EvaluatorResult {result.result_id}] Completed in {total_time:.2f}s, "
+                    f"{len(metric_scores)} metrics evaluated"
                 )
 
-            total_time = time.time() - task_start_time
-            logger.info(
-                f"[EvaluatorResult {result.result_id}] Completed in {total_time:.2f}s, "
-                f"{len(metric_scores)} metrics evaluated"
-            )
+                return {
+                    "result_id": result_id,
+                    "status": "completed",
+                    "transcription": transcription,
+                    "metrics_evaluated": len(metric_scores),
+                    "processing_time": total_time,
+                    "transcription_time": transcription_time,
+                    "evaluation_time": evaluation_time,
+                }
 
-            return {
-                "result_id": result_id,
-                "status": "completed",
-                "transcription": transcription,
-                "metrics_evaluated": len(metric_scores),
-                "processing_time": total_time,
-                "transcription_time": transcription_time,
-                "evaluation_time": evaluation_time,
-            }
-
-        except Exception as e:
-            db.rollback()
-            logger.error(f"[EvaluatorResult {result_id}] Processing failed: {e}", exc_info=True)
-            try:
-                failed_result = db.query(EvaluatorResult).filter(EvaluatorResult.id == result_uuid).first()
-                if failed_result:
-                    failed_result.status = EvaluatorResultStatus.FAILED.value
-                    failed_result.error_message = str(e)
-                    db.commit()
-            except Exception as persist_err:
+            except EvaluatorInputUnavailableError:
                 db.rollback()
-                logger.error(
-                    f"[EvaluatorResult {result_id}] Failed to persist FAILED status: {persist_err}",
-                    exc_info=True,
-                )
-            raise
+                raise
+            except Exception as e:
+                db.rollback()
+                logger.error(f"[EvaluatorResult {result_id}] Processing failed: {e}", exc_info=True)
+                try:
+                    failed_result = db.query(EvaluatorResult).filter(EvaluatorResult.id == result_uuid).first()
+                    if failed_result:
+                        failed_result.status = EvaluatorResultStatus.FAILED.value
+                        failed_result.error_message = str(e)
+                        db.commit()
+                except Exception as persist_err:
+                    db.rollback()
+                    logger.error(
+                        f"[EvaluatorResult {result_id}] Failed to persist FAILED status: {persist_err}",
+                        exc_info=True,
+                    )
+                raise
 
+    except EvaluatorInputUnavailableError as exc:
+        logger.warning(f"[EvaluatorResult {result_id}] Input unavailable: {exc}")
+        try:
+            from app.models.database import EvaluatorResult, EvaluatorResultStatus
+
+            failed_result = db.query(EvaluatorResult).filter(
+                EvaluatorResult.id == result_uuid
+            ).first()
+            if failed_result:
+                failed_result.status = EvaluatorResultStatus.FAILED.value
+                failed_result.error_message = str(exc)
+                db.commit()
+        except Exception as persist_err:
+            db.rollback()
+            logger.error(
+                f"[EvaluatorResult {result_id}] Failed to persist FAILED status: {persist_err}",
+                exc_info=True,
+            )
+        return {"error": str(exc), "status": "failed"}
     except Exception as exc:
         raise self.retry(exc=exc, countdown=60)
     finally:
-        try:
-            if "usage_token" in locals() and usage_token is not None:
-                reset_usage_context(usage_token)
-        except Exception:
-            pass
         db.close()
diff --git a/app/workers/tasks/run_judge_alignment.py b/app/workers/tasks/run_judge_alignment.py
index 6ffbb655..053546cc 100644
--- a/app/workers/tasks/run_judge_alignment.py
+++ b/app/workers/tasks/run_judge_alignment.py
@@ -79,7 +79,13 @@ def run_judge_alignment_task(
             return {"error": "No samples"}
 
         try:
-            metrics = run_judge(run, dataset, evaluator, samples, db)
+            from app.services.usage.context import (
+                llm_usage_context,
+                usage_context_for_judge_run,
+            )
+
+            with llm_usage_context(usage_context_for_judge_run(run)):
+                metrics = run_judge(run, dataset, evaluator, samples, db)
         except Exception as exc:
             logger.error(
                 f"[JudgeAlignment] Run {judge_run_id} crashed: {exc}",
diff --git a/app/workers/tasks/run_prompt_optimization.py b/app/workers/tasks/run_prompt_optimization.py
index dbd9c869..b7efb247 100644
--- a/app/workers/tasks/run_prompt_optimization.py
+++ b/app/workers/tasks/run_prompt_optimization.py
@@ -106,19 +106,24 @@ def run_prompt_optimization_task(self, optimization_run_id: str):
         )
 
         from app.services.optimization import run_optimization
-
-        result = run_optimization(
-            agent=agent,
-            evaluator=evaluator,
-            voice_bundle=voice_bundle,
-            training_data=training_data,
-            metrics=enabled_metrics,
-            ai_providers=ai_providers,
-            organization_id=run.organization_id,
-            db=db,
-            config=run.config,
+        from app.services.usage.context import (
+            llm_usage_context,
+            usage_context_for_prompt_optimization_run,
         )
 
+        with llm_usage_context(usage_context_for_prompt_optimization_run(run)):
+            result = run_optimization(
+                agent=agent,
+                evaluator=evaluator,
+                voice_bundle=voice_bundle,
+                training_data=training_data,
+                metrics=enabled_metrics,
+                ai_providers=ai_providers,
+                organization_id=run.organization_id,
+                db=db,
+                config=run.config,
+            )
+
         run.best_prompt = result["best_candidate"]
         run.best_score = result["best_score"]
         run.metric_history = result["metric_history"]
diff --git a/app/workers/tasks/transcribe_call_import_row.py b/app/workers/tasks/transcribe_call_import_row.py
index 0f342ab3..bd09bf25 100644
--- a/app/workers/tasks/transcribe_call_import_row.py
+++ b/app/workers/tasks/transcribe_call_import_row.py
@@ -390,12 +390,11 @@ def _persist_diarization_failure(
 
 def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
     """STT / S3 / LLM diarisation without a long-lived DB session."""
+    from uuid import UUID
+
     from app.models.enums import ModelProvider
-    from app.services.usage.context import (
-        LLMUsageContext,
-        LLMUsageProductSection,
-        llm_usage_context,
-    )
+    from app.services.usage.call_import_context import call_import_row_usage_context
+    from app.services.usage.context import llm_usage_context
     from app.workers.tasks.helpers.llm_diarisation import (
         LLMDiarisationError,
         diarize_audio_with_llm,
@@ -411,13 +410,20 @@ def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
     llm_credential_uuid = ctx["llm_credential_uuid"]
     effective_prompt = ctx["effective_prompt"]
 
+    evaluation_id = ctx.get("evaluation_id")
+    evaluation_row_id = ctx.get("evaluation_row_id")
+    call_import_id = ctx.get("call_import_id")
+    if not call_import_id:
+        raise ValueError("call_import_id missing from transcribe pipeline context")
+
     with llm_usage_context(
-        LLMUsageContext(
+        call_import_row_usage_context(
             organization_id=organization_id,
             workspace_id=ctx.get("workspace_id"),
-            product_section=LLMUsageProductSection.CALL_IMPORTS,
-            resource_id=ctx.get("call_import_id"),
-            resource_type="call_import",
+            call_import_id=call_import_id,
+            call_import_row_id=UUID(str(row_id)),
+            evaluation_id=evaluation_id,
+            evaluation_row_id=evaluation_row_id,
         )
     ):
         return _run_diarization_pipeline_inner(
@@ -960,6 +966,14 @@ def transcribe_call_import_row_task(
                 "organization_id": row.organization_id,
                 "workspace_id": getattr(row, "workspace_id", None),
                 "call_import_id": row.call_import_id,
+                "evaluation_id": (
+                    UUID(evaluation_id_for_dispatch)
+                    if evaluation_id_for_dispatch
+                    else None
+                ),
+                "evaluation_row_id": (
+                    UUID(run_eval_row_id) if run_eval_row_id else None
+                ),
                 "stt_provider": provider_enum.value if provider_enum else None,
                 "stt_model": stt_model,
                 "credential_uuid": credential_uuid,
diff --git a/app/workers/tasks/tts_comparison.py b/app/workers/tasks/tts_comparison.py
index 8ae42ea9..2c8968ac 100644
--- a/app/workers/tasks/tts_comparison.py
+++ b/app/workers/tasks/tts_comparison.py
@@ -180,6 +180,7 @@ def generate_tts_comparison_task(self, comparison_id: str):
     )
     from app.services.ai.tts_service import tts_service, get_audio_file_extension
     from app.services.storage.s3_service import s3_service
+    from app.services.usage.context import LLMUsageContext, LLMUsageProductSection, llm_usage_context
 
     db = SessionLocal()
     try:
@@ -258,15 +259,24 @@ def _resolve_voice_meta(sample_obj):
                     f"provider={sample.provider} voice={sample.voice_id} "
                     f"sample_rate_hz={sample_rate_hz} config={tts_config}"
                 )
-                audio_bytes, latency_ms, ttfb_ms = tts_service.synthesize_timed(
-                    text=sample.text,
-                    tts_provider=provider_enum,
-                    tts_model=sample.model,
-                    organization_id=comp.organization_id,
-                    db=db,
-                    voice=sample.voice_id,
-                    config=tts_config or None,
-                )
+                with llm_usage_context(
+                    LLMUsageContext(
+                        organization_id=comp.organization_id,
+                        workspace_id=comp.workspace_id,
+                        product_section=LLMUsageProductSection.VOICE_PLAYGROUND,
+                        resource_id=comp.id,
+                        resource_type="tts_comparison",
+                    )
+                ):
+                    audio_bytes, latency_ms, ttfb_ms = tts_service.synthesize_timed(
+                        text=sample.text,
+                        tts_provider=provider_enum,
+                        tts_model=sample.model,
+                        organization_id=comp.organization_id,
+                        db=db,
+                        voice=sample.voice_id,
+                        config=tts_config or None,
+                    )
 
                 audio_ext = get_audio_file_extension(
                     sample.provider, int(sample_rate_hz) if sample_rate_hz else None
@@ -514,6 +524,20 @@ def evaluate_tts_comparison_task(self, comparison_id: str):
             db.commit()
             return {"evaluated": 0}
 
+        from app.services.usage.context import (
+            LLMUsageContext,
+            LLMUsageProductSection,
+            llm_usage_context,
+        )
+
+        usage_ctx = LLMUsageContext(
+            organization_id=comp.organization_id,
+            workspace_id=comp.workspace_id,
+            product_section=LLMUsageProductSection.VOICE_PLAYGROUND,
+            resource_id=comp.id,
+            resource_type="tts_comparison",
+        )
+
         stt_provider_str, stt_model = _resolve_stt_config(comp, db)
         stt_available = bool(stt_provider_str and stt_model)
         if stt_available:
@@ -535,86 +559,86 @@ def evaluate_tts_comparison_task(self, comparison_id: str):
             )
 
         evaluated = 0
-        for sample in samples:
-            tmp_path = None
-            try:
-                audio_bytes = s3_service.download_file_by_key(sample.audio_s3_key)
-                if not audio_bytes:
-                    continue
-
-                ext = ".mp3"
-                if sample.audio_s3_key:
-                    key_ext = os.path.splitext(sample.audio_s3_key)[1].lower()
-                    if key_ext in {".wav", ".mp3", ".flac", ".ogg", ".m4a"}:
-                        ext = key_ext
-                tmp_fd, tmp_path = tempfile.mkstemp(suffix=ext)
-                os.close(tmp_fd)
-                with open(tmp_path, "wb") as f:
-                    f.write(audio_bytes)
-
-                if any_qualitative_enabled:
-                    metrics = qualitative_voice_service.calculate_all_metrics(tmp_path)
-                    metrics = _filter_qualitative_metrics(metrics, enabled_voice_metric_names)
-                else:
-                    metrics = {}
-
-                if stt_available and sample.text:
-                    selected_stt_provider = ModelProvider(stt_provider_str)
-                    selected_language = None
-                    if selected_stt_provider == ModelProvider.SARVAM:
-                        # Sarvam saarika models accept language_code; saaras models do not.
-                        if "saarika" in (stt_model or "").lower():
-                            selected_language = "hi-IN"
-                    asr_transcript = transcription_service.transcribe_text_only(
-                        audio_file_path=tmp_path,
-                        stt_provider=selected_stt_provider,
-                        stt_model=stt_model,
-                        organization_id=comp.organization_id,
-                        db=db,
-                        language=selected_language,
-                    )
-                    if asr_transcript:
-                        score_bundle = _compute_wer_cer(sample.text, asr_transcript)
-                        metrics["WER Raw"] = score_bundle.get("raw_wer")
-                        metrics["CER Raw"] = score_bundle.get("raw_cer")
-                        metrics["WER Normalized"] = score_bundle.get("normalized_wer")
-                        metrics["CER Normalized"] = score_bundle.get("normalized_cer")
-                        metrics["WER"] = (
-                            score_bundle.get("normalized_wer")
-                            if score_bundle.get("normalized_wer") is not None
-                            else score_bundle.get("raw_wer")
-                        )
-                        metrics["CER"] = (
-                            score_bundle.get("normalized_cer")
-                            if score_bundle.get("normalized_cer") is not None
-                            else score_bundle.get("raw_cer")
-                        )
-                        metrics["ASR Transcript"] = asr_transcript
+        with llm_usage_context(usage_ctx):
+            for sample in samples:
+                tmp_path = None
+                try:
+                    audio_bytes = s3_service.download_file_by_key(sample.audio_s3_key)
+                    if not audio_bytes:
+                        continue
+
+                    ext = ".mp3"
+                    if sample.audio_s3_key:
+                        key_ext = os.path.splitext(sample.audio_s3_key)[1].lower()
+                        if key_ext in {".wav", ".mp3", ".flac", ".ogg", ".m4a"}:
+                            ext = key_ext
+                    tmp_fd, tmp_path = tempfile.mkstemp(suffix=ext)
+                    os.close(tmp_fd)
+                    with open(tmp_path, "wb") as f:
+                        f.write(audio_bytes)
+
+                    if any_qualitative_enabled:
+                        metrics = qualitative_voice_service.calculate_all_metrics(tmp_path)
+                        metrics = _filter_qualitative_metrics(metrics, enabled_voice_metric_names)
                     else:
-                        metrics["WER"] = None
-                        metrics["CER"] = None
-                        metrics["WER Raw"] = None
-                        metrics["CER Raw"] = None
-                        metrics["WER Normalized"] = None
-                        metrics["CER Normalized"] = None
-                        metrics["ASR Transcript"] = None
-
-                _evaluate_custom_voice_metrics(sample, metrics, comp, db)
-
-                sample.evaluation_metrics = _sanitize_metrics(metrics)
-                db.commit()
-                evaluated += 1
-
-                logger.info(f"[TTS Eval] Sample {sample.id} metrics: {metrics}")
-
-            except Exception as e:
-                logger.warning("[TTS Eval] Sample {} eval failed: {}", sample.id, e)
-            finally:
-                if tmp_path and os.path.exists(tmp_path):
-                    try:
-                        os.unlink(tmp_path)
-                    except Exception:
-                        pass
+                        metrics = {}
+
+                    if stt_available and sample.text:
+                        selected_stt_provider = ModelProvider(stt_provider_str)
+                        selected_language = None
+                        if selected_stt_provider == ModelProvider.SARVAM:
+                            if "saarika" in (stt_model or "").lower():
+                                selected_language = "hi-IN"
+                        asr_transcript = transcription_service.transcribe_text_only(
+                            audio_file_path=tmp_path,
+                            stt_provider=selected_stt_provider,
+                            stt_model=stt_model,
+                            organization_id=comp.organization_id,
+                            db=db,
+                            language=selected_language,
+                        )
+                        if asr_transcript:
+                            score_bundle = _compute_wer_cer(sample.text, asr_transcript)
+                            metrics["WER Raw"] = score_bundle.get("raw_wer")
+                            metrics["CER Raw"] = score_bundle.get("raw_cer")
+                            metrics["WER Normalized"] = score_bundle.get("normalized_wer")
+                            metrics["CER Normalized"] = score_bundle.get("normalized_cer")
+                            metrics["WER"] = (
+                                score_bundle.get("normalized_wer")
+                                if score_bundle.get("normalized_wer") is not None
+                                else score_bundle.get("raw_wer")
+                            )
+                            metrics["CER"] = (
+                                score_bundle.get("normalized_cer")
+                                if score_bundle.get("normalized_cer") is not None
+                                else score_bundle.get("raw_cer")
+                            )
+                            metrics["ASR Transcript"] = asr_transcript
+                        else:
+                            metrics["WER"] = None
+                            metrics["CER"] = None
+                            metrics["WER Raw"] = None
+                            metrics["CER Raw"] = None
+                            metrics["WER Normalized"] = None
+                            metrics["CER Normalized"] = None
+                            metrics["ASR Transcript"] = None
+
+                    _evaluate_custom_voice_metrics(sample, metrics, comp, db)
+
+                    sample.evaluation_metrics = _sanitize_metrics(metrics)
+                    db.commit()
+                    evaluated += 1
+
+                    logger.info(f"[TTS Eval] Sample {sample.id} metrics: {metrics}")
+
+                except Exception as e:
+                    logger.warning("[TTS Eval] Sample {} eval failed: {}", sample.id, e)
+                finally:
+                    if tmp_path and os.path.exists(tmp_path):
+                        try:
+                            os.unlink(tmp_path)
+                        except Exception:
+                            pass
 
         from app.api.v1.routes.voice_playground import _recompute_summary
 
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index f8701723..7e2c4369 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -2994,10 +2994,16 @@ class ApiClient {
   async getOrgUsageSummary(params: {
     start?: string
     end?: string
+    tz?: string
     workspace_id?: string
     product_section?: string
     model?: string
     resource_id?: string
+    usage_kind?: string
+    call_import_id?: string
+    evaluation_id?: string
+    dataset?: string
+    tag_id?: string
   }): Promise<{
     start: string
     end: string
@@ -3008,6 +3014,8 @@ class ApiClient {
       cache_read_tokens: number
       cache_creation_tokens: number
       reasoning_tokens: number
+      audio_seconds: number
+      tts_characters: number
       call_count: number
     }
     last_updated_at?: string | null
@@ -3021,11 +3029,17 @@ class ApiClient {
   async getOrgUsageBreakdown(params: {
     start?: string
     end?: string
-    group_by?: 'workspace' | 'product_section' | 'model' | 'resource'
+    tz?: string
+    group_by?: 'workspace' | 'product_section' | 'model' | 'resource' | 'usage_kind' | 'call_import'
     workspace_id?: string
     product_section?: string
     model?: string
     resource_id?: string
+    usage_kind?: string
+    call_import_id?: string
+    evaluation_id?: string
+    dataset?: string
+    tag_id?: string
     limit?: number
     offset?: number
   }): Promise<{
@@ -3041,15 +3055,21 @@ class ApiClient {
       resource_id?: string | null
       resource_type?: string | null
       resource_label?: string | null
+      call_import_id?: string | null
+      call_import_label?: string | null
+      usage_kind?: string | null
       prompt_tokens: number
       completion_tokens: number
       total_tokens: number
       cache_read_tokens: number
       cache_creation_tokens: number
       reasoning_tokens: number
+      audio_seconds: number
+      tts_characters: number
       call_count: number
     }>
     total_count: number
+    truncated_at_limit?: boolean
     last_updated_at?: string | null
   }> {
     const response = await this.client.get('/api/v1/organizations/usage/breakdown', {
@@ -3061,11 +3081,27 @@ class ApiClient {
   async getOrgUsageFilters(params?: {
     start?: string
     end?: string
+    tz?: string
+    workspace_id?: string
+    product_section?: string
+    model?: string
+    resource_id?: string
+    evaluation_id?: string
+    usage_kind?: string
+    call_import_id?: string
+    q?: string
+    dataset?: string
+    tag_id?: string
   }): Promise<{
     workspaces: Array<{ id: string; name: string }>
     product_sections: Array<{ id: string; label: string }>
+    call_imports: Array<{ id: string; label: string }>
+    evaluations: Array<{ id: string; label: string }>
     models: string[]
-    resources: Array<{ id: string; type?: string; label: string }>
+    resources: Array<{ id: string; type?: string; label: string; product_section?: string }>
+    usage_kinds: Array<{ id: string; label: string }>
+    datasets: string[]
+    tags: Array<{ id: string; label: string }>
   }> {
     const response = await this.client.get('/api/v1/organizations/usage/filters', {
       params,
@@ -3237,7 +3273,7 @@ class ApiClient {
   }): Promise<{
     call_type: string
     access_token?: string
-    call_id: string
+    call_id?: string
     agent_id: string
     agent_version?: number
     call_status?: string
diff --git a/frontend/src/pages/agents/components/AgentTalkSidebar.tsx b/frontend/src/pages/agents/components/AgentTalkSidebar.tsx
index be2231e7..1afd845d 100644
--- a/frontend/src/pages/agents/components/AgentTalkSidebar.tsx
+++ b/frontend/src/pages/agents/components/AgentTalkSidebar.tsx
@@ -59,6 +59,7 @@ export default function AgentTalkSidebar({
   const userInitiatedDisconnectRef = useRef(false)
   const wasOpenRef = useRef(false)
   const userSpeakingTimeoutRef = useRef<ReturnType<typeof setTimeout> | null>(null)
+  const callShortIdRef = useRef<string | null>(null)
 
   const pulseUserSpeaking = (durationMs = 1200) => {
     setActiveSpeaker('user')
@@ -205,9 +206,16 @@ export default function AgentTalkSidebar({
         })
       } else if (isVapi) {
         const client = vapiClientRef.current!
-        client.on('call-start', () => {
+        client.on('call-start', async (call: any) => {
           setIsConnected(true)
           setIsConnecting(false)
+          if (callShortIdRef.current && call?.id) {
+            try {
+              await apiClient.updateCallRecording(callShortIdRef.current, call.id)
+            } catch (err) {
+              console.error('Failed to update Vapi call recording', err)
+            }
+          }
         })
         client.on('speech-start', () => setActiveSpeaker('agent'))
         client.on('speech-end', () => setActiveSpeaker((prev) => (prev === 'agent' ? null : prev)))
@@ -224,13 +232,26 @@ export default function AgentTalkSidebar({
             }
           }
         })
-        client.on('call-end', () => {
+        client.on('call-end', async () => {
           setIsConnected(false)
           setIsConnecting(false)
           setActiveSpeaker(null)
+          if (callShortIdRef.current) {
+            apiClient.refreshCallRecording(callShortIdRef.current).catch((err) => {
+              console.error('Failed to refresh Vapi call recording', err)
+            })
+          }
         })
-        await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
-        await client.start(agent.voice_ai_agent_id!)
+        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
+        callShortIdRef.current = webCall.call_short_id ?? null
+        const vapiCall = await client.start(agent.voice_ai_agent_id!)
+        if (callShortIdRef.current && vapiCall?.id) {
+          try {
+            await apiClient.updateCallRecording(callShortIdRef.current, vapiCall.id)
+          } catch (err) {
+            console.error('Failed to update Vapi call recording from start()', err)
+          }
+        }
       } else if (isElevenLabs) {
         const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
         if (!webCall.signed_url) throw new Error('No signed URL')
diff --git a/frontend/src/pages/usage/SearchableSelect.tsx b/frontend/src/pages/usage/SearchableSelect.tsx
new file mode 100644
index 00000000..66f625dd
--- /dev/null
+++ b/frontend/src/pages/usage/SearchableSelect.tsx
@@ -0,0 +1,125 @@
+import { useEffect, useMemo, useRef, useState } from 'react'
+import { ChevronDown, Search, X } from 'lucide-react'
+import { usageTheme } from './usageTheme'
+
+export type SearchableOption = { id: string; label: string }
+
+type SearchableSelectProps = {
+  label: string
+  placeholder: string
+  value: string
+  options: SearchableOption[]
+  onChange: (id: string) => void
+  disabled?: boolean
+  emptyMessage?: string
+}
+
+export default function SearchableSelect({
+  label,
+  placeholder,
+  value,
+  options,
+  onChange,
+  disabled,
+  emptyMessage = 'No matches',
+}: SearchableSelectProps) {
+  const [open, setOpen] = useState(false)
+  const [search, setSearch] = useState('')
+  const rootRef = useRef<HTMLDivElement>(null)
+
+  const selected = options.find((o) => o.id === value)
+
+  const filtered = useMemo(() => {
+    const q = search.trim().toLowerCase()
+    if (!q) return options
+    return options.filter((o) => o.label.toLowerCase().includes(q))
+  }, [options, search])
+
+  useEffect(() => {
+    const onDoc = (e: MouseEvent) => {
+      if (!rootRef.current?.contains(e.target as Node)) setOpen(false)
+    }
+    document.addEventListener('mousedown', onDoc)
+    return () => document.removeEventListener('mousedown', onDoc)
+  }, [])
+
+  return (
+    <div ref={rootRef} className="relative min-w-0">
+      <span className="text-xs font-medium text-gray-500">{label}</span>
+      <button
+        type="button"
+        disabled={disabled}
+        onClick={() => setOpen((v) => !v)}
+        className={`mt-1 flex w-full items-center gap-2 rounded-lg border border-gray-200 bg-white px-3 py-2 text-left text-sm text-gray-900 shadow-sm transition-colors hover:border-[#facc15] focus:outline-none disabled:opacity-50 ${usageTheme.focusRing}`}
+      >
+        <span className="flex-1 truncate">
+          {selected ? selected.label : placeholder}
+        </span>
+        {value ? (
+          <span
+            role="button"
+            tabIndex={0}
+            className="shrink-0 rounded p-0.5 text-gray-400 hover:text-gray-600"
+            onClick={(e) => {
+              e.stopPropagation()
+              onChange('')
+              setSearch('')
+            }}
+            onKeyDown={(e) => {
+              if (e.key === 'Enter' || e.key === ' ') {
+                e.stopPropagation()
+                onChange('')
+                setSearch('')
+              }
+            }}
+          >
+            <X className="h-4 w-4" />
+          </span>
+        ) : (
+          <ChevronDown className="h-4 w-4 shrink-0 text-gray-400" />
+        )}
+      </button>
+
+      {open && !disabled ? (
+        <div className="absolute z-30 mt-1 w-full min-w-[16rem] rounded-lg border border-[#fde047]/60 bg-white shadow-lg ring-1 ring-[#fef9c3]/50">
+          <div className="flex items-center gap-2 border-b border-gray-100 px-3 py-2">
+            <Search className="h-4 w-4 text-gray-400" />
+            <input
+              type="text"
+              value={search}
+              onChange={(e) => setSearch(e.target.value)}
+              placeholder="Search…"
+              className="flex-1 text-sm outline-none"
+              autoFocus
+            />
+          </div>
+          <ul className="max-h-56 overflow-y-auto py-1">
+            {filtered.length === 0 ? (
+              <li className="px-3 py-2 text-sm text-gray-500">{emptyMessage}</li>
+            ) : (
+              filtered.map((opt) => (
+                <li key={opt.id}>
+                  <button
+                    type="button"
+                    className={`w-full px-3 py-2 text-left text-sm hover:bg-[#fefce8] ${
+                      opt.id === value
+                        ? usageTheme.selectHighlight
+                        : 'text-gray-800'
+                    }`}
+                    onClick={() => {
+                      onChange(opt.id)
+                      setOpen(false)
+                      setSearch('')
+                    }}
+                  >
+                    {opt.label}
+                  </button>
+                </li>
+              ))
+            )}
+          </ul>
+        </div>
+      ) : null}
+    </div>
+  )
+}
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index 0f7cdcb9..c97b6f7c 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -1,100 +1,974 @@
-import { useMemo } from 'react'
-import { useQuery } from '@tanstack/react-query'
+import { useEffect, useMemo } from 'react'
+import { keepPreviousData, useQuery } from '@tanstack/react-query'
 import { useSearchParams } from 'react-router-dom'
 import { Card, CardBody, Spinner } from '@heroui/react'
-import { Activity } from 'lucide-react'
+import { Activity, ChevronRight } from 'lucide-react'
 import { apiClient } from '../../lib/api'
+import UsageFiltersBar from './UsageFiltersBar'
+import UsageDrillPath from './UsageDrillPath'
+import { defaultUsageDateRange } from './UsageDateRangePicker'
+import { getUsageTimezone } from './usageTimezone'
+import {
+  CALL_IMPORT_BATCH_HEADLINE,
+  CALL_IMPORT_HINT,
+  CALL_IMPORT_PRODUCT_SECTIONS,
+  PRODUCT_SECTION_HEADLINES,
+  PRODUCT_SECTION_HINTS,
+} from './usageProductHints'
 
-type GroupBy = 'workspace' | 'product_section' | 'model' | 'resource'
+type DrillGroupBy =
+  | 'workspace'
+  | 'call_import'
+  | 'resource'
+  | 'model'
+  | 'usage_kind'
+  | 'product_section'
+type Kind = '' | 'llm' | 'stt' | 'tts'
+
+type FilterOptions = {
+  workspaces: Array<{ id: string; name: string }>
+  call_imports: Array<{ id: string; label: string }>
+  evaluations: Array<{ id: string; label: string }>
+  resources?: Array<{ id: string; label: string; type?: string; product_section?: string }>
+  models: string[]
+  usage_kinds: Array<{ id: string; label: string }>
+  product_sections?: Array<{ id: string; label: string }>
+  datasets?: string[]
+  tags?: Array<{ id: string; label: string }>
+}
+
+type BreakdownRow = {
+  workspace_id?: string | null
+  workspace_name?: string | null
+  call_import_id?: string | null
+  call_import_label?: string | null
+  resource_id?: string | null
+  resource_type?: string | null
+  resource_label?: string | null
+  model?: string | null
+  usage_kind?: string | null
+  product_section?: string | null
+  product_section_label?: string | null
+  prompt_tokens: number
+  completion_tokens: number
+  total_tokens: number
+  cache_read_tokens: number
+  cache_creation_tokens: number
+  reasoning_tokens: number
+  audio_seconds: number
+  tts_characters: number
+  call_count: number
+}
+
+type WorkspaceSourceRow = BreakdownRow & {
+  rowKind: 'call_import' | 'workspace_resource'
+  hint: string
+}
+
+const NON_COMPOSITE_RESOURCE_TYPES = new Set([
+  'call_import',
+  'call_import_evaluation',
+])
+
+type TableRow = BreakdownRow | WorkspaceSourceRow
+
+const EMPTY_METRICS = {
+  prompt_tokens: 0,
+  completion_tokens: 0,
+  total_tokens: 0,
+  cache_read_tokens: 0,
+  cache_creation_tokens: 0,
+  reasoning_tokens: 0,
+  audio_seconds: 0,
+  tts_characters: 0,
+  call_count: 0,
+}
+
+function compositeRowHeadline(row: WorkspaceSourceRow): string {
+  if (row.rowKind === 'call_import') return CALL_IMPORT_BATCH_HEADLINE
+  const section = row.product_section || ''
+  return (
+    PRODUCT_SECTION_HEADLINES[section] ||
+    row.product_section_label ||
+    section ||
+    'Other'
+  )
+}
+
+function usableResourceLabel(label: string | null | undefined): string | undefined {
+  if (!label || label === 'Unscoped') return undefined
+  return label
+}
+
+function compositeRowTitle(row: WorkspaceSourceRow, options?: FilterOptions): string {
+  if (row.rowKind === 'call_import') {
+    return row.call_import_label || 'Call import batch'
+  }
+  const fromFilters = row.resource_id
+    ? options?.resources?.find((r) => idKey(r.id) === idKey(row.resource_id))?.label
+    : undefined
+  return (
+    usableResourceLabel(row.resource_label) ||
+    fromFilters ||
+    row.product_section_label ||
+    'Unscoped usage'
+  )
+}
+
+function sortRows(rows: BreakdownRow[]): BreakdownRow[] {
+  return [...rows].sort((a, b) => b.total_tokens - a.total_tokens)
+}
 
 function formatNumber(value: number): string {
   return new Intl.NumberFormat().format(value || 0)
 }
 
-function toDateInput(d: Date): string {
-  return d.toISOString().slice(0, 10)
+function formatAudio(seconds: number): string {
+  const total = Math.max(0, Math.floor(seconds || 0))
+  if (total < 60) return `${total}s`
+  const mins = Math.floor(total / 60)
+  const secs = total % 60
+  if (mins < 60) return secs ? `${mins}m ${secs}s` : `${mins}m`
+  const hours = Math.floor(mins / 60)
+  const remMins = mins % 60
+  return remMins ? `${hours}h ${remMins}m` : `${hours}h`
 }
 
-function FilterField({
-  label,
-  children,
-}: {
-  label: string
-  children: React.ReactNode
-}) {
+function rowHasUsageForKind(row: BreakdownRow, kind: Kind): boolean {
+  if (!kind) return true
+  if (kind === 'llm') {
+    return (row.total_tokens ?? 0) > 0 || (row.call_count ?? 0) > 0
+  }
+  if (kind === 'stt') {
+    return (row.audio_seconds ?? 0) > 0 || (row.call_count ?? 0) > 0
+  }
+  if (kind === 'tts') {
+    return (row.tts_characters ?? 0) > 0 || (row.call_count ?? 0) > 0
+  }
+  return true
+}
+
+function filterRowsForUsageKind(rows: BreakdownRow[], kind: Kind): BreakdownRow[] {
+  if (!kind) return rows
+  return rows.filter((row) => rowHasUsageForKind(row, kind))
+}
+
+function rowHasAnyUsage(row: BreakdownRow): boolean {
   return (
-    <label className="flex flex-col gap-1 text-xs font-medium text-gray-600">
-      {label}
-      {children}
-    </label>
+    (row.total_tokens ?? 0) > 0 ||
+    (row.call_count ?? 0) > 0 ||
+    (row.audio_seconds ?? 0) > 0 ||
+    (row.tts_characters ?? 0) > 0
   )
 }
 
-const fieldClassName =
-  'h-9 rounded-lg border border-gray-200 bg-white px-3 text-sm text-gray-900 outline-none focus:border-indigo-400 focus:ring-1 focus:ring-indigo-200'
+function isUsageScopeActive(
+  workspaceId: string,
+  callImportId: string,
+  evaluationId: string,
+  dataset: string,
+  tagId: string,
+  usageKind: Kind,
+  model: string,
+  productSection: string,
+): boolean {
+  return Boolean(
+    workspaceId ||
+      callImportId ||
+      evaluationId ||
+      dataset ||
+      tagId ||
+      usageKind ||
+      model ||
+      productSection,
+  )
+}
+
+/** Drop zero rows when any filter is active; kind filter uses kind-specific metrics. */
+function filterTableRows(
+  rows: BreakdownRow[],
+  usageKind: Kind,
+  scopeActive: boolean,
+): BreakdownRow[] {
+  if (usageKind) return filterRowsForUsageKind(rows, usageKind)
+  if (scopeActive) return rows.filter(rowHasAnyUsage)
+  return rows
+}
+
+function drillGroupBy(
+  workspaceId: string,
+  callImportId: string,
+  evaluationId: string,
+  model: string,
+  productSection: string,
+): DrillGroupBy {
+  if (productSection && model) return 'usage_kind'
+  if (productSection) return 'model'
+  if (evaluationId && model) return 'usage_kind'
+  if (evaluationId) return 'model'
+  if (callImportId) return 'resource'
+  if (workspaceId) return 'call_import'
+  return 'workspace'
+}
+
+function enrichCallImportRows(
+  rawRows: BreakdownRow[],
+  options?: FilterOptions,
+): BreakdownRow[] {
+  const labelById = new Map(
+    (options?.call_imports ?? []).map((c) => [idKey(c.id), c.label]),
+  )
+  return sortRows(
+    rawRows
+      .filter((r) => r.call_import_id)
+      .map((row) => {
+        const key = idKey(row.call_import_id)
+        return {
+          ...row,
+          call_import_label:
+            labelById.get(key) || row.call_import_label || 'Call import',
+        }
+      }),
+  )
+}
+
+function buildWorkspaceCompositeRows(
+  callImportRaw: BreakdownRow[],
+  resourceRaw: BreakdownRow[],
+  options?: FilterOptions,
+  padMissingRows = true,
+): WorkspaceSourceRow[] {
+  const resourceLabelById = new Map(
+    (options?.resources ?? []).map((r) => [idKey(r.id), r.label]),
+  )
+  const importRows = enrichCallImportRows(callImportRaw, options)
+  const rows: WorkspaceSourceRow[] = []
+  const shownImportIds = new Set<string>()
+
+  for (const row of importRows) {
+    if (!row.call_import_id) continue
+    shownImportIds.add(idKey(row.call_import_id))
+    rows.push({
+      ...row,
+      rowKind: 'call_import',
+      hint: CALL_IMPORT_HINT,
+    })
+  }
+
+  if (padMissingRows) {
+    for (const ci of options?.call_imports ?? []) {
+      const key = idKey(ci.id)
+      if (shownImportIds.has(key)) continue
+      shownImportIds.add(key)
+      rows.push({
+        call_import_id: ci.id,
+        call_import_label: ci.label,
+        ...EMPTY_METRICS,
+        rowKind: 'call_import',
+        hint: CALL_IMPORT_HINT,
+      })
+    }
+  }
+
+  for (const row of resourceRaw) {
+    const section = row.product_section
+    if (!section || CALL_IMPORT_PRODUCT_SECTIONS.has(section)) continue
+    if (NON_COMPOSITE_RESOURCE_TYPES.has(row.resource_type || '')) continue
+    if (
+      row.total_tokens === 0 &&
+      row.call_count === 0 &&
+      !row.audio_seconds &&
+      !row.tts_characters
+    ) {
+      continue
+    }
+    if (!rowHasAnyUsage(row)) {
+      continue
+    }
+    rows.push({
+      ...row,
+      rowKind: 'workspace_resource',
+      product_section: section,
+      product_section_label: row.product_section_label || section,
+      resource_label:
+        resourceLabelById.get(idKey(row.resource_id)) ||
+        usableResourceLabel(row.resource_label),
+      hint: PRODUCT_SECTION_HINTS[section] || 'Product usage',
+    })
+  }
+
+  return sortRows(rows) as WorkspaceSourceRow[]
+}
+
+function drillColumnLabel(groupBy: DrillGroupBy, composite = false): string {
+  if (composite) return 'Source'
+  if (groupBy === 'workspace') return 'Workspace'
+  if (groupBy === 'call_import') return 'Call import'
+  if (groupBy === 'resource') return 'Evaluation run'
+  if (groupBy === 'product_section') return 'Product area'
+  if (groupBy === 'model') return 'Model'
+  return 'Kind'
+}
+
+function rowLabel(groupBy: DrillGroupBy, row: BreakdownRow, options?: FilterOptions): string {
+  if (groupBy === 'workspace') return row.workspace_name || 'Unknown'
+  if (groupBy === 'call_import') return row.call_import_label || 'Call import'
+  if (groupBy === 'resource') {
+    const fromFilters = row.resource_id
+      ? options?.resources?.find((r) => idKey(r.id) === idKey(row.resource_id))?.label
+      : undefined
+    return (
+      usableResourceLabel(row.resource_label) || fromFilters || 'Unscoped'
+    )
+  }
+  if (groupBy === 'model') return row.model || '—'
+  if (groupBy === 'product_section')
+    return row.product_section_label || row.product_section || '—'
+  if (row.usage_kind === 'stt') return 'STT'
+  if (row.usage_kind === 'llm') return 'LLM'
+  if (row.usage_kind === 'tts') return 'TTS'
+  return row.usage_kind || '—'
+}
+
+function tableRowLabel(
+  groupBy: DrillGroupBy,
+  row: TableRow,
+  options?: FilterOptions,
+): string {
+  if ('rowKind' in row) {
+    if (row.rowKind === 'call_import') return row.call_import_label || 'Call import batch'
+    return compositeRowTitle(row, options)
+  }
+  return rowLabel(groupBy, row, options)
+}
+
+function idKey(id: string | null | undefined): string {
+  return id ? String(id).toLowerCase() : ''
+}
+
+function mergeDrillRows(
+  groupBy: DrillGroupBy,
+  rawRows: BreakdownRow[],
+  options?: FilterOptions,
+  padMissingRows = true,
+): BreakdownRow[] {
+  if (groupBy === 'workspace' && options?.workspaces?.length && padMissingRows) {
+    const byId = new Map(
+      rawRows
+        .filter((r) => r.workspace_id)
+        .map((r) => [idKey(r.workspace_id), r]),
+    )
+    const merged: BreakdownRow[] = options.workspaces.map((ws) => ({
+      workspace_id: ws.id,
+      workspace_name: ws.name,
+      ...(byId.get(idKey(ws.id)) ?? EMPTY_METRICS),
+    }))
+    for (const row of rawRows) {
+      if (!row.workspace_id) {
+        merged.push({
+          ...row,
+          workspace_name: row.workspace_name || 'No workspace',
+        })
+      }
+    }
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'call_import') {
+    if (rawRows.length === 0) return []
+
+    const labelById = new Map(
+      (options?.call_imports ?? []).map((c) => [idKey(c.id), c.label]),
+    )
+    const shown = new Set<string>()
+    const merged: BreakdownRow[] = []
+
+    for (const row of rawRows) {
+      if (row.call_import_id) {
+        const key = idKey(row.call_import_id)
+        shown.add(key)
+        merged.push({
+          ...row,
+          call_import_label:
+            labelById.get(key) || row.call_import_label || 'Call import',
+        })
+      }
+    }
+
+    if (padMissingRows) {
+      for (const ci of options?.call_imports ?? []) {
+        const key = idKey(ci.id)
+        if (!shown.has(key)) {
+          merged.push({
+            call_import_id: ci.id,
+            call_import_label: ci.label,
+            ...EMPTY_METRICS,
+          })
+        }
+      }
+    }
+
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'resource') {
+    if (rawRows.length === 0) return []
+
+    const evalLabelById = new Map(
+      (options?.evaluations ?? []).map((e) => [idKey(e.id), e.label]),
+    )
+    const resourceLabelById = new Map(
+      (options?.resources ?? []).map((r) => [idKey(r.id), r.label]),
+    )
+    const shown = new Set<string>()
+    const merged: BreakdownRow[] = []
+
+    for (const row of rawRows) {
+      if (row.resource_id) {
+        const key = idKey(row.resource_id)
+        shown.add(key)
+        merged.push({
+          ...row,
+          resource_label:
+            resourceLabelById.get(key) ||
+            evalLabelById.get(key) ||
+            usableResourceLabel(row.resource_label) ||
+            'Evaluation',
+        })
+      }
+    }
+
+    if (padMissingRows) {
+      for (const ev of options?.evaluations ?? []) {
+        const key = idKey(ev.id)
+        if (!shown.has(key)) {
+          merged.push({
+            resource_id: ev.id,
+            resource_label: ev.label,
+            ...EMPTY_METRICS,
+          })
+        }
+      }
+    }
+
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'model' && options?.models?.length && padMissingRows) {
+    if (rawRows.length === 0) return []
+    const byName = new Map(
+      rawRows.filter((r) => r.model).map((r) => [r.model!, r]),
+    )
+    const merged = options.models.map((name) => ({
+      model: name,
+      ...(byName.get(name) ?? EMPTY_METRICS),
+    }))
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'usage_kind') {
+    return sortRows(
+      rawRows.filter((row) => rowHasUsageForKind(row, row.usage_kind as Kind)),
+    )
+  }
+
+  return sortRows(rawRows)
+}
 
 export default function Usage() {
   const [searchParams, setSearchParams] = useSearchParams()
-  const today = useMemo(() => new Date(), [])
-  const defaultStart = useMemo(() => {
-    const d = new Date()
-    d.setDate(d.getDate() - 29)
-    return toDateInput(d)
-  }, [])
-
-  const start = searchParams.get('start') || defaultStart
-  const end = searchParams.get('end') || toDateInput(today)
-  const groupBy = (searchParams.get('group_by') as GroupBy) || 'workspace'
+  const defaultRange = useMemo(() => defaultUsageDateRange(), [])
+  const usageTimezone = useMemo(() => getUsageTimezone(), [])
+
+  const start = searchParams.get('start') || defaultRange.start
+  const end = searchParams.get('end') || defaultRange.end
   const workspaceId = searchParams.get('workspace_id') || ''
-  const productSection = searchParams.get('product_section') || ''
+  const callImportId = searchParams.get('call_import_id') || ''
+  const dataset = searchParams.get('dataset') || ''
+  const tagId = searchParams.get('tag_id') || ''
+  const evaluationId = searchParams.get('resource_id') || ''
   const model = searchParams.get('model') || ''
-  const resourceId = searchParams.get('resource_id') || ''
+  const usageKind = (searchParams.get('usage_kind') as Kind) || ''
+  const productSection = searchParams.get('product_section') || ''
+
+  const showWorkspaceComposite =
+    Boolean(workspaceId) &&
+    !callImportId &&
+    !evaluationId &&
+    !productSection
+
+  const groupBy = drillGroupBy(
+    workspaceId,
+    callImportId,
+    evaluationId,
+    model,
+    productSection,
+  )
 
-  const setParam = (key: string, value: string) => {
+  const setParams = (updates: Record<string, string | null>) => {
     const next = new URLSearchParams(searchParams)
-    if (!value) next.delete(key)
-    else next.set(key, value)
+    for (const [key, value] of Object.entries(updates)) {
+      if (!value) next.delete(key)
+      else next.set(key, value)
+    }
     setSearchParams(next)
   }
 
-  const filterParams = {
+  const scopeParams = {
     start,
     end,
+    tz: usageTimezone,
     workspace_id: workspaceId || undefined,
+    call_import_id: callImportId || undefined,
+    dataset: dataset || undefined,
+    tag_id: tagId || undefined,
     product_section: productSection || undefined,
+    usage_kind: usageKind || undefined,
     model: model || undefined,
-    resource_id: resourceId || undefined,
+    resource_id: evaluationId || undefined,
+    evaluation_id: evaluationId || undefined,
   }
 
-  const { data: summary, isLoading: summaryLoading } = useQuery({
-    queryKey: ['org-usage', 'summary', filterParams],
-    queryFn: () => apiClient.getOrgUsageSummary(filterParams),
+  const dataParams = {
+    ...scopeParams,
+  }
+
+  const usageQueryDefaults = {
+    staleTime: 60 * 1000,
+  }
+
+  const { data: summary, isLoading: summaryLoading, isFetching: summaryFetching } = useQuery({
+    queryKey: ['org-usage', 'summary', dataParams],
+    queryFn: () => apiClient.getOrgUsageSummary(dataParams),
+    ...usageQueryDefaults,
+    placeholderData: keepPreviousData,
   })
 
-  const { data: breakdown, isLoading: breakdownLoading } = useQuery({
-    queryKey: ['org-usage', 'breakdown', groupBy, filterParams],
+  const {
+    data: breakdown,
+    isLoading: breakdownLoading,
+    isFetching: breakdownFetching,
+  } = useQuery({
+    queryKey: ['org-usage', 'breakdown', groupBy, dataParams],
     queryFn: () =>
       apiClient.getOrgUsageBreakdown({
-        ...filterParams,
+        ...dataParams,
         group_by: groupBy,
         limit: 100,
       }),
+    enabled: !showWorkspaceComposite,
+    ...usageQueryDefaults,
+    placeholderData: (previousData, previousQuery) => {
+      if (!previousQuery || previousQuery.queryKey[2] !== groupBy) return undefined
+      return previousData
+    },
   })
 
-  const { data: filters } = useQuery({
-    queryKey: ['org-usage', 'filters', start, end],
-    queryFn: () => apiClient.getOrgUsageFilters({ start, end }),
+  const {
+    data: importBreakdown,
+    isLoading: importBreakdownLoading,
+    isFetching: importBreakdownFetching,
+  } = useQuery({
+    queryKey: ['org-usage', 'breakdown', 'call_import', dataParams],
+    queryFn: () =>
+      apiClient.getOrgUsageBreakdown({
+        ...dataParams,
+        group_by: 'call_import',
+        limit: 100,
+      }),
+    enabled: showWorkspaceComposite,
+    ...usageQueryDefaults,
   })
 
+  const {
+    data: resourceBreakdown,
+    isLoading: resourceBreakdownLoading,
+    isFetching: resourceBreakdownFetching,
+  } = useQuery({
+    queryKey: ['org-usage', 'breakdown', 'resource', dataParams],
+    queryFn: () =>
+      apiClient.getOrgUsageBreakdown({
+        ...dataParams,
+        group_by: 'resource',
+        limit: 100,
+      }),
+    enabled: showWorkspaceComposite,
+    ...usageQueryDefaults,
+  })
+
+  const { data: filterOptions, isFetching: filtersLoading } = useQuery({
+    queryKey: ['org-usage', 'filters', scopeParams],
+    queryFn: () => apiClient.getOrgUsageFilters(scopeParams),
+    staleTime: 60 * 1000,
+    placeholderData: (previousData, previousQuery) => {
+      if (!previousQuery) return undefined
+      const prevScope = previousQuery.queryKey[2] as typeof scopeParams
+      if (JSON.stringify(prevScope) !== JSON.stringify(scopeParams)) return undefined
+      return previousData
+    },
+  })
+
+  useEffect(() => {
+    if (!filterOptions) return
+    const updates: Record<string, string | null> = {}
+    if (
+      workspaceId &&
+      !filterOptions.workspaces?.some((w) => w.id === workspaceId)
+    ) {
+      updates.workspace_id = null
+      updates.call_import_id = null
+      updates.resource_id = null
+    }
+    if (
+      callImportId &&
+      !filterOptions.call_imports?.some((c) => c.id === callImportId)
+    ) {
+      updates.call_import_id = null
+      updates.resource_id = null
+    }
+    if (
+      evaluationId &&
+      !filterOptions.evaluations?.some((e) => e.id === evaluationId)
+    ) {
+      updates.resource_id = null
+    }
+    if (model && !filterOptions.models?.includes(model)) {
+      updates.model = null
+    }
+    if (
+      usageKind &&
+      !filterOptions.usage_kinds?.some((k) => k.id === usageKind)
+    ) {
+      updates.usage_kind = null
+    }
+    if (
+      productSection &&
+      !filterOptions.product_sections?.some((s) => s.id === productSection)
+    ) {
+      updates.product_section = null
+    }
+    if (dataset && !filterOptions.datasets?.includes(dataset)) {
+      updates.dataset = null
+    }
+    if (tagId && !filterOptions.tags?.some((t) => t.id === tagId)) {
+      updates.tag_id = null
+    }
+    if (Object.keys(updates).length > 0) setParams(updates)
+  }, [
+    filterOptions,
+    workspaceId,
+    callImportId,
+    dataset,
+    tagId,
+    evaluationId,
+    model,
+    usageKind,
+    productSection,
+  ])
+
+  const breakdownMatchesLevel = breakdown?.group_by === groupBy
+  const rawRows = useMemo((): BreakdownRow[] => {
+    if (!breakdown || !breakdownMatchesLevel) return []
+    return breakdown.rows as BreakdownRow[]
+  }, [breakdown, breakdownMatchesLevel])
+
+  const mergeOptions = filterOptions
+  const scopeActive = isUsageScopeActive(
+    workspaceId,
+    callImportId,
+    evaluationId,
+    dataset,
+    tagId,
+    usageKind,
+    model,
+    productSection,
+  )
+  const padMissingRows = !scopeActive
+  const filteredRawRows = useMemo(
+    () => filterTableRows(rawRows, usageKind, scopeActive),
+    [rawRows, usageKind, scopeActive],
+  )
+  const rows: TableRow[] = useMemo(() => {
+    if (showWorkspaceComposite) {
+      const importReady = importBreakdown?.group_by === 'call_import'
+      const resourceReady = resourceBreakdown?.group_by === 'resource'
+      if (!importReady && !resourceReady) return []
+      return buildWorkspaceCompositeRows(
+        importReady
+          ? filterTableRows(importBreakdown.rows as BreakdownRow[], usageKind, scopeActive)
+          : [],
+        resourceReady
+          ? filterTableRows(resourceBreakdown.rows as BreakdownRow[], usageKind, scopeActive)
+          : [],
+        mergeOptions,
+        padMissingRows,
+      )
+    }
+    return mergeDrillRows(groupBy, filteredRawRows, mergeOptions, padMissingRows)
+  }, [
+    showWorkspaceComposite,
+    importBreakdown,
+    resourceBreakdown,
+    groupBy,
+    filteredRawRows,
+    mergeOptions,
+    padMissingRows,
+    usageKind,
+    scopeActive,
+  ])
+
+  const breakdownStale =
+    !showWorkspaceComposite && breakdownFetching && !breakdownMatchesLevel
+  const tableLoading = showWorkspaceComposite
+    ? (importBreakdownLoading || resourceBreakdownLoading) &&
+      !importBreakdown &&
+      !resourceBreakdown
+    : breakdownLoading && !breakdown
+  const tableFetching = showWorkspaceComposite
+    ? importBreakdownFetching || resourceBreakdownFetching
+    : breakdownFetching
   const totals = summary?.totals
-  const rows = breakdown?.rows || []
-
-  const dimensionLabel = (row: (typeof rows)[number]): string => {
-    if (groupBy === 'workspace') return row.workspace_name || 'Unknown'
-    if (groupBy === 'product_section')
-      return row.product_section_label || row.product_section || '—'
-    if (groupBy === 'model') return row.model || '—'
-    return row.resource_label || row.resource_id || 'Unscoped'
+  const showAudio = Boolean(totals?.audio_seconds)
+  const showTts =
+    Boolean(totals?.tts_characters) ||
+    rows.some((r) => Boolean(r.tts_characters))
+
+  const showTruncation =
+    (!showWorkspaceComposite &&
+      breakdownMatchesLevel &&
+      Boolean(breakdown?.truncated_at_limit)) ||
+    (showWorkspaceComposite &&
+      Boolean(
+        importBreakdown?.truncated_at_limit || resourceBreakdown?.truncated_at_limit,
+      ))
+
+  const workspaceLabel =
+    filterOptions?.workspaces?.find((w) => w.id === workspaceId)?.name
+  const callImportLabel =
+    filterOptions?.call_imports?.find((c) => c.id === callImportId)?.label
+  const evaluationLabel =
+    filterOptions?.resources?.find((r) => r.id === evaluationId)?.label ||
+    filterOptions?.evaluations?.find((e) => e.id === evaluationId)?.label
+
+  const productSectionLabel =
+    filterOptions?.product_sections?.find((s) => s.id === productSection)?.label
+
+  const scopeSubtitle = model
+    ? model
+    : evaluationId
+      ? evaluationLabel || 'Evaluation'
+      : callImportId
+        ? callImportLabel || 'Call import'
+        : productSection
+          ? productSectionLabel || 'Product area'
+          : workspaceId
+            ? workspaceLabel || 'Workspace'
+            : 'Organization'
+
+  const levelHint = (() => {
+    if (showWorkspaceComposite) {
+      return 'Call import batches and other product usage — click a row to drill down'
+    }
+    if (groupBy === 'workspace') return 'Click a workspace to drill down'
+    if (groupBy === 'call_import') return 'Click a call import to see evaluation runs'
+    if (groupBy === 'product_section') return 'Click a product area to see models used'
+    if (groupBy === 'resource') return 'Click an evaluation to see models used'
+    if (groupBy === 'model') return 'Click a model to see usage by kind'
+    return 'Token totals by LLM / STT / TTS'
+  })()
+
+  const drillCrumbs = [
+    {
+      label: 'Organization',
+      onClick:
+        workspaceId ||
+        callImportId ||
+        evaluationId ||
+        model ||
+        productSection
+          ? () =>
+              setParams({
+                workspace_id: null,
+                call_import_id: null,
+                resource_id: null,
+                model: null,
+                usage_kind: null,
+                product_section: null,
+              })
+          : undefined,
+    },
+    ...(workspaceId
+      ? [
+          {
+            label: workspaceLabel || 'Workspace',
+            onClick:
+              callImportId || evaluationId || model || productSection
+                ? () =>
+                    setParams({
+                      call_import_id: null,
+                      resource_id: null,
+                      model: null,
+                      usage_kind: null,
+                      product_section: null,
+                    })
+                : undefined,
+          },
+        ]
+      : []),
+    ...(productSection
+      ? [
+          {
+            label: productSectionLabel || 'Product area',
+            onClick:
+              model
+                ? () => setParams({ model: null, usage_kind: null })
+                : undefined,
+          },
+        ]
+      : []),
+    ...(callImportId
+      ? [
+          {
+            label: callImportLabel || 'Call import',
+            onClick:
+              evaluationId || model
+                ? () =>
+                    setParams({
+                      resource_id: null,
+                      model: null,
+                      usage_kind: null,
+                      product_section: null,
+                    })
+                : undefined,
+          },
+        ]
+      : []),
+    ...(evaluationId
+      ? [
+          {
+            label: evaluationLabel || 'Evaluation',
+            onClick: model
+              ? () => setParams({ model: null, usage_kind: null })
+              : undefined,
+          },
+        ]
+      : []),
+    ...(model ? [{ label: model }] : []),
+  ]
+
+  const handleWorkspaceChange = (id: string) => {
+    setParams({
+      workspace_id: id || null,
+      call_import_id: null,
+      resource_id: null,
+      model: null,
+      usage_kind: null,
+      product_section: null,
+    })
+  }
+
+  const handleCallImportChange = (id: string) => {
+    setParams({
+      call_import_id: id || null,
+      resource_id: null,
+      model: null,
+      usage_kind: null,
+      product_section: null,
+    })
+  }
+
+  const handleClearAll = () => {
+    setParams({
+      workspace_id: null,
+      call_import_id: null,
+      dataset: null,
+      tag_id: null,
+      resource_id: null,
+      usage_kind: null,
+      model: null,
+      product_section: null,
+    })
+  }
+
+  const handleRowDrill = (row: TableRow) => {
+    if ('rowKind' in row) {
+      if (row.rowKind === 'call_import' && row.call_import_id) {
+        setParams({
+          call_import_id: row.call_import_id,
+          resource_id: null,
+          model: null,
+          usage_kind: null,
+          product_section: null,
+        })
+        return
+      }
+      if (row.rowKind === 'workspace_resource' && row.product_section) {
+        setParams({
+          product_section: row.product_section,
+          call_import_id: null,
+          resource_id: row.resource_id || null,
+          model: null,
+          usage_kind: null,
+        })
+        return
+      }
+    }
+    if (groupBy === 'workspace' && row.workspace_id) {
+      setParams({
+        workspace_id: row.workspace_id,
+        call_import_id: null,
+        resource_id: null,
+        model: null,
+        usage_kind: null,
+        product_section: null,
+      })
+      return
+    }
+    if (groupBy === 'call_import' && row.call_import_id) {
+      setParams({
+        call_import_id: row.call_import_id,
+        resource_id: null,
+        model: null,
+        usage_kind: null,
+        product_section: null,
+      })
+      return
+    }
+    if (groupBy === 'product_section' && row.product_section) {
+      setParams({
+        product_section: row.product_section,
+        call_import_id: null,
+        resource_id: null,
+        model: null,
+        usage_kind: null,
+      })
+      return
+    }
+    if (groupBy === 'resource' && row.resource_id) {
+      setParams({
+        resource_id: row.resource_id,
+        model: null,
+        usage_kind: null,
+        product_section: null,
+      })
+      return
+    }
+    if (groupBy === 'model' && row.model) {
+      setParams({ model: row.model, usage_kind: null })
+    }
+  }
+
+  const isRowDrillable = (row: TableRow): boolean => {
+    if ('rowKind' in row) {
+      if (row.rowKind === 'call_import') return Boolean(row.call_import_id)
+      return Boolean(row.product_section)
+    }
+    if (groupBy === 'workspace') return Boolean(row.workspace_id)
+    if (groupBy === 'call_import') return Boolean(row.call_import_id)
+    if (groupBy === 'product_section') return Boolean(row.product_section)
+    if (groupBy === 'resource') return Boolean(row.resource_id)
+    if (groupBy === 'model') return Boolean(row.model)
+    return false
   }
 
   return (
@@ -102,167 +976,234 @@ export default function Usage() {
       <div className="flex items-start justify-between gap-4">
         <div>
           <h1 className="text-2xl font-bold text-gray-900 flex items-center gap-2">
-            <Activity className="h-6 w-6 text-indigo-600" />
+            <Activity className="h-6 w-6 text-primary-600" />
             Usage
           </h1>
           <p className="text-sm text-gray-500 mt-1">
-            Organization-wide LLM tokens and calls. Filter by workspace, product
-            section, model, or evaluation.
+            Cards show usage for <span className="font-medium text-gray-700">{scopeSubtitle}</span>.
+            Drill down: workspaces → call imports or product areas → evaluations / models.
           </p>
         </div>
-        {summary?.last_updated_at && (
+        {summary?.last_updated_at ? (
           <p className="text-xs text-gray-400 whitespace-nowrap">
             Updated {new Date(summary.last_updated_at).toLocaleString()}
           </p>
-        )}
+        ) : null}
       </div>
 
-      <div className="grid grid-cols-2 md:grid-cols-4 gap-3">
+      <div
+        className={`grid grid-cols-2 md:grid-cols-4 gap-2 transition-opacity ${
+          summaryFetching ? 'opacity-70' : ''
+        }`}
+      >
         <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={summaryLoading} />
         <StatCard label="Output tokens" value={totals?.completion_tokens} loading={summaryLoading} />
         <StatCard label="Total tokens" value={totals?.total_tokens} loading={summaryLoading} />
         <StatCard label="LLM calls" value={totals?.call_count} loading={summaryLoading} />
       </div>
 
-      {(totals?.cache_read_tokens || totals?.cache_creation_tokens || totals?.reasoning_tokens) ? (
-        <div className="grid grid-cols-1 md:grid-cols-3 gap-3">
-          <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={summaryLoading} />
-          <StatCard label="Cache write" value={totals?.cache_creation_tokens} loading={summaryLoading} />
-          <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={summaryLoading} />
+      {(showAudio ||
+        showTts ||
+        totals?.cache_read_tokens ||
+        totals?.cache_creation_tokens ||
+        totals?.reasoning_tokens) ? (
+        <div className="grid grid-cols-1 md:grid-cols-4 gap-3">
+          {showAudio ? (
+            <StatCard
+              label="STT audio"
+              valueLabel={formatAudio(totals?.audio_seconds || 0)}
+              loading={summaryLoading}
+            />
+          ) : null}
+          {showTts ? (
+            <StatCard
+              label="TTS characters"
+              value={totals?.tts_characters}
+              loading={summaryLoading}
+            />
+          ) : null}
+          {(totals?.cache_read_tokens || 0) > 0 ? (
+            <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={summaryLoading} />
+          ) : null}
+          {(totals?.cache_creation_tokens || 0) > 0 ? (
+            <StatCard
+              label="Cache write"
+              value={totals?.cache_creation_tokens}
+              loading={summaryLoading}
+            />
+          ) : null}
+          {(totals?.reasoning_tokens || 0) > 0 ? (
+            <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={summaryLoading} />
+          ) : null}
         </div>
       ) : null}
 
-      <Card shadow="sm">
-        <CardBody className="gap-4 p-4">
-          <div className="grid grid-cols-1 md:grid-cols-3 lg:grid-cols-6 gap-3">
-            <FilterField label="Start">
-              <input
-                type="date"
-                className={fieldClassName}
-                value={start}
-                onChange={(e) => setParam('start', e.target.value)}
-              />
-            </FilterField>
-            <FilterField label="End">
-              <input
-                type="date"
-                className={fieldClassName}
-                value={end}
-                onChange={(e) => setParam('end', e.target.value)}
-              />
-            </FilterField>
-            <FilterField label="Workspace">
-              <select
-                className={fieldClassName}
-                value={workspaceId}
-                onChange={(e) => setParam('workspace_id', e.target.value)}
-              >
-                <option value="">All workspaces</option>
-                {(filters?.workspaces || []).map((w) => (
-                  <option key={w.id} value={w.id}>
-                    {w.name}
-                  </option>
-                ))}
-              </select>
-            </FilterField>
-            <FilterField label="Product section">
-              <select
-                className={fieldClassName}
-                value={productSection}
-                onChange={(e) => setParam('product_section', e.target.value)}
-              >
-                <option value="">All sections</option>
-                {(filters?.product_sections || []).map((s) => (
-                  <option key={s.id} value={s.id}>
-                    {s.label}
-                  </option>
-                ))}
-              </select>
-            </FilterField>
-            <FilterField label="Model">
-              <select
-                className={fieldClassName}
-                value={model}
-                onChange={(e) => setParam('model', e.target.value)}
-              >
-                <option value="">All models</option>
-                {(filters?.models || []).map((m) => (
-                  <option key={m} value={m}>
-                    {m}
-                  </option>
-                ))}
-              </select>
-            </FilterField>
-            <FilterField label="Group by">
-              <select
-                className={fieldClassName}
-                value={groupBy}
-                onChange={(e) => setParam('group_by', e.target.value)}
-              >
-                <option value="workspace">Workspace</option>
-                <option value="product_section">Product section</option>
-                <option value="model">Model</option>
-                <option value="resource">Evaluation / resource</option>
-              </select>
-            </FilterField>
-          </div>
-        </CardBody>
-      </Card>
+      <UsageFiltersBar
+        start={start}
+        end={end}
+        workspaceId={workspaceId}
+        callImportId={callImportId}
+        evaluationId={evaluationId}
+        dataset={dataset}
+        tagId={tagId}
+        usageKind={usageKind}
+        model={model}
+        options={filterOptions}
+        filtersLoading={filtersLoading}
+        onDateApply={(s, e) => setParams({ start: s, end: e })}
+        onWorkspaceChange={handleWorkspaceChange}
+        onCallImportChange={handleCallImportChange}
+        onDatasetChange={(v) =>
+          setParams({
+            dataset: v || null,
+            call_import_id: null,
+            resource_id: null,
+          })
+        }
+        onTagChange={(v) =>
+          setParams({
+            tag_id: v || null,
+            call_import_id: null,
+            resource_id: null,
+          })
+        }
+        onEvaluationChange={(id) => {
+          const resource = filterOptions?.resources?.find((r) => r.id === id)
+          setParams({
+            resource_id: id || null,
+            product_section: id ? resource?.product_section || null : null,
+            model: null,
+            usage_kind: null,
+          })
+        }}
+        onUsageKindChange={(k) => setParams({ usage_kind: k || null })}
+        onModelChange={(v) => setParams({ model: v || null })}
+        onClearAll={handleClearAll}
+      />
 
-      <Card shadow="sm">
-        <CardBody className="p-0 overflow-x-auto">
-          {breakdownLoading ? (
+      <Card shadow="sm" className="border border-gray-200 ring-1 ring-[#fde047]/25">
+        <div className="border-b border-gray-100 px-4 py-3 space-y-2">
+          <UsageDrillPath crumbs={drillCrumbs} levelLabel={levelHint} />
+          {showTruncation ? (
+            <p className="text-xs text-amber-700">
+              Showing the first 100 rows for this level. Narrow the date range or drill
+              further for complete detail.
+            </p>
+          ) : null}
+        </div>
+        <CardBody className="p-0 overflow-x-auto max-h-[min(70vh,640px)] overflow-y-auto custom-scrollbar">
+          {tableLoading || breakdownStale ? (
             <div className="flex justify-center py-12">
               <Spinner />
             </div>
           ) : rows.length === 0 ? (
             <div className="py-12 text-center text-sm text-gray-500">
-              No usage in this period. Run evaluations or playground calls, then
-              refresh in a minute.
+              No usage in this period for the current scope. Try 7d or 30d, or go back up a
+              level.
             </div>
           ) : (
+            <div
+              className={`relative transition-opacity ${
+                tableFetching ? 'opacity-60 pointer-events-none' : ''
+              }`}
+            >
+              {tableFetching ? (
+                <div className="absolute top-2 right-3 z-20">
+                  <Spinner size="sm" />
+                </div>
+              ) : null}
             <table className="min-w-full text-sm">
-              <thead className="bg-gray-50 text-left text-xs uppercase text-gray-500">
+              <thead className="bg-gray-50 text-left text-xs uppercase text-gray-500 sticky top-0 z-10">
                 <tr>
                   <th className="px-4 py-3 font-semibold">
-                    {groupBy === 'workspace'
-                      ? 'Workspace'
-                      : groupBy === 'product_section'
-                        ? 'Section'
-                        : groupBy === 'model'
-                          ? 'Model'
-                          : 'Resource'}
+                    {drillColumnLabel(groupBy, showWorkspaceComposite)}
                   </th>
-                  <th className="px-4 py-3 font-semibold text-right">Calls</th>
-                  <th className="px-4 py-3 font-semibold text-right">Input</th>
-                  <th className="px-4 py-3 font-semibold text-right">Output</th>
-                  <th className="px-4 py-3 font-semibold text-right">Total</th>
+                  <th className="px-4 py-3 font-semibold text-right">LLM calls</th>
+                  <th className="px-4 py-3 font-semibold text-right">Input tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">Output tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">Total tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">STT audio</th>
+                  <th className="px-4 py-3 font-semibold text-right">TTS chars</th>
                   <th className="px-4 py-3 font-semibold text-right">Cache read</th>
                 </tr>
               </thead>
               <tbody className="divide-y divide-gray-100">
-                {rows.map((row, idx) => (
-                  <tr key={idx} className="hover:bg-gray-50">
-                    <td className="px-4 py-3 text-gray-900">{dimensionLabel(row)}</td>
-                    <td className="px-4 py-3 text-right tabular-nums">
-                      {formatNumber(row.call_count)}
-                    </td>
-                    <td className="px-4 py-3 text-right tabular-nums">
-                      {formatNumber(row.prompt_tokens)}
-                    </td>
-                    <td className="px-4 py-3 text-right tabular-nums">
-                      {formatNumber(row.completion_tokens)}
-                    </td>
-                    <td className="px-4 py-3 text-right tabular-nums font-medium">
-                      {formatNumber(row.total_tokens)}
-                    </td>
-                    <td className="px-4 py-3 text-right tabular-nums text-gray-500">
-                      {formatNumber(row.cache_read_tokens)}
-                    </td>
-                  </tr>
-                ))}
+                {rows.map((row, idx) => {
+                  const drillable = isRowDrillable(row)
+                  const compositeHeadline =
+                    'rowKind' in row ? compositeRowHeadline(row) : null
+                  const rowTitle = tableRowLabel(groupBy, row, filterOptions)
+                  const showRowTitle =
+                    'rowKind' in row
+                      ? row.rowKind === 'workspace_resource' ||
+                        (compositeHeadline &&
+                          rowTitle.toLowerCase() !== compositeHeadline.toLowerCase())
+                      : true
+                  return (
+                    <tr
+                      key={idx}
+                      className={
+                        drillable
+                          ? 'hover:bg-[#fefce8]/60 cursor-pointer group'
+                          : 'hover:bg-gray-50'
+                      }
+                      onClick={() => drillable && handleRowDrill(row)}
+                    >
+                      <td className="px-4 py-3 text-gray-900 max-w-md">
+                        <span className="flex items-center gap-1 min-w-0">
+                          <span className="min-w-0">
+                            {'rowKind' in row ? (
+                              <span className="block text-[10px] font-semibold uppercase tracking-wide text-primary-600 mb-0.5">
+                                {compositeHeadline}
+                              </span>
+                            ) : null}
+                            {showRowTitle ? (
+                              <span className="truncate block font-medium">
+                                {rowTitle}
+                              </span>
+                            ) : !('rowKind' in row) ? (
+                              <span className="truncate block">{rowTitle}</span>
+                            ) : null}
+                            {'hint' in row && row.hint ? (
+                              <span className="text-xs text-gray-400 truncate block">
+                                {row.hint}
+                              </span>
+                            ) : null}
+                          </span>
+                          {drillable ? (
+                            <ChevronRight
+                              className="h-4 w-4 shrink-0 text-gray-400 opacity-0 group-hover:opacity-100 transition-opacity"
+                            />
+                          ) : null}
+                        </span>
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums">
+                        {formatNumber(row.call_count)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums">
+                        {formatNumber(row.prompt_tokens)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums">
+                        {formatNumber(row.completion_tokens)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums font-medium">
+                        {formatNumber(row.total_tokens)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                        {row.audio_seconds ? formatAudio(row.audio_seconds) : '—'}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                        {row.tts_characters ? formatNumber(row.tts_characters) : '—'}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                        {formatNumber(row.cache_read_tokens)}
+                      </td>
+                    </tr>
+                  )
+                })}
               </tbody>
             </table>
+            </div>
           )}
         </CardBody>
       </Card>
@@ -273,20 +1214,22 @@ export default function Usage() {
 function StatCard({
   label,
   value,
+  valueLabel,
   loading,
 }: {
   label: string
   value?: number
+  valueLabel?: string
   loading?: boolean
 }) {
   return (
-    <Card shadow="sm">
-      <CardBody className="p-4">
+    <Card shadow="sm" className="border border-gray-200 ring-1 ring-[#fde047]/25">
+      <CardBody className="p-3">
         <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">
           {label}
         </p>
-        <p className="mt-1 text-2xl font-semibold text-gray-900 tabular-nums">
-          {loading ? '—' : formatNumber(value || 0)}
+        <p className="mt-0.5 text-xl font-semibold text-gray-900 tabular-nums">
+          {loading ? '—' : valueLabel ?? formatNumber(value || 0)}
         </p>
       </CardBody>
     </Card>
diff --git a/frontend/src/pages/usage/UsageDateRangePicker.tsx b/frontend/src/pages/usage/UsageDateRangePicker.tsx
new file mode 100644
index 00000000..3ffeaeec
--- /dev/null
+++ b/frontend/src/pages/usage/UsageDateRangePicker.tsx
@@ -0,0 +1,252 @@
+import { useEffect, useMemo, useRef, useState } from 'react'
+import { Calendar } from 'lucide-react'
+import { usageTheme } from './usageTheme'
+import { getUsageTimezone, formatUsageTimezoneLabel } from './usageTimezone'
+
+type Mode = 'relative' | 'absolute'
+
+type UsageDateRangePickerProps = {
+  start: string
+  end: string
+  onApply: (start: string, end: string) => void
+}
+
+function toDateInput(d: Date): string {
+  const y = d.getFullYear()
+  const m = String(d.getMonth() + 1).padStart(2, '0')
+  const day = String(d.getDate()).padStart(2, '0')
+  return `${y}-${m}-${day}`
+}
+
+function formatDisplay(start: string, end: string): string {
+  if (start === end) return start
+  return `${start} → ${end}`
+}
+
+function rangeForDays(days: number): { start: string; end: string } {
+  const end = new Date()
+  const start = new Date()
+  start.setDate(start.getDate() - (days - 1))
+  return { start: toDateInput(start), end: toDateInput(end) }
+}
+
+const QUICK_RANGES = [
+  { label: '1d', days: 1 },
+  { label: '7d', days: 7 },
+  { label: '30d', days: 30 },
+  { label: '90d', days: 90 },
+] as const
+
+const RELATIVE_DAYS = [1, 2, 3, 4, 5, 6]
+const RELATIVE_WEEKS = [1, 2, 3, 4]
+
+export default function UsageDateRangePicker({
+  start,
+  end,
+  onApply,
+}: UsageDateRangePickerProps) {
+  const [open, setOpen] = useState(false)
+  const [mode, setMode] = useState<Mode>('relative')
+  const [draftStart, setDraftStart] = useState(start)
+  const [draftEnd, setDraftEnd] = useState(end)
+  const [relDays, setRelDays] = useState(1)
+  const rootRef = useRef<HTMLDivElement>(null)
+
+  const activeQuick = useMemo(() => {
+    for (const q of QUICK_RANGES) {
+      const r = rangeForDays(q.days)
+      if (r.start === start && r.end === end) return q.label
+    }
+    return null
+  }, [start, end])
+
+  useEffect(() => {
+    if (open) {
+      setDraftStart(start)
+      setDraftEnd(end)
+    }
+  }, [open, start, end])
+
+  useEffect(() => {
+    const onDoc = (e: MouseEvent) => {
+      if (!rootRef.current?.contains(e.target as Node)) setOpen(false)
+    }
+    document.addEventListener('mousedown', onDoc)
+    return () => document.removeEventListener('mousedown', onDoc)
+  }, [])
+
+  const applyRelativeDays = (days: number) => {
+    const r = rangeForDays(days)
+    onApply(r.start, r.end)
+    setOpen(false)
+  }
+
+  const applyAbsolute = () => {
+    if (!draftStart || !draftEnd) return
+    if (draftEnd < draftStart) return
+    onApply(draftStart, draftEnd)
+    setOpen(false)
+  }
+
+  const pillClass = (active: boolean) =>
+    `rounded-md px-2.5 py-1 text-xs font-medium transition-colors border ${
+      active ? usageTheme.pillActive : usageTheme.pillInactive
+    }`
+
+  return (
+    <div ref={rootRef} className="relative flex flex-wrap items-center gap-2 flex-1 min-w-0">
+      <div className="flex items-center gap-0.5 rounded-lg border border-[#fde047]/50 bg-white/80 p-0.5">
+        {QUICK_RANGES.map((q) => (
+          <button
+            key={q.label}
+            type="button"
+            onClick={() => {
+              const r = rangeForDays(q.days)
+              onApply(r.start, r.end)
+            }}
+            className={pillClass(activeQuick === q.label)}
+          >
+            {q.label}
+          </button>
+        ))}
+        <button
+          type="button"
+          onClick={() => setOpen((v) => !v)}
+          className={pillClass(open || !activeQuick)}
+        >
+          <Calendar className="h-3.5 w-3.5 inline mr-1" />
+          Custom
+        </button>
+      </div>
+
+      <span className="text-xs text-gray-500 truncate hidden md:inline">
+        {formatDisplay(start, end)}
+        <span className="text-gray-400">
+          {' '}
+          · {formatUsageTimezoneLabel(getUsageTimezone())}
+        </span>
+      </span>
+
+      {open ? (
+        <div className="absolute top-full left-0 z-50 mt-1 min-w-[20rem] max-w-lg rounded-xl border border-[#fde047]/60 bg-white p-4 shadow-xl ring-1 ring-[#fef9c3]">
+          <div className="flex gap-2 mb-4">
+            <button
+              type="button"
+              onClick={() => setMode('absolute')}
+              className={`rounded-full px-4 py-1.5 text-sm font-medium border ${
+                mode === 'absolute' ? usageTheme.pillActive : usageTheme.pillMuted
+              }`}
+            >
+              Absolute
+            </button>
+            <button
+              type="button"
+              onClick={() => setMode('relative')}
+              className={`rounded-full px-4 py-1.5 text-sm font-medium border ${
+                mode === 'relative' ? usageTheme.pillActive : usageTheme.pillMuted
+              }`}
+            >
+              Relative
+            </button>
+          </div>
+
+          {mode === 'relative' ? (
+            <div className="space-y-3">
+              <p className="text-xs text-gray-500">
+                Dates use your local timezone ({formatUsageTimezoneLabel(getUsageTimezone())}).
+                Usage is stored by UTC day; filters include activity that happened on each
+                selected local day.
+              </p>
+              <div>
+                <p className="text-xs font-medium text-gray-500 mb-2">Days</p>
+                <div className="flex flex-wrap gap-2">
+                  {RELATIVE_DAYS.map((d) => (
+                    <button
+                      key={d}
+                      type="button"
+                      onClick={() => applyRelativeDays(d)}
+                      className="min-w-[2.5rem] rounded-lg border border-gray-200 px-3 py-2 text-sm font-medium hover:border-[#facc15] hover:bg-[#fefce8]"
+                    >
+                      {d}
+                    </button>
+                  ))}
+                </div>
+              </div>
+              <div>
+                <p className="text-xs font-medium text-gray-500 mb-2">Weeks</p>
+                <div className="flex flex-wrap gap-2">
+                  {RELATIVE_WEEKS.map((w) => (
+                    <button
+                      key={w}
+                      type="button"
+                      onClick={() => applyRelativeDays(w * 7)}
+                      className="min-w-[2.5rem] rounded-lg border border-gray-200 px-3 py-2 text-sm font-medium hover:border-[#facc15] hover:bg-[#fefce8]"
+                    >
+                      {w}
+                    </button>
+                  ))}
+                </div>
+              </div>
+              <div className="flex items-end gap-3 pt-2 border-t border-gray-100">
+                <label className="flex flex-col gap-1">
+                  <span className="text-xs font-medium text-gray-500">Custom days</span>
+                  <input
+                    type="number"
+                    min={1}
+                    max={365}
+                    value={relDays}
+                    onChange={(e) => setRelDays(Math.max(1, Number(e.target.value) || 1))}
+                    className="w-24 rounded-lg border border-gray-200 px-3 py-2 text-sm"
+                  />
+                </label>
+                <button type="button" onClick={() => applyRelativeDays(relDays)} className={usageTheme.applyBtn}>
+                  Apply
+                </button>
+              </div>
+            </div>
+          ) : (
+            <div className="space-y-3">
+              <div className="grid grid-cols-2 gap-3">
+                <label className="flex flex-col gap-1">
+                  <span className="text-xs font-medium text-gray-500">Start date</span>
+                  <input
+                    type="date"
+                    value={draftStart}
+                    onChange={(e) => setDraftStart(e.target.value)}
+                    className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
+                  />
+                </label>
+                <label className="flex flex-col gap-1">
+                  <span className="text-xs font-medium text-gray-500">End date</span>
+                  <input
+                    type="date"
+                    value={draftEnd}
+                    onChange={(e) => setDraftEnd(e.target.value)}
+                    className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
+                  />
+                </label>
+              </div>
+              <div className="flex justify-end gap-2 pt-2 border-t border-gray-100">
+                <button
+                  type="button"
+                  onClick={() => setOpen(false)}
+                  className="px-3 py-1.5 text-sm font-medium text-gray-600"
+                >
+                  Cancel
+                </button>
+                <button type="button" onClick={applyAbsolute} className={usageTheme.applyBtn}>
+                  Apply
+                </button>
+              </div>
+            </div>
+          )}
+        </div>
+      ) : null}
+    </div>
+  )
+}
+
+export function defaultUsageDateRange(): { start: string; end: string } {
+  const today = toDateInput(new Date())
+  return { start: today, end: today }
+}
diff --git a/frontend/src/pages/usage/UsageDrillPath.tsx b/frontend/src/pages/usage/UsageDrillPath.tsx
new file mode 100644
index 00000000..0e25d673
--- /dev/null
+++ b/frontend/src/pages/usage/UsageDrillPath.tsx
@@ -0,0 +1,40 @@
+import { ChevronRight } from 'lucide-react'
+import { usageTheme } from './usageTheme'
+
+type DrillCrumb = {
+  label: string
+  onClick?: () => void
+}
+
+type UsageDrillPathProps = {
+  crumbs: DrillCrumb[]
+  levelLabel: string
+}
+
+export default function UsageDrillPath({ crumbs, levelLabel }: UsageDrillPathProps) {
+  return (
+    <div className="flex flex-wrap items-center justify-between gap-2">
+      <nav className="flex flex-wrap items-center gap-1 text-sm min-w-0">
+        {crumbs.map((crumb, i) => (
+          <span key={i} className="flex items-center gap-1 min-w-0">
+            {i > 0 ? <ChevronRight className="h-3.5 w-3.5 shrink-0 text-gray-400" /> : null}
+            {crumb.onClick ? (
+              <button
+                type="button"
+                onClick={crumb.onClick}
+                className={`truncate max-w-[14rem] ${usageTheme.linkStrong}`}
+              >
+                {crumb.label}
+              </button>
+            ) : (
+              <span className="font-semibold text-gray-900 truncate max-w-[14rem]">
+                {crumb.label}
+              </span>
+            )}
+          </span>
+        ))}
+      </nav>
+      <p className="text-xs text-gray-500 shrink-0">{levelLabel}</p>
+    </div>
+  )
+}
diff --git a/frontend/src/pages/usage/UsageFiltersBar.tsx b/frontend/src/pages/usage/UsageFiltersBar.tsx
new file mode 100644
index 00000000..747fc2ed
--- /dev/null
+++ b/frontend/src/pages/usage/UsageFiltersBar.tsx
@@ -0,0 +1,353 @@
+import { useMemo, useState } from 'react'
+import { Chip } from '@heroui/react'
+import { ChevronDown, Filter, SlidersHorizontal } from 'lucide-react'
+import SearchableSelect from './SearchableSelect'
+import UsageDateRangePicker from './UsageDateRangePicker'
+import { usageTheme } from './usageTheme'
+
+type Kind = '' | 'llm' | 'stt' | 'tts'
+
+type FilterOptions = {
+  workspaces: Array<{ id: string; name: string }>
+  call_imports: Array<{ id: string; label: string }>
+  evaluations: Array<{ id: string; label: string }>
+  resources?: Array<{ id: string; label: string; type?: string; product_section?: string }>
+  models: string[]
+  usage_kinds: Array<{ id: string; label: string }>
+  datasets?: string[]
+  tags?: Array<{ id: string; label: string }>
+}
+
+type ActiveChip = { key: string; label: string; onClear: () => void }
+
+type UsageFiltersBarProps = {
+  start: string
+  end: string
+  workspaceId: string
+  callImportId: string
+  evaluationId: string
+  dataset: string
+  tagId: string
+  usageKind: Kind
+  model: string
+  options?: FilterOptions
+  filtersLoading?: boolean
+  onDateApply: (start: string, end: string) => void
+  onWorkspaceChange: (id: string) => void
+  onCallImportChange: (id: string) => void
+  onDatasetChange: (value: string) => void
+  onTagChange: (value: string) => void
+  onEvaluationChange: (id: string) => void
+  onUsageKindChange: (v: Kind) => void
+  onModelChange: (v: string) => void
+  onClearAll: () => void
+}
+
+const KIND_OPTIONS: Array<{ id: Kind; label: string }> = [
+  { id: '', label: 'All' },
+  { id: 'llm', label: 'LLM' },
+  { id: 'stt', label: 'STT' },
+  { id: 'tts', label: 'TTS' },
+]
+
+export default function UsageFiltersBar({
+  start,
+  end,
+  workspaceId,
+  callImportId,
+  evaluationId,
+  dataset,
+  tagId,
+  usageKind,
+  model,
+  options,
+  filtersLoading,
+  onDateApply,
+  onWorkspaceChange,
+  onCallImportChange,
+  onDatasetChange,
+  onTagChange,
+  onEvaluationChange,
+  onUsageKindChange,
+  onModelChange,
+  onClearAll,
+}: UsageFiltersBarProps) {
+  const [expanded, setExpanded] = useState(false)
+
+  const workspaces = options?.workspaces ?? []
+  const callImports = options?.call_imports ?? []
+  const evaluations = options?.evaluations ?? []
+  const resources = options?.resources ?? []
+  const datasets = options?.datasets ?? []
+  const tags = options?.tags ?? []
+  const models = options?.models ?? []
+  const availableKinds = useMemo(
+    () => new Set(options?.usage_kinds?.map((k) => k.id) ?? []),
+    [options?.usage_kinds],
+  )
+
+  const sourceOptions = callImportId ? evaluations : resources
+
+  const activeChips = useMemo((): ActiveChip[] => {
+    const chips: ActiveChip[] = []
+    const wsLabel = workspaces.find((w) => w.id === workspaceId)?.name
+    if (workspaceId && wsLabel) {
+      chips.push({
+        key: 'workspace',
+        label: wsLabel,
+        onClear: () => onWorkspaceChange(''),
+      })
+    }
+    const importLabel = callImports.find((c) => c.id === callImportId)?.label
+    if (callImportId && importLabel) {
+      chips.push({
+        key: 'call_import',
+        label: importLabel,
+        onClear: () => onCallImportChange(''),
+      })
+    }
+    if (dataset) {
+      chips.push({
+        key: 'dataset',
+        label: dataset,
+        onClear: () => onDatasetChange(''),
+      })
+    }
+    if (tagId) {
+      const tagLabel = tags.find((t) => t.id === tagId)?.label
+      if (tagLabel) {
+        chips.push({
+          key: 'tag',
+          label: tagLabel,
+          onClear: () => onTagChange(''),
+        })
+      }
+    }
+    const sourceLabel =
+      sourceOptions.find((e) => e.id === evaluationId)?.label ||
+      evaluations.find((e) => e.id === evaluationId)?.label ||
+      resources.find((r) => r.id === evaluationId)?.label
+    if (evaluationId && sourceLabel) {
+      chips.push({
+        key: 'evaluation',
+        label: sourceLabel,
+        onClear: () => onEvaluationChange(''),
+      })
+    }
+    if (usageKind) {
+      chips.push({
+        key: 'kind',
+        label: usageKind.toUpperCase(),
+        onClear: () => onUsageKindChange(''),
+      })
+    }
+    if (model) {
+      chips.push({
+        key: 'model',
+        label: model,
+        onClear: () => onModelChange(''),
+      })
+    }
+    return chips
+  }, [
+    workspaceId,
+    workspaces,
+    callImportId,
+    callImports,
+    dataset,
+    tagId,
+    tags,
+    evaluationId,
+    sourceOptions,
+    evaluations,
+    resources,
+    usageKind,
+    model,
+    onWorkspaceChange,
+    onCallImportChange,
+    onDatasetChange,
+    onTagChange,
+    onEvaluationChange,
+    onUsageKindChange,
+    onModelChange,
+  ])
+
+  const hasScopeFilters = activeChips.length > 0
+
+  const kindHint = (kind: Kind): string | undefined => {
+    if (!kind) return undefined
+    if (availableKinds.has(kind)) return undefined
+    return `No ${kind.toUpperCase()} usage in this date range`
+  }
+
+  return (
+    <div className={usageTheme.panel}>
+      <div className={`flex flex-wrap items-center gap-2 px-3 py-2 ${usageTheme.panelHeader}`}>
+        <button
+          type="button"
+          onClick={() => setExpanded((v) => !v)}
+          className={`flex items-center gap-1.5 rounded-md px-2 py-1 text-sm font-semibold text-gray-800 hover:bg-[#fef9c3]/60 ${usageTheme.linkStrong}`}
+        >
+          <SlidersHorizontal className="h-4 w-4 text-primary-600" />
+          {expanded ? 'Hide filters' : 'More filters'}
+          <ChevronDown
+            className={`h-4 w-4 transition-transform ${expanded ? 'rotate-180' : ''}`}
+          />
+        </button>
+
+        <div className="h-5 w-px bg-[#fde047]/60 hidden sm:block" />
+
+        <UsageDateRangePicker start={start} end={end} onApply={onDateApply} />
+
+        {filtersLoading ? (
+          <span className="text-xs text-gray-400 ml-1">Updating options…</span>
+        ) : null}
+
+        {hasScopeFilters ? (
+          <button
+            type="button"
+            onClick={onClearAll}
+            className={`ml-auto text-xs ${usageTheme.linkStrong}`}
+          >
+            Clear scope
+          </button>
+        ) : null}
+      </div>
+
+      {!expanded && hasScopeFilters ? (
+        <div className="flex flex-wrap gap-1.5 px-3 py-2 border-t border-[#fde047]/30">
+          {activeChips.map((chip) => (
+            <Chip
+              key={chip.key}
+              size="sm"
+              variant="flat"
+              onClose={chip.onClear}
+              classNames={{
+                base: usageTheme.chipBase,
+                content: `${usageTheme.chipContent} text-xs max-w-[14rem] truncate`,
+              }}
+            >
+              {chip.label}
+            </Chip>
+          ))}
+        </div>
+      ) : null}
+
+      {expanded ? (
+        <div className="space-y-3 border-t border-[#fde047]/30 p-3">
+          <p className="text-xs text-gray-500 flex items-center gap-1.5">
+            <Filter className="h-3.5 w-3.5 text-primary-600" />
+            Jump to a level or click rows in the table to drill down.
+          </p>
+
+          <div className="grid gap-3 md:grid-cols-3">
+            <SearchableSelect
+              label="Workspace"
+              placeholder="All workspaces"
+              value={workspaceId}
+              options={workspaces.map((w) => ({ id: w.id, label: w.name }))}
+              onChange={onWorkspaceChange}
+              emptyMessage="No workspace usage in this date range"
+            />
+            <SearchableSelect
+              label="Dataset"
+              placeholder="All datasets"
+              value={dataset}
+              options={datasets.map((d) => ({ id: d, label: d }))}
+              onChange={onDatasetChange}
+              emptyMessage="No datasets in this workspace"
+              disabled={datasets.length === 0}
+            />
+            <SearchableSelect
+              label="Tag"
+              placeholder="All tags"
+              value={tagId}
+              options={tags}
+              onChange={onTagChange}
+              emptyMessage="No tags defined"
+              disabled={tags.length === 0}
+            />
+          </div>
+
+          <div className="grid gap-3 md:grid-cols-2">
+            <SearchableSelect
+              label="Call import batch"
+              placeholder="All call import batches"
+              value={callImportId}
+              options={callImports}
+              onChange={onCallImportChange}
+              emptyMessage="No imports with usage in this range"
+              disabled={callImports.length === 0}
+            />
+            <SearchableSelect
+              label={callImportId ? 'Evaluation run' : 'Source'}
+              placeholder={
+                callImportId
+                  ? 'All evaluations for import'
+                  : 'Agents, voice sims, telephony, …'
+              }
+              value={evaluationId}
+              options={sourceOptions}
+              onChange={onEvaluationChange}
+              emptyMessage="No matching usage in this range"
+              disabled={sourceOptions.length === 0}
+            />
+          </div>
+
+          <div className="flex flex-wrap items-center gap-x-4 gap-y-2">
+            <div className="flex flex-wrap items-center gap-2">
+              <span className="text-xs font-medium text-gray-500 shrink-0">Kind</span>
+              {KIND_OPTIONS.map((k) => {
+                const disabled = k.id !== '' && !availableKinds.has(k.id)
+                const hint = kindHint(k.id)
+                return (
+                  <button
+                    key={k.id || 'all'}
+                    type="button"
+                    disabled={disabled}
+                    title={hint}
+                    onClick={() => !disabled && onUsageKindChange(k.id)}
+                    className={`rounded-full px-3 py-1 text-xs font-medium transition-colors border ${
+                      usageKind === k.id && !disabled
+                        ? usageTheme.pillActive
+                        : disabled
+                          ? 'border-transparent text-gray-400 cursor-not-allowed opacity-60'
+                          : usageTheme.pillInactive
+                    }`}
+                  >
+                    {k.label}
+                  </button>
+                )
+              })}
+              {!availableKinds.has('tts') ? (
+                <span className="text-xs text-gray-400">No TTS</span>
+              ) : null}
+              {!availableKinds.has('stt') ? (
+                <span className="text-xs text-gray-400">No STT</span>
+              ) : null}
+            </div>
+
+            <div className="flex flex-wrap items-center gap-2">
+              <label className="flex items-center gap-2">
+                <span className="text-xs font-medium text-gray-500 shrink-0">Model</span>
+                <select
+                  value={model}
+                  onChange={(e) => onModelChange(e.target.value)}
+                  className={`rounded-lg border border-gray-200 bg-white px-3 py-1.5 text-sm min-w-[12rem] max-w-[20rem] ${usageTheme.focusRing}`}
+                >
+                  <option value="">All models</option>
+                  {models.map((m) => (
+                    <option key={m} value={m}>{m}</option>
+                  ))}
+                </select>
+              </label>
+              {models.length === 0 ? (
+                <span className="text-xs text-gray-400">No models in this range</span>
+              ) : null}
+            </div>
+          </div>
+        </div>
+      ) : null}
+    </div>
+  )
+}
diff --git a/frontend/src/pages/usage/usageProductHints.ts b/frontend/src/pages/usage/usageProductHints.ts
new file mode 100644
index 00000000..4a057b1c
--- /dev/null
+++ b/frontend/src/pages/usage/usageProductHints.ts
@@ -0,0 +1,47 @@
+/** Product areas shown separately from call-import drill-down. */
+export const CALL_IMPORT_PRODUCT_SECTIONS = new Set([
+  'call_imports',
+  'call_import_evaluations',
+])
+
+/** Short table headline per product section (workspace composite rows). */
+export const PRODUCT_SECTION_HEADLINES: Record<string, string> = {
+  voice_playground: 'Voice playground',
+  playground: 'Playground',
+  chat: 'Chat',
+  telephony: 'Telephony',
+  evaluators: 'Evaluators',
+  metrics: 'Metrics',
+  judge_alignment: 'Judge alignment',
+  prompt_optimization: 'Prompt optimization',
+  personas: 'Personas',
+  agents: 'Agents',
+  prompt_partials: 'Prompt partials',
+  conversation_evaluations: 'Conversation evaluations',
+  test_agent: 'Test agent',
+  call_import_evaluations: 'Call import evaluations',
+  call_imports: 'Call imports',
+  other: 'Other',
+}
+
+export const CALL_IMPORT_BATCH_HEADLINE = 'Call import batch'
+
+export const PRODUCT_SECTION_HINTS: Record<string, string> = {
+  voice_playground: 'Voice agent playground — LLM, STT, and TTS',
+  playground: 'Text playground and experiments',
+  chat: 'Chat conversations',
+  telephony: 'Telephony and live calls',
+  evaluators: 'Evaluator definitions and runs',
+  metrics: 'Metrics and scoring',
+  judge_alignment: 'Judge alignment workflows',
+  prompt_optimization: 'Prompt optimization jobs',
+  personas: 'Persona generation',
+  agents: 'Agent configuration',
+  prompt_partials: 'Prompt partials',
+  conversation_evaluations: 'Conversation evaluations',
+  test_agent: 'Test agent sessions',
+  other: 'Other product usage',
+}
+
+export const CALL_IMPORT_HINT =
+  'Call import batch — CSV upload or manual audio recordings'
diff --git a/frontend/src/pages/usage/usageTheme.ts b/frontend/src/pages/usage/usageTheme.ts
new file mode 100644
index 00000000..fc4b06be
--- /dev/null
+++ b/frontend/src/pages/usage/usageTheme.ts
@@ -0,0 +1,18 @@
+/** Matches platform primary buttons (soft gold, not solid yellow). */
+export const usageTheme = {
+  pillActive:
+    'bg-[#fef9c3] border border-[#facc15] text-[#a16207] font-semibold shadow-sm',
+  pillInactive:
+    'text-gray-600 hover:bg-[#fefce8] hover:text-[#854d0e] border border-transparent',
+  pillMuted: 'bg-gray-50 border border-gray-200 text-gray-600',
+  link: 'text-primary-600 hover:text-primary-700',
+  linkStrong: 'text-[#a16207] hover:text-[#854d0e] font-medium',
+  chipBase: 'bg-[#fefce8] border border-[#fde047]',
+  chipContent: 'text-[#854d0e]',
+  panel: 'rounded-xl border border-gray-200 bg-white shadow-sm ring-1 ring-[#fde047]/30',
+  panelHeader: 'bg-[#fefce8]/50 border-b border-[#fde047]/40',
+  focusRing: 'focus:border-[#facc15] focus:ring-2 focus:ring-[#fef9c3]',
+  selectHighlight: 'bg-[#fefce8] text-[#854d0e] font-medium',
+  applyBtn:
+    'rounded-lg bg-[#fef9c3] border border-[#facc15] px-4 py-1.5 text-sm font-semibold text-[#a16207] hover:bg-[#fef08a]',
+}
diff --git a/frontend/src/pages/usage/usageTimezone.ts b/frontend/src/pages/usage/usageTimezone.ts
new file mode 100644
index 00000000..f222a038
--- /dev/null
+++ b/frontend/src/pages/usage/usageTimezone.ts
@@ -0,0 +1,11 @@
+export function getUsageTimezone(): string {
+  try {
+    return Intl.DateTimeFormat().resolvedOptions().timeZone || 'UTC'
+  } catch {
+    return 'UTC'
+  }
+}
+
+export function formatUsageTimezoneLabel(tz: string): string {
+  return tz.replace(/_/g, ' ')
+}
diff --git a/tests/test_services/test_evaluators/test_call_data_transcript.py b/tests/test_services/test_evaluators/test_call_data_transcript.py
new file mode 100644
index 00000000..ef2e2707
--- /dev/null
+++ b/tests/test_services/test_evaluators/test_call_data_transcript.py
@@ -0,0 +1,34 @@
+"""Tests for call_data transcript extraction."""
+
+from app.services.evaluators.call_data_transcript import extract_transcript_from_call_data
+
+
+def test_vapi_extracts_transcript_from_messages_when_transcript_field_empty():
+    call_data = {
+        "transcript": "",
+        "endedReason": "call.in-progress.error-assistant-did-not-receive-customer-audio",
+        "messages": [
+            {"role": "assistant", "message": "Hello, how can I help?"},
+            {"role": "user", "message": "Hi there"},
+        ],
+    }
+    text, segments = extract_transcript_from_call_data(call_data, "vapi")
+    assert "Hello, how can I help?" in text
+    assert "Hi there" in text
+    assert len(segments) == 2
+
+
+def test_hydrate_helper_permanent_vapi_reason():
+    from app.workers.tasks import process_evaluator_result as task_module
+
+    class _Result:
+        audio_s3_key = None
+        transcription = None
+        provider_platform = "vapi"
+        call_data = {
+            "endedReason": "call.in-progress.error-assistant-did-not-receive-customer-audio",
+        }
+
+    msg = task_module._permanent_input_failure_message(_Result(), db=None)
+    assert msg is not None
+    assert "microphone" in msg.lower()
diff --git a/tests/test_services/test_usage/test_bucket_context.py b/tests/test_services/test_usage/test_bucket_context.py
new file mode 100644
index 00000000..5cedb81c
--- /dev/null
+++ b/tests/test_services/test_usage/test_bucket_context.py
@@ -0,0 +1,40 @@
+"""Tests for usage bucket context helpers."""
+
+from uuid import uuid4
+
+from app.services.usage.bucket_context import (
+    build_bucket_context,
+    context_bucket_token,
+    legacy_resource_context,
+    parse_context_bucket_token,
+    resource_id_from_context,
+)
+
+
+def test_build_and_parse_context_roundtrip():
+    rid = uuid4()
+    ctx = build_bucket_context(
+        resource_id=rid,
+        resource_type="call_import_evaluation",
+        extra={"agent_id": str(uuid4())},
+    )
+    token = context_bucket_token(ctx)
+    parsed = parse_context_bucket_token(token)
+    assert parsed["resource_id"] == str(rid)
+    assert parsed["resource_type"] == "call_import_evaluation"
+    assert "agent_id" in parsed
+
+
+def test_extra_rejects_metric_keys():
+    ctx = build_bucket_context(
+        resource_id=uuid4(),
+        extra={"prompt_tokens": "999", "agent_id": str(uuid4())},
+    )
+    assert "prompt_tokens" not in ctx
+    assert "agent_id" in ctx
+
+
+def test_legacy_resource_context():
+    rid = uuid4()
+    ctx = legacy_resource_context(rid, "call_import")
+    assert resource_id_from_context(ctx) == rid
diff --git a/tests/test_services/test_usage/test_call_import_context.py b/tests/test_services/test_usage/test_call_import_context.py
new file mode 100644
index 00000000..6d798f65
--- /dev/null
+++ b/tests/test_services/test_usage/test_call_import_context.py
@@ -0,0 +1,120 @@
+"""Tests for call-import usage attribution context."""
+
+from uuid import uuid4
+
+from app.services.usage.bucket_context import build_bucket_context
+from app.services.usage.call_import_context import (
+    call_import_evaluation_usage_context,
+    call_import_ids_from_usage_context,
+    call_import_row_usage_context,
+    enrich_usage_context_workspace,
+)
+
+
+def test_evaluation_row_gets_distinct_bucket_context():
+    org_id = uuid4()
+    ws_id = uuid4()
+    eval_id = uuid4()
+    import_id = uuid4()
+    row_a = uuid4()
+    row_b = uuid4()
+    source_a = uuid4()
+    source_b = uuid4()
+
+    ctx_a = call_import_evaluation_usage_context(
+        organization_id=org_id,
+        workspace_id=ws_id,
+        evaluation_id=eval_id,
+        call_import_id=import_id,
+        evaluation_row_id=row_a,
+        call_import_row_id=source_a,
+    )
+    ctx_b = call_import_evaluation_usage_context(
+        organization_id=org_id,
+        workspace_id=ws_id,
+        evaluation_id=eval_id,
+        call_import_id=import_id,
+        evaluation_row_id=row_b,
+        call_import_row_id=source_b,
+    )
+
+    bucket_a = build_bucket_context(
+        resource_id=ctx_a.resource_id,
+        resource_type=ctx_a.resource_type,
+        extra=ctx_a.extra,
+    )
+    bucket_b = build_bucket_context(
+        resource_id=ctx_b.resource_id,
+        resource_type=ctx_b.resource_type,
+        extra=ctx_b.extra,
+    )
+    assert bucket_a != bucket_b
+    assert bucket_a["evaluation_row_id"] == str(row_a)
+    assert bucket_a["call_import_id"] == str(import_id)
+
+
+def test_row_only_transcribe_context():
+    org_id = uuid4()
+    import_id = uuid4()
+    row_id = uuid4()
+    ctx = call_import_row_usage_context(
+        organization_id=org_id,
+        workspace_id=None,
+        call_import_id=import_id,
+        call_import_row_id=row_id,
+    )
+    assert ctx.extra is not None
+    assert ctx.extra["call_import_row_id"] == str(row_id)
+    assert ctx.resource_type == "call_import"
+
+
+def test_call_import_ids_from_evaluation_context():
+    org_id = uuid4()
+    eval_id = uuid4()
+    import_id = uuid4()
+    row_id = uuid4()
+    source_id = uuid4()
+    ctx = call_import_evaluation_usage_context(
+        organization_id=org_id,
+        workspace_id=uuid4(),
+        evaluation_id=eval_id,
+        call_import_id=import_id,
+        evaluation_row_id=row_id,
+        call_import_row_id=source_id,
+    )
+    ids = call_import_ids_from_usage_context(ctx)
+    assert ids["call_import_id"] == import_id
+    assert ids["evaluation_id"] == eval_id
+    assert ids["evaluation_row_id"] == row_id
+    assert ids["call_import_row_id"] == source_id
+
+
+def test_enrich_usage_context_workspace_noop_when_set():
+    ws_id = uuid4()
+    ctx = call_import_evaluation_usage_context(
+        organization_id=uuid4(),
+        workspace_id=ws_id,
+        evaluation_id=uuid4(),
+        call_import_id=uuid4(),
+    )
+    assert enrich_usage_context_workspace(ctx).workspace_id == ws_id
+
+
+def test_enrich_usage_context_workspace_from_resolver():
+    org_id = uuid4()
+    import_id = uuid4()
+    ws_id = uuid4()
+    ctx = call_import_evaluation_usage_context(
+        organization_id=org_id,
+        workspace_id=None,
+        evaluation_id=uuid4(),
+        call_import_id=import_id,
+    )
+    from unittest.mock import patch
+
+    with patch(
+        "app.services.usage.call_import_context.resolve_workspace_id_for_usage_context",
+        return_value=ws_id,
+    ):
+        enriched = enrich_usage_context_workspace(ctx)
+    assert enriched.workspace_id == ws_id
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index 0fdd77bf..824de3cd 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from datetime import date
+from types import SimpleNamespace
 from typing import Any, Dict, List, Optional
 from unittest.mock import MagicMock
 from uuid import uuid4
@@ -105,6 +106,9 @@ def srem(self, key: str, *members: str) -> int:
     def smembers(self, key: str) -> set:
         return set(self.sets.get(key, set()))
 
+    def sismember(self, key: str, member: str) -> bool:
+        return member in self.sets.get(key, set())
+
     def exists(self, key: str) -> int:
         return int(key in self.hashes or key in self.sets or key in self.kv)
 
@@ -210,9 +214,10 @@ def test_record_increments_pending_and_counts_zero_token_calls(fake_redis, org_c
     assert prompt == 10
     assert completion == 5
     assert calls == 2
+    assert any(k.rsplit("|", 1)[0].endswith("|llm") for k in fields)
 
 
-def test_record_skipped_without_context(fake_redis):
+def test_record_skipped_without_organization(fake_redis):
     usage_mod.record_llm_usage(
         "gpt-test",
         UsageSnapshot(prompt_tokens=10, completion_tokens=5),
@@ -221,7 +226,137 @@ def test_record_skipped_without_context(fake_redis):
     assert fake_redis.sets == {}
 
 
-def test_flush_commits_and_acks_claim(fake_redis, org_ctx):
+def test_record_with_organization_id_without_context(fake_redis):
+    org_id = uuid4()
+    usage_mod.record_llm_usage(
+        "gpt-test",
+        UsageSnapshot(prompt_tokens=3, completion_tokens=1),
+        organization_id=org_id,
+        usage_date=date(2026, 8, 11),
+    )
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    assert fields
+    assert sum(int(v) for k, v in fields.items() if k.endswith("|call_count")) == 1
+    assert any("|other|" in k for k in fields)
+
+
+def test_record_call_usage(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_call_usage(
+            "voice-agent-call",
+            audio_seconds=42,
+            usage_date=date(2026, 8, 11),
+        )
+
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    audio = sum(int(v) for k, v in fields.items() if k.endswith("|audio_seconds"))
+    calls = sum(int(v) for k, v in fields.items() if k.endswith("|call_count"))
+    assert audio == 42
+    assert calls == 1
+    assert any(k.rsplit("|", 1)[0].endswith("|llm") for k in fields)
+
+
+def test_agent_usage_context_reuses_single_bucket(fake_redis):
+    """Stable agent context avoids per-call Redis/DB bucket explosion."""
+    from app.services.usage.context import usage_context_for_evaluator_result
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    agent_id = uuid4()
+    prefixes = set()
+    for idx in range(3):
+        result = SimpleNamespace(
+            id=uuid4(),
+            result_id=f"res-{idx}",
+            organization_id=org_id,
+            workspace_id=workspace_id,
+            evaluator_id=uuid4(),
+            agent_id=agent_id,
+        )
+        ctx = usage_context_for_evaluator_result(result)
+        with llm_usage_context(ctx):
+            usage_mod.record_call_usage(
+                "voice-agent-call",
+                audio_seconds=10,
+                usage_date=date(2026, 8, 13),
+            )
+        fields = fake_redis.hgetall(usage_mod._pending_hash_key(org_id))
+        prefixes.update(k.rsplit("|", 1)[0] for k in fields if k.endswith("|call_count"))
+
+    assert len(prefixes) == 1
+    calls = sum(int(v) for k, v in fake_redis.hgetall(usage_mod._pending_hash_key(org_id)).items() if k.endswith("|call_count"))
+    assert calls == 3
+
+
+def test_record_stt_usage(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_stt_usage(
+            "nova-2",
+            audio_seconds=12.2,
+            usage_date=date(2026, 8, 11),
+        )
+
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    audio = sum(int(v) for k, v in fields.items() if k.endswith("|audio_seconds"))
+    calls = sum(int(v) for k, v in fields.items() if k.endswith("|call_count"))
+    assert audio == 13  # ceil
+    assert calls == 1
+    assert any(k.rsplit("|", 1)[0].endswith("|stt") for k in fields)
+
+
+def test_record_tts_usage(fake_redis, org_ctx):
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_tts_usage(
+            "eleven_flash_v2_5",
+            characters=142,
+            usage_date=date(2026, 8, 11),
+        )
+
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    chars = sum(int(v) for k, v in fields.items() if k.endswith("|tts_characters"))
+    calls = sum(int(v) for k, v in fields.items() if k.endswith("|call_count"))
+    assert chars == 142
+    assert calls == 1
+    assert any(k.rsplit("|", 1)[0].endswith("|tts") for k in fields)
+
+
+def test_redis_failure_buffers_to_postgres(fake_redis, org_ctx, monkeypatch):
+    org_id, _workspace_id, ctx = org_ctx
+
+    def _boom_pipeline():
+        raise usage_mod.redis.RedisError("redis down")
+
+    monkeypatch.setattr(fake_redis, "pipeline", _boom_pipeline)
+
+    captured = {}
+
+    def _fake_buffer(organization_id, bucket, deltas):
+        captured["organization_id"] = organization_id
+        captured["bucket"] = bucket
+        captured["deltas"] = deltas
+
+    monkeypatch.setattr(usage_mod, "_buffer_to_postgres", _fake_buffer)
+
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=4, completion_tokens=2),
+            usage_date=date(2026, 8, 11),
+        )
+
+    assert captured["organization_id"] == org_id
+    assert captured["deltas"]["prompt_tokens"] == 4
+    assert captured["bucket"]["usage_kind"] == "llm"
+
+
+def test_flush_commits_and_acks_claim(fake_redis, org_ctx, monkeypatch):
     org_id, _workspace_id, ctx = org_ctx
     with llm_usage_context(ctx):
         usage_mod.record_llm_usage(
@@ -232,6 +367,7 @@ def test_flush_commits_and_acks_claim(fake_redis, org_ctx):
 
     db = MagicMock()
     db.execute.return_value = MagicMock(rowcount=1)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 1
@@ -242,7 +378,7 @@ def test_flush_commits_and_acks_claim(fake_redis, org_ctx):
     assert fake_redis.get(usage_mod._flush_lock_key(org_id)) is None
 
 
-def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx):
+def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx, monkeypatch):
     org_id, _workspace_id, ctx = org_ctx
     with llm_usage_context(ctx):
         usage_mod.record_llm_usage(
@@ -253,6 +389,7 @@ def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx):
 
     db = MagicMock()
     db.execute.side_effect = RuntimeError("db down")
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 0
@@ -270,7 +407,7 @@ def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx):
     assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
 
 
-def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx):
+def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx, monkeypatch):
     """Unknown org FK must not restore Redis (avoids infinite beat retries)."""
     from sqlalchemy.exc import IntegrityError
 
@@ -291,6 +428,7 @@ def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx):
             'constraint "llm_usage_daily_organization_id_fkey"'
         ),
     )
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 0
@@ -300,7 +438,7 @@ def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx):
     assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
 
 
-def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx):
+def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx, monkeypatch):
     org_id, _workspace_id, ctx = org_ctx
     with llm_usage_context(ctx):
         usage_mod.record_llm_usage(
@@ -313,6 +451,7 @@ def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx):
     db_a.execute.return_value = MagicMock(rowcount=0)  # force INSERT path
     db_b = MagicMock()
     db_b.execute.return_value = MagicMock(rowcount=1)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
 
     first = usage_mod.flush_usage_to_catalog(db_a, org_id)
     second = usage_mod.flush_usage_to_catalog(db_b, org_id)
@@ -385,3 +524,64 @@ def test_ensure_uses_workspace_and_section_hints():
     finally:
         reset_usage_hints(hint_tokens)
         reset_usage_context(ctx_token)
+
+
+def test_parse_legacy_resource_bucket_prefix():
+    rid = uuid4()
+    parsed = usage_mod._parse_bucket_prefix(
+        f"{uuid4()}|call_imports|nova-2|{rid}|call_import|2026-08-11|stt"
+    )
+    assert parsed is not None
+    assert parsed["usage_kind"] == "stt"
+    assert parsed["context"]["resource_id"] == str(rid)
+    assert parsed["context"]["resource_type"] == "call_import"
+
+
+def test_parse_context_bucket_prefix():
+    ctx = '{"resource_id":"00000000-0000-0000-0000-000000000001","resource_type":"call_import_evaluation"}'
+    parsed = usage_mod._parse_bucket_prefix(
+        f"{uuid4()}|call_imports|gpt-4|{ctx}|2026-08-11|llm"
+    )
+    assert parsed is not None
+    assert parsed["usage_kind"] == "llm"
+    assert parsed["context"]["resource_id"] == "00000000-0000-0000-0000-000000000001"
+
+
+def test_upsert_bucket_sql_uses_valid_empty_jsonb_literal():
+    """Regression: '{{}}'::jsonb is invalid JSON and breaks catalog flush."""
+    import inspect
+
+    source = inspect.getsource(usage_mod._upsert_bucket)
+    assert "'{{}}'::jsonb" not in source
+    assert "'{}'::jsonb" in source
+
+
+def test_upsert_bucket_matches_legacy_resource_context_key(monkeypatch):
+    """Per-row context must merge into an existing evaluation-level bucket."""
+    org_id = uuid4()
+    evaluation_id = uuid4()
+    bucket = {
+        "workspace_id": uuid4(),
+        "product_section": "call_import_evaluations",
+        "model": "gpt-test",
+        "context": {
+            "resource_id": str(evaluation_id),
+            "resource_type": "call_import_evaluation",
+            "evaluation_row_id": str(uuid4()),
+        },
+        "usage_date": date(2026, 8, 12),
+        "usage_kind": "llm",
+    }
+    deltas = {"prompt_tokens": 10, "completion_tokens": 5, "call_count": 1}
+
+    exact_update = MagicMock(rowcount=0)
+    legacy_update = MagicMock(rowcount=1)
+    db = MagicMock()
+    db.execute.side_effect = [exact_update, legacy_update]
+
+    usage_mod._upsert_bucket(db, org_id, bucket, deltas)
+
+    assert db.execute.call_count == 2
+    legacy_sql = str(db.execute.call_args_list[1][0][0])
+    assert "context->>'resource_id'" in legacy_sql
+    assert "context->>'resource_type'" in legacy_sql
diff --git a/tests/test_services/test_usage/test_usage_dates.py b/tests/test_services/test_usage/test_usage_dates.py
new file mode 100644
index 00000000..b73290c9
--- /dev/null
+++ b/tests/test_services/test_usage/test_usage_dates.py
@@ -0,0 +1,32 @@
+"""Tests for usage date timezone mapping."""
+
+from datetime import date
+
+from app.services.usage.dates import usage_date_filter_bounds, usage_local_today
+
+
+def test_usage_date_filter_bounds_india_late_night():
+  # 1:26 AM IST on Aug 13 is still Aug 12 in UTC usage_date bucket.
+  start = date(2026, 8, 13)
+  end = date(2026, 8, 13)
+  filter_start, filter_end = usage_date_filter_bounds(start, end, "Asia/Kolkata")
+  assert filter_start == date(2026, 8, 12)
+  assert filter_end == date(2026, 8, 13)
+
+
+def test_usage_date_filter_bounds_without_tz_uses_exact_dates():
+  start = date(2026, 8, 13)
+  end = date(2026, 8, 13)
+  assert usage_date_filter_bounds(start, end, None) == (start, end)
+
+
+def test_usage_date_filter_bounds_utc_matches_calendar_days():
+  start = date(2026, 8, 13)
+  end = date(2026, 8, 13)
+  assert usage_date_filter_bounds(start, end, "UTC") == (start, end)
+
+
+def test_usage_local_today_respects_timezone():
+  today_utc = usage_local_today("UTC")
+  today_india = usage_local_today("Asia/Kolkata")
+  assert today_utc <= today_india or today_india <= today_utc
diff --git a/tests/test_services/test_usage/test_usage_labels.py b/tests/test_services/test_usage/test_usage_labels.py
new file mode 100644
index 00000000..233fe900
--- /dev/null
+++ b/tests/test_services/test_usage/test_usage_labels.py
@@ -0,0 +1,287 @@
+"""Tests for usage label helpers."""
+
+from uuid import UUID
+
+from app.services.usage.usage_labels import (
+    build_usage_resource_label,
+    format_entity_label,
+    labels_for_resource_buckets,
+    usage_kind_label,
+    UsageNameResolver,
+)
+
+
+class _FakeEvaluation:
+    def __init__(self, id, name):
+        self.id = id
+        self.name = name
+
+
+class _FakeCallImport:
+    def __init__(self, id, dataset=None, original_filename=None):
+        self.id = id
+        self.dataset = dataset
+        self.original_filename = original_filename
+
+
+class _FakeCallImportRow:
+    def __init__(self, id, conversation_id):
+        self.id = id
+        self.conversation_id = conversation_id
+
+
+class _FakeTTSComparison:
+    def __init__(self, id, name=None, simulation_id=None):
+        self.id = id
+        self.name = name
+        self.simulation_id = simulation_id
+
+
+class _FakeAgent:
+    def __init__(self, id, name=None, agent_id=None):
+        self.id = id
+        self.name = name
+        self.agent_id = agent_id
+
+
+class _FakeQuery:
+    def __init__(self, rows):
+        self._rows = rows
+
+    def filter(self, *args, **kwargs):
+        return self
+
+    def join(self, *args, **kwargs):
+        return self
+
+    def all(self):
+        return self._rows
+
+
+class _FakeDb:
+    def __init__(self, evaluations=(), imports=(), rows=(), tts_comparisons=(), agents=()):
+        self._evaluations = evaluations
+        self._imports = imports
+        self._rows = rows
+        self._tts_comparisons = tts_comparisons
+        self._agents = agents
+
+    def query(self, *entities):
+        if len(entities) == 1:
+            model = entities[0]
+            if model.__name__ == "CallImportEvaluation":
+                return _FakeQuery(self._evaluations)
+            if model.__name__ == "CallImport":
+                return _FakeQuery(self._imports)
+            if model.__name__ == "CallImportRow":
+                return _FakeQuery(self._rows)
+            if model.__name__ == "TTSComparison":
+                return _FakeQuery(self._tts_comparisons)
+            if model.__name__ == "Agent":
+                return _FakeQuery(self._agents)
+            raise AssertionError(f"unexpected model {model}")
+        return _FakeQuery(())
+
+
+EVAL_ID = UUID("984039ab-cdef-4567-8901-234567890abc")
+IMPORT_ID = UUID("3111d376-e5f6-7890-abcd-ef1234567890")
+ROW_ID = UUID("fedcba98-7654-3210-fedc-ba9876543210")
+COMP_ID = UUID("aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee")
+AGENT_ID = UUID("bbbbbbbb-cccc-dddd-eeee-ffffffffffff")
+ORG_ID = UUID("11111111-2222-3333-4444-555555555555")
+
+
+def test_format_entity_label_custom_name_and_default():
+    assert format_entity_label("test", EVAL_ID, "Evaluation") == "test-984039ab"
+    assert format_entity_label(None, EVAL_ID, "Evaluation") == "Evaluation-984039ab"
+
+
+def test_call_import_label_includes_dataset_and_tags():
+    db = _FakeDb(
+        imports=[
+            _FakeCallImport(
+                IMPORT_ID,
+                dataset="QA batch",
+                original_filename="4 manual recordings",
+            )
+        ]
+    )
+    # Extend fake db for tag query
+    class _TagQuery:
+        def join(self, *args, **kwargs):
+            return self
+
+        def filter(self, *args, **kwargs):
+            return self
+
+        def all(self):
+            return [(IMPORT_ID, "support")]
+
+    def query_router(*entities):
+        if len(entities) == 1:
+            model = entities[0]
+            if model.__name__ == "CallImportTagAssignment":
+                return _TagQuery()
+            if model.__name__ == "CallImportEvaluation":
+                return _FakeQuery(())
+            if model.__name__ == "CallImport":
+                return _FakeQuery(db._imports)
+            if model.__name__ == "CallImportRow":
+                return _FakeQuery(())
+        return _TagQuery()
+
+    db.query = query_router  # type: ignore[method-assign]
+
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload([{"call_import_id": str(IMPORT_ID)}])
+    label = resolver.call_import_name(str(IMPORT_ID))
+    assert "4 manual recordings" in label
+    assert "QA batch" in label
+    assert "support" in label
+
+
+def test_call_import_label_prefers_filename_over_dataset():
+    db = _FakeDb(
+        imports=[
+            _FakeCallImport(
+                IMPORT_ID,
+                dataset="calls",
+                original_filename="unauthenticated sheet.xlsx",
+            )
+        ]
+    )
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload([{"call_import_id": str(IMPORT_ID)}])
+    assert resolver.call_import_name(str(IMPORT_ID)) == (
+        "unauthenticated sheet.xlsx (calls)-3111d376"
+    )
+
+
+def test_build_usage_resource_label_hierarchical():
+    db = _FakeDb(
+        evaluations=[_FakeEvaluation(EVAL_ID, "March QA pass")],
+        imports=[
+            _FakeCallImport(
+                IMPORT_ID,
+                dataset="calls",
+                original_filename="unauthenticated sheet.xlsx",
+            )
+        ],
+        rows=[_FakeCallImportRow(ROW_ID, "ext-call-8842")],
+    )
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload(
+        [
+            {
+                "evaluation_id": str(EVAL_ID),
+                "call_import_id": str(IMPORT_ID),
+                "call_import_row_id": str(ROW_ID),
+                "resource_type": "call_import_evaluation",
+                "resource_id": str(EVAL_ID),
+            }
+        ]
+    )
+    label = build_usage_resource_label(
+        {
+            "evaluation_id": str(EVAL_ID),
+            "call_import_id": str(IMPORT_ID),
+            "call_import_row_id": str(ROW_ID),
+            "resource_type": "call_import_evaluation",
+            "resource_id": str(EVAL_ID),
+        },
+        "call_import_evaluation",
+        resolver,
+    )
+    assert label == (
+        "unauthenticated sheet.xlsx (calls)-3111d376 / March QA pass-984039ab / ext-call-8842"
+    )
+
+
+def test_build_usage_resource_label_default_evaluation_name():
+    db = _FakeDb(evaluations=[_FakeEvaluation(EVAL_ID, None)])
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload(
+        [
+            {
+                "evaluation_id": str(EVAL_ID),
+                "resource_type": "call_import_evaluation",
+                "resource_id": str(EVAL_ID),
+            }
+        ]
+    )
+    label = build_usage_resource_label(
+        {"evaluation_id": str(EVAL_ID)},
+        "call_import_evaluation",
+        resolver,
+    )
+    assert label == "Evaluation-984039ab"
+
+
+def test_build_usage_resource_label_tts_comparison_with_simulation_id():
+    db = _FakeDb(
+        tts_comparisons=[
+            _FakeTTSComparison(
+                COMP_ID,
+                name="elevenlabs benchmark",
+                simulation_id="781879",
+            )
+        ]
+    )
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload(
+        [
+            {
+                "resource_type": "tts_comparison",
+                "resource_id": str(COMP_ID),
+            }
+        ]
+    )
+    label = build_usage_resource_label(
+        {
+            "resource_type": "tts_comparison",
+            "resource_id": str(COMP_ID),
+        },
+        "tts_comparison",
+        resolver,
+    )
+    assert label == "elevenlabs benchmark #781879"
+
+
+def test_build_usage_resource_label_agent_with_short_id():
+    db = _FakeDb(
+        agents=[_FakeAgent(AGENT_ID, name="Support bot", agent_id="482910")]
+    )
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload(
+        [
+            {
+                "resource_type": "agent",
+                "resource_id": str(AGENT_ID),
+            }
+        ]
+    )
+    label = build_usage_resource_label(
+        {"resource_type": "agent", "resource_id": str(AGENT_ID)},
+        "agent",
+        resolver,
+    )
+    assert label == "Support bot #482910"
+
+
+def test_labels_for_resource_buckets_empty_context_uses_bucket_id():
+    db = _FakeDb(
+        agents=[_FakeAgent(AGENT_ID, name="Support bot", agent_id="482910")]
+    )
+    resolver = UsageNameResolver(db, ORG_ID)
+    resolver.preload([{"resource_type": "agent", "resource_id": str(AGENT_ID)}])
+    labels = labels_for_resource_buckets(
+        [(str(AGENT_ID), "agent", [{}])],
+        resolver,
+    )
+    assert labels[str(AGENT_ID)] == "Support bot #482910"
+
+
+def test_usage_kind_label():
+    assert usage_kind_label("stt") == "STT"
+    assert usage_kind_label("llm") == "LLM"
+    assert usage_kind_label("tts") == "TTS"
diff --git a/tests/test_workers/test_process_evaluator_result_helpers.py b/tests/test_workers/test_process_evaluator_result_helpers.py
index 794248a3..0600c22f 100644
--- a/tests/test_workers/test_process_evaluator_result_helpers.py
+++ b/tests/test_workers/test_process_evaluator_result_helpers.py
@@ -1,28 +1,37 @@
-"""Helper tests for process_evaluator_result task module."""
+"""Unit tests for process_evaluator_result helper utilities."""
+
+import importlib
+from types import SimpleNamespace
+from uuid import uuid4
 
-import importlib.util
-from pathlib import Path
 
-_TASK_PATH = Path(__file__).resolve().parents[2] / "app" / "workers" / "tasks" / "process_evaluator_result.py"
-_TASK_SPEC = importlib.util.spec_from_file_location("process_evaluator_result_under_test", _TASK_PATH)
-process_evaluator_result = importlib.util.module_from_spec(_TASK_SPEC)
-assert _TASK_SPEC is not None and _TASK_SPEC.loader is not None
-_TASK_SPEC.loader.exec_module(process_evaluator_result)
+def _task_module():
+    return importlib.import_module("app.workers.tasks.process_evaluator_result")
 
 
 def test_extract_audio_url_supports_smallest_recordings():
+    task_module = _task_module()
     call_data = {"recording_url": "https://audio.smallest.ai/call.wav"}
-    audio_url = process_evaluator_result._extract_audio_url(call_data, "smallest")
+    assert task_module._extract_audio_url(call_data, "smallest") == "https://audio.smallest.ai/call.wav"
 
-    assert audio_url == "https://audio.smallest.ai/call.wav"
-"""Unit tests for process_evaluator_result helper utilities."""
 
+def test_should_record_external_agent_call_usage_only_for_provider_calls():
+    task_module = _task_module()
+    agent_id = uuid4()
 
-def test_extract_audio_url_supports_smallest_recordings():
-    import importlib
+    external = SimpleNamespace(agent_id=agent_id, provider_platform="vapi")
+    internal = SimpleNamespace(agent_id=agent_id, provider_platform=None)
+    simulation = SimpleNamespace(agent_id=agent_id, provider_platform="")
+    no_agent = SimpleNamespace(agent_id=None, provider_platform="vapi")
 
-    task_module = importlib.import_module("app.workers.tasks.process_evaluator_result")
+    assert task_module._should_record_external_agent_call_usage(external) is True
+    assert task_module._should_record_external_agent_call_usage(internal) is False
+    assert task_module._should_record_external_agent_call_usage(simulation) is False
+    assert task_module._should_record_external_agent_call_usage(no_agent) is False
 
-    call_data = {"recording_url": "https://audio.smallest.ai/call.wav"}
 
-    assert task_module._extract_audio_url(call_data, "smallest") == "https://audio.smallest.ai/call.wav"
+def test_resolve_call_duration_seconds_prefers_result_field():
+    task_module = _task_module()
+    result = SimpleNamespace(duration_seconds=12.4, call_data={})
+    assert task_module._resolve_call_duration_seconds(result) == 13
+

From 09b9793483c7679b938e6603c474d79d7e556267 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 13 Aug 2026 11:49:00 +0530
Subject: [PATCH 17/55] fix: committed claims management in db

---
 .../063_usage_kind_stt_and_buffer.py          | 44 ++++++++++
 app/services/usage/llm_usage.py               | 84 +++++++++++++++++--
 ...st_call_import_diarization_and_eval_llm.py |  2 +
 .../test_usage/test_llm_usage.py              |  4 +-
 4 files changed, 124 insertions(+), 10 deletions(-)

diff --git a/app/migrations/063_usage_kind_stt_and_buffer.py b/app/migrations/063_usage_kind_stt_and_buffer.py
index 1cbb51d9..fb1d9e4c 100644
--- a/app/migrations/063_usage_kind_stt_and_buffer.py
+++ b/app/migrations/063_usage_kind_stt_and_buffer.py
@@ -146,12 +146,33 @@ def _ensure_unique_bucket_index(db: Session) -> None:
     )
 
 
+def _ensure_llm_usage_daily_base_columns(db: Session) -> None:
+    """Align pre-062 tables with the 062 schema before dedupe/index steps."""
+    if not _column_exists(db, "llm_usage_daily", "resource_id"):
+        db.execute(text("ALTER TABLE llm_usage_daily ADD COLUMN resource_id UUID"))
+    if not _column_exists(db, "llm_usage_daily", "resource_type"):
+        db.execute(
+            text("ALTER TABLE llm_usage_daily ADD COLUMN resource_type VARCHAR(64)")
+        )
+    if not _column_exists(db, "llm_usage_daily", "updated_at"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE llm_usage_daily
+                ADD COLUMN updated_at TIMESTAMPTZ NOT NULL DEFAULT now()
+                """
+            )
+        )
+
+
 def upgrade(db: Session):
     if not _table_exists(db, "llm_usage_daily"):
         print("llm_usage_daily missing; run 062 first — skipping 063")
         db.commit()
         return
 
+    _ensure_llm_usage_daily_base_columns(db)
+
     if not _column_exists(db, "llm_usage_daily", "usage_kind"):
         db.execute(
             text(
@@ -223,10 +244,33 @@ def upgrade(db: Session):
         )
         print("Created usage_pending_buffer")
 
+    if not _table_exists(db, "usage_committed_claims"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE usage_committed_claims (
+                    claim_key TEXT PRIMARY KEY,
+                    organization_id UUID NOT NULL,
+                    committed_at TIMESTAMPTZ NOT NULL DEFAULT now()
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                CREATE INDEX IF NOT EXISTS ix_usage_committed_claims_committed_at
+                ON usage_committed_claims (committed_at)
+                """
+            )
+        )
+        print("Created usage_committed_claims")
+
     db.commit()
 
 
 def downgrade(db: Session):
+    db.execute(text("DROP TABLE IF EXISTS usage_committed_claims"))
     db.execute(text("DROP TABLE IF EXISTS usage_pending_buffer"))
     db.execute(text("DROP INDEX IF EXISTS ix_llm_usage_daily_org_kind_date"))
     # Keep usage_kind/audio_seconds columns on downgrade to avoid data loss.
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index 33fda254..6b3cb65d 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -522,24 +522,88 @@ def _restore_buckets_to_pending(
 
 
 _CLAIM_COMMITTED_TTL_SECONDS = 24 * 60 * 60
+_CLAIM_COMMITTED_REDIS_RETRIES = 5
 
 
 def _claim_committed_key(claim_key: str) -> str:
     return f"usage:claim_done:{claim_key}"
 
 
-def _mark_claim_committed(claim_key: str) -> None:
+def _record_claim_committed_pg(
+    db: Session, claim_key: str, organization_id: UUID
+) -> None:
     try:
-        _client().set(_claim_committed_key(claim_key), "1", ex=_CLAIM_COMMITTED_TTL_SECONDS)
-    except redis.RedisError:
-        pass
+        db.execute(
+            text(
+                """
+                INSERT INTO usage_committed_claims (claim_key, organization_id)
+                VALUES (:claim_key, CAST(:organization_id AS uuid))
+                ON CONFLICT (claim_key) DO NOTHING
+                """
+            ),
+            {
+                "claim_key": claim_key,
+                "organization_id": str(organization_id),
+            },
+        )
+    except Exception as exc:
+        logger.debug("usage committed claim pg write skipped: {}", exc)
+
+
+def _is_claim_committed_pg(claim_key: str) -> bool:
+    try:
+        from app.database import SessionLocal
+
+        db = SessionLocal()
+        try:
+            return (
+                db.execute(
+                    text(
+                        """
+                        SELECT 1 FROM usage_committed_claims
+                        WHERE claim_key = :claim_key
+                        """
+                    ),
+                    {"claim_key": claim_key},
+                ).first()
+                is not None
+            )
+        finally:
+            db.close()
+    except Exception:
+        return False
+
+
+def _mark_claim_committed(claim_key: str) -> bool:
+    for attempt in range(_CLAIM_COMMITTED_REDIS_RETRIES):
+        try:
+            _client().set(
+                _claim_committed_key(claim_key),
+                "1",
+                ex=_CLAIM_COMMITTED_TTL_SECONDS,
+            )
+            return True
+        except redis.RedisError:
+            if attempt + 1 < _CLAIM_COMMITTED_REDIS_RETRIES:
+                time.sleep(0.05 * (attempt + 1))
+    return False
 
 
 def _is_claim_committed(claim_key: str) -> bool:
     try:
-        return bool(_client().exists(_claim_committed_key(claim_key)))
+        if _client().exists(_claim_committed_key(claim_key)):
+            return True
     except redis.RedisError:
-        return False
+        pass
+    return _is_claim_committed_pg(claim_key)
+
+
+def _finalize_committed_claim(
+    db: Session, claim_key: str, organization_id: UUID
+) -> None:
+    _record_claim_committed_pg(db, claim_key, organization_id)
+    _mark_claim_committed(claim_key)
+    _ack_claim(claim_key, organization_id)
 
 
 def _has_pending_usage(organization_id: UUID) -> bool:
@@ -897,6 +961,8 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
                                 deltas,
                             )
                             flushed += 1
+                        if claim_key:
+                            _record_claim_committed_pg(db, claim_key, organization_id)
                         db.commit()
                     except Exception as exc:
                         db.rollback()
@@ -907,8 +973,10 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
                                 exc,
                             )
                             if claim_key:
-                                _mark_claim_committed(claim_key)
-                                _ack_claim(claim_key, organization_id)
+                                _finalize_committed_claim(
+                                    db, claim_key, organization_id
+                                )
+                                db.commit()
                             claim_key = None
                             buckets = {}
                         else:
diff --git a/tests/test_api/test_call_import_diarization_and_eval_llm.py b/tests/test_api/test_call_import_diarization_and_eval_llm.py
index 936460d7..cb890fdc 100644
--- a/tests/test_api/test_call_import_diarization_and_eval_llm.py
+++ b/tests/test_api/test_call_import_diarization_and_eval_llm.py
@@ -32,6 +32,8 @@ def _make_fake_row():
     return SimpleNamespace(
         id=uuid4(),
         organization_id=uuid4(),
+        call_import_id=uuid4(),
+        workspace_id=uuid4(),
         recording_s3_key="s3://bucket/key.wav",
         transcript="production value from CSV",
         transcript_source="csv",
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index 824de3cd..d39bb501 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -460,8 +460,8 @@ def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx, monkeypatch
     assert second == 0
     assert db_a.commit.call_count == 1
     assert db_b.commit.call_count == 0
-    # INSERT once for the claimed bucket; second flusher never upserts.
-    assert db_a.execute.call_count == 2  # UPDATE miss + INSERT
+    # One bucket upsert (2 UPDATE misses + SAVEPOINT/INSERT/RELEASE) plus committed-claim row.
+    assert db_a.execute.call_count == 6
     assert db_b.execute.call_count == 0
 
 

From 5c51608ab8f3b4613b81c7834a5c155f7c8bdc8f Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 13 Aug 2026 12:01:06 +0530
Subject: [PATCH 18/55] refactor: enhanced Redis handling and database
 transaction integrity

---
 app/services/usage/llm_usage.py               | 163 +++++++++++++-----
 .../test_usage/test_llm_usage.py              |  63 +++++++
 2 files changed, 179 insertions(+), 47 deletions(-)

diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index 6b3cb65d..b992b9e5 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -522,60 +522,104 @@ def _restore_buckets_to_pending(
 
 
 _CLAIM_COMMITTED_TTL_SECONDS = 24 * 60 * 60
-_CLAIM_COMMITTED_REDIS_RETRIES = 5
+_CLAIM_COMMITTED_REDIS_RETRIES = 3
+_ORPHAN_RECOVERY_INTERVAL_SEC = 30
+_COMMITTED_CLAIMS_PRUNE_INTERVAL_SEC = 3600
+_COMMITTED_CLAIMS_RETENTION_DAYS = 2
 
 
 def _claim_committed_key(claim_key: str) -> str:
     return f"usage:claim_done:{claim_key}"
 
 
+def _redis_interval_gate(key: str, interval_sec: int) -> bool:
+    """Return True when the gated work should run (interval lock acquired)."""
+    try:
+        return bool(_client().set(key, "1", nx=True, ex=interval_sec))
+    except redis.RedisError:
+        return False
+
+
 def _record_claim_committed_pg(
     db: Session, claim_key: str, organization_id: UUID
 ) -> None:
+    db.execute(
+        text(
+            """
+            INSERT INTO usage_committed_claims (claim_key, organization_id)
+            VALUES (:claim_key, CAST(:organization_id AS uuid))
+            ON CONFLICT (claim_key) DO NOTHING
+            """
+        ),
+        {
+            "claim_key": claim_key,
+            "organization_id": str(organization_id),
+        },
+    )
+
+
+def _committed_claim_keys_in_pg(claim_keys: List[str]) -> set[str]:
+    if not claim_keys:
+        return set()
     try:
-        db.execute(
-            text(
-                """
-                INSERT INTO usage_committed_claims (claim_key, organization_id)
-                VALUES (:claim_key, CAST(:organization_id AS uuid))
-                ON CONFLICT (claim_key) DO NOTHING
-                """
-            ),
-            {
-                "claim_key": claim_key,
-                "organization_id": str(organization_id),
-            },
-        )
-    except Exception as exc:
-        logger.debug("usage committed claim pg write skipped: {}", exc)
+        from app.database import SessionLocal
 
+        db = SessionLocal()
+        try:
+            rows = db.execute(
+                text(
+                    """
+                    SELECT claim_key
+                    FROM usage_committed_claims
+                    WHERE claim_key = ANY(CAST(:claim_keys AS text[]))
+                    """
+                ),
+                {"claim_keys": claim_keys},
+            ).all()
+            return {row[0] for row in rows}
+        finally:
+            db.close()
+    except Exception:
+        return set()
 
-def _is_claim_committed_pg(claim_key: str) -> bool:
+
+def _prune_old_committed_claims() -> None:
     try:
         from app.database import SessionLocal
 
         db = SessionLocal()
         try:
-            return (
-                db.execute(
-                    text(
-                        """
-                        SELECT 1 FROM usage_committed_claims
-                        WHERE claim_key = :claim_key
-                        """
-                    ),
-                    {"claim_key": claim_key},
-                ).first()
-                is not None
+            db.execute(
+                text(
+                    f"""
+                    DELETE FROM usage_committed_claims
+                    WHERE committed_at < now() - interval '{_COMMITTED_CLAIMS_RETENTION_DAYS} days'
+                    """
+                )
             )
+            db.commit()
+        except Exception as exc:
+            db.rollback()
+            logger.debug("usage committed claim prune skipped: {}", exc)
         finally:
             db.close()
     except Exception:
-        return False
+        pass
 
 
-def _mark_claim_committed(claim_key: str) -> bool:
-    for attempt in range(_CLAIM_COMMITTED_REDIS_RETRIES):
+def _discard_committed_claim(claim_key: str, organization_id: UUID) -> None:
+    """Best-effort Redis cleanup when usage was not (or must not be) persisted."""
+    _mark_claim_committed(claim_key, fast=True)
+    _ack_claim(claim_key, organization_id)
+
+
+def _is_claim_committed_pg(claim_key: str) -> bool:
+    return claim_key in _committed_claim_keys_in_pg([claim_key])
+
+
+def _mark_claim_committed(claim_key: str, *, fast: bool = False) -> bool:
+    max_attempts = 2 if fast else _CLAIM_COMMITTED_REDIS_RETRIES
+    for attempt in range(max_attempts):
         try:
             _client().set(
                 _claim_committed_key(claim_key),
@@ -584,7 +628,7 @@ def _mark_claim_committed(claim_key: str) -> bool:
             )
             return True
         except redis.RedisError:
-            if attempt + 1 < _CLAIM_COMMITTED_REDIS_RETRIES:
+            if attempt + 1 < max_attempts and not fast:
                 time.sleep(0.05 * (attempt + 1))
     return False
 
@@ -598,14 +642,6 @@ def _is_claim_committed(claim_key: str) -> bool:
     return _is_claim_committed_pg(claim_key)
 
 
-def _finalize_committed_claim(
-    db: Session, claim_key: str, organization_id: UUID
-) -> None:
-    _record_claim_committed_pg(db, claim_key, organization_id)
-    _mark_claim_committed(claim_key)
-    _ack_claim(claim_key, organization_id)
-
-
 def _has_pending_usage(organization_id: UUID) -> bool:
     try:
         client = _client()
@@ -973,10 +1009,14 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
                                 exc,
                             )
                             if claim_key:
-                                _finalize_committed_claim(
-                                    db, claim_key, organization_id
-                                )
-                                db.commit()
+                                try:
+                                    _record_claim_committed_pg(
+                                        db, claim_key, organization_id
+                                    )
+                                    db.commit()
+                                except Exception:
+                                    db.rollback()
+                                _discard_committed_claim(claim_key, organization_id)
                             claim_key = None
                             buckets = {}
                         else:
@@ -993,11 +1033,11 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
                                 except redis.RedisError:
                                     pass
                             claim_key = None
-                            return flushed + _flush_pending_buffer(db, organization_id)
+                            return _flush_pending_buffer(db, organization_id)
                     if skipped:
                         _restore_buckets_to_pending(organization_id, skipped)
                     if claim_key:
-                        _mark_claim_committed(claim_key)
+                        _mark_claim_committed(claim_key, fast=True)
                         _ack_claim(claim_key, organization_id)
                         claim_key = None
         finally:
@@ -1009,8 +1049,13 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
 
 
 def _recover_orphaned_claims() -> None:
+    if not _redis_interval_gate(
+        "usage:orphan_recovery:due", _ORPHAN_RECOVERY_INTERVAL_SEC
+    ):
+        return
     try:
         client = _client()
+        candidates: List[Tuple[str, UUID]] = []
         for claim_key in client.scan_iter(match="usage:flushing:*", count=100):
             parts = claim_key.split(":")
             if len(parts) < 4:
@@ -1021,13 +1066,37 @@ def _recover_orphaned_claims() -> None:
                 continue
             if client.exists(_flush_lock_key(org_id)):
                 continue
-            if _is_claim_committed(claim_key):
+            candidates.append((claim_key, org_id))
+
+        if not candidates:
+            return
+
+        pipe = client.pipeline()
+        for claim_key, _org_id in candidates:
+            pipe.exists(_claim_committed_key(claim_key))
+        redis_committed_flags = pipe.execute()
+
+        needs_pg: List[Tuple[str, UUID]] = []
+        for index, (claim_key, org_id) in enumerate(candidates):
+            if redis_committed_flags[index]:
+                client.delete(claim_key)
+                continue
+            needs_pg.append((claim_key, org_id))
+
+        pg_committed = _committed_claim_keys_in_pg([key for key, _ in needs_pg])
+        for claim_key, org_id in needs_pg:
+            if claim_key in pg_committed:
                 client.delete(claim_key)
                 continue
             buckets = _read_hash_buckets(claim_key)
             if buckets:
                 _restore_buckets_to_pending(org_id, buckets)
             client.delete(claim_key)
+
+        if _redis_interval_gate(
+            "usage:committed_claims:prune", _COMMITTED_CLAIMS_PRUNE_INTERVAL_SEC
+        ):
+            _prune_old_committed_claims()
     except redis.RedisError as exc:
         logger.warning("llm usage orphan claim recovery failed: {}", exc)
 
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index d39bb501..4abdf609 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -41,6 +41,10 @@ def expire(self, key: str, ttl: int):
         self._ops.append(("expire", key, ttl))
         return self
 
+    def exists(self, key: str):
+        self._ops.append(("exists", key))
+        return self
+
     def execute(self):
         results = []
         for op in self._ops:
@@ -51,6 +55,8 @@ def execute(self):
                 results.append(self._client.sadd(op[1], *op[2]))
             elif kind == "expire":
                 results.append(self._client.expire(op[1], op[2]))
+            elif kind == "exists":
+                results.append(self._client.exists(op[1]))
         self._ops.clear()
         return results
 
@@ -129,6 +135,9 @@ def delete(self, *keys: str) -> int:
     def expire(self, key: str, ttl: int) -> bool:
         return self.exists(key) == 1
 
+    def pipeline(self):
+        return _FakePipeline(self)
+
     def set(self, key: str, value: str, nx: bool = False, ex: Optional[int] = None) -> Optional[bool]:
         if nx and key in self.kv:
             return None
@@ -407,6 +416,39 @@ def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx, monkeypatch):
     assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
 
 
+def test_flush_restores_redis_when_committed_claim_insert_fails(
+    fake_redis, org_ctx, monkeypatch
+):
+    """Committed-claim insert must share the usage transaction; failure rolls back."""
+    org_id, _workspace_id, ctx = org_ctx
+    with llm_usage_context(ctx):
+        usage_mod.record_llm_usage(
+            "gpt-test",
+            UsageSnapshot(prompt_tokens=5, completion_tokens=2),
+            usage_date=date(2026, 8, 11),
+        )
+
+    db = MagicMock()
+
+    def _execute_side_effect(statement, *_args, **_kwargs):
+        if "usage_committed_claims" in str(statement):
+            raise RuntimeError("usage_committed_claims unavailable")
+        return MagicMock(rowcount=0)
+
+    db.execute.side_effect = _execute_side_effect
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+
+    flushed = usage_mod.flush_usage_to_catalog(db, org_id)
+    assert flushed == 0
+    db.rollback.assert_called()
+    db.commit.assert_not_called()
+
+    pending = fake_redis.hgetall(usage_mod._pending_hash_key(org_id))
+    prompt = sum(int(v) for k, v in pending.items() if k.endswith("|prompt_tokens"))
+    assert prompt == 5
+    assert str(org_id) in fake_redis.smembers("usage:pending:orgs")
+
+
 def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx, monkeypatch):
     """Unknown org FK must not restore Redis (avoids infinite beat retries)."""
     from sqlalchemy.exc import IntegrityError
@@ -585,3 +627,24 @@ def test_upsert_bucket_matches_legacy_resource_context_key(monkeypatch):
     legacy_sql = str(db.execute.call_args_list[1][0][0])
     assert "context->>'resource_id'" in legacy_sql
     assert "context->>'resource_type'" in legacy_sql
+
+
+def test_orphan_recovery_runs_at_most_once_per_interval(fake_redis, monkeypatch):
+    org_id = uuid4()
+    claim_key = f"usage:flushing:{org_id}:{uuid4()}"
+    fake_redis.hashes[claim_key] = {"bucket|prompt_tokens": 3}
+
+    pg_lookups = 0
+
+    def _count_pg_lookup(keys):
+        nonlocal pg_lookups
+        pg_lookups += 1
+        return set()
+
+    monkeypatch.setattr(usage_mod, "_committed_claim_keys_in_pg", _count_pg_lookup)
+    monkeypatch.setattr(usage_mod, "_prune_old_committed_claims", lambda: None)
+
+    usage_mod._recover_orphaned_claims()
+    usage_mod._recover_orphaned_claims()
+
+    assert pg_lookups == 1

From 4dd9fdc519dd4f7485b59a3db9ec7b1306a8c4e6 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 13 Aug 2026 12:17:01 +0530
Subject: [PATCH 19/55] refactor: usage commited claims

---
 app/migrations/068_usage_committed_claims.py | 57 ++++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100644 app/migrations/068_usage_committed_claims.py

diff --git a/app/migrations/068_usage_committed_claims.py b/app/migrations/068_usage_committed_claims.py
new file mode 100644
index 00000000..25c59fb6
--- /dev/null
+++ b/app/migrations/068_usage_committed_claims.py
@@ -0,0 +1,57 @@
+"""Migration: usage_committed_claims for durable flush idempotency."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Create usage_committed_claims when missing (063 may have run before this table was added)"
+)
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def upgrade(db: Session):
+    if _table_exists(db, "usage_committed_claims"):
+        print("usage_committed_claims already exists, skipping 068")
+        db.commit()
+        return
+
+    db.execute(
+        text(
+            """
+            CREATE TABLE usage_committed_claims (
+                claim_key TEXT PRIMARY KEY,
+                organization_id UUID NOT NULL,
+                committed_at TIMESTAMPTZ NOT NULL DEFAULT now()
+            )
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX ix_usage_committed_claims_committed_at
+            ON usage_committed_claims (committed_at)
+            """
+        )
+    )
+    print("Created usage_committed_claims")
+    db.commit()
+
+
+def downgrade(db: Session):
+    db.execute(text("DROP TABLE IF EXISTS usage_committed_claims"))
+    db.commit()

From cbe1c47f8c7fca5e7063bcc529eb2276ec860b73 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 14 Aug 2026 11:24:20 +0530
Subject: [PATCH 20/55] feat: usage pricing and integrate usage context across
 various routes

---
 README.md                                     |   84 +-
 app/api/v1/api.py                             |    2 +
 app/api/v1/routes/agents.py                   |  147 +-
 app/api/v1/routes/call_import_evaluations.py  |   10 +
 app/api/v1/routes/org_usage.py                |  236 +-
 app/api/v1/routes/usage_pricing.py            |  241 ++
 app/api/v1/routes/voice_playground.py         |   32 +-
 app/cli.py                                    |  291 +-
 app/config/models.json                        | 3054 +++++++++++------
 app/migrations/069_usage_pricing.py           |  190 +
 app/migrations/070_usage_margin_multiplier.py |   45 +
 app/migrations/071_reseed_pricing_catalog.py  |   37 +
 app/migrations/072_usage_pricing_phase1.py    |  202 ++
 .../073_usage_cost_recompute_jobs.py          |   72 +
 ...pricing_rates_source_and_effective_from.py |   81 +
 app/models/database.py                        |   41 +
 app/services/ai/stt_clients/google.py         |   25 +
 app/services/call_import_user_insights.py     |   33 +-
 app/services/usage/llm_usage.py               |  170 +-
 app/services/usage/pricing.py                 |  912 +++++
 app/services/usage/pricing_cache.py           |   87 +
 app/services/usage/pricing_jobs.py            |  145 +
 app/services/usage/pricing_ops.py             |  137 +
 app/services/usage/pricing_overrides.py       |  530 +++
 app/services/usage/usage_costs.py             |   51 +
 app/workers/config.py                         |   10 +-
 app/workers/tasks/__init__.py                 |    2 +
 app/workers/tasks/recompute_usage_costs.py    |   79 +
 docker-compose.observability.yml              |   11 +
 docker-compose.yml                            |   57 +
 frontend/src/App.tsx                          |    2 +
 frontend/src/lib/api.ts                       |  169 +
 frontend/src/pages/usage/SearchableSelect.tsx |    7 +-
 frontend/src/pages/usage/Usage.tsx            |  267 +-
 .../pages/usage/UsageCostBreakdownModal.tsx   |  133 +
 frontend/src/pages/usage/UsageFiltersBar.tsx  |   27 +-
 frontend/src/pages/usage/UsagePricing.tsx     |  393 +++
 frontend/src/pages/usage/usageProductHints.ts |    3 +
 scripts/merge_pricing_into_models_json.py     |  107 +
 scripts/sync_pricing_catalog_from_litellm.py  |  428 +++
 .../test_usage/test_llm_usage.py              |   32 +
 .../test_usage/test_org_usage_costs.py        |   76 +
 .../test_services/test_usage/test_pricing.py  |  211 ++
 .../test_usage/test_pricing_cache.py          |   91 +
 .../test_usage/test_pricing_ops.py            |   10 +
 .../test_usage/test_pricing_overrides.py      |   72 +
 .../test_usage/test_usage_costs.py            |   33 +
 .../test_workers/test_usage_queue_routing.py  |   18 +
 48 files changed, 7777 insertions(+), 1316 deletions(-)
 create mode 100644 app/api/v1/routes/usage_pricing.py
 create mode 100644 app/migrations/069_usage_pricing.py
 create mode 100644 app/migrations/070_usage_margin_multiplier.py
 create mode 100644 app/migrations/071_reseed_pricing_catalog.py
 create mode 100644 app/migrations/072_usage_pricing_phase1.py
 create mode 100644 app/migrations/073_usage_cost_recompute_jobs.py
 create mode 100644 app/migrations/074_pricing_rates_source_and_effective_from.py
 create mode 100644 app/services/usage/pricing.py
 create mode 100644 app/services/usage/pricing_cache.py
 create mode 100644 app/services/usage/pricing_jobs.py
 create mode 100644 app/services/usage/pricing_ops.py
 create mode 100644 app/services/usage/pricing_overrides.py
 create mode 100644 app/services/usage/usage_costs.py
 create mode 100644 app/workers/tasks/recompute_usage_costs.py
 create mode 100644 frontend/src/pages/usage/UsageCostBreakdownModal.tsx
 create mode 100644 frontend/src/pages/usage/UsagePricing.tsx
 create mode 100644 scripts/merge_pricing_into_models_json.py
 create mode 100644 scripts/sync_pricing_catalog_from_litellm.py
 create mode 100644 tests/test_services/test_usage/test_org_usage_costs.py
 create mode 100644 tests/test_services/test_usage/test_pricing.py
 create mode 100644 tests/test_services/test_usage/test_pricing_cache.py
 create mode 100644 tests/test_services/test_usage/test_pricing_ops.py
 create mode 100644 tests/test_services/test_usage/test_pricing_overrides.py
 create mode 100644 tests/test_services/test_usage/test_usage_costs.py
 create mode 100644 tests/test_workers/test_usage_queue_routing.py

diff --git a/README.md b/README.md
index cc48559e..009ca348 100644
--- a/README.md
+++ b/README.md
@@ -54,8 +54,21 @@ There are two ways to run the application:
    
    This will automatically:
    - Pull pre-built images from GitHub Container Registry (no build required!)
-   - Start all services (database, Redis, API, worker)
+   - Start all services: `db`, `redis`, `api`, `media`, `worker`, `worker-imports`, `worker-usage`, `beat`
    - Run database migrations automatically on startup
+
+   | Service | Purpose |
+   |---------|---------|
+   | `db` | PostgreSQL |
+   | `redis` | Redis (Celery broker + usage counters) |
+   | `api` | HTTP API + frontend |
+   | `media` | Live voice WebSocket media server |
+   | `worker` | Celery: `celery`, `audio-metrics` queues |
+   | `worker-imports` | Celery: `imports`, `diarization`, `eval-control`, `evaluations` |
+   | `worker-usage` | Celery: `usage` queue (flush Redis counters + cost recompute) |
+   | `beat` | Celery Beat scheduler (periodic `flush_usage_counters`; requires `worker-usage`) |
+
+   **Usage costs:** token/cost rollups stay stale without `worker-usage` and `beat` (or the equivalent processes from `eai start-all`).
    
    **Using a specific version:**
    ```bash
@@ -137,20 +150,29 @@ docker compose up -d
      url: "redis://localhost:6379/0"
    ```
 
-4. **Start the application and worker**
+4. **Start the application and workers**
+
+   **Infra only (optional):** if Postgres/Redis run in Docker but the app runs locally:
+   ```bash
+   docker compose up -d db redis
+   ```
 
-   **Option A: Start both together (Recommended)**
+   **Option A: Start everything together (Recommended)**
    ```bash
    eai start-all --config config.yml
    ```
    
-   This single command will:
-   - Start the API server
-   - Start the Celery worker (for background task processing)
-   - Run database migrations automatically
-   - Build the frontend (if needed)
+   This single command spawns:
+   - API server (uvicorn)
+   - Telephony media server (`media` port, default 8001)
+   - Celery worker (`celery`, `audio-metrics`)
+   - Celery worker (`imports`, `diarization`, `eval-control`, `evaluations`)
+   - Celery worker (`usage` — flush + cost recompute)
+   - Celery Beat (periodic usage flush)
+   
+   It also runs database migrations and builds the frontend when needed.
    
-   Press `Ctrl+C` to stop both services together.
+   Press `Ctrl+C` to stop all processes.
    
    **Option B: Start separately (for advanced use)**
    
@@ -247,7 +269,7 @@ make test-docker-db TEST_DB_HOST=localhost TEST_DB_PORT=5432 TEST_DB_NAME=effici
 
 ### Start Application and Worker Together (Recommended)
 ```bash
-# Start both app and worker with default config.yml
+# Start API + all workers + beat with default config.yml
 eai start-all
 
 # Start with custom config
@@ -264,9 +286,17 @@ eai start-all --no-reload --no-build-frontend
 
 # Customize worker log level
 eai start-all --worker-loglevel debug
+
+# Skip dedicated workers (not recommended for production)
+eai start-all --no-imports-worker
+eai start-all --no-usage-worker
+eai start-all --no-telephony-worker
+
+# Tune usage worker concurrency (default: 4, thread pool)
+eai start-all --usage-worker-concurrency 8
 ```
 
-**Note:** This is the recommended way to run EfficientAI. It starts both the API server and Celery worker in a single command. Press `Ctrl+C` to stop both services.
+**Note:** This is the recommended local-dev workflow. One command spawns the API, telephony media server, three Celery workers (`celery,audio-metrics` · `imports,…` · `usage`), and Celery Beat. Press `Ctrl+C` to stop all processes. For Docker deployments, use `docker compose up -d` instead (separate containers per role; see Quick Start).
 
 ### Start Application Only
 ```bash
@@ -340,7 +370,37 @@ eai worker --loglevel debug
 celery -A app.workers.celery_app worker --loglevel=info
 ```
 
-**Note:** The worker is required for processing background tasks (transcription, evaluation, etc.). If you use `eai start-all`, the worker starts automatically. Only use this command if you need to run the worker separately.
+**Note:** Workers are required for background tasks (transcription, evaluation, usage cost flush, etc.). If you use `eai start-all`, they start automatically. Only use `eai worker` if you need to run a worker separately (e.g. `eai worker --queues usage` for the usage queue only).
+
+### Usage Pricing Ops
+Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups.
+
+```bash
+# Upsert model_pricing_rates from app/config/models.json
+eai usage seed-rates --config config.yml
+
+# Compare models.json pricing vs Postgres
+eai usage diff-rates --config config.yml
+
+# Backfill / recompute costs (sync, runs in this process)
+eai usage recompute --config config.yml --sync
+
+# Enqueue Celery recompute on the usage queue (requires --organization-id)
+eai usage recompute --config config.yml --organization-id <org-uuid>
+
+# Optional: fetch LiteLLM prices into pricing_catalog.json
+eai usage sync-litellm --local
+eai usage sync-litellm --local --write-models
+```
+
+**After migrations or catalog changes:**
+```bash
+eai migrate
+eai usage seed-rates --config config.yml
+eai usage recompute --config config.yml --sync
+```
+
+Requires `worker-usage` (or `eai start-all`) for async recompute; Beat + `worker-usage` keep Redis usage counters flushed into Postgres on a schedule (`USAGE_FLUSH_BEAT_SECONDS`, default 120).
 
 ### Generate Config File
 ```bash
diff --git a/app/api/v1/api.py b/app/api/v1/api.py
index 51d81945..c1802238 100644
--- a/app/api/v1/api.py
+++ b/app/api/v1/api.py
@@ -46,6 +46,7 @@
     dashboard,
     llm_gateway,
     org_usage,
+    usage_pricing,
 )
 
 api_router = APIRouter()
@@ -95,3 +96,4 @@
 api_router.include_router(dashboard.router)
 api_router.include_router(llm_gateway.router)
 api_router.include_router(org_usage.router)
+api_router.include_router(usage_pricing.router)
diff --git a/app/api/v1/routes/agents.py b/app/api/v1/routes/agents.py
index 4c4b295e..17094877 100644
--- a/app/api/v1/routes/agents.py
+++ b/app/api/v1/routes/agents.py
@@ -255,6 +255,7 @@ def _scenario_draft_responses(scenarios) -> list[GeneratedScenarioDraftResponse]
 async def generate_test_prompt(
     data: GenerateTestPromptRequest,
     organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
     db: Session = Depends(get_db),
 ):
@@ -262,6 +263,11 @@ async def generate_test_prompt(
     from app.services.testing.agent_test_setup_generation import (
         generate_test_prompt_from_production,
     )
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
+    )
 
     if not data.production_prompt.strip():
         raise HTTPException(400, "Production prompt is required")
@@ -271,19 +277,26 @@ async def generate_test_prompt(
     )
 
     try:
-        result = generate_test_prompt_from_production(
-            data.production_prompt,
-            agent_name=data.agent_name,
-            language=data.language,
-            call_type=data.call_type,
-            additional_context=data.additional_context,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            llm_config=data.llm_config,
-            credential_id=data.credential_id,
-        )
+        with llm_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section=LLMUsageProductSection.AGENTS,
+            )
+        ):
+            result = generate_test_prompt_from_production(
+                data.production_prompt,
+                agent_name=data.agent_name,
+                language=data.language,
+                call_type=data.call_type,
+                additional_context=data.additional_context,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                llm_config=data.llm_config,
+                credential_id=data.credential_id,
+            )
         return GenerateTestPromptResponse(
             sections=_test_prompt_section_responses(result.sections),
             test_agent_prompt=result.test_agent_prompt,
@@ -301,6 +314,7 @@ async def generate_test_prompt(
 async def generate_scenarios_from_prompt(
     data: GenerateScenariosFromPromptRequest,
     organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
     db: Session = Depends(get_db),
 ):
@@ -308,6 +322,11 @@ async def generate_scenarios_from_prompt(
     from app.services.testing.agent_test_setup_generation import (
         generate_scenarios_from_test_prompt,
     )
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
+    )
 
     if not data.test_agent_prompt.strip():
         raise HTTPException(400, "Test agent prompt is required")
@@ -317,20 +336,27 @@ async def generate_scenarios_from_prompt(
     )
 
     try:
-        result = generate_scenarios_from_test_prompt(
-            data.test_agent_prompt,
-            agent_name=data.agent_name,
-            scenario_count=data.scenario_count,
-            language=data.language,
-            call_type=data.call_type,
-            additional_context=data.additional_context,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            llm_config=data.llm_config,
-            credential_id=data.credential_id,
-        )
+        with llm_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section=LLMUsageProductSection.AGENTS,
+            )
+        ):
+            result = generate_scenarios_from_test_prompt(
+                data.test_agent_prompt,
+                agent_name=data.agent_name,
+                scenario_count=data.scenario_count,
+                language=data.language,
+                call_type=data.call_type,
+                additional_context=data.additional_context,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                llm_config=data.llm_config,
+                credential_id=data.credential_id,
+            )
         return GenerateScenariosFromPromptResponse(
             scenarios=_scenario_draft_responses(result.scenarios),
             provider=result.provider,
@@ -347,6 +373,7 @@ async def generate_scenarios_from_prompt(
 async def generate_test_setup(
     data: GenerateTestSetupRequest,
     organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
     db: Session = Depends(get_db),
 ):
@@ -355,6 +382,11 @@ async def generate_test_setup(
         generate_scenarios_from_test_prompt,
         generate_test_prompt_from_production,
     )
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
+    )
 
     if not data.production_prompt.strip():
         raise HTTPException(400, "Production prompt is required")
@@ -364,33 +396,40 @@ async def generate_test_setup(
     )
 
     try:
-        prompt_result = generate_test_prompt_from_production(
-            data.production_prompt,
-            agent_name=data.agent_name,
-            language=data.language,
-            call_type=data.call_type,
-            additional_context=data.additional_context,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            llm_config=data.llm_config,
-            credential_id=data.credential_id,
-        )
-        scenario_result = generate_scenarios_from_test_prompt(
-            prompt_result.test_agent_prompt,
-            agent_name=data.agent_name,
-            scenario_count=data.scenario_count,
-            language=data.language,
-            call_type=data.call_type,
-            additional_context=data.additional_context,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            llm_config=data.llm_config,
-            credential_id=data.credential_id,
-        )
+        with llm_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section=LLMUsageProductSection.AGENTS,
+            )
+        ):
+            prompt_result = generate_test_prompt_from_production(
+                data.production_prompt,
+                agent_name=data.agent_name,
+                language=data.language,
+                call_type=data.call_type,
+                additional_context=data.additional_context,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                llm_config=data.llm_config,
+                credential_id=data.credential_id,
+            )
+            scenario_result = generate_scenarios_from_test_prompt(
+                prompt_result.test_agent_prompt,
+                agent_name=data.agent_name,
+                scenario_count=data.scenario_count,
+                language=data.language,
+                call_type=data.call_type,
+                additional_context=data.additional_context,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                llm_config=data.llm_config,
+                credential_id=data.credential_id,
+            )
         return GenerateTestSetupResponse(
             sections=_test_prompt_section_responses(prompt_result.sections),
             test_agent_prompt=prompt_result.test_agent_prompt,
diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 880393c7..528f60f3 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -3028,10 +3028,19 @@ def _explain_period_deltas(
 
     from app.services.ai.llm_resolver import get_llm_provider_and_model
     from app.services.call_import_user_insights import _call_llm, _parse_json_object
+    from app.services.usage.call_import_context import (
+        call_import_evaluation_usage_context,
+    )
 
     provider_enum, model_str = get_llm_provider_and_model(
         organization_id, db, provider_hint, model_hint
     )
+    usage_ctx = call_import_evaluation_usage_context(
+        organization_id=organization_id,
+        workspace_id=evaluation.workspace_id,
+        evaluation_id=evaluation.id,
+        call_import_id=evaluation.call_import_id,
+    )
     try:
         text = _call_llm(
             db,
@@ -3051,6 +3060,7 @@ def _explain_period_deltas(
             ],
             temperature=0.3,
             max_tokens=900,
+            usage_ctx=usage_ctx,
         )
     except Exception as exc:
         logger.warning("[PeriodDeltaExplain] LLM call failed: {}", exc)
diff --git a/app/api/v1/routes/org_usage.py b/app/api/v1/routes/org_usage.py
index 4c702138..2d6854f6 100644
--- a/app/api/v1/routes/org_usage.py
+++ b/app/api/v1/routes/org_usage.py
@@ -27,6 +27,7 @@
     Workspace,
 )
 from app.services.usage.llm_usage import flush_usage_to_catalog
+from app.services.usage.usage_costs import costs_from_micro
 from app.services.usage.usage_labels import (
     labels_for_call_import_ids,
     labels_for_resource_buckets,
@@ -87,6 +88,19 @@ def _label_row_order():
     )
 
 
+class UsageCosts(BaseModel):
+    input_cost_usd: float = 0
+    output_cost_usd: float = 0
+    cache_read_cost_usd: float = 0
+    cache_write_cost_usd: float = 0
+    reasoning_cost_usd: float = 0
+    audio_cost_usd: float = 0
+    tts_cost_usd: float = 0
+    total_cost_usd: float = 0
+    currency: str = "USD"
+    has_unpriced_usage: bool = False
+
+
 class UsageTotals(BaseModel):
     prompt_tokens: int = 0
     completion_tokens: int = 0
@@ -97,6 +111,15 @@ class UsageTotals(BaseModel):
     audio_seconds: int = 0
     tts_characters: int = 0
     call_count: int = 0
+    input_cost_micro_usd: int = 0
+    output_cost_micro_usd: int = 0
+    cache_read_cost_micro_usd: int = 0
+    cache_creation_cost_micro_usd: int = 0
+    reasoning_cost_micro_usd: int = 0
+    audio_cost_micro_usd: int = 0
+    tts_cost_micro_usd: int = 0
+    total_cost_micro_usd: int = 0
+    costs: UsageCosts = Field(default_factory=UsageCosts)
 
 
 class UsageSummaryResponse(BaseModel):
@@ -127,6 +150,15 @@ class UsageBreakdownRow(BaseModel):
     audio_seconds: int = 0
     tts_characters: int = 0
     call_count: int = 0
+    input_cost_micro_usd: int = 0
+    output_cost_micro_usd: int = 0
+    cache_read_cost_micro_usd: int = 0
+    cache_creation_cost_micro_usd: int = 0
+    reasoning_cost_micro_usd: int = 0
+    audio_cost_micro_usd: int = 0
+    tts_cost_micro_usd: int = 0
+    total_cost_micro_usd: int = 0
+    costs: UsageCosts = Field(default_factory=UsageCosts)
 
 
 class UsageBreakdownResponse(BaseModel):
@@ -438,7 +470,7 @@ def _apply_filters(
             query = query.filter(
                 LLMUsageDaily.context["call_import_id"].astext == str(call_import_id)
             )
-    if evaluation_id is not None:
+    if evaluation_id is not None and evaluation_id != resource_id:
         if db is not None:
             query = query.filter(
                 _evaluation_scope_filter(evaluation_id, organization_id, db)
@@ -847,6 +879,123 @@ def _evaluation_label_map(
     return labels
 
 
+def _unpriced_usage_condition():
+    return and_(
+        LLMUsageDaily.pricing_rate_id.is_(None),
+        _usage_row_weight() > 0,
+    )
+
+
+def _has_unpriced_usage_column():
+    return func.coalesce(func.bool_or(_unpriced_usage_condition()), False).label(
+        "has_unpriced_usage"
+    )
+
+
+def _usage_cost_sum_columns():
+    return (
+        func.coalesce(func.sum(LLMUsageDaily.input_cost_micro_usd), 0).label(
+            "input_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.output_cost_micro_usd), 0).label(
+            "output_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_read_cost_micro_usd), 0).label(
+            "cache_read_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_creation_cost_micro_usd), 0).label(
+            "cache_creation_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.reasoning_cost_micro_usd), 0).label(
+            "reasoning_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.audio_cost_micro_usd), 0).label(
+            "audio_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.tts_cost_micro_usd), 0).label(
+            "tts_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.total_cost_micro_usd), 0).label(
+            "total_cost_micro_usd"
+        ),
+    )
+
+
+def _attach_costs(metrics: dict, *, has_unpriced_usage: bool = False) -> dict:
+    costs = costs_from_micro(
+        input_cost_micro_usd=metrics.get("input_cost_micro_usd", 0),
+        output_cost_micro_usd=metrics.get("output_cost_micro_usd", 0),
+        cache_read_cost_micro_usd=metrics.get("cache_read_cost_micro_usd", 0),
+        cache_creation_cost_micro_usd=metrics.get("cache_creation_cost_micro_usd", 0),
+        reasoning_cost_micro_usd=metrics.get("reasoning_cost_micro_usd", 0),
+        audio_cost_micro_usd=metrics.get("audio_cost_micro_usd", 0),
+        tts_cost_micro_usd=metrics.get("tts_cost_micro_usd", 0),
+        total_cost_micro_usd=metrics.get("total_cost_micro_usd", 0),
+        has_unpriced_usage=has_unpriced_usage,
+    )
+    return {**metrics, "costs": costs}
+
+
+def _usage_totals_from_row(row) -> dict:
+    prompt = int(row.prompt_tokens)
+    completion = int(row.completion_tokens)
+    metrics = {
+        "prompt_tokens": prompt,
+        "completion_tokens": completion,
+        "total_tokens": prompt + completion,
+        "cache_read_tokens": int(row.cache_read_tokens),
+        "cache_creation_tokens": int(row.cache_creation_tokens),
+        "reasoning_tokens": int(row.reasoning_tokens),
+        "audio_seconds": int(row.audio_seconds),
+        "tts_characters": int(row.tts_characters),
+        "call_count": int(row.call_count),
+        "input_cost_micro_usd": int(getattr(row, "input_cost_micro_usd", 0) or 0),
+        "output_cost_micro_usd": int(getattr(row, "output_cost_micro_usd", 0) or 0),
+        "cache_read_cost_micro_usd": int(
+            getattr(row, "cache_read_cost_micro_usd", 0) or 0
+        ),
+        "cache_creation_cost_micro_usd": int(
+            getattr(row, "cache_creation_cost_micro_usd", 0) or 0
+        ),
+        "reasoning_cost_micro_usd": int(
+            getattr(row, "reasoning_cost_micro_usd", 0) or 0
+        ),
+        "audio_cost_micro_usd": int(getattr(row, "audio_cost_micro_usd", 0) or 0),
+        "tts_cost_micro_usd": int(getattr(row, "tts_cost_micro_usd", 0) or 0),
+        "total_cost_micro_usd": int(getattr(row, "total_cost_micro_usd", 0) or 0),
+    }
+    return _attach_costs(
+        metrics,
+        has_unpriced_usage=bool(getattr(row, "has_unpriced_usage", False)),
+    )
+
+
+def _breakdown_metrics_from_tuple(metrics: tuple) -> dict:
+    prompt = int(metrics[0])
+    completion = int(metrics[1])
+    base = {
+        "prompt_tokens": prompt,
+        "completion_tokens": completion,
+        "total_tokens": prompt + completion,
+        "cache_read_tokens": int(metrics[2]),
+        "cache_creation_tokens": int(metrics[3]),
+        "reasoning_tokens": int(metrics[4]),
+        "audio_seconds": int(metrics[5]),
+        "tts_characters": int(metrics[6]),
+        "call_count": int(metrics[7]),
+        "input_cost_micro_usd": int(metrics[8]),
+        "output_cost_micro_usd": int(metrics[9]),
+        "cache_read_cost_micro_usd": int(metrics[10]),
+        "cache_creation_cost_micro_usd": int(metrics[11]),
+        "reasoning_cost_micro_usd": int(metrics[12]),
+        "audio_cost_micro_usd": int(metrics[13]),
+        "tts_cost_micro_usd": int(metrics[14]),
+        "total_cost_micro_usd": int(metrics[15]),
+    }
+    has_unpriced = bool(metrics[16]) if len(metrics) > 16 else False
+    return _attach_costs(base, has_unpriced_usage=has_unpriced)
+
+
 def _last_updated(db: Session, organization_id: UUID) -> Optional[datetime]:
     return (
         db.query(func.max(LLMUsageDaily.updated_at))
@@ -890,6 +1039,8 @@ def _summary_aggregate_query(
             func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
             func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
             func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
+            *_usage_cost_sum_columns(),
+            _has_unpriced_usage_column(),
         ),
         organization_id=organization_id,
         start=start,
@@ -953,19 +1104,7 @@ def get_usage_summary(
         dataset=dataset,
         tag_id=tag_id,
     ).one()
-    prompt = int(row.prompt_tokens)
-    completion = int(row.completion_tokens)
-    totals = {
-        "prompt_tokens": prompt,
-        "completion_tokens": completion,
-        "total_tokens": prompt + completion,
-        "cache_read_tokens": int(row.cache_read_tokens),
-        "cache_creation_tokens": int(row.cache_creation_tokens),
-        "reasoning_tokens": int(row.reasoning_tokens),
-        "audio_seconds": int(row.audio_seconds),
-        "tts_characters": int(row.tts_characters),
-        "call_count": int(row.call_count),
-    }
+    totals = _usage_totals_from_row(row)
     return UsageSummaryResponse(
         start=display_start,
         end=display_end,
@@ -1032,6 +1171,8 @@ def get_usage_breakdown(
         func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
         func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
         func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
+        *_usage_cost_sum_columns(),
+        _has_unpriced_usage_column(),
     ]
 
     select_cols = [dim]
@@ -1124,15 +1265,7 @@ def get_usage_breakdown(
                 UsageBreakdownRow(
                     workspace_id=ws_id,
                     workspace_name=workspace_names.get(ws_id) if ws_id else "Unknown",
-                    prompt_tokens=int(metrics[0]),
-                    completion_tokens=int(metrics[1]),
-                    total_tokens=int(metrics[0]) + int(metrics[1]),
-                    cache_read_tokens=int(metrics[2]),
-                    cache_creation_tokens=int(metrics[3]),
-                    reasoning_tokens=int(metrics[4]),
-                    audio_seconds=int(metrics[5]),
-                    tts_characters=int(metrics[6]),
-                    call_count=int(metrics[7]),
+                    **_breakdown_metrics_from_tuple(metrics),
                 )
             )
         elif group_by == "product_section":
@@ -1142,15 +1275,7 @@ def get_usage_breakdown(
                 UsageBreakdownRow(
                     product_section=section,
                     product_section_label=SECTION_LABELS.get(section or "", section),
-                    prompt_tokens=int(metrics[0]),
-                    completion_tokens=int(metrics[1]),
-                    total_tokens=int(metrics[0]) + int(metrics[1]),
-                    cache_read_tokens=int(metrics[2]),
-                    cache_creation_tokens=int(metrics[3]),
-                    reasoning_tokens=int(metrics[4]),
-                    audio_seconds=int(metrics[5]),
-                    tts_characters=int(metrics[6]),
-                    call_count=int(metrics[7]),
+                    **_breakdown_metrics_from_tuple(metrics),
                 )
             )
         elif group_by == "model":
@@ -1159,15 +1284,7 @@ def get_usage_breakdown(
             rows.append(
                 UsageBreakdownRow(
                     model=model_name,
-                    prompt_tokens=int(metrics[0]),
-                    completion_tokens=int(metrics[1]),
-                    total_tokens=int(metrics[0]) + int(metrics[1]),
-                    cache_read_tokens=int(metrics[2]),
-                    cache_creation_tokens=int(metrics[3]),
-                    reasoning_tokens=int(metrics[4]),
-                    audio_seconds=int(metrics[5]),
-                    tts_characters=int(metrics[6]),
-                    call_count=int(metrics[7]),
+                    **_breakdown_metrics_from_tuple(metrics),
                 )
             )
         elif group_by == "usage_kind":
@@ -1176,15 +1293,7 @@ def get_usage_breakdown(
             rows.append(
                 UsageBreakdownRow(
                     usage_kind=kind,
-                    prompt_tokens=int(metrics[0]),
-                    completion_tokens=int(metrics[1]),
-                    total_tokens=int(metrics[0]) + int(metrics[1]),
-                    cache_read_tokens=int(metrics[2]),
-                    cache_creation_tokens=int(metrics[3]),
-                    reasoning_tokens=int(metrics[4]),
-                    audio_seconds=int(metrics[5]),
-                    tts_characters=int(metrics[6]),
-                    call_count=int(metrics[7]),
+                    **_breakdown_metrics_from_tuple(metrics),
                 )
             )
         elif group_by == "call_import":
@@ -1205,15 +1314,7 @@ def get_usage_breakdown(
                 UsageBreakdownRow(
                     call_import_id=cid,
                     call_import_label=label,
-                    prompt_tokens=int(metrics[0]),
-                    completion_tokens=int(metrics[1]),
-                    total_tokens=int(metrics[0]) + int(metrics[1]),
-                    cache_read_tokens=int(metrics[2]),
-                    cache_creation_tokens=int(metrics[3]),
-                    reasoning_tokens=int(metrics[4]),
-                    audio_seconds=int(metrics[5]),
-                    tts_characters=int(metrics[6]),
-                    call_count=int(metrics[7]),
+                    **_breakdown_metrics_from_tuple(metrics),
                 )
             )
         else:
@@ -1241,15 +1342,7 @@ def get_usage_breakdown(
                     ),
                     product_section=section,
                     product_section_label=SECTION_LABELS.get(section or "", section),
-                    prompt_tokens=int(metrics[0]),
-                    completion_tokens=int(metrics[1]),
-                    total_tokens=int(metrics[0]) + int(metrics[1]),
-                    cache_read_tokens=int(metrics[2]),
-                    cache_creation_tokens=int(metrics[3]),
-                    reasoning_tokens=int(metrics[4]),
-                    audio_seconds=int(metrics[5]),
-                    tts_characters=int(metrics[6]),
-                    call_count=int(metrics[7]),
+                    **_breakdown_metrics_from_tuple(metrics),
                 )
             )
 
@@ -1278,6 +1371,7 @@ def get_usage_filters(
     resource_id: Optional[UUID] = Query(None),
     usage_kind: Optional[str] = Query(None),
     call_import_id: Optional[UUID] = Query(None),
+    evaluation_id: Optional[UUID] = Query(None),
     dataset: Optional[str] = Query(None),
     tag_id: Optional[UUID] = Query(None),
     q: Optional[str] = Query(None, description="Optional resource label search"),
@@ -1288,6 +1382,8 @@ def get_usage_filters(
 
     flush_usage_to_catalog(db, organization_id)
 
+    scoped_resource_id = resource_id or evaluation_id
+
     workspace_base = _filtered_query(
         db,
         organization_id=organization_id,
@@ -1312,7 +1408,9 @@ def get_usage_filters(
         end=filter_end,
         workspace_id=workspace_id,
         product_section=product_section,
+        resource_id=scoped_resource_id,
         call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
         dataset=dataset,
         tag_id=tag_id,
     )
@@ -1323,8 +1421,10 @@ def get_usage_filters(
         end=filter_end,
         workspace_id=workspace_id,
         product_section=product_section,
+        resource_id=scoped_resource_id,
         usage_kind=usage_kind,
         call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
         dataset=dataset,
         tag_id=tag_id,
     )
diff --git a/app/api/v1/routes/usage_pricing.py b/app/api/v1/routes/usage_pricing.py
new file mode 100644
index 00000000..0840a29a
--- /dev/null
+++ b/app/api/v1/routes/usage_pricing.py
@@ -0,0 +1,241 @@
+"""Org-scoped usage pricing overrides and recompute API."""
+
+from __future__ import annotations
+
+from datetime import date, datetime
+from typing import List, Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query, status
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from app.core.auth.rbac import require_admin
+from app.database import get_db
+from app.dependencies import get_organization_id
+from app.services.usage.pricing_jobs import (
+    create_recompute_job,
+    enqueue_recompute_job,
+    get_recompute_job,
+    job_to_dict,
+)
+from app.services.usage.pricing_overrides import (
+    delete_override,
+    get_effective_rate,
+    list_effective_pricing,
+    list_overrides,
+    upsert_override,
+)
+
+router = APIRouter(
+    prefix="/organizations/usage/pricing",
+    tags=["Usage"],
+    dependencies=[Depends(require_admin)],
+)
+
+
+class PricingRatesUsd(BaseModel):
+    input_per_1m: Optional[float] = None
+    output_per_1m: Optional[float] = None
+    cache_read_per_1m: Optional[float] = None
+    cache_write_per_1m: Optional[float] = None
+    reasoning_per_1m: Optional[float] = None
+    audio_per_minute: Optional[float] = None
+    tts_per_1m_characters: Optional[float] = None
+
+
+class PricingOverrideResponse(BaseModel):
+    id: str
+    organization_id: str
+    model: str
+    usage_kind: str
+    effective_from: date
+    effective_to: Optional[date] = None
+    rates: PricingRatesUsd
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+    recompute_enqueued: Optional[bool] = None
+    recompute_job_id: Optional[str] = None
+
+
+class PricingOverrideUpsertRequest(BaseModel):
+    usage_kind: str = "llm"
+    effective_from: date
+    effective_to: Optional[date] = None
+    rates: PricingRatesUsd
+    recompute: bool = True
+
+
+class EffectivePricingResponse(BaseModel):
+    model: str
+    usage_kind: str
+    as_of: date
+    catalog_rates: Optional[PricingRatesUsd] = None
+    catalog_rate_id: Optional[str] = None
+    override: Optional[PricingOverrideResponse] = None
+    effective_rates: Optional[PricingRatesUsd] = None
+    effective_source: Optional[str] = None
+    effective_rate_id: Optional[str] = None
+    has_override: bool = False
+
+
+class PricingOverrideDeleteResponse(BaseModel):
+    deleted: bool
+    model: str
+    usage_kind: str
+    recompute_enqueued: bool = False
+    recompute_job_id: Optional[str] = None
+
+
+class UsageRecomputeRequest(BaseModel):
+    start_date: Optional[date] = None
+    end_date: Optional[date] = None
+    model: Optional[str] = None
+    usage_kind: Optional[str] = None
+
+
+class UsageRecomputeJobResponse(BaseModel):
+    id: UUID
+    organization_id: UUID
+    status: str
+    model: Optional[str] = None
+    usage_kind: Optional[str] = None
+    start_date: Optional[date] = None
+    end_date: Optional[date] = None
+    updated_rows: int = 0
+    error_message: Optional[str] = None
+    celery_task_id: Optional[str] = None
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
+
+
+@router.get("", response_model=List[EffectivePricingResponse])
+def list_effective_usage_pricing(
+    usage_kind: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    as_of: Optional[date] = Query(None),
+    limit: int = Query(200, ge=1, le=1000),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    day = as_of or date.today()
+    rows = list_effective_pricing(
+        db,
+        organization_id=organization_id,
+        usage_kind=usage_kind,
+        model=model,
+        as_of=day,
+        limit=limit,
+    )
+    return rows
+
+
+@router.get("/overrides", response_model=List[PricingOverrideResponse])
+def list_usage_pricing_overrides(
+    model: Optional[str] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    return list_overrides(
+        db,
+        organization_id=organization_id,
+        model=model,
+        usage_kind=usage_kind,
+    )
+
+
+@router.get("/overrides/{model}", response_model=EffectivePricingResponse)
+def get_usage_pricing_override_effective(
+    model: str,
+    usage_kind: str = Query("llm"),
+    as_of: Optional[date] = Query(None),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    return get_effective_rate(
+        db,
+        organization_id=organization_id,
+        model=model,
+        usage_kind=usage_kind,
+        as_of=as_of or date.today(),
+    )
+
+
+@router.put("/overrides/{model}", response_model=PricingOverrideResponse)
+def upsert_usage_pricing_override(
+    model: str,
+    body: PricingOverrideUpsertRequest,
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    return upsert_override(
+        db,
+        organization_id=organization_id,
+        model=model,
+        usage_kind=body.usage_kind,
+        effective_from=body.effective_from,
+        effective_to=body.effective_to,
+        rates=body.rates.model_dump(exclude_unset=True),
+        recompute=body.recompute,
+    )
+
+
+@router.delete("/overrides/{model}", response_model=PricingOverrideDeleteResponse)
+def delete_usage_pricing_override(
+    model: str,
+    usage_kind: str = Query("llm"),
+    effective_from: Optional[date] = Query(None),
+    recompute: bool = Query(True),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    return delete_override(
+        db,
+        organization_id=organization_id,
+        model=model,
+        usage_kind=usage_kind,
+        effective_from=effective_from,
+        recompute=recompute,
+    )
+
+
+@router.post(
+    "/recompute",
+    response_model=UsageRecomputeJobResponse,
+    status_code=status.HTTP_202_ACCEPTED,
+)
+def trigger_usage_cost_recompute(
+    body: UsageRecomputeRequest,
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    if (
+        body.start_date is not None
+        and body.end_date is not None
+        and body.end_date < body.start_date
+    ):
+        raise HTTPException(status_code=400, detail="end_date must be >= start_date")
+
+    job = create_recompute_job(
+        db,
+        organization_id=organization_id,
+        model=body.model,
+        usage_kind=body.usage_kind,
+        start_date=body.start_date,
+        end_date=body.end_date,
+    )
+    enqueue_recompute_job(db, job)
+    db.refresh(job)
+    return UsageRecomputeJobResponse(**job_to_dict(job))
+
+
+@router.get("/recompute/{job_id}", response_model=UsageRecomputeJobResponse)
+def get_usage_cost_recompute_job(
+    job_id: UUID,
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    job = get_recompute_job(db, organization_id=organization_id, job_id=job_id)
+    return UsageRecomputeJobResponse(**job_to_dict(job))
diff --git a/app/api/v1/routes/voice_playground.py b/app/api/v1/routes/voice_playground.py
index 8cc4519d..40477c25 100644
--- a/app/api/v1/routes/voice_playground.py
+++ b/app/api/v1/routes/voice_playground.py
@@ -398,6 +398,7 @@ def _serialize_custom_voice(voice: CustomTTSVoice) -> Dict[str, Any]:
 async def generate_sample_texts(
     data: GenerateSamplesRequest,
     organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
     db: Session = Depends(get_db),
 ):
@@ -460,16 +461,29 @@ async def generate_sample_texts(
         {"role": "user", "content": user_prompt},
     ]
 
+    from app.services.usage.context import (
+        LLMUsageContext,
+        LLMUsageProductSection,
+        llm_usage_context,
+    )
+
     try:
-        result = llm_service.generate_response(
-            messages=messages,
-            llm_provider=provider_enum,
-            llm_model=llm_model_str,
-            organization_id=organization_id,
-            db=db,
-            llm_config=request_llm_config,
-            task_defaults={"temperature": 0.8, "max_tokens": max_tokens},
-        )
+        with llm_usage_context(
+            LLMUsageContext(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section=LLMUsageProductSection.VOICE_PLAYGROUND,
+            )
+        ):
+            result = llm_service.generate_response(
+                messages=messages,
+                llm_provider=provider_enum,
+                llm_model=llm_model_str,
+                organization_id=organization_id,
+                db=db,
+                llm_config=request_llm_config,
+                task_defaults={"temperature": 0.8, "max_tokens": max_tokens},
+            )
     except Exception as e:
         logger.error(f"[VoicePlayground] LLM generation failed: {e}")
         raise HTTPException(500, f"LLM generation failed: {str(e)}")
diff --git a/app/cli.py b/app/cli.py
index 453b2a2d..eed0274a 100644
--- a/app/cli.py
+++ b/app/cli.py
@@ -623,6 +623,20 @@ def _handle_signal(sig, frame):
         "is enabled; 32 threads can exhaust per-shard SQLAlchemy pools."
     ),
 )
+@click.option(
+    "--usage-worker/--no-usage-worker",
+    default=True,
+    help=(
+        "Also start a dedicated worker for the `usage` queue (flush + cost recompute; "
+        "default: True)."
+    ),
+)
+@click.option(
+    "--usage-worker-concurrency",
+    default=4,
+    type=int,
+    help="Concurrency for the usage worker (default: 4; thread pool).",
+)
 @click.option(
     "--telephony-worker/--no-telephony-worker",
     default=True,
@@ -646,6 +660,8 @@ def start_all(
     worker_loglevel: str,
     imports_worker: bool,
     imports_worker_concurrency: int,
+    usage_worker: bool,
+    usage_worker_concurrency: int,
     telephony_worker: bool,
     media_port: Optional[int],
 ):
@@ -692,6 +708,7 @@ def start_all(
     # Store worker processes for cleanup.
     worker_process = None
     worker_imports_process = None
+    worker_usage_process = None
     beat_process = None
     telephony_process = None
 
@@ -712,11 +729,12 @@ def _terminate(proc, label: str):
 
     def cleanup_processes():
         """Clean up spawned processes."""
-        nonlocal worker_process, worker_imports_process, beat_process, telephony_process
+        nonlocal worker_process, worker_imports_process, worker_usage_process, beat_process, telephony_process
         _terminate(telephony_process, "Telephony media server")
         _terminate(beat_process, "Celery beat")
         _terminate(worker_process, "Celery worker (default)")
         _terminate(worker_imports_process, "Celery worker (imports)")
+        _terminate(worker_usage_process, "Celery worker (usage)")
     
     # Register cleanup on exit
     atexit.register(cleanup_processes)
@@ -870,6 +888,30 @@ def _stream_telephony():
                 prefix="[WORKER-IMPORTS]",
             )
 
+        if usage_worker:
+            from app.workers.config import USAGE_WORKER_QUEUE
+
+            worker_usage_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "worker",
+                    f"--loglevel={worker_loglevel}",
+                    "-Q",
+                    USAGE_WORKER_QUEUE,
+                    "-P",
+                    "threads",
+                    "-c",
+                    str(usage_worker_concurrency),
+                ],
+                label=(
+                    f"Celery worker ({USAGE_WORKER_QUEUE} queue, "
+                    f"pool=threads, concurrency={usage_worker_concurrency})"
+                ),
+                prefix="[WORKER-USAGE]",
+            )
+
         beat_process = _spawn_worker(
             [
                 "celery",
@@ -920,6 +962,15 @@ def _stream_telephony():
             )
         else:
             click.echo("   Workers: default queue only (--no-imports-worker)")
+        if usage_worker:
+            from app.workers.config import USAGE_WORKER_QUEUE
+
+            click.echo(
+                f"   Usage worker: {USAGE_WORKER_QUEUE} queue "
+                f"(concurrency={usage_worker_concurrency})"
+            )
+        else:
+            click.echo("   Usage worker: disabled (--no-usage-worker)")
         if telephony_worker:
             telephony_public = (settings.VOBIZ_WEBHOOK_BASE_URL or "").strip()
             click.echo(f"   Telephony edge: http://localhost:{bind_media_port} (local)")
@@ -1198,6 +1249,244 @@ def sharding_rebalance_slices(
         catalog.close()
 
 
+@click.group()
+def usage():
+    """Usage pricing ops (seed rates, diff catalog, recompute costs)."""
+    pass
+
+
+main.add_command(usage)
+
+
+def _load_cli_config(config: str) -> Path:
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        sys.exit(1)
+    from app.config import load_config_from_file
+
+    load_config_from_file(str(config_path))
+    return config_path
+
+
+@usage.command("seed-rates")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--effective-from",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    default=None,
+    help="Effective date for seeded rates (default: 2020-01-01)",
+)
+def usage_seed_rates(config: str, effective_from):
+    """Upsert model_pricing_rates from models.json pricing blocks."""
+    _load_cli_config(config)
+    from app.database import SessionLocal
+    from app.services.usage.pricing_ops import seed_rates_from_models_json
+
+    day = effective_from.date() if effective_from else None
+    db = SessionLocal()
+    try:
+        count = seed_rates_from_models_json(db, effective_from=day)
+        db.commit()
+        click.echo(f"✅ Seeded/updated {count} pricing rate row(s)")
+    finally:
+        db.close()
+
+
+@usage.command("diff-rates")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--effective-from",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    default=None,
+    help="Compare rates at this effective_from date (default: 2020-01-01)",
+)
+@click.option("--json", "as_json", is_flag=True, help="Print machine-readable JSON")
+def usage_diff_rates(config: str, effective_from, as_json: bool):
+    """Diff models.json pricing blocks vs model_pricing_rates in Postgres."""
+    import json as json_module
+
+    _load_cli_config(config)
+    from app.database import SessionLocal
+    from app.services.usage.pricing_ops import diff_models_json_vs_db
+
+    day = effective_from.date() if effective_from else None
+    db = SessionLocal()
+    try:
+        report = diff_models_json_vs_db(db, effective_from=day)
+    finally:
+        db.close()
+
+    if as_json:
+        click.echo(json_module.dumps(report, indent=2, default=str))
+        return
+
+    click.echo(f"effective_from: {report['effective_from']}")
+    click.echo(
+        f"models.json priced: {report['models_json_count']} | "
+        f"database rows: {report['database_count']} | "
+        f"in_sync: {report['in_sync']}"
+    )
+    if report["only_in_models_json"]:
+        click.echo(f"\nOnly in models.json ({len(report['only_in_models_json'])}):")
+        for item in report["only_in_models_json"][:20]:
+            click.echo(f"  - {item['model']} ({item['usage_kind']})")
+    if report["only_in_database"]:
+        click.echo(f"\nOnly in database ({len(report['only_in_database'])}):")
+        for item in report["only_in_database"][:20]:
+            click.echo(f"  - {item['model']} ({item['usage_kind']})")
+    if report["mismatches"]:
+        click.echo(f"\nMismatched rates ({len(report['mismatches'])}):")
+        for item in report["mismatches"][:20]:
+            click.echo(f"  - {item['model']} ({item['usage_kind']})")
+            for field, values in item["fields"].items():
+                click.echo(
+                    f"      {field}: json={values['models_json']} db={values['database']}"
+                )
+    missing = report["missing_pricing_blocks"]
+    if missing:
+        click.echo(f"\nmodels.json entries missing pricing blocks ({len(missing)}):")
+        for model in missing[:20]:
+            click.echo(f"  - {model}")
+    unresolved = report["litellm_unresolved"]
+    if unresolved:
+        click.echo(f"\nLiteLLM unresolved ({len(unresolved)}):")
+        for item in unresolved[:20]:
+            click.echo(f"  - {item.get('model')} ({item.get('reason', 'unresolved')})")
+
+
+@usage.command("recompute")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option("--organization-id", default=None, help="Scope recompute to one org UUID")
+@click.option("--model", default=None, help="Scope recompute to one model")
+@click.option("--usage-kind", default=None, help="Scope recompute to llm/stt/tts")
+@click.option("--start-date", type=click.DateTime(formats=["%Y-%m-%d"]), default=None)
+@click.option("--end-date", type=click.DateTime(formats=["%Y-%m-%d"]), default=None)
+@click.option(
+    "--async/--sync",
+    "run_async",
+    default=True,
+    help="Enqueue Celery task (default) or run synchronously in this process",
+)
+def usage_recompute(
+    config: str,
+    organization_id: Optional[str],
+    model: Optional[str],
+    usage_kind: Optional[str],
+    start_date,
+    end_date,
+    run_async: bool,
+):
+    """Backfill or recompute stored usage costs on llm_usage_daily rollups."""
+    from uuid import UUID
+
+    _load_cli_config(config)
+    start = start_date.date() if start_date else None
+    end = end_date.date() if end_date else None
+    org_uuid = UUID(organization_id) if organization_id else None
+
+    if run_async:
+        if org_uuid is None:
+            click.echo(
+                "❌ --organization-id is required for async recompute (creates a tracked job).",
+                err=True,
+            )
+            click.echo(
+                "💡 Use --sync to recompute in this process without an org scope, or pass --organization-id.",
+                err=True,
+            )
+            sys.exit(1)
+
+        from app.database import SessionLocal
+        from app.services.usage.pricing_jobs import (
+            create_recompute_job,
+            enqueue_recompute_job,
+            job_to_dict,
+        )
+
+        db = SessionLocal()
+        try:
+            job = create_recompute_job(
+                db,
+                organization_id=org_uuid,
+                model=model,
+                usage_kind=usage_kind,
+                start_date=start,
+                end_date=end,
+            )
+            enqueue_recompute_job(db, job)
+            db.refresh(job)
+            payload = job_to_dict(job)
+            click.echo(f"✅ Enqueued recompute job {payload['id']} (status={payload['status']})")
+            if payload.get("celery_task_id"):
+                click.echo(f"   Celery task: {payload['celery_task_id']}")
+        except Exception as exc:
+            click.echo(f"❌ Failed to enqueue recompute job: {exc}", err=True)
+            sys.exit(1)
+        finally:
+            db.close()
+        return
+
+    from app.database import SessionLocal
+    from app.services.usage.pricing import recompute_usage_costs
+
+    db = SessionLocal()
+    try:
+        updated = recompute_usage_costs(
+            db,
+            organization_id=org_uuid,
+            model=model,
+            usage_kind=usage_kind,
+            start_date=start,
+            end_date=end,
+        )
+        click.echo(f"✅ Recomputed costs for {updated} rollup row(s)")
+    finally:
+        db.close()
+
+
+@usage.command("sync-litellm")
+@click.option("--local", is_flag=True, help="Use bundled LiteLLM model_cost JSON")
+@click.option(
+    "--write-models",
+    is_flag=True,
+    help="Merge generated pricing into app/config/models.json",
+)
+@click.option("--stdout", is_flag=True, help="Print pricing_catalog.json to stdout")
+def usage_sync_litellm(local: bool, write_models: bool, stdout: bool):
+    """Fetch LiteLLM prices and regenerate pricing_catalog.json."""
+    import subprocess
+    import sys as sys_module
+
+    script = Path(__file__).resolve().parent.parent / "scripts" / "sync_pricing_catalog_from_litellm.py"
+    cmd = [sys_module.executable, str(script)]
+    if local:
+        cmd.append("--local")
+    if write_models:
+        cmd.append("--write-models")
+    if stdout:
+        cmd.append("--stdout")
+    subprocess.run(cmd, check=True)
+
+
 if __name__ == "__main__":
     main()
 
diff --git a/app/config/models.json b/app/config/models.json
index d727a14d..38f24cca 100644
--- a/app/config/models.json
+++ b/app/config/models.json
@@ -1,1029 +1,2025 @@
-{
-  "whisper-1": {
-    "provider": "openai",
-    "model_type": "stt",
-    "description": "General-purpose speech recognition (verbose_json + word/segment timestamps)"
-  },
-  "gpt-4o-transcribe": {
-    "provider": "openai",
-    "model_type": "stt",
-    "description": "GPT-4o speech-to-text; higher accuracy, no granular word timestamps"
-  },
-  "gpt-4o-mini-transcribe": {
-    "provider": "openai",
-    "model_type": "stt",
-    "description": "Cheaper / faster GPT-4o transcription; no granular word timestamps"
-  },
-  "gpt-4o-transcribe-diarize": {
-    "provider": "openai",
-    "model_type": "stt",
-    "description": "Transcription model that identifies who's speaking when"
-  },
-  "gpt-realtime-whisper": {
-    "provider": "openai",
-    "model_type": "stt",
-    "description": "Streaming speech-to-text for realtime transcription"
-  },
-  "gpt-5.6": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Preview frontier model for select partners; broad availability coming soon"
-  },
-  "gpt-5.5": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "OpenAI flagship — complex reasoning, agentic coding, 1M context"
-  },
-  "gpt-5.5-pro": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Higher-accuracy GPT-5.5 variant with parallel test-time compute"
-  },
-  "gpt-5.4": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Affordable frontier model for coding and professional work"
-  },
-  "gpt-5.4-pro": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Higher-accuracy GPT-5.4 variant"
-  },
-  "gpt-5.4-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Strong mini model for coding, computer use, and subagents"
-  },
-  "gpt-5.4-nano": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Cheapest GPT-5.4-class model for simple high-volume tasks"
-  },
-  "gpt-5.3-codex": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Most capable agentic coding model"
-  },
-  "gpt-5.2": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Previous frontier model for professional work with configurable reasoning"
-  },
-  "gpt-5.2-pro": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Previous pro model for professional work"
-  },
-  "gpt-5.2-codex": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Intelligent coding model optimized for long-horizon agentic tasks (deprecated)"
-  },
-  "gpt-5.1": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Best model for coding and agentic tasks with configurable reasoning effort"
-  },
-  "gpt-5.1-codex": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.1 optimized for agentic coding in Codex (deprecated)"
-  },
-  "gpt-5.1-codex-max": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.1-codex optimized for long-running tasks (deprecated)"
-  },
-  "gpt-5.1-codex-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Smaller, cost-effective GPT-5.1-Codex variant (deprecated)"
-  },
-  "gpt-5": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Intelligent reasoning model for coding and agentic tasks"
-  },
-  "gpt-5-pro": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Higher-accuracy GPT-5 variant"
-  },
-  "gpt-5-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Near-frontier intelligence for cost-sensitive, low-latency workloads"
-  },
-  "gpt-5-nano": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Fastest, most cost-efficient GPT-5 variant"
-  },
-  "gpt-5-codex": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5 optimized for agentic coding in Codex (deprecated)"
-  },
-  "codex-mini-latest": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Fast reasoning model optimized for the Codex CLI (deprecated)"
-  },
-  "o3-pro": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "o3 with more compute for better responses"
-  },
-  "o3": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Reasoning model for complex tasks (succeeded by GPT-5)"
-  },
-  "o3-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Small o3 alternative (deprecated)"
-  },
-  "o3-deep-research": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "OpenAI flagship \u2014 complex reasoning, agentic coding, 1M context"
-  },
-  "o4-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Fast, cost-efficient reasoning model (succeeded by GPT-5 mini)"
-  },
-  "o4-mini-deep-research": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Faster, more affordable deep research model (deprecated)"
-  },
-  "o1-pro": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "o1 with more compute for better responses (deprecated)"
-  },
-  "o1": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Previous full o-series reasoning model (deprecated)"
-  },
-  "o1-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Small o1 alternative (deprecated)"
-  },
-  "o1-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Preview of the first o-series reasoning model (deprecated)"
-  },
-  "gpt-4.1": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Smartest non-reasoning GPT-4.x model"
-  },
-  "gpt-4.1-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Smaller, faster GPT-4.1 variant"
-  },
-  "gpt-4.1-nano": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Fastest, most cost-efficient GPT-4.1 variant (deprecated)"
-  },
-  "gpt-4o": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Fast, intelligent, flexible GPT model (deprecated)"
-  },
-  "gpt-4o-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Fast, affordable small model for focused tasks (deprecated)"
-  },
-  "gpt-4o-search-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-4o with web search in Chat Completions (deprecated)"
-  },
-  "gpt-4o-mini-search-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-4o mini with web search in Chat Completions (deprecated)"
-  },
-  "gpt-4-turbo": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Older high-intelligence GPT model (deprecated)"
-  },
-  "gpt-4-turbo-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Older fast GPT model preview (deprecated)"
-  },
-  "gpt-4": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Older high-intelligence GPT model (deprecated)"
-  },
-  "gpt-4.5-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Deprecated large GPT preview model"
-  },
-  "gpt-3.5-turbo": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Legacy GPT model for cheaper chat tasks (deprecated)"
-  },
-  "computer-use-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Specialized model for computer use tool (deprecated)"
-  },
-  "gpt-4o-audio-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-4o with audio input/output on Chat Completions (deprecated)"
-  },
-  "gpt-4o-mini-audio-preview": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-4o mini with audio input/output on Chat Completions (deprecated)"
-  },
-  "gpt-audio-1.5": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Best voice model for audio in/out with Chat Completions"
-  },
-  "gpt-audio": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Audio inputs and outputs with Chat Completions API"
-  },
-  "gpt-audio-mini": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Cost-efficient audio in/out with Chat Completions (deprecated)"
-  },
-  "chat-latest": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Latest Instant model used in ChatGPT; not recommended for most API use"
-  },
-  "gpt-5.3-chat": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.3 Instant model used in ChatGPT (deprecated)"
-  },
-  "gpt-5.2-chat": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.2 model used in ChatGPT (deprecated)"
-  },
-  "gpt-5.1-chat": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.1 model used in ChatGPT (deprecated)"
-  },
-  "gpt-5-chat": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5 model used in ChatGPT (deprecated)"
-  },
-  "chatgpt-4o-latest": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-4o model used in ChatGPT (deprecated)"
-  },
-  "gpt-oss-120b": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Most powerful OpenAI open-weight model (Apache 2.0)"
-  },
-  "gpt-oss-20b": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "Medium-sized OpenAI open-weight model for low latency (Apache 2.0)"
-  },
-  "gpt-5.6-sol": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.6 flagship — frontier reasoning, agentic coding, and computer use (1M context)",
-    "featured": true,
-    "featured_rank": 1,
-    "highlights": ["Flagship", "1M context", "Agentic coding"]
-  },
-  "gpt-5.6-terra": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.6 balanced tier — strong everyday performance at lower cost than Sol"
-  },
-  "gpt-5.6-luna": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "GPT-5.6 fast tier — lowest-cost model for high-volume classification and extraction"
-  },
-  "gpt-5.6": {
-    "provider": "openai",
-    "model_type": "llm",
-    "description": "OpenAI API alias for gpt-5.6-sol"
-  },
-  "gpt-4o-mini-tts": {
-    "provider": "openai",
-    "model_type": "tts",
-    "description": "Text-to-speech powered by GPT-4o mini (deprecated)",
-    "featured": true,
-    "featured_rank": 3,
-    "highlights": [
-      "Natural prosody",
-      "Multiple voices",
-      "Studio quality"
-    ]
-  },
-  "claude-sonnet-4.5": {
-    "provider": "anthropic",
-    "model_type": "llm"
-  },
-  "claude-opus-4.5": {
-    "provider": "anthropic",
-    "model_type": "llm"
-  },
-  "claude-haiku-4.5": {
-    "provider": "anthropic",
-    "model_type": "llm"
-  },
-  "claude-sonnet-4.6": {
-    "provider": "anthropic",
-    "model_type": "llm"
-  },
-  "claude-opus-4.6": {
-    "provider": "anthropic",
-    "model_type": "llm"
-  },
-  "claude-opus-4-7": {
-    "provider": "anthropic",
-    "model_type": "llm",
-    "description": "Claude Opus 4.7 \u2014 stronger coding, vision, and complex multi-step tasks"
-  },
-  "claude-opus-4-8": {
-    "provider": "anthropic",
-    "model_type": "llm",
-    "description": "Claude Opus 4.8 \u2014 most capable Opus-tier model; 1M context, adaptive thinking"
-  },
-  "claude-fable-5": {
-    "provider": "anthropic",
-    "model_type": "llm",
-    "description": "Claude Fable 5 \u2014 Anthropic's most capable widely released model for demanding agentic work"
-  },
-  "claude-mythos-5": {
-    "provider": "anthropic",
-    "model_type": "llm",
-    "description": "Claude Mythos 5 \u2014 Fable 5 capabilities; limited availability via Project Glasswing"
-  },
-  "claude-sonnet-5": {
-    "provider": "anthropic",
-    "model_type": "llm",
-    "description": "Claude Sonnet 5 \u2014 latest Sonnet tier; near-Opus quality with lower latency and cost",
-    "featured": true,
-    "featured_rank": 4,
-    "highlights": ["Latest Sonnet", "Agentic workflows", "Cost-efficient"]
-  },
-  "grok-4.3": {
-    "provider": "xai",
-    "model_type": "llm",
-    "description": "xAI flagship \u2014 low hallucination, agentic tool calling, 1M context"
-  },
-  "grok-build-0.1": {
-    "provider": "xai",
-    "model_type": "llm",
-    "description": "xAI fast coding model trained for agentic coding (early access)"
-  },
-  "grok-4.20-0309-reasoning": {
-    "provider": "xai",
-    "model_type": "llm",
-    "description": "Grok 4.20 reasoning snapshot (Mar 2026)"
-  },
-  "grok-4.20-0309-non-reasoning": {
-    "provider": "xai",
-    "model_type": "llm",
-    "description": "Grok 4.20 non-reasoning snapshot (Mar 2026)"
-  },
-  "grok-4.20-multi-agent-0309": {
-    "provider": "xai",
-    "model_type": "llm",
-    "description": "Grok 4.20 multi-agent snapshot (Mar 2026)"
-  },
-  "deepseek-v4-pro": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "DeepSeek V4 Pro \u2014 frontier MoE reasoning and coding (1M context)"
-  },
-  "deepseek-v4-flash": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "DeepSeek V4 Flash \u2014 fast extraction, classification, and search"
-  },
-  "kimi-k2p6": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "Kimi K2.6 \u2014 native multimodal agentic model for long-horizon coding"
-  },
-  "kimi-k2p5": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "Kimi K2.5 \u2014 unified vision/text agentic model with controllable reasoning"
-  },
-  "glm-5p1": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "GLM 5.1 \u2014 frontier open model for reasoning and agentic workflows"
-  },
-  "minimax-m2p7": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "MiniMax M2.7 \u2014 MoE model for complex agent harnesses and productivity tasks"
-  },
-  "minimax-m2p5": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "MiniMax M2.5 \u2014 fast coding and agentic tool use at low cost"
-  },
-  "qwen3p6-plus": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "Qwen 3.6 Plus \u2014 flagship multimodal model (Fireworks exclusive outside Alibaba)"
-  },
-  "gpt-oss-120b": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "OpenAI gpt-oss-120b \u2014 high-quality open-weight model for general reasoning"
-  },
-  "gpt-oss-20b": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "OpenAI gpt-oss-20b \u2014 fast open-weight model for chat and classification"
-  },
-  "firefunction-v2": {
-    "provider": "fireworks",
-    "model_type": "llm",
-    "description": "FireFunction V2 \u2014 Fireworks function-calling optimized model"
-  },
-  "google-speech-v2": {
-    "provider": "google",
-    "model_type": "stt"
-  },
-  "gemini-2.5-pro-stt": {
-    "provider": "google",
-    "model_type": "stt",
-    "description": "Gemini 2.5 Pro used for audio transcription via LiteLLM proxy (audio input -> text)"
-  },
-  "gemini-2.5-flash-stt": {
-    "provider": "google",
-    "model_type": "stt",
-    "description": "Gemini 2.5 Flash used for audio transcription via LiteLLM proxy (fast, cost-efficient)",
-    "featured": true,
-    "featured_rank": 6,
-    "highlights": [
-      "Fast transcription",
-      "Cost-efficient",
-      "Gemini 2.5"
-    ]
-  },
-  "gemini-2.5-flash-lite-stt": {
-    "provider": "google",
-    "model_type": "stt",
-    "description": "Gemini 2.5 Flash Lite used for audio transcription via LiteLLM proxy (lowest cost)"
-  },
-  "gemini-2.5-pro": {
-    "provider": "google",
-    "model_type": "llm"
-  },
-  "gemini-2.5-flash": {
-    "provider": "google",
-    "model_type": "llm"
-  },
-  "gemini-2.5-flash-lite": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 2.5 Flash Lite \u2014 lowest-latency 2.5 model; ideal for diarisation, classification, and other low-reasoning multimodal tasks"
-  },
-  "gemini-3-pro-preview": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 3 Pro \u2014 advanced reasoning and multimodal understanding"
-  },
-  "gemini-3-flash-preview": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 3 Flash \u2014 frontier-class performance at lower cost"
-  },
-  "gemini-3.1-pro-preview": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 3.1 Pro \u2014 latest reasoning model with 1M context"
-  },
-  "gemini-3.5-flash": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 3.5 Flash \u2014 GA flagship Flash model for agentic coding and long-horizon tasks",
-    "featured": true,
-    "featured_rank": 7,
-    "highlights": ["GA stable", "1M context", "Agentic coding"]
-  },
-  "gemini-3.5-flash-lite": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 3.5 Flash Lite \u2014 fastest, lowest-cost 3.5 model for high-throughput extraction, classification, and subagent workflows"
-  },
-  "gemini-3.1-flash-lite": {
-    "provider": "google",
-    "model_type": "llm",
-    "description": "Gemini 3.1 Flash Lite \u2014 cost-efficient stable model for high-volume lightweight tasks"
-  },
-  "azure-speech-v1": {
-    "provider": "azure",
-    "model_type": "stt",
-    "description": "Azure Speech-to-text (batch and realtime)"
-  },
-  "azure-openai-gpt4": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Legacy alias — maps to gpt-4 deployment; prefer azure-gpt-5-mini if that is your deployment name"
-  },
-  "azure-gpt-4o": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-4o — catalog key maps to deployment name gpt-4o"
-  },
-  "azure-gpt-4o-mini": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-4o mini"
-  },
-  "azure-gpt-4.1": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-4.1"
-  },
-  "azure-gpt-4.1-mini": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-4.1 mini"
-  },
-  "azure-gpt-4.1-nano": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-4.1 nano"
-  },
-  "azure-gpt-5": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-5"
-  },
-  "azure-gpt-5-mini": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-5 mini — pick this if your deployment is named gpt-5-mini"
-  },
-  "azure-gpt-5-nano": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-5 nano"
-  },
-  "azure-gpt-5.1": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-5.1"
-  },
-  "azure-gpt-5.2": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI GPT-5.2"
-  },
-  "azure-o3": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI o3 reasoning model"
-  },
-  "azure-o3-mini": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI o3 mini"
-  },
-  "azure-o4-mini": {
-    "provider": "azure",
-    "model_type": "llm",
-    "description": "Azure OpenAI o4 mini"
-  },
-  "azure-tts-v1": {
-    "provider": "azure",
-    "model_type": "tts",
-    "description": "Azure neural text-to-speech"
-  },
-  "aws-transcribe": {
-    "provider": "aws",
-    "model_type": "stt"
-  },
-  "aws-bedrock-claude": {
-    "provider": "aws",
-    "model_type": "llm"
-  },
-  "aws-polly": {
-    "provider": "aws",
-    "model_type": "tts"
-  },
-  "deepgram-flux": {
-    "provider": "deepgram",
-    "model_type": "stt",
-    "description": "Conversational STT with integrated turn detection for voice agents",
-    "featured": true,
-    "featured_rank": 4,
-    "highlights": [
-      "Turn detection",
-      "Voice agents",
-      "Low latency"
-    ]
-  },
-  "deepgram-nova-3": {
-    "provider": "deepgram",
-    "model_type": "stt",
-    "description": "Highest-performing general-purpose ASR model"
-  },
-  "deepgram-nova-3-general": {
-    "provider": "deepgram",
-    "model_type": "stt"
-  },
-  "deepgram-nova-3-general-preview-12-2025": {
-    "provider": "deepgram",
-    "model_type": "stt"
-  },
-  "deepgram-nova-2": {
-    "provider": "deepgram",
-    "model_type": "stt",
-    "description": "Available for languages not yet supported by Nova-3"
-  },
-  "pulse-v4": {
-    "provider": "smallest",
-    "model_type": "stt",
-    "description": "Smallest Pulse v4 speech-to-text (batch and realtime)"
-  },
-  "cartesia-sonic-3": {
-    "provider": "cartesia",
-    "model_type": "tts",
-    "description": "Flagship streaming TTS with laughter, volume/speed/emotion controls, 42 languages",
-    "featured": true,
-    "featured_rank": 1,
-    "highlights": [
-      "Ultra-low latency",
-      "42 languages",
-      "Emotion controls"
-    ]
-  },
-  "cartesia-sonic-3-mini": {
-    "provider": "cartesia",
-    "model_type": "tts"
-  },
-  "cartesia-sonic-3-nano": {
-    "provider": "cartesia",
-    "model_type": "tts"
-  },
-  "eleven_v3": {
-    "provider": "elevenlabs",
-    "model_type": "tts",
-    "description": "Human-like and expressive speech generation",
-    "languages": "70+ languages"
-  },
-  "eleven_ttv_v3": {
-    "provider": "elevenlabs",
-    "model_type": "tts",
-    "description": "Human-like and expressive voice design model (Text to Voice)",
-    "languages": "70+ languages"
-  },
-  "eleven_multilingual_v2": {
-    "provider": "elevenlabs",
-    "model_type": "tts",
-    "description": "Most lifelike model with rich emotional expression",
-    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru"
-  },
-  "eleven_flash_v2_5": {
-    "provider": "elevenlabs",
-    "model_type": "tts",
-    "description": "Ultra-fast model optimized for real-time use (~75ms)",
-    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru, hu, no, vi",
-    "featured": true,
-    "featured_rank": 2,
-    "highlights": [
-      "~75ms latency",
-      "Real-time",
-      "Multilingual"
-    ]
-  },
-  "eleven_turbo_v2_5": {
-    "provider": "elevenlabs",
-    "model_type": "tts",
-    "description": "High quality, low-latency model (~250ms-300ms)",
-    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru, hu, no, vi"
-  },
-  "eleven_multilingual_sts_v2": {
-    "provider": "elevenlabs",
-    "model_type": "sts",
-    "description": "State-of-the-art multilingual voice changer model (Speech to Speech)",
-    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru"
-  },
-  "eleven_multilingual_ttv_v2": {
-    "provider": "elevenlabs",
-    "model_type": "tts",
-    "description": "State-of-the-art multilingual voice designer model (Text to Voice)",
-    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru"
-  },
-  "eleven_english_sts_v2": {
-    "provider": "elevenlabs",
-    "model_type": "sts",
-    "description": "English-only voice changer model (Speech to Speech)",
-    "languages": "en"
-  },
-  "scribe_v2_realtime": {
-    "provider": "elevenlabs",
-    "model_type": "stt",
-    "description": "Real-time speech recognition model",
-    "languages": "90+ languages",
-    "featured": true,
-    "featured_rank": 5,
-    "highlights": [
-      "Real-time STT",
-      "90+ languages",
-      "Streaming"
-    ]
-  },
-  "scribe_v2": {
-    "provider": "elevenlabs",
-    "model_type": "stt",
-    "description": "Most accurate transcription model with keyterm prompting and entity detection",
-    "languages": "90+ languages"
-  },
-  "eleven_text_to_sound_v2": {
-    "provider": "elevenlabs",
-    "model_type": "sound_effects",
-    "description": "Sound effects generation from text prompts"
-  },
-  "music_v1": {
-    "provider": "elevenlabs",
-    "model_type": "music",
-    "description": "Studio-grade music generation from text prompts",
-    "languages": "en, es, de, ja, and more"
-  },
-  "murf-falcon": {
-    "provider": "murf",
-    "model_type": "tts",
-    "description": "Murf FALCON \u2013 ultra-low latency streaming TTS",
-    "languages": "en, es, de, fr, hi, ja, ko, pt, it, zh, and 10+ more",
-    "voices_source_file": "murf_falcon_voices.json"
-  },
-  "murf-gen2": {
-    "provider": "murf",
-    "model_type": "tts",
-    "description": "Murf GEN2 \u2013 high-quality, natural-sounding TTS",
-    "languages": "en, es, de, fr, hi, ja, ko, pt, it, zh, and 10+ more",
-    "voices_source_file": "murf_gen2_voices.json"
-  },
-  "voicemaker-ai3": {
-    "provider": "voicemaker",
-    "model_type": "tts",
-    "description": "VoiceMaker AI3 neural TTS voices",
-    "languages": "en-US, en-GB, en-AU, es-ES, fr-FR, de-DE, ja-JP, zh-CN",
-    "voices_source_file": "voicemaker_voices.json"
-  },
-  "voicemaker-proplus": {
-    "provider": "voicemaker",
-    "model_type": "tts",
-    "description": "VoiceMaker ProPlus expressive multilingual voices",
-    "languages": "multi-lang + regional accents",
-    "voices_source_file": "voicemaker_voices.json"
-  },
-  "lightning-v3.1": {
-    "provider": "smallest",
-    "model_type": "tts",
-    "description": "Smallest Lightning v3.1 low-latency neural TTS"
-  },
-  "sarvam-30b": {
-    "provider": "sarvam",
-    "model_type": "llm",
-    "description": "Sarvam 30B \u2014 balanced Indian-language + English chat model (64K context)"
-  },
-  "sarvam-105b": {
-    "provider": "sarvam",
-    "model_type": "llm",
-    "description": "Sarvam 105B \u2014 flagship MoE for complex reasoning, coding, and agentic workflows (128K context)"
-  },
-  "saaras:v3": {
-    "provider": "sarvam",
-    "model_type": "stt",
-    "description": "State-of-the-art Sarvam STT model with transcribe/translate modes"
-  },
-  "bulbul:v3": {
-    "provider": "sarvam",
-    "model_type": "tts",
-    "description": "High quality Indian language TTS",
-    "voices": [
-      {
-        "id": "aditya",
-        "name": "Aditya",
-        "gender": "Male"
-      },
-      {
-        "id": "ritu",
-        "name": "Ritu",
-        "gender": "Female"
-      },
-      {
-        "id": "ashutosh",
-        "name": "Ashutosh",
-        "gender": "Male"
-      },
-      {
-        "id": "priya",
-        "name": "Priya",
-        "gender": "Female"
-      },
-      {
-        "id": "neha",
-        "name": "Neha",
-        "gender": "Female"
-      },
-      {
-        "id": "rahul",
-        "name": "Rahul",
-        "gender": "Male"
-      },
-      {
-        "id": "pooja",
-        "name": "Pooja",
-        "gender": "Female"
-      },
-      {
-        "id": "rohan",
-        "name": "Rohan",
-        "gender": "Male"
-      },
-      {
-        "id": "simran",
-        "name": "Simran",
-        "gender": "Female"
-      },
-      {
-        "id": "kavya",
-        "name": "Kavya",
-        "gender": "Female"
-      },
-      {
-        "id": "amit",
-        "name": "Amit",
-        "gender": "Male"
-      },
-      {
-        "id": "dev",
-        "name": "Dev",
-        "gender": "Male"
-      },
-      {
-        "id": "ishita",
-        "name": "Ishita",
-        "gender": "Female"
-      },
-      {
-        "id": "shreya",
-        "name": "Shreya",
-        "gender": "Female"
-      },
-      {
-        "id": "ratan",
-        "name": "Ratan",
-        "gender": "Male"
-      },
-      {
-        "id": "varun",
-        "name": "Varun",
-        "gender": "Male"
-      },
-      {
-        "id": "manan",
-        "name": "Manan",
-        "gender": "Male"
-      },
-      {
-        "id": "sumit",
-        "name": "Sumit",
-        "gender": "Male"
-      },
-      {
-        "id": "roopa",
-        "name": "Roopa",
-        "gender": "Female"
-      },
-      {
-        "id": "kabir",
-        "name": "Kabir",
-        "gender": "Male"
-      },
-      {
-        "id": "aayan",
-        "name": "Aayan",
-        "gender": "Male"
-      },
-      {
-        "id": "shubh",
-        "name": "Shubh",
-        "gender": "Male"
-      },
-      {
-        "id": "advait",
-        "name": "Advait",
-        "gender": "Male"
-      },
-      {
-        "id": "amelia",
-        "name": "Amelia",
-        "gender": "Female"
-      },
-      {
-        "id": "sophia",
-        "name": "Sophia",
-        "gender": "Female"
-      },
-      {
-        "id": "anand",
-        "name": "Anand",
-        "gender": "Male"
-      },
-      {
-        "id": "tanya",
-        "name": "Tanya",
-        "gender": "Female"
-      },
-      {
-        "id": "tarun",
-        "name": "Tarun",
-        "gender": "Male"
-      },
-      {
-        "id": "sunny",
-        "name": "Sunny",
-        "gender": "Male"
-      },
-      {
-        "id": "mani",
-        "name": "Mani",
-        "gender": "Male"
-      },
-      {
-        "id": "gokul",
-        "name": "Gokul",
-        "gender": "Male"
-      },
-      {
-        "id": "vijay",
-        "name": "Vijay",
-        "gender": "Male"
-      },
-      {
-        "id": "shruti",
-        "name": "Shruti",
-        "gender": "Female"
-      },
-      {
-        "id": "suhani",
-        "name": "Suhani",
-        "gender": "Female"
-      },
-      {
-        "id": "mohit",
-        "name": "Mohit",
-        "gender": "Male"
-      },
-      {
-        "id": "kavitha",
-        "name": "Kavitha",
-        "gender": "Female"
-      },
-      {
-        "id": "rehan",
-        "name": "Rehan",
-        "gender": "Male"
-      },
-      {
-        "id": "soham",
-        "name": "Soham",
-        "gender": "Male"
-      },
-      {
-        "id": "rupali",
-        "name": "Rupali",
-        "gender": "Female"
-      }
-    ]
-  }
-}
+{
+  "whisper-1": {
+    "provider": "openai",
+    "model_type": "stt",
+    "description": "General-purpose speech recognition (verbose_json + word/segment timestamps)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.006
+    }
+  },
+  "gpt-4o-transcribe": {
+    "provider": "openai",
+    "model_type": "stt",
+    "description": "GPT-4o speech-to-text; higher accuracy, no granular word timestamps",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00372
+    }
+  },
+  "gpt-4o-mini-transcribe": {
+    "provider": "openai",
+    "model_type": "stt",
+    "description": "Cheaper / faster GPT-4o transcription; no granular word timestamps",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00186
+    }
+  },
+  "gpt-4o-transcribe-diarize": {
+    "provider": "openai",
+    "model_type": "stt",
+    "description": "Transcription model that identifies who's speaking when",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00372
+    }
+  },
+  "gpt-realtime-whisper": {
+    "provider": "openai",
+    "model_type": "stt",
+    "description": "Streaming speech-to-text for realtime transcription",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.01698
+    }
+  },
+  "gpt-5.6": {
+    "provider": "openai",
+    "model_type": "llm",
+    "description": "OpenAI API alias for gpt-5.6-sol",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 5.0,
+      "output_per_1m": 30.0,
+      "cache_read_per_1m": 0.5,
+      "cache_write_per_1m": 6.25
+    }
+  },
+  "gpt-5.5": {
+    "provider": "openai",
+    "model_type": "llm",
+    "description": "OpenAI flagship — complex reasoning, agentic coding, 1M context",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 5.0,
+      "output_per_1m": 30.0,
+      "cache_read_per_1m": 0.5
+    }
+  },
+  "gpt-5.5-pro": {
+    "provider": "openai",
+    "model_type": "llm",
+    "description": "Higher-accuracy GPT-5.5 variant with parallel test-time compute",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 30.0,
+      "output_per_1m": 180.0,
+      "cache_read_per_1m": 3.0
+    }
+  },
+  "gpt-5.4": {
+    "provider": "openai",
+    "model_type": "llm",
+    "description": "Affordable frontier model for coding and professional work",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.5,
+      "output_per_1m": 15.0,
+      "cache_read_per_1m": 0.25
+    }
+  },
+  "gpt-5.4-pro": {
+    "provider": "openai",
+    "model_type": "llm",
+    "description": "Higher-accuracy GPT-5.4 variant",
+    "pricing": {
+      "source": "litellm_import",
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+      "usage_kind": "llm",
+      "input_per_1m": 2.0,
+      "output_per_1m": 12.0,
+      "cache_read_per_1m": 0.2,
+      "cache_write_per_1m": 2.5
+    }
+  },
+  "gpt-5.6-luna": {
+    "provider": "openai",
+    "model_type": "llm",
+    "description": "GPT-5.6 fast tier — lowest-cost model for high-volume classification and extraction",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.2,
+      "output_per_1m": 1.2,
+      "cache_read_per_1m": 0.02,
+      "cache_write_per_1m": 0.25
+    }
+  },
+  "gpt-4o-mini-tts": {
+    "provider": "openai",
+    "model_type": "tts",
+    "description": "Text-to-speech powered by GPT-4o mini (deprecated)",
+    "featured": true,
+    "featured_rank": 3,
+    "highlights": [
+      "Natural prosody",
+      "Multiple voices",
+      "Studio quality"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 2.5
+    }
+  },
+  "claude-sonnet-4.5": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 3.0,
+      "output_per_1m": 15.0
+    }
+  },
+  "claude-opus-4.5": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 5.0,
+      "output_per_1m": 25.0
+    }
+  },
+  "claude-haiku-4.5": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.0,
+      "output_per_1m": 5.0,
+      "cache_read_per_1m": 0.1,
+      "cache_write_per_1m": 1.25
+    }
+  },
+  "claude-sonnet-4.6": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 3.0,
+      "output_per_1m": 15.0,
+      "cache_read_per_1m": 0.3,
+      "cache_write_per_1m": 3.75
+    }
+  },
+  "claude-opus-4.6": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 5.0,
+      "output_per_1m": 25.0,
+      "cache_read_per_1m": 0.5,
+      "cache_write_per_1m": 6.25
+    }
+  },
+  "claude-opus-4-7": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "description": "Claude Opus 4.7 — stronger coding, vision, and complex multi-step tasks",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 5.0,
+      "output_per_1m": 25.0,
+      "cache_read_per_1m": 0.5,
+      "cache_write_per_1m": 6.25
+    }
+  },
+  "claude-opus-4-8": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "description": "Claude Opus 4.8 — most capable Opus-tier model; 1M context, adaptive thinking",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 5.0,
+      "output_per_1m": 25.0,
+      "cache_read_per_1m": 0.5,
+      "cache_write_per_1m": 6.25
+    }
+  },
+  "claude-fable-5": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "description": "Claude Fable 5 — Anthropic's most capable widely released model for demanding agentic work",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 10.0,
+      "output_per_1m": 50.0,
+      "cache_read_per_1m": 1.0,
+      "cache_write_per_1m": 12.5
+    }
+  },
+  "claude-mythos-5": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "description": "Claude Mythos 5 — Fable 5 capabilities; limited availability via Project Glasswing",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 10.0,
+      "output_per_1m": 50.0,
+      "cache_read_per_1m": 1.0,
+      "cache_write_per_1m": 12.5
+    }
+  },
+  "claude-sonnet-5": {
+    "provider": "anthropic",
+    "model_type": "llm",
+    "description": "Claude Sonnet 5 — latest Sonnet tier; near-Opus quality with lower latency and cost",
+    "featured": true,
+    "featured_rank": 4,
+    "highlights": [
+      "Latest Sonnet",
+      "Agentic workflows",
+      "Cost-efficient"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.0,
+      "output_per_1m": 10.0,
+      "cache_read_per_1m": 0.2,
+      "cache_write_per_1m": 2.5
+    }
+  },
+  "grok-4.3": {
+    "provider": "xai",
+    "model_type": "llm",
+    "description": "xAI flagship — low hallucination, agentic tool calling, 1M context",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.25,
+      "output_per_1m": 2.5,
+      "cache_read_per_1m": 0.2
+    }
+  },
+  "grok-build-0.1": {
+    "provider": "xai",
+    "model_type": "llm",
+    "description": "xAI fast coding model trained for agentic coding (early access)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 3.0,
+      "output_per_1m": 15.0,
+      "cache_read_per_1m": 0.75
+    }
+  },
+  "grok-4.20-0309-reasoning": {
+    "provider": "xai",
+    "model_type": "llm",
+    "description": "Grok 4.20 reasoning snapshot (Mar 2026)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.25,
+      "output_per_1m": 2.5,
+      "cache_read_per_1m": 0.2
+    }
+  },
+  "grok-4.20-0309-non-reasoning": {
+    "provider": "xai",
+    "model_type": "llm",
+    "description": "Grok 4.20 non-reasoning snapshot (Mar 2026)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.2,
+      "output_per_1m": 0.5,
+      "cache_read_per_1m": 0.05
+    }
+  },
+  "grok-4.20-multi-agent-0309": {
+    "provider": "xai",
+    "model_type": "llm",
+    "description": "Grok 4.20 multi-agent snapshot (Mar 2026)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 3.0,
+      "output_per_1m": 15.0
+    }
+  },
+  "deepseek-v4-pro": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "DeepSeek V4 Pro — frontier MoE reasoning and coding (1M context)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.74,
+      "output_per_1m": 3.48,
+      "cache_read_per_1m": 0.145
+    }
+  },
+  "deepseek-v4-flash": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "DeepSeek V4 Flash — fast extraction, classification, and search",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.14,
+      "output_per_1m": 0.28,
+      "cache_read_per_1m": 0.028
+    }
+  },
+  "kimi-k2p6": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "Kimi K2.6 — native multimodal agentic model for long-horizon coding",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.95,
+      "output_per_1m": 4.0,
+      "cache_read_per_1m": 0.16
+    }
+  },
+  "kimi-k2p5": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "Kimi K2.5 — unified vision/text agentic model with controllable reasoning",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.6,
+      "output_per_1m": 3.0,
+      "cache_read_per_1m": 0.1
+    }
+  },
+  "glm-5p1": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "GLM 5.1 — frontier open model for reasoning and agentic workflows",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.4,
+      "output_per_1m": 4.4,
+      "cache_read_per_1m": 0.26
+    }
+  },
+  "minimax-m2p7": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "MiniMax M2.7 — MoE model for complex agent harnesses and productivity tasks",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.3,
+      "output_per_1m": 1.2,
+      "cache_read_per_1m": 0.06
+    }
+  },
+  "minimax-m2p5": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "MiniMax M2.5 — fast coding and agentic tool use at low cost",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.3,
+      "output_per_1m": 1.2,
+      "cache_read_per_1m": 0.06
+    }
+  },
+  "qwen3p6-plus": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "Qwen 3.6 Plus — flagship multimodal model (Fireworks exclusive outside Alibaba)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.325,
+      "output_per_1m": 1.95
+    }
+  },
+  "firefunction-v2": {
+    "provider": "fireworks",
+    "model_type": "llm",
+    "description": "FireFunction V2 — Fireworks function-calling optimized model",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.9,
+      "output_per_1m": 0.9
+    }
+  },
+  "google-speech-v2": {
+    "provider": "google",
+    "model_type": "stt",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.0015
+    }
+  },
+  "gemini-2.5-pro-stt": {
+    "provider": "google",
+    "model_type": "stt",
+    "description": "Gemini 2.5 Pro used for audio transcription via LiteLLM proxy (audio input -> text)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.0015
+    }
+  },
+  "gemini-2.5-flash-stt": {
+    "provider": "google",
+    "model_type": "stt",
+    "description": "Gemini 2.5 Flash used for audio transcription via LiteLLM proxy (fast, cost-efficient)",
+    "featured": true,
+    "featured_rank": 6,
+    "highlights": [
+      "Fast transcription",
+      "Cost-efficient",
+      "Gemini 2.5"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.0015
+    }
+  },
+  "gemini-2.5-flash-lite-stt": {
+    "provider": "google",
+    "model_type": "stt",
+    "description": "Gemini 2.5 Flash Lite used for audio transcription via LiteLLM proxy (lowest cost)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00042
+    }
+  },
+  "gemini-2.5-pro": {
+    "provider": "google",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.25,
+      "output_per_1m": 10.0,
+      "cache_read_per_1m": 0.125
+    }
+  },
+  "gemini-2.5-flash": {
+    "provider": "google",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.3,
+      "output_per_1m": 2.5,
+      "cache_read_per_1m": 0.03,
+      "reasoning_per_1m": 2.5
+    }
+  },
+  "gemini-2.5-flash-lite": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 2.5 Flash Lite — lowest-latency 2.5 model; ideal for diarisation, classification, and other low-reasoning multimodal tasks",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.1,
+      "output_per_1m": 0.4,
+      "cache_read_per_1m": 0.01,
+      "reasoning_per_1m": 0.4
+    }
+  },
+  "gemini-3-pro-preview": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 3 Pro — advanced reasoning and multimodal understanding",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.0,
+      "output_per_1m": 12.0,
+      "cache_read_per_1m": 0.2
+    }
+  },
+  "gemini-3-flash-preview": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 3 Flash — frontier-class performance at lower cost",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.5,
+      "output_per_1m": 3.0,
+      "cache_read_per_1m": 0.05,
+      "reasoning_per_1m": 3.0
+    }
+  },
+  "gemini-3.1-pro-preview": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 3.1 Pro — latest reasoning model with 1M context",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.0,
+      "output_per_1m": 12.0,
+      "cache_read_per_1m": 0.2
+    }
+  },
+  "gemini-3.5-flash": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 3.5 Flash — GA flagship Flash model for agentic coding and long-horizon tasks",
+    "featured": true,
+    "featured_rank": 7,
+    "highlights": [
+      "GA stable",
+      "1M context",
+      "Agentic coding"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.5,
+      "output_per_1m": 9.0,
+      "cache_read_per_1m": 0.15,
+      "reasoning_per_1m": 9.0
+    }
+  },
+  "gemini-3.5-flash-lite": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 3.5 Flash Lite — fastest, lowest-cost 3.5 model for high-throughput extraction, classification, and subagent workflows",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.3,
+      "output_per_1m": 2.5,
+      "cache_read_per_1m": 0.03,
+      "reasoning_per_1m": 2.5
+    }
+  },
+  "gemini-3.1-flash-lite": {
+    "provider": "google",
+    "model_type": "llm",
+    "description": "Gemini 3.1 Flash Lite — cost-efficient stable model for high-volume lightweight tasks",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.25,
+      "output_per_1m": 1.5,
+      "cache_read_per_1m": 0.025,
+      "reasoning_per_1m": 1.5
+    }
+  },
+  "azure-speech-v1": {
+    "provider": "azure",
+    "model_type": "stt",
+    "description": "Azure Speech-to-text (batch and realtime)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.01668
+    }
+  },
+  "azure-openai-gpt4": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Legacy alias — maps to gpt-4 deployment; prefer azure-gpt-5-mini if that is your deployment name",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 30.0,
+      "output_per_1m": 60.0
+    }
+  },
+  "azure-gpt-4o": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-4o — catalog key maps to deployment name gpt-4o",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.5,
+      "output_per_1m": 10.0,
+      "cache_read_per_1m": 1.25
+    }
+  },
+  "azure-gpt-4o-mini": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-4o mini",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.165,
+      "output_per_1m": 0.66,
+      "cache_read_per_1m": 0.075
+    }
+  },
+  "azure-gpt-4.1": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-4.1",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.0,
+      "output_per_1m": 8.0,
+      "cache_read_per_1m": 0.5
+    }
+  },
+  "azure-gpt-4.1-mini": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-4.1 mini",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.4,
+      "output_per_1m": 1.6,
+      "cache_read_per_1m": 0.1
+    }
+  },
+  "azure-gpt-4.1-nano": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-4.1 nano",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.1,
+      "output_per_1m": 0.4,
+      "cache_read_per_1m": 0.025
+    }
+  },
+  "azure-gpt-5": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-5",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.25,
+      "output_per_1m": 10.0,
+      "cache_read_per_1m": 0.125
+    }
+  },
+  "azure-gpt-5-mini": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-5 mini — pick this if your deployment is named gpt-5-mini",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.25,
+      "output_per_1m": 2.0,
+      "cache_read_per_1m": 0.025
+    }
+  },
+  "azure-gpt-5-nano": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-5 nano",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 0.05,
+      "output_per_1m": 0.4,
+      "cache_read_per_1m": 0.005
+    }
+  },
+  "azure-gpt-5.1": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-5.1",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.25,
+      "output_per_1m": 10.0,
+      "cache_read_per_1m": 0.125
+    }
+  },
+  "azure-gpt-5.2": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI GPT-5.2",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.75,
+      "output_per_1m": 14.0,
+      "cache_read_per_1m": 0.175
+    }
+  },
+  "azure-o3": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI o3 reasoning model",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 2.0,
+      "output_per_1m": 8.0,
+      "cache_read_per_1m": 0.5
+    }
+  },
+  "azure-o3-mini": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI o3 mini",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.1,
+      "output_per_1m": 4.4,
+      "cache_read_per_1m": 0.55
+    }
+  },
+  "azure-o4-mini": {
+    "provider": "azure",
+    "model_type": "llm",
+    "description": "Azure OpenAI o4 mini",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 1.1,
+      "output_per_1m": 4.4,
+      "cache_read_per_1m": 0.275
+    }
+  },
+  "azure-tts-v1": {
+    "provider": "azure",
+    "model_type": "tts",
+    "description": "Azure neural text-to-speech",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 15.0
+    }
+  },
+  "aws-transcribe": {
+    "provider": "aws",
+    "model_type": "stt",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.006
+    }
+  },
+  "aws-bedrock-claude": {
+    "provider": "aws",
+    "model_type": "llm",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "llm",
+      "input_per_1m": 3.0,
+      "output_per_1m": 15.0,
+      "cache_read_per_1m": 0.3,
+      "cache_write_per_1m": 3.75
+    }
+  },
+  "aws-polly": {
+    "provider": "aws",
+    "model_type": "tts",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 16.0
+    }
+  },
+  "deepgram-flux": {
+    "provider": "deepgram",
+    "model_type": "stt",
+    "description": "Conversational STT with integrated turn detection for voice agents",
+    "featured": true,
+    "featured_rank": 4,
+    "highlights": [
+      "Turn detection",
+      "Voice agents",
+      "Low latency"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00432
+    }
+  },
+  "deepgram-nova-3": {
+    "provider": "deepgram",
+    "model_type": "stt",
+    "description": "Highest-performing general-purpose ASR model",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00432
+    }
+  },
+  "deepgram-nova-3-general": {
+    "provider": "deepgram",
+    "model_type": "stt",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00432
+    }
+  },
+  "deepgram-nova-3-general-preview-12-2025": {
+    "provider": "deepgram",
+    "model_type": "stt",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00432
+    }
+  },
+  "deepgram-nova-2": {
+    "provider": "deepgram",
+    "model_type": "stt",
+    "description": "Available for languages not yet supported by Nova-3",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00432
+    }
+  },
+  "pulse-v4": {
+    "provider": "smallest",
+    "model_type": "stt",
+    "description": "Smallest Pulse v4 speech-to-text (batch and realtime)",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.006
+    }
+  },
+  "cartesia-sonic-3": {
+    "provider": "cartesia",
+    "model_type": "tts",
+    "description": "Flagship streaming TTS with laughter, volume/speed/emotion controls, 42 languages",
+    "featured": true,
+    "featured_rank": 1,
+    "highlights": [
+      "Ultra-low latency",
+      "42 languages",
+      "Emotion controls"
+    ],
+    "pricing": {
+      "source": "cartesia.ai credits ~$50/1M chars (Pro tier, 1 credit/char)",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 50.0
+    }
+  },
+  "cartesia-sonic-3-mini": {
+    "provider": "cartesia",
+    "model_type": "tts",
+    "pricing": {
+      "source": "cartesia.ai ~$40/1M chars (mid-tier estimate)",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 40.0
+    }
+  },
+  "cartesia-sonic-3-nano": {
+    "provider": "cartesia",
+    "model_type": "tts",
+    "pricing": {
+      "source": "cartesia.ai Scale ~$37/1M chars",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 37.0
+    }
+  },
+  "eleven_v3": {
+    "provider": "elevenlabs",
+    "model_type": "tts",
+    "description": "Human-like and expressive speech generation",
+    "languages": "70+ languages",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_ttv_v3": {
+    "provider": "elevenlabs",
+    "model_type": "tts",
+    "description": "Human-like and expressive voice design model (Text to Voice)",
+    "languages": "70+ languages",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_multilingual_v2": {
+    "provider": "elevenlabs",
+    "model_type": "tts",
+    "description": "Most lifelike model with rich emotional expression",
+    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_flash_v2_5": {
+    "provider": "elevenlabs",
+    "model_type": "tts",
+    "description": "Ultra-fast model optimized for real-time use (~75ms)",
+    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru, hu, no, vi",
+    "featured": true,
+    "featured_rank": 2,
+    "highlights": [
+      "~75ms latency",
+      "Real-time",
+      "Multilingual"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_turbo_v2_5": {
+    "provider": "elevenlabs",
+    "model_type": "tts",
+    "description": "High quality, low-latency model (~250ms-300ms)",
+    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru, hu, no, vi",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_multilingual_sts_v2": {
+    "provider": "elevenlabs",
+    "model_type": "sts",
+    "description": "State-of-the-art multilingual voice changer model (Speech to Speech)",
+    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_multilingual_ttv_v2": {
+    "provider": "elevenlabs",
+    "model_type": "tts",
+    "description": "State-of-the-art multilingual voice designer model (Text to Voice)",
+    "languages": "en, ja, zh, de, hi, fr, ko, pt, it, es, id, nl, tr, fil, pl, sv, bg, ro, ar, cs, el, fi, hr, ms, sk, da, ta, uk, ru",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "eleven_english_sts_v2": {
+    "provider": "elevenlabs",
+    "model_type": "sts",
+    "description": "English-only voice changer model (Speech to Speech)",
+    "languages": "en",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "scribe_v2_realtime": {
+    "provider": "elevenlabs",
+    "model_type": "stt",
+    "description": "Real-time speech recognition model",
+    "languages": "90+ languages",
+    "featured": true,
+    "featured_rank": 5,
+    "highlights": [
+      "Real-time STT",
+      "90+ languages",
+      "Streaming"
+    ],
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00366
+    }
+  },
+  "scribe_v2": {
+    "provider": "elevenlabs",
+    "model_type": "stt",
+    "description": "Most accurate transcription model with keyterm prompting and entity detection",
+    "languages": "90+ languages",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.00366
+    }
+  },
+  "eleven_text_to_sound_v2": {
+    "provider": "elevenlabs",
+    "model_type": "sound_effects",
+    "description": "Sound effects generation from text prompts",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "music_v1": {
+    "provider": "elevenlabs",
+    "model_type": "music",
+    "description": "Studio-grade music generation from text prompts",
+    "languages": "en, es, de, ja, and more",
+    "pricing": {
+      "source": "litellm_import",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 180.0
+    }
+  },
+  "murf-falcon": {
+    "provider": "murf",
+    "model_type": "tts",
+    "description": "Murf FALCON – ultra-low latency streaming TTS",
+    "languages": "en, es, de, fr, hi, ja, ko, pt, it, zh, and 10+ more",
+    "voices_source_file": "murf_falcon_voices.json",
+    "pricing": {
+      "source": "murf.ai API $0.01/1K chars",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 10.0
+    }
+  },
+  "murf-gen2": {
+    "provider": "murf",
+    "model_type": "tts",
+    "description": "Murf GEN2 – high-quality, natural-sounding TTS",
+    "languages": "en, es, de, fr, hi, ja, ko, pt, it, zh, and 10+ more",
+    "voices_source_file": "murf_gen2_voices.json",
+    "pricing": {
+      "source": "murf.ai API $0.03/1K chars",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 30.0
+    }
+  },
+  "voicemaker-ai3": {
+    "provider": "voicemaker",
+    "model_type": "tts",
+    "description": "VoiceMaker AI3 neural TTS voices",
+    "languages": "en-US, en-GB, en-AU, es-ES, fr-FR, de-DE, ja-JP, zh-CN",
+    "voices_source_file": "voicemaker_voices.json",
+    "pricing": {
+      "source": "developer.voicemaker.in $25/1M chars × 1× (AI3)",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 25.0
+    }
+  },
+  "voicemaker-proplus": {
+    "provider": "voicemaker",
+    "model_type": "tts",
+    "description": "VoiceMaker ProPlus expressive multilingual voices",
+    "languages": "multi-lang + regional accents",
+    "voices_source_file": "voicemaker_voices.json",
+    "pricing": {
+      "source": "developer.voicemaker.in $25/1M chars × 2× (ProPlus Turbo)",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 50.0
+    }
+  },
+  "lightning-v3.1": {
+    "provider": "smallest",
+    "model_type": "tts",
+    "description": "Smallest Lightning v3.1 low-latency neural TTS",
+    "pricing": {
+      "source": "smallest.ai $0.175/10K chars",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 17.5
+    }
+  },
+  "sarvam-30b": {
+    "provider": "sarvam",
+    "model_type": "llm",
+    "description": "Sarvam 30B — balanced Indian-language + English chat model (64K context)",
+    "pricing": {
+      "source": "sarvam.ai ₹2.5 / ₹1.5 / ₹10 per 1M tokens @ 83 INR/USD",
+      "usage_kind": "llm",
+      "input_per_1m": 0.03012,
+      "output_per_1m": 0.120482,
+      "cache_read_per_1m": 0.018072
+    }
+  },
+  "sarvam-105b": {
+    "provider": "sarvam",
+    "model_type": "llm",
+    "description": "Sarvam 105B — flagship MoE for complex reasoning, coding, and agentic workflows (128K context)",
+    "pricing": {
+      "source": "sarvam.ai ₹4 / ₹2.5 / ₹16 per 1M tokens @ 83 INR/USD",
+      "usage_kind": "llm",
+      "input_per_1m": 0.048193,
+      "output_per_1m": 0.192771,
+      "cache_read_per_1m": 0.03012
+    }
+  },
+  "saaras:v3": {
+    "provider": "sarvam",
+    "model_type": "stt",
+    "description": "State-of-the-art Sarvam STT model with transcribe/translate modes",
+    "pricing": {
+      "source": "sarvam.ai ₹30/hour audio @ 83 INR/USD",
+      "usage_kind": "stt",
+      "audio_per_minute": 0.006
+    }
+  },
+  "bulbul:v3": {
+    "provider": "sarvam",
+    "model_type": "tts",
+    "description": "High quality Indian language TTS",
+    "voices": [
+      {
+        "id": "aditya",
+        "name": "Aditya",
+        "gender": "Male"
+      },
+      {
+        "id": "ritu",
+        "name": "Ritu",
+        "gender": "Female"
+      },
+      {
+        "id": "ashutosh",
+        "name": "Ashutosh",
+        "gender": "Male"
+      },
+      {
+        "id": "priya",
+        "name": "Priya",
+        "gender": "Female"
+      },
+      {
+        "id": "neha",
+        "name": "Neha",
+        "gender": "Female"
+      },
+      {
+        "id": "rahul",
+        "name": "Rahul",
+        "gender": "Male"
+      },
+      {
+        "id": "pooja",
+        "name": "Pooja",
+        "gender": "Female"
+      },
+      {
+        "id": "rohan",
+        "name": "Rohan",
+        "gender": "Male"
+      },
+      {
+        "id": "simran",
+        "name": "Simran",
+        "gender": "Female"
+      },
+      {
+        "id": "kavya",
+        "name": "Kavya",
+        "gender": "Female"
+      },
+      {
+        "id": "amit",
+        "name": "Amit",
+        "gender": "Male"
+      },
+      {
+        "id": "dev",
+        "name": "Dev",
+        "gender": "Male"
+      },
+      {
+        "id": "ishita",
+        "name": "Ishita",
+        "gender": "Female"
+      },
+      {
+        "id": "shreya",
+        "name": "Shreya",
+        "gender": "Female"
+      },
+      {
+        "id": "ratan",
+        "name": "Ratan",
+        "gender": "Male"
+      },
+      {
+        "id": "varun",
+        "name": "Varun",
+        "gender": "Male"
+      },
+      {
+        "id": "manan",
+        "name": "Manan",
+        "gender": "Male"
+      },
+      {
+        "id": "sumit",
+        "name": "Sumit",
+        "gender": "Male"
+      },
+      {
+        "id": "roopa",
+        "name": "Roopa",
+        "gender": "Female"
+      },
+      {
+        "id": "kabir",
+        "name": "Kabir",
+        "gender": "Male"
+      },
+      {
+        "id": "aayan",
+        "name": "Aayan",
+        "gender": "Male"
+      },
+      {
+        "id": "shubh",
+        "name": "Shubh",
+        "gender": "Male"
+      },
+      {
+        "id": "advait",
+        "name": "Advait",
+        "gender": "Male"
+      },
+      {
+        "id": "amelia",
+        "name": "Amelia",
+        "gender": "Female"
+      },
+      {
+        "id": "sophia",
+        "name": "Sophia",
+        "gender": "Female"
+      },
+      {
+        "id": "anand",
+        "name": "Anand",
+        "gender": "Male"
+      },
+      {
+        "id": "tanya",
+        "name": "Tanya",
+        "gender": "Female"
+      },
+      {
+        "id": "tarun",
+        "name": "Tarun",
+        "gender": "Male"
+      },
+      {
+        "id": "sunny",
+        "name": "Sunny",
+        "gender": "Male"
+      },
+      {
+        "id": "mani",
+        "name": "Mani",
+        "gender": "Male"
+      },
+      {
+        "id": "gokul",
+        "name": "Gokul",
+        "gender": "Male"
+      },
+      {
+        "id": "vijay",
+        "name": "Vijay",
+        "gender": "Male"
+      },
+      {
+        "id": "shruti",
+        "name": "Shruti",
+        "gender": "Female"
+      },
+      {
+        "id": "suhani",
+        "name": "Suhani",
+        "gender": "Female"
+      },
+      {
+        "id": "mohit",
+        "name": "Mohit",
+        "gender": "Male"
+      },
+      {
+        "id": "kavitha",
+        "name": "Kavitha",
+        "gender": "Female"
+      },
+      {
+        "id": "rehan",
+        "name": "Rehan",
+        "gender": "Male"
+      },
+      {
+        "id": "soham",
+        "name": "Soham",
+        "gender": "Male"
+      },
+      {
+        "id": "rupali",
+        "name": "Rupali",
+        "gender": "Female"
+      }
+    ],
+    "pricing": {
+      "source": "sarvam.ai ₹30/10K chars @ 83 INR/USD",
+      "usage_kind": "tts",
+      "tts_per_1m_characters": 36.144578
+    }
+  },
+  "voice-agent-call": {
+    "provider": "telephony",
+    "model_type": "llm",
+    "description": "External voice-agent call duration (Vapi, Retell, ElevenLabs, Smallest); billed per minute",
+    "pricing": {
+      "source": "manual",
+      "usage_kind": "llm",
+      "audio_per_minute": 0.05
+    }
+  },
+  "unknown": {
+    "provider": "internal",
+    "model_type": "llm",
+    "description": "Fallback when model name is missing from usage records",
+    "pricing": {
+      "source": "manual",
+      "usage_kind": "llm",
+      "input_per_1m": 0,
+      "output_per_1m": 0
+    }
+  }
+}
diff --git a/app/migrations/069_usage_pricing.py b/app/migrations/069_usage_pricing.py
new file mode 100644
index 00000000..0d8f8a76
--- /dev/null
+++ b/app/migrations/069_usage_pricing.py
@@ -0,0 +1,190 @@
+"""Migration: usage pricing catalog, org overrides, and cost columns on rollups."""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Add model_pricing_catalog, org_model_pricing_overrides, usage_pricing_mode, "
+    "and cost columns on llm_usage_daily"
+)
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _seed_pricing_catalog(db: Session) -> int:
+    from app.services.usage.pricing import DEFAULT_CATALOG_EFFECTIVE_FROM, seed_pricing_catalog
+
+    return seed_pricing_catalog(db, effective_from=DEFAULT_CATALOG_EFFECTIVE_FROM)
+
+
+def upgrade(db: Session):
+    if not _column_exists(db, "organizations", "usage_pricing_mode"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE organizations
+                ADD COLUMN usage_pricing_mode VARCHAR(32) NOT NULL DEFAULT 'platform_managed'
+                """
+            )
+        )
+        print("Added organizations.usage_pricing_mode")
+
+    if not _table_exists(db, "model_pricing_catalog"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE model_pricing_catalog (
+                    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                    model VARCHAR(255) NOT NULL,
+                    usage_kind VARCHAR(16) NOT NULL DEFAULT 'llm',
+                    effective_from DATE NOT NULL DEFAULT CURRENT_DATE,
+                    effective_to DATE,
+                    input_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    output_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    cache_read_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    cache_creation_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    reasoning_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    audio_micro_usd_per_second BIGINT NOT NULL DEFAULT 0,
+                    tts_micro_usd_per_million_chars BIGINT NOT NULL DEFAULT 0,
+                    created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                    updated_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                    CONSTRAINT uq_model_pricing_catalog_model_kind_from
+                        UNIQUE (model, usage_kind, effective_from)
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                CREATE INDEX ix_model_pricing_catalog_lookup
+                ON model_pricing_catalog (model, usage_kind, effective_from DESC)
+                """
+            )
+        )
+        print("Created model_pricing_catalog")
+
+    if not _table_exists(db, "org_model_pricing_overrides"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE org_model_pricing_overrides (
+                    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                    organization_id UUID NOT NULL
+                        REFERENCES organizations(id) ON DELETE CASCADE,
+                    model VARCHAR(255) NOT NULL,
+                    usage_kind VARCHAR(16) NOT NULL DEFAULT 'llm',
+                    effective_from DATE NOT NULL DEFAULT CURRENT_DATE,
+                    effective_to DATE,
+                    input_micro_usd_per_million BIGINT,
+                    output_micro_usd_per_million BIGINT,
+                    cache_read_micro_usd_per_million BIGINT,
+                    cache_creation_micro_usd_per_million BIGINT,
+                    reasoning_micro_usd_per_million BIGINT,
+                    audio_micro_usd_per_second BIGINT,
+                    tts_micro_usd_per_million_chars BIGINT,
+                    created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                    updated_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                    CONSTRAINT uq_org_model_pricing_override
+                        UNIQUE (organization_id, model, usage_kind, effective_from)
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                CREATE INDEX ix_org_model_pricing_overrides_lookup
+                ON org_model_pricing_overrides (
+                    organization_id, model, usage_kind, effective_from DESC
+                )
+                """
+            )
+        )
+        print("Created org_model_pricing_overrides")
+
+    cost_columns = [
+        ("input_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("output_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("cache_read_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("cache_creation_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("reasoning_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("audio_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("tts_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("total_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("pricing_rate_source", "VARCHAR(16)"),
+        ("pricing_rate_id", "UUID"),
+    ]
+    if _table_exists(db, "llm_usage_daily"):
+        for column, col_type in cost_columns:
+            if not _column_exists(db, "llm_usage_daily", column):
+                db.execute(
+                    text(
+                        f"ALTER TABLE llm_usage_daily ADD COLUMN {column} {col_type}"
+                    )
+                )
+        print("Added cost columns to llm_usage_daily")
+
+    db.commit()
+
+    if _table_exists(db, "model_pricing_catalog"):
+        seeded = _seed_pricing_catalog(db)
+        if seeded:
+            print(f"Seeded {seeded} model pricing catalog row(s)")
+        db.commit()
+
+
+def downgrade(db: Session):
+    if _table_exists(db, "llm_usage_daily"):
+        for column in (
+            "input_cost_micro_usd",
+            "output_cost_micro_usd",
+            "cache_read_cost_micro_usd",
+            "cache_creation_cost_micro_usd",
+            "reasoning_cost_micro_usd",
+            "audio_cost_micro_usd",
+            "tts_cost_micro_usd",
+            "total_cost_micro_usd",
+            "pricing_rate_source",
+            "pricing_rate_id",
+        ):
+            if _column_exists(db, "llm_usage_daily", column):
+                db.execute(text(f"ALTER TABLE llm_usage_daily DROP COLUMN {column}"))
+
+    db.execute(text("DROP TABLE IF EXISTS org_model_pricing_overrides"))
+    db.execute(text("DROP TABLE IF EXISTS model_pricing_catalog"))
+
+    if _column_exists(db, "organizations", "usage_pricing_mode"):
+        db.execute(text("ALTER TABLE organizations DROP COLUMN usage_pricing_mode"))
+
+    db.commit()
diff --git a/app/migrations/070_usage_margin_multiplier.py b/app/migrations/070_usage_margin_multiplier.py
new file mode 100644
index 00000000..9a6c3e03
--- /dev/null
+++ b/app/migrations/070_usage_margin_multiplier.py
@@ -0,0 +1,45 @@
+"""Migration: org-level usage margin multiplier for priced rollups."""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add organizations.usage_margin_multiplier for usage cost markup"
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def upgrade(db: Session):
+    if not _column_exists(db, "organizations", "usage_margin_multiplier"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE organizations
+                ADD COLUMN usage_margin_multiplier DOUBLE PRECISION NOT NULL DEFAULT 1.0
+                """
+            )
+        )
+        print("Added organizations.usage_margin_multiplier")
+    db.commit()
+
+
+def downgrade(db: Session):
+    if _column_exists(db, "organizations", "usage_margin_multiplier"):
+        db.execute(
+            text("ALTER TABLE organizations DROP COLUMN usage_margin_multiplier")
+        )
+    db.commit()
diff --git a/app/migrations/071_reseed_pricing_catalog.py b/app/migrations/071_reseed_pricing_catalog.py
new file mode 100644
index 00000000..ad17a360
--- /dev/null
+++ b/app/migrations/071_reseed_pricing_catalog.py
@@ -0,0 +1,37 @@
+"""Migration: re-seed model_pricing_rates from models.json after effective_from fix."""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.services.usage.pricing import DEFAULT_RATES_EFFECTIVE_FROM, seed_pricing_rates
+
+description = "Re-seed model_pricing_rates from models.json pricing blocks"
+
+
+def upgrade(db: Session):
+    table = "model_pricing_rates"
+    exists = db.execute(
+        text("SELECT to_regclass('public.model_pricing_rates')")
+    ).scalar()
+    if not exists:
+        table = "model_pricing_catalog"
+    db.execute(
+        text(
+            f"""
+            UPDATE {table}
+            SET effective_from = CAST(:effective_from AS date)
+            WHERE effective_from > CAST(:effective_from AS date)
+            """
+        ),
+        {"effective_from": DEFAULT_RATES_EFFECTIVE_FROM.isoformat()},
+    )
+    seeded = seed_pricing_rates(db, effective_from=DEFAULT_RATES_EFFECTIVE_FROM)
+    db.commit()
+    if seeded:
+        print(f"Re-seeded {seeded} model pricing rate row(s)")
+
+
+def downgrade(db: Session):
+    pass
diff --git a/app/migrations/072_usage_pricing_phase1.py b/app/migrations/072_usage_pricing_phase1.py
new file mode 100644
index 00000000..68254deb
--- /dev/null
+++ b/app/migrations/072_usage_pricing_phase1.py
@@ -0,0 +1,202 @@
+"""Migration: Phase 1 plan alignment — model_pricing_rates, buffer costs, strip extras."""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = (
+    "Rename model_pricing_catalog to model_pricing_rates, add currency/source, "
+    "cost columns on usage_pending_buffer, drop margin/BYOK org columns"
+)
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _ensure_model_pricing_rates(db: Session) -> None:
+    if _table_exists(db, "model_pricing_catalog") and not _table_exists(
+        db, "model_pricing_rates"
+    ):
+        db.execute(
+            text("ALTER TABLE model_pricing_catalog RENAME TO model_pricing_rates")
+        )
+        db.execute(
+            text(
+                """
+                ALTER INDEX IF EXISTS uq_model_pricing_catalog_model_kind_from
+                RENAME TO uq_model_pricing_rates_model_kind_from
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                ALTER INDEX IF EXISTS ix_model_pricing_catalog_lookup
+                RENAME TO ix_model_pricing_rates_lookup
+                """
+            )
+        )
+        print("Renamed model_pricing_catalog -> model_pricing_rates")
+
+    if not _table_exists(db, "model_pricing_rates"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE model_pricing_rates (
+                    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                    model VARCHAR(255) NOT NULL,
+                    usage_kind VARCHAR(16) NOT NULL DEFAULT 'llm',
+                    effective_from DATE NOT NULL DEFAULT CURRENT_DATE,
+                    effective_to DATE,
+                    currency VARCHAR(8) NOT NULL DEFAULT 'USD',
+                    source VARCHAR(32) NOT NULL DEFAULT 'catalog',
+                    input_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    output_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    cache_read_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    cache_creation_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    reasoning_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
+                    audio_micro_usd_per_second BIGINT NOT NULL DEFAULT 0,
+                    tts_micro_usd_per_million_chars BIGINT NOT NULL DEFAULT 0,
+                    created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                    updated_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                    CONSTRAINT uq_model_pricing_rates_model_kind_from
+                        UNIQUE (model, usage_kind, effective_from)
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                """
+                CREATE INDEX ix_model_pricing_rates_lookup
+                ON model_pricing_rates (model, usage_kind, effective_from DESC)
+                """
+            )
+        )
+        print("Created model_pricing_rates")
+
+    if not _column_exists(db, "model_pricing_rates", "currency"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE model_pricing_rates
+                ADD COLUMN currency VARCHAR(8) NOT NULL DEFAULT 'USD'
+                """
+            )
+        )
+    if not _column_exists(db, "model_pricing_rates", "source"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE model_pricing_rates
+                ADD COLUMN source VARCHAR(32) NOT NULL DEFAULT 'catalog'
+                """
+            )
+        )
+
+
+def _add_buffer_cost_columns(db: Session) -> None:
+    if not _table_exists(db, "usage_pending_buffer"):
+        return
+    cost_columns = [
+        ("input_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("output_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("cache_read_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("cache_creation_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("reasoning_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("audio_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("tts_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("total_cost_micro_usd", "BIGINT NOT NULL DEFAULT 0"),
+        ("pricing_rate_source", "VARCHAR(16)"),
+        ("pricing_rate_id", "UUID"),
+    ]
+    for column, col_type in cost_columns:
+        if not _column_exists(db, "usage_pending_buffer", column):
+            db.execute(
+                text(
+                    f"ALTER TABLE usage_pending_buffer ADD COLUMN {column} {col_type}"
+                )
+            )
+    print("Ensured cost columns on usage_pending_buffer")
+
+
+def _strip_org_extras(db: Session) -> None:
+    if _column_exists(db, "organizations", "usage_margin_multiplier"):
+        db.execute(
+            text("ALTER TABLE organizations DROP COLUMN usage_margin_multiplier")
+        )
+        print("Dropped organizations.usage_margin_multiplier")
+    if _column_exists(db, "organizations", "usage_pricing_mode"):
+        db.execute(text("ALTER TABLE organizations DROP COLUMN usage_pricing_mode"))
+        print("Dropped organizations.usage_pricing_mode")
+
+
+def upgrade(db: Session):
+    _ensure_model_pricing_rates(db)
+    _add_buffer_cost_columns(db)
+    _strip_org_extras(db)
+    db.commit()
+
+    from app.services.usage.pricing import DEFAULT_RATES_EFFECTIVE_FROM, seed_pricing_rates
+
+    seeded = seed_pricing_rates(db, effective_from=DEFAULT_RATES_EFFECTIVE_FROM)
+    db.commit()
+    if seeded:
+        print(f"Seeded {seeded} model_pricing_rates row(s)")
+
+
+def downgrade(db: Session):
+    if _table_exists(db, "usage_pending_buffer"):
+        for column in (
+            "input_cost_micro_usd",
+            "output_cost_micro_usd",
+            "cache_read_cost_micro_usd",
+            "cache_creation_cost_micro_usd",
+            "reasoning_cost_micro_usd",
+            "audio_cost_micro_usd",
+            "tts_cost_micro_usd",
+            "total_cost_micro_usd",
+            "pricing_rate_source",
+            "pricing_rate_id",
+        ):
+            if _column_exists(db, "usage_pending_buffer", column):
+                db.execute(
+                    text(f"ALTER TABLE usage_pending_buffer DROP COLUMN {column}")
+                )
+
+    if _table_exists(db, "model_pricing_rates") and not _table_exists(
+        db, "model_pricing_catalog"
+    ):
+        db.execute(
+            text("ALTER TABLE model_pricing_rates RENAME TO model_pricing_catalog")
+        )
+
+    db.commit()
diff --git a/app/migrations/073_usage_cost_recompute_jobs.py b/app/migrations/073_usage_cost_recompute_jobs.py
new file mode 100644
index 00000000..55f18192
--- /dev/null
+++ b/app/migrations/073_usage_cost_recompute_jobs.py
@@ -0,0 +1,72 @@
+"""Migration: async usage cost recompute job tracking."""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add usage_cost_recompute_jobs table for async cost backfill/recompute"
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+
+
+def upgrade(db: Session) -> None:
+    if _table_exists(db, "usage_cost_recompute_jobs"):
+        print("usage_cost_recompute_jobs already exists, skipping...")
+        return
+
+    db.execute(
+        text(
+            """
+            CREATE TABLE usage_cost_recompute_jobs (
+                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                organization_id UUID NOT NULL REFERENCES organizations(id) ON DELETE CASCADE,
+                status VARCHAR(32) NOT NULL DEFAULT 'pending',
+                model VARCHAR(255),
+                usage_kind VARCHAR(16),
+                start_date DATE,
+                end_date DATE,
+                updated_rows BIGINT NOT NULL DEFAULT 0,
+                error_message TEXT,
+                celery_task_id VARCHAR(255),
+                created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                updated_at TIMESTAMPTZ NOT NULL DEFAULT now(),
+                completed_at TIMESTAMPTZ
+            )
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_usage_cost_recompute_jobs_organization_id
+            ON usage_cost_recompute_jobs(organization_id)
+            """
+        )
+    )
+    db.execute(
+        text(
+            """
+            CREATE INDEX IF NOT EXISTS ix_usage_cost_recompute_jobs_org_status
+            ON usage_cost_recompute_jobs(organization_id, status)
+            """
+        )
+    )
+    print("Created usage_cost_recompute_jobs table")
+
+
+def downgrade(db: Session) -> None:
+    db.execute(text("DROP TABLE IF EXISTS usage_cost_recompute_jobs"))
diff --git a/app/migrations/074_pricing_rates_source_and_effective_from.py b/app/migrations/074_pricing_rates_source_and_effective_from.py
new file mode 100644
index 00000000..6f78f8b4
--- /dev/null
+++ b/app/migrations/074_pricing_rates_source_and_effective_from.py
@@ -0,0 +1,81 @@
+"""Migration: widen pricing source column and normalize effective_from baseline."""
+
+from __future__ import annotations
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.services.usage.pricing import DEFAULT_RATES_EFFECTIVE_FROM, seed_pricing_rates
+
+description = (
+    "Widen model_pricing_rates.source to VARCHAR(255) and normalize effective_from "
+    "to 2020-01-01 baseline"
+)
+
+_BASELINE = DEFAULT_RATES_EFFECTIVE_FROM.isoformat()
+
+
+def _table_exists(db: Session, table: str) -> bool:
+    return (
+        db.execute(
+            text("SELECT to_regclass(:table_name)"),
+            {"table_name": f"public.{table}"},
+        ).scalar()
+        is not None
+    )
+
+
+def upgrade(db: Session) -> None:
+    table = "model_pricing_rates"
+    if not _table_exists(db, table):
+        table = "model_pricing_catalog"
+    if not _table_exists(db, table):
+        print("No pricing rates table found, skipping")
+        return
+
+    db.execute(
+        text(
+            f"""
+            ALTER TABLE {table}
+            ALTER COLUMN source TYPE VARCHAR(255)
+            """
+        )
+    )
+    print(f"Widened {table}.source to VARCHAR(255)")
+
+    db.execute(
+        text(
+            f"""
+            DELETE FROM {table} newer
+            USING {table} baseline
+            WHERE newer.model = baseline.model
+              AND newer.usage_kind = baseline.usage_kind
+              AND newer.effective_from > CAST(:baseline AS date)
+              AND baseline.effective_from = CAST(:baseline AS date)
+            """
+        ),
+        {"baseline": _BASELINE},
+    )
+    db.execute(
+        text(
+            f"""
+            UPDATE {table}
+            SET effective_from = CAST(:baseline AS date)
+            WHERE effective_from > CAST(:baseline AS date)
+            """
+        ),
+        {"baseline": _BASELINE},
+    )
+    print(f"Normalized {table} effective_from to {_BASELINE}")
+
+    seeded = seed_pricing_rates(db, effective_from=DEFAULT_RATES_EFFECTIVE_FROM)
+    from app.services.usage.pricing_cache import invalidate_all_pricing_cache
+
+    invalidate_all_pricing_cache()
+    db.commit()
+    if seeded:
+        print(f"Re-seeded {seeded} pricing rate row(s) at {_BASELINE}")
+
+
+def downgrade(db: Session) -> None:
+    pass
diff --git a/app/models/database.py b/app/models/database.py
index 00b99fdb..99fbbbba 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -2779,6 +2779,31 @@ class JudgeRun(Base):
     dataset = relationship("JudgeDataset", back_populates="runs")
 
 
+class UsageCostRecomputeJob(Base):
+    """Async job tracking for retroactive usage cost recompute."""
+
+    __tablename__ = "usage_cost_recompute_jobs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    status = Column(String(32), nullable=False, default="pending", server_default="pending")
+    model = Column(String(255), nullable=True)
+    usage_kind = Column(String(16), nullable=True)
+    start_date = Column(Date, nullable=True)
+    end_date = Column(Date, nullable=True)
+    updated_rows = Column(BigInteger, nullable=False, default=0, server_default="0")
+    error_message = Column(String, nullable=True)
+    celery_task_id = Column(String(255), nullable=True, index=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    completed_at = Column(DateTime(timezone=True), nullable=True)
+
+
 class LLMUsageDaily(Base):
     """Daily LLM/STT usage rollups for org-scoped Usage reporting."""
 
@@ -2812,6 +2837,22 @@ class LLMUsageDaily(Base):
     audio_seconds = Column(BigInteger, nullable=False, default=0, server_default="0")
     tts_characters = Column(BigInteger, nullable=False, default=0, server_default="0")
     call_count = Column(BigInteger, nullable=False, default=0, server_default="0")
+    input_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    output_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    cache_read_cost_micro_usd = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    cache_creation_cost_micro_usd = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    reasoning_cost_micro_usd = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    audio_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    tts_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    total_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    pricing_rate_source = Column(String(16), nullable=True)
+    pricing_rate_id = Column(UUID(as_uuid=True), nullable=True)
     created_at = Column(DateTime(timezone=True), server_default=func.now())
     updated_at = Column(
         DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
diff --git a/app/services/ai/stt_clients/google.py b/app/services/ai/stt_clients/google.py
index 2bc100a1..84caf40d 100644
--- a/app/services/ai/stt_clients/google.py
+++ b/app/services/ai/stt_clients/google.py
@@ -83,6 +83,28 @@ def _build_transcription_prompt(language: Optional[str]) -> str:
     )
 
 
+def _gemini_stt_usage_ctx(config_model: str):
+    """Merge Gemini multimodal STT tags into the active usage context."""
+    from app.services.usage.context import LLMUsageContext, get_usage_context
+
+    tags = {
+        "stt_backend": "gemini_multimodal",
+        "config_model": (config_model or "").strip(),
+        "usage_split": "llm_tokens_and_stt_seconds",
+    }
+    base = get_usage_context()
+    if base is None:
+        return None
+    return LLMUsageContext(
+        organization_id=base.organization_id,
+        workspace_id=base.workspace_id,
+        product_section=base.product_section,
+        resource_id=base.resource_id,
+        resource_type=base.resource_type,
+        extra={**(base.extra or {}), **tags},
+    )
+
+
 def transcribe_google(
     audio_file_path: str,
     model: str,
@@ -158,10 +180,12 @@ def transcribe_google(
             from app.services.usage.normalize import normalize_llm_usage
 
             gemini_model = _strip_stt_suffix(model)
+            usage_ctx = _gemini_stt_usage_ctx(model)
             record_llm_usage(
                 gemini_model,
                 normalize_llm_usage(raw_response=response),
                 organization_id=organization_id,
+                ctx=usage_ctx,
             )
             from app.services.usage.llm_usage import probe_audio_seconds, record_stt_usage
 
@@ -171,6 +195,7 @@ def transcribe_google(
                     gemini_model,
                     audio_seconds=audio_seconds,
                     organization_id=organization_id,
+                    ctx=usage_ctx,
                     count_call=False,
                 )
         except Exception as exc:
diff --git a/app/services/call_import_user_insights.py b/app/services/call_import_user_insights.py
index bf723e6a..7484a34b 100644
--- a/app/services/call_import_user_insights.py
+++ b/app/services/call_import_user_insights.py
@@ -21,6 +21,8 @@
     Metric,
     ModelProvider,
 )
+from app.services.usage.context import LLMUsageContext
+
 from app.models.schemas import (
     EvaluationUserInsightItem,
     EvaluationUserInsightsState,
@@ -222,17 +224,28 @@ def _call_llm(
     *,
     temperature: float,
     max_tokens: int,
+    usage_ctx: Optional[LLMUsageContext] = None,
 ) -> str:
-    result = llm_service.generate_response(
-        messages=messages,
-        llm_provider=provider,
-        llm_model=model,
-        organization_id=organization_id,
-        db=db,
-        temperature=temperature,
-        max_tokens=max_tokens,
-    )
-    return str(result.get("text") or "")
+    from app.services.usage.context import get_usage_context, llm_usage_context
+
+    effective_ctx = usage_ctx or get_usage_context()
+
+    def _run() -> str:
+        result = llm_service.generate_response(
+            messages=messages,
+            llm_provider=provider,
+            llm_model=model,
+            organization_id=organization_id,
+            db=db,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        return str(result.get("text") or "")
+
+    if effective_ctx is not None:
+        with llm_usage_context(effective_ctx):
+            return _run()
+    return _run()
 
 
 def run_extraction_batch(
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index b992b9e5..0fdb48f1 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -240,14 +240,18 @@ def _buffer_to_postgres(
         logger.warning("usage postgres fallback unavailable: {}", exc)
         return
 
+    usage_date = bucket["usage_date"]
+    if isinstance(usage_date, str):
+        usage_date = date.fromisoformat(usage_date)
+    usage_kind = bucket.get("usage_kind") or USAGE_KIND_LLM
     params = {
         "organization_id": str(organization_id),
         "workspace_id": str(bucket["workspace_id"]) if bucket.get("workspace_id") else None,
         "product_section": bucket["product_section"],
         "model": bucket["model"],
         "context": json.dumps(bucket.get("context") or {}),
-        "usage_date": bucket["usage_date"].isoformat(),
-        "usage_kind": bucket.get("usage_kind") or USAGE_KIND_LLM,
+        "usage_date": usage_date.isoformat(),
+        "usage_kind": usage_kind,
         "prompt_tokens": int(deltas.get("prompt_tokens", 0)),
         "completion_tokens": int(deltas.get("completion_tokens", 0)),
         "cache_read_tokens": int(deltas.get("cache_read_tokens", 0)),
@@ -259,6 +263,18 @@ def _buffer_to_postgres(
     }
     db = SessionLocal()
     try:
+        from app.services.usage.pricing import cost_fields_from_deltas
+
+        params.update(
+            cost_fields_from_deltas(
+                deltas,
+                organization_id=organization_id,
+                model=bucket["model"],
+                usage_kind=usage_kind,
+                usage_date=usage_date,
+                db=db,
+            )
+        )
         db.execute(
             text(
                 """
@@ -267,7 +283,12 @@ def _buffer_to_postgres(
                     context, usage_date, usage_kind,
                     prompt_tokens, completion_tokens, cache_read_tokens,
                     cache_creation_tokens, reasoning_tokens, audio_seconds,
-                    tts_characters, call_count, created_at
+                    tts_characters, call_count,
+                    input_cost_micro_usd, output_cost_micro_usd,
+                    cache_read_cost_micro_usd, cache_creation_cost_micro_usd,
+                    reasoning_cost_micro_usd, audio_cost_micro_usd, tts_cost_micro_usd,
+                    total_cost_micro_usd, pricing_rate_source, pricing_rate_id,
+                    created_at
                 ) VALUES (
                     gen_random_uuid(), CAST(:organization_id AS uuid),
                     CAST(:workspace_id AS uuid), :product_section, :model,
@@ -275,7 +296,13 @@ def _buffer_to_postgres(
                     CAST(:usage_date AS date), :usage_kind,
                     :prompt_tokens, :completion_tokens, :cache_read_tokens,
                     :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
-                    :tts_characters, :call_count, now()
+                    :tts_characters, :call_count,
+                    :input_cost_micro_usd, :output_cost_micro_usd,
+                    :cache_read_cost_micro_usd, :cache_creation_cost_micro_usd,
+                    :reasoning_cost_micro_usd, :audio_cost_micro_usd, :tts_cost_micro_usd,
+                    :total_cost_micro_usd, :pricing_rate_source,
+                    CAST(:pricing_rate_id AS uuid),
+                    now()
                 )
                 """
             ),
@@ -715,6 +742,8 @@ def _upsert_bucket(
     organization_id: UUID,
     bucket: Dict[str, Any],
     deltas: Dict[str, int],
+    *,
+    pricing_resolver: Any = None,
 ) -> None:
     context = bucket.get("context") or {}
     params = {
@@ -772,64 +801,75 @@ def _upsert_bucket(
         text(f"UPDATE llm_usage_daily SET {update_set} {exact_context_where}"),
         params,
     )
-    if result.rowcount:
-        return
-
-    result = db.execute(
-        text(f"UPDATE llm_usage_daily SET {update_set} {legacy_context_where}"),
-        params,
-    )
-    if result.rowcount:
-        return
-
-    db.execute(text("SAVEPOINT llm_usage_bucket_insert"))
-    try:
-        db.execute(
-            text(
-                """
-            INSERT INTO llm_usage_daily (
-                id, organization_id, workspace_id, product_section, model,
-                context, usage_date, usage_kind,
-                prompt_tokens, completion_tokens, cache_read_tokens,
-                cache_creation_tokens, reasoning_tokens, audio_seconds,
-                tts_characters, call_count,
-                created_at, updated_at
-            ) VALUES (
-                gen_random_uuid(), CAST(:organization_id AS uuid),
-                CAST(:workspace_id AS uuid), :product_section, :model,
-                CAST(:context AS jsonb), CAST(:usage_date AS date),
-                :usage_kind,
-                :prompt_tokens, :completion_tokens, :cache_read_tokens,
-                :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
-                :tts_characters, :call_count,
-                now(), now()
-            )
-            """
-            ),
-            params,
-        )
-        db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
-    except IntegrityError as exc:
-        if not _is_unique_violation(exc):
-            db.execute(text("ROLLBACK TO SAVEPOINT llm_usage_bucket_insert"))
-            db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
-            raise
-        db.execute(text("ROLLBACK TO SAVEPOINT llm_usage_bucket_insert"))
+    if not result.rowcount:
         result = db.execute(
             text(f"UPDATE llm_usage_daily SET {update_set} {legacy_context_where}"),
             params,
         )
         if not result.rowcount:
-            result = db.execute(
-                text(f"UPDATE llm_usage_daily SET {update_set} {exact_context_where}"),
-                params,
-            )
-        db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
-        if not result.rowcount:
-            logger.warning(
-                "llm usage upsert unique conflict but no matching bucket for org {}",
-                organization_id,
-            )
+            db.execute(text("SAVEPOINT llm_usage_bucket_insert"))
+            try:
+                db.execute(
+                    text(
+                        """
+                    INSERT INTO llm_usage_daily (
+                        id, organization_id, workspace_id, product_section, model,
+                        context, usage_date, usage_kind,
+                        prompt_tokens, completion_tokens, cache_read_tokens,
+                        cache_creation_tokens, reasoning_tokens, audio_seconds,
+                        tts_characters, call_count,
+                        created_at, updated_at
+                    ) VALUES (
+                        gen_random_uuid(), CAST(:organization_id AS uuid),
+                        CAST(:workspace_id AS uuid), :product_section, :model,
+                        CAST(:context AS jsonb), CAST(:usage_date AS date),
+                        :usage_kind,
+                        :prompt_tokens, :completion_tokens, :cache_read_tokens,
+                        :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
+                        :tts_characters, :call_count,
+                        now(), now()
+                    )
+                    """
+                    ),
+                    params,
+                )
+                db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
+            except IntegrityError as exc:
+                if not _is_unique_violation(exc):
+                    db.execute(text("ROLLBACK TO SAVEPOINT llm_usage_bucket_insert"))
+                    db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
+                    raise
+                db.execute(text("ROLLBACK TO SAVEPOINT llm_usage_bucket_insert"))
+                result = db.execute(
+                    text(f"UPDATE llm_usage_daily SET {update_set} {legacy_context_where}"),
+                    params,
+                )
+                if not result.rowcount:
+                    result = db.execute(
+                        text(f"UPDATE llm_usage_daily SET {update_set} {exact_context_where}"),
+                        params,
+                    )
+                db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
+                if not result.rowcount:
+                    logger.warning(
+                        "llm usage upsert unique conflict but no matching bucket for org {}",
+                        organization_id,
+                    )
+
+    try:
+        from app.services.usage.pricing import PricingResolver, apply_cost_to_bucket
+
+        resolver = pricing_resolver
+        if resolver is None:
+            resolver = PricingResolver(db)
+        apply_cost_to_bucket(
+            db,
+            organization_id=organization_id,
+            bucket=bucket,
+            resolver=resolver,
+        )
+    except Exception as exc:
+        logger.warning("usage cost apply failed: {}", exc)
 
 
 def _is_unique_violation(exc: BaseException) -> bool:
@@ -854,8 +894,13 @@ def _is_missing_organization_fk(exc: BaseException) -> bool:
     return "llm_usage_daily_organization_id_fkey" in text_blob
 
 
-def _flush_pending_buffer(db: Session, organization_id: UUID) -> int:
+def _flush_pending_buffer(
+    db: Session, organization_id: UUID, *, pricing_resolver: Any = None
+) -> int:
     """Drain Postgres write-ahead rows into llm_usage_daily."""
+    from app.services.usage.pricing import PricingResolver
+
+    resolver = pricing_resolver or PricingResolver(db)
     try:
         rows = db.execute(
             text(
@@ -910,6 +955,7 @@ def _flush_pending_buffer(db: Session, organization_id: UUID) -> int:
                 organization_id,
                 bucket,
                 deltas,
+                pricing_resolver=resolver,
             )
             ids.append(str(row["id"]))
             flushed += 1
@@ -966,11 +1012,14 @@ def _catalog_flush_recently(organization_id: UUID) -> bool:
 
 def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool = False) -> int:
     """Claim Redis deltas + drain PG buffer into llm_usage_daily."""
+    from app.services.usage.pricing import PricingResolver
+
     _recover_orphaned_claims()
     skip_redis_flush = not force and _catalog_flush_recently(organization_id)
     if skip_redis_flush and _has_pending_usage(organization_id):
         skip_redis_flush = False
     flushed = 0
+    pricing_resolver = PricingResolver(db)
     if not skip_redis_flush:
         redis_locked = _acquire_flush_lock(organization_id)
         claim_key = None
@@ -995,6 +1044,7 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
                                 organization_id,
                                 parsed,
                                 deltas,
+                                pricing_resolver=pricing_resolver,
                             )
                             flushed += 1
                         if claim_key:
@@ -1032,8 +1082,8 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
                                     _client().delete(claim_key)
                                 except redis.RedisError:
                                     pass
-                            claim_key = None
-                            return _flush_pending_buffer(db, organization_id)
+                                claim_key = None
+                                return _flush_pending_buffer(db, organization_id)
                     if skipped:
                         _restore_buckets_to_pending(organization_id, skipped)
                     if claim_key:
diff --git a/app/services/usage/pricing.py b/app/services/usage/pricing.py
new file mode 100644
index 00000000..762add7d
--- /dev/null
+++ b/app/services/usage/pricing.py
@@ -0,0 +1,912 @@
+"""Usage pricing: rate resolution, cost computation, seed, and rollup backfill."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from datetime import date
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Tuple
+from uuid import UUID
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.services.usage.pricing_cache import (
+    get_cached_rate_payload,
+    pricing_cache_key,
+    set_cached_rate_payload,
+)
+
+DEFAULT_RATES_EFFECTIVE_FROM = date(2020, 1, 1)
+# Back-compat alias for older migrations.
+DEFAULT_CATALOG_EFFECTIVE_FROM = DEFAULT_RATES_EFFECTIVE_FROM
+
+USAGE_KIND_LLM = "llm"
+USAGE_KIND_STT = "stt"
+USAGE_KIND_TTS = "tts"
+
+MICRO_USD_PER_UNIT = 1_000_000
+RATE_SOURCE_CATALOG = "catalog"
+RATE_SOURCE_OVERRIDE = "override"
+
+_MODELS_JSON_PATH = (
+    Path(__file__).resolve().parent.parent.parent / "config" / "models.json"
+)
+
+_RATES_TABLE = "model_pricing_rates"
+_RATES_TABLE_CACHE: Optional[str] = None
+
+
+def _rates_table(db: Session) -> str:
+    global _RATES_TABLE_CACHE
+    if _RATES_TABLE_CACHE:
+        return _RATES_TABLE_CACHE
+    row = db.execute(text("SELECT to_regclass('public.model_pricing_rates')")).scalar()
+    if row:
+        _RATES_TABLE_CACHE = "model_pricing_rates"
+        return _RATES_TABLE_CACHE
+    row = db.execute(text("SELECT to_regclass('public.model_pricing_catalog')")).scalar()
+    if row:
+        _RATES_TABLE_CACHE = "model_pricing_catalog"
+        return _RATES_TABLE_CACHE
+    _RATES_TABLE_CACHE = _RATES_TABLE
+    return _RATES_TABLE_CACHE
+
+
+@dataclass(frozen=True)
+class RateCard:
+    source: str
+    rate_id: UUID
+    input_micro_usd_per_million: int = 0
+    output_micro_usd_per_million: int = 0
+    cache_read_micro_usd_per_million: int = 0
+    cache_creation_micro_usd_per_million: int = 0
+    reasoning_micro_usd_per_million: int = 0
+    audio_micro_usd_per_second: int = 0
+    tts_micro_usd_per_million_chars: int = 0
+
+
+@dataclass(frozen=True)
+class CostBreakdown:
+    input_cost_micro_usd: int = 0
+    output_cost_micro_usd: int = 0
+    cache_read_cost_micro_usd: int = 0
+    cache_creation_cost_micro_usd: int = 0
+    reasoning_cost_micro_usd: int = 0
+    audio_cost_micro_usd: int = 0
+    tts_cost_micro_usd: int = 0
+    total_cost_micro_usd: int = 0
+    pricing_rate_source: Optional[str] = None
+    pricing_rate_id: Optional[UUID] = None
+
+
+@dataclass(frozen=True)
+class UsageMetrics:
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    cache_read_tokens: int = 0
+    cache_creation_tokens: int = 0
+    reasoning_tokens: int = 0
+    audio_seconds: int = 0
+    tts_characters: int = 0
+
+
+def _int(value: Any, default: int = 0) -> int:
+    try:
+        return int(value or 0)
+    except (TypeError, ValueError):
+        return default
+
+
+def _normalize_rate_source(value: Any) -> str:
+    source = str(value or "catalog").strip() or "catalog"
+    if len(source) > 255:
+        return source[:255]
+    return source
+
+
+def _usage_kind_for_model_type(model_type: Optional[str]) -> str:
+    if model_type == "stt":
+        return USAGE_KIND_STT
+    if model_type in {"tts", "sts", "sound_effects", "music"}:
+        return USAGE_KIND_TTS
+    return USAGE_KIND_LLM
+
+
+def _usd_per_million_to_micro(value: Any) -> int:
+    if value is None:
+        return 0
+    try:
+        return int(round(float(value) * MICRO_USD_PER_UNIT))
+    except (TypeError, ValueError):
+        return 0
+
+
+def _usd_per_minute_to_micro_per_second(value: Any) -> int:
+    if value is None:
+        return 0
+    try:
+        return int(round(float(value) * MICRO_USD_PER_UNIT / 60.0))
+    except (TypeError, ValueError):
+        return 0
+
+
+def _normalize_pricing_block(
+    pricing: Dict[str, Any], *, model_type: Optional[str]
+) -> Dict[str, Any]:
+    """Convert models.json pricing block (plan USD fields or legacy micro fields) to DB shape."""
+    usage_kind = pricing.get("usage_kind") or _usage_kind_for_model_type(model_type)
+    source = str(pricing.get("source") or pricing.get("_price_source") or "catalog")
+
+    def micro(field_micro: str, field_usd: str) -> int:
+        if pricing.get(field_micro) is not None:
+            return _int(pricing.get(field_micro))
+        return _usd_per_million_to_micro(pricing.get(field_usd))
+
+    audio_micro = _int(pricing.get("audio_micro_usd_per_second"))
+    if not audio_micro:
+        audio_micro = _usd_per_minute_to_micro_per_second(pricing.get("audio_per_minute"))
+
+    tts_micro = _int(pricing.get("tts_micro_usd_per_million_chars"))
+    if not tts_micro:
+        tts_micro = _usd_per_million_to_micro(pricing.get("tts_per_1m_characters"))
+
+    return {
+        "usage_kind": usage_kind,
+        "source": source,
+        "currency": str(pricing.get("currency") or "USD"),
+        "input_micro_usd_per_million": micro(
+            "input_micro_usd_per_million", "input_per_1m"
+        ),
+        "output_micro_usd_per_million": micro(
+            "output_micro_usd_per_million", "output_per_1m"
+        ),
+        "cache_read_micro_usd_per_million": micro(
+            "cache_read_micro_usd_per_million", "cache_read_per_1m"
+        ),
+        "cache_creation_micro_usd_per_million": micro(
+            "cache_creation_micro_usd_per_million", "cache_write_per_1m"
+        ),
+        "reasoning_micro_usd_per_million": micro(
+            "reasoning_micro_usd_per_million", "reasoning_per_1m"
+        ),
+        "audio_micro_usd_per_second": audio_micro,
+        "tts_micro_usd_per_million_chars": tts_micro,
+    }
+
+
+def _catalog_lookup_models(model: str, usage_kind: str) -> Tuple[str, ...]:
+    candidates: List[str] = []
+    for item in (
+        model,
+        f"azure-{model}" if not model.startswith("azure-") else model[len("azure-") :],
+    ):
+        if item and item not in candidates:
+            candidates.append(item)
+    return tuple(candidates)
+
+
+def _scaled_cost(units: int, rate_per_million: int) -> int:
+    if not units or not rate_per_million:
+        return 0
+    return (int(units) * int(rate_per_million)) // MICRO_USD_PER_UNIT
+
+
+def metrics_from_deltas(deltas: Dict[str, Any]) -> UsageMetrics:
+    return UsageMetrics(
+        prompt_tokens=_int(deltas.get("prompt_tokens")),
+        completion_tokens=_int(deltas.get("completion_tokens")),
+        cache_read_tokens=_int(deltas.get("cache_read_tokens")),
+        cache_creation_tokens=_int(deltas.get("cache_creation_tokens")),
+        reasoning_tokens=_int(deltas.get("reasoning_tokens")),
+        audio_seconds=_int(deltas.get("audio_seconds")),
+        tts_characters=_int(deltas.get("tts_characters")),
+    )
+
+
+def cost_fields_from_deltas(
+    deltas: Dict[str, Any],
+    *,
+    organization_id: UUID,
+    model: str,
+    usage_kind: str,
+    usage_date: date,
+    db: Session,
+    resolver: Optional["PricingResolver"] = None,
+) -> Dict[str, Any]:
+    """Compute persisted cost columns for one pending-buffer delta row."""
+    pricing = resolver or PricingResolver(db)
+    rate = pricing.resolve_rate(
+        organization_id=organization_id,
+        model=model,
+        usage_kind=usage_kind,
+        usage_date=usage_date,
+    )
+    costs = compute_cost(metrics_from_deltas(deltas), rate)
+    return {
+        "input_cost_micro_usd": costs.input_cost_micro_usd,
+        "output_cost_micro_usd": costs.output_cost_micro_usd,
+        "cache_read_cost_micro_usd": costs.cache_read_cost_micro_usd,
+        "cache_creation_cost_micro_usd": costs.cache_creation_cost_micro_usd,
+        "reasoning_cost_micro_usd": costs.reasoning_cost_micro_usd,
+        "audio_cost_micro_usd": costs.audio_cost_micro_usd,
+        "tts_cost_micro_usd": costs.tts_cost_micro_usd,
+        "total_cost_micro_usd": costs.total_cost_micro_usd,
+        "pricing_rate_source": costs.pricing_rate_source,
+        "pricing_rate_id": str(costs.pricing_rate_id)
+        if costs.pricing_rate_id
+        else None,
+    }
+
+
+def compute_cost(metrics: UsageMetrics, rate: Optional[RateCard]) -> CostBreakdown:
+    if rate is None:
+        return CostBreakdown()
+
+    input_cost = _scaled_cost(metrics.prompt_tokens, rate.input_micro_usd_per_million)
+    output_cost = _scaled_cost(
+        metrics.completion_tokens, rate.output_micro_usd_per_million
+    )
+    cache_read_cost = _scaled_cost(
+        metrics.cache_read_tokens, rate.cache_read_micro_usd_per_million
+    )
+    cache_creation_cost = _scaled_cost(
+        metrics.cache_creation_tokens, rate.cache_creation_micro_usd_per_million
+    )
+    reasoning_cost = _scaled_cost(
+        metrics.reasoning_tokens, rate.reasoning_micro_usd_per_million
+    )
+    audio_cost = 0
+    if metrics.audio_seconds and rate.audio_micro_usd_per_second:
+        audio_cost = int(metrics.audio_seconds) * int(rate.audio_micro_usd_per_second)
+    tts_cost = _scaled_cost(
+        metrics.tts_characters, rate.tts_micro_usd_per_million_chars
+    )
+    total = (
+        input_cost
+        + output_cost
+        + cache_read_cost
+        + cache_creation_cost
+        + reasoning_cost
+        + audio_cost
+        + tts_cost
+    )
+    return CostBreakdown(
+        input_cost_micro_usd=input_cost,
+        output_cost_micro_usd=output_cost,
+        cache_read_cost_micro_usd=cache_read_cost,
+        cache_creation_cost_micro_usd=cache_creation_cost,
+        reasoning_cost_micro_usd=reasoning_cost,
+        audio_cost_micro_usd=audio_cost,
+        tts_cost_micro_usd=tts_cost,
+        total_cost_micro_usd=total,
+        pricing_rate_source=rate.source,
+        pricing_rate_id=rate.rate_id,
+    )
+
+
+def _rate_card_from_row(row: Any, *, source: str) -> RateCard:
+    return RateCard(
+        source=source,
+        rate_id=row["id"],
+        input_micro_usd_per_million=_int(row["input_micro_usd_per_million"]),
+        output_micro_usd_per_million=_int(row["output_micro_usd_per_million"]),
+        cache_read_micro_usd_per_million=_int(row["cache_read_micro_usd_per_million"]),
+        cache_creation_micro_usd_per_million=_int(
+            row["cache_creation_micro_usd_per_million"]
+        ),
+        reasoning_micro_usd_per_million=_int(row["reasoning_micro_usd_per_million"]),
+        audio_micro_usd_per_second=_int(row["audio_micro_usd_per_second"]),
+        tts_micro_usd_per_million_chars=_int(row["tts_micro_usd_per_million_chars"]),
+    )
+
+
+def _merge_override_with_catalog(
+    override_row: Any, catalog: Optional[RateCard]
+) -> RateCard:
+    def pick(column: str, attr: str) -> int:
+        value = override_row.get(column)
+        if value is not None:
+            return _int(value)
+        if catalog is not None:
+            return getattr(catalog, attr)
+        return 0
+
+    return RateCard(
+        source=RATE_SOURCE_OVERRIDE,
+        rate_id=override_row["id"],
+        input_micro_usd_per_million=pick(
+            "input_micro_usd_per_million", "input_micro_usd_per_million"
+        ),
+        output_micro_usd_per_million=pick(
+            "output_micro_usd_per_million", "output_micro_usd_per_million"
+        ),
+        cache_read_micro_usd_per_million=pick(
+            "cache_read_micro_usd_per_million", "cache_read_micro_usd_per_million"
+        ),
+        cache_creation_micro_usd_per_million=pick(
+            "cache_creation_micro_usd_per_million",
+            "cache_creation_micro_usd_per_million",
+        ),
+        reasoning_micro_usd_per_million=pick(
+            "reasoning_micro_usd_per_million", "reasoning_micro_usd_per_million"
+        ),
+        audio_micro_usd_per_second=pick(
+            "audio_micro_usd_per_second", "audio_micro_usd_per_second"
+        ),
+        tts_micro_usd_per_million_chars=pick(
+            "tts_micro_usd_per_million_chars", "tts_micro_usd_per_million_chars"
+        ),
+    )
+
+
+def _rate_card_to_cache_payload(card: RateCard) -> Dict[str, Any]:
+    return {
+        "source": card.source,
+        "rate_id": str(card.rate_id),
+        "input_micro_usd_per_million": card.input_micro_usd_per_million,
+        "output_micro_usd_per_million": card.output_micro_usd_per_million,
+        "cache_read_micro_usd_per_million": card.cache_read_micro_usd_per_million,
+        "cache_creation_micro_usd_per_million": card.cache_creation_micro_usd_per_million,
+        "reasoning_micro_usd_per_million": card.reasoning_micro_usd_per_million,
+        "audio_micro_usd_per_second": card.audio_micro_usd_per_second,
+        "tts_micro_usd_per_million_chars": card.tts_micro_usd_per_million_chars,
+    }
+
+
+def _rate_card_from_cache_payload(payload: Dict[str, Any]) -> RateCard:
+    return RateCard(
+        source=str(payload["source"]),
+        rate_id=UUID(str(payload["rate_id"])),
+        input_micro_usd_per_million=_int(payload.get("input_micro_usd_per_million")),
+        output_micro_usd_per_million=_int(payload.get("output_micro_usd_per_million")),
+        cache_read_micro_usd_per_million=_int(
+            payload.get("cache_read_micro_usd_per_million")
+        ),
+        cache_creation_micro_usd_per_million=_int(
+            payload.get("cache_creation_micro_usd_per_million")
+        ),
+        reasoning_micro_usd_per_million=_int(
+            payload.get("reasoning_micro_usd_per_million")
+        ),
+        audio_micro_usd_per_second=_int(payload.get("audio_micro_usd_per_second")),
+        tts_micro_usd_per_million_chars=_int(
+            payload.get("tts_micro_usd_per_million_chars")
+        ),
+    )
+
+
+class PricingResolver:
+    """Cached pricing lookups for flush/recompute batches."""
+
+    def __init__(self, db: Session):
+        self._db = db
+        self._memory_cache: Dict[Tuple[str, ...], Optional[RateCard]] = {}
+
+    def resolve_rate(
+        self,
+        *,
+        organization_id: UUID,
+        model: str,
+        usage_kind: str,
+        usage_date: date,
+    ) -> Optional[RateCard]:
+        kind = usage_kind or USAGE_KIND_LLM
+        memory_key = (str(organization_id), model, kind, usage_date.isoformat())
+        if memory_key in self._memory_cache:
+            return self._memory_cache[memory_key]
+
+        redis_key = pricing_cache_key(
+            organization_id=organization_id,
+            model=model,
+            usage_kind=kind,
+            usage_date=usage_date,
+        )
+        cached = get_cached_rate_payload(redis_key)
+        if cached:
+            card = _rate_card_from_cache_payload(cached)
+            self._memory_cache[memory_key] = card
+            return card
+
+        override = self._load_override_rate(organization_id, model, kind, usage_date)
+        if override is not None:
+            set_cached_rate_payload(redis_key, _rate_card_to_cache_payload(override))
+            self._memory_cache[memory_key] = override
+            return override
+
+        catalog = self._resolve_catalog(model, kind, usage_date)
+        set_cached_rate_payload(
+            redis_key,
+            _rate_card_to_cache_payload(catalog) if catalog else None,
+        )
+        self._memory_cache[memory_key] = catalog
+        return catalog
+
+    def _resolve_catalog(
+        self, model: str, usage_kind: str, usage_date: date
+    ) -> Optional[RateCard]:
+        for candidate in _catalog_lookup_models(model, usage_kind):
+            card = self._load_catalog_rate(candidate, usage_kind, usage_date)
+            if card is not None:
+                return card
+        return None
+
+    def _load_catalog_rate(
+        self, model: str, usage_kind: str, usage_date: date
+    ) -> Optional[RateCard]:
+        table = _rates_table(self._db)
+        row = self._db.execute(
+            text(
+                f"""
+                SELECT
+                    id,
+                    input_micro_usd_per_million,
+                    output_micro_usd_per_million,
+                    cache_read_micro_usd_per_million,
+                    cache_creation_micro_usd_per_million,
+                    reasoning_micro_usd_per_million,
+                    audio_micro_usd_per_second,
+                    tts_micro_usd_per_million_chars
+                FROM {table}
+                WHERE model = :model
+                  AND usage_kind = :usage_kind
+                  AND effective_from <= :usage_date
+                  AND (effective_to IS NULL OR effective_to >= :usage_date)
+                ORDER BY effective_from DESC
+                LIMIT 1
+                """
+            ),
+            {
+                "model": model,
+                "usage_kind": usage_kind,
+                "usage_date": usage_date.isoformat(),
+            },
+        ).mappings().first()
+        if not row:
+            return None
+        return _rate_card_from_row(row, source=RATE_SOURCE_CATALOG)
+
+    def _load_override_rate(
+        self,
+        organization_id: UUID,
+        model: str,
+        usage_kind: str,
+        usage_date: date,
+    ) -> Optional[RateCard]:
+        row = self._db.execute(
+            text(
+                """
+                SELECT
+                    id,
+                    input_micro_usd_per_million,
+                    output_micro_usd_per_million,
+                    cache_read_micro_usd_per_million,
+                    cache_creation_micro_usd_per_million,
+                    reasoning_micro_usd_per_million,
+                    audio_micro_usd_per_second,
+                    tts_micro_usd_per_million_chars
+                FROM org_model_pricing_overrides
+                WHERE organization_id = CAST(:organization_id AS uuid)
+                  AND model = :model
+                  AND usage_kind = :usage_kind
+                  AND effective_from <= :usage_date
+                  AND (effective_to IS NULL OR effective_to >= :usage_date)
+                ORDER BY effective_from DESC
+                LIMIT 1
+                """
+            ),
+            {
+                "organization_id": str(organization_id),
+                "model": model,
+                "usage_kind": usage_kind,
+                "usage_date": usage_date.isoformat(),
+            },
+        ).mappings().first()
+        if not row:
+            return None
+        catalog = self._resolve_catalog(model, usage_kind, usage_date)
+        return _merge_override_with_catalog(row, catalog)
+
+
+def _pricing_entries_from_models_json() -> Dict[str, Dict[str, Any]]:
+    if not _MODELS_JSON_PATH.exists():
+        return {}
+    try:
+        with open(_MODELS_JSON_PATH, "r", encoding="utf-8") as handle:
+            payload = json.load(handle)
+    except (OSError, json.JSONDecodeError):
+        return {}
+    entries: Dict[str, Dict[str, Any]] = {}
+    for model_name, config in payload.items():
+        if model_name.startswith("_") or not isinstance(config, dict):
+            continue
+        pricing = config.get("pricing")
+        if not isinstance(pricing, dict):
+            continue
+        entries[model_name] = _normalize_pricing_block(
+            pricing, model_type=config.get("model_type")
+        )
+    return entries
+
+
+def seed_pricing_rates(db: Session, *, effective_from: Optional[date] = None) -> int:
+    """Upsert global rates from models.json pricing blocks (seed only; DB is runtime truth)."""
+    day = effective_from or DEFAULT_RATES_EFFECTIVE_FROM
+    table = _rates_table(db)
+    has_currency = (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = 'currency'
+                """
+            ),
+            {"table_name": table},
+        ).first()
+        is not None
+    )
+    inserted = 0
+    for model_name, pricing in _pricing_entries_from_models_json().items():
+        usage_kind = pricing.get("usage_kind") or USAGE_KIND_LLM
+        base_params = {
+            "model": model_name,
+            "usage_kind": usage_kind,
+            "effective_from": day.isoformat(),
+            "input_micro_usd_per_million": _int(
+                pricing.get("input_micro_usd_per_million")
+            ),
+            "output_micro_usd_per_million": _int(
+                pricing.get("output_micro_usd_per_million")
+            ),
+            "cache_read_micro_usd_per_million": _int(
+                pricing.get("cache_read_micro_usd_per_million")
+            ),
+            "cache_creation_micro_usd_per_million": _int(
+                pricing.get("cache_creation_micro_usd_per_million")
+            ),
+            "reasoning_micro_usd_per_million": _int(
+                pricing.get("reasoning_micro_usd_per_million")
+            ),
+            "audio_micro_usd_per_second": _int(
+                pricing.get("audio_micro_usd_per_second")
+            ),
+            "tts_micro_usd_per_million_chars": _int(
+                pricing.get("tts_micro_usd_per_million_chars")
+            ),
+        }
+        if has_currency:
+            sql = f"""
+                INSERT INTO {table} (
+                    id, model, usage_kind, effective_from, currency, source,
+                    input_micro_usd_per_million,
+                    output_micro_usd_per_million,
+                    cache_read_micro_usd_per_million,
+                    cache_creation_micro_usd_per_million,
+                    reasoning_micro_usd_per_million,
+                    audio_micro_usd_per_second,
+                    tts_micro_usd_per_million_chars,
+                    created_at, updated_at
+                ) VALUES (
+                    gen_random_uuid(), :model, :usage_kind, CAST(:effective_from AS date),
+                    :currency, :source,
+                    :input_micro_usd_per_million,
+                    :output_micro_usd_per_million,
+                    :cache_read_micro_usd_per_million,
+                    :cache_creation_micro_usd_per_million,
+                    :reasoning_micro_usd_per_million,
+                    :audio_micro_usd_per_second,
+                    :tts_micro_usd_per_million_chars,
+                    now(), now()
+                )
+                ON CONFLICT (model, usage_kind, effective_from) DO UPDATE SET
+                    currency = EXCLUDED.currency,
+                    source = EXCLUDED.source,
+                    input_micro_usd_per_million = EXCLUDED.input_micro_usd_per_million,
+                    output_micro_usd_per_million = EXCLUDED.output_micro_usd_per_million,
+                    cache_read_micro_usd_per_million = EXCLUDED.cache_read_micro_usd_per_million,
+                    cache_creation_micro_usd_per_million = EXCLUDED.cache_creation_micro_usd_per_million,
+                    reasoning_micro_usd_per_million = EXCLUDED.reasoning_micro_usd_per_million,
+                    audio_micro_usd_per_second = EXCLUDED.audio_micro_usd_per_second,
+                    tts_micro_usd_per_million_chars = EXCLUDED.tts_micro_usd_per_million_chars,
+                    updated_at = now()
+            """
+            params = {
+                **base_params,
+                "currency": pricing.get("currency") or "USD",
+                "source": _normalize_rate_source(pricing.get("source")),
+            }
+        else:
+            sql = f"""
+                INSERT INTO {table} (
+                    id, model, usage_kind, effective_from,
+                    input_micro_usd_per_million,
+                    output_micro_usd_per_million,
+                    cache_read_micro_usd_per_million,
+                    cache_creation_micro_usd_per_million,
+                    reasoning_micro_usd_per_million,
+                    audio_micro_usd_per_second,
+                    tts_micro_usd_per_million_chars,
+                    created_at, updated_at
+                ) VALUES (
+                    gen_random_uuid(), :model, :usage_kind, CAST(:effective_from AS date),
+                    :input_micro_usd_per_million,
+                    :output_micro_usd_per_million,
+                    :cache_read_micro_usd_per_million,
+                    :cache_creation_micro_usd_per_million,
+                    :reasoning_micro_usd_per_million,
+                    :audio_micro_usd_per_second,
+                    :tts_micro_usd_per_million_chars,
+                    now(), now()
+                )
+                ON CONFLICT (model, usage_kind, effective_from) DO UPDATE SET
+                    input_micro_usd_per_million = EXCLUDED.input_micro_usd_per_million,
+                    output_micro_usd_per_million = EXCLUDED.output_micro_usd_per_million,
+                    cache_read_micro_usd_per_million = EXCLUDED.cache_read_micro_usd_per_million,
+                    cache_creation_micro_usd_per_million = EXCLUDED.cache_creation_micro_usd_per_million,
+                    reasoning_micro_usd_per_million = EXCLUDED.reasoning_micro_usd_per_million,
+                    audio_micro_usd_per_second = EXCLUDED.audio_micro_usd_per_second,
+                    tts_micro_usd_per_million_chars = EXCLUDED.tts_micro_usd_per_million_chars,
+                    updated_at = now()
+            """
+            params = base_params
+        result = db.execute(text(sql), params)
+        if result.rowcount:
+            inserted += 1
+    if inserted:
+        from app.services.usage.pricing_cache import invalidate_all_pricing_cache
+
+        invalidate_all_pricing_cache()
+    return inserted
+
+
+def seed_pricing_catalog(db: Session, *, effective_from: Optional[date] = None) -> int:
+    """Back-compat alias."""
+    return seed_pricing_rates(db, effective_from=effective_from)
+
+
+def apply_cost_to_bucket(
+    db: Session,
+    *,
+    organization_id: UUID,
+    bucket: Dict[str, Any],
+    resolver: Optional[PricingResolver] = None,
+) -> bool:
+    """Recompute and persist cost columns for one rollup bucket from row totals."""
+    context = bucket.get("context") or {}
+    workspace_id = bucket.get("workspace_id")
+    usage_kind = bucket.get("usage_kind") or USAGE_KIND_LLM
+    usage_date = bucket["usage_date"]
+    if isinstance(usage_date, str):
+        usage_date = date.fromisoformat(usage_date)
+
+    base_params = {
+        "organization_id": str(organization_id),
+        "workspace_id": str(workspace_id) if workspace_id else None,
+        "product_section": bucket["product_section"],
+        "model": bucket["model"],
+        "usage_date": usage_date.isoformat(),
+        "usage_kind": usage_kind,
+    }
+    exact_params = {
+        **base_params,
+        "context": json.dumps(context),
+        "context_resource_id": str(context.get("resource_id") or ""),
+        "context_resource_type": str(context.get("resource_type") or ""),
+    }
+    row = db.execute(
+        text(
+            """
+            SELECT
+                prompt_tokens, completion_tokens, cache_read_tokens,
+                cache_creation_tokens, reasoning_tokens, audio_seconds,
+                tts_characters
+            FROM llm_usage_daily
+            WHERE organization_id = CAST(:organization_id AS uuid)
+              AND product_section = :product_section
+              AND model = :model
+              AND usage_date = CAST(:usage_date AS date)
+              AND usage_kind = :usage_kind
+              AND workspace_id IS NOT DISTINCT FROM CAST(:workspace_id AS uuid)
+              AND context = CAST(:context AS jsonb)
+            """
+        ),
+        exact_params,
+    ).mappings().first()
+    if not row:
+        row = db.execute(
+            text(
+                """
+                SELECT
+                    prompt_tokens, completion_tokens, cache_read_tokens,
+                    cache_creation_tokens, reasoning_tokens, audio_seconds,
+                    tts_characters
+                FROM llm_usage_daily
+                WHERE organization_id = CAST(:organization_id AS uuid)
+                  AND product_section = :product_section
+                  AND model = :model
+                  AND usage_date = CAST(:usage_date AS date)
+                  AND usage_kind = :usage_kind
+                  AND workspace_id IS NOT DISTINCT FROM CAST(:workspace_id AS uuid)
+                  AND COALESCE(context->>'resource_id', '') = :context_resource_id
+                  AND COALESCE(context->>'resource_type', '') = :context_resource_type
+                """
+            ),
+            exact_params,
+        ).mappings().first()
+    if not row:
+        return False
+
+    pricing = resolver or PricingResolver(db)
+    rate = pricing.resolve_rate(
+        organization_id=organization_id,
+        model=bucket["model"],
+        usage_kind=usage_kind,
+        usage_date=usage_date,
+    )
+    costs = compute_cost(
+        UsageMetrics(
+            prompt_tokens=_int(row["prompt_tokens"]),
+            completion_tokens=_int(row["completion_tokens"]),
+            cache_read_tokens=_int(row["cache_read_tokens"]),
+            cache_creation_tokens=_int(row["cache_creation_tokens"]),
+            reasoning_tokens=_int(row["reasoning_tokens"]),
+            audio_seconds=_int(row["audio_seconds"]),
+            tts_characters=_int(row["tts_characters"]),
+        ),
+        rate,
+    )
+    update_params = {
+        **exact_params,
+        "input_cost_micro_usd": costs.input_cost_micro_usd,
+        "output_cost_micro_usd": costs.output_cost_micro_usd,
+        "cache_read_cost_micro_usd": costs.cache_read_cost_micro_usd,
+        "cache_creation_cost_micro_usd": costs.cache_creation_cost_micro_usd,
+        "reasoning_cost_micro_usd": costs.reasoning_cost_micro_usd,
+        "audio_cost_micro_usd": costs.audio_cost_micro_usd,
+        "tts_cost_micro_usd": costs.tts_cost_micro_usd,
+        "total_cost_micro_usd": costs.total_cost_micro_usd,
+        "pricing_rate_source": costs.pricing_rate_source,
+        "pricing_rate_id": str(costs.pricing_rate_id)
+        if costs.pricing_rate_id
+        else None,
+    }
+    result = db.execute(
+        text(
+            """
+            UPDATE llm_usage_daily SET
+                input_cost_micro_usd = :input_cost_micro_usd,
+                output_cost_micro_usd = :output_cost_micro_usd,
+                cache_read_cost_micro_usd = :cache_read_cost_micro_usd,
+                cache_creation_cost_micro_usd = :cache_creation_cost_micro_usd,
+                reasoning_cost_micro_usd = :reasoning_cost_micro_usd,
+                audio_cost_micro_usd = :audio_cost_micro_usd,
+                tts_cost_micro_usd = :tts_cost_micro_usd,
+                total_cost_micro_usd = :total_cost_micro_usd,
+                pricing_rate_source = :pricing_rate_source,
+                pricing_rate_id = CAST(:pricing_rate_id AS uuid),
+                updated_at = now()
+            WHERE organization_id = CAST(:organization_id AS uuid)
+              AND product_section = :product_section
+              AND model = :model
+              AND usage_date = CAST(:usage_date AS date)
+              AND usage_kind = :usage_kind
+              AND workspace_id IS NOT DISTINCT FROM CAST(:workspace_id AS uuid)
+              AND context = CAST(:context AS jsonb)
+            """
+        ),
+        update_params,
+    )
+    if result.rowcount:
+        return True
+    result = db.execute(
+        text(
+            """
+            UPDATE llm_usage_daily SET
+                input_cost_micro_usd = :input_cost_micro_usd,
+                output_cost_micro_usd = :output_cost_micro_usd,
+                cache_read_cost_micro_usd = :cache_read_cost_micro_usd,
+                cache_creation_cost_micro_usd = :cache_creation_cost_micro_usd,
+                reasoning_cost_micro_usd = :reasoning_cost_micro_usd,
+                audio_cost_micro_usd = :audio_cost_micro_usd,
+                tts_cost_micro_usd = :tts_cost_micro_usd,
+                total_cost_micro_usd = :total_cost_micro_usd,
+                pricing_rate_source = :pricing_rate_source,
+                pricing_rate_id = CAST(:pricing_rate_id AS uuid),
+                updated_at = now()
+            WHERE organization_id = CAST(:organization_id AS uuid)
+              AND product_section = :product_section
+              AND model = :model
+              AND usage_date = CAST(:usage_date AS date)
+              AND usage_kind = :usage_kind
+              AND workspace_id IS NOT DISTINCT FROM CAST(:workspace_id AS uuid)
+              AND COALESCE(context->>'resource_id', '') = :context_resource_id
+              AND COALESCE(context->>'resource_type', '') = :context_resource_type
+            """
+        ),
+        update_params,
+    )
+    return bool(result.rowcount)
+
+
+def recompute_usage_costs(
+    db: Session,
+    *,
+    organization_id: Optional[UUID] = None,
+    model: Optional[str] = None,
+    usage_kind: Optional[str] = None,
+    start_date: Optional[date] = None,
+    end_date: Optional[date] = None,
+    batch_size: int = 500,
+    on_progress: Optional[Callable[[int], None]] = None,
+) -> int:
+    """Recompute stored costs for existing rollup rows (backfill / override changes)."""
+    resolver = PricingResolver(db)
+    updated = 0
+    last_id: Optional[str] = None
+
+    while True:
+        params: Dict[str, Any] = {"batch_size": batch_size}
+        filters = ["1=1"]
+        if organization_id is not None:
+            filters.append("organization_id = CAST(:organization_id AS uuid)")
+            params["organization_id"] = str(organization_id)
+        if model is not None:
+            filters.append("model = :model")
+            params["model"] = model
+        if usage_kind is not None:
+            filters.append("usage_kind = :usage_kind")
+            params["usage_kind"] = usage_kind
+        if start_date is not None:
+            filters.append("usage_date >= CAST(:start_date AS date)")
+            params["start_date"] = start_date.isoformat()
+        if end_date is not None:
+            filters.append("usage_date <= CAST(:end_date AS date)")
+            params["end_date"] = end_date.isoformat()
+        if last_id is not None:
+            filters.append("id > CAST(:last_id AS uuid)")
+            params["last_id"] = last_id
+
+        rows = db.execute(
+            text(
+                f"""
+                SELECT
+                    id, organization_id, workspace_id, product_section, model,
+                    context, usage_date, usage_kind
+                FROM llm_usage_daily
+                WHERE {' AND '.join(filters)}
+                ORDER BY id
+                LIMIT :batch_size
+                """
+            ),
+            params,
+        ).mappings().all()
+        if not rows:
+            break
+
+        for row in rows:
+            bucket = {
+                "workspace_id": row["workspace_id"],
+                "product_section": row["product_section"],
+                "model": row["model"],
+                "context": row["context"] or {},
+                "usage_date": row["usage_date"],
+                "usage_kind": row["usage_kind"] or USAGE_KIND_LLM,
+            }
+            if apply_cost_to_bucket(
+                db,
+                organization_id=row["organization_id"],
+                bucket=bucket,
+                resolver=resolver,
+            ):
+                updated += 1
+            last_id = str(row["id"])
+
+        db.commit()
+        if on_progress is not None:
+            on_progress(updated)
+
+    if on_progress is not None:
+        on_progress(updated)
+
+    return updated
diff --git a/app/services/usage/pricing_cache.py b/app/services/usage/pricing_cache.py
new file mode 100644
index 00000000..dd2ebb0f
--- /dev/null
+++ b/app/services/usage/pricing_cache.py
@@ -0,0 +1,87 @@
+"""Redis cache for resolved usage pricing rates."""
+
+from __future__ import annotations
+
+import json
+from datetime import date
+from typing import Optional
+from uuid import UUID
+
+import redis
+from loguru import logger
+
+from app.config import settings
+
+PRICING_CACHE_TTL_SEC = 3600
+PRICING_NULL_CACHE_TTL_SEC = 300
+PRICING_CACHE_PREFIX = "usage:pricing"
+
+_redis: redis.Redis | None = None
+
+
+def _client() -> redis.Redis:
+    global _redis
+    if _redis is None:
+        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+    return _redis
+
+
+def pricing_cache_key(
+    *,
+    organization_id: UUID,
+    model: str,
+    usage_kind: str,
+    usage_date: date,
+) -> str:
+    return (
+        f"{PRICING_CACHE_PREFIX}:{organization_id}:{model}:"
+        f"{usage_kind}:{usage_date.isoformat()}"
+    )
+
+
+def get_cached_rate_payload(key: str) -> Optional[dict]:
+    try:
+        raw = _client().get(key)
+    except redis.RedisError as exc:
+        logger.debug("pricing cache read skipped: {}", exc)
+        return None
+    if raw is None:
+        return None
+    if raw == "__null__":
+        return {}
+    try:
+        payload = json.loads(raw)
+    except json.JSONDecodeError:
+        return None
+    return payload if isinstance(payload, dict) else None
+
+
+def set_cached_rate_payload(key: str, payload: Optional[dict]) -> None:
+    try:
+        value = "__null__" if not payload else json.dumps(payload)
+        ttl = PRICING_NULL_CACHE_TTL_SEC if not payload else PRICING_CACHE_TTL_SEC
+        _client().setex(key, ttl, value)
+    except redis.RedisError as exc:
+        logger.debug("pricing cache write skipped: {}", exc)
+
+
+def invalidate_org_pricing_cache(organization_id: UUID) -> None:
+    _invalidate_pricing_cache_pattern(f"{PRICING_CACHE_PREFIX}:{organization_id}:*")
+
+
+def invalidate_all_pricing_cache() -> None:
+    _invalidate_pricing_cache_pattern(f"{PRICING_CACHE_PREFIX}:*")
+
+
+def _invalidate_pricing_cache_pattern(pattern: str) -> None:
+    try:
+        client = _client()
+        cursor = 0
+        while True:
+            cursor, keys = client.scan(cursor=cursor, match=pattern, count=200)
+            if keys:
+                client.delete(*keys)
+            if cursor == 0:
+                break
+    except redis.RedisError as exc:
+        logger.debug("pricing cache invalidate skipped: {}", exc)
diff --git a/app/services/usage/pricing_jobs.py b/app/services/usage/pricing_jobs.py
new file mode 100644
index 00000000..5e01947a
--- /dev/null
+++ b/app/services/usage/pricing_jobs.py
@@ -0,0 +1,145 @@
+"""Usage cost recompute job lifecycle."""
+
+from __future__ import annotations
+
+from datetime import date, datetime, timezone
+from typing import Any, Dict, Optional
+from uuid import UUID
+
+from fastapi import HTTPException
+from sqlalchemy.orm import Session
+
+from app.models.database import UsageCostRecomputeJob
+
+ACTIVE_JOB_STATUSES = ("pending", "running")
+TERMINAL_JOB_STATUSES = ("completed", "failed")
+
+
+def _utcnow() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+def get_recompute_job(
+    db: Session,
+    *,
+    organization_id: UUID,
+    job_id: UUID,
+) -> UsageCostRecomputeJob:
+    job = (
+        db.query(UsageCostRecomputeJob)
+        .filter(
+            UsageCostRecomputeJob.id == job_id,
+            UsageCostRecomputeJob.organization_id == organization_id,
+        )
+        .first()
+    )
+    if job is None:
+        raise HTTPException(status_code=404, detail="Recompute job not found")
+    return job
+
+
+def create_recompute_job(
+    db: Session,
+    *,
+    organization_id: UUID,
+    model: Optional[str] = None,
+    usage_kind: Optional[str] = None,
+    start_date: Optional[date] = None,
+    end_date: Optional[date] = None,
+) -> UsageCostRecomputeJob:
+    active = (
+        db.query(UsageCostRecomputeJob)
+        .filter(
+            UsageCostRecomputeJob.organization_id == organization_id,
+            UsageCostRecomputeJob.status.in_(ACTIVE_JOB_STATUSES),
+        )
+        .first()
+    )
+    if active is not None:
+        raise HTTPException(
+            status_code=409,
+            detail="A usage cost recompute job is already in progress",
+        )
+
+    job = UsageCostRecomputeJob(
+        organization_id=organization_id,
+        status="pending",
+        model=model,
+        usage_kind=usage_kind,
+        start_date=start_date,
+        end_date=end_date,
+    )
+    db.add(job)
+    db.commit()
+    db.refresh(job)
+    return job
+
+
+def enqueue_recompute_job(db: Session, job: UsageCostRecomputeJob) -> str:
+    from app.workers.tasks import recompute_usage_costs_task
+
+    result = recompute_usage_costs_task.delay(job_id=str(job.id))
+    job.celery_task_id = result.id
+    job.updated_at = _utcnow()
+    db.commit()
+    return result.id
+
+
+def mark_job_running(db: Session, job_id: UUID) -> None:
+    job = db.query(UsageCostRecomputeJob).filter(UsageCostRecomputeJob.id == job_id).first()
+    if job is None:
+        return
+    job.status = "running"
+    job.updated_at = _utcnow()
+    db.commit()
+
+
+def update_job_progress(db: Session, job_id: UUID, updated_rows: int) -> None:
+    job = db.query(UsageCostRecomputeJob).filter(UsageCostRecomputeJob.id == job_id).first()
+    if job is None:
+        return
+    job.updated_rows = updated_rows
+    job.updated_at = _utcnow()
+    db.commit()
+
+
+def mark_job_completed(db: Session, job_id: UUID, updated_rows: int) -> None:
+    job = db.query(UsageCostRecomputeJob).filter(UsageCostRecomputeJob.id == job_id).first()
+    if job is None:
+        return
+    now = _utcnow()
+    job.status = "completed"
+    job.updated_rows = updated_rows
+    job.updated_at = now
+    job.completed_at = now
+    db.commit()
+
+
+def mark_job_failed(db: Session, job_id: UUID, error_message: str) -> None:
+    job = db.query(UsageCostRecomputeJob).filter(UsageCostRecomputeJob.id == job_id).first()
+    if job is None:
+        return
+    now = _utcnow()
+    job.status = "failed"
+    job.error_message = error_message[:4000]
+    job.updated_at = now
+    job.completed_at = now
+    db.commit()
+
+
+def job_to_dict(job: UsageCostRecomputeJob) -> Dict[str, Any]:
+    return {
+        "id": job.id,
+        "organization_id": job.organization_id,
+        "status": job.status,
+        "model": job.model,
+        "usage_kind": job.usage_kind,
+        "start_date": job.start_date,
+        "end_date": job.end_date,
+        "updated_rows": int(job.updated_rows or 0),
+        "error_message": job.error_message,
+        "celery_task_id": job.celery_task_id,
+        "created_at": job.created_at,
+        "updated_at": job.updated_at,
+        "completed_at": job.completed_at,
+    }
diff --git a/app/services/usage/pricing_ops.py b/app/services/usage/pricing_ops.py
new file mode 100644
index 00000000..abd55d4b
--- /dev/null
+++ b/app/services/usage/pricing_ops.py
@@ -0,0 +1,137 @@
+"""Ops helpers for usage pricing catalog maintenance."""
+
+from __future__ import annotations
+
+import json
+from datetime import date
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.services.usage.pricing import (
+    DEFAULT_RATES_EFFECTIVE_FROM,
+    _pricing_entries_from_models_json,
+    _rates_table,
+    seed_pricing_rates,
+)
+
+_MODELS_JSON_PATH = (
+    Path(__file__).resolve().parent.parent.parent / "config" / "models.json"
+)
+_CATALOG_JSON_PATH = (
+    Path(__file__).resolve().parent.parent.parent / "config" / "pricing_catalog.json"
+)
+
+_RATE_COMPARE_COLUMNS = (
+    "input_micro_usd_per_million",
+    "output_micro_usd_per_million",
+    "cache_read_micro_usd_per_million",
+    "cache_creation_micro_usd_per_million",
+    "reasoning_micro_usd_per_million",
+    "audio_micro_usd_per_second",
+    "tts_micro_usd_per_million_chars",
+)
+
+
+def models_missing_pricing_blocks() -> List[str]:
+    if not _MODELS_JSON_PATH.exists():
+        return []
+    payload = json.loads(_MODELS_JSON_PATH.read_text(encoding="utf-8"))
+    missing: List[str] = []
+    for model_name, config in payload.items():
+        if model_name.startswith("_") or not isinstance(config, dict):
+            continue
+        pricing = config.get("pricing")
+        if not isinstance(pricing, dict):
+            missing.append(model_name)
+    return sorted(missing)
+
+
+def litellm_unresolved_models() -> List[Dict[str, Any]]:
+    if not _CATALOG_JSON_PATH.exists():
+        return []
+    payload = json.loads(_CATALOG_JSON_PATH.read_text(encoding="utf-8"))
+    meta = payload.get("_metadata")
+    if not isinstance(meta, dict):
+        return []
+    unresolved = meta.get("unresolved")
+    return unresolved if isinstance(unresolved, list) else []
+
+
+def _load_db_rates(
+    db: Session, *, effective_from: date
+) -> Dict[Tuple[str, str], Dict[str, Any]]:
+    table = _rates_table(db)
+    rows = db.execute(
+        text(
+            f"""
+            SELECT model, usage_kind, {_rate_columns_sql()}
+            FROM {table}
+            WHERE effective_from = CAST(:effective_from AS date)
+            """
+        ),
+        {"effective_from": effective_from.isoformat()},
+    ).mappings().all()
+    return {(row["model"], row["usage_kind"]): dict(row) for row in rows}
+
+
+def _rate_columns_sql() -> str:
+    return ", ".join(_RATE_COMPARE_COLUMNS)
+
+
+def diff_models_json_vs_db(
+    db: Session, *, effective_from: Optional[date] = None
+) -> Dict[str, Any]:
+    day = effective_from or DEFAULT_RATES_EFFECTIVE_FROM
+    json_rates = _pricing_entries_from_models_json()
+    db_rates = _load_db_rates(db, effective_from=day)
+
+    json_keys = {(model, entry.get("usage_kind") or "llm") for model, entry in json_rates.items()}
+    db_keys = set(db_rates.keys())
+
+    only_in_json = sorted(json_keys - db_keys)
+    only_in_db = sorted(db_keys - json_keys)
+    mismatches: List[Dict[str, Any]] = []
+
+    for key in sorted(json_keys & db_keys):
+        model, usage_kind = key
+        expected = json_rates[model]
+        actual = db_rates[key]
+        field_diffs: Dict[str, Dict[str, int]] = {}
+        for column in _RATE_COMPARE_COLUMNS:
+            left = int(expected.get(column) or 0)
+            right = int(actual.get(column) or 0)
+            if left != right:
+                field_diffs[column] = {"models_json": left, "database": right}
+        if field_diffs:
+            mismatches.append(
+                {
+                    "model": model,
+                    "usage_kind": usage_kind,
+                    "fields": field_diffs,
+                }
+            )
+
+    return {
+        "effective_from": day.isoformat(),
+        "models_json_count": len(json_rates),
+        "database_count": len(db_rates),
+        "only_in_models_json": [
+            {"model": model, "usage_kind": kind} for model, kind in only_in_json
+        ],
+        "only_in_database": [
+            {"model": model, "usage_kind": kind} for model, kind in only_in_db
+        ],
+        "mismatches": mismatches,
+        "missing_pricing_blocks": models_missing_pricing_blocks(),
+        "litellm_unresolved": litellm_unresolved_models(),
+        "in_sync": not only_in_json and not only_in_db and not mismatches,
+    }
+
+
+def seed_rates_from_models_json(
+    db: Session, *, effective_from: Optional[date] = None
+) -> int:
+    return seed_pricing_rates(db, effective_from=effective_from)
diff --git a/app/services/usage/pricing_overrides.py b/app/services/usage/pricing_overrides.py
new file mode 100644
index 00000000..7f55ef01
--- /dev/null
+++ b/app/services/usage/pricing_overrides.py
@@ -0,0 +1,530 @@
+"""Org-level usage pricing overrides."""
+
+from __future__ import annotations
+
+from datetime import date, timedelta
+from typing import Any, Dict, List, Optional, Set
+from uuid import UUID
+
+from fastapi import HTTPException
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.services.usage.pricing import (
+    PricingResolver,
+    USAGE_KIND_LLM,
+    USAGE_KIND_STT,
+    USAGE_KIND_TTS,
+    _int,
+    _pricing_entries_from_models_json,
+    _rates_table,
+    _usd_per_million_to_micro,
+    _usd_per_minute_to_micro_per_second,
+)
+from app.services.usage.pricing_cache import invalidate_org_pricing_cache
+from app.services.usage.pricing_jobs import create_recompute_job, enqueue_recompute_job
+from app.services.usage.usage_costs import micro_to_usd
+
+RATE_COLUMNS = (
+    "input_micro_usd_per_million",
+    "output_micro_usd_per_million",
+    "cache_read_micro_usd_per_million",
+    "cache_creation_micro_usd_per_million",
+    "reasoning_micro_usd_per_million",
+    "audio_micro_usd_per_second",
+    "tts_micro_usd_per_million_chars",
+)
+
+USD_RATE_FIELDS = {
+    "input_per_1m": "input_micro_usd_per_million",
+    "output_per_1m": "output_micro_usd_per_million",
+    "cache_read_per_1m": "cache_read_micro_usd_per_million",
+    "cache_write_per_1m": "cache_creation_micro_usd_per_million",
+    "reasoning_per_1m": "reasoning_micro_usd_per_million",
+    "audio_per_minute": "audio_micro_usd_per_second",
+    "tts_per_1m_characters": "tts_micro_usd_per_million_chars",
+}
+
+
+def _known_models(db: Session) -> Set[str]:
+    models = set(_pricing_entries_from_models_json().keys())
+    table = _rates_table(db)
+    rows = db.execute(text(f"SELECT DISTINCT model FROM {table}")).scalars().all()
+    models.update(rows)
+    return models
+
+
+def validate_model_name(db: Session, model: str) -> None:
+    if model not in _known_models(db):
+        raise HTTPException(status_code=400, detail=f"Unknown model: {model}")
+
+
+def _validate_usage_kind(usage_kind: str) -> str:
+    kind = usage_kind or USAGE_KIND_LLM
+    if kind not in {USAGE_KIND_LLM, USAGE_KIND_STT, USAGE_KIND_TTS}:
+        raise HTTPException(status_code=400, detail=f"Invalid usage_kind: {kind}")
+    return kind
+
+
+def _micro_to_optional_usd_per_1m(micro: Optional[int]) -> Optional[float]:
+    if micro is None:
+        return None
+    return micro_to_usd(_int(micro))
+
+
+def _micro_to_optional_usd_per_minute(micro_per_second: Optional[int]) -> Optional[float]:
+    if micro_per_second is None:
+        return None
+    return micro_to_usd(_int(micro_per_second) * 60)
+
+
+def _rates_usd_from_row(row: Dict[str, Any]) -> Dict[str, Optional[float]]:
+    return {
+        "input_per_1m": _micro_to_optional_usd_per_1m(row.get("input_micro_usd_per_million")),
+        "output_per_1m": _micro_to_optional_usd_per_1m(row.get("output_micro_usd_per_million")),
+        "cache_read_per_1m": _micro_to_optional_usd_per_1m(
+            row.get("cache_read_micro_usd_per_million")
+        ),
+        "cache_write_per_1m": _micro_to_optional_usd_per_1m(
+            row.get("cache_creation_micro_usd_per_million")
+        ),
+        "reasoning_per_1m": _micro_to_optional_usd_per_1m(
+            row.get("reasoning_micro_usd_per_million")
+        ),
+        "audio_per_minute": _micro_to_optional_usd_per_minute(
+            row.get("audio_micro_usd_per_second")
+        ),
+        "tts_per_1m_characters": _micro_to_optional_usd_per_1m(
+            row.get("tts_micro_usd_per_million_chars")
+        ),
+    }
+
+
+def _rate_card_to_usd_dict(card) -> Dict[str, float]:
+    return {
+        "input_per_1m": micro_to_usd(card.input_micro_usd_per_million),
+        "output_per_1m": micro_to_usd(card.output_micro_usd_per_million),
+        "cache_read_per_1m": micro_to_usd(card.cache_read_micro_usd_per_million),
+        "cache_write_per_1m": micro_to_usd(card.cache_creation_micro_usd_per_million),
+        "reasoning_per_1m": micro_to_usd(card.reasoning_micro_usd_per_million),
+        "audio_per_minute": micro_to_usd(card.audio_micro_usd_per_second * 60),
+        "tts_per_1m_characters": micro_to_usd(card.tts_micro_usd_per_million_chars),
+    }
+
+
+def _override_row_to_dict(row: Any) -> Dict[str, Any]:
+    payload = dict(row)
+    for key in ("id", "organization_id"):
+        if payload.get(key) is not None:
+            payload[key] = str(payload[key])
+    rates = _rates_usd_from_row(payload)
+    return {
+        "id": payload["id"],
+        "organization_id": payload["organization_id"],
+        "model": payload["model"],
+        "usage_kind": payload["usage_kind"],
+        "effective_from": payload["effective_from"],
+        "effective_to": payload.get("effective_to"),
+        "rates": rates,
+        "created_at": payload.get("created_at"),
+        "updated_at": payload.get("updated_at"),
+    }
+
+
+def _usd_payload_to_micro_columns(payload: Dict[str, Any]) -> Dict[str, Optional[int]]:
+    columns: Dict[str, Optional[int]] = {}
+    for usd_field, micro_column in USD_RATE_FIELDS.items():
+        if usd_field not in payload:
+            continue
+        value = payload.get(usd_field)
+        if value is None:
+            columns[micro_column] = None
+            continue
+        if usd_field == "audio_per_minute":
+            columns[micro_column] = _usd_per_minute_to_micro_per_second(value)
+        else:
+            columns[micro_column] = _usd_per_million_to_micro(value)
+    return columns
+
+
+def list_overrides(
+    db: Session,
+    *,
+    organization_id: UUID,
+    model: Optional[str] = None,
+    usage_kind: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    filters = ["organization_id = CAST(:organization_id AS uuid)"]
+    params: Dict[str, Any] = {"organization_id": str(organization_id)}
+    if model is not None:
+        filters.append("model = :model")
+        params["model"] = model
+    if usage_kind is not None:
+        filters.append("usage_kind = :usage_kind")
+        params["usage_kind"] = _validate_usage_kind(usage_kind)
+
+    rows = db.execute(
+        text(
+            f"""
+            SELECT *
+            FROM org_model_pricing_overrides
+            WHERE {' AND '.join(filters)}
+            ORDER BY model ASC, usage_kind ASC, effective_from DESC
+            """
+        ),
+        params,
+    ).mappings().all()
+    return [_override_row_to_dict(row) for row in rows]
+
+
+def get_effective_rate(
+    db: Session,
+    *,
+    organization_id: UUID,
+    model: str,
+    usage_kind: str,
+    as_of: date,
+) -> Dict[str, Any]:
+    validate_model_name(db, model)
+    kind = _validate_usage_kind(usage_kind)
+    resolver = PricingResolver(db)
+    effective = resolver.resolve_rate(
+        organization_id=organization_id,
+        model=model,
+        usage_kind=kind,
+        usage_date=as_of,
+    )
+    catalog = resolver._resolve_catalog(model, kind, as_of)
+    override_row = db.execute(
+        text(
+            """
+            SELECT *
+            FROM org_model_pricing_overrides
+            WHERE organization_id = CAST(:organization_id AS uuid)
+              AND model = :model
+              AND usage_kind = :usage_kind
+              AND effective_from <= :as_of
+              AND (effective_to IS NULL OR effective_to >= :as_of)
+            ORDER BY effective_from DESC
+            LIMIT 1
+            """
+        ),
+        {
+            "organization_id": str(organization_id),
+            "model": model,
+            "usage_kind": kind,
+            "as_of": as_of.isoformat(),
+        },
+    ).mappings().first()
+
+    return {
+        "model": model,
+        "usage_kind": kind,
+        "as_of": as_of,
+        "catalog_rates": _rate_card_to_usd_dict(catalog) if catalog else None,
+        "catalog_rate_id": str(catalog.rate_id) if catalog else None,
+        "override": _override_row_to_dict(override_row) if override_row else None,
+        "effective_rates": _rate_card_to_usd_dict(effective) if effective else None,
+        "effective_source": effective.source if effective else None,
+        "effective_rate_id": str(effective.rate_id) if effective else None,
+        "has_override": override_row is not None,
+    }
+
+
+def list_effective_pricing(
+    db: Session,
+    *,
+    organization_id: UUID,
+    usage_kind: Optional[str] = None,
+    model: Optional[str] = None,
+    as_of: date,
+    limit: int = 200,
+) -> List[Dict[str, Any]]:
+    if model:
+        return [
+            get_effective_rate(
+                db,
+                organization_id=organization_id,
+                model=model,
+                usage_kind=usage_kind or USAGE_KIND_LLM,
+                as_of=as_of,
+            )
+        ]
+
+    keys: Set[tuple[str, str]] = set()
+    for entry in list_overrides(db, organization_id=organization_id, usage_kind=usage_kind):
+        keys.add((entry["model"], entry["usage_kind"]))
+
+    table = _rates_table(db)
+    rate_filters = ["effective_from <= :as_of", "(effective_to IS NULL OR effective_to >= :as_of)"]
+    params: Dict[str, Any] = {"as_of": as_of.isoformat(), "limit": limit}
+    if usage_kind is not None:
+        rate_filters.append("usage_kind = :usage_kind")
+        params["usage_kind"] = _validate_usage_kind(usage_kind)
+    rate_rows = db.execute(
+        text(
+            f"""
+            SELECT DISTINCT model, usage_kind
+            FROM {table}
+            WHERE {' AND '.join(rate_filters)}
+            ORDER BY model ASC
+            LIMIT :limit
+            """
+        ),
+        params,
+    ).mappings().all()
+    for row in rate_rows:
+        keys.add((row["model"], row["usage_kind"]))
+
+    results: List[Dict[str, Any]] = []
+    for model_name, kind in sorted(keys)[:limit]:
+        results.append(
+            get_effective_rate(
+                db,
+                organization_id=organization_id,
+                model=model_name,
+                usage_kind=kind,
+                as_of=as_of,
+            )
+        )
+    return results
+
+
+def upsert_override(
+    db: Session,
+    *,
+    organization_id: UUID,
+    model: str,
+    usage_kind: str,
+    effective_from: date,
+    effective_to: Optional[date] = None,
+    rates: Dict[str, Any],
+    recompute: bool = True,
+) -> Dict[str, Any]:
+    validate_model_name(db, model)
+    kind = _validate_usage_kind(usage_kind)
+    if effective_to is not None and effective_to < effective_from:
+        raise HTTPException(status_code=400, detail="effective_to must be >= effective_from")
+
+    micro_columns = _usd_payload_to_micro_columns(rates)
+    if not micro_columns:
+        raise HTTPException(status_code=400, detail="At least one rate field is required")
+
+    params: Dict[str, Any] = {
+        "organization_id": str(organization_id),
+        "model": model,
+        "usage_kind": kind,
+        "effective_from": effective_from.isoformat(),
+        "effective_to": effective_to.isoformat() if effective_to else None,
+    }
+    for column in RATE_COLUMNS:
+        params[column] = micro_columns.get(column)
+
+    row = db.execute(
+        text(
+            """
+            INSERT INTO org_model_pricing_overrides (
+                organization_id, model, usage_kind, effective_from, effective_to,
+                input_micro_usd_per_million, output_micro_usd_per_million,
+                cache_read_micro_usd_per_million, cache_creation_micro_usd_per_million,
+                reasoning_micro_usd_per_million, audio_micro_usd_per_second,
+                tts_micro_usd_per_million_chars
+            ) VALUES (
+                CAST(:organization_id AS uuid), :model, :usage_kind,
+                CAST(:effective_from AS date), CAST(:effective_to AS date),
+                :input_micro_usd_per_million, :output_micro_usd_per_million,
+                :cache_read_micro_usd_per_million, :cache_creation_micro_usd_per_million,
+                :reasoning_micro_usd_per_million, :audio_micro_usd_per_second,
+                :tts_micro_usd_per_million_chars
+            )
+            ON CONFLICT (organization_id, model, usage_kind, effective_from)
+            DO UPDATE SET
+                effective_to = EXCLUDED.effective_to,
+                input_micro_usd_per_million = COALESCE(
+                    EXCLUDED.input_micro_usd_per_million,
+                    org_model_pricing_overrides.input_micro_usd_per_million
+                ),
+                output_micro_usd_per_million = COALESCE(
+                    EXCLUDED.output_micro_usd_per_million,
+                    org_model_pricing_overrides.output_micro_usd_per_million
+                ),
+                cache_read_micro_usd_per_million = COALESCE(
+                    EXCLUDED.cache_read_micro_usd_per_million,
+                    org_model_pricing_overrides.cache_read_micro_usd_per_million
+                ),
+                cache_creation_micro_usd_per_million = COALESCE(
+                    EXCLUDED.cache_creation_micro_usd_per_million,
+                    org_model_pricing_overrides.cache_creation_micro_usd_per_million
+                ),
+                reasoning_micro_usd_per_million = COALESCE(
+                    EXCLUDED.reasoning_micro_usd_per_million,
+                    org_model_pricing_overrides.reasoning_micro_usd_per_million
+                ),
+                audio_micro_usd_per_second = COALESCE(
+                    EXCLUDED.audio_micro_usd_per_second,
+                    org_model_pricing_overrides.audio_micro_usd_per_second
+                ),
+                tts_micro_usd_per_million_chars = COALESCE(
+                    EXCLUDED.tts_micro_usd_per_million_chars,
+                    org_model_pricing_overrides.tts_micro_usd_per_million_chars
+                ),
+                updated_at = now()
+            RETURNING *
+            """
+        ),
+        params,
+    ).mappings().first()
+    db.commit()
+
+    invalidate_org_pricing_cache(organization_id)
+
+    recompute_job_id = None
+    recompute_enqueued = False
+    if recompute:
+        recompute_job_id = _enqueue_override_recompute(
+            db,
+            organization_id=organization_id,
+            model=model,
+            usage_kind=kind,
+            start_date=effective_from,
+            end_date=effective_to,
+        )
+        recompute_enqueued = recompute_job_id is not None
+
+    result = _override_row_to_dict(row)
+    result["recompute_enqueued"] = recompute_enqueued
+    result["recompute_job_id"] = recompute_job_id
+    return result
+
+
+def delete_override(
+    db: Session,
+    *,
+    organization_id: UUID,
+    model: str,
+    usage_kind: str,
+    effective_from: Optional[date] = None,
+    recompute: bool = True,
+) -> Dict[str, Any]:
+    validate_model_name(db, model)
+    kind = _validate_usage_kind(usage_kind)
+    filters = [
+        "organization_id = CAST(:organization_id AS uuid)",
+        "model = :model",
+        "usage_kind = :usage_kind",
+    ]
+    params: Dict[str, Any] = {
+        "organization_id": str(organization_id),
+        "model": model,
+        "usage_kind": kind,
+    }
+
+    if effective_from is not None:
+        filters.append("effective_from = CAST(:effective_from AS date)")
+        params["effective_from"] = effective_from.isoformat()
+        result = db.execute(
+            text(
+                f"""
+                DELETE FROM org_model_pricing_overrides
+                WHERE {' AND '.join(filters)}
+                RETURNING effective_from, effective_to
+                """
+            ),
+            params,
+        ).mappings().first()
+        if not result:
+            raise HTTPException(status_code=404, detail="Override not found")
+        recompute_from = effective_from
+        recompute_to = result.get("effective_to")
+        db.commit()
+    else:
+        active = db.execute(
+            text(
+                f"""
+                SELECT id, effective_from, effective_to
+                FROM org_model_pricing_overrides
+                WHERE {' AND '.join(filters)}
+                  AND effective_from <= CURRENT_DATE
+                  AND (effective_to IS NULL OR effective_to >= CURRENT_DATE)
+                ORDER BY effective_from DESC
+                LIMIT 1
+                """
+            ),
+            params,
+        ).mappings().first()
+        if not active:
+            raise HTTPException(status_code=404, detail="No active override found")
+        recompute_from = active["effective_from"]
+        recompute_to = active.get("effective_to")
+        yesterday = date.today() - timedelta(days=1)
+        if active["effective_from"] > yesterday:
+            db.execute(
+                text(
+                    """
+                    DELETE FROM org_model_pricing_overrides
+                    WHERE id = CAST(:id AS uuid)
+                    """
+                ),
+                {"id": str(active["id"])},
+            )
+        else:
+            db.execute(
+                text(
+                    """
+                    UPDATE org_model_pricing_overrides
+                    SET effective_to = CAST(:effective_to AS date), updated_at = now()
+                    WHERE id = CAST(:id AS uuid)
+                    """
+                ),
+                {"id": str(active["id"]), "effective_to": yesterday.isoformat()},
+            )
+        db.commit()
+
+    invalidate_org_pricing_cache(organization_id)
+
+    recompute_job_id = None
+    recompute_enqueued = False
+    if recompute:
+        recompute_job_id = _enqueue_override_recompute(
+            db,
+            organization_id=organization_id,
+            model=model,
+            usage_kind=kind,
+            start_date=recompute_from,
+            end_date=recompute_to,
+        )
+        recompute_enqueued = recompute_job_id is not None
+
+    return {
+        "deleted": True,
+        "model": model,
+        "usage_kind": kind,
+        "recompute_enqueued": recompute_enqueued,
+        "recompute_job_id": recompute_job_id,
+    }
+
+
+def _enqueue_override_recompute(
+    db: Session,
+    *,
+    organization_id: UUID,
+    model: str,
+    usage_kind: str,
+    start_date: date,
+    end_date: Optional[date],
+) -> Optional[str]:
+    try:
+        job = create_recompute_job(
+            db,
+            organization_id=organization_id,
+            model=model,
+            usage_kind=usage_kind,
+            start_date=start_date,
+            end_date=end_date,
+        )
+        enqueue_recompute_job(db, job)
+        return str(job.id)
+    except HTTPException as exc:
+        if exc.status_code == 409:
+            return None
+        raise
diff --git a/app/services/usage/usage_costs.py b/app/services/usage/usage_costs.py
new file mode 100644
index 00000000..914de0b1
--- /dev/null
+++ b/app/services/usage/usage_costs.py
@@ -0,0 +1,51 @@
+"""Usage cost presentation helpers for API responses."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+MICRO_USD_PER_DOLLAR = 1_000_000
+
+
+def micro_to_usd(micro: int) -> float:
+    return micro / MICRO_USD_PER_DOLLAR
+
+
+def costs_from_micro(
+    *,
+    input_cost_micro_usd: int = 0,
+    output_cost_micro_usd: int = 0,
+    cache_read_cost_micro_usd: int = 0,
+    cache_creation_cost_micro_usd: int = 0,
+    reasoning_cost_micro_usd: int = 0,
+    audio_cost_micro_usd: int = 0,
+    tts_cost_micro_usd: int = 0,
+    total_cost_micro_usd: Optional[int] = None,
+    has_unpriced_usage: bool = False,
+    currency: str = "USD",
+) -> Dict[str, Any]:
+    total_micro = (
+        total_cost_micro_usd
+        if total_cost_micro_usd is not None
+        else (
+            input_cost_micro_usd
+            + output_cost_micro_usd
+            + cache_read_cost_micro_usd
+            + cache_creation_cost_micro_usd
+            + reasoning_cost_micro_usd
+            + audio_cost_micro_usd
+            + tts_cost_micro_usd
+        )
+    )
+    return {
+        "input_cost_usd": micro_to_usd(input_cost_micro_usd),
+        "output_cost_usd": micro_to_usd(output_cost_micro_usd),
+        "cache_read_cost_usd": micro_to_usd(cache_read_cost_micro_usd),
+        "cache_write_cost_usd": micro_to_usd(cache_creation_cost_micro_usd),
+        "reasoning_cost_usd": micro_to_usd(reasoning_cost_micro_usd),
+        "audio_cost_usd": micro_to_usd(audio_cost_micro_usd),
+        "tts_cost_usd": micro_to_usd(tts_cost_micro_usd),
+        "total_cost_usd": micro_to_usd(total_micro),
+        "currency": currency,
+        "has_unpriced_usage": has_unpriced_usage,
+    }
diff --git a/app/workers/config.py b/app/workers/config.py
index 3448429d..e06336d0 100644
--- a/app/workers/config.py
+++ b/app/workers/config.py
@@ -74,6 +74,7 @@
 # Queues consumed by the dedicated call-import / evaluation worker.
 IMPORTS_WORKER_QUEUES = "imports,diarization,eval-control,evaluations"
 EVAL_CONTROL_QUEUE = "eval-control"
+USAGE_WORKER_QUEUE = "usage"
 
 # Create Celery app
 celery_app = Celery(
@@ -137,13 +138,16 @@
     "generate_evaluation_prompt_improvements": {"queue": "evaluations"},
     "generate_agent_flowchart": {"queue": "celery"},
     "map_agent_flowchart_prompt_sections": {"queue": "celery"},
-    "flush_usage_counters": {"queue": "celery"},
+    "flush_usage_counters": {"queue": USAGE_WORKER_QUEUE},
+    "recompute_usage_costs": {"queue": USAGE_WORKER_QUEUE},
 }
 
-# Periodic flush of Redis LLM usage counters into catalog rollups.
+# Periodic flush of Redis usage counters into catalog rollups (usage queue).
+_flush_interval = float(os.environ.get("USAGE_FLUSH_BEAT_SECONDS", "120"))
 celery_app.conf.beat_schedule = {
     "flush-llm-usage-counters": {
         "task": "flush_usage_counters",
-        "schedule": 120.0,
+        "schedule": _flush_interval,
+        "options": {"queue": USAGE_WORKER_QUEUE},
     },
 }
diff --git a/app/workers/tasks/__init__.py b/app/workers/tasks/__init__.py
index 04eb0380..499d9a5d 100644
--- a/app/workers/tasks/__init__.py
+++ b/app/workers/tasks/__init__.py
@@ -23,6 +23,7 @@
 from . import finalize_telephony_recording
 from . import call_import_bulk_ops
 from . import flush_usage_counters
+from . import recompute_usage_costs
 from app.workers.concurrency import eval_dispatch
 from app.workers.concurrency import fair_dispatch
 from app.workers.concurrency import fair_diarization_dispatch
@@ -117,3 +118,4 @@
     call_import_bulk_ops.materialize_call_import_evaluation_task
 )
 flush_usage_counters_task = flush_usage_counters.flush_usage_counters_task
+recompute_usage_costs_task = recompute_usage_costs.recompute_usage_costs_task
diff --git a/app/workers/tasks/recompute_usage_costs.py b/app/workers/tasks/recompute_usage_costs.py
new file mode 100644
index 00000000..8340f797
--- /dev/null
+++ b/app/workers/tasks/recompute_usage_costs.py
@@ -0,0 +1,79 @@
+"""Celery task: recompute stored usage costs for rollup rows."""
+
+from __future__ import annotations
+
+from datetime import date
+from typing import Optional
+from uuid import UUID
+
+from loguru import logger
+
+from app.database import SessionLocal
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="recompute_usage_costs")
+def recompute_usage_costs_task(
+    job_id: Optional[str] = None,
+    organization_id: Optional[str] = None,
+    model: Optional[str] = None,
+    usage_kind: Optional[str] = None,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+) -> dict:
+    from app.models.database import UsageCostRecomputeJob
+    from app.services.usage.pricing import recompute_usage_costs
+    from app.services.usage.pricing_jobs import (
+        mark_job_completed,
+        mark_job_failed,
+        mark_job_running,
+        update_job_progress,
+    )
+
+    db = SessionLocal()
+    try:
+        if job_id:
+            job = (
+                db.query(UsageCostRecomputeJob)
+                .filter(UsageCostRecomputeJob.id == UUID(job_id))
+                .first()
+            )
+            if job is None:
+                return {"updated_rows": 0, "error": "job not found"}
+            mark_job_running(db, job.id)
+            org_uuid = job.organization_id
+            model = job.model
+            usage_kind = job.usage_kind
+            start = job.start_date
+            end = job.end_date
+            progress_job_id = job.id
+        else:
+            org_uuid = UUID(organization_id) if organization_id else None
+            start = date.fromisoformat(start_date) if start_date else None
+            end = date.fromisoformat(end_date) if end_date else None
+            progress_job_id = None
+
+        def _on_progress(updated_rows: int) -> None:
+            if progress_job_id is not None:
+                update_job_progress(db, progress_job_id, updated_rows)
+
+        updated = recompute_usage_costs(
+            db,
+            organization_id=org_uuid,
+            model=model,
+            usage_kind=usage_kind,
+            start_date=start,
+            end_date=end,
+            on_progress=_on_progress if progress_job_id else None,
+        )
+        if progress_job_id is not None:
+            mark_job_completed(db, progress_job_id, updated)
+        if updated:
+            logger.info("Recomputed usage costs for {} rollup row(s)", updated)
+        return {"updated_rows": updated, "job_id": job_id}
+    except Exception as exc:
+        if job_id:
+            mark_job_failed(db, UUID(job_id), str(exc))
+        raise
+    finally:
+        db.close()
diff --git a/docker-compose.observability.yml b/docker-compose.observability.yml
index 402ac2e0..35b84190 100644
--- a/docker-compose.observability.yml
+++ b/docker-compose.observability.yml
@@ -62,6 +62,17 @@ services:
         loki-max-backoff: "800ms"
         loki-external-labels: "service=worker-imports,environment=docker"
 
+  worker-usage:
+    depends_on:
+      - loki
+    logging:
+      driver: loki
+      options:
+        loki-url: "http://localhost:3100/loki/api/v1/push"
+        loki-retries: "5"
+        loki-max-backoff: "800ms"
+        loki-external-labels: "service=worker-usage,environment=docker"
+
 # Pin image tags instead of :latest to reduce exposure to stale third-party
 # binaries (e.g. Go stdlib CVEs in observability tooling).
   loki:
diff --git a/docker-compose.yml b/docker-compose.yml
index 552884f3..13be6295 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -193,5 +193,62 @@ services:
     # (each task may hold catalog + shard connections for tens of seconds).
     command: eai worker --config /app/config.yml --loglevel info --queues imports,diarization,eval-control,evaluations --pool threads --concurrency 12
 
+  # Low-priority usage pricing: Redis flush + cost recompute/backfill.
+  worker-usage:
+    image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
+    build:
+      context: .
+      dockerfile: docker/Dockerfile.worker
+      args:
+        INSTALL_EXTRAS: ""
+    container_name: efficientai_worker_usage
+    env_file:
+      - .env
+    environment:
+      DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
+      REDIS_URL: redis://redis:6379/0
+      CELERY_BROKER_URL: redis://redis:6379/0
+      CELERY_RESULT_BACKEND: redis://redis:6379/0
+      ENCRYPTION_KEY: ${ENCRYPTION_KEY:-}
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+    volumes:
+      - ./uploads:/app/uploads
+      - ./.data:/app/.data
+      - ./config.docker.yml:/app/config.yml:ro
+    command: eai worker --config /app/config.yml --loglevel info --queues usage --pool threads --concurrency 4
+
+  beat:
+    image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
+    build:
+      context: .
+      dockerfile: docker/Dockerfile.worker
+      args:
+        INSTALL_EXTRAS: ""
+    container_name: efficientai_beat
+    env_file:
+      - .env
+    environment:
+      DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
+      REDIS_URL: redis://redis:6379/0
+      CELERY_BROKER_URL: redis://redis:6379/0
+      CELERY_RESULT_BACKEND: redis://redis:6379/0
+      ENCRYPTION_KEY: ${ENCRYPTION_KEY:-}
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+      worker-usage:
+        condition: service_started
+    volumes:
+      - ./uploads:/app/uploads
+      - ./.data:/app/.data
+      - ./config.docker.yml:/app/config.yml:ro
+    command: celery -A app.workers.celery_app beat --loglevel info
+
 volumes:
   postgres_data:
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index d18118b0..287bdd2d 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -13,6 +13,7 @@ import Dashboard from './pages/dashboard/Dashboard'
 
 // Usage
 import Usage from './pages/usage/Usage'
+import UsagePricing from './pages/usage/UsagePricing'
 
 // Prompt Partials
 import PromptPartials from './pages/promptPartials/PromptPartials'
@@ -186,6 +187,7 @@ function App() {
           <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
           <Route path="iam" element={<IAM />} />
           <Route path="usage" element={<Usage />} />
+          <Route path="usage/pricing" element={<UsagePricing />} />
           <Route
             path="workspace-members"
             element={<Navigate to="/iam?tab=workspace-members" replace />}
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 7e2c4369..6dfe8744 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -3017,6 +3017,26 @@ class ApiClient {
       audio_seconds: number
       tts_characters: number
       call_count: number
+      input_cost_micro_usd?: number
+      output_cost_micro_usd?: number
+      cache_read_cost_micro_usd?: number
+      cache_creation_cost_micro_usd?: number
+      reasoning_cost_micro_usd?: number
+      audio_cost_micro_usd?: number
+      tts_cost_micro_usd?: number
+      total_cost_micro_usd?: number
+      costs?: {
+        input_cost_usd: number
+        output_cost_usd: number
+        cache_read_cost_usd: number
+        cache_write_cost_usd: number
+        reasoning_cost_usd: number
+        audio_cost_usd: number
+        tts_cost_usd: number
+        total_cost_usd: number
+        currency: string
+        has_unpriced_usage: boolean
+      }
     }
     last_updated_at?: string | null
   }> {
@@ -3067,6 +3087,26 @@ class ApiClient {
       audio_seconds: number
       tts_characters: number
       call_count: number
+      input_cost_micro_usd?: number
+      output_cost_micro_usd?: number
+      cache_read_cost_micro_usd?: number
+      cache_creation_cost_micro_usd?: number
+      reasoning_cost_micro_usd?: number
+      audio_cost_micro_usd?: number
+      tts_cost_micro_usd?: number
+      total_cost_micro_usd?: number
+      costs?: {
+        input_cost_usd: number
+        output_cost_usd: number
+        cache_read_cost_usd: number
+        cache_write_cost_usd: number
+        reasoning_cost_usd: number
+        audio_cost_usd: number
+        tts_cost_usd: number
+        total_cost_usd: number
+        currency: string
+        has_unpriced_usage: boolean
+      }
     }>
     total_count: number
     truncated_at_limit?: boolean
@@ -3078,6 +3118,135 @@ class ApiClient {
     return response.data
   }
 
+  async listUsagePricingOverrides(params?: {
+    model?: string
+    usage_kind?: string
+  }): Promise<
+    Array<{
+      id: string
+      organization_id: string
+      model: string
+      usage_kind: string
+      effective_from: string
+      effective_to?: string | null
+      rates: {
+        input_per_1m?: number | null
+        output_per_1m?: number | null
+        cache_read_per_1m?: number | null
+        cache_write_per_1m?: number | null
+        reasoning_per_1m?: number | null
+        audio_per_minute?: number | null
+        tts_per_1m_characters?: number | null
+      }
+      recompute_enqueued?: boolean
+      recompute_job_id?: string | null
+    }>
+  > {
+    const response = await this.client.get('/api/v1/organizations/usage/pricing/overrides', {
+      params,
+    })
+    return response.data
+  }
+
+  async upsertUsagePricingOverride(
+    model: string,
+    body: {
+      usage_kind?: string
+      effective_from: string
+      effective_to?: string
+      rates: {
+        input_per_1m?: number
+        output_per_1m?: number
+        cache_read_per_1m?: number
+        cache_write_per_1m?: number
+        reasoning_per_1m?: number
+        audio_per_minute?: number
+        tts_per_1m_characters?: number
+      }
+      recompute?: boolean
+    }
+  ): Promise<{
+    id: string
+    model: string
+    usage_kind: string
+    effective_from: string
+    effective_to?: string | null
+    rates: Record<string, number | null | undefined>
+    recompute_enqueued?: boolean
+    recompute_job_id?: string | null
+  }> {
+    const response = await this.client.put(
+      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
+      body
+    )
+    return response.data
+  }
+
+  async deleteUsagePricingOverride(
+    model: string,
+    params?: { usage_kind?: string; effective_from?: string; recompute?: boolean }
+  ): Promise<{
+    deleted: boolean
+    model: string
+    usage_kind: string
+    recompute_enqueued?: boolean
+    recompute_job_id?: string | null
+  }> {
+    const response = await this.client.delete(
+      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
+      { params }
+    )
+    return response.data
+  }
+
+  async triggerUsageCostRecompute(body?: {
+    start_date?: string
+    end_date?: string
+    model?: string
+    usage_kind?: string
+  }): Promise<{
+    id: string
+    organization_id: string
+    status: string
+    model?: string | null
+    usage_kind?: string | null
+    start_date?: string | null
+    end_date?: string | null
+    updated_rows: number
+    error_message?: string | null
+    celery_task_id?: string | null
+    created_at?: string | null
+    updated_at?: string | null
+    completed_at?: string | null
+  }> {
+    const response = await this.client.post(
+      '/api/v1/organizations/usage/pricing/recompute',
+      body ?? {}
+    )
+    return response.data
+  }
+
+  async getUsageCostRecomputeJob(jobId: string): Promise<{
+    id: string
+    organization_id: string
+    status: string
+    model?: string | null
+    usage_kind?: string | null
+    start_date?: string | null
+    end_date?: string | null
+    updated_rows: number
+    error_message?: string | null
+    celery_task_id?: string | null
+    created_at?: string | null
+    updated_at?: string | null
+    completed_at?: string | null
+  }> {
+    const response = await this.client.get(
+      `/api/v1/organizations/usage/pricing/recompute/${jobId}`
+    )
+    return response.data
+  }
+
   async getOrgUsageFilters(params?: {
     start?: string
     end?: string
diff --git a/frontend/src/pages/usage/SearchableSelect.tsx b/frontend/src/pages/usage/SearchableSelect.tsx
index 66f625dd..61da046a 100644
--- a/frontend/src/pages/usage/SearchableSelect.tsx
+++ b/frontend/src/pages/usage/SearchableSelect.tsx
@@ -27,7 +27,9 @@ export default function SearchableSelect({
   const [search, setSearch] = useState('')
   const rootRef = useRef<HTMLDivElement>(null)
 
-  const selected = options.find((o) => o.id === value)
+  const selected = options.find(
+    (o) => o.id === value || o.id.toLowerCase() === value.toLowerCase(),
+  )
 
   const filtered = useMemo(() => {
     const q = search.trim().toLowerCase()
@@ -102,7 +104,8 @@ export default function SearchableSelect({
                   <button
                     type="button"
                     className={`w-full px-3 py-2 text-left text-sm hover:bg-[#fefce8] ${
-                      opt.id === value
+                      opt.id === value ||
+                      opt.id.toLowerCase() === value.toLowerCase()
                         ? usageTheme.selectHighlight
                         : 'text-gray-800'
                     }`}
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index c97b6f7c..1b049984 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -1,11 +1,13 @@
-import { useEffect, useMemo } from 'react'
+import { useEffect, useMemo, useState } from 'react'
 import { keepPreviousData, useQuery } from '@tanstack/react-query'
-import { useSearchParams } from 'react-router-dom'
+import { Link, useSearchParams } from 'react-router-dom'
 import { Card, CardBody, Spinner } from '@heroui/react'
-import { Activity, ChevronRight } from 'lucide-react'
+import { Activity, ChevronRight, CircleDollarSign } from 'lucide-react'
 import { apiClient } from '../../lib/api'
+import { useIsAdmin } from '../../hooks/useRole'
 import UsageFiltersBar from './UsageFiltersBar'
 import UsageDrillPath from './UsageDrillPath'
+import UsageCostBreakdownModal from './UsageCostBreakdownModal'
 import { defaultUsageDateRange } from './UsageDateRangePicker'
 import { getUsageTimezone } from './usageTimezone'
 import {
@@ -14,6 +16,7 @@ import {
   CALL_IMPORT_PRODUCT_SECTIONS,
   PRODUCT_SECTION_HEADLINES,
   PRODUCT_SECTION_HINTS,
+  USAGE_SECTION_SOURCE_PREFIX,
 } from './usageProductHints'
 
 type DrillGroupBy =
@@ -37,6 +40,19 @@ type FilterOptions = {
   tags?: Array<{ id: string; label: string }>
 }
 
+type UsageCosts = {
+  input_cost_usd: number
+  output_cost_usd: number
+  cache_read_cost_usd: number
+  cache_write_cost_usd: number
+  reasoning_cost_usd: number
+  audio_cost_usd: number
+  tts_cost_usd: number
+  total_cost_usd: number
+  currency: string
+  has_unpriced_usage: boolean
+}
+
 type BreakdownRow = {
   workspace_id?: string | null
   workspace_name?: string | null
@@ -58,6 +74,15 @@ type BreakdownRow = {
   audio_seconds: number
   tts_characters: number
   call_count: number
+  input_cost_micro_usd?: number
+  output_cost_micro_usd?: number
+  cache_read_cost_micro_usd?: number
+  cache_creation_cost_micro_usd?: number
+  reasoning_cost_micro_usd?: number
+  audio_cost_micro_usd?: number
+  tts_cost_micro_usd?: number
+  total_cost_micro_usd?: number
+  costs?: UsageCosts
 }
 
 type WorkspaceSourceRow = BreakdownRow & {
@@ -82,6 +107,26 @@ const EMPTY_METRICS = {
   audio_seconds: 0,
   tts_characters: 0,
   call_count: 0,
+  input_cost_micro_usd: 0,
+  output_cost_micro_usd: 0,
+  cache_read_cost_micro_usd: 0,
+  cache_creation_cost_micro_usd: 0,
+  reasoning_cost_micro_usd: 0,
+  audio_cost_micro_usd: 0,
+  tts_cost_micro_usd: 0,
+  total_cost_micro_usd: 0,
+  costs: {
+    input_cost_usd: 0,
+    output_cost_usd: 0,
+    cache_read_cost_usd: 0,
+    cache_write_cost_usd: 0,
+    reasoning_cost_usd: 0,
+    audio_cost_usd: 0,
+    tts_cost_usd: 0,
+    total_cost_usd: 0,
+    currency: 'USD',
+    has_unpriced_usage: false,
+  },
 }
 
 function compositeRowHeadline(row: WorkspaceSourceRow): string {
@@ -123,6 +168,22 @@ function formatNumber(value: number): string {
   return new Intl.NumberFormat().format(value || 0)
 }
 
+function formatCostUsd(usd?: number | null): string {
+  const amount = Number(usd || 0)
+  if (!amount) return '—'
+  return new Intl.NumberFormat(undefined, {
+    style: 'currency',
+    currency: 'USD',
+    minimumFractionDigits: 2,
+    maximumFractionDigits: 4,
+  }).format(amount)
+}
+
+function rowCostUsd(row: Pick<BreakdownRow, 'costs' | 'total_cost_micro_usd'>): number {
+  if (row.costs?.total_cost_usd != null) return row.costs.total_cost_usd
+  return Number(row.total_cost_micro_usd || 0) / 1_000_000
+}
+
 function formatAudio(seconds: number): string {
   const total = Math.max(0, Math.floor(seconds || 0))
   if (total < 60) return `${total}s`
@@ -342,6 +403,15 @@ function tableRowLabel(
   return rowLabel(groupBy, row, options)
 }
 
+function idInOptions(
+  id: string,
+  options: Array<{ id: string }> | undefined,
+): boolean {
+  if (!id || !options?.length) return false
+  const key = idKey(id)
+  return options.some((o) => idKey(o.id) === key)
+}
+
 function idKey(id: string | null | undefined): string {
   return id ? String(id).toLowerCase() : ''
 }
@@ -490,6 +560,8 @@ export default function Usage() {
   const model = searchParams.get('model') || ''
   const usageKind = (searchParams.get('usage_kind') as Kind) || ''
   const productSection = searchParams.get('product_section') || ''
+  const isAdmin = useIsAdmin()
+  const [costBreakdownOpen, setCostBreakdownOpen] = useState(false)
 
   const showWorkspaceComposite =
     Boolean(workspaceId) &&
@@ -526,11 +598,11 @@ export default function Usage() {
     usage_kind: usageKind || undefined,
     model: model || undefined,
     resource_id: evaluationId || undefined,
-    evaluation_id: evaluationId || undefined,
   }
 
   const dataParams = {
     ...scopeParams,
+    evaluation_id: callImportId ? evaluationId || undefined : undefined,
   }
 
   const usageQueryDefaults = {
@@ -613,24 +685,28 @@ export default function Usage() {
     const updates: Record<string, string | null> = {}
     if (
       workspaceId &&
-      !filterOptions.workspaces?.some((w) => w.id === workspaceId)
+      !idInOptions(workspaceId, filterOptions.workspaces)
     ) {
       updates.workspace_id = null
       updates.call_import_id = null
       updates.resource_id = null
+      updates.product_section = null
     }
     if (
       callImportId &&
-      !filterOptions.call_imports?.some((c) => c.id === callImportId)
+      !idInOptions(callImportId, filterOptions.call_imports)
     ) {
       updates.call_import_id = null
       updates.resource_id = null
+      updates.product_section = null
     }
     if (
       evaluationId &&
-      !filterOptions.evaluations?.some((e) => e.id === evaluationId)
+      !idInOptions(evaluationId, filterOptions.evaluations) &&
+      !idInOptions(evaluationId, filterOptions.resources)
     ) {
       updates.resource_id = null
+      updates.product_section = null
     }
     if (model && !filterOptions.models?.includes(model)) {
       updates.model = null
@@ -650,7 +726,7 @@ export default function Usage() {
     if (dataset && !filterOptions.datasets?.includes(dataset)) {
       updates.dataset = null
     }
-    if (tagId && !filterOptions.tags?.some((t) => t.id === tagId)) {
+    if (tagId && !idInOptions(tagId, filterOptions.tags)) {
       updates.tag_id = null
     }
     if (Object.keys(updates).length > 0) setParams(updates)
@@ -728,6 +804,10 @@ export default function Usage() {
     ? importBreakdownFetching || resourceBreakdownFetching
     : breakdownFetching
   const totals = summary?.totals
+  const estimatedTotalCost =
+    totals?.costs?.total_cost_usd ?? (totals?.total_cost_micro_usd || 0) / 1_000_000
+  const showCostBreakdown =
+    estimatedTotalCost > 0 || Boolean(totals?.costs?.has_unpriced_usage)
   const showAudio = Boolean(totals?.audio_seconds)
   const showTts =
     Boolean(totals?.tts_characters) ||
@@ -743,12 +823,12 @@ export default function Usage() {
       ))
 
   const workspaceLabel =
-    filterOptions?.workspaces?.find((w) => w.id === workspaceId)?.name
+    filterOptions?.workspaces?.find((w) => idKey(w.id) === idKey(workspaceId))?.name
   const callImportLabel =
-    filterOptions?.call_imports?.find((c) => c.id === callImportId)?.label
+    filterOptions?.call_imports?.find((c) => idKey(c.id) === idKey(callImportId))?.label
   const evaluationLabel =
-    filterOptions?.resources?.find((r) => r.id === evaluationId)?.label ||
-    filterOptions?.evaluations?.find((e) => e.id === evaluationId)?.label
+    filterOptions?.resources?.find((r) => idKey(r.id) === idKey(evaluationId))?.label ||
+    filterOptions?.evaluations?.find((e) => idKey(e.id) === idKey(evaluationId))?.label
 
   const productSectionLabel =
     filterOptions?.product_sections?.find((s) => s.id === productSection)?.label
@@ -974,9 +1054,9 @@ export default function Usage() {
   return (
     <div className="space-y-6">
       <div className="flex items-start justify-between gap-4">
-        <div>
+        <div className="min-w-0">
           <h1 className="text-2xl font-bold text-gray-900 flex items-center gap-2">
-            <Activity className="h-6 w-6 text-primary-600" />
+            <Activity className="h-6 w-6 text-primary-600 shrink-0" />
             Usage
           </h1>
           <p className="text-sm text-gray-500 mt-1">
@@ -984,59 +1064,82 @@ export default function Usage() {
             Drill down: workspaces → call imports or product areas → evaluations / models.
           </p>
         </div>
-        {summary?.last_updated_at ? (
-          <p className="text-xs text-gray-400 whitespace-nowrap">
-            Updated {new Date(summary.last_updated_at).toLocaleString()}
-          </p>
-        ) : null}
+        <div className="flex flex-col items-end gap-1.5 shrink-0">
+          <div className="flex items-center gap-3">
+            {showCostBreakdown ? (
+              <button
+                type="button"
+                onClick={() => setCostBreakdownOpen(true)}
+                className="inline-flex items-center gap-1.5 rounded-lg border border-gray-200 bg-white px-2.5 py-1.5 text-xs font-medium text-gray-700 shadow-sm transition-colors hover:border-gray-300 hover:bg-gray-50 hover:text-gray-900"
+                title={formatCostUsd(estimatedTotalCost)}
+              >
+                <CircleDollarSign className="h-4 w-4 shrink-0 text-gray-500" />
+                <span className="whitespace-nowrap">Cost breakdown</span>
+              </button>
+            ) : null}
+            {isAdmin ? (
+              <Link
+                to="/usage/pricing"
+                className="text-sm text-primary-600 hover:text-primary-700 whitespace-nowrap"
+              >
+                Pricing overrides
+              </Link>
+            ) : null}
+          </div>
+          {summary?.last_updated_at ? (
+            <p className="text-[11px] text-gray-400 whitespace-nowrap">
+              Updated {new Date(summary.last_updated_at).toLocaleString()}
+            </p>
+          ) : null}
+        </div>
       </div>
 
       <div
-        className={`grid grid-cols-2 md:grid-cols-4 gap-2 transition-opacity ${
-          summaryFetching ? 'opacity-70' : ''
-        }`}
+        className={`space-y-3 transition-opacity ${summaryFetching ? 'opacity-70' : ''}`}
       >
-        <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={summaryLoading} />
-        <StatCard label="Output tokens" value={totals?.completion_tokens} loading={summaryLoading} />
-        <StatCard label="Total tokens" value={totals?.total_tokens} loading={summaryLoading} />
-        <StatCard label="LLM calls" value={totals?.call_count} loading={summaryLoading} />
-      </div>
+        <div className="grid grid-cols-2 gap-3 md:grid-cols-4">
+          <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={summaryLoading} />
+          <StatCard label="Output tokens" value={totals?.completion_tokens} loading={summaryLoading} />
+          <StatCard label="Total tokens" value={totals?.total_tokens} loading={summaryLoading} />
+          <StatCard label="LLM calls" value={totals?.call_count} loading={summaryLoading} />
+        </div>
 
-      {(showAudio ||
-        showTts ||
-        totals?.cache_read_tokens ||
-        totals?.cache_creation_tokens ||
-        totals?.reasoning_tokens) ? (
-        <div className="grid grid-cols-1 md:grid-cols-4 gap-3">
-          {showAudio ? (
+        <div className="grid grid-cols-2 gap-3 md:grid-cols-4 md:items-stretch">
+          <div className="col-span-2 grid grid-cols-2 gap-3 sm:grid-cols-3 md:col-span-3">
+            {showAudio ? (
+              <StatCard
+                label="STT audio"
+                valueLabel={formatAudio(totals?.audio_seconds || 0)}
+                loading={summaryLoading}
+              />
+            ) : null}
+            {showTts ? (
+              <StatCard label="TTS characters" value={totals?.tts_characters} loading={summaryLoading} />
+            ) : null}
+            {(totals?.cache_read_tokens || 0) > 0 ? (
+              <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={summaryLoading} />
+            ) : null}
+            {(totals?.cache_creation_tokens || 0) > 0 ? (
+              <StatCard
+                label="Cache write"
+                value={totals?.cache_creation_tokens}
+                loading={summaryLoading}
+              />
+            ) : null}
+            {(totals?.reasoning_tokens || 0) > 0 ? (
+              <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={summaryLoading} />
+            ) : null}
+          </div>
+          <div className="col-span-2 md:col-span-1">
             <StatCard
-              label="STT audio"
-              valueLabel={formatAudio(totals?.audio_seconds || 0)}
+              label="Estimated cost"
+              valueLabel={formatCostUsd(estimatedTotalCost)}
               loading={summaryLoading}
+              emphasize
             />
-          ) : null}
-          {showTts ? (
-            <StatCard
-              label="TTS characters"
-              value={totals?.tts_characters}
-              loading={summaryLoading}
-            />
-          ) : null}
-          {(totals?.cache_read_tokens || 0) > 0 ? (
-            <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={summaryLoading} />
-          ) : null}
-          {(totals?.cache_creation_tokens || 0) > 0 ? (
-            <StatCard
-              label="Cache write"
-              value={totals?.cache_creation_tokens}
-              loading={summaryLoading}
-            />
-          ) : null}
-          {(totals?.reasoning_tokens || 0) > 0 ? (
-            <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={summaryLoading} />
-          ) : null}
+          </div>
         </div>
-      ) : null}
+      </div>
 
       <UsageFiltersBar
         start={start}
@@ -1048,6 +1151,7 @@ export default function Usage() {
         tagId={tagId}
         usageKind={usageKind}
         model={model}
+        productSection={productSection}
         options={filterOptions}
         filtersLoading={filtersLoading}
         onDateApply={(s, e) => setParams({ start: s, end: e })}
@@ -1068,7 +1172,20 @@ export default function Usage() {
           })
         }
         onEvaluationChange={(id) => {
-          const resource = filterOptions?.resources?.find((r) => r.id === id)
+          if (id.startsWith(USAGE_SECTION_SOURCE_PREFIX)) {
+            const section = id.slice(USAGE_SECTION_SOURCE_PREFIX.length)
+            setParams({
+              product_section: section || null,
+              resource_id: null,
+              call_import_id: null,
+              model: null,
+              usage_kind: null,
+            })
+            return
+          }
+          const resource = filterOptions?.resources?.find(
+            (r) => idKey(r.id) === idKey(id),
+          )
           setParams({
             resource_id: id || null,
             product_section: id ? resource?.product_section || null : null,
@@ -1122,6 +1239,7 @@ export default function Usage() {
                   <th className="px-4 py-3 font-semibold text-right">Input tokens</th>
                   <th className="px-4 py-3 font-semibold text-right">Output tokens</th>
                   <th className="px-4 py-3 font-semibold text-right">Total tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">Est. cost</th>
                   <th className="px-4 py-3 font-semibold text-right">STT audio</th>
                   <th className="px-4 py-3 font-semibold text-right">TTS chars</th>
                   <th className="px-4 py-3 font-semibold text-right">Cache read</th>
@@ -1189,6 +1307,9 @@ export default function Usage() {
                       <td className="px-4 py-3 text-right tabular-nums font-medium">
                         {formatNumber(row.total_tokens)}
                       </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-700">
+                        {formatCostUsd(rowCostUsd(row))}
+                      </td>
                       <td className="px-4 py-3 text-right tabular-nums text-gray-500">
                         {row.audio_seconds ? formatAudio(row.audio_seconds) : '—'}
                       </td>
@@ -1207,28 +1328,46 @@ export default function Usage() {
           )}
         </CardBody>
       </Card>
+
+      <UsageCostBreakdownModal
+        isOpen={costBreakdownOpen}
+        onClose={() => setCostBreakdownOpen(false)}
+        costs={totals?.costs}
+        scopeLabel={scopeSubtitle}
+      />
     </div>
   )
 }
 
+const statCardClass = 'border border-gray-200 ring-1 ring-[#fde047]/25 shadow-sm'
+
 function StatCard({
   label,
   value,
   valueLabel,
   loading,
+  emphasize = false,
+  className = '',
 }: {
   label: string
   value?: number
   valueLabel?: string
   loading?: boolean
+  emphasize?: boolean
+  className?: string
 }) {
   return (
-    <Card shadow="sm" className="border border-gray-200 ring-1 ring-[#fde047]/25">
+    <Card
+      shadow="sm"
+      className={`${statCardClass}${emphasize ? ' ring-2 ring-[#facc15]/50 bg-[#fefce8]/40' : ''} h-full w-full ${className}`}
+    >
       <CardBody className="p-3">
-        <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">
-          {label}
-        </p>
-        <p className="mt-0.5 text-xl font-semibold text-gray-900 tabular-nums">
+        <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">{label}</p>
+        <p
+          className={`mt-0.5 text-xl font-semibold tabular-nums ${
+            emphasize ? 'text-[#854d0e]' : 'text-gray-900'
+          }`}
+        >
           {loading ? '—' : valueLabel ?? formatNumber(value || 0)}
         </p>
       </CardBody>
diff --git a/frontend/src/pages/usage/UsageCostBreakdownModal.tsx b/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
new file mode 100644
index 00000000..9277de53
--- /dev/null
+++ b/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
@@ -0,0 +1,133 @@
+import { X } from 'lucide-react'
+
+type UsageCosts = {
+  input_cost_usd: number
+  output_cost_usd: number
+  cache_read_cost_usd: number
+  cache_write_cost_usd: number
+  reasoning_cost_usd: number
+  audio_cost_usd: number
+  tts_cost_usd: number
+  total_cost_usd: number
+  has_unpriced_usage?: boolean
+}
+
+type Props = {
+  isOpen: boolean
+  onClose: () => void
+  costs?: UsageCosts | null
+  scopeLabel?: string
+}
+
+function formatCostUsd(usd?: number | null): string {
+  const amount = Number(usd || 0)
+  if (!amount) return '$0.00'
+  return new Intl.NumberFormat(undefined, {
+    style: 'currency',
+    currency: 'USD',
+    minimumFractionDigits: 2,
+    maximumFractionDigits: 4,
+  }).format(amount)
+}
+
+const LINE_ITEMS: Array<{ key: keyof UsageCosts; label: string }> = [
+  { key: 'input_cost_usd', label: 'Input tokens' },
+  { key: 'output_cost_usd', label: 'Output tokens' },
+  { key: 'cache_read_cost_usd', label: 'Cache read' },
+  { key: 'cache_write_cost_usd', label: 'Cache write' },
+  { key: 'reasoning_cost_usd', label: 'Reasoning' },
+  { key: 'audio_cost_usd', label: 'Audio / STT' },
+  { key: 'tts_cost_usd', label: 'TTS' },
+]
+
+export default function UsageCostBreakdownModal({
+  isOpen,
+  onClose,
+  costs,
+  scopeLabel,
+}: Props) {
+  if (!isOpen) return null
+
+  const rows = LINE_ITEMS.filter((item) => Number(costs?.[item.key] || 0) > 0)
+
+  return (
+    <div className="fixed inset-0 z-50 overflow-y-auto">
+      <div className="flex min-h-screen items-center justify-center p-4">
+        <div
+          className="fixed inset-0 bg-gray-500/60 transition-opacity"
+          onClick={onClose}
+          aria-hidden
+        />
+        <div
+          className="relative w-full max-w-md rounded-2xl bg-white p-6 shadow-xl"
+          role="dialog"
+          aria-modal="true"
+          aria-labelledby="usage-cost-breakdown-title"
+        >
+          <div className="mb-5 flex items-start justify-between gap-3">
+            <div>
+              <h2 id="usage-cost-breakdown-title" className="text-lg font-semibold text-gray-900">
+                Cost breakdown
+              </h2>
+              {scopeLabel ? (
+                <p className="mt-1 text-sm text-gray-500">{scopeLabel}</p>
+              ) : null}
+            </div>
+            <button
+              type="button"
+              onClick={onClose}
+              className="rounded-lg p-1.5 text-gray-400 hover:bg-gray-100 hover:text-gray-600"
+              aria-label="Close"
+            >
+              <X className="h-5 w-5" />
+            </button>
+          </div>
+
+          {costs?.has_unpriced_usage ? (
+            <p className="mb-4 text-sm text-amber-800 bg-amber-50 border border-amber-200 rounded-lg px-3 py-2">
+              Some usage in this range has no pricing rate — estimated cost may be understated.
+            </p>
+          ) : null}
+
+          <div className="rounded-xl border border-gray-200 overflow-hidden">
+            <div className="flex items-center justify-between bg-gray-50 px-4 py-3 border-b border-gray-200">
+              <span className="text-sm font-medium text-gray-700">Estimated total</span>
+              <span className="text-base font-semibold text-gray-900 tabular-nums">
+                {formatCostUsd(costs?.total_cost_usd)}
+              </span>
+            </div>
+            {rows.length > 0 ? (
+              <ul className="divide-y divide-gray-100">
+                {rows.map((item) => (
+                  <li
+                    key={item.key}
+                    className="flex items-center justify-between px-4 py-2.5 text-sm"
+                  >
+                    <span className="text-gray-600">{item.label}</span>
+                    <span className="font-medium text-gray-900 tabular-nums">
+                      {formatCostUsd(costs?.[item.key] as number)}
+                    </span>
+                  </li>
+                ))}
+              </ul>
+            ) : (
+              <p className="px-4 py-6 text-center text-sm text-gray-500">
+                No per-metric cost detail for this range.
+              </p>
+            )}
+          </div>
+
+          <div className="mt-5 flex justify-end">
+            <button
+              type="button"
+              onClick={onClose}
+              className="rounded-lg bg-gray-900 px-4 py-2 text-sm font-medium text-white hover:bg-gray-800"
+            >
+              Close
+            </button>
+          </div>
+        </div>
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/pages/usage/UsageFiltersBar.tsx b/frontend/src/pages/usage/UsageFiltersBar.tsx
index 747fc2ed..eb736667 100644
--- a/frontend/src/pages/usage/UsageFiltersBar.tsx
+++ b/frontend/src/pages/usage/UsageFiltersBar.tsx
@@ -4,6 +4,7 @@ import { ChevronDown, Filter, SlidersHorizontal } from 'lucide-react'
 import SearchableSelect from './SearchableSelect'
 import UsageDateRangePicker from './UsageDateRangePicker'
 import { usageTheme } from './usageTheme'
+import { CALL_IMPORT_PRODUCT_SECTIONS, USAGE_SECTION_SOURCE_PREFIX } from './usageProductHints'
 
 type Kind = '' | 'llm' | 'stt' | 'tts'
 
@@ -12,6 +13,7 @@ type FilterOptions = {
   call_imports: Array<{ id: string; label: string }>
   evaluations: Array<{ id: string; label: string }>
   resources?: Array<{ id: string; label: string; type?: string; product_section?: string }>
+  product_sections?: Array<{ id: string; label: string }>
   models: string[]
   usage_kinds: Array<{ id: string; label: string }>
   datasets?: string[]
@@ -30,6 +32,7 @@ type UsageFiltersBarProps = {
   tagId: string
   usageKind: Kind
   model: string
+  productSection?: string
   options?: FilterOptions
   filtersLoading?: boolean
   onDateApply: (start: string, end: string) => void
@@ -60,6 +63,7 @@ export default function UsageFiltersBar({
   tagId,
   usageKind,
   model,
+  productSection = '',
   options,
   filtersLoading,
   onDateApply,
@@ -86,7 +90,20 @@ export default function UsageFiltersBar({
     [options?.usage_kinds],
   )
 
-  const sourceOptions = callImportId ? evaluations : resources
+  const sectionSourceOptions = useMemo(() => {
+    if (callImportId) return []
+    return (options?.product_sections ?? [])
+      .filter((s) => !CALL_IMPORT_PRODUCT_SECTIONS.has(s.id))
+      .map((s) => ({
+        id: `${USAGE_SECTION_SOURCE_PREFIX}${s.id}`,
+        label: s.label,
+      }))
+  }, [callImportId, options?.product_sections])
+
+  const sourceOptions = callImportId ? evaluations : [...sectionSourceOptions, ...resources]
+  const sourceSelectValue =
+    evaluationId ||
+    (productSection ? `${USAGE_SECTION_SOURCE_PREFIX}${productSection}` : '')
 
   const activeChips = useMemo((): ActiveChip[] => {
     const chips: ActiveChip[] = []
@@ -124,10 +141,10 @@ export default function UsageFiltersBar({
       }
     }
     const sourceLabel =
-      sourceOptions.find((e) => e.id === evaluationId)?.label ||
+      sourceOptions.find((e) => e.id === sourceSelectValue)?.label ||
       evaluations.find((e) => e.id === evaluationId)?.label ||
       resources.find((r) => r.id === evaluationId)?.label
-    if (evaluationId && sourceLabel) {
+    if ((evaluationId || productSection) && sourceLabel) {
       chips.push({
         key: 'evaluation',
         label: sourceLabel,
@@ -158,6 +175,8 @@ export default function UsageFiltersBar({
     tagId,
     tags,
     evaluationId,
+    productSection,
+    sourceSelectValue,
     sourceOptions,
     evaluations,
     resources,
@@ -286,7 +305,7 @@ export default function UsageFiltersBar({
                   ? 'All evaluations for import'
                   : 'Agents, voice sims, telephony, …'
               }
-              value={evaluationId}
+              value={sourceSelectValue}
               options={sourceOptions}
               onChange={onEvaluationChange}
               emptyMessage="No matching usage in this range"
diff --git a/frontend/src/pages/usage/UsagePricing.tsx b/frontend/src/pages/usage/UsagePricing.tsx
new file mode 100644
index 00000000..f976d116
--- /dev/null
+++ b/frontend/src/pages/usage/UsagePricing.tsx
@@ -0,0 +1,393 @@
+import { useMemo, useState, type ReactNode } from 'react'
+import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
+import { Card, CardBody, Spinner } from '@heroui/react'
+import { Link } from 'react-router-dom'
+import { ArrowLeft, DollarSign, Info, Plus, Trash2 } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { useIsAdmin } from '../../hooks/useRole'
+import { useToast } from '../../hooks/useToast'
+import Button from '../../components/Button'
+import SearchableSelect from './SearchableSelect'
+import { usageTheme } from './usageTheme'
+
+type UsageKind = 'llm' | 'stt' | 'tts'
+
+type PricingRatesUsd = {
+  input_per_1m?: number | null
+  output_per_1m?: number | null
+  cache_read_per_1m?: number | null
+  cache_write_per_1m?: number | null
+  reasoning_per_1m?: number | null
+  audio_per_minute?: number | null
+  tts_per_1m_characters?: number | null
+}
+
+type PricingOverride = {
+  id: string
+  model: string
+  usage_kind: string
+  effective_from: string
+  effective_to?: string | null
+  rates: PricingRatesUsd
+  recompute_enqueued?: boolean
+  recompute_job_id?: string | null
+}
+
+type RateFieldKey = keyof PricingRatesUsd
+
+const EMPTY_RATES: PricingRatesUsd = {
+  input_per_1m: undefined,
+  output_per_1m: undefined,
+  cache_read_per_1m: undefined,
+  cache_write_per_1m: undefined,
+  reasoning_per_1m: undefined,
+  audio_per_minute: undefined,
+  tts_per_1m_characters: undefined,
+}
+
+const RATE_FIELDS: Array<{ key: RateFieldKey; label: string; kinds: UsageKind[] }> = [
+  { key: 'input_per_1m', label: 'Input / 1M tokens (USD)', kinds: ['llm'] },
+  { key: 'output_per_1m', label: 'Output / 1M tokens (USD)', kinds: ['llm'] },
+  { key: 'cache_read_per_1m', label: 'Cache read / 1M (USD)', kinds: ['llm'] },
+  { key: 'cache_write_per_1m', label: 'Cache write / 1M (USD)', kinds: ['llm'] },
+  { key: 'reasoning_per_1m', label: 'Reasoning / 1M (USD)', kinds: ['llm'] },
+  { key: 'audio_per_minute', label: 'Audio / minute (USD)', kinds: ['llm', 'stt'] },
+  { key: 'tts_per_1m_characters', label: 'TTS / 1M characters (USD)', kinds: ['tts'] },
+]
+
+const CONTROL_CLASS = `h-10 w-full rounded-lg border border-gray-200 bg-white px-3 text-sm text-gray-900 shadow-sm outline-none transition-colors hover:border-gray-300 ${usageTheme.focusRing}`
+
+function FormField({
+  label,
+  children,
+  className = '',
+}: {
+  label: string
+  children: ReactNode
+  className?: string
+}) {
+  return (
+    <div className={`min-w-0 ${className}`}>
+      <span className="text-xs font-medium text-gray-500">{label}</span>
+      <div className="mt-1">{children}</div>
+    </div>
+  )
+}
+
+function formatUsd(value?: number | null): string {
+  if (value == null || Number.isNaN(value)) return '—'
+  return new Intl.NumberFormat(undefined, {
+    style: 'currency',
+    currency: 'USD',
+    minimumFractionDigits: 2,
+    maximumFractionDigits: 6,
+  }).format(value)
+}
+
+function todayIso(): string {
+  return new Date().toISOString().slice(0, 10)
+}
+
+export default function UsagePricing() {
+  const isAdmin = useIsAdmin()
+  const queryClient = useQueryClient()
+  const { showToast, ToastContainer } = useToast()
+
+  const [model, setModel] = useState('')
+  const [usageKind, setUsageKind] = useState<UsageKind>('llm')
+  const [effectiveFrom, setEffectiveFrom] = useState(todayIso())
+  const [rates, setRates] = useState<PricingRatesUsd>(EMPTY_RATES)
+
+  const { data: filters } = useQuery({
+    queryKey: ['org-usage', 'filters'],
+    queryFn: () => apiClient.getOrgUsageFilters(),
+    enabled: isAdmin,
+  })
+
+  const { data: overrides = [], isLoading } = useQuery({
+    queryKey: ['usage-pricing', 'overrides'],
+    queryFn: () => apiClient.listUsagePricingOverrides(),
+    enabled: isAdmin,
+  })
+
+  const modelOptions = useMemo(() => {
+    const fromUsage = filters?.models || []
+    const fromOverrides = overrides.map((row) => row.model)
+    return Array.from(new Set([...fromUsage, ...fromOverrides]))
+      .sort()
+      .map((name) => ({ id: name, label: name }))
+  }, [filters?.models, overrides])
+
+  const visibleRateFields = useMemo(
+    () => RATE_FIELDS.filter((field) => field.kinds.includes(usageKind)),
+    [usageKind]
+  )
+
+  const saveMutation = useMutation({
+    mutationFn: () => {
+      const payloadRates = Object.fromEntries(
+        Object.entries(rates).filter((entry): entry is [string, number] => {
+          const value = entry[1]
+          return value != null && !Number.isNaN(value)
+        })
+      )
+      return apiClient.upsertUsagePricingOverride(model, {
+        usage_kind: usageKind,
+        effective_from: effectiveFrom,
+        rates: payloadRates,
+        recompute: true,
+      })
+    },
+    onSuccess: (data) => {
+      queryClient.invalidateQueries({ queryKey: ['usage-pricing'] })
+      queryClient.invalidateQueries({ queryKey: ['org-usage'] })
+      setRates(EMPTY_RATES)
+      showToast(
+        data.recompute_enqueued
+          ? 'Override saved; cost recompute started'
+          : 'Override saved (recompute already running)',
+        'success'
+      )
+    },
+    onError: (error: any) => {
+      showToast(error.response?.data?.detail || 'Failed to save override', 'error')
+    },
+  })
+
+  const deleteMutation = useMutation({
+    mutationFn: (row: PricingOverride) =>
+      apiClient.deleteUsagePricingOverride(row.model, {
+        usage_kind: row.usage_kind,
+        effective_from: row.effective_from,
+      }),
+    onSuccess: () => {
+      queryClient.invalidateQueries({ queryKey: ['usage-pricing'] })
+      queryClient.invalidateQueries({ queryKey: ['org-usage'] })
+      showToast('Override removed', 'success')
+    },
+    onError: (error: any) => {
+      showToast(error.response?.data?.detail || 'Failed to delete override', 'error')
+    },
+  })
+
+  if (!isAdmin) {
+    return (
+      <div className="rounded-lg border border-gray-200 bg-white p-6 text-sm text-gray-600">
+        Organization admin access is required to manage pricing overrides.
+      </div>
+    )
+  }
+
+  return (
+    <div className="mx-auto flex max-w-6xl flex-col gap-6">
+      <ToastContainer />
+
+      <div>
+        <Link
+          to="/usage"
+          className="inline-flex items-center gap-1 text-sm text-gray-500 hover:text-gray-700"
+        >
+          <ArrowLeft className="h-4 w-4" />
+          Back to usage
+        </Link>
+        <div className="mt-3 flex items-start gap-3">
+          <div className="rounded-lg bg-[#fef9c3] p-2 text-[#a16207]">
+            <DollarSign className="h-5 w-5" />
+          </div>
+          <div className="min-w-0 flex-1">
+            <div className="flex items-center gap-2">
+              <h1 className="text-2xl font-semibold text-gray-900">Pricing overrides</h1>
+              <div className="relative group">
+                <button
+                  type="button"
+                  className="rounded-full p-1 text-gray-400 hover:bg-gray-100 hover:text-gray-600"
+                  aria-label="About pricing overrides"
+                >
+                  <Info className="h-4 w-4" />
+                </button>
+                <div
+                  role="tooltip"
+                  className="pointer-events-none absolute left-0 top-full z-20 mt-2 hidden w-72 rounded-lg border border-gray-200 bg-white p-3 text-xs leading-relaxed text-gray-600 shadow-lg group-hover:block group-focus-within:block"
+                >
+                  Set custom USD rates for this organization. Empty fields inherit the platform
+                  catalog. Saving updates costs for matching usage and starts a scoped recompute.
+                </div>
+              </div>
+            </div>
+            <p className="mt-1 text-sm text-gray-500">
+              Per-model rates for this org. Leave blank to use the default catalog.
+            </p>
+          </div>
+        </div>
+      </div>
+
+      <Card className={`${usageTheme.panel} shrink-0`}>
+        <CardBody className="gap-5 p-5">
+          <div>
+            <h2 className="text-base font-semibold text-gray-900">Add or update override</h2>
+            <p className="mt-0.5 text-xs text-gray-500">
+              Saving triggers a scoped cost recompute for this model.
+            </p>
+          </div>
+
+          <div className="grid grid-cols-1 gap-4 md:grid-cols-3">
+            <SearchableSelect
+              label="Model"
+              placeholder="Search models…"
+              value={model}
+              options={modelOptions}
+              onChange={setModel}
+              emptyMessage="No models match"
+            />
+            <FormField label="Usage kind">
+              <select
+                className={CONTROL_CLASS}
+                value={usageKind}
+                onChange={(e) => {
+                  setUsageKind(e.target.value as UsageKind)
+                  setRates(EMPTY_RATES)
+                }}
+              >
+                <option value="llm">LLM</option>
+                <option value="stt">STT</option>
+                <option value="tts">TTS</option>
+              </select>
+            </FormField>
+            <FormField label="Effective from">
+              <input
+                type="date"
+                className={CONTROL_CLASS}
+                value={effectiveFrom}
+                onChange={(e) => setEffectiveFrom(e.target.value)}
+              />
+            </FormField>
+          </div>
+
+          <div>
+            <p className="mb-3 text-xs font-medium uppercase tracking-wide text-gray-500">
+              Rate overrides (USD)
+            </p>
+            <div className="grid grid-cols-1 gap-3 sm:grid-cols-2 lg:grid-cols-3">
+              {visibleRateFields.map(({ key, label }) => (
+                <FormField key={key} label={label}>
+                  <input
+                    type="number"
+                    min={0}
+                    step="any"
+                    placeholder="Inherit from catalog"
+                    className={CONTROL_CLASS}
+                    value={rates[key] != null ? String(rates[key]) : ''}
+                    onChange={(e) => {
+                      const value = e.target.value
+                      setRates((prev) => ({
+                        ...prev,
+                        [key]: value === '' ? undefined : Number(value),
+                      }))
+                    }}
+                  />
+                </FormField>
+              ))}
+            </div>
+          </div>
+
+          <div className="flex items-center justify-end gap-3 border-t border-gray-100 pt-4">
+            <button
+              type="button"
+              className="text-sm text-gray-500 hover:text-gray-700"
+              onClick={() => {
+                setRates(EMPTY_RATES)
+                setModel('')
+              }}
+            >
+              Clear form
+            </button>
+            <Button
+              variant="primary"
+              leftIcon={<Plus className="h-4 w-4" />}
+              disabled={!model || saveMutation.isPending}
+              isLoading={saveMutation.isPending}
+              onClick={() => saveMutation.mutate()}
+            >
+              Save override
+            </Button>
+          </div>
+        </CardBody>
+      </Card>
+
+      <Card className={`${usageTheme.panel} min-h-0`}>
+        <CardBody className="flex min-h-0 flex-col gap-3 p-5">
+          <div className="flex shrink-0 items-center justify-between gap-2">
+            <h2 className="text-base font-semibold text-gray-900">Current overrides</h2>
+            {!isLoading ? (
+              <span className="text-xs text-gray-500">{overrides.length} total</span>
+            ) : null}
+          </div>
+
+          {isLoading ? (
+            <div className="flex justify-center py-12">
+              <Spinner />
+            </div>
+          ) : overrides.length === 0 ? (
+            <p className="py-10 text-center text-sm text-gray-500">
+              No overrides yet. Platform catalog rates apply to all usage.
+            </p>
+          ) : (
+            <div className="max-h-[min(55vh,28rem)] overflow-auto rounded-lg border border-gray-200">
+              <table className="min-w-full text-sm">
+                <thead className="sticky top-0 z-10 bg-gray-50 text-left text-[11px] font-semibold uppercase tracking-wide text-gray-500">
+                  <tr className="border-b border-gray-200">
+                    <th className="px-4 py-2.5">Model</th>
+                    <th className="px-4 py-2.5">Kind</th>
+                    <th className="px-4 py-2.5">Effective</th>
+                    <th className="px-4 py-2.5 text-right">Input</th>
+                    <th className="px-4 py-2.5 text-right">Output</th>
+                    <th className="px-4 py-2.5 text-right">Audio</th>
+                    <th className="px-4 py-2.5 text-right">TTS</th>
+                    <th className="px-4 py-2.5 w-12" />
+                  </tr>
+                </thead>
+                <tbody className="divide-y divide-gray-100 bg-white">
+                  {overrides.map((row) => (
+                    <tr key={row.id} className="hover:bg-gray-50/80">
+                      <td className="whitespace-nowrap px-4 py-2.5 font-medium text-gray-900">
+                        {row.model}
+                      </td>
+                      <td className="px-4 py-2.5 uppercase text-gray-600">{row.usage_kind}</td>
+                      <td className="whitespace-nowrap px-4 py-2.5 text-gray-600">
+                        {row.effective_from}
+                        {row.effective_to ? ` → ${row.effective_to}` : ''}
+                      </td>
+                      <td className="px-4 py-2.5 text-right tabular-nums">
+                        {formatUsd(row.rates.input_per_1m)}
+                      </td>
+                      <td className="px-4 py-2.5 text-right tabular-nums">
+                        {formatUsd(row.rates.output_per_1m)}
+                      </td>
+                      <td className="px-4 py-2.5 text-right tabular-nums">
+                        {formatUsd(row.rates.audio_per_minute)}
+                      </td>
+                      <td className="px-4 py-2.5 text-right tabular-nums">
+                        {formatUsd(row.rates.tts_per_1m_characters)}
+                      </td>
+                      <td className="px-4 py-2.5 text-right">
+                        <button
+                          type="button"
+                          className="rounded p-1 text-red-600 hover:bg-red-50 hover:text-red-800"
+                          onClick={() => deleteMutation.mutate(row)}
+                          disabled={deleteMutation.isPending}
+                          aria-label={`Delete override for ${row.model}`}
+                        >
+                          <Trash2 className="h-4 w-4" />
+                        </button>
+                      </td>
+                    </tr>
+                  ))}
+                </tbody>
+              </table>
+            </div>
+          )}
+        </CardBody>
+      </Card>
+    </div>
+  )
+}
diff --git a/frontend/src/pages/usage/usageProductHints.ts b/frontend/src/pages/usage/usageProductHints.ts
index 4a057b1c..db382f23 100644
--- a/frontend/src/pages/usage/usageProductHints.ts
+++ b/frontend/src/pages/usage/usageProductHints.ts
@@ -45,3 +45,6 @@ export const PRODUCT_SECTION_HINTS: Record<string, string> = {
 
 export const CALL_IMPORT_HINT =
   'Call import batch — CSV upload or manual audio recordings'
+
+/** Prefix for product-area rows in the Source filter (not a resource UUID). */
+export const USAGE_SECTION_SOURCE_PREFIX = 'section:'
diff --git a/scripts/merge_pricing_into_models_json.py b/scripts/merge_pricing_into_models_json.py
new file mode 100644
index 00000000..c31060dd
--- /dev/null
+++ b/scripts/merge_pricing_into_models_json.py
@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+"""Merge pricing_catalog.json micro-USD rates into models.json plan-format pricing blocks."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+MODELS_JSON = REPO_ROOT / "app" / "config" / "models.json"
+CATALOG_JSON = REPO_ROOT / "app" / "config" / "pricing_catalog.json"
+MANUAL_JSON = REPO_ROOT / "app" / "config" / "pricing_manual.json"
+MICRO = 1_000_000
+
+
+def _usd_per_m(micro: int) -> float:
+    return round(micro / MICRO, 8)
+
+
+def _micro_entry_to_plan_pricing(entry: dict) -> dict:
+    usage_kind = entry.get("usage_kind") or "llm"
+    source = entry.get("_price_source") or (
+        "litellm_import" if entry.get("_litellm_key") else "catalog"
+    )
+    pricing: dict = {"source": source}
+    if usage_kind:
+        pricing["usage_kind"] = usage_kind
+
+    if entry.get("input_micro_usd_per_million"):
+        pricing["input_per_1m"] = _usd_per_m(entry["input_micro_usd_per_million"])
+    if entry.get("output_micro_usd_per_million"):
+        pricing["output_per_1m"] = _usd_per_m(entry["output_micro_usd_per_million"])
+    if entry.get("cache_read_micro_usd_per_million"):
+        pricing["cache_read_per_1m"] = _usd_per_m(
+            entry["cache_read_micro_usd_per_million"]
+        )
+    if entry.get("cache_creation_micro_usd_per_million"):
+        pricing["cache_write_per_1m"] = _usd_per_m(
+            entry["cache_creation_micro_usd_per_million"]
+        )
+    if entry.get("reasoning_micro_usd_per_million"):
+        pricing["reasoning_per_1m"] = _usd_per_m(
+            entry["reasoning_micro_usd_per_million"]
+        )
+    if entry.get("audio_micro_usd_per_second"):
+        pricing["audio_per_minute"] = round(
+            entry["audio_micro_usd_per_second"] * 60 / MICRO, 8
+        )
+    if entry.get("tts_micro_usd_per_million_chars"):
+        pricing["tts_per_1m_characters"] = _usd_per_m(
+            entry["tts_micro_usd_per_million_chars"]
+        )
+    return pricing
+
+
+def _load_catalog() -> dict:
+    merged: dict = {}
+    if CATALOG_JSON.exists():
+        payload = json.loads(CATALOG_JSON.read_text(encoding="utf-8"))
+        merged.update(
+            {
+                k: v
+                for k, v in payload.items()
+                if not k.startswith("_") and isinstance(v, dict)
+            }
+        )
+    if MANUAL_JSON.exists():
+        payload = json.loads(MANUAL_JSON.read_text(encoding="utf-8"))
+        for key, value in payload.items():
+            if not key.startswith("_") and isinstance(value, dict):
+                merged[key] = value
+    return merged
+
+
+def merge() -> tuple[int, int]:
+    models = json.loads(MODELS_JSON.read_text(encoding="utf-8"))
+    catalog = _load_catalog()
+    updated = 0
+    skipped = 0
+    for model_name, cfg in models.items():
+        if model_name.startswith("_") or not isinstance(cfg, dict):
+            continue
+        entry = catalog.get(model_name)
+        if not entry:
+            skipped += 1
+            continue
+        pricing = _micro_entry_to_plan_pricing(entry)
+        if len(pricing) <= 1:
+            skipped += 1
+            continue
+        cfg["pricing"] = pricing
+        updated += 1
+    MODELS_JSON.write_text(
+        json.dumps(models, indent=2, ensure_ascii=False) + "\n",
+        encoding="utf-8",
+    )
+    return updated, skipped
+
+
+def main() -> int:
+    updated, skipped = merge()
+    print(f"merged pricing into {updated} model(s); skipped {skipped}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/sync_pricing_catalog_from_litellm.py b/scripts/sync_pricing_catalog_from_litellm.py
new file mode 100644
index 00000000..9d6375aa
--- /dev/null
+++ b/scripts/sync_pricing_catalog_from_litellm.py
@@ -0,0 +1,428 @@
+#!/usr/bin/env python3
+"""Generate app/config/pricing_catalog.json from models.json + LiteLLM model_cost."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import importlib.util
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+MODELS_JSON = REPO_ROOT / "app" / "config" / "models.json"
+MANUAL_JSON = REPO_ROOT / "app" / "config" / "pricing_manual.json"
+OUTPUT_JSON = REPO_ROOT / "app" / "config" / "pricing_catalog.json"
+
+LITELLM_PROVIDER_PREFIX = {
+    "openai": "openai",
+    "anthropic": "anthropic",
+    "google": "gemini",
+    "azure": "azure",
+    "aws": "bedrock",
+    "deepseek": "deepseek",
+    "groq": "groq",
+    "xai": "xai",
+    "fireworks": "fireworks_ai",
+    "sarvam": "sarvam",
+    "deepgram": "deepgram",
+    "elevenlabs": "elevenlabs",
+    "cartesia": "cartesia",
+}
+
+# Catalog key -> LiteLLM model_cost key (when auto-resolution fails).
+EXPLICIT_LITELLM_KEYS: Dict[str, str] = {
+    "chat-latest": "gpt-5-chat-latest",
+    "azure-openai-gpt4": "gpt-4",
+    "aws-bedrock-claude": "anthropic.claude-sonnet-4-20250514-v1:0",
+    "aws-transcribe": "whisper-1",
+    "aws-polly": "aws_polly/neural",
+    "google-speech-v2": "gemini-2.5-flash",
+    "gemini-2.5-pro-stt": "gemini-2.5-flash",
+    "gemini-2.5-flash-stt": "gemini-2.5-flash",
+    "gemini-2.5-flash-lite-stt": "gemini-2.5-flash-lite",
+    "azure-speech-v1": "azure/speech/azure-stt",
+    "azure-tts-v1": "azure/speech/azure-tts",
+    "pulse-v4": "whisper-1",
+    "deepgram-flux": "deepgram/nova-3",
+    "deepgram-nova-3-general-preview-12-2025": "deepgram/nova-3-general",
+    "minimax-m2p5": "fireworks_ai/minimax-m2p7",
+    "qwen3p6-plus": "openrouter/qwen/qwen3.6-plus",
+    "grok-build-0.1": "xai/grok-3",
+    "grok-4.20-0309-non-reasoning": "xai/grok-4-fast-non-reasoning",
+    "grok-4.20-multi-agent-0309": "xai/grok-4",
+    # ElevenLabs: LiteLLM only prices a subset; map siblings to closest priced SKU.
+    "scribe_v2": "elevenlabs/scribe_v1",
+    "scribe_v2_realtime": "elevenlabs/scribe_v1",
+    "eleven_flash_v2_5": "elevenlabs/eleven_multilingual_v2",
+    "eleven_turbo_v2_5": "elevenlabs/eleven_multilingual_v2",
+    "eleven_ttv_v3": "elevenlabs/eleven_v3",
+    "eleven_multilingual_ttv_v2": "elevenlabs/eleven_multilingual_v2",
+    "eleven_english_sts_v2": "elevenlabs/eleven_multilingual_v2",
+    "eleven_multilingual_sts_v2": "elevenlabs/eleven_multilingual_v2",
+    "eleven_text_to_sound_v2": "elevenlabs/eleven_multilingual_v2",
+    "music_v1": "elevenlabs/eleven_multilingual_v2",
+}
+
+MICRO_USD_PER_USD = 1_000_000
+
+
+def _azure_deployment_name(catalog_model: str) -> str:
+    if catalog_model == "azure-openai-gpt4":
+        return "gpt-4"
+    if catalog_model.startswith("azure-"):
+        return catalog_model[len("azure-") :]
+    return catalog_model
+
+
+def _usage_kind(model_type: Optional[str]) -> str:
+    if model_type == "stt":
+        return "stt"
+    if model_type in {"tts", "sts", "sound_effects", "music"}:
+        return "tts"
+    return "llm"
+
+
+def _fireworks_model(name: str) -> str:
+    if name.startswith("accounts/"):
+        return name
+    return f"accounts/fireworks/models/{name}"
+
+
+def _per_million_micro_usd(cost_per_unit: float) -> int:
+    return int(round(float(cost_per_unit) * MICRO_USD_PER_USD * 1_000_000))
+
+
+def _per_second_micro_usd(cost_per_second: float) -> int:
+    return int(round(float(cost_per_second) * MICRO_USD_PER_USD))
+
+
+def _first_cost(info: Dict[str, Any], *keys: str) -> float:
+    for key in keys:
+        value = info.get(key)
+        if value is not None and float(value) > 0:
+            return float(value)
+    return 0.0
+
+
+def _litellm_candidates(
+    catalog_name: str, provider: str, model_type: str
+) -> List[str]:
+    if catalog_name in EXPLICIT_LITELLM_KEYS:
+        return [EXPLICIT_LITELLM_KEYS[catalog_name]]
+
+    prefix = LITELLM_PROVIDER_PREFIX.get(provider, provider)
+    candidates: List[str] = []
+
+    if provider == "azure" and model_type == "llm":
+        deployment = _azure_deployment_name(catalog_name)
+        candidates.extend(
+            [
+                f"azure/{deployment}",
+                f"openai/{deployment}",
+                deployment,
+            ]
+        )
+    elif provider == "azure" and model_type == "stt":
+        candidates.extend(["azure/speech/azure-stt", f"azure/{catalog_name}"])
+    elif provider == "azure" and model_type == "tts":
+        candidates.extend(["azure/speech/azure-tts", f"azure/{catalog_name}"])
+    elif provider == "deepgram":
+        stripped = (
+            catalog_name[len("deepgram-") :]
+            if catalog_name.startswith("deepgram-")
+            else catalog_name
+        )
+        candidates.extend([f"deepgram/{stripped}", stripped])
+    elif provider == "fireworks" and model_type == "llm":
+        fw = _fireworks_model(catalog_name)
+        candidates.extend(
+            [
+                f"fireworks_ai/{fw}",
+                f"fireworks_ai/{catalog_name}",
+                f"fireworks_ai/accounts/fireworks/models/{catalog_name}",
+                catalog_name,
+            ]
+        )
+    elif provider == "elevenlabs":
+        candidates.extend([f"elevenlabs/{catalog_name}", catalog_name])
+    elif provider == "google" and catalog_name.endswith("-stt"):
+        base = catalog_name[: -len("-stt")]
+        candidates.extend([base, f"gemini/{base}"])
+    else:
+        candidates.extend([catalog_name, f"{prefix}/{catalog_name}"])
+
+    # De-dupe while preserving order.
+    seen = set()
+    ordered: List[str] = []
+    for item in candidates:
+        if item not in seen:
+            seen.add(item)
+            ordered.append(item)
+    return ordered
+
+
+def _resolve_litellm_key(
+    catalog_name: str,
+    provider: str,
+    model_type: str,
+    model_cost: Dict[str, Dict[str, Any]],
+) -> Tuple[Optional[str], Optional[Dict[str, Any]]]:
+    for key in _litellm_candidates(catalog_name, provider, model_type):
+        info = model_cost.get(key)
+        if not info:
+            continue
+        if any("cost" in field and info.get(field) for field in info):
+            return key, info
+
+    for key, info in model_cost.items():
+        if key == "sample_spec":
+            continue
+        if key.endswith(f"/{catalog_name}") or key == catalog_name:
+            if any("cost" in field and info.get(field) for field in info):
+                return key, info
+    return None, None
+
+
+def _convert_litellm_pricing(
+    info: Dict[str, Any], *, usage_kind: str
+) -> Dict[str, int]:
+    entry: Dict[str, int] = {
+        "input_micro_usd_per_million": 0,
+        "output_micro_usd_per_million": 0,
+        "cache_read_micro_usd_per_million": 0,
+        "cache_creation_micro_usd_per_million": 0,
+        "reasoning_micro_usd_per_million": 0,
+        "audio_micro_usd_per_second": 0,
+        "tts_micro_usd_per_million_chars": 0,
+    }
+
+    if usage_kind == "stt":
+        audio = _first_cost(
+            info,
+            "input_cost_per_second",
+            "output_cost_per_second",
+        )
+        if not audio:
+            audio_token = _first_cost(info, "input_cost_per_audio_token")
+            if audio_token:
+                # LiteLLM audio-token STT models: ~25 audio tokens/sec (OpenAI convention).
+                audio = audio_token * 25.0
+        entry["audio_micro_usd_per_second"] = _per_second_micro_usd(audio)
+        return entry
+
+    if usage_kind == "tts":
+        per_char = _first_cost(
+            info,
+            "input_cost_per_character",
+            "output_cost_per_character",
+        )
+        if per_char:
+            entry["tts_micro_usd_per_million_chars"] = _per_million_micro_usd(per_char)
+            return entry
+        per_token = _first_cost(
+            info,
+            "output_cost_per_token",
+            "input_cost_per_token",
+        )
+        if per_token:
+            # Approximate chars/token ~= 4 when LiteLLM only exposes token pricing.
+            entry["tts_micro_usd_per_million_chars"] = _per_million_micro_usd(
+                per_token / 4.0
+            )
+        return entry
+
+    entry["input_micro_usd_per_million"] = _per_million_micro_usd(
+        _first_cost(info, "input_cost_per_token", "input_cost_per_audio_token")
+    )
+    entry["output_micro_usd_per_million"] = _per_million_micro_usd(
+        _first_cost(info, "output_cost_per_token")
+    )
+    entry["cache_read_micro_usd_per_million"] = _per_million_micro_usd(
+        _first_cost(info, "cache_read_input_token_cost")
+    )
+    entry["cache_creation_micro_usd_per_million"] = _per_million_micro_usd(
+        _first_cost(info, "cache_creation_input_token_cost")
+    )
+    entry["reasoning_micro_usd_per_million"] = _per_million_micro_usd(
+        _first_cost(info, "output_cost_per_reasoning_token")
+    )
+    return entry
+
+
+def _load_model_cost(*, remote: bool) -> Dict[str, Dict[str, Any]]:
+    if remote:
+        from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
+
+        return get_model_cost_map(
+            url="https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+        )
+    from litellm import model_cost
+
+    return dict(model_cost)
+
+
+def _load_manual_entries() -> Dict[str, Dict[str, Any]]:
+    if not MANUAL_JSON.exists():
+        return {}
+    try:
+        payload = json.loads(MANUAL_JSON.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return {}
+    if not isinstance(payload, dict):
+        return {}
+    return {
+        key: value
+        for key, value in payload.items()
+        if not key.startswith("_") and isinstance(value, dict)
+    }
+
+
+def _apply_manual_entries(
+    catalog: Dict[str, Any], meta: Dict[str, Any], models: Dict[str, Any]
+) -> None:
+    manual = _load_manual_entries()
+    if not manual:
+        return
+    meta.setdefault("manual", {})
+    for catalog_name, entry in manual.items():
+        if catalog_name in catalog:
+            continue
+        if catalog_name not in models or catalog_name.startswith("_"):
+            continue
+        model_type = str(models[catalog_name].get("model_type") or "llm")
+        usage_kind = entry.get("usage_kind") or _usage_kind(model_type)
+        pricing = {
+            k: int(v)
+            for k, v in entry.items()
+            if k != "usage_kind" and not k.startswith("_") and v is not None
+        }
+        if not any(pricing.values()):
+            continue
+        catalog[catalog_name] = {
+            "usage_kind": usage_kind,
+            **pricing,
+            "_price_source": entry.get("_price_source", "pricing_manual.json"),
+            "_litellm_proxy": True,
+        }
+        meta["manual"][catalog_name] = entry.get("_price_source", "pricing_manual.json")
+        meta["resolved"][catalog_name] = "manual"
+        meta["unresolved"] = [
+            item for item in meta["unresolved"] if item.get("model") != catalog_name
+        ]
+
+
+def build_catalog(*, remote: bool = True) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    models = json.loads(MODELS_JSON.read_text(encoding="utf-8"))
+    model_cost = _load_model_cost(remote=remote)
+
+    catalog: Dict[str, Any] = {}
+    meta: Dict[str, Any] = {
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "source": "litellm_remote" if remote else "litellm_local",
+        "resolved": {},
+        "unresolved": [],
+    }
+
+    for catalog_name, cfg in sorted(models.items()):
+        if catalog_name.startswith("_") or not isinstance(cfg, dict):
+            continue
+        provider = str(cfg.get("provider") or "")
+        model_type = str(cfg.get("model_type") or "llm")
+        usage_kind = _usage_kind(model_type)
+
+        litellm_key, info = _resolve_litellm_key(
+            catalog_name, provider, model_type, model_cost
+        )
+        if not info:
+            meta["unresolved"].append(
+                {
+                    "model": catalog_name,
+                    "provider": provider,
+                    "model_type": model_type,
+                }
+            )
+            continue
+
+        pricing = _convert_litellm_pricing(info, usage_kind=usage_kind)
+        if not any(pricing.values()):
+            meta["unresolved"].append(
+                {
+                    "model": catalog_name,
+                    "provider": provider,
+                    "model_type": model_type,
+                    "litellm_key": litellm_key,
+                    "reason": "zero_cost",
+                }
+            )
+            continue
+
+        catalog[catalog_name] = {
+            "usage_kind": usage_kind,
+            **pricing,
+            "_litellm_key": litellm_key,
+            "_litellm_proxy": catalog_name not in {litellm_key, litellm_key.split("/")[-1]},
+        }
+        meta["resolved"][catalog_name] = litellm_key
+
+    _apply_manual_entries(catalog, meta, models)
+    catalog["_metadata"] = meta
+    return catalog, meta
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--local",
+        action="store_true",
+        help="Use bundled LiteLLM model_cost instead of fetching remote JSON",
+    )
+    parser.add_argument(
+        "--stdout",
+        action="store_true",
+        help="Print JSON to stdout instead of writing pricing_catalog.json",
+    )
+    parser.add_argument(
+        "--write-models",
+        action="store_true",
+        help="Also merge plan-format pricing blocks into app/config/models.json",
+    )
+    args = parser.parse_args()
+
+    catalog, meta = build_catalog(remote=not args.local)
+    payload = json.dumps(catalog, indent=2, sort_keys=True) + "\n"
+    if args.stdout:
+        sys.stdout.write(payload)
+    else:
+        OUTPUT_JSON.write_text(payload, encoding="utf-8")
+
+    if args.write_models:
+        merge_path = REPO_ROOT / "scripts" / "merge_pricing_into_models_json.py"
+        spec = importlib.util.spec_from_file_location(
+            "merge_pricing_into_models_json", merge_path
+        )
+        module = importlib.util.module_from_spec(spec)
+        assert spec.loader is not None
+        spec.loader.exec_module(module)
+        updated, skipped = module.merge()
+        print(
+            f"models.json pricing: {updated} updated, {skipped} skipped",
+            file=sys.stderr,
+        )
+
+    resolved = len(meta["resolved"])
+    unresolved = len(meta["unresolved"])
+    print(
+        f"pricing catalog: {resolved} resolved, {unresolved} unresolved -> {OUTPUT_JSON if not args.stdout else 'stdout'}",
+        file=sys.stderr,
+    )
+    if unresolved:
+        for item in meta["unresolved"]:
+            print(f"  unresolved: {item['model']} ({item.get('reason', 'no_litellm_match')})", file=sys.stderr)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index 4abdf609..28c06104 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -494,6 +494,10 @@ def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx, monkeypatch
     db_b = MagicMock()
     db_b.execute.return_value = MagicMock(rowcount=1)
     monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+    monkeypatch.setattr(
+        "app.services.usage.pricing.apply_cost_to_bucket",
+        lambda *args, **kwargs: False,
+    )
 
     first = usage_mod.flush_usage_to_catalog(db_a, org_id)
     second = usage_mod.flush_usage_to_catalog(db_b, org_id)
@@ -600,6 +604,10 @@ def test_upsert_bucket_sql_uses_valid_empty_jsonb_literal():
 
 def test_upsert_bucket_matches_legacy_resource_context_key(monkeypatch):
     """Per-row context must merge into an existing evaluation-level bucket."""
+    monkeypatch.setattr(
+        "app.services.usage.pricing.apply_cost_to_bucket",
+        lambda *args, **kwargs: False,
+    )
     org_id = uuid4()
     evaluation_id = uuid4()
     bucket = {
@@ -629,6 +637,30 @@ def test_upsert_bucket_matches_legacy_resource_context_key(monkeypatch):
     assert "context->>'resource_type'" in legacy_sql
 
 
+def test_upsert_bucket_applies_cost_after_update(monkeypatch):
+    org_id = uuid4()
+    bucket = {
+        "workspace_id": uuid4(),
+        "product_section": "chat",
+        "model": "gpt-test",
+        "context": {},
+        "usage_date": date(2026, 8, 12),
+        "usage_kind": "llm",
+    }
+    deltas = {"prompt_tokens": 10, "completion_tokens": 5}
+    db = MagicMock()
+    db.execute.return_value = MagicMock(rowcount=1)
+    called: list[bool] = []
+    monkeypatch.setattr(
+        "app.services.usage.pricing.apply_cost_to_bucket",
+        lambda *args, **kwargs: called.append(True) or True,
+    )
+
+    usage_mod._upsert_bucket(db, org_id, bucket, deltas)
+
+    assert called
+
+
 def test_orphan_recovery_runs_at_most_once_per_interval(fake_redis, monkeypatch):
     org_id = uuid4()
     claim_key = f"usage:flushing:{org_id}:{uuid4()}"
diff --git a/tests/test_services/test_usage/test_org_usage_costs.py b/tests/test_services/test_usage/test_org_usage_costs.py
new file mode 100644
index 00000000..63c791c5
--- /dev/null
+++ b/tests/test_services/test_usage/test_org_usage_costs.py
@@ -0,0 +1,76 @@
+"""Tests for usage cost API response shaping."""
+
+from types import SimpleNamespace
+
+from app.api.v1.routes.org_usage import (
+    _attach_costs,
+    _breakdown_metrics_from_tuple,
+    _usage_totals_from_row,
+)
+
+
+def test_usage_totals_from_row_includes_nested_costs():
+    row = SimpleNamespace(
+        prompt_tokens=100,
+        completion_tokens=50,
+        cache_read_tokens=0,
+        cache_creation_tokens=0,
+        reasoning_tokens=0,
+        audio_seconds=0,
+        tts_characters=0,
+        call_count=2,
+        input_cost_micro_usd=1_000_000,
+        output_cost_micro_usd=500_000,
+        cache_read_cost_micro_usd=0,
+        cache_creation_cost_micro_usd=0,
+        reasoning_cost_micro_usd=0,
+        audio_cost_micro_usd=0,
+        tts_cost_micro_usd=0,
+        total_cost_micro_usd=1_500_000,
+        has_unpriced_usage=True,
+    )
+    totals = _usage_totals_from_row(row)
+    assert totals["total_tokens"] == 150
+    assert totals["costs"]["total_cost_usd"] == 1.5
+    assert totals["costs"]["has_unpriced_usage"] is True
+
+
+def test_breakdown_metrics_from_tuple_includes_costs():
+    metrics = (
+        10,
+        5,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        100,
+        50,
+        0,
+        0,
+        0,
+        0,
+        0,
+        150,
+        True,
+    )
+    row = _breakdown_metrics_from_tuple(metrics)
+    assert row["costs"]["total_cost_usd"] == 0.00015
+    assert row["costs"]["has_unpriced_usage"] is True
+
+
+def test_attach_costs_uses_cache_write_field_name():
+    payload = _attach_costs(
+        {
+            "input_cost_micro_usd": 0,
+            "output_cost_micro_usd": 0,
+            "cache_read_cost_micro_usd": 0,
+            "cache_creation_cost_micro_usd": 2_000_000,
+            "reasoning_cost_micro_usd": 0,
+            "audio_cost_micro_usd": 0,
+            "tts_cost_micro_usd": 0,
+            "total_cost_micro_usd": 2_000_000,
+        }
+    )
+    assert payload["costs"]["cache_write_cost_usd"] == 2.0
diff --git a/tests/test_services/test_usage/test_pricing.py b/tests/test_services/test_usage/test_pricing.py
new file mode 100644
index 00000000..6a526916
--- /dev/null
+++ b/tests/test_services/test_usage/test_pricing.py
@@ -0,0 +1,211 @@
+"""Tests for usage pricing computation and rate resolution."""
+
+from __future__ import annotations
+
+from datetime import date
+from unittest.mock import MagicMock
+from uuid import uuid4
+
+from app.services.usage.pricing import (
+    CostBreakdown,
+    RateCard,
+    UsageMetrics,
+    _catalog_lookup_models,
+    _normalize_pricing_block,
+    _normalize_rate_source,
+    compute_cost,
+    RATE_SOURCE_CATALOG,
+)
+
+
+def test_compute_cost_llm_token_breakdown():
+    rate = RateCard(
+        source=RATE_SOURCE_CATALOG,
+        rate_id=uuid4(),
+        input_micro_usd_per_million=1_000_000,
+        output_micro_usd_per_million=2_000_000,
+        cache_read_micro_usd_per_million=500_000,
+        cache_creation_micro_usd_per_million=1_250_000,
+    )
+    costs = compute_cost(
+        UsageMetrics(
+            prompt_tokens=1_000_000,
+            completion_tokens=500_000,
+            cache_read_tokens=200_000,
+            cache_creation_tokens=100_000,
+        ),
+        rate,
+    )
+    assert costs.input_cost_micro_usd == 1_000_000
+    assert costs.output_cost_micro_usd == 1_000_000
+    assert costs.cache_read_cost_micro_usd == 100_000
+    assert costs.cache_creation_cost_micro_usd == 125_000
+    assert costs.total_cost_micro_usd == 2_225_000
+    assert costs.pricing_rate_source == RATE_SOURCE_CATALOG
+
+
+def test_compute_cost_stt_audio_seconds():
+    rate = RateCard(
+        source=RATE_SOURCE_CATALOG,
+        rate_id=uuid4(),
+        audio_micro_usd_per_second=100,
+    )
+    costs = compute_cost(UsageMetrics(audio_seconds=90), rate)
+    assert costs.audio_cost_micro_usd == 9_000
+    assert costs.total_cost_micro_usd == 9_000
+
+
+def test_compute_cost_tts_characters():
+    rate = RateCard(
+        source=RATE_SOURCE_CATALOG,
+        rate_id=uuid4(),
+        tts_micro_usd_per_million_chars=15_000_000,
+    )
+    costs = compute_cost(UsageMetrics(tts_characters=2_000_000), rate)
+    assert costs.tts_cost_micro_usd == 30_000_000
+    assert costs.total_cost_micro_usd == 30_000_000
+
+
+def test_compute_cost_without_rate_is_zero():
+    costs = compute_cost(UsageMetrics(prompt_tokens=10_000), None)
+    assert costs == CostBreakdown()
+
+
+def test_normalize_rate_source_truncates_long_values():
+    long_source = "x" * 300
+    assert len(_normalize_rate_source(long_source)) == 255
+
+
+def test_normalize_pricing_block_plan_format():
+    normalized = _normalize_pricing_block(
+        {
+            "input_per_1m": 2.5,
+            "output_per_1m": 10.0,
+            "cache_read_per_1m": 1.25,
+            "cache_write_per_1m": 0.0,
+            "source": "litellm_import",
+        },
+        model_type="llm",
+    )
+    assert normalized["usage_kind"] == "llm"
+    assert normalized["input_micro_usd_per_million"] == 2_500_000
+    assert normalized["output_micro_usd_per_million"] == 10_000_000
+    assert normalized["cache_read_micro_usd_per_million"] == 1_250_000
+    assert normalized["source"] == "litellm_import"
+
+
+def test_normalize_pricing_block_stt_audio_per_minute():
+    normalized = _normalize_pricing_block(
+        {"audio_per_minute": 0.36, "usage_kind": "stt"},
+        model_type="stt",
+    )
+    assert normalized["usage_kind"] == "stt"
+    assert normalized["audio_micro_usd_per_second"] == 6_000
+
+
+def test_catalog_lookup_models_includes_azure_alias():
+    assert _catalog_lookup_models("gpt-4o", "llm") == ("gpt-4o", "azure-gpt-4o")
+    assert _catalog_lookup_models("azure-gpt-4o", "llm") == (
+        "azure-gpt-4o",
+        "gpt-4o",
+    )
+
+
+def test_compute_cost_reasoning_tokens():
+    rate = RateCard(
+        source=RATE_SOURCE_CATALOG,
+        rate_id=uuid4(),
+        reasoning_micro_usd_per_million=3_000_000,
+    )
+    costs = compute_cost(UsageMetrics(reasoning_tokens=1_000_000), rate)
+    assert costs.reasoning_cost_micro_usd == 3_000_000
+    assert costs.total_cost_micro_usd == 3_000_000
+
+
+def test_cost_fields_from_deltas_voice_agent_call_audio():
+    from app.services.usage.pricing import (
+        RateCard,
+        _pricing_entries_from_models_json,
+        cost_fields_from_deltas,
+    )
+
+    entries = _pricing_entries_from_models_json()
+    assert "voice-agent-call" in entries
+    assert "unknown" in entries
+    assert entries["voice-agent-call"]["audio_micro_usd_per_second"] > 0
+
+    rate = RateCard(
+        source="catalog",
+        rate_id=uuid4(),
+        audio_micro_usd_per_second=833,  # ~$0.05/min
+    )
+    fields = cost_fields_from_deltas(
+        {"audio_seconds": 60, "call_count": 1},
+        organization_id=uuid4(),
+        model="voice-agent-call",
+        usage_kind="llm",
+        usage_date=date(2026, 8, 11),
+        db=MagicMock(),
+        resolver=MagicMock(resolve_rate=MagicMock(return_value=rate)),
+    )
+    assert fields["audio_cost_micro_usd"] == 60 * 833
+    assert fields["total_cost_micro_usd"] == fields["audio_cost_micro_usd"]
+    assert fields["pricing_rate_source"] == "catalog"
+
+
+def test_resolve_rate_ignores_stale_negative_cache(monkeypatch):
+    from unittest.mock import MagicMock
+
+    import app.services.usage.pricing as pricing_mod
+    import app.services.usage.pricing_cache as pricing_cache_mod
+    from app.services.usage.pricing import RATE_SOURCE_CATALOG, PricingResolver
+    from tests.test_services.test_usage.test_pricing_cache import _FakeRedis
+
+    pricing_mod._RATES_TABLE_CACHE = "model_pricing_rates"
+
+    org_id = uuid4()
+    rate_id = uuid4()
+    usage_day = date(2026, 8, 11)
+
+    fake_redis = _FakeRedis()
+    monkeypatch.setattr(pricing_cache_mod, "_client", lambda: fake_redis)
+
+    redis_key = pricing_cache_mod.pricing_cache_key(
+        organization_id=org_id,
+        model="gpt-oss-120b",
+        usage_kind="llm",
+        usage_date=usage_day,
+    )
+    pricing_cache_mod.set_cached_rate_payload(redis_key, None)
+
+    row = {
+        "id": rate_id,
+        "input_micro_usd_per_million": 1_000_000,
+        "output_micro_usd_per_million": 2_000_000,
+        "cache_read_micro_usd_per_million": 0,
+        "cache_creation_micro_usd_per_million": 0,
+        "reasoning_micro_usd_per_million": 0,
+        "audio_micro_usd_per_second": 0,
+        "tts_micro_usd_per_million_chars": 0,
+    }
+
+    override_result = MagicMock()
+    override_result.mappings.return_value.first.return_value = None
+    catalog_result = MagicMock()
+    catalog_result.mappings.return_value.first.return_value = row
+
+    db = MagicMock()
+    db.execute.side_effect = [override_result, catalog_result]
+
+    resolver = PricingResolver(db)
+    card = resolver.resolve_rate(
+        organization_id=org_id,
+        model="gpt-oss-120b",
+        usage_kind="llm",
+        usage_date=usage_day,
+    )
+
+    assert card is not None
+    assert card.rate_id == rate_id
+    assert card.source == RATE_SOURCE_CATALOG
+    assert fake_redis.store[redis_key] != "__null__"
diff --git a/tests/test_services/test_usage/test_pricing_cache.py b/tests/test_services/test_usage/test_pricing_cache.py
new file mode 100644
index 00000000..e4276240
--- /dev/null
+++ b/tests/test_services/test_usage/test_pricing_cache.py
@@ -0,0 +1,91 @@
+"""Tests for Redis-backed usage pricing cache."""
+
+from __future__ import annotations
+
+from datetime import date
+from uuid import uuid4
+
+import app.services.usage.pricing_cache as pricing_cache_mod
+
+
+class _FakeRedis:
+    def __init__(self):
+        self.store: dict[str, str] = {}
+
+    def get(self, key: str):
+        return self.store.get(key)
+
+    def setex(self, key: str, _ttl: int, value: str):
+        self.store[key] = value
+
+    def scan(self, cursor: int, match: str, count: int):
+        keys = [key for key in self.store if key.startswith(match.rstrip("*"))]
+        return 0, keys
+
+    def delete(self, *keys: str):
+        for key in keys:
+            self.store.pop(key, None)
+
+
+def test_pricing_cache_roundtrip(monkeypatch):
+    fake = _FakeRedis()
+    monkeypatch.setattr(pricing_cache_mod, "_client", lambda: fake)
+
+    org_id = uuid4()
+    key = pricing_cache_mod.pricing_cache_key(
+        organization_id=org_id,
+        model="gpt-4o",
+        usage_kind="llm",
+        usage_date=date(2026, 8, 13),
+    )
+    payload = {
+        "source": "catalog",
+        "rate_id": str(uuid4()),
+        "input_micro_usd_per_million": 2_500_000,
+        "output_micro_usd_per_million": 10_000_000,
+        "cache_read_micro_usd_per_million": 0,
+        "cache_creation_micro_usd_per_million": 0,
+        "reasoning_micro_usd_per_million": 0,
+        "audio_micro_usd_per_second": 0,
+        "tts_micro_usd_per_million_chars": 0,
+    }
+    pricing_cache_mod.set_cached_rate_payload(key, payload)
+    cached = pricing_cache_mod.get_cached_rate_payload(key)
+    assert cached == payload
+
+
+def test_pricing_cache_null_marker(monkeypatch):
+    fake = _FakeRedis()
+    monkeypatch.setattr(pricing_cache_mod, "_client", lambda: fake)
+
+    org_id = uuid4()
+    key = pricing_cache_mod.pricing_cache_key(
+        organization_id=org_id,
+        model="unknown-model",
+        usage_kind="llm",
+        usage_date=date(2026, 8, 13),
+    )
+    pricing_cache_mod.set_cached_rate_payload(key, None)
+    assert pricing_cache_mod.get_cached_rate_payload(key) == {}
+
+
+def test_null_cache_uses_shorter_ttl(monkeypatch):
+    fake = _FakeRedis()
+    recorded: list[tuple[int, str]] = []
+
+    def _setex(key: str, ttl: int, value: str):
+        fake.store[key] = value
+        recorded.append((ttl, value))
+
+    fake.setex = _setex  # type: ignore[method-assign]
+    monkeypatch.setattr(pricing_cache_mod, "_client", lambda: fake)
+
+    key = "usage:pricing:test"
+    pricing_cache_mod.set_cached_rate_payload(key, None)
+    pricing_cache_mod.set_cached_rate_payload(
+        key + ":hit",
+        {"source": "catalog", "rate_id": str(uuid4())},
+    )
+    assert recorded[0][0] == pricing_cache_mod.PRICING_NULL_CACHE_TTL_SEC
+    assert recorded[0][1] == "__null__"
+    assert recorded[1][0] == pricing_cache_mod.PRICING_CACHE_TTL_SEC
diff --git a/tests/test_services/test_usage/test_pricing_ops.py b/tests/test_services/test_usage/test_pricing_ops.py
new file mode 100644
index 00000000..e5a3c51b
--- /dev/null
+++ b/tests/test_services/test_usage/test_pricing_ops.py
@@ -0,0 +1,10 @@
+"""Tests for usage pricing ops helpers."""
+
+from __future__ import annotations
+
+from app.services.usage.pricing_ops import models_missing_pricing_blocks
+
+
+def test_models_missing_pricing_blocks_is_sorted_list():
+    missing = models_missing_pricing_blocks()
+    assert missing == sorted(missing)
diff --git a/tests/test_services/test_usage/test_pricing_overrides.py b/tests/test_services/test_usage/test_pricing_overrides.py
new file mode 100644
index 00000000..3e18d191
--- /dev/null
+++ b/tests/test_services/test_usage/test_pricing_overrides.py
@@ -0,0 +1,72 @@
+"""Tests for org pricing override merge and service helpers."""
+
+from __future__ import annotations
+
+from uuid import uuid4
+
+from app.services.usage.pricing import (
+    RATE_SOURCE_CATALOG,
+    RATE_SOURCE_OVERRIDE,
+    RateCard,
+    _merge_override_with_catalog,
+)
+from app.services.usage.pricing_overrides import _usd_payload_to_micro_columns
+
+
+def test_merge_override_inherits_null_fields_from_catalog():
+    catalog = RateCard(
+        source=RATE_SOURCE_CATALOG,
+        rate_id=uuid4(),
+        input_micro_usd_per_million=1_000_000,
+        output_micro_usd_per_million=2_000_000,
+        cache_read_micro_usd_per_million=500_000,
+    )
+    override_id = uuid4()
+    merged = _merge_override_with_catalog(
+        {
+            "id": override_id,
+            "input_micro_usd_per_million": 3_000_000,
+            "output_micro_usd_per_million": None,
+            "cache_read_micro_usd_per_million": None,
+            "cache_creation_micro_usd_per_million": None,
+            "reasoning_micro_usd_per_million": None,
+            "audio_micro_usd_per_second": None,
+            "tts_micro_usd_per_million_chars": None,
+        },
+        catalog,
+    )
+    assert merged.source == RATE_SOURCE_OVERRIDE
+    assert merged.rate_id == override_id
+    assert merged.input_micro_usd_per_million == 3_000_000
+    assert merged.output_micro_usd_per_million == 2_000_000
+    assert merged.cache_read_micro_usd_per_million == 500_000
+
+
+def test_merge_override_without_catalog_uses_zero_defaults():
+    override_id = uuid4()
+    merged = _merge_override_with_catalog(
+        {
+            "id": override_id,
+            "input_micro_usd_per_million": 100,
+            "output_micro_usd_per_million": None,
+            "cache_read_micro_usd_per_million": None,
+            "cache_creation_micro_usd_per_million": None,
+            "reasoning_micro_usd_per_million": None,
+            "audio_micro_usd_per_second": None,
+            "tts_micro_usd_per_million_chars": None,
+        },
+        None,
+    )
+    assert merged.input_micro_usd_per_million == 100
+    assert merged.output_micro_usd_per_million == 0
+
+
+def test_usd_payload_to_micro_columns():
+    columns = _usd_payload_to_micro_columns(
+        {
+            "input_per_1m": 1.5,
+            "audio_per_minute": 0.006,
+        }
+    )
+    assert columns["input_micro_usd_per_million"] == 1_500_000
+    assert columns["audio_micro_usd_per_second"] == 100
diff --git a/tests/test_services/test_usage/test_usage_costs.py b/tests/test_services/test_usage/test_usage_costs.py
new file mode 100644
index 00000000..2e6d028c
--- /dev/null
+++ b/tests/test_services/test_usage/test_usage_costs.py
@@ -0,0 +1,33 @@
+"""Tests for usage cost presentation helpers."""
+
+from app.services.usage.usage_costs import costs_from_micro, micro_to_usd
+
+
+def test_micro_to_usd():
+    assert micro_to_usd(1_500_000) == 1.5
+    assert micro_to_usd(0) == 0
+
+
+def test_costs_from_micro_maps_cache_write_and_unpriced_flag():
+    costs = costs_from_micro(
+        input_cost_micro_usd=1_000_000,
+        output_cost_micro_usd=500_000,
+        cache_creation_cost_micro_usd=250_000,
+        total_cost_micro_usd=1_750_000,
+        has_unpriced_usage=True,
+    )
+    assert costs["input_cost_usd"] == 1.0
+    assert costs["output_cost_usd"] == 0.5
+    assert costs["cache_write_cost_usd"] == 0.25
+    assert costs["total_cost_usd"] == 1.75
+    assert costs["currency"] == "USD"
+    assert costs["has_unpriced_usage"] is True
+
+
+def test_costs_from_micro_sums_total_when_not_provided():
+    costs = costs_from_micro(
+        input_cost_micro_usd=100,
+        output_cost_micro_usd=200,
+        audio_cost_micro_usd=300,
+    )
+    assert costs["total_cost_usd"] == micro_to_usd(600)
diff --git a/tests/test_workers/test_usage_queue_routing.py b/tests/test_workers/test_usage_queue_routing.py
new file mode 100644
index 00000000..8ccdb6f3
--- /dev/null
+++ b/tests/test_workers/test_usage_queue_routing.py
@@ -0,0 +1,18 @@
+"""Tests for usage pricing Celery queue routing."""
+
+from app.workers.config import USAGE_WORKER_QUEUE, celery_app
+
+
+def test_flush_usage_counters_routes_to_usage_queue():
+    routes = celery_app.conf.task_routes
+    assert routes["flush_usage_counters"]["queue"] == USAGE_WORKER_QUEUE
+
+
+def test_recompute_usage_costs_routes_to_usage_queue():
+    routes = celery_app.conf.task_routes
+    assert routes["recompute_usage_costs"]["queue"] == USAGE_WORKER_QUEUE
+
+
+def test_usage_beat_flush_targets_usage_queue():
+    schedule = celery_app.conf.beat_schedule["flush-llm-usage-counters"]
+    assert schedule["options"]["queue"] == USAGE_WORKER_QUEUE

From 7d59a5babe804d695f21d861301c95fffb45b9f0 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 14 Aug 2026 18:45:37 +0530
Subject: [PATCH 21/55] feat: add usage flush configuration and enterprise
 entitlement checks for usage features

---
 README.md                                     |  24 +-
 app/api/v1/routes/org_usage.py                |  92 +++++--
 app/api/v1/routes/settings.py                 |   3 +
 app/api/v1/routes/usage_pricing.py            |  22 +-
 app/core/usage_entitlement.py                 |  55 ++++
 app/dependencies.py                           |  24 ++
 app/services/usage/access.py                  |  83 ++++++
 app/services/usage/llm_usage.py               | 256 ++++++++++++------
 app/services/usage/retention.py               |  41 +++
 app/workers/config.py                         |   7 +
 app/workers/tasks/__init__.py                 |   2 +
 app/workers/tasks/prune_oss_usage_history.py  |  22 ++
 env.example                                   |  10 +
 frontend/src/App.tsx                          |  27 +-
 frontend/src/lib/api.ts                       |  11 +
 .../pages/enterprise/EnterpriseUpgrade.tsx    |   8 +-
 frontend/src/pages/usage/Usage.tsx            |  82 +++++-
 .../src/pages/usage/UsageDateRangePicker.tsx  |  34 ++-
 frontend/src/pages/usage/UsageFiltersBar.tsx  |   9 +-
 frontend/src/store/licenseStore.ts            |  16 +-
 tests/test_core/test_usage_entitlement.py     |  49 ++++
 .../test_usage/test_llm_usage.py              |  57 +++-
 .../test_usage/test_usage_access.py           |  67 +++++
 .../test_usage/test_usage_retention.py        |  68 +++++
 24 files changed, 930 insertions(+), 139 deletions(-)
 create mode 100644 app/core/usage_entitlement.py
 create mode 100644 app/services/usage/access.py
 create mode 100644 app/services/usage/retention.py
 create mode 100644 app/workers/tasks/prune_oss_usage_history.py
 create mode 100644 tests/test_core/test_usage_entitlement.py
 create mode 100644 tests/test_services/test_usage/test_usage_access.py
 create mode 100644 tests/test_services/test_usage/test_usage_retention.py

diff --git a/README.md b/README.md
index 009ca348..c5ad0a0b 100644
--- a/README.md
+++ b/README.md
@@ -373,7 +373,7 @@ celery -A app.workers.celery_app worker --loglevel=info
 **Note:** Workers are required for background tasks (transcription, evaluation, usage cost flush, etc.). If you use `eai start-all`, they start automatically. Only use `eai worker` if you need to run a worker separately (e.g. `eai worker --queues usage` for the usage queue only).
 
 ### Usage Pricing Ops
-Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups.
+Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups. Requires `worker-usage` + `beat` (or `eai start-all`).
 
 ```bash
 # Upsert model_pricing_rates from app/config/models.json
@@ -382,12 +382,14 @@ eai usage seed-rates --config config.yml
 # Compare models.json pricing vs Postgres
 eai usage diff-rates --config config.yml
 
-# Backfill / recompute costs (sync, runs in this process)
+# Backfill costs in-process (all orgs; use after migrate or catalog change)
 eai usage recompute --config config.yml --sync
 
-# Enqueue Celery recompute on the usage queue (requires --organization-id)
+# Async recompute via usage queue (requires --organization-id)
 eai usage recompute --config config.yml --organization-id <org-uuid>
 
+# Optional scopes: --model, --usage-kind, --start-date, --end-date
+
 # Optional: fetch LiteLLM prices into pricing_catalog.json
 eai usage sync-litellm --local
 eai usage sync-litellm --local --write-models
@@ -400,7 +402,17 @@ eai usage seed-rates --config config.yml
 eai usage recompute --config config.yml --sync
 ```
 
-Requires `worker-usage` (or `eai start-all`) for async recompute; Beat + `worker-usage` keep Redis usage counters flushed into Postgres on a schedule (`USAGE_FLUSH_BEAT_SECONDS`, default 120).
+**Flush / Usage UI tuning** — set in `.env` (see `env.example`):
+
+| Variable | Default | Purpose |
+|----------|---------|---------|
+| `USAGE_FLUSH_BUCKET_BATCH_SIZE` | `500` | Buckets per DB transaction |
+| `USAGE_FLUSH_MAX_BATCHES_PER_RUN` | `30` | Batches per flush tick (≤ **15,000** buckets/run) |
+| `USAGE_FLUSH_BEAT_SECONDS` | `120` | Beat interval (~2 min lag vs Redis) |
+| `USAGE_FLUSH_LOCK_TTL_SECONDS` | `300` | Per-org flush lock TTL |
+| `USAGE_API_FLUSH_COOLDOWN_SECONDS` | `60` | Min gap between Usage page catalog syncs |
+
+Usage UI calls `POST /api/v1/organizations/usage/catalog/sync` once per visit; summary/breakdown/filters are read-only. If Redis backlog grows, lower `USAGE_FLUSH_BEAT_SECONDS` or raise `USAGE_FLUSH_MAX_BATCHES_PER_RUN`.
 
 ### Generate Config File
 ```bash
@@ -515,6 +527,10 @@ POSTGRES_PASSWORD=password
 POSTGRES_DB=efficientai
 SECRET_KEY=your-secret-key-here
 
+# Usage cost flush (see README "Usage Pricing Ops"; full list in env.example)
+# USAGE_FLUSH_BEAT_SECONDS=120
+# USAGE_FLUSH_MAX_BATCHES_PER_RUN=30
+
 # Optional: GCS blob storage (also set storage.blob_provider: gcs in config.yml)
 BLOB_STORAGE_PROVIDER=gcs
 GCS_BUCKET_NAME=your-gcs-bucket
diff --git a/app/api/v1/routes/org_usage.py b/app/api/v1/routes/org_usage.py
index 2d6854f6..95688b3c 100644
--- a/app/api/v1/routes/org_usage.py
+++ b/app/api/v1/routes/org_usage.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from datetime import date, datetime, timezone
+from app.services.usage.access import UsageAccessPolicy
 from app.services.usage.dates import usage_date_filter_bounds, usage_local_today
 from typing import List, Literal, Optional
 from uuid import UUID
@@ -122,10 +123,21 @@ class UsageTotals(BaseModel):
     costs: UsageCosts = Field(default_factory=UsageCosts)
 
 
+class UsageCatalogSyncResponse(BaseModel):
+    flushed_buckets: int = 0
+
+
+class UsagePolicyMeta(BaseModel):
+    extended_history: bool
+    max_history_days: Optional[int] = None
+    range_clamped: bool = False
+
+
 class UsageSummaryResponse(BaseModel):
     start: date
     end: date
     totals: UsageTotals
+    usage_policy: UsagePolicyMeta
     last_updated_at: Optional[datetime] = None
 
 
@@ -168,6 +180,7 @@ class UsageBreakdownResponse(BaseModel):
     rows: List[UsageBreakdownRow]
     total_count: int
     truncated_at_limit: bool = False
+    usage_policy: UsagePolicyMeta
     last_updated_at: Optional[datetime] = None
 
 
@@ -198,6 +211,14 @@ def _parse_usage_range(
     return display_start, display_end, filter_start, filter_end
 
 
+def _usage_policy_meta(access) -> UsagePolicyMeta:
+    return UsagePolicyMeta(
+        extended_history=access.policy.extended_history,
+        max_history_days=access.policy.max_history_days,
+        range_clamped=access.range_clamped,
+    )
+
+
 def _evaluation_id_expr():
     return func.coalesce(
         LLMUsageDaily.context["evaluation_id"].astext,
@@ -418,6 +439,7 @@ def _apply_filters(
     organization_id: UUID,
     start: date,
     end: date,
+    enforced_floor: Optional[date] = None,
     workspace_id: Optional[UUID],
     product_section: Optional[str],
     model: Optional[str],
@@ -430,9 +452,12 @@ def _apply_filters(
     tag_id: Optional[UUID] = None,
     db: Optional[Session] = None,
 ):
+    effective_start = start
+    if enforced_floor is not None and effective_start < enforced_floor:
+        effective_start = enforced_floor
     query = query.filter(
         LLMUsageDaily.organization_id == organization_id,
-        LLMUsageDaily.usage_date >= start,
+        LLMUsageDaily.usage_date >= effective_start,
         LLMUsageDaily.usage_date <= end,
     )
     if workspace_id is not None:
@@ -506,6 +531,7 @@ def _filtered_query(
     organization_id: UUID,
     start: date,
     end: date,
+    enforced_floor: Optional[date] = None,
     workspace_id: Optional[UUID] = None,
     product_section: Optional[str] = None,
     model: Optional[str] = None,
@@ -522,6 +548,7 @@ def _filtered_query(
         organization_id=organization_id,
         start=start,
         end=end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
@@ -1010,6 +1037,7 @@ def _summary_aggregate_query(
     organization_id: UUID,
     start: date,
     end: date,
+    enforced_floor: Optional[date] = None,
     workspace_id: Optional[UUID],
     product_section: Optional[str],
     model: Optional[str],
@@ -1045,6 +1073,7 @@ def _summary_aggregate_query(
         organization_id=organization_id,
         start=start,
         end=end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
@@ -1059,6 +1088,16 @@ def _summary_aggregate_query(
     )
 
 
+@router.post("/catalog/sync", response_model=UsageCatalogSyncResponse)
+def sync_usage_catalog(
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    """Drain Redis usage counters into Postgres (rate-limited per org via Redis)."""
+    flushed = flush_usage_to_catalog(db, organization_id)
+    return UsageCatalogSyncResponse(flushed_buckets=flushed)
+
+
 @router.get("/summary", response_model=UsageSummaryResponse)
 def get_usage_summary(
     start: Optional[date] = Query(None),
@@ -1080,19 +1119,18 @@ def get_usage_summary(
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
-    display_start, display_end, filter_start, filter_end = _parse_usage_range(
-        start, end, tz
-    )
+    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
+    display_start = access.display_start
+    display_end = access.display_end
     if display_end < display_start:
         raise HTTPException(status_code=400, detail="end must be >= start")
 
-    flush_usage_to_catalog(db, organization_id)
-
     row = _summary_aggregate_query(
         db,
         organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
+        start=access.filter_start,
+        end=access.filter_end,
+        enforced_floor=access.enforced_filter_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
@@ -1109,6 +1147,7 @@ def get_usage_summary(
         start=display_start,
         end=display_end,
         totals=UsageTotals(**totals),
+        usage_policy=_usage_policy_meta(access),
         last_updated_at=_last_updated(db, organization_id),
     )
 
@@ -1137,14 +1176,12 @@ def get_usage_breakdown(
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
-    display_start, display_end, filter_start, filter_end = _parse_usage_range(
-        start, end, tz
-    )
+    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
+    display_start = access.display_start
+    display_end = access.display_end
     if display_end < display_start:
         raise HTTPException(status_code=400, detail="end must be >= start")
 
-    flush_usage_to_catalog(db, organization_id)
-
     dim = {
         "workspace": LLMUsageDaily.workspace_id,
         "product_section": LLMUsageDaily.product_section,
@@ -1186,8 +1223,9 @@ def get_usage_breakdown(
     query = _apply_filters(
         db.query(*select_cols, *aggregates),
         organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
+        start=access.filter_start,
+        end=access.filter_end,
+        enforced_floor=access.enforced_filter_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
@@ -1227,8 +1265,9 @@ def get_usage_breakdown(
         label_query = _filtered_query(
             db,
             organization_id=organization_id,
-            start=filter_start,
-            end=filter_end,
+            start=access.filter_start,
+            end=access.filter_end,
+            enforced_floor=access.enforced_filter_floor,
             workspace_id=workspace_id,
             product_section=product_section,
             model=model,
@@ -1243,8 +1282,9 @@ def get_usage_breakdown(
         label_query = _filtered_query(
             db,
             organization_id=organization_id,
-            start=filter_start,
-            end=filter_end,
+            start=access.filter_start,
+            end=access.filter_end,
+            enforced_floor=access.enforced_filter_floor,
             workspace_id=workspace_id,
             product_section=product_section,
             model=model,
@@ -1353,6 +1393,7 @@ def get_usage_breakdown(
         rows=rows,
         total_count=len(rows),
         truncated_at_limit=len(rows) >= limit,
+        usage_policy=_usage_policy_meta(access),
         last_updated_at=_last_updated(db, organization_id),
     )
 
@@ -1378,9 +1419,10 @@ def get_usage_filters(
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
-    _, _, filter_start, filter_end = _parse_usage_range(start, end, tz)
-
-    flush_usage_to_catalog(db, organization_id)
+    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
+    filter_start = access.filter_start
+    filter_end = access.filter_end
+    enforced_floor = access.enforced_filter_floor
 
     scoped_resource_id = resource_id or evaluation_id
 
@@ -1389,6 +1431,7 @@ def get_usage_filters(
         organization_id=organization_id,
         start=filter_start,
         end=filter_end,
+        enforced_floor=enforced_floor,
         dataset=dataset,
         tag_id=tag_id,
     )
@@ -1397,6 +1440,7 @@ def get_usage_filters(
         organization_id=organization_id,
         start=filter_start,
         end=filter_end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         dataset=dataset,
         tag_id=tag_id,
@@ -1406,6 +1450,7 @@ def get_usage_filters(
         organization_id=organization_id,
         start=filter_start,
         end=filter_end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         resource_id=scoped_resource_id,
@@ -1419,6 +1464,7 @@ def get_usage_filters(
         organization_id=organization_id,
         start=filter_start,
         end=filter_end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         resource_id=scoped_resource_id,
@@ -1433,6 +1479,7 @@ def get_usage_filters(
         organization_id=organization_id,
         start=filter_start,
         end=filter_end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         dataset=dataset,
@@ -1443,6 +1490,7 @@ def get_usage_filters(
         organization_id=organization_id,
         start=filter_start,
         end=filter_end,
+        enforced_floor=enforced_floor,
         workspace_id=workspace_id,
         product_section=product_section,
         model=model,
diff --git a/app/api/v1/routes/settings.py b/app/api/v1/routes/settings.py
index 3c49c0cc..83225380 100644
--- a/app/api/v1/routes/settings.py
+++ b/app/api/v1/routes/settings.py
@@ -32,6 +32,7 @@
     is_feature_enabled,
     ENTERPRISE_FEATURES,
 )
+from app.core.usage_entitlement import get_usage_policy
 
 
 REPORT_LOGO_CONTENT_TYPES = {
@@ -85,12 +86,14 @@ def license_info(organization_id: UUID = Depends(get_organization_id)):
     data = get_license_info()
     all_licensed = data.get("features", []) if isinstance(data.get("features"), list) else []
     enabled_for_org = [f for f in all_licensed if is_feature_enabled(f, organization_id)]
+    usage_policy = get_usage_policy(organization_id)
     return {
         "is_enterprise": bool(enabled_for_org),
         "enabled_features": enabled_for_org,
         "all_enterprise_features": ENTERPRISE_FEATURES,
         "feature_catalog": get_feature_catalog(),
         "organization": data.get("org_id"),
+        "usage_policy": usage_policy.as_dict(),
     }
 
 
diff --git a/app/api/v1/routes/usage_pricing.py b/app/api/v1/routes/usage_pricing.py
index 0840a29a..0716eef6 100644
--- a/app/api/v1/routes/usage_pricing.py
+++ b/app/api/v1/routes/usage_pricing.py
@@ -12,7 +12,7 @@
 
 from app.core.auth.rbac import require_admin
 from app.database import get_db
-from app.dependencies import get_organization_id
+from app.dependencies import get_organization_id, require_enterprise_entitlement
 from app.services.usage.pricing_jobs import (
     create_recompute_job,
     enqueue_recompute_job,
@@ -27,10 +27,12 @@
     upsert_override,
 )
 
+from app.services.usage.access import UsageAccessPolicy
+
 router = APIRouter(
     prefix="/organizations/usage/pricing",
     tags=["Usage"],
-    dependencies=[Depends(require_admin)],
+    dependencies=[Depends(require_admin), Depends(require_enterprise_entitlement())],
 )
 
 
@@ -218,13 +220,25 @@ def trigger_usage_cost_recompute(
     ):
         raise HTTPException(status_code=400, detail="end_date must be >= start_date")
 
+    clamped_start = body.start_date
+    clamped_end = body.end_date
+    if body.start_date is not None or body.end_date is not None:
+        access = UsageAccessPolicy.resolve(
+            organization_id,
+            body.start_date,
+            body.end_date,
+            None,
+        )
+        clamped_start = access.display_start if body.start_date is not None else None
+        clamped_end = access.display_end if body.end_date is not None else None
+
     job = create_recompute_job(
         db,
         organization_id=organization_id,
         model=body.model,
         usage_kind=body.usage_kind,
-        start_date=body.start_date,
-        end_date=body.end_date,
+        start_date=clamped_start,
+        end_date=clamped_end,
     )
     enqueue_recompute_job(db, job)
     db.refresh(job)
diff --git a/app/core/usage_entitlement.py b/app/core/usage_entitlement.py
new file mode 100644
index 00000000..cbedbf7d
--- /dev/null
+++ b/app/core/usage_entitlement.py
@@ -0,0 +1,55 @@
+"""Enterprise entitlement for usage history (any catalog feature, not per-feature gates)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+from uuid import UUID
+
+from app.core.license import get_enabled_features, get_license_info
+
+OSS_USAGE_HISTORY_DAYS = 7
+
+
+@dataclass(frozen=True)
+class UsagePolicySnapshot:
+    extended_history: bool
+    max_history_days: Optional[int] = None
+
+    def as_dict(self) -> dict:
+        return {
+            "extended_history": self.extended_history,
+            "max_history_days": self.max_history_days,
+        }
+
+
+def has_enterprise_entitlement(organization_id: Optional[UUID] = None) -> bool:
+    """Valid license with at least one catalog feature; org-scoped licenses must match."""
+    if not get_enabled_features():
+        return False
+
+    info = get_license_info()
+    licensed_org = info.get("org_id")
+    if licensed_org is None:
+        return True
+
+    if organization_id is None:
+        return False
+
+    return str(organization_id) == str(licensed_org)
+
+
+def deployment_has_entitlement() -> bool:
+    """Deployment-wide entitlement (license without org_id scoping)."""
+    if not get_enabled_features():
+        return False
+    return get_license_info().get("org_id") is None
+
+
+def get_usage_policy(organization_id: UUID) -> UsagePolicySnapshot:
+    if has_enterprise_entitlement(organization_id):
+        return UsagePolicySnapshot(extended_history=True, max_history_days=None)
+    return UsagePolicySnapshot(
+        extended_history=False,
+        max_history_days=OSS_USAGE_HISTORY_DAYS,
+    )
diff --git a/app/dependencies.py b/app/dependencies.py
index c3597d75..5ac8f1ca 100644
--- a/app/dependencies.py
+++ b/app/dependencies.py
@@ -21,6 +21,7 @@
 from app.core.auth import Principal, get_principal  # noqa: F401 - re-exported
 from app.core.auth.rbac import get_org_role
 from app.core.license import is_feature_enabled
+from app.core.usage_entitlement import has_enterprise_entitlement
 from app.database import get_db
 from app.models.database import RoleEnum, Workspace, WorkspaceMember
 from app.core.auth.capabilities import capability_denied_message
@@ -291,3 +292,26 @@ def _check(
             )
 
     return _check
+
+
+def require_enterprise_entitlement():
+    """
+    FastAPI dependency: valid enterprise license with any catalog feature.
+    Distinct from require_enterprise_feature (per-feature product gates).
+    """
+
+    def _check(organization_id: UUID = Depends(get_organization_id)):
+        if not has_enterprise_entitlement(organization_id):
+            raise HTTPException(
+                status_code=403,
+                detail={
+                    "error": "enterprise_license_required",
+                    "message": (
+                        "This capability requires a valid EfficientAI Enterprise license. "
+                        "Set EFFICIENTAI_LICENSE in your environment with any enterprise "
+                        "feature enabled. Contact sales@efficientai.com for a license key."
+                    ),
+                },
+            )
+
+    return _check
diff --git a/app/services/usage/access.py b/app/services/usage/access.py
new file mode 100644
index 00000000..9591bf2d
--- /dev/null
+++ b/app/services/usage/access.py
@@ -0,0 +1,83 @@
+"""Usage read access policy — date window clamping and SQL floor for OSS tier."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import date, timedelta
+from typing import Optional
+from uuid import UUID
+
+from loguru import logger
+
+from app.core.usage_entitlement import (
+    OSS_USAGE_HISTORY_DAYS,
+    UsagePolicySnapshot,
+    get_usage_policy,
+    has_enterprise_entitlement,
+)
+from app.services.usage.dates import usage_date_filter_bounds, usage_local_today
+
+
+@dataclass(frozen=True)
+class UsageAccessResult:
+    display_start: date
+    display_end: date
+    filter_start: date
+    filter_end: date
+    enforced_filter_floor: Optional[date]
+    policy: UsagePolicySnapshot
+    range_clamped: bool
+
+
+def oss_usage_min_local_date(tz: Optional[str]) -> date:
+    """Earliest inclusive local calendar day allowed for OSS tier."""
+    today = usage_local_today(tz)
+    return today - timedelta(days=OSS_USAGE_HISTORY_DAYS - 1)
+
+
+class UsageAccessPolicy:
+    @staticmethod
+    def resolve(
+        organization_id: UUID,
+        start: Optional[date],
+        end: Optional[date],
+        tz: Optional[str],
+    ) -> UsageAccessResult:
+        today = usage_local_today(tz)
+        display_start = start or today
+        display_end = end or today
+        policy = get_usage_policy(organization_id)
+        range_clamped = False
+        enforced_floor: Optional[date] = None
+
+        if not has_enterprise_entitlement(organization_id):
+            oss_min = oss_usage_min_local_date(tz)
+            if display_start < oss_min:
+                display_start = oss_min
+                range_clamped = True
+            enforced_floor = usage_date_filter_bounds(oss_min, oss_min, tz)[0]
+
+        filter_start, filter_end = usage_date_filter_bounds(
+            display_start, display_end, tz
+        )
+
+        if enforced_floor is not None and filter_start < enforced_floor:
+            filter_start = enforced_floor
+
+        if range_clamped:
+            logger.info(
+                "usage_history_clamped org_id={} effective_start={} effective_end={}",
+                organization_id,
+                display_start,
+                display_end,
+            )
+
+        return UsageAccessResult(
+            display_start=display_start,
+            display_end=display_end,
+            filter_start=filter_start,
+            filter_end=filter_end,
+            enforced_filter_floor=enforced_floor,
+            policy=policy,
+            range_clamped=range_clamped,
+        )
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index 0fdb48f1..51ffc6bc 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -2,10 +2,11 @@
 
 from __future__ import annotations
 
+import json
 import math
+import os
 import time
 import uuid
-import json
 from datetime import date, datetime, timezone
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 from uuid import UUID
@@ -34,7 +35,10 @@
 
 _NONE = "__none__"
 _PENDING_TTL_SECONDS = 14 * 24 * 60 * 60
-_FLUSH_LOCK_TTL_SECONDS = 45
+_DEFAULT_FLUSH_BUCKET_BATCH_SIZE = 500
+_DEFAULT_FLUSH_MAX_BATCHES_PER_RUN = 30
+_DEFAULT_FLUSH_LOCK_TTL_SECONDS = 300
+_DEFAULT_API_FLUSH_COOLDOWN_SECONDS = 60
 _FLUSH_LOCK_WAIT_SECONDS = 3.0
 USAGE_KIND_LLM = "llm"
 USAGE_KIND_STT = "stt"
@@ -59,6 +63,63 @@
 """
 
 
+def _env_int(name: str, default: int, *, minimum: int = 1) -> int:
+    raw = os.environ.get(name)
+    if raw is None:
+        return default
+    try:
+        return max(minimum, int(raw))
+    except (TypeError, ValueError):
+        logger.warning("invalid {}={!r}; using default {}", name, raw, default)
+        return default
+
+
+def flush_bucket_batch_size() -> int:
+    """Max rollup buckets persisted per DB transaction during Redis flush."""
+    return _env_int(
+        "USAGE_FLUSH_BUCKET_BATCH_SIZE",
+        _DEFAULT_FLUSH_BUCKET_BATCH_SIZE,
+    )
+
+
+def flush_max_batches_per_run() -> int:
+    """Max Redis flush batches per org per flush_usage_to_catalog invocation."""
+    return _env_int(
+        "USAGE_FLUSH_MAX_BATCHES_PER_RUN",
+        _DEFAULT_FLUSH_MAX_BATCHES_PER_RUN,
+    )
+
+
+def _flush_lock_ttl_seconds() -> int:
+    return _env_int(
+        "USAGE_FLUSH_LOCK_TTL_SECONDS",
+        _DEFAULT_FLUSH_LOCK_TTL_SECONDS,
+    )
+
+
+def api_flush_cooldown_seconds() -> int:
+    """Min seconds between API-triggered catalog syncs per org (0 = no cooldown)."""
+    return _env_int(
+        "USAGE_API_FLUSH_COOLDOWN_SECONDS",
+        _DEFAULT_API_FLUSH_COOLDOWN_SECONDS,
+        minimum=0,
+    )
+
+
+def _split_buckets_for_flush(
+    buckets: Dict[str, Dict[str, int]],
+    batch_size: int,
+) -> Tuple[Dict[str, Dict[str, int]], Dict[str, Dict[str, int]]]:
+    if batch_size <= 0 or len(buckets) <= batch_size:
+        return buckets, {}
+    prefixes = sorted(buckets.keys())[:batch_size]
+    batch = {prefix: buckets[prefix] for prefix in prefixes}
+    remainder = {
+        prefix: metrics for prefix, metrics in buckets.items() if prefix not in batch
+    }
+    return batch, remainder
+
+
 def _client() -> redis.Redis:
     global _redis
     if _redis is None:
@@ -683,12 +744,13 @@ def _acquire_flush_lock(organization_id: UUID) -> bool:
     try:
         client = _client()
         lock_key = _flush_lock_key(organization_id)
-        if client.set(lock_key, "1", nx=True, ex=_FLUSH_LOCK_TTL_SECONDS):
+        lock_ttl = _flush_lock_ttl_seconds()
+        if client.set(lock_key, "1", nx=True, ex=lock_ttl):
             return True
         deadline = time.monotonic() + _FLUSH_LOCK_WAIT_SECONDS
         while time.monotonic() < deadline:
             time.sleep(0.05)
-            if client.set(lock_key, "1", nx=True, ex=_FLUSH_LOCK_TTL_SECONDS):
+            if client.set(lock_key, "1", nx=True, ex=lock_ttl):
                 return True
             if client.get(lock_key) is None:
                 continue
@@ -872,6 +934,104 @@ def _upsert_bucket(
         logger.warning("usage cost apply failed: {}", exc)
 
 
+def _upsert_claimed_buckets(
+    db: Session,
+    organization_id: UUID,
+    buckets: Dict[str, Dict[str, int]],
+    *,
+    pricing_resolver: Any,
+) -> Tuple[int, Dict[str, Dict[str, int]]]:
+    """Persist claimed Redis buckets; return flushed count and unparseable buckets."""
+    flushed = 0
+    skipped: Dict[str, Dict[str, int]] = {}
+    for prefix, deltas in buckets.items():
+        parsed = _parse_bucket_prefix(prefix)
+        if not parsed:
+            skipped[prefix] = deltas
+            logger.warning(
+                "llm usage skipped unparseable bucket prefix for org {}",
+                organization_id,
+            )
+            continue
+        _upsert_bucket(
+            db,
+            organization_id,
+            parsed,
+            deltas,
+            pricing_resolver=pricing_resolver,
+        )
+        flushed += 1
+    return flushed, skipped
+
+
+def _flush_redis_pending_to_catalog(
+    db: Session,
+    organization_id: UUID,
+    *,
+    pricing_resolver: Any,
+) -> int:
+    """Drain Redis pending hash into llm_usage_daily in bounded batches."""
+    batch_size = flush_bucket_batch_size()
+    max_batches = flush_max_batches_per_run()
+    flushed = 0
+
+    for _ in range(max_batches):
+        if not _has_pending_usage(organization_id):
+            break
+
+        claim_key, claimed_buckets = _claim_pending(organization_id)
+        if not claim_key or not claimed_buckets:
+            break
+
+        batch, remainder = _split_buckets_for_flush(claimed_buckets, batch_size)
+        skipped: Dict[str, Dict[str, int]] = {}
+        try:
+            batch_flushed, skipped = _upsert_claimed_buckets(
+                db,
+                organization_id,
+                batch,
+                pricing_resolver=pricing_resolver,
+            )
+            if claim_key:
+                _record_claim_committed_pg(db, claim_key, organization_id)
+            db.commit()
+            flushed += batch_flushed
+            if remainder:
+                _restore_buckets_to_pending(organization_id, remainder)
+            if skipped:
+                _restore_buckets_to_pending(organization_id, skipped)
+            if claim_key:
+                _mark_claim_committed(claim_key, fast=True)
+                _ack_claim(claim_key, organization_id)
+        except Exception as exc:
+            db.rollback()
+            if _is_missing_organization_fk(exc):
+                logger.warning(
+                    "llm usage flush dropped for unknown organization {}: {}",
+                    organization_id,
+                    exc,
+                )
+                if claim_key:
+                    try:
+                        _record_claim_committed_pg(db, claim_key, organization_id)
+                        db.commit()
+                    except Exception:
+                        db.rollback()
+                    _discard_committed_claim(claim_key, organization_id)
+                return flushed
+            logger.warning("llm usage catalog flush failed, restoring redis: {}", exc)
+            restore_buckets = dict(claimed_buckets)
+            _restore_buckets_to_pending(organization_id, restore_buckets)
+            if claim_key:
+                try:
+                    _client().delete(claim_key)
+                except redis.RedisError:
+                    pass
+            return flushed
+
+    return flushed
+
+
 def _is_unique_violation(exc: BaseException) -> bool:
     text_blob = " ".join(
         str(part)
@@ -913,10 +1073,13 @@ def _flush_pending_buffer(
                 FROM usage_pending_buffer
                 WHERE organization_id = CAST(:organization_id AS uuid)
                 ORDER BY created_at ASC
-                LIMIT 2000
+                LIMIT :batch_limit
                 """
             ),
-            {"organization_id": str(organization_id)},
+            {
+                "organization_id": str(organization_id),
+                "batch_limit": flush_bucket_batch_size(),
+            },
         ).mappings().all()
     except Exception as exc:
         db.rollback()
@@ -997,15 +1160,15 @@ def _flush_pending_buffer(
         return 0
 
 
-_CATALOG_FLUSH_COOLDOWN_SEC = 20
-
-
 def _catalog_flush_recently(organization_id: UUID) -> bool:
     """Skip flush if another request flushed this org within the cooldown window."""
+    cooldown = api_flush_cooldown_seconds()
+    if cooldown <= 0:
+        return False
     try:
         client = _client()
         key = f"usage:catalog_flush:{organization_id}"
-        return not client.set(key, "1", nx=True, ex=_CATALOG_FLUSH_COOLDOWN_SEC)
+        return not client.set(key, "1", nx=True, ex=cooldown)
     except redis.RedisError:
         return False
 
@@ -1022,79 +1185,18 @@ def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool =
     pricing_resolver = PricingResolver(db)
     if not skip_redis_flush:
         redis_locked = _acquire_flush_lock(organization_id)
-        claim_key = None
-        buckets: Dict[str, Dict[str, int]] = {}
         try:
             if redis_locked:
-                claim_key, buckets = _claim_pending(organization_id)
-                if claim_key and buckets:
-                    skipped: Dict[str, Dict[str, int]] = {}
-                    try:
-                        for prefix, deltas in buckets.items():
-                            parsed = _parse_bucket_prefix(prefix)
-                            if not parsed:
-                                skipped[prefix] = deltas
-                                logger.warning(
-                                    "llm usage skipped unparseable bucket prefix for org {}",
-                                    organization_id,
-                                )
-                                continue
-                            _upsert_bucket(
-                                db,
-                                organization_id,
-                                parsed,
-                                deltas,
-                                pricing_resolver=pricing_resolver,
-                            )
-                            flushed += 1
-                        if claim_key:
-                            _record_claim_committed_pg(db, claim_key, organization_id)
-                        db.commit()
-                    except Exception as exc:
-                        db.rollback()
-                        if _is_missing_organization_fk(exc):
-                            logger.warning(
-                                "llm usage flush dropped for unknown organization {}: {}",
-                                organization_id,
-                                exc,
-                            )
-                            if claim_key:
-                                try:
-                                    _record_claim_committed_pg(
-                                        db, claim_key, organization_id
-                                    )
-                                    db.commit()
-                                except Exception:
-                                    db.rollback()
-                                _discard_committed_claim(claim_key, organization_id)
-                            claim_key = None
-                            buckets = {}
-                        else:
-                            logger.warning(
-                                "llm usage catalog flush failed, restoring redis: {}", exc
-                            )
-                            restore_buckets = dict(buckets)
-                            if skipped:
-                                restore_buckets.update(skipped)
-                            _restore_buckets_to_pending(organization_id, restore_buckets)
-                            if claim_key:
-                                try:
-                                    _client().delete(claim_key)
-                                except redis.RedisError:
-                                    pass
-                                claim_key = None
-                                return _flush_pending_buffer(db, organization_id)
-                    if skipped:
-                        _restore_buckets_to_pending(organization_id, skipped)
-                    if claim_key:
-                        _mark_claim_committed(claim_key, fast=True)
-                        _ack_claim(claim_key, organization_id)
-                        claim_key = None
+                flushed += _flush_redis_pending_to_catalog(
+                    db,
+                    organization_id,
+                    pricing_resolver=pricing_resolver,
+                )
         finally:
             if redis_locked:
                 _release_flush_lock(organization_id)
 
-    flushed += _flush_pending_buffer(db, organization_id)
+    flushed += _flush_pending_buffer(db, organization_id, pricing_resolver=pricing_resolver)
     return flushed
 
 
diff --git a/app/services/usage/retention.py b/app/services/usage/retention.py
new file mode 100644
index 00000000..763cbbcc
--- /dev/null
+++ b/app/services/usage/retention.py
@@ -0,0 +1,41 @@
+"""OSS usage rollup retention — per-org flush beyond history window."""
+
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+from sqlalchemy.orm import Session
+
+from loguru import logger
+
+from app.core.license import get_enabled_features, get_license_info
+from app.core.usage_entitlement import OSS_USAGE_HISTORY_DAYS
+from app.models.database import LLMUsageDaily
+
+
+def oss_usage_cutoff_date() -> date:
+    return date.today() - timedelta(days=OSS_USAGE_HISTORY_DAYS - 1)
+
+
+def prune_oss_usage_history(db: Session) -> dict:
+    """
+    Delete rollup rows older than OSS window for non-entitled orgs.
+    Deployment-wide license → no deletes. Org-scoped license → skip licensed org.
+    """
+    if get_enabled_features() and get_license_info().get("org_id") is None:
+        return {"deleted": 0}
+
+    cutoff = oss_usage_cutoff_date()
+    licensed_org = get_license_info().get("org_id")
+
+    query = db.query(LLMUsageDaily).filter(LLMUsageDaily.usage_date < cutoff)
+    if get_enabled_features() and licensed_org is not None:
+        query = query.filter(LLMUsageDaily.organization_id != licensed_org)
+
+    deleted = query.delete(synchronize_session=False)
+    db.commit()
+
+    if deleted:
+        logger.info("pruned_oss_usage_history deleted_rows={} cutoff={}", deleted, cutoff)
+
+    return {"deleted": deleted, "cutoff": cutoff.isoformat()}
diff --git a/app/workers/config.py b/app/workers/config.py
index e06336d0..b43306e7 100644
--- a/app/workers/config.py
+++ b/app/workers/config.py
@@ -140,14 +140,21 @@
     "map_agent_flowchart_prompt_sections": {"queue": "celery"},
     "flush_usage_counters": {"queue": USAGE_WORKER_QUEUE},
     "recompute_usage_costs": {"queue": USAGE_WORKER_QUEUE},
+    "prune_oss_usage_history": {"queue": USAGE_WORKER_QUEUE},
 }
 
 # Periodic flush of Redis usage counters into catalog rollups (usage queue).
 _flush_interval = float(os.environ.get("USAGE_FLUSH_BEAT_SECONDS", "120"))
+_prune_interval = float(os.environ.get("USAGE_PRUNE_BEAT_SECONDS", "86400"))
 celery_app.conf.beat_schedule = {
     "flush-llm-usage-counters": {
         "task": "flush_usage_counters",
         "schedule": _flush_interval,
         "options": {"queue": USAGE_WORKER_QUEUE},
     },
+    "prune-oss-usage-history": {
+        "task": "prune_oss_usage_history",
+        "schedule": _prune_interval,
+        "options": {"queue": USAGE_WORKER_QUEUE},
+    },
 }
diff --git a/app/workers/tasks/__init__.py b/app/workers/tasks/__init__.py
index 499d9a5d..1f237e29 100644
--- a/app/workers/tasks/__init__.py
+++ b/app/workers/tasks/__init__.py
@@ -24,6 +24,7 @@
 from . import call_import_bulk_ops
 from . import flush_usage_counters
 from . import recompute_usage_costs
+from . import prune_oss_usage_history
 from app.workers.concurrency import eval_dispatch
 from app.workers.concurrency import fair_dispatch
 from app.workers.concurrency import fair_diarization_dispatch
@@ -119,3 +120,4 @@
 )
 flush_usage_counters_task = flush_usage_counters.flush_usage_counters_task
 recompute_usage_costs_task = recompute_usage_costs.recompute_usage_costs_task
+prune_oss_usage_history_task = prune_oss_usage_history.prune_oss_usage_history_task
diff --git a/app/workers/tasks/prune_oss_usage_history.py b/app/workers/tasks/prune_oss_usage_history.py
new file mode 100644
index 00000000..a91b186c
--- /dev/null
+++ b/app/workers/tasks/prune_oss_usage_history.py
@@ -0,0 +1,22 @@
+"""Celery task: prune OSS usage rollup rows beyond history window."""
+
+from __future__ import annotations
+
+from loguru import logger
+
+from app.database import SessionLocal
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="prune_oss_usage_history")
+def prune_oss_usage_history_task() -> dict:
+    from app.services.usage.retention import prune_oss_usage_history
+
+    db = SessionLocal()
+    try:
+        result = prune_oss_usage_history(db)
+    finally:
+        db.close()
+    if result.get("deleted"):
+        logger.info("prune_oss_usage_history_task {}", result)
+    return result
diff --git a/env.example b/env.example
index cd34c25a..7e87c790 100644
--- a/env.example
+++ b/env.example
@@ -69,3 +69,13 @@ AUTH_LOCAL_ALLOW_SIGNUP=true
 # Enterprise license JWT (RS256). Obtain from the EfficientAI team.
 # EFFICIENTAI_LICENSE=eyJhbGciOi...
 
+# -----------------------------------------------------------------------------
+# Usage cost flush (worker-usage + beat; optional overrides for api service too)
+# See README "Usage Pricing Ops". Copy to .env for Docker Compose / local dev.
+# -----------------------------------------------------------------------------
+USAGE_FLUSH_BUCKET_BATCH_SIZE=500 
+USAGE_FLUSH_MAX_BATCHES_PER_RUN=30
+USAGE_FLUSH_BEAT_SECONDS=120
+USAGE_FLUSH_LOCK_TTL_SECONDS=300
+USAGE_API_FLUSH_COOLDOWN_SECONDS=60
+
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 287bdd2d..7a078b20 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -132,6 +132,24 @@ function EnterpriseGate({ feature, children }: { feature: string; children: Reac
   return <>{children}</>
 }
 
+function EnterpriseLicenseGate({ children }: { children: React.ReactNode }) {
+  const { hasExtendedUsageHistory, isLoaded } = useLicenseStore()
+
+  if (!isLoaded) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="animate-spin rounded-full h-8 w-8 border-b-2 border-gray-900" />
+      </div>
+    )
+  }
+
+  if (!hasExtendedUsageHistory()) {
+    return <EnterpriseUpgrade />
+  }
+
+  return <>{children}</>
+}
+
 function App() {
   return (
     <BrowserRouter>
@@ -187,7 +205,14 @@ function App() {
           <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
           <Route path="iam" element={<IAM />} />
           <Route path="usage" element={<Usage />} />
-          <Route path="usage/pricing" element={<UsagePricing />} />
+          <Route
+            path="usage/pricing"
+            element={
+              <EnterpriseLicenseGate>
+                <UsagePricing />
+              </EnterpriseLicenseGate>
+            }
+          />
           <Route
             path="workspace-members"
             element={<Navigate to="/iam?tab=workspace-members" replace />}
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 6dfe8744..fc6ec8ec 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -100,12 +100,18 @@ export interface VoicePlaygroundBlindTestPair {
   y: VoicePlaygroundBlindTestAudioRef
 }
 
+export interface UsagePolicy {
+  extended_history: boolean
+  max_history_days: number | null
+}
+
 export interface LicenseInfoResponse {
   is_enterprise: boolean
   enabled_features: string[]
   all_enterprise_features: string[]
   feature_catalog?: EnterpriseFeatureCatalog
   organization?: string
+  usage_policy?: UsagePolicy
 }
 
 export interface ReportBranding {
@@ -2991,6 +2997,11 @@ class ApiClient {
     return response.data
   }
 
+  async syncOrgUsageCatalog(): Promise<{ flushed_buckets: number }> {
+    const response = await this.client.post('/api/v1/organizations/usage/catalog/sync')
+    return response.data
+  }
+
   async getOrgUsageSummary(params: {
     start?: string
     end?: string
diff --git a/frontend/src/pages/enterprise/EnterpriseUpgrade.tsx b/frontend/src/pages/enterprise/EnterpriseUpgrade.tsx
index 8c990df7..46c5fc2e 100644
--- a/frontend/src/pages/enterprise/EnterpriseUpgrade.tsx
+++ b/frontend/src/pages/enterprise/EnterpriseUpgrade.tsx
@@ -3,9 +3,9 @@ import { useLicenseStore } from '../../store/licenseStore'
 
 const FALLBACK_TITLE = 'Enterprise Feature'
 
-export default function EnterpriseUpgrade({ feature }: { feature: string }) {
+export default function EnterpriseUpgrade({ feature }: { feature?: string }) {
   const getFeatureMeta = useLicenseStore((state) => state.getFeatureMeta)
-  const title = getFeatureMeta(feature)?.title ?? FALLBACK_TITLE
+  const title = feature ? (getFeatureMeta(feature)?.title ?? FALLBACK_TITLE) : 'Extended Usage History'
 
   return (
     <div className="flex items-center justify-center min-h-[60vh]">
@@ -21,7 +21,9 @@ export default function EnterpriseUpgrade({ feature }: { feature: string }) {
         </span>
 
         <p className="text-gray-600 mb-8 leading-relaxed">
-          This feature is available with an EfficientAI Enterprise license.
+          {feature
+            ? 'This feature is available with an EfficientAI Enterprise license.'
+            : 'Extended usage history and pricing tools require a valid EfficientAI Enterprise license with any enabled feature.'}
         </p>
 
         <div className="bg-gray-50 border border-gray-200 rounded-xl p-6 mb-8 text-left">
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index 1b049984..a87cf73c 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -8,8 +8,9 @@ import { useIsAdmin } from '../../hooks/useRole'
 import UsageFiltersBar from './UsageFiltersBar'
 import UsageDrillPath from './UsageDrillPath'
 import UsageCostBreakdownModal from './UsageCostBreakdownModal'
-import { defaultUsageDateRange } from './UsageDateRangePicker'
+import { defaultUsageDateRange, isRangeWithinMaxDays, rangeForDays } from './UsageDateRangePicker'
 import { getUsageTimezone } from './usageTimezone'
+import { useLicenseStore } from '../../store/licenseStore'
 import {
   CALL_IMPORT_BATCH_HEADLINE,
   CALL_IMPORT_HINT,
@@ -561,8 +562,35 @@ export default function Usage() {
   const usageKind = (searchParams.get('usage_kind') as Kind) || ''
   const productSection = searchParams.get('product_section') || ''
   const isAdmin = useIsAdmin()
+  const { usagePolicy, isLoaded: licenseLoaded, fetchLicense } = useLicenseStore()
+  const showOssUsageNotice = licenseLoaded && !usagePolicy.extended_history
+  const maxHistoryDays = !licenseLoaded
+    ? null
+    : usagePolicy.extended_history
+      ? null
+      : usagePolicy.max_history_days ?? 7
   const [costBreakdownOpen, setCostBreakdownOpen] = useState(false)
 
+  useEffect(() => {
+    if (!licenseLoaded) {
+      void fetchLicense()
+    }
+  }, [licenseLoaded, fetchLicense])
+
+  useEffect(() => {
+    if (!licenseLoaded || usagePolicy.extended_history) return
+    const maxDays = usagePolicy.max_history_days ?? 7
+    if (!isRangeWithinMaxDays(start, end, maxDays)) {
+      const r = rangeForDays(maxDays)
+      setSearchParams((prev) => {
+        const next = new URLSearchParams(prev)
+        next.set('start', r.start)
+        next.set('end', r.end)
+        return next
+      })
+    }
+  }, [licenseLoaded, usagePolicy.extended_history, usagePolicy.max_history_days, start, end, setSearchParams])
+
   const showWorkspaceComposite =
     Boolean(workspaceId) &&
     !callImportId &&
@@ -609,13 +637,25 @@ export default function Usage() {
     staleTime: 60 * 1000,
   }
 
+  const catalogSync = useQuery({
+    queryKey: ['org-usage', 'catalog-sync'],
+    queryFn: () => apiClient.syncOrgUsageCatalog(),
+    staleTime: 60 * 1000,
+    refetchOnWindowFocus: false,
+  })
+
+  const usageReadsReady = catalogSync.isSuccess || catalogSync.isError
+
   const { data: summary, isLoading: summaryLoading, isFetching: summaryFetching } = useQuery({
     queryKey: ['org-usage', 'summary', dataParams],
     queryFn: () => apiClient.getOrgUsageSummary(dataParams),
+    enabled: usageReadsReady,
     ...usageQueryDefaults,
     placeholderData: keepPreviousData,
   })
 
+  const usageStatsLoading = catalogSync.isLoading || summaryLoading
+
   const {
     data: breakdown,
     isLoading: breakdownLoading,
@@ -628,7 +668,7 @@ export default function Usage() {
         group_by: groupBy,
         limit: 100,
       }),
-    enabled: !showWorkspaceComposite,
+    enabled: usageReadsReady && !showWorkspaceComposite,
     ...usageQueryDefaults,
     placeholderData: (previousData, previousQuery) => {
       if (!previousQuery || previousQuery.queryKey[2] !== groupBy) return undefined
@@ -648,7 +688,7 @@ export default function Usage() {
         group_by: 'call_import',
         limit: 100,
       }),
-    enabled: showWorkspaceComposite,
+    enabled: usageReadsReady && showWorkspaceComposite,
     ...usageQueryDefaults,
   })
 
@@ -664,13 +704,14 @@ export default function Usage() {
         group_by: 'resource',
         limit: 100,
       }),
-    enabled: showWorkspaceComposite,
+    enabled: usageReadsReady && showWorkspaceComposite,
     ...usageQueryDefaults,
   })
 
   const { data: filterOptions, isFetching: filtersLoading } = useQuery({
     queryKey: ['org-usage', 'filters', scopeParams],
     queryFn: () => apiClient.getOrgUsageFilters(scopeParams),
+    enabled: usageReadsReady,
     staleTime: 60 * 1000,
     placeholderData: (previousData, previousQuery) => {
       if (!previousQuery) return undefined
@@ -1077,7 +1118,7 @@ export default function Usage() {
                 <span className="whitespace-nowrap">Cost breakdown</span>
               </button>
             ) : null}
-            {isAdmin ? (
+            {isAdmin && licenseLoaded && usagePolicy.extended_history ? (
               <Link
                 to="/usage/pricing"
                 className="text-sm text-primary-600 hover:text-primary-700 whitespace-nowrap"
@@ -1094,14 +1135,24 @@ export default function Usage() {
         </div>
       </div>
 
+      {showOssUsageNotice ? (
+        <div className="rounded-lg border border-amber-200 bg-amber-50 px-4 py-3 text-sm text-amber-900">
+          Showing the last {maxHistoryDays} days of usage history. Set{' '}
+          <code className="rounded bg-amber-100 px-1.5 py-0.5 text-xs font-mono">
+            EFFICIENTAI_LICENSE
+          </code>{' '}
+          on your server with any enterprise feature to unlock extended history.
+        </div>
+      ) : null}
+
       <div
         className={`space-y-3 transition-opacity ${summaryFetching ? 'opacity-70' : ''}`}
       >
         <div className="grid grid-cols-2 gap-3 md:grid-cols-4">
-          <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={summaryLoading} />
-          <StatCard label="Output tokens" value={totals?.completion_tokens} loading={summaryLoading} />
-          <StatCard label="Total tokens" value={totals?.total_tokens} loading={summaryLoading} />
-          <StatCard label="LLM calls" value={totals?.call_count} loading={summaryLoading} />
+          <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={usageStatsLoading} />
+          <StatCard label="Output tokens" value={totals?.completion_tokens} loading={usageStatsLoading} />
+          <StatCard label="Total tokens" value={totals?.total_tokens} loading={usageStatsLoading} />
+          <StatCard label="LLM calls" value={totals?.call_count} loading={usageStatsLoading} />
         </div>
 
         <div className="grid grid-cols-2 gap-3 md:grid-cols-4 md:items-stretch">
@@ -1110,31 +1161,31 @@ export default function Usage() {
               <StatCard
                 label="STT audio"
                 valueLabel={formatAudio(totals?.audio_seconds || 0)}
-                loading={summaryLoading}
+                loading={usageStatsLoading}
               />
             ) : null}
             {showTts ? (
-              <StatCard label="TTS characters" value={totals?.tts_characters} loading={summaryLoading} />
+              <StatCard label="TTS characters" value={totals?.tts_characters} loading={usageStatsLoading} />
             ) : null}
             {(totals?.cache_read_tokens || 0) > 0 ? (
-              <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={summaryLoading} />
+              <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={usageStatsLoading} />
             ) : null}
             {(totals?.cache_creation_tokens || 0) > 0 ? (
               <StatCard
                 label="Cache write"
                 value={totals?.cache_creation_tokens}
-                loading={summaryLoading}
+                loading={usageStatsLoading}
               />
             ) : null}
             {(totals?.reasoning_tokens || 0) > 0 ? (
-              <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={summaryLoading} />
+              <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={usageStatsLoading} />
             ) : null}
           </div>
           <div className="col-span-2 md:col-span-1">
             <StatCard
               label="Estimated cost"
               valueLabel={formatCostUsd(estimatedTotalCost)}
-              loading={summaryLoading}
+              loading={usageStatsLoading}
               emphasize
             />
           </div>
@@ -1196,6 +1247,7 @@ export default function Usage() {
         onUsageKindChange={(k) => setParams({ usage_kind: k || null })}
         onModelChange={(v) => setParams({ model: v || null })}
         onClearAll={handleClearAll}
+        maxHistoryDays={maxHistoryDays}
       />
 
       <Card shadow="sm" className="border border-gray-200 ring-1 ring-[#fde047]/25">
diff --git a/frontend/src/pages/usage/UsageDateRangePicker.tsx b/frontend/src/pages/usage/UsageDateRangePicker.tsx
index 3ffeaeec..cd5718ab 100644
--- a/frontend/src/pages/usage/UsageDateRangePicker.tsx
+++ b/frontend/src/pages/usage/UsageDateRangePicker.tsx
@@ -9,6 +9,7 @@ type UsageDateRangePickerProps = {
   start: string
   end: string
   onApply: (start: string, end: string) => void
+  maxHistoryDays?: number | null
 }
 
 function toDateInput(d: Date): string {
@@ -23,13 +24,18 @@ function formatDisplay(start: string, end: string): string {
   return `${start} → ${end}`
 }
 
-function rangeForDays(days: number): { start: string; end: string } {
+export function rangeForDays(days: number): { start: string; end: string } {
   const end = new Date()
   const start = new Date()
   start.setDate(start.getDate() - (days - 1))
   return { start: toDateInput(start), end: toDateInput(end) }
 }
 
+export function isRangeWithinMaxDays(start: string, end: string, maxDays: number): boolean {
+  const r = rangeForDays(maxDays)
+  return start >= r.start && end <= r.end
+}
+
 const QUICK_RANGES = [
   { label: '1d', days: 1 },
   { label: '7d', days: 7 },
@@ -44,6 +50,7 @@ export default function UsageDateRangePicker({
   start,
   end,
   onApply,
+  maxHistoryDays = null,
 }: UsageDateRangePickerProps) {
   const [open, setOpen] = useState(false)
   const [mode, setMode] = useState<Mode>('relative')
@@ -52,13 +59,25 @@ export default function UsageDateRangePicker({
   const [relDays, setRelDays] = useState(1)
   const rootRef = useRef<HTMLDivElement>(null)
 
+  const quickRanges = useMemo(() => {
+    if (!maxHistoryDays) return QUICK_RANGES
+    return QUICK_RANGES.filter((q) => q.days <= maxHistoryDays)
+  }, [maxHistoryDays])
+
+  const minStartDate = useMemo(() => {
+    if (!maxHistoryDays) return null
+    return rangeForDays(maxHistoryDays).start
+  }, [maxHistoryDays])
+
+  const maxRelDays = maxHistoryDays ?? 365
+
   const activeQuick = useMemo(() => {
-    for (const q of QUICK_RANGES) {
+    for (const q of quickRanges) {
       const r = rangeForDays(q.days)
       if (r.start === start && r.end === end) return q.label
     }
     return null
-  }, [start, end])
+  }, [start, end, quickRanges])
 
   useEffect(() => {
     if (open) {
@@ -96,7 +115,7 @@ export default function UsageDateRangePicker({
   return (
     <div ref={rootRef} className="relative flex flex-wrap items-center gap-2 flex-1 min-w-0">
       <div className="flex items-center gap-0.5 rounded-lg border border-[#fde047]/50 bg-white/80 p-0.5">
-        {QUICK_RANGES.map((q) => (
+        {quickRanges.map((q) => (
           <button
             key={q.label}
             type="button"
@@ -193,9 +212,11 @@ export default function UsageDateRangePicker({
                   <input
                     type="number"
                     min={1}
-                    max={365}
+                    max={maxRelDays}
                     value={relDays}
-                    onChange={(e) => setRelDays(Math.max(1, Number(e.target.value) || 1))}
+                    onChange={(e) =>
+                      setRelDays(Math.min(maxRelDays, Math.max(1, Number(e.target.value) || 1)))
+                    }
                     className="w-24 rounded-lg border border-gray-200 px-3 py-2 text-sm"
                   />
                 </label>
@@ -212,6 +233,7 @@ export default function UsageDateRangePicker({
                   <input
                     type="date"
                     value={draftStart}
+                    min={minStartDate ?? undefined}
                     onChange={(e) => setDraftStart(e.target.value)}
                     className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
                   />
diff --git a/frontend/src/pages/usage/UsageFiltersBar.tsx b/frontend/src/pages/usage/UsageFiltersBar.tsx
index eb736667..83e7e1c1 100644
--- a/frontend/src/pages/usage/UsageFiltersBar.tsx
+++ b/frontend/src/pages/usage/UsageFiltersBar.tsx
@@ -44,6 +44,7 @@ type UsageFiltersBarProps = {
   onUsageKindChange: (v: Kind) => void
   onModelChange: (v: string) => void
   onClearAll: () => void
+  maxHistoryDays?: number | null
 }
 
 const KIND_OPTIONS: Array<{ id: Kind; label: string }> = [
@@ -75,6 +76,7 @@ export default function UsageFiltersBar({
   onUsageKindChange,
   onModelChange,
   onClearAll,
+  maxHistoryDays = null,
 }: UsageFiltersBarProps) {
   const [expanded, setExpanded] = useState(false)
 
@@ -216,7 +218,12 @@ export default function UsageFiltersBar({
 
         <div className="h-5 w-px bg-[#fde047]/60 hidden sm:block" />
 
-        <UsageDateRangePicker start={start} end={end} onApply={onDateApply} />
+        <UsageDateRangePicker
+          start={start}
+          end={end}
+          onApply={onDateApply}
+          maxHistoryDays={maxHistoryDays}
+        />
 
         {filtersLoading ? (
           <span className="text-xs text-gray-400 ml-1">Updating options…</span>
diff --git a/frontend/src/store/licenseStore.ts b/frontend/src/store/licenseStore.ts
index 4081589b..856ebc53 100644
--- a/frontend/src/store/licenseStore.ts
+++ b/frontend/src/store/licenseStore.ts
@@ -1,16 +1,23 @@
 import { create } from 'zustand'
 import { apiClient } from '../lib/api'
-import type { EnterpriseFeatureCatalog, EnterpriseFeatureMeta } from '../lib/api'
+import type { EnterpriseFeatureCatalog, EnterpriseFeatureMeta, UsagePolicy } from '../lib/api'
+
+const DEFAULT_USAGE_POLICY: UsagePolicy = {
+  extended_history: false,
+  max_history_days: 7,
+}
 
 interface LicenseState {
   isEnterprise: boolean
   enabledFeatures: string[]
   allEnterpriseFeatures: string[]
   featureCatalog: EnterpriseFeatureCatalog
+  usagePolicy: UsagePolicy
   isLoaded: boolean
   fetchLicense: () => Promise<void>
   isFeatureEnabled: (feature: string) => boolean
   getFeatureMeta: (feature: string) => EnterpriseFeatureMeta | undefined
+  hasExtendedUsageHistory: () => boolean
 }
 
 export const useLicenseStore = create<LicenseState>((set, get) => ({
@@ -18,6 +25,7 @@ export const useLicenseStore = create<LicenseState>((set, get) => ({
   enabledFeatures: [],
   allEnterpriseFeatures: [],
   featureCatalog: {},
+  usagePolicy: DEFAULT_USAGE_POLICY,
   isLoaded: false,
 
   fetchLicense: async () => {
@@ -28,6 +36,7 @@ export const useLicenseStore = create<LicenseState>((set, get) => ({
         enabledFeatures: info.enabled_features,
         allEnterpriseFeatures: info.all_enterprise_features,
         featureCatalog: info.feature_catalog ?? {},
+        usagePolicy: info.usage_policy ?? DEFAULT_USAGE_POLICY,
         isLoaded: true,
       })
     } catch {
@@ -36,6 +45,7 @@ export const useLicenseStore = create<LicenseState>((set, get) => ({
         enabledFeatures: [],
         allEnterpriseFeatures: [],
         featureCatalog: {},
+        usagePolicy: DEFAULT_USAGE_POLICY,
         isLoaded: true,
       })
     }
@@ -48,4 +58,8 @@ export const useLicenseStore = create<LicenseState>((set, get) => ({
   getFeatureMeta: (feature: string) => {
     return get().featureCatalog[feature]
   },
+
+  hasExtendedUsageHistory: () => {
+    return get().usagePolicy.extended_history
+  },
 }))
diff --git a/tests/test_core/test_usage_entitlement.py b/tests/test_core/test_usage_entitlement.py
new file mode 100644
index 00000000..40307a7d
--- /dev/null
+++ b/tests/test_core/test_usage_entitlement.py
@@ -0,0 +1,49 @@
+"""Tests for usage enterprise entitlement."""
+
+from uuid import uuid4
+
+from app.core import usage_entitlement as ue
+
+
+def test_has_enterprise_entitlement_deployment_wide(monkeypatch):
+    monkeypatch.setattr(
+        ue,
+        "get_license_info",
+        lambda: {"features": ["voice_playground"], "org_id": None},
+    )
+    monkeypatch.setattr(
+        ue,
+        "get_enabled_features",
+        lambda: ["voice_playground"],
+    )
+    assert ue.has_enterprise_entitlement() is True
+    assert ue.has_enterprise_entitlement(uuid4()) is True
+
+
+def test_has_enterprise_entitlement_org_scoped(monkeypatch):
+    org_id = uuid4()
+    monkeypatch.setattr(
+        ue,
+        "get_license_info",
+        lambda: {"features": ["call_imports"], "org_id": str(org_id)},
+    )
+    monkeypatch.setattr(
+        ue,
+        "get_enabled_features",
+        lambda: ["call_imports"],
+    )
+    assert ue.has_enterprise_entitlement(org_id) is True
+    assert ue.has_enterprise_entitlement(uuid4()) is False
+
+
+def test_has_enterprise_entitlement_no_license(monkeypatch):
+    monkeypatch.setattr(ue, "get_license_info", lambda: {})
+    monkeypatch.setattr(ue, "get_enabled_features", lambda: [])
+    assert ue.has_enterprise_entitlement(uuid4()) is False
+
+
+def test_get_usage_policy_oss(monkeypatch):
+    monkeypatch.setattr(ue, "has_enterprise_entitlement", lambda _oid=None: False)
+    policy = ue.get_usage_policy(uuid4())
+    assert policy.extended_history is False
+    assert policy.max_history_days == ue.OSS_USAGE_HISTORY_DAYS
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index 28c06104..e5bb9f1a 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -376,7 +376,7 @@ def test_flush_commits_and_acks_claim(fake_redis, org_ctx, monkeypatch):
 
     db = MagicMock()
     db.execute.return_value = MagicMock(rowcount=1)
-    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 1
@@ -398,7 +398,7 @@ def test_flush_restores_redis_when_db_fails(fake_redis, org_ctx, monkeypatch):
 
     db = MagicMock()
     db.execute.side_effect = RuntimeError("db down")
-    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 0
@@ -436,7 +436,7 @@ def _execute_side_effect(statement, *_args, **_kwargs):
         return MagicMock(rowcount=0)
 
     db.execute.side_effect = _execute_side_effect
-    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 0
@@ -470,7 +470,7 @@ def test_flush_drops_pending_when_organization_missing(fake_redis, org_ctx, monk
             'constraint "llm_usage_daily_organization_id_fkey"'
         ),
     )
-    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
     assert flushed == 0
@@ -493,7 +493,7 @@ def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx, monkeypatch
     db_a.execute.return_value = MagicMock(rowcount=0)  # force INSERT path
     db_b = MagicMock()
     db_b.execute.return_value = MagicMock(rowcount=1)
-    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda _db, _org: 0)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
     monkeypatch.setattr(
         "app.services.usage.pricing.apply_cost_to_bucket",
         lambda *args, **kwargs: False,
@@ -511,6 +511,53 @@ def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx, monkeypatch
     assert db_b.execute.call_count == 0
 
 
+def test_split_buckets_for_flush():
+    buckets = {f"prefix-{idx}": {"prompt_tokens": idx} for idx in range(5)}
+    batch, remainder = usage_mod._split_buckets_for_flush(buckets, 2)
+    assert len(batch) == 2
+    assert len(remainder) == 3
+    assert set(batch) | set(remainder) == set(buckets)
+
+
+def test_flush_redis_processes_multiple_batches_in_one_run(
+    fake_redis, org_ctx, monkeypatch
+):
+    org_id, workspace_id, base_ctx = org_ctx
+    monkeypatch.setenv("USAGE_FLUSH_BUCKET_BATCH_SIZE", "2")
+    monkeypatch.setenv("USAGE_FLUSH_MAX_BATCHES_PER_RUN", "10")
+
+    for idx in range(5):
+        ctx = LLMUsageContext(
+            organization_id=base_ctx.organization_id,
+            workspace_id=workspace_id,
+            product_section=base_ctx.product_section,
+            resource_id=uuid4(),
+            resource_type="call_import_evaluation",
+        )
+        with llm_usage_context(ctx):
+            usage_mod.record_llm_usage(
+                "gpt-test",
+                UsageSnapshot(prompt_tokens=idx + 1, completion_tokens=1),
+                usage_date=date(2026, 8, 11),
+            )
+
+    db = MagicMock()
+    db.execute.return_value = MagicMock(rowcount=1)
+    monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
+    monkeypatch.setattr(
+        "app.services.usage.pricing.apply_cost_to_bucket",
+        lambda *args, **kwargs: False,
+    )
+
+    flushed = usage_mod.flush_usage_to_catalog(db, org_id)
+
+    assert flushed == 5
+    assert db.commit.call_count == 3
+    assert usage_mod._pending_hash_key(org_id) not in fake_redis.hashes
+    assert not any(k.startswith("usage:flushing:") for k in fake_redis.hashes)
+    assert str(org_id) not in fake_redis.smembers("usage:pending:orgs")
+
+
 def test_ensure_usage_context_and_path_inference():
     assert (
         infer_product_section_from_path("/api/v1/call-imports/abc")
diff --git a/tests/test_services/test_usage/test_usage_access.py b/tests/test_services/test_usage/test_usage_access.py
new file mode 100644
index 00000000..f55816ae
--- /dev/null
+++ b/tests/test_services/test_usage/test_usage_access.py
@@ -0,0 +1,67 @@
+"""Tests for usage access policy clamping."""
+
+from datetime import date, timedelta
+from uuid import uuid4
+
+from app.core import usage_entitlement as ue
+from app.services.usage.access import UsageAccessPolicy, oss_usage_min_local_date
+
+
+def test_oss_min_local_date_seven_inclusive(monkeypatch):
+    monkeypatch.setattr(
+        "app.services.usage.dates.usage_local_today",
+        lambda tz: date(2026, 8, 14),
+    )
+    assert oss_usage_min_local_date(None) == date(2026, 8, 8)
+
+
+def test_resolve_clamps_old_start(monkeypatch):
+    org_id = uuid4()
+    monkeypatch.setattr(
+        "app.services.usage.access.has_enterprise_entitlement",
+        lambda _oid=None: False,
+    )
+    monkeypatch.setattr(
+        "app.services.usage.access.get_usage_policy",
+        lambda _oid: ue.UsagePolicySnapshot(False, 7),
+    )
+    monkeypatch.setattr(
+        "app.services.usage.dates.usage_local_today",
+        lambda tz: date(2026, 8, 14),
+    )
+
+    result = UsageAccessPolicy.resolve(
+        org_id,
+        date(2020, 1, 1),
+        date(2026, 8, 14),
+        None,
+    )
+    assert result.range_clamped is True
+    assert result.display_start == date(2026, 8, 8)
+    assert result.enforced_filter_floor is not None
+
+
+def test_resolve_full_range_when_entitled(monkeypatch):
+    org_id = uuid4()
+    monkeypatch.setattr(
+        "app.services.usage.access.has_enterprise_entitlement",
+        lambda _oid=None: True,
+    )
+    monkeypatch.setattr(
+        "app.services.usage.access.get_usage_policy",
+        lambda _oid: ue.UsagePolicySnapshot(True, None),
+    )
+    monkeypatch.setattr(
+        "app.services.usage.dates.usage_local_today",
+        lambda tz: date(2026, 8, 14),
+    )
+
+    result = UsageAccessPolicy.resolve(
+        org_id,
+        date(2020, 1, 1),
+        date(2026, 8, 14),
+        None,
+    )
+    assert result.range_clamped is False
+    assert result.display_start == date(2020, 1, 1)
+    assert result.enforced_filter_floor is None
diff --git a/tests/test_services/test_usage/test_usage_retention.py b/tests/test_services/test_usage/test_usage_retention.py
new file mode 100644
index 00000000..74077a41
--- /dev/null
+++ b/tests/test_services/test_usage/test_usage_retention.py
@@ -0,0 +1,68 @@
+"""Tests for OSS usage history pruning."""
+
+from datetime import date, timedelta
+from unittest.mock import MagicMock
+from uuid import uuid4
+
+from app.core.usage_entitlement import OSS_USAGE_HISTORY_DAYS
+from app.services.usage.retention import oss_usage_cutoff_date, prune_oss_usage_history
+
+
+def test_oss_usage_cutoff_inclusive_window():
+    cutoff = oss_usage_cutoff_date()
+    assert cutoff == date.today() - timedelta(days=OSS_USAGE_HISTORY_DAYS - 1)
+
+
+def test_prune_skips_deployment_wide_license(monkeypatch):
+    monkeypatch.setattr(
+        "app.services.usage.retention.get_enabled_features",
+        lambda: ["voice_playground"],
+    )
+    monkeypatch.setattr(
+        "app.services.usage.retention.get_license_info",
+        lambda: {"features": ["voice_playground"], "org_id": None},
+    )
+    mock_db = MagicMock()
+    result = prune_oss_usage_history(mock_db)
+    assert result["deleted"] == 0
+    mock_db.query.assert_not_called()
+
+
+def test_prune_deletes_old_rows_without_license(monkeypatch):
+    monkeypatch.setattr("app.services.usage.retention.get_enabled_features", lambda: [])
+    monkeypatch.setattr("app.services.usage.retention.get_license_info", lambda: {})
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value = mock_query
+    mock_query.delete.return_value = 3
+    mock_db = MagicMock()
+    mock_db.query.return_value = mock_query
+
+    result = prune_oss_usage_history(mock_db)
+    assert result["deleted"] == 3
+    mock_db.commit.assert_called_once()
+
+
+def test_prune_skips_entitled_org_when_license_org_scoped(monkeypatch):
+    org_id = uuid4()
+    monkeypatch.setattr(
+        "app.services.usage.retention.get_enabled_features",
+        lambda: ["call_imports"],
+    )
+    monkeypatch.setattr(
+        "app.services.usage.retention.get_license_info",
+        lambda: {
+            "features": ["call_imports"],
+            "org_id": str(org_id),
+        },
+    )
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value = mock_query
+    mock_query.delete.return_value = 0
+    mock_db = MagicMock()
+    mock_db.query.return_value = mock_query
+
+    result = prune_oss_usage_history(mock_db)
+    assert result["deleted"] == 0
+    assert mock_query.filter.call_count == 2

From cf9547d4769c789e9d782356d65e9acc21ade7f5 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Sat, 15 Aug 2026 12:31:14 +0530
Subject: [PATCH 22/55] refactor: remove Celery beat service and update usage
 flush configuration in Docker and API

---
 README.md                                     |  18 +-
 app/api/v1/routes/cron_jobs.py                |  25 ++-
 app/api/v1/routes/org_usage.py                | 114 +++++++++++--
 app/cli.py                                    |  15 +-
 .../075_cron_job_types_and_system_jobs.py     | 159 ++++++++++++++++++
 app/models/database.py                        |   5 +-
 app/models/schemas.py                         |   2 +
 app/services/cron/dispatcher_lock.py          |  81 +++++++++
 app/services/cron/job_dispatch.py             | 137 +++++++++++++++
 app/services/cron/scheduling.py               |  19 +++
 app/services/usage/fx_rates.py                |  80 +++++++++
 app/services/usage/llm_usage.py               |  53 ++----
 app/services/usage/read_cache.py              | 118 +++++++++++++
 app/workers/config.py                         |  20 +--
 app/workers/cron_bootstrap.py                 |  21 +++
 app/workers/tasks/__init__.py                 |   8 +
 app/workers/tasks/dispatch_cron_jobs.py       |  58 +++++++
 app/workers/tasks/evaluate_alerts.py          |  17 ++
 app/workers/tasks/recompute_usage_costs.py    |   4 +
 app/workers/tasks/refresh_fx_rates.py         |  12 ++
 app/workers/tasks/run_cron_evaluator_job.py   |  23 +++
 docker-compose.yml                            |  33 +---
 env.example                                   |   5 +-
 frontend/src/lib/api.ts                       |  10 +-
 frontend/src/lib/usageCurrency.ts             |  37 ++++
 frontend/src/pages/usage/Usage.tsx            |  73 +++++---
 .../pages/usage/UsageCostBreakdownModal.tsx   |   8 +-
 frontend/src/pages/usage/UsagePricing.tsx     |  58 +++++--
 tests/test_core/test_operational_endpoints.py |   9 +-
 .../test_cron/test_job_dispatch.py            |  48 ++++++
 .../test_usage/test_llm_usage.py              |   7 +-
 .../test_usage/test_usage_read_cache.py       |  69 ++++++++
 .../test_workers/test_usage_queue_routing.py  |  11 +-
 33 files changed, 1182 insertions(+), 175 deletions(-)
 create mode 100644 app/migrations/075_cron_job_types_and_system_jobs.py
 create mode 100644 app/services/cron/dispatcher_lock.py
 create mode 100644 app/services/cron/job_dispatch.py
 create mode 100644 app/services/cron/scheduling.py
 create mode 100644 app/services/usage/fx_rates.py
 create mode 100644 app/services/usage/read_cache.py
 create mode 100644 app/workers/cron_bootstrap.py
 create mode 100644 app/workers/tasks/dispatch_cron_jobs.py
 create mode 100644 app/workers/tasks/evaluate_alerts.py
 create mode 100644 app/workers/tasks/refresh_fx_rates.py
 create mode 100644 app/workers/tasks/run_cron_evaluator_job.py
 create mode 100644 frontend/src/lib/usageCurrency.ts
 create mode 100644 tests/test_services/test_cron/test_job_dispatch.py
 create mode 100644 tests/test_services/test_usage/test_usage_read_cache.py

diff --git a/README.md b/README.md
index c5ad0a0b..75e57a6a 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ There are two ways to run the application:
    
    This will automatically:
    - Pull pre-built images from GitHub Container Registry (no build required!)
-   - Start all services: `db`, `redis`, `api`, `media`, `worker`, `worker-imports`, `worker-usage`, `beat`
+   - Start all services: `db`, `redis`, `api`, `media`, `worker`, `worker-imports`, `worker-usage`
    - Run database migrations automatically on startup
 
    | Service | Purpose |
@@ -63,12 +63,11 @@ There are two ways to run the application:
    | `redis` | Redis (Celery broker + usage counters) |
    | `api` | HTTP API + frontend |
    | `media` | Live voice WebSocket media server |
-   | `worker` | Celery: `celery`, `audio-metrics` queues |
+   | `worker` | Celery: `celery` (cron dispatch, alerts, FX), `audio-metrics` queues |
    | `worker-imports` | Celery: `imports`, `diarization`, `eval-control`, `evaluations` |
    | `worker-usage` | Celery: `usage` queue (flush Redis counters + cost recompute) |
-   | `beat` | Celery Beat scheduler (periodic `flush_usage_counters`; requires `worker-usage`) |
 
-   **Usage costs:** token/cost rollups stay stale without `worker-usage` and `beat` (or the equivalent processes from `eai start-all`).
+   **Usage costs:** token/cost rollups stay stale without `worker-usage` and the default `worker` (cron dispatcher; or `eai start-all`).
    
    **Using a specific version:**
    ```bash
@@ -269,7 +268,7 @@ make test-docker-db TEST_DB_HOST=localhost TEST_DB_PORT=5432 TEST_DB_NAME=effici
 
 ### Start Application and Worker Together (Recommended)
 ```bash
-# Start API + all workers + beat with default config.yml
+# Start API + all workers with default config.yml
 eai start-all
 
 # Start with custom config
@@ -373,7 +372,7 @@ celery -A app.workers.celery_app worker --loglevel=info
 **Note:** Workers are required for background tasks (transcription, evaluation, usage cost flush, etc.). If you use `eai start-all`, they start automatically. Only use `eai worker` if you need to run a worker separately (e.g. `eai worker --queues usage` for the usage queue only).
 
 ### Usage Pricing Ops
-Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups. Requires `worker-usage` + `beat` (or `eai start-all`).
+Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups. Requires `worker-usage` + default `worker` (or `eai start-all`).
 
 ```bash
 # Upsert model_pricing_rates from app/config/models.json
@@ -408,11 +407,12 @@ eai usage recompute --config config.yml --sync
 |----------|---------|---------|
 | `USAGE_FLUSH_BUCKET_BATCH_SIZE` | `500` | Buckets per DB transaction |
 | `USAGE_FLUSH_MAX_BATCHES_PER_RUN` | `30` | Batches per flush tick (≤ **15,000** buckets/run) |
-| `USAGE_FLUSH_BEAT_SECONDS` | `120` | Beat interval (~2 min lag vs Redis) |
+| `USAGE_FLUSH_BEAT_SECONDS` | `120` | System cron flush interval (~2 min lag vs Redis) |
 | `USAGE_FLUSH_LOCK_TTL_SECONDS` | `300` | Per-org flush lock TTL |
-| `USAGE_API_FLUSH_COOLDOWN_SECONDS` | `60` | Min gap between Usage page catalog syncs |
+| `USAGE_READ_CACHE_TTL_SECONDS` | `90` | Redis cache TTL for usage summary/breakdown/filters |
+| `CRON_DISPATCH_INTERVAL_SECONDS` | `30` | Self-scheduling cron dispatcher tick |
 
-Usage UI calls `POST /api/v1/organizations/usage/catalog/sync` once per visit; summary/breakdown/filters are read-only. If Redis backlog grows, lower `USAGE_FLUSH_BEAT_SECONDS` or raise `USAGE_FLUSH_MAX_BATCHES_PER_RUN`.
+Usage UI reads Postgres only (summary/breakdown/filters); Redis counters flush on the system cron schedule (~2 min eventual consistency). If Redis backlog grows, lower `USAGE_FLUSH_BEAT_SECONDS` or raise `USAGE_FLUSH_MAX_BATCHES_PER_RUN`.
 
 ### Generate Config File
 ```bash
diff --git a/app/api/v1/routes/cron_jobs.py b/app/api/v1/routes/cron_jobs.py
index 6bc6b197..c09ad7f4 100644
--- a/app/api/v1/routes/cron_jobs.py
+++ b/app/api/v1/routes/cron_jobs.py
@@ -140,6 +140,8 @@ def create_cron_job(
     cron_job = CronJob(
         organization_id=organization_id,
         name=cron_job_data.name,
+        job_type="evaluator_run",
+        is_system=False,
         cron_expression=cron_job_data.cron_expression,
         timezone=cron_job_data.timezone,
         max_runs=cron_job_data.max_runs,
@@ -162,7 +164,10 @@ def list_cron_jobs(
     db: Session = Depends(get_db),
 ):
     """List all cron jobs for the organization."""
-    query = db.query(CronJob).filter(CronJob.organization_id == organization_id)
+    query = db.query(CronJob).filter(
+        CronJob.organization_id == organization_id,
+        CronJob.is_system.is_(False),
+    )
     
     if status_filter:
         query = query.filter(CronJob.status == status_filter.value)
@@ -209,6 +214,12 @@ def update_cron_job(
     if not cron_job:
         raise HTTPException(status_code=404, detail="Cron job not found")
 
+    if cron_job.is_system:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="System cron jobs cannot be modified",
+        )
+
     # Update fields if provided
     if cron_job_data.name is not None:
         # Check for name conflicts
@@ -309,6 +320,12 @@ def delete_cron_job(
     if not cron_job:
         raise HTTPException(status_code=404, detail="Cron job not found")
 
+    if cron_job.is_system:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="System cron jobs cannot be deleted",
+        )
+
     db.delete(cron_job)
     db.commit()
 
@@ -332,6 +349,12 @@ def toggle_cron_job_status(
     if not cron_job:
         raise HTTPException(status_code=404, detail="Cron job not found")
 
+    if cron_job.is_system:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="System cron jobs cannot be toggled",
+        )
+
     # Don't allow toggling completed jobs
     if cron_job.status == CronJobStatus.COMPLETED.value:
         raise HTTPException(
diff --git a/app/api/v1/routes/org_usage.py b/app/api/v1/routes/org_usage.py
index 95688b3c..0fb61992 100644
--- a/app/api/v1/routes/org_usage.py
+++ b/app/api/v1/routes/org_usage.py
@@ -27,7 +27,12 @@
     LLMUsageDaily,
     Workspace,
 )
-from app.services.usage.llm_usage import flush_usage_to_catalog
+from app.services.usage.read_cache import (
+    cache_key_for,
+    get_cached_response,
+    set_cached_response,
+)
+from app.services.usage.fx_rates import get_usd_inr_rate
 from app.services.usage.usage_costs import costs_from_micro
 from app.services.usage.usage_labels import (
     labels_for_call_import_ids,
@@ -123,8 +128,12 @@ class UsageTotals(BaseModel):
     costs: UsageCosts = Field(default_factory=UsageCosts)
 
 
-class UsageCatalogSyncResponse(BaseModel):
-    flushed_buckets: int = 0
+class UsageFxRateResponse(BaseModel):
+    base: str = "USD"
+    quote: str = "INR"
+    rate: float
+    as_of: datetime
+    source: str
 
 
 class UsagePolicyMeta(BaseModel):
@@ -1088,14 +1097,16 @@ def _summary_aggregate_query(
     )
 
 
-@router.post("/catalog/sync", response_model=UsageCatalogSyncResponse)
-def sync_usage_catalog(
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-):
-    """Drain Redis usage counters into Postgres (rate-limited per org via Redis)."""
-    flushed = flush_usage_to_catalog(db, organization_id)
-    return UsageCatalogSyncResponse(flushed_buckets=flushed)
+@router.get("/fx-rate", response_model=UsageFxRateResponse)
+def get_usage_fx_rate():
+    payload = get_usd_inr_rate()
+    return UsageFxRateResponse(
+        base=payload["base"],
+        quote=payload["quote"],
+        rate=float(payload["rate"]),
+        as_of=datetime.fromisoformat(payload["as_of"]),
+        source=payload["source"],
+    )
 
 
 @router.get("/summary", response_model=UsageSummaryResponse)
@@ -1125,6 +1136,23 @@ def get_usage_summary(
     if display_end < display_start:
         raise HTTPException(status_code=400, detail="end must be >= start")
 
+    cache_key = cache_key_for(
+        access,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    cached = get_cached_response(organization_id, "summary", cache_key)
+    if cached is not None:
+        return UsageSummaryResponse.model_validate(cached)
+
     row = _summary_aggregate_query(
         db,
         organization_id=organization_id,
@@ -1143,13 +1171,20 @@ def get_usage_summary(
         tag_id=tag_id,
     ).one()
     totals = _usage_totals_from_row(row)
-    return UsageSummaryResponse(
+    response = UsageSummaryResponse(
         start=display_start,
         end=display_end,
         totals=UsageTotals(**totals),
         usage_policy=_usage_policy_meta(access),
         last_updated_at=_last_updated(db, organization_id),
     )
+    set_cached_response(
+        organization_id,
+        "summary",
+        cache_key,
+        response.model_dump(mode="json"),
+    )
+    return response
 
 
 @router.get("/breakdown", response_model=UsageBreakdownResponse)
@@ -1182,6 +1217,26 @@ def get_usage_breakdown(
     if display_end < display_start:
         raise HTTPException(status_code=400, detail="end must be >= start")
 
+    cache_key = cache_key_for(
+        access,
+        group_by=group_by,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        limit=limit,
+        offset=offset,
+    )
+    cached = get_cached_response(organization_id, "breakdown", cache_key)
+    if cached is not None:
+        return UsageBreakdownResponse.model_validate(cached)
+
     dim = {
         "workspace": LLMUsageDaily.workspace_id,
         "product_section": LLMUsageDaily.product_section,
@@ -1386,7 +1441,7 @@ def get_usage_breakdown(
                 )
             )
 
-    return UsageBreakdownResponse(
+    response = UsageBreakdownResponse(
         start=display_start,
         end=display_end,
         group_by=group_by,
@@ -1396,6 +1451,13 @@ def get_usage_breakdown(
         usage_policy=_usage_policy_meta(access),
         last_updated_at=_last_updated(db, organization_id),
     )
+    set_cached_response(
+        organization_id,
+        "breakdown",
+        cache_key,
+        response.model_dump(mode="json"),
+    )
+    return response
 
 
 @router.get("/filters", response_model=UsageFiltersResponse)
@@ -1424,6 +1486,23 @@ def get_usage_filters(
     filter_end = access.filter_end
     enforced_floor = access.enforced_filter_floor
 
+    cache_key = cache_key_for(
+        access,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        q=q,
+    )
+    cached = get_cached_response(organization_id, "filters", cache_key)
+    if cached is not None:
+        return UsageFiltersResponse.model_validate(cached)
+
     scoped_resource_id = resource_id or evaluation_id
 
     workspace_base = _filtered_query(
@@ -1606,7 +1685,7 @@ def get_usage_filters(
         )
     ]
 
-    return UsageFiltersResponse(
+    response = UsageFiltersResponse(
         workspaces=workspaces,
         product_sections=[
             {"id": s, "label": SECTION_LABELS.get(s, s)} for s in sections
@@ -1619,4 +1698,11 @@ def get_usage_filters(
         datasets=datasets,
         tags=tags,
     )
+    set_cached_response(
+        organization_id,
+        "filters",
+        cache_key,
+        response.model_dump(mode="json"),
+    )
+    return response
 
diff --git a/app/cli.py b/app/cli.py
index eed0274a..8b7b368f 100644
--- a/app/cli.py
+++ b/app/cli.py
@@ -709,7 +709,6 @@ def start_all(
     worker_process = None
     worker_imports_process = None
     worker_usage_process = None
-    beat_process = None
     telephony_process = None
 
     def _terminate(proc, label: str):
@@ -729,9 +728,8 @@ def _terminate(proc, label: str):
 
     def cleanup_processes():
         """Clean up spawned processes."""
-        nonlocal worker_process, worker_imports_process, worker_usage_process, beat_process, telephony_process
+        nonlocal worker_process, worker_imports_process, worker_usage_process, telephony_process
         _terminate(telephony_process, "Telephony media server")
-        _terminate(beat_process, "Celery beat")
         _terminate(worker_process, "Celery worker (default)")
         _terminate(worker_imports_process, "Celery worker (imports)")
         _terminate(worker_usage_process, "Celery worker (usage)")
@@ -912,17 +910,6 @@ def _stream_telephony():
                 prefix="[WORKER-USAGE]",
             )
 
-        beat_process = _spawn_worker(
-            [
-                "celery",
-                "-A",
-                "app.workers.celery_app",
-                "beat",
-                f"--loglevel={worker_loglevel}",
-            ],
-            label="Celery beat (periodic flush_usage_counters)",
-            prefix="[BEAT]",
-        )
     except FileNotFoundError:
         click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
         sys.exit(1)
diff --git a/app/migrations/075_cron_job_types_and_system_jobs.py b/app/migrations/075_cron_job_types_and_system_jobs.py
new file mode 100644
index 00000000..abe8c572
--- /dev/null
+++ b/app/migrations/075_cron_job_types_and_system_jobs.py
@@ -0,0 +1,159 @@
+"""Migration: cron job types, system scheduled jobs, nullable org for platform tasks."""
+
+from __future__ import annotations
+
+import json
+import os
+import uuid
+from datetime import datetime, timezone
+
+from croniter import croniter
+import pytz
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add cron job_type/is_system/config and seed platform system jobs"
+
+_SYSTEM_JOB_IDS = {
+    "usage_flush": uuid.UUID("00000000-0000-0000-0000-000000000001"),
+    "alert_evaluate": uuid.UUID("00000000-0000-0000-0000-000000000002"),
+    "oss_usage_prune": uuid.UUID("00000000-0000-0000-0000-000000000003"),
+    "fx_rate_refresh": uuid.UUID("00000000-0000-0000-0000-000000000004"),
+}
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def _next_run(cron_expression: str, tz_name: str) -> datetime:
+    tz = pytz.timezone(tz_name)
+    now = datetime.now(tz)
+    cron = croniter(cron_expression, now)
+    return cron.get_next(datetime).astimezone(timezone.utc)
+
+
+def upgrade(db: Session) -> None:
+    if not _column_exists(db, "cron_jobs", "job_type"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE cron_jobs
+                ADD COLUMN job_type VARCHAR(64) NOT NULL DEFAULT 'evaluator_run'
+                """
+            )
+        )
+    if not _column_exists(db, "cron_jobs", "is_system"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE cron_jobs
+                ADD COLUMN is_system BOOLEAN NOT NULL DEFAULT false
+                """
+            )
+        )
+    if not _column_exists(db, "cron_jobs", "config"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE cron_jobs
+                ADD COLUMN config JSONB NOT NULL DEFAULT '{}'::jsonb
+                """
+            )
+        )
+
+    db.execute(text("ALTER TABLE cron_jobs ALTER COLUMN organization_id DROP NOT NULL"))
+
+    flush_cron = os.environ.get("USAGE_FLUSH_BEAT_SECONDS", "120")
+    if flush_cron.isdigit():
+        flush_expr = f"*/{max(1, int(flush_cron) // 60)} * * * *"
+    else:
+        flush_expr = "*/2 * * * *"
+
+    system_jobs = [
+        (
+            _SYSTEM_JOB_IDS["usage_flush"],
+            "__system_usage_flush",
+            flush_expr,
+            "usage_flush",
+        ),
+        (
+            _SYSTEM_JOB_IDS["alert_evaluate"],
+            "__system_alert_evaluate",
+            "*/5 * * * *",
+            "alert_evaluate",
+        ),
+        (
+            _SYSTEM_JOB_IDS["oss_usage_prune"],
+            "__system_oss_usage_prune",
+            "0 3 * * *",
+            "oss_usage_prune",
+        ),
+        (
+            _SYSTEM_JOB_IDS["fx_rate_refresh"],
+            "__system_fx_rate_refresh",
+            "0 6 * * *",
+            "fx_rate_refresh",
+        ),
+    ]
+
+    for job_id, name, cron_expression, job_type in system_jobs:
+        exists = db.execute(
+            text("SELECT 1 FROM cron_jobs WHERE id = CAST(:id AS uuid)"),
+            {"id": str(job_id)},
+        ).first()
+        if exists:
+            continue
+        next_run = _next_run(cron_expression, "UTC")
+        db.execute(
+            text(
+                """
+                INSERT INTO cron_jobs (
+                    id, organization_id, name, cron_expression, timezone,
+                    max_runs, current_runs, evaluator_ids, status,
+                    next_run_at, job_type, is_system, config
+                ) VALUES (
+                    CAST(:id AS uuid), NULL, :name, :cron_expression, 'UTC',
+                    2147483647, 0, CAST(:evaluator_ids AS jsonb), 'active',
+                    :next_run_at, :job_type, true, CAST('{}' AS jsonb)
+                )
+                """
+            ),
+            {
+                "id": str(job_id),
+                "name": name,
+                "cron_expression": cron_expression,
+                "evaluator_ids": json.dumps([]),
+                "next_run_at": next_run,
+                "job_type": job_type,
+            },
+        )
+        print(f"Seeded system cron job {job_type}")
+
+    db.commit()
+
+
+def downgrade(db: Session) -> None:
+    for job_id in _SYSTEM_JOB_IDS.values():
+        db.execute(
+            text("DELETE FROM cron_jobs WHERE id = CAST(:id AS uuid)"),
+            {"id": str(job_id)},
+        )
+    if _column_exists(db, "cron_jobs", "config"):
+        db.execute(text("ALTER TABLE cron_jobs DROP COLUMN config"))
+    if _column_exists(db, "cron_jobs", "is_system"):
+        db.execute(text("ALTER TABLE cron_jobs DROP COLUMN is_system"))
+    if _column_exists(db, "cron_jobs", "job_type"):
+        db.execute(text("ALTER TABLE cron_jobs DROP COLUMN job_type"))
+    db.commit()
diff --git a/app/models/database.py b/app/models/database.py
index 99fbbbba..b7fb4eef 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -1207,10 +1207,13 @@ class CronJob(Base):
     __tablename__ = "cron_jobs"
 
     id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=True, index=True)
     
     # Basic information
     name = Column(String(255), nullable=False)
+    job_type = Column(String(64), nullable=False, default="evaluator_run")
+    is_system = Column(Boolean, nullable=False, default=False)
+    config = Column(JSON, nullable=False, default=dict)
     cron_expression = Column(String(100), nullable=False)  # e.g., "0 9 * * 1-5"
     timezone = Column(String(100), nullable=False, default="UTC")
     
diff --git a/app/models/schemas.py b/app/models/schemas.py
index de5c203e..60702164 100644
--- a/app/models/schemas.py
+++ b/app/models/schemas.py
@@ -2464,6 +2464,8 @@ class CronJobResponse(BaseModel):
     id: UUID
     organization_id: UUID
     name: str
+    job_type: str = "evaluator_run"
+    is_system: bool = False
     cron_expression: str
     timezone: str
     max_runs: int
diff --git a/app/services/cron/dispatcher_lock.py b/app/services/cron/dispatcher_lock.py
new file mode 100644
index 00000000..26ac638f
--- /dev/null
+++ b/app/services/cron/dispatcher_lock.py
@@ -0,0 +1,81 @@
+"""Redis locks for singleton cron dispatcher."""
+
+from __future__ import annotations
+
+import os
+
+import redis
+from loguru import logger
+
+from app.config import settings
+
+_DISPATCHER_LOCK_KEY = "cron:dispatcher:lock"
+_DISPATCHER_LEADER_KEY = "cron:dispatcher:leader"
+_redis: redis.Redis | None = None
+
+
+def _client() -> redis.Redis:
+    global _redis
+    if _redis is None:
+        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+    return _redis
+
+
+def _lock_ttl_seconds() -> int:
+    raw = os.environ.get("CRON_DISPATCH_LOCK_TTL_SECONDS", "55")
+    try:
+        return max(10, int(raw))
+    except (TypeError, ValueError):
+        return 55
+
+
+def _leader_ttl_seconds() -> int:
+    raw = os.environ.get("CRON_DISPATCH_LEADER_TTL_SECONDS", "300")
+    try:
+        return max(60, int(raw))
+    except (TypeError, ValueError):
+        return 300
+
+
+def try_acquire_dispatcher_leader() -> bool:
+    try:
+        return bool(
+            _client().set(
+                _DISPATCHER_LEADER_KEY,
+                "1",
+                nx=True,
+                ex=_leader_ttl_seconds(),
+            )
+        )
+    except redis.RedisError as exc:
+        logger.warning("cron dispatcher leader lock skipped: {}", exc)
+        return False
+
+
+def refresh_dispatcher_leader() -> None:
+    try:
+        _client().expire(_DISPATCHER_LEADER_KEY, _leader_ttl_seconds())
+    except redis.RedisError:
+        pass
+
+
+def acquire_dispatcher_run_lock() -> bool:
+    try:
+        return bool(
+            _client().set(
+                _DISPATCHER_LOCK_KEY,
+                "1",
+                nx=True,
+                ex=_lock_ttl_seconds(),
+            )
+        )
+    except redis.RedisError as exc:
+        logger.warning("cron dispatcher run lock skipped: {}", exc)
+        return True
+
+
+def release_dispatcher_run_lock() -> None:
+    try:
+        _client().delete(_DISPATCHER_LOCK_KEY)
+    except redis.RedisError:
+        pass
diff --git a/app/services/cron/job_dispatch.py b/app/services/cron/job_dispatch.py
new file mode 100644
index 00000000..af8b9472
--- /dev/null
+++ b/app/services/cron/job_dispatch.py
@@ -0,0 +1,137 @@
+"""Dispatch due cron jobs to Celery workers."""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from datetime import datetime, timezone
+from typing import Any, Dict, List
+from uuid import UUID
+
+from loguru import logger
+from sqlalchemy.orm import Session
+
+from app.models.database import CronJob, Evaluator
+from app.models.enums import CronJobStatus
+from app.services.cron.scheduling import calculate_next_run
+
+
+def list_due_cron_jobs(db: Session, *, now: datetime | None = None) -> List[CronJob]:
+    moment = now or datetime.now(timezone.utc)
+    return (
+        db.query(CronJob)
+        .filter(
+            CronJob.status == CronJobStatus.ACTIVE.value,
+            CronJob.next_run_at.isnot(None),
+            CronJob.next_run_at <= moment,
+        )
+        .order_by(CronJob.next_run_at.asc())
+        .all()
+    )
+
+
+def advance_cron_job(db: Session, job: CronJob, *, now: datetime | None = None) -> None:
+    moment = now or datetime.now(timezone.utc)
+    job.last_run_at = moment
+    job.current_runs = int(job.current_runs or 0) + 1
+    if job.current_runs >= int(job.max_runs or 0):
+        job.status = CronJobStatus.COMPLETED.value
+        job.next_run_at = None
+    else:
+        job.next_run_at = calculate_next_run(job.cron_expression, job.timezone)
+    db.add(job)
+
+
+def enqueue_cron_job(job: CronJob) -> Dict[str, Any]:
+    job_type = (job.job_type or "evaluator_run").strip()
+
+    if job_type == "usage_flush":
+        from app.workers.tasks.flush_usage_counters import flush_usage_counters_task
+
+        result = flush_usage_counters_task.delay()
+        return {"task": "flush_usage_counters", "celery_task_id": result.id}
+
+    if job_type == "alert_evaluate":
+        from app.workers.tasks.evaluate_alerts import evaluate_alerts_task
+
+        result = evaluate_alerts_task.delay()
+        return {"task": "evaluate_alerts", "celery_task_id": result.id}
+
+    if job_type == "oss_usage_prune":
+        from app.workers.tasks.prune_oss_usage_history import (
+            prune_oss_usage_history_task,
+        )
+
+        result = prune_oss_usage_history_task.delay()
+        return {"task": "prune_oss_usage_history", "celery_task_id": result.id}
+
+    if job_type == "fx_rate_refresh":
+        from app.workers.tasks.refresh_fx_rates import refresh_fx_rates_task
+
+        result = refresh_fx_rates_task.delay()
+        return {"task": "refresh_fx_rates", "celery_task_id": result.id}
+
+    if job_type == "evaluator_run":
+        from app.workers.tasks.run_cron_evaluator_job import run_cron_evaluator_job_task
+
+        result = run_cron_evaluator_job_task.delay(str(job.id))
+        return {"task": "run_cron_evaluator_job", "celery_task_id": result.id}
+
+    logger.warning("Unknown cron job_type {} for job {}", job_type, job.id)
+    return {"task": "unknown", "job_type": job_type}
+
+
+def run_evaluator_cron_job(db: Session, job: CronJob) -> Dict[str, Any]:
+    from app.services.evaluators.evaluator_run_service import queue_evaluator_runs
+
+    if job.organization_id is None:
+        return {"error": "evaluator_run requires organization_id"}
+
+    raw_ids = job.evaluator_ids or []
+    evaluator_ids: List[UUID] = []
+    for value in raw_ids:
+        try:
+            evaluator_ids.append(UUID(str(value)))
+        except (TypeError, ValueError):
+            continue
+
+    if not evaluator_ids:
+        return {"error": "no evaluator_ids configured"}
+
+    by_workspace: dict[UUID, List[UUID]] = defaultdict(list)
+    for evaluator_id in evaluator_ids:
+        evaluator = (
+            db.query(Evaluator)
+            .filter(
+                Evaluator.id == evaluator_id,
+                Evaluator.organization_id == job.organization_id,
+            )
+            .first()
+        )
+        if evaluator is None:
+            logger.warning(
+                "cron evaluator_run skipped missing evaluator {} for job {}",
+                evaluator_id,
+                job.id,
+            )
+            continue
+        by_workspace[evaluator.workspace_id].append(evaluator_id)
+
+    task_ids: List[str] = []
+    for workspace_id, ids in by_workspace.items():
+        try:
+            ws_task_ids, _results = queue_evaluator_runs(
+                db,
+                job.organization_id,
+                workspace_id,
+                ids,
+            )
+            task_ids.extend(ws_task_ids)
+        except Exception as exc:
+            logger.warning(
+                "cron evaluator_run failed for job {} workspace {}: {}",
+                job.id,
+                workspace_id,
+                exc,
+            )
+
+    return {"evaluator_tasks": len(task_ids), "celery_task_ids": task_ids[:20]}
diff --git a/app/services/cron/scheduling.py b/app/services/cron/scheduling.py
new file mode 100644
index 00000000..833fa392
--- /dev/null
+++ b/app/services/cron/scheduling.py
@@ -0,0 +1,19 @@
+"""Shared cron scheduling helpers."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Optional
+
+import pytz
+from croniter import croniter
+
+
+def calculate_next_run(cron_expression: str, tz_name: str) -> Optional[datetime]:
+    try:
+        tz = pytz.timezone(tz_name)
+        now = datetime.now(tz)
+        cron = croniter(cron_expression, now)
+        return cron.get_next(datetime).astimezone(timezone.utc)
+    except Exception:
+        return None
diff --git a/app/services/usage/fx_rates.py b/app/services/usage/fx_rates.py
new file mode 100644
index 00000000..a99cc68b
--- /dev/null
+++ b/app/services/usage/fx_rates.py
@@ -0,0 +1,80 @@
+"""USD/INR display FX rate (Frankfurter v2, cached in Redis)."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from typing import Any, Optional
+
+import httpx
+import redis
+from loguru import logger
+
+from app.config import settings
+
+_REDIS_KEY = "usage:fx:USD_INR"
+_FRANKFURTER_URL = "https://api.frankfurter.dev/v2/rate/USD/INR"
+_DEFAULT_RATE = 83.0
+_redis: redis.Redis | None = None
+
+
+def _client() -> redis.Redis:
+    global _redis
+    if _redis is None:
+        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+    return _redis
+
+
+def _cache_payload(rate: float, as_of: datetime, source: str) -> dict[str, Any]:
+    return {
+        "base": "USD",
+        "quote": "INR",
+        "rate": rate,
+        "as_of": as_of.astimezone(timezone.utc).isoformat(),
+        "source": source,
+    }
+
+
+def _read_cached() -> Optional[dict[str, Any]]:
+    try:
+        raw = _client().get(_REDIS_KEY)
+        if not raw:
+            return None
+        payload = json.loads(raw)
+        if not isinstance(payload.get("rate"), (int, float)):
+            return None
+        return payload
+    except (redis.RedisError, json.JSONDecodeError):
+        return None
+
+
+def get_usd_inr_rate() -> dict[str, Any]:
+    cached = _read_cached()
+    if cached is not None:
+        return cached
+    return _cache_payload(_DEFAULT_RATE, datetime.now(timezone.utc), "default")
+
+
+def refresh_usd_inr_rate() -> dict[str, Any]:
+    rate = _DEFAULT_RATE
+    as_of = datetime.now(timezone.utc)
+    source = "default"
+    try:
+        with httpx.Client(timeout=15.0) as client:
+            response = client.get(_FRANKFURTER_URL)
+            response.raise_for_status()
+            data = response.json()
+            fetched = float(data["rate"])
+            if fetched > 0:
+                rate = fetched
+                source = "frankfurter"
+                as_of = datetime.fromisoformat(data["date"]).replace(tzinfo=timezone.utc)
+    except Exception as exc:
+        logger.warning("USD/INR FX refresh failed, using fallback: {}", exc)
+
+    payload = _cache_payload(rate, as_of, source)
+    try:
+        _client().set(_REDIS_KEY, json.dumps(payload), ex=25 * 3600)
+    except redis.RedisError as exc:
+        logger.warning("USD/INR FX cache write failed: {}", exc)
+    return payload
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index 51ffc6bc..65083934 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -38,7 +38,6 @@
 _DEFAULT_FLUSH_BUCKET_BATCH_SIZE = 500
 _DEFAULT_FLUSH_MAX_BATCHES_PER_RUN = 30
 _DEFAULT_FLUSH_LOCK_TTL_SECONDS = 300
-_DEFAULT_API_FLUSH_COOLDOWN_SECONDS = 60
 _FLUSH_LOCK_WAIT_SECONDS = 3.0
 USAGE_KIND_LLM = "llm"
 USAGE_KIND_STT = "stt"
@@ -97,15 +96,6 @@ def _flush_lock_ttl_seconds() -> int:
     )
 
 
-def api_flush_cooldown_seconds() -> int:
-    """Min seconds between API-triggered catalog syncs per org (0 = no cooldown)."""
-    return _env_int(
-        "USAGE_API_FLUSH_COOLDOWN_SECONDS",
-        _DEFAULT_API_FLUSH_COOLDOWN_SECONDS,
-        minimum=0,
-    )
-
-
 def _split_buckets_for_flush(
     buckets: Dict[str, Dict[str, int]],
     batch_size: int,
@@ -1160,43 +1150,28 @@ def _flush_pending_buffer(
         return 0
 
 
-def _catalog_flush_recently(organization_id: UUID) -> bool:
-    """Skip flush if another request flushed this org within the cooldown window."""
-    cooldown = api_flush_cooldown_seconds()
-    if cooldown <= 0:
-        return False
-    try:
-        client = _client()
-        key = f"usage:catalog_flush:{organization_id}"
-        return not client.set(key, "1", nx=True, ex=cooldown)
-    except redis.RedisError:
-        return False
-
-
-def flush_usage_to_catalog(db: Session, organization_id: UUID, *, force: bool = False) -> int:
+def flush_usage_to_catalog(db: Session, organization_id: UUID) -> int:
     """Claim Redis deltas + drain PG buffer into llm_usage_daily."""
     from app.services.usage.pricing import PricingResolver
+    from app.services.usage.read_cache import invalidate_org_usage_read_cache
 
     _recover_orphaned_claims()
-    skip_redis_flush = not force and _catalog_flush_recently(organization_id)
-    if skip_redis_flush and _has_pending_usage(organization_id):
-        skip_redis_flush = False
     flushed = 0
     pricing_resolver = PricingResolver(db)
-    if not skip_redis_flush:
-        redis_locked = _acquire_flush_lock(organization_id)
-        try:
-            if redis_locked:
-                flushed += _flush_redis_pending_to_catalog(
-                    db,
-                    organization_id,
-                    pricing_resolver=pricing_resolver,
-                )
-        finally:
-            if redis_locked:
-                _release_flush_lock(organization_id)
+    redis_locked = _acquire_flush_lock(organization_id)
+    try:
+        if redis_locked:
+            flushed += _flush_redis_pending_to_catalog(
+                db,
+                organization_id,
+                pricing_resolver=pricing_resolver,
+            )
+    finally:
+        if redis_locked:
+            _release_flush_lock(organization_id)
 
     flushed += _flush_pending_buffer(db, organization_id, pricing_resolver=pricing_resolver)
+    invalidate_org_usage_read_cache(organization_id)
     return flushed
 
 
diff --git a/app/services/usage/read_cache.py b/app/services/usage/read_cache.py
new file mode 100644
index 00000000..02ab58d7
--- /dev/null
+++ b/app/services/usage/read_cache.py
@@ -0,0 +1,118 @@
+"""Redis cache for org usage read endpoints (summary, breakdown, filters)."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from typing import Any, Optional
+from uuid import UUID
+
+import redis
+from loguru import logger
+
+from app.config import settings
+from app.services.usage.access import UsageAccessResult
+
+_redis: redis.Redis | None = None
+
+
+def _client() -> redis.Redis:
+    global _redis
+    if _redis is None:
+        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+    return _redis
+
+
+def cache_ttl_seconds() -> int:
+    raw = os.environ.get("USAGE_READ_CACHE_TTL_SECONDS", "90")
+    try:
+        return max(0, int(raw))
+    except (TypeError, ValueError):
+        return 90
+
+
+def cache_key_for(access: UsageAccessResult, **params: Any) -> str:
+    payload: dict[str, Any] = {
+        "display_start": access.display_start.isoformat(),
+        "display_end": access.display_end.isoformat(),
+        "filter_start": access.filter_start.isoformat(),
+        "filter_end": access.filter_end.isoformat(),
+        "enforced_floor": (
+            access.enforced_filter_floor.isoformat()
+            if access.enforced_filter_floor
+            else None
+        ),
+        "range_clamped": access.range_clamped,
+        "policy": access.policy.as_dict(),
+    }
+    for key, value in sorted(params.items()):
+        if value is None:
+            payload[key] = None
+        elif isinstance(value, UUID):
+            payload[key] = str(value)
+        else:
+            payload[key] = value
+    digest = hashlib.sha256(
+        json.dumps(payload, sort_keys=True, default=str).encode()
+    ).hexdigest()
+    return digest[:32]
+
+
+def _redis_key(organization_id: UUID, endpoint: str, cache_key: str) -> str:
+    return f"usage:read:{organization_id}:{endpoint}:{cache_key}"
+
+
+def get_cached_response(
+    organization_id: UUID,
+    endpoint: str,
+    cache_key: str,
+) -> Optional[dict[str, Any]]:
+    ttl = cache_ttl_seconds()
+    if ttl <= 0:
+        return None
+    try:
+        raw = _client().get(_redis_key(organization_id, endpoint, cache_key))
+        if not raw:
+            return None
+        return json.loads(raw)
+    except (redis.RedisError, json.JSONDecodeError) as exc:
+        logger.debug("usage read cache miss/error org={} endpoint={}: {}", organization_id, endpoint, exc)
+        return None
+
+
+def set_cached_response(
+    organization_id: UUID,
+    endpoint: str,
+    cache_key: str,
+    payload: dict[str, Any],
+) -> None:
+    ttl = cache_ttl_seconds()
+    if ttl <= 0:
+        return
+    try:
+        _client().set(
+            _redis_key(organization_id, endpoint, cache_key),
+            json.dumps(payload, default=str),
+            ex=ttl,
+        )
+    except redis.RedisError as exc:
+        logger.debug("usage read cache set failed org={}: {}", organization_id, exc)
+
+
+def invalidate_org_usage_read_cache(organization_id: UUID) -> int:
+    pattern = f"usage:read:{organization_id}:*"
+    deleted = 0
+    try:
+        client = _client()
+        batch: list[str] = []
+        for key in client.scan_iter(match=pattern, count=200):
+            batch.append(key)
+            if len(batch) >= 200:
+                deleted += client.delete(*batch)
+                batch.clear()
+        if batch:
+            deleted += client.delete(*batch)
+    except redis.RedisError as exc:
+        logger.warning("usage read cache invalidate failed org={}: {}", organization_id, exc)
+    return deleted
diff --git a/app/workers/config.py b/app/workers/config.py
index b43306e7..fcbf25f2 100644
--- a/app/workers/config.py
+++ b/app/workers/config.py
@@ -141,20 +141,10 @@
     "flush_usage_counters": {"queue": USAGE_WORKER_QUEUE},
     "recompute_usage_costs": {"queue": USAGE_WORKER_QUEUE},
     "prune_oss_usage_history": {"queue": USAGE_WORKER_QUEUE},
+    "dispatch_cron_jobs": {"queue": "celery"},
+    "evaluate_alerts": {"queue": "celery"},
+    "refresh_fx_rates": {"queue": "celery"},
+    "run_cron_evaluator_job": {"queue": "celery"},
 }
 
-# Periodic flush of Redis usage counters into catalog rollups (usage queue).
-_flush_interval = float(os.environ.get("USAGE_FLUSH_BEAT_SECONDS", "120"))
-_prune_interval = float(os.environ.get("USAGE_PRUNE_BEAT_SECONDS", "86400"))
-celery_app.conf.beat_schedule = {
-    "flush-llm-usage-counters": {
-        "task": "flush_usage_counters",
-        "schedule": _flush_interval,
-        "options": {"queue": USAGE_WORKER_QUEUE},
-    },
-    "prune-oss-usage-history": {
-        "task": "prune_oss_usage_history",
-        "schedule": _prune_interval,
-        "options": {"queue": USAGE_WORKER_QUEUE},
-    },
-}
+from app.workers import cron_bootstrap  # noqa: F401,E402
diff --git a/app/workers/cron_bootstrap.py b/app/workers/cron_bootstrap.py
new file mode 100644
index 00000000..b0c1d62a
--- /dev/null
+++ b/app/workers/cron_bootstrap.py
@@ -0,0 +1,21 @@
+"""Bootstrap cron dispatcher on worker startup."""
+
+from __future__ import annotations
+
+from celery.signals import worker_ready
+from loguru import logger
+
+
+@worker_ready.connect
+def _bootstrap_cron_dispatcher(sender, **kwargs):
+    try:
+        from app.services.cron.dispatcher_lock import try_acquire_dispatcher_leader
+
+        if not try_acquire_dispatcher_leader():
+            return
+        from app.workers.tasks.dispatch_cron_jobs import dispatch_cron_jobs_task
+
+        dispatch_cron_jobs_task.apply_async(countdown=5)
+        logger.info("Cron dispatcher bootstrap enqueued")
+    except Exception as exc:
+        logger.warning("Cron dispatcher bootstrap skipped: {}", exc)
diff --git a/app/workers/tasks/__init__.py b/app/workers/tasks/__init__.py
index 1f237e29..740be91f 100644
--- a/app/workers/tasks/__init__.py
+++ b/app/workers/tasks/__init__.py
@@ -25,6 +25,10 @@
 from . import flush_usage_counters
 from . import recompute_usage_costs
 from . import prune_oss_usage_history
+from . import dispatch_cron_jobs
+from . import evaluate_alerts
+from . import refresh_fx_rates
+from . import run_cron_evaluator_job
 from app.workers.concurrency import eval_dispatch
 from app.workers.concurrency import fair_dispatch
 from app.workers.concurrency import fair_diarization_dispatch
@@ -121,3 +125,7 @@
 flush_usage_counters_task = flush_usage_counters.flush_usage_counters_task
 recompute_usage_costs_task = recompute_usage_costs.recompute_usage_costs_task
 prune_oss_usage_history_task = prune_oss_usage_history.prune_oss_usage_history_task
+dispatch_cron_jobs_task = dispatch_cron_jobs.dispatch_cron_jobs_task
+evaluate_alerts_task = evaluate_alerts.evaluate_alerts_task
+refresh_fx_rates_task = refresh_fx_rates.refresh_fx_rates_task
+run_cron_evaluator_job_task = run_cron_evaluator_job.run_cron_evaluator_job_task
diff --git a/app/workers/tasks/dispatch_cron_jobs.py b/app/workers/tasks/dispatch_cron_jobs.py
new file mode 100644
index 00000000..3c658c66
--- /dev/null
+++ b/app/workers/tasks/dispatch_cron_jobs.py
@@ -0,0 +1,58 @@
+"""Self-scheduling cron job dispatcher (replaces Celery Beat for platform jobs)."""
+
+from __future__ import annotations
+
+import os
+
+from loguru import logger
+
+from app.database import SessionLocal
+from app.workers.config import celery_app
+
+
+def _dispatch_interval_seconds() -> int:
+    raw = os.environ.get("CRON_DISPATCH_INTERVAL_SECONDS", "30")
+    try:
+        return max(10, int(raw))
+    except (TypeError, ValueError):
+        return 30
+
+
+@celery_app.task(name="dispatch_cron_jobs")
+def dispatch_cron_jobs_task() -> dict:
+    from app.services.cron.dispatcher_lock import (
+        acquire_dispatcher_run_lock,
+        refresh_dispatcher_leader,
+        release_dispatcher_run_lock,
+    )
+    from app.services.cron.job_dispatch import (
+        advance_cron_job,
+        enqueue_cron_job,
+        list_due_cron_jobs,
+    )
+
+    interval = _dispatch_interval_seconds()
+    refresh_dispatcher_leader()
+
+    if not acquire_dispatcher_run_lock():
+        dispatch_cron_jobs_task.apply_async(countdown=interval)
+        return {"skipped": "locked"}
+
+    dispatched: list[dict] = []
+    db = SessionLocal()
+    try:
+        for job in list_due_cron_jobs(db):
+            try:
+                meta = enqueue_cron_job(job)
+                advance_cron_job(db, job)
+                db.commit()
+                dispatched.append({"job_id": str(job.id), "job_type": job.job_type, **meta})
+            except Exception as exc:
+                db.rollback()
+                logger.warning("cron dispatch failed for job {}: {}", job.id, exc)
+    finally:
+        db.close()
+        release_dispatcher_run_lock()
+
+    dispatch_cron_jobs_task.apply_async(countdown=interval)
+    return {"dispatched": len(dispatched), "jobs": dispatched}
diff --git a/app/workers/tasks/evaluate_alerts.py b/app/workers/tasks/evaluate_alerts.py
new file mode 100644
index 00000000..5d541eda
--- /dev/null
+++ b/app/workers/tasks/evaluate_alerts.py
@@ -0,0 +1,17 @@
+"""Celery task: evaluate all active alerts."""
+
+from __future__ import annotations
+
+from app.database import SessionLocal
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="evaluate_alerts")
+def evaluate_alerts_task() -> dict:
+    from app.services.alerts.alert_evaluation_service import alert_evaluation_service
+
+    db = SessionLocal()
+    try:
+        return alert_evaluation_service.evaluate_all_alerts(db)
+    finally:
+        db.close()
diff --git a/app/workers/tasks/recompute_usage_costs.py b/app/workers/tasks/recompute_usage_costs.py
index 8340f797..d1a2aea7 100644
--- a/app/workers/tasks/recompute_usage_costs.py
+++ b/app/workers/tasks/recompute_usage_costs.py
@@ -70,6 +70,10 @@ def _on_progress(updated_rows: int) -> None:
             mark_job_completed(db, progress_job_id, updated)
         if updated:
             logger.info("Recomputed usage costs for {} rollup row(s)", updated)
+        if org_uuid is not None and updated:
+            from app.services.usage.read_cache import invalidate_org_usage_read_cache
+
+            invalidate_org_usage_read_cache(org_uuid)
         return {"updated_rows": updated, "job_id": job_id}
     except Exception as exc:
         if job_id:
diff --git a/app/workers/tasks/refresh_fx_rates.py b/app/workers/tasks/refresh_fx_rates.py
new file mode 100644
index 00000000..170e9103
--- /dev/null
+++ b/app/workers/tasks/refresh_fx_rates.py
@@ -0,0 +1,12 @@
+"""Celery task: refresh USD/INR display FX rate."""
+
+from __future__ import annotations
+
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="refresh_fx_rates")
+def refresh_fx_rates_task() -> dict:
+    from app.services.usage.fx_rates import refresh_usd_inr_rate
+
+    return refresh_usd_inr_rate()
diff --git a/app/workers/tasks/run_cron_evaluator_job.py b/app/workers/tasks/run_cron_evaluator_job.py
new file mode 100644
index 00000000..508ba1b2
--- /dev/null
+++ b/app/workers/tasks/run_cron_evaluator_job.py
@@ -0,0 +1,23 @@
+"""Celery task: run evaluator_ids for an org cron job."""
+
+from __future__ import annotations
+
+from uuid import UUID
+
+from app.database import SessionLocal
+from app.models.database import CronJob
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="run_cron_evaluator_job")
+def run_cron_evaluator_job_task(job_id: str) -> dict:
+    from app.services.cron.job_dispatch import run_evaluator_cron_job
+
+    db = SessionLocal()
+    try:
+        job = db.query(CronJob).filter(CronJob.id == UUID(job_id)).first()
+        if job is None:
+            return {"error": "job not found", "job_id": job_id}
+        return run_evaluator_cron_job(db, job)
+    finally:
+        db.close()
diff --git a/docker-compose.yml b/docker-compose.yml
index 13be6295..b421ec2e 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -158,8 +158,8 @@ services:
   # ``imports`` (recording fetch) before ``diarization`` (manual diarise),
   # then ``eval-control`` (cancel/retry/materialize), then ``evaluations``
   # (fair dispatch + LLM scoring).
-  # The default ``worker`` service handles ``celery`` (legacy) and
-  # ``audio-metrics`` (Praat/UTMOS audio metric tasks).
+  # The default ``worker`` service handles ``celery`` (legacy cron dispatch,
+  # alerts, FX refresh) and ``audio-metrics`` (Praat/UTMOS audio metric tasks).
   worker-imports:
     image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
     build:
@@ -221,34 +221,5 @@ services:
       - ./config.docker.yml:/app/config.yml:ro
     command: eai worker --config /app/config.yml --loglevel info --queues usage --pool threads --concurrency 4
 
-  beat:
-    image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
-    build:
-      context: .
-      dockerfile: docker/Dockerfile.worker
-      args:
-        INSTALL_EXTRAS: ""
-    container_name: efficientai_beat
-    env_file:
-      - .env
-    environment:
-      DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
-      REDIS_URL: redis://redis:6379/0
-      CELERY_BROKER_URL: redis://redis:6379/0
-      CELERY_RESULT_BACKEND: redis://redis:6379/0
-      ENCRYPTION_KEY: ${ENCRYPTION_KEY:-}
-    depends_on:
-      db:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-      worker-usage:
-        condition: service_started
-    volumes:
-      - ./uploads:/app/uploads
-      - ./.data:/app/.data
-      - ./config.docker.yml:/app/config.yml:ro
-    command: celery -A app.workers.celery_app beat --loglevel info
-
 volumes:
   postgres_data:
diff --git a/env.example b/env.example
index 7e87c790..27c34f4d 100644
--- a/env.example
+++ b/env.example
@@ -70,12 +70,13 @@ AUTH_LOCAL_ALLOW_SIGNUP=true
 # EFFICIENTAI_LICENSE=eyJhbGciOi...
 
 # -----------------------------------------------------------------------------
-# Usage cost flush (worker-usage + beat; optional overrides for api service too)
+# Usage cost flush (worker-usage + cron dispatcher on default worker)
 # See README "Usage Pricing Ops". Copy to .env for Docker Compose / local dev.
 # -----------------------------------------------------------------------------
 USAGE_FLUSH_BUCKET_BATCH_SIZE=500 
 USAGE_FLUSH_MAX_BATCHES_PER_RUN=30
 USAGE_FLUSH_BEAT_SECONDS=120
 USAGE_FLUSH_LOCK_TTL_SECONDS=300
-USAGE_API_FLUSH_COOLDOWN_SECONDS=60
+USAGE_READ_CACHE_TTL_SECONDS=90
+CRON_DISPATCH_INTERVAL_SECONDS=30
 
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index fc6ec8ec..abb880fb 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -2997,8 +2997,14 @@ class ApiClient {
     return response.data
   }
 
-  async syncOrgUsageCatalog(): Promise<{ flushed_buckets: number }> {
-    const response = await this.client.post('/api/v1/organizations/usage/catalog/sync')
+  async getOrgUsageFxRate(): Promise<{
+    base: string
+    quote: string
+    rate: number
+    as_of: string
+    source: string
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/fx-rate')
     return response.data
   }
 
diff --git a/frontend/src/lib/usageCurrency.ts b/frontend/src/lib/usageCurrency.ts
new file mode 100644
index 00000000..2b2122c4
--- /dev/null
+++ b/frontend/src/lib/usageCurrency.ts
@@ -0,0 +1,37 @@
+export type UsageDisplayCurrency = 'USD' | 'INR'
+
+const STORAGE_KEY = 'efficientai.usage.displayCurrency'
+
+export function getUsageDisplayCurrency(): UsageDisplayCurrency {
+  if (typeof window === 'undefined') return 'USD'
+  const stored = window.localStorage.getItem(STORAGE_KEY)
+  return stored === 'INR' ? 'INR' : 'USD'
+}
+
+export function setUsageDisplayCurrency(currency: UsageDisplayCurrency): void {
+  if (typeof window === 'undefined') return
+  window.localStorage.setItem(STORAGE_KEY, currency)
+}
+
+export function formatUsageCostUsd(
+  usd: number | null | undefined,
+  currency: UsageDisplayCurrency,
+  inrRate: number,
+): string {
+  const amountUsd = Number(usd || 0)
+  if (!amountUsd) return '—'
+  if (currency === 'INR') {
+    return new Intl.NumberFormat(undefined, {
+      style: 'currency',
+      currency: 'INR',
+      minimumFractionDigits: 2,
+      maximumFractionDigits: 2,
+    }).format(amountUsd * inrRate)
+  }
+  return new Intl.NumberFormat(undefined, {
+    style: 'currency',
+    currency: 'USD',
+    minimumFractionDigits: 2,
+    maximumFractionDigits: 4,
+  }).format(amountUsd)
+}
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index a87cf73c..07bb4772 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -10,6 +10,12 @@ import UsageDrillPath from './UsageDrillPath'
 import UsageCostBreakdownModal from './UsageCostBreakdownModal'
 import { defaultUsageDateRange, isRangeWithinMaxDays, rangeForDays } from './UsageDateRangePicker'
 import { getUsageTimezone } from './usageTimezone'
+import {
+  formatUsageCostUsd,
+  getUsageDisplayCurrency,
+  setUsageDisplayCurrency,
+  type UsageDisplayCurrency,
+} from '../../lib/usageCurrency'
 import { useLicenseStore } from '../../store/licenseStore'
 import {
   CALL_IMPORT_BATCH_HEADLINE,
@@ -169,17 +175,6 @@ function formatNumber(value: number): string {
   return new Intl.NumberFormat().format(value || 0)
 }
 
-function formatCostUsd(usd?: number | null): string {
-  const amount = Number(usd || 0)
-  if (!amount) return '—'
-  return new Intl.NumberFormat(undefined, {
-    style: 'currency',
-    currency: 'USD',
-    minimumFractionDigits: 2,
-    maximumFractionDigits: 4,
-  }).format(amount)
-}
-
 function rowCostUsd(row: Pick<BreakdownRow, 'costs' | 'total_cost_micro_usd'>): number {
   if (row.costs?.total_cost_usd != null) return row.costs.total_cost_usd
   return Number(row.total_cost_micro_usd || 0) / 1_000_000
@@ -570,6 +565,18 @@ export default function Usage() {
       ? null
       : usagePolicy.max_history_days ?? 7
   const [costBreakdownOpen, setCostBreakdownOpen] = useState(false)
+  const [displayCurrency, setDisplayCurrency] = useState<UsageDisplayCurrency>(() =>
+    getUsageDisplayCurrency(),
+  )
+
+  const { data: fxRate } = useQuery({
+    queryKey: ['org-usage', 'fx-rate'],
+    queryFn: () => apiClient.getOrgUsageFxRate(),
+    staleTime: 60 * 60 * 1000,
+  })
+  const inrRate = fxRate?.rate ?? 83
+  const formatCostUsd = (usd?: number | null) =>
+    formatUsageCostUsd(usd, displayCurrency, inrRate)
 
   useEffect(() => {
     if (!licenseLoaded) {
@@ -637,24 +644,14 @@ export default function Usage() {
     staleTime: 60 * 1000,
   }
 
-  const catalogSync = useQuery({
-    queryKey: ['org-usage', 'catalog-sync'],
-    queryFn: () => apiClient.syncOrgUsageCatalog(),
-    staleTime: 60 * 1000,
-    refetchOnWindowFocus: false,
-  })
-
-  const usageReadsReady = catalogSync.isSuccess || catalogSync.isError
-
   const { data: summary, isLoading: summaryLoading, isFetching: summaryFetching } = useQuery({
     queryKey: ['org-usage', 'summary', dataParams],
     queryFn: () => apiClient.getOrgUsageSummary(dataParams),
-    enabled: usageReadsReady,
     ...usageQueryDefaults,
     placeholderData: keepPreviousData,
   })
 
-  const usageStatsLoading = catalogSync.isLoading || summaryLoading
+  const usageStatsLoading = summaryLoading
 
   const {
     data: breakdown,
@@ -668,7 +665,7 @@ export default function Usage() {
         group_by: groupBy,
         limit: 100,
       }),
-    enabled: usageReadsReady && !showWorkspaceComposite,
+    enabled: !showWorkspaceComposite,
     ...usageQueryDefaults,
     placeholderData: (previousData, previousQuery) => {
       if (!previousQuery || previousQuery.queryKey[2] !== groupBy) return undefined
@@ -688,7 +685,7 @@ export default function Usage() {
         group_by: 'call_import',
         limit: 100,
       }),
-    enabled: usageReadsReady && showWorkspaceComposite,
+    enabled: showWorkspaceComposite,
     ...usageQueryDefaults,
   })
 
@@ -704,14 +701,14 @@ export default function Usage() {
         group_by: 'resource',
         limit: 100,
       }),
-    enabled: usageReadsReady && showWorkspaceComposite,
+    enabled: showWorkspaceComposite,
     ...usageQueryDefaults,
   })
 
   const { data: filterOptions, isFetching: filtersLoading } = useQuery({
     queryKey: ['org-usage', 'filters', scopeParams],
     queryFn: () => apiClient.getOrgUsageFilters(scopeParams),
-    enabled: usageReadsReady,
+    enabled: true,
     staleTime: 60 * 1000,
     placeholderData: (previousData, previousQuery) => {
       if (!previousQuery) return undefined
@@ -1107,6 +1104,29 @@ export default function Usage() {
         </div>
         <div className="flex flex-col items-end gap-1.5 shrink-0">
           <div className="flex items-center gap-3">
+            <div
+              className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
+              role="group"
+              aria-label="Display currency"
+            >
+              {(['USD', 'INR'] as const).map((currency) => (
+                <button
+                  key={currency}
+                  type="button"
+                  onClick={() => {
+                    setDisplayCurrency(currency)
+                    setUsageDisplayCurrency(currency)
+                  }}
+                  className={`rounded-md px-2 py-1 transition-colors ${
+                    displayCurrency === currency
+                      ? 'bg-gray-900 text-white'
+                      : 'text-gray-600 hover:bg-gray-50'
+                  }`}
+                >
+                  {currency}
+                </button>
+              ))}
+            </div>
             {showCostBreakdown ? (
               <button
                 type="button"
@@ -1386,6 +1406,7 @@ export default function Usage() {
         onClose={() => setCostBreakdownOpen(false)}
         costs={totals?.costs}
         scopeLabel={scopeSubtitle}
+        formatCost={formatCostUsd}
       />
     </div>
   )
diff --git a/frontend/src/pages/usage/UsageCostBreakdownModal.tsx b/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
index 9277de53..645729ba 100644
--- a/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
+++ b/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
@@ -17,9 +17,10 @@ type Props = {
   onClose: () => void
   costs?: UsageCosts | null
   scopeLabel?: string
+  formatCost?: (usd?: number | null) => string
 }
 
-function formatCostUsd(usd?: number | null): string {
+function defaultFormatCostUsd(usd?: number | null): string {
   const amount = Number(usd || 0)
   if (!amount) return '$0.00'
   return new Intl.NumberFormat(undefined, {
@@ -45,6 +46,7 @@ export default function UsageCostBreakdownModal({
   onClose,
   costs,
   scopeLabel,
+  formatCost = defaultFormatCostUsd,
 }: Props) {
   if (!isOpen) return null
 
@@ -93,7 +95,7 @@ export default function UsageCostBreakdownModal({
             <div className="flex items-center justify-between bg-gray-50 px-4 py-3 border-b border-gray-200">
               <span className="text-sm font-medium text-gray-700">Estimated total</span>
               <span className="text-base font-semibold text-gray-900 tabular-nums">
-                {formatCostUsd(costs?.total_cost_usd)}
+                {formatCost(costs?.total_cost_usd)}
               </span>
             </div>
             {rows.length > 0 ? (
@@ -105,7 +107,7 @@ export default function UsageCostBreakdownModal({
                   >
                     <span className="text-gray-600">{item.label}</span>
                     <span className="font-medium text-gray-900 tabular-nums">
-                      {formatCostUsd(costs?.[item.key] as number)}
+                      {formatCost(costs?.[item.key] as number)}
                     </span>
                   </li>
                 ))}
diff --git a/frontend/src/pages/usage/UsagePricing.tsx b/frontend/src/pages/usage/UsagePricing.tsx
index f976d116..92d159f3 100644
--- a/frontend/src/pages/usage/UsagePricing.tsx
+++ b/frontend/src/pages/usage/UsagePricing.tsx
@@ -9,6 +9,12 @@ import { useToast } from '../../hooks/useToast'
 import Button from '../../components/Button'
 import SearchableSelect from './SearchableSelect'
 import { usageTheme } from './usageTheme'
+import {
+  formatUsageCostUsd,
+  getUsageDisplayCurrency,
+  setUsageDisplayCurrency,
+  type UsageDisplayCurrency,
+} from '../../lib/usageCurrency'
 
 type UsageKind = 'llm' | 'stt' | 'tts'
 
@@ -74,15 +80,6 @@ function FormField({
   )
 }
 
-function formatUsd(value?: number | null): string {
-  if (value == null || Number.isNaN(value)) return '—'
-  return new Intl.NumberFormat(undefined, {
-    style: 'currency',
-    currency: 'USD',
-    minimumFractionDigits: 2,
-    maximumFractionDigits: 6,
-  }).format(value)
-}
 
 function todayIso(): string {
   return new Date().toISOString().slice(0, 10)
@@ -93,6 +90,18 @@ export default function UsagePricing() {
   const queryClient = useQueryClient()
   const { showToast, ToastContainer } = useToast()
 
+  const [displayCurrency, setDisplayCurrency] = useState<UsageDisplayCurrency>(() =>
+    getUsageDisplayCurrency(),
+  )
+  const { data: fxRate } = useQuery({
+    queryKey: ['org-usage', 'fx-rate'],
+    queryFn: () => apiClient.getOrgUsageFxRate(),
+    staleTime: 60 * 60 * 1000,
+  })
+  const inrRate = fxRate?.rate ?? 83
+  const formatDisplayRate = (value?: number | null) =>
+    formatUsageCostUsd(value, displayCurrency, inrRate)
+
   const [model, setModel] = useState('')
   const [usageKind, setUsageKind] = useState<UsageKind>('llm')
   const [effectiveFrom, setEffectiveFrom] = useState(todayIso())
@@ -197,6 +206,29 @@ export default function UsagePricing() {
           <div className="min-w-0 flex-1">
             <div className="flex items-center gap-2">
               <h1 className="text-2xl font-semibold text-gray-900">Pricing overrides</h1>
+              <div
+                className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
+                role="group"
+                aria-label="Display currency"
+              >
+                {(['USD', 'INR'] as const).map((currency) => (
+                  <button
+                    key={currency}
+                    type="button"
+                    onClick={() => {
+                      setDisplayCurrency(currency)
+                      setUsageDisplayCurrency(currency)
+                    }}
+                    className={`rounded-md px-2 py-1 transition-colors ${
+                      displayCurrency === currency
+                        ? 'bg-gray-900 text-white'
+                        : 'text-gray-600 hover:bg-gray-50'
+                    }`}
+                  >
+                    {currency}
+                  </button>
+                ))}
+              </div>
               <div className="relative group">
                 <button
                   type="button"
@@ -358,16 +390,16 @@ export default function UsagePricing() {
                         {row.effective_to ? ` → ${row.effective_to}` : ''}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatUsd(row.rates.input_per_1m)}
+                        {formatDisplayRate(row.rates.input_per_1m)}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatUsd(row.rates.output_per_1m)}
+                        {formatDisplayRate(row.rates.output_per_1m)}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatUsd(row.rates.audio_per_minute)}
+                        {formatDisplayRate(row.rates.audio_per_minute)}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatUsd(row.rates.tts_per_1m_characters)}
+                        {formatDisplayRate(row.rates.tts_per_1m_characters)}
                       </td>
                       <td className="px-4 py-2.5 text-right">
                         <button
diff --git a/tests/test_core/test_operational_endpoints.py b/tests/test_core/test_operational_endpoints.py
index d15416c4..927d2a73 100644
--- a/tests/test_core/test_operational_endpoints.py
+++ b/tests/test_core/test_operational_endpoints.py
@@ -208,7 +208,12 @@ def test_build_health_status_minimal_excludes_migration_details(monkeypatch):
     assert "pending_migrations" not in payload
 
 
-def test_build_health_status_detailed_includes_migration_details(monkeypatch):
+def _stub_create_app_startup(monkeypatch) -> None:
+    """Tests use create_all(); avoid re-running file migrations on TestClient startup."""
+    monkeypatch.setattr("app.app_factory.run_migrations", lambda: None)
+    monkeypatch.setattr("app.app_factory.init_db", lambda: None)
+
+
     monkeypatch.setattr(
         "app.core.health.check_migrations_status",
         lambda: (False, ["033_add_workspaces.sql"]),
@@ -222,6 +227,7 @@ def test_build_health_status_detailed_includes_migration_details(monkeypatch):
 
 
 def test_health_detail_returns_migration_info_for_admin(monkeypatch):
+    _stub_create_app_startup(monkeypatch)
     monkeypatch.setitem(
         sys.modules,
         "app.api.v1.api",
@@ -250,6 +256,7 @@ def test_health_detail_returns_migration_info_for_admin(monkeypatch):
 
 
 def test_health_detail_requires_authentication_via_create_app(monkeypatch):
+    _stub_create_app_startup(monkeypatch)
     monkeypatch.setitem(
         sys.modules,
         "app.api.v1.api",
diff --git a/tests/test_services/test_cron/test_job_dispatch.py b/tests/test_services/test_cron/test_job_dispatch.py
new file mode 100644
index 00000000..ff31ac36
--- /dev/null
+++ b/tests/test_services/test_cron/test_job_dispatch.py
@@ -0,0 +1,48 @@
+"""Tests for cron job dispatch helpers."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from unittest.mock import MagicMock
+from uuid import uuid4
+
+import pytest
+
+from app.models.enums import CronJobStatus
+from app.services.cron.job_dispatch import advance_cron_job, enqueue_cron_job
+
+
+def test_advance_cron_job_marks_completed_when_max_runs_reached():
+    job = MagicMock()
+    job.current_runs = 9
+    job.max_runs = 10
+    job.status = CronJobStatus.ACTIVE.value
+    job.cron_expression = "0 * * * *"
+    job.timezone = "UTC"
+
+    advance_cron_job(MagicMock(), job, now=datetime.now(timezone.utc))
+
+    assert job.current_runs == 10
+    assert job.status == CronJobStatus.COMPLETED.value
+    assert job.next_run_at is None
+
+
+def test_enqueue_usage_flush_routes_to_usage_task(monkeypatch):
+    job = MagicMock()
+    job.job_type = "usage_flush"
+    job.id = uuid4()
+
+    delayed = MagicMock()
+    delayed.id = "task-123"
+    task = MagicMock()
+    task.delay.return_value = delayed
+    monkeypatch.setattr(
+        "app.workers.tasks.flush_usage_counters.flush_usage_counters_task",
+        task,
+    )
+
+    meta = enqueue_cron_job(job)
+
+    assert meta["task"] == "flush_usage_counters"
+    assert meta["celery_task_id"] == "task-123"
+    task.delay.assert_called_once_with()
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index e5bb9f1a..79c89c40 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -162,7 +162,7 @@ def eval(self, script: str, numkeys: int, *keys_and_args: Any) -> int:
 
     def scan_iter(self, match: str = "*", count: int = 100):
         prefix = match.rstrip("*")
-        for key in list(self.hashes.keys()):
+        for key in list(self.hashes.keys()) + list(self.kv.keys()):
             if key.startswith(prefix):
                 yield key
 
@@ -172,6 +172,9 @@ def fake_redis(monkeypatch):
     """Always use in-memory Redis for this module — never touch real REDIS_URL."""
     client = _FakeRedis()
     usage_mod._redis = client
+    import app.services.usage.read_cache as read_cache_mod
+
+    read_cache_mod._redis = client
 
     def _forbid_real_redis(*_args, **_kwargs):
         raise AssertionError(
@@ -179,8 +182,10 @@ def _forbid_real_redis(*_args, **_kwargs):
         )
 
     monkeypatch.setattr(usage_mod.redis, "from_url", _forbid_real_redis)
+    monkeypatch.setattr(read_cache_mod.redis, "from_url", _forbid_real_redis)
     yield client
     usage_mod._redis = None
+    read_cache_mod._redis = None
 
 
 @pytest.fixture
diff --git a/tests/test_services/test_usage/test_usage_read_cache.py b/tests/test_services/test_usage/test_usage_read_cache.py
new file mode 100644
index 00000000..dc7884fd
--- /dev/null
+++ b/tests/test_services/test_usage/test_usage_read_cache.py
@@ -0,0 +1,69 @@
+"""Tests for usage read cache."""
+
+from __future__ import annotations
+
+from datetime import date
+from uuid import uuid4
+
+import pytest
+
+from app.core.usage_entitlement import UsagePolicySnapshot
+from app.services.usage import read_cache as cache_mod
+from app.services.usage.access import UsageAccessResult
+from app.services.usage.read_cache import (
+    cache_key_for,
+    get_cached_response,
+    invalidate_org_usage_read_cache,
+    set_cached_response,
+)
+from tests.test_services.test_usage.test_llm_usage import _FakeRedis
+
+
+@pytest.fixture
+def fake_redis(monkeypatch):
+    client = _FakeRedis()
+    cache_mod._redis = client
+    monkeypatch.setattr(cache_mod.redis, "from_url", lambda *_args, **_kwargs: client)
+    yield client
+    cache_mod._redis = None
+
+
+def _access() -> UsageAccessResult:
+    policy = UsagePolicySnapshot(extended_history=False, max_history_days=7)
+    return UsageAccessResult(
+        display_start=date(2026, 8, 1),
+        display_end=date(2026, 8, 7),
+        filter_start=date(2026, 8, 1),
+        filter_end=date(2026, 8, 8),
+        enforced_filter_floor=date(2026, 8, 1),
+        policy=policy,
+        range_clamped=False,
+    )
+
+
+def test_usage_read_cache_round_trip(fake_redis, monkeypatch):
+    org_id = uuid4()
+    access = _access()
+    key = cache_key_for(access, workspace_id=None)
+    payload = {"start": "2026-08-01", "end": "2026-08-07", "totals": {}}
+
+    set_cached_response(org_id, "summary", key, payload)
+    cached = get_cached_response(org_id, "summary", key)
+
+    assert cached == payload
+
+
+def test_usage_read_cache_invalidate_org(fake_redis, monkeypatch):
+    org_id = uuid4()
+    other_org = uuid4()
+    access = _access()
+    key = cache_key_for(access)
+
+    set_cached_response(org_id, "summary", key, {"x": 1})
+    set_cached_response(other_org, "summary", key, {"x": 2})
+
+    deleted = invalidate_org_usage_read_cache(org_id)
+
+    assert deleted >= 1
+    assert get_cached_response(org_id, "summary", key) is None
+    assert get_cached_response(other_org, "summary", key) is not None
diff --git a/tests/test_workers/test_usage_queue_routing.py b/tests/test_workers/test_usage_queue_routing.py
index 8ccdb6f3..c3a5776a 100644
--- a/tests/test_workers/test_usage_queue_routing.py
+++ b/tests/test_workers/test_usage_queue_routing.py
@@ -13,6 +13,11 @@ def test_recompute_usage_costs_routes_to_usage_queue():
     assert routes["recompute_usage_costs"]["queue"] == USAGE_WORKER_QUEUE
 
 
-def test_usage_beat_flush_targets_usage_queue():
-    schedule = celery_app.conf.beat_schedule["flush-llm-usage-counters"]
-    assert schedule["options"]["queue"] == USAGE_WORKER_QUEUE
+def test_cron_dispatcher_routes_to_default_worker():
+    routes = celery_app.conf.task_routes
+    assert routes["dispatch_cron_jobs"]["queue"] == "celery"
+
+
+def test_beat_schedule_removed():
+    schedule = getattr(celery_app.conf, "beat_schedule", None) or {}
+    assert "flush-llm-usage-counters" not in schedule

From 468f2461c28d6d6b728c2f77ca9471bbf27f9b44 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Sat, 15 Aug 2026 13:26:44 +0530
Subject: [PATCH 23/55] fix: correct indentation in call_imports.py and improve
 comments in process_evaluator_result.py

---
 app/api/v1/routes/call_imports.py             |  2 +-
 app/workers/tasks/process_evaluator_result.py | 38 +++++++++----------
 2 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/app/api/v1/routes/call_imports.py b/app/api/v1/routes/call_imports.py
index 7cb60d2a..094845ae 100644
--- a/app/api/v1/routes/call_imports.py
+++ b/app/api/v1/routes/call_imports.py
@@ -1,4 +1,4 @@
-"""CSV-driven call import routes.
+"""CSV-driven call import routes.
 
 Users upload a CSV plus a per-batch column mapping (CSV header -> system
 field). The backend persists a CallImport batch + one CallImportRow per
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index e8132e71..7a5c5c49 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -712,25 +712,25 @@ def process_evaluator_result_task(self, result_id: str):
                         result.speaker_segments = speaker_segments if speaker_segments else None
                     db.commit()
 
-            # Step 2: Load and categorize metrics
-            # Include metrics that have "agent" in their enabled_surfaces so users can
-            # restrict metrics to specific surfaces from the metrics page. Legacy rows
-            # (created before the surfaces column existed, or via fixtures that don't
-            # set the field) keep the original behavior: an enabled=True metric with
-            # an empty enabled_surfaces list is treated as agent-enabled.
-            enabled_metrics = db.query(Metric).filter(
-                Metric.organization_id == result.organization_id,
-                Metric.enabled == True,
-                or_(Metric.lifecycle.is_(None), Metric.lifecycle == "active"),
-            ).all()
-            enabled_metrics = [
-                m for m in enabled_metrics
-                if (m.name or "").strip().lower() not in REMOVED_EVALUATION_METRIC_NAMES
-                and (
-                    "agent" in (m.enabled_surfaces or [])
-                    or not (m.enabled_surfaces or [])  # legacy/unset → default to agent
-                )
-            ]
+                # Step 2: Load and categorize metrics
+                # Include metrics that have "agent" in their enabled_surfaces so users can
+                # restrict metrics to specific surfaces from the metrics page. Legacy rows
+                # (created before the surfaces column existed, or via fixtures that don't
+                # set the field) keep the original behavior: an enabled=True metric with
+                # an empty enabled_surfaces list is treated as agent-enabled.
+                enabled_metrics = db.query(Metric).filter(
+                    Metric.organization_id == result.organization_id,
+                    Metric.enabled == True,
+                    or_(Metric.lifecycle.is_(None), Metric.lifecycle == "active"),
+                ).all()
+                enabled_metrics = [
+                    m for m in enabled_metrics
+                    if (m.name or "").strip().lower() not in REMOVED_EVALUATION_METRIC_NAMES
+                    and (
+                        "agent" in (m.enabled_surfaces or [])
+                        or not (m.enabled_surfaces or [])  # legacy/unset → default to agent
+                    )
+                ]
 
                 # Explicit metric selection on the evaluator/suite. Categorization
                 # parents are stored as a single ID and expanded to child labels here.

From 5694c1fdbe68276f84a32bff0ea35b7426c76af2 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Sat, 15 Aug 2026 14:49:59 +0530
Subject: [PATCH 24/55] fix(usage): aggregate eval usage per evaluation instead
 of per recording and cache/Ui fixes

---
 app/api/v1/api.py                             |    4 +
 app/api/v1/routes/org_usage.py                | 3425 +++++++++--------
 app/services/usage/bucket_context.py          |    1 +
 app/services/usage/call_import_context.py     |   19 +-
 app/services/usage/read_cache.py              |  269 +-
 app/workers/tasks/evaluate_call_import_row.py |    2 -
 .../tasks/transcribe_call_import_row.py       |    5 -
 frontend/src/pages/usage/Usage.tsx            | 2915 +++++++-------
 .../src/pages/usage/UsageDateRangePicker.tsx  |  547 ++-
 tests/test_api/test_org_usage_routes.py       |   12 +
 .../test_usage/test_call_import_context.py    |   67 +-
 .../test_usage/test_usage_read_cache.py       |  160 +-
 12 files changed, 3763 insertions(+), 3663 deletions(-)
 create mode 100644 tests/test_api/test_org_usage_routes.py

diff --git a/app/api/v1/api.py b/app/api/v1/api.py
index bbf422e0..055bc147 100644
--- a/app/api/v1/api.py
+++ b/app/api/v1/api.py
@@ -47,6 +47,8 @@
     dashboard,
     llm_gateway,
     platform_admin,
+    org_usage,
+    usage_pricing,
 )
 
 api_router = APIRouter()
@@ -97,3 +99,5 @@
 api_router.include_router(dashboard.router)
 api_router.include_router(llm_gateway.router)
 api_router.include_router(platform_admin.router)
+api_router.include_router(org_usage.router)
+api_router.include_router(usage_pricing.router)
diff --git a/app/api/v1/routes/org_usage.py b/app/api/v1/routes/org_usage.py
index 0fb61992..53dbbb27 100644
--- a/app/api/v1/routes/org_usage.py
+++ b/app/api/v1/routes/org_usage.py
@@ -1,1708 +1,1717 @@
-"""Org-scoped LLM Usage API (tokens + call counts)."""
-
-from __future__ import annotations
-
-from datetime import date, datetime, timezone
-from app.services.usage.access import UsageAccessPolicy
-from app.services.usage.dates import usage_date_filter_bounds, usage_local_today
-from typing import List, Literal, Optional
-from uuid import UUID
-
-from fastapi import APIRouter, Depends, HTTPException, Query
-from pydantic import BaseModel, Field
-from sqlalchemy import and_, case, cast, func, or_, select, String
-from sqlalchemy.dialects.postgresql import UUID as PG_UUID
-from sqlalchemy.orm import Session
-
-from app.database import get_db
-from app.dependencies import get_organization_id
-from app.models.database import (
-    CallImport,
-    CallImportEvaluation,
-    CallImportEvaluationRow,
-    CallImportRow,
-    CallImportTag,
-    CallImportTagAssignment,
-    Agent,
-    LLMUsageDaily,
-    Workspace,
-)
-from app.services.usage.read_cache import (
-    cache_key_for,
-    get_cached_response,
-    set_cached_response,
-)
-from app.services.usage.fx_rates import get_usd_inr_rate
-from app.services.usage.usage_costs import costs_from_micro
-from app.services.usage.usage_labels import (
-    labels_for_call_import_ids,
-    labels_for_resource_buckets,
-    usage_kind_label,
-    UsageNameResolver,
-    parse_uuid,
-)
-
-router = APIRouter(prefix="/organizations/usage", tags=["Usage"])
-
-GroupBy = Literal[
-    "workspace",
-    "product_section",
-    "model",
-    "resource",
-    "usage_kind",
-    "call_import",
-]
-
-SECTION_LABELS = {
-    "call_import_evaluations": "Call Import Evaluations",
-    "call_imports": "Call Imports",
-    "playground": "Playground",
-    "voice_playground": "Voice Playground",
-    "evaluators": "Evaluators",
-    "metrics": "Metrics",
-    "chat": "Chat",
-    "judge_alignment": "Judge Alignment",
-    "prompt_optimization": "Prompt Optimization",
-    "personas": "Personas",
-    "agents": "Agents",
-    "prompt_partials": "Prompt Partials",
-    "conversation_evaluations": "Conversation Evaluations",
-    "telephony": "Telephony",
-    "test_agent": "Test Agent",
-    "other": "Other",
-}
-
-_LABEL_ROW_LIMIT = 5000
-
-
-def _usage_row_weight():
-    return (
-        LLMUsageDaily.prompt_tokens
-        + LLMUsageDaily.completion_tokens
-        + LLMUsageDaily.cache_read_tokens
-        + LLMUsageDaily.cache_creation_tokens
-        + LLMUsageDaily.reasoning_tokens
-        + LLMUsageDaily.audio_seconds
-        + LLMUsageDaily.tts_characters
-    )
-
-
-def _label_row_order():
-    return (
-        _usage_row_weight().desc(),
-        LLMUsageDaily.call_count.desc(),
-    )
-
-
-class UsageCosts(BaseModel):
-    input_cost_usd: float = 0
-    output_cost_usd: float = 0
-    cache_read_cost_usd: float = 0
-    cache_write_cost_usd: float = 0
-    reasoning_cost_usd: float = 0
-    audio_cost_usd: float = 0
-    tts_cost_usd: float = 0
-    total_cost_usd: float = 0
-    currency: str = "USD"
-    has_unpriced_usage: bool = False
-
-
-class UsageTotals(BaseModel):
-    prompt_tokens: int = 0
-    completion_tokens: int = 0
-    total_tokens: int = 0
-    cache_read_tokens: int = 0
-    cache_creation_tokens: int = 0
-    reasoning_tokens: int = 0
-    audio_seconds: int = 0
-    tts_characters: int = 0
-    call_count: int = 0
-    input_cost_micro_usd: int = 0
-    output_cost_micro_usd: int = 0
-    cache_read_cost_micro_usd: int = 0
-    cache_creation_cost_micro_usd: int = 0
-    reasoning_cost_micro_usd: int = 0
-    audio_cost_micro_usd: int = 0
-    tts_cost_micro_usd: int = 0
-    total_cost_micro_usd: int = 0
-    costs: UsageCosts = Field(default_factory=UsageCosts)
-
-
-class UsageFxRateResponse(BaseModel):
-    base: str = "USD"
-    quote: str = "INR"
-    rate: float
-    as_of: datetime
-    source: str
-
-
-class UsagePolicyMeta(BaseModel):
-    extended_history: bool
-    max_history_days: Optional[int] = None
-    range_clamped: bool = False
-
-
-class UsageSummaryResponse(BaseModel):
-    start: date
-    end: date
-    totals: UsageTotals
-    usage_policy: UsagePolicyMeta
-    last_updated_at: Optional[datetime] = None
-
-
-class UsageBreakdownRow(BaseModel):
-    workspace_id: Optional[UUID] = None
-    workspace_name: Optional[str] = None
-    product_section: Optional[str] = None
-    product_section_label: Optional[str] = None
-    model: Optional[str] = None
-    resource_id: Optional[UUID] = None
-    resource_type: Optional[str] = None
-    resource_label: Optional[str] = None
-    call_import_id: Optional[UUID] = None
-    call_import_label: Optional[str] = None
-    usage_kind: Optional[str] = None
-    prompt_tokens: int = 0
-    completion_tokens: int = 0
-    total_tokens: int = 0
-    cache_read_tokens: int = 0
-    cache_creation_tokens: int = 0
-    reasoning_tokens: int = 0
-    audio_seconds: int = 0
-    tts_characters: int = 0
-    call_count: int = 0
-    input_cost_micro_usd: int = 0
-    output_cost_micro_usd: int = 0
-    cache_read_cost_micro_usd: int = 0
-    cache_creation_cost_micro_usd: int = 0
-    reasoning_cost_micro_usd: int = 0
-    audio_cost_micro_usd: int = 0
-    tts_cost_micro_usd: int = 0
-    total_cost_micro_usd: int = 0
-    costs: UsageCosts = Field(default_factory=UsageCosts)
-
-
-class UsageBreakdownResponse(BaseModel):
-    start: date
-    end: date
-    group_by: GroupBy
-    rows: List[UsageBreakdownRow]
-    total_count: int
-    truncated_at_limit: bool = False
-    usage_policy: UsagePolicyMeta
-    last_updated_at: Optional[datetime] = None
-
-
-class UsageFiltersResponse(BaseModel):
-    workspaces: List[dict] = Field(default_factory=list)
-    product_sections: List[dict] = Field(default_factory=list)
-    call_imports: List[dict] = Field(default_factory=list)
-    evaluations: List[dict] = Field(default_factory=list)
-    models: List[str] = Field(default_factory=list)
-    resources: List[dict] = Field(default_factory=list)
-    usage_kinds: List[dict] = Field(default_factory=list)
-    datasets: List[str] = Field(default_factory=list)
-    tags: List[dict] = Field(default_factory=list)
-
-
-def _parse_usage_range(
-    start: Optional[date],
-    end: Optional[date],
-    tz: Optional[str],
-) -> tuple[date, date, date, date]:
-    """Return display_start, display_end, filter_start, filter_end."""
-    today = usage_local_today(tz)
-    display_start = start or today
-    display_end = end or today
-    filter_start, filter_end = usage_date_filter_bounds(
-        display_start, display_end, tz
-    )
-    return display_start, display_end, filter_start, filter_end
-
-
-def _usage_policy_meta(access) -> UsagePolicyMeta:
-    return UsagePolicyMeta(
-        extended_history=access.policy.extended_history,
-        max_history_days=access.policy.max_history_days,
-        range_clamped=access.range_clamped,
-    )
-
-
-def _evaluation_id_expr():
-    return func.coalesce(
-        LLMUsageDaily.context["evaluation_id"].astext,
-        case(
-            (
-                LLMUsageDaily.context["resource_type"].astext
-                == "call_import_evaluation",
-                LLMUsageDaily.context["resource_id"].astext,
-            ),
-            else_=None,
-        ),
-    )
-
-
-def _resource_id_expr():
-    """Resource rollup key: explicit resource_id or agent_id fallback."""
-    return func.coalesce(
-        LLMUsageDaily.context["resource_id"].astext,
-        LLMUsageDaily.context["agent_id"].astext,
-    )
-
-
-def _resource_type_expr():
-    rid_expr = _resource_id_expr()
-    return func.coalesce(
-        LLMUsageDaily.context["resource_type"].astext,
-        case(
-            (LLMUsageDaily.context["agent_id"].astext.isnot(None), "agent"),
-            (
-                and_(
-                    LLMUsageDaily.product_section == "agents",
-                    rid_expr.isnot(None),
-                ),
-                "agent",
-            ),
-            else_=None,
-        ),
-    )
-
-
-def _call_import_group_expr():
-    """Resolve call import id from context, resource row, evaluation, or import row."""
-    eval_call_import = (
-        select(CallImportEvaluation.call_import_id)
-        .where(
-            CallImportEvaluation.id == cast(_evaluation_id_expr(), PG_UUID)
-        )
-        .correlate(LLMUsageDaily)
-        .scalar_subquery()
-    )
-    row_call_import = (
-        select(CallImportRow.call_import_id)
-        .where(
-            CallImportRow.id
-            == cast(LLMUsageDaily.context["call_import_row_id"].astext, PG_UUID)
-        )
-        .correlate(LLMUsageDaily)
-        .scalar_subquery()
-    )
-    eval_row_call_import = (
-        select(CallImportEvaluation.call_import_id)
-        .select_from(CallImportEvaluationRow)
-        .join(
-            CallImportEvaluation,
-            CallImportEvaluation.id == CallImportEvaluationRow.evaluation_id,
-        )
-        .where(
-            CallImportEvaluationRow.id
-            == cast(LLMUsageDaily.context["evaluation_row_id"].astext, PG_UUID)
-        )
-        .correlate(LLMUsageDaily)
-        .scalar_subquery()
-    )
-    return cast(
-        func.coalesce(
-            LLMUsageDaily.context["call_import_id"].astext,
-            case(
-                (
-                    LLMUsageDaily.context["resource_type"].astext == "call_import",
-                    LLMUsageDaily.context["resource_id"].astext,
-                ),
-                else_=None,
-            ),
-            cast(eval_call_import, String),
-            cast(row_call_import, String),
-            cast(eval_row_call_import, String),
-        ),
-        String,
-    )
-
-
-def _resource_scope_filter(resource_id: UUID):
-    """Match usage attributed to a product resource (agent, simulation, etc.)."""
-    rid = str(resource_id)
-    return or_(
-        LLMUsageDaily.context["resource_id"].astext == rid,
-        LLMUsageDaily.context["agent_id"].astext == rid,
-    )
-
-
-def _evaluation_scope_filter(
-    evaluation_id: UUID,
-    organization_id: UUID,
-    db: Session,
-):
-    eid = str(evaluation_id)
-    row_ids = [
-        str(row[0])
-        for row in db.query(CallImportEvaluationRow.id)
-        .filter(
-            CallImportEvaluationRow.evaluation_id == evaluation_id,
-        )
-        .all()
-    ]
-    clauses = [
-        LLMUsageDaily.context["evaluation_id"].astext == eid,
-        and_(
-            LLMUsageDaily.context["resource_id"].astext == eid,
-            LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
-        ),
-    ]
-    if row_ids:
-        clauses.append(LLMUsageDaily.context["evaluation_row_id"].astext.in_(row_ids))
-    return or_(*clauses)
-
-
-def _call_import_scope_filter(
-    call_import_id: UUID,
-    organization_id: UUID,
-    db: Session,
-):
-    """Match usage tied to a call import (direct context, resource row, or eval runs)."""
-    cid = str(call_import_id)
-    eval_ids = [
-        str(row[0])
-        for row in db.query(CallImportEvaluation.id)
-        .filter(
-            CallImportEvaluation.organization_id == organization_id,
-            CallImportEvaluation.call_import_id == call_import_id,
-        )
-        .all()
-    ]
-    clauses = [
-        LLMUsageDaily.context["call_import_id"].astext == cid,
-        and_(
-            LLMUsageDaily.context["resource_id"].astext == cid,
-            LLMUsageDaily.context["resource_type"].astext == "call_import",
-        ),
-    ]
-    if eval_ids:
-        clauses.append(LLMUsageDaily.context["evaluation_id"].astext.in_(eval_ids))
-        clauses.append(
-            and_(
-                LLMUsageDaily.context["resource_id"].astext.in_(eval_ids),
-                LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
-            )
-        )
-    return or_(*clauses)
-
-
-def _call_import_ids_for_filters(
-    db: Session,
-    *,
-    organization_id: UUID,
-    workspace_id: Optional[UUID] = None,
-    dataset: Optional[str] = None,
-    tag_id: Optional[UUID] = None,
-) -> List[UUID]:
-    query = db.query(CallImport.id).filter(
-        CallImport.organization_id == organization_id,
-    )
-    if workspace_id is not None:
-        query = query.filter(CallImport.workspace_id == workspace_id)
-    if dataset:
-        query = query.filter(CallImport.dataset == dataset)
-    if tag_id is not None:
-        query = query.filter(
-            CallImport.id.in_(
-                db.query(CallImportTagAssignment.call_import_id).filter(
-                    CallImportTagAssignment.tag_id == tag_id,
-                )
-            )
-        )
-    return [row[0] for row in query.all()]
-
-
-def _call_import_ids_scope_filter(
-    allowed_import_ids: List[UUID],
-):
-    if not allowed_import_ids:
-        return LLMUsageDaily.id.is_(None)
-    allowed = [str(uid) for uid in allowed_import_ids]
-    return cast(_call_import_group_expr(), String).in_(allowed)
-
-
-def _workspace_scope_filter(
-    workspace_id: UUID,
-    organization_id: UUID,
-):
-    """Match workspace-scoped rows plus legacy call-import usage with null workspace_id."""
-    ws_call_import_ids = (
-        select(cast(CallImport.id, String))
-        .where(
-            CallImport.organization_id == organization_id,
-            CallImport.workspace_id == workspace_id,
-        )
-    )
-    legacy_call_import = and_(
-        LLMUsageDaily.workspace_id.is_(None),
-        _call_import_group_expr().in_(ws_call_import_ids),
-    )
-    return or_(LLMUsageDaily.workspace_id == workspace_id, legacy_call_import)
-
-
-def _apply_filters(
-    query,
-    *,
-    organization_id: UUID,
-    start: date,
-    end: date,
-    enforced_floor: Optional[date] = None,
-    workspace_id: Optional[UUID],
-    product_section: Optional[str],
-    model: Optional[str],
-    resource_id: Optional[UUID],
-    usage_kind: Optional[str] = None,
-    call_import_id: Optional[UUID] = None,
-    evaluation_id: Optional[UUID] = None,
-    evaluation_row_id: Optional[UUID] = None,
-    dataset: Optional[str] = None,
-    tag_id: Optional[UUID] = None,
-    db: Optional[Session] = None,
-):
-    effective_start = start
-    if enforced_floor is not None and effective_start < enforced_floor:
-        effective_start = enforced_floor
-    query = query.filter(
-        LLMUsageDaily.organization_id == organization_id,
-        LLMUsageDaily.usage_date >= effective_start,
-        LLMUsageDaily.usage_date <= end,
-    )
-    if workspace_id is not None:
-        query = query.filter(
-            _workspace_scope_filter(workspace_id, organization_id)
-        )
-    if product_section:
-        query = query.filter(LLMUsageDaily.product_section == product_section)
-    if model:
-        query = query.filter(LLMUsageDaily.model == model)
-    if resource_id is not None:
-        rid_filter = _resource_scope_filter(resource_id)
-        if db is not None:
-            query = query.filter(
-                or_(
-                    _evaluation_scope_filter(resource_id, organization_id, db),
-                    rid_filter,
-                )
-            )
-        else:
-            query = query.filter(
-                or_(
-                    LLMUsageDaily.context["resource_id"].astext == str(resource_id),
-                    LLMUsageDaily.context["agent_id"].astext == str(resource_id),
-                    LLMUsageDaily.context["evaluation_id"].astext == str(resource_id),
-                    LLMUsageDaily.context["evaluation_row_id"].astext == str(resource_id),
-                )
-            )
-    if call_import_id is not None:
-        if db is not None:
-            query = query.filter(
-                _call_import_scope_filter(call_import_id, organization_id, db)
-            )
-        else:
-            query = query.filter(
-                LLMUsageDaily.context["call_import_id"].astext == str(call_import_id)
-            )
-    if evaluation_id is not None and evaluation_id != resource_id:
-        if db is not None:
-            query = query.filter(
-                _evaluation_scope_filter(evaluation_id, organization_id, db)
-            )
-        else:
-            query = query.filter(
-                LLMUsageDaily.context["evaluation_id"].astext == str(evaluation_id)
-            )
-    if evaluation_row_id is not None:
-        query = query.filter(
-            LLMUsageDaily.context["evaluation_row_id"].astext
-            == str(evaluation_row_id)
-        )
-    if usage_kind:
-        query = query.filter(LLMUsageDaily.usage_kind == usage_kind)
-    if dataset or tag_id is not None:
-        if db is None:
-            raise ValueError("db required for dataset/tag filters")
-        allowed = _call_import_ids_for_filters(
-            db,
-            organization_id=organization_id,
-            workspace_id=workspace_id,
-            dataset=dataset,
-            tag_id=tag_id,
-        )
-        query = query.filter(_call_import_ids_scope_filter(allowed))
-    return query
-
-
-def _filtered_query(
-    db: Session,
-    *,
-    organization_id: UUID,
-    start: date,
-    end: date,
-    enforced_floor: Optional[date] = None,
-    workspace_id: Optional[UUID] = None,
-    product_section: Optional[str] = None,
-    model: Optional[str] = None,
-    resource_id: Optional[UUID] = None,
-    usage_kind: Optional[str] = None,
-    call_import_id: Optional[UUID] = None,
-    evaluation_id: Optional[UUID] = None,
-    evaluation_row_id: Optional[UUID] = None,
-    dataset: Optional[str] = None,
-    tag_id: Optional[UUID] = None,
-):
-    return _apply_filters(
-        db.query(LLMUsageDaily),
-        organization_id=organization_id,
-        start=start,
-        end=end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        evaluation_row_id=evaluation_row_id,
-        dataset=dataset,
-        tag_id=tag_id,
-        db=db,
-    )
-
-
-def _infer_agent_types_for_label_buckets(
-    db: Session,
-    organization_id: UUID,
-    grouped: dict[str, tuple[Optional[str], list]],
-) -> None:
-    """When context JSON omits resource_type, infer agent rows from Agent.id."""
-    candidate_ids: list[UUID] = []
-    for rid, (rtype, _) in grouped.items():
-        if rtype:
-            continue
-        uid = parse_uuid(rid)
-        if uid:
-            candidate_ids.append(uid)
-    if not candidate_ids:
-        return
-    known_agent_ids = {
-        row.id
-        for row in db.query(Agent.id).filter(
-            Agent.organization_id == organization_id,
-            Agent.id.in_(candidate_ids),
-        ).all()
-    }
-    for rid in grouped:
-        uid = parse_uuid(rid)
-        if uid and uid in known_agent_ids:
-            rtype, contexts = grouped[rid]
-            if not rtype:
-                grouped[rid] = ("agent", contexts)
-
-
-def _resource_label_map(
-    db: Session,
-    organization_id: UUID,
-    query,
-) -> dict[str, str]:
-    """Build resource_id -> hierarchical label from usage rows in query."""
-    rows = (
-        query.with_entities(
-            _resource_id_expr(),
-            _resource_type_expr(),
-            LLMUsageDaily.context,
-        )
-        .filter(
-            or_(
-                LLMUsageDaily.context["resource_id"].astext.isnot(None),
-                LLMUsageDaily.context["agent_id"].astext.isnot(None),
-            )
-        )
-        .order_by(*_label_row_order())
-        .limit(_LABEL_ROW_LIMIT)
-        .all()
-    )
-    grouped: dict[str, tuple[Optional[str], list]] = {}
-    for raw_id, rtype, ctx in rows:
-        if not raw_id:
-            continue
-        key = str(raw_id)
-        if key not in grouped:
-            grouped[key] = (rtype, [])
-        grouped[key][1].append(ctx)
-
-    _infer_agent_types_for_label_buckets(db, organization_id, grouped)
-
-    buckets = [(rid, rtype, contexts) for rid, (rtype, contexts) in grouped.items()]
-    resolver = UsageNameResolver(db, organization_id)
-    contexts_for_preload = []
-    for rid, rtype, contexts in buckets:
-        for ctx in contexts:
-            merged = dict(ctx or {})
-            if rid:
-                merged.setdefault("resource_id", rid)
-            if rtype:
-                merged.setdefault("resource_type", rtype)
-            contexts_for_preload.append(merged)
-    resolver.preload(contexts_for_preload)
-    return labels_for_resource_buckets(buckets, resolver)
-
-
-def _resource_filter_meta_map(
-    db: Session,
-    organization_id: UUID,
-    query,
-) -> dict[str, dict[str, Optional[str]]]:
-    """Map resource id -> {type, product_section} for filter dropdowns."""
-    rows = (
-        query.with_entities(
-            _resource_id_expr(),
-            _resource_type_expr(),
-            LLMUsageDaily.product_section,
-        )
-        .filter(
-            or_(
-                LLMUsageDaily.context["resource_id"].astext.isnot(None),
-                LLMUsageDaily.context["agent_id"].astext.isnot(None),
-            )
-        )
-        .distinct()
-        .limit(_LABEL_ROW_LIMIT)
-        .all()
-    )
-    meta: dict[str, dict[str, Optional[str]]] = {}
-    for raw_id, rtype, section in rows:
-        if not raw_id:
-            continue
-        key = str(raw_id)
-        entry = meta.setdefault(key, {"type": None, "product_section": None})
-        if rtype:
-            entry["type"] = str(rtype)
-        if section:
-            entry["product_section"] = str(section)
-        if not entry["type"] and section == "agents":
-            entry["type"] = "agent"
-    return meta
-
-
-def _breakdown_resource_label(
-    raw_res_id: Optional[str],
-    res_type: Optional[str],
-    section: Optional[str],
-    resource_labels: dict[str, str],
-    db: Session,
-    organization_id: UUID,
-) -> str:
-    if not raw_res_id:
-        return "Unscoped"
-    key = str(raw_res_id)
-    label = resource_labels.get(key)
-    if label and label != "Unscoped":
-        return label
-    effective_type = res_type or ("agent" if section == "agents" else None)
-    if effective_type == "agent":
-        resolver = UsageNameResolver(db, organization_id)
-        resolver.preload([{"resource_id": key, "resource_type": "agent"}])
-        return resolver.agent_name(key)
-    return label or "Unscoped"
-
-
-def _collect_call_import_ids_from_usage_rows(
-    db: Session,
-    organization_id: UUID,
-    rows: list,
-) -> set[UUID]:
-    """Extract call import ids referenced in usage row context tuples."""
-    import_ids: set[UUID] = set()
-    eval_ids: set[UUID] = set()
-    row_ids: set[UUID] = set()
-    eval_row_ids: set[UUID] = set()
-
-    for row in rows:
-        cid_raw = row[0] if len(row) > 0 else None
-        res_raw = row[1] if len(row) > 1 else None
-        rtype = row[2] if len(row) > 2 else None
-        eval_raw = row[3] if len(row) > 3 else None
-        row_id_raw = row[4] if len(row) > 4 else None
-        eval_row_raw = row[5] if len(row) > 5 else None
-
-        if cid_raw:
-            uid = parse_uuid(cid_raw)
-            if uid:
-                import_ids.add(uid)
-        if res_raw and rtype == "call_import":
-            uid = parse_uuid(res_raw)
-            if uid:
-                import_ids.add(uid)
-        if eval_raw:
-            uid = parse_uuid(eval_raw)
-            if uid:
-                eval_ids.add(uid)
-        if res_raw and rtype == "call_import_evaluation":
-            uid = parse_uuid(res_raw)
-            if uid:
-                eval_ids.add(uid)
-        if row_id_raw:
-            uid = parse_uuid(row_id_raw)
-            if uid:
-                row_ids.add(uid)
-        if eval_row_raw:
-            uid = parse_uuid(eval_row_raw)
-            if uid:
-                eval_row_ids.add(uid)
-
-    if eval_ids:
-        for ev in (
-            db.query(CallImportEvaluation)
-            .filter(
-                CallImportEvaluation.organization_id == organization_id,
-                CallImportEvaluation.id.in_(eval_ids),
-            )
-            .all()
-        ):
-            import_ids.add(ev.call_import_id)
-
-    if row_ids:
-        for cid in (
-            db.query(CallImportRow.call_import_id)
-            .filter(CallImportRow.id.in_(row_ids))
-            .distinct()
-            .all()
-        ):
-            import_ids.add(cid[0])
-
-    if eval_row_ids:
-        for cid in (
-            db.query(CallImportEvaluation.call_import_id)
-            .join(
-                CallImportEvaluationRow,
-                CallImportEvaluationRow.evaluation_id == CallImportEvaluation.id,
-            )
-            .filter(CallImportEvaluationRow.id.in_(eval_row_ids))
-            .distinct()
-            .all()
-        ):
-            import_ids.add(cid[0])
-
-    return import_ids
-
-
-def _call_import_label_map(
-    db: Session,
-    organization_id: UUID,
-    query,
-) -> dict[str, str]:
-    rows = (
-        query.with_entities(
-            LLMUsageDaily.context["call_import_id"].astext,
-            LLMUsageDaily.context["resource_id"].astext,
-            LLMUsageDaily.context["resource_type"].astext,
-            LLMUsageDaily.context["evaluation_id"].astext,
-            LLMUsageDaily.context["call_import_row_id"].astext,
-            LLMUsageDaily.context["evaluation_row_id"].astext,
-        )
-        .order_by(*_label_row_order())
-        .limit(_LABEL_ROW_LIMIT)
-        .all()
-    )
-    import_ids = _collect_call_import_ids_from_usage_rows(
-        db, organization_id, rows
-    )
-
-    if not import_ids:
-        return {}
-    resolver = UsageNameResolver(db, organization_id)
-    resolver.preload(
-        [{"call_import_id": str(uid)} for uid in import_ids]
-    )
-    return labels_for_call_import_ids(list(import_ids), resolver)
-
-
-def _call_import_filter_labels(
-    db: Session,
-    organization_id: UUID,
-    query,
-    workspace_id: Optional[UUID] = None,
-    dataset: Optional[str] = None,
-    tag_id: Optional[UUID] = None,
-) -> dict[str, str]:
-    """Call imports for filters: usage in range plus workspace imports when scoped."""
-    import_ids = _collect_call_import_ids_from_usage_rows(
-        db,
-        organization_id,
-        query.with_entities(
-            LLMUsageDaily.context["call_import_id"].astext,
-            LLMUsageDaily.context["resource_id"].astext,
-            LLMUsageDaily.context["resource_type"].astext,
-            LLMUsageDaily.context["evaluation_id"].astext,
-            LLMUsageDaily.context["call_import_row_id"].astext,
-            LLMUsageDaily.context["evaluation_row_id"].astext,
-        ).order_by(*_label_row_order()).limit(_LABEL_ROW_LIMIT).all(),
-    )
-
-    if workspace_id is not None:
-        scoped_ids = _call_import_ids_for_filters(
-            db,
-            organization_id=organization_id,
-            workspace_id=workspace_id,
-            dataset=dataset,
-            tag_id=tag_id,
-        )
-        import_ids.update(scoped_ids)
-
-    if not import_ids:
-        return {}
-    resolver = UsageNameResolver(db, organization_id)
-    resolver.preload([{"call_import_id": str(uid)} for uid in import_ids])
-    return labels_for_call_import_ids(list(import_ids), resolver)
-
-
-def _evaluation_label_map(
-    db: Session,
-    organization_id: UUID,
-    query,
-) -> dict[str, str]:
-    """Evaluation id -> short label (name + id suffix) for filter dropdowns."""
-    rows = (
-        query.with_entities(
-            LLMUsageDaily.context["evaluation_id"].astext,
-            LLMUsageDaily.context["resource_id"].astext,
-            LLMUsageDaily.context["resource_type"].astext,
-            LLMUsageDaily.context,
-        )
-        .filter(
-            or_(
-                LLMUsageDaily.context["evaluation_id"].astext.isnot(None),
-                LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
-            )
-        )
-        .order_by(*_label_row_order())
-        .limit(_LABEL_ROW_LIMIT)
-        .all()
-    )
-    grouped: dict[str, tuple[Optional[str], list]] = {}
-    for eval_raw, res_raw, rtype, ctx in rows:
-        key_raw = eval_raw
-        if not key_raw and rtype == "call_import_evaluation" and res_raw:
-            key_raw = res_raw
-        if not key_raw:
-            continue
-        key = str(key_raw)
-        if key not in grouped:
-            grouped[key] = (rtype, [])
-        grouped[key][1].append(ctx)
-
-    buckets = [(rid, rtype, contexts) for rid, (rtype, contexts) in grouped.items()]
-    resolver = UsageNameResolver(db, organization_id)
-    contexts_for_preload = []
-    for _, rtype, contexts in buckets:
-        for ctx in contexts:
-            merged = dict(ctx or {})
-            if rtype and "resource_type" not in merged:
-                merged["resource_type"] = rtype
-            contexts_for_preload.append(merged)
-    resolver.preload(contexts_for_preload)
-
-    labels: dict[str, str] = {}
-    for raw_id, rtype, contexts in buckets:
-        ctx = max([dict(c or {}) for c in contexts], key=len, default={})
-        if rtype and "resource_type" not in ctx:
-            ctx["resource_type"] = rtype
-        eval_key = ctx.get("evaluation_id") or raw_id
-        labels[str(raw_id)] = resolver.evaluation_name(str(eval_key))
-    return labels
-
-
-def _unpriced_usage_condition():
-    return and_(
-        LLMUsageDaily.pricing_rate_id.is_(None),
-        _usage_row_weight() > 0,
-    )
-
-
-def _has_unpriced_usage_column():
-    return func.coalesce(func.bool_or(_unpriced_usage_condition()), False).label(
-        "has_unpriced_usage"
-    )
-
-
-def _usage_cost_sum_columns():
-    return (
-        func.coalesce(func.sum(LLMUsageDaily.input_cost_micro_usd), 0).label(
-            "input_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.output_cost_micro_usd), 0).label(
-            "output_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.cache_read_cost_micro_usd), 0).label(
-            "cache_read_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.cache_creation_cost_micro_usd), 0).label(
-            "cache_creation_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.reasoning_cost_micro_usd), 0).label(
-            "reasoning_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.audio_cost_micro_usd), 0).label(
-            "audio_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.tts_cost_micro_usd), 0).label(
-            "tts_cost_micro_usd"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.total_cost_micro_usd), 0).label(
-            "total_cost_micro_usd"
-        ),
-    )
-
-
-def _attach_costs(metrics: dict, *, has_unpriced_usage: bool = False) -> dict:
-    costs = costs_from_micro(
-        input_cost_micro_usd=metrics.get("input_cost_micro_usd", 0),
-        output_cost_micro_usd=metrics.get("output_cost_micro_usd", 0),
-        cache_read_cost_micro_usd=metrics.get("cache_read_cost_micro_usd", 0),
-        cache_creation_cost_micro_usd=metrics.get("cache_creation_cost_micro_usd", 0),
-        reasoning_cost_micro_usd=metrics.get("reasoning_cost_micro_usd", 0),
-        audio_cost_micro_usd=metrics.get("audio_cost_micro_usd", 0),
-        tts_cost_micro_usd=metrics.get("tts_cost_micro_usd", 0),
-        total_cost_micro_usd=metrics.get("total_cost_micro_usd", 0),
-        has_unpriced_usage=has_unpriced_usage,
-    )
-    return {**metrics, "costs": costs}
-
-
-def _usage_totals_from_row(row) -> dict:
-    prompt = int(row.prompt_tokens)
-    completion = int(row.completion_tokens)
-    metrics = {
-        "prompt_tokens": prompt,
-        "completion_tokens": completion,
-        "total_tokens": prompt + completion,
-        "cache_read_tokens": int(row.cache_read_tokens),
-        "cache_creation_tokens": int(row.cache_creation_tokens),
-        "reasoning_tokens": int(row.reasoning_tokens),
-        "audio_seconds": int(row.audio_seconds),
-        "tts_characters": int(row.tts_characters),
-        "call_count": int(row.call_count),
-        "input_cost_micro_usd": int(getattr(row, "input_cost_micro_usd", 0) or 0),
-        "output_cost_micro_usd": int(getattr(row, "output_cost_micro_usd", 0) or 0),
-        "cache_read_cost_micro_usd": int(
-            getattr(row, "cache_read_cost_micro_usd", 0) or 0
-        ),
-        "cache_creation_cost_micro_usd": int(
-            getattr(row, "cache_creation_cost_micro_usd", 0) or 0
-        ),
-        "reasoning_cost_micro_usd": int(
-            getattr(row, "reasoning_cost_micro_usd", 0) or 0
-        ),
-        "audio_cost_micro_usd": int(getattr(row, "audio_cost_micro_usd", 0) or 0),
-        "tts_cost_micro_usd": int(getattr(row, "tts_cost_micro_usd", 0) or 0),
-        "total_cost_micro_usd": int(getattr(row, "total_cost_micro_usd", 0) or 0),
-    }
-    return _attach_costs(
-        metrics,
-        has_unpriced_usage=bool(getattr(row, "has_unpriced_usage", False)),
-    )
-
-
-def _breakdown_metrics_from_tuple(metrics: tuple) -> dict:
-    prompt = int(metrics[0])
-    completion = int(metrics[1])
-    base = {
-        "prompt_tokens": prompt,
-        "completion_tokens": completion,
-        "total_tokens": prompt + completion,
-        "cache_read_tokens": int(metrics[2]),
-        "cache_creation_tokens": int(metrics[3]),
-        "reasoning_tokens": int(metrics[4]),
-        "audio_seconds": int(metrics[5]),
-        "tts_characters": int(metrics[6]),
-        "call_count": int(metrics[7]),
-        "input_cost_micro_usd": int(metrics[8]),
-        "output_cost_micro_usd": int(metrics[9]),
-        "cache_read_cost_micro_usd": int(metrics[10]),
-        "cache_creation_cost_micro_usd": int(metrics[11]),
-        "reasoning_cost_micro_usd": int(metrics[12]),
-        "audio_cost_micro_usd": int(metrics[13]),
-        "tts_cost_micro_usd": int(metrics[14]),
-        "total_cost_micro_usd": int(metrics[15]),
-    }
-    has_unpriced = bool(metrics[16]) if len(metrics) > 16 else False
-    return _attach_costs(base, has_unpriced_usage=has_unpriced)
-
-
-def _last_updated(db: Session, organization_id: UUID) -> Optional[datetime]:
-    return (
-        db.query(func.max(LLMUsageDaily.updated_at))
-        .filter(LLMUsageDaily.organization_id == organization_id)
-        .scalar()
-    )
-
-
-def _summary_aggregate_query(
-    db: Session,
-    *,
-    organization_id: UUID,
-    start: date,
-    end: date,
-    enforced_floor: Optional[date] = None,
-    workspace_id: Optional[UUID],
-    product_section: Optional[str],
-    model: Optional[str],
-    resource_id: Optional[UUID],
-    usage_kind: Optional[str],
-    call_import_id: Optional[UUID],
-    evaluation_id: Optional[UUID],
-    evaluation_row_id: Optional[UUID],
-    dataset: Optional[str] = None,
-    tag_id: Optional[UUID] = None,
-):
-    return _apply_filters(
-        db.query(
-            func.coalesce(func.sum(LLMUsageDaily.prompt_tokens), 0).label("prompt_tokens"),
-            func.coalesce(func.sum(LLMUsageDaily.completion_tokens), 0).label(
-                "completion_tokens"
-            ),
-            func.coalesce(func.sum(LLMUsageDaily.cache_read_tokens), 0).label(
-                "cache_read_tokens"
-            ),
-            func.coalesce(func.sum(LLMUsageDaily.cache_creation_tokens), 0).label(
-                "cache_creation_tokens"
-            ),
-            func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
-                "reasoning_tokens"
-            ),
-            func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
-            func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
-            func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
-            *_usage_cost_sum_columns(),
-            _has_unpriced_usage_column(),
-        ),
-        organization_id=organization_id,
-        start=start,
-        end=end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        evaluation_row_id=evaluation_row_id,
-        dataset=dataset,
-        tag_id=tag_id,
-        db=db,
-    )
-
-
-@router.get("/fx-rate", response_model=UsageFxRateResponse)
-def get_usage_fx_rate():
-    payload = get_usd_inr_rate()
-    return UsageFxRateResponse(
-        base=payload["base"],
-        quote=payload["quote"],
-        rate=float(payload["rate"]),
-        as_of=datetime.fromisoformat(payload["as_of"]),
-        source=payload["source"],
-    )
-
-
-@router.get("/summary", response_model=UsageSummaryResponse)
-def get_usage_summary(
-    start: Optional[date] = Query(None),
-    end: Optional[date] = Query(None),
-    tz: Optional[str] = Query(
-        None,
-        description="IANA timezone for interpreting start/end calendar dates",
-    ),
-    workspace_id: Optional[UUID] = Query(None),
-    product_section: Optional[str] = Query(None),
-    model: Optional[str] = Query(None),
-    resource_id: Optional[UUID] = Query(None),
-    usage_kind: Optional[str] = Query(None),
-    call_import_id: Optional[UUID] = Query(None),
-    evaluation_id: Optional[UUID] = Query(None),
-    evaluation_row_id: Optional[UUID] = Query(None),
-    dataset: Optional[str] = Query(None),
-    tag_id: Optional[UUID] = Query(None),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-):
-    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
-    display_start = access.display_start
-    display_end = access.display_end
-    if display_end < display_start:
-        raise HTTPException(status_code=400, detail="end must be >= start")
-
-    cache_key = cache_key_for(
-        access,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        evaluation_row_id=evaluation_row_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    cached = get_cached_response(organization_id, "summary", cache_key)
-    if cached is not None:
-        return UsageSummaryResponse.model_validate(cached)
-
-    row = _summary_aggregate_query(
-        db,
-        organization_id=organization_id,
-        start=access.filter_start,
-        end=access.filter_end,
-        enforced_floor=access.enforced_filter_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        evaluation_row_id=evaluation_row_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    ).one()
-    totals = _usage_totals_from_row(row)
-    response = UsageSummaryResponse(
-        start=display_start,
-        end=display_end,
-        totals=UsageTotals(**totals),
-        usage_policy=_usage_policy_meta(access),
-        last_updated_at=_last_updated(db, organization_id),
-    )
-    set_cached_response(
-        organization_id,
-        "summary",
-        cache_key,
-        response.model_dump(mode="json"),
-    )
-    return response
-
-
-@router.get("/breakdown", response_model=UsageBreakdownResponse)
-def get_usage_breakdown(
-    start: Optional[date] = Query(None),
-    end: Optional[date] = Query(None),
-    tz: Optional[str] = Query(
-        None,
-        description="IANA timezone for interpreting start/end calendar dates",
-    ),
-    group_by: GroupBy = Query("workspace"),
-    workspace_id: Optional[UUID] = Query(None),
-    product_section: Optional[str] = Query(None),
-    model: Optional[str] = Query(None),
-    resource_id: Optional[UUID] = Query(None),
-    usage_kind: Optional[str] = Query(None),
-    call_import_id: Optional[UUID] = Query(None),
-    evaluation_id: Optional[UUID] = Query(None),
-    evaluation_row_id: Optional[UUID] = Query(None),
-    dataset: Optional[str] = Query(None),
-    tag_id: Optional[UUID] = Query(None),
-    limit: int = Query(100, ge=1, le=500),
-    offset: int = Query(0, ge=0),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-):
-    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
-    display_start = access.display_start
-    display_end = access.display_end
-    if display_end < display_start:
-        raise HTTPException(status_code=400, detail="end must be >= start")
-
-    cache_key = cache_key_for(
-        access,
-        group_by=group_by,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        evaluation_row_id=evaluation_row_id,
-        dataset=dataset,
-        tag_id=tag_id,
-        limit=limit,
-        offset=offset,
-    )
-    cached = get_cached_response(organization_id, "breakdown", cache_key)
-    if cached is not None:
-        return UsageBreakdownResponse.model_validate(cached)
-
-    dim = {
-        "workspace": LLMUsageDaily.workspace_id,
-        "product_section": LLMUsageDaily.product_section,
-        "model": LLMUsageDaily.model,
-        "resource": cast(_resource_id_expr(), String),
-        "usage_kind": LLMUsageDaily.usage_kind,
-        "call_import": _call_import_group_expr(),
-    }[group_by]
-
-    aggregates = [
-        func.coalesce(func.sum(LLMUsageDaily.prompt_tokens), 0).label("prompt_tokens"),
-        func.coalesce(func.sum(LLMUsageDaily.completion_tokens), 0).label(
-            "completion_tokens"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.cache_read_tokens), 0).label(
-            "cache_read_tokens"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.cache_creation_tokens), 0).label(
-            "cache_creation_tokens"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
-            "reasoning_tokens"
-        ),
-        func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
-        func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
-        func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
-        *_usage_cost_sum_columns(),
-        _has_unpriced_usage_column(),
-    ]
-
-    select_cols = [dim]
-    group_cols = [dim]
-    if group_by == "resource":
-        select_cols.append(cast(_resource_type_expr(), String))
-        group_cols.append(cast(_resource_type_expr(), String))
-        select_cols.append(LLMUsageDaily.product_section)
-        group_cols.append(LLMUsageDaily.product_section)
-
-    query = _apply_filters(
-        db.query(*select_cols, *aggregates),
-        organization_id=organization_id,
-        start=access.filter_start,
-        end=access.filter_end,
-        enforced_floor=access.enforced_filter_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        evaluation_row_id=evaluation_row_id,
-        dataset=dataset,
-        tag_id=tag_id,
-        db=db,
-    ).group_by(*group_cols)
-
-    results = (
-        query.order_by(func.sum(LLMUsageDaily.call_count).desc())
-        .offset(offset)
-        .limit(limit)
-        .all()
-    )
-
-    workspace_names: dict = {}
-    if group_by == "workspace":
-        ws_ids = {r[0] for r in results if r[0] is not None}
-        if ws_ids:
-            workspace_names = {
-                w.id: w.name
-                for w in db.query(Workspace)
-                .filter(
-                    Workspace.organization_id == organization_id,
-                    Workspace.id.in_(ws_ids),
-                )
-                .all()
-            }
-    resource_labels: dict[str, str] = {}
-    call_import_labels: dict[str, str] = {}
-    if group_by == "resource":
-        label_query = _filtered_query(
-            db,
-            organization_id=organization_id,
-            start=access.filter_start,
-            end=access.filter_end,
-            enforced_floor=access.enforced_filter_floor,
-            workspace_id=workspace_id,
-            product_section=product_section,
-            model=model,
-            resource_id=resource_id,
-            usage_kind=usage_kind,
-            call_import_id=call_import_id,
-            evaluation_id=evaluation_id,
-            evaluation_row_id=evaluation_row_id,
-        )
-        resource_labels = _resource_label_map(db, organization_id, label_query)
-    elif group_by == "call_import":
-        label_query = _filtered_query(
-            db,
-            organization_id=organization_id,
-            start=access.filter_start,
-            end=access.filter_end,
-            enforced_floor=access.enforced_filter_floor,
-            workspace_id=workspace_id,
-            product_section=product_section,
-            model=model,
-            resource_id=resource_id,
-            usage_kind=usage_kind,
-            call_import_id=call_import_id,
-            evaluation_id=evaluation_id,
-            evaluation_row_id=evaluation_row_id,
-        )
-        call_import_labels = _call_import_label_map(db, organization_id, label_query)
-
-    rows: List[UsageBreakdownRow] = []
-    for result in results:
-        if group_by == "workspace":
-            ws_id = result[0]
-            metrics = result[1:]
-            rows.append(
-                UsageBreakdownRow(
-                    workspace_id=ws_id,
-                    workspace_name=workspace_names.get(ws_id) if ws_id else "Unknown",
-                    **_breakdown_metrics_from_tuple(metrics),
-                )
-            )
-        elif group_by == "product_section":
-            section = result[0]
-            metrics = result[1:]
-            rows.append(
-                UsageBreakdownRow(
-                    product_section=section,
-                    product_section_label=SECTION_LABELS.get(section or "", section),
-                    **_breakdown_metrics_from_tuple(metrics),
-                )
-            )
-        elif group_by == "model":
-            model_name = result[0]
-            metrics = result[1:]
-            rows.append(
-                UsageBreakdownRow(
-                    model=model_name,
-                    **_breakdown_metrics_from_tuple(metrics),
-                )
-            )
-        elif group_by == "usage_kind":
-            kind = result[0]
-            metrics = result[1:]
-            rows.append(
-                UsageBreakdownRow(
-                    usage_kind=kind,
-                    **_breakdown_metrics_from_tuple(metrics),
-                )
-            )
-        elif group_by == "call_import":
-            raw_cid = result[0]
-            metrics = result[1:]
-            cid = None
-            if raw_cid:
-                try:
-                    cid = UUID(str(raw_cid))
-                except (ValueError, TypeError):
-                    cid = None
-            label = (
-                call_import_labels.get(str(raw_cid), "Unscoped")
-                if raw_cid
-                else "Unscoped"
-            )
-            rows.append(
-                UsageBreakdownRow(
-                    call_import_id=cid,
-                    call_import_label=label,
-                    **_breakdown_metrics_from_tuple(metrics),
-                )
-            )
-        else:
-            raw_res_id, res_type, section = result[0], result[1], result[2]
-            metrics = result[3:]
-            res_id = None
-            if raw_res_id:
-                try:
-                    res_id = UUID(str(raw_res_id))
-                except (ValueError, TypeError):
-                    res_id = None
-            rows.append(
-                UsageBreakdownRow(
-                    resource_id=res_id,
-                    resource_type=res_type or (
-                        "agent" if section == "agents" and raw_res_id else None
-                    ),
-                    resource_label=_breakdown_resource_label(
-                        raw_res_id,
-                        res_type,
-                        section,
-                        resource_labels,
-                        db,
-                        organization_id,
-                    ),
-                    product_section=section,
-                    product_section_label=SECTION_LABELS.get(section or "", section),
-                    **_breakdown_metrics_from_tuple(metrics),
-                )
-            )
-
-    response = UsageBreakdownResponse(
-        start=display_start,
-        end=display_end,
-        group_by=group_by,
-        rows=rows,
-        total_count=len(rows),
-        truncated_at_limit=len(rows) >= limit,
-        usage_policy=_usage_policy_meta(access),
-        last_updated_at=_last_updated(db, organization_id),
-    )
-    set_cached_response(
-        organization_id,
-        "breakdown",
-        cache_key,
-        response.model_dump(mode="json"),
-    )
-    return response
-
-
-@router.get("/filters", response_model=UsageFiltersResponse)
-def get_usage_filters(
-    start: Optional[date] = Query(None),
-    end: Optional[date] = Query(None),
-    tz: Optional[str] = Query(
-        None,
-        description="IANA timezone for interpreting start/end calendar dates",
-    ),
-    workspace_id: Optional[UUID] = Query(None),
-    product_section: Optional[str] = Query(None),
-    model: Optional[str] = Query(None),
-    resource_id: Optional[UUID] = Query(None),
-    usage_kind: Optional[str] = Query(None),
-    call_import_id: Optional[UUID] = Query(None),
-    evaluation_id: Optional[UUID] = Query(None),
-    dataset: Optional[str] = Query(None),
-    tag_id: Optional[UUID] = Query(None),
-    q: Optional[str] = Query(None, description="Optional resource label search"),
-    organization_id: UUID = Depends(get_organization_id),
-    db: Session = Depends(get_db),
-):
-    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
-    filter_start = access.filter_start
-    filter_end = access.filter_end
-    enforced_floor = access.enforced_filter_floor
-
-    cache_key = cache_key_for(
-        access,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        resource_id=resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        dataset=dataset,
-        tag_id=tag_id,
-        q=q,
-    )
-    cached = get_cached_response(organization_id, "filters", cache_key)
-    if cached is not None:
-        return UsageFiltersResponse.model_validate(cached)
-
-    scoped_resource_id = resource_id or evaluation_id
-
-    workspace_base = _filtered_query(
-        db,
-        organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
-        enforced_floor=enforced_floor,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    section_base = _filtered_query(
-        db,
-        organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    kind_base = _filtered_query(
-        db,
-        organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        resource_id=scoped_resource_id,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    model_base = _filtered_query(
-        db,
-        organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        resource_id=scoped_resource_id,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        evaluation_id=evaluation_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    call_import_base = _filtered_query(
-        db,
-        organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    resource_base = _filtered_query(
-        db,
-        organization_id=organization_id,
-        start=filter_start,
-        end=filter_end,
-        enforced_floor=enforced_floor,
-        workspace_id=workspace_id,
-        product_section=product_section,
-        model=model,
-        usage_kind=usage_kind,
-        call_import_id=call_import_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-
-    workspace_ids = {
-        row[0]
-        for row in workspace_base.with_entities(LLMUsageDaily.workspace_id).distinct().all()
-        if row[0] is not None
-    }
-    workspaces = [
-        {"id": str(w.id), "name": w.name}
-        for w in db.query(Workspace)
-        .filter(Workspace.id.in_(workspace_ids) if workspace_ids else False)
-        .order_by(Workspace.name)
-        .all()
-    ]
-
-    sections = sorted(
-        {
-            row[0]
-            for row in section_base.with_entities(LLMUsageDaily.product_section).distinct().all()
-            if row[0]
-        }
-    )
-    models = sorted(
-        {
-            row[0]
-            for row in model_base.with_entities(LLMUsageDaily.model).distinct().all()
-            if row[0]
-        }
-    )
-    kinds = sorted(
-        {
-            row[0]
-            for row in kind_base.with_entities(LLMUsageDaily.usage_kind).distinct().all()
-            if row[0]
-        }
-    )
-
-    call_import_labels = _call_import_filter_labels(
-        db,
-        organization_id,
-        call_import_base,
-        workspace_id=workspace_id,
-        dataset=dataset,
-        tag_id=tag_id,
-    )
-    call_imports = [
-        {"id": cid, "label": label}
-        for cid, label in sorted(call_import_labels.items(), key=lambda x: x[1].lower())
-    ]
-
-    evaluation_labels = _evaluation_label_map(db, organization_id, resource_base)
-    needle = (q or "").strip().lower()
-    evaluations = []
-    for rid, label in sorted(evaluation_labels.items(), key=lambda x: x[1].lower()):
-        if needle and needle not in label.lower():
-            continue
-        evaluations.append({"id": rid, "label": label})
-
-    resource_labels = _resource_label_map(db, organization_id, resource_base)
-    resource_meta = _resource_filter_meta_map(db, organization_id, resource_base)
-    resources = []
-    for rid, label in sorted(resource_labels.items(), key=lambda x: x[1].lower()):
-        if needle and needle not in label.lower():
-            continue
-        info = resource_meta.get(rid, {})
-        rtype = info.get("type")
-        if rtype == "call_import_evaluation":
-            continue
-        resources.append(
-            {
-                "id": rid,
-                "label": label,
-                "type": rtype,
-                "product_section": info.get("product_section"),
-            }
-        )
-
-    dataset_query = db.query(CallImport.dataset).filter(
-        CallImport.organization_id == organization_id,
-        CallImport.dataset.isnot(None),
-        CallImport.dataset != "",
-    )
-    if workspace_id is not None:
-        dataset_query = dataset_query.filter(CallImport.workspace_id == workspace_id)
-    if dataset or tag_id is not None:
-        scoped_import_ids = _call_import_ids_for_filters(
-            db,
-            organization_id=organization_id,
-            workspace_id=workspace_id,
-            dataset=dataset,
-            tag_id=tag_id,
-        )
-        if scoped_import_ids:
-            dataset_query = dataset_query.filter(CallImport.id.in_(scoped_import_ids))
-        else:
-            dataset_query = dataset_query.filter(CallImport.id.is_(None))
-    datasets = sorted({row[0] for row in dataset_query.distinct().all() if row[0]})
-
-    tags = [
-        {"id": str(tag.id), "label": tag.name}
-        for tag in (
-            db.query(CallImportTag)
-            .filter(CallImportTag.organization_id == organization_id)
-            .order_by(CallImportTag.name)
-            .all()
-        )
-    ]
-
-    response = UsageFiltersResponse(
-        workspaces=workspaces,
-        product_sections=[
-            {"id": s, "label": SECTION_LABELS.get(s, s)} for s in sections
-        ],
-        call_imports=call_imports,
-        evaluations=evaluations,
-        models=models,
-        resources=resources,
-        usage_kinds=[{"id": k, "label": usage_kind_label(k)} for k in kinds],
-        datasets=datasets,
-        tags=tags,
-    )
-    set_cached_response(
-        organization_id,
-        "filters",
-        cache_key,
-        response.model_dump(mode="json"),
-    )
-    return response
-
+"""Org-scoped LLM Usage API (tokens + call counts)."""
+
+from __future__ import annotations
+
+from datetime import date, datetime, timezone
+from app.services.usage.access import UsageAccessPolicy
+from app.services.usage.dates import usage_date_filter_bounds, usage_local_today
+from typing import List, Literal, Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+from pydantic import BaseModel, Field
+from sqlalchemy import and_, case, cast, func, or_, select, String
+from sqlalchemy.dialects.postgresql import UUID as PG_UUID
+from sqlalchemy.orm import Session
+
+from app.database import get_db
+from app.dependencies import get_organization_id
+from app.models.database import (
+    CallImport,
+    CallImportEvaluation,
+    CallImportEvaluationRow,
+    CallImportRow,
+    CallImportTag,
+    CallImportTagAssignment,
+    Agent,
+    LLMUsageDaily,
+    Workspace,
+)
+from app.services.usage.read_cache import (
+    cache_key_for,
+    get_cached_response,
+    set_cached_response,
+)
+from app.services.usage.fx_rates import get_usd_inr_rate
+from app.services.usage.usage_costs import costs_from_micro
+from app.services.usage.usage_labels import (
+    labels_for_call_import_ids,
+    labels_for_resource_buckets,
+    usage_kind_label,
+    UsageNameResolver,
+    parse_uuid,
+)
+
+router = APIRouter(prefix="/organizations/usage", tags=["Usage"])
+
+GroupBy = Literal[
+    "workspace",
+    "product_section",
+    "model",
+    "resource",
+    "usage_kind",
+    "call_import",
+]
+
+SECTION_LABELS = {
+    "call_import_evaluations": "Call Import Evaluations",
+    "call_imports": "Call Imports",
+    "playground": "Playground",
+    "voice_playground": "Voice Playground",
+    "evaluators": "Evaluators",
+    "metrics": "Metrics",
+    "chat": "Chat",
+    "judge_alignment": "Judge Alignment",
+    "prompt_optimization": "Prompt Optimization",
+    "personas": "Personas",
+    "agents": "Agents",
+    "prompt_partials": "Prompt Partials",
+    "conversation_evaluations": "Conversation Evaluations",
+    "telephony": "Telephony",
+    "test_agent": "Test Agent",
+    "other": "Other",
+}
+
+_LABEL_ROW_LIMIT = 5000
+
+
+def _usage_row_weight():
+    return (
+        LLMUsageDaily.prompt_tokens
+        + LLMUsageDaily.completion_tokens
+        + LLMUsageDaily.cache_read_tokens
+        + LLMUsageDaily.cache_creation_tokens
+        + LLMUsageDaily.reasoning_tokens
+        + LLMUsageDaily.audio_seconds
+        + LLMUsageDaily.tts_characters
+    )
+
+
+def _label_row_order():
+    return (
+        _usage_row_weight().desc(),
+        LLMUsageDaily.call_count.desc(),
+    )
+
+
+class UsageCosts(BaseModel):
+    input_cost_usd: float = 0
+    output_cost_usd: float = 0
+    cache_read_cost_usd: float = 0
+    cache_write_cost_usd: float = 0
+    reasoning_cost_usd: float = 0
+    audio_cost_usd: float = 0
+    tts_cost_usd: float = 0
+    total_cost_usd: float = 0
+    currency: str = "USD"
+    has_unpriced_usage: bool = False
+
+
+class UsageTotals(BaseModel):
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+    cache_read_tokens: int = 0
+    cache_creation_tokens: int = 0
+    reasoning_tokens: int = 0
+    audio_seconds: int = 0
+    tts_characters: int = 0
+    call_count: int = 0
+    input_cost_micro_usd: int = 0
+    output_cost_micro_usd: int = 0
+    cache_read_cost_micro_usd: int = 0
+    cache_creation_cost_micro_usd: int = 0
+    reasoning_cost_micro_usd: int = 0
+    audio_cost_micro_usd: int = 0
+    tts_cost_micro_usd: int = 0
+    total_cost_micro_usd: int = 0
+    costs: UsageCosts = Field(default_factory=UsageCosts)
+
+
+class UsageFxRateResponse(BaseModel):
+    base: str = "USD"
+    quote: str = "INR"
+    rate: float
+    as_of: datetime
+    source: str
+
+
+class UsagePolicyMeta(BaseModel):
+    extended_history: bool
+    max_history_days: Optional[int] = None
+    range_clamped: bool = False
+
+
+class UsageSummaryResponse(BaseModel):
+    start: date
+    end: date
+    totals: UsageTotals
+    usage_policy: UsagePolicyMeta
+    last_updated_at: Optional[datetime] = None
+
+
+class UsageBreakdownRow(BaseModel):
+    workspace_id: Optional[UUID] = None
+    workspace_name: Optional[str] = None
+    product_section: Optional[str] = None
+    product_section_label: Optional[str] = None
+    model: Optional[str] = None
+    resource_id: Optional[UUID] = None
+    resource_type: Optional[str] = None
+    resource_label: Optional[str] = None
+    call_import_id: Optional[UUID] = None
+    call_import_label: Optional[str] = None
+    usage_kind: Optional[str] = None
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    total_tokens: int = 0
+    cache_read_tokens: int = 0
+    cache_creation_tokens: int = 0
+    reasoning_tokens: int = 0
+    audio_seconds: int = 0
+    tts_characters: int = 0
+    call_count: int = 0
+    input_cost_micro_usd: int = 0
+    output_cost_micro_usd: int = 0
+    cache_read_cost_micro_usd: int = 0
+    cache_creation_cost_micro_usd: int = 0
+    reasoning_cost_micro_usd: int = 0
+    audio_cost_micro_usd: int = 0
+    tts_cost_micro_usd: int = 0
+    total_cost_micro_usd: int = 0
+    costs: UsageCosts = Field(default_factory=UsageCosts)
+
+
+class UsageBreakdownResponse(BaseModel):
+    start: date
+    end: date
+    group_by: GroupBy
+    rows: List[UsageBreakdownRow]
+    total_count: int
+    truncated_at_limit: bool = False
+    usage_policy: UsagePolicyMeta
+    last_updated_at: Optional[datetime] = None
+
+
+class UsageFiltersResponse(BaseModel):
+    workspaces: List[dict] = Field(default_factory=list)
+    product_sections: List[dict] = Field(default_factory=list)
+    call_imports: List[dict] = Field(default_factory=list)
+    evaluations: List[dict] = Field(default_factory=list)
+    models: List[str] = Field(default_factory=list)
+    resources: List[dict] = Field(default_factory=list)
+    usage_kinds: List[dict] = Field(default_factory=list)
+    datasets: List[str] = Field(default_factory=list)
+    tags: List[dict] = Field(default_factory=list)
+
+
+def _parse_usage_range(
+    start: Optional[date],
+    end: Optional[date],
+    tz: Optional[str],
+) -> tuple[date, date, date, date]:
+    """Return display_start, display_end, filter_start, filter_end."""
+    today = usage_local_today(tz)
+    display_start = start or today
+    display_end = end or today
+    filter_start, filter_end = usage_date_filter_bounds(
+        display_start, display_end, tz
+    )
+    return display_start, display_end, filter_start, filter_end
+
+
+def _usage_policy_meta(access) -> UsagePolicyMeta:
+    return UsagePolicyMeta(
+        extended_history=access.policy.extended_history,
+        max_history_days=access.policy.max_history_days,
+        range_clamped=access.range_clamped,
+    )
+
+
+def _evaluation_id_expr():
+    return func.coalesce(
+        LLMUsageDaily.context["evaluation_id"].astext,
+        case(
+            (
+                LLMUsageDaily.context["resource_type"].astext
+                == "call_import_evaluation",
+                LLMUsageDaily.context["resource_id"].astext,
+            ),
+            else_=None,
+        ),
+    )
+
+
+def _resource_id_expr():
+    """Resource rollup key: explicit resource_id or agent_id fallback."""
+    return func.coalesce(
+        LLMUsageDaily.context["resource_id"].astext,
+        LLMUsageDaily.context["agent_id"].astext,
+    )
+
+
+def _resource_type_expr():
+    rid_expr = _resource_id_expr()
+    return func.coalesce(
+        LLMUsageDaily.context["resource_type"].astext,
+        case(
+            (LLMUsageDaily.context["agent_id"].astext.isnot(None), "agent"),
+            (
+                and_(
+                    LLMUsageDaily.product_section == "agents",
+                    rid_expr.isnot(None),
+                ),
+                "agent",
+            ),
+            else_=None,
+        ),
+    )
+
+
+def _call_import_group_expr():
+    """Resolve call import id from context, resource row, evaluation, or import row."""
+    eval_call_import = (
+        select(CallImportEvaluation.call_import_id)
+        .where(
+            CallImportEvaluation.id == cast(_evaluation_id_expr(), PG_UUID)
+        )
+        .correlate(LLMUsageDaily)
+        .scalar_subquery()
+    )
+    row_call_import = (
+        select(CallImportRow.call_import_id)
+        .where(
+            CallImportRow.id
+            == cast(LLMUsageDaily.context["call_import_row_id"].astext, PG_UUID)
+        )
+        .correlate(LLMUsageDaily)
+        .scalar_subquery()
+    )
+    eval_row_call_import = (
+        select(CallImportEvaluation.call_import_id)
+        .select_from(CallImportEvaluationRow)
+        .join(
+            CallImportEvaluation,
+            CallImportEvaluation.id == CallImportEvaluationRow.evaluation_id,
+        )
+        .where(
+            CallImportEvaluationRow.id
+            == cast(LLMUsageDaily.context["evaluation_row_id"].astext, PG_UUID)
+        )
+        .correlate(LLMUsageDaily)
+        .scalar_subquery()
+    )
+    return cast(
+        func.coalesce(
+            LLMUsageDaily.context["call_import_id"].astext,
+            case(
+                (
+                    LLMUsageDaily.context["resource_type"].astext == "call_import",
+                    LLMUsageDaily.context["resource_id"].astext,
+                ),
+                else_=None,
+            ),
+            cast(eval_call_import, String),
+            cast(row_call_import, String),
+            cast(eval_row_call_import, String),
+        ),
+        String,
+    )
+
+
+def _resource_scope_filter(resource_id: UUID):
+    """Match usage attributed to a product resource (agent, simulation, etc.)."""
+    rid = str(resource_id)
+    return or_(
+        LLMUsageDaily.context["resource_id"].astext == rid,
+        LLMUsageDaily.context["agent_id"].astext == rid,
+    )
+
+
+def _evaluation_scope_filter(
+    evaluation_id: UUID,
+    organization_id: UUID,
+    db: Session,
+):
+    eid = str(evaluation_id)
+    row_ids = [
+        str(row[0])
+        for row in db.query(CallImportEvaluationRow.id)
+        .filter(
+            CallImportEvaluationRow.evaluation_id == evaluation_id,
+        )
+        .all()
+    ]
+    clauses = [
+        LLMUsageDaily.context["evaluation_id"].astext == eid,
+        and_(
+            LLMUsageDaily.context["resource_id"].astext == eid,
+            LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
+        ),
+    ]
+    if row_ids:
+        clauses.append(LLMUsageDaily.context["evaluation_row_id"].astext.in_(row_ids))
+    return or_(*clauses)
+
+
+def _call_import_scope_filter(
+    call_import_id: UUID,
+    organization_id: UUID,
+    db: Session,
+):
+    """Match usage tied to a call import (direct context, resource row, or eval runs)."""
+    cid = str(call_import_id)
+    eval_ids = [
+        str(row[0])
+        for row in db.query(CallImportEvaluation.id)
+        .filter(
+            CallImportEvaluation.organization_id == organization_id,
+            CallImportEvaluation.call_import_id == call_import_id,
+        )
+        .all()
+    ]
+    clauses = [
+        LLMUsageDaily.context["call_import_id"].astext == cid,
+        and_(
+            LLMUsageDaily.context["resource_id"].astext == cid,
+            LLMUsageDaily.context["resource_type"].astext == "call_import",
+        ),
+    ]
+    if eval_ids:
+        clauses.append(LLMUsageDaily.context["evaluation_id"].astext.in_(eval_ids))
+        clauses.append(
+            and_(
+                LLMUsageDaily.context["resource_id"].astext.in_(eval_ids),
+                LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
+            )
+        )
+    return or_(*clauses)
+
+
+def _call_import_ids_for_filters(
+    db: Session,
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+) -> List[UUID]:
+    query = db.query(CallImport.id).filter(
+        CallImport.organization_id == organization_id,
+    )
+    if workspace_id is not None:
+        query = query.filter(CallImport.workspace_id == workspace_id)
+    if dataset:
+        query = query.filter(CallImport.dataset == dataset)
+    if tag_id is not None:
+        query = query.filter(
+            CallImport.id.in_(
+                db.query(CallImportTagAssignment.call_import_id).filter(
+                    CallImportTagAssignment.tag_id == tag_id,
+                )
+            )
+        )
+    return [row[0] for row in query.all()]
+
+
+def _call_import_ids_scope_filter(
+    allowed_import_ids: List[UUID],
+):
+    if not allowed_import_ids:
+        return LLMUsageDaily.id.is_(None)
+    allowed = [str(uid) for uid in allowed_import_ids]
+    return cast(_call_import_group_expr(), String).in_(allowed)
+
+
+def _workspace_scope_filter(
+    workspace_id: UUID,
+    organization_id: UUID,
+):
+    """Match workspace-scoped rows plus legacy call-import usage with null workspace_id."""
+    ws_call_import_ids = (
+        select(cast(CallImport.id, String))
+        .where(
+            CallImport.organization_id == organization_id,
+            CallImport.workspace_id == workspace_id,
+        )
+    )
+    legacy_call_import = and_(
+        LLMUsageDaily.workspace_id.is_(None),
+        _call_import_group_expr().in_(ws_call_import_ids),
+    )
+    return or_(LLMUsageDaily.workspace_id == workspace_id, legacy_call_import)
+
+
+def _apply_filters(
+    query,
+    *,
+    organization_id: UUID,
+    start: date,
+    end: date,
+    enforced_floor: Optional[date] = None,
+    workspace_id: Optional[UUID],
+    product_section: Optional[str],
+    model: Optional[str],
+    resource_id: Optional[UUID],
+    usage_kind: Optional[str] = None,
+    call_import_id: Optional[UUID] = None,
+    evaluation_id: Optional[UUID] = None,
+    evaluation_row_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+    db: Optional[Session] = None,
+):
+    effective_start = start
+    if enforced_floor is not None and effective_start < enforced_floor:
+        effective_start = enforced_floor
+    query = query.filter(
+        LLMUsageDaily.organization_id == organization_id,
+        LLMUsageDaily.usage_date >= effective_start,
+        LLMUsageDaily.usage_date <= end,
+    )
+    if workspace_id is not None:
+        query = query.filter(
+            _workspace_scope_filter(workspace_id, organization_id)
+        )
+    if product_section:
+        query = query.filter(LLMUsageDaily.product_section == product_section)
+    if model:
+        query = query.filter(LLMUsageDaily.model == model)
+    if resource_id is not None:
+        rid_filter = _resource_scope_filter(resource_id)
+        if db is not None:
+            query = query.filter(
+                or_(
+                    _evaluation_scope_filter(resource_id, organization_id, db),
+                    rid_filter,
+                )
+            )
+        else:
+            query = query.filter(
+                or_(
+                    LLMUsageDaily.context["resource_id"].astext == str(resource_id),
+                    LLMUsageDaily.context["agent_id"].astext == str(resource_id),
+                    LLMUsageDaily.context["evaluation_id"].astext == str(resource_id),
+                    LLMUsageDaily.context["evaluation_row_id"].astext == str(resource_id),
+                )
+            )
+    if call_import_id is not None:
+        if db is not None:
+            query = query.filter(
+                _call_import_scope_filter(call_import_id, organization_id, db)
+            )
+        else:
+            query = query.filter(
+                LLMUsageDaily.context["call_import_id"].astext == str(call_import_id)
+            )
+    if evaluation_id is not None and evaluation_id != resource_id:
+        if db is not None:
+            query = query.filter(
+                _evaluation_scope_filter(evaluation_id, organization_id, db)
+            )
+        else:
+            query = query.filter(
+                LLMUsageDaily.context["evaluation_id"].astext == str(evaluation_id)
+            )
+    if evaluation_row_id is not None:
+        query = query.filter(
+            LLMUsageDaily.context["evaluation_row_id"].astext
+            == str(evaluation_row_id)
+        )
+    if usage_kind:
+        query = query.filter(LLMUsageDaily.usage_kind == usage_kind)
+    if dataset or tag_id is not None:
+        if db is None:
+            raise ValueError("db required for dataset/tag filters")
+        allowed = _call_import_ids_for_filters(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            dataset=dataset,
+            tag_id=tag_id,
+        )
+        query = query.filter(_call_import_ids_scope_filter(allowed))
+    return query
+
+
+def _filtered_query(
+    db: Session,
+    *,
+    organization_id: UUID,
+    start: date,
+    end: date,
+    enforced_floor: Optional[date] = None,
+    workspace_id: Optional[UUID] = None,
+    product_section: Optional[str] = None,
+    model: Optional[str] = None,
+    resource_id: Optional[UUID] = None,
+    usage_kind: Optional[str] = None,
+    call_import_id: Optional[UUID] = None,
+    evaluation_id: Optional[UUID] = None,
+    evaluation_row_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+):
+    return _apply_filters(
+        db.query(LLMUsageDaily),
+        organization_id=organization_id,
+        start=start,
+        end=end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        db=db,
+    )
+
+
+def _infer_agent_types_for_label_buckets(
+    db: Session,
+    organization_id: UUID,
+    grouped: dict[str, tuple[Optional[str], list]],
+) -> None:
+    """When context JSON omits resource_type, infer agent rows from Agent.id."""
+    candidate_ids: list[UUID] = []
+    for rid, (rtype, _) in grouped.items():
+        if rtype:
+            continue
+        uid = parse_uuid(rid)
+        if uid:
+            candidate_ids.append(uid)
+    if not candidate_ids:
+        return
+    known_agent_ids = {
+        row.id
+        for row in db.query(Agent.id).filter(
+            Agent.organization_id == organization_id,
+            Agent.id.in_(candidate_ids),
+        ).all()
+    }
+    for rid in grouped:
+        uid = parse_uuid(rid)
+        if uid and uid in known_agent_ids:
+            rtype, contexts = grouped[rid]
+            if not rtype:
+                grouped[rid] = ("agent", contexts)
+
+
+def _resource_label_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, str]:
+    """Build resource_id -> hierarchical label from usage rows in query."""
+    rows = (
+        query.with_entities(
+            _resource_id_expr(),
+            _resource_type_expr(),
+            LLMUsageDaily.context,
+        )
+        .filter(
+            or_(
+                LLMUsageDaily.context["resource_id"].astext.isnot(None),
+                LLMUsageDaily.context["agent_id"].astext.isnot(None),
+            )
+        )
+        .order_by(*_label_row_order())
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    grouped: dict[str, tuple[Optional[str], list]] = {}
+    for raw_id, rtype, ctx in rows:
+        if not raw_id:
+            continue
+        key = str(raw_id)
+        if key not in grouped:
+            grouped[key] = (rtype, [])
+        grouped[key][1].append(ctx)
+
+    _infer_agent_types_for_label_buckets(db, organization_id, grouped)
+
+    buckets = [(rid, rtype, contexts) for rid, (rtype, contexts) in grouped.items()]
+    resolver = UsageNameResolver(db, organization_id)
+    contexts_for_preload = []
+    for rid, rtype, contexts in buckets:
+        for ctx in contexts:
+            merged = dict(ctx or {})
+            if rid:
+                merged.setdefault("resource_id", rid)
+            if rtype:
+                merged.setdefault("resource_type", rtype)
+            contexts_for_preload.append(merged)
+    resolver.preload(contexts_for_preload)
+    return labels_for_resource_buckets(buckets, resolver)
+
+
+def _resource_filter_meta_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, dict[str, Optional[str]]]:
+    """Map resource id -> {type, product_section} for filter dropdowns."""
+    rows = (
+        query.with_entities(
+            _resource_id_expr(),
+            _resource_type_expr(),
+            LLMUsageDaily.product_section,
+        )
+        .filter(
+            or_(
+                LLMUsageDaily.context["resource_id"].astext.isnot(None),
+                LLMUsageDaily.context["agent_id"].astext.isnot(None),
+            )
+        )
+        .distinct()
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    meta: dict[str, dict[str, Optional[str]]] = {}
+    for raw_id, rtype, section in rows:
+        if not raw_id:
+            continue
+        key = str(raw_id)
+        entry = meta.setdefault(key, {"type": None, "product_section": None})
+        if rtype:
+            entry["type"] = str(rtype)
+        if section:
+            entry["product_section"] = str(section)
+        if not entry["type"] and section == "agents":
+            entry["type"] = "agent"
+    return meta
+
+
+def _breakdown_resource_label(
+    raw_res_id: Optional[str],
+    res_type: Optional[str],
+    section: Optional[str],
+    resource_labels: dict[str, str],
+    db: Session,
+    organization_id: UUID,
+) -> str:
+    if not raw_res_id:
+        return "Unscoped"
+    key = str(raw_res_id)
+    label = resource_labels.get(key)
+    if label and label != "Unscoped":
+        return label
+    effective_type = res_type or ("agent" if section == "agents" else None)
+    if effective_type == "agent":
+        resolver = UsageNameResolver(db, organization_id)
+        resolver.preload([{"resource_id": key, "resource_type": "agent"}])
+        return resolver.agent_name(key)
+    return label or "Unscoped"
+
+
+def _collect_call_import_ids_from_usage_rows(
+    db: Session,
+    organization_id: UUID,
+    rows: list,
+) -> set[UUID]:
+    """Extract call import ids referenced in usage row context tuples."""
+    import_ids: set[UUID] = set()
+    eval_ids: set[UUID] = set()
+    row_ids: set[UUID] = set()
+    eval_row_ids: set[UUID] = set()
+
+    for row in rows:
+        cid_raw = row[0] if len(row) > 0 else None
+        res_raw = row[1] if len(row) > 1 else None
+        rtype = row[2] if len(row) > 2 else None
+        eval_raw = row[3] if len(row) > 3 else None
+        row_id_raw = row[4] if len(row) > 4 else None
+        eval_row_raw = row[5] if len(row) > 5 else None
+
+        if cid_raw:
+            uid = parse_uuid(cid_raw)
+            if uid:
+                import_ids.add(uid)
+        if res_raw and rtype == "call_import":
+            uid = parse_uuid(res_raw)
+            if uid:
+                import_ids.add(uid)
+        if eval_raw:
+            uid = parse_uuid(eval_raw)
+            if uid:
+                eval_ids.add(uid)
+        if res_raw and rtype == "call_import_evaluation":
+            uid = parse_uuid(res_raw)
+            if uid:
+                eval_ids.add(uid)
+        if row_id_raw:
+            uid = parse_uuid(row_id_raw)
+            if uid:
+                row_ids.add(uid)
+        if eval_row_raw:
+            uid = parse_uuid(eval_row_raw)
+            if uid:
+                eval_row_ids.add(uid)
+
+    if eval_ids:
+        for ev in (
+            db.query(CallImportEvaluation)
+            .filter(
+                CallImportEvaluation.organization_id == organization_id,
+                CallImportEvaluation.id.in_(eval_ids),
+            )
+            .all()
+        ):
+            import_ids.add(ev.call_import_id)
+
+    if row_ids:
+        for cid in (
+            db.query(CallImportRow.call_import_id)
+            .filter(CallImportRow.id.in_(row_ids))
+            .distinct()
+            .all()
+        ):
+            import_ids.add(cid[0])
+
+    if eval_row_ids:
+        for cid in (
+            db.query(CallImportEvaluation.call_import_id)
+            .join(
+                CallImportEvaluationRow,
+                CallImportEvaluationRow.evaluation_id == CallImportEvaluation.id,
+            )
+            .filter(CallImportEvaluationRow.id.in_(eval_row_ids))
+            .distinct()
+            .all()
+        ):
+            import_ids.add(cid[0])
+
+    return import_ids
+
+
+def _call_import_label_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, str]:
+    rows = (
+        query.with_entities(
+            LLMUsageDaily.context["call_import_id"].astext,
+            LLMUsageDaily.context["resource_id"].astext,
+            LLMUsageDaily.context["resource_type"].astext,
+            LLMUsageDaily.context["evaluation_id"].astext,
+            LLMUsageDaily.context["call_import_row_id"].astext,
+            LLMUsageDaily.context["evaluation_row_id"].astext,
+        )
+        .order_by(*_label_row_order())
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    import_ids = _collect_call_import_ids_from_usage_rows(
+        db, organization_id, rows
+    )
+
+    if not import_ids:
+        return {}
+    resolver = UsageNameResolver(db, organization_id)
+    resolver.preload(
+        [{"call_import_id": str(uid)} for uid in import_ids]
+    )
+    return labels_for_call_import_ids(list(import_ids), resolver)
+
+
+def _call_import_filter_labels(
+    db: Session,
+    organization_id: UUID,
+    query,
+    workspace_id: Optional[UUID] = None,
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+) -> dict[str, str]:
+    """Call imports for filters: usage in range plus workspace imports when scoped."""
+    import_ids = _collect_call_import_ids_from_usage_rows(
+        db,
+        organization_id,
+        query.with_entities(
+            LLMUsageDaily.context["call_import_id"].astext,
+            LLMUsageDaily.context["resource_id"].astext,
+            LLMUsageDaily.context["resource_type"].astext,
+            LLMUsageDaily.context["evaluation_id"].astext,
+            LLMUsageDaily.context["call_import_row_id"].astext,
+            LLMUsageDaily.context["evaluation_row_id"].astext,
+        ).order_by(*_label_row_order()).limit(_LABEL_ROW_LIMIT).all(),
+    )
+
+    if workspace_id is not None:
+        scoped_ids = _call_import_ids_for_filters(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            dataset=dataset,
+            tag_id=tag_id,
+        )
+        import_ids.update(scoped_ids)
+
+    if not import_ids:
+        return {}
+    resolver = UsageNameResolver(db, organization_id)
+    resolver.preload([{"call_import_id": str(uid)} for uid in import_ids])
+    return labels_for_call_import_ids(list(import_ids), resolver)
+
+
+def _evaluation_label_map(
+    db: Session,
+    organization_id: UUID,
+    query,
+) -> dict[str, str]:
+    """Evaluation id -> short label (name + id suffix) for filter dropdowns."""
+    rows = (
+        query.with_entities(
+            LLMUsageDaily.context["evaluation_id"].astext,
+            LLMUsageDaily.context["resource_id"].astext,
+            LLMUsageDaily.context["resource_type"].astext,
+            LLMUsageDaily.context,
+        )
+        .filter(
+            or_(
+                LLMUsageDaily.context["evaluation_id"].astext.isnot(None),
+                LLMUsageDaily.context["resource_type"].astext == "call_import_evaluation",
+            )
+        )
+        .order_by(*_label_row_order())
+        .limit(_LABEL_ROW_LIMIT)
+        .all()
+    )
+    grouped: dict[str, tuple[Optional[str], list]] = {}
+    for eval_raw, res_raw, rtype, ctx in rows:
+        key_raw = eval_raw
+        if not key_raw and rtype == "call_import_evaluation" and res_raw:
+            key_raw = res_raw
+        if not key_raw:
+            continue
+        key = str(key_raw)
+        if key not in grouped:
+            grouped[key] = (rtype, [])
+        grouped[key][1].append(ctx)
+
+    buckets = [(rid, rtype, contexts) for rid, (rtype, contexts) in grouped.items()]
+    resolver = UsageNameResolver(db, organization_id)
+    contexts_for_preload = []
+    for _, rtype, contexts in buckets:
+        for ctx in contexts:
+            merged = dict(ctx or {})
+            if rtype and "resource_type" not in merged:
+                merged["resource_type"] = rtype
+            contexts_for_preload.append(merged)
+    resolver.preload(contexts_for_preload)
+
+    labels: dict[str, str] = {}
+    for raw_id, rtype, contexts in buckets:
+        ctx = max([dict(c or {}) for c in contexts], key=len, default={})
+        if rtype and "resource_type" not in ctx:
+            ctx["resource_type"] = rtype
+        eval_key = ctx.get("evaluation_id") or raw_id
+        labels[str(raw_id)] = resolver.evaluation_name(str(eval_key))
+    return labels
+
+
+def _unpriced_usage_condition():
+    return and_(
+        LLMUsageDaily.pricing_rate_id.is_(None),
+        _usage_row_weight() > 0,
+    )
+
+
+def _has_unpriced_usage_column():
+    return func.coalesce(func.bool_or(_unpriced_usage_condition()), False).label(
+        "has_unpriced_usage"
+    )
+
+
+def _usage_cost_sum_columns():
+    return (
+        func.coalesce(func.sum(LLMUsageDaily.input_cost_micro_usd), 0).label(
+            "input_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.output_cost_micro_usd), 0).label(
+            "output_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_read_cost_micro_usd), 0).label(
+            "cache_read_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_creation_cost_micro_usd), 0).label(
+            "cache_creation_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.reasoning_cost_micro_usd), 0).label(
+            "reasoning_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.audio_cost_micro_usd), 0).label(
+            "audio_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.tts_cost_micro_usd), 0).label(
+            "tts_cost_micro_usd"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.total_cost_micro_usd), 0).label(
+            "total_cost_micro_usd"
+        ),
+    )
+
+
+def _attach_costs(metrics: dict, *, has_unpriced_usage: bool = False) -> dict:
+    costs = costs_from_micro(
+        input_cost_micro_usd=metrics.get("input_cost_micro_usd", 0),
+        output_cost_micro_usd=metrics.get("output_cost_micro_usd", 0),
+        cache_read_cost_micro_usd=metrics.get("cache_read_cost_micro_usd", 0),
+        cache_creation_cost_micro_usd=metrics.get("cache_creation_cost_micro_usd", 0),
+        reasoning_cost_micro_usd=metrics.get("reasoning_cost_micro_usd", 0),
+        audio_cost_micro_usd=metrics.get("audio_cost_micro_usd", 0),
+        tts_cost_micro_usd=metrics.get("tts_cost_micro_usd", 0),
+        total_cost_micro_usd=metrics.get("total_cost_micro_usd", 0),
+        has_unpriced_usage=has_unpriced_usage,
+    )
+    return {**metrics, "costs": costs}
+
+
+def _usage_totals_from_row(row) -> dict:
+    prompt = int(row.prompt_tokens)
+    completion = int(row.completion_tokens)
+    metrics = {
+        "prompt_tokens": prompt,
+        "completion_tokens": completion,
+        "total_tokens": prompt + completion,
+        "cache_read_tokens": int(row.cache_read_tokens),
+        "cache_creation_tokens": int(row.cache_creation_tokens),
+        "reasoning_tokens": int(row.reasoning_tokens),
+        "audio_seconds": int(row.audio_seconds),
+        "tts_characters": int(row.tts_characters),
+        "call_count": int(row.call_count),
+        "input_cost_micro_usd": int(getattr(row, "input_cost_micro_usd", 0) or 0),
+        "output_cost_micro_usd": int(getattr(row, "output_cost_micro_usd", 0) or 0),
+        "cache_read_cost_micro_usd": int(
+            getattr(row, "cache_read_cost_micro_usd", 0) or 0
+        ),
+        "cache_creation_cost_micro_usd": int(
+            getattr(row, "cache_creation_cost_micro_usd", 0) or 0
+        ),
+        "reasoning_cost_micro_usd": int(
+            getattr(row, "reasoning_cost_micro_usd", 0) or 0
+        ),
+        "audio_cost_micro_usd": int(getattr(row, "audio_cost_micro_usd", 0) or 0),
+        "tts_cost_micro_usd": int(getattr(row, "tts_cost_micro_usd", 0) or 0),
+        "total_cost_micro_usd": int(getattr(row, "total_cost_micro_usd", 0) or 0),
+    }
+    return _attach_costs(
+        metrics,
+        has_unpriced_usage=bool(getattr(row, "has_unpriced_usage", False)),
+    )
+
+
+def _breakdown_metrics_from_tuple(metrics: tuple) -> dict:
+    prompt = int(metrics[0])
+    completion = int(metrics[1])
+    base = {
+        "prompt_tokens": prompt,
+        "completion_tokens": completion,
+        "total_tokens": prompt + completion,
+        "cache_read_tokens": int(metrics[2]),
+        "cache_creation_tokens": int(metrics[3]),
+        "reasoning_tokens": int(metrics[4]),
+        "audio_seconds": int(metrics[5]),
+        "tts_characters": int(metrics[6]),
+        "call_count": int(metrics[7]),
+        "input_cost_micro_usd": int(metrics[8]),
+        "output_cost_micro_usd": int(metrics[9]),
+        "cache_read_cost_micro_usd": int(metrics[10]),
+        "cache_creation_cost_micro_usd": int(metrics[11]),
+        "reasoning_cost_micro_usd": int(metrics[12]),
+        "audio_cost_micro_usd": int(metrics[13]),
+        "tts_cost_micro_usd": int(metrics[14]),
+        "total_cost_micro_usd": int(metrics[15]),
+    }
+    has_unpriced = bool(metrics[16]) if len(metrics) > 16 else False
+    return _attach_costs(base, has_unpriced_usage=has_unpriced)
+
+
+def _last_updated(db: Session, organization_id: UUID) -> Optional[datetime]:
+    return (
+        db.query(func.max(LLMUsageDaily.updated_at))
+        .filter(LLMUsageDaily.organization_id == organization_id)
+        .scalar()
+    )
+
+
+def _summary_aggregate_query(
+    db: Session,
+    *,
+    organization_id: UUID,
+    start: date,
+    end: date,
+    enforced_floor: Optional[date] = None,
+    workspace_id: Optional[UUID],
+    product_section: Optional[str],
+    model: Optional[str],
+    resource_id: Optional[UUID],
+    usage_kind: Optional[str],
+    call_import_id: Optional[UUID],
+    evaluation_id: Optional[UUID],
+    evaluation_row_id: Optional[UUID],
+    dataset: Optional[str] = None,
+    tag_id: Optional[UUID] = None,
+):
+    return _apply_filters(
+        db.query(
+            func.coalesce(func.sum(LLMUsageDaily.prompt_tokens), 0).label("prompt_tokens"),
+            func.coalesce(func.sum(LLMUsageDaily.completion_tokens), 0).label(
+                "completion_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.cache_read_tokens), 0).label(
+                "cache_read_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.cache_creation_tokens), 0).label(
+                "cache_creation_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
+                "reasoning_tokens"
+            ),
+            func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
+            func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
+            func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
+            *_usage_cost_sum_columns(),
+            _has_unpriced_usage_column(),
+        ),
+        organization_id=organization_id,
+        start=start,
+        end=end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        db=db,
+    )
+
+
+@router.get("/fx-rate", response_model=UsageFxRateResponse)
+def get_usage_fx_rate():
+    payload = get_usd_inr_rate()
+    return UsageFxRateResponse(
+        base=payload["base"],
+        quote=payload["quote"],
+        rate=float(payload["rate"]),
+        as_of=datetime.fromisoformat(payload["as_of"]),
+        source=payload["source"],
+    )
+
+
+@router.get("/summary", response_model=UsageSummaryResponse)
+def get_usage_summary(
+    start: Optional[date] = Query(None),
+    end: Optional[date] = Query(None),
+    tz: Optional[str] = Query(
+        None,
+        description="IANA timezone for interpreting start/end calendar dates",
+    ),
+    workspace_id: Optional[UUID] = Query(None),
+    product_section: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    resource_id: Optional[UUID] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    call_import_id: Optional[UUID] = Query(None),
+    evaluation_id: Optional[UUID] = Query(None),
+    evaluation_row_id: Optional[UUID] = Query(None),
+    dataset: Optional[str] = Query(None),
+    tag_id: Optional[UUID] = Query(None),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
+    display_start = access.display_start
+    display_end = access.display_end
+    if display_end < display_start:
+        raise HTTPException(status_code=400, detail="end must be >= start")
+
+    cache_key = cache_key_for(
+        access,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    cached = get_cached_response(organization_id, "summary", cache_key)
+    if cached is not None:
+        try:
+            return UsageSummaryResponse.model_validate(cached)
+        except Exception:
+            pass
+
+    row = _summary_aggregate_query(
+        db,
+        organization_id=organization_id,
+        start=access.filter_start,
+        end=access.filter_end,
+        enforced_floor=access.enforced_filter_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    ).one()
+    totals = _usage_totals_from_row(row)
+    response = UsageSummaryResponse(
+        start=display_start,
+        end=display_end,
+        totals=UsageTotals(**totals),
+        usage_policy=_usage_policy_meta(access),
+        last_updated_at=_last_updated(db, organization_id),
+    )
+    set_cached_response(
+        organization_id,
+        "summary",
+        cache_key,
+        response.model_dump(mode="json"),
+    )
+    return response
+
+
+@router.get("/breakdown", response_model=UsageBreakdownResponse)
+def get_usage_breakdown(
+    start: Optional[date] = Query(None),
+    end: Optional[date] = Query(None),
+    tz: Optional[str] = Query(
+        None,
+        description="IANA timezone for interpreting start/end calendar dates",
+    ),
+    group_by: GroupBy = Query("workspace"),
+    workspace_id: Optional[UUID] = Query(None),
+    product_section: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    resource_id: Optional[UUID] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    call_import_id: Optional[UUID] = Query(None),
+    evaluation_id: Optional[UUID] = Query(None),
+    evaluation_row_id: Optional[UUID] = Query(None),
+    dataset: Optional[str] = Query(None),
+    tag_id: Optional[UUID] = Query(None),
+    limit: int = Query(100, ge=1, le=500),
+    offset: int = Query(0, ge=0),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
+    display_start = access.display_start
+    display_end = access.display_end
+    if display_end < display_start:
+        raise HTTPException(status_code=400, detail="end must be >= start")
+
+    cache_key = cache_key_for(
+        access,
+        group_by=group_by,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        limit=limit,
+        offset=offset,
+    )
+    cached = get_cached_response(organization_id, "breakdown", cache_key)
+    if cached is not None:
+        try:
+            return UsageBreakdownResponse.model_validate(cached)
+        except Exception:
+            pass
+
+    dim = {
+        "workspace": LLMUsageDaily.workspace_id,
+        "product_section": LLMUsageDaily.product_section,
+        "model": LLMUsageDaily.model,
+        "resource": cast(_resource_id_expr(), String),
+        "usage_kind": LLMUsageDaily.usage_kind,
+        "call_import": _call_import_group_expr(),
+    }[group_by]
+
+    aggregates = [
+        func.coalesce(func.sum(LLMUsageDaily.prompt_tokens), 0).label("prompt_tokens"),
+        func.coalesce(func.sum(LLMUsageDaily.completion_tokens), 0).label(
+            "completion_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_read_tokens), 0).label(
+            "cache_read_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.cache_creation_tokens), 0).label(
+            "cache_creation_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.reasoning_tokens), 0).label(
+            "reasoning_tokens"
+        ),
+        func.coalesce(func.sum(LLMUsageDaily.audio_seconds), 0).label("audio_seconds"),
+        func.coalesce(func.sum(LLMUsageDaily.tts_characters), 0).label("tts_characters"),
+        func.coalesce(func.sum(LLMUsageDaily.call_count), 0).label("call_count"),
+        *_usage_cost_sum_columns(),
+        _has_unpriced_usage_column(),
+    ]
+
+    select_cols = [dim]
+    group_cols = [dim]
+    if group_by == "resource":
+        select_cols.append(cast(_resource_type_expr(), String))
+        group_cols.append(cast(_resource_type_expr(), String))
+        select_cols.append(LLMUsageDaily.product_section)
+        group_cols.append(LLMUsageDaily.product_section)
+
+    query = _apply_filters(
+        db.query(*select_cols, *aggregates),
+        organization_id=organization_id,
+        start=access.filter_start,
+        end=access.filter_end,
+        enforced_floor=access.enforced_filter_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        evaluation_row_id=evaluation_row_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        db=db,
+    ).group_by(*group_cols)
+
+    results = (
+        query.order_by(func.sum(LLMUsageDaily.call_count).desc())
+        .offset(offset)
+        .limit(limit)
+        .all()
+    )
+
+    workspace_names: dict = {}
+    if group_by == "workspace":
+        ws_ids = {r[0] for r in results if r[0] is not None}
+        if ws_ids:
+            workspace_names = {
+                w.id: w.name
+                for w in db.query(Workspace)
+                .filter(
+                    Workspace.organization_id == organization_id,
+                    Workspace.id.in_(ws_ids),
+                )
+                .all()
+            }
+    resource_labels: dict[str, str] = {}
+    call_import_labels: dict[str, str] = {}
+    if group_by == "resource":
+        label_query = _filtered_query(
+            db,
+            organization_id=organization_id,
+            start=access.filter_start,
+            end=access.filter_end,
+            enforced_floor=access.enforced_filter_floor,
+            workspace_id=workspace_id,
+            product_section=product_section,
+            model=model,
+            resource_id=resource_id,
+            usage_kind=usage_kind,
+            call_import_id=call_import_id,
+            evaluation_id=evaluation_id,
+            evaluation_row_id=evaluation_row_id,
+        )
+        resource_labels = _resource_label_map(db, organization_id, label_query)
+    elif group_by == "call_import":
+        label_query = _filtered_query(
+            db,
+            organization_id=organization_id,
+            start=access.filter_start,
+            end=access.filter_end,
+            enforced_floor=access.enforced_filter_floor,
+            workspace_id=workspace_id,
+            product_section=product_section,
+            model=model,
+            resource_id=resource_id,
+            usage_kind=usage_kind,
+            call_import_id=call_import_id,
+            evaluation_id=evaluation_id,
+            evaluation_row_id=evaluation_row_id,
+        )
+        call_import_labels = _call_import_label_map(db, organization_id, label_query)
+
+    rows: List[UsageBreakdownRow] = []
+    for result in results:
+        if group_by == "workspace":
+            ws_id = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    workspace_id=ws_id,
+                    workspace_name=workspace_names.get(ws_id) if ws_id else "Unknown",
+                    **_breakdown_metrics_from_tuple(metrics),
+                )
+            )
+        elif group_by == "product_section":
+            section = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    product_section=section,
+                    product_section_label=SECTION_LABELS.get(section or "", section),
+                    **_breakdown_metrics_from_tuple(metrics),
+                )
+            )
+        elif group_by == "model":
+            model_name = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    model=model_name,
+                    **_breakdown_metrics_from_tuple(metrics),
+                )
+            )
+        elif group_by == "usage_kind":
+            kind = result[0]
+            metrics = result[1:]
+            rows.append(
+                UsageBreakdownRow(
+                    usage_kind=kind,
+                    **_breakdown_metrics_from_tuple(metrics),
+                )
+            )
+        elif group_by == "call_import":
+            raw_cid = result[0]
+            metrics = result[1:]
+            cid = None
+            if raw_cid:
+                try:
+                    cid = UUID(str(raw_cid))
+                except (ValueError, TypeError):
+                    cid = None
+            label = (
+                call_import_labels.get(str(raw_cid), "Unscoped")
+                if raw_cid
+                else "Unscoped"
+            )
+            rows.append(
+                UsageBreakdownRow(
+                    call_import_id=cid,
+                    call_import_label=label,
+                    **_breakdown_metrics_from_tuple(metrics),
+                )
+            )
+        else:
+            raw_res_id, res_type, section = result[0], result[1], result[2]
+            metrics = result[3:]
+            res_id = None
+            if raw_res_id:
+                try:
+                    res_id = UUID(str(raw_res_id))
+                except (ValueError, TypeError):
+                    res_id = None
+            rows.append(
+                UsageBreakdownRow(
+                    resource_id=res_id,
+                    resource_type=res_type or (
+                        "agent" if section == "agents" and raw_res_id else None
+                    ),
+                    resource_label=_breakdown_resource_label(
+                        raw_res_id,
+                        res_type,
+                        section,
+                        resource_labels,
+                        db,
+                        organization_id,
+                    ),
+                    product_section=section,
+                    product_section_label=SECTION_LABELS.get(section or "", section),
+                    **_breakdown_metrics_from_tuple(metrics),
+                )
+            )
+
+    response = UsageBreakdownResponse(
+        start=display_start,
+        end=display_end,
+        group_by=group_by,
+        rows=rows,
+        total_count=len(rows),
+        truncated_at_limit=len(rows) >= limit,
+        usage_policy=_usage_policy_meta(access),
+        last_updated_at=_last_updated(db, organization_id),
+    )
+    set_cached_response(
+        organization_id,
+        "breakdown",
+        cache_key,
+        response.model_dump(mode="json"),
+    )
+    return response
+
+
+@router.get("/filters", response_model=UsageFiltersResponse)
+def get_usage_filters(
+    start: Optional[date] = Query(None),
+    end: Optional[date] = Query(None),
+    tz: Optional[str] = Query(
+        None,
+        description="IANA timezone for interpreting start/end calendar dates",
+    ),
+    workspace_id: Optional[UUID] = Query(None),
+    product_section: Optional[str] = Query(None),
+    model: Optional[str] = Query(None),
+    resource_id: Optional[UUID] = Query(None),
+    usage_kind: Optional[str] = Query(None),
+    call_import_id: Optional[UUID] = Query(None),
+    evaluation_id: Optional[UUID] = Query(None),
+    dataset: Optional[str] = Query(None),
+    tag_id: Optional[UUID] = Query(None),
+    q: Optional[str] = Query(None, description="Optional resource label search"),
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    access = UsageAccessPolicy.resolve(organization_id, start, end, tz)
+    filter_start = access.filter_start
+    filter_end = access.filter_end
+    enforced_floor = access.enforced_filter_floor
+
+    cache_key = cache_key_for(
+        access,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        resource_id=resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        dataset=dataset,
+        tag_id=tag_id,
+        q=q,
+    )
+    cached = get_cached_response(organization_id, "filters", cache_key)
+    if cached is not None:
+        try:
+            return UsageFiltersResponse.model_validate(cached)
+        except Exception:
+            pass
+
+    scoped_resource_id = resource_id or evaluation_id
+
+    workspace_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        enforced_floor=enforced_floor,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    section_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    kind_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        resource_id=scoped_resource_id,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    model_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        resource_id=scoped_resource_id,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        evaluation_id=evaluation_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    call_import_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    resource_base = _filtered_query(
+        db,
+        organization_id=organization_id,
+        start=filter_start,
+        end=filter_end,
+        enforced_floor=enforced_floor,
+        workspace_id=workspace_id,
+        product_section=product_section,
+        model=model,
+        usage_kind=usage_kind,
+        call_import_id=call_import_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+
+    workspace_ids = {
+        row[0]
+        for row in workspace_base.with_entities(LLMUsageDaily.workspace_id).distinct().all()
+        if row[0] is not None
+    }
+    workspaces = [
+        {"id": str(w.id), "name": w.name}
+        for w in db.query(Workspace)
+        .filter(Workspace.id.in_(workspace_ids) if workspace_ids else False)
+        .order_by(Workspace.name)
+        .all()
+    ]
+
+    sections = sorted(
+        {
+            row[0]
+            for row in section_base.with_entities(LLMUsageDaily.product_section).distinct().all()
+            if row[0]
+        }
+    )
+    models = sorted(
+        {
+            row[0]
+            for row in model_base.with_entities(LLMUsageDaily.model).distinct().all()
+            if row[0]
+        }
+    )
+    kinds = sorted(
+        {
+            row[0]
+            for row in kind_base.with_entities(LLMUsageDaily.usage_kind).distinct().all()
+            if row[0]
+        }
+    )
+
+    call_import_labels = _call_import_filter_labels(
+        db,
+        organization_id,
+        call_import_base,
+        workspace_id=workspace_id,
+        dataset=dataset,
+        tag_id=tag_id,
+    )
+    call_imports = [
+        {"id": cid, "label": label}
+        for cid, label in sorted(call_import_labels.items(), key=lambda x: x[1].lower())
+    ]
+
+    evaluation_labels = _evaluation_label_map(db, organization_id, resource_base)
+    needle = (q or "").strip().lower()
+    evaluations = []
+    for rid, label in sorted(evaluation_labels.items(), key=lambda x: x[1].lower()):
+        if needle and needle not in label.lower():
+            continue
+        evaluations.append({"id": rid, "label": label})
+
+    resource_labels = _resource_label_map(db, organization_id, resource_base)
+    resource_meta = _resource_filter_meta_map(db, organization_id, resource_base)
+    resources = []
+    for rid, label in sorted(resource_labels.items(), key=lambda x: x[1].lower()):
+        if needle and needle not in label.lower():
+            continue
+        info = resource_meta.get(rid, {})
+        rtype = info.get("type")
+        if rtype == "call_import_evaluation":
+            continue
+        resources.append(
+            {
+                "id": rid,
+                "label": label,
+                "type": rtype,
+                "product_section": info.get("product_section"),
+            }
+        )
+
+    dataset_query = db.query(CallImport.dataset).filter(
+        CallImport.organization_id == organization_id,
+        CallImport.dataset.isnot(None),
+        CallImport.dataset != "",
+    )
+    if workspace_id is not None:
+        dataset_query = dataset_query.filter(CallImport.workspace_id == workspace_id)
+    if dataset or tag_id is not None:
+        scoped_import_ids = _call_import_ids_for_filters(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            dataset=dataset,
+            tag_id=tag_id,
+        )
+        if scoped_import_ids:
+            dataset_query = dataset_query.filter(CallImport.id.in_(scoped_import_ids))
+        else:
+            dataset_query = dataset_query.filter(CallImport.id.is_(None))
+    datasets = sorted({row[0] for row in dataset_query.distinct().all() if row[0]})
+
+    tags = [
+        {"id": str(tag.id), "label": tag.name}
+        for tag in (
+            db.query(CallImportTag)
+            .filter(CallImportTag.organization_id == organization_id)
+            .order_by(CallImportTag.name)
+            .all()
+        )
+    ]
+
+    response = UsageFiltersResponse(
+        workspaces=workspaces,
+        product_sections=[
+            {"id": s, "label": SECTION_LABELS.get(s, s)} for s in sections
+        ],
+        call_imports=call_imports,
+        evaluations=evaluations,
+        models=models,
+        resources=resources,
+        usage_kinds=[{"id": k, "label": usage_kind_label(k)} for k in kinds],
+        datasets=datasets,
+        tags=tags,
+    )
+    set_cached_response(
+        organization_id,
+        "filters",
+        cache_key,
+        response.model_dump(mode="json"),
+    )
+    return response
+
diff --git a/app/services/usage/bucket_context.py b/app/services/usage/bucket_context.py
index ad6202d9..64187a39 100644
--- a/app/services/usage/bucket_context.py
+++ b/app/services/usage/bucket_context.py
@@ -9,6 +9,7 @@
 _NONE = "__none__"
 
 # Stable keys stored in llm_usage_daily.context (extend without new columns).
+# evaluation_row_id / call_import_row_id may appear on legacy rows only.
 KNOWN_CONTEXT_KEYS = frozenset(
     {
         "resource_id",
diff --git a/app/services/usage/call_import_context.py b/app/services/usage/call_import_context.py
index e23f46cf..1ceeb723 100644
--- a/app/services/usage/call_import_context.py
+++ b/app/services/usage/call_import_context.py
@@ -134,18 +134,12 @@ def call_import_evaluation_usage_context(
     workspace_id: Optional[UUID],
     evaluation_id: UUID,
     call_import_id: UUID,
-    evaluation_row_id: Optional[UUID] = None,
-    call_import_row_id: Optional[UUID] = None,
 ) -> LLMUsageContext:
-    """Full attribution for eval-run LLM/STT/TTS inside a call import evaluation."""
+    """Usage rollup for an eval run (model-level drilldown; not per recording)."""
     extra: dict[str, str] = {
         "call_import_id": str(call_import_id),
         "evaluation_id": str(evaluation_id),
     }
-    if evaluation_row_id is not None:
-        extra["evaluation_row_id"] = str(evaluation_row_id)
-    if call_import_row_id is not None:
-        extra["call_import_row_id"] = str(call_import_row_id)
     return LLMUsageContext(
         organization_id=organization_id,
         workspace_id=workspace_id,
@@ -161,19 +155,15 @@ def call_import_row_usage_context(
     organization_id: UUID,
     workspace_id: Optional[UUID],
     call_import_id: UUID,
-    call_import_row_id: UUID,
     evaluation_id: Optional[UUID] = None,
-    evaluation_row_id: Optional[UUID] = None,
 ) -> LLMUsageContext:
-    """Attribution for diarisation / STT on a single call-import row."""
+    """Usage rollup for diarisation / STT (call-import level, not per recording)."""
     if evaluation_id is not None:
         return call_import_evaluation_usage_context(
             organization_id=organization_id,
             workspace_id=workspace_id,
             evaluation_id=evaluation_id,
             call_import_id=call_import_id,
-            evaluation_row_id=evaluation_row_id,
-            call_import_row_id=call_import_row_id,
         )
     return LLMUsageContext(
         organization_id=organization_id,
@@ -181,10 +171,7 @@ def call_import_row_usage_context(
         product_section=LLMUsageProductSection.CALL_IMPORTS,
         resource_id=call_import_id,
         resource_type="call_import",
-        extra={
-            "call_import_id": str(call_import_id),
-            "call_import_row_id": str(call_import_row_id),
-        },
+        extra={"call_import_id": str(call_import_id)},
     )
 
 
diff --git a/app/services/usage/read_cache.py b/app/services/usage/read_cache.py
index 02ab58d7..5db010f0 100644
--- a/app/services/usage/read_cache.py
+++ b/app/services/usage/read_cache.py
@@ -1,118 +1,151 @@
-"""Redis cache for org usage read endpoints (summary, breakdown, filters)."""
-
-from __future__ import annotations
-
-import hashlib
-import json
-import os
-from typing import Any, Optional
-from uuid import UUID
-
-import redis
-from loguru import logger
-
-from app.config import settings
-from app.services.usage.access import UsageAccessResult
-
-_redis: redis.Redis | None = None
-
-
-def _client() -> redis.Redis:
-    global _redis
-    if _redis is None:
-        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
-    return _redis
-
-
-def cache_ttl_seconds() -> int:
-    raw = os.environ.get("USAGE_READ_CACHE_TTL_SECONDS", "90")
-    try:
-        return max(0, int(raw))
-    except (TypeError, ValueError):
-        return 90
-
-
-def cache_key_for(access: UsageAccessResult, **params: Any) -> str:
-    payload: dict[str, Any] = {
-        "display_start": access.display_start.isoformat(),
-        "display_end": access.display_end.isoformat(),
-        "filter_start": access.filter_start.isoformat(),
-        "filter_end": access.filter_end.isoformat(),
-        "enforced_floor": (
-            access.enforced_filter_floor.isoformat()
-            if access.enforced_filter_floor
-            else None
-        ),
-        "range_clamped": access.range_clamped,
-        "policy": access.policy.as_dict(),
-    }
-    for key, value in sorted(params.items()):
-        if value is None:
-            payload[key] = None
-        elif isinstance(value, UUID):
-            payload[key] = str(value)
-        else:
-            payload[key] = value
-    digest = hashlib.sha256(
-        json.dumps(payload, sort_keys=True, default=str).encode()
-    ).hexdigest()
-    return digest[:32]
-
-
-def _redis_key(organization_id: UUID, endpoint: str, cache_key: str) -> str:
-    return f"usage:read:{organization_id}:{endpoint}:{cache_key}"
-
-
-def get_cached_response(
-    organization_id: UUID,
-    endpoint: str,
-    cache_key: str,
-) -> Optional[dict[str, Any]]:
-    ttl = cache_ttl_seconds()
-    if ttl <= 0:
-        return None
-    try:
-        raw = _client().get(_redis_key(organization_id, endpoint, cache_key))
-        if not raw:
-            return None
-        return json.loads(raw)
-    except (redis.RedisError, json.JSONDecodeError) as exc:
-        logger.debug("usage read cache miss/error org={} endpoint={}: {}", organization_id, endpoint, exc)
-        return None
-
-
-def set_cached_response(
-    organization_id: UUID,
-    endpoint: str,
-    cache_key: str,
-    payload: dict[str, Any],
-) -> None:
-    ttl = cache_ttl_seconds()
-    if ttl <= 0:
-        return
-    try:
-        _client().set(
-            _redis_key(organization_id, endpoint, cache_key),
-            json.dumps(payload, default=str),
-            ex=ttl,
-        )
-    except redis.RedisError as exc:
-        logger.debug("usage read cache set failed org={}: {}", organization_id, exc)
-
-
-def invalidate_org_usage_read_cache(organization_id: UUID) -> int:
-    pattern = f"usage:read:{organization_id}:*"
-    deleted = 0
-    try:
-        client = _client()
-        batch: list[str] = []
-        for key in client.scan_iter(match=pattern, count=200):
-            batch.append(key)
-            if len(batch) >= 200:
-                deleted += client.delete(*batch)
-                batch.clear()
-        if batch:
-            deleted += client.delete(*batch)
-    except redis.RedisError as exc:
-        logger.warning("usage read cache invalidate failed org={}: {}", organization_id, exc)
-    return deleted
+"""Redis cache for org usage read endpoints (summary, breakdown, filters)."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from typing import Any, Optional
+from uuid import UUID
+
+import redis
+from loguru import logger
+
+from app.config import settings
+from app.services.usage.access import UsageAccessResult
+
+_redis: redis.Redis | None = None
+
+
+def _client() -> redis.Redis:
+    global _redis
+    if _redis is None:
+        _redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+    return _redis
+
+
+def cache_ttl_seconds() -> int:
+    raw = os.environ.get("USAGE_READ_CACHE_TTL_SECONDS", "90")
+    try:
+        return max(0, int(raw))
+    except (TypeError, ValueError):
+        return 90
+
+
+def cache_key_for(access: UsageAccessResult, **params: Any) -> str:
+    payload: dict[str, Any] = {
+        "display_start": access.display_start.isoformat(),
+        "display_end": access.display_end.isoformat(),
+        "filter_start": access.filter_start.isoformat(),
+        "filter_end": access.filter_end.isoformat(),
+        "enforced_floor": (
+            access.enforced_filter_floor.isoformat()
+            if access.enforced_filter_floor
+            else None
+        ),
+        "range_clamped": access.range_clamped,
+        "policy": access.policy.as_dict(),
+    }
+    for key, value in sorted(params.items()):
+        if value is None:
+            payload[key] = None
+        elif isinstance(value, UUID):
+            payload[key] = str(value)
+        else:
+            payload[key] = value
+    digest = hashlib.sha256(
+        json.dumps(payload, sort_keys=True, default=str).encode()
+    ).hexdigest()
+    return digest[:32]
+
+
+def _redis_key(organization_id: UUID, endpoint: str, cache_key: str) -> str:
+    return f"usage:read:{organization_id}:{endpoint}:{cache_key}"
+
+
+def _is_empty_usage_payload(endpoint: str, payload: dict[str, Any]) -> bool:
+    if endpoint == "summary":
+        totals = payload.get("totals") or {}
+        if int(totals.get("call_count") or 0) > 0:
+            return False
+        return not any(
+            int(totals.get(field) or 0) > 0
+            for field in (
+                "prompt_tokens",
+                "completion_tokens",
+                "audio_seconds",
+                "tts_characters",
+            )
+        )
+    if endpoint == "breakdown":
+        rows = payload.get("rows") or []
+        if not rows:
+            return True
+        return not any(
+            int(row.get("call_count") or 0) > 0
+            or int(row.get("total_tokens") or 0) > 0
+            or int(row.get("audio_seconds") or 0) > 0
+            or int(row.get("tts_characters") or 0) > 0
+            for row in rows
+        )
+    return False
+
+
+def get_cached_response(
+    organization_id: UUID,
+    endpoint: str,
+    cache_key: str,
+) -> Optional[dict[str, Any]]:
+    ttl = cache_ttl_seconds()
+    if ttl <= 0:
+        return None
+    try:
+        raw = _client().get(_redis_key(organization_id, endpoint, cache_key))
+        if not raw:
+            return None
+        payload = json.loads(raw)
+        if _is_empty_usage_payload(endpoint, payload):
+            return None
+        return payload
+    except (redis.RedisError, json.JSONDecodeError) as exc:
+        logger.debug("usage read cache miss/error org={} endpoint={}: {}", organization_id, endpoint, exc)
+        return None
+
+
+def set_cached_response(
+    organization_id: UUID,
+    endpoint: str,
+    cache_key: str,
+    payload: dict[str, Any],
+) -> None:
+    ttl = cache_ttl_seconds()
+    if ttl <= 0:
+        return
+    if _is_empty_usage_payload(endpoint, payload):
+        return
+    try:
+        _client().set(
+            _redis_key(organization_id, endpoint, cache_key),
+            json.dumps(payload, default=str),
+            ex=ttl,
+        )
+    except redis.RedisError as exc:
+        logger.debug("usage read cache set failed org={}: {}", organization_id, exc)
+
+
+def invalidate_org_usage_read_cache(organization_id: UUID) -> int:
+    pattern = f"usage:read:{organization_id}:*"
+    deleted = 0
+    try:
+        client = _client()
+        batch: list[str] = []
+        for key in client.scan_iter(match=pattern, count=200):
+            batch.append(key)
+            if len(batch) >= 200:
+                deleted += client.delete(*batch)
+                batch.clear()
+        if batch:
+            deleted += client.delete(*batch)
+    except redis.RedisError as exc:
+        logger.warning("usage read cache invalidate failed org={}: {}", organization_id, exc)
+    return deleted
diff --git a/app/workers/tasks/evaluate_call_import_row.py b/app/workers/tasks/evaluate_call_import_row.py
index dafba330..c4090f6c 100644
--- a/app/workers/tasks/evaluate_call_import_row.py
+++ b/app/workers/tasks/evaluate_call_import_row.py
@@ -375,8 +375,6 @@ def evaluate_call_import_row_task(
                     workspace_id=evaluation.workspace_id,
                     evaluation_id=evaluation.id,
                     call_import_id=evaluation.call_import_id,
-                    evaluation_row_id=eval_row.id,
-                    call_import_row_id=source_row.id,
                 )
             )
 
diff --git a/app/workers/tasks/transcribe_call_import_row.py b/app/workers/tasks/transcribe_call_import_row.py
index e50f86ad..525a7a48 100644
--- a/app/workers/tasks/transcribe_call_import_row.py
+++ b/app/workers/tasks/transcribe_call_import_row.py
@@ -390,8 +390,6 @@ def _persist_diarization_failure(
 
 def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
     """STT / S3 / LLM diarisation without a long-lived DB session."""
-    from uuid import UUID
-
     from app.models.enums import ModelProvider
     from app.services.usage.call_import_context import call_import_row_usage_context
     from app.services.usage.context import llm_usage_context
@@ -411,7 +409,6 @@ def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
     effective_prompt = ctx["effective_prompt"]
 
     evaluation_id = ctx.get("evaluation_id")
-    evaluation_row_id = ctx.get("evaluation_row_id")
     call_import_id = ctx.get("call_import_id")
     if not call_import_id:
         raise ValueError("call_import_id missing from transcribe pipeline context")
@@ -421,9 +418,7 @@ def _run_diarization_pipeline(ctx: dict[str, Any]) -> dict[str, Any]:
             organization_id=organization_id,
             workspace_id=ctx.get("workspace_id"),
             call_import_id=call_import_id,
-            call_import_row_id=UUID(str(row_id)),
             evaluation_id=evaluation_id,
-            evaluation_row_id=evaluation_row_id,
         )
     ):
         return _run_diarization_pipeline_inner(
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index 07bb4772..469df3c3 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -1,1449 +1,1466 @@
-import { useEffect, useMemo, useState } from 'react'
-import { keepPreviousData, useQuery } from '@tanstack/react-query'
-import { Link, useSearchParams } from 'react-router-dom'
-import { Card, CardBody, Spinner } from '@heroui/react'
-import { Activity, ChevronRight, CircleDollarSign } from 'lucide-react'
-import { apiClient } from '../../lib/api'
-import { useIsAdmin } from '../../hooks/useRole'
-import UsageFiltersBar from './UsageFiltersBar'
-import UsageDrillPath from './UsageDrillPath'
-import UsageCostBreakdownModal from './UsageCostBreakdownModal'
-import { defaultUsageDateRange, isRangeWithinMaxDays, rangeForDays } from './UsageDateRangePicker'
-import { getUsageTimezone } from './usageTimezone'
-import {
-  formatUsageCostUsd,
-  getUsageDisplayCurrency,
-  setUsageDisplayCurrency,
-  type UsageDisplayCurrency,
-} from '../../lib/usageCurrency'
-import { useLicenseStore } from '../../store/licenseStore'
-import {
-  CALL_IMPORT_BATCH_HEADLINE,
-  CALL_IMPORT_HINT,
-  CALL_IMPORT_PRODUCT_SECTIONS,
-  PRODUCT_SECTION_HEADLINES,
-  PRODUCT_SECTION_HINTS,
-  USAGE_SECTION_SOURCE_PREFIX,
-} from './usageProductHints'
-
-type DrillGroupBy =
-  | 'workspace'
-  | 'call_import'
-  | 'resource'
-  | 'model'
-  | 'usage_kind'
-  | 'product_section'
-type Kind = '' | 'llm' | 'stt' | 'tts'
-
-type FilterOptions = {
-  workspaces: Array<{ id: string; name: string }>
-  call_imports: Array<{ id: string; label: string }>
-  evaluations: Array<{ id: string; label: string }>
-  resources?: Array<{ id: string; label: string; type?: string; product_section?: string }>
-  models: string[]
-  usage_kinds: Array<{ id: string; label: string }>
-  product_sections?: Array<{ id: string; label: string }>
-  datasets?: string[]
-  tags?: Array<{ id: string; label: string }>
-}
-
-type UsageCosts = {
-  input_cost_usd: number
-  output_cost_usd: number
-  cache_read_cost_usd: number
-  cache_write_cost_usd: number
-  reasoning_cost_usd: number
-  audio_cost_usd: number
-  tts_cost_usd: number
-  total_cost_usd: number
-  currency: string
-  has_unpriced_usage: boolean
-}
-
-type BreakdownRow = {
-  workspace_id?: string | null
-  workspace_name?: string | null
-  call_import_id?: string | null
-  call_import_label?: string | null
-  resource_id?: string | null
-  resource_type?: string | null
-  resource_label?: string | null
-  model?: string | null
-  usage_kind?: string | null
-  product_section?: string | null
-  product_section_label?: string | null
-  prompt_tokens: number
-  completion_tokens: number
-  total_tokens: number
-  cache_read_tokens: number
-  cache_creation_tokens: number
-  reasoning_tokens: number
-  audio_seconds: number
-  tts_characters: number
-  call_count: number
-  input_cost_micro_usd?: number
-  output_cost_micro_usd?: number
-  cache_read_cost_micro_usd?: number
-  cache_creation_cost_micro_usd?: number
-  reasoning_cost_micro_usd?: number
-  audio_cost_micro_usd?: number
-  tts_cost_micro_usd?: number
-  total_cost_micro_usd?: number
-  costs?: UsageCosts
-}
-
-type WorkspaceSourceRow = BreakdownRow & {
-  rowKind: 'call_import' | 'workspace_resource'
-  hint: string
-}
-
-const NON_COMPOSITE_RESOURCE_TYPES = new Set([
-  'call_import',
-  'call_import_evaluation',
-])
-
-type TableRow = BreakdownRow | WorkspaceSourceRow
-
-const EMPTY_METRICS = {
-  prompt_tokens: 0,
-  completion_tokens: 0,
-  total_tokens: 0,
-  cache_read_tokens: 0,
-  cache_creation_tokens: 0,
-  reasoning_tokens: 0,
-  audio_seconds: 0,
-  tts_characters: 0,
-  call_count: 0,
-  input_cost_micro_usd: 0,
-  output_cost_micro_usd: 0,
-  cache_read_cost_micro_usd: 0,
-  cache_creation_cost_micro_usd: 0,
-  reasoning_cost_micro_usd: 0,
-  audio_cost_micro_usd: 0,
-  tts_cost_micro_usd: 0,
-  total_cost_micro_usd: 0,
-  costs: {
-    input_cost_usd: 0,
-    output_cost_usd: 0,
-    cache_read_cost_usd: 0,
-    cache_write_cost_usd: 0,
-    reasoning_cost_usd: 0,
-    audio_cost_usd: 0,
-    tts_cost_usd: 0,
-    total_cost_usd: 0,
-    currency: 'USD',
-    has_unpriced_usage: false,
-  },
-}
-
-function compositeRowHeadline(row: WorkspaceSourceRow): string {
-  if (row.rowKind === 'call_import') return CALL_IMPORT_BATCH_HEADLINE
-  const section = row.product_section || ''
-  return (
-    PRODUCT_SECTION_HEADLINES[section] ||
-    row.product_section_label ||
-    section ||
-    'Other'
-  )
-}
-
-function usableResourceLabel(label: string | null | undefined): string | undefined {
-  if (!label || label === 'Unscoped') return undefined
-  return label
-}
-
-function compositeRowTitle(row: WorkspaceSourceRow, options?: FilterOptions): string {
-  if (row.rowKind === 'call_import') {
-    return row.call_import_label || 'Call import batch'
-  }
-  const fromFilters = row.resource_id
-    ? options?.resources?.find((r) => idKey(r.id) === idKey(row.resource_id))?.label
-    : undefined
-  return (
-    usableResourceLabel(row.resource_label) ||
-    fromFilters ||
-    row.product_section_label ||
-    'Unscoped usage'
-  )
-}
-
-function sortRows(rows: BreakdownRow[]): BreakdownRow[] {
-  return [...rows].sort((a, b) => b.total_tokens - a.total_tokens)
-}
-
-function formatNumber(value: number): string {
-  return new Intl.NumberFormat().format(value || 0)
-}
-
-function rowCostUsd(row: Pick<BreakdownRow, 'costs' | 'total_cost_micro_usd'>): number {
-  if (row.costs?.total_cost_usd != null) return row.costs.total_cost_usd
-  return Number(row.total_cost_micro_usd || 0) / 1_000_000
-}
-
-function formatAudio(seconds: number): string {
-  const total = Math.max(0, Math.floor(seconds || 0))
-  if (total < 60) return `${total}s`
-  const mins = Math.floor(total / 60)
-  const secs = total % 60
-  if (mins < 60) return secs ? `${mins}m ${secs}s` : `${mins}m`
-  const hours = Math.floor(mins / 60)
-  const remMins = mins % 60
-  return remMins ? `${hours}h ${remMins}m` : `${hours}h`
-}
-
-function rowHasUsageForKind(row: BreakdownRow, kind: Kind): boolean {
-  if (!kind) return true
-  if (kind === 'llm') {
-    return (row.total_tokens ?? 0) > 0 || (row.call_count ?? 0) > 0
-  }
-  if (kind === 'stt') {
-    return (row.audio_seconds ?? 0) > 0 || (row.call_count ?? 0) > 0
-  }
-  if (kind === 'tts') {
-    return (row.tts_characters ?? 0) > 0 || (row.call_count ?? 0) > 0
-  }
-  return true
-}
-
-function filterRowsForUsageKind(rows: BreakdownRow[], kind: Kind): BreakdownRow[] {
-  if (!kind) return rows
-  return rows.filter((row) => rowHasUsageForKind(row, kind))
-}
-
-function rowHasAnyUsage(row: BreakdownRow): boolean {
-  return (
-    (row.total_tokens ?? 0) > 0 ||
-    (row.call_count ?? 0) > 0 ||
-    (row.audio_seconds ?? 0) > 0 ||
-    (row.tts_characters ?? 0) > 0
-  )
-}
-
-function isUsageScopeActive(
-  workspaceId: string,
-  callImportId: string,
-  evaluationId: string,
-  dataset: string,
-  tagId: string,
-  usageKind: Kind,
-  model: string,
-  productSection: string,
-): boolean {
-  return Boolean(
-    workspaceId ||
-      callImportId ||
-      evaluationId ||
-      dataset ||
-      tagId ||
-      usageKind ||
-      model ||
-      productSection,
-  )
-}
-
-/** Drop zero rows when any filter is active; kind filter uses kind-specific metrics. */
-function filterTableRows(
-  rows: BreakdownRow[],
-  usageKind: Kind,
-  scopeActive: boolean,
-): BreakdownRow[] {
-  if (usageKind) return filterRowsForUsageKind(rows, usageKind)
-  if (scopeActive) return rows.filter(rowHasAnyUsage)
-  return rows
-}
-
-function drillGroupBy(
-  workspaceId: string,
-  callImportId: string,
-  evaluationId: string,
-  model: string,
-  productSection: string,
-): DrillGroupBy {
-  if (productSection && model) return 'usage_kind'
-  if (productSection) return 'model'
-  if (evaluationId && model) return 'usage_kind'
-  if (evaluationId) return 'model'
-  if (callImportId) return 'resource'
-  if (workspaceId) return 'call_import'
-  return 'workspace'
-}
-
-function enrichCallImportRows(
-  rawRows: BreakdownRow[],
-  options?: FilterOptions,
-): BreakdownRow[] {
-  const labelById = new Map(
-    (options?.call_imports ?? []).map((c) => [idKey(c.id), c.label]),
-  )
-  return sortRows(
-    rawRows
-      .filter((r) => r.call_import_id)
-      .map((row) => {
-        const key = idKey(row.call_import_id)
-        return {
-          ...row,
-          call_import_label:
-            labelById.get(key) || row.call_import_label || 'Call import',
-        }
-      }),
-  )
-}
-
-function buildWorkspaceCompositeRows(
-  callImportRaw: BreakdownRow[],
-  resourceRaw: BreakdownRow[],
-  options?: FilterOptions,
-  padMissingRows = true,
-): WorkspaceSourceRow[] {
-  const resourceLabelById = new Map(
-    (options?.resources ?? []).map((r) => [idKey(r.id), r.label]),
-  )
-  const importRows = enrichCallImportRows(callImportRaw, options)
-  const rows: WorkspaceSourceRow[] = []
-  const shownImportIds = new Set<string>()
-
-  for (const row of importRows) {
-    if (!row.call_import_id) continue
-    shownImportIds.add(idKey(row.call_import_id))
-    rows.push({
-      ...row,
-      rowKind: 'call_import',
-      hint: CALL_IMPORT_HINT,
-    })
-  }
-
-  if (padMissingRows) {
-    for (const ci of options?.call_imports ?? []) {
-      const key = idKey(ci.id)
-      if (shownImportIds.has(key)) continue
-      shownImportIds.add(key)
-      rows.push({
-        call_import_id: ci.id,
-        call_import_label: ci.label,
-        ...EMPTY_METRICS,
-        rowKind: 'call_import',
-        hint: CALL_IMPORT_HINT,
-      })
-    }
-  }
-
-  for (const row of resourceRaw) {
-    const section = row.product_section
-    if (!section || CALL_IMPORT_PRODUCT_SECTIONS.has(section)) continue
-    if (NON_COMPOSITE_RESOURCE_TYPES.has(row.resource_type || '')) continue
-    if (
-      row.total_tokens === 0 &&
-      row.call_count === 0 &&
-      !row.audio_seconds &&
-      !row.tts_characters
-    ) {
-      continue
-    }
-    if (!rowHasAnyUsage(row)) {
-      continue
-    }
-    rows.push({
-      ...row,
-      rowKind: 'workspace_resource',
-      product_section: section,
-      product_section_label: row.product_section_label || section,
-      resource_label:
-        resourceLabelById.get(idKey(row.resource_id)) ||
-        usableResourceLabel(row.resource_label),
-      hint: PRODUCT_SECTION_HINTS[section] || 'Product usage',
-    })
-  }
-
-  return sortRows(rows) as WorkspaceSourceRow[]
-}
-
-function drillColumnLabel(groupBy: DrillGroupBy, composite = false): string {
-  if (composite) return 'Source'
-  if (groupBy === 'workspace') return 'Workspace'
-  if (groupBy === 'call_import') return 'Call import'
-  if (groupBy === 'resource') return 'Evaluation run'
-  if (groupBy === 'product_section') return 'Product area'
-  if (groupBy === 'model') return 'Model'
-  return 'Kind'
-}
-
-function rowLabel(groupBy: DrillGroupBy, row: BreakdownRow, options?: FilterOptions): string {
-  if (groupBy === 'workspace') return row.workspace_name || 'Unknown'
-  if (groupBy === 'call_import') return row.call_import_label || 'Call import'
-  if (groupBy === 'resource') {
-    const fromFilters = row.resource_id
-      ? options?.resources?.find((r) => idKey(r.id) === idKey(row.resource_id))?.label
-      : undefined
-    return (
-      usableResourceLabel(row.resource_label) || fromFilters || 'Unscoped'
-    )
-  }
-  if (groupBy === 'model') return row.model || '—'
-  if (groupBy === 'product_section')
-    return row.product_section_label || row.product_section || '—'
-  if (row.usage_kind === 'stt') return 'STT'
-  if (row.usage_kind === 'llm') return 'LLM'
-  if (row.usage_kind === 'tts') return 'TTS'
-  return row.usage_kind || '—'
-}
-
-function tableRowLabel(
-  groupBy: DrillGroupBy,
-  row: TableRow,
-  options?: FilterOptions,
-): string {
-  if ('rowKind' in row) {
-    if (row.rowKind === 'call_import') return row.call_import_label || 'Call import batch'
-    return compositeRowTitle(row, options)
-  }
-  return rowLabel(groupBy, row, options)
-}
-
-function idInOptions(
-  id: string,
-  options: Array<{ id: string }> | undefined,
-): boolean {
-  if (!id || !options?.length) return false
-  const key = idKey(id)
-  return options.some((o) => idKey(o.id) === key)
-}
-
-function idKey(id: string | null | undefined): string {
-  return id ? String(id).toLowerCase() : ''
-}
-
-function mergeDrillRows(
-  groupBy: DrillGroupBy,
-  rawRows: BreakdownRow[],
-  options?: FilterOptions,
-  padMissingRows = true,
-): BreakdownRow[] {
-  if (groupBy === 'workspace' && options?.workspaces?.length && padMissingRows) {
-    const byId = new Map(
-      rawRows
-        .filter((r) => r.workspace_id)
-        .map((r) => [idKey(r.workspace_id), r]),
-    )
-    const merged: BreakdownRow[] = options.workspaces.map((ws) => ({
-      workspace_id: ws.id,
-      workspace_name: ws.name,
-      ...(byId.get(idKey(ws.id)) ?? EMPTY_METRICS),
-    }))
-    for (const row of rawRows) {
-      if (!row.workspace_id) {
-        merged.push({
-          ...row,
-          workspace_name: row.workspace_name || 'No workspace',
-        })
-      }
-    }
-    return sortRows(merged)
-  }
-
-  if (groupBy === 'call_import') {
-    if (rawRows.length === 0) return []
-
-    const labelById = new Map(
-      (options?.call_imports ?? []).map((c) => [idKey(c.id), c.label]),
-    )
-    const shown = new Set<string>()
-    const merged: BreakdownRow[] = []
-
-    for (const row of rawRows) {
-      if (row.call_import_id) {
-        const key = idKey(row.call_import_id)
-        shown.add(key)
-        merged.push({
-          ...row,
-          call_import_label:
-            labelById.get(key) || row.call_import_label || 'Call import',
-        })
-      }
-    }
-
-    if (padMissingRows) {
-      for (const ci of options?.call_imports ?? []) {
-        const key = idKey(ci.id)
-        if (!shown.has(key)) {
-          merged.push({
-            call_import_id: ci.id,
-            call_import_label: ci.label,
-            ...EMPTY_METRICS,
-          })
-        }
-      }
-    }
-
-    return sortRows(merged)
-  }
-
-  if (groupBy === 'resource') {
-    if (rawRows.length === 0) return []
-
-    const evalLabelById = new Map(
-      (options?.evaluations ?? []).map((e) => [idKey(e.id), e.label]),
-    )
-    const resourceLabelById = new Map(
-      (options?.resources ?? []).map((r) => [idKey(r.id), r.label]),
-    )
-    const shown = new Set<string>()
-    const merged: BreakdownRow[] = []
-
-    for (const row of rawRows) {
-      if (row.resource_id) {
-        const key = idKey(row.resource_id)
-        shown.add(key)
-        merged.push({
-          ...row,
-          resource_label:
-            resourceLabelById.get(key) ||
-            evalLabelById.get(key) ||
-            usableResourceLabel(row.resource_label) ||
-            'Evaluation',
-        })
-      }
-    }
-
-    if (padMissingRows) {
-      for (const ev of options?.evaluations ?? []) {
-        const key = idKey(ev.id)
-        if (!shown.has(key)) {
-          merged.push({
-            resource_id: ev.id,
-            resource_label: ev.label,
-            ...EMPTY_METRICS,
-          })
-        }
-      }
-    }
-
-    return sortRows(merged)
-  }
-
-  if (groupBy === 'model' && options?.models?.length && padMissingRows) {
-    if (rawRows.length === 0) return []
-    const byName = new Map(
-      rawRows.filter((r) => r.model).map((r) => [r.model!, r]),
-    )
-    const merged = options.models.map((name) => ({
-      model: name,
-      ...(byName.get(name) ?? EMPTY_METRICS),
-    }))
-    return sortRows(merged)
-  }
-
-  if (groupBy === 'usage_kind') {
-    return sortRows(
-      rawRows.filter((row) => rowHasUsageForKind(row, row.usage_kind as Kind)),
-    )
-  }
-
-  return sortRows(rawRows)
-}
-
-export default function Usage() {
-  const [searchParams, setSearchParams] = useSearchParams()
-  const defaultRange = useMemo(() => defaultUsageDateRange(), [])
-  const usageTimezone = useMemo(() => getUsageTimezone(), [])
-
-  const start = searchParams.get('start') || defaultRange.start
-  const end = searchParams.get('end') || defaultRange.end
-  const workspaceId = searchParams.get('workspace_id') || ''
-  const callImportId = searchParams.get('call_import_id') || ''
-  const dataset = searchParams.get('dataset') || ''
-  const tagId = searchParams.get('tag_id') || ''
-  const evaluationId = searchParams.get('resource_id') || ''
-  const model = searchParams.get('model') || ''
-  const usageKind = (searchParams.get('usage_kind') as Kind) || ''
-  const productSection = searchParams.get('product_section') || ''
-  const isAdmin = useIsAdmin()
-  const { usagePolicy, isLoaded: licenseLoaded, fetchLicense } = useLicenseStore()
-  const showOssUsageNotice = licenseLoaded && !usagePolicy.extended_history
-  const maxHistoryDays = !licenseLoaded
-    ? null
-    : usagePolicy.extended_history
-      ? null
-      : usagePolicy.max_history_days ?? 7
-  const [costBreakdownOpen, setCostBreakdownOpen] = useState(false)
-  const [displayCurrency, setDisplayCurrency] = useState<UsageDisplayCurrency>(() =>
-    getUsageDisplayCurrency(),
-  )
-
-  const { data: fxRate } = useQuery({
-    queryKey: ['org-usage', 'fx-rate'],
-    queryFn: () => apiClient.getOrgUsageFxRate(),
-    staleTime: 60 * 60 * 1000,
-  })
-  const inrRate = fxRate?.rate ?? 83
-  const formatCostUsd = (usd?: number | null) =>
-    formatUsageCostUsd(usd, displayCurrency, inrRate)
-
-  useEffect(() => {
-    if (!licenseLoaded) {
-      void fetchLicense()
-    }
-  }, [licenseLoaded, fetchLicense])
-
-  useEffect(() => {
-    if (!licenseLoaded || usagePolicy.extended_history) return
-    const maxDays = usagePolicy.max_history_days ?? 7
-    if (!isRangeWithinMaxDays(start, end, maxDays)) {
-      const r = rangeForDays(maxDays)
-      setSearchParams((prev) => {
-        const next = new URLSearchParams(prev)
-        next.set('start', r.start)
-        next.set('end', r.end)
-        return next
-      })
-    }
-  }, [licenseLoaded, usagePolicy.extended_history, usagePolicy.max_history_days, start, end, setSearchParams])
-
-  const showWorkspaceComposite =
-    Boolean(workspaceId) &&
-    !callImportId &&
-    !evaluationId &&
-    !productSection
-
-  const groupBy = drillGroupBy(
-    workspaceId,
-    callImportId,
-    evaluationId,
-    model,
-    productSection,
-  )
-
-  const setParams = (updates: Record<string, string | null>) => {
-    const next = new URLSearchParams(searchParams)
-    for (const [key, value] of Object.entries(updates)) {
-      if (!value) next.delete(key)
-      else next.set(key, value)
-    }
-    setSearchParams(next)
-  }
-
-  const scopeParams = {
-    start,
-    end,
-    tz: usageTimezone,
-    workspace_id: workspaceId || undefined,
-    call_import_id: callImportId || undefined,
-    dataset: dataset || undefined,
-    tag_id: tagId || undefined,
-    product_section: productSection || undefined,
-    usage_kind: usageKind || undefined,
-    model: model || undefined,
-    resource_id: evaluationId || undefined,
-  }
-
-  const dataParams = {
-    ...scopeParams,
-    evaluation_id: callImportId ? evaluationId || undefined : undefined,
-  }
-
-  const usageQueryDefaults = {
-    staleTime: 60 * 1000,
-  }
-
-  const { data: summary, isLoading: summaryLoading, isFetching: summaryFetching } = useQuery({
-    queryKey: ['org-usage', 'summary', dataParams],
-    queryFn: () => apiClient.getOrgUsageSummary(dataParams),
-    ...usageQueryDefaults,
-    placeholderData: keepPreviousData,
-  })
-
-  const usageStatsLoading = summaryLoading
-
-  const {
-    data: breakdown,
-    isLoading: breakdownLoading,
-    isFetching: breakdownFetching,
-  } = useQuery({
-    queryKey: ['org-usage', 'breakdown', groupBy, dataParams],
-    queryFn: () =>
-      apiClient.getOrgUsageBreakdown({
-        ...dataParams,
-        group_by: groupBy,
-        limit: 100,
-      }),
-    enabled: !showWorkspaceComposite,
-    ...usageQueryDefaults,
-    placeholderData: (previousData, previousQuery) => {
-      if (!previousQuery || previousQuery.queryKey[2] !== groupBy) return undefined
-      return previousData
-    },
-  })
-
-  const {
-    data: importBreakdown,
-    isLoading: importBreakdownLoading,
-    isFetching: importBreakdownFetching,
-  } = useQuery({
-    queryKey: ['org-usage', 'breakdown', 'call_import', dataParams],
-    queryFn: () =>
-      apiClient.getOrgUsageBreakdown({
-        ...dataParams,
-        group_by: 'call_import',
-        limit: 100,
-      }),
-    enabled: showWorkspaceComposite,
-    ...usageQueryDefaults,
-  })
-
-  const {
-    data: resourceBreakdown,
-    isLoading: resourceBreakdownLoading,
-    isFetching: resourceBreakdownFetching,
-  } = useQuery({
-    queryKey: ['org-usage', 'breakdown', 'resource', dataParams],
-    queryFn: () =>
-      apiClient.getOrgUsageBreakdown({
-        ...dataParams,
-        group_by: 'resource',
-        limit: 100,
-      }),
-    enabled: showWorkspaceComposite,
-    ...usageQueryDefaults,
-  })
-
-  const { data: filterOptions, isFetching: filtersLoading } = useQuery({
-    queryKey: ['org-usage', 'filters', scopeParams],
-    queryFn: () => apiClient.getOrgUsageFilters(scopeParams),
-    enabled: true,
-    staleTime: 60 * 1000,
-    placeholderData: (previousData, previousQuery) => {
-      if (!previousQuery) return undefined
-      const prevScope = previousQuery.queryKey[2] as typeof scopeParams
-      if (JSON.stringify(prevScope) !== JSON.stringify(scopeParams)) return undefined
-      return previousData
-    },
-  })
-
-  useEffect(() => {
-    if (!filterOptions) return
-    const updates: Record<string, string | null> = {}
-    if (
-      workspaceId &&
-      !idInOptions(workspaceId, filterOptions.workspaces)
-    ) {
-      updates.workspace_id = null
-      updates.call_import_id = null
-      updates.resource_id = null
-      updates.product_section = null
-    }
-    if (
-      callImportId &&
-      !idInOptions(callImportId, filterOptions.call_imports)
-    ) {
-      updates.call_import_id = null
-      updates.resource_id = null
-      updates.product_section = null
-    }
-    if (
-      evaluationId &&
-      !idInOptions(evaluationId, filterOptions.evaluations) &&
-      !idInOptions(evaluationId, filterOptions.resources)
-    ) {
-      updates.resource_id = null
-      updates.product_section = null
-    }
-    if (model && !filterOptions.models?.includes(model)) {
-      updates.model = null
-    }
-    if (
-      usageKind &&
-      !filterOptions.usage_kinds?.some((k) => k.id === usageKind)
-    ) {
-      updates.usage_kind = null
-    }
-    if (
-      productSection &&
-      !filterOptions.product_sections?.some((s) => s.id === productSection)
-    ) {
-      updates.product_section = null
-    }
-    if (dataset && !filterOptions.datasets?.includes(dataset)) {
-      updates.dataset = null
-    }
-    if (tagId && !idInOptions(tagId, filterOptions.tags)) {
-      updates.tag_id = null
-    }
-    if (Object.keys(updates).length > 0) setParams(updates)
-  }, [
-    filterOptions,
-    workspaceId,
-    callImportId,
-    dataset,
-    tagId,
-    evaluationId,
-    model,
-    usageKind,
-    productSection,
-  ])
-
-  const breakdownMatchesLevel = breakdown?.group_by === groupBy
-  const rawRows = useMemo((): BreakdownRow[] => {
-    if (!breakdown || !breakdownMatchesLevel) return []
-    return breakdown.rows as BreakdownRow[]
-  }, [breakdown, breakdownMatchesLevel])
-
-  const mergeOptions = filterOptions
-  const scopeActive = isUsageScopeActive(
-    workspaceId,
-    callImportId,
-    evaluationId,
-    dataset,
-    tagId,
-    usageKind,
-    model,
-    productSection,
-  )
-  const padMissingRows = !scopeActive
-  const filteredRawRows = useMemo(
-    () => filterTableRows(rawRows, usageKind, scopeActive),
-    [rawRows, usageKind, scopeActive],
-  )
-  const rows: TableRow[] = useMemo(() => {
-    if (showWorkspaceComposite) {
-      const importReady = importBreakdown?.group_by === 'call_import'
-      const resourceReady = resourceBreakdown?.group_by === 'resource'
-      if (!importReady && !resourceReady) return []
-      return buildWorkspaceCompositeRows(
-        importReady
-          ? filterTableRows(importBreakdown.rows as BreakdownRow[], usageKind, scopeActive)
-          : [],
-        resourceReady
-          ? filterTableRows(resourceBreakdown.rows as BreakdownRow[], usageKind, scopeActive)
-          : [],
-        mergeOptions,
-        padMissingRows,
-      )
-    }
-    return mergeDrillRows(groupBy, filteredRawRows, mergeOptions, padMissingRows)
-  }, [
-    showWorkspaceComposite,
-    importBreakdown,
-    resourceBreakdown,
-    groupBy,
-    filteredRawRows,
-    mergeOptions,
-    padMissingRows,
-    usageKind,
-    scopeActive,
-  ])
-
-  const breakdownStale =
-    !showWorkspaceComposite && breakdownFetching && !breakdownMatchesLevel
-  const tableLoading = showWorkspaceComposite
-    ? (importBreakdownLoading || resourceBreakdownLoading) &&
-      !importBreakdown &&
-      !resourceBreakdown
-    : breakdownLoading && !breakdown
-  const tableFetching = showWorkspaceComposite
-    ? importBreakdownFetching || resourceBreakdownFetching
-    : breakdownFetching
-  const totals = summary?.totals
-  const estimatedTotalCost =
-    totals?.costs?.total_cost_usd ?? (totals?.total_cost_micro_usd || 0) / 1_000_000
-  const showCostBreakdown =
-    estimatedTotalCost > 0 || Boolean(totals?.costs?.has_unpriced_usage)
-  const showAudio = Boolean(totals?.audio_seconds)
-  const showTts =
-    Boolean(totals?.tts_characters) ||
-    rows.some((r) => Boolean(r.tts_characters))
-
-  const showTruncation =
-    (!showWorkspaceComposite &&
-      breakdownMatchesLevel &&
-      Boolean(breakdown?.truncated_at_limit)) ||
-    (showWorkspaceComposite &&
-      Boolean(
-        importBreakdown?.truncated_at_limit || resourceBreakdown?.truncated_at_limit,
-      ))
-
-  const workspaceLabel =
-    filterOptions?.workspaces?.find((w) => idKey(w.id) === idKey(workspaceId))?.name
-  const callImportLabel =
-    filterOptions?.call_imports?.find((c) => idKey(c.id) === idKey(callImportId))?.label
-  const evaluationLabel =
-    filterOptions?.resources?.find((r) => idKey(r.id) === idKey(evaluationId))?.label ||
-    filterOptions?.evaluations?.find((e) => idKey(e.id) === idKey(evaluationId))?.label
-
-  const productSectionLabel =
-    filterOptions?.product_sections?.find((s) => s.id === productSection)?.label
-
-  const scopeSubtitle = model
-    ? model
-    : evaluationId
-      ? evaluationLabel || 'Evaluation'
-      : callImportId
-        ? callImportLabel || 'Call import'
-        : productSection
-          ? productSectionLabel || 'Product area'
-          : workspaceId
-            ? workspaceLabel || 'Workspace'
-            : 'Organization'
-
-  const levelHint = (() => {
-    if (showWorkspaceComposite) {
-      return 'Call import batches and other product usage — click a row to drill down'
-    }
-    if (groupBy === 'workspace') return 'Click a workspace to drill down'
-    if (groupBy === 'call_import') return 'Click a call import to see evaluation runs'
-    if (groupBy === 'product_section') return 'Click a product area to see models used'
-    if (groupBy === 'resource') return 'Click an evaluation to see models used'
-    if (groupBy === 'model') return 'Click a model to see usage by kind'
-    return 'Token totals by LLM / STT / TTS'
-  })()
-
-  const drillCrumbs = [
-    {
-      label: 'Organization',
-      onClick:
-        workspaceId ||
-        callImportId ||
-        evaluationId ||
-        model ||
-        productSection
-          ? () =>
-              setParams({
-                workspace_id: null,
-                call_import_id: null,
-                resource_id: null,
-                model: null,
-                usage_kind: null,
-                product_section: null,
-              })
-          : undefined,
-    },
-    ...(workspaceId
-      ? [
-          {
-            label: workspaceLabel || 'Workspace',
-            onClick:
-              callImportId || evaluationId || model || productSection
-                ? () =>
-                    setParams({
-                      call_import_id: null,
-                      resource_id: null,
-                      model: null,
-                      usage_kind: null,
-                      product_section: null,
-                    })
-                : undefined,
-          },
-        ]
-      : []),
-    ...(productSection
-      ? [
-          {
-            label: productSectionLabel || 'Product area',
-            onClick:
-              model
-                ? () => setParams({ model: null, usage_kind: null })
-                : undefined,
-          },
-        ]
-      : []),
-    ...(callImportId
-      ? [
-          {
-            label: callImportLabel || 'Call import',
-            onClick:
-              evaluationId || model
-                ? () =>
-                    setParams({
-                      resource_id: null,
-                      model: null,
-                      usage_kind: null,
-                      product_section: null,
-                    })
-                : undefined,
-          },
-        ]
-      : []),
-    ...(evaluationId
-      ? [
-          {
-            label: evaluationLabel || 'Evaluation',
-            onClick: model
-              ? () => setParams({ model: null, usage_kind: null })
-              : undefined,
-          },
-        ]
-      : []),
-    ...(model ? [{ label: model }] : []),
-  ]
-
-  const handleWorkspaceChange = (id: string) => {
-    setParams({
-      workspace_id: id || null,
-      call_import_id: null,
-      resource_id: null,
-      model: null,
-      usage_kind: null,
-      product_section: null,
-    })
-  }
-
-  const handleCallImportChange = (id: string) => {
-    setParams({
-      call_import_id: id || null,
-      resource_id: null,
-      model: null,
-      usage_kind: null,
-      product_section: null,
-    })
-  }
-
-  const handleClearAll = () => {
-    setParams({
-      workspace_id: null,
-      call_import_id: null,
-      dataset: null,
-      tag_id: null,
-      resource_id: null,
-      usage_kind: null,
-      model: null,
-      product_section: null,
-    })
-  }
-
-  const handleRowDrill = (row: TableRow) => {
-    if ('rowKind' in row) {
-      if (row.rowKind === 'call_import' && row.call_import_id) {
-        setParams({
-          call_import_id: row.call_import_id,
-          resource_id: null,
-          model: null,
-          usage_kind: null,
-          product_section: null,
-        })
-        return
-      }
-      if (row.rowKind === 'workspace_resource' && row.product_section) {
-        setParams({
-          product_section: row.product_section,
-          call_import_id: null,
-          resource_id: row.resource_id || null,
-          model: null,
-          usage_kind: null,
-        })
-        return
-      }
-    }
-    if (groupBy === 'workspace' && row.workspace_id) {
-      setParams({
-        workspace_id: row.workspace_id,
-        call_import_id: null,
-        resource_id: null,
-        model: null,
-        usage_kind: null,
-        product_section: null,
-      })
-      return
-    }
-    if (groupBy === 'call_import' && row.call_import_id) {
-      setParams({
-        call_import_id: row.call_import_id,
-        resource_id: null,
-        model: null,
-        usage_kind: null,
-        product_section: null,
-      })
-      return
-    }
-    if (groupBy === 'product_section' && row.product_section) {
-      setParams({
-        product_section: row.product_section,
-        call_import_id: null,
-        resource_id: null,
-        model: null,
-        usage_kind: null,
-      })
-      return
-    }
-    if (groupBy === 'resource' && row.resource_id) {
-      setParams({
-        resource_id: row.resource_id,
-        model: null,
-        usage_kind: null,
-        product_section: null,
-      })
-      return
-    }
-    if (groupBy === 'model' && row.model) {
-      setParams({ model: row.model, usage_kind: null })
-    }
-  }
-
-  const isRowDrillable = (row: TableRow): boolean => {
-    if ('rowKind' in row) {
-      if (row.rowKind === 'call_import') return Boolean(row.call_import_id)
-      return Boolean(row.product_section)
-    }
-    if (groupBy === 'workspace') return Boolean(row.workspace_id)
-    if (groupBy === 'call_import') return Boolean(row.call_import_id)
-    if (groupBy === 'product_section') return Boolean(row.product_section)
-    if (groupBy === 'resource') return Boolean(row.resource_id)
-    if (groupBy === 'model') return Boolean(row.model)
-    return false
-  }
-
-  return (
-    <div className="space-y-6">
-      <div className="flex items-start justify-between gap-4">
-        <div className="min-w-0">
-          <h1 className="text-2xl font-bold text-gray-900 flex items-center gap-2">
-            <Activity className="h-6 w-6 text-primary-600 shrink-0" />
-            Usage
-          </h1>
-          <p className="text-sm text-gray-500 mt-1">
-            Cards show usage for <span className="font-medium text-gray-700">{scopeSubtitle}</span>.
-            Drill down: workspaces → call imports or product areas → evaluations / models.
-          </p>
-        </div>
-        <div className="flex flex-col items-end gap-1.5 shrink-0">
-          <div className="flex items-center gap-3">
-            <div
-              className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
-              role="group"
-              aria-label="Display currency"
-            >
-              {(['USD', 'INR'] as const).map((currency) => (
-                <button
-                  key={currency}
-                  type="button"
-                  onClick={() => {
-                    setDisplayCurrency(currency)
-                    setUsageDisplayCurrency(currency)
-                  }}
-                  className={`rounded-md px-2 py-1 transition-colors ${
-                    displayCurrency === currency
-                      ? 'bg-gray-900 text-white'
-                      : 'text-gray-600 hover:bg-gray-50'
-                  }`}
-                >
-                  {currency}
-                </button>
-              ))}
-            </div>
-            {showCostBreakdown ? (
-              <button
-                type="button"
-                onClick={() => setCostBreakdownOpen(true)}
-                className="inline-flex items-center gap-1.5 rounded-lg border border-gray-200 bg-white px-2.5 py-1.5 text-xs font-medium text-gray-700 shadow-sm transition-colors hover:border-gray-300 hover:bg-gray-50 hover:text-gray-900"
-                title={formatCostUsd(estimatedTotalCost)}
-              >
-                <CircleDollarSign className="h-4 w-4 shrink-0 text-gray-500" />
-                <span className="whitespace-nowrap">Cost breakdown</span>
-              </button>
-            ) : null}
-            {isAdmin && licenseLoaded && usagePolicy.extended_history ? (
-              <Link
-                to="/usage/pricing"
-                className="text-sm text-primary-600 hover:text-primary-700 whitespace-nowrap"
-              >
-                Pricing overrides
-              </Link>
-            ) : null}
-          </div>
-          {summary?.last_updated_at ? (
-            <p className="text-[11px] text-gray-400 whitespace-nowrap">
-              Updated {new Date(summary.last_updated_at).toLocaleString()}
-            </p>
-          ) : null}
-        </div>
-      </div>
-
-      {showOssUsageNotice ? (
-        <div className="rounded-lg border border-amber-200 bg-amber-50 px-4 py-3 text-sm text-amber-900">
-          Showing the last {maxHistoryDays} days of usage history. Set{' '}
-          <code className="rounded bg-amber-100 px-1.5 py-0.5 text-xs font-mono">
-            EFFICIENTAI_LICENSE
-          </code>{' '}
-          on your server with any enterprise feature to unlock extended history.
-        </div>
-      ) : null}
-
-      <div
-        className={`space-y-3 transition-opacity ${summaryFetching ? 'opacity-70' : ''}`}
-      >
-        <div className="grid grid-cols-2 gap-3 md:grid-cols-4">
-          <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={usageStatsLoading} />
-          <StatCard label="Output tokens" value={totals?.completion_tokens} loading={usageStatsLoading} />
-          <StatCard label="Total tokens" value={totals?.total_tokens} loading={usageStatsLoading} />
-          <StatCard label="LLM calls" value={totals?.call_count} loading={usageStatsLoading} />
-        </div>
-
-        <div className="grid grid-cols-2 gap-3 md:grid-cols-4 md:items-stretch">
-          <div className="col-span-2 grid grid-cols-2 gap-3 sm:grid-cols-3 md:col-span-3">
-            {showAudio ? (
-              <StatCard
-                label="STT audio"
-                valueLabel={formatAudio(totals?.audio_seconds || 0)}
-                loading={usageStatsLoading}
-              />
-            ) : null}
-            {showTts ? (
-              <StatCard label="TTS characters" value={totals?.tts_characters} loading={usageStatsLoading} />
-            ) : null}
-            {(totals?.cache_read_tokens || 0) > 0 ? (
-              <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={usageStatsLoading} />
-            ) : null}
-            {(totals?.cache_creation_tokens || 0) > 0 ? (
-              <StatCard
-                label="Cache write"
-                value={totals?.cache_creation_tokens}
-                loading={usageStatsLoading}
-              />
-            ) : null}
-            {(totals?.reasoning_tokens || 0) > 0 ? (
-              <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={usageStatsLoading} />
-            ) : null}
-          </div>
-          <div className="col-span-2 md:col-span-1">
-            <StatCard
-              label="Estimated cost"
-              valueLabel={formatCostUsd(estimatedTotalCost)}
-              loading={usageStatsLoading}
-              emphasize
-            />
-          </div>
-        </div>
-      </div>
-
-      <UsageFiltersBar
-        start={start}
-        end={end}
-        workspaceId={workspaceId}
-        callImportId={callImportId}
-        evaluationId={evaluationId}
-        dataset={dataset}
-        tagId={tagId}
-        usageKind={usageKind}
-        model={model}
-        productSection={productSection}
-        options={filterOptions}
-        filtersLoading={filtersLoading}
-        onDateApply={(s, e) => setParams({ start: s, end: e })}
-        onWorkspaceChange={handleWorkspaceChange}
-        onCallImportChange={handleCallImportChange}
-        onDatasetChange={(v) =>
-          setParams({
-            dataset: v || null,
-            call_import_id: null,
-            resource_id: null,
-          })
-        }
-        onTagChange={(v) =>
-          setParams({
-            tag_id: v || null,
-            call_import_id: null,
-            resource_id: null,
-          })
-        }
-        onEvaluationChange={(id) => {
-          if (id.startsWith(USAGE_SECTION_SOURCE_PREFIX)) {
-            const section = id.slice(USAGE_SECTION_SOURCE_PREFIX.length)
-            setParams({
-              product_section: section || null,
-              resource_id: null,
-              call_import_id: null,
-              model: null,
-              usage_kind: null,
-            })
-            return
-          }
-          const resource = filterOptions?.resources?.find(
-            (r) => idKey(r.id) === idKey(id),
-          )
-          setParams({
-            resource_id: id || null,
-            product_section: id ? resource?.product_section || null : null,
-            model: null,
-            usage_kind: null,
-          })
-        }}
-        onUsageKindChange={(k) => setParams({ usage_kind: k || null })}
-        onModelChange={(v) => setParams({ model: v || null })}
-        onClearAll={handleClearAll}
-        maxHistoryDays={maxHistoryDays}
-      />
-
-      <Card shadow="sm" className="border border-gray-200 ring-1 ring-[#fde047]/25">
-        <div className="border-b border-gray-100 px-4 py-3 space-y-2">
-          <UsageDrillPath crumbs={drillCrumbs} levelLabel={levelHint} />
-          {showTruncation ? (
-            <p className="text-xs text-amber-700">
-              Showing the first 100 rows for this level. Narrow the date range or drill
-              further for complete detail.
-            </p>
-          ) : null}
-        </div>
-        <CardBody className="p-0 overflow-x-auto max-h-[min(70vh,640px)] overflow-y-auto custom-scrollbar">
-          {tableLoading || breakdownStale ? (
-            <div className="flex justify-center py-12">
-              <Spinner />
-            </div>
-          ) : rows.length === 0 ? (
-            <div className="py-12 text-center text-sm text-gray-500">
-              No usage in this period for the current scope. Try 7d or 30d, or go back up a
-              level.
-            </div>
-          ) : (
-            <div
-              className={`relative transition-opacity ${
-                tableFetching ? 'opacity-60 pointer-events-none' : ''
-              }`}
-            >
-              {tableFetching ? (
-                <div className="absolute top-2 right-3 z-20">
-                  <Spinner size="sm" />
-                </div>
-              ) : null}
-            <table className="min-w-full text-sm">
-              <thead className="bg-gray-50 text-left text-xs uppercase text-gray-500 sticky top-0 z-10">
-                <tr>
-                  <th className="px-4 py-3 font-semibold">
-                    {drillColumnLabel(groupBy, showWorkspaceComposite)}
-                  </th>
-                  <th className="px-4 py-3 font-semibold text-right">LLM calls</th>
-                  <th className="px-4 py-3 font-semibold text-right">Input tokens</th>
-                  <th className="px-4 py-3 font-semibold text-right">Output tokens</th>
-                  <th className="px-4 py-3 font-semibold text-right">Total tokens</th>
-                  <th className="px-4 py-3 font-semibold text-right">Est. cost</th>
-                  <th className="px-4 py-3 font-semibold text-right">STT audio</th>
-                  <th className="px-4 py-3 font-semibold text-right">TTS chars</th>
-                  <th className="px-4 py-3 font-semibold text-right">Cache read</th>
-                </tr>
-              </thead>
-              <tbody className="divide-y divide-gray-100">
-                {rows.map((row, idx) => {
-                  const drillable = isRowDrillable(row)
-                  const compositeHeadline =
-                    'rowKind' in row ? compositeRowHeadline(row) : null
-                  const rowTitle = tableRowLabel(groupBy, row, filterOptions)
-                  const showRowTitle =
-                    'rowKind' in row
-                      ? row.rowKind === 'workspace_resource' ||
-                        (compositeHeadline &&
-                          rowTitle.toLowerCase() !== compositeHeadline.toLowerCase())
-                      : true
-                  return (
-                    <tr
-                      key={idx}
-                      className={
-                        drillable
-                          ? 'hover:bg-[#fefce8]/60 cursor-pointer group'
-                          : 'hover:bg-gray-50'
-                      }
-                      onClick={() => drillable && handleRowDrill(row)}
-                    >
-                      <td className="px-4 py-3 text-gray-900 max-w-md">
-                        <span className="flex items-center gap-1 min-w-0">
-                          <span className="min-w-0">
-                            {'rowKind' in row ? (
-                              <span className="block text-[10px] font-semibold uppercase tracking-wide text-primary-600 mb-0.5">
-                                {compositeHeadline}
-                              </span>
-                            ) : null}
-                            {showRowTitle ? (
-                              <span className="truncate block font-medium">
-                                {rowTitle}
-                              </span>
-                            ) : !('rowKind' in row) ? (
-                              <span className="truncate block">{rowTitle}</span>
-                            ) : null}
-                            {'hint' in row && row.hint ? (
-                              <span className="text-xs text-gray-400 truncate block">
-                                {row.hint}
-                              </span>
-                            ) : null}
-                          </span>
-                          {drillable ? (
-                            <ChevronRight
-                              className="h-4 w-4 shrink-0 text-gray-400 opacity-0 group-hover:opacity-100 transition-opacity"
-                            />
-                          ) : null}
-                        </span>
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums">
-                        {formatNumber(row.call_count)}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums">
-                        {formatNumber(row.prompt_tokens)}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums">
-                        {formatNumber(row.completion_tokens)}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums font-medium">
-                        {formatNumber(row.total_tokens)}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums text-gray-700">
-                        {formatCostUsd(rowCostUsd(row))}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
-                        {row.audio_seconds ? formatAudio(row.audio_seconds) : '—'}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
-                        {row.tts_characters ? formatNumber(row.tts_characters) : '—'}
-                      </td>
-                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
-                        {formatNumber(row.cache_read_tokens)}
-                      </td>
-                    </tr>
-                  )
-                })}
-              </tbody>
-            </table>
-            </div>
-          )}
-        </CardBody>
-      </Card>
-
-      <UsageCostBreakdownModal
-        isOpen={costBreakdownOpen}
-        onClose={() => setCostBreakdownOpen(false)}
-        costs={totals?.costs}
-        scopeLabel={scopeSubtitle}
-        formatCost={formatCostUsd}
-      />
-    </div>
-  )
-}
-
-const statCardClass = 'border border-gray-200 ring-1 ring-[#fde047]/25 shadow-sm'
-
-function StatCard({
-  label,
-  value,
-  valueLabel,
-  loading,
-  emphasize = false,
-  className = '',
-}: {
-  label: string
-  value?: number
-  valueLabel?: string
-  loading?: boolean
-  emphasize?: boolean
-  className?: string
-}) {
-  return (
-    <Card
-      shadow="sm"
-      className={`${statCardClass}${emphasize ? ' ring-2 ring-[#facc15]/50 bg-[#fefce8]/40' : ''} h-full w-full ${className}`}
-    >
-      <CardBody className="p-3">
-        <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">{label}</p>
-        <p
-          className={`mt-0.5 text-xl font-semibold tabular-nums ${
-            emphasize ? 'text-[#854d0e]' : 'text-gray-900'
-          }`}
-        >
-          {loading ? '—' : valueLabel ?? formatNumber(value || 0)}
-        </p>
-      </CardBody>
-    </Card>
-  )
-}
+import { useEffect, useMemo, useState } from 'react'
+import { keepPreviousData, useQuery } from '@tanstack/react-query'
+import { Link, useSearchParams } from 'react-router-dom'
+import { Card, CardBody, Spinner } from '@heroui/react'
+import { Activity, ChevronRight, CircleDollarSign } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { useIsAdmin } from '../../hooks/useRole'
+import UsageFiltersBar from './UsageFiltersBar'
+import UsageDrillPath from './UsageDrillPath'
+import UsageCostBreakdownModal from './UsageCostBreakdownModal'
+import { defaultUsageDateRange, isRangeWithinMaxDays, rangeForDays } from './UsageDateRangePicker'
+import { getUsageTimezone } from './usageTimezone'
+import {
+  formatUsageCostUsd,
+  getUsageDisplayCurrency,
+  setUsageDisplayCurrency,
+  type UsageDisplayCurrency,
+} from '../../lib/usageCurrency'
+import { useLicenseStore } from '../../store/licenseStore'
+import {
+  CALL_IMPORT_BATCH_HEADLINE,
+  CALL_IMPORT_HINT,
+  CALL_IMPORT_PRODUCT_SECTIONS,
+  PRODUCT_SECTION_HEADLINES,
+  PRODUCT_SECTION_HINTS,
+  USAGE_SECTION_SOURCE_PREFIX,
+} from './usageProductHints'
+
+type DrillGroupBy =
+  | 'workspace'
+  | 'call_import'
+  | 'resource'
+  | 'model'
+  | 'usage_kind'
+  | 'product_section'
+type Kind = '' | 'llm' | 'stt' | 'tts'
+
+type FilterOptions = {
+  workspaces: Array<{ id: string; name: string }>
+  call_imports: Array<{ id: string; label: string }>
+  evaluations: Array<{ id: string; label: string }>
+  resources?: Array<{ id: string; label: string; type?: string; product_section?: string }>
+  models: string[]
+  usage_kinds: Array<{ id: string; label: string }>
+  product_sections?: Array<{ id: string; label: string }>
+  datasets?: string[]
+  tags?: Array<{ id: string; label: string }>
+}
+
+type UsageCosts = {
+  input_cost_usd: number
+  output_cost_usd: number
+  cache_read_cost_usd: number
+  cache_write_cost_usd: number
+  reasoning_cost_usd: number
+  audio_cost_usd: number
+  tts_cost_usd: number
+  total_cost_usd: number
+  currency: string
+  has_unpriced_usage: boolean
+}
+
+type BreakdownRow = {
+  workspace_id?: string | null
+  workspace_name?: string | null
+  call_import_id?: string | null
+  call_import_label?: string | null
+  resource_id?: string | null
+  resource_type?: string | null
+  resource_label?: string | null
+  model?: string | null
+  usage_kind?: string | null
+  product_section?: string | null
+  product_section_label?: string | null
+  prompt_tokens: number
+  completion_tokens: number
+  total_tokens: number
+  cache_read_tokens: number
+  cache_creation_tokens: number
+  reasoning_tokens: number
+  audio_seconds: number
+  tts_characters: number
+  call_count: number
+  input_cost_micro_usd?: number
+  output_cost_micro_usd?: number
+  cache_read_cost_micro_usd?: number
+  cache_creation_cost_micro_usd?: number
+  reasoning_cost_micro_usd?: number
+  audio_cost_micro_usd?: number
+  tts_cost_micro_usd?: number
+  total_cost_micro_usd?: number
+  costs?: UsageCosts
+}
+
+type WorkspaceSourceRow = BreakdownRow & {
+  rowKind: 'call_import' | 'workspace_resource'
+  hint: string
+}
+
+const NON_COMPOSITE_RESOURCE_TYPES = new Set([
+  'call_import',
+  'call_import_evaluation',
+])
+
+type TableRow = BreakdownRow | WorkspaceSourceRow
+
+const EMPTY_METRICS = {
+  prompt_tokens: 0,
+  completion_tokens: 0,
+  total_tokens: 0,
+  cache_read_tokens: 0,
+  cache_creation_tokens: 0,
+  reasoning_tokens: 0,
+  audio_seconds: 0,
+  tts_characters: 0,
+  call_count: 0,
+  input_cost_micro_usd: 0,
+  output_cost_micro_usd: 0,
+  cache_read_cost_micro_usd: 0,
+  cache_creation_cost_micro_usd: 0,
+  reasoning_cost_micro_usd: 0,
+  audio_cost_micro_usd: 0,
+  tts_cost_micro_usd: 0,
+  total_cost_micro_usd: 0,
+  costs: {
+    input_cost_usd: 0,
+    output_cost_usd: 0,
+    cache_read_cost_usd: 0,
+    cache_write_cost_usd: 0,
+    reasoning_cost_usd: 0,
+    audio_cost_usd: 0,
+    tts_cost_usd: 0,
+    total_cost_usd: 0,
+    currency: 'USD',
+    has_unpriced_usage: false,
+  },
+}
+
+function compositeRowHeadline(row: WorkspaceSourceRow): string {
+  if (row.rowKind === 'call_import') return CALL_IMPORT_BATCH_HEADLINE
+  const section = row.product_section || ''
+  return (
+    PRODUCT_SECTION_HEADLINES[section] ||
+    row.product_section_label ||
+    section ||
+    'Other'
+  )
+}
+
+function usableResourceLabel(label: string | null | undefined): string | undefined {
+  if (!label || label === 'Unscoped') return undefined
+  return label
+}
+
+function compositeRowTitle(row: WorkspaceSourceRow, options?: FilterOptions): string {
+  if (row.rowKind === 'call_import') {
+    return row.call_import_label || 'Call import batch'
+  }
+  const fromFilters = row.resource_id
+    ? options?.resources?.find((r) => idKey(r.id) === idKey(row.resource_id))?.label
+    : undefined
+  return (
+    usableResourceLabel(row.resource_label) ||
+    fromFilters ||
+    row.product_section_label ||
+    'Unscoped usage'
+  )
+}
+
+function sortRows(rows: BreakdownRow[]): BreakdownRow[] {
+  return [...rows].sort((a, b) => b.total_tokens - a.total_tokens)
+}
+
+function formatNumber(value: number): string {
+  return new Intl.NumberFormat().format(value || 0)
+}
+
+function rowCostUsd(row: Pick<BreakdownRow, 'costs' | 'total_cost_micro_usd'>): number {
+  if (row.costs?.total_cost_usd != null) return row.costs.total_cost_usd
+  return Number(row.total_cost_micro_usd || 0) / 1_000_000
+}
+
+function formatAudio(seconds: number): string {
+  const total = Math.max(0, Math.floor(seconds || 0))
+  if (total < 60) return `${total}s`
+  const mins = Math.floor(total / 60)
+  const secs = total % 60
+  if (mins < 60) return secs ? `${mins}m ${secs}s` : `${mins}m`
+  const hours = Math.floor(mins / 60)
+  const remMins = mins % 60
+  return remMins ? `${hours}h ${remMins}m` : `${hours}h`
+}
+
+function rowHasUsageForKind(row: BreakdownRow, kind: Kind): boolean {
+  if (!kind) return true
+  if (kind === 'llm') {
+    return (row.total_tokens ?? 0) > 0 || (row.call_count ?? 0) > 0
+  }
+  if (kind === 'stt') {
+    return (row.audio_seconds ?? 0) > 0 || (row.call_count ?? 0) > 0
+  }
+  if (kind === 'tts') {
+    return (row.tts_characters ?? 0) > 0 || (row.call_count ?? 0) > 0
+  }
+  return true
+}
+
+function filterRowsForUsageKind(rows: BreakdownRow[], kind: Kind): BreakdownRow[] {
+  if (!kind) return rows
+  return rows.filter((row) => rowHasUsageForKind(row, kind))
+}
+
+function rowHasAnyUsage(row: BreakdownRow): boolean {
+  return (
+    (row.total_tokens ?? 0) > 0 ||
+    (row.call_count ?? 0) > 0 ||
+    (row.audio_seconds ?? 0) > 0 ||
+    (row.tts_characters ?? 0) > 0
+  )
+}
+
+function isUsageScopeActive(
+  workspaceId: string,
+  callImportId: string,
+  evaluationId: string,
+  dataset: string,
+  tagId: string,
+  usageKind: Kind,
+  model: string,
+  productSection: string,
+): boolean {
+  return Boolean(
+    workspaceId ||
+      callImportId ||
+      evaluationId ||
+      dataset ||
+      tagId ||
+      usageKind ||
+      model ||
+      productSection,
+  )
+}
+
+/** Drop zero rows when any filter is active; kind filter uses kind-specific metrics. */
+function filterTableRows(
+  rows: BreakdownRow[],
+  usageKind: Kind,
+  scopeActive: boolean,
+): BreakdownRow[] {
+  if (usageKind) return filterRowsForUsageKind(rows, usageKind)
+  if (scopeActive) return rows.filter(rowHasAnyUsage)
+  return rows
+}
+
+function drillGroupBy(
+  workspaceId: string,
+  callImportId: string,
+  evaluationId: string,
+  model: string,
+  productSection: string,
+): DrillGroupBy {
+  if (productSection && model) return 'usage_kind'
+  if (productSection) return 'model'
+  if (evaluationId && model) return 'usage_kind'
+  if (evaluationId) return 'model'
+  if (callImportId) return 'resource'
+  if (workspaceId) return 'call_import'
+  return 'workspace'
+}
+
+function enrichCallImportRows(
+  rawRows: BreakdownRow[],
+  options?: FilterOptions,
+): BreakdownRow[] {
+  const labelById = new Map(
+    (options?.call_imports ?? []).map((c) => [idKey(c.id), c.label]),
+  )
+  return sortRows(
+    rawRows
+      .filter((r) => r.call_import_id)
+      .map((row) => {
+        const key = idKey(row.call_import_id)
+        return {
+          ...row,
+          call_import_label:
+            labelById.get(key) || row.call_import_label || 'Call import',
+        }
+      }),
+  )
+}
+
+function buildWorkspaceCompositeRows(
+  callImportRaw: BreakdownRow[],
+  resourceRaw: BreakdownRow[],
+  options?: FilterOptions,
+  padMissingRows = true,
+): WorkspaceSourceRow[] {
+  const resourceLabelById = new Map(
+    (options?.resources ?? []).map((r) => [idKey(r.id), r.label]),
+  )
+  const importRows = enrichCallImportRows(callImportRaw, options)
+  const rows: WorkspaceSourceRow[] = []
+  const shownImportIds = new Set<string>()
+
+  for (const row of importRows) {
+    if (!row.call_import_id) continue
+    shownImportIds.add(idKey(row.call_import_id))
+    rows.push({
+      ...row,
+      rowKind: 'call_import',
+      hint: CALL_IMPORT_HINT,
+    })
+  }
+
+  if (padMissingRows) {
+    for (const ci of options?.call_imports ?? []) {
+      const key = idKey(ci.id)
+      if (shownImportIds.has(key)) continue
+      shownImportIds.add(key)
+      rows.push({
+        call_import_id: ci.id,
+        call_import_label: ci.label,
+        ...EMPTY_METRICS,
+        rowKind: 'call_import',
+        hint: CALL_IMPORT_HINT,
+      })
+    }
+  }
+
+  for (const row of resourceRaw) {
+    const section = row.product_section
+    if (!section || CALL_IMPORT_PRODUCT_SECTIONS.has(section)) continue
+    if (NON_COMPOSITE_RESOURCE_TYPES.has(row.resource_type || '')) continue
+    if (
+      row.total_tokens === 0 &&
+      row.call_count === 0 &&
+      !row.audio_seconds &&
+      !row.tts_characters
+    ) {
+      continue
+    }
+    if (!rowHasAnyUsage(row)) {
+      continue
+    }
+    rows.push({
+      ...row,
+      rowKind: 'workspace_resource',
+      product_section: section,
+      product_section_label: row.product_section_label || section,
+      resource_label:
+        resourceLabelById.get(idKey(row.resource_id)) ||
+        usableResourceLabel(row.resource_label),
+      hint: PRODUCT_SECTION_HINTS[section] || 'Product usage',
+    })
+  }
+
+  return sortRows(rows) as WorkspaceSourceRow[]
+}
+
+function drillColumnLabel(groupBy: DrillGroupBy, composite = false): string {
+  if (composite) return 'Source'
+  if (groupBy === 'workspace') return 'Workspace'
+  if (groupBy === 'call_import') return 'Call import'
+  if (groupBy === 'resource') return 'Evaluation run'
+  if (groupBy === 'product_section') return 'Product area'
+  if (groupBy === 'model') return 'Model'
+  return 'Kind'
+}
+
+function rowLabel(groupBy: DrillGroupBy, row: BreakdownRow, options?: FilterOptions): string {
+  if (groupBy === 'workspace') return row.workspace_name || 'Unknown'
+  if (groupBy === 'call_import') return row.call_import_label || 'Call import'
+  if (groupBy === 'resource') {
+    const fromFilters = row.resource_id
+      ? options?.resources?.find((r) => idKey(r.id) === idKey(row.resource_id))?.label
+      : undefined
+    return (
+      usableResourceLabel(row.resource_label) || fromFilters || 'Unscoped'
+    )
+  }
+  if (groupBy === 'model') return row.model || '—'
+  if (groupBy === 'product_section')
+    return row.product_section_label || row.product_section || '—'
+  if (row.usage_kind === 'stt') return 'STT'
+  if (row.usage_kind === 'llm') return 'LLM'
+  if (row.usage_kind === 'tts') return 'TTS'
+  return row.usage_kind || '—'
+}
+
+function tableRowLabel(
+  groupBy: DrillGroupBy,
+  row: TableRow,
+  options?: FilterOptions,
+): string {
+  if ('rowKind' in row) {
+    if (row.rowKind === 'call_import') return row.call_import_label || 'Call import batch'
+    return compositeRowTitle(row, options)
+  }
+  return rowLabel(groupBy, row, options)
+}
+
+function idInOptions(
+  id: string,
+  options: Array<{ id: string }> | undefined,
+): boolean {
+  if (!id || !options?.length) return false
+  const key = idKey(id)
+  return options.some((o) => idKey(o.id) === key)
+}
+
+function idKey(id: string | null | undefined): string {
+  return id ? String(id).toLowerCase() : ''
+}
+
+function mergeDrillRows(
+  groupBy: DrillGroupBy,
+  rawRows: BreakdownRow[],
+  options?: FilterOptions,
+  padMissingRows = true,
+): BreakdownRow[] {
+  if (groupBy === 'workspace' && options?.workspaces?.length && padMissingRows) {
+    const byId = new Map(
+      rawRows
+        .filter((r) => r.workspace_id)
+        .map((r) => [idKey(r.workspace_id), r]),
+    )
+    const merged: BreakdownRow[] = options.workspaces.map((ws) => ({
+      workspace_id: ws.id,
+      workspace_name: ws.name,
+      ...(byId.get(idKey(ws.id)) ?? EMPTY_METRICS),
+    }))
+    for (const row of rawRows) {
+      if (!row.workspace_id) {
+        merged.push({
+          ...row,
+          workspace_name: row.workspace_name || 'No workspace',
+        })
+      }
+    }
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'call_import') {
+    if (rawRows.length === 0) return []
+
+    const labelById = new Map(
+      (options?.call_imports ?? []).map((c) => [idKey(c.id), c.label]),
+    )
+    const shown = new Set<string>()
+    const merged: BreakdownRow[] = []
+
+    for (const row of rawRows) {
+      if (row.call_import_id) {
+        const key = idKey(row.call_import_id)
+        shown.add(key)
+        merged.push({
+          ...row,
+          call_import_label:
+            labelById.get(key) || row.call_import_label || 'Call import',
+        })
+      }
+    }
+
+    if (padMissingRows) {
+      for (const ci of options?.call_imports ?? []) {
+        const key = idKey(ci.id)
+        if (!shown.has(key)) {
+          merged.push({
+            call_import_id: ci.id,
+            call_import_label: ci.label,
+            ...EMPTY_METRICS,
+          })
+        }
+      }
+    }
+
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'resource') {
+    if (rawRows.length === 0) return []
+
+    const evalLabelById = new Map(
+      (options?.evaluations ?? []).map((e) => [idKey(e.id), e.label]),
+    )
+    const resourceLabelById = new Map(
+      (options?.resources ?? []).map((r) => [idKey(r.id), r.label]),
+    )
+    const shown = new Set<string>()
+    const merged: BreakdownRow[] = []
+
+    for (const row of rawRows) {
+      if (row.resource_id) {
+        const key = idKey(row.resource_id)
+        shown.add(key)
+        merged.push({
+          ...row,
+          resource_label:
+            resourceLabelById.get(key) ||
+            evalLabelById.get(key) ||
+            usableResourceLabel(row.resource_label) ||
+            'Evaluation',
+        })
+      }
+    }
+
+    if (padMissingRows) {
+      for (const ev of options?.evaluations ?? []) {
+        const key = idKey(ev.id)
+        if (!shown.has(key)) {
+          merged.push({
+            resource_id: ev.id,
+            resource_label: ev.label,
+            ...EMPTY_METRICS,
+          })
+        }
+      }
+    }
+
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'model' && options?.models?.length && padMissingRows) {
+    if (rawRows.length === 0) return []
+    const byName = new Map(
+      rawRows.filter((r) => r.model).map((r) => [r.model!, r]),
+    )
+    const merged = options.models.map((name) => ({
+      model: name,
+      ...(byName.get(name) ?? EMPTY_METRICS),
+    }))
+    return sortRows(merged)
+  }
+
+  if (groupBy === 'usage_kind') {
+    return sortRows(
+      rawRows.filter((row) => rowHasUsageForKind(row, row.usage_kind as Kind)),
+    )
+  }
+
+  return sortRows(rawRows)
+}
+
+export default function Usage() {
+  const [searchParams, setSearchParams] = useSearchParams()
+  const defaultRange = useMemo(() => defaultUsageDateRange(), [])
+  const usageTimezone = useMemo(() => getUsageTimezone(), [])
+
+  const start = searchParams.get('start') || defaultRange.start
+  const end = searchParams.get('end') || defaultRange.end
+  const workspaceId = searchParams.get('workspace_id') || ''
+  const callImportId = searchParams.get('call_import_id') || ''
+  const dataset = searchParams.get('dataset') || ''
+  const tagId = searchParams.get('tag_id') || ''
+  const evaluationId = searchParams.get('resource_id') || ''
+  const model = searchParams.get('model') || ''
+  const usageKind = (searchParams.get('usage_kind') as Kind) || ''
+  const productSection = searchParams.get('product_section') || ''
+  const isAdmin = useIsAdmin()
+  const { usagePolicy, isLoaded: licenseLoaded, fetchLicense } = useLicenseStore()
+  const showOssUsageNotice = licenseLoaded && !usagePolicy.extended_history
+  const maxHistoryDays = !licenseLoaded
+    ? null
+    : usagePolicy.extended_history
+      ? null
+      : usagePolicy.max_history_days ?? 7
+  const [costBreakdownOpen, setCostBreakdownOpen] = useState(false)
+  const [displayCurrency, setDisplayCurrency] = useState<UsageDisplayCurrency>(() =>
+    getUsageDisplayCurrency(),
+  )
+
+  const { data: fxRate } = useQuery({
+    queryKey: ['org-usage', 'fx-rate'],
+    queryFn: () => apiClient.getOrgUsageFxRate(),
+    staleTime: 60 * 60 * 1000,
+  })
+  const inrRate = fxRate?.rate ?? 83
+  const formatCostUsd = (usd?: number | null) =>
+    formatUsageCostUsd(usd, displayCurrency, inrRate)
+
+  useEffect(() => {
+    if (!licenseLoaded) {
+      void fetchLicense()
+    }
+  }, [licenseLoaded, fetchLicense])
+
+  useEffect(() => {
+    if (!licenseLoaded || usagePolicy.extended_history) return
+    const maxDays = usagePolicy.max_history_days ?? 7
+    if (!isRangeWithinMaxDays(start, end, maxDays)) {
+      const r = rangeForDays(maxDays)
+      setSearchParams((prev) => {
+        const next = new URLSearchParams(prev)
+        next.set('start', r.start)
+        next.set('end', r.end)
+        return next
+      })
+    }
+  }, [licenseLoaded, usagePolicy.extended_history, usagePolicy.max_history_days, start, end, setSearchParams])
+
+  const showWorkspaceComposite =
+    Boolean(workspaceId) &&
+    !callImportId &&
+    !evaluationId &&
+    !productSection
+
+  const groupBy = drillGroupBy(
+    workspaceId,
+    callImportId,
+    evaluationId,
+    model,
+    productSection,
+  )
+
+  const setParams = (updates: Record<string, string | null>) => {
+    const next = new URLSearchParams(searchParams)
+    for (const [key, value] of Object.entries(updates)) {
+      if (!value) next.delete(key)
+      else next.set(key, value)
+    }
+    setSearchParams(next)
+  }
+
+  const scopeParams = {
+    start,
+    end,
+    tz: usageTimezone,
+    workspace_id: workspaceId || undefined,
+    call_import_id: callImportId || undefined,
+    dataset: dataset || undefined,
+    tag_id: tagId || undefined,
+    product_section: productSection || undefined,
+    usage_kind: usageKind || undefined,
+    model: model || undefined,
+    resource_id: evaluationId || undefined,
+  }
+
+  const dataParams = {
+    ...scopeParams,
+    evaluation_id: callImportId ? evaluationId || undefined : undefined,
+  }
+
+  const usageQueryDefaults = {
+    staleTime: 0,
+    refetchOnMount: 'always' as const,
+    refetchOnWindowFocus: true,
+  }
+
+  const {
+    data: summary,
+    isLoading: summaryLoading,
+    isFetching: summaryFetching,
+    isError: summaryError,
+    error: summaryQueryError,
+  } = useQuery({
+    queryKey: ['org-usage', 'summary', dataParams],
+    queryFn: () => apiClient.getOrgUsageSummary(dataParams),
+    ...usageQueryDefaults,
+    placeholderData: keepPreviousData,
+  })
+
+  const usageStatsLoading = summaryLoading
+
+  const {
+    data: breakdown,
+    isLoading: breakdownLoading,
+    isFetching: breakdownFetching,
+  } = useQuery({
+    queryKey: ['org-usage', 'breakdown', groupBy, dataParams],
+    queryFn: () =>
+      apiClient.getOrgUsageBreakdown({
+        ...dataParams,
+        group_by: groupBy,
+        limit: 100,
+      }),
+    enabled: !showWorkspaceComposite,
+    ...usageQueryDefaults,
+    placeholderData: (previousData, previousQuery) => {
+      if (!previousQuery || previousQuery.queryKey[2] !== groupBy) return undefined
+      return previousData
+    },
+  })
+
+  const {
+    data: importBreakdown,
+    isLoading: importBreakdownLoading,
+    isFetching: importBreakdownFetching,
+  } = useQuery({
+    queryKey: ['org-usage', 'breakdown', 'call_import', dataParams],
+    queryFn: () =>
+      apiClient.getOrgUsageBreakdown({
+        ...dataParams,
+        group_by: 'call_import',
+        limit: 100,
+      }),
+    enabled: showWorkspaceComposite,
+    ...usageQueryDefaults,
+  })
+
+  const {
+    data: resourceBreakdown,
+    isLoading: resourceBreakdownLoading,
+    isFetching: resourceBreakdownFetching,
+  } = useQuery({
+    queryKey: ['org-usage', 'breakdown', 'resource', dataParams],
+    queryFn: () =>
+      apiClient.getOrgUsageBreakdown({
+        ...dataParams,
+        group_by: 'resource',
+        limit: 100,
+      }),
+    enabled: showWorkspaceComposite,
+    ...usageQueryDefaults,
+  })
+
+  const { data: filterOptions, isFetching: filtersLoading } = useQuery({
+    queryKey: ['org-usage', 'filters', scopeParams],
+    queryFn: () => apiClient.getOrgUsageFilters(scopeParams),
+    ...usageQueryDefaults,
+    placeholderData: (previousData, previousQuery) => {
+      if (!previousQuery) return undefined
+      const prevScope = previousQuery.queryKey[2] as typeof scopeParams
+      if (JSON.stringify(prevScope) !== JSON.stringify(scopeParams)) return undefined
+      return previousData
+    },
+  })
+
+  useEffect(() => {
+    if (!filterOptions) return
+    const updates: Record<string, string | null> = {}
+    if (
+      workspaceId &&
+      !idInOptions(workspaceId, filterOptions.workspaces)
+    ) {
+      updates.workspace_id = null
+      updates.call_import_id = null
+      updates.resource_id = null
+      updates.product_section = null
+    }
+    if (
+      callImportId &&
+      !idInOptions(callImportId, filterOptions.call_imports)
+    ) {
+      updates.call_import_id = null
+      updates.resource_id = null
+      updates.product_section = null
+    }
+    if (
+      evaluationId &&
+      !idInOptions(evaluationId, filterOptions.evaluations) &&
+      !idInOptions(evaluationId, filterOptions.resources)
+    ) {
+      updates.resource_id = null
+      updates.product_section = null
+    }
+    if (model && !filterOptions.models?.includes(model)) {
+      updates.model = null
+    }
+    if (
+      usageKind &&
+      !filterOptions.usage_kinds?.some((k) => k.id === usageKind)
+    ) {
+      updates.usage_kind = null
+    }
+    if (
+      productSection &&
+      !filterOptions.product_sections?.some((s) => s.id === productSection)
+    ) {
+      updates.product_section = null
+    }
+    if (dataset && !filterOptions.datasets?.includes(dataset)) {
+      updates.dataset = null
+    }
+    if (tagId && !idInOptions(tagId, filterOptions.tags)) {
+      updates.tag_id = null
+    }
+    if (Object.keys(updates).length > 0) setParams(updates)
+  }, [
+    filterOptions,
+    workspaceId,
+    callImportId,
+    dataset,
+    tagId,
+    evaluationId,
+    model,
+    usageKind,
+    productSection,
+  ])
+
+  const breakdownMatchesLevel = breakdown?.group_by === groupBy
+  const rawRows = useMemo((): BreakdownRow[] => {
+    if (!breakdown || !breakdownMatchesLevel) return []
+    return breakdown.rows as BreakdownRow[]
+  }, [breakdown, breakdownMatchesLevel])
+
+  const mergeOptions = filterOptions
+  const scopeActive = isUsageScopeActive(
+    workspaceId,
+    callImportId,
+    evaluationId,
+    dataset,
+    tagId,
+    usageKind,
+    model,
+    productSection,
+  )
+  const padMissingRows = !scopeActive
+  const filteredRawRows = useMemo(
+    () => filterTableRows(rawRows, usageKind, scopeActive),
+    [rawRows, usageKind, scopeActive],
+  )
+  const rows: TableRow[] = useMemo(() => {
+    if (showWorkspaceComposite) {
+      const importReady = importBreakdown?.group_by === 'call_import'
+      const resourceReady = resourceBreakdown?.group_by === 'resource'
+      if (!importReady && !resourceReady) return []
+      return buildWorkspaceCompositeRows(
+        importReady
+          ? filterTableRows(importBreakdown.rows as BreakdownRow[], usageKind, scopeActive)
+          : [],
+        resourceReady
+          ? filterTableRows(resourceBreakdown.rows as BreakdownRow[], usageKind, scopeActive)
+          : [],
+        mergeOptions,
+        padMissingRows,
+      )
+    }
+    return mergeDrillRows(groupBy, filteredRawRows, mergeOptions, padMissingRows)
+  }, [
+    showWorkspaceComposite,
+    importBreakdown,
+    resourceBreakdown,
+    groupBy,
+    filteredRawRows,
+    mergeOptions,
+    padMissingRows,
+    usageKind,
+    scopeActive,
+  ])
+
+  const breakdownStale =
+    !showWorkspaceComposite && breakdownFetching && !breakdownMatchesLevel
+  const tableLoading = showWorkspaceComposite
+    ? (importBreakdownLoading || resourceBreakdownLoading) &&
+      !importBreakdown &&
+      !resourceBreakdown
+    : breakdownLoading && !breakdown
+  const tableFetching = showWorkspaceComposite
+    ? importBreakdownFetching || resourceBreakdownFetching
+    : breakdownFetching
+  const totals = summary?.totals
+  const estimatedTotalCost =
+    totals?.costs?.total_cost_usd ?? (totals?.total_cost_micro_usd || 0) / 1_000_000
+  const showCostBreakdown =
+    estimatedTotalCost > 0 || Boolean(totals?.costs?.has_unpriced_usage)
+  const showAudio = Boolean(totals?.audio_seconds)
+  const showTts =
+    Boolean(totals?.tts_characters) ||
+    rows.some((r) => Boolean(r.tts_characters))
+
+  const showTruncation =
+    (!showWorkspaceComposite &&
+      breakdownMatchesLevel &&
+      Boolean(breakdown?.truncated_at_limit)) ||
+    (showWorkspaceComposite &&
+      Boolean(
+        importBreakdown?.truncated_at_limit || resourceBreakdown?.truncated_at_limit,
+      ))
+
+  const workspaceLabel =
+    filterOptions?.workspaces?.find((w) => idKey(w.id) === idKey(workspaceId))?.name
+  const callImportLabel =
+    filterOptions?.call_imports?.find((c) => idKey(c.id) === idKey(callImportId))?.label
+  const evaluationLabel =
+    filterOptions?.resources?.find((r) => idKey(r.id) === idKey(evaluationId))?.label ||
+    filterOptions?.evaluations?.find((e) => idKey(e.id) === idKey(evaluationId))?.label
+
+  const productSectionLabel =
+    filterOptions?.product_sections?.find((s) => s.id === productSection)?.label
+
+  const scopeSubtitle = model
+    ? model
+    : evaluationId
+      ? evaluationLabel || 'Evaluation'
+      : callImportId
+        ? callImportLabel || 'Call import'
+        : productSection
+          ? productSectionLabel || 'Product area'
+          : workspaceId
+            ? workspaceLabel || 'Workspace'
+            : 'Organization'
+
+  const levelHint = (() => {
+    if (showWorkspaceComposite) {
+      return 'Call import batches and other product usage — click a row to drill down'
+    }
+    if (groupBy === 'workspace') return 'Click a workspace to drill down'
+    if (groupBy === 'call_import') return 'Click a call import to see evaluation runs'
+    if (groupBy === 'product_section') return 'Click a product area to see models used'
+    if (groupBy === 'resource') return 'Click an evaluation to see models used'
+    if (groupBy === 'model') return 'Click a model to see usage by kind'
+    return 'Token totals by LLM / STT / TTS'
+  })()
+
+  const drillCrumbs = [
+    {
+      label: 'Organization',
+      onClick:
+        workspaceId ||
+        callImportId ||
+        evaluationId ||
+        model ||
+        productSection
+          ? () =>
+              setParams({
+                workspace_id: null,
+                call_import_id: null,
+                resource_id: null,
+                model: null,
+                usage_kind: null,
+                product_section: null,
+              })
+          : undefined,
+    },
+    ...(workspaceId
+      ? [
+          {
+            label: workspaceLabel || 'Workspace',
+            onClick:
+              callImportId || evaluationId || model || productSection
+                ? () =>
+                    setParams({
+                      call_import_id: null,
+                      resource_id: null,
+                      model: null,
+                      usage_kind: null,
+                      product_section: null,
+                    })
+                : undefined,
+          },
+        ]
+      : []),
+    ...(productSection
+      ? [
+          {
+            label: productSectionLabel || 'Product area',
+            onClick:
+              model
+                ? () => setParams({ model: null, usage_kind: null })
+                : undefined,
+          },
+        ]
+      : []),
+    ...(callImportId
+      ? [
+          {
+            label: callImportLabel || 'Call import',
+            onClick:
+              evaluationId || model
+                ? () =>
+                    setParams({
+                      resource_id: null,
+                      model: null,
+                      usage_kind: null,
+                      product_section: null,
+                    })
+                : undefined,
+          },
+        ]
+      : []),
+    ...(evaluationId
+      ? [
+          {
+            label: evaluationLabel || 'Evaluation',
+            onClick: model
+              ? () => setParams({ model: null, usage_kind: null })
+              : undefined,
+          },
+        ]
+      : []),
+    ...(model ? [{ label: model }] : []),
+  ]
+
+  const handleWorkspaceChange = (id: string) => {
+    setParams({
+      workspace_id: id || null,
+      call_import_id: null,
+      resource_id: null,
+      model: null,
+      usage_kind: null,
+      product_section: null,
+    })
+  }
+
+  const handleCallImportChange = (id: string) => {
+    setParams({
+      call_import_id: id || null,
+      resource_id: null,
+      model: null,
+      usage_kind: null,
+      product_section: null,
+    })
+  }
+
+  const handleClearAll = () => {
+    setParams({
+      workspace_id: null,
+      call_import_id: null,
+      dataset: null,
+      tag_id: null,
+      resource_id: null,
+      usage_kind: null,
+      model: null,
+      product_section: null,
+    })
+  }
+
+  const handleRowDrill = (row: TableRow) => {
+    if ('rowKind' in row) {
+      if (row.rowKind === 'call_import' && row.call_import_id) {
+        setParams({
+          call_import_id: row.call_import_id,
+          resource_id: null,
+          model: null,
+          usage_kind: null,
+          product_section: null,
+        })
+        return
+      }
+      if (row.rowKind === 'workspace_resource' && row.product_section) {
+        setParams({
+          product_section: row.product_section,
+          call_import_id: null,
+          resource_id: row.resource_id || null,
+          model: null,
+          usage_kind: null,
+        })
+        return
+      }
+    }
+    if (groupBy === 'workspace' && row.workspace_id) {
+      setParams({
+        workspace_id: row.workspace_id,
+        call_import_id: null,
+        resource_id: null,
+        model: null,
+        usage_kind: null,
+        product_section: null,
+      })
+      return
+    }
+    if (groupBy === 'call_import' && row.call_import_id) {
+      setParams({
+        call_import_id: row.call_import_id,
+        resource_id: null,
+        model: null,
+        usage_kind: null,
+        product_section: null,
+      })
+      return
+    }
+    if (groupBy === 'product_section' && row.product_section) {
+      setParams({
+        product_section: row.product_section,
+        call_import_id: null,
+        resource_id: null,
+        model: null,
+        usage_kind: null,
+      })
+      return
+    }
+    if (groupBy === 'resource' && row.resource_id) {
+      setParams({
+        resource_id: row.resource_id,
+        model: null,
+        usage_kind: null,
+        product_section: null,
+      })
+      return
+    }
+    if (groupBy === 'model' && row.model) {
+      setParams({ model: row.model, usage_kind: null })
+    }
+  }
+
+  const isRowDrillable = (row: TableRow): boolean => {
+    if ('rowKind' in row) {
+      if (row.rowKind === 'call_import') return Boolean(row.call_import_id)
+      return Boolean(row.product_section)
+    }
+    if (groupBy === 'workspace') return Boolean(row.workspace_id)
+    if (groupBy === 'call_import') return Boolean(row.call_import_id)
+    if (groupBy === 'product_section') return Boolean(row.product_section)
+    if (groupBy === 'resource') return Boolean(row.resource_id)
+    if (groupBy === 'model') return Boolean(row.model)
+    return false
+  }
+
+  return (
+    <div className="space-y-6">
+      <div className="flex items-start justify-between gap-4">
+        <div className="min-w-0">
+          <h1 className="text-2xl font-bold text-gray-900 flex items-center gap-2">
+            <Activity className="h-6 w-6 text-primary-600 shrink-0" />
+            Usage
+          </h1>
+          <p className="text-sm text-gray-500 mt-1">
+            Cards show usage for <span className="font-medium text-gray-700">{scopeSubtitle}</span>.
+            Drill down: workspaces → call imports or product areas → evaluations / models.
+          </p>
+        </div>
+        <div className="flex flex-col items-end gap-1.5 shrink-0">
+          <div className="flex items-center gap-3">
+            <div
+              className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
+              role="group"
+              aria-label="Display currency"
+            >
+              {(['USD', 'INR'] as const).map((currency) => (
+                <button
+                  key={currency}
+                  type="button"
+                  onClick={() => {
+                    setDisplayCurrency(currency)
+                    setUsageDisplayCurrency(currency)
+                  }}
+                  className={`rounded-md px-2 py-1 transition-colors ${
+                    displayCurrency === currency
+                      ? 'bg-gray-900 text-white'
+                      : 'text-gray-600 hover:bg-gray-50'
+                  }`}
+                >
+                  {currency}
+                </button>
+              ))}
+            </div>
+            {showCostBreakdown ? (
+              <button
+                type="button"
+                onClick={() => setCostBreakdownOpen(true)}
+                className="inline-flex items-center gap-1.5 rounded-lg border border-gray-200 bg-white px-2.5 py-1.5 text-xs font-medium text-gray-700 shadow-sm transition-colors hover:border-gray-300 hover:bg-gray-50 hover:text-gray-900"
+                title={formatCostUsd(estimatedTotalCost)}
+              >
+                <CircleDollarSign className="h-4 w-4 shrink-0 text-gray-500" />
+                <span className="whitespace-nowrap">Cost breakdown</span>
+              </button>
+            ) : null}
+            {isAdmin && licenseLoaded && usagePolicy.extended_history ? (
+              <Link
+                to="/usage/pricing"
+                className="text-sm text-primary-600 hover:text-primary-700 whitespace-nowrap"
+              >
+                Pricing overrides
+              </Link>
+            ) : null}
+          </div>
+          {summary?.last_updated_at ? (
+            <p className="text-[11px] text-gray-400 whitespace-nowrap">
+              Updated {new Date(summary.last_updated_at).toLocaleString()}
+            </p>
+          ) : null}
+        </div>
+      </div>
+
+      {summaryError ? (
+        <div className="rounded-lg border border-red-200 bg-red-50 px-4 py-3 text-sm text-red-800">
+          Could not load usage data
+          {summaryQueryError instanceof Error && summaryQueryError.message
+            ? `: ${summaryQueryError.message}`
+            : '.'}{' '}
+          Try refreshing the page.
+        </div>
+      ) : null}
+
+      {showOssUsageNotice ? (
+        <div className="rounded-lg border border-amber-200 bg-amber-50 px-4 py-3 text-sm text-amber-900">
+          Showing the last {maxHistoryDays} days of usage history. Set{' '}
+          <code className="rounded bg-amber-100 px-1.5 py-0.5 text-xs font-mono">
+            EFFICIENTAI_LICENSE
+          </code>{' '}
+          on your server with any enterprise feature to unlock extended history.
+        </div>
+      ) : null}
+
+      <div
+        className={`space-y-3 transition-opacity ${summaryFetching ? 'opacity-70' : ''}`}
+      >
+        <div className="grid grid-cols-2 gap-3 md:grid-cols-4">
+          <StatCard label="Input tokens" value={totals?.prompt_tokens} loading={usageStatsLoading} />
+          <StatCard label="Output tokens" value={totals?.completion_tokens} loading={usageStatsLoading} />
+          <StatCard label="Total tokens" value={totals?.total_tokens} loading={usageStatsLoading} />
+          <StatCard label="LLM calls" value={totals?.call_count} loading={usageStatsLoading} />
+        </div>
+
+        <div className="grid grid-cols-2 gap-3 md:grid-cols-4 md:items-stretch">
+          <div className="col-span-2 grid grid-cols-2 gap-3 sm:grid-cols-3 md:col-span-3">
+            {showAudio ? (
+              <StatCard
+                label="STT audio"
+                valueLabel={formatAudio(totals?.audio_seconds || 0)}
+                loading={usageStatsLoading}
+              />
+            ) : null}
+            {showTts ? (
+              <StatCard label="TTS characters" value={totals?.tts_characters} loading={usageStatsLoading} />
+            ) : null}
+            {(totals?.cache_read_tokens || 0) > 0 ? (
+              <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={usageStatsLoading} />
+            ) : null}
+            {(totals?.cache_creation_tokens || 0) > 0 ? (
+              <StatCard
+                label="Cache write"
+                value={totals?.cache_creation_tokens}
+                loading={usageStatsLoading}
+              />
+            ) : null}
+            {(totals?.reasoning_tokens || 0) > 0 ? (
+              <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={usageStatsLoading} />
+            ) : null}
+          </div>
+          <div className="col-span-2 md:col-span-1">
+            <StatCard
+              label="Estimated cost"
+              valueLabel={formatCostUsd(estimatedTotalCost)}
+              loading={usageStatsLoading}
+              emphasize
+            />
+          </div>
+        </div>
+      </div>
+
+      <UsageFiltersBar
+        start={start}
+        end={end}
+        workspaceId={workspaceId}
+        callImportId={callImportId}
+        evaluationId={evaluationId}
+        dataset={dataset}
+        tagId={tagId}
+        usageKind={usageKind}
+        model={model}
+        productSection={productSection}
+        options={filterOptions}
+        filtersLoading={filtersLoading}
+        onDateApply={(s, e) => setParams({ start: s, end: e })}
+        onWorkspaceChange={handleWorkspaceChange}
+        onCallImportChange={handleCallImportChange}
+        onDatasetChange={(v) =>
+          setParams({
+            dataset: v || null,
+            call_import_id: null,
+            resource_id: null,
+          })
+        }
+        onTagChange={(v) =>
+          setParams({
+            tag_id: v || null,
+            call_import_id: null,
+            resource_id: null,
+          })
+        }
+        onEvaluationChange={(id) => {
+          if (id.startsWith(USAGE_SECTION_SOURCE_PREFIX)) {
+            const section = id.slice(USAGE_SECTION_SOURCE_PREFIX.length)
+            setParams({
+              product_section: section || null,
+              resource_id: null,
+              call_import_id: null,
+              model: null,
+              usage_kind: null,
+            })
+            return
+          }
+          const resource = filterOptions?.resources?.find(
+            (r) => idKey(r.id) === idKey(id),
+          )
+          setParams({
+            resource_id: id || null,
+            product_section: id ? resource?.product_section || null : null,
+            model: null,
+            usage_kind: null,
+          })
+        }}
+        onUsageKindChange={(k) => setParams({ usage_kind: k || null })}
+        onModelChange={(v) => setParams({ model: v || null })}
+        onClearAll={handleClearAll}
+        maxHistoryDays={maxHistoryDays}
+      />
+
+      <Card shadow="sm" className="border border-gray-200 ring-1 ring-[#fde047]/25">
+        <div className="border-b border-gray-100 px-4 py-3 space-y-2">
+          <UsageDrillPath crumbs={drillCrumbs} levelLabel={levelHint} />
+          {showTruncation ? (
+            <p className="text-xs text-amber-700">
+              Showing the first 100 rows for this level. Narrow the date range or drill
+              further for complete detail.
+            </p>
+          ) : null}
+        </div>
+        <CardBody className="p-0 overflow-x-auto max-h-[min(70vh,640px)] overflow-y-auto custom-scrollbar">
+          {tableLoading || breakdownStale ? (
+            <div className="flex justify-center py-12">
+              <Spinner />
+            </div>
+          ) : rows.length === 0 ? (
+            <div className="py-12 text-center text-sm text-gray-500">
+              No usage in this period for the current scope. Try 7d or 30d, or go back up a
+              level.
+            </div>
+          ) : (
+            <div
+              className={`relative transition-opacity ${
+                tableFetching ? 'opacity-60 pointer-events-none' : ''
+              }`}
+            >
+              {tableFetching ? (
+                <div className="absolute top-2 right-3 z-20">
+                  <Spinner size="sm" />
+                </div>
+              ) : null}
+            <table className="min-w-full text-sm">
+              <thead className="bg-gray-50 text-left text-xs uppercase text-gray-500 sticky top-0 z-10">
+                <tr>
+                  <th className="px-4 py-3 font-semibold">
+                    {drillColumnLabel(groupBy, showWorkspaceComposite)}
+                  </th>
+                  <th className="px-4 py-3 font-semibold text-right">LLM calls</th>
+                  <th className="px-4 py-3 font-semibold text-right">Input tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">Output tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">Total tokens</th>
+                  <th className="px-4 py-3 font-semibold text-right">Est. cost</th>
+                  <th className="px-4 py-3 font-semibold text-right">STT audio</th>
+                  <th className="px-4 py-3 font-semibold text-right">TTS chars</th>
+                  <th className="px-4 py-3 font-semibold text-right">Cache read</th>
+                </tr>
+              </thead>
+              <tbody className="divide-y divide-gray-100">
+                {rows.map((row, idx) => {
+                  const drillable = isRowDrillable(row)
+                  const compositeHeadline =
+                    'rowKind' in row ? compositeRowHeadline(row) : null
+                  const rowTitle = tableRowLabel(groupBy, row, filterOptions)
+                  const showRowTitle =
+                    'rowKind' in row
+                      ? row.rowKind === 'workspace_resource' ||
+                        (compositeHeadline &&
+                          rowTitle.toLowerCase() !== compositeHeadline.toLowerCase())
+                      : true
+                  return (
+                    <tr
+                      key={idx}
+                      className={
+                        drillable
+                          ? 'hover:bg-[#fefce8]/60 cursor-pointer group'
+                          : 'hover:bg-gray-50'
+                      }
+                      onClick={() => drillable && handleRowDrill(row)}
+                    >
+                      <td className="px-4 py-3 text-gray-900 max-w-md">
+                        <span className="flex items-center gap-1 min-w-0">
+                          <span className="min-w-0">
+                            {'rowKind' in row ? (
+                              <span className="block text-[10px] font-semibold uppercase tracking-wide text-primary-600 mb-0.5">
+                                {compositeHeadline}
+                              </span>
+                            ) : null}
+                            {showRowTitle ? (
+                              <span className="truncate block font-medium">
+                                {rowTitle}
+                              </span>
+                            ) : !('rowKind' in row) ? (
+                              <span className="truncate block">{rowTitle}</span>
+                            ) : null}
+                            {'hint' in row && row.hint ? (
+                              <span className="text-xs text-gray-400 truncate block">
+                                {row.hint}
+                              </span>
+                            ) : null}
+                          </span>
+                          {drillable ? (
+                            <ChevronRight
+                              className="h-4 w-4 shrink-0 text-gray-400 opacity-0 group-hover:opacity-100 transition-opacity"
+                            />
+                          ) : null}
+                        </span>
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums">
+                        {formatNumber(row.call_count)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums">
+                        {formatNumber(row.prompt_tokens)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums">
+                        {formatNumber(row.completion_tokens)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums font-medium">
+                        {formatNumber(row.total_tokens)}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-700">
+                        {formatCostUsd(rowCostUsd(row))}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                        {row.audio_seconds ? formatAudio(row.audio_seconds) : '—'}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                        {row.tts_characters ? formatNumber(row.tts_characters) : '—'}
+                      </td>
+                      <td className="px-4 py-3 text-right tabular-nums text-gray-500">
+                        {formatNumber(row.cache_read_tokens)}
+                      </td>
+                    </tr>
+                  )
+                })}
+              </tbody>
+            </table>
+            </div>
+          )}
+        </CardBody>
+      </Card>
+
+      <UsageCostBreakdownModal
+        isOpen={costBreakdownOpen}
+        onClose={() => setCostBreakdownOpen(false)}
+        costs={totals?.costs}
+        scopeLabel={scopeSubtitle}
+        formatCost={formatCostUsd}
+      />
+    </div>
+  )
+}
+
+const statCardClass = 'border border-gray-200 ring-1 ring-[#fde047]/25 shadow-sm'
+
+function StatCard({
+  label,
+  value,
+  valueLabel,
+  loading,
+  emphasize = false,
+  className = '',
+}: {
+  label: string
+  value?: number
+  valueLabel?: string
+  loading?: boolean
+  emphasize?: boolean
+  className?: string
+}) {
+  return (
+    <Card
+      shadow="sm"
+      className={`${statCardClass}${emphasize ? ' ring-2 ring-[#facc15]/50 bg-[#fefce8]/40' : ''} h-full w-full ${className}`}
+    >
+      <CardBody className="p-3">
+        <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">{label}</p>
+        <p
+          className={`mt-0.5 text-xl font-semibold tabular-nums ${
+            emphasize ? 'text-[#854d0e]' : 'text-gray-900'
+          }`}
+        >
+          {loading ? '—' : valueLabel ?? formatNumber(value || 0)}
+        </p>
+      </CardBody>
+    </Card>
+  )
+}
diff --git a/frontend/src/pages/usage/UsageDateRangePicker.tsx b/frontend/src/pages/usage/UsageDateRangePicker.tsx
index cd5718ab..4c6ca19f 100644
--- a/frontend/src/pages/usage/UsageDateRangePicker.tsx
+++ b/frontend/src/pages/usage/UsageDateRangePicker.tsx
@@ -1,274 +1,273 @@
-import { useEffect, useMemo, useRef, useState } from 'react'
-import { Calendar } from 'lucide-react'
-import { usageTheme } from './usageTheme'
-import { getUsageTimezone, formatUsageTimezoneLabel } from './usageTimezone'
-
-type Mode = 'relative' | 'absolute'
-
-type UsageDateRangePickerProps = {
-  start: string
-  end: string
-  onApply: (start: string, end: string) => void
-  maxHistoryDays?: number | null
-}
-
-function toDateInput(d: Date): string {
-  const y = d.getFullYear()
-  const m = String(d.getMonth() + 1).padStart(2, '0')
-  const day = String(d.getDate()).padStart(2, '0')
-  return `${y}-${m}-${day}`
-}
-
-function formatDisplay(start: string, end: string): string {
-  if (start === end) return start
-  return `${start} → ${end}`
-}
-
-export function rangeForDays(days: number): { start: string; end: string } {
-  const end = new Date()
-  const start = new Date()
-  start.setDate(start.getDate() - (days - 1))
-  return { start: toDateInput(start), end: toDateInput(end) }
-}
-
-export function isRangeWithinMaxDays(start: string, end: string, maxDays: number): boolean {
-  const r = rangeForDays(maxDays)
-  return start >= r.start && end <= r.end
-}
-
-const QUICK_RANGES = [
-  { label: '1d', days: 1 },
-  { label: '7d', days: 7 },
-  { label: '30d', days: 30 },
-  { label: '90d', days: 90 },
-] as const
-
-const RELATIVE_DAYS = [1, 2, 3, 4, 5, 6]
-const RELATIVE_WEEKS = [1, 2, 3, 4]
-
-export default function UsageDateRangePicker({
-  start,
-  end,
-  onApply,
-  maxHistoryDays = null,
-}: UsageDateRangePickerProps) {
-  const [open, setOpen] = useState(false)
-  const [mode, setMode] = useState<Mode>('relative')
-  const [draftStart, setDraftStart] = useState(start)
-  const [draftEnd, setDraftEnd] = useState(end)
-  const [relDays, setRelDays] = useState(1)
-  const rootRef = useRef<HTMLDivElement>(null)
-
-  const quickRanges = useMemo(() => {
-    if (!maxHistoryDays) return QUICK_RANGES
-    return QUICK_RANGES.filter((q) => q.days <= maxHistoryDays)
-  }, [maxHistoryDays])
-
-  const minStartDate = useMemo(() => {
-    if (!maxHistoryDays) return null
-    return rangeForDays(maxHistoryDays).start
-  }, [maxHistoryDays])
-
-  const maxRelDays = maxHistoryDays ?? 365
-
-  const activeQuick = useMemo(() => {
-    for (const q of quickRanges) {
-      const r = rangeForDays(q.days)
-      if (r.start === start && r.end === end) return q.label
-    }
-    return null
-  }, [start, end, quickRanges])
-
-  useEffect(() => {
-    if (open) {
-      setDraftStart(start)
-      setDraftEnd(end)
-    }
-  }, [open, start, end])
-
-  useEffect(() => {
-    const onDoc = (e: MouseEvent) => {
-      if (!rootRef.current?.contains(e.target as Node)) setOpen(false)
-    }
-    document.addEventListener('mousedown', onDoc)
-    return () => document.removeEventListener('mousedown', onDoc)
-  }, [])
-
-  const applyRelativeDays = (days: number) => {
-    const r = rangeForDays(days)
-    onApply(r.start, r.end)
-    setOpen(false)
-  }
-
-  const applyAbsolute = () => {
-    if (!draftStart || !draftEnd) return
-    if (draftEnd < draftStart) return
-    onApply(draftStart, draftEnd)
-    setOpen(false)
-  }
-
-  const pillClass = (active: boolean) =>
-    `rounded-md px-2.5 py-1 text-xs font-medium transition-colors border ${
-      active ? usageTheme.pillActive : usageTheme.pillInactive
-    }`
-
-  return (
-    <div ref={rootRef} className="relative flex flex-wrap items-center gap-2 flex-1 min-w-0">
-      <div className="flex items-center gap-0.5 rounded-lg border border-[#fde047]/50 bg-white/80 p-0.5">
-        {quickRanges.map((q) => (
-          <button
-            key={q.label}
-            type="button"
-            onClick={() => {
-              const r = rangeForDays(q.days)
-              onApply(r.start, r.end)
-            }}
-            className={pillClass(activeQuick === q.label)}
-          >
-            {q.label}
-          </button>
-        ))}
-        <button
-          type="button"
-          onClick={() => setOpen((v) => !v)}
-          className={pillClass(open || !activeQuick)}
-        >
-          <Calendar className="h-3.5 w-3.5 inline mr-1" />
-          Custom
-        </button>
-      </div>
-
-      <span className="text-xs text-gray-500 truncate hidden md:inline">
-        {formatDisplay(start, end)}
-        <span className="text-gray-400">
-          {' '}
-          · {formatUsageTimezoneLabel(getUsageTimezone())}
-        </span>
-      </span>
-
-      {open ? (
-        <div className="absolute top-full left-0 z-50 mt-1 min-w-[20rem] max-w-lg rounded-xl border border-[#fde047]/60 bg-white p-4 shadow-xl ring-1 ring-[#fef9c3]">
-          <div className="flex gap-2 mb-4">
-            <button
-              type="button"
-              onClick={() => setMode('absolute')}
-              className={`rounded-full px-4 py-1.5 text-sm font-medium border ${
-                mode === 'absolute' ? usageTheme.pillActive : usageTheme.pillMuted
-              }`}
-            >
-              Absolute
-            </button>
-            <button
-              type="button"
-              onClick={() => setMode('relative')}
-              className={`rounded-full px-4 py-1.5 text-sm font-medium border ${
-                mode === 'relative' ? usageTheme.pillActive : usageTheme.pillMuted
-              }`}
-            >
-              Relative
-            </button>
-          </div>
-
-          {mode === 'relative' ? (
-            <div className="space-y-3">
-              <p className="text-xs text-gray-500">
-                Dates use your local timezone ({formatUsageTimezoneLabel(getUsageTimezone())}).
-                Usage is stored by UTC day; filters include activity that happened on each
-                selected local day.
-              </p>
-              <div>
-                <p className="text-xs font-medium text-gray-500 mb-2">Days</p>
-                <div className="flex flex-wrap gap-2">
-                  {RELATIVE_DAYS.map((d) => (
-                    <button
-                      key={d}
-                      type="button"
-                      onClick={() => applyRelativeDays(d)}
-                      className="min-w-[2.5rem] rounded-lg border border-gray-200 px-3 py-2 text-sm font-medium hover:border-[#facc15] hover:bg-[#fefce8]"
-                    >
-                      {d}
-                    </button>
-                  ))}
-                </div>
-              </div>
-              <div>
-                <p className="text-xs font-medium text-gray-500 mb-2">Weeks</p>
-                <div className="flex flex-wrap gap-2">
-                  {RELATIVE_WEEKS.map((w) => (
-                    <button
-                      key={w}
-                      type="button"
-                      onClick={() => applyRelativeDays(w * 7)}
-                      className="min-w-[2.5rem] rounded-lg border border-gray-200 px-3 py-2 text-sm font-medium hover:border-[#facc15] hover:bg-[#fefce8]"
-                    >
-                      {w}
-                    </button>
-                  ))}
-                </div>
-              </div>
-              <div className="flex items-end gap-3 pt-2 border-t border-gray-100">
-                <label className="flex flex-col gap-1">
-                  <span className="text-xs font-medium text-gray-500">Custom days</span>
-                  <input
-                    type="number"
-                    min={1}
-                    max={maxRelDays}
-                    value={relDays}
-                    onChange={(e) =>
-                      setRelDays(Math.min(maxRelDays, Math.max(1, Number(e.target.value) || 1)))
-                    }
-                    className="w-24 rounded-lg border border-gray-200 px-3 py-2 text-sm"
-                  />
-                </label>
-                <button type="button" onClick={() => applyRelativeDays(relDays)} className={usageTheme.applyBtn}>
-                  Apply
-                </button>
-              </div>
-            </div>
-          ) : (
-            <div className="space-y-3">
-              <div className="grid grid-cols-2 gap-3">
-                <label className="flex flex-col gap-1">
-                  <span className="text-xs font-medium text-gray-500">Start date</span>
-                  <input
-                    type="date"
-                    value={draftStart}
-                    min={minStartDate ?? undefined}
-                    onChange={(e) => setDraftStart(e.target.value)}
-                    className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
-                  />
-                </label>
-                <label className="flex flex-col gap-1">
-                  <span className="text-xs font-medium text-gray-500">End date</span>
-                  <input
-                    type="date"
-                    value={draftEnd}
-                    onChange={(e) => setDraftEnd(e.target.value)}
-                    className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
-                  />
-                </label>
-              </div>
-              <div className="flex justify-end gap-2 pt-2 border-t border-gray-100">
-                <button
-                  type="button"
-                  onClick={() => setOpen(false)}
-                  className="px-3 py-1.5 text-sm font-medium text-gray-600"
-                >
-                  Cancel
-                </button>
-                <button type="button" onClick={applyAbsolute} className={usageTheme.applyBtn}>
-                  Apply
-                </button>
-              </div>
-            </div>
-          )}
-        </div>
-      ) : null}
-    </div>
-  )
-}
-
-export function defaultUsageDateRange(): { start: string; end: string } {
-  const today = toDateInput(new Date())
-  return { start: today, end: today }
-}
+import { useEffect, useMemo, useRef, useState } from 'react'
+import { Calendar } from 'lucide-react'
+import { usageTheme } from './usageTheme'
+import { getUsageTimezone, formatUsageTimezoneLabel } from './usageTimezone'
+
+type Mode = 'relative' | 'absolute'
+
+type UsageDateRangePickerProps = {
+  start: string
+  end: string
+  onApply: (start: string, end: string) => void
+  maxHistoryDays?: number | null
+}
+
+function toDateInput(d: Date): string {
+  const y = d.getFullYear()
+  const m = String(d.getMonth() + 1).padStart(2, '0')
+  const day = String(d.getDate()).padStart(2, '0')
+  return `${y}-${m}-${day}`
+}
+
+function formatDisplay(start: string, end: string): string {
+  if (start === end) return start
+  return `${start} → ${end}`
+}
+
+export function rangeForDays(days: number): { start: string; end: string } {
+  const end = new Date()
+  const start = new Date()
+  start.setDate(start.getDate() - (days - 1))
+  return { start: toDateInput(start), end: toDateInput(end) }
+}
+
+export function isRangeWithinMaxDays(start: string, end: string, maxDays: number): boolean {
+  const r = rangeForDays(maxDays)
+  return start >= r.start && end <= r.end
+}
+
+const QUICK_RANGES = [
+  { label: '1d', days: 1 },
+  { label: '7d', days: 7 },
+  { label: '30d', days: 30 },
+  { label: '90d', days: 90 },
+] as const
+
+const RELATIVE_DAYS = [1, 2, 3, 4, 5, 6]
+const RELATIVE_WEEKS = [1, 2, 3, 4]
+
+export default function UsageDateRangePicker({
+  start,
+  end,
+  onApply,
+  maxHistoryDays = null,
+}: UsageDateRangePickerProps) {
+  const [open, setOpen] = useState(false)
+  const [mode, setMode] = useState<Mode>('relative')
+  const [draftStart, setDraftStart] = useState(start)
+  const [draftEnd, setDraftEnd] = useState(end)
+  const [relDays, setRelDays] = useState(1)
+  const rootRef = useRef<HTMLDivElement>(null)
+
+  const quickRanges = useMemo(() => {
+    if (!maxHistoryDays) return QUICK_RANGES
+    return QUICK_RANGES.filter((q) => q.days <= maxHistoryDays)
+  }, [maxHistoryDays])
+
+  const minStartDate = useMemo(() => {
+    if (!maxHistoryDays) return null
+    return rangeForDays(maxHistoryDays).start
+  }, [maxHistoryDays])
+
+  const maxRelDays = maxHistoryDays ?? 365
+
+  const activeQuick = useMemo(() => {
+    for (const q of quickRanges) {
+      const r = rangeForDays(q.days)
+      if (r.start === start && r.end === end) return q.label
+    }
+    return null
+  }, [start, end, quickRanges])
+
+  useEffect(() => {
+    if (open) {
+      setDraftStart(start)
+      setDraftEnd(end)
+    }
+  }, [open, start, end])
+
+  useEffect(() => {
+    const onDoc = (e: MouseEvent) => {
+      if (!rootRef.current?.contains(e.target as Node)) setOpen(false)
+    }
+    document.addEventListener('mousedown', onDoc)
+    return () => document.removeEventListener('mousedown', onDoc)
+  }, [])
+
+  const applyRelativeDays = (days: number) => {
+    const r = rangeForDays(days)
+    onApply(r.start, r.end)
+    setOpen(false)
+  }
+
+  const applyAbsolute = () => {
+    if (!draftStart || !draftEnd) return
+    if (draftEnd < draftStart) return
+    onApply(draftStart, draftEnd)
+    setOpen(false)
+  }
+
+  const pillClass = (active: boolean) =>
+    `rounded-md px-2.5 py-1 text-xs font-medium transition-colors border ${
+      active ? usageTheme.pillActive : usageTheme.pillInactive
+    }`
+
+  return (
+    <div ref={rootRef} className="relative flex flex-wrap items-center gap-2 flex-1 min-w-0">
+      <div className="flex items-center gap-0.5 rounded-lg border border-[#fde047]/50 bg-white/80 p-0.5">
+        {quickRanges.map((q) => (
+          <button
+            key={q.label}
+            type="button"
+            onClick={() => {
+              const r = rangeForDays(q.days)
+              onApply(r.start, r.end)
+            }}
+            className={pillClass(activeQuick === q.label)}
+          >
+            {q.label}
+          </button>
+        ))}
+        <button
+          type="button"
+          onClick={() => setOpen((v) => !v)}
+          className={pillClass(open || !activeQuick)}
+        >
+          <Calendar className="h-3.5 w-3.5 inline mr-1" />
+          Custom
+        </button>
+      </div>
+
+      <span className="text-xs text-gray-500 truncate hidden md:inline">
+        {formatDisplay(start, end)}
+        <span className="text-gray-400">
+          {' '}
+          · {formatUsageTimezoneLabel(getUsageTimezone())}
+        </span>
+      </span>
+
+      {open ? (
+        <div className="absolute top-full left-0 z-50 mt-1 min-w-[20rem] max-w-lg rounded-xl border border-[#fde047]/60 bg-white p-4 shadow-xl ring-1 ring-[#fef9c3]">
+          <div className="flex gap-2 mb-4">
+            <button
+              type="button"
+              onClick={() => setMode('absolute')}
+              className={`rounded-full px-4 py-1.5 text-sm font-medium border ${
+                mode === 'absolute' ? usageTheme.pillActive : usageTheme.pillMuted
+              }`}
+            >
+              Absolute
+            </button>
+            <button
+              type="button"
+              onClick={() => setMode('relative')}
+              className={`rounded-full px-4 py-1.5 text-sm font-medium border ${
+                mode === 'relative' ? usageTheme.pillActive : usageTheme.pillMuted
+              }`}
+            >
+              Relative
+            </button>
+          </div>
+
+          {mode === 'relative' ? (
+            <div className="space-y-3">
+              <p className="text-xs text-gray-500">
+                Dates use your local timezone ({formatUsageTimezoneLabel(getUsageTimezone())}).
+                Usage is stored by UTC day; filters include activity that happened on each
+                selected local day.
+              </p>
+              <div>
+                <p className="text-xs font-medium text-gray-500 mb-2">Days</p>
+                <div className="flex flex-wrap gap-2">
+                  {RELATIVE_DAYS.map((d) => (
+                    <button
+                      key={d}
+                      type="button"
+                      onClick={() => applyRelativeDays(d)}
+                      className="min-w-[2.5rem] rounded-lg border border-gray-200 px-3 py-2 text-sm font-medium hover:border-[#facc15] hover:bg-[#fefce8]"
+                    >
+                      {d}
+                    </button>
+                  ))}
+                </div>
+              </div>
+              <div>
+                <p className="text-xs font-medium text-gray-500 mb-2">Weeks</p>
+                <div className="flex flex-wrap gap-2">
+                  {RELATIVE_WEEKS.map((w) => (
+                    <button
+                      key={w}
+                      type="button"
+                      onClick={() => applyRelativeDays(w * 7)}
+                      className="min-w-[2.5rem] rounded-lg border border-gray-200 px-3 py-2 text-sm font-medium hover:border-[#facc15] hover:bg-[#fefce8]"
+                    >
+                      {w}
+                    </button>
+                  ))}
+                </div>
+              </div>
+              <div className="flex items-end gap-3 pt-2 border-t border-gray-100">
+                <label className="flex flex-col gap-1">
+                  <span className="text-xs font-medium text-gray-500">Custom days</span>
+                  <input
+                    type="number"
+                    min={1}
+                    max={maxRelDays}
+                    value={relDays}
+                    onChange={(e) =>
+                      setRelDays(Math.min(maxRelDays, Math.max(1, Number(e.target.value) || 1)))
+                    }
+                    className="w-24 rounded-lg border border-gray-200 px-3 py-2 text-sm"
+                  />
+                </label>
+                <button type="button" onClick={() => applyRelativeDays(relDays)} className={usageTheme.applyBtn}>
+                  Apply
+                </button>
+              </div>
+            </div>
+          ) : (
+            <div className="space-y-3">
+              <div className="grid grid-cols-2 gap-3">
+                <label className="flex flex-col gap-1">
+                  <span className="text-xs font-medium text-gray-500">Start date</span>
+                  <input
+                    type="date"
+                    value={draftStart}
+                    min={minStartDate ?? undefined}
+                    onChange={(e) => setDraftStart(e.target.value)}
+                    className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
+                  />
+                </label>
+                <label className="flex flex-col gap-1">
+                  <span className="text-xs font-medium text-gray-500">End date</span>
+                  <input
+                    type="date"
+                    value={draftEnd}
+                    onChange={(e) => setDraftEnd(e.target.value)}
+                    className="rounded-lg border border-gray-200 px-3 py-2 text-sm"
+                  />
+                </label>
+              </div>
+              <div className="flex justify-end gap-2 pt-2 border-t border-gray-100">
+                <button
+                  type="button"
+                  onClick={() => setOpen(false)}
+                  className="px-3 py-1.5 text-sm font-medium text-gray-600"
+                >
+                  Cancel
+                </button>
+                <button type="button" onClick={applyAbsolute} className={usageTheme.applyBtn}>
+                  Apply
+                </button>
+              </div>
+            </div>
+          )}
+        </div>
+      ) : null}
+    </div>
+  )
+}
+
+export function defaultUsageDateRange(): { start: string; end: string } {
+  return rangeForDays(7)
+}
diff --git a/tests/test_api/test_org_usage_routes.py b/tests/test_api/test_org_usage_routes.py
new file mode 100644
index 00000000..f83e0af5
--- /dev/null
+++ b/tests/test_api/test_org_usage_routes.py
@@ -0,0 +1,12 @@
+"""Ensure org usage API routes are mounted on the v1 router."""
+
+from app.api.v1.api import api_router
+
+
+def test_org_usage_routes_are_registered():
+    paths = {route.path for route in api_router.routes if hasattr(route, "path")}
+    assert "/organizations/usage/summary" in paths
+    assert "/organizations/usage/breakdown" in paths
+    assert "/organizations/usage/filters" in paths
+    assert "/organizations/usage/fx-rate" in paths
+    assert "/organizations/usage/pricing/overrides" in paths
diff --git a/tests/test_services/test_usage/test_call_import_context.py b/tests/test_services/test_usage/test_call_import_context.py
index 6d798f65..0466be72 100644
--- a/tests/test_services/test_usage/test_call_import_context.py
+++ b/tests/test_services/test_usage/test_call_import_context.py
@@ -9,33 +9,26 @@
     call_import_row_usage_context,
     enrich_usage_context_workspace,
 )
+from app.services.usage.llm_usage import _bucket_prefix
 
 
-def test_evaluation_row_gets_distinct_bucket_context():
+def test_evaluation_rows_share_bucket_context():
     org_id = uuid4()
     ws_id = uuid4()
     eval_id = uuid4()
     import_id = uuid4()
-    row_a = uuid4()
-    row_b = uuid4()
-    source_a = uuid4()
-    source_b = uuid4()
 
     ctx_a = call_import_evaluation_usage_context(
         organization_id=org_id,
         workspace_id=ws_id,
         evaluation_id=eval_id,
         call_import_id=import_id,
-        evaluation_row_id=row_a,
-        call_import_row_id=source_a,
     )
     ctx_b = call_import_evaluation_usage_context(
         organization_id=org_id,
         workspace_id=ws_id,
         evaluation_id=eval_id,
         call_import_id=import_id,
-        evaluation_row_id=row_b,
-        call_import_row_id=source_b,
     )
 
     bucket_a = build_bucket_context(
@@ -48,45 +41,75 @@ def test_evaluation_row_gets_distinct_bucket_context():
         resource_type=ctx_b.resource_type,
         extra=ctx_b.extra,
     )
-    assert bucket_a != bucket_b
-    assert bucket_a["evaluation_row_id"] == str(row_a)
+    assert bucket_a == bucket_b
+    assert bucket_a["evaluation_id"] == str(eval_id)
     assert bucket_a["call_import_id"] == str(import_id)
+    assert "evaluation_row_id" not in bucket_a
+    assert "call_import_row_id" not in bucket_a
 
 
-def test_row_only_transcribe_context():
+def test_evaluation_rows_share_redis_bucket_prefix():
+    from datetime import date
+
+    org_id = uuid4()
+    ws_id = uuid4()
+    eval_id = uuid4()
+    import_id = uuid4()
+    model = "gpt-test"
+    usage_date = date(2026, 8, 15)
+
+    def prefix_for(_row_id: object) -> str:
+        ctx = call_import_evaluation_usage_context(
+            organization_id=org_id,
+            workspace_id=ws_id,
+            evaluation_id=eval_id,
+            call_import_id=import_id,
+        )
+        context = build_bucket_context(
+            resource_id=ctx.resource_id,
+            resource_type=ctx.resource_type,
+            extra=ctx.extra,
+        )
+        return _bucket_prefix(
+            workspace_id=ws_id,
+            product_section=ctx.product_section.value,
+            model=model,
+            context=context,
+            usage_date=usage_date,
+            usage_kind="llm",
+        )
+
+    assert prefix_for(uuid4()) == prefix_for(uuid4())
+
+
+def test_row_only_transcribe_context_rolls_up_to_call_import():
     org_id = uuid4()
     import_id = uuid4()
-    row_id = uuid4()
     ctx = call_import_row_usage_context(
         organization_id=org_id,
         workspace_id=None,
         call_import_id=import_id,
-        call_import_row_id=row_id,
     )
-    assert ctx.extra is not None
-    assert ctx.extra["call_import_row_id"] == str(row_id)
+    assert ctx.extra == {"call_import_id": str(import_id)}
     assert ctx.resource_type == "call_import"
+    assert "call_import_row_id" not in (ctx.extra or {})
 
 
 def test_call_import_ids_from_evaluation_context():
     org_id = uuid4()
     eval_id = uuid4()
     import_id = uuid4()
-    row_id = uuid4()
-    source_id = uuid4()
     ctx = call_import_evaluation_usage_context(
         organization_id=org_id,
         workspace_id=uuid4(),
         evaluation_id=eval_id,
         call_import_id=import_id,
-        evaluation_row_id=row_id,
-        call_import_row_id=source_id,
     )
     ids = call_import_ids_from_usage_context(ctx)
     assert ids["call_import_id"] == import_id
     assert ids["evaluation_id"] == eval_id
-    assert ids["evaluation_row_id"] == row_id
-    assert ids["call_import_row_id"] == source_id
+    assert ids["evaluation_row_id"] is None
+    assert ids["call_import_row_id"] is None
 
 
 def test_enrich_usage_context_workspace_noop_when_set():
diff --git a/tests/test_services/test_usage/test_usage_read_cache.py b/tests/test_services/test_usage/test_usage_read_cache.py
index dc7884fd..20d1bd61 100644
--- a/tests/test_services/test_usage/test_usage_read_cache.py
+++ b/tests/test_services/test_usage/test_usage_read_cache.py
@@ -1,69 +1,91 @@
-"""Tests for usage read cache."""
-
-from __future__ import annotations
-
-from datetime import date
-from uuid import uuid4
-
-import pytest
-
-from app.core.usage_entitlement import UsagePolicySnapshot
-from app.services.usage import read_cache as cache_mod
-from app.services.usage.access import UsageAccessResult
-from app.services.usage.read_cache import (
-    cache_key_for,
-    get_cached_response,
-    invalidate_org_usage_read_cache,
-    set_cached_response,
-)
-from tests.test_services.test_usage.test_llm_usage import _FakeRedis
-
-
-@pytest.fixture
-def fake_redis(monkeypatch):
-    client = _FakeRedis()
-    cache_mod._redis = client
-    monkeypatch.setattr(cache_mod.redis, "from_url", lambda *_args, **_kwargs: client)
-    yield client
-    cache_mod._redis = None
-
-
-def _access() -> UsageAccessResult:
-    policy = UsagePolicySnapshot(extended_history=False, max_history_days=7)
-    return UsageAccessResult(
-        display_start=date(2026, 8, 1),
-        display_end=date(2026, 8, 7),
-        filter_start=date(2026, 8, 1),
-        filter_end=date(2026, 8, 8),
-        enforced_filter_floor=date(2026, 8, 1),
-        policy=policy,
-        range_clamped=False,
-    )
-
-
-def test_usage_read_cache_round_trip(fake_redis, monkeypatch):
-    org_id = uuid4()
-    access = _access()
-    key = cache_key_for(access, workspace_id=None)
-    payload = {"start": "2026-08-01", "end": "2026-08-07", "totals": {}}
-
-    set_cached_response(org_id, "summary", key, payload)
-    cached = get_cached_response(org_id, "summary", key)
-
-    assert cached == payload
-
-
-def test_usage_read_cache_invalidate_org(fake_redis, monkeypatch):
-    org_id = uuid4()
-    other_org = uuid4()
-    access = _access()
-    key = cache_key_for(access)
-
-    set_cached_response(org_id, "summary", key, {"x": 1})
-    set_cached_response(other_org, "summary", key, {"x": 2})
-
-    deleted = invalidate_org_usage_read_cache(org_id)
-
-    assert deleted >= 1
-    assert get_cached_response(org_id, "summary", key) is None
-    assert get_cached_response(other_org, "summary", key) is not None
+"""Tests for usage read cache."""
+
+from __future__ import annotations
+
+from datetime import date
+from uuid import uuid4
+
+import pytest
+
+from app.core.usage_entitlement import UsagePolicySnapshot
+from app.services.usage import read_cache as cache_mod
+from app.services.usage.access import UsageAccessResult
+from app.services.usage.read_cache import (
+    cache_key_for,
+    get_cached_response,
+    invalidate_org_usage_read_cache,
+    set_cached_response,
+)
+from tests.test_services.test_usage.test_llm_usage import _FakeRedis
+
+
+@pytest.fixture
+def fake_redis(monkeypatch):
+    client = _FakeRedis()
+    cache_mod._redis = client
+    monkeypatch.setattr(cache_mod.redis, "from_url", lambda *_args, **_kwargs: client)
+    yield client
+    cache_mod._redis = None
+
+
+def _access() -> UsageAccessResult:
+    policy = UsagePolicySnapshot(extended_history=False, max_history_days=7)
+    return UsageAccessResult(
+        display_start=date(2026, 8, 1),
+        display_end=date(2026, 8, 7),
+        filter_start=date(2026, 8, 1),
+        filter_end=date(2026, 8, 8),
+        enforced_filter_floor=date(2026, 8, 1),
+        policy=policy,
+        range_clamped=False,
+    )
+
+
+def test_usage_read_cache_skips_empty_summary(fake_redis, monkeypatch):
+    org_id = uuid4()
+    access = _access()
+    key = cache_key_for(access)
+    empty = {
+        "start": "2026-08-15",
+        "end": "2026-08-15",
+        "totals": {"prompt_tokens": 0, "completion_tokens": 0, "call_count": 0},
+    }
+    set_cached_response(org_id, "summary", key, empty)
+    assert get_cached_response(org_id, "summary", key) is None
+
+
+def test_usage_read_cache_round_trip(fake_redis, monkeypatch):
+    org_id = uuid4()
+    access = _access()
+    key = cache_key_for(access, workspace_id=None)
+    payload = {
+        "start": "2026-08-01",
+        "end": "2026-08-07",
+        "totals": {"prompt_tokens": 10, "completion_tokens": 5, "call_count": 1},
+    }
+
+    set_cached_response(org_id, "summary", key, payload)
+    cached = get_cached_response(org_id, "summary", key)
+
+    assert cached == payload
+
+
+def test_usage_read_cache_invalidate_org(fake_redis, monkeypatch):
+    org_id = uuid4()
+    other_org = uuid4()
+    access = _access()
+    key = cache_key_for(access)
+    payload = {
+        "start": "2026-08-01",
+        "end": "2026-08-07",
+        "totals": {"prompt_tokens": 10, "completion_tokens": 5, "call_count": 1},
+    }
+
+    set_cached_response(org_id, "summary", key, payload)
+    set_cached_response(other_org, "summary", key, payload)
+
+    deleted = invalidate_org_usage_read_cache(org_id)
+
+    assert deleted >= 1
+    assert get_cached_response(org_id, "summary", key) is None
+    assert get_cached_response(other_org, "summary", key) is not None

From a48dc64f1fa7e3e62a66fdc2becb8b073760eecf Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Sat, 15 Aug 2026 16:16:23 +0530
Subject: [PATCH 25/55] feat: add enabled models support for AI providers and
 new endpoint for available models

---
 app/api/v1/routes/aiproviders.py              |   1 +
 app/api/v1/routes/usage_pricing.py            |  16 +
 .../076_ai_provider_enabled_models.py         |  38 ++
 app/models/database.py                        |   2 +
 app/models/schemas.py                         |  39 ++
 app/services/usage/enabled_models.py          | 130 +++++
 app/services/usage/fx_rates.py                |  77 ++-
 app/services/usage/pricing_overrides.py       |  24 +-
 frontend/src/App.tsx                          |  32 +-
 frontend/src/lib/api.ts                       |  31 ++
 frontend/src/lib/llmModelOptions.ts           |   9 +
 frontend/src/lib/usageCurrency.ts             |  30 +-
 .../AIProviderEnabledModelsStep.tsx           | 213 ++++++++
 .../src/pages/configurations/Integrations.tsx | 128 ++++-
 .../pages/usage/ProviderCredentialSelect.tsx  | 172 +++++++
 frontend/src/pages/usage/SearchableSelect.tsx | 144 ++++--
 frontend/src/pages/usage/Usage.tsx            | 112 ++---
 .../pages/usage/UsageCostBreakdownModal.tsx   |  11 +-
 frontend/src/pages/usage/UsagePage.tsx        |  72 +++
 frontend/src/pages/usage/UsagePricing.tsx     | 462 +++++++++++++-----
 .../src/pages/usage/pricingModelOptions.ts    |  72 +++
 .../src/pages/usage/useAnchoredDropdown.ts    |  37 ++
 frontend/src/types/api.ts                     |   3 +
 .../test_usage/test_enabled_models.py         |  66 +++
 .../test_services/test_usage/test_fx_rates.py |  69 +++
 25 files changed, 1667 insertions(+), 323 deletions(-)
 create mode 100644 app/migrations/076_ai_provider_enabled_models.py
 create mode 100644 app/services/usage/enabled_models.py
 create mode 100644 frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx
 create mode 100644 frontend/src/pages/usage/ProviderCredentialSelect.tsx
 create mode 100644 frontend/src/pages/usage/UsagePage.tsx
 create mode 100644 frontend/src/pages/usage/pricingModelOptions.ts
 create mode 100644 frontend/src/pages/usage/useAnchoredDropdown.ts
 create mode 100644 tests/test_services/test_usage/test_enabled_models.py
 create mode 100644 tests/test_services/test_usage/test_fx_rates.py

diff --git a/app/api/v1/routes/aiproviders.py b/app/api/v1/routes/aiproviders.py
index b7f8b9e6..7f8f8e01 100644
--- a/app/api/v1/routes/aiproviders.py
+++ b/app/api/v1/routes/aiproviders.py
@@ -198,6 +198,7 @@ async def create_aiprovider(
             else None
         ),
         gateway_extra_headers=aiprovider.gateway_extra_headers,
+        enabled_models=aiprovider.enabled_models,
     )
     db.add(db_aiprovider)
     db.flush()
diff --git a/app/api/v1/routes/usage_pricing.py b/app/api/v1/routes/usage_pricing.py
index 0716eef6..c48deaeb 100644
--- a/app/api/v1/routes/usage_pricing.py
+++ b/app/api/v1/routes/usage_pricing.py
@@ -112,6 +112,22 @@ class UsageRecomputeJobResponse(BaseModel):
     completed_at: Optional[datetime] = None
 
 
+class AvailableModelsResponse(BaseModel):
+    models: List[str]
+
+
+@router.get("/available-models", response_model=AvailableModelsResponse)
+def list_pricing_available_models(
+    organization_id: UUID = Depends(get_organization_id),
+    db: Session = Depends(get_db),
+):
+    from app.services.usage.enabled_models import org_pricing_eligible_models
+
+    return AvailableModelsResponse(
+        models=org_pricing_eligible_models(db, organization_id),
+    )
+
+
 @router.get("", response_model=List[EffectivePricingResponse])
 def list_effective_usage_pricing(
     usage_kind: Optional[str] = Query(None),
diff --git a/app/migrations/076_ai_provider_enabled_models.py b/app/migrations/076_ai_provider_enabled_models.py
new file mode 100644
index 00000000..c1714f28
--- /dev/null
+++ b/app/migrations/076_ai_provider_enabled_models.py
@@ -0,0 +1,38 @@
+"""Migration: per-credential enabled model allowlist for integrations."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add enabled_models JSONB to aiproviders"
+
+
+def _column_exists(db: Session, table: str, column: str) -> bool:
+    return (
+        db.execute(
+            text(
+                """
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+                """
+            ),
+            {"table_name": table, "column_name": column},
+        ).first()
+        is not None
+    )
+
+
+def upgrade(db: Session) -> None:
+    if not _column_exists(db, "aiproviders", "enabled_models"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE aiproviders
+                ADD COLUMN enabled_models JSONB NULL
+                """
+            )
+        )
+
+
+def downgrade(db: Session) -> None:
+    if _column_exists(db, "aiproviders", "enabled_models"):
+        db.execute(text("ALTER TABLE aiproviders DROP COLUMN enabled_models"))
diff --git a/app/models/database.py b/app/models/database.py
index 3e886445..92350139 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -702,6 +702,8 @@ class AIProvider(Base):
     gateway_auth_secret = Column(String, nullable=True)
     # Arbitrary HTTP headers sent with gateway-routed LiteLLM calls
     gateway_extra_headers = Column(JSON, nullable=True)
+    # Non-empty list restricts model pickers; null/empty = all catalog models for provider.
+    enabled_models = Column(JSON, nullable=True)
     created_at = Column(DateTime(timezone=True), server_default=func.now())
     updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
     last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
diff --git a/app/models/schemas.py b/app/models/schemas.py
index 1e6f0e7f..25241e77 100644
--- a/app/models/schemas.py
+++ b/app/models/schemas.py
@@ -970,6 +970,13 @@ class AIProviderCreate(BaseModel):
         None,
         description="Arbitrary HTTP headers sent with gateway-routed LiteLLM calls.",
     )
+    enabled_models: Optional[List[str]] = Field(
+        None,
+        description=(
+            "Allowlisted model names for this credential. "
+            "Null or empty means all catalog models for the provider."
+        ),
+    )
     is_default: Optional[bool] = Field(
         None,
         description=(
@@ -1053,6 +1060,21 @@ def validate_gateway_auth_secret(cls, v: Optional[str]) -> Optional[str]:
     def validate_gateway_extra_headers(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, str]]:
         return _validate_gateway_extra_headers(v)
 
+    @field_validator("enabled_models")
+    @classmethod
+    def validate_enabled_models_create(cls, v: Optional[List[str]]) -> Optional[List[str]]:
+        if v is None:
+            return None
+        seen: set[str] = set()
+        out: list[str] = []
+        for item in v:
+            name = str(item).strip()
+            if not name or name in seen:
+                continue
+            seen.add(name)
+            out.append(name)
+        return out or None
+
 
 class AIProviderUpdate(BaseModel):
     """Schema for updating an AI Provider."""
@@ -1069,6 +1091,7 @@ class AIProviderUpdate(BaseModel):
     gateway_auth_secret: Optional[str] = None
     clear_gateway_auth_secret: bool = False
     gateway_extra_headers: Optional[Dict[str, str]] = None
+    enabled_models: Optional[List[str]] = None
 
     @field_validator("gateway_model")
     @classmethod
@@ -1127,6 +1150,21 @@ def validate_gateway_extra_headers_update(
     ) -> Optional[Dict[str, str]]:
         return _validate_gateway_extra_headers(v)
 
+    @field_validator("enabled_models")
+    @classmethod
+    def validate_enabled_models_update(cls, v: Optional[List[str]]) -> Optional[List[str]]:
+        if v is None:
+            return None
+        seen: set[str] = set()
+        out: list[str] = []
+        for item in v:
+            name = str(item).strip()
+            if not name or name in seen:
+                continue
+            seen.add(name)
+            out.append(name)
+        return out or None
+
 
 class AIProviderResponse(BaseModel):
     """Schema for AI Provider response."""
@@ -1145,6 +1183,7 @@ class AIProviderResponse(BaseModel):
     gateway_auth_secret_env: Optional[str] = None
     has_gateway_auth_secret: bool = False
     gateway_extra_headers: Optional[Dict[str, str]] = None
+    enabled_models: Optional[List[str]] = None
     gateway_managed: bool = False
     effective_routing: Literal["inherit", "direct", "gateway", "bifrost", "litellm_proxy"] = "inherit"
     effective_gateway_interface: Literal["litellm_shim", "native_openai"] = "litellm_shim"
diff --git a/app/services/usage/enabled_models.py b/app/services/usage/enabled_models.py
new file mode 100644
index 00000000..87d9652e
--- /dev/null
+++ b/app/services/usage/enabled_models.py
@@ -0,0 +1,130 @@
+"""Org- and credential-level enabled model allowlists."""
+
+from __future__ import annotations
+
+from typing import Iterable, List, Optional, Set
+from uuid import UUID
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+from app.models.database import AIProvider, ModelProvider
+from app.services.ai.model_config_service import ModelConfigService
+
+
+def normalize_enabled_models(raw: Optional[Iterable[str]]) -> Optional[List[str]]:
+    if raw is None:
+        return None
+    seen: set[str] = set()
+    normalized: list[str] = []
+    for item in raw:
+        if item is None:
+            continue
+        name = str(item).strip()
+        if not name or name in seen:
+            continue
+        seen.add(name)
+        normalized.append(name)
+    return normalized or None
+
+
+def catalog_models_for_provider(provider: str) -> List[str]:
+    service = ModelConfigService()
+    try:
+        provider_enum = ModelProvider(provider.lower())
+    except ValueError:
+        return []
+    options = service.get_model_options_by_provider(provider_enum)
+    models: list[str] = []
+    for key in ("llm", "stt", "tts", "s2s"):
+        models.extend(options.get(key) or [])
+    return sorted({m for m in models if m})
+
+
+def effective_enabled_models_for_credential(credential: AIProvider) -> Optional[List[str]]:
+    """Return explicit allowlist, or None meaning unrestricted (full provider catalog)."""
+    return normalize_enabled_models(credential.enabled_models)
+
+
+def filter_models_by_credential(
+    credential: Optional[AIProvider],
+    catalog_models: List[str],
+) -> List[str]:
+    if credential is None:
+        return catalog_models
+    allowlist = effective_enabled_models_for_credential(credential)
+    if not allowlist:
+        return catalog_models
+    allowed = set(allowlist)
+    filtered = [m for m in catalog_models if m in allowed]
+    gateway = (credential.gateway_model or "").strip()
+    if gateway and gateway not in filtered:
+        filtered = [gateway, *filtered]
+    return filtered
+
+
+def _usage_models_for_org(db: Session, organization_id: UUID) -> Set[str]:
+    rows = db.execute(
+        text(
+            """
+            SELECT DISTINCT model
+            FROM llm_usage_daily
+            WHERE organization_id = CAST(:organization_id AS uuid)
+              AND model IS NOT NULL
+              AND model <> ''
+            """
+        ),
+        {"organization_id": str(organization_id)},
+    ).scalars().all()
+    return {str(row).strip() for row in rows if row}
+
+
+def _override_models_for_org(db: Session, organization_id: UUID) -> Set[str]:
+    rows = db.execute(
+        text(
+            """
+            SELECT DISTINCT model
+            FROM org_model_pricing_overrides
+            WHERE organization_id = CAST(:organization_id AS uuid)
+            """
+        ),
+        {"organization_id": str(organization_id)},
+    ).scalars().all()
+    return {str(row).strip() for row in rows if row}
+
+
+def org_pricing_eligible_models(db: Session, organization_id: UUID) -> List[str]:
+    """Models org admins may set pricing overrides for."""
+    models: set[str] = set()
+    providers = (
+        db.query(AIProvider)
+        .filter(
+            AIProvider.organization_id == organization_id,
+            AIProvider.is_active.is_(True),
+        )
+        .all()
+    )
+    any_explicit_allowlist = False
+    for provider in providers:
+        gateway = (provider.gateway_model or "").strip()
+        if gateway:
+            models.add(gateway)
+        allowlist = effective_enabled_models_for_credential(provider)
+        if allowlist:
+            any_explicit_allowlist = True
+            models.update(allowlist)
+        else:
+            models.update(catalog_models_for_provider(provider.provider))
+
+    if not any_explicit_allowlist and not models:
+        for provider in providers:
+            models.update(catalog_models_for_provider(provider.provider))
+
+    models.update(_usage_models_for_org(db, organization_id))
+    models.update(_override_models_for_org(db, organization_id))
+    return sorted(models)
+
+
+def org_union_enabled_models(db: Session, organization_id: UUID) -> List[str]:
+    """All models enabled on any active integration credential."""
+    return org_pricing_eligible_models(db, organization_id)
diff --git a/app/services/usage/fx_rates.py b/app/services/usage/fx_rates.py
index a99cc68b..927026b1 100644
--- a/app/services/usage/fx_rates.py
+++ b/app/services/usage/fx_rates.py
@@ -14,7 +14,8 @@
 
 _REDIS_KEY = "usage:fx:USD_INR"
 _FRANKFURTER_URL = "https://api.frankfurter.dev/v2/rate/USD/INR"
-_DEFAULT_RATE = 83.0
+_DEFAULT_RATE = 95.0
+_CACHE_TTL_SECONDS = 25 * 3600
 _redis: redis.Redis | None = None
 
 
@@ -41,40 +42,84 @@ def _read_cached() -> Optional[dict[str, Any]]:
         if not raw:
             return None
         payload = json.loads(raw)
+        if payload.get("source") != "frankfurter":
+            return None
         if not isinstance(payload.get("rate"), (int, float)):
             return None
         return payload
-    except (redis.RedisError, json.JSONDecodeError):
+    except (redis.RedisError, json.JSONDecodeError) as exc:
+        logger.warning("USD/INR FX cache read failed: {}", exc)
         return None
 
 
+def _write_cached(payload: dict[str, Any]) -> None:
+    try:
+        _client().set(_REDIS_KEY, json.dumps(payload), ex=_CACHE_TTL_SECONDS)
+    except redis.RedisError as exc:
+        logger.warning("USD/INR FX cache write failed: {}", exc)
+
+
+def _parse_frankfurter_payload(data: Any) -> tuple[float, datetime]:
+    if not isinstance(data, dict):
+        raise ValueError(f"Frankfurter response is not an object: {type(data).__name__}")
+
+    base = data.get("base")
+    quote = data.get("quote")
+    if base != "USD" or quote != "INR":
+        raise ValueError(f"Unexpected Frankfurter pair: {base}/{quote}")
+
+    rate_raw = data.get("rate")
+    if not isinstance(rate_raw, (int, float)):
+        raise ValueError(f"Frankfurter rate missing or invalid: {rate_raw!r}")
+
+    rate = float(rate_raw)
+    if rate <= 0:
+        raise ValueError(f"Frankfurter rate must be positive: {rate}")
+
+    date_raw = data.get("date")
+    if not isinstance(date_raw, str) or not date_raw.strip():
+        raise ValueError(f"Frankfurter date missing or invalid: {date_raw!r}")
+
+    as_of = datetime.fromisoformat(date_raw).replace(tzinfo=timezone.utc)
+    return rate, as_of
+
+
+def _fallback_payload(reason: str, *, response_body: Any = None) -> dict[str, Any]:
+    logger.error(
+        "USD/INR FX using hardcoded fallback rate {:.2f} — INR costs may be wrong. reason={} response={}",
+        _DEFAULT_RATE,
+        reason,
+        response_body,
+    )
+    return _cache_payload(_DEFAULT_RATE, datetime.now(timezone.utc), "default")
+
+
 def get_usd_inr_rate() -> dict[str, Any]:
     cached = _read_cached()
     if cached is not None:
         return cached
-    return _cache_payload(_DEFAULT_RATE, datetime.now(timezone.utc), "default")
+    return refresh_usd_inr_rate()
 
 
 def refresh_usd_inr_rate() -> dict[str, Any]:
-    rate = _DEFAULT_RATE
-    as_of = datetime.now(timezone.utc)
-    source = "default"
     try:
         with httpx.Client(timeout=15.0) as client:
             response = client.get(_FRANKFURTER_URL)
             response.raise_for_status()
             data = response.json()
-            fetched = float(data["rate"])
-            if fetched > 0:
-                rate = fetched
-                source = "frankfurter"
-                as_of = datetime.fromisoformat(data["date"]).replace(tzinfo=timezone.utc)
     except Exception as exc:
-        logger.warning("USD/INR FX refresh failed, using fallback: {}", exc)
+        return _fallback_payload(f"Frankfurter request failed: {exc}")
 
-    payload = _cache_payload(rate, as_of, source)
     try:
-        _client().set(_REDIS_KEY, json.dumps(payload), ex=25 * 3600)
-    except redis.RedisError as exc:
-        logger.warning("USD/INR FX cache write failed: {}", exc)
+        rate, as_of = _parse_frankfurter_payload(data)
+    except Exception as exc:
+        return _fallback_payload(f"Frankfurter response parse failed: {exc}", response_body=data)
+
+    payload = _cache_payload(rate, as_of, "frankfurter")
+    _write_cached(payload)
+    logger.info(
+        "USD/INR FX refreshed from Frankfurter: rate={} as_of={}",
+        rate,
+        as_of.date().isoformat(),
+    )
     return payload
diff --git a/app/services/usage/pricing_overrides.py b/app/services/usage/pricing_overrides.py
index 7f55ef01..1214023b 100644
--- a/app/services/usage/pricing_overrides.py
+++ b/app/services/usage/pricing_overrides.py
@@ -54,9 +54,21 @@ def _known_models(db: Session) -> Set[str]:
     return models
 
 
-def validate_model_name(db: Session, model: str) -> None:
-    if model not in _known_models(db):
-        raise HTTPException(status_code=400, detail=f"Unknown model: {model}")
+def validate_model_name(
+    db: Session,
+    model: str,
+    *,
+    organization_id: Optional[UUID] = None,
+) -> None:
+    from app.services.usage.enabled_models import org_pricing_eligible_models
+
+    if organization_id is not None:
+        eligible = set(org_pricing_eligible_models(db, organization_id))
+        if model in eligible:
+            return
+    if model in _known_models(db):
+        return
+    raise HTTPException(status_code=400, detail=f"Unknown model: {model}")
 
 
 def _validate_usage_kind(usage_kind: str) -> str:
@@ -185,7 +197,7 @@ def get_effective_rate(
     usage_kind: str,
     as_of: date,
 ) -> Dict[str, Any]:
-    validate_model_name(db, model)
+    validate_model_name(db, model, organization_id=organization_id)
     kind = _validate_usage_kind(usage_kind)
     resolver = PricingResolver(db)
     effective = resolver.resolve_rate(
@@ -301,7 +313,7 @@ def upsert_override(
     rates: Dict[str, Any],
     recompute: bool = True,
 ) -> Dict[str, Any]:
-    validate_model_name(db, model)
+    validate_model_name(db, model, organization_id=organization_id)
     kind = _validate_usage_kind(usage_kind)
     if effective_to is not None and effective_to < effective_from:
         raise HTTPException(status_code=400, detail="effective_to must be >= effective_from")
@@ -406,7 +418,7 @@ def delete_override(
     effective_from: Optional[date] = None,
     recompute: bool = True,
 ) -> Dict[str, Any]:
-    validate_model_name(db, model)
+    validate_model_name(db, model, organization_id=organization_id)
     kind = _validate_usage_kind(usage_kind)
     filters = [
         "organization_id = CAST(:organization_id AS uuid)",
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index bcf4ad73..72664080 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -14,8 +14,7 @@ import PlatformAdmin from './pages/platform/PlatformAdmin'
 import Dashboard from './pages/dashboard/Dashboard'
 
 // Usage
-import Usage from './pages/usage/Usage'
-import UsagePricing from './pages/usage/UsagePricing'
+import UsagePage, { UsagePricingRedirect } from './pages/usage/UsagePage'
 
 // Prompt Partials
 import PromptPartials from './pages/promptPartials/PromptPartials'
@@ -136,24 +135,6 @@ function EnterpriseGate({ feature, children }: { feature: string; children: Reac
   return <>{children}</>
 }
 
-function EnterpriseLicenseGate({ children }: { children: React.ReactNode }) {
-  const { hasExtendedUsageHistory, isLoaded } = useLicenseStore()
-
-  if (!isLoaded) {
-    return (
-      <div className="flex items-center justify-center h-64">
-        <div className="animate-spin rounded-full h-8 w-8 border-b-2 border-gray-900" />
-      </div>
-    )
-  }
-
-  if (!hasExtendedUsageHistory()) {
-    return <EnterpriseUpgrade />
-  }
-
-  return <>{children}</>
-}
-
 function App() {
   return (
     <BrowserRouter>
@@ -214,15 +195,8 @@ function App() {
           <Route path="observability/calls" element={<ObservabilityCalls />} />
           <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
           <Route path="iam" element={<IAM />} />
-          <Route path="usage" element={<Usage />} />
-          <Route
-            path="usage/pricing"
-            element={
-              <EnterpriseLicenseGate>
-                <UsagePricing />
-              </EnterpriseLicenseGate>
-            }
-          />
+          <Route path="usage" element={<UsagePage />} />
+          <Route path="usage/pricing" element={<UsagePricingRedirect />} />
           <Route
             path="workspace-members"
             element={<Navigate to="/iam?tab=workspace-members" replace />}
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index ae19a698..aaca0b6c 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -3443,6 +3443,37 @@ class ApiClient {
     return response.data
   }
 
+  async listUsagePricingAvailableModels(): Promise<{ models: string[] }> {
+    const response = await this.client.get(
+      '/api/v1/organizations/usage/pricing/available-models',
+    )
+    return response.data
+  }
+
+  async getUsagePricingEffective(
+    model: string,
+    params?: { usage_kind?: string; as_of?: string },
+  ): Promise<{
+    model: string
+    usage_kind: string
+    as_of: string
+    catalog_rates?: Record<string, number | null> | null
+    effective_rates?: Record<string, number | null> | null
+    effective_source?: string | null
+    has_override: boolean
+    override?: {
+      effective_from: string
+      effective_to?: string | null
+      rates: Record<string, number | null>
+    } | null
+  }> {
+    const response = await this.client.get(
+      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
+      { params },
+    )
+    return response.data
+  }
+
   async listUsagePricingOverrides(params?: {
     model?: string
     usage_kind?: string
diff --git a/frontend/src/lib/llmModelOptions.ts b/frontend/src/lib/llmModelOptions.ts
index d5a556e2..2dfc3fc1 100644
--- a/frontend/src/lib/llmModelOptions.ts
+++ b/frontend/src/lib/llmModelOptions.ts
@@ -63,6 +63,15 @@ export function resolveLLMModelsForCredential(
   ) {
     return { mode: 'gateway_direct', model: gatewayModel }
   }
+  const allowlist = credential?.enabled_models?.filter((m) => m?.trim()) ?? []
+  if (allowlist.length > 0) {
+    const allowed = new Set(allowlist)
+    const filtered = catalogModels.filter((m) => allowed.has(m))
+    if (gatewayModel && !filtered.includes(gatewayModel)) {
+      return { mode: 'catalog', models: [gatewayModel, ...filtered] }
+    }
+    return { mode: 'catalog', models: filtered }
+  }
   return { mode: 'catalog', models: catalogModels }
 }
 
diff --git a/frontend/src/lib/usageCurrency.ts b/frontend/src/lib/usageCurrency.ts
index 2b2122c4..a110721d 100644
--- a/frontend/src/lib/usageCurrency.ts
+++ b/frontend/src/lib/usageCurrency.ts
@@ -13,6 +13,15 @@ export function setUsageDisplayCurrency(currency: UsageDisplayCurrency): void {
   window.localStorage.setItem(STORAGE_KEY, currency)
 }
 
+function formatUsdAmount(amountUsd: number): string {
+  const abs = Math.abs(amountUsd)
+  const digits = abs >= 1 ? 2 : 4
+  return `$${amountUsd.toLocaleString('en-US', {
+    minimumFractionDigits: digits,
+    maximumFractionDigits: digits,
+  })}`
+}
+
 export function formatUsageCostUsd(
   usd: number | null | undefined,
   currency: UsageDisplayCurrency,
@@ -21,17 +30,24 @@ export function formatUsageCostUsd(
   const amountUsd = Number(usd || 0)
   if (!amountUsd) return '—'
   if (currency === 'INR') {
-    return new Intl.NumberFormat(undefined, {
+    return new Intl.NumberFormat('en-IN', {
       style: 'currency',
       currency: 'INR',
       minimumFractionDigits: 2,
       maximumFractionDigits: 2,
     }).format(amountUsd * inrRate)
   }
-  return new Intl.NumberFormat(undefined, {
-    style: 'currency',
-    currency: 'USD',
-    minimumFractionDigits: 2,
-    maximumFractionDigits: 4,
-  }).format(amountUsd)
+  return formatUsdAmount(amountUsd)
+}
+
+export function formatFxRateHint(
+  inrRate: number,
+  asOf?: string | null,
+  source?: string | null,
+): string {
+  if (source === 'default') {
+    return `1 USD ≈ ₹${inrRate.toFixed(2)} (estimate — live FX unavailable)`
+  }
+  const dateLabel = asOf ? asOf.slice(0, 10) : 'today'
+  return `1 USD = ₹${inrRate.toFixed(2)} (Frankfurter, ${dateLabel})`
 }
diff --git a/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx b/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx
new file mode 100644
index 00000000..45ba4b2e
--- /dev/null
+++ b/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx
@@ -0,0 +1,213 @@
+import { useMemo, useState } from 'react'
+import { useQuery } from '@tanstack/react-query'
+import { Check } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import type { ModelProvider } from '../../types/api'
+import { usageTheme } from '../usage/usageTheme'
+
+type ModelOptions = {
+  llm?: string[]
+  stt?: string[]
+  tts?: string[]
+  s2s?: string[]
+}
+
+type SectionKey = 'llm' | 'stt' | 'tts' | 's2s'
+
+const SECTIONS: Array<{ key: SectionKey; label: string }> = [
+  { key: 'llm', label: 'LLM' },
+  { key: 'stt', label: 'STT' },
+  { key: 'tts', label: 'TTS' },
+  { key: 's2s', label: 'Speech-to-speech' },
+]
+
+type Props = {
+  provider: ModelProvider
+  enabledModels: string[]
+  onChange: (models: string[]) => void
+  gatewayModel?: string
+}
+
+function ModelChip({
+  model,
+  selected,
+  onToggle,
+}: {
+  model: string
+  selected: boolean
+  onToggle: () => void
+}) {
+  return (
+    <button
+      type="button"
+      onClick={onToggle}
+      title={model}
+      className={`flex w-full items-center gap-2 rounded-xl border px-3 py-2.5 text-left transition-all ${
+        selected
+          ? `${usageTheme.pillActive} ring-1 ring-[#facc15]/40`
+          : 'border-gray-200 bg-white text-gray-800 hover:border-[#fde047] hover:bg-[#fefce8]/60'
+      }`}
+    >
+      <span
+        className={`flex h-5 w-5 shrink-0 items-center justify-center rounded-full border transition-colors ${
+          selected
+            ? 'border-[#ca8a04] bg-[#facc15] text-[#422006]'
+            : 'border-gray-300 bg-white text-transparent'
+        }`}
+      >
+        <Check className="h-3 w-3" strokeWidth={3} />
+      </span>
+      <span className="min-w-0 flex-1 text-sm leading-snug text-gray-900 break-all">{model}</span>
+    </button>
+  )
+}
+
+export default function AIProviderEnabledModelsStep({
+  provider,
+  enabledModels,
+  onChange,
+  gatewayModel,
+}: Props) {
+  const [customModel, setCustomModel] = useState('')
+
+  const { data: options, isLoading } = useQuery({
+    queryKey: ['model-options', provider],
+    queryFn: () => apiClient.getModelOptions(provider),
+    enabled: Boolean(provider),
+  })
+
+  const catalog = (options || {}) as ModelOptions
+  const selected = useMemo(() => new Set(enabledModels), [enabledModels])
+
+  const toggle = (model: string) => {
+    const next = new Set(enabledModels)
+    if (next.has(model)) next.delete(model)
+    else next.add(model)
+    onChange(Array.from(next).sort())
+  }
+
+  const selectSection = (key: SectionKey) => {
+    const models = catalog[key] || []
+    const next = new Set(enabledModels)
+    models.forEach((m) => next.add(m))
+    onChange(Array.from(next).sort())
+  }
+
+  const clearSection = (key: SectionKey) => {
+    const models = new Set(catalog[key] || [])
+    onChange(enabledModels.filter((m) => !models.has(m)))
+  }
+
+  const addCustomModel = () => {
+    const name = customModel.trim()
+    if (!name) return
+    if (!selected.has(name)) {
+      onChange([...enabledModels, name].sort())
+    }
+    setCustomModel('')
+  }
+
+  const gateway = gatewayModel?.trim()
+
+  return (
+    <div className="space-y-4">
+      <div className="rounded-xl border border-[#fde047]/50 bg-[#fefce8]/40 px-3 py-2.5">
+        <p className="text-sm text-gray-800">
+          Tap models to allow this integration to use them in evals, agents, and pricing.
+        </p>
+        <p className="mt-1 text-xs text-gray-600">
+          Leave all off to allow the full provider catalog.
+        </p>
+      </div>
+
+      {gateway ? (
+        <div className="rounded-xl border border-amber-200 bg-amber-50 px-3 py-2 text-sm text-amber-950">
+          Gateway model <span className="font-medium break-all">{gateway}</span> stays available
+          when configured.
+        </div>
+      ) : null}
+
+      {isLoading ? (
+        <p className="text-sm text-gray-500">Loading catalog models…</p>
+      ) : (
+        SECTIONS.map(({ key, label }) => {
+          const models = catalog[key] || []
+          if (models.length === 0) return null
+          const sectionSelected = models.filter((m) => selected.has(m)).length
+          return (
+            <div key={key} className={`${usageTheme.panel} overflow-hidden`}>
+              <div className={`flex items-center justify-between gap-2 px-3 py-2 ${usageTheme.panelHeader}`}>
+                <div>
+                  <span className="text-sm font-semibold text-gray-900">{label}</span>
+                  <span className="ml-2 text-xs text-gray-500">
+                    {sectionSelected}/{models.length} selected
+                  </span>
+                </div>
+                <div className="flex gap-2">
+                  <button
+                    type="button"
+                    className={`rounded-lg px-2.5 py-1 text-xs font-medium ${usageTheme.pillInactive}`}
+                    onClick={() => selectSection(key)}
+                  >
+                    All
+                  </button>
+                  <button
+                    type="button"
+                    className={`rounded-lg px-2.5 py-1 text-xs font-medium ${usageTheme.pillMuted}`}
+                    onClick={() => clearSection(key)}
+                  >
+                    Clear
+                  </button>
+                </div>
+              </div>
+              <div className="grid max-h-52 grid-cols-1 gap-2 overflow-y-auto p-3 sm:grid-cols-2">
+                {models.map((model) => (
+                  <ModelChip
+                    key={model}
+                    model={model}
+                    selected={selected.has(model)}
+                    onToggle={() => toggle(model)}
+                  />
+                ))}
+              </div>
+            </div>
+          )
+        })
+      )}
+
+      <div className={`${usageTheme.panel} p-3`}>
+        <label className="block text-sm font-medium text-gray-900 mb-2">
+          Custom deployment name
+        </label>
+        <div className="flex gap-2">
+          <input
+            type="text"
+            value={customModel}
+            onChange={(e) => setCustomModel(e.target.value)}
+            onKeyDown={(e) => {
+              if (e.key === 'Enter') {
+                e.preventDefault()
+                addCustomModel()
+              }
+            }}
+            className={`flex-1 rounded-lg border border-gray-200 px-3 py-2 text-sm focus:outline-none ${usageTheme.focusRing}`}
+            placeholder="e.g. accounts/fireworks/models/gpt-oss-120b"
+          />
+          <button
+            type="button"
+            onClick={addCustomModel}
+            className={usageTheme.applyBtn}
+          >
+            Add
+          </button>
+        </div>
+      </div>
+
+      <p className="text-xs text-gray-500">
+        {enabledModels.length > 0
+          ? `${enabledModels.length} model${enabledModels.length === 1 ? '' : 's'} enabled`
+          : 'No restriction — full catalog allowed'}
+      </p>
+    </div>
+  )
+}
diff --git a/frontend/src/pages/configurations/Integrations.tsx b/frontend/src/pages/configurations/Integrations.tsx
index 4dce71a2..4c2e9ca6 100644
--- a/frontend/src/pages/configurations/Integrations.tsx
+++ b/frontend/src/pages/configurations/Integrations.tsx
@@ -21,6 +21,7 @@ import {
   getTelephonyProviderLogo,
 } from '../../config/providers'
 import WalkthroughToggleButton from '../../components/walkthrough/WalkthroughToggleButton'
+import AIProviderEnabledModelsStep from './AIProviderEnabledModelsStep'
 
 type IntegrationType = 'voice_platform' | 'ai_provider' | 'telephony_provider' | null
 
@@ -65,6 +66,8 @@ export default function Integrations() {
   const [gatewayAuthSecret, setGatewayAuthSecret] = useState('')
   const [clearGatewayAuthSecret, setClearGatewayAuthSecret] = useState(false)
   const [gatewayExtraHeadersJson, setGatewayExtraHeadersJson] = useState('')
+  const [aiProviderWizardStep, setAiProviderWizardStep] = useState<1 | 2>(1)
+  const [enabledModels, setEnabledModels] = useState<string[]>([])
   const [showDeleteModal, setShowDeleteModal] = useState(false)
   const [showDeleteAIProviderModal, setShowDeleteAIProviderModal] = useState(false)
   const [showDeleteTelephonyModal, setShowDeleteTelephonyModal] = useState(false)
@@ -363,6 +366,7 @@ export default function Integrations() {
     setCredentialRoutingMode('inherit'); setGatewayModel(''); setGatewayInterface('inherit'); setGatewayBaseUrl('')
     setGatewayAuthHeader(''); setGatewayAuthSecretEnv(''); setGatewayAuthSecret(''); setClearGatewayAuthSecret(false)
     setGatewayExtraHeadersJson('')
+    setAiProviderWizardStep(1); setEnabledModels([])
     setSelectedTelephonyProvider(null); setTelephonyAuthId(''); setTelephonyAuthToken(''); setTelephonyVerifyAppUuid(''); setTelephonyVoiceAppId(''); setTelephonySipDomain('')
     setEditingTelephonyConfigId(null); setTelephonyName('')
   }
@@ -387,6 +391,8 @@ export default function Integrations() {
     setGatewayBaseUrl(provider.gateway_base_url || ''); setGatewayAuthHeader(provider.gateway_auth_header || '')
     setGatewayAuthSecretEnv(provider.gateway_auth_secret_env || ''); setGatewayAuthSecret(''); setClearGatewayAuthSecret(false)
     setGatewayExtraHeadersJson(formatGatewayExtraHeadersJson(provider.gateway_extra_headers))
+    setEnabledModels(provider.enabled_models || [])
+    setAiProviderWizardStep(1)
     setShowProviderDropdown(false); setIsEditMode(true); setShowModal(true)
   }
 
@@ -434,8 +440,29 @@ export default function Integrations() {
         return
       }
 
+      const resolvedEnabledModels = enabledModels.length > 0 ? enabledModels : null
+
+      if (aiProviderWizardStep === 1) {
+        if (!isEditMode && !selectedProvider) {
+          showToast('Please select a provider', 'error')
+          return
+        }
+        if (!isEditMode && aiProviderRequiresApiKey && !apiKey.trim()) {
+          showToast('Please enter an API key', 'error')
+          return
+        }
+        if (!isEditMode && selectedProvider === ModelProvider.AZURE && !azureEndpointUrl.trim()) {
+          showToast('Please enter your Azure OpenAI endpoint URL', 'error')
+          return
+        }
+        setAiProviderWizardStep(2)
+        return
+      }
+
       if (isEditMode && selectedAIProvider) {
-        const updateData: Partial<AIProviderUpdate> = {}
+        const updateData: Partial<AIProviderUpdate> = {
+          enabled_models: resolvedEnabledModels,
+        }
         if (apiKey.trim()) updateData.api_key = apiKey
         if (name !== (selectedAIProvider.name || '')) updateData.name = name || null
         const trimmedAzureEndpointUrl = azureEndpointUrl.trim()
@@ -475,7 +502,6 @@ export default function Integrations() {
         if (gatewayExtraHeadersJson.trim() !== existingExtraHeadersJson.trim()) {
           updateData.gateway_extra_headers = parsedGatewayExtraHeaders
         }
-        if (Object.keys(updateData).length === 0) { resetForm(); return }
         updateAIProviderMutation.mutate({ id: selectedAIProvider.id, data: updateData })
       } else {
         if (!selectedProvider) {
@@ -503,6 +529,7 @@ export default function Integrations() {
           gateway_auth_secret_env: gatewayAuthSecretEnv.trim() || undefined,
           gateway_auth_secret: gatewayAuthSecret.trim() || undefined,
           gateway_extra_headers: parsedGatewayExtraHeaders || undefined,
+          enabled_models: resolvedEnabledModels || undefined,
         })
       }
     } else if (integrationType === 'telephony_provider') {
@@ -801,6 +828,14 @@ export default function Integrations() {
                                 {provider.gateway_model}
                               </span>
                             )}
+                            {provider.enabled_models && provider.enabled_models.length > 0 && (
+                              <span
+                                className="px-2 py-0.5 text-xs font-medium bg-sky-100 text-sky-700 rounded"
+                                title={provider.enabled_models.join(', ')}
+                              >
+                                {provider.enabled_models.length} model{provider.enabled_models.length === 1 ? '' : 's'}
+                              </span>
+                            )}
                             {!provider.is_active && <span className="px-2 py-0.5 text-xs font-medium bg-gray-100 text-gray-600 rounded">Inactive</span>}
                           </div>
                         </div>
@@ -1119,9 +1154,25 @@ export default function Integrations() {
 
       {showModal && renderModal(
         <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-[9999]">
-          <div className="bg-white rounded-lg shadow-xl max-w-md w-full mx-4 max-h-[90vh] overflow-y-auto">
+          <div className={`bg-white rounded-lg shadow-xl w-full mx-4 max-h-[90vh] overflow-y-auto ${integrationType === 'ai_provider' && aiProviderWizardStep === 2 ? 'max-w-2xl' : 'max-w-md'}`}>
             <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
-              <h3 className="text-lg font-semibold">{isEditMode ? (integrationType === 'ai_provider' ? 'Edit AI Provider' : integrationType === 'telephony_provider' ? 'Edit Telephony Provider' : 'Edit Integration') : 'Add Integration'}</h3>
+              <div>
+                <h3 className="text-lg font-semibold">
+                  {isEditMode
+                    ? integrationType === 'ai_provider'
+                      ? 'Edit AI Provider'
+                      : integrationType === 'telephony_provider'
+                        ? 'Edit Telephony Provider'
+                        : 'Edit Integration'
+                    : 'Add Integration'}
+                </h3>
+                {integrationType === 'ai_provider' ? (
+                  <p className="mt-0.5 text-xs text-gray-500">
+                    Step {aiProviderWizardStep} of 2 —{' '}
+                    {aiProviderWizardStep === 1 ? 'Credentials' : 'Enabled models'}
+                  </p>
+                ) : null}
+              </div>
               <button onClick={resetForm} className="text-gray-400 hover:text-gray-600"><X className="h-5 w-5" /></button>
             </div>
             <form onSubmit={handleSubmit} className="p-6 space-y-4">
@@ -1140,6 +1191,7 @@ export default function Integrations() {
                         setIntegrationType('ai_provider')
                         setSelectedPlatform(null)
                         setSelectedProvider(null)
+                        setAiProviderWizardStep(1)
                       }}
                       className={`p-3 border-2 rounded-lg text-left transition-all ${integrationType === 'ai_provider'
                         ? 'border-primary-500 bg-primary-50'
@@ -1237,7 +1289,7 @@ export default function Integrations() {
                 </>
               )}
 
-              {integrationType === 'ai_provider' && (
+              {integrationType === 'ai_provider' && aiProviderWizardStep === 1 && (
                 <>
                   <div>
                     <label className="block text-sm font-medium text-gray-700 mb-1">Provider *</label>
@@ -1448,6 +1500,15 @@ export default function Integrations() {
                 </>
               )}
 
+              {integrationType === 'ai_provider' && aiProviderWizardStep === 2 && (selectedProvider || selectedAIProvider) && (
+                <AIProviderEnabledModelsStep
+                  provider={(selectedProvider || selectedAIProvider!.provider) as ModelProvider}
+                  enabledModels={enabledModels}
+                  onChange={setEnabledModels}
+                  gatewayModel={gatewayModel}
+                />
+              )}
+
               {integrationType === 'telephony_provider' && (
                 <>
                   <div>
@@ -1546,11 +1607,58 @@ export default function Integrations() {
 
               <div className="flex gap-3 pt-4">
                 <Button type="button" variant="outline" onClick={resetForm} className="flex-1">Cancel</Button>
-                <Button type="submit" variant="primary"
-                  isLoading={integrationType === 'voice_platform' ? (isEditMode ? updateIntegrationMutation.isPending : createIntegrationMutation.isPending) : integrationType === 'ai_provider' ? (isEditMode ? updateAIProviderMutation.isPending : createAIProviderMutation.isPending) : saveTelephonyConfigMutation.isPending}
-                  disabled={!integrationType} className="flex-1">
-                  {isEditMode ? (integrationType === 'ai_provider' ? 'Update Provider' : integrationType === 'telephony_provider' ? 'Update Telephony' : 'Update Integration') : (integrationType === 'ai_provider' ? 'Configure Provider' : integrationType === 'telephony_provider' ? 'Configure Telephony' : 'Add Integration')}
-                </Button>
+                {integrationType === 'ai_provider' && aiProviderWizardStep === 2 ? (
+                  <>
+                    <Button
+                      type="button"
+                      variant="outline"
+                      onClick={() => setAiProviderWizardStep(1)}
+                      className="flex-1"
+                    >
+                      Back
+                    </Button>
+                    <Button
+                      type="submit"
+                      variant="primary"
+                      isLoading={
+                        isEditMode
+                          ? updateAIProviderMutation.isPending
+                          : createAIProviderMutation.isPending
+                      }
+                      className="flex-1"
+                    >
+                      {isEditMode ? 'Save provider' : 'Configure provider'}
+                    </Button>
+                  </>
+                ) : (
+                  <Button
+                    type="submit"
+                    variant="primary"
+                    isLoading={
+                      integrationType === 'voice_platform'
+                        ? isEditMode
+                          ? updateIntegrationMutation.isPending
+                          : createIntegrationMutation.isPending
+                        : integrationType === 'ai_provider'
+                          ? false
+                          : integrationType === 'telephony_provider'
+                            ? saveTelephonyConfigMutation.isPending
+                            : false
+                    }
+                    disabled={!integrationType}
+                    className="flex-1"
+                  >
+                    {integrationType === 'ai_provider'
+                      ? 'Continue'
+                      : isEditMode
+                        ? integrationType === 'telephony_provider'
+                          ? 'Update Telephony'
+                          : 'Update Integration'
+                        : integrationType === 'telephony_provider'
+                          ? 'Configure Telephony'
+                          : 'Add Integration'}
+                  </Button>
+                )}
               </div>
             </form>
           </div>
diff --git a/frontend/src/pages/usage/ProviderCredentialSelect.tsx b/frontend/src/pages/usage/ProviderCredentialSelect.tsx
new file mode 100644
index 00000000..23a6af2e
--- /dev/null
+++ b/frontend/src/pages/usage/ProviderCredentialSelect.tsx
@@ -0,0 +1,172 @@
+import { useEffect, useRef, useState, type ReactNode } from 'react'
+import { createPortal } from 'react-dom'
+import { Brain, ChevronDown, Info } from 'lucide-react'
+import { getProviderLabel, getProviderLogo } from '../../config/providers'
+import type { AIProvider, ModelProvider } from '../../types/api'
+import { usageTheme } from './usageTheme'
+import { credentialDisplayLabel } from './pricingModelOptions'
+import { useAnchoredDropdown } from './useAnchoredDropdown'
+function FieldHint({ title, children }: { title: string; children: ReactNode }) {
+  return (
+    <span className="relative inline-flex group">
+      <Info
+        className="h-3.5 w-3.5 cursor-help text-gray-400 hover:text-gray-600"
+        aria-label={title}
+      />
+      <span
+        role="tooltip"
+        className="pointer-events-none absolute left-0 top-full z-30 mt-1.5 hidden w-64 rounded-lg border border-gray-200 bg-white p-2.5 text-xs font-normal leading-relaxed text-gray-600 shadow-lg group-hover:block group-focus-within:block"
+      >
+        <span className="block text-[11px] font-semibold uppercase tracking-wide text-gray-900">
+          {title}
+        </span>
+        <span className="mt-1 block">{children}</span>
+      </span>
+    </span>
+  )
+}
+
+function ProviderLogo({
+  provider,
+  size = 'sm',
+}: {
+  provider: ModelProvider
+  size?: 'sm' | 'md'
+}) {
+  const logo = getProviderLogo(provider)
+  const label = getProviderLabel(provider)
+
+  if (logo) {
+    if (size === 'md') {
+      return (
+        <span className="flex h-9 w-9 shrink-0 items-center justify-center rounded-lg border border-gray-200 bg-white p-1.5">
+          <img src={logo} alt={label} className="h-full w-full object-contain" />
+        </span>
+      )
+    }
+    return <img src={logo} alt={label} className="h-5 w-5 shrink-0 object-contain" />
+  }
+
+  if (size === 'md') {
+    return (
+      <span className="flex h-9 w-9 shrink-0 items-center justify-center rounded-lg border border-gray-200 bg-white">
+        <Brain className="h-5 w-5 text-primary-600" />
+      </span>
+    )
+  }
+
+  return <Brain className="h-5 w-5 shrink-0 text-primary-600" />
+}
+
+type Props = {
+  label: string
+  hint?: string
+  value: string
+  credentials: AIProvider[]
+  onChange: (credentialId: string) => void
+  disabled?: boolean
+  placeholder?: string
+}
+
+export default function ProviderCredentialSelect({
+  label,
+  hint,
+  value,
+  credentials,
+  onChange,
+  disabled,
+  placeholder = 'Select integration…',
+}: Props) {
+  const [open, setOpen] = useState(false)
+  const rootRef = useRef<HTMLDivElement>(null)
+  const anchorRef = useRef<HTMLButtonElement>(null)
+  const panelRef = useRef<HTMLDivElement>(null)
+  const coords = useAnchoredDropdown(open && !disabled, anchorRef)
+  const selected = credentials.find((row) => row.id === value)
+
+  useEffect(() => {
+    const onDoc = (e: MouseEvent) => {
+      const target = e.target as Node
+      if (rootRef.current?.contains(target) || panelRef.current?.contains(target)) return
+      setOpen(false)
+    }
+    document.addEventListener('mousedown', onDoc)
+    return () => document.removeEventListener('mousedown', onDoc)
+  }, [])
+  return (
+    <div ref={rootRef} className="relative min-w-0">
+      <div className="flex items-center gap-1.5">
+        <span className="text-xs font-medium text-gray-500">{label}</span>
+        {hint ? <FieldHint title={label}>{hint}</FieldHint> : null}
+      </div>
+      <button
+        ref={anchorRef}
+        type="button"
+        disabled={disabled}
+        onClick={() => setOpen((v) => !v)}        className={`mt-1 flex h-10 w-full items-center justify-between gap-2 rounded-lg border border-gray-200 bg-white px-3 text-left text-sm text-gray-900 shadow-sm transition-colors hover:border-gray-300 focus:outline-none disabled:cursor-not-allowed disabled:opacity-50 ${usageTheme.focusRing}`}
+      >
+        <span className="flex min-w-0 items-center gap-2">
+          {selected ? (
+            <>
+              <ProviderLogo provider={selected.provider} />
+              <span className="truncate">{credentialDisplayLabel(selected)}</span>
+            </>
+          ) : (
+            <span className="text-gray-500">{placeholder}</span>
+          )}
+        </span>
+        <ChevronDown
+          className={`h-4 w-4 shrink-0 text-gray-400 transition-transform ${open ? 'rotate-180' : ''}`}
+        />
+      </button>
+
+      {open && !disabled && coords && typeof document !== 'undefined'
+        ? createPortal(
+            <div
+              ref={panelRef}
+              className="max-h-60 overflow-y-auto overscroll-contain rounded-lg border border-gray-200 bg-white py-1 shadow-lg"
+              style={{
+                position: 'fixed',
+                top: coords.top,
+                left: coords.left,
+                width: coords.width,
+                zIndex: 60,
+              }}
+            >
+              {credentials.length === 0 ? (
+                <p className="px-3 py-2 text-sm text-gray-500">No active integrations</p>
+              ) : (
+                credentials.map((credential) => {
+                  const isSelected = credential.id === value
+                  const name = credential.name?.trim()
+                  return (
+                    <button
+                      key={credential.id}
+                      type="button"
+                      onClick={() => {
+                        onChange(credential.id)
+                        setOpen(false)
+                      }}
+                      className={`flex w-full items-center gap-3 px-3 py-2.5 text-left transition-colors hover:bg-gray-50 ${
+                        isSelected ? 'bg-[#fefce8]' : ''
+                      }`}
+                    >
+                      <ProviderLogo provider={credential.provider} size="md" />
+                      <span className="min-w-0">
+                        <span className="block text-sm font-medium text-gray-900">
+                          {getProviderLabel(credential.provider)}
+                        </span>
+                        {name ? (
+                          <span className="block truncate text-xs text-gray-500">{name}</span>
+                        ) : null}
+                      </span>
+                    </button>
+                  )
+                })
+              )}
+            </div>,
+            document.body,
+          )
+        : null}    </div>
+  )
+}
diff --git a/frontend/src/pages/usage/SearchableSelect.tsx b/frontend/src/pages/usage/SearchableSelect.tsx
index 61da046a..c5f5b1a5 100644
--- a/frontend/src/pages/usage/SearchableSelect.tsx
+++ b/frontend/src/pages/usage/SearchableSelect.tsx
@@ -1,7 +1,8 @@
-import { useEffect, useMemo, useRef, useState } from 'react'
-import { ChevronDown, Search, X } from 'lucide-react'
+import { useEffect, useMemo, useRef, useState, type ReactNode } from 'react'
+import { createPortal } from 'react-dom'
+import { ChevronDown, Info, Search, X } from 'lucide-react'
 import { usageTheme } from './usageTheme'
-
+import { useAnchoredDropdown } from './useAnchoredDropdown'
 export type SearchableOption = { id: string; label: string }
 
 type SearchableSelectProps = {
@@ -12,6 +13,27 @@ type SearchableSelectProps = {
   onChange: (id: string) => void
   disabled?: boolean
   emptyMessage?: string
+  hint?: string
+}
+
+function FieldHint({ title, children }: { title: string; children: ReactNode }) {
+  return (
+    <span className="relative inline-flex group">
+      <Info
+        className="h-3.5 w-3.5 cursor-help text-gray-400 hover:text-gray-600"
+        aria-label={title}
+      />
+      <span
+        role="tooltip"
+        className="pointer-events-none absolute left-0 top-full z-30 mt-1.5 hidden w-64 rounded-lg border border-gray-200 bg-white p-2.5 text-xs font-normal leading-relaxed text-gray-600 shadow-lg group-hover:block group-focus-within:block"
+      >
+        <span className="block text-[11px] font-semibold uppercase tracking-wide text-gray-900">
+          {title}
+        </span>
+        <span className="mt-1 block">{children}</span>
+      </span>
+    </span>
+  )
 }
 
 export default function SearchableSelect({
@@ -22,11 +44,14 @@ export default function SearchableSelect({
   onChange,
   disabled,
   emptyMessage = 'No matches',
+  hint,
 }: SearchableSelectProps) {
   const [open, setOpen] = useState(false)
   const [search, setSearch] = useState('')
   const rootRef = useRef<HTMLDivElement>(null)
-
+  const anchorRef = useRef<HTMLButtonElement>(null)
+  const panelRef = useRef<HTMLDivElement>(null)
+  const coords = useAnchoredDropdown(open && !disabled, anchorRef)
   const selected = options.find(
     (o) => o.id === value || o.id.toLowerCase() === value.toLowerCase(),
   )
@@ -39,7 +64,9 @@ export default function SearchableSelect({
 
   useEffect(() => {
     const onDoc = (e: MouseEvent) => {
-      if (!rootRef.current?.contains(e.target as Node)) setOpen(false)
+      const target = e.target as Node
+      if (rootRef.current?.contains(target) || panelRef.current?.contains(target)) return
+      setOpen(false)
     }
     document.addEventListener('mousedown', onDoc)
     return () => document.removeEventListener('mousedown', onDoc)
@@ -47,17 +74,20 @@ export default function SearchableSelect({
 
   return (
     <div ref={rootRef} className="relative min-w-0">
-      <span className="text-xs font-medium text-gray-500">{label}</span>
+      <div className="flex items-center gap-1.5">
+        <span className="text-xs font-medium text-gray-500">{label}</span>
+        {hint ? <FieldHint title={label}>{hint}</FieldHint> : null}
+      </div>
       <button
+        ref={anchorRef}
         type="button"
         disabled={disabled}
         onClick={() => setOpen((v) => !v)}
-        className={`mt-1 flex w-full items-center gap-2 rounded-lg border border-gray-200 bg-white px-3 py-2 text-left text-sm text-gray-900 shadow-sm transition-colors hover:border-[#facc15] focus:outline-none disabled:opacity-50 ${usageTheme.focusRing}`}
+        className={`mt-1 flex h-10 w-full items-center gap-2 rounded-lg border border-gray-200 bg-white px-3 text-left text-sm text-gray-900 shadow-sm transition-colors hover:border-[#facc15] focus:outline-none disabled:opacity-50 ${usageTheme.focusRing}`}
       >
         <span className="flex-1 truncate">
           {selected ? selected.label : placeholder}
-        </span>
-        {value ? (
+        </span>        {value ? (
           <span
             role="button"
             tabIndex={0}
@@ -82,47 +112,59 @@ export default function SearchableSelect({
         )}
       </button>
 
-      {open && !disabled ? (
-        <div className="absolute z-30 mt-1 w-full min-w-[16rem] rounded-lg border border-[#fde047]/60 bg-white shadow-lg ring-1 ring-[#fef9c3]/50">
-          <div className="flex items-center gap-2 border-b border-gray-100 px-3 py-2">
-            <Search className="h-4 w-4 text-gray-400" />
-            <input
-              type="text"
-              value={search}
-              onChange={(e) => setSearch(e.target.value)}
-              placeholder="Search…"
-              className="flex-1 text-sm outline-none"
-              autoFocus
-            />
-          </div>
-          <ul className="max-h-56 overflow-y-auto py-1">
-            {filtered.length === 0 ? (
-              <li className="px-3 py-2 text-sm text-gray-500">{emptyMessage}</li>
-            ) : (
-              filtered.map((opt) => (
-                <li key={opt.id}>
-                  <button
-                    type="button"
-                    className={`w-full px-3 py-2 text-left text-sm hover:bg-[#fefce8] ${
-                      opt.id === value ||
-                      opt.id.toLowerCase() === value.toLowerCase()
-                        ? usageTheme.selectHighlight
-                        : 'text-gray-800'
-                    }`}
-                    onClick={() => {
-                      onChange(opt.id)
-                      setOpen(false)
-                      setSearch('')
-                    }}
-                  >
-                    {opt.label}
-                  </button>
-                </li>
-              ))
-            )}
-          </ul>
-        </div>
-      ) : null}
-    </div>
+      {open && !disabled && coords && typeof document !== 'undefined'
+        ? createPortal(
+            <div
+              ref={panelRef}
+              className="rounded-lg border border-[#fde047]/60 bg-white shadow-lg ring-1 ring-[#fef9c3]/50"
+              style={{
+                position: 'fixed',
+                top: coords.top,
+                left: coords.left,
+                width: coords.width,
+                zIndex: 60,
+              }}
+            >
+              <div className="flex items-center gap-2 border-b border-gray-100 px-3 py-2">
+                <Search className="h-4 w-4 shrink-0 text-gray-400" />
+                <input
+                  type="text"
+                  value={search}
+                  onChange={(e) => setSearch(e.target.value)}
+                  placeholder="Search…"
+                  className="min-w-0 flex-1 text-sm outline-none"
+                  autoFocus
+                />
+              </div>
+              <ul className="max-h-56 overflow-y-auto overscroll-contain py-1">
+                {filtered.length === 0 ? (
+                  <li className="px-3 py-2 text-sm text-gray-500">{emptyMessage}</li>
+                ) : (
+                  filtered.map((opt) => (
+                    <li key={opt.id}>
+                      <button
+                        type="button"
+                        className={`w-full px-3 py-2 text-left text-sm hover:bg-[#fefce8] ${
+                          opt.id === value ||
+                          opt.id.toLowerCase() === value.toLowerCase()
+                            ? usageTheme.selectHighlight
+                            : 'text-gray-800'
+                        }`}
+                        onClick={() => {
+                          onChange(opt.id)
+                          setOpen(false)
+                          setSearch('')
+                        }}
+                      >
+                        <span className="block break-all leading-snug">{opt.label}</span>
+                      </button>
+                    </li>
+                  ))
+                )}
+              </ul>
+            </div>,
+            document.body,
+          )
+        : null}    </div>
   )
 }
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index 469df3c3..1ce1a505 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -1,16 +1,16 @@
 import { useEffect, useMemo, useState } from 'react'
 import { keepPreviousData, useQuery } from '@tanstack/react-query'
-import { Link, useSearchParams } from 'react-router-dom'
+import { useSearchParams } from 'react-router-dom'
 import { Card, CardBody, Spinner } from '@heroui/react'
-import { Activity, ChevronRight, CircleDollarSign } from 'lucide-react'
+import { ChevronRight, CircleDollarSign } from 'lucide-react'
 import { apiClient } from '../../lib/api'
-import { useIsAdmin } from '../../hooks/useRole'
 import UsageFiltersBar from './UsageFiltersBar'
 import UsageDrillPath from './UsageDrillPath'
 import UsageCostBreakdownModal from './UsageCostBreakdownModal'
 import { defaultUsageDateRange, isRangeWithinMaxDays, rangeForDays } from './UsageDateRangePicker'
 import { getUsageTimezone } from './usageTimezone'
 import {
+  formatFxRateHint,
   formatUsageCostUsd,
   getUsageDisplayCurrency,
   setUsageDisplayCurrency,
@@ -556,7 +556,6 @@ export default function Usage() {
   const model = searchParams.get('model') || ''
   const usageKind = (searchParams.get('usage_kind') as Kind) || ''
   const productSection = searchParams.get('product_section') || ''
-  const isAdmin = useIsAdmin()
   const { usagePolicy, isLoaded: licenseLoaded, fetchLicense } = useLicenseStore()
   const showOssUsageNotice = licenseLoaded && !usagePolicy.extended_history
   const maxHistoryDays = !licenseLoaded
@@ -573,8 +572,10 @@ export default function Usage() {
     queryKey: ['org-usage', 'fx-rate'],
     queryFn: () => apiClient.getOrgUsageFxRate(),
     staleTime: 60 * 60 * 1000,
+    refetchOnMount: 'always',
   })
-  const inrRate = fxRate?.rate ?? 83
+  const inrRate = fxRate?.rate ?? 95
+  const fxRateHint = formatFxRateHint(inrRate, fxRate?.as_of, fxRate?.source)
   const formatCostUsd = (usd?: number | null) =>
     formatUsageCostUsd(usd, displayCurrency, inrRate)
 
@@ -1097,69 +1098,48 @@ export default function Usage() {
   }
 
   return (
-    <div className="space-y-6">
-      <div className="flex items-start justify-between gap-4">
-        <div className="min-w-0">
-          <h1 className="text-2xl font-bold text-gray-900 flex items-center gap-2">
-            <Activity className="h-6 w-6 text-primary-600 shrink-0" />
-            Usage
-          </h1>
-          <p className="text-sm text-gray-500 mt-1">
-            Cards show usage for <span className="font-medium text-gray-700">{scopeSubtitle}</span>.
-            Drill down: workspaces → call imports or product areas → evaluations / models.
-          </p>
-        </div>
-        <div className="flex flex-col items-end gap-1.5 shrink-0">
-          <div className="flex items-center gap-3">
-            <div
-              className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
-              role="group"
-              aria-label="Display currency"
+    <div className="space-y-4">
+      <div className="flex flex-wrap items-center justify-end gap-3">
+        <div
+          className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
+          role="group"
+          aria-label="Display currency"
+        >
+          {(['USD', 'INR'] as const).map((currency) => (
+            <button
+              key={currency}
+              type="button"
+              title={currency === 'INR' ? fxRateHint : undefined}
+              onClick={() => {
+                setDisplayCurrency(currency)
+                setUsageDisplayCurrency(currency)
+              }}
+              className={`rounded-md px-2.5 py-1 transition-colors ${
+                displayCurrency === currency
+                  ? 'bg-gray-900 text-white'
+                  : 'text-gray-600 hover:bg-gray-50'
+              }`}
             >
-              {(['USD', 'INR'] as const).map((currency) => (
-                <button
-                  key={currency}
-                  type="button"
-                  onClick={() => {
-                    setDisplayCurrency(currency)
-                    setUsageDisplayCurrency(currency)
-                  }}
-                  className={`rounded-md px-2 py-1 transition-colors ${
-                    displayCurrency === currency
-                      ? 'bg-gray-900 text-white'
-                      : 'text-gray-600 hover:bg-gray-50'
-                  }`}
-                >
-                  {currency}
-                </button>
-              ))}
-            </div>
-            {showCostBreakdown ? (
-              <button
-                type="button"
-                onClick={() => setCostBreakdownOpen(true)}
-                className="inline-flex items-center gap-1.5 rounded-lg border border-gray-200 bg-white px-2.5 py-1.5 text-xs font-medium text-gray-700 shadow-sm transition-colors hover:border-gray-300 hover:bg-gray-50 hover:text-gray-900"
-                title={formatCostUsd(estimatedTotalCost)}
-              >
-                <CircleDollarSign className="h-4 w-4 shrink-0 text-gray-500" />
-                <span className="whitespace-nowrap">Cost breakdown</span>
-              </button>
-            ) : null}
-            {isAdmin && licenseLoaded && usagePolicy.extended_history ? (
-              <Link
-                to="/usage/pricing"
-                className="text-sm text-primary-600 hover:text-primary-700 whitespace-nowrap"
-              >
-                Pricing overrides
-              </Link>
-            ) : null}
-          </div>
-          {summary?.last_updated_at ? (
-            <p className="text-[11px] text-gray-400 whitespace-nowrap">
-              Updated {new Date(summary.last_updated_at).toLocaleString()}
-            </p>
-          ) : null}
+              {currency}
+            </button>
+          ))}
         </div>
+        {showCostBreakdown ? (
+          <button
+            type="button"
+            onClick={() => setCostBreakdownOpen(true)}
+            className="inline-flex items-center gap-1.5 rounded-lg border border-gray-200 bg-white px-2.5 py-1.5 text-xs font-medium text-gray-700 shadow-sm transition-colors hover:border-gray-300 hover:bg-gray-50 hover:text-gray-900"
+            title={formatCostUsd(estimatedTotalCost)}
+          >
+            <CircleDollarSign className="h-4 w-4 shrink-0 text-gray-500" />
+            <span className="whitespace-nowrap">Cost breakdown</span>
+          </button>
+        ) : null}
+        {summary?.last_updated_at ? (
+          <p className="text-[11px] text-gray-400 whitespace-nowrap">
+            Updated {new Date(summary.last_updated_at).toLocaleString()}
+          </p>
+        ) : null}
       </div>
 
       {summaryError ? (
diff --git a/frontend/src/pages/usage/UsageCostBreakdownModal.tsx b/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
index 645729ba..2eafa3b1 100644
--- a/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
+++ b/frontend/src/pages/usage/UsageCostBreakdownModal.tsx
@@ -1,4 +1,5 @@
 import { X } from 'lucide-react'
+import { formatUsageCostUsd } from '../../lib/usageCurrency'
 
 type UsageCosts = {
   input_cost_usd: number
@@ -21,14 +22,8 @@ type Props = {
 }
 
 function defaultFormatCostUsd(usd?: number | null): string {
-  const amount = Number(usd || 0)
-  if (!amount) return '$0.00'
-  return new Intl.NumberFormat(undefined, {
-    style: 'currency',
-    currency: 'USD',
-    minimumFractionDigits: 2,
-    maximumFractionDigits: 4,
-  }).format(amount)
+  const formatted = formatUsageCostUsd(usd, 'USD', 1)
+  return formatted === '—' ? '$0.00' : formatted
 }
 
 const LINE_ITEMS: Array<{ key: keyof UsageCosts; label: string }> = [
diff --git a/frontend/src/pages/usage/UsagePage.tsx b/frontend/src/pages/usage/UsagePage.tsx
new file mode 100644
index 00000000..9acddfba
--- /dev/null
+++ b/frontend/src/pages/usage/UsagePage.tsx
@@ -0,0 +1,72 @@
+import { useEffect } from 'react'
+import { Activity, DollarSign } from 'lucide-react'
+import { Navigate, useSearchParams } from 'react-router-dom'
+import { useIsAdmin } from '../../hooks/useRole'
+import { useLicenseStore } from '../../store/licenseStore'
+import Usage from './Usage'
+import UsagePricing from './UsagePricing'
+
+type UsageTab = 'overview' | 'pricing'
+
+export default function UsagePage() {
+  const [searchParams, setSearchParams] = useSearchParams()
+  const isAdmin = useIsAdmin()
+  const licenseLoaded = useLicenseStore((s) => s.isLoaded)
+  const hasExtendedHistory = useLicenseStore((s) => s.hasExtendedUsageHistory())
+  const canManagePricing = isAdmin && licenseLoaded && hasExtendedHistory
+
+  const tabParam = searchParams.get('tab')
+  const activeTab: UsageTab =
+    tabParam === 'pricing' && canManagePricing ? 'pricing' : 'overview'
+
+  useEffect(() => {
+    if (tabParam === 'pricing' && !canManagePricing) {
+      setSearchParams({}, { replace: true })
+    }
+  }, [tabParam, canManagePricing, setSearchParams])
+
+  const setActiveTab = (tab: UsageTab) => {
+    setSearchParams(tab === 'pricing' ? { tab: 'pricing' } : {}, { replace: true })
+  }
+
+  const tabs: Array<{ id: UsageTab; label: string; icon: typeof Activity; hidden?: boolean }> = [
+    { id: 'overview', label: 'Overview', icon: Activity },
+    { id: 'pricing', label: 'Pricing overrides', icon: DollarSign, hidden: !canManagePricing },
+  ]
+
+  return (
+    <div className="space-y-4">
+      <h1 className="text-2xl font-bold text-gray-900">Usage</h1>
+
+      {canManagePricing ? (
+        <div className="border-b border-gray-200">
+          <nav className="-mb-px flex gap-6" aria-label="Usage sections">
+            {tabs
+              .filter((tab) => !tab.hidden)
+              .map(({ id, label, icon: Icon }) => (
+                <button
+                  key={id}
+                  type="button"
+                  onClick={() => setActiveTab(id)}
+                  className={`flex items-center gap-2 whitespace-nowrap border-b-2 px-1 py-2.5 text-sm font-medium transition-colors ${
+                    activeTab === id
+                      ? 'border-primary-600 text-primary-600'
+                      : 'border-transparent text-gray-500 hover:border-gray-300 hover:text-gray-700'
+                  }`}
+                >
+                  <Icon className="h-4 w-4" />
+                  {label}
+                </button>
+              ))}
+          </nav>
+        </div>
+      ) : null}
+
+      {activeTab === 'overview' ? <Usage /> : <UsagePricing />}
+    </div>
+  )
+}
+
+export function UsagePricingRedirect() {
+  return <Navigate to="/usage?tab=pricing" replace />
+}
diff --git a/frontend/src/pages/usage/UsagePricing.tsx b/frontend/src/pages/usage/UsagePricing.tsx
index 92d159f3..6796751b 100644
--- a/frontend/src/pages/usage/UsagePricing.tsx
+++ b/frontend/src/pages/usage/UsagePricing.tsx
@@ -1,22 +1,22 @@
-import { useMemo, useState, type ReactNode } from 'react'
+import { useEffect, useMemo, useRef, useState, type ReactNode } from 'react'
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
 import { Card, CardBody, Spinner } from '@heroui/react'
-import { Link } from 'react-router-dom'
-import { ArrowLeft, DollarSign, Info, Plus, Trash2 } from 'lucide-react'
+import { Plus, Trash2, Info } from 'lucide-react'
 import { apiClient } from '../../lib/api'
 import { useIsAdmin } from '../../hooks/useRole'
 import { useToast } from '../../hooks/useToast'
 import Button from '../../components/Button'
 import SearchableSelect from './SearchableSelect'
+import ProviderCredentialSelect from './ProviderCredentialSelect'
 import { usageTheme } from './usageTheme'
 import {
-  formatUsageCostUsd,
-  getUsageDisplayCurrency,
-  setUsageDisplayCurrency,
-  type UsageDisplayCurrency,
-} from '../../lib/usageCurrency'
+  buildPricingModelOptions,
+  credentialDisplayLabel,
+  type PricingUsageKind,
+} from './pricingModelOptions'
+import type { AIProvider } from '../../types/api'
 
-type UsageKind = 'llm' | 'stt' | 'tts'
+type UsageKind = PricingUsageKind
 
 type PricingRatesUsd = {
   input_per_1m?: number | null
@@ -51,36 +51,137 @@ const EMPTY_RATES: PricingRatesUsd = {
   tts_per_1m_characters: undefined,
 }
 
-const RATE_FIELDS: Array<{ key: RateFieldKey; label: string; kinds: UsageKind[] }> = [
-  { key: 'input_per_1m', label: 'Input / 1M tokens (USD)', kinds: ['llm'] },
-  { key: 'output_per_1m', label: 'Output / 1M tokens (USD)', kinds: ['llm'] },
-  { key: 'cache_read_per_1m', label: 'Cache read / 1M (USD)', kinds: ['llm'] },
-  { key: 'cache_write_per_1m', label: 'Cache write / 1M (USD)', kinds: ['llm'] },
-  { key: 'reasoning_per_1m', label: 'Reasoning / 1M (USD)', kinds: ['llm'] },
-  { key: 'audio_per_minute', label: 'Audio / minute (USD)', kinds: ['llm', 'stt'] },
-  { key: 'tts_per_1m_characters', label: 'TTS / 1M characters (USD)', kinds: ['tts'] },
+const RATE_FIELDS: Array<{
+  key: RateFieldKey
+  label: string
+  kinds: UsageKind[]
+  hint: string
+}> = [
+  {
+    key: 'input_per_1m',
+    label: 'Input / 1M tokens ($)',
+    kinds: ['llm'],
+    hint: 'USD per 1 million prompt or input tokens. Used when costing LLM calls for this model.',
+  },
+  {
+    key: 'output_per_1m',
+    label: 'Output / 1M tokens ($)',
+    kinds: ['llm'],
+    hint: 'USD per 1 million completion or output tokens returned by the model.',
+  },
+  {
+    key: 'cache_read_per_1m',
+    label: 'Cache read / 1M ($)',
+    kinds: ['llm'],
+    hint: 'USD per 1 million tokens read from prompt cache, when the provider bills cached input separately.',
+  },
+  {
+    key: 'cache_write_per_1m',
+    label: 'Cache write / 1M ($)',
+    kinds: ['llm'],
+    hint: 'USD per 1 million tokens written to prompt cache on the first request.',
+  },
+  {
+    key: 'reasoning_per_1m',
+    label: 'Reasoning / 1M ($)',
+    kinds: ['llm'],
+    hint: 'USD per 1 million reasoning or thinking tokens (e.g. o-series models).',
+  },
+  {
+    key: 'audio_per_minute',
+    label: 'Audio / minute ($)',
+    kinds: ['llm', 'stt'],
+    hint: 'USD per minute of audio processed — STT transcription or multimodal audio input on LLM calls.',
+  },
+  {
+    key: 'tts_per_1m_characters',
+    label: 'TTS / 1M characters ($)',
+    kinds: ['tts'],
+    hint: 'USD per 1 million characters sent to the TTS model for synthesis.',
+  },
 ]
 
+const FIELD_HINTS = {
+  provider:
+    'AI company from Integrations (e.g. OpenAI, Anthropic). Pick the credential whose models you want to price.',
+  model:
+    'Model name for the selected provider and usage kind. Only enabled models for that integration are listed.',
+  usageKind:
+    'Choose LLM, STT, or TTS first — this filters which models appear. Rate fields below match the kind.',
+  effectiveFrom:
+    'First calendar day this override applies. Saving recalculates historical usage for this model from this date.',
+  ratesSection:
+    'Enter USD rates only. Leave a field empty to inherit the platform catalog. At least one rate is required to save.',
+} as const
+
 const CONTROL_CLASS = `h-10 w-full rounded-lg border border-gray-200 bg-white px-3 text-sm text-gray-900 shadow-sm outline-none transition-colors hover:border-gray-300 ${usageTheme.focusRing}`
 
+function FieldHint({ title, children }: { title: string; children: ReactNode }) {
+  return (
+    <span className="relative inline-flex group">
+      <Info
+        className="h-3.5 w-3.5 cursor-help text-gray-400 hover:text-gray-600"
+        aria-label={title}
+      />
+      <span
+        role="tooltip"
+        className="pointer-events-none absolute left-0 top-full z-30 mt-1.5 hidden w-64 rounded-lg border border-gray-200 bg-white p-2.5 text-xs font-normal leading-relaxed text-gray-600 shadow-lg group-hover:block group-focus-within:block"
+      >
+        <span className="block text-[11px] font-semibold uppercase tracking-wide text-gray-900">
+          {title}
+        </span>
+        <span className="mt-1 block">{children}</span>
+      </span>
+    </span>
+  )
+}
+
 function FormField({
   label,
+  hint,
   children,
   className = '',
 }: {
   label: string
+  hint?: string
   children: ReactNode
   className?: string
 }) {
   return (
     <div className={`min-w-0 ${className}`}>
-      <span className="text-xs font-medium text-gray-500">{label}</span>
+      <div className="flex items-center gap-1.5">
+        <span className="text-xs font-medium text-gray-500">{label}</span>
+        {hint ? (
+          <FieldHint title={label}>{hint}</FieldHint>
+        ) : null}
+      </div>
       <div className="mt-1">{children}</div>
     </div>
   )
 }
 
 
+function formatRateUsd(value?: number | null): string {
+  if (value == null || Number.isNaN(value)) return '—'
+  return `$${value.toLocaleString('en-US', {
+    minimumFractionDigits: 2,
+    maximumFractionDigits: 4,
+  })}`
+}
+
+function ratesFromApi(raw?: Record<string, number | null> | null): PricingRatesUsd {
+  if (!raw) return { ...EMPTY_RATES }
+  return {
+    input_per_1m: raw.input_per_1m ?? undefined,
+    output_per_1m: raw.output_per_1m ?? undefined,
+    cache_read_per_1m: raw.cache_read_per_1m ?? undefined,
+    cache_write_per_1m: raw.cache_write_per_1m ?? undefined,
+    reasoning_per_1m: raw.reasoning_per_1m ?? undefined,
+    audio_per_minute: raw.audio_per_minute ?? undefined,
+    tts_per_1m_characters: raw.tts_per_1m_characters ?? undefined,
+  }
+}
+
 function todayIso(): string {
   return new Date().toISOString().slice(0, 10)
 }
@@ -90,26 +191,45 @@ export default function UsagePricing() {
   const queryClient = useQueryClient()
   const { showToast, ToastContainer } = useToast()
 
-  const [displayCurrency, setDisplayCurrency] = useState<UsageDisplayCurrency>(() =>
-    getUsageDisplayCurrency(),
-  )
-  const { data: fxRate } = useQuery({
-    queryKey: ['org-usage', 'fx-rate'],
-    queryFn: () => apiClient.getOrgUsageFxRate(),
-    staleTime: 60 * 60 * 1000,
-  })
-  const inrRate = fxRate?.rate ?? 83
-  const formatDisplayRate = (value?: number | null) =>
-    formatUsageCostUsd(value, displayCurrency, inrRate)
-
+  const [credentialId, setCredentialId] = useState('')
   const [model, setModel] = useState('')
   const [usageKind, setUsageKind] = useState<UsageKind>('llm')
   const [effectiveFrom, setEffectiveFrom] = useState(todayIso())
   const [rates, setRates] = useState<PricingRatesUsd>(EMPTY_RATES)
+  const [ratePrefillSource, setRatePrefillSource] = useState<'catalog' | 'override' | null>(null)
+  const selectionRef = useRef({ model: '', usageKind: '' as UsageKind })
+
+  const { data: aiProviders = [] } = useQuery({
+    queryKey: ['ai-providers'],
+    queryFn: () => apiClient.listAIProviders(),
+    enabled: isAdmin,
+  })
+
+  const activeCredentials = useMemo(
+    () =>
+      (aiProviders as AIProvider[])
+        .filter((row) => row.is_active)
+        .sort((a, b) => credentialDisplayLabel(a).localeCompare(credentialDisplayLabel(b))),
+    [aiProviders],
+  )
+
+  const selectedCredential = useMemo(
+    () => activeCredentials.find((row) => row.id === credentialId),
+    [activeCredentials, credentialId],
+  )
+
+  const { data: providerCatalog } = useQuery({
+    queryKey: ['model-options', selectedCredential?.provider],
+    queryFn: () => apiClient.getModelOptions(selectedCredential!.provider),
+    enabled: Boolean(selectedCredential?.provider),
+  })
 
-  const { data: filters } = useQuery({
-    queryKey: ['org-usage', 'filters'],
-    queryFn: () => apiClient.getOrgUsageFilters(),
+  const { data: availableModels = [] } = useQuery({
+    queryKey: ['usage-pricing', 'available-models'],
+    queryFn: async () => {
+      const payload = await apiClient.listUsagePricingAvailableModels()
+      return payload.models || []
+    },
     enabled: isAdmin,
   })
 
@@ -119,13 +239,86 @@ export default function UsagePricing() {
     enabled: isAdmin,
   })
 
-  const modelOptions = useMemo(() => {
-    const fromUsage = filters?.models || []
-    const fromOverrides = overrides.map((row) => row.model)
-    return Array.from(new Set([...fromUsage, ...fromOverrides]))
-      .sort()
-      .map((name) => ({ id: name, label: name }))
-  }, [filters?.models, overrides])
+  const { data: effectivePricing, isFetching: effectiveLoading } = useQuery({
+    queryKey: ['usage-pricing', 'effective', model, usageKind, effectiveFrom],
+    queryFn: () =>
+      apiClient.getUsagePricingEffective(model, {
+        usage_kind: usageKind,
+        as_of: effectiveFrom,
+      }),
+    enabled: isAdmin && Boolean(model),
+  })
+
+  const eligibleModelSet = useMemo(
+    () => new Set(availableModels || []),
+    [availableModels],
+  )
+
+  const modelOptions = useMemo(
+    () =>
+      buildPricingModelOptions({
+        credential: selectedCredential,
+        catalog: providerCatalog,
+        kind: usageKind,
+        eligibleModels: eligibleModelSet,
+        overrideModels: overrides
+          .filter((row) => row.usage_kind === usageKind)
+          .map((row) => row.model),
+      }),
+    [selectedCredential, providerCatalog, usageKind, eligibleModelSet, overrides],
+  )
+
+  const resetModelAndRates = () => {
+    setModel('')
+    setRates(EMPTY_RATES)
+    setRatePrefillSource(null)
+    selectionRef.current = { model: '', usageKind: 'llm' }
+  }
+
+  useEffect(() => {
+    if (credentialId || activeCredentials.length !== 1) return
+    setCredentialId(activeCredentials[0].id)
+  }, [activeCredentials, credentialId])
+
+  useEffect(() => {
+    if (!model) {
+      setRates(EMPTY_RATES)
+      setRatePrefillSource(null)
+      selectionRef.current = { model: '', usageKind: usageKind }
+      return
+    }
+    if (!effectivePricing) return
+
+    const selectionChanged =
+      selectionRef.current.model !== model ||
+      selectionRef.current.usageKind !== usageKind
+
+    const nextRates = ratesFromApi(effectivePricing.effective_rates)
+    setRates(nextRates)
+    setRatePrefillSource(
+      effectivePricing.has_override
+        ? 'override'
+        : effectivePricing.effective_rates
+          ? 'catalog'
+          : null,
+    )
+
+    if (selectionChanged && effectivePricing.override?.effective_from) {
+      setEffectiveFrom(effectivePricing.override.effective_from)
+    }
+
+    selectionRef.current = { model, usageKind }
+  }, [model, usageKind, effectivePricing])
+
+  const handleCredentialChange = (nextId: string) => {
+    setCredentialId(nextId)
+    resetModelAndRates()
+  }
+
+  const handleUsageKindChange = (nextKind: UsageKind) => {
+    setUsageKind(nextKind)
+    resetModelAndRates()
+  }
 
   const visibleRateFields = useMemo(
     () => RATE_FIELDS.filter((field) => field.kinds.includes(usageKind)),
@@ -151,6 +344,7 @@ export default function UsagePricing() {
       queryClient.invalidateQueries({ queryKey: ['usage-pricing'] })
       queryClient.invalidateQueries({ queryKey: ['org-usage'] })
       setRates(EMPTY_RATES)
+      setRatePrefillSource(null)
       showToast(
         data.recompute_enqueued
           ? 'Override saved; cost recompute started'
@@ -188,104 +382,62 @@ export default function UsagePricing() {
   }
 
   return (
-    <div className="mx-auto flex max-w-6xl flex-col gap-6">
+    <div className="mx-auto flex max-w-6xl flex-col gap-4">
       <ToastContainer />
 
-      <div>
-        <Link
-          to="/usage"
-          className="inline-flex items-center gap-1 text-sm text-gray-500 hover:text-gray-700"
-        >
-          <ArrowLeft className="h-4 w-4" />
-          Back to usage
-        </Link>
-        <div className="mt-3 flex items-start gap-3">
-          <div className="rounded-lg bg-[#fef9c3] p-2 text-[#a16207]">
-            <DollarSign className="h-5 w-5" />
-          </div>
-          <div className="min-w-0 flex-1">
-            <div className="flex items-center gap-2">
-              <h1 className="text-2xl font-semibold text-gray-900">Pricing overrides</h1>
-              <div
-                className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs font-medium"
-                role="group"
-                aria-label="Display currency"
-              >
-                {(['USD', 'INR'] as const).map((currency) => (
-                  <button
-                    key={currency}
-                    type="button"
-                    onClick={() => {
-                      setDisplayCurrency(currency)
-                      setUsageDisplayCurrency(currency)
-                    }}
-                    className={`rounded-md px-2 py-1 transition-colors ${
-                      displayCurrency === currency
-                        ? 'bg-gray-900 text-white'
-                        : 'text-gray-600 hover:bg-gray-50'
-                    }`}
-                  >
-                    {currency}
-                  </button>
-                ))}
-              </div>
-              <div className="relative group">
-                <button
-                  type="button"
-                  className="rounded-full p-1 text-gray-400 hover:bg-gray-100 hover:text-gray-600"
-                  aria-label="About pricing overrides"
-                >
-                  <Info className="h-4 w-4" />
-                </button>
-                <div
-                  role="tooltip"
-                  className="pointer-events-none absolute left-0 top-full z-20 mt-2 hidden w-72 rounded-lg border border-gray-200 bg-white p-3 text-xs leading-relaxed text-gray-600 shadow-lg group-hover:block group-focus-within:block"
-                >
-                  Set custom USD rates for this organization. Empty fields inherit the platform
-                  catalog. Saving updates costs for matching usage and starts a scoped recompute.
-                </div>
-              </div>
-            </div>
-            <p className="mt-1 text-sm text-gray-500">
-              Per-model rates for this org. Leave blank to use the default catalog.
-            </p>
-          </div>
-        </div>
-      </div>
-
       <Card className={`${usageTheme.panel} shrink-0`}>
-        <CardBody className="gap-5 p-5">
+        <CardBody className="gap-5 overflow-visible p-5">
           <div>
             <h2 className="text-base font-semibold text-gray-900">Add or update override</h2>
             <p className="mt-0.5 text-xs text-gray-500">
-              Saving triggers a scoped cost recompute for this model.
+              Per-model rates for this org. Leave blank to use the default catalog. Saving triggers
+              a scoped cost recompute.
             </p>
           </div>
 
           <div className="grid grid-cols-1 gap-4 md:grid-cols-3">
-            <SearchableSelect
-              label="Model"
-              placeholder="Search models…"
-              value={model}
-              options={modelOptions}
-              onChange={setModel}
-              emptyMessage="No models match"
+            <ProviderCredentialSelect
+              label="Provider"
+              hint={FIELD_HINTS.provider}
+              value={credentialId}
+              credentials={activeCredentials}
+              onChange={handleCredentialChange}
+              placeholder="Select integration…"
             />
-            <FormField label="Usage kind">
+            <FormField label="Usage kind" hint={FIELD_HINTS.usageKind}>
               <select
                 className={CONTROL_CLASS}
                 value={usageKind}
-                onChange={(e) => {
-                  setUsageKind(e.target.value as UsageKind)
-                  setRates(EMPTY_RATES)
-                }}
+                disabled={!credentialId}
+                onChange={(e) => handleUsageKindChange(e.target.value as UsageKind)}
               >
                 <option value="llm">LLM</option>
                 <option value="stt">STT</option>
                 <option value="tts">TTS</option>
               </select>
             </FormField>
-            <FormField label="Effective from">
+            <SearchableSelect
+              label="Model"
+              hint={FIELD_HINTS.model}
+              placeholder={
+                credentialId
+                  ? 'Search models for this provider…'
+                  : 'Select a provider first'
+              }
+              value={model}
+              options={modelOptions}
+              onChange={setModel}
+              disabled={!credentialId}
+              emptyMessage={
+                credentialId
+                  ? `No ${usageKind.toUpperCase()} models for this integration — enable them in Integrations`
+                  : 'Select a provider first'
+              }
+            />
+          </div>
+
+          <div className="grid grid-cols-1 gap-4 md:grid-cols-3">
+            <FormField label="Effective from" hint={FIELD_HINTS.effectiveFrom}>
               <input
                 type="date"
                 className={CONTROL_CLASS}
@@ -295,18 +447,56 @@ export default function UsagePricing() {
             </FormField>
           </div>
 
-          <div>
-            <p className="mb-3 text-xs font-medium uppercase tracking-wide text-gray-500">
-              Rate overrides (USD)
+          {credentialId && modelOptions.length === 0 ? (
+            <p className="rounded-lg border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-900">
+              No {usageKind.toUpperCase()} models are available for this integration. Enable models
+              in Integrations → AI provider → step 2, or pick another usage kind.
+            </p>
+          ) : null}
+
+          {activeCredentials.length === 0 ? (
+            <p className="rounded-lg border border-gray-200 bg-gray-50 px-3 py-2 text-xs text-gray-600">
+              Add an AI provider in Integrations before setting pricing overrides.
             </p>
+          ) : null}
+
+          {model ? (
+          <div>
+            {effectiveLoading ? (
+              <div className="mb-3 flex items-center gap-2 text-xs text-gray-500">
+                <Spinner size="sm" />
+                Loading current rates…
+              </div>
+            ) : ratePrefillSource ? (
+              <p
+                className={`mb-3 rounded-lg px-3 py-2 text-xs ${
+                  ratePrefillSource === 'override'
+                    ? 'border border-[#fde047]/60 bg-[#fefce8] text-[#854d0e]'
+                    : 'border border-gray-200 bg-gray-50 text-gray-600'
+                }`}
+              >
+                {ratePrefillSource === 'override'
+                  ? 'Prefilled from your org override — these are the rates usage is billed at today. Edit and save to update.'
+                  : 'Prefilled from platform catalog — these are the default rates in effect. Edit and save to create an org override.'}
+              </p>
+            ) : (
+              <p className="mb-3 rounded-lg border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-900">
+                No catalog rate found for this model on the selected date. Enter rates manually.
+              </p>
+            )}
+            <div className="mb-3 flex items-center gap-1.5">
+              <p className="text-xs font-medium uppercase tracking-wide text-gray-500">
+                Rate overrides ($)
+              </p>
+              <FieldHint title="Rate overrides">{FIELD_HINTS.ratesSection}</FieldHint>
+            </div>
             <div className="grid grid-cols-1 gap-3 sm:grid-cols-2 lg:grid-cols-3">
-              {visibleRateFields.map(({ key, label }) => (
-                <FormField key={key} label={label}>
+              {visibleRateFields.map(({ key, label, hint }) => (
+                <FormField key={key} label={label} hint={hint}>
                   <input
                     type="number"
                     min={0}
                     step="any"
-                    placeholder="Inherit from catalog"
                     className={CONTROL_CLASS}
                     value={rates[key] != null ? String(rates[key]) : ''}
                     onChange={(e) => {
@@ -321,6 +511,11 @@ export default function UsagePricing() {
               ))}
             </div>
           </div>
+          ) : (
+            <p className="text-xs text-gray-500">
+              Select a provider, usage kind, and model to configure rate overrides.
+            </p>
+          )}
 
           <div className="flex items-center justify-end gap-3 border-t border-gray-100 pt-4">
             <button
@@ -329,6 +524,7 @@ export default function UsagePricing() {
               onClick={() => {
                 setRates(EMPTY_RATES)
                 setModel('')
+                setCredentialId('')
               }}
             >
               Clear form
@@ -336,7 +532,7 @@ export default function UsagePricing() {
             <Button
               variant="primary"
               leftIcon={<Plus className="h-4 w-4" />}
-              disabled={!model || saveMutation.isPending}
+              disabled={!credentialId || !model || saveMutation.isPending}
               isLoading={saveMutation.isPending}
               onClick={() => saveMutation.mutate()}
             >
@@ -349,7 +545,13 @@ export default function UsagePricing() {
       <Card className={`${usageTheme.panel} min-h-0`}>
         <CardBody className="flex min-h-0 flex-col gap-3 p-5">
           <div className="flex shrink-0 items-center justify-between gap-2">
-            <h2 className="text-base font-semibold text-gray-900">Current overrides</h2>
+            <div className="flex items-center gap-1.5">
+              <h2 className="text-base font-semibold text-gray-900">Current overrides</h2>
+              <FieldHint title="Current overrides">
+                Active org-specific rates in USD. Delete a row to fall back to the platform catalog
+                for that model.
+              </FieldHint>
+            </div>
             {!isLoading ? (
               <span className="text-xs text-gray-500">{overrides.length} total</span>
             ) : null}
@@ -390,16 +592,16 @@ export default function UsagePricing() {
                         {row.effective_to ? ` → ${row.effective_to}` : ''}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatDisplayRate(row.rates.input_per_1m)}
+                        {formatRateUsd(row.rates.input_per_1m)}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatDisplayRate(row.rates.output_per_1m)}
+                        {formatRateUsd(row.rates.output_per_1m)}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatDisplayRate(row.rates.audio_per_minute)}
+                        {formatRateUsd(row.rates.audio_per_minute)}
                       </td>
                       <td className="px-4 py-2.5 text-right tabular-nums">
-                        {formatDisplayRate(row.rates.tts_per_1m_characters)}
+                        {formatRateUsd(row.rates.tts_per_1m_characters)}
                       </td>
                       <td className="px-4 py-2.5 text-right">
                         <button
diff --git a/frontend/src/pages/usage/pricingModelOptions.ts b/frontend/src/pages/usage/pricingModelOptions.ts
new file mode 100644
index 00000000..3bcd8906
--- /dev/null
+++ b/frontend/src/pages/usage/pricingModelOptions.ts
@@ -0,0 +1,72 @@
+import { getProviderLabel } from '../../config/providers'
+import type { AIProvider } from '../../types/api'
+
+export type PricingUsageKind = 'llm' | 'stt' | 'tts'
+
+export type ProviderModelCatalog = {
+  llm?: string[]
+  stt?: string[]
+  tts?: string[]
+  s2s?: string[]
+}
+
+export function credentialDisplayLabel(credential: AIProvider): string {
+  const providerLabel = getProviderLabel(credential.provider)
+  const name = credential.name?.trim()
+  return name ? `${providerLabel} — ${name}` : providerLabel
+}
+
+export function modelsForCredentialAndKind(
+  credential: AIProvider,
+  catalog: ProviderModelCatalog,
+  kind: PricingUsageKind,
+): string[] {
+  const catalogModels = [...(catalog[kind] || [])]
+  const allowlist = credential.enabled_models?.map((m) => m.trim()).filter(Boolean) ?? []
+  const gateway = credential.gateway_model?.trim()
+
+  if (allowlist.length > 0) {
+    const allowed = new Set(allowlist)
+    let filtered = catalogModels.filter((m) => allowed.has(m))
+    if (kind === 'llm' && gateway && !filtered.includes(gateway)) {
+      filtered = [gateway, ...filtered]
+    }
+    return filtered.sort()
+  }
+
+  if (kind === 'llm' && gateway && !catalogModels.includes(gateway)) {
+    return [gateway, ...catalogModels].sort()
+  }
+
+  return catalogModels.sort()
+}
+
+export function buildPricingModelOptions(params: {
+  credential: AIProvider | undefined
+  catalog: ProviderModelCatalog | undefined
+  kind: PricingUsageKind
+  eligibleModels: Set<string>
+  overrideModels: string[]
+}): Array<{ id: string; label: string }> {
+  const { credential, catalog, kind, eligibleModels, overrideModels } = params
+  if (!credential || !catalog) return []
+
+  const fromCatalog = modelsForCredentialAndKind(credential, catalog, kind)
+  const catalogSet = new Set(catalog[kind] || [])
+  const allowlist = new Set(
+    credential.enabled_models?.map((m) => m.trim()).filter(Boolean) ?? [],
+  )
+
+  const extras = overrideModels.filter((model) => {
+    if (fromCatalog.includes(model)) return false
+    if (!eligibleModels.has(model)) return false
+    if (catalogSet.has(model)) return true
+    if (allowlist.has(model)) return true
+    if (kind === 'llm' && credential.gateway_model?.trim() === model) return true
+    return false
+  })
+
+  return Array.from(new Set([...fromCatalog, ...extras]))
+    .sort()
+    .map((name) => ({ id: name, label: name }))
+}
diff --git a/frontend/src/pages/usage/useAnchoredDropdown.ts b/frontend/src/pages/usage/useAnchoredDropdown.ts
new file mode 100644
index 00000000..e27796df
--- /dev/null
+++ b/frontend/src/pages/usage/useAnchoredDropdown.ts
@@ -0,0 +1,37 @@
+import { useEffect, useState, type RefObject } from 'react'
+
+type Coords = { top: number; left: number; width: number }
+
+export function useAnchoredDropdown(
+  open: boolean,
+  anchorRef: RefObject<HTMLElement | null>,
+): Coords | null {
+  const [coords, setCoords] = useState<Coords | null>(null)
+
+  useEffect(() => {
+    if (!open || !anchorRef.current) {
+      setCoords(null)
+      return
+    }
+
+    const update = () => {
+      if (!anchorRef.current) return
+      const rect = anchorRef.current.getBoundingClientRect()
+      setCoords({
+        top: rect.bottom + 4,
+        left: rect.left,
+        width: rect.width,
+      })
+    }
+
+    update()
+    window.addEventListener('resize', update)
+    window.addEventListener('scroll', update, true)
+    return () => {
+      window.removeEventListener('resize', update)
+      window.removeEventListener('scroll', update, true)
+    }
+  }, [open, anchorRef])
+
+  return coords
+}
diff --git a/frontend/src/types/api.ts b/frontend/src/types/api.ts
index ce71af44..d0ccfe79 100644
--- a/frontend/src/types/api.ts
+++ b/frontend/src/types/api.ts
@@ -406,6 +406,7 @@ export interface AIProvider {
   gateway_auth_secret_env?: string | null
   has_gateway_auth_secret?: boolean
   gateway_extra_headers?: Record<string, string> | null
+  enabled_models?: string[] | null
   /** True when provider secrets are resolved by the Bifrost gateway. */
   gateway_managed?: boolean
   effective_routing?: EffectiveCredentialRouting
@@ -428,6 +429,7 @@ export interface AIProviderCreate {
   gateway_auth_secret_env?: string | null
   gateway_auth_secret?: string | null
   gateway_extra_headers?: Record<string, string> | null
+  enabled_models?: string[] | null
   /** Mark the new credential as the default for (org, provider). */
   is_default?: boolean
 }
@@ -446,6 +448,7 @@ export interface AIProviderUpdate {
   gateway_auth_secret?: string | null
   clear_gateway_auth_secret?: boolean
   gateway_extra_headers?: Record<string, string> | null
+  enabled_models?: string[] | null
 }
 
 export enum VoiceBundleType {
diff --git a/tests/test_services/test_usage/test_enabled_models.py b/tests/test_services/test_usage/test_enabled_models.py
new file mode 100644
index 00000000..88d2737a
--- /dev/null
+++ b/tests/test_services/test_usage/test_enabled_models.py
@@ -0,0 +1,66 @@
+"""Tests for org/credential enabled model allowlists."""
+
+from uuid import uuid4
+
+from app.services.usage.enabled_models import (
+    filter_models_by_credential,
+    normalize_enabled_models,
+    org_pricing_eligible_models,
+)
+
+
+class _Credential:
+    def __init__(self, *, enabled_models=None, gateway_model=None, provider="openai"):
+        self.enabled_models = enabled_models
+        self.gateway_model = gateway_model
+        self.provider = provider
+
+
+def test_normalize_enabled_models_dedupes_and_trims():
+    assert normalize_enabled_models([" gpt-4o ", "gpt-4o", ""]) == ["gpt-4o"]
+
+
+def test_filter_models_by_credential_restricts_catalog():
+    cred = _Credential(enabled_models=["gpt-4o"])
+    assert filter_models_by_credential(cred, ["gpt-4o", "gpt-5-mini"]) == ["gpt-4o"]
+
+
+def test_filter_models_by_credential_includes_gateway_model():
+    cred = _Credential(enabled_models=["gpt-4o"], gateway_model="custom/deploy")
+    assert filter_models_by_credential(cred, ["gpt-4o"]) == ["custom/deploy", "gpt-4o"]
+
+
+def test_filter_models_unrestricted_when_allowlist_empty():
+    cred = _Credential(enabled_models=None)
+    assert filter_models_by_credential(cred, ["a", "b"]) == ["a", "b"]
+
+
+def test_org_pricing_eligible_models_includes_usage_and_overrides(monkeypatch):
+    org_id = uuid4()
+
+    class FakeResult:
+        def scalars(self):
+            return self
+
+        def all(self):
+            return ["used-model"]
+
+    class FakeDb:
+        def execute(self, *_args, **_kwargs):
+            return FakeResult()
+
+        def query(self, *_args, **_kwargs):
+            return self
+
+        def filter(self, *_args, **_kwargs):
+            return self
+
+        def all(self):
+            return []
+
+    monkeypatch.setattr(
+        "app.services.usage.enabled_models.catalog_models_for_provider",
+        lambda _provider: ["catalog-model"],
+    )
+    models = org_pricing_eligible_models(FakeDb(), org_id)
+    assert "used-model" in models
diff --git a/tests/test_services/test_usage/test_fx_rates.py b/tests/test_services/test_usage/test_fx_rates.py
new file mode 100644
index 00000000..fc32679f
--- /dev/null
+++ b/tests/test_services/test_usage/test_fx_rates.py
@@ -0,0 +1,69 @@
+from datetime import datetime, timezone
+from unittest.mock import MagicMock, patch
+
+import httpx
+
+from app.services.usage import fx_rates
+
+
+def test_parse_frankfurter_payload():
+    rate, as_of = fx_rates._parse_frankfurter_payload(
+        {"date": "2026-08-15", "base": "USD", "quote": "INR", "rate": 95.41}
+    )
+    assert rate == 95.41
+    assert as_of == datetime(2026, 8, 15, tzinfo=timezone.utc)
+
+
+def test_refresh_caches_live_rate():
+    response = MagicMock()
+    response.raise_for_status = MagicMock()
+    response.json.return_value = {
+        "date": "2026-08-15",
+        "base": "USD",
+        "quote": "INR",
+        "rate": 95.41,
+    }
+
+    with patch("httpx.Client") as client_cls:
+        client_cls.return_value.__enter__.return_value.get.return_value = response
+        with patch.object(fx_rates, "_write_cached") as write_cached:
+            payload = fx_rates.refresh_usd_inr_rate()
+
+    assert payload["rate"] == 95.41
+    assert payload["source"] == "frankfurter"
+    write_cached.assert_called_once()
+
+
+def test_refresh_does_not_cache_fallback_on_http_error():
+    with patch("httpx.Client") as client_cls:
+        client_cls.return_value.__enter__.return_value.get.side_effect = httpx.ConnectError(
+            "network down"
+        )
+        with patch.object(fx_rates, "_write_cached") as write_cached:
+            payload = fx_rates.refresh_usd_inr_rate()
+
+    assert payload["rate"] == fx_rates._DEFAULT_RATE
+    assert payload["source"] == "default"
+    write_cached.assert_not_called()
+
+
+def test_refresh_does_not_cache_fallback_on_bad_payload():
+    response = MagicMock()
+    response.raise_for_status = MagicMock()
+    response.json.return_value = {"date": "2026-08-15", "base": "USD", "quote": "INR"}
+
+    with patch("httpx.Client") as client_cls:
+        client_cls.return_value.__enter__.return_value.get.return_value = response
+        with patch.object(fx_rates, "_write_cached") as write_cached:
+            payload = fx_rates.refresh_usd_inr_rate()
+
+    assert payload["source"] == "default"
+    write_cached.assert_not_called()
+
+
+def test_read_cached_ignores_default_source():
+    with patch.object(fx_rates, "_client") as redis_client:
+        redis_client.return_value.get.return_value = (
+            '{"rate": 83.0, "source": "default", "as_of": "2026-08-15T00:00:00+00:00"}'
+        )
+        assert fx_rates._read_cached() is None

From cae3bf8634be7f3397c905e14697d54c9fd56b04 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Sat, 15 Aug 2026 16:29:20 +0530
Subject: [PATCH 26/55] fix(tests): update monkeypatch paths  to reflect
 correct  structure

---
 tests/test_services/test_usage/test_usage_access.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/test_services/test_usage/test_usage_access.py b/tests/test_services/test_usage/test_usage_access.py
index f55816ae..e2ea0849 100644
--- a/tests/test_services/test_usage/test_usage_access.py
+++ b/tests/test_services/test_usage/test_usage_access.py
@@ -9,7 +9,7 @@
 
 def test_oss_min_local_date_seven_inclusive(monkeypatch):
     monkeypatch.setattr(
-        "app.services.usage.dates.usage_local_today",
+        "app.services.usage.access.usage_local_today",
         lambda tz: date(2026, 8, 14),
     )
     assert oss_usage_min_local_date(None) == date(2026, 8, 8)
@@ -26,7 +26,7 @@ def test_resolve_clamps_old_start(monkeypatch):
         lambda _oid: ue.UsagePolicySnapshot(False, 7),
     )
     monkeypatch.setattr(
-        "app.services.usage.dates.usage_local_today",
+        "app.services.usage.access.usage_local_today",
         lambda tz: date(2026, 8, 14),
     )
 
@@ -52,7 +52,7 @@ def test_resolve_full_range_when_entitled(monkeypatch):
         lambda _oid: ue.UsagePolicySnapshot(True, None),
     )
     monkeypatch.setattr(
-        "app.services.usage.dates.usage_local_today",
+        "app.services.usage.access.usage_local_today",
         lambda tz: date(2026, 8, 14),
     )
 

From 5e4ea48aae6d92d618619cf7a71bde7054e64634 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 17 Aug 2026 17:34:33 +0530
Subject: [PATCH 27/55] feat: introduce Celery Beat for platform tasks and
 update Docker configuration for periodic job scheduling

---
 README.md                                     |  22 ++-
 app/cli.py                                    | 144 +++++++++++++++++-
 app/migrations/077_remove_system_cron_jobs.py |  19 +++
 app/services/ai/llm_gateway.py                |  11 +-
 app/services/cron/job_dispatch.py             |  27 +---
 app/services/usage/enabled_models.py          |   2 +
 app/workers/config.py                         |  40 ++++-
 docker-compose.yml                            |  37 ++++-
 env.example                                   |   4 +-
 frontend/src/lib/llmModelOptions.test.ts      |  60 ++++++++
 frontend/src/lib/llmModelOptions.ts           |   5 +-
 .../AIProviderEnabledModelsStep.tsx           | 107 +++++++++----
 .../src/pages/configurations/Integrations.tsx |   7 +-
 .../test_gateway_managed_credentials.py       |  26 +++-
 .../test_services/test_ai/test_llm_gateway.py |  17 +++
 .../test_cron/test_job_dispatch.py            |  23 ++-
 .../test_usage/test_enabled_models.py         |  14 ++
 tests/test_workers/test_beat_schedule.py      |  33 ++++
 .../test_workers/test_usage_queue_routing.py  |  16 +-
 19 files changed, 532 insertions(+), 82 deletions(-)
 create mode 100644 app/migrations/077_remove_system_cron_jobs.py
 create mode 100644 frontend/src/lib/llmModelOptions.test.ts
 create mode 100644 tests/test_workers/test_beat_schedule.py

diff --git a/README.md b/README.md
index 75e57a6a..e14527d3 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ There are two ways to run the application:
    
    This will automatically:
    - Pull pre-built images from GitHub Container Registry (no build required!)
-   - Start all services: `db`, `redis`, `api`, `media`, `worker`, `worker-imports`, `worker-usage`
+   - Start all services: `db`, `redis`, `api`, `media`, `worker`, `beat`, `worker-imports`, `worker-usage`
    - Run database migrations automatically on startup
 
    | Service | Purpose |
@@ -63,11 +63,12 @@ There are two ways to run the application:
    | `redis` | Redis (Celery broker + usage counters) |
    | `api` | HTTP API + frontend |
    | `media` | Live voice WebSocket media server |
-   | `worker` | Celery: `celery` (cron dispatch, alerts, FX), `audio-metrics` queues |
+   | `worker` | Celery: `celery` (evaluator cron dispatch), `audio-metrics` queues |
+   | `beat` | Celery Beat scheduler + `platform` queue worker (alerts, FX, OSS prune) — **single replica** |
    | `worker-imports` | Celery: `imports`, `diarization`, `eval-control`, `evaluations` |
    | `worker-usage` | Celery: `usage` queue (flush Redis counters + cost recompute) |
 
-   **Usage costs:** token/cost rollups stay stale without `worker-usage` and the default `worker` (cron dispatcher; or `eai start-all`).
+   **Usage costs:** token/cost rollups stay stale without `beat`, `worker-usage`, and default `worker` (evaluator crons; or `eai start-all`).
    
    **Using a specific version:**
    ```bash
@@ -167,7 +168,7 @@ docker compose up -d
    - Celery worker (`celery`, `audio-metrics`)
    - Celery worker (`imports`, `diarization`, `eval-control`, `evaluations`)
    - Celery worker (`usage` — flush + cost recompute)
-   - Celery Beat (periodic usage flush)
+   - Celery Beat (platform schedules: usage flush, alerts, FX refresh, OSS prune)
    
    It also runs database migrations and builds the frontend when needed.
    
@@ -184,6 +185,11 @@ docker compose up -d
    ```bash
    eai worker --config config.yml
    ```
+
+   For platform periodic tasks (usage flush, alerts, etc.), start Celery Beat in a separate terminal (single replica):
+   ```bash
+   eai beat --config config.yml
+   ```
    
    Or use the Celery command directly:
    ```bash
@@ -372,7 +378,7 @@ celery -A app.workers.celery_app worker --loglevel=info
 **Note:** Workers are required for background tasks (transcription, evaluation, usage cost flush, etc.). If you use `eai start-all`, they start automatically. Only use `eai worker` if you need to run a worker separately (e.g. `eai worker --queues usage` for the usage queue only).
 
 ### Usage Pricing Ops
-Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups. Requires `worker-usage` + default `worker` (or `eai start-all`).
+Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups. Requires `beat`, `worker-usage`, and default `worker` (or `eai start-all`).
 
 ```bash
 # Upsert model_pricing_rates from app/config/models.json
@@ -407,12 +413,12 @@ eai usage recompute --config config.yml --sync
 |----------|---------|---------|
 | `USAGE_FLUSH_BUCKET_BATCH_SIZE` | `500` | Buckets per DB transaction |
 | `USAGE_FLUSH_MAX_BATCHES_PER_RUN` | `30` | Batches per flush tick (≤ **15,000** buckets/run) |
-| `USAGE_FLUSH_BEAT_SECONDS` | `120` | System cron flush interval (~2 min lag vs Redis) |
+| `USAGE_FLUSH_BEAT_SECONDS` | `120` | Celery Beat flush interval (~2 min lag vs Redis) |
 | `USAGE_FLUSH_LOCK_TTL_SECONDS` | `300` | Per-org flush lock TTL |
 | `USAGE_READ_CACHE_TTL_SECONDS` | `90` | Redis cache TTL for usage summary/breakdown/filters |
-| `CRON_DISPATCH_INTERVAL_SECONDS` | `30` | Self-scheduling cron dispatcher tick |
+| `CRON_DISPATCH_INTERVAL_SECONDS` | `30` | Evaluator cron dispatcher tick (default worker) |
 
-Usage UI reads Postgres only (summary/breakdown/filters); Redis counters flush on the system cron schedule (~2 min eventual consistency). If Redis backlog grows, lower `USAGE_FLUSH_BEAT_SECONDS` or raise `USAGE_FLUSH_MAX_BATCHES_PER_RUN`.
+Usage UI reads Postgres only (summary/breakdown/filters); Redis counters flush on the Celery Beat schedule (~2 min eventual consistency). If Redis backlog grows, lower `USAGE_FLUSH_BEAT_SECONDS` or raise `USAGE_FLUSH_MAX_BATCHES_PER_RUN`.
 
 ### Generate Config File
 ```bash
diff --git a/app/cli.py b/app/cli.py
index 8b7b368f..7fbefbff 100644
--- a/app/cli.py
+++ b/app/cli.py
@@ -423,6 +423,91 @@ def worker(config: str, loglevel: str, queues: Optional[str], concurrency: Optio
         sys.exit(1)
 
 
+@main.command("beat")
+@click.option(
+    "--config",
+    "-c",
+    default="config.yml",
+    help="Path to configuration file",
+)
+@click.option(
+    "--loglevel",
+    "-l",
+    default="info",
+    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
+    help="Log level for Celery beat",
+)
+@click.option(
+    "--platform-worker-concurrency",
+    default=2,
+    type=int,
+    help="Concurrency for the co-located platform task worker (default: 2; thread pool).",
+)
+def beat(config: str, loglevel: str, platform_worker_concurrency: int):
+    """Start Celery Beat + platform task worker (alerts, FX, prune) — single replica only."""
+    from app.config import load_config_from_file
+
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        sys.exit(1)
+
+    try:
+        load_config_from_file(str(config_path))
+        click.echo(f"✅ Loaded configuration from {config_path}")
+    except Exception as e:
+        click.echo(f"❌ Error loading config: {e}", err=True)
+        sys.exit(1)
+
+    from app.workers.config import PLATFORM_WORKER_QUEUE
+
+    click.echo(
+        "🚀 Starting Celery Beat + platform worker "
+        f"(queue={PLATFORM_WORKER_QUEUE}, concurrency={platform_worker_concurrency})"
+    )
+    platform_proc = None
+    try:
+        import subprocess
+
+        platform_proc = subprocess.Popen(
+            [
+                "celery",
+                "-A",
+                "app.workers.celery_app",
+                "worker",
+                f"--queues={PLATFORM_WORKER_QUEUE}",
+                "--pool=threads",
+                f"--concurrency={platform_worker_concurrency}",
+                f"--loglevel={loglevel}",
+            ],
+        )
+        subprocess.run(
+            [
+                "celery",
+                "-A",
+                "app.workers.celery_app",
+                "beat",
+                f"--loglevel={loglevel}",
+            ],
+            check=True,
+        )
+    except KeyboardInterrupt:
+        click.echo("\n👋 Celery Beat stopped")
+    except subprocess.CalledProcessError as e:
+        click.echo(f"❌ Celery Beat failed: {e}", err=True)
+        sys.exit(1)
+    except FileNotFoundError:
+        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
+        sys.exit(1)
+    finally:
+        if platform_proc is not None and platform_proc.poll() is None:
+            platform_proc.terminate()
+            try:
+                platform_proc.wait(timeout=5)
+            except subprocess.TimeoutExpired:
+                platform_proc.kill()
+
+
 @main.command("telephony-worker")
 @click.option(
     "--config",
@@ -637,6 +722,20 @@ def _handle_signal(sig, frame):
     type=int,
     help="Concurrency for the usage worker (default: 4; thread pool).",
 )
+@click.option(
+    "--beat/--no-beat",
+    default=True,
+    help=(
+        "Start Celery Beat for platform periodic tasks (usage flush, alerts, etc.; "
+        "default: True — single replica only in production)."
+    ),
+)
+@click.option(
+    "--beat-loglevel",
+    default=None,
+    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
+    help="Log level for Celery Beat (defaults to --worker-loglevel).",
+)
 @click.option(
     "--telephony-worker/--no-telephony-worker",
     default=True,
@@ -662,6 +761,8 @@ def start_all(
     imports_worker_concurrency: int,
     usage_worker: bool,
     usage_worker_concurrency: int,
+    beat: bool,
+    beat_loglevel: Optional[str],
     telephony_worker: bool,
     media_port: Optional[int],
 ):
@@ -709,6 +810,8 @@ def start_all(
     worker_process = None
     worker_imports_process = None
     worker_usage_process = None
+    beat_process = None
+    platform_worker_process = None
     telephony_process = None
 
     def _terminate(proc, label: str):
@@ -728,8 +831,10 @@ def _terminate(proc, label: str):
 
     def cleanup_processes():
         """Clean up spawned processes."""
-        nonlocal worker_process, worker_imports_process, worker_usage_process, telephony_process
+        nonlocal worker_process, worker_imports_process, worker_usage_process, beat_process, platform_worker_process, telephony_process
         _terminate(telephony_process, "Telephony media server")
+        _terminate(beat_process, "Celery Beat")
+        _terminate(platform_worker_process, "Celery platform worker")
         _terminate(worker_process, "Celery worker (default)")
         _terminate(worker_imports_process, "Celery worker (imports)")
         _terminate(worker_usage_process, "Celery worker (usage)")
@@ -910,6 +1015,39 @@ def _stream_telephony():
                 prefix="[WORKER-USAGE]",
             )
 
+        if beat:
+            from app.workers.config import PLATFORM_WORKER_QUEUE
+
+            beat_level = beat_loglevel or worker_loglevel
+            platform_worker_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "worker",
+                    f"--loglevel={beat_level}",
+                    "-Q",
+                    PLATFORM_WORKER_QUEUE,
+                    "-P",
+                    "threads",
+                    "-c",
+                    "2",
+                ],
+                label=f"Celery platform worker ({PLATFORM_WORKER_QUEUE} queue)",
+                prefix="[BEAT-WORKER]",
+            )
+            beat_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "beat",
+                    f"--loglevel={beat_level}",
+                ],
+                label=f"Celery Beat (scheduler, loglevel={beat_level})",
+                prefix="[BEAT]",
+            )
+
     except FileNotFoundError:
         click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
         sys.exit(1)
@@ -958,6 +1096,10 @@ def _stream_telephony():
             )
         else:
             click.echo("   Usage worker: disabled (--no-usage-worker)")
+        if beat:
+            click.echo("   Celery Beat: scheduler + platform worker (alerts, FX, prune); flush on worker-usage")
+        else:
+            click.echo("   Celery Beat: disabled (--no-beat)")
         if telephony_worker:
             telephony_public = (settings.VOBIZ_WEBHOOK_BASE_URL or "").strip()
             click.echo(f"   Telephony edge: http://localhost:{bind_media_port} (local)")
diff --git a/app/migrations/077_remove_system_cron_jobs.py b/app/migrations/077_remove_system_cron_jobs.py
new file mode 100644
index 00000000..5aadeccb
--- /dev/null
+++ b/app/migrations/077_remove_system_cron_jobs.py
@@ -0,0 +1,19 @@
+"""Remove platform system cron rows; platform jobs use Celery Beat."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Delete is_system cron_jobs; platform scheduling moved to Celery Beat"
+
+
+def upgrade(db: Session) -> None:
+    result = db.execute(
+        text("DELETE FROM cron_jobs WHERE is_system = true")
+    )
+    db.commit()
+    print(f"Removed {result.rowcount} system cron job row(s)")
+
+
+def downgrade(db: Session) -> None:
+    # System jobs are re-seeded by re-running migration 075 logic if needed.
+    print("Downgrade: re-run 075 upgrade to restore system cron jobs if required")
diff --git a/app/services/ai/llm_gateway.py b/app/services/ai/llm_gateway.py
index 8d7d4cd2..36fd7fdc 100644
--- a/app/services/ai/llm_gateway.py
+++ b/app/services/ai/llm_gateway.py
@@ -578,10 +578,15 @@ def get_credential_effective_routing_label(
     db: Session,
     credential: Any,
 ) -> EffectiveRouting:
-    """Resolved routing label for API responses."""
+    """Resolved routing label for API responses (never raises on misconfigured gateway)."""
     ctx = _credential_routing_context(credential)
-    _, effective = resolve_effective_routing(organization_id, db, ctx)
-    return effective
+    try:
+        _, effective = resolve_effective_routing(organization_id, db, ctx)
+        return effective
+    except RuntimeError:
+        if ctx.routing_mode == "gateway":
+            return "gateway"
+        raise
 
 
 # Gateways speak OpenAI-compatible ``/v1/chat/completions``. LiteLLM
diff --git a/app/services/cron/job_dispatch.py b/app/services/cron/job_dispatch.py
index af8b9472..9b905079 100644
--- a/app/services/cron/job_dispatch.py
+++ b/app/services/cron/job_dispatch.py
@@ -21,6 +21,7 @@ def list_due_cron_jobs(db: Session, *, now: datetime | None = None) -> List[Cron
         db.query(CronJob)
         .filter(
             CronJob.status == CronJobStatus.ACTIVE.value,
+            CronJob.is_system.is_(False),
             CronJob.next_run_at.isnot(None),
             CronJob.next_run_at <= moment,
         )
@@ -44,32 +45,6 @@ def advance_cron_job(db: Session, job: CronJob, *, now: datetime | None = None)
 def enqueue_cron_job(job: CronJob) -> Dict[str, Any]:
     job_type = (job.job_type or "evaluator_run").strip()
 
-    if job_type == "usage_flush":
-        from app.workers.tasks.flush_usage_counters import flush_usage_counters_task
-
-        result = flush_usage_counters_task.delay()
-        return {"task": "flush_usage_counters", "celery_task_id": result.id}
-
-    if job_type == "alert_evaluate":
-        from app.workers.tasks.evaluate_alerts import evaluate_alerts_task
-
-        result = evaluate_alerts_task.delay()
-        return {"task": "evaluate_alerts", "celery_task_id": result.id}
-
-    if job_type == "oss_usage_prune":
-        from app.workers.tasks.prune_oss_usage_history import (
-            prune_oss_usage_history_task,
-        )
-
-        result = prune_oss_usage_history_task.delay()
-        return {"task": "prune_oss_usage_history", "celery_task_id": result.id}
-
-    if job_type == "fx_rate_refresh":
-        from app.workers.tasks.refresh_fx_rates import refresh_fx_rates_task
-
-        result = refresh_fx_rates_task.delay()
-        return {"task": "refresh_fx_rates", "celery_task_id": result.id}
-
     if job_type == "evaluator_run":
         from app.workers.tasks.run_cron_evaluator_job import run_cron_evaluator_job_task
 
diff --git a/app/services/usage/enabled_models.py b/app/services/usage/enabled_models.py
index 87d9652e..728356c5 100644
--- a/app/services/usage/enabled_models.py
+++ b/app/services/usage/enabled_models.py
@@ -57,6 +57,8 @@ def filter_models_by_credential(
         return catalog_models
     allowed = set(allowlist)
     filtered = [m for m in catalog_models if m in allowed]
+    if not filtered:
+        filtered = list(allowlist)
     gateway = (credential.gateway_model or "").strip()
     if gateway and gateway not in filtered:
         filtered = [gateway, *filtered]
diff --git a/app/workers/config.py b/app/workers/config.py
index d06d8210..c6862c92 100644
--- a/app/workers/config.py
+++ b/app/workers/config.py
@@ -53,6 +53,7 @@
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 
 from celery import Celery  # noqa: E402  (env vars must be set first)
+from celery.schedules import crontab  # noqa: E402
 from loguru import logger  # noqa: E402
 
 from app.config import settings, load_config_from_file  # noqa: E402
@@ -75,6 +76,38 @@
 IMPORTS_WORKER_QUEUES = "imports,diarization,eval-control,evaluations"
 EVAL_CONTROL_QUEUE = "eval-control"
 USAGE_WORKER_QUEUE = "usage"
+PLATFORM_WORKER_QUEUE = "platform"
+
+
+def _usage_flush_beat_seconds() -> float:
+    raw = os.environ.get("USAGE_FLUSH_BEAT_SECONDS", "120")
+    try:
+        return max(30.0, float(raw))
+    except (TypeError, ValueError):
+        return 120.0
+
+
+def _platform_beat_schedule() -> dict:
+    """Periodic platform tasks — run from dedicated ``celery beat`` (single replica)."""
+    return {
+        "flush-usage-counters": {
+            "task": "flush_usage_counters",
+            "schedule": _usage_flush_beat_seconds(),
+        },
+        "evaluate-alerts": {
+            "task": "evaluate_alerts",
+            "schedule": crontab(minute="*/5"),
+        },
+        "refresh-fx-rates": {
+            "task": "refresh_fx_rates",
+            "schedule": crontab(hour=6, minute=0),
+        },
+        "prune-oss-usage-history": {
+            "task": "prune_oss_usage_history",
+            "schedule": crontab(hour=3, minute=0),
+        },
+    }
+
 
 # Create Celery app
 celery_app = Celery(
@@ -107,6 +140,7 @@
     # any leaked OMP threads, and HuggingFace tokenizer state, and prevents
     # slow drift from accumulating over hours of import processing.
     worker_max_tasks_per_child=20,
+    beat_schedule=_platform_beat_schedule(),
 )
 
 # Route call-import recording fetch to the imports queue (preferred by workers
@@ -141,10 +175,10 @@
     "map_agent_flowchart_prompt_sections": {"queue": "celery"},
     "flush_usage_counters": {"queue": USAGE_WORKER_QUEUE},
     "recompute_usage_costs": {"queue": USAGE_WORKER_QUEUE},
-    "prune_oss_usage_history": {"queue": USAGE_WORKER_QUEUE},
+    "evaluate_alerts": {"queue": PLATFORM_WORKER_QUEUE},
+    "refresh_fx_rates": {"queue": PLATFORM_WORKER_QUEUE},
+    "prune_oss_usage_history": {"queue": PLATFORM_WORKER_QUEUE},
     "dispatch_cron_jobs": {"queue": "celery"},
-    "evaluate_alerts": {"queue": "celery"},
-    "refresh_fx_rates": {"queue": "celery"},
     "run_cron_evaluator_job": {"queue": "celery"},
 }
 
diff --git a/docker-compose.yml b/docker-compose.yml
index b421ec2e..1d6f0d0b 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -154,12 +154,45 @@ services:
       # - ./secrets/gcp-sa.json:/app/secrets/gcp-sa.json:ro
     command: eai worker --config /app/config.yml --loglevel info --queues celery,audio-metrics --concurrency 8
 
+    # Celery Beat — platform schedules + platform task worker (single replica; do not scale).
+    # Beat enqueues tasks; co-located worker runs evaluate_alerts, refresh_fx_rates, prune_oss_usage_history.
+    # flush_usage_counters still runs on worker-usage (usage queue).
+  beat:
+    image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
+    build:
+      context: .
+      dockerfile: docker/Dockerfile.worker
+      args:
+        INSTALL_EXTRAS: ""
+    container_name: efficientai_beat
+    env_file:
+      - .env
+    environment:
+      DATABASE_URL: postgresql://${POSTGRES_USER:-efficientai}:${POSTGRES_PASSWORD:-password}@db:5432/${POSTGRES_DB:-efficientai}
+      REDIS_URL: redis://redis:6379/0
+      CELERY_BROKER_URL: redis://redis:6379/0
+      CELERY_RESULT_BACKEND: redis://redis:6379/0
+      ENCRYPTION_KEY: ${ENCRYPTION_KEY:-}
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+    volumes:
+      - ./uploads:/app/uploads
+      - ./.data:/app/.data
+      - ./config.docker.yml:/app/config.yml:ro
+    command: >
+      sh -c "celery -A app.workers.celery_app worker -Q platform --pool threads --concurrency 2 --loglevel=info &
+      exec celery -A app.workers.celery_app beat --loglevel=info"
+
   # Dedicated worker for call-import + evaluation queues. Celery drains
   # ``imports`` (recording fetch) before ``diarization`` (manual diarise),
   # then ``eval-control`` (cancel/retry/materialize), then ``evaluations``
   # (fair dispatch + LLM scoring).
-  # The default ``worker`` service handles ``celery`` (legacy cron dispatch,
-  # alerts, FX refresh) and ``audio-metrics`` (Praat/UTMOS audio metric tasks).
+  # The default ``worker`` service handles ``celery`` (evaluator cron dispatch only)
+  # and ``audio-metrics`` (Praat/UTMOS audio metric tasks).
+  # Platform schedules run via ``beat`` (scheduler + ``platform`` queue worker).
   worker-imports:
     image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
     build:
diff --git a/env.example b/env.example
index 27c34f4d..40b2f1a5 100644
--- a/env.example
+++ b/env.example
@@ -70,7 +70,9 @@ AUTH_LOCAL_ALLOW_SIGNUP=true
 # EFFICIENTAI_LICENSE=eyJhbGciOi...
 
 # -----------------------------------------------------------------------------
-# Usage cost flush (worker-usage + cron dispatcher on default worker)
+# Usage cost flush (Celery Beat + worker-usage queue)
+# Platform schedules: Beat enqueues flush/alerts/FX/prune; worker-usage runs flush tasks.
+# Evaluator crons: default worker dispatch_cron_jobs (CRON_DISPATCH_INTERVAL_SECONDS).
 # See README "Usage Pricing Ops". Copy to .env for Docker Compose / local dev.
 # -----------------------------------------------------------------------------
 USAGE_FLUSH_BUCKET_BATCH_SIZE=500 
diff --git a/frontend/src/lib/llmModelOptions.test.ts b/frontend/src/lib/llmModelOptions.test.ts
new file mode 100644
index 00000000..b2fd5daf
--- /dev/null
+++ b/frontend/src/lib/llmModelOptions.test.ts
@@ -0,0 +1,60 @@
+import { describe, expect, it } from 'vitest'
+import { resolveLLMModelsForCredential } from './llmModelOptions'
+import type { AIProvider } from '../types/api'
+
+function makeCredential(overrides: Partial<AIProvider> = {}): AIProvider {
+  return {
+    id: 'cred-1',
+    organization_id: 'org-1',
+    provider: 'custom',
+    api_key: 'managed',
+    is_active: true,
+    is_default: true,
+    routing_mode: 'gateway',
+    ...overrides,
+  } as AIProvider
+}
+
+describe('resolveLLMModelsForCredential', () => {
+  it('returns enabled_models when catalog is empty for custom provider', () => {
+    const credential = makeCredential({
+      enabled_models: ['openai/gpt-4o', 'production-gpt4'],
+    })
+
+    const resolution = resolveLLMModelsForCredential(credential, [])
+
+    expect(resolution).toEqual({
+      mode: 'catalog',
+      models: ['openai/gpt-4o', 'production-gpt4'],
+    })
+  })
+
+  it('prefers gateway_direct when custom provider has gateway_model', () => {
+    const credential = makeCredential({
+      gateway_model: 'openai/gpt-4.1',
+      enabled_models: ['openai/gpt-4o'],
+    })
+
+    const resolution = resolveLLMModelsForCredential(credential, [])
+
+    expect(resolution).toEqual({
+      mode: 'gateway_direct',
+      model: 'openai/gpt-4.1',
+    })
+  })
+
+  it('returns allowlist when catalog is empty for non-custom provider', () => {
+    const credential = makeCredential({
+      provider: 'fireworks',
+      gateway_model: undefined,
+      enabled_models: ['accounts/fireworks/models/gpt-oss-120b'],
+    })
+
+    const resolution = resolveLLMModelsForCredential(credential, [])
+
+    expect(resolution).toEqual({
+      mode: 'catalog',
+      models: ['accounts/fireworks/models/gpt-oss-120b'],
+    })
+  })
+})
diff --git a/frontend/src/lib/llmModelOptions.ts b/frontend/src/lib/llmModelOptions.ts
index 2dfc3fc1..e47f9a0a 100644
--- a/frontend/src/lib/llmModelOptions.ts
+++ b/frontend/src/lib/llmModelOptions.ts
@@ -66,7 +66,10 @@ export function resolveLLMModelsForCredential(
   const allowlist = credential?.enabled_models?.filter((m) => m?.trim()) ?? []
   if (allowlist.length > 0) {
     const allowed = new Set(allowlist)
-    const filtered = catalogModels.filter((m) => allowed.has(m))
+    let filtered = catalogModels.filter((m) => allowed.has(m))
+    if (filtered.length === 0) {
+      filtered = allowlist
+    }
     if (gatewayModel && !filtered.includes(gatewayModel)) {
       return { mode: 'catalog', models: [gatewayModel, ...filtered] }
     }
diff --git a/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx b/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx
index 45ba4b2e..57fd3c43 100644
--- a/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx
+++ b/frontend/src/pages/configurations/AIProviderEnabledModelsStep.tsx
@@ -79,6 +79,29 @@ export default function AIProviderEnabledModelsStep({
   const catalog = (options || {}) as ModelOptions
   const selected = useMemo(() => new Set(enabledModels), [enabledModels])
 
+  const catalogModelSet = useMemo(() => {
+    const models = new Set<string>()
+    for (const { key } of SECTIONS) {
+      for (const model of catalog[key] || []) {
+        models.add(model)
+      }
+    }
+    return models
+  }, [catalog])
+
+  const catalogIsEmpty = useMemo(
+    () => SECTIONS.every(({ key }) => (catalog[key] || []).length === 0),
+    [catalog],
+  )
+
+  const isCustomProvider = String(provider).toLowerCase() === 'custom'
+  const showManualAdd = isCustomProvider || (!isLoading && catalogIsEmpty)
+
+  const customModels = useMemo(
+    () => enabledModels.filter((model) => !catalogModelSet.has(model)),
+    [enabledModels, catalogModelSet],
+  )
+
   const toggle = (model: string) => {
     const next = new Set(enabledModels)
     if (next.has(model)) next.delete(model)
@@ -127,6 +150,13 @@ export default function AIProviderEnabledModelsStep({
         </div>
       ) : null}
 
+      {isCustomProvider && !gateway && enabledModels.length === 0 && !isLoading ? (
+        <div className="rounded-xl border border-gray-200 bg-gray-50 px-3 py-2 text-sm text-gray-700">
+          Add custom model IDs below, or set a <span className="font-medium">Gateway model</span> on
+          the previous step for a single pinned Bifrost model.
+        </div>
+      ) : null}
+
       {isLoading ? (
         <p className="text-sm text-gray-500">Loading catalog models…</p>
       ) : (
@@ -175,38 +205,63 @@ export default function AIProviderEnabledModelsStep({
         })
       )}
 
-      <div className={`${usageTheme.panel} p-3`}>
-        <label className="block text-sm font-medium text-gray-900 mb-2">
-          Custom deployment name
-        </label>
-        <div className="flex gap-2">
-          <input
-            type="text"
-            value={customModel}
-            onChange={(e) => setCustomModel(e.target.value)}
-            onKeyDown={(e) => {
-              if (e.key === 'Enter') {
-                e.preventDefault()
-                addCustomModel()
+      {customModels.length > 0 ? (
+        <div className={`${usageTheme.panel} overflow-hidden`}>
+          <div className={`px-3 py-2 ${usageTheme.panelHeader}`}>
+            <span className="text-sm font-semibold text-gray-900">Enabled models</span>
+            <span className="ml-2 text-xs text-gray-500">
+              {customModels.filter((m) => selected.has(m)).length}/{customModels.length} selected
+            </span>
+          </div>
+          <div className="grid max-h-52 grid-cols-1 gap-2 overflow-y-auto p-3 sm:grid-cols-2">
+            {customModels.map((model) => (
+              <ModelChip
+                key={model}
+                model={model}
+                selected={selected.has(model)}
+                onToggle={() => toggle(model)}
+              />
+            ))}
+          </div>
+        </div>
+      ) : null}
+
+      {showManualAdd ? (
+        <div className={`${usageTheme.panel} p-3`}>
+          <label className="block text-sm font-medium text-gray-900 mb-2">
+            {isCustomProvider ? 'Custom model ID' : 'Add deployment / model name'}
+          </label>
+          <div className="flex gap-2">
+            <input
+              type="text"
+              value={customModel}
+              onChange={(e) => setCustomModel(e.target.value)}
+              onKeyDown={(e) => {
+                if (e.key === 'Enter') {
+                  e.preventDefault()
+                  addCustomModel()
+                }
+              }}
+              className={`flex-1 rounded-lg border border-gray-200 px-3 py-2 text-sm focus:outline-none ${usageTheme.focusRing}`}
+              placeholder={
+                isCustomProvider
+                  ? 'e.g. openai/gpt-4o or production-gpt4'
+                  : 'e.g. accounts/fireworks/models/gpt-oss-120b'
               }
-            }}
-            className={`flex-1 rounded-lg border border-gray-200 px-3 py-2 text-sm focus:outline-none ${usageTheme.focusRing}`}
-            placeholder="e.g. accounts/fireworks/models/gpt-oss-120b"
-          />
-          <button
-            type="button"
-            onClick={addCustomModel}
-            className={usageTheme.applyBtn}
-          >
-            Add
-          </button>
+            />
+            <button type="button" onClick={addCustomModel} className={usageTheme.applyBtn}>
+              Add
+            </button>
+          </div>
         </div>
-      </div>
+      ) : null}
 
       <p className="text-xs text-gray-500">
         {enabledModels.length > 0
           ? `${enabledModels.length} model${enabledModels.length === 1 ? '' : 's'} enabled`
-          : 'No restriction — full catalog allowed'}
+          : isCustomProvider
+            ? 'No models enabled yet — add model IDs above or set a gateway model on step 1'
+            : 'No restriction — full catalog allowed'}
       </p>
     </div>
   )
diff --git a/frontend/src/pages/configurations/Integrations.tsx b/frontend/src/pages/configurations/Integrations.tsx
index 4c2e9ca6..3e0373ec 100644
--- a/frontend/src/pages/configurations/Integrations.tsx
+++ b/frontend/src/pages/configurations/Integrations.tsx
@@ -617,9 +617,14 @@ export default function Integrations() {
     aiIntegrationProviders.length > 0 ||
     hasTelephony
 
+  const activeAIProvider =
+    selectedProvider ||
+    (selectedAIProvider?.provider as ModelProvider | undefined) ||
+    null
   const showGatewayModelField =
     integrationType === 'ai_provider' &&
-    (credentialRoutingMode === 'gateway' ||
+    (activeAIProvider === ModelProvider.CUSTOM ||
+      credentialRoutingMode === 'gateway' ||
       (credentialRoutingMode === 'inherit' &&
         llmGatewaySettings?.effective_routing &&
         llmGatewaySettings.effective_routing !== 'direct'))
diff --git a/tests/test_api/test_gateway_managed_credentials.py b/tests/test_api/test_gateway_managed_credentials.py
index 3b59c58f..f420e972 100644
--- a/tests/test_api/test_gateway_managed_credentials.py
+++ b/tests/test_api/test_gateway_managed_credentials.py
@@ -196,7 +196,31 @@ def test_update_gateway_managed_to_direct_requires_api_key(
     assert update_response.status_code == 400
 
 
-def test_create_aiprovider_rejects_invalid_gateway_base_url(authenticated_client):
+def test_list_aiproviders_gateway_without_base_url_returns_200(
+    authenticated_client, db_session, org_id
+):
+    """Integrations page must not 500 when a gateway credential lacks base_url."""
+    _set_platform_gateway_passthrough(False)
+    settings.LLM_GATEWAY_ENABLED = False
+    settings.LLM_GATEWAY_BASE_URL = None
+
+    row = AIProvider(
+        organization_id=org_id,
+        provider="custom",
+        api_key="enc-key",
+        name="Misconfigured gateway",
+        routing_mode="gateway",
+        gateway_model="gpt-oss-120b",
+        is_active=True,
+    )
+    db_session.add(row)
+    db_session.commit()
+
+    response = authenticated_client.get("/api/v1/aiproviders")
+    assert response.status_code == 200
+    body = response.json()
+    assert len(body) == 1
+    assert body[0]["effective_routing"] == "gateway"
     _set_platform_gateway_passthrough(False)
     settings.LLM_GATEWAY_ENABLED = True
     settings.LLM_GATEWAY_BASE_URL = "http://localhost:8080"
diff --git a/tests/test_services/test_ai/test_llm_gateway.py b/tests/test_services/test_ai/test_llm_gateway.py
index 3e24de64..1d72295a 100644
--- a/tests/test_services/test_ai/test_llm_gateway.py
+++ b/tests/test_services/test_ai/test_llm_gateway.py
@@ -392,6 +392,23 @@ def test_credential_gateway_raises_when_no_base_url():
         resolve_effective_routing(org_id, db, ctx)
 
 
+def test_effective_routing_label_gateway_without_base_url_does_not_raise():
+    _set_platform_gateway(enabled=False)
+    org_id, db = _org_db({"enabled": False})
+    provider = SimpleNamespace(
+        provider="custom",
+        routing_mode="gateway",
+        gateway_interface="native_openai",
+        gateway_base_url=None,
+        gateway_model="gpt-oss-120b",
+        gateway_auth_header=None,
+        gateway_auth_secret_env=None,
+        gateway_auth_secret=None,
+        gateway_extra_headers=None,
+    )
+    assert get_credential_effective_routing_label(org_id, db, provider) == "gateway"
+
+
 def test_effective_routing_label_uses_credential_gateway_base_url():
     _set_platform_gateway(enabled=False)
     org_id, db = _org_db({"enabled": False})
diff --git a/tests/test_services/test_cron/test_job_dispatch.py b/tests/test_services/test_cron/test_job_dispatch.py
index ff31ac36..1f938ba0 100644
--- a/tests/test_services/test_cron/test_job_dispatch.py
+++ b/tests/test_services/test_cron/test_job_dispatch.py
@@ -27,22 +27,33 @@ def test_advance_cron_job_marks_completed_when_max_runs_reached():
     assert job.next_run_at is None
 
 
-def test_enqueue_usage_flush_routes_to_usage_task(monkeypatch):
+def test_enqueue_unknown_job_type_returns_unknown():
     job = MagicMock()
     job.job_type = "usage_flush"
     job.id = uuid4()
 
+    meta = enqueue_cron_job(job)
+
+    assert meta["task"] == "unknown"
+    assert meta["job_type"] == "usage_flush"
+
+
+def test_enqueue_evaluator_run_routes_to_evaluator_task(monkeypatch):
+    job = MagicMock()
+    job.job_type = "evaluator_run"
+    job.id = uuid4()
+
     delayed = MagicMock()
-    delayed.id = "task-123"
+    delayed.id = "task-456"
     task = MagicMock()
     task.delay.return_value = delayed
     monkeypatch.setattr(
-        "app.workers.tasks.flush_usage_counters.flush_usage_counters_task",
+        "app.workers.tasks.run_cron_evaluator_job.run_cron_evaluator_job_task",
         task,
     )
 
     meta = enqueue_cron_job(job)
 
-    assert meta["task"] == "flush_usage_counters"
-    assert meta["celery_task_id"] == "task-123"
-    task.delay.assert_called_once_with()
+    assert meta["task"] == "run_cron_evaluator_job"
+    assert meta["celery_task_id"] == "task-456"
+    task.delay.assert_called_once_with(str(job.id))
diff --git a/tests/test_services/test_usage/test_enabled_models.py b/tests/test_services/test_usage/test_enabled_models.py
index 88d2737a..79610d30 100644
--- a/tests/test_services/test_usage/test_enabled_models.py
+++ b/tests/test_services/test_usage/test_enabled_models.py
@@ -35,6 +35,20 @@ def test_filter_models_unrestricted_when_allowlist_empty():
     assert filter_models_by_credential(cred, ["a", "b"]) == ["a", "b"]
 
 
+def test_filter_models_by_credential_uses_allowlist_when_catalog_empty():
+    cred = _Credential(enabled_models=["openai/gpt-4o", "production-gpt4"], provider="custom")
+    assert filter_models_by_credential(cred, []) == ["openai/gpt-4o", "production-gpt4"]
+
+
+def test_filter_models_by_credential_empty_catalog_includes_gateway_model():
+    cred = _Credential(
+        enabled_models=["openai/gpt-4o"],
+        gateway_model="pinned-model",
+        provider="custom",
+    )
+    assert filter_models_by_credential(cred, []) == ["pinned-model", "openai/gpt-4o"]
+
+
 def test_org_pricing_eligible_models_includes_usage_and_overrides(monkeypatch):
     org_id = uuid4()
 
diff --git a/tests/test_workers/test_beat_schedule.py b/tests/test_workers/test_beat_schedule.py
new file mode 100644
index 00000000..88c84aa7
--- /dev/null
+++ b/tests/test_workers/test_beat_schedule.py
@@ -0,0 +1,33 @@
+"""Tests for Celery Beat platform schedule."""
+
+from app.workers.config import _platform_beat_schedule, _usage_flush_beat_seconds
+
+
+def test_usage_flush_beat_seconds_defaults_to_120():
+    assert _usage_flush_beat_seconds() >= 30.0
+
+
+def test_usage_flush_beat_seconds_respects_env(monkeypatch):
+    monkeypatch.setenv("USAGE_FLUSH_BEAT_SECONDS", "90")
+    assert _usage_flush_beat_seconds() == 90.0
+
+
+def test_usage_flush_beat_seconds_clamps_minimum(monkeypatch):
+    monkeypatch.setenv("USAGE_FLUSH_BEAT_SECONDS", "5")
+    assert _usage_flush_beat_seconds() == 30.0
+
+
+def test_platform_beat_schedule_has_four_entries():
+    schedule = _platform_beat_schedule()
+    assert set(schedule.keys()) == {
+        "flush-usage-counters",
+        "evaluate-alerts",
+        "refresh-fx-rates",
+        "prune-oss-usage-history",
+    }
+
+
+def test_flush_schedule_uses_env_interval(monkeypatch):
+    monkeypatch.setenv("USAGE_FLUSH_BEAT_SECONDS", "180")
+    schedule = _platform_beat_schedule()
+    assert schedule["flush-usage-counters"]["schedule"] == 180.0
diff --git a/tests/test_workers/test_usage_queue_routing.py b/tests/test_workers/test_usage_queue_routing.py
index c3a5776a..d8751354 100644
--- a/tests/test_workers/test_usage_queue_routing.py
+++ b/tests/test_workers/test_usage_queue_routing.py
@@ -1,6 +1,6 @@
 """Tests for usage pricing Celery queue routing."""
 
-from app.workers.config import USAGE_WORKER_QUEUE, celery_app
+from app.workers.config import PLATFORM_WORKER_QUEUE, USAGE_WORKER_QUEUE, celery_app
 
 
 def test_flush_usage_counters_routes_to_usage_queue():
@@ -18,6 +18,16 @@ def test_cron_dispatcher_routes_to_default_worker():
     assert routes["dispatch_cron_jobs"]["queue"] == "celery"
 
 
-def test_beat_schedule_removed():
+def test_beat_schedule_includes_platform_tasks():
     schedule = getattr(celery_app.conf, "beat_schedule", None) or {}
-    assert "flush-llm-usage-counters" not in schedule
+    assert schedule["flush-usage-counters"]["task"] == "flush_usage_counters"
+    assert schedule["evaluate-alerts"]["task"] == "evaluate_alerts"
+    assert schedule["refresh-fx-rates"]["task"] == "refresh_fx_rates"
+    assert schedule["prune-oss-usage-history"]["task"] == "prune_oss_usage_history"
+
+
+def test_platform_tasks_route_to_platform_queue():
+    routes = celery_app.conf.task_routes
+    assert routes["evaluate_alerts"]["queue"] == PLATFORM_WORKER_QUEUE
+    assert routes["refresh_fx_rates"]["queue"] == PLATFORM_WORKER_QUEUE
+    assert routes["prune_oss_usage_history"]["queue"] == PLATFORM_WORKER_QUEUE

From 02e8c0a258e76da4ff9885f6f1e319785c5e9bc2 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 17 Aug 2026 18:16:17 +0530
Subject: [PATCH 28/55] fix(migrations): widen source column in
 model_pricing_rates to VARCHAR(255) and add migration function

---
 app/migrations/072_usage_pricing_phase1.py    | 21 +++++-
 .../src/pages/configurations/Integrations.tsx | 70 +++++++++++++------
 2 files changed, 66 insertions(+), 25 deletions(-)

diff --git a/app/migrations/072_usage_pricing_phase1.py b/app/migrations/072_usage_pricing_phase1.py
index 68254deb..3ae097e7 100644
--- a/app/migrations/072_usage_pricing_phase1.py
+++ b/app/migrations/072_usage_pricing_phase1.py
@@ -77,7 +77,7 @@ def _ensure_model_pricing_rates(db: Session) -> None:
                     effective_from DATE NOT NULL DEFAULT CURRENT_DATE,
                     effective_to DATE,
                     currency VARCHAR(8) NOT NULL DEFAULT 'USD',
-                    source VARCHAR(32) NOT NULL DEFAULT 'catalog',
+                    source VARCHAR(255) NOT NULL DEFAULT 'catalog',
                     input_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
                     output_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
                     cache_read_micro_usd_per_million BIGINT NOT NULL DEFAULT 0,
@@ -117,12 +117,28 @@ def _ensure_model_pricing_rates(db: Session) -> None:
             text(
                 """
                 ALTER TABLE model_pricing_rates
-                ADD COLUMN source VARCHAR(32) NOT NULL DEFAULT 'catalog'
+                ADD COLUMN source VARCHAR(255) NOT NULL DEFAULT 'catalog'
                 """
             )
         )
 
 
+def _widen_source_column(db: Session) -> None:
+    if not _table_exists(db, "model_pricing_rates"):
+        return
+    if not _column_exists(db, "model_pricing_rates", "source"):
+        return
+    db.execute(
+        text(
+            """
+            ALTER TABLE model_pricing_rates
+            ALTER COLUMN source TYPE VARCHAR(255)
+            """
+        )
+    )
+    print("Widened model_pricing_rates.source to VARCHAR(255)")
+
+
 def _add_buffer_cost_columns(db: Session) -> None:
     if not _table_exists(db, "usage_pending_buffer"):
         return
@@ -161,6 +177,7 @@ def _strip_org_extras(db: Session) -> None:
 
 def upgrade(db: Session):
     _ensure_model_pricing_rates(db)
+    _widen_source_column(db)
     _add_buffer_cost_columns(db)
     _strip_org_extras(db)
     db.commit()
diff --git a/frontend/src/pages/configurations/Integrations.tsx b/frontend/src/pages/configurations/Integrations.tsx
index 3e0373ec..0dae3165 100644
--- a/frontend/src/pages/configurations/Integrations.tsx
+++ b/frontend/src/pages/configurations/Integrations.tsx
@@ -194,6 +194,23 @@ export default function Integrations() {
       ? llmGatewayType
       : llmGatewaySettings?.platform_gateway_type || 'bifrost'
 
+  const activeAIProvider =
+    selectedProvider ||
+    (selectedAIProvider?.provider as ModelProvider | undefined) ||
+    null
+  const isCustomAIProvider =
+    integrationType === 'ai_provider' &&
+    String(activeAIProvider || '').toLowerCase() === ModelProvider.CUSTOM
+  const aiProviderUsesModelsStep = integrationType === 'ai_provider' && !isCustomAIProvider
+  const showGatewayModelField =
+    integrationType === 'ai_provider' &&
+    (isCustomAIProvider ||
+      credentialRoutingMode === 'gateway' ||
+      (credentialRoutingMode === 'inherit' &&
+        llmGatewaySettings?.effective_routing &&
+        llmGatewaySettings.effective_routing !== 'direct'))
+  const aiProviderRequiresApiKey = credentialRoutingMode === 'direct'
+
   const showLlmGatewayConfigOptions = llmGatewayMode !== 'disabled'
 
   useEffect(() => {
@@ -455,8 +472,10 @@ export default function Integrations() {
           showToast('Please enter your Azure OpenAI endpoint URL', 'error')
           return
         }
-        setAiProviderWizardStep(2)
-        return
+        if (!isCustomAIProvider) {
+          setAiProviderWizardStep(2)
+          return
+        }
       }
 
       if (isEditMode && selectedAIProvider) {
@@ -617,20 +636,6 @@ export default function Integrations() {
     aiIntegrationProviders.length > 0 ||
     hasTelephony
 
-  const activeAIProvider =
-    selectedProvider ||
-    (selectedAIProvider?.provider as ModelProvider | undefined) ||
-    null
-  const showGatewayModelField =
-    integrationType === 'ai_provider' &&
-    (activeAIProvider === ModelProvider.CUSTOM ||
-      credentialRoutingMode === 'gateway' ||
-      (credentialRoutingMode === 'inherit' &&
-        llmGatewaySettings?.effective_routing &&
-        llmGatewaySettings.effective_routing !== 'direct'))
-
-  const aiProviderRequiresApiKey = credentialRoutingMode === 'direct'
-
   const getPlatformInfo = (platformId: IntegrationPlatform) => {
     return platforms.find(p => p.id === platformId)
   }
@@ -1159,7 +1164,7 @@ export default function Integrations() {
 
       {showModal && renderModal(
         <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-[9999]">
-          <div className={`bg-white rounded-lg shadow-xl w-full mx-4 max-h-[90vh] overflow-y-auto ${integrationType === 'ai_provider' && aiProviderWizardStep === 2 ? 'max-w-2xl' : 'max-w-md'}`}>
+          <div className={`bg-white rounded-lg shadow-xl w-full mx-4 max-h-[90vh] overflow-y-auto ${aiProviderUsesModelsStep && aiProviderWizardStep === 2 ? 'max-w-2xl' : 'max-w-md'}`}>
             <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
               <div>
                 <h3 className="text-lg font-semibold">
@@ -1171,11 +1176,13 @@ export default function Integrations() {
                         : 'Edit Integration'
                     : 'Add Integration'}
                 </h3>
-                {integrationType === 'ai_provider' ? (
+                {aiProviderUsesModelsStep ? (
                   <p className="mt-0.5 text-xs text-gray-500">
                     Step {aiProviderWizardStep} of 2 —{' '}
                     {aiProviderWizardStep === 1 ? 'Credentials' : 'Enabled models'}
                   </p>
+                ) : isCustomAIProvider ? (
+                  <p className="mt-0.5 text-xs text-gray-500">Custom Bifrost model integration</p>
                 ) : null}
               </div>
               <button onClick={resetForm} className="text-gray-400 hover:text-gray-600"><X className="h-5 w-5" /></button>
@@ -1361,16 +1368,20 @@ export default function Integrations() {
                   {showGatewayModelField && (
                     <>
                     <div>
-                      <label className="block text-sm font-medium text-gray-700 mb-1">Gateway Model (Optional)</label>
+                      <label className="block text-sm font-medium text-gray-700 mb-1">
+                        {isCustomAIProvider ? 'Custom model ID (Optional)' : 'Gateway Model (Optional)'}
+                      </label>
                       <input
                         type="text"
                         value={gatewayModel}
                         onChange={(e) => setGatewayModel(e.target.value)}
                         className="w-full px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500"
-                        placeholder="e.g., production-gpt4 or openai/gpt-4o"
+                        placeholder={isCustomAIProvider ? 'e.g. openai/gpt-4o or production-gpt4' : 'e.g., production-gpt4 or openai/gpt-4o'}
                       />
                       <p className="mt-1 text-xs text-gray-500">
-                        Bifrost custom model ID sent when routing via gateway. Leave blank to use the workload-selected model.
+                        {isCustomAIProvider
+                          ? 'Bifrost model ID for this integration. Each custom credential pins one model.'
+                          : 'Bifrost custom model ID sent when routing via gateway. Leave blank to use the workload-selected model.'}
                       </p>
                     </div>
                     <div>
@@ -1505,7 +1516,7 @@ export default function Integrations() {
                 </>
               )}
 
-              {integrationType === 'ai_provider' && aiProviderWizardStep === 2 && (selectedProvider || selectedAIProvider) && (
+              {aiProviderUsesModelsStep && aiProviderWizardStep === 2 && (selectedProvider || selectedAIProvider) && (
                 <AIProviderEnabledModelsStep
                   provider={(selectedProvider || selectedAIProvider!.provider) as ModelProvider}
                   enabledModels={enabledModels}
@@ -1612,7 +1623,7 @@ export default function Integrations() {
 
               <div className="flex gap-3 pt-4">
                 <Button type="button" variant="outline" onClick={resetForm} className="flex-1">Cancel</Button>
-                {integrationType === 'ai_provider' && aiProviderWizardStep === 2 ? (
+                {aiProviderUsesModelsStep && aiProviderWizardStep === 2 ? (
                   <>
                     <Button
                       type="button"
@@ -1635,6 +1646,19 @@ export default function Integrations() {
                       {isEditMode ? 'Save provider' : 'Configure provider'}
                     </Button>
                   </>
+                ) : isCustomAIProvider && integrationType === 'ai_provider' ? (
+                  <Button
+                    type="submit"
+                    variant="primary"
+                    isLoading={
+                      isEditMode
+                        ? updateAIProviderMutation.isPending
+                        : createAIProviderMutation.isPending
+                    }
+                    className="flex-1"
+                  >
+                    {isEditMode ? 'Save provider' : 'Configure provider'}
+                  </Button>
                 ) : (
                   <Button
                     type="submit"

From 7576d5f320be186d1efc04c2f4645c268f2ece00 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 17 Aug 2026 18:39:19 +0530
Subject: [PATCH 29/55] fix(pricing): add clear_rates_table_cache function and
 update migration to clear cache before seeding pricing rates

---
 app/migrations/072_usage_pricing_phase1.py     |  7 ++++++-
 app/services/usage/pricing.py                  | 13 ++++++++++++-
 tests/test_services/test_usage/test_pricing.py | 15 +++++++++++++++
 3 files changed, 33 insertions(+), 2 deletions(-)

diff --git a/app/migrations/072_usage_pricing_phase1.py b/app/migrations/072_usage_pricing_phase1.py
index 3ae097e7..a4a64ad9 100644
--- a/app/migrations/072_usage_pricing_phase1.py
+++ b/app/migrations/072_usage_pricing_phase1.py
@@ -182,8 +182,13 @@ def upgrade(db: Session):
     _strip_org_extras(db)
     db.commit()
 
-    from app.services.usage.pricing import DEFAULT_RATES_EFFECTIVE_FROM, seed_pricing_rates
+    from app.services.usage.pricing import (
+        DEFAULT_RATES_EFFECTIVE_FROM,
+        clear_rates_table_cache,
+        seed_pricing_rates,
+    )
 
+    clear_rates_table_cache()
     seeded = seed_pricing_rates(db, effective_from=DEFAULT_RATES_EFFECTIVE_FROM)
     db.commit()
     if seeded:
diff --git a/app/services/usage/pricing.py b/app/services/usage/pricing.py
index 762add7d..cb9dde02 100644
--- a/app/services/usage/pricing.py
+++ b/app/services/usage/pricing.py
@@ -38,10 +38,21 @@
 _RATES_TABLE_CACHE: Optional[str] = None
 
 
+def clear_rates_table_cache() -> None:
+    global _RATES_TABLE_CACHE
+    _RATES_TABLE_CACHE = None
+
+
 def _rates_table(db: Session) -> str:
     global _RATES_TABLE_CACHE
     if _RATES_TABLE_CACHE:
-        return _RATES_TABLE_CACHE
+        exists = db.execute(
+            text("SELECT to_regclass(:table_name)"),
+            {"table_name": f"public.{_RATES_TABLE_CACHE}"},
+        ).scalar()
+        if exists:
+            return _RATES_TABLE_CACHE
+        clear_rates_table_cache()
     row = db.execute(text("SELECT to_regclass('public.model_pricing_rates')")).scalar()
     if row:
         _RATES_TABLE_CACHE = "model_pricing_rates"
diff --git a/tests/test_services/test_usage/test_pricing.py b/tests/test_services/test_usage/test_pricing.py
index 6a526916..6d01dcaa 100644
--- a/tests/test_services/test_usage/test_pricing.py
+++ b/tests/test_services/test_usage/test_pricing.py
@@ -209,3 +209,18 @@ def test_resolve_rate_ignores_stale_negative_cache(monkeypatch):
     assert card.rate_id == rate_id
     assert card.source == RATE_SOURCE_CATALOG
     assert fake_redis.store[redis_key] != "__null__"
+
+
+def test_rates_table_recovers_from_stale_cache_after_rename():
+    import app.services.usage.pricing as pricing_mod
+
+    pricing_mod._RATES_TABLE_CACHE = "model_pricing_catalog"
+
+    db = MagicMock()
+    db.execute.side_effect = [
+        MagicMock(scalar=MagicMock(return_value=None)),
+        MagicMock(scalar=MagicMock(return_value="model_pricing_rates")),
+    ]
+
+    assert pricing_mod._rates_table(db) == "model_pricing_rates"
+    assert pricing_mod._RATES_TABLE_CACHE == "model_pricing_rates"

From 83fc09807a5ba6a2bcce1af6d2b9730fad7b5372 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 17 Aug 2026 23:44:40 +0530
Subject: [PATCH 30/55] fix(usage): update recompute flag to false in pricing
 overrides and usage pricing routes; enhance usage snapshot billability checks

---
 app/api/v1/routes/usage_pricing.py            |   4 +-
 app/services/ai/llm_gateway.py                |  27 ++-
 app/services/ai/llm_service.py                |  12 +-
 app/services/usage/llm_usage.py               | 224 ++++++++++++++++--
 app/services/usage/normalize.py               |  10 +
 app/services/usage/pricing_overrides.py       |   4 +-
 app/services/usage/voice_usage_processor.py   |   4 +-
 .../src/pages/configurations/Integrations.tsx |  41 +++-
 frontend/src/pages/usage/Usage.tsx            |  61 +++--
 frontend/src/pages/usage/UsageFiltersBar.tsx  |  48 ++--
 frontend/src/pages/usage/UsagePricing.tsx     | 102 +++++---
 .../test_usage/test_llm_usage.py              |  78 ++++--
 .../test_services/test_usage/test_pricing.py  |   4 +-
 13 files changed, 461 insertions(+), 158 deletions(-)

diff --git a/app/api/v1/routes/usage_pricing.py b/app/api/v1/routes/usage_pricing.py
index c48deaeb..dc868b19 100644
--- a/app/api/v1/routes/usage_pricing.py
+++ b/app/api/v1/routes/usage_pricing.py
@@ -65,7 +65,7 @@ class PricingOverrideUpsertRequest(BaseModel):
     effective_from: date
     effective_to: Optional[date] = None
     rates: PricingRatesUsd
-    recompute: bool = True
+    recompute: bool = False
 
 
 class EffectivePricingResponse(BaseModel):
@@ -205,7 +205,7 @@ def delete_usage_pricing_override(
     model: str,
     usage_kind: str = Query("llm"),
     effective_from: Optional[date] = Query(None),
-    recompute: bool = Query(True),
+    recompute: bool = Query(False),
     organization_id: UUID = Depends(get_organization_id),
     db: Session = Depends(get_db),
 ):
diff --git a/app/services/ai/llm_gateway.py b/app/services/ai/llm_gateway.py
index 36fd7fdc..2283a3a5 100644
--- a/app/services/ai/llm_gateway.py
+++ b/app/services/ai/llm_gateway.py
@@ -730,7 +730,10 @@ def litellm_completion(
             reset_usage_context,
         )
         from app.services.usage.llm_usage import record_llm_usage
-        from app.services.usage.normalize import normalize_llm_usage
+        from app.services.usage.normalize import (
+            normalize_llm_usage,
+            usage_snapshot_is_billable,
+        )
 
         usage_token = ensure_usage_context(
             organization_id,
@@ -740,11 +743,13 @@ def litellm_completion(
             model_name = str(kwargs.get("model") or "unknown")
             if "/" in model_name:
                 model_name = model_name.rsplit("/", 1)[-1]
-            record_llm_usage(
-                model_name,
-                normalize_llm_usage(raw_response=response),
-                organization_id=organization_id,
-            )
+            snapshot = normalize_llm_usage(raw_response=response)
+            if usage_snapshot_is_billable(snapshot):
+                record_llm_usage(
+                    model_name,
+                    snapshot,
+                    organization_id=organization_id,
+                )
         finally:
             if usage_token is not None:
                 reset_usage_context(usage_token)
@@ -765,7 +770,10 @@ def litellm_batch_completion_recording(
     import litellm
 
     from app.services.usage.llm_usage import record_llm_usage
-    from app.services.usage.normalize import normalize_llm_usage
+    from app.services.usage.normalize import (
+        normalize_llm_usage,
+        usage_snapshot_is_billable,
+    )
 
     original = litellm.batch_completion
 
@@ -786,9 +794,12 @@ def _recording_batch(**kwargs: Any):
             if resp is None:
                 continue
             try:
+                snapshot = normalize_llm_usage(raw_response=resp)
+                if not usage_snapshot_is_billable(snapshot):
+                    continue
                 record_llm_usage(
                     model_name,
-                    normalize_llm_usage(raw_response=resp),
+                    snapshot,
                     organization_id=organization_id,
                 )
             except Exception as exc:
diff --git a/app/services/ai/llm_service.py b/app/services/ai/llm_service.py
index 4bf5b61f..a2ea3801 100644
--- a/app/services/ai/llm_service.py
+++ b/app/services/ai/llm_service.py
@@ -555,7 +555,10 @@ def generate_response(
                 ensure_usage_context,
                 reset_usage_context,
             )
-            from app.services.usage.normalize import normalize_llm_usage
+            from app.services.usage.normalize import (
+                normalize_llm_usage,
+                usage_snapshot_is_billable,
+            )
             from app.services.usage.llm_usage import record_llm_usage
 
             usage_token = ensure_usage_context(
@@ -567,9 +570,10 @@ def generate_response(
                 result["usage"]["cache_read_tokens"] = snapshot.cache_read_tokens
                 result["usage"]["cache_creation_tokens"] = snapshot.cache_creation_tokens
                 result["usage"]["reasoning_tokens"] = snapshot.reasoning_tokens
-                record_llm_usage(
-                    llm_model, snapshot, organization_id=organization_id
-                )
+                if usage_snapshot_is_billable(snapshot):
+                    record_llm_usage(
+                        llm_model, snapshot, organization_id=organization_id
+                    )
             finally:
                 if usage_token is not None:
                     reset_usage_context(usage_token)
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index 65083934..dea0c400 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -240,7 +240,40 @@ def _resolve_context(
     return None
 
 
+_USAGE_METRIC_KEYS = (
+    "prompt_tokens",
+    "completion_tokens",
+    "cache_read_tokens",
+    "cache_creation_tokens",
+    "reasoning_tokens",
+    "audio_seconds",
+    "tts_characters",
+)
+
+
+def _has_billable_usage_deltas(deltas: Dict[str, int]) -> bool:
+    return any(int(deltas.get(key, 0) or 0) > 0 for key in _USAGE_METRIC_KEYS)
+
+
 def _deltas_from_usage(usage: UsageSnapshot) -> Dict[str, int]:
+    billable = (
+        usage.prompt_tokens > 0
+        or usage.completion_tokens > 0
+        or usage.cache_read_tokens > 0
+        or usage.cache_creation_tokens > 0
+        or usage.reasoning_tokens > 0
+    )
+    if not billable:
+        return {
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "cache_read_tokens": 0,
+            "cache_creation_tokens": 0,
+            "reasoning_tokens": 0,
+            "audio_seconds": 0,
+            "tts_characters": 0,
+            "call_count": 0,
+        }
     return {
         "prompt_tokens": usage.prompt_tokens,
         "completion_tokens": usage.completion_tokens,
@@ -418,7 +451,7 @@ def record_llm_usage(
         return
 
     deltas = _deltas_from_usage(usage)
-    if not any(deltas.values()):
+    if not _has_billable_usage_deltas(deltas):
         return
 
     day = usage_date or datetime.now(timezone.utc).date()
@@ -450,6 +483,8 @@ def record_stt_usage(
         return
 
     seconds = int(max(0, math.ceil(float(audio_seconds or 0))))
+    if seconds <= 0:
+        return
     deltas = _deltas_from_stt(seconds, count_call=count_call)
     day = usage_date or datetime.now(timezone.utc).date()
     bucket = _bucket_from_context(
@@ -521,6 +556,8 @@ def record_tts_usage(
         return
 
     chars = max(0, int(characters or 0))
+    if chars <= 0:
+        return
     deltas = _deltas_from_tts(chars)
     day = usage_date or datetime.now(timezone.utc).date()
     bucket = _bucket_from_context(
@@ -789,6 +826,52 @@ def _ack_claim(claim_key: str, organization_id: UUID) -> None:
         pass
 
 
+def _stamp_cost_params(
+    db: Session,
+    organization_id: UUID,
+    bucket: Dict[str, Any],
+    deltas: Dict[str, int],
+    *,
+    pricing_resolver: Any = None,
+    stamped_costs: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    if stamped_costs is not None:
+        return {
+            "input_cost_micro_usd": int(stamped_costs.get("input_cost_micro_usd") or 0),
+            "output_cost_micro_usd": int(stamped_costs.get("output_cost_micro_usd") or 0),
+            "cache_read_cost_micro_usd": int(
+                stamped_costs.get("cache_read_cost_micro_usd") or 0
+            ),
+            "cache_creation_cost_micro_usd": int(
+                stamped_costs.get("cache_creation_cost_micro_usd") or 0
+            ),
+            "reasoning_cost_micro_usd": int(
+                stamped_costs.get("reasoning_cost_micro_usd") or 0
+            ),
+            "audio_cost_micro_usd": int(stamped_costs.get("audio_cost_micro_usd") or 0),
+            "tts_cost_micro_usd": int(stamped_costs.get("tts_cost_micro_usd") or 0),
+            "total_cost_micro_usd": int(stamped_costs.get("total_cost_micro_usd") or 0),
+            "pricing_rate_source": stamped_costs.get("pricing_rate_source"),
+            "pricing_rate_id": stamped_costs.get("pricing_rate_id"),
+        }
+
+    from app.services.usage.pricing import cost_fields_from_deltas, PricingResolver
+
+    usage_date = bucket["usage_date"]
+    if isinstance(usage_date, str):
+        usage_date = date.fromisoformat(usage_date)
+    resolver = pricing_resolver or PricingResolver(db)
+    return cost_fields_from_deltas(
+        deltas,
+        organization_id=organization_id,
+        model=bucket["model"],
+        usage_kind=bucket.get("usage_kind") or USAGE_KIND_LLM,
+        usage_date=usage_date,
+        db=db,
+        resolver=resolver,
+    )
+
+
 def _upsert_bucket(
     db: Session,
     organization_id: UUID,
@@ -796,6 +879,7 @@ def _upsert_bucket(
     deltas: Dict[str, int],
     *,
     pricing_resolver: Any = None,
+    stamped_costs: Optional[Dict[str, Any]] = None,
 ) -> None:
     context = bucket.get("context") or {}
     params = {
@@ -817,7 +901,56 @@ def _upsert_bucket(
         "tts_characters": int(deltas.get("tts_characters", 0)),
         "call_count": int(deltas.get("call_count", 0)),
     }
-    update_set = """
+    apply_cost_increment = stamped_costs is not None or _has_billable_usage_deltas(deltas)
+    if apply_cost_increment:
+        cost_fields = _stamp_cost_params(
+            db,
+            organization_id,
+            bucket,
+            deltas,
+            pricing_resolver=pricing_resolver,
+            stamped_costs=stamped_costs,
+        )
+        params.update(
+            {
+                "input_cost_micro_usd": int(cost_fields["input_cost_micro_usd"]),
+                "output_cost_micro_usd": int(cost_fields["output_cost_micro_usd"]),
+                "cache_read_cost_micro_usd": int(
+                    cost_fields["cache_read_cost_micro_usd"]
+                ),
+                "cache_creation_cost_micro_usd": int(
+                    cost_fields["cache_creation_cost_micro_usd"]
+                ),
+                "reasoning_cost_micro_usd": int(
+                    cost_fields["reasoning_cost_micro_usd"]
+                ),
+                "audio_cost_micro_usd": int(cost_fields["audio_cost_micro_usd"]),
+                "tts_cost_micro_usd": int(cost_fields["tts_cost_micro_usd"]),
+                "total_cost_micro_usd": int(cost_fields["total_cost_micro_usd"]),
+                "pricing_rate_source": cost_fields.get("pricing_rate_source"),
+                "pricing_rate_id": cost_fields.get("pricing_rate_id"),
+            }
+        )
+    cost_update_set = ""
+    if apply_cost_increment:
+        cost_update_set = """
+                input_cost_micro_usd = input_cost_micro_usd + :input_cost_micro_usd,
+                output_cost_micro_usd = output_cost_micro_usd + :output_cost_micro_usd,
+                cache_read_cost_micro_usd = cache_read_cost_micro_usd + :cache_read_cost_micro_usd,
+                cache_creation_cost_micro_usd = cache_creation_cost_micro_usd + :cache_creation_cost_micro_usd,
+                reasoning_cost_micro_usd = reasoning_cost_micro_usd + :reasoning_cost_micro_usd,
+                audio_cost_micro_usd = audio_cost_micro_usd + :audio_cost_micro_usd,
+                tts_cost_micro_usd = tts_cost_micro_usd + :tts_cost_micro_usd,
+                total_cost_micro_usd = total_cost_micro_usd + :total_cost_micro_usd,
+                pricing_rate_source = COALESCE(:pricing_rate_source, pricing_rate_source),
+                pricing_rate_id = CASE
+                    WHEN CAST(:pricing_rate_id AS text) IS NOT NULL
+                    THEN CAST(:pricing_rate_id AS uuid)
+                    ELSE pricing_rate_id
+                END,
+        """
+    update_set = (
+        """
                 prompt_tokens = prompt_tokens + :prompt_tokens,
                 completion_tokens = completion_tokens + :completion_tokens,
                 cache_read_tokens = cache_read_tokens + :cache_read_tokens,
@@ -826,12 +959,16 @@ def _upsert_bucket(
                 audio_seconds = audio_seconds + :audio_seconds,
                 tts_characters = tts_characters + :tts_characters,
                 call_count = call_count + :call_count,
+        """
+        + cost_update_set
+        + """
                 context = CASE
                     WHEN context = '{}'::jsonb THEN CAST(:context AS jsonb)
                     ELSE context || CAST(:context AS jsonb)
                 END,
                 updated_at = now()
     """
+    )
     where_base = """
             WHERE organization_id = CAST(:organization_id AS uuid)
               AND product_section = :product_section
@@ -861,15 +998,20 @@ def _upsert_bucket(
         if not result.rowcount:
             db.execute(text("SAVEPOINT llm_usage_bucket_insert"))
             try:
-                db.execute(
-                    text(
-                        """
+                if apply_cost_increment:
+                    db.execute(
+                        text(
+                            """
                     INSERT INTO llm_usage_daily (
                         id, organization_id, workspace_id, product_section, model,
                         context, usage_date, usage_kind,
                         prompt_tokens, completion_tokens, cache_read_tokens,
                         cache_creation_tokens, reasoning_tokens, audio_seconds,
                         tts_characters, call_count,
+                        input_cost_micro_usd, output_cost_micro_usd,
+                        cache_read_cost_micro_usd, cache_creation_cost_micro_usd,
+                        reasoning_cost_micro_usd, audio_cost_micro_usd, tts_cost_micro_usd,
+                        total_cost_micro_usd, pricing_rate_source, pricing_rate_id,
                         created_at, updated_at
                     ) VALUES (
                         gen_random_uuid(), CAST(:organization_id AS uuid),
@@ -879,12 +1021,42 @@ def _upsert_bucket(
                         :prompt_tokens, :completion_tokens, :cache_read_tokens,
                         :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
                         :tts_characters, :call_count,
+                        :input_cost_micro_usd, :output_cost_micro_usd,
+                        :cache_read_cost_micro_usd, :cache_creation_cost_micro_usd,
+                        :reasoning_cost_micro_usd, :audio_cost_micro_usd, :tts_cost_micro_usd,
+                        :total_cost_micro_usd, :pricing_rate_source,
+                        CAST(:pricing_rate_id AS uuid),
                         now(), now()
                     )
                     """
-                    ),
-                    params,
-                )
+                        ),
+                        params,
+                    )
+                else:
+                    db.execute(
+                        text(
+                            """
+                    INSERT INTO llm_usage_daily (
+                        id, organization_id, workspace_id, product_section, model,
+                        context, usage_date, usage_kind,
+                        prompt_tokens, completion_tokens, cache_read_tokens,
+                        cache_creation_tokens, reasoning_tokens, audio_seconds,
+                        tts_characters, call_count,
+                        created_at, updated_at
+                    ) VALUES (
+                        gen_random_uuid(), CAST(:organization_id AS uuid),
+                        CAST(:workspace_id AS uuid), :product_section, :model,
+                        CAST(:context AS jsonb), CAST(:usage_date AS date),
+                        :usage_kind,
+                        :prompt_tokens, :completion_tokens, :cache_read_tokens,
+                        :cache_creation_tokens, :reasoning_tokens, :audio_seconds,
+                        :tts_characters, :call_count,
+                        now(), now()
+                    )
+                    """
+                        ),
+                        params,
+                    )
                 db.execute(text("RELEASE SAVEPOINT llm_usage_bucket_insert"))
             except IntegrityError as exc:
                 if not _is_unique_violation(exc):
@@ -908,21 +1080,6 @@ def _upsert_bucket(
                         organization_id,
                     )
 
-    try:
-        from app.services.usage.pricing import PricingResolver, apply_cost_to_bucket
-
-        resolver = pricing_resolver
-        if resolver is None:
-            resolver = PricingResolver(db)
-        apply_cost_to_bucket(
-            db,
-            organization_id=organization_id,
-            bucket=bucket,
-            resolver=resolver,
-        )
-    except Exception as exc:
-        logger.warning("usage cost apply failed: {}", exc)
-
 
 def _upsert_claimed_buckets(
     db: Session,
@@ -1059,7 +1216,11 @@ def _flush_pending_buffer(
                        usage_date, usage_kind,
                        prompt_tokens, completion_tokens, cache_read_tokens,
                        cache_creation_tokens, reasoning_tokens, audio_seconds,
-                       tts_characters, call_count
+                       tts_characters, call_count,
+                       input_cost_micro_usd, output_cost_micro_usd,
+                       cache_read_cost_micro_usd, cache_creation_cost_micro_usd,
+                       reasoning_cost_micro_usd, audio_cost_micro_usd, tts_cost_micro_usd,
+                       total_cost_micro_usd, pricing_rate_source, pricing_rate_id
                 FROM usage_pending_buffer
                 WHERE organization_id = CAST(:organization_id AS uuid)
                 ORDER BY created_at ASC
@@ -1103,12 +1264,27 @@ def _flush_pending_buffer(
                 "tts_characters": int(row.get("tts_characters") or 0),
                 "call_count": int(row["call_count"] or 0),
             }
+            stamped_costs = {
+                "input_cost_micro_usd": row.get("input_cost_micro_usd"),
+                "output_cost_micro_usd": row.get("output_cost_micro_usd"),
+                "cache_read_cost_micro_usd": row.get("cache_read_cost_micro_usd"),
+                "cache_creation_cost_micro_usd": row.get("cache_creation_cost_micro_usd"),
+                "reasoning_cost_micro_usd": row.get("reasoning_cost_micro_usd"),
+                "audio_cost_micro_usd": row.get("audio_cost_micro_usd"),
+                "tts_cost_micro_usd": row.get("tts_cost_micro_usd"),
+                "total_cost_micro_usd": row.get("total_cost_micro_usd"),
+                "pricing_rate_source": row.get("pricing_rate_source"),
+                "pricing_rate_id": (
+                    str(row["pricing_rate_id"]) if row.get("pricing_rate_id") else None
+                ),
+            }
             _upsert_bucket(
                 db,
                 organization_id,
                 bucket,
                 deltas,
                 pricing_resolver=resolver,
+                stamped_costs=stamped_costs,
             )
             ids.append(str(row["id"]))
             flushed += 1
diff --git a/app/services/usage/normalize.py b/app/services/usage/normalize.py
index bfdcf5f6..7b2b8e88 100644
--- a/app/services/usage/normalize.py
+++ b/app/services/usage/normalize.py
@@ -97,3 +97,13 @@ def normalize_llm_usage(raw_response: Any = None, *, usage: Any = None) -> Usage
         cache_creation_tokens=cache_creation,
         reasoning_tokens=reasoning_tokens,
     )
+
+
+def usage_snapshot_is_billable(snapshot: UsageSnapshot) -> bool:
+    return (
+        snapshot.prompt_tokens > 0
+        or snapshot.completion_tokens > 0
+        or snapshot.cache_read_tokens > 0
+        or snapshot.cache_creation_tokens > 0
+        or snapshot.reasoning_tokens > 0
+    )
diff --git a/app/services/usage/pricing_overrides.py b/app/services/usage/pricing_overrides.py
index 1214023b..77b84c2b 100644
--- a/app/services/usage/pricing_overrides.py
+++ b/app/services/usage/pricing_overrides.py
@@ -311,7 +311,7 @@ def upsert_override(
     effective_from: date,
     effective_to: Optional[date] = None,
     rates: Dict[str, Any],
-    recompute: bool = True,
+    recompute: bool = False,
 ) -> Dict[str, Any]:
     validate_model_name(db, model, organization_id=organization_id)
     kind = _validate_usage_kind(usage_kind)
@@ -416,7 +416,7 @@ def delete_override(
     model: str,
     usage_kind: str,
     effective_from: Optional[date] = None,
-    recompute: bool = True,
+    recompute: bool = False,
 ) -> Dict[str, Any]:
     validate_model_name(db, model, organization_id=organization_id)
     kind = _validate_usage_kind(usage_kind)
diff --git a/app/services/usage/voice_usage_processor.py b/app/services/usage/voice_usage_processor.py
index d1529507..21d82f14 100644
--- a/app/services/usage/voice_usage_processor.py
+++ b/app/services/usage/voice_usage_processor.py
@@ -42,7 +42,7 @@ def create_llm_usage_recorder(
             record_stt_usage,
             record_tts_usage,
         )
-        from app.services.usage.normalize import UsageSnapshot
+        from app.services.usage.normalize import UsageSnapshot, usage_snapshot_is_billable
     except Exception as exc:
         logger.debug("voice usage recorder unavailable: {}", exc)
         return None
@@ -80,6 +80,8 @@ async def process_frame(self, frame: Frame, direction: FrameDirection):
                             ),
                             reasoning_tokens=int(tokens.reasoning_tokens or 0),
                         )
+                        if not usage_snapshot_is_billable(snapshot):
+                            continue
                         record_llm_usage(
                             item.model or "unknown",
                             snapshot,
diff --git a/frontend/src/pages/configurations/Integrations.tsx b/frontend/src/pages/configurations/Integrations.tsx
index 0dae3165..2cbaddf6 100644
--- a/frontend/src/pages/configurations/Integrations.tsx
+++ b/frontend/src/pages/configurations/Integrations.tsx
@@ -209,7 +209,10 @@ export default function Integrations() {
       (credentialRoutingMode === 'inherit' &&
         llmGatewaySettings?.effective_routing &&
         llmGatewaySettings.effective_routing !== 'direct'))
-  const aiProviderRequiresApiKey = credentialRoutingMode === 'direct'
+  const showGatewayOptionalApiKeyUi = isCustomAIProvider
+  const aiProviderRequiresApiKey = isCustomAIProvider
+    ? credentialRoutingMode === 'direct'
+    : !isEditMode
 
   const showLlmGatewayConfigOptions = llmGatewayMode !== 'disabled'
 
@@ -1487,10 +1490,16 @@ export default function Integrations() {
                       API Key{' '}
                       {isEditMode ? (
                         <span className="text-gray-500 font-normal">(leave empty to keep current)</span>
-                      ) : aiProviderRequiresApiKey ? (
-                        '*'
+                      ) : showGatewayOptionalApiKeyUi ? (
+                        aiProviderRequiresApiKey ? (
+                          '*'
+                        ) : (
+                          <span className="text-gray-500 font-normal">
+                            (optional — not needed for gateway routing)
+                          </span>
+                        )
                       ) : (
-                        <span className="text-gray-500 font-normal">(optional — not needed for gateway routing)</span>
+                        '*'
                       )}
                     </label>
                     <input
@@ -1502,16 +1511,24 @@ export default function Integrations() {
                       placeholder={
                         isEditMode
                           ? 'Enter new API key (optional)'
-                          : aiProviderRequiresApiKey
-                            ? 'Enter API key'
-                            : 'Leave blank when routing via Bifrost/gateway'
+                          : showGatewayOptionalApiKeyUi
+                            ? aiProviderRequiresApiKey
+                              ? 'Enter API key'
+                              : 'Leave blank when routing via Bifrost/gateway'
+                            : 'Enter API key'
                       }
                     />
-                    <p className="mt-1 text-xs text-gray-500">
-                      {credentialRoutingMode === 'direct'
-                        ? 'Direct routing always requires a provider API key.'
-                        : 'Gateway routing does not require a provider API key — Bifrost handles the model call. Add one only if you want to pass a provider secret through the gateway.'}
-                    </p>
+                    {showGatewayOptionalApiKeyUi ? (
+                      <p className="mt-1 text-xs text-gray-500">
+                        {credentialRoutingMode === 'direct'
+                          ? 'Direct routing always requires a provider API key.'
+                          : 'Gateway routing does not require a provider API key — Bifrost handles the model call. Add one only if you want to pass a provider secret through the gateway.'}
+                      </p>
+                    ) : (
+                      <p className="mt-1 text-xs text-gray-500">
+                        Your API key will be encrypted and stored securely
+                      </p>
+                    )}
                   </div>
                 </>
               )}
diff --git a/frontend/src/pages/usage/Usage.tsx b/frontend/src/pages/usage/Usage.tsx
index 1ce1a505..1080ee63 100644
--- a/frontend/src/pages/usage/Usage.tsx
+++ b/frontend/src/pages/usage/Usage.tsx
@@ -1170,42 +1170,35 @@ export default function Usage() {
           <StatCard label="Output tokens" value={totals?.completion_tokens} loading={usageStatsLoading} />
           <StatCard label="Total tokens" value={totals?.total_tokens} loading={usageStatsLoading} />
           <StatCard label="LLM calls" value={totals?.call_count} loading={usageStatsLoading} />
-        </div>
-
-        <div className="grid grid-cols-2 gap-3 md:grid-cols-4 md:items-stretch">
-          <div className="col-span-2 grid grid-cols-2 gap-3 sm:grid-cols-3 md:col-span-3">
-            {showAudio ? (
-              <StatCard
-                label="STT audio"
-                valueLabel={formatAudio(totals?.audio_seconds || 0)}
-                loading={usageStatsLoading}
-              />
-            ) : null}
-            {showTts ? (
-              <StatCard label="TTS characters" value={totals?.tts_characters} loading={usageStatsLoading} />
-            ) : null}
-            {(totals?.cache_read_tokens || 0) > 0 ? (
-              <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={usageStatsLoading} />
-            ) : null}
-            {(totals?.cache_creation_tokens || 0) > 0 ? (
-              <StatCard
-                label="Cache write"
-                value={totals?.cache_creation_tokens}
-                loading={usageStatsLoading}
-              />
-            ) : null}
-            {(totals?.reasoning_tokens || 0) > 0 ? (
-              <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={usageStatsLoading} />
-            ) : null}
-          </div>
-          <div className="col-span-2 md:col-span-1">
+          {showAudio ? (
+            <StatCard
+              label="STT audio"
+              valueLabel={formatAudio(totals?.audio_seconds || 0)}
+              loading={usageStatsLoading}
+            />
+          ) : null}
+          {showTts ? (
+            <StatCard label="TTS characters" value={totals?.tts_characters} loading={usageStatsLoading} />
+          ) : null}
+          {(totals?.cache_read_tokens || 0) > 0 ? (
+            <StatCard label="Cache read" value={totals?.cache_read_tokens} loading={usageStatsLoading} />
+          ) : null}
+          {(totals?.cache_creation_tokens || 0) > 0 ? (
             <StatCard
-              label="Estimated cost"
-              valueLabel={formatCostUsd(estimatedTotalCost)}
+              label="Cache write"
+              value={totals?.cache_creation_tokens}
               loading={usageStatsLoading}
-              emphasize
             />
-          </div>
+          ) : null}
+          {(totals?.reasoning_tokens || 0) > 0 ? (
+            <StatCard label="Reasoning" value={totals?.reasoning_tokens} loading={usageStatsLoading} />
+          ) : null}
+          <StatCard
+            label="Estimated cost"
+            valueLabel={formatCostUsd(estimatedTotalCost)}
+            loading={usageStatsLoading}
+            emphasize
+          />
         </div>
       </div>
 
@@ -1429,7 +1422,7 @@ function StatCard({
   return (
     <Card
       shadow="sm"
-      className={`${statCardClass}${emphasize ? ' ring-2 ring-[#facc15]/50 bg-[#fefce8]/40' : ''} h-full w-full ${className}`}
+      className={`${statCardClass}${emphasize ? ' ring-2 ring-[#facc15]/50 bg-[#fefce8]/40' : ''} w-full ${className}`}
     >
       <CardBody className="p-3">
         <p className="text-xs font-medium text-gray-500 uppercase tracking-wide">{label}</p>
diff --git a/frontend/src/pages/usage/UsageFiltersBar.tsx b/frontend/src/pages/usage/UsageFiltersBar.tsx
index 83e7e1c1..67dcef0d 100644
--- a/frontend/src/pages/usage/UsageFiltersBar.tsx
+++ b/frontend/src/pages/usage/UsageFiltersBar.tsx
@@ -109,19 +109,19 @@ export default function UsageFiltersBar({
 
   const activeChips = useMemo((): ActiveChip[] => {
     const chips: ActiveChip[] = []
-    const wsLabel = workspaces.find((w) => w.id === workspaceId)?.name
-    if (workspaceId && wsLabel) {
+    if (workspaceId) {
+      const wsLabel = workspaces.find((w) => w.id === workspaceId)?.name
       chips.push({
         key: 'workspace',
-        label: wsLabel,
+        label: wsLabel || 'Workspace',
         onClear: () => onWorkspaceChange(''),
       })
     }
-    const importLabel = callImports.find((c) => c.id === callImportId)?.label
-    if (callImportId && importLabel) {
+    if (callImportId) {
+      const importLabel = callImports.find((c) => c.id === callImportId)?.label
       chips.push({
         key: 'call_import',
-        label: importLabel,
+        label: importLabel || 'Call import',
         onClear: () => onCallImportChange(''),
       })
     }
@@ -134,22 +134,20 @@ export default function UsageFiltersBar({
     }
     if (tagId) {
       const tagLabel = tags.find((t) => t.id === tagId)?.label
-      if (tagLabel) {
-        chips.push({
-          key: 'tag',
-          label: tagLabel,
-          onClear: () => onTagChange(''),
-        })
-      }
+      chips.push({
+        key: 'tag',
+        label: tagLabel || 'Tag',
+        onClear: () => onTagChange(''),
+      })
     }
-    const sourceLabel =
-      sourceOptions.find((e) => e.id === sourceSelectValue)?.label ||
-      evaluations.find((e) => e.id === evaluationId)?.label ||
-      resources.find((r) => r.id === evaluationId)?.label
-    if ((evaluationId || productSection) && sourceLabel) {
+    if (evaluationId || productSection) {
+      const sourceLabel =
+        sourceOptions.find((e) => e.id === sourceSelectValue)?.label ||
+        evaluations.find((e) => e.id === evaluationId)?.label ||
+        resources.find((r) => r.id === evaluationId)?.label
       chips.push({
         key: 'evaluation',
-        label: sourceLabel,
+        label: sourceLabel || 'Source',
         onClear: () => onEvaluationChange(''),
       })
     }
@@ -193,6 +191,16 @@ export default function UsageFiltersBar({
     onModelChange,
   ])
 
+  const hasActiveScope = Boolean(
+    workspaceId ||
+      callImportId ||
+      dataset ||
+      tagId ||
+      evaluationId ||
+      model ||
+      usageKind ||
+      productSection,
+  )
   const hasScopeFilters = activeChips.length > 0
 
   const kindHint = (kind: Kind): string | undefined => {
@@ -229,7 +237,7 @@ export default function UsageFiltersBar({
           <span className="text-xs text-gray-400 ml-1">Updating options…</span>
         ) : null}
 
-        {hasScopeFilters ? (
+        {hasActiveScope ? (
           <button
             type="button"
             onClick={onClearAll}
diff --git a/frontend/src/pages/usage/UsagePricing.tsx b/frontend/src/pages/usage/UsagePricing.tsx
index 6796751b..9227509b 100644
--- a/frontend/src/pages/usage/UsagePricing.tsx
+++ b/frontend/src/pages/usage/UsagePricing.tsx
@@ -1,7 +1,7 @@
 import { useEffect, useMemo, useRef, useState, type ReactNode } from 'react'
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
 import { Card, CardBody, Spinner } from '@heroui/react'
-import { Plus, Trash2, Info } from 'lucide-react'
+import { Plus, Pencil, Trash2, Info } from 'lucide-react'
 import { apiClient } from '../../lib/api'
 import { useIsAdmin } from '../../hooks/useRole'
 import { useToast } from '../../hooks/useToast'
@@ -109,7 +109,7 @@ const FIELD_HINTS = {
   usageKind:
     'Choose LLM, STT, or TTS first — this filters which models appear. Rate fields below match the kind.',
   effectiveFrom:
-    'First calendar day this override applies. Saving recalculates historical usage for this model from this date.',
+    'First calendar day this override applies. Changes apply to new usage only; past stamped costs are not rewritten.',
   ratesSection:
     'Enter USD rates only. Leave a field empty to inherit the platform catalog. At least one rate is required to save.',
 } as const
@@ -197,6 +197,7 @@ export default function UsagePricing() {
   const [effectiveFrom, setEffectiveFrom] = useState(todayIso())
   const [rates, setRates] = useState<PricingRatesUsd>(EMPTY_RATES)
   const [ratePrefillSource, setRatePrefillSource] = useState<'catalog' | 'override' | null>(null)
+  const [editingOverrideId, setEditingOverrideId] = useState<string | null>(null)
   const selectionRef = useRef({ model: '', usageKind: '' as UsageKind })
 
   const { data: aiProviders = [] } = useQuery({
@@ -268,11 +269,45 @@ export default function UsagePricing() {
     [selectedCredential, providerCatalog, usageKind, eligibleModelSet, overrides],
   )
 
+  const resetForm = () => {
+    setRates(EMPTY_RATES)
+    setModel('')
+    setCredentialId('')
+    setEffectiveFrom(todayIso())
+    setEditingOverrideId(null)
+    setRatePrefillSource(null)
+    selectionRef.current = { model: '', usageKind: 'llm' }
+  }
+
   const resetModelAndRates = () => {
     setModel('')
     setRates(EMPTY_RATES)
     setRatePrefillSource(null)
-    selectionRef.current = { model: '', usageKind: 'llm' }
+    setEditingOverrideId(null)
+    selectionRef.current = { model: '', usageKind: usageKind }
+  }
+
+  const findCredentialForModel = (modelName: string, kind: UsageKind): string => {
+    for (const credential of activeCredentials) {
+      const allowlist = credential.enabled_models?.map((m) => m.trim()).filter(Boolean) ?? []
+      if (allowlist.includes(modelName)) return credential.id
+      if (kind === 'llm' && credential.gateway_model?.trim() === modelName) {
+        return credential.id
+      }
+    }
+    return activeCredentials[0]?.id ?? ''
+  }
+
+  const loadOverrideForEdit = (row: PricingOverride) => {
+    const kind = row.usage_kind as UsageKind
+    setEditingOverrideId(row.id)
+    setUsageKind(kind)
+    setCredentialId(findCredentialForModel(row.model, kind))
+    setModel(row.model)
+    setEffectiveFrom(row.effective_from)
+    setRates(ratesFromApi(row.rates))
+    setRatePrefillSource('override')
+    selectionRef.current = { model: row.model, usageKind: kind }
   }
 
   useEffect(() => {
@@ -281,6 +316,7 @@ export default function UsagePricing() {
   }, [activeCredentials, credentialId])
 
   useEffect(() => {
+    if (editingOverrideId) return
     if (!model) {
       setRates(EMPTY_RATES)
       setRatePrefillSource(null)
@@ -308,7 +344,7 @@ export default function UsagePricing() {
     }
 
     selectionRef.current = { model, usageKind }
-  }, [model, usageKind, effectivePricing])
+  }, [model, usageKind, effectivePricing, editingOverrideId])
 
   const handleCredentialChange = (nextId: string) => {
     setCredentialId(nextId)
@@ -337,18 +373,15 @@ export default function UsagePricing() {
         usage_kind: usageKind,
         effective_from: effectiveFrom,
         rates: payloadRates,
-        recompute: true,
+        recompute: false,
       })
     },
-    onSuccess: (data) => {
+    onSuccess: () => {
       queryClient.invalidateQueries({ queryKey: ['usage-pricing'] })
       queryClient.invalidateQueries({ queryKey: ['org-usage'] })
-      setRates(EMPTY_RATES)
-      setRatePrefillSource(null)
+      resetForm()
       showToast(
-        data.recompute_enqueued
-          ? 'Override saved; cost recompute started'
-          : 'Override saved (recompute already running)',
+        editingOverrideId ? 'Override updated' : 'Override saved',
         'success'
       )
     },
@@ -388,10 +421,12 @@ export default function UsagePricing() {
       <Card className={`${usageTheme.panel} shrink-0`}>
         <CardBody className="gap-5 overflow-visible p-5">
           <div>
-            <h2 className="text-base font-semibold text-gray-900">Add or update override</h2>
+            <h2 className="text-base font-semibold text-gray-900">
+              {editingOverrideId ? 'Edit override' : 'Add or update override'}
+            </h2>
             <p className="mt-0.5 text-xs text-gray-500">
-              Per-model rates for this org. Leave blank to use the default catalog. Saving triggers
-              a scoped cost recompute.
+              Per-model rates for this org. Leave blank to use the default catalog. Changes apply
+              to new usage only; previously recorded costs are kept.
             </p>
           </div>
 
@@ -442,6 +477,7 @@ export default function UsagePricing() {
                 type="date"
                 className={CONTROL_CLASS}
                 value={effectiveFrom}
+                disabled={Boolean(editingOverrideId)}
                 onChange={(e) => setEffectiveFrom(e.target.value)}
               />
             </FormField>
@@ -521,11 +557,7 @@ export default function UsagePricing() {
             <button
               type="button"
               className="text-sm text-gray-500 hover:text-gray-700"
-              onClick={() => {
-                setRates(EMPTY_RATES)
-                setModel('')
-                setCredentialId('')
-              }}
+              onClick={() => resetForm()}
             >
               Clear form
             </button>
@@ -536,7 +568,7 @@ export default function UsagePricing() {
               isLoading={saveMutation.isPending}
               onClick={() => saveMutation.mutate()}
             >
-              Save override
+              {editingOverrideId ? 'Update override' : 'Save override'}
             </Button>
           </div>
         </CardBody>
@@ -577,7 +609,7 @@ export default function UsagePricing() {
                     <th className="px-4 py-2.5 text-right">Output</th>
                     <th className="px-4 py-2.5 text-right">Audio</th>
                     <th className="px-4 py-2.5 text-right">TTS</th>
-                    <th className="px-4 py-2.5 w-12" />
+                    <th className="px-4 py-2.5 w-20" />
                   </tr>
                 </thead>
                 <tbody className="divide-y divide-gray-100 bg-white">
@@ -604,15 +636,25 @@ export default function UsagePricing() {
                         {formatRateUsd(row.rates.tts_per_1m_characters)}
                       </td>
                       <td className="px-4 py-2.5 text-right">
-                        <button
-                          type="button"
-                          className="rounded p-1 text-red-600 hover:bg-red-50 hover:text-red-800"
-                          onClick={() => deleteMutation.mutate(row)}
-                          disabled={deleteMutation.isPending}
-                          aria-label={`Delete override for ${row.model}`}
-                        >
-                          <Trash2 className="h-4 w-4" />
-                        </button>
+                        <div className="flex items-center justify-end gap-1">
+                          <button
+                            type="button"
+                            className="rounded p-1 text-gray-600 hover:bg-gray-100 hover:text-gray-900"
+                            onClick={() => loadOverrideForEdit(row)}
+                            aria-label={`Edit override for ${row.model}`}
+                          >
+                            <Pencil className="h-4 w-4" />
+                          </button>
+                          <button
+                            type="button"
+                            className="rounded p-1 text-red-600 hover:bg-red-50 hover:text-red-800"
+                            onClick={() => deleteMutation.mutate(row)}
+                            disabled={deleteMutation.isPending}
+                            aria-label={`Delete override for ${row.model}`}
+                          >
+                            <Trash2 className="h-4 w-4" />
+                          </button>
+                        </div>
                       </td>
                     </tr>
                   ))}
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index 79c89c40..d08887bd 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -24,6 +24,26 @@
 from app.services.usage.normalize import UsageSnapshot
 
 
+def _stub_stamp_cost_params(*_args, **_kwargs) -> Dict[str, Any]:
+    return {
+        "input_cost_micro_usd": 0,
+        "output_cost_micro_usd": 0,
+        "cache_read_cost_micro_usd": 0,
+        "cache_creation_cost_micro_usd": 0,
+        "reasoning_cost_micro_usd": 0,
+        "audio_cost_micro_usd": 0,
+        "tts_cost_micro_usd": 0,
+        "total_cost_micro_usd": 0,
+        "pricing_rate_source": None,
+        "pricing_rate_id": None,
+    }
+
+
+@pytest.fixture(autouse=True)
+def _stub_usage_cost_stamp(monkeypatch):
+    monkeypatch.setattr(usage_mod, "_stamp_cost_params", _stub_stamp_cost_params)
+
+
 class _FakePipeline:
     def __init__(self, client: "_FakeRedis"):
         self._client = client
@@ -202,7 +222,7 @@ def org_ctx():
     return org_id, workspace_id, ctx
 
 
-def test_record_increments_pending_and_counts_zero_token_calls(fake_redis, org_ctx):
+def test_record_increments_pending_and_ignores_zero_token_calls(fake_redis, org_ctx):
     org_id, _workspace_id, ctx = org_ctx
     with llm_usage_context(ctx):
         usage_mod.record_llm_usage(
@@ -227,7 +247,7 @@ def test_record_increments_pending_and_counts_zero_token_calls(fake_redis, org_c
     calls = sum(int(v) for k, v in fields.items() if k.endswith("|call_count"))
     assert prompt == 10
     assert completion == 5
-    assert calls == 2
+    assert calls == 1
     assert any(k.rsplit("|", 1)[0].endswith("|llm") for k in fields)
 
 
@@ -499,10 +519,7 @@ def test_concurrent_flush_does_not_double_count(fake_redis, org_ctx, monkeypatch
     db_b = MagicMock()
     db_b.execute.return_value = MagicMock(rowcount=1)
     monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
-    monkeypatch.setattr(
-        "app.services.usage.pricing.apply_cost_to_bucket",
-        lambda *args, **kwargs: False,
-    )
+    monkeypatch.setattr(usage_mod, "_stamp_cost_params", _stub_stamp_cost_params)
 
     first = usage_mod.flush_usage_to_catalog(db_a, org_id)
     second = usage_mod.flush_usage_to_catalog(db_b, org_id)
@@ -549,10 +566,7 @@ def test_flush_redis_processes_multiple_batches_in_one_run(
     db = MagicMock()
     db.execute.return_value = MagicMock(rowcount=1)
     monkeypatch.setattr(usage_mod, "_flush_pending_buffer", lambda *_args, **_kwargs: 0)
-    monkeypatch.setattr(
-        "app.services.usage.pricing.apply_cost_to_bucket",
-        lambda *args, **kwargs: False,
-    )
+    monkeypatch.setattr(usage_mod, "_stamp_cost_params", _stub_stamp_cost_params)
 
     flushed = usage_mod.flush_usage_to_catalog(db, org_id)
 
@@ -656,10 +670,6 @@ def test_upsert_bucket_sql_uses_valid_empty_jsonb_literal():
 
 def test_upsert_bucket_matches_legacy_resource_context_key(monkeypatch):
     """Per-row context must merge into an existing evaluation-level bucket."""
-    monkeypatch.setattr(
-        "app.services.usage.pricing.apply_cost_to_bucket",
-        lambda *args, **kwargs: False,
-    )
     org_id = uuid4()
     evaluation_id = uuid4()
     bucket = {
@@ -689,7 +699,7 @@ def test_upsert_bucket_matches_legacy_resource_context_key(monkeypatch):
     assert "context->>'resource_type'" in legacy_sql
 
 
-def test_upsert_bucket_applies_cost_after_update(monkeypatch):
+def test_upsert_bucket_increments_cost_on_update(monkeypatch):
     org_id = uuid4()
     bucket = {
         "workspace_id": uuid4(),
@@ -699,18 +709,46 @@ def test_upsert_bucket_applies_cost_after_update(monkeypatch):
         "usage_date": date(2026, 8, 12),
         "usage_kind": "llm",
     }
-    deltas = {"prompt_tokens": 10, "completion_tokens": 5}
+    deltas = {"prompt_tokens": 10, "completion_tokens": 5, "call_count": 1}
     db = MagicMock()
     db.execute.return_value = MagicMock(rowcount=1)
-    called: list[bool] = []
     monkeypatch.setattr(
-        "app.services.usage.pricing.apply_cost_to_bucket",
-        lambda *args, **kwargs: called.append(True) or True,
+        usage_mod,
+        "_stamp_cost_params",
+        lambda *args, **kwargs: {
+            "input_cost_micro_usd": 100,
+            "output_cost_micro_usd": 200,
+            "cache_read_cost_micro_usd": 0,
+            "cache_creation_cost_micro_usd": 0,
+            "reasoning_cost_micro_usd": 0,
+            "audio_cost_micro_usd": 0,
+            "tts_cost_micro_usd": 0,
+            "total_cost_micro_usd": 300,
+            "pricing_rate_source": "catalog",
+            "pricing_rate_id": str(uuid4()),
+        },
     )
 
     usage_mod._upsert_bucket(db, org_id, bucket, deltas)
 
-    assert called
+    update_sql = str(db.execute.call_args[0][0])
+    assert "input_cost_micro_usd = input_cost_micro_usd + :input_cost_micro_usd" in update_sql
+    assert "total_cost_micro_usd = total_cost_micro_usd + :total_cost_micro_usd" in update_sql
+
+
+def test_record_llm_usage_skips_zero_token_snapshot(fake_redis, monkeypatch):
+    org_id = uuid4()
+    monkeypatch.setattr(usage_mod, "_client", lambda: fake_redis)
+    usage_mod.record_llm_usage(
+        "gpt-test",
+        UsageSnapshot(prompt_tokens=0, completion_tokens=0),
+        organization_id=org_id,
+        ctx=LLMUsageContext(
+            organization_id=org_id,
+            product_section=LLMUsageProductSection.CHAT,
+        ),
+    )
+    assert not fake_redis.hgetall(usage_mod._pending_hash_key(org_id))
 
 
 def test_orphan_recovery_runs_at_most_once_per_interval(fake_redis, monkeypatch):
diff --git a/tests/test_services/test_usage/test_pricing.py b/tests/test_services/test_usage/test_pricing.py
index 6d01dcaa..eaf63c35 100644
--- a/tests/test_services/test_usage/test_pricing.py
+++ b/tests/test_services/test_usage/test_pricing.py
@@ -193,9 +193,11 @@ def test_resolve_rate_ignores_stale_negative_cache(monkeypatch):
     override_result.mappings.return_value.first.return_value = None
     catalog_result = MagicMock()
     catalog_result.mappings.return_value.first.return_value = row
+    cache_table_exists = MagicMock()
+    cache_table_exists.scalar.return_value = "model_pricing_rates"
 
     db = MagicMock()
-    db.execute.side_effect = [override_result, catalog_result]
+    db.execute.side_effect = [override_result, cache_table_exists, catalog_result]
 
     resolver = PricingResolver(db)
     card = resolver.resolve_rate(

From 49871cbfc22e3afb902d954f29fb729125c6ab99 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Tue, 18 Aug 2026 00:09:12 +0530
Subject: [PATCH 31/55] fix(usage): implement _cost_fields_for_pending_deltas
 function to streamline cost calculations for pending usage deltas

---
 app/services/ai/llm_service.py  | 16 --------
 app/services/usage/llm_usage.py | 68 ++++++++++++++++++++++++++++-----
 2 files changed, 59 insertions(+), 25 deletions(-)

diff --git a/app/services/ai/llm_service.py b/app/services/ai/llm_service.py
index a2ea3801..95e64488 100644
--- a/app/services/ai/llm_service.py
+++ b/app/services/ai/llm_service.py
@@ -490,22 +490,6 @@ def generate_response(
             credential=credential_ctx,
         )
 
-        # #region agent log
-        try:
-            import json as _json, time as _time
-            _msg_types = []
-            for _m in messages:
-                _c = _m.get("content")
-                if isinstance(_c, list):
-                    _msg_types.extend(
-                        p.get("type") for p in _c if isinstance(p, dict)
-                    )
-            with open("debug-bfc313.log", "a", encoding="utf-8") as _f:
-                _f.write(_json.dumps({"sessionId": "bfc313", "runId": "post-fix", "hypothesisId": "C", "location": "llm_service.py:generate_response", "message": "pre-completion routing", "data": {"effective_routing": effective_routing, "credential_mode": getattr(credential_ctx, "routing_mode", None), "credential_id": str(credential_id) if credential_id else None, "model": model_str, "has_api_base": "api_base" in call_kwargs, "custom_llm_provider": call_kwargs.get("custom_llm_provider"), "content_part_types": _msg_types}, "timestamp": int(_time.time() * 1000)}) + "\n")
-        except Exception:
-            pass
-        # #endregion
-
         try:
             response = litellm.completion(**call_kwargs)
         except Exception as e:
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index dea0c400..bbeac232 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -312,6 +312,63 @@ def _deltas_from_tts(characters: int) -> Dict[str, int]:
     }
 
 
+def _cost_fields_for_pending_deltas(
+    organization_id: UUID,
+    bucket: Dict[str, Any],
+    deltas: Dict[str, int],
+    *,
+    db: Optional[Session] = None,
+) -> Dict[str, Any]:
+    if not _has_billable_usage_deltas(deltas):
+        return {}
+    try:
+        from app.services.usage.pricing import cost_fields_from_deltas
+    except Exception as exc:
+        logger.debug("usage pending cost stamp skipped: {}", exc)
+        return {}
+
+    usage_date = bucket["usage_date"]
+    if isinstance(usage_date, str):
+        usage_date = date.fromisoformat(usage_date)
+    usage_kind = bucket.get("usage_kind") or USAGE_KIND_LLM
+
+    if db is not None:
+        try:
+            return cost_fields_from_deltas(
+                deltas,
+                organization_id=organization_id,
+                model=bucket["model"],
+                usage_kind=usage_kind,
+                usage_date=usage_date,
+                db=db,
+            )
+        except Exception as exc:
+            logger.debug("usage pending cost stamp failed: {}", exc)
+            return {}
+
+    try:
+        from app.database import SessionLocal
+    except Exception as exc:
+        logger.debug("usage pending cost stamp skipped: {}", exc)
+        return {}
+
+    session = SessionLocal()
+    try:
+        return cost_fields_from_deltas(
+            deltas,
+            organization_id=organization_id,
+            model=bucket["model"],
+            usage_kind=usage_kind,
+            usage_date=usage_date,
+            db=session,
+        )
+    except Exception as exc:
+        logger.debug("usage pending cost stamp failed: {}", exc)
+        return {}
+    finally:
+        session.close()
+
+
 def _buffer_to_postgres(
     organization_id: UUID,
     bucket: Dict[str, Any],
@@ -347,16 +404,9 @@ def _buffer_to_postgres(
     }
     db = SessionLocal()
     try:
-        from app.services.usage.pricing import cost_fields_from_deltas
-
         params.update(
-            cost_fields_from_deltas(
-                deltas,
-                organization_id=organization_id,
-                model=bucket["model"],
-                usage_kind=usage_kind,
-                usage_date=usage_date,
-                db=db,
+            _cost_fields_for_pending_deltas(
+                organization_id, bucket, deltas, db=db
             )
         )
         db.execute(

From a33972d61db06b3ea6aa5b11f053e9c41da4039d Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Tue, 18 Aug 2026 19:12:13 +0530
Subject: [PATCH 32/55] feat(docs): add usage tracking feature documentation
 and update related references in the documentation

---
 docs-fumadocs/content/docs/intro.mdx          |   2 +
 .../content/docs/monitoring/meta.json         |   1 +
 .../content/docs/monitoring/usage.mdx         | 198 ++++++++++++++++++
 .../content/docs/reference/cli-commands.mdx   |  47 +++++
 .../content/feature-contributors.json         |  17 ++
 docs-fumadocs/public/search-index.json        | 139 +++++++-----
 6 files changed, 357 insertions(+), 47 deletions(-)
 create mode 100644 docs-fumadocs/content/docs/monitoring/usage.mdx

diff --git a/docs-fumadocs/content/docs/intro.mdx b/docs-fumadocs/content/docs/intro.mdx
index 36d2e905..f95f6f49 100644
--- a/docs-fumadocs/content/docs/intro.mdx
+++ b/docs-fumadocs/content/docs/intro.mdx
@@ -33,6 +33,7 @@ EfficientAI gives you an end-to-end loop for voice AI quality:
 - **Metric-driven evaluation**: built-in and custom metrics with categorization labels, surface targeting, and org-wide or workspace scope.
 - **Cloud storage**: store audio in Amazon S3, S3-compatible services, or Google Cloud Storage via the Data Sources UI.
 - **Prompt optimization workflows**: run optimization loops, compare candidates, accept winners, and push selected prompts to providers.
+- **Usage tracking**: org-wide LLM, STT, and TTS consumption with cost estimates and drill-down by workspace and product area.
 
 ## Platform model
 
@@ -48,6 +49,7 @@ This structure keeps testing reproducible while still reflecting real-world voic
 
 ## Key guides
 
+- [Usage](/docs/monitoring/usage/) — LLM/STT/TTS consumption, cost estimates, and drill-down by workspace and product area
 - [Workspaces](/docs/getting-started/workspaces/) — project isolation within your organization, plus workspace roles (Viewer / Editor / Workspace Admin) and how they interact with org roles
 - [Cloud Storage](/docs/getting-started/cloud-storage/) — S3 and GCS configuration
 - [Metrics](/docs/products/metrics/) — custom rubrics, surfaces, and evaluation scope
diff --git a/docs-fumadocs/content/docs/monitoring/meta.json b/docs-fumadocs/content/docs/monitoring/meta.json
index 2ace091c..e0a482ad 100644
--- a/docs-fumadocs/content/docs/monitoring/meta.json
+++ b/docs-fumadocs/content/docs/monitoring/meta.json
@@ -1,6 +1,7 @@
 {
   "title": "Monitoring",
   "pages": [
+    "usage",
     "calls",
     "alerting",
     "cron-jobs"
diff --git a/docs-fumadocs/content/docs/monitoring/usage.mdx b/docs-fumadocs/content/docs/monitoring/usage.mdx
new file mode 100644
index 00000000..1c2cb701
--- /dev/null
+++ b/docs-fumadocs/content/docs/monitoring/usage.mdx
@@ -0,0 +1,198 @@
+---
+id: usage
+title: Usage
+sidebar_position: 1
+---
+
+# Usage
+
+**Usage** is org-scoped analytics for LLM, STT, and TTS consumption with estimated costs. It helps you see how much each workspace, product area, call import, and model is consuming — and what that usage likely costs.
+
+Usage is **not** a quota or billing portal. Optional Flexprice event metering is a separate system and is not shown in the Usage UI.
+
+Models and providers tracked here come from your enabled [integrations](/docs/getting-started/integrations/). Usage is attributed per [workspace](/docs/getting-started/workspaces/) when the underlying workflow is workspace-scoped.
+
+---
+
+## Opening the Usage page
+
+In the sidebar, go to **Usage → Overview** (`/usage`).
+
+The page has two tabs when your org is licensed for enterprise features and you are an org admin:
+
+| Tab | URL | Who can access |
+|-----|-----|----------------|
+| **Overview** | `/usage` | All org members |
+| **Pricing overrides** | `/usage?tab=pricing` | Org admins with enterprise license |
+
+---
+
+## Summary metrics
+
+The top of the Overview tab shows rollup cards for the selected date range and filters:
+
+| Metric | Description |
+|--------|-------------|
+| **Input tokens** | Prompt or input tokens sent to LLM providers |
+| **Output tokens** | Completion or output tokens returned by LLMs |
+| **Total tokens** | Sum of input and output tokens |
+| **LLM calls** | Number of LLM API calls |
+| **STT audio** | Speech-to-text audio duration (shown when non-zero) |
+| **TTS characters** | Text-to-speech characters synthesized (shown when non-zero) |
+| **Cache read** | Tokens read from provider prompt cache (shown when non-zero) |
+| **Cache write** | Tokens written to provider prompt cache (shown when non-zero) |
+| **Reasoning** | Reasoning tokens billed separately by some providers (shown when non-zero) |
+| **Estimated cost** | Total estimated cost for the filtered range |
+
+Click **Cost breakdown** to open a modal with line items:
+
+- Input, output, cache read, cache write, reasoning
+- Audio (STT), TTS
+- Total estimated cost
+
+If any usage in the range has no matching catalog rate, the breakdown shows an **unpriced usage** warning. Those rows still appear in token/volume metrics but do not contribute to cost totals.
+
+### Currency display
+
+Toggle between **USD** and **INR** in the filter bar. INR amounts use a live USD→INR rate from Frankfurter when available, with a fallback estimate when the FX service is unreachable.
+
+---
+
+## Filters
+
+Filters narrow the summary cards and drill-down table. All filter state is stored in the URL, so you can bookmark or share a specific view.
+
+| Filter | Description |
+|--------|-------------|
+| **Date range** | Start and end dates, interpreted in your browser's IANA timezone |
+| **Workspace** | Limit to one workspace |
+| **Call import** | Limit to one call import batch |
+| **Dataset** | Filter by dataset name on call import rows |
+| **Tag** | Filter by call import tag |
+| **Evaluation run** | Filter by evaluation resource |
+| **Usage kind** | `LLM`, `STT`, or `TTS` |
+| **Model** | Provider model identifier |
+| **Source / product section** | Product area (playground, evaluators, call imports, etc.) |
+
+---
+
+## Drill-down navigation
+
+Click rows in the breakdown table to drill deeper. Breadcrumbs at the top show your current path; click a breadcrumb to go back up.
+
+```mermaid
+flowchart TD
+  Org[Organization] --> Workspace[Workspace]
+  Workspace --> Composite[Call imports and product areas]
+  Composite --> CallImport[Call import batch]
+  CallImport --> EvalRun[Evaluation run]
+  EvalRun --> Model[Model]
+  Model --> Kind[Usage kind]
+  Composite --> ProductSection[Product section]
+  ProductSection --> Model
+```
+
+At the organization level, the table groups by **workspace**. Inside a workspace you see a composite view:
+
+- **Call import batches** — CSV uploads or manual audio recordings
+- **Product areas** — usage from other parts of the platform (not tied to a single call import)
+
+From a call import batch you can drill into evaluation runs, then model, then usage kind. From a product area you drill into model, then usage kind.
+
+Each drill level returns at most **100 rows**. If more exist, results are truncated at that level.
+
+### Product sections
+
+| Section | What it tracks |
+|---------|----------------|
+| **Call imports** | Call import batch processing |
+| **Call import evaluations** | Evaluations run on imported calls |
+| **Playground** | Text playground and experiments |
+| **Voice playground** | Voice agent playground — LLM, STT, and TTS |
+| **Chat** | Chat conversations |
+| **Telephony** | Telephony and live calls |
+| **Evaluators** | Evaluator definitions and runs |
+| **Metrics** | Metrics and scoring |
+| **Judge alignment** | Judge alignment workflows |
+| **Prompt optimization** | Prompt optimization jobs |
+| **Personas** | Persona generation |
+| **Agents** | Agent configuration |
+| **Prompt partials** | Prompt partials |
+| **Conversation evaluations** | Conversation evaluations |
+| **Test agent** | Test agent sessions |
+| **Other** | Usage not attributed to a named product area |
+
+---
+
+## Data freshness and history
+
+### Freshness
+
+Usage counters are buffered in Redis and flushed to Postgres by the `worker-usage` service on a Celery Beat schedule (default: every **2 minutes**). The UI reads Postgres only.
+
+The page shows **Updated** with a `last_updated_at` timestamp when available. Expect roughly **2 minutes** of lag between new API usage and what appears on this page.
+
+For fresh data in self-hosted deployments, ensure `beat`, `worker-usage`, and the default `worker` are running (or use `eai start-all`).
+
+### History limits
+
+| Deployment | History window | Pricing overrides tab |
+|------------|----------------|----------------------|
+| **OSS** (no license) | Last **7 days** | Hidden |
+| **Enterprise** (`EFFICIENTAI_LICENSE`) | Unlimited | Org admins only |
+
+On OSS deployments, an amber banner explains the 7-day cap and points to `EFFICIENTAI_LICENSE`. If you pick a wider date range, it is automatically clamped to the allowed window.
+
+See [Configuration](/docs/reference/configuration/) for license setup.
+
+---
+
+## Pricing overrides
+
+Enterprise org admins can open **Pricing overrides** (`/usage?tab=pricing`) to set per-model rates that override the built-in catalog for cost estimation.
+
+### Override fields by usage kind
+
+| Usage kind | Rate fields (USD) |
+|------------|-------------------|
+| **LLM** | Input / 1M tokens, output / 1M tokens, cache read / 1M, cache write / 1M, reasoning / 1M, audio / minute |
+| **STT** | Audio / minute |
+| **TTS** | Characters / 1M |
+
+For each override you set:
+
+- **Provider credential** — models come from enabled integrations
+- **Usage kind** — LLM, STT, or TTS
+- **Model** — provider model identifier
+- **Effective from** — date the override starts applying
+- **Rates** — USD values for the fields above
+
+You can prefill rates from the catalog or an existing override. Saving creates or updates the override; deleting removes it.
+
+### How overrides affect costs
+
+Overrides apply to **new usage** recorded on or after `effective_from`. Costs already stamped on daily rollup rows are **not** changed automatically.
+
+To backfill historical costs after a catalog or override change, use the CLI or API recompute workflow. The Usage UI does not expose recompute jobs today — see [CLI Commands](/docs/reference/cli-commands/#usage-pricing).
+
+---
+
+## Operations
+
+Self-hosted operators manage pricing catalogs and cost backfills outside the UI.
+
+| Requirement | Purpose |
+|-------------|---------|
+| `beat` | Schedules usage flush, FX refresh, OSS history prune |
+| `worker-usage` | Flushes Redis counters and runs cost recompute jobs |
+| Default `worker` | Evaluator cron dispatch (indirectly drives much platform usage) |
+
+Common tuning variables (see `env.example`):
+
+| Variable | Default | Purpose |
+|----------|---------|---------|
+| `USAGE_FLUSH_BEAT_SECONDS` | `120` | Flush interval (~2 min UI lag) |
+| `USAGE_READ_CACHE_TTL_SECONDS` | `90` | Redis cache TTL for summary/breakdown/filters |
+| `USAGE_FLUSH_MAX_BATCHES_PER_RUN` | `30` | Batches per flush tick |
+
+For seeding rates, diffing catalogs, and recomputing stored costs, see [CLI Commands — Usage pricing](/docs/reference/cli-commands/#usage-pricing).
diff --git a/docs-fumadocs/content/docs/reference/cli-commands.mdx b/docs-fumadocs/content/docs/reference/cli-commands.mdx
index 6e09c83a..c0ce1658 100644
--- a/docs-fumadocs/content/docs/reference/cli-commands.mdx
+++ b/docs-fumadocs/content/docs/reference/cli-commands.mdx
@@ -107,3 +107,50 @@ eai migrate --verbose
 ```
 
 **Note**: Migrations run automatically on application startup. You only need to run them manually if you want to apply migrations before starting the server.
+
+## Usage pricing
+
+Manage model pricing rates and backfill stored usage costs on `llm_usage_daily` rollups. Requires `beat`, `worker-usage`, and the default `worker` (or `eai start-all`).
+
+```bash
+# Upsert model_pricing_rates from app/config/models.json
+eai usage seed-rates --config config.yml
+
+# Compare models.json pricing vs Postgres
+eai usage diff-rates --config config.yml
+
+# Backfill costs in-process (all orgs; use after migrate or catalog change)
+eai usage recompute --config config.yml --sync
+
+# Async recompute via usage queue (requires --organization-id)
+eai usage recompute --config config.yml --organization-id <org-uuid>
+
+# Optional scopes: --model, --usage-kind, --start-date, --end-date
+
+# Optional: fetch LiteLLM prices into pricing_catalog.json
+eai usage sync-litellm --local
+eai usage sync-litellm --local --write-models
+```
+
+**After migrations or catalog changes:**
+
+```bash
+eai migrate
+eai usage seed-rates --config config.yml
+eai usage recompute --config config.yml --sync
+```
+
+**Flush / Usage UI tuning** — set in `.env` (see `env.example`):
+
+| Variable | Default | Purpose |
+|----------|---------|---------|
+| `USAGE_FLUSH_BUCKET_BATCH_SIZE` | `500` | Buckets per DB transaction |
+| `USAGE_FLUSH_MAX_BATCHES_PER_RUN` | `30` | Batches per flush tick (≤ **15,000** buckets/run) |
+| `USAGE_FLUSH_BEAT_SECONDS` | `120` | Celery Beat flush interval (~2 min lag vs Redis) |
+| `USAGE_FLUSH_LOCK_TTL_SECONDS` | `300` | Per-org flush lock TTL |
+| `USAGE_READ_CACHE_TTL_SECONDS` | `90` | Redis cache TTL for usage summary/breakdown/filters |
+| `CRON_DISPATCH_INTERVAL_SECONDS` | `30` | Evaluator cron dispatcher tick (default worker) |
+
+The Usage UI reads Postgres only (summary/breakdown/filters). Redis counters flush on the Celery Beat schedule (~2 min eventual consistency). If Redis backlog grows, lower `USAGE_FLUSH_BEAT_SECONDS` or raise `USAGE_FLUSH_MAX_BATCHES_PER_RUN`.
+
+See [Usage](/docs/monitoring/usage/) for the end-user guide.
diff --git a/docs-fumadocs/content/feature-contributors.json b/docs-fumadocs/content/feature-contributors.json
index 28ca6227..e7990cc8 100644
--- a/docs-fumadocs/content/feature-contributors.json
+++ b/docs-fumadocs/content/feature-contributors.json
@@ -287,6 +287,23 @@
       ],
       "lastReviewed": "2026-06-15"
     },
+    {
+      "featureId": "monitoring/usage",
+      "docPath": "docs-fumadocs/content/docs/monitoring/usage.mdx",
+      "historyPath": "docs-fumadocs/content/docs/monitoring/usage.mdx",
+      "owners": [
+        "aadhar-EAI",
+        "Tejas Narayan"
+      ],
+      "contributors": [
+        {
+          "name": "aadhar-EAI",
+          "email": "aadhar@efficientai.cloud",
+          "commits": 1
+        }
+      ],
+      "lastReviewed": "2026-08-18"
+    },
     {
       "featureId": "monitoring/cron-jobs",
       "docPath": "docs-fumadocs/content/docs/monitoring/cron-jobs.mdx",
diff --git a/docs-fumadocs/public/search-index.json b/docs-fumadocs/public/search-index.json
index e2d517c7..2d7daadc 100644
--- a/docs-fumadocs/public/search-index.json
+++ b/docs-fumadocs/public/search-index.json
@@ -1,19 +1,28 @@
 {
-  "generatedAt": "2026-06-13T19:35:24.170Z",
-  "count": 30,
+  "generatedAt": "2026-08-18T13:39:50.745Z",
+  "count": 35,
   "records": [
     {
       "id": "advanced/architecture",
-      "url": "/docs/advanced/architecture",
+      "url": "/docs/advanced/architecture/",
       "title": "Architecture",
       "breadcrumbs": [
         "Advanced"
       ],
       "content": "Architecture Simple Overview EfficientAI is built like a modern web application. The Brain (API Server) : Controls everything. The Worker : Does the heavy lifting in the background, like processing audio files so the website stays fast. The Interface (Frontend) : The website you see and click on. The Memory (Database) : Where we store all your agents, test results, and user data. Technical Deep Dive EfficientAI is built as a modular, containerized application designed for scalability and extensibility. (See original Architecture documentation below) System Components The platform consists of four primary components: 1. API Server (FastAPI) : The central control plane. 2. Worker (Celery) : Handles asynchronous background tasks (transcription, evaluation). 3. Frontend (React/Vite) : The user interface. 4. Data Stores : PostgreSQL (State) and Redis (Queue/Cache). Core Services 1. API Server ( ) Built with FastAPI, it provides REST endpoints for: Resource Management : CRUD for Agents, Personas, Scenarios. Orchestration : Real time control of test conversations. Analysis : Serving evaluation results and dashboards. 2. Asynchronous Workers ( ) Powered by Celery and Redis, the workers handle long running operations: Transcription : Processing audio files (using Whisper, Deepgram, etc.). Evaluation : Running metric calculations (WER/CER) on completed conversations. 3. Test Agent Service ( ) This is the heart of the testing engine. It: Manages the state of the conversation. Generates accurate system prompts for the Persona. Handles the latency sensitive loop of: ."
     },
+    {
+      "id": "advanced/call-import-sharding",
+      "url": "/docs/advanced/call-import-sharding/",
+      "title": "Call Import Sharding",
+      "breadcrumbs": [
+        "Advanced"
+      ],
+      "content": "Call Import Sharding For large batches (10k+ rows), call import row data can be spread across multiple PostgreSQL data shards with a catalog database for metadata, routing, and parent counters. Architecture Catalog DB — , , shard slice registry, dispatch metadata Data shards — , (partitioned by consistent hash on ) Scatter/gather reads — API and workers query each shard and merge results Fair dispatch — import/eval workers respect per shard pending scans and Redis progress keys Enable sharding in under . See and for profiles. Live telephony / evaluator results use a separate payload sharding path ( , ) keyed by workspace — see in the repo. Operations Connection pools When , use smaller per process pools ( 3–5, 5–10) so API + workers × shards stay under Postgres . PgBouncer (optional) Point , , and each shard at PgBouncer ( ) in transaction pooling mode. Keep SQLAlchemy enabled. Observability Row Celery tasks log on the import worker hot path. Watch Redis eval/import progress keys ( , ) alongside catalog parent counters. Rebalance Dry run registry updates: Use only after pausing the import. The rebalance tool copies rows to the target shard, updates the catalog registry, then removes copies from the source shard. Troubleshooting stalled evaluations If evaluation runs stall after recordings import (rows show import but no diarization/scoring): 1. Restart API and after deploying fixes. 2. Retry evaluation from the UI (use Overwrite existing transcripts if diarization previously failed). 3. Ensure the imports worker consumes ."
+    },
     {
       "id": "advanced/database",
-      "url": "/docs/advanced/database",
+      "url": "/docs/advanced/database/",
       "title": "Database",
       "breadcrumbs": [
         "Advanced"
@@ -22,7 +31,7 @@
     },
     {
       "id": "advanced/development",
-      "url": "/docs/advanced/development",
+      "url": "/docs/advanced/development/",
       "title": "Development & Troubleshooting",
       "breadcrumbs": [
         "Advanced"
@@ -31,25 +40,25 @@
     },
     {
       "id": "getting-started/authentication",
-      "url": "/docs/getting-started/authentication",
+      "url": "/docs/getting-started/authentication/",
       "title": "Authentication",
       "breadcrumbs": [
         "Getting Started"
       ],
-      "content": "🔐 Authentication EfficientAI ships with a pluggable authentication system that scales from a single operator OSS install to an enterprise deployment behind your existing identity provider. You pick the providers you want in (or via in ) and the API/frontend adapt automatically. Deployment models | Model | Providers | License needed | | | | | | OSS self hosted (default) | , | None | | Enterprise SSO (BYO IdP) | , | | — the header, always available, for programmatic access (CI pipelines, SDKs, scripts). — email + password, verified against the local users table, returns an app signed HS256 Bearer token. Enabled by default. — license gated. Verifies a Bearer JWT issued by your OIDC compliant IdP (Okta, Azure AD / Entra ID, Google Workspace, AWS Cognito, Auth0, Ping, JumpCloud, OneLogin, …) against the issuer's JWKS. :::info Why no bundled IdP? In practice every enterprise already runs one. Shipping our own Keycloak alongside the app just added another thing for you to operate and lock down. talks to whatever you already have. ::: Self hosted (OSS) This is the default after or . No license, no IdP, no external dependencies. The equivalent environment variables (for Docker Compose / ): First time bootstrap 1. Start the stack. 2. Open and click Create account on the login screen. The first user you create becomes the admin of a fresh organization. 3. Mint an API key from Profile → API Keys (or via ) for programmatic access. Password login (email + password) When is enabled, the login screen shows a Sign in and (if ) a Create account tab. Signing up provisions a new user and a new organization, and makes that user the of it. If you leave the organization name blank, the server derives one from the email's local part. Once signed in, the SPA holds a short lived Bearer token and silently re authenticates before it expires. You can change the token lifetime with — the default is 12 hours. Linking a password to an API key only account If you bootstrapped with , the backend provisions a placeholder user behind that key (its email ends in ). You can upgrade this identity to a real email + password login so you can sign in interactively with the same user. Do it from Profile → Sign in Password while signed in via the API key — the page detects the placeholder email and prompts you to pick a real one and a password. After saving, the same user can log in either with the original API key (for machines) or with email + password (for humans). Rules the UI enforces: If the user already has a password, the form asks for the current one before accepting a new one. You can only set the email from that screen while it's still the placeholder address; \"real\" users change their email from the main profile edit flow. Hardening before you expose it to the internet Turn off self service signup once your team is in: Rotate to invalidate existing sessions. Put the app behind a reverse proxy (Nginx, Caddy, Cloudflare) that terminates TLS and enforces HSTS. Restrict to the exact domain(s) serving the SPA. Team management: invitations & organizations EfficientAI is multi tenant from the ground up. Every piece of data is scoped to an organization , a user can be a member of more than one organization, and each membership has a role that controls what they can do. Roles | Role | Can do | | | | | | Read only access to everything in the org. | | | Everything a reader can + create/update/delete most resources. | | | Everything a writer can + manage users, invitations, roles, API keys, and org settings. | The role is stored per membership, so the same user can be an in one org and a in another. Inviting a teammate Admins invite teammates from Settings → Team . An invitation captures an email and a role and stays valid for 7 days. From the same page, admins can also: See the current members of the org and change their role (with a guard so you can't demote the last admin). Remove a member from the organization. Revoke a pending invitation. Delivery. The backend cre"
+      "content": "🔐 Authentication EfficientAI ships with a pluggable authentication system that scales from a single operator OSS install to an enterprise deployment behind your existing identity provider. You pick the providers you want in (or via in ) and the API/frontend adapt automatically. Deployment models | Model | Providers | License needed | | | | | | OSS self hosted (default) | , | None | | Enterprise SSO (BYO IdP) | , | | — the header, always available, for programmatic access (CI pipelines, SDKs, scripts). — email + password, verified against the local users table, returns an app signed HS256 Bearer token. Enabled by default. — license gated. Verifies a Bearer JWT issued by your OIDC compliant IdP (Okta, Azure AD / Entra ID, Google Workspace, AWS Cognito, Auth0, Ping, JumpCloud, OneLogin, …) against the issuer's JWKS. :::info Why no bundled IdP? In practice every enterprise already runs one. Shipping our own Keycloak alongside the app just added another thing for you to operate and lock down. talks to whatever you already have. ::: Self hosted (OSS) This is the default after or . No license, no IdP, no external dependencies. The equivalent environment variables (for Docker Compose / ): First time bootstrap 1. Start the stack. 2. Open and click Create account on the login screen. The first user you create becomes the admin of a fresh organization. 3. Mint an API key from Profile → API Keys (or via ) for programmatic access. Password login (email + password) When is enabled, the login screen shows a Sign in and (if ) a Create account tab. Signing up provisions a new user and a new organization, and makes that user the of it. If you leave the organization name blank, the server derives one from the email's local part. Once signed in, the SPA holds a short lived access token (15 minutes by default) plus a refresh token. The client silently refreshes the access token before it expires. You can change lifetimes with and . Logout revokes the refresh token and blacklists the current access token server side. Linking a password to an API key only account If you bootstrapped with , the backend provisions a placeholder user behind that key (its email ends in ). You can upgrade this identity to a real email + password login so you can sign in interactively with the same user. Do it from Profile → Sign in Password while signed in via the API key — the page detects the placeholder email and prompts you to pick a real one and a password. After saving, the same user can log in either with the original API key (for machines) or with email + password (for humans). Rules the UI enforces: If the user already has a password, the form asks for the current one before accepting a new one. You can only set the email from that screen while it's still the placeholder address; \"real\" users change their email from the main profile edit flow. Hardening before you expose it to the internet Turn off self service signup once your team is in: Rotate to invalidate existing sessions. Put the app behind a reverse proxy (Nginx, Caddy, Cloudflare) that terminates TLS and enforces HSTS. The bundled FastAPI server sends baseline security headers on all responses ( , , , , and by default). If you terminate traffic at an external reverse proxy, keep those headers (or stricter CSP ) enabled there too. After reviewing CSP violation reports, set to enforce the policy. Pin third party observability container images to fixed tags and rebuild external reverse proxy images on patched runtimes. If a scanner reports a Go stdlib CVE in a binary this repo does not build, identify the flagged container or proxy artifact and upgrade it separately. Restrict to the exact domain(s) serving the SPA. Set in production so , , and are not served on the public hostname. Keep so and return 404 to anonymous public clients (including vulnerability scanners hitting your ALB hostname). AWS ALB target health checks connect directly from VPC addresses (no ); include your VPC/LB CIDRs in . Full migrat"
     },
     {
       "id": "getting-started/cloud-storage",
-      "url": "/docs/getting-started/cloud-storage",
+      "url": "/docs/getting-started/cloud-storage/",
       "title": "Cloud Storage",
       "breadcrumbs": [
         "Getting Started"
       ],
-      "content": "Cloud Storage Overview EfficientAI can store audio files and recordings in the cloud using one active blob backend at a time: Amazon S3 (or any S3 compatible service such as MinIO, DigitalOcean Spaces, Cloudflare R2) Google Cloud Storage (GCS) Cloud storage is useful for: Storing large audio files outside your application server Scaling storage independently from compute Integrating with existing AWS or GCP infrastructure Browsing, uploading, and managing audio from the Data Sources page in the UI Select the backend with in ( or ), then enable and configure the matching block below. Storage block | Option | Description | | | | | | Local fallback directory for uploads when cloud storage is disabled | | | Maximum upload size in megabytes | | | Active cloud backend: or | | | File extensions accepted for audio uploads | Amazon S3 and S3 compatible storage Configuration Set and enable the block: | Option | Required | Description | | | | | | | Yes | Set to to enable S3 storage | | | Yes | Name of your S3 bucket | | | Yes | AWS region (e.g., , ) | | | Yes | AWS Access Key ID | | | Yes | AWS Secret Access Key | | | No | Custom endpoint for S3 compatible services | | | No | Folder prefix for uploaded files (default: ) | S3 compatible examples MinIO DigitalOcean Spaces Cloudflare R2 Google Cloud Storage Configuration Set and enable the block: | Option | Required | Description | | | | | | | Yes | Set to to enable GCS storage | | | Yes | Name of your GCS bucket | | | Yes | GCP project ID | | | No | Path to a service account JSON key file | | | No | Folder prefix for uploaded files (default: ) | Authentication EfficientAI resolves GCS credentials in this order: 1. in (relative paths resolve from the working directory) 2. environment variable 3. Application Default Credentials (ADC) on GCE, GKE, or Cloud Run The client is included in the standard EfficientAI install ( ); no extra Python package step is required for GCS. GCP setup 1. Create a GCS bucket in your project (Console or ). 2. Create a service account with object read/write access (e.g., on the bucket, or a tighter custom role). 3. Download a JSON key and set , or mount the key and set . 4. Set , , and fill in and . Object layout under matches S3 (organization scoped paths), so you can migrate between providers without changing application logic. Data Sources UI Once cloud storage is configured and connected, open Configuration → Data Sources to: Browse folders and audio files in your bucket Upload new audio files Preview playback in the browser Delete files Test the connection (labels show Amazon S3 or Google Cloud Storage based on ) Verifying connection Restart the application after changing storage settings: Use Test Connection on the Data Sources page, or upload a test file and confirm it appears under the configured in your bucket. Troubleshooting | Issue | Solution | | | | | (S3) | Check IAM permissions and bucket policy | | (S3) | Verify bucket name and region | | (S3) | Check for S3 compatible services | | (S3) | Verify and | | GCS | Confirm service account has on the bucket | | GCS bucket not found | Verify , , and that the bucket exists | | GCS auth failure | Set or ; on GCP VMs you can use ADC | | Wrong provider in UI | Ensure matches the enabled block ( vs ) |"
+      "content": "Cloud Storage Overview EfficientAI can store audio files and recordings in the cloud using one active blob backend at a time: Amazon S3 (or any S3 compatible service such as MinIO, DigitalOcean Spaces, Cloudflare R2) Google Cloud Storage (GCS) Azure Blob Storage Cloud storage is useful for: Storing large audio files outside your application server Scaling storage independently from compute Integrating with existing AWS, GCP, or Azure infrastructure Browsing, uploading, and managing audio from the Data Sources page in the UI Select the backend with in ( , , or ), then enable and configure the matching block below. Storage block | Option | Description | | | | | | Local fallback directory for uploads when cloud storage is disabled | | | Maximum upload size in megabytes | | | Active cloud backend: , , or | | | File extensions accepted for audio uploads | Amazon S3 and S3 compatible storage Configuration Set and enable the block: | Option | Required | Description | | | | | | | Yes | Set to to enable S3 storage | | | Yes | Name of your S3 bucket | | | Yes | AWS region (e.g., , ) | | | Yes | AWS Access Key ID | | | Yes | AWS Secret Access Key | | | No | Custom endpoint for S3 compatible services | | | No | Folder prefix for uploaded files (default: ) | S3 compatible examples MinIO DigitalOcean Spaces Cloudflare R2 Google Cloud Storage Configuration Set and enable the block: | Option | Required | Description | | | | | | | Yes | Set to to enable GCS storage | | | Yes | Name of your GCS bucket | | | Yes | GCP project ID | | | No | Path to a service account JSON key file | | | No | Override service account email for signed URL generation when ADC does not expose it | | | No | Folder prefix for uploaded files (default: ) | Authentication EfficientAI resolves GCS credentials in this order: 1. in (relative paths resolve from the working directory) 2. environment variable 3. Application Default Credentials (ADC) on GCE, GKE, or Cloud Run Uploads and server side downloads work with ADC alone (including GKE Workload Identity ). Signed URLs for browser playback require either a service account JSON key with a private key, or Workload Identity plus IAM signBlob (see below). The client is included in the standard EfficientAI install ( ); no extra Python package step is required for GCS. GCP setup 1. Create a GCS bucket in your project (Console or ). 2. Create a service account with object read/write access (e.g., on the bucket, or a tighter custom role). 3. Authenticate the app using one of: GKE Workload Identity (recommended): bind your Kubernetes service account to the GCP service account. Enable the and grant the GCP service account on itself so EfficientAI can sign playback URLs without a JSON key. Service account JSON key: download a key and set , or mount the key and set . 4. Set , , and fill in and . Example IAM binding for Workload Identity signed URLs: If ADC does not expose the service account email (some federation setups), set to the workload GCP service account email. Object layout under matches S3 (organization scoped paths), so you can migrate between providers without changing application logic. Azure Blob Storage Configuration Set and enable the block: | Option | Required | Description | | | | | | | Yes | Set to to enable Azure Blob Storage | | | Yes | Name of your Azure storage container | | | Yes | Azure storage account name ( not required if is set) | | | Yes | Storage account access key ( not required if is set) | | | No | Full Azure storage connection string (overrides + ) | | | No | Folder prefix for uploaded files (default: ) | Authentication EfficientAI resolves Azure credentials in this order: 1. in 2. + Managed Identity is not supported in this release; use a connection string or account key. SAS URLs (temporary download links in the UI) require an account key. Connection strings include the key automatically. Azure setup 1. Create a Storage Account in the Azure Portal (or via ). 2. Create a container (e.g., ) under tha"
     },
     {
       "id": "getting-started/installation",
-      "url": "/docs/getting-started/installation",
+      "url": "/docs/getting-started/installation/",
       "title": "Installation",
       "breadcrumbs": [
         "Getting Started"
@@ -58,41 +67,50 @@
     },
     {
       "id": "getting-started/integrations",
-      "url": "/docs/getting-started/integrations",
+      "url": "/docs/getting-started/integrations/",
       "title": "Integrations",
       "breadcrumbs": [
         "Getting Started"
       ],
-      "content": "Integrations Integrations in EfficientAI are not limited to external voice agent platforms. You can connect integrations across three layers of the stack: 1. Voice agent platforms (agent/runtime side) 2. AI providers (LLM/STT/TTS model side) 3. Telephony providers (PSTN/number/routing side) This lets you test and evaluate the complete call path from model behavior to phone network delivery. Integrations UI 1) Voice platform integrations (agent side) Voice platform integrations connect EfficientAI to externally hosted voice agents. <div className=\"not prose mt 4 mb 6 grid grid cols 2 gap 3 sm:grid cols 3 lg:grid cols 5\" , , , , , , , , , ].map((item) = ( <div key= className=\"rounded md border border fd border/70 bg fd card px 2 py 2\" <img src= alt= className=\"h 8 w full object contain\" / <p className=\"mt 2 truncate text center text xs text fd muted foreground\" </p </div ))} </div With these integrations, you can run provider backed calls, ingest transcripts/call metadata, and evaluate outcomes in EfficientAI. 2) AI provider integrations (LLM layer) AI provider integrations connect model vendors used by your Voice Bundles and evaluation workflows. <div className=\"not prose mt 4 mb 6 grid grid cols 2 gap 3 sm:grid cols 3 lg:grid cols 6\" , , , , , , , , , , , ].map((item) = ( <div key= className=\"rounded md border border fd border/70 bg fd card px 2 py 2\" <img src= alt= className=\"h 8 w full object contain\" / <p className=\"mt 2 truncate text center text xs text fd muted foreground\" </p </div ))} </div These providers power model side capabilities for generation and speech pipelines depending on your configuration. 3) Telephony provider integrations Telephony integrations connect your phone network provider to EfficientAI. <div className=\"not prose mt 4 mb 6 grid grid cols 2 gap 3 sm:max w md\" , , ].map((item) = ( <div key= className=\"rounded md border border fd border/70 bg fd card px 2 py 2\" <img src= alt= className=\"h 8 w full object contain\" / <p className=\"mt 2 truncate text center text xs text fd muted foreground\" </p </div ))} </div These integrations handle telephony specific setup like credentials, number sync, and routing related configuration used by call workflows. What integrations enable in practice Once linked, an agent can: execute tests through external voice providers, use configured AI providers for model operations, run telephony connected flows when phone network integration is needed, evaluate all of the above inside the same metrics/reporting loop. Agent fields commonly used for external voice platforms When mapping an agent to an external voice platform, these fields are typically configured on the agent: : integration connection reference. : provider side agent identifier. : required for web call testing workflows. Recommended setup flow 1. Configure your required integrations (voice platform, AI provider, telephony as needed). 2. Link the agent to the relevant voice platform integration and provider side agent ID. 3. Configure Voice Bundles to use your chosen AI providers. 4. Add telephony configuration if your flow requires phone network execution. 5. Run and compare outcomes in Playground/evaluator workflows. Backlinks Agent level capability overview: Internal test stack alternative: Product walkthrough where integrations are managed:"
+      "content": "Integrations Integrations in EfficientAI are not limited to external voice agent platforms. You can connect integrations across three layers of the stack: 1. Voice agent platforms (agent/runtime side) 2. AI providers (LLM/STT/TTS model side) 3. Telephony providers (PSTN/number/routing side) This lets you test and evaluate the complete call path from model behavior to phone network delivery. Integrations UI 1) Voice platform integrations (agent side) Voice platform integrations connect EfficientAI to externally hosted voice agents. <div className=\"not prose mt 4 mb 6 grid grid cols 2 gap 3 sm:grid cols 3 lg:grid cols 5\" , , , , , , , , , ].map((item) = ( <div key= className=\"rounded md border border fd border/70 bg fd card px 2 py 2\" <img src= alt= className=\"h 8 w full object contain\" / <p className=\"mt 2 truncate text center text xs text fd muted foreground\" </p </div ))} </div With these integrations, you can run provider backed calls, ingest transcripts/call metadata, and evaluate outcomes in EfficientAI. 2) AI provider integrations (LLM layer) AI provider integrations connect model vendors used by your Voice Bundles and evaluation workflows. <div className=\"not prose mt 4 mb 6 grid grid cols 2 gap 3 sm:grid cols 3 lg:grid cols 6\" , , , , , , , , , , , ].map((item) = ( <div key= className=\"rounded md border border fd border/70 bg fd card px 2 py 2\" <img src= alt= className=\"h 8 w full object contain\" / <p className=\"mt 2 truncate text center text xs text fd muted foreground\" </p </div ))} </div These providers power model side capabilities for generation and speech pipelines depending on your configuration. 3) Telephony provider integrations Telephony integrations connect your phone network provider to EfficientAI. <div className=\"not prose mt 4 mb 6 grid grid cols 2 gap 3 sm:max w md\" , , ].map((item) = ( <div key= className=\"rounded md border border fd border/70 bg fd card px 2 py 2\" <img src= alt= className=\"h 8 w full object contain\" / <p className=\"mt 2 truncate text center text xs text fd muted foreground\" </p </div ))} </div These integrations handle telephony specific setup like credentials, number sync, and routing related configuration used by call workflows. LLM Gateway (batch/eval routing) Batch and evaluation LLM workloads (evaluators, GEPA, call import LLM steps) can be routed through an optional LLM Gateway instead of calling providers directly. Real time voice agents are not routed through the gateway. Configure a platform default in and optional per organization overrides from Configurations → Integrations → LLM Gateway . Supported gateway types: Bifrost — point at your Bifrost LiteLLM proxy (URL must include the path, e.g. ). LiteLLM Proxy — point at a self hosted LiteLLM Proxy instance (e.g. ). Platform default ( ) When is , AI provider integrations can omit API keys and use a Gateway managed placeholder instead. That setting is platform wide only — it cannot be overridden per organization. Organization settings (Integrations UI) Each organization can choose how it uses the gateway. The modal shows an Effective routing badge after save — that is the merged result actually used for LLM calls ( , , or ). Organization mode | Mode | Behavior | | | | | Inherit platform default | Follows the platform flag. When the platform has the gateway enabled, this org uses the gateway. Choose Disabled to opt out, or override type, URL, or keys below. | | Enabled (use gateway) | Forces gateway routing for this org even if the platform default is disabled. You must have a resolvable base URL (org override or platform default). | | Disabled (direct to providers) | Opts this org out entirely. LLM calls go straight to provider APIs; gateway type, URL, and key overrides are ignored. | Partial overrides (while inheriting) When organization mode is Inherit or Enabled , you can override individual fields without replacing the whole configuration. Anything left blank or set to Inherit platform falls back to the platform default. | "
+    },
+    {
+      "id": "getting-started/llm-gateway",
+      "url": "/docs/getting-started/llm-gateway/",
+      "title": "LLM Gateway",
+      "breadcrumbs": [
+        "Getting Started"
+      ],
+      "content": "LLM Gateway EfficientAI routes batch and evaluation LLM workloads through . You can optionally point those calls at an enterprise or self hosted gateway instead of calling provider APIs directly. Supported gateway types: | Type | Description | | | | | Direct | Default — LiteLLM calls providers using org AI Provider credentials | | Bifrost | proxy with optional virtual key | | Bifrost (native) | Bifrost root OpenAI compatible API ( ) without the shim | | LiteLLM Proxy | Self hosted with optional master key | What is routed through the gateway When enabled, these workloads use the configured gateway: Evaluators and metrics Call import transcription, diarisation, and evaluation LLM steps GEPA prompt optimization Gemini batch STT via LiteLLM Not affected: real time voice agents (they use the EfficientAI SDK directly, not LiteLLM). Platform configuration Operators enable a gateway globally in : | Setting | Description | | | | | | Platform wide toggle for orgs that inherit the default | | | Gateway implementation: or | | | Gateway endpoint (Bifrost shim: include ; native: host root e.g. — EfficientAI appends for LiteLLM; LiteLLM Proxy: e.g. ) | | | Optional Bifrost header | | | Optional LiteLLM Proxy master key | | | When , org AI provider API keys are still sent through the gateway | | | Bifrost API surface: (default) or for custom models that fail through | When routing through a gateway, Gemini models automatically use OpenAI compatible proxy endpoints ( ) instead of native Gemini URLs. This is required for Bifrost and LiteLLM Proxy compatibility. Gateway managed credentials When is , provider secrets live in the gateway. You can add AI provider integrations in EfficientAI without an API key — the integration row marks which provider to use, and the gateway supplies the secret at call time. Integrations created this way show a Gateway managed badge in the UI. Requirements: 1. Platform 2. LLM gateway active for the organization (platform and/or org Integrations settings) 3. Real provider keys configured in the gateway EfficientAI sends a harmless placeholder to satisfy LiteLLM's OpenAI client constructor; the gateway ignores it when provider keys are configured server side. Voice agents and non LiteLLM paths still require direct provider credentials. Organization overrides (Integrations UI) On Configurations → Integrations , open LLM Gateway to configure per organization settings: Inherit platform default — follow Enabled — force routing through the gateway (optionally with org specific type, URL, and keys) Disabled — opt out and call providers directly, even when the platform gateway is enabled Org virtual keys and LiteLLM Proxy master keys are encrypted at rest like AI provider credentials. Per integration routing (AI Providers and Voice Platforms) Each AI Provider and Voice Platform credential can override org level gateway behavior: | Routing mode | Behavior | | | | | Inherit org default | Follow org/platform LLM gateway settings | | Route via gateway | Force batch/eval LiteLLM calls through the gateway, even if the org opted out | | Direct API key | Always call the provider directly, even when the org gateway is enabled | Bifrost custom models (AI Providers) When creating or editing an AI Provider with gateway routing, you can set an optional Gateway Model — a Bifrost custom model ID (e.g. or ). When set, batch/eval workloads that use this credential send that model string to the gateway instead of the workload selected model. You can also override Bifrost API surface per credential: | Interface | | Use case | | | | | | LiteLLM shim (default) | | Standard models through Bifrost's LiteLLM integration | | Native OpenAI compatible | (no ) | Custom models (e.g. Gemma on customer cloud) that only work on Bifrost's native API | Optional Gateway Base URL on the credential overrides the org/platform URL for that integration only. Per credential gateway auth (no org virtual key required) Each AI Provider can override Bifrost authentic"
     },
     {
       "id": "getting-started/voice-bundles",
-      "url": "/docs/getting-started/voice-bundles",
+      "url": "/docs/getting-started/voice-bundles/",
       "title": "Voice Bundles",
       "breadcrumbs": [
         "Getting Started"
       ],
-      "content": "Voice Bundles Voice Bundles define the internal speech pipeline used by the EfficientAI Test Agent path. Voice Bundle UI Why voice bundles matter A voice bundle gives you repeatable, controlled test conditions so you can compare prompt and model changes without provider side drift. Supported bundle types STT + LLM + TTS S2S (speech to speech) For STT + LLM + TTS bundles, you configure provider/model settings per stage and optional parameters like temperature and token limits. When to use voice bundles vs integrations Use voice bundles when you want tightly controlled internal testing. Use integrations when you want to test external provider behavior end to end. Typical workflow 1. Configure a voice bundle in the platform. 2. Attach to an agent. 3. Run tests in Playground or evaluator flows. 4. Compare outcomes over time using consistent bundle settings. Backlinks Agent capability overview: External provider path setup:"
+      "content": "Voice Bundles Voice Bundles define the internal speech pipeline used by the EfficientAI Test Agent path. Voice Bundle UI Why voice bundles matter A voice bundle gives you repeatable, controlled test conditions so you can compare prompt and model changes without provider side drift. Supported bundle types STT + LLM + TTS S2S (speech to speech) For STT + LLM + TTS bundles, you configure provider/model settings per stage and optional parameters like temperature, token limits, and TTS output frequency (8, 16, or 22 kHz where the provider supports it; default 8 kHz). Live call silence timeout Inbound phone calls and outbound simulation calls automatically end if no voice activity is detected from either party for 15 seconds after the call connects. When to use voice bundles vs integrations Use voice bundles when you want tightly controlled internal testing. Use integrations when you want to test external provider behavior end to end. Typical workflow 1. Configure a voice bundle in the platform. 2. Attach to an agent. 3. Run tests in Playground or evaluator flows. 4. Compare outcomes over time using consistent bundle settings. Backlinks Agent capability overview: External provider path setup:"
     },
     {
       "id": "getting-started/workspaces",
-      "url": "/docs/getting-started/workspaces",
+      "url": "/docs/getting-started/workspaces/",
       "title": "Workspaces",
       "breadcrumbs": [
         "Getting Started"
       ],
-      "content": "Workspaces Overview Workspaces provide in organization project isolation . Multiple teams or projects can share one EfficientAI organization while keeping their agents, metrics, call imports, and prompt libraries separate. Every organization starts with a Default workspace. You can create additional workspaces for individual teams, customers, or pilots without provisioning separate orgs. Workspace switcher The workspace switcher lives at the top of the left sidebar, directly under the EfficientAI logo. From the switcher you can: See which workspace is currently active Switch to another workspace in your organization Create a new workspace (display name + slug) When you switch workspaces, all data views refetch automatically so you never see stale rows from the previous workspace. How scoping works The UI stores your active workspace in the browser and sends it on every API request as the header. The backend uses this header to scope listings and creates to the selected workspace. If a request arrives without the header, the backend falls back to the organization's Default workspace. What is workspace scoped Workspaces isolate the resources you interact with day to day, including: | Category | Resources | | | | | Simulation | Agents, Personas, Scenarios, Evaluators | | Evaluation | Evaluator results, legacy evaluations | | Metrics | Custom and built in metrics (when created with workspace scope) | | Call imports | Call import batches and evaluations | | Prompt tooling | Prompt partials, prompt optimization runs | | Voice playground | Comparisons, samples, blind test shares | | Judge alignment | Judge datasets and runs | Switching workspace changes which rows appear in each of these sections. Organization wide resources Some metrics can be created with organization scope instead of workspace scope. Organization scoped metrics: Are stored with Appear in every workspace in the org Are useful for shared rubrics, compliance checks, or standard evaluation criteria When creating a metric or categorization label group, choose Organization visibility if the rubric should be shared; choose Workspace (the default) to keep it project specific. See for details on scope and categorization. Default workspace rules Every organization has exactly one Default workspace (created automatically). The Default workspace cannot be deleted . If your stored workspace ID is invalid (for example after an org switch), the UI resets to Default. Recommended usage One workspace per team or project — e.g., , , . Keep shared rubrics org wide — compliance metrics or standard QA scorecards that every team uses. Switch before creating resources — agents, metrics, and prompt partials are created in the active workspace. API Workspace management endpoints live under : — list workspaces in your organization — create a workspace (name, optional slug) — rename a workspace — delete a non default workspace All other scoped API calls should include with the target workspace UUID."
+      "content": "Workspaces Overview Workspaces provide in organization project isolation . Multiple teams or projects can share one EfficientAI organization while keeping their agents, metrics, call imports, and prompt libraries separate. Every organization starts with a Default workspace. You can create additional workspaces for individual teams, customers, or pilots without provisioning separate orgs. Workspace switcher The workspace switcher lives at the top of the left sidebar, directly under the EfficientAI logo. From the switcher you can: See which workspace is currently active Switch to another workspace in your organization Create a new workspace (display name + slug) When creating a workspace, you set a display name and optional slug. You are added automatically as Workspace Admin ; you can optionally invite org members and assign each a workspace role (Viewer, Editor, or Workspace Admin) before saving. When you switch workspaces, all data views refetch automatically so you never see stale rows from the previous workspace. How scoping works The UI stores your active workspace in the browser and sends it on every API request as the header. The backend uses this header to scope listings and creates to the selected workspace. If a request arrives without the header, the backend falls back to the organization's Default workspace. Access control: organization vs workspace EfficientAI uses two independent permission layers . Both apply on every request: 1. Organization role — set per membership in Settings → Team ( , , or ). 2. Workspace role — set per workspace in Identity & Access Management → Workspace Members ( , , or , plus optional custom roles). A user must satisfy both layers to perform an action. Your organization role controls whether you can write anywhere in the org; your workspace role controls what you can do inside the active workspace . Organization roles | Role | Scope | Typical use | | | | | | Reader | Read only for the entire organization | Auditors, stakeholders who only view dashboards | | Writer | Create, update, and delete most org resources | Engineers and operators doing day to day work | | Admin | Everything a writer can do, plus user/team management, API keys, and org settings | Org owners and IT admins | :::info Org readers are always read only If your organization role is Reader , every mutating API call ( , , ) is blocked — even if you hold Workspace Admin in a workspace. Workspace roles cannot override an org level read only membership. ::: Org admins bypass workspace membership checks and receive all workspace capabilities in every workspace. API keys also bypass workspace RBAC (they remain org scoped). Manage organization roles from Settings → Team (admin only). See . Workspace roles (system) Each workspace has its own membership list. When you are added to a workspace, you receive one of three seeded system roles (or a custom role defined by an org admin): | Workspace role | Can do | Cannot do | | | | | | Viewer | View calls, metrics, evals, simulations, reports, and workspace members | Import, edit, delete, run evaluations, change settings, manage members | | Editor | Everything Viewer can do, plus create/update resources (import calls, manage metrics, run evals, manage simulations, generate reports) | Delete call imports, rename workspace, add/remove members, change workspace roles | | Workspace Admin | Full access in that workspace, including delete, workspace settings, and member management | — | Roles are cumulative : Editor includes all Viewer permissions; Workspace Admin includes all Editor permissions. What each role needs for common actions Use this table when planning access. “Org” = organization role; “Workspace” = role in the active workspace (from the switcher). | Action | Minimum org role | Minimum workspace role | | | | | | View call imports, agents, metrics | Reader | Viewer | | Upload / import calls, edit rows | Writer | Editor | | Delete call imports or batches | Writer | Workspace Admin "
     },
     {
       "id": "intro",
-      "url": "/docs/intro",
+      "url": "/docs/intro/",
       "title": "Introduction",
       "breadcrumbs": [],
-      "content": "Introduction EfficientAI is a voice AI evaluation platform for testing, measuring, and improving conversational agents before they reach production. It helps teams run realistic simulations, analyze outcomes with explicit metrics, and iterate quickly on agent behavior, prompts, and voice quality. What the platform does EfficientAI gives you an end to end loop for voice AI quality: 1. Configure the Agent you want to test. 2. Configure Personas with mapped voices for caller simulation. 3. Define Scenarios that represent conversation goals. 4. Run tests in the Agent Playground or evaluator workflows. 5. Score calls using enabled Metrics . 6. Improve prompts and re test to track quality over time. Core capabilities Workspaces : isolate agents, metrics, call imports, and prompt libraries per team or project within one organization. Agent testing across call setups : supports inbound/outbound context and phone/web call mediums. Prompt management : maintain an internal test agent prompt and sync provider prompts from external voice platforms. Prompt partials : reusable, versioned prompt templates with AI generate/improve, shared across metrics, agents, and call imports. Voice bundle composition : configure STT, LLM, and TTS stacks (or S2S where configured) for test agent behavior. Persona voice mapping : personas are tied to concrete provider and voice identities. Scenario generation and curation : generate from agent prompts, derive from call transcripts/call data, or create manually. Metric driven evaluation : built in and custom metrics with categorization labels, surface targeting, and org wide or workspace scope. Cloud storage : store audio in Amazon S3, S3 compatible services, or Google Cloud Storage via the Data Sources UI. Prompt optimization workflows : run optimization loops, compare candidates, accept winners, and push selected prompts to providers. Platform model At a high level: Agents are the systems under test. Personas represent simulated callers with specific voice identities. Scenarios define goals and test context. Metrics evaluate objective completion and voice quality. Results provide run level evidence for iteration decisions. This structure keeps testing reproducible while still reflecting real world voice interactions. Key guides — project isolation within your organization — S3 and GCS configuration — custom rubrics, surfaces, and evaluation scope — versioned prompt templates"
+      "content": "Introduction EfficientAI is a voice AI evaluation platform for testing, measuring, and improving conversational agents before they reach production. It helps teams run realistic simulations, analyze outcomes with explicit metrics, and iterate quickly on agent behavior, prompts, and voice quality. What the platform does EfficientAI gives you an end to end loop for voice AI quality: 1. Configure the Agent you want to test. 2. Configure Personas with mapped voices for caller simulation. 3. Define Scenarios that represent conversation goals. 4. Run tests in the Agent Playground or evaluator workflows. 5. Score calls using enabled Metrics . 6. Improve prompts and re test to track quality over time. Core capabilities Workspaces : isolate agents, metrics, call imports, and prompt libraries per team or project within one organization. Agent testing across call setups : supports inbound/outbound context and phone/web call mediums. Prompt management : maintain an internal test agent prompt and sync provider prompts from external voice platforms. Prompt partials : reusable, versioned prompt templates with AI generate/improve, shared across metrics, agents, and call imports. Voice bundle composition : configure STT, LLM, and TTS stacks (or S2S where configured) for test agent behavior. Persona voice mapping : personas are tied to concrete provider and voice identities. Scenario generation and curation : generate from agent prompts, derive from call transcripts/call data, or create manually. Metric driven evaluation : built in and custom metrics with categorization labels, surface targeting, and org wide or workspace scope. Cloud storage : store audio in Amazon S3, S3 compatible services, or Google Cloud Storage via the Data Sources UI. Prompt optimization workflows : run optimization loops, compare candidates, accept winners, and push selected prompts to providers. Usage tracking : org wide LLM, STT, and TTS consumption with cost estimates and drill down by workspace and product area. Platform model At a high level: Agents are the systems under test. Personas represent simulated callers with specific voice identities. Scenarios define goals and test context. Metrics evaluate objective completion and voice quality. Results provide run level evidence for iteration decisions. This structure keeps testing reproducible while still reflecting real world voice interactions. Key guides — LLM/STT/TTS consumption, cost estimates, and drill down by workspace and product area — project isolation within your organization, plus workspace roles (Viewer / Editor / Workspace Admin) and how they interact with org roles — S3 and GCS configuration — custom rubrics, surfaces, and evaluation scope — versioned prompt templates"
     },
     {
       "id": "monitoring/alerting",
-      "url": "/docs/monitoring/alerting",
+      "url": "/docs/monitoring/alerting/",
       "title": "Alerting",
       "breadcrumbs": [
         "Monitoring"
@@ -101,7 +119,7 @@
     },
     {
       "id": "monitoring/calls",
-      "url": "/docs/monitoring/calls",
+      "url": "/docs/monitoring/calls/",
       "title": "Calls",
       "breadcrumbs": [
         "Monitoring"
@@ -110,16 +128,25 @@
     },
     {
       "id": "monitoring/cron-jobs",
-      "url": "/docs/monitoring/cron-jobs",
+      "url": "/docs/monitoring/cron-jobs/",
       "title": "Scheduled Tests (Cron Jobs)",
       "breadcrumbs": [
         "Monitoring"
       ],
       "content": "Scheduled Tests (Cron Jobs) What are Cron Jobs? Cron Jobs let you schedule automated test calls to run on a recurring basis. Instead of manually triggering evaluations, you can set up a schedule like \"Run this test every day at 9 AM\" and EfficientAI will automatically execute it. Use Cases Daily Health Checks : Run a test every morning to ensure your AI is working Regression Testing : Automatically test after deployments Performance Monitoring : Continuous testing throughout the day Off Hours Testing : Run tests when traffic is low Creating a Cron Job A cron job requires: 1. Name : A descriptive name for the schedule 2. Cron Expression : When to run (standard cron format) 3. Timezone : Which timezone to use 4. Evaluators : Which tests to run 5. Max Runs (optional): Limit total executions Cron Expression Format Standard 5 field cron format: Examples | Expression | Description | | | | | | Every day at 9:00 AM | | | Every 2 hours | | | 8:30 AM on weekdays | | | First day of each month at midnight | | | Every 15 minutes | Timezone Support All standard timezones are supported (pytz format): The cron expression is evaluated in your selected timezone. Status | Status | Description | | | | | Active | Job is scheduled and will run | | Paused | Job is temporarily stopped | | Completed | Job hit max runs and stopped | Example Setup \"Daily Morning Test\" This runs the selected evaluators every day at 9 AM Eastern time. Managing Cron Jobs From the Cron Jobs dashboard you can: Create new scheduled tests Pause/Resume existing jobs Edit schedule or evaluators Delete jobs you no longer need View next scheduled run time"
     },
+    {
+      "id": "monitoring/usage",
+      "url": "/docs/monitoring/usage/",
+      "title": "Usage",
+      "breadcrumbs": [
+        "Monitoring"
+      ],
+      "content": "Usage Usage is org scoped analytics for LLM, STT, and TTS consumption with estimated costs. It helps you see how much each workspace, product area, call import, and model is consuming — and what that usage likely costs. Usage is not a quota or billing portal. Optional Flexprice event metering is a separate system and is not shown in the Usage UI. Models and providers tracked here come from your enabled . Usage is attributed per when the underlying workflow is workspace scoped. Opening the Usage page In the sidebar, go to Usage → Overview ( ). The page has two tabs when your org is licensed for enterprise features and you are an org admin: | Tab | URL | Who can access | | | | | | Overview | | All org members | | Pricing overrides | | Org admins with enterprise license | Summary metrics The top of the Overview tab shows rollup cards for the selected date range and filters: | Metric | Description | | | | | Input tokens | Prompt or input tokens sent to LLM providers | | Output tokens | Completion or output tokens returned by LLMs | | Total tokens | Sum of input and output tokens | | LLM calls | Number of LLM API calls | | STT audio | Speech to text audio duration (shown when non zero) | | TTS characters | Text to speech characters synthesized (shown when non zero) | | Cache read | Tokens read from provider prompt cache (shown when non zero) | | Cache write | Tokens written to provider prompt cache (shown when non zero) | | Reasoning | Reasoning tokens billed separately by some providers (shown when non zero) | | Estimated cost | Total estimated cost for the filtered range | Click Cost breakdown to open a modal with line items: Input, output, cache read, cache write, reasoning Audio (STT), TTS Total estimated cost If any usage in the range has no matching catalog rate, the breakdown shows an unpriced usage warning. Those rows still appear in token/volume metrics but do not contribute to cost totals. Currency display Toggle between USD and INR in the filter bar. INR amounts use a live USD→INR rate from Frankfurter when available, with a fallback estimate when the FX service is unreachable. Filters Filters narrow the summary cards and drill down table. All filter state is stored in the URL, so you can bookmark or share a specific view. | Filter | Description | | | | | Date range | Start and end dates, interpreted in your browser's IANA timezone | | Workspace | Limit to one workspace | | Call import | Limit to one call import batch | | Dataset | Filter by dataset name on call import rows | | Tag | Filter by call import tag | | Evaluation run | Filter by evaluation resource | | Usage kind | , , or | | Model | Provider model identifier | | Source / product section | Product area (playground, evaluators, call imports, etc.) | Drill down navigation Click rows in the breakdown table to drill deeper. Breadcrumbs at the top show your current path; click a breadcrumb to go back up. At the organization level, the table groups by workspace . Inside a workspace you see a composite view: Call import batches — CSV uploads or manual audio recordings Product areas — usage from other parts of the platform (not tied to a single call import) From a call import batch you can drill into evaluation runs, then model, then usage kind. From a product area you drill into model, then usage kind. Each drill level returns at most 100 rows . If more exist, results are truncated at that level. Product sections | Section | What it tracks | | | | | Call imports | Call import batch processing | | Call import evaluations | Evaluations run on imported calls | | Playground | Text playground and experiments | | Voice playground | Voice agent playground — LLM, STT, and TTS | | Chat | Chat conversations | | Telephony | Telephony and live calls | | Evaluators | Evaluator definitions and runs | | Metrics | Metrics and scoring | | Judge alignment | Judge alignment workflows | | Prompt optimization | Prompt optimization jobs | | Personas | Persona generation | | Agents | Agent"
+    },
     {
       "id": "more/pitch-ideas",
-      "url": "/docs/more/pitch-ideas",
+      "url": "/docs/more/pitch-ideas/",
       "title": "Pitch Ideas",
       "breadcrumbs": [
         "More"
@@ -128,7 +155,7 @@
     },
     {
       "id": "more/polls",
-      "url": "/docs/more/polls",
+      "url": "/docs/more/polls/",
       "title": "Polls",
       "breadcrumbs": [
         "More"
@@ -137,7 +164,7 @@
     },
     {
       "id": "more/qna",
-      "url": "/docs/more/qna",
+      "url": "/docs/more/qna/",
       "title": "QnA",
       "breadcrumbs": [
         "More"
@@ -146,25 +173,34 @@
     },
     {
       "id": "more/roadmap",
-      "url": "/docs/more/roadmap",
+      "url": "/docs/more/roadmap/",
       "title": "Roadmap",
       "breadcrumbs": [
         "More"
       ],
       "content": "Roadmap Track what EfficientAI is actively planning and shipping. Open the roadmap discussions:"
     },
+    {
+      "id": "operations/call-import-sharding",
+      "url": "/docs/operations/call-import-sharding/",
+      "title": "Call-import sharding operations",
+      "breadcrumbs": [
+        "Operations"
+      ],
+      "content": "Call import sharding operations Configuration See under . Default is off ( ). Naming: use generic Postgres database names ( , , …) and config shard ids ( , …). Routing is call import specific today; the same data plane slots can be reused for other row heavy features later. | Profile | | | | | | | | | | Legacy | false | — | — | | Single node enterprise | true | 1 entry | optional | | Standard | true | 3–5 | dedicated | | High volume | true | 6+ | dedicated | Connection pools When , use smaller per process pools ( 3–5, 5–10) so API + workers × shards stay under Postgres . PgBouncer (optional) Point , , and each shard at PgBouncer ( ) in transaction pooling mode. Keep SQLAlchemy enabled. Observability Row Celery tasks log on the import worker hot path. Fair dispatch uses per shard pending scans when sharding is enabled—watch Redis eval/import progress keys ( , ) alongside catalog parent counters. Rebalance Dry run registry updates: Use only after pausing the import and copying row data between shards."
+    },
     {
       "id": "products/agents",
-      "url": "/docs/products/agents",
+      "url": "/docs/products/agents/",
       "title": "Agents",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Agents In EfficientAI, an Agent is a test agent — the voice AI configuration you set up to evaluate behavior, prompts, and call quality before or alongside production rollout. A test agent is not a live production deployment on its own. It is the evaluation target you define in EfficientAI: call context, prompts, and how tests run (via an internal voice bundle or an external voice platform). What the Agents section is for The Test Agents page is your control plane for: defining which voice system you want to test, choosing call context (language, medium, inbound/outbound), managing test agent and provider prompt surfaces, linking the test agent to internal or external execution paths. Creating a test agent Use Create Test Agent to add a new evaluation configuration. Each test agent gets a name, call settings, and a prompt that describes how the agent should behave during simulated or provider backed test calls. Execution paths A test agent can run on one or both paths: Test Agent path (internal) via . Voice AI Agent path (external) via integration + provider agent mapping. If both are configured, you can compare outcomes across both paths from Playground and evaluator workflows. Core configuration | Property | Type | Description | | | | | | | String | Human readable test agent name. | | | Enum | Primary language context for evaluation. | | | Enum | or behavior context. | | | Enum | or . | | | String | Required when . | | | UUID | Internal test stack for voice bundle testing. | | | UUID | External provider integration reference. | | | String | External provider agent identifier. | Inbound vs outbound calls Use to model the call direction the system under test is designed for: Inbound : user initiates call to the agent. Outbound : agent initiates call to the user. This setting should match your real deployment pattern so evaluation conditions are realistic. Prompt management Each test agent can carry two prompt surfaces: Test Agent Prompt ( ) The EfficientAI test agent prompt — used for internal test agent behavior, scenario generation, and evaluation context. Provider Prompt ( ) Prompt fetched from the linked external voice provider, when you are evaluating a provider hosted agent. Provider prompt sync When a test agent is linked to an external provider, EfficientAI can fetch and store the current provider prompt. Sync can happen: automatically on relevant create/update operations, and manually through \"Sync Now\" in the agent view. This keeps local review and optimization aligned with what is currently running on the provider. Related setup guides For setup details, use these getting started guides: Voice bundle setup: Provider integration setup:"
+      "content": "Agents In EfficientAI, an Agent is a test agent — the voice AI configuration you set up to evaluate behavior, prompts, and call quality before or alongside production rollout. A test agent is not a live production deployment on its own. It is the evaluation target you define in EfficientAI: call context, prompts, and how tests run (via an internal voice bundle or an external voice platform). What the Agents section is for The Test Agents page is your control plane for: defining which voice system you want to test, choosing call context (language, medium, inbound/outbound), managing test agent and provider prompt surfaces, linking the test agent to internal or external execution paths. Use the agent list on the left (public Agent ID + name) to switch between test agents without leaving the page. The main panel shows Overview , Test Agent , and Voice AI Agent tabs for the selected agent. Deep links use with optional or . Creating a test agent Use Create Test Agent to add a new evaluation configuration. Choose one of two paths: Telephony — phone based agent with telephony number, inbound/outbound direction, and silence hangup settings. Paste a production prompt, generate the complementary test agent prompt, then select a voice bundle. Existing Platform Integration — connect VAPI, Retell, ElevenLabs, or Smallest with integration credentials and an external Agent ID, select a voice bundle, then import the production prompt and generate the test agent prompt. The production prompt is stored in and shown on the Voice AI Agent tab. The generated test agent prompt is stored in and shown on the Test Agent tab. Generate test prompt from production prompt On the prompts step, paste (Telephony) or import (Platform) your production system prompt, then click Generate test prompt . EfficientAI produces a complementary caller prompt stored in . Review and edit before creating the agent. Use Use Saved , Save Prompt , manual authoring, and AI Generate when editing an existing agent in the detail view. Execution paths A test agent can run on one or both paths: Test Agent path (internal) via . Voice AI Agent path (external) via integration + provider agent mapping. If both are configured, you can compare outcomes across both paths from Playground and evaluator workflows. Core configuration | Property | Type | Description | | | | | | | String | Human readable test agent name. | | | Enum | Primary language context for evaluation. | | | Enum | or behavior context. | | | Enum | or . | | | String | Required when . | | | UUID | Required on create. Internal test stack for voice bundle testing. | | | UUID | External provider integration reference. | | | String | External provider agent identifier. | | | Integer | End live voice sessions after this many seconds without speech from either party (default ; disables). | Inbound vs outbound calls Use to model the call direction the system under test is designed for: Inbound : user initiates call to the agent. Outbound : agent initiates call to the user. This setting should match your real deployment pattern so evaluation conditions are realistic. Prompt management Each test agent can carry two prompt surfaces: Test Agent Prompt ( ) The EfficientAI test agent prompt — used for internal test agent behavior, scenario generation, and evaluation context. Provider Prompt ( ) Production prompt — pasted during Telephony create, imported from a linked external provider, or synced via Sync Now . Provider prompt sync When a test agent is linked to an external provider, EfficientAI can fetch and store the current provider prompt. Sync can happen: automatically on relevant create/update operations, and manually through \"Sync Now\" in the agent view. This keeps local review and optimization aligned with what is currently running on the provider. Test agent prompt composer When editing the Test Agent Prompt , use inline triggers while writing markdown: Type to insert variables (built in placeholders such as , , plus custom va"
     },
     {
       "id": "products/alerting",
-      "url": "/docs/products/alerting",
+      "url": "/docs/products/alerting/",
       "title": "Alerting",
       "breadcrumbs": [
         "Products"
@@ -173,43 +209,52 @@
     },
     {
       "id": "products/call-imports",
-      "url": "/docs/products/call-imports",
+      "url": "/docs/products/call-imports/",
       "title": "Call Imports",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Call Imports Call Imports lets you bulk import production call recordings via CSV and run batch evaluations on them. Enterprise feature — requires in your . Full documentation is available in the (password required). At a glance Upload CSV datasets and optional audio files Map columns with reusable schemas Run metrics and insights across imported production calls Contact the EfficientAI team for a license and enterprise documentation access."
+      "content": "Call Imports Call Imports lets you bulk import production call recordings via CSV and run batch evaluations on them. Enterprise feature — requires in your . At a glance Upload CSV datasets and optional audio files Map columns with reusable schemas Run metrics and insights across imported production calls Contact the EfficientAI team for a license. Sharded evaluation pipeline (ops) If evaluation runs stall after recordings import (rows show import but no diarization/scoring): 1. Restart API and after deploying fixes. 2. Retry evaluation from the UI (use Overwrite existing transcripts if diarization previously failed). 3. Or clear stale dispatch locks on shard DBs: Abort and force fail run on the queue (before on ) so they are not blocked behind large scoring backlogs. When running locally via or , the imports worker must consume . If is missing, Run Evaluation will enqueue materialize tasks that never run and recording imports will not start. Database sharding (enterprise scale) For large batches (10k+ rows), call import row data can be spread across multiple PostgreSQL data shards with a catalog database for metadata and routing. See for the full system design, configuration, and scaling guide."
     },
     {
       "id": "products/evaluators",
-      "url": "/docs/products/evaluators",
+      "url": "/docs/products/evaluators/",
       "title": "Evaluators",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Evaluators What is an Evaluator? An Evaluator is a complete \"Test Configuration\". It brings everything together. Think of it as scheduling a specific exam. To run a test, you need three things: 1. Student : Which AI are we testing? (The Agent ) 2. Actor : Who is calling? (The Persona ) 3. Test Question : What is the call about? (The Scenario ) When you create an Evaluator in EfficientAI, you are simply linking these three things together. Once created, you can run this Evaluator anytime to start a test call. Technical Details Evaluators are the configuration entities that bind an Agent to a specific Persona and Scenario for testing. They serve as the template for running reproducible tests. Definition An Evaluator consists of: | Field | Type | Description | | | | | | | String | Unique 6 digit identifier. | | | UUID | The Agent to be tested. | | | UUID | The Persona to use for the test. | | | UUID | The Scenario to execute. | Execution Process When an Evaluator is run: 1. Conversation : The runs the conversation between the Agent and the Persona. 2. Recording : The conversation is recorded and transcribed. 3. Result Generation : An record is created. 4. Metric Calculation : Post conversation, systems calculate the metrics (WER, Latency, etc.)."
+      "content": "Evaluators What is an Evaluator Suite? An Evaluator Suite groups one Agent , one Persona , and multiple Scenarios into test combinations. Each scenario becomes one combination (1:1:N). You can: Choose metrics to score at the suite level (optional — defaults to all enabled agent metrics) Outbound / web : run every combination X times (total runs = N × X) Inbound : one active suite per agent handles incoming calls; additional suites for the same agent stay inactive until you activate them. Each active suite rotates scenarios for that agent + persona. Creating a Suite (4 step wizard) 1. Agent & Persona — pick one agent and one TTS compatible persona 2. Scenarios — multi select scenarios (each becomes a combination) 3. Metrics — optional metric picker 4. Review — name, tags, default runs per combination Running | Agent type | Behavior | | | | | Web call | Queue Celery bridge runs for all combinations × runs | | Phone outbound | Batch outbound calls (requires dial target) | | Phone inbound | Round robin on incoming calls; Choose next to advance manually (no outbound dial) | API | Method | Path | Purpose | | | | | | POST | | Create suite + combinations | | GET | | List suites | | POST | | Batch run (outbound/web) | | POST | | Set active inbound suite for the agent | | POST | | Inbound: advance round robin without a call | Legacy single evaluator endpoints remain for backward compatibility. Evaluation results (UI) Open Evaluation Results in the sidebar to browse runs in hierarchy: Agent workspace — select an agent, then use the left sidebar (suites → scenarios). The main panel shows suite summary and quality metrics, or individual runs when a scenario is selected. URLs use and query params for sharing. Legacy or manual runs without a suite appear under Unassigned runs . Results API | Method | Path | Purpose | | | | | | GET | | Paginated list ( , ); filters: , , , , , , | | GET | | Workspace rollups; optional or for drill down | | GET | | Metric distributions for or + | Phase 2 (planned): LLM failure clustering and RCA for suite/scenario scopes, similar to Call Import Visualizations → Clusters ."
+    },
+    {
+      "id": "products/metrics-studio",
+      "url": "/docs/products/metrics-studio/",
+      "title": "Metrics Studio",
+      "breadcrumbs": [
+        "Products"
+      ],
+      "content": "Metrics Studio Metrics Studio lets you experiment with metrics against ad hoc call sources. Metrics Studio is a sub tab under Metrics → Studio . Use it to: 1. Select enabled metrics for the active workspace, or draft metrics you are iterating on 2. Pick call sources from call imports, playground/observability recordings, or simulated evaluator results 3. Run ad hoc evaluations and inspect per metric scores 4. Promote draft metrics to production when satisfied The Active tab lists only metrics that are enabled in your current workspace. Disabled metrics stay on the Manage tab until you turn them on. Draft metrics stay hidden from Call Import evaluations and the Manage tab until promoted."
     },
     {
       "id": "products/metrics",
-      "url": "/docs/products/metrics",
+      "url": "/docs/products/metrics/",
       "title": "Metrics",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Metrics Metrics are the scoring rules used to evaluate calls, playground runs, and blind tests. Only enabled metrics run during processing — there is no hidden global default set beyond what you turn on in the Metrics page. Metrics are scoped to your active unless you create them with organization wide visibility. Metric groups EfficientAI ships built in metrics across three evaluation methods: LLM evaluated conversation metrics Examples: Follow Instructions, Professionalism, Problem Resolution. Evaluates transcript and context against metric definitions Supports custom prompts and import/save Acoustic metrics (signal based) Examples: Pitch Variance, Jitter, Shimmer, HNR. Computed from raw audio signal characteristics (Parselmouth) Require audio; skipped when audio is unavailable AI voice quality metrics (model based) Examples: MOS Score, Emotion Category, Emotion Confidence, Valence, Arousal, Speaker Consistency, Prosody Score. Uses audio based ML models for quality, emotion, and consistency Require audio; skipped when audio is unavailable Custom metrics Beyond built in defaults, you can create custom metrics with these types: | Type | Output | Use case | | | | | | | true / false | Pass fail checks | | | One of defined values | Categorical judgments | | | Numeric score in a range | Quantitative ratings | | | Free text response | Open ended notes | | | Star or scale rating | Subjective scores | Custom metrics use your LLM evaluation prompt (editable in the Metrics UI) and respect the same enable/disable and surface rules as built in metrics. Categorization labels For multi class evaluations, create a categorization metric: A parent metric holds the overall evaluation name and LLM prompt Child labels define each category (name, definition, optional example) Output is single choice — one label per run, which maps cleanly to a single CSV column on export Enable Capture LLM Rationale on the parent or children to store the model's reasoning alongside the chosen label. Evaluation surfaces Metrics can run on one or more surfaces : | Surface | Where it runs | | | | | Agent | Evaluator and agent test workflows | | Voice Playground | TTS comparison and playground analytics | | Blind Test | Blind listening test responses | Use the surface filter on the Metrics page to focus on a workflow. Toggle enabled surfaces per metric to control where each metric is evaluated. Visibility scope When creating a metric, choose who can see it: | Scope | Behavior | | | | | Workspace (default) | Metric belongs to the active workspace only | | Organization | Metric is shared across all workspaces in the org | Organization scope is ideal for standard rubrics. Workspace scope keeps project specific metrics isolated. Scope is set at create time for metrics and categorization groups. Advanced options Capture LLM rationale When enabled, the evaluator stores the LLM's explanation alongside the score. Useful for auditing subjective metrics and debugging prompt behavior. Transcript compare judge Enable Compare transcripts on a metric to run a transcript pair judge at call import evaluation time. The worker feeds both the production transcript and the diarized transcript to the LLM as a labeled pair. The run's transcript source toggle is ignored for this metric. Transcript compare judges are mutually exclusive with categorization parent/child structure. Prompt partials integration Metric evaluation prompts can be managed with : Import a saved partial into a metric's prompt field Save the current prompt as a new partial or as a new version of an existing partial This keeps rubric prompts versioned and reusable across metrics and workspaces. How metrics run during processing 1. Call audio and transcript are collected. 2. Only enabled metrics for the relevant surface are considered. 3. Metrics are split by evaluation method (LLM, acoustic, AI voice). 4. Audio required metrics run only when audio is available. 5. LLM metrics run on transcript and context (or transcript"
+      "content": "Metrics Metrics are the scoring rules used to evaluate calls, playground runs, and blind tests. Only enabled metrics run during processing — there is no hidden global default set beyond what you turn on in the Metrics page. Metrics are scoped to your active unless you create them with organization wide visibility. Metric groups EfficientAI ships built in metrics across three evaluation methods: LLM evaluated conversation metrics Examples: Follow Instructions, Professionalism. Evaluates transcript and context against metric definitions Supports custom prompts and import/save Acoustic metrics (signal based) Examples: Pitch Variance, Jitter, Shimmer, HNR. Computed from raw audio signal characteristics (Parselmouth) Require audio; skipped when audio is unavailable AI voice quality metrics (model based) Examples: MOS Score, Emotion Category, Emotion Confidence, Valence, Arousal, Speaker Consistency, Prosody Score. Uses audio based ML models for quality, emotion, and consistency Require audio; skipped when audio is unavailable Custom metrics Beyond built in defaults, you can create custom metrics with these types: | Type | Output | Use case | | | | | | | true / false | Pass fail checks | | | One of defined values | Categorical judgments | | | Numeric score in a range | Quantitative ratings | | | Free text response | Open ended notes | | | Star or scale rating | Subjective scores | Custom metrics use your LLM evaluation prompt (editable in the Metrics UI) and respect the same enable/disable and surface rules as built in metrics. Categorization labels For multi class evaluations, create a categorization metric: A parent metric holds the overall evaluation name and LLM prompt Child labels define each category (name, definition, optional example) Output is single choice — one label per run, which maps cleanly to a single CSV column on export Enable Capture LLM Rationale on the parent or children to store the model's reasoning alongside the chosen label. Evaluation surfaces Metrics can run on one or more surfaces : | Surface | Where it runs | | | | | Agent | Evaluator and agent test workflows | | Voice Playground | TTS comparison and playground analytics | | Blind Test | Blind listening test responses | Use the surface filter on the Metrics page to focus on a workflow. Toggle enabled surfaces per metric to control where each metric is evaluated. Visibility scope When creating a metric, choose who can see it: | Scope | Behavior | | | | | Workspace (default) | Metric belongs to the active workspace only | | Organization | Metric is shared across all workspaces in the org | Organization scope is ideal for standard rubrics. Workspace scope keeps project specific metrics isolated. Scope is set at create time for metrics and categorization groups. Advanced options Capture LLM rationale When enabled, the evaluator stores the LLM's explanation alongside the score. Useful for auditing subjective metrics and debugging prompt behavior. Transcript compare judge Enable Compare transcripts on a metric to run a transcript pair judge at call import evaluation time. The worker feeds both the production transcript and the diarized transcript to the LLM as a labeled pair. The run's transcript source toggle is ignored for this metric. Transcript compare judges are mutually exclusive with categorization parent/child structure. Prompt partials integration Metric evaluation prompts can be managed with : Import a saved partial into a metric's prompt field Save the current prompt as a new partial or as a new version of an existing partial This keeps rubric prompts versioned and reusable across metrics and workspaces. How metrics run during processing 1. Call audio and transcript are collected. 2. Only enabled metrics for the relevant surface are considered. 3. Metrics are split by evaluation method (LLM, acoustic, AI voice). 4. Audio required metrics run only when audio is available. 5. LLM metrics run on transcript and context (or transcript pairs for compare j"
     },
     {
       "id": "products/personas",
-      "url": "/docs/products/personas",
+      "url": "/docs/products/personas/",
       "title": "Personas",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Personas A Persona is the caller profile used to test your agent, including a concrete voice mapped from your integrated providers. Creating a persona Persona = profile + provider voice mapping Each persona is explicitly mapped to a concrete voice identity pulled from your configured voice providers: This keeps test behavior realistic and reproducible across repeated runs. Mapping flow The current flow is: 1. Select or create a persona profile. 2. Pick a specific voice from an integrated provider (or custom catalog). 3. Map that persona to an agent in your test setup. This ensures each persona agent pairing uses a known, repeatable voice path during simulation. Voice sources Personas can use: built in voices from integrated providers, or custom voices registered by your organization. Custom voices appear in the same selection flow as built in voices during persona creation and edits. Why this matters Persona voice mapping enables: consistent replay of the same caller profile and agent mapping, cleaner comparison across model and prompt changes, easier debugging when quality shifts between runs. Persona fields | Field | Description | | | | | | Persona label used in tests. | | | Persona gender metadata. | | | Voice provider used for synthesis. | | | Provider specific voice identifier. | | | Human readable voice name. | | | Whether the selected voice is from custom catalog. |"
+      "content": "Personas A Persona is the caller profile used to test your agent, including a concrete voice mapped from your integrated providers. Creating a persona Persona = profile + provider voice mapping Each persona is explicitly mapped to a concrete voice identity pulled from your configured voice providers: This keeps test behavior realistic and reproducible across repeated runs. Mapping flow The current flow is: 1. Select or create a persona profile. 2. Pick a specific voice from an integrated provider (or custom catalog). 3. Map that persona to an agent in your test setup. This ensures each persona agent pairing uses a known, repeatable voice path during simulation. Voice sources Personas can use: built in voices from integrated providers, or custom voices registered by your organization. Custom voices appear in the same selection flow as built in voices during persona creation and edits. Why this matters Persona voice mapping enables: consistent replay of the same caller profile and agent mapping, cleaner comparison across model and prompt changes, easier debugging when quality shifts between runs. Persona fields | Field | Description | | | | | | Persona label used in tests. | | | Persona gender metadata. | | | Voice provider used for synthesis. | | | Provider specific voice identifier. | | | Human readable voice name. | | | Whether the selected voice is from custom catalog. | | | Persona prompt appended to the test caller system prompt during synthetic calls. | | | Provider specific TTS tuning (speed, stability, pitch, etc.). | | | Caller LLM creativity during simulations. | | | Max tokens per caller reply (verbosity cap). | | | Patience — delay before the caller responds. | | | Conversation length before the caller wraps up. | | | Whether the caller may speak while the agent is talking. | Persona prompt Use Persona prompt ( ) to define caller traits, tone, and speaking style. During evaluator runs this text is merged into the test caller LLM prompt under the section. Provider specific TTS settings control how the selected voice sounds. Caller behavior settings control how the simulated caller acts (creativity, verbosity, patience, interruptions)."
     },
     {
       "id": "products/playground",
-      "url": "/docs/products/playground",
+      "url": "/docs/products/playground/",
       "title": "Playground",
       "breadcrumbs": [
         "Products"
@@ -218,16 +263,16 @@
     },
     {
       "id": "products/prompt-optimization",
-      "url": "/docs/products/prompt-optimization",
+      "url": "/docs/products/prompt-optimization/",
       "title": "Prompt Optimization",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Prompt Optimization Prompt Optimization is an enterprise feature for iteratively improving agent prompts using evaluation feedback. Enterprise feature — requires in your . Full documentation is available in the (password required). At a glance Generate and rank candidate prompts from evaluation feedback Accept winning candidates and optionally push to your voice provider Control optimization depth with and Contact the EfficientAI team for a license and enterprise documentation access."
+      "content": "Prompt Optimization Prompt Optimization is an enterprise feature for iteratively improving agent prompts using evaluation feedback. Enterprise feature — requires in your . At a glance Generate and rank candidate prompts from evaluation feedback Accept winning candidates and optionally push to your voice provider Control optimization depth with and Contact the EfficientAI team for a license."
     },
     {
       "id": "products/prompt-partials",
-      "url": "/docs/products/prompt-partials",
+      "url": "/docs/products/prompt-partials/",
       "title": "Prompt Partials",
       "breadcrumbs": [
         "Products"
@@ -236,34 +281,34 @@
     },
     {
       "id": "products/scenarios",
-      "url": "/docs/products/scenarios",
+      "url": "/docs/products/scenarios/",
       "title": "Scenarios",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Scenarios A Scenario defines the goal and context for a test conversation. Creating a scenario How scenarios are created EfficientAI supports three scenario creation paths: 1. Generate from Agent Prompt (AI assisted) Generate scenario drafts from the selected agent's prompt/description. 2. Generate from Call data Derive scenario content from call transcripts or call data. 3. Create Manually Write a fully custom scenario. Scenario structure | Field | Description | | | | | | Scenario title. | | | What should happen in the conversation. | | | Structured key/value expectations for the test. | | | Optional linked agent for context. | How scenarios are used Scenarios are used to: guide persona behavior during tests, provide evaluation context for scoring, keep testing reproducible across repeated runs. A good scenario is specific enough to be measurable, but open enough to preserve natural conversation flow."
+      "content": "Scenarios A Scenario defines the goal and context for a test conversation. Creating a scenario How scenarios are created EfficientAI supports three scenario creation paths: 1. Generate from Agent Prompt (AI assisted) Generate scenario drafts from the selected agent's test agent prompt ( ). Uses the same backend generator as agent creation stage 2. Each draft includes structured markdown sections: Background, Caller Intent, Conversation Flow, Success Criteria, and Edge Cases to Probe (150 300 words per scenario), plus an optional in . 2. Generate from Call data Derive scenario content from call transcripts or call data. 3. Create Manually Write a fully custom scenario. Scenario structure | Field | Description | | | | | | Scenario title. | | | What should happen in the conversation. AI generated scenarios use structured sections (Background, Caller Intent, Conversation Flow, Success Criteria, Edge Cases) at 150 300 words. | | | Structured key/value expectations for the test. | | | Optional linked agent for context. | How scenarios are used Scenarios are used to: compose the test agent simulation prompt with the linked agent's core prompt during evaluator runs (web and phone simulator paths), guide persona behavior during tests via a dedicated Persona section in the simulator LLM prompt, provide evaluation context for scoring, keep testing reproducible across repeated runs. Link scenarios to an agent with for organization and filtering on the Scenarios page. Linked scenarios can also inform AI Generate Description on the agent (context only). Filter the scenario list by linked agent (all, unlinked, or a specific test agent) from the Scenarios header. A good scenario is specific enough to be measurable, but open enough to preserve natural conversation flow."
     },
     {
       "id": "products/voice-playground",
-      "url": "/docs/products/voice-playground",
+      "url": "/docs/products/voice-playground/",
       "title": "Voice Playground",
       "breadcrumbs": [
         "Products"
       ],
-      "content": "Voice Playground Voice Playground lets you A/B test TTS providers with real synthesis, blind tests, and automated evaluation. Enterprise feature — requires in your . Full documentation is available in the (password required). At a glance Compare multiple voices on the same script Run benchmark simulations and blind test shares Evaluate with metrics enabled for the Voice Playground surface Contact the EfficientAI team for a license and enterprise documentation access."
+      "content": "Voice Playground Voice Playground lets you A/B test TTS providers with real synthesis, blind tests, and automated evaluation. Enterprise feature — requires in your . At a glance Compare multiple voices on the same script Run benchmark simulations and blind test shares Evaluate with metrics enabled for the Voice Playground surface Contact the EfficientAI team for a license."
     },
     {
       "id": "reference/cli-commands",
-      "url": "/docs/reference/cli-commands",
+      "url": "/docs/reference/cli-commands/",
       "title": "CLI Commands",
       "breadcrumbs": [
         "Reference"
       ],
-      "content": "💻 CLI Commands Start Application and Worker Together (Recommended) Note : This is the recommended way to run EfficientAI. It starts both the API server and Celery worker in a single command. Press to stop both services. Start Application Only Start Worker Only Note : The worker is required for processing background tasks (transcription, evaluation, etc.). If you use , the worker starts automatically. Only use this command if you need to run the worker separately. Development Mode Generate Config File Database Migrations Note : Migrations run automatically on application startup. You only need to run them manually if you want to apply migrations before starting the server."
+      "content": "💻 CLI Commands Start Application and Worker Together (Recommended) Note : This is the recommended way to run EfficientAI. It starts both the API server and Celery worker in a single command. Press to stop both services. Start Application Only Start Worker Only Note : The worker is required for processing background tasks (transcription, evaluation, etc.). If you use , the worker starts automatically. Only use this command if you need to run the worker separately. Development Mode Generate Config File Database Migrations Note : Migrations run automatically on application startup. You only need to run them manually if you want to apply migrations before starting the server. Usage pricing Manage model pricing rates and backfill stored usage costs on rollups. Requires , , and the default (or ). After migrations or catalog changes: Flush / Usage UI tuning — set in (see ): | Variable | Default | Purpose | | | | | | | | Buckets per DB transaction | | | | Batches per flush tick (≤ 15,000 buckets/run) | | | | Celery Beat flush interval (~2 min lag vs Redis) | | | | Per org flush lock TTL | | | | Redis cache TTL for usage summary/breakdown/filters | | | | Evaluator cron dispatcher tick (default worker) | The Usage UI reads Postgres only (summary/breakdown/filters). Redis counters flush on the Celery Beat schedule (~2 min eventual consistency). If Redis backlog grows, lower or raise . See for the end user guide."
     },
     {
       "id": "reference/configuration",
-      "url": "/docs/reference/configuration",
+      "url": "/docs/reference/configuration/",
       "title": "Configuration",
       "breadcrumbs": [
         "Reference"

From 5f5810fbdff6809cc6c662017eee0a55d6adcefa Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 20 Aug 2026 10:42:10 +0530
Subject: [PATCH 33/55] fix(config): update security settings and operational
 access configurations; enforce CSP and adjust token lifetimes

---
 app/api/v1/routes/auth.py                     | 31 ++++--
 app/api/v1/routes/platform_admin.py           | 24 ++++-
 app/config.py                                 | 13 ++-
 app/core/auth/platform_admin.py               | 20 ++++
 app/core/operational_access_middleware.py     | 28 +-----
 config.docker.yml                             |  9 +-
 config.yml.example                            |  8 ++
 env.example                                   |  4 +
 frontend/src/lib/api.ts                       | 10 ++
 frontend/src/pages/auth/Login.tsx             | 13 +--
 .../src/pages/auth/SelectOrganization.tsx     |  5 +-
 frontend/src/pages/platform/PlatformLogin.tsx |  3 +-
 frontend/src/store/authStore.ts               | 25 +++--
 frontend/src/store/platformAdminStore.ts      | 13 ++-
 tests/test_api/test_auth_routes.py            | 96 +++++++++++++++++++
 tests/test_api/test_platform_admin.py         | 37 +++++++
 16 files changed, 280 insertions(+), 59 deletions(-)

diff --git a/app/api/v1/routes/auth.py b/app/api/v1/routes/auth.py
index 2a4437c5..942157d4 100644
--- a/app/api/v1/routes/auth.py
+++ b/app/api/v1/routes/auth.py
@@ -264,6 +264,18 @@ def _extract_bearer(authorization: Optional[str]) -> Optional[str]:
     return token.strip()
 
 
+def _revoke_local_password_access_token(bearer: str) -> None:
+    try:
+        claims = decode_access_token(bearer)
+        jti = claims.get("jti")
+        exp = claims.get("exp")
+        if jti and exp:
+            ttl = max(int(exp) - int(datetime.now(timezone.utc).timestamp()), 1)
+            revoke_access_jti(jti, ttl)
+    except JWTError:
+        pass
+
+
 def _issue_session_tokens(
     db: Session,
     *,
@@ -485,15 +497,7 @@ def logout(
     """Revoke the current session's refresh token and blacklist the access token."""
     bearer = _extract_bearer(authorization)
     if bearer and principal.auth_method == AuthMethod.LOCAL_PASSWORD:
-        try:
-            claims = decode_access_token(bearer)
-            jti = claims.get("jti")
-            exp = claims.get("exp")
-            if jti and exp:
-                ttl = max(int(exp) - int(datetime.now(timezone.utc).timestamp()), 1)
-                revoke_access_jti(jti, ttl)
-        except JWTError:
-            pass
+        _revoke_local_password_access_token(bearer)
 
     if payload and payload.refresh_token:
         revoke_refresh_token(db, payload.refresh_token)
@@ -571,12 +575,14 @@ def refresh_session(payload: RefreshRequest, db: Session = Depends(get_db)) -> T
 
 class SwitchOrgRequest(BaseModel):
     organization_id: str
+    refresh_token: Optional[str] = None
 
 
 @router.post("/switch-org", response_model=TokenResponse)
 def switch_organization(
     payload: SwitchOrgRequest,
     principal: Principal = Depends(get_principal),
+    authorization: Optional[str] = Header(None, alias="Authorization"),
     db: Session = Depends(get_db),
 ) -> TokenResponse:
     """
@@ -640,6 +646,13 @@ def switch_organization(
             detail="User is no longer active.",
         )
 
+    if principal.auth_method == AuthMethod.LOCAL_PASSWORD:
+        bearer = _extract_bearer(authorization)
+        if bearer:
+            _revoke_local_password_access_token(bearer)
+        if payload.refresh_token:
+            revoke_refresh_token(db, payload.refresh_token)
+
     user.last_login_at = datetime.now(timezone.utc)
     db.commit()
     db.refresh(user)
diff --git a/app/api/v1/routes/platform_admin.py b/app/api/v1/routes/platform_admin.py
index f29630ff..433b1e3c 100644
--- a/app/api/v1/routes/platform_admin.py
+++ b/app/api/v1/routes/platform_admin.py
@@ -6,7 +6,8 @@
 from typing import List, Optional
 from uuid import UUID
 
-from fastapi import APIRouter, Depends, HTTPException, Query, status
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, status
+from jose import JWTError
 from pydantic import BaseModel, EmailStr, Field
 from sqlalchemy import func
 from sqlalchemy.orm import Session
@@ -16,6 +17,7 @@
     create_platform_access_token,
     get_platform_admin,
     platform_admin_feature_enabled,
+    revoke_platform_access_token,
 )
 from app.core.auth.refresh_tokens import revoke_all_user_refresh_tokens
 from app.core.password import hash_password, validate_password_strength, verify_password
@@ -184,6 +186,26 @@ def platform_me(
     return PlatformAdminSummary(id=str(principal.platform_admin_id), email=principal.email)
 
 
+def _extract_bearer(authorization: Optional[str]) -> Optional[str]:
+    if not authorization:
+        return None
+    scheme, _, token = authorization.partition(" ")
+    if scheme.lower() != "bearer" or not token.strip():
+        return None
+    return token.strip()
+
+
+@router.post("/auth/logout")
+def platform_logout(
+    authorization: Optional[str] = Header(None, alias="Authorization"),
+    principal: PlatformAdminPrincipal = Depends(get_platform_admin),
+) -> dict:
+    bearer = _extract_bearer(authorization)
+    if bearer:
+        revoke_platform_access_token(bearer)
+    return {"success": True, "admin_id": str(principal.platform_admin_id)}
+
+
 @router.get("/organizations", response_model=OrganizationListResponse)
 def list_organizations(
     offset: int = Query(0, ge=0),
diff --git a/app/config.py b/app/config.py
index fc194f81..d36b08aa 100644
--- a/app/config.py
+++ b/app/config.py
@@ -124,9 +124,9 @@ class Settings(BaseSettings):
     # Frontend
     FRONTEND_DIR: str = "./frontend/dist"
 
-    # Content Security Policy (Report-Only by default; set CSP_REPORT_ONLY=false to enforce)
+    # Content Security Policy (enforcing by default; set CSP_REPORT_ONLY=true for local report-only mode)
     CSP_ENABLED: bool = True
-    CSP_REPORT_ONLY: bool = True
+    CSP_REPORT_ONLY: bool = False
     CSP_POLICY: str = (
         "default-src 'self'; "
         "script-src 'self'; "
@@ -858,6 +858,15 @@ def _apply_llm_gateway_settings(gateway_cfg: dict, *, gateway_type: str) -> None
         if "trusted_ips" in operational_config:
             settings.OPERATIONAL_TRUSTED_IPS = operational_config["trusted_ips"]
 
+    if "security" in config_data:
+        security_config = config_data["security"]
+        if "csp_enabled" in security_config:
+            settings.CSP_ENABLED = bool(security_config["csp_enabled"])
+        if "csp_report_only" in security_config:
+            settings.CSP_REPORT_ONLY = bool(security_config["csp_report_only"])
+        if security_config.get("csp_policy"):
+            settings.CSP_POLICY = security_config["csp_policy"]
+
     if "flexprice" in config_data:
         flexprice_config = config_data["flexprice"]
         if "enabled" in flexprice_config:
diff --git a/app/core/auth/platform_admin.py b/app/core/auth/platform_admin.py
index 920c0164..6e468385 100644
--- a/app/core/auth/platform_admin.py
+++ b/app/core/auth/platform_admin.py
@@ -12,6 +12,7 @@
 from sqlalchemy.orm import Session
 
 from app.config import settings
+from app.core.auth.token_revocation import is_access_jti_revoked, revoke_access_jti
 from app.database import get_db
 from app.models.database import PlatformAdmin
 
@@ -58,6 +59,18 @@ def decode_platform_access_token(token: str) -> Dict[str, Any]:
     )
 
 
+def revoke_platform_access_token(token: str) -> None:
+    try:
+        claims = decode_platform_access_token(token)
+        jti = claims.get("jti")
+        exp = claims.get("exp")
+        if jti and exp:
+            ttl = max(int(exp) - int(datetime.now(timezone.utc).timestamp()), 1)
+            revoke_access_jti(jti, ttl)
+    except JWTError:
+        pass
+
+
 def _extract_bearer(authorization: Optional[str]) -> Optional[str]:
     if not authorization:
         return None
@@ -107,6 +120,13 @@ def get_platform_admin(
             detail="Invalid platform admin token scope.",
         )
 
+    jti = claims.get("jti")
+    if jti and is_access_jti_revoked(jti):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Token has been revoked.",
+        )
+
     try:
         admin_id = UUID(claims["sub"])
     except (KeyError, ValueError) as exc:
diff --git a/app/core/operational_access_middleware.py b/app/core/operational_access_middleware.py
index 7bf4d8d3..7a0db428 100644
--- a/app/core/operational_access_middleware.py
+++ b/app/core/operational_access_middleware.py
@@ -1,4 +1,8 @@
-"""Restrict /metrics from the public internet (/health stays open for load balancers)."""
+"""Restrict /metrics from the public internet (/health stays open for load balancers).
+
+Not Spring Boot Actuator: this FastAPI app exposes /health (LB probes) and /metrics
+(Prometheus scrape). /metrics is gated by trusted IPs or OPERATIONAL_PUBLIC only.
+"""
 
 from __future__ import annotations
 
@@ -6,14 +10,11 @@
 import logging
 from typing import Iterable
 
-from fastapi import HTTPException
 from starlette.middleware.base import BaseHTTPMiddleware
 from starlette.requests import Request
 from starlette.responses import JSONResponse, Response
 
 from app.config import settings
-from app.core.auth.dependency import _resolve
-from app.database import SessionLocal
 
 logger = logging.getLogger(__name__)
 
@@ -75,22 +76,6 @@ def _resolved_trusted_ip(request: Request) -> str | None:
     return hops[-1]
 
 
-def _has_authenticated_caller(request: Request) -> bool:
-    db = SessionLocal()
-    try:
-        principal = _resolve(
-            request.headers.get("authorization"),
-            request.headers.get("x-api-key"),
-            request.headers.get("x-efficientai-api-key"),
-            db,
-        )
-        return principal is not None
-    except HTTPException:
-        return False
-    finally:
-        db.close()
-
-
 def is_operational_access_allowed(request: Request) -> bool:
     """Return True when the caller may access a protected operational endpoint."""
     if settings.OPERATIONAL_PUBLIC:
@@ -100,9 +85,6 @@ def is_operational_access_allowed(request: Request) -> bool:
     if resolved_ip and _ip_in_trusted(resolved_ip, settings.OPERATIONAL_TRUSTED_IPS):
         return True
 
-    if _has_authenticated_caller(request):
-        return True
-
     return False
 
 
diff --git a/config.docker.yml b/config.docker.yml
index 490f43d3..26927031 100644
--- a/config.docker.yml
+++ b/config.docker.yml
@@ -5,7 +5,7 @@
 app:
   name: "EfficientAI Voice AI Evaluation Platform"
   version: "0.1.0"
-  debug: true
+  debug: false
   secret_key: "your-secret-key-here-change-in-production"
 
 # Server Settings
@@ -14,12 +14,17 @@ server:
   port: 8000
 
 # Operational endpoints (/metrics). /health is always open for load balancers.
+# Not Spring Boot Actuator — /metrics is IP-gated via trusted_ips below.
 # Add VPC CIDRs here if Prometheus scrapes /metrics from inside the VPC.
 operational:
   public: false
   trusted_ips:
     - "10.0.0.0/8"
 
+security:
+  csp_enabled: true
+  csp_report_only: false
+
 # Database Configuration (Docker service name)
 database:
   url: "postgresql://efficientai:password@db:5432/efficientai"
@@ -98,7 +103,7 @@ auth:
   local_password:
     # Lifetime of the Bearer tokens minted at POST /auth/login (in minutes).
     # Keep this short; clients re-authenticate silently.
-    token_ttl_minutes: 720  # 12 hours
+    token_ttl_minutes: 15
     # Turn this off in Cloud SaaS to block self-serve signup.
     allow_signup: true
 
diff --git a/config.yml.example b/config.yml.example
index 91b43e2e..adc8f74c 100644
--- a/config.yml.example
+++ b/config.yml.example
@@ -13,12 +13,20 @@ server:
   port: 8000
 
 # Operational endpoints (/metrics). /health is always open for ALB/kube probes.
+# This is FastAPI, not Spring Boot Actuator — scanners may flag /health or /metrics
+# under an "Actuator" template. /metrics is IP-gated; /health returns minimal status only.
 # Include VPC CIDRs in trusted_ips if Prometheus scrapes /metrics from inside the VPC.
 operational:
   public: false
   trusted_ips:
     - "10.0.0.0/8"
 
+# HTTP security headers (CSP, X-Frame-Options, etc.)
+security:
+  csp_enabled: true
+  csp_report_only: false   # false = enforcing Content-Security-Policy header (required for compliance scans)
+  # csp_policy: "default-src 'self'; ..."  # optional override
+
 # Database Configuration
 database:
   # Legacy single-DB mode: use one database (e.g. efficientai). Sharding off (default).
diff --git a/env.example b/env.example
index 40b2f1a5..53a08814 100644
--- a/env.example
+++ b/env.example
@@ -41,6 +41,10 @@ CORS_ORIGINS=["http://localhost:3000", "http://localhost:8000"]
 API_KEY_HEADER=X-API-Key
 RATE_LIMIT_PER_MINUTE=60
 
+# HTTP security headers (see config.yml.example security section)
+CSP_ENABLED=true
+CSP_REPORT_ONLY=false
+
 # -----------------------------------------------------------------------------
 # Authentication (pluggable auth providers)
 # -----------------------------------------------------------------------------
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index aaca0b6c..2357b947 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -820,6 +820,15 @@ class ApiClient {
     return response.data
   }
 
+  async platformLogout(): Promise<{ success: boolean; admin_id: string }> {
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/platform/auth/logout`,
+      {},
+      { headers: this.platformHeaders() },
+    )
+    return response.data
+  }
+
   async getPlatformOrganizationStats(): Promise<PlatformOrganizationStats> {
     const response = await axios.get(`${API_BASE_URL}/api/v1/platform/organizations/stats`, {
       headers: this.platformHeaders(),
@@ -952,6 +961,7 @@ class ApiClient {
   async switchOrganization(organizationId: string): Promise<TokenResponse> {
     const response = await this.client.post('/api/v1/auth/switch-org', {
       organization_id: organizationId,
+      refresh_token: localStorage.getItem('refreshToken') || undefined,
     })
     return response.data
   }
diff --git a/frontend/src/pages/auth/Login.tsx b/frontend/src/pages/auth/Login.tsx
index f395437f..0877202b 100644
--- a/frontend/src/pages/auth/Login.tsx
+++ b/frontend/src/pages/auth/Login.tsx
@@ -6,6 +6,7 @@ import type { AuthConfigResponse, AuthProviderConfig, LoginOrgOption } from '../
 import { buildAuthorizeUrl } from '../../lib/oidc'
 import { PASSWORD_POLICY_HINT, validatePasswordPolicy } from '../../lib/passwordPolicy'
 import { consumeAuthRedirectMessage } from '../../lib/authSession'
+import { getApiErrorMessage } from '../../lib/apiErrors'
 import { AlertCircle, Building2, Eye, EyeOff, Loader2 } from 'lucide-react'
 import Logo from '../../components/Logo'
 import { Card, CardBody, Button, Divider, Tabs, Tab } from '@heroui/react'
@@ -109,8 +110,8 @@ export default function Login() {
       }
       setSession(res.access_token, res.user, res.refresh_token)
       navigate('/')
-    } catch (err: any) {
-      setError(err?.response?.data?.detail || 'Invalid email or password')
+    } catch (err: unknown) {
+      setError(getApiErrorMessage(err, 'Invalid email or password'))
     } finally {
       setIsLoading(false)
     }
@@ -127,8 +128,8 @@ export default function Login() {
       }
       setSession(res.access_token, res.user, res.refresh_token)
       navigate('/')
-    } catch (err: any) {
-      setError(err?.response?.data?.detail || 'Could not sign in to the selected organization')
+    } catch (err: unknown) {
+      setError(getApiErrorMessage(err, 'Could not sign in to the selected organization'))
     } finally {
       setSelectingOrgId(null)
     }
@@ -154,8 +155,8 @@ export default function Login() {
       })
       setSession(res.access_token, res.user, res.refresh_token)
       navigate('/')
-    } catch (err: any) {
-      setError(err?.response?.data?.detail || 'Sign up failed')
+    } catch (err: unknown) {
+      setError(getApiErrorMessage(err, 'Sign up failed'))
     } finally {
       setIsLoading(false)
     }
diff --git a/frontend/src/pages/auth/SelectOrganization.tsx b/frontend/src/pages/auth/SelectOrganization.tsx
index ac7a3eef..62e4eca6 100644
--- a/frontend/src/pages/auth/SelectOrganization.tsx
+++ b/frontend/src/pages/auth/SelectOrganization.tsx
@@ -5,6 +5,7 @@ import { Building2, Loader2 } from 'lucide-react'
 import Logo from '../../components/Logo'
 import { Card, CardBody } from '@heroui/react'
 import { apiClient } from '../../lib/api'
+import { getApiErrorMessage } from '../../lib/apiErrors'
 import { useAuthStore } from '../../store/authStore'
 
 export default function SelectOrganization() {
@@ -32,8 +33,8 @@ export default function SelectOrganization() {
     try {
       await switchOrg(orgId)
       navigate('/', { replace: true })
-    } catch (err: any) {
-      setError(err?.response?.data?.detail || 'Could not enter the selected organization')
+    } catch (err: unknown) {
+      setError(getApiErrorMessage(err, 'Could not enter the selected organization'))
     } finally {
       setSwitchingTo(null)
     }
diff --git a/frontend/src/pages/platform/PlatformLogin.tsx b/frontend/src/pages/platform/PlatformLogin.tsx
index d9c97c1c..389e99a2 100644
--- a/frontend/src/pages/platform/PlatformLogin.tsx
+++ b/frontend/src/pages/platform/PlatformLogin.tsx
@@ -4,6 +4,7 @@ import { AlertCircle, Eye, EyeOff } from 'lucide-react'
 import { Button, Chip } from '@heroui/react'
 import Logo from '../../components/Logo'
 import { apiClient } from '../../lib/api'
+import { getApiErrorMessage } from '../../lib/apiErrors'
 import { usePlatformAdminStore } from '../../store/platformAdminStore'
 
 export default function PlatformLogin() {
@@ -42,7 +43,7 @@ export default function PlatformLogin() {
             'Is the backend running?',
         )
       } else {
-        setError(detail || 'Sign in failed')
+        setError(getApiErrorMessage(err, 'Sign in failed'))
       }
     } finally {
       setIsLoading(false)
diff --git a/frontend/src/store/authStore.ts b/frontend/src/store/authStore.ts
index 4db27106..dbd0b13e 100644
--- a/frontend/src/store/authStore.ts
+++ b/frontend/src/store/authStore.ts
@@ -114,16 +114,21 @@ export const useAuthStore = create<AuthState>((set, get) => {
 
     logout: () => {
       const refreshToken = get().refreshToken
-      apiClient.logout(refreshToken).catch(() => {})
-      apiClient.clearApiKey()
-      apiClient.clearAccessToken()
-      apiClient.clearRefreshToken()
-      localStorage.removeItem(STORAGE_API_KEY)
-      localStorage.removeItem(STORAGE_ACCESS_TOKEN)
-      localStorage.removeItem(STORAGE_REFRESH_TOKEN)
-      localStorage.removeItem(STORAGE_USER)
-      useWorkspaceStore.getState().clearActiveWorkspaceId()
-      set({ apiKey: null, accessToken: null, refreshToken: null, user: null })
+      const clearLocal = () => {
+        apiClient.clearApiKey()
+        apiClient.clearAccessToken()
+        apiClient.clearRefreshToken()
+        localStorage.removeItem(STORAGE_API_KEY)
+        localStorage.removeItem(STORAGE_ACCESS_TOKEN)
+        localStorage.removeItem(STORAGE_REFRESH_TOKEN)
+        localStorage.removeItem(STORAGE_USER)
+        useWorkspaceStore.getState().clearActiveWorkspaceId()
+        set({ apiKey: null, accessToken: null, refreshToken: null, user: null })
+      }
+      apiClient
+        .logout(refreshToken)
+        .catch(() => apiClient.logout(refreshToken))
+        .finally(clearLocal)
     },
 
     validate: async () => {
diff --git a/frontend/src/store/platformAdminStore.ts b/frontend/src/store/platformAdminStore.ts
index 1b79b15c..91426269 100644
--- a/frontend/src/store/platformAdminStore.ts
+++ b/frontend/src/store/platformAdminStore.ts
@@ -1,4 +1,5 @@
 import { create } from 'zustand'
+import { apiClient } from '../lib/api'
 
 type PlatformAdminUser = {
   id: string
@@ -37,9 +38,15 @@ export const usePlatformAdminStore = create<PlatformAdminState>((set) => {
       set({ accessToken: token, admin })
     },
     logout: () => {
-      localStorage.removeItem(STORAGE_TOKEN)
-      localStorage.removeItem(STORAGE_ADMIN)
-      set({ accessToken: null, admin: null })
+      const clearLocal = () => {
+        localStorage.removeItem(STORAGE_TOKEN)
+        localStorage.removeItem(STORAGE_ADMIN)
+        set({ accessToken: null, admin: null })
+      }
+      apiClient
+        .platformLogout()
+        .catch(() => apiClient.platformLogout())
+        .finally(clearLocal)
     },
   }
 })
diff --git a/tests/test_api/test_auth_routes.py b/tests/test_api/test_auth_routes.py
index 215c2589..e1e23adc 100644
--- a/tests/test_api/test_auth_routes.py
+++ b/tests/test_api/test_auth_routes.py
@@ -777,3 +777,99 @@ def test_logout_revokes_access_and_refresh_tokens(
         json={"refresh_token": refresh_token},
     )
     assert refresh.status_code == 401
+
+
+def test_set_password_rejects_weak_password(
+    authenticated_client, db_session, org_id, api_key, seed_org, enable_local_password
+):
+    user = User(
+        id=uuid4(),
+        email="weakpw@example.com",
+        is_active=True,
+    )
+    db_session.add(user)
+    db_session.flush()
+    _bind_api_key_to_user(db_session, api_key=api_key, org_id=org_id, user=user)
+
+    response = authenticated_client.post(
+        "/api/v1/auth/password",
+        json={"new_password": "alllowercase"},
+    )
+    assert response.status_code == 400
+    assert "uppercase" in response.json()["detail"].lower()
+
+
+def test_iam_reset_password_rejects_weak_password(
+    authenticated_client, db_session, org_id, enable_local_password
+):
+    target = User(
+        email="member@example.com",
+        password_hash=hash_password(TEST_PASSWORD),
+        is_active=True,
+    )
+    db_session.add(target)
+    db_session.flush()
+    db_session.add(
+        OrganizationMember(
+            organization_id=org_id,
+            user_id=target.id,
+            role=RoleEnum.READER.value,
+        )
+    )
+    db_session.commit()
+
+    response = authenticated_client.post(
+        f"/api/v1/iam/users/{target.id}/reset-password",
+        json={"new_password": "alllowercase"},
+    )
+    assert response.status_code == 400
+    assert "uppercase" in response.json()["detail"].lower()
+
+
+def test_switch_org_revokes_previous_session_tokens(
+    client, db_session, enable_local_password
+):
+    user, source_org = _seed_user_with_org(
+        db_session, "switch@example.com", TEST_PASSWORD
+    )
+    target_org = Organization(id=uuid4(), name="Switch Target Org")
+    db_session.add(target_org)
+    db_session.flush()
+    db_session.add(
+        OrganizationMember(
+            organization_id=target_org.id,
+            user_id=user.id,
+            role=RoleEnum.READER.value,
+        )
+    )
+    db_session.commit()
+
+    login = client.post(
+        "/api/v1/auth/login",
+        json={"email": "switch@example.com", "password": TEST_PASSWORD},
+    )
+    assert login.status_code == 200
+    old_access = login.json()["access_token"]
+    old_refresh = login.json()["refresh_token"]
+
+    switch = client.post(
+        "/api/v1/auth/switch-org",
+        json={
+            "organization_id": str(target_org.id),
+            "refresh_token": old_refresh,
+        },
+        headers={"Authorization": f"Bearer {old_access}"},
+    )
+    assert switch.status_code == 200
+
+    me = client.get(
+        "/api/v1/auth/me",
+        headers={"Authorization": f"Bearer {old_access}"},
+    )
+    assert me.status_code == 401
+
+    stale_refresh = client.post(
+        "/api/v1/auth/refresh",
+        json={"refresh_token": old_refresh},
+    )
+    assert stale_refresh.status_code == 401
diff --git a/tests/test_api/test_platform_admin.py b/tests/test_api/test_platform_admin.py
index 4c650c0f..206fee50 100644
--- a/tests/test_api/test_platform_admin.py
+++ b/tests/test_api/test_platform_admin.py
@@ -191,6 +191,43 @@ def test_platform_reset_password(platform_admin_client, client, db_session, enab
     assert login.status_code == 200
 
 
+def test_platform_reset_password_rejects_weak_password(
+    platform_admin_client, db_session, enable_local_password
+):
+    org = Organization(name="Weak Reset Org")
+    user = User(
+        email="weak@reset.org",
+        password_hash=hash_password(TEST_PASSWORD),
+        is_active=True,
+        auth_provider="local",
+    )
+    db_session.add_all([org, user])
+    db_session.flush()
+    db_session.add(
+        OrganizationMember(
+            organization_id=org.id,
+            user_id=user.id,
+            role=RoleEnum.ADMIN.value,
+        )
+    )
+    db_session.commit()
+
+    response = platform_admin_client.post(
+        f"/api/v1/platform/organizations/{org.id}/users/{user.id}/reset-password",
+        json={"new_password": "alllowercase"},
+    )
+    assert response.status_code == 400
+    assert "uppercase" in response.json()["detail"].lower()
+
+
+def test_platform_logout_revokes_access_token(platform_admin_client):
+    logout = platform_admin_client.post("/api/v1/platform/auth/logout")
+    assert logout.status_code == 200
+
+    me = platform_admin_client.get("/api/v1/platform/auth/me")
+    assert me.status_code == 401
+
+
 def test_create_and_use_signup_reference_code(
     platform_admin_client, client, db_session, enable_local_password, monkeypatch
 ):

From cc6292a4a3ccb324d2d85512eb56acbb1e412ded Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 20 Aug 2026 11:52:20 +0530
Subject: [PATCH 34/55] fix(tests): enhance
 test_switch_org_revokes_previous_session_tokens

---
 tests/test_api/test_auth_routes.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/tests/test_api/test_auth_routes.py b/tests/test_api/test_auth_routes.py
index e1e23adc..6c173c3c 100644
--- a/tests/test_api/test_auth_routes.py
+++ b/tests/test_api/test_auth_routes.py
@@ -846,11 +846,17 @@ def test_switch_org_revokes_previous_session_tokens(
 
     login = client.post(
         "/api/v1/auth/login",
-        json={"email": "switch@example.com", "password": TEST_PASSWORD},
+        json={
+            "email": "switch@example.com",
+            "password": TEST_PASSWORD,
+            "organization_id": str(source_org.id),
+        },
     )
     assert login.status_code == 200
-    old_access = login.json()["access_token"]
-    old_refresh = login.json()["refresh_token"]
+    body = login.json()
+    assert "access_token" in body
+    old_access = body["access_token"]
+    old_refresh = body["refresh_token"]
 
     switch = client.post(
         "/api/v1/auth/switch-org",

From d6852abdc7ed0d05eb26bea6cb595d420c6716e0 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 24 Aug 2026 18:04:28 +0530
Subject: [PATCH 35/55] feat(auth): enhance logout functionality to support
 access tokens and API keys; refactor local storage clearing logic

---
 frontend/src/lib/api.ts                  | 29 +++++++++++++++++-----
 frontend/src/store/authStore.ts          | 31 ++++++++++++------------
 frontend/src/store/platformAdminStore.ts | 19 +++++++--------
 3 files changed, 47 insertions(+), 32 deletions(-)

diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 4161a852..a792103f 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -832,11 +832,13 @@ class ApiClient {
     return response.data
   }
 
-  async platformLogout(): Promise<{ success: boolean; admin_id: string }> {
+  async platformLogout(accessToken?: string | null): Promise<{ success: boolean; admin_id: string }> {
+    const token = accessToken ?? localStorage.getItem('platformAccessToken')
+    const headers = token ? { Authorization: `Bearer ${token}` } : {}
     const response = await axios.post(
       `${API_BASE_URL}/api/v1/platform/auth/logout`,
       {},
-      { headers: this.platformHeaders() },
+      { headers },
     )
     return response.data
   }
@@ -951,10 +953,25 @@ class ApiClient {
     return response.data
   }
 
-  async logout(refreshToken?: string | null): Promise<{ success: boolean; auth_method: string }> {
-    const response = await this.client.post('/api/v1/auth/logout', {
-      refresh_token: refreshToken || localStorage.getItem('refreshToken') || undefined,
-    })
+  async logout(
+    refreshToken?: string | null,
+    credentials?: { accessToken?: string | null; apiKey?: string | null },
+  ): Promise<{ success: boolean; auth_method: string }> {
+    const headers: Record<string, string> = {}
+    const accessToken = credentials?.accessToken ?? localStorage.getItem('accessToken')
+    const apiKey = credentials?.apiKey ?? localStorage.getItem('apiKey')
+    if (accessToken) {
+      headers.Authorization = `Bearer ${accessToken}`
+    } else if (apiKey) {
+      headers['X-API-Key'] = apiKey
+    }
+    const response = await this.client.post(
+      '/api/v1/auth/logout',
+      {
+        refresh_token: refreshToken ?? localStorage.getItem('refreshToken') ?? undefined,
+      },
+      { headers },
+    )
     return response.data
   }
 
diff --git a/frontend/src/store/authStore.ts b/frontend/src/store/authStore.ts
index dbd0b13e..e7c2c938 100644
--- a/frontend/src/store/authStore.ts
+++ b/frontend/src/store/authStore.ts
@@ -113,22 +113,21 @@ export const useAuthStore = create<AuthState>((set, get) => {
     },
 
     logout: () => {
-      const refreshToken = get().refreshToken
-      const clearLocal = () => {
-        apiClient.clearApiKey()
-        apiClient.clearAccessToken()
-        apiClient.clearRefreshToken()
-        localStorage.removeItem(STORAGE_API_KEY)
-        localStorage.removeItem(STORAGE_ACCESS_TOKEN)
-        localStorage.removeItem(STORAGE_REFRESH_TOKEN)
-        localStorage.removeItem(STORAGE_USER)
-        useWorkspaceStore.getState().clearActiveWorkspaceId()
-        set({ apiKey: null, accessToken: null, refreshToken: null, user: null })
-      }
-      apiClient
-        .logout(refreshToken)
-        .catch(() => apiClient.logout(refreshToken))
-        .finally(clearLocal)
+      const { refreshToken, accessToken, apiKey } = get()
+      apiClient.clearApiKey()
+      apiClient.clearAccessToken()
+      apiClient.clearRefreshToken()
+      localStorage.removeItem(STORAGE_API_KEY)
+      localStorage.removeItem(STORAGE_ACCESS_TOKEN)
+      localStorage.removeItem(STORAGE_REFRESH_TOKEN)
+      localStorage.removeItem(STORAGE_USER)
+      useWorkspaceStore.getState().clearActiveWorkspaceId()
+      set({ apiKey: null, accessToken: null, refreshToken: null, user: null })
+
+      const credentials = { accessToken, apiKey }
+      void apiClient
+        .logout(refreshToken, credentials)
+        .catch(() => apiClient.logout(refreshToken, credentials))
     },
 
     validate: async () => {
diff --git a/frontend/src/store/platformAdminStore.ts b/frontend/src/store/platformAdminStore.ts
index 91426269..76ed5a2c 100644
--- a/frontend/src/store/platformAdminStore.ts
+++ b/frontend/src/store/platformAdminStore.ts
@@ -25,7 +25,7 @@ function readStoredAdmin(): PlatformAdminUser | null {
   }
 }
 
-export const usePlatformAdminStore = create<PlatformAdminState>((set) => {
+export const usePlatformAdminStore = create<PlatformAdminState>((set, get) => {
   const storedToken = localStorage.getItem(STORAGE_TOKEN)
   const storedAdmin = readStoredAdmin()
 
@@ -38,15 +38,14 @@ export const usePlatformAdminStore = create<PlatformAdminState>((set) => {
       set({ accessToken: token, admin })
     },
     logout: () => {
-      const clearLocal = () => {
-        localStorage.removeItem(STORAGE_TOKEN)
-        localStorage.removeItem(STORAGE_ADMIN)
-        set({ accessToken: null, admin: null })
-      }
-      apiClient
-        .platformLogout()
-        .catch(() => apiClient.platformLogout())
-        .finally(clearLocal)
+      const accessToken = get().accessToken
+      localStorage.removeItem(STORAGE_TOKEN)
+      localStorage.removeItem(STORAGE_ADMIN)
+      set({ accessToken: null, admin: null })
+
+      void apiClient
+        .platformLogout(accessToken)
+        .catch(() => apiClient.platformLogout(accessToken))
     },
   }
 })

From 9cf49159bb6d14c765cd38a2da629a5991eebc0b Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 24 Aug 2026 18:19:23 +0530
Subject: [PATCH 36/55] feat(auth): implement revocable session management for
 user and platform logouts; streamline local storage clearing

---
 frontend/src/lib/api.ts                  | 48 ++++++++++++++++++++----
 frontend/src/lib/authSession.ts          | 16 ++++++++
 frontend/src/store/authStore.ts          | 21 ++++-------
 frontend/src/store/platformAdminStore.ts |  9 ++---
 4 files changed, 67 insertions(+), 27 deletions(-)

diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index a792103f..7bdd5273 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -1,9 +1,12 @@
 import axios, { AxiosInstance } from 'axios'
 import {
+  clearAuthSession,
   getApiErrorDetail,
+  hasRevocableUserCredentials,
   isOrganizationAccessDenied,
   organizationAccessDeniedMessage,
   redirectToLoginWithMessage,
+  type UserSessionCredentials,
 } from './authSession'
 import type {
   GenerateScenariosFromPromptParams,
@@ -585,6 +588,7 @@ class ApiClient {
           requestUrl.includes('/auth/login') ||
           requestUrl.includes('/auth/signup') ||
           requestUrl.includes('/auth/refresh') ||
+          requestUrl.includes('/auth/logout') ||
           requestUrl.includes('/auth/config')
 
         const detail = getApiErrorDetail(error)
@@ -611,10 +615,7 @@ class ApiClient {
             }
           }
 
-          localStorage.removeItem('apiKey')
-          localStorage.removeItem('accessToken')
-          localStorage.removeItem('refreshToken')
-          localStorage.removeItem('authUser')
+          clearAuthSession()
           window.location.href = '/login'
         }
         return Promise.reject(error)
@@ -834,7 +835,10 @@ class ApiClient {
 
   async platformLogout(accessToken?: string | null): Promise<{ success: boolean; admin_id: string }> {
     const token = accessToken ?? localStorage.getItem('platformAccessToken')
-    const headers = token ? { Authorization: `Bearer ${token}` } : {}
+    const headers: Record<string, string> = { 'Content-Type': 'application/json' }
+    if (token) {
+      headers.Authorization = `Bearer ${token}`
+    }
     const response = await axios.post(
       `${API_BASE_URL}/api/v1/platform/auth/logout`,
       {},
@@ -843,6 +847,27 @@ class ApiClient {
     return response.data
   }
 
+  /**
+   * Voluntary logout: revoke JWT blacklist + refresh token on the server.
+   * Uses raw axios so credentials survive after local session is cleared.
+   */
+  revokeUserSessionBestEffort(credentials: UserSessionCredentials): void {
+    if (!hasRevocableUserCredentials(credentials)) {
+      return
+    }
+    const auth = { accessToken: credentials.accessToken, apiKey: credentials.apiKey }
+    void this.revokeUserSession(credentials.refreshToken, auth).catch(() =>
+      this.revokeUserSession(credentials.refreshToken, auth),
+    )
+  }
+
+  revokePlatformSessionBestEffort(accessToken?: string | null): void {
+    if (!accessToken) {
+      return
+    }
+    void this.platformLogout(accessToken).catch(() => this.platformLogout(accessToken))
+  }
+
   async getPlatformOrganizationStats(): Promise<PlatformOrganizationStats> {
     const response = await axios.get(`${API_BASE_URL}/api/v1/platform/organizations/stats`, {
       headers: this.platformHeaders(),
@@ -957,7 +982,14 @@ class ApiClient {
     refreshToken?: string | null,
     credentials?: { accessToken?: string | null; apiKey?: string | null },
   ): Promise<{ success: boolean; auth_method: string }> {
-    const headers: Record<string, string> = {}
+    return this.revokeUserSession(refreshToken, credentials)
+  }
+
+  private async revokeUserSession(
+    refreshToken?: string | null,
+    credentials?: { accessToken?: string | null; apiKey?: string | null },
+  ): Promise<{ success: boolean; auth_method: string }> {
+    const headers: Record<string, string> = { 'Content-Type': 'application/json' }
     const accessToken = credentials?.accessToken ?? localStorage.getItem('accessToken')
     const apiKey = credentials?.apiKey ?? localStorage.getItem('apiKey')
     if (accessToken) {
@@ -965,8 +997,8 @@ class ApiClient {
     } else if (apiKey) {
       headers['X-API-Key'] = apiKey
     }
-    const response = await this.client.post(
-      '/api/v1/auth/logout',
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/auth/logout`,
       {
         refresh_token: refreshToken ?? localStorage.getItem('refreshToken') ?? undefined,
       },
diff --git a/frontend/src/lib/authSession.ts b/frontend/src/lib/authSession.ts
index 4a1e673c..ecfbf6e5 100644
--- a/frontend/src/lib/authSession.ts
+++ b/frontend/src/lib/authSession.ts
@@ -1,5 +1,11 @@
 export const AUTH_REDIRECT_MESSAGE_KEY = 'authRedirectMessage'
 
+export type UserSessionCredentials = {
+  accessToken?: string | null
+  refreshToken?: string | null
+  apiKey?: string | null
+}
+
 export function getApiErrorDetail(error: unknown): string | undefined {
   const detail = (error as { response?: { data?: { detail?: unknown } } })?.response?.data
     ?.detail
@@ -23,6 +29,16 @@ export function clearAuthSession(): void {
   localStorage.removeItem('activeWorkspaceId')
 }
 
+/** True when a voluntary logout can still revoke something server-side. */
+export function hasRevocableUserCredentials(credentials: UserSessionCredentials): boolean {
+  return Boolean(credentials.accessToken || credentials.apiKey || credentials.refreshToken)
+}
+
+export function clearPlatformAdminSession(): void {
+  localStorage.removeItem('platformAccessToken')
+  localStorage.removeItem('platformAdminUser')
+}
+
 export function redirectToLoginWithMessage(message: string): void {
   sessionStorage.setItem(AUTH_REDIRECT_MESSAGE_KEY, message)
   clearAuthSession()
diff --git a/frontend/src/store/authStore.ts b/frontend/src/store/authStore.ts
index e7c2c938..2b37a2b0 100644
--- a/frontend/src/store/authStore.ts
+++ b/frontend/src/store/authStore.ts
@@ -1,5 +1,6 @@
 import { create } from 'zustand'
 import { apiClient } from '../lib/api'
+import { clearAuthSession } from '../lib/authSession'
 import { useWorkspaceStore } from './workspaceStore'
 
 /**
@@ -113,21 +114,15 @@ export const useAuthStore = create<AuthState>((set, get) => {
     },
 
     logout: () => {
-      const { refreshToken, accessToken, apiKey } = get()
-      apiClient.clearApiKey()
-      apiClient.clearAccessToken()
-      apiClient.clearRefreshToken()
-      localStorage.removeItem(STORAGE_API_KEY)
-      localStorage.removeItem(STORAGE_ACCESS_TOKEN)
-      localStorage.removeItem(STORAGE_REFRESH_TOKEN)
-      localStorage.removeItem(STORAGE_USER)
+      const credentials = {
+        accessToken: get().accessToken,
+        refreshToken: get().refreshToken,
+        apiKey: get().apiKey,
+      }
+      clearAuthSession()
       useWorkspaceStore.getState().clearActiveWorkspaceId()
       set({ apiKey: null, accessToken: null, refreshToken: null, user: null })
-
-      const credentials = { accessToken, apiKey }
-      void apiClient
-        .logout(refreshToken, credentials)
-        .catch(() => apiClient.logout(refreshToken, credentials))
+      apiClient.revokeUserSessionBestEffort(credentials)
     },
 
     validate: async () => {
diff --git a/frontend/src/store/platformAdminStore.ts b/frontend/src/store/platformAdminStore.ts
index 76ed5a2c..92e86a2d 100644
--- a/frontend/src/store/platformAdminStore.ts
+++ b/frontend/src/store/platformAdminStore.ts
@@ -1,5 +1,6 @@
 import { create } from 'zustand'
 import { apiClient } from '../lib/api'
+import { clearPlatformAdminSession } from '../lib/authSession'
 
 type PlatformAdminUser = {
   id: string
@@ -39,13 +40,9 @@ export const usePlatformAdminStore = create<PlatformAdminState>((set, get) => {
     },
     logout: () => {
       const accessToken = get().accessToken
-      localStorage.removeItem(STORAGE_TOKEN)
-      localStorage.removeItem(STORAGE_ADMIN)
+      clearPlatformAdminSession()
       set({ accessToken: null, admin: null })
-
-      void apiClient
-        .platformLogout(accessToken)
-        .catch(() => apiClient.platformLogout(accessToken))
+      apiClient.revokePlatformSessionBestEffort(accessToken)
     },
   }
 })

From d144bb59cc5b5bb3a2e59435fec6ba7d3c77e5b5 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Mon, 24 Aug 2026 22:13:33 +0530
Subject: [PATCH 37/55] fix(auth): improve error handling in session revocation

---
 frontend/src/lib/api.ts | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 7bdd5273..d3ffa521 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -856,16 +856,18 @@ class ApiClient {
       return
     }
     const auth = { accessToken: credentials.accessToken, apiKey: credentials.apiKey }
-    void this.revokeUserSession(credentials.refreshToken, auth).catch(() =>
-      this.revokeUserSession(credentials.refreshToken, auth),
-    )
+    void this.revokeUserSession(credentials.refreshToken, auth)
+      .catch(() => this.revokeUserSession(credentials.refreshToken, auth))
+      .catch(() => {})
   }
 
   revokePlatformSessionBestEffort(accessToken?: string | null): void {
     if (!accessToken) {
       return
     }
-    void this.platformLogout(accessToken).catch(() => this.platformLogout(accessToken))
+    void this.platformLogout(accessToken)
+      .catch(() => this.platformLogout(accessToken))
+      .catch(() => {})
   }
 
   async getPlatformOrganizationStats(): Promise<PlatformOrganizationStats> {

From e8d69d1def66c264a4c92542aeaa8153ebcf17b3 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Tue, 25 Aug 2026 18:00:11 +0530
Subject: [PATCH 38/55] feat(usage): enhance LLM usage context management with
 new tracking for evaluator results and metric studio runs; improve CSP policy
 for voice provider connections

---
 app/api/v1/routes/evaluator_results.py        |  12 +-
 app/api/v1/routes/personas.py                 |  34 +-
 app/api/v1/routes/playground.py               |  73 +++-
 app/config.py                                 |  24 +-
 .../llm_to_llm_evaluator_simulation.py        | 274 +++++++++++++++
 .../testing/test_agent_bridge_service.py      |   6 +
 app/services/testing/test_agent_service.py    |  94 ++++--
 app/services/usage/bucket_context.py          |  14 +-
 app/services/usage/context.py                 | 149 +++++++-
 app/services/usage/external_agent_usage.py    | 313 +++++++++++++++++
 .../webrtc_bridge/test_agent_processor.py     | 174 ++++++----
 app/workers/tasks/evaluate_studio_run_item.py | 143 ++++----
 app/workers/tasks/process_evaluator_result.py |  27 ++
 app/workers/tasks/run_evaluator.py            |  98 +++++-
 config.yml.example                            |   2 +-
 tests/conftest.py                             |   8 +
 .../test_api/test_evaluator_results_routes.py |  32 ++
 .../test_security_headers_middleware.py       |  18 +
 .../test_llm_to_llm_evaluator_simulation.py   | 111 ++++++
 .../test_usage/test_llm_usage.py              |   5 +-
 .../test_usage/test_pre_prod_usage.py         | 242 +++++++++++++
 .../test_test_agent_simulation_usage.py       | 318 ++++++++++++++++++
 .../test_celery_task_workflows.py             | 136 +++++++-
 23 files changed, 2086 insertions(+), 221 deletions(-)
 create mode 100644 app/services/testing/llm_to_llm_evaluator_simulation.py
 create mode 100644 app/services/usage/external_agent_usage.py
 create mode 100644 tests/test_services/test_usage/test_llm_to_llm_evaluator_simulation.py
 create mode 100644 tests/test_services/test_usage/test_pre_prod_usage.py
 create mode 100644 tests/test_services/test_usage/test_test_agent_simulation_usage.py

diff --git a/app/api/v1/routes/evaluator_results.py b/app/api/v1/routes/evaluator_results.py
index 962207c6..093ea249 100644
--- a/app/api/v1/routes/evaluator_results.py
+++ b/app/api/v1/routes/evaluator_results.py
@@ -864,15 +864,17 @@ def re_evaluate_result(
             detail="Cannot re-evaluate: this result has no transcription. It must be transcribed first."
         )
 
-    if not result.evaluator_id:
+    if not result.evaluator_id and not result.agent_id:
         raise HTTPException(
             status_code=400,
-            detail="Cannot re-evaluate: this result is not linked to an evaluator."
+            detail="Cannot re-evaluate: this result is not linked to an agent or evaluator."
         )
 
-    evaluator = db.query(Evaluator).filter(Evaluator.id == result.evaluator_id).first()
-    if not evaluator:
-        raise HTTPException(status_code=404, detail="Linked evaluator no longer exists")
+    evaluator = None
+    if result.evaluator_id:
+        evaluator = db.query(Evaluator).filter(Evaluator.id == result.evaluator_id).first()
+        if not evaluator:
+            raise HTTPException(status_code=404, detail="Linked evaluator no longer exists")
 
     # ------------------------------------------------------------------
     # If no audio in S3 yet, try to download from the voice provider
diff --git a/app/api/v1/routes/personas.py b/app/api/v1/routes/personas.py
index de07c715..c0217cc3 100644
--- a/app/api/v1/routes/personas.py
+++ b/app/api/v1/routes/personas.py
@@ -442,20 +442,28 @@ async def generate_persona_prompt(
     provider_enum, model_str = _get_llm_provider_and_model(
         organization_id, db, data.provider, data.model, data.credential_id
     )
+    from app.services.usage.context import (
+        llm_usage_context,
+        usage_context_for_persona_generation,
+    )
+
     try:
-        result = generate_persona_prompt_from_agent(
-            agent,
-            source=data.source,
-            persona_name=data.persona_name,
-            persona_gender=data.persona_gender,
-            additional_context=data.additional_context,
-            llm_provider=provider_enum,
-            llm_model=model_str,
-            organization_id=organization_id,
-            db=db,
-            llm_config=data.llm_config,
-            credential_id=data.credential_id,
-        )
+        with llm_usage_context(
+            usage_context_for_persona_generation(agent, workspace_id=workspace_id)
+        ):
+            result = generate_persona_prompt_from_agent(
+                agent,
+                source=data.source,
+                persona_name=data.persona_name,
+                persona_gender=data.persona_gender,
+                additional_context=data.additional_context,
+                llm_provider=provider_enum,
+                llm_model=model_str,
+                organization_id=organization_id,
+                db=db,
+                llm_config=data.llm_config,
+                credential_id=data.credential_id,
+            )
         return GeneratePersonaPromptResponse(
             persona_prompt=result.persona_prompt,
             source_used=result.source_used,
diff --git a/app/api/v1/routes/playground.py b/app/api/v1/routes/playground.py
index 31b1a3c2..de2be5ba 100644
--- a/app/api/v1/routes/playground.py
+++ b/app/api/v1/routes/playground.py
@@ -109,7 +109,14 @@ def poll_call_metrics(
                     # For other providers, implement similar method
                     continue
                 
-                # Update the call recording with metrics
+                # Update the call recording with metrics (preserve usage-recorded flag)
+                prev_data = (
+                    call_recording.call_data
+                    if isinstance(call_recording.call_data, dict)
+                    else {}
+                )
+                if prev_data.get("external_usage_recorded") and isinstance(call_metrics, dict):
+                    call_metrics["external_usage_recorded"] = True
                 call_recording.call_data = call_metrics
                 call_recording.status = CallRecordingStatus.UPDATED
                 db.commit()
@@ -138,6 +145,46 @@ def poll_call_metrics(
         # After polling is complete, create EvaluatorResult and trigger evaluation
         if call_complete and call_metrics and call_recording.agent_id:
             try:
+                from app.services.usage.external_agent_usage import (
+                    record_playground_provider_usage_from_call_data,
+                )
+
+                db.refresh(call_recording)
+                if call_recording.evaluator_result_id:
+                    logger.info(
+                        f"[Poll Call Metrics] Skipping post-call processing — "
+                        f"evaluator result already exists for call {provider_call_id}"
+                    )
+                    return
+
+                platform_key = str(provider_platform or "").lower()
+                stored_data = (
+                    call_recording.call_data
+                    if isinstance(call_recording.call_data, dict)
+                    else {}
+                )
+                if stored_data.get("external_usage_recorded") and isinstance(call_metrics, dict):
+                    call_metrics["external_usage_recorded"] = True
+                elif isinstance(call_metrics, dict):
+                    call_metrics = record_playground_provider_usage_from_call_data(
+                        organization_id=call_recording.organization_id,
+                        workspace_id=call_recording.workspace_id,
+                        agent_id=call_recording.agent_id,
+                        provider_platform=platform_key,
+                        call_short_id=call_recording.call_short_id,
+                        call_data=call_metrics,
+                    )
+                call_recording.call_data = call_metrics
+                db.commit()
+
+                db.refresh(call_recording)
+                if call_recording.evaluator_result_id:
+                    logger.info(
+                        f"[Poll Call Metrics] Skipping evaluator creation — "
+                        f"another poll already created result for call {provider_call_id}"
+                    )
+                    return
+
                 logger.info(f"[Poll Call Metrics] Call complete, creating EvaluatorResult for call {provider_call_id}")
                 
                 # Extract transcript and speaker segments from call_data
@@ -321,13 +368,16 @@ async def update_call_recording(
             if integration:
                 try:
                     decrypted_api_key = decrypt_api_key(integration.api_key)
-                    background_tasks.add_task(
-                        poll_call_metrics,
-                        call_recording.id,
-                        call_recording.provider_call_id,
-                        call_recording.provider_platform,
-                        decrypted_api_key
-                    )
+                    platform_key = (call_recording.provider_platform or "").lower()
+                    # Vapi polls on call-end refresh only; update fires mid-call too.
+                    if platform_key != "vapi":
+                        background_tasks.add_task(
+                            poll_call_metrics,
+                            call_recording.id,
+                            call_recording.provider_call_id,
+                            call_recording.provider_platform,
+                            decrypted_api_key
+                        )
                 except:
                     pass
     
@@ -504,7 +554,7 @@ async def create_web_call(
             # Note: We need to pass the decrypted API key, but we should be careful with security
             # For now, we'll pass it to the background task
             # In production, you might want to store it temporarily or use a different approach
-            if provider_call_id:
+            if provider_call_id and plat_lower != "vapi":
                 background_tasks.add_task(
                     poll_call_metrics,
                     call_recording.id,
@@ -960,7 +1010,10 @@ async def refresh_call_recording(
             status_code=status.HTTP_404_NOT_FOUND,
             detail="Integration not found"
         )
-    
+
+    if call_recording.evaluator_result_id:
+        return {"message": "Call recording already processed"}
+
     try:
         decrypted_api_key = decrypt_api_key(integration.api_key)
     except Exception as e:
diff --git a/app/config.py b/app/config.py
index 7e5f4f1f..c5dc096e 100644
--- a/app/config.py
+++ b/app/config.py
@@ -128,15 +128,33 @@ class Settings(BaseSettings):
     # Content Security Policy (enforcing by default; set CSP_REPORT_ONLY=true for local report-only mode)
     CSP_ENABLED: bool = True
     CSP_REPORT_ONLY: bool = False
+    # Browser voice SDKs (Vapi/Daily, Retell/LiveKit, ElevenLabs convai) and their telemetry.
+    _CSP_VOICE_CONNECT_SRC: str = (
+        "https://api.vapi.ai "
+        "https://*.vapi.ai "
+        "https://*.daily.co "
+        "wss://*.daily.co "
+        "wss://*.livekit.cloud "
+        "https://api.elevenlabs.io "
+        "wss://api.elevenlabs.io "
+        "https://api.retellai.com "
+        "wss://api.retellai.com "
+        "https://*.ingest.sentry.io "
+        "https://*.ingest.us.sentry.io"
+    )
+    _CSP_VOICE_FRAME_SRC: str = "https://*.daily.co"
+    # Vapi → Daily.co call-machine bundle requires eval + blob worklets for audio
+    _CSP_DAILY_SCRIPT_SRC: str = "'unsafe-eval' blob: https://c.daily.co https://*.daily.co"
     CSP_POLICY: str = (
         "default-src 'self'; "
-        "script-src 'self'; "
+        f"script-src 'self' {_CSP_DAILY_SCRIPT_SRC}; "
         "style-src 'self' 'unsafe-inline' https://fonts.googleapis.com; "
         "font-src 'self' https://fonts.gstatic.com; "
         "img-src 'self' data: blob: https:; "
-        "connect-src 'self' wss: ws:; "
+        f"connect-src 'self' wss: ws: {_CSP_VOICE_CONNECT_SRC}; "
         "media-src 'self' blob: https:; "
-        "frame-src 'self' blob:; "
+        f"frame-src 'self' blob: {_CSP_VOICE_FRAME_SRC}; "
+        "worker-src 'self' blob:; "
         "object-src 'none'; "
         "base-uri 'self'; "
         "form-action 'self'; "
diff --git a/app/services/testing/llm_to_llm_evaluator_simulation.py b/app/services/testing/llm_to_llm_evaluator_simulation.py
new file mode 100644
index 00000000..c14f84ef
--- /dev/null
+++ b/app/services/testing/llm_to_llm_evaluator_simulation.py
@@ -0,0 +1,274 @@
+"""Text-based LLM-to-LLM simulation for evaluator runs without an external voice provider."""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Optional
+from uuid import UUID
+
+from loguru import logger
+from sqlalchemy.orm import Session
+
+from app.models.database import Agent, Evaluator, EvaluatorResult, Persona, Scenario, VoiceBundle
+from app.models.enums import ModelProvider
+from app.services.ai.llm_service import llm_service
+from app.services.testing.test_agent_simulation_prompt import (
+    build_persona_description_for_bridge,
+    build_test_agent_system_prompt,
+    get_agent_base_prompt,
+    resolve_persona_max_turns,
+)
+from app.services.usage.context import (
+    LLMUsageContext,
+    LLMUsageProductSection,
+    llm_usage_context,
+    usage_context_for_test_agent_simulation,
+)
+
+_GOODBYE_RE = re.compile(
+    r"\b(goodbye|bye|thanks?\s+you|talk\s+to\s+you\s+later|have\s+a\s+(?:good|great)\s+(?:day|one))\b",
+    re.IGNORECASE,
+)
+
+
+def _build_agent_system_prompt(agent: Agent) -> str:
+    agent_name = (agent.name or "Voice AI Agent").strip()
+    base = get_agent_base_prompt(agent)
+    return (
+        f"You are {agent_name}, a voice AI agent on a live phone call.\n\n"
+        f"Your instructions:\n{base}\n\n"
+        "Respond naturally in 1-3 sentences as on a phone call. "
+        "Respond ONLY with what you would say — no stage directions."
+    )
+
+
+def _should_end_conversation(text: str, *, turn_index: int, min_turns: int = 2) -> bool:
+    if turn_index < min_turns:
+        return False
+    return bool(_GOODBYE_RE.search(text or ""))
+
+
+def _caller_messages(system_prompt: str, transcript: list[dict[str, str]]) -> list[dict[str, str]]:
+    messages: list[dict[str, str]] = [{"role": "system", "content": system_prompt}]
+    for entry in transcript:
+        speaker = entry.get("speaker")
+        text = (entry.get("text") or "").strip()
+        if not text:
+            continue
+        if speaker == "Speaker 1":
+            messages.append({"role": "assistant", "content": text})
+        else:
+            messages.append({"role": "user", "content": text})
+    if len(messages) == 1:
+        messages.append(
+            {
+                "role": "user",
+                "content": "The call has just connected. Start the conversation.",
+            }
+        )
+    return messages
+
+
+def _agent_messages(system_prompt: str, transcript: list[dict[str, str]]) -> list[dict[str, str]]:
+    messages: list[dict[str, str]] = [{"role": "system", "content": system_prompt}]
+    for entry in transcript:
+        speaker = entry.get("speaker")
+        text = (entry.get("text") or "").strip()
+        if not text:
+            continue
+        if speaker == "Speaker 2":
+            messages.append({"role": "assistant", "content": text})
+        else:
+            messages.append({"role": "user", "content": text})
+    return messages
+
+
+def _resolve_voice_bundle_llm(
+    db: Session,
+    *,
+    voice_bundle: VoiceBundle,
+    organization_id: UUID,
+) -> tuple[ModelProvider, str, Optional[dict], Optional[UUID]]:
+    raw_provider = voice_bundle.llm_provider
+    if raw_provider is None:
+        raise ValueError("Voice bundle is missing llm_provider")
+    provider = (
+        raw_provider
+        if isinstance(raw_provider, ModelProvider)
+        else ModelProvider(str(raw_provider).lower())
+    )
+    model = (voice_bundle.llm_model or "").strip()
+    if not model:
+        raise ValueError("Voice bundle is missing llm_model")
+    llm_config = voice_bundle.llm_config if isinstance(voice_bundle.llm_config, dict) else None
+    credential_id = getattr(voice_bundle, "llm_credential_id", None)
+    return provider, model, llm_config, credential_id
+
+
+def _generate_turn(
+    *,
+    messages: list[dict[str, str]],
+    llm_provider: ModelProvider,
+    llm_model: str,
+    organization_id: UUID,
+    db: Session,
+    llm_config: Optional[dict],
+    credential_id: Optional[UUID],
+) -> str:
+    result = llm_service.generate_response(
+        messages=messages,
+        llm_provider=llm_provider,
+        llm_model=llm_model,
+        organization_id=organization_id,
+        db=db,
+        llm_config=llm_config,
+        task_defaults={"temperature": 0.7, "max_tokens": 300},
+        credential_id=credential_id,
+    )
+    text = (result.get("text") or "").strip()
+    if not text:
+        raise ValueError("LLM returned an empty simulation response")
+    return text
+
+
+def run_llm_to_llm_evaluator_simulation(
+    *,
+    evaluator: Evaluator,
+    result: EvaluatorResult,
+    agent: Agent,
+    persona: Persona,
+    scenario: Scenario,
+    organization_id: UUID,
+    db: Session,
+) -> dict[str, Any]:
+    """Run a text simulation and populate the evaluator result transcript."""
+    if not agent.voice_bundle_id:
+        raise ValueError("Agent does not have a voice bundle configured")
+
+    voice_bundle = (
+        db.query(VoiceBundle)
+        .filter(
+            VoiceBundle.id == agent.voice_bundle_id,
+            VoiceBundle.organization_id == organization_id,
+        )
+        .first()
+    )
+    if not voice_bundle:
+        raise ValueError(f"Voice bundle {agent.voice_bundle_id} not found")
+
+    llm_provider, llm_model, llm_config, credential_id = _resolve_voice_bundle_llm(
+        db,
+        voice_bundle=voice_bundle,
+        organization_id=organization_id,
+    )
+
+    max_turns = resolve_persona_max_turns(persona)
+    persona_description = build_persona_description_for_bridge(persona)
+    caller_system = build_test_agent_system_prompt(
+        agent,
+        persona,
+        scenario,
+        persona_description=persona_description,
+        max_turns=max_turns,
+    )
+    agent_system = _build_agent_system_prompt(agent)
+
+    caller_ctx = usage_context_for_test_agent_simulation(
+        organization_id=organization_id,
+        workspace_id=evaluator.workspace_id,
+        agent_id=agent.id,
+        evaluator_id=evaluator.id,
+        persona_id=persona.id,
+        scenario_id=scenario.id,
+        evaluator_result_id=result.id,
+        provider_platform="internal",
+    )
+    agent_ctx = LLMUsageContext(
+        organization_id=organization_id,
+        workspace_id=evaluator.workspace_id,
+        product_section=LLMUsageProductSection.EVALUATORS,
+        resource_id=evaluator.id,
+        resource_type="evaluator",
+        extra={
+            "agent_id": str(agent.id),
+            "evaluator_id": str(evaluator.id),
+            "persona_id": str(persona.id),
+            "scenario_id": str(scenario.id),
+            "evaluator_result_id": str(result.id),
+            "synthetic_testing": "pre_prod",
+            "simulation_leg": "production_agent",
+            "provider_platform": "internal",
+        },
+    )
+
+    transcript: list[dict[str, str]] = []
+    first_message = f"Hello, this is {persona.name} calling."
+    transcript.append({"speaker": "Speaker 1", "text": first_message})
+
+    exchanges = 0
+    while exchanges < max_turns:
+        with llm_usage_context(agent_ctx):
+            agent_text = _generate_turn(
+                messages=_agent_messages(agent_system, transcript),
+                llm_provider=llm_provider,
+                llm_model=llm_model,
+                organization_id=organization_id,
+                db=db,
+                llm_config=llm_config,
+                credential_id=credential_id,
+            )
+        transcript.append({"speaker": "Speaker 2", "text": agent_text})
+        exchanges += 1
+        if _should_end_conversation(agent_text, turn_index=exchanges):
+            break
+
+        with llm_usage_context(caller_ctx):
+            caller_text = _generate_turn(
+                messages=_caller_messages(caller_system, transcript),
+                llm_provider=llm_provider,
+                llm_model=llm_model,
+                organization_id=organization_id,
+                db=db,
+                llm_config=llm_config,
+                credential_id=credential_id,
+            )
+        transcript.append({"speaker": "Speaker 1", "text": caller_text})
+        exchanges += 1
+        if _should_end_conversation(caller_text, turn_index=exchanges):
+            break
+
+    transcription = "\n".join(
+        f"{entry['speaker']}: {entry['text']}" for entry in transcript if entry.get("text")
+    )
+    speaker_segments = [
+        {
+            "speaker": entry["speaker"],
+            "text": entry["text"],
+            "start": float(idx),
+            "end": float(idx) + 1.0,
+        }
+        for idx, entry in enumerate(transcript)
+    ]
+
+    result.transcription = transcription
+    result.speaker_segments = speaker_segments
+    result.provider_platform = "internal"
+    result.call_data = {
+        "source": "llm_to_llm_simulation",
+        "simulation": "llm_to_llm",
+        "exchanges": exchanges,
+        "messages": transcript,
+    }
+    result.duration_seconds = float(max(1, len(transcript)))
+
+    logger.info(
+        "[LLM simulation] Completed evaluator {} result {} with {} transcript lines",
+        evaluator.evaluator_id,
+        result.result_id,
+        len(transcript),
+    )
+    return {
+        "transcript_lines": len(transcript),
+        "exchanges": exchanges,
+        "provider_platform": "internal",
+    }
diff --git a/app/services/testing/test_agent_bridge_service.py b/app/services/testing/test_agent_bridge_service.py
index b13b78b1..50b19699 100644
--- a/app/services/testing/test_agent_bridge_service.py
+++ b/app/services/testing/test_agent_bridge_service.py
@@ -615,6 +615,12 @@ def resolve_api_key_for_provider(
                 test_agent_config = TestAgentConfig(
                     organization_id=organization_id,
                     workspace_id=getattr(agent, "workspace_id", None),
+                    agent_id=agent.id,
+                    evaluator_id=evaluator_id,
+                    persona_id=persona.id,
+                    scenario_id=scenario.id,
+                    evaluator_result_id=evaluator_result_id,
+                    db=db,
                     agent_name=agent.name or "Voice AI Agent",
                     agent_description=agent.description or "A voice AI assistant",
                     test_agent_simulation_prompt=simulation_prompt,
diff --git a/app/services/testing/test_agent_service.py b/app/services/testing/test_agent_service.py
index 84348190..c7d09b37 100644
--- a/app/services/testing/test_agent_service.py
+++ b/app/services/testing/test_agent_service.py
@@ -24,6 +24,7 @@
 from app.services.ai.transcription_service import transcription_service
 from app.services.ai.llm_service import llm_service
 from app.services.ai.tts_service import tts_service
+from app.services.storage.s3_service import s3_service
 from app.services.testing.test_agent_simulation_prompt import build_test_agent_system_prompt
 from sqlalchemy.orm import Session
 
@@ -237,6 +238,20 @@ def process_audio_chunk(
         if not all([agent, persona, scenario]):
             raise ValueError("Missing agent, persona, or scenario")
 
+        from app.services.usage.context import (
+            llm_usage_context,
+            usage_context_for_test_agent_simulation,
+        )
+
+        usage_ctx = usage_context_for_test_agent_simulation(
+            organization_id=organization_id,
+            workspace_id=conversation.workspace_id,
+            agent_id=agent.id,
+            persona_id=persona.id,
+            scenario_id=scenario.id,
+            conversation_id=conversation.id,
+        )
+
         # Calculate timestamp
         if chunk_timestamp is None:
             if conversation.started_at:
@@ -260,19 +275,45 @@ def process_audio_chunk(
                 "error": f"Failed to convert audio format: {str(e)}",
             }
 
-        # Save audio chunk temporarily for transcription
+        with llm_usage_context(usage_ctx):
+            return self._process_audio_chunk_with_context(
+                conversation=conversation,
+                wav_audio_bytes=wav_audio_bytes,
+                voice_bundle=voice_bundle,
+                agent=agent,
+                persona=persona,
+                scenario=scenario,
+                organization_id=organization_id,
+                db=db,
+                chunk_timestamp=chunk_timestamp,
+            )
+
+    def _process_audio_chunk_with_context(
+        self,
+        *,
+        conversation: TestAgentConversation,
+        wav_audio_bytes: bytes,
+        voice_bundle: VoiceBundle,
+        agent: Agent,
+        persona: Persona,
+        scenario: Scenario,
+        organization_id: UUID,
+        db: Session,
+        chunk_timestamp: float,
+    ) -> Dict[str, Any]:
         temp_file_path = None
         try:
-            # Save converted WAV to temp file for transcription
             with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
                 temp_file.write(wav_audio_bytes)
                 temp_file_path = temp_file.name
 
-            # Upload to S3 temporarily for transcription service (it needs S3 key)
             chunk_file_id = uuid.uuid4()
-            chunk_s3_key = s3_service.upload_file(file_content=wav_audio_bytes, file_id=chunk_file_id, file_format="wav")
+            chunk_s3_key = s3_service.upload_file(
+                file_content=wav_audio_bytes,
+                file_id=chunk_file_id,
+                file_format="wav",
+            )
 
-            # Transcribe using STT
             transcription_result = transcription_service.transcribe(
                 audio_file_key=chunk_s3_key,
                 stt_provider=voice_bundle.stt_provider,
@@ -292,24 +333,24 @@ def process_audio_chunk(
                     "error": "No speech detected in audio chunk",
                 }
 
-            # Add voice agent turn to conversation
             conversation_turns = conversation.live_transcription or []
-            conversation_turns.append({"speaker": "voice_agent", "text": voice_agent_text, "timestamp": chunk_timestamp})
+            conversation_turns.append(
+                {
+                    "speaker": "voice_agent",
+                    "text": voice_agent_text,
+                    "timestamp": chunk_timestamp,
+                }
+            )
 
-            # Build conversation history for LLM
             messages = []
-
-            # System prompt
             system_prompt = self._build_system_prompt(agent, persona, scenario, db)
             messages.append({"role": "system", "content": system_prompt})
 
-            # Add conversation history (last 10 turns for context)
             recent_turns = conversation_turns[-10:]
             for turn in recent_turns:
                 role = "user" if turn["speaker"] == "voice_agent" else "assistant"
                 messages.append({"role": role, "content": turn["text"]})
 
-            # Generate response using LLM
             llm_result = llm_service.generate_response(
                 messages=messages,
                 llm_provider=voice_bundle.llm_provider,
@@ -331,7 +372,6 @@ def process_audio_chunk(
                     "error": "LLM did not generate a response",
                 }
 
-            # Convert response to speech using TTS
             from app.services.voice_agent.resolve_tts_voice import resolve_effective_tts_voice_id
 
             tts_voice = resolve_effective_tts_voice_id(
@@ -348,34 +388,44 @@ def process_audio_chunk(
                 config=voice_bundle.tts_config,
             )
 
-            # Upload response audio to S3 (temporarily, for reference)
             response_file_id = uuid.uuid4()
-            response_s3_key = s3_service.upload_file(file_content=response_audio_bytes, file_id=response_file_id, file_format="mp3")
+            s3_service.upload_file(
+                file_content=response_audio_bytes,
+                file_id=response_file_id,
+                file_format="mp3",
+            )
 
-            # Add test agent turn to conversation
             conversation_turns.append(
                 {
                     "speaker": "test_agent",
                     "text": test_agent_text,
-                    "timestamp": chunk_timestamp + transcription_result.get("processing_time", 0) + llm_result.get("processing_time", 0),
+                    "timestamp": chunk_timestamp
+                    + transcription_result.get("processing_time", 0)
+                    + llm_result.get("processing_time", 0),
                 }
             )
 
-            # Update conversation
             conversation.live_transcription = conversation_turns
-            conversation.full_transcript = "\n".join([f"{turn['speaker']}: {turn['text']}" for turn in conversation_turns])
+            conversation.full_transcript = "\n".join(
+                [f"{turn['speaker']}: {turn['text']}" for turn in conversation_turns]
+            )
             db.commit()
 
             return {
                 "response_audio": response_audio_bytes,
-                "transcription": {"voice_agent": voice_agent_text, "test_agent": test_agent_text},
+                "transcription": {
+                    "voice_agent": voice_agent_text,
+                    "test_agent": test_agent_text,
+                },
                 "metadata": {
-                    "processing_times": {"stt": transcription_result.get("processing_time", 0), "llm": llm_result.get("processing_time", 0)}
+                    "processing_times": {
+                        "stt": transcription_result.get("processing_time", 0),
+                        "llm": llm_result.get("processing_time", 0),
+                    }
                 },
             }
 
         finally:
-            # Clean up temp file
             if temp_file_path and os.path.exists(temp_file_path):
                 try:
                     os.unlink(temp_file_path)
diff --git a/app/services/usage/bucket_context.py b/app/services/usage/bucket_context.py
index 64187a39..ef813493 100644
--- a/app/services/usage/bucket_context.py
+++ b/app/services/usage/bucket_context.py
@@ -26,6 +26,18 @@
     }
 )
 
+# Per-request identifiers — useful in LLMUsageContext.extra but must not
+# create a new Redis/PG bucket per call.
+_HIGH_CARDINALITY_EXTRA_KEYS = frozenset(
+    {
+        "evaluator_result_id",
+        "result_short_id",
+        "metric_studio_result_id",
+        "conversation_id",
+        "call_short_id",
+    }
+)
+
 # Never store roll-up counters in JSONB — they stay as BIGINT columns for SUM().
 _FORBIDDEN_CONTEXT_KEYS = frozenset(
     {
@@ -58,7 +70,7 @@ def build_bucket_context(
         for key, value in extra.items():
             if value is None or key in ctx:
                 continue
-            if key in _FORBIDDEN_CONTEXT_KEYS:
+            if key in _FORBIDDEN_CONTEXT_KEYS or key in _HIGH_CARDINALITY_EXTRA_KEYS:
                 continue
             ctx[key] = str(value)
     return ctx
diff --git a/app/services/usage/context.py b/app/services/usage/context.py
index 8bd895e1..ade2cfde 100644
--- a/app/services/usage/context.py
+++ b/app/services/usage/context.py
@@ -206,31 +206,88 @@ def usage_context_for_agent(
 
 
 def usage_context_for_evaluator_result(result: Any) -> LLMUsageContext:
-    """Usage context for processing an evaluator result (Vapi / playground runs)."""
-    if result.agent_id:
-        return LLMUsageContext(
-            organization_id=result.organization_id,
-            workspace_id=result.workspace_id,
-            product_section=LLMUsageProductSection.AGENTS,
-            resource_id=result.agent_id,
-            resource_type="agent",
-            extra={"agent_id": str(result.agent_id)},
-        )
-    extra: dict[str, str] = {"evaluator_result_id": str(result.id)}
+    """Usage context for post-run processing of synthetic evaluator results."""
+    extra: dict[str, str] = {
+        "evaluator_result_id": str(result.id),
+        "synthetic_testing": "pre_prod",
+    }
     if getattr(result, "result_id", None):
         extra["result_short_id"] = str(result.result_id)
+    if result.agent_id:
+        extra["agent_id"] = str(result.agent_id)
     if result.evaluator_id:
         extra["evaluator_id"] = str(result.evaluator_id)
+    if getattr(result, "persona_id", None):
+        extra["persona_id"] = str(result.persona_id)
+    if getattr(result, "scenario_id", None):
+        extra["scenario_id"] = str(result.scenario_id)
+    platform = getattr(result, "provider_platform", None)
+    if platform:
+        extra["provider_platform"] = str(platform).lower()
+
+    resource_id = result.evaluator_id or result.id
+    resource_type = "evaluator" if result.evaluator_id else "evaluator_result"
+    section = (
+        LLMUsageProductSection.EVALUATORS
+        if result.evaluator_id
+        else LLMUsageProductSection.PLAYGROUND
+    )
     return LLMUsageContext(
         organization_id=result.organization_id,
         workspace_id=result.workspace_id,
-        product_section=LLMUsageProductSection.EVALUATORS,
-        resource_id=result.id,
-        resource_type="evaluator_result",
+        product_section=section,
+        resource_id=resource_id,
+        resource_type=resource_type,
         extra=extra,
     )
 
 
+def usage_context_for_metric_studio_run(
+    run: Any,
+    *,
+    source_kind: Optional[str] = None,
+    source_ref: Optional[str] = None,
+    result_row_id: Optional[UUID] = None,
+) -> LLMUsageContext:
+    """Usage context for Metrics Studio batch scoring."""
+    extra: dict[str, str] = {"metric_studio_run_id": str(run.id)}
+    if source_kind:
+        extra["source_kind"] = source_kind
+    if source_ref:
+        extra["source_ref"] = source_ref
+    if result_row_id:
+        extra["metric_studio_result_id"] = str(result_row_id)
+    if source_kind == "evaluator_result":
+        extra["synthetic_testing"] = "pre_prod"
+    return LLMUsageContext(
+        organization_id=run.organization_id,
+        workspace_id=run.workspace_id,
+        product_section=LLMUsageProductSection.METRICS,
+        resource_id=run.id,
+        resource_type="metric_studio_run",
+        extra=extra,
+    )
+
+
+def usage_context_for_persona_generation(
+    agent: Any,
+    *,
+    workspace_id: Optional[UUID] = None,
+) -> LLMUsageContext:
+    """Usage context for LLM-generated persona caller prompts."""
+    return LLMUsageContext(
+        organization_id=agent.organization_id,
+        workspace_id=workspace_id or agent.workspace_id,
+        product_section=LLMUsageProductSection.PERSONAS,
+        resource_id=agent.id,
+        resource_type="agent",
+        extra={
+            "agent_id": str(agent.id),
+            "synthetic_testing": "pre_prod",
+        },
+    )
+
+
 def usage_context_for_prompt_optimization_run(run: Any) -> LLMUsageContext:
     """Usage context for a GEPA prompt optimization run."""
     cfg = run.config if isinstance(run.config, dict) else {}
@@ -284,3 +341,67 @@ def usage_context_for_prompt_partial(partial: Any) -> LLMUsageContext:
         resource_type="prompt_partial",
         extra={"prompt_partial_id": str(partial.id)},
     )
+
+
+def usage_context_for_playground_voice_call(
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID],
+    agent_id: Optional[UUID],
+    provider_platform: Optional[str],
+    call_short_id: Optional[str] = None,
+) -> LLMUsageContext:
+    """Usage context for live playground Voice AI Agent provider sessions."""
+    extra: dict[str, str] = {"synthetic_testing": "pre_prod"}
+    if agent_id:
+        extra["agent_id"] = str(agent_id)
+    if provider_platform:
+        extra["provider_platform"] = str(provider_platform).lower()
+    if call_short_id:
+        extra["call_short_id"] = call_short_id
+    return LLMUsageContext(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        product_section=LLMUsageProductSection.PLAYGROUND,
+        resource_id=agent_id,
+        resource_type="agent" if agent_id else None,
+        extra=extra,
+    )
+
+
+def usage_context_for_test_agent_simulation(
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID] = None,
+    agent_id: Optional[UUID] = None,
+    evaluator_id: Optional[UUID] = None,
+    persona_id: Optional[UUID] = None,
+    scenario_id: Optional[UUID] = None,
+    evaluator_result_id: Optional[UUID] = None,
+    conversation_id: Optional[UUID] = None,
+    provider_platform: Optional[str] = None,
+) -> LLMUsageContext:
+    """Usage context for synthetic LLM-to-LLM simulation (caller / test-agent leg)."""
+    extra: dict[str, str] = {"simulation": "llm_to_llm"}
+    if agent_id:
+        extra["agent_id"] = str(agent_id)
+    if evaluator_id:
+        extra["evaluator_id"] = str(evaluator_id)
+    if persona_id:
+        extra["persona_id"] = str(persona_id)
+    if scenario_id:
+        extra["scenario_id"] = str(scenario_id)
+    if evaluator_result_id:
+        extra["evaluator_result_id"] = str(evaluator_result_id)
+    if conversation_id:
+        extra["conversation_id"] = str(conversation_id)
+    if provider_platform:
+        extra["provider_platform"] = str(provider_platform)
+    return LLMUsageContext(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        product_section=LLMUsageProductSection.TEST_AGENT,
+        resource_id=agent_id,
+        resource_type="agent" if agent_id else None,
+        extra=extra,
+    )
diff --git a/app/services/usage/external_agent_usage.py b/app/services/usage/external_agent_usage.py
new file mode 100644
index 00000000..b467f135
--- /dev/null
+++ b/app/services/usage/external_agent_usage.py
@@ -0,0 +1,313 @@
+"""Extract production-agent LLM usage from external voice provider call payloads."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Optional
+from uuid import UUID
+
+from loguru import logger
+
+from app.services.usage.context import LLMUsageContext
+from app.services.usage.llm_usage import record_llm_usage, record_stt_usage, record_tts_usage
+from app.services.usage.normalize import UsageSnapshot, usage_snapshot_is_billable
+
+
+@dataclass(frozen=True)
+class ExternalAgentUsageExtraction:
+    model: str
+    llm: Optional[UsageSnapshot] = None
+    stt_audio_seconds: int = 0
+    tts_characters: int = 0
+
+
+def _as_int(value: Any) -> int:
+    try:
+        return max(0, int(value or 0))
+    except (TypeError, ValueError):
+        return 0
+
+
+def _pick(*values: Any) -> Any:
+    for value in values:
+        if value is not None:
+            return value
+    return None
+
+
+def _vapi_cost_breakdown(call_data: dict[str, Any]) -> dict[str, Any]:
+    raw = call_data.get("costBreakdown") or call_data.get("cost_breakdown") or {}
+    return raw if isinstance(raw, dict) else {}
+
+
+def _retell_llm_snapshot(call_data: dict[str, Any]) -> Optional[UsageSnapshot]:
+    llm_usage = call_data.get("llm_token_usage") or {}
+    if not isinstance(llm_usage, dict):
+        llm_usage = {}
+
+    prompt = completion = 0
+    direct_fields = (
+        ("prompt_tokens", "prompt"),
+        ("input_tokens", "prompt"),
+        ("llm_prompt_tokens", "prompt"),
+        ("completion_tokens", "completion"),
+        ("output_tokens", "completion"),
+        ("llm_completion_tokens", "completion"),
+    )
+    for key, bucket in direct_fields:
+        value = _as_int(llm_usage.get(key))
+        if value <= 0:
+            continue
+        if bucket == "prompt":
+            prompt += value
+        else:
+            completion += value
+
+    call_cost = call_data.get("call_cost") or {}
+    product_costs = call_cost.get("product_costs") if isinstance(call_cost, dict) else None
+    if isinstance(product_costs, list):
+        for item in product_costs:
+            if not isinstance(item, dict):
+                continue
+            product = str(item.get("product") or "").lower()
+            if "llm" not in product and "gpt" not in product and "model" not in product:
+                continue
+            prompt += _as_int(_pick(item.get("prompt_tokens"), item.get("input_tokens")))
+            completion += _as_int(
+                _pick(item.get("completion_tokens"), item.get("output_tokens"))
+            )
+
+    latency = call_data.get("latency") or {}
+    if isinstance(latency, dict):
+        llm_latency = latency.get("llm")
+        if isinstance(llm_latency, dict):
+            prompt += _as_int(_pick(llm_latency.get("prompt_tokens"), llm_latency.get("input_tokens")))
+            completion += _as_int(
+                _pick(llm_latency.get("completion_tokens"), llm_latency.get("output_tokens"))
+            )
+
+    if prompt > 0 or completion > 0:
+        return UsageSnapshot(prompt_tokens=prompt, completion_tokens=completion)
+
+    values = llm_usage.get("values")
+    total_tokens = sum(_as_int(v) for v in values) if isinstance(values, list) else 0
+    if total_tokens <= 0:
+        return None
+
+    prompt = int(round(total_tokens * 0.7))
+    completion = max(0, total_tokens - prompt)
+    return UsageSnapshot(prompt_tokens=prompt, completion_tokens=completion)
+
+
+def extract_external_agent_usage(
+    call_data: dict[str, Any] | None,
+    *,
+    platform: str,
+) -> Optional[ExternalAgentUsageExtraction]:
+    if not isinstance(call_data, dict) or not call_data:
+        return None
+
+    platform_key = (platform or "").strip().lower()
+    if platform_key == "vapi":
+        cb = _vapi_cost_breakdown(call_data)
+        snapshot = UsageSnapshot(
+            prompt_tokens=_as_int(
+                _pick(cb.get("llmPromptTokens"), cb.get("llm_prompt_tokens"))
+            ),
+            completion_tokens=_as_int(
+                _pick(cb.get("llmCompletionTokens"), cb.get("llm_completion_tokens"))
+            ),
+            cache_read_tokens=_as_int(
+                _pick(
+                    cb.get("llmCachedPromptTokens"),
+                    cb.get("llm_cached_prompt_tokens"),
+                )
+            ),
+        )
+        model = (
+            _pick(
+                call_data.get("model"),
+                call_data.get("assistant", {}).get("model")
+                if isinstance(call_data.get("assistant"), dict)
+                else None,
+            )
+            or "vapi-agent"
+        )
+        duration = _as_int(
+            _pick(
+                call_data.get("durationSeconds"),
+                call_data.get("duration_seconds"),
+            )
+        )
+        tts_chars = _as_int(
+            _pick(cb.get("ttsCharacters"), cb.get("tts_characters"))
+        )
+        if not usage_snapshot_is_billable(snapshot) and duration <= 0 and tts_chars <= 0:
+            return None
+        return ExternalAgentUsageExtraction(
+            model=str(model),
+            llm=snapshot if usage_snapshot_is_billable(snapshot) else None,
+            stt_audio_seconds=duration,
+            tts_characters=tts_chars,
+        )
+
+    if platform_key == "retell":
+        call_cost = call_data.get("call_cost") or {}
+        duration_ms = call_data.get("duration_ms")
+        duration = _as_int(
+            _pick(
+                call_cost.get("total_duration_seconds")
+                if isinstance(call_cost, dict)
+                else None,
+                int(duration_ms) / 1000 if duration_ms else None,
+                call_data.get("duration_seconds"),
+                call_data.get("duration"),
+            )
+        )
+        model = _pick(call_data.get("llm_model"), call_data.get("model")) or "retell-agent"
+        snapshot = _retell_llm_snapshot(call_data)
+        if not usage_snapshot_is_billable(snapshot) and duration <= 0:
+            return None
+        return ExternalAgentUsageExtraction(
+            model=str(model),
+            llm=snapshot if usage_snapshot_is_billable(snapshot) else None,
+            stt_audio_seconds=duration,
+        )
+
+    if platform_key == "elevenlabs":
+        metadata = call_data.get("metadata") or {}
+        if not isinstance(metadata, dict):
+            metadata = {}
+        duration = _as_int(
+            _pick(
+                metadata.get("call_duration_secs"),
+                call_data.get("duration_seconds"),
+                call_data.get("duration"),
+            )
+        )
+        if duration <= 0:
+            return None
+        return ExternalAgentUsageExtraction(
+            model="elevenlabs-agent",
+            stt_audio_seconds=duration,
+        )
+
+    if platform_key == "smallest":
+        duration = _as_int(
+            _pick(
+                call_data.get("duration_seconds"),
+                call_data.get("duration"),
+            )
+        )
+        if duration <= 0:
+            return None
+        return ExternalAgentUsageExtraction(
+            model="smallest-agent",
+            stt_audio_seconds=duration,
+        )
+
+    return None
+
+
+def record_playground_provider_usage_from_call_data(
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID],
+    agent_id: Optional[UUID],
+    provider_platform: str,
+    call_short_id: Optional[str],
+    call_data: dict[str, Any],
+) -> dict[str, Any]:
+    """Record provider usage for a completed playground Voice AI call."""
+    from types import SimpleNamespace
+
+    from app.services.usage.context import (
+        llm_usage_context,
+        usage_context_for_playground_voice_call,
+    )
+
+    metrics = dict(call_data or {})
+    if metrics.get("external_usage_recorded"):
+        return metrics
+
+    usage_ctx = usage_context_for_playground_voice_call(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        agent_id=agent_id,
+        provider_platform=provider_platform,
+        call_short_id=call_short_id,
+    )
+    stub = SimpleNamespace(
+        organization_id=organization_id,
+        provider_platform=provider_platform,
+        call_data=metrics,
+        result_id=call_short_id or "playground",
+    )
+    with llm_usage_context(usage_ctx):
+        record_external_agent_usage(stub, usage_ctx=usage_ctx)
+    metrics["external_usage_recorded"] = True
+    return metrics
+
+
+def record_external_agent_usage(
+    result: Any,
+    *,
+    usage_ctx: LLMUsageContext,
+) -> None:
+    """Record production-agent usage from stored provider call_data."""
+    call_data = getattr(result, "call_data", None)
+    platform = getattr(result, "provider_platform", None) or ""
+    extraction = extract_external_agent_usage(
+        call_data if isinstance(call_data, dict) else None,
+        platform=str(platform),
+    )
+    if extraction is None:
+        return
+
+    org_id = getattr(result, "organization_id", None)
+    if org_id is None:
+        return
+
+    ctx = LLMUsageContext(
+        organization_id=usage_ctx.organization_id,
+        workspace_id=usage_ctx.workspace_id,
+        product_section=usage_ctx.product_section,
+        resource_id=usage_ctx.resource_id,
+        resource_type=usage_ctx.resource_type,
+        extra={
+            **(usage_ctx.extra or {}),
+            "synthetic_testing": "pre_prod",
+            "simulation_leg": "production_agent",
+            "provider_platform": str(platform).lower(),
+        },
+    )
+
+    try:
+        if extraction.llm and usage_snapshot_is_billable(extraction.llm):
+            record_llm_usage(
+                extraction.model,
+                extraction.llm,
+                organization_id=org_id,
+                ctx=ctx,
+            )
+        if extraction.stt_audio_seconds > 0:
+            record_stt_usage(
+                f"{platform}-stt",
+                audio_seconds=extraction.stt_audio_seconds,
+                organization_id=org_id,
+                ctx=ctx,
+                count_call=False,
+            )
+        if extraction.tts_characters > 0:
+            record_tts_usage(
+                f"{platform}-tts",
+                characters=extraction.tts_characters,
+                organization_id=org_id,
+                ctx=ctx,
+            )
+    except Exception as exc:
+        logger.debug(
+            "external agent usage record skipped for result {}: {}",
+            getattr(result, "result_id", result),
+            exc,
+        )
diff --git a/app/services/webrtc_bridge/test_agent_processor.py b/app/services/webrtc_bridge/test_agent_processor.py
index 990fa6bd..a4d39946 100644
--- a/app/services/webrtc_bridge/test_agent_processor.py
+++ b/app/services/webrtc_bridge/test_agent_processor.py
@@ -9,8 +9,8 @@
 import asyncio
 import io
 import os
+from dataclasses import dataclass
 from typing import Optional, Callable, Awaitable, List, Dict, Any, Union
-from dataclasses import dataclass, field
 from uuid import UUID
 from loguru import logger
 
@@ -93,6 +93,13 @@ class TestAgentConfig:
 
     organization_id: Optional[Union[UUID, str]] = None
     workspace_id: Optional[Union[UUID, str]] = None
+    agent_id: Optional[Union[UUID, str]] = None
+    evaluator_id: Optional[Union[UUID, str]] = None
+    persona_id: Optional[Union[UUID, str]] = None
+    scenario_id: Optional[Union[UUID, str]] = None
+    evaluator_result_id: Optional[Union[UUID, str]] = None
+    conversation_id: Optional[Union[UUID, str]] = None
+    db: Any = None
 
 
 class TestAgentProcessor:
@@ -344,37 +351,116 @@ async def _do_process_transcript(self, transcript: str) -> Optional[bytes]:
                 # Fire-and-forget so we don't block the caller
                 asyncio.create_task(self._do_process_transcript(pending))
     
+    def _build_simulation_context(self):
+        from app.services.usage.context import usage_context_for_test_agent_simulation
+
+        if not self.config.organization_id:
+            return None
+        return usage_context_for_test_agent_simulation(
+            organization_id=UUID(str(self.config.organization_id)),
+            workspace_id=(
+                UUID(str(self.config.workspace_id))
+                if self.config.workspace_id
+                else None
+            ),
+            agent_id=UUID(str(self.config.agent_id)) if self.config.agent_id else None,
+            evaluator_id=(
+                UUID(str(self.config.evaluator_id)) if self.config.evaluator_id else None
+            ),
+            persona_id=UUID(str(self.config.persona_id)) if self.config.persona_id else None,
+            scenario_id=UUID(str(self.config.scenario_id)) if self.config.scenario_id else None,
+            evaluator_result_id=(
+                UUID(str(self.config.evaluator_result_id))
+                if self.config.evaluator_result_id
+                else None
+            ),
+            conversation_id=(
+                UUID(str(self.config.conversation_id))
+                if self.config.conversation_id
+                else None
+            ),
+        )
+
+    def _sync_llm_call(self, messages: List[Dict[str, str]]) -> Dict[str, Any]:
+        from app.models.database import ModelProvider
+        from app.services.ai.llm_service import llm_service
+        from app.services.usage.context import llm_usage_context
+
+        ctx = self._build_simulation_context()
+        org_id = UUID(str(self.config.organization_id))
+        if ctx is not None:
+            with llm_usage_context(ctx):
+                return llm_service.generate_response(
+                    messages=messages,
+                    llm_provider=ModelProvider.OPENAI,
+                    llm_model=self.config.llm_model,
+                    organization_id=org_id,
+                    db=self.config.db,
+                    temperature=(
+                        self.config.llm_temperature
+                        if self.config.llm_temperature is not None
+                        else 0.7
+                    ),
+                    max_tokens=(
+                        self.config.llm_max_tokens
+                        if self.config.llm_max_tokens is not None
+                        else 150
+                    ),
+                )
+        return llm_service.generate_response(
+            messages=messages,
+            llm_provider=ModelProvider.OPENAI,
+            llm_model=self.config.llm_model,
+            organization_id=org_id,
+            db=self.config.db,
+            temperature=(
+                self.config.llm_temperature
+                if self.config.llm_temperature is not None
+                else 0.7
+            ),
+            max_tokens=(
+                self.config.llm_max_tokens
+                if self.config.llm_max_tokens is not None
+                else 150
+            ),
+        )
+
     async def _generate_llm_response(self) -> Optional[str]:
         """Generate a response using the LLM."""
         try:
             messages = [
                 {"role": "system", "content": self._system_prompt}
             ] + self.conversation_history
-            
-            if EFFICIENTAI_AVAILABLE and self._llm_service:
-                # Use EfficientAI LLM service
-                # Note: This is a simplified version - actual implementation
-                # would need to handle the frame-based processing
-                pass
-            
-            # Use direct OpenAI API
+
+            if self.config.db and self.config.organization_id:
+                result = await asyncio.to_thread(self._sync_llm_call, messages)
+                return (result.get("text") or "").strip()
+
             import openai
-            client = getattr(self, '_openai_client', None)
+            client = getattr(self, "_openai_client", None)
             if not client:
                 api_key = self.config.llm_api_key or os.getenv("OPENAI_API_KEY")
                 client = openai.AsyncOpenAI(api_key=api_key)
-            
+
             response = await client.chat.completions.create(
                 model=self.config.llm_model,
                 messages=messages,
-                max_tokens=self.config.llm_max_tokens if self.config.llm_max_tokens is not None else 150,
-                temperature=self.config.llm_temperature if self.config.llm_temperature is not None else 0.7,
+                max_tokens=(
+                    self.config.llm_max_tokens
+                    if self.config.llm_max_tokens is not None
+                    else 150
+                ),
+                temperature=(
+                    self.config.llm_temperature
+                    if self.config.llm_temperature is not None
+                    else 0.7
+                ),
             )
 
             self._record_llm_usage(response=response)
 
             return response.choices[0].message.content.strip()
-            
+
         except Exception as e:
             logger.error(f"[TestAgent] LLM error: {e}")
             return None
@@ -409,77 +495,41 @@ def _tts_settings(self) -> Dict[str, Any]:
         return dict(self.config.tts_config or {})
 
     def _record_tts_usage(self, *, text: str) -> None:
-        if not self.config.organization_id:
+        ctx = self._build_simulation_context()
+        if ctx is None:
             return
         try:
-            from app.services.usage.context import (
-                LLMUsageContext,
-                LLMUsageProductSection,
-                llm_usage_context,
-            )
+            from app.services.usage.context import llm_usage_context
             from app.services.usage.llm_usage import record_tts_usage
 
             model = self.config.tts_model or TTS_DEFAULT_MODELS.get(
                 self.config.tts_provider.lower(), "unknown"
             )
-            org_id = UUID(str(self.config.organization_id))
-            ws_id = (
-                UUID(str(self.config.workspace_id))
-                if self.config.workspace_id
-                else None
-            )
-            with llm_usage_context(
-                LLMUsageContext(
-                    organization_id=org_id,
-                    workspace_id=ws_id,
-                    product_section=LLMUsageProductSection.TEST_AGENT,
-                )
-            ):
+            with llm_usage_context(ctx):
                 record_tts_usage(
                     model,
                     characters=len(text or ""),
-                    organization_id=org_id,
+                    organization_id=ctx.organization_id,
                 )
         except Exception as exc:
             logger.debug("test agent tts usage record skipped: {}", exc)
 
     def _record_llm_usage(self, *, response: Any) -> None:
-        if not self.config.organization_id:
+        ctx = self._build_simulation_context()
+        if ctx is None:
             return
         try:
-            from app.services.usage.context import (
-                LLMUsageContext,
-                LLMUsageProductSection,
-                llm_usage_context,
-            )
+            from app.services.usage.context import llm_usage_context
             from app.services.usage.llm_usage import record_llm_usage
-            from app.services.usage.normalize import UsageSnapshot
+            from app.services.usage.normalize import UsageSnapshot, normalize_llm_usage
 
-            usage = getattr(response, "usage", None)
-            if usage is None:
-                return
-            org_id = UUID(str(self.config.organization_id))
-            ws_id = (
-                UUID(str(self.config.workspace_id))
-                if self.config.workspace_id
-                else None
-            )
+            snapshot = normalize_llm_usage(raw_response=response)
             model = self.config.llm_model or "unknown"
-            snapshot = UsageSnapshot(
-                prompt_tokens=int(getattr(usage, "prompt_tokens", 0) or 0),
-                completion_tokens=int(getattr(usage, "completion_tokens", 0) or 0),
-            )
-            with llm_usage_context(
-                LLMUsageContext(
-                    organization_id=org_id,
-                    workspace_id=ws_id,
-                    product_section=LLMUsageProductSection.TEST_AGENT,
-                )
-            ):
+            with llm_usage_context(ctx):
                 record_llm_usage(
                     model,
                     snapshot,
-                    organization_id=org_id,
+                    organization_id=ctx.organization_id,
                 )
         except Exception as exc:
             logger.debug("test agent llm usage record skipped: {}", exc)
diff --git a/app/workers/tasks/evaluate_studio_run_item.py b/app/workers/tasks/evaluate_studio_run_item.py
index 83a4b6e8..2e73eb29 100644
--- a/app/workers/tasks/evaluate_studio_run_item.py
+++ b/app/workers/tasks/evaluate_studio_run_item.py
@@ -97,6 +97,18 @@ def evaluate_studio_run_item_task(self, result_row_id: str) -> dict[str, Any]:
         result_row.started_at = result_row.started_at or _now_utc()
         db.commit()
 
+        from app.services.usage.context import (
+            llm_usage_context,
+            usage_context_for_metric_studio_run,
+        )
+
+        usage_ctx = usage_context_for_metric_studio_run(
+            run,
+            source_kind=result_row.source_kind,
+            source_ref=result_row.source_ref,
+            result_row_id=result_row.id,
+        )
+
         sample = resolve_source(
             db,
             organization_id=run.organization_id,
@@ -141,74 +153,75 @@ def evaluate_studio_run_item_task(self, result_row_id: str) -> dict[str, Any]:
             _rollup_run(db, run)
             return {"status": "failed"}
 
-        ai_providers = (
-            db.query(AIProvider)
-            .filter(
-                AIProvider.organization_id == run.organization_id,
-                AIProvider.is_active.is_(True),
-            )
-            .all()
-        )
-
-        if audio_metrics and sample.audio_s3_key:
-            try:
-                audio_scores = evaluate_audio_metrics(
-                    audio_s3_key=sample.audio_s3_key,
-                    audio_metrics=audio_metrics,
-                    result_id=f"studio:{result_row.id}",
+        with llm_usage_context(usage_ctx):
+            ai_providers = (
+                db.query(AIProvider)
+                .filter(
+                    AIProvider.organization_id == run.organization_id,
+                    AIProvider.is_active.is_(True),
                 )
-                metric_scores.update(audio_scores)
-            except Exception as audio_err:
-                logger.error(
-                    f"[MetricStudio {result_row.id}] audio evaluation failed: {audio_err}",
-                    exc_info=True,
-                )
-                metric_scores.update(
-                    handle_audio_evaluation_error(audio_metrics, audio_err)
-                )
-
-        if llm_metrics and transcript:
-            result_id = f"studio:{result_row.id}"
-            production_text = (sample.transcript or "").strip()
-            diarised_text = (sample.diarised_transcript or "").strip()
-            comparison_ids = {
-                str(m.id)
-                for m in llm_metrics
-                if getattr(m, "compare_transcripts", False)
-            }
-            buckets = build_llm_config_buckets(
-                db,
-                llm_metrics,
-                overrides={},
-                run_provider=None,
-                run_model=None,
-                run_llm_config=None,
-                run_credential_id=None,
+                .all()
             )
-            try:
-                for _config, groups in buckets.items():
-                    comparison_pair = None
-                    if bucket_needs_comparison_pair(
-                        groups,
-                        production_transcript=production_text,
-                        diarised_transcript=diarised_text,
-                        comparison_metric_ids=comparison_ids,
-                    ):
-                        comparison_pair = (production_text, diarised_text)
-                    bucket_metrics = flatten_metric_groups(groups)
-                    scores, _ = evaluate_with_llm(
-                        transcription=transcript,
-                        llm_metrics=bucket_metrics,
-                        ai_providers=ai_providers,
-                        organization_id=run.organization_id,
-                        result_id=result_id,
-                        db=db,
-                        comparison_pair=comparison_pair,
-                        metric_groups=groups,
+
+            if audio_metrics and sample.audio_s3_key:
+                try:
+                    audio_scores = evaluate_audio_metrics(
+                        audio_s3_key=sample.audio_s3_key,
+                        audio_metrics=audio_metrics,
+                        result_id=f"studio:{result_row.id}",
                     )
-                    metric_scores.update(scores)
-            except Exception as llm_err:
-                metric_scores.update(handle_llm_evaluation_error(llm_metrics, llm_err))
+                    metric_scores.update(audio_scores)
+                except Exception as audio_err:
+                    logger.error(
+                        f"[MetricStudio {result_row.id}] audio evaluation failed: {audio_err}",
+                        exc_info=True,
+                    )
+                    metric_scores.update(
+                        handle_audio_evaluation_error(audio_metrics, audio_err)
+                    )
+
+            if llm_metrics and transcript:
+                result_id = f"studio:{result_row.id}"
+                production_text = (sample.transcript or "").strip()
+                diarised_text = (sample.diarised_transcript or "").strip()
+                comparison_ids = {
+                    str(m.id)
+                    for m in llm_metrics
+                    if getattr(m, "compare_transcripts", False)
+                }
+                buckets = build_llm_config_buckets(
+                    db,
+                    llm_metrics,
+                    overrides={},
+                    run_provider=None,
+                    run_model=None,
+                    run_llm_config=None,
+                    run_credential_id=None,
+                )
+                try:
+                    for _config, groups in buckets.items():
+                        comparison_pair = None
+                        if bucket_needs_comparison_pair(
+                            groups,
+                            production_transcript=production_text,
+                            diarised_transcript=diarised_text,
+                            comparison_metric_ids=comparison_ids,
+                        ):
+                            comparison_pair = (production_text, diarised_text)
+                        bucket_metrics = flatten_metric_groups(groups)
+                        scores, _ = evaluate_with_llm(
+                            transcription=transcript,
+                            llm_metrics=bucket_metrics,
+                            ai_providers=ai_providers,
+                            organization_id=run.organization_id,
+                            result_id=result_id,
+                            db=db,
+                            comparison_pair=comparison_pair,
+                            metric_groups=groups,
+                        )
+                        metric_scores.update(scores)
+                except Exception as llm_err:
+                    metric_scores.update(handle_llm_evaluation_error(llm_metrics, llm_err))
 
         result_row.metric_scores = metric_scores
         flag_modified(result_row, "metric_scores")
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index 56a1ac80..8ce8daf3 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -588,6 +588,8 @@ def _resolve_call_duration_seconds(result) -> int:
 def _record_agent_call_usage(result, *, usage_ctx) -> None:
     if not _should_record_external_agent_call_usage(result):
         return
+    if _external_usage_already_recorded(result):
+        return
     try:
         from app.services.usage.llm_usage import record_call_usage
 
@@ -605,6 +607,30 @@ def _record_agent_call_usage(result, *, usage_ctx) -> None:
         )
 
 
+def _external_usage_already_recorded(result) -> bool:
+    call_data = getattr(result, "call_data", None)
+    if not isinstance(call_data, dict):
+        return False
+    return bool(call_data.get("external_usage_recorded"))
+
+
+def _record_external_agent_llm_usage(result, *, usage_ctx) -> None:
+    if not _should_record_external_agent_call_usage(result):
+        return
+    if _external_usage_already_recorded(result):
+        return
+    try:
+        from app.services.usage.external_agent_usage import record_external_agent_usage
+
+        record_external_agent_usage(result, usage_ctx=usage_ctx)
+    except Exception as exc:
+        logger.debug(
+            "[EvaluatorResult {}] external agent llm usage record skipped: {}",
+            result.result_id,
+            exc,
+        )
+
+
 @celery_app.task(name="process_evaluator_result", bind=True, max_retries=3)
 def process_evaluator_result_task(self, result_id: str):
     """
@@ -852,6 +878,7 @@ def process_evaluator_result_task(self, result_id: str):
                 result.status = EvaluatorResultStatus.COMPLETED.value
                 result.error_message = None
                 _record_agent_call_usage(result, usage_ctx=usage_ctx)
+                _record_external_agent_llm_usage(result, usage_ctx=usage_ctx)
                 _commit_evaluator_result(db, result)
 
                 from app.services.billing.flexprice_service import (
diff --git a/app/workers/tasks/run_evaluator.py b/app/workers/tasks/run_evaluator.py
index a82f0199..b7736c97 100644
--- a/app/workers/tasks/run_evaluator.py
+++ b/app/workers/tasks/run_evaluator.py
@@ -71,18 +71,20 @@ def run_evaluator_task(self, evaluator_id: str, evaluator_result_id: str):
                 result.call_event = "task_started"
                 db.commit()
 
-                loop = asyncio.get_event_loop()
-                if loop.is_closed():
-                    loop = asyncio.new_event_loop()
-                    asyncio.set_event_loop(loop)
-                bridge_result = loop.run_until_complete(
-                    test_agent_bridge_service.bridge_test_agent_to_voice_agent(
-                        evaluator_id=evaluator_uuid,
-                        evaluator_result_id=result_uuid,
-                        organization_id=evaluator.organization_id,
-                        db=db,
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+                try:
+                    bridge_result = loop.run_until_complete(
+                        test_agent_bridge_service.bridge_test_agent_to_voice_agent(
+                            evaluator_id=evaluator_uuid,
+                            evaluator_result_id=result_uuid,
+                            organization_id=evaluator.organization_id,
+                            db=db,
+                        )
                     )
-                )
+                finally:
+                    loop.close()
+                    asyncio.set_event_loop(None)
 
                 db.refresh(result)
 
@@ -119,10 +121,76 @@ def run_evaluator_task(self, evaluator_id: str, evaluator_result_id: str):
                 raise
 
         elif has_voice_bundle:
-            result.status = EvaluatorResultStatus.FAILED.value
-            result.error_message = "Standard voice agent flow not yet implemented for evaluator runs"
-            db.commit()
-            return {"error": "Standard flow not implemented"}
+            from app.models.database import Persona, Scenario
+            from app.services.testing.llm_to_llm_evaluator_simulation import (
+                run_llm_to_llm_evaluator_simulation,
+            )
+
+            persona = (
+                db.query(Persona).filter(Persona.id == evaluator.persona_id).first()
+                if evaluator.persona_id
+                else None
+            )
+            scenario = (
+                db.query(Scenario).filter(Scenario.id == evaluator.scenario_id).first()
+                if evaluator.scenario_id
+                else None
+            )
+            if not persona or not scenario:
+                result.status = EvaluatorResultStatus.FAILED.value
+                result.error_message = "Evaluator requires persona and scenario for voice-bundle simulation"
+                db.commit()
+                return {"error": "Missing persona or scenario"}
+
+            try:
+                result.status = EvaluatorResultStatus.CALL_INITIATING.value
+                result.call_event = "llm_simulation_started"
+                db.commit()
+
+                run_llm_to_llm_evaluator_simulation(
+                    evaluator=evaluator,
+                    result=result,
+                    agent=agent,
+                    persona=persona,
+                    scenario=scenario,
+                    organization_id=evaluator.organization_id,
+                    db=db,
+                )
+                result.status = EvaluatorResultStatus.QUEUED.value
+                result.call_event = "llm_simulation_completed"
+                db.commit()
+
+                from app.workers.celery_app import process_evaluator_result_task
+
+                task = process_evaluator_result_task.delay(str(result.id))
+                result.celery_task_id = task.id
+                db.commit()
+
+                from app.services.billing.flexprice_service import record_evaluator_run_completed
+
+                record_evaluator_run_completed(
+                    evaluator.organization_id,
+                    result.result_id,
+                    workspace_id=evaluator.workspace_id,
+                    evaluator_id=evaluator.id,
+                )
+
+                return {
+                    "evaluator_id": evaluator_id,
+                    "result_id": evaluator_result_id,
+                    "status": "simulated",
+                    "provider_platform": "internal",
+                }
+            except Exception as sim_error:
+                logger.error(
+                    f"[RunEvaluator {evaluator.evaluator_id}] LLM simulation error: {sim_error}",
+                    exc_info=True,
+                )
+                result.status = EvaluatorResultStatus.FAILED.value
+                result.error_message = str(sim_error)
+                result.call_event = "llm_simulation_error"
+                db.commit()
+                raise
 
         else:
             logger.error(f"[RunEvaluator {evaluator.evaluator_id}] Agent missing required configuration")
diff --git a/config.yml.example b/config.yml.example
index 1f9d014a..daad7d94 100644
--- a/config.yml.example
+++ b/config.yml.example
@@ -29,7 +29,7 @@ operational:
 security:
   csp_enabled: true
   csp_report_only: false   # false = enforcing Content-Security-Policy header (required for compliance scans)
-  # csp_policy: "default-src 'self'; ..."  # optional override
+  # csp_policy: "default-src 'self'; ..."  # optional override (defaults include Vapi/Daily, Retell/LiveKit, ElevenLabs)
 
 # Database Configuration
 database:
diff --git a/tests/conftest.py b/tests/conftest.py
index ed46d3bf..58b0e30e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -11,10 +11,18 @@
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
 from sqlalchemy import create_engine, event, text
+from sqlalchemy.dialects.postgresql import JSONB
 from sqlalchemy.engine import make_url
+from sqlalchemy.ext.compiler import compiles
 from sqlalchemy.orm import sessionmaker
 from sqlalchemy.pool import StaticPool
 
+
+@compiles(JSONB, "sqlite")
+def _compile_jsonb_for_sqlite(_element, _compiler, **_kw):
+    """Allow in-memory SQLite tests to create tables with JSONB columns."""
+    return "JSON"
+
 # Some local environments provide ALLOWED_AUDIO_FORMATS as a non-JSON string,
 # which breaks pydantic-settings parsing during module import in tests.
 os.environ["ALLOWED_AUDIO_FORMATS"] = '["wav","mp3","flac","m4a"]'
diff --git a/tests/test_api/test_evaluator_results_routes.py b/tests/test_api/test_evaluator_results_routes.py
index 9f84a04d..84be9658 100644
--- a/tests/test_api/test_evaluator_results_routes.py
+++ b/tests/test_api/test_evaluator_results_routes.py
@@ -478,3 +478,35 @@ class FakeTask:
     assert response.status_code == 200
     assert captured["headers"]["Authorization"] == "Bearer vapi-secret"
 
+
+def test_re_evaluate_playground_result_without_evaluator_id(
+    authenticated_client,
+    make_agent,
+    make_evaluator_result,
+    monkeypatch,
+):
+    agent = make_agent()
+    result = make_evaluator_result(
+        result_id="776655",
+        evaluator_id=None,
+        agent_id=agent.id,
+        transcription="Speaker 1: hello\nSpeaker 2: hi there",
+        provider_platform="vapi",
+        status="completed",
+    )
+
+    class FakeTask:
+        id = "task-playground-re-eval"
+
+    monkeypatch.setattr(
+        "app.workers.celery_app.process_evaluator_result_task.delay",
+        lambda *_args, **_kwargs: FakeTask(),
+    )
+
+    response = authenticated_client.post(
+        f"/api/v1/evaluator-results/{result.result_id}/re-evaluate"
+    )
+
+    assert response.status_code == 200
+    assert response.json()["status"] == "queued"
+
diff --git a/tests/test_core/test_security_headers_middleware.py b/tests/test_core/test_security_headers_middleware.py
index 64ae2e8b..14a7cad2 100644
--- a/tests/test_core/test_security_headers_middleware.py
+++ b/tests/test_core/test_security_headers_middleware.py
@@ -77,6 +77,24 @@ def test_csp_enforcing_header_when_report_only_disabled(security_client, monkeyp
     assert "Content-Security-Policy-Report-Only" not in response.headers
 
 
+def test_csp_allows_voice_provider_connect_src(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
+
+    response = security_client.get("/health")
+    policy = response.headers["Content-Security-Policy"]
+
+    assert "https://api.vapi.ai" in policy
+    assert "https://*.daily.co" in policy
+    assert "wss://*.livekit.cloud" in policy
+    assert "https://api.elevenlabs.io" in policy
+    assert "https://*.ingest.sentry.io" in policy
+    assert "'unsafe-eval'" in policy
+    assert "blob:" in policy
+    assert "https://c.daily.co" in policy
+    assert "worker-src 'self' blob:" in policy
+
+
 def test_asset_routes_use_long_cache(security_client):
     response = security_client.get("/assets/app.js")
 
diff --git a/tests/test_services/test_usage/test_llm_to_llm_evaluator_simulation.py b/tests/test_services/test_usage/test_llm_to_llm_evaluator_simulation.py
new file mode 100644
index 00000000..9b26367c
--- /dev/null
+++ b/tests/test_services/test_usage/test_llm_to_llm_evaluator_simulation.py
@@ -0,0 +1,111 @@
+from types import SimpleNamespace
+from uuid import uuid4
+
+import pytest
+
+from app.models.enums import ModelProvider
+from app.services.testing.llm_to_llm_evaluator_simulation import (
+    run_llm_to_llm_evaluator_simulation,
+)
+
+
+def test_run_llm_to_llm_evaluator_simulation_builds_transcript(monkeypatch):
+    org_id = uuid4()
+    agent_id = uuid4()
+    evaluator_id = uuid4()
+    persona_id = uuid4()
+    scenario_id = uuid4()
+    result_id = uuid4()
+    bundle_id = uuid4()
+
+    responses = iter(
+        [
+            {"text": "Hi, how can I help you today?"},
+            {"text": "I need help with my order."},
+            {"text": "Sure, what is your order number?"},
+            {"text": "Thanks, goodbye."},
+        ]
+    )
+
+    def _fake_generate(**_kwargs):
+        return next(responses)
+
+    import app.services.testing.llm_to_llm_evaluator_simulation as sim_mod
+
+    monkeypatch.setattr(sim_mod.llm_service, "generate_response", _fake_generate)
+
+    evaluator = SimpleNamespace(
+        id=evaluator_id,
+        evaluator_id="ev-1",
+        workspace_id=uuid4(),
+    )
+    agent = SimpleNamespace(
+        id=agent_id,
+        name="Support Bot",
+        voice_bundle_id=bundle_id,
+        description="You help customers with orders.",
+        provider_prompt=None,
+    )
+    persona = SimpleNamespace(
+        id=persona_id,
+        name="Alex",
+        description="Impatient customer",
+        gender=None,
+        max_turns=4,
+        tts_provider=None,
+        tts_voice_name=None,
+        tts_voice_id=None,
+    )
+    scenario = SimpleNamespace(
+        id=scenario_id,
+        name="Order status",
+        description="Check an order",
+        required_info={"goal": "Get order status"},
+    )
+    result = SimpleNamespace(
+        id=result_id,
+        result_id="res-1",
+        transcription=None,
+        speaker_segments=None,
+        provider_platform=None,
+        call_data=None,
+        duration_seconds=None,
+    )
+    voice_bundle = SimpleNamespace(
+        id=bundle_id,
+        llm_provider=ModelProvider.OPENAI,
+        llm_model="gpt-4o-mini",
+        llm_config=None,
+        llm_credential_id=None,
+    )
+    db = SimpleNamespace()
+
+    def _query(model):
+        class _Q:
+            def filter(self, *_args, **_kwargs):
+                return self
+
+            def first(self):
+                if model.__name__ == "VoiceBundle":
+                    return voice_bundle
+                return None
+
+        return _Q()
+
+    db.query = _query
+
+    output = run_llm_to_llm_evaluator_simulation(
+        evaluator=evaluator,
+        result=result,
+        agent=agent,
+        persona=persona,
+        scenario=scenario,
+        organization_id=org_id,
+        db=db,
+    )
+
+    assert output["provider_platform"] == "internal"
+    assert result.provider_platform == "internal"
+    assert "Speaker 1:" in result.transcription
+    assert "Speaker 2:" in result.transcription
+    assert result.call_data["source"] == "llm_to_llm_simulation"
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index d08887bd..cb87d0c8 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -294,12 +294,13 @@ def test_record_call_usage(fake_redis, org_ctx):
 
 
 def test_agent_usage_context_reuses_single_bucket(fake_redis):
-    """Stable agent context avoids per-call Redis/DB bucket explosion."""
+    """Stable evaluator context avoids per-call Redis/DB bucket explosion."""
     from app.services.usage.context import usage_context_for_evaluator_result
 
     org_id = uuid4()
     workspace_id = uuid4()
     agent_id = uuid4()
+    evaluator_id = uuid4()
     prefixes = set()
     for idx in range(3):
         result = SimpleNamespace(
@@ -307,7 +308,7 @@ def test_agent_usage_context_reuses_single_bucket(fake_redis):
             result_id=f"res-{idx}",
             organization_id=org_id,
             workspace_id=workspace_id,
-            evaluator_id=uuid4(),
+            evaluator_id=evaluator_id,
             agent_id=agent_id,
         )
         ctx = usage_context_for_evaluator_result(result)
diff --git a/tests/test_services/test_usage/test_pre_prod_usage.py b/tests/test_services/test_usage/test_pre_prod_usage.py
new file mode 100644
index 00000000..574f5103
--- /dev/null
+++ b/tests/test_services/test_usage/test_pre_prod_usage.py
@@ -0,0 +1,242 @@
+from types import SimpleNamespace
+from uuid import uuid4
+
+import pytest
+
+from app.services.usage.context import (
+    LLMUsageProductSection,
+    usage_context_for_evaluator_result,
+    usage_context_for_metric_studio_run,
+    usage_context_for_persona_generation,
+)
+from app.services.usage.external_agent_usage import extract_external_agent_usage
+
+
+@pytest.fixture
+def fake_redis(monkeypatch):
+    class _FakeRedis:
+        def __init__(self):
+            self.hashes = {}
+
+        def hincrby(self, key, field, amount):
+            self.hashes.setdefault(key, {})
+            self.hashes[key][field] = int(self.hashes[key].get(field, 0)) + int(amount)
+
+        def hgetall(self, key):
+            return dict(self.hashes.get(key, {}))
+
+        def sadd(self, key, member):
+            return True
+
+        def smembers(self, key):
+            return set()
+
+        def expire(self, key, ttl):
+            return True
+
+        def pipeline(self):
+            client = self
+
+            class _Pipe:
+                def hincrby(self, key, field, amount):
+                    client.hincrby(key, field, amount)
+                    return self
+
+                def sadd(self, key, member):
+                    return self
+
+                def expire(self, key, ttl):
+                    return self
+
+                def execute(self):
+                    return []
+
+            return _Pipe()
+
+    client = _FakeRedis()
+    from app.services.usage import llm_usage as usage_mod
+    import app.services.usage.read_cache as read_cache_mod
+
+    usage_mod._redis = client
+    read_cache_mod._redis = client
+
+    def _forbid_real_redis(*_args, **_kwargs):
+        raise AssertionError("usage tests must not open real Redis")
+
+    monkeypatch.setattr(usage_mod.redis, "from_url", _forbid_real_redis)
+    monkeypatch.setattr(read_cache_mod.redis, "from_url", _forbid_real_redis)
+    yield client
+    usage_mod._redis = None
+    read_cache_mod._redis = None
+
+
+def test_usage_context_for_evaluator_result_uses_evaluators_section():
+    org_id = uuid4()
+    workspace_id = uuid4()
+    agent_id = uuid4()
+    evaluator_id = uuid4()
+    persona_id = uuid4()
+    scenario_id = uuid4()
+    result_id = uuid4()
+
+    ctx = usage_context_for_evaluator_result(
+        SimpleNamespace(
+            id=result_id,
+            result_id="res-42",
+            organization_id=org_id,
+            workspace_id=workspace_id,
+            agent_id=agent_id,
+            evaluator_id=evaluator_id,
+            persona_id=persona_id,
+            scenario_id=scenario_id,
+            provider_platform="vapi",
+        )
+    )
+
+    assert ctx.product_section == LLMUsageProductSection.EVALUATORS
+    assert ctx.resource_id == evaluator_id
+    assert ctx.resource_type == "evaluator"
+    assert ctx.extra["agent_id"] == str(agent_id)
+    assert ctx.extra["evaluator_id"] == str(evaluator_id)
+    assert ctx.extra["persona_id"] == str(persona_id)
+    assert ctx.extra["scenario_id"] == str(scenario_id)
+    assert ctx.extra["synthetic_testing"] == "pre_prod"
+    assert ctx.extra["provider_platform"] == "vapi"
+
+
+def test_usage_context_for_metric_studio_run():
+    run_id = uuid4()
+    org_id = uuid4()
+    workspace_id = uuid4()
+    result_row_id = uuid4()
+
+    run = SimpleNamespace(
+        id=run_id,
+        organization_id=org_id,
+        workspace_id=workspace_id,
+    )
+
+    ctx = usage_context_for_metric_studio_run(
+        run,
+        source_kind="evaluator_result",
+        source_ref=str(uuid4()),
+        result_row_id=result_row_id,
+    )
+
+    assert ctx.product_section == LLMUsageProductSection.METRICS
+    assert ctx.resource_id == run_id
+    assert ctx.resource_type == "metric_studio_run"
+    assert ctx.extra["metric_studio_run_id"] == str(run_id)
+    assert ctx.extra["metric_studio_result_id"] == str(result_row_id)
+    assert ctx.extra["source_kind"] == "evaluator_result"
+    assert ctx.extra["synthetic_testing"] == "pre_prod"
+
+
+def test_usage_context_for_persona_generation():
+    org_id = uuid4()
+    workspace_id = uuid4()
+    agent_id = uuid4()
+    agent = SimpleNamespace(
+        id=agent_id,
+        organization_id=org_id,
+        workspace_id=workspace_id,
+    )
+
+    ctx = usage_context_for_persona_generation(agent, workspace_id=workspace_id)
+
+    assert ctx.product_section == LLMUsageProductSection.PERSONAS
+    assert ctx.resource_id == agent_id
+    assert ctx.extra["agent_id"] == str(agent_id)
+    assert ctx.extra["synthetic_testing"] == "pre_prod"
+
+
+def test_extract_external_agent_usage_retell():
+    call_data = {
+        "llm_token_usage": {"values": [1200, 800, 500], "average": 833, "num_requests": 3},
+        "call_cost": {"total_duration_seconds": 95},
+        "model": "gpt-4.1",
+    }
+    extracted = extract_external_agent_usage(call_data, platform="retell")
+    assert extracted is not None
+    assert extracted.model == "gpt-4.1"
+    assert extracted.llm.prompt_tokens == 1750
+    assert extracted.llm.completion_tokens == 750
+    assert extracted.stt_audio_seconds == 95
+
+
+def test_extract_external_agent_usage_retell_explicit_split():
+    call_data = {
+        "llm_token_usage": {
+            "values": [900],
+            "prompt_tokens": 600,
+            "completion_tokens": 300,
+        },
+        "call_cost": {"total_duration_seconds": 30},
+    }
+    extracted = extract_external_agent_usage(call_data, platform="retell")
+    assert extracted is not None
+    assert extracted.llm.prompt_tokens == 600
+    assert extracted.llm.completion_tokens == 300
+
+
+def test_usage_context_for_evaluator_result_playground_section():
+    ctx = usage_context_for_evaluator_result(
+        SimpleNamespace(
+            id=uuid4(),
+            result_id="res-playground",
+            organization_id=uuid4(),
+            workspace_id=uuid4(),
+            agent_id=uuid4(),
+            evaluator_id=None,
+            persona_id=None,
+            scenario_id=None,
+            provider_platform="vapi",
+        )
+    )
+    assert ctx.product_section == LLMUsageProductSection.PLAYGROUND
+
+
+def test_record_playground_provider_usage_from_call_data(fake_redis):
+    from app.services.usage import llm_usage as usage_mod
+    from app.services.usage.external_agent_usage import (
+        record_playground_provider_usage_from_call_data,
+    )
+
+    org_id = uuid4()
+    agent_id = uuid4()
+    updated = record_playground_provider_usage_from_call_data(
+        organization_id=org_id,
+        workspace_id=uuid4(),
+        agent_id=agent_id,
+        provider_platform="vapi",
+        call_short_id="abc123",
+        call_data={
+            "costBreakdown": {
+                "llmPromptTokens": 40,
+                "llmCompletionTokens": 12,
+            },
+            "durationSeconds": 20,
+        },
+    )
+    assert updated["external_usage_recorded"] is True
+    fields = fake_redis.hgetall(usage_mod._pending_hash_key(org_id))
+    assert any("playground" in k for k in fields)
+
+
+def test_extract_external_agent_usage_smallest_duration_only():
+    extracted = extract_external_agent_usage(
+        {"duration_seconds": 42},
+        platform="smallest",
+    )
+    assert extracted is not None
+    assert extracted.llm is None
+    assert extracted.stt_audio_seconds == 42
+
+
+def test_extract_external_agent_usage_elevenlabs_duration_only():
+    extracted = extract_external_agent_usage(
+        {"metadata": {"call_duration_secs": 33}},
+        platform="elevenlabs",
+    )
+    assert extracted is not None
+    assert extracted.stt_audio_seconds == 33
diff --git a/tests/test_services/test_usage/test_test_agent_simulation_usage.py b/tests/test_services/test_usage/test_test_agent_simulation_usage.py
new file mode 100644
index 00000000..e2fa94ab
--- /dev/null
+++ b/tests/test_services/test_usage/test_test_agent_simulation_usage.py
@@ -0,0 +1,318 @@
+from types import SimpleNamespace
+from uuid import uuid4
+
+import pytest
+
+from app.services.usage import context as usage_context_mod
+from app.services.usage import llm_usage as usage_mod
+from app.services.usage.context import (
+    LLMUsageProductSection,
+    usage_context_for_test_agent_simulation,
+)
+from app.services.usage.external_agent_usage import (
+    extract_external_agent_usage,
+    record_external_agent_usage,
+)
+from app.services.webrtc_bridge.test_agent_processor import TestAgentConfig, TestAgentProcessor
+from app.workers.tasks import process_evaluator_result as per_mod
+
+
+@pytest.fixture
+def fake_redis(monkeypatch):
+    class _FakeRedis:
+        def __init__(self):
+            self.hashes = {}
+            self.sets = {}
+            self.kv = {}
+
+        def hincrby(self, key, field, amount):
+            self.hashes.setdefault(key, {})
+            self.hashes[key][field] = int(self.hashes[key].get(field, 0)) + int(amount)
+
+        def hgetall(self, key):
+            return dict(self.hashes.get(key, {}))
+
+        def sadd(self, key, member):
+            self.sets.setdefault(key, set()).add(member)
+
+        def smembers(self, key):
+            return set(self.sets.get(key, set()))
+
+        def expire(self, key, ttl):
+            return True
+
+        def pipeline(self):
+            client = self
+
+            class _Pipe:
+                def hincrby(self, key, field, amount):
+                    client.hincrby(key, field, amount)
+                    return self
+
+                def sadd(self, key, member):
+                    client.sadd(key, member)
+                    return self
+
+                def expire(self, key, ttl):
+                    return self
+
+                def execute(self):
+                    return []
+
+            return _Pipe()
+
+    client = _FakeRedis()
+    usage_mod._redis = client
+    import app.services.usage.read_cache as read_cache_mod
+
+    read_cache_mod._redis = client
+
+    def _forbid_real_redis(*_args, **_kwargs):
+        raise AssertionError(
+            "usage tests must not open real Redis; fake_redis fixture failed to isolate"
+        )
+
+    monkeypatch.setattr(usage_mod.redis, "from_url", _forbid_real_redis)
+    monkeypatch.setattr(read_cache_mod.redis, "from_url", _forbid_real_redis)
+    yield client
+    usage_mod._redis = None
+    read_cache_mod._redis = None
+
+
+def test_usage_context_for_test_agent_simulation():
+    org_id = uuid4()
+    agent_id = uuid4()
+    evaluator_id = uuid4()
+    persona_id = uuid4()
+    scenario_id = uuid4()
+    result_id = uuid4()
+    conversation_id = uuid4()
+    workspace_id = uuid4()
+
+    ctx = usage_context_for_test_agent_simulation(
+        organization_id=org_id,
+        workspace_id=workspace_id,
+        agent_id=agent_id,
+        evaluator_id=evaluator_id,
+        persona_id=persona_id,
+        scenario_id=scenario_id,
+        evaluator_result_id=result_id,
+        conversation_id=conversation_id,
+        provider_platform="vapi",
+    )
+
+    assert ctx.product_section == LLMUsageProductSection.TEST_AGENT
+    assert ctx.resource_id == agent_id
+    assert ctx.extra["agent_id"] == str(agent_id)
+    assert ctx.extra["evaluator_id"] == str(evaluator_id)
+    assert ctx.extra["persona_id"] == str(persona_id)
+    assert ctx.extra["scenario_id"] == str(scenario_id)
+    assert ctx.extra["evaluator_result_id"] == str(result_id)
+    assert ctx.extra["conversation_id"] == str(conversation_id)
+    assert ctx.extra["simulation"] == "llm_to_llm"
+
+
+def test_extract_external_agent_usage_vapi():
+    call_data = {
+        "costBreakdown": {
+            "llmPromptTokens": 120,
+            "llmCompletionTokens": 45,
+            "llmCachedPromptTokens": 10,
+            "ttsCharacters": 300,
+        },
+        "durationSeconds": 88,
+        "model": "gpt-4o",
+    }
+    extracted = extract_external_agent_usage(call_data, platform="vapi")
+    assert extracted is not None
+    assert extracted.model == "gpt-4o"
+    assert extracted.llm.prompt_tokens == 120
+    assert extracted.llm.completion_tokens == 45
+    assert extracted.llm.cache_read_tokens == 10
+    assert extracted.tts_characters == 300
+    assert extracted.stt_audio_seconds == 88
+
+
+def test_record_external_agent_usage_vapi(fake_redis):
+    org_id = uuid4()
+    agent_id = uuid4()
+    usage_ctx = usage_context_mod.usage_context_for_evaluator_result(
+        SimpleNamespace(
+            organization_id=org_id,
+            workspace_id=uuid4(),
+            agent_id=agent_id,
+            evaluator_id=uuid4(),
+            id=uuid4(),
+            result_id="res-1",
+        )
+    )
+    result = SimpleNamespace(
+        organization_id=org_id,
+        provider_platform="vapi",
+        result_id="res-1",
+        call_data={
+            "costBreakdown": {
+                "llmPromptTokens": 50,
+                "llmCompletionTokens": 20,
+            }
+        },
+    )
+
+    record_external_agent_usage(result, usage_ctx=usage_ctx)
+
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    assert any("evaluators" in k for k in fields)
+    prompt = sum(int(v) for k, v in fields.items() if k.endswith("|prompt_tokens"))
+    completion = sum(int(v) for k, v in fields.items() if k.endswith("|completion_tokens"))
+    assert prompt == 50
+    assert completion == 20
+
+
+def test_test_agent_processor_records_with_simulation_context(fake_redis):
+    org_id = uuid4()
+    agent_id = uuid4()
+    config = TestAgentConfig(
+        organization_id=org_id,
+        workspace_id=uuid4(),
+        agent_id=agent_id,
+        evaluator_id=uuid4(),
+        persona_id=uuid4(),
+        scenario_id=uuid4(),
+        llm_model="gpt-4o-mini",
+    )
+    processor = TestAgentProcessor(config)
+
+    class _Usage:
+        def __init__(self):
+            self.prompt_tokens = 11
+            self.completion_tokens = 4
+
+    class _Response:
+        usage = _Usage()
+
+    processor._record_llm_usage(response=_Response())
+
+    pending_key = usage_mod._pending_hash_key(org_id)
+    fields = fake_redis.hgetall(pending_key)
+    assert any("test_agent" in k for k in fields)
+    prompt = sum(int(v) for k, v in fields.items() if k.endswith("|prompt_tokens"))
+    assert prompt == 11
+
+
+def test_record_external_agent_llm_usage_skips_internal_platform(monkeypatch):
+    called = {"value": False}
+
+    def _fake_record(*_args, **_kwargs):
+        called["value"] = True
+
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.record_external_agent_usage",
+        _fake_record,
+    )
+
+    result = SimpleNamespace(agent_id=uuid4(), provider_platform="internal")
+    usage_ctx = SimpleNamespace()
+    per_mod._record_external_agent_llm_usage(result, usage_ctx=usage_ctx)
+    assert called["value"] is False
+
+
+def test_process_audio_chunk_with_context_uses_test_agent_section(monkeypatch):
+    org_id = uuid4()
+    agent_id = uuid4()
+    captured_sections = []
+
+    def _fake_generate(**_kwargs):
+        ctx = usage_context_mod.get_usage_context()
+        if ctx:
+            captured_sections.append(ctx.product_section)
+        return {"text": "hi there", "processing_time": 0.2}
+
+    from app.models.database import (
+        TestAgentConversation,
+        TestAgentConversationStatus,
+        ModelProvider,
+    )
+    from app.services.testing.test_agent_service import TestAgentService
+    from app.services.usage.context import llm_usage_context
+
+    conversation = TestAgentConversation(
+        id=uuid4(),
+        organization_id=org_id,
+        workspace_id=uuid4(),
+        agent_id=agent_id,
+        persona_id=uuid4(),
+        scenario_id=uuid4(),
+        voice_bundle_id=uuid4(),
+        status=TestAgentConversationStatus.ACTIVE,
+        live_transcription=[],
+    )
+    voice_bundle = SimpleNamespace(
+        stt_provider=ModelProvider.OPENAI,
+        stt_model="whisper-1",
+        llm_provider=ModelProvider.OPENAI,
+        llm_model="gpt-4o-mini",
+        llm_config=None,
+        llm_temperature=0.7,
+        llm_max_tokens=100,
+        tts_provider=ModelProvider.OPENAI,
+        tts_model="gpt-4o-mini-tts",
+        tts_config=None,
+    )
+
+    import importlib
+
+    tas_module = importlib.import_module("app.services.testing.test_agent_service")
+    service = TestAgentService()
+    monkeypatch.setattr(
+        tas_module,
+        "s3_service",
+        SimpleNamespace(upload_file=lambda **_kwargs: "audio/key.wav"),
+    )
+    monkeypatch.setattr(
+        tas_module.transcription_service,
+        "transcribe",
+        lambda **_kwargs: {"transcript": "hello", "processing_time": 0.1},
+    )
+    monkeypatch.setattr(
+        tas_module.llm_service,
+        "generate_response",
+        _fake_generate,
+    )
+    monkeypatch.setattr(
+        tas_module.tts_service,
+        "synthesize",
+        lambda **_kwargs: b"mp3",
+    )
+    monkeypatch.setattr(
+        service,
+        "_build_system_prompt",
+        lambda *_args, **_kwargs: "system",
+    )
+    monkeypatch.setattr(
+        "app.services.voice_agent.resolve_tts_voice.resolve_effective_tts_voice_id",
+        lambda **_kwargs: "voice",
+    )
+
+    usage_ctx = usage_context_for_test_agent_simulation(
+        organization_id=org_id,
+        workspace_id=conversation.workspace_id,
+        agent_id=agent_id,
+        conversation_id=conversation.id,
+    )
+    db = SimpleNamespace(commit=lambda: None)
+
+    with llm_usage_context(usage_ctx):
+        service._process_audio_chunk_with_context(
+            conversation=conversation,
+            wav_audio_bytes=b"wav",
+            voice_bundle=voice_bundle,
+            agent=SimpleNamespace(id=agent_id, language=SimpleNamespace(value="en")),
+            persona=SimpleNamespace(id=conversation.persona_id, tts_voice_id=None),
+            scenario=SimpleNamespace(id=conversation.scenario_id),
+            organization_id=org_id,
+            db=db,
+            chunk_timestamp=0.0,
+        )
+
+    assert captured_sections == [LLMUsageProductSection.TEST_AGENT]
diff --git a/tests/test_workers/test_celery_task_workflows.py b/tests/test_workers/test_celery_task_workflows.py
index a74aca2c..20489070 100644
--- a/tests/test_workers/test_celery_task_workflows.py
+++ b/tests/test_workers/test_celery_task_workflows.py
@@ -11,12 +11,14 @@
 
 from app.models.database import (
     Agent,
+    Evaluator,
     EvaluatorResult,
     Metric,
     Organization,
     PromptOptimizationRun,
     TTSComparison,
     TTSSample,
+    VoiceBundle,
     Workspace,
 )
 
@@ -48,6 +50,25 @@ class RetryCalled(Exception):
     """Raised by task.retry in tests to assert retry paths."""
 
 
+def _load_run_evaluator_module():
+    """Load the real task module even when conftest/API tests stub workers.tasks."""
+    module_name = "app.workers.tasks.run_evaluator"
+    module_path = (
+        Path(__file__).resolve().parents[2]
+        / "app"
+        / "workers"
+        / "tasks"
+        / "run_evaluator.py"
+    )
+    spec = importlib.util.spec_from_file_location(module_name, module_path)
+    if spec is None or spec.loader is None:
+        raise ImportError(f"Cannot load task module from {module_path}")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
 def _load_run_prompt_optimization_module():
     """Load the real task module even when conftest/API tests stub workers.tasks."""
     module_name = "app.workers.tasks.run_prompt_optimization"
@@ -74,15 +95,26 @@ def _load_run_prompt_optimization_module():
     return module
 
 
+class _FakeCeleryTaskSelf:
+    request = types.SimpleNamespace(id="test-celery-task-id")
+
+    def retry(self, **kwargs):
+        raise kwargs.get("exc")
+
+
 def _invoke_bound_task(task, *args):
     """Call a bind=True Celery task under real or conftest-fake decorators."""
     run = getattr(task, "run", task)
-    try:
-        return run(*args)
-    except TypeError as exc:
-        if "missing 1 required positional argument" in str(exc):
-            return run(None, *args)
-        raise
+    last_exc = None
+    for call_args in (args, (_FakeCeleryTaskSelf(), *args)):
+        try:
+            return run(*call_args)
+        except TypeError as exc:
+            last_exc = exc
+            continue
+    if last_exc is not None:
+        raise last_exc
+    raise RuntimeError("bind=True task invocation failed")
 
 
 def _seed_org(db_session):
@@ -525,8 +557,92 @@ def test_process_evaluator_result_categorizes_audio_metrics_as_skipped_without_a
     assert skipped_scores[str(audio_metric.id)]["skipped"] == "audio_required"
 
 
+def test_run_evaluator_bridge_runs_without_existing_event_loop(db_session, monkeypatch):
+    task_module = _load_run_evaluator_module()
+
+    org = _seed_org(db_session)
+    workspace_id = _default_workspace_id(db_session, org.id)
+    voice_bundle = VoiceBundle(
+        id=uuid4(),
+        organization_id=org.id,
+        name="Bridge Bundle",
+        bundle_type="stt_llm_tts",
+        stt_provider="openai",
+        stt_model="whisper-1",
+        llm_provider="openai",
+        llm_model="gpt-4o-mini",
+        tts_provider="openai",
+        tts_model="tts-1",
+        tts_voice="alloy",
+    )
+    db_session.add(voice_bundle)
+    db_session.flush()
+
+    agent = Agent(
+        id=uuid4(),
+        organization_id=org.id,
+        workspace_id=workspace_id,
+        name="Bridge Agent",
+        language="en",
+        description="Bridge test agent",
+        call_type="outbound",
+        call_medium="phone_call",
+        voice_bundle_id=voice_bundle.id,
+        voice_ai_integration_id=uuid4(),
+        voice_ai_agent_id="provider-agent-1",
+    )
+    evaluator = Evaluator(
+        id=uuid4(),
+        evaluator_id="bridge-eval",
+        organization_id=org.id,
+        workspace_id=workspace_id,
+        name="Bridge Evaluator",
+        agent_id=agent.id,
+        persona_id=None,
+        scenario_id=None,
+    )
+    eval_result = EvaluatorResult(
+        id=uuid4(),
+        result_id="bridge-result",
+        organization_id=org.id,
+        workspace_id=workspace_id,
+        evaluator_id=evaluator.id,
+        agent_id=agent.id,
+        status="queued",
+    )
+    db_session.add_all([agent, evaluator, eval_result])
+    db_session.commit()
+
+    async def fake_bridge(**_kwargs):
+        return {"status": "bridged"}
+
+    fake_bridge_module = types.ModuleType("app.services.testing.test_agent_bridge_service")
+    fake_bridge_module.test_agent_bridge_service = types.SimpleNamespace(
+        bridge_test_agent_to_voice_agent=fake_bridge
+    )
+    monkeypatch.setitem(
+        sys.modules,
+        "app.services.testing.test_agent_bridge_service",
+        fake_bridge_module,
+    )
+    monkeypatch.setattr(task_module, "SessionLocal", lambda: _worker_db(db_session))
+    monkeypatch.setattr(
+        "app.services.billing.flexprice_service.record_evaluator_run_completed",
+        lambda *_args, **_kwargs: None,
+    )
+
+    result = _invoke_bound_task(
+        task_module.run_evaluator_task,
+        str(evaluator.id),
+        str(eval_result.id),
+    )
+
+    assert result["status"] == "initiated"
+    assert result["bridge_result"] == {"status": "bridged"}
+
+
 def test_run_evaluator_returns_error_when_evaluator_missing(db_session, monkeypatch):
-    from app.workers.tasks import run_evaluator as task_module
+    task_module = _load_run_evaluator_module()
 
     org = _seed_org(db_session)
     eval_result = EvaluatorResult(
@@ -544,7 +660,11 @@ def test_run_evaluator_returns_error_when_evaluator_missing(db_session, monkeypa
     monkeypatch.setitem(sys.modules, "app.services.testing.test_agent_bridge_service", fake_bridge_module)
     monkeypatch.setattr(task_module, "SessionLocal", lambda: _worker_db(db_session))
 
-    result = task_module.run_evaluator_task.run(str(uuid4()), str(eval_result.id))
+    result = _invoke_bound_task(
+        task_module.run_evaluator_task,
+        str(uuid4()),
+        str(eval_result.id),
+    )
 
     assert result == {"error": "Evaluator not found"}
 

From 5fc705dbcad7470d879af6303e1e93a9405a3314 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Tue, 25 Aug 2026 22:18:51 +0530
Subject: [PATCH 39/55] feat(flexprice billing): implement tracking for call
 import evaluation events and audio billing; enhance CSP policy for frame
 sources

---
 app/api/v1/routes/call_import_evaluations.py  |  46 +-
 app/config.py                                 |  11 +-
 app/services/billing/flexprice_service.py     |  83 ++++
 .../tasks/evaluate_call_import_row_audio.py   |   7 +
 .../tasks/evaluate_call_import_row_core.py    | 135 ++++++
 .../CallImportEvaluationDetail.tsx            |  16 +-
 .../src/pages/callImports/metricScoresMeta.ts |  33 ++
 frontend/tsconfig.json                        |   1 -
 scripts/setup_flexprice_meters.py             | 445 ++++++++++++++++--
 .../test_security_headers_middleware.py       |  16 +
 .../test_setup_flexprice_meters.py            |  22 +
 tests/test_services/test_flexprice_service.py | 123 +++++
 12 files changed, 901 insertions(+), 37 deletions(-)
 create mode 100644 frontend/src/pages/callImports/metricScoresMeta.ts
 create mode 100644 tests/test_scripts/test_setup_flexprice_meters.py

diff --git a/app/api/v1/routes/call_import_evaluations.py b/app/api/v1/routes/call_import_evaluations.py
index 18749f6d..54188b08 100644
--- a/app/api/v1/routes/call_import_evaluations.py
+++ b/app/api/v1/routes/call_import_evaluations.py
@@ -1281,6 +1281,20 @@ def _name_for_source(source: str) -> Optional[str]:
                 transcribe_overwrite=payload.transcribe_overwrite,
             )
 
+    from app.services.billing.flexprice_service import (
+        record_call_import_evaluation_started,
+    )
+
+    for evaluation in created_evaluations:
+        record_call_import_evaluation_started(
+            organization_id,
+            evaluation.id,
+            workspace_id=evaluation.workspace_id,
+            call_import_id=call_import.id,
+            total_rows=int(evaluation.total_rows or 0),
+            metric_count=len(leaf_metric_ids),
+        )
+
     for evaluation in created_evaluations:
         db.refresh(evaluation)
 
@@ -3980,6 +3994,18 @@ async def generate_call_import_evaluation_pdf_report(
             detail="Failed to store PDF report due to a concurrent duplicate request.",
         ) from None
     db.refresh(pdf_report)
+    from app.services.billing.flexprice_service import (
+        record_call_import_pdf_report_generated,
+    )
+
+    record_call_import_pdf_report_generated(
+        organization_id,
+        pdf_report.id,
+        workspace_id=pdf_report.workspace_id,
+        evaluation_id=evaluation.id,
+        call_import_id=call_import_id,
+        report_type=pdf_report.report_type,
+    )
     return _pdf_report_response_from_row(pdf_report)
 
 
@@ -6926,6 +6952,16 @@ def _resolve_alias(alias_map: Dict[str, str], key: str) -> str:
 # in ``app/workers/tasks/helpers/llm_evaluation.py`` — kept local here to
 # avoid a worker import cycle from the routes module.
 DISCOVERED_METRICS_KEY = "__discovered_metrics__"
+METRIC_SCORES_META_KEYS = frozenset({DISCOVERED_METRICS_KEY, "_billing"})
+
+
+def _is_metric_scores_meta_key(key: str) -> bool:
+    normalized = str(key or "").strip().lower()
+    if not normalized:
+        return True
+    if normalized in {item.lower() for item in METRIC_SCORES_META_KEYS}:
+        return True
+    return normalized.endswith("__discovered")
 
 # Allowed values for an LLM-suggested top-level metric type. Kept in
 # sync with ``DiscoveredMetricSuggestedType`` in
@@ -8674,7 +8710,8 @@ def _reset_eval_row_for_retry(
         eval_row.metric_scores = {
             key: value
             for key, value in existing.items()
-            if str(key).lower() not in target_keys
+            if _is_metric_scores_meta_key(str(key))
+            or str(key).lower() not in target_keys
         }
     else:
         eval_row.metric_scores = {}
@@ -9181,12 +9218,15 @@ async def retry_call_import_evaluation(
         payload.metric_ids if payload else None
     )
     if metric_ids is not None:
+        metric_ids = [
+            mid for mid in metric_ids if not _is_metric_scores_meta_key(str(mid))
+        ]
         if not metric_ids:
             raise HTTPException(
                 status_code=400,
                 detail=(
-                    "metric_ids must be a non-empty list. Omit the "
-                    "field to re-run all metrics."
+                    "metric_ids must be a non-empty list of metric UUIDs. "
+                    "Omit the field to re-run all metrics."
                 ),
             )
 
diff --git a/app/config.py b/app/config.py
index c5dc096e..0e9154e0 100644
--- a/app/config.py
+++ b/app/config.py
@@ -142,7 +142,14 @@ class Settings(BaseSettings):
         "https://*.ingest.sentry.io "
         "https://*.ingest.us.sentry.io"
     )
-    _CSP_VOICE_FRAME_SRC: str = "https://*.daily.co"
+    _CSP_FRAME_SRC: str = (
+        "https://*.daily.co "
+        "https://*.s3.amazonaws.com "
+        "https://*.amazonaws.com "
+        "https://*.cloudfront.net "
+        "https://storage.googleapis.com "
+        "https://*.blob.core.windows.net"
+    )
     # Vapi → Daily.co call-machine bundle requires eval + blob worklets for audio
     _CSP_DAILY_SCRIPT_SRC: str = "'unsafe-eval' blob: https://c.daily.co https://*.daily.co"
     CSP_POLICY: str = (
@@ -153,7 +160,7 @@ class Settings(BaseSettings):
         "img-src 'self' data: blob: https:; "
         f"connect-src 'self' wss: ws: {_CSP_VOICE_CONNECT_SRC}; "
         "media-src 'self' blob: https:; "
-        f"frame-src 'self' blob: {_CSP_VOICE_FRAME_SRC}; "
+        f"frame-src 'self' blob: {_CSP_FRAME_SRC}; "
         "worker-src 'self' blob:; "
         "object-src 'none'; "
         "base-uri 'self'; "
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index 80cb9bdd..d3088874 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -36,6 +36,8 @@
 CALL_IMPORT_EVALUATION_STARTED = "call_import.evaluation_started"
 CALL_IMPORT_EVALUATION_COMPLETED = "call_import.evaluation_completed"
 CALL_IMPORT_EVALUATION_ROW_COMPLETED = "call_import.evaluation_row_completed"
+CALL_IMPORT_AUDIO_MINUTES_BILLED = "call_import.audio_minutes_billed"
+CALL_IMPORT_PDF_REPORT_GENERATED = "call_import.pdf_report_generated"
 PLAYGROUND_WEB_CALL_STARTED = "playground.web_call_started"
 PLAYGROUND_WEBSOCKET_SESSION_STARTED = "playground.websocket_session_started"
 PLAYGROUND_CALL_EVALUATED = "playground.call_evaluated"
@@ -459,6 +461,31 @@ def record_call_import_batch_created(
 # --- Call imports (evaluations) ---
 
 
+def record_call_import_evaluation_started(
+    organization_id: UUID,
+    evaluation_id: UUID,
+    *,
+    workspace_id: UUID,
+    call_import_id: UUID,
+    total_rows: int,
+    metric_count: int = 0,
+) -> None:
+    record_event(
+        CALL_IMPORT_EVALUATION_STARTED,
+        organization_id,
+        evaluation_id,
+        properties={
+            "workspace_id": workspace_id,
+            "feature": FEATURE_CALL_IMPORTS,
+            "call_import_id": call_import_id,
+            "evaluation_id": evaluation_id,
+            "total_rows": total_rows,
+            "metric_count": metric_count,
+            "quantity": 1,
+        },
+    )
+
+
 def record_call_import_evaluation_completed(
     organization_id: UUID,
     evaluation_id: UUID,
@@ -467,6 +494,7 @@ def record_call_import_evaluation_completed(
     call_import_id: UUID,
     rows_billed: int,
     completed_total: int,
+    total_rows: int = 0,
     metric_count: int = 0,
 ) -> bool:
     """Bill one pass of an evaluation run for newly completed rows."""
@@ -481,12 +509,67 @@ def record_call_import_evaluation_completed(
             "evaluation_id": evaluation_id,
             "rows_billed": rows_billed,
             "completed_total": completed_total,
+            "total_rows": total_rows,
             "metric_count": metric_count,
             "quantity": rows_billed,
         },
     )
 
 
+def record_call_import_audio_minutes_billed(
+    organization_id: UUID,
+    evaluation_row_id: UUID,
+    *,
+    workspace_id: UUID,
+    evaluation_id: UUID,
+    call_import_id: UUID,
+    audio_seconds: int,
+    billable_minutes: int,
+) -> bool:
+    """Bill audio duration for one completed evaluation row."""
+    if billable_minutes <= 0:
+        return False
+    return record_event(
+        CALL_IMPORT_AUDIO_MINUTES_BILLED,
+        organization_id,
+        evaluation_row_id,
+        properties={
+            "workspace_id": workspace_id,
+            "feature": FEATURE_CALL_IMPORTS,
+            "call_import_id": call_import_id,
+            "evaluation_id": evaluation_id,
+            "evaluation_row_id": evaluation_row_id,
+            "audio_seconds": audio_seconds,
+            "quantity": billable_minutes,
+        },
+    )
+
+
+def record_call_import_pdf_report_generated(
+    organization_id: UUID,
+    pdf_report_id: UUID,
+    *,
+    workspace_id: UUID,
+    evaluation_id: UUID,
+    call_import_id: UUID,
+    report_type: str,
+) -> None:
+    record_event(
+        CALL_IMPORT_PDF_REPORT_GENERATED,
+        organization_id,
+        pdf_report_id,
+        properties={
+            "workspace_id": workspace_id,
+            "feature": FEATURE_CALL_IMPORTS,
+            "call_import_id": call_import_id,
+            "evaluation_id": evaluation_id,
+            "pdf_report_id": pdf_report_id,
+            "report_type": report_type,
+            "quantity": 1,
+        },
+    )
+
+
 # --- Agent playground ---
 
 
diff --git a/app/workers/tasks/evaluate_call_import_row_audio.py b/app/workers/tasks/evaluate_call_import_row_audio.py
index fb746b59..c80ba6db 100644
--- a/app/workers/tasks/evaluate_call_import_row_audio.py
+++ b/app/workers/tasks/evaluate_call_import_row_audio.py
@@ -140,6 +140,13 @@ def evaluate_call_import_row_audio_task(
             result_id = f"call-import-eval:{eval_row.id}"
             audio_failed = False
 
+            if recording_s3_key:
+                from app.workers.tasks.evaluate_call_import_row_core import (
+                    resolve_eval_row_audio_seconds,
+                )
+
+                resolve_eval_row_audio_seconds(catalog_db, eval_row, source_row)
+
             if audio_metrics and recording_s3_key:
                 from app.workers.tasks.helpers.audio_evaluation import (
                     evaluate_audio_metrics,
diff --git a/app/workers/tasks/evaluate_call_import_row_core.py b/app/workers/tasks/evaluate_call_import_row_core.py
index 30148887..4c42f5da 100644
--- a/app/workers/tasks/evaluate_call_import_row_core.py
+++ b/app/workers/tasks/evaluate_call_import_row_core.py
@@ -2,6 +2,9 @@
 
 from __future__ import annotations
 
+import math
+import os
+import tempfile
 from datetime import datetime, timezone
 from typing import Any, List, Optional
 from uuid import UUID
@@ -21,6 +24,8 @@
 
 EVAL_CANCELLED_BY_USER_ERROR: str = "Evaluation cancelled by user"
 
+_BILLING_META_KEY = "_billing"
+
 _ALL_COLUMNS_BLOCK_MAX_CHARS = 16_000
 _ALL_COLUMNS_CELL_MAX_CHARS = 4_000
 
@@ -507,6 +512,18 @@ def commit_terminal_row_and_rollup(
     catalog_db: Session | None = None,
 ) -> None:
     parent_db = catalog_db if catalog_db is not None and catalog_db is not row_db else row_db
+    if (eval_row.status or "").lower() == "completed":
+        source_row = (
+            row_db.query(CallImportRow)
+            .filter(CallImportRow.id == eval_row.call_import_row_id)
+            .first()
+        )
+        maybe_bill_completed_eval_row_flexprice(
+            row_db,
+            evaluation,
+            eval_row,
+            source_row=source_row,
+        )
     row_db.commit()
     if parent_db is not row_db:
         evaluation = (
@@ -596,6 +613,7 @@ def rollup_parent(
             call_import_id=evaluation.call_import_id,
             rows_billed=delta,
             completed_total=completed,
+            total_rows=int(evaluation.total_rows or 0),
             metric_count=metric_count,
         )
         if billing_accepted:
@@ -709,3 +727,120 @@ def row_needs_llm_phase(
         db, evaluation, source_row, metrics
     )
     return bool(transcript_metrics or comparison_metrics)
+
+
+def cache_eval_row_audio_seconds(
+    eval_row: CallImportEvaluationRow,
+    audio_seconds: int,
+) -> None:
+    merged = dict(eval_row.metric_scores or {})
+    billing = dict(merged.get(_BILLING_META_KEY) or {})
+    billing["audio_seconds"] = max(0, int(audio_seconds))
+    merged[_BILLING_META_KEY] = billing
+    eval_row.metric_scores = merged
+
+
+def get_cached_eval_row_audio_seconds(eval_row: CallImportEvaluationRow) -> int:
+    merged = eval_row.metric_scores if isinstance(eval_row.metric_scores, dict) else {}
+    billing = merged.get(_BILLING_META_KEY) or {}
+    if not isinstance(billing, dict):
+        return 0
+    try:
+        return max(0, int(billing.get("audio_seconds") or 0))
+    except (TypeError, ValueError):
+        return 0
+
+
+def billable_audio_minutes(audio_seconds: int) -> int:
+    seconds = max(0, int(audio_seconds or 0))
+    if seconds <= 0:
+        return 0
+    return max(1, math.ceil(seconds / 60))
+
+
+def probe_recording_audio_seconds(recording_s3_key: str) -> int:
+    from app.services.storage.s3_service import s3_service
+    from app.services.usage.llm_usage import probe_audio_seconds
+
+    key = (recording_s3_key or "").strip()
+    if not key:
+        return 0
+    audio_bytes = s3_service.download_file_by_key(key)
+    if not audio_bytes:
+        return 0
+    tmp_fd, tmp_path = tempfile.mkstemp(suffix=".mp3")
+    os.close(tmp_fd)
+    try:
+        with open(tmp_path, "wb") as handle:
+            handle.write(audio_bytes)
+        return probe_audio_seconds(tmp_path)
+    finally:
+        try:
+            os.unlink(tmp_path)
+        except OSError:
+            pass
+
+
+def resolve_eval_row_audio_seconds(
+    db: Session,
+    eval_row: CallImportEvaluationRow,
+    source_row: CallImportRow | None,
+) -> int:
+    cached = get_cached_eval_row_audio_seconds(eval_row)
+    if cached > 0:
+        return cached
+    if source_row is None:
+        source_row = (
+            db.query(CallImportRow)
+            .filter(CallImportRow.id == eval_row.call_import_row_id)
+            .first()
+        )
+    if source_row is None:
+        return 0
+    recording_s3_key = (source_row.recording_s3_key or "").strip()
+    if not recording_s3_key:
+        return 0
+    seconds = probe_recording_audio_seconds(recording_s3_key)
+    if seconds > 0:
+        cache_eval_row_audio_seconds(eval_row, seconds)
+    return seconds
+
+
+def maybe_bill_completed_eval_row_flexprice(
+    db: Session,
+    evaluation: CallImportEvaluation,
+    eval_row: CallImportEvaluationRow,
+    *,
+    source_row: CallImportRow | None = None,
+) -> None:
+    if (eval_row.status or "").lower() != "completed":
+        return
+    from app.services.billing.flexprice_service import (
+        record_call_import_audio_minutes_billed,
+    )
+
+    if source_row is None:
+        source_row = (
+            db.query(CallImportRow)
+            .filter(CallImportRow.id == eval_row.call_import_row_id)
+            .first()
+        )
+    if source_row is None:
+        return
+    if not (source_row.recording_s3_key or "").strip():
+        return
+
+    audio_seconds = resolve_eval_row_audio_seconds(db, eval_row, source_row)
+    minutes = billable_audio_minutes(audio_seconds)
+    if minutes <= 0:
+        return
+
+    record_call_import_audio_minutes_billed(
+        evaluation.organization_id,
+        eval_row.id,
+        workspace_id=eval_row.workspace_id or evaluation.workspace_id,
+        evaluation_id=evaluation.id,
+        call_import_id=evaluation.call_import_id,
+        audio_seconds=audio_seconds,
+        billable_minutes=minutes,
+    )
diff --git a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
index 55b70c81..be8a2d56 100644
--- a/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
+++ b/frontend/src/pages/callImports/CallImportEvaluationDetail.tsx
@@ -111,6 +111,10 @@ import {
   evaluationBulkOperationLabel,
   type BulkEvaluationOperation,
 } from './evaluationBulkOperation'
+import {
+  formatApiErrorDetail,
+  isReservedMetricScoreKey,
+} from './metricScoresMeta'
 
 const PIE_COLORS = [
   '#6366f1',
@@ -1282,12 +1286,16 @@ export default function CallImportEvaluationDetail() {
       setRerunMetricsOpen(false)
     },
     onError: (err: any) => {
+      const fallback = 'Failed to re-run the selected metrics.'
       setRerunError(
         err?.response?.status === 409
-          ? err?.response?.data?.detail || bulkOperationConflictMessage
-          : err?.response?.data?.detail ||
+          ? formatApiErrorDetail(
+              err?.response?.data?.detail,
+              bulkOperationConflictMessage,
+            )
+          : formatApiErrorDetail(err?.response?.data?.detail, fallback) ||
               err?.message ||
-              'Failed to re-run the selected metrics.',
+              fallback,
       )
     },
   })
@@ -1477,7 +1485,7 @@ export default function CallImportEvaluationDetail() {
       const scores = row.metric_scores
       if (!scores || typeof scores !== 'object') continue
       for (const [metricId, entry] of Object.entries(scores)) {
-        if (!metricId) continue
+        if (!metricId || isReservedMetricScoreKey(metricId)) continue
         if (childrenInGroups.has(metricId)) continue
         const fallbackName =
           entry && typeof entry === 'object' && 'metric_name' in entry
diff --git a/frontend/src/pages/callImports/metricScoresMeta.ts b/frontend/src/pages/callImports/metricScoresMeta.ts
new file mode 100644
index 00000000..a3fdfa3c
--- /dev/null
+++ b/frontend/src/pages/callImports/metricScoresMeta.ts
@@ -0,0 +1,33 @@
+/** Keys stored in ``metric_scores`` that are not real Metric IDs. */
+const RESERVED_METRIC_SCORE_KEYS = new Set(['_billing', '__discovered_metrics__'])
+
+export function isReservedMetricScoreKey(key: string): boolean {
+  if (!key) return true
+  if (RESERVED_METRIC_SCORE_KEYS.has(key)) return true
+  if (key.endsWith('__discovered')) return true
+  return false
+}
+
+export function formatApiErrorDetail(detail: unknown, fallback: string): string {
+  if (typeof detail === 'string' && detail.trim()) return detail
+  if (Array.isArray(detail)) {
+    const parts = detail
+      .map((item) => {
+        if (item && typeof item === 'object' && 'msg' in item) {
+          const msg = String((item as { msg?: unknown }).msg ?? '')
+          const loc = (item as { loc?: unknown }).loc
+          if (Array.isArray(loc) && loc.length > 0) {
+            return `${loc.join('.')}: ${msg}`
+          }
+          return msg
+        }
+        return typeof item === 'string' ? item : null
+      })
+      .filter((part): part is string => !!part && part.trim().length > 0)
+    if (parts.length > 0) return parts.join('; ')
+  }
+  if (detail && typeof detail === 'object' && 'msg' in detail) {
+    return String((detail as { msg?: unknown }).msg ?? fallback)
+  }
+  return fallback
+}
diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json
index 4b233ae6..228cd0cb 100644
--- a/frontend/tsconfig.json
+++ b/frontend/tsconfig.json
@@ -21,7 +21,6 @@
     "noFallthroughCasesInSwitch": true,
 
     /* Path aliases */
-    "baseUrl": ".",
     "paths": {
       "@/*": ["./src/*"]
     }
diff --git a/scripts/setup_flexprice_meters.py b/scripts/setup_flexprice_meters.py
index 533526c7..df9ab8e8 100644
--- a/scripts/setup_flexprice_meters.py
+++ b/scripts/setup_flexprice_meters.py
@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import argparse
 import json
 import sys
 from pathlib import Path
@@ -15,6 +16,11 @@
 
 CONFIG_PATH = Path(__file__).resolve().parent.parent / "config.yml"
 
+CALL_IMPORT_BATCH_EVENT = "call_import.batch_created"
+CALL_IMPORT_BATCH_METER_NAME = "Call Imports"
+CALL_IMPORT_BATCH_AGG_TYPE = "SUM"
+CALL_IMPORT_BATCH_AGG_FIELD = "quantity"
+
 # (event_name, display_name, aggregation_type, aggregation_field|None)
 METERS: list[tuple[str, str, str, str | None]] = [
     # Voice playground
@@ -24,9 +30,11 @@
     ("tts.sample_synthesized", "TTS Sample Synthesized", "SUM", "quantity"),
     ("tts.report_requested", "TTS Report Requested", "COUNT", None),
     ("tts.report_completed", "TTS Report Completed", "COUNT", None),
-    # Call imports
-    ("call_import.batch_created", "Call Import Batch Created", "SUM", "quantity"),
+    # Call imports (batch_created meter comes from the call_imports license feature)
+    ("call_import.evaluation_started", "Call Import Evaluation Started", "COUNT", None),
     ("call_import.evaluation_completed", "Call Import Evaluation Completed", "SUM", "quantity"),
+    ("call_import.audio_minutes_billed", "Call Import Audio Minutes Billed", "SUM", "quantity"),
+    ("call_import.pdf_report_generated", "Call Import PDF Report Generated", "COUNT", None),
     # Agent playground
     ("playground.web_call_started", "Playground Web Call Started", "COUNT", None),
     ("playground.websocket_session_started", "Playground Websocket Session Started", "COUNT", None),
@@ -60,10 +68,10 @@
         "name": "Call Imports",
         "lookup_key": "call_imports",
         "description": "CSV/audio call import batches, row processing, and evaluations",
-        "unit_singular": "batch",
-        "unit_plural": "batches",
-        "event_name": "call_import.batch_created",
-        "aggregation": {"type": "COUNT"},
+        "unit_singular": "row",
+        "unit_plural": "rows",
+        "event_name": CALL_IMPORT_BATCH_EVENT,
+        "aggregation": {"type": CALL_IMPORT_BATCH_AGG_TYPE, "field": CALL_IMPORT_BATCH_AGG_FIELD},
     },
     {
         "name": "Voice Playground",
@@ -85,6 +93,8 @@
     },
 ]
 
+FEATURE_OWNED_EVENT_NAMES = frozenset(spec["event_name"] for spec in LICENSE_FEATURES)
+
 
 def _headers() -> dict[str, str]:
     return {"x-api-key": settings.FLEXPRICE_API_KEY or "", "Content-Type": "application/json"}
@@ -106,8 +116,26 @@ def _meter_payload(name: str, event_name: str, agg_type: str, field: str | None)
     }
 
 
-def _list_meters(client: httpx.Client) -> dict[str, dict]:
-    existing: dict[str, dict] = {}
+def meter_aggregation_matches(
+    meter: dict[str, Any],
+    *,
+    agg_type: str,
+    agg_field: str | None = None,
+) -> bool:
+    aggregation = meter.get("aggregation") or {}
+    if (aggregation.get("type") or "").upper() != agg_type.upper():
+        return False
+    if agg_field is None:
+        return not aggregation.get("field")
+    return (aggregation.get("field") or "") == agg_field
+
+
+def _is_active_meter(meter: dict[str, Any]) -> bool:
+    return (meter.get("status") or "published").lower() == "published"
+
+
+def _list_all_meters(client: httpx.Client, *, active_only: bool = False) -> list[dict[str, Any]]:
+    items: list[dict[str, Any]] = []
     offset = 0
     while True:
         resp = client.get(
@@ -117,17 +145,26 @@ def _list_meters(client: httpx.Client) -> dict[str, dict]:
         )
         resp.raise_for_status()
         data = resp.json()
-        items = data.get("items") or []
-        for item in items:
-            event_name = item.get("event_name")
-            if event_name:
-                existing[event_name] = item
+        batch = data.get("items") or []
+        if active_only:
+            batch = [meter for meter in batch if _is_active_meter(meter)]
+        items.extend(batch)
         pagination = data.get("pagination") or {}
         total = pagination.get("total")
-        offset += len(items)
-        if not items or (total is not None and offset >= total):
+        offset += len(data.get("items") or [])
+        if not data.get("items") or (total is not None and offset >= total):
             break
-    return existing
+    return items
+
+
+def _list_meters_by_event(client: httpx.Client, *, active_only: bool = False) -> dict[str, list[dict[str, Any]]]:
+    grouped: dict[str, list[dict[str, Any]]] = {}
+    for meter in _list_all_meters(client, active_only=active_only):
+        event_name = meter.get("event_name")
+        if not event_name:
+            continue
+        grouped.setdefault(event_name, []).append(meter)
+    return grouped
 
 
 def _create_meter(client: httpx.Client, event_name: str, name: str, agg_type: str, field: str | None) -> dict:
@@ -141,6 +178,319 @@ def _create_meter(client: httpx.Client, event_name: str, name: str, agg_type: st
     return resp.json()
 
 
+def _delete_meter(client: httpx.Client, meter_id: str) -> None:
+    resp = client.delete(f"{_base_url()}/meters/{meter_id}", headers=_headers())
+    resp.raise_for_status()
+
+
+def _list_prices(client: httpx.Client) -> list[dict[str, Any]]:
+    items: list[dict[str, Any]] = []
+    offset = 0
+    while True:
+        resp = client.get(
+            f"{_base_url()}/prices",
+            headers=_headers(),
+            params={"limit": 200, "offset": offset},
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        batch = data.get("items") or []
+        items.extend(batch)
+        pagination = data.get("pagination") or {}
+        total = pagination.get("total")
+        offset += len(batch)
+        if not batch or (total is not None and offset >= total):
+            break
+    return items
+
+
+def _active_prices_for_meter(prices: list[dict[str, Any]], meter_id: str) -> list[dict[str, Any]]:
+    active: list[dict[str, Any]] = []
+    for price in prices:
+        if price.get("meter_id") != meter_id:
+            continue
+        if price.get("end_date"):
+            continue
+        if (price.get("status") or "").lower() in {"deleted", "archived", "disabled"}:
+            continue
+        active.append(price)
+    return active
+
+
+def _create_usage_price_from_template(
+    client: httpx.Client,
+    *,
+    template: dict[str, Any],
+    meter_id: str,
+) -> dict[str, Any]:
+    payload: dict[str, Any] = {
+        "entity_type": template.get("entity_type"),
+        "entity_id": template.get("entity_id"),
+        "type": template.get("type", "USAGE"),
+        "billing_model": template.get("billing_model", "FLAT_FEE"),
+        "billing_period": template.get("billing_period", "MONTHLY"),
+        "billing_period_count": template.get("billing_period_count", 1),
+        "billing_cadence": template.get("billing_cadence", "RECURRING"),
+        "currency": template.get("currency", "usd"),
+        "invoice_cadence": template.get("invoice_cadence", "ARREAR"),
+        "price_unit_type": template.get("price_unit_type", "FIAT"),
+        "meter_id": meter_id,
+        "amount": template.get("amount", "1"),
+        "display_name": template.get("display_name") or CALL_IMPORT_BATCH_METER_NAME,
+    }
+    if template.get("description"):
+        payload["description"] = template["description"]
+    if template.get("lookup_key"):
+        payload["lookup_key"] = template["lookup_key"]
+    if template.get("transform_quantity"):
+        payload["transform_quantity"] = template["transform_quantity"]
+    resp = client.post(f"{_base_url()}/prices", headers=_headers(), json=payload)
+    resp.raise_for_status()
+    return resp.json()
+
+
+def _terminate_price(client: httpx.Client, price_id: str) -> None:
+    resp = client.request("DELETE", f"{_base_url()}/prices/{price_id}", headers=_headers(), json={})
+    if resp.status_code == 404:
+        return
+    resp.raise_for_status()
+
+
+def _dedupe_canonical_prices(
+    client: httpx.Client,
+    *,
+    canonical_meter_id: str,
+    prices: list[dict[str, Any]],
+    result: dict[str, Any],
+) -> None:
+    active = _active_prices_for_meter(prices, canonical_meter_id)
+    if len(active) <= 1:
+        return
+    active.sort(key=lambda price: price.get("created_at") or "", reverse=True)
+    for duplicate in active[1:]:
+        _terminate_price(client, duplicate["id"])
+        result["terminated_prices"].append(duplicate["id"])
+        result["subscription_resync_required"] = True
+
+
+def _terminate_orphaned_batch_prices(
+    client: httpx.Client,
+    *,
+    prices: list[dict[str, Any]],
+    active_batch_meter_ids: set[str],
+    result: dict[str, Any],
+) -> None:
+    batch_meter_ids = {
+        meter["id"]
+        for meter in _list_all_meters(client)
+        if meter.get("event_name") == CALL_IMPORT_BATCH_EVENT
+    }
+    for price in prices:
+        meter_id = price.get("meter_id")
+        if not meter_id or meter_id not in batch_meter_ids:
+            continue
+        if meter_id in active_batch_meter_ids:
+            continue
+        if price.get("end_date"):
+            continue
+        if (price.get("status") or "").lower() in {"deleted", "archived", "disabled"}:
+            continue
+        _terminate_price(client, price["id"])
+        result["terminated_prices"].append(price["id"])
+        result["subscription_resync_required"] = True
+
+
+PLAN_USAGE_RESTORE_NAMES = ("Voice Playground", "Blind Test")
+# Fallback archived template price IDs when /prices list omits terminated prices.
+PLAN_USAGE_ARCHIVE_TEMPLATE_IDS: dict[str, str] = {
+    "Voice Playground": "price_01KVT8P9T8E52KHB1CA6HCVPV0",
+    "Blind Test": "price_01KW9E4W02NND35R6W87Y7863K",
+}
+
+
+def _fetch_price(client: httpx.Client, price_id: str) -> dict[str, Any] | None:
+    resp = client.get(f"{_base_url()}/prices/{price_id}", headers=_headers())
+    if resp.status_code == 404:
+        return None
+    resp.raise_for_status()
+    return resp.json()
+
+
+def restore_missing_plan_usage_prices(client: httpx.Client) -> dict[str, Any]:
+    """Recreate plan usage prices that were terminated but still have archived templates."""
+    result: dict[str, Any] = {"restored": [], "skipped": []}
+    all_prices = _list_prices(client)
+    active_names = {
+        price.get("display_name")
+        for price in all_prices
+        if price.get("entity_type") == "PLAN"
+        and price.get("type") == "USAGE"
+        and not price.get("end_date")
+        and (price.get("status") or "published").lower() == "published"
+    }
+
+    for display_name in PLAN_USAGE_RESTORE_NAMES:
+        if display_name in active_names:
+            result["skipped"].append(display_name)
+            continue
+        templates = [
+            price
+            for price in all_prices
+            if price.get("display_name") == display_name
+            and price.get("entity_type") == "PLAN"
+            and price.get("type") == "USAGE"
+            and price.get("end_date")
+        ]
+        template: dict[str, Any] | None = None
+        if templates:
+            templates.sort(key=lambda price: price.get("end_date") or "", reverse=True)
+            template = templates[0]
+        else:
+            archive_id = PLAN_USAGE_ARCHIVE_TEMPLATE_IDS.get(display_name)
+            if archive_id:
+                template = _fetch_price(client, archive_id)
+        if not template:
+            result["skipped"].append(display_name)
+            continue
+        restored = _create_usage_price_from_template(
+            client,
+            template=template,
+            meter_id=template["meter_id"],
+        )
+        result["restored"].append({"display_name": display_name, "price_id": restored.get("id")})
+    return result
+
+
+def repair_call_import_batch_meter(client: httpx.Client) -> dict[str, Any]:
+    """Ensure one SUM(quantity) meter for call_import.batch_created and remove duplicates."""
+    result: dict[str, Any] = {
+        "deleted_duplicate_meters": [],
+        "deleted_legacy_meters": [],
+        "terminated_prices": [],
+        "created_meter_id": None,
+        "created_price_ids": [],
+        "kept_meter_id": None,
+        "subscription_resync_required": False,
+    }
+
+    all_prices = _list_prices(client)
+    batch_meters = _list_meters_by_event(client, active_only=True).get(CALL_IMPORT_BATCH_EVENT, [])
+
+    correct_meters = [
+        meter
+        for meter in batch_meters
+        if meter_aggregation_matches(
+            meter,
+            agg_type=CALL_IMPORT_BATCH_AGG_TYPE,
+            agg_field=CALL_IMPORT_BATCH_AGG_FIELD,
+        )
+    ]
+    incorrect_meters = [meter for meter in batch_meters if meter not in correct_meters]
+
+    canonical_meter: dict[str, Any] | None = None
+    if correct_meters:
+        priced_correct = [
+            meter
+            for meter in correct_meters
+            if _active_prices_for_meter(all_prices, meter["id"])
+        ]
+        canonical_meter = priced_correct[0] if priced_correct else correct_meters[0]
+        result["kept_meter_id"] = canonical_meter["id"]
+
+    prices_to_recreate: list[dict[str, Any]] = []
+    for meter in incorrect_meters:
+        meter_id = meter["id"]
+        attached_prices = _active_prices_for_meter(all_prices, meter_id)
+        if attached_prices:
+            prices_to_recreate.extend(attached_prices)
+        else:
+            _delete_meter(client, meter_id)
+            result["deleted_duplicate_meters"].append(
+                {"id": meter_id, "name": meter.get("name")}
+            )
+
+    if canonical_meter is None or prices_to_recreate:
+        if canonical_meter is None:
+            canonical_meter = _create_meter(
+                client,
+                CALL_IMPORT_BATCH_EVENT,
+                CALL_IMPORT_BATCH_METER_NAME,
+                CALL_IMPORT_BATCH_AGG_TYPE,
+                CALL_IMPORT_BATCH_AGG_FIELD,
+            )
+            result["created_meter_id"] = canonical_meter.get("id")
+        result["kept_meter_id"] = canonical_meter["id"]
+
+        existing_canonical_prices = _active_prices_for_meter(all_prices, canonical_meter["id"])
+        for old_price in prices_to_recreate:
+            if existing_canonical_prices:
+                _terminate_price(client, old_price["id"])
+                result["terminated_prices"].append(old_price["id"])
+                result["subscription_resync_required"] = True
+                continue
+            new_price = _create_usage_price_from_template(
+                client,
+                template=old_price,
+                meter_id=canonical_meter["id"],
+            )
+            result["created_price_ids"].append(new_price.get("id"))
+            existing_canonical_prices = [new_price]
+            _terminate_price(client, old_price["id"])
+            result["terminated_prices"].append(old_price["id"])
+            result["subscription_resync_required"] = True
+
+        for meter in incorrect_meters:
+            meter_id = meter["id"]
+            if meter_id == canonical_meter["id"]:
+                continue
+            _delete_meter(client, meter_id)
+            result["deleted_legacy_meters"].append(
+                {"id": meter_id, "name": meter.get("name")}
+            )
+
+    extra_correct = [
+        meter
+        for meter in correct_meters
+        if canonical_meter and meter["id"] != canonical_meter["id"]
+    ]
+    for meter in extra_correct:
+        meter_id = meter["id"]
+        attached_prices = _active_prices_for_meter(all_prices, meter_id)
+        if attached_prices:
+            continue
+        _delete_meter(client, meter_id)
+        result["deleted_duplicate_meters"].append(
+            {"id": meter_id, "name": meter.get("name")}
+        )
+
+    if canonical_meter:
+        refreshed_prices = _list_prices(client)
+        active_batch_meter_ids = {
+            meter["id"]
+            for meter in _list_meters_by_event(client, active_only=True).get(CALL_IMPORT_BATCH_EVENT, [])
+            if meter_aggregation_matches(
+                meter,
+                agg_type=CALL_IMPORT_BATCH_AGG_TYPE,
+                agg_field=CALL_IMPORT_BATCH_AGG_FIELD,
+            )
+        }
+        _terminate_orphaned_batch_prices(
+            client,
+            prices=refreshed_prices,
+            active_batch_meter_ids=active_batch_meter_ids,
+            result=result,
+        )
+        refreshed_prices = _list_prices(client)
+        _dedupe_canonical_prices(
+            client,
+            canonical_meter_id=canonical_meter["id"],
+            prices=refreshed_prices,
+            result=result,
+        )
+
+    return result
+
+
 def _list_feature_lookup_keys(sdk: Flexprice) -> set[str]:
     keys: set[str] = set()
     offset = 0
@@ -181,14 +531,7 @@ def _create_license_feature(sdk: Flexprice, spec: dict[str, Any]) -> dict:
         raise
 
 
-def main() -> int:
-    if CONFIG_PATH.exists():
-        load_config_from_file(str(CONFIG_PATH))
-
-    if not settings.FLEXPRICE_ENABLED or not settings.FLEXPRICE_API_KEY:
-        print("Flexprice is not enabled or FLEXPRICE_API_KEY is missing.", file=sys.stderr)
-        return 1
-
+def bootstrap_catalog() -> dict[str, Any]:
     created_meters: list[str] = []
     skipped_meters: list[str] = []
     failed_meters: list[str] = []
@@ -197,9 +540,12 @@ def main() -> int:
     failed_features: list[str] = []
 
     with httpx.Client(timeout=60.0) as http_client:
-        existing_meters = _list_meters(http_client)
+        existing_by_event = _list_meters_by_event(http_client)
         for event_name, name, agg_type, field in METERS:
-            if event_name in existing_meters:
+            if event_name in FEATURE_OWNED_EVENT_NAMES:
+                skipped_meters.append(event_name)
+                continue
+            if event_name in existing_by_event:
                 skipped_meters.append(event_name)
                 continue
             try:
@@ -230,11 +576,56 @@ def main() -> int:
             except Exception as exc:
                 failed_features.append(f"{key}: {exc}")
 
-    summary = {
+    return {
         "meters": {"created": created_meters, "skipped": skipped_meters, "failed": failed_meters},
         "features": {"created": created_features, "skipped": skipped_features, "failed": failed_features},
     }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Bootstrap or repair Flexprice metering catalog.")
+    parser.add_argument(
+        "--repair-call-imports",
+        action="store_true",
+        help="Remove duplicate batch_created meters and migrate pricing to SUM(quantity).",
+    )
+    parser.add_argument(
+        "--restore-plan-usage-prices",
+        action="store_true",
+        help="Recreate missing Voice Playground and Blind Test plan usage prices from archived templates.",
+    )
+    args = parser.parse_args()
+
+    if CONFIG_PATH.exists():
+        load_config_from_file(str(CONFIG_PATH))
+
+    if not settings.FLEXPRICE_ENABLED or not settings.FLEXPRICE_API_KEY:
+        print("Flexprice is not enabled or FLEXPRICE_API_KEY is missing.", file=sys.stderr)
+        return 1
+
+    summary: dict[str, Any] = {}
+    with httpx.Client(timeout=60.0) as http_client:
+        if args.repair_call_imports:
+            summary["call_import_batch_repair"] = repair_call_import_batch_meter(http_client)
+        if args.restore_plan_usage_prices:
+            summary["plan_usage_restore"] = restore_missing_plan_usage_prices(http_client)
+
+    bootstrap_summary = bootstrap_catalog()
+    summary.update(bootstrap_summary)
+
     print(json.dumps(summary, indent=2))
+
+    repair = summary.get("call_import_batch_repair") or {}
+    if repair.get("subscription_resync_required"):
+        print(
+            "\nNOTE: Plan prices were recreated. In Flexprice, open the plan and click "
+            "'Sync Usage Charges', then refresh the customer subscription so line items "
+            "reference the new price IDs.",
+            file=sys.stderr,
+        )
+
+    failed_meters = bootstrap_summary["meters"]["failed"]
+    failed_features = bootstrap_summary["features"]["failed"]
     return 1 if (failed_meters or failed_features) else 0
 
 
diff --git a/tests/test_core/test_security_headers_middleware.py b/tests/test_core/test_security_headers_middleware.py
index 14a7cad2..5b7f9af3 100644
--- a/tests/test_core/test_security_headers_middleware.py
+++ b/tests/test_core/test_security_headers_middleware.py
@@ -95,6 +95,22 @@ def test_csp_allows_voice_provider_connect_src(security_client, monkeypatch):
     assert "worker-src 'self' blob:" in policy
 
 
+def test_csp_allows_frame_src_for_pdf_preview_and_voice(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
+
+    response = security_client.get("/health")
+    policy = response.headers["Content-Security-Policy"]
+
+    assert "frame-src 'self' blob:" in policy
+    assert "https://*.daily.co" in policy
+    assert "https://*.s3.amazonaws.com" in policy
+    assert "https://*.amazonaws.com" in policy
+    assert "https://*.cloudfront.net" in policy
+    assert "https://storage.googleapis.com" in policy
+    assert "https://*.blob.core.windows.net" in policy
+
+
 def test_asset_routes_use_long_cache(security_client):
     response = security_client.get("/assets/app.js")
 
diff --git a/tests/test_scripts/test_setup_flexprice_meters.py b/tests/test_scripts/test_setup_flexprice_meters.py
new file mode 100644
index 00000000..be12234a
--- /dev/null
+++ b/tests/test_scripts/test_setup_flexprice_meters.py
@@ -0,0 +1,22 @@
+from scripts.setup_flexprice_meters import meter_aggregation_matches
+from app.api.v1.routes.call_import_evaluations import (
+    DISCOVERED_METRICS_KEY,
+    _is_metric_scores_meta_key,
+)
+
+
+def test_meter_aggregation_matches_sum_quantity():
+    meter = {"aggregation": {"type": "SUM", "field": "quantity"}}
+    assert meter_aggregation_matches(meter, agg_type="SUM", agg_field="quantity") is True
+
+
+def test_meter_aggregation_matches_rejects_count():
+    meter = {"aggregation": {"type": "COUNT"}}
+    assert meter_aggregation_matches(meter, agg_type="SUM", agg_field="quantity") is False
+
+
+def test_metric_scores_meta_keys():
+    assert _is_metric_scores_meta_key("_billing") is True
+    assert _is_metric_scores_meta_key(DISCOVERED_METRICS_KEY) is True
+    assert _is_metric_scores_meta_key("parent-id__discovered") is True
+    assert _is_metric_scores_meta_key("550e8400-e29b-41d4-a716-446655440000") is False
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index 714cead5..3eb56b4c 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -220,6 +220,7 @@ def test_record_call_import_evaluation_completed_meters_pass_delta(mock_flexpric
         call_import_id=call_import_id,
         rows_billed=50,
         completed_total=1950,
+        total_rows=2000,
         metric_count=5,
     )
 
@@ -235,6 +236,7 @@ def test_record_call_import_evaluation_completed_meters_pass_delta(mock_flexpric
             "evaluation_id": str(evaluation_id),
             "rows_billed": "50",
             "completed_total": "1950",
+            "total_rows": "2000",
             "metric_count": "5",
             "quantity": "50",
         },
@@ -242,6 +244,127 @@ def test_record_call_import_evaluation_completed_meters_pass_delta(mock_flexpric
     assert accepted is True
 
 
+@patch("flexprice.Flexprice")
+def test_record_call_import_evaluation_started(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    evaluation_id = uuid4()
+    workspace_id = uuid4()
+    call_import_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_call_import_evaluation_started(
+        org_id,
+        evaluation_id,
+        workspace_id=workspace_id,
+        call_import_id=call_import_id,
+        total_rows=100,
+        metric_count=3,
+    )
+
+    mock_client.events.ingest_event.assert_called_once_with(
+        event_name="call_import.evaluation_started",
+        external_customer_id=str(org_id),
+        event_id=str(evaluation_id),
+        source="efficientai",
+        properties={
+            "workspace_id": str(workspace_id),
+            "feature": "call_imports",
+            "call_import_id": str(call_import_id),
+            "evaluation_id": str(evaluation_id),
+            "total_rows": "100",
+            "metric_count": "3",
+            "quantity": "1",
+        },
+    )
+
+
+@patch("flexprice.Flexprice")
+def test_record_call_import_audio_minutes_billed(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    eval_row_id = uuid4()
+    evaluation_id = uuid4()
+    workspace_id = uuid4()
+    call_import_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    accepted = svc.record_call_import_audio_minutes_billed(
+        org_id,
+        eval_row_id,
+        workspace_id=workspace_id,
+        evaluation_id=evaluation_id,
+        call_import_id=call_import_id,
+        audio_seconds=90,
+        billable_minutes=2,
+    )
+
+    mock_client.events.ingest_event.assert_called_once_with(
+        event_name="call_import.audio_minutes_billed",
+        external_customer_id=str(org_id),
+        event_id=str(eval_row_id),
+        source="efficientai",
+        properties={
+            "workspace_id": str(workspace_id),
+            "feature": "call_imports",
+            "call_import_id": str(call_import_id),
+            "evaluation_id": str(evaluation_id),
+            "evaluation_row_id": str(eval_row_id),
+            "audio_seconds": "90",
+            "quantity": "2",
+        },
+    )
+    assert accepted is True
+
+
+@patch("flexprice.Flexprice")
+def test_record_call_import_pdf_report_generated(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    pdf_report_id = uuid4()
+    evaluation_id = uuid4()
+    workspace_id = uuid4()
+    call_import_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_call_import_pdf_report_generated(
+        org_id,
+        pdf_report_id,
+        workspace_id=workspace_id,
+        evaluation_id=evaluation_id,
+        call_import_id=call_import_id,
+        report_type="external",
+    )
+
+    mock_client.events.ingest_event.assert_called_once_with(
+        event_name="call_import.pdf_report_generated",
+        external_customer_id=str(org_id),
+        event_id=str(pdf_report_id),
+        source="efficientai",
+        properties={
+            "workspace_id": str(workspace_id),
+            "feature": "call_imports",
+            "call_import_id": str(call_import_id),
+            "evaluation_id": str(evaluation_id),
+            "pdf_report_id": str(pdf_report_id),
+            "report_type": "external",
+            "quantity": "1",
+        },
+    )
+
+
 @patch("flexprice.Flexprice")
 def test_record_event_returns_false_when_disabled(mock_flexprice):
     settings.FLEXPRICE_ENABLED = False

From 35b69a21320d0832b5e1cab4369a675a0658cff7 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 00:41:49 +0530
Subject: [PATCH 40/55] feat(flexprice): add support for flexprice usage
 metering in various API routes and services;

---
 app/api/v1/routes/agents.py                   |  27 +-
 app/api/v1/routes/chat.py                     |   2 +
 app/api/v1/routes/metric_studio.py            |  24 +-
 app/api/v1/routes/voice_agent.py              |  74 ++-
 app/services/billing/flexprice_service.py     | 255 ++++++++--
 app/services/metric_studio/run_rollup.py      |  61 +++
 .../tasks/evaluate_call_import_row_core.py    |  14 +-
 app/workers/tasks/evaluate_studio_run_item.py |  37 +-
 config.docker.yml                             |   8 +-
 scripts/setup_flexprice_meters.py             |  85 +++-
 .../test_setup_flexprice_meters.py            |  47 +-
 tests/test_services/test_flexprice_service.py | 456 +++++++++++++++++-
 .../test_flexprice_tracking_wiring.py         | 136 ++++++
 13 files changed, 1128 insertions(+), 98 deletions(-)
 create mode 100644 app/services/metric_studio/run_rollup.py
 create mode 100644 tests/test_services/test_flexprice_tracking_wiring.py

diff --git a/app/api/v1/routes/agents.py b/app/api/v1/routes/agents.py
index 17094877..884769b9 100644
--- a/app/api/v1/routes/agents.py
+++ b/app/api/v1/routes/agents.py
@@ -2,16 +2,19 @@
 Agents API Routes
 Complete CRUD operations for test agents
 """
-from fastapi import APIRouter, Depends, HTTPException, status, Query
+from uuid import uuid4
+
+from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, status, Query
 from fastapi.responses import JSONResponse, Response
 from sqlalchemy.orm import Session
 from typing import List, Optional
-from uuid import UUID
+from uuid import UUID, uuid4
 import random
 from pydantic import BaseModel
 from loguru import logger
 
 from app.dependencies import get_db, get_organization_id, get_workspace_id, get_api_key
+from app.services.billing.flexprice_service import record_scenario_ai_text_generated
 from app.models.database import (
     Agent, ConversationEvaluation, TestAgentConversation, VoiceBundle,
     AIProvider, Integration, IntegrationPlatform, CallMediumEnum,
@@ -313,6 +316,7 @@ async def generate_test_prompt(
 @router.post("/generate-scenarios-from-prompt", response_model=GenerateScenariosFromPromptResponse)
 async def generate_scenarios_from_prompt(
     data: GenerateScenariosFromPromptRequest,
+    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
     workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
@@ -357,6 +361,15 @@ async def generate_scenarios_from_prompt(
                 llm_config=data.llm_config,
                 credential_id=data.credential_id,
             )
+        background_tasks.add_task(
+            record_scenario_ai_text_generated,
+            organization_id,
+            uuid4(),
+            workspace_id=workspace_id,
+            model=result.model,
+            purpose="scenarios_from_agent_prompt",
+            scenario_count=len(result.scenarios),
+        )
         return GenerateScenariosFromPromptResponse(
             scenarios=_scenario_draft_responses(result.scenarios),
             provider=result.provider,
@@ -372,6 +385,7 @@ async def generate_scenarios_from_prompt(
 @router.post("/generate-test-setup", response_model=GenerateTestSetupResponse)
 async def generate_test_setup(
     data: GenerateTestSetupRequest,
+    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
     workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
@@ -430,6 +444,15 @@ async def generate_test_setup(
                 llm_config=data.llm_config,
                 credential_id=data.credential_id,
             )
+        background_tasks.add_task(
+            record_scenario_ai_text_generated,
+            organization_id,
+            uuid4(),
+            workspace_id=workspace_id,
+            model=scenario_result.model,
+            purpose="scenarios_from_agent_prompt",
+            scenario_count=len(scenario_result.scenarios),
+        )
         return GenerateTestSetupResponse(
             sections=_test_prompt_section_responses(prompt_result.sections),
             test_agent_prompt=prompt_result.test_agent_prompt,
diff --git a/app/api/v1/routes/chat.py b/app/api/v1/routes/chat.py
index 6fc026f8..6f023654 100644
--- a/app/api/v1/routes/chat.py
+++ b/app/api/v1/routes/chat.py
@@ -28,6 +28,7 @@ class ChatRequest(BaseModel):
     temperature: Optional[float] = 0.7
     max_tokens: Optional[int] = None
     llm_config: Optional[Dict[str, Any]] = None
+    usage_purpose: Optional[str] = "scenario_description"
 
 
 class ChatResponse(BaseModel):
@@ -80,6 +81,7 @@ async def chat_completion(
             uuid4(),
             workspace_id=workspace_id,
             model=result.get("model", request.model),
+            purpose=request.usage_purpose or "scenario_description",
         )
 
         return ChatResponse(
diff --git a/app/api/v1/routes/metric_studio.py b/app/api/v1/routes/metric_studio.py
index aeb155f3..10b407ca 100644
--- a/app/api/v1/routes/metric_studio.py
+++ b/app/api/v1/routes/metric_studio.py
@@ -25,6 +25,7 @@
     MetricStudioRunRetryRequest,
 )
 from app.services.metric_studio.metric_selection import expand_studio_metric_selection
+from app.services.metric_studio.run_rollup import rollup_metric_studio_run
 from app.services.metric_studio.source_resolver import resolve_source
 
 router = APIRouter(prefix="/metric-studio", tags=["metric-studio"])
@@ -117,26 +118,7 @@ def _serialize_result(
 
 
 def _rollup_run_status(db: Session, run: MetricStudioRun) -> None:
-    results = (
-        db.query(MetricStudioRunResult)
-        .filter(MetricStudioRunResult.run_id == run.id)
-        .all()
-    )
-    completed = sum(1 for r in results if r.status == "completed")
-    failed = sum(1 for r in results if r.status == "failed")
-    pending = sum(1 for r in results if r.status in {"pending", "running"})
-    run.completed_items = completed
-    run.failed_items = failed
-    if pending:
-        run.status = "running"
-    elif failed and completed:
-        run.status = "partial"
-    elif failed:
-        run.status = "failed"
-    else:
-        run.status = "completed"
-        run.finished_at = datetime.now(timezone.utc)
-    db.flush()
+    rollup_metric_studio_run(db, run, emit_flexprice=True, commit=False)
 
 
 @router.post(
@@ -297,6 +279,8 @@ def get_metric_studio_run(
     )
     if not run:
         raise HTTPException(status_code=404, detail="Studio run not found.")
+    rollup_metric_studio_run(db, run, emit_flexprice=True, commit=True)
+    db.refresh(run)
     return _serialize_run(run)
 
 
diff --git a/app/api/v1/routes/voice_agent.py b/app/api/v1/routes/voice_agent.py
index e6d553b5..9656d7af 100644
--- a/app/api/v1/routes/voice_agent.py
+++ b/app/api/v1/routes/voice_agent.py
@@ -5,7 +5,7 @@
 
 from fastapi import APIRouter, Depends, HTTPException, WebSocket, WebSocketDisconnect, Request, status
 from sqlalchemy.orm import Session
-from uuid import UUID
+from uuid import UUID, uuid4
 from typing import Dict, Any, Optional, List
 from loguru import logger
 
@@ -478,8 +478,21 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
         
         # Run the bot with the appropriate pipeline
         call_metadata = None
+        session_uuid: Optional[UUID] = None
         from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
 
+        if agent_id and workspace_id and not test_agent_bridge_mode:
+            session_uuid = uuid4()
+            from app.services.billing.flexprice_service import record_test_agent_conversation_started
+
+            record_test_agent_conversation_started(
+                organization_id,
+                session_uuid,
+                workspace_id=workspace_id,
+                result_id=result_id,
+                agent_id=UUID(agent_id),
+            )
+
         agent_silence_hangup_secs = resolve_agent_silence_hangup_secs(agent)
         try:
             if use_voice_bundle_pipeline:
@@ -608,9 +621,17 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
             # evaluator_id is optional - can be None if no persona/scenario
             if result_id and agent_id:
                 try:
-                    from app.models.database import EvaluatorResult, EvaluatorResultStatus
+                    from app.models.database import (
+                        CallRecording,
+                        CallRecordingSource,
+                        EvaluatorResult,
+                        EvaluatorResultStatus,
+                    )
+                    from app.models.enums import CallRecordingStatus
+                    from app.utils.call_recordings import generate_unique_call_short_id
                     from app.workers.celery_app import process_evaluator_result_task
-                    
+                    from app.services.billing.flexprice_service import record_test_agent_conversation_ended
+
                     # Determine name for the result
                     if scenario_name and scenario_name != "Test Call":
                         result_name = scenario_name
@@ -618,9 +639,22 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
                         result_name = f"Test Call - {agent.name}"
                     else:
                         result_name = "Test Call"
-                    
+
                     logger.info(f"Creating evaluator result: result_id={result_id}, agent_id={agent_id}, persona_id={persona_id}, scenario_id={scenario_id}, s3_key={call_metadata.get('s3_key')}")
-                    
+
+                    call_short_id = generate_unique_call_short_id(db)
+                    speaker_segments = call_metadata.get("speaker_segments") or []
+                    if not isinstance(speaker_segments, list):
+                        speaker_segments = []
+                    playground_call_data = {
+                        "source": "voice_bundle",
+                        "result_id": result_id,
+                        "transcript": call_metadata.get("transcription"),
+                        "speaker_segments": speaker_segments,
+                        "recording_s3_key": call_metadata.get("s3_key"),
+                        "duration_seconds": call_metadata.get("duration"),
+                    }
+
                     # Create evaluator result with QUEUED status
                     # persona_id and scenario_id can be None for test calls without persona/scenario
                     evaluator_result = EvaluatorResult(
@@ -636,11 +670,39 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
                         status=EvaluatorResultStatus.QUEUED.value,  # Use .value to get the string
                         audio_s3_key=call_metadata.get("s3_key"),
                         transcription=call_metadata.get("transcription"),
-                        speaker_segments=call_metadata.get("speaker_segments"),
+                        speaker_segments=speaker_segments or None,
                     )
                     db.add(evaluator_result)
+                    db.flush()
+
+                    call_recording = CallRecording(
+                        organization_id=organization_id,
+                        workspace_id=workspace_id,
+                        call_short_id=call_short_id,
+                        status=CallRecordingStatus.UPDATED,
+                        source=CallRecordingSource.PLAYGROUND,
+                        call_data=playground_call_data,
+                        provider_call_id=f"voice_bundle_{result_id}",
+                        provider_platform="voice_bundle",
+                        agent_id=UUID(agent_id),
+                        evaluator_result_id=evaluator_result.id,
+                    )
+                    db.add(call_recording)
                     db.commit()
                     db.refresh(evaluator_result)
+
+                    if session_uuid and workspace_id:
+                        turn_count = len(speaker_segments)
+                        record_test_agent_conversation_ended(
+                            organization_id,
+                            session_uuid,
+                            workspace_id=workspace_id,
+                            duration_seconds=call_metadata.get("duration"),
+                            turn_count=turn_count,
+                            result_id=result_id,
+                            agent_id=UUID(agent_id),
+                            call_short_id=call_short_id,
+                        )
                     
                     logger.info(f"✅ Evaluator result created in database: id={evaluator_result.id}, result_id={result_id}")
                     
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index d3088874..1c746faf 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -4,6 +4,31 @@
 Every event uses ``external_customer_id=str(organization.id)`` and a stable
 ``event_id`` for idempotency. ``properties`` should include ``workspace_id`` and
 ``feature`` (license key) when the surface is gated.
+
+Plan pricing (Flexprice UI): bill on **successful delivery**, not attempts.
+Primary charge meters per product (add plan usage charges on these only):
+
+- call_imports: ``call_import.batch_created``, ``call_import.evaluation_completed`` (one
+  event per finished evaluation pass; ``quantity`` = newly completed rows),
+  ``call_import.recording_minutes_billed`` (one per evaluated call with audio;
+  ``billable_minutes`` = rounded-up recording duration in minutes),
+  ``call_import.pdf_report_generated``
+- agent_playground: ``playground.evaluation_completed`` (main); optional secondary
+  ``test_agent.conversation_ended`` for test-agent-only flows — not both on the
+  same conversation
+- voice_playground: ``tts.sample_synthesized``, ``tts.report_completed``; optional
+  ``blind_test.share_created`` or ``blind_test.response_submitted``
+- evaluators: ``evaluator.run_completed`` (not ``run_requested``)
+- gepa_optimization: ``prompt_optimization.run_completed``
+- judge_alignment: ``judge_alignment.run_completed`` (not ``run_started``)
+- metrics_ai_assist: ``metrics.ai_assist`` (AI metric builder / bulk parse)
+- metric_studio: ``metric_studio.run_completed`` (main); ``metric_studio.item_evaluated`` optional
+- scenario_ai: ``scenario.ai_text_generated`` (AI text generation, e.g. scenario descriptions)
+
+Track-only (analytics, do not price): ``*_started``, ``call_evaluated``,
+``conversation_started``, ``web_call_started``, ``evaluation_started``, etc.
+``metric_count`` and ``duration_seconds`` in properties are metadata unless a
+meter explicitly SUMs those fields (call-import audio minutes does).
 """
 
 from __future__ import annotations
@@ -18,8 +43,14 @@
 
 EVENT_SOURCE = "efficientai"
 FEATURE_CALL_IMPORTS = "call_imports"
+FEATURE_AGENT_PLAYGROUND = "agent_playground"
 FEATURE_VOICE_PLAYGROUND = "voice_playground"
 FEATURE_GEPA = "gepa_optimization"
+FEATURE_EVALUATORS = "evaluators"
+FEATURE_JUDGE_ALIGNMENT = "judge_alignment"
+FEATURE_METRICS_AI_ASSIST = "metrics_ai_assist"
+FEATURE_METRIC_STUDIO = "metric_studio"
+FEATURE_SCENARIO_AI = "scenario_ai"
 
 # Log once when metering is inactive so AWS/worker misconfig is obvious.
 _disabled_skip_logged = False
@@ -32,11 +63,10 @@
 TTS_REPORT_REQUESTED = "tts.report_requested"
 TTS_REPORT_COMPLETED = "tts.report_completed"
 CALL_IMPORT_BATCH_CREATED = "call_import.batch_created"
-CALL_IMPORT_ROW_IMPORTED = "call_import.row_imported"
 CALL_IMPORT_EVALUATION_STARTED = "call_import.evaluation_started"
 CALL_IMPORT_EVALUATION_COMPLETED = "call_import.evaluation_completed"
-CALL_IMPORT_EVALUATION_ROW_COMPLETED = "call_import.evaluation_row_completed"
-CALL_IMPORT_AUDIO_MINUTES_BILLED = "call_import.audio_minutes_billed"
+CALL_IMPORT_RECORDING_MINUTES_BILLED = "call_import.recording_minutes_billed"
+CALL_IMPORT_AUDIO_MINUTES_BILLED = CALL_IMPORT_RECORDING_MINUTES_BILLED
 CALL_IMPORT_PDF_REPORT_GENERATED = "call_import.pdf_report_generated"
 PLAYGROUND_WEB_CALL_STARTED = "playground.web_call_started"
 PLAYGROUND_WEBSOCKET_SESSION_STARTED = "playground.websocket_session_started"
@@ -54,8 +84,13 @@
 OBSERVABILITY_CALL_EVALUATED = "observability.call_evaluated"
 TEST_AGENT_CONVERSATION_STARTED = "test_agent.conversation_started"
 TEST_AGENT_CONVERSATION_ENDED = "test_agent.conversation_ended"
-METRICS_LLM_ASSIST = "metrics.llm_assist"
-CHAT_COMPLETION = "chat.completion"
+METRICS_AI_ASSIST = "metrics.ai_assist"
+METRIC_STUDIO_ITEM_EVALUATED = "metric_studio.item_evaluated"
+METRIC_STUDIO_RUN_COMPLETED = "metric_studio.run_completed"
+SCENARIO_AI_TEXT_GENERATED = "scenario.ai_text_generated"
+# Legacy aliases (Flexprice meters may still exist under old names)
+METRICS_LLM_ASSIST = METRICS_AI_ASSIST
+CHAT_COMPLETION = SCENARIO_AI_TEXT_GENERATED
 
 
 def _verbose_logging() -> bool:
@@ -430,7 +465,7 @@ def record_tts_report_completed(
     )
 
 
-# --- Call imports ---
+# --- Call imports (tracking complete: batch, eval lifecycle, audio minutes, PDF) ---
 
 
 def record_call_import_batch_created(
@@ -497,7 +532,7 @@ def record_call_import_evaluation_completed(
     total_rows: int = 0,
     metric_count: int = 0,
 ) -> bool:
-    """Bill one pass of an evaluation run for newly completed rows."""
+    """Bill one finished evaluation pass for newly completed rows (not per row)."""
     return record_event(
         CALL_IMPORT_EVALUATION_COMPLETED,
         organization_id,
@@ -516,7 +551,7 @@ def record_call_import_evaluation_completed(
     )
 
 
-def record_call_import_audio_minutes_billed(
+def record_call_import_recording_minutes_billed(
     organization_id: UUID,
     evaluation_row_id: UUID,
     *,
@@ -526,11 +561,11 @@ def record_call_import_audio_minutes_billed(
     audio_seconds: int,
     billable_minutes: int,
 ) -> bool:
-    """Bill audio duration for one completed evaluation row."""
+    """Bill recording duration for one successfully evaluated call-import row."""
     if billable_minutes <= 0:
         return False
     return record_event(
-        CALL_IMPORT_AUDIO_MINUTES_BILLED,
+        CALL_IMPORT_RECORDING_MINUTES_BILLED,
         organization_id,
         evaluation_row_id,
         properties={
@@ -540,11 +575,35 @@ def record_call_import_audio_minutes_billed(
             "evaluation_id": evaluation_id,
             "evaluation_row_id": evaluation_row_id,
             "audio_seconds": audio_seconds,
+            "billable_minutes": billable_minutes,
+            "billing_unit": "minute",
+            # Flexprice generic field; same value as billable_minutes for older meters.
             "quantity": billable_minutes,
         },
     )
 
 
+def record_call_import_audio_minutes_billed(
+    organization_id: UUID,
+    evaluation_row_id: UUID,
+    *,
+    workspace_id: UUID,
+    evaluation_id: UUID,
+    call_import_id: UUID,
+    audio_seconds: int,
+    billable_minutes: int,
+) -> bool:
+    return record_call_import_recording_minutes_billed(
+        organization_id,
+        evaluation_row_id,
+        workspace_id=workspace_id,
+        evaluation_id=evaluation_id,
+        call_import_id=call_import_id,
+        audio_seconds=audio_seconds,
+        billable_minutes=billable_minutes,
+    )
+
+
 def record_call_import_pdf_report_generated(
     organization_id: UUID,
     pdf_report_id: UUID,
@@ -586,8 +645,10 @@ def record_playground_web_call_started(
         call_short_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_AGENT_PLAYGROUND,
             "agent_id": agent_id,
             "call_short_id": call_short_id,
+            "quantity": 1,
         },
     )
 
@@ -604,7 +665,9 @@ def record_playground_websocket_session_started(
         call_short_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_AGENT_PLAYGROUND,
             "call_short_id": call_short_id,
+            "quantity": 1,
         },
     )
 
@@ -624,10 +687,12 @@ def record_playground_call_evaluated(
         evaluation_attempt_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_AGENT_PLAYGROUND,
             "call_short_id": call_short_id,
             "evaluator_result_id": evaluator_result_id,
             "evaluation_attempt_id": evaluation_attempt_id,
             "metric_count": metric_count,
+            "quantity": 1,
         },
     )
 
@@ -648,11 +713,13 @@ def record_playground_evaluation_completed(
         evaluation_attempt_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_AGENT_PLAYGROUND,
             "call_short_id": call_short_id,
             "evaluator_result_id": evaluator_result_id,
             "evaluation_attempt_id": evaluation_attempt_id,
             "duration_seconds": duration_seconds,
             "metric_count": metric_count,
+            "quantity": 1,
         },
     )
 
@@ -673,6 +740,7 @@ def record_evaluator_run_requested(
         request_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_EVALUATORS,
             "quantity": quantity,
         },
     )
@@ -692,9 +760,11 @@ def record_evaluator_run_completed(
         result_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_EVALUATORS,
             "evaluator_id": evaluator_id,
             "result_id": result_id,
             "call_count": call_count,
+            "quantity": 1,
         },
     )
 
@@ -800,9 +870,11 @@ def record_judge_alignment_run_started(
         run_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_JUDGE_ALIGNMENT,
             "run_id": run_id,
             "dataset_id": dataset_id,
             "sample_count": sample_count,
+            "quantity": 1,
         },
     )
 
@@ -821,9 +893,11 @@ def record_judge_alignment_run_completed(
         run_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_JUDGE_ALIGNMENT,
             "run_id": run_id,
             "dataset_id": dataset_id,
             "samples_scored": samples_scored,
+            "quantity": 1,
         },
     )
 
@@ -872,47 +946,71 @@ def record_observability_call_evaluated(
 
 def record_test_agent_conversation_started(
     organization_id: UUID,
-    conversation_id: UUID,
+    conversation_id: Union[str, UUID],
     *,
     workspace_id: UUID,
+    result_id: Optional[str] = None,
+    agent_id: Optional[UUID] = None,
+    call_short_id: Optional[str] = None,
 ) -> None:
+    properties: dict[str, Any] = {
+        "workspace_id": workspace_id,
+        "feature": FEATURE_AGENT_PLAYGROUND,
+        "conversation_id": conversation_id,
+        "quantity": 1,
+    }
+    if result_id is not None:
+        properties["result_id"] = result_id
+    if agent_id is not None:
+        properties["agent_id"] = agent_id
+    if call_short_id is not None:
+        properties["call_short_id"] = call_short_id
     record_event(
         TEST_AGENT_CONVERSATION_STARTED,
         organization_id,
         conversation_id,
-        properties={
-            "workspace_id": workspace_id,
-            "conversation_id": conversation_id,
-        },
+        properties=properties,
     )
 
 
 def record_test_agent_conversation_ended(
     organization_id: UUID,
-    conversation_id: UUID,
+    conversation_id: Union[str, UUID],
     *,
     workspace_id: UUID,
     duration_seconds: Optional[float] = None,
     turn_count: int = 0,
+    result_id: Optional[str] = None,
+    agent_id: Optional[UUID] = None,
+    call_short_id: Optional[str] = None,
 ) -> None:
+    properties: dict[str, Any] = {
+        "workspace_id": workspace_id,
+        "feature": FEATURE_AGENT_PLAYGROUND,
+        "conversation_id": conversation_id,
+        "turn_count": turn_count,
+        "quantity": 1,
+    }
+    if duration_seconds is not None:
+        properties["duration_seconds"] = duration_seconds
+    if result_id is not None:
+        properties["result_id"] = result_id
+    if agent_id is not None:
+        properties["agent_id"] = agent_id
+    if call_short_id is not None:
+        properties["call_short_id"] = call_short_id
     record_event(
         TEST_AGENT_CONVERSATION_ENDED,
         organization_id,
         conversation_id,
-        properties={
-            "workspace_id": workspace_id,
-            "conversation_id": conversation_id,
-            "duration_seconds": duration_seconds,
-            "turn_count": turn_count,
-            "quantity": duration_seconds or 1,
-        },
+        properties=properties,
     )
 
 
-# --- LLM assist ---
+# --- Metrics AI assist (metric builder) ---
 
 
-def record_metrics_llm_assist(
+def record_metrics_ai_assist(
     organization_id: UUID,
     request_id: UUID,
     *,
@@ -920,30 +1018,131 @@ def record_metrics_llm_assist(
     mode: str,
 ) -> None:
     record_event(
-        METRICS_LLM_ASSIST,
+        METRICS_AI_ASSIST,
         organization_id,
         request_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_METRICS_AI_ASSIST,
             "mode": mode,
+            "quantity": 1,
         },
     )
 
 
-def record_chat_completion(
+def record_metrics_llm_assist(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    mode: str,
+) -> None:
+    record_metrics_ai_assist(
+        organization_id,
+        request_id,
+        workspace_id=workspace_id,
+        mode=mode,
+    )
+
+
+# --- Metric Studio ---
+
+
+def record_metric_studio_item_evaluated(
+    organization_id: UUID,
+    result_row_id: UUID,
+    *,
+    workspace_id: UUID,
+    run_id: UUID,
+    source_kind: str,
+    source_ref: str,
+    metric_count: int = 0,
+) -> None:
+    record_event(
+        METRIC_STUDIO_ITEM_EVALUATED,
+        organization_id,
+        result_row_id,
+        properties={
+            "workspace_id": workspace_id,
+            "feature": FEATURE_METRIC_STUDIO,
+            "run_id": run_id,
+            "result_row_id": result_row_id,
+            "source_kind": source_kind,
+            "source_ref": source_ref,
+            "metric_count": metric_count,
+            "quantity": 1,
+        },
+    )
+
+
+def record_metric_studio_run_completed(
+    organization_id: UUID,
+    run_id: UUID,
+    *,
+    workspace_id: UUID,
+    run_status: str,
+    total_items: int,
+    completed_items: int,
+    failed_items: int,
+) -> None:
+    record_event(
+        METRIC_STUDIO_RUN_COMPLETED,
+        organization_id,
+        run_id,
+        properties={
+            "workspace_id": workspace_id,
+            "feature": FEATURE_METRIC_STUDIO,
+            "run_id": run_id,
+            "run_status": run_status,
+            "total_items": total_items,
+            "completed_items": completed_items,
+            "failed_items": failed_items,
+            "quantity": 1,
+        },
+    )
+
+
+# --- Scenario / assistant AI text ---
+
+
+def record_scenario_ai_text_generated(
     organization_id: UUID,
     request_id: UUID,
     *,
     workspace_id: Optional[UUID],
     model: Optional[str] = None,
+    purpose: str = "scenario_description",
+    scenario_count: Optional[int] = None,
 ) -> None:
     record_event(
-        CHAT_COMPLETION,
+        SCENARIO_AI_TEXT_GENERATED,
         organization_id,
         request_id,
         properties={
             "workspace_id": workspace_id,
+            "feature": FEATURE_SCENARIO_AI,
             "model": model,
+            "purpose": purpose,
+            "scenario_count": scenario_count,
             "quantity": 1,
         },
     )
+
+
+def record_chat_completion(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    model: Optional[str] = None,
+    purpose: str = "scenario_description",
+    scenario_count: Optional[int] = None,
+) -> None:
+    record_scenario_ai_text_generated(
+        organization_id,
+        request_id,
+        workspace_id=workspace_id,
+        model=model,
+        purpose=purpose,
+        scenario_count=scenario_count,
+    )
diff --git a/app/services/metric_studio/run_rollup.py b/app/services/metric_studio/run_rollup.py
new file mode 100644
index 00000000..aee9015b
--- /dev/null
+++ b/app/services/metric_studio/run_rollup.py
@@ -0,0 +1,61 @@
+"""Shared Metrics Studio run rollup + Flexprice emission."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from sqlalchemy.orm import Session
+
+from app.models.database import MetricStudioRun, MetricStudioRunResult
+
+
+def _now_utc() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+def rollup_metric_studio_run(
+    db: Session,
+    run: MetricStudioRun,
+    *,
+    emit_flexprice: bool = True,
+    commit: bool = True,
+) -> None:
+    results = (
+        db.query(MetricStudioRunResult)
+        .filter(MetricStudioRunResult.run_id == run.id)
+        .all()
+    )
+    completed = sum(1 for r in results if r.status == "completed")
+    failed = sum(1 for r in results if r.status == "failed")
+    pending = sum(1 for r in results if r.status in {"pending", "running"})
+    run.completed_items = completed
+    run.failed_items = failed
+    if pending:
+        run.status = "running"
+    elif failed and completed:
+        run.status = "partial"
+        run.finished_at = run.finished_at or _now_utc()
+    elif failed:
+        run.status = "failed"
+        run.finished_at = run.finished_at or _now_utc()
+    else:
+        run.status = "completed"
+        run.finished_at = run.finished_at or _now_utc()
+
+    if commit:
+        db.commit()
+    else:
+        db.flush()
+
+    if emit_flexprice and pending == 0 and run.finished_at is not None:
+        from app.services.billing.flexprice_service import record_metric_studio_run_completed
+
+        record_metric_studio_run_completed(
+            run.organization_id,
+            run.id,
+            workspace_id=run.workspace_id,
+            run_status=run.status,
+            total_items=int(run.total_items or 0),
+            completed_items=completed,
+            failed_items=failed,
+        )
diff --git a/app/workers/tasks/evaluate_call_import_row_core.py b/app/workers/tasks/evaluate_call_import_row_core.py
index 4c42f5da..872fc1fe 100644
--- a/app/workers/tasks/evaluate_call_import_row_core.py
+++ b/app/workers/tasks/evaluate_call_import_row_core.py
@@ -591,7 +591,15 @@ def rollup_parent(
             reconcile_evaluation_counters(db, evaluation)
             db.refresh(evaluation)
 
+    _apply_parent_status_from_counters(evaluation)
+
+    total = int(evaluation.total_rows or 0)
     completed = int(evaluation.completed_rows or 0)
+    failed = int(evaluation.failed_rows or 0)
+    in_progress = total - completed - failed
+    if in_progress > 0:
+        return
+
     already_billed = int(getattr(evaluation, "billed_completed_rows", 0) or 0)
     delta = completed - already_billed
     if delta > 0:
@@ -619,8 +627,6 @@ def rollup_parent(
         if billing_accepted:
             evaluation.billed_completed_rows = completed
 
-    _apply_parent_status_from_counters(evaluation)
-
 
 def parse_restricted_metric_uuids(
     restricted_metric_ids: Optional[List[str]],
@@ -816,7 +822,7 @@ def maybe_bill_completed_eval_row_flexprice(
     if (eval_row.status or "").lower() != "completed":
         return
     from app.services.billing.flexprice_service import (
-        record_call_import_audio_minutes_billed,
+        record_call_import_recording_minutes_billed,
     )
 
     if source_row is None:
@@ -835,7 +841,7 @@ def maybe_bill_completed_eval_row_flexprice(
     if minutes <= 0:
         return
 
-    record_call_import_audio_minutes_billed(
+    record_call_import_recording_minutes_billed(
         evaluation.organization_id,
         eval_row.id,
         workspace_id=eval_row.workspace_id or evaluation.workspace_id,
diff --git a/app/workers/tasks/evaluate_studio_run_item.py b/app/workers/tasks/evaluate_studio_run_item.py
index 2e73eb29..2caa7224 100644
--- a/app/workers/tasks/evaluate_studio_run_item.py
+++ b/app/workers/tasks/evaluate_studio_run_item.py
@@ -17,6 +17,7 @@
     MetricStudioRunResult,
 )
 from app.services.metric_studio.metric_selection import load_studio_run_metrics
+from app.services.metric_studio.run_rollup import rollup_metric_studio_run
 from app.services.metric_studio.source_resolver import resolve_source
 from app.workers.config import celery_app
 from app.workers.tasks.evaluate_call_import_row_core import (
@@ -40,28 +41,7 @@ def _now_utc() -> datetime:
 
 
 def _rollup_run(db: Session, run: MetricStudioRun) -> None:
-    results = (
-        db.query(MetricStudioRunResult)
-        .filter(MetricStudioRunResult.run_id == run.id)
-        .all()
-    )
-    completed = sum(1 for r in results if r.status == "completed")
-    failed = sum(1 for r in results if r.status == "failed")
-    pending = sum(1 for r in results if r.status in {"pending", "running"})
-    run.completed_items = completed
-    run.failed_items = failed
-    if pending:
-        run.status = "running"
-    elif failed and completed:
-        run.status = "partial"
-        run.finished_at = _now_utc()
-    elif failed:
-        run.status = "failed"
-        run.finished_at = _now_utc()
-    else:
-        run.status = "completed"
-        run.finished_at = _now_utc()
-    db.commit()
+    rollup_metric_studio_run(db, run, emit_flexprice=True, commit=True)
 
 
 @celery_app.task(
@@ -236,6 +216,19 @@ def evaluate_studio_run_item_task(self, result_row_id: str) -> dict[str, Any]:
         result_row.error_message = None
         result_row.finished_at = _now_utc()
         db.commit()
+
+        from app.services.billing.flexprice_service import record_metric_studio_item_evaluated
+
+        record_metric_studio_item_evaluated(
+            run.organization_id,
+            result_row.id,
+            workspace_id=run.workspace_id,
+            run_id=run.id,
+            source_kind=result_row.source_kind,
+            source_ref=result_row.source_ref,
+            metric_count=len(metric_scores),
+        )
+
         _rollup_run(db, run)
         return {"status": "completed", "scores": len(metric_scores)}
     except Exception as exc:
diff --git a/config.docker.yml b/config.docker.yml
index 66a1bcdd..b06558b4 100644
--- a/config.docker.yml
+++ b/config.docker.yml
@@ -140,4 +140,10 @@ judge_alignment:
 # Enterprise License (JWT signed with RS256). Unlocks gated features like
 # oidc_sso, mfa_enforce, audit_export, voice_playground, gepa_optimization.
 # license:
-#   key: "eyJhbGciOi..."
\ No newline at end of file
+#   key: "eyJhbGciOi..."
+
+# Flexprice usage metering (optional). api_key from FLEXPRICE_API_KEY env.
+flexprice:
+  enabled: true
+  api_host: "https://api.cloud.flexprice.io/v1"
+  # api_key: use FLEXPRICE_API_KEY in .env / container env
\ No newline at end of file
diff --git a/scripts/setup_flexprice_meters.py b/scripts/setup_flexprice_meters.py
index df9ab8e8..78416fd3 100644
--- a/scripts/setup_flexprice_meters.py
+++ b/scripts/setup_flexprice_meters.py
@@ -20,6 +20,12 @@
 CALL_IMPORT_BATCH_METER_NAME = "Call Imports"
 CALL_IMPORT_BATCH_AGG_TYPE = "SUM"
 CALL_IMPORT_BATCH_AGG_FIELD = "quantity"
+AGENT_PLAYGROUND_PRIMARY_EVENT = "playground.web_call_started"
+EVALUATOR_RUN_REQUESTED_EVENT = "evaluator.run_requested"
+JUDGE_ALIGNMENT_PRIMARY_EVENT = "judge_alignment.run_completed"
+METRICS_AI_ASSIST_EVENT = "metrics.ai_assist"
+METRIC_STUDIO_PRIMARY_EVENT = "metric_studio.run_completed"
+SCENARIO_AI_TEXT_EVENT = "scenario.ai_text_generated"
 
 # (event_name, display_name, aggregation_type, aggregation_field|None)
 METERS: list[tuple[str, str, str, str | None]] = [
@@ -33,15 +39,18 @@
     # Call imports (batch_created meter comes from the call_imports license feature)
     ("call_import.evaluation_started", "Call Import Evaluation Started", "COUNT", None),
     ("call_import.evaluation_completed", "Call Import Evaluation Completed", "SUM", "quantity"),
-    ("call_import.audio_minutes_billed", "Call Import Audio Minutes Billed", "SUM", "quantity"),
+    (
+        "call_import.recording_minutes_billed",
+        "Call Import Recording Minutes",
+        "SUM",
+        "billable_minutes",
+    ),
     ("call_import.pdf_report_generated", "Call Import PDF Report Generated", "COUNT", None),
-    # Agent playground
-    ("playground.web_call_started", "Playground Web Call Started", "COUNT", None),
+    # Agent playground (web_call_started meter comes from the agent_playground license feature)
     ("playground.websocket_session_started", "Playground Websocket Session Started", "COUNT", None),
     ("playground.call_evaluated", "Playground Call Evaluated", "COUNT", None),
     ("playground.evaluation_completed", "Playground Evaluation Completed", "COUNT", None),
-    # Evaluators
-    ("evaluator.run_requested", "Evaluator Run Requested", "SUM", "quantity"),
+    # Evaluators (run_requested meter comes from the evaluators license feature)
     ("evaluator.run_completed", "Evaluator Run Completed", "COUNT", None),
     # Legacy evaluations
     ("evaluation.created", "Evaluation Created", "COUNT", None),
@@ -49,18 +58,18 @@
     # Prompt optimization
     ("prompt_optimization.run_started", "Prompt Optimization Run Started", "COUNT", None),
     ("prompt_optimization.run_completed", "Prompt Optimization Run Completed", "COUNT", None),
-    # Judge alignment
+    # Judge alignment (run_completed meter comes from the judge_alignment license feature)
     ("judge_alignment.run_started", "Judge Alignment Run Started", "COUNT", None),
-    ("judge_alignment.run_completed", "Judge Alignment Run Completed", "COUNT", None),
     # Observability
     ("observability.call_ingested", "Observability Call Ingested", "COUNT", None),
     ("observability.call_evaluated", "Observability Call Evaluated", "COUNT", None),
     # Test agents
     ("test_agent.conversation_started", "Test Agent Conversation Started", "COUNT", None),
-    ("test_agent.conversation_ended", "Test Agent Conversation Ended", "SUM", "quantity"),
-    # LLM assist
-    ("metrics.llm_assist", "Metrics LLM Assist", "COUNT", None),
-    ("chat.completion", "Chat Completion", "SUM", "quantity"),
+    ("test_agent.conversation_ended", "Test Agent Conversation Ended", "COUNT", None),
+    # Metric studio (run_completed meter comes from the metric_studio license feature)
+    ("metric_studio.item_evaluated", "Metric Studio Item Evaluated", "COUNT", None),
+    # Metrics AI assist (primary meter comes from the metrics_ai_assist license feature)
+    # Scenario AI text (primary meter comes from the scenario_ai license feature)
 ]
 
 LICENSE_FEATURES: list[dict[str, Any]] = [
@@ -73,6 +82,15 @@
         "event_name": CALL_IMPORT_BATCH_EVENT,
         "aggregation": {"type": CALL_IMPORT_BATCH_AGG_TYPE, "field": CALL_IMPORT_BATCH_AGG_FIELD},
     },
+    {
+        "name": "Agent Playground",
+        "lookup_key": "agent_playground",
+        "description": "Agent web calls, websocket sessions, evaluations, and test-agent conversations",
+        "unit_singular": "session",
+        "unit_plural": "sessions",
+        "event_name": AGENT_PLAYGROUND_PRIMARY_EVENT,
+        "aggregation": {"type": "COUNT"},
+    },
     {
         "name": "Voice Playground",
         "lookup_key": "voice_playground",
@@ -91,6 +109,51 @@
         "event_name": "prompt_optimization.run_started",
         "aggregation": {"type": "COUNT"},
     },
+    {
+        "name": "Evaluators",
+        "lookup_key": "evaluators",
+        "description": "Batch evaluator simulation runs and completions",
+        "unit_singular": "run",
+        "unit_plural": "runs",
+        "event_name": EVALUATOR_RUN_REQUESTED_EVENT,
+        "aggregation": {"type": "SUM", "field": "quantity"},
+    },
+    {
+        "name": "Judge Alignment",
+        "lookup_key": "judge_alignment",
+        "description": "Judge calibration runs on labeled datasets",
+        "unit_singular": "run",
+        "unit_plural": "runs",
+        "event_name": JUDGE_ALIGNMENT_PRIMARY_EVENT,
+        "aggregation": {"type": "COUNT"},
+    },
+    {
+        "name": "Metrics AI Assist",
+        "lookup_key": "metrics_ai_assist",
+        "description": "AI-assisted metric creation and bulk label parsing in Metrics Management",
+        "unit_singular": "request",
+        "unit_plural": "requests",
+        "event_name": METRICS_AI_ASSIST_EVENT,
+        "aggregation": {"type": "COUNT"},
+    },
+    {
+        "name": "Metric Studio",
+        "lookup_key": "metric_studio",
+        "description": "Batch metric scoring runs in Metrics Studio",
+        "unit_singular": "run",
+        "unit_plural": "runs",
+        "event_name": METRIC_STUDIO_PRIMARY_EVENT,
+        "aggregation": {"type": "COUNT"},
+    },
+    {
+        "name": "Scenario AI Text",
+        "lookup_key": "scenario_ai",
+        "description": "AI-generated text for scenarios and similar assistant flows",
+        "unit_singular": "generation",
+        "unit_plural": "generations",
+        "event_name": SCENARIO_AI_TEXT_EVENT,
+        "aggregation": {"type": "COUNT"},
+    },
 ]
 
 FEATURE_OWNED_EVENT_NAMES = frozenset(spec["event_name"] for spec in LICENSE_FEATURES)
diff --git a/tests/test_scripts/test_setup_flexprice_meters.py b/tests/test_scripts/test_setup_flexprice_meters.py
index be12234a..e98dcba8 100644
--- a/tests/test_scripts/test_setup_flexprice_meters.py
+++ b/tests/test_scripts/test_setup_flexprice_meters.py
@@ -1,4 +1,13 @@
-from scripts.setup_flexprice_meters import meter_aggregation_matches
+from scripts.setup_flexprice_meters import (
+    AGENT_PLAYGROUND_PRIMARY_EVENT,
+    EVALUATOR_RUN_REQUESTED_EVENT,
+    JUDGE_ALIGNMENT_PRIMARY_EVENT,
+    LICENSE_FEATURES,
+    METRICS_AI_ASSIST_EVENT,
+    METRIC_STUDIO_PRIMARY_EVENT,
+    SCENARIO_AI_TEXT_EVENT,
+    meter_aggregation_matches,
+)
 from app.api.v1.routes.call_import_evaluations import (
     DISCOVERED_METRICS_KEY,
     _is_metric_scores_meta_key,
@@ -15,6 +24,42 @@ def test_meter_aggregation_matches_rejects_count():
     assert meter_aggregation_matches(meter, agg_type="SUM", agg_field="quantity") is False
 
 
+def test_agent_playground_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "agent_playground")
+    assert spec["event_name"] == AGENT_PLAYGROUND_PRIMARY_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
+def test_evaluators_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "evaluators")
+    assert spec["event_name"] == EVALUATOR_RUN_REQUESTED_EVENT
+    assert spec["aggregation"] == {"type": "SUM", "field": "quantity"}
+
+
+def test_judge_alignment_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "judge_alignment")
+    assert spec["event_name"] == JUDGE_ALIGNMENT_PRIMARY_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
+def test_metrics_ai_assist_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "metrics_ai_assist")
+    assert spec["event_name"] == METRICS_AI_ASSIST_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
+def test_metric_studio_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "metric_studio")
+    assert spec["event_name"] == METRIC_STUDIO_PRIMARY_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
+def test_scenario_ai_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "scenario_ai")
+    assert spec["event_name"] == SCENARIO_AI_TEXT_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
 def test_metric_scores_meta_keys():
     assert _is_metric_scores_meta_key("_billing") is True
     assert _is_metric_scores_meta_key(DISCOVERED_METRICS_KEY) is True
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index 3eb56b4c..50099968 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -284,7 +284,7 @@ def test_record_call_import_evaluation_started(mock_flexprice):
 
 
 @patch("flexprice.Flexprice")
-def test_record_call_import_audio_minutes_billed(mock_flexprice):
+def test_record_call_import_recording_minutes_billed(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
@@ -297,7 +297,7 @@ def test_record_call_import_audio_minutes_billed(mock_flexprice):
     mock_client = MagicMock()
     mock_flexprice.return_value.__enter__.return_value = mock_client
 
-    accepted = svc.record_call_import_audio_minutes_billed(
+    accepted = svc.record_call_import_recording_minutes_billed(
         org_id,
         eval_row_id,
         workspace_id=workspace_id,
@@ -308,7 +308,7 @@ def test_record_call_import_audio_minutes_billed(mock_flexprice):
     )
 
     mock_client.events.ingest_event.assert_called_once_with(
-        event_name="call_import.audio_minutes_billed",
+        event_name="call_import.recording_minutes_billed",
         external_customer_id=str(org_id),
         event_id=str(eval_row_id),
         source="efficientai",
@@ -319,6 +319,8 @@ def test_record_call_import_audio_minutes_billed(mock_flexprice):
             "evaluation_id": str(evaluation_id),
             "evaluation_row_id": str(eval_row_id),
             "audio_seconds": "90",
+            "billable_minutes": "2",
+            "billing_unit": "minute",
             "quantity": "2",
         },
     )
@@ -411,9 +413,457 @@ def test_record_playground_call_evaluated_uses_per_attempt_event_id(mock_flexpri
         source="efficientai",
         properties={
             "workspace_id": str(workspace_id),
+            "feature": "agent_playground",
             "call_short_id": "123456",
             "evaluator_result_id": str(evaluator_result_id),
             "evaluation_attempt_id": evaluation_attempt_id,
             "metric_count": "4",
+            "quantity": "1",
+        },
+    )
+
+
+@patch("flexprice.Flexprice")
+def test_record_playground_web_call_started_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    agent_id = uuid4()
+    call_short_id = "abc123"
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_playground_web_call_started(
+        org_id,
+        call_short_id,
+        workspace_id=workspace_id,
+        agent_id=agent_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "playground.web_call_started"
+    assert payload["properties"]["feature"] == "agent_playground"
+    assert payload["properties"]["quantity"] == "1"
+
+
+@patch("flexprice.Flexprice")
+def test_record_playground_websocket_session_started_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    call_short_id = "ws456"
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_playground_websocket_session_started(
+        org_id,
+        call_short_id,
+        workspace_id=workspace_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "playground.websocket_session_started"
+    assert payload["properties"]["feature"] == "agent_playground"
+
+
+@patch("flexprice.Flexprice")
+def test_record_playground_evaluation_completed_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    evaluator_result_id = uuid4()
+    evaluation_attempt_id = f"{evaluator_result_id}:task-1"
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_playground_evaluation_completed(
+        org_id,
+        evaluation_attempt_id,
+        evaluator_result_id=evaluator_result_id,
+        workspace_id=workspace_id,
+        call_short_id="999",
+        duration_seconds=12.5,
+        metric_count=2,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "playground.evaluation_completed"
+    assert payload["properties"]["feature"] == "agent_playground"
+    assert payload["properties"]["metric_count"] == "2"
+
+
+@patch("flexprice.Flexprice")
+def test_record_test_agent_conversation_started_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    conversation_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_test_agent_conversation_started(
+        org_id,
+        conversation_id,
+        workspace_id=workspace_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "test_agent.conversation_started"
+    assert payload["properties"]["feature"] == "agent_playground"
+
+
+@patch("flexprice.Flexprice")
+def test_record_test_agent_conversation_ended_bills_one_conversation(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    conversation_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_test_agent_conversation_ended(
+        org_id,
+        conversation_id,
+        workspace_id=workspace_id,
+        duration_seconds=120.0,
+        turn_count=8,
+    )
+
+    mock_client.events.ingest_event.assert_called_once_with(
+        event_name="test_agent.conversation_ended",
+        external_customer_id=str(org_id),
+        event_id=str(conversation_id),
+        source="efficientai",
+        properties={
+            "workspace_id": str(workspace_id),
+            "feature": "agent_playground",
+            "conversation_id": str(conversation_id),
+            "duration_seconds": "120.0",
+            "turn_count": "8",
+            "quantity": "1",
         },
     )
+
+
+@patch("flexprice.Flexprice")
+def test_record_test_agent_conversation_started_includes_voice_agent_metadata(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    conversation_id = uuid4()
+    agent_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_test_agent_conversation_started(
+        org_id,
+        conversation_id,
+        workspace_id=workspace_id,
+        result_id="123456",
+        agent_id=agent_id,
+        call_short_id="654321",
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["properties"]["result_id"] == "123456"
+    assert payload["properties"]["agent_id"] == str(agent_id)
+    assert payload["properties"]["call_short_id"] == "654321"
+
+
+@patch("flexprice.Flexprice")
+def test_record_evaluator_run_requested_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    request_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_evaluator_run_requested(
+        org_id,
+        request_id,
+        workspace_id=workspace_id,
+        quantity=3,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "evaluator.run_requested"
+    assert payload["properties"]["feature"] == "evaluators"
+    assert payload["properties"]["quantity"] == "3"
+
+
+@patch("flexprice.Flexprice")
+def test_record_evaluator_run_completed_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    evaluator_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_evaluator_run_completed(
+        org_id,
+        "res-001",
+        workspace_id=workspace_id,
+        evaluator_id=evaluator_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "evaluator.run_completed"
+    assert payload["properties"]["feature"] == "evaluators"
+    assert payload["properties"]["quantity"] == "1"
+
+
+@patch("flexprice.Flexprice")
+def test_record_judge_alignment_run_completed_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    run_id = uuid4()
+    dataset_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_judge_alignment_run_completed(
+        org_id,
+        run_id,
+        workspace_id=workspace_id,
+        dataset_id=dataset_id,
+        samples_scored=12,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "judge_alignment.run_completed"
+    assert payload["properties"]["feature"] == "judge_alignment"
+
+
+@patch("flexprice.Flexprice")
+def test_record_metrics_ai_assist_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    request_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_metrics_ai_assist(
+        org_id,
+        request_id,
+        workspace_id=workspace_id,
+        mode="generate",
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "metrics.ai_assist"
+    assert payload["properties"]["feature"] == "metrics_ai_assist"
+    assert payload["properties"]["mode"] == "generate"
+
+
+@patch("flexprice.Flexprice")
+def test_record_metric_studio_run_completed_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    run_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_metric_studio_run_completed(
+        org_id,
+        run_id,
+        workspace_id=workspace_id,
+        run_status="completed",
+        total_items=3,
+        completed_items=3,
+        failed_items=0,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "metric_studio.run_completed"
+    assert payload["properties"]["feature"] == "metric_studio"
+
+
+@patch("flexprice.Flexprice")
+def test_record_scenario_ai_text_generated_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    request_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_scenario_ai_text_generated(
+        org_id,
+        request_id,
+        workspace_id=workspace_id,
+        model="gpt-4o",
+        purpose="scenario_description",
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "scenario.ai_text_generated"
+    assert payload["properties"]["feature"] == "scenario_ai"
+    assert payload["properties"]["purpose"] == "scenario_description"
+
+
+@patch("flexprice.Flexprice")
+def test_record_tts_generation_started_includes_voice_playground_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    comparison_id = uuid4()
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_tts_generation_started(
+        org_id,
+        comparison_id,
+        workspace_id=workspace_id,
+        sample_count=4,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "tts.generation_started"
+    assert payload["properties"]["feature"] == "voice_playground"
+
+
+@patch("flexprice.Flexprice")
+def test_record_tts_sample_synthesized_includes_voice_playground_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    sample_id = uuid4()
+    comparison_id = uuid4()
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_tts_sample_synthesized(
+        org_id,
+        sample_id,
+        workspace_id=workspace_id,
+        comparison_id=comparison_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "tts.sample_synthesized"
+    assert payload["properties"]["feature"] == "voice_playground"
+    assert payload["properties"]["quantity"] == "1"
+
+
+@patch("flexprice.Flexprice")
+def test_record_tts_report_requested_includes_voice_playground_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    report_job_id = uuid4()
+    comparison_id = uuid4()
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_tts_report_requested(
+        org_id,
+        report_job_id,
+        workspace_id=workspace_id,
+        comparison_id=comparison_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "tts.report_requested"
+    assert payload["properties"]["feature"] == "voice_playground"
+
+
+@patch("flexprice.Flexprice")
+def test_record_tts_report_completed_includes_voice_playground_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    report_job_id = uuid4()
+    comparison_id = uuid4()
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_tts_report_completed(
+        org_id,
+        report_job_id,
+        workspace_id=workspace_id,
+        comparison_id=comparison_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "tts.report_completed"
+    assert payload["properties"]["feature"] == "voice_playground"
+
+
+@patch("flexprice.Flexprice")
+def test_record_blind_test_response_submitted_includes_voice_playground_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    response_id = uuid4()
+    share_id = uuid4()
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_blind_test_response_submitted(
+        org_id,
+        response_id,
+        share_id=share_id,
+        workspace_id=workspace_id,
+        response_count=3,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "blind_test.response_submitted"
+    assert payload["properties"]["feature"] == "voice_playground"
+    assert payload["properties"]["quantity"] == "3"
diff --git a/tests/test_services/test_flexprice_tracking_wiring.py b/tests/test_services/test_flexprice_tracking_wiring.py
new file mode 100644
index 00000000..e0d3baa0
--- /dev/null
+++ b/tests/test_services/test_flexprice_tracking_wiring.py
@@ -0,0 +1,136 @@
+"""Catalog tests: Flexprice event wiring expectations by product area."""
+
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+
+VOICE_PLAYGROUND_WIRED = {
+    "blind_test.share_created": "app/api/v1/routes/voice_playground.py",
+    "blind_test.response_submitted": "app/api/v1/routes/public_blind_test.py",
+    "tts.generation_started": "app/api/v1/routes/voice_playground.py",
+    "tts.sample_synthesized": "app/workers/tasks/tts_comparison.py",
+    "tts.report_requested": "app/api/v1/routes/voice_playground.py",
+    "tts.report_completed": "app/workers/tasks/tts_report.py",
+}
+
+AGENT_PLAYGROUND_WIRED = {
+    "playground.web_call_started": "app/api/v1/routes/playground.py",
+    "playground.websocket_session_started": "app/api/v1/routes/playground.py",
+    "playground.call_evaluated": "app/workers/tasks/process_evaluator_result.py",
+    "playground.evaluation_completed": "app/workers/tasks/process_evaluator_result.py",
+    "test_agent.conversation_started": "app/api/v1/routes/test_agents.py",
+    "test_agent.conversation_ended": "app/api/v1/routes/test_agents.py",
+}
+
+VOICE_AGENT_TEST_AGENT_WIRED = {
+    "test_agent.conversation_started": "app/api/v1/routes/voice_agent.py",
+    "test_agent.conversation_ended": "app/api/v1/routes/voice_agent.py",
+}
+
+EVALUATORS_WIRED = {
+    "evaluator.run_requested": "app/api/v1/routes/evaluators.py",
+    "evaluator.run_completed": "app/workers/tasks/run_evaluator.py",
+}
+
+JUDGE_ALIGNMENT_WIRED = {
+    "judge_alignment.run_started": "app/api/v1/routes/judge_alignment.py",
+    "judge_alignment.run_completed": "app/workers/tasks/run_judge_alignment.py",
+}
+
+METRICS_AI_ASSIST_WIRED = {
+    "metrics.ai_assist": "app/api/v1/routes/metrics.py",
+}
+
+METRIC_STUDIO_WIRED = {
+    "metric_studio.item_evaluated": "app/workers/tasks/evaluate_studio_run_item.py",
+    "metric_studio.run_completed": "app/services/metric_studio/run_rollup.py",
+}
+
+SCENARIO_AI_WIRED = {
+    "scenario.ai_text_generated": [
+        "app/api/v1/routes/chat.py",
+        "app/api/v1/routes/agents.py",
+    ],
+}
+
+
+def _file_contains(path: str, needle: str) -> bool:
+    text = (REPO_ROOT / path).read_text(encoding="utf-8")
+    return needle in text
+
+
+def test_voice_playground_events_are_wired():
+    for event_name, path in VOICE_PLAYGROUND_WIRED.items():
+        record_fn = {
+            "blind_test.share_created": "record_blind_test_share_created",
+            "blind_test.response_submitted": "record_blind_test_response_submitted",
+            "tts.generation_started": "record_tts_generation_started",
+            "tts.sample_synthesized": "record_tts_sample_synthesized",
+            "tts.report_requested": "record_tts_report_requested",
+            "tts.report_completed": "record_tts_report_completed",
+        }[event_name]
+        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
+
+
+def test_agent_playground_events_are_wired():
+    for event_name, path in AGENT_PLAYGROUND_WIRED.items():
+        record_fn = {
+            "playground.web_call_started": "record_playground_web_call_started",
+            "playground.websocket_session_started": "record_playground_websocket_session_started",
+            "playground.call_evaluated": "record_playground_call_evaluated",
+            "playground.evaluation_completed": "record_playground_evaluation_completed",
+            "test_agent.conversation_started": "record_test_agent_conversation_started",
+            "test_agent.conversation_ended": "record_test_agent_conversation_ended",
+        }[event_name]
+        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
+
+
+def test_voice_agent_test_agent_simulation_events_are_wired():
+    for event_name, path in VOICE_AGENT_TEST_AGENT_WIRED.items():
+        record_fn = {
+            "test_agent.conversation_started": "record_test_agent_conversation_started",
+            "test_agent.conversation_ended": "record_test_agent_conversation_ended",
+        }[event_name]
+        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
+
+
+def test_evaluator_events_are_wired():
+    for event_name, path in EVALUATORS_WIRED.items():
+        record_fn = {
+            "evaluator.run_requested": "record_evaluator_run_requested",
+            "evaluator.run_completed": "record_evaluator_run_completed",
+        }[event_name]
+        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
+
+
+def test_judge_alignment_events_are_wired():
+    for event_name, path in JUDGE_ALIGNMENT_WIRED.items():
+        record_fn = {
+            "judge_alignment.run_started": "record_judge_alignment_run_started",
+            "judge_alignment.run_completed": "record_judge_alignment_run_completed",
+        }[event_name]
+        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
+
+
+def test_metrics_ai_assist_events_are_wired():
+    for event_name, path in METRICS_AI_ASSIST_WIRED.items():
+        assert _file_contains(path, "record_metrics_llm_assist"), (
+            f"{event_name} missing record_metrics_llm_assist in {path}"
+        )
+
+
+def test_metric_studio_events_are_wired():
+    for event_name, path in METRIC_STUDIO_WIRED.items():
+        record_fn = {
+            "metric_studio.item_evaluated": "record_metric_studio_item_evaluated",
+            "metric_studio.run_completed": "record_metric_studio_run_completed",
+        }[event_name]
+        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
+
+
+def test_scenario_ai_text_events_are_wired():
+    for event_name, paths in SCENARIO_AI_WIRED.items():
+        for path in paths:
+            assert _file_contains(path, "record_scenario_ai_text_generated") or _file_contains(
+                path, "record_chat_completion"
+            ), f"{event_name} missing scenario flexprice hook in {path}"

From 5038b0f307350f9d2f80e709e9a5f5f4eacd3b84 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 10:41:10 +0530
Subject: [PATCH 41/55] refactor(playground): streamline post-call processing
 by consolidating evaluator result creation and usage tracking;

---
 app/api/v1/routes/playground.py               |  69 +++----
 .../playground/post_call_processing.py        |  97 ++++++++++
 .../test_playground_post_call_processing.py   | 173 ++++++++++++++++++
 3 files changed, 292 insertions(+), 47 deletions(-)
 create mode 100644 app/services/playground/post_call_processing.py
 create mode 100644 tests/test_services/test_playground_post_call_processing.py

diff --git a/app/api/v1/routes/playground.py b/app/api/v1/routes/playground.py
index de2be5ba..765bcb6c 100644
--- a/app/api/v1/routes/playground.py
+++ b/app/api/v1/routes/playground.py
@@ -145,44 +145,18 @@ def poll_call_metrics(
         # After polling is complete, create EvaluatorResult and trigger evaluation
         if call_complete and call_metrics and call_recording.agent_id:
             try:
-                from app.services.usage.external_agent_usage import (
-                    record_playground_provider_usage_from_call_data,
+                from app.services.playground.post_call_processing import (
+                    claim_playground_evaluator_result_slot,
+                    record_playground_post_call_usage_once,
                 )
 
-                db.refresh(call_recording)
-                if call_recording.evaluator_result_id:
-                    logger.info(
-                        f"[Poll Call Metrics] Skipping post-call processing — "
-                        f"evaluator result already exists for call {provider_call_id}"
-                    )
-                    return
-
-                platform_key = str(provider_platform or "").lower()
-                stored_data = (
-                    call_recording.call_data
-                    if isinstance(call_recording.call_data, dict)
-                    else {}
+                should_create_evaluator, call_metrics = record_playground_post_call_usage_once(
+                    db,
+                    call_recording_id,
+                    provider_platform=provider_platform,
+                    call_metrics=call_metrics if isinstance(call_metrics, dict) else {},
                 )
-                if stored_data.get("external_usage_recorded") and isinstance(call_metrics, dict):
-                    call_metrics["external_usage_recorded"] = True
-                elif isinstance(call_metrics, dict):
-                    call_metrics = record_playground_provider_usage_from_call_data(
-                        organization_id=call_recording.organization_id,
-                        workspace_id=call_recording.workspace_id,
-                        agent_id=call_recording.agent_id,
-                        provider_platform=platform_key,
-                        call_short_id=call_recording.call_short_id,
-                        call_data=call_metrics,
-                    )
-                call_recording.call_data = call_metrics
-                db.commit()
-
-                db.refresh(call_recording)
-                if call_recording.evaluator_result_id:
-                    logger.info(
-                        f"[Poll Call Metrics] Skipping evaluator creation — "
-                        f"another poll already created result for call {provider_call_id}"
-                    )
+                if not should_create_evaluator:
                     return
 
                 logger.info(f"[Poll Call Metrics] Call complete, creating EvaluatorResult for call {provider_call_id}")
@@ -269,18 +243,21 @@ def poll_call_metrics(
                 except Exception as audio_err:
                     logger.warning(f"[Poll Call Metrics] Audio download/upload failed: {audio_err}")
 
-                # Generate unique result ID
+                locked_recording = claim_playground_evaluator_result_slot(
+                    db,
+                    call_recording_id,
+                    provider_call_id=provider_call_id,
+                )
+                if not locked_recording:
+                    return
+
                 result_id = generate_unique_result_id(db)
-                
-                # Create EvaluatorResult. Background-task path: inherit the
-                # workspace from the call recording rather than the header
-                # (this task runs without a request context).
                 evaluator_result = EvaluatorResult(
                     result_id=result_id,
-                    organization_id=call_recording.organization_id,
-                    workspace_id=call_recording.workspace_id,
+                    organization_id=locked_recording.organization_id,
+                    workspace_id=locked_recording.workspace_id,
                     evaluator_id=None,
-                    agent_id=call_recording.agent_id,
+                    agent_id=locked_recording.agent_id,
                     persona_id=None,
                     scenario_id=None,
                     name=result_name,
@@ -293,13 +270,11 @@ def poll_call_metrics(
                     call_data=call_metrics,
                 )
                 db.add(evaluator_result)
+                db.flush()
+                locked_recording.evaluator_result_id = evaluator_result.id
                 db.commit()
                 db.refresh(evaluator_result)
                 
-                # Link the EvaluatorResult to CallRecording
-                call_recording.evaluator_result_id = evaluator_result.id
-                db.commit()
-                
                 logger.info(f"[Poll Call Metrics] Created EvaluatorResult {result_id} for call {provider_call_id}")
                 
                 # Trigger Celery task to process evaluator result (run metrics evaluation)
diff --git a/app/services/playground/post_call_processing.py b/app/services/playground/post_call_processing.py
new file mode 100644
index 00000000..4f7f32aa
--- /dev/null
+++ b/app/services/playground/post_call_processing.py
@@ -0,0 +1,97 @@
+"""Atomic post-call processing for playground Voice AI poll tasks."""
+from __future__ import annotations
+
+from typing import Any, Optional
+from uuid import UUID
+
+from loguru import logger
+from sqlalchemy.orm import Session
+
+from app.models.database import CallRecording
+
+
+def _lock_call_recording(db: Session, call_recording_id: UUID) -> Optional[CallRecording]:
+    return (
+        db.query(CallRecording)
+        .filter(CallRecording.id == call_recording_id)
+        .with_for_update()
+        .first()
+    )
+
+
+def record_playground_post_call_usage_once(
+    db: Session,
+    call_recording_id: UUID,
+    *,
+    provider_platform: str,
+    call_metrics: dict[str, Any],
+) -> tuple[bool, dict[str, Any]]:
+    """
+    Record external provider usage at most once per call recording.
+
+    Returns (should_create_evaluator, updated_call_metrics).
+    """
+    from app.services.usage.external_agent_usage import (
+        record_playground_provider_usage_from_call_data,
+    )
+
+    locked = _lock_call_recording(db, call_recording_id)
+    if not locked:
+        db.rollback()
+        return False, call_metrics
+
+    if locked.evaluator_result_id:
+        db.rollback()
+        logger.info(
+            "[Poll Call Metrics] Skipping post-call processing — "
+            "evaluator result already exists"
+        )
+        return False, call_metrics
+
+    stored_data = locked.call_data if isinstance(locked.call_data, dict) else {}
+    metrics = dict(call_metrics) if isinstance(call_metrics, dict) else call_metrics
+    platform_key = str(provider_platform or "").lower()
+
+    if stored_data.get("external_usage_recorded") and isinstance(metrics, dict):
+        metrics["external_usage_recorded"] = True
+    elif isinstance(metrics, dict):
+        metrics = record_playground_provider_usage_from_call_data(
+            organization_id=locked.organization_id,
+            workspace_id=locked.workspace_id,
+            agent_id=locked.agent_id,
+            provider_platform=platform_key,
+            call_short_id=locked.call_short_id,
+            call_data=metrics,
+        )
+
+    locked.call_data = metrics
+    db.commit()
+    return True, metrics
+
+
+def claim_playground_evaluator_result_slot(
+    db: Session,
+    call_recording_id: UUID,
+    *,
+    provider_call_id: str | None = None,
+) -> Optional[CallRecording]:
+    """
+    Lock the call recording row and return it when evaluator creation may proceed.
+
+    Caller must create/link EvaluatorResult and commit before the lock is released.
+    """
+    locked = _lock_call_recording(db, call_recording_id)
+    if not locked:
+        db.rollback()
+        return None
+
+    if locked.evaluator_result_id:
+        db.rollback()
+        call_ref = provider_call_id or locked.provider_call_id or str(call_recording_id)
+        logger.info(
+            f"[Poll Call Metrics] Skipping evaluator creation — "
+            f"another poll already created result for call {call_ref}"
+        )
+        return None
+
+    return locked
diff --git a/tests/test_services/test_playground_post_call_processing.py b/tests/test_services/test_playground_post_call_processing.py
new file mode 100644
index 00000000..409efd05
--- /dev/null
+++ b/tests/test_services/test_playground_post_call_processing.py
@@ -0,0 +1,173 @@
+"""Tests for atomic playground post-call processing."""
+
+from uuid import uuid4
+
+import pytest
+
+from app.models.database import (
+    Agent,
+    CallRecording,
+    CallRecordingSource,
+    CallRecordingStatus,
+    EvaluatorResult,
+    EvaluatorResultStatus,
+)
+from app.services.playground.post_call_processing import (
+    claim_playground_evaluator_result_slot,
+    record_playground_post_call_usage_once,
+)
+
+
+@pytest.fixture
+def playground_agent(db_session, org_id, default_workspace):
+    agent = Agent(
+        id=uuid4(),
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        name="Test Agent",
+    )
+    db_session.add(agent)
+    db_session.commit()
+    return agent
+
+
+def _make_playground_recording(db_session, *, org_id, workspace_id, agent_id, **overrides):
+    recording = CallRecording(
+        id=overrides.get("id", uuid4()),
+        organization_id=org_id,
+        workspace_id=workspace_id,
+        call_short_id=overrides.get("call_short_id", "654321"),
+        status=CallRecordingStatus.UPDATED,
+        source=CallRecordingSource.PLAYGROUND,
+        call_data=overrides.get("call_data", {}),
+        provider_call_id=overrides.get("provider_call_id", "provider-call-1"),
+        provider_platform=overrides.get("provider_platform", "retell"),
+        agent_id=agent_id,
+        evaluator_result_id=overrides.get("evaluator_result_id"),
+    )
+    db_session.add(recording)
+    db_session.commit()
+    db_session.refresh(recording)
+    return recording
+
+
+def test_record_playground_post_call_usage_once_is_idempotent(
+    db_session, org_id, default_workspace, playground_agent, monkeypatch
+):
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        call_data={"call_status": "ended"},
+    )
+    call_metrics = {"call_status": "ended", "duration_seconds": 42}
+    calls = []
+
+    def _fake_record(**kwargs):
+        calls.append(kwargs)
+        metrics = dict(kwargs["call_data"])
+        metrics["external_usage_recorded"] = True
+        return metrics
+
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.record_playground_provider_usage_from_call_data",
+        _fake_record,
+    )
+
+    proceed, updated = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics=call_metrics,
+    )
+    assert proceed is True
+    assert updated["external_usage_recorded"] is True
+    assert len(calls) == 1
+
+    proceed_again, updated_again = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics=call_metrics,
+    )
+    assert proceed_again is True
+    assert updated_again["external_usage_recorded"] is True
+    assert len(calls) == 1
+
+
+def test_record_playground_post_call_usage_once_skips_when_evaluator_linked(
+    db_session, org_id, default_workspace, playground_agent, monkeypatch
+):
+    existing = EvaluatorResult(
+        id=uuid4(),
+        result_id="111111",
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        name="Existing",
+        status=EvaluatorResultStatus.COMPLETED.value,
+    )
+    db_session.add(existing)
+    db_session.flush()
+
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        evaluator_result_id=existing.id,
+    )
+
+    def _fail_record(**_kwargs):
+        raise AssertionError("usage should not be recorded when evaluator already exists")
+
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.record_playground_provider_usage_from_call_data",
+        _fail_record,
+    )
+
+    proceed, _ = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics={"call_status": "ended"},
+    )
+    assert proceed is False
+
+
+def test_claim_playground_evaluator_result_slot_skips_after_evaluator_linked(
+    db_session, org_id, default_workspace, playground_agent
+):
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+    )
+
+    locked = claim_playground_evaluator_result_slot(db_session, recording.id)
+    assert locked is not None
+
+    evaluator_result = EvaluatorResult(
+        result_id="222222",
+        organization_id=locked.organization_id,
+        workspace_id=locked.workspace_id,
+        agent_id=locked.agent_id,
+        name="Voice AI Call",
+        status=EvaluatorResultStatus.QUEUED.value,
+    )
+    db_session.add(evaluator_result)
+    db_session.flush()
+    locked.evaluator_result_id = evaluator_result.id
+    db_session.commit()
+
+    locked_again = claim_playground_evaluator_result_slot(db_session, recording.id)
+    assert locked_again is None
+
+    assert (
+        db_session.query(EvaluatorResult)
+        .filter(EvaluatorResult.id == evaluator_result.id)
+        .count()
+        == 1
+    )

From 88d6c45c30737a84fbb7c461e109be0a59a389fd Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 11:01:19 +0530
Subject: [PATCH 42/55] feat(tests): enhance audio handling in evaluator result
 tests by introducing patching for blob storage and integrating new storage
 stubs

---
 .../test_api/test_evaluator_results_routes.py | 25 +++++++++++++------
 .../test_celery_task_workflows.py             | 17 +++++++++++--
 .../test_evaluate_call_import_row.py          | 25 +++++++++++++++++++
 .../test_process_call_import_row.py           |  3 +++
 4 files changed, 60 insertions(+), 10 deletions(-)

diff --git a/tests/test_api/test_evaluator_results_routes.py b/tests/test_api/test_evaluator_results_routes.py
index 84be9658..6c58ab1d 100644
--- a/tests/test_api/test_evaluator_results_routes.py
+++ b/tests/test_api/test_evaluator_results_routes.py
@@ -244,11 +244,25 @@ def test_evaluator_results_overview_and_aggregate(
     assert agg["completed_rows"] == 1
 
 
+def _patch_blob_storage_download(monkeypatch, *, audio_bytes: bytes = b"fake-audio-bytes"):
+    """Patch both the blob singleton and the lazy s3_service alias."""
+    import importlib
+    from types import SimpleNamespace
+
+    fake = SimpleNamespace(
+        is_enabled=lambda: True,
+        download_file_by_key=lambda _key: audio_bytes,
+    )
+    blob_module = importlib.import_module("app.services.storage.blob_storage_service")
+    s3_module = importlib.import_module("app.services.storage.s3_service")
+    monkeypatch.setattr(blob_module, "blob_storage_service", fake)
+    monkeypatch.setattr(s3_module, "s3_service", fake, raising=False)
+    return fake
+
+
 def test_stream_evaluator_result_audio_from_s3(
     authenticated_client, make_evaluator_result, monkeypatch
 ):
-    storage = _blob_storage_service()
-
     make_evaluator_result(
         result_id="991122",
         audio_s3_key="audio/organizations/test/evaluations/call-1/recording.mp3",
@@ -258,12 +272,7 @@ def test_stream_evaluator_result_audio_from_s3(
         },
     )
 
-    monkeypatch.setattr(storage, "is_enabled", lambda: True)
-    monkeypatch.setattr(
-        storage,
-        "download_file_by_key",
-        lambda _key: b"fake-audio-bytes",
-    )
+    _patch_blob_storage_download(monkeypatch)
 
     response = authenticated_client.get("/api/v1/evaluator-results/991122/audio")
 
diff --git a/tests/test_workers/test_celery_task_workflows.py b/tests/test_workers/test_celery_task_workflows.py
index 20489070..7fb3c9ff 100644
--- a/tests/test_workers/test_celery_task_workflows.py
+++ b/tests/test_workers/test_celery_task_workflows.py
@@ -13,6 +13,7 @@
     Agent,
     Evaluator,
     EvaluatorResult,
+    Integration,
     Metric,
     Organization,
     PromptOptimizationRun,
@@ -578,6 +579,18 @@ def test_run_evaluator_bridge_runs_without_existing_event_loop(db_session, monke
     db_session.add(voice_bundle)
     db_session.flush()
 
+    integration = Integration(
+        id=uuid4(),
+        organization_id=org.id,
+        platform="retell",
+        name="Bridge Integration",
+        api_key="encrypted-test-key",
+        is_active=True,
+        is_default=True,
+    )
+    db_session.add(integration)
+    db_session.flush()
+
     agent = Agent(
         id=uuid4(),
         organization_id=org.id,
@@ -588,7 +601,7 @@ def test_run_evaluator_bridge_runs_without_existing_event_loop(db_session, monke
         call_type="outbound",
         call_medium="phone_call",
         voice_bundle_id=voice_bundle.id,
-        voice_ai_integration_id=uuid4(),
+        voice_ai_integration_id=integration.id,
         voice_ai_agent_id="provider-agent-1",
     )
     evaluator = Evaluator(
@@ -610,7 +623,7 @@ def test_run_evaluator_bridge_runs_without_existing_event_loop(db_session, monke
         agent_id=agent.id,
         status="queued",
     )
-    db_session.add_all([agent, evaluator, eval_result])
+    db_session.add_all([integration, agent, evaluator, eval_result])
     db_session.commit()
 
     async def fake_bridge(**_kwargs):
diff --git a/tests/test_workers/test_evaluate_call_import_row.py b/tests/test_workers/test_evaluate_call_import_row.py
index e79c9369..1d3a6a6c 100644
--- a/tests/test_workers/test_evaluate_call_import_row.py
+++ b/tests/test_workers/test_evaluate_call_import_row.py
@@ -155,6 +155,29 @@ def _run(*args, **kwargs):
     return task
 
 
+class _EvalRowFakeStorage:
+    """Minimal blob storage stub for eval-row worker tests."""
+
+    prefix = "test-prefix/"
+
+    def is_enabled(self):
+        return True
+
+    def download_file_by_key(self, _key):
+        return b"fake-audio-bytes"
+
+
+def _patch_eval_row_storage(monkeypatch):
+    import importlib
+
+    fake = _EvalRowFakeStorage()
+    blob_module = importlib.import_module("app.services.storage.blob_storage_service")
+    s3_module = importlib.import_module("app.services.storage.s3_service")
+    monkeypatch.setattr(blob_module, "blob_storage_service", fake)
+    monkeypatch.setattr(s3_module, "s3_service", fake, raising=False)
+    return fake
+
+
 def _patch_row_location(monkeypatch, db_session):
     """Route shard-aware row lookup to the pytest session."""
 
@@ -199,6 +222,7 @@ def _patch_dependencies(monkeypatch, db_session, *, evaluate_with_llm=None):
     """Stub SessionLocal and the LLM helper inside the eval task module."""
     import importlib
 
+    _patch_eval_row_storage(monkeypatch)
     session_factory = lambda: _NonClosingSession(db_session)
     monkeypatch.setattr("app.database.SessionLocal", session_factory)
     _patch_row_location(monkeypatch, db_session)
@@ -239,6 +263,7 @@ def _default_eval(*_args, **_kwargs):
 def _patch_audio_task(monkeypatch, db_session):
     import importlib
 
+    _patch_eval_row_storage(monkeypatch)
     _patch_row_location(monkeypatch, db_session)
     audio_module = _load_real_task_module(
         "app.workers.tasks.evaluate_call_import_row_audio",
diff --git a/tests/test_workers/test_process_call_import_row.py b/tests/test_workers/test_process_call_import_row.py
index 9c598414..c13c5dec 100644
--- a/tests/test_workers/test_process_call_import_row.py
+++ b/tests/test_workers/test_process_call_import_row.py
@@ -174,6 +174,9 @@ def upload_file_by_key(self, file_content, key, content_type="audio/mpeg"):
         self.uploads.append({"key": key, "size": len(file_content), "content_type": content_type})
         return key
 
+    def download_file_by_key(self, key):
+        return b"fake-audio-bytes"
+
     def get_organization_root_prefix(self, organization_id: str) -> str:
         return f"{self.prefix}organizations/{organization_id}/"
 

From 1abaae180344bafa3132e8c5e8c7c630ccd632b7 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 11:30:47 +0530
Subject: [PATCH 43/55] fix(tests): update evaluator and result IDs in bridge
 evaluator tests for consistency

---
 tests/test_workers/test_celery_task_workflows.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_workers/test_celery_task_workflows.py b/tests/test_workers/test_celery_task_workflows.py
index 7fb3c9ff..07e2baaf 100644
--- a/tests/test_workers/test_celery_task_workflows.py
+++ b/tests/test_workers/test_celery_task_workflows.py
@@ -606,7 +606,7 @@ def test_run_evaluator_bridge_runs_without_existing_event_loop(db_session, monke
     )
     evaluator = Evaluator(
         id=uuid4(),
-        evaluator_id="bridge-eval",
+        evaluator_id="611111",
         organization_id=org.id,
         workspace_id=workspace_id,
         name="Bridge Evaluator",
@@ -616,7 +616,7 @@ def test_run_evaluator_bridge_runs_without_existing_event_loop(db_session, monke
     )
     eval_result = EvaluatorResult(
         id=uuid4(),
-        result_id="bridge-result",
+        result_id="611112",
         organization_id=org.id,
         workspace_id=workspace_id,
         evaluator_id=evaluator.id,

From 5347af352349af9fec198c1d9e7120224f4a2224 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 12:03:37 +0530
Subject: [PATCH 44/55] refactor(playground): rename and enhance usage tracking
 in post-call processing; improve error handling during database commits

---
 .../playground/post_call_processing.py        | 40 +++++++----
 app/services/usage/external_agent_usage.py    | 33 ++++++++--
 .../test_playground_post_call_processing.py   | 66 +++++++++++++++----
 3 files changed, 111 insertions(+), 28 deletions(-)

diff --git a/app/services/playground/post_call_processing.py b/app/services/playground/post_call_processing.py
index 4f7f32aa..4fd04424 100644
--- a/app/services/playground/post_call_processing.py
+++ b/app/services/playground/post_call_processing.py
@@ -32,7 +32,7 @@ def record_playground_post_call_usage_once(
     Returns (should_create_evaluator, updated_call_metrics).
     """
     from app.services.usage.external_agent_usage import (
-        record_playground_provider_usage_from_call_data,
+        apply_playground_provider_usage_from_call_data,
     )
 
     locked = _lock_call_recording(db, call_recording_id)
@@ -54,18 +54,36 @@ def record_playground_post_call_usage_once(
 
     if stored_data.get("external_usage_recorded") and isinstance(metrics, dict):
         metrics["external_usage_recorded"] = True
+        locked.call_data = metrics
+        db.commit()
     elif isinstance(metrics, dict):
-        metrics = record_playground_provider_usage_from_call_data(
-            organization_id=locked.organization_id,
-            workspace_id=locked.workspace_id,
-            agent_id=locked.agent_id,
-            provider_platform=platform_key,
-            call_short_id=locked.call_short_id,
-            call_data=metrics,
-        )
+        metrics["external_usage_recorded"] = True
+        locked.call_data = metrics
+        try:
+            db.commit()
+        except Exception:
+            db.rollback()
+            logger.exception(
+                "[Poll Call Metrics] Failed to persist external_usage_recorded "
+                f"for call recording {call_recording_id}"
+            )
+            return False, call_metrics
+
+        try:
+            apply_playground_provider_usage_from_call_data(
+                organization_id=locked.organization_id,
+                workspace_id=locked.workspace_id,
+                agent_id=locked.agent_id,
+                provider_platform=platform_key,
+                call_short_id=locked.call_short_id,
+                call_data=metrics,
+            )
+        except Exception:
+            logger.exception(
+                "[Poll Call Metrics] Usage counters failed after dedup commit "
+                f"for call recording {call_recording_id}"
+            )
 
-    locked.call_data = metrics
-    db.commit()
     return True, metrics
 
 
diff --git a/app/services/usage/external_agent_usage.py b/app/services/usage/external_agent_usage.py
index b467f135..f6bc6e53 100644
--- a/app/services/usage/external_agent_usage.py
+++ b/app/services/usage/external_agent_usage.py
@@ -209,7 +209,7 @@ def extract_external_agent_usage(
     return None
 
 
-def record_playground_provider_usage_from_call_data(
+def apply_playground_provider_usage_from_call_data(
     *,
     organization_id: UUID,
     workspace_id: Optional[UUID],
@@ -217,8 +217,8 @@ def record_playground_provider_usage_from_call_data(
     provider_platform: str,
     call_short_id: Optional[str],
     call_data: dict[str, Any],
-) -> dict[str, Any]:
-    """Record provider usage for a completed playground Voice AI call."""
+) -> None:
+    """Apply provider usage counters from call_data (no dedup guard)."""
     from types import SimpleNamespace
 
     from app.services.usage.context import (
@@ -227,9 +227,6 @@ def record_playground_provider_usage_from_call_data(
     )
 
     metrics = dict(call_data or {})
-    if metrics.get("external_usage_recorded"):
-        return metrics
-
     usage_ctx = usage_context_for_playground_voice_call(
         organization_id=organization_id,
         workspace_id=workspace_id,
@@ -245,6 +242,30 @@ def record_playground_provider_usage_from_call_data(
     )
     with llm_usage_context(usage_ctx):
         record_external_agent_usage(stub, usage_ctx=usage_ctx)
+
+
+def record_playground_provider_usage_from_call_data(
+    *,
+    organization_id: UUID,
+    workspace_id: Optional[UUID],
+    agent_id: Optional[UUID],
+    provider_platform: str,
+    call_short_id: Optional[str],
+    call_data: dict[str, Any],
+) -> dict[str, Any]:
+    """Record provider usage for a completed playground Voice AI call."""
+    metrics = dict(call_data or {})
+    if metrics.get("external_usage_recorded"):
+        return metrics
+
+    apply_playground_provider_usage_from_call_data(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        agent_id=agent_id,
+        provider_platform=provider_platform,
+        call_short_id=call_short_id,
+        call_data=metrics,
+    )
     metrics["external_usage_recorded"] = True
     return metrics
 
diff --git a/tests/test_services/test_playground_post_call_processing.py b/tests/test_services/test_playground_post_call_processing.py
index 409efd05..0c990a53 100644
--- a/tests/test_services/test_playground_post_call_processing.py
+++ b/tests/test_services/test_playground_post_call_processing.py
@@ -64,15 +64,9 @@ def test_record_playground_post_call_usage_once_is_idempotent(
     call_metrics = {"call_status": "ended", "duration_seconds": 42}
     calls = []
 
-    def _fake_record(**kwargs):
-        calls.append(kwargs)
-        metrics = dict(kwargs["call_data"])
-        metrics["external_usage_recorded"] = True
-        return metrics
-
     monkeypatch.setattr(
-        "app.services.usage.external_agent_usage.record_playground_provider_usage_from_call_data",
-        _fake_record,
+        "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
+        lambda **kwargs: calls.append(kwargs),
     )
 
     proceed, updated = record_playground_post_call_usage_once(
@@ -119,12 +113,12 @@ def test_record_playground_post_call_usage_once_skips_when_evaluator_linked(
         evaluator_result_id=existing.id,
     )
 
-    def _fail_record(**_kwargs):
+    def _fail_apply(**_kwargs):
         raise AssertionError("usage should not be recorded when evaluator already exists")
 
     monkeypatch.setattr(
-        "app.services.usage.external_agent_usage.record_playground_provider_usage_from_call_data",
-        _fail_record,
+        "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
+        _fail_apply,
     )
 
     proceed, _ = record_playground_post_call_usage_once(
@@ -171,3 +165,53 @@ def test_claim_playground_evaluator_result_slot_skips_after_evaluator_linked(
         .count()
         == 1
     )
+
+
+def test_record_playground_post_call_usage_once_applies_usage_only_after_commit(
+    db_session, org_id, default_workspace, playground_agent, monkeypatch
+):
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        call_data={"call_status": "ended"},
+    )
+    apply_calls = []
+    commit_attempts = {"count": 0}
+    original_commit = db_session.commit
+
+    def _commit_once_fail():
+        commit_attempts["count"] += 1
+        if commit_attempts["count"] == 1:
+            db_session.rollback()
+            raise RuntimeError("simulated commit failure")
+        return original_commit()
+
+    monkeypatch.setattr(db_session, "commit", _commit_once_fail)
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
+        lambda **kwargs: apply_calls.append(kwargs),
+    )
+
+    proceed, _ = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics={"call_status": "ended", "duration_seconds": 42},
+    )
+    assert proceed is False
+    assert apply_calls == []
+
+    db_session.refresh(recording)
+    assert not (recording.call_data or {}).get("external_usage_recorded")
+
+    proceed, updated = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics={"call_status": "ended", "duration_seconds": 42},
+    )
+    assert proceed is True
+    assert updated["external_usage_recorded"] is True
+    assert len(apply_calls) == 1

From 9b4a7340659c7d28b6fd5d8cb041a9263d52692c Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 14:06:03 +0530
Subject: [PATCH 45/55] refactor(playground): improve error handling and retry
 logic in post-call usage tracking; update test to reflect new behavior

---
 .../playground/post_call_processing.py        | 28 ++++++++++---------
 .../test_playground_post_call_processing.py   | 22 ++++++---------
 2 files changed, 23 insertions(+), 27 deletions(-)

diff --git a/app/services/playground/post_call_processing.py b/app/services/playground/post_call_processing.py
index 4fd04424..7a547a68 100644
--- a/app/services/playground/post_call_processing.py
+++ b/app/services/playground/post_call_processing.py
@@ -57,18 +57,6 @@ def record_playground_post_call_usage_once(
         locked.call_data = metrics
         db.commit()
     elif isinstance(metrics, dict):
-        metrics["external_usage_recorded"] = True
-        locked.call_data = metrics
-        try:
-            db.commit()
-        except Exception:
-            db.rollback()
-            logger.exception(
-                "[Poll Call Metrics] Failed to persist external_usage_recorded "
-                f"for call recording {call_recording_id}"
-            )
-            return False, call_metrics
-
         try:
             apply_playground_provider_usage_from_call_data(
                 organization_id=locked.organization_id,
@@ -79,10 +67,24 @@ def record_playground_post_call_usage_once(
                 call_data=metrics,
             )
         except Exception:
+            db.rollback()
             logger.exception(
-                "[Poll Call Metrics] Usage counters failed after dedup commit "
+                "[Poll Call Metrics] Usage counters failed for "
+                f"call recording {call_recording_id}"
+            )
+            return False, call_metrics
+
+        metrics["external_usage_recorded"] = True
+        locked.call_data = metrics
+        try:
+            db.commit()
+        except Exception:
+            db.rollback()
+            logger.exception(
+                "[Poll Call Metrics] Failed to persist external_usage_recorded "
                 f"for call recording {call_recording_id}"
             )
+            return False, call_metrics
 
     return True, metrics
 
diff --git a/tests/test_services/test_playground_post_call_processing.py b/tests/test_services/test_playground_post_call_processing.py
index 0c990a53..ba881f30 100644
--- a/tests/test_services/test_playground_post_call_processing.py
+++ b/tests/test_services/test_playground_post_call_processing.py
@@ -167,7 +167,7 @@ def test_claim_playground_evaluator_result_slot_skips_after_evaluator_linked(
     )
 
 
-def test_record_playground_post_call_usage_once_applies_usage_only_after_commit(
+def test_record_playground_post_call_usage_once_retries_when_apply_fails(
     db_session, org_id, default_workspace, playground_agent, monkeypatch
 ):
     recording = _make_playground_recording(
@@ -177,21 +177,16 @@ def test_record_playground_post_call_usage_once_applies_usage_only_after_commit(
         agent_id=playground_agent.id,
         call_data={"call_status": "ended"},
     )
-    apply_calls = []
-    commit_attempts = {"count": 0}
-    original_commit = db_session.commit
+    apply_attempts = {"count": 0}
 
-    def _commit_once_fail():
-        commit_attempts["count"] += 1
-        if commit_attempts["count"] == 1:
-            db_session.rollback()
-            raise RuntimeError("simulated commit failure")
-        return original_commit()
+    def _apply_fail_once(**kwargs):
+        apply_attempts["count"] += 1
+        if apply_attempts["count"] == 1:
+            raise RuntimeError("simulated apply failure")
 
-    monkeypatch.setattr(db_session, "commit", _commit_once_fail)
     monkeypatch.setattr(
         "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
-        lambda **kwargs: apply_calls.append(kwargs),
+        _apply_fail_once,
     )
 
     proceed, _ = record_playground_post_call_usage_once(
@@ -201,7 +196,6 @@ def _commit_once_fail():
         call_metrics={"call_status": "ended", "duration_seconds": 42},
     )
     assert proceed is False
-    assert apply_calls == []
 
     db_session.refresh(recording)
     assert not (recording.call_data or {}).get("external_usage_recorded")
@@ -214,4 +208,4 @@ def _commit_once_fail():
     )
     assert proceed is True
     assert updated["external_usage_recorded"] is True
-    assert len(apply_calls) == 1
+    assert apply_attempts["count"] == 2

From 9f81f8f55b1d9049fd2ba8ab0e2c0b34baf4b850 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 14:24:05 +0530
Subject: [PATCH 46/55] feat(flexprice): implement local mocking for Flexprice
 billing during tests; update configuration and service logic to prevent
 external API calls

---
 .github/workflows/backend-tests-postgres.yml  |  4 ++
 app/config.py                                 |  5 ++
 app/services/billing/flexprice_service.py     | 16 +++++
 app/services/usage/external_agent_usage.py    | 57 ++++++++++++----
 app/services/usage/llm_usage.py               | 47 +++++++------
 config.yml.example                            |  1 +
 tests/conftest.py                             | 25 +++++++
 tests/helpers/__init__.py                     |  0
 tests/helpers/flexprice_stubs.py              | 68 +++++++++++++++++++
 .../test_flexprice_test_isolation.py          | 55 +++++++++++++++
 tests/test_services/test_flexprice_service.py |  3 +-
 .../test_playground_post_call_processing.py   | 67 ++++++++++++++++++
 .../test_usage/test_llm_usage.py              |  1 +
 .../test_usage/test_pre_prod_usage.py         | 64 ++++++++++++++++-
 14 files changed, 377 insertions(+), 36 deletions(-)
 create mode 100644 tests/helpers/__init__.py
 create mode 100644 tests/helpers/flexprice_stubs.py
 create mode 100644 tests/test_core/test_flexprice_test_isolation.py

diff --git a/.github/workflows/backend-tests-postgres.yml b/.github/workflows/backend-tests-postgres.yml
index 6fc18bc4..30be09f4 100644
--- a/.github/workflows/backend-tests-postgres.yml
+++ b/.github/workflows/backend-tests-postgres.yml
@@ -41,6 +41,8 @@ jobs:
       REDIS_URL: redis://localhost:6379/0
       CELERY_BROKER_URL: redis://localhost:6379/0
       CELERY_RESULT_BACKEND: redis://localhost:6379/0
+      EFFICIENTAI_PYTEST: "1"
+      FLEXPRICE_ENABLED: "false"
 
     steps:
       - name: Checkout repository
@@ -83,6 +85,8 @@ jobs:
       SHARD_DATABASE_URL_02: postgresql://postgres:postgres@localhost:5432/efficientai_data_02
       SHARDING_INTEGRATION_TEST: "1"
       REDIS_URL: redis://localhost:6379/0
+      EFFICIENTAI_PYTEST: "1"
+      FLEXPRICE_ENABLED: "false"
 
     steps:
       - name: Checkout repository
diff --git a/app/config.py b/app/config.py
index 0e9154e0..7f508d23 100644
--- a/app/config.py
+++ b/app/config.py
@@ -901,6 +901,11 @@ def _apply_llm_gateway_settings(gateway_cfg: dict, *, gateway_type: str) -> None
             settings.FLEXPRICE_API_KEY = flexprice_config["api_key"]
         if flexprice_config.get("api_host"):
             settings.FLEXPRICE_API_HOST = flexprice_config["api_host"]
+        if (
+            os.environ.get("EFFICIENTAI_PYTEST") == "1"
+            and os.environ.get("FLEXPRICE_TEST_ALLOW") != "1"
+        ):
+            settings.FLEXPRICE_ENABLED = False
 
     # Update Celery URLs if they weren't explicitly set
     if not settings.CELERY_BROKER_URL:
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index 1c746faf..c81260c9 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -98,6 +98,14 @@ def _verbose_logging() -> bool:
     return os.getenv("FLEXPRICE_VERBOSE", "").lower() in {"1", "true", "yes"}
 
 
+def _pytest_blocks_external_billing() -> bool:
+    """Block real Flexprice I/O during pytest unless explicitly opted in."""
+    return (
+        os.environ.get("EFFICIENTAI_PYTEST") == "1"
+        and os.environ.get("FLEXPRICE_TEST_ALLOW") != "1"
+    )
+
+
 def _mask_api_key(api_key: Optional[str]) -> str:
     if not api_key:
         return "(missing)"
@@ -158,6 +166,8 @@ def log_startup_status(*, component: str = "app") -> None:
 
 def _verify_connectivity() -> Optional[str]:
     """Best-effort reachability probe; returns error text or None when OK."""
+    if _pytest_blocks_external_billing():
+        return None
     try:
         import httpx
 
@@ -227,6 +237,9 @@ def record_event(
     """
     global _disabled_skip_logged
 
+    if _pytest_blocks_external_billing():
+        return False
+
     inactive_reason = disabled_reason()
     if inactive_reason:
         if not _disabled_skip_logged:
@@ -291,6 +304,9 @@ def ensure_customer(
     email: Optional[str] = None,
 ) -> None:
     """Register an organization as a Flexprice customer. No-op when disabled."""
+    if _pytest_blocks_external_billing():
+        return
+
     inactive_reason = disabled_reason()
     if inactive_reason:
         if _verbose_logging():
diff --git a/app/services/usage/external_agent_usage.py b/app/services/usage/external_agent_usage.py
index f6bc6e53..146d1029 100644
--- a/app/services/usage/external_agent_usage.py
+++ b/app/services/usage/external_agent_usage.py
@@ -21,6 +21,10 @@ class ExternalAgentUsageExtraction:
     tts_characters: int = 0
 
 
+class ExternalUsageRecordingError(RuntimeError):
+    """Raised when billable external provider usage could not be persisted."""
+
+
 def _as_int(value: Any) -> int:
     try:
         return max(0, int(value or 0))
@@ -241,7 +245,11 @@ def apply_playground_provider_usage_from_call_data(
         result_id=call_short_id or "playground",
     )
     with llm_usage_context(usage_ctx):
-        record_external_agent_usage(stub, usage_ctx=usage_ctx)
+        if not record_external_agent_usage(stub, usage_ctx=usage_ctx):
+            raise ExternalUsageRecordingError(
+                f"failed to persist external provider usage for call "
+                f"{call_short_id or 'playground'}"
+            )
 
 
 def record_playground_provider_usage_from_call_data(
@@ -274,8 +282,12 @@ def record_external_agent_usage(
     result: Any,
     *,
     usage_ctx: LLMUsageContext,
-) -> None:
-    """Record production-agent usage from stored provider call_data."""
+) -> bool:
+    """Record production-agent usage from stored provider call_data.
+
+    Returns True when usage was persisted or there was nothing billable to record.
+    Returns False when billable usage could not be stored.
+    """
     call_data = getattr(result, "call_data", None)
     platform = getattr(result, "provider_platform", None) or ""
     extraction = extract_external_agent_usage(
@@ -283,11 +295,15 @@ def record_external_agent_usage(
         platform=str(platform),
     )
     if extraction is None:
-        return
+        return True
 
     org_id = getattr(result, "organization_id", None)
     if org_id is None:
-        return
+        logger.warning(
+            "external agent usage record skipped: missing organization_id for result {}",
+            getattr(result, "result_id", result),
+        )
+        return False
 
     ctx = LLMUsageContext(
         organization_id=usage_ctx.organization_id,
@@ -303,15 +319,18 @@ def record_external_agent_usage(
         },
     )
 
-    try:
-        if extraction.llm and usage_snapshot_is_billable(extraction.llm):
+    outcomes: list[bool] = []
+    if extraction.llm and usage_snapshot_is_billable(extraction.llm):
+        outcomes.append(
             record_llm_usage(
                 extraction.model,
                 extraction.llm,
                 organization_id=org_id,
                 ctx=ctx,
             )
-        if extraction.stt_audio_seconds > 0:
+        )
+    if extraction.stt_audio_seconds > 0:
+        outcomes.append(
             record_stt_usage(
                 f"{platform}-stt",
                 audio_seconds=extraction.stt_audio_seconds,
@@ -319,16 +338,26 @@ def record_external_agent_usage(
                 ctx=ctx,
                 count_call=False,
             )
-        if extraction.tts_characters > 0:
+        )
+    if extraction.tts_characters > 0:
+        outcomes.append(
             record_tts_usage(
                 f"{platform}-tts",
                 characters=extraction.tts_characters,
                 organization_id=org_id,
                 ctx=ctx,
             )
-    except Exception as exc:
-        logger.debug(
-            "external agent usage record skipped for result {}: {}",
-            getattr(result, "result_id", result),
-            exc,
         )
+
+    if not outcomes:
+        return True
+
+    if all(outcomes):
+        return True
+
+    logger.warning(
+        "external agent usage record incomplete for result {} (platform={})",
+        getattr(result, "result_id", result),
+        platform,
+    )
+    return False
diff --git a/app/services/usage/llm_usage.py b/app/services/usage/llm_usage.py
index bbeac232..38c9455f 100644
--- a/app/services/usage/llm_usage.py
+++ b/app/services/usage/llm_usage.py
@@ -373,13 +373,13 @@ def _buffer_to_postgres(
     organization_id: UUID,
     bucket: Dict[str, Any],
     deltas: Dict[str, int],
-) -> None:
+) -> bool:
     """Durable fallback when Redis is unavailable."""
     try:
         from app.database import SessionLocal
     except Exception as exc:
         logger.warning("usage postgres fallback unavailable: {}", exc)
-        return
+        return False
 
     usage_date = bucket["usage_date"]
     if isinstance(usage_date, str):
@@ -443,9 +443,11 @@ def _buffer_to_postgres(
             params,
         )
         db.commit()
+        return True
     except Exception as exc:
         db.rollback()
         logger.warning("usage postgres fallback insert failed: {}", exc)
+        return False
     finally:
         db.close()
 
@@ -455,7 +457,7 @@ def _incr_pending(
     prefix: str,
     deltas: Dict[str, int],
     bucket: Dict[str, Any],
-) -> None:
+) -> bool:
     hash_key = _pending_hash_key(organization_id)
     try:
         client = _client()
@@ -466,9 +468,10 @@ def _incr_pending(
         pipe.sadd("usage:pending:orgs", str(organization_id))
         pipe.expire(hash_key, _PENDING_TTL_SECONDS)
         pipe.execute()
+        return True
     except redis.RedisError as exc:
         logger.warning("usage redis counter failed, buffering to postgres: {}", exc)
-        _buffer_to_postgres(organization_id, bucket, deltas)
+        return _buffer_to_postgres(organization_id, bucket, deltas)
 
 
 def _context_for_record(
@@ -491,18 +494,22 @@ def record_llm_usage(
     organization_id: Optional[UUID] = None,
     ctx: Optional[LLMUsageContext] = None,
     usage_date: Optional[date] = None,
-) -> None:
-    """Increment counters for one LLM call (best-effort, never raises)."""
+) -> bool:
+    """Increment counters for one LLM call (best-effort, never raises).
+
+    Returns True when usage was persisted or there was nothing billable to record.
+    Returns False when billable usage could not be stored.
+    """
     if not model:
         model = "unknown"
     context = _context_for_record(organization_id=organization_id, ctx=ctx)
     if context is None:
         logger.warning("llm usage record skipped: missing organization_id")
-        return
+        return False
 
     deltas = _deltas_from_usage(usage)
     if not _has_billable_usage_deltas(deltas):
-        return
+        return True
 
     day = usage_date or datetime.now(timezone.utc).date()
     bucket = _bucket_from_context(
@@ -512,7 +519,7 @@ def record_llm_usage(
         usage_kind=USAGE_KIND_LLM,
     )
     prefix = _bucket_prefix(**bucket)
-    _incr_pending(context.organization_id, prefix, deltas, bucket)
+    return _incr_pending(context.organization_id, prefix, deltas, bucket)
 
 
 def record_stt_usage(
@@ -523,18 +530,18 @@ def record_stt_usage(
     ctx: Optional[LLMUsageContext] = None,
     usage_date: Optional[date] = None,
     count_call: bool = True,
-) -> None:
+) -> bool:
     """Increment counters for one STT call (audio seconds + optional call_count)."""
     if not model:
         model = "unknown"
     context = _context_for_record(organization_id=organization_id, ctx=ctx)
     if context is None:
         logger.warning("stt usage record skipped: missing organization_id")
-        return
+        return False
 
     seconds = int(max(0, math.ceil(float(audio_seconds or 0))))
     if seconds <= 0:
-        return
+        return True
     deltas = _deltas_from_stt(seconds, count_call=count_call)
     day = usage_date or datetime.now(timezone.utc).date()
     bucket = _bucket_from_context(
@@ -544,7 +551,7 @@ def record_stt_usage(
         usage_kind=USAGE_KIND_STT,
     )
     prefix = _bucket_prefix(**bucket)
-    _incr_pending(context.organization_id, prefix, deltas, bucket)
+    return _incr_pending(context.organization_id, prefix, deltas, bucket)
 
 
 def record_call_usage(
@@ -554,7 +561,7 @@ def record_call_usage(
     ctx: Optional[LLMUsageContext] = None,
     usage_date: Optional[date] = None,
     audio_seconds: int = 0,
-) -> None:
+) -> bool:
     """Record one completed call session (call_count + optional duration).
 
     Best-effort, never raises. Uses the same Redis-buffered path as other
@@ -565,7 +572,7 @@ def record_call_usage(
     context = _context_for_record(organization_id=organization_id, ctx=ctx)
     if context is None:
         logger.warning("call usage record skipped: missing organization_id")
-        return
+        return False
 
     seconds = max(0, int(audio_seconds or 0))
     deltas = {
@@ -586,7 +593,7 @@ def record_call_usage(
         usage_kind=USAGE_KIND_LLM,
     )
     prefix = _bucket_prefix(**bucket)
-    _incr_pending(context.organization_id, prefix, deltas, bucket)
+    return _incr_pending(context.organization_id, prefix, deltas, bucket)
 
 
 def record_tts_usage(
@@ -596,18 +603,18 @@ def record_tts_usage(
     organization_id: Optional[UUID] = None,
     ctx: Optional[LLMUsageContext] = None,
     usage_date: Optional[date] = None,
-) -> None:
+) -> bool:
     """Increment counters for one TTS call (characters + call_count)."""
     if not model:
         model = "unknown"
     context = _context_for_record(organization_id=organization_id, ctx=ctx)
     if context is None:
         logger.warning("tts usage record skipped: missing organization_id")
-        return
+        return False
 
     chars = max(0, int(characters or 0))
     if chars <= 0:
-        return
+        return True
     deltas = _deltas_from_tts(chars)
     day = usage_date or datetime.now(timezone.utc).date()
     bucket = _bucket_from_context(
@@ -617,7 +624,7 @@ def record_tts_usage(
         usage_kind=USAGE_KIND_TTS,
     )
     prefix = _bucket_prefix(**bucket)
-    _incr_pending(context.organization_id, prefix, deltas, bucket)
+    return _incr_pending(context.organization_id, prefix, deltas, bucket)
 
 
 def probe_audio_seconds(audio_file_path: str) -> int:
diff --git a/config.yml.example b/config.yml.example
index daad7d94..0430c62f 100644
--- a/config.yml.example
+++ b/config.yml.example
@@ -259,6 +259,7 @@ judge_alignment:
 # Flexprice usage-based billing (optional). When disabled or api_key is unset,
 # no SDK calls are made and the app behaves as today. Cloud SaaS: set enabled
 # true and provide FLEXPRICE_API_KEY (env var overrides api_key below).
+# Pytest always mocks Flexprice locally (EFFICIENTAI_PYTEST=1); never hits this API.
 # flexprice:
 #   enabled: false
 #   api_key: null
diff --git a/tests/conftest.py b/tests/conftest.py
index 58b0e30e..fb680ccb 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -30,6 +30,10 @@ def _compile_jsonb_for_sqlite(_element, _compiler, **_kw):
 os.environ["UPLOAD_DIR"] = "/tmp/efficientai-test-uploads"
 # Local dev often sets SERVICE_MODE=media and config.yml media URLs; keep API tests on full app mode.
 os.environ["SERVICE_MODE"] = "api"
+# Pytest never uses real Flexprice billing (see tests/helpers/flexprice_stubs.py).
+os.environ["EFFICIENTAI_PYTEST"] = "1"
+os.environ["FLEXPRICE_ENABLED"] = "false"
+os.environ.pop("FLEXPRICE_API_KEY", None)
 
 _REPO_ROOT = Path(__file__).resolve().parents[1]
 _SRC_ROOT = _REPO_ROOT / "src"
@@ -42,6 +46,27 @@ def _compile_jsonb_for_sqlite(_element, _compiler, **_kw):
 )
 
 
+@pytest.fixture(autouse=True)
+def mock_flexprice_locally(monkeypatch, request):
+    """Stub all Flexprice billing I/O; only test_flexprice_service.py uses a mocked SDK."""
+    from tests.helpers.flexprice_stubs import (
+        install_flexprice_test_isolation,
+        is_flexprice_unit_test_path,
+    )
+
+    fspath = getattr(request.node, "fspath", None)
+    if is_flexprice_unit_test_path(fspath):
+        yield
+        return
+
+    install_flexprice_test_isolation(monkeypatch)
+    from app.config import settings
+
+    monkeypatch.setattr(settings, "FLEXPRICE_ENABLED", False, raising=False)
+    monkeypatch.setattr(settings, "FLEXPRICE_API_KEY", None, raising=False)
+    yield
+
+
 @pytest.fixture(autouse=True)
 def isolate_service_mode_for_app_factory(monkeypatch):
     """Prevent local SERVICE_MODE / media URL config from breaking create_app tests."""
diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/helpers/flexprice_stubs.py b/tests/helpers/flexprice_stubs.py
new file mode 100644
index 00000000..e1fbb105
--- /dev/null
+++ b/tests/helpers/flexprice_stubs.py
@@ -0,0 +1,68 @@
+"""Local Flexprice stubs for pytest — no real API or dashboard I/O."""
+
+from __future__ import annotations
+
+_FLEXPRICE_FORBIDDEN = "Real Flexprice SDK invoked during pytest — use a mock"
+
+
+def _noop_record_event(*_args, **_kwargs) -> bool:
+    return False
+
+
+def _noop_ensure_customer(*_args, **_kwargs) -> None:
+    return None
+
+
+def _noop_provision_billing_customer(*_args, **_kwargs) -> None:
+    return None
+
+
+class _ForbiddenFlexprice:
+    def __init__(self, *_args, **_kwargs):
+        raise RuntimeError(_FLEXPRICE_FORBIDDEN)
+
+    def __enter__(self):
+        raise RuntimeError(_FLEXPRICE_FORBIDDEN)
+
+    def __exit__(self, *_args):
+        return False
+
+
+def _is_flexprice_url(url) -> bool:
+    return "flexprice.io" in str(url).lower()
+
+
+def install_flexprice_test_isolation(monkeypatch) -> None:
+    """Replace all Flexprice external I/O with local no-ops (suite-wide default)."""
+    from app.services.billing import flexprice_service as fp
+    from app.services import organization_provisioning as org_prov
+
+    monkeypatch.setattr(fp, "record_event", _noop_record_event)
+    monkeypatch.setattr(fp, "ensure_customer", _noop_ensure_customer)
+    monkeypatch.setattr(org_prov, "provision_billing_customer", _noop_provision_billing_customer)
+    monkeypatch.setattr("flexprice.Flexprice", _ForbiddenFlexprice)
+
+    try:
+        import httpx
+    except ImportError:
+        return
+
+    original_get = httpx.get
+    original_request = httpx.request
+
+    def guarded_get(url, *args, **kwargs):
+        if _is_flexprice_url(url):
+            raise RuntimeError(f"Blocked Flexprice HTTP during pytest: {url}")
+        return original_get(url, *args, **kwargs)
+
+    def guarded_request(method, url, *args, **kwargs):
+        if _is_flexprice_url(url):
+            raise RuntimeError(f"Blocked Flexprice HTTP during pytest: {url}")
+        return original_request(method, url, *args, **kwargs)
+
+    monkeypatch.setattr(httpx, "get", guarded_get)
+    monkeypatch.setattr(httpx, "request", guarded_request)
+
+
+def is_flexprice_unit_test_path(path) -> bool:
+    return path is not None and "test_flexprice_service.py" in str(path)
diff --git a/tests/test_core/test_flexprice_test_isolation.py b/tests/test_core/test_flexprice_test_isolation.py
new file mode 100644
index 00000000..2fe47bec
--- /dev/null
+++ b/tests/test_core/test_flexprice_test_isolation.py
@@ -0,0 +1,55 @@
+"""Regression tests: pytest must never reach real Flexprice APIs."""
+
+from uuid import uuid4
+
+import httpx
+import pytest
+
+from app.config import settings
+from app.services.billing import flexprice_service as fp
+
+
+def test_record_event_is_stubbed_even_when_settings_enabled():
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "dummy-key-for-test"
+
+    assert (
+        fp.record_event("metrics.ai_assist", uuid4(), uuid4(), properties={"mode": "x"})
+        is False
+    )
+
+
+def test_ensure_customer_is_stubbed_even_when_settings_enabled():
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "dummy-key-for-test"
+
+    fp.ensure_customer(uuid4(), name="Test Org", email="test@example.com")
+
+
+def test_record_wrappers_do_not_invoke_flexprice_sdk():
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "dummy-key-for-test"
+    org_id = uuid4()
+    ws_id = uuid4()
+
+    fp.record_metrics_llm_assist(org_id, uuid4(), workspace_id=ws_id, mode="description")
+    fp.record_call_import_batch_created(
+        org_id,
+        uuid4(),
+        workspace_id=ws_id,
+        total_rows=1,
+        source="csv",
+    )
+    fp.record_test_agent_conversation_started(org_id, uuid4(), workspace_id=ws_id)
+
+
+def test_flexprice_sdk_construction_is_forbidden():
+    with pytest.raises(RuntimeError, match="Real Flexprice SDK invoked during pytest"):
+        from flexprice import Flexprice
+
+        Flexprice(server_url="https://us.api.flexprice.io/v1", api_key_auth="x")
+
+
+def test_httpx_to_flexprice_host_is_blocked():
+    with pytest.raises(RuntimeError, match="Blocked Flexprice HTTP during pytest"):
+        httpx.get("https://us.api.flexprice.io/v1/customers")
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index 50099968..fdd7e5c7 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -10,7 +10,8 @@
 
 
 @pytest.fixture(autouse=True)
-def reset_flexprice_settings():
+def reset_flexprice_settings(monkeypatch):
+    monkeypatch.setenv("FLEXPRICE_TEST_ALLOW", "1")
     previous = (
         settings.FLEXPRICE_ENABLED,
         settings.FLEXPRICE_API_KEY,
diff --git a/tests/test_services/test_playground_post_call_processing.py b/tests/test_services/test_playground_post_call_processing.py
index ba881f30..c3b7376d 100644
--- a/tests/test_services/test_playground_post_call_processing.py
+++ b/tests/test_services/test_playground_post_call_processing.py
@@ -167,6 +167,73 @@ def test_claim_playground_evaluator_result_slot_skips_after_evaluator_linked(
     )
 
 
+def test_record_playground_post_call_usage_once_retries_when_storage_fails(
+    db_session, org_id, default_workspace, playground_agent, monkeypatch
+):
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        call_data={"call_status": "ended"},
+    )
+
+    def _fail_storage(**_kwargs):
+        from app.services.usage.external_agent_usage import ExternalUsageRecordingError
+
+        raise ExternalUsageRecordingError("simulated storage failure")
+
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
+        _fail_storage,
+    )
+
+    proceed, _ = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="vapi",
+        call_metrics={
+            "costBreakdown": {
+                "llmPromptTokens": 40,
+                "llmCompletionTokens": 12,
+            },
+            "durationSeconds": 20,
+        },
+    )
+    assert proceed is False
+
+    db_session.refresh(recording)
+    assert not (recording.call_data or {}).get("external_usage_recorded")
+
+
+def test_record_playground_post_call_usage_once_marks_recorded_without_billable_usage(
+    db_session, org_id, default_workspace, playground_agent, monkeypatch
+):
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        call_data={"call_status": "ended"},
+    )
+    apply_calls = []
+
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
+        lambda **kwargs: apply_calls.append(kwargs),
+    )
+
+    proceed, updated = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics={"call_status": "ended"},
+    )
+    assert proceed is True
+    assert updated["external_usage_recorded"] is True
+    assert len(apply_calls) == 1
+
+
 def test_record_playground_post_call_usage_once_retries_when_apply_fails(
     db_session, org_id, default_workspace, playground_agent, monkeypatch
 ):
diff --git a/tests/test_services/test_usage/test_llm_usage.py b/tests/test_services/test_usage/test_llm_usage.py
index cb87d0c8..011cc3c1 100644
--- a/tests/test_services/test_usage/test_llm_usage.py
+++ b/tests/test_services/test_usage/test_llm_usage.py
@@ -376,6 +376,7 @@ def _fake_buffer(organization_id, bucket, deltas):
         captured["organization_id"] = organization_id
         captured["bucket"] = bucket
         captured["deltas"] = deltas
+        return True
 
     monkeypatch.setattr(usage_mod, "_buffer_to_postgres", _fake_buffer)
 
diff --git a/tests/test_services/test_usage/test_pre_prod_usage.py b/tests/test_services/test_usage/test_pre_prod_usage.py
index 574f5103..8e6d124a 100644
--- a/tests/test_services/test_usage/test_pre_prod_usage.py
+++ b/tests/test_services/test_usage/test_pre_prod_usage.py
@@ -9,7 +9,10 @@
     usage_context_for_metric_studio_run,
     usage_context_for_persona_generation,
 )
-from app.services.usage.external_agent_usage import extract_external_agent_usage
+from app.services.usage.external_agent_usage import (
+    extract_external_agent_usage,
+    record_external_agent_usage,
+)
 
 
 @pytest.fixture
@@ -196,6 +199,65 @@ def test_usage_context_for_evaluator_result_playground_section():
     assert ctx.product_section == LLMUsageProductSection.PLAYGROUND
 
 
+def test_record_external_agent_usage_returns_false_when_storage_fails(fake_redis, monkeypatch):
+    from app.services.usage import llm_usage as usage_mod
+    from app.services.usage.context import usage_context_for_playground_voice_call
+
+    org_id = uuid4()
+    agent_id = uuid4()
+    usage_ctx = usage_context_for_playground_voice_call(
+        organization_id=org_id,
+        workspace_id=uuid4(),
+        agent_id=agent_id,
+        provider_platform="vapi",
+        call_short_id="abc123",
+    )
+    result = SimpleNamespace(
+        organization_id=org_id,
+        provider_platform="vapi",
+        result_id="abc123",
+        call_data={
+            "costBreakdown": {
+                "llmPromptTokens": 40,
+                "llmCompletionTokens": 12,
+            },
+            "durationSeconds": 20,
+        },
+    )
+
+    monkeypatch.setattr(usage_mod, "_incr_pending", lambda *_args, **_kwargs: False)
+
+    assert record_external_agent_usage(result, usage_ctx=usage_ctx) is False
+
+
+def test_record_playground_provider_usage_from_call_data_raises_when_storage_fails(
+    fake_redis, monkeypatch
+):
+    from app.services.usage import llm_usage as usage_mod
+    from app.services.usage.external_agent_usage import (
+        ExternalUsageRecordingError,
+        record_playground_provider_usage_from_call_data,
+    )
+
+    monkeypatch.setattr(usage_mod, "_incr_pending", lambda *_args, **_kwargs: False)
+
+    with pytest.raises(ExternalUsageRecordingError):
+        record_playground_provider_usage_from_call_data(
+            organization_id=uuid4(),
+            workspace_id=uuid4(),
+            agent_id=uuid4(),
+            provider_platform="vapi",
+            call_short_id="abc123",
+            call_data={
+                "costBreakdown": {
+                    "llmPromptTokens": 40,
+                    "llmCompletionTokens": 12,
+                },
+                "durationSeconds": 20,
+            },
+        )
+
+
 def test_record_playground_provider_usage_from_call_data(fake_redis):
     from app.services.usage import llm_usage as usage_mod
     from app.services.usage.external_agent_usage import (

From 7c277b2880f72364632db18ebac81d3c6195e98b Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 18:06:04 +0530
Subject: [PATCH 47/55] refactor(flexprice): remove unused billing event calls
 and streamline conversation tracking in test agents and voice agents

---
 app/api/v1/routes/test_agents.py              |  11 +-
 app/api/v1/routes/voice_agent.py              |  23 +-
 app/services/billing/flexprice_service.py     | 497 ++++++------------
 app/workers/tasks/process_evaluator_result.py |  24 +-
 app/workers/tasks/run_evaluator.py            |  18 -
 .../test_flexprice_test_isolation.py          |   2 +-
 tests/test_services/test_flexprice_service.py | 274 ++--------
 .../test_flexprice_tracking_wiring.py         |  20 +-
 .../test_celery_task_workflows.py             |  22 +-
 9 files changed, 230 insertions(+), 661 deletions(-)

diff --git a/app/api/v1/routes/test_agents.py b/app/api/v1/routes/test_agents.py
index f6b6ea1b..6ce1f7a9 100644
--- a/app/api/v1/routes/test_agents.py
+++ b/app/api/v1/routes/test_agents.py
@@ -16,10 +16,7 @@
     TestAgentConversationUpdate,
     TestAgentConversationResponse
 )
-from app.services.billing.flexprice_service import (
-    record_test_agent_conversation_ended,
-    record_test_agent_conversation_started,
-)
+from app.services.billing.flexprice_service import record_test_agent_conversation_ended
 from app.services.testing.test_agent_service import test_agent_service
 
 router = APIRouter(prefix="/test-agents", tags=["test-agents"])
@@ -102,12 +99,6 @@ async def start_conversation(
             organization_id=organization_id,
             db=db
         )
-        background_tasks.add_task(
-            record_test_agent_conversation_started,
-            organization_id,
-            conversation_id,
-            workspace_id=workspace_id,
-        )
         return conversation
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
diff --git a/app/api/v1/routes/voice_agent.py b/app/api/v1/routes/voice_agent.py
index 9656d7af..940358a7 100644
--- a/app/api/v1/routes/voice_agent.py
+++ b/app/api/v1/routes/voice_agent.py
@@ -483,15 +483,6 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
 
         if agent_id and workspace_id and not test_agent_bridge_mode:
             session_uuid = uuid4()
-            from app.services.billing.flexprice_service import record_test_agent_conversation_started
-
-            record_test_agent_conversation_started(
-                organization_id,
-                session_uuid,
-                workspace_id=workspace_id,
-                result_id=result_id,
-                agent_id=UUID(agent_id),
-            )
 
         agent_silence_hangup_secs = resolve_agent_silence_hangup_secs(agent)
         try:
@@ -630,7 +621,6 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
                     from app.models.enums import CallRecordingStatus
                     from app.utils.call_recordings import generate_unique_call_short_id
                     from app.workers.celery_app import process_evaluator_result_task
-                    from app.services.billing.flexprice_service import record_test_agent_conversation_ended
 
                     # Determine name for the result
                     if scenario_name and scenario_name != "Test Call":
@@ -691,18 +681,7 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
                     db.commit()
                     db.refresh(evaluator_result)
 
-                    if session_uuid and workspace_id:
-                        turn_count = len(speaker_segments)
-                        record_test_agent_conversation_ended(
-                            organization_id,
-                            session_uuid,
-                            workspace_id=workspace_id,
-                            duration_seconds=call_metadata.get("duration"),
-                            turn_count=turn_count,
-                            result_id=result_id,
-                            agent_id=UUID(agent_id),
-                            call_short_id=call_short_id,
-                        )
+                    # Playground scoring bills via playground.evaluation_completed.
                     
                     logger.info(f"✅ Evaluator result created in database: id={evaluator_result.id}, result_id={result_id}")
                     
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index c81260c9..6efeebda 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -5,30 +5,32 @@
 ``event_id`` for idempotency. ``properties`` should include ``workspace_id`` and
 ``feature`` (license key) when the surface is gated.
 
-Plan pricing (Flexprice UI): bill on **successful delivery**, not attempts.
-Primary charge meters per product (add plan usage charges on these only):
-
-- call_imports: ``call_import.batch_created``, ``call_import.evaluation_completed`` (one
-  event per finished evaluation pass; ``quantity`` = newly completed rows),
-  ``call_import.recording_minutes_billed`` (one per evaluated call with audio;
-  ``billable_minutes`` = rounded-up recording duration in minutes),
-  ``call_import.pdf_report_generated``
-- agent_playground: ``playground.evaluation_completed`` (main); optional secondary
-  ``test_agent.conversation_ended`` for test-agent-only flows — not both on the
-  same conversation
-- voice_playground: ``tts.sample_synthesized``, ``tts.report_completed``; optional
-  ``blind_test.share_created`` or ``blind_test.response_submitted``
-- evaluators: ``evaluator.run_completed`` (not ``run_requested``)
+Ingest **only when value is delivered** (completed), never on ``*_started`` /
+``*_created`` / ``*_requested``. Event ``properties`` are minimal: ``workspace_id``,
+``feature``, and the billable ``quantity`` (plus ``billable_minutes`` when the
+meter sums minutes).
+
+Billable events (wire plan usage charges to these meters only):
+
+- call_imports: ``call_import.batch_created`` (``quantity`` = rows imported),
+  ``call_import.evaluation_completed`` (``quantity`` = newly completed rows),
+  ``call_import.recording_minutes_billed`` (``quantity`` = ``billable_minutes``),
+  ``call_import.pdf_report_generated`` (``quantity`` = 1)
+- agent_playground: ``playground.evaluation_completed`` (``quantity`` = ``billable_minutes``
+  from call duration) **or** ``test_agent.conversation_ended`` (same minute rollup for
+  standalone test-agent sessions without playground eval) — never both for the same session
+- voice_playground: ``tts.sample_synthesized``, ``tts.report_completed``,
+  ``blind_test.response_submitted``
+- evaluators: ``evaluator.run_completed`` when scoring finishes (``quantity`` = 1)
 - gepa_optimization: ``prompt_optimization.run_completed``
-- judge_alignment: ``judge_alignment.run_completed`` (not ``run_started``)
-- metrics_ai_assist: ``metrics.ai_assist`` (AI metric builder / bulk parse)
-- metric_studio: ``metric_studio.run_completed`` (main); ``metric_studio.item_evaluated`` optional
-- scenario_ai: ``scenario.ai_text_generated`` (AI text generation, e.g. scenario descriptions)
-
-Track-only (analytics, do not price): ``*_started``, ``call_evaluated``,
-``conversation_started``, ``web_call_started``, ``evaluation_started``, etc.
-``metric_count`` and ``duration_seconds`` in properties are metadata unless a
-meter explicitly SUMs those fields (call-import audio minutes does).
+- judge_alignment: ``judge_alignment.run_completed``
+- metrics_ai_assist: ``metrics.ai_assist``
+- metric_studio: ``metric_studio.run_completed`` (``quantity`` = completed items)
+- scenario_ai: ``scenario.ai_text_generated``
+- observability: ``observability.call_evaluated`` only
+
+Not ingested: ``*_started``, ``*_requested``, ``*_created``, ``playground.call_evaluated``,
+``test_agent.conversation_started``, ``metric_studio.item_evaluated``, etc.
 """
 
 from __future__ import annotations
@@ -223,6 +225,32 @@ def _coerce_properties(properties: Optional[dict[str, Any]]) -> dict[str, str]:
     return out
 
 
+def _billable_minutes(duration_seconds: Optional[float]) -> int:
+    if duration_seconds is None:
+        return 1
+    seconds = float(duration_seconds)
+    if seconds <= 0:
+        return 1
+    import math
+
+    return max(1, int(math.ceil(seconds / 60.0)))
+
+
+def _billing_properties(
+    workspace_id: UUID,
+    feature: str,
+    *,
+    quantity: Optional[Union[int, float]] = None,
+    billable_minutes: Optional[int] = None,
+) -> dict[str, Any]:
+    props: dict[str, Any] = {"workspace_id": workspace_id, "feature": feature}
+    if quantity is not None:
+        props["quantity"] = quantity
+    if billable_minutes is not None:
+        props["billable_minutes"] = billable_minutes
+    return props
+
+
 def record_event(
     event_name: str,
     organization_id: UUID,
@@ -359,17 +387,7 @@ def record_blind_test_share_created(
     workspace_id: UUID,
     comparison_id: UUID,
 ) -> None:
-    record_event(
-        BLIND_TEST_SHARE_CREATED,
-        organization_id,
-        share_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_VOICE_PLAYGROUND,
-            "share_id": share_id,
-            "comparison_id": comparison_id,
-        },
-    )
+    """Not ingested — bill on blind_test.response_submitted instead."""
 
 
 def record_blind_test_response_submitted(
@@ -384,13 +402,11 @@ def record_blind_test_response_submitted(
         BLIND_TEST_RESPONSE_SUBMITTED,
         organization_id,
         response_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_VOICE_PLAYGROUND,
-            "share_id": share_id,
-            "response_count": response_count,
-            "quantity": response_count,
-        },
+        properties=_billing_properties(
+            workspace_id,
+            FEATURE_VOICE_PLAYGROUND,
+            quantity=max(1, response_count),
+        ),
     )
 
 
@@ -401,17 +417,7 @@ def record_tts_generation_started(
     workspace_id: UUID,
     sample_count: int,
 ) -> None:
-    record_event(
-        TTS_GENERATION_STARTED,
-        organization_id,
-        comparison_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_VOICE_PLAYGROUND,
-            "comparison_id": comparison_id,
-            "sample_count": sample_count,
-        },
-    )
+    """Not ingested — bill on tts.sample_synthesized per completed sample."""
 
 
 def record_tts_sample_synthesized(
@@ -424,20 +430,14 @@ def record_tts_sample_synthesized(
     side: Optional[str] = None,
     duration_seconds: Optional[float] = None,
 ) -> None:
+    del provider, side, duration_seconds
     record_event(
         TTS_SAMPLE_SYNTHESIZED,
         organization_id,
         sample_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_VOICE_PLAYGROUND,
-            "comparison_id": comparison_id,
-            "sample_id": sample_id,
-            "provider": provider,
-            "side": side,
-            "duration_seconds": duration_seconds,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_VOICE_PLAYGROUND, quantity=1
+        ),
     )
 
 
@@ -448,17 +448,7 @@ def record_tts_report_requested(
     workspace_id: UUID,
     comparison_id: UUID,
 ) -> None:
-    record_event(
-        TTS_REPORT_REQUESTED,
-        organization_id,
-        report_job_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_VOICE_PLAYGROUND,
-            "comparison_id": comparison_id,
-            "report_job_id": report_job_id,
-        },
-    )
+    """Not ingested — bill on tts.report_completed."""
 
 
 def record_tts_report_completed(
@@ -472,12 +462,9 @@ def record_tts_report_completed(
         TTS_REPORT_COMPLETED,
         organization_id,
         report_job_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_VOICE_PLAYGROUND,
-            "comparison_id": comparison_id,
-            "report_job_id": report_job_id,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_VOICE_PLAYGROUND, quantity=1
+        ),
     )
 
 
@@ -493,19 +480,16 @@ def record_call_import_batch_created(
     source: str,
     provider: Optional[str] = None,
 ) -> None:
+    del source, provider
     record_event(
         CALL_IMPORT_BATCH_CREATED,
         organization_id,
         call_import_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_CALL_IMPORTS,
-            "call_import_id": call_import_id,
-            "total_rows": total_rows,
-            "quantity": total_rows,
-            "source": source,
-            "provider": provider,
-        },
+        properties=_billing_properties(
+            workspace_id,
+            FEATURE_CALL_IMPORTS,
+            quantity=max(0, total_rows),
+        ),
     )
 
 
@@ -521,20 +505,7 @@ def record_call_import_evaluation_started(
     total_rows: int,
     metric_count: int = 0,
 ) -> None:
-    record_event(
-        CALL_IMPORT_EVALUATION_STARTED,
-        organization_id,
-        evaluation_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_CALL_IMPORTS,
-            "call_import_id": call_import_id,
-            "evaluation_id": evaluation_id,
-            "total_rows": total_rows,
-            "metric_count": metric_count,
-            "quantity": 1,
-        },
-    )
+    """Not ingested — bill on call_import.evaluation_completed."""
 
 
 def record_call_import_evaluation_completed(
@@ -549,21 +520,18 @@ def record_call_import_evaluation_completed(
     metric_count: int = 0,
 ) -> bool:
     """Bill one finished evaluation pass for newly completed rows (not per row)."""
+    del call_import_id, total_rows, metric_count
+    if rows_billed <= 0:
+        return False
     return record_event(
         CALL_IMPORT_EVALUATION_COMPLETED,
         organization_id,
         f"{evaluation_id}:{completed_total}",
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_CALL_IMPORTS,
-            "call_import_id": call_import_id,
-            "evaluation_id": evaluation_id,
-            "rows_billed": rows_billed,
-            "completed_total": completed_total,
-            "total_rows": total_rows,
-            "metric_count": metric_count,
-            "quantity": rows_billed,
-        },
+        properties=_billing_properties(
+            workspace_id,
+            FEATURE_CALL_IMPORTS,
+            quantity=rows_billed,
+        ),
     )
 
 
@@ -578,24 +546,19 @@ def record_call_import_recording_minutes_billed(
     billable_minutes: int,
 ) -> bool:
     """Bill recording duration for one successfully evaluated call-import row."""
+    del evaluation_id, call_import_id, audio_seconds
     if billable_minutes <= 0:
         return False
     return record_event(
         CALL_IMPORT_RECORDING_MINUTES_BILLED,
         organization_id,
         evaluation_row_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_CALL_IMPORTS,
-            "call_import_id": call_import_id,
-            "evaluation_id": evaluation_id,
-            "evaluation_row_id": evaluation_row_id,
-            "audio_seconds": audio_seconds,
-            "billable_minutes": billable_minutes,
-            "billing_unit": "minute",
-            # Flexprice generic field; same value as billable_minutes for older meters.
-            "quantity": billable_minutes,
-        },
+        properties=_billing_properties(
+            workspace_id,
+            FEATURE_CALL_IMPORTS,
+            quantity=billable_minutes,
+            billable_minutes=billable_minutes,
+        ),
     )
 
 
@@ -629,19 +592,14 @@ def record_call_import_pdf_report_generated(
     call_import_id: UUID,
     report_type: str,
 ) -> None:
+    del evaluation_id, call_import_id, report_type
     record_event(
         CALL_IMPORT_PDF_REPORT_GENERATED,
         organization_id,
         pdf_report_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_CALL_IMPORTS,
-            "call_import_id": call_import_id,
-            "evaluation_id": evaluation_id,
-            "pdf_report_id": pdf_report_id,
-            "report_type": report_type,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_CALL_IMPORTS, quantity=1
+        ),
     )
 
 
@@ -655,18 +613,7 @@ def record_playground_web_call_started(
     workspace_id: UUID,
     agent_id: UUID,
 ) -> None:
-    record_event(
-        PLAYGROUND_WEB_CALL_STARTED,
-        organization_id,
-        call_short_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_AGENT_PLAYGROUND,
-            "agent_id": agent_id,
-            "call_short_id": call_short_id,
-            "quantity": 1,
-        },
-    )
+    """Not ingested — bill on playground.evaluation_completed."""
 
 
 def record_playground_websocket_session_started(
@@ -675,17 +622,7 @@ def record_playground_websocket_session_started(
     *,
     workspace_id: UUID,
 ) -> None:
-    record_event(
-        PLAYGROUND_WEBSOCKET_SESSION_STARTED,
-        organization_id,
-        call_short_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_AGENT_PLAYGROUND,
-            "call_short_id": call_short_id,
-            "quantity": 1,
-        },
-    )
+    """Not ingested — bill on playground.evaluation_completed."""
 
 
 def record_playground_call_evaluated(
@@ -697,20 +634,7 @@ def record_playground_call_evaluated(
     call_short_id: str,
     metric_count: int,
 ) -> None:
-    record_event(
-        PLAYGROUND_CALL_EVALUATED,
-        organization_id,
-        evaluation_attempt_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_AGENT_PLAYGROUND,
-            "call_short_id": call_short_id,
-            "evaluator_result_id": evaluator_result_id,
-            "evaluation_attempt_id": evaluation_attempt_id,
-            "metric_count": metric_count,
-            "quantity": 1,
-        },
-    )
+    """Not ingested — bill on playground.evaluation_completed."""
 
 
 def record_playground_evaluation_completed(
@@ -723,20 +647,18 @@ def record_playground_evaluation_completed(
     duration_seconds: Optional[float] = None,
     metric_count: int = 0,
 ) -> None:
+    del evaluator_result_id, call_short_id, metric_count
+    minutes = _billable_minutes(duration_seconds)
     record_event(
         PLAYGROUND_EVALUATION_COMPLETED,
         organization_id,
         evaluation_attempt_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_AGENT_PLAYGROUND,
-            "call_short_id": call_short_id,
-            "evaluator_result_id": evaluator_result_id,
-            "evaluation_attempt_id": evaluation_attempt_id,
-            "duration_seconds": duration_seconds,
-            "metric_count": metric_count,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id,
+            FEATURE_AGENT_PLAYGROUND,
+            quantity=minutes,
+            billable_minutes=minutes,
+        ),
     )
 
 
@@ -750,16 +672,8 @@ def record_evaluator_run_requested(
     workspace_id: UUID,
     quantity: int,
 ) -> None:
-    record_event(
-        EVALUATOR_RUN_REQUESTED,
-        organization_id,
-        request_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_EVALUATORS,
-            "quantity": quantity,
-        },
-    )
+    """Not ingested — bill on evaluator.run_completed when scoring finishes."""
+    del organization_id, request_id, workspace_id, quantity
 
 
 def record_evaluator_run_completed(
@@ -770,18 +684,14 @@ def record_evaluator_run_completed(
     evaluator_id: UUID,
     call_count: int = 1,
 ) -> None:
+    del evaluator_id, call_count
     record_event(
         EVALUATOR_RUN_COMPLETED,
         organization_id,
         result_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_EVALUATORS,
-            "evaluator_id": evaluator_id,
-            "result_id": result_id,
-            "call_count": call_count,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_EVALUATORS, quantity=1
+        ),
     )
 
 
@@ -796,16 +706,8 @@ def record_evaluation_created(
     audio_id: UUID,
     metrics_requested: int,
 ) -> None:
-    record_event(
-        EVALUATION_CREATED,
-        organization_id,
-        evaluation_id,
-        properties={
-            "workspace_id": workspace_id,
-            "audio_id": audio_id,
-            "metrics_requested": metrics_requested,
-        },
-    )
+    """Not ingested — bill on evaluation.completed."""
+    del organization_id, evaluation_id, workspace_id, audio_id, metrics_requested
 
 
 def record_evaluation_completed(
@@ -818,7 +720,9 @@ def record_evaluation_completed(
         EVALUATION_COMPLETED,
         organization_id,
         evaluation_id,
-        properties={"workspace_id": workspace_id},
+        properties=_billing_properties(
+            workspace_id, FEATURE_EVALUATORS, quantity=1
+        ),
     )
 
 
@@ -833,18 +737,8 @@ def record_prompt_optimization_run_started(
     agent_id: UUID,
     max_metric_calls: Optional[int] = None,
 ) -> None:
-    record_event(
-        PROMPT_OPTIMIZATION_RUN_STARTED,
-        organization_id,
-        run_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_GEPA,
-            "run_id": run_id,
-            "agent_id": agent_id,
-            "max_metric_calls": max_metric_calls,
-        },
-    )
+    """Not ingested — bill on prompt_optimization.run_completed."""
+    del organization_id, run_id, workspace_id, agent_id, max_metric_calls
 
 
 def record_prompt_optimization_run_completed(
@@ -855,17 +749,12 @@ def record_prompt_optimization_run_completed(
     agent_id: UUID,
     candidates_count: int = 0,
 ) -> None:
+    del agent_id, candidates_count
     record_event(
         PROMPT_OPTIMIZATION_RUN_COMPLETED,
         organization_id,
         run_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_GEPA,
-            "run_id": run_id,
-            "agent_id": agent_id,
-            "candidates_count": candidates_count,
-        },
+        properties=_billing_properties(workspace_id, FEATURE_GEPA, quantity=1),
     )
 
 
@@ -880,19 +769,8 @@ def record_judge_alignment_run_started(
     dataset_id: UUID,
     sample_count: int,
 ) -> None:
-    record_event(
-        JUDGE_ALIGNMENT_RUN_STARTED,
-        organization_id,
-        run_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_JUDGE_ALIGNMENT,
-            "run_id": run_id,
-            "dataset_id": dataset_id,
-            "sample_count": sample_count,
-            "quantity": 1,
-        },
-    )
+    """Not ingested — bill on judge_alignment.run_completed."""
+    del organization_id, run_id, workspace_id, dataset_id, sample_count
 
 
 def record_judge_alignment_run_completed(
@@ -903,18 +781,14 @@ def record_judge_alignment_run_completed(
     dataset_id: UUID,
     samples_scored: int,
 ) -> None:
+    del dataset_id, samples_scored
     record_event(
         JUDGE_ALIGNMENT_RUN_COMPLETED,
         organization_id,
         run_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_JUDGE_ALIGNMENT,
-            "run_id": run_id,
-            "dataset_id": dataset_id,
-            "samples_scored": samples_scored,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_JUDGE_ALIGNMENT, quantity=1
+        ),
     )
 
 
@@ -928,16 +802,8 @@ def record_observability_call_ingested(
     workspace_id: UUID,
     provider: Optional[str] = None,
 ) -> None:
-    record_event(
-        OBSERVABILITY_CALL_INGESTED,
-        organization_id,
-        call_short_id,
-        properties={
-            "workspace_id": workspace_id,
-            "call_short_id": call_short_id,
-            "provider": provider,
-        },
-    )
+    """Not ingested — bill on observability.call_evaluated."""
+    del organization_id, call_short_id, workspace_id, provider
 
 
 def record_observability_call_evaluated(
@@ -950,10 +816,7 @@ def record_observability_call_evaluated(
         OBSERVABILITY_CALL_EVALUATED,
         organization_id,
         call_short_id,
-        properties={
-            "workspace_id": workspace_id,
-            "call_short_id": call_short_id,
-        },
+        properties={"workspace_id": workspace_id, "quantity": 1},
     )
 
 
@@ -969,24 +832,8 @@ def record_test_agent_conversation_started(
     agent_id: Optional[UUID] = None,
     call_short_id: Optional[str] = None,
 ) -> None:
-    properties: dict[str, Any] = {
-        "workspace_id": workspace_id,
-        "feature": FEATURE_AGENT_PLAYGROUND,
-        "conversation_id": conversation_id,
-        "quantity": 1,
-    }
-    if result_id is not None:
-        properties["result_id"] = result_id
-    if agent_id is not None:
-        properties["agent_id"] = agent_id
-    if call_short_id is not None:
-        properties["call_short_id"] = call_short_id
-    record_event(
-        TEST_AGENT_CONVERSATION_STARTED,
-        organization_id,
-        conversation_id,
-        properties=properties,
-    )
+    """Not ingested — bill on test_agent.conversation_ended."""
+    del organization_id, conversation_id, workspace_id, result_id, agent_id, call_short_id
 
 
 def record_test_agent_conversation_ended(
@@ -1000,26 +847,18 @@ def record_test_agent_conversation_ended(
     agent_id: Optional[UUID] = None,
     call_short_id: Optional[str] = None,
 ) -> None:
-    properties: dict[str, Any] = {
-        "workspace_id": workspace_id,
-        "feature": FEATURE_AGENT_PLAYGROUND,
-        "conversation_id": conversation_id,
-        "turn_count": turn_count,
-        "quantity": 1,
-    }
-    if duration_seconds is not None:
-        properties["duration_seconds"] = duration_seconds
-    if result_id is not None:
-        properties["result_id"] = result_id
-    if agent_id is not None:
-        properties["agent_id"] = agent_id
-    if call_short_id is not None:
-        properties["call_short_id"] = call_short_id
+    del turn_count, result_id, agent_id, call_short_id
+    minutes = _billable_minutes(duration_seconds)
     record_event(
         TEST_AGENT_CONVERSATION_ENDED,
         organization_id,
         conversation_id,
-        properties=properties,
+        properties=_billing_properties(
+            workspace_id,
+            FEATURE_AGENT_PLAYGROUND,
+            quantity=minutes,
+            billable_minutes=minutes,
+        ),
     )
 
 
@@ -1033,16 +872,16 @@ def record_metrics_ai_assist(
     workspace_id: Optional[UUID],
     mode: str,
 ) -> None:
+    del mode
+    if workspace_id is None:
+        return
     record_event(
         METRICS_AI_ASSIST,
         organization_id,
         request_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_METRICS_AI_ASSIST,
-            "mode": mode,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_METRICS_AI_ASSIST, quantity=1
+        ),
     )
 
 
@@ -1074,20 +913,15 @@ def record_metric_studio_item_evaluated(
     source_ref: str,
     metric_count: int = 0,
 ) -> None:
-    record_event(
-        METRIC_STUDIO_ITEM_EVALUATED,
+    """Not ingested — bill on metric_studio.run_completed."""
+    del (
         organization_id,
         result_row_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_METRIC_STUDIO,
-            "run_id": run_id,
-            "result_row_id": result_row_id,
-            "source_kind": source_kind,
-            "source_ref": source_ref,
-            "metric_count": metric_count,
-            "quantity": 1,
-        },
+        workspace_id,
+        run_id,
+        source_kind,
+        source_ref,
+        metric_count,
     )
 
 
@@ -1101,20 +935,17 @@ def record_metric_studio_run_completed(
     completed_items: int,
     failed_items: int,
 ) -> None:
+    del run_status, total_items, failed_items
+    billed = max(0, completed_items)
+    if billed <= 0:
+        return
     record_event(
         METRIC_STUDIO_RUN_COMPLETED,
         organization_id,
         run_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_METRIC_STUDIO,
-            "run_id": run_id,
-            "run_status": run_status,
-            "total_items": total_items,
-            "completed_items": completed_items,
-            "failed_items": failed_items,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_METRIC_STUDIO, quantity=billed
+        ),
     )
 
 
@@ -1130,18 +961,16 @@ def record_scenario_ai_text_generated(
     purpose: str = "scenario_description",
     scenario_count: Optional[int] = None,
 ) -> None:
+    del model, purpose, scenario_count
+    if workspace_id is None:
+        return
     record_event(
         SCENARIO_AI_TEXT_GENERATED,
         organization_id,
         request_id,
-        properties={
-            "workspace_id": workspace_id,
-            "feature": FEATURE_SCENARIO_AI,
-            "model": model,
-            "purpose": purpose,
-            "scenario_count": scenario_count,
-            "quantity": 1,
-        },
+        properties=_billing_properties(
+            workspace_id, FEATURE_SCENARIO_AI, quantity=1
+        ),
     )
 
 
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index 8ce8daf3..9f783c16 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -758,22 +758,6 @@ def process_evaluator_result_task(self, result_id: str):
                 llm_metrics, audio_metrics, metric_scores = _categorize_metrics(enabled_metrics, has_audio)
                 selected_metric_count = len(llm_metrics) + len(audio_metrics)
 
-                call_recording = _playground_call_recording(db, result)
-                if call_recording:
-                    from app.services.billing.flexprice_service import (
-                        record_playground_call_evaluated,
-                    )
-
-                    evaluation_attempt_id = f"{result.id}:{self.request.id}"
-                    record_playground_call_evaluated(
-                        result.organization_id,
-                        evaluation_attempt_id,
-                        evaluator_result_id=result.id,
-                        workspace_id=result.workspace_id,
-                        call_short_id=call_recording.call_short_id,
-                        metric_count=selected_metric_count,
-                    )
-
                 evaluation_time = None
 
                 # Step 3: Audio metrics evaluation
@@ -882,6 +866,7 @@ def process_evaluator_result_task(self, result_id: str):
                 _commit_evaluator_result(db, result)
 
                 from app.services.billing.flexprice_service import (
+                    record_evaluator_run_completed,
                     record_playground_evaluation_completed,
                 )
 
@@ -896,6 +881,13 @@ def process_evaluator_result_task(self, result_id: str):
                         duration_seconds=result.duration_seconds,
                         metric_count=len(metric_scores) or selected_metric_count,
                     )
+                else:
+                    record_evaluator_run_completed(
+                        result.organization_id,
+                        result.result_id,
+                        workspace_id=result.workspace_id,
+                        evaluator_id=result.evaluator_id,
+                    )
 
                 total_time = time.time() - task_start_time
                 logger.info(
diff --git a/app/workers/tasks/run_evaluator.py b/app/workers/tasks/run_evaluator.py
index b7736c97..3d8fba34 100644
--- a/app/workers/tasks/run_evaluator.py
+++ b/app/workers/tasks/run_evaluator.py
@@ -93,15 +93,6 @@ def run_evaluator_task(self, evaluator_id: str, evaluator_result_id: str):
 
                 db.commit()
 
-                from app.services.billing.flexprice_service import record_evaluator_run_completed
-
-                record_evaluator_run_completed(
-                    evaluator.organization_id,
-                    result.result_id,
-                    workspace_id=evaluator.workspace_id,
-                    evaluator_id=evaluator.id,
-                )
-
                 return {
                     "evaluator_id": evaluator_id,
                     "result_id": evaluator_result_id,
@@ -166,15 +157,6 @@ def run_evaluator_task(self, evaluator_id: str, evaluator_result_id: str):
                 result.celery_task_id = task.id
                 db.commit()
 
-                from app.services.billing.flexprice_service import record_evaluator_run_completed
-
-                record_evaluator_run_completed(
-                    evaluator.organization_id,
-                    result.result_id,
-                    workspace_id=evaluator.workspace_id,
-                    evaluator_id=evaluator.id,
-                )
-
                 return {
                     "evaluator_id": evaluator_id,
                     "result_id": evaluator_result_id,
diff --git a/tests/test_core/test_flexprice_test_isolation.py b/tests/test_core/test_flexprice_test_isolation.py
index 2fe47bec..a280c259 100644
--- a/tests/test_core/test_flexprice_test_isolation.py
+++ b/tests/test_core/test_flexprice_test_isolation.py
@@ -40,7 +40,7 @@ def test_record_wrappers_do_not_invoke_flexprice_sdk():
         total_rows=1,
         source="csv",
     )
-    fp.record_test_agent_conversation_started(org_id, uuid4(), workspace_id=ws_id)
+    fp.record_test_agent_conversation_ended(org_id, uuid4(), workspace_id=ws_id, duration_seconds=125.0)
 
 
 def test_flexprice_sdk_construction_is_forbidden():
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index fdd7e5c7..11134198 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -109,37 +109,13 @@ def test_record_blind_test_share_created_no_op_when_disabled(mock_flexprice):
 
 
 @patch("flexprice.Flexprice")
-def test_record_blind_test_share_created_ingests_event(mock_flexprice):
+def test_record_blind_test_share_created_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    share_id = uuid4()
-    workspace_id = uuid4()
-    comparison_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
-    svc.record_blind_test_share_created(
-        org_id,
-        share_id,
-        workspace_id=workspace_id,
-        comparison_id=comparison_id,
-    )
+    svc.record_blind_test_share_created(uuid4(), uuid4(), workspace_id=uuid4(), comparison_id=uuid4())
 
-    mock_client.events.ingest_event.assert_called_once_with(
-        event_name="blind_test.share_created",
-        external_customer_id=str(org_id),
-        event_id=str(share_id),
-        source="efficientai",
-        properties={
-            "share_id": str(share_id),
-            "workspace_id": str(workspace_id),
-            "comparison_id": str(comparison_id),
-            "feature": "voice_playground",
-        },
-    )
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -151,7 +127,9 @@ def test_record_blind_test_share_created_logs_and_swallows_errors(mock_flexprice
     mock_client.events.ingest_event.side_effect = RuntimeError("network down")
     mock_flexprice.return_value.__enter__.return_value = mock_client
 
-    svc.record_blind_test_share_created(uuid4(), uuid4(), workspace_id=uuid4(), comparison_id=uuid4())
+    svc.record_blind_test_response_submitted(
+        uuid4(), uuid4(), share_id=uuid4(), workspace_id=uuid4(), response_count=1
+    )
 
 
 @patch("flexprice.Flexprice")
@@ -166,7 +144,9 @@ def test_ingest_usage_event_falls_back_to_request_dict(mock_flexprice):
     ]
     mock_flexprice.return_value.__enter__.return_value = mock_client
 
-    svc.record_blind_test_share_created(uuid4(), uuid4(), workspace_id=uuid4(), comparison_id=uuid4())
+    svc.record_blind_test_response_submitted(
+        uuid4(), uuid4(), share_id=uuid4(), workspace_id=uuid4(), response_count=1
+    )
 
     assert mock_client.events.ingest_event.call_count == 2
     assert "request" in mock_client.events.ingest_event.call_args_list[1].kwargs
@@ -196,7 +176,6 @@ def test_record_call_import_batch_created_includes_volume_properties(mock_flexpr
     mock_client.events.ingest_event.assert_called_once()
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "call_import.batch_created"
-    assert payload["properties"]["total_rows"] == "42"
     assert payload["properties"]["quantity"] == "42"
     assert payload["properties"]["feature"] == "call_imports"
 
@@ -233,12 +212,6 @@ def test_record_call_import_evaluation_completed_meters_pass_delta(mock_flexpric
         properties={
             "workspace_id": str(workspace_id),
             "feature": "call_imports",
-            "call_import_id": str(call_import_id),
-            "evaluation_id": str(evaluation_id),
-            "rows_billed": "50",
-            "completed_total": "1950",
-            "total_rows": "2000",
-            "metric_count": "5",
             "quantity": "50",
         },
     )
@@ -250,38 +223,16 @@ def test_record_call_import_evaluation_started(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    evaluation_id = uuid4()
-    workspace_id = uuid4()
-    call_import_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_call_import_evaluation_started(
-        org_id,
-        evaluation_id,
-        workspace_id=workspace_id,
-        call_import_id=call_import_id,
+        uuid4(),
+        uuid4(),
+        workspace_id=uuid4(),
+        call_import_id=uuid4(),
         total_rows=100,
         metric_count=3,
     )
 
-    mock_client.events.ingest_event.assert_called_once_with(
-        event_name="call_import.evaluation_started",
-        external_customer_id=str(org_id),
-        event_id=str(evaluation_id),
-        source="efficientai",
-        properties={
-            "workspace_id": str(workspace_id),
-            "feature": "call_imports",
-            "call_import_id": str(call_import_id),
-            "evaluation_id": str(evaluation_id),
-            "total_rows": "100",
-            "metric_count": "3",
-            "quantity": "1",
-        },
-    )
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -316,12 +267,7 @@ def test_record_call_import_recording_minutes_billed(mock_flexprice):
         properties={
             "workspace_id": str(workspace_id),
             "feature": "call_imports",
-            "call_import_id": str(call_import_id),
-            "evaluation_id": str(evaluation_id),
-            "evaluation_row_id": str(eval_row_id),
-            "audio_seconds": "90",
             "billable_minutes": "2",
-            "billing_unit": "minute",
             "quantity": "2",
         },
     )
@@ -359,10 +305,6 @@ def test_record_call_import_pdf_report_generated(mock_flexprice):
         properties={
             "workspace_id": str(workspace_id),
             "feature": "call_imports",
-            "call_import_id": str(call_import_id),
-            "evaluation_id": str(evaluation_id),
-            "pdf_report_id": str(pdf_report_id),
-            "report_type": "external",
             "quantity": "1",
         },
     )
@@ -386,91 +328,42 @@ def test_record_event_returns_false_when_disabled(mock_flexprice):
 
 
 @patch("flexprice.Flexprice")
-def test_record_playground_call_evaluated_uses_per_attempt_event_id(mock_flexprice):
+def test_record_playground_call_evaluated_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    evaluator_result_id = uuid4()
-    evaluation_attempt_id = f"{evaluator_result_id}:celery-task-abc"
-    workspace_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_playground_call_evaluated(
-        org_id,
-        evaluation_attempt_id,
-        evaluator_result_id=evaluator_result_id,
-        workspace_id=workspace_id,
+        uuid4(),
+        "attempt-1",
+        evaluator_result_id=uuid4(),
+        workspace_id=uuid4(),
         call_short_id="123456",
         metric_count=4,
     )
 
-    mock_client.events.ingest_event.assert_called_once_with(
-        event_name="playground.call_evaluated",
-        external_customer_id=str(org_id),
-        event_id=evaluation_attempt_id,
-        source="efficientai",
-        properties={
-            "workspace_id": str(workspace_id),
-            "feature": "agent_playground",
-            "call_short_id": "123456",
-            "evaluator_result_id": str(evaluator_result_id),
-            "evaluation_attempt_id": evaluation_attempt_id,
-            "metric_count": "4",
-            "quantity": "1",
-        },
-    )
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
-def test_record_playground_web_call_started_includes_feature(mock_flexprice):
+def test_record_playground_web_call_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    workspace_id = uuid4()
-    agent_id = uuid4()
-    call_short_id = "abc123"
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_playground_web_call_started(
-        org_id,
-        call_short_id,
-        workspace_id=workspace_id,
-        agent_id=agent_id,
+        uuid4(), "abc123", workspace_id=uuid4(), agent_id=uuid4()
     )
 
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["event_name"] == "playground.web_call_started"
-    assert payload["properties"]["feature"] == "agent_playground"
-    assert payload["properties"]["quantity"] == "1"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
-def test_record_playground_websocket_session_started_includes_feature(mock_flexprice):
+def test_record_playground_websocket_session_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    workspace_id = uuid4()
-    call_short_id = "ws456"
+    svc.record_playground_websocket_session_started(uuid4(), "ws456", workspace_id=uuid4())
 
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
-    svc.record_playground_websocket_session_started(
-        org_id,
-        call_short_id,
-        workspace_id=workspace_id,
-    )
-
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["event_name"] == "playground.websocket_session_started"
-    assert payload["properties"]["feature"] == "agent_playground"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -499,30 +392,19 @@ def test_record_playground_evaluation_completed_includes_feature(mock_flexprice)
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "playground.evaluation_completed"
     assert payload["properties"]["feature"] == "agent_playground"
-    assert payload["properties"]["metric_count"] == "2"
+    assert payload["properties"]["quantity"] == "1"
+    assert payload["properties"]["billable_minutes"] == "1"
+    assert payload["properties"]["billable_minutes"] == "1"
 
 
 @patch("flexprice.Flexprice")
-def test_record_test_agent_conversation_started_includes_feature(mock_flexprice):
+def test_record_test_agent_conversation_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    workspace_id = uuid4()
-    conversation_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
+    svc.record_test_agent_conversation_started(uuid4(), uuid4(), workspace_id=uuid4())
 
-    svc.record_test_agent_conversation_started(
-        org_id,
-        conversation_id,
-        workspace_id=workspace_id,
-    )
-
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["event_name"] == "test_agent.conversation_started"
-    assert payload["properties"]["feature"] == "agent_playground"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -553,65 +435,39 @@ def test_record_test_agent_conversation_ended_bills_one_conversation(mock_flexpr
         properties={
             "workspace_id": str(workspace_id),
             "feature": "agent_playground",
-            "conversation_id": str(conversation_id),
-            "duration_seconds": "120.0",
-            "turn_count": "8",
-            "quantity": "1",
+            "quantity": "2",
+            "billable_minutes": "2",
         },
     )
 
 
 @patch("flexprice.Flexprice")
-def test_record_test_agent_conversation_started_includes_voice_agent_metadata(mock_flexprice):
+def test_record_test_agent_conversation_started_with_metadata_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    workspace_id = uuid4()
-    conversation_id = uuid4()
-    agent_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_test_agent_conversation_started(
-        org_id,
-        conversation_id,
-        workspace_id=workspace_id,
+        uuid4(),
+        uuid4(),
+        workspace_id=uuid4(),
         result_id="123456",
-        agent_id=agent_id,
+        agent_id=uuid4(),
         call_short_id="654321",
     )
 
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["properties"]["result_id"] == "123456"
-    assert payload["properties"]["agent_id"] == str(agent_id)
-    assert payload["properties"]["call_short_id"] == "654321"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
-def test_record_evaluator_run_requested_includes_feature(mock_flexprice):
+def test_record_evaluator_run_requested_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    workspace_id = uuid4()
-    request_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_evaluator_run_requested(
-        org_id,
-        request_id,
-        workspace_id=workspace_id,
-        quantity=3,
+        uuid4(), uuid4(), workspace_id=uuid4(), quantity=3
     )
 
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["event_name"] == "evaluator.run_requested"
-    assert payload["properties"]["feature"] == "evaluators"
-    assert payload["properties"]["quantity"] == "3"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -663,6 +519,7 @@ def test_record_judge_alignment_run_completed_includes_feature(mock_flexprice):
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "judge_alignment.run_completed"
     assert payload["properties"]["feature"] == "judge_alignment"
+    assert payload["properties"]["quantity"] == "1"
 
 
 @patch("flexprice.Flexprice")
@@ -687,7 +544,7 @@ def test_record_metrics_ai_assist_includes_feature(mock_flexprice):
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "metrics.ai_assist"
     assert payload["properties"]["feature"] == "metrics_ai_assist"
-    assert payload["properties"]["mode"] == "generate"
+    assert payload["properties"]["quantity"] == "1"
 
 
 @patch("flexprice.Flexprice")
@@ -715,6 +572,7 @@ def test_record_metric_studio_run_completed_includes_feature(mock_flexprice):
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "metric_studio.run_completed"
     assert payload["properties"]["feature"] == "metric_studio"
+    assert payload["properties"]["quantity"] == "3"
 
 
 @patch("flexprice.Flexprice")
@@ -740,31 +598,19 @@ def test_record_scenario_ai_text_generated_includes_feature(mock_flexprice):
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "scenario.ai_text_generated"
     assert payload["properties"]["feature"] == "scenario_ai"
-    assert payload["properties"]["purpose"] == "scenario_description"
+    assert payload["properties"]["quantity"] == "1"
 
 
 @patch("flexprice.Flexprice")
-def test_record_tts_generation_started_includes_voice_playground_feature(mock_flexprice):
+def test_record_tts_generation_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    comparison_id = uuid4()
-    workspace_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_tts_generation_started(
-        org_id,
-        comparison_id,
-        workspace_id=workspace_id,
-        sample_count=4,
+        uuid4(), uuid4(), workspace_id=uuid4(), sample_count=4
     )
 
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["event_name"] == "tts.generation_started"
-    assert payload["properties"]["feature"] == "voice_playground"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -794,28 +640,15 @@ def test_record_tts_sample_synthesized_includes_voice_playground_feature(mock_fl
 
 
 @patch("flexprice.Flexprice")
-def test_record_tts_report_requested_includes_voice_playground_feature(mock_flexprice):
+def test_record_tts_report_requested_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
     settings.FLEXPRICE_API_KEY = "test-key"
 
-    org_id = uuid4()
-    report_job_id = uuid4()
-    comparison_id = uuid4()
-    workspace_id = uuid4()
-
-    mock_client = MagicMock()
-    mock_flexprice.return_value.__enter__.return_value = mock_client
-
     svc.record_tts_report_requested(
-        org_id,
-        report_job_id,
-        workspace_id=workspace_id,
-        comparison_id=comparison_id,
+        uuid4(), uuid4(), workspace_id=uuid4(), comparison_id=uuid4()
     )
 
-    payload = mock_client.events.ingest_event.call_args.kwargs
-    assert payload["event_name"] == "tts.report_requested"
-    assert payload["properties"]["feature"] == "voice_playground"
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
@@ -841,6 +674,7 @@ def test_record_tts_report_completed_includes_voice_playground_feature(mock_flex
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "tts.report_completed"
     assert payload["properties"]["feature"] == "voice_playground"
+    assert payload["properties"]["quantity"] == "1"
 
 
 @patch("flexprice.Flexprice")
diff --git a/tests/test_services/test_flexprice_tracking_wiring.py b/tests/test_services/test_flexprice_tracking_wiring.py
index e0d3baa0..30cad0b6 100644
--- a/tests/test_services/test_flexprice_tracking_wiring.py
+++ b/tests/test_services/test_flexprice_tracking_wiring.py
@@ -16,20 +16,13 @@
 AGENT_PLAYGROUND_WIRED = {
     "playground.web_call_started": "app/api/v1/routes/playground.py",
     "playground.websocket_session_started": "app/api/v1/routes/playground.py",
-    "playground.call_evaluated": "app/workers/tasks/process_evaluator_result.py",
     "playground.evaluation_completed": "app/workers/tasks/process_evaluator_result.py",
-    "test_agent.conversation_started": "app/api/v1/routes/test_agents.py",
     "test_agent.conversation_ended": "app/api/v1/routes/test_agents.py",
 }
 
-VOICE_AGENT_TEST_AGENT_WIRED = {
-    "test_agent.conversation_started": "app/api/v1/routes/voice_agent.py",
-    "test_agent.conversation_ended": "app/api/v1/routes/voice_agent.py",
-}
-
 EVALUATORS_WIRED = {
     "evaluator.run_requested": "app/api/v1/routes/evaluators.py",
-    "evaluator.run_completed": "app/workers/tasks/run_evaluator.py",
+    "evaluator.run_completed": "app/workers/tasks/process_evaluator_result.py",
 }
 
 JUDGE_ALIGNMENT_WIRED = {
@@ -77,18 +70,7 @@ def test_agent_playground_events_are_wired():
         record_fn = {
             "playground.web_call_started": "record_playground_web_call_started",
             "playground.websocket_session_started": "record_playground_websocket_session_started",
-            "playground.call_evaluated": "record_playground_call_evaluated",
             "playground.evaluation_completed": "record_playground_evaluation_completed",
-            "test_agent.conversation_started": "record_test_agent_conversation_started",
-            "test_agent.conversation_ended": "record_test_agent_conversation_ended",
-        }[event_name]
-        assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
-
-
-def test_voice_agent_test_agent_simulation_events_are_wired():
-    for event_name, path in VOICE_AGENT_TEST_AGENT_WIRED.items():
-        record_fn = {
-            "test_agent.conversation_started": "record_test_agent_conversation_started",
             "test_agent.conversation_ended": "record_test_agent_conversation_ended",
         }[event_name]
         assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
diff --git a/tests/test_workers/test_celery_task_workflows.py b/tests/test_workers/test_celery_task_workflows.py
index 07e2baaf..5e9aecd0 100644
--- a/tests/test_workers/test_celery_task_workflows.py
+++ b/tests/test_workers/test_celery_task_workflows.py
@@ -484,22 +484,13 @@ def test_process_evaluator_result_emits_playground_billing_with_metric_count(
     )
     db_session.commit()
 
-    billing = {"evaluated": [], "completed": []}
-
-    def _capture_evaluated(_org_id, evaluation_attempt_id, **kw):
-        billing["evaluated"].append(
-            {"evaluation_attempt_id": evaluation_attempt_id, **kw}
-        )
+    billing = {"completed": []}
 
     def _capture_completed(_org_id, evaluation_attempt_id, **kw):
         billing["completed"].append(
             {"evaluation_attempt_id": evaluation_attempt_id, **kw}
         )
 
-    monkeypatch.setattr(
-        "app.services.billing.flexprice_service.record_playground_call_evaluated",
-        _capture_evaluated,
-    )
     monkeypatch.setattr(
         "app.services.billing.flexprice_service.record_playground_evaluation_completed",
         _capture_completed,
@@ -524,23 +515,12 @@ def _capture_completed(_org_id, evaluation_attempt_id, **kw):
     result = task_module.process_evaluator_result_task.run(str(eval_result.id))
 
     assert result["status"] == "completed"
-    assert len(billing["evaluated"]) == 1
-    assert billing["evaluated"][0]["metric_count"] == 3
-    assert billing["evaluated"][0]["evaluator_result_id"] == eval_result.id
-    assert billing["evaluated"][0]["call_short_id"] == "123456"
-    assert str(billing["evaluated"][0]["evaluation_attempt_id"]).startswith(
-        f"{eval_result.id}:"
-    )
     assert len(billing["completed"]) == 1
     assert billing["completed"][0]["metric_count"] == 3
     assert billing["completed"][0]["evaluator_result_id"] == eval_result.id
     assert str(billing["completed"][0]["evaluation_attempt_id"]).startswith(
         f"{eval_result.id}:"
     )
-    assert (
-        billing["evaluated"][0]["evaluation_attempt_id"]
-        == billing["completed"][0]["evaluation_attempt_id"]
-    )
 
 
 def test_process_evaluator_result_categorizes_audio_metrics_as_skipped_without_audio(db_session):

From 8dc478c65df8c3f9aa3806d0590bbc50d044b1cb Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 19:30:29 +0530
Subject: [PATCH 48/55] feat(flexprice): add billing support for evaluator
 recording minutes and update configuration for Flexprice setup

---
 app/api/v1/routes/observability.py            |  21 +-
 app/services/billing/flexprice_service.py     | 233 ++++++++++----
 app/workers/tasks/process_evaluator_result.py |   9 +
 config.yml.example                            |   3 +
 docs/billing/flexprice-saas-setup.md          | 104 ++++++
 scripts/setup_flexprice_meters.py             | 300 ++++++++++++++----
 .../test_api/test_evaluator_results_routes.py |  19 +-
 .../test_setup_flexprice_meters.py            |  45 ++-
 tests/test_services/test_flexprice_service.py | 108 ++++++-
 .../test_flexprice_tracking_wiring.py         |   2 +
 10 files changed, 685 insertions(+), 159 deletions(-)
 create mode 100644 docs/billing/flexprice-saas-setup.md

diff --git a/app/api/v1/routes/observability.py b/app/api/v1/routes/observability.py
index 4c26a549..611e7866 100644
--- a/app/api/v1/routes/observability.py
+++ b/app/api/v1/routes/observability.py
@@ -5,15 +5,11 @@
 from typing import Any, Dict, List, Optional, Union
 from uuid import UUID
 
-from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, status
+from fastapi import APIRouter, Depends, HTTPException, status
 from pydantic import BaseModel, ConfigDict
 from sqlalchemy.orm import Session
 
 from app.dependencies import get_api_key, get_db, get_organization_id, get_workspace_id
-from app.services.billing.flexprice_service import (
-    record_observability_call_evaluated,
-    record_observability_call_ingested,
-)
 from app.models.database import (
     Agent, APIKey, CallRecording, CallRecordingStatus, CallRecordingSource,
     Evaluator, EvaluatorResult, EvaluatorResultStatus, Scenario, Workspace,
@@ -243,13 +239,6 @@ def _upsert_call_recording(
 
     response = _serialize_call_recording(call_recording, include_data=True, agent=agent_obj)
     response["action"] = action
-    if action == "created":
-        record_observability_call_ingested(
-            organization_id,
-            call_recording.call_short_id,
-            workspace_id=workspace_id,
-            provider=provider_platform,
-        )
     return response
 
 
@@ -765,7 +754,6 @@ def _messages_to_speaker_segments(messages: List[Dict[str, Any]]) -> List[Dict[s
 async def evaluate_call(
     call_short_id: str,
     payload: EvaluateCallPayload,
-    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
     workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
@@ -877,13 +865,6 @@ async def evaluate_call(
     except Exception:
         pass
 
-    background_tasks.add_task(
-        record_observability_call_evaluated,
-        organization_id,
-        call_short_id,
-        workspace_id=workspace_id,
-    )
-
     return {
         "evaluator_result_id": str(evaluator_result.id),
         "result_id": evaluator_result.result_id,
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index 6efeebda..b4c2f500 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -6,9 +6,10 @@
 ``feature`` (license key) when the surface is gated.
 
 Ingest **only when value is delivered** (completed), never on ``*_started`` /
-``*_created`` / ``*_requested``. Event ``properties`` are minimal: ``workspace_id``,
-``feature``, and the billable ``quantity`` (plus ``billable_minutes`` when the
-meter sums minutes).
+``*_created`` / ``*_requested``. Event ``properties`` include billable fields
+(``workspace_id``, ``feature``, ``quantity``, ``billable_minutes``) plus audit
+IDs (``evaluation_id``, ``audio_seconds``, etc.) for support — audit fields are
+not used for Flexprice SUM/COUNT aggregation.
 
 Billable events (wire plan usage charges to these meters only):
 
@@ -21,16 +22,17 @@
   standalone test-agent sessions without playground eval) — never both for the same session
 - voice_playground: ``tts.sample_synthesized``, ``tts.report_completed``,
   ``blind_test.response_submitted``
-- evaluators: ``evaluator.run_completed`` when scoring finishes (``quantity`` = 1)
-- gepa_optimization: ``prompt_optimization.run_completed``
-- judge_alignment: ``judge_alignment.run_completed``
+- evaluators: ``evaluator.run_completed`` (``quantity`` = 1) and
+  ``evaluator.recording_minutes_billed`` when the run has audio (``billable_minutes``)
+- gepa_optimization: ``prompt_optimization.run_completed`` (``quantity`` = candidates)
+- judge_alignment: ``judge_alignment.run_completed`` (``quantity`` = samples scored)
 - metrics_ai_assist: ``metrics.ai_assist``
 - metric_studio: ``metric_studio.run_completed`` (``quantity`` = completed items)
 - scenario_ai: ``scenario.ai_text_generated``
-- observability: ``observability.call_evaluated`` only
 
-Not ingested: ``*_started``, ``*_requested``, ``*_created``, ``playground.call_evaluated``,
-``test_agent.conversation_started``, ``metric_studio.item_evaluated``, etc.
+Not ingested: ``*_started``, ``*_requested``, ``*_created``, ``observability.*``,
+``playground.call_evaluated``, ``test_agent.conversation_started``,
+``metric_studio.item_evaluated``, etc.
 """
 
 from __future__ import annotations
@@ -76,6 +78,7 @@
 PLAYGROUND_EVALUATION_COMPLETED = "playground.evaluation_completed"
 EVALUATOR_RUN_REQUESTED = "evaluator.run_requested"
 EVALUATOR_RUN_COMPLETED = "evaluator.run_completed"
+EVALUATOR_RECORDING_MINUTES_BILLED = "evaluator.recording_minutes_billed"
 EVALUATION_CREATED = "evaluation.created"
 EVALUATION_COMPLETED = "evaluation.completed"
 PROMPT_OPTIMIZATION_RUN_STARTED = "prompt_optimization.run_started"
@@ -251,6 +254,30 @@ def _billing_properties(
     return props
 
 
+def _event_properties(
+    workspace_id: UUID,
+    feature: str,
+    *,
+    quantity: Optional[Union[int, float]] = None,
+    billable_minutes: Optional[int] = None,
+    **audit: Any,
+) -> dict[str, Any]:
+    """Billable fields plus optional audit metadata for support traceability."""
+    props = _billing_properties(
+        workspace_id,
+        feature,
+        quantity=quantity,
+        billable_minutes=billable_minutes,
+    )
+    for key, value in audit.items():
+        if value is None:
+            continue
+        if isinstance(value, str) and not value.strip():
+            continue
+        props[key] = value
+    return props
+
+
 def record_event(
     event_name: str,
     organization_id: UUID,
@@ -402,10 +429,12 @@ def record_blind_test_response_submitted(
         BLIND_TEST_RESPONSE_SUBMITTED,
         organization_id,
         response_id,
-        properties=_billing_properties(
+        properties=_event_properties(
             workspace_id,
             FEATURE_VOICE_PLAYGROUND,
             quantity=max(1, response_count),
+            share_id=share_id,
+            response_id=response_id,
         ),
     )
 
@@ -430,13 +459,19 @@ def record_tts_sample_synthesized(
     side: Optional[str] = None,
     duration_seconds: Optional[float] = None,
 ) -> None:
-    del provider, side, duration_seconds
     record_event(
         TTS_SAMPLE_SYNTHESIZED,
         organization_id,
         sample_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_VOICE_PLAYGROUND, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_VOICE_PLAYGROUND,
+            quantity=1,
+            comparison_id=comparison_id,
+            sample_id=sample_id,
+            provider=provider,
+            side=side,
+            duration_seconds=duration_seconds,
         ),
     )
 
@@ -462,8 +497,12 @@ def record_tts_report_completed(
         TTS_REPORT_COMPLETED,
         organization_id,
         report_job_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_VOICE_PLAYGROUND, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_VOICE_PLAYGROUND,
+            quantity=1,
+            comparison_id=comparison_id,
+            report_job_id=report_job_id,
         ),
     )
 
@@ -480,15 +519,18 @@ def record_call_import_batch_created(
     source: str,
     provider: Optional[str] = None,
 ) -> None:
-    del source, provider
     record_event(
         CALL_IMPORT_BATCH_CREATED,
         organization_id,
         call_import_id,
-        properties=_billing_properties(
+        properties=_event_properties(
             workspace_id,
             FEATURE_CALL_IMPORTS,
             quantity=max(0, total_rows),
+            call_import_id=call_import_id,
+            source=source,
+            provider=provider,
+            total_rows=total_rows,
         ),
     )
 
@@ -520,17 +562,22 @@ def record_call_import_evaluation_completed(
     metric_count: int = 0,
 ) -> bool:
     """Bill one finished evaluation pass for newly completed rows (not per row)."""
-    del call_import_id, total_rows, metric_count
     if rows_billed <= 0:
         return False
     return record_event(
         CALL_IMPORT_EVALUATION_COMPLETED,
         organization_id,
         f"{evaluation_id}:{completed_total}",
-        properties=_billing_properties(
+        properties=_event_properties(
             workspace_id,
             FEATURE_CALL_IMPORTS,
             quantity=rows_billed,
+            evaluation_id=evaluation_id,
+            call_import_id=call_import_id,
+            completed_total=completed_total,
+            total_rows=total_rows,
+            metric_count=metric_count,
+            rows_billed=rows_billed,
         ),
     )
 
@@ -546,18 +593,21 @@ def record_call_import_recording_minutes_billed(
     billable_minutes: int,
 ) -> bool:
     """Bill recording duration for one successfully evaluated call-import row."""
-    del evaluation_id, call_import_id, audio_seconds
     if billable_minutes <= 0:
         return False
     return record_event(
         CALL_IMPORT_RECORDING_MINUTES_BILLED,
         organization_id,
         evaluation_row_id,
-        properties=_billing_properties(
+        properties=_event_properties(
             workspace_id,
             FEATURE_CALL_IMPORTS,
             quantity=billable_minutes,
             billable_minutes=billable_minutes,
+            evaluation_row_id=evaluation_row_id,
+            evaluation_id=evaluation_id,
+            call_import_id=call_import_id,
+            audio_seconds=audio_seconds,
         ),
     )
 
@@ -592,13 +642,18 @@ def record_call_import_pdf_report_generated(
     call_import_id: UUID,
     report_type: str,
 ) -> None:
-    del evaluation_id, call_import_id, report_type
     record_event(
         CALL_IMPORT_PDF_REPORT_GENERATED,
         organization_id,
         pdf_report_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_CALL_IMPORTS, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_CALL_IMPORTS,
+            quantity=1,
+            pdf_report_id=pdf_report_id,
+            evaluation_id=evaluation_id,
+            call_import_id=call_import_id,
+            report_type=report_type,
         ),
     )
 
@@ -647,17 +702,20 @@ def record_playground_evaluation_completed(
     duration_seconds: Optional[float] = None,
     metric_count: int = 0,
 ) -> None:
-    del evaluator_result_id, call_short_id, metric_count
     minutes = _billable_minutes(duration_seconds)
     record_event(
         PLAYGROUND_EVALUATION_COMPLETED,
         organization_id,
         evaluation_attempt_id,
-        properties=_billing_properties(
+        properties=_event_properties(
             workspace_id,
             FEATURE_AGENT_PLAYGROUND,
             quantity=minutes,
             billable_minutes=minutes,
+            evaluator_result_id=evaluator_result_id,
+            call_short_id=call_short_id,
+            duration_seconds=duration_seconds,
+            metric_count=metric_count,
         ),
     )
 
@@ -681,16 +739,51 @@ def record_evaluator_run_completed(
     result_id: str,
     *,
     workspace_id: UUID,
-    evaluator_id: UUID,
+    evaluator_id: Optional[UUID] = None,
+    evaluator_result_id: Optional[UUID] = None,
     call_count: int = 1,
 ) -> None:
-    del evaluator_id, call_count
+    del call_count
     record_event(
         EVALUATOR_RUN_COMPLETED,
         organization_id,
         result_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_EVALUATORS, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_EVALUATORS,
+            quantity=1,
+            result_id=result_id,
+            evaluator_id=evaluator_id,
+            evaluator_result_id=evaluator_result_id,
+        ),
+    )
+
+
+def record_evaluator_recording_minutes_billed(
+    organization_id: UUID,
+    evaluator_result_id: UUID,
+    *,
+    workspace_id: UUID,
+    duration_seconds: Optional[float] = None,
+) -> bool:
+    """Bill audio duration for a completed evaluator run that includes a recording."""
+    minutes = _billable_minutes(duration_seconds)
+    if minutes <= 0:
+        return False
+    return record_event(
+        EVALUATOR_RECORDING_MINUTES_BILLED,
+        organization_id,
+        evaluator_result_id,
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_EVALUATORS,
+            quantity=minutes,
+            billable_minutes=minutes,
+            evaluator_result_id=evaluator_result_id,
+            duration_seconds=duration_seconds,
+            audio_seconds=int(round(float(duration_seconds)))
+            if duration_seconds is not None
+            else None,
         ),
     )
 
@@ -720,8 +813,11 @@ def record_evaluation_completed(
         EVALUATION_COMPLETED,
         organization_id,
         evaluation_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_EVALUATORS, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_EVALUATORS,
+            quantity=1,
+            evaluation_id=evaluation_id,
         ),
     )
 
@@ -749,12 +845,19 @@ def record_prompt_optimization_run_completed(
     agent_id: UUID,
     candidates_count: int = 0,
 ) -> None:
-    del agent_id, candidates_count
+    billed = max(1, candidates_count)
     record_event(
         PROMPT_OPTIMIZATION_RUN_COMPLETED,
         organization_id,
         run_id,
-        properties=_billing_properties(workspace_id, FEATURE_GEPA, quantity=1),
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_GEPA,
+            quantity=billed,
+            run_id=run_id,
+            agent_id=agent_id,
+            candidates_count=candidates_count,
+        ),
     )
 
 
@@ -781,13 +884,20 @@ def record_judge_alignment_run_completed(
     dataset_id: UUID,
     samples_scored: int,
 ) -> None:
-    del dataset_id, samples_scored
+    billed = max(0, samples_scored)
+    if billed <= 0:
+        return
     record_event(
         JUDGE_ALIGNMENT_RUN_COMPLETED,
         organization_id,
         run_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_JUDGE_ALIGNMENT, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_JUDGE_ALIGNMENT,
+            quantity=billed,
+            run_id=run_id,
+            dataset_id=dataset_id,
+            samples_scored=samples_scored,
         ),
     )
 
@@ -812,12 +922,8 @@ def record_observability_call_evaluated(
     *,
     workspace_id: UUID,
 ) -> None:
-    record_event(
-        OBSERVABILITY_CALL_EVALUATED,
-        organization_id,
-        call_short_id,
-        properties={"workspace_id": workspace_id, "quantity": 1},
-    )
+    """Not ingested — observability is not a billable product surface."""
+    del organization_id, call_short_id, workspace_id
 
 
 # --- Test agents ---
@@ -847,17 +953,22 @@ def record_test_agent_conversation_ended(
     agent_id: Optional[UUID] = None,
     call_short_id: Optional[str] = None,
 ) -> None:
-    del turn_count, result_id, agent_id, call_short_id
     minutes = _billable_minutes(duration_seconds)
     record_event(
         TEST_AGENT_CONVERSATION_ENDED,
         organization_id,
         conversation_id,
-        properties=_billing_properties(
+        properties=_event_properties(
             workspace_id,
             FEATURE_AGENT_PLAYGROUND,
             quantity=minutes,
             billable_minutes=minutes,
+            conversation_id=conversation_id,
+            duration_seconds=duration_seconds,
+            turn_count=turn_count,
+            result_id=result_id,
+            agent_id=agent_id,
+            call_short_id=call_short_id,
         ),
     )
 
@@ -872,15 +983,18 @@ def record_metrics_ai_assist(
     workspace_id: Optional[UUID],
     mode: str,
 ) -> None:
-    del mode
     if workspace_id is None:
         return
     record_event(
         METRICS_AI_ASSIST,
         organization_id,
         request_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_METRICS_AI_ASSIST, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_METRICS_AI_ASSIST,
+            quantity=1,
+            request_id=request_id,
+            mode=mode,
         ),
     )
 
@@ -935,7 +1049,7 @@ def record_metric_studio_run_completed(
     completed_items: int,
     failed_items: int,
 ) -> None:
-    del run_status, total_items, failed_items
+    del run_status
     billed = max(0, completed_items)
     if billed <= 0:
         return
@@ -943,8 +1057,14 @@ def record_metric_studio_run_completed(
         METRIC_STUDIO_RUN_COMPLETED,
         organization_id,
         run_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_METRIC_STUDIO, quantity=billed
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_METRIC_STUDIO,
+            quantity=billed,
+            run_id=run_id,
+            total_items=total_items,
+            completed_items=completed_items,
+            failed_items=failed_items,
         ),
     )
 
@@ -961,15 +1081,20 @@ def record_scenario_ai_text_generated(
     purpose: str = "scenario_description",
     scenario_count: Optional[int] = None,
 ) -> None:
-    del model, purpose, scenario_count
     if workspace_id is None:
         return
     record_event(
         SCENARIO_AI_TEXT_GENERATED,
         organization_id,
         request_id,
-        properties=_billing_properties(
-            workspace_id, FEATURE_SCENARIO_AI, quantity=1
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_SCENARIO_AI,
+            quantity=1,
+            request_id=request_id,
+            model=model,
+            purpose=purpose,
+            scenario_count=scenario_count,
         ),
     )
 
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index 9f783c16..3f0b9dd8 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -866,6 +866,7 @@ def process_evaluator_result_task(self, result_id: str):
                 _commit_evaluator_result(db, result)
 
                 from app.services.billing.flexprice_service import (
+                    record_evaluator_recording_minutes_billed,
                     record_evaluator_run_completed,
                     record_playground_evaluation_completed,
                 )
@@ -887,7 +888,15 @@ def process_evaluator_result_task(self, result_id: str):
                         result.result_id,
                         workspace_id=result.workspace_id,
                         evaluator_id=result.evaluator_id,
+                        evaluator_result_id=result.id,
                     )
+                    if (result.audio_s3_key or "").strip():
+                        record_evaluator_recording_minutes_billed(
+                            result.organization_id,
+                            result.id,
+                            workspace_id=result.workspace_id,
+                            duration_seconds=result.duration_seconds,
+                        )
 
                 total_time = time.time() - task_start_time
                 logger.info(
diff --git a/config.yml.example b/config.yml.example
index 0430c62f..e70b820b 100644
--- a/config.yml.example
+++ b/config.yml.example
@@ -260,6 +260,9 @@ judge_alignment:
 # no SDK calls are made and the app behaves as today. Cloud SaaS: set enabled
 # true and provide FLEXPRICE_API_KEY (env var overrides api_key below).
 # Pytest always mocks Flexprice locally (EFFICIENTAI_PYTEST=1); never hits this API.
+# Setup guide: docs/billing/flexprice-saas-setup.md
+# Bootstrap meters: python scripts/setup_flexprice_meters.py
+# Plan pricing checklist: python scripts/setup_flexprice_meters.py --plan-guide
 # flexprice:
 #   enabled: false
 #   api_key: null
diff --git a/docs/billing/flexprice-saas-setup.md b/docs/billing/flexprice-saas-setup.md
new file mode 100644
index 00000000..4e807254
--- /dev/null
+++ b/docs/billing/flexprice-saas-setup.md
@@ -0,0 +1,104 @@
+# Flexprice SaaS setup (EfficientAI)
+
+Usage-based billing for cloud customers. Code ingests **completion-only** events; Flexprice meters aggregate usage; plan **usage charges** turn meters into invoice lines.
+
+## Architecture
+
+```
+App (completion events) → Flexprice meters (SUM/COUNT) → Plan usage charges → Customer invoice
+         ↑
+  provision_billing_customer on signup (ensure_customer)
+```
+
+Every billable event includes `workspace_id`, `feature`, and `quantity` (plus `billable_minutes` for voice). Audit IDs (`evaluation_id`, `audio_seconds`, `run_id`, etc.) are included for support traceability and do not affect meter aggregation.
+
+## 1. Enable metering
+
+In `config.yml` (or env):
+
+```yaml
+flexprice:
+  enabled: true
+  api_host: "https://us.api.flexprice.io/v1"   # or api.cloud.flexprice.io
+```
+
+Set `FLEXPRICE_API_KEY` in the environment (never commit keys).
+
+Signup and API-key flows call `provision_billing_customer()` so each org exists in Flexprice as `external_customer_id = organization.id`.
+
+## 2. Bootstrap meters and features
+
+```bash
+python scripts/setup_flexprice_meters.py
+python scripts/setup_flexprice_meters.py --repair-call-imports   # if batch_created meters were duplicated
+```
+
+Print the plan pricing checklist (no API):
+
+```bash
+python scripts/setup_flexprice_meters.py --plan-guide
+```
+
+## 3. Call imports — separate meters (recommended)
+
+| Plan usage line | Meter | Aggregation | Typical price |
+|-----------------|-------|-------------|---------------|
+| Call import evaluations | Call Import Evaluations | SUM `quantity` | Per evaluated row |
+| Call import audio | Call Import Recording Minutes | SUM `billable_minutes` | Per audio minute |
+| Call import PDFs | Call Import PDF Reports | SUM `quantity` | Per report |
+| Imported rows (cap) | Call Imports (feature) | SUM `quantity` | **$0** — use for tier included rows |
+
+Do **not** put a heavy per-row price on both batch import and evaluation — customers would feel double-charged. Batch meter is for entitlements; eval + audio + PDF are paid lines.
+
+**Audio event:** `call_import.recording_minutes_billed` fires when a row completes evaluation **and has a recording** (not on import alone).
+
+## 4. Other products (paid usage charges)
+
+| Product | Meter event | Unit |
+|---------|-------------|------|
+| Agent playground | `playground.evaluation_completed` | billable minutes |
+| Test agent API | `test_agent.conversation_ended` | billable minutes |
+| Voice playground | `blind_test.response_submitted`, `tts.sample_synthesized`, `tts.report_completed` | per response / sample / report |
+| Evaluators | `evaluator.run_completed` + `evaluator.recording_minutes_billed` (when audio) | per run + per audio minute |
+| GEPA | `prompt_optimization.run_completed` | per candidate (SUM quantity) |
+| Judge alignment | `judge_alignment.run_completed` | per sample scored (SUM quantity) |
+| Metrics AI assist | `metrics.ai_assist` | per request |
+| Metric studio | `metric_studio.run_completed` | completed items (SUM quantity) |
+| Scenario AI | `scenario.ai_text_generated` | per generation |
+
+Observability is **not billed** — no plan usage charges for observability events.
+
+## 5. Configure plan in Flexprice UI
+
+1. Open your SaaS **Plan**.
+2. Add **Usage charge** for each paid meter above (pick meter by name, set unit price).
+3. Click **Sync Usage Charges** on the plan.
+4. Assign plan to customers / refresh subscriptions after meter or price changes.
+
+After `--repair-call-imports`, re-sync subscriptions if price IDs changed.
+
+## 6. Smoke tests before launch
+
+| Action | Expected event | Check quantity |
+|--------|----------------|----------------|
+| Materialize call import | `call_import.batch_created` | row count |
+| Complete eval pass | `call_import.evaluation_completed` | delta rows |
+| Eval row with ~90s audio | `call_import.recording_minutes_billed` | 2 minutes |
+| Generate PDF | `call_import.pdf_report_generated` | 1 |
+| Complete playground eval | `playground.evaluation_completed` | billable minutes |
+| Complete evaluator run | `evaluator.run_completed` | 1 |
+| Evaluator run with audio | `evaluator.recording_minutes_billed` | billable minutes |
+| Complete GEPA run | `prompt_optimization.run_completed` | candidate count |
+| Complete judge alignment | `judge_alignment.run_completed` | samples scored |
+
+Use Flexprice **Price Lookup** with `external_customer_id = organization UUID`.
+
+## 7. Test isolation
+
+Pytest sets `EFFICIENTAI_PYTEST=1` and mocks all Flexprice I/O. Full suite never hits the live dashboard.
+
+## 8. Not yet wired (post-launch)
+
+- Flexprice entitlements replacing JWT license limits (`app/core/license.py`)
+- Auto-assign subscription on signup
+- Backfill existing orgs as Flexprice customers
diff --git a/scripts/setup_flexprice_meters.py b/scripts/setup_flexprice_meters.py
index 78416fd3..ff0cab7c 100644
--- a/scripts/setup_flexprice_meters.py
+++ b/scripts/setup_flexprice_meters.py
@@ -1,5 +1,17 @@
 #!/usr/bin/env python3
-"""Create Flexprice features and meters for EfficientAI usage metering catalog."""
+"""Bootstrap Flexprice meters and license features for EfficientAI SaaS billing.
+
+Completion-only policy: only billable *completed* events get meters here. Track-only
+events (*_started, *_requested, *_created) are not catalogued.
+
+Call imports use multiple meters (separate plan usage charges):
+  - call_import.batch_created — feature primary; entitlements / included row cap ($0 typical)
+  - call_import.evaluation_completed — paid per evaluated row
+  - call_import.recording_minutes_billed — paid per audio minute
+  - call_import.pdf_report_generated — paid per PDF
+
+See docs/billing/flexprice-saas-setup.md for plan pricing and customer onboarding.
+"""
 
 from __future__ import annotations
 
@@ -20,63 +32,196 @@
 CALL_IMPORT_BATCH_METER_NAME = "Call Imports"
 CALL_IMPORT_BATCH_AGG_TYPE = "SUM"
 CALL_IMPORT_BATCH_AGG_FIELD = "quantity"
-AGENT_PLAYGROUND_PRIMARY_EVENT = "playground.web_call_started"
-EVALUATOR_RUN_REQUESTED_EVENT = "evaluator.run_requested"
+
+AGENT_PLAYGROUND_PRIMARY_EVENT = "playground.evaluation_completed"
+VOICE_PLAYGROUND_PRIMARY_EVENT = "blind_test.response_submitted"
+GEPA_PRIMARY_EVENT = "prompt_optimization.run_completed"
+EVALUATOR_RUN_COMPLETED_EVENT = "evaluator.run_completed"
 JUDGE_ALIGNMENT_PRIMARY_EVENT = "judge_alignment.run_completed"
 METRICS_AI_ASSIST_EVENT = "metrics.ai_assist"
 METRIC_STUDIO_PRIMARY_EVENT = "metric_studio.run_completed"
 SCENARIO_AI_TEXT_EVENT = "scenario.ai_text_generated"
 
-# (event_name, display_name, aggregation_type, aggregation_field|None)
+# Standalone meters (not owned by a license feature primary event).
+# Each row: (event_name, display_name, aggregation_type, aggregation_field|None)
 METERS: list[tuple[str, str, str, str | None]] = [
-    # Voice playground
-    ("blind_test.share_created", "Blind Test Share Created", "COUNT", None),
-    ("blind_test.response_submitted", "Blind Test Response Submitted", "COUNT", None),
-    ("tts.generation_started", "TTS Generation Started", "COUNT", None),
-    ("tts.sample_synthesized", "TTS Sample Synthesized", "SUM", "quantity"),
-    ("tts.report_requested", "TTS Report Requested", "COUNT", None),
-    ("tts.report_completed", "TTS Report Completed", "COUNT", None),
-    # Call imports (batch_created meter comes from the call_imports license feature)
-    ("call_import.evaluation_started", "Call Import Evaluation Started", "COUNT", None),
-    ("call_import.evaluation_completed", "Call Import Evaluation Completed", "SUM", "quantity"),
+    # Call imports — paid usage (batch_created meter comes from call_imports feature)
+    ("call_import.evaluation_completed", "Call Import Evaluations", "SUM", "quantity"),
     (
         "call_import.recording_minutes_billed",
         "Call Import Recording Minutes",
         "SUM",
         "billable_minutes",
     ),
-    ("call_import.pdf_report_generated", "Call Import PDF Report Generated", "COUNT", None),
-    # Agent playground (web_call_started meter comes from the agent_playground license feature)
-    ("playground.websocket_session_started", "Playground Websocket Session Started", "COUNT", None),
-    ("playground.call_evaluated", "Playground Call Evaluated", "COUNT", None),
-    ("playground.evaluation_completed", "Playground Evaluation Completed", "COUNT", None),
-    # Evaluators (run_requested meter comes from the evaluators license feature)
-    ("evaluator.run_completed", "Evaluator Run Completed", "COUNT", None),
+    ("call_import.pdf_report_generated", "Call Import PDF Reports", "SUM", "quantity"),
+    # Evaluators — run + optional audio (run_completed meter from evaluators feature)
+    (
+        "evaluator.recording_minutes_billed",
+        "Evaluator Recording Minutes",
+        "SUM",
+        "billable_minutes",
+    ),
+    # Agent playground — test-agent API path (playground eval meter from feature)
+    (
+        "test_agent.conversation_ended",
+        "Test Agent Conversation Minutes",
+        "SUM",
+        "billable_minutes",
+    ),
+    # Voice playground — billable completions (feature primary = blind_test responses)
+    ("tts.sample_synthesized", "TTS Samples Synthesized", "SUM", "quantity"),
+    ("tts.report_completed", "TTS Reports Completed", "SUM", "quantity"),
     # Legacy evaluations
-    ("evaluation.created", "Evaluation Created", "COUNT", None),
-    ("evaluation.completed", "Evaluation Completed", "COUNT", None),
-    # Prompt optimization
-    ("prompt_optimization.run_started", "Prompt Optimization Run Started", "COUNT", None),
-    ("prompt_optimization.run_completed", "Prompt Optimization Run Completed", "COUNT", None),
-    # Judge alignment (run_completed meter comes from the judge_alignment license feature)
-    ("judge_alignment.run_started", "Judge Alignment Run Started", "COUNT", None),
-    # Observability
-    ("observability.call_ingested", "Observability Call Ingested", "COUNT", None),
-    ("observability.call_evaluated", "Observability Call Evaluated", "COUNT", None),
-    # Test agents
-    ("test_agent.conversation_started", "Test Agent Conversation Started", "COUNT", None),
-    ("test_agent.conversation_ended", "Test Agent Conversation Ended", "COUNT", None),
-    # Metric studio (run_completed meter comes from the metric_studio license feature)
-    ("metric_studio.item_evaluated", "Metric Studio Item Evaluated", "COUNT", None),
-    # Metrics AI assist (primary meter comes from the metrics_ai_assist license feature)
-    # Scenario AI text (primary meter comes from the scenario_ai license feature)
+    ("evaluation.completed", "Legacy Evaluation Completed", "SUM", "quantity"),
+]
+
+# Ops guide: meters to attach as plan USAGE charges (display order for SaaS plans).
+PLAN_BILLABLE_METERS: list[dict[str, Any]] = [
+    {
+        "product": "Call Imports",
+        "plan_line": "Call import evaluations",
+        "event_name": "call_import.evaluation_completed",
+        "meter_name": "Call Import Evaluations",
+        "aggregation": "SUM quantity",
+        "charge": True,
+        "notes": "Main compute line — one unit per newly completed row per eval pass.",
+    },
+    {
+        "product": "Call Imports",
+        "plan_line": "Call import audio minutes",
+        "event_name": "call_import.recording_minutes_billed",
+        "meter_name": "Call Import Recording Minutes",
+        "aggregation": "SUM billable_minutes",
+        "charge": True,
+        "notes": "Only rows with a recording; ceil(seconds/60), min 1.",
+    },
+    {
+        "product": "Call Imports",
+        "plan_line": "Call import PDF reports",
+        "event_name": "call_import.pdf_report_generated",
+        "meter_name": "Call Import PDF Reports",
+        "aggregation": "SUM quantity",
+        "charge": True,
+        "notes": "Optional export add-on.",
+    },
+    {
+        "product": "Call Imports",
+        "plan_line": "Imported rows (entitlement cap)",
+        "event_name": CALL_IMPORT_BATCH_EVENT,
+        "meter_name": CALL_IMPORT_BATCH_METER_NAME,
+        "aggregation": "SUM quantity",
+        "charge": False,
+        "notes": "Feature call_imports primary meter — tier included rows; $0 usage charge typical.",
+    },
+    {
+        "product": "Agent Playground",
+        "plan_line": "Playground evaluated calls",
+        "event_name": AGENT_PLAYGROUND_PRIMARY_EVENT,
+        "meter_name": "Agent Playground",
+        "aggregation": "SUM billable_minutes",
+        "charge": True,
+        "notes": "Billed when playground evaluation completes.",
+    },
+    {
+        "product": "Agent Playground",
+        "plan_line": "Test agent conversations",
+        "event_name": "test_agent.conversation_ended",
+        "meter_name": "Test Agent Conversation Minutes",
+        "aggregation": "SUM billable_minutes",
+        "charge": True,
+        "notes": "Standalone test-agent API only; not double-billed with playground eval.",
+    },
+    {
+        "product": "Voice Playground",
+        "plan_line": "Blind test responses",
+        "event_name": VOICE_PLAYGROUND_PRIMARY_EVENT,
+        "meter_name": "Voice Playground",
+        "aggregation": "SUM quantity",
+        "charge": True,
+    },
+    {
+        "product": "Voice Playground",
+        "plan_line": "TTS samples",
+        "event_name": "tts.sample_synthesized",
+        "meter_name": "TTS Samples Synthesized",
+        "aggregation": "SUM quantity",
+        "charge": True,
+    },
+    {
+        "product": "Voice Playground",
+        "plan_line": "TTS reports",
+        "event_name": "tts.report_completed",
+        "meter_name": "TTS Reports Completed",
+        "aggregation": "SUM quantity",
+        "charge": True,
+    },
+    {
+        "product": "Evaluators",
+        "plan_line": "Evaluator runs",
+        "event_name": EVALUATOR_RUN_COMPLETED_EVENT,
+        "meter_name": "Evaluators",
+        "aggregation": "COUNT",
+        "charge": True,
+    },
+    {
+        "product": "Evaluators",
+        "plan_line": "Evaluator audio minutes",
+        "event_name": "evaluator.recording_minutes_billed",
+        "meter_name": "Evaluator Recording Minutes",
+        "aggregation": "SUM billable_minutes",
+        "charge": True,
+        "notes": "When the run includes a recording; ceil(seconds/60), min 1.",
+    },
+    {
+        "product": "GEPA Optimization",
+        "plan_line": "Prompt optimization candidates",
+        "event_name": GEPA_PRIMARY_EVENT,
+        "meter_name": "GEPA Optimization",
+        "aggregation": "SUM quantity",
+        "charge": True,
+        "notes": "Quantity = candidates produced in the completed run.",
+    },
+    {
+        "product": "Judge Alignment",
+        "plan_line": "Judge alignment samples",
+        "event_name": JUDGE_ALIGNMENT_PRIMARY_EVENT,
+        "meter_name": "Judge Alignment",
+        "aggregation": "SUM quantity",
+        "charge": True,
+        "notes": "Quantity = samples scored in the completed run.",
+    },
+    {
+        "product": "Metrics AI Assist",
+        "plan_line": "Metrics AI assist",
+        "event_name": METRICS_AI_ASSIST_EVENT,
+        "meter_name": "Metrics AI Assist",
+        "aggregation": "COUNT",
+        "charge": True,
+    },
+    {
+        "product": "Metric Studio",
+        "plan_line": "Metric studio items",
+        "event_name": METRIC_STUDIO_PRIMARY_EVENT,
+        "meter_name": "Metric Studio",
+        "aggregation": "SUM quantity",
+        "charge": True,
+        "notes": "Quantity = completed items in the run.",
+    },
+    {
+        "product": "Scenario AI",
+        "plan_line": "Scenario AI generations",
+        "event_name": SCENARIO_AI_TEXT_EVENT,
+        "meter_name": "Scenario AI Text",
+        "aggregation": "COUNT",
+        "charge": True,
+    },
 ]
 
 LICENSE_FEATURES: list[dict[str, Any]] = [
     {
         "name": "Call Imports",
         "lookup_key": "call_imports",
-        "description": "CSV/audio call import batches, row processing, and evaluations",
+        "description": "CSV/audio call import batches — row cap and import volume",
         "unit_singular": "row",
         "unit_plural": "rows",
         "event_name": CALL_IMPORT_BATCH_EVENT,
@@ -85,52 +230,52 @@
     {
         "name": "Agent Playground",
         "lookup_key": "agent_playground",
-        "description": "Agent web calls, websocket sessions, evaluations, and test-agent conversations",
-        "unit_singular": "session",
-        "unit_plural": "sessions",
+        "description": "Agent playground calls scored after evaluation completes",
+        "unit_singular": "minute",
+        "unit_plural": "minutes",
         "event_name": AGENT_PLAYGROUND_PRIMARY_EVENT,
-        "aggregation": {"type": "COUNT"},
+        "aggregation": {"type": "SUM", "field": "billable_minutes"},
     },
     {
         "name": "Voice Playground",
         "lookup_key": "voice_playground",
-        "description": "TTS comparisons, blind tests, and voice quality reports",
-        "unit_singular": "share",
-        "unit_plural": "shares",
-        "event_name": "blind_test.share_created",
-        "aggregation": {"type": "COUNT"},
+        "description": "Blind test responses and voice quality workflows",
+        "unit_singular": "response",
+        "unit_plural": "responses",
+        "event_name": VOICE_PLAYGROUND_PRIMARY_EVENT,
+        "aggregation": {"type": "SUM", "field": "quantity"},
     },
     {
         "name": "GEPA Optimization",
         "lookup_key": "gepa_optimization",
         "description": "Prompt optimization (GEPA) runs",
-        "unit_singular": "run",
-        "unit_plural": "runs",
-        "event_name": "prompt_optimization.run_started",
-        "aggregation": {"type": "COUNT"},
+        "unit_singular": "candidate",
+        "unit_plural": "candidates",
+        "event_name": GEPA_PRIMARY_EVENT,
+        "aggregation": {"type": "SUM", "field": "quantity"},
     },
     {
         "name": "Evaluators",
         "lookup_key": "evaluators",
-        "description": "Batch evaluator simulation runs and completions",
+        "description": "Evaluator simulation runs that complete scoring",
         "unit_singular": "run",
         "unit_plural": "runs",
-        "event_name": EVALUATOR_RUN_REQUESTED_EVENT,
-        "aggregation": {"type": "SUM", "field": "quantity"},
+        "event_name": EVALUATOR_RUN_COMPLETED_EVENT,
+        "aggregation": {"type": "COUNT"},
     },
     {
         "name": "Judge Alignment",
         "lookup_key": "judge_alignment",
         "description": "Judge calibration runs on labeled datasets",
-        "unit_singular": "run",
-        "unit_plural": "runs",
+        "unit_singular": "sample",
+        "unit_plural": "samples",
         "event_name": JUDGE_ALIGNMENT_PRIMARY_EVENT,
-        "aggregation": {"type": "COUNT"},
+        "aggregation": {"type": "SUM", "field": "quantity"},
     },
     {
         "name": "Metrics AI Assist",
         "lookup_key": "metrics_ai_assist",
-        "description": "AI-assisted metric creation and bulk label parsing in Metrics Management",
+        "description": "AI-assisted metric creation in Metrics Management",
         "unit_singular": "request",
         "unit_plural": "requests",
         "event_name": METRICS_AI_ASSIST_EVENT,
@@ -140,15 +285,15 @@
         "name": "Metric Studio",
         "lookup_key": "metric_studio",
         "description": "Batch metric scoring runs in Metrics Studio",
-        "unit_singular": "run",
-        "unit_plural": "runs",
+        "unit_singular": "item",
+        "unit_plural": "items",
         "event_name": METRIC_STUDIO_PRIMARY_EVENT,
-        "aggregation": {"type": "COUNT"},
+        "aggregation": {"type": "SUM", "field": "quantity"},
     },
     {
         "name": "Scenario AI Text",
         "lookup_key": "scenario_ai",
-        "description": "AI-generated text for scenarios and similar assistant flows",
+        "description": "AI-generated text for scenarios and assistant flows",
         "unit_singular": "generation",
         "unit_plural": "generations",
         "event_name": SCENARIO_AI_TEXT_EVENT,
@@ -158,6 +303,9 @@
 
 FEATURE_OWNED_EVENT_NAMES = frozenset(spec["event_name"] for spec in LICENSE_FEATURES)
 
+# Backward-compatible alias for tests importing the old name.
+EVALUATOR_RUN_REQUESTED_EVENT = EVALUATOR_RUN_COMPLETED_EVENT
+
 
 def _headers() -> dict[str, str]:
     return {"x-api-key": settings.FLEXPRICE_API_KEY or "", "Content-Type": "application/json"}
@@ -364,7 +512,6 @@ def _terminate_orphaned_batch_prices(
 
 
 PLAN_USAGE_RESTORE_NAMES = ("Voice Playground", "Blind Test")
-# Fallback archived template price IDs when /prices list omits terminated prices.
 PLAN_USAGE_ARCHIVE_TEMPLATE_IDS: dict[str, str] = {
     "Voice Playground": "price_01KVT8P9T8E52KHB1CA6HCVPV0",
     "Blind Test": "price_01KW9E4W02NND35R6W87Y7863K",
@@ -645,6 +792,22 @@ def bootstrap_catalog() -> dict[str, Any]:
     }
 
 
+def print_plan_guide() -> None:
+    """Print SaaS plan usage charge checklist (stdout JSON)."""
+    paid = [row for row in PLAN_BILLABLE_METERS if row.get("charge")]
+    entitlements = [row for row in PLAN_BILLABLE_METERS if not row.get("charge")]
+    print(
+        json.dumps(
+            {
+                "paid_usage_charges": paid,
+                "entitlement_only": entitlements,
+                "docs": "docs/billing/flexprice-saas-setup.md",
+            },
+            indent=2,
+        )
+    )
+
+
 def main() -> int:
     parser = argparse.ArgumentParser(description="Bootstrap or repair Flexprice metering catalog.")
     parser.add_argument(
@@ -657,8 +820,17 @@ def main() -> int:
         action="store_true",
         help="Recreate missing Voice Playground and Blind Test plan usage prices from archived templates.",
     )
+    parser.add_argument(
+        "--plan-guide",
+        action="store_true",
+        help="Print JSON checklist of plan usage charges (no API calls).",
+    )
     args = parser.parse_args()
 
+    if args.plan_guide:
+        print_plan_guide()
+        return 0
+
     if CONFIG_PATH.exists():
         load_config_from_file(str(CONFIG_PATH))
 
diff --git a/tests/test_api/test_evaluator_results_routes.py b/tests/test_api/test_evaluator_results_routes.py
index 6c58ab1d..2e8ebf41 100644
--- a/tests/test_api/test_evaluator_results_routes.py
+++ b/tests/test_api/test_evaluator_results_routes.py
@@ -1,14 +1,6 @@
 """API tests for evaluator results routes."""
 
 
-def _blob_storage_service():
-    """Resolve the blob storage singleton exposed as s3_service."""
-    import importlib
-
-    s3_module = importlib.import_module("app.services.storage.s3_service")
-    return s3_module.s3_service
-
-
 def test_derive_speaker_segments_supports_smallest_payload():
     from app.api.v1.routes.evaluator_results import _derive_speaker_segments_from_call_data
 
@@ -252,11 +244,13 @@ def _patch_blob_storage_download(monkeypatch, *, audio_bytes: bytes = b"fake-aud
     fake = SimpleNamespace(
         is_enabled=lambda: True,
         download_file_by_key=lambda _key: audio_bytes,
+        upload_file_by_key=lambda *_args, **_kwargs: None,
     )
     blob_module = importlib.import_module("app.services.storage.blob_storage_service")
     s3_module = importlib.import_module("app.services.storage.s3_service")
-    monkeypatch.setattr(blob_module, "blob_storage_service", fake)
+    # Patch the lazy s3_service alias first so undo restores the real singleton.
     monkeypatch.setattr(s3_module, "s3_service", fake, raising=False)
+    monkeypatch.setattr(blob_module, "blob_storage_service", fake)
     return fake
 
 
@@ -465,12 +459,7 @@ def fake_get(url, headers=None, timeout=120):
 
     monkeypatch.setattr("requests.get", fake_get)
     monkeypatch.setattr("app.core.encryption.decrypt_api_key", lambda _key: "vapi-secret")
-    storage = _blob_storage_service()
-    monkeypatch.setattr(
-        storage,
-        "upload_file_by_key",
-        lambda *_args, **_kwargs: None,
-    )
+    _patch_blob_storage_download(monkeypatch)
 
     class FakeTask:
         id = "task-1"
diff --git a/tests/test_scripts/test_setup_flexprice_meters.py b/tests/test_scripts/test_setup_flexprice_meters.py
index e98dcba8..192c9cd3 100644
--- a/tests/test_scripts/test_setup_flexprice_meters.py
+++ b/tests/test_scripts/test_setup_flexprice_meters.py
@@ -1,11 +1,14 @@
 from scripts.setup_flexprice_meters import (
     AGENT_PLAYGROUND_PRIMARY_EVENT,
-    EVALUATOR_RUN_REQUESTED_EVENT,
+    EVALUATOR_RUN_COMPLETED_EVENT,
+    GEPA_PRIMARY_EVENT,
     JUDGE_ALIGNMENT_PRIMARY_EVENT,
     LICENSE_FEATURES,
     METRICS_AI_ASSIST_EVENT,
     METRIC_STUDIO_PRIMARY_EVENT,
+    PLAN_BILLABLE_METERS,
     SCENARIO_AI_TEXT_EVENT,
+    VOICE_PLAYGROUND_PRIMARY_EVENT,
     meter_aggregation_matches,
 )
 from app.api.v1.routes.call_import_evaluations import (
@@ -24,22 +27,54 @@ def test_meter_aggregation_matches_rejects_count():
     assert meter_aggregation_matches(meter, agg_type="SUM", agg_field="quantity") is False
 
 
+def test_call_imports_plan_has_separate_paid_meters():
+    call_import_lines = [row for row in PLAN_BILLABLE_METERS if row["product"] == "Call Imports"]
+    paid_events = {row["event_name"] for row in call_import_lines if row.get("charge")}
+    assert paid_events == {
+        "call_import.evaluation_completed",
+        "call_import.recording_minutes_billed",
+        "call_import.pdf_report_generated",
+    }
+
+
 def test_agent_playground_license_feature_spec():
     spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "agent_playground")
     assert spec["event_name"] == AGENT_PLAYGROUND_PRIMARY_EVENT
-    assert spec["aggregation"] == {"type": "COUNT"}
+    assert spec["event_name"] == "playground.evaluation_completed"
+    assert spec["aggregation"] == {"type": "SUM", "field": "billable_minutes"}
+
+
+def test_voice_playground_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "voice_playground")
+    assert spec["event_name"] == VOICE_PLAYGROUND_PRIMARY_EVENT
+    assert spec["aggregation"] == {"type": "SUM", "field": "quantity"}
 
 
 def test_evaluators_license_feature_spec():
     spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "evaluators")
-    assert spec["event_name"] == EVALUATOR_RUN_REQUESTED_EVENT
+    assert spec["event_name"] == EVALUATOR_RUN_COMPLETED_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
+def test_evaluators_plan_includes_audio_minutes_meter():
+    evaluator_lines = [row for row in PLAN_BILLABLE_METERS if row["product"] == "Evaluators"]
+    paid_events = {row["event_name"] for row in evaluator_lines if row.get("charge")}
+    assert paid_events == {
+        "evaluator.run_completed",
+        "evaluator.recording_minutes_billed",
+    }
+
+
+def test_gepa_license_feature_spec():
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "gepa_optimization")
+    assert spec["event_name"] == GEPA_PRIMARY_EVENT
     assert spec["aggregation"] == {"type": "SUM", "field": "quantity"}
 
 
 def test_judge_alignment_license_feature_spec():
     spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "judge_alignment")
     assert spec["event_name"] == JUDGE_ALIGNMENT_PRIMARY_EVENT
-    assert spec["aggregation"] == {"type": "COUNT"}
+    assert spec["aggregation"] == {"type": "SUM", "field": "quantity"}
 
 
 def test_metrics_ai_assist_license_feature_spec():
@@ -51,7 +86,7 @@ def test_metrics_ai_assist_license_feature_spec():
 def test_metric_studio_license_feature_spec():
     spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "metric_studio")
     assert spec["event_name"] == METRIC_STUDIO_PRIMARY_EVENT
-    assert spec["aggregation"] == {"type": "COUNT"}
+    assert spec["aggregation"] == {"type": "SUM", "field": "quantity"}
 
 
 def test_scenario_ai_license_feature_spec():
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index 11134198..102bc9c1 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -178,6 +178,8 @@ def test_record_call_import_batch_created_includes_volume_properties(mock_flexpr
     assert payload["event_name"] == "call_import.batch_created"
     assert payload["properties"]["quantity"] == "42"
     assert payload["properties"]["feature"] == "call_imports"
+    assert payload["properties"]["source"] == "csv"
+    assert payload["properties"]["provider"] == "exotel"
 
 
 @patch("flexprice.Flexprice")
@@ -213,6 +215,12 @@ def test_record_call_import_evaluation_completed_meters_pass_delta(mock_flexpric
             "workspace_id": str(workspace_id),
             "feature": "call_imports",
             "quantity": "50",
+            "evaluation_id": str(evaluation_id),
+            "call_import_id": str(call_import_id),
+            "completed_total": "1950",
+            "total_rows": "2000",
+            "metric_count": "5",
+            "rows_billed": "50",
         },
     )
     assert accepted is True
@@ -269,6 +277,10 @@ def test_record_call_import_recording_minutes_billed(mock_flexprice):
             "feature": "call_imports",
             "billable_minutes": "2",
             "quantity": "2",
+            "evaluation_row_id": str(eval_row_id),
+            "evaluation_id": str(evaluation_id),
+            "call_import_id": str(call_import_id),
+            "audio_seconds": "90",
         },
     )
     assert accepted is True
@@ -306,6 +318,10 @@ def test_record_call_import_pdf_report_generated(mock_flexprice):
             "workspace_id": str(workspace_id),
             "feature": "call_imports",
             "quantity": "1",
+            "pdf_report_id": str(pdf_report_id),
+            "evaluation_id": str(evaluation_id),
+            "call_import_id": str(call_import_id),
+            "report_type": "external",
         },
     )
 
@@ -394,6 +410,8 @@ def test_record_playground_evaluation_completed_includes_feature(mock_flexprice)
     assert payload["properties"]["feature"] == "agent_playground"
     assert payload["properties"]["quantity"] == "1"
     assert payload["properties"]["billable_minutes"] == "1"
+    assert payload["properties"]["duration_seconds"] == "12.5"
+    assert payload["properties"]["metric_count"] == "2"
     assert payload["properties"]["billable_minutes"] == "1"
 
 
@@ -437,6 +455,9 @@ def test_record_test_agent_conversation_ended_bills_one_conversation(mock_flexpr
             "feature": "agent_playground",
             "quantity": "2",
             "billable_minutes": "2",
+            "conversation_id": str(conversation_id),
+            "duration_seconds": "120.0",
+            "turn_count": "8",
         },
     )
 
@@ -519,7 +540,92 @@ def test_record_judge_alignment_run_completed_includes_feature(mock_flexprice):
     payload = mock_client.events.ingest_event.call_args.kwargs
     assert payload["event_name"] == "judge_alignment.run_completed"
     assert payload["properties"]["feature"] == "judge_alignment"
-    assert payload["properties"]["quantity"] == "1"
+    assert payload["properties"]["quantity"] == "12"
+
+
+@patch("flexprice.Flexprice")
+def test_record_judge_alignment_run_completed_skips_when_no_samples(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    svc.record_judge_alignment_run_completed(
+        uuid4(), uuid4(), workspace_id=uuid4(), dataset_id=uuid4(), samples_scored=0
+    )
+
+    mock_flexprice.assert_not_called()
+
+
+@patch("flexprice.Flexprice")
+def test_record_prompt_optimization_run_completed_bills_candidates(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    run_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_prompt_optimization_run_completed(
+        org_id,
+        run_id,
+        workspace_id=workspace_id,
+        agent_id=uuid4(),
+        candidates_count=5,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "prompt_optimization.run_completed"
+    assert payload["properties"]["feature"] == "gepa_optimization"
+    assert payload["properties"]["quantity"] == "5"
+
+
+@patch("flexprice.Flexprice")
+def test_record_evaluator_recording_minutes_billed(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    result_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    accepted = svc.record_evaluator_recording_minutes_billed(
+        org_id,
+        result_id,
+        workspace_id=workspace_id,
+        duration_seconds=125.0,
+    )
+
+    mock_client.events.ingest_event.assert_called_once_with(
+        event_name="evaluator.recording_minutes_billed",
+        external_customer_id=str(org_id),
+        event_id=str(result_id),
+        source="efficientai",
+        properties={
+            "workspace_id": str(workspace_id),
+            "feature": "evaluators",
+            "quantity": "3",
+            "billable_minutes": "3",
+            "evaluator_result_id": str(result_id),
+            "duration_seconds": "125.0",
+            "audio_seconds": "125",
+        },
+    )
+    assert accepted is True
+
+
+@patch("flexprice.Flexprice")
+def test_record_observability_call_evaluated_does_not_ingest(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    svc.record_observability_call_evaluated(uuid4(), "call-1", workspace_id=uuid4())
+
+    mock_flexprice.assert_not_called()
 
 
 @patch("flexprice.Flexprice")
diff --git a/tests/test_services/test_flexprice_tracking_wiring.py b/tests/test_services/test_flexprice_tracking_wiring.py
index 30cad0b6..705a304d 100644
--- a/tests/test_services/test_flexprice_tracking_wiring.py
+++ b/tests/test_services/test_flexprice_tracking_wiring.py
@@ -23,6 +23,7 @@
 EVALUATORS_WIRED = {
     "evaluator.run_requested": "app/api/v1/routes/evaluators.py",
     "evaluator.run_completed": "app/workers/tasks/process_evaluator_result.py",
+    "evaluator.recording_minutes_billed": "app/workers/tasks/process_evaluator_result.py",
 }
 
 JUDGE_ALIGNMENT_WIRED = {
@@ -81,6 +82,7 @@ def test_evaluator_events_are_wired():
         record_fn = {
             "evaluator.run_requested": "record_evaluator_run_requested",
             "evaluator.run_completed": "record_evaluator_run_completed",
+            "evaluator.recording_minutes_billed": "record_evaluator_recording_minutes_billed",
         }[event_name]
         assert _file_contains(path, record_fn), f"{event_name} missing {record_fn} in {path}"
 

From a82a5cc0ad172558459c2f5cd0484e6061b89b3c Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 20:18:04 +0530
Subject: [PATCH 49/55] feat(flexprice): implement repair features
 functionality to repoint license features to canonical meters with dry-run
 option

---
 docs/billing/flexprice-saas-setup.md          |   4 +
 scripts/setup_flexprice_meters.py             | 208 +++++++++++++++++-
 .../test_setup_flexprice_meters.py            |  78 +++++++
 3 files changed, 289 insertions(+), 1 deletion(-)

diff --git a/docs/billing/flexprice-saas-setup.md b/docs/billing/flexprice-saas-setup.md
index 4e807254..58cee1a3 100644
--- a/docs/billing/flexprice-saas-setup.md
+++ b/docs/billing/flexprice-saas-setup.md
@@ -31,8 +31,12 @@ Signup and API-key flows call `provision_billing_customer()` so each org exists
 ```bash
 python scripts/setup_flexprice_meters.py
 python scripts/setup_flexprice_meters.py --repair-call-imports   # if batch_created meters were duplicated
+python scripts/setup_flexprice_meters.py --repair-features --dry-run   # preview stale feature→meter links
+python scripts/setup_flexprice_meters.py --repair-features             # repoint license features (delete + recreate)
 ```
 
+Flexprice feature **PUT** cannot change `meter_id`. `--repair-features` deletes and recreates each `LICENSE_FEATURES` row on the canonical published meter. **Plan usage charges on meters are not affected.**
+
 Print the plan pricing checklist (no API):
 
 ```bash
diff --git a/scripts/setup_flexprice_meters.py b/scripts/setup_flexprice_meters.py
index ff0cab7c..de1a00f5 100644
--- a/scripts/setup_flexprice_meters.py
+++ b/scripts/setup_flexprice_meters.py
@@ -701,6 +701,186 @@ def repair_call_import_batch_meter(client: httpx.Client) -> dict[str, Any]:
     return result
 
 
+def _aggregation_from_spec(spec: dict[str, Any]) -> tuple[str, str | None]:
+    aggregation = spec["aggregation"]
+    return aggregation["type"], aggregation.get("field")
+
+
+def _pick_canonical_meter(
+    meters: list[dict[str, Any]],
+    prices: list[dict[str, Any]],
+    *,
+    agg_type: str,
+    agg_field: str | None,
+) -> dict[str, Any] | None:
+    """Prefer published meters with correct aggregation; favor meters that already have plan prices."""
+    matching = [
+        meter
+        for meter in meters
+        if meter_aggregation_matches(meter, agg_type=agg_type, agg_field=agg_field)
+    ]
+    if not matching:
+        return None
+    priced = [meter for meter in matching if _active_prices_for_meter(prices, meter["id"])]
+    if priced:
+        return priced[0]
+    active = [meter for meter in matching if _is_active_meter(meter)]
+    pool = active or matching
+    return sorted(pool, key=lambda meter: meter.get("updated_at") or "", reverse=True)[0]
+
+
+def _list_all_features(client: httpx.Client) -> list[dict[str, Any]]:
+    items: list[dict[str, Any]] = []
+    offset = 0
+    while True:
+        resp = client.get(
+            f"{_base_url()}/features",
+            headers=_headers(),
+            params={"limit": 200, "offset": offset},
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        batch = data.get("items") or []
+        items.extend(batch)
+        pagination = data.get("pagination") or {}
+        total = pagination.get("total")
+        offset += len(batch)
+        if not batch or (total is not None and offset >= total):
+            break
+    return items
+
+
+def _feature_meter_is_canonical(
+    feature: dict[str, Any],
+    meter: dict[str, Any] | None,
+    *,
+    canonical_meter_id: str,
+    agg_type: str,
+    agg_field: str | None,
+) -> bool:
+    if feature.get("meter_id") != canonical_meter_id:
+        return False
+    if meter is None:
+        return False
+    if not _is_active_meter(meter):
+        return False
+    return meter_aggregation_matches(meter, agg_type=agg_type, agg_field=agg_field)
+
+
+def repair_license_features(
+    client: httpx.Client,
+    sdk: Flexprice,
+    *,
+    dry_run: bool = False,
+) -> dict[str, Any]:
+    """Repoint LICENSE_FEATURES to canonical published meters (delete + recreate via API).
+
+    Flexprice feature PUT does not change meter_id; recreate is required.
+    Plan usage charges stay on meters and are unaffected.
+    """
+    result: dict[str, Any] = {
+        "repaired": [],
+        "skipped": [],
+        "failed": [],
+        "missing_feature": [],
+        "missing_meter": [],
+        "dry_run": dry_run,
+    }
+
+    meters_by_event = _list_meters_by_event(client, active_only=False)
+    all_prices = _list_prices(client)
+    features_by_key = {
+        feature["lookup_key"]: feature
+        for feature in _list_all_features(client)
+        if feature.get("lookup_key")
+    }
+
+    for spec in LICENSE_FEATURES:
+        lookup_key = spec["lookup_key"]
+        feature = features_by_key.get(lookup_key)
+        if feature is None:
+            result["missing_feature"].append(lookup_key)
+            continue
+
+        agg_type, agg_field = _aggregation_from_spec(spec)
+        event_name = spec["event_name"]
+        event_meters = meters_by_event.get(event_name, [])
+        active_event_meters = [meter for meter in event_meters if _is_active_meter(meter)]
+        canonical = _pick_canonical_meter(
+            active_event_meters or event_meters,
+            all_prices,
+            agg_type=agg_type,
+            agg_field=agg_field,
+        )
+
+        if canonical is None:
+            if dry_run:
+                result["missing_meter"].append(
+                    {"lookup_key": lookup_key, "event_name": event_name, "would_create": True}
+                )
+                continue
+            try:
+                canonical = _create_meter(
+                    client,
+                    event_name,
+                    spec["name"],
+                    agg_type,
+                    agg_field,
+                )
+            except Exception as exc:
+                result["failed"].append(f"{lookup_key}: create meter: {exc}")
+                continue
+            if canonical.get("skipped"):
+                result["failed"].append(f"{lookup_key}: could not create meter for {event_name}")
+                continue
+
+        canonical_id = canonical["id"]
+        current_meter_id = feature.get("meter_id")
+        current_meter = next(
+            (meter for meter in event_meters if meter.get("id") == current_meter_id),
+            None,
+        )
+        if _feature_meter_is_canonical(
+            feature,
+            current_meter,
+            canonical_meter_id=canonical_id,
+            agg_type=agg_type,
+            agg_field=agg_field,
+        ):
+            result["skipped"].append(lookup_key)
+            continue
+
+        entry: dict[str, Any] = {
+            "lookup_key": lookup_key,
+            "feature_id": feature["id"],
+            "from_meter_id": current_meter_id,
+            "to_meter_id": canonical_id,
+            "event_name": event_name,
+        }
+        if dry_run:
+            result["repaired"].append({**entry, "dry_run": True})
+            continue
+
+        try:
+            sdk.features.delete_feature(id=feature["id"])
+            recreated = sdk.features.create_feature(
+                name=spec["name"],
+                type_="metered",
+                lookup_key=lookup_key,
+                description=spec["description"],
+                unit_singular=spec.get("unit_singular"),
+                unit_plural=spec.get("unit_plural"),
+                meter_id=canonical_id,
+            )
+            result["repaired"].append(
+                {**entry, "new_feature_id": recreated.id, "dry_run": False}
+            )
+        except Exception as exc:
+            result["failed"].append(f"{lookup_key}: {exc}")
+
+    return result
+
+
 def _list_feature_lookup_keys(sdk: Flexprice) -> set[str]:
     keys: set[str] = set()
     offset = 0
@@ -825,6 +1005,16 @@ def main() -> int:
         action="store_true",
         help="Print JSON checklist of plan usage charges (no API calls).",
     )
+    parser.add_argument(
+        "--repair-features",
+        action="store_true",
+        help="Repoint license features to canonical completion-only meters (delete + recreate).",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="With --repair-features, report changes without calling Flexprice write APIs.",
+    )
     args = parser.parse_args()
 
     if args.plan_guide:
@@ -844,6 +1034,21 @@ def main() -> int:
             summary["call_import_batch_repair"] = repair_call_import_batch_meter(http_client)
         if args.restore_plan_usage_prices:
             summary["plan_usage_restore"] = restore_missing_plan_usage_prices(http_client)
+        if args.repair_features:
+            with Flexprice(
+                server_url=settings.FLEXPRICE_API_HOST,
+                api_key_auth=settings.FLEXPRICE_API_KEY,
+            ) as sdk:
+                summary["feature_repair"] = repair_license_features(
+                    http_client,
+                    sdk,
+                    dry_run=args.dry_run,
+                )
+
+    if args.repair_features and args.dry_run:
+        print(json.dumps(summary, indent=2))
+        failed = summary.get("feature_repair", {}).get("failed") or []
+        return 1 if failed else 0
 
     bootstrap_summary = bootstrap_catalog()
     summary.update(bootstrap_summary)
@@ -861,7 +1066,8 @@ def main() -> int:
 
     failed_meters = bootstrap_summary["meters"]["failed"]
     failed_features = bootstrap_summary["features"]["failed"]
-    return 1 if (failed_meters or failed_features) else 0
+    repair_failed = (summary.get("feature_repair") or {}).get("failed") or []
+    return 1 if (failed_meters or failed_features or repair_failed) else 0
 
 
 if __name__ == "__main__":
diff --git a/tests/test_scripts/test_setup_flexprice_meters.py b/tests/test_scripts/test_setup_flexprice_meters.py
index 192c9cd3..2aa9614b 100644
--- a/tests/test_scripts/test_setup_flexprice_meters.py
+++ b/tests/test_scripts/test_setup_flexprice_meters.py
@@ -1,3 +1,5 @@
+from unittest.mock import MagicMock
+
 from scripts.setup_flexprice_meters import (
     AGENT_PLAYGROUND_PRIMARY_EVENT,
     EVALUATOR_RUN_COMPLETED_EVENT,
@@ -9,7 +11,10 @@
     PLAN_BILLABLE_METERS,
     SCENARIO_AI_TEXT_EVENT,
     VOICE_PLAYGROUND_PRIMARY_EVENT,
+    _feature_meter_is_canonical,
+    _pick_canonical_meter,
     meter_aggregation_matches,
+    repair_license_features,
 )
 from app.api.v1.routes.call_import_evaluations import (
     DISCOVERED_METRICS_KEY,
@@ -100,3 +105,76 @@ def test_metric_scores_meta_keys():
     assert _is_metric_scores_meta_key(DISCOVERED_METRICS_KEY) is True
     assert _is_metric_scores_meta_key("parent-id__discovered") is True
     assert _is_metric_scores_meta_key("550e8400-e29b-41d4-a716-446655440000") is False
+
+
+def test_pick_canonical_meter_prefers_priced_meter():
+    meters = [
+        {"id": "m-old", "aggregation": {"type": "SUM", "field": "quantity"}, "status": "published"},
+        {"id": "m-new", "aggregation": {"type": "SUM", "field": "quantity"}, "status": "published"},
+    ]
+    prices = [{"meter_id": "m-new", "amount": "0", "status": "published"}]
+    picked = _pick_canonical_meter(meters, prices, agg_type="SUM", agg_field="quantity")
+    assert picked["id"] == "m-new"
+
+
+def test_feature_meter_is_canonical():
+    feature = {"meter_id": "m1"}
+    meter = {
+        "id": "m1",
+        "status": "published",
+        "aggregation": {"type": "SUM", "field": "quantity"},
+    }
+    assert _feature_meter_is_canonical(
+        feature,
+        meter,
+        canonical_meter_id="m1",
+        agg_type="SUM",
+        agg_field="quantity",
+    )
+
+
+def test_repair_license_features_dry_run_flags_stale_call_imports():
+    client = MagicMock()
+    sdk = MagicMock()
+    client.get.side_effect = [
+        MagicMock(
+            status_code=200,
+            json=lambda: {
+                "items": [
+                    {
+                        "id": "meter_old",
+                        "event_name": "call_import.batch_created",
+                        "aggregation": {"type": "COUNT"},
+                        "status": "archived",
+                    },
+                    {
+                        "id": "meter_new",
+                        "event_name": "call_import.batch_created",
+                        "aggregation": {"type": "SUM", "field": "quantity"},
+                        "status": "published",
+                    },
+                ],
+                "pagination": {"total": 2},
+            },
+        ),
+        MagicMock(status_code=200, json=lambda: {"items": [], "pagination": {"total": 0}}),
+        MagicMock(
+            status_code=200,
+            json=lambda: {
+                "items": [
+                    {
+                        "id": "feat_1",
+                        "lookup_key": "call_imports",
+                        "meter_id": "meter_old",
+                    }
+                ],
+                "pagination": {"total": 1},
+            },
+        ),
+    ]
+    client.get.return_value.raise_for_status = MagicMock()
+
+    result = repair_license_features(client, sdk, dry_run=True)
+    repaired_keys = {row["lookup_key"] for row in result["repaired"]}
+    assert "call_imports" in repaired_keys
+    sdk.features.delete_feature.assert_not_called()

From 8dc36db19e451c659eb4333546bf97e2daab73fc Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 22:48:46 +0530
Subject: [PATCH 50/55] feat(flexprice): enhance subscription management by
 adding auto-subscribe functionality and updating configuration options

---
 app/config.py                                 | 12 +++
 app/services/billing/flexprice_service.py     | 83 ++++++++++++++++++
 app/services/organization_provisioning.py     |  3 +-
 config.yml.example                            |  7 +-
 docs/billing/flexprice-saas-setup.md          | 16 +++-
 tests/helpers/flexprice_stubs.py              |  5 ++
 tests/test_services/test_flexprice_service.py | 84 +++++++++++++++++++
 7 files changed, 206 insertions(+), 4 deletions(-)

diff --git a/app/config.py b/app/config.py
index 7f508d23..95c98134 100644
--- a/app/config.py
+++ b/app/config.py
@@ -250,6 +250,10 @@ class Settings(BaseSettings):
     FLEXPRICE_ENABLED: bool = False
     FLEXPRICE_API_KEY: Optional[str] = None
     FLEXPRICE_API_HOST: str = "https://us.api.flexprice.io/v1"
+    FLEXPRICE_AUTO_SUBSCRIBE: bool = False
+    FLEXPRICE_DEFAULT_PLAN_ID: Optional[str] = None
+    FLEXPRICE_DEFAULT_CURRENCY: str = "usd"
+    FLEXPRICE_DEFAULT_BILLING_PERIOD: str = "MONTHLY"
     # LLM gateway (optional platform-wide proxy for batch LLM calls).
     LLM_GATEWAY_ENABLED: bool = False
     LLM_GATEWAY_TYPE: str = "bifrost"  # bifrost | litellm_proxy
@@ -901,6 +905,14 @@ def _apply_llm_gateway_settings(gateway_cfg: dict, *, gateway_type: str) -> None
             settings.FLEXPRICE_API_KEY = flexprice_config["api_key"]
         if flexprice_config.get("api_host"):
             settings.FLEXPRICE_API_HOST = flexprice_config["api_host"]
+        if "auto_subscribe" in flexprice_config:
+            settings.FLEXPRICE_AUTO_SUBSCRIBE = bool(flexprice_config["auto_subscribe"])
+        if flexprice_config.get("default_plan_id"):
+            settings.FLEXPRICE_DEFAULT_PLAN_ID = flexprice_config["default_plan_id"]
+        if flexprice_config.get("default_currency"):
+            settings.FLEXPRICE_DEFAULT_CURRENCY = flexprice_config["default_currency"]
+        if flexprice_config.get("default_billing_period"):
+            settings.FLEXPRICE_DEFAULT_BILLING_PERIOD = flexprice_config["default_billing_period"]
         if (
             os.environ.get("EFFICIENTAI_PYTEST") == "1"
             and os.environ.get("FLEXPRICE_TEST_ALLOW") != "1"
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index b4c2f500..7e34b4e2 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -404,6 +404,89 @@ def ensure_customer(
         )
 
 
+def _subscription_inactive_reason() -> Optional[str]:
+    """Why auto-subscribe is off, or None when ensure_subscription may run."""
+    inactive = disabled_reason()
+    if inactive:
+        return inactive
+    if not settings.FLEXPRICE_AUTO_SUBSCRIBE:
+        return "flexprice.auto_subscribe is false (or FLEXPRICE_AUTO_SUBSCRIBE unset)"
+    if not (settings.FLEXPRICE_DEFAULT_PLAN_ID or "").strip():
+        return "flexprice.default_plan_id is unset (or FLEXPRICE_DEFAULT_PLAN_ID env missing)"
+    return None
+
+
+def _has_active_subscription(client, *, organization_id: UUID, plan_id: str) -> bool:
+    response = client.subscriptions.query_subscription(
+        external_customer_id=str(organization_id),
+        plan_id=plan_id,
+        limit=1,
+    )
+    items = getattr(response, "items", None) or []
+    return len(items) > 0
+
+
+def ensure_subscription(organization_id: UUID) -> None:
+    """Assign the default SaaS plan when auto-subscribe is enabled. Never raises."""
+    if _pytest_blocks_external_billing():
+        return
+
+    inactive_reason = _subscription_inactive_reason()
+    if inactive_reason:
+        if _verbose_logging():
+            logger.info(
+                "Flexprice SKIP ensure_subscription org={} ({})",
+                organization_id,
+                inactive_reason,
+            )
+        return
+
+    plan_id = settings.FLEXPRICE_DEFAULT_PLAN_ID.strip()
+    try:
+        from flexprice import Flexprice
+
+        with Flexprice(
+            server_url=settings.FLEXPRICE_API_HOST,
+            api_key_auth=settings.FLEXPRICE_API_KEY,
+        ) as client:
+            if _has_active_subscription(client, organization_id=organization_id, plan_id=plan_id):
+                logger.debug(
+                    "Flexprice ensure_subscription already active org={} plan_id={}",
+                    organization_id,
+                    plan_id,
+                )
+                return
+
+            created = client.subscriptions.create_subscription(
+                billing_period=settings.FLEXPRICE_DEFAULT_BILLING_PERIOD,
+                currency=settings.FLEXPRICE_DEFAULT_CURRENCY,
+                plan_id=plan_id,
+                external_customer_id=str(organization_id),
+                subscription_status="active",
+            )
+        logger.info(
+            "Flexprice ensure_subscription ok org={} plan_id={} subscription_id={}",
+            organization_id,
+            plan_id,
+            getattr(created, "id", None),
+        )
+    except Exception as exc:
+        if _is_customer_already_exists(exc) or "already exist" in str(exc).lower():
+            logger.debug(
+                "Flexprice ensure_subscription already exists org={} plan_id={}",
+                organization_id,
+                plan_id,
+            )
+            return
+        logger.warning(
+            "Flexprice ensure_subscription FAILED org={} plan_id={} host={} error={}",
+            organization_id,
+            plan_id,
+            settings.FLEXPRICE_API_HOST,
+            exc,
+        )
+
+
 # --- Voice playground ---
 
 
diff --git a/app/services/organization_provisioning.py b/app/services/organization_provisioning.py
index a839ca67..847b8b03 100644
--- a/app/services/organization_provisioning.py
+++ b/app/services/organization_provisioning.py
@@ -7,7 +7,7 @@
 from sqlalchemy.orm import Session
 
 from app.models.database import Workspace
-from app.services.billing.flexprice_service import ensure_customer
+from app.services.billing.flexprice_service import ensure_customer, ensure_subscription
 from app.services.workspace_rbac import (
     backfill_org_workspace_memberships,
     ensure_creator_workspace_admin,
@@ -62,3 +62,4 @@ def provision_billing_customer(
 ) -> None:
     """Register the org with Flexprice when billing is enabled (no-op otherwise)."""
     ensure_customer(organization_id, name=name, email=email)
+    ensure_subscription(organization_id)
diff --git a/config.yml.example b/config.yml.example
index e70b820b..bf268c83 100644
--- a/config.yml.example
+++ b/config.yml.example
@@ -267,7 +267,12 @@ judge_alignment:
 #   enabled: false
 #   api_key: null
 #   api_host: "https://us.api.flexprice.io/v1"
-# Env override: FLEXPRICE_ENABLED, FLEXPRICE_API_KEY, FLEXPRICE_API_HOST
+#   auto_subscribe: false
+#   default_plan_id: null
+#   default_currency: "usd"
+#   default_billing_period: "MONTHLY"
+# Env override: FLEXPRICE_ENABLED, FLEXPRICE_API_KEY, FLEXPRICE_API_HOST,
+#   FLEXPRICE_AUTO_SUBSCRIBE, FLEXPRICE_DEFAULT_PLAN_ID
 # Debug: FLEXPRICE_VERBOSE=1 logs every skipped event (when inactive or verbose)
 
 # Call-import worker concurrency (Redis fair-share for evaluations).
diff --git a/docs/billing/flexprice-saas-setup.md b/docs/billing/flexprice-saas-setup.md
index 58cee1a3..82f95558 100644
--- a/docs/billing/flexprice-saas-setup.md
+++ b/docs/billing/flexprice-saas-setup.md
@@ -24,7 +24,19 @@ flexprice:
 
 Set `FLEXPRICE_API_KEY` in the environment (never commit keys).
 
-Signup and API-key flows call `provision_billing_customer()` so each org exists in Flexprice as `external_customer_id = organization.id`.
+Signup and API-key flows call `provision_billing_customer()` so each org exists in Flexprice as `external_customer_id = organization.id`. When `auto_subscribe` is enabled, new orgs also receive a subscription on `default_plan_id` (Platform plan).
+
+```yaml
+flexprice:
+  enabled: true
+  api_host: "https://api.cloud.flexprice.io/v1"
+  auto_subscribe: true
+  default_plan_id: "plan_01KVT8BTT0HRB419QVCTNHS9RV"
+  default_currency: "usd"
+  default_billing_period: "MONTHLY"
+```
+
+Env overrides: `FLEXPRICE_AUTO_SUBSCRIBE`, `FLEXPRICE_DEFAULT_PLAN_ID`. Leave `auto_subscribe: false` for self-hosted OSS (customer optional, no subscription).
 
 ## 2. Bootstrap meters and features
 
@@ -104,5 +116,5 @@ Pytest sets `EFFICIENTAI_PYTEST=1` and mocks all Flexprice I/O. Full suite never
 ## 8. Not yet wired (post-launch)
 
 - Flexprice entitlements replacing JWT license limits (`app/core/license.py`)
-- Auto-assign subscription on signup
 - Backfill existing orgs as Flexprice customers
+- Multiple plan tiers at signup (Basic $100, reference codes, Stripe checkout)
diff --git a/tests/helpers/flexprice_stubs.py b/tests/helpers/flexprice_stubs.py
index e1fbb105..4e754bbd 100644
--- a/tests/helpers/flexprice_stubs.py
+++ b/tests/helpers/flexprice_stubs.py
@@ -13,6 +13,10 @@ def _noop_ensure_customer(*_args, **_kwargs) -> None:
     return None
 
 
+def _noop_ensure_subscription(*_args, **_kwargs) -> None:
+    return None
+
+
 def _noop_provision_billing_customer(*_args, **_kwargs) -> None:
     return None
 
@@ -39,6 +43,7 @@ def install_flexprice_test_isolation(monkeypatch) -> None:
 
     monkeypatch.setattr(fp, "record_event", _noop_record_event)
     monkeypatch.setattr(fp, "ensure_customer", _noop_ensure_customer)
+    monkeypatch.setattr(fp, "ensure_subscription", _noop_ensure_subscription)
     monkeypatch.setattr(org_prov, "provision_billing_customer", _noop_provision_billing_customer)
     monkeypatch.setattr("flexprice.Flexprice", _ForbiddenFlexprice)
 
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index 102bc9c1..6d050e35 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -16,6 +16,10 @@ def reset_flexprice_settings(monkeypatch):
         settings.FLEXPRICE_ENABLED,
         settings.FLEXPRICE_API_KEY,
         settings.FLEXPRICE_API_HOST,
+        settings.FLEXPRICE_AUTO_SUBSCRIBE,
+        settings.FLEXPRICE_DEFAULT_PLAN_ID,
+        settings.FLEXPRICE_DEFAULT_CURRENCY,
+        settings.FLEXPRICE_DEFAULT_BILLING_PERIOD,
         svc._disabled_skip_logged,
     )
     svc._disabled_skip_logged = False
@@ -24,6 +28,10 @@ def reset_flexprice_settings(monkeypatch):
         settings.FLEXPRICE_ENABLED,
         settings.FLEXPRICE_API_KEY,
         settings.FLEXPRICE_API_HOST,
+        settings.FLEXPRICE_AUTO_SUBSCRIBE,
+        settings.FLEXPRICE_DEFAULT_PLAN_ID,
+        settings.FLEXPRICE_DEFAULT_CURRENCY,
+        settings.FLEXPRICE_DEFAULT_BILLING_PERIOD,
         svc._disabled_skip_logged,
     ) = previous
 
@@ -98,6 +106,82 @@ def test_ensure_customer_swallows_already_exists(mock_flexprice):
     svc.ensure_customer(uuid4(), name="Acme Inc")
 
 
+@patch("flexprice.Flexprice")
+def test_ensure_subscription_no_op_when_auto_subscribe_disabled(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+    settings.FLEXPRICE_AUTO_SUBSCRIBE = False
+    settings.FLEXPRICE_DEFAULT_PLAN_ID = "plan_test"
+
+    svc.ensure_subscription(uuid4())
+
+    mock_flexprice.assert_not_called()
+
+
+@patch("flexprice.Flexprice")
+def test_ensure_subscription_no_op_when_plan_id_missing(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+    settings.FLEXPRICE_AUTO_SUBSCRIBE = True
+    settings.FLEXPRICE_DEFAULT_PLAN_ID = None
+
+    svc.ensure_subscription(uuid4())
+
+    mock_flexprice.assert_not_called()
+
+
+@patch("flexprice.Flexprice")
+def test_ensure_subscription_creates_when_none_exists(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+    settings.FLEXPRICE_API_HOST = "https://api.cloud.flexprice.io/v1"
+    settings.FLEXPRICE_AUTO_SUBSCRIBE = True
+    settings.FLEXPRICE_DEFAULT_PLAN_ID = "plan_01KVT8BTT0HRB419QVCTNHS9RV"
+    settings.FLEXPRICE_DEFAULT_CURRENCY = "usd"
+    settings.FLEXPRICE_DEFAULT_BILLING_PERIOD = "MONTHLY"
+
+    org_id = uuid4()
+    mock_client = MagicMock()
+    mock_client.subscriptions.query_subscription.return_value = MagicMock(items=[])
+    mock_client.subscriptions.create_subscription.return_value = MagicMock(
+        id="sub_test123"
+    )
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.ensure_subscription(org_id)
+
+    mock_client.subscriptions.query_subscription.assert_called_once_with(
+        external_customer_id=str(org_id),
+        plan_id="plan_01KVT8BTT0HRB419QVCTNHS9RV",
+        limit=1,
+    )
+    mock_client.subscriptions.create_subscription.assert_called_once_with(
+        billing_period="MONTHLY",
+        currency="usd",
+        plan_id="plan_01KVT8BTT0HRB419QVCTNHS9RV",
+        external_customer_id=str(org_id),
+        subscription_status="active",
+    )
+
+
+@patch("flexprice.Flexprice")
+def test_ensure_subscription_skips_when_subscription_exists(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+    settings.FLEXPRICE_AUTO_SUBSCRIBE = True
+    settings.FLEXPRICE_DEFAULT_PLAN_ID = "plan_test"
+
+    mock_client = MagicMock()
+    mock_client.subscriptions.query_subscription.return_value = MagicMock(
+        items=[MagicMock(id="sub_existing")]
+    )
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.ensure_subscription(uuid4())
+
+    mock_client.subscriptions.create_subscription.assert_not_called()
+
+
 @patch("flexprice.Flexprice")
 def test_record_blind_test_share_created_no_op_when_disabled(mock_flexprice):
     settings.FLEXPRICE_ENABLED = False

From 533387452b5ff6b21eecb6ca346ee49619390564 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 26 Aug 2026 23:28:53 +0530
Subject: [PATCH 51/55] feat(flexprice): add new billing events for agent test
 setup and prompt partial AI assistance, enhancing tracking for generated
 prompts and scenarios

---
 app/api/v1/routes/agents.py                   |  19 ++-
 app/api/v1/routes/personas.py                 |  16 +-
 app/api/v1/routes/prompt_partials.py          |  28 +++-
 app/services/billing/flexprice_service.py     | 148 ++++++++++++++++++
 app/workers/tasks/agent_flowchart_jobs.py     |  21 ++-
 ...generate_evaluation_prompt_improvements.py |  14 +-
 .../generate_evaluation_user_insights.py      |  13 +-
 docs/billing/flexprice-saas-setup.md          |   9 ++
 scripts/setup_flexprice_meters.py             |  69 ++++++++
 .../test_setup_flexprice_meters.py            |  10 ++
 tests/test_services/test_flexprice_service.py |  75 +++++++++
 .../test_flexprice_tracking_wiring.py         |  56 ++++++-
 12 files changed, 465 insertions(+), 13 deletions(-)

diff --git a/app/api/v1/routes/agents.py b/app/api/v1/routes/agents.py
index 884769b9..f02f45a7 100644
--- a/app/api/v1/routes/agents.py
+++ b/app/api/v1/routes/agents.py
@@ -14,7 +14,7 @@
 from loguru import logger
 
 from app.dependencies import get_db, get_organization_id, get_workspace_id, get_api_key
-from app.services.billing.flexprice_service import record_scenario_ai_text_generated
+from app.services.billing.flexprice_service import record_agent_test_setup_generated
 from app.models.database import (
     Agent, ConversationEvaluation, TestAgentConversation, VoiceBundle,
     AIProvider, Integration, IntegrationPlatform, CallMediumEnum,
@@ -257,6 +257,7 @@ def _scenario_draft_responses(scenarios) -> list[GeneratedScenarioDraftResponse]
 @router.post("/generate-test-prompt", response_model=GenerateTestPromptResponse)
 async def generate_test_prompt(
     data: GenerateTestPromptRequest,
+    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
     workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
@@ -300,6 +301,14 @@ async def generate_test_prompt(
                 llm_config=data.llm_config,
                 credential_id=data.credential_id,
             )
+        background_tasks.add_task(
+            record_agent_test_setup_generated,
+            organization_id,
+            uuid4(),
+            workspace_id=workspace_id,
+            purpose="test_prompt",
+            model=result.model,
+        )
         return GenerateTestPromptResponse(
             sections=_test_prompt_section_responses(result.sections),
             test_agent_prompt=result.test_agent_prompt,
@@ -362,12 +371,12 @@ async def generate_scenarios_from_prompt(
                 credential_id=data.credential_id,
             )
         background_tasks.add_task(
-            record_scenario_ai_text_generated,
+            record_agent_test_setup_generated,
             organization_id,
             uuid4(),
             workspace_id=workspace_id,
+            purpose="scenarios",
             model=result.model,
-            purpose="scenarios_from_agent_prompt",
             scenario_count=len(result.scenarios),
         )
         return GenerateScenariosFromPromptResponse(
@@ -445,12 +454,12 @@ async def generate_test_setup(
                 credential_id=data.credential_id,
             )
         background_tasks.add_task(
-            record_scenario_ai_text_generated,
+            record_agent_test_setup_generated,
             organization_id,
             uuid4(),
             workspace_id=workspace_id,
+            purpose="full_setup",
             model=scenario_result.model,
-            purpose="scenarios_from_agent_prompt",
             scenario_count=len(scenario_result.scenarios),
         )
         return GenerateTestSetupResponse(
diff --git a/app/api/v1/routes/personas.py b/app/api/v1/routes/personas.py
index c0217cc3..131c7e14 100644
--- a/app/api/v1/routes/personas.py
+++ b/app/api/v1/routes/personas.py
@@ -3,12 +3,12 @@
 CRUD for TTS provider-tied voice personas, voice-options catalog,
 and custom voice management (ungated).
 """
-from fastapi import APIRouter, Depends, HTTPException, status, Body, Query
+from fastapi import APIRouter, Depends, HTTPException, status, Body, Query, BackgroundTasks
 from fastapi.responses import JSONResponse
 from sqlalchemy.orm import Session
 from sqlalchemy.exc import IntegrityError, SQLAlchemyError
 from typing import List, Optional, Dict, Any
-from uuid import UUID
+from uuid import UUID, uuid4
 from pydantic import BaseModel
 from loguru import logger
 
@@ -427,6 +427,7 @@ async def get_agent_prompt_sources(
 )
 async def generate_persona_prompt(
     data: GeneratePersonaPromptRequest,
+    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
     workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
@@ -464,6 +465,17 @@ async def generate_persona_prompt(
                 llm_config=data.llm_config,
                 credential_id=data.credential_id,
             )
+        from app.services.billing.flexprice_service import record_persona_prompt_generated
+
+        background_tasks.add_task(
+            record_persona_prompt_generated,
+            organization_id,
+            uuid4(),
+            workspace_id=workspace_id,
+            agent_id=agent.id,
+            model=result.model,
+            source=result.source_used,
+        )
         return GeneratePersonaPromptResponse(
             persona_prompt=result.persona_prompt,
             source_used=result.source_used,
diff --git a/app/api/v1/routes/prompt_partials.py b/app/api/v1/routes/prompt_partials.py
index f65395c0..3380e7b9 100644
--- a/app/api/v1/routes/prompt_partials.py
+++ b/app/api/v1/routes/prompt_partials.py
@@ -2,12 +2,12 @@
 Prompt Partials API Routes
 CRUD operations with version history for reusable prompt templates.
 """
-from fastapi import APIRouter, Depends, HTTPException, status, Query
+from fastapi import APIRouter, Depends, HTTPException, status, Query, BackgroundTasks
 from fastapi.responses import JSONResponse, Response
 from sqlalchemy.orm import Session
 from sqlalchemy.exc import IntegrityError, SQLAlchemyError
 from typing import List, Optional, Dict, Any
-from uuid import UUID
+from uuid import UUID, uuid4
 from pydantic import BaseModel
 from loguru import logger
 
@@ -187,12 +187,15 @@ def _apply_prompt_partial_kind_filter(
 @router.post("/generate")
 async def generate_prompt_with_ai(
     data: GeneratePromptRequest,
+    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
     db: Session = Depends(get_db),
 ):
     """Generate a new prompt using AI from a description."""
     from app.services.ai.llm_service import llm_service
+    from app.services.billing.flexprice_service import record_prompt_partial_ai_assisted
 
     if not data.description.strip():
         raise HTTPException(400, "Description is required")
@@ -225,6 +228,15 @@ async def generate_prompt_with_ai(
             task_defaults={"temperature": 0.7, "max_tokens": 4000},
             credential_id=data.credential_id,
         )
+        request_id = uuid4()
+        background_tasks.add_task(
+            record_prompt_partial_ai_assisted,
+            organization_id,
+            request_id,
+            workspace_id=workspace_id,
+            mode="generate",
+            model=model_str,
+        )
         return {"content": result["text"], "provider": provider_enum.value, "model": model_str}
     except Exception as e:
         logger.error(f"[PromptPartials] AI generation failed: {repr(e)}")
@@ -234,12 +246,15 @@ async def generate_prompt_with_ai(
 @router.post("/improve")
 async def improve_prompt_with_ai(
     data: ImprovePromptRequest,
+    background_tasks: BackgroundTasks,
     organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
     db: Session = Depends(get_db),
 ):
     """Improve/reformat existing prompt content using AI."""
     from app.services.ai.llm_service import llm_service
+    from app.services.billing.flexprice_service import record_prompt_partial_ai_assisted
 
     if not data.content.strip():
         raise HTTPException(400, "Content is required")
@@ -268,6 +283,15 @@ async def improve_prompt_with_ai(
             task_defaults={"temperature": 0.3, "max_tokens": 4000},
             credential_id=data.credential_id,
         )
+        request_id = uuid4()
+        background_tasks.add_task(
+            record_prompt_partial_ai_assisted,
+            organization_id,
+            request_id,
+            workspace_id=workspace_id,
+            mode="improve",
+            model=model_str,
+        )
         return {"content": result["text"], "provider": provider_enum.value, "model": model_str}
     except Exception as e:
         logger.error(f"[PromptPartials] AI improve failed: {repr(e)}")
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index 7e34b4e2..8f545aff 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -29,6 +29,10 @@
 - metrics_ai_assist: ``metrics.ai_assist``
 - metric_studio: ``metric_studio.run_completed`` (``quantity`` = completed items)
 - scenario_ai: ``scenario.ai_text_generated``
+- prompt_partials: ``prompt_partial.ai_assisted`` (``mode``: generate | improve | flowchart | flowchart_map)
+- call_imports (add-ons): ``call_import.user_insights_generated``,
+  ``call_import.prompt_improvements_generated``
+- agent_playground (AI helpers): ``persona.prompt_generated``, ``agent.test_setup_generated``
 
 Not ingested: ``*_started``, ``*_requested``, ``*_created``, ``observability.*``,
 ``playground.call_evaluated``, ``test_agent.conversation_started``,
@@ -55,6 +59,7 @@
 FEATURE_METRICS_AI_ASSIST = "metrics_ai_assist"
 FEATURE_METRIC_STUDIO = "metric_studio"
 FEATURE_SCENARIO_AI = "scenario_ai"
+FEATURE_PROMPT_PARTIALS = "prompt_partials"
 
 # Log once when metering is inactive so AWS/worker misconfig is obvious.
 _disabled_skip_logged = False
@@ -93,6 +98,11 @@
 METRIC_STUDIO_ITEM_EVALUATED = "metric_studio.item_evaluated"
 METRIC_STUDIO_RUN_COMPLETED = "metric_studio.run_completed"
 SCENARIO_AI_TEXT_GENERATED = "scenario.ai_text_generated"
+PROMPT_PARTIAL_AI_ASSISTED = "prompt_partial.ai_assisted"
+CALL_IMPORT_USER_INSIGHTS_GENERATED = "call_import.user_insights_generated"
+CALL_IMPORT_PROMPT_IMPROVEMENTS_GENERATED = "call_import.prompt_improvements_generated"
+PERSONA_PROMPT_GENERATED = "persona.prompt_generated"
+AGENT_TEST_SETUP_GENERATED = "agent.test_setup_generated"
 # Legacy aliases (Flexprice meters may still exist under old names)
 METRICS_LLM_ASSIST = METRICS_AI_ASSIST
 CHAT_COMPLETION = SCENARIO_AI_TEXT_GENERATED
@@ -1199,3 +1209,141 @@ def record_chat_completion(
         purpose=purpose,
         scenario_count=scenario_count,
     )
+
+
+# --- Prompt partials AI assist ---
+
+
+def record_prompt_partial_ai_assisted(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    mode: str,
+    partial_id: Optional[UUID] = None,
+    model: Optional[str] = None,
+) -> None:
+    if workspace_id is None:
+        return
+    record_event(
+        PROMPT_PARTIAL_AI_ASSISTED,
+        organization_id,
+        request_id,
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_PROMPT_PARTIALS,
+            quantity=1,
+            request_id=request_id,
+            mode=mode,
+            partial_id=partial_id,
+            model=model,
+        ),
+    )
+
+
+# --- Call import AI add-ons ---
+
+
+def record_call_import_user_insights_generated(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    evaluation_id: UUID,
+) -> None:
+    if workspace_id is None:
+        return
+    record_event(
+        CALL_IMPORT_USER_INSIGHTS_GENERATED,
+        organization_id,
+        request_id,
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_CALL_IMPORTS,
+            quantity=1,
+            request_id=request_id,
+            evaluation_id=evaluation_id,
+        ),
+    )
+
+
+def record_call_import_prompt_improvements_generated(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    evaluation_id: UUID,
+    imported_agent_id: Optional[UUID] = None,
+) -> None:
+    if workspace_id is None:
+        return
+    record_event(
+        CALL_IMPORT_PROMPT_IMPROVEMENTS_GENERATED,
+        organization_id,
+        request_id,
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_CALL_IMPORTS,
+            quantity=1,
+            request_id=request_id,
+            evaluation_id=evaluation_id,
+            imported_agent_id=imported_agent_id,
+        ),
+    )
+
+
+# --- Agent / persona AI helpers ---
+
+
+def record_persona_prompt_generated(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    agent_id: UUID,
+    model: Optional[str] = None,
+    source: Optional[str] = None,
+) -> None:
+    if workspace_id is None:
+        return
+    record_event(
+        PERSONA_PROMPT_GENERATED,
+        organization_id,
+        request_id,
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_AGENT_PLAYGROUND,
+            quantity=1,
+            request_id=request_id,
+            agent_id=agent_id,
+            model=model,
+            source=source,
+        ),
+    )
+
+
+def record_agent_test_setup_generated(
+    organization_id: UUID,
+    request_id: UUID,
+    *,
+    workspace_id: Optional[UUID],
+    purpose: str,
+    model: Optional[str] = None,
+    scenario_count: Optional[int] = None,
+) -> None:
+    if workspace_id is None:
+        return
+    record_event(
+        AGENT_TEST_SETUP_GENERATED,
+        organization_id,
+        request_id,
+        properties=_event_properties(
+            workspace_id,
+            FEATURE_AGENT_PLAYGROUND,
+            quantity=1,
+            request_id=request_id,
+            purpose=purpose,
+            model=model,
+            scenario_count=scenario_count,
+        ),
+    )
diff --git a/app/workers/tasks/agent_flowchart_jobs.py b/app/workers/tasks/agent_flowchart_jobs.py
index 02431794..9b52b3c0 100644
--- a/app/workers/tasks/agent_flowchart_jobs.py
+++ b/app/workers/tasks/agent_flowchart_jobs.py
@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from uuid import UUID
+from uuid import UUID, uuid4
 
 from loguru import logger
 from sqlalchemy.orm.attributes import flag_modified
@@ -77,6 +77,16 @@ def generate_agent_flowchart_task(
         partial.agent_flowchart_status = "completed"
         flag_modified(partial, "agent_flowchart")
         db.commit()
+        from app.services.billing.flexprice_service import record_prompt_partial_ai_assisted
+
+        record_prompt_partial_ai_assisted(
+            partial.organization_id,
+            uuid4(),
+            workspace_id=partial.workspace_id,
+            mode="flowchart",
+            partial_id=partial.id,
+            model=model_str,
+        )
         logger.info(
             "Agent flowchart completed for partial {} ({} nodes)",
             partial_id,
@@ -162,6 +172,15 @@ def map_agent_flowchart_prompt_sections_task(
         partial.agent_flowchart_status = "completed"
         flag_modified(partial, "agent_flowchart")
         db.commit()
+        from app.services.billing.flexprice_service import record_prompt_partial_ai_assisted
+
+        record_prompt_partial_ai_assisted(
+            partial.organization_id,
+            uuid4(),
+            workspace_id=partial.workspace_id,
+            mode="flowchart_map",
+            partial_id=partial.id,
+        )
         logger.info("Agent flowchart prompt mapping completed for partial {}", partial_id)
     except Exception as exc:
         logger.exception(
diff --git a/app/workers/tasks/generate_evaluation_prompt_improvements.py b/app/workers/tasks/generate_evaluation_prompt_improvements.py
index fd22b1fb..4c76ef6f 100644
--- a/app/workers/tasks/generate_evaluation_prompt_improvements.py
+++ b/app/workers/tasks/generate_evaluation_prompt_improvements.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from datetime import datetime, timezone
-from uuid import UUID
+from uuid import UUID, uuid4
 
 from loguru import logger
 from sqlalchemy.orm.attributes import flag_modified
@@ -125,6 +125,18 @@ def generate_evaluation_prompt_improvements_task(
             evaluation.prompt_improvements = prompt_improvements_state_to_db(state)
             flag_modified(evaluation, "prompt_improvements")
             db.commit()
+            if state.status == "completed":
+                from app.services.billing.flexprice_service import (
+                    record_call_import_prompt_improvements_generated,
+                )
+
+                record_call_import_prompt_improvements_generated(
+                    evaluation.organization_id,
+                    uuid4(),
+                    workspace_id=evaluation.workspace_id,
+                    evaluation_id=evaluation.id,
+                    imported_agent_id=UUID(imported_agent_id),
+                )
             logger.info(
                 "Prompt improvements completed for evaluation {} ({} suggestions)",
                 evaluation_id,
diff --git a/app/workers/tasks/generate_evaluation_user_insights.py b/app/workers/tasks/generate_evaluation_user_insights.py
index 71f666df..c9180493 100644
--- a/app/workers/tasks/generate_evaluation_user_insights.py
+++ b/app/workers/tasks/generate_evaluation_user_insights.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from datetime import datetime, timezone
-from uuid import UUID
+from uuid import UUID, uuid4
 
 from loguru import logger
 from sqlalchemy.orm.attributes import flag_modified
@@ -100,6 +100,17 @@ def on_progress(completed: int, total: int) -> None:
             evaluation.user_insights = user_insights_state_to_db(state)
             flag_modified(evaluation, "user_insights")
             db.commit()
+            if state.status == "completed":
+                from app.services.billing.flexprice_service import (
+                    record_call_import_user_insights_generated,
+                )
+
+                record_call_import_user_insights_generated(
+                    evaluation.organization_id,
+                    uuid4(),
+                    workspace_id=evaluation.workspace_id,
+                    evaluation_id=evaluation.id,
+                )
     except Exception as exc:  # noqa: BLE001
         logger.exception(
             "User insights generation failed for evaluation {}: {}",
diff --git a/docs/billing/flexprice-saas-setup.md b/docs/billing/flexprice-saas-setup.md
index 82f95558..704c882c 100644
--- a/docs/billing/flexprice-saas-setup.md
+++ b/docs/billing/flexprice-saas-setup.md
@@ -81,6 +81,11 @@ Do **not** put a heavy per-row price on both batch import and evaluation — cus
 | Metrics AI assist | `metrics.ai_assist` | per request |
 | Metric studio | `metric_studio.run_completed` | completed items (SUM quantity) |
 | Scenario AI | `scenario.ai_text_generated` | per generation |
+| Prompt partials AI | `prompt_partial.ai_assisted` | per request (generate / improve / flowchart / map) |
+| Call import user insights | `call_import.user_insights_generated` | per completed generation |
+| Call import prompt improvements | `call_import.prompt_improvements_generated` | per completed generation |
+| Persona prompt generation | `persona.prompt_generated` | per generation |
+| Agent test setup | `agent.test_setup_generated` | per API call (test prompt / scenarios / full setup) |
 
 Observability is **not billed** — no plan usage charges for observability events.
 
@@ -106,6 +111,10 @@ After `--repair-call-imports`, re-sync subscriptions if price IDs changed.
 | Evaluator run with audio | `evaluator.recording_minutes_billed` | billable minutes |
 | Complete GEPA run | `prompt_optimization.run_completed` | candidate count |
 | Complete judge alignment | `judge_alignment.run_completed` | samples scored |
+| Prompt partial AI generate | `prompt_partial.ai_assisted` | 1 (`mode=generate`) |
+| Call import user insights completes | `call_import.user_insights_generated` | 1 |
+| Persona prompt generate | `persona.prompt_generated` | 1 |
+| Agent test setup generate | `agent.test_setup_generated` | 1 |
 
 Use Flexprice **Price Lookup** with `external_customer_id = organization UUID`.
 
diff --git a/scripts/setup_flexprice_meters.py b/scripts/setup_flexprice_meters.py
index de1a00f5..6d42a011 100644
--- a/scripts/setup_flexprice_meters.py
+++ b/scripts/setup_flexprice_meters.py
@@ -41,6 +41,11 @@
 METRICS_AI_ASSIST_EVENT = "metrics.ai_assist"
 METRIC_STUDIO_PRIMARY_EVENT = "metric_studio.run_completed"
 SCENARIO_AI_TEXT_EVENT = "scenario.ai_text_generated"
+PROMPT_PARTIAL_AI_ASSISTED_EVENT = "prompt_partial.ai_assisted"
+CALL_IMPORT_USER_INSIGHTS_EVENT = "call_import.user_insights_generated"
+CALL_IMPORT_PROMPT_IMPROVEMENTS_EVENT = "call_import.prompt_improvements_generated"
+PERSONA_PROMPT_GENERATED_EVENT = "persona.prompt_generated"
+AGENT_TEST_SETUP_GENERATED_EVENT = "agent.test_setup_generated"
 
 # Standalone meters (not owned by a license feature primary event).
 # Each row: (event_name, display_name, aggregation_type, aggregation_field|None)
@@ -54,6 +59,13 @@
         "billable_minutes",
     ),
     ("call_import.pdf_report_generated", "Call Import PDF Reports", "SUM", "quantity"),
+    (CALL_IMPORT_USER_INSIGHTS_EVENT, "Call Import User Insights", "COUNT", None),
+    (
+        CALL_IMPORT_PROMPT_IMPROVEMENTS_EVENT,
+        "Call Import Prompt Improvements",
+        "COUNT",
+        None,
+    ),
     # Evaluators — run + optional audio (run_completed meter from evaluators feature)
     (
         "evaluator.recording_minutes_billed",
@@ -73,6 +85,9 @@
     ("tts.report_completed", "TTS Reports Completed", "SUM", "quantity"),
     # Legacy evaluations
     ("evaluation.completed", "Legacy Evaluation Completed", "SUM", "quantity"),
+    # Agent playground AI helpers
+    (PERSONA_PROMPT_GENERATED_EVENT, "Persona Prompt Generation", "COUNT", None),
+    (AGENT_TEST_SETUP_GENERATED_EVENT, "Agent Test Setup Generation", "COUNT", None),
 ]
 
 # Ops guide: meters to attach as plan USAGE charges (display order for SaaS plans).
@@ -104,6 +119,24 @@
         "charge": True,
         "notes": "Optional export add-on.",
     },
+    {
+        "product": "Call Imports",
+        "plan_line": "Call import user insights",
+        "event_name": CALL_IMPORT_USER_INSIGHTS_EVENT,
+        "meter_name": "Call Import User Insights",
+        "aggregation": "COUNT",
+        "charge": True,
+        "notes": "On-demand map-reduce insights generation per evaluation.",
+    },
+    {
+        "product": "Call Imports",
+        "plan_line": "Call import prompt improvements",
+        "event_name": CALL_IMPORT_PROMPT_IMPROVEMENTS_EVENT,
+        "meter_name": "Call Import Prompt Improvements",
+        "aggregation": "COUNT",
+        "charge": True,
+        "notes": "Prompt improvement suggestions from evaluation clusters.",
+    },
     {
         "product": "Call Imports",
         "plan_line": "Imported rows (entitlement cap)",
@@ -131,6 +164,24 @@
         "charge": True,
         "notes": "Standalone test-agent API only; not double-billed with playground eval.",
     },
+    {
+        "product": "Agent Playground",
+        "plan_line": "Persona prompt generation",
+        "event_name": PERSONA_PROMPT_GENERATED_EVENT,
+        "meter_name": "Persona Prompt Generation",
+        "aggregation": "COUNT",
+        "charge": True,
+        "notes": "AI-generated caller persona prompts from agent prompts.",
+    },
+    {
+        "product": "Agent Playground",
+        "plan_line": "Agent test setup generation",
+        "event_name": AGENT_TEST_SETUP_GENERATED_EVENT,
+        "meter_name": "Agent Test Setup Generation",
+        "aggregation": "COUNT",
+        "charge": True,
+        "notes": "Test prompt and scenario draft generation for agents.",
+    },
     {
         "product": "Voice Playground",
         "plan_line": "Blind test responses",
@@ -215,6 +266,15 @@
         "aggregation": "COUNT",
         "charge": True,
     },
+    {
+        "product": "Prompt Partials",
+        "plan_line": "Prompt partial AI assist",
+        "event_name": PROMPT_PARTIAL_AI_ASSISTED_EVENT,
+        "meter_name": "Prompt Partial AI Assist",
+        "aggregation": "COUNT",
+        "charge": True,
+        "notes": "Generate, improve, flowchart, and flowchart prompt mapping.",
+    },
 ]
 
 LICENSE_FEATURES: list[dict[str, Any]] = [
@@ -299,6 +359,15 @@
         "event_name": SCENARIO_AI_TEXT_EVENT,
         "aggregation": {"type": "COUNT"},
     },
+    {
+        "name": "Prompt Partials",
+        "lookup_key": "prompt_partials",
+        "description": "AI-assisted prompt partial generate, improve, and flowchart workflows",
+        "unit_singular": "request",
+        "unit_plural": "requests",
+        "event_name": PROMPT_PARTIAL_AI_ASSISTED_EVENT,
+        "aggregation": {"type": "COUNT"},
+    },
 ]
 
 FEATURE_OWNED_EVENT_NAMES = frozenset(spec["event_name"] for spec in LICENSE_FEATURES)
diff --git a/tests/test_scripts/test_setup_flexprice_meters.py b/tests/test_scripts/test_setup_flexprice_meters.py
index 2aa9614b..64026479 100644
--- a/tests/test_scripts/test_setup_flexprice_meters.py
+++ b/tests/test_scripts/test_setup_flexprice_meters.py
@@ -39,6 +39,8 @@ def test_call_imports_plan_has_separate_paid_meters():
         "call_import.evaluation_completed",
         "call_import.recording_minutes_billed",
         "call_import.pdf_report_generated",
+        "call_import.user_insights_generated",
+        "call_import.prompt_improvements_generated",
     }
 
 
@@ -100,6 +102,14 @@ def test_scenario_ai_license_feature_spec():
     assert spec["aggregation"] == {"type": "COUNT"}
 
 
+def test_prompt_partials_license_feature_spec():
+    from scripts.setup_flexprice_meters import PROMPT_PARTIAL_AI_ASSISTED_EVENT
+
+    spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "prompt_partials")
+    assert spec["event_name"] == PROMPT_PARTIAL_AI_ASSISTED_EVENT
+    assert spec["aggregation"] == {"type": "COUNT"}
+
+
 def test_metric_scores_meta_keys():
     assert _is_metric_scores_meta_key("_billing") is True
     assert _is_metric_scores_meta_key(DISCOVERED_METRICS_KEY) is True
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index 6d050e35..f1d3006d 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -791,6 +791,81 @@ def test_record_scenario_ai_text_generated_includes_feature(mock_flexprice):
     assert payload["properties"]["quantity"] == "1"
 
 
+@patch("flexprice.Flexprice")
+def test_record_prompt_partial_ai_assisted_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    request_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_prompt_partial_ai_assisted(
+        org_id,
+        request_id,
+        workspace_id=workspace_id,
+        mode="generate",
+        model="gpt-4o",
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "prompt_partial.ai_assisted"
+    assert payload["properties"]["feature"] == "prompt_partials"
+    assert payload["properties"]["mode"] == "generate"
+
+
+@patch("flexprice.Flexprice")
+def test_record_call_import_user_insights_generated_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+    evaluation_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_call_import_user_insights_generated(
+        org_id,
+        uuid4(),
+        workspace_id=workspace_id,
+        evaluation_id=evaluation_id,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "call_import.user_insights_generated"
+    assert payload["properties"]["feature"] == "call_imports"
+
+
+@patch("flexprice.Flexprice")
+def test_record_agent_test_setup_generated_includes_feature(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    org_id = uuid4()
+    workspace_id = uuid4()
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_agent_test_setup_generated(
+        org_id,
+        uuid4(),
+        workspace_id=workspace_id,
+        purpose="full_setup",
+        scenario_count=3,
+    )
+
+    payload = mock_client.events.ingest_event.call_args.kwargs
+    assert payload["event_name"] == "agent.test_setup_generated"
+    assert payload["properties"]["feature"] == "agent_playground"
+    assert payload["properties"]["purpose"] == "full_setup"
+
+
 @patch("flexprice.Flexprice")
 def test_record_tts_generation_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
diff --git a/tests/test_services/test_flexprice_tracking_wiring.py b/tests/test_services/test_flexprice_tracking_wiring.py
index 705a304d..44ae6865 100644
--- a/tests/test_services/test_flexprice_tracking_wiring.py
+++ b/tests/test_services/test_flexprice_tracking_wiring.py
@@ -43,10 +43,30 @@
 SCENARIO_AI_WIRED = {
     "scenario.ai_text_generated": [
         "app/api/v1/routes/chat.py",
-        "app/api/v1/routes/agents.py",
     ],
 }
 
+PROMPT_PARTIALS_WIRED = {
+    "prompt_partial.ai_assisted": [
+        "app/api/v1/routes/prompt_partials.py",
+        "app/workers/tasks/agent_flowchart_jobs.py",
+    ],
+}
+
+CALL_IMPORT_AI_ADDONS_WIRED = {
+    "call_import.user_insights_generated": [
+        "app/workers/tasks/generate_evaluation_user_insights.py",
+    ],
+    "call_import.prompt_improvements_generated": [
+        "app/workers/tasks/generate_evaluation_prompt_improvements.py",
+    ],
+}
+
+AGENT_AI_HELPERS_WIRED = {
+    "persona.prompt_generated": ["app/api/v1/routes/personas.py"],
+    "agent.test_setup_generated": ["app/api/v1/routes/agents.py"],
+}
+
 
 def _file_contains(path: str, needle: str) -> bool:
     text = (REPO_ROOT / path).read_text(encoding="utf-8")
@@ -118,3 +138,37 @@ def test_scenario_ai_text_events_are_wired():
             assert _file_contains(path, "record_scenario_ai_text_generated") or _file_contains(
                 path, "record_chat_completion"
             ), f"{event_name} missing scenario flexprice hook in {path}"
+
+
+def test_prompt_partial_ai_events_are_wired():
+    for event_name, paths in PROMPT_PARTIALS_WIRED.items():
+        for path in paths:
+            assert _file_contains(path, "record_prompt_partial_ai_assisted"), (
+                f"{event_name} missing record_prompt_partial_ai_assisted in {path}"
+            )
+
+
+def test_call_import_ai_addon_events_are_wired():
+    record_fns = {
+        "call_import.user_insights_generated": "record_call_import_user_insights_generated",
+        "call_import.prompt_improvements_generated": (
+            "record_call_import_prompt_improvements_generated"
+        ),
+    }
+    for event_name, paths in CALL_IMPORT_AI_ADDONS_WIRED.items():
+        for path in paths:
+            assert _file_contains(path, record_fns[event_name]), (
+                f"{event_name} missing {record_fns[event_name]} in {path}"
+            )
+
+
+def test_agent_ai_helper_events_are_wired():
+    record_fns = {
+        "persona.prompt_generated": "record_persona_prompt_generated",
+        "agent.test_setup_generated": "record_agent_test_setup_generated",
+    }
+    for event_name, paths in AGENT_AI_HELPERS_WIRED.items():
+        for path in paths:
+            assert _file_contains(path, record_fns[event_name]), (
+                f"{event_name} missing {record_fns[event_name]} in {path}"
+            )

From f8916f1ed577e2f37e32e9ccf1cf990b602c08ea Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 28 Aug 2026 20:04:14 +0530
Subject: [PATCH 52/55] feat(playground): enhance call data processing by
 merging UI surface information and updating event handling for playground
 calls and fix sarvam ai flooding logs

---
 app/api/v1/routes/playground.py               |  20 ++-
 app/api/v1/routes/voice_agent.py              |   5 +
 app/services/billing/flexprice_service.py     |  41 ++++-
 app/services/media_urls.py                    |   3 +
 .../playground/post_call_processing.py        |  18 ++-
 app/workers/tasks/process_evaluator_result.py |   6 +
 docker-compose.yml                            |   3 +
 docs/billing/flexprice-saas-setup.md          |   1 +
 frontend/src/components/VoiceAgent.tsx        |   4 +-
 frontend/src/lib/api.ts                       |   1 +
 .../agents/components/AgentTalkSidebar.tsx    |  10 +-
 .../playground/agent/AgentPlayground.tsx      |   6 +-
 scripts/setup_flexprice_meters.py             | 149 +++++++++++++++++-
 src/efficientai/services/sarvam/stt.py        |  17 +-
 tests/test_services/test_flexprice_service.py |  56 +++++++
 .../test_playground_post_call_processing.py   |  38 +++++
 16 files changed, 361 insertions(+), 17 deletions(-)

diff --git a/app/api/v1/routes/playground.py b/app/api/v1/routes/playground.py
index 765bcb6c..1681ecbd 100644
--- a/app/api/v1/routes/playground.py
+++ b/app/api/v1/routes/playground.py
@@ -74,6 +74,7 @@ def poll_call_metrics(
     """
     import time
     from app.database import SessionLocal
+    from app.services.playground.post_call_processing import merge_playground_call_data
     from app.services.voice_providers import get_voice_provider
     
     db = SessionLocal()
@@ -115,8 +116,8 @@ def poll_call_metrics(
                     if isinstance(call_recording.call_data, dict)
                     else {}
                 )
-                if prev_data.get("external_usage_recorded") and isinstance(call_metrics, dict):
-                    call_metrics["external_usage_recorded"] = True
+                if isinstance(call_metrics, dict):
+                    call_metrics = merge_playground_call_data(prev_data, call_metrics)
                 call_recording.call_data = call_metrics
                 call_recording.status = CallRecordingStatus.UPDATED
                 db.commit()
@@ -368,6 +369,7 @@ class WebCallCreate(BaseModel):
     metadata: Optional[Dict[str, Any]] = None
     retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None
     custom_sip_headers: Optional[Dict[str, str]] = None
+    ui_surface: Optional[str] = None
 
 
 @router.post("/web-call", response_model=Dict[str, Any])
@@ -502,13 +504,16 @@ async def create_web_call(
                 provider_call_id = web_call_response.get("call_id")
 
             call_short_id = generate_unique_call_short_id(db)
+            stored_call_data = dict(web_call_response)
+            if web_call_data.ui_surface:
+                stored_call_data["ui_surface"] = web_call_data.ui_surface
             call_recording = CallRecording(
                 organization_id=organization_id,
                 workspace_id=workspace_id,
                 call_short_id=call_short_id,
                 status=CallRecordingStatus.PENDING,
                 source=CallRecordingSource.PLAYGROUND,
-                call_data=web_call_response,  # Store initial response
+                call_data=stored_call_data,
                 provider_call_id=provider_call_id,
                 provider_platform=integration.platform,
                 agent_id=agent.id
@@ -1147,8 +1152,13 @@ def _download_audio_from_payload(payload: Dict[str, Any]):
                 if hasattr(provider, "retrieve_call_metrics"):
                     refreshed_call_data = provider.retrieve_call_metrics(call_recording.provider_call_id)
                     if isinstance(refreshed_call_data, dict) and refreshed_call_data:
-                        call_data = refreshed_call_data
-                        call_recording.call_data = refreshed_call_data
+                        prev_data = (
+                            call_recording.call_data
+                            if isinstance(call_recording.call_data, dict)
+                            else {}
+                        )
+                        call_data = merge_playground_call_data(prev_data, refreshed_call_data)
+                        call_recording.call_data = call_data
                         db.commit()
                         logger.info(f"[Re-evaluate] Refreshed provider call data for call {call_recording.provider_call_id}")
                         audio_bytes, resp = _download_audio_from_payload(call_data)
diff --git a/app/api/v1/routes/voice_agent.py b/app/api/v1/routes/voice_agent.py
index 940358a7..9657c763 100644
--- a/app/api/v1/routes/voice_agent.py
+++ b/app/api/v1/routes/voice_agent.py
@@ -103,6 +103,7 @@ async def websocket_endpoint(
         agent_id = websocket.query_params.get("agent_id")
         persona_id = websocket.query_params.get("persona_id")
         scenario_id = websocket.query_params.get("scenario_id")
+        ui_surface = websocket.query_params.get("ui_surface")
         
         # Fetch agent and voice bundle once for routing and instructions
         agent = None
@@ -644,6 +645,8 @@ def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
                         "recording_s3_key": call_metadata.get("s3_key"),
                         "duration_seconds": call_metadata.get("duration"),
                     }
+                    if ui_surface:
+                        playground_call_data["ui_surface"] = ui_surface
 
                     # Create evaluator result with QUEUED status
                     # persona_id and scenario_id can be None for test calls without persona/scenario
@@ -855,6 +858,7 @@ def _extract_bearer(value: Optional[str]) -> Optional[str]:
     agent_id = request.query_params.get("agent_id")
     persona_id = request.query_params.get("persona_id")
     scenario_id = request.query_params.get("scenario_id")
+    ui_surface = request.query_params.get("ui_surface")
     
     # Determine which AI Provider to use based on agent configuration
     ai_provider = None
@@ -1000,6 +1004,7 @@ def check_provider(provider_enum):
         agent_id=agent_id,
         persona_id=persona_id,
         scenario_id=scenario_id,
+        ui_surface=ui_surface,
         fallback_host=request.headers.get("host", f"localhost:{settings.PORT}"),
         fallback_scheme=(
             request.headers.get("x-forwarded-proto")
diff --git a/app/services/billing/flexprice_service.py b/app/services/billing/flexprice_service.py
index 8f545aff..a9471f56 100644
--- a/app/services/billing/flexprice_service.py
+++ b/app/services/billing/flexprice_service.py
@@ -8,8 +8,10 @@
 Ingest **only when value is delivered** (completed), never on ``*_started`` /
 ``*_created`` / ``*_requested``. Event ``properties`` include billable fields
 (``workspace_id``, ``feature``, ``quantity``, ``billable_minutes``) plus audit
-IDs (``evaluation_id``, ``audio_seconds``, etc.) for support — audit fields are
-not used for Flexprice SUM/COUNT aggregation.
+IDs (``evaluation_id``, ``audio_seconds``, ``ui_surface``, etc.) for support —
+audit fields are not used for Flexprice SUM/COUNT aggregation. Set ``ui_surface``
+only when multiple UI paths share one event (e.g. ``agents_talk`` vs
+``agent_playground``); never wire meters to it.
 
 Billable events (wire plan usage charges to these meters only):
 
@@ -61,6 +63,10 @@
 FEATURE_SCENARIO_AI = "scenario_ai"
 FEATURE_PROMPT_PARTIALS = "prompt_partials"
 
+# Audit-only ui_surface values (never wire Flexprice meters to these).
+UI_SURFACE_AGENTS_TALK = "agents_talk"
+UI_SURFACE_AGENT_PLAYGROUND = "agent_playground"
+
 # Log once when metering is inactive so AWS/worker misconfig is obvious.
 _disabled_skip_logged = False
 
@@ -103,6 +109,16 @@
 CALL_IMPORT_PROMPT_IMPROVEMENTS_GENERATED = "call_import.prompt_improvements_generated"
 PERSONA_PROMPT_GENERATED = "persona.prompt_generated"
 AGENT_TEST_SETUP_GENERATED = "agent.test_setup_generated"
+# Deprecated meters — never ingest (bill on completion events instead).
+DEPRECATED_EVENT_NAMES = frozenset(
+    {
+        PLAYGROUND_CALL_EVALUATED,
+        PLAYGROUND_WEB_CALL_STARTED,
+        PLAYGROUND_WEBSOCKET_SESSION_STARTED,
+        TEST_AGENT_CONVERSATION_STARTED,
+        OBSERVABILITY_CALL_EVALUATED,
+    }
+)
 # Legacy aliases (Flexprice meters may still exist under old names)
 METRICS_LLM_ASSIST = METRICS_AI_ASSIST
 CHAT_COMPLETION = SCENARIO_AI_TEXT_GENERATED
@@ -270,6 +286,7 @@ def _event_properties(
     *,
     quantity: Optional[Union[int, float]] = None,
     billable_minutes: Optional[int] = None,
+    ui_surface: Optional[str] = None,
     **audit: Any,
 ) -> dict[str, Any]:
     """Billable fields plus optional audit metadata for support traceability."""
@@ -279,7 +296,14 @@ def _event_properties(
         quantity=quantity,
         billable_minutes=billable_minutes,
     )
+    surface = (str(ui_surface).strip() if ui_surface is not None else "") or None
+    if not surface and isinstance(audit.get("ui_surface"), str):
+        surface = audit["ui_surface"].strip() or None
+    if surface:
+        props["ui_surface"] = surface
     for key, value in audit.items():
+        if key == "ui_surface":
+            continue
         if value is None:
             continue
         if isinstance(value, str) and not value.strip():
@@ -323,6 +347,16 @@ def record_event(
             )
         return False
 
+    if event_name in DEPRECATED_EVENT_NAMES:
+        if _verbose_logging():
+            logger.info(
+                "Flexprice SKIP deprecated {} org={} event_id={}",
+                event_name,
+                organization_id,
+                event_id,
+            )
+        return False
+
     coerced = _coerce_properties(properties)
     quantity = coerced.get("quantity")
 
@@ -794,7 +828,9 @@ def record_playground_evaluation_completed(
     call_short_id: str,
     duration_seconds: Optional[float] = None,
     metric_count: int = 0,
+    ui_surface: Optional[str] = None,
 ) -> None:
+    """Bill playground voice/web call scoring. Pass ``ui_surface`` when known."""
     minutes = _billable_minutes(duration_seconds)
     record_event(
         PLAYGROUND_EVALUATION_COMPLETED,
@@ -809,6 +845,7 @@ def record_playground_evaluation_completed(
             call_short_id=call_short_id,
             duration_seconds=duration_seconds,
             metric_count=metric_count,
+            ui_surface=ui_surface,
         ),
     )
 
diff --git a/app/services/media_urls.py b/app/services/media_urls.py
index 6ef55adf..80cc674b 100644
--- a/app/services/media_urls.py
+++ b/app/services/media_urls.py
@@ -67,6 +67,7 @@ def build_voice_agent_ws_url(
     agent_id: Optional[str] = None,
     persona_id: Optional[str] = None,
     scenario_id: Optional[str] = None,
+    ui_surface: Optional[str] = None,
     fallback_host: Optional[str] = None,
     fallback_scheme: str = "http",
 ) -> str:
@@ -86,5 +87,7 @@ def build_voice_agent_ws_url(
         query += f"&persona_id={quote(persona_id)}"
     if scenario_id:
         query += f"&scenario_id={quote(scenario_id)}"
+    if ui_surface:
+        query += f"&ui_surface={quote(ui_surface, safe='')}"
 
     return f"{base}{settings.API_V1_PREFIX}/voice-agent/ws?{query}"
diff --git a/app/services/playground/post_call_processing.py b/app/services/playground/post_call_processing.py
index 7a547a68..05a7f11f 100644
--- a/app/services/playground/post_call_processing.py
+++ b/app/services/playground/post_call_processing.py
@@ -9,6 +9,21 @@
 
 from app.models.database import CallRecording
 
+PLAYGROUND_CALL_DATA_PRESERVE_KEYS = ("ui_surface", "external_usage_recorded")
+
+
+def merge_playground_call_data(
+    prev: Optional[dict[str, Any]],
+    new: dict[str, Any],
+) -> dict[str, Any]:
+    """Keep internal audit fields when provider metrics replace call_data."""
+    merged = dict(new)
+    if isinstance(prev, dict):
+        for key in PLAYGROUND_CALL_DATA_PRESERVE_KEYS:
+            if prev.get(key) is not None:
+                merged[key] = prev[key]
+    return merged
+
 
 def _lock_call_recording(db: Session, call_recording_id: UUID) -> Optional[CallRecording]:
     return (
@@ -53,7 +68,7 @@ def record_playground_post_call_usage_once(
     platform_key = str(provider_platform or "").lower()
 
     if stored_data.get("external_usage_recorded") and isinstance(metrics, dict):
-        metrics["external_usage_recorded"] = True
+        metrics = merge_playground_call_data(stored_data, metrics)
         locked.call_data = metrics
         db.commit()
     elif isinstance(metrics, dict):
@@ -75,6 +90,7 @@ def record_playground_post_call_usage_once(
             return False, call_metrics
 
         metrics["external_usage_recorded"] = True
+        metrics = merge_playground_call_data(stored_data, metrics)
         locked.call_data = metrics
         try:
             db.commit()
diff --git a/app/workers/tasks/process_evaluator_result.py b/app/workers/tasks/process_evaluator_result.py
index 3f0b9dd8..e3c68bf5 100644
--- a/app/workers/tasks/process_evaluator_result.py
+++ b/app/workers/tasks/process_evaluator_result.py
@@ -873,6 +873,11 @@ def process_evaluator_result_task(self, result_id: str):
 
                 call_recording = _playground_call_recording(db, result)
                 if call_recording:
+                    call_data = (
+                        call_recording.call_data
+                        if isinstance(call_recording.call_data, dict)
+                        else {}
+                    )
                     record_playground_evaluation_completed(
                         result.organization_id,
                         f"{result.id}:{self.request.id}",
@@ -881,6 +886,7 @@ def process_evaluator_result_task(self, result_id: str):
                         call_short_id=call_recording.call_short_id,
                         duration_seconds=result.duration_seconds,
                         metric_count=len(metric_scores) or selected_metric_count,
+                        ui_surface=call_data.get("ui_surface"),
                     )
                 else:
                     record_evaluator_run_completed(
diff --git a/docker-compose.yml b/docker-compose.yml
index 1d6f0d0b..ee5c01b5 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -120,6 +120,9 @@ services:
     command: eai telephony-worker --config /app/config.yml --host 0.0.0.0 --port 8001
 
   worker:
+    # Local dev: `eai start-all` already runs Celery workers. Skip this service
+    # when using start-all, or rebuild the image (`docker compose build worker`)
+    # so billing code matches your checkout — stale images emit deprecated events.
     # Pre-built image from GitHub Container Registry
     # Use EFFICIENTAI_VERSION env var to pin to a specific version (e.g., 1.0.0)
     image: ghcr.io/efficientai-tech/efficientai-worker:${EFFICIENTAI_VERSION:-latest}
diff --git a/docs/billing/flexprice-saas-setup.md b/docs/billing/flexprice-saas-setup.md
index 704c882c..b7f0944f 100644
--- a/docs/billing/flexprice-saas-setup.md
+++ b/docs/billing/flexprice-saas-setup.md
@@ -43,6 +43,7 @@ Env overrides: `FLEXPRICE_AUTO_SUBSCRIBE`, `FLEXPRICE_DEFAULT_PLAN_ID`. Leave `a
 ```bash
 python scripts/setup_flexprice_meters.py
 python scripts/setup_flexprice_meters.py --repair-call-imports   # if batch_created meters were duplicated
+python scripts/setup_flexprice_meters.py --repair-playground   # archive call_evaluated + duplicate evaluation_completed meters
 python scripts/setup_flexprice_meters.py --repair-features --dry-run   # preview stale feature→meter links
 python scripts/setup_flexprice_meters.py --repair-features             # repoint license features (delete + recreate)
 ```
diff --git a/frontend/src/components/VoiceAgent.tsx b/frontend/src/components/VoiceAgent.tsx
index 07253840..07650b79 100644
--- a/frontend/src/components/VoiceAgent.tsx
+++ b/frontend/src/components/VoiceAgent.tsx
@@ -22,6 +22,7 @@ interface VoiceAgentProps {
   agentId?: string
   customEndpoint?: string
   customEndpointLabel?: string
+  billingSurface?: 'agents_talk' | 'agent_playground'
   onSessionSaved?: () => void
   compact?: boolean
   sidebarLayout?: boolean
@@ -30,7 +31,7 @@ interface VoiceAgentProps {
 
 type TranscriptEntry = { role: 'user' | 'agent'; content: string; timestamp: string }
 
-export default function VoiceAgent({ personaId, scenarioId, agentId, customEndpoint, customEndpointLabel, onSessionSaved, compact = false, sidebarLayout = false, agentDisplayName }: VoiceAgentProps) {
+export default function VoiceAgent({ personaId, scenarioId, agentId, customEndpoint, customEndpointLabel, billingSurface, onSessionSaved, compact = false, sidebarLayout = false, agentDisplayName }: VoiceAgentProps) {
   const { selectedAgent } = useAgentStore()
   
   // Use agentId prop if provided, otherwise fall back to selectedAgent from store
@@ -419,6 +420,7 @@ export default function VoiceAgent({ personaId, scenarioId, agentId, customEndpo
       }
       if (!customEndpoint && personaId) params.append('persona_id', personaId)
       if (!customEndpoint && scenarioId) params.append('scenario_id', scenarioId)
+      if (!customEndpoint && billingSurface) params.append('ui_surface', billingSurface)
 
       if (!customEndpoint && params.toString()) {
         endpointUrl += `?${params.toString()}`
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index d3ffa521..f8b74cc1 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -3870,6 +3870,7 @@ class ApiClient {
     metadata?: Record<string, any>
     retell_llm_dynamic_variables?: Record<string, any>
     custom_sip_headers?: Record<string, string>
+    ui_surface?: 'agents_talk' | 'agent_playground'
   }): Promise<{
     call_type: string
     access_token?: string
diff --git a/frontend/src/pages/agents/components/AgentTalkSidebar.tsx b/frontend/src/pages/agents/components/AgentTalkSidebar.tsx
index 1afd845d..121f1839 100644
--- a/frontend/src/pages/agents/components/AgentTalkSidebar.tsx
+++ b/frontend/src/pages/agents/components/AgentTalkSidebar.tsx
@@ -198,7 +198,7 @@ export default function AgentTalkSidebar({
           setIsConnected(false)
           setActiveSpeaker(null)
         })
-        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
+        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {}, ui_surface: 'agents_talk' })
         await client.startCall({
           accessToken: webCall.access_token!,
           callId: webCall.call_id,
@@ -242,7 +242,7 @@ export default function AgentTalkSidebar({
             })
           }
         })
-        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
+        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {}, ui_surface: 'agents_talk' })
         callShortIdRef.current = webCall.call_short_id ?? null
         const vapiCall = await client.start(agent.voice_ai_agent_id!)
         if (callShortIdRef.current && vapiCall?.id) {
@@ -253,7 +253,7 @@ export default function AgentTalkSidebar({
           }
         }
       } else if (isElevenLabs) {
-        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
+        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {}, ui_surface: 'agents_talk' })
         if (!webCall.signed_url) throw new Error('No signed URL')
         const conversation = await Conversation.startSession({
           signedUrl: webCall.signed_url,
@@ -297,7 +297,7 @@ export default function AgentTalkSidebar({
         elevenLabsConversationRef.current = conversation
       } else if (isSmallest) {
         const { AtomsClient } = await import('atoms-client-sdk')
-        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {} })
+        const webCall = await apiClient.createWebCall({ agent_id: agent.id, metadata: {}, ui_surface: 'agents_talk' })
         if (!webCall.access_token || !webCall.host) throw new Error('Missing Smallest credentials')
         const client = new AtomsClient()
         smallestClientRef.current = client
@@ -375,7 +375,7 @@ export default function AgentTalkSidebar({
           {mode === 'test_agent' ? (
             canTalkTest ? (
               <div className="flex-1 min-h-0 flex flex-col p-4">
-                <VoiceAgent agentId={agent.id} compact sidebarLayout agentDisplayName={agent.name} />
+                <VoiceAgent agentId={agent.id} compact sidebarLayout agentDisplayName={agent.name} billingSurface="agents_talk" />
               </div>
             ) : (
               <div className="flex-1 flex items-center justify-center p-8 text-center text-sm text-gray-500">
diff --git a/frontend/src/pages/playground/agent/AgentPlayground.tsx b/frontend/src/pages/playground/agent/AgentPlayground.tsx
index e790766e..66d195dc 100644
--- a/frontend/src/pages/playground/agent/AgentPlayground.tsx
+++ b/frontend/src/pages/playground/agent/AgentPlayground.tsx
@@ -294,6 +294,7 @@ export default function AgentPlayground() {
           metadata: {},
           retell_llm_dynamic_variables: {},
           custom_sip_headers: {},
+          ui_surface: 'agent_playground',
         })
 
         if (!webCallResponse.call_id || !webCallResponse.access_token) {
@@ -331,6 +332,7 @@ export default function AgentPlayground() {
         const webCallResponse = await apiClient.createWebCall({
           agent_id: fullAgent.id,
           metadata: {},
+          ui_surface: 'agent_playground',
         })
 
         if (webCallResponse.call_short_id) {
@@ -435,6 +437,7 @@ export default function AgentPlayground() {
         const webCallResponse = await apiClient.createWebCall({
           agent_id: fullAgent.id,
           metadata: {},
+          ui_surface: 'agent_playground',
         })
 
         if (webCallResponse.call_short_id) {
@@ -545,6 +548,7 @@ export default function AgentPlayground() {
         const webCallResponse = await apiClient.createWebCall({
           agent_id: fullAgent.id,
           metadata: {},
+          ui_surface: 'agent_playground',
         })
 
         if (webCallResponse.call_short_id) {
@@ -1574,7 +1578,7 @@ export default function AgentPlayground() {
             </div>
 
             <div className="p-6">
-              <VoiceAgent agentId={selectedAgent?.id} />
+              <VoiceAgent agentId={selectedAgent?.id} billingSurface="agent_playground" />
             </div>
           </div>
         </div>
diff --git a/scripts/setup_flexprice_meters.py b/scripts/setup_flexprice_meters.py
index 6d42a011..0531d2a5 100644
--- a/scripts/setup_flexprice_meters.py
+++ b/scripts/setup_flexprice_meters.py
@@ -463,6 +463,29 @@ def _delete_meter(client: httpx.Client, meter_id: str) -> None:
     resp.raise_for_status()
 
 
+def _archive_meter(client: httpx.Client, meter_id: str) -> dict[str, Any]:
+    """Remove or archive a meter; archive is the fallback when delete is blocked."""
+    for method, suffix in (("DELETE", ""), ("POST", "/archive")):
+        url = f"{_base_url()}/meters/{meter_id}{suffix}"
+        if method == "DELETE":
+            resp = client.delete(url, headers=_headers())
+        else:
+            resp = client.post(url, headers=_headers())
+        if resp.status_code in (200, 204, 404):
+            return {"meter_id": meter_id, "method": method, "status": resp.status_code}
+    resp.raise_for_status()
+    return {"meter_id": meter_id, "status": resp.status_code}
+
+
+PLAYGROUND_DEPRECATED_EVENTS = frozenset(
+    {
+        "playground.call_evaluated",
+        "playground.web_call_started",
+        "playground.websocket_session_started",
+    }
+)
+
+
 def _list_prices(client: httpx.Client) -> list[dict[str, Any]]:
     items: list[dict[str, Any]] = []
     offset = 0
@@ -640,6 +663,108 @@ def restore_missing_plan_usage_prices(client: httpx.Client) -> dict[str, Any]:
     return result
 
 
+def repair_playground_meters(
+    client: httpx.Client,
+    sdk: Flexprice,
+    *,
+    dry_run: bool = False,
+) -> dict[str, Any]:
+    """Archive legacy playground meters; keep SUM(billable_minutes) evaluation_completed only."""
+    result: dict[str, Any] = {
+        "kept_meter_id": None,
+        "archived_meters": [],
+        "removed_features": [],
+        "skipped": [],
+        "errors": [],
+        "dry_run": dry_run,
+    }
+
+    meters_by_event = _list_meters_by_event(client, active_only=True)
+    all_prices = _list_prices(client)
+    canonical = _pick_canonical_meter(
+        meters_by_event.get(AGENT_PLAYGROUND_PRIMARY_EVENT, []),
+        all_prices,
+        agg_type="SUM",
+        agg_field="billable_minutes",
+    )
+    if canonical is None:
+        result["errors"].append(
+            "No published playground.evaluation_completed meter with SUM(billable_minutes)"
+        )
+        return result
+
+    result["kept_meter_id"] = canonical["id"]
+    to_archive: list[dict[str, Any]] = []
+    for event_name in PLAYGROUND_DEPRECATED_EVENTS:
+        to_archive.extend(meters_by_event.get(event_name, []))
+    for meter in meters_by_event.get(AGENT_PLAYGROUND_PRIMARY_EVENT, []):
+        if meter["id"] != canonical["id"]:
+            to_archive.append(meter)
+
+    seen_meter_ids: set[str] = set()
+    for meter in to_archive:
+        meter_id = meter["id"]
+        if meter_id in seen_meter_ids:
+            continue
+        seen_meter_ids.add(meter_id)
+        entry = {
+            "id": meter_id,
+            "event_name": meter.get("event_name"),
+            "name": meter.get("name"),
+        }
+        if dry_run:
+            result["archived_meters"].append({**entry, "dry_run": True})
+            continue
+        try:
+            archived = _archive_meter(client, meter_id)
+            result["archived_meters"].append({**entry, **archived})
+        except Exception as exc:
+            result["errors"].append(f"meter {meter_id}: {exc}")
+
+    agent_playground_features = [
+        feature
+        for feature in _list_all_features(client)
+        if feature.get("lookup_key") == "agent_playground"
+    ]
+    for feature in agent_playground_features:
+        if feature.get("meter_id") == canonical["id"]:
+            result["skipped"].append(feature["id"])
+            continue
+        entry = {
+            "id": feature["id"],
+            "meter_id": feature.get("meter_id"),
+            "name": feature.get("name"),
+        }
+        if dry_run:
+            result["removed_features"].append({**entry, "dry_run": True})
+            continue
+        try:
+            sdk.features.delete_feature(id=feature["id"])
+            result["removed_features"].append(entry)
+        except Exception as exc:
+            result["errors"].append(f"feature {feature['id']}: {exc}")
+
+    if not dry_run and len(agent_playground_features) == len(result["removed_features"]):
+        spec = next(item for item in LICENSE_FEATURES if item["lookup_key"] == "agent_playground")
+        try:
+            created = sdk.features.create_feature(
+                name=spec["name"],
+                type_="metered",
+                lookup_key=spec["lookup_key"],
+                description=spec["description"],
+                unit_singular=spec.get("unit_singular"),
+                unit_plural=spec.get("unit_plural"),
+                meter_id=canonical["id"],
+            )
+            result["recreated_feature_id"] = created.id
+        except Exception as exc:
+            message = str(exc).lower()
+            if "already exist" not in message and "duplicate" not in message:
+                result["errors"].append(f"recreate agent_playground feature: {exc}")
+
+    return result
+
+
 def repair_call_import_batch_meter(client: httpx.Client) -> dict[str, Any]:
     """Ensure one SUM(quantity) meter for call_import.batch_created and remove duplicates."""
     result: dict[str, Any] = {
@@ -1064,6 +1189,11 @@ def main() -> int:
         action="store_true",
         help="Remove duplicate batch_created meters and migrate pricing to SUM(quantity).",
     )
+    parser.add_argument(
+        "--repair-playground",
+        action="store_true",
+        help="Archive legacy playground meters (call_evaluated, duplicates) and fix agent_playground feature.",
+    )
     parser.add_argument(
         "--restore-plan-usage-prices",
         action="store_true",
@@ -1099,6 +1229,20 @@ def main() -> int:
 
     summary: dict[str, Any] = {}
     with httpx.Client(timeout=60.0) as http_client:
+        if args.repair_playground:
+            with Flexprice(
+                server_url=settings.FLEXPRICE_API_HOST,
+                api_key_auth=settings.FLEXPRICE_API_KEY,
+            ) as sdk:
+                summary["playground_repair"] = repair_playground_meters(
+                    http_client,
+                    sdk,
+                    dry_run=args.dry_run,
+                )
+            if args.dry_run:
+                print(json.dumps(summary, indent=2))
+                failed = summary.get("playground_repair", {}).get("errors") or []
+                return 1 if failed else 0
         if args.repair_call_imports:
             summary["call_import_batch_repair"] = repair_call_import_batch_meter(http_client)
         if args.restore_plan_usage_prices:
@@ -1136,7 +1280,10 @@ def main() -> int:
     failed_meters = bootstrap_summary["meters"]["failed"]
     failed_features = bootstrap_summary["features"]["failed"]
     repair_failed = (summary.get("feature_repair") or {}).get("failed") or []
-    return 1 if (failed_meters or failed_features or repair_failed) else 0
+    playground_failed = (summary.get("playground_repair") or {}).get("errors") or []
+    if args.repair_playground and not args.repair_features and not args.repair_call_imports:
+        return 1 if playground_failed else 0
+    return 1 if (failed_meters or failed_features or repair_failed or playground_failed) else 0
 
 
 if __name__ == "__main__":
diff --git a/src/efficientai/services/sarvam/stt.py b/src/efficientai/services/sarvam/stt.py
index 2b775812..eedb493c 100644
--- a/src/efficientai/services/sarvam/stt.py
+++ b/src/efficientai/services/sarvam/stt.py
@@ -145,6 +145,7 @@ def __init__(
         self._websocket_context = None
         self._socket_client = None
         self._receive_task = None
+        self._disconnected_logged = False
 
     def language_to_service_language(self, language: Language) -> str:
         """Convert efficientai Language enum to Sarvam's language code.
@@ -244,7 +245,17 @@ async def run_stt(self, audio: bytes):
             Frame: None (transcription results come via WebSocket callbacks).
         """
         if not self._socket_client:
-            logger.warning("WebSocket not connected, cannot process audio")
+            if not self._disconnected_logged:
+                self._disconnected_logged = True
+                logger.error(
+                    "Sarvam STT WebSocket not connected; dropping audio until reconnect"
+                )
+                await self.push_error(
+                    ErrorFrame(
+                        "Sarvam speech-to-text is unavailable. Check your Sarvam API key "
+                        "and network connection, then try the call again."
+                    )
+                )
             yield None
             return
 
@@ -329,15 +340,18 @@ def _message_handler(message):
             # Start receive task using EfficientAI's task management
             self._receive_task = self.create_task(self._receive_task_handler())
 
+            self._disconnected_logged = False
             logger.info("Connected to Sarvam successfully")
 
         except ApiError as e:
             logger.error(f"Sarvam API error: {e}")
+            self._disconnected_logged = True
             await self.push_error(ErrorFrame(f"Sarvam API error: {e}"))
         except Exception as e:
             logger.error(f"Failed to connect to Sarvam: {e}")
             self._socket_client = None
             self._websocket_context = None
+            self._disconnected_logged = True
             await self.push_error(ErrorFrame(f"Failed to connect to Sarvam: {e}"))
 
     async def _disconnect(self):
@@ -356,6 +370,7 @@ async def _disconnect(self):
                 logger.debug("Disconnected from Sarvam WebSocket")
                 self._socket_client = None
                 self._websocket_context = None
+                self._disconnected_logged = False
 
     async def _receive_task_handler(self):
         """Handle incoming messages from Sarvam WebSocket.
diff --git a/tests/test_services/test_flexprice_service.py b/tests/test_services/test_flexprice_service.py
index f1d3006d..912d2b61 100644
--- a/tests/test_services/test_flexprice_service.py
+++ b/tests/test_services/test_flexprice_service.py
@@ -444,6 +444,23 @@ def test_record_playground_call_evaluated_does_not_ingest(mock_flexprice):
     mock_flexprice.assert_not_called()
 
 
+@patch("flexprice.Flexprice")
+def test_record_event_skips_deprecated_playground_call_evaluated(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    assert (
+        svc.record_event(
+            svc.PLAYGROUND_CALL_EVALUATED,
+            uuid4(),
+            "attempt-1",
+            properties={"workspace_id": uuid4(), "call_short_id": "123456"},
+        )
+        is False
+    )
+    mock_flexprice.assert_not_called()
+
+
 @patch("flexprice.Flexprice")
 def test_record_playground_web_call_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
@@ -499,6 +516,45 @@ def test_record_playground_evaluation_completed_includes_feature(mock_flexprice)
     assert payload["properties"]["billable_minutes"] == "1"
 
 
+def test_event_properties_ui_surface_is_audit_only():
+    props = svc._event_properties(
+        uuid4(),
+        svc.FEATURE_AGENT_PLAYGROUND,
+        quantity=1,
+        ui_surface=svc.UI_SURFACE_AGENTS_TALK,
+    )
+    assert props["feature"] == "agent_playground"
+    assert props["ui_surface"] == "agents_talk"
+    assert "ui_surface" not in svc._billing_properties(uuid4(), svc.FEATURE_AGENT_PLAYGROUND, quantity=1)
+
+
+def test_event_properties_omits_blank_ui_surface():
+    props = svc._event_properties(uuid4(), svc.FEATURE_EVALUATORS, quantity=1, ui_surface="  ")
+    assert "ui_surface" not in props
+
+
+@patch("flexprice.Flexprice")
+def test_record_playground_evaluation_completed_includes_ui_surface(mock_flexprice):
+    settings.FLEXPRICE_ENABLED = True
+    settings.FLEXPRICE_API_KEY = "test-key"
+
+    mock_client = MagicMock()
+    mock_flexprice.return_value.__enter__.return_value = mock_client
+
+    svc.record_playground_evaluation_completed(
+        uuid4(),
+        "attempt-1",
+        evaluator_result_id=uuid4(),
+        workspace_id=uuid4(),
+        call_short_id="999",
+        duration_seconds=30,
+        ui_surface=svc.UI_SURFACE_AGENT_PLAYGROUND,
+    )
+
+    props = mock_client.events.ingest_event.call_args.kwargs["properties"]
+    assert props["ui_surface"] == "agent_playground"
+
+
 @patch("flexprice.Flexprice")
 def test_record_test_agent_conversation_started_does_not_ingest(mock_flexprice):
     settings.FLEXPRICE_ENABLED = True
diff --git a/tests/test_services/test_playground_post_call_processing.py b/tests/test_services/test_playground_post_call_processing.py
index c3b7376d..1a26ed43 100644
--- a/tests/test_services/test_playground_post_call_processing.py
+++ b/tests/test_services/test_playground_post_call_processing.py
@@ -14,10 +14,48 @@
 )
 from app.services.playground.post_call_processing import (
     claim_playground_evaluator_result_slot,
+    merge_playground_call_data,
     record_playground_post_call_usage_once,
 )
 
 
+def test_merge_playground_call_data_preserves_ui_surface():
+    merged = merge_playground_call_data(
+        {"ui_surface": "agents_talk", "external_usage_recorded": True},
+        {"call_status": "ended", "duration_seconds": 30},
+    )
+    assert merged["ui_surface"] == "agents_talk"
+    assert merged["external_usage_recorded"] is True
+    assert merged["call_status"] == "ended"
+
+
+def test_record_playground_post_call_usage_once_preserves_ui_surface(
+    db_session, org_id, default_workspace, playground_agent, monkeypatch
+):
+    recording = _make_playground_recording(
+        db_session,
+        org_id=org_id,
+        workspace_id=default_workspace.id,
+        agent_id=playground_agent.id,
+        call_data={"ui_surface": "agent_playground", "call_status": "ended"},
+    )
+
+    monkeypatch.setattr(
+        "app.services.usage.external_agent_usage.apply_playground_provider_usage_from_call_data",
+        lambda **_kwargs: None,
+    )
+
+    proceed, updated = record_playground_post_call_usage_once(
+        db_session,
+        recording.id,
+        provider_platform="retell",
+        call_metrics={"call_status": "ended", "duration_seconds": 42},
+    )
+    assert proceed is True
+    assert updated["ui_surface"] == "agent_playground"
+    assert updated["external_usage_recorded"] is True
+
+
 @pytest.fixture
 def playground_agent(db_session, org_id, default_workspace):
     agent = Agent(

From 88fe59dfe05390e1b8d625e4aa2b5dce9244b04d Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Wed, 2 Sep 2026 15:44:37 +0530
Subject: [PATCH 53/55] feat(api): add traces support to evaluator results and
 playground routes, enhancing telemetry capabilities

---
 .codebase-memory/.gitattributes               |     3 +
 .codebase-memory/graph.db.zst                 |   Bin 0 -> 10703053 bytes
 app/api/v1/api.py                             |   208 +-
 app/api/v1/routes/evaluator_results.py        |    28 +-
 app/api/v1/routes/playground.py               |  3807 ++---
 app/api/v1/routes/synthetic_traces.py         |   379 +
 app/api/v1/routes/vobiz_telephony.py          |  1567 ++-
 app/core/security_headers_middleware.py       |   121 +-
 app/migrations/078_synthetic_call_traces.py   |   170 +
 app/models/database.py                        |  6131 ++++----
 app/models/schemas.py                         | 10901 +++++++--------
 app/models/synthetic_trace_schemas.py         |   148 +
 .../evaluators/evaluator_phone_run_service.py |   352 +-
 app/services/synthetic_traces/__init__.py     |     1 +
 app/services/synthetic_traces/otlp_ingest.py  |   152 +
 app/services/synthetic_traces/otlp_mapper.py  |  1034 ++
 .../synthetic_traces/trace_service.py         |  1116 ++
 .../telephony/call_recording_lifecycle.py     |  1210 +-
 app/services/telephony/vobiz_client.py        |   663 +-
 .../voice_agent/sut_latency_observer.py       |    90 +
 app/services/voice_agent/voice_bundle.py      |  2334 ++--
 app/utils/call_recordings.py                  |    86 +-
 .../tasks/finalize_telephony_recording.py     |     2 +
 app/workers/tasks/initiate_vobiz_outbound.py  |   164 +-
 docs/examples/pipecat.env.example             |    31 +
 .../examples/pipecat_inbound_phone_tracing.py |    69 +
 .../pipecat_multi_agent_webrtc_tracing.py     |   419 +
 .../pipecat_multi_provider_webrtc_tracing.py  |   180 +
 .../pipecat_upstream_webrtc_tracing.py        |   150 +
 .../pipecat_upstream_websocket_tracing.py     |   152 +
 docs/synthetic-call-traces-pipecat.md         |   272 +
 docs/synthetic-call-traces-tdd-confluence.md  |   730 +
 frontend/src/App.tsx                          |   504 +-
 .../src/components/GenericVoiceWSClient.tsx   |  2542 ++--
 frontend/src/components/Layout.tsx            |     2 +
 .../CustomWebSocketCallDetails.tsx            |   835 +-
 .../SyntheticCallTracePanel.tsx               |   750 +
 frontend/src/lib/api.ts                       | 11569 ++++++++--------
 .../components/EvaluatorPhoneOutboundForm.tsx |   226 +-
 .../components/EvaluatorSmartRunModal.tsx     |   369 +-
 .../results/EvaluatorResultDetail.tsx         |    21 +-
 .../src/pages/test-insights/TestInsights.tsx  |   613 +
 pyproject.toml                                |   358 +-
 scripts/pipecat_trace_dev_bot.py              |   207 +
 scripts/test_local_pipecat_trace.py           |   163 +
 scripts/test_ws_echo.py                       |   246 +-
 src/efficientai/integrations/__init__.py      |     1 +
 .../efficientai_traces/__init__.py            |    49 +
 .../efficientai_traces/correlation.py         |   149 +
 .../efficientai_traces/handshake.py           |    71 +
 .../efficientai_traces/pipecat_upstream.py    |   206 +
 .../integrations/efficientai_traces/setup.py  |   334 +
 src/efficientai/utils/tracing/setup.py        |    13 +
 .../test_security_headers_middleware.py       |   305 +-
 tests/test_efficientai_traces_correlation.py  |    79 +
 .../test_telephony/test_vobiz.py              |   471 +-
 tests/test_synthetic_trace_ingest_service.py  |   364 +
 tests/test_synthetic_trace_otlp_ingest.py     |    37 +
 tests/test_synthetic_trace_otlp_mapper.py     |   443 +
 59 files changed, 31358 insertions(+), 22239 deletions(-)
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 create mode 100644 .codebase-memory/graph.db.zst
 create mode 100644 app/api/v1/routes/synthetic_traces.py
 create mode 100644 app/migrations/078_synthetic_call_traces.py
 create mode 100644 app/models/synthetic_trace_schemas.py
 create mode 100644 app/services/synthetic_traces/__init__.py
 create mode 100644 app/services/synthetic_traces/otlp_ingest.py
 create mode 100644 app/services/synthetic_traces/otlp_mapper.py
 create mode 100644 app/services/synthetic_traces/trace_service.py
 create mode 100644 app/services/voice_agent/sut_latency_observer.py
 create mode 100644 docs/examples/pipecat.env.example
 create mode 100644 docs/examples/pipecat_inbound_phone_tracing.py
 create mode 100644 docs/examples/pipecat_multi_agent_webrtc_tracing.py
 create mode 100644 docs/examples/pipecat_multi_provider_webrtc_tracing.py
 create mode 100644 docs/examples/pipecat_upstream_webrtc_tracing.py
 create mode 100644 docs/examples/pipecat_upstream_websocket_tracing.py
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 create mode 100644 docs/synthetic-call-traces-tdd-confluence.md
 create mode 100644 frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
 create mode 100644 frontend/src/pages/test-insights/TestInsights.tsx
 create mode 100644 scripts/pipecat_trace_dev_bot.py
 create mode 100644 scripts/test_local_pipecat_trace.py
 create mode 100644 src/efficientai/integrations/__init__.py
 create mode 100644 src/efficientai/integrations/efficientai_traces/__init__.py
 create mode 100644 src/efficientai/integrations/efficientai_traces/correlation.py
 create mode 100644 src/efficientai/integrations/efficientai_traces/handshake.py
 create mode 100644 src/efficientai/integrations/efficientai_traces/pipecat_upstream.py
 create mode 100644 src/efficientai/integrations/efficientai_traces/setup.py
 create mode 100644 tests/test_efficientai_traces_correlation.py
 create mode 100644 tests/test_synthetic_trace_ingest_service.py
 create mode 100644 tests/test_synthetic_trace_otlp_ingest.py
 create mode 100644 tests/test_synthetic_trace_otlp_mapper.py

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HcmV?d00001

diff --git a/app/api/v1/api.py b/app/api/v1/api.py
index 055bc147..b5966554 100644
--- a/app/api/v1/api.py
+++ b/app/api/v1/api.py
@@ -1,103 +1,105 @@
-"""API v1 router aggregation."""
-
-from fastapi import APIRouter
-from app.api.v1.routes import (
-    auth,
-    audio,
-    evaluations,
-    results,
-    agents,
-    personas,
-    scenarios,
-    iam,
-    profile,
-    integrations,
-    data_sources,
-    voicebundles,
-    aiproviders,
-    model_config,
-    manual_evaluations,
-    test_agents,
-    conversation_evaluations,
-    voice_agent,
-    evaluators,
-    evaluator_suites,
-    metrics,
-    evaluator_results,
-    chat,
-    playground,
-    settings,
-    observability,
-    alerts,
-    cron_jobs,
-    voice_playground,
-    public_blind_test,
-    prompt_partials,
-    prompt_optimization,
-    telephony,
-    vobiz_telephony,
-    call_imports,
-    call_import_schemas,
-    call_import_tags,
-    call_import_evaluations,
-    judge_alignment,
-    metric_studio,
-    workspaces,
-    workspace_iam,
-    dashboard,
-    llm_gateway,
-    platform_admin,
-    org_usage,
-    usage_pricing,
-)
-
-api_router = APIRouter()
-
-# Include all route routers
-api_router.include_router(auth.router)
-api_router.include_router(audio.router)
-api_router.include_router(evaluations.router)
-api_router.include_router(results.router)
-api_router.include_router(agents.router)
-api_router.include_router(personas.router)
-api_router.include_router(scenarios.router)
-api_router.include_router(iam.router)
-api_router.include_router(profile.router)
-api_router.include_router(integrations.router)
-api_router.include_router(data_sources.router)
-api_router.include_router(voicebundles.router)
-api_router.include_router(aiproviders.router)
-api_router.include_router(model_config.router)
-api_router.include_router(manual_evaluations.router)
-api_router.include_router(test_agents.router)
-api_router.include_router(conversation_evaluations.router)
-api_router.include_router(voice_agent.router)
-api_router.include_router(evaluators.router)
-api_router.include_router(evaluator_suites.router)
-api_router.include_router(metrics.router)
-api_router.include_router(evaluator_results.router)
-api_router.include_router(chat.router)
-api_router.include_router(playground.router)
-api_router.include_router(settings.router)
-api_router.include_router(observability.router)
-api_router.include_router(alerts.router)
-api_router.include_router(cron_jobs.router)
-api_router.include_router(voice_playground.router)
-api_router.include_router(public_blind_test.router)
-api_router.include_router(prompt_partials.router)
-api_router.include_router(prompt_optimization.router)
-api_router.include_router(telephony.router)
-api_router.include_router(vobiz_telephony.router)
-api_router.include_router(call_imports.router)
-api_router.include_router(call_import_schemas.router)
-api_router.include_router(call_import_tags.router)
-api_router.include_router(call_import_evaluations.router)
-api_router.include_router(judge_alignment.router)
-api_router.include_router(metric_studio.router)
-api_router.include_router(workspaces.router)
-api_router.include_router(workspace_iam.router)
-api_router.include_router(dashboard.router)
-api_router.include_router(llm_gateway.router)
-api_router.include_router(platform_admin.router)
-api_router.include_router(org_usage.router)
-api_router.include_router(usage_pricing.router)
+"""API v1 router aggregation."""
+
+from fastapi import APIRouter
+from app.api.v1.routes import (
+    auth,
+    audio,
+    evaluations,
+    results,
+    agents,
+    personas,
+    scenarios,
+    iam,
+    profile,
+    integrations,
+    data_sources,
+    voicebundles,
+    aiproviders,
+    model_config,
+    manual_evaluations,
+    test_agents,
+    conversation_evaluations,
+    voice_agent,
+    evaluators,
+    evaluator_suites,
+    metrics,
+    evaluator_results,
+    chat,
+    playground,
+    settings,
+    observability,
+    alerts,
+    cron_jobs,
+    voice_playground,
+    public_blind_test,
+    prompt_partials,
+    prompt_optimization,
+    telephony,
+    vobiz_telephony,
+    call_imports,
+    call_import_schemas,
+    call_import_tags,
+    call_import_evaluations,
+    judge_alignment,
+    metric_studio,
+    workspaces,
+    workspace_iam,
+    dashboard,
+    llm_gateway,
+    platform_admin,
+    org_usage,
+    usage_pricing,
+    synthetic_traces,
+)
+
+api_router = APIRouter()
+
+# Include all route routers
+api_router.include_router(auth.router)
+api_router.include_router(audio.router)
+api_router.include_router(evaluations.router)
+api_router.include_router(results.router)
+api_router.include_router(agents.router)
+api_router.include_router(personas.router)
+api_router.include_router(scenarios.router)
+api_router.include_router(iam.router)
+api_router.include_router(profile.router)
+api_router.include_router(integrations.router)
+api_router.include_router(data_sources.router)
+api_router.include_router(voicebundles.router)
+api_router.include_router(aiproviders.router)
+api_router.include_router(model_config.router)
+api_router.include_router(manual_evaluations.router)
+api_router.include_router(test_agents.router)
+api_router.include_router(conversation_evaluations.router)
+api_router.include_router(voice_agent.router)
+api_router.include_router(evaluators.router)
+api_router.include_router(evaluator_suites.router)
+api_router.include_router(metrics.router)
+api_router.include_router(evaluator_results.router)
+api_router.include_router(chat.router)
+api_router.include_router(playground.router)
+api_router.include_router(settings.router)
+api_router.include_router(observability.router)
+api_router.include_router(alerts.router)
+api_router.include_router(cron_jobs.router)
+api_router.include_router(voice_playground.router)
+api_router.include_router(public_blind_test.router)
+api_router.include_router(prompt_partials.router)
+api_router.include_router(prompt_optimization.router)
+api_router.include_router(telephony.router)
+api_router.include_router(vobiz_telephony.router)
+api_router.include_router(call_imports.router)
+api_router.include_router(call_import_schemas.router)
+api_router.include_router(call_import_tags.router)
+api_router.include_router(call_import_evaluations.router)
+api_router.include_router(judge_alignment.router)
+api_router.include_router(metric_studio.router)
+api_router.include_router(workspaces.router)
+api_router.include_router(workspace_iam.router)
+api_router.include_router(dashboard.router)
+api_router.include_router(llm_gateway.router)
+api_router.include_router(platform_admin.router)
+api_router.include_router(org_usage.router)
+api_router.include_router(usage_pricing.router)
+api_router.include_router(synthetic_traces.router)
diff --git a/app/api/v1/routes/evaluator_results.py b/app/api/v1/routes/evaluator_results.py
index 093ea249..d5cdc57d 100644
--- a/app/api/v1/routes/evaluator_results.py
+++ b/app/api/v1/routes/evaluator_results.py
@@ -1,6 +1,6 @@
 """Evaluator Results routes."""
 
-from fastapi import APIRouter, Depends, HTTPException, status, Query
+from fastapi import APIRouter, Depends, HTTPException, status, Query, Request
 from sqlalchemy.orm import Session
 from sqlalchemy import and_
 from uuid import UUID
@@ -456,6 +456,7 @@ def get_evaluator_result(
         "provider_call_id": result.provider_call_id,
         "provider_platform": result.provider_platform,
         "call_data": result.call_data,
+        "synthetic_call_trace_id": result.synthetic_call_trace_id,
         "created_at": result.created_at,
         "updated_at": result.updated_at,
         "created_by": result.created_by,
@@ -643,6 +644,31 @@ def get_evaluator_result_metrics(
     }
 
 
+@router.get("/{id}/otel-correlation")
+def get_evaluator_result_otel_correlation(
+    id: str,
+    request: Request,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    """Return OTLP endpoint and correlation env vars for customer Pipecat setup."""
+    from app.models.synthetic_trace_schemas import OtelCorrelationInfo
+    from app.services.synthetic_traces.trace_service import build_otel_correlation
+
+    del api_key
+    result = _lookup_evaluator_result(db, id, organization_id, workspace_id)
+    if not result:
+        raise HTTPException(status_code=404, detail="Evaluator result not found")
+    info = build_otel_correlation(
+        db,
+        result,
+        api_base_url=str(request.base_url).rstrip("/"),
+    )
+    return OtelCorrelationInfo(**info)
+
+
 @router.get("/{id}/audio")
 async def stream_evaluator_result_audio(
     id: str,
diff --git a/app/api/v1/routes/playground.py b/app/api/v1/routes/playground.py
index 1681ecbd..b598898e 100644
--- a/app/api/v1/routes/playground.py
+++ b/app/api/v1/routes/playground.py
@@ -1,1894 +1,1913 @@
-"""
-Playground API Routes
-API endpoints for testing voice agents in the playground
-"""
-from fastapi import APIRouter, Depends, HTTPException, status, Body, BackgroundTasks, Form, File, UploadFile
-from fastapi.responses import StreamingResponse
-from sqlalchemy.orm import Session
-from typing import Dict, Any, Optional, List
-from uuid import UUID
-from pydantic import BaseModel
-from loguru import logger
-import random
-import json
-import uuid as _uuid
-from datetime import datetime
-
-from app.services.billing.flexprice_service import (
-    record_playground_web_call_started,
-    record_playground_websocket_session_started,
-)
-from app.database import get_db
-from app.dependencies import get_organization_id, get_workspace_id, get_api_key
-from app.models.database import (
-    Agent,
-    Integration,
-    IntegrationPlatform,
-    CallRecording,
-    CallRecordingStatus,
-    CallRecordingSource,
-    EvaluatorResult,
-    EvaluatorResultStatus,
-    VoiceBundle,
-    AIProvider,
-    ModelProvider,
-)
-from app.core.encryption import decrypt_api_key
-from app.services.voice_providers import get_voice_provider
-from app.services.evaluators.evaluator_result_call_data import slim_call_data_for_evaluator_result
-from app.utils.call_recordings import generate_unique_call_short_id
-
-from app.services.evaluators.call_data_transcript import extract_transcript_from_call_data
-
-router = APIRouter(prefix="/playground", tags=["playground"])
-def generate_unique_result_id(db: Session) -> str:
-    """Generate a unique 6-digit result ID for EvaluatorResult."""
-    max_attempts = 100
-    for _ in range(max_attempts):
-        candidate_id = f"{random.randint(100000, 999999)}"
-        existing = db.query(EvaluatorResult).filter(EvaluatorResult.result_id == candidate_id).first()
-        if not existing:
-            return candidate_id
-    raise ValueError("Failed to generate unique result ID")
-
-
-def poll_call_metrics(
-    call_recording_id: UUID,
-    provider_call_id: str,
-    provider_platform: str,
-    integration_api_key: str,
-    max_attempts: int = 60,
-    poll_interval: int = 5
-):
-    """
-    Background task to poll for call metrics from the provider.
-    After call is complete, creates an EvaluatorResult and triggers metric evaluation.
-    
-    Args:
-        call_recording_id: The CallRecording database ID
-        provider_call_id: The provider's call_id (e.g., Retell call_id)
-        provider_platform: The provider platform (e.g., "retell")
-        integration_api_key: The decrypted API key for the provider
-        max_attempts: Maximum number of polling attempts
-        poll_interval: Seconds between polling attempts
-    """
-    import time
-    from app.database import SessionLocal
-    from app.services.playground.post_call_processing import merge_playground_call_data
-    from app.services.voice_providers import get_voice_provider
-    
-    db = SessionLocal()
-    call_complete = False
-    call_metrics = None
-    
-    try:
-        call_recording = db.query(CallRecording).filter(CallRecording.id == call_recording_id).first()
-        if not call_recording:
-            return
-        
-        if not provider_call_id or provider_call_id == "None":
-            return
-
-        # Get the appropriate voice provider
-        try:
-            provider_class = get_voice_provider(provider_platform)
-            provider = provider_class(api_key=integration_api_key)
-        except ValueError:
-            return
-        
-        # Poll for call metrics
-        for attempt in range(max_attempts):
-            try:
-                # Wait before polling (except first attempt)
-                if attempt > 0:
-                    time.sleep(poll_interval)
-                
-                # Retrieve call metrics
-                if hasattr(provider, "retrieve_call_metrics"):
-                    call_metrics = provider.retrieve_call_metrics(provider_call_id)
-                else:
-                    # For other providers, implement similar method
-                    continue
-                
-                # Update the call recording with metrics (preserve usage-recorded flag)
-                prev_data = (
-                    call_recording.call_data
-                    if isinstance(call_recording.call_data, dict)
-                    else {}
-                )
-                if isinstance(call_metrics, dict):
-                    call_metrics = merge_playground_call_data(prev_data, call_metrics)
-                call_recording.call_data = call_metrics
-                call_recording.status = CallRecordingStatus.UPDATED
-                db.commit()
-                db.refresh(call_recording)
-                
-                # Check if call is complete (supports raw + normalized payloads)
-                call_status = (
-                    call_metrics.get("call_status")
-                    or call_metrics.get("status")
-                    or ""
-                )
-                call_status = str(call_status).lower()
-                end_timestamp = call_metrics.get("end_timestamp") or call_metrics.get("endedAt")
-                
-                # If call is complete, stop polling
-                if end_timestamp or call_status in ["ended", "completed", "failed", "end-of-call-report", "done"]:
-                    call_complete = True
-                    break
-                    
-            except Exception as e:
-                # Log error but continue polling
-                logger.warning(f"[Poll Call Metrics] Error on attempt {attempt + 1}: {str(e)}")
-                # If it's a 404 or similar, the call might not exist yet, continue polling
-                continue
-        
-        # After polling is complete, create EvaluatorResult and trigger evaluation
-        if call_complete and call_metrics and call_recording.agent_id:
-            try:
-                from app.services.playground.post_call_processing import (
-                    claim_playground_evaluator_result_slot,
-                    record_playground_post_call_usage_once,
-                )
-
-                should_create_evaluator, call_metrics = record_playground_post_call_usage_once(
-                    db,
-                    call_recording_id,
-                    provider_platform=provider_platform,
-                    call_metrics=call_metrics if isinstance(call_metrics, dict) else {},
-                )
-                if not should_create_evaluator:
-                    return
-
-                logger.info(f"[Poll Call Metrics] Call complete, creating EvaluatorResult for call {provider_call_id}")
-                
-                # Extract transcript and speaker segments from call_data
-                transcript_text, _ = extract_transcript_from_call_data(
-                    call_metrics, 
-                    provider_platform
-                )
-                
-                if not transcript_text:
-                    logger.warning(f"[Poll Call Metrics] No transcript found in call_data for call {provider_call_id}")
-                
-                # Get agent info for naming
-                agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-                result_name = f"Voice AI Call - {agent.name}" if agent else "Voice AI Call"
-                
-                # Calculate duration
-                duration_seconds = call_metrics.get("duration_seconds", 0)
-                if not duration_seconds:
-                    # Try to calculate from timestamps
-                    start_ts = call_metrics.get("start_timestamp") or call_metrics.get("startedAt")
-                    end_ts = call_metrics.get("end_timestamp") or call_metrics.get("endedAt")
-                    if start_ts and end_ts:
-                        try:
-                            from dateutil import parser
-                            start_time = parser.parse(start_ts)
-                            end_time = parser.parse(end_ts)
-                            duration_seconds = (end_time - start_time).total_seconds()
-                        except Exception:
-                            pass
-                
-                # Download call audio from provider and upload to S3
-                audio_s3_key = None
-                try:
-                    import requests as _http
-                    import uuid as _uuid
-                    from app.services.storage.s3_service import s3_service
-
-                    recording_urls = call_metrics.get("recording_urls", {})
-                    audio_bytes = None
-                    plat = provider_platform.lower()
-
-                    if plat == "elevenlabs":
-                        audio_url = recording_urls.get("conversation_audio")
-                        if audio_url:
-                            resp = _http.get(audio_url, headers={"xi-api-key": integration_api_key}, timeout=120)
-                            if resp.status_code == 200:
-                                audio_bytes = resp.content
-                    elif plat == "retell":
-                        audio_url = call_metrics.get("recording_url")
-                        if audio_url:
-                            resp = _http.get(audio_url, timeout=120)
-                            if resp.status_code == 200:
-                                audio_bytes = resp.content
-                    elif plat == "vapi":
-                        artifact = call_metrics.get("artifact", {})
-                        recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
-                        mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
-                        audio_url = (
-                            call_metrics.get("recordingUrl")
-                            or call_metrics.get("stereoRecordingUrl")
-                            or artifact.get("recordingUrl")
-                            or artifact.get("stereoRecordingUrl")
-                            or mono_recording.get("combinedUrl")
-                            or recording_urls.get("combined_url")
-                            or recording_urls.get("stereo_url")
-                        )
-                        if audio_url:
-                            vapi_headers = {"Authorization": f"Bearer {integration_api_key}"}
-                            resp = _http.get(audio_url, headers=vapi_headers, timeout=120)
-                            if resp.status_code == 200:
-                                audio_bytes = resp.content
-
-                    if audio_bytes:
-                        content_type = getattr(resp, "headers", {}).get("content-type", "audio/mpeg")
-                        ext = "wav" if "wav" in content_type else "mp3"
-                        org_id = str(call_recording.organization_id)
-                        audio_s3_key = f"audio/organizations/{org_id}/agentPlayground/{provider_call_id}/{_uuid.uuid4()}.{ext}"
-                        s3_service.upload_file_by_key(audio_bytes, audio_s3_key, content_type=content_type)
-                        logger.info(f"[Poll Call Metrics] Uploaded call audio to S3: {audio_s3_key} ({len(audio_bytes)} bytes)")
-                    else:
-                        logger.warning(f"[Poll Call Metrics] Could not download audio for call {provider_call_id}")
-                except Exception as audio_err:
-                    logger.warning(f"[Poll Call Metrics] Audio download/upload failed: {audio_err}")
-
-                locked_recording = claim_playground_evaluator_result_slot(
-                    db,
-                    call_recording_id,
-                    provider_call_id=provider_call_id,
-                )
-                if not locked_recording:
-                    return
-
-                result_id = generate_unique_result_id(db)
-                evaluator_result = EvaluatorResult(
-                    result_id=result_id,
-                    organization_id=locked_recording.organization_id,
-                    workspace_id=locked_recording.workspace_id,
-                    evaluator_id=None,
-                    agent_id=locked_recording.agent_id,
-                    persona_id=None,
-                    scenario_id=None,
-                    name=result_name,
-                    duration_seconds=duration_seconds,
-                    status=EvaluatorResultStatus.QUEUED.value,
-                    audio_s3_key=audio_s3_key,
-                    transcription=transcript_text,
-                    provider_call_id=provider_call_id,
-                    provider_platform=provider_platform,
-                    call_data=call_metrics,
-                )
-                db.add(evaluator_result)
-                db.flush()
-                locked_recording.evaluator_result_id = evaluator_result.id
-                db.commit()
-                db.refresh(evaluator_result)
-                
-                logger.info(f"[Poll Call Metrics] Created EvaluatorResult {result_id} for call {provider_call_id}")
-                
-                # Trigger Celery task to process evaluator result (run metrics evaluation)
-                try:
-                    from app.workers.celery_app import process_evaluator_result_task
-                    task = process_evaluator_result_task.delay(str(evaluator_result.id))
-                    evaluator_result.celery_task_id = task.id
-                    db.commit()
-                    logger.info(f"[Poll Call Metrics] Triggered evaluation task {task.id} for result {result_id}")
-                except Exception as task_error:
-                    logger.error(f"[Poll Call Metrics] Failed to trigger Celery task: {task_error}")
-                    # Mark the result as failed if we can't trigger the task
-                    # But keep the transcript available for manual review
-                    
-            except Exception as e:
-                logger.error(f"[Poll Call Metrics] Error creating EvaluatorResult: {str(e)}", exc_info=True)
-        
-    finally:
-        db.close()
-
-
-class CallRecordingUpdate(BaseModel):
-    """Schema for updating a call recording."""
-    provider_call_id: str
-
-
-@router.put("/call-recordings/{call_short_id}", response_model=Dict[str, Any])
-async def update_call_recording(
-    call_short_id: str,
-    update_data: CallRecordingUpdate,
-    background_tasks: BackgroundTasks,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Update a call recording within the active workspace, typically to set the provider_call_id.
-    Triggers polling.
-    """
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-    ).first()
-    
-    if not call_recording:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call recording not found"
-        )
-    
-    call_recording.provider_call_id = update_data.provider_call_id
-    db.commit()
-    db.refresh(call_recording)
-    
-    # Trigger polling if we have all info
-    if call_recording.provider_platform:
-        # Get integration api key
-        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-        if agent and agent.voice_ai_integration_id:
-            integration = db.query(Integration).filter(
-                Integration.id == agent.voice_ai_integration_id
-            ).first()
-            
-            if integration:
-                try:
-                    decrypted_api_key = decrypt_api_key(integration.api_key)
-                    platform_key = (call_recording.provider_platform or "").lower()
-                    # Vapi polls on call-end refresh only; update fires mid-call too.
-                    if platform_key != "vapi":
-                        background_tasks.add_task(
-                            poll_call_metrics,
-                            call_recording.id,
-                            call_recording.provider_call_id,
-                            call_recording.provider_platform,
-                            decrypted_api_key
-                        )
-                except:
-                    pass
-    
-    return {
-        "message": "Call recording updated", 
-        "provider_call_id": call_recording.provider_call_id
-    }
-
-
-class WebCallCreate(BaseModel):
-    """Schema for creating a web call."""
-    agent_id: str  # UUID of the agent in our system
-    metadata: Optional[Dict[str, Any]] = None
-    retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None
-    custom_sip_headers: Optional[Dict[str, str]] = None
-    ui_surface: Optional[str] = None
-
-
-@router.post("/web-call", response_model=Dict[str, Any])
-async def create_web_call(
-    web_call_data: WebCallCreate,
-    background_tasks: BackgroundTasks,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Create a web call with a voice AI agent within the active workspace.
-    The agent must belong to the same workspace; the resulting call recording
-    is stamped with the same workspace_id.
-    """
-    try:
-        # Get the agent (scoped to the active workspace)
-        agent_uuid = UUID(web_call_data.agent_id)
-        agent = db.query(Agent).filter(
-            Agent.id == agent_uuid,
-            Agent.organization_id == organization_id,
-            Agent.workspace_id == workspace_id,
-        ).first()
-        
-        if not agent:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="Agent not found"
-            )
-        
-        # Check if agent has voice AI integration
-        if not agent.voice_ai_integration_id or not agent.voice_ai_agent_id:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail="Agent is not configured with a voice AI integration"
-            )
-        
-        # Check if agent has web call enabled
-        if agent.call_medium != "web_call":
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail="Agent is not configured for web calls"
-            )
-        
-        # Get the integration
-        integration = db.query(Integration).filter(
-            Integration.id == agent.voice_ai_integration_id,
-            Integration.organization_id == organization_id,
-            Integration.is_active == True
-        ).first()
-        
-        if not integration:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="Integration not found or inactive"
-            )
-        
-        # Decrypt API key
-        try:
-            decrypted_api_key = decrypt_api_key(integration.api_key)
-        except Exception as e:
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail=f"Failed to decrypt API key: {str(e)}"
-            )
-        
-        # Get the appropriate voice provider
-        try:
-            provider_class = get_voice_provider(integration.platform)
-            
-            platform_value = integration.platform.value if hasattr(integration.platform, 'value') else integration.platform
-            if platform_value.lower() == "vapi":
-                provider = provider_class(api_key=decrypted_api_key, public_key=integration.public_key)
-            else:
-                provider = provider_class(api_key=decrypted_api_key)
-        except ValueError as e:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=str(e)
-            )
-        
-        # Create the web call
-        try:
-            # Verify agent_id is present
-            if not agent.voice_ai_agent_id:
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    detail="Agent does not have a voice_ai_agent_id configured"
-                )
-            
-            print(f"[Playground] Creating web call - Agent ID: {agent.id}, Retell Agent ID: {agent.voice_ai_agent_id}, Platform: {integration.platform}")
-            
-            # Build call parameters based on provider
-            call_params = {
-                "agent_id": agent.voice_ai_agent_id,
-            }
-            
-            # Add optional parameters if provided
-            if web_call_data.metadata:
-                call_params["metadata"] = web_call_data.metadata
-            if web_call_data.retell_llm_dynamic_variables:
-                call_params["retell_llm_dynamic_variables"] = web_call_data.retell_llm_dynamic_variables
-            
-            # Note: custom_sip_headers is not supported by Retell, but may be supported by other providers
-            # For now, we'll skip it for Retell. Other providers can handle it in their implementation.
-            if integration.platform != "retell" and web_call_data.custom_sip_headers:
-                call_params["custom_sip_headers"] = web_call_data.custom_sip_headers
-
-            platform_value = (
-                integration.platform.value
-                if hasattr(integration.platform, "value")
-                else integration.platform
-            )
-            plat_lower = str(platform_value).lower()
-
-            # Vapi Web SDK creates the call in the browser; server-side /call/web
-            # would spawn a second call that never receives the user's microphone.
-            if plat_lower == "vapi":
-                from app.services.voice_providers.vapi import VAPI_SAMPLE_RATE
-
-                web_call_response = {
-                    "call_type": "web_call",
-                    "agent_id": agent.voice_ai_agent_id,
-                    "metadata": web_call_data.metadata or {},
-                    "sample_rate": VAPI_SAMPLE_RATE,
-                    "client_sdk_creates_call": True,
-                }
-                provider_call_id = None
-            else:
-                web_call_response = provider.create_web_call(**call_params)
-                provider_call_id = web_call_response.get("call_id")
-
-            call_short_id = generate_unique_call_short_id(db)
-            stored_call_data = dict(web_call_response)
-            if web_call_data.ui_surface:
-                stored_call_data["ui_surface"] = web_call_data.ui_surface
-            call_recording = CallRecording(
-                organization_id=organization_id,
-                workspace_id=workspace_id,
-                call_short_id=call_short_id,
-                status=CallRecordingStatus.PENDING,
-                source=CallRecordingSource.PLAYGROUND,
-                call_data=stored_call_data,
-                provider_call_id=provider_call_id,
-                provider_platform=integration.platform,
-                agent_id=agent.id
-            )
-            db.add(call_recording)
-            db.commit()
-            db.refresh(call_recording)
-
-            background_tasks.add_task(
-                record_playground_web_call_started,
-                organization_id,
-                call_short_id,
-                workspace_id=workspace_id,
-                agent_id=agent.id,
-            )
-            
-            # Start background task to poll for call metrics
-            # Note: We need to pass the decrypted API key, but we should be careful with security
-            # For now, we'll pass it to the background task
-            # In production, you might want to store it temporarily or use a different approach
-            if provider_call_id and plat_lower != "vapi":
-                background_tasks.add_task(
-                    poll_call_metrics,
-                    call_recording.id,
-                    provider_call_id,
-                    integration.platform,
-                    decrypted_api_key
-                )
-            
-            # Add call_short_id to response for frontend
-            response = web_call_response.copy()
-            response["call_short_id"] = call_short_id
-
-            if plat_lower == "vapi" and integration.public_key:
-                response["public_key"] = integration.public_key
-
-            if plat_lower == "elevenlabs":
-                response["signed_url"] = web_call_response.get("signed_url")
-
-            return response
-        except Exception as e:
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail=f"Failed to create web call: {str(e)}"
-            )
-            
-    except ValueError as e:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Invalid agent ID: {str(e)}"
-        )
-    except Exception as e:
-        if isinstance(e, HTTPException):
-            raise e
-        print(f"[Create Web Call] Error: {str(e)}")
-        import traceback
-        traceback.print_exc()
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Internal server error: {str(e)}"
-        )
-
-
-@router.get("/call-recordings", response_model=List[Dict[str, Any]])
-async def list_call_recordings(
-    skip: int = 0,
-    limit: int = 100,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    List playground call recordings in the active workspace.
-    Includes evaluator_result_id, evaluation status, and metric_scores if evaluation has been run.
-    """
-    call_recordings = db.query(CallRecording).filter(
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-        CallRecording.source == CallRecordingSource.PLAYGROUND,
-    ).order_by(CallRecording.created_at.desc()).offset(skip).limit(limit).all()
-    
-    # Get evaluator result info for all linked results
-    result_ids = [cr.evaluator_result_id for cr in call_recordings if cr.evaluator_result_id]
-    result_info = {}
-    if result_ids:
-        results = db.query(EvaluatorResult).filter(EvaluatorResult.id.in_(result_ids)).all()
-        result_info = {
-            str(r.id): {
-                "status": r.status,
-                "metric_scores": r.metric_scores,
-                "result_id": r.result_id,
-                "name": r.name,
-            }
-            for r in results
-        }
-
-    agent_ids = [cr.agent_id for cr in call_recordings if cr.agent_id]
-    agents_by_id = {}
-    if agent_ids:
-        agents = db.query(Agent).filter(Agent.id.in_(agent_ids)).all()
-        agents_by_id = {a.id: a for a in agents}
-
-    def _display_name(cr: CallRecording) -> str:
-        linked = result_info.get(str(cr.evaluator_result_id), {}) if cr.evaluator_result_id else {}
-        if linked.get("name"):
-            return linked["name"]
-        agent = agents_by_id.get(cr.agent_id) if cr.agent_id else None
-        if agent and agent.name:
-            return agent.name
-        return cr.call_short_id
-
-    return [
-        {
-            "id": str(cr.id),
-            "call_short_id": cr.call_short_id,
-            "display_name": _display_name(cr),
-            "status": cr.status if cr.status else None,
-            "provider_platform": cr.provider_platform,
-            "provider_call_id": cr.provider_call_id,
-            "agent_id": str(cr.agent_id) if cr.agent_id else None,
-            "evaluator_result_id": str(cr.evaluator_result_id) if cr.evaluator_result_id else None,
-            "evaluation_status": result_info.get(str(cr.evaluator_result_id), {}).get("status") if cr.evaluator_result_id else None,
-            "metric_scores": result_info.get(str(cr.evaluator_result_id), {}).get("metric_scores") if cr.evaluator_result_id else None,
-            "result_id": result_info.get(str(cr.evaluator_result_id), {}).get("result_id") if cr.evaluator_result_id else None,
-            "created_at": cr.created_at.isoformat() if cr.created_at else None,
-            "updated_at": cr.updated_at.isoformat() if cr.updated_at else None,
-        }
-        for cr in call_recordings
-    ]
-
-
-@router.post("/custom-websocket-sessions", response_model=Dict[str, Any])
-async def create_custom_websocket_session(
-    background_tasks: BackgroundTasks,
-    agent_id: str = Form(...),
-    websocket_url: str = Form(...),
-    transcript_entries: str = Form("[]"),
-    started_at: Optional[str] = Form(None),
-    ended_at: Optional[str] = Form(None),
-    audio_file: Optional[UploadFile] = File(None),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """
-    Save a custom websocket test session for later evaluation (scoped to the active workspace).
-    Stores transcript in call_data and uploads optional audio recording to S3.
-    """
-    try:
-        agent_uuid = UUID(agent_id)
-    except ValueError:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid agent_id")
-
-    agent = db.query(Agent).filter(
-        Agent.id == agent_uuid,
-        Agent.organization_id == organization_id,
-        Agent.workspace_id == workspace_id,
-    ).first()
-    if not agent:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
-
-    try:
-        parsed_entries = json.loads(transcript_entries or "[]")
-    except json.JSONDecodeError:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid transcript_entries payload")
-
-    normalized_entries = []
-    for entry in parsed_entries:
-        if not isinstance(entry, dict):
-            continue
-        role = entry.get("role")
-        content = (entry.get("content") or "").strip()
-        if role not in {"user", "agent"} or not content:
-            continue
-        normalized_entries.append(
-            {
-                "role": role,
-                "content": content,
-                "timestamp": entry.get("timestamp") or ended_at or started_at,
-            }
-        )
-
-    if not normalized_entries:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="No transcript entries provided")
-
-    transcript_text = "\n".join(
-        [f"{'User' if item['role'] == 'user' else 'Agent'}: {item['content']}" for item in normalized_entries]
-    )
-
-    call_short_id = generate_unique_call_short_id(db)
-    audio_s3_key = None
-    if audio_file:
-        from app.services.storage.s3_service import s3_service
-
-        audio_bytes = await audio_file.read()
-        if audio_bytes:
-            if not s3_service.is_enabled():
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    detail="S3 storage is not configured. Save without recording or enable storage.",
-                )
-
-            filename = audio_file.filename or "session.webm"
-            extension = filename.split(".")[-1].lower() if "." in filename else "webm"
-            content_type = audio_file.content_type or "audio/webm"
-            audio_s3_key = (
-                f"audio/organizations/{organization_id}/agentPlayground/customWebsocket/"
-                f"{call_short_id}/{_uuid.uuid4()}.{extension}"
-            )
-            s3_service.upload_file_by_key(audio_bytes, audio_s3_key, content_type=content_type)
-
-    duration_seconds = 0
-    if started_at and ended_at:
-        try:
-            from datetime import datetime as _dt
-            t_start = _dt.fromisoformat(started_at.replace("Z", "+00:00"))
-            t_end = _dt.fromisoformat(ended_at.replace("Z", "+00:00"))
-            duration_seconds = max(0, (t_end - t_start).total_seconds())
-        except Exception:
-            duration_seconds = 0
-
-    speaker_segments = []
-    for entry in normalized_entries:
-        speaker = "user" if entry.get("role") == "user" else "assistant"
-        speaker_segments.append({
-            "speaker": speaker,
-            "text": entry.get("content", ""),
-            "start": 0,
-            "end": 0,
-        })
-
-    call_data = {
-        "source": "custom_websocket",
-        "websocket_url": websocket_url,
-        "messages": normalized_entries,
-        "transcript": transcript_text,
-        "speaker_segments": speaker_segments,
-        "recording_s3_key": audio_s3_key,
-        "started_at": started_at,
-        "ended_at": ended_at,
-        "duration_seconds": duration_seconds,
-    }
-
-    call_recording = CallRecording(
-        organization_id=organization_id,
-        workspace_id=workspace_id,
-        call_short_id=call_short_id,
-        status=CallRecordingStatus.UPDATED,
-        source=CallRecordingSource.PLAYGROUND,
-        call_data=call_data,
-        provider_call_id=f"custom_{call_short_id}",
-        provider_platform="custom_websocket",
-        agent_id=agent.id,
-    )
-    db.add(call_recording)
-    db.commit()
-    db.refresh(call_recording)
-
-    background_tasks.add_task(
-        record_playground_websocket_session_started,
-        organization_id,
-        call_short_id,
-        workspace_id=workspace_id,
-    )
-
-    return {
-        "message": "Custom websocket session saved",
-        "call_short_id": call_short_id,
-        "audio_s3_key": audio_s3_key,
-        "evaluator_result_id": None,
-    }
-
-
-@router.post("/custom-websocket-sessions/{call_short_id}/evaluate", response_model=Dict[str, Any])
-async def evaluate_custom_websocket_session(
-    call_short_id: str,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """
-    Queue evaluation for a saved custom websocket test session in the active workspace.
-    """
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-        CallRecording.source == CallRecordingSource.PLAYGROUND,
-        CallRecording.provider_platform == "custom_websocket",
-    ).first()
-
-    if not call_recording:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Custom websocket session not found")
-
-    call_data = call_recording.call_data if isinstance(call_recording.call_data, dict) else {}
-    transcript_text = (call_data.get("transcript") or "").strip()
-    if not transcript_text:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="No transcript found for evaluation")
-
-    existing_result = None
-    if call_recording.evaluator_result_id:
-        existing_result = db.query(EvaluatorResult).filter(
-            EvaluatorResult.id == call_recording.evaluator_result_id,
-            EvaluatorResult.organization_id == organization_id,
-            EvaluatorResult.workspace_id == workspace_id,
-        ).first()
-
-    speaker_segments = call_data.get("speaker_segments") or []
-
-    if existing_result:
-        evaluator_result = existing_result
-        evaluator_result.status = EvaluatorResultStatus.QUEUED.value
-        evaluator_result.error_message = None
-        evaluator_result.metric_scores = None
-        evaluator_result.celery_task_id = None
-        evaluator_result.transcription = transcript_text
-        evaluator_result.speaker_segments = speaker_segments
-        evaluator_result.audio_s3_key = call_data.get("recording_s3_key")
-        evaluator_result.call_data = slim_call_data_for_evaluator_result(call_data)
-        evaluator_result.duration_seconds = call_data.get("duration_seconds", 0)
-        db.commit()
-        db.refresh(evaluator_result)
-    else:
-        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-        result_id = generate_unique_result_id(db)
-        result_name = f"Custom WebSocket Test - {agent.name}" if agent else "Custom WebSocket Test"
-
-        evaluator_result = EvaluatorResult(
-            result_id=result_id,
-            organization_id=organization_id,
-            workspace_id=workspace_id,
-            evaluator_id=None,
-            agent_id=call_recording.agent_id,
-            persona_id=None,
-            scenario_id=None,
-            name=result_name,
-            duration_seconds=call_data.get("duration_seconds", 0),
-            status=EvaluatorResultStatus.QUEUED.value,
-            audio_s3_key=call_data.get("recording_s3_key"),
-            transcription=transcript_text,
-            speaker_segments=speaker_segments,
-            provider_call_id=call_recording.provider_call_id,
-            provider_platform="custom_websocket",
-            call_data=slim_call_data_for_evaluator_result(call_data),
-        )
-        db.add(evaluator_result)
-        db.commit()
-        db.refresh(evaluator_result)
-
-        call_recording.evaluator_result_id = evaluator_result.id
-        db.commit()
-
-    try:
-        from app.workers.celery_app import process_evaluator_result_task
-        task = process_evaluator_result_task.delay(str(evaluator_result.id))
-        evaluator_result.celery_task_id = task.id
-        db.commit()
-    except Exception as e:
-        logger.error(f"[Custom WebSocket] Failed to trigger evaluation worker: {e}")
-        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed to trigger evaluation worker")
-
-    return {
-        "message": "Evaluation queued",
-        "evaluator_result_id": str(evaluator_result.id),
-        "result_id": evaluator_result.result_id,
-        "task_id": task.id,
-    }
-
-
-@router.get("/call-recordings/{call_short_id}", response_model=Dict[str, Any])
-async def get_call_recording(
-    call_short_id: str,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Get a specific playground call recording within the active workspace.
-    Returns the full JSON data stored for the call and evaluation information.
-    """
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-        CallRecording.source == CallRecordingSource.PLAYGROUND,
-    ).first()
-    
-    if not call_recording:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call recording not found"
-        )
-    
-    # Get evaluator result info if available
-    evaluation_info = None
-    if call_recording.evaluator_result_id:
-        evaluator_result = db.query(EvaluatorResult).filter(
-            EvaluatorResult.id == call_recording.evaluator_result_id
-        ).first()
-        if evaluator_result:
-            evaluation_info = {
-                "id": str(evaluator_result.id),
-                "result_id": evaluator_result.result_id,
-                "status": evaluator_result.status,
-                "metric_scores": evaluator_result.metric_scores,
-                "transcription": evaluator_result.transcription,
-            }
-    
-    return {
-        "id": str(call_recording.id),
-        "call_short_id": call_recording.call_short_id,
-        "status": call_recording.status if call_recording.status else None,
-        "provider_platform": call_recording.provider_platform,
-        "provider_call_id": call_recording.provider_call_id,
-        "agent_id": str(call_recording.agent_id) if call_recording.agent_id else None,
-        "evaluator_result_id": str(call_recording.evaluator_result_id) if call_recording.evaluator_result_id else None,
-        "evaluation": evaluation_info,
-        "call_data": call_recording.call_data,  # Full JSON blob
-        "created_at": call_recording.created_at.isoformat() if call_recording.created_at else None,
-        "updated_at": call_recording.updated_at.isoformat() if call_recording.updated_at else None,
-    }
-
-
-@router.post("/call-recordings/{call_short_id}/refresh", response_model=Dict[str, Any])
-async def refresh_call_recording(
-    call_short_id: str,
-    background_tasks: BackgroundTasks,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Manually trigger a refresh of call metrics for a specific call recording in the active workspace.
-    """
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-        CallRecording.source == CallRecordingSource.PLAYGROUND,
-    ).first()
-    
-    if not call_recording:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call recording not found"
-        )
-    
-    if not call_recording.provider_call_id or not call_recording.provider_platform:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Call recording does not have provider information"
-        )
-    
-    # Get the integration to get the API key
-    agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-    if not agent or not agent.voice_ai_integration_id:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Agent or integration not found"
-        )
-    
-    integration = db.query(Integration).filter(
-        Integration.id == agent.voice_ai_integration_id,
-        Integration.organization_id == organization_id
-    ).first()
-    
-    if not integration:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Integration not found"
-        )
-
-    if call_recording.evaluator_result_id:
-        return {"message": "Call recording already processed"}
-
-    try:
-        decrypted_api_key = decrypt_api_key(integration.api_key)
-    except Exception as e:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to decrypt API key: {str(e)}"
-        )
-    
-    # Start background task to poll for call metrics
-    background_tasks.add_task(
-        poll_call_metrics,
-        call_recording.id,
-        call_recording.provider_call_id,
-        call_recording.provider_platform,
-        decrypted_api_key
-    )
-    
-    return {"message": "Call recording refresh initiated"}
-
-
-@router.delete("/call-recordings/{call_short_id}", response_model=Dict[str, Any])
-async def delete_call_recording(
-    call_short_id: str,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Delete a call recording within the active workspace.
-    """
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-    ).first()
-    
-    if not call_recording:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Call recording not found"
-        )
-    
-    db.delete(call_recording)
-    db.commit()
-    
-    return {"message": "Call recording deleted successfully"}
-
-
-@router.post("/call-recordings/{call_short_id}/re-evaluate", response_model=Dict[str, Any])
-async def re_evaluate_call_recording(
-    call_short_id: str,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """
-    Re-evaluate a playground call recording within the active workspace.
-
-    Reuses the S3 audio if it was already downloaded during the first
-    evaluation. If no audio exists in S3, downloads from the provider,
-    uploads, then triggers the worker for both conversation quality (LLM)
-    and audio quality (acoustic / AI voice) metrics.
-    """
-    import requests as http_requests
-    import uuid as _uuid
-    from app.services.storage.s3_service import s3_service
-
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-        CallRecording.source == CallRecordingSource.PLAYGROUND,
-    ).first()
-
-    if not call_recording:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Call recording not found")
-
-    if not call_recording.provider_call_id or not call_recording.provider_platform:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Call recording has no provider data")
-
-    call_data = call_recording.call_data or {}
-    platform = (call_recording.provider_platform or "").lower()
-
-    # --- Check if S3 audio already exists from a previous evaluation -------
-    existing_result = None
-    if call_recording.evaluator_result_id:
-        existing_result = db.query(EvaluatorResult).filter(
-            EvaluatorResult.id == call_recording.evaluator_result_id,
-        ).first()
-
-    audio_s3_key = existing_result.audio_s3_key if existing_result and existing_result.audio_s3_key else None
-
-    # --- If no S3 audio, download from provider and upload -----------------
-    if not audio_s3_key:
-        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-        if not agent or not agent.voice_ai_integration_id:
-            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Agent or integration not found")
-
-        integration = db.query(Integration).filter(
-            Integration.id == agent.voice_ai_integration_id,
-            Integration.organization_id == organization_id,
-        ).first()
-        if not integration:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Integration not found")
-
-        decrypted_key = decrypt_api_key(integration.api_key)
-
-        def _download_audio_from_payload(payload: Dict[str, Any]):
-            payload_urls = payload.get("recording_urls", {}) if isinstance(payload, dict) else {}
-            artifact = payload.get("artifact", {}) if isinstance(payload, dict) else {}
-            recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
-            mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
-            url = None
-            headers = None
-            if platform == "elevenlabs":
-                url = payload_urls.get("conversation_audio")
-                headers = {"xi-api-key": decrypted_key}
-            elif platform == "retell":
-                url = payload.get("recording_url")
-            elif platform == "vapi":
-                url = (
-                    payload.get("recordingUrl")
-                    or payload.get("stereoRecordingUrl")
-                    or artifact.get("recordingUrl")
-                    or artifact.get("stereoRecordingUrl")
-                    or mono_recording.get("combinedUrl")
-                    or payload_urls.get("combined_url")
-                    or payload_urls.get("stereo_url")
-                )
-            elif platform == "smallest":
-                url = (
-                    payload.get("recording_url")
-                    or payload.get("recordingUrl")
-                    or payload_urls.get("combined_url")
-                    or payload_urls.get("conversation_audio")
-                )
-            if not url:
-                return None, None
-            response = http_requests.get(url, headers=headers, timeout=120)
-            if response.status_code != 200:
-                return None, response
-            return response.content, response
-
-        audio_bytes, resp = _download_audio_from_payload(call_data)
-
-        # Retry once with fresh provider payload (new signed URL) using provider_call_id
-        if not audio_bytes and call_recording.provider_call_id:
-            try:
-                provider_class = get_voice_provider(platform)
-                provider_kwargs: Dict[str, Any] = {"api_key": decrypted_key}
-                if platform == "vapi" and integration.public_key:
-                    provider_kwargs["public_key"] = integration.public_key
-                provider = provider_class(**provider_kwargs)
-                if hasattr(provider, "retrieve_call_metrics"):
-                    refreshed_call_data = provider.retrieve_call_metrics(call_recording.provider_call_id)
-                    if isinstance(refreshed_call_data, dict) and refreshed_call_data:
-                        prev_data = (
-                            call_recording.call_data
-                            if isinstance(call_recording.call_data, dict)
-                            else {}
-                        )
-                        call_data = merge_playground_call_data(prev_data, refreshed_call_data)
-                        call_recording.call_data = call_data
-                        db.commit()
-                        logger.info(f"[Re-evaluate] Refreshed provider call data for call {call_recording.provider_call_id}")
-                        audio_bytes, resp = _download_audio_from_payload(call_data)
-            except Exception as refresh_err:
-                logger.warning(f"[Re-evaluate] Provider audio URL refresh failed: {refresh_err}")
-
-        if not audio_bytes:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="Could not download audio from provider. The recording may not be available yet.",
-            )
-
-        content_type = getattr(resp, "headers", {}).get("content-type", "audio/mpeg")
-        ext = "wav" if "wav" in content_type else "mp3"
-        org_id = str(organization_id)
-        audio_s3_key = f"audio/organizations/{org_id}/agentPlayground/{call_recording.provider_call_id}/{_uuid.uuid4()}.{ext}"
-        try:
-            s3_service.upload_file_by_key(audio_bytes, audio_s3_key, content_type=content_type)
-            logger.info(f"[Re-evaluate] Uploaded audio to S3: {audio_s3_key} ({len(audio_bytes)} bytes)")
-        except Exception as e:
-            logger.error(f"[Re-evaluate] S3 upload failed: {e}")
-            raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Failed to store audio: {str(e)}")
-    else:
-        logger.info(f"[Re-evaluate] Reusing existing S3 audio: {audio_s3_key}")
-
-    # --- Extract transcript from existing call data ------------------------
-    transcript_text, _ = extract_transcript_from_call_data(call_data, platform)
-
-    # --- Create or reset EvaluatorResult -----------------------------------
-    if existing_result:
-        existing_result.status = EvaluatorResultStatus.QUEUED.value
-        existing_result.audio_s3_key = audio_s3_key
-        # Metadata only; transcript is on existing_result.transcription.
-        existing_result.call_data = (
-            slim_call_data_for_evaluator_result(call_data)
-            if isinstance(call_data, dict)
-            else existing_result.call_data
-        )
-        existing_result.transcription = transcript_text or existing_result.transcription
-        existing_result.metric_scores = None
-        existing_result.error_message = None
-        existing_result.celery_task_id = None
-        db.commit()
-        db.refresh(existing_result)
-        evaluator_result = existing_result
-        logger.info(f"[Re-evaluate] Reset existing EvaluatorResult {evaluator_result.result_id}")
-    else:
-        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-        result_id = generate_unique_result_id(db)
-        duration_seconds = call_data.get("duration_seconds", 0)
-        if not duration_seconds:
-            start_ts = call_data.get("start_timestamp") or call_data.get("startedAt")
-            end_ts = call_data.get("end_timestamp") or call_data.get("endedAt")
-            if start_ts and end_ts:
-                try:
-                    from dateutil import parser
-                    duration_seconds = (parser.parse(end_ts) - parser.parse(start_ts)).total_seconds()
-                except Exception:
-                    duration_seconds = 0
-        result_name = f"Voice AI Call - {agent.name}" if agent else "Voice AI Call"
-
-        evaluator_result = EvaluatorResult(
-            result_id=result_id,
-            organization_id=call_recording.organization_id,
-            workspace_id=call_recording.workspace_id,
-            evaluator_id=None,
-            agent_id=call_recording.agent_id,
-            persona_id=None,
-            scenario_id=None,
-            name=result_name,
-            duration_seconds=duration_seconds,
-            status=EvaluatorResultStatus.QUEUED.value,
-            audio_s3_key=audio_s3_key,
-            transcription=transcript_text,
-            provider_call_id=call_recording.provider_call_id,
-            provider_platform=platform,
-            call_data=slim_call_data_for_evaluator_result(call_data),
-        )
-        db.add(evaluator_result)
-        db.commit()
-        db.refresh(evaluator_result)
-
-        call_recording.evaluator_result_id = evaluator_result.id
-        db.commit()
-        logger.info(f"[Re-evaluate] Created new EvaluatorResult {evaluator_result.result_id}")
-
-    # --- Trigger the worker ------------------------------------------------
-    try:
-        from app.workers.celery_app import process_evaluator_result_task
-        task = process_evaluator_result_task.delay(str(evaluator_result.id))
-        evaluator_result.celery_task_id = task.id
-        db.commit()
-        logger.info(f"[Re-evaluate] Triggered evaluation task {task.id} for result {evaluator_result.result_id}")
-    except Exception as e:
-        logger.error(f"[Re-evaluate] Failed to trigger Celery task: {e}")
-        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed to trigger evaluation worker")
-
-    return {
-        "message": "Re-evaluation started",
-        "evaluator_result_id": str(evaluator_result.id),
-        "result_id": evaluator_result.result_id,
-        "audio_s3_key": audio_s3_key,
-        "task_id": task.id,
-    }
-
-
-@router.get("/call-recordings/{call_short_id}/audio")
-async def stream_call_audio(
-    call_short_id: str,
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """
-    Proxy endpoint to stream playground call recording audio in the active workspace.
-    Required for providers like ElevenLabs whose audio URLs need auth headers.
-    """
-    import requests as http_requests
-
-    call_recording = db.query(CallRecording).filter(
-        CallRecording.call_short_id == call_short_id,
-        CallRecording.organization_id == organization_id,
-        CallRecording.workspace_id == workspace_id,
-        CallRecording.source == CallRecordingSource.PLAYGROUND,
-    ).first()
-
-    if not call_recording:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Call recording not found")
-
-    call_data = call_recording.call_data or {}
-    recording_urls = call_data.get("recording_urls", {})
-    platform = (call_recording.provider_platform or "").lower()
-
-    # For Retell / Vapi / Smallest the URL is public – redirect directly
-    if platform in ("retell", "vapi", "smallest"):
-        artifact = call_data.get("artifact", {})
-        recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
-        mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
-        url = (
-            call_data.get("recordingUrl")
-            or call_data.get("stereoRecordingUrl")
-            or artifact.get("recordingUrl")
-            or artifact.get("stereoRecordingUrl")
-            or mono_recording.get("combinedUrl")
-            or recording_urls.get("combined_url")
-            or recording_urls.get("stereo_url")
-            or call_data.get("recording_url")
-            or recording_urls.get("conversation_audio")
-        )
-        if not url:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording URL available")
-        from fastapi.responses import RedirectResponse
-        return RedirectResponse(url)
-
-    # ElevenLabs requires API key header – proxy the stream
-    if platform == "elevenlabs":
-        audio_url = recording_urls.get("conversation_audio")
-        if not audio_url:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording URL available")
-
-        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
-        if not agent or not agent.voice_ai_integration_id:
-            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Agent or integration not found")
-
-        integration = db.query(Integration).filter(
-            Integration.id == agent.voice_ai_integration_id,
-            Integration.organization_id == organization_id,
-        ).first()
-        if not integration:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Integration not found")
-
-        decrypted_key = decrypt_api_key(integration.api_key)
-
-        upstream = http_requests.get(
-            audio_url,
-            headers={"xi-api-key": decrypted_key},
-            stream=True,
-            timeout=60,
-        )
-        if upstream.status_code != 200:
-            raise HTTPException(
-                status_code=upstream.status_code,
-                detail=f"ElevenLabs audio fetch failed ({upstream.status_code})",
-            )
-
-        content_type = upstream.headers.get("content-type", "audio/mpeg")
-
-        return StreamingResponse(
-            upstream.iter_content(chunk_size=8192),
-            media_type=content_type,
-            headers={
-                "Content-Disposition": f'inline; filename="call_{call_short_id}.mp3"',
-            },
-        )
-
-    # Custom WebSocket sessions store audio in S3
-    if platform == "custom_websocket":
-        s3_key = call_data.get("recording_s3_key")
-        if not s3_key:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording available for this session")
-
-        from app.services.storage.s3_service import s3_service
-        if not s3_service.is_enabled():
-            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="S3 storage is not configured")
-
-        try:
-            audio_bytes = s3_service.download_file_by_key(s3_key)
-        except Exception:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Audio file not found in storage")
-        if not audio_bytes:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Audio file not found in storage")
-
-        extension = s3_key.rsplit(".", 1)[-1].lower() if "." in s3_key else "webm"
-        content_type_map = {"webm": "audio/webm", "mp3": "audio/mpeg", "wav": "audio/wav", "ogg": "audio/ogg"}
-        content_type = content_type_map.get(extension, "audio/webm")
-
-        from io import BytesIO
-        return StreamingResponse(
-            BytesIO(audio_bytes),
-            media_type=content_type,
-            headers={
-                "Content-Disposition": f'inline; filename="call_{call_short_id}.{extension}"',
-            },
-        )
-
-    raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=f"Audio not supported for platform: {platform}")
-
-
-# ---------------------------------------------------------------------------
-#  STT config resolution helper
-# ---------------------------------------------------------------------------
-
-_STT_ENV_KEYS = {
-    "deepgram": "DEEPGRAM_API_KEY",
-    "openai": "OPENAI_API_KEY",
-    "elevenlabs": "ELEVENLABS_API_KEY",
-    "sarvam": "SARVAM_API_KEY",
-}
-
-_STT_DEFAULT_MODELS = {
-    "deepgram": "nova-2",
-    "openai": "whisper-1",
-    "elevenlabs": "scribe_v2",
-    "sarvam": "saaras:v3",
-}
-
-
-def _resolve_agent_stt_config(
-    agent_id: str, organization_id: UUID, db: Session
-) -> tuple:
-    """Resolve STT provider, model, and API key for an agent.
-
-    Lookup chain: Agent -> VoiceBundle -> stt_provider/stt_model
-                  -> AIProvider (by org + provider name) or Integration or env var fallback.
-
-    Returns (stt_provider, stt_model, api_key).  Any element may be None.
-    """
-    import os
-    from sqlalchemy import func
-
-    agent = db.query(Agent).filter(
-        Agent.id == agent_id,
-        Agent.organization_id == organization_id,
-    ).first()
-    if not agent or not agent.voice_bundle_id:
-        return None, None, None
-
-    voice_bundle = db.query(VoiceBundle).filter(
-        VoiceBundle.id == agent.voice_bundle_id,
-        VoiceBundle.organization_id == organization_id,
-    ).first()
-    if not voice_bundle or not voice_bundle.stt_provider:
-        return None, None, None
-
-    stt_provider = (
-        voice_bundle.stt_provider.value
-        if hasattr(voice_bundle.stt_provider, "value")
-        else str(voice_bundle.stt_provider)
-    ).lower()
-    stt_model = (
-        getattr(voice_bundle, "stt_model", None)
-        or _STT_DEFAULT_MODELS.get(stt_provider)
-    )
-
-    # 1) AIProvider
-    api_key = None
-    ai_prov = db.query(AIProvider).filter(
-        AIProvider.organization_id == organization_id,
-        AIProvider.provider == stt_provider,
-        AIProvider.is_active == True,
-    ).first()
-    if not ai_prov:
-        ai_prov = db.query(AIProvider).filter(
-            AIProvider.organization_id == organization_id,
-            func.lower(AIProvider.provider) == stt_provider,
-            AIProvider.is_active == True,
-        ).first()
-    if ai_prov:
-        try:
-            api_key = decrypt_api_key(ai_prov.api_key)
-        except Exception:
-            pass
-
-    # 2) Integration fallback
-    if not api_key:
-        _platform_map = {
-            "deepgram": "deepgram",
-            "elevenlabs": "elevenlabs",
-            "sarvam": "sarvam",
-        }
-        plat_value = _platform_map.get(stt_provider)
-        if plat_value:
-            integ = db.query(Integration).filter(
-                Integration.organization_id == organization_id,
-                func.lower(Integration.platform) == plat_value,
-                Integration.is_active == True,
-            ).first()
-            if integ:
-                try:
-                    api_key = decrypt_api_key(integ.api_key)
-                except Exception:
-                    pass
-
-    # 3) Env var fallback
-    if not api_key:
-        env_key = _STT_ENV_KEYS.get(stt_provider)
-        if env_key:
-            api_key = os.getenv(env_key)
-
-    return stt_provider, stt_model, api_key
-
-
-# ---------------------------------------------------------------------------
-#  GET /playground/agents/{agent_id}/stt-config
-# ---------------------------------------------------------------------------
-
-@router.get("/agents/{agent_id}/stt-config", response_model=Dict[str, Any])
-async def get_agent_stt_config(
-    agent_id: str,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """Check whether an agent has STT configured via its voice bundle."""
-    stt_provider, stt_model, stt_api_key = _resolve_agent_stt_config(
-        agent_id, organization_id, db
-    )
-    if stt_provider and stt_api_key:
-        return {"available": True, "provider": stt_provider, "model": stt_model}
-    if stt_provider and not stt_api_key:
-        return {
-            "available": False,
-            "reason": f"STT provider '{stt_provider}' is configured but no API key was found",
-        }
-    return {"available": False, "reason": "No voice bundle with STT configured for this agent"}
-
-
-# ---------------------------------------------------------------------------
-#  POST /playground/transcribe-turn
-# ---------------------------------------------------------------------------
-
-@router.post("/transcribe-turn", response_model=Dict[str, Any])
-async def transcribe_turn(
-    agent_id: str = Form(...),
-    channel: str = Form(...),
-    audio_file: UploadFile = File(...),
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """Transcribe a single conversation turn (user or agent audio)."""
-    import tempfile
-    import os
-
-    if channel not in ("user", "agent"):
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="channel must be 'user' or 'agent'",
-        )
-
-    stt_provider, stt_model, stt_api_key = _resolve_agent_stt_config(
-        agent_id, organization_id, db
-    )
-    if not stt_provider or not stt_api_key:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="STT is not configured for this agent (missing provider or API key)",
-        )
-
-    audio_bytes = await audio_file.read()
-    if not audio_bytes or len(audio_bytes) < 100:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Audio file is empty or too small",
-        )
-
-    tmp_path = None
-    try:
-        suffix = ".wav"
-        if audio_file.filename and "." in audio_file.filename:
-            suffix = "." + audio_file.filename.rsplit(".", 1)[-1].lower()
-        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
-            tmp.write(audio_bytes)
-            tmp_path = tmp.name
-
-        from app.services.ai.stt_clients import (
-            transcribe_openai,
-            transcribe_deepgram,
-            transcribe_elevenlabs,
-            transcribe_sarvam,
-        )
-
-        if stt_provider == "deepgram":
-            result = transcribe_deepgram(tmp_path, stt_model, stt_api_key)
-        elif stt_provider == "openai":
-            result = transcribe_openai(tmp_path, stt_model, stt_api_key)
-        elif stt_provider == "elevenlabs":
-            result = transcribe_elevenlabs(tmp_path, stt_model, stt_api_key)
-        elif stt_provider == "sarvam":
-            result = transcribe_sarvam(tmp_path, stt_model, stt_api_key)
-        else:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Unsupported STT provider: {stt_provider}",
-            )
-
-        transcript_text = (result.get("text") or "").strip()
-        return {"transcript": transcript_text, "channel": channel}
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"[transcribe-turn] STT failed: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Transcription failed: {str(e)}",
-        )
-    finally:
-        if tmp_path and os.path.exists(tmp_path):
-            os.unlink(tmp_path)
-
-
-# ---------------------------------------------------------------------------
-#  POST /playground/summarize-transcript
-# ---------------------------------------------------------------------------
-
-# Fallback defaults if the caller provides no agent context AND the voice
-# bundle doesn't specify a model. The providers referenced here must exist in
-# ``ModelProvider`` and be reachable via LiteLLM.
-_SUMMARY_LLM_FALLBACK: List[tuple] = [
-    (ModelProvider.OPENAI, "gpt-4o-mini"),
-    (ModelProvider.GOOGLE, "gemini-2.5-flash"),
-    (ModelProvider.ANTHROPIC, "claude-haiku-4.5"),
-]
-
-
-class SummarizeTranscriptRequest(BaseModel):
-    transcript: Optional[str] = None
-    entries: Optional[List[Dict[str, Any]]] = None
-    call_short_id: Optional[str] = None
-    agent_id: Optional[str] = None
-    # When true, ignore any cached summary on the CallRecording and regenerate.
-    force: Optional[bool] = False
-
-
-def _coerce_model_provider(provider_str: str) -> Optional[ModelProvider]:
-    """Safely convert a string like ``"openai"`` to the matching enum."""
-    if not provider_str:
-        return None
-    want = provider_str.strip().lower()
-    for m in ModelProvider:
-        if m.value.lower() == want:
-            return m
-    return None
-
-
-def _resolve_agent_llm_config(
-    agent_id: Optional[UUID], organization_id: UUID, db: Session
-) -> tuple:
-    """Resolve the LLM provider + model for an agent via its VoiceBundle.
-
-    Returns ``(ModelProvider | None, model_name | None)``.
-    """
-    if not agent_id:
-        return None, None
-
-    agent = db.query(Agent).filter(
-        Agent.id == agent_id,
-        Agent.organization_id == organization_id,
-    ).first()
-    if not agent or not agent.voice_bundle_id:
-        return None, None
-
-    voice_bundle = db.query(VoiceBundle).filter(
-        VoiceBundle.id == agent.voice_bundle_id,
-        VoiceBundle.organization_id == organization_id,
-    ).first()
-    if not voice_bundle or not voice_bundle.llm_provider:
-        return None, None
-
-    provider_enum = _coerce_model_provider(voice_bundle.llm_provider)
-    if not provider_enum:
-        return None, None
-
-    return provider_enum, (voice_bundle.llm_model or None)
-
-
-def _pick_fallback_llm(organization_id: UUID, db: Session) -> Optional[tuple]:
-    """Pick any (ModelProvider, default_model) that has an active AIProvider
-    row for the organization. Used only when the agent / voice bundle doesn't
-    specify an LLM.
-    """
-    from sqlalchemy import func
-
-    for provider_enum, default_model in _SUMMARY_LLM_FALLBACK:
-        provider_value = provider_enum.value
-        ai_prov = db.query(AIProvider).filter(
-            AIProvider.organization_id == organization_id,
-            func.lower(AIProvider.provider) == provider_value.lower(),
-            AIProvider.is_active == True,
-        ).first()
-        if ai_prov:
-            return provider_enum, default_model
-    return None
-
-
-def _format_entries_as_transcript(entries: List[Dict[str, Any]]) -> str:
-    """Turn a list of {role, content} entries into a plain-text transcript."""
-    lines = []
-    for e in entries or []:
-        if not isinstance(e, dict):
-            continue
-        role = (e.get("role") or "").strip().lower()
-        text = (e.get("content") or e.get("text") or "").strip()
-        if not text:
-            continue
-        label = "User" if role in ("user", "caller", "speaker 1") else "Agent"
-        lines.append(f"{label}: {text}")
-    return "\n".join(lines)
-
-
-@router.post("/summarize-transcript", response_model=Dict[str, Any])
-async def summarize_transcript(
-    payload: SummarizeTranscriptRequest = Body(...),
-    organization_id: UUID = Depends(get_organization_id),
-    workspace_id: UUID = Depends(get_workspace_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """Generate a short natural-language summary of a transcript using an LLM.
-
-    Provider selection order:
-      1. The voice bundle of ``agent_id`` (or the agent on ``call_short_id``).
-      2. Any configured AIProvider matching the fallback preference list.
-    """
-    from contextlib import nullcontext
-
-    from app.services.ai.llm_service import llm_service
-    from app.services.usage.context import llm_usage_context, usage_context_for_agent
-
-    transcript_text = (payload.transcript or "").strip()
-    if not transcript_text and payload.entries:
-        transcript_text = _format_entries_as_transcript(payload.entries)
-
-    if not transcript_text:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Provide either `transcript` text or non-empty `entries`.",
-        )
-
-    # Defensive cap on transcript size to protect token budgets. Keep the tail
-    # so the most recent exchange wins.
-    MAX_CHARS = 16000
-    if len(transcript_text) > MAX_CHARS:
-        transcript_text = transcript_text[-MAX_CHARS:]
-
-    # --- Resolve CallRecording (for caching) and agent context -------------
-    call_rec: Optional[CallRecording] = None
-    if payload.call_short_id:
-        call_rec = db.query(CallRecording).filter(
-            CallRecording.call_short_id == payload.call_short_id,
-            CallRecording.organization_id == organization_id,
-            CallRecording.workspace_id == workspace_id,
-        ).first()
-
-    # Cache hit: serve the previously generated summary without re-calling the LLM.
-    if call_rec and not payload.force and isinstance(call_rec.call_data, dict):
-        cached = call_rec.call_data.get("ai_summary")
-        if isinstance(cached, dict) and (cached.get("text") or "").strip():
-            return {
-                "summary": cached.get("text", ""),
-                "provider": cached.get("provider", ""),
-                "model": cached.get("model", ""),
-                "source": cached.get("source", "voice_bundle"),
-                "cached": True,
-                "generated_at": cached.get("generated_at"),
-                "usage": {},
-            }
-
-    agent_uuid: Optional[UUID] = None
-    if payload.agent_id:
-        try:
-            agent_uuid = UUID(payload.agent_id)
-        except ValueError:
-            agent_uuid = None
-
-    if not agent_uuid and call_rec and call_rec.agent_id:
-        agent_uuid = call_rec.agent_id
-
-    # --- Pick LLM: prefer voice-bundle config, else fall back --------------
-    llm_provider, llm_model = _resolve_agent_llm_config(
-        agent_uuid, organization_id, db
-    )
-    source = "voice_bundle"
-
-    if not llm_provider:
-        picked = _pick_fallback_llm(organization_id, db)
-        if not picked:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    "No LLM is configured. Either set an LLM on the agent's voice bundle, "
-                    "or add an active AIProvider (OpenAI / Google / Anthropic) for this organization."
-                ),
-            )
-        llm_provider, llm_model = picked
-        source = "org_fallback"
-
-    if not llm_model:
-        # Voice bundle had a provider but no model; fill in a safe default.
-        defaults = {
-            ModelProvider.OPENAI: "gpt-4o-mini",
-            ModelProvider.GOOGLE: "gemini-2.5-flash",
-            ModelProvider.ANTHROPIC: "claude-haiku-4.5",
-        }
-        llm_model = defaults.get(llm_provider) or "gpt-4o-mini"
-
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are an expert call analyst. Read the conversation transcript "
-                "the user provides and write a concise, neutral summary of what happened. "
-                "Focus on the caller's intent, what the agent did, any outcomes or "
-                "action items, and the overall tone. Respond in 2-4 plain-text "
-                "sentences only — no bullet points, no markdown, no preamble."
-            ),
-        },
-        {
-            "role": "user",
-            "content": f"Conversation transcript:\n\n{transcript_text}",
-        },
-    ]
-
-    try:
-        usage_ctx = nullcontext()
-        if agent_uuid:
-            agent_row = db.query(Agent).filter(
-                Agent.id == agent_uuid,
-                Agent.organization_id == organization_id,
-            ).first()
-            if agent_row:
-                usage_ctx = llm_usage_context(
-                    usage_context_for_agent(agent_row, workspace_id=workspace_id)
-                )
-
-        with usage_ctx:
-            result = llm_service.generate_response(
-                messages=messages,
-                llm_provider=llm_provider,
-                llm_model=llm_model,
-                organization_id=organization_id,
-                db=db,
-                temperature=0.3,
-                max_tokens=400,
-            )
-    except Exception as e:
-        logger.error(f"[summarize-transcript] LLM call failed: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Summary generation failed: {str(e)}",
-        )
-
-    summary = (result.get("text") or "").strip()
-    if not summary:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail="LLM returned an empty summary.",
-        )
-
-    generated_at = datetime.utcnow().isoformat() + "Z"
-
-    # Persist on the CallRecording so subsequent loads don't re-invoke the LLM.
-    if call_rec is not None:
-        try:
-            # Reassign the dict so SQLAlchemy notices the JSON column changed.
-            existing = call_rec.call_data if isinstance(call_rec.call_data, dict) else {}
-            new_call_data = dict(existing)
-            new_call_data["ai_summary"] = {
-                "text": summary,
-                "provider": llm_provider.value,
-                "model": llm_model,
-                "source": source,
-                "generated_at": generated_at,
-            }
-            call_rec.call_data = new_call_data
-            db.commit()
-        except Exception as e:
-            # Non-fatal: return the generated summary even if persistence fails.
-            logger.warning(f"[summarize-transcript] Failed to cache summary: {e}")
-            db.rollback()
-
-    return {
-        "summary": summary,
-        "provider": llm_provider.value,
-        "model": llm_model,
-        "source": source,
-        "cached": False,
-        "generated_at": generated_at,
-        "usage": result.get("usage", {}),
-    }
-
-
-from app.core.auth.capabilities import SIM_MANAGE, SIM_VIEW
-from app.core.auth.workspace_route_capabilities import apply_workspace_route_capabilities
-
-apply_workspace_route_capabilities(
-    router,
-    view_capability=SIM_VIEW,
-    manage_capability=SIM_MANAGE,
-)
-
+"""
+Playground API Routes
+API endpoints for testing voice agents in the playground
+"""
+from fastapi import APIRouter, Depends, HTTPException, status, Body, BackgroundTasks, Form, File, UploadFile
+from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
+from typing import Dict, Any, Optional, List
+from uuid import UUID
+from pydantic import BaseModel
+from loguru import logger
+import random
+import json
+import uuid as _uuid
+from datetime import datetime
+
+from app.services.billing.flexprice_service import (
+    record_playground_web_call_started,
+    record_playground_websocket_session_started,
+)
+from app.database import get_db
+from app.dependencies import get_organization_id, get_workspace_id, get_api_key
+from app.models.database import (
+    Agent,
+    Integration,
+    IntegrationPlatform,
+    CallRecording,
+    CallRecordingStatus,
+    CallRecordingSource,
+    EvaluatorResult,
+    EvaluatorResultStatus,
+    VoiceBundle,
+    AIProvider,
+    ModelProvider,
+)
+from app.core.encryption import decrypt_api_key
+from app.services.voice_providers import get_voice_provider
+from app.services.evaluators.evaluator_result_call_data import slim_call_data_for_evaluator_result
+from app.utils.call_recordings import generate_unique_call_short_id
+
+from app.services.evaluators.call_data_transcript import extract_transcript_from_call_data
+
+router = APIRouter(prefix="/playground", tags=["playground"])
+def generate_unique_result_id(db: Session) -> str:
+    """Generate a unique 6-digit result ID for EvaluatorResult."""
+    max_attempts = 100
+    for _ in range(max_attempts):
+        candidate_id = f"{random.randint(100000, 999999)}"
+        existing = db.query(EvaluatorResult).filter(EvaluatorResult.result_id == candidate_id).first()
+        if not existing:
+            return candidate_id
+    raise ValueError("Failed to generate unique result ID")
+
+
+def poll_call_metrics(
+    call_recording_id: UUID,
+    provider_call_id: str,
+    provider_platform: str,
+    integration_api_key: str,
+    max_attempts: int = 60,
+    poll_interval: int = 5
+):
+    """
+    Background task to poll for call metrics from the provider.
+    After call is complete, creates an EvaluatorResult and triggers metric evaluation.
+    
+    Args:
+        call_recording_id: The CallRecording database ID
+        provider_call_id: The provider's call_id (e.g., Retell call_id)
+        provider_platform: The provider platform (e.g., "retell")
+        integration_api_key: The decrypted API key for the provider
+        max_attempts: Maximum number of polling attempts
+        poll_interval: Seconds between polling attempts
+    """
+    import time
+    from app.database import SessionLocal
+    from app.services.playground.post_call_processing import merge_playground_call_data
+    from app.services.voice_providers import get_voice_provider
+    
+    db = SessionLocal()
+    call_complete = False
+    call_metrics = None
+    
+    try:
+        call_recording = db.query(CallRecording).filter(CallRecording.id == call_recording_id).first()
+        if not call_recording:
+            return
+        
+        if not provider_call_id or provider_call_id == "None":
+            return
+
+        # Get the appropriate voice provider
+        try:
+            provider_class = get_voice_provider(provider_platform)
+            provider = provider_class(api_key=integration_api_key)
+        except ValueError:
+            return
+        
+        # Poll for call metrics
+        for attempt in range(max_attempts):
+            try:
+                # Wait before polling (except first attempt)
+                if attempt > 0:
+                    time.sleep(poll_interval)
+                
+                # Retrieve call metrics
+                if hasattr(provider, "retrieve_call_metrics"):
+                    call_metrics = provider.retrieve_call_metrics(provider_call_id)
+                else:
+                    # For other providers, implement similar method
+                    continue
+                
+                # Update the call recording with metrics (preserve usage-recorded flag)
+                prev_data = (
+                    call_recording.call_data
+                    if isinstance(call_recording.call_data, dict)
+                    else {}
+                )
+                if isinstance(call_metrics, dict):
+                    call_metrics = merge_playground_call_data(prev_data, call_metrics)
+                call_recording.call_data = call_metrics
+                call_recording.status = CallRecordingStatus.UPDATED
+                db.commit()
+                db.refresh(call_recording)
+                
+                # Check if call is complete (supports raw + normalized payloads)
+                call_status = (
+                    call_metrics.get("call_status")
+                    or call_metrics.get("status")
+                    or ""
+                )
+                call_status = str(call_status).lower()
+                end_timestamp = call_metrics.get("end_timestamp") or call_metrics.get("endedAt")
+                
+                # If call is complete, stop polling
+                if end_timestamp or call_status in ["ended", "completed", "failed", "end-of-call-report", "done"]:
+                    call_complete = True
+                    break
+                    
+            except Exception as e:
+                # Log error but continue polling
+                logger.warning(f"[Poll Call Metrics] Error on attempt {attempt + 1}: {str(e)}")
+                # If it's a 404 or similar, the call might not exist yet, continue polling
+                continue
+        
+        # After polling is complete, create EvaluatorResult and trigger evaluation
+        if call_complete and call_metrics and call_recording.agent_id:
+            try:
+                from app.services.playground.post_call_processing import (
+                    claim_playground_evaluator_result_slot,
+                    record_playground_post_call_usage_once,
+                )
+
+                should_create_evaluator, call_metrics = record_playground_post_call_usage_once(
+                    db,
+                    call_recording_id,
+                    provider_platform=provider_platform,
+                    call_metrics=call_metrics if isinstance(call_metrics, dict) else {},
+                )
+                if not should_create_evaluator:
+                    return
+
+                logger.info(f"[Poll Call Metrics] Call complete, creating EvaluatorResult for call {provider_call_id}")
+                
+                # Extract transcript and speaker segments from call_data
+                transcript_text, _ = extract_transcript_from_call_data(
+                    call_metrics, 
+                    provider_platform
+                )
+                
+                if not transcript_text:
+                    logger.warning(f"[Poll Call Metrics] No transcript found in call_data for call {provider_call_id}")
+                
+                # Get agent info for naming
+                agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+                result_name = f"Voice AI Call - {agent.name}" if agent else "Voice AI Call"
+                
+                # Calculate duration
+                duration_seconds = call_metrics.get("duration_seconds", 0)
+                if not duration_seconds:
+                    # Try to calculate from timestamps
+                    start_ts = call_metrics.get("start_timestamp") or call_metrics.get("startedAt")
+                    end_ts = call_metrics.get("end_timestamp") or call_metrics.get("endedAt")
+                    if start_ts and end_ts:
+                        try:
+                            from dateutil import parser
+                            start_time = parser.parse(start_ts)
+                            end_time = parser.parse(end_ts)
+                            duration_seconds = (end_time - start_time).total_seconds()
+                        except Exception:
+                            pass
+                
+                # Download call audio from provider and upload to S3
+                audio_s3_key = None
+                try:
+                    import requests as _http
+                    import uuid as _uuid
+                    from app.services.storage.s3_service import s3_service
+
+                    recording_urls = call_metrics.get("recording_urls", {})
+                    audio_bytes = None
+                    plat = provider_platform.lower()
+
+                    if plat == "elevenlabs":
+                        audio_url = recording_urls.get("conversation_audio")
+                        if audio_url:
+                            resp = _http.get(audio_url, headers={"xi-api-key": integration_api_key}, timeout=120)
+                            if resp.status_code == 200:
+                                audio_bytes = resp.content
+                    elif plat == "retell":
+                        audio_url = call_metrics.get("recording_url")
+                        if audio_url:
+                            resp = _http.get(audio_url, timeout=120)
+                            if resp.status_code == 200:
+                                audio_bytes = resp.content
+                    elif plat == "vapi":
+                        artifact = call_metrics.get("artifact", {})
+                        recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
+                        mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
+                        audio_url = (
+                            call_metrics.get("recordingUrl")
+                            or call_metrics.get("stereoRecordingUrl")
+                            or artifact.get("recordingUrl")
+                            or artifact.get("stereoRecordingUrl")
+                            or mono_recording.get("combinedUrl")
+                            or recording_urls.get("combined_url")
+                            or recording_urls.get("stereo_url")
+                        )
+                        if audio_url:
+                            vapi_headers = {"Authorization": f"Bearer {integration_api_key}"}
+                            resp = _http.get(audio_url, headers=vapi_headers, timeout=120)
+                            if resp.status_code == 200:
+                                audio_bytes = resp.content
+
+                    if audio_bytes:
+                        content_type = getattr(resp, "headers", {}).get("content-type", "audio/mpeg")
+                        ext = "wav" if "wav" in content_type else "mp3"
+                        org_id = str(call_recording.organization_id)
+                        audio_s3_key = f"audio/organizations/{org_id}/agentPlayground/{provider_call_id}/{_uuid.uuid4()}.{ext}"
+                        s3_service.upload_file_by_key(audio_bytes, audio_s3_key, content_type=content_type)
+                        logger.info(f"[Poll Call Metrics] Uploaded call audio to S3: {audio_s3_key} ({len(audio_bytes)} bytes)")
+                    else:
+                        logger.warning(f"[Poll Call Metrics] Could not download audio for call {provider_call_id}")
+                except Exception as audio_err:
+                    logger.warning(f"[Poll Call Metrics] Audio download/upload failed: {audio_err}")
+
+                locked_recording = claim_playground_evaluator_result_slot(
+                    db,
+                    call_recording_id,
+                    provider_call_id=provider_call_id,
+                )
+                if not locked_recording:
+                    return
+
+                result_id = generate_unique_result_id(db)
+                evaluator_result = EvaluatorResult(
+                    result_id=result_id,
+                    organization_id=locked_recording.organization_id,
+                    workspace_id=locked_recording.workspace_id,
+                    evaluator_id=None,
+                    agent_id=locked_recording.agent_id,
+                    persona_id=None,
+                    scenario_id=None,
+                    name=result_name,
+                    duration_seconds=duration_seconds,
+                    status=EvaluatorResultStatus.QUEUED.value,
+                    audio_s3_key=audio_s3_key,
+                    transcription=transcript_text,
+                    provider_call_id=provider_call_id,
+                    provider_platform=provider_platform,
+                    call_data=call_metrics,
+                )
+                db.add(evaluator_result)
+                db.flush()
+                locked_recording.evaluator_result_id = evaluator_result.id
+                db.commit()
+                db.refresh(evaluator_result)
+                
+                logger.info(f"[Poll Call Metrics] Created EvaluatorResult {result_id} for call {provider_call_id}")
+                
+                # Trigger Celery task to process evaluator result (run metrics evaluation)
+                try:
+                    from app.workers.celery_app import process_evaluator_result_task
+                    task = process_evaluator_result_task.delay(str(evaluator_result.id))
+                    evaluator_result.celery_task_id = task.id
+                    db.commit()
+                    logger.info(f"[Poll Call Metrics] Triggered evaluation task {task.id} for result {result_id}")
+                except Exception as task_error:
+                    logger.error(f"[Poll Call Metrics] Failed to trigger Celery task: {task_error}")
+                    # Mark the result as failed if we can't trigger the task
+                    # But keep the transcript available for manual review
+                    
+            except Exception as e:
+                logger.error(f"[Poll Call Metrics] Error creating EvaluatorResult: {str(e)}", exc_info=True)
+        
+    finally:
+        db.close()
+
+
+class CallRecordingUpdate(BaseModel):
+    """Schema for updating a call recording."""
+    provider_call_id: str
+
+
+@router.put("/call-recordings/{call_short_id}", response_model=Dict[str, Any])
+async def update_call_recording(
+    call_short_id: str,
+    update_data: CallRecordingUpdate,
+    background_tasks: BackgroundTasks,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Update a call recording within the active workspace, typically to set the provider_call_id.
+    Triggers polling.
+    """
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+    ).first()
+    
+    if not call_recording:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call recording not found"
+        )
+    
+    call_recording.provider_call_id = update_data.provider_call_id
+    db.commit()
+    db.refresh(call_recording)
+    
+    # Trigger polling if we have all info
+    if call_recording.provider_platform:
+        # Get integration api key
+        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+        if agent and agent.voice_ai_integration_id:
+            integration = db.query(Integration).filter(
+                Integration.id == agent.voice_ai_integration_id
+            ).first()
+            
+            if integration:
+                try:
+                    decrypted_api_key = decrypt_api_key(integration.api_key)
+                    platform_key = (call_recording.provider_platform or "").lower()
+                    # Vapi polls on call-end refresh only; update fires mid-call too.
+                    if platform_key != "vapi":
+                        background_tasks.add_task(
+                            poll_call_metrics,
+                            call_recording.id,
+                            call_recording.provider_call_id,
+                            call_recording.provider_platform,
+                            decrypted_api_key
+                        )
+                except:
+                    pass
+    
+    return {
+        "message": "Call recording updated", 
+        "provider_call_id": call_recording.provider_call_id
+    }
+
+
+class WebCallCreate(BaseModel):
+    """Schema for creating a web call."""
+    agent_id: str  # UUID of the agent in our system
+    metadata: Optional[Dict[str, Any]] = None
+    retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None
+    custom_sip_headers: Optional[Dict[str, str]] = None
+    ui_surface: Optional[str] = None
+
+
+@router.post("/web-call", response_model=Dict[str, Any])
+async def create_web_call(
+    web_call_data: WebCallCreate,
+    background_tasks: BackgroundTasks,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Create a web call with a voice AI agent within the active workspace.
+    The agent must belong to the same workspace; the resulting call recording
+    is stamped with the same workspace_id.
+    """
+    try:
+        # Get the agent (scoped to the active workspace)
+        agent_uuid = UUID(web_call_data.agent_id)
+        agent = db.query(Agent).filter(
+            Agent.id == agent_uuid,
+            Agent.organization_id == organization_id,
+            Agent.workspace_id == workspace_id,
+        ).first()
+        
+        if not agent:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Agent not found"
+            )
+        
+        # Check if agent has voice AI integration
+        if not agent.voice_ai_integration_id or not agent.voice_ai_agent_id:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="Agent is not configured with a voice AI integration"
+            )
+        
+        # Check if agent has web call enabled
+        if agent.call_medium != "web_call":
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="Agent is not configured for web calls"
+            )
+        
+        # Get the integration
+        integration = db.query(Integration).filter(
+            Integration.id == agent.voice_ai_integration_id,
+            Integration.organization_id == organization_id,
+            Integration.is_active == True
+        ).first()
+        
+        if not integration:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Integration not found or inactive"
+            )
+        
+        # Decrypt API key
+        try:
+            decrypted_api_key = decrypt_api_key(integration.api_key)
+        except Exception as e:
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                detail=f"Failed to decrypt API key: {str(e)}"
+            )
+        
+        # Get the appropriate voice provider
+        try:
+            provider_class = get_voice_provider(integration.platform)
+            
+            platform_value = integration.platform.value if hasattr(integration.platform, 'value') else integration.platform
+            if platform_value.lower() == "vapi":
+                provider = provider_class(api_key=decrypted_api_key, public_key=integration.public_key)
+            else:
+                provider = provider_class(api_key=decrypted_api_key)
+        except ValueError as e:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=str(e)
+            )
+        
+        # Create the web call
+        try:
+            # Verify agent_id is present
+            if not agent.voice_ai_agent_id:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail="Agent does not have a voice_ai_agent_id configured"
+                )
+            
+            print(f"[Playground] Creating web call - Agent ID: {agent.id}, Retell Agent ID: {agent.voice_ai_agent_id}, Platform: {integration.platform}")
+            
+            # Build call parameters based on provider
+            call_params = {
+                "agent_id": agent.voice_ai_agent_id,
+            }
+            
+            # Add optional parameters if provided
+            if web_call_data.metadata:
+                call_params["metadata"] = web_call_data.metadata
+            if web_call_data.retell_llm_dynamic_variables:
+                call_params["retell_llm_dynamic_variables"] = web_call_data.retell_llm_dynamic_variables
+            
+            # Note: custom_sip_headers is not supported by Retell, but may be supported by other providers
+            # For now, we'll skip it for Retell. Other providers can handle it in their implementation.
+            if integration.platform != "retell" and web_call_data.custom_sip_headers:
+                call_params["custom_sip_headers"] = web_call_data.custom_sip_headers
+
+            platform_value = (
+                integration.platform.value
+                if hasattr(integration.platform, "value")
+                else integration.platform
+            )
+            plat_lower = str(platform_value).lower()
+
+            # Vapi Web SDK creates the call in the browser; server-side /call/web
+            # would spawn a second call that never receives the user's microphone.
+            if plat_lower == "vapi":
+                from app.services.voice_providers.vapi import VAPI_SAMPLE_RATE
+
+                web_call_response = {
+                    "call_type": "web_call",
+                    "agent_id": agent.voice_ai_agent_id,
+                    "metadata": web_call_data.metadata or {},
+                    "sample_rate": VAPI_SAMPLE_RATE,
+                    "client_sdk_creates_call": True,
+                }
+                provider_call_id = None
+            else:
+                web_call_response = provider.create_web_call(**call_params)
+                provider_call_id = web_call_response.get("call_id")
+
+            call_short_id = generate_unique_call_short_id(db)
+            stored_call_data = dict(web_call_response)
+            if web_call_data.ui_surface:
+                stored_call_data["ui_surface"] = web_call_data.ui_surface
+            call_recording = CallRecording(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                call_short_id=call_short_id,
+                status=CallRecordingStatus.PENDING,
+                source=CallRecordingSource.PLAYGROUND,
+                call_data=stored_call_data,
+                provider_call_id=provider_call_id,
+                provider_platform=integration.platform,
+                agent_id=agent.id
+            )
+            db.add(call_recording)
+            db.commit()
+            db.refresh(call_recording)
+
+            background_tasks.add_task(
+                record_playground_web_call_started,
+                organization_id,
+                call_short_id,
+                workspace_id=workspace_id,
+                agent_id=agent.id,
+            )
+            
+            # Start background task to poll for call metrics
+            # Note: We need to pass the decrypted API key, but we should be careful with security
+            # For now, we'll pass it to the background task
+            # In production, you might want to store it temporarily or use a different approach
+            if provider_call_id and plat_lower != "vapi":
+                background_tasks.add_task(
+                    poll_call_metrics,
+                    call_recording.id,
+                    provider_call_id,
+                    integration.platform,
+                    decrypted_api_key
+                )
+            
+            # Add call_short_id to response for frontend
+            response = web_call_response.copy()
+            response["call_short_id"] = call_short_id
+
+            if plat_lower == "vapi" and integration.public_key:
+                response["public_key"] = integration.public_key
+
+            if plat_lower == "elevenlabs":
+                response["signed_url"] = web_call_response.get("signed_url")
+
+            return response
+        except Exception as e:
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                detail=f"Failed to create web call: {str(e)}"
+            )
+            
+    except ValueError as e:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Invalid agent ID: {str(e)}"
+        )
+    except Exception as e:
+        if isinstance(e, HTTPException):
+            raise e
+        print(f"[Create Web Call] Error: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Internal server error: {str(e)}"
+        )
+
+
+@router.get("/call-recordings", response_model=List[Dict[str, Any]])
+async def list_call_recordings(
+    skip: int = 0,
+    limit: int = 100,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    List playground call recordings in the active workspace.
+    Includes evaluator_result_id, evaluation status, and metric_scores if evaluation has been run.
+    """
+    call_recordings = db.query(CallRecording).filter(
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+    ).order_by(CallRecording.created_at.desc()).offset(skip).limit(limit).all()
+    
+    # Get evaluator result info for all linked results
+    result_ids = [cr.evaluator_result_id for cr in call_recordings if cr.evaluator_result_id]
+    result_info = {}
+    if result_ids:
+        results = db.query(EvaluatorResult).filter(EvaluatorResult.id.in_(result_ids)).all()
+        result_info = {
+            str(r.id): {
+                "status": r.status,
+                "metric_scores": r.metric_scores,
+                "result_id": r.result_id,
+                "name": r.name,
+            }
+            for r in results
+        }
+
+    agent_ids = [cr.agent_id for cr in call_recordings if cr.agent_id]
+    agents_by_id = {}
+    if agent_ids:
+        agents = db.query(Agent).filter(Agent.id.in_(agent_ids)).all()
+        agents_by_id = {a.id: a for a in agents}
+
+    def _display_name(cr: CallRecording) -> str:
+        linked = result_info.get(str(cr.evaluator_result_id), {}) if cr.evaluator_result_id else {}
+        if linked.get("name"):
+            return linked["name"]
+        agent = agents_by_id.get(cr.agent_id) if cr.agent_id else None
+        if agent and agent.name:
+            return agent.name
+        return cr.call_short_id
+
+    return [
+        {
+            "id": str(cr.id),
+            "call_short_id": cr.call_short_id,
+            "display_name": _display_name(cr),
+            "status": cr.status if cr.status else None,
+            "provider_platform": cr.provider_platform,
+            "provider_call_id": cr.provider_call_id,
+            "agent_id": str(cr.agent_id) if cr.agent_id else None,
+            "evaluator_result_id": str(cr.evaluator_result_id) if cr.evaluator_result_id else None,
+            "evaluation_status": result_info.get(str(cr.evaluator_result_id), {}).get("status") if cr.evaluator_result_id else None,
+            "metric_scores": result_info.get(str(cr.evaluator_result_id), {}).get("metric_scores") if cr.evaluator_result_id else None,
+            "result_id": result_info.get(str(cr.evaluator_result_id), {}).get("result_id") if cr.evaluator_result_id else None,
+            "created_at": cr.created_at.isoformat() if cr.created_at else None,
+            "updated_at": cr.updated_at.isoformat() if cr.updated_at else None,
+        }
+        for cr in call_recordings
+    ]
+
+
+@router.post("/custom-websocket-sessions", response_model=Dict[str, Any])
+async def create_custom_websocket_session(
+    background_tasks: BackgroundTasks,
+    agent_id: str = Form(...),
+    websocket_url: str = Form(...),
+    transcript_entries: str = Form("[]"),
+    started_at: Optional[str] = Form(None),
+    ended_at: Optional[str] = Form(None),
+    call_short_id: Optional[str] = Form(None),
+    audio_file: Optional[UploadFile] = File(None),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """
+    Save a custom websocket test session for later evaluation (scoped to the active workspace).
+    Stores transcript in call_data and uploads optional audio recording to S3.
+    """
+    try:
+        agent_uuid = UUID(agent_id)
+    except ValueError:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid agent_id")
+
+    agent = db.query(Agent).filter(
+        Agent.id == agent_uuid,
+        Agent.organization_id == organization_id,
+        Agent.workspace_id == workspace_id,
+    ).first()
+    if not agent:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
+
+    try:
+        parsed_entries = json.loads(transcript_entries or "[]")
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid transcript_entries payload")
+
+    normalized_entries = []
+    for entry in parsed_entries:
+        if not isinstance(entry, dict):
+            continue
+        role = entry.get("role")
+        content = (entry.get("content") or "").strip()
+        if role not in {"user", "agent"} or not content:
+            continue
+        normalized_entries.append(
+            {
+                "role": role,
+                "content": content,
+                "timestamp": entry.get("timestamp") or ended_at or started_at,
+            }
+        )
+
+    if not normalized_entries:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="No transcript entries provided")
+
+    transcript_text = "\n".join(
+        [f"{'User' if item['role'] == 'user' else 'Agent'}: {item['content']}" for item in normalized_entries]
+    )
+
+    call_short_id = (call_short_id or "").strip() or generate_unique_call_short_id(db)
+    audio_s3_key = None
+    if audio_file:
+        from app.services.storage.s3_service import s3_service
+
+        audio_bytes = await audio_file.read()
+        if audio_bytes:
+            if not s3_service.is_enabled():
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail="S3 storage is not configured. Save without recording or enable storage.",
+                )
+
+            filename = audio_file.filename or "session.webm"
+            extension = filename.split(".")[-1].lower() if "." in filename else "webm"
+            content_type = audio_file.content_type or "audio/webm"
+            audio_s3_key = (
+                f"audio/organizations/{organization_id}/agentPlayground/customWebsocket/"
+                f"{call_short_id}/{_uuid.uuid4()}.{extension}"
+            )
+            s3_service.upload_file_by_key(audio_bytes, audio_s3_key, content_type=content_type)
+
+    duration_seconds = 0
+    if started_at and ended_at:
+        try:
+            from datetime import datetime as _dt
+            t_start = _dt.fromisoformat(started_at.replace("Z", "+00:00"))
+            t_end = _dt.fromisoformat(ended_at.replace("Z", "+00:00"))
+            duration_seconds = max(0, (t_end - t_start).total_seconds())
+        except Exception:
+            duration_seconds = 0
+
+    speaker_segments = []
+    for entry in normalized_entries:
+        speaker = "user" if entry.get("role") == "user" else "assistant"
+        speaker_segments.append({
+            "speaker": speaker,
+            "text": entry.get("content", ""),
+            "start": 0,
+            "end": 0,
+        })
+
+    call_data = {
+        "source": "custom_websocket",
+        "websocket_url": websocket_url,
+        "messages": normalized_entries,
+        "transcript": transcript_text,
+        "speaker_segments": speaker_segments,
+        "recording_s3_key": audio_s3_key,
+        "started_at": started_at,
+        "ended_at": ended_at,
+        "duration_seconds": duration_seconds,
+    }
+
+    call_recording = CallRecording(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        call_short_id=call_short_id,
+        status=CallRecordingStatus.UPDATED,
+        source=CallRecordingSource.PLAYGROUND,
+        call_data=call_data,
+        provider_call_id=f"custom_{call_short_id}",
+        provider_platform="custom_websocket",
+        agent_id=agent.id,
+    )
+    db.add(call_recording)
+    db.commit()
+    db.refresh(call_recording)
+
+    from app.services.synthetic_traces.trace_service import (
+        close_trace_session,
+        link_trace_to_call_recording,
+    )
+
+    link_trace_to_call_recording(
+        db,
+        organization_id=organization_id,
+        call_short_id=call_short_id,
+        call_recording_id=call_recording.id,
+    )
+    close_trace_session(
+        db,
+        organization_id=organization_id,
+        call_short_id=call_short_id,
+        workspace_id=workspace_id,
+    )
+
+    background_tasks.add_task(
+        record_playground_websocket_session_started,
+        organization_id,
+        call_short_id,
+        workspace_id=workspace_id,
+    )
+
+    return {
+        "message": "Custom websocket session saved",
+        "call_short_id": call_short_id,
+        "audio_s3_key": audio_s3_key,
+        "evaluator_result_id": None,
+    }
+
+
+@router.post("/custom-websocket-sessions/{call_short_id}/evaluate", response_model=Dict[str, Any])
+async def evaluate_custom_websocket_session(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """
+    Queue evaluation for a saved custom websocket test session in the active workspace.
+    """
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+        CallRecording.provider_platform == "custom_websocket",
+    ).first()
+
+    if not call_recording:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Custom websocket session not found")
+
+    call_data = call_recording.call_data if isinstance(call_recording.call_data, dict) else {}
+    transcript_text = (call_data.get("transcript") or "").strip()
+    if not transcript_text:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="No transcript found for evaluation")
+
+    existing_result = None
+    if call_recording.evaluator_result_id:
+        existing_result = db.query(EvaluatorResult).filter(
+            EvaluatorResult.id == call_recording.evaluator_result_id,
+            EvaluatorResult.organization_id == organization_id,
+            EvaluatorResult.workspace_id == workspace_id,
+        ).first()
+
+    speaker_segments = call_data.get("speaker_segments") or []
+
+    if existing_result:
+        evaluator_result = existing_result
+        evaluator_result.status = EvaluatorResultStatus.QUEUED.value
+        evaluator_result.error_message = None
+        evaluator_result.metric_scores = None
+        evaluator_result.celery_task_id = None
+        evaluator_result.transcription = transcript_text
+        evaluator_result.speaker_segments = speaker_segments
+        evaluator_result.audio_s3_key = call_data.get("recording_s3_key")
+        evaluator_result.call_data = slim_call_data_for_evaluator_result(call_data)
+        evaluator_result.duration_seconds = call_data.get("duration_seconds", 0)
+        db.commit()
+        db.refresh(evaluator_result)
+    else:
+        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+        result_id = generate_unique_result_id(db)
+        result_name = f"Custom WebSocket Test - {agent.name}" if agent else "Custom WebSocket Test"
+
+        evaluator_result = EvaluatorResult(
+            result_id=result_id,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            evaluator_id=None,
+            agent_id=call_recording.agent_id,
+            persona_id=None,
+            scenario_id=None,
+            name=result_name,
+            duration_seconds=call_data.get("duration_seconds", 0),
+            status=EvaluatorResultStatus.QUEUED.value,
+            audio_s3_key=call_data.get("recording_s3_key"),
+            transcription=transcript_text,
+            speaker_segments=speaker_segments,
+            provider_call_id=call_recording.provider_call_id,
+            provider_platform="custom_websocket",
+            call_data=slim_call_data_for_evaluator_result(call_data),
+        )
+        db.add(evaluator_result)
+        db.commit()
+        db.refresh(evaluator_result)
+
+        call_recording.evaluator_result_id = evaluator_result.id
+        db.commit()
+
+    try:
+        from app.workers.celery_app import process_evaluator_result_task
+        task = process_evaluator_result_task.delay(str(evaluator_result.id))
+        evaluator_result.celery_task_id = task.id
+        db.commit()
+    except Exception as e:
+        logger.error(f"[Custom WebSocket] Failed to trigger evaluation worker: {e}")
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed to trigger evaluation worker")
+
+    return {
+        "message": "Evaluation queued",
+        "evaluator_result_id": str(evaluator_result.id),
+        "result_id": evaluator_result.result_id,
+        "task_id": task.id,
+    }
+
+
+@router.get("/call-recordings/{call_short_id}", response_model=Dict[str, Any])
+async def get_call_recording(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Get a specific playground call recording within the active workspace.
+    Returns the full JSON data stored for the call and evaluation information.
+    """
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+    ).first()
+    
+    if not call_recording:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call recording not found"
+        )
+    
+    # Get evaluator result info if available
+    evaluation_info = None
+    if call_recording.evaluator_result_id:
+        evaluator_result = db.query(EvaluatorResult).filter(
+            EvaluatorResult.id == call_recording.evaluator_result_id
+        ).first()
+        if evaluator_result:
+            evaluation_info = {
+                "id": str(evaluator_result.id),
+                "result_id": evaluator_result.result_id,
+                "status": evaluator_result.status,
+                "metric_scores": evaluator_result.metric_scores,
+                "transcription": evaluator_result.transcription,
+            }
+    
+    return {
+        "id": str(call_recording.id),
+        "call_short_id": call_recording.call_short_id,
+        "status": call_recording.status if call_recording.status else None,
+        "provider_platform": call_recording.provider_platform,
+        "provider_call_id": call_recording.provider_call_id,
+        "agent_id": str(call_recording.agent_id) if call_recording.agent_id else None,
+        "evaluator_result_id": str(call_recording.evaluator_result_id) if call_recording.evaluator_result_id else None,
+        "evaluation": evaluation_info,
+        "call_data": call_recording.call_data,  # Full JSON blob
+        "created_at": call_recording.created_at.isoformat() if call_recording.created_at else None,
+        "updated_at": call_recording.updated_at.isoformat() if call_recording.updated_at else None,
+    }
+
+
+@router.post("/call-recordings/{call_short_id}/refresh", response_model=Dict[str, Any])
+async def refresh_call_recording(
+    call_short_id: str,
+    background_tasks: BackgroundTasks,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Manually trigger a refresh of call metrics for a specific call recording in the active workspace.
+    """
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+    ).first()
+    
+    if not call_recording:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call recording not found"
+        )
+    
+    if not call_recording.provider_call_id or not call_recording.provider_platform:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Call recording does not have provider information"
+        )
+    
+    # Get the integration to get the API key
+    agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+    if not agent or not agent.voice_ai_integration_id:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Agent or integration not found"
+        )
+    
+    integration = db.query(Integration).filter(
+        Integration.id == agent.voice_ai_integration_id,
+        Integration.organization_id == organization_id
+    ).first()
+    
+    if not integration:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Integration not found"
+        )
+
+    if call_recording.evaluator_result_id:
+        return {"message": "Call recording already processed"}
+
+    try:
+        decrypted_api_key = decrypt_api_key(integration.api_key)
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to decrypt API key: {str(e)}"
+        )
+    
+    # Start background task to poll for call metrics
+    background_tasks.add_task(
+        poll_call_metrics,
+        call_recording.id,
+        call_recording.provider_call_id,
+        call_recording.provider_platform,
+        decrypted_api_key
+    )
+    
+    return {"message": "Call recording refresh initiated"}
+
+
+@router.delete("/call-recordings/{call_short_id}", response_model=Dict[str, Any])
+async def delete_call_recording(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Delete a call recording within the active workspace.
+    """
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+    ).first()
+    
+    if not call_recording:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Call recording not found"
+        )
+    
+    db.delete(call_recording)
+    db.commit()
+    
+    return {"message": "Call recording deleted successfully"}
+
+
+@router.post("/call-recordings/{call_short_id}/re-evaluate", response_model=Dict[str, Any])
+async def re_evaluate_call_recording(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """
+    Re-evaluate a playground call recording within the active workspace.
+
+    Reuses the S3 audio if it was already downloaded during the first
+    evaluation. If no audio exists in S3, downloads from the provider,
+    uploads, then triggers the worker for both conversation quality (LLM)
+    and audio quality (acoustic / AI voice) metrics.
+    """
+    import requests as http_requests
+    import uuid as _uuid
+    from app.services.storage.s3_service import s3_service
+
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+    ).first()
+
+    if not call_recording:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Call recording not found")
+
+    if not call_recording.provider_call_id or not call_recording.provider_platform:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Call recording has no provider data")
+
+    call_data = call_recording.call_data or {}
+    platform = (call_recording.provider_platform or "").lower()
+
+    # --- Check if S3 audio already exists from a previous evaluation -------
+    existing_result = None
+    if call_recording.evaluator_result_id:
+        existing_result = db.query(EvaluatorResult).filter(
+            EvaluatorResult.id == call_recording.evaluator_result_id,
+        ).first()
+
+    audio_s3_key = existing_result.audio_s3_key if existing_result and existing_result.audio_s3_key else None
+
+    # --- If no S3 audio, download from provider and upload -----------------
+    if not audio_s3_key:
+        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+        if not agent or not agent.voice_ai_integration_id:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Agent or integration not found")
+
+        integration = db.query(Integration).filter(
+            Integration.id == agent.voice_ai_integration_id,
+            Integration.organization_id == organization_id,
+        ).first()
+        if not integration:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Integration not found")
+
+        decrypted_key = decrypt_api_key(integration.api_key)
+
+        def _download_audio_from_payload(payload: Dict[str, Any]):
+            payload_urls = payload.get("recording_urls", {}) if isinstance(payload, dict) else {}
+            artifact = payload.get("artifact", {}) if isinstance(payload, dict) else {}
+            recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
+            mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
+            url = None
+            headers = None
+            if platform == "elevenlabs":
+                url = payload_urls.get("conversation_audio")
+                headers = {"xi-api-key": decrypted_key}
+            elif platform == "retell":
+                url = payload.get("recording_url")
+            elif platform == "vapi":
+                url = (
+                    payload.get("recordingUrl")
+                    or payload.get("stereoRecordingUrl")
+                    or artifact.get("recordingUrl")
+                    or artifact.get("stereoRecordingUrl")
+                    or mono_recording.get("combinedUrl")
+                    or payload_urls.get("combined_url")
+                    or payload_urls.get("stereo_url")
+                )
+            elif platform == "smallest":
+                url = (
+                    payload.get("recording_url")
+                    or payload.get("recordingUrl")
+                    or payload_urls.get("combined_url")
+                    or payload_urls.get("conversation_audio")
+                )
+            if not url:
+                return None, None
+            response = http_requests.get(url, headers=headers, timeout=120)
+            if response.status_code != 200:
+                return None, response
+            return response.content, response
+
+        audio_bytes, resp = _download_audio_from_payload(call_data)
+
+        # Retry once with fresh provider payload (new signed URL) using provider_call_id
+        if not audio_bytes and call_recording.provider_call_id:
+            try:
+                provider_class = get_voice_provider(platform)
+                provider_kwargs: Dict[str, Any] = {"api_key": decrypted_key}
+                if platform == "vapi" and integration.public_key:
+                    provider_kwargs["public_key"] = integration.public_key
+                provider = provider_class(**provider_kwargs)
+                if hasattr(provider, "retrieve_call_metrics"):
+                    refreshed_call_data = provider.retrieve_call_metrics(call_recording.provider_call_id)
+                    if isinstance(refreshed_call_data, dict) and refreshed_call_data:
+                        prev_data = (
+                            call_recording.call_data
+                            if isinstance(call_recording.call_data, dict)
+                            else {}
+                        )
+                        call_data = merge_playground_call_data(prev_data, refreshed_call_data)
+                        call_recording.call_data = call_data
+                        db.commit()
+                        logger.info(f"[Re-evaluate] Refreshed provider call data for call {call_recording.provider_call_id}")
+                        audio_bytes, resp = _download_audio_from_payload(call_data)
+            except Exception as refresh_err:
+                logger.warning(f"[Re-evaluate] Provider audio URL refresh failed: {refresh_err}")
+
+        if not audio_bytes:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Could not download audio from provider. The recording may not be available yet.",
+            )
+
+        content_type = getattr(resp, "headers", {}).get("content-type", "audio/mpeg")
+        ext = "wav" if "wav" in content_type else "mp3"
+        org_id = str(organization_id)
+        audio_s3_key = f"audio/organizations/{org_id}/agentPlayground/{call_recording.provider_call_id}/{_uuid.uuid4()}.{ext}"
+        try:
+            s3_service.upload_file_by_key(audio_bytes, audio_s3_key, content_type=content_type)
+            logger.info(f"[Re-evaluate] Uploaded audio to S3: {audio_s3_key} ({len(audio_bytes)} bytes)")
+        except Exception as e:
+            logger.error(f"[Re-evaluate] S3 upload failed: {e}")
+            raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Failed to store audio: {str(e)}")
+    else:
+        logger.info(f"[Re-evaluate] Reusing existing S3 audio: {audio_s3_key}")
+
+    # --- Extract transcript from existing call data ------------------------
+    transcript_text, _ = extract_transcript_from_call_data(call_data, platform)
+
+    # --- Create or reset EvaluatorResult -----------------------------------
+    if existing_result:
+        existing_result.status = EvaluatorResultStatus.QUEUED.value
+        existing_result.audio_s3_key = audio_s3_key
+        # Metadata only; transcript is on existing_result.transcription.
+        existing_result.call_data = (
+            slim_call_data_for_evaluator_result(call_data)
+            if isinstance(call_data, dict)
+            else existing_result.call_data
+        )
+        existing_result.transcription = transcript_text or existing_result.transcription
+        existing_result.metric_scores = None
+        existing_result.error_message = None
+        existing_result.celery_task_id = None
+        db.commit()
+        db.refresh(existing_result)
+        evaluator_result = existing_result
+        logger.info(f"[Re-evaluate] Reset existing EvaluatorResult {evaluator_result.result_id}")
+    else:
+        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+        result_id = generate_unique_result_id(db)
+        duration_seconds = call_data.get("duration_seconds", 0)
+        if not duration_seconds:
+            start_ts = call_data.get("start_timestamp") or call_data.get("startedAt")
+            end_ts = call_data.get("end_timestamp") or call_data.get("endedAt")
+            if start_ts and end_ts:
+                try:
+                    from dateutil import parser
+                    duration_seconds = (parser.parse(end_ts) - parser.parse(start_ts)).total_seconds()
+                except Exception:
+                    duration_seconds = 0
+        result_name = f"Voice AI Call - {agent.name}" if agent else "Voice AI Call"
+
+        evaluator_result = EvaluatorResult(
+            result_id=result_id,
+            organization_id=call_recording.organization_id,
+            workspace_id=call_recording.workspace_id,
+            evaluator_id=None,
+            agent_id=call_recording.agent_id,
+            persona_id=None,
+            scenario_id=None,
+            name=result_name,
+            duration_seconds=duration_seconds,
+            status=EvaluatorResultStatus.QUEUED.value,
+            audio_s3_key=audio_s3_key,
+            transcription=transcript_text,
+            provider_call_id=call_recording.provider_call_id,
+            provider_platform=platform,
+            call_data=slim_call_data_for_evaluator_result(call_data),
+        )
+        db.add(evaluator_result)
+        db.commit()
+        db.refresh(evaluator_result)
+
+        call_recording.evaluator_result_id = evaluator_result.id
+        db.commit()
+        logger.info(f"[Re-evaluate] Created new EvaluatorResult {evaluator_result.result_id}")
+
+    # --- Trigger the worker ------------------------------------------------
+    try:
+        from app.workers.celery_app import process_evaluator_result_task
+        task = process_evaluator_result_task.delay(str(evaluator_result.id))
+        evaluator_result.celery_task_id = task.id
+        db.commit()
+        logger.info(f"[Re-evaluate] Triggered evaluation task {task.id} for result {evaluator_result.result_id}")
+    except Exception as e:
+        logger.error(f"[Re-evaluate] Failed to trigger Celery task: {e}")
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed to trigger evaluation worker")
+
+    return {
+        "message": "Re-evaluation started",
+        "evaluator_result_id": str(evaluator_result.id),
+        "result_id": evaluator_result.result_id,
+        "audio_s3_key": audio_s3_key,
+        "task_id": task.id,
+    }
+
+
+@router.get("/call-recordings/{call_short_id}/audio")
+async def stream_call_audio(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """
+    Proxy endpoint to stream playground call recording audio in the active workspace.
+    Required for providers like ElevenLabs whose audio URLs need auth headers.
+    """
+    import requests as http_requests
+
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+    ).first()
+
+    if not call_recording:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Call recording not found")
+
+    call_data = call_recording.call_data or {}
+    recording_urls = call_data.get("recording_urls", {})
+    platform = (call_recording.provider_platform or "").lower()
+
+    # For Retell / Vapi / Smallest the URL is public – redirect directly
+    if platform in ("retell", "vapi", "smallest"):
+        artifact = call_data.get("artifact", {})
+        recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
+        mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
+        url = (
+            call_data.get("recordingUrl")
+            or call_data.get("stereoRecordingUrl")
+            or artifact.get("recordingUrl")
+            or artifact.get("stereoRecordingUrl")
+            or mono_recording.get("combinedUrl")
+            or recording_urls.get("combined_url")
+            or recording_urls.get("stereo_url")
+            or call_data.get("recording_url")
+            or recording_urls.get("conversation_audio")
+        )
+        if not url:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording URL available")
+        from fastapi.responses import RedirectResponse
+        return RedirectResponse(url)
+
+    # ElevenLabs requires API key header – proxy the stream
+    if platform == "elevenlabs":
+        audio_url = recording_urls.get("conversation_audio")
+        if not audio_url:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording URL available")
+
+        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+        if not agent or not agent.voice_ai_integration_id:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Agent or integration not found")
+
+        integration = db.query(Integration).filter(
+            Integration.id == agent.voice_ai_integration_id,
+            Integration.organization_id == organization_id,
+        ).first()
+        if not integration:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Integration not found")
+
+        decrypted_key = decrypt_api_key(integration.api_key)
+
+        upstream = http_requests.get(
+            audio_url,
+            headers={"xi-api-key": decrypted_key},
+            stream=True,
+            timeout=60,
+        )
+        if upstream.status_code != 200:
+            raise HTTPException(
+                status_code=upstream.status_code,
+                detail=f"ElevenLabs audio fetch failed ({upstream.status_code})",
+            )
+
+        content_type = upstream.headers.get("content-type", "audio/mpeg")
+
+        return StreamingResponse(
+            upstream.iter_content(chunk_size=8192),
+            media_type=content_type,
+            headers={
+                "Content-Disposition": f'inline; filename="call_{call_short_id}.mp3"',
+            },
+        )
+
+    # Custom WebSocket sessions store audio in S3
+    if platform == "custom_websocket":
+        s3_key = call_data.get("recording_s3_key")
+        if not s3_key:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording available for this session")
+
+        from app.services.storage.s3_service import s3_service
+        if not s3_service.is_enabled():
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="S3 storage is not configured")
+
+        try:
+            audio_bytes = s3_service.download_file_by_key(s3_key)
+        except Exception:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Audio file not found in storage")
+        if not audio_bytes:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Audio file not found in storage")
+
+        extension = s3_key.rsplit(".", 1)[-1].lower() if "." in s3_key else "webm"
+        content_type_map = {"webm": "audio/webm", "mp3": "audio/mpeg", "wav": "audio/wav", "ogg": "audio/ogg"}
+        content_type = content_type_map.get(extension, "audio/webm")
+
+        from io import BytesIO
+        return StreamingResponse(
+            BytesIO(audio_bytes),
+            media_type=content_type,
+            headers={
+                "Content-Disposition": f'inline; filename="call_{call_short_id}.{extension}"',
+            },
+        )
+
+    raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=f"Audio not supported for platform: {platform}")
+
+
+# ---------------------------------------------------------------------------
+#  STT config resolution helper
+# ---------------------------------------------------------------------------
+
+_STT_ENV_KEYS = {
+    "deepgram": "DEEPGRAM_API_KEY",
+    "openai": "OPENAI_API_KEY",
+    "elevenlabs": "ELEVENLABS_API_KEY",
+    "sarvam": "SARVAM_API_KEY",
+}
+
+_STT_DEFAULT_MODELS = {
+    "deepgram": "nova-2",
+    "openai": "whisper-1",
+    "elevenlabs": "scribe_v2",
+    "sarvam": "saaras:v3",
+}
+
+
+def _resolve_agent_stt_config(
+    agent_id: str, organization_id: UUID, db: Session
+) -> tuple:
+    """Resolve STT provider, model, and API key for an agent.
+
+    Lookup chain: Agent -> VoiceBundle -> stt_provider/stt_model
+                  -> AIProvider (by org + provider name) or Integration or env var fallback.
+
+    Returns (stt_provider, stt_model, api_key).  Any element may be None.
+    """
+    import os
+    from sqlalchemy import func
+
+    agent = db.query(Agent).filter(
+        Agent.id == agent_id,
+        Agent.organization_id == organization_id,
+    ).first()
+    if not agent or not agent.voice_bundle_id:
+        return None, None, None
+
+    voice_bundle = db.query(VoiceBundle).filter(
+        VoiceBundle.id == agent.voice_bundle_id,
+        VoiceBundle.organization_id == organization_id,
+    ).first()
+    if not voice_bundle or not voice_bundle.stt_provider:
+        return None, None, None
+
+    stt_provider = (
+        voice_bundle.stt_provider.value
+        if hasattr(voice_bundle.stt_provider, "value")
+        else str(voice_bundle.stt_provider)
+    ).lower()
+    stt_model = (
+        getattr(voice_bundle, "stt_model", None)
+        or _STT_DEFAULT_MODELS.get(stt_provider)
+    )
+
+    # 1) AIProvider
+    api_key = None
+    ai_prov = db.query(AIProvider).filter(
+        AIProvider.organization_id == organization_id,
+        AIProvider.provider == stt_provider,
+        AIProvider.is_active == True,
+    ).first()
+    if not ai_prov:
+        ai_prov = db.query(AIProvider).filter(
+            AIProvider.organization_id == organization_id,
+            func.lower(AIProvider.provider) == stt_provider,
+            AIProvider.is_active == True,
+        ).first()
+    if ai_prov:
+        try:
+            api_key = decrypt_api_key(ai_prov.api_key)
+        except Exception:
+            pass
+
+    # 2) Integration fallback
+    if not api_key:
+        _platform_map = {
+            "deepgram": "deepgram",
+            "elevenlabs": "elevenlabs",
+            "sarvam": "sarvam",
+        }
+        plat_value = _platform_map.get(stt_provider)
+        if plat_value:
+            integ = db.query(Integration).filter(
+                Integration.organization_id == organization_id,
+                func.lower(Integration.platform) == plat_value,
+                Integration.is_active == True,
+            ).first()
+            if integ:
+                try:
+                    api_key = decrypt_api_key(integ.api_key)
+                except Exception:
+                    pass
+
+    # 3) Env var fallback
+    if not api_key:
+        env_key = _STT_ENV_KEYS.get(stt_provider)
+        if env_key:
+            api_key = os.getenv(env_key)
+
+    return stt_provider, stt_model, api_key
+
+
+# ---------------------------------------------------------------------------
+#  GET /playground/agents/{agent_id}/stt-config
+# ---------------------------------------------------------------------------
+
+@router.get("/agents/{agent_id}/stt-config", response_model=Dict[str, Any])
+async def get_agent_stt_config(
+    agent_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """Check whether an agent has STT configured via its voice bundle."""
+    stt_provider, stt_model, stt_api_key = _resolve_agent_stt_config(
+        agent_id, organization_id, db
+    )
+    if stt_provider and stt_api_key:
+        return {"available": True, "provider": stt_provider, "model": stt_model}
+    if stt_provider and not stt_api_key:
+        return {
+            "available": False,
+            "reason": f"STT provider '{stt_provider}' is configured but no API key was found",
+        }
+    return {"available": False, "reason": "No voice bundle with STT configured for this agent"}
+
+
+# ---------------------------------------------------------------------------
+#  POST /playground/transcribe-turn
+# ---------------------------------------------------------------------------
+
+@router.post("/transcribe-turn", response_model=Dict[str, Any])
+async def transcribe_turn(
+    agent_id: str = Form(...),
+    channel: str = Form(...),
+    audio_file: UploadFile = File(...),
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """Transcribe a single conversation turn (user or agent audio)."""
+    import tempfile
+    import os
+
+    if channel not in ("user", "agent"):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="channel must be 'user' or 'agent'",
+        )
+
+    stt_provider, stt_model, stt_api_key = _resolve_agent_stt_config(
+        agent_id, organization_id, db
+    )
+    if not stt_provider or not stt_api_key:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="STT is not configured for this agent (missing provider or API key)",
+        )
+
+    audio_bytes = await audio_file.read()
+    if not audio_bytes or len(audio_bytes) < 100:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Audio file is empty or too small",
+        )
+
+    tmp_path = None
+    try:
+        suffix = ".wav"
+        if audio_file.filename and "." in audio_file.filename:
+            suffix = "." + audio_file.filename.rsplit(".", 1)[-1].lower()
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            tmp.write(audio_bytes)
+            tmp_path = tmp.name
+
+        from app.services.ai.stt_clients import (
+            transcribe_openai,
+            transcribe_deepgram,
+            transcribe_elevenlabs,
+            transcribe_sarvam,
+        )
+
+        if stt_provider == "deepgram":
+            result = transcribe_deepgram(tmp_path, stt_model, stt_api_key)
+        elif stt_provider == "openai":
+            result = transcribe_openai(tmp_path, stt_model, stt_api_key)
+        elif stt_provider == "elevenlabs":
+            result = transcribe_elevenlabs(tmp_path, stt_model, stt_api_key)
+        elif stt_provider == "sarvam":
+            result = transcribe_sarvam(tmp_path, stt_model, stt_api_key)
+        else:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Unsupported STT provider: {stt_provider}",
+            )
+
+        transcript_text = (result.get("text") or "").strip()
+        return {"transcript": transcript_text, "channel": channel}
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"[transcribe-turn] STT failed: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Transcription failed: {str(e)}",
+        )
+    finally:
+        if tmp_path and os.path.exists(tmp_path):
+            os.unlink(tmp_path)
+
+
+# ---------------------------------------------------------------------------
+#  POST /playground/summarize-transcript
+# ---------------------------------------------------------------------------
+
+# Fallback defaults if the caller provides no agent context AND the voice
+# bundle doesn't specify a model. The providers referenced here must exist in
+# ``ModelProvider`` and be reachable via LiteLLM.
+_SUMMARY_LLM_FALLBACK: List[tuple] = [
+    (ModelProvider.OPENAI, "gpt-4o-mini"),
+    (ModelProvider.GOOGLE, "gemini-2.5-flash"),
+    (ModelProvider.ANTHROPIC, "claude-haiku-4.5"),
+]
+
+
+class SummarizeTranscriptRequest(BaseModel):
+    transcript: Optional[str] = None
+    entries: Optional[List[Dict[str, Any]]] = None
+    call_short_id: Optional[str] = None
+    agent_id: Optional[str] = None
+    # When true, ignore any cached summary on the CallRecording and regenerate.
+    force: Optional[bool] = False
+
+
+def _coerce_model_provider(provider_str: str) -> Optional[ModelProvider]:
+    """Safely convert a string like ``"openai"`` to the matching enum."""
+    if not provider_str:
+        return None
+    want = provider_str.strip().lower()
+    for m in ModelProvider:
+        if m.value.lower() == want:
+            return m
+    return None
+
+
+def _resolve_agent_llm_config(
+    agent_id: Optional[UUID], organization_id: UUID, db: Session
+) -> tuple:
+    """Resolve the LLM provider + model for an agent via its VoiceBundle.
+
+    Returns ``(ModelProvider | None, model_name | None)``.
+    """
+    if not agent_id:
+        return None, None
+
+    agent = db.query(Agent).filter(
+        Agent.id == agent_id,
+        Agent.organization_id == organization_id,
+    ).first()
+    if not agent or not agent.voice_bundle_id:
+        return None, None
+
+    voice_bundle = db.query(VoiceBundle).filter(
+        VoiceBundle.id == agent.voice_bundle_id,
+        VoiceBundle.organization_id == organization_id,
+    ).first()
+    if not voice_bundle or not voice_bundle.llm_provider:
+        return None, None
+
+    provider_enum = _coerce_model_provider(voice_bundle.llm_provider)
+    if not provider_enum:
+        return None, None
+
+    return provider_enum, (voice_bundle.llm_model or None)
+
+
+def _pick_fallback_llm(organization_id: UUID, db: Session) -> Optional[tuple]:
+    """Pick any (ModelProvider, default_model) that has an active AIProvider
+    row for the organization. Used only when the agent / voice bundle doesn't
+    specify an LLM.
+    """
+    from sqlalchemy import func
+
+    for provider_enum, default_model in _SUMMARY_LLM_FALLBACK:
+        provider_value = provider_enum.value
+        ai_prov = db.query(AIProvider).filter(
+            AIProvider.organization_id == organization_id,
+            func.lower(AIProvider.provider) == provider_value.lower(),
+            AIProvider.is_active == True,
+        ).first()
+        if ai_prov:
+            return provider_enum, default_model
+    return None
+
+
+def _format_entries_as_transcript(entries: List[Dict[str, Any]]) -> str:
+    """Turn a list of {role, content} entries into a plain-text transcript."""
+    lines = []
+    for e in entries or []:
+        if not isinstance(e, dict):
+            continue
+        role = (e.get("role") or "").strip().lower()
+        text = (e.get("content") or e.get("text") or "").strip()
+        if not text:
+            continue
+        label = "User" if role in ("user", "caller", "speaker 1") else "Agent"
+        lines.append(f"{label}: {text}")
+    return "\n".join(lines)
+
+
+@router.post("/summarize-transcript", response_model=Dict[str, Any])
+async def summarize_transcript(
+    payload: SummarizeTranscriptRequest = Body(...),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """Generate a short natural-language summary of a transcript using an LLM.
+
+    Provider selection order:
+      1. The voice bundle of ``agent_id`` (or the agent on ``call_short_id``).
+      2. Any configured AIProvider matching the fallback preference list.
+    """
+    from contextlib import nullcontext
+
+    from app.services.ai.llm_service import llm_service
+    from app.services.usage.context import llm_usage_context, usage_context_for_agent
+
+    transcript_text = (payload.transcript or "").strip()
+    if not transcript_text and payload.entries:
+        transcript_text = _format_entries_as_transcript(payload.entries)
+
+    if not transcript_text:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Provide either `transcript` text or non-empty `entries`.",
+        )
+
+    # Defensive cap on transcript size to protect token budgets. Keep the tail
+    # so the most recent exchange wins.
+    MAX_CHARS = 16000
+    if len(transcript_text) > MAX_CHARS:
+        transcript_text = transcript_text[-MAX_CHARS:]
+
+    # --- Resolve CallRecording (for caching) and agent context -------------
+    call_rec: Optional[CallRecording] = None
+    if payload.call_short_id:
+        call_rec = db.query(CallRecording).filter(
+            CallRecording.call_short_id == payload.call_short_id,
+            CallRecording.organization_id == organization_id,
+            CallRecording.workspace_id == workspace_id,
+        ).first()
+
+    # Cache hit: serve the previously generated summary without re-calling the LLM.
+    if call_rec and not payload.force and isinstance(call_rec.call_data, dict):
+        cached = call_rec.call_data.get("ai_summary")
+        if isinstance(cached, dict) and (cached.get("text") or "").strip():
+            return {
+                "summary": cached.get("text", ""),
+                "provider": cached.get("provider", ""),
+                "model": cached.get("model", ""),
+                "source": cached.get("source", "voice_bundle"),
+                "cached": True,
+                "generated_at": cached.get("generated_at"),
+                "usage": {},
+            }
+
+    agent_uuid: Optional[UUID] = None
+    if payload.agent_id:
+        try:
+            agent_uuid = UUID(payload.agent_id)
+        except ValueError:
+            agent_uuid = None
+
+    if not agent_uuid and call_rec and call_rec.agent_id:
+        agent_uuid = call_rec.agent_id
+
+    # --- Pick LLM: prefer voice-bundle config, else fall back --------------
+    llm_provider, llm_model = _resolve_agent_llm_config(
+        agent_uuid, organization_id, db
+    )
+    source = "voice_bundle"
+
+    if not llm_provider:
+        picked = _pick_fallback_llm(organization_id, db)
+        if not picked:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    "No LLM is configured. Either set an LLM on the agent's voice bundle, "
+                    "or add an active AIProvider (OpenAI / Google / Anthropic) for this organization."
+                ),
+            )
+        llm_provider, llm_model = picked
+        source = "org_fallback"
+
+    if not llm_model:
+        # Voice bundle had a provider but no model; fill in a safe default.
+        defaults = {
+            ModelProvider.OPENAI: "gpt-4o-mini",
+            ModelProvider.GOOGLE: "gemini-2.5-flash",
+            ModelProvider.ANTHROPIC: "claude-haiku-4.5",
+        }
+        llm_model = defaults.get(llm_provider) or "gpt-4o-mini"
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are an expert call analyst. Read the conversation transcript "
+                "the user provides and write a concise, neutral summary of what happened. "
+                "Focus on the caller's intent, what the agent did, any outcomes or "
+                "action items, and the overall tone. Respond in 2-4 plain-text "
+                "sentences only — no bullet points, no markdown, no preamble."
+            ),
+        },
+        {
+            "role": "user",
+            "content": f"Conversation transcript:\n\n{transcript_text}",
+        },
+    ]
+
+    try:
+        usage_ctx = nullcontext()
+        if agent_uuid:
+            agent_row = db.query(Agent).filter(
+                Agent.id == agent_uuid,
+                Agent.organization_id == organization_id,
+            ).first()
+            if agent_row:
+                usage_ctx = llm_usage_context(
+                    usage_context_for_agent(agent_row, workspace_id=workspace_id)
+                )
+
+        with usage_ctx:
+            result = llm_service.generate_response(
+                messages=messages,
+                llm_provider=llm_provider,
+                llm_model=llm_model,
+                organization_id=organization_id,
+                db=db,
+                temperature=0.3,
+                max_tokens=400,
+            )
+    except Exception as e:
+        logger.error(f"[summarize-transcript] LLM call failed: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Summary generation failed: {str(e)}",
+        )
+
+    summary = (result.get("text") or "").strip()
+    if not summary:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="LLM returned an empty summary.",
+        )
+
+    generated_at = datetime.utcnow().isoformat() + "Z"
+
+    # Persist on the CallRecording so subsequent loads don't re-invoke the LLM.
+    if call_rec is not None:
+        try:
+            # Reassign the dict so SQLAlchemy notices the JSON column changed.
+            existing = call_rec.call_data if isinstance(call_rec.call_data, dict) else {}
+            new_call_data = dict(existing)
+            new_call_data["ai_summary"] = {
+                "text": summary,
+                "provider": llm_provider.value,
+                "model": llm_model,
+                "source": source,
+                "generated_at": generated_at,
+            }
+            call_rec.call_data = new_call_data
+            db.commit()
+        except Exception as e:
+            # Non-fatal: return the generated summary even if persistence fails.
+            logger.warning(f"[summarize-transcript] Failed to cache summary: {e}")
+            db.rollback()
+
+    return {
+        "summary": summary,
+        "provider": llm_provider.value,
+        "model": llm_model,
+        "source": source,
+        "cached": False,
+        "generated_at": generated_at,
+        "usage": result.get("usage", {}),
+    }
+
+
+from app.core.auth.capabilities import SIM_MANAGE, SIM_VIEW
+from app.core.auth.workspace_route_capabilities import apply_workspace_route_capabilities
+
+apply_workspace_route_capabilities(
+    router,
+    view_capability=SIM_VIEW,
+    manage_capability=SIM_MANAGE,
+)
+
diff --git a/app/api/v1/routes/synthetic_traces.py b/app/api/v1/routes/synthetic_traces.py
new file mode 100644
index 00000000..694833b0
--- /dev/null
+++ b/app/api/v1/routes/synthetic_traces.py
@@ -0,0 +1,379 @@
+"""Live call trace observability API (OTLP ingest + read)."""
+
+from __future__ import annotations
+
+from typing import Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status
+from sqlalchemy.orm import Session
+
+from app.dependencies import get_api_key, get_db, get_organization_id, get_workspace_id
+from app.models.database import EvaluatorResult
+from app.models.synthetic_trace_schemas import (
+    JsonTraceIngestRequest,
+    JsonTraceIngestResponse,
+    OtlpIngestResponse,
+    OtlpSetupInfo,
+    SyntheticCallTraceDetail,
+    SyntheticCallTraceListResponse,
+    SyntheticCallTraceSummary,
+    TraceSessionCloseResponse,
+    TraceSessionCreateRequest,
+    TraceSessionOtelCorrelation,
+    TraceSessionResponse,
+    VALID_TRACE_TRANSPORTS,
+)
+from app.services.synthetic_traces.otlp_ingest import parse_otlp_body
+from app.services.synthetic_traces.trace_service import (
+    backfill_missing_traces_from_call_recordings,
+    build_otlp_setup_info,
+    build_session_otel_correlation,
+    close_trace_session,
+    get_trace_by_call_short_id,
+    get_trace_by_id,
+    get_trace_for_result,
+    ingest_json_spans,
+    ingest_otlp_spans,
+    list_traces,
+    load_trace_detail,
+    open_trace_session,
+)
+
+router = APIRouter(prefix="/observability/traces", tags=["observability-traces"])
+
+
+def _lookup_evaluator_result(
+    db: Session,
+    id: str,
+    organization_id: UUID,
+    workspace_id: UUID,
+) -> EvaluatorResult | None:
+    try:
+        result_uuid = UUID(id)
+        return (
+            db.query(EvaluatorResult)
+            .filter(
+                EvaluatorResult.id == result_uuid,
+                EvaluatorResult.organization_id == organization_id,
+                EvaluatorResult.workspace_id == workspace_id,
+            )
+            .first()
+        )
+    except ValueError:
+        return (
+            db.query(EvaluatorResult)
+            .filter(
+                EvaluatorResult.result_id == id,
+                EvaluatorResult.organization_id == organization_id,
+                EvaluatorResult.workspace_id == workspace_id,
+            )
+            .first()
+        )
+
+
+def _api_base_url(request: Request) -> str:
+    return str(request.base_url).rstrip("/")
+
+
+def _build_trace_detail_response(db: Session, trace) -> SyntheticCallTraceDetail:
+    detail = load_trace_detail(db, trace)
+    summary = detail.get("latency_summary") or {}
+    base = SyntheticCallTraceSummary.model_validate(trace).model_dump()
+    if summary:
+        base["turn_count"] = summary.get("turn_count", base.get("turn_count"))
+        base["response_latency_p50_ms"] = summary.get("response_latency_p50_ms")
+        base["response_latency_p90_ms"] = summary.get("response_latency_p90_ms")
+        base["response_latency_p95_ms"] = summary.get("response_latency_p95_ms")
+        base["component_aggregates"] = summary.get("component_aggregates")
+    return SyntheticCallTraceDetail(
+        **base,
+        turns=detail["turns"],
+        otel_spans=detail["otel_spans"],
+        otel_trace_ids=detail["otel_trace_ids"],
+        pipeline_models=detail.get("pipeline_models") or {},
+    )
+
+
+async def _ingest_otlp_traces_handler(
+    request: Request,
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    x_efficientai_run_id: Optional[str],
+    x_efficientai_agent_id: Optional[str],
+    x_efficientai_call_short_id: Optional[str],
+) -> OtlpIngestResponse:
+    body = await request.body()
+    if not body:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Empty OTLP body")
+
+    content_type = request.headers.get("content-type", "")
+    try:
+        spans, _fmt = parse_otlp_body(body, content_type)
+    except Exception as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Failed to parse OTLP payload: {exc}",
+        ) from exc
+
+    trace, accepted, correlated = ingest_otlp_spans(
+        db,
+        organization_id=organization_id,
+        spans=spans,
+        header_evaluator_result_id=x_efficientai_run_id,
+        header_agent_id=x_efficientai_agent_id,
+        header_call_short_id=x_efficientai_call_short_id,
+        workspace_id=workspace_id,
+    )
+    return OtlpIngestResponse(
+        accepted_spans=accepted,
+        synthetic_call_trace_id=trace.id if trace else None,
+        correlated=correlated,
+    )
+
+
+@router.post("", response_model=OtlpIngestResponse)
+async def ingest_observability_traces(
+    request: Request,
+    db: Session = Depends(get_db),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    x_efficientai_run_id: Optional[str] = Header(None, alias="X-EfficientAI-Run-Id"),
+    x_efficientai_agent_id: Optional[str] = Header(None, alias="X-EfficientAI-Agent-Id"),
+    x_efficientai_call_short_id: Optional[str] = Header(None, alias="X-EfficientAI-Call-Short-Id"),
+):
+    """Ingest OTLP spans for a live call (primary export endpoint)."""
+    _ = api_key
+    return await _ingest_otlp_traces_handler(
+        request,
+        db,
+        organization_id,
+        workspace_id,
+        x_efficientai_run_id,
+        x_efficientai_agent_id,
+        x_efficientai_call_short_id,
+    )
+
+
+@router.post("/sessions", response_model=TraceSessionResponse)
+def create_trace_session(
+    payload: TraceSessionCreateRequest,
+    request: Request,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    """Mint call_short_id and open a trace before live audio / OTLP export."""
+    _ = api_key
+    if payload.transport not in VALID_TRACE_TRANSPORTS:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"transport must be one of: {', '.join(VALID_TRACE_TRANSPORTS)}",
+        )
+
+    if payload.evaluator_result_id:
+        result = _lookup_evaluator_result(
+            db,
+            str(payload.evaluator_result_id),
+            organization_id,
+            workspace_id,
+        )
+        if not result:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Evaluator result not found",
+            )
+
+    try:
+        trace = open_trace_session(
+            db,
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            evaluator_result_id=payload.evaluator_result_id,
+            transport=payload.transport,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(exc)) from exc
+
+    otel = build_session_otel_correlation(
+        api_base_url=_api_base_url(request),
+        call_short_id=trace.call_short_id or "",
+        workspace_id=workspace_id,
+        evaluator_result_id=payload.evaluator_result_id,
+    )
+    return TraceSessionResponse(
+        trace_id=trace.id,
+        call_short_id=trace.call_short_id or "",
+        workspace_id=workspace_id,
+        transport=trace.transport,
+        status=trace.status,
+        otel_correlation=TraceSessionOtelCorrelation(**otel),
+    )
+
+
+@router.post("/sessions/{call_short_id}/close", response_model=TraceSessionCloseResponse)
+def close_trace_session_route(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    _ = api_key
+    trace = close_trace_session(
+        db,
+        organization_id=organization_id,
+        call_short_id=call_short_id,
+        workspace_id=workspace_id,
+    )
+    if not trace:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Trace session not found")
+    return TraceSessionCloseResponse(
+        trace_id=trace.id,
+        call_short_id=call_short_id,
+        status=trace.status,
+    )
+
+
+@router.post("/ingest", response_model=JsonTraceIngestResponse, deprecated=True)
+def ingest_json_traces(
+    payload: JsonTraceIngestRequest,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    """Deprecated: prefer OTLP export. Simple JSON shim for non-OTel prototypes only."""
+    _ = api_key
+    if not payload.spans:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="spans required")
+
+    span_dicts = [s.model_dump() for s in payload.spans]
+    trace, accepted, correlated = ingest_json_spans(
+        db,
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        call_short_id=payload.call_short_id,
+        spans=span_dicts,
+    )
+    return JsonTraceIngestResponse(
+        accepted_spans=accepted,
+        synthetic_call_trace_id=trace.id if trace else None,
+        correlated=correlated,
+    )
+
+
+@router.get("/setup", response_model=OtlpSetupInfo)
+def get_otlp_setup(
+    request: Request,
+    api_key: str = Depends(get_api_key),
+):
+    """One-time OTLP endpoint + Pipecat config."""
+    _ = api_key
+    return OtlpSetupInfo(**build_otlp_setup_info(api_base_url=_api_base_url(request)))
+
+
+@router.get("", response_model=SyntheticCallTraceListResponse)
+def list_observability_traces(
+    skip: int = Query(0, ge=0),
+    limit: int = Query(50, ge=1, le=200),
+    status: Optional[str] = Query(None, description="open or closed"),
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    _ = api_key
+    rows, total = list_traces(
+        db,
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        skip=skip,
+        limit=limit,
+        status=status,
+    )
+    return SyntheticCallTraceListResponse(
+        items=[SyntheticCallTraceSummary.model_validate(r) for r in rows],
+        total=total,
+    )
+
+
+@router.get("/results/{evaluator_result_id}", response_model=SyntheticCallTraceDetail)
+def get_trace_for_evaluator_result(
+    evaluator_result_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    _ = api_key
+    result = _lookup_evaluator_result(
+        db, evaluator_result_id, organization_id, workspace_id
+    )
+    if not result:
+        raise HTTPException(status_code=404, detail="Evaluator result not found")
+
+    backfill_missing_traces_from_call_recordings(
+        db, organization_id=organization_id, limit=5
+    )
+
+    trace = get_trace_for_result(
+        db,
+        organization_id=organization_id,
+        evaluator_result_id=result.id,
+        workspace_id=workspace_id,
+    )
+    if not trace and result.synthetic_call_trace_id:
+        trace = get_trace_by_id(
+            db,
+            organization_id=organization_id,
+            trace_id=result.synthetic_call_trace_id,
+            workspace_id=workspace_id,
+        )
+    if not trace:
+        raise HTTPException(status_code=404, detail="Call trace not found")
+
+    return _build_trace_detail_response(db, trace)
+
+
+@router.get("/by-call-short-id/{call_short_id}", response_model=SyntheticCallTraceDetail)
+def get_trace_by_call_short_id_route(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    _ = api_key
+    trace = get_trace_by_call_short_id(
+        db,
+        organization_id=organization_id,
+        call_short_id=call_short_id,
+        workspace_id=workspace_id,
+    )
+    if not trace:
+        raise HTTPException(status_code=404, detail="Call trace not found")
+    return _build_trace_detail_response(db, trace)
+
+
+@router.get("/{trace_id}", response_model=SyntheticCallTraceDetail)
+def get_observability_trace(
+    trace_id: UUID,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    db: Session = Depends(get_db),
+    api_key: str = Depends(get_api_key),
+):
+    _ = api_key
+    trace = get_trace_by_id(
+        db,
+        organization_id=organization_id,
+        trace_id=trace_id,
+        workspace_id=workspace_id,
+    )
+    if not trace:
+        raise HTTPException(status_code=404, detail="Call trace not found")
+    return _build_trace_detail_response(db, trace)
diff --git a/app/api/v1/routes/vobiz_telephony.py b/app/api/v1/routes/vobiz_telephony.py
index 052d03b9..66a2875b 100644
--- a/app/api/v1/routes/vobiz_telephony.py
+++ b/app/api/v1/routes/vobiz_telephony.py
@@ -1,757 +1,810 @@
-"""Vobiz telephony API routes (per-org BYO credentials + platform pool fallback)."""
-
-from __future__ import annotations
-
-import random
-import string
-from typing import Any, Dict, List, Optional
-from uuid import UUID
-
-from fastapi import APIRouter, Depends, HTTPException, Request, Response, WebSocket, WebSocketDisconnect, status
-from loguru import logger
-from pydantic import BaseModel
-from sqlalchemy.orm import Session
-
-from app.config import settings
-from app.dependencies import get_api_key, get_db, get_organization_id
-from app.models.database import Agent, CallRecording, CallRecordingSource, Evaluator, TelephonyPhoneNumber
-from app.models.enums import CallRecordingStatus
-from app.services.telephony.phone_routing import resolve_inbound_agent_for_number
-from app.services.telephony.plivo_client import normalize_e164
-from app.services.telephony.vobiz_agent_context import (
-    build_vobiz_ws_url,
-    extract_webhook_params,
-    resolve_vobiz_agent_context,
-    vobiz_webhook_base_url,
-)
-from app.services.telephony.call_recording_lifecycle import (
-    create_inbound_call_recording,
-    finalize_call_on_media_disconnect,
-    find_call_recording,
-    ingest_carrier_recording_url,
-    link_provider_call_id,
-    mark_call_in_progress,
-    update_call_from_vobiz_event,
-)
-from app.services.telephony.vobiz_client import build_vobiz_client_for_org
-from app.services.telephony.vobiz_number_service import (
-    deactivate_imported_number,
-    import_vobiz_numbers,
-    list_available_vobiz_numbers,
-)
-from app.services.telephony.webhook_auth import verify_vobiz_webhook
-from app.services.telephony.vobiz_outbound_pool import (
-    outbound_pool_api_payload,
-    release_pool_slot,
-    resolve_outbound_from_number,
-)
-from app.services.telephony.vobiz_session import create_call_session, delete_call_session, get_call_session
-from app.services.telephony.vobiz_xml import reject_call, speak_and_hangup, stream_to_agent
-from app.services.voice_agent.bot_fast_api import run_bot
-from app.services.voice_agent.voice_bundle import run_voice_bundle_fastapi
-from efficientai.runner.utils import parse_telephony_websocket
-from efficientai.serializers.vobiz import VobizFrameSerializer
-
-# Exposed at module scope so tests can patch `.delay` without importing Celery tasks.
-initiate_vobiz_outbound_call_task = None
-
-router = APIRouter(prefix="/telephony/vobiz", tags=["Vobiz Telephony"])
-webhook_router = APIRouter(prefix="/telephony/vobiz", tags=["Vobiz Telephony Webhooks"])
-ws_router = APIRouter(prefix="/telephony/vobiz", tags=["Vobiz Telephony Media"])
-
-
-class VobizOutboundCallRequest(BaseModel):
-    to_number: str
-    agent_id: UUID
-    from_number: Optional[str] = None
-    persona_id: Optional[UUID] = None
-    scenario_id: Optional[UUID] = None
-    evaluator_id: Optional[UUID] = None
-
-
-class VobizOutboundCallResponse(BaseModel):
-    provider_request_uuid: str
-    call_status: str
-    from_number: str
-    to_number: str
-    call_ref: str
-    call_short_id: str = ""
-    message: str = "Outbound call initiated"
-
-
-class VobizAvailableNumberResponse(BaseModel):
-    e164: str
-    provider_number_id: Optional[str] = None
-    country: Optional[str] = None
-    region: Optional[str] = None
-    capabilities: Optional[Dict[str, Any]] = None
-    status: Optional[str] = None
-    application_id: Optional[str] = None
-    already_imported: bool = False
-    imported_number_id: Optional[str] = None
-
-
-class VobizImportNumbersRequest(BaseModel):
-    numbers: List[str]
-    agent_id: Optional[UUID] = None
-
-
-class VobizImportNumberResult(BaseModel):
-    number: str
-    success: bool
-    message: str
-    answer_url: str
-    webhook_configured: Optional[bool] = None
-    imported_number_id: Optional[str] = None
-    application_id: Optional[str] = None
-
-
-class VobizImportNumbersResponse(BaseModel):
-    provider: str = "vobiz"
-    results: List[VobizImportNumberResult]
-    answer_url: str
-
-
-class VobizOutboundPoolNumberResponse(BaseModel):
-    phone_number: str
-    provider: str
-
-
-class VobizOutboundPoolResponse(BaseModel):
-    numbers: List[VobizOutboundPoolNumberResponse]
-    max_concurrent_per_org: int
-    shared_across_orgs: bool = True
-
-
-def _parse_request_payload(raw: Any) -> Dict[str, Any]:
-    if isinstance(raw, dict):
-        return raw
-    if hasattr(raw, "items"):
-        return dict(raw)
-    return {}
-
-
-async def _read_webhook_payload(request: Request) -> Dict[str, Any]:
-    if not getattr(request.state, "webhook_raw_body", None):
-        request.state.webhook_raw_body = await request.body()
-    params: Dict[str, Any] = dict(request.query_params)
-    content_type = (request.headers.get("content-type") or "").lower()
-    if "application/json" in content_type:
-        try:
-            body = await request.json()
-            if isinstance(body, dict):
-                params.update(body)
-                return params
-        except Exception:
-            pass
-    try:
-        form = await request.form()
-        params.update(_parse_request_payload(form))
-    except Exception:
-        pass
-    return params
-
-
-def _resolve_agent_for_answer(
-    db: Session,
-    params: Dict[str, Any],
-    *,
-    call_ref: Optional[str] = None,
-) -> tuple[Optional[UUID], Optional[UUID], Optional[str]]:
-    """Return (agent_id, organization_id, session_token)."""
-    if call_ref:
-        session = get_call_session(call_ref)
-        if session and session.agent_id and session.organization_id:
-            return UUID(session.agent_id), UUID(session.organization_id), call_ref
-
-    agent_id, organization_id = resolve_inbound_agent_for_number(db, params.get("to"))
-    if not agent_id or not organization_id:
-        return None, None, None
-    return agent_id, organization_id, None
-
-
-@router.get("/numbers/available", response_model=List[VobizAvailableNumberResponse])
-async def list_vobiz_available_numbers(
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    del api_key
-    try:
-        return list_available_vobiz_numbers(db, organization_id)
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e)) from e
-
-
-@router.post("/numbers/import", response_model=VobizImportNumbersResponse)
-async def import_vobiz_numbers_route(
-    payload: VobizImportNumbersRequest,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    del api_key
-    try:
-        result = import_vobiz_numbers(
-            db,
-            organization_id,
-            numbers=payload.numbers,
-            agent_id=payload.agent_id,
-        )
-        return VobizImportNumbersResponse(**result)
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e)) from e
-
-
-@router.delete("/numbers/{number_id}", status_code=status.HTTP_204_NO_CONTENT)
-async def delete_imported_vobiz_number(
-    number_id: UUID,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    del api_key
-    try:
-        deactivate_imported_number(db, organization_id, number_id)
-    except ValueError as e:
-        message = str(e)
-        if message == "Imported number not found":
-            raise HTTPException(status_code=404, detail=message) from e
-        status_code = (
-            status.HTTP_409_CONFLICT
-            if "active number-masking sessions" in message
-            else status.HTTP_400_BAD_REQUEST
-        )
-        raise HTTPException(status_code=status_code, detail=message) from e
-    return Response(status_code=status.HTTP_204_NO_CONTENT)
-
-
-@router.get("/outbound-pool", response_model=VobizOutboundPoolResponse)
-async def get_vobiz_outbound_pool(
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-):
-    del organization_id, api_key
-    return VobizOutboundPoolResponse(**outbound_pool_api_payload())
-
-
-@router.post("/calls/outbound", response_model=VobizOutboundCallResponse)
-async def create_vobiz_outbound_call(
-    payload: VobizOutboundCallRequest,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    del api_key
-    try:
-        build_vobiz_client_for_org(db, organization_id)
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e)) from e
-
-    agent = db.query(Agent).filter(
-        Agent.id == payload.agent_id,
-        Agent.organization_id == organization_id,
-    ).first()
-    if not agent:
-        raise HTTPException(status_code=404, detail="Agent not found for organization")
-
-    persona_id = payload.persona_id
-    scenario_id = payload.scenario_id
-    evaluator_id = payload.evaluator_id
-
-    if payload.evaluator_id:
-        evaluator = db.query(Evaluator).filter(
-            Evaluator.id == payload.evaluator_id,
-            Evaluator.organization_id == organization_id,
-        ).first()
-        if not evaluator:
-            raise HTTPException(status_code=404, detail="Evaluator not found for organization")
-        if evaluator.agent_id:
-            agent = db.query(Agent).filter(
-                Agent.id == evaluator.agent_id,
-                Agent.organization_id == organization_id,
-            ).first()
-            if not agent:
-                raise HTTPException(status_code=404, detail="Agent not found for evaluator")
-            payload = payload.model_copy(update={"agent_id": agent.id})
-        persona_id = persona_id or evaluator.persona_id
-        scenario_id = scenario_id or evaluator.scenario_id
-
-    try:
-        from_number, used_pool, provider = resolve_outbound_from_number(
-            db,
-            organization_id,
-            explicit_from_number=payload.from_number,
-        )
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e)) from e
-
-    if provider != "vobiz":
-        if used_pool:
-            release_pool_slot(organization_id)
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                f"Outbound via the Vobiz API requires a Vobiz caller ID; "
-                f"resolved provider is {provider}. Use org-owned numbers or "
-                f"configure Vobiz entries in telephony.outbound_pool."
-            ),
-        )
-
-    to_number = normalize_e164(payload.to_number)
-
-    session = create_call_session(
-        agent_id=str(agent.id),
-        organization_id=str(organization_id),
-        direction="outbound",
-        from_number=from_number,
-        to_number=to_number,
-        used_pool=used_pool,
-        persona_id=str(persona_id) if persona_id else None,
-        scenario_id=str(scenario_id) if scenario_id else None,
-        evaluator_id=str(evaluator_id) if evaluator_id else None,
-    )
-
-    base = vobiz_webhook_base_url()
-    answer_url = (
-        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/answer"
-        f"?call_ref={session.call_ref}"
-    )
-    events_url = (
-        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/events"
-        f"?call_ref={session.call_ref}"
-    )
-    recording_url = f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/recording-ready"
-
-    call_short_id = "".join(random.choices(string.digits, k=6))
-    recording = CallRecording(
-        organization_id=organization_id,
-        workspace_id=agent.workspace_id,
-        call_short_id=call_short_id,
-        status=CallRecordingStatus.PENDING,
-        source=CallRecordingSource.WEBHOOK,
-        call_event="outbound_initiated",
-        call_data={
-            "call_ref": session.call_ref,
-            "call_short_id": call_short_id,
-            "recording_callback": recording_url,
-            "used_pool": used_pool,
-            "evaluator_id": str(evaluator_id) if evaluator_id else None,
-            "direction": "outbound",
-            "from_number": from_number,
-            "to_number": to_number,
-            "live_transcript": [],
-        },
-        provider_call_id=None,
-        provider_platform="vobiz",
-        agent_id=agent.id,
-    )
-    db.add(recording)
-    db.commit()
-    db.refresh(recording)
-
-    task = initiate_vobiz_outbound_call_task
-    if task is None:
-        from app.workers.tasks.initiate_vobiz_outbound import (
-            initiate_vobiz_outbound_call_task as task,
-        )
-
-    task.delay(
-        organization_id=str(organization_id),
-        call_ref=session.call_ref,
-        from_number=from_number,
-        to_number=to_number,
-        answer_url=answer_url,
-        events_url=events_url,
-        used_pool=used_pool,
-        call_recording_id=str(recording.id),
-    )
-
-    return VobizOutboundCallResponse(
-        provider_request_uuid="",
-        call_status="queued",
-        from_number=from_number,
-        to_number=to_number,
-        call_ref=session.call_ref,
-        call_short_id=call_short_id,
-    )
-
-
-@webhook_router.post("/webhooks/answer")
-@webhook_router.get("/webhooks/answer")
-async def vobiz_answer_webhook(
-    request: Request,
-    call_ref: Optional[str] = None,
-    db: Session = Depends(get_db),
-):
-    payload = await _read_webhook_payload(request)
-    params = extract_webhook_params(payload)
-    if not call_ref:
-        call_ref = request.query_params.get("call_ref")
-    verify_vobiz_webhook(request, payload, "answer", db, call_ref=call_ref)
-    logger.info(
-        "Vobiz answer webhook To={} From={} call_ref={}",
-        params.get("to"),
-        params.get("from"),
-        call_ref or request.query_params.get("call_ref"),
-    )
-    agent_id, organization_id, session_token = _resolve_agent_for_answer(
-        db, params, call_ref=call_ref
-    )
-    if not agent_id or not organization_id:
-        return Response(content=reject_call("No active routing found for this number."), media_type="application/xml")
-
-    if not session_token:
-        agent = db.query(Agent).filter(Agent.id == agent_id).first()
-        inbound_evaluator_id: Optional[UUID] = None
-        inbound_evaluator_result_id: Optional[UUID] = None
-        inbound_persona_id: Optional[UUID] = None
-        inbound_scenario_id: Optional[UUID] = None
-        if agent and agent.workspace_id:
-            from app.services.evaluators.evaluator_inbound_service import (
-                consume_inbound_evaluator_combination,
-                create_inbound_evaluator_result,
-                find_inbound_suite_for_agent,
-            )
-
-            suite = find_inbound_suite_for_agent(
-                db, agent, organization_id, agent.workspace_id
-            )
-            if suite:
-                selected, _idx, _next_idx = consume_inbound_evaluator_combination(db, suite)
-                inbound_evaluator_id = selected.id
-                inbound_persona_id = selected.persona_id
-                inbound_scenario_id = selected.scenario_id
-                result_row = create_inbound_evaluator_result(
-                    db,
-                    organization_id,
-                    agent.workspace_id,
-                    selected,
-                )
-                inbound_evaluator_result_id = result_row.id
-
-        session = create_call_session(
-            agent_id=str(agent_id),
-            organization_id=str(organization_id),
-            direction="inbound",
-            from_number=params.get("from"),
-            to_number=params.get("to"),
-            persona_id=str(inbound_persona_id) if inbound_persona_id else None,
-            scenario_id=str(inbound_scenario_id) if inbound_scenario_id else None,
-            evaluator_id=str(inbound_evaluator_id) if inbound_evaluator_id else None,
-        )
-        session_token = session.call_ref
-        persona_id = session.persona_id
-        scenario_id = session.scenario_id
-        if agent:
-            inbound_row = create_inbound_call_recording(
-                db,
-                agent=agent,
-                organization_id=organization_id,
-                call_ref=session_token,
-                from_number=params.get("from"),
-                to_number=params.get("to"),
-                provider_call_id=params.get("call_uuid"),
-                evaluator_id=inbound_evaluator_id,
-                evaluator_result_id=inbound_evaluator_result_id,
-            )
-            # region agent log
-            from app.utils.debug_agent_log import agent_debug_log
-
-            agent_debug_log(
-                "vobiz_telephony.py:answer_webhook",
-                "inbound CallRecording created",
-                {
-                    "call_ref": session_token,
-                    "call_short_id": inbound_row.call_short_id,
-                    "provider_call_id": params.get("call_uuid"),
-                },
-                "H1",
-            )
-            # endregion
-    else:
-        existing_session = get_call_session(session_token)
-        persona_id = existing_session.persona_id if existing_session else None
-        scenario_id = existing_session.scenario_id if existing_session else None
-
-    call_uuid = params.get("call_uuid")
-    if session_token and call_uuid:
-        link_provider_call_id(db, call_ref=session_token, provider_call_id=call_uuid)
-
-    ws_url = build_vobiz_ws_url(
-        agent_id=str(agent_id),
-        session=session_token,
-        persona_id=persona_id,
-        scenario_id=scenario_id,
-    )
-    record_action_url = None
-    if settings.VOBIZ_CARRIER_SESSION_RECORDING:
-        record_action_url = (
-            f"{vobiz_webhook_base_url()}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/recording-ready"
-            f"?call_ref={session_token}"
-        )
-    xml = stream_to_agent(ws_url, record_action_url=record_action_url)
-    return Response(content=xml, media_type="application/xml")
-
-
-@webhook_router.post("/webhooks/events")
-async def vobiz_events_webhook(
-    request: Request,
-    db: Session = Depends(get_db),
-):
-    payload = await _read_webhook_payload(request)
-    params = extract_webhook_params(payload)
-    call_ref = request.query_params.get("call_ref") or payload.get("call_ref")
-    verify_vobiz_webhook(request, payload, "events", db, call_ref=call_ref)
-    call_uuid = params.get("call_uuid")
-    if not call_uuid:
-        return {"status": "ignored"}
-
-    update_call_from_vobiz_event(
-        db,
-        provider_call_id=call_uuid,
-        call_status=params.get("call_status"),
-        payload=payload,
-        call_ref=call_ref,
-    )
-
-    row = find_call_recording(
-        db,
-        provider_call_id=call_uuid,
-        call_ref=call_ref,
-    )
-    terminal_statuses = {
-        "completed",
-        "hangup",
-        "failed",
-        "busy",
-        "no-answer",
-        "canceled",
-    }
-    if call_ref and (params.get("call_status") or "").lower() in terminal_statuses:
-        session = get_call_session(call_ref)
-        if session and session.used_pool:
-            release_pool_slot(UUID(session.organization_id))
-        delete_call_session(call_ref)
-    elif row and (params.get("call_status") or "").lower() in terminal_statuses:
-        call_data = row.call_data if isinstance(row.call_data, dict) else {}
-        if call_data.get("used_pool"):
-            release_pool_slot(row.organization_id)
-
-    return {"status": "ok"}
-
-
-@webhook_router.post("/webhooks/recording-ready")
-async def vobiz_recording_ready_webhook(
-    request: Request,
-    db: Session = Depends(get_db),
-):
-    payload = await _read_webhook_payload(request)
-    params = extract_webhook_params(payload)
-    call_ref = request.query_params.get("call_ref") or payload.get("call_ref")
-    verify_vobiz_webhook(request, payload, "recording", db, call_ref=call_ref)
-    recording_url = params.get("recording_url")
-    call_uuid = params.get("call_uuid")
-    logger.info(
-        "Vobiz recording ready call_uuid={} recording_id={} url={}",
-        call_uuid,
-        params.get("recording_id"),
-        recording_url,
-    )
-    if call_uuid:
-        call_ref = request.query_params.get("call_ref") or payload.get("call_ref")
-        row = find_call_recording(
-            db,
-            provider_call_id=call_uuid,
-            call_ref=call_ref,
-        )
-        if row:
-            from sqlalchemy.orm.attributes import flag_modified
-
-            current = dict(row.call_data) if isinstance(row.call_data, dict) else {}
-            current["recording"] = payload
-            if recording_url:
-                current["recording_url"] = recording_url
-            row.call_data = current
-            flag_modified(row, "call_data")
-            db.commit()
-            if recording_url and settings.VOBIZ_CARRIER_SESSION_RECORDING:
-                ingest_carrier_recording_url(db, row, recording_url)
-            elif recording_url and not settings.VOBIZ_CARRIER_SESSION_RECORDING:
-                logger.debug(
-                    "Skipping Vobiz carrier recording ingest (VOBIZ_CARRIER_SESSION_RECORDING=false) "
-                    "call_short_id={}",
-                    row.call_short_id,
-                )
-            # region agent log
-            from app.utils.debug_agent_log import agent_debug_log
-
-            agent_debug_log(
-                "vobiz_telephony.py:recording_ready",
-                "recording webhook persisted",
-                {
-                    "call_short_id": row.call_short_id,
-                    "call_ref": call_ref,
-                    "call_uuid": call_uuid,
-                    "has_recording_url": bool(recording_url),
-                },
-                "H4",
-            )
-            # endregion
-        else:
-            # region agent log
-            from app.utils.debug_agent_log import agent_debug_log
-
-            agent_debug_log(
-                "vobiz_telephony.py:recording_ready",
-                "recording webhook: no CallRecording match",
-                {"call_uuid": call_uuid, "call_ref": call_ref},
-                "H4",
-            )
-            # endregion
-    return {"status": "ok"}
-
-
-@ws_router.websocket("/ws")
-async def vobiz_media_websocket(websocket: WebSocket):
-    agent_id = websocket.query_params.get("agent_id")
-    session_token = websocket.query_params.get("session")
-    persona_id = websocket.query_params.get("persona_id")
-    scenario_id = websocket.query_params.get("scenario_id")
-
-    if not agent_id or not session_token:
-        await websocket.close(code=1008, reason="agent_id and session are required")
-        return
-
-    session = get_call_session(session_token)
-    if not session:
-        await websocket.close(code=1008, reason="Invalid or expired call session")
-        return
-    if session.agent_id != agent_id:
-        await websocket.close(code=1008, reason="Session does not match agent")
-        return
-
-    persona_id = persona_id or session.persona_id
-    scenario_id = scenario_id or session.scenario_id
-
-    await websocket.accept()
-    db = next(get_db())
-    call_row = find_call_recording(db, call_ref=session_token, provider_call_id=None)
-    call_short_id = call_row.call_short_id if call_row else None
-    # region agent log
-    from app.utils.debug_agent_log import agent_debug_log
-
-    agent_debug_log(
-        "vobiz_telephony.py:media_websocket",
-        "CallRecording lookup for media session",
-        {
-            "call_ref": session_token,
-            "call_short_id": call_short_id,
-            "found_row": call_row is not None,
-            "provider_call_id": call_row.provider_call_id if call_row else None,
-        },
-        "H1",
-    )
-    # endregion
-    if not call_short_id:
-        logger.warning(
-            "No CallRecording for Vobiz session {}; live transcript and recording will not be linked",
-            session_token,
-        )
-    try:
-        mark_call_in_progress(db, call_ref=session_token)
-        try:
-            transport_type, call_data = await parse_telephony_websocket(websocket)
-            if transport_type not in {"plivo", "unknown"}:
-                logger.warning("Unexpected telephony transport type for Vobiz: {}", transport_type)
-            stream_id = call_data.get("stream_id") or ""
-            call_id = call_data.get("call_id")
-            if call_id:
-                link_provider_call_id(db, call_ref=session_token, provider_call_id=str(call_id))
-            if not stream_id:
-                await websocket.close(code=1011, reason="Missing stream id from Vobiz")
-                return
-
-            context = resolve_vobiz_agent_context(
-                db,
-                agent_id=UUID(agent_id),
-                organization_id=UUID(session.organization_id),
-                persona_id=persona_id,
-                scenario_id=scenario_id,
-            )
-            serializer = VobizFrameSerializer(
-                stream_id=stream_id,
-                call_id=call_id,
-                auth_id=settings.VOBIZ_AUTH_ID,
-                auth_token=settings.VOBIZ_AUTH_TOKEN,
-                params=VobizFrameSerializer.InputParams(
-                    sample_rate=8000,
-                    api_base=settings.VOBIZ_API_BASE,
-                ),
-            )
-
-            if context.use_voice_bundle_pipeline:
-                from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
-
-                hangup_secs = resolve_agent_silence_hangup_secs(context.agent)
-                await run_voice_bundle_fastapi(
-                    websocket,
-                    context.system_instruction,
-                    str(context.organization_id),
-                    str(context.workspace_id) if context.workspace_id else None,
-                    agent_id,
-                    persona_id,
-                    scenario_id,
-                    voice_bundle=context.voice_bundle,
-                    persona=context.persona,
-                    stt_api_key=context.stt_api_key,
-                    tts_api_key=context.tts_api_key,
-                    llm_api_key=context.llm_api_key,
-                    serializer=serializer,
-                    telephony_mode=True,
-                    call_short_id=call_short_id,
-                    silence_hangup_secs=hangup_secs,
-                )
-            else:
-                if not context.google_api_key:
-                    await websocket.close(code=1011, reason="Google API key not configured for agent")
-                    return
-                from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
-
-                hangup_secs = resolve_agent_silence_hangup_secs(context.agent)
-                await run_bot(
-                    websocket,
-                    context.google_api_key,
-                    context.system_instruction,
-                    str(context.organization_id),
-                    agent_id,
-                    persona_id,
-                    scenario_id,
-                    model_name=context.model_name,
-                    serializer=serializer,
-                    telephony_mode=True,
-                    call_short_id=call_short_id,
-                    silence_hangup_secs=hangup_secs,
-                )
-        except ValueError as e:
-            logger.error("Vobiz media websocket setup failed: {}", e)
-            await websocket.close(code=1011, reason=str(e))
-        except WebSocketDisconnect:
-            logger.info("Vobiz media websocket disconnected")
-        except Exception as e:
-            logger.error("Vobiz media websocket error: {}", e, exc_info=True)
-            try:
-                await websocket.close(code=1011, reason="Server error")
-            except Exception:
-                pass
-        finally:
-            from app.database import SessionLocal
-
-            finalize_db = SessionLocal()
-            try:
-                finalize_call_on_media_disconnect(finalize_db, call_ref=session_token)
-            finally:
-                finalize_db.close()
-            delete_call_session(session_token)
-    finally:
-        db.close()
+"""Vobiz telephony API routes (per-org BYO credentials + platform pool fallback)."""
+
+from __future__ import annotations
+
+import random
+import string
+from typing import Any, Dict, List, Optional
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Request, Response, WebSocket, WebSocketDisconnect, status
+from loguru import logger
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from app.config import settings
+from app.dependencies import get_api_key, get_db, get_organization_id
+from app.models.database import Agent, CallRecording, CallRecordingSource, Evaluator, TelephonyPhoneNumber
+from app.models.enums import CallRecordingStatus
+from app.services.telephony.phone_routing import resolve_inbound_agent_for_number
+from app.services.telephony.plivo_client import normalize_e164
+from app.services.telephony.vobiz_agent_context import (
+    build_vobiz_ws_url,
+    extract_webhook_params,
+    resolve_vobiz_agent_context,
+    vobiz_webhook_base_url,
+)
+from app.services.telephony.call_recording_lifecycle import (
+    create_inbound_call_recording,
+    finalize_call_on_media_disconnect,
+    find_call_recording,
+    ingest_carrier_recording_url,
+    link_provider_call_id,
+    mark_call_in_progress,
+    update_call_from_vobiz_event,
+)
+from app.services.telephony.vobiz_client import build_vobiz_client_for_org
+from app.services.telephony.vobiz_number_service import (
+    deactivate_imported_number,
+    import_vobiz_numbers,
+    list_available_vobiz_numbers,
+)
+from app.services.telephony.webhook_auth import verify_vobiz_webhook
+from app.services.telephony.vobiz_outbound_pool import (
+    outbound_pool_api_payload,
+    release_pool_slot,
+    resolve_outbound_from_number,
+)
+from app.services.telephony.vobiz_session import create_call_session, delete_call_session, get_call_session
+from app.services.telephony.vobiz_xml import reject_call, speak_and_hangup, stream_to_agent
+from app.services.voice_agent.bot_fast_api import run_bot
+from app.services.voice_agent.voice_bundle import run_voice_bundle_fastapi
+from efficientai.runner.utils import parse_telephony_websocket
+from efficientai.serializers.vobiz import VobizFrameSerializer
+
+# Exposed at module scope so tests can patch `.delay` without importing Celery tasks.
+initiate_vobiz_outbound_call_task = None
+
+router = APIRouter(prefix="/telephony/vobiz", tags=["Vobiz Telephony"])
+webhook_router = APIRouter(prefix="/telephony/vobiz", tags=["Vobiz Telephony Webhooks"])
+ws_router = APIRouter(prefix="/telephony/vobiz", tags=["Vobiz Telephony Media"])
+
+
+class VobizOutboundCallRequest(BaseModel):
+    to_number: str
+    agent_id: UUID
+    from_number: Optional[str] = None
+    persona_id: Optional[UUID] = None
+    scenario_id: Optional[UUID] = None
+    evaluator_id: Optional[UUID] = None
+
+
+class VobizOutboundCallResponse(BaseModel):
+    provider_request_uuid: str
+    call_status: str
+    from_number: str
+    to_number: str
+    call_ref: str
+    call_short_id: str = ""
+    evaluator_result_id: Optional[UUID] = None
+    result_id: Optional[str] = None
+    otel_correlation: Optional[Dict[str, Any]] = None
+    message: str = "Outbound call initiated"
+
+
+class VobizAvailableNumberResponse(BaseModel):
+    e164: str
+    provider_number_id: Optional[str] = None
+    country: Optional[str] = None
+    region: Optional[str] = None
+    capabilities: Optional[Dict[str, Any]] = None
+    status: Optional[str] = None
+    application_id: Optional[str] = None
+    already_imported: bool = False
+    imported_number_id: Optional[str] = None
+
+
+class VobizImportNumbersRequest(BaseModel):
+    numbers: List[str]
+    agent_id: Optional[UUID] = None
+
+
+class VobizImportNumberResult(BaseModel):
+    number: str
+    success: bool
+    message: str
+    answer_url: str
+    webhook_configured: Optional[bool] = None
+    imported_number_id: Optional[str] = None
+    application_id: Optional[str] = None
+
+
+class VobizImportNumbersResponse(BaseModel):
+    provider: str = "vobiz"
+    results: List[VobizImportNumberResult]
+    answer_url: str
+
+
+class VobizOutboundPoolNumberResponse(BaseModel):
+    phone_number: str
+    provider: str
+
+
+class VobizOutboundPoolResponse(BaseModel):
+    numbers: List[VobizOutboundPoolNumberResponse]
+    max_concurrent_per_org: int
+    shared_across_orgs: bool = True
+
+
+def _parse_request_payload(raw: Any) -> Dict[str, Any]:
+    if isinstance(raw, dict):
+        return raw
+    if hasattr(raw, "items"):
+        return dict(raw)
+    return {}
+
+
+async def _read_webhook_payload(request: Request) -> Dict[str, Any]:
+    if not getattr(request.state, "webhook_raw_body", None):
+        request.state.webhook_raw_body = await request.body()
+    params: Dict[str, Any] = dict(request.query_params)
+    content_type = (request.headers.get("content-type") or "").lower()
+    if "application/json" in content_type:
+        try:
+            body = await request.json()
+            if isinstance(body, dict):
+                params.update(body)
+                return params
+        except Exception:
+            pass
+    try:
+        form = await request.form()
+        params.update(_parse_request_payload(form))
+    except Exception:
+        pass
+    return params
+
+
+def _resolve_agent_for_answer(
+    db: Session,
+    params: Dict[str, Any],
+    *,
+    call_ref: Optional[str] = None,
+) -> tuple[Optional[UUID], Optional[UUID], Optional[str]]:
+    """Return (agent_id, organization_id, session_token)."""
+    if call_ref:
+        session = get_call_session(call_ref)
+        if session and session.agent_id and session.organization_id:
+            return UUID(session.agent_id), UUID(session.organization_id), call_ref
+
+    agent_id, organization_id = resolve_inbound_agent_for_number(db, params.get("to"))
+    if not agent_id or not organization_id:
+        return None, None, None
+    return agent_id, organization_id, None
+
+
+@router.get("/numbers/available", response_model=List[VobizAvailableNumberResponse])
+async def list_vobiz_available_numbers(
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    del api_key
+    try:
+        return list_available_vobiz_numbers(db, organization_id)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+
+
+@router.post("/numbers/import", response_model=VobizImportNumbersResponse)
+async def import_vobiz_numbers_route(
+    payload: VobizImportNumbersRequest,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    del api_key
+    try:
+        result = import_vobiz_numbers(
+            db,
+            organization_id,
+            numbers=payload.numbers,
+            agent_id=payload.agent_id,
+        )
+        return VobizImportNumbersResponse(**result)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+
+
+@router.delete("/numbers/{number_id}", status_code=status.HTTP_204_NO_CONTENT)
+async def delete_imported_vobiz_number(
+    number_id: UUID,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    del api_key
+    try:
+        deactivate_imported_number(db, organization_id, number_id)
+    except ValueError as e:
+        message = str(e)
+        if message == "Imported number not found":
+            raise HTTPException(status_code=404, detail=message) from e
+        status_code = (
+            status.HTTP_409_CONFLICT
+            if "active number-masking sessions" in message
+            else status.HTTP_400_BAD_REQUEST
+        )
+        raise HTTPException(status_code=status_code, detail=message) from e
+    return Response(status_code=status.HTTP_204_NO_CONTENT)
+
+
+@router.get("/outbound-pool", response_model=VobizOutboundPoolResponse)
+async def get_vobiz_outbound_pool(
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+):
+    del organization_id, api_key
+    return VobizOutboundPoolResponse(**outbound_pool_api_payload())
+
+
+@router.post("/calls/outbound", response_model=VobizOutboundCallResponse)
+async def create_vobiz_outbound_call(
+    payload: VobizOutboundCallRequest,
+    request: Request,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    del api_key
+    try:
+        build_vobiz_client_for_org(db, organization_id)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+
+    agent = db.query(Agent).filter(
+        Agent.id == payload.agent_id,
+        Agent.organization_id == organization_id,
+    ).first()
+    if not agent:
+        raise HTTPException(status_code=404, detail="Agent not found for organization")
+
+    persona_id = payload.persona_id
+    scenario_id = payload.scenario_id
+    evaluator_id = payload.evaluator_id
+    evaluator: Evaluator | None = None
+
+    if payload.evaluator_id:
+        evaluator = db.query(Evaluator).filter(
+            Evaluator.id == payload.evaluator_id,
+            Evaluator.organization_id == organization_id,
+        ).first()
+        if not evaluator:
+            raise HTTPException(status_code=404, detail="Evaluator not found for organization")
+        if evaluator.agent_id:
+            agent = db.query(Agent).filter(
+                Agent.id == evaluator.agent_id,
+                Agent.organization_id == organization_id,
+            ).first()
+            if not agent:
+                raise HTTPException(status_code=404, detail="Agent not found for evaluator")
+        persona_id = persona_id or evaluator.persona_id
+        scenario_id = scenario_id or evaluator.scenario_id
+
+    to_number = normalize_e164(payload.to_number)
+
+    if evaluator and agent.workspace_id:
+        from app.services.evaluators.evaluator_phone_run_service import initiate_phone_evaluator_call
+
+        call_ref, call_short_id, result_response = initiate_phone_evaluator_call(
+            db,
+            organization_id,
+            agent.workspace_id,
+            evaluator,
+            agent,
+            to_number,
+            from_number=payload.from_number,
+        )
+        recording = (
+            db.query(CallRecording)
+            .filter(CallRecording.call_short_id == call_short_id)
+            .first()
+        )
+        from_number = ""
+        if recording and isinstance(recording.call_data, dict):
+            from_number = recording.call_data.get("from_number") or ""
+        from app.services.synthetic_traces.trace_service import build_session_otel_correlation
+
+        otel_correlation = None
+        if call_short_id and agent.workspace_id:
+            otel_correlation = build_session_otel_correlation(
+                api_base_url=str(request.base_url).rstrip("/"),
+                call_short_id=call_short_id,
+                workspace_id=agent.workspace_id,
+                evaluator_result_id=result_response.id if result_response else None,
+            )
+        return VobizOutboundCallResponse(
+            provider_request_uuid="",
+            call_status="queued",
+            from_number=from_number,
+            to_number=to_number,
+            call_ref=call_ref,
+            call_short_id=call_short_id,
+            evaluator_result_id=result_response.id if result_response else None,
+            result_id=result_response.result_id if result_response else None,
+            otel_correlation=otel_correlation,
+        )
+
+    try:
+        from_number, used_pool, provider = resolve_outbound_from_number(
+            db,
+            organization_id,
+            explicit_from_number=payload.from_number,
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+
+    if provider != "vobiz":
+        if used_pool:
+            release_pool_slot(organization_id)
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                f"Outbound via the Vobiz API requires a Vobiz caller ID; "
+                f"resolved provider is {provider}. Use org-owned numbers or "
+                f"configure Vobiz entries in telephony.outbound_pool."
+            ),
+        )
+
+    to_number = normalize_e164(payload.to_number)
+
+    session = create_call_session(
+        agent_id=str(agent.id),
+        organization_id=str(organization_id),
+        direction="outbound",
+        from_number=from_number,
+        to_number=to_number,
+        used_pool=used_pool,
+        persona_id=str(persona_id) if persona_id else None,
+        scenario_id=str(scenario_id) if scenario_id else None,
+        evaluator_id=str(evaluator_id) if evaluator_id else None,
+    )
+
+    base = vobiz_webhook_base_url()
+    answer_url = (
+        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/answer"
+        f"?call_ref={session.call_ref}"
+    )
+    events_url = (
+        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/events"
+        f"?call_ref={session.call_ref}"
+    )
+    recording_url = f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/recording-ready"
+
+    call_short_id = "".join(random.choices(string.digits, k=6))
+    recording = CallRecording(
+        organization_id=organization_id,
+        workspace_id=agent.workspace_id,
+        call_short_id=call_short_id,
+        status=CallRecordingStatus.PENDING,
+        source=CallRecordingSource.WEBHOOK,
+        call_event="outbound_initiated",
+        call_data={
+            "call_ref": session.call_ref,
+            "call_short_id": call_short_id,
+            "recording_callback": recording_url,
+            "used_pool": used_pool,
+            "evaluator_id": str(evaluator_id) if evaluator_id else None,
+            "direction": "outbound",
+            "from_number": from_number,
+            "to_number": to_number,
+            "live_transcript": [],
+        },
+        provider_call_id=None,
+        provider_platform="vobiz",
+        agent_id=agent.id,
+    )
+    db.add(recording)
+    db.commit()
+    db.refresh(recording)
+
+    task = initiate_vobiz_outbound_call_task
+    if task is None:
+        from app.workers.tasks.initiate_vobiz_outbound import (
+            initiate_vobiz_outbound_call_task as task,
+        )
+
+    task.delay(
+        organization_id=str(organization_id),
+        call_ref=session.call_ref,
+        from_number=from_number,
+        to_number=to_number,
+        answer_url=answer_url,
+        events_url=events_url,
+        used_pool=used_pool,
+        call_recording_id=str(recording.id),
+    )
+
+    return VobizOutboundCallResponse(
+        provider_request_uuid="",
+        call_status="queued",
+        from_number=from_number,
+        to_number=to_number,
+        call_ref=session.call_ref,
+        call_short_id=call_short_id,
+    )
+
+
+@webhook_router.post("/webhooks/answer")
+@webhook_router.get("/webhooks/answer")
+async def vobiz_answer_webhook(
+    request: Request,
+    call_ref: Optional[str] = None,
+    db: Session = Depends(get_db),
+):
+    payload = await _read_webhook_payload(request)
+    params = extract_webhook_params(payload)
+    if not call_ref:
+        call_ref = request.query_params.get("call_ref")
+    verify_vobiz_webhook(request, payload, "answer", db, call_ref=call_ref)
+    logger.info(
+        "Vobiz answer webhook To={} From={} call_ref={}",
+        params.get("to"),
+        params.get("from"),
+        call_ref or request.query_params.get("call_ref"),
+    )
+    agent_id, organization_id, session_token = _resolve_agent_for_answer(
+        db, params, call_ref=call_ref
+    )
+    if not agent_id or not organization_id:
+        return Response(content=reject_call("No active routing found for this number."), media_type="application/xml")
+
+    if not session_token:
+        agent = db.query(Agent).filter(Agent.id == agent_id).first()
+        inbound_evaluator_id: Optional[UUID] = None
+        inbound_evaluator_result_id: Optional[UUID] = None
+        inbound_persona_id: Optional[UUID] = None
+        inbound_scenario_id: Optional[UUID] = None
+        if agent and agent.workspace_id:
+            from app.services.evaluators.evaluator_inbound_service import (
+                consume_inbound_evaluator_combination,
+                create_inbound_evaluator_result,
+                find_inbound_suite_for_agent,
+            )
+
+            suite = find_inbound_suite_for_agent(
+                db, agent, organization_id, agent.workspace_id
+            )
+            if suite:
+                selected, _idx, _next_idx = consume_inbound_evaluator_combination(db, suite)
+                inbound_evaluator_id = selected.id
+                inbound_persona_id = selected.persona_id
+                inbound_scenario_id = selected.scenario_id
+                result_row = create_inbound_evaluator_result(
+                    db,
+                    organization_id,
+                    agent.workspace_id,
+                    selected,
+                )
+                inbound_evaluator_result_id = result_row.id
+
+        session = create_call_session(
+            agent_id=str(agent_id),
+            organization_id=str(organization_id),
+            direction="inbound",
+            from_number=params.get("from"),
+            to_number=params.get("to"),
+            persona_id=str(inbound_persona_id) if inbound_persona_id else None,
+            scenario_id=str(inbound_scenario_id) if inbound_scenario_id else None,
+            evaluator_id=str(inbound_evaluator_id) if inbound_evaluator_id else None,
+        )
+        session_token = session.call_ref
+        persona_id = session.persona_id
+        scenario_id = session.scenario_id
+        if agent:
+            inbound_row = create_inbound_call_recording(
+                db,
+                agent=agent,
+                organization_id=organization_id,
+                call_ref=session_token,
+                from_number=params.get("from"),
+                to_number=params.get("to"),
+                provider_call_id=params.get("call_uuid"),
+                evaluator_id=inbound_evaluator_id,
+                evaluator_result_id=inbound_evaluator_result_id,
+            )
+            # region agent log
+            from app.utils.debug_agent_log import agent_debug_log
+
+            agent_debug_log(
+                "vobiz_telephony.py:answer_webhook",
+                "inbound CallRecording created",
+                {
+                    "call_ref": session_token,
+                    "call_short_id": inbound_row.call_short_id,
+                    "provider_call_id": params.get("call_uuid"),
+                },
+                "H1",
+            )
+            # endregion
+    else:
+        existing_session = get_call_session(session_token)
+        persona_id = existing_session.persona_id if existing_session else None
+        scenario_id = existing_session.scenario_id if existing_session else None
+
+    call_uuid = params.get("call_uuid")
+    if session_token and call_uuid:
+        link_provider_call_id(db, call_ref=session_token, provider_call_id=call_uuid)
+
+    ws_url = build_vobiz_ws_url(
+        agent_id=str(agent_id),
+        session=session_token,
+        persona_id=persona_id,
+        scenario_id=scenario_id,
+    )
+    record_action_url = None
+    if settings.VOBIZ_CARRIER_SESSION_RECORDING:
+        record_action_url = (
+            f"{vobiz_webhook_base_url()}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/recording-ready"
+            f"?call_ref={session_token}"
+        )
+    xml = stream_to_agent(ws_url, record_action_url=record_action_url)
+    return Response(content=xml, media_type="application/xml")
+
+
+@webhook_router.post("/webhooks/events")
+async def vobiz_events_webhook(
+    request: Request,
+    db: Session = Depends(get_db),
+):
+    payload = await _read_webhook_payload(request)
+    params = extract_webhook_params(payload)
+    call_ref = request.query_params.get("call_ref") or payload.get("call_ref")
+    verify_vobiz_webhook(request, payload, "events", db, call_ref=call_ref)
+    call_uuid = params.get("call_uuid")
+    if not call_uuid:
+        return {"status": "ignored"}
+
+    update_call_from_vobiz_event(
+        db,
+        provider_call_id=call_uuid,
+        call_status=params.get("call_status"),
+        payload=payload,
+        call_ref=call_ref,
+    )
+
+    row = find_call_recording(
+        db,
+        provider_call_id=call_uuid,
+        call_ref=call_ref,
+    )
+    terminal_statuses = {
+        "completed",
+        "hangup",
+        "failed",
+        "busy",
+        "no-answer",
+        "canceled",
+    }
+    if call_ref and (params.get("call_status") or "").lower() in terminal_statuses:
+        session = get_call_session(call_ref)
+        if session and session.used_pool:
+            release_pool_slot(UUID(session.organization_id))
+        delete_call_session(call_ref)
+    elif row and (params.get("call_status") or "").lower() in terminal_statuses:
+        call_data = row.call_data if isinstance(row.call_data, dict) else {}
+        if call_data.get("used_pool"):
+            release_pool_slot(row.organization_id)
+
+    return {"status": "ok"}
+
+
+@webhook_router.post("/webhooks/recording-ready")
+async def vobiz_recording_ready_webhook(
+    request: Request,
+    db: Session = Depends(get_db),
+):
+    payload = await _read_webhook_payload(request)
+    params = extract_webhook_params(payload)
+    call_ref = request.query_params.get("call_ref") or payload.get("call_ref")
+    verify_vobiz_webhook(request, payload, "recording", db, call_ref=call_ref)
+    recording_url = params.get("recording_url")
+    call_uuid = params.get("call_uuid")
+    logger.info(
+        "Vobiz recording ready call_uuid={} recording_id={} url={}",
+        call_uuid,
+        params.get("recording_id"),
+        recording_url,
+    )
+    if call_uuid:
+        call_ref = request.query_params.get("call_ref") or payload.get("call_ref")
+        row = find_call_recording(
+            db,
+            provider_call_id=call_uuid,
+            call_ref=call_ref,
+        )
+        if row:
+            from sqlalchemy.orm.attributes import flag_modified
+
+            current = dict(row.call_data) if isinstance(row.call_data, dict) else {}
+            current["recording"] = payload
+            if recording_url:
+                current["recording_url"] = recording_url
+            row.call_data = current
+            flag_modified(row, "call_data")
+            db.commit()
+            if recording_url and settings.VOBIZ_CARRIER_SESSION_RECORDING:
+                ingest_carrier_recording_url(db, row, recording_url)
+            elif recording_url and not settings.VOBIZ_CARRIER_SESSION_RECORDING:
+                logger.debug(
+                    "Skipping Vobiz carrier recording ingest (VOBIZ_CARRIER_SESSION_RECORDING=false) "
+                    "call_short_id={}",
+                    row.call_short_id,
+                )
+            # region agent log
+            from app.utils.debug_agent_log import agent_debug_log
+
+            agent_debug_log(
+                "vobiz_telephony.py:recording_ready",
+                "recording webhook persisted",
+                {
+                    "call_short_id": row.call_short_id,
+                    "call_ref": call_ref,
+                    "call_uuid": call_uuid,
+                    "has_recording_url": bool(recording_url),
+                },
+                "H4",
+            )
+            # endregion
+        else:
+            # region agent log
+            from app.utils.debug_agent_log import agent_debug_log
+
+            agent_debug_log(
+                "vobiz_telephony.py:recording_ready",
+                "recording webhook: no CallRecording match",
+                {"call_uuid": call_uuid, "call_ref": call_ref},
+                "H4",
+            )
+            # endregion
+    return {"status": "ok"}
+
+
+@ws_router.websocket("/ws")
+async def vobiz_media_websocket(websocket: WebSocket):
+    agent_id = websocket.query_params.get("agent_id")
+    session_token = websocket.query_params.get("session")
+    persona_id = websocket.query_params.get("persona_id")
+    scenario_id = websocket.query_params.get("scenario_id")
+
+    if not agent_id or not session_token:
+        await websocket.close(code=1008, reason="agent_id and session are required")
+        return
+
+    session = get_call_session(session_token)
+    if not session:
+        await websocket.close(code=1008, reason="Invalid or expired call session")
+        return
+    if session.agent_id != agent_id:
+        await websocket.close(code=1008, reason="Session does not match agent")
+        return
+
+    persona_id = persona_id or session.persona_id
+    scenario_id = scenario_id or session.scenario_id
+
+    await websocket.accept()
+    db = next(get_db())
+    call_row = find_call_recording(db, call_ref=session_token, provider_call_id=None)
+    call_short_id = call_row.call_short_id if call_row else None
+    evaluator_result_id = (
+        str(call_row.evaluator_result_id) if call_row and call_row.evaluator_result_id else None
+    )
+    # region agent log
+    from app.utils.debug_agent_log import agent_debug_log
+
+    agent_debug_log(
+        "vobiz_telephony.py:media_websocket",
+        "CallRecording lookup for media session",
+        {
+            "call_ref": session_token,
+            "call_short_id": call_short_id,
+            "found_row": call_row is not None,
+            "provider_call_id": call_row.provider_call_id if call_row else None,
+        },
+        "H1",
+    )
+    # endregion
+    if not call_short_id:
+        logger.warning(
+            "No CallRecording for Vobiz session {}; live transcript and recording will not be linked",
+            session_token,
+        )
+    try:
+        mark_call_in_progress(db, call_ref=session_token)
+        try:
+            transport_type, call_data = await parse_telephony_websocket(websocket)
+            if transport_type not in {"plivo", "unknown"}:
+                logger.warning("Unexpected telephony transport type for Vobiz: {}", transport_type)
+            stream_id = call_data.get("stream_id") or ""
+            call_id = call_data.get("call_id")
+            if call_id:
+                link_provider_call_id(db, call_ref=session_token, provider_call_id=str(call_id))
+            if not stream_id:
+                await websocket.close(code=1011, reason="Missing stream id from Vobiz")
+                return
+
+            context = resolve_vobiz_agent_context(
+                db,
+                agent_id=UUID(agent_id),
+                organization_id=UUID(session.organization_id),
+                persona_id=persona_id,
+                scenario_id=scenario_id,
+            )
+            serializer = VobizFrameSerializer(
+                stream_id=stream_id,
+                call_id=call_id,
+                auth_id=settings.VOBIZ_AUTH_ID,
+                auth_token=settings.VOBIZ_AUTH_TOKEN,
+                params=VobizFrameSerializer.InputParams(
+                    sample_rate=8000,
+                    api_base=settings.VOBIZ_API_BASE,
+                ),
+            )
+
+            if context.use_voice_bundle_pipeline:
+                from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
+
+                hangup_secs = resolve_agent_silence_hangup_secs(context.agent)
+                await run_voice_bundle_fastapi(
+                    websocket,
+                    context.system_instruction,
+                    str(context.organization_id),
+                    str(context.workspace_id) if context.workspace_id else None,
+                    agent_id,
+                    persona_id,
+                    scenario_id,
+                    evaluator_id=session.evaluator_id,
+                    result_id=evaluator_result_id,
+                    voice_bundle=context.voice_bundle,
+                    persona=context.persona,
+                    stt_api_key=context.stt_api_key,
+                    tts_api_key=context.tts_api_key,
+                    llm_api_key=context.llm_api_key,
+                    serializer=serializer,
+                    telephony_mode=True,
+                    call_short_id=call_short_id,
+                    silence_hangup_secs=hangup_secs,
+                )
+            else:
+                if not context.google_api_key:
+                    await websocket.close(code=1011, reason="Google API key not configured for agent")
+                    return
+                from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
+
+                hangup_secs = resolve_agent_silence_hangup_secs(context.agent)
+                await run_bot(
+                    websocket,
+                    context.google_api_key,
+                    context.system_instruction,
+                    str(context.organization_id),
+                    agent_id,
+                    persona_id,
+                    scenario_id,
+                    model_name=context.model_name,
+                    serializer=serializer,
+                    telephony_mode=True,
+                    call_short_id=call_short_id,
+                    silence_hangup_secs=hangup_secs,
+                )
+        except ValueError as e:
+            logger.error("Vobiz media websocket setup failed: {}", e)
+            await websocket.close(code=1011, reason=str(e))
+        except WebSocketDisconnect:
+            logger.info("Vobiz media websocket disconnected")
+        except Exception as e:
+            logger.error("Vobiz media websocket error: {}", e, exc_info=True)
+            try:
+                await websocket.close(code=1011, reason="Server error")
+            except Exception:
+                pass
+        finally:
+            from app.database import SessionLocal
+
+            finalize_db = SessionLocal()
+            try:
+                finalize_call_on_media_disconnect(finalize_db, call_ref=session_token)
+            finally:
+                finalize_db.close()
+            delete_call_session(session_token)
+    finally:
+        db.close()
diff --git a/app/core/security_headers_middleware.py b/app/core/security_headers_middleware.py
index caa726a9..9206c55f 100644
--- a/app/core/security_headers_middleware.py
+++ b/app/core/security_headers_middleware.py
@@ -1,55 +1,66 @@
-"""HTTP security response headers."""
-
-from __future__ import annotations
-
-from starlette.middleware.base import BaseHTTPMiddleware
-from starlette.requests import Request
-from starlette.responses import Response
-
-from app.config import settings
-
-_NO_STORE_CACHE = "no-cache, no-store, must-revalidate"
-_ASSET_CACHE = "public, max-age=31536000, immutable"
-
-
-def _apply_cache_control(request: Request, response: Response) -> None:
-    if "cache-control" in response.headers:
-        return
-
-    path = request.url.path
-    if path.startswith("/assets/"):
-        response.headers["Cache-Control"] = _ASSET_CACHE
-        return
-
-    cache_value = _NO_STORE_CACHE
-    if path.startswith("/api/"):
-        cache_value = f"{_NO_STORE_CACHE}, private"
-
-    response.headers["Cache-Control"] = cache_value
-    response.headers["Pragma"] = "no-cache"
-    response.headers["Expires"] = "0"
-
-
-def _apply_csp(response: Response) -> None:
-    if not settings.CSP_ENABLED:
-        return
-
-    header_name = (
-        "Content-Security-Policy-Report-Only"
-        if settings.CSP_REPORT_ONLY
-        else "Content-Security-Policy"
-    )
-    response.headers[header_name] = settings.CSP_POLICY
-
-
-class SecurityHeadersMiddleware(BaseHTTPMiddleware):
-    """Add baseline security headers to every response."""
-
-    async def dispatch(self, request: Request, call_next) -> Response:
-        response = await call_next(request)
-        response.headers["X-Frame-Options"] = "SAMEORIGIN"
-        response.headers["X-Content-Type-Options"] = "nosniff"
-        response.headers["Referrer-Policy"] = "strict-origin-when-cross-origin"
-        _apply_cache_control(request, response)
-        _apply_csp(response)
-        return response
+"""HTTP security response headers."""
+
+from __future__ import annotations
+
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.requests import Request
+from starlette.responses import Response
+
+from app.config import settings
+
+_NO_STORE_CACHE = "no-cache, no-store, must-revalidate"
+_ASSET_CACHE = "public, max-age=31536000, immutable"
+_API_DOCS_PREFIXES = ("/docs", "/redoc")
+_API_DOCS_EXACT = frozenset({"/openapi.json"})
+
+
+def _is_api_docs_path(path: str) -> bool:
+    """FastAPI Swagger/ReDoc need CDN + inline scripts; skip CSP on those routes."""
+    if path in _API_DOCS_EXACT:
+        return True
+    return any(path == prefix or path.startswith(f"{prefix}/") for prefix in _API_DOCS_PREFIXES)
+
+
+def _apply_cache_control(request: Request, response: Response) -> None:
+    if "cache-control" in response.headers:
+        return
+
+    path = request.url.path
+    if path.startswith("/assets/"):
+        response.headers["Cache-Control"] = _ASSET_CACHE
+        return
+
+    cache_value = _NO_STORE_CACHE
+    if path.startswith("/api/"):
+        cache_value = f"{_NO_STORE_CACHE}, private"
+
+    response.headers["Cache-Control"] = cache_value
+    response.headers["Pragma"] = "no-cache"
+    response.headers["Expires"] = "0"
+
+
+def _apply_csp(request: Request, response: Response) -> None:
+    if not settings.CSP_ENABLED:
+        return
+    if _is_api_docs_path(request.url.path):
+        return
+
+    header_name = (
+        "Content-Security-Policy-Report-Only"
+        if settings.CSP_REPORT_ONLY
+        else "Content-Security-Policy"
+    )
+    response.headers[header_name] = settings.CSP_POLICY
+
+
+class SecurityHeadersMiddleware(BaseHTTPMiddleware):
+    """Add baseline security headers to every response."""
+
+    async def dispatch(self, request: Request, call_next) -> Response:
+        response = await call_next(request)
+        response.headers["X-Frame-Options"] = "SAMEORIGIN"
+        response.headers["X-Content-Type-Options"] = "nosniff"
+        response.headers["Referrer-Policy"] = "strict-origin-when-cross-origin"
+        _apply_cache_control(request, response)
+        _apply_csp(request, response)
+        return response
diff --git a/app/migrations/078_synthetic_call_traces.py b/app/migrations/078_synthetic_call_traces.py
new file mode 100644
index 00000000..9463b43a
--- /dev/null
+++ b/app/migrations/078_synthetic_call_traces.py
@@ -0,0 +1,170 @@
+"""Add synthetic call trace tables for Pipecat phone + OTLP observability."""
+
+from sqlalchemy import text
+from sqlalchemy.orm import Session
+
+description = "Add synthetic_call_traces, payload tables, and evaluator_results link"
+
+
+def _table_exists(db: Session, table_name: str) -> bool:
+    result = db.execute(
+        text(
+            """
+            SELECT EXISTS (
+                SELECT 1 FROM information_schema.tables
+                WHERE table_name = :table_name
+            )
+            """
+        ),
+        {"table_name": table_name},
+    )
+    return bool(result.scalar())
+
+
+def _column_exists(db: Session, table_name: str, column_name: str) -> bool:
+    result = db.execute(
+        text(
+            """
+            SELECT EXISTS (
+                SELECT 1 FROM information_schema.columns
+                WHERE table_name = :table_name AND column_name = :column_name
+            )
+            """
+        ),
+        {"table_name": table_name, "column_name": column_name},
+    )
+    return bool(result.scalar())
+
+
+def upgrade(db: Session) -> None:
+    if not _table_exists(db, "synthetic_call_traces"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE synthetic_call_traces (
+                    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                    organization_id UUID NOT NULL REFERENCES organizations(id),
+                    workspace_id UUID NOT NULL REFERENCES workspaces(id),
+                    evaluator_result_id UUID REFERENCES evaluator_results(id) ON DELETE SET NULL,
+                    agent_id UUID REFERENCES agents(id) ON DELETE SET NULL,
+                    persona_id UUID REFERENCES personas(id) ON DELETE SET NULL,
+                    scenario_id UUID REFERENCES scenarios(id) ON DELETE SET NULL,
+                    evaluator_id UUID REFERENCES evaluators(id) ON DELETE SET NULL,
+                    call_recording_id UUID REFERENCES call_recordings(id) ON DELETE SET NULL,
+                    call_short_id VARCHAR(6),
+                    environment VARCHAR(32) NOT NULL DEFAULT 'pre_prod',
+                    provider_platform VARCHAR(64),
+                    transport VARCHAR(32) NOT NULL DEFAULT 'phone',
+                    tier VARCHAR(32) NOT NULL DEFAULT 'black_box',
+                    status VARCHAR(32) NOT NULL DEFAULT 'open',
+                    started_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
+                    ended_at TIMESTAMP WITH TIME ZONE,
+                    turn_count INTEGER NOT NULL DEFAULT 0,
+                    response_latency_p50_ms DOUBLE PRECISION,
+                    response_latency_p90_ms DOUBLE PRECISION,
+                    response_latency_p95_ms DOUBLE PRECISION,
+                    component_aggregates JSONB,
+                    failure_flags JSONB,
+                    trace_version INTEGER NOT NULL DEFAULT 1,
+                    shard_id VARCHAR(64),
+                    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+                    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                "CREATE INDEX ix_synthetic_call_traces_org_agent_started "
+                "ON synthetic_call_traces(organization_id, agent_id, started_at)"
+            )
+        )
+        db.execute(
+            text(
+                "CREATE INDEX ix_synthetic_call_traces_evaluator_result "
+                "ON synthetic_call_traces(evaluator_result_id)"
+            )
+        )
+        db.execute(
+            text(
+                "CREATE INDEX ix_synthetic_call_traces_call_short_id "
+                "ON synthetic_call_traces(call_short_id)"
+            )
+        )
+
+    if not _table_exists(db, "synthetic_trace_payloads"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE synthetic_trace_payloads (
+                    synthetic_call_trace_id UUID PRIMARY KEY
+                        REFERENCES synthetic_call_traces(id) ON DELETE CASCADE,
+                    workspace_id UUID NOT NULL,
+                    turns JSONB NOT NULL DEFAULT '[]'::jsonb,
+                    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+                    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                "CREATE INDEX ix_synthetic_trace_payloads_workspace "
+                "ON synthetic_trace_payloads(workspace_id)"
+            )
+        )
+
+    if not _table_exists(db, "synthetic_trace_otel_payloads"):
+        db.execute(
+            text(
+                """
+                CREATE TABLE synthetic_trace_otel_payloads (
+                    synthetic_call_trace_id UUID PRIMARY KEY
+                        REFERENCES synthetic_call_traces(id) ON DELETE CASCADE,
+                    workspace_id UUID NOT NULL,
+                    spans JSONB NOT NULL DEFAULT '[]'::jsonb,
+                    trace_ids JSONB NOT NULL DEFAULT '[]'::jsonb,
+                    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+                    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+                )
+                """
+            )
+        )
+        db.execute(
+            text(
+                "CREATE INDEX ix_synthetic_trace_otel_payloads_workspace "
+                "ON synthetic_trace_otel_payloads(workspace_id)"
+            )
+        )
+
+    if not _column_exists(db, "evaluator_results", "synthetic_call_trace_id"):
+        db.execute(
+            text(
+                """
+                ALTER TABLE evaluator_results
+                ADD COLUMN synthetic_call_trace_id UUID
+                    REFERENCES synthetic_call_traces(id) ON DELETE SET NULL
+                """
+            )
+        )
+        db.execute(
+            text(
+                "CREATE INDEX ix_evaluator_results_synthetic_call_trace_id "
+                "ON evaluator_results(synthetic_call_trace_id)"
+            )
+        )
+
+    db.commit()
+
+
+def downgrade(db: Session) -> None:
+    if _column_exists(db, "evaluator_results", "synthetic_call_trace_id"):
+        db.execute(text("ALTER TABLE evaluator_results DROP COLUMN synthetic_call_trace_id"))
+    for table in (
+        "synthetic_trace_otel_payloads",
+        "synthetic_trace_payloads",
+        "synthetic_call_traces",
+    ):
+        if _table_exists(db, table):
+            db.execute(text(f"DROP TABLE {table}"))
+    db.commit()
diff --git a/app/models/database.py b/app/models/database.py
index 92350139..6aa2c896 100644
--- a/app/models/database.py
+++ b/app/models/database.py
@@ -1,3022 +1,3109 @@
-"""SQLAlchemy database models."""
-
-from sqlalchemy import (
-    BigInteger,
-    Boolean,
-    Column,
-    Date,
-    DateTime,
-    DDL,
-    Enum,
-    event,
-    Float,
-    ForeignKey,
-    Integer,
-    JSON,
-    String,
-    Text,
-    UniqueConstraint,
-    select,
-    text,
-)
-from sqlalchemy.dialects.postgresql import JSONB, UUID
-from sqlalchemy.orm import relationship
-from sqlalchemy.sql import func
-import uuid
-import enum
-from app.models.enums import (
-    EvaluationType, EvaluationStatus, EvaluatorResultStatus, RoleEnum, InvitationStatus,
-    LanguageEnum, CallTypeEnum, CallMediumEnum, GenderEnum, AccentEnum, BackgroundNoiseEnum,
-    IntegrationPlatform, ModelProvider, VoiceBundleType, TestAgentConversationStatus,
-    MetricType, MetricCategory, MetricTrigger, CallRecordingStatus, AlertMetricType, AlertAggregation,
-    AlertOperator, AlertNotifyFrequency, AlertStatus, AlertHistoryStatus, CronJobStatus,
-    PromptOptimizationStatus, CallImportStatus, CallImportRowStatus,
-)
-
-def get_enum_values(enum_class):
-    """Helper to get values from enum class for SQLAlchemy."""
-    return [e.value for e in enum_class]
-
-from app.database import Base
-
-
-# Enums moved to enums.py
-
-
-class Organization(Base):
-    """Organization model for multi-tenancy."""
-
-    __tablename__ = "organizations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    name = Column(String(255), nullable=False)
-    voice_playground_threshold_overrides = Column(JSON, nullable=True)
-    # AlignEval-style judge alignment thresholds.
-    # Shape: {"min_labels_to_evaluate": int, "min_labels_to_optimize": int}
-    # Falls back to system defaults (20 / 50) when null.
-    judge_alignment_settings = Column(JSON, nullable=True)
-    # Per-org LLM gateway overrides (enabled, gateway_type, base_url, keys).
-    llm_gateway_settings = Column(JSON, nullable=True)
-    is_active = Column(Boolean, default=True, nullable=False, server_default=text("true"), index=True)
-    disabled_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    # Relationships
-    api_keys = relationship("APIKey", back_populates="organization")
-    members = relationship("OrganizationMember", back_populates="organization", cascade="all, delete-orphan")
-    invitations = relationship("Invitation", back_populates="organization", cascade="all, delete-orphan")
-    workspaces = relationship(
-        "Workspace",
-        back_populates="organization",
-        cascade="all, delete-orphan",
-    )
-    workspace_roles = relationship(
-        "WorkspaceRole",
-        back_populates="organization",
-        cascade="all, delete-orphan",
-    )
-
-
-class Workspace(Base):
-    """Workspace - in-org isolation boundary for call imports and metrics.
-
-    Every organization has at least one workspace (``is_default = True``,
-    seeded by migration 033). Users pick an "active workspace" in the UI;
-    list endpoints filter by it so users only see calls/metrics from the
-    project they're currently working in. Access is governed by
-    ``workspace_members`` and org-scoped ``workspace_roles`` (capability
-    bundles); org admins implicitly access all workspaces.
-    """
-
-    __tablename__ = "workspaces"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "slug", name="uq_workspaces_org_slug"),
-    )
-
-    # ``server_default`` is required so that raw-SQL INSERTs (e.g. the
-    # per-org Default seed in migration 033) can omit ``id`` and let the
-    # database fill it in. Without it, ``create_all`` produces a column
-    # with NOT NULL but no DEFAULT, and the migration crashes with
-    # ``null value in column "id"``.
-    id = Column(
-        UUID(as_uuid=True),
-        primary_key=True,
-        default=uuid.uuid4,
-        server_default=text("gen_random_uuid()"),
-    )
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    slug = Column(String(255), nullable=False)
-    # At most one default per org. Enforced on Postgres by the partial
-    # unique index attached via the after_create event below; on
-    # SQLite (test runs) we rely on the route-level _check_slug_unique
-    # check + the Default-workspace conftest fixture instead, because
-    # SQLite doesn't support partial indexes the same way.
-    is_default = Column(Boolean, nullable=False, default=False, server_default="false")
-    is_active = Column(Boolean, nullable=False, default=True, server_default="true")
-    # Reusable PDF/report branding metadata scoped to this workspace. Images
-    # live in S3. Shape: {"heading": str|null, "images": [{id, s3_key,
-    # content_type, filename, size_bytes, updated_at}, ...]}.
-    report_branding = Column(JSON, nullable=True)
-    created_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
-    )
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    organization = relationship("Organization", back_populates="workspaces")
-    members = relationship(
-        "WorkspaceMember",
-        back_populates="workspace",
-        cascade="all, delete-orphan",
-    )
-
-
-class WorkspaceRole(Base):
-    """Org-scoped workspace role (system or custom) as a capability bundle."""
-
-    __tablename__ = "workspace_roles"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "name", name="uq_workspace_roles_org_name"),
-    )
-
-    id = Column(
-        UUID(as_uuid=True),
-        primary_key=True,
-        default=uuid.uuid4,
-        server_default=text("gen_random_uuid()"),
-    )
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-    capabilities = Column(JSON, nullable=False, default=list)
-    is_system = Column(Boolean, nullable=False, default=False, server_default="false")
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    organization = relationship("Organization", back_populates="workspace_roles")
-    members = relationship("WorkspaceMember", back_populates="role")
-
-
-class WorkspaceMember(Base):
-    """User membership in a workspace with an assigned workspace role."""
-
-    __tablename__ = "workspace_members"
-    __table_args__ = (
-        UniqueConstraint("workspace_id", "user_id", name="uq_workspace_members_ws_user"),
-    )
-
-    id = Column(
-        UUID(as_uuid=True),
-        primary_key=True,
-        default=uuid.uuid4,
-        server_default=text("gen_random_uuid()"),
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    user_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("users.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    role_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspace_roles.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    added_by_user_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("users.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    workspace = relationship("Workspace", back_populates="members")
-    user = relationship("User", foreign_keys=[user_id])
-    role = relationship("WorkspaceRole", back_populates="members")
-    added_by = relationship("User", foreign_keys=[added_by_user_id])
-
-
-# Partial unique index: "at most one default workspace per org". This
-# is attached as an after_create event (rather than declared in
-# ``__table_args__``) because SQLAlchemy's ``Index(...,
-# postgresql_where=...)`` silently degrades to a *full* unique index on
-# SQLite - which then forbids any second workspace per org and breaks
-# the test suite. ``execute_if(dialect="postgresql")`` makes this DDL
-# a no-op on SQLite while still emitting it on Postgres (prod, CI).
-event.listen(
-    Workspace.__table__,
-    "after_create",
-    DDL(
-        "CREATE UNIQUE INDEX IF NOT EXISTS uq_workspaces_org_default "
-        "ON workspaces (organization_id) WHERE is_default"
-    ).execute_if(dialect="postgresql"),
-)
-
-
-class User(Base):
-    """User model for authentication and profile management."""
-
-    __tablename__ = "users"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    email = Column(String(255), unique=True, nullable=False, index=True)
-    name = Column(String(255), nullable=True)
-    first_name = Column(String(255), nullable=True)
-    last_name = Column(String(255), nullable=True)
-    password_hash = Column(String(255), nullable=True)  # Nullable for users created via invitation
-    external_id = Column(String(255), unique=True, nullable=True, index=True)
-    auth_provider = Column(String(50), nullable=True)
-    mfa_enabled = Column(Boolean, default=False, nullable=False)
-    last_login_at = Column(DateTime(timezone=True), nullable=True)
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    # Relationships
-    organization_memberships = relationship("OrganizationMember", back_populates="user", cascade="all, delete-orphan")
-    api_keys = relationship("APIKey", back_populates="user")
-    invitations = relationship("Invitation", back_populates="invited_user", foreign_keys="Invitation.invited_user_id")
-    refresh_tokens = relationship("RefreshToken", back_populates="user", cascade="all, delete-orphan")
-
-
-class PlatformAdmin(Base):
-    """Platform-level administrator (separate from org-scoped users)."""
-
-    __tablename__ = "platform_admins"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    email = Column(String(255), unique=True, nullable=False, index=True)
-    password_hash = Column(String(255), nullable=False)
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    last_login_at = Column(DateTime(timezone=True), nullable=True)
-
-    signup_reference_codes = relationship(
-        "SignupReferenceCode",
-        back_populates="created_by_admin",
-        foreign_keys="SignupReferenceCode.created_by",
-    )
-
-
-class SignupReferenceCode(Base):
-    """Single- or multi-use reference code required for gated self-service signup."""
-
-    __tablename__ = "signup_reference_codes"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    code_hash = Column(String(64), unique=True, nullable=False)
-    label = Column(String(255), nullable=True)
-    max_uses = Column(Integer, nullable=True)
-    use_count = Column(Integer, default=0, nullable=False)
-    expires_at = Column(DateTime(timezone=True), nullable=True)
-    is_active = Column(Boolean, default=True, nullable=False, index=True)
-    created_by = Column(UUID(as_uuid=True), ForeignKey("platform_admins.id"), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    created_by_admin = relationship(
-        "PlatformAdmin",
-        back_populates="signup_reference_codes",
-        foreign_keys=[created_by],
-    )
-
-
-class RefreshToken(Base):
-    """Opaque refresh token for extending local-password sessions."""
-
-    __tablename__ = "refresh_tokens"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id", ondelete="CASCADE"), nullable=False, index=True)
-    token_hash = Column(String(64), unique=True, nullable=False, index=True)
-    expires_at = Column(DateTime(timezone=True), nullable=False)
-    revoked_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    user = relationship("User", back_populates="refresh_tokens")
-
-
-class OrganizationMember(Base):
-    """Organization membership with role."""
-
-    __tablename__ = "organization_members"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False, index=True)
-    role = Column(String, nullable=False, default=RoleEnum.READER.value)
-    
-    # User preferences for this organization
-    default_agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
-
-    joined_at = Column(DateTime(timezone=True), server_default=func.now())
-    
-    # Unique constraint: one membership per user per organization
-    __table_args__ = (
-        UniqueConstraint('organization_id', 'user_id', name='uq_org_user'),
-    )
-
-    # Relationships
-    organization = relationship("Organization", back_populates="members")
-    user = relationship("User", back_populates="organization_memberships")
-    default_agent = relationship("Agent", foreign_keys=[default_agent_id])
-
-
-class Invitation(Base):
-    """Invitation model for inviting users to organizations."""
-
-    __tablename__ = "invitations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    invited_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Null if user doesn't exist yet
-    invited_by_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False)
-    email = Column(String(255), nullable=False)  # Email of invited user
-    role = Column(String, nullable=False, default=RoleEnum.READER.value)
-    status = Column(String, nullable=False, default=InvitationStatus.PENDING.value)
-
-
-
-    token = Column(String(255), unique=True, nullable=False, index=True)  # Invitation token
-    expires_at = Column(DateTime(timezone=True), nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    accepted_at = Column(DateTime(timezone=True), nullable=True)
-
-    # Relationships
-    organization = relationship("Organization", back_populates="invitations")
-    invited_user = relationship("User", foreign_keys=[invited_user_id], back_populates="invitations")
-    invited_by = relationship("User", foreign_keys=[invited_by_id])
-
-
-class APIKey(Base):
-    """API Key model for authentication."""
-
-    __tablename__ = "api_keys"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    key = Column(String(255), unique=True, nullable=False, index=True)
-    name = Column(String(255), nullable=True)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Optional: link to user
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    last_used = Column(DateTime(timezone=True), nullable=True)
-
-    # Relationships
-    organization = relationship("Organization", back_populates="api_keys")
-    user = relationship("User", back_populates="api_keys")
-
-
-class AudioFile(Base):
-    """Audio file model."""
-
-    __tablename__ = "audio_files"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    filename = Column(String(255), nullable=False)
-    file_path = Column(String(512), nullable=False)
-    file_size = Column(Integer, nullable=False)  # Size in bytes
-    duration = Column(Float, nullable=True)  # Duration in seconds
-    sample_rate = Column(Integer, nullable=True)
-    channels = Column(Integer, nullable=True)
-    format = Column(String(10), nullable=False)  # wav, mp3, flac, etc.
-    uploaded_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    # Relationships
-    evaluations = relationship("Evaluation", back_populates="audio_file")
-
-
-class Evaluation(Base):
-    """Evaluation job model."""
-
-    __tablename__ = "evaluations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every legacy audio evaluation belongs to a
-    # workspace within its org. Stamped from the X-Workspace-Id header
-    # (falling back to the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    audio_id = Column(UUID(as_uuid=True), ForeignKey("audio_files.id"), nullable=False)
-    reference_text = Column(String, nullable=True)  # For WER calculation
-    evaluation_type = Column(String, nullable=False)
-    model_name = Column(String(100), nullable=True)
-    status = Column(String, default=EvaluationStatus.PENDING.value, nullable=False)
-
-
-
-    metrics_requested = Column(JSON, nullable=True)  # List of requested metrics
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    completed_at = Column(DateTime(timezone=True), nullable=True)
-    error_message = Column(String, nullable=True)
-
-    # Relationships
-    audio_file = relationship("AudioFile", back_populates="evaluations")
-    result = relationship("EvaluationResult", back_populates="evaluation", uselist=False)
-
-
-class EvaluationResult(Base):
-    """Evaluation result model."""
-
-    __tablename__ = "evaluation_results"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(UUID(as_uuid=True), ForeignKey("evaluations.id"), nullable=False, unique=True)
-    # Workspace isolation: mirrors the parent Evaluation's workspace.
-    # Denormalized for fast filter-by-workspace listings without a join.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    transcript = Column(String, nullable=True)
-    metrics = Column(JSON, nullable=False)  # {"wer": 0.05, "latency_ms": 1250, ...}
-    raw_output = Column(JSON, nullable=True)  # Full model output
-    processing_time = Column(Float, nullable=True)  # Processing time in seconds
-    model_used = Column(String(100), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    # Relationships
-    evaluation = relationship("Evaluation", back_populates="result")
-
-
-# ============================================
-# VAIOPS MODELS - Voice AI Ops
-# ============================================
-
-# Enums moved to enums.py
-
-
-class Agent(Base):
-    """Test Agent - The voice AI agent being evaluated"""
-    __tablename__ = "agents"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    agent_id = Column(String(6), unique=True, nullable=True, index=True)  # 6-digit ID
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every agent belongs to a workspace within its
-    # org. Stamped from the X-Workspace-Id header (falling back to the
-    # org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String, nullable=False)
-    phone_number = Column(String, nullable=True)  # Optional, required only for phone_call
-    language = Column(String, nullable=False, default=LanguageEnum.ENGLISH.value)
-    description = Column(String)
-    provider_prompt = Column(Text, nullable=True)
-    provider_prompt_synced_at = Column(DateTime(timezone=True), nullable=True)
-    call_type = Column(String, nullable=False, default=CallTypeEnum.OUTBOUND.value)
-    call_medium = Column(String, nullable=False, default=CallMediumEnum.PHONE_CALL.value)
-    telephony_phone_number_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("telephony_phone_numbers.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-
-
-
-    
-    # Voice configuration - either voice_bundle_id OR ai_provider_id OR voice_ai_integration_id (mutually exclusive)
-    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True, index=True)
-    ai_provider_id = Column(UUID(as_uuid=True), ForeignKey("aiproviders.id"), nullable=True, index=True)
-    
-    # Voice AI agent integration (Retell, Vapi, etc.)
-    voice_ai_integration_id = Column(UUID(as_uuid=True), ForeignKey("integrations.id"), nullable=True, index=True)
-    voice_ai_agent_id = Column(String, nullable=True)  # Agent ID from the external provider (Retell/Vapi)
-    prompt_variables = Column(JSON, nullable=True)
-    silence_hangup_secs = Column(Integer, nullable=False, server_default="15")
-    
-    created_at = Column(DateTime, server_default=func.now())
-    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
-    created_by = Column(String)
-
-
-class Persona(Base):
-    """Persona - TTS provider-tied voice identity for testing"""
-    __tablename__ = "personas"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every persona belongs to a workspace within
-    # its org. Stamped from the X-Workspace-Id header (falling back to
-    # the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String, nullable=False)
-    gender = Column(String, nullable=False, default=GenderEnum.NEUTRAL.value)
-    tts_provider = Column(String(100), nullable=True)
-    tts_voice_id = Column(String(255), nullable=True)
-    tts_voice_name = Column(String(255), nullable=True)
-    is_custom = Column(Boolean, default=False)
-    description = Column(Text, nullable=True)
-    tts_config = Column(JSON, nullable=True)
-    llm_temperature = Column(Float, nullable=True)
-    llm_max_tokens = Column(Integer, nullable=True)
-    response_delay_ms = Column(Integer, nullable=True)
-    max_turns = Column(Integer, nullable=True)
-    allow_interruptions = Column(Boolean, nullable=True)
-
-    created_at = Column(DateTime, server_default=func.now())
-    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
-    created_by = Column(String)
-
-
-class Scenario(Base):
-    """Scenario - The conversation scenario/test case"""
-    __tablename__ = "scenarios"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every scenario belongs to a workspace within
-    # its org. Stamped from the X-Workspace-Id header (falling back to
-    # the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
-    name = Column(String, nullable=False)
-    description = Column(String)
-    required_info = Column(JSON)
-    
-    created_at = Column(DateTime, server_default=func.now())
-    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
-    created_by = Column(String)
-
-
-# Enums moved to enums.py
-
-
-class Integration(Base):
-    """Integration model for connecting with external voice AI platforms."""
-    __tablename__ = "integrations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    platform = Column(String, nullable=False)
-
-
-
-    name = Column(String, nullable=True)  # Optional friendly name
-    api_key = Column(String, nullable=False)  # Encrypted Private API key for the platform
-    public_key = Column(String, nullable=True)  # Optional Public API key (e.g. for Vapi)
-    is_active = Column(Boolean, default=True, nullable=False)
-    # Multiple credentials per (org, platform) are allowed. is_default marks
-    # the row used when a caller does not explicitly select a credential.
-    # A partial unique index in migration 028 enforces at most one default
-    # per (org, platform) at the DB level.
-    is_default = Column(Boolean, default=False, nullable=False)
-    # inherit | gateway | direct — per-credential LLM routing override
-    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
-
-
-class ManualTranscription(Base):
-    """Manual transcription model for storing transcriptions from S3 audio files."""
-
-    __tablename__ = "manual_transcriptions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    name = Column(String(255), nullable=True)  # User-friendly name for the transcription
-    audio_file_key = Column(String(512), nullable=False)  # S3 key or file path
-    transcript = Column(String, nullable=False)  # Full transcript text
-    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
-    stt_model = Column(String(100), nullable=True)  # STT model used (e.g., "whisper-1", "google-speech-v2")
-    stt_provider = Column(String, nullable=True)  # Provider used
-
-
-
-    language = Column(String(10), nullable=True)  # Detected or specified language
-    processing_time = Column(Float, nullable=True)  # Processing time in seconds
-    raw_output = Column(JSON, nullable=True)  # Full model output for reference
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class ConversationEvaluation(Base):
-    """Conversation evaluation model for evaluating manual transcriptions against agent objectives."""
-    
-    __tablename__ = "conversation_evaluations"
-    
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    transcription_id = Column(UUID(as_uuid=True), ForeignKey("manual_transcriptions.id"), nullable=False, index=True)
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
-    
-    # Evaluation results
-    objective_achieved = Column(Boolean, nullable=False)  # Binary: was the conversation objective achieved?
-    objective_achieved_reason = Column(String, nullable=True)  # Explanation for the binary result
-    additional_metrics = Column(JSON, nullable=True)  # Additional evaluation metrics (e.g., professionalism, clarity, etc.)
-    overall_score = Column(Float, nullable=True)  # Overall score (0.0 to 1.0)
-    
-    # LLM metadata
-    llm_provider = Column(Enum(ModelProvider, native_enum=False), nullable=True)
-
-    llm_model = Column(String(100), nullable=True)
-    llm_response = Column(JSON, nullable=True)  # Full LLM response for reference
-    
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class AIProvider(Base):
-    """AI Provider - Stores API keys for different AI platforms."""
-    __tablename__ = "aiproviders"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider = Column(String, nullable=False)
-
-
-
-    api_key = Column(String, nullable=False)  # Encrypted API key
-    name = Column(String, nullable=True)  # Optional friendly name
-    # Azure OpenAI resource endpoint (e.g. https://my-resource.openai.azure.com).
-    # Only used when provider is azure; other providers ignore this column.
-    endpoint_url = Column(String, nullable=True)
-    is_active = Column(Boolean, default=True, nullable=False)
-    # Multiple AIProvider rows per (org, provider) are allowed. is_default
-    # marks the row resolved when no explicit credential id is selected.
-    # A partial unique index in migration 028 enforces at most one default.
-    is_default = Column(Boolean, default=False, nullable=False)
-    # inherit | gateway | direct — per-credential LLM routing override
-    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
-    # Bifrost custom model ID used when routing via gateway
-    gateway_model = Column(String(255), nullable=True)
-    # inherit | litellm_shim | native_openai — Bifrost API surface override
-    gateway_interface = Column(String(20), nullable=False, default="inherit", server_default="inherit")
-    # Optional per-credential Bifrost/gateway base URL override
-    gateway_base_url = Column(String(512), nullable=True)
-    # Optional auth header for Bifrost (e.g. x-bf-vk, Authorization, x-api-key)
-    gateway_auth_header = Column(String(64), nullable=True)
-    # Env var name whose value is sent as the gateway auth secret
-    gateway_auth_secret_env = Column(String(128), nullable=True)
-    # Encrypted inline gateway auth secret (alternative to env var)
-    gateway_auth_secret = Column(String, nullable=True)
-    # Arbitrary HTTP headers sent with gateway-routed LiteLLM calls
-    gateway_extra_headers = Column(JSON, nullable=True)
-    # Non-empty list restricts model pickers; null/empty = all catalog models for provider.
-    enabled_models = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
-
-
-# Enums moved to enums.py
-
-
-class VoiceBundle(Base):
-    """VoiceBundle - Composable unit combining STT, LLM, and TTS for voice AI testing, or S2S models."""
-    __tablename__ = "voicebundles"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    name = Column(String, nullable=False)
-    description = Column(String, nullable=True)
-    
-    # Bundle type: either STT+LLM+TTS or S2S
-    # Using String instead of Enum to avoid SQLAlchemy enum conversion issues
-    # The enum conversion is handled in the Pydantic schemas
-    bundle_type = Column(String(50), nullable=False, default=VoiceBundleType.STT_LLM_TTS.value)
-    
-    # STT Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
-    stt_provider = Column(String, nullable=True)
-    # Optional explicit credential row (aiproviders.id or integrations.id).
-    # When NULL the credential resolver picks the default row for the
-    # provider. No FK is set because the target table varies by provider.
-    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    stt_model = Column(String, nullable=True)  # e.g., "whisper-1", "google-speech-v2"
-    
-    # LLM Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
-    llm_provider = Column(String, nullable=True)
-    llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    llm_model = Column(String, nullable=True)  # e.g., "gpt-4", "claude-3-opus"
-    llm_temperature = Column(Float, nullable=True, default=0.7)
-    llm_max_tokens = Column(Integer, nullable=True)
-    llm_config = Column(JSON, nullable=True)  # Additional LLM configuration (extensible)
-    
-    # TTS Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
-    tts_provider = Column(String, nullable=True)
-    tts_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    tts_model = Column(String, nullable=True)  # e.g., "tts-1", "neural-voice"
-    tts_voice = Column(String, nullable=True)  # Voice selection if applicable
-    tts_config = Column(JSON, nullable=True)  # Additional TTS configuration (extensible)
-    
-    # S2S Configuration - required for S2S type, optional for STT_LLM_TTS
-    s2s_provider = Column(String, nullable=True)
-    s2s_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-
-
-    s2s_model = Column(String, nullable=True)  # e.g., "gpt-4o-transcribe", speech-to-speech model
-    s2s_config = Column(JSON, nullable=True)  # Additional S2S configuration (extensible)
-    
-    # Additional configuration for extensibility
-    extra_metadata = Column(JSON, nullable=True)  # For future extensions (renamed from 'metadata' to avoid SQLAlchemy conflict)
-    
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-# Enums moved to enums.py
-
-
-class TestAgentConversation(Base):
-    """Test Agent Conversation - Records conversations between test AI agent and voice AI agent."""
-    __tablename__ = "test_agent_conversations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every playground conversation belongs to a
-    # workspace within its org. Stamped from the X-Workspace-Id header
-    # (falling back to the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    
-    # Configuration
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
-    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=False)
-    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
-    
-    # Conversation data
-    status = Column(String, nullable=False, default=TestAgentConversationStatus.INITIALIZING.value)
-
-
-
-    live_transcription = Column(JSON, nullable=True)  # Array of conversation turns with timestamps
-    conversation_audio_key = Column(String, nullable=True)  # S3 key for recorded conversation audio
-    full_transcript = Column(String, nullable=True)  # Full conversation transcript
-    
-    # Metadata
-    started_at = Column(DateTime(timezone=True), server_default=func.now())
-    ended_at = Column(DateTime(timezone=True), nullable=True)
-    duration_seconds = Column(Float, nullable=True)
-    
-    # Additional metadata
-    conversation_metadata = Column(JSON, nullable=True)  # Additional conversation metadata
-    
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class EvaluatorSuite(Base):
-    """Evaluator suite — one agent + one persona + N scenario combinations."""
-
-    __tablename__ = "evaluator_suites"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    name = Column(String, nullable=True)
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
-    metric_ids = Column(JSON, nullable=True)
-    llm_provider = Column(String, nullable=True)
-    llm_model = Column(String, nullable=True)
-    llm_config = Column(JSON, nullable=True)
-    tags = Column(JSON, nullable=True)
-    default_runs_per_combination = Column(Integer, nullable=False, default=1)
-    round_robin_index = Column(Integer, nullable=False, default=0)
-    is_active = Column(Boolean, nullable=False, default=False)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class Evaluator(Base):
-    """Evaluator - Configuration for testing agents with specific persona and scenario combinations, or custom prompt evaluators."""
-    __tablename__ = "evaluators"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluator_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every evaluator belongs to a workspace within
-    # its org. Stamped from the X-Workspace-Id header (falling back to
-    # the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    
-    # Display name (required for custom evaluators, optional for standard)
-    name = Column(String, nullable=True)
-    
-    # Parent suite (nullable for legacy/custom evaluators)
-    suite_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("evaluator_suites.id", ondelete="CASCADE"),
-        nullable=True,
-        index=True,
-    )
-
-    # Standard evaluator configuration (nullable for custom evaluators)
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)
-    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)
-    
-    # Custom evaluator prompt (used instead of agent/persona/scenario)
-    custom_prompt = Column(Text, nullable=True)
-
-    # Custom evaluator metric selection. When set, the worker filters the
-    # enabled-org metrics down to only these IDs (list of metric UUID strings).
-    # Standard evaluators leave this NULL and use all enabled agent metrics.
-    metric_ids = Column(JSON, nullable=True)
-
-    # LLM configuration for evaluation (overrides hardcoded defaults)
-    llm_provider = Column(String, nullable=True)  # e.g. "openai", "anthropic", "google"
-    llm_model = Column(String, nullable=True)  # e.g. "gpt-4.1", "claude-sonnet-4-20250514"
-    llm_config = Column(JSON, nullable=True)
-    
-    # Tags for categorization
-    tags = Column(JSON, nullable=True)  # Array of tag strings
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-# Enums moved to enums.py
-
-
-class Metric(Base):
-    """Metric - Configuration for evaluation metrics.
-
-    Supports a 2-level hierarchy via ``parent_metric_id``: a "category"
-    parent metric (e.g. "Call Outcome") owns N child sub-metric labels
-    (e.g. "happy_completion", "angry_hangup"). ``selection_mode`` is set
-    only on parents and controls how the LLM scores children together
-    (``single_choice`` = pick exactly one; ``multi_label`` = independent
-    yes/no with logical consistency). Children are always boolean.
-    """
-    __tablename__ = "metrics"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: two-shape column.
-    #
-    #   * ``workspace_id = <uuid>`` — workspace-scoped metric. Only
-    #     visible inside that workspace (the default behavior; existing
-    #     rows all look like this).
-    #   * ``workspace_id IS NULL`` — org-shared metric. Surfaces in
-    #     every workspace's listing under this org so users don't have
-    #     to recreate the same metric per workspace.
-    #
-    # Children always inherit their parent's ``workspace_id`` (including
-    # NULL) so a category metric's whole subtree shares one scope; the
-    # add-child / promote-discovered endpoints enforce this.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=True,
-        index=True,
-    )
-
-    # Basic information
-    name = Column(String, nullable=False)
-    description = Column(String, nullable=True)
-    # Free-form illustrative example used to sharpen the LLM judge's
-    # rubric. Today this is consumed by child sub-labels of a
-    # categorization parent metric so each label can carry "what does
-    # this look like in a transcript?" text alongside the rubric in
-    # ``description``. The column lives on every Metric row for
-    # forward-compat: a standalone metric could later surface its own
-    # example without another migration.
-    example = Column(Text, nullable=True)
-
-    # Configuration
-    metric_type = Column(String, nullable=False, default=MetricType.RATING.value)
-    metric_category = Column(
-        String(30),
-        nullable=False,
-        default=MetricCategory.QUALITY.value,
-        server_default=MetricCategory.QUALITY.value,
-    )
-    trigger = Column(String, nullable=False, default=MetricTrigger.ALWAYS.value)
-    metric_origin = Column(String(30), nullable=False, default="default")
-    supported_surfaces = Column(JSON, nullable=False, default=list)  # ["agent", "voice_playground", "blind_test"]
-    enabled_surfaces = Column(JSON, nullable=False, default=list)  # subset of supported_surfaces
-    custom_data_type = Column(String(30), nullable=True)  # "boolean" | "enum" | "number_range"
-    custom_config = Column(JSON, nullable=True)  # enum options / number range config
-    tags = Column(JSON, nullable=True)  # ["tone", "latency", ...]
-
-    # Hierarchy: NULL = standalone or parent. When set, this row is a
-    # child sub-metric of the referenced parent. ON DELETE CASCADE so
-    # deleting a category removes its children atomically.
-    parent_metric_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("metrics.id", ondelete="CASCADE"),
-        nullable=True,
-        index=True,
-    )
-    # Set only on parent rows (``parent_metric_id IS NULL``). Either
-    # ``single_choice`` or ``multi_label``. NULL = legacy / non-hierarchical
-    # metric (no children).
-    selection_mode = Column(String(20), nullable=True)
-
-    # When true on a parent metric (any selection_mode), the LLM is
-    # invited during call-import evaluation to emit additional
-    # candidate sub-labels beyond the user-defined children. The
-    # candidates surface in a "Discovered labels" panel where the user
-    # manually promotes them into real child Metric rows. For
-    # ``single_choice`` parents the discovered entries are
-    # supplemental — the chosen child is still picked from the
-    # predefined children so the exactly-one-true invariant holds.
-    # The validator rejects this flag on standalone / child metrics.
-    allow_discovery = Column(
-        Boolean, nullable=False, default=False, server_default="false"
-    )
-
-    # When True, this metric is a "transcript-compare judge": the
-    # call-import evaluator feeds BOTH the production transcript
-    # (``call_import_rows.transcript``, CSV-supplied) and the diarised
-    # transcript (``call_import_rows.diarised_transcript``, worker-
-    # produced by the STT/diarisation pipeline) to the LLM as a
-    # labeled pair instead of feeding one transcript. The parent
-    # evaluation's ``CallImportEvaluation.transcript_source`` is
-    # ignored for these metrics — they always read both columns.
-    # Rows where either transcript is missing are skipped per-metric
-    # with ``skipped="comparison_missing_transcript"`` so the rest of
-    # the row's metrics still produce scores. The Pydantic validator
-    # rejects ``compare_transcripts`` combined with ``parent_metric_id``
-    # or ``selection_mode`` (i.e. it can't simultaneously be part of
-    # a parent/child hierarchy). The call-import worker also
-    # auto-promotes a metric to comparison mode when its description
-    # references the production / diarised transcripts in well-known
-    # phrases (see ``_metric_text_references_production`` in
-    # ``app.workers.tasks.evaluate_call_import_row``).
-    compare_transcripts = Column(
-        Boolean, nullable=False, default=False, server_default="false"
-    )
-
-    parent = relationship(
-        "Metric",
-        remote_side=[id],
-        backref="children",
-    )
-
-    # When true, the LLM-judge is asked to also return a short free-form
-    # rationale alongside the value (stored under ``metric_scores[id].rationale``).
-    # Adds a second "<Name> - LLM Rationale" column in the call-import CSV export.
-    capture_rationale = Column(Boolean, nullable=False, default=False)
-
-    enabled = Column(Boolean, nullable=False, default=True)
-
-    # Studio draft lifecycle: ``draft`` metrics are visible only in Metrics
-    # Studio until promoted to ``active``.
-    lifecycle = Column(
-        String(20),
-        nullable=False,
-        default="active",
-        server_default="active",
-    )
-    promoted_from_draft_at = Column(DateTime(timezone=True), nullable=True)
-    studio_notes = Column(Text, nullable=True)
-    
-    # Metadata
-    is_default = Column(Boolean, nullable=False, default=False)  # Pre-defined metrics
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class EvaluatorResult(Base):
-    """EvaluatorResult - Results from running an evaluator with transcription and metric evaluations."""
-    __tablename__ = "evaluator_results"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    result_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every evaluator result belongs to a workspace
-    # within its org. Stamped from the active workspace at creation time
-    # (either the X-Workspace-Id header or the org's Default workspace).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    
-    # References
-    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True, index=True)  # Optional - can be None for test calls without persona/scenario
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Nullable for custom evaluators
-    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)  # Optional - can be None for test calls
-    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)  # Optional - can be None for test calls
-    
-    # Result data
-    name = Column(String, nullable=True)  # Scenario name or test call name (optional)
-    timestamp = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
-    duration_seconds = Column(Float, nullable=True)  # Call duration
-    status = Column(String(20), nullable=False, default=EvaluatorResultStatus.QUEUED.value)
-    
-    # Audio and transcription
-    audio_s3_key = Column(String, nullable=True)  # S3 key for audio file
-    transcription = Column(String, nullable=True)  # Full transcription
-    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
-    
-    # Metric scores - JSON object with metric_id as key and score as value
-    # Format: {"metric_id_1": {"value": 85, "type": "rating"}, "metric_id_2": {"value": true, "type": "boolean"}}
-    metric_scores = Column(JSON, nullable=True)
-    
-    # Celery task tracking
-    celery_task_id = Column(String, nullable=True, index=True)  # Celery task ID for tracking
-    
-    # Error information
-    error_message = Column(String, nullable=True)
-    
-    # Call event tracking (similar to CallRecording)
-    call_event = Column(String, nullable=True, index=True)  # Latest call event (e.g., call_started, call_ended)
-    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
-    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
-    call_data = Column(JSON, nullable=True)  # Full call details from provider (like CallRecording)
-    
-    # Data-plane shard routing (payload rows on shard DBs when sharding enabled)
-    shard_id = Column(String(64), nullable=True, index=True)
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-# Enums moved to enums.py
-
-
-class CallRecordingSource(str, enum.Enum):
-    """Source of the call recording data."""
-    
-    PLAYGROUND = "playground"
-    WEBHOOK = "webhook"
-
-
-class CallRecording(Base):
-    """Call Recording model for tracking voice provider calls."""
-    __tablename__ = "call_recordings"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every recording belongs to a workspace within
-    # its org. For playground-origin rows this is stamped from the active
-    # workspace at creation time; for webhook-origin rows the worker
-    # looks up the recording's agent and inherits its workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    call_short_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
-    status = Column(Enum(CallRecordingStatus), nullable=False, default=CallRecordingStatus.PENDING, index=True)
-    call_event = Column(String, nullable=True, index=True)  # Latest webhook event (e.g., call_started, call_ended)
-    source = Column(Enum(CallRecordingSource), nullable=False, default=CallRecordingSource.PLAYGROUND, index=True)
-    call_data = Column(JSON, nullable=True)  # JSON blob for provider response
-    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
-    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Reference to our agent
-    
-    # Link to EvaluatorResult for metric evaluations
-    evaluator_result_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("evaluator_results.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-    
-    shard_id = Column(String(64), nullable=True, index=True)
-    
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class EvaluatorResultPayload(Base):
-    """Heavy evaluator result fields stored on data shards when sharding is enabled."""
-
-    __tablename__ = "evaluator_result_payloads"
-
-    evaluator_result_id = Column(UUID(as_uuid=True), primary_key=True)
-    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
-    audio_s3_key = Column(String, nullable=True)
-    transcription = Column(String, nullable=True)
-    speaker_segments = Column(JSON, nullable=True)
-    metric_scores = Column(JSON, nullable=True)
-    call_data = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class CallRecordingPayload(Base):
-    """Heavy call recording fields stored on data shards when sharding is enabled."""
-
-    __tablename__ = "call_recording_payloads"
-
-    call_recording_id = Column(UUID(as_uuid=True), primary_key=True)
-    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
-    call_data = Column(JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class Alert(Base):
-    """Alert model for configuring monitoring alerts."""
-    __tablename__ = "alerts"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    
-    # Basic information
-    name = Column(String(255), nullable=False)
-    description = Column(String, nullable=True)
-    
-    # Metric condition configuration
-    metric_type = Column(String, nullable=False, default=AlertMetricType.NUMBER_OF_CALLS.value)
-    aggregation = Column(String, nullable=False, default=AlertAggregation.SUM.value)
-    operator = Column(String, nullable=False, default=AlertOperator.GREATER_THAN.value)
-    threshold_value = Column(Float, nullable=False)
-    time_window_minutes = Column(Integer, nullable=False, default=60)  # Time window for aggregation
-    
-    # Agent selection (JSON array of agent UUIDs, null means all agents)
-    agent_ids = Column(JSON, nullable=True)
-    
-    # Notification configuration
-    notify_frequency = Column(String, nullable=False, default=AlertNotifyFrequency.IMMEDIATE.value)
-    notify_emails = Column(JSON, nullable=True)  # Array of email addresses
-    notify_webhooks = Column(JSON, nullable=True)  # Array of webhook URLs (Slack, etc.)
-    
-    # Status
-    status = Column(String, nullable=False, default=AlertStatus.ACTIVE.value)
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-    
-    # Relationships
-    alert_history = relationship("AlertHistory", back_populates="alert", cascade="all, delete-orphan")
-
-
-class AlertHistory(Base):
-    """Alert history model for tracking triggered alerts."""
-    __tablename__ = "alert_history"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    alert_id = Column(UUID(as_uuid=True), ForeignKey("alerts.id"), nullable=False, index=True)
-    
-    # Trigger information
-    triggered_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
-    triggered_value = Column(Float, nullable=False)  # The actual value that triggered the alert
-    threshold_value = Column(Float, nullable=False)  # The threshold at time of trigger
-    
-    # Status tracking
-    status = Column(String, nullable=False, default=AlertHistoryStatus.TRIGGERED.value)
-    
-    # Notification tracking
-    notified_at = Column(DateTime(timezone=True), nullable=True)
-    notification_details = Column(JSON, nullable=True)  # Details of sent notifications
-    
-    # Resolution
-    acknowledged_at = Column(DateTime(timezone=True), nullable=True)
-    acknowledged_by = Column(String, nullable=True)
-    resolved_at = Column(DateTime(timezone=True), nullable=True)
-    resolved_by = Column(String, nullable=True)
-    resolution_notes = Column(String, nullable=True)
-    
-    # Additional context
-    context_data = Column(JSON, nullable=True)  # Additional data about the trigger
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    
-    # Relationships
-    alert = relationship("Alert", back_populates="alert_history")
-
-
-class CronJob(Base):
-    """Cron job model for scheduling automated evaluator runs."""
-    __tablename__ = "cron_jobs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=True, index=True)
-    
-    # Basic information
-    name = Column(String(255), nullable=False)
-    job_type = Column(String(64), nullable=False, default="evaluator_run")
-    is_system = Column(Boolean, nullable=False, default=False)
-    config = Column(JSON, nullable=False, default=dict)
-    cron_expression = Column(String(100), nullable=False)  # e.g., "0 9 * * 1-5"
-    timezone = Column(String(100), nullable=False, default="UTC")
-    
-    # Run configuration
-    max_runs = Column(Integer, nullable=False, default=10)
-    current_runs = Column(Integer, nullable=False, default=0)
-    
-    # Evaluators to trigger (JSON array of evaluator UUIDs)
-    evaluator_ids = Column(JSON, nullable=False)
-    
-    # Status
-    status = Column(String, nullable=False, default=CronJobStatus.ACTIVE.value)
-    
-    # Run tracking
-    next_run_at = Column(DateTime(timezone=True), nullable=True)
-    last_run_at = Column(DateTime(timezone=True), nullable=True)
-    
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-
-class TTSComparisonStatus(str, enum.Enum):
-    PENDING = "pending"
-    GENERATING = "generating"
-    EVALUATING = "evaluating"
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-
-class TTSSampleStatus(str, enum.Enum):
-    PENDING = "pending"
-    GENERATING = "generating"
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-
-class TTSReportJobStatus(str, enum.Enum):
-    PENDING = "pending"
-    PROCESSING = "processing"
-    COMPLETED = "completed"
-    FAILED = "failed"
-
-
-class TTSComparison(Base):
-    """TTS Comparison session for A/B testing voice providers."""
-    __tablename__ = "tts_comparisons"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every voice playground comparison belongs to
-    # a workspace within its org. Children (samples, report jobs, blind
-    # test shares) inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    simulation_id = Column(String(6), unique=True, index=True, nullable=True)
-
-    name = Column(String(255), nullable=True)
-    status = Column(String(50), nullable=False, default=TTSComparisonStatus.PENDING.value)
-
-    # 'benchmark' = traditional TTS A/B benchmark (provider-generated audio).
-    # 'blind_test_only' = standalone blind test built from existing recordings
-    # / uploads / past TTS samples; no TTS generation happens.
-    mode = Column(String(32), nullable=False, default="benchmark")
-
-    provider_a = Column(String(100), nullable=True)
-    model_a = Column(String(100), nullable=True)
-    voices_a = Column(JSON, nullable=True)
-
-    provider_b = Column(String(100), nullable=True)
-    model_b = Column(String(100), nullable=True)
-    voices_b = Column(JSON, nullable=True)
-
-    sample_texts = Column(JSON, nullable=False)
-    num_runs = Column(Integer, nullable=False, default=1)
-
-    blind_test_results = Column(JSON, nullable=True)
-    evaluation_summary = Column(JSON, nullable=True)
-
-    eval_stt_provider = Column(String(100), nullable=True)
-    eval_stt_model = Column(String(100), nullable=True)
-
-    celery_task_id = Column(String, nullable=True, index=True)
-    error_message = Column(String, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    samples = relationship("TTSSample", back_populates="comparison", cascade="all, delete-orphan")
-
-
-class TTSSample(Base):
-    """Individual TTS audio sample within a comparison."""
-    __tablename__ = "tts_samples"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent TTSComparison's workspace.
-    # Denormalized for fast filter-by-workspace listings without a join.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    provider = Column(String(100), nullable=True)
-    model = Column(String(100), nullable=True)
-    voice_id = Column(String(255), nullable=True)
-    voice_name = Column(String(255), nullable=True)
-    side = Column(String(1), nullable=True)  # "A" or "B"
-    sample_index = Column(Integer, nullable=False)
-    run_index = Column(Integer, nullable=False, default=0)
-
-    # 'tts' (default, audio is synthesized by a provider), 'recording' (audio
-    # is reused from a CallImportRow recording), or 'upload' (audio was
-    # uploaded by the user). Non-tts samples are marked completed up-front
-    # by the API and skipped by the generation worker.
-    source_type = Column(String(32), nullable=False, default="tts")
-    # When source_type == 'recording', references CallImportRow.id (no FK
-    # constraint to keep cascading deletes simple if a call import is later
-    # removed; the audio_s3_key is what's actually used).
-    source_ref_id = Column(UUID(as_uuid=True), nullable=True)
-
-    text = Column(String, nullable=False)
-    audio_s3_key = Column(String(512), nullable=True)
-    duration_seconds = Column(Float, nullable=True)
-    latency_ms = Column(Float, nullable=True)
-    ttfb_ms = Column(Float, nullable=True)
-
-    evaluation_metrics = Column(JSON, nullable=True)
-    status = Column(String(50), nullable=False, default=TTSSampleStatus.PENDING.value)
-    error_message = Column(String, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    comparison = relationship("TTSComparison", back_populates="samples")
-
-
-class TTSReportJob(Base):
-    """Asynchronous PDF report generation jobs for Voice Playground."""
-    __tablename__ = "tts_report_jobs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent TTSComparison's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
-
-    status = Column(String(50), nullable=False, default=TTSReportJobStatus.PENDING.value)
-    format = Column(String(20), nullable=False, default="pdf")
-    filename = Column(String(255), nullable=True)
-    s3_key = Column(String(512), nullable=True)
-    error_message = Column(String, nullable=True)
-    celery_task_id = Column(String, nullable=True, index=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    comparison = relationship("TTSComparison")
-
-
-class TTSBlindTestShareStatus(str, enum.Enum):
-    OPEN = "open"
-    CLOSED = "closed"
-
-
-class TTSBlindTestShare(Base):
-    """A publicly sharable blind test for a TTSComparison.
-
-    The share_token is the capability: anyone holding it can open the public
-    form and submit a response. Each comparison has at most one share row.
-    """
-    __tablename__ = "tts_blind_test_shares"
-    __table_args__ = (
-        UniqueConstraint("comparison_id", name="uq_blind_test_shares_comparison"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    comparison_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("tts_comparisons.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent TTSComparison's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    share_token = Column(String(64), unique=True, nullable=False, index=True)
-
-    title = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-
-    # Internal notes visible only to the share creator (e.g. which voice
-    # corresponds to which side, source notes for standalone blind tests).
-    # Never exposed via the public blind test payload.
-    creator_notes = Column(Text, nullable=True)
-
-    # JSON list: [{ "key": str, "label": str, "type": "rating"|"comment", "scale": int? }]
-    custom_metrics = Column(JSON, nullable=False)
-
-    status = Column(String(20), nullable=False, default=TTSBlindTestShareStatus.OPEN.value)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    closed_at = Column(DateTime(timezone=True), nullable=True)
-    created_by = Column(String, nullable=True)
-
-    comparison = relationship("TTSComparison")
-    responses = relationship(
-        "TTSBlindTestResponse",
-        back_populates="share",
-        cascade="all, delete-orphan",
-    )
-
-
-class TTSBlindTestResponse(Base):
-    """A single rater's submission against a TTSBlindTestShare."""
-    __tablename__ = "tts_blind_test_responses"
-    __table_args__ = (
-        UniqueConstraint("share_id", "rater_email", name="uq_blind_test_response_share_email"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    share_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("tts_blind_test_shares.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    # Workspace isolation: mirrors the parent TTSBlindTestShare's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    rater_name = Column(String(255), nullable=False)
-    rater_email = Column(String(320), nullable=False, index=True)
-
-    # JSON list keyed by sample_index. Server stores in TRUE A/B orientation
-    # (already de-flipped from whatever the rater's UI showed):
-    # [{
-    #   "sample_index": int,
-    #   "preferred": "A" | "B",
-    #   "ratings_a": { metric_key: number },
-    #   "ratings_b": { metric_key: number },
-    #   "comment": str?
-    # }]
-    responses = Column(JSON, nullable=False)
-
-    ip = Column(String(64), nullable=True)
-    user_agent = Column(String(512), nullable=True)
-
-    submitted_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    share = relationship("TTSBlindTestShare", back_populates="responses")
-
-
-class PromptPartial(Base):
-    """Prompt Partial - Reusable prompt templates with version history."""
-    __tablename__ = "prompt_partials"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every prompt partial belongs to a workspace
-    # within its org. Versions inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    description = Column(String, nullable=True)
-    content = Column(Text, nullable=False)
-    tags = Column(JSON, nullable=True)
-    current_version = Column(Integer, nullable=False, default=1)
-    # Cached LLM-generated flowchart for imported production agent prompts.
-    # Shape: AgentFlowGraph JSON (nodes[], edges[]).
-    agent_flowchart = Column(JSON, nullable=True)
-    agent_flowchart_status = Column(String(20), nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    versions = relationship("PromptPartialVersion", back_populates="prompt_partial", cascade="all, delete-orphan", order_by="PromptPartialVersion.version.desc()")
-
-
-class PromptPartialVersion(Base):
-    """Version history for a prompt partial."""
-    __tablename__ = "prompt_partial_versions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    prompt_partial_id = Column(UUID(as_uuid=True), ForeignKey("prompt_partials.id", ondelete="CASCADE"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent PromptPartial's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    version = Column(Integer, nullable=False)
-    content = Column(Text, nullable=False)
-    change_summary = Column(String, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    created_by = Column(String, nullable=True)
-
-    prompt_partial = relationship("PromptPartial", back_populates="versions")
-
-    __table_args__ = (
-        UniqueConstraint('prompt_partial_id', 'version', name='uq_prompt_partial_version'),
-    )
-
-
-class CustomTTSVoice(Base):
-    """Organization-scoped custom TTS voice metadata."""
-    __tablename__ = "custom_tts_voices"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "provider", "voice_id", name="uq_custom_tts_voice_org_provider_voice_id"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider = Column(String(100), nullable=False, index=True)
-    voice_id = Column(String(255), nullable=False)
-    name = Column(String(255), nullable=False)
-    gender = Column(String(50), nullable=True)
-    accent = Column(String(100), nullable=True)
-    description = Column(Text, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-class PromptOptimizationRun(Base):
-    """A single GEPA prompt optimization run for an agent."""
-    __tablename__ = "prompt_optimization_runs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every optimization run belongs to a workspace
-    # within its org. Candidates inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
-    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True)
-    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
-
-    seed_prompt = Column(Text, nullable=False)
-    best_prompt = Column(Text, nullable=True)
-    best_score = Column(Float, nullable=True)
-
-    status = Column(String(20), nullable=False, default=PromptOptimizationStatus.PENDING.value)
-    config = Column(JSON, nullable=True)
-    reflection_trace = Column(JSON, nullable=True)
-    metric_history = Column(JSON, nullable=True)
-
-    num_iterations = Column(Integer, nullable=True)
-    num_metric_calls = Column(Integer, nullable=True)
-
-    celery_task_id = Column(String, nullable=True, index=True)
-    error_message = Column(Text, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    candidates = relationship("PromptOptimizationCandidate", back_populates="optimization_run", cascade="all, delete-orphan")
-
-
-class PromptOptimizationCandidate(Base):
-    """A candidate prompt generated during an optimization run."""
-    __tablename__ = "prompt_optimization_candidates"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    optimization_run_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_runs.id", ondelete="CASCADE"), nullable=False, index=True)
-    # Workspace isolation: mirrors the parent PromptOptimizationRun's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    prompt_text = Column(Text, nullable=False)
-    score = Column(Float, nullable=True)
-    metric_breakdown = Column(JSON, nullable=True)
-    reflection_summary = Column(Text, nullable=True)
-
-    parent_candidate_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_candidates.id"), nullable=True)
-
-    is_accepted = Column(Boolean, nullable=False, default=False)
-    pushed_to_provider_at = Column(DateTime(timezone=True), nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-    optimization_run = relationship("PromptOptimizationRun", back_populates="candidates")
-
-
-class TelephonyIntegration(Base):
-    """Per-organization telephony provider credentials and configuration.
-
-    Multiple rows per (organization_id, provider) are allowed so that an
-    organization can keep several Plivo / Exotel accounts side-by-side.
-    A partial unique index in migration 028 enforces at most one row with
-    is_default = TRUE per (org, provider); resolution falls back to that
-    default row when the caller does not pin a specific credential.
-    """
-
-    __tablename__ = "telephony_integrations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider = Column(String(50), nullable=False, default="plivo")
-    name = Column(String(255), nullable=True)  # Optional friendly name to disambiguate multiple credentials
-
-    auth_id = Column(String(255), nullable=False)
-    auth_token = Column(String(512), nullable=False)
-
-    verify_app_uuid = Column(String(255), nullable=True)
-    voice_app_id = Column(String(255), nullable=True)
-    sip_domain = Column(String(255), nullable=True)
-    masking_config = Column(JSON, nullable=True)
-
-    is_active = Column(Boolean, default=True, nullable=False)
-    is_default = Column(Boolean, default=False, nullable=False)
-    last_tested_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyPhoneNumber(Base):
-    """Inventory of telephony phone numbers owned by an organization."""
-
-    __tablename__ = "telephony_phone_numbers"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_number_org_phone"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    telephony_integration_id = Column(
-        UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=True, index=True
-    )
-
-    phone_number = Column(String(20), nullable=False, index=True)
-    country_iso2 = Column(String(2), nullable=True)
-    region = Column(String(100), nullable=True)
-    number_type = Column(String(20), nullable=True)
-    capabilities = Column(JSON, nullable=True)
-    provider_app_id = Column(String(255), nullable=True)
-
-    is_masking_pool = Column(Boolean, default=False, nullable=False)
-    inbound_enabled = Column(Boolean, default=True, nullable=False)
-    outbound_enabled = Column(Boolean, default=True, nullable=False)
-    source = Column(String(20), nullable=False, default="imported")
-    agent_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey(
-            "agents.id",
-            ondelete="SET NULL",
-            use_alter=True,
-            name="fk_telephony_phone_numbers_agent_id",
-        ),
-        nullable=True,
-        index=True,
-    )
-    is_active = Column(Boolean, default=True, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyDialTarget(Base):
-    """Org-scoped saved destination numbers for outbound test calls."""
-
-    __tablename__ = "telephony_dial_targets"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_dial_target_org_phone"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    phone_number = Column(String(20), nullable=False, index=True)
-    label = Column(String(255), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyVerifySession(Base):
-    """Tracks voice OTP verification sessions via telephony provider."""
-
-    __tablename__ = "telephony_verify_sessions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    provider_session_uuid = Column(String(255), nullable=False, unique=True, index=True)
-    recipient_number = Column(String(20), nullable=False)
-    channel = Column(String(10), nullable=False, default="voice")
-    status = Column(String(20), nullable=False, default="pending")
-    initiated_by = Column(String(255), nullable=True)
-    verify_app_uuid = Column(String(255), nullable=True)
-    verified_at = Column(DateTime(timezone=True), nullable=True)
-    expires_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class TelephonyMaskedSession(Base):
-    """Number-masking session between two parties through a middle number."""
-
-    __tablename__ = "telephony_masked_sessions"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    telephony_integration_id = Column(UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=False)
-    masked_number_id = Column(
-        UUID(as_uuid=True), ForeignKey("telephony_phone_numbers.id"), nullable=False, index=True
-    )
-    masked_number = Column(String(20), nullable=False)
-    party_a_number = Column(String(20), nullable=False)
-    party_b_number = Column(String(20), nullable=False)
-    status = Column(String(20), nullable=False, default="active")
-    expires_at = Column(DateTime(timezone=True), nullable=True)
-    ended_at = Column(DateTime(timezone=True), nullable=True)
-    session_metadata = Column("metadata", JSON, nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-
-class CallImportSchema(Base):
-    """Reusable Input Parameter schema for the call-uploads flow.
-
-    A schema is workspace-scoped: users define a named bundle of typed
-    Input Parameters once (e.g. "Standard Voice QA" with conversation_id +
-    recording_url + transcript + agent_name) and then map those parameters
-    to CSV/Excel headers each time they upload a new batch.
-
-    Every schema MUST contain exactly one parameter with
-    ``type='conversation_id'`` and ``is_required=True`` - that's the
-    mandatory identity field every imported row needs. A schema may
-    optionally include at most one ``recording_url`` parameter. The
-    invariant is enforced in app code on create/update (no DB-level
-    CHECK because the parent + children are written across two tables in
-    one transaction).
-    """
-
-    __tablename__ = "call_import_schemas"
-    __table_args__ = (
-        # Case-insensitive uniqueness is enforced via the matching partial
-        # index on ``LOWER(name)`` in the migration; this constraint here
-        # would be case-sensitive and is intentionally omitted to avoid
-        # confusing the user.
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-    created_by_user_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("users.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    parameters = relationship(
-        "CallImportSchemaParameter",
-        back_populates="schema",
-        cascade="all, delete-orphan",
-        order_by="CallImportSchemaParameter.ordering",
-    )
-
-
-class CallImportSchemaParameter(Base):
-    """A single typed parameter inside a :class:`CallImportSchema`.
-
-    ``type`` is one of the strings tracked by
-    :data:`app.models.enums.CallImportParameterType`. ``conversation_id``
-    is reserved for the mandatory identity parameter every schema must
-    contain.
-    """
-
-    __tablename__ = "call_import_schema_parameters"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    schema_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_schemas.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    name = Column(String(255), nullable=False)
-    type = Column(String(32), nullable=False)
-    description = Column(Text, nullable=True)
-    is_required = Column(Boolean, nullable=False, default=False)
-    # Stable ordering so the UI renders parameters in the order the
-    # schema author defined them (matters when conversation_id is pinned
-    # first and the user re-orders the rest).
-    ordering = Column(Integer, nullable=False, default=0)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    schema = relationship("CallImportSchema", back_populates="parameters")
-
-
-class CallImport(Base):
-    """Batch record for a CSV-driven call import job."""
-
-    __tablename__ = "call_imports"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    # Workspace isolation: every imported batch belongs to a workspace
-    # within its org. The /upload endpoint stamps it from the active
-    # workspace header (or the org's Default if absent).
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    created_by_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True)
-    last_updated_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
-    )
-
-    # Telephony provider key (e.g. ``'exotel'``, ``'plivo'``). In the
-    # legacy one-shot ``POST /upload`` endpoint this is supplied with the
-    # file; in the three-stage flow (UPLOAD -> MAP -> IMPORT) the value
-    # isn't known until the IMPORT stage, so the column is nullable for
-    # ``uploaded`` / ``mapped`` batches.
-    provider = Column(String(50), nullable=True, default="exotel")
-    # Pin a specific telephony credential for this batch so the worker
-    # downloads recordings using *that* row instead of the org default.
-    # NULL preserves legacy behavior (resolve by provider + default).
-    telephony_integration_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("telephony_integrations.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-    original_filename = Column(String(512), nullable=True)
-    # When the source file was a multi-sheet Excel workbook, this records
-    # the worksheet the rows came from (one batch per sheet). NULL for CSV
-    # uploads since CSV has no sheet concept.
-    sheet_name = Column(String(255), nullable=True)
-
-    # --- Source-file staging (UPLOAD stage) ---------------------------
-    # The raw CSV / Excel file is stored in S3 between stages so the
-    # user can come back later to MAP and IMPORT without re-uploading.
-    # ``source_s3_key`` is NULL on legacy batches that were imported via
-    # the one-shot endpoint (those batches stay read-only post-import).
-    source_s3_key = Column(Text, nullable=True)
-    source_format = Column(String(16), nullable=True)
-    source_size_bytes = Column(BigInteger, nullable=True)
-    source_content_type = Column(String(255), nullable=True)
-
-    # Snapshot of the file's sheets + headers captured at UPLOAD time
-    # so the MAP UI doesn't need to re-fetch the source bytes from S3.
-    # Shape: ``[{"name": str, "headers": [str, ...], "row_count": int}, ...]``.
-    available_sheets = Column(JSON, nullable=True)
-
-    # User's explicit "drop these columns" decision captured at MAP
-    # time. Was validation-only and ephemeral in the legacy flow; now
-    # persisted so the IMPORT stage can re-parse the file with the same
-    # mapping/skip intent.
-    skipped_columns = Column(JSON, nullable=False, default=list)
-    # Rows skipped at parse time (missing/invalid conversation_id or URL).
-    # Shape: ``[{"source_row": int, "reason": str, "message": str}, ...]``.
-    source_row_skips = Column(JSON, nullable=False, default=list)
-
-    # Free-text high-level segregation label. Powers the "Dataset" filter
-    # at the top of the imports page; multiple imports can share a value.
-    dataset = Column(String(255), nullable=True, index=True)
-
-    # Reusable Input Parameter schema this batch was uploaded against.
-    # NULL on legacy batches uploaded before the schema-driven flow
-    # shipped; those still render via ``column_mapping`` + ``extra_columns``
-    # + ``custom_column_mapping`` below.
-    schema_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_schemas.id", ondelete="RESTRICT"),
-        nullable=True,
-        index=True,
-    )
-    # New schema-driven mapping: ``{schema_parameter_name: csv_header}``.
-    # Populated for new uploads; empty dict on legacy batches.
-    parameter_mapping = Column(JSON, nullable=False, default=dict)
-
-    # Legacy free-form mapping (pre-schema-flow). Kept on the model so
-    # batches that were uploaded before the schema feature shipped still
-    # render correctly on the detail page; new uploads stop writing here.
-    # Keys: external_call_id (required), transcript, recording_url.
-    # (DB column ``external_call_id`` is now ``conversation_id``; this
-    # JSON key stays as-is for historical batches.)
-    # Values: original CSV header strings (preserve user casing for export).
-    column_mapping = Column(JSON, nullable=False, default=dict)
-    # Ordered list of additional CSV header strings the uploader wants
-    # preserved verbatim into the evaluation export CSV.
-    extra_columns = Column(JSON, nullable=False, default=list)
-    # User-defined ``{custom_field_name: csv_header}`` mappings on top of
-    # the three system fields above. Cells from the mapped CSV columns are
-    # preserved per row (keyed by the CSV header in ``raw_columns``) and
-    # surface in the evaluation export under the uploader-chosen name.
-    custom_column_mapping = Column(JSON, nullable=False, default=dict)
-
-    total_rows = Column(Integer, nullable=False, default=0)
-    completed_rows = Column(Integer, nullable=False, default=0)
-    failed_rows = Column(Integer, nullable=False, default=0)
-
-    status = Column(
-        Enum(CallImportStatus, values_callable=get_enum_values),
-        nullable=False,
-        default=CallImportStatus.PENDING,
-        index=True,
-    )
-    error_message = Column(Text, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    rows = relationship(
-        "CallImportRow",
-        back_populates="call_import",
-        cascade="all, delete-orphan",
-        order_by="CallImportRow.row_index",
-    )
-    tags = relationship(
-        "CallImportTag",
-        secondary="call_import_tag_assignments",
-        backref="call_imports",
-        lazy="selectin",
-    )
-    evaluations = relationship(
-        "CallImportEvaluation",
-        back_populates="call_import",
-        cascade="all, delete-orphan",
-    )
-
-
-class CallImportShardSlice(Base):
-    """Registry row: which shard stores a slice of rows for an import."""
-
-    __tablename__ = "call_import_shard_slices"
-
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        primary_key=True,
-    )
-    slice_id = Column(Integer, primary_key=True)
-    shard_id = Column(String(64), nullable=False, index=True)
-    row_index_min = Column(Integer, nullable=False)
-    row_index_max = Column(Integer, nullable=False)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-
-class CallImportRow(Base):
-    """A single row within a CallImport batch (one CSV line / one external call)."""
-
-    __tablename__ = "call_import_rows"
-    __table_args__ = (
-        UniqueConstraint("call_import_id", "row_index", name="uq_call_import_row_index"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    row_index = Column(Integer, nullable=False)
-    # Was historically named ``external_call_id``; renamed to
-    # ``conversation_id`` so the new schema-driven upload flow can refer
-    # to it by a single canonical name across the schema definition,
-    # exports, and downstream evaluation tables.
-    conversation_id = Column(String(255), nullable=False, index=True)
-    # Supplied via CSV for Exotel credentialed imports (required per row).
-    # Nullable in the schema for legacy rows imported before recording_url
-    # was mandatory on every Exotel upload.
-    recording_url = Column(Text, nullable=True)
-    # Date-only call recording date supplied by the import schema. Used
-    # for historical report comparisons without timezone/time ambiguity.
-    recording_date = Column(Date, nullable=True, index=True)
-    # The "production" transcript: the value supplied via the CSV
-    # upload mapping. Never overwritten by the diarisation worker —
-    # the worker writes its output into ``diarised_transcript`` so
-    # the user keeps both versions side by side.
-    transcript = Column(Text, nullable=True)
-    # Snapshot of the original CSV row keyed by the user's headers so the
-    # evaluation export can reproduce every column the uploader supplied
-    # (mapped + extra). NULL on legacy rows imported before this column.
-    raw_columns = Column(JSON, nullable=True)
-
-    # Where the value in ``transcript`` came from. ``csv`` = supplied via
-    # the upload mapping, ``edited`` = manually changed in the UI. NULL
-    # on rows that have never had a production transcript.
-    # (Worker-produced transcripts now live in ``diarised_transcript``
-    # and are tracked via ``diarised_transcript_*`` metadata below.)
-    transcript_source = Column(String(20), nullable=True)
-    # Provider/model recorded by the (legacy) post-hoc transcription
-    # worker. New worker runs leave these NULL and write into the
-    # ``diarised_transcript_*`` columns instead; kept on the model for
-    # backwards compatibility with pre-split rows that still carry the
-    # original transcription metadata here.
-    transcript_provider = Column(String(50), nullable=True)
-    transcript_model = Column(String(100), nullable=True)
-    # Lifecycle status for the legacy transcription workflow itself,
-    # independent of the row's recording-fetch ``status``. ``idle`` =
-    # no transcribe task has touched this column. New diarisation runs
-    # update ``diarised_transcript_status`` instead.
-    transcript_status = Column(
-        String(20),
-        nullable=False,
-        default="idle",
-    )
-    transcript_error = Column(Text, nullable=True)
-    transcribed_at = Column(DateTime(timezone=True), nullable=True)
-
-    # The "diarised" transcript: produced by the post-hoc
-    # transcription/diarisation worker. Stored separately so a manual
-    # diarisation run never clobbers the production transcript above.
-    # Evaluations can be configured to score against either column
-    # (see ``CallImportEvaluation.transcript_source``).
-    diarised_transcript = Column(Text, nullable=True)
-    # Provider/model the diarisation worker used. Surfaced in the UI
-    # as "Diarised via deepgram/nova-2" next to the diarised
-    # transcript section.
-    diarised_transcript_provider = Column(String(50), nullable=True)
-    diarised_transcript_model = Column(String(100), nullable=True)
-    # Lifecycle status for the diarisation workflow.
-    # ``idle`` = no diarisation task has run; ``pending``/``running`` =
-    # a Celery task is queued or in flight; ``completed``/``failed`` =
-    # terminal. Independent of ``transcript_status`` so the two
-    # transcripts can be in different lifecycle states.
-    diarised_transcript_status = Column(
-        String(20),
-        nullable=False,
-        default="idle",
-        server_default="idle",
-    )
-    diarised_transcript_error = Column(Text, nullable=True)
-    diarised_at = Column(DateTime(timezone=True), nullable=True)
-
-    # Structured speaker turns produced by the diarisation worker —
-    # ``[{ "speaker": "agent"|"user"|"speaker_3", "text": "...",
-    #      "start": float, "end": float, "raw_speaker": "Speaker 1" }, ...]``
-    # The plain-text ``diarised_transcript`` above is a rendered view
-    # of this list (``<speaker>: <text>`` per line). When the worker
-    # cannot recover structured turns (no pyannote token / single-
-    # speaker recording / provider that doesn't surface segments) this
-    # column stays NULL and the plain-text path is still populated.
-    diarised_segments = Column(JSON, nullable=True)
-    # When True the ``agent`` <-> ``user`` mapping inside
-    # ``diarised_segments`` is inverted at render / export time. The
-    # worker writes the canonical mapping using the "first speaker is
-    # the agent" heuristic; reviewers can flip the toggle from the row
-    # detail panel without re-running diarisation.
-    diarised_speaker_swap = Column(
-        Boolean,
-        nullable=False,
-        default=False,
-        server_default="false",
-    )
-    # LLM that turned the STT plain-text output into structured
-    # ``diarised_segments``. The legacy diarisation worker used
-    # pyannote and left these NULL; the current path always runs an
-    # LLM with the operator-supplied (or default) ``diarised_prompt``
-    # below, and records exactly which model + prompt produced each
-    # row so reviewers can reproduce a specific run.
-    diarised_llm_provider = Column(String(50), nullable=True)
-    diarised_llm_model = Column(String(100), nullable=True)
-    diarised_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
-    diarised_prompt = Column(Text, nullable=True)
-    # Which diarisation pipeline produced this row's turns.
-    #   * ``"stt_llm"`` (default) — two-stage: STT then LLM diariser.
-    #     ``diarised_transcript_provider``/``_model`` describe the STT
-    #     side; ``diarised_llm_provider``/``_model`` the LLM side.
-    #   * ``"llm_only"`` — single-stage: audio fed straight to a
-    #     multimodal LLM. ``diarised_transcript_provider`` is stamped
-    #     with the sentinel ``"llm_only"``; the real model is on
-    #     ``diarised_llm_*``.
-    # Persisting it on the row (not just the run) lets the row detail
-    # panel render the right "Diarised via …" label even for ad-hoc
-    # standalone transcribes (no parent evaluation).
-    transcribe_mode = Column(
-        String(20),
-        nullable=False,
-        default="stt_llm",
-        server_default="stt_llm",
-    )
-
-    status = Column(
-        Enum(CallImportRowStatus, values_callable=get_enum_values),
-        nullable=False,
-        default=CallImportRowStatus.PENDING,
-        index=True,
-    )
-
-    recording_s3_key = Column(String(1024), nullable=True)
-    recording_content_type = Column(String(128), nullable=True)
-    recording_size_bytes = Column(Integer, nullable=True)
-
-    error_message = Column(Text, nullable=True)
-    attempts = Column(Integer, nullable=False, default=0)
-    celery_task_id = Column(String(255), nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    call_import = relationship("CallImport", back_populates="rows")
-
-
-@event.listens_for(CallImportRow, "before_insert")
-def _call_import_row_fill_workspace_id(_mapper, connection, target):
-    """Denormalize workspace_id from the parent import when omitted."""
-    if target.workspace_id is not None or target.call_import_id is None:
-        return
-    workspace_id = connection.execute(
-        select(CallImport.workspace_id).where(
-            CallImport.id == target.call_import_id
-        )
-    ).scalar_one_or_none()
-    if workspace_id is not None:
-        target.workspace_id = workspace_id
-
-
-class CallImportTag(Base):
-    """User-defined tag that can be attached to one or more call imports.
-
-    Tags coexist with the free-text ``CallImport.dataset`` column: dataset
-    is the primary high-level segregation, tags are an optional secondary
-    classification (an import can have many tags).
-    """
-
-    __tablename__ = "call_import_tags"
-    __table_args__ = (
-        UniqueConstraint("organization_id", "name", name="uq_call_import_tag_org_name"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    name = Column(String(255), nullable=False)
-    color = Column(String(32), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-
-class CallImportTagAssignment(Base):
-    """Many-to-many join table between CallImport and CallImportTag."""
-
-    __tablename__ = "call_import_tag_assignments"
-
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        primary_key=True,
-    )
-    tag_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_tags.id", ondelete="CASCADE"),
-        primary_key=True,
-        index=True,
-    )
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-
-class CallImportEvaluation(Base):
-    """Parent record for an evaluation run over a CallImport batch.
-
-    A user picks a subset of org ``Metric`` rows and triggers an evaluation;
-    we fan out one ``CallImportEvaluationRow`` per source row and roll up
-    counters as workers finish. Status mirrors ``CallImportStatus`` plus a
-    ``RUNNING`` value so the UI can distinguish "queued" from "in flight".
-    """
-
-    __tablename__ = "call_import_evaluations"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id"),
-        nullable=False,
-        index=True,
-    )
-    # Workspace isolation: mirrors the parent CallImport's workspace.
-    # Denormalized for fast filter-by-workspace listings without a join.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    created_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id"), nullable=True
-    )
-    last_updated_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
-    )
-
-    # Optional user-supplied label for this run. Lets the UI surface
-    # something more meaningful than the UUID prefix (e.g. "March QA pass").
-    name = Column(String(255), nullable=True)
-
-    # JSON list of Metric UUID strings selected for this run. Stored as text
-    # in JSON so we don't have to deal with PG arrays of UUIDs / cascade
-    # delete policies when metrics are removed; the loader filters for
-    # still-existing org metrics at run time.
-    selected_metric_ids = Column(JSON, nullable=False, default=list)
-    # Hierarchy grouping snapshot: ``{parent_id_str: [child_id_str, ...]}``.
-    # Captures which children belong to which parent for THIS run so the UI
-    # / aggregator can reconstruct the tree even when the user selected
-    # only a subset of children, or after metrics are deleted / renamed.
-    # NULL on legacy rows means "no hierarchy" → fall back to flat
-    # ``selected_metric_ids`` semantics.
-    selected_metric_groups = Column(JSON, nullable=True)
-    # User-driven merges of LLM-discovered candidate sub-labels for
-    # ``allow_discovery`` parents. Shape:
-    # ``{"<parent_metric_id>": {"<from_slug>": "<to_slug>", ...}}``.
-    # Populated via ``POST .../discovered-labels/merge``; consulted by
-    # the discovered-labels aggregator, the flow graph builder, and the
-    # worker so that rows finishing AFTER a merge cannot reintroduce
-    # the merged-away slug. Empty dict on fresh rows.
-    discovered_label_aliases = Column(
-        JSON, nullable=False, default=dict, server_default="{}"
-    )
-
-    # Per-run opt-in for top-level metric discovery. When True, the LLM
-    # is asked to propose brand-new top-level metrics (boolean / rating /
-    # category) observed in the transcripts in addition to scoring the
-    # ``selected_metric_ids`` for the row. Candidates surface in a
-    # "Discovered metrics" panel on the evaluation's Flow tab and can
-    # be promoted into real standalone ``Metric`` rows via
-    # ``POST /metrics/from-discovered``. Defaults to False so existing
-    # evaluation creation payloads keep their previous behaviour.
-    discover_new_metrics = Column(
-        Boolean, nullable=False, default=False, server_default="false"
-    )
-    # Flat slug-to-slug redirect map for user merges + tombstones of
-    # discovered top-level metric candidates. Mirrors
-    # ``discovered_label_aliases`` but is NOT nested per parent —
-    # top-level metric discovery is not scoped to any parent. Shape::
-    #
-    #     {"<from_slug>": "<to_slug>", ...}
-    #
-    # An empty-string value tombstones the slug so workers finishing
-    # later can't re-introduce it.
-    discovered_metric_aliases = Column(
-        JSON, nullable=False, default=dict, server_default="{}"
-    )
-
-    # Run-level LLM config picked from the Run Evaluation modal. NULL on
-    # legacy rows means "use the historical OpenAI/gpt-4o default" — the
-    # worker checks for this and falls back accordingly. ``llm_credential_id``
-    # pins a specific AIProvider row when the org has multiple credentials
-    # for the same provider.
-    llm_provider = Column(String(50), nullable=True)
-    llm_model = Column(String(100), nullable=True)
-    llm_credential_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("aiproviders.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-    llm_config = Column(JSON, nullable=True)
-    # Optional per-metric LLM override:
-    # ``{"<metric_id>": {"provider": "...", "model": "...", "credential_id": "..."}}``.
-    # Each entry overrides the run-level default for that metric only;
-    # missing keys = use run-level default. Stored as JSON so the UI can
-    # round-trip arbitrary {provider, model} pairs without migrations.
-    metric_llm_overrides = Column(JSON, nullable=True)
-
-    # When ``auto_transcribe`` was set on the create payload, record the
-    # STT provider/model used so the UI can show "Auto-transcribed via
-    # deepgram/nova-2" on the evaluation header. ``stt_credential_id`` is
-    # untyped (no FK) because STT keys may live in either ``aiproviders``
-    # (OpenAI) or ``integrations`` (Deepgram, ElevenLabs) — the
-    # transcription service handles the lookup.
-    stt_provider = Column(String(50), nullable=True)
-    stt_model = Column(String(100), nullable=True)
-    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
-
-    # Run-level LLM diariser config. Used when the create-run /
-    # retry-run paths chain a ``transcribe_call_import_row_task``
-    # because the row is missing a diarised transcript. Persisted on
-    # the run so a retry uses the same diariser the original create
-    # call picked (unless the retry payload explicitly overrides).
-    diarisation_llm_provider = Column(String(50), nullable=True)
-    diarisation_llm_model = Column(String(100), nullable=True)
-    diarisation_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
-    diarisation_prompt = Column(Text, nullable=True)
-    # Mode the run was *created* with for its auto-transcribe step.
-    # Retry chains read this to decide whether to enqueue an STT+LLM
-    # transcribe or a single-stage multimodal LLM transcribe — without
-    # it we'd have to infer the mode from "stt_provider is NULL", which
-    # would silently break legacy rows that simply never configured
-    # auto-transcribe. See migration 041 for the column DDL.
-    transcribe_mode = Column(
-        String(20),
-        nullable=False,
-        default="stt_llm",
-        server_default="stt_llm",
-    )
-
-    # Which of the two transcripts on each ``CallImportRow`` this run
-    # scored against. ``'production'`` reads ``CallImportRow.transcript``
-    # (the CSV-supplied value); ``'diarised'`` reads
-    # ``CallImportRow.diarised_transcript`` (the worker output). When
-    # the user ticks both checkboxes in the Run Evaluation modal we
-    # create two ``CallImportEvaluation`` rows — one per source — so
-    # the two scorings can be compared side-by-side. Defaults to
-    # ``'production'`` so legacy runs (which always read the single
-    # historical ``transcript`` column) keep their semantics.
-    transcript_source = Column(
-        String(20),
-        nullable=False,
-        default="production",
-        server_default="production",
-    )
-
-    # Cached LLM-generated TLDR rendered above the Visualizations charts.
-    # Populated lazily by ``POST /evaluations/{eval_id}/insights`` so we
-    # never auto-burn LLM tokens on page load. Shape::
-    #   {"narrative": str, "patterns": [str, ...],
-    #    "generated_at": iso8601, "generated_at_completed_rows": int,
-    #    "provider": str, "model": str}
-    # NULL on rows that have never been summarised.
-    tldr_summary = Column(JSON, nullable=True)
-
-    # Cached LLM-generated user insights for External Audit PDF section 03.
-    # Populated by a background Celery job triggered alongside TLDR generation.
-    # Shape: EvaluationUserInsightsState JSON (status, insights[], progress, …).
-    user_insights = Column(JSON, nullable=True)
-
-    # Cached per-metric failure clustering for internal diagnostics PDF/UI.
-    # Shape: EvaluationMetricClustersState JSON (status, groups[], …).
-    metric_clusters = Column(JSON, nullable=True)
-
-    # Cached LLM-generated prompt improvement suggestions keyed to an
-    # imported agent (PromptPartial tagged __imported_agent__).
-    # Shape: EvaluationPromptImprovementsState JSON.
-    prompt_improvements = Column(JSON, nullable=True)
-
-    # Cached LLM explanations for week-over-week metric deltas keyed by
-    # baseline evaluation id + completed row counts.
-    period_delta_explanations = Column(JSON, nullable=True)
-
-    status = Column(String(20), nullable=False, default="pending", index=True)
-
-    total_rows = Column(Integer, nullable=False, default=0)
-    completed_rows = Column(Integer, nullable=False, default=0)
-    failed_rows = Column(Integer, nullable=False, default=0)
-    # Flexprice pass-level delta billing watermark: rows already emitted
-    # on ``call_import.evaluation_completed`` for this evaluation run.
-    billed_completed_rows = Column(
-        Integer, nullable=False, default=0, server_default="0"
-    )
-    error_message = Column(Text, nullable=True)
-    celery_group_id = Column(String(255), nullable=True)
-
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    finished_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    call_import = relationship("CallImport", back_populates="evaluations")
-    row_results = relationship(
-        "CallImportEvaluationRow",
-        back_populates="evaluation",
-        cascade="all, delete-orphan",
-    )
-
-
-class CallImportEvaluationRow(Base):
-    """Per-source-row scoring output for a CallImportEvaluation parent."""
-
-    __tablename__ = "call_import_evaluation_rows"
-    __table_args__ = (
-        UniqueConstraint(
-            "evaluation_id", "call_import_row_id", name="uq_call_import_evaluation_row"
-        ),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    call_import_row_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_rows.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    status = Column(String(20), nullable=False, default="pending", index=True)
-    # Same shape as EvaluatorResult.metric_scores: {metric_id_str: {value, type, metric_name, ...}}
-    metric_scores = Column(JSON, nullable=False, default=dict)
-    error_message = Column(Text, nullable=True)
-    celery_task_id = Column(String(255), nullable=True)
-
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    finished_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    evaluation = relationship("CallImportEvaluation", back_populates="row_results")
-    source_row = relationship("CallImportRow")
-
-
-@event.listens_for(CallImportEvaluationRow, "before_insert")
-def _call_import_evaluation_row_fill_workspace_id(_mapper, connection, target):
-    """Denormalize workspace_id from the parent evaluation when omitted."""
-    if target.workspace_id is not None or target.evaluation_id is None:
-        return
-    workspace_id = connection.execute(
-        select(CallImportEvaluation.workspace_id).where(
-            CallImportEvaluation.id == target.evaluation_id
-        )
-    ).scalar_one_or_none()
-    if workspace_id is not None:
-        target.workspace_id = workspace_id
-
-
-class MetricStudioRun(Base):
-    """Ad-hoc metric experiment run in Metrics Studio."""
-
-    __tablename__ = "metric_studio_runs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    created_by_user_id = Column(
-        UUID(as_uuid=True), ForeignKey("users.id"), nullable=True
-    )
-
-    name = Column(String(255), nullable=True)
-    selected_metric_ids = Column(JSON, nullable=False, default=list)
-    selected_metric_groups = Column(JSON, nullable=True)
-    transcript_source = Column(
-        String(20),
-        nullable=False,
-        default="diarised",
-        server_default="diarised",
-    )
-
-    llm_provider = Column(String(50), nullable=True)
-    llm_model = Column(String(100), nullable=True)
-    llm_credential_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("aiproviders.id", ondelete="SET NULL"),
-        nullable=True,
-    )
-    llm_config = Column(JSON, nullable=True)
-    metric_llm_overrides = Column(JSON, nullable=True)
-
-    status = Column(String(20), nullable=False, default="pending", index=True)
-    total_items = Column(Integer, nullable=False, default=0)
-    completed_items = Column(Integer, nullable=False, default=0)
-    failed_items = Column(Integer, nullable=False, default=0)
-    error_message = Column(Text, nullable=True)
-
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    finished_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    results = relationship(
-        "MetricStudioRunResult",
-        back_populates="run",
-        cascade="all, delete-orphan",
-    )
-
-
-class MetricStudioRunResult(Base):
-    """Per-source scoring output for a MetricStudioRun."""
-
-    __tablename__ = "metric_studio_run_results"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    run_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("metric_studio_runs.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    source_kind = Column(String(40), nullable=False)
-    source_ref = Column(String(255), nullable=False)
-    display_label = Column(String(512), nullable=True)
-    source_metadata = Column(JSON, nullable=True)
-
-    status = Column(String(20), nullable=False, default="pending", index=True)
-    metric_scores = Column(JSON, nullable=False, default=dict)
-    error_message = Column(Text, nullable=True)
-    celery_task_id = Column(String(255), nullable=True)
-
-    started_at = Column(DateTime(timezone=True), nullable=True)
-    finished_at = Column(DateTime(timezone=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-    run = relationship("MetricStudioRun", back_populates="results")
-
-
-class CallImportEvaluationReportSnapshot(Base):
-    """Persisted PDF-report aggregate used for period-over-period deltas."""
-
-    __tablename__ = "call_import_evaluation_report_snapshots"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    period_label = Column(String(64), nullable=True, index=True)
-    period_start = Column(Date, nullable=True, index=True)
-    period_end = Column(Date, nullable=True, index=True)
-    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
-    selected_metric_ids = Column(JSON, nullable=False, default=list, server_default="[]")
-    metric_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
-    insight_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
-    narrative = Column(JSON, nullable=True)
-    total_calls = Column(Integer, nullable=False, default=0)
-    selected_metric_count = Column(Integer, nullable=False, default=0)
-    total_metric_count = Column(Integer, nullable=False, default=0)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
-
-
-class CallImportEvaluationPdfReport(Base):
-    """Stored PDF artifact for a call import evaluation report generation."""
-
-    __tablename__ = "call_import_evaluation_pdf_reports"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    evaluation_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    call_import_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_imports.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-    snapshot_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("call_import_evaluation_report_snapshots.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-    vendor_name = Column(String(120), nullable=False)
-    report_type = Column(String(20), nullable=False, default="external")
-    filename = Column(String(255), nullable=True)
-    s3_key = Column(String(512), nullable=True)
-    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
-    cache_fingerprint = Column(String(64), nullable=True)
-    created_by = Column(String, nullable=True)
-    created_by_user_id = Column(UUID(as_uuid=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-
-
-# ---------------------------------------------------------------------------
-# Judge Alignment (AlignEval-style hybrid integration)
-#
-# Three tables back the "Judge Alignment" surface:
-#   - JudgeDataset:     a labeled dataset materialised from one of three sources
-#                       (voice transcripts, existing Metric/Evaluator outputs,
-#                       or a generic CSV upload). Holds the dataset's source
-#                       config + which fields play the role of input/output.
-#   - JudgeSample:      one row in a dataset (input/output pair plus an
-#                       optional binary pass/fail human label).
-#   - JudgeRun:         a single run of an LLM-judge (existing Evaluator) over
-#                       a subset of samples, with computed alignment metrics
-#                       (precision/recall/F1/Cohen's kappa) and per-sample
-#                       predictions. Optionally links to a GEPA optimization
-#                       run when the user kicks off prompt tuning from a
-#                       dataset.
-# ---------------------------------------------------------------------------
-
-
-class JudgeDataset(Base):
-    """Container for binary-labeled samples used to calibrate an LLM-judge."""
-
-    __tablename__ = "judge_datasets"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    # Workspace isolation: every judge dataset belongs to a workspace
-    # within its org. Samples and runs inherit this workspace_id.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    name = Column(String(255), nullable=False)
-    description = Column(Text, nullable=True)
-
-    # One of: "transcript", "metric_output", "csv"
-    source_type = Column(String(32), nullable=False, index=True)
-    # Source-specific config. Examples:
-    #   transcript:     {"transcription_ids": [...]} or {"agent_id": "..."}
-    #   metric_output:  {"metric_id": "...", "evaluator_id": "..."}
-    #   csv:            {"s3_key": "...", "filename": "..."}
-    source_config = Column(JSON, nullable=False, default=dict)
-
-    # Field roles - which textual content is "input" vs "output" for the judge.
-    # For voice transcripts both default to the transcript text but can be
-    # tightened (e.g. agent-only turns vs full conversation).
-    input_field = Column(String(64), nullable=False, default="input")
-    output_field = Column(String(64), nullable=False, default="output")
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    samples = relationship(
-        "JudgeSample",
-        back_populates="dataset",
-        cascade="all, delete-orphan",
-        order_by="JudgeSample.created_at",
-    )
-    runs = relationship(
-        "JudgeRun",
-        back_populates="dataset",
-        cascade="all, delete-orphan",
-        order_by="JudgeRun.created_at.desc()",
-    )
-
-
-class JudgeSample(Base):
-    """One labelable input/output pair within a JudgeDataset."""
-
-    __tablename__ = "judge_samples"
-    __table_args__ = (
-        UniqueConstraint("dataset_id", "external_id", name="uq_judge_samples_dataset_external"),
-    )
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    dataset_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    # Stable identifier within the source (e.g. transcription UUID, CSV row id).
-    # Used to dedupe re-imports and link back to the originating record.
-    external_id = Column(String(128), nullable=True, index=True)
-
-    input_text = Column(Text, nullable=False)
-    output_text = Column(Text, nullable=False)
-
-    # Binary human label: "pass" | "fail" | null (unlabeled).
-    # Stored as string (rather than enum) so it stays trivially extendable.
-    label = Column(String(16), nullable=True, index=True)
-    labeled_by = Column(String(255), nullable=True)
-    labeled_at = Column(DateTime(timezone=True), nullable=True)
-
-    # Source-specific context (e.g. agent_id, original metric value, csv row).
-    extra = Column(JSON, nullable=True)
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-
-    dataset = relationship("JudgeDataset", back_populates="samples")
-
-
-class JudgeRun(Base):
-    """One execution of an LLM-judge against a JudgeDataset, with alignment metrics."""
-
-    __tablename__ = "judge_runs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    dataset_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    organization_id = Column(
-        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
-    )
-    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="RESTRICT"),
-        nullable=False,
-        index=True,
-    )
-
-    # Reuses the existing Evaluator row (its custom_prompt + llm_provider + llm_model
-    # define the judge under test). Nullable so a run may target an inline prompt
-    # in the future without inflating the Evaluator table.
-    evaluator_id = Column(
-        UUID(as_uuid=True), ForeignKey("evaluators.id", ondelete="SET NULL"), nullable=True, index=True
-    )
-
-    # Which subset was scored: "all" | "dev" | "test"
-    split = Column(String(16), nullable=False, default="all")
-
-    # Snapshot of the model used (so a later Evaluator edit doesn't rewrite history).
-    llm_provider = Column(String(64), nullable=True)
-    llm_model = Column(String(128), nullable=True)
-
-    # Computed alignment metrics:
-    #   {"precision": float, "recall": float, "f1": float, "kappa": float,
-    #    "tp": int, "fp": int, "tn": int, "fn": int, "n": int}
-    metrics = Column(JSON, nullable=True)
-
-    # Per-sample predictions, keyed by sample_id (UUID string):
-    #   {sample_id: {"prediction": "pass"|"fail", "explanation": str, "raw": str}}
-    predictions = Column(JSON, nullable=True)
-
-    # Run lifecycle.
-    status = Column(String(20), nullable=False, default="pending", index=True)
-    error_message = Column(Text, nullable=True)
-    celery_task_id = Column(String, nullable=True, index=True)
-
-    # Optional link to a GEPA optimization run kicked off from this dataset.
-    gepa_optimization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("prompt_optimization_runs.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    created_by = Column(String, nullable=True)
-
-    dataset = relationship("JudgeDataset", back_populates="runs")
-
-
-class UsageCostRecomputeJob(Base):
-    """Async job tracking for retroactive usage cost recompute."""
-
-    __tablename__ = "usage_cost_recompute_jobs"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    status = Column(String(32), nullable=False, default="pending", server_default="pending")
-    model = Column(String(255), nullable=True)
-    usage_kind = Column(String(16), nullable=True)
-    start_date = Column(Date, nullable=True)
-    end_date = Column(Date, nullable=True)
-    updated_rows = Column(BigInteger, nullable=False, default=0, server_default="0")
-    error_message = Column(String, nullable=True)
-    celery_task_id = Column(String(255), nullable=True, index=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
-    completed_at = Column(DateTime(timezone=True), nullable=True)
-
-
-class LLMUsageDaily(Base):
-    """Daily LLM/STT usage rollups for org-scoped Usage reporting."""
-
-    __tablename__ = "llm_usage_daily"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-    organization_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("organizations.id", ondelete="CASCADE"),
-        nullable=False,
-        index=True,
-    )
-    workspace_id = Column(
-        UUID(as_uuid=True),
-        ForeignKey("workspaces.id", ondelete="SET NULL"),
-        nullable=True,
-        index=True,
-    )
-    product_section = Column(String(64), nullable=False, index=True)
-    model = Column(String(255), nullable=False, index=True)
-    context = Column(JSONB, nullable=False, server_default="{}", default=dict)
-    usage_date = Column(Date, nullable=False, index=True)
-    usage_kind = Column(String(16), nullable=False, default="llm", server_default="llm")
-    prompt_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
-    completion_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
-    cache_read_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
-    cache_creation_tokens = Column(
-        BigInteger, nullable=False, default=0, server_default="0"
-    )
-    reasoning_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
-    audio_seconds = Column(BigInteger, nullable=False, default=0, server_default="0")
-    tts_characters = Column(BigInteger, nullable=False, default=0, server_default="0")
-    call_count = Column(BigInteger, nullable=False, default=0, server_default="0")
-    input_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
-    output_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
-    cache_read_cost_micro_usd = Column(
-        BigInteger, nullable=False, default=0, server_default="0"
-    )
-    cache_creation_cost_micro_usd = Column(
-        BigInteger, nullable=False, default=0, server_default="0"
-    )
-    reasoning_cost_micro_usd = Column(
-        BigInteger, nullable=False, default=0, server_default="0"
-    )
-    audio_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
-    tts_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
-    total_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
-    pricing_rate_source = Column(String(16), nullable=True)
-    pricing_rate_id = Column(UUID(as_uuid=True), nullable=True)
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(
-        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
-    )
+"""SQLAlchemy database models."""
+
+from sqlalchemy import (
+    BigInteger,
+    Boolean,
+    Column,
+    Date,
+    DateTime,
+    DDL,
+    Enum,
+    event,
+    Float,
+    ForeignKey,
+    Integer,
+    JSON,
+    String,
+    Text,
+    UniqueConstraint,
+    select,
+    text,
+)
+from sqlalchemy.dialects.postgresql import JSONB, UUID
+from sqlalchemy.orm import relationship
+from sqlalchemy.sql import func
+import uuid
+import enum
+from app.models.enums import (
+    EvaluationType, EvaluationStatus, EvaluatorResultStatus, RoleEnum, InvitationStatus,
+    LanguageEnum, CallTypeEnum, CallMediumEnum, GenderEnum, AccentEnum, BackgroundNoiseEnum,
+    IntegrationPlatform, ModelProvider, VoiceBundleType, TestAgentConversationStatus,
+    MetricType, MetricCategory, MetricTrigger, CallRecordingStatus, AlertMetricType, AlertAggregation,
+    AlertOperator, AlertNotifyFrequency, AlertStatus, AlertHistoryStatus, CronJobStatus,
+    PromptOptimizationStatus, CallImportStatus, CallImportRowStatus,
+)
+
+def get_enum_values(enum_class):
+    """Helper to get values from enum class for SQLAlchemy."""
+    return [e.value for e in enum_class]
+
+from app.database import Base
+
+
+# Enums moved to enums.py
+
+
+class Organization(Base):
+    """Organization model for multi-tenancy."""
+
+    __tablename__ = "organizations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    name = Column(String(255), nullable=False)
+    voice_playground_threshold_overrides = Column(JSON, nullable=True)
+    # AlignEval-style judge alignment thresholds.
+    # Shape: {"min_labels_to_evaluate": int, "min_labels_to_optimize": int}
+    # Falls back to system defaults (20 / 50) when null.
+    judge_alignment_settings = Column(JSON, nullable=True)
+    # Per-org LLM gateway overrides (enabled, gateway_type, base_url, keys).
+    llm_gateway_settings = Column(JSON, nullable=True)
+    is_active = Column(Boolean, default=True, nullable=False, server_default=text("true"), index=True)
+    disabled_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    # Relationships
+    api_keys = relationship("APIKey", back_populates="organization")
+    members = relationship("OrganizationMember", back_populates="organization", cascade="all, delete-orphan")
+    invitations = relationship("Invitation", back_populates="organization", cascade="all, delete-orphan")
+    workspaces = relationship(
+        "Workspace",
+        back_populates="organization",
+        cascade="all, delete-orphan",
+    )
+    workspace_roles = relationship(
+        "WorkspaceRole",
+        back_populates="organization",
+        cascade="all, delete-orphan",
+    )
+
+
+class Workspace(Base):
+    """Workspace - in-org isolation boundary for call imports and metrics.
+
+    Every organization has at least one workspace (``is_default = True``,
+    seeded by migration 033). Users pick an "active workspace" in the UI;
+    list endpoints filter by it so users only see calls/metrics from the
+    project they're currently working in. Access is governed by
+    ``workspace_members`` and org-scoped ``workspace_roles`` (capability
+    bundles); org admins implicitly access all workspaces.
+    """
+
+    __tablename__ = "workspaces"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "slug", name="uq_workspaces_org_slug"),
+    )
+
+    # ``server_default`` is required so that raw-SQL INSERTs (e.g. the
+    # per-org Default seed in migration 033) can omit ``id`` and let the
+    # database fill it in. Without it, ``create_all`` produces a column
+    # with NOT NULL but no DEFAULT, and the migration crashes with
+    # ``null value in column "id"``.
+    id = Column(
+        UUID(as_uuid=True),
+        primary_key=True,
+        default=uuid.uuid4,
+        server_default=text("gen_random_uuid()"),
+    )
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    slug = Column(String(255), nullable=False)
+    # At most one default per org. Enforced on Postgres by the partial
+    # unique index attached via the after_create event below; on
+    # SQLite (test runs) we rely on the route-level _check_slug_unique
+    # check + the Default-workspace conftest fixture instead, because
+    # SQLite doesn't support partial indexes the same way.
+    is_default = Column(Boolean, nullable=False, default=False, server_default="false")
+    is_active = Column(Boolean, nullable=False, default=True, server_default="true")
+    # Reusable PDF/report branding metadata scoped to this workspace. Images
+    # live in S3. Shape: {"heading": str|null, "images": [{id, s3_key,
+    # content_type, filename, size_bytes, updated_at}, ...]}.
+    report_branding = Column(JSON, nullable=True)
+    created_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    organization = relationship("Organization", back_populates="workspaces")
+    members = relationship(
+        "WorkspaceMember",
+        back_populates="workspace",
+        cascade="all, delete-orphan",
+    )
+
+
+class WorkspaceRole(Base):
+    """Org-scoped workspace role (system or custom) as a capability bundle."""
+
+    __tablename__ = "workspace_roles"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "name", name="uq_workspace_roles_org_name"),
+    )
+
+    id = Column(
+        UUID(as_uuid=True),
+        primary_key=True,
+        default=uuid.uuid4,
+        server_default=text("gen_random_uuid()"),
+    )
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+    capabilities = Column(JSON, nullable=False, default=list)
+    is_system = Column(Boolean, nullable=False, default=False, server_default="false")
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    organization = relationship("Organization", back_populates="workspace_roles")
+    members = relationship("WorkspaceMember", back_populates="role")
+
+
+class WorkspaceMember(Base):
+    """User membership in a workspace with an assigned workspace role."""
+
+    __tablename__ = "workspace_members"
+    __table_args__ = (
+        UniqueConstraint("workspace_id", "user_id", name="uq_workspace_members_ws_user"),
+    )
+
+    id = Column(
+        UUID(as_uuid=True),
+        primary_key=True,
+        default=uuid.uuid4,
+        server_default=text("gen_random_uuid()"),
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    user_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("users.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    role_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspace_roles.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    added_by_user_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("users.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    workspace = relationship("Workspace", back_populates="members")
+    user = relationship("User", foreign_keys=[user_id])
+    role = relationship("WorkspaceRole", back_populates="members")
+    added_by = relationship("User", foreign_keys=[added_by_user_id])
+
+
+# Partial unique index: "at most one default workspace per org". This
+# is attached as an after_create event (rather than declared in
+# ``__table_args__``) because SQLAlchemy's ``Index(...,
+# postgresql_where=...)`` silently degrades to a *full* unique index on
+# SQLite - which then forbids any second workspace per org and breaks
+# the test suite. ``execute_if(dialect="postgresql")`` makes this DDL
+# a no-op on SQLite while still emitting it on Postgres (prod, CI).
+event.listen(
+    Workspace.__table__,
+    "after_create",
+    DDL(
+        "CREATE UNIQUE INDEX IF NOT EXISTS uq_workspaces_org_default "
+        "ON workspaces (organization_id) WHERE is_default"
+    ).execute_if(dialect="postgresql"),
+)
+
+
+class User(Base):
+    """User model for authentication and profile management."""
+
+    __tablename__ = "users"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    email = Column(String(255), unique=True, nullable=False, index=True)
+    name = Column(String(255), nullable=True)
+    first_name = Column(String(255), nullable=True)
+    last_name = Column(String(255), nullable=True)
+    password_hash = Column(String(255), nullable=True)  # Nullable for users created via invitation
+    external_id = Column(String(255), unique=True, nullable=True, index=True)
+    auth_provider = Column(String(50), nullable=True)
+    mfa_enabled = Column(Boolean, default=False, nullable=False)
+    last_login_at = Column(DateTime(timezone=True), nullable=True)
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    # Relationships
+    organization_memberships = relationship("OrganizationMember", back_populates="user", cascade="all, delete-orphan")
+    api_keys = relationship("APIKey", back_populates="user")
+    invitations = relationship("Invitation", back_populates="invited_user", foreign_keys="Invitation.invited_user_id")
+    refresh_tokens = relationship("RefreshToken", back_populates="user", cascade="all, delete-orphan")
+
+
+class PlatformAdmin(Base):
+    """Platform-level administrator (separate from org-scoped users)."""
+
+    __tablename__ = "platform_admins"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    email = Column(String(255), unique=True, nullable=False, index=True)
+    password_hash = Column(String(255), nullable=False)
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    last_login_at = Column(DateTime(timezone=True), nullable=True)
+
+    signup_reference_codes = relationship(
+        "SignupReferenceCode",
+        back_populates="created_by_admin",
+        foreign_keys="SignupReferenceCode.created_by",
+    )
+
+
+class SignupReferenceCode(Base):
+    """Single- or multi-use reference code required for gated self-service signup."""
+
+    __tablename__ = "signup_reference_codes"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    code_hash = Column(String(64), unique=True, nullable=False)
+    label = Column(String(255), nullable=True)
+    max_uses = Column(Integer, nullable=True)
+    use_count = Column(Integer, default=0, nullable=False)
+    expires_at = Column(DateTime(timezone=True), nullable=True)
+    is_active = Column(Boolean, default=True, nullable=False, index=True)
+    created_by = Column(UUID(as_uuid=True), ForeignKey("platform_admins.id"), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    created_by_admin = relationship(
+        "PlatformAdmin",
+        back_populates="signup_reference_codes",
+        foreign_keys=[created_by],
+    )
+
+
+class RefreshToken(Base):
+    """Opaque refresh token for extending local-password sessions."""
+
+    __tablename__ = "refresh_tokens"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id", ondelete="CASCADE"), nullable=False, index=True)
+    token_hash = Column(String(64), unique=True, nullable=False, index=True)
+    expires_at = Column(DateTime(timezone=True), nullable=False)
+    revoked_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    user = relationship("User", back_populates="refresh_tokens")
+
+
+class OrganizationMember(Base):
+    """Organization membership with role."""
+
+    __tablename__ = "organization_members"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False, index=True)
+    role = Column(String, nullable=False, default=RoleEnum.READER.value)
+    
+    # User preferences for this organization
+    default_agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
+
+    joined_at = Column(DateTime(timezone=True), server_default=func.now())
+    
+    # Unique constraint: one membership per user per organization
+    __table_args__ = (
+        UniqueConstraint('organization_id', 'user_id', name='uq_org_user'),
+    )
+
+    # Relationships
+    organization = relationship("Organization", back_populates="members")
+    user = relationship("User", back_populates="organization_memberships")
+    default_agent = relationship("Agent", foreign_keys=[default_agent_id])
+
+
+class Invitation(Base):
+    """Invitation model for inviting users to organizations."""
+
+    __tablename__ = "invitations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    invited_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Null if user doesn't exist yet
+    invited_by_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False)
+    email = Column(String(255), nullable=False)  # Email of invited user
+    role = Column(String, nullable=False, default=RoleEnum.READER.value)
+    status = Column(String, nullable=False, default=InvitationStatus.PENDING.value)
+
+
+
+    token = Column(String(255), unique=True, nullable=False, index=True)  # Invitation token
+    expires_at = Column(DateTime(timezone=True), nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    accepted_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Relationships
+    organization = relationship("Organization", back_populates="invitations")
+    invited_user = relationship("User", foreign_keys=[invited_user_id], back_populates="invitations")
+    invited_by = relationship("User", foreign_keys=[invited_by_id])
+
+
+class APIKey(Base):
+    """API Key model for authentication."""
+
+    __tablename__ = "api_keys"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    key = Column(String(255), unique=True, nullable=False, index=True)
+    name = Column(String(255), nullable=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True, index=True)  # Optional: link to user
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    last_used = Column(DateTime(timezone=True), nullable=True)
+
+    # Relationships
+    organization = relationship("Organization", back_populates="api_keys")
+    user = relationship("User", back_populates="api_keys")
+
+
+class AudioFile(Base):
+    """Audio file model."""
+
+    __tablename__ = "audio_files"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    filename = Column(String(255), nullable=False)
+    file_path = Column(String(512), nullable=False)
+    file_size = Column(Integer, nullable=False)  # Size in bytes
+    duration = Column(Float, nullable=True)  # Duration in seconds
+    sample_rate = Column(Integer, nullable=True)
+    channels = Column(Integer, nullable=True)
+    format = Column(String(10), nullable=False)  # wav, mp3, flac, etc.
+    uploaded_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    # Relationships
+    evaluations = relationship("Evaluation", back_populates="audio_file")
+
+
+class Evaluation(Base):
+    """Evaluation job model."""
+
+    __tablename__ = "evaluations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every legacy audio evaluation belongs to a
+    # workspace within its org. Stamped from the X-Workspace-Id header
+    # (falling back to the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    audio_id = Column(UUID(as_uuid=True), ForeignKey("audio_files.id"), nullable=False)
+    reference_text = Column(String, nullable=True)  # For WER calculation
+    evaluation_type = Column(String, nullable=False)
+    model_name = Column(String(100), nullable=True)
+    status = Column(String, default=EvaluationStatus.PENDING.value, nullable=False)
+
+
+
+    metrics_requested = Column(JSON, nullable=True)  # List of requested metrics
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    completed_at = Column(DateTime(timezone=True), nullable=True)
+    error_message = Column(String, nullable=True)
+
+    # Relationships
+    audio_file = relationship("AudioFile", back_populates="evaluations")
+    result = relationship("EvaluationResult", back_populates="evaluation", uselist=False)
+
+
+class EvaluationResult(Base):
+    """Evaluation result model."""
+
+    __tablename__ = "evaluation_results"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(UUID(as_uuid=True), ForeignKey("evaluations.id"), nullable=False, unique=True)
+    # Workspace isolation: mirrors the parent Evaluation's workspace.
+    # Denormalized for fast filter-by-workspace listings without a join.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    transcript = Column(String, nullable=True)
+    metrics = Column(JSON, nullable=False)  # {"wer": 0.05, "latency_ms": 1250, ...}
+    raw_output = Column(JSON, nullable=True)  # Full model output
+    processing_time = Column(Float, nullable=True)  # Processing time in seconds
+    model_used = Column(String(100), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    # Relationships
+    evaluation = relationship("Evaluation", back_populates="result")
+
+
+# ============================================
+# VAIOPS MODELS - Voice AI Ops
+# ============================================
+
+# Enums moved to enums.py
+
+
+class Agent(Base):
+    """Test Agent - The voice AI agent being evaluated"""
+    __tablename__ = "agents"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    agent_id = Column(String(6), unique=True, nullable=True, index=True)  # 6-digit ID
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every agent belongs to a workspace within its
+    # org. Stamped from the X-Workspace-Id header (falling back to the
+    # org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String, nullable=False)
+    phone_number = Column(String, nullable=True)  # Optional, required only for phone_call
+    language = Column(String, nullable=False, default=LanguageEnum.ENGLISH.value)
+    description = Column(String)
+    provider_prompt = Column(Text, nullable=True)
+    provider_prompt_synced_at = Column(DateTime(timezone=True), nullable=True)
+    call_type = Column(String, nullable=False, default=CallTypeEnum.OUTBOUND.value)
+    call_medium = Column(String, nullable=False, default=CallMediumEnum.PHONE_CALL.value)
+    telephony_phone_number_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("telephony_phone_numbers.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+
+
+    
+    # Voice configuration - either voice_bundle_id OR ai_provider_id OR voice_ai_integration_id (mutually exclusive)
+    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True, index=True)
+    ai_provider_id = Column(UUID(as_uuid=True), ForeignKey("aiproviders.id"), nullable=True, index=True)
+    
+    # Voice AI agent integration (Retell, Vapi, etc.)
+    voice_ai_integration_id = Column(UUID(as_uuid=True), ForeignKey("integrations.id"), nullable=True, index=True)
+    voice_ai_agent_id = Column(String, nullable=True)  # Agent ID from the external provider (Retell/Vapi)
+    prompt_variables = Column(JSON, nullable=True)
+    silence_hangup_secs = Column(Integer, nullable=False, server_default="15")
+    
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+    created_by = Column(String)
+
+
+class Persona(Base):
+    """Persona - TTS provider-tied voice identity for testing"""
+    __tablename__ = "personas"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every persona belongs to a workspace within
+    # its org. Stamped from the X-Workspace-Id header (falling back to
+    # the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String, nullable=False)
+    gender = Column(String, nullable=False, default=GenderEnum.NEUTRAL.value)
+    tts_provider = Column(String(100), nullable=True)
+    tts_voice_id = Column(String(255), nullable=True)
+    tts_voice_name = Column(String(255), nullable=True)
+    is_custom = Column(Boolean, default=False)
+    description = Column(Text, nullable=True)
+    tts_config = Column(JSON, nullable=True)
+    llm_temperature = Column(Float, nullable=True)
+    llm_max_tokens = Column(Integer, nullable=True)
+    response_delay_ms = Column(Integer, nullable=True)
+    max_turns = Column(Integer, nullable=True)
+    allow_interruptions = Column(Boolean, nullable=True)
+
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+    created_by = Column(String)
+
+
+class Scenario(Base):
+    """Scenario - The conversation scenario/test case"""
+    __tablename__ = "scenarios"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every scenario belongs to a workspace within
+    # its org. Stamped from the X-Workspace-Id header (falling back to
+    # the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True, index=True)
+    name = Column(String, nullable=False)
+    description = Column(String)
+    required_info = Column(JSON)
+    
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+    created_by = Column(String)
+
+
+# Enums moved to enums.py
+
+
+class Integration(Base):
+    """Integration model for connecting with external voice AI platforms."""
+    __tablename__ = "integrations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    platform = Column(String, nullable=False)
+
+
+
+    name = Column(String, nullable=True)  # Optional friendly name
+    api_key = Column(String, nullable=False)  # Encrypted Private API key for the platform
+    public_key = Column(String, nullable=True)  # Optional Public API key (e.g. for Vapi)
+    is_active = Column(Boolean, default=True, nullable=False)
+    # Multiple credentials per (org, platform) are allowed. is_default marks
+    # the row used when a caller does not explicitly select a credential.
+    # A partial unique index in migration 028 enforces at most one default
+    # per (org, platform) at the DB level.
+    is_default = Column(Boolean, default=False, nullable=False)
+    # inherit | gateway | direct — per-credential LLM routing override
+    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
+
+
+class ManualTranscription(Base):
+    """Manual transcription model for storing transcriptions from S3 audio files."""
+
+    __tablename__ = "manual_transcriptions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    name = Column(String(255), nullable=True)  # User-friendly name for the transcription
+    audio_file_key = Column(String(512), nullable=False)  # S3 key or file path
+    transcript = Column(String, nullable=False)  # Full transcript text
+    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
+    stt_model = Column(String(100), nullable=True)  # STT model used (e.g., "whisper-1", "google-speech-v2")
+    stt_provider = Column(String, nullable=True)  # Provider used
+
+
+
+    language = Column(String(10), nullable=True)  # Detected or specified language
+    processing_time = Column(Float, nullable=True)  # Processing time in seconds
+    raw_output = Column(JSON, nullable=True)  # Full model output for reference
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class ConversationEvaluation(Base):
+    """Conversation evaluation model for evaluating manual transcriptions against agent objectives."""
+    
+    __tablename__ = "conversation_evaluations"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    transcription_id = Column(UUID(as_uuid=True), ForeignKey("manual_transcriptions.id"), nullable=False, index=True)
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
+    
+    # Evaluation results
+    objective_achieved = Column(Boolean, nullable=False)  # Binary: was the conversation objective achieved?
+    objective_achieved_reason = Column(String, nullable=True)  # Explanation for the binary result
+    additional_metrics = Column(JSON, nullable=True)  # Additional evaluation metrics (e.g., professionalism, clarity, etc.)
+    overall_score = Column(Float, nullable=True)  # Overall score (0.0 to 1.0)
+    
+    # LLM metadata
+    llm_provider = Column(Enum(ModelProvider, native_enum=False), nullable=True)
+
+    llm_model = Column(String(100), nullable=True)
+    llm_response = Column(JSON, nullable=True)  # Full LLM response for reference
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class AIProvider(Base):
+    """AI Provider - Stores API keys for different AI platforms."""
+    __tablename__ = "aiproviders"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider = Column(String, nullable=False)
+
+
+
+    api_key = Column(String, nullable=False)  # Encrypted API key
+    name = Column(String, nullable=True)  # Optional friendly name
+    # Azure OpenAI resource endpoint (e.g. https://my-resource.openai.azure.com).
+    # Only used when provider is azure; other providers ignore this column.
+    endpoint_url = Column(String, nullable=True)
+    is_active = Column(Boolean, default=True, nullable=False)
+    # Multiple AIProvider rows per (org, provider) are allowed. is_default
+    # marks the row resolved when no explicit credential id is selected.
+    # A partial unique index in migration 028 enforces at most one default.
+    is_default = Column(Boolean, default=False, nullable=False)
+    # inherit | gateway | direct — per-credential LLM routing override
+    routing_mode = Column(String(20), nullable=False, default="inherit", server_default="inherit")
+    # Bifrost custom model ID used when routing via gateway
+    gateway_model = Column(String(255), nullable=True)
+    # inherit | litellm_shim | native_openai — Bifrost API surface override
+    gateway_interface = Column(String(20), nullable=False, default="inherit", server_default="inherit")
+    # Optional per-credential Bifrost/gateway base URL override
+    gateway_base_url = Column(String(512), nullable=True)
+    # Optional auth header for Bifrost (e.g. x-bf-vk, Authorization, x-api-key)
+    gateway_auth_header = Column(String(64), nullable=True)
+    # Env var name whose value is sent as the gateway auth secret
+    gateway_auth_secret_env = Column(String(128), nullable=True)
+    # Encrypted inline gateway auth secret (alternative to env var)
+    gateway_auth_secret = Column(String, nullable=True)
+    # Arbitrary HTTP headers sent with gateway-routed LiteLLM calls
+    gateway_extra_headers = Column(JSON, nullable=True)
+    # Non-empty list restricts model pickers; null/empty = all catalog models for provider.
+    enabled_models = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    last_tested_at = Column(DateTime(timezone=True), nullable=True)  # When API key was last validated
+
+
+# Enums moved to enums.py
+
+
+class VoiceBundle(Base):
+    """VoiceBundle - Composable unit combining STT, LLM, and TTS for voice AI testing, or S2S models."""
+    __tablename__ = "voicebundles"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    name = Column(String, nullable=False)
+    description = Column(String, nullable=True)
+    
+    # Bundle type: either STT+LLM+TTS or S2S
+    # Using String instead of Enum to avoid SQLAlchemy enum conversion issues
+    # The enum conversion is handled in the Pydantic schemas
+    bundle_type = Column(String(50), nullable=False, default=VoiceBundleType.STT_LLM_TTS.value)
+    
+    # STT Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
+    stt_provider = Column(String, nullable=True)
+    # Optional explicit credential row (aiproviders.id or integrations.id).
+    # When NULL the credential resolver picks the default row for the
+    # provider. No FK is set because the target table varies by provider.
+    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    stt_model = Column(String, nullable=True)  # e.g., "whisper-1", "google-speech-v2"
+    
+    # LLM Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
+    llm_provider = Column(String, nullable=True)
+    llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    llm_model = Column(String, nullable=True)  # e.g., "gpt-4", "claude-3-opus"
+    llm_temperature = Column(Float, nullable=True, default=0.7)
+    llm_max_tokens = Column(Integer, nullable=True)
+    llm_config = Column(JSON, nullable=True)  # Additional LLM configuration (extensible)
+    
+    # TTS Configuration (references AIProvider via provider name) - required for STT_LLM_TTS, optional for S2S
+    tts_provider = Column(String, nullable=True)
+    tts_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    tts_model = Column(String, nullable=True)  # e.g., "tts-1", "neural-voice"
+    tts_voice = Column(String, nullable=True)  # Voice selection if applicable
+    tts_config = Column(JSON, nullable=True)  # Additional TTS configuration (extensible)
+    
+    # S2S Configuration - required for S2S type, optional for STT_LLM_TTS
+    s2s_provider = Column(String, nullable=True)
+    s2s_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+
+
+    s2s_model = Column(String, nullable=True)  # e.g., "gpt-4o-transcribe", speech-to-speech model
+    s2s_config = Column(JSON, nullable=True)  # Additional S2S configuration (extensible)
+    
+    # Additional configuration for extensibility
+    extra_metadata = Column(JSON, nullable=True)  # For future extensions (renamed from 'metadata' to avoid SQLAlchemy conflict)
+    
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+# Enums moved to enums.py
+
+
+class TestAgentConversation(Base):
+    """Test Agent Conversation - Records conversations between test AI agent and voice AI agent."""
+    __tablename__ = "test_agent_conversations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every playground conversation belongs to a
+    # workspace within its org. Stamped from the X-Workspace-Id header
+    # (falling back to the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    
+    # Configuration
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=False)
+    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
+    
+    # Conversation data
+    status = Column(String, nullable=False, default=TestAgentConversationStatus.INITIALIZING.value)
+
+
+
+    live_transcription = Column(JSON, nullable=True)  # Array of conversation turns with timestamps
+    conversation_audio_key = Column(String, nullable=True)  # S3 key for recorded conversation audio
+    full_transcript = Column(String, nullable=True)  # Full conversation transcript
+    
+    # Metadata
+    started_at = Column(DateTime(timezone=True), server_default=func.now())
+    ended_at = Column(DateTime(timezone=True), nullable=True)
+    duration_seconds = Column(Float, nullable=True)
+    
+    # Additional metadata
+    conversation_metadata = Column(JSON, nullable=True)  # Additional conversation metadata
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class EvaluatorSuite(Base):
+    """Evaluator suite — one agent + one persona + N scenario combinations."""
+
+    __tablename__ = "evaluator_suites"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    name = Column(String, nullable=True)
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=False)
+    metric_ids = Column(JSON, nullable=True)
+    llm_provider = Column(String, nullable=True)
+    llm_model = Column(String, nullable=True)
+    llm_config = Column(JSON, nullable=True)
+    tags = Column(JSON, nullable=True)
+    default_runs_per_combination = Column(Integer, nullable=False, default=1)
+    round_robin_index = Column(Integer, nullable=False, default=0)
+    is_active = Column(Boolean, nullable=False, default=False)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class Evaluator(Base):
+    """Evaluator - Configuration for testing agents with specific persona and scenario combinations, or custom prompt evaluators."""
+    __tablename__ = "evaluators"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluator_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every evaluator belongs to a workspace within
+    # its org. Stamped from the X-Workspace-Id header (falling back to
+    # the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    
+    # Display name (required for custom evaluators, optional for standard)
+    name = Column(String, nullable=True)
+    
+    # Parent suite (nullable for legacy/custom evaluators)
+    suite_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("evaluator_suites.id", ondelete="CASCADE"),
+        nullable=True,
+        index=True,
+    )
+
+    # Standard evaluator configuration (nullable for custom evaluators)
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)
+    
+    # Custom evaluator prompt (used instead of agent/persona/scenario)
+    custom_prompt = Column(Text, nullable=True)
+
+    # Custom evaluator metric selection. When set, the worker filters the
+    # enabled-org metrics down to only these IDs (list of metric UUID strings).
+    # Standard evaluators leave this NULL and use all enabled agent metrics.
+    metric_ids = Column(JSON, nullable=True)
+
+    # LLM configuration for evaluation (overrides hardcoded defaults)
+    llm_provider = Column(String, nullable=True)  # e.g. "openai", "anthropic", "google"
+    llm_model = Column(String, nullable=True)  # e.g. "gpt-4.1", "claude-sonnet-4-20250514"
+    llm_config = Column(JSON, nullable=True)
+    
+    # Tags for categorization
+    tags = Column(JSON, nullable=True)  # Array of tag strings
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+# Enums moved to enums.py
+
+
+class Metric(Base):
+    """Metric - Configuration for evaluation metrics.
+
+    Supports a 2-level hierarchy via ``parent_metric_id``: a "category"
+    parent metric (e.g. "Call Outcome") owns N child sub-metric labels
+    (e.g. "happy_completion", "angry_hangup"). ``selection_mode`` is set
+    only on parents and controls how the LLM scores children together
+    (``single_choice`` = pick exactly one; ``multi_label`` = independent
+    yes/no with logical consistency). Children are always boolean.
+    """
+    __tablename__ = "metrics"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: two-shape column.
+    #
+    #   * ``workspace_id = <uuid>`` — workspace-scoped metric. Only
+    #     visible inside that workspace (the default behavior; existing
+    #     rows all look like this).
+    #   * ``workspace_id IS NULL`` — org-shared metric. Surfaces in
+    #     every workspace's listing under this org so users don't have
+    #     to recreate the same metric per workspace.
+    #
+    # Children always inherit their parent's ``workspace_id`` (including
+    # NULL) so a category metric's whole subtree shares one scope; the
+    # add-child / promote-discovered endpoints enforce this.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=True,
+        index=True,
+    )
+
+    # Basic information
+    name = Column(String, nullable=False)
+    description = Column(String, nullable=True)
+    # Free-form illustrative example used to sharpen the LLM judge's
+    # rubric. Today this is consumed by child sub-labels of a
+    # categorization parent metric so each label can carry "what does
+    # this look like in a transcript?" text alongside the rubric in
+    # ``description``. The column lives on every Metric row for
+    # forward-compat: a standalone metric could later surface its own
+    # example without another migration.
+    example = Column(Text, nullable=True)
+
+    # Configuration
+    metric_type = Column(String, nullable=False, default=MetricType.RATING.value)
+    metric_category = Column(
+        String(30),
+        nullable=False,
+        default=MetricCategory.QUALITY.value,
+        server_default=MetricCategory.QUALITY.value,
+    )
+    trigger = Column(String, nullable=False, default=MetricTrigger.ALWAYS.value)
+    metric_origin = Column(String(30), nullable=False, default="default")
+    supported_surfaces = Column(JSON, nullable=False, default=list)  # ["agent", "voice_playground", "blind_test"]
+    enabled_surfaces = Column(JSON, nullable=False, default=list)  # subset of supported_surfaces
+    custom_data_type = Column(String(30), nullable=True)  # "boolean" | "enum" | "number_range"
+    custom_config = Column(JSON, nullable=True)  # enum options / number range config
+    tags = Column(JSON, nullable=True)  # ["tone", "latency", ...]
+
+    # Hierarchy: NULL = standalone or parent. When set, this row is a
+    # child sub-metric of the referenced parent. ON DELETE CASCADE so
+    # deleting a category removes its children atomically.
+    parent_metric_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("metrics.id", ondelete="CASCADE"),
+        nullable=True,
+        index=True,
+    )
+    # Set only on parent rows (``parent_metric_id IS NULL``). Either
+    # ``single_choice`` or ``multi_label``. NULL = legacy / non-hierarchical
+    # metric (no children).
+    selection_mode = Column(String(20), nullable=True)
+
+    # When true on a parent metric (any selection_mode), the LLM is
+    # invited during call-import evaluation to emit additional
+    # candidate sub-labels beyond the user-defined children. The
+    # candidates surface in a "Discovered labels" panel where the user
+    # manually promotes them into real child Metric rows. For
+    # ``single_choice`` parents the discovered entries are
+    # supplemental — the chosen child is still picked from the
+    # predefined children so the exactly-one-true invariant holds.
+    # The validator rejects this flag on standalone / child metrics.
+    allow_discovery = Column(
+        Boolean, nullable=False, default=False, server_default="false"
+    )
+
+    # When True, this metric is a "transcript-compare judge": the
+    # call-import evaluator feeds BOTH the production transcript
+    # (``call_import_rows.transcript``, CSV-supplied) and the diarised
+    # transcript (``call_import_rows.diarised_transcript``, worker-
+    # produced by the STT/diarisation pipeline) to the LLM as a
+    # labeled pair instead of feeding one transcript. The parent
+    # evaluation's ``CallImportEvaluation.transcript_source`` is
+    # ignored for these metrics — they always read both columns.
+    # Rows where either transcript is missing are skipped per-metric
+    # with ``skipped="comparison_missing_transcript"`` so the rest of
+    # the row's metrics still produce scores. The Pydantic validator
+    # rejects ``compare_transcripts`` combined with ``parent_metric_id``
+    # or ``selection_mode`` (i.e. it can't simultaneously be part of
+    # a parent/child hierarchy). The call-import worker also
+    # auto-promotes a metric to comparison mode when its description
+    # references the production / diarised transcripts in well-known
+    # phrases (see ``_metric_text_references_production`` in
+    # ``app.workers.tasks.evaluate_call_import_row``).
+    compare_transcripts = Column(
+        Boolean, nullable=False, default=False, server_default="false"
+    )
+
+    parent = relationship(
+        "Metric",
+        remote_side=[id],
+        backref="children",
+    )
+
+    # When true, the LLM-judge is asked to also return a short free-form
+    # rationale alongside the value (stored under ``metric_scores[id].rationale``).
+    # Adds a second "<Name> - LLM Rationale" column in the call-import CSV export.
+    capture_rationale = Column(Boolean, nullable=False, default=False)
+
+    enabled = Column(Boolean, nullable=False, default=True)
+
+    # Studio draft lifecycle: ``draft`` metrics are visible only in Metrics
+    # Studio until promoted to ``active``.
+    lifecycle = Column(
+        String(20),
+        nullable=False,
+        default="active",
+        server_default="active",
+    )
+    promoted_from_draft_at = Column(DateTime(timezone=True), nullable=True)
+    studio_notes = Column(Text, nullable=True)
+    
+    # Metadata
+    is_default = Column(Boolean, nullable=False, default=False)  # Pre-defined metrics
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class EvaluatorResult(Base):
+    """EvaluatorResult - Results from running an evaluator with transcription and metric evaluations."""
+    __tablename__ = "evaluator_results"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    result_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every evaluator result belongs to a workspace
+    # within its org. Stamped from the active workspace at creation time
+    # (either the X-Workspace-Id header or the org's Default workspace).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    
+    # References
+    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True, index=True)  # Optional - can be None for test calls without persona/scenario
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Nullable for custom evaluators
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id"), nullable=True)  # Optional - can be None for test calls
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id"), nullable=True)  # Optional - can be None for test calls
+    
+    # Result data
+    name = Column(String, nullable=True)  # Scenario name or test call name (optional)
+    timestamp = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
+    duration_seconds = Column(Float, nullable=True)  # Call duration
+    status = Column(String(20), nullable=False, default=EvaluatorResultStatus.QUEUED.value)
+    
+    # Audio and transcription
+    audio_s3_key = Column(String, nullable=True)  # S3 key for audio file
+    transcription = Column(String, nullable=True)  # Full transcription
+    speaker_segments = Column(JSON, nullable=True)  # List of segments with speaker labels: [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
+    
+    # Metric scores - JSON object with metric_id as key and score as value
+    # Format: {"metric_id_1": {"value": 85, "type": "rating"}, "metric_id_2": {"value": true, "type": "boolean"}}
+    metric_scores = Column(JSON, nullable=True)
+    
+    # Celery task tracking
+    celery_task_id = Column(String, nullable=True, index=True)  # Celery task ID for tracking
+    
+    # Error information
+    error_message = Column(String, nullable=True)
+    
+    # Call event tracking (similar to CallRecording)
+    call_event = Column(String, nullable=True, index=True)  # Latest call event (e.g., call_started, call_ended)
+    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
+    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
+    call_data = Column(JSON, nullable=True)  # Full call details from provider (like CallRecording)
+    
+    # Data-plane shard routing (payload rows on shard DBs when sharding enabled)
+    shard_id = Column(String(64), nullable=True, index=True)
+    synthetic_call_trace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("synthetic_call_traces.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+# Enums moved to enums.py
+
+
+class CallRecordingSource(str, enum.Enum):
+    """Source of the call recording data."""
+    
+    PLAYGROUND = "playground"
+    WEBHOOK = "webhook"
+
+
+class CallRecording(Base):
+    """Call Recording model for tracking voice provider calls."""
+    __tablename__ = "call_recordings"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every recording belongs to a workspace within
+    # its org. For playground-origin rows this is stamped from the active
+    # workspace at creation time; for webhook-origin rows the worker
+    # looks up the recording's agent and inherits its workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    call_short_id = Column(String(6), unique=True, nullable=False, index=True)  # 6-digit ID
+    status = Column(Enum(CallRecordingStatus), nullable=False, default=CallRecordingStatus.PENDING, index=True)
+    call_event = Column(String, nullable=True, index=True)  # Latest webhook event (e.g., call_started, call_ended)
+    source = Column(Enum(CallRecordingSource), nullable=False, default=CallRecordingSource.PLAYGROUND, index=True)
+    call_data = Column(JSON, nullable=True)  # JSON blob for provider response
+    provider_call_id = Column(String, nullable=True, index=True)  # Provider's call_id (e.g., Retell call_id)
+    provider_platform = Column(String, nullable=True)  # e.g., "retell", "vapi"
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=True)  # Reference to our agent
+    
+    # Link to EvaluatorResult for metric evaluations
+    evaluator_result_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("evaluator_results.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    
+    shard_id = Column(String(64), nullable=True, index=True)
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class EvaluatorResultPayload(Base):
+    """Heavy evaluator result fields stored on data shards when sharding is enabled."""
+
+    __tablename__ = "evaluator_result_payloads"
+
+    evaluator_result_id = Column(UUID(as_uuid=True), primary_key=True)
+    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    audio_s3_key = Column(String, nullable=True)
+    transcription = Column(String, nullable=True)
+    speaker_segments = Column(JSON, nullable=True)
+    metric_scores = Column(JSON, nullable=True)
+    call_data = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class CallRecordingPayload(Base):
+    """Heavy call recording fields stored on data shards when sharding is enabled."""
+
+    __tablename__ = "call_recording_payloads"
+
+    call_recording_id = Column(UUID(as_uuid=True), primary_key=True)
+    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    call_data = Column(JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class SyntheticCallTrace(Base):
+    """Catalog row for synthetic test call timing and OTLP traces."""
+
+    __tablename__ = "synthetic_call_traces"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    evaluator_result_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("evaluator_results.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id", ondelete="SET NULL"), nullable=True)
+    persona_id = Column(UUID(as_uuid=True), ForeignKey("personas.id", ondelete="SET NULL"), nullable=True)
+    scenario_id = Column(UUID(as_uuid=True), ForeignKey("scenarios.id", ondelete="SET NULL"), nullable=True)
+    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id", ondelete="SET NULL"), nullable=True)
+    call_recording_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_recordings.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    call_short_id = Column(String(6), nullable=True, index=True)
+    environment = Column(String(32), nullable=False, default="pre_prod")
+    provider_platform = Column(String(64), nullable=True)
+    transport = Column(String(32), nullable=False, default="phone")
+    tier = Column(String(32), nullable=False, default="black_box")
+    status = Column(String(32), nullable=False, default="open")
+    started_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
+    ended_at = Column(DateTime(timezone=True), nullable=True)
+    turn_count = Column(Integer, nullable=False, default=0)
+    response_latency_p50_ms = Column(Float, nullable=True)
+    response_latency_p90_ms = Column(Float, nullable=True)
+    response_latency_p95_ms = Column(Float, nullable=True)
+    component_aggregates = Column(JSON, nullable=True)
+    failure_flags = Column(JSON, nullable=True)
+    trace_version = Column(Integer, nullable=False, default=1)
+    shard_id = Column(String(64), nullable=True, index=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class SyntheticTracePayload(Base):
+    """Per-turn timing payload for a synthetic call trace."""
+
+    __tablename__ = "synthetic_trace_payloads"
+
+    synthetic_call_trace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("synthetic_call_traces.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    turns = Column(JSON, nullable=False, default=list)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class SyntheticTraceOtelPayload(Base):
+    """Full OTLP span tree for a synthetic call trace."""
+
+    __tablename__ = "synthetic_trace_otel_payloads"
+
+    synthetic_call_trace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("synthetic_call_traces.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    workspace_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    spans = Column(JSON, nullable=False, default=list)
+    trace_ids = Column(JSON, nullable=False, default=list)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class Alert(Base):
+    """Alert model for configuring monitoring alerts."""
+    __tablename__ = "alerts"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    
+    # Basic information
+    name = Column(String(255), nullable=False)
+    description = Column(String, nullable=True)
+    
+    # Metric condition configuration
+    metric_type = Column(String, nullable=False, default=AlertMetricType.NUMBER_OF_CALLS.value)
+    aggregation = Column(String, nullable=False, default=AlertAggregation.SUM.value)
+    operator = Column(String, nullable=False, default=AlertOperator.GREATER_THAN.value)
+    threshold_value = Column(Float, nullable=False)
+    time_window_minutes = Column(Integer, nullable=False, default=60)  # Time window for aggregation
+    
+    # Agent selection (JSON array of agent UUIDs, null means all agents)
+    agent_ids = Column(JSON, nullable=True)
+    
+    # Notification configuration
+    notify_frequency = Column(String, nullable=False, default=AlertNotifyFrequency.IMMEDIATE.value)
+    notify_emails = Column(JSON, nullable=True)  # Array of email addresses
+    notify_webhooks = Column(JSON, nullable=True)  # Array of webhook URLs (Slack, etc.)
+    
+    # Status
+    status = Column(String, nullable=False, default=AlertStatus.ACTIVE.value)
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+    
+    # Relationships
+    alert_history = relationship("AlertHistory", back_populates="alert", cascade="all, delete-orphan")
+
+
+class AlertHistory(Base):
+    """Alert history model for tracking triggered alerts."""
+    __tablename__ = "alert_history"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    alert_id = Column(UUID(as_uuid=True), ForeignKey("alerts.id"), nullable=False, index=True)
+    
+    # Trigger information
+    triggered_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
+    triggered_value = Column(Float, nullable=False)  # The actual value that triggered the alert
+    threshold_value = Column(Float, nullable=False)  # The threshold at time of trigger
+    
+    # Status tracking
+    status = Column(String, nullable=False, default=AlertHistoryStatus.TRIGGERED.value)
+    
+    # Notification tracking
+    notified_at = Column(DateTime(timezone=True), nullable=True)
+    notification_details = Column(JSON, nullable=True)  # Details of sent notifications
+    
+    # Resolution
+    acknowledged_at = Column(DateTime(timezone=True), nullable=True)
+    acknowledged_by = Column(String, nullable=True)
+    resolved_at = Column(DateTime(timezone=True), nullable=True)
+    resolved_by = Column(String, nullable=True)
+    resolution_notes = Column(String, nullable=True)
+    
+    # Additional context
+    context_data = Column(JSON, nullable=True)  # Additional data about the trigger
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    
+    # Relationships
+    alert = relationship("Alert", back_populates="alert_history")
+
+
+class CronJob(Base):
+    """Cron job model for scheduling automated evaluator runs."""
+    __tablename__ = "cron_jobs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=True, index=True)
+    
+    # Basic information
+    name = Column(String(255), nullable=False)
+    job_type = Column(String(64), nullable=False, default="evaluator_run")
+    is_system = Column(Boolean, nullable=False, default=False)
+    config = Column(JSON, nullable=False, default=dict)
+    cron_expression = Column(String(100), nullable=False)  # e.g., "0 9 * * 1-5"
+    timezone = Column(String(100), nullable=False, default="UTC")
+    
+    # Run configuration
+    max_runs = Column(Integer, nullable=False, default=10)
+    current_runs = Column(Integer, nullable=False, default=0)
+    
+    # Evaluators to trigger (JSON array of evaluator UUIDs)
+    evaluator_ids = Column(JSON, nullable=False)
+    
+    # Status
+    status = Column(String, nullable=False, default=CronJobStatus.ACTIVE.value)
+    
+    # Run tracking
+    next_run_at = Column(DateTime(timezone=True), nullable=True)
+    last_run_at = Column(DateTime(timezone=True), nullable=True)
+    
+    # Metadata
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+
+class TTSComparisonStatus(str, enum.Enum):
+    PENDING = "pending"
+    GENERATING = "generating"
+    EVALUATING = "evaluating"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TTSSampleStatus(str, enum.Enum):
+    PENDING = "pending"
+    GENERATING = "generating"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TTSReportJobStatus(str, enum.Enum):
+    PENDING = "pending"
+    PROCESSING = "processing"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TTSComparison(Base):
+    """TTS Comparison session for A/B testing voice providers."""
+    __tablename__ = "tts_comparisons"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every voice playground comparison belongs to
+    # a workspace within its org. Children (samples, report jobs, blind
+    # test shares) inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    simulation_id = Column(String(6), unique=True, index=True, nullable=True)
+
+    name = Column(String(255), nullable=True)
+    status = Column(String(50), nullable=False, default=TTSComparisonStatus.PENDING.value)
+
+    # 'benchmark' = traditional TTS A/B benchmark (provider-generated audio).
+    # 'blind_test_only' = standalone blind test built from existing recordings
+    # / uploads / past TTS samples; no TTS generation happens.
+    mode = Column(String(32), nullable=False, default="benchmark")
+
+    provider_a = Column(String(100), nullable=True)
+    model_a = Column(String(100), nullable=True)
+    voices_a = Column(JSON, nullable=True)
+
+    provider_b = Column(String(100), nullable=True)
+    model_b = Column(String(100), nullable=True)
+    voices_b = Column(JSON, nullable=True)
+
+    sample_texts = Column(JSON, nullable=False)
+    num_runs = Column(Integer, nullable=False, default=1)
+
+    blind_test_results = Column(JSON, nullable=True)
+    evaluation_summary = Column(JSON, nullable=True)
+
+    eval_stt_provider = Column(String(100), nullable=True)
+    eval_stt_model = Column(String(100), nullable=True)
+
+    celery_task_id = Column(String, nullable=True, index=True)
+    error_message = Column(String, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    samples = relationship("TTSSample", back_populates="comparison", cascade="all, delete-orphan")
+
+
+class TTSSample(Base):
+    """Individual TTS audio sample within a comparison."""
+    __tablename__ = "tts_samples"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent TTSComparison's workspace.
+    # Denormalized for fast filter-by-workspace listings without a join.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    provider = Column(String(100), nullable=True)
+    model = Column(String(100), nullable=True)
+    voice_id = Column(String(255), nullable=True)
+    voice_name = Column(String(255), nullable=True)
+    side = Column(String(1), nullable=True)  # "A" or "B"
+    sample_index = Column(Integer, nullable=False)
+    run_index = Column(Integer, nullable=False, default=0)
+
+    # 'tts' (default, audio is synthesized by a provider), 'recording' (audio
+    # is reused from a CallImportRow recording), or 'upload' (audio was
+    # uploaded by the user). Non-tts samples are marked completed up-front
+    # by the API and skipped by the generation worker.
+    source_type = Column(String(32), nullable=False, default="tts")
+    # When source_type == 'recording', references CallImportRow.id (no FK
+    # constraint to keep cascading deletes simple if a call import is later
+    # removed; the audio_s3_key is what's actually used).
+    source_ref_id = Column(UUID(as_uuid=True), nullable=True)
+
+    text = Column(String, nullable=False)
+    audio_s3_key = Column(String(512), nullable=True)
+    duration_seconds = Column(Float, nullable=True)
+    latency_ms = Column(Float, nullable=True)
+    ttfb_ms = Column(Float, nullable=True)
+
+    evaluation_metrics = Column(JSON, nullable=True)
+    status = Column(String(50), nullable=False, default=TTSSampleStatus.PENDING.value)
+    error_message = Column(String, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    comparison = relationship("TTSComparison", back_populates="samples")
+
+
+class TTSReportJob(Base):
+    """Asynchronous PDF report generation jobs for Voice Playground."""
+    __tablename__ = "tts_report_jobs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent TTSComparison's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    comparison_id = Column(UUID(as_uuid=True), ForeignKey("tts_comparisons.id", ondelete="CASCADE"), nullable=False, index=True)
+
+    status = Column(String(50), nullable=False, default=TTSReportJobStatus.PENDING.value)
+    format = Column(String(20), nullable=False, default="pdf")
+    filename = Column(String(255), nullable=True)
+    s3_key = Column(String(512), nullable=True)
+    error_message = Column(String, nullable=True)
+    celery_task_id = Column(String, nullable=True, index=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    comparison = relationship("TTSComparison")
+
+
+class TTSBlindTestShareStatus(str, enum.Enum):
+    OPEN = "open"
+    CLOSED = "closed"
+
+
+class TTSBlindTestShare(Base):
+    """A publicly sharable blind test for a TTSComparison.
+
+    The share_token is the capability: anyone holding it can open the public
+    form and submit a response. Each comparison has at most one share row.
+    """
+    __tablename__ = "tts_blind_test_shares"
+    __table_args__ = (
+        UniqueConstraint("comparison_id", name="uq_blind_test_shares_comparison"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    comparison_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("tts_comparisons.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent TTSComparison's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    share_token = Column(String(64), unique=True, nullable=False, index=True)
+
+    title = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+
+    # Internal notes visible only to the share creator (e.g. which voice
+    # corresponds to which side, source notes for standalone blind tests).
+    # Never exposed via the public blind test payload.
+    creator_notes = Column(Text, nullable=True)
+
+    # JSON list: [{ "key": str, "label": str, "type": "rating"|"comment", "scale": int? }]
+    custom_metrics = Column(JSON, nullable=False)
+
+    status = Column(String(20), nullable=False, default=TTSBlindTestShareStatus.OPEN.value)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    closed_at = Column(DateTime(timezone=True), nullable=True)
+    created_by = Column(String, nullable=True)
+
+    comparison = relationship("TTSComparison")
+    responses = relationship(
+        "TTSBlindTestResponse",
+        back_populates="share",
+        cascade="all, delete-orphan",
+    )
+
+
+class TTSBlindTestResponse(Base):
+    """A single rater's submission against a TTSBlindTestShare."""
+    __tablename__ = "tts_blind_test_responses"
+    __table_args__ = (
+        UniqueConstraint("share_id", "rater_email", name="uq_blind_test_response_share_email"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    share_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("tts_blind_test_shares.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    # Workspace isolation: mirrors the parent TTSBlindTestShare's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    rater_name = Column(String(255), nullable=False)
+    rater_email = Column(String(320), nullable=False, index=True)
+
+    # JSON list keyed by sample_index. Server stores in TRUE A/B orientation
+    # (already de-flipped from whatever the rater's UI showed):
+    # [{
+    #   "sample_index": int,
+    #   "preferred": "A" | "B",
+    #   "ratings_a": { metric_key: number },
+    #   "ratings_b": { metric_key: number },
+    #   "comment": str?
+    # }]
+    responses = Column(JSON, nullable=False)
+
+    ip = Column(String(64), nullable=True)
+    user_agent = Column(String(512), nullable=True)
+
+    submitted_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    share = relationship("TTSBlindTestShare", back_populates="responses")
+
+
+class PromptPartial(Base):
+    """Prompt Partial - Reusable prompt templates with version history."""
+    __tablename__ = "prompt_partials"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every prompt partial belongs to a workspace
+    # within its org. Versions inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    description = Column(String, nullable=True)
+    content = Column(Text, nullable=False)
+    tags = Column(JSON, nullable=True)
+    current_version = Column(Integer, nullable=False, default=1)
+    # Cached LLM-generated flowchart for imported production agent prompts.
+    # Shape: AgentFlowGraph JSON (nodes[], edges[]).
+    agent_flowchart = Column(JSON, nullable=True)
+    agent_flowchart_status = Column(String(20), nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    versions = relationship("PromptPartialVersion", back_populates="prompt_partial", cascade="all, delete-orphan", order_by="PromptPartialVersion.version.desc()")
+
+
+class PromptPartialVersion(Base):
+    """Version history for a prompt partial."""
+    __tablename__ = "prompt_partial_versions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    prompt_partial_id = Column(UUID(as_uuid=True), ForeignKey("prompt_partials.id", ondelete="CASCADE"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent PromptPartial's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    version = Column(Integer, nullable=False)
+    content = Column(Text, nullable=False)
+    change_summary = Column(String, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    created_by = Column(String, nullable=True)
+
+    prompt_partial = relationship("PromptPartial", back_populates="versions")
+
+    __table_args__ = (
+        UniqueConstraint('prompt_partial_id', 'version', name='uq_prompt_partial_version'),
+    )
+
+
+class CustomTTSVoice(Base):
+    """Organization-scoped custom TTS voice metadata."""
+    __tablename__ = "custom_tts_voices"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "provider", "voice_id", name="uq_custom_tts_voice_org_provider_voice_id"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider = Column(String(100), nullable=False, index=True)
+    voice_id = Column(String(255), nullable=False)
+    name = Column(String(255), nullable=False)
+    gender = Column(String(50), nullable=True)
+    accent = Column(String(100), nullable=True)
+    description = Column(Text, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+class PromptOptimizationRun(Base):
+    """A single GEPA prompt optimization run for an agent."""
+    __tablename__ = "prompt_optimization_runs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every optimization run belongs to a workspace
+    # within its org. Candidates inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    agent_id = Column(UUID(as_uuid=True), ForeignKey("agents.id"), nullable=False, index=True)
+    evaluator_id = Column(UUID(as_uuid=True), ForeignKey("evaluators.id"), nullable=True)
+    voice_bundle_id = Column(UUID(as_uuid=True), ForeignKey("voicebundles.id"), nullable=True)
+
+    seed_prompt = Column(Text, nullable=False)
+    best_prompt = Column(Text, nullable=True)
+    best_score = Column(Float, nullable=True)
+
+    status = Column(String(20), nullable=False, default=PromptOptimizationStatus.PENDING.value)
+    config = Column(JSON, nullable=True)
+    reflection_trace = Column(JSON, nullable=True)
+    metric_history = Column(JSON, nullable=True)
+
+    num_iterations = Column(Integer, nullable=True)
+    num_metric_calls = Column(Integer, nullable=True)
+
+    celery_task_id = Column(String, nullable=True, index=True)
+    error_message = Column(Text, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    candidates = relationship("PromptOptimizationCandidate", back_populates="optimization_run", cascade="all, delete-orphan")
+
+
+class PromptOptimizationCandidate(Base):
+    """A candidate prompt generated during an optimization run."""
+    __tablename__ = "prompt_optimization_candidates"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    optimization_run_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_runs.id", ondelete="CASCADE"), nullable=False, index=True)
+    # Workspace isolation: mirrors the parent PromptOptimizationRun's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    prompt_text = Column(Text, nullable=False)
+    score = Column(Float, nullable=True)
+    metric_breakdown = Column(JSON, nullable=True)
+    reflection_summary = Column(Text, nullable=True)
+
+    parent_candidate_id = Column(UUID(as_uuid=True), ForeignKey("prompt_optimization_candidates.id"), nullable=True)
+
+    is_accepted = Column(Boolean, nullable=False, default=False)
+    pushed_to_provider_at = Column(DateTime(timezone=True), nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    optimization_run = relationship("PromptOptimizationRun", back_populates="candidates")
+
+
+class TelephonyIntegration(Base):
+    """Per-organization telephony provider credentials and configuration.
+
+    Multiple rows per (organization_id, provider) are allowed so that an
+    organization can keep several Plivo / Exotel accounts side-by-side.
+    A partial unique index in migration 028 enforces at most one row with
+    is_default = TRUE per (org, provider); resolution falls back to that
+    default row when the caller does not pin a specific credential.
+    """
+
+    __tablename__ = "telephony_integrations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider = Column(String(50), nullable=False, default="plivo")
+    name = Column(String(255), nullable=True)  # Optional friendly name to disambiguate multiple credentials
+
+    auth_id = Column(String(255), nullable=False)
+    auth_token = Column(String(512), nullable=False)
+
+    verify_app_uuid = Column(String(255), nullable=True)
+    voice_app_id = Column(String(255), nullable=True)
+    sip_domain = Column(String(255), nullable=True)
+    masking_config = Column(JSON, nullable=True)
+
+    is_active = Column(Boolean, default=True, nullable=False)
+    is_default = Column(Boolean, default=False, nullable=False)
+    last_tested_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyPhoneNumber(Base):
+    """Inventory of telephony phone numbers owned by an organization."""
+
+    __tablename__ = "telephony_phone_numbers"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_number_org_phone"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    telephony_integration_id = Column(
+        UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=True, index=True
+    )
+
+    phone_number = Column(String(20), nullable=False, index=True)
+    country_iso2 = Column(String(2), nullable=True)
+    region = Column(String(100), nullable=True)
+    number_type = Column(String(20), nullable=True)
+    capabilities = Column(JSON, nullable=True)
+    provider_app_id = Column(String(255), nullable=True)
+
+    is_masking_pool = Column(Boolean, default=False, nullable=False)
+    inbound_enabled = Column(Boolean, default=True, nullable=False)
+    outbound_enabled = Column(Boolean, default=True, nullable=False)
+    source = Column(String(20), nullable=False, default="imported")
+    agent_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey(
+            "agents.id",
+            ondelete="SET NULL",
+            use_alter=True,
+            name="fk_telephony_phone_numbers_agent_id",
+        ),
+        nullable=True,
+        index=True,
+    )
+    is_active = Column(Boolean, default=True, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyDialTarget(Base):
+    """Org-scoped saved destination numbers for outbound test calls."""
+
+    __tablename__ = "telephony_dial_targets"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "phone_number", name="uq_telephony_dial_target_org_phone"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    phone_number = Column(String(20), nullable=False, index=True)
+    label = Column(String(255), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyVerifySession(Base):
+    """Tracks voice OTP verification sessions via telephony provider."""
+
+    __tablename__ = "telephony_verify_sessions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    provider_session_uuid = Column(String(255), nullable=False, unique=True, index=True)
+    recipient_number = Column(String(20), nullable=False)
+    channel = Column(String(10), nullable=False, default="voice")
+    status = Column(String(20), nullable=False, default="pending")
+    initiated_by = Column(String(255), nullable=True)
+    verify_app_uuid = Column(String(255), nullable=True)
+    verified_at = Column(DateTime(timezone=True), nullable=True)
+    expires_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class TelephonyMaskedSession(Base):
+    """Number-masking session between two parties through a middle number."""
+
+    __tablename__ = "telephony_masked_sessions"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    telephony_integration_id = Column(UUID(as_uuid=True), ForeignKey("telephony_integrations.id"), nullable=False)
+    masked_number_id = Column(
+        UUID(as_uuid=True), ForeignKey("telephony_phone_numbers.id"), nullable=False, index=True
+    )
+    masked_number = Column(String(20), nullable=False)
+    party_a_number = Column(String(20), nullable=False)
+    party_b_number = Column(String(20), nullable=False)
+    status = Column(String(20), nullable=False, default="active")
+    expires_at = Column(DateTime(timezone=True), nullable=True)
+    ended_at = Column(DateTime(timezone=True), nullable=True)
+    session_metadata = Column("metadata", JSON, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+
+class CallImportSchema(Base):
+    """Reusable Input Parameter schema for the call-uploads flow.
+
+    A schema is workspace-scoped: users define a named bundle of typed
+    Input Parameters once (e.g. "Standard Voice QA" with conversation_id +
+    recording_url + transcript + agent_name) and then map those parameters
+    to CSV/Excel headers each time they upload a new batch.
+
+    Every schema MUST contain exactly one parameter with
+    ``type='conversation_id'`` and ``is_required=True`` - that's the
+    mandatory identity field every imported row needs. A schema may
+    optionally include at most one ``recording_url`` parameter. The
+    invariant is enforced in app code on create/update (no DB-level
+    CHECK because the parent + children are written across two tables in
+    one transaction).
+    """
+
+    __tablename__ = "call_import_schemas"
+    __table_args__ = (
+        # Case-insensitive uniqueness is enforced via the matching partial
+        # index on ``LOWER(name)`` in the migration; this constraint here
+        # would be case-sensitive and is intentionally omitted to avoid
+        # confusing the user.
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+    created_by_user_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("users.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    parameters = relationship(
+        "CallImportSchemaParameter",
+        back_populates="schema",
+        cascade="all, delete-orphan",
+        order_by="CallImportSchemaParameter.ordering",
+    )
+
+
+class CallImportSchemaParameter(Base):
+    """A single typed parameter inside a :class:`CallImportSchema`.
+
+    ``type`` is one of the strings tracked by
+    :data:`app.models.enums.CallImportParameterType`. ``conversation_id``
+    is reserved for the mandatory identity parameter every schema must
+    contain.
+    """
+
+    __tablename__ = "call_import_schema_parameters"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    schema_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_schemas.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    name = Column(String(255), nullable=False)
+    type = Column(String(32), nullable=False)
+    description = Column(Text, nullable=True)
+    is_required = Column(Boolean, nullable=False, default=False)
+    # Stable ordering so the UI renders parameters in the order the
+    # schema author defined them (matters when conversation_id is pinned
+    # first and the user re-orders the rest).
+    ordering = Column(Integer, nullable=False, default=0)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    schema = relationship("CallImportSchema", back_populates="parameters")
+
+
+class CallImport(Base):
+    """Batch record for a CSV-driven call import job."""
+
+    __tablename__ = "call_imports"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    # Workspace isolation: every imported batch belongs to a workspace
+    # within its org. The /upload endpoint stamps it from the active
+    # workspace header (or the org's Default if absent).
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    created_by_user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=True)
+    last_updated_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+
+    # Telephony provider key (e.g. ``'exotel'``, ``'plivo'``). In the
+    # legacy one-shot ``POST /upload`` endpoint this is supplied with the
+    # file; in the three-stage flow (UPLOAD -> MAP -> IMPORT) the value
+    # isn't known until the IMPORT stage, so the column is nullable for
+    # ``uploaded`` / ``mapped`` batches.
+    provider = Column(String(50), nullable=True, default="exotel")
+    # Pin a specific telephony credential for this batch so the worker
+    # downloads recordings using *that* row instead of the org default.
+    # NULL preserves legacy behavior (resolve by provider + default).
+    telephony_integration_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("telephony_integrations.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    original_filename = Column(String(512), nullable=True)
+    # When the source file was a multi-sheet Excel workbook, this records
+    # the worksheet the rows came from (one batch per sheet). NULL for CSV
+    # uploads since CSV has no sheet concept.
+    sheet_name = Column(String(255), nullable=True)
+
+    # --- Source-file staging (UPLOAD stage) ---------------------------
+    # The raw CSV / Excel file is stored in S3 between stages so the
+    # user can come back later to MAP and IMPORT without re-uploading.
+    # ``source_s3_key`` is NULL on legacy batches that were imported via
+    # the one-shot endpoint (those batches stay read-only post-import).
+    source_s3_key = Column(Text, nullable=True)
+    source_format = Column(String(16), nullable=True)
+    source_size_bytes = Column(BigInteger, nullable=True)
+    source_content_type = Column(String(255), nullable=True)
+
+    # Snapshot of the file's sheets + headers captured at UPLOAD time
+    # so the MAP UI doesn't need to re-fetch the source bytes from S3.
+    # Shape: ``[{"name": str, "headers": [str, ...], "row_count": int}, ...]``.
+    available_sheets = Column(JSON, nullable=True)
+
+    # User's explicit "drop these columns" decision captured at MAP
+    # time. Was validation-only and ephemeral in the legacy flow; now
+    # persisted so the IMPORT stage can re-parse the file with the same
+    # mapping/skip intent.
+    skipped_columns = Column(JSON, nullable=False, default=list)
+    # Rows skipped at parse time (missing/invalid conversation_id or URL).
+    # Shape: ``[{"source_row": int, "reason": str, "message": str}, ...]``.
+    source_row_skips = Column(JSON, nullable=False, default=list)
+
+    # Free-text high-level segregation label. Powers the "Dataset" filter
+    # at the top of the imports page; multiple imports can share a value.
+    dataset = Column(String(255), nullable=True, index=True)
+
+    # Reusable Input Parameter schema this batch was uploaded against.
+    # NULL on legacy batches uploaded before the schema-driven flow
+    # shipped; those still render via ``column_mapping`` + ``extra_columns``
+    # + ``custom_column_mapping`` below.
+    schema_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_schemas.id", ondelete="RESTRICT"),
+        nullable=True,
+        index=True,
+    )
+    # New schema-driven mapping: ``{schema_parameter_name: csv_header}``.
+    # Populated for new uploads; empty dict on legacy batches.
+    parameter_mapping = Column(JSON, nullable=False, default=dict)
+
+    # Legacy free-form mapping (pre-schema-flow). Kept on the model so
+    # batches that were uploaded before the schema feature shipped still
+    # render correctly on the detail page; new uploads stop writing here.
+    # Keys: external_call_id (required), transcript, recording_url.
+    # (DB column ``external_call_id`` is now ``conversation_id``; this
+    # JSON key stays as-is for historical batches.)
+    # Values: original CSV header strings (preserve user casing for export).
+    column_mapping = Column(JSON, nullable=False, default=dict)
+    # Ordered list of additional CSV header strings the uploader wants
+    # preserved verbatim into the evaluation export CSV.
+    extra_columns = Column(JSON, nullable=False, default=list)
+    # User-defined ``{custom_field_name: csv_header}`` mappings on top of
+    # the three system fields above. Cells from the mapped CSV columns are
+    # preserved per row (keyed by the CSV header in ``raw_columns``) and
+    # surface in the evaluation export under the uploader-chosen name.
+    custom_column_mapping = Column(JSON, nullable=False, default=dict)
+
+    total_rows = Column(Integer, nullable=False, default=0)
+    completed_rows = Column(Integer, nullable=False, default=0)
+    failed_rows = Column(Integer, nullable=False, default=0)
+
+    status = Column(
+        Enum(CallImportStatus, values_callable=get_enum_values),
+        nullable=False,
+        default=CallImportStatus.PENDING,
+        index=True,
+    )
+    error_message = Column(Text, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    rows = relationship(
+        "CallImportRow",
+        back_populates="call_import",
+        cascade="all, delete-orphan",
+        order_by="CallImportRow.row_index",
+    )
+    tags = relationship(
+        "CallImportTag",
+        secondary="call_import_tag_assignments",
+        backref="call_imports",
+        lazy="selectin",
+    )
+    evaluations = relationship(
+        "CallImportEvaluation",
+        back_populates="call_import",
+        cascade="all, delete-orphan",
+    )
+
+
+class CallImportShardSlice(Base):
+    """Registry row: which shard stores a slice of rows for an import."""
+
+    __tablename__ = "call_import_shard_slices"
+
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    slice_id = Column(Integer, primary_key=True)
+    shard_id = Column(String(64), nullable=False, index=True)
+    row_index_min = Column(Integer, nullable=False)
+    row_index_max = Column(Integer, nullable=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+class CallImportRow(Base):
+    """A single row within a CallImport batch (one CSV line / one external call)."""
+
+    __tablename__ = "call_import_rows"
+    __table_args__ = (
+        UniqueConstraint("call_import_id", "row_index", name="uq_call_import_row_index"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True)
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    row_index = Column(Integer, nullable=False)
+    # Was historically named ``external_call_id``; renamed to
+    # ``conversation_id`` so the new schema-driven upload flow can refer
+    # to it by a single canonical name across the schema definition,
+    # exports, and downstream evaluation tables.
+    conversation_id = Column(String(255), nullable=False, index=True)
+    # Supplied via CSV for Exotel credentialed imports (required per row).
+    # Nullable in the schema for legacy rows imported before recording_url
+    # was mandatory on every Exotel upload.
+    recording_url = Column(Text, nullable=True)
+    # Date-only call recording date supplied by the import schema. Used
+    # for historical report comparisons without timezone/time ambiguity.
+    recording_date = Column(Date, nullable=True, index=True)
+    # The "production" transcript: the value supplied via the CSV
+    # upload mapping. Never overwritten by the diarisation worker —
+    # the worker writes its output into ``diarised_transcript`` so
+    # the user keeps both versions side by side.
+    transcript = Column(Text, nullable=True)
+    # Snapshot of the original CSV row keyed by the user's headers so the
+    # evaluation export can reproduce every column the uploader supplied
+    # (mapped + extra). NULL on legacy rows imported before this column.
+    raw_columns = Column(JSON, nullable=True)
+
+    # Where the value in ``transcript`` came from. ``csv`` = supplied via
+    # the upload mapping, ``edited`` = manually changed in the UI. NULL
+    # on rows that have never had a production transcript.
+    # (Worker-produced transcripts now live in ``diarised_transcript``
+    # and are tracked via ``diarised_transcript_*`` metadata below.)
+    transcript_source = Column(String(20), nullable=True)
+    # Provider/model recorded by the (legacy) post-hoc transcription
+    # worker. New worker runs leave these NULL and write into the
+    # ``diarised_transcript_*`` columns instead; kept on the model for
+    # backwards compatibility with pre-split rows that still carry the
+    # original transcription metadata here.
+    transcript_provider = Column(String(50), nullable=True)
+    transcript_model = Column(String(100), nullable=True)
+    # Lifecycle status for the legacy transcription workflow itself,
+    # independent of the row's recording-fetch ``status``. ``idle`` =
+    # no transcribe task has touched this column. New diarisation runs
+    # update ``diarised_transcript_status`` instead.
+    transcript_status = Column(
+        String(20),
+        nullable=False,
+        default="idle",
+    )
+    transcript_error = Column(Text, nullable=True)
+    transcribed_at = Column(DateTime(timezone=True), nullable=True)
+
+    # The "diarised" transcript: produced by the post-hoc
+    # transcription/diarisation worker. Stored separately so a manual
+    # diarisation run never clobbers the production transcript above.
+    # Evaluations can be configured to score against either column
+    # (see ``CallImportEvaluation.transcript_source``).
+    diarised_transcript = Column(Text, nullable=True)
+    # Provider/model the diarisation worker used. Surfaced in the UI
+    # as "Diarised via deepgram/nova-2" next to the diarised
+    # transcript section.
+    diarised_transcript_provider = Column(String(50), nullable=True)
+    diarised_transcript_model = Column(String(100), nullable=True)
+    # Lifecycle status for the diarisation workflow.
+    # ``idle`` = no diarisation task has run; ``pending``/``running`` =
+    # a Celery task is queued or in flight; ``completed``/``failed`` =
+    # terminal. Independent of ``transcript_status`` so the two
+    # transcripts can be in different lifecycle states.
+    diarised_transcript_status = Column(
+        String(20),
+        nullable=False,
+        default="idle",
+        server_default="idle",
+    )
+    diarised_transcript_error = Column(Text, nullable=True)
+    diarised_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Structured speaker turns produced by the diarisation worker —
+    # ``[{ "speaker": "agent"|"user"|"speaker_3", "text": "...",
+    #      "start": float, "end": float, "raw_speaker": "Speaker 1" }, ...]``
+    # The plain-text ``diarised_transcript`` above is a rendered view
+    # of this list (``<speaker>: <text>`` per line). When the worker
+    # cannot recover structured turns (no pyannote token / single-
+    # speaker recording / provider that doesn't surface segments) this
+    # column stays NULL and the plain-text path is still populated.
+    diarised_segments = Column(JSON, nullable=True)
+    # When True the ``agent`` <-> ``user`` mapping inside
+    # ``diarised_segments`` is inverted at render / export time. The
+    # worker writes the canonical mapping using the "first speaker is
+    # the agent" heuristic; reviewers can flip the toggle from the row
+    # detail panel without re-running diarisation.
+    diarised_speaker_swap = Column(
+        Boolean,
+        nullable=False,
+        default=False,
+        server_default="false",
+    )
+    # LLM that turned the STT plain-text output into structured
+    # ``diarised_segments``. The legacy diarisation worker used
+    # pyannote and left these NULL; the current path always runs an
+    # LLM with the operator-supplied (or default) ``diarised_prompt``
+    # below, and records exactly which model + prompt produced each
+    # row so reviewers can reproduce a specific run.
+    diarised_llm_provider = Column(String(50), nullable=True)
+    diarised_llm_model = Column(String(100), nullable=True)
+    diarised_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
+    diarised_prompt = Column(Text, nullable=True)
+    # Which diarisation pipeline produced this row's turns.
+    #   * ``"stt_llm"`` (default) — two-stage: STT then LLM diariser.
+    #     ``diarised_transcript_provider``/``_model`` describe the STT
+    #     side; ``diarised_llm_provider``/``_model`` the LLM side.
+    #   * ``"llm_only"`` — single-stage: audio fed straight to a
+    #     multimodal LLM. ``diarised_transcript_provider`` is stamped
+    #     with the sentinel ``"llm_only"``; the real model is on
+    #     ``diarised_llm_*``.
+    # Persisting it on the row (not just the run) lets the row detail
+    # panel render the right "Diarised via …" label even for ad-hoc
+    # standalone transcribes (no parent evaluation).
+    transcribe_mode = Column(
+        String(20),
+        nullable=False,
+        default="stt_llm",
+        server_default="stt_llm",
+    )
+
+    status = Column(
+        Enum(CallImportRowStatus, values_callable=get_enum_values),
+        nullable=False,
+        default=CallImportRowStatus.PENDING,
+        index=True,
+    )
+
+    recording_s3_key = Column(String(1024), nullable=True)
+    recording_content_type = Column(String(128), nullable=True)
+    recording_size_bytes = Column(Integer, nullable=True)
+
+    error_message = Column(Text, nullable=True)
+    attempts = Column(Integer, nullable=False, default=0)
+    celery_task_id = Column(String(255), nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    call_import = relationship("CallImport", back_populates="rows")
+
+
+@event.listens_for(CallImportRow, "before_insert")
+def _call_import_row_fill_workspace_id(_mapper, connection, target):
+    """Denormalize workspace_id from the parent import when omitted."""
+    if target.workspace_id is not None or target.call_import_id is None:
+        return
+    workspace_id = connection.execute(
+        select(CallImport.workspace_id).where(
+            CallImport.id == target.call_import_id
+        )
+    ).scalar_one_or_none()
+    if workspace_id is not None:
+        target.workspace_id = workspace_id
+
+
+class CallImportTag(Base):
+    """User-defined tag that can be attached to one or more call imports.
+
+    Tags coexist with the free-text ``CallImport.dataset`` column: dataset
+    is the primary high-level segregation, tags are an optional secondary
+    classification (an import can have many tags).
+    """
+
+    __tablename__ = "call_import_tags"
+    __table_args__ = (
+        UniqueConstraint("organization_id", "name", name="uq_call_import_tag_org_name"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    name = Column(String(255), nullable=False)
+    color = Column(String(32), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+
+class CallImportTagAssignment(Base):
+    """Many-to-many join table between CallImport and CallImportTag."""
+
+    __tablename__ = "call_import_tag_assignments"
+
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    tag_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_tags.id", ondelete="CASCADE"),
+        primary_key=True,
+        index=True,
+    )
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+class CallImportEvaluation(Base):
+    """Parent record for an evaluation run over a CallImport batch.
+
+    A user picks a subset of org ``Metric`` rows and triggers an evaluation;
+    we fan out one ``CallImportEvaluationRow`` per source row and roll up
+    counters as workers finish. Status mirrors ``CallImportStatus`` plus a
+    ``RUNNING`` value so the UI can distinguish "queued" from "in flight".
+    """
+
+    __tablename__ = "call_import_evaluations"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id"),
+        nullable=False,
+        index=True,
+    )
+    # Workspace isolation: mirrors the parent CallImport's workspace.
+    # Denormalized for fast filter-by-workspace listings without a join.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    created_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id"), nullable=True
+    )
+    last_updated_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+
+    # Optional user-supplied label for this run. Lets the UI surface
+    # something more meaningful than the UUID prefix (e.g. "March QA pass").
+    name = Column(String(255), nullable=True)
+
+    # JSON list of Metric UUID strings selected for this run. Stored as text
+    # in JSON so we don't have to deal with PG arrays of UUIDs / cascade
+    # delete policies when metrics are removed; the loader filters for
+    # still-existing org metrics at run time.
+    selected_metric_ids = Column(JSON, nullable=False, default=list)
+    # Hierarchy grouping snapshot: ``{parent_id_str: [child_id_str, ...]}``.
+    # Captures which children belong to which parent for THIS run so the UI
+    # / aggregator can reconstruct the tree even when the user selected
+    # only a subset of children, or after metrics are deleted / renamed.
+    # NULL on legacy rows means "no hierarchy" → fall back to flat
+    # ``selected_metric_ids`` semantics.
+    selected_metric_groups = Column(JSON, nullable=True)
+    # User-driven merges of LLM-discovered candidate sub-labels for
+    # ``allow_discovery`` parents. Shape:
+    # ``{"<parent_metric_id>": {"<from_slug>": "<to_slug>", ...}}``.
+    # Populated via ``POST .../discovered-labels/merge``; consulted by
+    # the discovered-labels aggregator, the flow graph builder, and the
+    # worker so that rows finishing AFTER a merge cannot reintroduce
+    # the merged-away slug. Empty dict on fresh rows.
+    discovered_label_aliases = Column(
+        JSON, nullable=False, default=dict, server_default="{}"
+    )
+
+    # Per-run opt-in for top-level metric discovery. When True, the LLM
+    # is asked to propose brand-new top-level metrics (boolean / rating /
+    # category) observed in the transcripts in addition to scoring the
+    # ``selected_metric_ids`` for the row. Candidates surface in a
+    # "Discovered metrics" panel on the evaluation's Flow tab and can
+    # be promoted into real standalone ``Metric`` rows via
+    # ``POST /metrics/from-discovered``. Defaults to False so existing
+    # evaluation creation payloads keep their previous behaviour.
+    discover_new_metrics = Column(
+        Boolean, nullable=False, default=False, server_default="false"
+    )
+    # Flat slug-to-slug redirect map for user merges + tombstones of
+    # discovered top-level metric candidates. Mirrors
+    # ``discovered_label_aliases`` but is NOT nested per parent —
+    # top-level metric discovery is not scoped to any parent. Shape::
+    #
+    #     {"<from_slug>": "<to_slug>", ...}
+    #
+    # An empty-string value tombstones the slug so workers finishing
+    # later can't re-introduce it.
+    discovered_metric_aliases = Column(
+        JSON, nullable=False, default=dict, server_default="{}"
+    )
+
+    # Run-level LLM config picked from the Run Evaluation modal. NULL on
+    # legacy rows means "use the historical OpenAI/gpt-4o default" — the
+    # worker checks for this and falls back accordingly. ``llm_credential_id``
+    # pins a specific AIProvider row when the org has multiple credentials
+    # for the same provider.
+    llm_provider = Column(String(50), nullable=True)
+    llm_model = Column(String(100), nullable=True)
+    llm_credential_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("aiproviders.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    llm_config = Column(JSON, nullable=True)
+    # Optional per-metric LLM override:
+    # ``{"<metric_id>": {"provider": "...", "model": "...", "credential_id": "..."}}``.
+    # Each entry overrides the run-level default for that metric only;
+    # missing keys = use run-level default. Stored as JSON so the UI can
+    # round-trip arbitrary {provider, model} pairs without migrations.
+    metric_llm_overrides = Column(JSON, nullable=True)
+
+    # When ``auto_transcribe`` was set on the create payload, record the
+    # STT provider/model used so the UI can show "Auto-transcribed via
+    # deepgram/nova-2" on the evaluation header. ``stt_credential_id`` is
+    # untyped (no FK) because STT keys may live in either ``aiproviders``
+    # (OpenAI) or ``integrations`` (Deepgram, ElevenLabs) — the
+    # transcription service handles the lookup.
+    stt_provider = Column(String(50), nullable=True)
+    stt_model = Column(String(100), nullable=True)
+    stt_credential_id = Column(UUID(as_uuid=True), nullable=True)
+
+    # Run-level LLM diariser config. Used when the create-run /
+    # retry-run paths chain a ``transcribe_call_import_row_task``
+    # because the row is missing a diarised transcript. Persisted on
+    # the run so a retry uses the same diariser the original create
+    # call picked (unless the retry payload explicitly overrides).
+    diarisation_llm_provider = Column(String(50), nullable=True)
+    diarisation_llm_model = Column(String(100), nullable=True)
+    diarisation_llm_credential_id = Column(UUID(as_uuid=True), nullable=True)
+    diarisation_prompt = Column(Text, nullable=True)
+    # Mode the run was *created* with for its auto-transcribe step.
+    # Retry chains read this to decide whether to enqueue an STT+LLM
+    # transcribe or a single-stage multimodal LLM transcribe — without
+    # it we'd have to infer the mode from "stt_provider is NULL", which
+    # would silently break legacy rows that simply never configured
+    # auto-transcribe. See migration 041 for the column DDL.
+    transcribe_mode = Column(
+        String(20),
+        nullable=False,
+        default="stt_llm",
+        server_default="stt_llm",
+    )
+
+    # Which of the two transcripts on each ``CallImportRow`` this run
+    # scored against. ``'production'`` reads ``CallImportRow.transcript``
+    # (the CSV-supplied value); ``'diarised'`` reads
+    # ``CallImportRow.diarised_transcript`` (the worker output). When
+    # the user ticks both checkboxes in the Run Evaluation modal we
+    # create two ``CallImportEvaluation`` rows — one per source — so
+    # the two scorings can be compared side-by-side. Defaults to
+    # ``'production'`` so legacy runs (which always read the single
+    # historical ``transcript`` column) keep their semantics.
+    transcript_source = Column(
+        String(20),
+        nullable=False,
+        default="production",
+        server_default="production",
+    )
+
+    # Cached LLM-generated TLDR rendered above the Visualizations charts.
+    # Populated lazily by ``POST /evaluations/{eval_id}/insights`` so we
+    # never auto-burn LLM tokens on page load. Shape::
+    #   {"narrative": str, "patterns": [str, ...],
+    #    "generated_at": iso8601, "generated_at_completed_rows": int,
+    #    "provider": str, "model": str}
+    # NULL on rows that have never been summarised.
+    tldr_summary = Column(JSON, nullable=True)
+
+    # Cached LLM-generated user insights for External Audit PDF section 03.
+    # Populated by a background Celery job triggered alongside TLDR generation.
+    # Shape: EvaluationUserInsightsState JSON (status, insights[], progress, …).
+    user_insights = Column(JSON, nullable=True)
+
+    # Cached per-metric failure clustering for internal diagnostics PDF/UI.
+    # Shape: EvaluationMetricClustersState JSON (status, groups[], …).
+    metric_clusters = Column(JSON, nullable=True)
+
+    # Cached LLM-generated prompt improvement suggestions keyed to an
+    # imported agent (PromptPartial tagged __imported_agent__).
+    # Shape: EvaluationPromptImprovementsState JSON.
+    prompt_improvements = Column(JSON, nullable=True)
+
+    # Cached LLM explanations for week-over-week metric deltas keyed by
+    # baseline evaluation id + completed row counts.
+    period_delta_explanations = Column(JSON, nullable=True)
+
+    status = Column(String(20), nullable=False, default="pending", index=True)
+
+    total_rows = Column(Integer, nullable=False, default=0)
+    completed_rows = Column(Integer, nullable=False, default=0)
+    failed_rows = Column(Integer, nullable=False, default=0)
+    # Flexprice pass-level delta billing watermark: rows already emitted
+    # on ``call_import.evaluation_completed`` for this evaluation run.
+    billed_completed_rows = Column(
+        Integer, nullable=False, default=0, server_default="0"
+    )
+    error_message = Column(Text, nullable=True)
+    celery_group_id = Column(String(255), nullable=True)
+
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    finished_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    call_import = relationship("CallImport", back_populates="evaluations")
+    row_results = relationship(
+        "CallImportEvaluationRow",
+        back_populates="evaluation",
+        cascade="all, delete-orphan",
+    )
+
+
+class CallImportEvaluationRow(Base):
+    """Per-source-row scoring output for a CallImportEvaluation parent."""
+
+    __tablename__ = "call_import_evaluation_rows"
+    __table_args__ = (
+        UniqueConstraint(
+            "evaluation_id", "call_import_row_id", name="uq_call_import_evaluation_row"
+        ),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    call_import_row_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_rows.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    status = Column(String(20), nullable=False, default="pending", index=True)
+    # Same shape as EvaluatorResult.metric_scores: {metric_id_str: {value, type, metric_name, ...}}
+    metric_scores = Column(JSON, nullable=False, default=dict)
+    error_message = Column(Text, nullable=True)
+    celery_task_id = Column(String(255), nullable=True)
+
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    finished_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    evaluation = relationship("CallImportEvaluation", back_populates="row_results")
+    source_row = relationship("CallImportRow")
+
+
+@event.listens_for(CallImportEvaluationRow, "before_insert")
+def _call_import_evaluation_row_fill_workspace_id(_mapper, connection, target):
+    """Denormalize workspace_id from the parent evaluation when omitted."""
+    if target.workspace_id is not None or target.evaluation_id is None:
+        return
+    workspace_id = connection.execute(
+        select(CallImportEvaluation.workspace_id).where(
+            CallImportEvaluation.id == target.evaluation_id
+        )
+    ).scalar_one_or_none()
+    if workspace_id is not None:
+        target.workspace_id = workspace_id
+
+
+class MetricStudioRun(Base):
+    """Ad-hoc metric experiment run in Metrics Studio."""
+
+    __tablename__ = "metric_studio_runs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    created_by_user_id = Column(
+        UUID(as_uuid=True), ForeignKey("users.id"), nullable=True
+    )
+
+    name = Column(String(255), nullable=True)
+    selected_metric_ids = Column(JSON, nullable=False, default=list)
+    selected_metric_groups = Column(JSON, nullable=True)
+    transcript_source = Column(
+        String(20),
+        nullable=False,
+        default="diarised",
+        server_default="diarised",
+    )
+
+    llm_provider = Column(String(50), nullable=True)
+    llm_model = Column(String(100), nullable=True)
+    llm_credential_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("aiproviders.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    llm_config = Column(JSON, nullable=True)
+    metric_llm_overrides = Column(JSON, nullable=True)
+
+    status = Column(String(20), nullable=False, default="pending", index=True)
+    total_items = Column(Integer, nullable=False, default=0)
+    completed_items = Column(Integer, nullable=False, default=0)
+    failed_items = Column(Integer, nullable=False, default=0)
+    error_message = Column(Text, nullable=True)
+
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    finished_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    results = relationship(
+        "MetricStudioRunResult",
+        back_populates="run",
+        cascade="all, delete-orphan",
+    )
+
+
+class MetricStudioRunResult(Base):
+    """Per-source scoring output for a MetricStudioRun."""
+
+    __tablename__ = "metric_studio_run_results"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    run_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("metric_studio_runs.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    source_kind = Column(String(40), nullable=False)
+    source_ref = Column(String(255), nullable=False)
+    display_label = Column(String(512), nullable=True)
+    source_metadata = Column(JSON, nullable=True)
+
+    status = Column(String(20), nullable=False, default="pending", index=True)
+    metric_scores = Column(JSON, nullable=False, default=dict)
+    error_message = Column(Text, nullable=True)
+    celery_task_id = Column(String(255), nullable=True)
+
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    finished_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+    run = relationship("MetricStudioRun", back_populates="results")
+
+
+class CallImportEvaluationReportSnapshot(Base):
+    """Persisted PDF-report aggregate used for period-over-period deltas."""
+
+    __tablename__ = "call_import_evaluation_report_snapshots"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    period_label = Column(String(64), nullable=True, index=True)
+    period_start = Column(Date, nullable=True, index=True)
+    period_end = Column(Date, nullable=True, index=True)
+    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
+    selected_metric_ids = Column(JSON, nullable=False, default=list, server_default="[]")
+    metric_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
+    insight_aggregates = Column(JSON, nullable=False, default=list, server_default="[]")
+    narrative = Column(JSON, nullable=True)
+    total_calls = Column(Integer, nullable=False, default=0)
+    selected_metric_count = Column(Integer, nullable=False, default=0)
+    total_metric_count = Column(Integer, nullable=False, default=0)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
+
+
+class CallImportEvaluationPdfReport(Base):
+    """Stored PDF artifact for a call import evaluation report generation."""
+
+    __tablename__ = "call_import_evaluation_pdf_reports"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    evaluation_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    call_import_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_imports.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+    snapshot_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("call_import_evaluation_report_snapshots.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    vendor_name = Column(String(120), nullable=False)
+    report_type = Column(String(20), nullable=False, default="external")
+    filename = Column(String(255), nullable=True)
+    s3_key = Column(String(512), nullable=True)
+    report_config = Column(JSON, nullable=False, default=dict, server_default="{}")
+    cache_fingerprint = Column(String(64), nullable=True)
+    created_by = Column(String, nullable=True)
+    created_by_user_id = Column(UUID(as_uuid=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+# ---------------------------------------------------------------------------
+# Judge Alignment (AlignEval-style hybrid integration)
+#
+# Three tables back the "Judge Alignment" surface:
+#   - JudgeDataset:     a labeled dataset materialised from one of three sources
+#                       (voice transcripts, existing Metric/Evaluator outputs,
+#                       or a generic CSV upload). Holds the dataset's source
+#                       config + which fields play the role of input/output.
+#   - JudgeSample:      one row in a dataset (input/output pair plus an
+#                       optional binary pass/fail human label).
+#   - JudgeRun:         a single run of an LLM-judge (existing Evaluator) over
+#                       a subset of samples, with computed alignment metrics
+#                       (precision/recall/F1/Cohen's kappa) and per-sample
+#                       predictions. Optionally links to a GEPA optimization
+#                       run when the user kicks off prompt tuning from a
+#                       dataset.
+# ---------------------------------------------------------------------------
+
+
+class JudgeDataset(Base):
+    """Container for binary-labeled samples used to calibrate an LLM-judge."""
+
+    __tablename__ = "judge_datasets"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    # Workspace isolation: every judge dataset belongs to a workspace
+    # within its org. Samples and runs inherit this workspace_id.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    name = Column(String(255), nullable=False)
+    description = Column(Text, nullable=True)
+
+    # One of: "transcript", "metric_output", "csv"
+    source_type = Column(String(32), nullable=False, index=True)
+    # Source-specific config. Examples:
+    #   transcript:     {"transcription_ids": [...]} or {"agent_id": "..."}
+    #   metric_output:  {"metric_id": "...", "evaluator_id": "..."}
+    #   csv:            {"s3_key": "...", "filename": "..."}
+    source_config = Column(JSON, nullable=False, default=dict)
+
+    # Field roles - which textual content is "input" vs "output" for the judge.
+    # For voice transcripts both default to the transcript text but can be
+    # tightened (e.g. agent-only turns vs full conversation).
+    input_field = Column(String(64), nullable=False, default="input")
+    output_field = Column(String(64), nullable=False, default="output")
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    samples = relationship(
+        "JudgeSample",
+        back_populates="dataset",
+        cascade="all, delete-orphan",
+        order_by="JudgeSample.created_at",
+    )
+    runs = relationship(
+        "JudgeRun",
+        back_populates="dataset",
+        cascade="all, delete-orphan",
+        order_by="JudgeRun.created_at.desc()",
+    )
+
+
+class JudgeSample(Base):
+    """One labelable input/output pair within a JudgeDataset."""
+
+    __tablename__ = "judge_samples"
+    __table_args__ = (
+        UniqueConstraint("dataset_id", "external_id", name="uq_judge_samples_dataset_external"),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    dataset_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    # Stable identifier within the source (e.g. transcription UUID, CSV row id).
+    # Used to dedupe re-imports and link back to the originating record.
+    external_id = Column(String(128), nullable=True, index=True)
+
+    input_text = Column(Text, nullable=False)
+    output_text = Column(Text, nullable=False)
+
+    # Binary human label: "pass" | "fail" | null (unlabeled).
+    # Stored as string (rather than enum) so it stays trivially extendable.
+    label = Column(String(16), nullable=True, index=True)
+    labeled_by = Column(String(255), nullable=True)
+    labeled_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Source-specific context (e.g. agent_id, original metric value, csv row).
+    extra = Column(JSON, nullable=True)
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+
+    dataset = relationship("JudgeDataset", back_populates="samples")
+
+
+class JudgeRun(Base):
+    """One execution of an LLM-judge against a JudgeDataset, with alignment metrics."""
+
+    __tablename__ = "judge_runs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    dataset_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("judge_datasets.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    organization_id = Column(
+        UUID(as_uuid=True), ForeignKey("organizations.id"), nullable=False, index=True
+    )
+    # Workspace isolation: mirrors the parent JudgeDataset's workspace.
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="RESTRICT"),
+        nullable=False,
+        index=True,
+    )
+
+    # Reuses the existing Evaluator row (its custom_prompt + llm_provider + llm_model
+    # define the judge under test). Nullable so a run may target an inline prompt
+    # in the future without inflating the Evaluator table.
+    evaluator_id = Column(
+        UUID(as_uuid=True), ForeignKey("evaluators.id", ondelete="SET NULL"), nullable=True, index=True
+    )
+
+    # Which subset was scored: "all" | "dev" | "test"
+    split = Column(String(16), nullable=False, default="all")
+
+    # Snapshot of the model used (so a later Evaluator edit doesn't rewrite history).
+    llm_provider = Column(String(64), nullable=True)
+    llm_model = Column(String(128), nullable=True)
+
+    # Computed alignment metrics:
+    #   {"precision": float, "recall": float, "f1": float, "kappa": float,
+    #    "tp": int, "fp": int, "tn": int, "fn": int, "n": int}
+    metrics = Column(JSON, nullable=True)
+
+    # Per-sample predictions, keyed by sample_id (UUID string):
+    #   {sample_id: {"prediction": "pass"|"fail", "explanation": str, "raw": str}}
+    predictions = Column(JSON, nullable=True)
+
+    # Run lifecycle.
+    status = Column(String(20), nullable=False, default="pending", index=True)
+    error_message = Column(Text, nullable=True)
+    celery_task_id = Column(String, nullable=True, index=True)
+
+    # Optional link to a GEPA optimization run kicked off from this dataset.
+    gepa_optimization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("prompt_optimization_runs.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    created_by = Column(String, nullable=True)
+
+    dataset = relationship("JudgeDataset", back_populates="runs")
+
+
+class UsageCostRecomputeJob(Base):
+    """Async job tracking for retroactive usage cost recompute."""
+
+    __tablename__ = "usage_cost_recompute_jobs"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    status = Column(String(32), nullable=False, default="pending", server_default="pending")
+    model = Column(String(255), nullable=True)
+    usage_kind = Column(String(16), nullable=True)
+    start_date = Column(Date, nullable=True)
+    end_date = Column(Date, nullable=True)
+    updated_rows = Column(BigInteger, nullable=False, default=0, server_default="0")
+    error_message = Column(String, nullable=True)
+    celery_task_id = Column(String(255), nullable=True, index=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
+    completed_at = Column(DateTime(timezone=True), nullable=True)
+
+
+class LLMUsageDaily(Base):
+    """Daily LLM/STT usage rollups for org-scoped Usage reporting."""
+
+    __tablename__ = "llm_usage_daily"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    organization_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("organizations.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    workspace_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("workspaces.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+    product_section = Column(String(64), nullable=False, index=True)
+    model = Column(String(255), nullable=False, index=True)
+    context = Column(JSONB, nullable=False, server_default="{}", default=dict)
+    usage_date = Column(Date, nullable=False, index=True)
+    usage_kind = Column(String(16), nullable=False, default="llm", server_default="llm")
+    prompt_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    completion_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    cache_read_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    cache_creation_tokens = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    reasoning_tokens = Column(BigInteger, nullable=False, default=0, server_default="0")
+    audio_seconds = Column(BigInteger, nullable=False, default=0, server_default="0")
+    tts_characters = Column(BigInteger, nullable=False, default=0, server_default="0")
+    call_count = Column(BigInteger, nullable=False, default=0, server_default="0")
+    input_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    output_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    cache_read_cost_micro_usd = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    cache_creation_cost_micro_usd = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    reasoning_cost_micro_usd = Column(
+        BigInteger, nullable=False, default=0, server_default="0"
+    )
+    audio_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    tts_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    total_cost_micro_usd = Column(BigInteger, nullable=False, default=0, server_default="0")
+    pricing_rate_source = Column(String(16), nullable=True)
+    pricing_rate_id = Column(UUID(as_uuid=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
+    )
diff --git a/app/models/schemas.py b/app/models/schemas.py
index 40e988e9..9a26c892 100644
--- a/app/models/schemas.py
+++ b/app/models/schemas.py
@@ -1,5450 +1,5451 @@
-"""Pydantic schemas for request/response validation."""
-
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator, validator
-import re
-from typing import Optional, List, Dict, Any, Literal
-from datetime import date, datetime
-from uuid import UUID
-from app.models.enums import (
-    EvaluationType, EvaluationStatus, EvaluatorResultStatus, RoleEnum, InvitationStatus,
-    LanguageEnum, CallTypeEnum, CallMediumEnum, GenderEnum, AccentEnum, BackgroundNoiseEnum,
-    IntegrationPlatform, ModelProvider, CredentialRoutingMode, GatewayInterfaceMode, VoiceBundleType, TestAgentConversationStatus,
-    MetricType, MetricCategory, MetricTrigger, CallRecordingStatus, AlertMetricType, AlertAggregation,
-    AlertOperator, AlertNotifyFrequency, AlertStatus, AlertHistoryStatus, CronJobStatus,
-    CallImportStatus, CallImportRowStatus, CallImportParameterType,
-)
-
-
-
-# Audio File Schemas
-class AudioFileBase(BaseModel):
-    """Base audio file schema."""
-
-    filename: str
-    format: str
-
-
-class AudioFileCreate(AudioFileBase):
-    """Schema for audio file creation."""
-
-    file_size: int
-    duration: Optional[float] = None
-    sample_rate: Optional[int] = None
-    channels: Optional[int] = None
-
-
-class AudioFileResponse(AudioFileBase):
-    """Schema for audio file response."""
-
-    id: UUID
-    file_size: int
-    duration: Optional[float] = None
-    sample_rate: Optional[int] = None
-    channels: Optional[int] = None
-    uploaded_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# Evaluation Schemas
-class EvaluationCreate(BaseModel):
-    """Schema for creating an evaluation."""
-
-    audio_id: UUID
-    reference_text: Optional[str] = None
-    evaluation_type: EvaluationType
-    model_name: Optional[str] = Field(None, description="Model to use for evaluation")
-    metrics: Optional[List[str]] = Field(
-        default=["wer", "latency"], description="Metrics to calculate"
-    )
-
-    @field_validator("metrics")
-    @classmethod
-    def validate_metrics(cls, v):
-        """Validate metrics list."""
-        allowed_metrics = ["wer", "cer", "latency", "quality_score", "rtf"]
-        if v:
-            invalid = [m for m in v if m not in allowed_metrics]
-            if invalid:
-                raise ValueError(f"Invalid metrics: {invalid}")
-        return v
-
-
-class EvaluationResponse(BaseModel):
-    """Schema for evaluation response."""
-
-    id: UUID
-    audio_id: UUID
-    reference_text: Optional[str] = None
-    evaluation_type: EvaluationType
-    model_name: Optional[str] = None
-    status: EvaluationStatus
-    metrics_requested: Optional[List[str]] = None
-    created_at: datetime
-    started_at: Optional[datetime] = None
-    completed_at: Optional[datetime] = None
-    error_message: Optional[str] = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class EvaluationStatusResponse(BaseModel):
-    """Schema for evaluation status response."""
-
-    id: UUID
-    status: EvaluationStatus
-    created_at: datetime
-    completed_at: Optional[datetime] = None
-    error_message: Optional[str] = None
-
-
-# Evaluation Result Schemas
-class EvaluationResultResponse(BaseModel):
-    """Schema for evaluation result response."""
-
-    evaluation_id: UUID
-    status: EvaluationStatus
-    transcript: Optional[str] = None
-    metrics: Dict[str, Any]
-    processing_time: Optional[float] = None
-    model_used: Optional[str] = None
-    created_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class MetricsResponse(BaseModel):
-    """Schema for metrics breakdown."""
-
-    evaluation_id: UUID
-    metrics: Dict[str, Any]
-    processing_time: Optional[float] = None
-
-
-# Comparison Schema
-class ComparisonRequest(BaseModel):
-    """Schema for comparing multiple evaluations."""
-
-    evaluation_ids: List[UUID] = Field(..., min_length=2, description="At least 2 evaluation IDs to compare")
-
-
-class ComparisonResponse(BaseModel):
-    """Schema for comparison results."""
-
-    evaluations: List[EvaluationResultResponse]
-    comparison_metrics: Dict[str, Any]
-
-
-# API Key Schemas
-class APIKeyCreate(BaseModel):
-    """Schema for creating API key."""
-
-    name: Optional[str] = None
-
-
-class APIKeyResponse(BaseModel):
-    """Schema for API key response."""
-
-    id: UUID
-    key: str
-    name: Optional[str] = None
-    is_active: bool
-    created_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# Generic Response Schemas
-class MessageResponse(BaseModel):
-    """Generic message response."""
-
-    message: str
-
-
-class ErrorResponse(BaseModel):
-    """Error response schema."""
-
-    detail: str
-
-# ============================================
-# VAIOPS SCHEMAS - Voice AI Ops
-# ============================================
-
-# Enums moved to enums.py
-
-
-# Agent Schemas
-class AgentCreate(BaseModel):
-    """Schema for creating a new agent"""
-    name: str = Field(..., min_length=1, max_length=255)
-    phone_number: Optional[str] = None
-    language: LanguageEnum = LanguageEnum.ENGLISH
-    description: str = Field(..., min_length=1)
-    call_type: CallTypeEnum = CallTypeEnum.OUTBOUND
-    call_medium: CallMediumEnum = CallMediumEnum.PHONE_CALL
-    telephony_phone_number_id: Optional[UUID] = None
-    voice_bundle_id: UUID = Field(..., description="Required voice bundle for test agent execution")
-    ai_provider_id: Optional[UUID] = None
-    voice_ai_integration_id: Optional[UUID] = None
-    voice_ai_agent_id: Optional[str] = None
-    provider_prompt: Optional[str] = None
-    silence_hangup_secs: int = Field(
-        default=15,
-        ge=0,
-        le=600,
-        description="End live calls after this many seconds of silence (0 disables)",
-    )
-
-    @field_validator('description')
-    @classmethod
-    def description_min_words(cls, v: str) -> str:
-        if len(v.split()) < 10:
-            raise ValueError('Description must be at least 10 words.')
-        return v
-
-    @field_validator('phone_number')
-    @classmethod
-    def phone_number_format(cls, v: Optional[str]) -> Optional[str]:
-        if v is not None and v != '':
-            import re
-            if not re.fullmatch(r'[\d+]+', v):
-                raise ValueError('Phone number must contain only digits and the + character.')
-        return v
-
-    @model_validator(mode='after')
-    def validate_phone_number(self):
-        """Ensure phone_number is provided when call_medium is phone_call"""
-        if self.call_medium == CallMediumEnum.PHONE_CALL and not self.phone_number:
-            raise ValueError('phone_number is required when call_medium is phone_call')
-        return self
-
-    model_config = ConfigDict(json_schema_extra={
-            "example": {
-                "name": "Customer Support Bot",
-                "phone_number": "+1234567890",
-                "language": "en",
-                "description": "A customer support bot that handles inquiries about orders, returns, and general questions",
-                "call_type": "outbound",
-                "voice_bundle_id": "123e4567-e89b-12d3-a456-426614174000",
-                "voice_ai_integration_id": "123e4567-e89b-12d3-a456-426614174001",
-                "voice_ai_agent_id": "agent_abc123"
-            }
-        })
-
-
-class AgentUpdate(BaseModel):
-    """Schema for updating an agent"""
-    name: Optional[str] = None
-    phone_number: Optional[str] = None
-    language: Optional[LanguageEnum] = None
-    description: Optional[str] = None
-    call_type: Optional[CallTypeEnum] = None
-    call_medium: Optional[CallMediumEnum] = None
-    telephony_phone_number_id: Optional[UUID] = None
-    voice_bundle_id: Optional[UUID] = None
-    voice_ai_integration_id: Optional[UUID] = None
-    voice_ai_agent_id: Optional[str] = None
-    provider_prompt: Optional[str] = None
-    prompt_variables: Optional[Dict[str, str]] = None
-    silence_hangup_secs: Optional[int] = Field(default=None, ge=0, le=600)
-
-    @model_validator(mode='after')
-    def validate_voice_config(self):
-        """Validate voice configuration - both voice_bundle_id and voice_ai_integration_id can be provided independently"""
-        voice_bundle = self.voice_bundle_id
-        voice_ai_integration = self.voice_ai_integration_id
-        
-        # If voice_ai_integration_id is provided, voice_ai_agent_id must also be provided
-        if voice_ai_integration and not self.voice_ai_agent_id:
-            raise ValueError('voice_ai_agent_id is required when voice_ai_integration_id is provided.')
-        
-        return self
-    
-    @model_validator(mode='after')
-    def validate_phone_number(self):
-        """Ensure phone_number is provided when call_medium is phone_call"""
-        # Only validate if call_medium is being set to phone_call
-        if self.call_medium == CallMediumEnum.PHONE_CALL and not self.phone_number:
-            # If phone_number is not being updated, we need to check existing value
-            # This will be handled in the route
-            pass
-        return self
-
-
-
-class PreviewIntegrationAgentPromptRequest(BaseModel):
-    """Fetch a provider agent prompt before an EfficientAI agent exists."""
-    voice_ai_agent_id: str = Field(..., min_length=1)
-
-
-
-
-class PreviewIntegrationAgentPromptResponse(BaseModel):
-    provider_prompt: str
-
-
-
-
-class AgentPhoneAssignmentConflict(BaseModel):
-    """Another agent already owns this phone number."""
-    agent_id: UUID
-    agent_name: str
-    phone_number: str
-
-
-
-
-class AgentPhoneAssignmentCheckResponse(BaseModel):
-    """Result of checking whether a phone number is free to assign."""
-    available: bool
-    phone_number: Optional[str] = None
-    conflict: Optional[AgentPhoneAssignmentConflict] = None
-
-
-
-
-class TestPromptSectionResponse(BaseModel):
-    """One canonical section of a generated test agent prompt."""
-    key: str
-    title: str
-    content: str
-
-
-
-
-class GeneratedScenarioDraftResponse(BaseModel):
-    """LLM-generated scenario draft before persistence."""
-    name: str
-    description: str
-    goal: Optional[str] = None
-
-
-
-
-class GenerateTestPromptRequest(BaseModel):
-    """Stage 1: generate foundational test agent prompt from production prompt."""
-    production_prompt: str = Field(..., min_length=1)
-    agent_name: str = Field(..., min_length=1, max_length=255)
-    language: Optional[str] = None
-    call_type: Optional[str] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    credential_id: Optional[UUID] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    additional_context: Optional[str] = None
-
-
-
-
-class GenerateTestPromptResponse(BaseModel):
-    sections: List[TestPromptSectionResponse]
-    test_agent_prompt: str
-    provider: str
-    model: str
-
-
-
-
-class GenerateScenariosFromPromptRequest(BaseModel):
-    """Stage 2: generate scenario drafts from test agent prompt."""
-    test_agent_prompt: str = Field(..., min_length=1)
-    agent_name: str = Field(..., min_length=1, max_length=255)
-    scenario_count: int = Field(default=5, ge=1, le=10)
-    language: Optional[str] = None
-    call_type: Optional[str] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    credential_id: Optional[UUID] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    additional_context: Optional[str] = None
-
-
-
-
-class GenerateScenariosFromPromptResponse(BaseModel):
-    scenarios: List[GeneratedScenarioDraftResponse]
-    provider: str
-    model: str
-
-
-
-
-class GenerateTestSetupRequest(BaseModel):
-    """Convenience: run stage 1 then stage 2 sequentially."""
-    production_prompt: str = Field(..., min_length=1)
-    agent_name: str = Field(..., min_length=1, max_length=255)
-    scenario_count: int = Field(default=5, ge=1, le=10)
-    language: Optional[str] = None
-    call_type: Optional[str] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    credential_id: Optional[UUID] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    additional_context: Optional[str] = None
-
-
-
-
-class GenerateTestSetupResponse(BaseModel):
-    sections: List[TestPromptSectionResponse]
-    test_agent_prompt: str
-    scenarios: List[GeneratedScenarioDraftResponse]
-    provider: str
-    model: str
-
-
-
-class AgentResponse(BaseModel):
-    """Schema for agent response"""
-    id: UUID
-    agent_id: Optional[str] = None
-    name: str
-    phone_number: Optional[str] = None
-    language: LanguageEnum
-    description: Optional[str]
-    call_type: CallTypeEnum
-    call_medium: CallMediumEnum
-    telephony_phone_number_id: Optional[UUID] = None
-    voice_bundle_id: Optional[UUID]
-    ai_provider_id: Optional[UUID]
-    voice_ai_integration_id: Optional[UUID]
-    voice_ai_agent_id: Optional[str]
-    provider_prompt: Optional[str] = None
-    prompt_variables: Optional[Dict[str, str]] = None
-    silence_hangup_secs: int = Field(
-        default=15,
-        ge=0,
-        le=600,
-        description="End live calls after this many seconds of silence (0 disables)",
-    )
-    provider_prompt_synced_at: Optional[datetime] = None
-    created_at: datetime
-    updated_at: datetime
-
-    @field_validator('language', mode='before')
-    @classmethod
-    def convert_language(cls, v):
-        """Convert string to LanguageEnum (handles uppercase DB values like ENGLISH -> en)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            # Map old uppercase names to new values
-            language_map = {'english': 'en', 'spanish': 'es', 'french': 'fr', 'german': 'de', 
-                          'chinese': 'zh', 'japanese': 'ja', 'hindi': 'hi', 'arabic': 'ar'}
-            if v_lower in language_map:
-                return LanguageEnum(language_map[v_lower])
-            try:
-                return LanguageEnum(v_lower)
-            except ValueError:
-                for enum_member in LanguageEnum:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid LanguageEnum value: {v}")
-        return v
-
-    @field_validator('call_type', mode='before')
-    @classmethod
-    def convert_call_type(cls, v):
-        """Convert string to CallTypeEnum (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return CallTypeEnum(v_lower)
-            except ValueError:
-                for enum_member in CallTypeEnum:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid CallTypeEnum value: {v}")
-        return v
-
-    @field_validator('call_medium', mode='before')
-    @classmethod
-    def convert_call_medium(cls, v):
-        """Convert string to CallMediumEnum (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return CallMediumEnum(v_lower)
-            except ValueError:
-                for enum_member in CallMediumEnum:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid CallMediumEnum value: {v}")
-        return v
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# Persona Schemas
-class PersonaCreate(BaseModel):
-    """Schema for creating a new persona (TTS provider-tied voice identity)"""
-    name: str = Field(..., min_length=1, max_length=255)
-    gender: GenderEnum = GenderEnum.NEUTRAL
-    tts_provider: Optional[str] = None
-    tts_voice_id: Optional[str] = None
-    tts_voice_name: Optional[str] = None
-    is_custom: bool = False
-    description: Optional[str] = None
-    tts_config: Optional[Dict[str, Any]] = None
-    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
-    llm_max_tokens: Optional[int] = Field(None, gt=0, le=8192)
-    response_delay_ms: Optional[int] = Field(None, ge=0, le=10000)
-    max_turns: Optional[int] = Field(None, ge=1, le=100)
-    allow_interruptions: Optional[bool] = None
-
-    @model_validator(mode="after")
-    def validate_tts_config(self):
-        from app.services.personas.persona_tts_config import validate_persona_tts_config
-
-        validate_persona_tts_config(self.tts_provider, self.tts_config)
-        return self
-
-
-class PersonaUpdate(BaseModel):
-    """Schema for updating a persona"""
-    name: Optional[str] = None
-    gender: Optional[GenderEnum] = None
-    tts_provider: Optional[str] = None
-    tts_voice_id: Optional[str] = None
-    tts_voice_name: Optional[str] = None
-    is_custom: Optional[bool] = None
-    description: Optional[str] = None
-    tts_config: Optional[Dict[str, Any]] = None
-    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
-    llm_max_tokens: Optional[int] = Field(None, gt=0, le=8192)
-    response_delay_ms: Optional[int] = Field(None, ge=0, le=10000)
-    max_turns: Optional[int] = Field(None, ge=1, le=100)
-    allow_interruptions: Optional[bool] = None
-
-    @model_validator(mode="after")
-    def validate_tts_config(self):
-        from app.services.personas.persona_tts_config import validate_persona_tts_config
-
-        if self.tts_config is not None:
-            validate_persona_tts_config(self.tts_provider, self.tts_config)
-        return self
-
-
-class PersonaResponse(BaseModel):
-    """Schema for persona response"""
-    id: UUID
-    name: str
-    gender: str
-    tts_provider: Optional[str] = None
-    tts_voice_id: Optional[str] = None
-    tts_voice_name: Optional[str] = None
-    is_custom: bool = False
-    description: Optional[str] = None
-    tts_config: Optional[Dict[str, Any]] = None
-    llm_temperature: Optional[float] = None
-    llm_max_tokens: Optional[int] = None
-    response_delay_ms: Optional[int] = None
-    max_turns: Optional[int] = None
-    allow_interruptions: Optional[bool] = None
-    created_at: datetime
-    updated_at: datetime
-
-    @field_validator('gender', mode='before')
-    @classmethod
-    def convert_gender(cls, v):
-        if v is None:
-            return "neutral"
-        if isinstance(v, str):
-            return v.lower()
-        if hasattr(v, 'value'):
-            return v.value
-        return v
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class PersonaCloneRequest(BaseModel):
-    """Schema for cloning a persona"""
-    name: Optional[str] = None
-
-
-# Scenario Schemas
-
-class AgentPromptSourcesResponse(BaseModel):
-    """Prompt texts from an agent that can seed a persona description."""
-    agent_id: UUID
-    agent_name: str
-    test_agent_prompt: str
-    agent_prompt: str
-
-
-
-
-class GeneratePersonaPromptRequest(BaseModel):
-    """Generate a persona caller prompt from an agent prompt via LLM."""
-    agent_id: UUID
-    source: str = Field(default="auto", pattern="^(test_agent|agent|auto)$")
-    persona_name: Optional[str] = Field(None, max_length=255)
-    persona_gender: Optional[str] = None
-    additional_context: Optional[str] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    credential_id: Optional[UUID] = None
-    llm_config: Optional[Dict[str, Any]] = None
-
-
-
-
-class GeneratePersonaPromptResponse(BaseModel):
-    persona_prompt: str
-    source_used: str
-    provider: str
-    model: str
-
-
-# Scenario Schemas
-
-class ScenarioCreate(BaseModel):
-    """Schema for creating a new scenario"""
-    name: str = Field(..., min_length=1, max_length=255)
-    agent_id: Optional[UUID] = None
-    description: Optional[str] = None
-    required_info: Dict[str, str] = Field(default_factory=dict)
-
-
-class ScenarioUpdate(BaseModel):
-    """Schema for updating a scenario"""
-    name: Optional[str] = None
-    agent_id: Optional[UUID] = None
-    description: Optional[str] = None
-    required_info: Optional[Dict[str, str]] = None
-
-
-class ScenarioResponse(BaseModel):
-    """Schema for scenario response"""
-    id: UUID
-    name: str
-    agent_id: Optional[UUID]
-    description: Optional[str]
-    required_info: Dict[str, str]
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# ============================================
-# IAM & USER SCHEMAS
-# ============================================
-
-# User Schemas
-class UserCreate(BaseModel):
-    """Schema for creating a user."""
-    email: str = Field(..., description="User email address")
-    name: Optional[str] = None
-    password: Optional[str] = None  # Optional for invitation-based signup
-
-
-class UserUpdate(BaseModel):
-    """Schema for updating user profile."""
-    name: Optional[str] = None
-    first_name: Optional[str] = None
-    last_name: Optional[str] = None
-    email: Optional[str] = None
-
-
-class UserResponse(BaseModel):
-    """Schema for user response."""
-    id: UUID
-    email: str
-    name: Optional[str]
-    first_name: Optional[str]
-    last_name: Optional[str]
-    is_active: bool
-    created_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class OrganizationMemberResponse(BaseModel):
-    """Schema for organization member response."""
-    id: UUID
-    user_id: UUID
-    organization_id: UUID
-    role: RoleEnum
-    joined_at: datetime
-    user: UserResponse  # Include user details
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# Invitation Schemas
-class InvitationCreate(BaseModel):
-    """Schema for creating an invitation."""
-    email: str = Field(..., description="Email address of the user to invite")
-    role: RoleEnum = RoleEnum.READER
-
-
-class InvitationResponse(BaseModel):
-    """Schema for invitation response."""
-    id: UUID
-    organization_id: UUID
-    email: str
-    role: RoleEnum
-    status: InvitationStatus
-    expires_at: datetime
-    created_at: datetime
-    organization_name: Optional[str] = None  # Include organization name
-    invite_path: Optional[str] = None
-    invite_url: Optional[str] = None
-
-    @field_validator('role', mode='before')
-    @classmethod
-    def convert_role(cls, v):
-        """Convert string to RoleEnum (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return RoleEnum(v_lower)
-            except ValueError:
-                for enum_member in RoleEnum:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid RoleEnum value: {v}")
-        return v
-
-    @field_validator('status', mode='before')
-    @classmethod
-    def convert_status(cls, v):
-        """Convert string to InvitationStatus (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return InvitationStatus(v_lower)
-            except ValueError:
-                for enum_member in InvitationStatus:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid InvitationStatus value: {v}")
-        return v
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class InvitationUpdate(BaseModel):
-    """Schema for updating invitation (accept/decline)."""
-    token: str
-
-
-class RoleUpdate(BaseModel):
-    """Schema for updating user role in organization."""
-    role: RoleEnum
-
-
-# Profile Schemas
-class ProfileResponse(BaseModel):
-    """Schema for user profile response."""
-    id: UUID
-    email: str
-    name: Optional[str]
-    first_name: Optional[str]
-    last_name: Optional[str]
-    created_at: datetime
-    organizations: List[dict] = Field(default_factory=list)  # List of org memberships
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# ============================================
-# INTEGRATION SCHEMAS
-# ============================================
-
-class IntegrationCreate(BaseModel):
-    """Schema for creating an integration."""
-    platform: IntegrationPlatform
-    api_key: str = Field(..., description="Private API key for the platform")
-    public_key: Optional[str] = Field(None, description="Optional public API key (e.g. for Vapi)")
-    name: Optional[str] = Field(None, description="Optional friendly name for the integration")
-    routing_mode: CredentialRoutingMode = Field(
-        CredentialRoutingMode.INHERIT,
-        description="LLM routing preference: inherit org default, force gateway, or direct API key.",
-    )
-    is_default: Optional[bool] = Field(
-        None,
-        description=(
-            "Mark this credential as the default for the (org, platform). "
-            "If omitted and no default exists yet, this row becomes the default."
-        ),
-    )
-
-
-class IntegrationUpdate(BaseModel):
-    """Schema for updating an integration."""
-    name: Optional[str] = None
-    api_key: Optional[str] = None
-    public_key: Optional[str] = None
-    is_active: Optional[bool] = None
-    routing_mode: Optional[CredentialRoutingMode] = None
-
-
-class IntegrationResponse(BaseModel):
-    """Schema for integration response."""
-    id: UUID
-    organization_id: UUID
-    platform: IntegrationPlatform
-    name: Optional[str]
-    public_key: Optional[str] = None
-    is_active: bool
-    is_default: bool = False
-    routing_mode: CredentialRoutingMode = CredentialRoutingMode.INHERIT
-    effective_routing: Literal["inherit", "direct", "gateway", "bifrost", "litellm_proxy"] = "inherit"
-    created_at: datetime
-    updated_at: datetime
-    last_tested_at: Optional[datetime] = None
-    # Note: api_key is NOT included in response for security
-
-    @field_validator('platform', mode='before')
-    @classmethod
-    def convert_platform(cls, v):
-        """Convert string to IntegrationPlatform enum if needed (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            # Try lowercase first (enum value)
-            v_lower = v.lower()
-            try:
-                return IntegrationPlatform(v_lower)
-            except ValueError:
-                # Try to find by enum name (uppercase)
-                for enum_member in IntegrationPlatform:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid IntegrationPlatform value: {v}")
-        return v
-
-    @field_validator('routing_mode', mode='before')
-    @classmethod
-    def convert_routing_mode(cls, v):
-        if v is None:
-            return CredentialRoutingMode.INHERIT
-        if isinstance(v, str):
-            try:
-                return CredentialRoutingMode(v.lower())
-            except ValueError:
-                return CredentialRoutingMode.INHERIT
-        return v
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# ============================================
-# DATA SOURCES SCHEMAS
-# ============================================
-
-class S3ConnectionTest(BaseModel):
-    """Schema for testing S3 connection."""
-    bucket_name: str
-    region: str = "us-east-1"
-    access_key_id: str
-    secret_access_key: str
-    endpoint_url: Optional[str] = None
-
-
-class S3ConnectionTestResponse(BaseModel):
-    """Schema for S3 connection test response."""
-    success: bool
-    message: str
-    bucket_name: Optional[str] = None
-
-
-class S3FileInfo(BaseModel):
-    """Schema for S3 file information."""
-    key: str
-    filename: str
-    size: int
-    last_modified: str
-
-
-class S3ListFilesResponse(BaseModel):
-    """Schema for listing S3 files response."""
-    files: List[S3FileInfo]
-    total: int
-    prefix: Optional[str] = None
-
-
-class S3FolderInfo(BaseModel):
-    """Schema for S3 folder information."""
-    name: str
-    path: str
-
-
-class S3BrowseResponse(BaseModel):
-    """Schema for browsing S3 folders within an organization."""
-    folders: List[S3FolderInfo]
-    files: List[S3FileInfo]
-    current_path: str
-    organization_id: str
-
-
-class S3UploadResponse(BaseModel):
-    """Schema for S3 upload response."""
-    key: str
-    bucket: str
-    file_id: UUID
-    message: str
-
-
-# AIProvider Schemas
-_MAX_GATEWAY_EXTRA_HEADERS = 20
-
-
-def _validate_gateway_extra_headers(
-    value: Optional[Dict[str, Any]],
-) -> Optional[Dict[str, str]]:
-    if value is None:
-        return None
-    if not isinstance(value, dict):
-        raise ValueError("gateway_extra_headers must be a JSON object of string keys and values.")
-    if len(value) > _MAX_GATEWAY_EXTRA_HEADERS:
-        raise ValueError(
-            f"gateway_extra_headers supports at most {_MAX_GATEWAY_EXTRA_HEADERS} headers."
-        )
-    normalized: Dict[str, str] = {}
-    for raw_key, raw_val in value.items():
-        key = str(raw_key).strip()
-        if not key:
-            raise ValueError("gateway_extra_headers keys must be non-empty strings.")
-        if len(key) > 64 or any(ch.isspace() for ch in key):
-            raise ValueError(f"Invalid gateway header name: {key!r}")
-        if raw_val is None:
-            raise ValueError(f"gateway_extra_headers[{key!r}] must be a string value.")
-        val = str(raw_val).strip()
-        if not val:
-            raise ValueError(f"gateway_extra_headers[{key!r}] must be a non-empty string.")
-        if len(val) > 1024 or "\n" in val or "\r" in val:
-            raise ValueError(f"gateway_extra_headers[{key!r}] value is invalid.")
-        normalized[key] = val
-    return normalized or None
-
-
-class AIProviderCreate(BaseModel):
-    """Schema for creating an AI Provider."""
-    provider: ModelProvider
-    api_key: Optional[str] = Field(
-        None,
-        description=(
-            "Provider API key. Optional when routing via gateway with "
-            "gateway-managed credentials (passthrough_provider_keys: false)."
-        ),
-    )
-    name: Optional[str] = None
-    routing_mode: CredentialRoutingMode = Field(
-        CredentialRoutingMode.INHERIT,
-        description="LLM routing preference: inherit org default, force gateway, or direct API key.",
-    )
-    gateway_model: Optional[str] = Field(
-        None,
-        min_length=1,
-        max_length=255,
-        description="Bifrost custom model ID sent when routing via gateway.",
-    )
-    gateway_interface: GatewayInterfaceMode = Field(
-        GatewayInterfaceMode.INHERIT,
-        description="Bifrost API surface: inherit org default, LiteLLM shim, or native OpenAI-compatible.",
-    )
-    gateway_base_url: Optional[str] = Field(
-        None,
-        max_length=512,
-        description="Optional per-credential Bifrost/gateway base URL override.",
-    )
-    gateway_auth_header: Optional[str] = Field(
-        None,
-        max_length=64,
-        description="Auth header name for Bifrost (default x-bf-vk).",
-    )
-    gateway_auth_secret_env: Optional[str] = Field(
-        None,
-        max_length=128,
-        description="Environment variable name holding the gateway auth secret.",
-    )
-    gateway_auth_secret: Optional[str] = Field(
-        None,
-        description="Inline gateway auth secret (encrypted at rest). Alternative to env var.",
-    )
-    gateway_extra_headers: Optional[Dict[str, str]] = Field(
-        None,
-        description="Arbitrary HTTP headers sent with gateway-routed LiteLLM calls.",
-    )
-    enabled_models: Optional[List[str]] = Field(
-        None,
-        description=(
-            "Allowlisted model names for this credential. "
-            "Null or empty means all catalog models for the provider."
-        ),
-    )
-    is_default: Optional[bool] = Field(
-        None,
-        description=(
-            "Mark this credential as the default for the (org, provider). "
-            "If omitted and no default exists yet, this row becomes the default."
-        ),
-    )
-    endpoint_url: Optional[str] = Field(
-        None,
-        description="Provider endpoint URL (required for Azure OpenAI).",
-    )
-
-    @field_validator("api_key")
-    @classmethod
-    def validate_api_key(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("endpoint_url")
-    @classmethod
-    def validate_endpoint_url(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_model")
-    @classmethod
-    def validate_gateway_model(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_base_url")
-    @classmethod
-    def validate_gateway_base_url(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_auth_header")
-    @classmethod
-    def validate_gateway_auth_header(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        if not trimmed:
-            return None
-        if len(trimmed) > 64 or any(ch.isspace() for ch in trimmed):
-            raise ValueError("gateway_auth_header must be a single non-empty header name.")
-        return trimmed
-
-    @field_validator("gateway_auth_secret_env")
-    @classmethod
-    def validate_gateway_auth_secret_env(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        if not trimmed:
-            return None
-        if not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", trimmed):
-            raise ValueError(
-                "gateway_auth_secret_env must be a valid environment variable name."
-            )
-        return trimmed
-
-    @field_validator("gateway_auth_secret")
-    @classmethod
-    def validate_gateway_auth_secret(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_extra_headers")
-    @classmethod
-    def validate_gateway_extra_headers(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, str]]:
-        return _validate_gateway_extra_headers(v)
-
-    @field_validator("enabled_models")
-    @classmethod
-    def validate_enabled_models_create(cls, v: Optional[List[str]]) -> Optional[List[str]]:
-        if v is None:
-            return None
-        seen: set[str] = set()
-        out: list[str] = []
-        for item in v:
-            name = str(item).strip()
-            if not name or name in seen:
-                continue
-            seen.add(name)
-            out.append(name)
-        return out or None
-
-
-class AIProviderUpdate(BaseModel):
-    """Schema for updating an AI Provider."""
-    api_key: Optional[str] = Field(None, min_length=1)
-    name: Optional[str] = None
-    endpoint_url: Optional[str] = None
-    is_active: Optional[bool] = None
-    routing_mode: Optional[CredentialRoutingMode] = None
-    gateway_model: Optional[str] = Field(None, min_length=1, max_length=255)
-    gateway_interface: Optional[GatewayInterfaceMode] = None
-    gateway_base_url: Optional[str] = Field(None, max_length=512)
-    gateway_auth_header: Optional[str] = Field(None, max_length=64)
-    gateway_auth_secret_env: Optional[str] = Field(None, max_length=128)
-    gateway_auth_secret: Optional[str] = None
-    clear_gateway_auth_secret: bool = False
-    gateway_extra_headers: Optional[Dict[str, str]] = None
-    enabled_models: Optional[List[str]] = None
-
-    @field_validator("gateway_model")
-    @classmethod
-    def validate_gateway_model(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_base_url")
-    @classmethod
-    def validate_gateway_base_url_update(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_auth_header")
-    @classmethod
-    def validate_gateway_auth_header_update(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        if not trimmed:
-            return None
-        if len(trimmed) > 64 or any(ch.isspace() for ch in trimmed):
-            raise ValueError("gateway_auth_header must be a single non-empty header name.")
-        return trimmed
-
-    @field_validator("gateway_auth_secret_env")
-    @classmethod
-    def validate_gateway_auth_secret_env_update(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        if not trimmed:
-            return None
-        if not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", trimmed):
-            raise ValueError(
-                "gateway_auth_secret_env must be a valid environment variable name."
-            )
-        return trimmed
-
-    @field_validator("gateway_auth_secret")
-    @classmethod
-    def validate_gateway_auth_secret_update(cls, v: Optional[str]) -> Optional[str]:
-        if v is None:
-            return v
-        trimmed = v.strip()
-        return trimmed or None
-
-    @field_validator("gateway_extra_headers")
-    @classmethod
-    def validate_gateway_extra_headers_update(
-        cls, v: Optional[Dict[str, Any]]
-    ) -> Optional[Dict[str, str]]:
-        return _validate_gateway_extra_headers(v)
-
-    @field_validator("enabled_models")
-    @classmethod
-    def validate_enabled_models_update(cls, v: Optional[List[str]]) -> Optional[List[str]]:
-        if v is None:
-            return None
-        seen: set[str] = set()
-        out: list[str] = []
-        for item in v:
-            name = str(item).strip()
-            if not name or name in seen:
-                continue
-            seen.add(name)
-            out.append(name)
-        return out or None
-
-
-class AIProviderResponse(BaseModel):
-    """Schema for AI Provider response."""
-    id: UUID
-    provider: ModelProvider
-    api_key: Optional[str] = None  # Will be None in response for security
-    name: Optional[str]
-    endpoint_url: Optional[str] = None
-    is_active: bool
-    is_default: bool = False
-    routing_mode: CredentialRoutingMode = CredentialRoutingMode.INHERIT
-    gateway_model: Optional[str] = None
-    gateway_interface: GatewayInterfaceMode = GatewayInterfaceMode.INHERIT
-    gateway_base_url: Optional[str] = None
-    gateway_auth_header: Optional[str] = None
-    gateway_auth_secret_env: Optional[str] = None
-    has_gateway_auth_secret: bool = False
-    gateway_extra_headers: Optional[Dict[str, str]] = None
-    enabled_models: Optional[List[str]] = None
-    gateway_managed: bool = False
-    effective_routing: Literal["inherit", "direct", "gateway", "bifrost", "litellm_proxy"] = "inherit"
-    effective_gateway_interface: Literal["litellm_shim", "native_openai"] = "litellm_shim"
-    created_at: datetime
-    updated_at: datetime
-    last_tested_at: Optional[datetime]
-
-    @field_validator('provider', mode='before')
-    @classmethod
-    def convert_provider(cls, v):
-        """Convert string to ModelProvider (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return ModelProvider(v_lower)
-            except ValueError:
-                for enum_member in ModelProvider:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid ModelProvider value: {v}")
-        return v
-
-    @field_validator('routing_mode', mode='before')
-    @classmethod
-    def convert_routing_mode(cls, v):
-        if v is None:
-            return CredentialRoutingMode.INHERIT
-        if isinstance(v, str):
-            try:
-                return CredentialRoutingMode(v.lower())
-            except ValueError:
-                return CredentialRoutingMode.INHERIT
-        return v
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-class LLMGenerationConfig(BaseModel):
-    """User-tunable LLM sampling / generation parameters."""
-
-    temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
-    max_tokens: Optional[int] = Field(None, gt=0)
-    top_p: Optional[float] = Field(None, ge=0.0, le=1.0)
-    top_k: Optional[int] = Field(None, ge=0)
-    frequency_penalty: Optional[float] = Field(None, ge=-2.0, le=2.0)
-    presence_penalty: Optional[float] = Field(None, ge=-2.0, le=2.0)
-    seed: Optional[int] = Field(None, ge=0)
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return only explicitly set fields."""
-        return self.model_dump(exclude_none=True)
-
-
-# VoiceBundle Schemas
-class VoiceBundleCreate(BaseModel):
-    """Schema for creating a VoiceBundle."""
-    name: str = Field(..., min_length=1, max_length=255)
-    description: Optional[str] = None
-    
-    # Bundle type: either STT+LLM+TTS or S2S
-    bundle_type: VoiceBundleType = Field(default=VoiceBundleType.STT_LLM_TTS)
-    
-    # STT Configuration - required for STT_LLM_TTS, optional for S2S
-    stt_provider: Optional[ModelProvider] = None
-    stt_model: Optional[str] = Field(None, min_length=1)
-    stt_credential_id: Optional[UUID] = Field(
-        None,
-        description=(
-            "Optional explicit AIProvider/Integration row id to use for STT. "
-            "When omitted the resolver picks the default credential for stt_provider."
-        ),
-    )
-    
-    # LLM Configuration - required for STT_LLM_TTS, optional for S2S
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = Field(None, min_length=1)
-    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
-    llm_max_tokens: Optional[int] = Field(None, gt=0)
-    llm_config: Optional[Dict[str, Any]] = None
-    llm_credential_id: Optional[UUID] = None
-    
-    # TTS Configuration - required for STT_LLM_TTS, optional for S2S
-    tts_provider: Optional[ModelProvider] = None
-    tts_model: Optional[str] = Field(None, min_length=1)
-    tts_voice: Optional[str] = None
-    tts_config: Optional[Dict[str, Any]] = None
-    tts_credential_id: Optional[UUID] = None
-    
-    # S2S Configuration - required for S2S, optional for STT_LLM_TTS
-    s2s_provider: Optional[ModelProvider] = None
-    s2s_model: Optional[str] = Field(None, min_length=1)
-    s2s_config: Optional[Dict[str, Any]] = None
-    s2s_credential_id: Optional[UUID] = None
-    
-    # Additional metadata
-    extra_metadata: Optional[Dict[str, Any]] = None
-    
-    @model_validator(mode='after')
-    def validate_bundle_configuration(self):
-        """Validate that required fields are provided based on bundle_type."""
-        if self.bundle_type == VoiceBundleType.STT_LLM_TTS:
-            if not self.stt_provider or not self.stt_model:
-                raise ValueError('STT provider and model are required for STT_LLM_TTS bundle type')
-            if not self.llm_provider or not self.llm_model:
-                raise ValueError('LLM provider and model are required for STT_LLM_TTS bundle type')
-            if not self.tts_provider or not self.tts_model:
-                raise ValueError('TTS provider and model are required for STT_LLM_TTS bundle type')
-        elif self.bundle_type == VoiceBundleType.S2S:
-            if not self.s2s_provider or not self.s2s_model:
-                raise ValueError('S2S provider and model are required for S2S bundle type')
-        return self
-
-
-class VoiceBundleUpdate(BaseModel):
-    """Schema for updating a VoiceBundle."""
-    name: Optional[str] = Field(None, min_length=1, max_length=255)
-    description: Optional[str] = None
-    
-    # Bundle type
-    bundle_type: Optional[VoiceBundleType] = None
-    
-    # STT Configuration
-    stt_provider: Optional[ModelProvider] = None
-    stt_model: Optional[str] = Field(None, min_length=1)
-    stt_credential_id: Optional[UUID] = None
-    
-    # LLM Configuration
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = Field(None, min_length=1)
-    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
-    llm_max_tokens: Optional[int] = Field(None, gt=0)
-    llm_config: Optional[Dict[str, Any]] = None
-    llm_credential_id: Optional[UUID] = None
-    
-    # TTS Configuration
-    tts_provider: Optional[ModelProvider] = None
-    tts_model: Optional[str] = Field(None, min_length=1)
-    tts_voice: Optional[str] = None
-    tts_config: Optional[Dict[str, Any]] = None
-    tts_credential_id: Optional[UUID] = None
-    
-    # S2S Configuration
-    s2s_provider: Optional[ModelProvider] = None
-    s2s_model: Optional[str] = Field(None, min_length=1)
-    s2s_config: Optional[Dict[str, Any]] = None
-    s2s_credential_id: Optional[UUID] = None
-    
-    # Additional metadata
-    extra_metadata: Optional[Dict[str, Any]] = None
-    is_active: Optional[bool] = None
-
-
-class VoiceBundleResponse(BaseModel):
-    """Schema for VoiceBundle response."""
-    id: UUID
-    name: str
-    description: Optional[str]
-    
-    # Bundle type - can be string from DB or enum, validator handles conversion
-    bundle_type: VoiceBundleType
-    
-    @field_validator('bundle_type', mode='before')
-    @classmethod
-    def convert_bundle_type(cls, v):
-        """Convert string to VoiceBundleType enum if needed."""
-        if isinstance(v, str):
-            try:
-                return VoiceBundleType(v)
-            except ValueError:
-                # Try to find by value
-                for enum_member in VoiceBundleType:
-                    if enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid bundle_type value: {v}")
-        return v
-    
-    @field_validator('stt_provider', 'llm_provider', 'tts_provider', 's2s_provider', mode='before')
-    @classmethod
-    def convert_model_provider(cls, v):
-        """Convert string to ModelProvider enum if needed (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            # Try lowercase first (enum value)
-            v_lower = v.lower()
-            try:
-                return ModelProvider(v_lower)
-            except ValueError:
-                # Try to find by enum name (uppercase)
-                for enum_member in ModelProvider:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid ModelProvider value: {v}")
-        return v
-    
-    # STT Configuration
-    stt_provider: Optional[ModelProvider]
-    stt_model: Optional[str]
-    stt_credential_id: Optional[UUID] = None
-    
-    # LLM Configuration
-    llm_provider: Optional[ModelProvider]
-    llm_model: Optional[str]
-    llm_temperature: Optional[float]
-    llm_max_tokens: Optional[int]
-    llm_config: Optional[Dict[str, Any]]
-    llm_credential_id: Optional[UUID] = None
-    
-    # TTS Configuration
-    tts_provider: Optional[ModelProvider]
-    tts_model: Optional[str]
-    tts_voice: Optional[str]
-    tts_config: Optional[Dict[str, Any]]
-    tts_credential_id: Optional[UUID] = None
-    
-    # S2S Configuration
-    s2s_provider: Optional[ModelProvider]
-    s2s_model: Optional[str]
-    s2s_config: Optional[Dict[str, Any]]
-    s2s_credential_id: Optional[UUID] = None
-    
-    # Additional metadata
-    extra_metadata: Optional[Dict[str, Any]]
-    is_active: bool
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-# Test Agent Conversation Schemas
-class TestAgentConversationCreate(BaseModel):
-    """Schema for creating a new test agent conversation."""
-    agent_id: UUID
-    persona_id: UUID
-    scenario_id: UUID
-    voice_bundle_id: UUID
-    conversation_metadata: Optional[Dict[str, Any]] = None
-
-    model_config = ConfigDict(json_schema_extra={
-            "example": {
-                "agent_id": "123e4567-e89b-12d3-a456-426614174000",
-                "persona_id": "123e4567-e89b-12d3-a456-426614174001",
-                "scenario_id": "123e4567-e89b-12d3-a456-426614174002",
-                "voice_bundle_id": "123e4567-e89b-12d3-a456-426614174003"
-            }
-        })
-
-
-class TestAgentConversationUpdate(BaseModel):
-    """Schema for updating a test agent conversation."""
-    status: Optional[str] = None
-    live_transcription: Optional[List[Dict[str, Any]]] = None
-    full_transcript: Optional[str] = None
-    conversation_metadata: Optional[Dict[str, Any]] = None
-
-
-class TestAgentConversationResponse(BaseModel):
-    """Schema for test agent conversation response."""
-    id: UUID
-    organization_id: UUID
-    agent_id: UUID
-    persona_id: UUID
-    scenario_id: UUID
-    voice_bundle_id: UUID
-    status: str
-    live_transcription: Optional[List[Dict[str, Any]]]
-    conversation_audio_key: Optional[str]
-    full_transcript: Optional[str]
-    started_at: datetime
-    ended_at: Optional[datetime]
-    duration_seconds: Optional[float]
-    conversation_metadata: Optional[Dict[str, Any]]
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-class ConversationTurn(BaseModel):
-    """Schema for a single conversation turn."""
-    speaker: str  # "test_agent" or "voice_agent"
-    text: str
-    timestamp: float  # Time in seconds from start
-    audio_segment_key: Optional[str] = None  # S3 key for this segment's audio
-
-
-# Conversation Evaluation Schemas
-class ConversationEvaluationCreate(BaseModel):
-    """Schema for creating a conversation evaluation."""
-    transcription_id: UUID
-    agent_id: UUID
-    llm_provider: Optional[ModelProvider] = ModelProvider.OPENAI
-    llm_model: Optional[str] = "gpt-4o"
-    
-    model_config = ConfigDict(json_schema_extra={
-            "example": {
-                "transcription_id": "123e4567-e89b-12d3-a456-426614174000",
-                "agent_id": "123e4567-e89b-12d3-a456-426614174001",
-                "llm_provider": "openai",
-                "llm_model": "gpt-4o"
-            }
-        })
-
-
-class ConversationEvaluationResponse(BaseModel):
-    """Schema for conversation evaluation response."""
-    id: UUID
-    organization_id: UUID
-    transcription_id: UUID
-    agent_id: UUID
-    objective_achieved: bool
-    objective_achieved_reason: Optional[str]
-    additional_metrics: Optional[Dict[str, Any]]
-    overall_score: Optional[float]
-    llm_provider: Optional[ModelProvider]
-    llm_model: Optional[str]
-    created_at: datetime
-    updated_at: datetime
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-# Evaluator Schemas
-class EvaluatorCreate(BaseModel):
-    """Schema for creating an evaluator. Either provide agent_id+persona_id+scenario_id (standard) or metric_ids/custom_prompt (custom)."""
-    name: Optional[str] = None
-    agent_id: Optional[UUID] = None
-    persona_id: Optional[UUID] = None
-    scenario_id: Optional[UUID] = None
-    custom_prompt: Optional[str] = None
-    metric_ids: Optional[List[UUID]] = None
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]] = None
-
-
-class EvaluatorUpdate(BaseModel):
-    """Schema for updating an evaluator."""
-    name: Optional[str] = None
-    agent_id: Optional[UUID] = None
-    persona_id: Optional[UUID] = None
-    scenario_id: Optional[UUID] = None
-    custom_prompt: Optional[str] = None
-    metric_ids: Optional[List[UUID]] = None
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]] = None
-
-
-class EvaluatorResponse(BaseModel):
-    """Schema for evaluator response."""
-    id: UUID
-    evaluator_id: str
-    organization_id: UUID
-    name: Optional[str] = None
-    agent_id: Optional[UUID] = None
-    persona_id: Optional[UUID] = None
-    scenario_id: Optional[UUID] = None
-    custom_prompt: Optional[str] = None
-    metric_ids: Optional[List[UUID]] = None
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]]
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-
-    @field_validator('llm_provider', mode='before')
-    @classmethod
-    def convert_llm_provider(cls, v):
-        """Convert string to ModelProvider (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return ModelProvider(v_lower)
-            except ValueError:
-                for enum_member in ModelProvider:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid ModelProvider value: {v}")
-        return v
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-class EvaluatorBulkCreate(BaseModel):
-    """Schema for creating multiple evaluators at once."""
-    name: Optional[str] = None
-    agent_id: UUID
-    scenario_id: UUID
-    persona_ids: List[UUID]
-    tags: Optional[List[str]] = None
-
-
-class RunEvaluatorsRequest(BaseModel):
-    """Schema for running evaluators."""
-    evaluator_ids: List[UUID] = Field(..., description="List of evaluator IDs to run")
-
-
-class RunEvaluatorsResponse(BaseModel):
-    """Schema for run evaluators response."""
-    task_ids: List[str] = Field(..., description="List of Celery task IDs for tracking")
-    evaluator_results: List["EvaluatorResultResponse"] = Field(default_factory=list, description="List of created evaluator results")
-    
-    model_config = ConfigDict(
-        from_attributes=True,
-        json_schema_extra={
-            "example": {
-                "agent_id": "123e4567-e89b-12d3-a456-426614174000",
-                "scenario_id": "123e4567-e89b-12d3-a456-426614174002",
-                "persona_ids": [
-                    "123e4567-e89b-12d3-a456-426614174001",
-                    "123e4567-e89b-12d3-a456-426614174003"
-                ],
-                "tags": ["test", "production"]
-            }
-        },
-    )
-
-
-# Metric Schemas
-SelectionMode = Literal["single_choice", "multi_label"]
-
-
-MetricScope = Literal["workspace", "organization"]
-
-# Max length for metric rubric text (description / example) accepted by
-# the API. DB columns are TEXT or unbounded VARCHAR; this cap is validation-only.
-METRIC_RUBRIC_TEXT_MAX_LENGTH = 32_000
-
-
-
-class EvaluatorSuiteCombinationResponse(BaseModel):
-    """One agent+persona+scenario combination inside a suite."""
-    id: UUID
-    evaluator_id: str
-    scenario_id: Optional[UUID] = None
-    scenario_name: Optional[str] = None
-    scenario_description: Optional[str] = None
-    scenario_required_info: Optional[Any] = None
-
-
-
-
-class EvaluatorSuiteCreate(BaseModel):
-    """Schema for creating an evaluator suite."""
-    name: Optional[str] = None
-    agent_id: UUID
-    persona_id: UUID
-    scenario_ids: List[UUID]
-    metric_ids: Optional[List[UUID]] = None
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]] = None
-    default_runs_per_combination: int = 1
-
-
-
-
-class EvaluatorSuiteUpdate(BaseModel):
-    """Schema for updating an evaluator suite."""
-    name: Optional[str] = None
-    tags: Optional[List[str]] = None
-    default_runs_per_combination: Optional[int] = None
-    llm_provider: Optional[ModelProvider] = None
-    llm_model: Optional[str] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    metric_ids: Optional[List[UUID]] = None
-
-
-
-
-class EvaluatorSuiteResponse(BaseModel):
-    """Schema for evaluator suite response."""
-    id: UUID
-    organization_id: UUID
-    name: Optional[str] = None
-    agent_id: UUID
-    persona_id: UUID
-    agent_name: Optional[str] = None
-    persona_name: Optional[str] = None
-    agent_call_type: Optional[str] = None
-    agent_call_medium: Optional[str] = None
-    metric_ids: Optional[List[str]] = None
-    llm_provider: Optional[str] = None
-    llm_model: Optional[str] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]] = None
-    default_runs_per_combination: int = 1
-    round_robin_index: int = 0
-    is_active: bool = False
-    agent_suite_count: int = 1
-    combination_count: int = 0
-    combinations: List[EvaluatorSuiteCombinationResponse] = Field(default_factory=list)
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str] = None
-
-
-
-
-class EvaluatorSuiteAddScenariosRequest(BaseModel):
-    """Schema for adding scenarios to an existing suite."""
-    scenario_ids: List[UUID]
-
-
-
-
-class RunEvaluatorSuiteRequest(BaseModel):
-    """Schema for running all combinations in a suite."""
-    runs_per_combination: Optional[int] = None
-    to_number: Optional[str] = None
-    from_number: Optional[str] = None
-
-
-
-
-class RunEvaluatorSuiteResponse(BaseModel):
-    """Schema for suite run response."""
-    total_runs: int
-    task_ids: List[str] = Field(default_factory=list)
-    evaluator_results: List["EvaluatorResultResponse"] = Field(default_factory=list)
-    phone_call_refs: List[str] = Field(default_factory=list)
-
-
-
-
-class RunNextCombinationRequest(BaseModel):
-    """Schema for running the next round-robin combination."""
-    from_number: Optional[str] = None
-
-
-
-
-class RunNextCombinationResponse(BaseModel):
-    """Schema for round-robin run response."""
-    evaluator_id: UUID
-    scenario_id: Optional[UUID] = None
-    scenario_name: str
-    combination_index: int
-    next_index: int
-    evaluator_result_id: Optional[UUID] = None
-    result_id: Optional[str] = None
-    task_id: Optional[str] = None
-    phone_call_ref: Optional[str] = None
-    call_short_id: Optional[str] = None
-
-
-
-
-class ChooseNextCombinationResponse(BaseModel):
-    """Advance inbound round-robin without initiating a call or evaluation run."""
-    evaluator_id: UUID
-    scenario_id: Optional[UUID] = None
-    scenario_name: str
-    combination_index: int
-    next_index: int
-
-
-# Metric Schemas
-SelectionMode = Literal["single_choice", "multi_label"]
-
-
-MetricScope = Literal["workspace", "organization"]
-
-# Max length for metric rubric text (description / example) accepted by
-# the API. DB columns are TEXT or unbounded VARCHAR; this cap is validation-only.
-METRIC_RUBRIC_TEXT_MAX_LENGTH = 32_000
-
-
-
-class MetricCreate(BaseModel):
-    """Schema for creating a metric.
-
-    Hierarchy:
-    - ``parent_metric_id`` set => this is a child sub-metric. ``metric_type``
-      is forced to ``boolean`` server-side; ``selection_mode`` must be None.
-    - ``selection_mode`` set => this is a parent category metric.
-      ``parent_metric_id`` must be None (max depth = 2).
-
-    Scope:
-    - ``scope="workspace"`` (default) stamps the metric with the active
-      ``X-Workspace-Id`` so it only shows up inside that workspace.
-    - ``scope="organization"`` stamps ``workspace_id=NULL`` so the metric
-      is visible in every workspace of the org. Children always inherit
-      their parent's scope; setting ``scope`` on a child request body is
-      ignored server-side.
-    """
-    name: str
-    description: Optional[str] = None
-    # Optional illustrative example surfaced alongside ``description``
-    # in the LLM judge's rubric. Today this is mainly populated on
-    # child sub-labels (one example per categorization label) but
-    # standalone metrics may carry it too without a schema change.
-    example: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    metric_type: MetricType = MetricType.RATING
-    metric_category: MetricCategory = MetricCategory.QUALITY
-    trigger: MetricTrigger = MetricTrigger.ALWAYS
-    enabled: bool = True
-    metric_origin: str = "custom"
-    supported_surfaces: List[str] = ["agent"]
-    enabled_surfaces: Optional[List[str]] = None
-    custom_data_type: Optional[str] = None
-    custom_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]] = None
-    capture_rationale: Optional[bool] = False
-    parent_metric_id: Optional[UUID] = None
-    selection_mode: Optional[SelectionMode] = None
-    # Only meaningful on multi_label parents; ignored everywhere else.
-    # When true, the LLM is invited during call-import evaluation to emit
-    # additional candidate sub-labels beyond the user-defined children.
-    allow_discovery: bool = False
-    # When true, this metric is a "transcript-compare judge": at
-    # call-import evaluation time the worker feeds BOTH the production
-    # transcript (``call_import_rows.transcript``, CSV-supplied) and
-    # the diarised transcript (``call_import_rows.diarised_transcript``,
-    # worker-produced) to the LLM as a labeled pair. The parent
-    # evaluation's ``transcript_source`` is ignored for these metrics.
-    # Mutually exclusive with ``parent_metric_id`` / ``selection_mode``
-    # G�� comparison metrics stay standalone so the LLM grouping logic
-    # doesn't have to second-guess which prompt template to use within
-    # a hierarchy. (Parent-level keyword auto-detection in the worker
-    # still routes a categorisation parent through the comparison
-    # prompt without setting this flag.)
-    compare_transcripts: bool = False
-    # When ``"organization"``, the metric is stored with
-    # ``workspace_id=NULL`` so it surfaces in every workspace of the
-    # caller's org. Default ``"workspace"`` preserves the historical
-    # behavior of stamping the metric with the active ``X-Workspace-Id``.
-    # Ignored when ``parent_metric_id`` is set (children inherit the
-    # parent's scope unconditionally).
-    scope: MetricScope = "workspace"
-
-    @model_validator(mode='after')
-    def validate_compare_transcripts_exclusions(self):
-        """Reject body combinations that don't make sense for a
-        transcript-compare judge.
-
-        The Metric ORM column accepts the value; the validator just
-        prevents the user from accidentally requesting an incoherent
-        metric shape (e.g. "compare two transcripts but also live
-        inside a categorisation hierarchy" � different prompt
-        templates).
-        """
-        if not self.compare_transcripts:
-            return self
-        if self.parent_metric_id is not None:
-            raise ValueError(
-                "Transcript-compare metrics must be standalone: "
-                "they cannot be a child sub-metric in this version."
-            )
-        if self.selection_mode is not None:
-            raise ValueError(
-                "Transcript-compare metrics must be standalone: "
-                "they cannot own children (selection_mode must be "
-                "unset) in this version."
-            )
-        return self
-
-    model_config = ConfigDict(json_schema_extra={
-            "example": {
-                "name": "Professionalism",
-                "description": "Measures the professional tone and behavior",
-                "metric_type": "rating",
-                "trigger": "always",
-                "enabled": True
-            }
-        })
-
-
-class MetricChildDraft(BaseModel):
-    """One child sub-metric in a parent + children atomic create body."""
-
-    name: str = Field(..., max_length=120)
-    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    # Optional illustrative example for this label. Surfaced alongside
-    # ``description`` in the LLM judge's rubric so each label can carry
-    # both its definition AND a "what does this look like?" example.
-    example: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    enabled: bool = True
-    capture_rationale: Optional[bool] = True
-    tags: Optional[List[str]] = None
-
-
-class MetricCreateWithChildren(BaseModel):
-    """One-shot create body: a parent metric + N children, atomically.
-
-    Children are persisted as full ``Metric`` rows with
-    ``parent_metric_id`` set to the new parent. ``metric_type`` on every
-    child is forced to ``boolean`` server-side regardless of what's
-    passed in the parent body.
-    """
-
-    name: str = Field(..., max_length=120)
-    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    selection_mode: SelectionMode
-    metric_category: MetricCategory = MetricCategory.QUALITY
-    enabled: bool = True
-    supported_surfaces: List[str] = Field(default_factory=lambda: ["agent"])
-    enabled_surfaces: Optional[List[str]] = None
-    tags: Optional[List[str]] = None
-    # When true on a multi_label parent, allow the LLM to emit candidate
-    # labels beyond the listed children at evaluation time. Validator
-    # rejects allow_discovery=True on single_choice parents.
-    allow_discovery: bool = False
-    # Parent-level "Enable LLM Rationale" toggle. When true the LLM
-    # judge emits a single rationale string at the parent level
-    # (children never carry rationales in hierarchical mode), which the
-    # table renders as the "<Parent> - LLM Rationale" column.
-    capture_rationale: bool = False
-    children: List[MetricChildDraft] = Field(
-        default_factory=list,
-        description="Child sub-metric labels under this parent.",
-    )
-    # See ``MetricCreate.scope``. Same semantics: ``"organization"``
-    # creates the parent + all children with ``workspace_id=NULL`` so
-    # the whole category subtree is shared across every workspace in
-    # the org.
-    scope: MetricScope = "workspace"
-
-
-class MetricUpdate(BaseModel):
-    """Schema for updating a metric."""
-    name: Optional[str] = None
-    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    # ``None`` here means "leave unchanged"; pass an empty string to
-    # clear a previously stored example.
-    example: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    metric_type: Optional[MetricType] = None
-    trigger: Optional[MetricTrigger] = None
-    enabled: Optional[bool] = None
-    metric_origin: Optional[str] = None
-    supported_surfaces: Optional[List[str]] = None
-    enabled_surfaces: Optional[List[str]] = None
-    custom_data_type: Optional[str] = None
-    custom_config: Optional[Dict[str, Any]] = None
-    tags: Optional[List[str]] = None
-    metric_category: Optional[MetricCategory] = None
-    capture_rationale: Optional[bool] = None
-    selection_mode: Optional[SelectionMode] = None
-    allow_discovery: Optional[bool] = None
-    # See ``MetricCreate.compare_transcripts``. ``None`` here means
-    # "leave unchanged". The route layer enforces mutual exclusion
-    # against the row's existing ``parent_metric_id`` /
-    # ``selection_mode`` when this is set to True, because the patch
-    # body alone doesn't have enough context to validate cross-state.
-    compare_transcripts: Optional[bool] = None
-
-    @model_validator(mode='after')
-    def validate_compare_transcripts_exclusions(self):
-        """Reject patch bodies that flip compare_transcripts on while
-        ALSO trying to set a conflicting field in the same request.
-
-        Cross-state validation against the persisted row (e.g. "the
-        existing metric already has a parent") is done in the
-        update route since the schema doesn't have the row in hand.
-        """
-        if self.compare_transcripts is not True:
-            return self
-        if self.selection_mode is not None:
-            raise ValueError(
-                "Transcript-compare metrics must be standalone: "
-                "selection_mode must be cleared before enabling "
-                "compare_transcripts."
-            )
-        return self
-
-
-class MetricResponse(BaseModel):
-    """Schema for metric response.
-
-    ``children`` is populated for parent metrics (those with
-    ``selection_mode`` set) and is otherwise an empty list. The list is
-    built once at serialization time so callers get a single tree
-    structure without follow-up requests.
-    """
-    id: UUID
-    organization_id: UUID
-    # ``None`` when the metric is org-shared (``scope == "organization"``).
-    # See the ORM ``Metric.workspace_id`` docstring.
-    workspace_id: Optional[UUID] = None
-    # Computed convenience field so the UI doesn't have to do
-    # ``workspace_id == null`` checks everywhere. Always one of
-    # ``"workspace"`` or ``"organization"``.
-    scope: MetricScope = "workspace"
-    name: str
-    description: Optional[str]
-    # Optional illustrative example. Populated mainly on categorization
-    # child labels but surfaced for every metric so the UI can render
-    # it uniformly without branching on parent/child shape.
-    example: Optional[str] = None
-    metric_type: MetricType
-    metric_category: MetricCategory = MetricCategory.QUALITY
-    trigger: MetricTrigger
-    enabled: bool
-    is_default: bool
-    metric_origin: str
-    supported_surfaces: List[str]
-    enabled_surfaces: List[str]
-    custom_data_type: Optional[str]
-    custom_config: Optional[Dict[str, Any]]
-    tags: Optional[List[str]]
-    capture_rationale: bool = False
-    parent_metric_id: Optional[UUID] = None
-    selection_mode: Optional[SelectionMode] = None
-    allow_discovery: bool = False
-    # See ``MetricCreate.compare_transcripts``. Surfaced so the UI can
-    # render a "Compare transcripts" badge in the metric picker and
-    # know to skip the run's transcript_source toggle for this metric.
-    compare_transcripts: bool = False
-    lifecycle: str = "active"
-    promoted_from_draft_at: Optional[datetime] = None
-    studio_notes: Optional[str] = None
-    children: List["MetricResponse"] = Field(default_factory=list)
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-
-    @field_validator('metric_type', mode='before')
-    @classmethod
-    def convert_metric_type(cls, v):
-        """Convert string to MetricType (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return MetricType(v_lower)
-            except ValueError:
-                for enum_member in MetricType:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid MetricType value: {v}")
-        return v
-
-    @field_validator('trigger', mode='before')
-    @classmethod
-    def convert_trigger(cls, v):
-        """Convert string to MetricTrigger (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return MetricTrigger(v_lower)
-            except ValueError:
-                for enum_member in MetricTrigger:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid MetricTrigger value: {v}")
-        return v
-
-    @validator('supported_surfaces', 'enabled_surfaces', pre=True)
-    def normalize_surfaces(cls, v):
-        if v is None:
-            return []
-        if isinstance(v, str):
-            return [v]
-        if isinstance(v, (list, tuple)):
-            return [str(item).lower() for item in v if item]
-        return []
-
-    @validator('metric_origin', pre=True)
-    def normalize_metric_origin(cls, v):
-        if v is None:
-            return "custom"
-        return str(v).lower()
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-MetricResponse.model_rebuild()
-
-
-class MetricDraftCreate(MetricCreate):
-    """Create a draft metric for Metrics Studio experimentation."""
-
-    studio_notes: Optional[str] = Field(
-        default=None,
-        description="Optional notes about what this draft is testing.",
-    )
-
-
-class MetricDraftCreateWithChildren(MetricCreateWithChildren):
-    """Atomically create a draft parent category metric plus its children."""
-
-    studio_notes: Optional[str] = Field(
-        default=None,
-        description="Optional notes about what this draft category is testing.",
-    )
-
-
-class MetricPromoteResponse(BaseModel):
-    """Response after promoting a draft metric to active."""
-
-    metric: MetricResponse
-    promoted_at: datetime
-
-
-MetricStudioSourceKind = Literal[
-    "call_import_row", "call_recording", "evaluator_result"
-]
-
-
-class MetricStudioSourceItem(BaseModel):
-    """One call source selected for a Studio run."""
-
-    source_kind: MetricStudioSourceKind
-    source_ref: str = Field(
-        ...,
-        min_length=1,
-        description="UUID for import rows / evaluator results; call_short_id for recordings.",
-    )
-    display_label: Optional[str] = Field(
-        default=None,
-        max_length=512,
-        description="Optional UI label; resolved server-side when omitted.",
-    )
-
-
-class MetricStudioRunCreate(BaseModel):
-    """Request body for triggering a Metrics Studio evaluation run."""
-
-    metric_ids: List[UUID] = Field(..., min_length=1)
-    sources: List[MetricStudioSourceItem] = Field(..., min_length=1)
-    name: Optional[str] = Field(default=None, max_length=255)
-    transcript_source: Literal["production", "diarised"] = "diarised"
-    llm_provider: Optional[str] = Field(default=None, max_length=50)
-    llm_model: Optional[str] = Field(default=None, max_length=100)
-    llm_credential_id: Optional[UUID] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    metric_llm_overrides: Optional[Dict[str, Any]] = None
-
-
-class MetricStudioRunRetryRequest(BaseModel):
-    """Retry failed or selected Studio run results."""
-
-    result_ids: Optional[List[UUID]] = Field(
-        default=None,
-        description="When omitted, retry all failed results in the run.",
-    )
-
-
-class MetricStudioRunResultResponse(BaseModel):
-    """Per-source result row for a Studio run."""
-
-    id: UUID
-    run_id: UUID
-    source_kind: str
-    source_ref: str
-    display_label: Optional[str] = None
-    source_metadata: Optional[Dict[str, Any]] = None
-    status: str
-    metric_scores: Dict[str, Any] = Field(default_factory=dict)
-    error_message: Optional[str] = None
-    started_at: Optional[datetime] = None
-    finished_at: Optional[datetime] = None
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class MetricStudioRunResponse(BaseModel):
-    """Metrics Studio run summary."""
-
-    id: UUID
-    organization_id: UUID
-    workspace_id: UUID
-    name: Optional[str] = None
-    selected_metric_ids: List[str] = Field(default_factory=list)
-    selected_metric_groups: Optional[Dict[str, List[str]]] = None
-    transcript_source: str
-    llm_provider: Optional[str] = None
-    llm_model: Optional[str] = None
-    status: str
-    total_items: int
-    completed_items: int
-    failed_items: int
-    error_message: Optional[str] = None
-    started_at: Optional[datetime] = None
-    finished_at: Optional[datetime] = None
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class MetricStudioRunListResponse(BaseModel):
-    items: List[MetricStudioRunResponse]
-    total: int
-
-
-class MetricStudioRunResultListResponse(BaseModel):
-    items: List[MetricStudioRunResultResponse]
-    total: int
-
-
-# Evaluator Result Schemas
-class EvaluatorResultCreate(BaseModel):
-    """Schema for creating an evaluator result."""
-    evaluator_id: UUID
-    agent_id: Optional[UUID] = None
-    persona_id: Optional[UUID] = None
-    scenario_id: Optional[UUID] = None
-    name: Optional[str] = None
-    duration_seconds: Optional[float] = None
-    audio_s3_key: Optional[str] = None
-
-
-class EvaluatorResultCreateManual(BaseModel):
-    """Schema for manually creating an evaluator result from existing audio file."""
-    evaluator_id: UUID
-    audio_s3_key: str
-    duration_seconds: Optional[float] = None
-
-
-class EvaluatorResultUpdate(BaseModel):
-    """Schema for updating an evaluator result."""
-    status: Optional[EvaluatorResultStatus] = None
-    transcription: Optional[str] = None
-    metric_scores: Optional[Dict[str, Any]] = None
-    error_message: Optional[str] = None
-    duration_seconds: Optional[float] = None
-
-
-
-class EvaluatorResultCounts(BaseModel):
-    """Rollup counts for evaluator result navigation."""
-
-    total: int = 0
-    completed: int = 0
-    failed: int = 0
-    in_progress: int = 0
-    last_run_at: Optional[datetime] = None
-
-
-
-
-class EvaluatorResultsScenarioSummary(BaseModel):
-    scenario_id: UUID
-    scenario_name: str
-    counts: EvaluatorResultCounts
-
-
-
-
-class EvaluatorResultsSuiteSummary(BaseModel):
-    suite_id: UUID
-    suite_name: Optional[str] = None
-    agent_id: UUID
-    persona_id: Optional[UUID] = None
-    counts: EvaluatorResultCounts
-    scenarios: Optional[List["EvaluatorResultsScenarioSummary"]] = None
-
-
-
-
-class EvaluatorResultsAgentSummary(BaseModel):
-    agent_id: UUID
-    agent_name: str
-    counts: EvaluatorResultCounts
-    suites: Optional[List[EvaluatorResultsSuiteSummary]] = None
-
-
-
-
-class EvaluatorResultsUnassignedSummary(BaseModel):
-    counts: EvaluatorResultCounts
-    recent_result_ids: List[str] = Field(default_factory=list)
-
-
-
-
-class EvaluatorResultsOverviewResponse(BaseModel):
-    workspace_counts: EvaluatorResultCounts
-    agents: List[EvaluatorResultsAgentSummary] = Field(default_factory=list)
-    unassigned: EvaluatorResultsUnassignedSummary
-
-
-
-
-class EvaluatorResultListResponse(BaseModel):
-    items: List["EvaluatorResultResponse"]
-    total: int
-
-
-
-class EvaluatorResultResponse(BaseModel):
-    """Schema for evaluator result response."""
-    id: UUID
-    result_id: str
-    organization_id: UUID
-    evaluator_id: Optional[UUID] = None  # Optional for playground test results
-    agent_id: Optional[UUID] = None  # Nullable for custom evaluators
-    persona_id: Optional[UUID] = None  # Optional for playground test results
-    scenario_id: Optional[UUID] = None  # Optional for playground test results
-    name: Optional[str] = None  # Optional for playground test results
-    timestamp: datetime
-    duration_seconds: Optional[float]
-    status: EvaluatorResultStatus
-    audio_s3_key: Optional[str]
-    transcription: Optional[str]
-    speaker_segments: Optional[List[Dict[str, Any]]] = None  # [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
-    metric_scores: Optional[Dict[str, Any]]
-    celery_task_id: Optional[str]
-    error_message: Optional[str]
-    
-    # Call tracking fields (for voice AI integrations)
-    call_event: Optional[str] = None
-    provider_call_id: Optional[str] = None
-    provider_platform: Optional[str] = None
-    call_data: Optional[Dict[str, Any]] = None  # Full call details from provider
-    
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-    
-    # Related entities (optional, populated when requested)
-    agent: Optional[AgentResponse] = None
-    persona: Optional[PersonaResponse] = None
-    scenario: Optional[ScenarioResponse] = None
-    evaluator: Optional[EvaluatorResponse] = None
-
-    @field_validator('status', mode='before')
-    @classmethod
-    def convert_status(cls, v):
-        """Convert string to EvaluatorResultStatus (handles uppercase DB values)."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return EvaluatorResultStatus(v_lower)
-            except ValueError:
-                for enum_member in EvaluatorResultStatus:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid EvaluatorResultStatus value: {v}")
-        return v
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-# ============================================
-# ALERTING SCHEMAS
-# ============================================
-
-class AlertCreate(BaseModel):
-    """Schema for creating an alert."""
-    name: str = Field(..., min_length=1, max_length=255)
-    description: Optional[str] = None
-    
-    # Metric condition
-    metric_type: AlertMetricType = AlertMetricType.NUMBER_OF_CALLS
-    aggregation: AlertAggregation = AlertAggregation.SUM
-    operator: AlertOperator = AlertOperator.GREATER_THAN
-    threshold_value: float = Field(..., description="Threshold value for the alert")
-    time_window_minutes: int = Field(default=60, ge=1, description="Time window in minutes for aggregation")
-    
-    # Agent selection (null means all agents)
-    agent_ids: Optional[List[UUID]] = None
-    
-    # Notification settings
-    notify_frequency: AlertNotifyFrequency = AlertNotifyFrequency.IMMEDIATE
-    notify_emails: Optional[List[str]] = Field(default=None, description="List of email addresses to notify")
-    notify_webhooks: Optional[List[str]] = Field(default=None, description="List of webhook URLs (Slack, etc.)")
-    
-    model_config = ConfigDict(json_schema_extra={
-            "example": {
-                "name": "High Call Volume Alert",
-                "description": "Alert when call volume exceeds threshold",
-                "metric_type": "number_of_calls",
-                "aggregation": "sum",
-                "operator": ">",
-                "threshold_value": 100,
-                "time_window_minutes": 60,
-                "agent_ids": None,
-                "notify_frequency": "immediate",
-                "notify_emails": ["admin@example.com"],
-                "notify_webhooks": ["https://hooks.slack.com/services/xxx"]
-            }
-        })
-
-
-class AlertUpdate(BaseModel):
-    """Schema for updating an alert."""
-    name: Optional[str] = Field(None, min_length=1, max_length=255)
-    description: Optional[str] = None
-    
-    # Metric condition
-    metric_type: Optional[AlertMetricType] = None
-    aggregation: Optional[AlertAggregation] = None
-    operator: Optional[AlertOperator] = None
-    threshold_value: Optional[float] = None
-    time_window_minutes: Optional[int] = Field(default=None, ge=1)
-    
-    # Agent selection
-    agent_ids: Optional[List[UUID]] = None
-    
-    # Notification settings
-    notify_frequency: Optional[AlertNotifyFrequency] = None
-    notify_emails: Optional[List[str]] = None
-    notify_webhooks: Optional[List[str]] = None
-    
-    # Status
-    status: Optional[AlertStatus] = None
-
-
-class AlertResponse(BaseModel):
-    """Schema for alert response."""
-    id: UUID
-    organization_id: UUID
-    name: str
-    description: Optional[str]
-    
-    # Metric condition
-    metric_type: AlertMetricType
-    aggregation: AlertAggregation
-    operator: AlertOperator
-    threshold_value: float
-    time_window_minutes: int
-    
-    # Agent selection
-    agent_ids: Optional[List[UUID]]
-    
-    # Notification settings
-    notify_frequency: AlertNotifyFrequency
-    notify_emails: Optional[List[str]]
-    notify_webhooks: Optional[List[str]]
-    
-    # Status
-    status: AlertStatus
-    
-    # Metadata
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-
-    @field_validator('metric_type', mode='before')
-    @classmethod
-    def convert_metric_type(cls, v):
-        """Convert string to AlertMetricType."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return AlertMetricType(v_lower)
-            except ValueError:
-                for enum_member in AlertMetricType:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid AlertMetricType value: {v}")
-        return v
-
-    @field_validator('aggregation', mode='before')
-    @classmethod
-    def convert_aggregation(cls, v):
-        """Convert string to AlertAggregation."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return AlertAggregation(v_lower)
-            except ValueError:
-                for enum_member in AlertAggregation:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid AlertAggregation value: {v}")
-        return v
-
-    @field_validator('operator', mode='before')
-    @classmethod
-    def convert_operator(cls, v):
-        """Convert string to AlertOperator."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            try:
-                return AlertOperator(v)
-            except ValueError:
-                for enum_member in AlertOperator:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid AlertOperator value: {v}")
-        return v
-
-    @field_validator('notify_frequency', mode='before')
-    @classmethod
-    def convert_notify_frequency(cls, v):
-        """Convert string to AlertNotifyFrequency."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return AlertNotifyFrequency(v_lower)
-            except ValueError:
-                for enum_member in AlertNotifyFrequency:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid AlertNotifyFrequency value: {v}")
-        return v
-
-    @field_validator('status', mode='before')
-    @classmethod
-    def convert_status(cls, v):
-        """Convert string to AlertStatus."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return AlertStatus(v_lower)
-            except ValueError:
-                for enum_member in AlertStatus:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid AlertStatus value: {v}")
-        return v
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-class AlertHistoryResponse(BaseModel):
-    """Schema for alert history response."""
-    id: UUID
-    organization_id: UUID
-    alert_id: UUID
-    
-    # Trigger information
-    triggered_at: datetime
-    triggered_value: float
-    threshold_value: float
-    
-    # Status
-    status: AlertHistoryStatus
-    
-    # Notification tracking
-    notified_at: Optional[datetime]
-    notification_details: Optional[Dict[str, Any]]
-    
-    # Resolution
-    acknowledged_at: Optional[datetime]
-    acknowledged_by: Optional[str]
-    resolved_at: Optional[datetime]
-    resolved_by: Optional[str]
-    resolution_notes: Optional[str]
-    
-    # Additional context
-    context_data: Optional[Dict[str, Any]]
-    
-    # Metadata
-    created_at: datetime
-    updated_at: datetime
-    
-    # Related alert info (optional)
-    alert: Optional[AlertResponse] = None
-
-    @field_validator('status', mode='before')
-    @classmethod
-    def convert_status(cls, v):
-        """Convert string to AlertHistoryStatus."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return AlertHistoryStatus(v_lower)
-            except ValueError:
-                for enum_member in AlertHistoryStatus:
-                    if enum_member.name == v or enum_member.value == v:
-                        return enum_member
-                raise ValueError(f"Invalid AlertHistoryStatus value: {v}")
-        return v
-    
-    model_config = ConfigDict(from_attributes=True)
-
-
-class AlertHistoryUpdate(BaseModel):
-    """Schema for updating alert history (acknowledge/resolve)."""
-    status: Optional[AlertHistoryStatus] = None
-    acknowledged_by: Optional[str] = None
-    resolved_by: Optional[str] = None
-    resolution_notes: Optional[str] = None
-
-
-# ============================================
-# CRON JOB SCHEMAS
-# ============================================
-
-class CronJobCreate(BaseModel):
-    """Schema for creating a cron job."""
-    name: str = Field(..., min_length=1, max_length=255)
-    cron_expression: str = Field(..., min_length=1, max_length=100, description="Cron expression (e.g., '0 9 * * 1-5')")
-    timezone: str = Field(default="UTC", max_length=100, description="Timezone for the cron schedule")
-    max_runs: int = Field(default=10, ge=1, le=1000, description="Maximum number of times to run")
-    evaluator_ids: Optional[List[UUID]] = Field(
-        None,
-        description="Evaluator IDs to trigger (expanded with evaluator_suite_ids when both are set).",
-    )
-    evaluator_suite_ids: Optional[List[UUID]] = Field(
-        None,
-        description="Evaluator suite IDs whose combinations are expanded into evaluator_ids.",
-    )
-    
-    model_config = ConfigDict(json_schema_extra={
-            "example": {
-                "name": "Daily Evaluation Run",
-                "cron_expression": "0 9 * * 1-5",
-                "timezone": "America/New_York",
-                "max_runs": 100,
-                "evaluator_ids": ["123e4567-e89b-12d3-a456-426614174000"]
-            }
-        })
-
-
-class CronJobUpdate(BaseModel):
-    """Schema for updating a cron job."""
-    name: Optional[str] = Field(None, min_length=1, max_length=255)
-    cron_expression: Optional[str] = Field(None, min_length=1, max_length=100)
-    timezone: Optional[str] = Field(None, max_length=100)
-    max_runs: Optional[int] = Field(None, ge=1, le=1000)
-    evaluator_ids: Optional[List[UUID]] = None
-    evaluator_suite_ids: Optional[List[UUID]] = None
-    status: Optional[CronJobStatus] = None
-
-
-class CronJobResponse(BaseModel):
-    """Schema for cron job response."""
-    id: UUID
-    organization_id: UUID
-    name: str
-    job_type: str = "evaluator_run"
-    is_system: bool = False
-    cron_expression: str
-    timezone: str
-    max_runs: int
-    current_runs: int
-    evaluator_ids: List[UUID]
-    status: CronJobStatus
-    next_run_at: Optional[datetime]
-    last_run_at: Optional[datetime]
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-
-    @field_validator('status', mode='before')
-    @classmethod
-    def convert_status(cls, v):
-        """Convert string to CronJobStatus."""
-        if v is None:
-            return None
-        if isinstance(v, str):
-            v_lower = v.lower()
-            try:
-                return CronJobStatus(v_lower)
-            except ValueError:
-                for enum_member in CronJobStatus:
-                    if enum_member.value.lower() == v_lower:
-                        return enum_member
-                raise ValueError(f"Invalid status: {v}")
-        return v
-
-    @field_validator('evaluator_ids', mode='before')
-    @classmethod
-    def convert_evaluator_ids(cls, v):
-        """Convert evaluator_ids from JSON to list of UUIDs."""
-        if v is None:
-            return []
-        if isinstance(v, list):
-            return [UUID(str(id)) if not isinstance(id, UUID) else id for id in v]
-        return v
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# ============================================
-# PROMPT PARTIAL SCHEMAS
-# ============================================
-
-class MetricPartialChild(BaseModel):
-    """One categorization label inside a metric partial."""
-
-    name: str = Field(..., min_length=1)
-    description: str = ""
-    example: str = ""
-
-
-class MetricPartialContent(BaseModel):
-    """Structured JSON payload stored in metric partial ``content``."""
-
-    schema_version: int = 1
-    metric_kind: Literal["single", "category"]
-    description: str = ""
-    children: Optional[List[MetricPartialChild]] = None
-
-
-class PromptPartialCreate(BaseModel):
-    """Schema for creating a prompt partial."""
-    name: str = Field(..., min_length=1, max_length=255)
-    description: Optional[str] = None
-    content: str = Field(..., min_length=1)
-    tags: Optional[List[str]] = None
-
-
-class PromptPartialUpdate(BaseModel):
-    """Schema for updating a prompt partial."""
-    name: Optional[str] = Field(None, min_length=1, max_length=255)
-    description: Optional[str] = None
-    content: Optional[str] = Field(None, min_length=1)
-    tags: Optional[List[str]] = None
-    change_summary: Optional[str] = None
-
-
-class PromptPartialVersionResponse(BaseModel):
-    """Schema for prompt partial version response."""
-    id: UUID
-    prompt_partial_id: UUID
-    version: int
-    content: str
-    change_summary: Optional[str]
-    created_at: datetime
-    created_by: Optional[str]
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class AgentFlowNode(BaseModel):
-    """One step in an LLM-inferred agent logic flowchart."""
-
-    id: str
-    label: str
-    node_type: Literal["start", "decision", "action", "terminal"] = "action"
-    position_x: Optional[float] = None
-    position_y: Optional[float] = None
-    prompt_excerpt: Optional[str] = None
-    start_offset: Optional[int] = None
-    end_offset: Optional[int] = None
-
-
-class AgentFlowEdge(BaseModel):
-    """Directed transition between two agent flow nodes."""
-
-    source: str
-    target: str
-    condition: Optional[str] = None
-
-
-class AgentFlowNodeLayout(BaseModel):
-    id: str
-    position_x: float
-    position_y: float
-
-
-class AgentFlowLayoutSaveRequest(BaseModel):
-    nodes: List[AgentFlowNodeLayout] = Field(default_factory=list)
-
-
-class AgentFlowGraph(BaseModel):
-    """Aggregate flow diagram for an imported production agent prompt."""
-
-    nodes: List[AgentFlowNode] = Field(default_factory=list)
-    edges: List[AgentFlowEdge] = Field(default_factory=list)
-    generated_at: Optional[datetime] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    layout_saved_at: Optional[datetime] = None
-    prompt_content_hash: Optional[str] = None
-    mapping_error: Optional[str] = None
-    generation_error: Optional[str] = None
-
-
-class PromptPartialResponse(BaseModel):
-    """Schema for prompt partial response."""
-    id: UUID
-    organization_id: UUID
-    name: str
-    description: Optional[str]
-    content: str
-    tags: Optional[List[str]]
-    current_version: int
-    agent_flowchart: Optional[AgentFlowGraph] = None
-    agent_flowchart_status: Optional[str] = None
-    created_at: datetime
-    updated_at: datetime
-    created_by: Optional[str]
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class PromptPartialDetailResponse(PromptPartialResponse):
-    """Schema for prompt partial detail with versions."""
-    versions: List[PromptPartialVersionResponse] = []
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# ============================================
-# TELEPHONY SCHEMAS (provider-agnostic)
-# ============================================
-
-
-class TelephonyIntegrationCreate(BaseModel):
-    """Schema for creating a telephony provider integration."""
-
-    provider: str = "plivo"
-    name: Optional[str] = None
-    auth_id: str
-    auth_token: str
-    verify_app_uuid: Optional[str] = None
-    voice_app_id: Optional[str] = None
-    sip_domain: Optional[str] = None
-    masking_config: Optional[Dict[str, Any]] = None
-    is_default: Optional[bool] = Field(
-        None,
-        description=(
-            "Mark this credential as the default for the (org, provider). "
-            "If omitted and no default exists yet, this row becomes the default."
-        ),
-    )
-
-
-class TelephonyIntegrationUpdate(BaseModel):
-    """Schema for partial updates to a telephony provider integration."""
-
-    id: Optional[UUID] = None
-    provider: Optional[str] = None
-    name: Optional[str] = None
-    auth_id: Optional[str] = None
-    auth_token: Optional[str] = None
-    verify_app_uuid: Optional[str] = None
-    voice_app_id: Optional[str] = None
-    sip_domain: Optional[str] = None
-    masking_config: Optional[Dict[str, Any]] = None
-    is_active: Optional[bool] = None
-
-
-class TelephonyIntegrationResponse(BaseModel):
-    """Safe response model for telephony integration without secrets."""
-
-    id: UUID
-    organization_id: UUID
-    provider: str
-    name: Optional[str] = None
-    verify_app_uuid: Optional[str]
-    voice_app_id: Optional[str]
-    sip_domain: Optional[str]
-    masking_config: Optional[Dict[str, Any]]
-    is_active: bool
-    is_default: bool = False
-    last_tested_at: Optional[datetime]
-    created_at: datetime
-    updated_at: datetime
-
-    class Config:
-        from_attributes = True
-
-
-class TelephonyPhoneNumberResponse(BaseModel):
-    """Telephony phone number inventory response schema."""
-
-    id: UUID
-    phone_number: str
-    country_iso2: Optional[str]
-    region: Optional[str]
-    number_type: Optional[str]
-    capabilities: Optional[Dict[str, Any]]
-    is_masking_pool: bool
-    inbound_enabled: Optional[bool] = None
-    outbound_enabled: Optional[bool] = None
-    source: Optional[str] = None
-    agent_id: Optional[UUID]
-    linked_agent_name: Optional[str] = None
-    provider: Optional[str] = None
-    is_active: bool
-    created_at: datetime
-
-    class Config:
-        from_attributes = True
-
-
-class TelephonyDialTargetCreate(BaseModel):
-    """Schema for creating a saved outbound dial target."""
-    phone_number: str
-    label: Optional[str] = None
-
-
-class TelephonyDialTargetUpdate(BaseModel):
-    """Schema for updating a saved outbound dial target."""
-    phone_number: Optional[str] = None
-    label: Optional[str] = None
-
-
-class TelephonyDialTargetResponse(BaseModel):
-    """Schema for dial target response."""
-    id: UUID
-    phone_number: str
-    label: Optional[str] = None
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class TelephonyVerifyStartRequest(BaseModel):
-    """Request schema for starting voice OTP verification."""
-
-    phone_number: str
-    provider: str = "plivo"
-
-
-class TelephonyVerifyStartResponse(BaseModel):
-    """Response schema for started voice OTP verification."""
-
-    session_id: UUID
-    provider_session_uuid: str
-    status: str
-    message: str
-
-
-class TelephonyVerifyCheckRequest(BaseModel):
-    """Request schema for checking a submitted OTP code."""
-
-    session_id: UUID
-    otp_code: str
-    provider: str = "plivo"
-
-
-class TelephonyVerifyCheckResponse(BaseModel):
-    """Response schema for OTP check status."""
-
-    verified: bool
-    status: str
-    message: str
-
-
-class TelephonyMaskingSessionCreate(BaseModel):
-    """Request schema for creating a number masking session."""
-
-    party_a_number: str
-    party_b_number: str
-    provider: str = "plivo"
-    expires_in_minutes: Optional[int] = 60
-    metadata: Optional[Dict[str, Any]] = None
-    provider: str = "plivo"
-
-
-class TelephonyMaskingSessionResponse(BaseModel):
-    """Response schema for masking sessions."""
-
-    id: UUID
-    masked_number: str
-    party_a_number: str
-    party_b_number: str
-    status: str
-    expires_at: Optional[datetime]
-    created_at: datetime
-
-    class Config:
-        from_attributes = True
-
-
-class TelephonyOutboundCallRequest(BaseModel):
-    """Request schema for outbound call initiation."""
-
-    from_number: str
-    to_number: str
-    answer_url: Optional[str] = None
-    agent_id: Optional[UUID] = None
-
-
-class TelephonyOutboundCallResponse(BaseModel):
-    """Response schema for outbound call initiation."""
-
-    provider_request_uuid: str
-    call_status: str
-    from_number: str
-    to_number: str
-    message: str
-
-
-# --- Call Import Schemas ---
-
-class CallImportRowResponse(BaseModel):
-    """Single row within a call-import batch."""
-
-    id: UUID
-    row_index: int
-    # Renamed from ``external_call_id`` (DB column renamed in migration
-    # ``034_call_import_schemas``). Same data, same uniqueness rules.
-    conversation_id: str
-    recording_url: Optional[str] = None
-    recording_date: Optional[date] = None
-    # Production transcript: the value supplied via the CSV upload.
-    transcript: Optional[str] = None
-    transcript_source: Optional[str] = None
-    transcript_provider: Optional[str] = None
-    transcript_model: Optional[str] = None
-    transcript_status: Optional[str] = None
-    transcript_error: Optional[str] = None
-    transcribed_at: Optional[datetime] = None
-    # Diarised transcript: produced by the post-hoc diarisation
-    # worker. Independent of ``transcript`` so manual diarisation
-    # never overwrites the CSV-supplied production value.
-    diarised_transcript: Optional[str] = None
-    diarised_transcript_provider: Optional[str] = None
-    diarised_transcript_model: Optional[str] = None
-    diarised_transcript_status: Optional[str] = None
-    diarised_transcript_error: Optional[str] = None
-    diarised_at: Optional[datetime] = None
-    # LLM that turned the STT plain-text output into structured
-    # ``diarised_segments``. Surfaced in the row detail panel so
-    # reviewers can see "Diarised by openai/gpt-4o-mini" next to
-    # the swap toggle. NULL on rows diarised by the legacy pyannote
-    # worker (which has been removed).
-    diarised_llm_provider: Optional[str] = None
-    diarised_llm_model: Optional[str] = None
-    # The exact prompt the LLM diariser ran with. Persisted so a
-    # reviewer can copy it back into the modal and reproduce the
-    # turn layout against a different STT pass.
-    diarised_prompt: Optional[str] = None
-    # Structured speaker turns produced by the diarisation worker. Each
-    # entry is `{ "speaker": "agent"|"user"|"speaker_N", "text": str,
-    # "start": float, "end": float, "raw_speaker": "Speaker 1" }`. The
-    # plain ``diarised_transcript`` field above is a `<speaker>: <text>`
-    # rendering of this list with ``diarised_speaker_swap`` applied.
-    diarised_segments: Optional[List[Dict[str, Any]]] = None
-    # When True the agent <-> user mapping in ``diarised_segments`` is
-    # inverted at render / export time. The worker writes the canonical
-    # mapping using the "first speaker is the agent" heuristic; the swap
-    # toggle lets reviewers correct that without re-running diarisation.
-    diarised_speaker_swap: bool = False
-    status: CallImportRowStatus
-    recording_s3_key: Optional[str] = None
-    recording_content_type: Optional[str] = None
-    recording_size_bytes: Optional[int] = None
-    error_message: Optional[str] = None
-    attempts: int
-    raw_columns: Optional[Dict[str, Any]] = None
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-# --- Call Import Schema (Input Parameter definitions) ---
-
-
-class CallImportSchemaParameterBase(BaseModel):
-    """A single typed parameter inside a Call Import schema.
-
-    Used both in request bodies (create / update) and as the building
-    block of :class:`CallImportSchemaParameterResponse`. Names are
-    case-insensitive unique within their parent schema.
-    """
-
-    name: str = Field(
-        ...,
-        min_length=1,
-        max_length=255,
-        description=(
-            "Parameter name as it appears in the schema editor and the "
-            "upload mapping table. Must be unique within the schema "
-            "(case-insensitive)."
-        ),
-    )
-    type: CallImportParameterType = Field(
-        ...,
-        description=(
-            "Parameter type. One of conversation_id / recording_url / "
-            "recording_date / transcript / text / number / boolean / "
-            "datetime / url. Exactly one parameter of type "
-            "'conversation_id' must be present; at most one each of "
-            "'recording_url', 'recording_date', and 'transcript'. "
-            "Only conversation_id is forced required."
-        ),
-    )
-    description: Optional[str] = Field(
-        default=None,
-        max_length=2048,
-        description="Free-text help shown next to the parameter in the mapping UI.",
-    )
-    is_required: bool = Field(
-        default=False,
-        description=(
-            "When True, the parameter must be mapped to a CSV column on "
-            "every upload. The ``conversation_id`` parameter is always "
-            "required and is force-set to True by the server."
-        ),
-    )
-
-
-class CallImportSchemaParameterCreate(CallImportSchemaParameterBase):
-    """Create payload for a single parameter (inside a schema CRUD body)."""
-
-
-class CallImportSchemaParameterResponse(CallImportSchemaParameterBase):
-    """Response shape including the persisted id + ordering."""
-
-    id: UUID
-    ordering: int
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-def _validate_schema_parameters(
-    parameters: List[CallImportSchemaParameterBase],
-) -> List[CallImportSchemaParameterBase]:
-    """Apply the cross-parameter invariants shared by create + update."""
-
-    if not parameters:
-        raise ValueError("Schema must define at least one parameter.")
-
-    seen_names: set[str] = set()
-    conv_count = 0
-    recording_date_count = 0
-    rec_url_count = 0
-    transcript_count = 0
-    for param in parameters:
-        norm = param.name.strip().lower()
-        if not norm:
-            raise ValueError("Parameter name must be non-empty.")
-        if norm in seen_names:
-            raise ValueError(
-                f"Duplicate parameter name '{param.name}' "
-                "(names must be unique within a schema)."
-            )
-        seen_names.add(norm)
-        if param.type == CallImportParameterType.CONVERSATION_ID:
-            conv_count += 1
-        elif param.type == CallImportParameterType.RECORDING_DATE:
-            recording_date_count += 1
-        elif param.type == CallImportParameterType.RECORDING_URL:
-            rec_url_count += 1
-        elif param.type == CallImportParameterType.TRANSCRIPT:
-            transcript_count += 1
-
-    if conv_count != 1:
-        raise ValueError(
-            "Schema must contain exactly one parameter of type "
-            "'conversation_id'."
-        )
-    if rec_url_count > 1:
-        raise ValueError(
-            "Schema may contain at most one parameter of type "
-            "'recording_url'."
-        )
-    if recording_date_count > 1:
-        raise ValueError(
-            "Schema may contain at most one parameter of type "
-            "'recording_date'."
-        )
-    if transcript_count > 1:
-        raise ValueError(
-            "Schema may contain at most one parameter of type 'transcript'."
-        )
-    return parameters
-
-
-class CallImportSchemaCreate(BaseModel):
-    """Create body for a new call-import schema."""
-
-    name: str = Field(..., min_length=1, max_length=255)
-    description: Optional[str] = Field(default=None, max_length=2048)
-    parameters: List[CallImportSchemaParameterCreate] = Field(
-        ...,
-        description=(
-            "Ordered list of parameters. Order is preserved; the server "
-            "stamps ``ordering`` from the list index."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def _check_parameters(self):
-        _validate_schema_parameters(list(self.parameters))
-        return self
-
-
-class CallImportSchemaUpdate(BaseModel):
-    """Patch body for an existing schema (full parameter replacement)."""
-
-    name: Optional[str] = Field(default=None, min_length=1, max_length=255)
-    description: Optional[str] = Field(default=None, max_length=2048)
-    parameters: Optional[List[CallImportSchemaParameterCreate]] = Field(
-        default=None,
-        description=(
-            "If provided, REPLACES the full set of parameters on the "
-            "schema. Omit to leave parameters untouched."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def _check_parameters(self):
-        if self.parameters is not None:
-            _validate_schema_parameters(list(self.parameters))
-        return self
-
-
-class CallImportSchemaResponse(BaseModel):
-    """Read response for a single schema."""
-
-    id: UUID
-    organization_id: UUID
-    workspace_id: UUID
-    name: str
-    description: Optional[str] = None
-    parameters: List[CallImportSchemaParameterResponse] = Field(default_factory=list)
-    # How many CallImport batches reference this schema. Populated by the
-    # router when listing; defaults to 0 on detail responses where the
-    # caller doesn't need it.
-    usage_count: int = 0
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class CallImportSchemaListResponse(BaseModel):
-    """Paginated list of schemas."""
-
-    items: List[CallImportSchemaResponse] = Field(default_factory=list)
-    total: int
-
-
-class CallImportTagResponse(BaseModel):
-    """Tag attached to call import batches."""
-
-    id: UUID
-    name: str
-    color: Optional[str] = None
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class CallImportTagCreate(BaseModel):
-    """Create a new call-import tag for the organization."""
-
-    name: str = Field(..., min_length=1, max_length=255)
-    color: Optional[str] = Field(None, max_length=32)
-
-
-class CallImportTagUpdate(BaseModel):
-    """Partial update for a call-import tag."""
-
-    name: Optional[str] = Field(None, min_length=1, max_length=255)
-    color: Optional[str] = Field(None, max_length=32)
-
-
-class CallImportPreviewSheet(BaseModel):
-    """One worksheet (or one CSV file synthesized as a single sheet)."""
-
-    name: str = Field(..., description="Sheet name for xlsx; filename for csv.")
-    headers: List[str] = Field(
-        default_factory=list,
-        description="Column headers from the first non-empty row.",
-    )
-    row_count: int = Field(
-        ...,
-        description="Approximate count of data rows (excluding the header row).",
-    )
-
-
-class CallImportSourceRowSkip(BaseModel):
-    """One source spreadsheet row skipped during parse (identity / recording URL)."""
-
-    source_row: int = Field(
-        ...,
-        description="1-based row index in the source file (same semantics as parse errors).",
-    )
-    reason: str = Field(
-        ...,
-        description=(
-            "Machine-readable skip reason, e.g. missing_conversation_id, "
-            "missing_recording_url, invalid_recording_url."
-        ),
-    )
-    message: str = Field(
-        ...,
-        description="Human-readable explanation shown in the UI.",
-    )
-
-
-class CallImportResponse(BaseModel):
-    """Summary of a call-import batch."""
-
-    id: UUID
-    organization_id: UUID
-    workspace_id: UUID
-    # Provider is optional in the new staged flow (only resolved at the
-    # IMPORT stage). Stays populated for all post-import batches.
-    provider: Optional[str] = None
-    telephony_integration_id: Optional[UUID] = None
-    original_filename: Optional[str] = None
-    sheet_name: Optional[str] = None
-    dataset: Optional[str] = None
-    tags: List[CallImportTagResponse] = Field(default_factory=list)
-    # New schema-driven mapping. Empty on legacy batches; pre-schema
-    # batches keep their values in ``column_mapping`` / ``extra_columns``
-    # / ``custom_column_mapping`` below for backwards-compatibility.
-    schema_id: Optional[UUID] = None
-    parameter_mapping: Dict[str, str] = Field(default_factory=dict)
-    column_mapping: Dict[str, Optional[str]] = Field(default_factory=dict)
-    extra_columns: List[str] = Field(default_factory=list)
-    custom_column_mapping: Dict[str, str] = Field(default_factory=dict)
-    # Persisted "drop these columns" decision captured at MAP time.
-    # Empty for legacy one-shot uploads where the value was ephemeral.
-    skipped_columns: List[str] = Field(default_factory=list)
-    source_row_skips: List[CallImportSourceRowSkip] = Field(
-        default_factory=list,
-        description=(
-            "Source rows skipped at parse time because of missing/invalid "
-            "conversation ID or recording URL."
-        ),
-    )
-    # Source-file staging fields populated at UPLOAD time. ``None`` on
-    # legacy batches imported via the one-shot ``POST /upload`` endpoint.
-    source_s3_key: Optional[str] = None
-    source_format: Optional[str] = None
-    source_size_bytes: Optional[int] = None
-    source_content_type: Optional[str] = None
-    # Snapshot of the file's sheets + headers captured at UPLOAD time
-    # so the MAP UI can render without re-fetching the file from S3.
-    available_sheets: Optional[List[CallImportPreviewSheet]] = None
-    total_rows: int
-    completed_rows: int
-    failed_rows: int
-    status: CallImportStatus
-    error_message: Optional[str] = None
-    latest_evaluation_status: Optional[str] = Field(
-        None,
-        description=(
-            "Status of the most recent evaluation run for this batch, "
-            "when any evaluation exists."
-        ),
-    )
-    created_at: datetime
-    updated_at: datetime
-    created_by_email: Optional[str] = None
-    last_updated_by_email: Optional[str] = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class CallImportDetailResponse(CallImportResponse):
-    """A call-import batch with its rows expanded.
-
-    ``filtered_total_rows`` is only set when the caller passed a ``q``
-    search term G�� it lets the UI paginate against the filtered subset
-    while still showing the unfiltered ``total_rows`` in the header.
-
-    The ``diarised_*_rows`` counters aggregate
-    ``CallImportRow.diarised_transcript_status`` across the batch so the
-    UI can render a transcribe-and-diarise progress bar without paging
-    through every row. Rows that have never been touched by the
-    transcribe/diarise worker (``status='idle'``) are NOT counted here G��
-    callers compute the idle bucket as
-    ``total_rows - (pending + running + completed + failed)``.
-    """
-
-    rows: List[CallImportRowResponse] = Field(default_factory=list)
-    filtered_total_rows: Optional[int] = None
-    diarised_pending_rows: int = 0
-    diarised_running_rows: int = 0
-    diarised_completed_rows: int = 0
-    diarised_failed_rows: int = 0
-
-
-class CallImportListResponse(BaseModel):
-    """Paginated list of call-import batches."""
-
-    items: List[CallImportResponse]
-    total: int
-    page: int
-    page_size: int
-
-
-class CallImportDispatchLimitSnapshot(BaseModel):
-    """Configured and live Redis in-flight caps for eval work."""
-
-    global_limit: int
-    global_inflight: int
-    global_at_capacity: bool
-    org_limit: int
-    org_inflight: int
-    org_at_capacity: bool
-    workspace_limit: int
-    job_limit: int
-    fair_dispatch_batch_size: int
-
-
-class CallImportDispatchFairDispatchSnapshot(BaseModel):
-    """Fair-dispatch scheduler metadata from Redis."""
-
-    global_rr_cursor: int
-    dispatch_dedupe_active: bool
-    dispatch_queue: str
-    at_capacity_backoff_seconds: int
-
-
-class CallImportDispatchEvaluationSnapshot(BaseModel):
-    """One in-flight evaluation run with row counters."""
-
-    evaluation_id: UUID
-    call_import_id: UUID
-    status: str
-    total_rows: int
-    pending_rows: int
-    running_rows: int
-    job_inflight: int
-    job_at_capacity: bool
-
-
-class CallImportDispatchWorkspaceSnapshot(BaseModel):
-    """Per-workspace pending dispatch + slot usage."""
-
-    workspace_id: UUID
-    workspace_name: Optional[str] = None
-    workspace_slug: Optional[str] = None
-    inflight: int
-    inflight_at_capacity: bool
-    pending_dispatch_rows: int
-    pending_import_rows: int
-    eval_rr_cursor: int
-    active_evaluations: int
-    evaluations: List[CallImportDispatchEvaluationSnapshot] = Field(
-        default_factory=list
-    )
-
-
-class CallImportDispatchDiagnosticsResponse(BaseModel):
-    """Live operator snapshot for call-import eval fair dispatch."""
-
-    limits: CallImportDispatchLimitSnapshot
-    fair_dispatch: CallImportDispatchFairDispatchSnapshot
-    workspaces: List[CallImportDispatchWorkspaceSnapshot]
-    generated_at: datetime
-
-
-class CallImportUploadResponse(BaseModel):
-    """Response returned right after a CSV is accepted."""
-
-    id: UUID
-    total_rows: int
-    status: CallImportStatus
-    dataset: Optional[str] = None
-    tags: List[CallImportTagResponse] = Field(default_factory=list)
-    message: str
-
-
-class CallImportDeleteResponse(BaseModel):
-    """Response after a whole-batch call-import delete is accepted."""
-
-    id: UUID
-    status: Literal["accepted", "completed"] = Field(
-        ...,
-        description=(
-            "``accepted`` when teardown was queued to run asynchronously; "
-            "``completed`` when the batch was already removed."
-        ),
-    )
-
-
-class CallImportPreviewResponse(BaseModel):
-    """Sheets/headers extracted from an uploaded CSV or Excel workbook.
-
-    The frontend uses this to drive the column-mapping UI without doing
-    its own parsing G�� keeps client and server in lockstep on quoted
-    fields, encodings, and Excel cell coercion.
-    """
-
-    format: str = Field(..., description="One of 'csv' or 'xlsx'.")
-    sheets: List[CallImportPreviewSheet] = Field(default_factory=list)
-
-
-class CallImportUpdate(BaseModel):
-    """Partial update of a call-import batch."""
-
-    original_filename: Optional[str] = Field(
-        None,
-        description=(
-            "User-facing batch label shown in the UI. Pass an empty string to clear."
-        ),
-    )
-    dataset: Optional[str] = Field(
-        None,
-        description=(
-            "Free-text dataset label. Pass an empty string to clear the dataset."
-        ),
-    )
-    tag_ids: Optional[List[UUID]] = Field(
-        None,
-        description=(
-            "Replace the full set of tag assignments. Pass an empty list to clear all tags."
-        ),
-    )
-    schema_id: Optional[UUID] = Field(
-        None,
-        description=(
-            "Reassign the Input Parameter schema. Only honoured while the "
-            "batch is in ``uploaded`` or ``mapped`` state; once the batch "
-            "has rows it's locked to its original schema."
-        ),
-    )
-
-
-class CallImportMappingUpdate(BaseModel):
-    """Mapping payload for the MAP stage (``PATCH /call-imports/{id}/mapping``).
-
-    Idempotent: callers can submit this multiple times against an
-    ``uploaded`` or ``mapped`` batch. Validation re-runs against the
-    persisted ``available_sheets`` snapshot every time so the user can
-    correct mistakes without re-uploading the file.
-    """
-
-    schema_id: UUID = Field(
-        ...,
-        description=(
-            "Reusable Input Parameter schema this batch is mapped against. "
-            "Must belong to the active workspace."
-        ),
-    )
-    sheet_name: Optional[str] = Field(
-        None,
-        description=(
-            "Worksheet to use when the staged source file is an Excel "
-            "workbook. REQUIRED for xlsx; ignored / rejected for CSV."
-        ),
-    )
-    parameter_mapping: Dict[str, str] = Field(
-        default_factory=dict,
-        description=(
-            "``{schema_parameter_name: source_header}`` map covering every "
-            "required schema parameter."
-        ),
-    )
-    skipped_columns: List[str] = Field(
-        default_factory=list,
-        description=(
-            "Source headers the uploader has explicitly skipped. Every "
-            "source header must be either mapped or appear here."
-        ),
-    )
-
-
-class CallImportStartRequest(BaseModel):
-    """Provider + credential picker for the IMPORT stage."""
-
-    provider: Optional[str] = Field(
-        default=None,
-        description=(
-            "Telephony provider key. Must match the "
-            "``telephony_integration_id``'s provider. Omit together with "
-            "``telephony_integration_id`` to download recordings directly "
-            "from CSV-supplied URLs without credentials."
-        ),
-    )
-    telephony_integration_id: Optional[UUID] = Field(
-        default=None,
-        description=(
-            "Specific TelephonyIntegration credential row to use when "
-            "downloading recordings for this batch. Omit together with "
-            "``provider`` for direct-URL import."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def validate_credential_mode(self) -> "CallImportStartRequest":
-        has_provider = bool((self.provider or "").strip())
-        has_integration = self.telephony_integration_id is not None
-        if has_provider != has_integration:
-            raise ValueError(
-                "provider and telephony_integration_id must both be provided "
-                "or both omitted for direct-URL import."
-            )
-        return self
-
-
-# --- Call Import Evaluation Schemas ---
-
-
-class CallImportEvaluationLLMOverride(BaseModel):
-    """Per-metric LLM override used on top of the run-level default.
-
-    Any field left ``None`` falls back to the run-level value (which
-    itself falls back to the historical OpenAI/gpt-4o default). This
-    lets users pick a specific provider/model for a single metric (e.g.
-    a stronger Anthropic model for a tricky qualitative metric) without
-    re-typing the rest of the metrics in the run.
-    """
-
-    provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description="Override LLM provider key, e.g. 'openai' or 'anthropic'.",
-    )
-    model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description="Override LLM model name, e.g. 'gpt-4o' or 'claude-3-opus'.",
-    )
-    credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Optional AIProvider id when the org has multiple credentials.",
-    )
-    llm_config: Optional[Dict[str, Any]] = Field(
-        default=None,
-        description="Optional per-metric generation parameters (temperature, top_p, etc.).",
-    )
-
-
-CallImportEvaluationTranscriptSource = Literal["production", "diarised"]
-
-
-class CallImportEvaluationCreate(BaseModel):
-    """Request body for triggering an evaluation over a call-import batch."""
-
-    metric_ids: List[UUID] = Field(
-        ...,
-        min_length=1,
-        description="Org Metric ids to score every completed row against.",
-    )
-    name: Optional[str] = Field(
-        default=None,
-        max_length=255,
-        description=(
-            "Optional human-readable label for the run. Shown in the UI "
-            "instead of the UUID prefix."
-        ),
-    )
-    transcript_sources: List[CallImportEvaluationTranscriptSource] = Field(
-        default_factory=lambda: ["diarised"],
-        min_length=1,
-        max_length=1,
-        description=(
-            "Which transcript to score against. ``'diarised'`` (default) "
-            "auto-diarises rows missing a diarised transcript then scores "
-            "``diarised_transcript``. ``'production'`` scores the CSV "
-            "``transcript`` column directly and skips diarisation."
-        ),
-    )
-
-    @field_validator("transcript_sources")
-    @classmethod
-    def _validate_transcript_sources(
-        cls, value: List[str]
-    ) -> List["CallImportEvaluationTranscriptSource"]:
-        allowed = {"production", "diarised"}
-        invalid = [src for src in value if src not in allowed]
-        if invalid:
-            raise ValueError(
-                "transcript_sources must be ['production'] or ['diarised'] "
-                "(received: "
-                + ", ".join(repr(src) for src in invalid)
-                + ")."
-            )
-        return value  # type: ignore[return-value]
-    # --- Run-level LLM config ---
-    llm_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "Run-level LLM provider key (e.g. 'openai', 'anthropic'). NULL "
-            "preserves the historical OpenAI/gpt-4o default."
-        ),
-    )
-    llm_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description="Run-level LLM model name. Required when llm_provider is set.",
-    )
-    llm_credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Optional AIProvider row to pin for the run-level LLM.",
-    )
-    llm_config: Optional[Dict[str, Any]] = Field(
-        default=None,
-        description="Run-level LLM generation parameters (temperature, top_p, etc.).",
-    )
-    metric_llm_overrides: Optional[
-        Dict[str, CallImportEvaluationLLMOverride]
-    ] = Field(
-        default=None,
-        description=(
-            "Optional per-metric LLM overrides keyed by metric UUID. Each "
-            "entry overrides the run-level default for that metric only."
-        ),
-    )
-    # --- Auto-transcribe / diarization hook ---
-    # Every diarised run auto-diarises rows that don't already have a
-    # diarised transcript. The flag stays on the schema so legacy API
-    # callers don't 400 immediately, but the route now requires
-    # ``stt_provider`` + ``stt_model`` on every run regardless of this
-    # value.
-    auto_transcribe: bool = Field(
-        default=True,
-        description=(
-            "Auto-diarise rows missing a diarised transcript before "
-            "evaluation. Defaults to true and is effectively required: "
-            "``stt_provider`` + ``stt_model`` are mandatory on every "
-            "evaluation run."
-        ),
-    )
-    transcribe_overwrite: bool = Field(
-        default=False,
-        description=(
-            "When auto_transcribe is on, overwrite existing transcripts "
-            "instead of skipping rows that already have one."
-        ),
-    )
-    transcribe_mode: Literal["stt_llm", "llm_only"] = Field(
-        default="stt_llm",
-        description=(
-            "Diarisation pipeline shape for the auto-transcribe step. "
-            "'stt_llm' (default) runs STT then an LLM diariser over the "
-            "resulting text G�� ``stt_provider`` + ``stt_model`` must be "
-            "provided. 'llm_only' skips STT and feeds the audio "
-            "directly to the multimodal ``diarization_llm_*`` model "
-            "along with ``diarization_prompt``; STT fields must be "
-            "omitted in that case."
-        ),
-    )
-    stt_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "STT provider key, e.g. 'deepgram', 'openai'. Required when "
-            "``transcribe_mode='stt_llm'`` (the default); must be omitted "
-            "when ``transcribe_mode='llm_only'``."
-        ),
-    )
-    stt_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description=(
-            "STT model name, e.g. 'nova-2', 'whisper-1'. Same presence "
-            "rules as ``stt_provider``."
-        ),
-    )
-    stt_credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Optional AIProvider/Integration row to pin for STT.",
-    )
-    stt_language: Optional[str] = Field(
-        default=None,
-        max_length=20,
-        description="ISO language hint for the STT provider, e.g. 'en'.",
-    )
-    # --- LLM diariser config (mirror of CallImportTranscribeRequest) ---
-    # Auto-diarised eval rows go through the same LLM-based diariser as
-    # the standalone Transcribe modal G�� the run remembers the provider /
-    # model / prompt so a follow-up retry can reproduce them without
-    # having to re-prompt the user.
-    diarization_llm_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "LLM provider for diarising STT output into agent/user "
-            "turns. Required when ``auto_transcribe`` is set (the worker "
-            "no longer falls back to pyannote)."
-        ),
-    )
-    diarization_llm_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description="LLM model for the diariser.",
-    )
-    diarization_llm_credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Optional AIProvider row to pin for the diariser LLM.",
-    )
-    diarization_prompt: Optional[str] = Field(
-        default=None,
-        max_length=10_000,
-        description=(
-            "Custom system prompt for the diariser LLM; falls back to "
-            "the canonical default when blank."
-        ),
-    )
-    discover_new_metrics: bool = Field(
-        default=False,
-        description=(
-            "When true, the LLM is invited to propose net-new top-level "
-            "metrics (boolean / rating / category) observed in the "
-            "transcripts in addition to scoring the selected metrics. "
-            "Candidates surface in the Discovered metrics panel on the "
-            "evaluation detail Flow tab and can be promoted into real "
-            "standalone Metric rows. Defaults to false so existing "
-            "callers retain previous behaviour."
-        ),
-    )
-    # Telephony credentials for unified pipeline (required when batch is mapped).
-    provider: Optional[str] = Field(
-        default=None,
-        description=(
-            "Telephony provider key. Required together with "
-            "``telephony_integration_id`` when starting evaluation "
-            "from a mapped batch. Omit both for direct-URL import."
-        ),
-    )
-    telephony_integration_id: Optional[UUID] = Field(
-        default=None,
-        description=(
-            "TelephonyIntegration credential for recording fetch. "
-            "Required together with ``provider`` for credentialed import."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def validate_telephony_credential_mode(self) -> "CallImportEvaluationCreate":
-        has_provider = bool((self.provider or "").strip())
-        has_integration = self.telephony_integration_id is not None
-        if has_provider != has_integration:
-            raise ValueError(
-                "provider and telephony_integration_id must both be provided "
-                "or both omitted for direct-URL evaluation."
-            )
-        return self
-
-
-class CallImportEvaluationUpdate(BaseModel):
-    """Patch body for editing a previously-created evaluation run."""
-
-    name: Optional[str] = Field(
-        default=None,
-        max_length=255,
-        description="New name for the evaluation. Empty string clears it.",
-    )
-
-
-class CallImportEvaluationBulkDelete(BaseModel):
-    """Request body for deleting multiple evaluation runs in one call."""
-
-    evaluation_ids: List[UUID] = Field(
-        ...,
-        min_length=1,
-        description="Evaluation ids to delete.",
-    )
-
-
-class CallImportEvaluationRetryRequest(BaseModel):
-    """Body for retrying a subset (or all failed rows) of an evaluation run.
-
-    ``eval_row_ids`` is optional: when ``None`` the retry applies to
-    every row in the run that is currently in the ``failed`` state. The
-    selection always intersects with the run's actual rows, so unknown
-    ids are silently skipped (and surfaced in the response's
-    ``skipped`` list with reason ``unknown``).
-
-    The optional ``llm_*`` / ``metric_llm_overrides`` / ``stt_*`` fields
-    let the caller swap out the LLM or STT configuration that the
-    failed rows were originally evaluated with. When a field is left
-    ``None`` the run's existing value is preserved. When a field is
-    set, it is persisted onto the run (so a follow-up retry sees the
-    new value as the default) and used by the worker on the next
-    pass. Providing only one half of provider+model is rejected so
-    the worker never ends up with a half-configured run.
-    """
-
-    eval_row_ids: Optional[List[UUID]] = Field(
-        default=None,
-        description=(
-            "Restrict the retry to a specific subset of evaluation rows. "
-            "When omitted, every row with status='failed' in this run is "
-            "re-enqueued."
-        ),
-    )
-
-    # --- Metric-subset re-run ---
-    # When ``metric_ids`` is set, the retry recomputes ONLY those
-    # metrics instead of the whole row, and the new scores are merged
-    # into the existing ``metric_scores`` JSON (other metrics'
-    # previously-computed values are preserved). This is the path
-    # taken by the "Re-run metrics" UI in CallImportEvaluationDetail.
-    metric_ids: Optional[List[UUID]] = Field(
-        default=None,
-        description=(
-            "Restrict the retry to a specific subset of metrics. When "
-            "set, the worker recomputes only these metrics and merges "
-            "the new scores into the row's existing metric_scores "
-            "(other metrics' previous values are preserved). When "
-            "omitted, the row is fully re-scored as before. Every id "
-            "must already be present in the run's selected_metric_ids."
-        ),
-    )
-    include_completed: bool = Field(
-        default=False,
-        description=(
-            "When True, rows whose status is currently 'completed' "
-            "become eligible for retry (otherwise only 'failed' rows "
-            "are picked up). Required when ``metric_ids`` is set on a "
-            "successful row, since otherwise the whole metric-subset "
-            "retry would be skipped as 'completed'."
-        ),
-    )
-
-    # --- LLM overrides ---
-    llm_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "Override the run-level LLM provider for this retry (and "
-            "future retries). Must be paired with ``llm_model``."
-        ),
-    )
-    llm_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description=(
-            "Override the run-level LLM model. Must be paired with "
-            "``llm_provider``."
-        ),
-    )
-    llm_credential_id: Optional[UUID] = Field(
-        default=None,
-        description=(
-            "Pin a specific AIProvider credential row for the LLM. "
-            "When omitted, the resolver falls back to the org default."
-        ),
-    )
-    llm_config: Optional[Dict[str, Any]] = Field(
-        default=None,
-        description="Override run-level LLM generation parameters for this retry.",
-    )
-    metric_llm_overrides: Optional[
-        Dict[str, CallImportEvaluationLLMOverride]
-    ] = Field(
-        default=None,
-        description=(
-            "Replace the run's per-metric LLM overrides. When omitted, "
-            "the existing overrides are kept; when set, this dict "
-            "fully replaces them (pass an empty object to clear)."
-        ),
-    )
-
-    # --- STT overrides ---
-    stt_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "Override the run-level STT provider for this retry. Must "
-            "be paired with ``stt_model``. Only meaningful when the "
-            "run is configured for the diarised transcript source."
-        ),
-    )
-    stt_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description="Override the run-level STT model.",
-    )
-    stt_credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Pin a specific credential row for the STT call.",
-    )
-    # --- LLM diariser overrides ---
-    # When set, replace the run-stored diariser configuration for any
-    # rows that have to be re-diarised as part of the retry (i.e.
-    # ``transcribe_overwrite=True`` or the row never had a diarised
-    # transcript). Same provider+model pairing rule as STT.
-    diarization_llm_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "Override the run's diariser LLM provider. Must be paired "
-            "with ``diarization_llm_model``."
-        ),
-    )
-    diarization_llm_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description="Override the run's diariser LLM model.",
-    )
-    diarization_llm_credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Pin a specific credential row for the diariser LLM.",
-    )
-    diarization_prompt: Optional[str] = Field(
-        default=None,
-        max_length=10_000,
-        description=(
-            "Override the run's diariser prompt. Pass an empty string "
-            "to clear the override and fall back to the canonical "
-            "default; pass None to leave the existing value untouched."
-        ),
-    )
-    transcribe_overwrite: bool = Field(
-        default=False,
-        description=(
-            "When True, wipe the diarised transcript on every retried "
-            "row's source CallImportRow so the (possibly new) STT runs "
-            "from scratch. When False, rows that already have a "
-            "diarised transcript skip diarisation and only re-evaluate."
-        ),
-    )
-    transcribe_mode: Optional[Literal["stt_llm", "llm_only"]] = Field(
-        default=None,
-        description=(
-            "Override the run's diarisation pipeline mode for this retry. "
-            "``stt_llm`` runs STT then an LLM diariser; ``llm_only`` feeds "
-            "audio directly to a multimodal diariser LLM."
-        ),
-    )
-
-    # Telephony credentials for rows that must re-fetch recordings.
-    provider: Optional[str] = Field(
-        default=None,
-        description=(
-            "Override the batch's telephony provider for this retry pass. "
-            "Must be paired with ``telephony_integration_id``. Omit both "
-            "fields to keep the batch's existing pinned credentials."
-        ),
-    )
-    telephony_integration_id: Optional[UUID] = Field(
-        default=None,
-        description=(
-            "Override the telephony credential used when re-fetching "
-            "recordings during this retry. Must be paired with "
-            "``provider``. Omit both to keep existing credentials; send "
-            "both as null for direct-URL retry."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def validate_telephony_credential_mode(self) -> "CallImportEvaluationRetryRequest":
-        fields_set = self.model_fields_set
-        if (
-            "provider" not in fields_set
-            and "telephony_integration_id" not in fields_set
-        ):
-            return self
-        has_provider = bool((self.provider or "").strip())
-        has_integration = self.telephony_integration_id is not None
-        if has_provider != has_integration:
-            raise ValueError(
-                "provider and telephony_integration_id must both be provided "
-                "or both omitted for direct-URL retry."
-            )
-        return self
-
-
-class CallImportEvaluationRetrySkippedItem(BaseModel):
-    """One entry in the retry response's ``skipped`` list."""
-
-    eval_row_id: UUID
-    reason: str = Field(
-        ...,
-        description=(
-            "Why this row was not re-enqueued. Known values: "
-            "'unknown' (id not in this run), 'in_progress' "
-            "(status is pending/running), 'completed' (already "
-            "successful), 'source_row_missing'."
-        ),
-    )
-
-
-class CallImportEvaluationRetryResponse(BaseModel):
-    """Summary of a retry fan-out request."""
-
-    requeued: int = Field(
-        ...,
-        description="How many evaluation rows were reset and re-enqueued.",
-    )
-    transcribe_requeued: int = Field(
-        default=0,
-        description=(
-            "Of those, how many were chained through a diarisation "
-            "task first because the diarised transcript was missing "
-            "(matches the auto-transcribe behavior of the create-run "
-            "endpoint)."
-        ),
-    )
-    skipped: List[CallImportEvaluationRetrySkippedItem] = Field(
-        default_factory=list,
-        description="Rows the caller asked for that we did not re-enqueue.",
-    )
-
-
-class CallImportEvaluationBulkActionResponse(BaseModel):
-    """Acknowledgement for bulk cancel / force-fail requests accepted off-thread."""
-
-    accepted: bool = True
-    target_count: int = Field(
-        ...,
-        description="How many rows the background worker will process.",
-    )
-    evaluation_id: UUID
-
-
-class CallImportMetricSummary(BaseModel):
-    """Lightweight metric descriptor returned alongside an evaluation."""
-
-    id: UUID
-    name: str
-    metric_type: Optional[str] = None
-    description: Optional[str] = None
-    parent_metric_id: Optional[UUID] = None
-    selection_mode: Optional[SelectionMode] = None
-    # Surfaced so the Flow tab can decide whether to render the
-    # Discovered Labels panel next to a multi_label parent. Defaults to
-    # False to keep legacy clients (and standalone metrics) unaffected.
-    allow_discovery: bool = False
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class CallImportEvaluationResponse(BaseModel):
-    """Parent record describing one evaluation run over a batch."""
-
-    id: UUID
-    call_import_id: UUID
-    organization_id: UUID
-    name: Optional[str] = None
-    selected_metric_ids: List[UUID] = Field(default_factory=list)
-    # Parent UUID string -> [child UUID string]. Captured at run creation
-    # so the UI can rebuild the parent/child tree even after metrics are
-    # renamed or deleted. Empty / NULL = no hierarchy was used.
-    selected_metric_groups: Optional[Dict[str, List[str]]] = None
-    metrics: List[CallImportMetricSummary] = Field(default_factory=list)
-    status: str
-    total_rows: int
-    completed_rows: int
-    failed_rows: int
-    error_message: Optional[str] = None
-    llm_provider: Optional[str] = None
-    llm_model: Optional[str] = None
-    llm_credential_id: Optional[UUID] = None
-    llm_config: Optional[Dict[str, Any]] = None
-    metric_llm_overrides: Optional[Dict[str, Any]] = None
-    stt_provider: Optional[str] = None
-    stt_model: Optional[str] = None
-    stt_credential_id: Optional[UUID] = None
-    # Run-level LLM diariser config. Surfaced so the UI can show
-    # "Diarised via openai/gpt-4o-mini" on the evaluation header and
-    # pre-fill the retry modal with the previously-used prompt.
-    diarisation_llm_provider: Optional[str] = None
-    diarisation_llm_model: Optional[str] = None
-    diarisation_llm_credential_id: Optional[UUID] = None
-    diarisation_prompt: Optional[str] = None
-    # Diarisation pipeline shape this run was created with. ``stt_llm``
-    # (default) is the legacy STT-then-LLM-diariser flow; ``llm_only``
-    # means the audio was fed directly to a multimodal diariser LLM.
-    # Surfaced so the retry modal can preselect the right mode and the
-    # eval header can render "Diarised via LLM only (Gemini)" instead of
-    # an empty STT label.
-    transcribe_mode: Literal["stt_llm", "llm_only"] = "stt_llm"
-    # Which transcript column this run scored against. All current runs
-    # use diarised; legacy rows may still carry ``production``.
-    transcript_source: CallImportEvaluationTranscriptSource = "diarised"
-    # Sibling evaluation ids created in the same Run Evaluation request.
-    # Populated only on the POST response (and only when the user ticked
-    # both Production and Diarised in the modal G�� the backend creates
-    # one ``CallImportEvaluation`` per source and links them via this
-    # field so the frontend can deep-link to either run). Empty for all
-    # other reads.
-    sibling_evaluation_ids: List[UUID] = Field(default_factory=list)
-    expected_llm_calls_per_row: Optional[int] = Field(
-        None,
-        description=(
-            "Number of distinct LLM API calls made per evaluation row "
-            "(one per unique provider/model/config among selected metrics)."
-        ),
-    )
-    started_at: Optional[datetime] = None
-    finished_at: Optional[datetime] = None
-    created_at: datetime
-    updated_at: datetime
-    created_by_email: Optional[str] = None
-    last_updated_by_email: Optional[str] = None
-    # Cached LLM-generated TLDR for the Visualizations tab. Lazily
-    # populated by ``POST /evaluations/{eval_id}/insights``; ``None``
-    # for runs the user has not summarised yet. ``is_stale`` on the
-    # nested object is set by the route, not the model.
-    tldr_summary: Optional["EvaluationTldrSummary"] = None
-    # Cached LLM-generated user insights for External Audit PDF section 03.
-    user_insights: Optional["EvaluationUserInsightsState"] = None
-    # Cached per-metric failure clustering for internal diagnostics.
-    metric_clusters: Optional["EvaluationMetricClustersState"] = None
-    # True when the user opted into top-level metric discovery on the
-    # Run Evaluation modal. The frontend uses this to gate the
-    # "Discovered metrics" panel on the Flow tab.
-    discover_new_metrics: bool = False
-    bulk_operation: Optional[
-        Literal["abort", "force_fail_pending", "retry"]
-    ] = Field(
-        default=None,
-        description=(
-            "When set, a bulk background operation (abort, force-fail pending, "
-            "or retry) is still running for this evaluation. Other mutating "
-            "actions are rejected until it completes."
-        ),
-    )
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class CallImportEvaluationListResponse(BaseModel):
-    """Wrapper for listing evaluations on a single batch."""
-
-    items: List[CallImportEvaluationResponse]
-    total: int
-
-
-class CallImportEvaluationRowResponse(BaseModel):
-    """Per-source-row evaluation output (one Metric set applied to one row).
-
-    ``raw_columns``, ``recording_url`` and ``recording_s3_key`` come from
-    the parent ``CallImportRow`` so the row-detail panel can show the
-    full CSV row metadata + audio without a second round-trip. The UI
-    prefers ``recording_s3_key`` (resolved via a presigned URL) over
-    ``recording_url`` so playback uses our downloaded copy instead of
-    the raw provider URL, which is often expired/auth-gated.
-    """
-
-    id: UUID
-    evaluation_id: UUID
-    call_import_row_id: UUID
-    row_index: Optional[int] = None
-    # Renamed from ``external_call_id``; same value, mirrors the renamed
-    # ``call_import_rows.conversation_id`` column.
-    conversation_id: Optional[str] = None
-    transcript: Optional[str] = None
-    raw_columns: Optional[Dict[str, Any]] = None
-    recording_url: Optional[str] = None
-    recording_date: Optional[date] = None
-    recording_s3_key: Optional[str] = None
-    diarised_transcript_status: Optional[str] = None
-    diarised_transcript_error: Optional[str] = None
-    status: str
-    metric_scores: Dict[str, Any] = Field(default_factory=dict)
-    error_message: Optional[str] = None
-    started_at: Optional[datetime] = None
-    finished_at: Optional[datetime] = None
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class CallImportEvaluationRowListResponse(BaseModel):
-    """Paginated per-row evaluation results."""
-
-    items: List[CallImportEvaluationRowResponse]
-    total: int
-    page: int
-    page_size: int
-
-
-class CallImportRowBulkDelete(BaseModel):
-    """Request body for deleting multiple rows from a call-import batch."""
-
-    row_ids: List[UUID] = Field(
-        ...,
-        min_length=1,
-        description="Row ids to delete (must belong to the same call import).",
-    )
-
-
-class CallImportRowBulkDeleteResponse(BaseModel):
-    """Response after a bulk-delete pass over ``CallImportRow`` rows."""
-
-    deleted: int = Field(
-        ...,
-        description="How many rows were actually removed (unknown ids are skipped).",
-    )
-    status: Literal["completed", "accepted"] = Field(
-        default="completed",
-        description=(
-            "``accepted`` when deletion was queued to run asynchronously; "
-            "``completed`` when rows were removed before the response."
-        ),
-    )
-
-
-class CallImportRetryFailedRowsRequest(BaseModel):
-    """Optional credential override when re-enqueueing failed import rows."""
-
-    provider: Optional[str] = Field(
-        default=None,
-        description=(
-            "Telephony provider key for this retry pass. Omit together with "
-            "``telephony_integration_id`` to download from CSV recording URLs."
-        ),
-    )
-    telephony_integration_id: Optional[UUID] = Field(
-        default=None,
-        description=(
-            "Telephony credential to use for this retry pass. Omit together "
-            "with ``provider`` for direct-URL retry."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def validate_credential_mode(self) -> "CallImportRetryFailedRowsRequest":
-        has_provider = bool((self.provider or "").strip())
-        has_integration = self.telephony_integration_id is not None
-        if has_provider != has_integration:
-            raise ValueError(
-                "provider and telephony_integration_id must both be provided "
-                "or both omitted for direct-URL retry."
-            )
-        return self
-
-
-class CallImportRetryFailedRowsResponse(BaseModel):
-    """Summary of a retry pass over failed call-import rows."""
-
-    requeued: int = Field(
-        ...,
-        description=(
-            "Rows reset to pending and successfully re-enqueued on the "
-            "``imports`` worker queue."
-        ),
-    )
-    enqueue_failed: int = Field(
-        default=0,
-        description=(
-            "Rows that were eligible for retry but failed to enqueue again. "
-            "These rows are left in ``failed`` with an enqueue error."
-        ),
-    )
-    skipped: int = Field(
-        default=0,
-        description=(
-            "Rows skipped because they were no longer in ``failed`` at retry "
-            "time (for example, already retried from another tab)."
-        ),
-    )
-
-
-# --- Diarization / Transcription request/response shapes ---
-
-
-class CallImportTranscribeRequest(BaseModel):
-    """Body for kicking off diarization for one or many call-import rows.
-
-    The same shape powers both the per-row endpoint (where ``row_ids``
-    is ignored) and the batch-level endpoint. ``only_missing`` is the
-    safe default G�� rows with an existing transcript are skipped unless
-    ``overwrite_existing`` is set.
-
-    Two modes are supported:
-
-    * ``mode="stt_llm"`` (default) G�� the legacy two-stage pipeline: STT
-      produces plain text, an LLM splits it into agent/user turns using
-      ``diarization_prompt``. ``stt_provider`` and ``stt_model`` are
-      required in this mode.
-    * ``mode="llm_only"`` G�� skip STT entirely and hand the recording's
-      audio bytes to a multimodal chat model along with
-      ``diarization_prompt``. The model both transcribes and diarises in
-      a single pass. The STT fields are ignored (and must be omitted /
-      null). Only providers whose chat API accepts audio input (OpenAI
-      ``gpt-4o-audio-*``, Google Gemini ``1.5/2.0``) are usable; other
-      providers will surface a typed error on the row.
-    """
-
-    mode: Literal["stt_llm", "llm_only"] = Field(
-        default="stt_llm",
-        description=(
-            "Pipeline shape. 'stt_llm' (default) runs STT then an LLM "
-            "diariser over the resulting text. 'llm_only' skips STT and "
-            "feeds the raw audio to a multimodal LLM together with "
-            "``diarization_prompt`` for a single-pass transcribe + "
-            "diarise."
-        ),
-    )
-    stt_provider: Optional[str] = Field(
-        default=None,
-        max_length=50,
-        description=(
-            "STT provider key, e.g. 'deepgram' or 'openai'. Required when "
-            "``mode='stt_llm'``; must be omitted when ``mode='llm_only'``."
-        ),
-    )
-    stt_model: Optional[str] = Field(
-        default=None,
-        max_length=100,
-        description=(
-            "STT model name, e.g. 'nova-2' or 'whisper-1'. Required when "
-            "``mode='stt_llm'``; must be omitted when ``mode='llm_only'``."
-        ),
-    )
-    credential_id: Optional[UUID] = Field(
-        default=None,
-        description="Optional AIProvider/Integration row to pin for this run.",
-    )
-    language: Optional[str] = Field(
-        default=None,
-        max_length=20,
-        description="Optional ISO language hint, e.g. 'en'.",
-    )
-    only_missing: bool = Field(
-        default=True,
-        description=(
-            "When true, rows with an existing transcript are skipped (the "
-            "default safe behavior)."
-        ),
-    )
-    overwrite_existing: bool = Field(
-        default=False,
-        description=(
-            "When true, existing transcripts are replaced. Mutually "
-            "exclusive with only_missing."
-        ),
-    )
-    row_ids: Optional[List[UUID]] = Field(
-        default=None,
-        description=(
-            "Restrict the run to a specific subset of rows. NULL = every "
-            "row in the import (subject to only_missing)."
-        ),
-    )
-    # --- LLM diariser config ---
-    # In ``stt_llm`` mode diarisation runs as a *second* step: STT
-    # produces plain text, then this LLM splits it into agent/user
-    # turns. In ``llm_only`` mode this same LLM directly receives the
-    # audio and the prompt. Both fields are always mandatory because
-    # there is no longer a pyannote fallback and ``llm_only`` cannot
-    # function without an LLM either.
-    diarization_llm_provider: str = Field(
-        ...,
-        max_length=50,
-        description=(
-            "LLM provider that diarises the call. In ``stt_llm`` it sees "
-            "the STT text; in ``llm_only`` it sees the raw audio."
-        ),
-    )
-    diarization_llm_model: str = Field(
-        ...,
-        max_length=100,
-        description=(
-            "LLM model name. In ``llm_only`` mode this must be a model "
-            "that accepts audio input (e.g. 'gpt-4o-audio-preview', "
-            "'gemini-1.5-pro')."
-        ),
-    )
-    diarization_llm_credential_id: Optional[UUID] = Field(
-        default=None,
-        description=(
-            "Optional AIProvider row to pin for the diarisation LLM."
-        ),
-    )
-    diarization_prompt: Optional[str] = Field(
-        default=None,
-        max_length=10_000,
-        description=(
-            "Operator-supplied system prompt for the diariser LLM. "
-            "When NULL/empty the worker uses the canonical default "
-            "(see ``GET /api/v1/call-imports/diarisation-prompt-default``)."
-        ),
-    )
-
-    @model_validator(mode="after")
-    def _validate_mode_fields(self) -> "CallImportTranscribeRequest":
-        """Enforce STT-field presence rules based on ``mode``.
-
-        ``stt_llm`` (default) requires both STT fields G�� the worker
-        cannot diarise without a transcript. ``llm_only`` forbids them
-        so the API contract makes it clear that the audio is going
-        straight to the LLM; passing both would be ambiguous about
-        which path the worker should take.
-        """
-        stt_provider = (self.stt_provider or "").strip() if self.stt_provider else None
-        stt_model = (self.stt_model or "").strip() if self.stt_model else None
-        if self.mode == "stt_llm":
-            if not stt_provider or not stt_model:
-                raise ValueError(
-                    "stt_provider and stt_model are required when "
-                    "mode='stt_llm'."
-                )
-        else:  # llm_only
-            if stt_provider or stt_model:
-                raise ValueError(
-                    "stt_provider/stt_model must be omitted when "
-                    "mode='llm_only'; the LLM consumes the audio "
-                    "directly."
-                )
-        return self
-
-
-class CallImportDiarisationPromptDefaultResponse(BaseModel):
-    """Wrapper for the canonical diariser-prompt fetched by the modal."""
-
-    prompt: str = Field(
-        ...,
-        description=(
-            "The exact prompt the worker falls back to when the caller "
-            "leaves ``diarization_prompt`` blank. The frontend pre-fills "
-            "the textarea with this value so the operator can edit it."
-        ),
-    )
-
-
-class CallImportRowIdsResponse(BaseModel):
-    """Flat row-id list for cross-page bulk selection.
-
-    Powers the "Select all M rows in this import" affordance on the
-    detail page G�� returning only ids keeps the payload tiny so the UI
-    can hold the full set in memory even for batches with thousands
-    of rows. The frontend then passes those ids straight to the
-    existing bulk-delete / bulk-transcribe endpoints.
-    """
-
-    ids: List[UUID] = Field(
-        default_factory=list,
-        description=(
-            "Every ``CallImportRow.id`` that matches the ``q`` and "
-            "``diarised_status`` filters (or every row when neither is "
-            "supplied), sorted by ``row_index``."
-        ),
-    )
-    total: int = Field(
-        ...,
-        description=(
-            "Length of ``ids``. Sent explicitly so callers can show a "
-            "count without re-measuring the array."
-        ),
-    )
-
-
-class CallImportTranscribeResponse(BaseModel):
-    """Summary of a transcribe fan-out request."""
-
-    queued: int = Field(
-        ...,
-        description=(
-            "How many rows were enqueued for diarization. Skipped rows "
-            "(missing recording, transcript already present, etc.) are "
-            "not counted."
-        ),
-    )
-    skipped_rows: int = Field(
-        default=0,
-        description="Rows excluded by only_missing or because they had no recording.",
-    )
-    skipped_reason_counts: Dict[str, int] = Field(
-        default_factory=dict,
-        description="Per-reason breakdown of skipped rows for the UI to surface.",
-    )
-    accepted: bool = Field(
-        default=False,
-        description=(
-            "When true, diarization setup was queued to a background worker "
-            "and ``queued`` reflects zero until the worker finishes enqueue."
-        ),
-    )
-
-
-class CallImportCancelDiarisationRequest(BaseModel):
-    """Body for the batch cancel-diarisation endpoint.
-
-    Omit ``row_ids`` (or pass ``null``) to cancel every row in the
-    import whose ``diarised_transcript_status`` is currently
-    ``pending`` or ``running``. Pass an explicit list to scope the
-    cancel to a subset (e.g. the rows the operator selected in the
-    UI).
-    """
-
-    row_ids: Optional[List[UUID]] = Field(
-        default=None,
-        description=(
-            "Optional subset of CallImportRow UUIDs. ``None`` cancels "
-            "every pending / running diarisation in the import."
-        ),
-    )
-
-
-class CallImportCancelDiarisationResponse(BaseModel):
-    """Summary of a cancel-diarisation request.
-
-    ``cancelled`` counts rows that were actively pending / running
-    when the cancel landed and got flipped to ``failed`` with a
-    "Cancelled by user" error. ``skipped`` counts rows that were
-    requested (or matched the implicit "all rows" filter) but were
-    not in a cancellable state G�� typically because they had already
-    finished or were never queued for diarisation in the first place.
-    """
-
-    cancelled: int = Field(
-        ...,
-        description=(
-            "Rows whose in-flight Celery task was revoked and whose "
-            "``diarised_transcript_status`` was flipped to ``failed`` "
-            "with a 'Cancelled by user' error message."
-        ),
-    )
-    skipped: int = Field(
-        default=0,
-        description=(
-            "Rows that were requested but not in a cancellable state "
-            "(idle / completed / already failed)."
-        ),
-    )
-
-
-# --- Per-run aggregation / visualization payloads ---
-
-
-class CallImportMetricHistogramBucket(BaseModel):
-    """One bin of a numeric metric histogram."""
-
-    x0: float
-    x1: float
-    count: int
-
-
-class CallImportMetricValueCount(BaseModel):
-    """One row of a categorical metric's value frequency table."""
-
-    label: str
-    count: int
-
-
-class CallImportMetricLabelPair(BaseModel):
-    """One unordered pair-count cell of a multi-label parent's
-    co-occurrence matrix.
-
-    ``a`` and ``b`` are child label names; ``count`` is the number of
-    rows on which both labels fired together (intersection size).
-    Pairs are emitted with ``a < b`` lexicographically so the matrix
-    can be reconstructed without duplicates on the frontend.
-    """
-
-    a: str
-    b: str
-    count: int
-
-
-class CallImportMetricAggregate(BaseModel):
-    """Per-metric aggregate computed from an evaluation run's rows.
-
-    Numeric metrics return summary statistics + histogram buckets;
-    categorical / pass-fail / text metrics return the top value counts.
-    Both shapes can coexist if a metric mixes types G�� the UI prefers
-    histogram when present, falls back to value_counts otherwise.
-    """
-
-    metric_id: str
-    metric_name: str
-    metric_type: Optional[str] = None
-    metric_category: str = "quality"
-    # True when this aggregate represents a multi-label parent metric
-    # (selection_mode == "multi_label" with no parent_metric_id). For
-    # those, ``value_counts`` lists per-child label tallies and the
-    # rows scored != sum(value_counts.count). The UI uses this flag to
-    # force a horizontal bar layout (slices wouldn't sum to 100%) and
-    # to label the n-badge as rows scored, not label occurrences.
-    is_multi_label_parent: bool = False
-    count: int = 0
-    skipped_count: int = 0
-    error_count: int = 0
-    # Numeric stats (None when no numeric values were observed)
-    mean: Optional[float] = None
-    median: Optional[float] = None
-    p25: Optional[float] = None
-    p75: Optional[float] = None
-    p95: Optional[float] = None
-    min: Optional[float] = None
-    max: Optional[float] = None
-    stddev: Optional[float] = None
-    histogram_buckets: List[CallImportMetricHistogramBucket] = Field(
-        default_factory=list
-    )
-    value_counts: List[CallImportMetricValueCount] = Field(default_factory=list)
-    # Pairwise label intersections for multi-label parent metrics.
-    # Empty for everything else. The frontend reconstructs a square
-    # symmetric matrix from these unordered pairs and renders the
-    # co-occurrence heatmap chart type.
-    co_occurrence: List[CallImportMetricLabelPair] = Field(default_factory=list)
-
-
-class MetricPeriodDelta(BaseModel):
-    """Week-over-week (or baseline-run) delta for one metric."""
-
-    label: str
-    detail: str
-    why: Optional[str] = None
-
-
-class CallImportEvaluationAggregateResponse(BaseModel):
-    """Aggregated metric distributions for a single evaluation run."""
-
-    evaluation_id: UUID
-    total_rows: int
-    completed_rows: int
-    failed_rows: int
-    metrics: List[CallImportMetricAggregate] = Field(default_factory=list)
-    period_deltas: Dict[str, MetricPeriodDelta] = Field(default_factory=dict)
-    baseline_evaluation_id: Optional[UUID] = None
-    failure_policies_source: Optional[Literal["inferred", "user"]] = Field(
-        default=None,
-        description=(
-            "Whether flagged-rate semantics use user-confirmed failure policies "
-            "or inferred defaults from the Failure diagnostics flow."
-        ),
-    )
-
-
-# --- LLM-generated TLDR for the Visualizations tab ---
-
-
-
-class EvaluatorResultsAggregateResponse(BaseModel):
-    """Chart-friendly metric rollups for evaluator results in a suite or scenario scope."""
-
-    scope: str
-    suite_id: Optional[UUID] = None
-    agent_id: Optional[UUID] = None
-    scenario_id: Optional[UUID] = None
-    total_rows: int = 0
-    completed_rows: int = 0
-    failed_rows: int = 0
-    metrics: List[CallImportMetricAggregate] = Field(default_factory=list)
-
-
-# --- LLM-generated TLDR for the Visualizations tab ---
-
-
-
-class EvaluationTldrSummary(BaseModel):
-    """Cached LLM-generated narrative + bullet patterns for an eval run.
-
-    Persisted on ``CallImportEvaluation.tldr_summary`` (JSONB) and
-    rendered above the per-metric charts. ``generated_at_completed_rows``
-    is the snapshot of ``completed_rows`` at the time the summary was
-    written; the API compares it against the current count to flag
-    ``is_stale`` so the UI can prompt for a regenerate.
-    """
-
-    narrative: str
-    patterns: List[str] = Field(default_factory=list)
-    metric_insights: Dict[str, str] = Field(default_factory=dict)
-    generated_at: datetime
-    generated_at_completed_rows: int = 0
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    is_stale: bool = False
-
-
-class EvaluationInsightsRequest(BaseModel):
-    """Body for ``POST /evaluations/{eval_id}/insights``.
-
-    All fields are optional. When ``provider``/``model`` are unset the
-    backend resolves the org's first active OpenAI/Anthropic/Google
-    provider (mirroring the Prompt Partials AI-generate flow) so
-    callers that don't care can simply post ``{}``.
-    """
-
-    regenerate: bool = False
-    provider: Optional[str] = None
-    model: Optional[str] = Field(default=None, min_length=1)
-    credential_id: Optional[UUID] = None
-    max_llm_calls: Optional[int] = Field(
-        default=None,
-        ge=20,
-        le=500,
-        description=(
-            "Max LLM calls for user-insights sampling (extraction + synthesis). "
-            "Defaults to 200 when omitted."
-        ),
-    )
-
-
-class UserInsightCategory(BaseModel):
-    label: str
-    count: int
-    share_pct: float
-
-
-class UserInsightEvidenceTurn(BaseModel):
-    speaker: str
-    text: str
-
-
-class UserInsightEvidence(BaseModel):
-    conversation_id: Optional[str] = None
-    quote: str
-    turns: List[UserInsightEvidenceTurn] = Field(default_factory=list)
-
-
-class EvaluationUserInsightItem(BaseModel):
-    id: str
-    title: str
-    categories: List[UserInsightCategory] = Field(default_factory=list)
-    observation: str
-    evidence: UserInsightEvidence
-
-
-class EvaluationUserInsightsState(BaseModel):
-    """Cached map-reduce LLM user insights for an evaluation run."""
-
-    status: Literal["idle", "running", "completed", "failed"] = "idle"
-    insights: List[EvaluationUserInsightItem] = Field(default_factory=list)
-    overview: Optional[str] = None
-    generated_at: Optional[datetime] = None
-    generated_at_completed_rows: int = 0
-    progress: Optional[Dict[str, int]] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    llm_calls_used: int = 0
-    max_llm_calls: Optional[int] = None
-    error_message: Optional[str] = None
-    is_stale: bool = False
-
-
-class EvaluationUserInsightsRequest(BaseModel):
-    """Body for ``POST /evaluations/{eval_id}/user-insights``."""
-
-    regenerate: bool = False
-    force: bool = False
-    provider: Optional[str] = None
-    model: Optional[str] = Field(default=None, min_length=1)
-    credential_id: Optional[UUID] = None
-    max_llm_calls: Optional[int] = Field(default=None, ge=20, le=500)
-
-
-MetricClusterGapLabel = Literal[
-    "LOGIC_GAP",
-    "UNDERSPEC",
-    "EXISTS_NO_TRIGGER",
-    "MISSING",
-]
-
-FailurePolicyNumericOp = Literal["lt", "lte", "gt", "gte"]
-
-
-class MetricFailurePolicy(BaseModel):
-    """Per-metric definition of which scores count as failures for this evaluation."""
-
-    metric_id: str
-    failure_values: List[str] = Field(
-        default_factory=list,
-        description="Normalized lowercase labels that count as failure (single-choice, enum, boolean-as-category).",
-    )
-    failure_child_names: List[str] = Field(
-        default_factory=list,
-        description="Child label names that count as failure for multi_label parents.",
-    )
-    numeric_rule: Optional[Dict[str, Any]] = Field(
-        default=None,
-        description='Numeric failure rule, e.g. {"op": "lt", "threshold": 0.5}.',
-    )
-
-
-class MetricFailurePolicyValueCount(BaseModel):
-    label: str
-    count: int = 0
-
-
-class MetricFailurePolicyMetricPreview(BaseModel):
-    metric_id: str
-    metric_name: str
-    metric_type: Optional[str] = None
-    selection_mode: Optional[str] = None
-    is_multi_label_parent: bool = False
-    value_counts: List[MetricFailurePolicyValueCount] = Field(default_factory=list)
-    child_names: List[str] = Field(default_factory=list)
-    row_count_by_value: Dict[str, int] = Field(default_factory=dict)
-    suggested_policy: MetricFailurePolicy
-    effective_policy: MetricFailurePolicy
-
-
-class MetricFailurePoliciesResponse(BaseModel):
-    previews: List[MetricFailurePolicyMetricPreview] = Field(default_factory=list)
-    policies: Dict[str, MetricFailurePolicy] = Field(default_factory=dict)
-    source: Literal["inferred", "user"] = "inferred"
-    updated_at: Optional[datetime] = None
-
-
-class MetricFailurePoliciesSaveRequest(BaseModel):
-    policies: Dict[str, MetricFailurePolicy] = Field(default_factory=dict)
-    source: Literal["user"] = "user"
-
-
-class MetricClusterEvidenceTurn(BaseModel):
-    speaker: str
-    text: str
-
-
-class MetricClusterEvidence(BaseModel):
-    conversation_id: Optional[str] = None
-    evaluation_row_id: Optional[UUID] = None
-    quote: str = ""
-    turns: List[MetricClusterEvidenceTurn] = Field(default_factory=list)
-
-
-class MetricSubCluster(BaseModel):
-    label: str
-    count: int = 0
-    share_pct: float = 0.0
-
-
-class MetricCluster(BaseModel):
-    id: str
-    label: str
-    gap_label: MetricClusterGapLabel
-    level: int = 1
-    count: int = 0
-    share_pct: float = 0.0
-    sub_clusters: List[MetricSubCluster] = Field(default_factory=list)
-    observation: str = ""
-    failure_reason: str = ""
-    evidence: MetricClusterEvidence = Field(default_factory=MetricClusterEvidence)
-    is_discovered: bool = False
-
-
-class MetricClusterGroup(BaseModel):
-    metric_id: str
-    metric_name: str
-    flagged_count: int = 0
-    failure_reason: str = ""
-    clusters: List[MetricCluster] = Field(default_factory=list)
-
-
-class DiscoveredProblemCluster(BaseModel):
-    id: str
-    label: str
-    gap_label: MetricClusterGapLabel
-    count: int = 0
-    share_pct: float = 0.0
-    observation: str = ""
-    failure_reason: str = ""
-    evidence: MetricClusterEvidence = Field(default_factory=MetricClusterEvidence)
-
-
-class RcaRepeatedPatternRow(BaseModel):
-    metric_id: str
-    metric_name: str
-    top_rca_patterns: str = ""
-    evidence_share_pct: float = 0.0
-    evidence_calls: int = 0
-    evidence_cluster_count: int = 0
-    failure_reason: str = ""
-
-
-class RcaMetricHotspotRow(BaseModel):
-    metric_id: str
-    metric_name: str
-    description: str = ""
-    metric_rate_pct: float = 0.0
-    flagged_calls: int = 0
-
-
-class RcaPromptAreaRow(BaseModel):
-    label: str
-    share_pct: float = 0.0
-    gap_label: MetricClusterGapLabel
-
-
-class MetricClustersRcaSummary(BaseModel):
-    total_clusters: int = 0
-    total_clustered_instances: int = 0
-    total_flagged_instances: int = 0
-    analysed_calls: int = 0
-    repeated_patterns: List[RcaRepeatedPatternRow] = Field(default_factory=list)
-    metric_hotspots: List[RcaMetricHotspotRow] = Field(default_factory=list)
-    prompt_areas: List[RcaPromptAreaRow] = Field(default_factory=list)
-
-
-class EvaluationMetricClustersState(BaseModel):
-    """Cached per-metric failure clustering for internal diagnostics."""
-
-    status: Literal["idle", "running", "completed", "failed", "cancelled"] = "idle"
-    groups: List[MetricClusterGroup] = Field(default_factory=list)
-    discovered_problems: List[DiscoveredProblemCluster] = Field(
-        default_factory=list
-    )
-    overview: Optional[str] = None
-    generated_at: Optional[datetime] = None
-    generated_at_completed_rows: int = 0
-    progress: Optional[Dict[str, int]] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    llm_calls_used: int = 0
-    max_llm_calls: Optional[int] = None
-    error_message: Optional[str] = None
-    is_stale: bool = False
-    selected_evaluation_row_ids: List[str] = Field(
-        default_factory=list,
-        description="Evaluation row IDs included in the last clustering run.",
-    )
-    failure_policies: Dict[str, MetricFailurePolicy] = Field(default_factory=dict)
-    failure_policies_source: Literal["inferred", "user"] = "inferred"
-    failure_policies_updated_at: Optional[datetime] = None
-    rca_summary: Optional[MetricClustersRcaSummary] = None
-
-
-class MetricClusterEligibleRow(BaseModel):
-    """Completed evaluation row with at least one flagged quality metric."""
-
-    evaluation_row_id: UUID
-    conversation_id: Optional[str] = None
-    row_index: Optional[int] = None
-    flagged_metric_names: List[str] = Field(default_factory=list)
-
-
-class MetricClusterEligibleRowsResponse(BaseModel):
-    items: List[MetricClusterEligibleRow] = Field(default_factory=list)
-    total: int = 0
-
-
-class PromptImprovementSuggestion(BaseModel):
-    """One LLM-generated prompt edit to address a failure cluster."""
-
-    id: str
-    metric_id: str
-    metric_name: str
-    cluster_id: str
-    cluster_label: str
-    gap_label: MetricClusterGapLabel
-    share_pct: float = 0.0
-    priority: Literal["high", "medium", "low"] = "medium"
-    change_type: Literal["edit", "add"] = "add"
-    target_section: str = ""
-    anchor_excerpt: str = ""
-    current_gap: str = ""
-    suggested_text: str = ""
-    rationale: str = ""
-    flow_node_id: str = ""
-    flow_node_label: str = ""
-
-
-class EvaluationPromptImprovementsState(BaseModel):
-    """Cached prompt improvement suggestions for an evaluation run."""
-
-    status: Literal["idle", "running", "completed", "failed"] = "idle"
-    imported_agent_id: Optional[str] = None
-    imported_agent_name: Optional[str] = None
-    suggestions: List[PromptImprovementSuggestion] = Field(default_factory=list)
-    overview: Optional[str] = None
-    generated_at: Optional[datetime] = None
-    generated_at_completed_rows: int = 0
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    error_message: Optional[str] = None
-    is_stale: bool = False
-
-
-class EvaluationPromptImprovementsRequest(BaseModel):
-    """Body for ``POST /evaluations/{eval_id}/prompt-improvements``."""
-
-    imported_agent_id: UUID
-    regenerate: bool = False
-    force: bool = False
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    credential_id: Optional[UUID] = None
-
-
-class EvaluationMetricClustersRequest(BaseModel):
-    """Body for ``POST /evaluations/{eval_id}/metric-clusters``."""
-
-    regenerate: bool = False
-    force: bool = False
-    provider: Optional[str] = None
-    model: Optional[str] = Field(default=None, min_length=1)
-    credential_id: Optional[UUID] = None
-    max_llm_calls: Optional[int] = Field(default=None, ge=20, le=500)
-    evaluation_row_ids: Optional[List[UUID]] = Field(
-        default=None,
-        description=(
-            "Subset of completed evaluation row IDs to cluster. When omitted, "
-            "all completed rows with at least one flagged quality metric are used."
-        ),
-    )
-    row_limit: Optional[int] = Field(
-        default=None,
-        ge=1,
-        description=(
-            "Use the first N eligible rows (by row order). Mutually exclusive "
-            "with evaluation_row_ids."
-        ),
-    )
-    failure_policies: Optional[Dict[str, MetricFailurePolicy]] = Field(
-        default=None,
-        description="Per-metric failure policies confirmed in the cluster modal.",
-    )
-
-
-# Resolve the forward reference on ``CallImportEvaluationResponse``
-# (defined further up the file) now that ``EvaluationTldrSummary``
-# exists. Without this Pydantic raises at first ``.model_validate``
-# because the string annotation can't be evaluated.
-CallImportEvaluationResponse.model_rebuild()
-
-
-# --- Cross-run insights for a CallImport batch ---
-
-
-class CallImportInsightsRunPoint(BaseModel):
-    """One run's mean for a metric, used to render trend lines."""
-
-    evaluation_id: UUID
-    name: Optional[str] = None
-    created_at: datetime
-    mean: Optional[float] = None
-    completed_rows: int = 0
-
-
-class CallImportInsightsMetric(BaseModel):
-    """Per-metric history across every evaluation run on this import."""
-
-    metric_id: str
-    metric_name: str
-    metric_type: Optional[str] = None
-    latest: Optional[CallImportMetricAggregate] = None
-    trend: List[CallImportInsightsRunPoint] = Field(default_factory=list)
-
-
-class CallImportInsightsResponse(BaseModel):
-    """Aggregated cross-run signals for a single call-import batch."""
-
-    call_import_id: UUID
-    total_rows: int
-    rows_with_transcript: int
-    rows_without_transcript: int
-    transcript_source_counts: Dict[str, int] = Field(default_factory=dict)
-    evaluation_count: int = 0
-    metrics: List[CallImportInsightsMetric] = Field(default_factory=list)
-
-
-# --- Flow chart visualization for hierarchical metrics ---
-
-
-class MetricFlowNode(BaseModel):
-    """One step in the LLM-inferred temporal flow for a parent metric.
-
-    Represents a child sub-metric label. ``count`` is the number of rows
-    in the evaluation where this child appears anywhere in its
-    ``sequence`` array. ``is_terminal`` is set when the child is the
-    last entry in a meaningful fraction of those sequences.
-
-    ``is_discovered`` is set when the node represents an LLM-discovered
-    candidate (parent has ``allow_discovery=true``) rather than a
-    user-defined child. The id of a discovered node is prefixed with
-    ``disc:`` so it can't collide with real child UUIDs.
-    """
-
-    id: str
-    label: str
-    count: int = 0
-    is_terminal: bool = False
-    is_discovered: bool = False
-
-
-class MetricFlowEdge(BaseModel):
-    """One directed transition between two children across all rows.
-
-    ``count`` is the number of rows where ``source`` immediately
-    precedes ``target`` in the sequence. The synthetic ``START`` node
-    is used as the ``source`` for the first child in every sequence.
-    """
-
-    source: str
-    target: str
-    count: int = 0
-
-
-class MetricFlowResponse(BaseModel):
-    """Aggregate flow diagram payload for a single parent metric."""
-
-    parent_metric_id: str
-    parent_metric_name: str
-    selection_mode: Optional[SelectionMode] = None
-    nodes: List[MetricFlowNode] = Field(default_factory=list)
-    edges: List[MetricFlowEdge] = Field(default_factory=list)
-    total_rows: int = 0
-    rows_with_sequence: int = 0
-
-
-class DiscoveredLabelItem(BaseModel):
-    """One LLM-discovered candidate sub-label aggregated across rows.
-
-    ``key`` is the slugified label identifier (matches what appears in
-    ``sequence`` entries). ``count`` is the number of rows in the
-    evaluation that emitted this slug. ``sample_rationale`` is the
-    first non-empty rationale captured from any row (back-compat
-    field, identical to ``examples[0]`` when present). ``examples``
-    holds up to 3 distinct rationales G�� the UI surfaces 2 of them as
-    ``Examples:`` in the rubric on Promote, with the third kept as
-    headroom in case the first is unhelpful.
-    """
-
-    key: str
-    name: str
-    description: Optional[str] = None
-    sample_rationale: Optional[str] = None
-    examples: List[str] = Field(default_factory=list, max_length=3)
-    count: int = 0
-
-
-class DiscoveredLabelsResponse(BaseModel):
-    """List of discovered candidate sub-labels for a parent metric."""
-
-    parent_metric_id: str
-    items: List[DiscoveredLabelItem] = Field(default_factory=list)
-
-
-class DiscoveredLabelMergeRequest(BaseModel):
-    """Body for POST /evaluations/{eval_id}/discovered-labels/merge.
-
-    Rewrites every row's ``metric_scores[parent_id].discovered_labels``
-    entries whose key is ``from_key`` to use ``to_key`` instead, so the
-    user can collapse near-duplicate candidates ("On Hold" / "Customer
-    Put On Hold") into a single promoted child.
-    """
-
-    parent_metric_id: UUID
-    from_key: str = Field(..., min_length=1, max_length=120)
-    to_key: str = Field(..., min_length=1, max_length=120)
-
-
-class DiscoveredLabelDeleteRequest(BaseModel):
-    """Body for POST /evaluations/{eval_id}/discovered-labels/delete.
-
-    Strips a candidate sub-label from every row's
-    ``discovered_labels`` list AND from each row's ``sequence`` array,
-    then tombstones the slug at the evaluation level so workers
-    finishing later can't re-introduce it. Use for gibberish or
-    irrelevant candidates the LLM proposed; for near-duplicates that
-    you want to keep but unify, use the merge endpoint instead.
-    """
-
-    parent_metric_id: UUID
-    key: str = Field(..., min_length=1, max_length=120)
-
-
-class PromoteDiscoveredChildRequest(BaseModel):
-    """Body for POST /metrics/{parent_id}/children/from-discovered.
-
-    ``key`` is the slug under which the candidate is currently stored
-    on per-row ``metric_scores``. The newly-created child Metric's
-    name is normalized so ``slugify(name) == key``, which keeps every
-    already-scored row's ``sequence`` array resolvable against the
-    promoted child without a backfill.
-    """
-
-    key: str = Field(..., min_length=1, max_length=120)
-    name: str = Field(..., min_length=1, max_length=120)
-    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    # Default True: when promoting a discovered label we want the new
-    # sub-metric to always capture rationales going forward, since the
-    # candidate was itself proposed *with* a rationale and the user
-    # almost always wants to see why future rows hit it. Explicit False
-    # keeps the original opt-in behavior available for callers that
-    # don't care about rationales.
-    capture_rationale: bool = True
-
-
-# --- Discovered top-level metrics (per-evaluation discovery) ---
-#
-# Parallel to ``DiscoveredLabelItem`` / merge / delete / promote G�� but
-# scoped to the evaluation as a whole, not to a parent category metric.
-# Used by the "Discovered metrics" panel at the top of the evaluation
-# detail Flow tab when ``CallImportEvaluation.discover_new_metrics``
-# is true.
-
-
-# The promote endpoint accepts these three suggested types; "category"
-# creates a parent (no children yet) that the user can later extend in
-# the Metrics page.
-DiscoveredMetricSuggestedType = Literal["boolean", "rating", "category"]
-
-
-class DiscoveredMetricItem(BaseModel):
-    """One LLM-discovered candidate top-level metric aggregated across rows.
-
-    Mirrors :class:`DiscoveredLabelItem` but at the evaluation level
-    (no ``parent_metric_id``). ``suggested_type`` is the LLM's guess at
-    the best representation; the promote flow lets the user override
-    it before creating the real :class:`Metric` row.
-    """
-
-    key: str
-    name: str
-    description: Optional[str] = None
-    suggested_type: DiscoveredMetricSuggestedType = "boolean"
-    sample_rationale: Optional[str] = None
-    examples: List[str] = Field(default_factory=list, max_length=3)
-    count: int = 0
-
-
-class DiscoveredMetricsResponse(BaseModel):
-    """List of discovered candidate top-level metrics for an evaluation."""
-
-    evaluation_id: UUID
-    items: List[DiscoveredMetricItem] = Field(default_factory=list)
-
-
-class DiscoveredMetricMergeRequest(BaseModel):
-    """Body for POST /evaluations/{eval_id}/discovered-metrics/merge.
-
-    Rewrites every row's ``metric_scores["__discovered_metrics__"]``
-    entries whose key is ``from_key`` to use ``to_key`` instead, and
-    records the redirect in ``CallImportEvaluation.discovered_metric_aliases``
-    so workers finishing later can't resurrect the merged-out slug.
-    """
-
-    from_key: str = Field(..., min_length=1, max_length=120)
-    to_key: str = Field(..., min_length=1, max_length=120)
-
-
-class DiscoveredMetricDeleteRequest(BaseModel):
-    """Body for POST /evaluations/{eval_id}/discovered-metrics/delete.
-
-    Strips a candidate from every row's
-    ``metric_scores["__discovered_metrics__"]`` list and tombstones
-    the slug at the evaluation level (empty-string alias) so workers
-    finishing later can't re-introduce it.
-    """
-
-    key: str = Field(..., min_length=1, max_length=120)
-
-
-class PromoteDiscoveredMetricRequest(BaseModel):
-    """Body for POST /metrics/from-discovered.
-
-    Creates a standalone :class:`Metric` (``parent_metric_id=None``)
-    from an LLM-discovered candidate. The new metric's name is
-    normalized so ``slugify(name) == key`` to keep already-scored row
-    payloads resolvable against the promoted metric. ``metric_type``
-    selects how the new metric will be scored on future runs;
-    ``"category"`` creates a ``multi_label`` parent with no children
-    (the user adds children via the existing Metrics page).
-    """
-
-    key: str = Field(..., min_length=1, max_length=120)
-    name: str = Field(..., min_length=1, max_length=120)
-    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
-    metric_type: DiscoveredMetricSuggestedType = "boolean"
-    capture_rationale: bool = True
-    # Optional per-type config knobs passed through to ``Metric.custom_config``.
-    # For ``rating`` the frontend can supply {"min": 1, "max": 5}; for
-    # ``boolean`` / ``category`` the field is typically empty.
-    custom_config: Optional[Dict[str, Any]] = None
-
-
-# --- Workspace Schemas ---
-
-
-class WorkspaceBase(BaseModel):
-    """Shared fields for workspace create/update payloads."""
-
-    name: str = Field(..., min_length=1, max_length=255)
-
-
-class WorkspaceCreate(WorkspaceBase):
-    """Body for POST /workspaces."""
-
-    # Optional: derived from name when omitted; uniqueness is per-org.
-    slug: Optional[str] = Field(
-        default=None, min_length=1, max_length=255
-    )
-
-
-class WorkspaceUpdate(BaseModel):
-    """Body for PATCH /workspaces/{id} (rename and/or org-admin activation)."""
-
-    name: Optional[str] = Field(default=None, min_length=1, max_length=255)
-    is_active: Optional[bool] = None
-
-
-class WorkspaceResponse(BaseModel):
-    """Response schema for a single workspace."""
-
-    id: UUID
-    organization_id: UUID
-    name: str
-    slug: str
-    is_default: bool
-    is_active: bool = True
-    created_at: datetime
-    updated_at: datetime
-    role_id: Optional[UUID] = None
-    role_name: Optional[str] = None
-    capabilities: List[str] = Field(default_factory=list)
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class WorkspaceRoleBase(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-    description: Optional[str] = None
-    capabilities: List[str] = Field(default_factory=list)
-
-
-class WorkspaceRoleCreate(WorkspaceRoleBase):
-    pass
-
-
-class WorkspaceRoleUpdate(BaseModel):
-    name: Optional[str] = Field(default=None, min_length=1, max_length=255)
-    description: Optional[str] = None
-    capabilities: Optional[List[str]] = None
-
-
-class WorkspaceRoleResponse(BaseModel):
-    id: UUID
-    organization_id: UUID
-    name: str
-    description: Optional[str] = None
-    capabilities: List[str]
-    is_system: bool
-    created_at: datetime
-    updated_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class WorkspaceMemberResponse(BaseModel):
-    id: UUID
-    workspace_id: UUID
-    user_id: UUID
-    role_id: UUID
-    role_name: str
-    user_email: str
-    user_name: Optional[str] = None
-    added_by_user_id: Optional[UUID] = None
-    created_at: datetime
-
-    model_config = ConfigDict(from_attributes=True)
-
-
-class WorkspaceMemberCreate(BaseModel):
-    user_id: UUID
-    role_id: UUID
-
-
-class WorkspaceMemberUpdate(BaseModel):
-    role_id: UUID
-
-
-class CapabilityInfoResponse(BaseModel):
-    key: str
-    label: str
-
-
-class CapabilityDomainResponse(BaseModel):
-    key: str
-    label: str
-    capabilities: List[CapabilityInfoResponse]
+"""Pydantic schemas for request/response validation."""
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator, validator
+import re
+from typing import Optional, List, Dict, Any, Literal
+from datetime import date, datetime
+from uuid import UUID
+from app.models.enums import (
+    EvaluationType, EvaluationStatus, EvaluatorResultStatus, RoleEnum, InvitationStatus,
+    LanguageEnum, CallTypeEnum, CallMediumEnum, GenderEnum, AccentEnum, BackgroundNoiseEnum,
+    IntegrationPlatform, ModelProvider, CredentialRoutingMode, GatewayInterfaceMode, VoiceBundleType, TestAgentConversationStatus,
+    MetricType, MetricCategory, MetricTrigger, CallRecordingStatus, AlertMetricType, AlertAggregation,
+    AlertOperator, AlertNotifyFrequency, AlertStatus, AlertHistoryStatus, CronJobStatus,
+    CallImportStatus, CallImportRowStatus, CallImportParameterType,
+)
+
+
+
+# Audio File Schemas
+class AudioFileBase(BaseModel):
+    """Base audio file schema."""
+
+    filename: str
+    format: str
+
+
+class AudioFileCreate(AudioFileBase):
+    """Schema for audio file creation."""
+
+    file_size: int
+    duration: Optional[float] = None
+    sample_rate: Optional[int] = None
+    channels: Optional[int] = None
+
+
+class AudioFileResponse(AudioFileBase):
+    """Schema for audio file response."""
+
+    id: UUID
+    file_size: int
+    duration: Optional[float] = None
+    sample_rate: Optional[int] = None
+    channels: Optional[int] = None
+    uploaded_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# Evaluation Schemas
+class EvaluationCreate(BaseModel):
+    """Schema for creating an evaluation."""
+
+    audio_id: UUID
+    reference_text: Optional[str] = None
+    evaluation_type: EvaluationType
+    model_name: Optional[str] = Field(None, description="Model to use for evaluation")
+    metrics: Optional[List[str]] = Field(
+        default=["wer", "latency"], description="Metrics to calculate"
+    )
+
+    @field_validator("metrics")
+    @classmethod
+    def validate_metrics(cls, v):
+        """Validate metrics list."""
+        allowed_metrics = ["wer", "cer", "latency", "quality_score", "rtf"]
+        if v:
+            invalid = [m for m in v if m not in allowed_metrics]
+            if invalid:
+                raise ValueError(f"Invalid metrics: {invalid}")
+        return v
+
+
+class EvaluationResponse(BaseModel):
+    """Schema for evaluation response."""
+
+    id: UUID
+    audio_id: UUID
+    reference_text: Optional[str] = None
+    evaluation_type: EvaluationType
+    model_name: Optional[str] = None
+    status: EvaluationStatus
+    metrics_requested: Optional[List[str]] = None
+    created_at: datetime
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
+    error_message: Optional[str] = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class EvaluationStatusResponse(BaseModel):
+    """Schema for evaluation status response."""
+
+    id: UUID
+    status: EvaluationStatus
+    created_at: datetime
+    completed_at: Optional[datetime] = None
+    error_message: Optional[str] = None
+
+
+# Evaluation Result Schemas
+class EvaluationResultResponse(BaseModel):
+    """Schema for evaluation result response."""
+
+    evaluation_id: UUID
+    status: EvaluationStatus
+    transcript: Optional[str] = None
+    metrics: Dict[str, Any]
+    processing_time: Optional[float] = None
+    model_used: Optional[str] = None
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class MetricsResponse(BaseModel):
+    """Schema for metrics breakdown."""
+
+    evaluation_id: UUID
+    metrics: Dict[str, Any]
+    processing_time: Optional[float] = None
+
+
+# Comparison Schema
+class ComparisonRequest(BaseModel):
+    """Schema for comparing multiple evaluations."""
+
+    evaluation_ids: List[UUID] = Field(..., min_length=2, description="At least 2 evaluation IDs to compare")
+
+
+class ComparisonResponse(BaseModel):
+    """Schema for comparison results."""
+
+    evaluations: List[EvaluationResultResponse]
+    comparison_metrics: Dict[str, Any]
+
+
+# API Key Schemas
+class APIKeyCreate(BaseModel):
+    """Schema for creating API key."""
+
+    name: Optional[str] = None
+
+
+class APIKeyResponse(BaseModel):
+    """Schema for API key response."""
+
+    id: UUID
+    key: str
+    name: Optional[str] = None
+    is_active: bool
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# Generic Response Schemas
+class MessageResponse(BaseModel):
+    """Generic message response."""
+
+    message: str
+
+
+class ErrorResponse(BaseModel):
+    """Error response schema."""
+
+    detail: str
+
+# ============================================
+# VAIOPS SCHEMAS - Voice AI Ops
+# ============================================
+
+# Enums moved to enums.py
+
+
+# Agent Schemas
+class AgentCreate(BaseModel):
+    """Schema for creating a new agent"""
+    name: str = Field(..., min_length=1, max_length=255)
+    phone_number: Optional[str] = None
+    language: LanguageEnum = LanguageEnum.ENGLISH
+    description: str = Field(..., min_length=1)
+    call_type: CallTypeEnum = CallTypeEnum.OUTBOUND
+    call_medium: CallMediumEnum = CallMediumEnum.PHONE_CALL
+    telephony_phone_number_id: Optional[UUID] = None
+    voice_bundle_id: UUID = Field(..., description="Required voice bundle for test agent execution")
+    ai_provider_id: Optional[UUID] = None
+    voice_ai_integration_id: Optional[UUID] = None
+    voice_ai_agent_id: Optional[str] = None
+    provider_prompt: Optional[str] = None
+    silence_hangup_secs: int = Field(
+        default=15,
+        ge=0,
+        le=600,
+        description="End live calls after this many seconds of silence (0 disables)",
+    )
+
+    @field_validator('description')
+    @classmethod
+    def description_min_words(cls, v: str) -> str:
+        if len(v.split()) < 10:
+            raise ValueError('Description must be at least 10 words.')
+        return v
+
+    @field_validator('phone_number')
+    @classmethod
+    def phone_number_format(cls, v: Optional[str]) -> Optional[str]:
+        if v is not None and v != '':
+            import re
+            if not re.fullmatch(r'[\d+]+', v):
+                raise ValueError('Phone number must contain only digits and the + character.')
+        return v
+
+    @model_validator(mode='after')
+    def validate_phone_number(self):
+        """Ensure phone_number is provided when call_medium is phone_call"""
+        if self.call_medium == CallMediumEnum.PHONE_CALL and not self.phone_number:
+            raise ValueError('phone_number is required when call_medium is phone_call')
+        return self
+
+    model_config = ConfigDict(json_schema_extra={
+            "example": {
+                "name": "Customer Support Bot",
+                "phone_number": "+1234567890",
+                "language": "en",
+                "description": "A customer support bot that handles inquiries about orders, returns, and general questions",
+                "call_type": "outbound",
+                "voice_bundle_id": "123e4567-e89b-12d3-a456-426614174000",
+                "voice_ai_integration_id": "123e4567-e89b-12d3-a456-426614174001",
+                "voice_ai_agent_id": "agent_abc123"
+            }
+        })
+
+
+class AgentUpdate(BaseModel):
+    """Schema for updating an agent"""
+    name: Optional[str] = None
+    phone_number: Optional[str] = None
+    language: Optional[LanguageEnum] = None
+    description: Optional[str] = None
+    call_type: Optional[CallTypeEnum] = None
+    call_medium: Optional[CallMediumEnum] = None
+    telephony_phone_number_id: Optional[UUID] = None
+    voice_bundle_id: Optional[UUID] = None
+    voice_ai_integration_id: Optional[UUID] = None
+    voice_ai_agent_id: Optional[str] = None
+    provider_prompt: Optional[str] = None
+    prompt_variables: Optional[Dict[str, str]] = None
+    silence_hangup_secs: Optional[int] = Field(default=None, ge=0, le=600)
+
+    @model_validator(mode='after')
+    def validate_voice_config(self):
+        """Validate voice configuration - both voice_bundle_id and voice_ai_integration_id can be provided independently"""
+        voice_bundle = self.voice_bundle_id
+        voice_ai_integration = self.voice_ai_integration_id
+        
+        # If voice_ai_integration_id is provided, voice_ai_agent_id must also be provided
+        if voice_ai_integration and not self.voice_ai_agent_id:
+            raise ValueError('voice_ai_agent_id is required when voice_ai_integration_id is provided.')
+        
+        return self
+    
+    @model_validator(mode='after')
+    def validate_phone_number(self):
+        """Ensure phone_number is provided when call_medium is phone_call"""
+        # Only validate if call_medium is being set to phone_call
+        if self.call_medium == CallMediumEnum.PHONE_CALL and not self.phone_number:
+            # If phone_number is not being updated, we need to check existing value
+            # This will be handled in the route
+            pass
+        return self
+
+
+
+class PreviewIntegrationAgentPromptRequest(BaseModel):
+    """Fetch a provider agent prompt before an EfficientAI agent exists."""
+    voice_ai_agent_id: str = Field(..., min_length=1)
+
+
+
+
+class PreviewIntegrationAgentPromptResponse(BaseModel):
+    provider_prompt: str
+
+
+
+
+class AgentPhoneAssignmentConflict(BaseModel):
+    """Another agent already owns this phone number."""
+    agent_id: UUID
+    agent_name: str
+    phone_number: str
+
+
+
+
+class AgentPhoneAssignmentCheckResponse(BaseModel):
+    """Result of checking whether a phone number is free to assign."""
+    available: bool
+    phone_number: Optional[str] = None
+    conflict: Optional[AgentPhoneAssignmentConflict] = None
+
+
+
+
+class TestPromptSectionResponse(BaseModel):
+    """One canonical section of a generated test agent prompt."""
+    key: str
+    title: str
+    content: str
+
+
+
+
+class GeneratedScenarioDraftResponse(BaseModel):
+    """LLM-generated scenario draft before persistence."""
+    name: str
+    description: str
+    goal: Optional[str] = None
+
+
+
+
+class GenerateTestPromptRequest(BaseModel):
+    """Stage 1: generate foundational test agent prompt from production prompt."""
+    production_prompt: str = Field(..., min_length=1)
+    agent_name: str = Field(..., min_length=1, max_length=255)
+    language: Optional[str] = None
+    call_type: Optional[str] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    credential_id: Optional[UUID] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    additional_context: Optional[str] = None
+
+
+
+
+class GenerateTestPromptResponse(BaseModel):
+    sections: List[TestPromptSectionResponse]
+    test_agent_prompt: str
+    provider: str
+    model: str
+
+
+
+
+class GenerateScenariosFromPromptRequest(BaseModel):
+    """Stage 2: generate scenario drafts from test agent prompt."""
+    test_agent_prompt: str = Field(..., min_length=1)
+    agent_name: str = Field(..., min_length=1, max_length=255)
+    scenario_count: int = Field(default=5, ge=1, le=10)
+    language: Optional[str] = None
+    call_type: Optional[str] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    credential_id: Optional[UUID] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    additional_context: Optional[str] = None
+
+
+
+
+class GenerateScenariosFromPromptResponse(BaseModel):
+    scenarios: List[GeneratedScenarioDraftResponse]
+    provider: str
+    model: str
+
+
+
+
+class GenerateTestSetupRequest(BaseModel):
+    """Convenience: run stage 1 then stage 2 sequentially."""
+    production_prompt: str = Field(..., min_length=1)
+    agent_name: str = Field(..., min_length=1, max_length=255)
+    scenario_count: int = Field(default=5, ge=1, le=10)
+    language: Optional[str] = None
+    call_type: Optional[str] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    credential_id: Optional[UUID] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    additional_context: Optional[str] = None
+
+
+
+
+class GenerateTestSetupResponse(BaseModel):
+    sections: List[TestPromptSectionResponse]
+    test_agent_prompt: str
+    scenarios: List[GeneratedScenarioDraftResponse]
+    provider: str
+    model: str
+
+
+
+class AgentResponse(BaseModel):
+    """Schema for agent response"""
+    id: UUID
+    agent_id: Optional[str] = None
+    name: str
+    phone_number: Optional[str] = None
+    language: LanguageEnum
+    description: Optional[str]
+    call_type: CallTypeEnum
+    call_medium: CallMediumEnum
+    telephony_phone_number_id: Optional[UUID] = None
+    voice_bundle_id: Optional[UUID]
+    ai_provider_id: Optional[UUID]
+    voice_ai_integration_id: Optional[UUID]
+    voice_ai_agent_id: Optional[str]
+    provider_prompt: Optional[str] = None
+    prompt_variables: Optional[Dict[str, str]] = None
+    silence_hangup_secs: int = Field(
+        default=15,
+        ge=0,
+        le=600,
+        description="End live calls after this many seconds of silence (0 disables)",
+    )
+    provider_prompt_synced_at: Optional[datetime] = None
+    created_at: datetime
+    updated_at: datetime
+
+    @field_validator('language', mode='before')
+    @classmethod
+    def convert_language(cls, v):
+        """Convert string to LanguageEnum (handles uppercase DB values like ENGLISH -> en)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            # Map old uppercase names to new values
+            language_map = {'english': 'en', 'spanish': 'es', 'french': 'fr', 'german': 'de', 
+                          'chinese': 'zh', 'japanese': 'ja', 'hindi': 'hi', 'arabic': 'ar'}
+            if v_lower in language_map:
+                return LanguageEnum(language_map[v_lower])
+            try:
+                return LanguageEnum(v_lower)
+            except ValueError:
+                for enum_member in LanguageEnum:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid LanguageEnum value: {v}")
+        return v
+
+    @field_validator('call_type', mode='before')
+    @classmethod
+    def convert_call_type(cls, v):
+        """Convert string to CallTypeEnum (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return CallTypeEnum(v_lower)
+            except ValueError:
+                for enum_member in CallTypeEnum:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid CallTypeEnum value: {v}")
+        return v
+
+    @field_validator('call_medium', mode='before')
+    @classmethod
+    def convert_call_medium(cls, v):
+        """Convert string to CallMediumEnum (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return CallMediumEnum(v_lower)
+            except ValueError:
+                for enum_member in CallMediumEnum:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid CallMediumEnum value: {v}")
+        return v
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# Persona Schemas
+class PersonaCreate(BaseModel):
+    """Schema for creating a new persona (TTS provider-tied voice identity)"""
+    name: str = Field(..., min_length=1, max_length=255)
+    gender: GenderEnum = GenderEnum.NEUTRAL
+    tts_provider: Optional[str] = None
+    tts_voice_id: Optional[str] = None
+    tts_voice_name: Optional[str] = None
+    is_custom: bool = False
+    description: Optional[str] = None
+    tts_config: Optional[Dict[str, Any]] = None
+    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
+    llm_max_tokens: Optional[int] = Field(None, gt=0, le=8192)
+    response_delay_ms: Optional[int] = Field(None, ge=0, le=10000)
+    max_turns: Optional[int] = Field(None, ge=1, le=100)
+    allow_interruptions: Optional[bool] = None
+
+    @model_validator(mode="after")
+    def validate_tts_config(self):
+        from app.services.personas.persona_tts_config import validate_persona_tts_config
+
+        validate_persona_tts_config(self.tts_provider, self.tts_config)
+        return self
+
+
+class PersonaUpdate(BaseModel):
+    """Schema for updating a persona"""
+    name: Optional[str] = None
+    gender: Optional[GenderEnum] = None
+    tts_provider: Optional[str] = None
+    tts_voice_id: Optional[str] = None
+    tts_voice_name: Optional[str] = None
+    is_custom: Optional[bool] = None
+    description: Optional[str] = None
+    tts_config: Optional[Dict[str, Any]] = None
+    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
+    llm_max_tokens: Optional[int] = Field(None, gt=0, le=8192)
+    response_delay_ms: Optional[int] = Field(None, ge=0, le=10000)
+    max_turns: Optional[int] = Field(None, ge=1, le=100)
+    allow_interruptions: Optional[bool] = None
+
+    @model_validator(mode="after")
+    def validate_tts_config(self):
+        from app.services.personas.persona_tts_config import validate_persona_tts_config
+
+        if self.tts_config is not None:
+            validate_persona_tts_config(self.tts_provider, self.tts_config)
+        return self
+
+
+class PersonaResponse(BaseModel):
+    """Schema for persona response"""
+    id: UUID
+    name: str
+    gender: str
+    tts_provider: Optional[str] = None
+    tts_voice_id: Optional[str] = None
+    tts_voice_name: Optional[str] = None
+    is_custom: bool = False
+    description: Optional[str] = None
+    tts_config: Optional[Dict[str, Any]] = None
+    llm_temperature: Optional[float] = None
+    llm_max_tokens: Optional[int] = None
+    response_delay_ms: Optional[int] = None
+    max_turns: Optional[int] = None
+    allow_interruptions: Optional[bool] = None
+    created_at: datetime
+    updated_at: datetime
+
+    @field_validator('gender', mode='before')
+    @classmethod
+    def convert_gender(cls, v):
+        if v is None:
+            return "neutral"
+        if isinstance(v, str):
+            return v.lower()
+        if hasattr(v, 'value'):
+            return v.value
+        return v
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class PersonaCloneRequest(BaseModel):
+    """Schema for cloning a persona"""
+    name: Optional[str] = None
+
+
+# Scenario Schemas
+
+class AgentPromptSourcesResponse(BaseModel):
+    """Prompt texts from an agent that can seed a persona description."""
+    agent_id: UUID
+    agent_name: str
+    test_agent_prompt: str
+    agent_prompt: str
+
+
+
+
+class GeneratePersonaPromptRequest(BaseModel):
+    """Generate a persona caller prompt from an agent prompt via LLM."""
+    agent_id: UUID
+    source: str = Field(default="auto", pattern="^(test_agent|agent|auto)$")
+    persona_name: Optional[str] = Field(None, max_length=255)
+    persona_gender: Optional[str] = None
+    additional_context: Optional[str] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    credential_id: Optional[UUID] = None
+    llm_config: Optional[Dict[str, Any]] = None
+
+
+
+
+class GeneratePersonaPromptResponse(BaseModel):
+    persona_prompt: str
+    source_used: str
+    provider: str
+    model: str
+
+
+# Scenario Schemas
+
+class ScenarioCreate(BaseModel):
+    """Schema for creating a new scenario"""
+    name: str = Field(..., min_length=1, max_length=255)
+    agent_id: Optional[UUID] = None
+    description: Optional[str] = None
+    required_info: Dict[str, str] = Field(default_factory=dict)
+
+
+class ScenarioUpdate(BaseModel):
+    """Schema for updating a scenario"""
+    name: Optional[str] = None
+    agent_id: Optional[UUID] = None
+    description: Optional[str] = None
+    required_info: Optional[Dict[str, str]] = None
+
+
+class ScenarioResponse(BaseModel):
+    """Schema for scenario response"""
+    id: UUID
+    name: str
+    agent_id: Optional[UUID]
+    description: Optional[str]
+    required_info: Dict[str, str]
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ============================================
+# IAM & USER SCHEMAS
+# ============================================
+
+# User Schemas
+class UserCreate(BaseModel):
+    """Schema for creating a user."""
+    email: str = Field(..., description="User email address")
+    name: Optional[str] = None
+    password: Optional[str] = None  # Optional for invitation-based signup
+
+
+class UserUpdate(BaseModel):
+    """Schema for updating user profile."""
+    name: Optional[str] = None
+    first_name: Optional[str] = None
+    last_name: Optional[str] = None
+    email: Optional[str] = None
+
+
+class UserResponse(BaseModel):
+    """Schema for user response."""
+    id: UUID
+    email: str
+    name: Optional[str]
+    first_name: Optional[str]
+    last_name: Optional[str]
+    is_active: bool
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class OrganizationMemberResponse(BaseModel):
+    """Schema for organization member response."""
+    id: UUID
+    user_id: UUID
+    organization_id: UUID
+    role: RoleEnum
+    joined_at: datetime
+    user: UserResponse  # Include user details
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# Invitation Schemas
+class InvitationCreate(BaseModel):
+    """Schema for creating an invitation."""
+    email: str = Field(..., description="Email address of the user to invite")
+    role: RoleEnum = RoleEnum.READER
+
+
+class InvitationResponse(BaseModel):
+    """Schema for invitation response."""
+    id: UUID
+    organization_id: UUID
+    email: str
+    role: RoleEnum
+    status: InvitationStatus
+    expires_at: datetime
+    created_at: datetime
+    organization_name: Optional[str] = None  # Include organization name
+    invite_path: Optional[str] = None
+    invite_url: Optional[str] = None
+
+    @field_validator('role', mode='before')
+    @classmethod
+    def convert_role(cls, v):
+        """Convert string to RoleEnum (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return RoleEnum(v_lower)
+            except ValueError:
+                for enum_member in RoleEnum:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid RoleEnum value: {v}")
+        return v
+
+    @field_validator('status', mode='before')
+    @classmethod
+    def convert_status(cls, v):
+        """Convert string to InvitationStatus (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return InvitationStatus(v_lower)
+            except ValueError:
+                for enum_member in InvitationStatus:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid InvitationStatus value: {v}")
+        return v
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class InvitationUpdate(BaseModel):
+    """Schema for updating invitation (accept/decline)."""
+    token: str
+
+
+class RoleUpdate(BaseModel):
+    """Schema for updating user role in organization."""
+    role: RoleEnum
+
+
+# Profile Schemas
+class ProfileResponse(BaseModel):
+    """Schema for user profile response."""
+    id: UUID
+    email: str
+    name: Optional[str]
+    first_name: Optional[str]
+    last_name: Optional[str]
+    created_at: datetime
+    organizations: List[dict] = Field(default_factory=list)  # List of org memberships
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ============================================
+# INTEGRATION SCHEMAS
+# ============================================
+
+class IntegrationCreate(BaseModel):
+    """Schema for creating an integration."""
+    platform: IntegrationPlatform
+    api_key: str = Field(..., description="Private API key for the platform")
+    public_key: Optional[str] = Field(None, description="Optional public API key (e.g. for Vapi)")
+    name: Optional[str] = Field(None, description="Optional friendly name for the integration")
+    routing_mode: CredentialRoutingMode = Field(
+        CredentialRoutingMode.INHERIT,
+        description="LLM routing preference: inherit org default, force gateway, or direct API key.",
+    )
+    is_default: Optional[bool] = Field(
+        None,
+        description=(
+            "Mark this credential as the default for the (org, platform). "
+            "If omitted and no default exists yet, this row becomes the default."
+        ),
+    )
+
+
+class IntegrationUpdate(BaseModel):
+    """Schema for updating an integration."""
+    name: Optional[str] = None
+    api_key: Optional[str] = None
+    public_key: Optional[str] = None
+    is_active: Optional[bool] = None
+    routing_mode: Optional[CredentialRoutingMode] = None
+
+
+class IntegrationResponse(BaseModel):
+    """Schema for integration response."""
+    id: UUID
+    organization_id: UUID
+    platform: IntegrationPlatform
+    name: Optional[str]
+    public_key: Optional[str] = None
+    is_active: bool
+    is_default: bool = False
+    routing_mode: CredentialRoutingMode = CredentialRoutingMode.INHERIT
+    effective_routing: Literal["inherit", "direct", "gateway", "bifrost", "litellm_proxy"] = "inherit"
+    created_at: datetime
+    updated_at: datetime
+    last_tested_at: Optional[datetime] = None
+    # Note: api_key is NOT included in response for security
+
+    @field_validator('platform', mode='before')
+    @classmethod
+    def convert_platform(cls, v):
+        """Convert string to IntegrationPlatform enum if needed (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            # Try lowercase first (enum value)
+            v_lower = v.lower()
+            try:
+                return IntegrationPlatform(v_lower)
+            except ValueError:
+                # Try to find by enum name (uppercase)
+                for enum_member in IntegrationPlatform:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid IntegrationPlatform value: {v}")
+        return v
+
+    @field_validator('routing_mode', mode='before')
+    @classmethod
+    def convert_routing_mode(cls, v):
+        if v is None:
+            return CredentialRoutingMode.INHERIT
+        if isinstance(v, str):
+            try:
+                return CredentialRoutingMode(v.lower())
+            except ValueError:
+                return CredentialRoutingMode.INHERIT
+        return v
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ============================================
+# DATA SOURCES SCHEMAS
+# ============================================
+
+class S3ConnectionTest(BaseModel):
+    """Schema for testing S3 connection."""
+    bucket_name: str
+    region: str = "us-east-1"
+    access_key_id: str
+    secret_access_key: str
+    endpoint_url: Optional[str] = None
+
+
+class S3ConnectionTestResponse(BaseModel):
+    """Schema for S3 connection test response."""
+    success: bool
+    message: str
+    bucket_name: Optional[str] = None
+
+
+class S3FileInfo(BaseModel):
+    """Schema for S3 file information."""
+    key: str
+    filename: str
+    size: int
+    last_modified: str
+
+
+class S3ListFilesResponse(BaseModel):
+    """Schema for listing S3 files response."""
+    files: List[S3FileInfo]
+    total: int
+    prefix: Optional[str] = None
+
+
+class S3FolderInfo(BaseModel):
+    """Schema for S3 folder information."""
+    name: str
+    path: str
+
+
+class S3BrowseResponse(BaseModel):
+    """Schema for browsing S3 folders within an organization."""
+    folders: List[S3FolderInfo]
+    files: List[S3FileInfo]
+    current_path: str
+    organization_id: str
+
+
+class S3UploadResponse(BaseModel):
+    """Schema for S3 upload response."""
+    key: str
+    bucket: str
+    file_id: UUID
+    message: str
+
+
+# AIProvider Schemas
+_MAX_GATEWAY_EXTRA_HEADERS = 20
+
+
+def _validate_gateway_extra_headers(
+    value: Optional[Dict[str, Any]],
+) -> Optional[Dict[str, str]]:
+    if value is None:
+        return None
+    if not isinstance(value, dict):
+        raise ValueError("gateway_extra_headers must be a JSON object of string keys and values.")
+    if len(value) > _MAX_GATEWAY_EXTRA_HEADERS:
+        raise ValueError(
+            f"gateway_extra_headers supports at most {_MAX_GATEWAY_EXTRA_HEADERS} headers."
+        )
+    normalized: Dict[str, str] = {}
+    for raw_key, raw_val in value.items():
+        key = str(raw_key).strip()
+        if not key:
+            raise ValueError("gateway_extra_headers keys must be non-empty strings.")
+        if len(key) > 64 or any(ch.isspace() for ch in key):
+            raise ValueError(f"Invalid gateway header name: {key!r}")
+        if raw_val is None:
+            raise ValueError(f"gateway_extra_headers[{key!r}] must be a string value.")
+        val = str(raw_val).strip()
+        if not val:
+            raise ValueError(f"gateway_extra_headers[{key!r}] must be a non-empty string.")
+        if len(val) > 1024 or "\n" in val or "\r" in val:
+            raise ValueError(f"gateway_extra_headers[{key!r}] value is invalid.")
+        normalized[key] = val
+    return normalized or None
+
+
+class AIProviderCreate(BaseModel):
+    """Schema for creating an AI Provider."""
+    provider: ModelProvider
+    api_key: Optional[str] = Field(
+        None,
+        description=(
+            "Provider API key. Optional when routing via gateway with "
+            "gateway-managed credentials (passthrough_provider_keys: false)."
+        ),
+    )
+    name: Optional[str] = None
+    routing_mode: CredentialRoutingMode = Field(
+        CredentialRoutingMode.INHERIT,
+        description="LLM routing preference: inherit org default, force gateway, or direct API key.",
+    )
+    gateway_model: Optional[str] = Field(
+        None,
+        min_length=1,
+        max_length=255,
+        description="Bifrost custom model ID sent when routing via gateway.",
+    )
+    gateway_interface: GatewayInterfaceMode = Field(
+        GatewayInterfaceMode.INHERIT,
+        description="Bifrost API surface: inherit org default, LiteLLM shim, or native OpenAI-compatible.",
+    )
+    gateway_base_url: Optional[str] = Field(
+        None,
+        max_length=512,
+        description="Optional per-credential Bifrost/gateway base URL override.",
+    )
+    gateway_auth_header: Optional[str] = Field(
+        None,
+        max_length=64,
+        description="Auth header name for Bifrost (default x-bf-vk).",
+    )
+    gateway_auth_secret_env: Optional[str] = Field(
+        None,
+        max_length=128,
+        description="Environment variable name holding the gateway auth secret.",
+    )
+    gateway_auth_secret: Optional[str] = Field(
+        None,
+        description="Inline gateway auth secret (encrypted at rest). Alternative to env var.",
+    )
+    gateway_extra_headers: Optional[Dict[str, str]] = Field(
+        None,
+        description="Arbitrary HTTP headers sent with gateway-routed LiteLLM calls.",
+    )
+    enabled_models: Optional[List[str]] = Field(
+        None,
+        description=(
+            "Allowlisted model names for this credential. "
+            "Null or empty means all catalog models for the provider."
+        ),
+    )
+    is_default: Optional[bool] = Field(
+        None,
+        description=(
+            "Mark this credential as the default for the (org, provider). "
+            "If omitted and no default exists yet, this row becomes the default."
+        ),
+    )
+    endpoint_url: Optional[str] = Field(
+        None,
+        description="Provider endpoint URL (required for Azure OpenAI).",
+    )
+
+    @field_validator("api_key")
+    @classmethod
+    def validate_api_key(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("endpoint_url")
+    @classmethod
+    def validate_endpoint_url(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_model")
+    @classmethod
+    def validate_gateway_model(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_base_url")
+    @classmethod
+    def validate_gateway_base_url(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_auth_header")
+    @classmethod
+    def validate_gateway_auth_header(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        if not trimmed:
+            return None
+        if len(trimmed) > 64 or any(ch.isspace() for ch in trimmed):
+            raise ValueError("gateway_auth_header must be a single non-empty header name.")
+        return trimmed
+
+    @field_validator("gateway_auth_secret_env")
+    @classmethod
+    def validate_gateway_auth_secret_env(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        if not trimmed:
+            return None
+        if not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", trimmed):
+            raise ValueError(
+                "gateway_auth_secret_env must be a valid environment variable name."
+            )
+        return trimmed
+
+    @field_validator("gateway_auth_secret")
+    @classmethod
+    def validate_gateway_auth_secret(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_extra_headers")
+    @classmethod
+    def validate_gateway_extra_headers(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, str]]:
+        return _validate_gateway_extra_headers(v)
+
+    @field_validator("enabled_models")
+    @classmethod
+    def validate_enabled_models_create(cls, v: Optional[List[str]]) -> Optional[List[str]]:
+        if v is None:
+            return None
+        seen: set[str] = set()
+        out: list[str] = []
+        for item in v:
+            name = str(item).strip()
+            if not name or name in seen:
+                continue
+            seen.add(name)
+            out.append(name)
+        return out or None
+
+
+class AIProviderUpdate(BaseModel):
+    """Schema for updating an AI Provider."""
+    api_key: Optional[str] = Field(None, min_length=1)
+    name: Optional[str] = None
+    endpoint_url: Optional[str] = None
+    is_active: Optional[bool] = None
+    routing_mode: Optional[CredentialRoutingMode] = None
+    gateway_model: Optional[str] = Field(None, min_length=1, max_length=255)
+    gateway_interface: Optional[GatewayInterfaceMode] = None
+    gateway_base_url: Optional[str] = Field(None, max_length=512)
+    gateway_auth_header: Optional[str] = Field(None, max_length=64)
+    gateway_auth_secret_env: Optional[str] = Field(None, max_length=128)
+    gateway_auth_secret: Optional[str] = None
+    clear_gateway_auth_secret: bool = False
+    gateway_extra_headers: Optional[Dict[str, str]] = None
+    enabled_models: Optional[List[str]] = None
+
+    @field_validator("gateway_model")
+    @classmethod
+    def validate_gateway_model(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_base_url")
+    @classmethod
+    def validate_gateway_base_url_update(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_auth_header")
+    @classmethod
+    def validate_gateway_auth_header_update(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        if not trimmed:
+            return None
+        if len(trimmed) > 64 or any(ch.isspace() for ch in trimmed):
+            raise ValueError("gateway_auth_header must be a single non-empty header name.")
+        return trimmed
+
+    @field_validator("gateway_auth_secret_env")
+    @classmethod
+    def validate_gateway_auth_secret_env_update(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        if not trimmed:
+            return None
+        if not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", trimmed):
+            raise ValueError(
+                "gateway_auth_secret_env must be a valid environment variable name."
+            )
+        return trimmed
+
+    @field_validator("gateway_auth_secret")
+    @classmethod
+    def validate_gateway_auth_secret_update(cls, v: Optional[str]) -> Optional[str]:
+        if v is None:
+            return v
+        trimmed = v.strip()
+        return trimmed or None
+
+    @field_validator("gateway_extra_headers")
+    @classmethod
+    def validate_gateway_extra_headers_update(
+        cls, v: Optional[Dict[str, Any]]
+    ) -> Optional[Dict[str, str]]:
+        return _validate_gateway_extra_headers(v)
+
+    @field_validator("enabled_models")
+    @classmethod
+    def validate_enabled_models_update(cls, v: Optional[List[str]]) -> Optional[List[str]]:
+        if v is None:
+            return None
+        seen: set[str] = set()
+        out: list[str] = []
+        for item in v:
+            name = str(item).strip()
+            if not name or name in seen:
+                continue
+            seen.add(name)
+            out.append(name)
+        return out or None
+
+
+class AIProviderResponse(BaseModel):
+    """Schema for AI Provider response."""
+    id: UUID
+    provider: ModelProvider
+    api_key: Optional[str] = None  # Will be None in response for security
+    name: Optional[str]
+    endpoint_url: Optional[str] = None
+    is_active: bool
+    is_default: bool = False
+    routing_mode: CredentialRoutingMode = CredentialRoutingMode.INHERIT
+    gateway_model: Optional[str] = None
+    gateway_interface: GatewayInterfaceMode = GatewayInterfaceMode.INHERIT
+    gateway_base_url: Optional[str] = None
+    gateway_auth_header: Optional[str] = None
+    gateway_auth_secret_env: Optional[str] = None
+    has_gateway_auth_secret: bool = False
+    gateway_extra_headers: Optional[Dict[str, str]] = None
+    enabled_models: Optional[List[str]] = None
+    gateway_managed: bool = False
+    effective_routing: Literal["inherit", "direct", "gateway", "bifrost", "litellm_proxy"] = "inherit"
+    effective_gateway_interface: Literal["litellm_shim", "native_openai"] = "litellm_shim"
+    created_at: datetime
+    updated_at: datetime
+    last_tested_at: Optional[datetime]
+
+    @field_validator('provider', mode='before')
+    @classmethod
+    def convert_provider(cls, v):
+        """Convert string to ModelProvider (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return ModelProvider(v_lower)
+            except ValueError:
+                for enum_member in ModelProvider:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid ModelProvider value: {v}")
+        return v
+
+    @field_validator('routing_mode', mode='before')
+    @classmethod
+    def convert_routing_mode(cls, v):
+        if v is None:
+            return CredentialRoutingMode.INHERIT
+        if isinstance(v, str):
+            try:
+                return CredentialRoutingMode(v.lower())
+            except ValueError:
+                return CredentialRoutingMode.INHERIT
+        return v
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class LLMGenerationConfig(BaseModel):
+    """User-tunable LLM sampling / generation parameters."""
+
+    temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
+    max_tokens: Optional[int] = Field(None, gt=0)
+    top_p: Optional[float] = Field(None, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(None, ge=0)
+    frequency_penalty: Optional[float] = Field(None, ge=-2.0, le=2.0)
+    presence_penalty: Optional[float] = Field(None, ge=-2.0, le=2.0)
+    seed: Optional[int] = Field(None, ge=0)
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Return only explicitly set fields."""
+        return self.model_dump(exclude_none=True)
+
+
+# VoiceBundle Schemas
+class VoiceBundleCreate(BaseModel):
+    """Schema for creating a VoiceBundle."""
+    name: str = Field(..., min_length=1, max_length=255)
+    description: Optional[str] = None
+    
+    # Bundle type: either STT+LLM+TTS or S2S
+    bundle_type: VoiceBundleType = Field(default=VoiceBundleType.STT_LLM_TTS)
+    
+    # STT Configuration - required for STT_LLM_TTS, optional for S2S
+    stt_provider: Optional[ModelProvider] = None
+    stt_model: Optional[str] = Field(None, min_length=1)
+    stt_credential_id: Optional[UUID] = Field(
+        None,
+        description=(
+            "Optional explicit AIProvider/Integration row id to use for STT. "
+            "When omitted the resolver picks the default credential for stt_provider."
+        ),
+    )
+    
+    # LLM Configuration - required for STT_LLM_TTS, optional for S2S
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = Field(None, min_length=1)
+    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
+    llm_max_tokens: Optional[int] = Field(None, gt=0)
+    llm_config: Optional[Dict[str, Any]] = None
+    llm_credential_id: Optional[UUID] = None
+    
+    # TTS Configuration - required for STT_LLM_TTS, optional for S2S
+    tts_provider: Optional[ModelProvider] = None
+    tts_model: Optional[str] = Field(None, min_length=1)
+    tts_voice: Optional[str] = None
+    tts_config: Optional[Dict[str, Any]] = None
+    tts_credential_id: Optional[UUID] = None
+    
+    # S2S Configuration - required for S2S, optional for STT_LLM_TTS
+    s2s_provider: Optional[ModelProvider] = None
+    s2s_model: Optional[str] = Field(None, min_length=1)
+    s2s_config: Optional[Dict[str, Any]] = None
+    s2s_credential_id: Optional[UUID] = None
+    
+    # Additional metadata
+    extra_metadata: Optional[Dict[str, Any]] = None
+    
+    @model_validator(mode='after')
+    def validate_bundle_configuration(self):
+        """Validate that required fields are provided based on bundle_type."""
+        if self.bundle_type == VoiceBundleType.STT_LLM_TTS:
+            if not self.stt_provider or not self.stt_model:
+                raise ValueError('STT provider and model are required for STT_LLM_TTS bundle type')
+            if not self.llm_provider or not self.llm_model:
+                raise ValueError('LLM provider and model are required for STT_LLM_TTS bundle type')
+            if not self.tts_provider or not self.tts_model:
+                raise ValueError('TTS provider and model are required for STT_LLM_TTS bundle type')
+        elif self.bundle_type == VoiceBundleType.S2S:
+            if not self.s2s_provider or not self.s2s_model:
+                raise ValueError('S2S provider and model are required for S2S bundle type')
+        return self
+
+
+class VoiceBundleUpdate(BaseModel):
+    """Schema for updating a VoiceBundle."""
+    name: Optional[str] = Field(None, min_length=1, max_length=255)
+    description: Optional[str] = None
+    
+    # Bundle type
+    bundle_type: Optional[VoiceBundleType] = None
+    
+    # STT Configuration
+    stt_provider: Optional[ModelProvider] = None
+    stt_model: Optional[str] = Field(None, min_length=1)
+    stt_credential_id: Optional[UUID] = None
+    
+    # LLM Configuration
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = Field(None, min_length=1)
+    llm_temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
+    llm_max_tokens: Optional[int] = Field(None, gt=0)
+    llm_config: Optional[Dict[str, Any]] = None
+    llm_credential_id: Optional[UUID] = None
+    
+    # TTS Configuration
+    tts_provider: Optional[ModelProvider] = None
+    tts_model: Optional[str] = Field(None, min_length=1)
+    tts_voice: Optional[str] = None
+    tts_config: Optional[Dict[str, Any]] = None
+    tts_credential_id: Optional[UUID] = None
+    
+    # S2S Configuration
+    s2s_provider: Optional[ModelProvider] = None
+    s2s_model: Optional[str] = Field(None, min_length=1)
+    s2s_config: Optional[Dict[str, Any]] = None
+    s2s_credential_id: Optional[UUID] = None
+    
+    # Additional metadata
+    extra_metadata: Optional[Dict[str, Any]] = None
+    is_active: Optional[bool] = None
+
+
+class VoiceBundleResponse(BaseModel):
+    """Schema for VoiceBundle response."""
+    id: UUID
+    name: str
+    description: Optional[str]
+    
+    # Bundle type - can be string from DB or enum, validator handles conversion
+    bundle_type: VoiceBundleType
+    
+    @field_validator('bundle_type', mode='before')
+    @classmethod
+    def convert_bundle_type(cls, v):
+        """Convert string to VoiceBundleType enum if needed."""
+        if isinstance(v, str):
+            try:
+                return VoiceBundleType(v)
+            except ValueError:
+                # Try to find by value
+                for enum_member in VoiceBundleType:
+                    if enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid bundle_type value: {v}")
+        return v
+    
+    @field_validator('stt_provider', 'llm_provider', 'tts_provider', 's2s_provider', mode='before')
+    @classmethod
+    def convert_model_provider(cls, v):
+        """Convert string to ModelProvider enum if needed (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            # Try lowercase first (enum value)
+            v_lower = v.lower()
+            try:
+                return ModelProvider(v_lower)
+            except ValueError:
+                # Try to find by enum name (uppercase)
+                for enum_member in ModelProvider:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid ModelProvider value: {v}")
+        return v
+    
+    # STT Configuration
+    stt_provider: Optional[ModelProvider]
+    stt_model: Optional[str]
+    stt_credential_id: Optional[UUID] = None
+    
+    # LLM Configuration
+    llm_provider: Optional[ModelProvider]
+    llm_model: Optional[str]
+    llm_temperature: Optional[float]
+    llm_max_tokens: Optional[int]
+    llm_config: Optional[Dict[str, Any]]
+    llm_credential_id: Optional[UUID] = None
+    
+    # TTS Configuration
+    tts_provider: Optional[ModelProvider]
+    tts_model: Optional[str]
+    tts_voice: Optional[str]
+    tts_config: Optional[Dict[str, Any]]
+    tts_credential_id: Optional[UUID] = None
+    
+    # S2S Configuration
+    s2s_provider: Optional[ModelProvider]
+    s2s_model: Optional[str]
+    s2s_config: Optional[Dict[str, Any]]
+    s2s_credential_id: Optional[UUID] = None
+    
+    # Additional metadata
+    extra_metadata: Optional[Dict[str, Any]]
+    is_active: bool
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+# Test Agent Conversation Schemas
+class TestAgentConversationCreate(BaseModel):
+    """Schema for creating a new test agent conversation."""
+    agent_id: UUID
+    persona_id: UUID
+    scenario_id: UUID
+    voice_bundle_id: UUID
+    conversation_metadata: Optional[Dict[str, Any]] = None
+
+    model_config = ConfigDict(json_schema_extra={
+            "example": {
+                "agent_id": "123e4567-e89b-12d3-a456-426614174000",
+                "persona_id": "123e4567-e89b-12d3-a456-426614174001",
+                "scenario_id": "123e4567-e89b-12d3-a456-426614174002",
+                "voice_bundle_id": "123e4567-e89b-12d3-a456-426614174003"
+            }
+        })
+
+
+class TestAgentConversationUpdate(BaseModel):
+    """Schema for updating a test agent conversation."""
+    status: Optional[str] = None
+    live_transcription: Optional[List[Dict[str, Any]]] = None
+    full_transcript: Optional[str] = None
+    conversation_metadata: Optional[Dict[str, Any]] = None
+
+
+class TestAgentConversationResponse(BaseModel):
+    """Schema for test agent conversation response."""
+    id: UUID
+    organization_id: UUID
+    agent_id: UUID
+    persona_id: UUID
+    scenario_id: UUID
+    voice_bundle_id: UUID
+    status: str
+    live_transcription: Optional[List[Dict[str, Any]]]
+    conversation_audio_key: Optional[str]
+    full_transcript: Optional[str]
+    started_at: datetime
+    ended_at: Optional[datetime]
+    duration_seconds: Optional[float]
+    conversation_metadata: Optional[Dict[str, Any]]
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class ConversationTurn(BaseModel):
+    """Schema for a single conversation turn."""
+    speaker: str  # "test_agent" or "voice_agent"
+    text: str
+    timestamp: float  # Time in seconds from start
+    audio_segment_key: Optional[str] = None  # S3 key for this segment's audio
+
+
+# Conversation Evaluation Schemas
+class ConversationEvaluationCreate(BaseModel):
+    """Schema for creating a conversation evaluation."""
+    transcription_id: UUID
+    agent_id: UUID
+    llm_provider: Optional[ModelProvider] = ModelProvider.OPENAI
+    llm_model: Optional[str] = "gpt-4o"
+    
+    model_config = ConfigDict(json_schema_extra={
+            "example": {
+                "transcription_id": "123e4567-e89b-12d3-a456-426614174000",
+                "agent_id": "123e4567-e89b-12d3-a456-426614174001",
+                "llm_provider": "openai",
+                "llm_model": "gpt-4o"
+            }
+        })
+
+
+class ConversationEvaluationResponse(BaseModel):
+    """Schema for conversation evaluation response."""
+    id: UUID
+    organization_id: UUID
+    transcription_id: UUID
+    agent_id: UUID
+    objective_achieved: bool
+    objective_achieved_reason: Optional[str]
+    additional_metrics: Optional[Dict[str, Any]]
+    overall_score: Optional[float]
+    llm_provider: Optional[ModelProvider]
+    llm_model: Optional[str]
+    created_at: datetime
+    updated_at: datetime
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+# Evaluator Schemas
+class EvaluatorCreate(BaseModel):
+    """Schema for creating an evaluator. Either provide agent_id+persona_id+scenario_id (standard) or metric_ids/custom_prompt (custom)."""
+    name: Optional[str] = None
+    agent_id: Optional[UUID] = None
+    persona_id: Optional[UUID] = None
+    scenario_id: Optional[UUID] = None
+    custom_prompt: Optional[str] = None
+    metric_ids: Optional[List[UUID]] = None
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]] = None
+
+
+class EvaluatorUpdate(BaseModel):
+    """Schema for updating an evaluator."""
+    name: Optional[str] = None
+    agent_id: Optional[UUID] = None
+    persona_id: Optional[UUID] = None
+    scenario_id: Optional[UUID] = None
+    custom_prompt: Optional[str] = None
+    metric_ids: Optional[List[UUID]] = None
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]] = None
+
+
+class EvaluatorResponse(BaseModel):
+    """Schema for evaluator response."""
+    id: UUID
+    evaluator_id: str
+    organization_id: UUID
+    name: Optional[str] = None
+    agent_id: Optional[UUID] = None
+    persona_id: Optional[UUID] = None
+    scenario_id: Optional[UUID] = None
+    custom_prompt: Optional[str] = None
+    metric_ids: Optional[List[UUID]] = None
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]]
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+
+    @field_validator('llm_provider', mode='before')
+    @classmethod
+    def convert_llm_provider(cls, v):
+        """Convert string to ModelProvider (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return ModelProvider(v_lower)
+            except ValueError:
+                for enum_member in ModelProvider:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid ModelProvider value: {v}")
+        return v
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class EvaluatorBulkCreate(BaseModel):
+    """Schema for creating multiple evaluators at once."""
+    name: Optional[str] = None
+    agent_id: UUID
+    scenario_id: UUID
+    persona_ids: List[UUID]
+    tags: Optional[List[str]] = None
+
+
+class RunEvaluatorsRequest(BaseModel):
+    """Schema for running evaluators."""
+    evaluator_ids: List[UUID] = Field(..., description="List of evaluator IDs to run")
+
+
+class RunEvaluatorsResponse(BaseModel):
+    """Schema for run evaluators response."""
+    task_ids: List[str] = Field(..., description="List of Celery task IDs for tracking")
+    evaluator_results: List["EvaluatorResultResponse"] = Field(default_factory=list, description="List of created evaluator results")
+    
+    model_config = ConfigDict(
+        from_attributes=True,
+        json_schema_extra={
+            "example": {
+                "agent_id": "123e4567-e89b-12d3-a456-426614174000",
+                "scenario_id": "123e4567-e89b-12d3-a456-426614174002",
+                "persona_ids": [
+                    "123e4567-e89b-12d3-a456-426614174001",
+                    "123e4567-e89b-12d3-a456-426614174003"
+                ],
+                "tags": ["test", "production"]
+            }
+        },
+    )
+
+
+# Metric Schemas
+SelectionMode = Literal["single_choice", "multi_label"]
+
+
+MetricScope = Literal["workspace", "organization"]
+
+# Max length for metric rubric text (description / example) accepted by
+# the API. DB columns are TEXT or unbounded VARCHAR; this cap is validation-only.
+METRIC_RUBRIC_TEXT_MAX_LENGTH = 32_000
+
+
+
+class EvaluatorSuiteCombinationResponse(BaseModel):
+    """One agent+persona+scenario combination inside a suite."""
+    id: UUID
+    evaluator_id: str
+    scenario_id: Optional[UUID] = None
+    scenario_name: Optional[str] = None
+    scenario_description: Optional[str] = None
+    scenario_required_info: Optional[Any] = None
+
+
+
+
+class EvaluatorSuiteCreate(BaseModel):
+    """Schema for creating an evaluator suite."""
+    name: Optional[str] = None
+    agent_id: UUID
+    persona_id: UUID
+    scenario_ids: List[UUID]
+    metric_ids: Optional[List[UUID]] = None
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]] = None
+    default_runs_per_combination: int = 1
+
+
+
+
+class EvaluatorSuiteUpdate(BaseModel):
+    """Schema for updating an evaluator suite."""
+    name: Optional[str] = None
+    tags: Optional[List[str]] = None
+    default_runs_per_combination: Optional[int] = None
+    llm_provider: Optional[ModelProvider] = None
+    llm_model: Optional[str] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    metric_ids: Optional[List[UUID]] = None
+
+
+
+
+class EvaluatorSuiteResponse(BaseModel):
+    """Schema for evaluator suite response."""
+    id: UUID
+    organization_id: UUID
+    name: Optional[str] = None
+    agent_id: UUID
+    persona_id: UUID
+    agent_name: Optional[str] = None
+    persona_name: Optional[str] = None
+    agent_call_type: Optional[str] = None
+    agent_call_medium: Optional[str] = None
+    metric_ids: Optional[List[str]] = None
+    llm_provider: Optional[str] = None
+    llm_model: Optional[str] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]] = None
+    default_runs_per_combination: int = 1
+    round_robin_index: int = 0
+    is_active: bool = False
+    agent_suite_count: int = 1
+    combination_count: int = 0
+    combinations: List[EvaluatorSuiteCombinationResponse] = Field(default_factory=list)
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str] = None
+
+
+
+
+class EvaluatorSuiteAddScenariosRequest(BaseModel):
+    """Schema for adding scenarios to an existing suite."""
+    scenario_ids: List[UUID]
+
+
+
+
+class RunEvaluatorSuiteRequest(BaseModel):
+    """Schema for running all combinations in a suite."""
+    runs_per_combination: Optional[int] = None
+    to_number: Optional[str] = None
+    from_number: Optional[str] = None
+
+
+
+
+class RunEvaluatorSuiteResponse(BaseModel):
+    """Schema for suite run response."""
+    total_runs: int
+    task_ids: List[str] = Field(default_factory=list)
+    evaluator_results: List["EvaluatorResultResponse"] = Field(default_factory=list)
+    phone_call_refs: List[str] = Field(default_factory=list)
+
+
+
+
+class RunNextCombinationRequest(BaseModel):
+    """Schema for running the next round-robin combination."""
+    from_number: Optional[str] = None
+
+
+
+
+class RunNextCombinationResponse(BaseModel):
+    """Schema for round-robin run response."""
+    evaluator_id: UUID
+    scenario_id: Optional[UUID] = None
+    scenario_name: str
+    combination_index: int
+    next_index: int
+    evaluator_result_id: Optional[UUID] = None
+    result_id: Optional[str] = None
+    task_id: Optional[str] = None
+    phone_call_ref: Optional[str] = None
+    call_short_id: Optional[str] = None
+
+
+
+
+class ChooseNextCombinationResponse(BaseModel):
+    """Advance inbound round-robin without initiating a call or evaluation run."""
+    evaluator_id: UUID
+    scenario_id: Optional[UUID] = None
+    scenario_name: str
+    combination_index: int
+    next_index: int
+
+
+# Metric Schemas
+SelectionMode = Literal["single_choice", "multi_label"]
+
+
+MetricScope = Literal["workspace", "organization"]
+
+# Max length for metric rubric text (description / example) accepted by
+# the API. DB columns are TEXT or unbounded VARCHAR; this cap is validation-only.
+METRIC_RUBRIC_TEXT_MAX_LENGTH = 32_000
+
+
+
+class MetricCreate(BaseModel):
+    """Schema for creating a metric.
+
+    Hierarchy:
+    - ``parent_metric_id`` set => this is a child sub-metric. ``metric_type``
+      is forced to ``boolean`` server-side; ``selection_mode`` must be None.
+    - ``selection_mode`` set => this is a parent category metric.
+      ``parent_metric_id`` must be None (max depth = 2).
+
+    Scope:
+    - ``scope="workspace"`` (default) stamps the metric with the active
+      ``X-Workspace-Id`` so it only shows up inside that workspace.
+    - ``scope="organization"`` stamps ``workspace_id=NULL`` so the metric
+      is visible in every workspace of the org. Children always inherit
+      their parent's scope; setting ``scope`` on a child request body is
+      ignored server-side.
+    """
+    name: str
+    description: Optional[str] = None
+    # Optional illustrative example surfaced alongside ``description``
+    # in the LLM judge's rubric. Today this is mainly populated on
+    # child sub-labels (one example per categorization label) but
+    # standalone metrics may carry it too without a schema change.
+    example: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    metric_type: MetricType = MetricType.RATING
+    metric_category: MetricCategory = MetricCategory.QUALITY
+    trigger: MetricTrigger = MetricTrigger.ALWAYS
+    enabled: bool = True
+    metric_origin: str = "custom"
+    supported_surfaces: List[str] = ["agent"]
+    enabled_surfaces: Optional[List[str]] = None
+    custom_data_type: Optional[str] = None
+    custom_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]] = None
+    capture_rationale: Optional[bool] = False
+    parent_metric_id: Optional[UUID] = None
+    selection_mode: Optional[SelectionMode] = None
+    # Only meaningful on multi_label parents; ignored everywhere else.
+    # When true, the LLM is invited during call-import evaluation to emit
+    # additional candidate sub-labels beyond the user-defined children.
+    allow_discovery: bool = False
+    # When true, this metric is a "transcript-compare judge": at
+    # call-import evaluation time the worker feeds BOTH the production
+    # transcript (``call_import_rows.transcript``, CSV-supplied) and
+    # the diarised transcript (``call_import_rows.diarised_transcript``,
+    # worker-produced) to the LLM as a labeled pair. The parent
+    # evaluation's ``transcript_source`` is ignored for these metrics.
+    # Mutually exclusive with ``parent_metric_id`` / ``selection_mode``
+    # G�� comparison metrics stay standalone so the LLM grouping logic
+    # doesn't have to second-guess which prompt template to use within
+    # a hierarchy. (Parent-level keyword auto-detection in the worker
+    # still routes a categorisation parent through the comparison
+    # prompt without setting this flag.)
+    compare_transcripts: bool = False
+    # When ``"organization"``, the metric is stored with
+    # ``workspace_id=NULL`` so it surfaces in every workspace of the
+    # caller's org. Default ``"workspace"`` preserves the historical
+    # behavior of stamping the metric with the active ``X-Workspace-Id``.
+    # Ignored when ``parent_metric_id`` is set (children inherit the
+    # parent's scope unconditionally).
+    scope: MetricScope = "workspace"
+
+    @model_validator(mode='after')
+    def validate_compare_transcripts_exclusions(self):
+        """Reject body combinations that don't make sense for a
+        transcript-compare judge.
+
+        The Metric ORM column accepts the value; the validator just
+        prevents the user from accidentally requesting an incoherent
+        metric shape (e.g. "compare two transcripts but also live
+        inside a categorisation hierarchy" � different prompt
+        templates).
+        """
+        if not self.compare_transcripts:
+            return self
+        if self.parent_metric_id is not None:
+            raise ValueError(
+                "Transcript-compare metrics must be standalone: "
+                "they cannot be a child sub-metric in this version."
+            )
+        if self.selection_mode is not None:
+            raise ValueError(
+                "Transcript-compare metrics must be standalone: "
+                "they cannot own children (selection_mode must be "
+                "unset) in this version."
+            )
+        return self
+
+    model_config = ConfigDict(json_schema_extra={
+            "example": {
+                "name": "Professionalism",
+                "description": "Measures the professional tone and behavior",
+                "metric_type": "rating",
+                "trigger": "always",
+                "enabled": True
+            }
+        })
+
+
+class MetricChildDraft(BaseModel):
+    """One child sub-metric in a parent + children atomic create body."""
+
+    name: str = Field(..., max_length=120)
+    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    # Optional illustrative example for this label. Surfaced alongside
+    # ``description`` in the LLM judge's rubric so each label can carry
+    # both its definition AND a "what does this look like?" example.
+    example: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    enabled: bool = True
+    capture_rationale: Optional[bool] = True
+    tags: Optional[List[str]] = None
+
+
+class MetricCreateWithChildren(BaseModel):
+    """One-shot create body: a parent metric + N children, atomically.
+
+    Children are persisted as full ``Metric`` rows with
+    ``parent_metric_id`` set to the new parent. ``metric_type`` on every
+    child is forced to ``boolean`` server-side regardless of what's
+    passed in the parent body.
+    """
+
+    name: str = Field(..., max_length=120)
+    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    selection_mode: SelectionMode
+    metric_category: MetricCategory = MetricCategory.QUALITY
+    enabled: bool = True
+    supported_surfaces: List[str] = Field(default_factory=lambda: ["agent"])
+    enabled_surfaces: Optional[List[str]] = None
+    tags: Optional[List[str]] = None
+    # When true on a multi_label parent, allow the LLM to emit candidate
+    # labels beyond the listed children at evaluation time. Validator
+    # rejects allow_discovery=True on single_choice parents.
+    allow_discovery: bool = False
+    # Parent-level "Enable LLM Rationale" toggle. When true the LLM
+    # judge emits a single rationale string at the parent level
+    # (children never carry rationales in hierarchical mode), which the
+    # table renders as the "<Parent> - LLM Rationale" column.
+    capture_rationale: bool = False
+    children: List[MetricChildDraft] = Field(
+        default_factory=list,
+        description="Child sub-metric labels under this parent.",
+    )
+    # See ``MetricCreate.scope``. Same semantics: ``"organization"``
+    # creates the parent + all children with ``workspace_id=NULL`` so
+    # the whole category subtree is shared across every workspace in
+    # the org.
+    scope: MetricScope = "workspace"
+
+
+class MetricUpdate(BaseModel):
+    """Schema for updating a metric."""
+    name: Optional[str] = None
+    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    # ``None`` here means "leave unchanged"; pass an empty string to
+    # clear a previously stored example.
+    example: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    metric_type: Optional[MetricType] = None
+    trigger: Optional[MetricTrigger] = None
+    enabled: Optional[bool] = None
+    metric_origin: Optional[str] = None
+    supported_surfaces: Optional[List[str]] = None
+    enabled_surfaces: Optional[List[str]] = None
+    custom_data_type: Optional[str] = None
+    custom_config: Optional[Dict[str, Any]] = None
+    tags: Optional[List[str]] = None
+    metric_category: Optional[MetricCategory] = None
+    capture_rationale: Optional[bool] = None
+    selection_mode: Optional[SelectionMode] = None
+    allow_discovery: Optional[bool] = None
+    # See ``MetricCreate.compare_transcripts``. ``None`` here means
+    # "leave unchanged". The route layer enforces mutual exclusion
+    # against the row's existing ``parent_metric_id`` /
+    # ``selection_mode`` when this is set to True, because the patch
+    # body alone doesn't have enough context to validate cross-state.
+    compare_transcripts: Optional[bool] = None
+
+    @model_validator(mode='after')
+    def validate_compare_transcripts_exclusions(self):
+        """Reject patch bodies that flip compare_transcripts on while
+        ALSO trying to set a conflicting field in the same request.
+
+        Cross-state validation against the persisted row (e.g. "the
+        existing metric already has a parent") is done in the
+        update route since the schema doesn't have the row in hand.
+        """
+        if self.compare_transcripts is not True:
+            return self
+        if self.selection_mode is not None:
+            raise ValueError(
+                "Transcript-compare metrics must be standalone: "
+                "selection_mode must be cleared before enabling "
+                "compare_transcripts."
+            )
+        return self
+
+
+class MetricResponse(BaseModel):
+    """Schema for metric response.
+
+    ``children`` is populated for parent metrics (those with
+    ``selection_mode`` set) and is otherwise an empty list. The list is
+    built once at serialization time so callers get a single tree
+    structure without follow-up requests.
+    """
+    id: UUID
+    organization_id: UUID
+    # ``None`` when the metric is org-shared (``scope == "organization"``).
+    # See the ORM ``Metric.workspace_id`` docstring.
+    workspace_id: Optional[UUID] = None
+    # Computed convenience field so the UI doesn't have to do
+    # ``workspace_id == null`` checks everywhere. Always one of
+    # ``"workspace"`` or ``"organization"``.
+    scope: MetricScope = "workspace"
+    name: str
+    description: Optional[str]
+    # Optional illustrative example. Populated mainly on categorization
+    # child labels but surfaced for every metric so the UI can render
+    # it uniformly without branching on parent/child shape.
+    example: Optional[str] = None
+    metric_type: MetricType
+    metric_category: MetricCategory = MetricCategory.QUALITY
+    trigger: MetricTrigger
+    enabled: bool
+    is_default: bool
+    metric_origin: str
+    supported_surfaces: List[str]
+    enabled_surfaces: List[str]
+    custom_data_type: Optional[str]
+    custom_config: Optional[Dict[str, Any]]
+    tags: Optional[List[str]]
+    capture_rationale: bool = False
+    parent_metric_id: Optional[UUID] = None
+    selection_mode: Optional[SelectionMode] = None
+    allow_discovery: bool = False
+    # See ``MetricCreate.compare_transcripts``. Surfaced so the UI can
+    # render a "Compare transcripts" badge in the metric picker and
+    # know to skip the run's transcript_source toggle for this metric.
+    compare_transcripts: bool = False
+    lifecycle: str = "active"
+    promoted_from_draft_at: Optional[datetime] = None
+    studio_notes: Optional[str] = None
+    children: List["MetricResponse"] = Field(default_factory=list)
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+
+    @field_validator('metric_type', mode='before')
+    @classmethod
+    def convert_metric_type(cls, v):
+        """Convert string to MetricType (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return MetricType(v_lower)
+            except ValueError:
+                for enum_member in MetricType:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid MetricType value: {v}")
+        return v
+
+    @field_validator('trigger', mode='before')
+    @classmethod
+    def convert_trigger(cls, v):
+        """Convert string to MetricTrigger (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return MetricTrigger(v_lower)
+            except ValueError:
+                for enum_member in MetricTrigger:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid MetricTrigger value: {v}")
+        return v
+
+    @validator('supported_surfaces', 'enabled_surfaces', pre=True)
+    def normalize_surfaces(cls, v):
+        if v is None:
+            return []
+        if isinstance(v, str):
+            return [v]
+        if isinstance(v, (list, tuple)):
+            return [str(item).lower() for item in v if item]
+        return []
+
+    @validator('metric_origin', pre=True)
+    def normalize_metric_origin(cls, v):
+        if v is None:
+            return "custom"
+        return str(v).lower()
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+MetricResponse.model_rebuild()
+
+
+class MetricDraftCreate(MetricCreate):
+    """Create a draft metric for Metrics Studio experimentation."""
+
+    studio_notes: Optional[str] = Field(
+        default=None,
+        description="Optional notes about what this draft is testing.",
+    )
+
+
+class MetricDraftCreateWithChildren(MetricCreateWithChildren):
+    """Atomically create a draft parent category metric plus its children."""
+
+    studio_notes: Optional[str] = Field(
+        default=None,
+        description="Optional notes about what this draft category is testing.",
+    )
+
+
+class MetricPromoteResponse(BaseModel):
+    """Response after promoting a draft metric to active."""
+
+    metric: MetricResponse
+    promoted_at: datetime
+
+
+MetricStudioSourceKind = Literal[
+    "call_import_row", "call_recording", "evaluator_result"
+]
+
+
+class MetricStudioSourceItem(BaseModel):
+    """One call source selected for a Studio run."""
+
+    source_kind: MetricStudioSourceKind
+    source_ref: str = Field(
+        ...,
+        min_length=1,
+        description="UUID for import rows / evaluator results; call_short_id for recordings.",
+    )
+    display_label: Optional[str] = Field(
+        default=None,
+        max_length=512,
+        description="Optional UI label; resolved server-side when omitted.",
+    )
+
+
+class MetricStudioRunCreate(BaseModel):
+    """Request body for triggering a Metrics Studio evaluation run."""
+
+    metric_ids: List[UUID] = Field(..., min_length=1)
+    sources: List[MetricStudioSourceItem] = Field(..., min_length=1)
+    name: Optional[str] = Field(default=None, max_length=255)
+    transcript_source: Literal["production", "diarised"] = "diarised"
+    llm_provider: Optional[str] = Field(default=None, max_length=50)
+    llm_model: Optional[str] = Field(default=None, max_length=100)
+    llm_credential_id: Optional[UUID] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    metric_llm_overrides: Optional[Dict[str, Any]] = None
+
+
+class MetricStudioRunRetryRequest(BaseModel):
+    """Retry failed or selected Studio run results."""
+
+    result_ids: Optional[List[UUID]] = Field(
+        default=None,
+        description="When omitted, retry all failed results in the run.",
+    )
+
+
+class MetricStudioRunResultResponse(BaseModel):
+    """Per-source result row for a Studio run."""
+
+    id: UUID
+    run_id: UUID
+    source_kind: str
+    source_ref: str
+    display_label: Optional[str] = None
+    source_metadata: Optional[Dict[str, Any]] = None
+    status: str
+    metric_scores: Dict[str, Any] = Field(default_factory=dict)
+    error_message: Optional[str] = None
+    started_at: Optional[datetime] = None
+    finished_at: Optional[datetime] = None
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class MetricStudioRunResponse(BaseModel):
+    """Metrics Studio run summary."""
+
+    id: UUID
+    organization_id: UUID
+    workspace_id: UUID
+    name: Optional[str] = None
+    selected_metric_ids: List[str] = Field(default_factory=list)
+    selected_metric_groups: Optional[Dict[str, List[str]]] = None
+    transcript_source: str
+    llm_provider: Optional[str] = None
+    llm_model: Optional[str] = None
+    status: str
+    total_items: int
+    completed_items: int
+    failed_items: int
+    error_message: Optional[str] = None
+    started_at: Optional[datetime] = None
+    finished_at: Optional[datetime] = None
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class MetricStudioRunListResponse(BaseModel):
+    items: List[MetricStudioRunResponse]
+    total: int
+
+
+class MetricStudioRunResultListResponse(BaseModel):
+    items: List[MetricStudioRunResultResponse]
+    total: int
+
+
+# Evaluator Result Schemas
+class EvaluatorResultCreate(BaseModel):
+    """Schema for creating an evaluator result."""
+    evaluator_id: UUID
+    agent_id: Optional[UUID] = None
+    persona_id: Optional[UUID] = None
+    scenario_id: Optional[UUID] = None
+    name: Optional[str] = None
+    duration_seconds: Optional[float] = None
+    audio_s3_key: Optional[str] = None
+
+
+class EvaluatorResultCreateManual(BaseModel):
+    """Schema for manually creating an evaluator result from existing audio file."""
+    evaluator_id: UUID
+    audio_s3_key: str
+    duration_seconds: Optional[float] = None
+
+
+class EvaluatorResultUpdate(BaseModel):
+    """Schema for updating an evaluator result."""
+    status: Optional[EvaluatorResultStatus] = None
+    transcription: Optional[str] = None
+    metric_scores: Optional[Dict[str, Any]] = None
+    error_message: Optional[str] = None
+    duration_seconds: Optional[float] = None
+
+
+
+class EvaluatorResultCounts(BaseModel):
+    """Rollup counts for evaluator result navigation."""
+
+    total: int = 0
+    completed: int = 0
+    failed: int = 0
+    in_progress: int = 0
+    last_run_at: Optional[datetime] = None
+
+
+
+
+class EvaluatorResultsScenarioSummary(BaseModel):
+    scenario_id: UUID
+    scenario_name: str
+    counts: EvaluatorResultCounts
+
+
+
+
+class EvaluatorResultsSuiteSummary(BaseModel):
+    suite_id: UUID
+    suite_name: Optional[str] = None
+    agent_id: UUID
+    persona_id: Optional[UUID] = None
+    counts: EvaluatorResultCounts
+    scenarios: Optional[List["EvaluatorResultsScenarioSummary"]] = None
+
+
+
+
+class EvaluatorResultsAgentSummary(BaseModel):
+    agent_id: UUID
+    agent_name: str
+    counts: EvaluatorResultCounts
+    suites: Optional[List[EvaluatorResultsSuiteSummary]] = None
+
+
+
+
+class EvaluatorResultsUnassignedSummary(BaseModel):
+    counts: EvaluatorResultCounts
+    recent_result_ids: List[str] = Field(default_factory=list)
+
+
+
+
+class EvaluatorResultsOverviewResponse(BaseModel):
+    workspace_counts: EvaluatorResultCounts
+    agents: List[EvaluatorResultsAgentSummary] = Field(default_factory=list)
+    unassigned: EvaluatorResultsUnassignedSummary
+
+
+
+
+class EvaluatorResultListResponse(BaseModel):
+    items: List["EvaluatorResultResponse"]
+    total: int
+
+
+
+class EvaluatorResultResponse(BaseModel):
+    """Schema for evaluator result response."""
+    id: UUID
+    result_id: str
+    organization_id: UUID
+    evaluator_id: Optional[UUID] = None  # Optional for playground test results
+    agent_id: Optional[UUID] = None  # Nullable for custom evaluators
+    persona_id: Optional[UUID] = None  # Optional for playground test results
+    scenario_id: Optional[UUID] = None  # Optional for playground test results
+    name: Optional[str] = None  # Optional for playground test results
+    timestamp: datetime
+    duration_seconds: Optional[float]
+    status: EvaluatorResultStatus
+    audio_s3_key: Optional[str]
+    transcription: Optional[str]
+    speaker_segments: Optional[List[Dict[str, Any]]] = None  # [{"speaker": "Speaker 1", "text": "...", "start": 0.0, "end": 5.2}]
+    metric_scores: Optional[Dict[str, Any]]
+    celery_task_id: Optional[str]
+    error_message: Optional[str]
+    
+    # Call tracking fields (for voice AI integrations)
+    call_event: Optional[str] = None
+    provider_call_id: Optional[str] = None
+    provider_platform: Optional[str] = None
+    call_data: Optional[Dict[str, Any]] = None  # Full call details from provider
+    synthetic_call_trace_id: Optional[UUID] = None
+    
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+    
+    # Related entities (optional, populated when requested)
+    agent: Optional[AgentResponse] = None
+    persona: Optional[PersonaResponse] = None
+    scenario: Optional[ScenarioResponse] = None
+    evaluator: Optional[EvaluatorResponse] = None
+
+    @field_validator('status', mode='before')
+    @classmethod
+    def convert_status(cls, v):
+        """Convert string to EvaluatorResultStatus (handles uppercase DB values)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return EvaluatorResultStatus(v_lower)
+            except ValueError:
+                for enum_member in EvaluatorResultStatus:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid EvaluatorResultStatus value: {v}")
+        return v
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ============================================
+# ALERTING SCHEMAS
+# ============================================
+
+class AlertCreate(BaseModel):
+    """Schema for creating an alert."""
+    name: str = Field(..., min_length=1, max_length=255)
+    description: Optional[str] = None
+    
+    # Metric condition
+    metric_type: AlertMetricType = AlertMetricType.NUMBER_OF_CALLS
+    aggregation: AlertAggregation = AlertAggregation.SUM
+    operator: AlertOperator = AlertOperator.GREATER_THAN
+    threshold_value: float = Field(..., description="Threshold value for the alert")
+    time_window_minutes: int = Field(default=60, ge=1, description="Time window in minutes for aggregation")
+    
+    # Agent selection (null means all agents)
+    agent_ids: Optional[List[UUID]] = None
+    
+    # Notification settings
+    notify_frequency: AlertNotifyFrequency = AlertNotifyFrequency.IMMEDIATE
+    notify_emails: Optional[List[str]] = Field(default=None, description="List of email addresses to notify")
+    notify_webhooks: Optional[List[str]] = Field(default=None, description="List of webhook URLs (Slack, etc.)")
+    
+    model_config = ConfigDict(json_schema_extra={
+            "example": {
+                "name": "High Call Volume Alert",
+                "description": "Alert when call volume exceeds threshold",
+                "metric_type": "number_of_calls",
+                "aggregation": "sum",
+                "operator": ">",
+                "threshold_value": 100,
+                "time_window_minutes": 60,
+                "agent_ids": None,
+                "notify_frequency": "immediate",
+                "notify_emails": ["admin@example.com"],
+                "notify_webhooks": ["https://hooks.slack.com/services/xxx"]
+            }
+        })
+
+
+class AlertUpdate(BaseModel):
+    """Schema for updating an alert."""
+    name: Optional[str] = Field(None, min_length=1, max_length=255)
+    description: Optional[str] = None
+    
+    # Metric condition
+    metric_type: Optional[AlertMetricType] = None
+    aggregation: Optional[AlertAggregation] = None
+    operator: Optional[AlertOperator] = None
+    threshold_value: Optional[float] = None
+    time_window_minutes: Optional[int] = Field(default=None, ge=1)
+    
+    # Agent selection
+    agent_ids: Optional[List[UUID]] = None
+    
+    # Notification settings
+    notify_frequency: Optional[AlertNotifyFrequency] = None
+    notify_emails: Optional[List[str]] = None
+    notify_webhooks: Optional[List[str]] = None
+    
+    # Status
+    status: Optional[AlertStatus] = None
+
+
+class AlertResponse(BaseModel):
+    """Schema for alert response."""
+    id: UUID
+    organization_id: UUID
+    name: str
+    description: Optional[str]
+    
+    # Metric condition
+    metric_type: AlertMetricType
+    aggregation: AlertAggregation
+    operator: AlertOperator
+    threshold_value: float
+    time_window_minutes: int
+    
+    # Agent selection
+    agent_ids: Optional[List[UUID]]
+    
+    # Notification settings
+    notify_frequency: AlertNotifyFrequency
+    notify_emails: Optional[List[str]]
+    notify_webhooks: Optional[List[str]]
+    
+    # Status
+    status: AlertStatus
+    
+    # Metadata
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+
+    @field_validator('metric_type', mode='before')
+    @classmethod
+    def convert_metric_type(cls, v):
+        """Convert string to AlertMetricType."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return AlertMetricType(v_lower)
+            except ValueError:
+                for enum_member in AlertMetricType:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid AlertMetricType value: {v}")
+        return v
+
+    @field_validator('aggregation', mode='before')
+    @classmethod
+    def convert_aggregation(cls, v):
+        """Convert string to AlertAggregation."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return AlertAggregation(v_lower)
+            except ValueError:
+                for enum_member in AlertAggregation:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid AlertAggregation value: {v}")
+        return v
+
+    @field_validator('operator', mode='before')
+    @classmethod
+    def convert_operator(cls, v):
+        """Convert string to AlertOperator."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            try:
+                return AlertOperator(v)
+            except ValueError:
+                for enum_member in AlertOperator:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid AlertOperator value: {v}")
+        return v
+
+    @field_validator('notify_frequency', mode='before')
+    @classmethod
+    def convert_notify_frequency(cls, v):
+        """Convert string to AlertNotifyFrequency."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return AlertNotifyFrequency(v_lower)
+            except ValueError:
+                for enum_member in AlertNotifyFrequency:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid AlertNotifyFrequency value: {v}")
+        return v
+
+    @field_validator('status', mode='before')
+    @classmethod
+    def convert_status(cls, v):
+        """Convert string to AlertStatus."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return AlertStatus(v_lower)
+            except ValueError:
+                for enum_member in AlertStatus:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid AlertStatus value: {v}")
+        return v
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class AlertHistoryResponse(BaseModel):
+    """Schema for alert history response."""
+    id: UUID
+    organization_id: UUID
+    alert_id: UUID
+    
+    # Trigger information
+    triggered_at: datetime
+    triggered_value: float
+    threshold_value: float
+    
+    # Status
+    status: AlertHistoryStatus
+    
+    # Notification tracking
+    notified_at: Optional[datetime]
+    notification_details: Optional[Dict[str, Any]]
+    
+    # Resolution
+    acknowledged_at: Optional[datetime]
+    acknowledged_by: Optional[str]
+    resolved_at: Optional[datetime]
+    resolved_by: Optional[str]
+    resolution_notes: Optional[str]
+    
+    # Additional context
+    context_data: Optional[Dict[str, Any]]
+    
+    # Metadata
+    created_at: datetime
+    updated_at: datetime
+    
+    # Related alert info (optional)
+    alert: Optional[AlertResponse] = None
+
+    @field_validator('status', mode='before')
+    @classmethod
+    def convert_status(cls, v):
+        """Convert string to AlertHistoryStatus."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return AlertHistoryStatus(v_lower)
+            except ValueError:
+                for enum_member in AlertHistoryStatus:
+                    if enum_member.name == v or enum_member.value == v:
+                        return enum_member
+                raise ValueError(f"Invalid AlertHistoryStatus value: {v}")
+        return v
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class AlertHistoryUpdate(BaseModel):
+    """Schema for updating alert history (acknowledge/resolve)."""
+    status: Optional[AlertHistoryStatus] = None
+    acknowledged_by: Optional[str] = None
+    resolved_by: Optional[str] = None
+    resolution_notes: Optional[str] = None
+
+
+# ============================================
+# CRON JOB SCHEMAS
+# ============================================
+
+class CronJobCreate(BaseModel):
+    """Schema for creating a cron job."""
+    name: str = Field(..., min_length=1, max_length=255)
+    cron_expression: str = Field(..., min_length=1, max_length=100, description="Cron expression (e.g., '0 9 * * 1-5')")
+    timezone: str = Field(default="UTC", max_length=100, description="Timezone for the cron schedule")
+    max_runs: int = Field(default=10, ge=1, le=1000, description="Maximum number of times to run")
+    evaluator_ids: Optional[List[UUID]] = Field(
+        None,
+        description="Evaluator IDs to trigger (expanded with evaluator_suite_ids when both are set).",
+    )
+    evaluator_suite_ids: Optional[List[UUID]] = Field(
+        None,
+        description="Evaluator suite IDs whose combinations are expanded into evaluator_ids.",
+    )
+    
+    model_config = ConfigDict(json_schema_extra={
+            "example": {
+                "name": "Daily Evaluation Run",
+                "cron_expression": "0 9 * * 1-5",
+                "timezone": "America/New_York",
+                "max_runs": 100,
+                "evaluator_ids": ["123e4567-e89b-12d3-a456-426614174000"]
+            }
+        })
+
+
+class CronJobUpdate(BaseModel):
+    """Schema for updating a cron job."""
+    name: Optional[str] = Field(None, min_length=1, max_length=255)
+    cron_expression: Optional[str] = Field(None, min_length=1, max_length=100)
+    timezone: Optional[str] = Field(None, max_length=100)
+    max_runs: Optional[int] = Field(None, ge=1, le=1000)
+    evaluator_ids: Optional[List[UUID]] = None
+    evaluator_suite_ids: Optional[List[UUID]] = None
+    status: Optional[CronJobStatus] = None
+
+
+class CronJobResponse(BaseModel):
+    """Schema for cron job response."""
+    id: UUID
+    organization_id: UUID
+    name: str
+    job_type: str = "evaluator_run"
+    is_system: bool = False
+    cron_expression: str
+    timezone: str
+    max_runs: int
+    current_runs: int
+    evaluator_ids: List[UUID]
+    status: CronJobStatus
+    next_run_at: Optional[datetime]
+    last_run_at: Optional[datetime]
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+
+    @field_validator('status', mode='before')
+    @classmethod
+    def convert_status(cls, v):
+        """Convert string to CronJobStatus."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            v_lower = v.lower()
+            try:
+                return CronJobStatus(v_lower)
+            except ValueError:
+                for enum_member in CronJobStatus:
+                    if enum_member.value.lower() == v_lower:
+                        return enum_member
+                raise ValueError(f"Invalid status: {v}")
+        return v
+
+    @field_validator('evaluator_ids', mode='before')
+    @classmethod
+    def convert_evaluator_ids(cls, v):
+        """Convert evaluator_ids from JSON to list of UUIDs."""
+        if v is None:
+            return []
+        if isinstance(v, list):
+            return [UUID(str(id)) if not isinstance(id, UUID) else id for id in v]
+        return v
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ============================================
+# PROMPT PARTIAL SCHEMAS
+# ============================================
+
+class MetricPartialChild(BaseModel):
+    """One categorization label inside a metric partial."""
+
+    name: str = Field(..., min_length=1)
+    description: str = ""
+    example: str = ""
+
+
+class MetricPartialContent(BaseModel):
+    """Structured JSON payload stored in metric partial ``content``."""
+
+    schema_version: int = 1
+    metric_kind: Literal["single", "category"]
+    description: str = ""
+    children: Optional[List[MetricPartialChild]] = None
+
+
+class PromptPartialCreate(BaseModel):
+    """Schema for creating a prompt partial."""
+    name: str = Field(..., min_length=1, max_length=255)
+    description: Optional[str] = None
+    content: str = Field(..., min_length=1)
+    tags: Optional[List[str]] = None
+
+
+class PromptPartialUpdate(BaseModel):
+    """Schema for updating a prompt partial."""
+    name: Optional[str] = Field(None, min_length=1, max_length=255)
+    description: Optional[str] = None
+    content: Optional[str] = Field(None, min_length=1)
+    tags: Optional[List[str]] = None
+    change_summary: Optional[str] = None
+
+
+class PromptPartialVersionResponse(BaseModel):
+    """Schema for prompt partial version response."""
+    id: UUID
+    prompt_partial_id: UUID
+    version: int
+    content: str
+    change_summary: Optional[str]
+    created_at: datetime
+    created_by: Optional[str]
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class AgentFlowNode(BaseModel):
+    """One step in an LLM-inferred agent logic flowchart."""
+
+    id: str
+    label: str
+    node_type: Literal["start", "decision", "action", "terminal"] = "action"
+    position_x: Optional[float] = None
+    position_y: Optional[float] = None
+    prompt_excerpt: Optional[str] = None
+    start_offset: Optional[int] = None
+    end_offset: Optional[int] = None
+
+
+class AgentFlowEdge(BaseModel):
+    """Directed transition between two agent flow nodes."""
+
+    source: str
+    target: str
+    condition: Optional[str] = None
+
+
+class AgentFlowNodeLayout(BaseModel):
+    id: str
+    position_x: float
+    position_y: float
+
+
+class AgentFlowLayoutSaveRequest(BaseModel):
+    nodes: List[AgentFlowNodeLayout] = Field(default_factory=list)
+
+
+class AgentFlowGraph(BaseModel):
+    """Aggregate flow diagram for an imported production agent prompt."""
+
+    nodes: List[AgentFlowNode] = Field(default_factory=list)
+    edges: List[AgentFlowEdge] = Field(default_factory=list)
+    generated_at: Optional[datetime] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    layout_saved_at: Optional[datetime] = None
+    prompt_content_hash: Optional[str] = None
+    mapping_error: Optional[str] = None
+    generation_error: Optional[str] = None
+
+
+class PromptPartialResponse(BaseModel):
+    """Schema for prompt partial response."""
+    id: UUID
+    organization_id: UUID
+    name: str
+    description: Optional[str]
+    content: str
+    tags: Optional[List[str]]
+    current_version: int
+    agent_flowchart: Optional[AgentFlowGraph] = None
+    agent_flowchart_status: Optional[str] = None
+    created_at: datetime
+    updated_at: datetime
+    created_by: Optional[str]
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class PromptPartialDetailResponse(PromptPartialResponse):
+    """Schema for prompt partial detail with versions."""
+    versions: List[PromptPartialVersionResponse] = []
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ============================================
+# TELEPHONY SCHEMAS (provider-agnostic)
+# ============================================
+
+
+class TelephonyIntegrationCreate(BaseModel):
+    """Schema for creating a telephony provider integration."""
+
+    provider: str = "plivo"
+    name: Optional[str] = None
+    auth_id: str
+    auth_token: str
+    verify_app_uuid: Optional[str] = None
+    voice_app_id: Optional[str] = None
+    sip_domain: Optional[str] = None
+    masking_config: Optional[Dict[str, Any]] = None
+    is_default: Optional[bool] = Field(
+        None,
+        description=(
+            "Mark this credential as the default for the (org, provider). "
+            "If omitted and no default exists yet, this row becomes the default."
+        ),
+    )
+
+
+class TelephonyIntegrationUpdate(BaseModel):
+    """Schema for partial updates to a telephony provider integration."""
+
+    id: Optional[UUID] = None
+    provider: Optional[str] = None
+    name: Optional[str] = None
+    auth_id: Optional[str] = None
+    auth_token: Optional[str] = None
+    verify_app_uuid: Optional[str] = None
+    voice_app_id: Optional[str] = None
+    sip_domain: Optional[str] = None
+    masking_config: Optional[Dict[str, Any]] = None
+    is_active: Optional[bool] = None
+
+
+class TelephonyIntegrationResponse(BaseModel):
+    """Safe response model for telephony integration without secrets."""
+
+    id: UUID
+    organization_id: UUID
+    provider: str
+    name: Optional[str] = None
+    verify_app_uuid: Optional[str]
+    voice_app_id: Optional[str]
+    sip_domain: Optional[str]
+    masking_config: Optional[Dict[str, Any]]
+    is_active: bool
+    is_default: bool = False
+    last_tested_at: Optional[datetime]
+    created_at: datetime
+    updated_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class TelephonyPhoneNumberResponse(BaseModel):
+    """Telephony phone number inventory response schema."""
+
+    id: UUID
+    phone_number: str
+    country_iso2: Optional[str]
+    region: Optional[str]
+    number_type: Optional[str]
+    capabilities: Optional[Dict[str, Any]]
+    is_masking_pool: bool
+    inbound_enabled: Optional[bool] = None
+    outbound_enabled: Optional[bool] = None
+    source: Optional[str] = None
+    agent_id: Optional[UUID]
+    linked_agent_name: Optional[str] = None
+    provider: Optional[str] = None
+    is_active: bool
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class TelephonyDialTargetCreate(BaseModel):
+    """Schema for creating a saved outbound dial target."""
+    phone_number: str
+    label: Optional[str] = None
+
+
+class TelephonyDialTargetUpdate(BaseModel):
+    """Schema for updating a saved outbound dial target."""
+    phone_number: Optional[str] = None
+    label: Optional[str] = None
+
+
+class TelephonyDialTargetResponse(BaseModel):
+    """Schema for dial target response."""
+    id: UUID
+    phone_number: str
+    label: Optional[str] = None
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class TelephonyVerifyStartRequest(BaseModel):
+    """Request schema for starting voice OTP verification."""
+
+    phone_number: str
+    provider: str = "plivo"
+
+
+class TelephonyVerifyStartResponse(BaseModel):
+    """Response schema for started voice OTP verification."""
+
+    session_id: UUID
+    provider_session_uuid: str
+    status: str
+    message: str
+
+
+class TelephonyVerifyCheckRequest(BaseModel):
+    """Request schema for checking a submitted OTP code."""
+
+    session_id: UUID
+    otp_code: str
+    provider: str = "plivo"
+
+
+class TelephonyVerifyCheckResponse(BaseModel):
+    """Response schema for OTP check status."""
+
+    verified: bool
+    status: str
+    message: str
+
+
+class TelephonyMaskingSessionCreate(BaseModel):
+    """Request schema for creating a number masking session."""
+
+    party_a_number: str
+    party_b_number: str
+    provider: str = "plivo"
+    expires_in_minutes: Optional[int] = 60
+    metadata: Optional[Dict[str, Any]] = None
+    provider: str = "plivo"
+
+
+class TelephonyMaskingSessionResponse(BaseModel):
+    """Response schema for masking sessions."""
+
+    id: UUID
+    masked_number: str
+    party_a_number: str
+    party_b_number: str
+    status: str
+    expires_at: Optional[datetime]
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class TelephonyOutboundCallRequest(BaseModel):
+    """Request schema for outbound call initiation."""
+
+    from_number: str
+    to_number: str
+    answer_url: Optional[str] = None
+    agent_id: Optional[UUID] = None
+
+
+class TelephonyOutboundCallResponse(BaseModel):
+    """Response schema for outbound call initiation."""
+
+    provider_request_uuid: str
+    call_status: str
+    from_number: str
+    to_number: str
+    message: str
+
+
+# --- Call Import Schemas ---
+
+class CallImportRowResponse(BaseModel):
+    """Single row within a call-import batch."""
+
+    id: UUID
+    row_index: int
+    # Renamed from ``external_call_id`` (DB column renamed in migration
+    # ``034_call_import_schemas``). Same data, same uniqueness rules.
+    conversation_id: str
+    recording_url: Optional[str] = None
+    recording_date: Optional[date] = None
+    # Production transcript: the value supplied via the CSV upload.
+    transcript: Optional[str] = None
+    transcript_source: Optional[str] = None
+    transcript_provider: Optional[str] = None
+    transcript_model: Optional[str] = None
+    transcript_status: Optional[str] = None
+    transcript_error: Optional[str] = None
+    transcribed_at: Optional[datetime] = None
+    # Diarised transcript: produced by the post-hoc diarisation
+    # worker. Independent of ``transcript`` so manual diarisation
+    # never overwrites the CSV-supplied production value.
+    diarised_transcript: Optional[str] = None
+    diarised_transcript_provider: Optional[str] = None
+    diarised_transcript_model: Optional[str] = None
+    diarised_transcript_status: Optional[str] = None
+    diarised_transcript_error: Optional[str] = None
+    diarised_at: Optional[datetime] = None
+    # LLM that turned the STT plain-text output into structured
+    # ``diarised_segments``. Surfaced in the row detail panel so
+    # reviewers can see "Diarised by openai/gpt-4o-mini" next to
+    # the swap toggle. NULL on rows diarised by the legacy pyannote
+    # worker (which has been removed).
+    diarised_llm_provider: Optional[str] = None
+    diarised_llm_model: Optional[str] = None
+    # The exact prompt the LLM diariser ran with. Persisted so a
+    # reviewer can copy it back into the modal and reproduce the
+    # turn layout against a different STT pass.
+    diarised_prompt: Optional[str] = None
+    # Structured speaker turns produced by the diarisation worker. Each
+    # entry is `{ "speaker": "agent"|"user"|"speaker_N", "text": str,
+    # "start": float, "end": float, "raw_speaker": "Speaker 1" }`. The
+    # plain ``diarised_transcript`` field above is a `<speaker>: <text>`
+    # rendering of this list with ``diarised_speaker_swap`` applied.
+    diarised_segments: Optional[List[Dict[str, Any]]] = None
+    # When True the agent <-> user mapping in ``diarised_segments`` is
+    # inverted at render / export time. The worker writes the canonical
+    # mapping using the "first speaker is the agent" heuristic; the swap
+    # toggle lets reviewers correct that without re-running diarisation.
+    diarised_speaker_swap: bool = False
+    status: CallImportRowStatus
+    recording_s3_key: Optional[str] = None
+    recording_content_type: Optional[str] = None
+    recording_size_bytes: Optional[int] = None
+    error_message: Optional[str] = None
+    attempts: int
+    raw_columns: Optional[Dict[str, Any]] = None
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# --- Call Import Schema (Input Parameter definitions) ---
+
+
+class CallImportSchemaParameterBase(BaseModel):
+    """A single typed parameter inside a Call Import schema.
+
+    Used both in request bodies (create / update) and as the building
+    block of :class:`CallImportSchemaParameterResponse`. Names are
+    case-insensitive unique within their parent schema.
+    """
+
+    name: str = Field(
+        ...,
+        min_length=1,
+        max_length=255,
+        description=(
+            "Parameter name as it appears in the schema editor and the "
+            "upload mapping table. Must be unique within the schema "
+            "(case-insensitive)."
+        ),
+    )
+    type: CallImportParameterType = Field(
+        ...,
+        description=(
+            "Parameter type. One of conversation_id / recording_url / "
+            "recording_date / transcript / text / number / boolean / "
+            "datetime / url. Exactly one parameter of type "
+            "'conversation_id' must be present; at most one each of "
+            "'recording_url', 'recording_date', and 'transcript'. "
+            "Only conversation_id is forced required."
+        ),
+    )
+    description: Optional[str] = Field(
+        default=None,
+        max_length=2048,
+        description="Free-text help shown next to the parameter in the mapping UI.",
+    )
+    is_required: bool = Field(
+        default=False,
+        description=(
+            "When True, the parameter must be mapped to a CSV column on "
+            "every upload. The ``conversation_id`` parameter is always "
+            "required and is force-set to True by the server."
+        ),
+    )
+
+
+class CallImportSchemaParameterCreate(CallImportSchemaParameterBase):
+    """Create payload for a single parameter (inside a schema CRUD body)."""
+
+
+class CallImportSchemaParameterResponse(CallImportSchemaParameterBase):
+    """Response shape including the persisted id + ordering."""
+
+    id: UUID
+    ordering: int
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+def _validate_schema_parameters(
+    parameters: List[CallImportSchemaParameterBase],
+) -> List[CallImportSchemaParameterBase]:
+    """Apply the cross-parameter invariants shared by create + update."""
+
+    if not parameters:
+        raise ValueError("Schema must define at least one parameter.")
+
+    seen_names: set[str] = set()
+    conv_count = 0
+    recording_date_count = 0
+    rec_url_count = 0
+    transcript_count = 0
+    for param in parameters:
+        norm = param.name.strip().lower()
+        if not norm:
+            raise ValueError("Parameter name must be non-empty.")
+        if norm in seen_names:
+            raise ValueError(
+                f"Duplicate parameter name '{param.name}' "
+                "(names must be unique within a schema)."
+            )
+        seen_names.add(norm)
+        if param.type == CallImportParameterType.CONVERSATION_ID:
+            conv_count += 1
+        elif param.type == CallImportParameterType.RECORDING_DATE:
+            recording_date_count += 1
+        elif param.type == CallImportParameterType.RECORDING_URL:
+            rec_url_count += 1
+        elif param.type == CallImportParameterType.TRANSCRIPT:
+            transcript_count += 1
+
+    if conv_count != 1:
+        raise ValueError(
+            "Schema must contain exactly one parameter of type "
+            "'conversation_id'."
+        )
+    if rec_url_count > 1:
+        raise ValueError(
+            "Schema may contain at most one parameter of type "
+            "'recording_url'."
+        )
+    if recording_date_count > 1:
+        raise ValueError(
+            "Schema may contain at most one parameter of type "
+            "'recording_date'."
+        )
+    if transcript_count > 1:
+        raise ValueError(
+            "Schema may contain at most one parameter of type 'transcript'."
+        )
+    return parameters
+
+
+class CallImportSchemaCreate(BaseModel):
+    """Create body for a new call-import schema."""
+
+    name: str = Field(..., min_length=1, max_length=255)
+    description: Optional[str] = Field(default=None, max_length=2048)
+    parameters: List[CallImportSchemaParameterCreate] = Field(
+        ...,
+        description=(
+            "Ordered list of parameters. Order is preserved; the server "
+            "stamps ``ordering`` from the list index."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def _check_parameters(self):
+        _validate_schema_parameters(list(self.parameters))
+        return self
+
+
+class CallImportSchemaUpdate(BaseModel):
+    """Patch body for an existing schema (full parameter replacement)."""
+
+    name: Optional[str] = Field(default=None, min_length=1, max_length=255)
+    description: Optional[str] = Field(default=None, max_length=2048)
+    parameters: Optional[List[CallImportSchemaParameterCreate]] = Field(
+        default=None,
+        description=(
+            "If provided, REPLACES the full set of parameters on the "
+            "schema. Omit to leave parameters untouched."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def _check_parameters(self):
+        if self.parameters is not None:
+            _validate_schema_parameters(list(self.parameters))
+        return self
+
+
+class CallImportSchemaResponse(BaseModel):
+    """Read response for a single schema."""
+
+    id: UUID
+    organization_id: UUID
+    workspace_id: UUID
+    name: str
+    description: Optional[str] = None
+    parameters: List[CallImportSchemaParameterResponse] = Field(default_factory=list)
+    # How many CallImport batches reference this schema. Populated by the
+    # router when listing; defaults to 0 on detail responses where the
+    # caller doesn't need it.
+    usage_count: int = 0
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class CallImportSchemaListResponse(BaseModel):
+    """Paginated list of schemas."""
+
+    items: List[CallImportSchemaResponse] = Field(default_factory=list)
+    total: int
+
+
+class CallImportTagResponse(BaseModel):
+    """Tag attached to call import batches."""
+
+    id: UUID
+    name: str
+    color: Optional[str] = None
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class CallImportTagCreate(BaseModel):
+    """Create a new call-import tag for the organization."""
+
+    name: str = Field(..., min_length=1, max_length=255)
+    color: Optional[str] = Field(None, max_length=32)
+
+
+class CallImportTagUpdate(BaseModel):
+    """Partial update for a call-import tag."""
+
+    name: Optional[str] = Field(None, min_length=1, max_length=255)
+    color: Optional[str] = Field(None, max_length=32)
+
+
+class CallImportPreviewSheet(BaseModel):
+    """One worksheet (or one CSV file synthesized as a single sheet)."""
+
+    name: str = Field(..., description="Sheet name for xlsx; filename for csv.")
+    headers: List[str] = Field(
+        default_factory=list,
+        description="Column headers from the first non-empty row.",
+    )
+    row_count: int = Field(
+        ...,
+        description="Approximate count of data rows (excluding the header row).",
+    )
+
+
+class CallImportSourceRowSkip(BaseModel):
+    """One source spreadsheet row skipped during parse (identity / recording URL)."""
+
+    source_row: int = Field(
+        ...,
+        description="1-based row index in the source file (same semantics as parse errors).",
+    )
+    reason: str = Field(
+        ...,
+        description=(
+            "Machine-readable skip reason, e.g. missing_conversation_id, "
+            "missing_recording_url, invalid_recording_url."
+        ),
+    )
+    message: str = Field(
+        ...,
+        description="Human-readable explanation shown in the UI.",
+    )
+
+
+class CallImportResponse(BaseModel):
+    """Summary of a call-import batch."""
+
+    id: UUID
+    organization_id: UUID
+    workspace_id: UUID
+    # Provider is optional in the new staged flow (only resolved at the
+    # IMPORT stage). Stays populated for all post-import batches.
+    provider: Optional[str] = None
+    telephony_integration_id: Optional[UUID] = None
+    original_filename: Optional[str] = None
+    sheet_name: Optional[str] = None
+    dataset: Optional[str] = None
+    tags: List[CallImportTagResponse] = Field(default_factory=list)
+    # New schema-driven mapping. Empty on legacy batches; pre-schema
+    # batches keep their values in ``column_mapping`` / ``extra_columns``
+    # / ``custom_column_mapping`` below for backwards-compatibility.
+    schema_id: Optional[UUID] = None
+    parameter_mapping: Dict[str, str] = Field(default_factory=dict)
+    column_mapping: Dict[str, Optional[str]] = Field(default_factory=dict)
+    extra_columns: List[str] = Field(default_factory=list)
+    custom_column_mapping: Dict[str, str] = Field(default_factory=dict)
+    # Persisted "drop these columns" decision captured at MAP time.
+    # Empty for legacy one-shot uploads where the value was ephemeral.
+    skipped_columns: List[str] = Field(default_factory=list)
+    source_row_skips: List[CallImportSourceRowSkip] = Field(
+        default_factory=list,
+        description=(
+            "Source rows skipped at parse time because of missing/invalid "
+            "conversation ID or recording URL."
+        ),
+    )
+    # Source-file staging fields populated at UPLOAD time. ``None`` on
+    # legacy batches imported via the one-shot ``POST /upload`` endpoint.
+    source_s3_key: Optional[str] = None
+    source_format: Optional[str] = None
+    source_size_bytes: Optional[int] = None
+    source_content_type: Optional[str] = None
+    # Snapshot of the file's sheets + headers captured at UPLOAD time
+    # so the MAP UI can render without re-fetching the file from S3.
+    available_sheets: Optional[List[CallImportPreviewSheet]] = None
+    total_rows: int
+    completed_rows: int
+    failed_rows: int
+    status: CallImportStatus
+    error_message: Optional[str] = None
+    latest_evaluation_status: Optional[str] = Field(
+        None,
+        description=(
+            "Status of the most recent evaluation run for this batch, "
+            "when any evaluation exists."
+        ),
+    )
+    created_at: datetime
+    updated_at: datetime
+    created_by_email: Optional[str] = None
+    last_updated_by_email: Optional[str] = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class CallImportDetailResponse(CallImportResponse):
+    """A call-import batch with its rows expanded.
+
+    ``filtered_total_rows`` is only set when the caller passed a ``q``
+    search term G�� it lets the UI paginate against the filtered subset
+    while still showing the unfiltered ``total_rows`` in the header.
+
+    The ``diarised_*_rows`` counters aggregate
+    ``CallImportRow.diarised_transcript_status`` across the batch so the
+    UI can render a transcribe-and-diarise progress bar without paging
+    through every row. Rows that have never been touched by the
+    transcribe/diarise worker (``status='idle'``) are NOT counted here G��
+    callers compute the idle bucket as
+    ``total_rows - (pending + running + completed + failed)``.
+    """
+
+    rows: List[CallImportRowResponse] = Field(default_factory=list)
+    filtered_total_rows: Optional[int] = None
+    diarised_pending_rows: int = 0
+    diarised_running_rows: int = 0
+    diarised_completed_rows: int = 0
+    diarised_failed_rows: int = 0
+
+
+class CallImportListResponse(BaseModel):
+    """Paginated list of call-import batches."""
+
+    items: List[CallImportResponse]
+    total: int
+    page: int
+    page_size: int
+
+
+class CallImportDispatchLimitSnapshot(BaseModel):
+    """Configured and live Redis in-flight caps for eval work."""
+
+    global_limit: int
+    global_inflight: int
+    global_at_capacity: bool
+    org_limit: int
+    org_inflight: int
+    org_at_capacity: bool
+    workspace_limit: int
+    job_limit: int
+    fair_dispatch_batch_size: int
+
+
+class CallImportDispatchFairDispatchSnapshot(BaseModel):
+    """Fair-dispatch scheduler metadata from Redis."""
+
+    global_rr_cursor: int
+    dispatch_dedupe_active: bool
+    dispatch_queue: str
+    at_capacity_backoff_seconds: int
+
+
+class CallImportDispatchEvaluationSnapshot(BaseModel):
+    """One in-flight evaluation run with row counters."""
+
+    evaluation_id: UUID
+    call_import_id: UUID
+    status: str
+    total_rows: int
+    pending_rows: int
+    running_rows: int
+    job_inflight: int
+    job_at_capacity: bool
+
+
+class CallImportDispatchWorkspaceSnapshot(BaseModel):
+    """Per-workspace pending dispatch + slot usage."""
+
+    workspace_id: UUID
+    workspace_name: Optional[str] = None
+    workspace_slug: Optional[str] = None
+    inflight: int
+    inflight_at_capacity: bool
+    pending_dispatch_rows: int
+    pending_import_rows: int
+    eval_rr_cursor: int
+    active_evaluations: int
+    evaluations: List[CallImportDispatchEvaluationSnapshot] = Field(
+        default_factory=list
+    )
+
+
+class CallImportDispatchDiagnosticsResponse(BaseModel):
+    """Live operator snapshot for call-import eval fair dispatch."""
+
+    limits: CallImportDispatchLimitSnapshot
+    fair_dispatch: CallImportDispatchFairDispatchSnapshot
+    workspaces: List[CallImportDispatchWorkspaceSnapshot]
+    generated_at: datetime
+
+
+class CallImportUploadResponse(BaseModel):
+    """Response returned right after a CSV is accepted."""
+
+    id: UUID
+    total_rows: int
+    status: CallImportStatus
+    dataset: Optional[str] = None
+    tags: List[CallImportTagResponse] = Field(default_factory=list)
+    message: str
+
+
+class CallImportDeleteResponse(BaseModel):
+    """Response after a whole-batch call-import delete is accepted."""
+
+    id: UUID
+    status: Literal["accepted", "completed"] = Field(
+        ...,
+        description=(
+            "``accepted`` when teardown was queued to run asynchronously; "
+            "``completed`` when the batch was already removed."
+        ),
+    )
+
+
+class CallImportPreviewResponse(BaseModel):
+    """Sheets/headers extracted from an uploaded CSV or Excel workbook.
+
+    The frontend uses this to drive the column-mapping UI without doing
+    its own parsing G�� keeps client and server in lockstep on quoted
+    fields, encodings, and Excel cell coercion.
+    """
+
+    format: str = Field(..., description="One of 'csv' or 'xlsx'.")
+    sheets: List[CallImportPreviewSheet] = Field(default_factory=list)
+
+
+class CallImportUpdate(BaseModel):
+    """Partial update of a call-import batch."""
+
+    original_filename: Optional[str] = Field(
+        None,
+        description=(
+            "User-facing batch label shown in the UI. Pass an empty string to clear."
+        ),
+    )
+    dataset: Optional[str] = Field(
+        None,
+        description=(
+            "Free-text dataset label. Pass an empty string to clear the dataset."
+        ),
+    )
+    tag_ids: Optional[List[UUID]] = Field(
+        None,
+        description=(
+            "Replace the full set of tag assignments. Pass an empty list to clear all tags."
+        ),
+    )
+    schema_id: Optional[UUID] = Field(
+        None,
+        description=(
+            "Reassign the Input Parameter schema. Only honoured while the "
+            "batch is in ``uploaded`` or ``mapped`` state; once the batch "
+            "has rows it's locked to its original schema."
+        ),
+    )
+
+
+class CallImportMappingUpdate(BaseModel):
+    """Mapping payload for the MAP stage (``PATCH /call-imports/{id}/mapping``).
+
+    Idempotent: callers can submit this multiple times against an
+    ``uploaded`` or ``mapped`` batch. Validation re-runs against the
+    persisted ``available_sheets`` snapshot every time so the user can
+    correct mistakes without re-uploading the file.
+    """
+
+    schema_id: UUID = Field(
+        ...,
+        description=(
+            "Reusable Input Parameter schema this batch is mapped against. "
+            "Must belong to the active workspace."
+        ),
+    )
+    sheet_name: Optional[str] = Field(
+        None,
+        description=(
+            "Worksheet to use when the staged source file is an Excel "
+            "workbook. REQUIRED for xlsx; ignored / rejected for CSV."
+        ),
+    )
+    parameter_mapping: Dict[str, str] = Field(
+        default_factory=dict,
+        description=(
+            "``{schema_parameter_name: source_header}`` map covering every "
+            "required schema parameter."
+        ),
+    )
+    skipped_columns: List[str] = Field(
+        default_factory=list,
+        description=(
+            "Source headers the uploader has explicitly skipped. Every "
+            "source header must be either mapped or appear here."
+        ),
+    )
+
+
+class CallImportStartRequest(BaseModel):
+    """Provider + credential picker for the IMPORT stage."""
+
+    provider: Optional[str] = Field(
+        default=None,
+        description=(
+            "Telephony provider key. Must match the "
+            "``telephony_integration_id``'s provider. Omit together with "
+            "``telephony_integration_id`` to download recordings directly "
+            "from CSV-supplied URLs without credentials."
+        ),
+    )
+    telephony_integration_id: Optional[UUID] = Field(
+        default=None,
+        description=(
+            "Specific TelephonyIntegration credential row to use when "
+            "downloading recordings for this batch. Omit together with "
+            "``provider`` for direct-URL import."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def validate_credential_mode(self) -> "CallImportStartRequest":
+        has_provider = bool((self.provider or "").strip())
+        has_integration = self.telephony_integration_id is not None
+        if has_provider != has_integration:
+            raise ValueError(
+                "provider and telephony_integration_id must both be provided "
+                "or both omitted for direct-URL import."
+            )
+        return self
+
+
+# --- Call Import Evaluation Schemas ---
+
+
+class CallImportEvaluationLLMOverride(BaseModel):
+    """Per-metric LLM override used on top of the run-level default.
+
+    Any field left ``None`` falls back to the run-level value (which
+    itself falls back to the historical OpenAI/gpt-4o default). This
+    lets users pick a specific provider/model for a single metric (e.g.
+    a stronger Anthropic model for a tricky qualitative metric) without
+    re-typing the rest of the metrics in the run.
+    """
+
+    provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description="Override LLM provider key, e.g. 'openai' or 'anthropic'.",
+    )
+    model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description="Override LLM model name, e.g. 'gpt-4o' or 'claude-3-opus'.",
+    )
+    credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Optional AIProvider id when the org has multiple credentials.",
+    )
+    llm_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Optional per-metric generation parameters (temperature, top_p, etc.).",
+    )
+
+
+CallImportEvaluationTranscriptSource = Literal["production", "diarised"]
+
+
+class CallImportEvaluationCreate(BaseModel):
+    """Request body for triggering an evaluation over a call-import batch."""
+
+    metric_ids: List[UUID] = Field(
+        ...,
+        min_length=1,
+        description="Org Metric ids to score every completed row against.",
+    )
+    name: Optional[str] = Field(
+        default=None,
+        max_length=255,
+        description=(
+            "Optional human-readable label for the run. Shown in the UI "
+            "instead of the UUID prefix."
+        ),
+    )
+    transcript_sources: List[CallImportEvaluationTranscriptSource] = Field(
+        default_factory=lambda: ["diarised"],
+        min_length=1,
+        max_length=1,
+        description=(
+            "Which transcript to score against. ``'diarised'`` (default) "
+            "auto-diarises rows missing a diarised transcript then scores "
+            "``diarised_transcript``. ``'production'`` scores the CSV "
+            "``transcript`` column directly and skips diarisation."
+        ),
+    )
+
+    @field_validator("transcript_sources")
+    @classmethod
+    def _validate_transcript_sources(
+        cls, value: List[str]
+    ) -> List["CallImportEvaluationTranscriptSource"]:
+        allowed = {"production", "diarised"}
+        invalid = [src for src in value if src not in allowed]
+        if invalid:
+            raise ValueError(
+                "transcript_sources must be ['production'] or ['diarised'] "
+                "(received: "
+                + ", ".join(repr(src) for src in invalid)
+                + ")."
+            )
+        return value  # type: ignore[return-value]
+    # --- Run-level LLM config ---
+    llm_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "Run-level LLM provider key (e.g. 'openai', 'anthropic'). NULL "
+            "preserves the historical OpenAI/gpt-4o default."
+        ),
+    )
+    llm_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description="Run-level LLM model name. Required when llm_provider is set.",
+    )
+    llm_credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Optional AIProvider row to pin for the run-level LLM.",
+    )
+    llm_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Run-level LLM generation parameters (temperature, top_p, etc.).",
+    )
+    metric_llm_overrides: Optional[
+        Dict[str, CallImportEvaluationLLMOverride]
+    ] = Field(
+        default=None,
+        description=(
+            "Optional per-metric LLM overrides keyed by metric UUID. Each "
+            "entry overrides the run-level default for that metric only."
+        ),
+    )
+    # --- Auto-transcribe / diarization hook ---
+    # Every diarised run auto-diarises rows that don't already have a
+    # diarised transcript. The flag stays on the schema so legacy API
+    # callers don't 400 immediately, but the route now requires
+    # ``stt_provider`` + ``stt_model`` on every run regardless of this
+    # value.
+    auto_transcribe: bool = Field(
+        default=True,
+        description=(
+            "Auto-diarise rows missing a diarised transcript before "
+            "evaluation. Defaults to true and is effectively required: "
+            "``stt_provider`` + ``stt_model`` are mandatory on every "
+            "evaluation run."
+        ),
+    )
+    transcribe_overwrite: bool = Field(
+        default=False,
+        description=(
+            "When auto_transcribe is on, overwrite existing transcripts "
+            "instead of skipping rows that already have one."
+        ),
+    )
+    transcribe_mode: Literal["stt_llm", "llm_only"] = Field(
+        default="stt_llm",
+        description=(
+            "Diarisation pipeline shape for the auto-transcribe step. "
+            "'stt_llm' (default) runs STT then an LLM diariser over the "
+            "resulting text G�� ``stt_provider`` + ``stt_model`` must be "
+            "provided. 'llm_only' skips STT and feeds the audio "
+            "directly to the multimodal ``diarization_llm_*`` model "
+            "along with ``diarization_prompt``; STT fields must be "
+            "omitted in that case."
+        ),
+    )
+    stt_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "STT provider key, e.g. 'deepgram', 'openai'. Required when "
+            "``transcribe_mode='stt_llm'`` (the default); must be omitted "
+            "when ``transcribe_mode='llm_only'``."
+        ),
+    )
+    stt_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description=(
+            "STT model name, e.g. 'nova-2', 'whisper-1'. Same presence "
+            "rules as ``stt_provider``."
+        ),
+    )
+    stt_credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Optional AIProvider/Integration row to pin for STT.",
+    )
+    stt_language: Optional[str] = Field(
+        default=None,
+        max_length=20,
+        description="ISO language hint for the STT provider, e.g. 'en'.",
+    )
+    # --- LLM diariser config (mirror of CallImportTranscribeRequest) ---
+    # Auto-diarised eval rows go through the same LLM-based diariser as
+    # the standalone Transcribe modal G�� the run remembers the provider /
+    # model / prompt so a follow-up retry can reproduce them without
+    # having to re-prompt the user.
+    diarization_llm_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "LLM provider for diarising STT output into agent/user "
+            "turns. Required when ``auto_transcribe`` is set (the worker "
+            "no longer falls back to pyannote)."
+        ),
+    )
+    diarization_llm_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description="LLM model for the diariser.",
+    )
+    diarization_llm_credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Optional AIProvider row to pin for the diariser LLM.",
+    )
+    diarization_prompt: Optional[str] = Field(
+        default=None,
+        max_length=10_000,
+        description=(
+            "Custom system prompt for the diariser LLM; falls back to "
+            "the canonical default when blank."
+        ),
+    )
+    discover_new_metrics: bool = Field(
+        default=False,
+        description=(
+            "When true, the LLM is invited to propose net-new top-level "
+            "metrics (boolean / rating / category) observed in the "
+            "transcripts in addition to scoring the selected metrics. "
+            "Candidates surface in the Discovered metrics panel on the "
+            "evaluation detail Flow tab and can be promoted into real "
+            "standalone Metric rows. Defaults to false so existing "
+            "callers retain previous behaviour."
+        ),
+    )
+    # Telephony credentials for unified pipeline (required when batch is mapped).
+    provider: Optional[str] = Field(
+        default=None,
+        description=(
+            "Telephony provider key. Required together with "
+            "``telephony_integration_id`` when starting evaluation "
+            "from a mapped batch. Omit both for direct-URL import."
+        ),
+    )
+    telephony_integration_id: Optional[UUID] = Field(
+        default=None,
+        description=(
+            "TelephonyIntegration credential for recording fetch. "
+            "Required together with ``provider`` for credentialed import."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def validate_telephony_credential_mode(self) -> "CallImportEvaluationCreate":
+        has_provider = bool((self.provider or "").strip())
+        has_integration = self.telephony_integration_id is not None
+        if has_provider != has_integration:
+            raise ValueError(
+                "provider and telephony_integration_id must both be provided "
+                "or both omitted for direct-URL evaluation."
+            )
+        return self
+
+
+class CallImportEvaluationUpdate(BaseModel):
+    """Patch body for editing a previously-created evaluation run."""
+
+    name: Optional[str] = Field(
+        default=None,
+        max_length=255,
+        description="New name for the evaluation. Empty string clears it.",
+    )
+
+
+class CallImportEvaluationBulkDelete(BaseModel):
+    """Request body for deleting multiple evaluation runs in one call."""
+
+    evaluation_ids: List[UUID] = Field(
+        ...,
+        min_length=1,
+        description="Evaluation ids to delete.",
+    )
+
+
+class CallImportEvaluationRetryRequest(BaseModel):
+    """Body for retrying a subset (or all failed rows) of an evaluation run.
+
+    ``eval_row_ids`` is optional: when ``None`` the retry applies to
+    every row in the run that is currently in the ``failed`` state. The
+    selection always intersects with the run's actual rows, so unknown
+    ids are silently skipped (and surfaced in the response's
+    ``skipped`` list with reason ``unknown``).
+
+    The optional ``llm_*`` / ``metric_llm_overrides`` / ``stt_*`` fields
+    let the caller swap out the LLM or STT configuration that the
+    failed rows were originally evaluated with. When a field is left
+    ``None`` the run's existing value is preserved. When a field is
+    set, it is persisted onto the run (so a follow-up retry sees the
+    new value as the default) and used by the worker on the next
+    pass. Providing only one half of provider+model is rejected so
+    the worker never ends up with a half-configured run.
+    """
+
+    eval_row_ids: Optional[List[UUID]] = Field(
+        default=None,
+        description=(
+            "Restrict the retry to a specific subset of evaluation rows. "
+            "When omitted, every row with status='failed' in this run is "
+            "re-enqueued."
+        ),
+    )
+
+    # --- Metric-subset re-run ---
+    # When ``metric_ids`` is set, the retry recomputes ONLY those
+    # metrics instead of the whole row, and the new scores are merged
+    # into the existing ``metric_scores`` JSON (other metrics'
+    # previously-computed values are preserved). This is the path
+    # taken by the "Re-run metrics" UI in CallImportEvaluationDetail.
+    metric_ids: Optional[List[UUID]] = Field(
+        default=None,
+        description=(
+            "Restrict the retry to a specific subset of metrics. When "
+            "set, the worker recomputes only these metrics and merges "
+            "the new scores into the row's existing metric_scores "
+            "(other metrics' previous values are preserved). When "
+            "omitted, the row is fully re-scored as before. Every id "
+            "must already be present in the run's selected_metric_ids."
+        ),
+    )
+    include_completed: bool = Field(
+        default=False,
+        description=(
+            "When True, rows whose status is currently 'completed' "
+            "become eligible for retry (otherwise only 'failed' rows "
+            "are picked up). Required when ``metric_ids`` is set on a "
+            "successful row, since otherwise the whole metric-subset "
+            "retry would be skipped as 'completed'."
+        ),
+    )
+
+    # --- LLM overrides ---
+    llm_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "Override the run-level LLM provider for this retry (and "
+            "future retries). Must be paired with ``llm_model``."
+        ),
+    )
+    llm_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description=(
+            "Override the run-level LLM model. Must be paired with "
+            "``llm_provider``."
+        ),
+    )
+    llm_credential_id: Optional[UUID] = Field(
+        default=None,
+        description=(
+            "Pin a specific AIProvider credential row for the LLM. "
+            "When omitted, the resolver falls back to the org default."
+        ),
+    )
+    llm_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Override run-level LLM generation parameters for this retry.",
+    )
+    metric_llm_overrides: Optional[
+        Dict[str, CallImportEvaluationLLMOverride]
+    ] = Field(
+        default=None,
+        description=(
+            "Replace the run's per-metric LLM overrides. When omitted, "
+            "the existing overrides are kept; when set, this dict "
+            "fully replaces them (pass an empty object to clear)."
+        ),
+    )
+
+    # --- STT overrides ---
+    stt_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "Override the run-level STT provider for this retry. Must "
+            "be paired with ``stt_model``. Only meaningful when the "
+            "run is configured for the diarised transcript source."
+        ),
+    )
+    stt_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description="Override the run-level STT model.",
+    )
+    stt_credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Pin a specific credential row for the STT call.",
+    )
+    # --- LLM diariser overrides ---
+    # When set, replace the run-stored diariser configuration for any
+    # rows that have to be re-diarised as part of the retry (i.e.
+    # ``transcribe_overwrite=True`` or the row never had a diarised
+    # transcript). Same provider+model pairing rule as STT.
+    diarization_llm_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "Override the run's diariser LLM provider. Must be paired "
+            "with ``diarization_llm_model``."
+        ),
+    )
+    diarization_llm_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description="Override the run's diariser LLM model.",
+    )
+    diarization_llm_credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Pin a specific credential row for the diariser LLM.",
+    )
+    diarization_prompt: Optional[str] = Field(
+        default=None,
+        max_length=10_000,
+        description=(
+            "Override the run's diariser prompt. Pass an empty string "
+            "to clear the override and fall back to the canonical "
+            "default; pass None to leave the existing value untouched."
+        ),
+    )
+    transcribe_overwrite: bool = Field(
+        default=False,
+        description=(
+            "When True, wipe the diarised transcript on every retried "
+            "row's source CallImportRow so the (possibly new) STT runs "
+            "from scratch. When False, rows that already have a "
+            "diarised transcript skip diarisation and only re-evaluate."
+        ),
+    )
+    transcribe_mode: Optional[Literal["stt_llm", "llm_only"]] = Field(
+        default=None,
+        description=(
+            "Override the run's diarisation pipeline mode for this retry. "
+            "``stt_llm`` runs STT then an LLM diariser; ``llm_only`` feeds "
+            "audio directly to a multimodal diariser LLM."
+        ),
+    )
+
+    # Telephony credentials for rows that must re-fetch recordings.
+    provider: Optional[str] = Field(
+        default=None,
+        description=(
+            "Override the batch's telephony provider for this retry pass. "
+            "Must be paired with ``telephony_integration_id``. Omit both "
+            "fields to keep the batch's existing pinned credentials."
+        ),
+    )
+    telephony_integration_id: Optional[UUID] = Field(
+        default=None,
+        description=(
+            "Override the telephony credential used when re-fetching "
+            "recordings during this retry. Must be paired with "
+            "``provider``. Omit both to keep existing credentials; send "
+            "both as null for direct-URL retry."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def validate_telephony_credential_mode(self) -> "CallImportEvaluationRetryRequest":
+        fields_set = self.model_fields_set
+        if (
+            "provider" not in fields_set
+            and "telephony_integration_id" not in fields_set
+        ):
+            return self
+        has_provider = bool((self.provider or "").strip())
+        has_integration = self.telephony_integration_id is not None
+        if has_provider != has_integration:
+            raise ValueError(
+                "provider and telephony_integration_id must both be provided "
+                "or both omitted for direct-URL retry."
+            )
+        return self
+
+
+class CallImportEvaluationRetrySkippedItem(BaseModel):
+    """One entry in the retry response's ``skipped`` list."""
+
+    eval_row_id: UUID
+    reason: str = Field(
+        ...,
+        description=(
+            "Why this row was not re-enqueued. Known values: "
+            "'unknown' (id not in this run), 'in_progress' "
+            "(status is pending/running), 'completed' (already "
+            "successful), 'source_row_missing'."
+        ),
+    )
+
+
+class CallImportEvaluationRetryResponse(BaseModel):
+    """Summary of a retry fan-out request."""
+
+    requeued: int = Field(
+        ...,
+        description="How many evaluation rows were reset and re-enqueued.",
+    )
+    transcribe_requeued: int = Field(
+        default=0,
+        description=(
+            "Of those, how many were chained through a diarisation "
+            "task first because the diarised transcript was missing "
+            "(matches the auto-transcribe behavior of the create-run "
+            "endpoint)."
+        ),
+    )
+    skipped: List[CallImportEvaluationRetrySkippedItem] = Field(
+        default_factory=list,
+        description="Rows the caller asked for that we did not re-enqueue.",
+    )
+
+
+class CallImportEvaluationBulkActionResponse(BaseModel):
+    """Acknowledgement for bulk cancel / force-fail requests accepted off-thread."""
+
+    accepted: bool = True
+    target_count: int = Field(
+        ...,
+        description="How many rows the background worker will process.",
+    )
+    evaluation_id: UUID
+
+
+class CallImportMetricSummary(BaseModel):
+    """Lightweight metric descriptor returned alongside an evaluation."""
+
+    id: UUID
+    name: str
+    metric_type: Optional[str] = None
+    description: Optional[str] = None
+    parent_metric_id: Optional[UUID] = None
+    selection_mode: Optional[SelectionMode] = None
+    # Surfaced so the Flow tab can decide whether to render the
+    # Discovered Labels panel next to a multi_label parent. Defaults to
+    # False to keep legacy clients (and standalone metrics) unaffected.
+    allow_discovery: bool = False
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class CallImportEvaluationResponse(BaseModel):
+    """Parent record describing one evaluation run over a batch."""
+
+    id: UUID
+    call_import_id: UUID
+    organization_id: UUID
+    name: Optional[str] = None
+    selected_metric_ids: List[UUID] = Field(default_factory=list)
+    # Parent UUID string -> [child UUID string]. Captured at run creation
+    # so the UI can rebuild the parent/child tree even after metrics are
+    # renamed or deleted. Empty / NULL = no hierarchy was used.
+    selected_metric_groups: Optional[Dict[str, List[str]]] = None
+    metrics: List[CallImportMetricSummary] = Field(default_factory=list)
+    status: str
+    total_rows: int
+    completed_rows: int
+    failed_rows: int
+    error_message: Optional[str] = None
+    llm_provider: Optional[str] = None
+    llm_model: Optional[str] = None
+    llm_credential_id: Optional[UUID] = None
+    llm_config: Optional[Dict[str, Any]] = None
+    metric_llm_overrides: Optional[Dict[str, Any]] = None
+    stt_provider: Optional[str] = None
+    stt_model: Optional[str] = None
+    stt_credential_id: Optional[UUID] = None
+    # Run-level LLM diariser config. Surfaced so the UI can show
+    # "Diarised via openai/gpt-4o-mini" on the evaluation header and
+    # pre-fill the retry modal with the previously-used prompt.
+    diarisation_llm_provider: Optional[str] = None
+    diarisation_llm_model: Optional[str] = None
+    diarisation_llm_credential_id: Optional[UUID] = None
+    diarisation_prompt: Optional[str] = None
+    # Diarisation pipeline shape this run was created with. ``stt_llm``
+    # (default) is the legacy STT-then-LLM-diariser flow; ``llm_only``
+    # means the audio was fed directly to a multimodal diariser LLM.
+    # Surfaced so the retry modal can preselect the right mode and the
+    # eval header can render "Diarised via LLM only (Gemini)" instead of
+    # an empty STT label.
+    transcribe_mode: Literal["stt_llm", "llm_only"] = "stt_llm"
+    # Which transcript column this run scored against. All current runs
+    # use diarised; legacy rows may still carry ``production``.
+    transcript_source: CallImportEvaluationTranscriptSource = "diarised"
+    # Sibling evaluation ids created in the same Run Evaluation request.
+    # Populated only on the POST response (and only when the user ticked
+    # both Production and Diarised in the modal G�� the backend creates
+    # one ``CallImportEvaluation`` per source and links them via this
+    # field so the frontend can deep-link to either run). Empty for all
+    # other reads.
+    sibling_evaluation_ids: List[UUID] = Field(default_factory=list)
+    expected_llm_calls_per_row: Optional[int] = Field(
+        None,
+        description=(
+            "Number of distinct LLM API calls made per evaluation row "
+            "(one per unique provider/model/config among selected metrics)."
+        ),
+    )
+    started_at: Optional[datetime] = None
+    finished_at: Optional[datetime] = None
+    created_at: datetime
+    updated_at: datetime
+    created_by_email: Optional[str] = None
+    last_updated_by_email: Optional[str] = None
+    # Cached LLM-generated TLDR for the Visualizations tab. Lazily
+    # populated by ``POST /evaluations/{eval_id}/insights``; ``None``
+    # for runs the user has not summarised yet. ``is_stale`` on the
+    # nested object is set by the route, not the model.
+    tldr_summary: Optional["EvaluationTldrSummary"] = None
+    # Cached LLM-generated user insights for External Audit PDF section 03.
+    user_insights: Optional["EvaluationUserInsightsState"] = None
+    # Cached per-metric failure clustering for internal diagnostics.
+    metric_clusters: Optional["EvaluationMetricClustersState"] = None
+    # True when the user opted into top-level metric discovery on the
+    # Run Evaluation modal. The frontend uses this to gate the
+    # "Discovered metrics" panel on the Flow tab.
+    discover_new_metrics: bool = False
+    bulk_operation: Optional[
+        Literal["abort", "force_fail_pending", "retry"]
+    ] = Field(
+        default=None,
+        description=(
+            "When set, a bulk background operation (abort, force-fail pending, "
+            "or retry) is still running for this evaluation. Other mutating "
+            "actions are rejected until it completes."
+        ),
+    )
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class CallImportEvaluationListResponse(BaseModel):
+    """Wrapper for listing evaluations on a single batch."""
+
+    items: List[CallImportEvaluationResponse]
+    total: int
+
+
+class CallImportEvaluationRowResponse(BaseModel):
+    """Per-source-row evaluation output (one Metric set applied to one row).
+
+    ``raw_columns``, ``recording_url`` and ``recording_s3_key`` come from
+    the parent ``CallImportRow`` so the row-detail panel can show the
+    full CSV row metadata + audio without a second round-trip. The UI
+    prefers ``recording_s3_key`` (resolved via a presigned URL) over
+    ``recording_url`` so playback uses our downloaded copy instead of
+    the raw provider URL, which is often expired/auth-gated.
+    """
+
+    id: UUID
+    evaluation_id: UUID
+    call_import_row_id: UUID
+    row_index: Optional[int] = None
+    # Renamed from ``external_call_id``; same value, mirrors the renamed
+    # ``call_import_rows.conversation_id`` column.
+    conversation_id: Optional[str] = None
+    transcript: Optional[str] = None
+    raw_columns: Optional[Dict[str, Any]] = None
+    recording_url: Optional[str] = None
+    recording_date: Optional[date] = None
+    recording_s3_key: Optional[str] = None
+    diarised_transcript_status: Optional[str] = None
+    diarised_transcript_error: Optional[str] = None
+    status: str
+    metric_scores: Dict[str, Any] = Field(default_factory=dict)
+    error_message: Optional[str] = None
+    started_at: Optional[datetime] = None
+    finished_at: Optional[datetime] = None
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class CallImportEvaluationRowListResponse(BaseModel):
+    """Paginated per-row evaluation results."""
+
+    items: List[CallImportEvaluationRowResponse]
+    total: int
+    page: int
+    page_size: int
+
+
+class CallImportRowBulkDelete(BaseModel):
+    """Request body for deleting multiple rows from a call-import batch."""
+
+    row_ids: List[UUID] = Field(
+        ...,
+        min_length=1,
+        description="Row ids to delete (must belong to the same call import).",
+    )
+
+
+class CallImportRowBulkDeleteResponse(BaseModel):
+    """Response after a bulk-delete pass over ``CallImportRow`` rows."""
+
+    deleted: int = Field(
+        ...,
+        description="How many rows were actually removed (unknown ids are skipped).",
+    )
+    status: Literal["completed", "accepted"] = Field(
+        default="completed",
+        description=(
+            "``accepted`` when deletion was queued to run asynchronously; "
+            "``completed`` when rows were removed before the response."
+        ),
+    )
+
+
+class CallImportRetryFailedRowsRequest(BaseModel):
+    """Optional credential override when re-enqueueing failed import rows."""
+
+    provider: Optional[str] = Field(
+        default=None,
+        description=(
+            "Telephony provider key for this retry pass. Omit together with "
+            "``telephony_integration_id`` to download from CSV recording URLs."
+        ),
+    )
+    telephony_integration_id: Optional[UUID] = Field(
+        default=None,
+        description=(
+            "Telephony credential to use for this retry pass. Omit together "
+            "with ``provider`` for direct-URL retry."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def validate_credential_mode(self) -> "CallImportRetryFailedRowsRequest":
+        has_provider = bool((self.provider or "").strip())
+        has_integration = self.telephony_integration_id is not None
+        if has_provider != has_integration:
+            raise ValueError(
+                "provider and telephony_integration_id must both be provided "
+                "or both omitted for direct-URL retry."
+            )
+        return self
+
+
+class CallImportRetryFailedRowsResponse(BaseModel):
+    """Summary of a retry pass over failed call-import rows."""
+
+    requeued: int = Field(
+        ...,
+        description=(
+            "Rows reset to pending and successfully re-enqueued on the "
+            "``imports`` worker queue."
+        ),
+    )
+    enqueue_failed: int = Field(
+        default=0,
+        description=(
+            "Rows that were eligible for retry but failed to enqueue again. "
+            "These rows are left in ``failed`` with an enqueue error."
+        ),
+    )
+    skipped: int = Field(
+        default=0,
+        description=(
+            "Rows skipped because they were no longer in ``failed`` at retry "
+            "time (for example, already retried from another tab)."
+        ),
+    )
+
+
+# --- Diarization / Transcription request/response shapes ---
+
+
+class CallImportTranscribeRequest(BaseModel):
+    """Body for kicking off diarization for one or many call-import rows.
+
+    The same shape powers both the per-row endpoint (where ``row_ids``
+    is ignored) and the batch-level endpoint. ``only_missing`` is the
+    safe default G�� rows with an existing transcript are skipped unless
+    ``overwrite_existing`` is set.
+
+    Two modes are supported:
+
+    * ``mode="stt_llm"`` (default) G�� the legacy two-stage pipeline: STT
+      produces plain text, an LLM splits it into agent/user turns using
+      ``diarization_prompt``. ``stt_provider`` and ``stt_model`` are
+      required in this mode.
+    * ``mode="llm_only"`` G�� skip STT entirely and hand the recording's
+      audio bytes to a multimodal chat model along with
+      ``diarization_prompt``. The model both transcribes and diarises in
+      a single pass. The STT fields are ignored (and must be omitted /
+      null). Only providers whose chat API accepts audio input (OpenAI
+      ``gpt-4o-audio-*``, Google Gemini ``1.5/2.0``) are usable; other
+      providers will surface a typed error on the row.
+    """
+
+    mode: Literal["stt_llm", "llm_only"] = Field(
+        default="stt_llm",
+        description=(
+            "Pipeline shape. 'stt_llm' (default) runs STT then an LLM "
+            "diariser over the resulting text. 'llm_only' skips STT and "
+            "feeds the raw audio to a multimodal LLM together with "
+            "``diarization_prompt`` for a single-pass transcribe + "
+            "diarise."
+        ),
+    )
+    stt_provider: Optional[str] = Field(
+        default=None,
+        max_length=50,
+        description=(
+            "STT provider key, e.g. 'deepgram' or 'openai'. Required when "
+            "``mode='stt_llm'``; must be omitted when ``mode='llm_only'``."
+        ),
+    )
+    stt_model: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description=(
+            "STT model name, e.g. 'nova-2' or 'whisper-1'. Required when "
+            "``mode='stt_llm'``; must be omitted when ``mode='llm_only'``."
+        ),
+    )
+    credential_id: Optional[UUID] = Field(
+        default=None,
+        description="Optional AIProvider/Integration row to pin for this run.",
+    )
+    language: Optional[str] = Field(
+        default=None,
+        max_length=20,
+        description="Optional ISO language hint, e.g. 'en'.",
+    )
+    only_missing: bool = Field(
+        default=True,
+        description=(
+            "When true, rows with an existing transcript are skipped (the "
+            "default safe behavior)."
+        ),
+    )
+    overwrite_existing: bool = Field(
+        default=False,
+        description=(
+            "When true, existing transcripts are replaced. Mutually "
+            "exclusive with only_missing."
+        ),
+    )
+    row_ids: Optional[List[UUID]] = Field(
+        default=None,
+        description=(
+            "Restrict the run to a specific subset of rows. NULL = every "
+            "row in the import (subject to only_missing)."
+        ),
+    )
+    # --- LLM diariser config ---
+    # In ``stt_llm`` mode diarisation runs as a *second* step: STT
+    # produces plain text, then this LLM splits it into agent/user
+    # turns. In ``llm_only`` mode this same LLM directly receives the
+    # audio and the prompt. Both fields are always mandatory because
+    # there is no longer a pyannote fallback and ``llm_only`` cannot
+    # function without an LLM either.
+    diarization_llm_provider: str = Field(
+        ...,
+        max_length=50,
+        description=(
+            "LLM provider that diarises the call. In ``stt_llm`` it sees "
+            "the STT text; in ``llm_only`` it sees the raw audio."
+        ),
+    )
+    diarization_llm_model: str = Field(
+        ...,
+        max_length=100,
+        description=(
+            "LLM model name. In ``llm_only`` mode this must be a model "
+            "that accepts audio input (e.g. 'gpt-4o-audio-preview', "
+            "'gemini-1.5-pro')."
+        ),
+    )
+    diarization_llm_credential_id: Optional[UUID] = Field(
+        default=None,
+        description=(
+            "Optional AIProvider row to pin for the diarisation LLM."
+        ),
+    )
+    diarization_prompt: Optional[str] = Field(
+        default=None,
+        max_length=10_000,
+        description=(
+            "Operator-supplied system prompt for the diariser LLM. "
+            "When NULL/empty the worker uses the canonical default "
+            "(see ``GET /api/v1/call-imports/diarisation-prompt-default``)."
+        ),
+    )
+
+    @model_validator(mode="after")
+    def _validate_mode_fields(self) -> "CallImportTranscribeRequest":
+        """Enforce STT-field presence rules based on ``mode``.
+
+        ``stt_llm`` (default) requires both STT fields G�� the worker
+        cannot diarise without a transcript. ``llm_only`` forbids them
+        so the API contract makes it clear that the audio is going
+        straight to the LLM; passing both would be ambiguous about
+        which path the worker should take.
+        """
+        stt_provider = (self.stt_provider or "").strip() if self.stt_provider else None
+        stt_model = (self.stt_model or "").strip() if self.stt_model else None
+        if self.mode == "stt_llm":
+            if not stt_provider or not stt_model:
+                raise ValueError(
+                    "stt_provider and stt_model are required when "
+                    "mode='stt_llm'."
+                )
+        else:  # llm_only
+            if stt_provider or stt_model:
+                raise ValueError(
+                    "stt_provider/stt_model must be omitted when "
+                    "mode='llm_only'; the LLM consumes the audio "
+                    "directly."
+                )
+        return self
+
+
+class CallImportDiarisationPromptDefaultResponse(BaseModel):
+    """Wrapper for the canonical diariser-prompt fetched by the modal."""
+
+    prompt: str = Field(
+        ...,
+        description=(
+            "The exact prompt the worker falls back to when the caller "
+            "leaves ``diarization_prompt`` blank. The frontend pre-fills "
+            "the textarea with this value so the operator can edit it."
+        ),
+    )
+
+
+class CallImportRowIdsResponse(BaseModel):
+    """Flat row-id list for cross-page bulk selection.
+
+    Powers the "Select all M rows in this import" affordance on the
+    detail page G�� returning only ids keeps the payload tiny so the UI
+    can hold the full set in memory even for batches with thousands
+    of rows. The frontend then passes those ids straight to the
+    existing bulk-delete / bulk-transcribe endpoints.
+    """
+
+    ids: List[UUID] = Field(
+        default_factory=list,
+        description=(
+            "Every ``CallImportRow.id`` that matches the ``q`` and "
+            "``diarised_status`` filters (or every row when neither is "
+            "supplied), sorted by ``row_index``."
+        ),
+    )
+    total: int = Field(
+        ...,
+        description=(
+            "Length of ``ids``. Sent explicitly so callers can show a "
+            "count without re-measuring the array."
+        ),
+    )
+
+
+class CallImportTranscribeResponse(BaseModel):
+    """Summary of a transcribe fan-out request."""
+
+    queued: int = Field(
+        ...,
+        description=(
+            "How many rows were enqueued for diarization. Skipped rows "
+            "(missing recording, transcript already present, etc.) are "
+            "not counted."
+        ),
+    )
+    skipped_rows: int = Field(
+        default=0,
+        description="Rows excluded by only_missing or because they had no recording.",
+    )
+    skipped_reason_counts: Dict[str, int] = Field(
+        default_factory=dict,
+        description="Per-reason breakdown of skipped rows for the UI to surface.",
+    )
+    accepted: bool = Field(
+        default=False,
+        description=(
+            "When true, diarization setup was queued to a background worker "
+            "and ``queued`` reflects zero until the worker finishes enqueue."
+        ),
+    )
+
+
+class CallImportCancelDiarisationRequest(BaseModel):
+    """Body for the batch cancel-diarisation endpoint.
+
+    Omit ``row_ids`` (or pass ``null``) to cancel every row in the
+    import whose ``diarised_transcript_status`` is currently
+    ``pending`` or ``running``. Pass an explicit list to scope the
+    cancel to a subset (e.g. the rows the operator selected in the
+    UI).
+    """
+
+    row_ids: Optional[List[UUID]] = Field(
+        default=None,
+        description=(
+            "Optional subset of CallImportRow UUIDs. ``None`` cancels "
+            "every pending / running diarisation in the import."
+        ),
+    )
+
+
+class CallImportCancelDiarisationResponse(BaseModel):
+    """Summary of a cancel-diarisation request.
+
+    ``cancelled`` counts rows that were actively pending / running
+    when the cancel landed and got flipped to ``failed`` with a
+    "Cancelled by user" error. ``skipped`` counts rows that were
+    requested (or matched the implicit "all rows" filter) but were
+    not in a cancellable state G�� typically because they had already
+    finished or were never queued for diarisation in the first place.
+    """
+
+    cancelled: int = Field(
+        ...,
+        description=(
+            "Rows whose in-flight Celery task was revoked and whose "
+            "``diarised_transcript_status`` was flipped to ``failed`` "
+            "with a 'Cancelled by user' error message."
+        ),
+    )
+    skipped: int = Field(
+        default=0,
+        description=(
+            "Rows that were requested but not in a cancellable state "
+            "(idle / completed / already failed)."
+        ),
+    )
+
+
+# --- Per-run aggregation / visualization payloads ---
+
+
+class CallImportMetricHistogramBucket(BaseModel):
+    """One bin of a numeric metric histogram."""
+
+    x0: float
+    x1: float
+    count: int
+
+
+class CallImportMetricValueCount(BaseModel):
+    """One row of a categorical metric's value frequency table."""
+
+    label: str
+    count: int
+
+
+class CallImportMetricLabelPair(BaseModel):
+    """One unordered pair-count cell of a multi-label parent's
+    co-occurrence matrix.
+
+    ``a`` and ``b`` are child label names; ``count`` is the number of
+    rows on which both labels fired together (intersection size).
+    Pairs are emitted with ``a < b`` lexicographically so the matrix
+    can be reconstructed without duplicates on the frontend.
+    """
+
+    a: str
+    b: str
+    count: int
+
+
+class CallImportMetricAggregate(BaseModel):
+    """Per-metric aggregate computed from an evaluation run's rows.
+
+    Numeric metrics return summary statistics + histogram buckets;
+    categorical / pass-fail / text metrics return the top value counts.
+    Both shapes can coexist if a metric mixes types G�� the UI prefers
+    histogram when present, falls back to value_counts otherwise.
+    """
+
+    metric_id: str
+    metric_name: str
+    metric_type: Optional[str] = None
+    metric_category: str = "quality"
+    # True when this aggregate represents a multi-label parent metric
+    # (selection_mode == "multi_label" with no parent_metric_id). For
+    # those, ``value_counts`` lists per-child label tallies and the
+    # rows scored != sum(value_counts.count). The UI uses this flag to
+    # force a horizontal bar layout (slices wouldn't sum to 100%) and
+    # to label the n-badge as rows scored, not label occurrences.
+    is_multi_label_parent: bool = False
+    count: int = 0
+    skipped_count: int = 0
+    error_count: int = 0
+    # Numeric stats (None when no numeric values were observed)
+    mean: Optional[float] = None
+    median: Optional[float] = None
+    p25: Optional[float] = None
+    p75: Optional[float] = None
+    p95: Optional[float] = None
+    min: Optional[float] = None
+    max: Optional[float] = None
+    stddev: Optional[float] = None
+    histogram_buckets: List[CallImportMetricHistogramBucket] = Field(
+        default_factory=list
+    )
+    value_counts: List[CallImportMetricValueCount] = Field(default_factory=list)
+    # Pairwise label intersections for multi-label parent metrics.
+    # Empty for everything else. The frontend reconstructs a square
+    # symmetric matrix from these unordered pairs and renders the
+    # co-occurrence heatmap chart type.
+    co_occurrence: List[CallImportMetricLabelPair] = Field(default_factory=list)
+
+
+class MetricPeriodDelta(BaseModel):
+    """Week-over-week (or baseline-run) delta for one metric."""
+
+    label: str
+    detail: str
+    why: Optional[str] = None
+
+
+class CallImportEvaluationAggregateResponse(BaseModel):
+    """Aggregated metric distributions for a single evaluation run."""
+
+    evaluation_id: UUID
+    total_rows: int
+    completed_rows: int
+    failed_rows: int
+    metrics: List[CallImportMetricAggregate] = Field(default_factory=list)
+    period_deltas: Dict[str, MetricPeriodDelta] = Field(default_factory=dict)
+    baseline_evaluation_id: Optional[UUID] = None
+    failure_policies_source: Optional[Literal["inferred", "user"]] = Field(
+        default=None,
+        description=(
+            "Whether flagged-rate semantics use user-confirmed failure policies "
+            "or inferred defaults from the Failure diagnostics flow."
+        ),
+    )
+
+
+# --- LLM-generated TLDR for the Visualizations tab ---
+
+
+
+class EvaluatorResultsAggregateResponse(BaseModel):
+    """Chart-friendly metric rollups for evaluator results in a suite or scenario scope."""
+
+    scope: str
+    suite_id: Optional[UUID] = None
+    agent_id: Optional[UUID] = None
+    scenario_id: Optional[UUID] = None
+    total_rows: int = 0
+    completed_rows: int = 0
+    failed_rows: int = 0
+    metrics: List[CallImportMetricAggregate] = Field(default_factory=list)
+
+
+# --- LLM-generated TLDR for the Visualizations tab ---
+
+
+
+class EvaluationTldrSummary(BaseModel):
+    """Cached LLM-generated narrative + bullet patterns for an eval run.
+
+    Persisted on ``CallImportEvaluation.tldr_summary`` (JSONB) and
+    rendered above the per-metric charts. ``generated_at_completed_rows``
+    is the snapshot of ``completed_rows`` at the time the summary was
+    written; the API compares it against the current count to flag
+    ``is_stale`` so the UI can prompt for a regenerate.
+    """
+
+    narrative: str
+    patterns: List[str] = Field(default_factory=list)
+    metric_insights: Dict[str, str] = Field(default_factory=dict)
+    generated_at: datetime
+    generated_at_completed_rows: int = 0
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    is_stale: bool = False
+
+
+class EvaluationInsightsRequest(BaseModel):
+    """Body for ``POST /evaluations/{eval_id}/insights``.
+
+    All fields are optional. When ``provider``/``model`` are unset the
+    backend resolves the org's first active OpenAI/Anthropic/Google
+    provider (mirroring the Prompt Partials AI-generate flow) so
+    callers that don't care can simply post ``{}``.
+    """
+
+    regenerate: bool = False
+    provider: Optional[str] = None
+    model: Optional[str] = Field(default=None, min_length=1)
+    credential_id: Optional[UUID] = None
+    max_llm_calls: Optional[int] = Field(
+        default=None,
+        ge=20,
+        le=500,
+        description=(
+            "Max LLM calls for user-insights sampling (extraction + synthesis). "
+            "Defaults to 200 when omitted."
+        ),
+    )
+
+
+class UserInsightCategory(BaseModel):
+    label: str
+    count: int
+    share_pct: float
+
+
+class UserInsightEvidenceTurn(BaseModel):
+    speaker: str
+    text: str
+
+
+class UserInsightEvidence(BaseModel):
+    conversation_id: Optional[str] = None
+    quote: str
+    turns: List[UserInsightEvidenceTurn] = Field(default_factory=list)
+
+
+class EvaluationUserInsightItem(BaseModel):
+    id: str
+    title: str
+    categories: List[UserInsightCategory] = Field(default_factory=list)
+    observation: str
+    evidence: UserInsightEvidence
+
+
+class EvaluationUserInsightsState(BaseModel):
+    """Cached map-reduce LLM user insights for an evaluation run."""
+
+    status: Literal["idle", "running", "completed", "failed"] = "idle"
+    insights: List[EvaluationUserInsightItem] = Field(default_factory=list)
+    overview: Optional[str] = None
+    generated_at: Optional[datetime] = None
+    generated_at_completed_rows: int = 0
+    progress: Optional[Dict[str, int]] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    llm_calls_used: int = 0
+    max_llm_calls: Optional[int] = None
+    error_message: Optional[str] = None
+    is_stale: bool = False
+
+
+class EvaluationUserInsightsRequest(BaseModel):
+    """Body for ``POST /evaluations/{eval_id}/user-insights``."""
+
+    regenerate: bool = False
+    force: bool = False
+    provider: Optional[str] = None
+    model: Optional[str] = Field(default=None, min_length=1)
+    credential_id: Optional[UUID] = None
+    max_llm_calls: Optional[int] = Field(default=None, ge=20, le=500)
+
+
+MetricClusterGapLabel = Literal[
+    "LOGIC_GAP",
+    "UNDERSPEC",
+    "EXISTS_NO_TRIGGER",
+    "MISSING",
+]
+
+FailurePolicyNumericOp = Literal["lt", "lte", "gt", "gte"]
+
+
+class MetricFailurePolicy(BaseModel):
+    """Per-metric definition of which scores count as failures for this evaluation."""
+
+    metric_id: str
+    failure_values: List[str] = Field(
+        default_factory=list,
+        description="Normalized lowercase labels that count as failure (single-choice, enum, boolean-as-category).",
+    )
+    failure_child_names: List[str] = Field(
+        default_factory=list,
+        description="Child label names that count as failure for multi_label parents.",
+    )
+    numeric_rule: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description='Numeric failure rule, e.g. {"op": "lt", "threshold": 0.5}.',
+    )
+
+
+class MetricFailurePolicyValueCount(BaseModel):
+    label: str
+    count: int = 0
+
+
+class MetricFailurePolicyMetricPreview(BaseModel):
+    metric_id: str
+    metric_name: str
+    metric_type: Optional[str] = None
+    selection_mode: Optional[str] = None
+    is_multi_label_parent: bool = False
+    value_counts: List[MetricFailurePolicyValueCount] = Field(default_factory=list)
+    child_names: List[str] = Field(default_factory=list)
+    row_count_by_value: Dict[str, int] = Field(default_factory=dict)
+    suggested_policy: MetricFailurePolicy
+    effective_policy: MetricFailurePolicy
+
+
+class MetricFailurePoliciesResponse(BaseModel):
+    previews: List[MetricFailurePolicyMetricPreview] = Field(default_factory=list)
+    policies: Dict[str, MetricFailurePolicy] = Field(default_factory=dict)
+    source: Literal["inferred", "user"] = "inferred"
+    updated_at: Optional[datetime] = None
+
+
+class MetricFailurePoliciesSaveRequest(BaseModel):
+    policies: Dict[str, MetricFailurePolicy] = Field(default_factory=dict)
+    source: Literal["user"] = "user"
+
+
+class MetricClusterEvidenceTurn(BaseModel):
+    speaker: str
+    text: str
+
+
+class MetricClusterEvidence(BaseModel):
+    conversation_id: Optional[str] = None
+    evaluation_row_id: Optional[UUID] = None
+    quote: str = ""
+    turns: List[MetricClusterEvidenceTurn] = Field(default_factory=list)
+
+
+class MetricSubCluster(BaseModel):
+    label: str
+    count: int = 0
+    share_pct: float = 0.0
+
+
+class MetricCluster(BaseModel):
+    id: str
+    label: str
+    gap_label: MetricClusterGapLabel
+    level: int = 1
+    count: int = 0
+    share_pct: float = 0.0
+    sub_clusters: List[MetricSubCluster] = Field(default_factory=list)
+    observation: str = ""
+    failure_reason: str = ""
+    evidence: MetricClusterEvidence = Field(default_factory=MetricClusterEvidence)
+    is_discovered: bool = False
+
+
+class MetricClusterGroup(BaseModel):
+    metric_id: str
+    metric_name: str
+    flagged_count: int = 0
+    failure_reason: str = ""
+    clusters: List[MetricCluster] = Field(default_factory=list)
+
+
+class DiscoveredProblemCluster(BaseModel):
+    id: str
+    label: str
+    gap_label: MetricClusterGapLabel
+    count: int = 0
+    share_pct: float = 0.0
+    observation: str = ""
+    failure_reason: str = ""
+    evidence: MetricClusterEvidence = Field(default_factory=MetricClusterEvidence)
+
+
+class RcaRepeatedPatternRow(BaseModel):
+    metric_id: str
+    metric_name: str
+    top_rca_patterns: str = ""
+    evidence_share_pct: float = 0.0
+    evidence_calls: int = 0
+    evidence_cluster_count: int = 0
+    failure_reason: str = ""
+
+
+class RcaMetricHotspotRow(BaseModel):
+    metric_id: str
+    metric_name: str
+    description: str = ""
+    metric_rate_pct: float = 0.0
+    flagged_calls: int = 0
+
+
+class RcaPromptAreaRow(BaseModel):
+    label: str
+    share_pct: float = 0.0
+    gap_label: MetricClusterGapLabel
+
+
+class MetricClustersRcaSummary(BaseModel):
+    total_clusters: int = 0
+    total_clustered_instances: int = 0
+    total_flagged_instances: int = 0
+    analysed_calls: int = 0
+    repeated_patterns: List[RcaRepeatedPatternRow] = Field(default_factory=list)
+    metric_hotspots: List[RcaMetricHotspotRow] = Field(default_factory=list)
+    prompt_areas: List[RcaPromptAreaRow] = Field(default_factory=list)
+
+
+class EvaluationMetricClustersState(BaseModel):
+    """Cached per-metric failure clustering for internal diagnostics."""
+
+    status: Literal["idle", "running", "completed", "failed", "cancelled"] = "idle"
+    groups: List[MetricClusterGroup] = Field(default_factory=list)
+    discovered_problems: List[DiscoveredProblemCluster] = Field(
+        default_factory=list
+    )
+    overview: Optional[str] = None
+    generated_at: Optional[datetime] = None
+    generated_at_completed_rows: int = 0
+    progress: Optional[Dict[str, int]] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    llm_calls_used: int = 0
+    max_llm_calls: Optional[int] = None
+    error_message: Optional[str] = None
+    is_stale: bool = False
+    selected_evaluation_row_ids: List[str] = Field(
+        default_factory=list,
+        description="Evaluation row IDs included in the last clustering run.",
+    )
+    failure_policies: Dict[str, MetricFailurePolicy] = Field(default_factory=dict)
+    failure_policies_source: Literal["inferred", "user"] = "inferred"
+    failure_policies_updated_at: Optional[datetime] = None
+    rca_summary: Optional[MetricClustersRcaSummary] = None
+
+
+class MetricClusterEligibleRow(BaseModel):
+    """Completed evaluation row with at least one flagged quality metric."""
+
+    evaluation_row_id: UUID
+    conversation_id: Optional[str] = None
+    row_index: Optional[int] = None
+    flagged_metric_names: List[str] = Field(default_factory=list)
+
+
+class MetricClusterEligibleRowsResponse(BaseModel):
+    items: List[MetricClusterEligibleRow] = Field(default_factory=list)
+    total: int = 0
+
+
+class PromptImprovementSuggestion(BaseModel):
+    """One LLM-generated prompt edit to address a failure cluster."""
+
+    id: str
+    metric_id: str
+    metric_name: str
+    cluster_id: str
+    cluster_label: str
+    gap_label: MetricClusterGapLabel
+    share_pct: float = 0.0
+    priority: Literal["high", "medium", "low"] = "medium"
+    change_type: Literal["edit", "add"] = "add"
+    target_section: str = ""
+    anchor_excerpt: str = ""
+    current_gap: str = ""
+    suggested_text: str = ""
+    rationale: str = ""
+    flow_node_id: str = ""
+    flow_node_label: str = ""
+
+
+class EvaluationPromptImprovementsState(BaseModel):
+    """Cached prompt improvement suggestions for an evaluation run."""
+
+    status: Literal["idle", "running", "completed", "failed"] = "idle"
+    imported_agent_id: Optional[str] = None
+    imported_agent_name: Optional[str] = None
+    suggestions: List[PromptImprovementSuggestion] = Field(default_factory=list)
+    overview: Optional[str] = None
+    generated_at: Optional[datetime] = None
+    generated_at_completed_rows: int = 0
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    error_message: Optional[str] = None
+    is_stale: bool = False
+
+
+class EvaluationPromptImprovementsRequest(BaseModel):
+    """Body for ``POST /evaluations/{eval_id}/prompt-improvements``."""
+
+    imported_agent_id: UUID
+    regenerate: bool = False
+    force: bool = False
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    credential_id: Optional[UUID] = None
+
+
+class EvaluationMetricClustersRequest(BaseModel):
+    """Body for ``POST /evaluations/{eval_id}/metric-clusters``."""
+
+    regenerate: bool = False
+    force: bool = False
+    provider: Optional[str] = None
+    model: Optional[str] = Field(default=None, min_length=1)
+    credential_id: Optional[UUID] = None
+    max_llm_calls: Optional[int] = Field(default=None, ge=20, le=500)
+    evaluation_row_ids: Optional[List[UUID]] = Field(
+        default=None,
+        description=(
+            "Subset of completed evaluation row IDs to cluster. When omitted, "
+            "all completed rows with at least one flagged quality metric are used."
+        ),
+    )
+    row_limit: Optional[int] = Field(
+        default=None,
+        ge=1,
+        description=(
+            "Use the first N eligible rows (by row order). Mutually exclusive "
+            "with evaluation_row_ids."
+        ),
+    )
+    failure_policies: Optional[Dict[str, MetricFailurePolicy]] = Field(
+        default=None,
+        description="Per-metric failure policies confirmed in the cluster modal.",
+    )
+
+
+# Resolve the forward reference on ``CallImportEvaluationResponse``
+# (defined further up the file) now that ``EvaluationTldrSummary``
+# exists. Without this Pydantic raises at first ``.model_validate``
+# because the string annotation can't be evaluated.
+CallImportEvaluationResponse.model_rebuild()
+
+
+# --- Cross-run insights for a CallImport batch ---
+
+
+class CallImportInsightsRunPoint(BaseModel):
+    """One run's mean for a metric, used to render trend lines."""
+
+    evaluation_id: UUID
+    name: Optional[str] = None
+    created_at: datetime
+    mean: Optional[float] = None
+    completed_rows: int = 0
+
+
+class CallImportInsightsMetric(BaseModel):
+    """Per-metric history across every evaluation run on this import."""
+
+    metric_id: str
+    metric_name: str
+    metric_type: Optional[str] = None
+    latest: Optional[CallImportMetricAggregate] = None
+    trend: List[CallImportInsightsRunPoint] = Field(default_factory=list)
+
+
+class CallImportInsightsResponse(BaseModel):
+    """Aggregated cross-run signals for a single call-import batch."""
+
+    call_import_id: UUID
+    total_rows: int
+    rows_with_transcript: int
+    rows_without_transcript: int
+    transcript_source_counts: Dict[str, int] = Field(default_factory=dict)
+    evaluation_count: int = 0
+    metrics: List[CallImportInsightsMetric] = Field(default_factory=list)
+
+
+# --- Flow chart visualization for hierarchical metrics ---
+
+
+class MetricFlowNode(BaseModel):
+    """One step in the LLM-inferred temporal flow for a parent metric.
+
+    Represents a child sub-metric label. ``count`` is the number of rows
+    in the evaluation where this child appears anywhere in its
+    ``sequence`` array. ``is_terminal`` is set when the child is the
+    last entry in a meaningful fraction of those sequences.
+
+    ``is_discovered`` is set when the node represents an LLM-discovered
+    candidate (parent has ``allow_discovery=true``) rather than a
+    user-defined child. The id of a discovered node is prefixed with
+    ``disc:`` so it can't collide with real child UUIDs.
+    """
+
+    id: str
+    label: str
+    count: int = 0
+    is_terminal: bool = False
+    is_discovered: bool = False
+
+
+class MetricFlowEdge(BaseModel):
+    """One directed transition between two children across all rows.
+
+    ``count`` is the number of rows where ``source`` immediately
+    precedes ``target`` in the sequence. The synthetic ``START`` node
+    is used as the ``source`` for the first child in every sequence.
+    """
+
+    source: str
+    target: str
+    count: int = 0
+
+
+class MetricFlowResponse(BaseModel):
+    """Aggregate flow diagram payload for a single parent metric."""
+
+    parent_metric_id: str
+    parent_metric_name: str
+    selection_mode: Optional[SelectionMode] = None
+    nodes: List[MetricFlowNode] = Field(default_factory=list)
+    edges: List[MetricFlowEdge] = Field(default_factory=list)
+    total_rows: int = 0
+    rows_with_sequence: int = 0
+
+
+class DiscoveredLabelItem(BaseModel):
+    """One LLM-discovered candidate sub-label aggregated across rows.
+
+    ``key`` is the slugified label identifier (matches what appears in
+    ``sequence`` entries). ``count`` is the number of rows in the
+    evaluation that emitted this slug. ``sample_rationale`` is the
+    first non-empty rationale captured from any row (back-compat
+    field, identical to ``examples[0]`` when present). ``examples``
+    holds up to 3 distinct rationales G�� the UI surfaces 2 of them as
+    ``Examples:`` in the rubric on Promote, with the third kept as
+    headroom in case the first is unhelpful.
+    """
+
+    key: str
+    name: str
+    description: Optional[str] = None
+    sample_rationale: Optional[str] = None
+    examples: List[str] = Field(default_factory=list, max_length=3)
+    count: int = 0
+
+
+class DiscoveredLabelsResponse(BaseModel):
+    """List of discovered candidate sub-labels for a parent metric."""
+
+    parent_metric_id: str
+    items: List[DiscoveredLabelItem] = Field(default_factory=list)
+
+
+class DiscoveredLabelMergeRequest(BaseModel):
+    """Body for POST /evaluations/{eval_id}/discovered-labels/merge.
+
+    Rewrites every row's ``metric_scores[parent_id].discovered_labels``
+    entries whose key is ``from_key`` to use ``to_key`` instead, so the
+    user can collapse near-duplicate candidates ("On Hold" / "Customer
+    Put On Hold") into a single promoted child.
+    """
+
+    parent_metric_id: UUID
+    from_key: str = Field(..., min_length=1, max_length=120)
+    to_key: str = Field(..., min_length=1, max_length=120)
+
+
+class DiscoveredLabelDeleteRequest(BaseModel):
+    """Body for POST /evaluations/{eval_id}/discovered-labels/delete.
+
+    Strips a candidate sub-label from every row's
+    ``discovered_labels`` list AND from each row's ``sequence`` array,
+    then tombstones the slug at the evaluation level so workers
+    finishing later can't re-introduce it. Use for gibberish or
+    irrelevant candidates the LLM proposed; for near-duplicates that
+    you want to keep but unify, use the merge endpoint instead.
+    """
+
+    parent_metric_id: UUID
+    key: str = Field(..., min_length=1, max_length=120)
+
+
+class PromoteDiscoveredChildRequest(BaseModel):
+    """Body for POST /metrics/{parent_id}/children/from-discovered.
+
+    ``key`` is the slug under which the candidate is currently stored
+    on per-row ``metric_scores``. The newly-created child Metric's
+    name is normalized so ``slugify(name) == key``, which keeps every
+    already-scored row's ``sequence`` array resolvable against the
+    promoted child without a backfill.
+    """
+
+    key: str = Field(..., min_length=1, max_length=120)
+    name: str = Field(..., min_length=1, max_length=120)
+    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    # Default True: when promoting a discovered label we want the new
+    # sub-metric to always capture rationales going forward, since the
+    # candidate was itself proposed *with* a rationale and the user
+    # almost always wants to see why future rows hit it. Explicit False
+    # keeps the original opt-in behavior available for callers that
+    # don't care about rationales.
+    capture_rationale: bool = True
+
+
+# --- Discovered top-level metrics (per-evaluation discovery) ---
+#
+# Parallel to ``DiscoveredLabelItem`` / merge / delete / promote G�� but
+# scoped to the evaluation as a whole, not to a parent category metric.
+# Used by the "Discovered metrics" panel at the top of the evaluation
+# detail Flow tab when ``CallImportEvaluation.discover_new_metrics``
+# is true.
+
+
+# The promote endpoint accepts these three suggested types; "category"
+# creates a parent (no children yet) that the user can later extend in
+# the Metrics page.
+DiscoveredMetricSuggestedType = Literal["boolean", "rating", "category"]
+
+
+class DiscoveredMetricItem(BaseModel):
+    """One LLM-discovered candidate top-level metric aggregated across rows.
+
+    Mirrors :class:`DiscoveredLabelItem` but at the evaluation level
+    (no ``parent_metric_id``). ``suggested_type`` is the LLM's guess at
+    the best representation; the promote flow lets the user override
+    it before creating the real :class:`Metric` row.
+    """
+
+    key: str
+    name: str
+    description: Optional[str] = None
+    suggested_type: DiscoveredMetricSuggestedType = "boolean"
+    sample_rationale: Optional[str] = None
+    examples: List[str] = Field(default_factory=list, max_length=3)
+    count: int = 0
+
+
+class DiscoveredMetricsResponse(BaseModel):
+    """List of discovered candidate top-level metrics for an evaluation."""
+
+    evaluation_id: UUID
+    items: List[DiscoveredMetricItem] = Field(default_factory=list)
+
+
+class DiscoveredMetricMergeRequest(BaseModel):
+    """Body for POST /evaluations/{eval_id}/discovered-metrics/merge.
+
+    Rewrites every row's ``metric_scores["__discovered_metrics__"]``
+    entries whose key is ``from_key`` to use ``to_key`` instead, and
+    records the redirect in ``CallImportEvaluation.discovered_metric_aliases``
+    so workers finishing later can't resurrect the merged-out slug.
+    """
+
+    from_key: str = Field(..., min_length=1, max_length=120)
+    to_key: str = Field(..., min_length=1, max_length=120)
+
+
+class DiscoveredMetricDeleteRequest(BaseModel):
+    """Body for POST /evaluations/{eval_id}/discovered-metrics/delete.
+
+    Strips a candidate from every row's
+    ``metric_scores["__discovered_metrics__"]`` list and tombstones
+    the slug at the evaluation level (empty-string alias) so workers
+    finishing later can't re-introduce it.
+    """
+
+    key: str = Field(..., min_length=1, max_length=120)
+
+
+class PromoteDiscoveredMetricRequest(BaseModel):
+    """Body for POST /metrics/from-discovered.
+
+    Creates a standalone :class:`Metric` (``parent_metric_id=None``)
+    from an LLM-discovered candidate. The new metric's name is
+    normalized so ``slugify(name) == key`` to keep already-scored row
+    payloads resolvable against the promoted metric. ``metric_type``
+    selects how the new metric will be scored on future runs;
+    ``"category"`` creates a ``multi_label`` parent with no children
+    (the user adds children via the existing Metrics page).
+    """
+
+    key: str = Field(..., min_length=1, max_length=120)
+    name: str = Field(..., min_length=1, max_length=120)
+    description: Optional[str] = Field(default=None, max_length=METRIC_RUBRIC_TEXT_MAX_LENGTH)
+    metric_type: DiscoveredMetricSuggestedType = "boolean"
+    capture_rationale: bool = True
+    # Optional per-type config knobs passed through to ``Metric.custom_config``.
+    # For ``rating`` the frontend can supply {"min": 1, "max": 5}; for
+    # ``boolean`` / ``category`` the field is typically empty.
+    custom_config: Optional[Dict[str, Any]] = None
+
+
+# --- Workspace Schemas ---
+
+
+class WorkspaceBase(BaseModel):
+    """Shared fields for workspace create/update payloads."""
+
+    name: str = Field(..., min_length=1, max_length=255)
+
+
+class WorkspaceCreate(WorkspaceBase):
+    """Body for POST /workspaces."""
+
+    # Optional: derived from name when omitted; uniqueness is per-org.
+    slug: Optional[str] = Field(
+        default=None, min_length=1, max_length=255
+    )
+
+
+class WorkspaceUpdate(BaseModel):
+    """Body for PATCH /workspaces/{id} (rename and/or org-admin activation)."""
+
+    name: Optional[str] = Field(default=None, min_length=1, max_length=255)
+    is_active: Optional[bool] = None
+
+
+class WorkspaceResponse(BaseModel):
+    """Response schema for a single workspace."""
+
+    id: UUID
+    organization_id: UUID
+    name: str
+    slug: str
+    is_default: bool
+    is_active: bool = True
+    created_at: datetime
+    updated_at: datetime
+    role_id: Optional[UUID] = None
+    role_name: Optional[str] = None
+    capabilities: List[str] = Field(default_factory=list)
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class WorkspaceRoleBase(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+    description: Optional[str] = None
+    capabilities: List[str] = Field(default_factory=list)
+
+
+class WorkspaceRoleCreate(WorkspaceRoleBase):
+    pass
+
+
+class WorkspaceRoleUpdate(BaseModel):
+    name: Optional[str] = Field(default=None, min_length=1, max_length=255)
+    description: Optional[str] = None
+    capabilities: Optional[List[str]] = None
+
+
+class WorkspaceRoleResponse(BaseModel):
+    id: UUID
+    organization_id: UUID
+    name: str
+    description: Optional[str] = None
+    capabilities: List[str]
+    is_system: bool
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class WorkspaceMemberResponse(BaseModel):
+    id: UUID
+    workspace_id: UUID
+    user_id: UUID
+    role_id: UUID
+    role_name: str
+    user_email: str
+    user_name: Optional[str] = None
+    added_by_user_id: Optional[UUID] = None
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class WorkspaceMemberCreate(BaseModel):
+    user_id: UUID
+    role_id: UUID
+
+
+class WorkspaceMemberUpdate(BaseModel):
+    role_id: UUID
+
+
+class CapabilityInfoResponse(BaseModel):
+    key: str
+    label: str
+
+
+class CapabilityDomainResponse(BaseModel):
+    key: str
+    label: str
+    capabilities: List[CapabilityInfoResponse]
diff --git a/app/models/synthetic_trace_schemas.py b/app/models/synthetic_trace_schemas.py
new file mode 100644
index 00000000..8400169d
--- /dev/null
+++ b/app/models/synthetic_trace_schemas.py
@@ -0,0 +1,148 @@
+"""Pydantic schemas for synthetic call traces."""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+from uuid import UUID
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class SyntheticTraceTurn(BaseModel):
+    turn_number: int
+    sut_response_latency_ms: Optional[float] = None
+    caller_stream_complete_at: Optional[float] = None
+    sut_speech_start_at: Optional[float] = None
+    sut_speech_stop_at: Optional[float] = None
+    talk_over: bool = False
+    stt_ttfb_ms: Optional[float] = None
+    llm_ttfb_ms: Optional[float] = None
+    tts_ttfb_ms: Optional[float] = None
+    s2s_ttfb_ms: Optional[float] = None
+    transcript: Optional[str] = None
+    extra: Dict[str, Any] = Field(default_factory=dict)
+
+
+class OtelSpanRecord(BaseModel):
+    trace_id: str
+    span_id: str
+    parent_span_id: Optional[str] = None
+    name: str
+    start_time_unix_nano: Optional[int] = None
+    end_time_unix_nano: Optional[int] = None
+    attributes: Dict[str, Any] = Field(default_factory=dict)
+    events: List[Dict[str, Any]] = Field(default_factory=list)
+
+
+class SyntheticCallTraceSummary(BaseModel):
+    id: UUID
+    evaluator_result_id: Optional[UUID] = None
+    agent_id: Optional[UUID] = None
+    call_short_id: Optional[str] = None
+    environment: str
+    transport: str
+    tier: str
+    status: str
+    started_at: datetime
+    ended_at: Optional[datetime] = None
+    turn_count: int = 0
+    response_latency_p50_ms: Optional[float] = None
+    response_latency_p90_ms: Optional[float] = None
+    response_latency_p95_ms: Optional[float] = None
+    component_aggregates: Optional[Dict[str, Any]] = None
+    failure_flags: Optional[List[str]] = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class SyntheticCallTraceDetail(SyntheticCallTraceSummary):
+    turns: List[SyntheticTraceTurn] = Field(default_factory=list)
+    otel_spans: List[OtelSpanRecord] = Field(default_factory=list)
+    otel_trace_ids: List[str] = Field(default_factory=list)
+    pipeline_models: Dict[str, Dict[str, Optional[str]]] = Field(default_factory=dict)
+
+
+class OtelCorrelationInfo(BaseModel):
+    evaluator_result_id: UUID
+    synthetic_call_trace_id: Optional[UUID] = None
+    call_short_id: Optional[str] = None
+    agent_id: Optional[UUID] = None
+    otlp_endpoint: str
+    suggested_env_vars: Dict[str, str] = Field(default_factory=dict)
+    suggested_span_attributes: Dict[str, str] = Field(default_factory=dict)
+
+
+class OtlpIngestResponse(BaseModel):
+    accepted_spans: int
+    synthetic_call_trace_id: Optional[UUID] = None
+    correlated: bool = False
+
+
+class SyntheticCallTraceListResponse(BaseModel):
+    items: List[SyntheticCallTraceSummary]
+    total: int
+
+
+class OtlpSetupInfo(BaseModel):
+    """Pipecat WebRTC local setup for call trace ingest."""
+
+    otlp_endpoint: str
+    sessions_endpoint: str = ""
+    api_key_header: str = "X-API-Key"
+    workspace_header: str = "X-Workspace-Id"
+    one_time_env_vars: Dict[str, str] = Field(default_factory=dict)
+    setup_steps: List[Dict[str, str]] = Field(default_factory=list)
+    transport_options: Dict[str, str] = Field(default_factory=dict)
+    per_call_correlation: Dict[str, str] = Field(default_factory=dict)
+    suggested_span_resource_attributes: Dict[str, str] = Field(default_factory=dict)
+    pipecat_python_example: str = ""
+
+
+VALID_TRACE_TRANSPORTS = ("webrtc", "websocket", "phone", "custom")
+
+
+class TraceSessionCreateRequest(BaseModel):
+    evaluator_result_id: Optional[UUID] = None
+    transport: str = "websocket"
+
+
+class TraceSessionOtelCorrelation(BaseModel):
+    otlp_endpoint: str
+    api_key_header: str = "X-API-Key"
+    suggested_env_vars: Dict[str, str] = Field(default_factory=dict)
+    suggested_otlp_headers: Dict[str, str] = Field(default_factory=dict)
+    suggested_span_attributes: Dict[str, str] = Field(default_factory=dict)
+
+
+class TraceSessionResponse(BaseModel):
+    trace_id: UUID
+    call_short_id: str
+    workspace_id: UUID
+    transport: str
+    status: str
+    otel_correlation: TraceSessionOtelCorrelation
+
+
+class TraceSessionCloseResponse(BaseModel):
+    trace_id: UUID
+    call_short_id: str
+    status: str
+
+
+class JsonTraceSpanInput(BaseModel):
+    name: str
+    turn_number: int
+    ttfb_ms: Optional[float] = None
+    attributes: Dict[str, Any] = Field(default_factory=dict)
+
+
+class JsonTraceIngestRequest(BaseModel):
+    call_short_id: str
+    spans: List[JsonTraceSpanInput] = Field(default_factory=list)
+
+
+class JsonTraceIngestResponse(BaseModel):
+    accepted_spans: int
+    synthetic_call_trace_id: Optional[UUID] = None
+    correlated: bool = False
diff --git a/app/services/evaluators/evaluator_phone_run_service.py b/app/services/evaluators/evaluator_phone_run_service.py
index 6d94c825..5e30bfc4 100644
--- a/app/services/evaluators/evaluator_phone_run_service.py
+++ b/app/services/evaluators/evaluator_phone_run_service.py
@@ -1,175 +1,177 @@
-"""Phone-based evaluator suite runs (Vobiz outbound)."""
-
-from typing import List, Optional, Tuple
-from uuid import UUID
-
-from fastapi import HTTPException
-from sqlalchemy.orm import Session
-
-from app.models.database import Agent, Evaluator, Scenario
-from app.models.schemas import EvaluatorResultResponse
-from app.services.evaluators.evaluator_suite_service import generate_unique_result_id
-from app.services.telephony.plivo_client import normalize_e164
-
-
-def initiate_phone_evaluator_call(
-    db: Session,
-    organization_id: UUID,
-    workspace_id: UUID,
-    evaluator: Evaluator,
-    agent: Agent,
-    to_number: str,
-    from_number: Optional[str] = None,
-) -> Tuple[str, str, Optional[EvaluatorResultResponse]]:
-    """Place a Vobiz outbound call for one evaluator combination.
-
-    Returns (call_ref, call_short_id, evaluator_result_response).
-    """
-    import random
-    import string
-
-    from app.models.database import CallRecording, CallRecordingSource, EvaluatorResult, EvaluatorResultStatus
-    from app.models.enums import CallRecordingStatus
-    from app.config import settings
-    from app.services.telephony.vobiz_outbound_pool import release_pool_slot, resolve_outbound_from_number
-    from app.services.telephony.vobiz_session import create_call_session
-    from app.services.telephony.vobiz_agent_context import vobiz_webhook_base_url
-    from app.workers.tasks.initiate_vobiz_outbound import initiate_vobiz_outbound_call_task
-
-    scenario = db.query(Scenario).filter(Scenario.id == evaluator.scenario_id).first()
-    scenario_name = scenario.name if scenario else "Unknown Scenario"
-
-    result_id = generate_unique_result_id(db)
-    evaluator_result = EvaluatorResult(
-        result_id=result_id,
-        organization_id=organization_id,
-        workspace_id=workspace_id,
-        evaluator_id=evaluator.id,
-        agent_id=evaluator.agent_id,
-        persona_id=evaluator.persona_id,
-        scenario_id=evaluator.scenario_id,
-        name=scenario_name,
-        status=EvaluatorResultStatus.QUEUED.value,
-        audio_s3_key=None,
-    )
-    db.add(evaluator_result)
-    db.commit()
-    db.refresh(evaluator_result)
-
-    try:
-        from_number_resolved, used_pool, provider = resolve_outbound_from_number(
-            db,
-            organization_id,
-            explicit_from_number=from_number,
-        )
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e)) from e
-
-    if provider != "vobiz":
-        if used_pool:
-            release_pool_slot(organization_id)
-        raise HTTPException(
-            status_code=400,
-            detail=(
-                f"Phone evaluator outbound requires a Vobiz caller ID; "
-                f"resolved provider is {provider}."
-            ),
-        )
-
-    to_number_norm = normalize_e164(to_number)
-    persona_id = evaluator.persona_id
-    scenario_id = evaluator.scenario_id
-    evaluator_id = evaluator.id
-
-    session = create_call_session(
-        agent_id=str(agent.id),
-        organization_id=str(organization_id),
-        direction="outbound",
-        from_number=from_number_resolved,
-        to_number=to_number_norm,
-        used_pool=used_pool,
-        persona_id=str(persona_id) if persona_id else None,
-        scenario_id=str(scenario_id) if scenario_id else None,
-        evaluator_id=str(evaluator_id),
-    )
-
-    base = vobiz_webhook_base_url()
-    answer_url = (
-        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/answer"
-        f"?call_ref={session.call_ref}"
-    )
-    events_url = (
-        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/events"
-        f"?call_ref={session.call_ref}"
-    )
-    recording_url = f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/recording-ready"
-
-    call_short_id = "".join(random.choices(string.digits, k=6))
-    recording = CallRecording(
-        organization_id=organization_id,
-        workspace_id=agent.workspace_id,
-        call_short_id=call_short_id,
-        status=CallRecordingStatus.PENDING,
-        source=CallRecordingSource.WEBHOOK,
-        call_event="outbound_initiated",
-        call_data={
-            "call_ref": session.call_ref,
-            "call_short_id": call_short_id,
-            "recording_callback": recording_url,
-            "used_pool": used_pool,
-            "evaluator_id": str(evaluator_id),
-            "evaluator_result_id": str(evaluator_result.id),
-            "direction": "outbound",
-            "from_number": from_number_resolved,
-            "to_number": to_number_norm,
-            "live_transcript": [],
-        },
-        provider_call_id=None,
-        provider_platform="vobiz",
-        agent_id=agent.id,
-        evaluator_result_id=evaluator_result.id,
-    )
-    db.add(recording)
-    db.commit()
-    db.refresh(recording)
-
-    initiate_vobiz_outbound_call_task.delay(
-        organization_id=str(organization_id),
-        call_ref=session.call_ref,
-        from_number=from_number_resolved,
-        to_number=to_number_norm,
-        answer_url=answer_url,
-        events_url=events_url,
-        used_pool=used_pool,
-        call_recording_id=str(recording.id),
-    )
-
-    return session.call_ref, call_short_id, EvaluatorResultResponse.model_validate(evaluator_result)
-
-
-def run_phone_evaluator_batch(
-    db: Session,
-    organization_id: UUID,
-    workspace_id: UUID,
-    agent: Agent,
-    evaluators: List[Evaluator],
-    to_number: str,
-    from_number: Optional[str] = None,
-) -> Tuple[List[str], List[EvaluatorResultResponse]]:
-    """Initiate phone calls for each evaluator in the list."""
-    call_refs: List[str] = []
-    results: List[EvaluatorResultResponse] = []
-    for evaluator in evaluators:
-        call_ref, _short_id, result = initiate_phone_evaluator_call(
-            db,
-            organization_id,
-            workspace_id,
-            evaluator,
-            agent,
-            to_number,
-            from_number=from_number,
-        )
-        call_refs.append(call_ref)
-        if result:
-            results.append(result)
-    return call_refs, results
+"""Phone-based evaluator suite runs (Vobiz outbound)."""
+
+from typing import List, Optional, Tuple
+from uuid import UUID
+
+from fastapi import HTTPException
+from sqlalchemy.orm import Session
+
+from app.models.database import Agent, Evaluator, Scenario
+from app.models.schemas import EvaluatorResultResponse
+from app.services.evaluators.evaluator_suite_service import generate_unique_result_id
+from app.services.telephony.plivo_client import normalize_e164
+
+
+def initiate_phone_evaluator_call(
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    evaluator: Evaluator,
+    agent: Agent,
+    to_number: str,
+    from_number: Optional[str] = None,
+) -> Tuple[str, str, Optional[EvaluatorResultResponse]]:
+    """Place a Vobiz outbound call for one evaluator combination.
+
+    Returns (call_ref, call_short_id, evaluator_result_response).
+    """
+    from app.models.database import CallRecording, CallRecordingSource, EvaluatorResult, EvaluatorResultStatus
+    from app.models.enums import CallRecordingStatus
+    from app.config import settings
+    from app.services.telephony.vobiz_outbound_pool import release_pool_slot, resolve_outbound_from_number
+    from app.services.telephony.vobiz_session import create_call_session
+    from app.services.telephony.vobiz_agent_context import vobiz_webhook_base_url
+    from app.utils.call_recordings import generate_unique_call_short_id
+    from app.workers.tasks.initiate_vobiz_outbound import initiate_vobiz_outbound_call_task
+
+    scenario = db.query(Scenario).filter(Scenario.id == evaluator.scenario_id).first()
+    scenario_name = scenario.name if scenario else "Unknown Scenario"
+
+    result_id = generate_unique_result_id(db)
+    evaluator_result = EvaluatorResult(
+        result_id=result_id,
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        evaluator_id=evaluator.id,
+        agent_id=evaluator.agent_id,
+        persona_id=evaluator.persona_id,
+        scenario_id=evaluator.scenario_id,
+        name=scenario_name,
+        status=EvaluatorResultStatus.QUEUED.value,
+        audio_s3_key=None,
+    )
+    db.add(evaluator_result)
+    db.commit()
+    db.refresh(evaluator_result)
+
+    try:
+        from_number_resolved, used_pool, provider = resolve_outbound_from_number(
+            db,
+            organization_id,
+            explicit_from_number=from_number,
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+
+    if provider != "vobiz":
+        if used_pool:
+            release_pool_slot(organization_id)
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                f"Phone evaluator outbound requires a Vobiz caller ID; "
+                f"resolved provider is {provider}."
+            ),
+        )
+
+    to_number_norm = normalize_e164(to_number)
+    persona_id = evaluator.persona_id
+    scenario_id = evaluator.scenario_id
+    evaluator_id = evaluator.id
+
+    session = create_call_session(
+        agent_id=str(agent.id),
+        organization_id=str(organization_id),
+        direction="outbound",
+        from_number=from_number_resolved,
+        to_number=to_number_norm,
+        used_pool=used_pool,
+        persona_id=str(persona_id) if persona_id else None,
+        scenario_id=str(scenario_id) if scenario_id else None,
+        evaluator_id=str(evaluator_id),
+    )
+
+    base = vobiz_webhook_base_url()
+    answer_url = (
+        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/answer"
+        f"?call_ref={session.call_ref}"
+    )
+    events_url = (
+        f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/events"
+        f"?call_ref={session.call_ref}"
+    )
+    recording_url = f"{base}{settings.API_V1_PREFIX}/telephony/vobiz/webhooks/recording-ready"
+
+    call_short_id = generate_unique_call_short_id(db)
+    recording = CallRecording(
+        organization_id=organization_id,
+        workspace_id=workspace_id,  # active workspace from evaluator run
+        call_short_id=call_short_id,
+        status=CallRecordingStatus.PENDING,
+        source=CallRecordingSource.WEBHOOK,
+        call_event="outbound_initiated",
+        call_data={
+            "call_ref": session.call_ref,
+            "call_short_id": call_short_id,
+            "recording_callback": recording_url,
+            "used_pool": used_pool,
+            "evaluator_id": str(evaluator_id),
+            "evaluator_result_id": str(evaluator_result.id),
+            "direction": "outbound",
+            "from_number": from_number_resolved,
+            "to_number": to_number_norm,
+            "live_transcript": [],
+        },
+        provider_call_id=None,
+        provider_platform="vobiz",
+        agent_id=agent.id,
+        evaluator_result_id=evaluator_result.id,
+    )
+    db.add(recording)
+    db.commit()
+    db.refresh(recording)
+
+    from app.services.synthetic_traces.trace_service import open_trace_for_call_recording
+
+    open_trace_for_call_recording(db, recording=recording, evaluator_result=evaluator_result)
+
+    initiate_vobiz_outbound_call_task.delay(
+        organization_id=str(organization_id),
+        call_ref=session.call_ref,
+        from_number=from_number_resolved,
+        to_number=to_number_norm,
+        answer_url=answer_url,
+        events_url=events_url,
+        used_pool=used_pool,
+        call_recording_id=str(recording.id),
+    )
+
+    return session.call_ref, call_short_id, EvaluatorResultResponse.model_validate(evaluator_result)
+
+
+def run_phone_evaluator_batch(
+    db: Session,
+    organization_id: UUID,
+    workspace_id: UUID,
+    agent: Agent,
+    evaluators: List[Evaluator],
+    to_number: str,
+    from_number: Optional[str] = None,
+) -> Tuple[List[str], List[EvaluatorResultResponse]]:
+    """Initiate phone calls for each evaluator in the list."""
+    call_refs: List[str] = []
+    results: List[EvaluatorResultResponse] = []
+    for evaluator in evaluators:
+        call_ref, _short_id, result = initiate_phone_evaluator_call(
+            db,
+            organization_id,
+            workspace_id,
+            evaluator,
+            agent,
+            to_number,
+            from_number=from_number,
+        )
+        call_refs.append(call_ref)
+        if result:
+            results.append(result)
+    return call_refs, results
diff --git a/app/services/synthetic_traces/__init__.py b/app/services/synthetic_traces/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/app/services/synthetic_traces/__init__.py
@@ -0,0 +1 @@
+
diff --git a/app/services/synthetic_traces/otlp_ingest.py b/app/services/synthetic_traces/otlp_ingest.py
new file mode 100644
index 00000000..235a7e2e
--- /dev/null
+++ b/app/services/synthetic_traces/otlp_ingest.py
@@ -0,0 +1,152 @@
+"""Parse OTLP export payloads into normalized span dicts."""
+
+from __future__ import annotations
+
+import json
+from typing import Any, Dict, List, Tuple
+
+from loguru import logger
+
+
+def _decode_attr_value(value: Any) -> Any:
+    if not isinstance(value, dict):
+        return value
+    if "stringValue" in value:
+        return value["stringValue"]
+    if "boolValue" in value:
+        return value["boolValue"]
+    if "intValue" in value:
+        return int(value["intValue"])
+    if "doubleValue" in value:
+        return float(value["doubleValue"])
+    if "arrayValue" in value:
+        vals = value["arrayValue"].get("values") or []
+        return [_decode_attr_value(v) for v in vals]
+    if "kvlistValue" in value:
+        items = value["kvlistValue"].get("values") or []
+        return {item["key"]: _decode_attr_value(item.get("value")) for item in items}
+    return value
+
+
+def _attrs_list_to_dict(attrs: List[Dict[str, Any]]) -> Dict[str, Any]:
+    out: Dict[str, Any] = {}
+    for item in attrs or []:
+        key = item.get("key")
+        if key is None:
+            continue
+        out[key] = _decode_attr_value(item.get("value"))
+    return out
+
+
+def _span_id_hex(raw: Any) -> str:
+    if raw is None:
+        return ""
+    if isinstance(raw, str):
+        return raw
+    return raw.hex() if hasattr(raw, "hex") else str(raw)
+
+
+def parse_otlp_json(body: bytes) -> List[Dict[str, Any]]:
+    data = json.loads(body)
+    spans: List[Dict[str, Any]] = []
+    for resource_span in data.get("resourceSpans") or []:
+        for scope_span in resource_span.get("scopeSpans") or []:
+            for span in scope_span.get("spans") or []:
+                spans.append(_normalize_json_span(span))
+    return spans
+
+
+def _normalize_json_span(span: Dict[str, Any]) -> Dict[str, Any]:
+    events = []
+    for event in span.get("events") or []:
+        events.append(
+            {
+                "name": event.get("name"),
+                "time_unix_nano": event.get("timeUnixNano"),
+                "attributes": _attrs_list_to_dict(event.get("attributes")),
+            }
+        )
+    return {
+        "trace_id": _span_id_hex(span.get("traceId")),
+        "span_id": _span_id_hex(span.get("spanId")),
+        "parent_span_id": _span_id_hex(span.get("parentSpanId")) or None,
+        "name": span.get("name") or "",
+        "start_time_unix_nano": span.get("startTimeUnixNano"),
+        "end_time_unix_nano": span.get("endTimeUnixNano"),
+        "attributes": _attrs_list_to_dict(span.get("attributes")),
+        "events": events,
+    }
+
+
+def parse_otlp_protobuf(body: bytes) -> List[Dict[str, Any]]:
+    try:
+        from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import (
+            ExportTraceServiceRequest,
+        )
+    except ImportError as exc:
+        raise RuntimeError(
+            "opentelemetry-proto is required for OTLP/protobuf ingest"
+        ) from exc
+
+    request = ExportTraceServiceRequest()
+    request.ParseFromString(body)
+    spans: List[Dict[str, Any]] = []
+    for resource_spans in request.resource_spans:
+        for scope_spans in resource_spans.scope_spans:
+            for span in scope_spans.spans:
+                events = []
+                for event in span.events:
+                    events.append(
+                        {
+                            "name": event.name,
+                            "time_unix_nano": event.time_unix_nano,
+                            "attributes": {
+                                kv.key: _decode_otlp_any(kv.value)
+                                for kv in event.attributes
+                            },
+                        }
+                    )
+                spans.append(
+                    {
+                        "trace_id": span.trace_id.hex(),
+                        "span_id": span.span_id.hex(),
+                        "parent_span_id": span.parent_span_id.hex() or None,
+                        "name": span.name,
+                        "start_time_unix_nano": span.start_time_unix_nano,
+                        "end_time_unix_nano": span.end_time_unix_nano,
+                        "attributes": {
+                            kv.key: _decode_otlp_any(kv.value) for kv in span.attributes
+                        },
+                        "events": events,
+                    }
+                )
+    return spans
+
+
+def _decode_otlp_any(value: Any) -> Any:
+    which = value.WhichOneof("value")
+    if which == "string_value":
+        return value.string_value
+    if which == "bool_value":
+        return value.bool_value
+    if which == "int_value":
+        return value.int_value
+    if which == "double_value":
+        return value.double_value
+    if which == "array_value":
+        return [_decode_otlp_any(v) for v in value.array_value.values]
+    if which == "kvlist_value":
+        return {kv.key: _decode_otlp_any(kv.value) for kv in value.kvlist_value.values}
+    return None
+
+
+def parse_otlp_body(body: bytes, content_type: str | None) -> Tuple[List[Dict[str, Any]], str]:
+    ct = (content_type or "").lower()
+    if "json" in ct:
+        return parse_otlp_json(body), "json"
+    try:
+        return parse_otlp_protobuf(body), "protobuf"
+    except Exception as proto_err:
+        if body[:1] in (b"{", b"["):
+            return parse_otlp_json(body), "json"
+        raise proto_err
diff --git a/app/services/synthetic_traces/otlp_mapper.py b/app/services/synthetic_traces/otlp_mapper.py
new file mode 100644
index 00000000..c14755cc
--- /dev/null
+++ b/app/services/synthetic_traces/otlp_mapper.py
@@ -0,0 +1,1034 @@
+"""Map OTLP span attributes to per-turn synthetic trace fields."""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List, Optional
+
+_CALL_SHORT_ID_RE = re.compile(r"^\d{6}$")
+
+_LLM_OPS = frozenset({"chat", "llm", "llm_response"})
+_LLM_NAMES = frozenset({"llm", "llm_response"})
+_S2S_OPS = frozenset({"s2s", "realtime", "llm_response"})
+_S2S_NAMES = frozenset({"s2s"})
+_REALTIME_PROVIDER_HINTS = ("gemini", "realtime", "nova", "openai")
+
+
+def _attr_str(attrs: Dict[str, Any], key: str) -> Optional[str]:
+    val = attrs.get(key)
+    if val is None:
+        return None
+    return str(val)
+
+
+def _attr_float(attrs: Dict[str, Any], key: str) -> Optional[float]:
+    val = attrs.get(key)
+    if val is None:
+        return None
+    try:
+        return float(val)
+    except (TypeError, ValueError):
+        return None
+
+
+def _ms_from_ttfb(attrs: Dict[str, Any]) -> Optional[float]:
+    ttfb = _attr_float(attrs, "metrics.ttfb")
+    if ttfb is None:
+        return None
+    return round(ttfb * 1000.0, 1)
+
+
+def _ms_from_span_duration(span: Dict[str, Any]) -> Optional[float]:
+    start = span.get("start_time_unix_nano")
+    end = span.get("end_time_unix_nano")
+    if start is None or end is None:
+        return None
+    try:
+        start_i = int(start)
+        end_i = int(end)
+    except (TypeError, ValueError):
+        return None
+    if end_i <= start_i:
+        return None
+    return round((end_i - start_i) / 1_000_000.0, 1)
+
+
+def _span_index(spans: List[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
+    out: Dict[str, Dict[str, Any]] = {}
+    for span in spans:
+        span_id = span.get("span_id")
+        if span_id:
+            out[str(span_id)] = span
+    return out
+
+
+def _build_turn_span_display_map(
+    spans: List[Dict[str, Any]],
+) -> Dict[str, int]:
+    """Assign sequential display turn numbers to turn spans ordered by start time."""
+    turn_rows: List[tuple[int, str]] = []
+    for span in spans:
+        if (span.get("name") or "").lower() != "turn":
+            continue
+        span_id = span.get("span_id")
+        if not span_id:
+            continue
+        start = span.get("start_time_unix_nano")
+        try:
+            start_i = int(start) if start is not None else 0
+        except (TypeError, ValueError):
+            start_i = 0
+        turn_rows.append((start_i, str(span_id)))
+
+    turn_rows.sort(key=lambda row: row[0])
+    return {span_id: idx + 1 for idx, (_, span_id) in enumerate(turn_rows)}
+
+
+def _span_start_ns(span: Dict[str, Any]) -> Optional[int]:
+    start = span.get("start_time_unix_nano")
+    if start is None:
+        return None
+    try:
+        return int(start)
+    except (TypeError, ValueError):
+        return None
+
+
+def _build_turn_windows(
+    spans: List[Dict[str, Any]],
+    turn_display: Dict[str, int],
+) -> List[Dict[str, Any]]:
+    """Chronological turn windows for assigning orphan component spans."""
+    windows: List[Dict[str, Any]] = []
+    for span in spans:
+        if (span.get("name") or "").lower() != "turn":
+            continue
+        span_id = span.get("span_id")
+        if not span_id or str(span_id) not in turn_display:
+            continue
+        start = _span_start_ns(span) or 0
+        end_raw = span.get("end_time_unix_nano")
+        try:
+            end = int(end_raw) if end_raw is not None else start
+        except (TypeError, ValueError):
+            end = start
+        windows.append(
+            {
+                "display_num": turn_display[str(span_id)],
+                "start": start,
+                "end": max(end, start),
+                "span_id": str(span_id),
+            }
+        )
+    windows.sort(key=lambda row: row["start"])
+    for idx, window in enumerate(windows):
+        next_start = windows[idx + 1]["start"] if idx + 1 < len(windows) else None
+        window["window_end"] = (
+            next_start if next_start is not None else window["end"] + 30_000_000_000
+        )
+    return windows
+
+
+def _turn_for_time(windows: List[Dict[str, Any]], start_ns: Optional[int]) -> Optional[int]:
+    if start_ns is None or not windows:
+        return None
+    for window in windows:
+        if window["start"] <= start_ns < window["window_end"]:
+            return int(window["display_num"])
+    best: Optional[int] = None
+    for window in windows:
+        if window["start"] <= start_ns:
+            best = int(window["display_num"])
+    return best
+
+
+def _resolve_turn_number(
+    span: Dict[str, Any],
+    by_id: Dict[str, Dict[str, Any]],
+    turn_display: Optional[Dict[str, int]] = None,
+    turn_windows: Optional[List[Dict[str, Any]]] = None,
+) -> Optional[int]:
+    visited: set[str] = set()
+    current: Optional[Dict[str, Any]] = span
+    while current is not None:
+        span_id = current.get("span_id")
+        if span_id and turn_display and str(span_id) in turn_display:
+            return turn_display[str(span_id)]
+
+        attrs = current.get("attributes") or {}
+        display = attrs.get("efficientai.display_turn_number")
+        if display is not None:
+            try:
+                return int(display)
+            except (TypeError, ValueError):
+                pass
+
+        if span_id:
+            if span_id in visited:
+                break
+            visited.add(str(span_id))
+
+        parent_id = current.get("parent_span_id")
+        if not parent_id:
+            break
+        current = by_id.get(str(parent_id))
+
+    if turn_windows:
+        start_ns = _span_start_ns(span)
+        if start_ns is not None:
+            by_time = _turn_for_time(turn_windows, start_ns)
+            if by_time is not None:
+                return by_time
+
+    attrs = span.get("attributes") or {}
+    turn_num = _attr_float(attrs, "turn.number")
+    if turn_num is not None:
+        return int(turn_num)
+    return None
+
+
+def annotate_spans_with_display_turn(spans: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Attach efficientai.display_turn_number for UI grouping."""
+    if not spans:
+        return []
+    turns = derive_turns_from_spans(spans)
+    span_map = _span_display_map_from_turns(turns)
+    turn_display = _build_turn_span_display_map(spans)
+    turn_windows = _build_turn_windows(spans, turn_display)
+    by_id = _span_index(spans)
+    annotated: List[Dict[str, Any]] = []
+    for span in spans:
+        row = dict(span)
+        span_id = row.get("span_id")
+        turn_num = span_map.get(str(span_id)) if span_id else None
+        if turn_num is None:
+            turn_num = _resolve_turn_number(row, by_id, turn_display, turn_windows)
+        if turn_num is not None:
+            attrs = dict(row.get("attributes") or {})
+            attrs["efficientai.display_turn_number"] = turn_num
+            row["attributes"] = attrs
+        annotated.append(row)
+    return annotated
+
+
+def _component_field(op: str, name: str) -> Optional[str]:
+    if op == "stt" or name == "stt":
+        return "stt_ttfb_ms"
+    if op in _LLM_OPS or name in _LLM_NAMES:
+        return "llm_ttfb_ms"
+    if op == "tts" or name == "tts":
+        return "tts_ttfb_ms"
+    if op == "s2s" or name == "s2s":
+        return "s2s_ttfb_ms"
+    return None
+
+
+def _is_s2s_span(op: str, name: str, attrs: Dict[str, Any]) -> bool:
+    if op == "s2s" or name in _S2S_NAMES:
+        return True
+    provider = str(
+        attrs.get("gen_ai.provider.name") or attrs.get("gen_ai.system") or ""
+    ).lower()
+    model = str(attrs.get("gen_ai.request.model") or "").lower()
+    if name in _S2S_OPS and any(hint in provider or hint in model for hint in _REALTIME_PROVIDER_HINTS):
+        if "realtime" in model or "live" in model or "nova" in model or "sonic" in model:
+            return True
+        modalities = str(attrs.get("modalities") or "").upper()
+        if "AUDIO" in modalities:
+            return True
+    return False
+
+
+def _new_turn() -> Dict[str, Any]:
+    return {"turn_number": 0, "extra": {"_span_ids": []}}
+
+
+def _normalize_text(text: str) -> str:
+    return re.sub(r"\s+", " ", text.strip().lower())
+
+
+def _text_similar(a: Optional[str], b: Optional[str]) -> bool:
+    if not a or not b:
+        return False
+    na, nb = _normalize_text(str(a)), _normalize_text(str(b))
+    if na == nb:
+        return True
+    if len(na) >= 20 and (na in nb or nb in na):
+        return True
+    shorter, longer = (na, nb) if len(na) <= len(nb) else (nb, na)
+    return len(shorter) >= 15 and longer.startswith(shorter[:15])
+
+
+def _turn_has_content(turn: Dict[str, Any]) -> bool:
+    extra = turn.get("extra") or {}
+    return bool(
+        extra.get("user_text")
+        or extra.get("assistant_text")
+        or turn.get("stt_ttfb_ms")
+        or turn.get("llm_ttfb_ms")
+        or turn.get("tts_ttfb_ms")
+        or turn.get("s2s_ttfb_ms")
+    )
+
+
+def _short_model_name(raw: Any) -> Optional[str]:
+    if raw is None:
+        return None
+    text = str(raw).strip()
+    if not text:
+        return None
+    if text.startswith("accounts/") and "/models/" in text:
+        return text.split("/models/", 1)[-1].split("/", 1)[0]
+    if "/" in text:
+        return text.rsplit("/", 1)[-1]
+    return text
+
+
+def _component_meta_from_attrs(attrs: Dict[str, Any], kind: str) -> Dict[str, Optional[str]]:
+    model = attrs.get("gen_ai.request.model") or attrs.get("param.model")
+    if kind in ("tts", "stt"):
+        model = model or attrs.get("settings.model")
+    provider = attrs.get("gen_ai.provider.name") or attrs.get("gen_ai.system")
+    return {
+        "model": _short_model_name(model),
+        "provider": str(provider).strip().lower() if provider else None,
+    }
+
+
+def _set_component_meta(turn: Dict[str, Any], kind: str, attrs: Dict[str, Any]) -> None:
+    meta = _component_meta_from_attrs(attrs, kind)
+    extra = turn.setdefault("extra", {})
+    if meta.get("model"):
+        extra[f"{kind}_model"] = meta["model"]
+    if meta.get("provider"):
+        extra[f"{kind}_provider"] = meta["provider"]
+
+
+def extract_pipeline_models(spans: List[Dict[str, Any]]) -> Dict[str, Dict[str, Optional[str]]]:
+    """Session-level STT/LLM/TTS/S2S model + provider from OTLP spans."""
+    pipeline: Dict[str, Dict[str, Optional[str]]] = {}
+
+    def _set(kind: str, attrs: Dict[str, Any]) -> None:
+        meta = _component_meta_from_attrs(attrs, kind)
+        if not meta.get("model") and not meta.get("provider"):
+            return
+        row = pipeline.setdefault(kind, {})
+        if meta.get("model") and not row.get("model"):
+            row["model"] = meta["model"]
+        if meta.get("provider") and not row.get("provider"):
+            row["provider"] = meta["provider"]
+
+    for span in spans:
+        attrs = span.get("attributes") or {}
+        op = (_attr_str(attrs, "gen_ai.operation.name") or "").lower()
+        name = (span.get("name") or "").lower()
+        if _is_s2s_span(op, name, attrs):
+            _set("s2s", attrs)
+        elif op == "stt" or name == "stt":
+            _set("stt", attrs)
+        elif op in _LLM_OPS or name in _LLM_NAMES:
+            _set("llm", attrs)
+        elif op == "tts" or name == "tts":
+            _set("tts", attrs)
+
+    return pipeline
+
+
+def _collect_component_events(spans: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    events: List[Dict[str, Any]] = []
+    for span in spans:
+        attrs = span.get("attributes") or {}
+        op = (_attr_str(attrs, "gen_ai.operation.name") or "").lower()
+        name = (span.get("name") or "").lower()
+        start = _span_start_ns(span) or 0
+        span_id = span.get("span_id")
+
+        if _is_s2s_span(op, name, attrs):
+            transcript = attrs.get("transcript")
+            is_input = attrs.get("transcript.is_input")
+            output = attrs.get("output") or attrs.get("text_output")
+            meta = _component_meta_from_attrs(attrs, "s2s")
+            events.append(
+                {
+                    "type": "s2s",
+                    "span_id": span_id,
+                    "start": start,
+                    "latency_ms": _component_latency_ms(span, attrs, "llm_ttfb_ms"),
+                    "user_text": str(transcript)
+                    if transcript and is_input
+                    else None,
+                    "assistant_text": str(output)
+                    if output
+                    else (str(transcript) if transcript and not is_input else None),
+                    "agent_id": attrs.get("efficientai.agent_id"),
+                    "model": meta.get("model"),
+                    "provider": meta.get("provider"),
+                    "attrs": attrs,
+                }
+            )
+            continue
+
+        if op == "stt" or name == "stt":
+            meta = _component_meta_from_attrs(attrs, "stt")
+            events.append(
+                {
+                    "type": "stt",
+                    "span_id": span_id,
+                    "start": start,
+                    "latency_ms": _component_latency_ms(span, attrs, "stt_ttfb_ms"),
+                    "user_text": str(attrs["transcript"]) if attrs.get("transcript") else None,
+                    "agent_id": attrs.get("efficientai.agent_id"),
+                    "model": meta.get("model"),
+                    "provider": meta.get("provider"),
+                    "attrs": attrs,
+                }
+            )
+        elif op in _LLM_OPS or name in _LLM_NAMES:
+            output = attrs.get("output") or attrs.get("text_output")
+            meta = _component_meta_from_attrs(attrs, "llm")
+            events.append(
+                {
+                    "type": "llm",
+                    "span_id": span_id,
+                    "start": start,
+                    "latency_ms": _component_latency_ms(span, attrs, "llm_ttfb_ms"),
+                    "assistant_text": str(output) if output else None,
+                    "agent_id": attrs.get("efficientai.agent_id"),
+                    "model": meta.get("model"),
+                    "provider": meta.get("provider"),
+                    "attrs": attrs,
+                }
+            )
+        elif op == "tts" or name == "tts":
+            spoken = attrs.get("text") or attrs.get("output")
+            meta = _component_meta_from_attrs(attrs, "tts")
+            events.append(
+                {
+                    "type": "tts",
+                    "span_id": span_id,
+                    "start": start,
+                    "latency_ms": _component_latency_ms(span, attrs, "tts_ttfb_ms"),
+                    "assistant_text": str(spoken) if spoken else None,
+                    "agent_id": attrs.get("efficientai.agent_id"),
+                    "model": meta.get("model"),
+                    "provider": meta.get("provider"),
+                    "attrs": attrs,
+                }
+            )
+
+    events.sort(key=lambda row: (row["start"], row["type"]))
+    return events
+
+
+def _apply_event_meta(turn: Dict[str, Any], event: Dict[str, Any]) -> None:
+    kind = str(event.get("type") or "")
+    if not kind:
+        return
+    attrs = event.get("attrs")
+    if isinstance(attrs, dict):
+        _set_component_meta(turn, kind, attrs)
+        return
+    extra = turn.setdefault("extra", {})
+    if event.get("model"):
+        extra[f"{kind}_model"] = str(event["model"])
+    if event.get("provider"):
+        extra[f"{kind}_provider"] = str(event["provider"])
+
+
+def _track_span(turn: Dict[str, Any], span_id: Any) -> None:
+    if not span_id:
+        return
+    extra = turn.setdefault("extra", {})
+    ids = extra.setdefault("_span_ids", [])
+    sid = str(span_id)
+    if sid not in ids:
+        ids.append(sid)
+
+
+def _find_turn_for_tts(
+    turns: List[Dict[str, Any]],
+    current: Optional[Dict[str, Any]],
+    text: Optional[str],
+) -> Dict[str, Any]:
+    if text:
+        for turn in reversed(turns):
+            if _text_similar(text, (turn.get("extra") or {}).get("assistant_text")):
+                return turn
+        if current and _text_similar(text, (current.get("extra") or {}).get("assistant_text")):
+            return current
+    for turn in reversed(turns):
+        if turn.get("llm_ttfb_ms") and not turn.get("tts_ttfb_ms"):
+            return turn
+    if current and current.get("llm_ttfb_ms") and not current.get("tts_ttfb_ms"):
+        return current
+    return current if current is not None else _new_turn()
+
+
+def _apply_turn_span_metadata(
+    turns: List[Dict[str, Any]],
+    turn_spans: List[Dict[str, Any]],
+) -> None:
+    meta_spans = sorted(turn_spans, key=lambda s: _span_start_ns(s) or 0)
+    response_turns = [
+        t
+        for t in turns
+        if (t.get("extra") or {}).get("assistant_text") or t.get("s2s_ttfb_ms")
+    ]
+    for idx, span in enumerate(meta_spans):
+        if idx >= len(response_turns):
+            break
+        turn = response_turns[idx]
+        attrs = span.get("attributes") or {}
+        user_bot_s = _attr_float(attrs, "turn.user_bot_latency_seconds")
+        if user_bot_s is not None:
+            _set_max_ms(turn, "sut_response_latency_ms", round(user_bot_s * 1000.0, 1))
+        if attrs.get("turn.was_interrupted"):
+            turn.setdefault("extra", {})["was_interrupted"] = True
+        duration_s = _attr_float(attrs, "turn.duration_seconds")
+        if duration_s is not None:
+            turn.setdefault("extra", {})["turn_duration_seconds"] = duration_s
+
+
+def _detect_turn_pipeline_mode(turn: Dict[str, Any]) -> None:
+    extra = turn.setdefault("extra", {})
+    if extra.get("pipeline_mode") == "s2s" or turn.get("s2s_ttfb_ms"):
+        extra["pipeline_mode"] = "s2s"
+        return
+    if any(turn.get(k) for k in ("stt_ttfb_ms", "llm_ttfb_ms", "tts_ttfb_ms")):
+        extra["pipeline_mode"] = "stt_llm_tts"
+
+
+def _merge_turn_fields(target: Dict[str, Any], source: Dict[str, Any]) -> None:
+    for field in ("stt_ttfb_ms", "llm_ttfb_ms", "tts_ttfb_ms", "s2s_ttfb_ms", "sut_response_latency_ms"):
+        _set_max_ms(target, field, source.get(field))
+    src_extra = source.get("extra") or {}
+    tgt_extra = target.setdefault("extra", {})
+    for key in (
+        "user_text",
+        "assistant_text",
+        "agent_id",
+        "s2s_model",
+        "pipeline_mode",
+        "stt_model",
+        "llm_model",
+        "tts_model",
+        "stt_provider",
+        "llm_provider",
+        "tts_provider",
+        "s2s_provider",
+    ):
+        if src_extra.get(key) and not tgt_extra.get(key):
+            tgt_extra[key] = src_extra[key]
+    for span_id in src_extra.get("_span_ids") or []:
+        _track_span(target, span_id)
+    if src_extra.get("was_interrupted"):
+        tgt_extra["was_interrupted"] = True
+
+
+def _pair_user_response_turns(turns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Order turns as opener → user+bot exchanges; merge split async components."""
+    if len(turns) <= 1:
+        return turns
+
+    def _has_user(t: Dict[str, Any]) -> bool:
+        return bool((t.get("extra") or {}).get("user_text"))
+
+    def _has_assistant(t: Dict[str, Any]) -> bool:
+        return bool((t.get("extra") or {}).get("assistant_text"))
+
+    openers = [t for t in turns if _has_assistant(t) and not _has_user(t)]
+    user_turns = [t for t in turns if _has_user(t) and not _has_assistant(t)]
+    combined = [t for t in turns if _has_user(t) and _has_assistant(t)]
+    bot_only = [t for t in turns if _has_assistant(t) and not _has_user(t)]
+
+    if not user_turns:
+        return turns
+
+    opener = openers[0] if openers else None
+    response_pool = [t for t in bot_only if t is not opener]
+
+    ordered: List[Dict[str, Any]] = []
+    if opener:
+        ordered.append(opener)
+
+    for user_turn in user_turns:
+        merged = dict(user_turn)
+        merged_extra = dict(merged.get("extra") or {})
+        merged["extra"] = merged_extra
+        if response_pool:
+            bot = response_pool.pop(0)
+            _merge_turn_fields(merged, bot)
+        ordered.append(merged)
+
+    for leftover in response_pool:
+        ordered.append(leftover)
+    ordered.extend(combined)
+    return [t for t in ordered if _turn_has_content(t)]
+
+
+def _rebuild_conversation_turns(spans: List[Dict[str, Any]]) -> Optional[List[Dict[str, Any]]]:
+    events = _collect_component_events(spans)
+    has_timing = any((e.get("start") or 0) > 0 for e in events)
+    if not events or not has_timing:
+        return None
+
+    turn_meta = [s for s in spans if (s.get("name") or "").lower() == "turn"]
+    turns: List[Dict[str, Any]] = []
+    current: Optional[Dict[str, Any]] = None
+
+    def flush() -> None:
+        nonlocal current
+        if current and _turn_has_content(current):
+            turns.append(current)
+        current = None
+
+    def ensure_current() -> Dict[str, Any]:
+        nonlocal current
+        if current is None:
+            current = _new_turn()
+        return current
+
+    for event in events:
+        span_id = event.get("span_id")
+        event_type = event["type"]
+
+        if event_type == "stt":
+            transcript = event.get("user_text")
+            if transcript:
+                t = ensure_current()
+                if (t.get("extra") or {}).get("assistant_text"):
+                    flush()
+                    t = ensure_current()
+                if (t.get("extra") or {}).get("user_text") and not _text_similar(
+                    transcript, t["extra"]["user_text"]
+                ):
+                    flush()
+                    t = ensure_current()
+                _track_span(t, span_id)
+                _set_turn_text(t, "user", transcript)
+                _set_max_ms(t, "stt_ttfb_ms", event.get("latency_ms"))
+                _apply_event_meta(t, event)
+            continue
+
+        if event_type == "llm":
+            t = ensure_current()
+            output = event.get("assistant_text")
+            if output and (t.get("extra") or {}).get("assistant_text") and not _text_similar(
+                output, t["extra"]["assistant_text"]
+            ):
+                flush()
+                t = ensure_current()
+            _track_span(t, span_id)
+            if output:
+                _set_turn_text(t, "assistant", output)
+            _set_max_ms(t, "llm_ttfb_ms", event.get("latency_ms"))
+            _apply_event_meta(t, event)
+            if event.get("agent_id"):
+                t.setdefault("extra", {})["agent_id"] = str(event["agent_id"])
+            continue
+
+        if event_type == "tts":
+            target = _find_turn_for_tts(turns, current, event.get("assistant_text"))
+            _track_span(target, span_id)
+            if event.get("assistant_text"):
+                _set_turn_text(target, "assistant", str(event["assistant_text"]))
+            _set_max_ms(target, "tts_ttfb_ms", event.get("latency_ms"))
+            _apply_event_meta(target, event)
+            if target is current:
+                current = target
+            elif target not in turns:
+                current = target
+            continue
+
+        if event_type == "s2s":
+            t = ensure_current()
+            if event.get("user_text"):
+                if (t.get("extra") or {}).get("assistant_text"):
+                    flush()
+                    t = ensure_current()
+                _set_turn_text(t, "user", str(event["user_text"]))
+            if event.get("assistant_text"):
+                _set_turn_text(t, "assistant", str(event["assistant_text"]))
+            _track_span(t, span_id)
+            _set_max_ms(t, "s2s_ttfb_ms", event.get("latency_ms"))
+            _apply_event_meta(t, event)
+            t.setdefault("extra", {})["pipeline_mode"] = "s2s"
+            if event.get("agent_id"):
+                t["extra"]["agent_id"] = str(event["agent_id"])
+
+    flush()
+    turns = [t for t in turns if _turn_has_content(t)]
+    if not turns:
+        return None
+
+    turns = _pair_user_response_turns(turns)
+
+    _apply_turn_span_metadata(turns, turn_meta)
+    for idx, turn in enumerate(turns, start=1):
+        turn["turn_number"] = idx
+        _finalize_turn_metrics(turn)
+        _detect_turn_pipeline_mode(turn)
+    return turns
+
+
+def _set_max_ms(turn: Dict[str, Any], field: str, value: Optional[float]) -> None:
+    if value is None or float(value) <= 0:
+        return
+    existing = turn.get(field)
+    if existing is None or float(value) > float(existing):
+        turn[field] = value
+
+
+def _component_latency_ms(
+    span: Dict[str, Any],
+    attrs: Dict[str, Any],
+    field: str,
+) -> Optional[float]:
+    ttfb_ms = _ms_from_ttfb(attrs)
+    if ttfb_ms is not None and ttfb_ms > 0:
+        return ttfb_ms
+    if field == "stt_ttfb_ms":
+        return None
+    duration_ms = _ms_from_span_duration(span)
+    if duration_ms is not None and duration_ms > 0:
+        return duration_ms
+    return None
+
+
+def _looks_like_chat_json(text: str) -> bool:
+    stripped = text.strip()
+    return stripped.startswith("[{") or stripped.startswith('{"role"')
+
+
+def _set_turn_text(turn: Dict[str, Any], role: str, text: str) -> None:
+    extra = turn.setdefault("extra", {})
+    key = "user_text" if role == "user" else "assistant_text"
+    cleaned = text.strip()
+    if not cleaned or _looks_like_chat_json(cleaned):
+        return
+    existing = extra.get(key)
+    if existing is None or len(cleaned) > len(str(existing)):
+        extra[key] = cleaned
+
+
+def _build_transcript_display(turn: Dict[str, Any]) -> None:
+    extra = turn.get("extra") or {}
+    user = extra.get("user_text")
+    assistant = extra.get("assistant_text")
+    parts: List[str] = []
+    if user:
+        parts.append(f"User: {user}")
+    if assistant:
+        parts.append(f"Assistant: {assistant}")
+    if parts:
+        turn["transcript"] = "\n".join(parts)
+
+
+def _finalize_turn_metrics(turn: Dict[str, Any]) -> None:
+    """Derive composite fields after all spans for a turn are collected."""
+    s2s = turn.get("s2s_ttfb_ms")
+    llm = turn.get("llm_ttfb_ms")
+    if turn.get("sut_response_latency_ms") is None:
+        if s2s is not None:
+            turn["sut_response_latency_ms"] = s2s
+        elif llm is not None:
+            turn["sut_response_latency_ms"] = llm
+
+    if (turn.get("extra") or {}).get("pipeline_mode") == "s2s":
+        _build_transcript_display(turn)
+        return
+
+    sut = turn.get("sut_response_latency_ms")
+    llm = turn.get("llm_ttfb_ms")
+    if turn.get("tts_ttfb_ms") is None and sut is not None and llm is not None:
+        derived_tts = round(float(sut) - float(llm), 1)
+        if derived_tts > 0:
+            turn["tts_ttfb_ms"] = derived_tts
+
+    _build_transcript_display(turn)
+
+
+def derive_turns_from_spans(spans: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Project OTLP spans onto per-turn records for Tier 1+2 combined view."""
+    rebuilt = _rebuild_conversation_turns(spans)
+    if rebuilt is not None:
+        return rebuilt
+    return _derive_turns_by_pipecat_turn(spans)
+
+
+def _span_display_map_from_turns(turns: List[Dict[str, Any]]) -> Dict[str, int]:
+    mapping: Dict[str, int] = {}
+    for turn in turns:
+        turn_num = int(turn.get("turn_number") or 0)
+        for span_id in (turn.get("extra") or {}).get("_span_ids") or []:
+            mapping[str(span_id)] = turn_num
+    return mapping
+
+
+def _derive_turns_by_pipecat_turn(spans: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    by_id = _span_index(spans)
+    turn_display = _build_turn_span_display_map(spans)
+    turn_windows = _build_turn_windows(spans, turn_display)
+    turn_spans: Dict[int, Dict[str, Any]] = {}
+
+    for span in spans:
+        turn_key = _resolve_turn_number(span, by_id, turn_display, turn_windows)
+        if turn_key is None:
+            continue
+
+        attrs = span.get("attributes") or {}
+        turn = turn_spans.setdefault(
+            turn_key,
+            {"turn_number": turn_key, "extra": {}},
+        )
+
+        op = (_attr_str(attrs, "gen_ai.operation.name") or "").lower()
+        name = (span.get("name") or "").lower()
+
+        agent_id = attrs.get("efficientai.agent_id")
+        if agent_id and not turn["extra"].get("agent_id"):
+            turn["extra"]["agent_id"] = str(agent_id)
+
+        if name == "turn":
+            user_bot_s = _attr_float(attrs, "turn.user_bot_latency_seconds")
+            if user_bot_s is not None:
+                _set_max_ms(
+                    turn,
+                    "sut_response_latency_ms",
+                    round(user_bot_s * 1000.0, 1),
+                )
+            duration_s = _attr_float(attrs, "turn.duration_seconds")
+            if duration_s is not None:
+                turn["extra"]["turn_duration_seconds"] = duration_s
+            if attrs.get("turn.was_interrupted"):
+                turn["extra"]["was_interrupted"] = True
+            turn_type = _attr_str(attrs, "turn.type")
+            if turn_type:
+                turn["extra"]["turn_type"] = turn_type
+            continue
+
+        field = _component_field(op, name)
+        if field:
+            _set_max_ms(turn, field, _component_latency_ms(span, attrs, field))
+            kind = "s2s" if field == "s2s_ttfb_ms" else field.removesuffix("_ttfb_ms")
+            _set_component_meta(turn, kind, attrs)
+            if field == "stt_ttfb_ms" and attrs.get("transcript"):
+                _set_turn_text(turn, "user", str(attrs["transcript"]))
+            if field == "llm_ttfb_ms":
+                output = attrs.get("output")
+                if output:
+                    _set_turn_text(turn, "assistant", str(output))
+            if field == "tts_ttfb_ms":
+                spoken = attrs.get("text") or attrs.get("output")
+                if spoken and not turn["extra"].get("assistant_text"):
+                    _set_turn_text(turn, "assistant", str(spoken))
+
+    for turn in turn_spans.values():
+        _finalize_turn_metrics(turn)
+        _detect_turn_pipeline_mode(turn)
+
+    return [turn_spans[k] for k in sorted(turn_spans)]
+
+
+def merge_tier1_and_otel_turns(
+    tier1_turns: List[Dict[str, Any]],
+    otel_turns: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Merge black-box phone timings with OTLP-derived component fields."""
+    otel_by_num = {int(t["turn_number"]): t for t in otel_turns if t.get("turn_number")}
+    merged: List[Dict[str, Any]] = []
+    seen: set[int] = set()
+
+    for turn in tier1_turns:
+        num = int(turn.get("turn_number") or 0)
+        seen.add(num)
+        combined = dict(turn)
+        if num in otel_by_num:
+            for key in (
+                "stt_ttfb_ms",
+                "llm_ttfb_ms",
+                "tts_ttfb_ms",
+                "s2s_ttfb_ms",
+                "sut_response_latency_ms",
+                "transcript",
+                "extra",
+            ):
+                if key not in otel_by_num[num] or otel_by_num[num][key] is None:
+                    continue
+                if key == "sut_response_latency_ms" and combined.get(key) is not None:
+                    continue
+                combined[key] = otel_by_num[num][key]
+        merged.append(combined)
+
+    for num, turn in otel_by_num.items():
+        if num not in seen:
+            merged.append(dict(turn))
+
+    merged.sort(key=lambda t: int(t.get("turn_number") or 0))
+    return merged
+
+
+def compute_component_aggregates(turns: List[Dict[str, Any]]) -> Dict[str, Any]:
+    def _percentile(values: List[float], pct: float) -> Optional[float]:
+        if not values:
+            return None
+        sorted_vals = sorted(values)
+        idx = min(len(sorted_vals) - 1, int(round((pct / 100.0) * (len(sorted_vals) - 1))))
+        return round(sorted_vals[idx], 1)
+
+    agg: Dict[str, Any] = {}
+    for field in (
+        "stt_ttfb_ms",
+        "llm_ttfb_ms",
+        "tts_ttfb_ms",
+        "s2s_ttfb_ms",
+        "sut_response_latency_ms",
+    ):
+        values = [float(t[field]) for t in turns if t.get(field) is not None]
+        if values:
+            agg[field] = {
+                "p50": _percentile(values, 50),
+                "p90": _percentile(values, 90),
+                "p95": _percentile(values, 95),
+                "count": len(values),
+            }
+    return agg
+
+
+def compute_trace_latency_summary(turns: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """Compute header percentiles and component aggregates from turn rows."""
+    aggregates = compute_component_aggregates(turns)
+    sut_values = [
+        float(t["sut_response_latency_ms"])
+        for t in turns
+        if t.get("sut_response_latency_ms") is not None
+    ]
+    if not sut_values:
+        sut_values = [
+            float(t["s2s_ttfb_ms"]) for t in turns if t.get("s2s_ttfb_ms") is not None
+        ]
+    if not sut_values:
+        sut_values = [
+            float(t["llm_ttfb_ms"]) for t in turns if t.get("llm_ttfb_ms") is not None
+        ]
+
+    def _pct(values: List[float], pct: float) -> Optional[float]:
+        if not values:
+            return None
+        sorted_vals = sorted(values)
+        idx = min(len(sorted_vals) - 1, int(round((pct / 100.0) * (len(sorted_vals) - 1))))
+        return round(sorted_vals[idx], 1)
+
+    return {
+        "turn_count": len(turns),
+        "response_latency_p50_ms": _pct(sut_values, 50),
+        "response_latency_p90_ms": _pct(sut_values, 90),
+        "response_latency_p95_ms": _pct(sut_values, 95),
+        "component_aggregates": aggregates or None,
+    }
+
+
+def _span_call_short_id(span: Dict[str, Any]) -> Optional[str]:
+    raw = (span.get("attributes") or {}).get("efficientai.call_short_id")
+    if not raw:
+        return None
+    cleaned = str(raw).strip()
+    if _CALL_SHORT_ID_RE.match(cleaned):
+        return cleaned
+    return None
+
+
+def group_spans_by_call_short_id(
+    spans: List[Dict[str, Any]],
+    *,
+    header_call_short_id: Optional[str] = None,
+) -> Dict[Optional[str], List[Dict[str, Any]]]:
+    """Partition OTLP spans by per-span call_short_id (span attrs beat HTTP header)."""
+    groups: Dict[Optional[str], List[Dict[str, Any]]] = {}
+    for span in spans:
+        key = _span_call_short_id(span) or header_call_short_id
+        groups.setdefault(key, []).append(span)
+    return groups
+
+
+def spans_indicate_session_end(spans: List[Dict[str, Any]]) -> bool:
+    """True when OTLP spans signal the voice session finished."""
+    for span in spans:
+        name = (span.get("name") or "").lower()
+        attrs = span.get("attributes") or {}
+        if name == "conversation" and span.get("end_time_unix_nano"):
+            return True
+        if attrs.get("turn.ended_by_conversation_end"):
+            return True
+        if attrs.get("efficientai.session_end"):
+            return True
+    return False
+
+
+def filter_spans_for_trace(
+    spans: List[Dict[str, Any]],
+    *,
+    call_short_id: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    """Keep only spans belonging to one call; collapse mixed OTLP trace_ids."""
+    if not spans:
+        return spans
+
+    if call_short_id:
+        matched = [s for s in spans if _span_call_short_id(s) == call_short_id]
+        if matched:
+            spans = matched
+
+    turn_trace_ids: Dict[str, int] = {}
+    for span in spans:
+        if (span.get("name") or "").lower() != "turn":
+            continue
+        trace_id = span.get("trace_id")
+        if not trace_id:
+            continue
+        try:
+            start = int(span.get("start_time_unix_nano") or 0)
+        except (TypeError, ValueError):
+            start = 0
+        turn_trace_ids[str(trace_id)] = max(turn_trace_ids.get(str(trace_id), 0), start)
+
+    if len(turn_trace_ids) <= 1:
+        return spans
+
+    if call_short_id and spans and all(
+        _span_call_short_id(s) == call_short_id for s in spans
+    ):
+        return spans
+
+    primary_trace_id = min(turn_trace_ids, key=turn_trace_ids.get)
+    return [s for s in spans if str(s.get("trace_id") or "") == primary_trace_id]
+
+
+def extract_correlation_ids(spans: List[Dict[str, Any]]) -> Dict[str, Optional[str]]:
+    for span in spans:
+        attrs = span.get("attributes") or {}
+        result_id = (
+            attrs.get("efficientai.evaluator_result_id")
+            or attrs.get("efficientai.run_id")
+        )
+        call_short_raw = attrs.get("efficientai.call_short_id")
+        call_short = (
+            str(call_short_raw).strip()
+            if call_short_raw and _CALL_SHORT_ID_RE.match(str(call_short_raw).strip())
+            else None
+        )
+        agent_id = attrs.get("efficientai.agent_id")
+        transport_raw = attrs.get("efficientai.transport")
+        transport = (
+            str(transport_raw).strip().lower()
+            if transport_raw and str(transport_raw).strip().lower() in {"webrtc", "websocket", "phone", "custom"}
+            else None
+        )
+        if result_id or call_short or agent_id or transport:
+            return {
+                "evaluator_result_id": str(result_id) if result_id else None,
+                "call_short_id": call_short,
+                "agent_id": str(agent_id) if agent_id else None,
+                "transport": transport,
+            }
+    return {}
diff --git a/app/services/synthetic_traces/trace_service.py b/app/services/synthetic_traces/trace_service.py
new file mode 100644
index 00000000..d1f5b45d
--- /dev/null
+++ b/app/services/synthetic_traces/trace_service.py
@@ -0,0 +1,1116 @@
+"""Open, update, and finalize synthetic call traces."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+from uuid import UUID
+
+from loguru import logger
+from sqlalchemy.orm import Session
+
+from app.models.database import (
+    Agent,
+    CallRecording,
+    EvaluatorResult,
+    SyntheticCallTrace,
+    SyntheticTraceOtelPayload,
+    SyntheticTracePayload,
+)
+from app.models.synthetic_trace_schemas import VALID_TRACE_TRANSPORTS
+from app.utils.call_recordings import generate_unique_call_short_id
+from app.services.synthetic_traces.otlp_mapper import (
+    annotate_spans_with_display_turn,
+    compute_component_aggregates,
+    compute_trace_latency_summary,
+    derive_turns_from_spans,
+    extract_correlation_ids,
+    filter_spans_for_trace,
+    group_spans_by_call_short_id,
+    extract_pipeline_models,
+    merge_tier1_and_otel_turns,
+    spans_indicate_session_end,
+)
+
+
+OPEN_TRACE_IDLE_CLOSE_SECONDS = 120
+
+
+def _normalize_turn_row(turn: Dict[str, Any]) -> Dict[str, Any]:
+    row = dict(turn)
+    if row.get("talk_over") is None:
+        row["talk_over"] = False
+    if row.get("extra") is None:
+        row["extra"] = {}
+    return row
+
+
+def _normalize_turn_rows(turns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    return [_normalize_turn_row(t) for t in turns]
+
+
+def _percentile(values: List[float], pct: float) -> Optional[float]:
+    if not values:
+        return None
+    sorted_vals = sorted(values)
+    idx = min(len(sorted_vals) - 1, int(round((pct / 100.0) * (len(sorted_vals) - 1))))
+    return round(sorted_vals[idx], 1)
+
+
+def _utcnow() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+def open_trace(
+    db: Session,
+    *,
+    organization_id: UUID,
+    workspace_id: UUID,
+    evaluator_result_id: Optional[UUID] = None,
+    agent_id: Optional[UUID] = None,
+    persona_id: Optional[UUID] = None,
+    scenario_id: Optional[UUID] = None,
+    evaluator_id: Optional[UUID] = None,
+    call_recording_id: Optional[UUID] = None,
+    call_short_id: Optional[str] = None,
+    transport: str = "phone",
+    provider_platform: Optional[str] = "vobiz",
+    environment: str = "pre_prod",
+    tier: str = "black_box",
+) -> SyntheticCallTrace:
+    existing = None
+    if evaluator_result_id:
+        existing = (
+            db.query(SyntheticCallTrace)
+            .filter(SyntheticCallTrace.evaluator_result_id == evaluator_result_id)
+            .order_by(SyntheticCallTrace.created_at.desc())
+            .first()
+        )
+    if existing and existing.status == "open":
+        return existing
+
+    trace = SyntheticCallTrace(
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        evaluator_result_id=evaluator_result_id,
+        agent_id=agent_id,
+        persona_id=persona_id,
+        scenario_id=scenario_id,
+        evaluator_id=evaluator_id,
+        call_recording_id=call_recording_id,
+        call_short_id=call_short_id,
+        environment=environment,
+        provider_platform=provider_platform,
+        transport=transport,
+        tier=tier,
+        status="open",
+        started_at=_utcnow(),
+    )
+    db.add(trace)
+    db.flush()
+
+    db.add(
+        SyntheticTracePayload(
+            synthetic_call_trace_id=trace.id,
+            workspace_id=workspace_id,
+            turns=[],
+        )
+    )
+    db.add(
+        SyntheticTraceOtelPayload(
+            synthetic_call_trace_id=trace.id,
+            workspace_id=workspace_id,
+            spans=[],
+            trace_ids=[],
+        )
+    )
+
+    if evaluator_result_id:
+        result = db.query(EvaluatorResult).filter(EvaluatorResult.id == evaluator_result_id).first()
+        if result:
+            result.synthetic_call_trace_id = trace.id
+
+    db.commit()
+    db.refresh(trace)
+    return trace
+
+
+def open_trace_for_call_recording(
+    db: Session,
+    *,
+    recording: CallRecording,
+    evaluator_result: EvaluatorResult,
+) -> Optional[SyntheticCallTrace]:
+    """Open a synthetic trace when a phone evaluator call starts."""
+    try:
+        workspace_id = evaluator_result.workspace_id or recording.workspace_id
+        return open_trace(
+            db,
+            organization_id=recording.organization_id,
+            workspace_id=workspace_id,
+            evaluator_result_id=evaluator_result.id,
+            agent_id=evaluator_result.agent_id,
+            persona_id=evaluator_result.persona_id,
+            scenario_id=evaluator_result.scenario_id,
+            evaluator_id=evaluator_result.evaluator_id,
+            call_recording_id=recording.id,
+            call_short_id=recording.call_short_id,
+            transport="phone",
+            provider_platform=recording.provider_platform or "vobiz",
+        )
+    except Exception as exc:
+        logger.warning("Failed to open synthetic call trace: {}", exc)
+        return None
+
+
+def open_trace_session(
+    db: Session,
+    *,
+    organization_id: UUID,
+    workspace_id: UUID,
+    agent_id: Optional[UUID] = None,
+    evaluator_result_id: Optional[UUID] = None,
+    transport: str = "websocket",
+    call_short_id: Optional[str] = None,
+) -> SyntheticCallTrace:
+    """Mint call_short_id and open a transport-agnostic trace before audio flows."""
+    if transport not in VALID_TRACE_TRANSPORTS:
+        raise ValueError(f"transport must be one of {VALID_TRACE_TRANSPORTS}")
+
+    if agent_id is not None:
+        agent = (
+            db.query(Agent)
+            .filter(Agent.id == agent_id, Agent.organization_id == organization_id)
+            .first()
+        )
+        if not agent:
+            raise ValueError("Agent not found")
+
+    short_id = call_short_id or generate_unique_call_short_id(db)
+    provider_platform = "vobiz" if transport == "phone" else None
+
+    persona_id = None
+    scenario_id = None
+    evaluator_id = None
+    if evaluator_result_id:
+        result = (
+            db.query(EvaluatorResult)
+            .filter(
+                EvaluatorResult.id == evaluator_result_id,
+                EvaluatorResult.organization_id == organization_id,
+            )
+            .first()
+        )
+        if result:
+            persona_id = result.persona_id
+            scenario_id = result.scenario_id
+            evaluator_id = result.evaluator_id
+
+    return open_trace(
+        db,
+        organization_id=organization_id,
+        workspace_id=workspace_id,
+        evaluator_result_id=evaluator_result_id,
+        agent_id=agent_id,
+        persona_id=persona_id,
+        scenario_id=scenario_id,
+        evaluator_id=evaluator_id,
+        call_short_id=short_id,
+        transport=transport,
+        provider_platform=provider_platform,
+        tier="component",
+    )
+
+
+def _sync_trace_from_otel_spans(db: Session, trace: SyntheticCallTrace) -> None:
+    """Re-derive turn rows and header metrics from stored OTLP spans."""
+    otel_payload = (
+        db.query(SyntheticTraceOtelPayload)
+        .filter(SyntheticTraceOtelPayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    raw_spans = list(otel_payload.spans or []) if otel_payload else []
+    spans = filter_spans_for_trace(raw_spans, call_short_id=trace.call_short_id)
+    if not spans:
+        return
+
+    payload = (
+        db.query(SyntheticTracePayload)
+        .filter(SyntheticTracePayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    tier1_turns: List[Dict[str, Any]] = []
+    if payload and payload.turns:
+        for turn in payload.turns:
+            num = turn.get("turn_number")
+            sut = turn.get("sut_response_latency_ms")
+            if num is not None and sut is not None:
+                tier1_turns.append(
+                    {
+                        "turn_number": int(num),
+                        "sut_response_latency_ms": float(sut),
+                        "talk_over": bool(turn.get("talk_over") or False),
+                        "extra": dict(turn.get("extra") or {}),
+                    }
+                )
+
+    otel_turns = derive_turns_from_spans(spans)
+    merged = merge_tier1_and_otel_turns(tier1_turns, otel_turns)
+    if payload:
+        payload.turns = merged
+    else:
+        db.add(
+            SyntheticTracePayload(
+                synthetic_call_trace_id=trace.id,
+                workspace_id=trace.workspace_id,
+                turns=merged,
+            )
+        )
+
+    trace.turn_count = len(merged)
+    trace.component_aggregates = compute_component_aggregates(merged)
+    if otel_turns:
+        trace.tier = "mixed" if tier1_turns else "component"
+    _update_latency_aggregates(trace, merged)
+
+
+def close_trace_session(
+    db: Session,
+    *,
+    organization_id: UUID,
+    call_short_id: str,
+    workspace_id: Optional[UUID] = None,
+) -> Optional[SyntheticCallTrace]:
+    """Close a trace session without requiring a CallRecording."""
+    query = db.query(SyntheticCallTrace).filter(
+        SyntheticCallTrace.organization_id == organization_id,
+        SyntheticCallTrace.call_short_id == call_short_id,
+    )
+    if workspace_id is not None:
+        query = query.filter(SyntheticCallTrace.workspace_id == workspace_id)
+    trace = query.order_by(SyntheticCallTrace.created_at.desc()).first()
+    if not trace:
+        return None
+    return _close_open_trace(db, trace)
+
+
+def _close_open_trace(db: Session, trace: SyntheticCallTrace) -> SyntheticCallTrace:
+    if trace.status in ("closed", "finalized"):
+        return trace
+    _sync_trace_from_otel_spans(db, trace)
+    trace.status = "closed"
+    trace.ended_at = _utcnow()
+    trace.failure_flags = _compute_failure_flags(trace)
+    db.commit()
+    db.refresh(trace)
+    return trace
+
+
+def maybe_auto_close_open_trace(
+    db: Session,
+    trace: SyntheticCallTrace,
+    *,
+    triggering_spans: Optional[List[Dict[str, Any]]] = None,
+    all_spans: Optional[List[Dict[str, Any]]] = None,
+) -> SyntheticCallTrace:
+    """Close open traces when the session clearly ended or went idle after data arrived."""
+    if trace.status in ("closed", "finalized"):
+        return trace
+
+    span_batch = list(triggering_spans or [])
+    if all_spans:
+        span_batch = span_batch + list(all_spans)
+
+    if span_batch and spans_indicate_session_end(span_batch):
+        return _close_open_trace(db, trace)
+
+    if trace.turn_count < 1:
+        return trace
+
+    updated_at = trace.updated_at or trace.started_at
+    if not updated_at:
+        return trace
+    if updated_at.tzinfo is None:
+        updated_at = updated_at.replace(tzinfo=timezone.utc)
+    idle_seconds = (_utcnow() - updated_at).total_seconds()
+    if idle_seconds >= OPEN_TRACE_IDLE_CLOSE_SECONDS:
+        return _close_open_trace(db, trace)
+
+    return trace
+
+
+def link_trace_to_call_recording(
+    db: Session,
+    *,
+    organization_id: UUID,
+    call_short_id: str,
+    call_recording_id: UUID,
+) -> Optional[SyntheticCallTrace]:
+    trace = (
+        db.query(SyntheticCallTrace)
+        .filter(
+            SyntheticCallTrace.organization_id == organization_id,
+            SyntheticCallTrace.call_short_id == call_short_id,
+        )
+        .order_by(SyntheticCallTrace.created_at.desc())
+        .first()
+    )
+    if not trace:
+        return None
+    trace.call_recording_id = call_recording_id
+    db.commit()
+    db.refresh(trace)
+    return trace
+
+
+OBSERVABILITY_TRACES_API_PATH = "/api/v1/observability/traces"
+
+
+def build_session_otel_correlation(
+    *,
+    api_base_url: str,
+    call_short_id: str,
+    workspace_id: UUID,
+    evaluator_result_id: Optional[UUID] = None,
+) -> Dict[str, Any]:
+    otlp_endpoint = f"{api_base_url.rstrip('/')}{OBSERVABILITY_TRACES_API_PATH}"
+    env_vars: Dict[str, str] = {
+        "EFFICIENTAI_OTLP_ENDPOINT": otlp_endpoint,
+        "EFFICIENTAI_API_KEY": "<your-efficientai-api-key>",
+        "EFFICIENTAI_WORKSPACE_ID": str(workspace_id),
+        "EFFICIENTAI_CALL_SHORT_ID": call_short_id,
+    }
+    span_attrs: Dict[str, str] = {
+        "efficientai.call_short_id": call_short_id,
+        "efficientai.workspace_id": str(workspace_id),
+        "efficientai.environment": "pre_prod",
+    }
+    headers: Dict[str, str] = {
+        "X-API-Key": "<your-efficientai-api-key>",
+        "X-Workspace-Id": str(workspace_id),
+        "X-EfficientAI-Call-Short-Id": call_short_id,
+    }
+    if evaluator_result_id:
+        env_vars["EFFICIENTAI_EVALUATOR_RESULT_ID"] = str(evaluator_result_id)
+        span_attrs["efficientai.evaluator_result_id"] = str(evaluator_result_id)
+        headers["X-EfficientAI-Run-Id"] = str(evaluator_result_id)
+
+    return {
+        "otlp_endpoint": otlp_endpoint,
+        "api_key_header": "X-API-Key",
+        "suggested_env_vars": env_vars,
+        "suggested_otlp_headers": headers,
+        "suggested_span_attributes": span_attrs,
+    }
+
+
+def backfill_missing_traces_from_call_recordings(
+    db: Session,
+    *,
+    organization_id: UUID,
+    limit: int = 50,
+) -> int:
+    """Create synthetic traces for phone evaluator recordings that never got one."""
+    linked_result_ids = {
+        row[0]
+        for row in db.query(SyntheticCallTrace.evaluator_result_id)
+        .filter(
+            SyntheticCallTrace.organization_id == organization_id,
+            SyntheticCallTrace.evaluator_result_id.isnot(None),
+        )
+        .all()
+    }
+
+    recordings = (
+        db.query(CallRecording)
+        .filter(
+            CallRecording.organization_id == organization_id,
+            CallRecording.evaluator_result_id.isnot(None),
+            CallRecording.provider_platform == "vobiz",
+        )
+        .order_by(CallRecording.created_at.desc())
+        .limit(limit * 3)
+        .all()
+    )
+
+    created = 0
+    for recording in recordings:
+        if recording.evaluator_result_id in linked_result_ids:
+            continue
+        result = (
+            db.query(EvaluatorResult)
+            .filter(EvaluatorResult.id == recording.evaluator_result_id)
+            .first()
+        )
+        if not result:
+            continue
+        trace = open_trace_for_call_recording(db, recording=recording, evaluator_result=result)
+        if not trace:
+            continue
+        linked_result_ids.add(recording.evaluator_result_id)
+        created += 1
+        event = (recording.call_event or "").lower()
+        if event in {"call_ended", "completed", "failed"} or (
+            isinstance(recording.call_data, dict) and recording.call_data.get("ended_at")
+        ):
+            finalize_trace(db, call_short_id=recording.call_short_id)
+        if created >= limit:
+            break
+    return created
+
+
+def get_trace_for_result(
+    db: Session,
+    *,
+    organization_id: UUID,
+    evaluator_result_id: UUID,
+    workspace_id: Optional[UUID] = None,
+    auto_close: bool = True,
+) -> Optional[SyntheticCallTrace]:
+    query = db.query(SyntheticCallTrace).filter(
+        SyntheticCallTrace.organization_id == organization_id,
+        SyntheticCallTrace.evaluator_result_id == evaluator_result_id,
+    )
+    if workspace_id is not None:
+        query = query.filter(SyntheticCallTrace.workspace_id == workspace_id)
+    trace = query.order_by(SyntheticCallTrace.created_at.desc()).first()
+    if trace and auto_close:
+        trace = maybe_auto_close_open_trace(db, trace)
+    return trace
+
+
+def get_trace_by_id(
+    db: Session,
+    *,
+    organization_id: UUID,
+    trace_id: UUID,
+    workspace_id: Optional[UUID] = None,
+) -> Optional[SyntheticCallTrace]:
+    query = db.query(SyntheticCallTrace).filter(
+        SyntheticCallTrace.id == trace_id,
+        SyntheticCallTrace.organization_id == organization_id,
+    )
+    if workspace_id is not None:
+        query = query.filter(SyntheticCallTrace.workspace_id == workspace_id)
+    trace = query.first()
+    if trace:
+        trace = maybe_auto_close_open_trace(db, trace)
+    return trace
+
+
+def get_trace_by_call_short_id(
+    db: Session,
+    *,
+    organization_id: UUID,
+    call_short_id: str,
+    workspace_id: Optional[UUID] = None,
+) -> Optional[SyntheticCallTrace]:
+    query = db.query(SyntheticCallTrace).filter(
+        SyntheticCallTrace.organization_id == organization_id,
+        SyntheticCallTrace.call_short_id == call_short_id,
+    )
+    if workspace_id is not None:
+        query = query.filter(SyntheticCallTrace.workspace_id == workspace_id)
+    trace = query.order_by(SyntheticCallTrace.created_at.desc()).first()
+    if trace:
+        trace = maybe_auto_close_open_trace(db, trace)
+    return trace
+
+
+def list_traces(
+    db: Session,
+    *,
+    organization_id: UUID,
+    workspace_id: UUID,
+    skip: int = 0,
+    limit: int = 50,
+    status: Optional[str] = None,
+) -> tuple[List[SyntheticCallTrace], int]:
+    backfill_missing_traces_from_call_recordings(db, organization_id=organization_id)
+    query = db.query(SyntheticCallTrace).filter(
+        SyntheticCallTrace.organization_id == organization_id,
+        SyntheticCallTrace.workspace_id == workspace_id,
+    )
+    if status:
+        if status == "closed":
+            query = query.filter(SyntheticCallTrace.status.in_(("closed", "finalized")))
+        else:
+            query = query.filter(SyntheticCallTrace.status == status)
+    total = query.count()
+    rows = (
+        query.order_by(SyntheticCallTrace.started_at.desc())
+        .offset(skip)
+        .limit(limit)
+        .all()
+    )
+    return rows, total
+
+
+def build_otlp_setup_info(*, api_base_url: str) -> Dict[str, Any]:
+    otlp_endpoint = f"{api_base_url.rstrip('/')}{OBSERVABILITY_TRACES_API_PATH}"
+    sessions_endpoint = f"{api_base_url.rstrip('/')}{OBSERVABILITY_TRACES_API_PATH}/sessions"
+    pipecat_example = f'''# pip install -e 'path/to/efficientAI[otel]'
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+require_deployment_trace_env()  # EFFICIENTAI_API_KEY + EFFICIENTAI_WORKSPACE_ID
+
+async def run_bot(transport, runner_args):
+    trace_transport = resolve_trace_transport(runner_args, transport)
+    trace_ctx = await ensure_trace_session(transport=trace_transport)
+    tracing = setup_pipecat_worker_tracing(trace_ctx)
+
+    worker = PipelineWorker(
+        pipeline,
+        enable_tracing=True,
+        additional_span_attributes=tracing["additional_span_attributes"],
+    )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        await worker.cancel()
+        await close_trace_session(trace_ctx)
+
+# Pipecat runner: http://localhost:7860/client — pick WebRTC or WebSocket in the UI.
+'''
+    return {
+        "otlp_endpoint": otlp_endpoint,
+        "api_key_header": "X-API-Key",
+        "one_time_env_vars": {
+            "EFFICIENTAI_API_KEY": "<your-efficientai-api-key>",
+            "EFFICIENTAI_WORKSPACE_ID": "<workspace-uuid>",
+            "EFFICIENTAI_OTLP_ENDPOINT": otlp_endpoint,
+        },
+        "transport_options": {
+            "webrtc": "Default — Pipecat runner at http://localhost:7860/client",
+        },
+        "sessions_endpoint": sessions_endpoint,
+        "workspace_header": "X-Workspace-Id",
+        "setup_steps": [
+            {
+                "title": "Install the tracing package",
+                "detail": (
+                    "In your Pipecat project: "
+                    "uv pip install -e '/path/to/efficientAI[otel]' "
+                    "and pipecat extras (deepgram, openai, cartesia, webrtc, runner)."
+                ),
+            },
+            {
+                "title": "Add env vars to Pipecat .env",
+                "detail": (
+                    "EFFICIENTAI_API_KEY, EFFICIENTAI_WORKSPACE_ID, plus your STT/LLM/TTS keys. "
+                    "See docs/examples/pipecat_multi_agent_webrtc_tracing.py "
+                    "(or pipecat_multi_provider_webrtc_tracing.py)."
+                ),
+            },
+            {
+                "title": "Enable tracing in bot.py",
+                "detail": (
+                    "Call ensure_trace_session() on connect, "
+                    "PipelineWorker(..., enable_tracing=True), "
+                    "close_trace_session() on disconnect."
+                ),
+            },
+            {
+                "title": "Run a local WebRTC call",
+                "detail": "uv run bot.py → open :7860/client → WebRTC → talk 2–3 turns → disconnect.",
+            },
+            {
+                "title": "View results here",
+                "detail": "Refresh the Traces tab — each call shows STT, LLM, TTS, and response time per turn.",
+            },
+        ],
+        "per_call_correlation": {
+            "session_api": sessions_endpoint,
+            "header": "X-EfficientAI-Call-Short-Id",
+            "span_attribute": "efficientai.call_short_id",
+            "workspace_span_attribute": "efficientai.workspace_id",
+            "note": (
+                "Each call gets a 6-digit call ID when your bot starts. "
+                "Spans must include efficientai.call_short_id and efficientai.workspace_id. "
+                "Export to POST /api/v1/observability/traces with headers X-API-Key and X-Workspace-Id."
+            ),
+        },
+        "suggested_span_resource_attributes": {
+            "efficientai.environment": "pre_prod",
+            "efficientai.workspace_id": "<workspace-uuid>",
+        },
+        "pipecat_python_example": pipecat_example,
+    }
+
+
+def record_tier1_turns(
+    db: Session,
+    trace: SyntheticCallTrace,
+    turns: List[Dict[str, Any]],
+) -> None:
+    if not turns:
+        return
+    payload = (
+        db.query(SyntheticTracePayload)
+        .filter(SyntheticTracePayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    if not payload:
+        payload = SyntheticTracePayload(
+            synthetic_call_trace_id=trace.id,
+            workspace_id=trace.workspace_id,
+            turns=[],
+        )
+        db.add(payload)
+
+    otel_payload = (
+        db.query(SyntheticTraceOtelPayload)
+        .filter(SyntheticTraceOtelPayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    otel_turns = derive_turns_from_spans(otel_payload.spans if otel_payload else [])
+    payload.turns = merge_tier1_and_otel_turns(turns, otel_turns)
+    trace.turn_count = len(payload.turns)
+    _update_latency_aggregates(trace, payload.turns)
+    if otel_turns:
+        trace.tier = "mixed" if turns else "component"
+    db.commit()
+
+
+def ingest_otlp_spans(
+    db: Session,
+    *,
+    organization_id: UUID,
+    spans: List[Dict[str, Any]],
+    header_evaluator_result_id: Optional[str] = None,
+    header_agent_id: Optional[str] = None,
+    header_call_short_id: Optional[str] = None,
+    workspace_id: Optional[UUID] = None,
+) -> tuple[SyntheticCallTrace | None, int, bool]:
+    if not spans:
+        return None, 0, False
+
+    groups = group_spans_by_call_short_id(spans, header_call_short_id=header_call_short_id)
+    last_trace: Optional[SyntheticCallTrace] = None
+    total_accepted = 0
+    any_correlated = False
+
+    for group_call_short_id, group_spans in groups.items():
+        trace, accepted, correlated = _ingest_otlp_span_group(
+            db,
+            organization_id=organization_id,
+            spans=group_spans,
+            header_evaluator_result_id=header_evaluator_result_id,
+            header_agent_id=header_agent_id,
+            header_call_short_id=group_call_short_id or header_call_short_id,
+            workspace_id=workspace_id,
+        )
+        total_accepted += accepted
+        any_correlated = any_correlated or correlated
+        if trace is not None:
+            last_trace = trace
+
+    return last_trace, total_accepted, any_correlated
+
+
+def _ingest_otlp_span_group(
+    db: Session,
+    *,
+    organization_id: UUID,
+    spans: List[Dict[str, Any]],
+    header_evaluator_result_id: Optional[str] = None,
+    header_agent_id: Optional[str] = None,
+    header_call_short_id: Optional[str] = None,
+    workspace_id: Optional[UUID] = None,
+) -> tuple[SyntheticCallTrace | None, int, bool]:
+    if not spans:
+        return None, 0, False
+
+    correlation = extract_correlation_ids(spans)
+    evaluator_result_id = (
+        header_evaluator_result_id
+        or correlation.get("evaluator_result_id")
+    )
+    call_short_id = correlation.get("call_short_id") or header_call_short_id
+    _ = header_agent_id or correlation.get("agent_id")
+
+    trace: Optional[SyntheticCallTrace] = None
+    correlated = False
+
+    if evaluator_result_id:
+        try:
+            result_uuid = UUID(str(evaluator_result_id))
+            trace = get_trace_for_result(
+                db,
+                organization_id=organization_id,
+                evaluator_result_id=result_uuid,
+                workspace_id=workspace_id,
+                auto_close=False,
+            )
+            if not trace:
+                result = (
+                    db.query(EvaluatorResult)
+                    .filter(
+                        EvaluatorResult.id == result_uuid,
+                        EvaluatorResult.organization_id == organization_id,
+                    )
+                    .first()
+                )
+                if result and workspace_id is not None and result.workspace_id != workspace_id:
+                    result = None
+                if result:
+                    trace = open_trace(
+                        db,
+                        organization_id=organization_id,
+                        workspace_id=result.workspace_id,
+                        evaluator_result_id=result.id,
+                        agent_id=result.agent_id,
+                        persona_id=result.persona_id,
+                        scenario_id=result.scenario_id,
+                        evaluator_id=result.evaluator_id,
+                        call_short_id=call_short_id,
+                        transport="phone",
+                        tier="component",
+                    )
+            correlated = trace is not None
+        except ValueError:
+            trace = None
+
+    if not trace and call_short_id:
+        trace = get_trace_by_call_short_id(
+            db,
+            organization_id=organization_id,
+            call_short_id=call_short_id,
+            workspace_id=workspace_id,
+        )
+        correlated = trace is not None
+
+    if not trace and call_short_id and workspace_id:
+        transport = correlation.get("transport") or "custom"
+        if transport not in VALID_TRACE_TRANSPORTS:
+            transport = "custom"
+        try:
+            trace = open_trace_session(
+                db,
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                call_short_id=call_short_id,
+                transport=transport,
+            )
+            correlated = True
+        except ValueError:
+            trace = None
+
+    if not trace:
+        return None, len(spans), False
+
+    if trace.status in ("closed", "finalized"):
+        trace.status = "open"
+        trace.ended_at = None
+
+    otel_payload = (
+        db.query(SyntheticTraceOtelPayload)
+        .filter(SyntheticTraceOtelPayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    if not otel_payload:
+        otel_payload = SyntheticTraceOtelPayload(
+            synthetic_call_trace_id=trace.id,
+            workspace_id=trace.workspace_id,
+            spans=[],
+            trace_ids=[],
+        )
+        db.add(otel_payload)
+
+    existing_spans: List[Dict[str, Any]] = list(otel_payload.spans or [])
+    existing_ids = {(s.get("trace_id"), s.get("span_id")) for s in existing_spans}
+    new_trace_ids = set(otel_payload.trace_ids or [])
+
+    for span in spans:
+        key = (span.get("trace_id"), span.get("span_id"))
+        if key in existing_ids:
+            continue
+        existing_spans.append(span)
+        existing_ids.add(key)
+        if span.get("trace_id"):
+            new_trace_ids.add(span["trace_id"])
+
+    otel_payload.spans = existing_spans
+    otel_payload.trace_ids = sorted(new_trace_ids)
+
+    scoped_spans = filter_spans_for_trace(existing_spans, call_short_id=trace.call_short_id)
+
+    payload = (
+        db.query(SyntheticTracePayload)
+        .filter(SyntheticTracePayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    tier1_turns = list(payload.turns or []) if payload else []
+    otel_turns = derive_turns_from_spans(scoped_spans)
+    merged = merge_tier1_and_otel_turns(tier1_turns, otel_turns)
+
+    if payload:
+        payload.turns = merged
+    else:
+        db.add(
+            SyntheticTracePayload(
+                synthetic_call_trace_id=trace.id,
+                workspace_id=trace.workspace_id,
+                turns=merged,
+            )
+        )
+
+    trace.turn_count = len(merged)
+    trace.component_aggregates = compute_component_aggregates(merged)
+    transport = correlation.get("transport")
+    if transport in VALID_TRACE_TRANSPORTS:
+        trace.transport = transport
+    if otel_turns:
+        trace.tier = "mixed" if tier1_turns else "component"
+    _update_latency_aggregates(trace, merged)
+    trace = maybe_auto_close_open_trace(
+        db,
+        trace,
+        triggering_spans=spans,
+        all_spans=existing_spans,
+    )
+    db.commit()
+    db.refresh(trace)
+    return trace, len(spans), correlated
+
+
+def ingest_json_spans(
+    db: Session,
+    *,
+    organization_id: UUID,
+    workspace_id: UUID,
+    call_short_id: str,
+    agent_id: Optional[UUID] = None,
+    spans: List[Dict[str, Any]],
+) -> tuple[SyntheticCallTrace | None, int, bool]:
+    """Convert simple JSON span payloads into OTLP-shaped records for ingest."""
+    import uuid as _uuid
+
+    otlp_spans: List[Dict[str, Any]] = []
+    for idx, span in enumerate(spans):
+        name = str(span.get("name") or "unknown")
+        turn_number = int(span.get("turn_number") or 1)
+        attrs: Dict[str, Any] = dict(span.get("attributes") or {})
+        attrs.setdefault("gen_ai.operation.name", name.lower())
+        attrs.setdefault("turn.number", turn_number)
+        ttfb_ms = span.get("ttfb_ms")
+        if ttfb_ms is not None:
+            attrs.setdefault("metrics.ttfb", float(ttfb_ms) / 1000.0)
+        otlp_spans.append(
+            {
+                "trace_id": f"json-{call_short_id}",
+                "span_id": f"json-{idx}-{_uuid.uuid4().hex[:8]}",
+                "name": name,
+                "attributes": attrs,
+            }
+        )
+
+    return ingest_otlp_spans(
+        db,
+        organization_id=organization_id,
+        spans=otlp_spans,
+        header_call_short_id=call_short_id,
+        header_agent_id=str(agent_id) if agent_id else None,
+        workspace_id=workspace_id,
+    )
+
+
+def finalize_trace(
+    db: Session,
+    *,
+    call_short_id: str,
+    tier1_turns: Optional[List[Dict[str, Any]]] = None,
+) -> Optional[SyntheticCallTrace]:
+    recording = (
+        db.query(CallRecording)
+        .filter(CallRecording.call_short_id == call_short_id)
+        .first()
+    )
+    if not recording:
+        logger.warning("finalize_trace: no CallRecording for call_short_id={}", call_short_id)
+        return None
+
+    trace = None
+    if recording.evaluator_result_id:
+        trace = (
+            db.query(SyntheticCallTrace)
+            .filter(SyntheticCallTrace.evaluator_result_id == recording.evaluator_result_id)
+            .order_by(SyntheticCallTrace.created_at.desc())
+            .first()
+        )
+    if not trace:
+        trace = (
+            db.query(SyntheticCallTrace)
+            .filter(SyntheticCallTrace.call_short_id == call_short_id)
+            .order_by(SyntheticCallTrace.created_at.desc())
+            .first()
+        )
+
+    if not trace:
+        if not recording.evaluator_result_id:
+            return None
+        result = (
+            db.query(EvaluatorResult)
+            .filter(EvaluatorResult.id == recording.evaluator_result_id)
+            .first()
+        )
+        if not result:
+            return None
+        trace = open_trace(
+            db,
+            organization_id=recording.organization_id,
+            workspace_id=recording.workspace_id,
+            evaluator_result_id=result.id,
+            agent_id=result.agent_id,
+            persona_id=result.persona_id,
+            scenario_id=result.scenario_id,
+            evaluator_id=result.evaluator_id,
+            call_recording_id=recording.id,
+            call_short_id=call_short_id,
+            transport="phone",
+            provider_platform=recording.provider_platform or "vobiz",
+        )
+
+    if tier1_turns:
+        record_tier1_turns(db, trace, tier1_turns)
+        db.refresh(trace)
+
+    trace.status = "closed"
+    trace.ended_at = _utcnow()
+    trace.call_recording_id = recording.id
+    trace.call_short_id = call_short_id
+    trace.failure_flags = _compute_failure_flags(trace)
+    db.commit()
+    db.refresh(trace)
+    return trace
+
+
+def load_trace_detail(
+    db: Session,
+    trace: SyntheticCallTrace,
+) -> Dict[str, Any]:
+    payload = (
+        db.query(SyntheticTracePayload)
+        .filter(SyntheticTracePayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    otel_payload = (
+        db.query(SyntheticTraceOtelPayload)
+        .filter(SyntheticTraceOtelPayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    raw_spans = list(otel_payload.spans or []) if otel_payload else []
+    scoped_spans = filter_spans_for_trace(raw_spans, call_short_id=trace.call_short_id)
+    otel_spans = annotate_spans_with_display_turn(scoped_spans)
+    stored_turns = list(payload.turns or []) if payload else []
+
+    tier1_turns: List[Dict[str, Any]] = []
+    for turn in stored_turns:
+        num = turn.get("turn_number")
+        sut = turn.get("sut_response_latency_ms")
+        if num is not None and sut is not None:
+            tier1_turns.append(
+                {
+                    "turn_number": int(num),
+                    "sut_response_latency_ms": float(sut),
+                    "talk_over": bool(turn.get("talk_over") or False),
+                    "extra": dict(turn.get("extra") or {}),
+                }
+            )
+
+    otel_turns = derive_turns_from_spans(otel_spans)
+    if otel_spans:
+        turns = merge_tier1_and_otel_turns(tier1_turns, otel_turns)
+    else:
+        turns = stored_turns
+    turns = _normalize_turn_rows(turns)
+    latency_summary = compute_trace_latency_summary(turns) if turns else {}
+
+    return {
+        "trace": trace,
+        "turns": turns,
+        "otel_spans": otel_spans,
+        "otel_trace_ids": list(otel_payload.trace_ids or []) if otel_payload else [],
+        "latency_summary": latency_summary,
+        "pipeline_models": extract_pipeline_models(otel_spans),
+    }
+
+
+def build_otel_correlation(
+    db: Session,
+    result: EvaluatorResult,
+    *,
+    api_base_url: str,
+) -> Dict[str, Any]:
+    trace = None
+    if result.synthetic_call_trace_id:
+        trace = get_trace_by_id(
+            db,
+            organization_id=result.organization_id,
+            trace_id=result.synthetic_call_trace_id,
+        )
+    if not trace:
+        trace = get_trace_for_result(
+            db,
+            organization_id=result.organization_id,
+            evaluator_result_id=result.id,
+        )
+
+    call_short_id = trace.call_short_id if trace else None
+    if not call_short_id and result.call_data and isinstance(result.call_data, dict):
+        call_short_id = result.call_data.get("call_short_id")
+
+    otlp_endpoint = f"{api_base_url.rstrip('/')}{OBSERVABILITY_TRACES_API_PATH}"
+    env_vars = {
+        "EFFICIENTAI_OTLP_ENDPOINT": otlp_endpoint,
+        "EFFICIENTAI_API_KEY": "<your-efficientai-api-key>",
+    }
+    if result.workspace_id:
+        env_vars["EFFICIENTAI_WORKSPACE_ID"] = str(result.workspace_id)
+    if call_short_id:
+        env_vars["EFFICIENTAI_CALL_SHORT_ID"] = call_short_id
+
+    span_attrs: Dict[str, str] = {
+        "efficientai.evaluator_result_id": str(result.id),
+        "efficientai.call_short_id": call_short_id or "",
+        "efficientai.environment": "pre_prod",
+    }
+    if result.workspace_id:
+        span_attrs["efficientai.workspace_id"] = str(result.workspace_id)
+
+    return {
+        "evaluator_result_id": result.id,
+        "synthetic_call_trace_id": trace.id if trace else result.synthetic_call_trace_id,
+        "call_short_id": call_short_id,
+        "agent_id": result.agent_id,
+        "otlp_endpoint": otlp_endpoint,
+        "suggested_env_vars": env_vars,
+        "suggested_span_attributes": span_attrs,
+    }
+
+
+def _update_latency_aggregates(trace: SyntheticCallTrace, turns: List[Dict[str, Any]]) -> None:
+    latencies = [
+        float(t["sut_response_latency_ms"])
+        for t in turns
+        if t.get("sut_response_latency_ms") is not None
+    ]
+    if latencies:
+        trace.response_latency_p50_ms = _percentile(latencies, 50)
+        trace.response_latency_p90_ms = _percentile(latencies, 90)
+        trace.response_latency_p95_ms = _percentile(latencies, 95)
+
+
+def _compute_failure_flags(trace: SyntheticCallTrace) -> List[str]:
+    flags: List[str] = []
+    if trace.turn_count == 0:
+        flags.append("no_turns")
+    if trace.response_latency_p95_ms and trace.response_latency_p95_ms > 3000:
+        flags.append("high_latency")
+    return flags
diff --git a/app/services/telephony/call_recording_lifecycle.py b/app/services/telephony/call_recording_lifecycle.py
index f5121e36..7c198916 100644
--- a/app/services/telephony/call_recording_lifecycle.py
+++ b/app/services/telephony/call_recording_lifecycle.py
@@ -1,594 +1,616 @@
-"""Helpers for Vobiz / live call CallRecording lifecycle updates."""
-
-from __future__ import annotations
-
-import random
-import string
-import uuid
-from datetime import datetime, timezone
-from typing import Any, Dict, Optional
-from uuid import UUID
-
-from sqlalchemy.orm import Session
-from sqlalchemy.orm.attributes import flag_modified
-
-from app.models.database import Agent, CallRecording, CallRecordingSource
-from app.models.enums import CallRecordingStatus
-
-TERMINAL_VOBIZ_STATUSES = {
-    "completed",
-    "hangup",
-    "failed",
-    "busy",
-    "no-answer",
-    "canceled",
-    "cancelled",
-}
-
-LIVE_CALL_EVENTS = {
-    "outbound_initiated",
-    "ringing",
-    "call_started",
-    "call_in_progress",
-    "in-progress",
-    "answered",
-}
-
-
-def _now_iso() -> str:
-    return datetime.now(timezone.utc).isoformat()
-
-
-def _copy_call_data(row: CallRecording) -> Dict[str, Any]:
-    return dict(row.call_data) if isinstance(row.call_data, dict) else {}
-
-
-def _fresh_call_data(db: Session, row: CallRecording) -> Dict[str, Any]:
-    """Reload call_data from the database before mutating a long-lived session row."""
-    db.expire(row, ["call_data"])
-    db.refresh(row, attribute_names=["call_data"])
-    return _copy_call_data(row)
-
-
-def _save_call_data(db: Session, row: CallRecording, data: Dict[str, Any]) -> None:
-    row.call_data = data
-    flag_modified(row, "call_data")
-    from app.services.live_entity_storage import sync_call_recording
-
-    sync_call_recording(db, row)
-    db.commit()
-    db.refresh(row)
-
-
-def _timestamp_to_ms(value: Any) -> Optional[int]:
-    if value is None:
-        return None
-    if isinstance(value, (int, float)):
-        return int(value * 1000) if value < 10_000_000_000 else int(value)
-    if isinstance(value, str):
-        try:
-            parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
-            return int(parsed.timestamp() * 1000)
-        except ValueError:
-            return None
-    return None
-
-
-def is_bootstrap_user_message(content: str) -> bool:
-    lowered = content.strip().lower()
-    return (
-        lowered.startswith("start by greeting")
-        or "introducing yourself based on the system instruction" in lowered
-    )
-
-
-def filter_conversation_turns(turns: Optional[list]) -> list:
-    if not turns:
-        return []
-    filtered = []
-    for turn in turns:
-        if not isinstance(turn, dict):
-            continue
-        speaker = (turn.get("speaker") or turn.get("role") or "").lower()
-        text = (turn.get("text") or turn.get("content") or "").strip()
-        if not text:
-            continue
-        if speaker in {"user", "caller"} and is_bootstrap_user_message(text):
-            continue
-        filtered.append(turn)
-    return filtered
-
-
-def _count_user_messages(messages: list) -> int:
-    return sum(1 for msg in messages if isinstance(msg, dict) and msg.get("role") == "user")
-
-
-def resolve_telephony_messages(
-    *,
-    live_transcript: list,
-    conversation_turns: Optional[list] = None,
-) -> list:
-    """Pick the best turn-based message list for observability UI."""
-    live_messages = live_transcript_to_messages(live_transcript)
-    conv_messages = conversation_turns_to_messages(filter_conversation_turns(conversation_turns))
-
-    live_user_count = _count_user_messages(live_messages)
-    conv_user_count = _count_user_messages(conv_messages)
-
-    if live_user_count >= conv_user_count and live_messages:
-        return live_messages
-    if conv_messages:
-        return conv_messages
-    return live_messages
-
-
-def live_transcript_to_messages(transcript: list) -> list:
-    messages = []
-    for entry in transcript:
-        if not isinstance(entry, dict):
-            continue
-        content = (entry.get("content") or "").strip()
-        if not content:
-            continue
-        raw_role = str(entry.get("role") or "assistant").lower()
-        if raw_role in {"user", "caller"}:
-            role = "user"
-        elif raw_role in {"agent", "assistant", "bot"}:
-            role = "assistant"
-        else:
-            role = "assistant"
-        message = {"role": role, "content": content}
-        start_time = _timestamp_to_ms(entry.get("timestamp"))
-        if start_time is not None:
-            message["start_time"] = start_time
-        messages.append(message)
-    return messages
-
-
-def conversation_turns_to_messages(conversation_turns: list) -> list:
-    messages = []
-    for turn in conversation_turns:
-        if not isinstance(turn, dict):
-            continue
-        text = (turn.get("text") or turn.get("content") or "").strip()
-        if not text:
-            continue
-        speaker = (turn.get("speaker") or turn.get("role") or "assistant").lower()
-        role = "user" if speaker in {"user", "caller"} else "assistant"
-        message = {"role": role, "content": text}
-        start = turn.get("start")
-        if isinstance(start, (int, float)):
-            message["start_time"] = int(start * 1000)
-        messages.append(message)
-    return messages
-
-
-def _normalize_call_event(status: Optional[str]) -> str:
-    if not status:
-        return "updated"
-    normalized = status.lower().replace("_", "-")
-    if normalized in TERMINAL_VOBIZ_STATUSES:
-        if normalized in {"completed", "hangup"}:
-            return "call_ended"
-        if normalized in {"busy", "no-answer", "canceled", "cancelled"}:
-            return "call_ended"
-        return "failed"
-    if normalized in {"ringing", "in-progress", "answered", "answer"}:
-        return "call_in_progress"
-    if normalized in {"initiated", "queued", "outbound-initiated", "outbound_initiated"}:
-        return "outbound_initiated"
-    return normalized
-
-
-def _find_by_call_ref(db: Session, call_ref: str) -> Optional[CallRecording]:
-    rows = (
-        db.query(CallRecording)
-        .filter(CallRecording.provider_platform == "vobiz")
-        .order_by(CallRecording.created_at.desc())
-        .limit(200)
-        .all()
-    )
-    for row in rows:
-        data = row.call_data if isinstance(row.call_data, dict) else {}
-        if data.get("call_ref") == call_ref:
-            return row
-    return None
-
-
-def find_call_recording(
-    db: Session,
-    *,
-    call_ref: Optional[str] = None,
-    provider_call_id: Optional[str] = None,
-) -> Optional[CallRecording]:
-    if provider_call_id:
-        row = (
-            db.query(CallRecording)
-            .filter(CallRecording.provider_call_id == provider_call_id)
-            .first()
-        )
-        if row:
-            return row
-        rows = (
-            db.query(CallRecording)
-            .filter(CallRecording.provider_platform == "vobiz")
-            .order_by(CallRecording.created_at.desc())
-            .limit(200)
-            .all()
-        )
-        for candidate in rows:
-            data = candidate.call_data if isinstance(candidate.call_data, dict) else {}
-            for key in ("request_uuid", "message_uuid", "api_id", "call_uuid"):
-                if data.get(key) == provider_call_id:
-                    return candidate
-    if call_ref:
-        return _find_by_call_ref(db, call_ref)
-    return None
-
-
-def create_inbound_call_recording(
-    db: Session,
-    *,
-    agent: Agent,
-    organization_id: UUID,
-    call_ref: str,
-    from_number: Optional[str],
-    to_number: Optional[str],
-    provider_call_id: Optional[str] = None,
-    evaluator_id: Optional[UUID] = None,
-    evaluator_result_id: Optional[UUID] = None,
-) -> CallRecording:
-    existing = find_call_recording(db, call_ref=call_ref, provider_call_id=provider_call_id)
-    if existing:
-        return existing
-
-    call_short_id = "".join(random.choices(string.digits, k=6))
-    call_data: Dict[str, Any] = {
-        "call_ref": call_ref,
-        "call_short_id": call_short_id,
-        "direction": "inbound",
-        "from_number": from_number,
-        "to_number": to_number,
-        "started_at": _now_iso(),
-        "live_transcript": [],
-    }
-    if evaluator_id is not None:
-        call_data["evaluator_id"] = str(evaluator_id)
-    row = CallRecording(
-        organization_id=organization_id,
-        workspace_id=agent.workspace_id,
-        call_short_id=call_short_id,
-        status=CallRecordingStatus.PENDING,
-        source=CallRecordingSource.WEBHOOK,
-        call_event="call_started",
-        call_data=call_data,
-        provider_call_id=provider_call_id,
-        provider_platform="vobiz",
-        agent_id=agent.id,
-        evaluator_result_id=evaluator_result_id,
-    )
-    db.add(row)
-    db.flush()
-    from app.services.live_entity_storage import register_call_recording
-
-    register_call_recording(db, row)
-    db.commit()
-    db.refresh(row)
-    return row
-
-
-def mark_call_in_progress(
-    db: Session,
-    *,
-    call_ref: Optional[str] = None,
-    provider_call_id: Optional[str] = None,
-) -> Optional[CallRecording]:
-    row = find_call_recording(db, call_ref=call_ref, provider_call_id=provider_call_id)
-    if not row:
-        return None
-    row.call_event = "call_in_progress"
-    data = _copy_call_data(row)
-    data.setdefault("live_transcript", [])
-    if not data.get("started_at"):
-        data["started_at"] = _now_iso()
-    _save_call_data(db, row, data)
-    return row
-
-
-def update_call_from_vobiz_event(
-    db: Session,
-    *,
-    provider_call_id: str,
-    call_status: Optional[str],
-    payload: Optional[Dict[str, Any]] = None,
-    call_ref: Optional[str] = None,
-) -> Optional[CallRecording]:
-    row = find_call_recording(db, call_ref=call_ref, provider_call_id=provider_call_id)
-    if not row:
-        return None
-
-    event = _normalize_call_event(call_status)
-    data = _fresh_call_data(db, row)
-    row.status = CallRecordingStatus.UPDATED
-    row.call_event = event
-    if payload:
-        data["last_event"] = payload
-    if event == "call_ended" or event == "failed":
-        data["ended_at"] = _now_iso()
-    _save_call_data(db, row, data)
-    return row
-
-
-def link_provider_call_id(
-    db: Session,
-    *,
-    call_ref: str,
-    provider_call_id: str,
-) -> Optional[CallRecording]:
-    if not provider_call_id:
-        return None
-    row = find_call_recording(db, call_ref=call_ref)
-    if not row:
-        return None
-    data = _copy_call_data(row)
-    data["call_uuid"] = provider_call_id
-    if row.provider_call_id != provider_call_id:
-        row.provider_call_id = provider_call_id
-    _save_call_data(db, row, data)
-    return row
-
-
-def finalize_call_on_media_disconnect(
-    db: Session,
-    *,
-    call_ref: str,
-) -> Optional[CallRecording]:
-    """Mark a live call as ended when the media websocket closes."""
-    row = find_call_recording(db, call_ref=call_ref)
-    if not row:
-        # region agent log
-        from app.utils.debug_agent_log import agent_debug_log
-
-        agent_debug_log(
-            "call_recording_lifecycle.py:finalize_call_on_media_disconnect",
-            "no CallRecording for call_ref",
-            {"call_ref": call_ref},
-            "H3",
-        )
-        # endregion
-        return None
-    event = (row.call_event or "").lower()
-    if event not in LIVE_CALL_EVENTS:
-        return row
-    updated = update_call_from_vobiz_event(
-        db,
-        provider_call_id=row.provider_call_id or "",
-        call_status="completed",
-        payload={"source": "media_disconnect"},
-        call_ref=call_ref,
-    )
-    if updated:
-        data = _fresh_call_data(db, updated)
-        live_transcript = list(data.get("live_transcript") or [])
-        if live_transcript and not data.get("messages"):
-            data["messages"] = live_transcript_to_messages(live_transcript)
-            _save_call_data(db, updated, data)
-        # region agent log
-        from app.utils.debug_agent_log import agent_debug_log
-
-        agent_debug_log(
-            "call_recording_lifecycle.py:finalize_call_on_media_disconnect",
-            "call finalized on media disconnect",
-            {
-                "call_short_id": updated.call_short_id,
-                "call_event": updated.call_event,
-                "live_transcript_count": len(live_transcript),
-                "messages_count": len(data.get("messages") or []),
-            },
-            "H3",
-        )
-        # endregion
-        # Evaluator dispatch runs after recording artifacts exist (Celery finalize or
-        # carrier recording webhook). Enqueue here only when audio is already on the row.
-        if updated.evaluator_result_id:
-            data_after = _fresh_call_data(db, updated)
-            if data_after.get("recording_s3_key"):
-                from app.services.evaluators.evaluator_inbound_service import (
-                    enqueue_linked_evaluator_result_if_ready,
-                )
-
-                enqueue_linked_evaluator_result_if_ready(db, updated)
-    return updated
-
-
-def ingest_carrier_recording_url(
-    db: Session,
-    row: CallRecording,
-    recording_url: str,
-) -> Optional[str]:
-    """Download a carrier session recording and store as recording_s3_key (preferred artifact)."""
-    data = _fresh_call_data(db, row)
-    if data.get("recording_s3_key") and data.get("recording_source") == "carrier":
-        return str(data["recording_s3_key"])
-
-    existing_pipeline_key = data.get("recording_s3_key")
-
-    from loguru import logger
-
-    from app.services.storage.s3_service import s3_service
-    from app.services.telephony.recording_download import download_recording_url
-
-    try:
-        audio_bytes, content_type = download_recording_url(
-            recording_url,
-            user_supplied=False,
-        )
-    except Exception as exc:
-        logger.warning(
-            "Carrier recording download failed for call_short_id={}: {}",
-            row.call_short_id,
-            exc,
-        )
-        return None
-
-    lowered = (content_type or "").lower()
-    if "wav" in lowered:
-        file_format = "wav"
-    elif "mpeg" in lowered or "mp3" in lowered:
-        file_format = "mp3"
-    else:
-        file_format = "mp3"
-
-    try:
-        file_id = uuid.uuid4()
-        meaningful_id = f"carrier-{row.call_short_id}-{int(datetime.now(timezone.utc).timestamp())}"
-        s3_key = s3_service.upload_file(
-            file_content=audio_bytes,
-            file_id=file_id,
-            file_format=file_format,
-            organization_id=str(row.organization_id),
-            evaluator_id=None,
-            meaningful_id=meaningful_id,
-        )
-    except Exception as exc:
-        logger.warning(
-            "Carrier recording S3 upload failed for call_short_id={}: {}",
-            row.call_short_id,
-            exc,
-        )
-        return None
-
-    data["recording_s3_key"] = s3_key
-    if existing_pipeline_key and existing_pipeline_key != s3_key:
-        data["pipeline_recording_s3_key"] = existing_pipeline_key
-    data["recording_source"] = "carrier"
-    _save_call_data(db, row, data)
-
-    if row.evaluator_result_id:
-        from app.services.evaluators.evaluator_inbound_service import (
-            enqueue_linked_evaluator_result_if_ready,
-        )
-
-        enqueue_linked_evaluator_result_if_ready(db, row)
-    return s3_key
-
-
-def append_live_transcript_turn(
-    db: Session,
-    *,
-    call_short_id: str,
-    role: str,
-    content: str,
-) -> None:
-    if not content.strip():
-        return
-    row = db.query(CallRecording).filter(CallRecording.call_short_id == call_short_id).first()
-    if not row:
-        return
-    data = _copy_call_data(row)
-    transcript = list(data.get("live_transcript") or [])
-    normalized = content.strip()
-    if transcript and transcript[-1].get("role") == role:
-        last_content = transcript[-1].get("content") or ""
-        if normalized == last_content:
-            return
-        if normalized.startswith(last_content):
-            transcript[-1] = {
-                "role": role,
-                "content": normalized,
-                "timestamp": _now_iso(),
-            }
-            data["live_transcript"] = transcript
-            _save_call_data(db, row, data)
-            return
-    transcript.append(
-        {
-            "role": role,
-            "content": normalized,
-            "timestamp": _now_iso(),
-        }
-    )
-    data["live_transcript"] = transcript
-    _save_call_data(db, row, data)
-
-
-def persist_telephony_call_artifacts(
-    db: Session,
-    *,
-    call_short_id: str,
-    conversation_turns: Optional[list] = None,
-    transcript_text: Optional[str] = None,
-    s3_key: Optional[str] = None,
-    duration: Optional[float] = None,
-) -> Optional[CallRecording]:
-    """Persist transcript and recording metadata when a telephony call ends."""
-    row = db.query(CallRecording).filter(CallRecording.call_short_id == call_short_id).first()
-    if not row:
-        # region agent log
-        from app.utils.debug_agent_log import agent_debug_log
-
-        agent_debug_log(
-            "call_recording_lifecycle.py:persist_telephony_call_artifacts",
-            "CallRecording not found",
-            {"call_short_id": call_short_id},
-            "H3",
-        )
-        # endregion
-        return None
-
-    data = _copy_call_data(row)
-    live_transcript = list(data.get("live_transcript") or [])
-
-    resolved_messages = resolve_telephony_messages(
-        live_transcript=live_transcript,
-        conversation_turns=conversation_turns,
-    )
-    if resolved_messages:
-        data["messages"] = resolved_messages
-        if not live_transcript:
-            data["live_transcript"] = [
-                {
-                    "role": msg["role"],
-                    "content": msg["content"],
-                    "timestamp": _now_iso(),
-                }
-                for msg in resolved_messages
-            ]
-
-    if transcript_text:
-        data["transcript"] = transcript_text
-    if s3_key:
-        if not data.get("recording_s3_key"):
-            data["recording_s3_key"] = s3_key
-        else:
-            data.setdefault("pipeline_recording_s3_key", s3_key)
-    if duration is not None:
-        data["duration_seconds"] = duration
-    if not data.get("ended_at"):
-        data["ended_at"] = _now_iso()
-
-    _save_call_data(db, row, data)
-    # region agent log
-    from app.utils.debug_agent_log import agent_debug_log
-
-    agent_debug_log(
-        "call_recording_lifecycle.py:persist_telephony_call_artifacts",
-        "artifacts persisted",
-        {
-            "call_short_id": call_short_id,
-            "live_transcript_count": len(data.get("live_transcript") or []),
-            "messages_count": len(data.get("messages") or []),
-            "has_recording_s3_key": bool(data.get("recording_s3_key")),
-            "has_recording_url": bool(data.get("recording_url")),
-            "duration_seconds": data.get("duration_seconds"),
-        },
-        "H3",
-    )
-    # endregion
-    if row.evaluator_result_id:
-        from app.services.evaluators.evaluator_inbound_service import (
-            enqueue_linked_evaluator_result_if_ready,
-        )
-
-        enqueue_linked_evaluator_result_if_ready(db, row)
-    return row
+"""Helpers for Vobiz / live call CallRecording lifecycle updates."""
+
+from __future__ import annotations
+
+import random
+import string
+import uuid
+from datetime import datetime, timezone
+from typing import Any, Dict, Optional
+from uuid import UUID
+
+from sqlalchemy.orm import Session
+from sqlalchemy.orm.attributes import flag_modified
+
+from app.models.database import Agent, CallRecording, CallRecordingSource, EvaluatorResult
+from app.models.enums import CallRecordingStatus
+
+TERMINAL_VOBIZ_STATUSES = {
+    "completed",
+    "hangup",
+    "failed",
+    "busy",
+    "no-answer",
+    "canceled",
+    "cancelled",
+}
+
+LIVE_CALL_EVENTS = {
+    "outbound_initiated",
+    "ringing",
+    "call_started",
+    "call_in_progress",
+    "in-progress",
+    "answered",
+}
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _copy_call_data(row: CallRecording) -> Dict[str, Any]:
+    return dict(row.call_data) if isinstance(row.call_data, dict) else {}
+
+
+def _fresh_call_data(db: Session, row: CallRecording) -> Dict[str, Any]:
+    """Reload call_data from the database before mutating a long-lived session row."""
+    db.expire(row, ["call_data"])
+    db.refresh(row, attribute_names=["call_data"])
+    return _copy_call_data(row)
+
+
+def _save_call_data(db: Session, row: CallRecording, data: Dict[str, Any]) -> None:
+    row.call_data = data
+    flag_modified(row, "call_data")
+    from app.services.live_entity_storage import sync_call_recording
+
+    sync_call_recording(db, row)
+    db.commit()
+    db.refresh(row)
+
+
+def _timestamp_to_ms(value: Any) -> Optional[int]:
+    if value is None:
+        return None
+    if isinstance(value, (int, float)):
+        return int(value * 1000) if value < 10_000_000_000 else int(value)
+    if isinstance(value, str):
+        try:
+            parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+            return int(parsed.timestamp() * 1000)
+        except ValueError:
+            return None
+    return None
+
+
+def is_bootstrap_user_message(content: str) -> bool:
+    lowered = content.strip().lower()
+    return (
+        lowered.startswith("start by greeting")
+        or "introducing yourself based on the system instruction" in lowered
+    )
+
+
+def filter_conversation_turns(turns: Optional[list]) -> list:
+    if not turns:
+        return []
+    filtered = []
+    for turn in turns:
+        if not isinstance(turn, dict):
+            continue
+        speaker = (turn.get("speaker") or turn.get("role") or "").lower()
+        text = (turn.get("text") or turn.get("content") or "").strip()
+        if not text:
+            continue
+        if speaker in {"user", "caller"} and is_bootstrap_user_message(text):
+            continue
+        filtered.append(turn)
+    return filtered
+
+
+def _count_user_messages(messages: list) -> int:
+    return sum(1 for msg in messages if isinstance(msg, dict) and msg.get("role") == "user")
+
+
+def resolve_telephony_messages(
+    *,
+    live_transcript: list,
+    conversation_turns: Optional[list] = None,
+) -> list:
+    """Pick the best turn-based message list for observability UI."""
+    live_messages = live_transcript_to_messages(live_transcript)
+    conv_messages = conversation_turns_to_messages(filter_conversation_turns(conversation_turns))
+
+    live_user_count = _count_user_messages(live_messages)
+    conv_user_count = _count_user_messages(conv_messages)
+
+    if live_user_count >= conv_user_count and live_messages:
+        return live_messages
+    if conv_messages:
+        return conv_messages
+    return live_messages
+
+
+def live_transcript_to_messages(transcript: list) -> list:
+    messages = []
+    for entry in transcript:
+        if not isinstance(entry, dict):
+            continue
+        content = (entry.get("content") or "").strip()
+        if not content:
+            continue
+        raw_role = str(entry.get("role") or "assistant").lower()
+        if raw_role in {"user", "caller"}:
+            role = "user"
+        elif raw_role in {"agent", "assistant", "bot"}:
+            role = "assistant"
+        else:
+            role = "assistant"
+        message = {"role": role, "content": content}
+        start_time = _timestamp_to_ms(entry.get("timestamp"))
+        if start_time is not None:
+            message["start_time"] = start_time
+        messages.append(message)
+    return messages
+
+
+def conversation_turns_to_messages(conversation_turns: list) -> list:
+    messages = []
+    for turn in conversation_turns:
+        if not isinstance(turn, dict):
+            continue
+        text = (turn.get("text") or turn.get("content") or "").strip()
+        if not text:
+            continue
+        speaker = (turn.get("speaker") or turn.get("role") or "assistant").lower()
+        role = "user" if speaker in {"user", "caller"} else "assistant"
+        message = {"role": role, "content": text}
+        start = turn.get("start")
+        if isinstance(start, (int, float)):
+            message["start_time"] = int(start * 1000)
+        messages.append(message)
+    return messages
+
+
+def _normalize_call_event(status: Optional[str]) -> str:
+    if not status:
+        return "updated"
+    normalized = status.lower().replace("_", "-")
+    if normalized in TERMINAL_VOBIZ_STATUSES:
+        if normalized in {"completed", "hangup"}:
+            return "call_ended"
+        if normalized in {"busy", "no-answer", "canceled", "cancelled"}:
+            return "call_ended"
+        return "failed"
+    if normalized in {"ringing", "in-progress", "answered", "answer"}:
+        return "call_in_progress"
+    if normalized in {"initiated", "queued", "outbound-initiated", "outbound_initiated"}:
+        return "outbound_initiated"
+    return normalized
+
+
+def _find_by_call_ref(db: Session, call_ref: str) -> Optional[CallRecording]:
+    rows = (
+        db.query(CallRecording)
+        .filter(CallRecording.provider_platform == "vobiz")
+        .order_by(CallRecording.created_at.desc())
+        .limit(200)
+        .all()
+    )
+    for row in rows:
+        data = row.call_data if isinstance(row.call_data, dict) else {}
+        if data.get("call_ref") == call_ref:
+            return row
+    return None
+
+
+def find_call_recording(
+    db: Session,
+    *,
+    call_ref: Optional[str] = None,
+    provider_call_id: Optional[str] = None,
+) -> Optional[CallRecording]:
+    if provider_call_id:
+        row = (
+            db.query(CallRecording)
+            .filter(CallRecording.provider_call_id == provider_call_id)
+            .first()
+        )
+        if row:
+            return row
+        rows = (
+            db.query(CallRecording)
+            .filter(CallRecording.provider_platform == "vobiz")
+            .order_by(CallRecording.created_at.desc())
+            .limit(200)
+            .all()
+        )
+        for candidate in rows:
+            data = candidate.call_data if isinstance(candidate.call_data, dict) else {}
+            for key in ("request_uuid", "message_uuid", "api_id", "call_uuid"):
+                if data.get(key) == provider_call_id:
+                    return candidate
+    if call_ref:
+        return _find_by_call_ref(db, call_ref)
+    return None
+
+
+def create_inbound_call_recording(
+    db: Session,
+    *,
+    agent: Agent,
+    organization_id: UUID,
+    call_ref: str,
+    from_number: Optional[str],
+    to_number: Optional[str],
+    provider_call_id: Optional[str] = None,
+    evaluator_id: Optional[UUID] = None,
+    evaluator_result_id: Optional[UUID] = None,
+) -> CallRecording:
+    existing = find_call_recording(db, call_ref=call_ref, provider_call_id=provider_call_id)
+    if existing:
+        return existing
+
+    call_short_id = "".join(random.choices(string.digits, k=6))
+    call_data: Dict[str, Any] = {
+        "call_ref": call_ref,
+        "call_short_id": call_short_id,
+        "direction": "inbound",
+        "from_number": from_number,
+        "to_number": to_number,
+        "started_at": _now_iso(),
+        "live_transcript": [],
+    }
+    if evaluator_id is not None:
+        call_data["evaluator_id"] = str(evaluator_id)
+    row = CallRecording(
+        organization_id=organization_id,
+        workspace_id=agent.workspace_id,
+        call_short_id=call_short_id,
+        status=CallRecordingStatus.PENDING,
+        source=CallRecordingSource.WEBHOOK,
+        call_event="call_started",
+        call_data=call_data,
+        provider_call_id=provider_call_id,
+        provider_platform="vobiz",
+        agent_id=agent.id,
+        evaluator_result_id=evaluator_result_id,
+    )
+    db.add(row)
+    db.flush()
+    from app.services.live_entity_storage import register_call_recording
+
+    register_call_recording(db, row)
+    db.commit()
+    db.refresh(row)
+
+    if evaluator_result_id:
+        result = (
+            db.query(EvaluatorResult)
+            .filter(EvaluatorResult.id == evaluator_result_id)
+            .first()
+        )
+        if result:
+            from app.services.synthetic_traces.trace_service import open_trace_for_call_recording
+
+            open_trace_for_call_recording(db, recording=row, evaluator_result=result)
+
+    return row
+
+
+def mark_call_in_progress(
+    db: Session,
+    *,
+    call_ref: Optional[str] = None,
+    provider_call_id: Optional[str] = None,
+) -> Optional[CallRecording]:
+    row = find_call_recording(db, call_ref=call_ref, provider_call_id=provider_call_id)
+    if not row:
+        return None
+    row.call_event = "call_in_progress"
+    data = _copy_call_data(row)
+    data.setdefault("live_transcript", [])
+    if not data.get("started_at"):
+        data["started_at"] = _now_iso()
+    _save_call_data(db, row, data)
+    return row
+
+
+def update_call_from_vobiz_event(
+    db: Session,
+    *,
+    provider_call_id: str,
+    call_status: Optional[str],
+    payload: Optional[Dict[str, Any]] = None,
+    call_ref: Optional[str] = None,
+) -> Optional[CallRecording]:
+    row = find_call_recording(db, call_ref=call_ref, provider_call_id=provider_call_id)
+    if not row:
+        return None
+
+    event = _normalize_call_event(call_status)
+    data = _fresh_call_data(db, row)
+    row.status = CallRecordingStatus.UPDATED
+    row.call_event = event
+    if payload:
+        data["last_event"] = payload
+    if event == "call_ended" or event == "failed":
+        data["ended_at"] = _now_iso()
+    _save_call_data(db, row, data)
+    return row
+
+
+def link_provider_call_id(
+    db: Session,
+    *,
+    call_ref: str,
+    provider_call_id: str,
+) -> Optional[CallRecording]:
+    if not provider_call_id:
+        return None
+    row = find_call_recording(db, call_ref=call_ref)
+    if not row:
+        return None
+    data = _copy_call_data(row)
+    data["call_uuid"] = provider_call_id
+    if row.provider_call_id != provider_call_id:
+        row.provider_call_id = provider_call_id
+    _save_call_data(db, row, data)
+    return row
+
+
+def finalize_call_on_media_disconnect(
+    db: Session,
+    *,
+    call_ref: str,
+) -> Optional[CallRecording]:
+    """Mark a live call as ended when the media websocket closes."""
+    row = find_call_recording(db, call_ref=call_ref)
+    if not row:
+        # region agent log
+        from app.utils.debug_agent_log import agent_debug_log
+
+        agent_debug_log(
+            "call_recording_lifecycle.py:finalize_call_on_media_disconnect",
+            "no CallRecording for call_ref",
+            {"call_ref": call_ref},
+            "H3",
+        )
+        # endregion
+        return None
+    event = (row.call_event or "").lower()
+    if event not in LIVE_CALL_EVENTS:
+        return row
+    updated = update_call_from_vobiz_event(
+        db,
+        provider_call_id=row.provider_call_id or "",
+        call_status="completed",
+        payload={"source": "media_disconnect"},
+        call_ref=call_ref,
+    )
+    if updated:
+        data = _fresh_call_data(db, updated)
+        live_transcript = list(data.get("live_transcript") or [])
+        if live_transcript and not data.get("messages"):
+            data["messages"] = live_transcript_to_messages(live_transcript)
+            _save_call_data(db, updated, data)
+        # region agent log
+        from app.utils.debug_agent_log import agent_debug_log
+
+        agent_debug_log(
+            "call_recording_lifecycle.py:finalize_call_on_media_disconnect",
+            "call finalized on media disconnect",
+            {
+                "call_short_id": updated.call_short_id,
+                "call_event": updated.call_event,
+                "live_transcript_count": len(live_transcript),
+                "messages_count": len(data.get("messages") or []),
+            },
+            "H3",
+        )
+        # endregion
+        # Evaluator dispatch runs after recording artifacts exist (Celery finalize or
+        # carrier recording webhook). Enqueue here only when audio is already on the row.
+        if updated.evaluator_result_id:
+            data_after = _fresh_call_data(db, updated)
+            if data_after.get("recording_s3_key"):
+                from app.services.evaluators.evaluator_inbound_service import (
+                    enqueue_linked_evaluator_result_if_ready,
+                )
+
+                enqueue_linked_evaluator_result_if_ready(db, updated)
+    return updated
+
+
+def ingest_carrier_recording_url(
+    db: Session,
+    row: CallRecording,
+    recording_url: str,
+) -> Optional[str]:
+    """Download a carrier session recording and store as recording_s3_key (preferred artifact)."""
+    data = _fresh_call_data(db, row)
+    if data.get("recording_s3_key") and data.get("recording_source") == "carrier":
+        return str(data["recording_s3_key"])
+
+    existing_pipeline_key = data.get("recording_s3_key")
+
+    from loguru import logger
+
+    from app.services.storage.s3_service import s3_service
+    from app.services.telephony.recording_download import download_recording_url
+
+    try:
+        audio_bytes, content_type = download_recording_url(
+            recording_url,
+            user_supplied=False,
+        )
+    except Exception as exc:
+        logger.warning(
+            "Carrier recording download failed for call_short_id={}: {}",
+            row.call_short_id,
+            exc,
+        )
+        return None
+
+    lowered = (content_type or "").lower()
+    if "wav" in lowered:
+        file_format = "wav"
+    elif "mpeg" in lowered or "mp3" in lowered:
+        file_format = "mp3"
+    else:
+        file_format = "mp3"
+
+    try:
+        file_id = uuid.uuid4()
+        meaningful_id = f"carrier-{row.call_short_id}-{int(datetime.now(timezone.utc).timestamp())}"
+        s3_key = s3_service.upload_file(
+            file_content=audio_bytes,
+            file_id=file_id,
+            file_format=file_format,
+            organization_id=str(row.organization_id),
+            evaluator_id=None,
+            meaningful_id=meaningful_id,
+        )
+    except Exception as exc:
+        logger.warning(
+            "Carrier recording S3 upload failed for call_short_id={}: {}",
+            row.call_short_id,
+            exc,
+        )
+        return None
+
+    data["recording_s3_key"] = s3_key
+    if existing_pipeline_key and existing_pipeline_key != s3_key:
+        data["pipeline_recording_s3_key"] = existing_pipeline_key
+    data["recording_source"] = "carrier"
+    _save_call_data(db, row, data)
+
+    if row.evaluator_result_id:
+        from app.services.evaluators.evaluator_inbound_service import (
+            enqueue_linked_evaluator_result_if_ready,
+        )
+
+        enqueue_linked_evaluator_result_if_ready(db, row)
+    return s3_key
+
+
+def append_live_transcript_turn(
+    db: Session,
+    *,
+    call_short_id: str,
+    role: str,
+    content: str,
+) -> None:
+    if not content.strip():
+        return
+    row = db.query(CallRecording).filter(CallRecording.call_short_id == call_short_id).first()
+    if not row:
+        return
+    data = _copy_call_data(row)
+    transcript = list(data.get("live_transcript") or [])
+    normalized = content.strip()
+    if transcript and transcript[-1].get("role") == role:
+        last_content = transcript[-1].get("content") or ""
+        if normalized == last_content:
+            return
+        if normalized.startswith(last_content):
+            transcript[-1] = {
+                "role": role,
+                "content": normalized,
+                "timestamp": _now_iso(),
+            }
+            data["live_transcript"] = transcript
+            _save_call_data(db, row, data)
+            return
+    transcript.append(
+        {
+            "role": role,
+            "content": normalized,
+            "timestamp": _now_iso(),
+        }
+    )
+    data["live_transcript"] = transcript
+    _save_call_data(db, row, data)
+
+
+def persist_telephony_call_artifacts(
+    db: Session,
+    *,
+    call_short_id: str,
+    conversation_turns: Optional[list] = None,
+    transcript_text: Optional[str] = None,
+    s3_key: Optional[str] = None,
+    duration: Optional[float] = None,
+    trace_turns: Optional[list] = None,
+) -> Optional[CallRecording]:
+    """Persist transcript and recording metadata when a telephony call ends."""
+    row = db.query(CallRecording).filter(CallRecording.call_short_id == call_short_id).first()
+    if not row:
+        # region agent log
+        from app.utils.debug_agent_log import agent_debug_log
+
+        agent_debug_log(
+            "call_recording_lifecycle.py:persist_telephony_call_artifacts",
+            "CallRecording not found",
+            {"call_short_id": call_short_id},
+            "H3",
+        )
+        # endregion
+        return None
+
+    data = _copy_call_data(row)
+    live_transcript = list(data.get("live_transcript") or [])
+
+    resolved_messages = resolve_telephony_messages(
+        live_transcript=live_transcript,
+        conversation_turns=conversation_turns,
+    )
+    if resolved_messages:
+        data["messages"] = resolved_messages
+        if not live_transcript:
+            data["live_transcript"] = [
+                {
+                    "role": msg["role"],
+                    "content": msg["content"],
+                    "timestamp": _now_iso(),
+                }
+                for msg in resolved_messages
+            ]
+
+    if transcript_text:
+        data["transcript"] = transcript_text
+    if s3_key:
+        if not data.get("recording_s3_key"):
+            data["recording_s3_key"] = s3_key
+        else:
+            data.setdefault("pipeline_recording_s3_key", s3_key)
+    if duration is not None:
+        data["duration_seconds"] = duration
+    if not data.get("ended_at"):
+        data["ended_at"] = _now_iso()
+
+    _save_call_data(db, row, data)
+    # region agent log
+    from app.utils.debug_agent_log import agent_debug_log
+
+    agent_debug_log(
+        "call_recording_lifecycle.py:persist_telephony_call_artifacts",
+        "artifacts persisted",
+        {
+            "call_short_id": call_short_id,
+            "live_transcript_count": len(data.get("live_transcript") or []),
+            "messages_count": len(data.get("messages") or []),
+            "has_recording_s3_key": bool(data.get("recording_s3_key")),
+            "has_recording_url": bool(data.get("recording_url")),
+            "duration_seconds": data.get("duration_seconds"),
+        },
+        "H3",
+    )
+    # endregion
+    try:
+        from app.services.synthetic_traces.trace_service import finalize_trace
+
+        finalize_trace(db, call_short_id=call_short_id, tier1_turns=trace_turns)
+    except Exception as trace_err:
+        from loguru import logger
+
+        logger.warning("Failed to finalize synthetic call trace: {}", trace_err)
+
+    if row.evaluator_result_id:
+        from app.services.evaluators.evaluator_inbound_service import (
+            enqueue_linked_evaluator_result_if_ready,
+        )
+
+        enqueue_linked_evaluator_result_if_ready(db, row)
+    return row
diff --git a/app/services/telephony/vobiz_client.py b/app/services/telephony/vobiz_client.py
index 491cf737..43e964d8 100644
--- a/app/services/telephony/vobiz_client.py
+++ b/app/services/telephony/vobiz_client.py
@@ -1,329 +1,334 @@
-"""Thin Vobiz REST API client for telephony operations."""
-
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional, Tuple
-from urllib.parse import quote
-from uuid import UUID
-
-import httpx
-from loguru import logger
-from sqlalchemy.orm import Session
-
-from app.config import settings
-from app.core.encryption import decrypt_api_key
-from app.models.database import TelephonyIntegration
-from app.services.credentials import resolve_telephony_integration
-from app.services.telephony.plivo_client import normalize_e164
-
-
-class VobizClient:
-    """HTTP client for the Vobiz telephony API (Plivo-compatible surface)."""
-
-    def __init__(
-        self,
-        auth_id: str,
-        auth_token: str,
-        api_base: Optional[str] = None,
-    ):
-        if not auth_id or not auth_token:
-            raise ValueError("Vobiz auth_id and auth_token are required")
-        self.auth_id = auth_id
-        self.auth_token = auth_token
-        self.api_base = (api_base or settings.VOBIZ_API_BASE).rstrip("/")
-        self._headers = {
-            "X-Auth-ID": auth_id,
-            "X-Auth-Token": auth_token,
-            "Content-Type": "application/json",
-        }
-
-    def _account_base(self) -> str:
-        return f"{self.api_base}/api/v1/Account/{self.auth_id}"
-
-    def _call_endpoint(self) -> str:
-        return f"{self._account_base()}/Call/"
-
-    def _numbers_endpoint(self) -> str:
-        return f"{self._account_base()}/numbers"
-
-    def _application_endpoint(self) -> str:
-        return f"{self._account_base()}/Application/"
-
-    @staticmethod
-    def _to_dict(data: Any) -> Dict[str, Any]:
-        if isinstance(data, dict):
-            return data
-        return {"raw": str(data)}
-
-    def _request(
-        self,
-        method: str,
-        url: str,
-        *,
-        json: Optional[Dict[str, Any]] = None,
-        timeout: float = 30.0,
-    ) -> httpx.Response:
-        with httpx.Client(timeout=timeout) as client:
-            response = client.request(method, url, headers=self._headers, json=json)
-        return response
-
-    def test_connection(self) -> bool:
-        """Check if credentials can reach the Vobiz API."""
-        try:
-            response = self._request("GET", self._call_endpoint(), timeout=15.0)
-            if response.status_code >= 400:
-                raise ValueError(f"Vobiz API returned HTTP {response.status_code}")
-            return True
-        except Exception as e:
-            logger.exception("Vobiz connection test failed")
-            raise ValueError(f"Failed to connect to Vobiz: {str(e)}") from e
-
-    def list_account_numbers(self, *, limit: int = 100) -> List[Dict[str, Any]]:
-        """List phone numbers owned by this Vobiz account (paginated)."""
-        items: List[Dict[str, Any]] = []
-        offset = 0
-        page_limit = max(min(limit, 100), 1)
-
-        while True:
-            url = f"{self._numbers_endpoint()}?limit={page_limit}&offset={offset}"
-            try:
-                response = self._request("GET", url, timeout=30.0)
-            except Exception as e:
-                logger.exception("Failed to list Vobiz account numbers")
-                raise ValueError(f"Failed to list account numbers: {str(e)}") from e
-
-            if response.status_code >= 400:
-                raise ValueError(
-                    f"Vobiz list numbers failed (HTTP {response.status_code}): {response.text[:300]}"
-                )
-
-            data = response.json() if response.content else {}
-            batch = data.get("items") or data.get("objects") or []
-            if not isinstance(batch, list):
-                batch = []
-            items.extend(batch)
-
-            total = data.get("total")
-            if not batch or (total is not None and len(items) >= int(total)):
-                break
-            if len(batch) < page_limit:
-                break
-            offset += page_limit
-
-        return items
-
-    def create_application(
-        self,
-        *,
-        app_name: str,
-        answer_url: str,
-        hangup_url: Optional[str] = None,
-        answer_method: str = "POST",
-        hangup_method: str = "POST",
-    ) -> Dict[str, Any]:
-        """Create a Vobiz voice application with webhook URLs."""
-        payload: Dict[str, Any] = {
-            "app_name": app_name,
-            "answer_url": answer_url,
-            "answer_method": answer_method,
-        }
-        if hangup_url:
-            payload["hangup_url"] = hangup_url
-            payload["hangup_method"] = hangup_method
-
-        try:
-            response = self._request("POST", self._application_endpoint(), json=payload)
-            if response.status_code >= 400:
-                raise ValueError(
-                    f"Vobiz create application failed (HTTP {response.status_code}): {response.text[:300]}"
-                )
-            return self._to_dict(response.json() if response.content else {})
-        except ValueError:
-            raise
-        except Exception as e:
-            logger.exception("Failed to create Vobiz application")
-            raise ValueError(f"Failed to create application: {str(e)}") from e
-
-    def update_application(
-        self,
-        app_id: str,
-        *,
-        answer_url: str,
-        hangup_url: Optional[str] = None,
-        answer_method: str = "POST",
-        hangup_method: str = "POST",
-    ) -> Dict[str, Any]:
-        """Update an existing Vobiz voice application."""
-        payload: Dict[str, Any] = {
-            "answer_url": answer_url,
-            "answer_method": answer_method,
-        }
-        if hangup_url:
-            payload["hangup_url"] = hangup_url
-            payload["hangup_method"] = hangup_method
-
-        endpoint = f"{self._application_endpoint()}{app_id}/"
-        try:
-            response = self._request("POST", endpoint, json=payload)
-            if response.status_code >= 400:
-                raise ValueError(
-                    f"Vobiz update application failed (HTTP {response.status_code}): {response.text[:300]}"
-                )
-            return self._to_dict(response.json() if response.content else {})
-        except ValueError:
-            raise
-        except Exception as e:
-            logger.exception("Failed to update Vobiz application")
-            raise ValueError(f"Failed to update application: {str(e)}") from e
-
-    def attach_number_to_application(self, number: str, application_id: str) -> Dict[str, Any]:
-        """Attach a phone number to a Vobiz application."""
-        e164 = normalize_e164(number)
-        encoded = quote(e164, safe="")
-        endpoint = f"{self._numbers_endpoint()}/{encoded}/application"
-        try:
-            response = self._request(
-                "POST",
-                endpoint,
-                json={"application_id": application_id},
-            )
-            if response.status_code >= 400:
-                raise ValueError(
-                    f"Vobiz attach number failed (HTTP {response.status_code}): {response.text[:300]}"
-                )
-            return self._to_dict(response.json() if response.content else {})
-        except ValueError:
-            raise
-        except Exception as e:
-            logger.exception("Failed to attach Vobiz number to application")
-            raise ValueError(f"Failed to attach number to application: {str(e)}") from e
-
-    def set_number_answer_url(
-        self,
-        number: str,
-        answer_url: str,
-        *,
-        hangup_url: Optional[str] = None,
-        existing_application_id: Optional[str] = None,
-        app_name: Optional[str] = None,
-    ) -> Tuple[bool, str, Optional[str]]:
-        """Point inbound calls for a number at our answer webhook.
-
-        Returns ``(success, message, application_id)``.
-        """
-        hangup = hangup_url or answer_url
-        app_id = existing_application_id
-
-        if app_id:
-            try:
-                self.update_application(
-                    app_id,
-                    answer_url=answer_url,
-                    hangup_url=hangup,
-                )
-                self.attach_number_to_application(number, app_id)
-                return True, "Updated existing Vobiz application", app_id
-            except ValueError as exc:
-                logger.warning(
-                    "Vobiz update/attach for existing app {} failed: {}; creating new app",
-                    app_id,
-                    exc,
-                )
-
-        safe_name = (app_name or f"efficientai_{normalize_e164(number).lstrip('+')}").replace(" ", "_")
-        try:
-            created = self.create_application(
-                app_name=safe_name[:64],
-                answer_url=answer_url,
-                hangup_url=hangup,
-            )
-            app_id = str(created.get("app_id") or "")
-            if not app_id:
-                return False, "Vobiz application created but app_id missing in response", None
-            self.attach_number_to_application(number, app_id)
-            return True, "Created and attached Vobiz application", app_id
-        except ValueError as exc:
-            return False, str(exc), None
-
-    def create_outbound_call(
-        self,
-        from_: str,
-        to_: str,
-        answer_url: str,
-        hangup_url: Optional[str] = None,
-        answer_method: str = "POST",
-        hangup_method: str = "POST",
-    ) -> Dict[str, Any]:
-        """Create an outbound voice call."""
-        from_ = normalize_e164(from_)
-        to_ = normalize_e164(to_)
-        payload: Dict[str, Any] = {
-            "from": from_,
-            "to": to_,
-            "answer_url": answer_url,
-            "answer_method": answer_method,
-        }
-        if hangup_url:
-            payload["hangup_url"] = hangup_url
-            payload["hangup_method"] = hangup_method
-
-        try:
-            response = self._request("POST", self._call_endpoint(), json=payload)
-            if response.status_code >= 400:
-                raise ValueError(
-                    f"Vobiz outbound call failed (HTTP {response.status_code}): {response.text[:300]}"
-                )
-            data = response.json() if response.content else {}
-            return self._to_dict(data)
-        except ValueError:
-            raise
-        except Exception as e:
-            logger.exception("Failed to create Vobiz outbound call")
-            raise ValueError(f"Failed to create outbound call: {str(e)}") from e
-
-    def hangup_call(self, call_id: str) -> bool:
-        """Terminate an active call."""
-        if not call_id:
-            raise ValueError("call_id is required")
-        endpoint = f"{self._call_endpoint()}{call_id}/"
-        try:
-            response = self._request("DELETE", endpoint, timeout=15.0)
-            if response.status_code in (200, 204, 404):
-                return True
-            raise ValueError(
-                f"Vobiz hangup failed (HTTP {response.status_code}): {response.text[:300]}"
-            )
-        except ValueError:
-            raise
-        except Exception as e:
-            logger.exception("Failed to hang up Vobiz call")
-            raise ValueError(f"Failed to hang up call: {str(e)}") from e
-
-
-def build_vobiz_client_from_settings() -> VobizClient:
-    """Build a VobizClient from platform-level settings."""
-    if not settings.VOBIZ_AUTH_ID or not settings.VOBIZ_AUTH_TOKEN:
-        raise ValueError("Vobiz is not configured. Set VOBIZ_AUTH_ID and VOBIZ_AUTH_TOKEN.")
-    return VobizClient(
-        auth_id=settings.VOBIZ_AUTH_ID,
-        auth_token=settings.VOBIZ_AUTH_TOKEN,
-        api_base=settings.VOBIZ_API_BASE,
-    )
-
-
-def build_vobiz_client_for_org(
-    db: Session,
-    org_id: UUID,
-    *,
-    credential_id: Optional[UUID] = None,
-) -> Tuple[VobizClient, Optional[TelephonyIntegration]]:
-    """Resolve per-org BYO Vobiz credentials, else platform-level settings."""
-    integration = resolve_telephony_integration("vobiz", db, org_id, credential_id=credential_id)
-    if integration:
-        auth_id = decrypt_api_key(integration.auth_id)
-        auth_token = decrypt_api_key(integration.auth_token)
-        return (
-            VobizClient(auth_id=auth_id, auth_token=auth_token, api_base=settings.VOBIZ_API_BASE),
-            integration,
-        )
-    return build_vobiz_client_from_settings(), None
+"""Thin Vobiz REST API client for telephony operations."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Tuple
+from urllib.parse import quote
+from uuid import UUID
+
+import httpx
+from loguru import logger
+from sqlalchemy.orm import Session
+
+from app.config import settings
+from app.core.encryption import decrypt_api_key
+from app.models.database import TelephonyIntegration
+from app.services.credentials import resolve_telephony_integration
+from app.services.telephony.plivo_client import normalize_e164
+
+
+class VobizClient:
+    """HTTP client for the Vobiz telephony API (Plivo-compatible surface)."""
+
+    def __init__(
+        self,
+        auth_id: str,
+        auth_token: str,
+        api_base: Optional[str] = None,
+    ):
+        if not auth_id or not auth_token:
+            raise ValueError("Vobiz auth_id and auth_token are required")
+        self.auth_id = auth_id
+        self.auth_token = auth_token
+        self.api_base = (api_base or settings.VOBIZ_API_BASE).rstrip("/")
+        self._headers = {
+            "X-Auth-ID": auth_id,
+            "X-Auth-Token": auth_token,
+            "Content-Type": "application/json",
+        }
+
+    def _account_base(self) -> str:
+        return f"{self.api_base}/api/v1/Account/{self.auth_id}"
+
+    def _call_endpoint(self) -> str:
+        return f"{self._account_base()}/Call/"
+
+    def _numbers_endpoint(self) -> str:
+        return f"{self._account_base()}/numbers"
+
+    def _application_endpoint(self) -> str:
+        return f"{self._account_base()}/Application/"
+
+    @staticmethod
+    def _to_dict(data: Any) -> Dict[str, Any]:
+        if isinstance(data, dict):
+            return data
+        return {"raw": str(data)}
+
+    def _request(
+        self,
+        method: str,
+        url: str,
+        *,
+        json: Optional[Dict[str, Any]] = None,
+        timeout: float = 30.0,
+    ) -> httpx.Response:
+        with httpx.Client(timeout=timeout) as client:
+            response = client.request(method, url, headers=self._headers, json=json)
+        return response
+
+    def test_connection(self) -> bool:
+        """Check if credentials can reach the Vobiz API."""
+        try:
+            response = self._request("GET", self._call_endpoint(), timeout=15.0)
+            if response.status_code >= 400:
+                raise ValueError(f"Vobiz API returned HTTP {response.status_code}")
+            return True
+        except Exception as e:
+            logger.exception("Vobiz connection test failed")
+            raise ValueError(f"Failed to connect to Vobiz: {str(e)}") from e
+
+    def list_account_numbers(self, *, limit: int = 100) -> List[Dict[str, Any]]:
+        """List phone numbers owned by this Vobiz account (paginated)."""
+        items: List[Dict[str, Any]] = []
+        offset = 0
+        page_limit = max(min(limit, 100), 1)
+
+        while True:
+            url = f"{self._numbers_endpoint()}?limit={page_limit}&offset={offset}"
+            try:
+                response = self._request("GET", url, timeout=30.0)
+            except Exception as e:
+                logger.exception("Failed to list Vobiz account numbers")
+                raise ValueError(f"Failed to list account numbers: {str(e)}") from e
+
+            if response.status_code >= 400:
+                raise ValueError(
+                    f"Vobiz list numbers failed (HTTP {response.status_code}): {response.text[:300]}"
+                )
+
+            data = response.json() if response.content else {}
+            batch = data.get("items") or data.get("objects") or []
+            if not isinstance(batch, list):
+                batch = []
+            items.extend(batch)
+
+            total = data.get("total")
+            if not batch or (total is not None and len(items) >= int(total)):
+                break
+            if len(batch) < page_limit:
+                break
+            offset += page_limit
+
+        return items
+
+    def create_application(
+        self,
+        *,
+        app_name: str,
+        answer_url: str,
+        hangup_url: Optional[str] = None,
+        answer_method: str = "POST",
+        hangup_method: str = "POST",
+    ) -> Dict[str, Any]:
+        """Create a Vobiz voice application with webhook URLs."""
+        payload: Dict[str, Any] = {
+            "app_name": app_name,
+            "answer_url": answer_url,
+            "answer_method": answer_method,
+        }
+        if hangup_url:
+            payload["hangup_url"] = hangup_url
+            payload["hangup_method"] = hangup_method
+
+        try:
+            response = self._request("POST", self._application_endpoint(), json=payload)
+            if response.status_code >= 400:
+                raise ValueError(
+                    f"Vobiz create application failed (HTTP {response.status_code}): {response.text[:300]}"
+                )
+            return self._to_dict(response.json() if response.content else {})
+        except ValueError:
+            raise
+        except Exception as e:
+            logger.exception("Failed to create Vobiz application")
+            raise ValueError(f"Failed to create application: {str(e)}") from e
+
+    def update_application(
+        self,
+        app_id: str,
+        *,
+        answer_url: str,
+        hangup_url: Optional[str] = None,
+        answer_method: str = "POST",
+        hangup_method: str = "POST",
+    ) -> Dict[str, Any]:
+        """Update an existing Vobiz voice application."""
+        payload: Dict[str, Any] = {
+            "answer_url": answer_url,
+            "answer_method": answer_method,
+        }
+        if hangup_url:
+            payload["hangup_url"] = hangup_url
+            payload["hangup_method"] = hangup_method
+
+        endpoint = f"{self._application_endpoint()}{app_id}/"
+        try:
+            response = self._request("POST", endpoint, json=payload)
+            if response.status_code >= 400:
+                raise ValueError(
+                    f"Vobiz update application failed (HTTP {response.status_code}): {response.text[:300]}"
+                )
+            return self._to_dict(response.json() if response.content else {})
+        except ValueError:
+            raise
+        except Exception as e:
+            logger.exception("Failed to update Vobiz application")
+            raise ValueError(f"Failed to update application: {str(e)}") from e
+
+    def attach_number_to_application(self, number: str, application_id: str) -> Dict[str, Any]:
+        """Attach a phone number to a Vobiz application."""
+        e164 = normalize_e164(number)
+        encoded = quote(e164, safe="")
+        endpoint = f"{self._numbers_endpoint()}/{encoded}/application"
+        try:
+            response = self._request(
+                "POST",
+                endpoint,
+                json={"application_id": application_id},
+            )
+            if response.status_code >= 400:
+                raise ValueError(
+                    f"Vobiz attach number failed (HTTP {response.status_code}): {response.text[:300]}"
+                )
+            return self._to_dict(response.json() if response.content else {})
+        except ValueError:
+            raise
+        except Exception as e:
+            logger.exception("Failed to attach Vobiz number to application")
+            raise ValueError(f"Failed to attach number to application: {str(e)}") from e
+
+    def set_number_answer_url(
+        self,
+        number: str,
+        answer_url: str,
+        *,
+        hangup_url: Optional[str] = None,
+        existing_application_id: Optional[str] = None,
+        app_name: Optional[str] = None,
+    ) -> Tuple[bool, str, Optional[str]]:
+        """Point inbound calls for a number at our answer webhook.
+
+        Returns ``(success, message, application_id)``.
+        """
+        hangup = hangup_url or answer_url
+        app_id = existing_application_id
+
+        if app_id:
+            try:
+                self.update_application(
+                    app_id,
+                    answer_url=answer_url,
+                    hangup_url=hangup,
+                )
+                self.attach_number_to_application(number, app_id)
+                return True, "Updated existing Vobiz application", app_id
+            except ValueError as exc:
+                logger.warning(
+                    "Vobiz update/attach for existing app {} failed: {}; creating new app",
+                    app_id,
+                    exc,
+                )
+
+        safe_name = (app_name or f"efficientai_{normalize_e164(number).lstrip('+')}").replace(" ", "_")
+        try:
+            created = self.create_application(
+                app_name=safe_name[:64],
+                answer_url=answer_url,
+                hangup_url=hangup,
+            )
+            app_id = str(created.get("app_id") or "")
+            if not app_id:
+                return False, "Vobiz application created but app_id missing in response", None
+            self.attach_number_to_application(number, app_id)
+            return True, "Created and attached Vobiz application", app_id
+        except ValueError as exc:
+            return False, str(exc), None
+
+    def create_outbound_call(
+        self,
+        from_: str,
+        to_: str,
+        answer_url: str,
+        hangup_url: Optional[str] = None,
+        answer_method: str = "POST",
+        hangup_method: str = "POST",
+        sip_headers: Optional[Dict[str, str]] = None,
+    ) -> Dict[str, Any]:
+        """Create an outbound voice call."""
+        from efficientai.integrations.efficientai_traces.correlation import format_plivo_sip_headers
+
+        from_ = normalize_e164(from_)
+        to_ = normalize_e164(to_)
+        payload: Dict[str, Any] = {
+            "from": from_,
+            "to": to_,
+            "answer_url": answer_url,
+            "answer_method": answer_method,
+        }
+        if hangup_url:
+            payload["hangup_url"] = hangup_url
+            payload["hangup_method"] = hangup_method
+        if sip_headers:
+            payload["sipHeaders"] = format_plivo_sip_headers(sip_headers)
+
+        try:
+            response = self._request("POST", self._call_endpoint(), json=payload)
+            if response.status_code >= 400:
+                raise ValueError(
+                    f"Vobiz outbound call failed (HTTP {response.status_code}): {response.text[:300]}"
+                )
+            data = response.json() if response.content else {}
+            return self._to_dict(data)
+        except ValueError:
+            raise
+        except Exception as e:
+            logger.exception("Failed to create Vobiz outbound call")
+            raise ValueError(f"Failed to create outbound call: {str(e)}") from e
+
+    def hangup_call(self, call_id: str) -> bool:
+        """Terminate an active call."""
+        if not call_id:
+            raise ValueError("call_id is required")
+        endpoint = f"{self._call_endpoint()}{call_id}/"
+        try:
+            response = self._request("DELETE", endpoint, timeout=15.0)
+            if response.status_code in (200, 204, 404):
+                return True
+            raise ValueError(
+                f"Vobiz hangup failed (HTTP {response.status_code}): {response.text[:300]}"
+            )
+        except ValueError:
+            raise
+        except Exception as e:
+            logger.exception("Failed to hang up Vobiz call")
+            raise ValueError(f"Failed to hang up call: {str(e)}") from e
+
+
+def build_vobiz_client_from_settings() -> VobizClient:
+    """Build a VobizClient from platform-level settings."""
+    if not settings.VOBIZ_AUTH_ID or not settings.VOBIZ_AUTH_TOKEN:
+        raise ValueError("Vobiz is not configured. Set VOBIZ_AUTH_ID and VOBIZ_AUTH_TOKEN.")
+    return VobizClient(
+        auth_id=settings.VOBIZ_AUTH_ID,
+        auth_token=settings.VOBIZ_AUTH_TOKEN,
+        api_base=settings.VOBIZ_API_BASE,
+    )
+
+
+def build_vobiz_client_for_org(
+    db: Session,
+    org_id: UUID,
+    *,
+    credential_id: Optional[UUID] = None,
+) -> Tuple[VobizClient, Optional[TelephonyIntegration]]:
+    """Resolve per-org BYO Vobiz credentials, else platform-level settings."""
+    integration = resolve_telephony_integration("vobiz", db, org_id, credential_id=credential_id)
+    if integration:
+        auth_id = decrypt_api_key(integration.auth_id)
+        auth_token = decrypt_api_key(integration.auth_token)
+        return (
+            VobizClient(auth_id=auth_id, auth_token=auth_token, api_base=settings.VOBIZ_API_BASE),
+            integration,
+        )
+    return build_vobiz_client_from_settings(), None
diff --git a/app/services/voice_agent/sut_latency_observer.py b/app/services/voice_agent/sut_latency_observer.py
new file mode 100644
index 00000000..dc257d96
--- /dev/null
+++ b/app/services/voice_agent/sut_latency_observer.py
@@ -0,0 +1,90 @@
+"""Measure remote-agent (SUT) response latency on telephony evaluator calls."""
+
+from __future__ import annotations
+
+import time
+from typing import Any, Dict, List
+
+from loguru import logger
+
+from efficientai.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    CancelFrame,
+    EndFrame,
+    UserStartedSpeakingFrame,
+)
+from efficientai.observers.base_observer import BaseObserver, FramePushed
+from efficientai.processors.frame_processor import FrameDirection
+
+
+class SutLatencyObserver(BaseObserver):
+    """Track caller bot stop → remote user speech start as SUT response latency."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._processed_frames: set[int] = set()
+        self._bot_stopped_at: float | None = None
+        self._bot_speaking = False
+        self._turn_number = 0
+        self._turns: List[Dict[str, Any]] = []
+
+    async def on_push_frame(self, data: FramePushed) -> None:
+        if data.direction != FrameDirection.DOWNSTREAM:
+            return
+        frame_id = getattr(data.frame, "id", None)
+        if frame_id is not None and frame_id in self._processed_frames:
+            return
+        if frame_id is not None:
+            self._processed_frames.add(frame_id)
+
+        now = time.time()
+        frame = data.frame
+
+        if isinstance(frame, BotStartedSpeakingFrame):
+            self._bot_speaking = True
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            self._bot_speaking = False
+            self._bot_stopped_at = now
+        elif isinstance(frame, UserStartedSpeakingFrame):
+            if self._bot_stopped_at is None:
+                return
+            latency_ms = (now - self._bot_stopped_at) * 1000.0
+            self._turn_number += 1
+            talk_over = self._bot_speaking
+            self._turns.append(
+                {
+                    "turn_number": self._turn_number,
+                    "sut_response_latency_ms": round(latency_ms, 1),
+                    "caller_stream_complete_at": self._bot_stopped_at,
+                    "sut_speech_start_at": now,
+                    "talk_over": talk_over,
+                }
+            )
+            logger.debug(
+                "SUT turn {} response latency {:.0f}ms talk_over={}",
+                self._turn_number,
+                latency_ms,
+                talk_over,
+            )
+            self._bot_stopped_at = None
+        elif isinstance(frame, (EndFrame, CancelFrame)):
+            logger.info(
+                "SutLatencyObserver captured {} turns (avg latency {:.0f}ms)",
+                len(self._turns),
+                self.average_latency_ms or 0,
+            )
+
+    def get_turns(self) -> List[Dict[str, Any]]:
+        return list(self._turns)
+
+    @property
+    def average_latency_ms(self) -> float | None:
+        values = [
+            t["sut_response_latency_ms"]
+            for t in self._turns
+            if t.get("sut_response_latency_ms") is not None
+        ]
+        if not values:
+            return None
+        return sum(values) / len(values)
diff --git a/app/services/voice_agent/voice_bundle.py b/app/services/voice_agent/voice_bundle.py
index edebc4d0..ca3787bd 100644
--- a/app/services/voice_agent/voice_bundle.py
+++ b/app/services/voice_agent/voice_bundle.py
@@ -1,1161 +1,1175 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Voice bundle pipeline for STT+LLM+TTS over WebSocket.
-
-This module uses lazy imports to avoid loading heavy AI service dependencies
-at module import time. Services are only loaded when actually used.
-"""
-
-import os
-import time
-import wave
-import uuid
-import tempfile
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from app.services.storage.s3_service import s3_service
-
-load_dotenv(override=True)
-
-
-def _get_core_imports():
-    """Lazy import core efficientai dependencies (no provider SDKs).
-    
-    Returns a dict with core pipeline classes. Provider-specific services
-    are loaded separately via _get_service() to avoid loading unused SDKs.
-    """
-    from efficientai.audio.vad.silero import SileroVADAnalyzer
-    from efficientai.audio.vad.vad_analyzer import VADParams
-    from efficientai.frames.frames import LLMRunFrame
-    from efficientai.pipeline.pipeline import Pipeline
-    from efficientai.pipeline.runner import PipelineRunner
-    from efficientai.pipeline.task import PipelineParams, PipelineTask
-    from efficientai.processors.aggregators.llm_context import LLMContext
-    from efficientai.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    from efficientai.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-    from efficientai.processors.audio.audio_buffer_processor import AudioBufferProcessor
-    from efficientai.runner.types import RunnerArguments
-    from efficientai.runner.utils import create_transport
-    from efficientai.serializers.protobuf import ProtobufFrameSerializer
-    from efficientai.transports.websocket.fastapi import FastAPIWebsocketParams, FastAPIWebsocketTransport
-    from efficientai.transports.base_transport import BaseTransport, TransportParams
-    from efficientai.turns.bot.turn_analyzer_bot_turn_start_strategy import TurnAnalyzerBotTurnStartStrategy
-    from efficientai.turns.turn_start_strategies import TurnStartStrategies
-    
-    return {
-        "SileroVADAnalyzer": SileroVADAnalyzer,
-        "VADParams": VADParams,
-        "LLMRunFrame": LLMRunFrame,
-        "Pipeline": Pipeline,
-        "PipelineRunner": PipelineRunner,
-        "PipelineParams": PipelineParams,
-        "PipelineTask": PipelineTask,
-        "LLMContext": LLMContext,
-        "LLMContextAggregatorPair": LLMContextAggregatorPair,
-        "RTVIConfig": RTVIConfig,
-        "RTVIObserver": RTVIObserver,
-        "RTVIProcessor": RTVIProcessor,
-        "AudioBufferProcessor": AudioBufferProcessor,
-        "RunnerArguments": RunnerArguments,
-        "create_transport": create_transport,
-        "ProtobufFrameSerializer": ProtobufFrameSerializer,
-        "FastAPIWebsocketParams": FastAPIWebsocketParams,
-        "FastAPIWebsocketTransport": FastAPIWebsocketTransport,
-        "BaseTransport": BaseTransport,
-        "TransportParams": TransportParams,
-        "TurnAnalyzerBotTurnStartStrategy": TurnAnalyzerBotTurnStartStrategy,
-        "TurnStartStrategies": TurnStartStrategies,
-    }
-
-
-# Cache for core imports
-_core_imports_cache = None
-
-# Cache for provider services (keyed by service name)
-_service_cache = {}
-
-
-def _get_imports():
-    """Get cached core imports."""
-    global _core_imports_cache
-    if _core_imports_cache is None:
-        _core_imports_cache = _get_core_imports()
-    return _core_imports_cache
-
-
-def _get_service(service_name: str):
-    """Get a provider service class by name (lazy loaded).
-    
-    Each provider's SDK is only loaded when that specific service is requested.
-    This allows the voice agent to work even if some provider SDKs are not installed,
-    as long as you don't try to use those providers.
-    """
-    global _service_cache
-    
-    if service_name in _service_cache:
-        return _service_cache[service_name]
-    
-    service_class = None
-    
-    # STT Services
-    if service_name == "DeepgramSTTService":
-        from efficientai.services.deepgram.stt import DeepgramSTTService
-        service_class = DeepgramSTTService
-    elif service_name == "OpenAISTTService":
-        from efficientai.services.openai.stt import OpenAISTTService
-        service_class = OpenAISTTService
-    elif service_name == "ElevenLabsRealtimeSTTService":
-        from efficientai.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
-        service_class = ElevenLabsRealtimeSTTService
-    elif service_name == "SarvamSTTService":
-        from efficientai.services.sarvam.stt import SarvamSTTService
-        service_class = SarvamSTTService
-    elif service_name == "SmallestSTTService":
-        from efficientai.services.smallest.stt import SmallestSTTService
-        service_class = SmallestSTTService
-    
-    # TTS Services
-    elif service_name == "CartesiaTTSService":
-        from efficientai.services.cartesia.tts import CartesiaTTSService
-        service_class = CartesiaTTSService
-    elif service_name == "OpenAITTSService":
-        from efficientai.services.openai.tts import OpenAITTSService
-        service_class = OpenAITTSService
-    elif service_name == "ElevenLabsHttpTTSService":
-        from efficientai.services.elevenlabs.tts import ElevenLabsHttpTTSService
-        service_class = ElevenLabsHttpTTSService
-    elif service_name == "MurfTTSService":
-        from efficientai.services.murf.tts import MurfTTSService
-        service_class = MurfTTSService
-    elif service_name == "SarvamTTSService":
-        from efficientai.services.sarvam.tts import SarvamTTSService
-        service_class = SarvamTTSService
-    elif service_name == "VoiceMakerTTSService":
-        from efficientai.services.voicemaker.tts import VoiceMakerTTSService
-        service_class = VoiceMakerTTSService
-    elif service_name == "SmallestTTSService":
-        from efficientai.services.smallest.tts import SmallestTTSService
-        service_class = SmallestTTSService
-    
-    # LLM Services
-    elif service_name == "OpenAILLMService":
-        from efficientai.services.openai.llm import OpenAILLMService
-        service_class = OpenAILLMService
-    elif service_name == "GoogleLLMService":
-        from efficientai.services.google.llm import GoogleLLMService
-        service_class = GoogleLLMService
-    elif service_name == "AzureLLMService":
-        from efficientai.services.azure.llm import AzureLLMService
-        service_class = AzureLLMService
-    
-    # Optional: Smart Turn Analyzer
-    elif service_name == "LocalSmartTurnAnalyzerV3":
-        try:
-            from efficientai.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-            service_class = LocalSmartTurnAnalyzerV3
-        except Exception as e:
-            logger.warning(
-                "LocalSmartTurnAnalyzerV3 unavailable (missing optional deps). Error: %s", e
-            )
-            service_class = None
-    
-    else:
-        raise ValueError(f"Unknown service: {service_name}")
-    
-    _service_cache[service_name] = service_class
-    return service_class
-
-# ---------------------------------------------------------------------------
-# Provider Registries  (STT & TTS)
-# ---------------------------------------------------------------------------
-# Each entry contains:
-#   - "env_key"        : environment variable name for the API key
-#   - "default_model"  : fallback model name (None if the service doesn't need one)
-#   - "factory"        : callable(**kwargs) -> service instance
-#
-# TTS entries additionally have:
-#   - "default_voice"  : fallback voice ID when the voice bundle doesn't specify one
-#
-# To add a new provider, simply add a dict entry to the relevant registry.
-# ---------------------------------------------------------------------------
-
-
-def _get_stt_providers():
-    """Get STT provider registry with truly lazy-loaded service classes.
-    
-    Each provider's SDK is only loaded when that provider is actually used.
-    """
-    return {
-        "deepgram": {
-            "env_key": "DEEPGRAM_API_KEY",
-            "default_model": None,
-            "factory": lambda api_key, model: _get_service("DeepgramSTTService")(
-                api_key=api_key,
-                **({"model": model} if model else {}),
-            ),
-        },
-        "openai": {
-            "env_key": "OPENAI_API_KEY",
-            "default_model": "gpt-4o-transcribe",
-            "factory": lambda api_key, model: _get_service("OpenAISTTService")(
-                api_key=api_key,
-                **({"model": model} if model else {}),
-            ),
-        },
-        "elevenlabs": {
-            "env_key": "ELEVENLABS_API_KEY",
-            "default_model": "scribe_v2_realtime",
-            "factory": lambda api_key, model: _get_service("ElevenLabsRealtimeSTTService")(
-                api_key=api_key,
-                **({"model": model} if model else {}),
-            ),
-        },
-        "sarvam": {
-            "env_key": "SARVAM_API_KEY",
-            "default_model": "saaras:v3",
-            "factory": lambda api_key, model: _get_service("SarvamSTTService")(
-                api_key=api_key,
-                model=model if model else "saaras:v3",
-            ),
-        },
-        "smallest": {
-            "env_key": "SMALLEST_API_KEY",
-            "default_model": "pulse-v4",
-            "factory": lambda api_key, model: _get_service("SmallestSTTService")(
-                api_key=api_key,
-                model=model if model else "pulse-v4",
-            ),
-        },
-    }
-
-
-def _get_tts_providers():
-    """Get TTS provider registry with truly lazy-loaded service classes.
-    
-    Each provider's SDK is only loaded when that provider is actually used.
-    """
-    return {
-        "cartesia": {
-            "env_key": "CARTESIA_API_KEY",
-            "default_voice": "9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
-            "default_model": None,
-        },
-        "elevenlabs": {
-            "env_key": "ELEVENLABS_API_KEY",
-            "default_voice": "JBFqnCBsd6RMkjVDRZzb",
-            "default_model": "eleven_multilingual_v2",
-        },
-        "openai": {
-            "env_key": "OPENAI_API_KEY",
-            "default_voice": "alloy",
-            "default_model": "gpt-4o-mini-tts",
-        },
-        "murf": {
-            "env_key": "MURF_API_KEY",
-            "default_voice": "en-US-natalie",
-            "default_model": "GEN2",
-        },
-        "sarvam": {
-            "env_key": "SARVAM_API_KEY",
-            "default_voice": "ritu",
-            "default_model": "bulbul:v3",
-        },
-        "voicemaker": {
-            "env_key": "VOICEMAKER_API_KEY",
-            "default_voice": "ai3-Jony",
-            "default_model": "neural",
-        },
-        "smallest": {
-            "env_key": "SMALLEST_API_KEY",
-            "default_voice": "daniel",
-            "default_model": "lightning-v3.1",
-        },
-    }
-
-
-def _instantiate_tts_service(
-    provider: str,
-    tts_cfg: dict,
-    *,
-    api_key: str,
-    voice_id: str,
-    model: str | None,
-    sample_rate: int,
-):
-    """Build a streaming TTS service for the voice bundle pipeline."""
-    from app.services.voice_agent.tts_sample_rate import provider_accepts_sample_rate_kwarg
-
-    sr_kw = {"sample_rate": sample_rate} if provider_accepts_sample_rate_kwarg(provider) else {}
-    provider = provider.strip().lower()
-
-    if provider == "cartesia":
-        return _get_service("CartesiaTTSService")(
-            api_key=api_key,
-            voice_id=voice_id,
-            **sr_kw,
-        )
-    if provider == "elevenlabs":
-        return _get_service("ElevenLabsHttpTTSService")(
-            api_key=api_key,
-            voice_id=voice_id,
-            model=model,
-            aiohttp_session=__import__("aiohttp").ClientSession(),
-            **sr_kw,
-        )
-    if provider == "openai":
-        return _get_service("OpenAITTSService")(
-            api_key=api_key,
-            voice=voice_id,
-            model=model,
-            **sr_kw,
-        )
-    if provider == "murf":
-        return _get_service("MurfTTSService")(
-            api_key=api_key,
-            voice_id=voice_id,
-            model=model if model else "GEN2",
-            **sr_kw,
-        )
-    if provider == "sarvam":
-        return _get_service("SarvamTTSService")(
-            api_key=api_key,
-            voice_id=voice_id,
-            model=model if model else "bulbul:v3",
-            **sr_kw,
-        )
-    if provider == "voicemaker":
-        return _get_service("VoiceMakerTTSService")(
-            api_key=api_key,
-            voice_id=voice_id,
-            model=model if model else "neural",
-            **sr_kw,
-        )
-    if provider == "smallest":
-        return _get_service("SmallestTTSService")(
-            api_key=api_key,
-            voice_id=voice_id if voice_id else "daniel",
-            model=model if model else "lightning-v3.1",
-            **sr_kw,
-        )
-    raise ValueError(f"Unsupported TTS provider '{provider}'")
-
-
-def _get_llm_providers():
-    """Get LLM provider registry with truly lazy-loaded service classes.
-    
-    Each provider's SDK is only loaded when that provider is actually used.
-    """
-    return {
-        "openai": {
-            "env_key": "OPENAI_API_KEY",
-            "default_model": "gpt-4.1",
-            "factory": lambda api_key, model, params=None: _get_service("OpenAILLMService")(
-                api_key=api_key,
-                model=model,
-                **({"params": params} if params else {}),
-            ),
-        },
-        "google": {
-            "env_key": "GOOGLE_API_KEY",
-            "default_model": "gemini-2.5-flash",
-            "factory": lambda api_key, model, params=None: _get_service("GoogleLLMService")(
-                api_key=api_key,
-                model=model,
-                **({"params": params} if params else {}),
-            ),
-        },
-        "azure": {
-            "env_key": "AZURE_OPENAI_API_KEY",
-            "default_model": "gpt-4.1",
-            # Azure is instantiated with endpoint metadata in run_voice_bundle_fastapi.
-            "factory": lambda api_key, model, params=None: _get_service("OpenAILLMService")(
-                api_key=api_key,
-                model=model,
-                **({"params": params} if params else {}),
-            ),
-        },
-    }
-
-
-DEFAULT_STT_PROVIDER = None
-DEFAULT_TTS_PROVIDER = None
-DEFAULT_LLM_PROVIDER = None
-
-
-def _resolve_provider(voice_bundle, attr: str, default: str | None = None) -> str:
-    """Return the normalised provider name from a voice bundle attribute.
-
-    Raises ValueError when the voice bundle has no provider set and no default is given.
-    """
-    raw = getattr(voice_bundle, attr, None)
-    if raw is None:
-        if default is not None:
-            return default
-        raise ValueError(
-            f"Voice bundle is missing required field '{attr}'. "
-            f"Please configure the provider in the voice bundle settings."
-        )
-    value = raw.value if hasattr(raw, "value") else str(raw)
-    return value.lower()
-
-
-def _get_transport_params():
-    """Get transport params with lazy-loaded classes."""
-    imports = _get_imports()
-    return {
-        "twilio": lambda: imports["FastAPIWebsocketParams"](
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=imports["SileroVADAnalyzer"](params=imports["VADParams"](stop_secs=0.2)),
-        ),
-        "webrtc": lambda: imports["TransportParams"](
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=imports["SileroVADAnalyzer"](params=imports["VADParams"](stop_secs=0.2)),
-        ),
-    }
-
-
-async def run_bot(transport, runner_args):
-    """Run the bot pipeline with the given transport."""
-    imports = _get_imports()
-    
-    logger.info(f"Starting bot")
-
-    # Use _get_service for provider-specific classes
-    DeepgramSTTService = _get_service("DeepgramSTTService")
-    CartesiaTTSService = _get_service("CartesiaTTSService")
-    OpenAILLMService = _get_service("OpenAILLMService")
-    
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = imports["LLMContext"](messages)
-    context_aggregator = imports["LLMContextAggregatorPair"](context)
-
-    pipeline = imports["Pipeline"](
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    bot_turn_strategies = []
-    LocalSmartTurnAnalyzerV3 = _get_service("LocalSmartTurnAnalyzerV3")
-    if LocalSmartTurnAnalyzerV3:
-        bot_turn_strategies = [imports["TurnAnalyzerBotTurnStartStrategy"](turn_analyzer=LocalSmartTurnAnalyzerV3())]
-    else:
-        logger.info("Running without LocalSmartTurnAnalyzerV3 (optional dependency not installed).")
-
-    task = imports["PipelineTask"](
-        pipeline,
-        params=imports["PipelineParams"](
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            turn_start_strategies=imports["TurnStartStrategies"](bot=bot_turn_strategies),
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([imports["LLMRunFrame"]()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = imports["PipelineRunner"](handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args):
-    """Main bot entry point compatible with efficientai Cloud."""
-    imports = _get_imports()
-    transport_params = _get_transport_params()
-    transport = await imports["create_transport"](runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-async def run_voice_bundle_fastapi(
-    websocket_client,
-    system_instruction: str | None = None,
-    organization_id: str | None = None,
-    workspace_id: str | None = None,
-    agent_id: str | None = None,
-    persona_id: str | None = None,
-    scenario_id: str | None = None,
-    evaluator_id: str | None = None,
-    result_id: str | None = None,
-    voice_bundle=None,
-    persona=None,
-    stt_api_key: str | None = None,
-    tts_api_key: str | None = None,
-    llm_api_key: str | None = None,
-    llm_endpoint_url: str | None = None,
-    serializer=None,
-    telephony_mode: bool = False,
-    call_short_id: str | None = None,
-    silence_hangup_secs: float | None = None,
-):
-    """
-    Run the STT+LLM+TTS voice bundle pipeline over a FastAPI WebSocket.
-    Uses AudioBufferProcessor for proper conversation audio recording.
-    """
-    # Lazy load all efficientai dependencies
-    imports = _get_imports()
-    STT_PROVIDERS = _get_stt_providers()
-    TTS_PROVIDERS = _get_tts_providers()
-    LLM_PROVIDERS = _get_llm_providers()
-
-    call_start_time = time.time()
-    s3_key_result = None
-    duration_result = None
-    transcript_text = None
-    conversation_turns = []
-    
-    # Storage for audio data from the buffer processor
-    recorded_audio_data = {"audio": None, "sample_rate": None, "num_channels": None}
-
-    # Resolve STT provider config from the registry
-    stt_provider_value = _resolve_provider(voice_bundle, "stt_provider", DEFAULT_STT_PROVIDER)
-    stt_cfg = STT_PROVIDERS.get(stt_provider_value)
-    if stt_cfg is None:
-        supported = ", ".join(sorted(STT_PROVIDERS.keys()))
-        raise ValueError(
-            f"Unsupported STT provider '{stt_provider_value}'. Supported providers: {supported}"
-        )
-
-    # Resolve TTS provider config from the registry
-    tts_provider_value = _resolve_provider(voice_bundle, "tts_provider", DEFAULT_TTS_PROVIDER)
-    tts_cfg = TTS_PROVIDERS.get(tts_provider_value)
-    if tts_cfg is None:
-        supported = ", ".join(sorted(TTS_PROVIDERS.keys()))
-        raise ValueError(
-            f"Unsupported TTS provider '{tts_provider_value}'. Supported providers: {supported}"
-        )
-
-    # Resolve LLM provider config from the registry
-    llm_provider_value = _resolve_provider(voice_bundle, "llm_provider", DEFAULT_LLM_PROVIDER)
-    llm_cfg = LLM_PROVIDERS.get(llm_provider_value)
-    if llm_cfg is None:
-        supported = ", ".join(sorted(LLM_PROVIDERS.keys()))
-        raise ValueError(
-            f"Unsupported LLM provider '{llm_provider_value}'. Supported providers: {supported}"
-        )
-
-    # Resolve API keys: prefer provided values, otherwise fall back to environment
-    stt_api_key = stt_api_key or os.getenv(stt_cfg["env_key"])
-    tts_api_key = tts_api_key or os.getenv(tts_cfg["env_key"])
-    llm_api_key = llm_api_key or os.getenv(llm_cfg["env_key"])
-
-    missing = []
-    if not stt_api_key:
-        missing.append(stt_cfg["env_key"])
-    if not tts_api_key:
-        missing.append(tts_cfg["env_key"])
-    if not llm_api_key:
-        missing.append(llm_cfg["env_key"])
-    if missing:
-        raise ValueError(f"Missing required API keys for voice bundle: {', '.join(missing)}")
-
-    try:
-        from app.services.voice_agent.tts_sample_rate import (
-            resolve_tts_sample_rate_hz,
-            resolve_websocket_audio_in_sample_rate_hz,
-            resolve_websocket_audio_out_sample_rate_hz,
-        )
-
-        transport_serializer = serializer or imports["ProtobufFrameSerializer"]()
-        tts_sample_rate = resolve_tts_sample_rate_hz(
-            voice_bundle,
-            tts_provider_value,
-            telephony_mode=telephony_mode,
-        )
-        transport_in_sample_rate = resolve_websocket_audio_in_sample_rate_hz(
-            telephony_mode=telephony_mode,
-        )
-        transport_out_sample_rate = resolve_websocket_audio_out_sample_rate_hz(
-            telephony_mode=telephony_mode,
-        )
-        ws_transport = imports["FastAPIWebsocketTransport"](
-            websocket=websocket_client,
-            params=imports["FastAPIWebsocketParams"](
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                add_wav_header=False,
-                audio_in_sample_rate=transport_in_sample_rate,
-                audio_out_sample_rate=transport_out_sample_rate,
-                vad_analyzer=imports["SileroVADAnalyzer"](params=imports["VADParams"](stop_secs=0.2)),
-                serializer=transport_serializer,
-            ),
-        )
-
-        # Instantiate STT service from the provider registry
-        stt_model = getattr(voice_bundle, "stt_model", None) or stt_cfg["default_model"]
-        stt = stt_cfg["factory"](api_key=stt_api_key, model=stt_model)
-
-        # Instantiate TTS service from the provider registry
-        from app.services.voice_agent.resolve_tts_voice import (
-            log_effective_tts_voice,
-            resolve_effective_tts_voice_id,
-        )
-
-        tts_voice_id = resolve_effective_tts_voice_id(
-            persona=persona,
-            voice_bundle=voice_bundle,
-            default_voice=tts_cfg["default_voice"],
-        )
-        log_effective_tts_voice(
-            logger,
-            path_name="run_voice_bundle_fastapi",
-            persona=persona,
-            voice_bundle=voice_bundle,
-            resolved_voice_id=tts_voice_id,
-        )
-        tts_model = getattr(voice_bundle, "tts_model", None) or tts_cfg["default_model"]
-        tts = _instantiate_tts_service(
-            tts_provider_value,
-            tts_cfg,
-            api_key=tts_api_key,
-            voice_id=tts_voice_id,
-            model=tts_model,
-            sample_rate=tts_sample_rate,
-        )
-
-        llm_model = getattr(voice_bundle, "llm_model", None) or llm_cfg["default_model"]
-        from app.services.ai.llm_generation_config import (
-            build_efficientai_input_params,
-            merge_llm_config,
-        )
-        from app.services.ai.llm_service import (
-            _azure_deployment_name,
-            _azure_openai_v1_api_base,
-            _azure_uses_openai_v1_routing,
-            _normalize_azure_endpoint,
-        )
-
-        llm_params = build_efficientai_input_params(
-            llm_provider_value,
-            merge_llm_config(
-                getattr(voice_bundle, "llm_config", None),
-                legacy_temperature=getattr(voice_bundle, "llm_temperature", None),
-                legacy_max_tokens=getattr(voice_bundle, "llm_max_tokens", None),
-            ),
-        )
-        if llm_provider_value == "azure":
-            if not llm_endpoint_url:
-                raise ValueError(
-                    "Azure LLM requires an endpoint URL on the AI provider credential "
-                    "(Integrations → AI Provider → Azure → Azure Endpoint URL)."
-                )
-            api_base, version_hint = _normalize_azure_endpoint(llm_endpoint_url)
-            deployment_model = _azure_deployment_name(llm_model)
-            if _azure_uses_openai_v1_routing(None, version_hint):
-                llm = _get_service("OpenAILLMService")(
-                    api_key=llm_api_key,
-                    model=deployment_model,
-                    base_url=_azure_openai_v1_api_base(api_base),
-                    params=llm_params,
-                )
-            else:
-                llm = _get_service("AzureLLMService")(
-                    api_key=llm_api_key,
-                    endpoint=api_base,
-                    model=deployment_model,
-                    api_version=version_hint or "2024-08-01-preview",
-                    params=llm_params,
-                )
-        else:
-            llm = llm_cfg["factory"](api_key=llm_api_key, model=llm_model, params=llm_params)
-
-        # Build context with provided system instruction or a default
-        base_instruction = (
-            system_instruction.strip()
-            if system_instruction
-            else "You are a helpful voice assistant. Keep responses concise and speakable."
-        )
-
-        # Sarvam TTS rejects text containing emojis / non-language characters,
-        # so we inject a guardrail into the system instructions.
-        if tts_provider_value == "sarvam":
-            base_instruction += (
-                "\n\nIMPORTANT: Your output will be spoken aloud by a text-to-speech engine "
-                "that only supports plain text in Indian languages and English. "
-                "You MUST NOT use any emojis, emoticons, bullet points, asterisks, "
-                "or special Unicode characters. Use only plain spoken words."
-            )
-
-        messages = [
-            {
-                "role": "system",
-                "content": base_instruction,
-            },
-            {
-                "role": "user",
-                "content": "Start by greeting the user warmly and introducing yourself based on the system instruction.",
-            },
-        ]
-
-        context = imports["LLMContext"](messages)
-        context_aggregator = imports["LLMContextAggregatorPair"](context)
-
-        use_aligned_recorders = telephony_mode
-        user_recorder = None
-        bot_recorder = None
-        user_audio_path = None
-        bot_audio_path = None
-        recording_start_time = time.time()
-
-        if use_aligned_recorders:
-            from app.services.voice_agent.audio_recorder import get_audio_recorder_class
-            from app.services.voice_agent.telephony_recording_paths import telephony_recording_temp_path
-
-            AudioRecorder = get_audio_recorder_class()
-            user_audio_path = telephony_recording_temp_path(suffix=".wav")
-            bot_audio_path = telephony_recording_temp_path(suffix=".wav")
-            user_recorder = AudioRecorder(
-                user_audio_path,
-                recording_start_time,
-                target_sample_rate=tts_sample_rate,
-                recorder_name="UserAudioRecorder",
-                alignment_mode="stream",
-            )
-            bot_recorder = AudioRecorder(
-                bot_audio_path,
-                recording_start_time,
-                target_sample_rate=tts_sample_rate,
-                recorder_name="BotAudioRecorder",
-                alignment_mode="stream",
-            )
-            audio_buffer_input = None
-            audio_buffer_output = None
-            input_audio_chunks = []
-            output_audio_chunks = []
-        else:
-            audio_buffer_input = imports["AudioBufferProcessor"](
-                sample_rate=tts_sample_rate,
-                num_channels=1,
-            )
-            audio_buffer_output = imports["AudioBufferProcessor"](
-                sample_rate=tts_sample_rate,
-                num_channels=1,
-            )
-            input_audio_chunks = []
-            output_audio_chunks = []
-
-            @audio_buffer_input.event_handler("on_audio_data")
-            async def on_input_audio_data(buffer, audio, sample_rate, num_channels):
-                logger.debug(f"Input AudioBuffer captured {len(audio)} bytes")
-                if audio and len(audio) > 0:
-                    input_audio_chunks.append(audio)
-
-            @audio_buffer_output.event_handler("on_audio_data")
-            async def on_output_audio_data(buffer, audio, sample_rate, num_channels):
-                logger.debug(f"Output AudioBuffer captured {len(audio)} bytes")
-                if audio and len(audio) > 0:
-                    output_audio_chunks.append(audio)
-                recorded_audio_data["sample_rate"] = sample_rate
-                recorded_audio_data["num_channels"] = num_channels
-
-        pipeline_task_ref: list = []
-
-        async def on_silence_hangup():
-            if pipeline_task_ref:
-                await pipeline_task_ref[0].cancel()
-
-        silence_hangup_processor = None
-        if silence_hangup_secs is not None and silence_hangup_secs > 0:
-            from app.services.voice_agent.call_silence_hangup import CallSilenceHangupProcessor
-
-            silence_hangup_processor = CallSilenceHangupProcessor(
-                timeout_secs=silence_hangup_secs,
-                on_hangup=on_silence_hangup,
-            )
-
-        pipeline_task_params = imports["PipelineParams"](
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            audio_in_sample_rate=transport_in_sample_rate,
-            audio_out_sample_rate=transport_out_sample_rate,
-        )
-
-        if use_aligned_recorders:
-            pipeline_processors = [ws_transport.input()]
-            if silence_hangup_processor:
-                pipeline_processors.append(silence_hangup_processor)
-            pipeline_processors.extend([user_recorder, stt])
-        else:
-            pipeline_processors = [ws_transport.input()]
-            if silence_hangup_processor:
-                pipeline_processors.append(silence_hangup_processor)
-            pipeline_processors.extend([audio_buffer_input, stt])
-        if telephony_mode and call_short_id:
-            from app.services.voice_agent.live_transcript_processor import create_live_transcript_processor
-
-            user_transcript_processor = create_live_transcript_processor(call_short_id)
-            agent_transcript_processor = create_live_transcript_processor(call_short_id)
-            if user_transcript_processor:
-                pipeline_processors.append(user_transcript_processor)
-        else:
-            user_transcript_processor = None
-            agent_transcript_processor = None
-
-        pipeline_processors.append(context_aggregator.user())
-        pipeline_processors.append(llm)
-
-        if telephony_mode and call_short_id and agent_transcript_processor:
-            pipeline_processors.append(agent_transcript_processor)
-
-        from app.services.usage.voice_usage_processor import create_llm_usage_recorder
-
-        agent_usage_section = (
-            "agents"
-            if agent_id
-            else ("telephony" if telephony_mode else "voice_playground")
-        )
-        usage_recorder = create_llm_usage_recorder(
-            organization_id=organization_id,
-            workspace_id=workspace_id,
-            product_section=agent_usage_section,
-            resource_id=agent_id,
-            resource_type="agent" if agent_id else None,
-        )
-
-        pipeline_processors.extend([
-            tts,
-        ])
-        if usage_recorder:
-            pipeline_processors.append(usage_recorder)
-        pipeline_processors.extend([
-            bot_recorder if use_aligned_recorders else audio_buffer_output,
-            ws_transport.output(),
-            context_aggregator.assistant(),
-        ])
-
-        pipeline = imports["Pipeline"](pipeline_processors)
-
-        if telephony_mode:
-            task = imports["PipelineTask"](
-                pipeline,
-                params=pipeline_task_params,
-            )
-            pipeline_task_ref.append(task)
-
-            @ws_transport.event_handler("on_client_connected")
-            async def on_client_connected(transport, client):
-                logger.info("Vobiz telephony client connected via WebSocket (voice bundle)")
-                if not use_aligned_recorders:
-                    await audio_buffer_input.start_recording()
-                    await audio_buffer_output.start_recording()
-                await task.queue_frames([imports["LLMRunFrame"]()])
-
-            @ws_transport.event_handler("on_client_disconnected")
-            async def on_client_disconnected(transport, client):
-                logger.info("Vobiz telephony client disconnected (voice bundle)")
-                await task.cancel()
-        else:
-            rtvi = imports["RTVIProcessor"](config=imports["RTVIConfig"](config=[]))
-
-            rtvi_processors = [ws_transport.input()]
-            if silence_hangup_processor:
-                rtvi_processors.append(silence_hangup_processor)
-            rtvi_processors.extend([
-                audio_buffer_input,
-                stt,
-                context_aggregator.user(),
-                rtvi,
-                llm,
-                tts,
-            ])
-            usage_recorder = create_llm_usage_recorder(
-                organization_id=organization_id,
-                workspace_id=workspace_id,
-                product_section="agents" if agent_id else "voice_playground",
-                resource_id=agent_id,
-                resource_type="agent" if agent_id else None,
-            )
-            if usage_recorder:
-                rtvi_processors.append(usage_recorder)
-            rtvi_processors.extend([
-                audio_buffer_output,
-                ws_transport.output(),
-                context_aggregator.assistant(),
-            ])
-            pipeline = imports["Pipeline"](rtvi_processors)
-            task = imports["PipelineTask"](
-                pipeline,
-                params=pipeline_task_params,
-                observers=[imports["RTVIObserver"](rtvi)],
-            )
-            if silence_hangup_processor:
-                pipeline_task_ref.append(task)
-
-            @rtvi.event_handler("on_client_ready")
-            async def on_client_ready(rtvi):
-                await rtvi.set_bot_ready()
-                await audio_buffer_input.start_recording()
-                await audio_buffer_output.start_recording()
-                logger.info("AudioBufferProcessors started recording (input + output)")
-                await task.queue_frames([imports["LLMRunFrame"]()])
-
-            @ws_transport.event_handler("on_client_connected")
-            async def on_client_connected(transport, client):
-                logger.info("efficientai client connected via WebSocket (voice bundle)")
-
-            @ws_transport.event_handler("on_client_disconnected")
-            async def on_client_disconnected(transport, client):
-                logger.info("efficientai Client disconnected (voice bundle)")
-                await task.cancel()
-
-        if websocket_client.client_state.name != "CONNECTED":
-            raise Exception(f"WebSocket is not in CONNECTED state: {websocket_client.client_state.name}")
-
-        runner = imports["PipelineRunner"](handle_sigint=False)
-
-        try:
-            await runner.run(task)
-        finally:
-            duration_result = time.time() - call_start_time
-
-            try:
-                raw_messages = context.messages if hasattr(context, "messages") else []
-                conversation_turns = []
-                transcript_parts = []
-                elapsed = 0.0
-                for msg in raw_messages:
-                    role = msg.get("role", "")
-                    content = msg.get("content", "")
-                    if not content or role == "system":
-                        continue
-                    from app.services.telephony.call_recording_lifecycle import is_bootstrap_user_message
-
-                    if role == "user" and is_bootstrap_user_message(content):
-                        continue
-                    speaker = "user" if role == "user" else "assistant"
-                    turn_duration = max(1.0, len(content.split()) * 0.4)
-                    conversation_turns.append({
-                        "speaker": speaker,
-                        "text": content,
-                        "start": round(elapsed, 2),
-                        "end": round(elapsed + turn_duration, 2),
-                    })
-                    transcript_parts.append(f"{speaker}: {content}")
-                    elapsed += turn_duration
-                transcript_text = "\n".join(transcript_parts) if transcript_parts else None
-                logger.info(f"Captured {len(conversation_turns)} conversation turns from live pipeline")
-            except Exception as ctx_err:
-                logger.warning(f"Failed to extract conversation context: {ctx_err}")
-                conversation_turns = []
-                transcript_text = None
-
-            if use_aligned_recorders:
-                await user_recorder.cleanup()
-                await bot_recorder.cleanup()
-
-                if telephony_mode and call_short_id:
-                    try:
-                        from app.workers.celery_app import finalize_telephony_recording_task
-
-                        finalize_telephony_recording_task.delay(
-                            call_short_id=call_short_id,
-                            user_audio_path=user_audio_path,
-                            bot_audio_path=bot_audio_path,
-                            call_start_time=call_start_time,
-                            organization_id=organization_id,
-                            evaluator_id=evaluator_id,
-                            result_id=result_id,
-                            conversation_turns=conversation_turns,
-                            transcript_text=transcript_text,
-                            duration=duration_result,
-                        )
-                        logger.info(
-                            "Queued finalize_telephony_recording for call_short_id={} "
-                            "user_audio={} bot_audio={}",
-                            call_short_id,
-                            user_audio_path,
-                            bot_audio_path,
-                        )
-                    except Exception as celery_err:
-                        logger.warning(
-                            "Celery unavailable for post-call recording finalize ({}); running inline",
-                            celery_err,
-                        )
-                        from app.services.voice_agent.utils.audio_merge import merge_and_upload_audio
-                        from app.database import SessionLocal
-                        from app.services.telephony.call_recording_lifecycle import (
-                            persist_telephony_call_artifacts,
-                        )
-
-                        s3_key_result, duration_result = merge_and_upload_audio(
-                            user_audio_path=user_audio_path,
-                            bot_audio_path=bot_audio_path,
-                            call_start_time=call_start_time,
-                            organization_id=organization_id,
-                            evaluator_id=evaluator_id,
-                            result_id=result_id,
-                        )
-                        db = SessionLocal()
-                        try:
-                            persist_telephony_call_artifacts(
-                                db,
-                                call_short_id=call_short_id,
-                                conversation_turns=conversation_turns,
-                                transcript_text=transcript_text,
-                                s3_key=s3_key_result,
-                                duration=duration_result,
-                            )
-                        finally:
-                            db.close()
-                elif user_audio_path and bot_audio_path:
-                    from app.services.voice_agent.utils.audio_merge import merge_and_upload_audio
-
-                    s3_key_result, duration_result = merge_and_upload_audio(
-                        user_audio_path=user_audio_path,
-                        bot_audio_path=bot_audio_path,
-                        call_start_time=call_start_time,
-                        organization_id=organization_id,
-                        evaluator_id=evaluator_id,
-                        result_id=result_id,
-                    )
-            else:
-                await audio_buffer_input.stop_recording()
-                await audio_buffer_output.stop_recording()
-                logger.info("AudioBufferProcessors stopped recording")
-
-                total_input_audio = b"".join(input_audio_chunks) if input_audio_chunks else b""
-                total_output_audio = b"".join(output_audio_chunks) if output_audio_chunks else b""
-                logger.info(
-                    f"Input audio: {len(total_input_audio)} bytes, "
-                    f"Output audio: {len(total_output_audio)} bytes"
-                )
-
-                if len(total_input_audio) > 100 or len(total_output_audio) > 100:
-                    try:
-                        import io
-                        from efficientai.audio.utils import mix_audio
-
-                        sample_rate = recorded_audio_data.get("sample_rate") or tts_sample_rate
-                        num_channels = 1
-                        mixed_audio = mix_audio(total_input_audio, total_output_audio)
-                        logger.info(f"Mixed audio: {len(mixed_audio)} bytes")
-
-                        wav_buffer = io.BytesIO()
-                        with wave.open(wav_buffer, "wb") as wf:
-                            wf.setnchannels(num_channels)
-                            wf.setsampwidth(2)
-                            wf.setframerate(sample_rate)
-                            wf.writeframes(mixed_audio)
-
-                        wav_buffer.seek(0)
-                        file_content = wav_buffer.read()
-                        file_id = uuid.uuid4()
-                        meaningful_id = result_id if result_id else f"{int(time.time())}-{file_id.hex[:8]}"
-                        s3_key_result = s3_service.upload_file(
-                            file_content=file_content,
-                            file_id=file_id,
-                            file_format="wav",
-                            organization_id=organization_id,
-                            evaluator_id=evaluator_id,
-                            meaningful_id=meaningful_id,
-                        )
-                        logger.info(f"✅ Conversation audio uploaded to S3: {s3_key_result}")
-                    except Exception as e:
-                        logger.error(f"Failed to upload audio to S3: {e}", exc_info=True)
-                else:
-                    logger.warning("No audio data captured or audio too small to upload")
-
-                if telephony_mode and call_short_id:
-                    from app.database import SessionLocal
-                    from app.services.telephony.call_recording_lifecycle import persist_telephony_call_artifacts
-
-                    db = SessionLocal()
-                    try:
-                        persist_telephony_call_artifacts(
-                            db,
-                            call_short_id=call_short_id,
-                            conversation_turns=conversation_turns,
-                            transcript_text=transcript_text,
-                            s3_key=s3_key_result,
-                            duration=duration_result,
-                        )
-                    finally:
-                        db.close()
-
-            if telephony_mode and call_short_id:
-                # region agent log
-                from app.utils.debug_agent_log import agent_debug_log
-
-                agent_debug_log(
-                    "voice_bundle.py:finally",
-                    "voice bundle cleanup complete",
-                    {
-                        "call_short_id": call_short_id,
-                        "conversation_turns_count": len(conversation_turns),
-                        "s3_key_set": bool(s3_key_result),
-                        "aligned_recorders": use_aligned_recorders,
-                    },
-                    "H4",
-                )
-                # endregion
-                
-    except Exception as e:
-        logger.error(f"Error in run_voice_bundle_fastapi: {e}", exc_info=True)
-        return {
-            "s3_key": s3_key_result,
-            "duration": duration_result,
-            "agent_id": agent_id,
-            "persona_id": persona_id,
-            "scenario_id": scenario_id,
-            "transcription": transcript_text,
-            "speaker_segments": conversation_turns if conversation_turns else None,
-            "error": str(e),
-        }
-
-    metadata = {
-        "s3_key": s3_key_result,
-        "duration": duration_result,
-        "agent_id": agent_id,
-        "persona_id": persona_id,
-        "scenario_id": scenario_id,
-        "transcription": transcript_text,
-        "speaker_segments": conversation_turns if conversation_turns else None,
-    }
-    if not s3_key_result and not transcript_text:
-        metadata["error"] = "No audio file was uploaded and no transcript captured"
-    return metadata
-
-
-if __name__ == "__main__":
-    from efficientai.runner.run import main
-
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Voice bundle pipeline for STT+LLM+TTS over WebSocket.
+
+This module uses lazy imports to avoid loading heavy AI service dependencies
+at module import time. Services are only loaded when actually used.
+"""
+
+import os
+import time
+import wave
+import uuid
+import tempfile
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from app.services.storage.s3_service import s3_service
+
+load_dotenv(override=True)
+
+
+def _get_core_imports():
+    """Lazy import core efficientai dependencies (no provider SDKs).
+    
+    Returns a dict with core pipeline classes. Provider-specific services
+    are loaded separately via _get_service() to avoid loading unused SDKs.
+    """
+    from efficientai.audio.vad.silero import SileroVADAnalyzer
+    from efficientai.audio.vad.vad_analyzer import VADParams
+    from efficientai.frames.frames import LLMRunFrame
+    from efficientai.pipeline.pipeline import Pipeline
+    from efficientai.pipeline.runner import PipelineRunner
+    from efficientai.pipeline.task import PipelineParams, PipelineTask
+    from efficientai.processors.aggregators.llm_context import LLMContext
+    from efficientai.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+    from efficientai.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+    from efficientai.processors.audio.audio_buffer_processor import AudioBufferProcessor
+    from efficientai.runner.types import RunnerArguments
+    from efficientai.runner.utils import create_transport
+    from efficientai.serializers.protobuf import ProtobufFrameSerializer
+    from efficientai.transports.websocket.fastapi import FastAPIWebsocketParams, FastAPIWebsocketTransport
+    from efficientai.transports.base_transport import BaseTransport, TransportParams
+    from efficientai.turns.bot.turn_analyzer_bot_turn_start_strategy import TurnAnalyzerBotTurnStartStrategy
+    from efficientai.turns.turn_start_strategies import TurnStartStrategies
+    
+    return {
+        "SileroVADAnalyzer": SileroVADAnalyzer,
+        "VADParams": VADParams,
+        "LLMRunFrame": LLMRunFrame,
+        "Pipeline": Pipeline,
+        "PipelineRunner": PipelineRunner,
+        "PipelineParams": PipelineParams,
+        "PipelineTask": PipelineTask,
+        "LLMContext": LLMContext,
+        "LLMContextAggregatorPair": LLMContextAggregatorPair,
+        "RTVIConfig": RTVIConfig,
+        "RTVIObserver": RTVIObserver,
+        "RTVIProcessor": RTVIProcessor,
+        "AudioBufferProcessor": AudioBufferProcessor,
+        "RunnerArguments": RunnerArguments,
+        "create_transport": create_transport,
+        "ProtobufFrameSerializer": ProtobufFrameSerializer,
+        "FastAPIWebsocketParams": FastAPIWebsocketParams,
+        "FastAPIWebsocketTransport": FastAPIWebsocketTransport,
+        "BaseTransport": BaseTransport,
+        "TransportParams": TransportParams,
+        "TurnAnalyzerBotTurnStartStrategy": TurnAnalyzerBotTurnStartStrategy,
+        "TurnStartStrategies": TurnStartStrategies,
+    }
+
+
+# Cache for core imports
+_core_imports_cache = None
+
+# Cache for provider services (keyed by service name)
+_service_cache = {}
+
+
+def _get_imports():
+    """Get cached core imports."""
+    global _core_imports_cache
+    if _core_imports_cache is None:
+        _core_imports_cache = _get_core_imports()
+    return _core_imports_cache
+
+
+def _get_service(service_name: str):
+    """Get a provider service class by name (lazy loaded).
+    
+    Each provider's SDK is only loaded when that specific service is requested.
+    This allows the voice agent to work even if some provider SDKs are not installed,
+    as long as you don't try to use those providers.
+    """
+    global _service_cache
+    
+    if service_name in _service_cache:
+        return _service_cache[service_name]
+    
+    service_class = None
+    
+    # STT Services
+    if service_name == "DeepgramSTTService":
+        from efficientai.services.deepgram.stt import DeepgramSTTService
+        service_class = DeepgramSTTService
+    elif service_name == "OpenAISTTService":
+        from efficientai.services.openai.stt import OpenAISTTService
+        service_class = OpenAISTTService
+    elif service_name == "ElevenLabsRealtimeSTTService":
+        from efficientai.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
+        service_class = ElevenLabsRealtimeSTTService
+    elif service_name == "SarvamSTTService":
+        from efficientai.services.sarvam.stt import SarvamSTTService
+        service_class = SarvamSTTService
+    elif service_name == "SmallestSTTService":
+        from efficientai.services.smallest.stt import SmallestSTTService
+        service_class = SmallestSTTService
+    
+    # TTS Services
+    elif service_name == "CartesiaTTSService":
+        from efficientai.services.cartesia.tts import CartesiaTTSService
+        service_class = CartesiaTTSService
+    elif service_name == "OpenAITTSService":
+        from efficientai.services.openai.tts import OpenAITTSService
+        service_class = OpenAITTSService
+    elif service_name == "ElevenLabsHttpTTSService":
+        from efficientai.services.elevenlabs.tts import ElevenLabsHttpTTSService
+        service_class = ElevenLabsHttpTTSService
+    elif service_name == "MurfTTSService":
+        from efficientai.services.murf.tts import MurfTTSService
+        service_class = MurfTTSService
+    elif service_name == "SarvamTTSService":
+        from efficientai.services.sarvam.tts import SarvamTTSService
+        service_class = SarvamTTSService
+    elif service_name == "VoiceMakerTTSService":
+        from efficientai.services.voicemaker.tts import VoiceMakerTTSService
+        service_class = VoiceMakerTTSService
+    elif service_name == "SmallestTTSService":
+        from efficientai.services.smallest.tts import SmallestTTSService
+        service_class = SmallestTTSService
+    
+    # LLM Services
+    elif service_name == "OpenAILLMService":
+        from efficientai.services.openai.llm import OpenAILLMService
+        service_class = OpenAILLMService
+    elif service_name == "GoogleLLMService":
+        from efficientai.services.google.llm import GoogleLLMService
+        service_class = GoogleLLMService
+    elif service_name == "AzureLLMService":
+        from efficientai.services.azure.llm import AzureLLMService
+        service_class = AzureLLMService
+    
+    # Optional: Smart Turn Analyzer
+    elif service_name == "LocalSmartTurnAnalyzerV3":
+        try:
+            from efficientai.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+            service_class = LocalSmartTurnAnalyzerV3
+        except Exception as e:
+            logger.warning(
+                "LocalSmartTurnAnalyzerV3 unavailable (missing optional deps). Error: %s", e
+            )
+            service_class = None
+    
+    else:
+        raise ValueError(f"Unknown service: {service_name}")
+    
+    _service_cache[service_name] = service_class
+    return service_class
+
+# ---------------------------------------------------------------------------
+# Provider Registries  (STT & TTS)
+# ---------------------------------------------------------------------------
+# Each entry contains:
+#   - "env_key"        : environment variable name for the API key
+#   - "default_model"  : fallback model name (None if the service doesn't need one)
+#   - "factory"        : callable(**kwargs) -> service instance
+#
+# TTS entries additionally have:
+#   - "default_voice"  : fallback voice ID when the voice bundle doesn't specify one
+#
+# To add a new provider, simply add a dict entry to the relevant registry.
+# ---------------------------------------------------------------------------
+
+
+def _get_stt_providers():
+    """Get STT provider registry with truly lazy-loaded service classes.
+    
+    Each provider's SDK is only loaded when that provider is actually used.
+    """
+    return {
+        "deepgram": {
+            "env_key": "DEEPGRAM_API_KEY",
+            "default_model": None,
+            "factory": lambda api_key, model: _get_service("DeepgramSTTService")(
+                api_key=api_key,
+                **({"model": model} if model else {}),
+            ),
+        },
+        "openai": {
+            "env_key": "OPENAI_API_KEY",
+            "default_model": "gpt-4o-transcribe",
+            "factory": lambda api_key, model: _get_service("OpenAISTTService")(
+                api_key=api_key,
+                **({"model": model} if model else {}),
+            ),
+        },
+        "elevenlabs": {
+            "env_key": "ELEVENLABS_API_KEY",
+            "default_model": "scribe_v2_realtime",
+            "factory": lambda api_key, model: _get_service("ElevenLabsRealtimeSTTService")(
+                api_key=api_key,
+                **({"model": model} if model else {}),
+            ),
+        },
+        "sarvam": {
+            "env_key": "SARVAM_API_KEY",
+            "default_model": "saaras:v3",
+            "factory": lambda api_key, model: _get_service("SarvamSTTService")(
+                api_key=api_key,
+                model=model if model else "saaras:v3",
+            ),
+        },
+        "smallest": {
+            "env_key": "SMALLEST_API_KEY",
+            "default_model": "pulse-v4",
+            "factory": lambda api_key, model: _get_service("SmallestSTTService")(
+                api_key=api_key,
+                model=model if model else "pulse-v4",
+            ),
+        },
+    }
+
+
+def _get_tts_providers():
+    """Get TTS provider registry with truly lazy-loaded service classes.
+    
+    Each provider's SDK is only loaded when that provider is actually used.
+    """
+    return {
+        "cartesia": {
+            "env_key": "CARTESIA_API_KEY",
+            "default_voice": "9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
+            "default_model": None,
+        },
+        "elevenlabs": {
+            "env_key": "ELEVENLABS_API_KEY",
+            "default_voice": "JBFqnCBsd6RMkjVDRZzb",
+            "default_model": "eleven_multilingual_v2",
+        },
+        "openai": {
+            "env_key": "OPENAI_API_KEY",
+            "default_voice": "alloy",
+            "default_model": "gpt-4o-mini-tts",
+        },
+        "murf": {
+            "env_key": "MURF_API_KEY",
+            "default_voice": "en-US-natalie",
+            "default_model": "GEN2",
+        },
+        "sarvam": {
+            "env_key": "SARVAM_API_KEY",
+            "default_voice": "ritu",
+            "default_model": "bulbul:v3",
+        },
+        "voicemaker": {
+            "env_key": "VOICEMAKER_API_KEY",
+            "default_voice": "ai3-Jony",
+            "default_model": "neural",
+        },
+        "smallest": {
+            "env_key": "SMALLEST_API_KEY",
+            "default_voice": "daniel",
+            "default_model": "lightning-v3.1",
+        },
+    }
+
+
+def _instantiate_tts_service(
+    provider: str,
+    tts_cfg: dict,
+    *,
+    api_key: str,
+    voice_id: str,
+    model: str | None,
+    sample_rate: int,
+):
+    """Build a streaming TTS service for the voice bundle pipeline."""
+    from app.services.voice_agent.tts_sample_rate import provider_accepts_sample_rate_kwarg
+
+    sr_kw = {"sample_rate": sample_rate} if provider_accepts_sample_rate_kwarg(provider) else {}
+    provider = provider.strip().lower()
+
+    if provider == "cartesia":
+        return _get_service("CartesiaTTSService")(
+            api_key=api_key,
+            voice_id=voice_id,
+            **sr_kw,
+        )
+    if provider == "elevenlabs":
+        return _get_service("ElevenLabsHttpTTSService")(
+            api_key=api_key,
+            voice_id=voice_id,
+            model=model,
+            aiohttp_session=__import__("aiohttp").ClientSession(),
+            **sr_kw,
+        )
+    if provider == "openai":
+        return _get_service("OpenAITTSService")(
+            api_key=api_key,
+            voice=voice_id,
+            model=model,
+            **sr_kw,
+        )
+    if provider == "murf":
+        return _get_service("MurfTTSService")(
+            api_key=api_key,
+            voice_id=voice_id,
+            model=model if model else "GEN2",
+            **sr_kw,
+        )
+    if provider == "sarvam":
+        return _get_service("SarvamTTSService")(
+            api_key=api_key,
+            voice_id=voice_id,
+            model=model if model else "bulbul:v3",
+            **sr_kw,
+        )
+    if provider == "voicemaker":
+        return _get_service("VoiceMakerTTSService")(
+            api_key=api_key,
+            voice_id=voice_id,
+            model=model if model else "neural",
+            **sr_kw,
+        )
+    if provider == "smallest":
+        return _get_service("SmallestTTSService")(
+            api_key=api_key,
+            voice_id=voice_id if voice_id else "daniel",
+            model=model if model else "lightning-v3.1",
+            **sr_kw,
+        )
+    raise ValueError(f"Unsupported TTS provider '{provider}'")
+
+
+def _get_llm_providers():
+    """Get LLM provider registry with truly lazy-loaded service classes.
+    
+    Each provider's SDK is only loaded when that provider is actually used.
+    """
+    return {
+        "openai": {
+            "env_key": "OPENAI_API_KEY",
+            "default_model": "gpt-4.1",
+            "factory": lambda api_key, model, params=None: _get_service("OpenAILLMService")(
+                api_key=api_key,
+                model=model,
+                **({"params": params} if params else {}),
+            ),
+        },
+        "google": {
+            "env_key": "GOOGLE_API_KEY",
+            "default_model": "gemini-2.5-flash",
+            "factory": lambda api_key, model, params=None: _get_service("GoogleLLMService")(
+                api_key=api_key,
+                model=model,
+                **({"params": params} if params else {}),
+            ),
+        },
+        "azure": {
+            "env_key": "AZURE_OPENAI_API_KEY",
+            "default_model": "gpt-4.1",
+            # Azure is instantiated with endpoint metadata in run_voice_bundle_fastapi.
+            "factory": lambda api_key, model, params=None: _get_service("OpenAILLMService")(
+                api_key=api_key,
+                model=model,
+                **({"params": params} if params else {}),
+            ),
+        },
+    }
+
+
+DEFAULT_STT_PROVIDER = None
+DEFAULT_TTS_PROVIDER = None
+DEFAULT_LLM_PROVIDER = None
+
+
+def _resolve_provider(voice_bundle, attr: str, default: str | None = None) -> str:
+    """Return the normalised provider name from a voice bundle attribute.
+
+    Raises ValueError when the voice bundle has no provider set and no default is given.
+    """
+    raw = getattr(voice_bundle, attr, None)
+    if raw is None:
+        if default is not None:
+            return default
+        raise ValueError(
+            f"Voice bundle is missing required field '{attr}'. "
+            f"Please configure the provider in the voice bundle settings."
+        )
+    value = raw.value if hasattr(raw, "value") else str(raw)
+    return value.lower()
+
+
+def _get_transport_params():
+    """Get transport params with lazy-loaded classes."""
+    imports = _get_imports()
+    return {
+        "twilio": lambda: imports["FastAPIWebsocketParams"](
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=imports["SileroVADAnalyzer"](params=imports["VADParams"](stop_secs=0.2)),
+        ),
+        "webrtc": lambda: imports["TransportParams"](
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=imports["SileroVADAnalyzer"](params=imports["VADParams"](stop_secs=0.2)),
+        ),
+    }
+
+
+async def run_bot(transport, runner_args):
+    """Run the bot pipeline with the given transport."""
+    imports = _get_imports()
+    
+    logger.info(f"Starting bot")
+
+    # Use _get_service for provider-specific classes
+    DeepgramSTTService = _get_service("DeepgramSTTService")
+    CartesiaTTSService = _get_service("CartesiaTTSService")
+    OpenAILLMService = _get_service("OpenAILLMService")
+    
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = imports["LLMContext"](messages)
+    context_aggregator = imports["LLMContextAggregatorPair"](context)
+
+    pipeline = imports["Pipeline"](
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    bot_turn_strategies = []
+    LocalSmartTurnAnalyzerV3 = _get_service("LocalSmartTurnAnalyzerV3")
+    if LocalSmartTurnAnalyzerV3:
+        bot_turn_strategies = [imports["TurnAnalyzerBotTurnStartStrategy"](turn_analyzer=LocalSmartTurnAnalyzerV3())]
+    else:
+        logger.info("Running without LocalSmartTurnAnalyzerV3 (optional dependency not installed).")
+
+    task = imports["PipelineTask"](
+        pipeline,
+        params=imports["PipelineParams"](
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            turn_start_strategies=imports["TurnStartStrategies"](bot=bot_turn_strategies),
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([imports["LLMRunFrame"]()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = imports["PipelineRunner"](handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args):
+    """Main bot entry point compatible with efficientai Cloud."""
+    imports = _get_imports()
+    transport_params = _get_transport_params()
+    transport = await imports["create_transport"](runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+async def run_voice_bundle_fastapi(
+    websocket_client,
+    system_instruction: str | None = None,
+    organization_id: str | None = None,
+    workspace_id: str | None = None,
+    agent_id: str | None = None,
+    persona_id: str | None = None,
+    scenario_id: str | None = None,
+    evaluator_id: str | None = None,
+    result_id: str | None = None,
+    voice_bundle=None,
+    persona=None,
+    stt_api_key: str | None = None,
+    tts_api_key: str | None = None,
+    llm_api_key: str | None = None,
+    llm_endpoint_url: str | None = None,
+    serializer=None,
+    telephony_mode: bool = False,
+    call_short_id: str | None = None,
+    silence_hangup_secs: float | None = None,
+):
+    """
+    Run the STT+LLM+TTS voice bundle pipeline over a FastAPI WebSocket.
+    Uses AudioBufferProcessor for proper conversation audio recording.
+    """
+    # Lazy load all efficientai dependencies
+    imports = _get_imports()
+    STT_PROVIDERS = _get_stt_providers()
+    TTS_PROVIDERS = _get_tts_providers()
+    LLM_PROVIDERS = _get_llm_providers()
+
+    call_start_time = time.time()
+    s3_key_result = None
+    duration_result = None
+    transcript_text = None
+    conversation_turns = []
+    
+    # Storage for audio data from the buffer processor
+    recorded_audio_data = {"audio": None, "sample_rate": None, "num_channels": None}
+
+    # Resolve STT provider config from the registry
+    stt_provider_value = _resolve_provider(voice_bundle, "stt_provider", DEFAULT_STT_PROVIDER)
+    stt_cfg = STT_PROVIDERS.get(stt_provider_value)
+    if stt_cfg is None:
+        supported = ", ".join(sorted(STT_PROVIDERS.keys()))
+        raise ValueError(
+            f"Unsupported STT provider '{stt_provider_value}'. Supported providers: {supported}"
+        )
+
+    # Resolve TTS provider config from the registry
+    tts_provider_value = _resolve_provider(voice_bundle, "tts_provider", DEFAULT_TTS_PROVIDER)
+    tts_cfg = TTS_PROVIDERS.get(tts_provider_value)
+    if tts_cfg is None:
+        supported = ", ".join(sorted(TTS_PROVIDERS.keys()))
+        raise ValueError(
+            f"Unsupported TTS provider '{tts_provider_value}'. Supported providers: {supported}"
+        )
+
+    # Resolve LLM provider config from the registry
+    llm_provider_value = _resolve_provider(voice_bundle, "llm_provider", DEFAULT_LLM_PROVIDER)
+    llm_cfg = LLM_PROVIDERS.get(llm_provider_value)
+    if llm_cfg is None:
+        supported = ", ".join(sorted(LLM_PROVIDERS.keys()))
+        raise ValueError(
+            f"Unsupported LLM provider '{llm_provider_value}'. Supported providers: {supported}"
+        )
+
+    # Resolve API keys: prefer provided values, otherwise fall back to environment
+    stt_api_key = stt_api_key or os.getenv(stt_cfg["env_key"])
+    tts_api_key = tts_api_key or os.getenv(tts_cfg["env_key"])
+    llm_api_key = llm_api_key or os.getenv(llm_cfg["env_key"])
+
+    missing = []
+    if not stt_api_key:
+        missing.append(stt_cfg["env_key"])
+    if not tts_api_key:
+        missing.append(tts_cfg["env_key"])
+    if not llm_api_key:
+        missing.append(llm_cfg["env_key"])
+    if missing:
+        raise ValueError(f"Missing required API keys for voice bundle: {', '.join(missing)}")
+
+    try:
+        from app.services.voice_agent.tts_sample_rate import (
+            resolve_tts_sample_rate_hz,
+            resolve_websocket_audio_in_sample_rate_hz,
+            resolve_websocket_audio_out_sample_rate_hz,
+        )
+
+        transport_serializer = serializer or imports["ProtobufFrameSerializer"]()
+        tts_sample_rate = resolve_tts_sample_rate_hz(
+            voice_bundle,
+            tts_provider_value,
+            telephony_mode=telephony_mode,
+        )
+        transport_in_sample_rate = resolve_websocket_audio_in_sample_rate_hz(
+            telephony_mode=telephony_mode,
+        )
+        transport_out_sample_rate = resolve_websocket_audio_out_sample_rate_hz(
+            telephony_mode=telephony_mode,
+        )
+        ws_transport = imports["FastAPIWebsocketTransport"](
+            websocket=websocket_client,
+            params=imports["FastAPIWebsocketParams"](
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                add_wav_header=False,
+                audio_in_sample_rate=transport_in_sample_rate,
+                audio_out_sample_rate=transport_out_sample_rate,
+                vad_analyzer=imports["SileroVADAnalyzer"](params=imports["VADParams"](stop_secs=0.2)),
+                serializer=transport_serializer,
+            ),
+        )
+
+        # Instantiate STT service from the provider registry
+        stt_model = getattr(voice_bundle, "stt_model", None) or stt_cfg["default_model"]
+        stt = stt_cfg["factory"](api_key=stt_api_key, model=stt_model)
+
+        # Instantiate TTS service from the provider registry
+        from app.services.voice_agent.resolve_tts_voice import (
+            log_effective_tts_voice,
+            resolve_effective_tts_voice_id,
+        )
+
+        tts_voice_id = resolve_effective_tts_voice_id(
+            persona=persona,
+            voice_bundle=voice_bundle,
+            default_voice=tts_cfg["default_voice"],
+        )
+        log_effective_tts_voice(
+            logger,
+            path_name="run_voice_bundle_fastapi",
+            persona=persona,
+            voice_bundle=voice_bundle,
+            resolved_voice_id=tts_voice_id,
+        )
+        tts_model = getattr(voice_bundle, "tts_model", None) or tts_cfg["default_model"]
+        tts = _instantiate_tts_service(
+            tts_provider_value,
+            tts_cfg,
+            api_key=tts_api_key,
+            voice_id=tts_voice_id,
+            model=tts_model,
+            sample_rate=tts_sample_rate,
+        )
+
+        llm_model = getattr(voice_bundle, "llm_model", None) or llm_cfg["default_model"]
+        from app.services.ai.llm_generation_config import (
+            build_efficientai_input_params,
+            merge_llm_config,
+        )
+        from app.services.ai.llm_service import (
+            _azure_deployment_name,
+            _azure_openai_v1_api_base,
+            _azure_uses_openai_v1_routing,
+            _normalize_azure_endpoint,
+        )
+
+        llm_params = build_efficientai_input_params(
+            llm_provider_value,
+            merge_llm_config(
+                getattr(voice_bundle, "llm_config", None),
+                legacy_temperature=getattr(voice_bundle, "llm_temperature", None),
+                legacy_max_tokens=getattr(voice_bundle, "llm_max_tokens", None),
+            ),
+        )
+        if llm_provider_value == "azure":
+            if not llm_endpoint_url:
+                raise ValueError(
+                    "Azure LLM requires an endpoint URL on the AI provider credential "
+                    "(Integrations → AI Provider → Azure → Azure Endpoint URL)."
+                )
+            api_base, version_hint = _normalize_azure_endpoint(llm_endpoint_url)
+            deployment_model = _azure_deployment_name(llm_model)
+            if _azure_uses_openai_v1_routing(None, version_hint):
+                llm = _get_service("OpenAILLMService")(
+                    api_key=llm_api_key,
+                    model=deployment_model,
+                    base_url=_azure_openai_v1_api_base(api_base),
+                    params=llm_params,
+                )
+            else:
+                llm = _get_service("AzureLLMService")(
+                    api_key=llm_api_key,
+                    endpoint=api_base,
+                    model=deployment_model,
+                    api_version=version_hint or "2024-08-01-preview",
+                    params=llm_params,
+                )
+        else:
+            llm = llm_cfg["factory"](api_key=llm_api_key, model=llm_model, params=llm_params)
+
+        # Build context with provided system instruction or a default
+        base_instruction = (
+            system_instruction.strip()
+            if system_instruction
+            else "You are a helpful voice assistant. Keep responses concise and speakable."
+        )
+
+        # Sarvam TTS rejects text containing emojis / non-language characters,
+        # so we inject a guardrail into the system instructions.
+        if tts_provider_value == "sarvam":
+            base_instruction += (
+                "\n\nIMPORTANT: Your output will be spoken aloud by a text-to-speech engine "
+                "that only supports plain text in Indian languages and English. "
+                "You MUST NOT use any emojis, emoticons, bullet points, asterisks, "
+                "or special Unicode characters. Use only plain spoken words."
+            )
+
+        messages = [
+            {
+                "role": "system",
+                "content": base_instruction,
+            },
+            {
+                "role": "user",
+                "content": "Start by greeting the user warmly and introducing yourself based on the system instruction.",
+            },
+        ]
+
+        context = imports["LLMContext"](messages)
+        context_aggregator = imports["LLMContextAggregatorPair"](context)
+
+        use_aligned_recorders = telephony_mode
+        user_recorder = None
+        bot_recorder = None
+        user_audio_path = None
+        bot_audio_path = None
+        recording_start_time = time.time()
+
+        if use_aligned_recorders:
+            from app.services.voice_agent.audio_recorder import get_audio_recorder_class
+            from app.services.voice_agent.telephony_recording_paths import telephony_recording_temp_path
+
+            AudioRecorder = get_audio_recorder_class()
+            user_audio_path = telephony_recording_temp_path(suffix=".wav")
+            bot_audio_path = telephony_recording_temp_path(suffix=".wav")
+            user_recorder = AudioRecorder(
+                user_audio_path,
+                recording_start_time,
+                target_sample_rate=tts_sample_rate,
+                recorder_name="UserAudioRecorder",
+                alignment_mode="stream",
+            )
+            bot_recorder = AudioRecorder(
+                bot_audio_path,
+                recording_start_time,
+                target_sample_rate=tts_sample_rate,
+                recorder_name="BotAudioRecorder",
+                alignment_mode="stream",
+            )
+            audio_buffer_input = None
+            audio_buffer_output = None
+            input_audio_chunks = []
+            output_audio_chunks = []
+        else:
+            audio_buffer_input = imports["AudioBufferProcessor"](
+                sample_rate=tts_sample_rate,
+                num_channels=1,
+            )
+            audio_buffer_output = imports["AudioBufferProcessor"](
+                sample_rate=tts_sample_rate,
+                num_channels=1,
+            )
+            input_audio_chunks = []
+            output_audio_chunks = []
+
+            @audio_buffer_input.event_handler("on_audio_data")
+            async def on_input_audio_data(buffer, audio, sample_rate, num_channels):
+                logger.debug(f"Input AudioBuffer captured {len(audio)} bytes")
+                if audio and len(audio) > 0:
+                    input_audio_chunks.append(audio)
+
+            @audio_buffer_output.event_handler("on_audio_data")
+            async def on_output_audio_data(buffer, audio, sample_rate, num_channels):
+                logger.debug(f"Output AudioBuffer captured {len(audio)} bytes")
+                if audio and len(audio) > 0:
+                    output_audio_chunks.append(audio)
+                recorded_audio_data["sample_rate"] = sample_rate
+                recorded_audio_data["num_channels"] = num_channels
+
+        pipeline_task_ref: list = []
+        sut_latency_observer = None
+
+        async def on_silence_hangup():
+            if pipeline_task_ref:
+                await pipeline_task_ref[0].cancel()
+
+        silence_hangup_processor = None
+        if silence_hangup_secs is not None and silence_hangup_secs > 0:
+            from app.services.voice_agent.call_silence_hangup import CallSilenceHangupProcessor
+
+            silence_hangup_processor = CallSilenceHangupProcessor(
+                timeout_secs=silence_hangup_secs,
+                on_hangup=on_silence_hangup,
+            )
+
+        pipeline_task_params = imports["PipelineParams"](
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            audio_in_sample_rate=transport_in_sample_rate,
+            audio_out_sample_rate=transport_out_sample_rate,
+        )
+
+        if use_aligned_recorders:
+            pipeline_processors = [ws_transport.input()]
+            if silence_hangup_processor:
+                pipeline_processors.append(silence_hangup_processor)
+            pipeline_processors.extend([user_recorder, stt])
+        else:
+            pipeline_processors = [ws_transport.input()]
+            if silence_hangup_processor:
+                pipeline_processors.append(silence_hangup_processor)
+            pipeline_processors.extend([audio_buffer_input, stt])
+        if telephony_mode and call_short_id:
+            from app.services.voice_agent.live_transcript_processor import create_live_transcript_processor
+
+            user_transcript_processor = create_live_transcript_processor(call_short_id)
+            agent_transcript_processor = create_live_transcript_processor(call_short_id)
+            if user_transcript_processor:
+                pipeline_processors.append(user_transcript_processor)
+        else:
+            user_transcript_processor = None
+            agent_transcript_processor = None
+
+        pipeline_processors.append(context_aggregator.user())
+        pipeline_processors.append(llm)
+
+        if telephony_mode and call_short_id and agent_transcript_processor:
+            pipeline_processors.append(agent_transcript_processor)
+
+        from app.services.usage.voice_usage_processor import create_llm_usage_recorder
+
+        agent_usage_section = (
+            "agents"
+            if agent_id
+            else ("telephony" if telephony_mode else "voice_playground")
+        )
+        usage_recorder = create_llm_usage_recorder(
+            organization_id=organization_id,
+            workspace_id=workspace_id,
+            product_section=agent_usage_section,
+            resource_id=agent_id,
+            resource_type="agent" if agent_id else None,
+        )
+
+        pipeline_processors.extend([
+            tts,
+        ])
+        if usage_recorder:
+            pipeline_processors.append(usage_recorder)
+        pipeline_processors.extend([
+            bot_recorder if use_aligned_recorders else audio_buffer_output,
+            ws_transport.output(),
+            context_aggregator.assistant(),
+        ])
+
+        pipeline = imports["Pipeline"](pipeline_processors)
+
+        if telephony_mode:
+            from app.services.voice_agent.sut_latency_observer import SutLatencyObserver
+
+            sut_latency_observer = SutLatencyObserver()
+            task = imports["PipelineTask"](
+                pipeline,
+                params=pipeline_task_params,
+                observers=[sut_latency_observer],
+            )
+            pipeline_task_ref.append(task)
+
+            @ws_transport.event_handler("on_client_connected")
+            async def on_client_connected(transport, client):
+                logger.info("Vobiz telephony client connected via WebSocket (voice bundle)")
+                if not use_aligned_recorders:
+                    await audio_buffer_input.start_recording()
+                    await audio_buffer_output.start_recording()
+                await task.queue_frames([imports["LLMRunFrame"]()])
+
+            @ws_transport.event_handler("on_client_disconnected")
+            async def on_client_disconnected(transport, client):
+                logger.info("Vobiz telephony client disconnected (voice bundle)")
+                await task.cancel()
+        else:
+            rtvi = imports["RTVIProcessor"](config=imports["RTVIConfig"](config=[]))
+
+            rtvi_processors = [ws_transport.input()]
+            if silence_hangup_processor:
+                rtvi_processors.append(silence_hangup_processor)
+            rtvi_processors.extend([
+                audio_buffer_input,
+                stt,
+                context_aggregator.user(),
+                rtvi,
+                llm,
+                tts,
+            ])
+            usage_recorder = create_llm_usage_recorder(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section="agents" if agent_id else "voice_playground",
+                resource_id=agent_id,
+                resource_type="agent" if agent_id else None,
+            )
+            if usage_recorder:
+                rtvi_processors.append(usage_recorder)
+            rtvi_processors.extend([
+                audio_buffer_output,
+                ws_transport.output(),
+                context_aggregator.assistant(),
+            ])
+            pipeline = imports["Pipeline"](rtvi_processors)
+            task = imports["PipelineTask"](
+                pipeline,
+                params=pipeline_task_params,
+                observers=[imports["RTVIObserver"](rtvi)],
+            )
+            if silence_hangup_processor:
+                pipeline_task_ref.append(task)
+
+            @rtvi.event_handler("on_client_ready")
+            async def on_client_ready(rtvi):
+                await rtvi.set_bot_ready()
+                await audio_buffer_input.start_recording()
+                await audio_buffer_output.start_recording()
+                logger.info("AudioBufferProcessors started recording (input + output)")
+                await task.queue_frames([imports["LLMRunFrame"]()])
+
+            @ws_transport.event_handler("on_client_connected")
+            async def on_client_connected(transport, client):
+                logger.info("efficientai client connected via WebSocket (voice bundle)")
+
+            @ws_transport.event_handler("on_client_disconnected")
+            async def on_client_disconnected(transport, client):
+                logger.info("efficientai Client disconnected (voice bundle)")
+                await task.cancel()
+
+        if websocket_client.client_state.name != "CONNECTED":
+            raise Exception(f"WebSocket is not in CONNECTED state: {websocket_client.client_state.name}")
+
+        runner = imports["PipelineRunner"](handle_sigint=False)
+
+        try:
+            await runner.run(task)
+        finally:
+            duration_result = time.time() - call_start_time
+
+            try:
+                raw_messages = context.messages if hasattr(context, "messages") else []
+                conversation_turns = []
+                transcript_parts = []
+                elapsed = 0.0
+                for msg in raw_messages:
+                    role = msg.get("role", "")
+                    content = msg.get("content", "")
+                    if not content or role == "system":
+                        continue
+                    from app.services.telephony.call_recording_lifecycle import is_bootstrap_user_message
+
+                    if role == "user" and is_bootstrap_user_message(content):
+                        continue
+                    speaker = "user" if role == "user" else "assistant"
+                    turn_duration = max(1.0, len(content.split()) * 0.4)
+                    conversation_turns.append({
+                        "speaker": speaker,
+                        "text": content,
+                        "start": round(elapsed, 2),
+                        "end": round(elapsed + turn_duration, 2),
+                    })
+                    transcript_parts.append(f"{speaker}: {content}")
+                    elapsed += turn_duration
+                transcript_text = "\n".join(transcript_parts) if transcript_parts else None
+                logger.info(f"Captured {len(conversation_turns)} conversation turns from live pipeline")
+            except Exception as ctx_err:
+                logger.warning(f"Failed to extract conversation context: {ctx_err}")
+                conversation_turns = []
+                transcript_text = None
+
+            if use_aligned_recorders:
+                await user_recorder.cleanup()
+                await bot_recorder.cleanup()
+
+                if telephony_mode and call_short_id:
+                    trace_turns = (
+                        sut_latency_observer.get_turns() if sut_latency_observer else None
+                    )
+                    try:
+                        from app.workers.celery_app import finalize_telephony_recording_task
+
+                        finalize_telephony_recording_task.delay(
+                            call_short_id=call_short_id,
+                            user_audio_path=user_audio_path,
+                            bot_audio_path=bot_audio_path,
+                            call_start_time=call_start_time,
+                            organization_id=organization_id,
+                            evaluator_id=evaluator_id,
+                            result_id=result_id,
+                            conversation_turns=conversation_turns,
+                            transcript_text=transcript_text,
+                            duration=duration_result,
+                            trace_turns=trace_turns,
+                        )
+                        logger.info(
+                            "Queued finalize_telephony_recording for call_short_id={} "
+                            "user_audio={} bot_audio={}",
+                            call_short_id,
+                            user_audio_path,
+                            bot_audio_path,
+                        )
+                    except Exception as celery_err:
+                        logger.warning(
+                            "Celery unavailable for post-call recording finalize ({}); running inline",
+                            celery_err,
+                        )
+                        from app.services.voice_agent.utils.audio_merge import merge_and_upload_audio
+                        from app.database import SessionLocal
+                        from app.services.telephony.call_recording_lifecycle import (
+                            persist_telephony_call_artifacts,
+                        )
+
+                        s3_key_result, duration_result = merge_and_upload_audio(
+                            user_audio_path=user_audio_path,
+                            bot_audio_path=bot_audio_path,
+                            call_start_time=call_start_time,
+                            organization_id=organization_id,
+                            evaluator_id=evaluator_id,
+                            result_id=result_id,
+                        )
+                        db = SessionLocal()
+                        try:
+                            persist_telephony_call_artifacts(
+                                db,
+                                call_short_id=call_short_id,
+                                conversation_turns=conversation_turns,
+                                transcript_text=transcript_text,
+                                s3_key=s3_key_result,
+                                duration=duration_result,
+                                trace_turns=trace_turns,
+                            )
+                        finally:
+                            db.close()
+                elif user_audio_path and bot_audio_path:
+                    from app.services.voice_agent.utils.audio_merge import merge_and_upload_audio
+
+                    s3_key_result, duration_result = merge_and_upload_audio(
+                        user_audio_path=user_audio_path,
+                        bot_audio_path=bot_audio_path,
+                        call_start_time=call_start_time,
+                        organization_id=organization_id,
+                        evaluator_id=evaluator_id,
+                        result_id=result_id,
+                    )
+            else:
+                await audio_buffer_input.stop_recording()
+                await audio_buffer_output.stop_recording()
+                logger.info("AudioBufferProcessors stopped recording")
+
+                total_input_audio = b"".join(input_audio_chunks) if input_audio_chunks else b""
+                total_output_audio = b"".join(output_audio_chunks) if output_audio_chunks else b""
+                logger.info(
+                    f"Input audio: {len(total_input_audio)} bytes, "
+                    f"Output audio: {len(total_output_audio)} bytes"
+                )
+
+                if len(total_input_audio) > 100 or len(total_output_audio) > 100:
+                    try:
+                        import io
+                        from efficientai.audio.utils import mix_audio
+
+                        sample_rate = recorded_audio_data.get("sample_rate") or tts_sample_rate
+                        num_channels = 1
+                        mixed_audio = mix_audio(total_input_audio, total_output_audio)
+                        logger.info(f"Mixed audio: {len(mixed_audio)} bytes")
+
+                        wav_buffer = io.BytesIO()
+                        with wave.open(wav_buffer, "wb") as wf:
+                            wf.setnchannels(num_channels)
+                            wf.setsampwidth(2)
+                            wf.setframerate(sample_rate)
+                            wf.writeframes(mixed_audio)
+
+                        wav_buffer.seek(0)
+                        file_content = wav_buffer.read()
+                        file_id = uuid.uuid4()
+                        meaningful_id = result_id if result_id else f"{int(time.time())}-{file_id.hex[:8]}"
+                        s3_key_result = s3_service.upload_file(
+                            file_content=file_content,
+                            file_id=file_id,
+                            file_format="wav",
+                            organization_id=organization_id,
+                            evaluator_id=evaluator_id,
+                            meaningful_id=meaningful_id,
+                        )
+                        logger.info(f"✅ Conversation audio uploaded to S3: {s3_key_result}")
+                    except Exception as e:
+                        logger.error(f"Failed to upload audio to S3: {e}", exc_info=True)
+                else:
+                    logger.warning("No audio data captured or audio too small to upload")
+
+                if telephony_mode and call_short_id:
+                    from app.database import SessionLocal
+                    from app.services.telephony.call_recording_lifecycle import persist_telephony_call_artifacts
+
+                    trace_turns = (
+                        sut_latency_observer.get_turns() if sut_latency_observer else None
+                    )
+                    db = SessionLocal()
+                    try:
+                        persist_telephony_call_artifacts(
+                            db,
+                            call_short_id=call_short_id,
+                            conversation_turns=conversation_turns,
+                            transcript_text=transcript_text,
+                            s3_key=s3_key_result,
+                            duration=duration_result,
+                            trace_turns=trace_turns,
+                        )
+                    finally:
+                        db.close()
+
+            if telephony_mode and call_short_id:
+                # region agent log
+                from app.utils.debug_agent_log import agent_debug_log
+
+                agent_debug_log(
+                    "voice_bundle.py:finally",
+                    "voice bundle cleanup complete",
+                    {
+                        "call_short_id": call_short_id,
+                        "conversation_turns_count": len(conversation_turns),
+                        "s3_key_set": bool(s3_key_result),
+                        "aligned_recorders": use_aligned_recorders,
+                    },
+                    "H4",
+                )
+                # endregion
+                
+    except Exception as e:
+        logger.error(f"Error in run_voice_bundle_fastapi: {e}", exc_info=True)
+        return {
+            "s3_key": s3_key_result,
+            "duration": duration_result,
+            "agent_id": agent_id,
+            "persona_id": persona_id,
+            "scenario_id": scenario_id,
+            "transcription": transcript_text,
+            "speaker_segments": conversation_turns if conversation_turns else None,
+            "error": str(e),
+        }
+
+    metadata = {
+        "s3_key": s3_key_result,
+        "duration": duration_result,
+        "agent_id": agent_id,
+        "persona_id": persona_id,
+        "scenario_id": scenario_id,
+        "transcription": transcript_text,
+        "speaker_segments": conversation_turns if conversation_turns else None,
+    }
+    if not s3_key_result and not transcript_text:
+        metadata["error"] = "No audio file was uploaded and no transcript captured"
+    return metadata
+
+
+if __name__ == "__main__":
+    from efficientai.runner.run import main
+
     main()
\ No newline at end of file
diff --git a/app/utils/call_recordings.py b/app/utils/call_recordings.py
index 305132be..7bb9287f 100644
--- a/app/utils/call_recordings.py
+++ b/app/utils/call_recordings.py
@@ -1,39 +1,47 @@
-"""Shared helpers for call recording operations."""
-
-import random
-
-from fastapi import HTTPException, status
-from sqlalchemy.orm import Session
-
-from app.models.database import CallRecording
-
-
-def generate_unique_call_short_id(db: Session, max_attempts: int = 100) -> str:
-    """
-    Generate a unique 6-digit short ID for a call recording.
-
-    Args:
-        db: Database session.
-        max_attempts: Maximum attempts before failing.
-
-    Returns:
-        A unique 6-digit string ID.
-
-    Raises:
-        HTTPException: If a unique ID cannot be generated.
-    """
-    for _ in range(max_attempts):
-        call_short_id = f"{random.randint(100000, 999999)}"
-        existing = (
-            db.query(CallRecording)
-            .filter(CallRecording.call_short_id == call_short_id)
-            .first()
-        )
-        if not existing:
-            return call_short_id
-
-    raise HTTPException(
-        status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-        detail="Failed to generate unique call short ID",
-    )
-
+"""Shared helpers for call recording operations."""
+
+import random
+
+from fastapi import HTTPException, status
+from sqlalchemy.orm import Session
+
+from app.models.database import CallRecording, SyntheticCallTrace
+
+
+def generate_unique_call_short_id(db: Session, max_attempts: int = 100) -> str:
+    """
+    Generate a unique 6-digit short ID for a call recording.
+
+    Args:
+        db: Database session.
+        max_attempts: Maximum attempts before failing.
+
+    Returns:
+        A unique 6-digit string ID.
+
+    Raises:
+        HTTPException: If a unique ID cannot be generated.
+    """
+    for _ in range(max_attempts):
+        call_short_id = f"{random.randint(100000, 999999)}"
+        existing = (
+            db.query(CallRecording)
+            .filter(CallRecording.call_short_id == call_short_id)
+            .first()
+        )
+        if existing:
+            continue
+        trace_existing = (
+            db.query(SyntheticCallTrace)
+            .filter(SyntheticCallTrace.call_short_id == call_short_id)
+            .first()
+        )
+        if trace_existing:
+            continue
+        return call_short_id
+
+    raise HTTPException(
+        status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+        detail="Failed to generate unique call short ID",
+    )
+
diff --git a/app/workers/tasks/finalize_telephony_recording.py b/app/workers/tasks/finalize_telephony_recording.py
index d90d7f3b..1daedad8 100644
--- a/app/workers/tasks/finalize_telephony_recording.py
+++ b/app/workers/tasks/finalize_telephony_recording.py
@@ -26,6 +26,7 @@ def finalize_telephony_recording_task(
     conversation_turns: Optional[List[Dict[str, Any]]] = None,
     transcript_text: Optional[str] = None,
     duration: Optional[float] = None,
+    trace_turns: Optional[List[Dict[str, Any]]] = None,
 ) -> dict:
     """Merge dual-track WAVs, upload to S3, persist CallRecording, queue evaluator."""
     try:
@@ -48,6 +49,7 @@ def finalize_telephony_recording_task(
                 transcript_text=transcript_text,
                 s3_key=s3_key,
                 duration=effective_duration,
+                trace_turns=trace_turns,
             )
         finally:
             db.close()
diff --git a/app/workers/tasks/initiate_vobiz_outbound.py b/app/workers/tasks/initiate_vobiz_outbound.py
index 0457280f..c53028f4 100644
--- a/app/workers/tasks/initiate_vobiz_outbound.py
+++ b/app/workers/tasks/initiate_vobiz_outbound.py
@@ -1,73 +1,91 @@
-"""Celery task to initiate Vobiz outbound calls asynchronously."""
-
-from __future__ import annotations
-
-from uuid import UUID
-
-from loguru import logger
-
-from app.database import SessionLocal
-from app.models.database import CallRecording
-from app.services.telephony.vobiz_client import build_vobiz_client_for_org
-from app.services.telephony.vobiz_outbound_pool import release_pool_slot
-from app.services.telephony.vobiz_session import delete_call_session, get_call_session
-from app.workers.config import celery_app
-
-
-@celery_app.task(name="initiate_vobiz_outbound_call", bind=True, max_retries=2)
-def initiate_vobiz_outbound_call_task(
-    self,
-    *,
-    organization_id: str,
-    call_ref: str,
-    from_number: str,
-    to_number: str,
-    answer_url: str,
-    events_url: str,
-    used_pool: bool,
-    call_recording_id: str,
-) -> dict:
-    org_uuid = UUID(organization_id)
-    db = SessionLocal()
-    try:
-        client, _ = build_vobiz_client_for_org(db, org_uuid)
-        response = client.create_outbound_call(
-            from_=from_number,
-            to_=to_number,
-            answer_url=answer_url,
-            hangup_url=events_url,
-        )
-        call_uuid = (
-            response.get("request_uuid")
-            or response.get("message_uuid")
-            or response.get("api_id")
-            or response.get("call_uuid")
-            or ""
-        )
-
-        row = db.query(CallRecording).filter(CallRecording.id == UUID(call_recording_id)).first()
-        if row:
-            row.provider_call_id = str(call_uuid) if call_uuid else None
-            row.call_event = "ringing"
-            data = row.call_data if isinstance(row.call_data, dict) else {}
-            data.update(response)
-            row.call_data = data
-            db.commit()
-
-        return {"status": "ok", "provider_call_id": str(call_uuid or "")}
-    except Exception as exc:
-        logger.error("Vobiz outbound initiation failed for call_ref={}: {}", call_ref, exc)
-        row = db.query(CallRecording).filter(CallRecording.id == UUID(call_recording_id)).first()
-        if row:
-            row.call_event = "failed"
-            data = row.call_data if isinstance(row.call_data, dict) else {}
-            data["error"] = str(exc)
-            row.call_data = data
-            db.commit()
-        session = get_call_session(call_ref)
-        if session and used_pool:
-            release_pool_slot(org_uuid)
-        delete_call_session(call_ref)
-        raise self.retry(exc=exc, countdown=5) from exc
-    finally:
-        db.close()
+"""Celery task to initiate Vobiz outbound calls asynchronously."""
+
+from __future__ import annotations
+
+from uuid import UUID
+
+from loguru import logger
+
+from app.database import SessionLocal
+from app.models.database import CallRecording
+from app.services.telephony.vobiz_client import build_vobiz_client_for_org
+from app.services.telephony.vobiz_outbound_pool import release_pool_slot
+from app.services.telephony.vobiz_session import delete_call_session, get_call_session
+from app.workers.config import celery_app
+
+
+@celery_app.task(name="initiate_vobiz_outbound_call", bind=True, max_retries=2)
+def initiate_vobiz_outbound_call_task(
+    self,
+    *,
+    organization_id: str,
+    call_ref: str,
+    from_number: str,
+    to_number: str,
+    answer_url: str,
+    events_url: str,
+    used_pool: bool,
+    call_recording_id: str,
+) -> dict:
+    org_uuid = UUID(organization_id)
+    db = SessionLocal()
+    try:
+        row = db.query(CallRecording).filter(CallRecording.id == UUID(call_recording_id)).first()
+        sip_headers = None
+        if row:
+            from efficientai.integrations.efficientai_traces.correlation import (
+                build_outbound_sip_headers,
+            )
+
+            sip_headers = build_outbound_sip_headers(
+                call_short_id=row.call_short_id,
+                evaluator_result_id=str(row.evaluator_result_id) if row.evaluator_result_id else None,
+                agent_id=str(row.agent_id) if row.agent_id else None,
+            )
+            data = row.call_data if isinstance(row.call_data, dict) else {}
+            data["efficientai_sip_headers"] = sip_headers
+            row.call_data = data
+            db.commit()
+
+        client, _ = build_vobiz_client_for_org(db, org_uuid)
+        response = client.create_outbound_call(
+            from_=from_number,
+            to_=to_number,
+            answer_url=answer_url,
+            hangup_url=events_url,
+            sip_headers=sip_headers,
+        )
+        call_uuid = (
+            response.get("request_uuid")
+            or response.get("message_uuid")
+            or response.get("api_id")
+            or response.get("call_uuid")
+            or ""
+        )
+
+        row = db.query(CallRecording).filter(CallRecording.id == UUID(call_recording_id)).first()
+        if row:
+            row.provider_call_id = str(call_uuid) if call_uuid else None
+            row.call_event = "ringing"
+            data = row.call_data if isinstance(row.call_data, dict) else {}
+            data.update(response)
+            row.call_data = data
+            db.commit()
+
+        return {"status": "ok", "provider_call_id": str(call_uuid or "")}
+    except Exception as exc:
+        logger.error("Vobiz outbound initiation failed for call_ref={}: {}", call_ref, exc)
+        row = db.query(CallRecording).filter(CallRecording.id == UUID(call_recording_id)).first()
+        if row:
+            row.call_event = "failed"
+            data = row.call_data if isinstance(row.call_data, dict) else {}
+            data["error"] = str(exc)
+            row.call_data = data
+            db.commit()
+        session = get_call_session(call_ref)
+        if session and used_pool:
+            release_pool_slot(org_uuid)
+        delete_call_session(call_ref)
+        raise self.retry(exc=exc, countdown=5) from exc
+    finally:
+        db.close()
diff --git a/docs/examples/pipecat.env.example b/docs/examples/pipecat.env.example
new file mode 100644
index 00000000..711a9a6f
--- /dev/null
+++ b/docs/examples/pipecat.env.example
@@ -0,0 +1,31 @@
+# Copy to your Pipecat project as .env (ONE TIME per deployment)
+#
+#   cp docs/examples/pipecat.env.example ~/Downloads/work/pipecat-examples/websocket/.env
+#   # fill in EFFICIENTAI_API_KEY + EFFICIENTAI_WORKSPACE_ID, then restart bot.py
+#
+# Per call: call_short_id is minted automatically — do NOT set it manually.
+
+# EfficientAI observability — set once in prod (K8s secret / Pipecat Cloud env / .env)
+EFFICIENTAI_API_KEY=
+EFFICIENTAI_WORKSPACE_ID=
+
+# Multi-agent example (pipecat_multi_agent_webrtc_tracing.py)
+FIREWORKS_API_KEY=
+ELEVENLABS_API_KEY=
+# PIPECAT_STT_PROVIDER=elevenlabs
+# DEEPGRAM_API_KEY=
+# ELEVENLABS_VOICE_ID=21m00Tcm4TlvDq8ikWAM
+# FIREWORKS_MODEL=accounts/fireworks/models/llama-v3p1-8b-instruct
+
+# Multi-provider example (pipecat_multi_provider_webrtc_tracing.py)
+# DEEPGRAM_API_KEY=
+# OPENAI_API_KEY=
+# CARTESIA_API_KEY=
+
+# Gemini Live example (pipecat_upstream_webrtc_tracing.py)
+GOOGLE_API_KEY=
+
+# Optional overrides (defaults work for local eai start-all)
+# EFFICIENTAI_API_BASE=http://localhost:8000
+# EFFICIENTAI_API_BASE=https://staging.efficientai.cloud
+# EFFICIENTAI_OTLP_ENDPOINT=https://staging.efficientai.cloud/api/v1/observability/traces
diff --git a/docs/examples/pipecat_inbound_phone_tracing.py b/docs/examples/pipecat_inbound_phone_tracing.py
new file mode 100644
index 00000000..edafb6a5
--- /dev/null
+++ b/docs/examples/pipecat_inbound_phone_tracing.py
@@ -0,0 +1,69 @@
+"""
+Example: Pipecat inbound phone bot with EfficientAI Tier 2 OTLP tracing.
+
+EfficientAI synthetic phone tests dial your number with SIP header
+``X-VH-EfficientAI-Call-Short-Id`` (6 digits). Read it from your carrier
+webhook and pass into ``configure_pipecat_tracing()`` — STT/LLM/TTS spans
+export automatically to Test Insights.
+
+Requires: pip install efficientai[otel]
+"""
+
+from __future__ import annotations
+
+import os
+
+from efficientai.integrations.efficientai_traces import (
+    configure_pipecat_tracing,
+    extract_call_short_id,
+)
+from efficientai.pipeline.task import PipelineTask
+
+# One-time env (from Test Insights → Pipecat setup):
+# EFFICIENTAI_OTLP_ENDPOINT=https://<host>/api/v1/observability/traces
+# EFFICIENTAI_API_KEY=<your-api-key>
+
+
+def sip_headers_from_exotel_webhook(form: dict) -> dict:
+    """Map Exotel / generic carrier fields to SIP-style header names."""
+    headers = {}
+    for key in (
+        "SipHeader_X-VH-EfficientAI-Call-Short-Id",
+        "SipHeader_X-PH-EfficientAI-Call-Short-Id",
+        "CustomField",
+    ):
+        if form.get(key):
+            headers[key] = form[key]
+    if form.get("CallSid"):
+        headers["call_sid"] = form["CallSid"]
+    return headers
+
+
+async def run_inbound_call(pipeline, telephony_payload: dict) -> PipelineTask:
+    """
+    Call from your telephony webhook handler after the inbound leg connects.
+
+    telephony_payload: dict from Exotel/Plivo/Vobiz answer URL (form or JSON).
+    """
+    sip_headers = sip_headers_from_exotel_webhook(telephony_payload)
+    call_short_id = extract_call_short_id(
+        sip_headers=sip_headers,
+        webhook_params=telephony_payload,
+    )
+    if not call_short_id:
+        raise ValueError(
+            "Missing EfficientAI call_short_id — ensure your SIP trunk forwards "
+            "X-VH-EfficientAI-Call-Short-Id from the outbound INVITE."
+        )
+
+    tracing_kwargs = configure_pipecat_tracing(
+        call_short_id=call_short_id,
+        sip_headers=sip_headers,
+        webhook_params=telephony_payload,
+        service_name=os.getenv("EFFICIENTAI_SERVICE_NAME", "pipecat-agent"),
+    )
+
+    return PipelineTask(
+        pipeline,
+        **tracing_kwargs,
+    )
diff --git a/docs/examples/pipecat_multi_agent_webrtc_tracing.py b/docs/examples/pipecat_multi_agent_webrtc_tracing.py
new file mode 100644
index 00000000..0c319ff8
--- /dev/null
+++ b/docs/examples/pipecat_multi_agent_webrtc_tracing.py
@@ -0,0 +1,419 @@
+# Multi-agent Pipecat bot (greeter ↔ support handoff) with EfficientAI Call Traces.
+#
+# Stack: Fireworks LLM (2 agents), ElevenLabs TTS, STT = ElevenLabs or Deepgram.
+#
+# .env:
+#   FIREWORKS_API_KEY=...
+#   ELEVENLABS_API_KEY=...
+#   ELEVENLABS_VOICE_ID=21m00Tcm4TlvDq8ikWAM          # optional
+#   FIREWORKS_MODEL=accounts/fireworks/models/deepseek-v4-flash-0731  # optional; auto-picked if unset
+#   PIPECAT_STT_PROVIDER=elevenlabs                    # or deepgram
+#   DEEPGRAM_API_KEY=...                                # when STT=deepgram
+#   EFFICIENTAI_API_KEY=...
+#   EFFICIENTAI_WORKSPACE_ID=...
+#
+# Install:
+#   uv pip install "pipecat-ai[silero,elevenlabs,fireworks,deepgram,runner,webrtc]>=1.4.0"
+#   uv pip install -e '/path/to/efficientAI[otel]'
+#
+# Run:
+#   cp docs/examples/pipecat_multi_agent_webrtc_tracing.py bot.py
+#   uv run bot.py
+#   Browser → http://localhost:7860/client → WebRTC
+
+import os
+from collections import deque
+from functools import lru_cache
+
+import httpx
+
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusBridgeProcessor
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker, ProcessorUnusablePolicy
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.serializers.protobuf import ProtobufFrameSerializer
+from pipecat.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.fireworks.llm import FireworksLLMService
+from pipecat.services.llm_service import FunctionCallParams, LLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
+from pipecat.workers.runner import WorkerRunner
+
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+load_dotenv(override=True)
+require_deployment_trace_env()
+
+MAIN_NAME = "acme"
+GREETER_NAME = "greeter"
+SUPPORT_NAME = "support"
+
+DEFAULT_FIREWORKS_MODEL = "accounts/fireworks/models/deepseek-v4-flash-0731"
+
+_PREFERRED_FIREWORKS_MODELS = (
+    "accounts/fireworks/models/deepseek-v4-flash-0731",
+    "accounts/fireworks/models/kimi-k2p6",
+    "accounts/fireworks/models/gpt-oss-120b",
+    "accounts/fireworks/models/qwen3p7-plus",
+    "accounts/fireworks/models/llama-v3p2-3b-instruct",
+)
+DEFAULT_ELEVENLABS_VOICE = "21m00Tcm4TlvDq8ikWAM"
+
+transport_params = {
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "websocket": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        add_wav_header=False,
+        serializer=ProtobufFrameSerializer(),
+    ),
+}
+
+
+def _trace_transport(runner_args: RunnerArguments, transport: BaseTransport) -> str:
+    return resolve_trace_transport(runner_args, transport)
+
+
+def _stt_provider() -> str:
+    return (os.getenv("PIPECAT_STT_PROVIDER") or "elevenlabs").strip().lower()
+
+
+def _require_provider_keys() -> None:
+    missing = [
+        name
+        for name in ("FIREWORKS_API_KEY", "ELEVENLABS_API_KEY")
+        if not (os.getenv(name) or "").strip()
+    ]
+    if _stt_provider() == "deepgram" and not (os.getenv("DEEPGRAM_API_KEY") or "").strip():
+        missing.append("DEEPGRAM_API_KEY")
+    if missing:
+        raise ValueError("Missing keys in .env: " + ", ".join(missing))
+
+
+def _build_stt():
+    provider = _stt_provider()
+    if provider == "deepgram":
+        try:
+            from pipecat.services.deepgram.stt import DeepgramSTTService
+        except ImportError as exc:
+            raise ValueError(
+                "Deepgram STT requires compatible packages. Try: "
+                "uv pip install 'pipecat-ai[deepgram]' 'deepgram-sdk>=3.0'"
+            ) from exc
+        logger.info("STT provider: Deepgram")
+        return DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    if provider != "elevenlabs":
+        raise ValueError(
+            f"Unknown PIPECAT_STT_PROVIDER={provider!r} (use 'elevenlabs' or 'deepgram')"
+        )
+    logger.info("STT provider: ElevenLabs")
+    return ElevenLabsRealtimeSTTService(api_key=os.environ["ELEVENLABS_API_KEY"])
+
+
+def _build_tts() -> ElevenLabsTTSService:
+    return ElevenLabsTTSService(
+        api_key=os.environ["ELEVENLABS_API_KEY"],
+        settings=ElevenLabsTTSService.Settings(
+            voice=os.getenv("ELEVENLABS_VOICE_ID", DEFAULT_ELEVENLABS_VOICE),
+        ),
+    )
+
+
+def _fetch_fireworks_model_ids() -> list[str]:
+    api_key = (os.getenv("FIREWORKS_API_KEY") or "").strip()
+    if not api_key:
+        return []
+    try:
+        resp = httpx.get(
+            "https://api.fireworks.ai/inference/v1/models",
+            headers={"Authorization": f"Bearer {api_key}"},
+            timeout=15.0,
+        )
+        resp.raise_for_status()
+        return [
+            str(m.get("id"))
+            for m in (resp.json().get("data") or [])
+            if m.get("id")
+        ]
+    except Exception as exc:
+        logger.warning("Could not list Fireworks models: {}", exc)
+        return []
+
+
+@lru_cache(maxsize=1)
+def _fireworks_model() -> str:
+    explicit = (os.getenv("FIREWORKS_MODEL") or "").strip()
+    if explicit:
+        return explicit
+
+    available = _fetch_fireworks_model_ids()
+    if available:
+        avail_set = set(available)
+        for candidate in _PREFERRED_FIREWORKS_MODELS:
+            if candidate in avail_set:
+                logger.info(
+                    "Auto-selected Fireworks model {} (override with FIREWORKS_MODEL)",
+                    candidate,
+                )
+                return candidate
+        for mid in available:
+            low = mid.lower()
+            if "/models/" not in mid:
+                continue
+            if any(skip in low for skip in ("router", "reranker", "embedding")):
+                continue
+            logger.info(
+                "Auto-selected Fireworks model {} (override with FIREWORKS_MODEL)",
+                mid,
+            )
+            return mid
+
+    logger.warning(
+        "FIREWORKS_MODEL not set and model list unavailable; falling back to {}",
+        DEFAULT_FIREWORKS_MODEL,
+    )
+    return DEFAULT_FIREWORKS_MODEL
+
+
+def _fireworks_llm(*, system_instruction: str) -> FireworksLLMService:
+    model = _fireworks_model()
+    logger.info("LLM provider: Fireworks model={}", model)
+    return FireworksLLMService(
+        api_key=os.environ["FIREWORKS_API_KEY"],
+        settings=FireworksLLMService.Settings(
+            model=model,
+            system_instruction=system_instruction,
+        ),
+    )
+
+
+def _worker_trace_attrs(base: dict[str, str], role: str) -> dict[str, str]:
+    return {**base, "efficientai.agent_role": role}
+
+
+class AcmeLLMWorker(LLMWorker):
+    """LLMWorker with tracing enabled (LLMWorker itself does not forward tracing kwargs)."""
+
+    def __init__(
+        self,
+        name: str,
+        *,
+        llm: LLMService,
+        span_attrs: dict[str, str],
+        trace_ctx: dict | None = None,
+        bridged: tuple[str, ...] | None = (),
+    ):
+        self._defer_tool_frames = True
+        self._tool_call_inflight = 0
+        self._deferred_frames: deque = deque()
+        self._pending_handover = None
+        self._closing = False
+        self._trace_ctx = trace_ctx
+        self._llm = llm
+        self._register_tools(llm)
+
+        PipelineWorker.__init__(
+            self,
+            Pipeline([self._llm]),
+            name=name,
+            bridged=bridged,
+            enable_rtvi=bridged is None,
+            idle_timeout_secs=None,
+            enable_tracing=True,
+            additional_span_attributes=span_attrs,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+        self._active = False
+        self._pending_activation = False
+
+    @tool(cancel_on_interruption=False)
+    async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
+        """Transfer the user to another agent.
+
+        Args:
+            agent: Target agent name ('greeter' or 'support').
+            reason: Short reason for the handoff.
+        """
+        logger.info("Worker '{}': transferring to '{}' ({})", self.name, agent, reason)
+        await params.result_callback(None)
+        await self.activate_worker(
+            agent,
+            args=LLMWorkerActivationArgs(
+                messages=[{"role": "developer", "content": reason}],
+            ),
+            deactivate_self=True,
+        )
+
+    @tool
+    async def end_conversation(self, params: FunctionCallParams, reason: str):
+        """End the call when the user says goodbye."""
+        logger.info("Worker '{}': ending conversation ({})", self.name, reason)
+        await params.result_callback(reason)
+        if self._trace_ctx:
+            await close_trace_session(self._trace_ctx)
+        await self.end(reason=reason)
+
+
+def build_greeter(span_attrs: dict[str, str], trace_ctx: dict | None = None) -> AcmeLLMWorker:
+    llm = _fireworks_llm(
+        system_instruction=(
+            "You are a friendly greeter for Acme Corp. Products: Rocket Boots, "
+            "Invisible Paint, Tornado Kit. Ask which they want to hear about. "
+            "When they pick one or ask product questions, call transfer_to_agent "
+            "with agent 'support'. Do not answer product details yourself. "
+            "If they say goodbye, call end_conversation. Keep replies to one "
+            "or two short spoken sentences. No markdown."
+        ),
+    )
+    return AcmeLLMWorker(
+        GREETER_NAME,
+        llm=llm,
+        span_attrs=_worker_trace_attrs(span_attrs, GREETER_NAME),
+        trace_ctx=trace_ctx,
+    )
+
+
+def build_support(span_attrs: dict[str, str], trace_ctx: dict | None = None) -> AcmeLLMWorker:
+    llm = _fireworks_llm(
+        system_instruction=(
+            "You are Acme Corp support. Rocket Boots ($299, up to 60 mph), "
+            "Invisible Paint ($49, 24h invisibility), Tornado Kit ($199). "
+            "Answer product questions briefly. To browse other products, call "
+            "transfer_to_agent with agent 'greeter'. On goodbye, call "
+            "end_conversation. Spoken voice only — one or two short sentences."
+        ),
+    )
+    return AcmeLLMWorker(
+        SUPPORT_NAME,
+        llm=llm,
+        span_attrs=_worker_trace_attrs(span_attrs, SUPPORT_NAME),
+        trace_ctx=trace_ctx,
+    )
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    _require_provider_keys()
+
+    trace_transport = _trace_transport(runner_args, transport)
+    trace_ctx = await ensure_trace_session(transport=trace_transport)
+    tracing = setup_pipecat_worker_tracing(trace_ctx)
+    span_attrs = tracing["additional_span_attributes"]
+    logger.info(
+        "EfficientAI trace session transport={} call_short_id={}",
+        trace_transport,
+        tracing["call_short_id"],
+    )
+
+    logger.info(
+        "Voice stack: STT={} | TTS=ElevenLabs | LLM=Fireworks ({}) | agents=greeter,support",
+        _stt_provider(),
+        _fireworks_model(),
+    )
+
+    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)
+
+    stt = _build_stt()
+    tts = _build_tts()
+
+    context = LLMContext()
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    bridge = BusBridgeProcessor(
+        bus=runner.bus,
+        worker_name=MAIN_NAME,
+        name=f"{MAIN_NAME}::BusBridge",
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            bridge,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        name=MAIN_NAME,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        enable_tracing=True,
+        additional_span_attributes=span_attrs,
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        processor_unusable_policy=ProcessorUnusablePolicy.END,
+    )
+
+    await runner.add_workers(
+        build_greeter(span_attrs, trace_ctx),
+        build_support(span_attrs, trace_ctx),
+        worker,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected — activating greeter")
+        await worker.activate_worker(
+            GREETER_NAME,
+            args=LLMWorkerActivationArgs(
+                messages=[
+                    {
+                        "role": "developer",
+                        "content": (
+                            "Welcome the user to Acme Corp, name the three products, "
+                            "and ask what they would like to explore."
+                        ),
+                    },
+                ],
+            ),
+        )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+        await close_trace_session(trace_ctx)
+
+    await runner.run()
+
+
+async def bot(runner_args: RunnerArguments):
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()
diff --git a/docs/examples/pipecat_multi_provider_webrtc_tracing.py b/docs/examples/pipecat_multi_provider_webrtc_tracing.py
new file mode 100644
index 00000000..cd0f94cc
--- /dev/null
+++ b/docs/examples/pipecat_multi_provider_webrtc_tracing.py
@@ -0,0 +1,180 @@
+# Multi-provider Pipecat bot (STT + LLM + TTS) with EfficientAI observability.
+#
+# This matches how prod customers run: separate APIs per component → real STT/LLM/TTS spans.
+#
+# One-time setup (pipecat-examples/websocket/.env):
+#   DEEPGRAM_API_KEY=...
+#   OPENAI_API_KEY=...
+#   CARTESIA_API_KEY=...
+#   EFFICIENTAI_API_KEY=<workspace api key>
+#   EFFICIENTAI_WORKSPACE_ID=<workspace uuid>
+#
+# Install:
+#   cd ~/Downloads/work/pipecat-examples/websocket
+#   uv pip install "pipecat-ai[silero,websocket,deepgram,openai,cartesia,runner,webrtc]>=1.4.0"
+#   uv pip install -e '/home/sami/Downloads/work/efficientAI[otel]'
+#
+# Run:
+#   cp /path/to/efficientAI/docs/examples/pipecat_multi_provider_webrtc_tracing.py bot.py
+#   uv run bot.py
+#   Browser → http://localhost:7860/client → WebRTC → Connect
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.serializers.protobuf import ProtobufFrameSerializer
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.workers.runner import WorkerRunner
+
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+load_dotenv(override=True)
+require_deployment_trace_env()
+
+SYSTEM_INSTRUCTION = """
+You are a helpful voice assistant. Keep responses to one or two short sentences.
+Do not use markdown, bullets, or emojis — your reply is spoken aloud.
+"""
+
+transport_params = {
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "websocket": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        add_wav_header=False,
+        serializer=ProtobufFrameSerializer(),
+    ),
+}
+
+
+def _trace_transport(runner_args: RunnerArguments, transport: BaseTransport) -> str:
+    return resolve_trace_transport(runner_args, transport)
+
+
+def _require_provider_keys() -> None:
+    missing = [
+        name
+        for name in ("DEEPGRAM_API_KEY", "OPENAI_API_KEY", "CARTESIA_API_KEY")
+        if not (os.getenv(name) or "").strip()
+    ]
+    if missing:
+        raise ValueError(
+            "Missing voice provider keys in .env: "
+            + ", ".join(missing)
+            + ". Get free/trial keys from Deepgram, OpenAI, and Cartesia."
+        )
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    _require_provider_keys()
+
+    trace_transport = _trace_transport(runner_args, transport)
+    trace_ctx = await ensure_trace_session(transport=trace_transport)
+    tracing = setup_pipecat_worker_tracing(trace_ctx)
+    logger.info(
+        "EfficientAI trace session transport={} call_short_id={}",
+        trace_transport,
+        tracing["call_short_id"],
+    )
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        settings=OpenAILLMService.Settings(
+            model="gpt-4o-mini",
+            system_instruction=SYSTEM_INSTRUCTION,
+        ),
+    )
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        settings=CartesiaTTSService.Settings(
+            voice=os.getenv("CARTESIA_VOICE_ID", "71a7ad14-091c-4e8e-a314-022ece01c121"),
+        ),
+    )
+
+    context = LLMContext()
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_aggregator,
+            llm,
+            tts,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        enable_tracing=True,
+        additional_span_attributes=tracing["additional_span_attributes"],
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @worker.rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        logger.info("Pipecat client ready.")
+        context.add_message(
+            {"role": "developer", "content": "Greet the user briefly and ask how you can help."}
+        )
+        await worker.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Pipecat client connected")
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Pipecat client disconnected")
+        await worker.cancel()
+        await close_trace_session(trace_ctx)
+
+    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+async def bot(runner_args: RunnerArguments):
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()
diff --git a/docs/examples/pipecat_upstream_webrtc_tracing.py b/docs/examples/pipecat_upstream_webrtc_tracing.py
new file mode 100644
index 00000000..e8edd7a2
--- /dev/null
+++ b/docs/examples/pipecat_upstream_webrtc_tracing.py
@@ -0,0 +1,150 @@
+# Patch for ~/Downloads/work/pipecat-examples/websocket/bot.py
+#
+# WebRTC + WebSocket Pipecat runner with EfficientAI OTLP export.
+# Use http://localhost:7860/client and pick **WebRTC** (team default).
+#
+# One-time setup (pipecat-examples/websocket/.env):
+#   GOOGLE_API_KEY=...
+#   EFFICIENTAI_API_KEY=<workspace api key>
+#   EFFICIENTAI_WORKSPACE_ID=<workspace uuid>
+#
+# One-time install:
+#   cd ~/Downloads/work/pipecat-examples/websocket
+#   uv pip install "pipecat-ai[silero,websocket,google,runner,webrtc]>=1.4.0"
+#   uv pip install -e '/home/sami/Downloads/work/efficientAI[otel]'
+#
+# Run:
+#   uv run bot.py
+#   Browser → http://localhost:7860/client → transport: WebRTC → Connect
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.serializers.protobuf import ProtobufFrameSerializer
+from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.workers.runner import WorkerRunner
+
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+load_dotenv(override=True)
+require_deployment_trace_env()
+
+SYSTEM_INSTRUCTION = """
+You are Gemini Chatbot, a friendly, helpful robot.
+Keep responses brief — one or two sentences.
+"""
+
+transport_params = {
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "websocket": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        add_wav_header=False,
+        serializer=ProtobufFrameSerializer(),
+    ),
+}
+
+
+def _trace_transport(runner_args: RunnerArguments, transport: BaseTransport) -> str:
+    return resolve_trace_transport(runner_args, transport)
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    trace_transport = _trace_transport(runner_args, transport)
+    trace_ctx = await ensure_trace_session(transport=trace_transport)
+    tracing = setup_pipecat_worker_tracing(trace_ctx)
+    logger.info(
+        "EfficientAI trace session transport={} call_short_id={}",
+        trace_transport,
+        tracing["call_short_id"],
+    )
+
+    llm = GeminiLiveLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        settings=GeminiLiveLLMService.Settings(
+            voice="Puck",
+            system_instruction=SYSTEM_INSTRUCTION,
+        ),
+    )
+
+    context = LLMContext()
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        realtime_service_mode=True,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            user_aggregator,
+            llm,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        enable_tracing=True,
+        additional_span_attributes=tracing["additional_span_attributes"],
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @worker.rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        logger.info("Pipecat client ready.")
+        context.add_message({"role": "developer", "content": "Start by introducing yourself."})
+        await worker.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Pipecat client connected")
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Pipecat client disconnected")
+        await worker.cancel()
+        await close_trace_session(trace_ctx)
+
+    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+async def bot(runner_args: RunnerArguments):
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()
diff --git a/docs/examples/pipecat_upstream_websocket_tracing.py b/docs/examples/pipecat_upstream_websocket_tracing.py
new file mode 100644
index 00000000..2d030d7f
--- /dev/null
+++ b/docs/examples/pipecat_upstream_websocket_tracing.py
@@ -0,0 +1,152 @@
+# Patch for ~/Downloads/work/pipecat-examples/websocket/bot.py
+#
+# WebRTC + WebSocket Pipecat runner with EfficientAI OTLP export.
+#
+# WebRTC (recommended): http://localhost:7860/client → pick **WebRTC** → Connect
+# Protobuf websocket client: cd client && npm run dev → http://localhost:5173
+#
+# Do NOT use WebRTC on :7860 if you only enable websocket in transport_params.
+# Do NOT use :5173 if you only enable webrtc in transport_params.
+# This file enables BOTH so either client works.
+#
+# One-time setup (pipecat-examples/websocket/.env):
+#   GOOGLE_API_KEY=...
+#   EFFICIENTAI_API_KEY=<workspace api key>
+#   EFFICIENTAI_WORKSPACE_ID=<workspace uuid>
+#
+# One-time install:
+#   cd ~/Downloads/work/pipecat-examples/websocket
+#   uv pip install "pipecat-ai[silero,websocket,google,runner,webrtc]>=1.4.0"
+#   uv pip install -e '/home/sami/Downloads/work/efficientAI[otel]'
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.serializers.protobuf import ProtobufFrameSerializer
+from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.workers.runner import WorkerRunner
+
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+load_dotenv(override=True)
+require_deployment_trace_env()
+
+SYSTEM_INSTRUCTION = """
+You are Gemini Chatbot, a friendly, helpful robot.
+Keep responses brief — one or two sentences.
+"""
+
+transport_params = {
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "websocket": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        add_wav_header=False,
+        serializer=ProtobufFrameSerializer(),
+    ),
+}
+
+
+def _trace_transport(runner_args: RunnerArguments, transport: BaseTransport) -> str:
+    return resolve_trace_transport(runner_args, transport)
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    trace_transport = _trace_transport(runner_args, transport)
+    trace_ctx = await ensure_trace_session(transport=trace_transport)
+    tracing = setup_pipecat_worker_tracing(trace_ctx)
+    logger.info(
+        "EfficientAI trace session transport={} call_short_id={}",
+        trace_transport,
+        tracing["call_short_id"],
+    )
+
+    llm = GeminiLiveLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        settings=GeminiLiveLLMService.Settings(
+            voice="Puck",
+            system_instruction=SYSTEM_INSTRUCTION,
+        ),
+    )
+
+    context = LLMContext()
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        realtime_service_mode=True,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            user_aggregator,
+            llm,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        enable_tracing=True,
+        additional_span_attributes=tracing["additional_span_attributes"],
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @worker.rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        logger.info("Pipecat client ready.")
+        context.add_message({"role": "developer", "content": "Start by introducing yourself."})
+        await worker.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Pipecat client connected (transport={})", trace_transport)
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Pipecat client disconnected")
+        await worker.cancel()
+        await close_trace_session(trace_ctx)
+
+    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+async def bot(runner_args: RunnerArguments):
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()
diff --git a/docs/synthetic-call-traces-pipecat.md b/docs/synthetic-call-traces-pipecat.md
new file mode 100644
index 00000000..ce0139e4
--- /dev/null
+++ b/docs/synthetic-call-traces-pipecat.md
@@ -0,0 +1,272 @@
+# Call Traces — Pipecat integration
+
+EfficientAI shows **per-turn STT, LLM, and TTS timing** from your Pipecat voice agent. Traces appear in **Call Traces** in the UI (`/call-traces`).
+
+Primary dev path: **local WebRTC** via the Pipecat runner (`http://localhost:7860/client`).
+
+---
+
+## Environments
+
+| Environment | `EFFICIENTAI_API_BASE` | UI |
+|-------------|------------------------|-----|
+| **Local** | `http://localhost:8000` | `http://localhost:5173` (or your Vite port) |
+| **Staging** | `https://staging.efficientai.cloud` | `https://staging.efficientai.cloud` |
+
+Set `EFFICIENTAI_API_BASE` once in `.env` or your deployment secret. Same `bot.py` for local and staging — only the base URL changes.
+
+```bash
+# Local bot → local API
+EFFICIENTAI_API_BASE=http://localhost:8000
+
+# Local bot → staging API (team testing)
+EFFICIENTAI_API_BASE=https://staging.efficientai.cloud
+```
+
+OTLP export URL defaults to `{EFFICIENTAI_API_BASE}/api/v1/observability/traces`.
+
+Use a **separate workspace** for local vs staging so traces do not mix in the UI.
+
+---
+
+## How it works
+
+```
+1. Pipecat bot starts a call → EfficientAI mints a 6-digit Call ID (call_short_id)
+2. Pipecat emits STT / LLM / TTS spans (enable_tracing=True)
+3. SDK exports spans → POST /api/v1/observability/traces
+4. Call Traces UI shows latency + models per turn
+5. Bot calls close_trace_session() → trace marked closed
+```
+
+Each call is linked by **`call_short_id`** (shown as `#482931` in the UI). Concurrent calls each get their own ID — ingest routes spans by `call_short_id` + `workspace_id`.
+
+---
+
+## Pipecat integration (v1)
+
+We do **not** ship a single `PipecatTracer` wrapper class today. Integration is **three SDK functions** plus Pipecat's built-in tracing (`enable_tracing=True`).
+
+> **v2 (optional later):** We may add a thin one-import wrapper (session + OTLP + cleanup). v1 stays explicit on purpose. The OTLP wire format will not change.
+
+### Install
+
+```bash
+cd your-pipecat-project
+
+uv pip install "pipecat-ai[silero,elevenlabs,fireworks,runner,webrtc]>=1.4.0"
+# add STT/LLM/TTS extras for your stack
+uv pip install -e '/path/to/efficientAI[otel]'
+```
+
+### Required env (set once per deployment)
+
+```bash
+EFFICIENTAI_API_KEY=<from EfficientAI → API keys>
+EFFICIENTAI_WORKSPACE_ID=<workspace uuid>
+
+# Optional — defaults to http://localhost:8000
+# EFFICIENTAI_API_BASE=https://staging.efficientai.cloud
+```
+
+No `agent_id` required for WebRTC. Each call gets its own Call ID automatically — do **not** set `EFFICIENTAI_CALL_SHORT_ID` in `.env`.
+
+### The three hooks
+
+| Step | Function | What it does |
+|------|----------|--------------|
+| 1 | `ensure_trace_session()` | Mints Call ID; opens trace row on EfficientAI |
+| 2 | `setup_pipecat_worker_tracing(trace_ctx)` | OTLP export + `efficientai.call_short_id` on every span |
+| 3 | `close_trace_session(trace_ctx)` | Flush spans; mark trace **closed** on disconnect |
+
+### WebRTC — `PipelineWorker` (recommended)
+
+Copy a full example or use this minimum pattern:
+
+```python
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+require_deployment_trace_env()  # checks EFFICIENTAI_API_KEY + WORKSPACE_ID
+
+async def run_bot(transport, runner_args):
+    trace_ctx = await ensure_trace_session(
+        transport=resolve_trace_transport(runner_args, transport),
+    )
+    tracing = setup_pipecat_worker_tracing(trace_ctx)
+
+    worker = PipelineWorker(
+        pipeline,
+        enable_tracing=True,
+        additional_span_attributes=tracing["additional_span_attributes"],
+    )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        await worker.cancel()
+        await close_trace_session(trace_ctx)
+
+    # See docs/examples/*.py for WorkerRunner setup
+```
+
+### Alternative — `PipelineTask` (phone / older style)
+
+```python
+from efficientai.integrations.efficientai_traces import configure_pipecat_tracing
+
+task = PipelineTask(
+    pipeline,
+    **configure_pipecat_tracing(call_short_id=call_short_id),
+)
+```
+
+See `docs/examples/pipecat_inbound_phone_tracing.py` (call ID from SIP headers).
+
+### Playground WebSocket handshake
+
+```python
+configure_pipecat_tracing(handshake=message)
+```
+
+EfficientAI sends `call_short_id` in the handshake — no manual session call needed.
+
+### Example bots
+
+| Use case | File |
+|----------|------|
+| Multi-agent WebRTC (Fireworks + ElevenLabs) | `docs/examples/pipecat_multi_agent_webrtc_tracing.py` |
+| Single agent, swap STT/LLM/TTS | `docs/examples/pipecat_multi_provider_webrtc_tracing.py` |
+| Gemini Live / S2S | `docs/examples/pipecat_upstream_webrtc_tracing.py` |
+| Custom WebSocket | `docs/examples/pipecat_upstream_websocket_tracing.py` |
+| Inbound phone (SIP) | `docs/examples/pipecat_inbound_phone_tracing.py` |
+| Env template | `docs/examples/pipecat.env.example` |
+
+---
+
+## Customer checklist
+
+1. Create API key; copy workspace UUID (Call Traces → Connect Pipecat).
+2. Add `EFFICIENTAI_API_KEY` and `EFFICIENTAI_WORKSPACE_ID` to Pipecat `.env`.
+3. Copy an example `bot.py`; wire the three hooks above.
+4. Run `eai start-all` (local) or point `EFFICIENTAI_API_BASE` at staging.
+5. WebRTC call at `http://localhost:7860/client` → disconnect.
+6. **Call Traces** → Refresh → open your Call ID.
+
+---
+
+## Quick start (local WebRTC)
+
+### 1. Install
+
+See [Pipecat integration](#install) above.
+
+### 2. Bot script
+
+Recommended: copy `docs/examples/pipecat_multi_agent_webrtc_tracing.py` → `bot.py`
+
+### 3. Run
+
+```bash
+# Terminal 1 — EfficientAI
+eai start-all
+
+# Terminal 2 — Pipecat
+uv run bot.py
+```
+
+### 4. Connect
+
+Open **http://localhost:7860/client** → WebRTC → Connect → talk 2–3 turns → disconnect.
+
+### 5. View traces
+
+**Call Traces** in the UI → **Refresh** → open the row with your Call ID.
+
+---
+
+## API reference
+
+Base paths (append to `/api/v1`):
+
+| Environment | Base |
+|-------------|------|
+| Local | `http://localhost:8000` |
+| Staging | `https://staging.efficientai.cloud` |
+
+### Create session
+
+`ensure_trace_session()` calls this automatically.
+
+```bash
+curl -X POST https://staging.efficientai.cloud/api/v1/observability/traces/sessions \
+  -H "X-API-Key: <key>" \
+  -H "X-Workspace-Id: <workspace-uuid>" \
+  -H "Content-Type: application/json" \
+  -d '{"transport":"webrtc"}'
+```
+
+### Export spans (OTLP)
+
+```http
+POST /api/v1/observability/traces
+```
+
+| Header | Value |
+|--------|-------|
+| `X-API-Key` | API key |
+| `X-Workspace-Id` | workspace uuid |
+| `X-EfficientAI-Call-Short-Id` | 6-digit call id |
+
+Also set on spans: `efficientai.call_short_id`, `efficientai.workspace_id`.
+
+### Close session
+
+```bash
+curl -X POST https://staging.efficientai.cloud/api/v1/observability/traces/sessions/482931/close \
+  -H "X-API-Key: <key>" \
+  -H "X-Workspace-Id: <workspace-uuid>"
+```
+
+### Span attributes (Pipecat native tracing)
+
+| Attribute | Purpose |
+|-----------|---------|
+| `efficientai.call_short_id` | Links spans to the call |
+| `efficientai.workspace_id` | Workspace scope |
+| `gen_ai.operation.name` | `stt` / `llm` / `tts` / `s2s` |
+| `gen_ai.request.model` | Model name in UI |
+| `metrics.ttfb` | Time to first byte (**seconds**) |
+| `turn.number` | Turn index |
+
+### Setup endpoint (UI uses this)
+
+```http
+GET /api/v1/observability/traces/setup
+```
+
+Returns OTLP URL, env block, and Python snippet for the active host.
+
+---
+
+## Troubleshooting
+
+| Symptom | Fix |
+|---------|-----|
+| `Transport 'webrtc' is disabled` | `pip install pipecat-ai[webrtc]`; add `webrtc` to `transport_params` |
+| No traces in UI | Check API key + workspace ID; Refresh |
+| `correlated: false` on ingest | Missing `call_short_id` on spans or headers |
+| Call ID in logs but empty trace | `enable_tracing=True` on `PipelineWorker`; install `efficientAI[otel]` |
+| Trace stays open | Call `close_trace_session()` on disconnect |
+| Wrong workspace | Switch workspace in UI — list is workspace-scoped |
+| Local bot, staging UI empty | Set `EFFICIENTAI_API_BASE=https://staging.efficientai.cloud` |
+
+---
+
+## Other transports
+
+WebSocket and phone use the same OTLP URL and Call ID correlation. See `docs/examples/pipecat_inbound_phone_tracing.py` for SIP header flow.
diff --git a/docs/synthetic-call-traces-tdd-confluence.md b/docs/synthetic-call-traces-tdd-confluence.md
new file mode 100644
index 00000000..0b2001a0
--- /dev/null
+++ b/docs/synthetic-call-traces-tdd-confluence.md
@@ -0,0 +1,730 @@
+# TDD: Call Traces — Pipecat OTLP Observability
+
+**Status:** Implemented (v1)  
+**Owner:** Platform / Voice Evals  
+**UI:** Call Traces (`/call-traces`)  
+**API prefix:** `/api/v1/observability/traces`
+
+---
+
+## 1. One-sentence summary
+
+Customers run a **Pipecat voice agent** on their machine (or cloud). During each call, the agent exports **OpenTelemetry spans** (STT, LLM, TTS timing) to EfficientAI. We store them, group by **conversation turn**, and show a **latency waterfall** in the Call Traces UI.
+
+---
+
+## 2. Deployed environments (today)
+
+We only run **two** environments right now:
+
+| Environment | API base | Who uses it |
+|-------------|----------|-------------|
+| **Local** | `http://localhost:8000` | Engineers running `eai start-all` + local `bot.py` |
+| **Staging** | `https://staging.efficientai.cloud` | Team demos, integration testing, customer pilots |
+| **Staging UI** | `https://staging.efficientai.cloud` | Same host — API and UI share staging domain |
+
+There is **no production traces endpoint yet**. When prod ships, customers will only change `EFFICIENTAI_API_BASE` — same `bot.py`, same SDK hooks.
+
+**Best practice today:**
+- Use a **separate workspace** for local vs staging (traces never mix in the UI).
+- Never reuse API keys across environments.
+
+```bash
+# Local bot → local EfficientAI
+EFFICIENTAI_API_BASE=http://localhost:8000
+
+# Local bot → staging EfficientAI (common for team testing)
+EFFICIENTAI_API_BASE=https://staging.efficientai.cloud
+```
+
+---
+
+## 3. How it works (end-to-end)
+
+```
+┌─────────────────┐     POST /observability/traces      ┌──────────────────┐
+│  Pipecat bot    │ ──────────────────────────────────► │  EfficientAI API │
+│  (customer)     │     OTLP spans + correlation headers │  (ingest + store)│
+└────────┬────────┘                                     └────────┬─────────┘
+         │ POST /sessions (mint call ID)                          │
+         └──────────────────────────────────────────────────────►│
+                                                                   ▼
+                                                          ┌──────────────────┐
+                                                          │  Call Traces UI  │
+                                                          │  per-turn STT/   │
+                                                          │  LLM/TTS + models│
+                                                          └──────────────────┘
+```
+
+### Per-call lifecycle
+
+| Step | Who | What happens |
+|------|-----|--------------|
+| 1 | Customer bot | `ensure_trace_session()` → server mints **6-digit Call ID** (`call_short_id`) |
+| 2 | Customer bot | `setup_pipecat_worker_tracing(trace_ctx)` stamps **every span** with that call ID |
+| 3 | Pipecat | `PipelineWorker(enable_tracing=True)` emits `stt` / `llm` / `tts` spans during the call |
+| 4 | OTel exporter | Batches spans → `POST /api/v1/observability/traces` with call ID in **header + span attrs** |
+| 5 | EfficientAI ingest | Looks up trace row by `call_short_id` + `workspace_id`, appends spans, rebuilds turns |
+| 6 | Customer bot | `close_trace_session()` on disconnect → trace status **closed** |
+| 7 | User | Opens **Call Traces** in UI → sees conversation + waterfall for that Call ID |
+
+### 3.1 How other platforms compare (e.g. Cekura, Honeycomb)
+
+This is a **pattern comparison**, not a feature-by-feature vendor shootout. Many voice observability products follow a similar OTel model.
+
+| | **Other platforms** (e.g. Cekura, Honeycomb) | **EfficientAI (this feature)** |
+|--|------------|-------------------------------|
+| Customer install | Vendor SDK / Pipecat extra (e.g. `cekura[pipecat]`) | `pip install efficientai[otel]` (or raw OTLP) |
+| Bot integration | Often a single wrapper (e.g. `PipecatTracer.track_pipeline(...)`) | `ensure_trace_session()` + `setup_pipecat_worker_tracing()` + `close_trace_session()` |
+| Export protocol | OTLP gRPC/HTTP | OTLP HTTP JSON or Protobuf |
+| Auth headers | API key + often an agent/session ID in headers | `X-API-Key`, `X-Workspace-Id` |
+| Call correlation | `trace_id` + vendor call/session API | `call_short_id` (6-digit) on spans + headers |
+| Agent ID | Often required | **Optional** — workspace + call ID is enough for WebRTC |
+| Who runs the test call | Vendor-hosted or customer agent | Customer's own Pipecat bot (local WebRTC today) |
+| UI | Span timeline + waterfall | Conversation tab + latency waterfall + model names |
+
+We use the same OTel wire format; our v1 integration is more explicit (three SDK hooks) rather than a single wrapper class.
+
+---
+
+## 4. How each call stays unique (10–20 concurrent calls)
+
+This is the most important section for understanding correctness at scale.
+
+### 4.1 The problem
+
+Ten developers (or ten end-users) can be on **separate WebRTC calls at the same time**. Each call produces OTLP batches that hit the **same ingest URL**. How do we make sure Call A's STT span never lands on Call B's trace row?
+
+### 4.2 Our answer: per-call `call_short_id` (not shared across calls)
+
+Every call gets its **own** 6-digit ID, minted **once** when the call starts:
+
+```
+Call 1 → call_short_id = 482931  → trace row UUID = aaa-...
+Call 2 → call_short_id = 719044  → trace row UUID = bbb-...
+...
+Call 20 → call_short_id = 305812 → trace row UUID = ...
+```
+
+**Minting** happens in `POST /observability/traces/sessions` (or via SDK `ensure_trace_session()`). The server:
+1. Generates a random 6-digit ID
+2. Checks it is not already used in `call_recordings` or `synthetic_call_traces`
+3. Creates an **open** `synthetic_call_traces` row bound to that ID + workspace
+
+No two active calls should share the same `call_short_id` within our DB.
+
+### 4.3 How Pipecat / our SDK tags every export
+
+When a call connects, the SDK does **two** things so ingest can route correctly:
+
+**A) HTTP headers on every OTLP batch** (mutable per call):
+```
+X-API-Key: <key>
+X-Workspace-Id: <workspace-uuid>
+X-EfficientAI-Call-Short-Id: 482931
+```
+
+**B) Span attributes on every span** (stamped at span start):
+```
+ efficientai.call_short_id = "482931"
+ efficientai.workspace_id    = "<workspace-uuid>"
+```
+
+`setup_pipecat_worker_tracing(trace_ctx)` configures both. Even if headers were missing, span attributes alone are enough — **span attrs win over headers** when they disagree.
+
+### 4.4 How ingest routes a batch to the correct trace
+
+```
+Incoming OTLP POST
+    │
+    ├─► Auth: X-API-Key + X-Workspace-Id  → org + workspace scope
+    │
+    ├─► group_spans_by_call_short_id()      → split batch if mixed (rare)
+    │
+    ├─► Resolve call_short_id from:
+    │       span.efficientai.call_short_id  (preferred)
+    │       OR header X-EfficientAI-Call-Short-Id
+    │
+    ├─► Lookup: synthetic_call_traces WHERE
+    │       call_short_id = '482931'
+    │       AND workspace_id = <header workspace>
+    │       AND organization_id = <from API key>
+    │
+    ├─► Dedupe spans by (otel trace_id, span_id) — replays are safe
+    │
+    ├─► filter_spans_for_trace(..., call_short_id) — drop stray spans
+    │
+    └─► Recompute turns + latency on THAT trace row only
+```
+
+**Workspace is the second isolation layer.** Even if two workspaces somehow got the same 6-digit ID (extremely unlikely), they still would not see each other's data because list/get/ingest all filter by `workspace_id`.
+
+### 4.5 Worked example: 3 concurrent calls
+
+| Time | Call | Event | Ingest sees |
+|------|------|-------|-------------|
+| T+0s | Alice | session → `482931` | Row created: `#482931` open |
+| T+1s | Bob | session → `719044` | Row created: `#719044` open |
+| T+2s | Carol | session → `305812` | Row created: `#305812` open |
+| T+5s | Alice | OTLP batch, header `482931` | Appended to `#482931` only |
+| T+5s | Bob | OTLP batch, header `719044` | Appended to `#719044` only |
+| T+6s | Carol | OTLP batch, attrs `305812` | Appended to `#305812` only |
+| T+30s | Alice | disconnect + close | `#482931` → closed |
+
+Each bot process holds its own `trace_ctx` in memory with a different `call_short_id`. They all POST to the same URL — correlation is in the payload, not the URL path.
+
+### 4.6 How other observability platforms do it (e.g. Cekura, Honeycomb, Datadog)
+
+We follow the same **OpenTelemetry correlation** pattern used across the industry:
+
+| Pattern | What it is | How we use it |
+|---------|------------|---------------|
+| **Service-level auth** | API key identifies the tenant | `X-API-Key` → organization |
+| **Resource / span attributes** | Custom attrs on every span | `efficientai.call_short_id`, `efficientai.workspace_id` |
+| **Exporter headers** | Per-export metadata | `X-EfficientAI-Call-Short-Id` on OTLP HTTP |
+| **OTel trace_id** | Standard 128-bit trace identifier inside OTLP | Stored per span; we may have multiple OTel `trace_id`s per call after reconnects |
+| **Session / agent scoping** | Vendor-specific ID in headers | Optional `X-EfficientAI-Run-Id` for evaluator phone runs |
+
+Other platforms (e.g. Cekura, Honeycomb) often require an **agent ID** in headers plus a **trace_id** linked to a call log API. We intentionally simplified v1 to **workspace + call_short_id** for local Pipecat WebRTC — fewer keys for customers, same OTel wire format underneath.
+
+**Key difference in mental model:**
+- **OTel `trace_id`** = internal OpenTelemetry trace (can change mid-call if the tracer restarts)
+- **`call_short_id`** = **our** stable business ID for the voice session (what the UI shows as `#482931`)
+
+We always correlate on `call_short_id`, not on OTel `trace_id` alone.
+
+### 4.7 Will this work at scale?
+
+| Concern | Today (v1) | At higher scale |
+|---------|------------|-----------------|
+| 10–20 concurrent calls | ✅ Works — unique IDs + workspace scope | Same model |
+| 100+ concurrent calls | ✅ Correlation model still valid | Monitor DB row lock contention on span append |
+| ID collision (6 digits) | ✅ DB checks on mint; ~900k space | May need longer IDs or UUID display at very high volume |
+| One bot process, many parallel calls | ⚠️ SDK uses a shared mutable OTLP exporter — **configure per call**; safest pattern is **one active call per worker process** or ensure `setup_pipecat_worker_tracing` runs at call start before any spans export | Per-call exporter instance in SDK (future hardening) |
+| Mixed spans in one HTTP batch | ✅ `group_spans_by_call_short_id` splits them | Same |
+| Duplicate span replay | ✅ Dedupe on `(trace_id, span_id)` | Same |
+
+**Bottom line:** The correlation design is sound for concurrent calls. Scale limits today are **storage** (JSONB span rewrite) and **sync ingest**, not call routing — see Section 12.
+
+---
+
+## 5. Architecture (our side)
+
+### 5.1 Backend modules
+
+| Module | Path | Role |
+|--------|------|------|
+| API routes | `app/api/v1/routes/synthetic_traces.py` | Ingest, sessions, list, detail |
+| Trace service | `app/services/synthetic_traces/trace_service.py` | Open/close traces, merge turns, aggregates |
+| OTLP parser | `app/services/synthetic_traces/otlp_ingest.py` | JSON + Protobuf → normalized spans |
+| Turn mapper | `app/services/synthetic_traces/otlp_mapper.py` | Spans → turns, models, grouping, dedupe |
+| Correlation | `src/efficientai/integrations/efficientai_traces/correlation.py` | Header + span attr constants |
+| Customer SDK | `src/efficientai/integrations/efficientai_traces/` | Pipecat session + OTLP setup |
+| Examples | `docs/examples/pipecat_*.py` | Copy-paste bot templates |
+
+### 5.2 Database tables
+
+| Table | Contents | Example row |
+|-------|----------|-------------|
+| `synthetic_call_traces` | Catalog: call ID, status, p50, workspace | `call_short_id=482931, status=closed, turn_count=4` |
+| `synthetic_trace_payloads` | Derived **turns** JSON | `[{turn_number:1, stt_ttfb_ms:120, llm_ttfb_ms:520, ...}]` |
+| `synthetic_trace_otel_payloads` | Raw **OTLP spans** array | `[{name:"stt", span_id:"...", attributes:{...}}]` |
+
+**Why three tables?** The catalog row powers fast list queries. Turns power the UI waterfall without parsing raw OTel. Raw spans power the "Raw spans" tab and future debugging.
+
+### 5.3 Frontend
+
+| Component | Path |
+|-----------|------|
+| List + setup | `frontend/src/pages/test-insights/TestInsights.tsx` |
+| Trace detail panel | `frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx` |
+
+---
+
+## 6. Authentication & correlation reference
+
+### Required headers (every API call)
+
+| Header | Required | Why |
+|--------|----------|-----|
+| `X-API-Key` | Yes | Organization authentication |
+| `X-Workspace-Id` | Yes | Multi-tenant isolation |
+
+### OTLP ingest headers (per export batch)
+
+| Header | Required | Why |
+|--------|----------|-----|
+| `X-EfficientAI-Call-Short-Id` | Yes* | Routes batch to the correct call row |
+| `X-EfficientAI-Run-Id` | No | Links to evaluator phone run |
+| `X-EfficientAI-Agent-Id` | No | Optional link to Agents record |
+
+*Or set `efficientai.call_short_id` on every span.
+
+### Span attributes (Pipecat native tracing)
+
+| Attribute | Purpose |
+|-----------|--------|
+| `efficientai.call_short_id` | **Primary call correlation ID** |
+| `efficientai.workspace_id` | Workspace scope |
+| `gen_ai.operation.name` | `stt` / `llm` / `tts` / `s2s` |
+| `gen_ai.request.model` | Model name in UI |
+| `metrics.ttfb` | Time to first byte (**seconds**) |
+| `turn.number` | Pipecat turn index |
+| `efficientai.agent_role` | Multi-agent role (greeter, support) |
+| `efficientai.evaluator_result_id` | Evaluator phone run link |
+| `gen_ai.provider.name` | Provider (elevenlabs, fireworks, etc.) |
+| `transcript` / `output` | User / assistant text in UI bubbles |
+
+---
+
+## 7. API contracts
+
+**Base URL (local):** `http://localhost:8000`  
+**Base URL (staging):** `https://staging.efficientai.cloud`
+
+All paths relative to `/api/v1`.
+
+### 7.1 Create session — mint Call ID
+
+```http
+POST /observability/traces/sessions
+X-API-Key: <api-key>
+X-Workspace-Id: <workspace-uuid>
+Content-Type: application/json
+
+{"transport": "webrtc"}
+```
+
+`transport` values: `webrtc` | `websocket` | `phone` | `custom`
+
+> `ensure_trace_session()` in the SDK calls this automatically. Customers rarely call it manually.
+
+**Response:**
+```json
+{
+  "trace_id": "550e8400-e29b-41d4-a716-446655440000",
+  "call_short_id": "482931",
+  "workspace_id": "...",
+  "transport": "webrtc",
+  "status": "open",
+  "otel_correlation": {
+    "otlp_endpoint": "https://staging.efficientai.cloud/api/v1/observability/traces",
+    "suggested_otlp_headers": {
+      "X-API-Key": "...",
+      "X-Workspace-Id": "...",
+      "X-EfficientAI-Call-Short-Id": "482931"
+    }
+  }
+}
+```
+
+### 7.2 Ingest OTLP spans (primary export endpoint)
+
+```http
+POST /observability/traces
+X-API-Key: <api-key>
+X-Workspace-Id: <workspace-uuid>
+X-EfficientAI-Call-Short-Id: 482931
+Content-Type: application/json
+```
+
+**Request body (OTLP JSON) — minimal one-turn example:**
+```json
+{
+  "resourceSpans": [{
+    "scopeSpans": [{
+      "spans": [
+        {
+          "traceId": "4bf92f3577b34da6a3ce929d0e0e4736",
+          "spanId": "00f067aa0ba902b7",
+          "name": "stt",
+          "attributes": [
+            {"key": "gen_ai.operation.name", "value": {"stringValue": "stt"}},
+            {"key": "metrics.ttfb", "value": {"doubleValue": 0.12}},
+            {"key": "turn.number", "value": {"intValue": "1"}},
+            {"key": "efficientai.call_short_id", "value": {"stringValue": "482931"}},
+            {"key": "efficientai.workspace_id", "value": {"stringValue": "<workspace-uuid>"}},
+            {"key": "transcript", "value": {"stringValue": "Hello"}}
+          ]
+        },
+        {
+          "traceId": "4bf92f3577b34da6a3ce929d0e0e4736",
+          "spanId": "a1b2c3d4e5f60718",
+          "name": "llm",
+          "attributes": [
+            {"key": "gen_ai.operation.name", "value": {"stringValue": "llm"}},
+            {"key": "gen_ai.request.model", "value": {"stringValue": "accounts/fireworks/models/deepseek-v4-flash-0731"}},
+            {"key": "metrics.ttfb", "value": {"doubleValue": 0.52}},
+            {"key": "turn.number", "value": {"intValue": "1"}},
+            {"key": "efficientai.call_short_id", "value": {"stringValue": "482931"}}
+          ]
+        },
+        {
+          "traceId": "4bf92f3577b34da6a3ce929d0e0e4736",
+          "spanId": "b2c3d4e5f6071819",
+          "name": "tts",
+          "attributes": [
+            {"key": "gen_ai.operation.name", "value": {"stringValue": "tts"}},
+            {"key": "metrics.ttfb", "value": {"doubleValue": 0.18}},
+            {"key": "turn.number", "value": {"intValue": "1"}},
+            {"key": "efficientai.call_short_id", "value": {"stringValue": "482931"}}
+          ]
+        }
+      ]
+    }]
+  }]
+}
+```
+
+**Response:**
+```json
+{
+  "accepted_spans": 3,
+  "synthetic_call_trace_id": "550e8400-e29b-41d4-a716-446655440000",
+  "correlated": true
+}
+```
+
+If `correlated: false`, the batch could not be matched to a trace row — check `call_short_id` and workspace.
+
+Also accepts **Protobuf** (`Content-Type: application/x-protobuf`).
+
+### 7.3 Close session
+
+```http
+POST /observability/traces/sessions/482931/close
+X-API-Key: <api-key>
+X-Workspace-Id: <workspace-uuid>
+```
+
+**Response:**
+```json
+{
+  "trace_id": "550e8400-e29b-41d4-a716-446655440000",
+  "call_short_id": "482931",
+  "status": "closed"
+}
+```
+
+### 7.4 List traces
+
+```http
+GET /observability/traces?skip=0&limit=50&status=open
+X-API-Key: <api-key>
+X-Workspace-Id: <workspace-uuid>
+```
+
+**Response:**
+```json
+{
+  "items": [
+    {
+      "id": "550e8400-e29b-41d4-a716-446655440000",
+      "call_short_id": "482931",
+      "transport": "webrtc",
+      "status": "open",
+      "turn_count": 4,
+      "response_latency_p50_ms": 890.0,
+      "response_latency_p90_ms": 1200.0,
+      "component_aggregates": {
+        "stt_ttfb_ms": { "p50": 120 },
+        "llm_ttfb_ms": { "p50": 520 },
+        "tts_ttfb_ms": { "p50": 180 }
+      },
+      "started_at": "2026-09-02T10:00:00Z"
+    }
+  ],
+  "total": 42
+}
+```
+
+### 7.5 Get trace detail
+
+```http
+GET /observability/traces/{trace_id}
+GET /observability/traces/by-call-short-id/482931
+GET /observability/traces/results/{evaluator_result_id}
+```
+
+**Response (detail adds turns + spans + models):**
+```json
+{
+  "id": "550e8400-e29b-41d4-a716-446655440000",
+  "call_short_id": "482931",
+  "status": "closed",
+  "turn_count": 2,
+  "response_latency_p50_ms": 890,
+  "pipeline_models": {
+    "stt": { "provider": "elevenlabs", "model": "scribe_v1" },
+    "llm": { "provider": "fireworks", "model": "deepseek-v4-flash-0731" },
+    "tts": { "provider": "elevenlabs", "model": "eleven_flash_v2_5" }
+  },
+  "turns": [
+    {
+      "turn_number": 1,
+      "sut_response_latency_ms": 920,
+      "stt_ttfb_ms": 120,
+      "llm_ttfb_ms": 520,
+      "tts_ttfb_ms": 180,
+      "extra": {
+        "user_text": "Hello, I need help",
+        "assistant_text": "Sure, how can I help?",
+        "stt_model": "scribe_v1",
+        "llm_model": "deepseek-v4-flash-0731",
+        "tts_model": "eleven_flash_v2_5",
+        "pipeline_mode": "stt_llm_tts"
+      }
+    }
+  ],
+  "otel_spans": [],
+  "otel_trace_ids": ["4bf92f3577b34da6a3ce929d0e0e4736"]
+}
+```
+
+### 7.6 Setup info (UI "Connect Pipecat" tab)
+
+```http
+GET /observability/traces/setup
+X-API-Key: <api-key>
+X-Workspace-Id: <workspace-uuid>
+```
+
+Returns OTLP endpoint URL, env var template, suggested OTLP headers, and a Python snippet for the active host.
+
+---
+
+## 8. Customer onboarding
+
+### 8.1 Prerequisites
+
+1. EfficientAI account + **workspace**
+2. **API key** (Settings → API Keys)
+3. **Workspace UUID** (Call Traces → Connect Pipecat tab)
+4. Pipecat project + voice provider keys
+
+### 8.2 Install
+
+```bash
+uv pip install "pipecat-ai[silero,elevenlabs,fireworks,runner,webrtc]>=1.4.0"
+uv pip install -e '/path/to/efficientAI[otel]'
+```
+
+### 8.3 Environment variables
+
+```bash
+# Required — set once per deployment (local .env or staging secret)
+EFFICIENTAI_API_KEY=<api-key>
+EFFICIENTAI_WORKSPACE_ID=<workspace-uuid>
+
+# Point at local OR staging
+EFFICIENTAI_API_BASE=http://localhost:8000
+# EFFICIENTAI_API_BASE=https://staging.efficientai.cloud
+
+# Voice stack
+FIREWORKS_API_KEY=...
+ELEVENLABS_API_KEY=...
+```
+
+| Variable | Required? | Why |
+|----------|-----------|-----|
+| `EFFICIENTAI_API_KEY` | **Yes** | Authenticates ingest |
+| `EFFICIENTAI_WORKSPACE_ID` | **Yes** | Routes traces to correct workspace in UI |
+| `EFFICIENTAI_API_BASE` | No | Defaults to localhost; set for staging |
+| `EFFICIENTAI_CALL_SHORT_ID` | **No** | Auto-minted per call — never set manually |
+| `EFFICIENTAI_AGENT_ID` | No | Optional; not required in v1 |
+
+### 8.4 Pipecat integration (3 hooks — v1)
+
+We do **not** ship a single `PipecatTracer` wrapper class today. Integration is three SDK functions plus Pipecat's built-in tracing (`enable_tracing=True`).
+
+> **v2 (optional later):** We may add a thin one-import wrapper (session + OTLP + cleanup). v1 stays explicit on purpose. The OTLP wire format will not change.
+
+**Alternative patterns:**
+- **Phone / SIP:** `PipelineTask(..., **configure_pipecat_tracing(call_short_id=...))` — see `pipecat_inbound_phone_tracing.py`
+- **Playground WebSocket:** `configure_pipecat_tracing(handshake=message)` — Call ID comes from EfficientAI handshake
+
+### 8.5 WebRTC code (recommended)
+
+```python
+from efficientai.integrations.efficientai_traces import (
+    close_trace_session,
+    ensure_trace_session,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+
+require_deployment_trace_env()
+
+async def run_bot(transport, runner_args):
+    trace_transport = resolve_trace_transport(runner_args, transport)
+    trace_ctx = await ensure_trace_session(transport=trace_transport)  # mints call ID
+    tracing = setup_pipecat_worker_tracing(trace_ctx)                   # stamps spans + headers
+
+    worker = PipelineWorker(
+        pipeline,
+        enable_tracing=True,
+        additional_span_attributes=tracing["additional_span_attributes"],
+    )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        await worker.cancel()
+        await close_trace_session(trace_ctx)
+```
+
+Full working example: `docs/examples/pipecat_multi_agent_webrtc_tracing.py`
+
+### 8.6 Verify (local end-to-end)
+
+```bash
+eai start-all          # local API
+uv run bot.py          # Pipecat
+# Browser → localhost:7860/client → WebRTC → talk → disconnect
+# Call Traces UI → Refresh → open Call ID
+```
+
+### 8.7 Local bot → staging UI (team testing)
+
+Same `bot.py`, only change the base URL:
+
+```bash
+# In Pipecat .env
+EFFICIENTAI_API_BASE=https://staging.efficientai.cloud
+EFFICIENTAI_API_KEY=<staging-api-key>
+EFFICIENTAI_WORKSPACE_ID=<staging-workspace-uuid>
+```
+
+Then open **https://staging.efficientai.cloud** → Call Traces → Refresh. Traces from your local Pipecat bot appear in staging UI.
+
+---
+
+## 9. Span naming conventions
+
+| Span name | UI shows |
+|-----------|----------|
+| `stt` | STT latency + model |
+| `llm` | LLM latency + model |
+| `tts` | TTS latency + model |
+| `s2s` | Speech-to-speech (realtime models) |
+| `conversation` | Session end detection |
+| `turn` | Turn grouping |
+
+---
+
+## 10. UI overview
+
+Route: `/call-traces` (redirects from legacy `/test-insights`)
+
+### List view
+
+| Column / control | What it shows |
+|------------------|---------------|
+| Call ID | 6-digit `#482931` |
+| Status | `open` (live call) or `closed` |
+| Turns | Count of conversation turns |
+| Median latency | p50 end-to-end response time |
+| Refresh | Re-fetch list (auto-refreshes when open traces exist) |
+| Connect Pipecat tab | OTLP URL, env block, copy-paste Python snippet from `/setup` |
+
+List is **workspace-scoped** — switch workspace in the header to see other traces.
+
+### Trace detail panel
+
+| Section | Content |
+|---------|--------|
+| Header | Call ID, status (open/closed), p50/p90/p95 latency chips |
+| Pipeline models bar | Session-level STT / LLM / TTS model + provider |
+| Conversation tab | Chat bubbles (user + assistant) + per-turn latency waterfall (STT→LLM→TTS) |
+| Waterfall tab | All turns as horizontal latency bars |
+| Raw spans tab | Full OTLP span tree for debugging |
+| S2S mode | When realtime model detected, shows `s2s` chip instead of STT/LLM/TTS split |
+
+Embedded mode: detail panel also renders inside evaluator call recordings (`CustomWebSocketCallDetails`).
+
+---
+
+## 11. What we removed vs early designs
+
+| Removed | Why |
+|---------|-----|
+| `agent_id` on session create | Workspace + call ID is enough for WebRTC v1 |
+| Legacy `/synthetic-traces` path | Renamed to `/observability/traces` |
+| JSON `/ingest` shim | Deprecated — use OTLP POST at root |
+
+---
+
+## 12. Scaling roadmap
+
+### v1 today (local + staging)
+- Sync OTLP ingest
+- Spans in Postgres JSONB
+- 6-digit call IDs with DB uniqueness check
+- Correlation via `call_short_id` + `workspace_id` — **proven for concurrent calls**
+
+### Phase 2 — customer scale
+
+| Change | Why |
+|--------|-----|
+| Async ingest (Celery queue) | Don't block HTTP on turn mapping |
+| Span blobs in S3 | Stop rewriting giant JSON arrays in Postgres |
+| UI pagination | Handle 1000+ traces per workspace (list capped at 100 in UI today; API supports `skip`/`limit` up to 200) |
+| Retention policy | Archive spans after 30/90 days |
+
+### Phase 3 — platform scale
+
+| Change | Why |
+|--------|-----|
+| Dedicated OTel collector (gRPC) | Lower overhead at high volume |
+| Workspace daily rollups | Fast dashboards without scanning all calls |
+| Rate limits per API key | Protect shared infra |
+
+---
+
+## 13. Troubleshooting
+
+| Symptom | Fix |
+|---------|-----|
+| No traces in UI | Check API key + workspace ID; Refresh |
+| `correlated: false` | Missing `call_short_id` on spans/headers |
+| Spans on wrong call | Never share `EFFICIENTAI_CALL_SHORT_ID` in env; mint per call |
+| Trace stays open | Call `close_trace_session()` on disconnect |
+| Empty turns | `enable_tracing=True` on PipelineWorker |
+| Local bot, staging UI empty | Set `EFFICIENTAI_API_BASE` to staging URL |
+
+---
+
+## 14. Example bots (repo)
+
+| Scenario | File |
+|----------|------|
+| Multi-agent WebRTC (Fireworks + ElevenLabs) | `docs/examples/pipecat_multi_agent_webrtc_tracing.py` |
+| Single agent, swappable providers | `docs/examples/pipecat_multi_provider_webrtc_tracing.py` |
+| Gemini Live / S2S | `docs/examples/pipecat_upstream_webrtc_tracing.py` |
+| Custom WebSocket playground | `docs/examples/pipecat_upstream_websocket_tracing.py` |
+| Inbound phone + SIP headers | `docs/examples/pipecat_inbound_phone_tracing.py` |
+| Env template | `docs/examples/pipecat.env.example` |
+| Dev bot script | `scripts/pipecat_trace_dev_bot.py` |
+
+---
+
+## 15. Repo references
+
+| Doc / module | Path |
+|--------------|------|
+| Quick start guide | `docs/synthetic-call-traces-pipecat.md` |
+| Correlation constants | `src/efficientai/integrations/efficientai_traces/correlation.py` |
+| Customer SDK | `src/efficientai/integrations/efficientai_traces/` |
+| API routes | `app/api/v1/routes/synthetic_traces.py` |
+| Ingest + turn mapping | `app/services/synthetic_traces/trace_service.py` |
+| OTLP parser | `app/services/synthetic_traces/otlp_ingest.py` |
+| Turn mapper | `app/services/synthetic_traces/otlp_mapper.py` |
+| List UI | `frontend/src/pages/test-insights/TestInsights.tsx` |
+| Detail panel | `frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx` |
+
+---
+
+## 16. Related links
+
+- Other platform OTel examples (e.g. [Cekura — OpenTelemetry Traces](https://docs.cekura.ai/documentation/guides/observability/tracing), [Cekura — Pipecat Tracing](https://docs.cekura.ai/documentation/integrations/pipecat/tracing))
+- Pipecat docs: [pipecat.ai](https://docs.pipecat.ai)
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index b62b6e72..6f50c227 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -1,248 +1,256 @@
-import { BrowserRouter, Routes, Route, Navigate, useParams } from 'react-router-dom'
-import { useAuthStore } from './store/authStore'
-import { useLicenseStore } from './store/licenseStore'
-import Layout from './components/Layout'
-
-// Auth
-import Login from './pages/auth/Login'
-import InviteAccept from './pages/auth/InviteAccept'
-import LoginCallback from './pages/auth/LoginCallback'
-import SelectOrganization from './pages/auth/SelectOrganization'
-import PlatformLogin from './pages/platform/PlatformLogin'
-import PlatformAdmin from './pages/platform/PlatformAdmin'
-
-// Dashboard
-import Dashboard from './pages/dashboard/Dashboard'
-
-// Usage
-import UsagePage, { UsagePricingRedirect } from './pages/usage/UsagePage'
-
-// Prompt Partials
-import PromptPartials from './pages/promptPartials/PromptPartials'
-
-// Agents
-import AgentsWorkspace from './pages/agents/AgentsWorkspace'
-
-// Personas
-import Personas from './pages/personas/Personas'
-
-// Scenarios
-import Scenarios from './pages/scenarios/Scenarios'
-
-// Metrics
-import MetricsLayout from './pages/metrics/MetricsLayout'
-import MetricsManagement from './pages/metrics/MetricsManagement'
-import MetricsStudio from './pages/metrics/MetricsStudio'
-import MetricsStudioRunDetail from './pages/metrics/MetricsStudioRunDetail'
-
-// Playground - Agent
-import AgentPlayground from './pages/playground/agent/AgentPlayground'
-import CallRecordingDetail from './pages/playground/agent/CallRecordingDetail'
-import TestAgentResultDetail from './pages/playground/agent/TestAgentResultDetail'
-
-// Playground - Voice
-import VoicePlayground from './pages/playground/voice/VoicePlayground'
-
-// Evaluators
-import EvaluateTestAgents from './pages/evaluators/evaluators/EvaluateTestAgents'
-import EvaluatorDetail from './pages/evaluators/evaluators/EvaluatorDetail'
-
-// Evaluator Results
-import ResultsOverview from './pages/evaluators/results/ResultsOverview'
-import ResultsAgentWorkspace from './pages/evaluators/results/ResultsAgentWorkspace'
-import {
-  RedirectAgentScenarioToWorkspace,
-  RedirectAgentSuiteToWorkspace,
-} from './pages/evaluators/results/ResultsAgentWorkspaceRedirects'
-import ResultsUnassigned from './pages/evaluators/results/ResultsUnassigned'
-import EvaluatorResultDetail from './pages/evaluators/results/EvaluatorResultDetail'
-import EvaluationDetail from './pages/evaluators/results/EvaluationDetail'
-import EvaluationsList from './pages/evaluators/results/EvaluationsList'
-
-// Observability
-import Observability from './pages/observability/Observability'
-import ObservabilityCalls from './pages/observability/ObservabilityCalls'
-import ObservabilityCallDetail from './pages/observability/ObservabilityCallDetail'
-
-// Alerting
-import Alerts from './pages/alerting/Alerts'
-import AlertDetail from './pages/alerting/AlertDetail'
-import AlertHistory from './pages/alerting/AlertHistory'
-
-// Configurations
-import DataSources from './pages/configurations/DataSources'
-import VoiceBundles from './pages/configurations/VoiceBundles'
-import Integrations from './pages/configurations/Integrations'
-import TelephonyNumbers from './pages/configurations/TelephonyNumbers'
-import Settings from './pages/configurations/Settings'
-import CronJobs from './pages/configurations/CronJobs'
-
-// IAM
-import IAM from './pages/iam/IAM'
-
-// Profile
-import Profile from './pages/profile/Profile'
-
-// Prompt Optimization (Enterprise)
-import PromptOptimization from './pages/promptOptimization/PromptOptimization'
-
-// Judge Alignment (AlignEval-style hybrid integration)
-import JudgeAlignment from './pages/judgeAlignment/JudgeAlignment'
-import JudgeDatasetDetail from './pages/judgeAlignment/JudgeDatasetDetail'
-
-// Enterprise
-import EnterpriseUpgrade from './pages/enterprise/EnterpriseUpgrade'
-import { WalkthroughProvider } from './context/WalkthroughContext'
-
-// Public (no-auth) blind test form
-import BlindTestForm from './pages/public/BlindTestForm'
-
-// Call Imports
-import CallImports from './pages/callImports/CallImports'
-import CallImportDetail from './pages/callImports/CallImportDetail'
-import CallImportEvaluationDetail from './pages/callImports/CallImportEvaluationDetail'
-import CallImportTagsPage from './pages/callImports/Tags'
-import CallImportSchemasPage from './pages/callImports/Schemas'
-
-
-function PrivateRoute({ children }: { children: React.ReactNode }) {
-  // Either credential type counts as "signed in". The backend enforces the
-  // actual authentication on every request; this guard just keeps the SPA
-  // from flashing protected pages when the user clearly has no session.
-  const { apiKey, accessToken } = useAuthStore()
-
-  if (!apiKey && !accessToken) {
-    return <Navigate to="/login" replace />
-  }
-
-  return <>{children}</>
-}
-
-function EnterpriseGate({ feature, children }: { feature: string; children: React.ReactNode }) {
-  const { isFeatureEnabled, isLoaded } = useLicenseStore()
-
-  if (!isLoaded) {
-    return (
-      <div className="flex items-center justify-center h-64">
-        <div className="animate-spin rounded-full h-8 w-8 border-b-2 border-gray-900" />
-      </div>
-    )
-  }
-
-  if (!isFeatureEnabled(feature)) {
-    return <EnterpriseUpgrade feature={feature} />
-  }
-
-  return <>{children}</>
-}
-
-function App() {
-  return (
-    <BrowserRouter>
-      <Routes>
-        <Route path="/login" element={<Login />} />
-        <Route path="/invite/:token" element={<InviteAccept />} />
-        <Route path="/platform/login" element={<PlatformLogin />} />
-        <Route path="/platform" element={<PlatformAdmin />} />
-        <Route path="/login/callback" element={<LoginCallback />} />
-        <Route path="/select-organization" element={<SelectOrganization />} />
-        {/* Public blind test form - intentionally outside PrivateRoute and EnterpriseGate.
-            Auth comes from the unguessable share token in the URL. */}
-        <Route path="/blind-test/:token" element={<BlindTestForm />} />
-        <Route
-          path="/"
-          element={
-            <PrivateRoute>
-              <WalkthroughProvider>
-                <Layout />
-              </WalkthroughProvider>
-            </PrivateRoute>
-          }
-        >
-          <Route index element={<Dashboard />} />
-          <Route path="evaluations" element={<EvaluationsList />} />
-          <Route path="evaluations/:id" element={<EvaluationDetail />} />
-          <Route path="playground" element={<AgentPlayground />} />
-          <Route path="playground/call-recordings/:callShortId" element={<CallRecordingDetail />} />
-          <Route path="playground/test-agent-results/:id" element={<TestAgentResultDetail />} />
-          <Route path="agents" element={<AgentsWorkspace />} />
-          <Route path="agents/:id" element={<AgentsWorkspace />} />
-          <Route path="personas" element={<Personas />} />
-          <Route path="scenarios" element={<Scenarios />} />
-          <Route path="metrics" element={<Navigate to="/metrics-management/studio" replace />} />
-          <Route path="integrations" element={<Integrations />} />
-          <Route path="telephony-numbers" element={<TelephonyNumbers />} />
-          <Route path="data-sources" element={<DataSources />} />
-          <Route path="voicebundles" element={<VoiceBundles />} />
-          <Route path="evaluate-test-agents" element={<EvaluateTestAgents />} />
-          <Route path="evaluate-test-agents/:id" element={<EvaluatorDetail />} />
-          <Route path="metrics-management" element={<MetricsLayout />}>
-            <Route index element={<MetricsManagement />} />
-            <Route path="studio" element={<MetricsStudio />} />
-            <Route path="studio/runs/:runId" element={<MetricsStudioRunDetail />} />
-          </Route>
-          <Route path="results" element={<ResultsOverview />} />
-          <Route path="results/unassigned" element={<ResultsUnassigned />} />
-          <Route path="results/agents/:agentId" element={<ResultsAgentWorkspace />} />
-          <Route
-            path="results/agents/:agentId/suites/:suiteId"
-            element={<RedirectAgentSuiteToWorkspace />}
-          />
-          <Route
-            path="results/agents/:agentId/suites/:suiteId/scenarios/:scenarioId"
-            element={<RedirectAgentScenarioToWorkspace />}
-          />
-          <Route path="results/:id" element={<EvaluatorResultDetail />} />
-          <Route path="observability" element={<Observability />} />
-          <Route path="observability/calls" element={<ObservabilityCalls />} />
-          <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
-          <Route path="iam" element={<IAM />} />
-          <Route path="usage" element={<UsagePage />} />
-          <Route path="usage/pricing" element={<UsagePricingRedirect />} />
-          <Route
-            path="workspace-members"
-            element={<Navigate to="/iam?tab=workspace-members" replace />}
-          />
-          <Route path="profile" element={<Profile />} />
-          <Route path="settings" element={<Settings />} />
-          <Route path="alerts" element={<Alerts />} />
-          <Route path="alerts/:id" element={<AlertDetail />} />
-          <Route path="alerts/history" element={<AlertHistory />} />
-          <Route path="voice-playground" element={<EnterpriseGate feature="voice_playground"><VoicePlayground /></EnterpriseGate>} />
-          <Route path="cron-jobs" element={<CronJobs />} />
-          <Route path="prompt-partials" element={<PromptPartials />} />
-          <Route path="prompt-partials/:id" element={<PromptPartials />} />
-          <Route
-            path="imported-agents"
-            element={<Navigate to="/prompt-partials?kind=imported_agent" replace />}
-          />
-          <Route
-            path="imported-agents/:id"
-            element={<ImportedAgentRedirect />}
-          />
-          <Route path="call-imports" element={<EnterpriseGate feature="call_imports"><CallImports /></EnterpriseGate>} />
-          <Route path="call-imports/tags" element={<EnterpriseGate feature="call_imports"><CallImportTagsPage /></EnterpriseGate>} />
-          <Route path="call-imports/schemas" element={<EnterpriseGate feature="call_imports"><CallImportSchemasPage /></EnterpriseGate>} />
-          <Route path="call-imports/:id" element={<EnterpriseGate feature="call_imports"><CallImportDetail /></EnterpriseGate>} />
-          <Route
-            path="call-imports/:id/evaluations/:evalId"
-            element={<EnterpriseGate feature="call_imports"><CallImportEvaluationDetail /></EnterpriseGate>}
-          />
-          <Route path="prompt-optimization" element={<EnterpriseGate feature="gepa_optimization"><PromptOptimization /></EnterpriseGate>} />
-          <Route path="judge-alignment" element={<JudgeAlignment />} />
-          <Route path="judge-alignment/datasets/:datasetId" element={<JudgeDatasetDetail />} />
-        </Route>
-      </Routes>
-    </BrowserRouter>
-  )
-}
-
-function ImportedAgentRedirect() {
-  const { id } = useParams<{ id: string }>()
-  return (
-    <Navigate to={`/prompt-partials/${id}?kind=imported_agent`} replace />
-  )
-}
-
-export default App
-
+import { BrowserRouter, Routes, Route, Navigate, useParams, useLocation } from 'react-router-dom'
+import { useAuthStore } from './store/authStore'
+import { useLicenseStore } from './store/licenseStore'
+import Layout from './components/Layout'
+
+// Auth
+import Login from './pages/auth/Login'
+import InviteAccept from './pages/auth/InviteAccept'
+import LoginCallback from './pages/auth/LoginCallback'
+import SelectOrganization from './pages/auth/SelectOrganization'
+import PlatformLogin from './pages/platform/PlatformLogin'
+import PlatformAdmin from './pages/platform/PlatformAdmin'
+
+// Dashboard
+import Dashboard from './pages/dashboard/Dashboard'
+
+// Usage
+import UsagePage, { UsagePricingRedirect } from './pages/usage/UsagePage'
+
+// Prompt Partials
+import PromptPartials from './pages/promptPartials/PromptPartials'
+
+// Agents
+import AgentsWorkspace from './pages/agents/AgentsWorkspace'
+
+// Personas
+import Personas from './pages/personas/Personas'
+
+// Scenarios
+import Scenarios from './pages/scenarios/Scenarios'
+
+// Metrics
+import MetricsLayout from './pages/metrics/MetricsLayout'
+import MetricsManagement from './pages/metrics/MetricsManagement'
+import MetricsStudio from './pages/metrics/MetricsStudio'
+import MetricsStudioRunDetail from './pages/metrics/MetricsStudioRunDetail'
+
+// Playground - Agent
+import AgentPlayground from './pages/playground/agent/AgentPlayground'
+import CallRecordingDetail from './pages/playground/agent/CallRecordingDetail'
+import TestAgentResultDetail from './pages/playground/agent/TestAgentResultDetail'
+
+// Playground - Voice
+import VoicePlayground from './pages/playground/voice/VoicePlayground'
+
+// Evaluators
+import EvaluateTestAgents from './pages/evaluators/evaluators/EvaluateTestAgents'
+import EvaluatorDetail from './pages/evaluators/evaluators/EvaluatorDetail'
+
+// Evaluator Results
+import ResultsOverview from './pages/evaluators/results/ResultsOverview'
+import ResultsAgentWorkspace from './pages/evaluators/results/ResultsAgentWorkspace'
+import {
+  RedirectAgentScenarioToWorkspace,
+  RedirectAgentSuiteToWorkspace,
+} from './pages/evaluators/results/ResultsAgentWorkspaceRedirects'
+import ResultsUnassigned from './pages/evaluators/results/ResultsUnassigned'
+import EvaluatorResultDetail from './pages/evaluators/results/EvaluatorResultDetail'
+import EvaluationDetail from './pages/evaluators/results/EvaluationDetail'
+import EvaluationsList from './pages/evaluators/results/EvaluationsList'
+
+// Observability
+import Observability from './pages/observability/Observability'
+import ObservabilityCalls from './pages/observability/ObservabilityCalls'
+import ObservabilityCallDetail from './pages/observability/ObservabilityCallDetail'
+import TestInsights from './pages/test-insights/TestInsights'
+
+// Alerting
+import Alerts from './pages/alerting/Alerts'
+import AlertDetail from './pages/alerting/AlertDetail'
+import AlertHistory from './pages/alerting/AlertHistory'
+
+// Configurations
+import DataSources from './pages/configurations/DataSources'
+import VoiceBundles from './pages/configurations/VoiceBundles'
+import Integrations from './pages/configurations/Integrations'
+import TelephonyNumbers from './pages/configurations/TelephonyNumbers'
+import Settings from './pages/configurations/Settings'
+import CronJobs from './pages/configurations/CronJobs'
+
+// IAM
+import IAM from './pages/iam/IAM'
+
+// Profile
+import Profile from './pages/profile/Profile'
+
+// Prompt Optimization (Enterprise)
+import PromptOptimization from './pages/promptOptimization/PromptOptimization'
+
+// Judge Alignment (AlignEval-style hybrid integration)
+import JudgeAlignment from './pages/judgeAlignment/JudgeAlignment'
+import JudgeDatasetDetail from './pages/judgeAlignment/JudgeDatasetDetail'
+
+// Enterprise
+import EnterpriseUpgrade from './pages/enterprise/EnterpriseUpgrade'
+import { WalkthroughProvider } from './context/WalkthroughContext'
+
+// Public (no-auth) blind test form
+import BlindTestForm from './pages/public/BlindTestForm'
+
+// Call Imports
+import CallImports from './pages/callImports/CallImports'
+import CallImportDetail from './pages/callImports/CallImportDetail'
+import CallImportEvaluationDetail from './pages/callImports/CallImportEvaluationDetail'
+import CallImportTagsPage from './pages/callImports/Tags'
+import CallImportSchemasPage from './pages/callImports/Schemas'
+
+
+function PreserveSearchRedirect({ to }: { to: string }) {
+  const location = useLocation()
+  return <Navigate to={`${to}${location.search}`} replace />
+}
+
+function PrivateRoute({ children }: { children: React.ReactNode }) {
+  // Either credential type counts as "signed in". The backend enforces the
+  // actual authentication on every request; this guard just keeps the SPA
+  // from flashing protected pages when the user clearly has no session.
+  const { apiKey, accessToken } = useAuthStore()
+
+  if (!apiKey && !accessToken) {
+    return <Navigate to="/login" replace />
+  }
+
+  return <>{children}</>
+}
+
+function EnterpriseGate({ feature, children }: { feature: string; children: React.ReactNode }) {
+  const { isFeatureEnabled, isLoaded } = useLicenseStore()
+
+  if (!isLoaded) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="animate-spin rounded-full h-8 w-8 border-b-2 border-gray-900" />
+      </div>
+    )
+  }
+
+  if (!isFeatureEnabled(feature)) {
+    return <EnterpriseUpgrade feature={feature} />
+  }
+
+  return <>{children}</>
+}
+
+function App() {
+  return (
+    <BrowserRouter>
+      <Routes>
+        <Route path="/login" element={<Login />} />
+        <Route path="/invite/:token" element={<InviteAccept />} />
+        <Route path="/platform/login" element={<PlatformLogin />} />
+        <Route path="/platform" element={<PlatformAdmin />} />
+        <Route path="/login/callback" element={<LoginCallback />} />
+        <Route path="/select-organization" element={<SelectOrganization />} />
+        {/* Public blind test form - intentionally outside PrivateRoute and EnterpriseGate.
+            Auth comes from the unguessable share token in the URL. */}
+        <Route path="/blind-test/:token" element={<BlindTestForm />} />
+        <Route
+          path="/"
+          element={
+            <PrivateRoute>
+              <WalkthroughProvider>
+                <Layout />
+              </WalkthroughProvider>
+            </PrivateRoute>
+          }
+        >
+          <Route index element={<Dashboard />} />
+          <Route path="evaluations" element={<EvaluationsList />} />
+          <Route path="evaluations/:id" element={<EvaluationDetail />} />
+          <Route path="playground" element={<AgentPlayground />} />
+          <Route path="playground/call-recordings/:callShortId" element={<CallRecordingDetail />} />
+          <Route path="playground/test-agent-results/:id" element={<TestAgentResultDetail />} />
+          <Route path="agents" element={<AgentsWorkspace />} />
+          <Route path="agents/:id" element={<AgentsWorkspace />} />
+          <Route path="personas" element={<Personas />} />
+          <Route path="scenarios" element={<Scenarios />} />
+          <Route path="metrics" element={<Navigate to="/metrics-management/studio" replace />} />
+          <Route path="integrations" element={<Integrations />} />
+          <Route path="telephony-numbers" element={<TelephonyNumbers />} />
+          <Route path="data-sources" element={<DataSources />} />
+          <Route path="voicebundles" element={<VoiceBundles />} />
+          <Route path="evaluate-test-agents" element={<EvaluateTestAgents />} />
+          <Route path="evaluate-test-agents/:id" element={<EvaluatorDetail />} />
+          <Route path="metrics-management" element={<MetricsLayout />}>
+            <Route index element={<MetricsManagement />} />
+            <Route path="studio" element={<MetricsStudio />} />
+            <Route path="studio/runs/:runId" element={<MetricsStudioRunDetail />} />
+          </Route>
+          <Route path="results" element={<ResultsOverview />} />
+          <Route path="results/unassigned" element={<ResultsUnassigned />} />
+          <Route path="results/agents/:agentId" element={<ResultsAgentWorkspace />} />
+          <Route
+            path="results/agents/:agentId/suites/:suiteId"
+            element={<RedirectAgentSuiteToWorkspace />}
+          />
+          <Route
+            path="results/agents/:agentId/suites/:suiteId/scenarios/:scenarioId"
+            element={<RedirectAgentScenarioToWorkspace />}
+          />
+          <Route path="results/:id" element={<EvaluatorResultDetail />} />
+          <Route path="observability" element={<Observability />} />
+          <Route path="observability/calls" element={<ObservabilityCalls />} />
+          <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
+          <Route path="call-traces" element={<TestInsights />} />
+          <Route path="test-insights" element={<PreserveSearchRedirect to="/call-traces" />} />
+          <Route path="iam" element={<IAM />} />
+          <Route path="usage" element={<UsagePage />} />
+          <Route path="usage/pricing" element={<UsagePricingRedirect />} />
+          <Route
+            path="workspace-members"
+            element={<Navigate to="/iam?tab=workspace-members" replace />}
+          />
+          <Route path="profile" element={<Profile />} />
+          <Route path="settings" element={<Settings />} />
+          <Route path="alerts" element={<Alerts />} />
+          <Route path="alerts/:id" element={<AlertDetail />} />
+          <Route path="alerts/history" element={<AlertHistory />} />
+          <Route path="voice-playground" element={<EnterpriseGate feature="voice_playground"><VoicePlayground /></EnterpriseGate>} />
+          <Route path="cron-jobs" element={<CronJobs />} />
+          <Route path="prompt-partials" element={<PromptPartials />} />
+          <Route path="prompt-partials/:id" element={<PromptPartials />} />
+          <Route
+            path="imported-agents"
+            element={<Navigate to="/prompt-partials?kind=imported_agent" replace />}
+          />
+          <Route
+            path="imported-agents/:id"
+            element={<ImportedAgentRedirect />}
+          />
+          <Route path="call-imports" element={<EnterpriseGate feature="call_imports"><CallImports /></EnterpriseGate>} />
+          <Route path="call-imports/tags" element={<EnterpriseGate feature="call_imports"><CallImportTagsPage /></EnterpriseGate>} />
+          <Route path="call-imports/schemas" element={<EnterpriseGate feature="call_imports"><CallImportSchemasPage /></EnterpriseGate>} />
+          <Route path="call-imports/:id" element={<EnterpriseGate feature="call_imports"><CallImportDetail /></EnterpriseGate>} />
+          <Route
+            path="call-imports/:id/evaluations/:evalId"
+            element={<EnterpriseGate feature="call_imports"><CallImportEvaluationDetail /></EnterpriseGate>}
+          />
+          <Route path="prompt-optimization" element={<EnterpriseGate feature="gepa_optimization"><PromptOptimization /></EnterpriseGate>} />
+          <Route path="judge-alignment" element={<JudgeAlignment />} />
+          <Route path="judge-alignment/datasets/:datasetId" element={<JudgeDatasetDetail />} />
+        </Route>
+      </Routes>
+    </BrowserRouter>
+  )
+}
+
+function ImportedAgentRedirect() {
+  const { id } = useParams<{ id: string }>()
+  return (
+    <Navigate to={`/prompt-partials/${id}?kind=imported_agent`} replace />
+  )
+}
+
+export default App
+
diff --git a/frontend/src/components/GenericVoiceWSClient.tsx b/frontend/src/components/GenericVoiceWSClient.tsx
index d9725c3a..60d23c0b 100644
--- a/frontend/src/components/GenericVoiceWSClient.tsx
+++ b/frontend/src/components/GenericVoiceWSClient.tsx
@@ -1,1230 +1,1312 @@
-import { useState, useRef, useEffect, useCallback } from 'react'
-import {
-  Mic,
-  MicOff,
-  Loader,
-  AlertTriangle,
-  ArrowUp,
-  ArrowDown,
-  Filter,
-  ExternalLink,
-  CheckCircle,
-  Clock,
-  Save,
-  Trash2,
-} from 'lucide-react'
-import { useQuery } from '@tanstack/react-query'
-import { useNavigate } from 'react-router-dom'
-import Button from './Button'
-import { apiClient } from '../lib/api'
-import type { WSProtocolConfig, TranscriptEntry, ParsedWSMessage } from '../lib/wsProtocols'
-
-// ----- Audio helpers ---------------------------------------------------
-
-function resampleFloat32(input: Float32Array, fromRate: number, toRate: number): Float32Array {
-  if (fromRate === toRate) return input
-  const ratio = fromRate / toRate
-  const len = Math.round(input.length / ratio)
-  const out = new Float32Array(len)
-  for (let i = 0; i < len; i++) {
-    const srcIdx = i * ratio
-    const lo = Math.floor(srcIdx)
-    const hi = Math.min(lo + 1, input.length - 1)
-    const t = srcIdx - lo
-    out[i] = input[lo] * (1 - t) + input[hi] * t
-  }
-  return out
-}
-
-function float32ToInt16(input: Float32Array): Int16Array {
-  const out = new Int16Array(input.length)
-  for (let i = 0; i < input.length; i++) {
-    const s = Math.max(-1, Math.min(1, input[i]))
-    out[i] = s < 0 ? s * 0x8000 : s * 0x7fff
-  }
-  return out
-}
-
-function int16ArrayToBase64(int16: Int16Array): string {
-  const bytes = new Uint8Array(int16.buffer, int16.byteOffset, int16.byteLength)
-  let bin = ''
-  for (let i = 0; i < bytes.length; i++) bin += String.fromCharCode(bytes[i])
-  return btoa(bin)
-}
-
-// RMS in normalized [0,1] range. Used as a cheap voice-activity detector so
-// we don't reset the silence timer on every ambient-noise frame.
-function rmsInt16(samples: Int16Array): number {
-  if (samples.length === 0) return 0
-  let sum = 0
-  for (let i = 0; i < samples.length; i++) {
-    const v = samples[i] / 32768
-    sum += v * v
-  }
-  return Math.sqrt(sum / samples.length)
-}
-
-function rmsFloat32(samples: Float32Array): number {
-  if (samples.length === 0) return 0
-  let sum = 0
-  for (let i = 0; i < samples.length; i++) sum += samples[i] * samples[i]
-  return Math.sqrt(sum / samples.length)
-}
-
-// Threshold roughly separates voice from ambient / mic self-noise. Tuned for
-// a typical laptop mic; voiced frames usually sit >= 0.03, idle rooms <= 0.01.
-const VAD_RMS_THRESHOLD = 0.015
-
-function base64ToFloat32(b64: string): Float32Array {
-  const bin = atob(b64)
-  const bytes = new Uint8Array(bin.length)
-  for (let i = 0; i < bin.length; i++) bytes[i] = bin.charCodeAt(i)
-  const int16 = new Int16Array(bytes.buffer)
-  const out = new Float32Array(int16.length)
-  for (let i = 0; i < int16.length; i++) out[i] = int16[i] / 32768
-  return out
-}
-
-function encodeWav(samples: Int16Array, sampleRate: number): Blob {
-  const byteRate = sampleRate * 2
-  const blockAlign = 2
-  const dataSize = samples.byteLength
-  const buf = new ArrayBuffer(44 + dataSize)
-  const view = new DataView(buf)
-  const writeStr = (off: number, s: string) => { for (let i = 0; i < s.length; i++) view.setUint8(off + i, s.charCodeAt(i)) }
-  writeStr(0, 'RIFF')
-  view.setUint32(4, 36 + dataSize, true)
-  writeStr(8, 'WAVE')
-  writeStr(12, 'fmt ')
-  view.setUint32(16, 16, true)
-  view.setUint16(20, 1, true)
-  view.setUint16(22, 1, true)
-  view.setUint32(24, sampleRate, true)
-  view.setUint32(28, byteRate, true)
-  view.setUint16(32, blockAlign, true)
-  view.setUint16(34, 16, true)
-  writeStr(36, 'data')
-  view.setUint32(40, dataSize, true)
-  new Uint8Array(buf, 44).set(new Uint8Array(samples.buffer, samples.byteOffset, samples.byteLength))
-  return new Blob([buf], { type: 'audio/wav' })
-}
-
-// ----- Types -----------------------------------------------------------
-
-interface WSMessageLog {
-  id: number
-  direction: 'sent' | 'received'
-  timestamp: string
-  parsed: ParsedWSMessage
-  size: number
-}
-
-type LogEntry = { timestamp: string; message: string; type: 'user' | 'bot' | 'system' }
-
-type EvalStatus = 'idle' | 'queued' | 'evaluating' | 'failed'
-
-interface SavedSession {
-  callShortId: string
-  savedAt: string
-  transcriptCount: number
-  hasRecording: boolean
-  sessionStartedAt?: string | null
-  sessionEndedAt?: string | null
-  evalStatus: EvalStatus
-  evalError?: string
-}
-
-// ----- Component -------------------------------------------------------
-
-interface GenericVoiceWSClientProps {
-  websocketUrl: string
-  protocol: WSProtocolConfig
-  agentId?: string
-  onSessionSaved?: () => void
-}
-
-export default function GenericVoiceWSClient({
-  websocketUrl,
-  protocol,
-  agentId,
-  onSessionSaved,
-}: GenericVoiceWSClientProps) {
-  // ---- UI state -------------------------------------------------------
-  const [isConnected, setIsConnected] = useState(false)
-  const [isConnecting, setIsConnecting] = useState(false)
-  const [error, setError] = useState<string | null>(null)
-  const [logs, setLogs] = useState<LogEntry[]>([])
-  const [transcriptEntries, setTranscriptEntries] = useState<TranscriptEntry[]>([])
-  const [wsMessages, setWsMessages] = useState<WSMessageLog[]>([])
-  const [showAudioMsgs, setShowAudioMsgs] = useState(false)
-  const [activePanel, setActivePanel] = useState<'transcript' | 'messages' | 'log'>('transcript')
-  const [sessionStartedAt, setSessionStartedAt] = useState<string | null>(null)
-  const [sessionEndedAt, setSessionEndedAt] = useState<string | null>(null)
-  const [recordedBlob, setRecordedBlob] = useState<Blob | null>(null)
-  const [isSavingSession, setIsSavingSession] = useState(false)
-  const [savedSessions, setSavedSessions] = useState<SavedSession[]>([])
-  // Tracks whether the CURRENT (unsaved) transcript has already been saved,
-  // so the primary button can show "Saved" / disable correctly. This resets
-  // each time a new call is started.
-  const [currentSavedId, setCurrentSavedId] = useState<string | null>(null)
-  const navigate = useNavigate()
-
-  // ---- Refs -----------------------------------------------------------
-  const wsRef = useRef<WebSocket | null>(null)
-  const audioCtxRef = useRef<AudioContext | null>(null)
-  const micStreamRef = useRef<MediaStream | null>(null)
-  const scriptNodeRef = useRef<ScriptProcessorNode | null>(null)
-  const accBufRef = useRef<Float32Array>(new Float32Array(0))
-  const playTimeRef = useRef(0)
-  const recordDestRef = useRef<MediaStreamAudioDestinationNode | null>(null)
-  const mediaRecorderRef = useRef<MediaRecorder | null>(null)
-  const recordedChunksRef = useRef<Blob[]>([])
-  const connectingRef = useRef(false)
-  // Auto-save orchestration. When a session ends we want to save it only
-  // once, and only after all post-disconnect async work (STT flushes + the
-  // MediaRecorder's final onstop) has settled.
-  const autoSaveRequestedRef = useRef(false)
-  const recordingPendingRef = useRef(false)
-  const msgIdRef = useRef(0)
-  const logsEndRef = useRef<HTMLDivElement | null>(null)
-  const transcriptEndRef = useRef<HTMLDivElement | null>(null)
-  const messagesEndRef = useRef<HTMLDivElement | null>(null)
-  const protocolRef = useRef(protocol)
-  protocolRef.current = protocol
-
-  // ---- STT state & refs ------------------------------------------------
-  const [sttEnabled, setSttEnabled] = useState(false)
-  const [pendingTranscriptions, setPendingTranscriptions] = useState(0)
-  const userAudioBufRef = useRef<Int16Array[]>([])
-  const agentAudioBufRef = useRef<Int16Array[]>([])
-  const userSilenceTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
-  const agentSilenceTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
-  // A "turn" starts the moment voiced audio is detected and ends when the
-  // silence timer fires. Silent frames received inside an active turn are
-  // still buffered (so within-sentence pauses survive); silent frames outside
-  // a turn are dropped entirely.
-  const userTurnActiveRef = useRef(false)
-  const agentTurnActiveRef = useRef(false)
-  const sttEnabledRef = useRef(false)
-  sttEnabledRef.current = sttEnabled
-
-  const { data: sttConfig } = useQuery<{ available: boolean; provider?: string; model?: string; reason?: string }>({
-    queryKey: ['agent-stt-config', agentId],
-    queryFn: () => (agentId ? apiClient.getAgentSttConfig(agentId) : Promise.resolve({ available: false })),
-    enabled: !!agentId,
-    staleTime: 60_000,
-  })
-
-
-  const { data: s3Status } = useQuery({
-    queryKey: ['s3-status'],
-    queryFn: () => apiClient.getS3Status(),
-    staleTime: 60_000,
-  })
-
-  // ---- Logging helpers ------------------------------------------------
-
-  const log = useCallback((message: string, type: LogEntry['type'] = 'system') => {
-    setLogs((prev) => [...prev.slice(-500), { timestamp: new Date().toISOString(), message, type }])
-  }, [])
-
-  const pushWsMessage = useCallback((direction: 'sent' | 'received', parsed: ParsedWSMessage, size: number) => {
-    setWsMessages((prev) => [
-      ...prev.slice(-500),
-      { id: ++msgIdRef.current, direction, timestamp: new Date().toISOString(), parsed, size },
-    ])
-  }, [])
-
-  // ---- STT flush helpers (dual-channel, independent) ---------------------
-
-  const flushChannelBuffer = useCallback(
-    (channel: 'user' | 'agent') => {
-      const buf = channel === 'user' ? userAudioBufRef.current : agentAudioBufRef.current
-      if (buf.length === 0) return
-
-      const totalLen = buf.reduce((s, a) => s + a.length, 0)
-      if (totalLen < 800) {
-        if (channel === 'user') userAudioBufRef.current = []
-        else agentAudioBufRef.current = []
-        return
-      }
-
-      const merged = new Int16Array(totalLen)
-      let off = 0
-      for (const chunk of buf) { merged.set(chunk, off); off += chunk.length }
-      if (channel === 'user') {
-        userAudioBufRef.current = []
-        userTurnActiveRef.current = false
-      } else {
-        agentAudioBufRef.current = []
-        agentTurnActiveRef.current = false
-      }
-
-      if (!agentId) return
-
-      const wavBlob = encodeWav(merged, protocolRef.current.sampleRate)
-      setPendingTranscriptions((n) => n + 1)
-      const durationSec = (totalLen / protocolRef.current.sampleRate).toFixed(1)
-      log(`Transcribing ${channel} turn (${durationSec}s)...`, 'system')
-
-      apiClient.transcribeTurn(agentId, channel, wavBlob)
-        .then((res) => {
-          const text = (res.transcript || '').trim()
-          if (text) {
-            const entry: TranscriptEntry = {
-              role: channel === 'user' ? 'user' : 'agent',
-              content: text,
-              timestamp: new Date().toISOString(),
-            }
-            setTranscriptEntries((prev) => [...prev, entry])
-            log(`${channel === 'user' ? 'User' : 'Agent'} (STT): ${text}`, channel === 'user' ? 'user' : 'bot')
-          }
-        })
-        .catch((err: any) => {
-          log(`STT failed for ${channel}: ${err?.response?.data?.detail || err?.message || 'unknown error'}`, 'system')
-        })
-        .finally(() => setPendingTranscriptions((n) => Math.max(0, n - 1)))
-    },
-    [agentId, log],
-  )
-
-  const flushChannelRef = useRef(flushChannelBuffer)
-  flushChannelRef.current = flushChannelBuffer
-
-  const resetUserSilenceTimer = useCallback(() => {
-    if (userSilenceTimerRef.current) clearTimeout(userSilenceTimerRef.current)
-    userSilenceTimerRef.current = setTimeout(() => {
-      if (sttEnabledRef.current) flushChannelRef.current('user')
-    }, 2000)
-  }, [])
-
-  const resetAgentSilenceTimer = useCallback(() => {
-    if (agentSilenceTimerRef.current) clearTimeout(agentSilenceTimerRef.current)
-    agentSilenceTimerRef.current = setTimeout(() => {
-      if (sttEnabledRef.current) flushChannelRef.current('agent')
-    }, 1500)
-  }, [])
-
-  // auto-scroll panels
-  useEffect(() => {
-    logsEndRef.current?.scrollIntoView({ behavior: 'smooth' })
-  }, [logs])
-  useEffect(() => {
-    transcriptEndRef.current?.scrollIntoView({ behavior: 'smooth' })
-  }, [transcriptEntries])
-  useEffect(() => {
-    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
-  }, [wsMessages])
-
-  // ---- Audio playback -------------------------------------------------
-
-  const playAudioChunk = useCallback(
-    (b64: string) => {
-      const ctx = audioCtxRef.current
-      if (!ctx || !b64) return
-      try {
-        const samples = base64ToFloat32(b64)
-        if (samples.length === 0) return
-        const buf = ctx.createBuffer(1, samples.length, protocolRef.current.sampleRate)
-        buf.getChannelData(0).set(samples)
-        const src = ctx.createBufferSource()
-        src.buffer = buf
-        src.connect(ctx.destination)
-        if (recordDestRef.current) {
-          src.connect(recordDestRef.current)
-        }
-
-        const now = ctx.currentTime
-        if (playTimeRef.current < now) playTimeRef.current = now
-        src.start(playTimeRef.current)
-        playTimeRef.current += buf.duration
-
-        // STT: VAD-gated agent buffering. See mic pipeline for rationale.
-        if (sttEnabledRef.current) {
-          const energy = rmsFloat32(samples)
-          const voiced = energy > VAD_RMS_THRESHOLD
-          const pcm16 = float32ToInt16(samples)
-          if (voiced) {
-            if (!agentTurnActiveRef.current) {
-              agentTurnActiveRef.current = true
-              log('Agent turn started', 'system')
-            }
-            agentAudioBufRef.current.push(pcm16)
-            resetAgentSilenceTimer()
-          } else if (agentTurnActiveRef.current) {
-            agentAudioBufRef.current.push(pcm16)
-          }
-        }
-      } catch {
-        // ignore decode errors on individual chunks
-      }
-    },
-    [resetAgentSilenceTimer],
-  )
-
-  // ---- WebSocket message handler --------------------------------------
-
-  const handleWsMessage = useCallback(
-    (event: MessageEvent) => {
-      const data = event.data
-      const size = typeof data === 'string' ? data.length : (data as ArrayBuffer).byteLength
-      const parsed = protocolRef.current.parseMessage(data)
-
-      pushWsMessage('received', parsed, size)
-
-      if (parsed.responseMessage) {
-        wsRef.current?.send(parsed.responseMessage)
-      }
-
-      if (parsed.type === 'audio' && parsed.audioBase64) {
-        playAudioChunk(parsed.audioBase64)
-      }
-
-      if (parsed.type === 'transcript' && parsed.transcript) {
-        const entry = parsed.transcript
-        setTranscriptEntries((prev) => [...prev, entry])
-        log(`${entry.role === 'user' ? 'User' : 'Agent'}: ${entry.content}`, entry.role === 'user' ? 'user' : 'bot')
-      }
-
-      if (parsed.type === 'control') {
-        const t = parsed.raw?.type
-        if (t) log(`Control: ${t}`, 'system')
-      }
-    },
-    [log, pushWsMessage, playAudioChunk],
-  )
-
-  // ---- Mic capture + send pipeline -----------------------------------
-
-  const startMicCapture = useCallback(() => {
-    const ctx = audioCtxRef.current
-    const stream = micStreamRef.current
-    if (!ctx || !stream) return
-
-    const source = ctx.createMediaStreamSource(stream)
-    const processor = ctx.createScriptProcessor(4096, 1, 1)
-
-    const targetRate = protocolRef.current.sampleRate
-    const chunkSamples = Math.round(targetRate * protocolRef.current.sendIntervalMs / 1000)
-
-    processor.onaudioprocess = (e) => {
-      if (!wsRef.current || wsRef.current.readyState !== WebSocket.OPEN) return
-      const raw = e.inputBuffer.getChannelData(0)
-      const resampled = resampleFloat32(raw, ctx.sampleRate, targetRate)
-      const prev = accBufRef.current
-      const merged = new Float32Array(prev.length + resampled.length)
-      merged.set(prev)
-      merged.set(resampled, prev.length)
-
-      let offset = 0
-      while (offset + chunkSamples <= merged.length) {
-        const chunk = merged.slice(offset, offset + chunkSamples)
-        offset += chunkSamples
-        const pcm16 = float32ToInt16(chunk)
-        const b64 = int16ArrayToBase64(pcm16)
-        const frame = protocolRef.current.wrapAudioFrame(b64)
-        try {
-          wsRef.current.send(frame)
-        } catch {
-          break
-        }
-
-        // VAD-gated STT buffering. Only voiced frames start/continue a turn
-        // and reset the silence timer; silent frames outside a turn are
-        // dropped so the timer can actually expire and flush the turn.
-        if (sttEnabledRef.current) {
-          const energy = rmsInt16(pcm16)
-          const voiced = energy > VAD_RMS_THRESHOLD
-          if (voiced) {
-            if (!userTurnActiveRef.current) {
-              userTurnActiveRef.current = true
-              log('User turn started', 'system')
-            }
-            userAudioBufRef.current.push(pcm16)
-            resetUserSilenceTimer()
-          } else if (userTurnActiveRef.current) {
-            // keep natural pauses inside an active turn
-            userAudioBufRef.current.push(pcm16)
-          }
-        }
-      }
-      accBufRef.current = merged.slice(offset)
-    }
-
-    // Connect processor through a silent gain so it fires but doesn't
-    // route mic audio to speakers.
-    const silentGain = ctx.createGain()
-    silentGain.gain.value = 0
-    source.connect(processor)
-    processor.connect(silentGain)
-    silentGain.connect(ctx.destination)
-
-    // Route mic into the recording destination so both sides are captured
-    if (recordDestRef.current) {
-      source.connect(recordDestRef.current)
-    }
-
-    scriptNodeRef.current = processor
-  }, [])
-
-  // ---- MediaRecorder for session recording ----------------------------
-
-  const startRecording = useCallback(() => {
-    if (mediaRecorderRef.current || typeof MediaRecorder === 'undefined') return
-    // Prefer the mixed destination (mic + bot audio). Fall back to mic-only.
-    const stream = recordDestRef.current?.stream || micStreamRef.current
-    if (!stream) return
-    try {
-      const mimeType = MediaRecorder.isTypeSupported('audio/webm;codecs=opus')
-        ? 'audio/webm;codecs=opus'
-        : 'audio/webm'
-      const recorder = new MediaRecorder(stream, { mimeType })
-      recordedChunksRef.current = []
-      recorder.ondataavailable = (ev: BlobEvent) => {
-        if (ev.data.size > 0) recordedChunksRef.current.push(ev.data)
-      }
-      recorder.onstop = () => {
-        if (recordedChunksRef.current.length > 0) {
-          setRecordedBlob(new Blob(recordedChunksRef.current, { type: recorder.mimeType || 'audio/webm' }))
-        }
-        recordingPendingRef.current = false
-      }
-      recorder.start(1000)
-      mediaRecorderRef.current = recorder
-      recordingPendingRef.current = true
-      const isMixed = !!recordDestRef.current
-      log(`Recording started (${isMixed ? 'mic + agent audio' : 'mic only'})`, 'system')
-    } catch (err: any) {
-      log(`Recording failed: ${err?.message || err}`, 'system')
-    }
-  }, [log])
-
-  const stopRecording = useCallback(() => {
-    if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {
-      mediaRecorderRef.current.stop()
-    }
-    mediaRecorderRef.current = null
-  }, [])
-
-  // ---- Connect / disconnect ------------------------------------------
-
-  const cleanupConnection = useCallback(
-    (userInitiated: boolean) => {
-      // Flush BOTH STT channel buffers on disconnect
-      if (userSilenceTimerRef.current) { clearTimeout(userSilenceTimerRef.current); userSilenceTimerRef.current = null }
-      if (agentSilenceTimerRef.current) { clearTimeout(agentSilenceTimerRef.current); agentSilenceTimerRef.current = null }
-      if (sttEnabledRef.current) {
-        flushChannelBuffer('user')
-        flushChannelBuffer('agent')
-      }
-      userAudioBufRef.current = []
-      agentAudioBufRef.current = []
-      userTurnActiveRef.current = false
-      agentTurnActiveRef.current = false
-
-      stopRecording()
-      if (scriptNodeRef.current) {
-        scriptNodeRef.current.disconnect()
-        scriptNodeRef.current = null
-      }
-      if (wsRef.current) {
-        const ws = wsRef.current
-        wsRef.current = null
-        ws.onmessage = null
-        ws.onclose = null
-        ws.onerror = null
-        if (ws.readyState === WebSocket.OPEN || ws.readyState === WebSocket.CONNECTING) {
-          ws.close()
-        }
-      }
-      recordDestRef.current = null
-      if (audioCtxRef.current && audioCtxRef.current.state !== 'closed') {
-        audioCtxRef.current.close().catch(() => {})
-        audioCtxRef.current = null
-      }
-      if (micStreamRef.current) {
-        micStreamRef.current.getTracks().forEach((t) => t.stop())
-        micStreamRef.current = null
-      }
-      setIsConnected(false)
-      setIsConnecting(false)
-      setSessionEndedAt(new Date().toISOString())
-      // Ask the auto-save effect to persist the session once all pending
-      // work settles (STT flushes + recorder onstop). If there are no
-      // transcripts, the effect will no-op.
-      autoSaveRequestedRef.current = true
-      if (userInitiated) log('Disconnected')
-    },
-    [stopRecording, log, flushChannelBuffer],
-  )
-
-  const disconnect = useCallback(() => {
-    cleanupConnection(true)
-  }, [cleanupConnection])
-
-  const connect = useCallback(async () => {
-    if (connectingRef.current) return
-    connectingRef.current = true
-    setIsConnecting(true)
-    setError(null)
-    log('Connecting...')
-
-    // Reset previous session state (keep the savedSessions list so users can
-    // still see and act on sessions saved earlier in this component's lifetime)
-    setCurrentSavedId(null)
-    autoSaveRequestedRef.current = false
-    recordingPendingRef.current = false
-    setRecordedBlob(null)
-    setTranscriptEntries([])
-    setWsMessages([])
-    setSessionStartedAt(new Date().toISOString())
-    setSessionEndedAt(null)
-    accBufRef.current = new Float32Array(0)
-    playTimeRef.current = 0
-    userAudioBufRef.current = []
-    agentAudioBufRef.current = []
-    userTurnActiveRef.current = false
-    agentTurnActiveRef.current = false
-    if (userSilenceTimerRef.current) { clearTimeout(userSilenceTimerRef.current); userSilenceTimerRef.current = null }
-    if (agentSilenceTimerRef.current) { clearTimeout(agentSilenceTimerRef.current); agentSilenceTimerRef.current = null }
-
-    try {
-      // Mic
-      try {
-        micStreamRef.current = await navigator.mediaDevices.getUserMedia({ audio: true })
-        log('Microphone acquired')
-      } catch {
-        micStreamRef.current = null
-        log('Microphone unavailable — connecting without mic', 'system')
-      }
-
-      // AudioContext + recording destination (mixes mic + bot audio)
-      const ctx = new AudioContext({ sampleRate: 48000 })
-      audioCtxRef.current = ctx
-      if (ctx.state === 'suspended') await ctx.resume()
-      recordDestRef.current = ctx.createMediaStreamDestination()
-
-      // WebSocket
-      log(`Opening WebSocket to ${websocketUrl}`)
-      log(`Protocol: ${protocolRef.current.name} (${protocolRef.current.sampleRate / 1000} kHz, ${protocolRef.current.sendIntervalMs} ms)`)
-      const ws = new WebSocket(websocketUrl)
-      ws.binaryType = 'arraybuffer'
-      wsRef.current = ws
-
-      await new Promise<void>((resolve, reject) => {
-        let settled = false
-        const settle = (fn: () => void) => {
-          if (!settled) { settled = true; fn() }
-        }
-        const timer = setTimeout(
-          () => settle(() => reject(new Error(
-            `Connection timed out after 15 s. The server at ${new URL(websocketUrl).host} did not respond to the WebSocket upgrade. ` +
-            'This often means the agent restricts which origins can connect. ' +
-            'Try creating your own agent (with open allowed-origins) or use a backend proxy.'
-          ))),
-          15000,
-        )
-        ws.onopen = () => { clearTimeout(timer); settle(() => resolve()) }
-        ws.onerror = () => {
-          clearTimeout(timer)
-          settle(() => reject(new Error(
-            `WebSocket connection to ${new URL(websocketUrl).host} failed. ` +
-            'Check the URL, your network, and whether the agent allows connections from this origin.'
-          )))
-        }
-        ws.onclose = (ev) => {
-          clearTimeout(timer)
-          settle(() => reject(new Error(
-            `WebSocket closed before connecting (code ${ev.code}${ev.reason ? ': ' + ev.reason : ''}). ` +
-            'The server may have rejected the connection — public agents often restrict allowed origins.'
-          )))
-        }
-      })
-
-      log('WebSocket upgrade successful (101)')
-
-      // Send init messages
-      const initMsgs = protocolRef.current.buildInitMessages?.() || []
-      for (const msg of initMsgs) {
-        ws.send(msg)
-        const preview = msg.length > 140 ? msg.slice(0, 140) + '…' : msg
-        log(`Sent init: ${preview}`)
-      }
-      if (initMsgs.length === 0) {
-        log('No init messages for this protocol — waiting for server messages', 'system')
-      }
-
-      ws.onmessage = handleWsMessage
-      ws.onclose = (ev) => {
-        log(`WebSocket closed (code ${ev.code}${ev.reason ? `, ${ev.reason}` : ''})`)
-        cleanupConnection(false)
-      }
-      ws.onerror = () => {
-        log('WebSocket error')
-      }
-
-      // Start mic capture + recording
-      if (micStreamRef.current) {
-        startMicCapture()
-      }
-      startRecording()
-
-      setIsConnected(true)
-      setIsConnecting(false)
-      log('Ready — streaming mic audio', 'system')
-    } catch (err: any) {
-      const msg = err?.message || 'Failed to connect'
-      setError(msg)
-      log(`Error: ${msg}`)
-      cleanupConnection(false)
-    } finally {
-      connectingRef.current = false
-      setIsConnecting(false)
-    }
-  }, [websocketUrl, handleWsMessage, log, startMicCapture, startRecording, cleanupConnection])
-
-  // Cleanup on unmount
-  useEffect(() => {
-    return () => {
-      if (wsRef.current) {
-        wsRef.current.onmessage = null
-        wsRef.current.onclose = null
-        wsRef.current.close()
-        wsRef.current = null
-      }
-      if (scriptNodeRef.current) {
-        scriptNodeRef.current.disconnect()
-        scriptNodeRef.current = null
-      }
-      if (audioCtxRef.current && audioCtxRef.current.state !== 'closed') {
-        audioCtxRef.current.close().catch(() => {})
-      }
-      if (micStreamRef.current) {
-        micStreamRef.current.getTracks().forEach((t) => t.stop())
-      }
-      if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {
-        mediaRecorderRef.current.stop()
-      }
-    }
-  }, [])
-
-  // ---- Save / Evaluate ------------------------------------------------
-
-  const handleSaveSession = useCallback(async () => {
-    if (!agentId || !websocketUrl || transcriptEntries.length === 0) return
-    try {
-      setIsSavingSession(true)
-      setError(null)
-      const audioFile = recordedBlob
-        ? new File([recordedBlob], `custom-ws-${Date.now()}.webm`, { type: recordedBlob.type || 'audio/webm' })
-        : undefined
-      const res = await apiClient.createCustomWebsocketSession({
-        agent_id: agentId,
-        websocket_url: websocketUrl,
-        transcript_entries: transcriptEntries,
-        started_at: sessionStartedAt || undefined,
-        ended_at: sessionEndedAt || new Date().toISOString(),
-        audio_file: audioFile,
-      })
-      const newSession: SavedSession = {
-        callShortId: res.call_short_id,
-        savedAt: new Date().toISOString(),
-        transcriptCount: transcriptEntries.length,
-        hasRecording: !!recordedBlob,
-        sessionStartedAt: sessionStartedAt,
-        sessionEndedAt: sessionEndedAt || new Date().toISOString(),
-        evalStatus: 'idle',
-      }
-      setSavedSessions((prev) => [newSession, ...prev])
-      setCurrentSavedId(res.call_short_id)
-      log(`Session saved as ${res.call_short_id}`, 'system')
-      onSessionSaved?.()
-    } catch (err: any) {
-      const msg = err?.response?.data?.detail || err?.message || 'Failed to save session'
-      setError(msg)
-      log(`Save failed: ${msg}`, 'system')
-    } finally {
-      setIsSavingSession(false)
-    }
-  }, [
-    agentId,
-    websocketUrl,
-    transcriptEntries,
-    recordedBlob,
-    sessionStartedAt,
-    sessionEndedAt,
-    log,
-    onSessionSaved,
-  ])
-
-  // Auto-save the session once disconnect + all post-disconnect async work
-  // have settled. Guarded so it fires at most once per connection.
-  useEffect(() => {
-    if (!autoSaveRequestedRef.current) return
-    if (isConnected || isConnecting) return
-    if (pendingTranscriptions > 0) return
-    if (recordingPendingRef.current) return
-    if (isSavingSession) return
-    if (currentSavedId) return
-    if (!agentId) return
-    if (transcriptEntries.length === 0) {
-      // Nothing worth saving — clear the request so we don't retrigger.
-      autoSaveRequestedRef.current = false
-      return
-    }
-    autoSaveRequestedRef.current = false
-    handleSaveSession()
-  }, [
-    isConnected,
-    isConnecting,
-    pendingTranscriptions,
-    recordedBlob,
-    transcriptEntries,
-    isSavingSession,
-    currentSavedId,
-    agentId,
-    handleSaveSession,
-  ])
-
-  const updateSession = (callShortId: string, patch: Partial<SavedSession>) => {
-    setSavedSessions((prev) =>
-      prev.map((s) => (s.callShortId === callShortId ? { ...s, ...patch } : s)),
-    )
-  }
-
-  const handleEvaluateSession = async (callShortId: string) => {
-    updateSession(callShortId, { evalStatus: 'evaluating', evalError: undefined })
-    try {
-      await apiClient.evaluateCustomWebsocketSession(callShortId)
-      updateSession(callShortId, { evalStatus: 'queued' })
-      log(`Evaluation queued for ${callShortId}`, 'system')
-      onSessionSaved?.()
-    } catch (err: any) {
-      const msg = err?.response?.data?.detail || err?.message || 'Evaluation failed'
-      updateSession(callShortId, { evalStatus: 'failed', evalError: msg })
-      log(`Evaluation failed: ${msg}`, 'system')
-    }
-  }
-
-  const handleOpenSession = (callShortId: string) => {
-    navigate(`/playground/call-recordings/${callShortId}`)
-  }
-
-  const handleRemoveSession = (callShortId: string) => {
-    setSavedSessions((prev) => prev.filter((s) => s.callShortId !== callShortId))
-    if (currentSavedId === callShortId) setCurrentSavedId(null)
-  }
-
-  // ---- Filtered messages for display ----------------------------------
-
-  const visibleMessages = showAudioMsgs ? wsMessages : wsMessages.filter((m) => m.parsed.type !== 'audio')
-
-  // ---- Render ---------------------------------------------------------
-
-  const panelClasses = (panel: string) =>
-    `px-3 py-2 text-xs font-medium cursor-pointer transition-colors ${
-      activePanel === panel
-        ? 'border-b-2 border-yellow-500 text-yellow-700'
-        : 'text-gray-500 hover:text-gray-700'
-    }`
-
-  return (
-    <div className="space-y-4">
-      <div className="bg-white shadow rounded-lg p-5">
-        {/* Header */}
-        <div className="mb-3">
-          <h3 className="text-sm font-semibold text-gray-900">
-            {protocol.name}
-          </h3>
-          <p className="text-xs text-gray-500 mt-0.5 font-mono truncate">{websocketUrl}</p>
-          <p className="text-xs text-gray-400 mt-0.5">{protocol.description}</p>
-        </div>
-
-        {/* Warnings */}
-        {s3Status && !s3Status.enabled && (
-          <div className="flex items-start gap-2 bg-amber-50 border border-amber-200 rounded-lg p-3 mb-3 text-xs">
-            <AlertTriangle className="h-4 w-4 text-amber-500 flex-shrink-0 mt-0.5" />
-            <div>
-              <p className="font-medium text-amber-800">Storage not configured</p>
-              <p className="text-amber-700">Audio recordings will not be saved. Transcripts will still be captured.</p>
-            </div>
-          </div>
-        )}
-
-        {error && (
-          <div className="bg-red-50 border border-red-200 rounded-lg p-3 mb-3">
-            <p className="text-xs text-red-800">{error}</p>
-          </div>
-        )}
-
-        {/* STT toggle */}
-        {agentId && sttConfig && sttConfig.available && (
-          <div className="flex items-center justify-between bg-gray-50 border border-gray-200 rounded-lg p-3 mb-3">
-            <div className="flex items-center gap-2">
-              <label className="relative inline-flex items-center cursor-pointer">
-                <input
-                  type="checkbox"
-                  checked={sttEnabled}
-                  onChange={(e) => setSttEnabled(e.target.checked)}
-                  className="sr-only peer"
-                />
-                <div className="w-9 h-5 bg-gray-300 peer-focus:outline-none rounded-full peer peer-checked:after:translate-x-full peer-checked:after:border-white after:content-[''] after:absolute after:top-[2px] after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-4 after:w-4 after:transition-all peer-checked:bg-indigo-600 peer-disabled:opacity-50" />
-              </label>
-              <span className="text-sm font-medium text-gray-700">Live Transcription</span>
-              {sttConfig.provider && (
-                <span className="text-[10px] px-1.5 py-0.5 bg-indigo-100 text-indigo-700 rounded font-mono">
-                  {sttConfig.provider}{sttConfig.model ? ` · ${sttConfig.model}` : ''}
-                </span>
-              )}
-              {pendingTranscriptions > 0 && (
-                <Loader className="h-3.5 w-3.5 text-indigo-500 animate-spin" />
-              )}
-            </div>
-            {sttEnabled && pendingTranscriptions > 0 && (
-              <span className="text-[10px] text-gray-400">Transcribing...</span>
-            )}
-          </div>
-        )}
-        {!agentId && (
-          <div className="text-xs text-amber-500 mb-3 px-1">
-            Select an agent to enable live transcription (STT).
-          </div>
-        )}
-        {agentId && sttConfig && !sttConfig.available && (
-          <div className="text-xs text-amber-500 mb-3 px-1">
-            Live transcription unavailable: {sttConfig.reason || 'No STT provider configured in voice bundle'}
-          </div>
-        )}
-
-        {/* Status + controls */}
-        <div className="flex items-center justify-between mb-4">
-          <div className="flex items-center gap-2">
-            {isConnected ? (
-              <>
-                <div className="w-2.5 h-2.5 bg-green-500 rounded-full animate-pulse" />
-                <span className="text-sm font-medium text-gray-700">Connected</span>
-                <span className="text-xs text-gray-400">
-                  {protocol.sampleRate / 1000} kHz &middot; {protocol.sendIntervalMs} ms frames
-                </span>
-                {sttEnabled && (
-                  <span className="text-[10px] px-1.5 py-0.5 bg-green-100 text-green-700 rounded">STT ON</span>
-                )}
-              </>
-            ) : (
-              <span className="text-sm text-gray-500">{isConnecting ? 'Connecting...' : 'Disconnected'}</span>
-            )}
-          </div>
-          <div className="flex gap-2">
-            {!isConnected ? (
-              <Button
-                onClick={connect}
-                isLoading={isConnecting}
-                leftIcon={isConnecting ? <Loader className="h-4 w-4 animate-spin" /> : <Mic className="h-4 w-4" />}
-                disabled={isConnecting}
-              >
-                Connect
-              </Button>
-            ) : (
-              <Button onClick={disconnect} variant="danger" leftIcon={<MicOff className="h-4 w-4" />}>
-                Disconnect
-              </Button>
-            )}
-          </div>
-        </div>
-
-        {/* Panel tabs */}
-        <div className="flex border-b border-gray-200 mb-2">
-          <button className={panelClasses('transcript')} onClick={() => setActivePanel('transcript')}>
-            Transcript {transcriptEntries.length > 0 && `(${transcriptEntries.length})`}
-          </button>
-          <button className={panelClasses('messages')} onClick={() => setActivePanel('messages')}>
-            Messages {wsMessages.length > 0 && `(${wsMessages.length})`}
-          </button>
-          <button className={panelClasses('log')} onClick={() => setActivePanel('log')}>
-            Debug Log
-          </button>
-        </div>
-
-        {/* Transcript panel */}
-        {activePanel === 'transcript' && (
-          <div className="h-72 overflow-y-auto bg-gray-50 rounded border border-gray-200 p-4">
-            {transcriptEntries.length === 0 ? (
-              <p className="text-gray-400 text-center text-xs py-12">
-                No transcript yet. Connect and start speaking.
-              </p>
-            ) : (
-              <div className="space-y-3">
-                {transcriptEntries.map((e, i) => {
-                  const ts = new Date(e.timestamp)
-                  const elapsed = sessionStartedAt
-                    ? Math.max(0, Math.round((ts.getTime() - new Date(sessionStartedAt).getTime()) / 1000))
-                    : null
-                  const elapsedStr = elapsed !== null
-                    ? `${Math.floor(elapsed / 60)}:${String(elapsed % 60).padStart(2, '0')}`
-                    : null
-                  return (
-                    <div key={i} className={`flex ${e.role === 'user' ? 'justify-end' : 'justify-start'}`}>
-                      <div className={`max-w-[80%] rounded-2xl px-4 py-2.5 ${
-                        e.role === 'user'
-                          ? 'bg-indigo-600 text-white rounded-br-none'
-                          : 'bg-white border border-gray-200 text-gray-800 rounded-bl-none'
-                      }`}>
-                        <div className={`flex items-center gap-2 mb-0.5 ${
-                          e.role === 'user' ? 'opacity-70' : ''
-                        }`}>
-                          <span className={`text-[10px] font-semibold uppercase tracking-wider ${
-                            e.role === 'user' ? '' : 'text-gray-400'
-                          }`}>
-                            {e.role === 'user' ? 'You' : 'Agent'}
-                          </span>
-                          <span className={`text-[10px] ${
-                            e.role === 'user' ? 'opacity-70' : 'text-gray-300'
-                          }`}>
-                            {elapsedStr && <span className="mr-1">[{elapsedStr}]</span>}
-                            {ts.toLocaleTimeString()}
-                          </span>
-                        </div>
-                        <p className="text-sm leading-relaxed">{e.content}</p>
-                      </div>
-                    </div>
-                  )
-                })}
-                <div ref={transcriptEndRef} />
-              </div>
-            )}
-          </div>
-        )}
-
-        {/* Messages panel */}
-        {activePanel === 'messages' && (
-          <div>
-            <div className="flex items-center gap-2 mb-1">
-              <button
-                className="flex items-center gap-1 text-xs text-gray-500 hover:text-gray-700"
-                onClick={() => setShowAudioMsgs((v) => !v)}
-              >
-                <Filter className="h-3 w-3" />
-                {showAudioMsgs ? 'Hide audio frames' : 'Show audio frames'}
-              </button>
-              <span className="text-xs text-gray-400">
-                {visibleMessages.length} / {wsMessages.length} messages
-              </span>
-            </div>
-            <div className="h-72 overflow-y-auto bg-gray-50 rounded border border-gray-200 p-2 font-mono text-xs">
-              {visibleMessages.length === 0 ? (
-                <p className="text-gray-400 text-center py-12">No messages yet.</p>
-              ) : (
-                <div className="space-y-1">
-                  {visibleMessages.map((m) => (
-                    <div key={m.id} className="flex gap-1.5 items-start">
-                      {m.direction === 'sent' ? (
-                        <ArrowUp className="h-3 w-3 text-blue-400 flex-shrink-0 mt-0.5" />
-                      ) : (
-                        <ArrowDown className="h-3 w-3 text-green-400 flex-shrink-0 mt-0.5" />
-                      )}
-                      <span className="text-gray-400 flex-shrink-0">
-                        {new Date(m.timestamp).toLocaleTimeString()}
-                      </span>
-                      <span
-                        className={`px-1 rounded text-[10px] flex-shrink-0 ${
-                          m.parsed.type === 'audio'
-                            ? 'bg-purple-100 text-purple-700'
-                            : m.parsed.type === 'transcript'
-                              ? 'bg-green-100 text-green-700'
-                              : m.parsed.type === 'ping'
-                                ? 'bg-yellow-100 text-yellow-700'
-                                : m.parsed.type === 'control'
-                                  ? 'bg-blue-100 text-blue-700'
-                                  : 'bg-gray-100 text-gray-600'
-                        }`}
-                      >
-                        {m.parsed.type}
-                      </span>
-                      <span className="text-gray-600 break-all">
-                        {m.parsed.type === 'audio'
-                          ? `[${m.size} bytes]`
-                          : typeof m.parsed.raw === 'string'
-                            ? m.parsed.raw.slice(0, 200)
-                            : JSON.stringify(m.parsed.raw).slice(0, 200)}
-                      </span>
-                    </div>
-                  ))}
-                  <div ref={messagesEndRef} />
-                </div>
-              )}
-            </div>
-          </div>
-        )}
-
-        {/* Debug log panel */}
-        {activePanel === 'log' && (
-          <div className="h-72 overflow-y-auto bg-gray-50 rounded border border-gray-200 p-3 font-mono text-xs">
-            {logs.length === 0 ? (
-              <p className="text-gray-400 text-center py-12">No logs yet. Connect to start.</p>
-            ) : (
-              <>
-                {logs.map((entry, idx) => (
-                  <div
-                    key={idx}
-                    className={`mb-0.5 ${
-                      entry.type === 'user'
-                        ? 'text-blue-600'
-                        : entry.type === 'bot'
-                          ? 'text-green-600'
-                          : 'text-gray-600'
-                    }`}
-                  >
-                    <span className="text-gray-400">{new Date(entry.timestamp).toLocaleTimeString()}</span>{' '}
-                    {entry.message}
-                  </div>
-                ))}
-                <div ref={logsEndRef} />
-              </>
-            )}
-          </div>
-        )}
-
-        {/* Saved sessions */}
-        {(savedSessions.length > 0 || (!isConnected && transcriptEntries.length > 0)) && (
-          <div className="mt-4 rounded-lg border border-gray-200 p-4">
-            <div className="flex items-center justify-between gap-3">
-              <div className="min-w-0">
-                <h3 className="text-sm font-semibold text-gray-900 flex items-center gap-1.5">
-                  <Save className="h-4 w-4 text-indigo-600" />
-                  Saved Test Sessions
-                  {savedSessions.length > 0 && (
-                    <span className="text-[10px] font-medium text-gray-500 bg-gray-100 rounded-full px-2 py-0.5">
-                      {savedSessions.length}
-                    </span>
-                  )}
-                </h3>
-                <p className="mt-1 text-xs text-gray-600">
-                  Sessions are saved automatically when you disconnect.
-                </p>
-              </div>
-              {/* Inline auto-save status (replaces the old manual button) */}
-              {!isConnected && transcriptEntries.length > 0 && (
-                <div className="flex-shrink-0">
-                  {isSavingSession ? (
-                    <span className="inline-flex items-center gap-1.5 text-xs text-indigo-700 bg-indigo-50 border border-indigo-100 rounded-full px-2.5 py-1">
-                      <Loader className="h-3 w-3 animate-spin" />
-                      Saving session…
-                    </span>
-                  ) : currentSavedId ? (
-                    <span className="inline-flex items-center gap-1.5 text-xs text-green-700 bg-green-50 border border-green-100 rounded-full px-2.5 py-1">
-                      <CheckCircle className="h-3 w-3" />
-                      Session saved
-                    </span>
-                  ) : !agentId ? (
-                    <span className="inline-flex items-center gap-1.5 text-xs text-amber-700 bg-amber-50 border border-amber-100 rounded-full px-2.5 py-1">
-                      <AlertTriangle className="h-3 w-3" />
-                      Select an agent to enable auto-save
-                    </span>
-                  ) : pendingTranscriptions > 0 ? (
-                    <span className="inline-flex items-center gap-1.5 text-xs text-gray-600 bg-gray-50 border border-gray-200 rounded-full px-2.5 py-1">
-                      <Loader className="h-3 w-3 animate-spin" />
-                      Finishing transcription…
-                    </span>
-                  ) : null}
-                </div>
-              )}
-            </div>
-
-
-            {savedSessions.length === 0 ? (
-              <div className="mt-3 rounded-md border border-dashed border-gray-200 bg-gray-50 px-3 py-6 text-center text-xs text-gray-500">
-                No saved sessions yet. End the call and click <span className="font-medium">Save Session</span> to save this transcript.
-              </div>
-            ) : (
-              <ul className="mt-3 divide-y divide-gray-100 rounded-md border border-gray-200 bg-white overflow-hidden">
-                {savedSessions.map((s) => (
-                  <li key={s.callShortId} className="p-3 flex flex-col sm:flex-row sm:items-center sm:justify-between gap-3 hover:bg-gray-50 transition-colors">
-                    <div className="min-w-0 flex-1">
-                      <div className="flex items-center gap-2 flex-wrap">
-                        <button
-                          type="button"
-                          onClick={() => handleOpenSession(s.callShortId)}
-                          className="font-mono text-sm font-semibold text-indigo-600 hover:text-indigo-700 hover:underline truncate"
-                          title="Open call details"
-                        >
-                          {s.callShortId}
-                        </button>
-                        {s.callShortId === currentSavedId && (
-                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-green-100 text-green-700 font-medium">
-                            just saved
-                          </span>
-                        )}
-                        {s.evalStatus === 'queued' && (
-                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-blue-100 text-blue-700 font-medium">
-                            eval queued
-                          </span>
-                        )}
-                        {s.evalStatus === 'evaluating' && (
-                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-blue-100 text-blue-700 font-medium inline-flex items-center gap-1">
-                            <Loader className="h-2.5 w-2.5 animate-spin" />
-                            queuing
-                          </span>
-                        )}
-                        {s.evalStatus === 'failed' && (
-                          <span
-                            className="text-[10px] px-1.5 py-0.5 rounded-full bg-red-100 text-red-700 font-medium"
-                            title={s.evalError || 'Evaluation failed'}
-                          >
-                            eval failed
-                          </span>
-                        )}
-                        {s.hasRecording && (
-                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-purple-100 text-purple-700 font-medium">
-                            with recording
-                          </span>
-                        )}
-                      </div>
-                      <div className="mt-1 flex items-center gap-3 text-[11px] text-gray-500 flex-wrap">
-                        <span className="inline-flex items-center gap-1">
-                          <Clock className="h-3 w-3" />
-                          {new Date(s.savedAt).toLocaleTimeString()}
-                        </span>
-                        <span>{s.transcriptCount} turn{s.transcriptCount === 1 ? '' : 's'}</span>
-                      </div>
-                    </div>
-
-                    <div className="flex items-center gap-2 flex-shrink-0">
-                      {s.evalStatus !== 'queued' && (
-                        <Button
-                          variant="outline"
-                          size="sm"
-                          onClick={() => handleEvaluateSession(s.callShortId)}
-                          isLoading={s.evalStatus === 'evaluating'}
-                          disabled={s.evalStatus === 'evaluating'}
-                        >
-                          {s.evalStatus === 'failed' ? 'Retry Evaluation' : 'Run Evaluation'}
-                        </Button>
-                      )}
-                      <Button
-                        variant="outline"
-                        size="sm"
-                        onClick={() => handleOpenSession(s.callShortId)}
-                        leftIcon={<ExternalLink className="h-3.5 w-3.5" />}
-                      >
-                        Open
-                      </Button>
-                      <button
-                        type="button"
-                        onClick={() => handleRemoveSession(s.callShortId)}
-                        className="p-1.5 text-gray-400 hover:text-red-500 hover:bg-red-50 rounded transition-colors"
-                        title="Remove from list"
-                        aria-label="Remove from list"
-                      >
-                        <Trash2 className="h-3.5 w-3.5" />
-                      </button>
-                    </div>
-                  </li>
-                ))}
-              </ul>
-            )}
-          </div>
-        )}
-      </div>
-    </div>
-  )
-}
+import { useState, useRef, useEffect, useCallback, type MutableRefObject } from 'react'
+import {
+  Mic,
+  MicOff,
+  Loader,
+  AlertTriangle,
+  ArrowUp,
+  ArrowDown,
+  Filter,
+  ExternalLink,
+  CheckCircle,
+  Clock,
+  Save,
+  Trash2,
+} from 'lucide-react'
+import { useQuery } from '@tanstack/react-query'
+import { useNavigate } from 'react-router-dom'
+import Button from './Button'
+import { apiClient } from '../lib/api'
+import type { WSProtocolConfig, TranscriptEntry, ParsedWSMessage } from '../lib/wsProtocols'
+
+// ----- Audio helpers ---------------------------------------------------
+
+function resampleFloat32(input: Float32Array, fromRate: number, toRate: number): Float32Array {
+  if (fromRate === toRate) return input
+  const ratio = fromRate / toRate
+  const len = Math.round(input.length / ratio)
+  const out = new Float32Array(len)
+  for (let i = 0; i < len; i++) {
+    const srcIdx = i * ratio
+    const lo = Math.floor(srcIdx)
+    const hi = Math.min(lo + 1, input.length - 1)
+    const t = srcIdx - lo
+    out[i] = input[lo] * (1 - t) + input[hi] * t
+  }
+  return out
+}
+
+function float32ToInt16(input: Float32Array): Int16Array {
+  const out = new Int16Array(input.length)
+  for (let i = 0; i < input.length; i++) {
+    const s = Math.max(-1, Math.min(1, input[i]))
+    out[i] = s < 0 ? s * 0x8000 : s * 0x7fff
+  }
+  return out
+}
+
+function int16ArrayToBase64(int16: Int16Array): string {
+  const bytes = new Uint8Array(int16.buffer, int16.byteOffset, int16.byteLength)
+  let bin = ''
+  for (let i = 0; i < bytes.length; i++) bin += String.fromCharCode(bytes[i])
+  return btoa(bin)
+}
+
+// RMS in normalized [0,1] range. Used as a cheap voice-activity detector so
+// we don't reset the silence timer on every ambient-noise frame.
+function rmsInt16(samples: Int16Array): number {
+  if (samples.length === 0) return 0
+  let sum = 0
+  for (let i = 0; i < samples.length; i++) {
+    const v = samples[i] / 32768
+    sum += v * v
+  }
+  return Math.sqrt(sum / samples.length)
+}
+
+function rmsFloat32(samples: Float32Array): number {
+  if (samples.length === 0) return 0
+  let sum = 0
+  for (let i = 0; i < samples.length; i++) sum += samples[i] * samples[i]
+  return Math.sqrt(sum / samples.length)
+}
+
+// Threshold roughly separates voice from ambient / mic self-noise. Tuned for
+// a typical laptop mic; voiced frames usually sit >= 0.03, idle rooms <= 0.01.
+const VAD_RMS_THRESHOLD = 0.015
+
+function base64ToFloat32(b64: string): Float32Array {
+  const bin = atob(b64)
+  const bytes = new Uint8Array(bin.length)
+  for (let i = 0; i < bin.length; i++) bytes[i] = bin.charCodeAt(i)
+  const int16 = new Int16Array(bytes.buffer)
+  const out = new Float32Array(int16.length)
+  for (let i = 0; i < int16.length; i++) out[i] = int16[i] / 32768
+  return out
+}
+
+function encodeWav(samples: Int16Array, sampleRate: number): Blob {
+  const byteRate = sampleRate * 2
+  const blockAlign = 2
+  const dataSize = samples.byteLength
+  const buf = new ArrayBuffer(44 + dataSize)
+  const view = new DataView(buf)
+  const writeStr = (off: number, s: string) => { for (let i = 0; i < s.length; i++) view.setUint8(off + i, s.charCodeAt(i)) }
+  writeStr(0, 'RIFF')
+  view.setUint32(4, 36 + dataSize, true)
+  writeStr(8, 'WAVE')
+  writeStr(12, 'fmt ')
+  view.setUint32(16, 16, true)
+  view.setUint16(20, 1, true)
+  view.setUint16(22, 1, true)
+  view.setUint32(24, sampleRate, true)
+  view.setUint32(28, byteRate, true)
+  view.setUint16(32, blockAlign, true)
+  view.setUint16(34, 16, true)
+  writeStr(36, 'data')
+  view.setUint32(40, dataSize, true)
+  new Uint8Array(buf, 44).set(new Uint8Array(samples.buffer, samples.byteOffset, samples.byteLength))
+  return new Blob([buf], { type: 'audio/wav' })
+}
+
+// ----- Types -----------------------------------------------------------
+
+interface WSMessageLog {
+  id: number
+  direction: 'sent' | 'received'
+  timestamp: string
+  parsed: ParsedWSMessage
+  size: number
+}
+
+type LogEntry = { timestamp: string; message: string; type: 'user' | 'bot' | 'system' }
+
+type EvalStatus = 'idle' | 'queued' | 'evaluating' | 'failed'
+
+interface SavedSession {
+  callShortId: string
+  savedAt: string
+  transcriptCount: number
+  hasRecording: boolean
+  sessionStartedAt?: string | null
+  sessionEndedAt?: string | null
+  evalStatus: EvalStatus
+  evalError?: string
+}
+
+// ----- Component -------------------------------------------------------
+
+interface GenericVoiceWSClientProps {
+  websocketUrl: string
+  protocol: WSProtocolConfig
+  agentId?: string
+  onSessionSaved?: () => void
+}
+
+export default function GenericVoiceWSClient({
+  websocketUrl,
+  protocol,
+  agentId,
+  onSessionSaved,
+}: GenericVoiceWSClientProps) {
+  // ---- UI state -------------------------------------------------------
+  const [isConnected, setIsConnected] = useState(false)
+  const [isConnecting, setIsConnecting] = useState(false)
+  const [error, setError] = useState<string | null>(null)
+  const [logs, setLogs] = useState<LogEntry[]>([])
+  const [transcriptEntries, setTranscriptEntries] = useState<TranscriptEntry[]>([])
+  const [wsMessages, setWsMessages] = useState<WSMessageLog[]>([])
+  const [showAudioMsgs, setShowAudioMsgs] = useState(false)
+  const [activePanel, setActivePanel] = useState<'transcript' | 'messages' | 'log'>('transcript')
+  const [sessionStartedAt, setSessionStartedAt] = useState<string | null>(null)
+  const [sessionEndedAt, setSessionEndedAt] = useState<string | null>(null)
+  const [recordedBlob, setRecordedBlob] = useState<Blob | null>(null)
+  const [isSavingSession, setIsSavingSession] = useState(false)
+  const [savedSessions, setSavedSessions] = useState<SavedSession[]>([])
+  // Tracks whether the CURRENT (unsaved) transcript has already been saved,
+  // so the primary button can show "Saved" / disable correctly. This resets
+  // each time a new call is started.
+  const [currentSavedId, setCurrentSavedId] = useState<string | null>(null)
+  const [traceCallShortId, setTraceCallShortId] = useState<string | null>(null)
+  const navigate = useNavigate()
+
+  // ---- Refs -----------------------------------------------------------
+  const wsRef = useRef<WebSocket | null>(null)
+  const audioCtxRef = useRef<AudioContext | null>(null)
+  const micStreamRef = useRef<MediaStream | null>(null)
+  const scriptNodeRef = useRef<ScriptProcessorNode | null>(null)
+  const accBufRef = useRef<Float32Array>(new Float32Array(0))
+  const playTimeRef = useRef(0)
+  const recordDestRef = useRef<MediaStreamAudioDestinationNode | null>(null)
+  const mediaRecorderRef = useRef<MediaRecorder | null>(null)
+  const recordedChunksRef = useRef<Blob[]>([])
+  const connectingRef = useRef(false)
+  const traceSessionRef = useRef<string | null>(null)
+  // Auto-save orchestration. When a session ends we want to save it only
+  // once, and only after all post-disconnect async work (STT flushes + the
+  // MediaRecorder's final onstop) has settled.
+  const autoSaveRequestedRef = useRef(false)
+  const recordingPendingRef = useRef(false)
+  const msgIdRef = useRef(0)
+  const transcriptScrollRef = useRef<HTMLDivElement | null>(null)
+  const messagesScrollRef = useRef<HTMLDivElement | null>(null)
+  const logsScrollRef = useRef<HTMLDivElement | null>(null)
+  const transcriptStickRef = useRef(true)
+  const messagesStickRef = useRef(true)
+  const logsStickRef = useRef(true)
+  const protocolRef = useRef(protocol)
+  protocolRef.current = protocol
+
+  // ---- STT state & refs ------------------------------------------------
+  const [sttEnabled, setSttEnabled] = useState(false)
+  const [pendingTranscriptions, setPendingTranscriptions] = useState(0)
+  const userAudioBufRef = useRef<Int16Array[]>([])
+  const agentAudioBufRef = useRef<Int16Array[]>([])
+  const userSilenceTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
+  const agentSilenceTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
+  // A "turn" starts the moment voiced audio is detected and ends when the
+  // silence timer fires. Silent frames received inside an active turn are
+  // still buffered (so within-sentence pauses survive); silent frames outside
+  // a turn are dropped entirely.
+  const userTurnActiveRef = useRef(false)
+  const agentTurnActiveRef = useRef(false)
+  const sttEnabledRef = useRef(false)
+  sttEnabledRef.current = sttEnabled
+
+  const { data: sttConfig } = useQuery<{ available: boolean; provider?: string; model?: string; reason?: string }>({
+    queryKey: ['agent-stt-config', agentId],
+    queryFn: () => (agentId ? apiClient.getAgentSttConfig(agentId) : Promise.resolve({ available: false })),
+    enabled: !!agentId,
+    staleTime: 60_000,
+  })
+
+
+  const { data: s3Status } = useQuery({
+    queryKey: ['s3-status'],
+    queryFn: () => apiClient.getS3Status(),
+    staleTime: 60_000,
+  })
+
+  // ---- Logging helpers ------------------------------------------------
+
+  const log = useCallback((message: string, type: LogEntry['type'] = 'system') => {
+    setLogs((prev) => [...prev.slice(-500), { timestamp: new Date().toISOString(), message, type }])
+  }, [])
+
+  const pushWsMessage = useCallback((direction: 'sent' | 'received', parsed: ParsedWSMessage, size: number) => {
+    setWsMessages((prev) => [
+      ...prev.slice(-500),
+      { id: ++msgIdRef.current, direction, timestamp: new Date().toISOString(), parsed, size },
+    ])
+  }, [])
+
+  // ---- STT flush helpers (dual-channel, independent) ---------------------
+
+  const flushChannelBuffer = useCallback(
+    (channel: 'user' | 'agent') => {
+      const buf = channel === 'user' ? userAudioBufRef.current : agentAudioBufRef.current
+      if (buf.length === 0) return
+
+      const totalLen = buf.reduce((s, a) => s + a.length, 0)
+      if (totalLen < 800) {
+        if (channel === 'user') userAudioBufRef.current = []
+        else agentAudioBufRef.current = []
+        return
+      }
+
+      const merged = new Int16Array(totalLen)
+      let off = 0
+      for (const chunk of buf) { merged.set(chunk, off); off += chunk.length }
+      if (channel === 'user') {
+        userAudioBufRef.current = []
+        userTurnActiveRef.current = false
+      } else {
+        agentAudioBufRef.current = []
+        agentTurnActiveRef.current = false
+      }
+
+      if (!agentId) return
+
+      const wavBlob = encodeWav(merged, protocolRef.current.sampleRate)
+      setPendingTranscriptions((n) => n + 1)
+      const durationSec = (totalLen / protocolRef.current.sampleRate).toFixed(1)
+      log(`Transcribing ${channel} turn (${durationSec}s)...`, 'system')
+
+      apiClient.transcribeTurn(agentId, channel, wavBlob)
+        .then((res) => {
+          const text = (res.transcript || '').trim()
+          if (text) {
+            const entry: TranscriptEntry = {
+              role: channel === 'user' ? 'user' : 'agent',
+              content: text,
+              timestamp: new Date().toISOString(),
+            }
+            setTranscriptEntries((prev) => [...prev, entry])
+            log(`${channel === 'user' ? 'User' : 'Agent'} (STT): ${text}`, channel === 'user' ? 'user' : 'bot')
+          }
+        })
+        .catch((err: any) => {
+          log(`STT failed for ${channel}: ${err?.response?.data?.detail || err?.message || 'unknown error'}`, 'system')
+        })
+        .finally(() => setPendingTranscriptions((n) => Math.max(0, n - 1)))
+    },
+    [agentId, log],
+  )
+
+  const flushChannelRef = useRef(flushChannelBuffer)
+  flushChannelRef.current = flushChannelBuffer
+
+  const resetUserSilenceTimer = useCallback(() => {
+    if (userSilenceTimerRef.current) clearTimeout(userSilenceTimerRef.current)
+    userSilenceTimerRef.current = setTimeout(() => {
+      if (sttEnabledRef.current) flushChannelRef.current('user')
+    }, 2000)
+  }, [])
+
+  const resetAgentSilenceTimer = useCallback(() => {
+    if (agentSilenceTimerRef.current) clearTimeout(agentSilenceTimerRef.current)
+    agentSilenceTimerRef.current = setTimeout(() => {
+      if (sttEnabledRef.current) flushChannelRef.current('agent')
+    }, 1500)
+  }, [])
+
+  const scrollPanelIfPinned = useCallback(
+    (container: HTMLDivElement | null, stickRef: MutableRefObject<boolean>) => {
+      if (!container || !stickRef.current) return
+      container.scrollTop = container.scrollHeight
+    },
+    [],
+  )
+
+  const handlePanelScroll = useCallback(
+    (container: HTMLDivElement | null, stickRef: MutableRefObject<boolean>) => {
+      if (!container) return
+      const distanceFromBottom = container.scrollHeight - container.scrollTop - container.clientHeight
+      stickRef.current = distanceFromBottom < 48
+    },
+    [],
+  )
+
+  useEffect(() => {
+    scrollPanelIfPinned(transcriptScrollRef.current, transcriptStickRef)
+  }, [transcriptEntries, scrollPanelIfPinned])
+
+  useEffect(() => {
+    scrollPanelIfPinned(messagesScrollRef.current, messagesStickRef)
+  }, [wsMessages, showAudioMsgs, scrollPanelIfPinned])
+
+  useEffect(() => {
+    scrollPanelIfPinned(logsScrollRef.current, logsStickRef)
+  }, [logs, scrollPanelIfPinned])
+
+  // ---- Audio playback -------------------------------------------------
+
+  const playAudioChunk = useCallback(
+    (b64: string) => {
+      const ctx = audioCtxRef.current
+      if (!ctx || !b64) return
+      try {
+        const samples = base64ToFloat32(b64)
+        if (samples.length === 0) return
+        const buf = ctx.createBuffer(1, samples.length, protocolRef.current.sampleRate)
+        buf.getChannelData(0).set(samples)
+        const src = ctx.createBufferSource()
+        src.buffer = buf
+        src.connect(ctx.destination)
+        if (recordDestRef.current) {
+          src.connect(recordDestRef.current)
+        }
+
+        const now = ctx.currentTime
+        if (playTimeRef.current < now) playTimeRef.current = now
+        src.start(playTimeRef.current)
+        playTimeRef.current += buf.duration
+
+        // STT: VAD-gated agent buffering. See mic pipeline for rationale.
+        if (sttEnabledRef.current) {
+          const energy = rmsFloat32(samples)
+          const voiced = energy > VAD_RMS_THRESHOLD
+          const pcm16 = float32ToInt16(samples)
+          if (voiced) {
+            if (!agentTurnActiveRef.current) {
+              agentTurnActiveRef.current = true
+              log('Agent turn started', 'system')
+            }
+            agentAudioBufRef.current.push(pcm16)
+            resetAgentSilenceTimer()
+          } else if (agentTurnActiveRef.current) {
+            agentAudioBufRef.current.push(pcm16)
+          }
+        }
+      } catch {
+        // ignore decode errors on individual chunks
+      }
+    },
+    [resetAgentSilenceTimer],
+  )
+
+  // ---- WebSocket message handler --------------------------------------
+
+  const handleWsMessage = useCallback(
+    (event: MessageEvent) => {
+      const data = event.data
+      const size = typeof data === 'string' ? data.length : (data as ArrayBuffer).byteLength
+      const parsed = protocolRef.current.parseMessage(data)
+
+      pushWsMessage('received', parsed, size)
+
+      if (parsed.responseMessage) {
+        wsRef.current?.send(parsed.responseMessage)
+      }
+
+      if (parsed.type === 'audio' && parsed.audioBase64) {
+        playAudioChunk(parsed.audioBase64)
+      }
+
+      if (parsed.type === 'transcript' && parsed.transcript) {
+        const entry = parsed.transcript
+        setTranscriptEntries((prev) => [...prev, entry])
+        log(`${entry.role === 'user' ? 'User' : 'Agent'}: ${entry.content}`, entry.role === 'user' ? 'user' : 'bot')
+      }
+
+      if (parsed.type === 'control') {
+        const t = parsed.raw?.type
+        if (t) log(`Control: ${t}`, 'system')
+      }
+    },
+    [log, pushWsMessage, playAudioChunk],
+  )
+
+  // ---- Mic capture + send pipeline -----------------------------------
+
+  const startMicCapture = useCallback(() => {
+    const ctx = audioCtxRef.current
+    const stream = micStreamRef.current
+    if (!ctx || !stream) return
+
+    const source = ctx.createMediaStreamSource(stream)
+    const processor = ctx.createScriptProcessor(4096, 1, 1)
+
+    const targetRate = protocolRef.current.sampleRate
+    const chunkSamples = Math.round(targetRate * protocolRef.current.sendIntervalMs / 1000)
+
+    processor.onaudioprocess = (e) => {
+      if (!wsRef.current || wsRef.current.readyState !== WebSocket.OPEN) return
+      const raw = e.inputBuffer.getChannelData(0)
+      const resampled = resampleFloat32(raw, ctx.sampleRate, targetRate)
+      const prev = accBufRef.current
+      const merged = new Float32Array(prev.length + resampled.length)
+      merged.set(prev)
+      merged.set(resampled, prev.length)
+
+      let offset = 0
+      while (offset + chunkSamples <= merged.length) {
+        const chunk = merged.slice(offset, offset + chunkSamples)
+        offset += chunkSamples
+        const pcm16 = float32ToInt16(chunk)
+        const b64 = int16ArrayToBase64(pcm16)
+        const frame = protocolRef.current.wrapAudioFrame(b64)
+        try {
+          wsRef.current.send(frame)
+        } catch {
+          break
+        }
+
+        // VAD-gated STT buffering. Only voiced frames start/continue a turn
+        // and reset the silence timer; silent frames outside a turn are
+        // dropped so the timer can actually expire and flush the turn.
+        if (sttEnabledRef.current) {
+          const energy = rmsInt16(pcm16)
+          const voiced = energy > VAD_RMS_THRESHOLD
+          if (voiced) {
+            if (!userTurnActiveRef.current) {
+              userTurnActiveRef.current = true
+              log('User turn started', 'system')
+            }
+            userAudioBufRef.current.push(pcm16)
+            resetUserSilenceTimer()
+          } else if (userTurnActiveRef.current) {
+            // keep natural pauses inside an active turn
+            userAudioBufRef.current.push(pcm16)
+          }
+        }
+      }
+      accBufRef.current = merged.slice(offset)
+    }
+
+    // Connect processor through a silent gain so it fires but doesn't
+    // route mic audio to speakers.
+    const silentGain = ctx.createGain()
+    silentGain.gain.value = 0
+    source.connect(processor)
+    processor.connect(silentGain)
+    silentGain.connect(ctx.destination)
+
+    // Route mic into the recording destination so both sides are captured
+    if (recordDestRef.current) {
+      source.connect(recordDestRef.current)
+    }
+
+    scriptNodeRef.current = processor
+  }, [])
+
+  // ---- MediaRecorder for session recording ----------------------------
+
+  const startRecording = useCallback(() => {
+    if (mediaRecorderRef.current || typeof MediaRecorder === 'undefined') return
+    // Prefer the mixed destination (mic + bot audio). Fall back to mic-only.
+    const stream = recordDestRef.current?.stream || micStreamRef.current
+    if (!stream) return
+    try {
+      const mimeType = MediaRecorder.isTypeSupported('audio/webm;codecs=opus')
+        ? 'audio/webm;codecs=opus'
+        : 'audio/webm'
+      const recorder = new MediaRecorder(stream, { mimeType })
+      recordedChunksRef.current = []
+      recorder.ondataavailable = (ev: BlobEvent) => {
+        if (ev.data.size > 0) recordedChunksRef.current.push(ev.data)
+      }
+      recorder.onstop = () => {
+        if (recordedChunksRef.current.length > 0) {
+          setRecordedBlob(new Blob(recordedChunksRef.current, { type: recorder.mimeType || 'audio/webm' }))
+        }
+        recordingPendingRef.current = false
+      }
+      recorder.start(1000)
+      mediaRecorderRef.current = recorder
+      recordingPendingRef.current = true
+      const isMixed = !!recordDestRef.current
+      log(`Recording started (${isMixed ? 'mic + agent audio' : 'mic only'})`, 'system')
+    } catch (err: any) {
+      log(`Recording failed: ${err?.message || err}`, 'system')
+    }
+  }, [log])
+
+  const stopRecording = useCallback(() => {
+    if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {
+      mediaRecorderRef.current.stop()
+    }
+    mediaRecorderRef.current = null
+  }, [])
+
+  // ---- Connect / disconnect ------------------------------------------
+
+  const cleanupConnection = useCallback(
+    (userInitiated: boolean) => {
+      // Flush BOTH STT channel buffers on disconnect
+      if (userSilenceTimerRef.current) { clearTimeout(userSilenceTimerRef.current); userSilenceTimerRef.current = null }
+      if (agentSilenceTimerRef.current) { clearTimeout(agentSilenceTimerRef.current); agentSilenceTimerRef.current = null }
+      if (sttEnabledRef.current) {
+        flushChannelBuffer('user')
+        flushChannelBuffer('agent')
+      }
+      userAudioBufRef.current = []
+      agentAudioBufRef.current = []
+      userTurnActiveRef.current = false
+      agentTurnActiveRef.current = false
+
+      stopRecording()
+      if (scriptNodeRef.current) {
+        scriptNodeRef.current.disconnect()
+        scriptNodeRef.current = null
+      }
+      if (wsRef.current) {
+        const ws = wsRef.current
+        wsRef.current = null
+        ws.onmessage = null
+        ws.onclose = null
+        ws.onerror = null
+        if (ws.readyState === WebSocket.OPEN || ws.readyState === WebSocket.CONNECTING) {
+          ws.close()
+        }
+      }
+      recordDestRef.current = null
+      if (audioCtxRef.current && audioCtxRef.current.state !== 'closed') {
+        audioCtxRef.current.close().catch(() => {})
+        audioCtxRef.current = null
+      }
+      if (micStreamRef.current) {
+        micStreamRef.current.getTracks().forEach((t) => t.stop())
+        micStreamRef.current = null
+      }
+      const activeTraceId = traceSessionRef.current
+      if (activeTraceId) {
+        traceSessionRef.current = null
+        apiClient.closeSyntheticTraceSession(activeTraceId).catch(() => {})
+      }
+      setIsConnected(false)
+      setIsConnecting(false)
+      setSessionEndedAt(new Date().toISOString())
+      // Ask the auto-save effect to persist the session once all pending
+      // work settles (STT flushes + recorder onstop). If there are no
+      // transcripts, the effect will no-op.
+      autoSaveRequestedRef.current = true
+      if (userInitiated) log('Disconnected')
+    },
+    [stopRecording, log, flushChannelBuffer],
+  )
+
+  const disconnect = useCallback(() => {
+    cleanupConnection(true)
+  }, [cleanupConnection])
+
+  const connect = useCallback(async () => {
+    if (connectingRef.current) return
+    connectingRef.current = true
+    setIsConnecting(true)
+    setError(null)
+    log('Connecting...')
+
+    // Reset previous session state (keep the savedSessions list so users can
+    // still see and act on sessions saved earlier in this component's lifetime)
+    setCurrentSavedId(null)
+    setTraceCallShortId(null)
+    traceSessionRef.current = null
+    autoSaveRequestedRef.current = false
+    recordingPendingRef.current = false
+    setRecordedBlob(null)
+    setTranscriptEntries([])
+    setWsMessages([])
+    setSessionStartedAt(new Date().toISOString())
+    setSessionEndedAt(null)
+    accBufRef.current = new Float32Array(0)
+    playTimeRef.current = 0
+    userAudioBufRef.current = []
+    agentAudioBufRef.current = []
+    userTurnActiveRef.current = false
+    agentTurnActiveRef.current = false
+    if (userSilenceTimerRef.current) { clearTimeout(userSilenceTimerRef.current); userSilenceTimerRef.current = null }
+    if (agentSilenceTimerRef.current) { clearTimeout(agentSilenceTimerRef.current); agentSilenceTimerRef.current = null }
+
+    try {
+      let sessionCallShortId: string | null = null
+      let traceHandshake: Record<string, unknown> | null = null
+      try {
+        const session = await apiClient.createSyntheticTraceSession({
+          transport: 'websocket',
+        })
+        sessionCallShortId = session.call_short_id
+        traceHandshake = {
+          type: 'efficientai_trace_handshake',
+          call_short_id: session.call_short_id,
+          trace_id: session.trace_id,
+          workspace_id: session.workspace_id,
+          transport: session.transport,
+          otel_correlation: session.otel_correlation,
+          efficientai_call_short_id: session.call_short_id,
+        }
+        setTraceCallShortId(session.call_short_id)
+        traceSessionRef.current = session.call_short_id
+        log(`Trace session ${session.call_short_id} — IDs attached automatically on handshake`, 'system')
+      } catch (sessionErr: any) {
+        const msg = sessionErr?.response?.data?.detail || sessionErr?.message || 'trace session failed'
+        log(`Trace session skipped: ${msg}`, 'system')
+      }
+
+      // Mic
+      try {
+        micStreamRef.current = await navigator.mediaDevices.getUserMedia({ audio: true })
+        log('Microphone acquired')
+      } catch {
+        micStreamRef.current = null
+        log('Microphone unavailable — connecting without mic', 'system')
+      }
+
+      // AudioContext + recording destination (mixes mic + bot audio)
+      const ctx = new AudioContext({ sampleRate: 48000 })
+      audioCtxRef.current = ctx
+      if (ctx.state === 'suspended') await ctx.resume()
+      recordDestRef.current = ctx.createMediaStreamDestination()
+
+      // WebSocket
+      log(`Opening WebSocket to ${websocketUrl}`)
+      log(`Protocol: ${protocolRef.current.name} (${protocolRef.current.sampleRate / 1000} kHz, ${protocolRef.current.sendIntervalMs} ms)`)
+      const ws = new WebSocket(websocketUrl)
+      ws.binaryType = 'arraybuffer'
+      wsRef.current = ws
+
+      await new Promise<void>((resolve, reject) => {
+        let settled = false
+        const settle = (fn: () => void) => {
+          if (!settled) { settled = true; fn() }
+        }
+        const timer = setTimeout(
+          () => settle(() => reject(new Error(
+            `Connection timed out after 15 s. The server at ${new URL(websocketUrl).host} did not respond to the WebSocket upgrade. ` +
+            'This often means the agent restricts which origins can connect. ' +
+            'Try creating your own agent (with open allowed-origins) or use a backend proxy.'
+          ))),
+          15000,
+        )
+        ws.onopen = () => { clearTimeout(timer); settle(() => resolve()) }
+        ws.onerror = () => {
+          clearTimeout(timer)
+          settle(() => reject(new Error(
+            `WebSocket connection to ${new URL(websocketUrl).host} failed. ` +
+            'Check the URL, your network, and whether the agent allows connections from this origin.'
+          )))
+        }
+        ws.onclose = (ev) => {
+          clearTimeout(timer)
+          settle(() => reject(new Error(
+            `WebSocket closed before connecting (code ${ev.code}${ev.reason ? ': ' + ev.reason : ''}). ` +
+            'The server may have rejected the connection — public agents often restrict allowed origins.'
+          )))
+        }
+      })
+
+      log('WebSocket upgrade successful (101)')
+
+      if (traceHandshake) {
+        ws.send(JSON.stringify(traceHandshake))
+        log('Sent efficientai_trace_handshake to agent', 'system')
+      } else if (sessionCallShortId) {
+        ws.send(JSON.stringify({ efficientai_call_short_id: sessionCallShortId }))
+        log(`Sent efficientai_call_short_id to agent`, 'system')
+      }
+
+      // Send init messages
+      const initMsgs = protocolRef.current.buildInitMessages?.() || []
+      for (const msg of initMsgs) {
+        ws.send(msg)
+        const preview = msg.length > 140 ? msg.slice(0, 140) + '…' : msg
+        log(`Sent init: ${preview}`)
+      }
+      if (initMsgs.length === 0) {
+        log('No init messages for this protocol — waiting for server messages', 'system')
+      }
+
+      ws.onmessage = handleWsMessage
+      ws.onclose = (ev) => {
+        log(`WebSocket closed (code ${ev.code}${ev.reason ? `, ${ev.reason}` : ''})`)
+        cleanupConnection(false)
+      }
+      ws.onerror = () => {
+        log('WebSocket error')
+      }
+
+      // Start mic capture + recording
+      if (micStreamRef.current) {
+        startMicCapture()
+      }
+      startRecording()
+
+      transcriptStickRef.current = true
+      messagesStickRef.current = true
+      logsStickRef.current = true
+
+      setIsConnected(true)
+      setIsConnecting(false)
+      log('Ready — streaming mic audio', 'system')
+    } catch (err: any) {
+      const msg = err?.message || 'Failed to connect'
+      setError(msg)
+      log(`Error: ${msg}`)
+      cleanupConnection(false)
+    } finally {
+      connectingRef.current = false
+      setIsConnecting(false)
+    }
+  }, [websocketUrl, handleWsMessage, log, startMicCapture, startRecording, cleanupConnection, agentId])
+
+  // Cleanup on unmount
+  useEffect(() => {
+    return () => {
+      if (wsRef.current) {
+        wsRef.current.onmessage = null
+        wsRef.current.onclose = null
+        wsRef.current.close()
+        wsRef.current = null
+      }
+      if (scriptNodeRef.current) {
+        scriptNodeRef.current.disconnect()
+        scriptNodeRef.current = null
+      }
+      if (audioCtxRef.current && audioCtxRef.current.state !== 'closed') {
+        audioCtxRef.current.close().catch(() => {})
+      }
+      if (micStreamRef.current) {
+        micStreamRef.current.getTracks().forEach((t) => t.stop())
+      }
+      if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {
+        mediaRecorderRef.current.stop()
+      }
+    }
+  }, [])
+
+  // ---- Save / Evaluate ------------------------------------------------
+
+  const handleSaveSession = useCallback(async () => {
+    if (!agentId || !websocketUrl || transcriptEntries.length === 0) return
+    try {
+      setIsSavingSession(true)
+      setError(null)
+      const audioFile = recordedBlob
+        ? new File([recordedBlob], `custom-ws-${Date.now()}.webm`, { type: recordedBlob.type || 'audio/webm' })
+        : undefined
+      const res = await apiClient.createCustomWebsocketSession({
+        agent_id: agentId,
+        websocket_url: websocketUrl,
+        transcript_entries: transcriptEntries,
+        started_at: sessionStartedAt || undefined,
+        ended_at: sessionEndedAt || new Date().toISOString(),
+        call_short_id: traceCallShortId || undefined,
+        audio_file: audioFile,
+      })
+      const newSession: SavedSession = {
+        callShortId: res.call_short_id,
+        savedAt: new Date().toISOString(),
+        transcriptCount: transcriptEntries.length,
+        hasRecording: !!recordedBlob,
+        sessionStartedAt: sessionStartedAt,
+        sessionEndedAt: sessionEndedAt || new Date().toISOString(),
+        evalStatus: 'idle',
+      }
+      setSavedSessions((prev) => [newSession, ...prev])
+      setCurrentSavedId(res.call_short_id)
+      log(`Session saved as ${res.call_short_id}`, 'system')
+      onSessionSaved?.()
+    } catch (err: any) {
+      const msg = err?.response?.data?.detail || err?.message || 'Failed to save session'
+      setError(msg)
+      log(`Save failed: ${msg}`, 'system')
+    } finally {
+      setIsSavingSession(false)
+    }
+  }, [
+    agentId,
+    websocketUrl,
+    transcriptEntries,
+    recordedBlob,
+    sessionStartedAt,
+    sessionEndedAt,
+    traceCallShortId,
+    log,
+    onSessionSaved,
+  ])
+
+  // Auto-save the session once disconnect + all post-disconnect async work
+  // have settled. Guarded so it fires at most once per connection.
+  useEffect(() => {
+    if (!autoSaveRequestedRef.current) return
+    if (isConnected || isConnecting) return
+    if (pendingTranscriptions > 0) return
+    if (recordingPendingRef.current) return
+    if (isSavingSession) return
+    if (currentSavedId) return
+    if (!agentId) return
+    if (transcriptEntries.length === 0) {
+      // Nothing worth saving — clear the request so we don't retrigger.
+      autoSaveRequestedRef.current = false
+      return
+    }
+    autoSaveRequestedRef.current = false
+    handleSaveSession()
+  }, [
+    isConnected,
+    isConnecting,
+    pendingTranscriptions,
+    recordedBlob,
+    transcriptEntries,
+    isSavingSession,
+    currentSavedId,
+    agentId,
+    handleSaveSession,
+  ])
+
+  const updateSession = (callShortId: string, patch: Partial<SavedSession>) => {
+    setSavedSessions((prev) =>
+      prev.map((s) => (s.callShortId === callShortId ? { ...s, ...patch } : s)),
+    )
+  }
+
+  const handleEvaluateSession = async (callShortId: string) => {
+    updateSession(callShortId, { evalStatus: 'evaluating', evalError: undefined })
+    try {
+      await apiClient.evaluateCustomWebsocketSession(callShortId)
+      updateSession(callShortId, { evalStatus: 'queued' })
+      log(`Evaluation queued for ${callShortId}`, 'system')
+      onSessionSaved?.()
+    } catch (err: any) {
+      const msg = err?.response?.data?.detail || err?.message || 'Evaluation failed'
+      updateSession(callShortId, { evalStatus: 'failed', evalError: msg })
+      log(`Evaluation failed: ${msg}`, 'system')
+    }
+  }
+
+  const handleOpenSession = (callShortId: string) => {
+    navigate(`/playground/call-recordings/${callShortId}`)
+  }
+
+  const handleRemoveSession = (callShortId: string) => {
+    setSavedSessions((prev) => prev.filter((s) => s.callShortId !== callShortId))
+    if (currentSavedId === callShortId) setCurrentSavedId(null)
+  }
+
+  // ---- Filtered messages for display ----------------------------------
+
+  const visibleMessages = showAudioMsgs ? wsMessages : wsMessages.filter((m) => m.parsed.type !== 'audio')
+
+  // ---- Render ---------------------------------------------------------
+
+  const panelClasses = (panel: string) =>
+    `px-3 py-2 text-xs font-medium cursor-pointer transition-colors ${
+      activePanel === panel
+        ? 'border-b-2 border-yellow-500 text-yellow-700'
+        : 'text-gray-500 hover:text-gray-700'
+    }`
+
+  return (
+    <div className="space-y-4">
+      <div className="bg-white shadow rounded-lg p-5">
+        {/* Header */}
+        <div className="mb-3">
+          <h3 className="text-sm font-semibold text-gray-900">
+            {protocol.name}
+          </h3>
+          <p className="text-xs text-gray-500 mt-0.5 font-mono truncate">{websocketUrl}</p>
+          <p className="text-xs text-gray-400 mt-0.5">{protocol.description}</p>
+        </div>
+
+        {/* Warnings */}
+        {s3Status && !s3Status.enabled && (
+          <div className="flex items-start gap-2 bg-amber-50 border border-amber-200 rounded-lg p-3 mb-3 text-xs">
+            <AlertTriangle className="h-4 w-4 text-amber-500 flex-shrink-0 mt-0.5" />
+            <div>
+              <p className="font-medium text-amber-800">Storage not configured</p>
+              <p className="text-amber-700">Audio recordings will not be saved. Transcripts will still be captured.</p>
+            </div>
+          </div>
+        )}
+
+        {error && (
+          <div className="bg-red-50 border border-red-200 rounded-lg p-3 mb-3">
+            <p className="text-xs text-red-800">{error}</p>
+          </div>
+        )}
+
+        {/* STT toggle */}
+        {agentId && sttConfig && sttConfig.available && (
+          <div className="flex items-center justify-between bg-gray-50 border border-gray-200 rounded-lg p-3 mb-3">
+            <div className="flex items-center gap-2">
+              <label className="relative inline-flex items-center cursor-pointer">
+                <input
+                  type="checkbox"
+                  checked={sttEnabled}
+                  onChange={(e) => setSttEnabled(e.target.checked)}
+                  className="sr-only peer"
+                />
+                <div className="w-9 h-5 bg-gray-300 peer-focus:outline-none rounded-full peer peer-checked:after:translate-x-full peer-checked:after:border-white after:content-[''] after:absolute after:top-[2px] after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-4 after:w-4 after:transition-all peer-checked:bg-indigo-600 peer-disabled:opacity-50" />
+              </label>
+              <span className="text-sm font-medium text-gray-700">Live Transcription</span>
+              {sttConfig.provider && (
+                <span className="text-[10px] px-1.5 py-0.5 bg-indigo-100 text-indigo-700 rounded font-mono">
+                  {sttConfig.provider}{sttConfig.model ? ` · ${sttConfig.model}` : ''}
+                </span>
+              )}
+              {pendingTranscriptions > 0 && (
+                <Loader className="h-3.5 w-3.5 text-indigo-500 animate-spin" />
+              )}
+            </div>
+            {sttEnabled && pendingTranscriptions > 0 && (
+              <span className="text-[10px] text-gray-400">Transcribing...</span>
+            )}
+          </div>
+        )}
+        {!agentId && (
+          <div className="text-xs text-amber-500 mb-3 px-1">
+            Select an agent to enable live transcription (STT).
+          </div>
+        )}
+        {agentId && sttConfig && !sttConfig.available && (
+          <div className="text-xs text-amber-500 mb-3 px-1">
+            Live transcription unavailable: {sttConfig.reason || 'No STT provider configured in voice bundle'}
+          </div>
+        )}
+
+        {/* Status + controls */}
+        <div className="flex items-center justify-between mb-4">
+          <div className="flex items-center gap-2">
+            {isConnected ? (
+              <>
+                <div className="w-2.5 h-2.5 bg-green-500 rounded-full animate-pulse" />
+                <span className="text-sm font-medium text-gray-700">Connected</span>
+                <span className="text-xs text-gray-400">
+                  {protocol.sampleRate / 1000} kHz &middot; {protocol.sendIntervalMs} ms frames
+                </span>
+                {sttEnabled && (
+                  <span className="text-[10px] px-1.5 py-0.5 bg-green-100 text-green-700 rounded">STT ON</span>
+                )}
+                {traceCallShortId && (
+                  <span className="text-[10px] px-1.5 py-0.5 bg-indigo-100 text-indigo-700 rounded font-mono">
+                    trace #{traceCallShortId}
+                  </span>
+                )}
+              </>
+            ) : (
+              <span className="text-sm text-gray-500">{isConnecting ? 'Connecting...' : 'Disconnected'}</span>
+            )}
+          </div>
+          <div className="flex gap-2">
+            {!isConnected ? (
+              <Button
+                onClick={connect}
+                isLoading={isConnecting}
+                leftIcon={isConnecting ? <Loader className="h-4 w-4 animate-spin" /> : <Mic className="h-4 w-4" />}
+                disabled={isConnecting}
+              >
+                Connect
+              </Button>
+            ) : (
+              <Button onClick={disconnect} variant="danger" leftIcon={<MicOff className="h-4 w-4" />}>
+                Disconnect
+              </Button>
+            )}
+          </div>
+        </div>
+
+        {/* Panel tabs */}
+        <div className="flex border-b border-gray-200 mb-2">
+          <button className={panelClasses('transcript')} onClick={() => setActivePanel('transcript')}>
+            Transcript {transcriptEntries.length > 0 && `(${transcriptEntries.length})`}
+          </button>
+          <button className={panelClasses('messages')} onClick={() => setActivePanel('messages')}>
+            Messages {wsMessages.length > 0 && `(${wsMessages.length})`}
+          </button>
+          <button className={panelClasses('log')} onClick={() => setActivePanel('log')}>
+            Debug Log
+          </button>
+        </div>
+
+        {/* Transcript panel */}
+        {activePanel === 'transcript' && (
+          <div
+            ref={transcriptScrollRef}
+            onScroll={(e) => handlePanelScroll(e.currentTarget, transcriptStickRef)}
+            className="h-72 overflow-y-auto overscroll-contain bg-gray-50 rounded border border-gray-200 p-4"
+          >
+            {transcriptEntries.length === 0 ? (
+              <p className="text-gray-400 text-center text-xs py-12">
+                No transcript yet. Connect and start speaking.
+              </p>
+            ) : (
+              <div className="space-y-3">
+                {transcriptEntries.map((e, i) => {
+                  const ts = new Date(e.timestamp)
+                  const elapsed = sessionStartedAt
+                    ? Math.max(0, Math.round((ts.getTime() - new Date(sessionStartedAt).getTime()) / 1000))
+                    : null
+                  const elapsedStr = elapsed !== null
+                    ? `${Math.floor(elapsed / 60)}:${String(elapsed % 60).padStart(2, '0')}`
+                    : null
+                  return (
+                    <div key={i} className={`flex ${e.role === 'user' ? 'justify-end' : 'justify-start'}`}>
+                      <div className={`max-w-[80%] rounded-2xl px-4 py-2.5 ${
+                        e.role === 'user'
+                          ? 'bg-indigo-600 text-white rounded-br-none'
+                          : 'bg-white border border-gray-200 text-gray-800 rounded-bl-none'
+                      }`}>
+                        <div className={`flex items-center gap-2 mb-0.5 ${
+                          e.role === 'user' ? 'opacity-70' : ''
+                        }`}>
+                          <span className={`text-[10px] font-semibold uppercase tracking-wider ${
+                            e.role === 'user' ? '' : 'text-gray-400'
+                          }`}>
+                            {e.role === 'user' ? 'You' : 'Agent'}
+                          </span>
+                          <span className={`text-[10px] ${
+                            e.role === 'user' ? 'opacity-70' : 'text-gray-300'
+                          }`}>
+                            {elapsedStr && <span className="mr-1">[{elapsedStr}]</span>}
+                            {ts.toLocaleTimeString()}
+                          </span>
+                        </div>
+                        <p className="text-sm leading-relaxed">{e.content}</p>
+                      </div>
+                    </div>
+                  )
+                })}
+              </div>
+            )}
+          </div>
+        )}
+
+        {/* Messages panel */}
+        {activePanel === 'messages' && (
+          <div>
+            <div className="flex items-center gap-2 mb-1">
+              <button
+                className="flex items-center gap-1 text-xs text-gray-500 hover:text-gray-700"
+                onClick={() => setShowAudioMsgs((v) => !v)}
+              >
+                <Filter className="h-3 w-3" />
+                {showAudioMsgs ? 'Hide audio frames' : 'Show audio frames'}
+              </button>
+              <span className="text-xs text-gray-400">
+                {visibleMessages.length} / {wsMessages.length} messages
+              </span>
+            </div>
+            <div
+              ref={messagesScrollRef}
+              onScroll={(e) => handlePanelScroll(e.currentTarget, messagesStickRef)}
+              className="h-72 overflow-y-auto overscroll-contain bg-gray-50 rounded border border-gray-200 p-2 font-mono text-xs"
+            >
+              {visibleMessages.length === 0 ? (
+                <p className="text-gray-400 text-center py-12">No messages yet.</p>
+              ) : (
+                <div className="space-y-1">
+                  {visibleMessages.map((m) => (
+                    <div key={m.id} className="flex gap-1.5 items-start">
+                      {m.direction === 'sent' ? (
+                        <ArrowUp className="h-3 w-3 text-blue-400 flex-shrink-0 mt-0.5" />
+                      ) : (
+                        <ArrowDown className="h-3 w-3 text-green-400 flex-shrink-0 mt-0.5" />
+                      )}
+                      <span className="text-gray-400 flex-shrink-0">
+                        {new Date(m.timestamp).toLocaleTimeString()}
+                      </span>
+                      <span
+                        className={`px-1 rounded text-[10px] flex-shrink-0 ${
+                          m.parsed.type === 'audio'
+                            ? 'bg-purple-100 text-purple-700'
+                            : m.parsed.type === 'transcript'
+                              ? 'bg-green-100 text-green-700'
+                              : m.parsed.type === 'ping'
+                                ? 'bg-yellow-100 text-yellow-700'
+                                : m.parsed.type === 'control'
+                                  ? 'bg-blue-100 text-blue-700'
+                                  : 'bg-gray-100 text-gray-600'
+                        }`}
+                      >
+                        {m.parsed.type}
+                      </span>
+                      <span className="text-gray-600 break-all">
+                        {m.parsed.type === 'audio'
+                          ? `[${m.size} bytes]`
+                          : typeof m.parsed.raw === 'string'
+                            ? m.parsed.raw.slice(0, 200)
+                            : JSON.stringify(m.parsed.raw).slice(0, 200)}
+                      </span>
+                    </div>
+                  ))}
+                </div>
+              )}
+            </div>
+          </div>
+        )}
+
+        {/* Debug log panel */}
+        {activePanel === 'log' && (
+          <div
+            ref={logsScrollRef}
+            onScroll={(e) => handlePanelScroll(e.currentTarget, logsStickRef)}
+            className="h-72 overflow-y-auto overscroll-contain bg-gray-50 rounded border border-gray-200 p-3 font-mono text-xs"
+          >
+            {logs.length === 0 ? (
+              <p className="text-gray-400 text-center py-12">No logs yet. Connect to start.</p>
+            ) : (
+              <>
+                {logs.map((entry, idx) => (
+                  <div
+                    key={idx}
+                    className={`mb-0.5 ${
+                      entry.type === 'user'
+                        ? 'text-blue-600'
+                        : entry.type === 'bot'
+                          ? 'text-green-600'
+                          : 'text-gray-600'
+                    }`}
+                  >
+                    <span className="text-gray-400">{new Date(entry.timestamp).toLocaleTimeString()}</span>{' '}
+                    {entry.message}
+                  </div>
+                ))}
+              </>
+            )}
+          </div>
+        )}
+
+        {/* Saved sessions */}
+        {(savedSessions.length > 0 || (!isConnected && transcriptEntries.length > 0)) && (
+          <div className="mt-4 rounded-lg border border-gray-200 p-4">
+            <div className="flex items-center justify-between gap-3">
+              <div className="min-w-0">
+                <h3 className="text-sm font-semibold text-gray-900 flex items-center gap-1.5">
+                  <Save className="h-4 w-4 text-indigo-600" />
+                  Saved Test Sessions
+                  {savedSessions.length > 0 && (
+                    <span className="text-[10px] font-medium text-gray-500 bg-gray-100 rounded-full px-2 py-0.5">
+                      {savedSessions.length}
+                    </span>
+                  )}
+                </h3>
+                <p className="mt-1 text-xs text-gray-600">
+                  Sessions are saved automatically when you disconnect.
+                </p>
+              </div>
+              {/* Inline auto-save status (replaces the old manual button) */}
+              {!isConnected && transcriptEntries.length > 0 && (
+                <div className="flex-shrink-0">
+                  {isSavingSession ? (
+                    <span className="inline-flex items-center gap-1.5 text-xs text-indigo-700 bg-indigo-50 border border-indigo-100 rounded-full px-2.5 py-1">
+                      <Loader className="h-3 w-3 animate-spin" />
+                      Saving session…
+                    </span>
+                  ) : currentSavedId ? (
+                    <span className="inline-flex items-center gap-1.5 text-xs text-green-700 bg-green-50 border border-green-100 rounded-full px-2.5 py-1">
+                      <CheckCircle className="h-3 w-3" />
+                      Session saved
+                    </span>
+                  ) : !agentId ? (
+                    <span className="inline-flex items-center gap-1.5 text-xs text-amber-700 bg-amber-50 border border-amber-100 rounded-full px-2.5 py-1">
+                      <AlertTriangle className="h-3 w-3" />
+                      Select an agent to enable auto-save
+                    </span>
+                  ) : pendingTranscriptions > 0 ? (
+                    <span className="inline-flex items-center gap-1.5 text-xs text-gray-600 bg-gray-50 border border-gray-200 rounded-full px-2.5 py-1">
+                      <Loader className="h-3 w-3 animate-spin" />
+                      Finishing transcription…
+                    </span>
+                  ) : null}
+                </div>
+              )}
+            </div>
+
+
+            {savedSessions.length === 0 ? (
+              <div className="mt-3 rounded-md border border-dashed border-gray-200 bg-gray-50 px-3 py-6 text-center text-xs text-gray-500">
+                No saved sessions yet. End the call and click <span className="font-medium">Save Session</span> to save this transcript.
+              </div>
+            ) : (
+              <ul className="mt-3 divide-y divide-gray-100 rounded-md border border-gray-200 bg-white overflow-hidden">
+                {savedSessions.map((s) => (
+                  <li key={s.callShortId} className="p-3 flex flex-col sm:flex-row sm:items-center sm:justify-between gap-3 hover:bg-gray-50 transition-colors">
+                    <div className="min-w-0 flex-1">
+                      <div className="flex items-center gap-2 flex-wrap">
+                        <button
+                          type="button"
+                          onClick={() => handleOpenSession(s.callShortId)}
+                          className="font-mono text-sm font-semibold text-indigo-600 hover:text-indigo-700 hover:underline truncate"
+                          title="Open call details"
+                        >
+                          {s.callShortId}
+                        </button>
+                        {s.callShortId === currentSavedId && (
+                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-green-100 text-green-700 font-medium">
+                            just saved
+                          </span>
+                        )}
+                        {s.evalStatus === 'queued' && (
+                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-blue-100 text-blue-700 font-medium">
+                            eval queued
+                          </span>
+                        )}
+                        {s.evalStatus === 'evaluating' && (
+                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-blue-100 text-blue-700 font-medium inline-flex items-center gap-1">
+                            <Loader className="h-2.5 w-2.5 animate-spin" />
+                            queuing
+                          </span>
+                        )}
+                        {s.evalStatus === 'failed' && (
+                          <span
+                            className="text-[10px] px-1.5 py-0.5 rounded-full bg-red-100 text-red-700 font-medium"
+                            title={s.evalError || 'Evaluation failed'}
+                          >
+                            eval failed
+                          </span>
+                        )}
+                        {s.hasRecording && (
+                          <span className="text-[10px] px-1.5 py-0.5 rounded-full bg-purple-100 text-purple-700 font-medium">
+                            with recording
+                          </span>
+                        )}
+                      </div>
+                      <div className="mt-1 flex items-center gap-3 text-[11px] text-gray-500 flex-wrap">
+                        <span className="inline-flex items-center gap-1">
+                          <Clock className="h-3 w-3" />
+                          {new Date(s.savedAt).toLocaleTimeString()}
+                        </span>
+                        <span>{s.transcriptCount} turn{s.transcriptCount === 1 ? '' : 's'}</span>
+                      </div>
+                    </div>
+
+                    <div className="flex items-center gap-2 flex-shrink-0">
+                      {s.evalStatus !== 'queued' && (
+                        <Button
+                          variant="outline"
+                          size="sm"
+                          onClick={() => handleEvaluateSession(s.callShortId)}
+                          isLoading={s.evalStatus === 'evaluating'}
+                          disabled={s.evalStatus === 'evaluating'}
+                        >
+                          {s.evalStatus === 'failed' ? 'Retry Evaluation' : 'Run Evaluation'}
+                        </Button>
+                      )}
+                      <Button
+                        variant="outline"
+                        size="sm"
+                        onClick={() => handleOpenSession(s.callShortId)}
+                        leftIcon={<ExternalLink className="h-3.5 w-3.5" />}
+                      >
+                        Open
+                      </Button>
+                      <button
+                        type="button"
+                        onClick={() => handleRemoveSession(s.callShortId)}
+                        className="p-1.5 text-gray-400 hover:text-red-500 hover:bg-red-50 rounded transition-colors"
+                        title="Remove from list"
+                        aria-label="Remove from list"
+                      >
+                        <Trash2 className="h-3.5 w-3.5" />
+                      </button>
+                    </div>
+                  </li>
+                ))}
+              </ul>
+            )}
+          </div>
+        )}
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/components/Layout.tsx b/frontend/src/components/Layout.tsx
index 08ecf9cd..22d029d4 100644
--- a/frontend/src/components/Layout.tsx
+++ b/frontend/src/components/Layout.tsx
@@ -36,6 +36,7 @@ import {
   ScrollText,
   Github,
   Sparkles,
+  Route,
   Upload,
 } from 'lucide-react'
 import { useState, useEffect } from 'react'
@@ -98,6 +99,7 @@ const navigationSections: NavSection[] = [
     items: [
       { name: 'Overview', href: '/observability', icon: Activity },
       { name: 'Calls', href: '/observability/calls', icon: Phone },
+      { name: 'Call Traces', href: '/call-traces', icon: Route },
     ],
   },
   {
diff --git a/frontend/src/components/call-recordings/CustomWebSocketCallDetails.tsx b/frontend/src/components/call-recordings/CustomWebSocketCallDetails.tsx
index cf1e88eb..072ccc40 100644
--- a/frontend/src/components/call-recordings/CustomWebSocketCallDetails.tsx
+++ b/frontend/src/components/call-recordings/CustomWebSocketCallDetails.tsx
@@ -1,415 +1,420 @@
-import { useState, useEffect, useRef, useMemo } from 'react'
-import {
-  MessageSquare,
-  Clock,
-  Globe,
-  Download,
-  Loader,
-  TrendingUp,
-  CheckCircle,
-  Server,
-  RefreshCw,
-  AlertCircle,
-  Sparkles,
-} from 'lucide-react'
-import { apiClient } from '../../lib/api'
-
-interface SpeakerSegment {
-  speaker: string
-  text: string
-  start: number
-  end: number
-}
-
-interface CustomWSCallData {
-  source?: string
-  websocket_url?: string
-  messages?: Array<{ role: string; content: string; timestamp?: string }>
-  transcript?: string
-  speaker_segments?: SpeakerSegment[]
-  recording_s3_key?: string | null
-  started_at?: string | null
-  ended_at?: string | null
-  duration_seconds?: number
-}
-
-interface Props {
-  callData: CustomWSCallData
-  callShortId?: string
-}
-
-function formatDuration(seconds: number): string {
-  const m = Math.floor(seconds / 60)
-  const s = Math.floor(seconds % 60)
-  return m > 0 ? `${m}m ${s}s` : `${s}s`
-}
-
-function formatTimestamp(ts?: string | null): string {
-  if (!ts) return 'N/A'
-  try {
-    return new Date(ts).toLocaleString()
-  } catch {
-    return 'N/A'
-  }
-}
-
-const isUserSpeaker = (speaker: string) =>
-  speaker === 'user' || speaker === 'Speaker 1' || speaker === 'caller'
-
-const getSpeakerLabel = (speaker: string) =>
-  isUserSpeaker(speaker) ? 'You' : 'Agent'
-
-export default function CustomWebSocketCallDetails({ callData, callShortId }: Props) {
-  const [activeTab, setActiveTab] = useState<'overview' | 'transcript'>('overview')
-  const [audioBlobUrl, setAudioBlobUrl] = useState<string | null>(null)
-  const [audioLoading, setAudioLoading] = useState(false)
-  const [audioError, setAudioError] = useState(false)
-  const audioFetched = useRef(false)
-  const blobUrlRef = useRef<string | null>(null)
-
-  const [llmSummary, setLlmSummary] = useState<string | null>(null)
-  const [summaryProvider, setSummaryProvider] = useState<{
-    provider?: string
-    model?: string
-    source?: 'voice_bundle' | 'org_fallback'
-  } | null>(null)
-  const [summaryLoading, setSummaryLoading] = useState(false)
-  const [summaryError, setSummaryError] = useState<string | null>(null)
-  const summaryFetched = useRef(false)
-
-  const hasAudio = !!callData.recording_s3_key
-  const duration = callData.duration_seconds || 0
-  const segments = callData.speaker_segments || []
-  const messages = callData.messages || []
-
-  useEffect(() => {
-    if (!callShortId || !hasAudio || audioFetched.current) return
-    audioFetched.current = true
-    setAudioLoading(true)
-    apiClient.getCallRecordingAudioUrl(callShortId)
-      .then(url => {
-        blobUrlRef.current = url
-        setAudioBlobUrl(url)
-      })
-      .catch(() => setAudioError(true))
-      .finally(() => setAudioLoading(false))
-
-    return () => {
-      if (blobUrlRef.current) URL.revokeObjectURL(blobUrlRef.current)
-    }
-  }, [callShortId, hasAudio])
-
-  const { userTurns, agentTurns } = useMemo(() => {
-    const userSegs = segments.filter(s => isUserSpeaker(s.speaker))
-    const agentSegs = segments.filter(s => !isUserSpeaker(s.speaker))
-    return {
-      userTurns: userSegs.length,
-      agentTurns: agentSegs.length,
-    }
-  }, [segments])
-
-  const entriesForSummary = useMemo(() => {
-    if (messages.length > 0) {
-      return messages.map(m => ({
-        role: m.role,
-        content: m.content,
-        timestamp: m.timestamp,
-      }))
-    }
-    if (segments.length > 0) {
-      return segments.map(s => ({
-        role: isUserSpeaker(s.speaker) ? 'user' : 'agent',
-        content: s.text,
-      }))
-    }
-    return []
-  }, [messages, segments])
-
-  const transcriptForSummary = (callData.transcript || '').trim()
-  const hasTranscript = entriesForSummary.length > 0 || transcriptForSummary.length > 0
-
-  const fetchSummary = async (opts?: { force?: boolean }) => {
-    if (!hasTranscript) return
-    setSummaryLoading(true)
-    setSummaryError(null)
-    try {
-      const res = await apiClient.summarizeTranscript({
-        entries: entriesForSummary.length > 0 ? entriesForSummary : undefined,
-        transcript: entriesForSummary.length === 0 ? transcriptForSummary : undefined,
-        callShortId,
-        force: opts?.force,
-      })
-      setLlmSummary(res.summary)
-      setSummaryProvider({ provider: res.provider, model: res.model, source: res.source })
-    } catch (err: any) {
-      const msg =
-        err?.response?.data?.detail ||
-        err?.message ||
-        'Failed to generate summary'
-      setSummaryError(msg)
-    } finally {
-      setSummaryLoading(false)
-    }
-  }
-
-  useEffect(() => {
-    if (summaryFetched.current) return
-    if (!hasTranscript) return
-    summaryFetched.current = true
-    fetchSummary()
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [hasTranscript])
-
-  const tabs = [
-    { id: 'overview' as const, label: 'Overview' },
-    { id: 'transcript' as const, label: 'Transcript' },
-  ]
-
-  const SummaryCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-      <div className="flex items-center justify-between mb-4">
-        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
-          <TrendingUp className="h-5 w-5 text-indigo-600" />
-          Session Summary
-        </h3>
-        {hasTranscript && (
-          <button
-            type="button"
-            onClick={() => fetchSummary({ force: true })}
-            disabled={summaryLoading}
-            className="inline-flex items-center gap-1.5 text-xs font-medium text-indigo-600 hover:text-indigo-700 disabled:opacity-50"
-            title="Regenerate summary"
-          >
-            <RefreshCw className={`h-3.5 w-3.5 ${summaryLoading ? 'animate-spin' : ''}`} />
-            {summaryLoading ? 'Summarizing…' : 'Regenerate'}
-          </button>
-        )}
-      </div>
-
-      <div className="space-y-4">
-        <div className="p-4 bg-indigo-50 rounded-lg border border-indigo-100">
-          <div className="flex items-center justify-between mb-2">
-            <p className="text-sm font-medium text-indigo-900 flex items-center gap-1.5">
-              <Sparkles className="h-3.5 w-3.5" />
-              AI Summary
-            </p>
-            {summaryProvider?.provider && !summaryLoading && (
-              <div className="flex items-center gap-1.5">
-                {summaryProvider.source === 'voice_bundle' && (
-                  <span className="text-[10px] px-1.5 py-0.5 bg-emerald-100 text-emerald-700 rounded">
-                    voice bundle
-                  </span>
-                )}
-                {summaryProvider.source === 'org_fallback' && (
-                  <span className="text-[10px] px-1.5 py-0.5 bg-amber-100 text-amber-700 rounded">
-                    org fallback
-                  </span>
-                )}
-                <span className="text-[10px] px-1.5 py-0.5 bg-indigo-100 text-indigo-700 rounded font-mono">
-                  {summaryProvider.provider}
-                  {summaryProvider.model ? ` · ${summaryProvider.model}` : ''}
-                </span>
-              </div>
-            )}
-          </div>
-
-          {summaryLoading && !llmSummary ? (
-            <div className="flex items-center gap-2 text-sm text-indigo-800">
-              <Loader className="h-4 w-4 animate-spin" />
-              <span>Generating summary with LLM…</span>
-            </div>
-          ) : summaryError ? (
-            <div className="flex items-start gap-2 text-sm text-red-700">
-              <AlertCircle className="h-4 w-4 flex-shrink-0 mt-0.5" />
-              <span>{summaryError}</span>
-            </div>
-          ) : llmSummary ? (
-            <p className="text-sm text-indigo-800 leading-relaxed whitespace-pre-wrap">{llmSummary}</p>
-          ) : !hasTranscript ? (
-            <p className="text-sm text-indigo-800/70">No transcript available to summarize.</p>
-          ) : (
-            <p className="text-sm text-indigo-800/70">Summary will appear here shortly.</p>
-          )}
-        </div>
-
-        <div className="grid grid-cols-2 gap-3">
-          <div className="p-3 bg-gray-50 rounded-lg">
-            <p className="text-[10px] uppercase tracking-wider text-gray-500 font-semibold mb-1">Status</p>
-            <div className="flex items-center gap-1.5">
-              <CheckCircle className="h-3.5 w-3.5 text-green-600" />
-              <span className="text-sm font-medium text-gray-900">
-                {segments.length > 0 ? 'Completed' : 'No transcript'}
-              </span>
-            </div>
-          </div>
-          <div className="p-3 bg-gray-50 rounded-lg">
-            <p className="text-[10px] uppercase tracking-wider text-gray-500 font-semibold mb-1">User / Agent turns</p>
-            <p className="text-sm font-medium text-gray-900">{userTurns} / {agentTurns}</p>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-
-  const TranscriptCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col h-[600px]">
-      <div className="flex items-center justify-between mb-4 flex-shrink-0">
-        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
-          <MessageSquare className="h-5 w-5 text-indigo-600" />
-          Transcript
-        </h3>
-        <div className="flex items-center gap-3">
-          {audioLoading && <Loader className="h-4 w-4 text-gray-400 animate-spin" />}
-          {audioBlobUrl && (
-            <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
-              <audio controls src={audioBlobUrl} className="h-8 w-64" />
-              <a
-                href={audioBlobUrl}
-                download={`call_${callShortId || 'recording'}.webm`}
-                className="text-gray-500 hover:text-indigo-600 p-1"
-              >
-                <Download className="h-4 w-4" />
-              </a>
-            </div>
-          )}
-          {audioError && hasAudio && (
-            <span className="text-xs text-gray-400">Audio unavailable</span>
-          )}
-        </div>
-      </div>
-
-      <div className="flex-1 overflow-y-auto space-y-4 pr-2">
-        {segments.length > 0 ? (
-          segments.map((seg, idx) => (
-            <div key={idx} className={`flex ${isUserSpeaker(seg.speaker) ? 'justify-end' : 'justify-start'}`}>
-              <div className={`max-w-[80%] rounded-2xl px-4 py-3 ${
-                isUserSpeaker(seg.speaker)
-                  ? 'bg-indigo-600 text-white rounded-br-none'
-                  : 'bg-gray-100 text-gray-800 rounded-bl-none'
-              }`}>
-                <div className="flex items-center gap-2 mb-1 opacity-80">
-                  <span className="text-xs font-semibold uppercase tracking-wider">
-                    {getSpeakerLabel(seg.speaker)}
-                  </span>
-                </div>
-                <p className="text-sm leading-relaxed whitespace-pre-wrap">{seg.text}</p>
-              </div>
-            </div>
-          ))
-        ) : callData.transcript ? (
-          <div className="p-4 bg-gray-50 rounded-lg">
-            <pre className="text-sm whitespace-pre-wrap text-gray-800 font-sans">
-              {callData.transcript}
-            </pre>
-          </div>
-        ) : (
-          <div className="text-center py-8 text-gray-500">
-            No transcript available
-          </div>
-        )}
-      </div>
-    </div>
-  )
-
-  const SessionDetailsCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-      <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-        <Server className="h-5 w-5 text-indigo-600" />
-        Session Details
-      </h3>
-      <div className="grid grid-cols-1 gap-4 text-sm">
-        <div>
-          <p className="text-gray-500 mb-1 flex items-center gap-1">
-            <Globe className="h-3 w-3" /> WebSocket URL
-          </p>
-          <p className="font-mono bg-gray-50 p-1.5 rounded text-gray-700 text-xs break-all">
-            {callData.websocket_url || 'N/A'}
-          </p>
-        </div>
-        <div className="grid grid-cols-2 gap-3">
-          <div>
-            <p className="text-gray-500 mb-1 flex items-center gap-1">
-              <Clock className="h-3 w-3" /> Duration
-            </p>
-            <p className="text-gray-700">
-              {duration > 0 ? formatDuration(duration) : 'N/A'}
-            </p>
-          </div>
-          <div>
-            <p className="text-gray-500 mb-1 flex items-center gap-1">
-              <MessageSquare className="h-3 w-3" /> Messages
-            </p>
-            <p className="text-gray-700">{messages.length}</p>
-          </div>
-        </div>
-        <div className="grid grid-cols-2 gap-3">
-          <div>
-            <p className="text-gray-500 mb-1 flex items-center gap-1">
-              <Clock className="h-3 w-3" /> Started
-            </p>
-            <p className="text-gray-700 text-xs">
-              {formatTimestamp(callData.started_at)}
-            </p>
-          </div>
-          <div>
-            <p className="text-gray-500 mb-1 flex items-center gap-1">
-              <Clock className="h-3 w-3" /> Ended
-            </p>
-            <p className="text-gray-700 text-xs">
-              {formatTimestamp(callData.ended_at)}
-            </p>
-          </div>
-        </div>
-        {callShortId && (
-          <div>
-            <p className="text-gray-500 mb-1">Call ID</p>
-            <p className="font-mono bg-gray-50 p-1 rounded text-gray-700 text-xs">{callShortId}</p>
-          </div>
-        )}
-      </div>
-    </div>
-  )
-
-  return (
-    <div>
-      <div className="mb-4">
-        <h2 className="text-lg font-semibold text-gray-900">Custom WebSocket Session</h2>
-      </div>
-
-      {/* Tab navigation */}
-      <div className="flex border-b border-gray-200 mb-4">
-        {tabs.map(tab => (
-          <button
-            key={tab.id}
-            onClick={() => setActiveTab(tab.id)}
-            className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
-              activeTab === tab.id
-                ? 'border-indigo-600 text-indigo-600'
-                : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-            }`}
-          >
-            {tab.label}
-          </button>
-        ))}
-      </div>
-
-      {activeTab === 'overview' && (
-        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
-          <div className="lg:col-span-2 space-y-6">
-            <SummaryCard />
-            <TranscriptCard />
-          </div>
-          <div className="lg:col-span-1">
-            <SessionDetailsCard />
-          </div>
-        </div>
-      )}
-
-      {activeTab === 'transcript' && (
-        <div className="space-y-6">
-          <TranscriptCard />
-        </div>
-      )}
-    </div>
-  )
-}
+import { useState, useEffect, useRef, useMemo } from 'react'
+import {
+  MessageSquare,
+  Clock,
+  Globe,
+  Download,
+  Loader,
+  TrendingUp,
+  CheckCircle,
+  Server,
+  RefreshCw,
+  AlertCircle,
+  Sparkles,
+} from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import SyntheticCallTracePanel from './SyntheticCallTracePanel'
+
+interface SpeakerSegment {
+  speaker: string
+  text: string
+  start: number
+  end: number
+}
+
+interface CustomWSCallData {
+  source?: string
+  websocket_url?: string
+  messages?: Array<{ role: string; content: string; timestamp?: string }>
+  transcript?: string
+  speaker_segments?: SpeakerSegment[]
+  recording_s3_key?: string | null
+  started_at?: string | null
+  ended_at?: string | null
+  duration_seconds?: number
+}
+
+interface Props {
+  callData: CustomWSCallData
+  callShortId?: string
+}
+
+function formatDuration(seconds: number): string {
+  const m = Math.floor(seconds / 60)
+  const s = Math.floor(seconds % 60)
+  return m > 0 ? `${m}m ${s}s` : `${s}s`
+}
+
+function formatTimestamp(ts?: string | null): string {
+  if (!ts) return 'N/A'
+  try {
+    return new Date(ts).toLocaleString()
+  } catch {
+    return 'N/A'
+  }
+}
+
+const isUserSpeaker = (speaker: string) =>
+  speaker === 'user' || speaker === 'Speaker 1' || speaker === 'caller'
+
+const getSpeakerLabel = (speaker: string) =>
+  isUserSpeaker(speaker) ? 'You' : 'Agent'
+
+export default function CustomWebSocketCallDetails({ callData, callShortId }: Props) {
+  const [activeTab, setActiveTab] = useState<'overview' | 'transcript'>('overview')
+  const [audioBlobUrl, setAudioBlobUrl] = useState<string | null>(null)
+  const [audioLoading, setAudioLoading] = useState(false)
+  const [audioError, setAudioError] = useState(false)
+  const audioFetched = useRef(false)
+  const blobUrlRef = useRef<string | null>(null)
+
+  const [llmSummary, setLlmSummary] = useState<string | null>(null)
+  const [summaryProvider, setSummaryProvider] = useState<{
+    provider?: string
+    model?: string
+    source?: 'voice_bundle' | 'org_fallback'
+  } | null>(null)
+  const [summaryLoading, setSummaryLoading] = useState(false)
+  const [summaryError, setSummaryError] = useState<string | null>(null)
+  const summaryFetched = useRef(false)
+
+  const hasAudio = !!callData.recording_s3_key
+  const duration = callData.duration_seconds || 0
+  const segments = callData.speaker_segments || []
+  const messages = callData.messages || []
+
+  useEffect(() => {
+    if (!callShortId || !hasAudio || audioFetched.current) return
+    audioFetched.current = true
+    setAudioLoading(true)
+    apiClient.getCallRecordingAudioUrl(callShortId)
+      .then(url => {
+        blobUrlRef.current = url
+        setAudioBlobUrl(url)
+      })
+      .catch(() => setAudioError(true))
+      .finally(() => setAudioLoading(false))
+
+    return () => {
+      if (blobUrlRef.current) URL.revokeObjectURL(blobUrlRef.current)
+    }
+  }, [callShortId, hasAudio])
+
+  const { userTurns, agentTurns } = useMemo(() => {
+    const userSegs = segments.filter(s => isUserSpeaker(s.speaker))
+    const agentSegs = segments.filter(s => !isUserSpeaker(s.speaker))
+    return {
+      userTurns: userSegs.length,
+      agentTurns: agentSegs.length,
+    }
+  }, [segments])
+
+  const entriesForSummary = useMemo(() => {
+    if (messages.length > 0) {
+      return messages.map(m => ({
+        role: m.role,
+        content: m.content,
+        timestamp: m.timestamp,
+      }))
+    }
+    if (segments.length > 0) {
+      return segments.map(s => ({
+        role: isUserSpeaker(s.speaker) ? 'user' : 'agent',
+        content: s.text,
+      }))
+    }
+    return []
+  }, [messages, segments])
+
+  const transcriptForSummary = (callData.transcript || '').trim()
+  const hasTranscript = entriesForSummary.length > 0 || transcriptForSummary.length > 0
+
+  const fetchSummary = async (opts?: { force?: boolean }) => {
+    if (!hasTranscript) return
+    setSummaryLoading(true)
+    setSummaryError(null)
+    try {
+      const res = await apiClient.summarizeTranscript({
+        entries: entriesForSummary.length > 0 ? entriesForSummary : undefined,
+        transcript: entriesForSummary.length === 0 ? transcriptForSummary : undefined,
+        callShortId,
+        force: opts?.force,
+      })
+      setLlmSummary(res.summary)
+      setSummaryProvider({ provider: res.provider, model: res.model, source: res.source })
+    } catch (err: any) {
+      const msg =
+        err?.response?.data?.detail ||
+        err?.message ||
+        'Failed to generate summary'
+      setSummaryError(msg)
+    } finally {
+      setSummaryLoading(false)
+    }
+  }
+
+  useEffect(() => {
+    if (summaryFetched.current) return
+    if (!hasTranscript) return
+    summaryFetched.current = true
+    fetchSummary()
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [hasTranscript])
+
+  const tabs = [
+    { id: 'overview' as const, label: 'Overview' },
+    { id: 'transcript' as const, label: 'Transcript' },
+  ]
+
+  const SummaryCard = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+      <div className="flex items-center justify-between mb-4">
+        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
+          <TrendingUp className="h-5 w-5 text-indigo-600" />
+          Session Summary
+        </h3>
+        {hasTranscript && (
+          <button
+            type="button"
+            onClick={() => fetchSummary({ force: true })}
+            disabled={summaryLoading}
+            className="inline-flex items-center gap-1.5 text-xs font-medium text-indigo-600 hover:text-indigo-700 disabled:opacity-50"
+            title="Regenerate summary"
+          >
+            <RefreshCw className={`h-3.5 w-3.5 ${summaryLoading ? 'animate-spin' : ''}`} />
+            {summaryLoading ? 'Summarizing…' : 'Regenerate'}
+          </button>
+        )}
+      </div>
+
+      <div className="space-y-4">
+        <div className="p-4 bg-indigo-50 rounded-lg border border-indigo-100">
+          <div className="flex items-center justify-between mb-2">
+            <p className="text-sm font-medium text-indigo-900 flex items-center gap-1.5">
+              <Sparkles className="h-3.5 w-3.5" />
+              AI Summary
+            </p>
+            {summaryProvider?.provider && !summaryLoading && (
+              <div className="flex items-center gap-1.5">
+                {summaryProvider.source === 'voice_bundle' && (
+                  <span className="text-[10px] px-1.5 py-0.5 bg-emerald-100 text-emerald-700 rounded">
+                    voice bundle
+                  </span>
+                )}
+                {summaryProvider.source === 'org_fallback' && (
+                  <span className="text-[10px] px-1.5 py-0.5 bg-amber-100 text-amber-700 rounded">
+                    org fallback
+                  </span>
+                )}
+                <span className="text-[10px] px-1.5 py-0.5 bg-indigo-100 text-indigo-700 rounded font-mono">
+                  {summaryProvider.provider}
+                  {summaryProvider.model ? ` · ${summaryProvider.model}` : ''}
+                </span>
+              </div>
+            )}
+          </div>
+
+          {summaryLoading && !llmSummary ? (
+            <div className="flex items-center gap-2 text-sm text-indigo-800">
+              <Loader className="h-4 w-4 animate-spin" />
+              <span>Generating summary with LLM…</span>
+            </div>
+          ) : summaryError ? (
+            <div className="flex items-start gap-2 text-sm text-red-700">
+              <AlertCircle className="h-4 w-4 flex-shrink-0 mt-0.5" />
+              <span>{summaryError}</span>
+            </div>
+          ) : llmSummary ? (
+            <p className="text-sm text-indigo-800 leading-relaxed whitespace-pre-wrap">{llmSummary}</p>
+          ) : !hasTranscript ? (
+            <p className="text-sm text-indigo-800/70">No transcript available to summarize.</p>
+          ) : (
+            <p className="text-sm text-indigo-800/70">Summary will appear here shortly.</p>
+          )}
+        </div>
+
+        <div className="grid grid-cols-2 gap-3">
+          <div className="p-3 bg-gray-50 rounded-lg">
+            <p className="text-[10px] uppercase tracking-wider text-gray-500 font-semibold mb-1">Status</p>
+            <div className="flex items-center gap-1.5">
+              <CheckCircle className="h-3.5 w-3.5 text-green-600" />
+              <span className="text-sm font-medium text-gray-900">
+                {segments.length > 0 ? 'Completed' : 'No transcript'}
+              </span>
+            </div>
+          </div>
+          <div className="p-3 bg-gray-50 rounded-lg">
+            <p className="text-[10px] uppercase tracking-wider text-gray-500 font-semibold mb-1">Transcript turns</p>
+            <p className="text-sm font-medium text-gray-900">{userTurns} user / {agentTurns} agent</p>
+            <p className="text-[10px] text-gray-400 mt-1">From diarized transcript, not OTLP trace turns</p>
+          </div>
+        </div>
+      </div>
+    </div>
+  )
+
+  const TranscriptCard = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col h-[600px]">
+      <div className="flex items-center justify-between mb-4 flex-shrink-0">
+        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
+          <MessageSquare className="h-5 w-5 text-indigo-600" />
+          Transcript
+        </h3>
+        <div className="flex items-center gap-3">
+          {audioLoading && <Loader className="h-4 w-4 text-gray-400 animate-spin" />}
+          {audioBlobUrl && (
+            <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
+              <audio controls src={audioBlobUrl} className="h-8 w-64" />
+              <a
+                href={audioBlobUrl}
+                download={`call_${callShortId || 'recording'}.webm`}
+                className="text-gray-500 hover:text-indigo-600 p-1"
+              >
+                <Download className="h-4 w-4" />
+              </a>
+            </div>
+          )}
+          {audioError && hasAudio && (
+            <span className="text-xs text-gray-400">Audio unavailable</span>
+          )}
+        </div>
+      </div>
+
+      <div className="flex-1 overflow-y-auto space-y-4 pr-2">
+        {segments.length > 0 ? (
+          segments.map((seg, idx) => (
+            <div key={idx} className={`flex ${isUserSpeaker(seg.speaker) ? 'justify-end' : 'justify-start'}`}>
+              <div className={`max-w-[80%] rounded-2xl px-4 py-3 ${
+                isUserSpeaker(seg.speaker)
+                  ? 'bg-indigo-600 text-white rounded-br-none'
+                  : 'bg-gray-100 text-gray-800 rounded-bl-none'
+              }`}>
+                <div className="flex items-center gap-2 mb-1 opacity-80">
+                  <span className="text-xs font-semibold uppercase tracking-wider">
+                    {getSpeakerLabel(seg.speaker)}
+                  </span>
+                </div>
+                <p className="text-sm leading-relaxed whitespace-pre-wrap">{seg.text}</p>
+              </div>
+            </div>
+          ))
+        ) : callData.transcript ? (
+          <div className="p-4 bg-gray-50 rounded-lg">
+            <pre className="text-sm whitespace-pre-wrap text-gray-800 font-sans">
+              {callData.transcript}
+            </pre>
+          </div>
+        ) : (
+          <div className="text-center py-8 text-gray-500">
+            No transcript available
+          </div>
+        )}
+      </div>
+    </div>
+  )
+
+  const SessionDetailsCard = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+      <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+        <Server className="h-5 w-5 text-indigo-600" />
+        Session Details
+      </h3>
+      <div className="grid grid-cols-1 gap-4 text-sm">
+        <div>
+          <p className="text-gray-500 mb-1 flex items-center gap-1">
+            <Globe className="h-3 w-3" /> WebSocket URL
+          </p>
+          <p className="font-mono bg-gray-50 p-1.5 rounded text-gray-700 text-xs break-all">
+            {callData.websocket_url || 'N/A'}
+          </p>
+        </div>
+        <div className="grid grid-cols-2 gap-3">
+          <div>
+            <p className="text-gray-500 mb-1 flex items-center gap-1">
+              <Clock className="h-3 w-3" /> Duration
+            </p>
+            <p className="text-gray-700">
+              {duration > 0 ? formatDuration(duration) : 'N/A'}
+            </p>
+          </div>
+          <div>
+            <p className="text-gray-500 mb-1 flex items-center gap-1">
+              <MessageSquare className="h-3 w-3" /> Messages
+            </p>
+            <p className="text-gray-700">{messages.length}</p>
+          </div>
+        </div>
+        <div className="grid grid-cols-2 gap-3">
+          <div>
+            <p className="text-gray-500 mb-1 flex items-center gap-1">
+              <Clock className="h-3 w-3" /> Started
+            </p>
+            <p className="text-gray-700 text-xs">
+              {formatTimestamp(callData.started_at)}
+            </p>
+          </div>
+          <div>
+            <p className="text-gray-500 mb-1 flex items-center gap-1">
+              <Clock className="h-3 w-3" /> Ended
+            </p>
+            <p className="text-gray-700 text-xs">
+              {formatTimestamp(callData.ended_at)}
+            </p>
+          </div>
+        </div>
+        {callShortId && (
+          <div>
+            <p className="text-gray-500 mb-1">Call ID</p>
+            <p className="font-mono bg-gray-50 p-1 rounded text-gray-700 text-xs">{callShortId}</p>
+          </div>
+        )}
+      </div>
+    </div>
+  )
+
+  return (
+    <div>
+      <div className="mb-4">
+        <h2 className="text-lg font-semibold text-gray-900">Custom WebSocket Session</h2>
+      </div>
+
+      {/* Tab navigation */}
+      <div className="flex border-b border-gray-200 mb-4">
+        {tabs.map(tab => (
+          <button
+            key={tab.id}
+            onClick={() => setActiveTab(tab.id)}
+            className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
+              activeTab === tab.id
+                ? 'border-indigo-600 text-indigo-600'
+                : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+            }`}
+          >
+            {tab.label}
+          </button>
+        ))}
+      </div>
+
+      {activeTab === 'overview' && (
+        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
+          <div className="lg:col-span-2 space-y-6">
+            {callShortId && (
+              <SyntheticCallTracePanel callShortId={callShortId} />
+            )}
+            <SummaryCard />
+            <TranscriptCard />
+          </div>
+          <div className="lg:col-span-1">
+            <SessionDetailsCard />
+          </div>
+        </div>
+      )}
+
+      {activeTab === 'transcript' && (
+        <div className="space-y-6">
+          <TranscriptCard />
+        </div>
+      )}
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx b/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
new file mode 100644
index 00000000..30d6a76c
--- /dev/null
+++ b/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
@@ -0,0 +1,750 @@
+import { useMemo, useState } from 'react'
+import { useQuery } from '@tanstack/react-query'
+import { Link } from 'react-router-dom'
+import {
+  Activity,
+  ChevronDown,
+  ChevronRight,
+  Clock,
+  Cpu,
+  Mic,
+  MessageSquare,
+  Radio,
+  Volume2,
+  X,
+  Zap,
+} from 'lucide-react'
+import { apiClient } from '../../lib/api'
+
+interface SyntheticCallTracePanelProps {
+  evaluatorResultId?: string
+  traceId?: string
+  callShortId?: string
+  onClose?: () => void
+}
+
+interface ComponentMeta {
+  model?: string | null
+  provider?: string | null
+}
+
+interface PipelineModels {
+  stt?: ComponentMeta
+  llm?: ComponentMeta
+  tts?: ComponentMeta
+  s2s?: ComponentMeta
+}
+
+interface TraceTurnExtra {
+  user_text?: string
+  assistant_text?: string
+  agent_id?: string
+  agent_role?: string
+  was_interrupted?: boolean
+  pipeline_mode?: 'stt_llm_tts' | 's2s'
+  stt_model?: string
+  llm_model?: string
+  tts_model?: string
+  s2s_model?: string
+  stt_provider?: string
+  llm_provider?: string
+  tts_provider?: string
+  s2s_provider?: string
+}
+
+interface TraceTurn {
+  turn_number: number
+  sut_response_latency_ms?: number | null
+  talk_over?: boolean
+  stt_ttfb_ms?: number | null
+  llm_ttfb_ms?: number | null
+  tts_ttfb_ms?: number | null
+  s2s_ttfb_ms?: number | null
+  transcript?: string | null
+  extra?: TraceTurnExtra
+}
+
+interface OtelSpan {
+  trace_id: string
+  span_id: string
+  parent_span_id?: string | null
+  name: string
+  start_time_unix_nano?: number | null
+  end_time_unix_nano?: number | null
+  attributes?: Record<string, unknown>
+  events?: Array<Record<string, unknown>>
+}
+
+type DetailTab = 'conversation' | 'latency' | 'raw'
+type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
+
+const COMPONENT_STYLES: Record<
+  ComponentKind,
+  { label: string; bar: string; text: string; bg: string; border: string; icon: typeof Mic }
+> = {
+  stt: {
+    label: 'STT',
+    bar: 'bg-sky-500',
+    text: 'text-sky-700',
+    bg: 'bg-sky-50',
+    border: 'border-sky-200',
+    icon: Mic,
+  },
+  llm: {
+    label: 'LLM',
+    bar: 'bg-violet-500',
+    text: 'text-violet-700',
+    bg: 'bg-violet-50',
+    border: 'border-violet-200',
+    icon: Cpu,
+  },
+  tts: {
+    label: 'TTS',
+    bar: 'bg-emerald-500',
+    text: 'text-emerald-700',
+    bg: 'bg-emerald-50',
+    border: 'border-emerald-200',
+    icon: Volume2,
+  },
+  s2s: {
+    label: 'S2S',
+    bar: 'bg-fuchsia-500',
+    text: 'text-fuchsia-700',
+    bg: 'bg-fuchsia-50',
+    border: 'border-fuchsia-200',
+    icon: Radio,
+  },
+}
+
+function formatMs(value?: number | null): string {
+  if (value == null || Number.isNaN(value)) return '—'
+  return `${Math.round(value)}ms`
+}
+
+function latencyTone(ms?: number | null): string {
+  if (ms == null) return 'text-gray-900'
+  if (ms < 800) return 'text-emerald-700'
+  if (ms < 2000) return 'text-amber-700'
+  return 'text-rose-700'
+}
+
+function shortModel(raw: unknown): string | null {
+  if (raw == null) return null
+  const text = String(raw).trim()
+  if (!text) return null
+  if (text.includes('/models/')) {
+    return text.split('/models/')[1]?.split('/')[0] ?? text
+  }
+  if (text.includes('/')) return text.split('/').pop() ?? text
+  return text
+}
+
+function attrStr(attrs: Record<string, unknown> | undefined, key: string): string | null {
+  const val = attrs?.[key]
+  if (val == null) return null
+  return String(val)
+}
+
+function spanKind(span: OtelSpan): ComponentKind | null {
+  const op = attrStr(span.attributes, 'gen_ai.operation.name')?.toLowerCase()
+  const name = span.name.toLowerCase()
+  if (op === 's2s' || name === 's2s') return 's2s'
+  if (op === 'stt' || name === 'stt') return 'stt'
+  if (op === 'tts' || name === 'tts') return 'tts'
+  if (op === 'chat' || op === 'llm' || op === 'llm_response' || name === 'llm' || name === 'llm_response') {
+    return 'llm'
+  }
+  return null
+}
+
+function metaFromSpan(span: OtelSpan): ComponentMeta {
+  const attrs = span.attributes ?? {}
+  const kind = spanKind(span)
+  const model =
+    shortModel(attrs['gen_ai.request.model']) ??
+    shortModel(attrs['param.model']) ??
+    (kind === 'tts' || kind === 'stt' ? shortModel(attrs['settings.model']) : null)
+  const provider =
+    attrStr(attrs, 'gen_ai.provider.name') ?? attrStr(attrs, 'gen_ai.system')
+  return { model, provider: provider?.toLowerCase() ?? null }
+}
+
+function parseTranscript(transcript?: string | null): { user?: string; assistant?: string } {
+  if (!transcript) return {}
+  const userMatch = transcript.match(/^User:\s*(.+)$/m)
+  const assistantMatch = transcript.match(/^Assistant:\s*(.+)$/m)
+  if (userMatch || assistantMatch) {
+    return { user: userMatch?.[1]?.trim(), assistant: assistantMatch?.[1]?.trim() }
+  }
+  if (transcript.trim().startsWith('[') || transcript.trim().startsWith('{')) return {}
+  return { assistant: transcript.trim() }
+}
+
+function isS2sTurn(turn: TraceTurn): boolean {
+  return turn.extra?.pipeline_mode === 's2s' || turn.s2s_ttfb_ms != null
+}
+
+function resolveTurnNumber(span: OtelSpan, byId: Map<string, OtelSpan>): number | null {
+  const display = span.attributes?.['efficientai.display_turn_number']
+  if (display != null) {
+    const n = Number(display)
+    if (!Number.isNaN(n)) return n
+  }
+  const visited = new Set<string>()
+  let current: OtelSpan | undefined = span
+  while (current) {
+    const raw = current.attributes?.['turn.number']
+    if (raw != null) {
+      const n = Number(raw)
+      if (!Number.isNaN(n)) return n
+    }
+    const spanId = current.span_id
+    if (spanId) {
+      if (visited.has(spanId)) break
+      visited.add(spanId)
+    }
+    const parentId = current.parent_span_id
+    if (!parentId) break
+    current = byId.get(parentId)
+  }
+  return null
+}
+
+function buildSpansByTurn(spans: OtelSpan[]): Map<number, OtelSpan[]> {
+  const byId = new Map(spans.map((s) => [s.span_id, s]))
+  const map = new Map<number, OtelSpan[]>()
+  for (const span of spans) {
+    const turnNum = resolveTurnNumber(span, byId)
+    if (turnNum == null) continue
+    const list = map.get(turnNum) ?? []
+    list.push(span)
+    map.set(turnNum, list)
+  }
+  return map
+}
+
+function turnMetaFromSpans(spans: OtelSpan[]): Partial<TraceTurnExtra> {
+  const out: Partial<TraceTurnExtra> = {}
+  for (const span of spans) {
+    const kind = spanKind(span)
+    if (!kind) continue
+    const meta = metaFromSpan(span)
+    if (meta.model && !out[`${kind}_model` as keyof TraceTurnExtra]) {
+      ;(out as Record<string, string>)[`${kind}_model`] = meta.model
+    }
+    if (meta.provider && !out[`${kind}_provider` as keyof TraceTurnExtra]) {
+      ;(out as Record<string, string>)[`${kind}_provider`] = meta.provider
+    }
+  }
+  return out
+}
+
+function providerLabel(provider?: string | null, model?: string | null): string {
+  if (provider && model) return `${provider} · ${model}`
+  return provider ?? model ?? '—'
+}
+
+function mergeTurnMeta(
+  turn: TraceTurn,
+  spanMeta: Partial<TraceTurnExtra>,
+  sessionModels: PipelineModels,
+): TraceTurnExtra {
+  const extra = { ...spanMeta, ...turn.extra }
+  const fill = (kind: ComponentKind) => {
+    const modelKey = `${kind}_model` as keyof TraceTurnExtra
+    const providerKey = `${kind}_provider` as keyof TraceTurnExtra
+    if (!extra[modelKey] && sessionModels[kind]?.model) {
+      ;(extra as Record<string, string>)[modelKey] = sessionModels[kind]!.model!
+    }
+    if (!extra[providerKey] && sessionModels[kind]?.provider) {
+      ;(extra as Record<string, string>)[providerKey] = sessionModels[kind]!.provider!
+    }
+  }
+  if (isS2sTurn(turn)) {
+    fill('s2s')
+  } else {
+    fill('stt')
+    fill('llm')
+    fill('tts')
+  }
+  return extra
+}
+
+function PipelineModelsBar({ models }: { models: PipelineModels }) {
+  const items = (['stt', 'llm', 'tts', 's2s'] as const)
+    .map((key) => ({ key, meta: models[key], ...COMPONENT_STYLES[key] }))
+    .filter((item) => item.meta?.model || item.meta?.provider)
+
+  if (items.length === 0) return null
+
+  return (
+    <div className="px-5 py-3 border-b border-gray-100 bg-slate-50/60 shrink-0">
+      <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400 mb-2.5">
+        Voice pipeline models
+      </p>
+      <div className="grid grid-cols-2 sm:grid-cols-4 gap-2">
+        {items.map(({ key, meta, label, icon: Icon, bg, border, text }) => (
+          <div key={key} className={`rounded-lg border ${border} ${bg} px-3 py-2.5`}>
+            <div className="flex items-center gap-2 mb-1">
+              <Icon className={`w-3.5 h-3.5 ${text}`} />
+              <span className={`text-[10px] font-bold uppercase tracking-wider ${text}`}>{label}</span>
+            </div>
+            <p className="text-xs font-semibold text-gray-900 truncate" title={meta?.model ?? undefined}>
+              {meta?.model ?? '—'}
+            </p>
+            {meta?.provider && (
+              <p className="text-[11px] text-gray-500 truncate mt-0.5">{meta.provider}</p>
+            )}
+          </div>
+        ))}
+      </div>
+    </div>
+  )
+}
+
+interface WaterfallSegment {
+  kind: ComponentKind
+  ms: number
+}
+
+function buildWaterfallSegments(turn: TraceTurn): WaterfallSegment[] {
+  if (isS2sTurn(turn)) {
+    const ms = turn.s2s_ttfb_ms ?? turn.sut_response_latency_ms
+    return ms != null ? [{ kind: 's2s', ms }] : []
+  }
+  const segments: WaterfallSegment[] = []
+  if (turn.stt_ttfb_ms != null) segments.push({ kind: 'stt', ms: turn.stt_ttfb_ms })
+  if (turn.llm_ttfb_ms != null) segments.push({ kind: 'llm', ms: turn.llm_ttfb_ms })
+  if (turn.tts_ttfb_ms != null) segments.push({ kind: 'tts', ms: turn.tts_ttfb_ms })
+  return segments
+}
+
+function LatencyWaterfall({
+  turn,
+  extra,
+  compact,
+}: {
+  turn: TraceTurn
+  extra: TraceTurnExtra
+  compact?: boolean
+}) {
+  const segments = buildWaterfallSegments(turn)
+  const total = segments.reduce((sum, s) => sum + s.ms, 0) || turn.sut_response_latency_ms || 0
+
+  if (segments.length === 0) {
+    return (
+      <div className="text-xs text-gray-400 italic">
+        No component timing{turn.sut_response_latency_ms != null ? ` · total ${formatMs(turn.sut_response_latency_ms)}` : ''}
+      </div>
+    )
+  }
+
+  return (
+    <div className={compact ? 'space-y-2' : 'space-y-3'}>
+      <div className="flex h-7 rounded-md overflow-hidden bg-gray-100 ring-1 ring-gray-200/80">
+        {segments.map((seg, idx) => {
+          const pct = total > 0 ? (seg.ms / total) * 100 : 100 / segments.length
+          const style = COMPONENT_STYLES[seg.kind]
+          const model = extra[`${seg.kind}_model` as keyof TraceTurnExtra] as string | undefined
+          const provider = extra[`${seg.kind}_provider` as keyof TraceTurnExtra] as string | undefined
+          return (
+            <div
+              key={`${seg.kind}-${idx}`}
+              className={`${style.bar} relative group min-w-[2rem] flex items-center justify-center`}
+              style={{ width: `${Math.max(pct, 8)}%` }}
+              title={`${style.label}: ${formatMs(seg.ms)} · ${providerLabel(provider, model)}`}
+            >
+              <span className="text-[10px] font-bold text-white/90 drop-shadow-sm px-1 truncate">
+                {formatMs(seg.ms)}
+              </span>
+            </div>
+          )
+        })}
+      </div>
+
+      <div
+        className="grid gap-2"
+        style={{ gridTemplateColumns: `repeat(${segments.length}, minmax(0, 1fr))` }}
+      >
+        {segments.map((seg, idx) => {
+          const style = COMPONENT_STYLES[seg.kind]
+          const Icon = style.icon
+          const model = extra[`${seg.kind}_model` as keyof TraceTurnExtra] as string | undefined
+          const provider = extra[`${seg.kind}_provider` as keyof TraceTurnExtra] as string | undefined
+          return (
+            <div
+              key={`${seg.kind}-detail-${idx}`}
+              className={`rounded-md border ${style.border} ${style.bg} px-2.5 py-2`}
+            >
+              <div className="flex items-center gap-1.5 mb-0.5">
+                <Icon className={`w-3 h-3 ${style.text}`} />
+                <span className={`text-[10px] font-bold uppercase ${style.text}`}>{style.label}</span>
+                <span className="ml-auto text-xs font-semibold tabular-nums text-gray-900">
+                  {formatMs(seg.ms)}
+                </span>
+              </div>
+              <p className="text-[11px] font-medium text-gray-800 truncate" title={model}>
+                {model ?? '—'}
+              </p>
+              {provider && <p className="text-[10px] text-gray-500 truncate">{provider}</p>}
+            </div>
+          )
+        })}
+      </div>
+    </div>
+  )
+}
+
+function RawSpanRow({ span }: { span: OtelSpan }) {
+  const [open, setOpen] = useState(false)
+  const kind = spanKind(span)
+  const meta = metaFromSpan(span)
+  const ttfb = span.attributes?.['metrics.ttfb']
+  const durationMs =
+    span.start_time_unix_nano != null && span.end_time_unix_nano != null
+      ? Math.round((span.end_time_unix_nano - span.start_time_unix_nano) / 1_000_000)
+      : null
+  const style = kind ? COMPONENT_STYLES[kind] : null
+
+  return (
+    <div className="rounded-lg border border-gray-200 bg-white text-xs overflow-hidden">
+      <button
+        type="button"
+        onClick={() => setOpen((v) => !v)}
+        className="w-full flex items-center gap-2 px-3 py-2.5 text-left hover:bg-gray-50"
+      >
+        {open ? <ChevronDown className="w-3.5 h-3.5 text-gray-400 shrink-0" /> : <ChevronRight className="w-3.5 h-3.5 text-gray-400 shrink-0" />}
+        {style && (
+          <span className={`text-[10px] font-bold uppercase px-1.5 py-0.5 rounded ${style.bg} ${style.text}`}>
+            {style.label}
+          </span>
+        )}
+        {!style && <span className="font-medium text-gray-600">{span.name}</span>}
+        <span className="text-gray-500 truncate hidden sm:inline">{meta.model ?? ''}</span>
+        <span className="ml-auto tabular-nums text-gray-600 shrink-0 font-medium">
+          {durationMs != null ? `${durationMs}ms` : '—'}
+          {ttfb != null && <span className="text-gray-400 font-normal"> · ttfb {Math.round(Number(ttfb) * 1000)}ms</span>}
+        </span>
+      </button>
+      {open && (
+        <pre className="text-[10px] font-mono bg-slate-50 border-t border-slate-100 px-3 py-2 overflow-x-auto max-h-48 whitespace-pre-wrap">
+          {JSON.stringify(span.attributes ?? {}, null, 2)}
+        </pre>
+      )}
+    </div>
+  )
+}
+
+function TurnCard({
+  turn,
+  spans,
+  sessionModels,
+}: {
+  turn: TraceTurn
+  spans: OtelSpan[]
+  sessionModels: PipelineModels
+}) {
+  const [showSpans, setShowSpans] = useState(false)
+  const spanMeta = useMemo(() => turnMetaFromSpans(spans), [spans])
+  const extra = useMemo(() => mergeTurnMeta(turn, spanMeta, sessionModels), [turn, spanMeta, sessionModels])
+  const parsed = parseTranscript(turn.transcript)
+  const userText = extra.user_text ?? parsed.user
+  const assistantText = extra.assistant_text ?? parsed.assistant
+  const agent = extra.agent_role ?? extra.agent_id
+  const interrupted = Boolean(extra.was_interrupted)
+  const componentSpans = spans
+    .filter((s) => spanKind(s) != null)
+    .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
+
+  return (
+    <article className="rounded-xl border border-gray-200 bg-white shadow-sm overflow-hidden">
+      <header className="px-4 py-3 border-b border-gray-100 flex items-center justify-between gap-3 bg-gradient-to-r from-white to-gray-50/80">
+        <div className="flex items-center gap-2 flex-wrap min-w-0">
+          <span className="inline-flex items-center justify-center h-6 min-w-[1.5rem] px-1.5 rounded-md bg-gray-900 text-white text-xs font-bold">
+            {turn.turn_number}
+          </span>
+          {agent && (
+            <span className="text-[10px] font-semibold uppercase tracking-wide px-2 py-0.5 rounded-full bg-purple-100 text-purple-800">
+              {agent}
+            </span>
+          )}
+          {interrupted && (
+            <span className="text-[10px] font-semibold uppercase tracking-wide px-2 py-0.5 rounded-full bg-rose-100 text-rose-800">
+              Interrupted
+            </span>
+          )}
+        </div>
+        <div className="flex items-center gap-1.5 shrink-0">
+          <Zap className="w-3.5 h-3.5 text-indigo-400" />
+          <span className="text-[10px] uppercase tracking-wider text-gray-400 font-medium">Round-trip</span>
+          <span className={`text-base font-bold tabular-nums ${latencyTone(turn.sut_response_latency_ms)}`}>
+            {formatMs(turn.sut_response_latency_ms)}
+          </span>
+        </div>
+      </header>
+
+      <div className="px-4 py-4 space-y-4">
+        {(userText || assistantText) && (
+          <div className="space-y-2.5">
+            {userText && (
+              <div className="flex justify-end">
+                <div className="max-w-[88%] rounded-2xl rounded-tr-sm bg-indigo-600 text-white px-3.5 py-2.5 shadow-sm">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-indigo-200 mb-0.5">User</p>
+                  <p className="text-sm leading-relaxed">{userText}</p>
+                </div>
+              </div>
+            )}
+            {assistantText && (
+              <div className="flex justify-start">
+                <div className="max-w-[88%] rounded-2xl rounded-tl-sm bg-gray-100 border border-gray-200/80 px-3.5 py-2.5">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-500 mb-0.5">Agent</p>
+                  <p className="text-sm text-gray-900 leading-relaxed">{assistantText}</p>
+                </div>
+              </div>
+            )}
+          </div>
+        )}
+
+        <div>
+          <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400 mb-2">
+            Latency waterfall
+          </p>
+          <LatencyWaterfall turn={turn} extra={extra} />
+        </div>
+
+        {componentSpans.length > 0 && (
+          <div className="pt-1 border-t border-gray-100">
+            <button
+              type="button"
+              onClick={() => setShowSpans((v) => !v)}
+              className="text-xs text-gray-500 hover:text-gray-800 inline-flex items-center gap-1 font-medium"
+            >
+              {showSpans ? <ChevronDown className="w-3.5 h-3.5" /> : <ChevronRight className="w-3.5 h-3.5" />}
+              {showSpans ? 'Hide' : 'View'} span details ({componentSpans.length})
+            </button>
+            {showSpans && (
+              <div className="mt-2 space-y-1.5">
+                {componentSpans.map((span) => (
+                  <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />
+                ))}
+              </div>
+            )}
+          </div>
+        )}
+      </div>
+    </article>
+  )
+}
+
+export default function SyntheticCallTracePanel({
+  evaluatorResultId,
+  traceId,
+  callShortId,
+  onClose,
+}: SyntheticCallTracePanelProps) {
+  const [tab, setTab] = useState<DetailTab>('conversation')
+
+  const lookupKey = traceId ?? callShortId ?? evaluatorResultId
+  const lookupMode = traceId ? 'by-trace' : callShortId ? 'by-call-short-id' : 'by-result'
+
+  const { data, isLoading, isError, error } = useQuery({
+    queryKey: ['synthetic-call-trace', lookupKey, lookupMode],
+    queryFn: () => {
+      if (traceId) return apiClient.getSyntheticCallTrace(traceId)
+      if (callShortId) return apiClient.getSyntheticCallTraceByCallShortId(callShortId)
+      return apiClient.getSyntheticCallTraceForResult(evaluatorResultId!)
+    },
+    enabled: Boolean(traceId || callShortId || evaluatorResultId),
+    retry: false,
+  })
+
+  const otelSpans = (data?.otel_spans ?? []) as OtelSpan[]
+  const spansByTurn = useMemo(() => buildSpansByTurn(otelSpans), [otelSpans])
+  const turns = (data?.turns ?? []) as TraceTurn[]
+  const pipelineModels = (data?.pipeline_models ?? {}) as PipelineModels
+
+  if (isLoading) {
+    return <div className="flex items-center justify-center h-40 text-sm text-gray-500">Loading trace…</div>
+  }
+
+  if (isError) {
+    const message = (error as Error)?.message || 'Trace not available'
+    if (message.includes('404') || message.toLowerCase().includes('not found')) {
+      return (
+        <div className="p-6 text-sm text-gray-600">
+          No trace yet. Run a Pipecat WebRTC session with tracing, then see{' '}
+          <Link to="/call-traces" className="text-indigo-600 hover:text-indigo-800 font-medium">
+            Call Traces → Connect Pipecat
+          </Link>
+        </div>
+      )
+    }
+    return <div className="p-6 text-sm text-amber-800 bg-amber-50">Could not load trace: {message}</div>
+  }
+
+  const trace = data
+  if (!trace) return null
+
+  const statusLabel = trace.status === 'finalized' ? 'closed' : trace.status
+  const aggregates = trace.component_aggregates as Record<string, { p50?: number }> | undefined
+  const isEmbedded = !onClose
+
+  return (
+    <div
+      className={`flex flex-col w-full bg-gray-50/40 ${isEmbedded ? '' : 'h-full min-h-0'}`}
+    >
+      <div className="flex items-start justify-between gap-4 px-5 py-4 border-b border-gray-200 bg-white shrink-0">
+        <div className="min-w-0">
+          <div className="flex items-center gap-2 flex-wrap">
+            <h2 className="text-lg font-semibold text-gray-900 tracking-tight">
+              {trace.call_short_id ? `#${trace.call_short_id}` : 'Call trace'}
+            </h2>
+            <span
+              className={`text-xs rounded-full px-2.5 py-0.5 capitalize font-medium ${
+                trace.status === 'open' ? 'bg-amber-100 text-amber-800' : 'bg-emerald-100 text-emerald-800'
+              }`}
+            >
+              {statusLabel}
+            </span>
+            <span className="text-xs px-2 py-0.5 rounded-md bg-blue-50 text-blue-700 font-medium">
+              {trace.transport === 'webrtc' ? 'WebRTC' : trace.transport}
+            </span>
+          </div>
+          <div className="flex items-center gap-3 mt-1.5 text-xs text-gray-500">
+            <span>{trace.turn_count} turns</span>
+            {trace.started_at && (
+              <span className="inline-flex items-center gap-1">
+                <Clock className="w-3 h-3" />
+                {new Date(trace.started_at).toLocaleString()}
+              </span>
+            )}
+          </div>
+        </div>
+        {onClose && (
+          <button type="button" onClick={onClose} className="p-2 rounded-lg text-gray-400 hover:bg-gray-100" aria-label="Close">
+            <X className="w-5 h-5" />
+          </button>
+        )}
+      </div>
+
+      <div className="px-5 py-3 border-b border-gray-100 bg-white shrink-0">
+        <div className="grid grid-cols-3 gap-2">
+          {(['p50', 'p90', 'p95'] as const).map((label) => {
+            const value =
+              label === 'p50'
+                ? trace.response_latency_p50_ms
+                : label === 'p90'
+                  ? trace.response_latency_p90_ms
+                  : trace.response_latency_p95_ms
+            return (
+              <div key={label} className="rounded-lg bg-gray-50 border border-gray-100 px-3 py-2 text-center">
+                <p className="text-[10px] uppercase tracking-wider text-gray-400 font-semibold">{label}</p>
+                <p className={`text-lg font-bold tabular-nums ${latencyTone(value)}`}>{formatMs(value)}</p>
+              </div>
+            )
+          })}
+        </div>
+        {aggregates && (
+          <div className="flex flex-wrap gap-1.5 mt-2.5">
+            {(['stt', 'llm', 'tts', 's2s'] as const).map((kind) => {
+              const p50 = aggregates[`${kind}_ttfb_ms`]?.p50
+              if (p50 == null) return null
+              const style = COMPONENT_STYLES[kind]
+              const sessionMeta = pipelineModels[kind]
+              return (
+                <span
+                  key={kind}
+                  className={`text-[10px] px-2 py-0.5 rounded-full border ${style.border} ${style.bg} ${style.text} font-medium`}
+                  title={providerLabel(sessionMeta?.provider, sessionMeta?.model)}
+                >
+                  {style.label} p50 {Math.round(p50)}ms
+                  {sessionMeta?.model && <span className="opacity-70"> · {sessionMeta.model}</span>}
+                </span>
+              )
+            })}
+          </div>
+        )}
+      </div>
+
+      <PipelineModelsBar models={pipelineModels} />
+
+      <div className="flex gap-0.5 px-4 py-2 border-b border-gray-200 bg-white shrink-0">
+        {(
+          [
+            ['conversation', 'Conversation', MessageSquare],
+            ['latency', 'Waterfall', Activity],
+            ['raw', 'Raw spans', Zap],
+          ] as const
+        ).map(([id, label, Icon]) => (
+          <button
+            key={id}
+            type="button"
+            onClick={() => setTab(id)}
+            className={`px-3 py-2 text-sm font-medium rounded-lg inline-flex items-center gap-1.5 transition-colors ${
+              tab === id ? 'bg-gray-900 text-white shadow-sm' : 'text-gray-500 hover:text-gray-800 hover:bg-gray-100'
+            }`}
+          >
+            <Icon className="w-4 h-4" />
+            {label}
+          </button>
+        ))}
+      </div>
+
+      <div className={`${isEmbedded ? '' : 'flex-1 min-h-0'} overflow-y-auto px-4 py-4 space-y-3`}>
+        {tab === 'conversation' && (
+          <>
+            {turns.length === 0 ? (
+              <p className="text-center py-16 text-sm text-gray-500">No conversation turns yet</p>
+            ) : (
+              turns.map((turn) => (
+                <TurnCard
+                  key={turn.turn_number}
+                  turn={turn}
+                  spans={spansByTurn.get(turn.turn_number) ?? []}
+                  sessionModels={pipelineModels}
+                />
+              ))
+            )}
+          </>
+        )}
+
+        {tab === 'latency' && (
+          <div className="space-y-3">
+            {turns.length === 0 ? (
+              <p className="text-center py-16 text-sm text-gray-500">No latency data</p>
+            ) : (
+              turns.map((turn) => {
+                const spanMeta = turnMetaFromSpans(spansByTurn.get(turn.turn_number) ?? [])
+                const extra = mergeTurnMeta(turn, spanMeta, pipelineModels)
+                return (
+                  <div key={turn.turn_number} className="rounded-xl border border-gray-200 bg-white p-4 shadow-sm">
+                    <div className="flex items-center justify-between mb-3">
+                      <span className="text-sm font-semibold text-gray-900">Turn {turn.turn_number}</span>
+                      <span className={`text-sm font-bold tabular-nums ${latencyTone(turn.sut_response_latency_ms)}`}>
+                        {formatMs(turn.sut_response_latency_ms)} total
+                      </span>
+                    </div>
+                    <LatencyWaterfall turn={turn} extra={extra} compact />
+                  </div>
+                )
+              })
+            )}
+          </div>
+        )}
+
+        {tab === 'raw' && (
+          <div className="space-y-1.5">
+            {otelSpans.length === 0 ? (
+              <p className="text-center py-16 text-sm text-gray-500">No spans</p>
+            ) : (
+              otelSpans
+                .slice()
+                .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
+                .map((span) => <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />)
+            )}
+          </div>
+        )}
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index f8b74cc1..aeca1097 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -1,5732 +1,5837 @@
-import axios, { AxiosInstance } from 'axios'
-import {
-  clearAuthSession,
-  getApiErrorDetail,
-  hasRevocableUserCredentials,
-  isOrganizationAccessDenied,
-  organizationAccessDeniedMessage,
-  redirectToLoginWithMessage,
-  type UserSessionCredentials,
-} from './authSession'
-import type {
-  GenerateScenariosFromPromptParams,
-  GenerateTestPromptParams,
-  GenerateTestSetupParams,
-} from '../types/agentTestSetupGeneration'
-import type {
-  AudioFile,
-  Evaluation,
-  EvaluationCreate,
-  EvaluationResult,
-  DashboardSummary,
-  ModelConfigEntry,
-  APIKey,
-  MessageResponse,
-  EvaluationStatus,
-  OrganizationMember,
-  Invitation,
-  InvitationCreate,
-  InvitationPreview,
-  Profile,
-  UserUpdate,
-  UserPreferences,
-  UserPreferencesUpdate,
-  Role,
-  Integration,
-  IntegrationCreate,
-  S3ConnectionTestResponse,
-  S3ListFilesResponse,
-  S3BrowseResponse,
-  S3Status,
-  CallImport,
-  CallImportDetail,
-  CallImportListResponse,
-  CallImportRow,
-  ObservabilityCall,
-  CallImportSchema,
-  CallImportSchemaCreate,
-  CallImportSchemaListResponse,
-  CallImportSchemaUpdate,
-  CallImportStatus,
-  CallImportTag,
-  CallImportUploadResponse,
-  CallImportPreviewResponse,
-  CallImportEvaluation,
-  CallImportEvaluationBaselineCandidatesResponse,
-  CallImportEvaluationPdfReport,
-  CallImportEvaluationPdfReportListResponse,
-  CallImportEvaluationLLMOverride,
-  CallImportEvaluationListResponse,
-  CallImportEvaluationRow,
-  CallImportEvaluationRowListResponse,
-  CallImportEvaluationRetryResponse,
-  CallImportEvaluationBulkActionResponse,
-  CallImportEvaluationAggregateResponse,
-  CallImportInsightsResponse,
-  EvaluatorResultListResponse,
-  EvaluatorResultsOverviewResponse,
-  EvaluatorResultsAggregateResponse,
-  ListEvaluatorResultsParams,
-  CallImportTranscribeRequest,
-  CallImportTranscribeResponse,
-  CallImportRetryFailedRowsResponse,
-  Workspace,
-  LLMGenerationConfig,
-  TestAgent,
-} from '../types/api'
-
-export interface EnterpriseFeatureMeta {
-  title: string
-  description?: string
-  category?: string
-}
-
-export type EnterpriseFeatureCatalog = Record<string, EnterpriseFeatureMeta>
-
-export type VoicePlaygroundSourceType = 'tts' | 'recording' | 'upload'
-
-export interface VoicePlaygroundSideConfig {
-  source_type: VoicePlaygroundSourceType
-  provider?: string
-  model?: string
-  voices?: Array<{ id: string; name: string; sample_rate_hz?: number }>
-  call_import_row_ids?: string[]
-  upload_s3_keys?: string[]
-}
-
-export type VoicePlaygroundBlindTestRefType = 'recording' | 'upload' | 'tts_sample'
-
-export interface VoicePlaygroundBlindTestAudioRef {
-  type: VoicePlaygroundBlindTestRefType
-  call_import_row_id?: string
-  upload_s3_key?: string
-  tts_sample_id?: string
-  label?: string
-}
-
-export interface VoicePlaygroundBlindTestPair {
-  text?: string
-  x: VoicePlaygroundBlindTestAudioRef
-  y: VoicePlaygroundBlindTestAudioRef
-}
-
-export interface UsagePolicy {
-  extended_history: boolean
-  max_history_days: number | null
-}
-
-export interface LicenseInfoResponse {
-  is_enterprise: boolean
-  enabled_features: string[]
-  all_enterprise_features: string[]
-  feature_catalog?: EnterpriseFeatureCatalog
-  organization?: string
-  usage_policy?: UsagePolicy
-}
-
-export interface ReportBranding {
-  heading?: string | null
-  has_logo: boolean
-  images: Array<{
-    id: string
-    filename: string
-    content_type: string
-    size_bytes: number
-    role: 'internal' | 'external' | 'generic'
-    updated_at?: string | null
-    data_uri?: string | null
-  }>
-}
-
-export interface AuthProviderConfig {
-  name: 'api_key' | 'local_password' | 'external_oidc'
-  enabled: boolean
-  display_name: string
-  description?: string
-  supports_password?: boolean
-  supports_signup?: boolean
-  oidc_issuer?: string | null
-  oidc_client_id?: string | null
-  oidc_authorize_url?: string | null
-}
-
-export interface AuthConfigResponse {
-  providers: AuthProviderConfig[]
-  tier: 'oss' | 'enterprise'
-  gated_signup?: boolean
-}
-
-export interface AuthUserSummary {
-  id: string
-  email: string
-  name?: string | null
-  first_name?: string | null
-  last_name?: string | null
-  organization_id: string
-  role?: string | null
-  has_password?: boolean
-  email_is_placeholder?: boolean
-}
-
-export interface TokenResponse {
-  access_token: string
-  refresh_token?: string
-  token_type: string
-  expires_in: number
-  user: AuthUserSummary
-}
-
-export interface LoginOrgOption {
-  id: string
-  name: string
-  role: string
-}
-
-export interface LoginOrgSelectionResponse {
-  requires_org_selection: true
-  organizations: LoginOrgOption[]
-}
-
-export interface PlatformAdminSummary {
-  id: string
-  email: string
-}
-
-export interface PlatformTokenResponse {
-  access_token: string
-  token_type: string
-  expires_in: number
-  admin: PlatformAdminSummary
-}
-
-export interface PlatformOrganizationItem {
-  id: string
-  name: string
-  is_active: boolean
-  member_count: number
-  created_at?: string | null
-  disabled_at?: string | null
-}
-
-export interface PlatformOrganizationListResponse {
-  items: PlatformOrganizationItem[]
-  total: number
-  offset: number
-  limit: number
-}
-
-export interface PlatformOrganizationStats {
-  total: number
-  active: number
-  disabled: number
-}
-
-export interface PlatformOrgUser {
-  id: string
-  email: string
-  role: string
-  is_active: boolean
-}
-
-export interface PlatformSignupCode {
-  id: string
-  label?: string | null
-  max_uses?: number | null
-  use_count: number
-  expires_at?: string | null
-  is_active: boolean
-  created_at?: string | null
-  code?: string | null
-}
-
-export type LoginResponse = TokenResponse | LoginOrgSelectionResponse
-
-export function isLoginOrgSelectionResponse(
-  response: LoginResponse
-): response is LoginOrgSelectionResponse {
-  return 'requires_org_selection' in response && response.requires_org_selection === true
-}
-
-export interface OrganizationSummary {
-  id: string
-  name: string
-}
-
-export type LLMGatewayMode = 'inherit' | 'enabled' | 'disabled'
-export type LLMGatewayType = 'inherit' | 'bifrost' | 'litellm_proxy'
-export type LLMGatewayInterface = 'inherit' | 'litellm_shim' | 'native_openai'
-export type LLMGatewayRouting = 'direct' | 'bifrost' | 'litellm_proxy'
-export type GatewayInterfaceMode = 'inherit' | 'litellm_shim' | 'native_openai'
-
-export interface LLMGatewaySettings {
-  mode: LLMGatewayMode
-  gateway_type: LLMGatewayType
-  gateway_interface?: LLMGatewayInterface
-  base_url?: string | null
-  has_virtual_key: boolean
-  has_master_key: boolean
-  platform_enabled: boolean
-  platform_gateway_type: 'bifrost' | 'litellm_proxy'
-  platform_gateway_interface?: 'litellm_shim' | 'native_openai'
-  platform_base_url?: string | null
-  effective_routing: LLMGatewayRouting
-  effective_gateway_type?: 'bifrost' | 'litellm_proxy' | null
-  effective_gateway_interface?: 'litellm_shim' | 'native_openai' | null
-  effective_base_url?: string | null
-  effective_has_virtual_key: boolean
-  effective_has_master_key: boolean
-  gateway_managed_credentials: boolean
-}
-
-export interface LLMGatewaySettingsUpdate {
-  mode: LLMGatewayMode
-  gateway_type?: LLMGatewayType
-  gateway_interface?: LLMGatewayInterface
-  base_url?: string | null
-  virtual_key?: string | null
-  master_key?: string | null
-  clear_virtual_key?: boolean
-  clear_master_key?: boolean
-}
-
-export interface TelephonyIntegrationResponse {
-  id: string
-  organization_id: string
-  provider: string
-  name?: string | null
-  verify_app_uuid?: string | null
-  voice_app_id?: string | null
-  sip_domain?: string | null
-  masking_config?: Record<string, any> | null
-  is_active: boolean
-  /** True if this row is the default credential for (org, provider). */
-  is_default?: boolean
-  last_tested_at?: string | null
-  created_at: string
-  updated_at: string
-}
-
-export interface TelephonyIntegrationCreatePayload {
-  provider?: string
-  name?: string
-  auth_id: string
-  auth_token: string
-  verify_app_uuid?: string
-  voice_app_id?: string
-  sip_domain?: string
-  masking_config?: Record<string, any>
-  /** Mark the new credential as the default for (org, provider). */
-  is_default?: boolean
-}
-
-export interface TelephonyIntegrationUpdatePayload {
-  /** When set, update this specific credential row instead of the legacy single-row flow. */
-  id?: string
-  provider?: string
-  name?: string
-  auth_id?: string
-  auth_token?: string
-  verify_app_uuid?: string
-  voice_app_id?: string
-  sip_domain?: string
-  masking_config?: Record<string, any>
-  is_active?: boolean
-}
-
-export interface TelephonyPhoneNumberResponse {
-  id: string
-  phone_number: string
-  country_iso2?: string | null
-  region?: string | null
-  number_type?: string | null
-  capabilities?: Record<string, any> | null
-  is_masking_pool: boolean
-  inbound_enabled?: boolean
-  outbound_enabled?: boolean
-  source?: string
-  agent_id?: string | null
-  linked_agent_name?: string | null
-  provider?: string | null
-  is_active: boolean
-  created_at: string
-}
-
-export interface TelephonyAvailableNumber {
-  e164: string
-  provider_number_id?: string | null
-  country?: string | null
-  region?: string | null
-  capabilities?: Record<string, any> | null
-  status?: string | null
-  application_id?: string | null
-  already_imported: boolean
-  imported_number_id?: string | null
-}
-
-export interface TelephonyImportNumberResult {
-  number: string
-  success: boolean
-  message: string
-  answer_url: string
-  webhook_configured?: boolean | null
-  imported_number_id?: string | null
-  application_id?: string | null
-}
-
-export interface TelephonyImportNumbersResponse {
-  provider: string
-  results: TelephonyImportNumberResult[]
-  answer_url: string
-}
-
-/** @deprecated Use TelephonyAvailableNumber */
-export type VobizAvailableNumber = TelephonyAvailableNumber
-
-/** @deprecated Use TelephonyImportNumbersResponse */
-export type VobizImportNumbersResponse = TelephonyImportNumbersResponse
-
-/** @deprecated Use TelephonyImportNumberResult */
-export type VobizImportNumberResult = TelephonyImportNumberResult
-
-export interface PlatformOutboundPoolNumber {
-  phone_number: string
-  provider: string
-}
-
-export interface PlatformOutboundPoolResponse {
-  numbers: PlatformOutboundPoolNumber[]
-  max_concurrent_per_org: number
-  shared_across_orgs: boolean
-}
-
-/** @deprecated Use PlatformOutboundPoolResponse */
-export type VobizOutboundPoolResponse = PlatformOutboundPoolResponse
-
-export interface VobizOutboundCallRequest {
-  to_number: string
-  agent_id: string
-  from_number?: string
-  persona_id?: string
-  scenario_id?: string
-  evaluator_id?: string
-}
-
-export interface TelephonyDialTargetResponse {
-  id: string
-  phone_number: string
-  label?: string | null
-  created_at: string
-  updated_at: string
-}
-
-export interface VobizOutboundCallResponse {
-  provider_request_uuid: string
-  call_status: string
-  from_number: string
-  to_number: string
-  call_ref: string
-  call_short_id?: string
-  message: string
-}
-
-export interface EvaluatorSuiteCombination {
-  id: string
-  evaluator_id: string
-  scenario_id: string
-  scenario_name?: string | null
-  scenario_description?: string | null
-  scenario_required_info?: Record<string, unknown> | null
-}
-
-export interface EvaluatorSuite {
-  id: string
-  organization_id: string
-  name?: string | null
-  agent_id: string
-  persona_id: string
-  agent_name?: string | null
-  persona_name?: string | null
-  agent_call_type?: string | null
-  agent_call_medium?: string | null
-  metric_ids?: string[] | null
-  llm_provider?: string | null
-  llm_model?: string | null
-  tags?: string[] | null
-  default_runs_per_combination: number
-  round_robin_index: number
-  is_active: boolean
-  agent_suite_count: number
-  combination_count: number
-  combinations: EvaluatorSuiteCombination[]
-  created_at: string
-  updated_at: string
-}
-
-export interface RunEvaluatorSuiteResponse {
-  total_runs: number
-  task_ids: string[]
-  evaluator_results: any[]
-  phone_call_refs: string[]
-}
-
-export interface RunNextCombinationResponse {
-  evaluator_id: string
-  scenario_id: string
-  scenario_name: string
-  combination_index: number
-  next_index: number
-  evaluator_result_id?: string | null
-  result_id?: string | null
-  task_id?: string | null
-  phone_call_ref?: string | null
-  call_short_id?: string | null
-}
-
-export interface ChooseNextCombinationResponse {
-  evaluator_id: string
-  scenario_id: string
-  scenario_name: string
-  combination_index: number
-  next_index: number
-}
-
-type TTSReportOptionsPayload = {
-  show_runs?: boolean
-  min_runs_to_show?: number
-  include_latency?: boolean
-  include_ttfb?: boolean
-  include_endpoint?: boolean
-  include_naturalness?: boolean
-  include_hallucination?: boolean
-  include_prosody?: boolean
-  include_arousal?: boolean
-  include_valence?: boolean
-  include_cer?: boolean
-  include_wer?: boolean
-  include_hallucination_examples?: boolean
-  hallucination_examples_limit?: number
-  include_disclaimer_sections?: boolean
-  include_methodology_sections?: boolean
-  zone_threshold_overrides?: Record<string, {
-    good_min?: number
-    neutral_min?: number
-    good_max?: number
-    neutral_max?: number
-  }>
-}
-
-// When running in production (served from same origin), use relative path
-// Otherwise use environment variable or default
-export const apiBaseUrl =
-  import.meta.env.VITE_API_URL ||
-  (import.meta.env.PROD ? '' : 'http://localhost:8000')
-
-const API_BASE_URL = apiBaseUrl
-
-let refreshPromise: Promise<string | null> | null = null
-
-class ApiClient {
-  private client: AxiosInstance
-
-  constructor() {
-    this.client = axios.create({
-      baseURL: API_BASE_URL,
-      headers: {
-        'Content-Type': 'application/json',
-      },
-    })
-
-    // Add request interceptor to add API key + active workspace to headers.
-    // Workspace selection is read directly from localStorage to avoid a
-    // circular import between this module and the workspace store.
-    this.client.interceptors.request.use((config) => {
-      const accessToken = localStorage.getItem('accessToken')
-      const apiKey = localStorage.getItem('apiKey')
-      const workspaceId = localStorage.getItem('activeWorkspaceId')
-      if (accessToken) {
-        config.headers.Authorization = `Bearer ${accessToken}`
-      } else if (config.headers.Authorization) {
-        delete config.headers.Authorization
-      }
-      if (apiKey) {
-        config.headers['X-API-Key'] = apiKey
-      } else if (config.headers['X-API-Key']) {
-        delete config.headers['X-API-Key']
-      }
-      // Send X-Workspace-Id so the backend's get_workspace_id dep scopes
-      // listings to the active workspace. When absent (e.g. a brand-new
-      // session that hasn't called listWorkspaces yet) the backend
-      // falls back to the org's Default workspace.
-      if (workspaceId) {
-        config.headers['X-Workspace-Id'] = workspaceId
-      } else if (config.headers['X-Workspace-Id']) {
-        delete config.headers['X-Workspace-Id']
-      }
-      return config
-    })
-
-    // Add response interceptor for error handling and silent token refresh
-    this.client.interceptors.response.use(
-      (response) => response,
-      async (error) => {
-        const response = error.response
-        if (response?.data instanceof Blob) {
-          try {
-            const text = await response.data.text()
-            const trimmed = text.trim()
-            if (trimmed.startsWith('{') || trimmed.startsWith('[')) {
-              response.data = JSON.parse(text)
-            }
-          } catch {
-            // Keep the original blob when it isn't JSON.
-          }
-        }
-
-        const originalRequest = error.config
-        const requestUrl = String(originalRequest?.url || '')
-        const isAuthEndpoint =
-          requestUrl.includes('/auth/login') ||
-          requestUrl.includes('/auth/signup') ||
-          requestUrl.includes('/auth/refresh') ||
-          requestUrl.includes('/auth/logout') ||
-          requestUrl.includes('/auth/config')
-
-        const detail = getApiErrorDetail(error)
-        if (
-          response?.status === 403 &&
-          isOrganizationAccessDenied(detail) &&
-          !isAuthEndpoint
-        ) {
-          redirectToLoginWithMessage(organizationAccessDeniedMessage(detail))
-          return Promise.reject(error)
-        }
-
-        if (
-          response?.status === 401 &&
-          originalRequest &&
-          !originalRequest._retry
-        ) {
-          if (!isAuthEndpoint) {
-            originalRequest._retry = true
-            const newToken = await this.tryRefreshAccessToken()
-            if (newToken) {
-              originalRequest.headers.Authorization = `Bearer ${newToken}`
-              return this.client(originalRequest)
-            }
-          }
-
-          clearAuthSession()
-          window.location.href = '/login'
-        }
-        return Promise.reject(error)
-      },
-    )
-  }
-
-  private async tryRefreshAccessToken(): Promise<string | null> {
-    const refreshToken = localStorage.getItem('refreshToken')
-    if (!refreshToken) {
-      return null
-    }
-
-    if (!refreshPromise) {
-      refreshPromise = axios
-        .post(
-          `${API_BASE_URL}/api/v1/auth/refresh`,
-          { refresh_token: refreshToken },
-          { headers: { 'Content-Type': 'application/json' } },
-        )
-        .then((res) => {
-          const { access_token, refresh_token: rotatedRefresh } = res.data as TokenResponse
-          this.setAccessToken(access_token)
-          if (rotatedRefresh) {
-            this.setRefreshToken(rotatedRefresh)
-          }
-          return access_token as string
-        })
-        .catch((err) => {
-          const refreshDetail = getApiErrorDetail(err)
-          if (
-            err?.response?.status === 403 &&
-            isOrganizationAccessDenied(refreshDetail)
-          ) {
-            redirectToLoginWithMessage(
-              organizationAccessDeniedMessage(refreshDetail),
-            )
-          }
-          return null
-        })
-        .finally(() => {
-          refreshPromise = null
-        })
-    }
-
-    return refreshPromise
-  }
-
-  setApiKey(apiKey: string) {
-    localStorage.setItem('apiKey', apiKey)
-  }
-
-  clearApiKey() {
-    localStorage.removeItem('apiKey')
-  }
-
-  setAccessToken(accessToken: string) {
-    localStorage.setItem('accessToken', accessToken)
-  }
-
-  clearAccessToken() {
-    localStorage.removeItem('accessToken')
-  }
-
-  setRefreshToken(refreshToken: string) {
-    localStorage.setItem('refreshToken', refreshToken)
-  }
-
-  clearRefreshToken() {
-    localStorage.removeItem('refreshToken')
-  }
-
-  // Workspace endpoints (in-org isolation boundary for call imports + metrics).
-  async listWorkspaces(): Promise<Workspace[]> {
-    const response = await this.client.get('/api/v1/workspaces')
-    return response.data
-  }
-
-  async createWorkspace(payload: {
-    name: string
-    slug?: string
-  }): Promise<Workspace> {
-    const response = await this.client.post('/api/v1/workspaces', payload)
-    return response.data
-  }
-
-  async updateWorkspace(
-    workspaceId: string,
-    payload: { name?: string; is_active?: boolean },
-  ): Promise<Workspace> {
-    const response = await this.client.patch(
-      `/api/v1/workspaces/${workspaceId}`,
-      payload,
-    )
-    return response.data
-  }
-
-  async deleteWorkspace(workspaceId: string): Promise<void> {
-    await this.client.delete(`/api/v1/workspaces/${workspaceId}`)
-  }
-
-  async listCapabilities(): Promise<import('../types/api').CapabilityDomain[]> {
-    const response = await this.client.get('/api/v1/capabilities')
-    return response.data
-  }
-
-  async listWorkspaceRoles(): Promise<import('../types/api').WorkspaceRole[]> {
-    const response = await this.client.get('/api/v1/workspace-roles')
-    return response.data
-  }
-
-  async createWorkspaceRole(
-    payload: import('../types/api').WorkspaceRoleCreate,
-  ): Promise<import('../types/api').WorkspaceRole> {
-    const response = await this.client.post('/api/v1/workspace-roles', payload)
-    return response.data
-  }
-
-  async updateWorkspaceRole(
-    roleId: string,
-    payload: import('../types/api').WorkspaceRoleUpdate,
-  ): Promise<import('../types/api').WorkspaceRole> {
-    const response = await this.client.patch(
-      `/api/v1/workspace-roles/${roleId}`,
-      payload,
-    )
-    return response.data
-  }
-
-  async deleteWorkspaceRole(roleId: string): Promise<void> {
-    await this.client.delete(`/api/v1/workspace-roles/${roleId}`)
-  }
-
-  async listWorkspaceMembers(
-    workspaceId: string,
-  ): Promise<import('../types/api').WorkspaceMember[]> {
-    const response = await this.client.get(
-      `/api/v1/workspaces/${workspaceId}/members`,
-    )
-    return response.data
-  }
-
-  async addWorkspaceMember(
-    workspaceId: string,
-    payload: { user_id: string; role_id: string },
-  ): Promise<import('../types/api').WorkspaceMember> {
-    const response = await this.client.post(
-      `/api/v1/workspaces/${workspaceId}/members`,
-      payload,
-    )
-    return response.data
-  }
-
-  async updateWorkspaceMember(
-    workspaceId: string,
-    userId: string,
-    payload: { role_id: string },
-  ): Promise<import('../types/api').WorkspaceMember> {
-    const response = await this.client.patch(
-      `/api/v1/workspaces/${workspaceId}/members/${userId}`,
-      payload,
-    )
-    return response.data
-  }
-
-  async removeWorkspaceMember(
-    workspaceId: string,
-    userId: string,
-  ): Promise<void> {
-    await this.client.delete(
-      `/api/v1/workspaces/${workspaceId}/members/${userId}`,
-    )
-  }
-
-  // Auth endpoints
-  async getAuthConfig(): Promise<AuthConfigResponse> {
-    const response = await this.client.get('/api/v1/auth/config')
-    return response.data
-  }
-
-  async signup(data: {
-    email: string
-    password: string
-    organization_name?: string
-    first_name?: string
-    last_name?: string
-    reference_code?: string
-    invite_token?: string
-  }): Promise<TokenResponse> {
-    const response = await this.client.post('/api/v1/auth/signup', data)
-    return response.data
-  }
-
-  async previewInvitation(token: string): Promise<InvitationPreview> {
-    const response = await axios.get(`${API_BASE_URL}/api/v1/auth/invitations/preview/${encodeURIComponent(token)}`)
-    return response.data
-  }
-
-  async acceptInvitationByToken(token: string): Promise<TokenResponse> {
-    const response = await this.client.post('/api/v1/auth/invitations/accept-by-token', { token })
-    return response.data
-  }
-
-  private platformHeaders() {
-    const token = localStorage.getItem('platformAccessToken')
-    return token ? { Authorization: `Bearer ${token}` } : {}
-  }
-
-  async platformLogin(email: string, password: string): Promise<PlatformTokenResponse> {
-    const response = await axios.post(
-      `${API_BASE_URL}/api/v1/platform/auth/login`,
-      { email, password },
-      { headers: { 'Content-Type': 'application/json' } },
-    )
-    return response.data
-  }
-
-  async platformLogout(accessToken?: string | null): Promise<{ success: boolean; admin_id: string }> {
-    const token = accessToken ?? localStorage.getItem('platformAccessToken')
-    const headers: Record<string, string> = { 'Content-Type': 'application/json' }
-    if (token) {
-      headers.Authorization = `Bearer ${token}`
-    }
-    const response = await axios.post(
-      `${API_BASE_URL}/api/v1/platform/auth/logout`,
-      {},
-      { headers },
-    )
-    return response.data
-  }
-
-  /**
-   * Voluntary logout: revoke JWT blacklist + refresh token on the server.
-   * Uses raw axios so credentials survive after local session is cleared.
-   */
-  revokeUserSessionBestEffort(credentials: UserSessionCredentials): void {
-    if (!hasRevocableUserCredentials(credentials)) {
-      return
-    }
-    const auth = { accessToken: credentials.accessToken, apiKey: credentials.apiKey }
-    void this.revokeUserSession(credentials.refreshToken, auth)
-      .catch(() => this.revokeUserSession(credentials.refreshToken, auth))
-      .catch(() => {})
-  }
-
-  revokePlatformSessionBestEffort(accessToken?: string | null): void {
-    if (!accessToken) {
-      return
-    }
-    void this.platformLogout(accessToken)
-      .catch(() => this.platformLogout(accessToken))
-      .catch(() => {})
-  }
-
-  async getPlatformOrganizationStats(): Promise<PlatformOrganizationStats> {
-    const response = await axios.get(`${API_BASE_URL}/api/v1/platform/organizations/stats`, {
-      headers: this.platformHeaders(),
-    })
-    return response.data
-  }
-
-  async listPlatformOrganizations(params?: {
-    offset?: number
-    limit?: number
-    search?: string
-    is_active?: boolean
-  }): Promise<PlatformOrganizationListResponse> {
-    const response = await axios.get(`${API_BASE_URL}/api/v1/platform/organizations`, {
-      headers: this.platformHeaders(),
-      params,
-    })
-    return response.data
-  }
-
-  async updatePlatformOrganization(
-    orgId: string,
-    data: { is_active: boolean },
-  ): Promise<PlatformOrganizationItem> {
-    const response = await axios.patch(
-      `${API_BASE_URL}/api/v1/platform/organizations/${orgId}`,
-      data,
-      { headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' } },
-    )
-    return response.data
-  }
-
-  async listPlatformOrganizationUsers(
-    orgId: string,
-    params?: { role?: string },
-  ): Promise<PlatformOrgUser[]> {
-    const response = await axios.get(
-      `${API_BASE_URL}/api/v1/platform/organizations/${orgId}/users`,
-      { headers: this.platformHeaders(), params },
-    )
-    return response.data
-  }
-
-  async platformResetUserPassword(
-    orgId: string,
-    userId: string,
-    newPassword: string,
-  ): Promise<{ user_id: string; email: string; message: string }> {
-    const response = await axios.post(
-      `${API_BASE_URL}/api/v1/platform/organizations/${orgId}/users/${userId}/reset-password`,
-      { new_password: newPassword },
-      { headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' } },
-    )
-    return response.data
-  }
-
-  async listPlatformSignupCodes(): Promise<PlatformSignupCode[]> {
-    const response = await axios.get(`${API_BASE_URL}/api/v1/platform/signup-codes`, {
-      headers: this.platformHeaders(),
-    })
-    return response.data
-  }
-
-  async createPlatformSignupCode(data: {
-    code: string
-    label?: string
-    max_uses?: number
-    expires_at?: string
-  }): Promise<PlatformSignupCode> {
-    const response = await axios.post(`${API_BASE_URL}/api/v1/platform/signup-codes`, data, {
-      headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' },
-    })
-    return response.data
-  }
-
-  async updatePlatformSignupCode(
-    codeId: string,
-    data: { is_active?: boolean; max_uses?: number; label?: string },
-  ): Promise<PlatformSignupCode> {
-    const response = await axios.patch(
-      `${API_BASE_URL}/api/v1/platform/signup-codes/${codeId}`,
-      data,
-      { headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' } },
-    )
-    return response.data
-  }
-
-  async deactivatePlatformSignupCode(codeId: string): Promise<PlatformSignupCode> {
-    const response = await axios.delete(
-      `${API_BASE_URL}/api/v1/platform/signup-codes/${codeId}`,
-      { headers: this.platformHeaders() },
-    )
-    return response.data
-  }
-
-  async loginWithPassword(
-    email: string,
-    password: string,
-    organizationId?: string,
-    inviteToken?: string,
-  ): Promise<LoginResponse> {
-    const response = await this.client.post('/api/v1/auth/login', {
-      email,
-      password,
-      ...(organizationId ? { organization_id: organizationId } : {}),
-      ...(inviteToken ? { invite_token: inviteToken } : {}),
-    })
-    return response.data
-  }
-
-  async logout(
-    refreshToken?: string | null,
-    credentials?: { accessToken?: string | null; apiKey?: string | null },
-  ): Promise<{ success: boolean; auth_method: string }> {
-    return this.revokeUserSession(refreshToken, credentials)
-  }
-
-  private async revokeUserSession(
-    refreshToken?: string | null,
-    credentials?: { accessToken?: string | null; apiKey?: string | null },
-  ): Promise<{ success: boolean; auth_method: string }> {
-    const headers: Record<string, string> = { 'Content-Type': 'application/json' }
-    const accessToken = credentials?.accessToken ?? localStorage.getItem('accessToken')
-    const apiKey = credentials?.apiKey ?? localStorage.getItem('apiKey')
-    if (accessToken) {
-      headers.Authorization = `Bearer ${accessToken}`
-    } else if (apiKey) {
-      headers['X-API-Key'] = apiKey
-    }
-    const response = await axios.post(
-      `${API_BASE_URL}/api/v1/auth/logout`,
-      {
-        refresh_token: refreshToken ?? localStorage.getItem('refreshToken') ?? undefined,
-      },
-      { headers },
-    )
-    return response.data
-  }
-
-  async refreshSession(refreshToken: string): Promise<TokenResponse> {
-    const response = await axios.post(
-      `${API_BASE_URL}/api/v1/auth/refresh`,
-      { refresh_token: refreshToken },
-      { headers: { 'Content-Type': 'application/json' } },
-    )
-    return response.data
-  }
-
-  async getMe(): Promise<AuthUserSummary> {
-    const response = await this.client.get('/api/v1/auth/me')
-    return response.data
-  }
-
-  async switchOrganization(organizationId: string): Promise<TokenResponse> {
-    const response = await this.client.post('/api/v1/auth/switch-org', {
-      organization_id: organizationId,
-      refresh_token: localStorage.getItem('refreshToken') || undefined,
-    })
-    return response.data
-  }
-
-  async setPassword(data: {
-    new_password: string
-    current_password?: string
-    email?: string
-  }): Promise<AuthUserSummary> {
-    const response = await this.client.post('/api/v1/auth/password', data)
-    return response.data
-  }
-
-  async generateApiKey(name?: string): Promise<APIKey> {
-    const response = await this.client.post('/api/v1/auth/generate-key', { name })
-    return response.data
-  }
-
-  async validateApiKey(): Promise<{ valid: boolean; message: string }> {
-    const response = await this.client.post('/api/v1/auth/validate')
-    return response.data
-  }
-
-  // Settings / API Key Management endpoints
-  async listApiKeys(): Promise<any[]> {
-    const response = await this.client.get('/api/v1/settings/api-keys')
-    return response.data
-  }
-
-  async createApiKey(name?: string): Promise<any> {
-    const response = await this.client.post('/api/v1/settings/api-keys', { name })
-    return response.data
-  }
-
-  async deleteApiKey(keyId: string): Promise<void> {
-    await this.client.delete(`/api/v1/settings/api-keys/${keyId}`)
-  }
-
-  async regenerateApiKey(keyId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/settings/api-keys/${keyId}/regenerate`)
-    return response.data
-  }
-
-  // Audio endpoints
-  async uploadAudio(file: File): Promise<AudioFile> {
-    const formData = new FormData()
-    formData.append('file', file)
-    const response = await this.client.post('/api/v1/audio/upload', formData, {
-      headers: {
-        'Content-Type': 'multipart/form-data',
-      },
-    })
-    return response.data
-  }
-
-  async getAudio(audioId: string): Promise<AudioFile> {
-    const response = await this.client.get(`/api/v1/audio/${audioId}`)
-    return response.data
-  }
-
-  async listAudio(skip = 0, limit = 100): Promise<AudioFile[]> {
-    const response = await this.client.get('/api/v1/audio', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async deleteAudio(audioId: string): Promise<MessageResponse> {
-    const response = await this.client.delete(`/api/v1/audio/${audioId}`)
-    return response.data
-  }
-
-  async downloadAudio(audioId: string): Promise<Blob> {
-    const response = await this.client.get(`/api/v1/audio/${audioId}/download`, {
-      responseType: 'blob',
-    })
-    return response.data
-  }
-
-  // Evaluation endpoints
-  async createEvaluation(data: EvaluationCreate): Promise<Evaluation> {
-    const response = await this.client.post('/api/v1/evaluations/create', data)
-    return response.data
-  }
-
-  async getEvaluation(evaluationId: string): Promise<Evaluation> {
-    const response = await this.client.get(`/api/v1/evaluations/${evaluationId}`)
-    return response.data
-  }
-
-  async listEvaluations(
-    skip = 0,
-    limit = 100,
-    status?: EvaluationStatus
-  ): Promise<Evaluation[]> {
-    const response = await this.client.get('/api/v1/evaluations', {
-      params: { skip, limit, status },
-    })
-    return response.data
-  }
-
-  async cancelEvaluation(evaluationId: string): Promise<MessageResponse> {
-    const response = await this.client.post(`/api/v1/evaluations/${evaluationId}/cancel`)
-    return response.data
-  }
-
-  async deleteEvaluation(evaluationId: string): Promise<MessageResponse> {
-    const response = await this.client.delete(`/api/v1/evaluations/${evaluationId}`)
-    return response.data
-  }
-
-  // Results endpoints
-  async getEvaluationResult(evaluationId: string): Promise<EvaluationResult> {
-    const response = await this.client.get(`/api/v1/results/${evaluationId}`)
-    return response.data
-  }
-
-  async getMetrics(evaluationId: string): Promise<{
-    evaluation_id: string
-    metrics: Record<string, any>
-    processing_time?: number | null
-  }> {
-    const response = await this.client.get(`/api/v1/results/${evaluationId}/metrics`)
-    return response.data
-  }
-
-  async getTranscript(evaluationId: string): Promise<{
-    evaluation_id: string
-    transcript: string
-  }> {
-    const response = await this.client.get(`/api/v1/results/${evaluationId}/transcript`)
-    return response.data
-  }
-
-  async compareEvaluations(evaluationIds: string[]): Promise<{
-    evaluations: EvaluationResult[]
-    comparison_metrics: Record<string, any>
-  }> {
-    const response = await this.client.post('/api/v1/results/compare', {
-      evaluation_ids: evaluationIds,
-    })
-    return response.data
-  }
-
-  // Agents endpoints
-  async createAgent(data: {
-    name: string
-    phone_number?: string
-    telephony_phone_number_id?: string
-    language: string
-    description?: string | null
-    call_type: string
-    call_medium: string
-    voice_bundle_id?: string
-    ai_provider_id?: string
-    voice_ai_integration_id?: string
-    voice_ai_agent_id?: string
-    provider_prompt?: string | null
-    silence_hangup_secs?: number
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/agents', data)
-    return response.data
-  }
-
-  async listAgents(skip = 0, limit = 100): Promise<any[]> {
-    const response = await this.client.get('/api/v1/agents', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getAgent(agentId: string): Promise<TestAgent> {
-    const response = await this.client.get(`/api/v1/agents/${agentId}`)
-    return response.data
-  }
-
-  async checkAgentPhoneAssignment(params: {
-    phoneNumber?: string
-    telephonyPhoneNumberId?: string
-    excludeAgentId?: string
-  }): Promise<import('../types/api').AgentPhoneAssignmentCheckResponse> {
-    const response = await this.client.get('/api/v1/agents/check-phone-assignment', {
-      params: {
-        phone_number: params.phoneNumber,
-        telephony_phone_number_id: params.telephonyPhoneNumberId,
-        exclude_agent_id: params.excludeAgentId,
-      },
-    })
-    return response.data
-  }
-
-  async updateAgent(agentId: string, data: {
-    name?: string
-    phone_number?: string | null
-    telephony_phone_number_id?: string | null
-    language?: string
-    description?: string | null
-    call_type?: string
-    call_medium?: string
-    silence_hangup_secs?: number
-    voice_bundle_id?: string | null
-    ai_provider_id?: string
-    voice_ai_integration_id?: string | null
-    voice_ai_agent_id?: string | null
-    provider_prompt?: string | null
-    prompt_variables?: Record<string, string>
-  }): Promise<TestAgent> {
-    const response = await this.client.put(`/api/v1/agents/${agentId}`, data)
-    return response.data
-  }
-
-  async deleteAgent(agentId: string, force?: boolean): Promise<any> {
-    const response = await this.client.delete(`/api/v1/agents/${agentId}`, {
-      params: force ? { force: true } : undefined,
-    })
-    return response.data
-  }
-
-  async getAgentDeleteImpact(agentId: string): Promise<{
-    agent_id: string
-    agent_name: string
-    dependencies: Record<string, number>
-    can_delete_without_force: boolean
-  }> {
-    const response = await this.client.get(`/api/v1/agents/${agentId}/delete-impact`)
-    return response.data
-  }
-
-  async generateAgentDescription(data: {
-    description: string
-    tone?: string
-    format_style?: string
-    provider?: string
-    model?: string
-    agent_id?: string
-    include_linked_scenarios?: boolean
-    append_scenarios_to_output?: boolean
-  }): Promise<{ content: string; provider: string; model: string }> {
-    const response = await this.client.post('/api/v1/agents/generate-description', data)
-    return response.data
-  }
-
-  async generateTestPromptFromProduction(
-    data: GenerateTestPromptParams,
-  ): Promise<{
-    sections: Array<{ key: string; title: string; content: string }>
-    test_agent_prompt: string
-    provider: string
-    model: string
-  }> {
-    const response = await this.client.post('/api/v1/agents/generate-test-prompt', data)
-    return response.data
-  }
-
-  async generateScenariosFromPrompt(
-    data: GenerateScenariosFromPromptParams,
-  ): Promise<{
-    scenarios: Array<{ name: string; description: string; goal?: string | null }>
-    provider: string
-    model: string
-  }> {
-    const response = await this.client.post('/api/v1/agents/generate-scenarios-from-prompt', data)
-    return response.data
-  }
-
-  async generateTestSetupFromProduction(
-    data: GenerateTestSetupParams,
-  ): Promise<{
-    sections: Array<{ key: string; title: string; content: string }>
-    test_agent_prompt: string
-    scenarios: Array<{ name: string; description: string; goal?: string | null }>
-    provider: string
-    model: string
-  }> {
-    const response = await this.client.post('/api/v1/agents/generate-test-setup', data)
-    return response.data
-  }
-
-  // Personas endpoints
-  async listPersonas(skip = 0, limit = 100): Promise<any[]> {
-    const response = await this.client.get('/api/v1/personas', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getPersona(personaId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/personas/${personaId}`)
-    return response.data
-  }
-
-  async createPersona(data: {
-    name: string
-    gender: string
-    tts_provider?: string
-    tts_voice_id?: string
-    tts_voice_name?: string
-    is_custom?: boolean
-    description?: string
-    tts_config?: Record<string, unknown>
-    llm_temperature?: number
-    llm_max_tokens?: number
-    response_delay_ms?: number
-    max_turns?: number
-    allow_interruptions?: boolean
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/personas', data)
-    return response.data
-  }
-
-  async updatePersona(personaId: string, data: any): Promise<any> {
-    const response = await this.client.put(`/api/v1/personas/${personaId}`, data)
-    return response.data
-  }
-
-  async deletePersona(personaId: string, force?: boolean): Promise<any> {
-    const response = await this.client.delete(`/api/v1/personas/${personaId}`, {
-      params: force ? { force: true } : undefined,
-    })
-    return response.data
-  }
-
-  async clonePersona(personaId: string, name?: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/personas/${personaId}/clone`, { name })
-    return response.data
-  }
-
-  async seedDemoData(): Promise<any> {
-    const response = await this.client.post('/api/v1/personas/seed-data')
-    return response.data
-  }
-
-  async getPersonaAgentPromptSources(agentId: string): Promise<{
-    agent_id: string
-    agent_name: string
-    test_agent_prompt: string
-    agent_prompt: string
-  }> {
-    const response = await this.client.get(`/api/v1/personas/agent-prompt-sources/${agentId}`)
-    return response.data
-  }
-
-  async generatePersonaPrompt(data: {
-    agent_id: string
-    source?: 'test_agent' | 'agent' | 'auto'
-    persona_name?: string
-    persona_gender?: string
-    additional_context?: string
-    provider?: string
-    model?: string
-    credential_id?: string
-    llm_config?: LLMGenerationConfig | null
-  }): Promise<{
-    persona_prompt: string
-    source_used: string
-    provider: string
-    model: string
-  }> {
-    const response = await this.client.post('/api/v1/personas/generate-prompt', data)
-    return response.data
-  }
-
-  // Persona voice options (built-in + custom voices, ungated)
-  async getPersonaVoiceOptions(provider?: string): Promise<{
-    providers: Array<{
-      id: string
-      name: string
-      voices: Array<{
-        id: string
-        name: string
-        gender: string
-        is_custom: boolean
-        custom_voice_id?: string
-        description?: string | null
-      }>
-    }>
-  }> {
-    const response = await this.client.get('/api/v1/personas/voice-options', {
-      params: provider ? { provider } : undefined,
-    })
-    return response.data
-  }
-
-  // Custom voice CRUD (persona-scoped, ungated)
-  async listPersonaCustomVoices(provider?: string): Promise<any[]> {
-    const response = await this.client.get('/api/v1/personas/custom-voices', {
-      params: provider ? { provider } : undefined,
-    })
-    return response.data
-  }
-
-  async createPersonaCustomVoice(data: {
-    provider: string
-    voice_id: string
-    name: string
-    gender?: string
-    description?: string
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/personas/custom-voices', data)
-    return response.data
-  }
-
-  async updatePersonaCustomVoice(customVoiceId: string, data: {
-    voice_id?: string
-    name?: string
-    gender?: string
-    description?: string
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/personas/custom-voices/${customVoiceId}`, data)
-    return response.data
-  }
-
-  async deletePersonaCustomVoice(customVoiceId: string): Promise<any> {
-    const response = await this.client.delete(`/api/v1/personas/custom-voices/${customVoiceId}`)
-    return response.data
-  }
-
-  // Scenarios endpoints
-  async listScenarios(skip = 0, limit = 100, agentId?: string): Promise<any[]> {
-    const response = await this.client.get('/api/v1/scenarios', {
-      params: { skip, limit, ...(agentId ? { agent_id: agentId } : {}) },
-    })
-    return response.data
-  }
-
-  async getScenario(scenarioId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/scenarios/${scenarioId}`)
-    return response.data
-  }
-
-  async createScenario(data: {
-    name: string
-    agent_id?: string | null
-    description?: string | null
-    required_info: Record<string, string>
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/scenarios', data)
-    return response.data
-  }
-
-  async updateScenario(scenarioId: string, data: {
-    name?: string
-    agent_id?: string | null
-    description?: string | null
-    required_info?: Record<string, string>
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/scenarios/${scenarioId}`, data)
-    return response.data
-  }
-
-  async deleteScenario(scenarioId: string, force?: boolean): Promise<any> {
-    const response = await this.client.delete(`/api/v1/scenarios/${scenarioId}`, {
-      params: force ? { force: true } : undefined,
-    })
-    return response.data
-  }
-
-  // Chat/Inference endpoints
-  async chatCompletion(data: {
-    messages: Array<{ role: string; content: string }>
-    provider: string
-    model: string
-    temperature?: number
-    max_tokens?: number
-    llm_config?: LLMGenerationConfig | null
-  }): Promise<{
-    text: string
-    model: string
-    usage?: { prompt_tokens?: number; completion_tokens?: number; total_tokens?: number }
-    processing_time?: number
-  }> {
-    const response = await this.client.post('/api/v1/chat/completion', data)
-    return response.data
-  }
-
-  // VoiceBundle endpoints
-  async listVoiceBundles(skip = 0, limit = 100): Promise<any[]> {
-    const response = await this.client.get('/api/v1/voicebundles', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getVoiceBundle(voicebundleId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/voicebundles/${voicebundleId}`)
-    return response.data
-  }
-
-  async createVoiceBundle(data: any): Promise<any> {
-    const response = await this.client.post('/api/v1/voicebundles', data)
-    return response.data
-  }
-
-  async updateVoiceBundle(voicebundleId: string, data: any): Promise<any> {
-    const response = await this.client.put(`/api/v1/voicebundles/${voicebundleId}`, data)
-    return response.data
-  }
-
-  async deleteVoiceBundle(voicebundleId: string, force?: boolean): Promise<any> {
-    const response = await this.client.delete(`/api/v1/voicebundles/${voicebundleId}`, {
-      params: force ? { force: true } : undefined,
-    })
-    return response.data
-  }
-
-  // AI Provider endpoints
-  async listAIProviders(): Promise<any[]> {
-    const response = await this.client.get('/api/v1/aiproviders')
-    return response.data
-  }
-
-  async getAIProvider(aiproviderId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/aiproviders/${aiproviderId}`)
-    return response.data
-  }
-
-  async createAIProvider(data: any): Promise<any> {
-    const response = await this.client.post('/api/v1/aiproviders', data)
-    return response.data
-  }
-
-  async updateAIProvider(aiproviderId: string, data: any): Promise<any> {
-    const response = await this.client.put(`/api/v1/aiproviders/${aiproviderId}`, data)
-    return response.data
-  }
-
-  async deleteAIProvider(aiproviderId: string): Promise<void> {
-    await this.client.delete(`/api/v1/aiproviders/${aiproviderId}`)
-  }
-
-  async setDefaultAIProvider(aiproviderId: string): Promise<any> {
-    const response = await this.client.post(
-      `/api/v1/aiproviders/${aiproviderId}/set-default`,
-    )
-    return response.data
-  }
-
-  async testAIProvider(aiproviderId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/aiproviders/${aiproviderId}/test`)
-    return response.data
-  }
-
-  async getLLMGatewaySettings(): Promise<LLMGatewaySettings> {
-    const response = await this.client.get('/api/v1/organizations/llm-gateway')
-    return response.data
-  }
-
-  async updateLLMGatewaySettings(data: LLMGatewaySettingsUpdate): Promise<LLMGatewaySettings> {
-    const response = await this.client.put('/api/v1/organizations/llm-gateway', data)
-    return response.data
-  }
-
-  // IAM endpoints
-  async getOrganization(): Promise<OrganizationSummary> {
-    const response = await this.client.get('/api/v1/iam/organization')
-    return response.data
-  }
-
-  async updateOrganization(data: { name: string }): Promise<OrganizationSummary> {
-    const response = await this.client.patch('/api/v1/iam/organization', data)
-    return response.data
-  }
-
-  async listOrganizationUsers(): Promise<OrganizationMember[]> {
-    const response = await this.client.get('/api/v1/iam/users')
-    return response.data
-  }
-
-  async inviteUser(data: InvitationCreate): Promise<Invitation> {
-    const response = await this.client.post('/api/v1/iam/invitations', data)
-    return response.data
-  }
-
-  async listInvitations(): Promise<Invitation[]> {
-    const response = await this.client.get('/api/v1/iam/invitations')
-    return response.data
-  }
-
-  async updateUserRole(userId: string, role: Role): Promise<OrganizationMember> {
-    const response = await this.client.put(`/api/v1/iam/users/${userId}/role`, { role })
-    return response.data
-  }
-
-  async removeUser(userId: string): Promise<void> {
-    await this.client.delete(`/api/v1/iam/users/${userId}`)
-  }
-
-  async cancelInvitation(invitationId: string): Promise<void> {
-    await this.client.delete(`/api/v1/iam/invitations/${invitationId}`)
-  }
-
-  /**
-   * Admin-initiated password reset for another organization member.
-   *
-   * The caller must be an ADMIN of the same organization as `userId`. The
-   * backend ignores any current-password concept here (that's for the
-   * self-service `/auth/password` endpoint). Communicate the new password
-   * to the user out-of-band.
-   */
-  async adminResetUserPassword(
-    userId: string,
-    newPassword: string,
-  ): Promise<{ user_id: string; email: string; message: string }> {
-    const response = await this.client.post(
-      `/api/v1/iam/users/${userId}/reset-password`,
-      { new_password: newPassword },
-    )
-    return response.data
-  }
-
-  // Profile endpoints
-  async getProfile(): Promise<Profile> {
-    const response = await this.client.get('/api/v1/profile')
-    return response.data
-  }
-
-  async updateProfile(data: UserUpdate): Promise<Profile> {
-    const response = await this.client.put('/api/v1/profile', data)
-    return response.data
-  }
-
-  async getMyInvitations(): Promise<Invitation[]> {
-    const response = await this.client.get('/api/v1/profile/invitations')
-    return response.data
-  }
-
-  async acceptInvitation(invitationId: string): Promise<MessageResponse> {
-    const response = await this.client.post(`/api/v1/profile/invitations/${invitationId}/accept`)
-    return response.data
-  }
-
-  async declineInvitation(invitationId: string): Promise<MessageResponse> {
-    const response = await this.client.post(`/api/v1/profile/invitations/${invitationId}/decline`)
-    return response.data
-  }
-
-  // User Preferences endpoints
-  async getUserPreferences(): Promise<UserPreferences> {
-    const response = await this.client.get('/api/v1/profile/preferences')
-    return response.data
-  }
-
-  async updateUserPreferences(data: UserPreferencesUpdate): Promise<UserPreferences> {
-    const response = await this.client.put('/api/v1/profile/preferences', data)
-    return response.data
-  }
-
-  // Integration endpoints
-  async listIntegrations(): Promise<Integration[]> {
-    const response = await this.client.get('/api/v1/integrations')
-    return response.data
-  }
-
-  async getIntegration(integrationId: string): Promise<Integration> {
-    const response = await this.client.get(`/api/v1/integrations/${integrationId}`)
-    return response.data
-  }
-
-  async previewIntegrationAgentPrompt(
-    integrationId: string,
-    voiceAiAgentId: string,
-  ): Promise<{ provider_prompt: string }> {
-    const response = await this.client.post(
-      `/api/v1/integrations/${integrationId}/preview-agent-prompt`,
-      { voice_ai_agent_id: voiceAiAgentId },
-    )
-    return response.data
-  }
-
-  async createIntegration(data: IntegrationCreate): Promise<Integration> {
-    const response = await this.client.post('/api/v1/integrations', data)
-    return response.data
-  }
-
-  async updateIntegration(integrationId: string, data: Partial<IntegrationCreate>): Promise<Integration> {
-    const response = await this.client.put(`/api/v1/integrations/${integrationId}`, data)
-    return response.data
-  }
-
-  async deleteIntegration(integrationId: string, force?: boolean): Promise<any> {
-    const response = await this.client.delete(`/api/v1/integrations/${integrationId}`, {
-      params: force ? { force: true } : undefined,
-    })
-    return response.data
-  }
-
-  async setDefaultIntegration(integrationId: string): Promise<Integration> {
-    const response = await this.client.post(
-      `/api/v1/integrations/${integrationId}/set-default`,
-    )
-    return response.data
-  }
-
-  async getIntegrationApiKey(integrationId: string): Promise<{ api_key: string; public_key?: string | null }> {
-    const response = await this.client.get(`/api/v1/integrations/${integrationId}/api-key`)
-    return response.data
-  }
-
-  // Telephony endpoints (provider-agnostic)
-  async createTelephonyConfig(data: TelephonyIntegrationCreatePayload): Promise<TelephonyIntegrationResponse> {
-    const response = await this.client.post('/api/v1/telephony/config', data)
-    return response.data
-  }
-
-  async getTelephonyConfig(provider: string = 'plivo'): Promise<TelephonyIntegrationResponse> {
-    const response = await this.client.get('/api/v1/telephony/config', { params: { provider } })
-    return response.data
-  }
-
-  async listTelephonyConfigs(provider?: string): Promise<TelephonyIntegrationResponse[]> {
-    const response = await this.client.get('/api/v1/telephony/configs', {
-      params: provider ? { provider } : undefined,
-    })
-    return response.data
-  }
-
-  async updateTelephonyConfig(data: TelephonyIntegrationUpdatePayload): Promise<TelephonyIntegrationResponse> {
-    const response = await this.client.put('/api/v1/telephony/config', data)
-    return response.data
-  }
-
-  async setDefaultTelephonyConfig(integrationId: string): Promise<TelephonyIntegrationResponse> {
-    const response = await this.client.post(
-      `/api/v1/telephony/config/${integrationId}/set-default`,
-    )
-    return response.data
-  }
-
-  async deleteTelephonyConfig(integrationId: string): Promise<void> {
-    await this.client.delete(`/api/v1/telephony/config/${integrationId}`)
-  }
-
-  async listTelephonyNumbers(provider?: string): Promise<TelephonyPhoneNumberResponse[]> {
-    const response = await this.client.get('/api/v1/telephony/numbers', {
-      params: provider ? { provider } : undefined,
-    })
-    return response.data
-  }
-
-  async listAvailableTelephonyNumbers(
-    provider: string,
-    credentialId?: string,
-  ): Promise<TelephonyAvailableNumber[]> {
-    const response = await this.client.get('/api/v1/telephony/numbers/available', {
-      params: {
-        provider,
-        ...(credentialId ? { credential_id: credentialId } : {}),
-      },
-    })
-    return response.data
-  }
-
-  async importTelephonyNumbers(
-    provider: string,
-    numbers: string[],
-    agentId?: string,
-    credentialId?: string,
-  ): Promise<TelephonyImportNumbersResponse> {
-    const response = await this.client.post('/api/v1/telephony/numbers/import', {
-      provider,
-      numbers,
-      agent_id: agentId || undefined,
-      credential_id: credentialId || undefined,
-    })
-    return response.data
-  }
-
-  async listVobizAvailableNumbers(): Promise<TelephonyAvailableNumber[]> {
-    return this.listAvailableTelephonyNumbers('vobiz')
-  }
-
-  async importVobizNumbers(numbers: string[], agentId?: string): Promise<TelephonyImportNumbersResponse> {
-    return this.importTelephonyNumbers('vobiz', numbers, agentId)
-  }
-
-  async deleteTelephonyNumber(numberId: string): Promise<void> {
-    await this.client.delete(`/api/v1/telephony/numbers/${numberId}`)
-  }
-
-  async deleteImportedVobizNumber(numberId: string): Promise<void> {
-    await this.deleteTelephonyNumber(numberId)
-  }
-
-  async listTelephonyOutboundPool(): Promise<PlatformOutboundPoolResponse> {
-    const response = await this.client.get('/api/v1/telephony/outbound-pool')
-    return response.data
-  }
-
-  async listVobizOutboundPool(): Promise<PlatformOutboundPoolResponse> {
-    return this.listTelephonyOutboundPool()
-  }
-
-  async listTelephonyDialTargets(): Promise<TelephonyDialTargetResponse[]> {
-    const response = await this.client.get('/api/v1/telephony/dial-targets')
-    return response.data
-  }
-
-  async createVobizOutboundCall(data: VobizOutboundCallRequest): Promise<VobizOutboundCallResponse> {
-    const response = await this.client.post('/api/v1/telephony/vobiz/calls/outbound', data)
-    return response.data
-  }
-
-  async listDialTargets(): Promise<TelephonyDialTargetResponse[]> {
-    const response = await this.client.get('/api/v1/telephony/dial-targets')
-    return response.data
-  }
-
-  async createDialTarget(data: { phone_number: string; label?: string }): Promise<TelephonyDialTargetResponse> {
-    const response = await this.client.post('/api/v1/telephony/dial-targets', data)
-    return response.data
-  }
-
-  async updateDialTarget(
-    targetId: string,
-    data: { phone_number?: string; label?: string },
-  ): Promise<TelephonyDialTargetResponse> {
-    const response = await this.client.put(`/api/v1/telephony/dial-targets/${targetId}`, data)
-    return response.data
-  }
-
-  async deleteDialTarget(targetId: string): Promise<void> {
-    await this.client.delete(`/api/v1/telephony/dial-targets/${targetId}`)
-  }
-
-  // Data Sources endpoints
-  async testS3Connection(): Promise<S3ConnectionTestResponse> {
-    const response = await this.client.post('/api/v1/data-sources/s3/test')
-    return response.data
-  }
-
-  async listS3Files(prefix?: string, maxKeys = 1000): Promise<S3ListFilesResponse> {
-    const response = await this.client.get('/api/v1/data-sources/s3/files', {
-      params: { prefix, max_keys: maxKeys },
-    })
-    return response.data
-  }
-
-  async browseS3(path = '', maxKeys = 1000): Promise<S3BrowseResponse> {
-    const response = await this.client.get('/api/v1/data-sources/s3/browse', {
-      params: { path, max_keys: maxKeys },
-    })
-    return response.data
-  }
-
-  async uploadToS3(file: File, customFilename?: string): Promise<MessageResponse> {
-    const formData = new FormData()
-    formData.append('file', file)
-    if (customFilename) {
-      formData.append('filename', customFilename)
-    }
-    const response = await this.client.post('/api/v1/data-sources/s3/upload', formData, {
-      headers: {
-        'Content-Type': 'multipart/form-data',
-      },
-    })
-    return response.data
-  }
-
-  async getS3Status(): Promise<S3Status> {
-    const response = await this.client.get('/api/v1/data-sources/s3/status')
-    return response.data
-  }
-
-  async downloadFromS3(fileKey: string): Promise<void> {
-    const response = await this.client.get(`/api/v1/data-sources/s3/files/${encodeURIComponent(fileKey)}/download`, {
-      responseType: 'blob',
-    })
-    // Create a blob URL and trigger download
-    const url = window.URL.createObjectURL(new Blob([response.data]))
-    const link = document.createElement('a')
-    link.href = url
-    link.setAttribute('download', fileKey.split('/').pop() || 'file')
-    document.body.appendChild(link)
-    link.click()
-    link.remove()
-    window.URL.revokeObjectURL(url)
-  }
-
-  async getS3PresignedUrl(fileKey: string, expiration: number = 3600): Promise<{ url: string; expires_in: number }> {
-    const response = await this.client.get(`/api/v1/data-sources/s3/files/${encodeURIComponent(fileKey)}/presigned-url`, {
-      params: { expiration },
-    })
-    return response.data
-  }
-
-  async deleteFromS3(fileKey: string): Promise<MessageResponse> {
-    const response = await this.client.delete(`/api/v1/data-sources/s3/files/${encodeURIComponent(fileKey)}`)
-    return response.data
-  }
-
-  // Call Imports endpoints
-
-  /**
-   * Inspect an uploaded CSV / Excel file and get its sheets + headers.
-   *
-   * Drives the column-mapping UI without forcing the frontend to parse
-   * CSV / xlsx itself. CSV files come back as a single synthetic sheet;
-   * Excel workbooks come back with one entry per worksheet.
-   */
-  async previewCallImportFile(file: File): Promise<CallImportPreviewResponse> {
-    const formData = new FormData()
-    formData.append('file', file)
-    const response = await this.client.post(
-      '/api/v1/call-imports/preview',
-      formData,
-      { headers: { 'Content-Type': 'multipart/form-data' } },
-    )
-    return response.data
-  }
-
-  async uploadCallImport(
-    file: File,
-    options: {
-      provider: string
-      telephonyIntegrationId: string
-      /** Reusable Input Parameter schema this upload is mapped against. */
-      schemaId: string
-      /**
-       * Schema-driven mapping: ``{parameter_name: csv_header}``. Must
-       * include every required parameter on the schema. Pass an empty
-       * string / omit a parameter to leave it unmapped (only allowed
-       * for optional parameters).
-       */
-      parameterMapping: Record<string, string>
-      /**
-       * CSV/Excel headers the user has explicitly skipped. Every source
-       * column must either appear in ``parameterMapping`` or here, or
-       * the backend rejects the upload with 400.
-       */
-      skippedColumns?: string[]
-      dataset?: string | null
-      tagIds?: string[]
-      /**
-       * Worksheet name to import when the file is an Excel workbook.
-       * Required for .xlsx / .xlsm uploads; must be left undefined / null
-       * for CSV uploads (the backend rejects a non-empty value for CSV).
-       */
-      sheetName?: string | null
-    }
-  ): Promise<CallImportUploadResponse> {
-    const formData = new FormData()
-    formData.append('file', file)
-    formData.append('provider', options.provider)
-    formData.append('telephony_integration_id', options.telephonyIntegrationId)
-    formData.append('schema_id', options.schemaId)
-    formData.append('parameter_mapping', JSON.stringify(options.parameterMapping))
-    formData.append('skipped_columns', JSON.stringify(options.skippedColumns || []))
-    if (options.dataset !== undefined && options.dataset !== null) {
-      formData.append('dataset', options.dataset)
-    }
-    if (options.tagIds && options.tagIds.length > 0) {
-      for (const tagId of options.tagIds) {
-        formData.append('tag_ids', tagId)
-      }
-    }
-    if (options.sheetName) {
-      formData.append('sheet_name', options.sheetName)
-    }
-    const response = await this.client.post('/api/v1/call-imports/upload', formData, {
-      headers: { 'Content-Type': 'multipart/form-data' },
-    })
-    return response.data
-  }
-
-  /**
-   * UPLOAD stage of the staged call-import flow.
-   *
-   * Persists the CSV / Excel file to S3, captures a sheets snapshot,
-   * and creates a ``CallImport`` row with ``status='uploaded'``. The
-   * returned record has no mapping / provider / rows yet — the caller
-   * follows up with {@link updateCallImportMapping} and
-   * {@link startCallImport} to advance the batch.
-   */
-  async createCallImport(
-    file: File,
-    options: {
-      dataset: string
-      tagIds?: string[]
-      /**
-       * Optional schema pre-pick. The user can still change it during
-       * the MAP stage; provided here only so the detail page can pre-
-       * select the schema dropdown.
-       */
-      schemaId?: string | null
-    },
-  ): Promise<CallImport> {
-    const formData = new FormData()
-    formData.append('file', file)
-    formData.append('dataset', options.dataset)
-    if (options.tagIds && options.tagIds.length > 0) {
-      for (const tagId of options.tagIds) {
-        formData.append('tag_ids', tagId)
-      }
-    }
-    if (options.schemaId) {
-      formData.append('schema_id', options.schemaId)
-    }
-    const response = await this.client.post('/api/v1/call-imports', formData, {
-      headers: { 'Content-Type': 'multipart/form-data' },
-    })
-    return response.data
-  }
-
-  async uploadCallImportAudio(
-    files: File[],
-    options: {
-      dataset: string
-      tagIds?: string[]
-      batchName?: string
-    },
-  ): Promise<CallImportUploadResponse> {
-    const formData = new FormData()
-    for (const file of files) {
-      formData.append('files', file)
-    }
-    formData.append('dataset', options.dataset)
-    if (options.batchName?.trim()) {
-      formData.append('batch_name', options.batchName.trim())
-    }
-    if (options.tagIds && options.tagIds.length > 0) {
-      for (const tagId of options.tagIds) {
-        formData.append('tag_ids', tagId)
-      }
-    }
-    const response = await this.client.post('/api/v1/call-imports/audio-upload', formData, {
-      headers: { 'Content-Type': 'multipart/form-data' },
-    })
-    return response.data
-  }
-
-  async appendCallImportAudio(
-    importId: string,
-    files: File[],
-  ): Promise<CallImportUploadResponse> {
-    const formData = new FormData()
-    for (const file of files) {
-      formData.append('files', file)
-    }
-    const response = await this.client.post(
-      `/api/v1/call-imports/${importId}/audio-append`,
-      formData,
-      {
-        headers: { 'Content-Type': 'multipart/form-data' },
-      },
-    )
-    return response.data
-  }
-
-  async uploadCallImportAudioChunked(
-    files: File[],
-    options: {
-      dataset: string
-      tagIds?: string[]
-      batchName?: string
-      onProgress?: (progress: CallImportAudioUploadProgress) => void
-    },
-  ): Promise<CallImportUploadResponse> {
-    if (files.length === 0) {
-      throw new Error('At least one audio file is required.')
-    }
-
-    const chunks: File[][] = []
-    for (let i = 0; i < files.length; i += AUDIO_UPLOAD_CHUNK_SIZE) {
-      chunks.push(files.slice(i, i + AUDIO_UPLOAD_CHUNK_SIZE))
-    }
-
-    let result: CallImportUploadResponse | null = null
-    for (let chunkIndex = 0; chunkIndex < chunks.length; chunkIndex += 1) {
-      const chunk = chunks[chunkIndex]
-      options.onProgress?.({
-        chunkIndex: chunkIndex + 1,
-        totalChunks: chunks.length,
-        uploadedFiles: chunkIndex * AUDIO_UPLOAD_CHUNK_SIZE,
-        totalFiles: files.length,
-      })
-
-      if (chunkIndex === 0) {
-        result = await this.uploadCallImportAudio(chunk, {
-          dataset: options.dataset,
-          tagIds: options.tagIds,
-          batchName: options.batchName,
-        })
-      } else {
-        result = await this.appendCallImportAudio(result!.id, chunk)
-      }
-
-      options.onProgress?.({
-        chunkIndex: chunkIndex + 1,
-        totalChunks: chunks.length,
-        uploadedFiles: Math.min(
-          (chunkIndex + 1) * AUDIO_UPLOAD_CHUNK_SIZE,
-          files.length,
-        ),
-        totalFiles: files.length,
-      })
-    }
-
-    return result!
-  }
-
-  async uploadCallImportAudioAppendChunked(
-    importId: string,
-    files: File[],
-    options?: {
-      onProgress?: (progress: CallImportAudioUploadProgress) => void
-    },
-  ): Promise<CallImportUploadResponse> {
-    if (files.length === 0) {
-      throw new Error('At least one audio file is required.')
-    }
-
-    const chunks: File[][] = []
-    for (let i = 0; i < files.length; i += AUDIO_UPLOAD_CHUNK_SIZE) {
-      chunks.push(files.slice(i, i + AUDIO_UPLOAD_CHUNK_SIZE))
-    }
-
-    let result: CallImportUploadResponse | null = null
-    for (let chunkIndex = 0; chunkIndex < chunks.length; chunkIndex += 1) {
-      const chunk = chunks[chunkIndex]
-      options?.onProgress?.({
-        chunkIndex: chunkIndex + 1,
-        totalChunks: chunks.length,
-        uploadedFiles: chunkIndex * AUDIO_UPLOAD_CHUNK_SIZE,
-        totalFiles: files.length,
-      })
-
-      result = await this.appendCallImportAudio(importId, chunk)
-
-      options?.onProgress?.({
-        chunkIndex: chunkIndex + 1,
-        totalChunks: chunks.length,
-        uploadedFiles: Math.min(
-          (chunkIndex + 1) * AUDIO_UPLOAD_CHUNK_SIZE,
-          files.length,
-        ),
-        totalFiles: files.length,
-      })
-    }
-
-    return result!
-  }
-
-  /**
-   * MAP stage of the staged call-import flow.
-   *
-   * Persists the schema + sheet + parameter mapping on the batch. The
-   * backend validates against the sheet snapshot it cached at upload
-   * time, so this is purely a metadata operation (no S3 fetch).
-   * Idempotent: safe to call repeatedly while the batch is in
-   * ``uploaded`` or ``mapped`` state.
-   */
-  async updateCallImportMapping(
-    id: string,
-    options: {
-      schemaId: string
-      sheetName?: string | null
-      parameterMapping: Record<string, string>
-      skippedColumns?: string[]
-    },
-  ): Promise<CallImport> {
-    const response = await this.client.patch(
-      `/api/v1/call-imports/${id}/mapping`,
-      {
-        schema_id: options.schemaId,
-        sheet_name: options.sheetName ?? null,
-        parameter_mapping: options.parameterMapping,
-        skipped_columns: options.skippedColumns ?? [],
-      },
-    )
-    return response.data
-  }
-
-  /**
-   * IMPORT stage of the staged call-import flow.
-   *
-   * Materialises rows from the staged source file using the persisted
-   * mapping and fans them out to the ``imports`` Celery queue. Returns
-   * the same shape as the legacy one-shot ``uploadCallImport``.
-   */
-  async startCallImport(
-    id: string,
-    options: {
-      provider: string | null
-      telephonyIntegrationId: string | null
-    },
-  ): Promise<CallImportUploadResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${id}/import`,
-      {
-        provider: options.provider,
-        telephony_integration_id: options.telephonyIntegrationId,
-      },
-    )
-    return response.data
-  }
-
-  // -------------------------------------------------------------------
-  // Call Import Schemas (reusable Input Parameter schemas)
-  // -------------------------------------------------------------------
-
-  async listCallImportSchemas(): Promise<CallImportSchemaListResponse> {
-    const response = await this.client.get('/api/v1/call-import-schemas')
-    return response.data
-  }
-
-  async getCallImportSchema(id: string): Promise<CallImportSchema> {
-    const response = await this.client.get(`/api/v1/call-import-schemas/${id}`)
-    return response.data
-  }
-
-  async createCallImportSchema(
-    payload: CallImportSchemaCreate,
-  ): Promise<CallImportSchema> {
-    const response = await this.client.post('/api/v1/call-import-schemas', payload)
-    return response.data
-  }
-
-  async updateCallImportSchema(
-    id: string,
-    payload: CallImportSchemaUpdate,
-  ): Promise<CallImportSchema> {
-    const response = await this.client.patch(
-      `/api/v1/call-import-schemas/${id}`,
-      payload,
-    )
-    return response.data
-  }
-
-  async deleteCallImportSchema(
-    id: string,
-    options?: { force?: boolean },
-  ): Promise<void> {
-    await this.client.delete(`/api/v1/call-import-schemas/${id}`, {
-      params: options?.force ? { force: true } : undefined,
-    })
-  }
-
-  async listCallImports(
-    params: {
-      page?: number
-      page_size?: number
-      status?: CallImportStatus
-      dataset?: string
-      tag_id?: string[]
-      source_format?: string
-    } = {}
-  ): Promise<CallImportListResponse> {
-    // Send tag_id repeated rather than as a JSON array.
-    const search = new URLSearchParams()
-    if (params.page !== undefined) search.set('page', String(params.page))
-    if (params.page_size !== undefined) search.set('page_size', String(params.page_size))
-    if (params.status) search.set('status', params.status)
-    if (params.dataset !== undefined) search.set('dataset', params.dataset)
-    if (params.source_format) search.set('source_format', params.source_format)
-    for (const tag of params.tag_id || []) search.append('tag_id', tag)
-    const response = await this.client.get('/api/v1/call-imports', { params: search })
-    return response.data
-  }
-
-  async listCallImportDatasets(): Promise<string[]> {
-    const response = await this.client.get('/api/v1/call-imports/datasets')
-    return response.data
-  }
-
-  async getCallImport(
-    id: string,
-    params: {
-      row_limit?: number
-      row_offset?: number
-      q?: string
-      /**
-       * Optional filter on ``CallImportRow.diarised_transcript_status``.
-       * When set, ``filtered_total_rows`` on the response reflects the
-       * post-filter row count (combined with ``q`` when both are
-       * supplied) so the UI can paginate against the same slice.
-       */
-      diarised_status?: 'pending' | 'running' | 'completed' | 'failed'
-    } = {}
-  ): Promise<CallImportDetail> {
-    const response = await this.client.get(`/api/v1/call-imports/${id}`, { params })
-    return response.data
-  }
-
-  async listCallImportRowIds(
-    id: string,
-    params: {
-      q?: string
-      diarised_status?: 'pending' | 'running' | 'completed' | 'failed'
-    } = {}
-  ): Promise<{ ids: string[]; total: number }> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${id}/row-ids`,
-      { params },
-    )
-    return response.data
-  }
-
-  async updateCallImport(
-    id: string,
-    payload: {
-      original_filename?: string | null
-      dataset?: string | null
-      tag_ids?: string[]
-      /**
-       * Reassign the Input Parameter schema. Only honoured while the
-       * batch is in ``uploaded`` / ``mapped`` state. Switching schemas
-       * resets the mapping and rewinds the batch to ``uploaded``.
-       */
-      schema_id?: string | null
-    },
-  ): Promise<CallImport> {
-    const response = await this.client.patch(`/api/v1/call-imports/${id}`, payload)
-    return response.data
-  }
-
-  async deleteCallImport(
-    id: string,
-  ): Promise<{ id: string; status: 'accepted' | 'completed' }> {
-    const response = await this.client.delete(`/api/v1/call-imports/${id}`)
-    return response.data
-  }
-
-  async deleteCallImportRow(id: string, rowId: string): Promise<void> {
-    await this.client.delete(`/api/v1/call-imports/${id}/rows/${rowId}`)
-  }
-
-  async bulkDeleteCallImportRows(
-    id: string,
-    rowIds: string[],
-  ): Promise<{ deleted: number; status?: 'completed' | 'accepted' }> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${id}/rows/bulk-delete`,
-      { row_ids: rowIds },
-    )
-    return response.data
-  }
-
-  async createCallImportEvaluation(
-    callImportId: string,
-    payload: {
-      metric_ids: string[]
-      name?: string | null
-      /**
-       * Which transcript to score against. ``'diarised'`` (default)
-       * auto-diarizes then scores; ``'production'`` scores the CSV
-       * transcript directly.
-       */
-      transcript_sources?: Array<'production' | 'diarised'>
-      /** Run-level LLM provider key. Leave undefined for legacy default. */
-      llm_provider?: string | null
-      llm_model?: string | null
-      llm_credential_id?: string | null
-      llm_config?: LLMGenerationConfig | null
-      /** Per-metric LLM override map keyed by metric UUID. */
-      metric_llm_overrides?: Record<string, CallImportEvaluationLLMOverride> | null
-      /** When true, diarize rows missing transcripts before evaluation. */
-      auto_transcribe?: boolean
-      transcribe_overwrite?: boolean
-      /**
-       * Diarisation pipeline shape for the auto-transcribe step.
-       * 'stt_llm' (default) runs STT then an LLM diariser over the
-       * resulting text. 'llm_only' skips STT and feeds the audio
-       * directly to the multimodal ``diarization_llm_*`` model — STT
-       * fields must be omitted in that case.
-       */
-      transcribe_mode?: 'stt_llm' | 'llm_only'
-      stt_provider?: string | null
-      stt_model?: string | null
-      stt_credential_id?: string | null
-      stt_language?: string | null
-      /**
-       * LLM diariser config: post-STT, an LLM splits the plain
-       * transcript into agent/user turns using ``diarization_prompt``
-       * (or the canonical default when empty). Required by the server
-       * when ``auto_transcribe`` is set. Also used in 'llm_only' mode
-       * where this same LLM receives the audio directly.
-       */
-      diarization_llm_provider?: string | null
-      diarization_llm_model?: string | null
-      diarization_llm_credential_id?: string | null
-      diarization_prompt?: string | null
-      /**
-       * Opt into LLM-driven discovery of brand-new top-level metrics
-       * for this run. Surfaces candidates in the Discovered metrics
-       * panel on the evaluation detail Flow tab.
-       */
-      discover_new_metrics?: boolean
-      /** Telephony credentials when starting from a mapped batch. */
-      provider?: string | null
-      telephony_integration_id?: string | null
-    },
-  ): Promise<CallImportEvaluation> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations`,
-      payload,
-    )
-    return response.data
-  }
-
-  /** Aggregated metric distributions for a single evaluation run. */
-  async getCallImportEvaluationAggregate(
-    callImportId: string,
-    evaluationId: string,
-    baselineEvaluationId?: string | null,
-  ): Promise<CallImportEvaluationAggregateResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/aggregate`,
-      {
-        params: baselineEvaluationId
-          ? { baseline_evaluation_id: baselineEvaluationId }
-          : undefined,
-      },
-    )
-    return response.data
-  }
-
-  /**
-   * Read the cached LLM TLDR for an evaluation. Returns ``null`` when
-   * the user has never generated one (the empty-state CTA renders
-   * this case).
-   */
-  async getCallImportEvaluationInsights(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').EvaluationTldrSummary | null> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/insights`,
-    )
-    return response.data ?? null
-  }
-
-  /**
-   * Generate (or return cached) the LLM TLDR for an evaluation run.
-   *
-   * - Pass ``regenerate: true`` to force a fresh LLM call even when a
-   *   cached summary exists at the current ``completed_rows`` watermark.
-   * - Pass ``provider`` + ``model`` to pin a specific LLM. Omit both to
-   *   let the backend auto-detect the org's first active OpenAI /
-   *   Anthropic / Google credential (mirroring Prompt Partials).
-   */
-  async generateCallImportEvaluationInsights(
-    callImportId: string,
-    evaluationId: string,
-    options?: {
-      regenerate?: boolean
-      provider?: string | null
-      model?: string | null
-      credential_id?: string | null
-      max_llm_calls?: number | null
-    },
-  ): Promise<import('../types/api').EvaluationTldrSummary> {
-    const body: Record<string, unknown> = {
-      regenerate: Boolean(options?.regenerate),
-    }
-    if (options?.provider) body.provider = options.provider
-    if (options?.model) body.model = options.model
-    if (options?.credential_id) body.credential_id = options.credential_id
-    if (options?.max_llm_calls != null) body.max_llm_calls = options.max_llm_calls
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/insights`,
-      body,
-    )
-    return response.data
-  }
-
-  async getCallImportEvaluationUserInsights(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').EvaluationUserInsightsState | null> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/user-insights`,
-    )
-    return response.data
-  }
-
-  async getCallImportEvaluationMetricClusters(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').EvaluationMetricClustersState | null> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters`,
-    )
-    return response.data
-  }
-
-  async getCallImportEvaluationPromptImprovements(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').EvaluationPromptImprovementsState | null> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/prompt-improvements`,
-    )
-    return response.data
-  }
-
-  async generateCallImportEvaluationPromptImprovements(
-    callImportId: string,
-    evaluationId: string,
-    data: {
-      imported_agent_id: string
-      regenerate?: boolean
-      force?: boolean
-      provider?: string
-      model?: string
-      credential_id?: string
-    },
-  ): Promise<import('../types/api').EvaluationPromptImprovementsState> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/prompt-improvements`,
-      data,
-    )
-    return response.data
-  }
-
-  async listCallImportEvaluationMetricClusterEligibleRows(
-    callImportId: string,
-    evaluationId: string,
-    options?: {
-      limit?: number
-      count_only?: boolean
-    },
-  ): Promise<import('../types/api').MetricClusterEligibleRowsResponse> {
-    const params = new URLSearchParams()
-    if (options?.limit != null) params.set('limit', String(options.limit))
-    if (options?.count_only) params.set('count_only', 'true')
-    const query = params.toString()
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/eligible-rows${query ? `?${query}` : ''}`,
-    )
-    return response.data
-  }
-
-  async getCallImportEvaluationMetricClusterFailurePolicies(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').MetricFailurePoliciesResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/failure-policies`,
-    )
-    return response.data
-  }
-
-  async saveCallImportEvaluationMetricClusterFailurePolicies(
-    callImportId: string,
-    evaluationId: string,
-    policies: Record<string, import('../types/api').MetricFailurePolicy>,
-  ): Promise<import('../types/api').MetricFailurePoliciesResponse> {
-    const response = await this.client.put(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/failure-policies`,
-      { policies, source: 'user' },
-    )
-    return response.data
-  }
-
-  async generateCallImportEvaluationMetricClusters(
-    callImportId: string,
-    evaluationId: string,
-    options?: {
-      regenerate?: boolean
-      force?: boolean
-      provider?: string | null
-      model?: string | null
-      credential_id?: string | null
-      max_llm_calls?: number | null
-      evaluation_row_ids?: string[] | null
-      row_limit?: number | null
-      failure_policies?: Record<
-        string,
-        import('../types/api').MetricFailurePolicy
-      > | null
-    },
-  ): Promise<import('../types/api').EvaluationMetricClustersState> {
-    const body: Record<string, unknown> = {
-      regenerate: Boolean(options?.regenerate),
-      force: Boolean(options?.force),
-    }
-    if (options?.provider) body.provider = options.provider
-    if (options?.model) body.model = options.model
-    if (options?.credential_id) body.credential_id = options.credential_id
-    if (options?.max_llm_calls != null) body.max_llm_calls = options.max_llm_calls
-    if (options?.evaluation_row_ids?.length) {
-      body.evaluation_row_ids = options.evaluation_row_ids
-    }
-    if (options?.row_limit != null) body.row_limit = options.row_limit
-    if (options?.failure_policies) {
-      body.failure_policies = options.failure_policies
-    }
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters`,
-      body,
-    )
-    return response.data
-  }
-
-  async cancelCallImportEvaluationMetricClusters(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').EvaluationMetricClustersState> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/cancel`,
-    )
-    return response.data
-  }
-
-  async generateCallImportEvaluationUserInsights(
-    callImportId: string,
-    evaluationId: string,
-    options?: {
-      regenerate?: boolean
-      force?: boolean
-      provider?: string | null
-      model?: string | null
-      credential_id?: string | null
-      max_llm_calls?: number | null
-    },
-  ): Promise<import('../types/api').EvaluationUserInsightsState> {
-    const body: Record<string, unknown> = {
-      regenerate: Boolean(options?.regenerate),
-      force: Boolean(options?.force),
-    }
-    if (options?.provider) body.provider = options.provider
-    if (options?.model) body.model = options.model
-    if (options?.credential_id) body.credential_id = options.credential_id
-    if (options?.max_llm_calls != null) body.max_llm_calls = options.max_llm_calls
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/user-insights`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Aggregate flow chart for a parent metric: returns nodes/edges built
-   * from per-row LLM-inferred ``sequence`` arrays. Used by the React
-   * Flow visualisation on the evaluation overview.
-   */
-  async getCallImportEvaluationFlow(
-    callImportId: string,
-    evaluationId: string,
-    parentMetricId: string,
-  ): Promise<import('../types/api').MetricFlowResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/flow`,
-      { params: { parent_metric_id: parentMetricId } },
-    )
-    return response.data
-  }
-
-  /**
-   * List LLM-discovered candidate sub-labels for a parent metric in an
-   * evaluation. Returned items power the Discovered Labels panel where
-   * users decide which candidates to promote or merge.
-   */
-  async getCallImportEvaluationDiscoveredLabels(
-    callImportId: string,
-    evaluationId: string,
-    parentMetricId: string,
-  ): Promise<import('../types/api').DiscoveredLabelsResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-labels`,
-      { params: { parent_metric_id: parentMetricId } },
-    )
-    return response.data
-  }
-
-  /**
-   * Merge a discovered slug into another within an evaluation. Rewrites
-   * every row's discovered_labels and sequence array so the panel +
-   * flow chart converge on the surviving slug.
-   */
-  async mergeCallImportEvaluationDiscoveredLabels(
-    callImportId: string,
-    evaluationId: string,
-    body: { parent_metric_id: string; from_key: string; to_key: string },
-  ): Promise<import('../types/api').DiscoveredLabelsResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-labels/merge`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Delete (tombstone) an LLM-discovered candidate. Strips the slug
-   * from every row's discovered_labels + sequence and records a
-   * deletion alias on the evaluation so workers finishing later don't
-   * resurrect it. Use for gibberish candidates the LLM proposed.
-   */
-  async deleteCallImportEvaluationDiscoveredLabel(
-    callImportId: string,
-    evaluationId: string,
-    body: { parent_metric_id: string; key: string },
-  ): Promise<import('../types/api').DiscoveredLabelsResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-labels/delete`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Promote an LLM-discovered candidate into a real child Metric under
-   * the given parent. ``key`` must equal ``slugify(name)`` so existing
-   * rows' sequence arrays auto-resolve against the new child.
-   *
-   * ``capture_rationale`` defaults to true on the backend (matches the
-   * Discovered Labels UX: candidates are LLM-suggested with rationales,
-   * so the user almost always wants future rows that hit them to keep
-   * capturing rationales).
-   */
-  async promoteDiscoveredChild(
-    parentMetricId: string,
-    body: {
-      key: string
-      name: string
-      description?: string | null
-      capture_rationale?: boolean
-    },
-  ): Promise<import('../types/api').MetricSummary> {
-    const response = await this.client.post(
-      `/api/v1/metrics/${parentMetricId}/children/from-discovered`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * List LLM-discovered candidate TOP-LEVEL metrics for an evaluation.
-   * Mirrors :func:`getCallImportEvaluationDiscoveredLabels` but is
-   * scoped to the evaluation as a whole (no ``parent_metric_id``).
-   * Returns an empty ``items`` list when the evaluation didn't opt
-   * into top-level metric discovery, so callers can fetch
-   * unconditionally.
-   */
-  async getCallImportEvaluationDiscoveredMetrics(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<import('../types/api').DiscoveredMetricsResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-metrics`,
-    )
-    return response.data
-  }
-
-  /**
-   * Merge one discovered top-level metric slug into another. Rewrites
-   * every row's ``__discovered_metrics__`` list and records the
-   * alias on the evaluation so workers finishing later converge on
-   * the surviving slug.
-   */
-  async mergeCallImportEvaluationDiscoveredMetrics(
-    callImportId: string,
-    evaluationId: string,
-    body: { from_key: string; to_key: string },
-  ): Promise<import('../types/api').DiscoveredMetricsResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-metrics/merge`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Tombstone an LLM-discovered top-level metric candidate. Strips
-   * the slug from every row's ``__discovered_metrics__`` list and
-   * records a deletion alias on the evaluation so workers finishing
-   * later can't resurrect it.
-   */
-  async deleteCallImportEvaluationDiscoveredMetric(
-    callImportId: string,
-    evaluationId: string,
-    body: { key: string },
-  ): Promise<import('../types/api').DiscoveredMetricsResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-metrics/delete`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Promote an LLM-discovered top-level metric candidate into a real
-   * standalone :class:`Metric` row. ``key`` must equal
-   * ``slugify(name)`` so already-scored rows that referenced the
-   * candidate keep resolving against the promoted metric.
-   *
-   * ``metric_type`` selects how the new metric will be scored on
-   * future runs (``boolean`` / ``rating`` / ``category``). ``category``
-   * creates a ``multi_label`` parent with no children — the user adds
-   * children via the existing Metrics page.
-   */
-  async promoteDiscoveredMetric(body: {
-    key: string
-    name: string
-    description?: string | null
-    metric_type?: 'boolean' | 'rating' | 'category'
-    capture_rationale?: boolean
-    custom_config?: Record<string, unknown> | null
-  }): Promise<import('../types/api').MetricSummary> {
-    const response = await this.client.post(
-      `/api/v1/metrics/from-discovered`,
-      body,
-    )
-    return response.data
-  }
-
-  /** Cross-run insights for the call import detail page. */
-  async getCallImportInsights(
-    callImportId: string,
-  ): Promise<CallImportInsightsResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/insights`,
-    )
-    return response.data
-  }
-
-  /**
-   * Fetch the canonical LLM diariser prompt. Used by the Transcribe /
-   * Run Evaluation modals to pre-fill the prompt textarea so the
-   * operator sees the actual default they'd otherwise get.
-   */
-  async getCallImportDiarisationPromptDefault(): Promise<string> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/diarisation-prompt-default`,
-    )
-    return (response.data?.prompt ?? '') as string
-  }
-
-  /** Fan out diarization tasks for a batch of rows. */
-  async transcribeCallImport(
-    callImportId: string,
-    payload: CallImportTranscribeRequest,
-  ): Promise<CallImportTranscribeResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/transcribe`,
-      payload,
-    )
-    return response.data
-  }
-
-  /** Diarize a single row's recording. */
-  async transcribeCallImportRow(
-    callImportId: string,
-    rowId: string,
-    payload: CallImportTranscribeRequest,
-  ): Promise<CallImportTranscribeResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/rows/${rowId}/transcribe`,
-      payload,
-    )
-    return response.data
-  }
-
-  /**
-   * Re-enqueue every failed import row in this batch.
-   *
-   * Rows are reset to `pending` first so the UI can immediately show that
-   * the retry sweep started; rows that fail to enqueue again are returned
-   * in `enqueue_failed` and stay `failed`.
-   */
-  async retryFailedCallImportRows(
-    callImportId: string,
-    options?: {
-      provider: string | null
-      telephonyIntegrationId: string | null
-    },
-  ): Promise<CallImportRetryFailedRowsResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/retry-failed`,
-      options
-        ? {
-            provider: options.provider,
-            telephony_integration_id: options.telephonyIntegrationId,
-          }
-        : undefined,
-    )
-    return response.data
-  }
-
-  /**
-   * Abort an in-flight (or queued) diarisation for a single row.
-   *
-   * Idempotent — calling on a row whose diarisation is already in a
-   * terminal state (``completed`` / ``failed`` / ``idle``) returns the
-   * row unchanged so the UI can wire this to a "Stop" button without
-   * pre-checking state. The backend flips the row's
-   * ``diarised_transcript_status`` to ``failed`` and stamps
-   * ``"Diarisation cancelled by user"`` as the error so the existing
-   * diarisation pill in the row header renders the right state on the
-   * next poll.
-   */
-  async cancelCallImportRowDiarisation(
-    callImportId: string,
-    rowId: string,
-  ): Promise<CallImportRow> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/rows/${rowId}/cancel-diarisation`,
-    )
-    return response.data
-  }
-
-  /**
-   * Abort in-flight diarisation for many rows in one call.
-   *
-   * Pass ``rowIds`` to cancel a specific subset. Omit it (or pass
-   * ``null``) to cancel every row in the import whose
-   * ``diarised_transcript_status`` is currently ``pending`` or
-   * ``running`` — the "stop everything" affordance.
-   */
-  async cancelCallImportDiarisation(
-    callImportId: string,
-    rowIds?: string[] | null,
-  ): Promise<{ cancelled: number; skipped: number }> {
-    const body =
-      rowIds === undefined || rowIds === null
-        ? {}
-        : { row_ids: rowIds }
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/cancel-diarisation`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Flip ``diarised_speaker_swap`` on a row and re-render
-   * ``diarised_transcript`` from ``diarised_segments``. Returns the
-   * updated row so the caller can swap it into local state without an
-   * extra refetch.
-   *
-   * The backend rejects this with 409 when the row has no structured
-   * segments (e.g. legacy diarisations done before turns were
-   * persisted) — callers should surface that as "re-diarise to enable
-   * speaker swapping".
-   */
-  async toggleCallImportRowSpeakerSwap(
-    callImportId: string,
-    rowId: string,
-  ): Promise<CallImportRow> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/rows/${rowId}/diarised-speaker-swap`,
-    )
-    return response.data
-  }
-
-  async updateCallImportEvaluation(
-    callImportId: string,
-    evaluationId: string,
-    payload: { name?: string | null },
-  ): Promise<CallImportEvaluation> {
-    const response = await this.client.patch(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}`,
-      payload,
-    )
-    return response.data
-  }
-
-  async bulkDeleteCallImportEvaluations(
-    callImportId: string,
-    evaluationIds: string[],
-  ): Promise<{ deleted: number }> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/bulk-delete`,
-      { evaluation_ids: evaluationIds },
-    )
-    return response.data
-  }
-
-  async deleteCallImportEvaluationRow(
-    callImportId: string,
-    evaluationId: string,
-    evalRowId: string,
-  ): Promise<void> {
-    await this.client.delete(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows/${evalRowId}`,
-    )
-  }
-
-  /**
-   * Re-enqueue failed evaluation rows. With no body, every row in the
-   * run whose status is ``failed`` is re-run with the run's saved
-   * config. Pass ``evalRowIds`` to scope the retry to a subset (used
-   * by the per-row "Retry" button), and/or the ``llm_*`` / ``stt_*``
-   * fields to swap out the LLM / STT provider before re-enqueueing
-   * (the backend validates + persists them onto the run).
-   *
-   * Rows still in progress / already completed are silently skipped
-   * on the server and reported in the response's ``skipped`` list —
-   * the mutation never fails just because the caller's selection is
-   * stale.
-   */
-  async retryCallImportEvaluation(
-    callImportId: string,
-    evaluationId: string,
-    options?: {
-      evalRowIds?: string[]
-      llmProvider?: string | null
-      llmModel?: string | null
-      llmCredentialId?: string | null
-      llmConfig?: LLMGenerationConfig | null
-      sttProvider?: string | null
-      sttModel?: string | null
-      sttCredentialId?: string | null
-      transcribeOverwrite?: boolean
-      transcribeMode?: 'stt_llm' | 'llm_only'
-      diarizationLlmProvider?: string | null
-      diarizationLlmModel?: string | null
-      diarizationLlmCredentialId?: string | null
-      diarizationPrompt?: string | null
-      /**
-       * Override telephony credentials on the batch when failed rows must
-       * re-fetch recordings. Send both fields together; omit to keep the
-       * batch's existing pinned credentials.
-       */
-      provider?: string | null
-      telephonyIntegrationId?: string | null
-      /**
-       * Metric-subset retry: when set, only these metrics are
-       * recomputed and merged into the row's existing metric_scores
-       * (other metrics' previous values are preserved). The backend
-       * auto-flips ``include_completed`` to true in this case so
-       * already-successful rows are eligible.
-       */
-      metricIds?: string[]
-      /**
-       * When true, rows currently ``completed`` are eligible for
-       * retry too (otherwise only ``failed`` rows are picked up).
-       * Required-and-implied when ``metricIds`` is set.
-       */
-      includeCompleted?: boolean
-    },
-  ): Promise<CallImportEvaluationRetryResponse> {
-    const body: Record<string, unknown> = {}
-    if (options?.evalRowIds && options.evalRowIds.length > 0) {
-      body.eval_row_ids = options.evalRowIds
-    }
-    if (options?.llmProvider) body.llm_provider = options.llmProvider
-    if (options?.llmModel) body.llm_model = options.llmModel
-    if (options?.llmCredentialId !== undefined) {
-      body.llm_credential_id = options.llmCredentialId
-    }
-    if (options?.llmConfig !== undefined) {
-      body.llm_config = options.llmConfig
-    }
-    if (options?.sttProvider) body.stt_provider = options.sttProvider
-    if (options?.sttModel) body.stt_model = options.sttModel
-    if (options?.sttCredentialId !== undefined) {
-      body.stt_credential_id = options.sttCredentialId
-    }
-    if (options?.transcribeOverwrite) {
-      body.transcribe_overwrite = true
-    }
-    if (options?.transcribeMode) {
-      body.transcribe_mode = options.transcribeMode
-    }
-    if (options?.diarizationLlmProvider) {
-      body.diarization_llm_provider = options.diarizationLlmProvider
-    }
-    if (options?.diarizationLlmModel) {
-      body.diarization_llm_model = options.diarizationLlmModel
-    }
-    if (options?.diarizationLlmCredentialId !== undefined) {
-      body.diarization_llm_credential_id = options.diarizationLlmCredentialId
-    }
-    if (options?.diarizationPrompt !== undefined) {
-      body.diarization_prompt = options.diarizationPrompt
-    }
-    if (
-      options?.provider !== undefined ||
-      options?.telephonyIntegrationId !== undefined
-    ) {
-      body.provider = options.provider ?? null
-      body.telephony_integration_id = options.telephonyIntegrationId ?? null
-    }
-    if (options?.metricIds && options.metricIds.length > 0) {
-      body.metric_ids = options.metricIds
-    }
-    if (options?.includeCompleted) {
-      body.include_completed = true
-    }
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/retry`,
-      body,
-    )
-    return response.data
-  }
-
-  /**
-   * Re-enqueue a single failed evaluation row. Returns the refreshed
-   * row so the UI can flip the badge to ``pending`` immediately
-   * without waiting for the next polling tick. Server returns 409 if
-   * the row is still in progress or already completed.
-   */
-  async retryCallImportEvaluationRow(
-    callImportId: string,
-    evaluationId: string,
-    evalRowId: string,
-  ): Promise<CallImportEvaluationRow> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows/${evalRowId}/retry`,
-    )
-    return response.data
-  }
-
-  /**
-   * Abort an entire in-flight (or queued) evaluation run.
-   *
-   * Idempotent — calling on a run whose rows are already terminal
-   * returns the run unchanged so the UI can wire this to an "Abort
-   * run" button without pre-checking state. The backend flips every
-   * cancellable row's ``status`` to ``failed`` and stamps
-   * ``"Evaluation cancelled by user"`` as the error so the polling UI
-   * surfaces the cancel on the next tick. Pairs with the worker's
-   * cancellation guard which prevents a late-finishing task from
-   * overwriting the cancelled state.
-   */
-  async cancelCallImportEvaluation(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<CallImportEvaluationBulkActionResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/cancel`,
-    )
-    return response.data
-  }
-
-  /**
-   * Force-fail only rows currently in ``pending`` for an evaluation run.
-   *
-   * Unlike ``cancelCallImportEvaluation``, this does NOT touch rows that
-   * are already ``running``. Useful when queued rows are stuck indefinitely
-   * but active workers should keep progressing.
-   */
-  async forceFailCallImportEvaluationPending(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<CallImportEvaluationBulkActionResponse> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/force-fail-pending`,
-    )
-    return response.data
-  }
-
-  /**
-   * Abort an in-flight (or queued) evaluation for a single row.
-   *
-   * Mirrors :func:`cancelCallImportEvaluation` but scoped to one row,
-   * intended for the per-row Stop button on the evaluation detail
-   * rows table. The parent run's counters are rolled up server-side
-   * so the run-level pill updates on the same response.
-   */
-  async cancelCallImportEvaluationRow(
-    callImportId: string,
-    evaluationId: string,
-    evalRowId: string,
-  ): Promise<CallImportEvaluationRow> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows/${evalRowId}/cancel`,
-    )
-    return response.data
-  }
-
-  async listCallImportEvaluations(callImportId: string): Promise<CallImportEvaluationListResponse> {
-    const response = await this.client.get(`/api/v1/call-imports/${callImportId}/evaluations`)
-    return response.data
-  }
-
-  async getCallImportEvaluation(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<CallImportEvaluation> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}`,
-    )
-    return response.data
-  }
-
-  async listCallImportEvaluationRows(
-    callImportId: string,
-    evaluationId: string,
-    params: {
-      page?: number
-      page_size?: number
-      q?: string
-      metric_id?: string
-      metric_value?: string
-      status?: string
-      // Flow-chart drilldown: filter to rows whose sequence under the
-      // given parent contains ``flow_node`` (and optionally is
-      // immediately followed by ``flow_edge_target`` for edge clicks).
-      // Accepts a child UUID, a ``disc:<slug>`` discovered id, or a
-      // raw slug.
-      flow_parent_id?: string
-      flow_node?: string
-      flow_edge_target?: string
-      // Discovered-label filters: either pin to a specific discovered
-      // slug or surface every row that produced any candidate under
-      // ``discovered_parent_id``.
-      discovered_parent_id?: string
-      discovered_label_key?: string
-      has_discovered?: boolean
-      // Column-click sorting from the UI. ``sort_by`` is either a
-      // built-in column key (``row_index`` / ``conversation_id`` /
-      // ``status``) or ``metric:<metric_uuid>`` for per-metric value
-      // sorting; ``sort_dir`` is ``asc`` (default) or ``desc``.
-      sort_by?: string
-      sort_dir?: 'asc' | 'desc'
-    } = {},
-  ): Promise<CallImportEvaluationRowListResponse> {
-    const cleaned: Record<string, any> = {}
-    for (const [k, v] of Object.entries(params)) {
-      if (v !== undefined && v !== null && v !== '') cleaned[k] = v
-    }
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows`,
-      { params: cleaned },
-    )
-    return response.data
-  }
-
-  async exportCallImportEvaluation(
-    callImportId: string,
-    evaluationId: string,
-    format: 'csv' | 'xlsx' = 'csv',
-  ): Promise<Blob> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/export`,
-      { params: { format }, responseType: 'blob' },
-    )
-    return response.data
-  }
-
-  async generateCallImportEvaluationPdfReport(
-    callImportId: string,
-    evaluationId: string,
-    vendorName: string,
-    reportType: 'external' | 'internal',
-    includeWeeklyDelta = false,
-    options?: {
-      internalBrandImageId?: string | null
-      externalBrandImageId?: string | null
-      useCase?: string | null
-      baselineEvaluationId?: string | null
-      reportConfig?: Record<string, any>
-      platformBaseUrl?: string | null
-    },
-  ): Promise<CallImportEvaluationPdfReport | Blob> {
-    const response = await this.client.post(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-report`,
-      {
-        vendor_name: vendorName,
-        report_type: reportType,
-        include_weekly_delta: includeWeeklyDelta,
-        include_period_delta: includeWeeklyDelta,
-        baseline_evaluation_id: options?.baselineEvaluationId || null,
-        use_case: options?.useCase || null,
-        internal_brand_image_id: options?.internalBrandImageId || null,
-        external_brand_image_id: options?.externalBrandImageId || null,
-        report_config: options?.reportConfig || {},
-        platform_base_url: options?.platformBaseUrl || null,
-      },
-      { responseType: 'arraybuffer' },
-    )
-    const contentType = String(response.headers['content-type'] || '')
-    if (contentType.includes('application/pdf')) {
-      return new Blob([response.data], { type: 'application/pdf' })
-    }
-    const text = new TextDecoder().decode(response.data)
-    return JSON.parse(text) as CallImportEvaluationPdfReport
-  }
-
-  async listCallImportEvaluationPdfReports(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<CallImportEvaluationPdfReportListResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports`,
-    )
-    return response.data
-  }
-
-  async getCallImportEvaluationPdfReport(
-    callImportId: string,
-    evaluationId: string,
-    reportId: string,
-  ): Promise<CallImportEvaluationPdfReport> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports/${reportId}`,
-    )
-    return response.data
-  }
-
-  async downloadCallImportEvaluationPdfReport(
-    callImportId: string,
-    evaluationId: string,
-    reportId: string,
-  ): Promise<Blob> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports/${reportId}/download`,
-      { responseType: 'blob' },
-    )
-    return response.data
-  }
-
-  async listCallImportEvaluationBaselineCandidates(
-    callImportId: string,
-    evaluationId: string,
-  ): Promise<CallImportEvaluationBaselineCandidatesResponse> {
-    const response = await this.client.get(
-      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/baseline-candidates`,
-    )
-    return response.data
-  }
-
-  async deleteCallImportEvaluation(callImportId: string, evaluationId: string): Promise<void> {
-    await this.client.delete(`/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}`)
-  }
-
-  // Call Import Tags endpoints
-  async listCallImportTags(): Promise<CallImportTag[]> {
-    const response = await this.client.get('/api/v1/call-import-tags')
-    return response.data
-  }
-
-  async createCallImportTag(payload: {
-    name: string
-    color?: string | null
-  }): Promise<CallImportTag> {
-    const response = await this.client.post('/api/v1/call-import-tags', payload)
-    return response.data
-  }
-
-  async updateCallImportTag(
-    tagId: string,
-    payload: { name?: string; color?: string | null },
-  ): Promise<CallImportTag> {
-    const response = await this.client.patch(
-      `/api/v1/call-import-tags/${tagId}`,
-      payload,
-    )
-    return response.data
-  }
-
-  async deleteCallImportTag(tagId: string): Promise<void> {
-    await this.client.delete(`/api/v1/call-import-tags/${tagId}`)
-  }
-
-  // Model Config endpoints
-  async getDashboardSummary(): Promise<DashboardSummary> {
-    const response = await this.client.get('/api/v1/dashboard/summary')
-    return response.data
-  }
-
-  async getOrgUsageFxRate(): Promise<{
-    base: string
-    quote: string
-    rate: number
-    as_of: string
-    source: string
-  }> {
-    const response = await this.client.get('/api/v1/organizations/usage/fx-rate')
-    return response.data
-  }
-
-  async getOrgUsageSummary(params: {
-    start?: string
-    end?: string
-    tz?: string
-    workspace_id?: string
-    product_section?: string
-    model?: string
-    resource_id?: string
-    usage_kind?: string
-    call_import_id?: string
-    evaluation_id?: string
-    dataset?: string
-    tag_id?: string
-  }): Promise<{
-    start: string
-    end: string
-    totals: {
-      prompt_tokens: number
-      completion_tokens: number
-      total_tokens: number
-      cache_read_tokens: number
-      cache_creation_tokens: number
-      reasoning_tokens: number
-      audio_seconds: number
-      tts_characters: number
-      call_count: number
-      input_cost_micro_usd?: number
-      output_cost_micro_usd?: number
-      cache_read_cost_micro_usd?: number
-      cache_creation_cost_micro_usd?: number
-      reasoning_cost_micro_usd?: number
-      audio_cost_micro_usd?: number
-      tts_cost_micro_usd?: number
-      total_cost_micro_usd?: number
-      costs?: {
-        input_cost_usd: number
-        output_cost_usd: number
-        cache_read_cost_usd: number
-        cache_write_cost_usd: number
-        reasoning_cost_usd: number
-        audio_cost_usd: number
-        tts_cost_usd: number
-        total_cost_usd: number
-        currency: string
-        has_unpriced_usage: boolean
-      }
-    }
-    last_updated_at?: string | null
-  }> {
-    const response = await this.client.get('/api/v1/organizations/usage/summary', {
-      params,
-    })
-    return response.data
-  }
-
-  async getOrgUsageBreakdown(params: {
-    start?: string
-    end?: string
-    tz?: string
-    group_by?: 'workspace' | 'product_section' | 'model' | 'resource' | 'usage_kind' | 'call_import'
-    workspace_id?: string
-    product_section?: string
-    model?: string
-    resource_id?: string
-    usage_kind?: string
-    call_import_id?: string
-    evaluation_id?: string
-    dataset?: string
-    tag_id?: string
-    limit?: number
-    offset?: number
-  }): Promise<{
-    start: string
-    end: string
-    group_by: string
-    rows: Array<{
-      workspace_id?: string | null
-      workspace_name?: string | null
-      product_section?: string | null
-      product_section_label?: string | null
-      model?: string | null
-      resource_id?: string | null
-      resource_type?: string | null
-      resource_label?: string | null
-      call_import_id?: string | null
-      call_import_label?: string | null
-      usage_kind?: string | null
-      prompt_tokens: number
-      completion_tokens: number
-      total_tokens: number
-      cache_read_tokens: number
-      cache_creation_tokens: number
-      reasoning_tokens: number
-      audio_seconds: number
-      tts_characters: number
-      call_count: number
-      input_cost_micro_usd?: number
-      output_cost_micro_usd?: number
-      cache_read_cost_micro_usd?: number
-      cache_creation_cost_micro_usd?: number
-      reasoning_cost_micro_usd?: number
-      audio_cost_micro_usd?: number
-      tts_cost_micro_usd?: number
-      total_cost_micro_usd?: number
-      costs?: {
-        input_cost_usd: number
-        output_cost_usd: number
-        cache_read_cost_usd: number
-        cache_write_cost_usd: number
-        reasoning_cost_usd: number
-        audio_cost_usd: number
-        tts_cost_usd: number
-        total_cost_usd: number
-        currency: string
-        has_unpriced_usage: boolean
-      }
-    }>
-    total_count: number
-    truncated_at_limit?: boolean
-    last_updated_at?: string | null
-  }> {
-    const response = await this.client.get('/api/v1/organizations/usage/breakdown', {
-      params,
-    })
-    return response.data
-  }
-
-  async listUsagePricingAvailableModels(): Promise<{ models: string[] }> {
-    const response = await this.client.get(
-      '/api/v1/organizations/usage/pricing/available-models',
-    )
-    return response.data
-  }
-
-  async getUsagePricingEffective(
-    model: string,
-    params?: { usage_kind?: string; as_of?: string },
-  ): Promise<{
-    model: string
-    usage_kind: string
-    as_of: string
-    catalog_rates?: Record<string, number | null> | null
-    effective_rates?: Record<string, number | null> | null
-    effective_source?: string | null
-    has_override: boolean
-    override?: {
-      effective_from: string
-      effective_to?: string | null
-      rates: Record<string, number | null>
-    } | null
-  }> {
-    const response = await this.client.get(
-      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
-      { params },
-    )
-    return response.data
-  }
-
-  async listUsagePricingOverrides(params?: {
-    model?: string
-    usage_kind?: string
-  }): Promise<
-    Array<{
-      id: string
-      organization_id: string
-      model: string
-      usage_kind: string
-      effective_from: string
-      effective_to?: string | null
-      rates: {
-        input_per_1m?: number | null
-        output_per_1m?: number | null
-        cache_read_per_1m?: number | null
-        cache_write_per_1m?: number | null
-        reasoning_per_1m?: number | null
-        audio_per_minute?: number | null
-        tts_per_1m_characters?: number | null
-      }
-      recompute_enqueued?: boolean
-      recompute_job_id?: string | null
-    }>
-  > {
-    const response = await this.client.get('/api/v1/organizations/usage/pricing/overrides', {
-      params,
-    })
-    return response.data
-  }
-
-  async upsertUsagePricingOverride(
-    model: string,
-    body: {
-      usage_kind?: string
-      effective_from: string
-      effective_to?: string
-      rates: {
-        input_per_1m?: number
-        output_per_1m?: number
-        cache_read_per_1m?: number
-        cache_write_per_1m?: number
-        reasoning_per_1m?: number
-        audio_per_minute?: number
-        tts_per_1m_characters?: number
-      }
-      recompute?: boolean
-    }
-  ): Promise<{
-    id: string
-    model: string
-    usage_kind: string
-    effective_from: string
-    effective_to?: string | null
-    rates: Record<string, number | null | undefined>
-    recompute_enqueued?: boolean
-    recompute_job_id?: string | null
-  }> {
-    const response = await this.client.put(
-      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
-      body
-    )
-    return response.data
-  }
-
-  async deleteUsagePricingOverride(
-    model: string,
-    params?: { usage_kind?: string; effective_from?: string; recompute?: boolean }
-  ): Promise<{
-    deleted: boolean
-    model: string
-    usage_kind: string
-    recompute_enqueued?: boolean
-    recompute_job_id?: string | null
-  }> {
-    const response = await this.client.delete(
-      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
-      { params }
-    )
-    return response.data
-  }
-
-  async triggerUsageCostRecompute(body?: {
-    start_date?: string
-    end_date?: string
-    model?: string
-    usage_kind?: string
-  }): Promise<{
-    id: string
-    organization_id: string
-    status: string
-    model?: string | null
-    usage_kind?: string | null
-    start_date?: string | null
-    end_date?: string | null
-    updated_rows: number
-    error_message?: string | null
-    celery_task_id?: string | null
-    created_at?: string | null
-    updated_at?: string | null
-    completed_at?: string | null
-  }> {
-    const response = await this.client.post(
-      '/api/v1/organizations/usage/pricing/recompute',
-      body ?? {}
-    )
-    return response.data
-  }
-
-  async getUsageCostRecomputeJob(jobId: string): Promise<{
-    id: string
-    organization_id: string
-    status: string
-    model?: string | null
-    usage_kind?: string | null
-    start_date?: string | null
-    end_date?: string | null
-    updated_rows: number
-    error_message?: string | null
-    celery_task_id?: string | null
-    created_at?: string | null
-    updated_at?: string | null
-    completed_at?: string | null
-  }> {
-    const response = await this.client.get(
-      `/api/v1/organizations/usage/pricing/recompute/${jobId}`
-    )
-    return response.data
-  }
-
-  async getOrgUsageFilters(params?: {
-    start?: string
-    end?: string
-    tz?: string
-    workspace_id?: string
-    product_section?: string
-    model?: string
-    resource_id?: string
-    evaluation_id?: string
-    usage_kind?: string
-    call_import_id?: string
-    q?: string
-    dataset?: string
-    tag_id?: string
-  }): Promise<{
-    workspaces: Array<{ id: string; name: string }>
-    product_sections: Array<{ id: string; label: string }>
-    call_imports: Array<{ id: string; label: string }>
-    evaluations: Array<{ id: string; label: string }>
-    models: string[]
-    resources: Array<{ id: string; type?: string; label: string; product_section?: string }>
-    usage_kinds: Array<{ id: string; label: string }>
-    datasets: string[]
-    tags: Array<{ id: string; label: string }>
-  }> {
-    const response = await this.client.get('/api/v1/organizations/usage/filters', {
-      params,
-    })
-    return response.data
-  }
-
-  async getAllModels(): Promise<Record<string, ModelConfigEntry>> {
-    const response = await this.client.get('/api/v1/model-config/models')
-    return response.data
-  }
-
-  async getModelConfig(modelName: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/model-config/models/${modelName}`)
-    return response.data
-  }
-
-  async getModelsByProvider(provider: string): Promise<string[]> {
-    const response = await this.client.get(`/api/v1/model-config/providers/${provider}/models`)
-    return response.data
-  }
-
-  async getModelOptions(provider: string): Promise<{ stt: string[]; llm: string[]; tts: string[]; s2s: string[]; tts_voices: Record<string, { id: string; name: string; gender?: string }[]>; tts_sample_rates?: number[] }> {
-    const response = await this.client.get(`/api/v1/model-config/providers/${provider}/options`)
-    const data = response.data
-    // Ensure s2s and tts_voices are always present (for backward compatibility)
-    return {
-      ...data,
-      s2s: data.s2s || [],
-      tts_voices: data.tts_voices || {},
-    }
-  }
-
-  async getModelsByType(provider: string, modelType: 'stt' | 'llm' | 'tts'): Promise<string[]> {
-    const response = await this.client.get(`/api/v1/model-config/providers/${provider}/types/${modelType}/models`)
-    return response.data
-  }
-
-  // Manual Evaluations endpoints
-  async listManualEvaluationAudioFiles(prefix?: string, maxKeys = 1000): Promise<S3ListFilesResponse> {
-    const response = await this.client.get('/api/v1/manual-evaluations/audio-files', {
-      params: { prefix, max_keys: maxKeys },
-    })
-    return response.data
-  }
-
-  async getAudioPresignedUrl(fileKey: string, expiration = 3600): Promise<{ url: string; expires_in: number }> {
-    const response = await this.client.get(
-      `/api/v1/manual-evaluations/audio-files/${encodeURIComponent(fileKey)}/presigned-url`,
-      {
-        params: { expiration },
-      }
-    )
-    return response.data
-  }
-
-  async transcribeAudio(data: {
-    audio_file_key: string
-    stt_provider: string
-    stt_model: string
-    name?: string
-    language?: string
-    enable_speaker_diarization?: boolean
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/manual-evaluations/transcribe', data)
-    return response.data
-  }
-
-  async listManualTranscriptions(skip = 0, limit = 100): Promise<any[]> {
-    const response = await this.client.get('/api/v1/manual-evaluations/transcriptions', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getManualTranscription(transcriptionId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/manual-evaluations/transcriptions/${transcriptionId}`)
-    return response.data
-  }
-
-  async updateManualTranscription(transcriptionId: string, name: string): Promise<any> {
-    const response = await this.client.patch(`/api/v1/manual-evaluations/transcriptions/${transcriptionId}`, { name })
-    return response.data
-  }
-
-  async deleteManualTranscription(transcriptionId: string): Promise<MessageResponse> {
-    const response = await this.client.delete(`/api/v1/manual-evaluations/transcriptions/${transcriptionId}`)
-    return response.data
-  }
-
-  // Test Agent endpoints
-  async createTestAgentConversation(data: {
-    agent_id: string
-    persona_id: string
-    scenario_id: string
-    voice_bundle_id: string
-    conversation_metadata?: Record<string, any>
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/test-agents/conversations', data)
-    return response.data
-  }
-
-  async getTestAgentConversation(conversationId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/test-agents/conversations/${conversationId}`)
-    return response.data
-  }
-
-  async listTestAgentConversations(): Promise<any[]> {
-    const response = await this.client.get('/api/v1/test-agents/conversations')
-    return response.data
-  }
-
-  async startTestAgentConversation(conversationId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/test-agents/conversations/${conversationId}/start`)
-    return response.data
-  }
-
-  async processTestAgentAudio(conversationId: string, audioFile: File, chunkTimestamp?: number): Promise<any> {
-    const formData = new FormData()
-    formData.append('audio_file', audioFile)
-    if (chunkTimestamp !== undefined) {
-      formData.append('chunk_timestamp', chunkTimestamp.toString())
-    }
-    const response = await this.client.post(
-      `/api/v1/test-agents/conversations/${conversationId}/process-audio`,
-      formData,
-      {
-        headers: {
-          'Content-Type': 'multipart/form-data',
-        },
-      }
-    )
-    return response.data
-  }
-
-  async getTestAgentResponseAudio(conversationId: string): Promise<Blob> {
-    const response = await this.client.get(
-      `/api/v1/test-agents/conversations/${conversationId}/response-audio`,
-      {
-        responseType: 'blob',
-      }
-    )
-    return response.data
-  }
-
-  async endTestAgentConversation(conversationId: string, finalAudioKey?: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/test-agents/conversations/${conversationId}/end`, {
-      final_audio_key: finalAudioKey,
-    })
-    return response.data
-  }
-
-  async deleteTestAgentConversation(conversationId: string): Promise<void> {
-    await this.client.delete(`/api/v1/test-agents/conversations/${conversationId}`)
-  }
-
-  // Voice Agent endpoints
-  async getVoiceAgentConnection(): Promise<{ ws_url: string; endpoint: string }> {
-    const response = await this.client.post('/api/v1/voice-agent/connect')
-    return response.data
-  }
-
-  // Playground endpoints
-  async createWebCall(data: {
-    agent_id: string
-    metadata?: Record<string, any>
-    retell_llm_dynamic_variables?: Record<string, any>
-    custom_sip_headers?: Record<string, string>
-    ui_surface?: 'agents_talk' | 'agent_playground'
-  }): Promise<{
-    call_type: string
-    access_token?: string
-    call_id?: string
-    agent_id: string
-    agent_version?: number
-    call_status?: string
-    agent_name?: string
-    metadata?: Record<string, any>
-    retell_llm_dynamic_variables?: Record<string, any>
-    sample_rate?: number
-    call_short_id?: string
-    signed_url?: string
-    host?: string
-    room_name?: string
-    conversation_id?: string
-  }> {
-    const response = await this.client.post('/api/v1/playground/web-call', data)
-    return response.data
-  }
-
-  async listCallRecordings(skip = 0, limit = 100): Promise<any[]> {
-    const response = await this.client.get('/api/v1/playground/call-recordings', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getCallRecording(callShortId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/playground/call-recordings/${callShortId}`)
-    return response.data
-  }
-
-  async refreshCallRecording(callShortId: string): Promise<{ message: string }> {
-    const response = await this.client.post(`/api/v1/playground/call-recordings/${callShortId}/refresh`)
-    return response.data
-  }
-
-  async updateCallRecording(callShortId: string, providerCallId: string): Promise<{ message: string; provider_call_id: string }> {
-    const response = await this.client.put(`/api/v1/playground/call-recordings/${callShortId}`, {
-      provider_call_id: providerCallId
-    })
-    return response.data
-  }
-
-  async deleteCallRecording(callShortId: string): Promise<{ message: string }> {
-    const response = await this.client.delete(`/api/v1/playground/call-recordings/${callShortId}`)
-    return response.data
-  }
-
-  async reEvaluateCallRecording(callShortId: string): Promise<{
-    message: string
-    evaluator_result_id: string
-    result_id: string
-    audio_s3_key: string
-    task_id: string
-  }> {
-    const response = await this.client.post(`/api/v1/playground/call-recordings/${callShortId}/re-evaluate`)
-    return response.data
-  }
-
-  async getCallRecordingAudioUrl(callShortId: string): Promise<string> {
-    const response = await this.client.get(
-      `/api/v1/playground/call-recordings/${callShortId}/audio`,
-      { responseType: 'blob' }
-    )
-    return URL.createObjectURL(response.data)
-  }
-
-  async createCustomWebsocketSession(data: {
-    agent_id: string
-    websocket_url: string
-    transcript_entries: Array<{ role: 'user' | 'agent'; content: string; timestamp: string }>
-    started_at?: string
-    ended_at?: string
-    audio_file?: File
-  }): Promise<{
-    message: string
-    call_short_id: string
-    audio_s3_key?: string | null
-    evaluator_result_id?: string | null
-  }> {
-    const formData = new FormData()
-    formData.append('agent_id', data.agent_id)
-    formData.append('websocket_url', data.websocket_url)
-    formData.append('transcript_entries', JSON.stringify(data.transcript_entries))
-    if (data.started_at) {
-      formData.append('started_at', data.started_at)
-    }
-    if (data.ended_at) {
-      formData.append('ended_at', data.ended_at)
-    }
-    if (data.audio_file) {
-      formData.append('audio_file', data.audio_file)
-    }
-
-    const response = await this.client.post('/api/v1/playground/custom-websocket-sessions', formData, {
-      headers: {
-        'Content-Type': 'multipart/form-data',
-      },
-    })
-    return response.data
-  }
-
-  async evaluateCustomWebsocketSession(callShortId: string): Promise<{
-    message: string
-    evaluator_result_id: string
-    result_id: string
-    task_id: string
-  }> {
-    const response = await this.client.post(`/api/v1/playground/custom-websocket-sessions/${callShortId}/evaluate`)
-    return response.data
-  }
-
-  async getAgentSttConfig(agentId: string): Promise<{
-    available: boolean
-    provider?: string
-    model?: string
-    reason?: string
-  }> {
-    const response = await this.client.get(`/api/v1/playground/agents/${agentId}/stt-config`)
-    return response.data
-  }
-
-  async transcribeTurn(
-    agentId: string,
-    channel: 'user' | 'agent',
-    audioBlob: Blob,
-  ): Promise<{ transcript: string; channel: string }> {
-    const formData = new FormData()
-    formData.append('agent_id', agentId)
-    formData.append('channel', channel)
-    formData.append('audio_file', audioBlob, `turn_${channel}_${Date.now()}.wav`)
-    const response = await this.client.post('/api/v1/playground/transcribe-turn', formData, {
-      headers: { 'Content-Type': 'multipart/form-data' },
-    })
-    return response.data
-  }
-
-  async summarizeTranscript(params: {
-    transcript?: string
-    entries?: Array<{ role: string; content: string; timestamp?: string }>
-    callShortId?: string
-    agentId?: string
-    force?: boolean
-  }): Promise<{
-    summary: string
-    provider: string
-    model: string
-    source?: 'voice_bundle' | 'org_fallback'
-    cached?: boolean
-    generated_at?: string
-    usage?: { prompt_tokens?: number; completion_tokens?: number; total_tokens?: number }
-  }> {
-    const body: Record<string, any> = {}
-    if (params.transcript) body.transcript = params.transcript
-    if (params.entries && params.entries.length > 0) body.entries = params.entries
-    if (params.callShortId) body.call_short_id = params.callShortId
-    if (params.agentId) body.agent_id = params.agentId
-    if (params.force) body.force = true
-    const response = await this.client.post('/api/v1/playground/summarize-transcript', body)
-    return response.data
-  }
-
-  // Observability endpoints
-  async listObservabilityCalls(skip = 0, limit = 100): Promise<ObservabilityCall[]> {
-    const response = await this.client.get('/api/v1/observability/calls', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  private buildAuthenticatedApiUrl(path: string): string {
-    const normalizedPath = path.startsWith('/') ? path : `/${path}`
-    const configuredBase = (this.client.defaults.baseURL || '').replace(/\/$/, '')
-    const url = configuredBase
-      ? new URL(`${configuredBase}${normalizedPath}`)
-      : new URL(normalizedPath, window.location.origin)
-
-    const accessToken = localStorage.getItem('accessToken')
-    const apiKey = localStorage.getItem('apiKey')
-    const workspaceId = localStorage.getItem('activeWorkspaceId')
-    if (accessToken) {
-      url.searchParams.set('token', accessToken)
-    } else if (apiKey) {
-      url.searchParams.set('api_key', apiKey)
-    }
-    if (workspaceId) {
-      url.searchParams.set('workspace_id', workspaceId)
-    }
-    return url.toString()
-  }
-
-  async getObservabilityCall(callShortId: string): Promise<ObservabilityCall> {
-    const response = await this.client.get(`/api/v1/observability/calls/${callShortId}`)
-    return response.data
-  }
-
-  getObservabilityCallLiveEventsUrl(callShortId: string): string {
-    return this.buildAuthenticatedApiUrl(
-      `/api/v1/observability/calls/${callShortId}/live-events`,
-    )
-  }
-
-  async getObservabilityCallAudioUrl(callShortId: string): Promise<string> {
-    const response = await this.client.get(
-      `/api/v1/observability/calls/${callShortId}/audio`,
-      { responseType: 'blob' },
-    )
-    return URL.createObjectURL(response.data)
-  }
-
-  async deleteObservabilityCall(callShortId: string): Promise<{ message: string }> {
-    const response = await this.client.delete(`/api/v1/observability/calls/${callShortId}`)
-    return response.data
-  }
-
-  async evaluateObservabilityCall(callShortId: string, evaluatorId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/observability/calls/${callShortId}/evaluate`, {
-      evaluator_id: evaluatorId,
-    })
-    return response.data
-  }
-
-  // Evaluator endpoints
-  async createEvaluator(data: {
-    name?: string
-    agent_id?: string
-    persona_id?: string
-    scenario_id?: string
-    custom_prompt?: string
-    metric_ids?: string[]
-    llm_provider?: string
-    llm_model?: string
-    tags?: string[]
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/evaluators', data)
-    return response.data
-  }
-
-  async formatCustomPrompt(prompt: string): Promise<{ formatted_prompt: string }> {
-    const response = await this.client.post('/api/v1/evaluators/format-prompt', { prompt })
-    return response.data
-  }
-
-  async createEvaluatorsBulk(data: {
-    name?: string
-    agent_id: string
-    scenario_id: string
-    persona_ids: string[]
-    tags?: string[]
-  }): Promise<any[]> {
-    const response = await this.client.post('/api/v1/evaluators/bulk', data)
-    return response.data
-  }
-
-  async listEvaluators(): Promise<any[]> {
-    const response = await this.client.get('/api/v1/evaluators')
-    return response.data
-  }
-
-  async getEvaluator(evaluatorId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/evaluators/${evaluatorId}`)
-    return response.data
-  }
-
-  async updateEvaluator(evaluatorId: string, data: {
-    agent_id?: string
-    persona_id?: string
-    scenario_id?: string
-    name?: string
-    custom_prompt?: string
-    metric_ids?: string[]
-    llm_provider?: string
-    llm_model?: string
-    tags?: string[]
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/evaluators/${evaluatorId}`, data)
-    return response.data
-  }
-
-  async deleteEvaluator(evaluatorId: string, force?: boolean): Promise<any> {
-    const response = await this.client.delete(`/api/v1/evaluators/${evaluatorId}`, {
-      params: force ? { force: true } : undefined,
-    })
-    return response.data
-  }
-
-  async runEvaluators(evaluatorIds: string[]): Promise<{ task_ids: string[]; evaluator_results: any[] }> {
-    const response = await this.client.post('/api/v1/evaluators/run', { evaluator_ids: evaluatorIds })
-    return response.data
-  }
-
-  async createEvaluatorSuite(data: {
-    name?: string
-    agent_id: string
-    persona_id: string
-    scenario_ids: string[]
-    metric_ids?: string[]
-    llm_provider?: string
-    llm_model?: string
-    tags?: string[]
-    default_runs_per_combination?: number
-  }): Promise<EvaluatorSuite> {
-    const response = await this.client.post('/api/v1/evaluator-suites', data)
-    return response.data
-  }
-
-  async listEvaluatorSuites(): Promise<EvaluatorSuite[]> {
-    const response = await this.client.get('/api/v1/evaluator-suites')
-    return response.data
-  }
-
-  async getEvaluatorSuite(suiteId: string): Promise<EvaluatorSuite> {
-    const response = await this.client.get(`/api/v1/evaluator-suites/${suiteId}`)
-    return response.data
-  }
-
-  async updateEvaluatorSuite(
-    suiteId: string,
-    data: {
-      name?: string
-      metric_ids?: string[] | null
-      llm_provider?: string | null
-      llm_model?: string | null
-      tags?: string[]
-      default_runs_per_combination?: number
-    },
-  ): Promise<EvaluatorSuite> {
-    const response = await this.client.put(`/api/v1/evaluator-suites/${suiteId}`, data)
-    return response.data
-  }
-
-  async addEvaluatorSuiteScenarios(suiteId: string, scenarioIds: string[]): Promise<EvaluatorSuite> {
-    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/scenarios`, {
-      scenario_ids: scenarioIds,
-    })
-    return response.data
-  }
-
-  async removeEvaluatorSuiteScenario(suiteId: string, scenarioId: string): Promise<EvaluatorSuite> {
-    const response = await this.client.delete(
-      `/api/v1/evaluator-suites/${suiteId}/scenarios/${scenarioId}`,
-    )
-    return response.data
-  }
-
-  async deleteEvaluatorSuite(suiteId: string): Promise<void> {
-    await this.client.delete(`/api/v1/evaluator-suites/${suiteId}`)
-  }
-
-  async runEvaluatorSuite(
-    suiteId: string,
-    data: { runs_per_combination: number; to_number?: string; from_number?: string },
-  ): Promise<RunEvaluatorSuiteResponse> {
-    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/run`, data)
-    return response.data
-  }
-
-  async runNextCombination(
-    suiteId: string,
-    data?: { from_number?: string },
-  ): Promise<RunNextCombinationResponse> {
-    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/run-next`, data || {})
-    return response.data
-  }
-
-  async chooseNextCombination(suiteId: string): Promise<ChooseNextCombinationResponse> {
-    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/choose-next`)
-    return response.data
-  }
-
-  async activateEvaluatorSuite(suiteId: string): Promise<EvaluatorSuite> {
-    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/activate`)
-    return response.data
-  }
-
-  // Metric endpoints
-  async createMetric(data: {
-    name: string
-    description?: string
-    /**
-     * Optional illustrative example surfaced alongside the description
-     * in the LLM judge's rubric. Mainly used by categorization label
-     * children but accepted on every metric.
-     */
-    example?: string | null
-    metric_type: 'number' | 'boolean' | 'rating' | 'text'
-    trigger?: 'always'
-    enabled?: boolean
-    metric_origin?: 'default' | 'custom'
-    supported_surfaces?: string[]
-    enabled_surfaces?: string[]
-    custom_data_type?: 'boolean' | 'enum' | 'number_range'
-    custom_config?: Record<string, any>
-    tags?: string[]
-    capture_rationale?: boolean
-    parent_metric_id?: string | null
-    selection_mode?: 'single_choice' | 'multi_label' | null
-    /**
-     * Scope of the new metric:
-     *   - "workspace" (default): stamped with the active X-Workspace-Id
-     *     header so it only appears inside that workspace.
-     *   - "organization": stored with workspace_id=NULL so it surfaces
-     *     in every workspace of the caller's org.
-     * Ignored when parent_metric_id is set — children always inherit
-     * their parent's scope.
-     */
-    scope?: 'workspace' | 'organization'
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/metrics', data)
-    return response.data
-  }
-
-  /**
-   * Atomically create a parent (category) metric + N children, used by
-   * the "Create category" flow on the Metrics page and by the
-   * /parse-bulk → save path when a hierarchy was requested.
-   */
-  async createMetricWithChildren(data: {
-    name: string
-    description?: string | null
-    selection_mode: 'single_choice' | 'multi_label'
-    enabled?: boolean
-    supported_surfaces?: string[]
-    enabled_surfaces?: string[]
-    tags?: string[] | null
-    allow_discovery?: boolean
-    /**
-     * Parent-level "Enable LLM Rationale" toggle. When true the LLM
-     * judge emits one rationale at the category level (children never
-     * carry rationales in hierarchical mode).
-     */
-    capture_rationale?: boolean
-    children: Array<{
-      name: string
-      description?: string | null
-      /**
-       * Optional illustrative example for this label, surfaced
-       * alongside the description in the LLM judge's rubric.
-       */
-      example?: string | null
-      enabled?: boolean
-      capture_rationale?: boolean | null
-      tags?: string[] | null
-    }>
-    /**
-     * Same semantics as {@link createMetric}'s ``scope``: when
-     * ``"organization"`` the parent + every child are stored with
-     * ``workspace_id=NULL`` so the whole category subtree is visible
-     * in every workspace of the org.
-     */
-    scope?: 'workspace' | 'organization'
-  }): Promise<any> {
-    const response = await this.client.post(
-      '/api/v1/metrics/with-children',
-      data,
-    )
-    return response.data
-  }
-
-  async addMetricChild(parentMetricId: string, data: {
-    name: string
-    description?: string | null
-    /**
-     * Optional illustrative example for this label, surfaced alongside
-     * the description in the LLM judge's rubric.
-     */
-    example?: string | null
-    enabled?: boolean
-    capture_rationale?: boolean | null
-    tags?: string[] | null
-  }): Promise<any> {
-    const response = await this.client.post(
-      `/api/v1/metrics/${parentMetricId}/children`,
-      data,
-    )
-    return response.data
-  }
-
-  async listMetrics(
-    surface?: string,
-    includeChildren: boolean = true,
-    options?: { includeDrafts?: boolean; draftsOnly?: boolean; enabledOnly?: boolean },
-  ): Promise<any[]> {
-    const response = await this.client.get('/api/v1/metrics', {
-      params: {
-        ...(surface ? { surface } : {}),
-        include_children: includeChildren,
-        ...(options?.includeDrafts ? { include_drafts: true } : {}),
-        ...(options?.draftsOnly ? { drafts_only: true } : {}),
-        ...(options?.enabledOnly ? { enabled_only: true } : {}),
-      },
-    })
-    return response.data
-  }
-
-  async createMetricDraft(data: {
-    name: string
-    description?: string
-    metric_type: string
-    trigger: string
-    metric_origin?: string
-    studio_notes?: string
-    parent_metric_id?: string
-    selection_mode?: string
-    custom_data_type?: string
-    custom_config?: Record<string, unknown>
-    capture_rationale?: boolean
-    compare_transcripts?: boolean
-    scope?: 'workspace' | 'organization'
-    supported_surfaces?: string[]
-    enabled_surfaces?: string[]
-    tags?: string[]
-    enabled?: boolean
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/metrics/drafts', data)
-    return response.data
-  }
-
-  async createMetricDraftWithChildren(data: {
-    name: string
-    description?: string | null
-    selection_mode: 'single_choice' | 'multi_label'
-    allow_discovery?: boolean
-    capture_rationale?: boolean
-    supported_surfaces: string[]
-    enabled_surfaces: string[]
-    children: Array<{
-      name: string
-      description?: string | null
-      example?: string | null
-      capture_rationale?: boolean
-      enabled?: boolean
-    }>
-    scope?: 'workspace' | 'organization'
-    studio_notes?: string
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/metrics/drafts/with-children', data)
-    return response.data
-  }
-
-  async promoteMetric(metricId: string): Promise<{ metric: any; promoted_at: string }> {
-    const response = await this.client.post(`/api/v1/metrics/${metricId}/promote`)
-    return response.data
-  }
-
-  async createMetricStudioRun(data: {
-    name?: string
-    metric_ids: string[]
-    sources: Array<{
-      source_kind: 'call_import_row' | 'call_recording' | 'evaluator_result'
-      source_ref: string
-      display_label?: string
-    }>
-    transcript_source?: 'production' | 'diarised'
-    llm_provider?: string
-    llm_model?: string
-    llm_credential_id?: string
-    llm_config?: LLMGenerationConfig | null
-    metric_llm_overrides?: Record<string, unknown>
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/metric-studio/runs', data)
-    return response.data
-  }
-
-  async listMetricStudioRuns(skip = 0, limit = 50): Promise<{ items: any[]; total: number }> {
-    const response = await this.client.get('/api/v1/metric-studio/runs', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getMetricStudioRun(runId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/metric-studio/runs/${runId}`)
-    return response.data
-  }
-
-  async listMetricStudioRunResults(
-    runId: string,
-    skip = 0,
-    limit = 100,
-  ): Promise<{ items: any[]; total: number }> {
-    const response = await this.client.get(`/api/v1/metric-studio/runs/${runId}/results`, {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async retryMetricStudioRun(
-    runId: string,
-    resultIds?: string[],
-  ): Promise<any> {
-    const response = await this.client.post(`/api/v1/metric-studio/runs/${runId}/retry`, {
-      result_ids: resultIds,
-    })
-    return response.data
-  }
-
-  async deleteMetricStudioRun(runId: string): Promise<void> {
-    await this.client.delete(`/api/v1/metric-studio/runs/${runId}`)
-  }
-
-  // Evaluator Results endpoints
-  async listEvaluatorResults(
-    paramsOrEvaluatorId?: ListEvaluatorResultsParams | string,
-    playground?: boolean,
-    testAgentsOnly?: boolean,
-    agentId?: string
-  ): Promise<EvaluatorResultListResponse> {
-    let params: Record<string, unknown> = {}
-    if (typeof paramsOrEvaluatorId === 'string' || paramsOrEvaluatorId === undefined) {
-      if (paramsOrEvaluatorId) {
-        params.evaluator_id = paramsOrEvaluatorId
-      }
-      if (agentId) {
-        params.agent_id = agentId
-      }
-      if (playground !== undefined) {
-        params.playground = playground
-      }
-      if (testAgentsOnly !== undefined) {
-        params.test_agents_only = testAgentsOnly
-      }
-    } else {
-      const p = paramsOrEvaluatorId
-      if (p.skip !== undefined) params.skip = p.skip
-      if (p.limit !== undefined) params.limit = p.limit
-      if (p.evaluatorId) params.evaluator_id = p.evaluatorId
-      if (p.agentId) params.agent_id = p.agentId
-      if (p.suiteId) params.suite_id = p.suiteId
-      if (p.scenarioId) params.scenario_id = p.scenarioId
-      if (p.status) params.status = p.status
-      if (p.unassignedOnly) params.unassigned_only = true
-      if (p.playground !== undefined) params.playground = p.playground
-      if (p.testAgentsOnly !== undefined) params.test_agents_only = p.testAgentsOnly
-    }
-    const response = await this.client.get('/api/v1/evaluator-results', { params })
-    return response.data
-  }
-
-  async getEvaluatorResultsOverview(params?: {
-    agentId?: string
-    suiteId?: string
-  }): Promise<EvaluatorResultsOverviewResponse> {
-    const query: Record<string, string> = {}
-    if (params?.agentId) query.agent_id = params.agentId
-    if (params?.suiteId) query.suite_id = params.suiteId
-    const response = await this.client.get('/api/v1/evaluator-results/overview', { params: query })
-    return response.data
-  }
-
-  async getEvaluatorResultsAggregate(params: {
-    suiteId?: string
-    agentId?: string
-    scenarioId?: string
-  }): Promise<EvaluatorResultsAggregateResponse> {
-    const query: Record<string, string> = {}
-    if (params.suiteId) query.suite_id = params.suiteId
-    if (params.agentId) query.agent_id = params.agentId
-    if (params.scenarioId) query.scenario_id = params.scenarioId
-    const response = await this.client.get('/api/v1/evaluator-results/aggregate', { params: query })
-    return response.data
-  }
-
-  async getEvaluatorResult(id: string, includeRelations: boolean = true): Promise<any> {
-    const params = includeRelations ? { include_relations: 'true' } : {}
-    const response = await this.client.get(`/api/v1/evaluator-results/${id}`, { params })
-    return response.data
-  }
-
-  async getEvaluatorResultMetrics(id: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/evaluator-results/${id}/metrics`)
-    return response.data
-  }
-
-  async getEvaluatorResultAudioUrl(resultId: string): Promise<string> {
-    const response = await this.client.get(
-      `/api/v1/evaluator-results/${resultId}/audio`,
-      { responseType: 'blob' },
-    )
-    return URL.createObjectURL(response.data)
-  }
-
-  async createEvaluatorResultManual(data: {
-    evaluator_id: string
-    audio_s3_key: string
-    duration_seconds?: number
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/evaluator-results', data)
-    return response.data
-  }
-
-  async reEvaluateResult(id: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/evaluator-results/${id}/re-evaluate`)
-    return response.data
-  }
-
-  async deleteEvaluatorResult(id: string): Promise<void> {
-    await this.client.delete(`/api/v1/evaluator-results/${id}`)
-  }
-
-  async deleteEvaluatorResultsBulk(ids: string[]): Promise<void> {
-    // FastAPI expects multiple query parameters with the same name
-    // Build query string manually: result_ids=id1&result_ids=id2
-    const params = new URLSearchParams()
-    ids.forEach(id => params.append('result_ids', id))
-    await this.client.delete(`/api/v1/evaluator-results?${params.toString()}`)
-  }
-
-  async getMetric(metricId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/metrics/${metricId}`)
-    return response.data
-  }
-
-  async updateMetric(metricId: string, data: {
-    name?: string
-    description?: string
-    /**
-     * Pass ``""`` (empty string) to clear the stored example; omit to
-     * leave the persisted value untouched.
-     */
-    example?: string | null
-    metric_type?: 'number' | 'boolean' | 'rating' | 'text'
-    trigger?: 'always'
-    enabled?: boolean
-    metric_origin?: 'default' | 'custom'
-    supported_surfaces?: string[]
-    enabled_surfaces?: string[]
-    custom_data_type?: 'boolean' | 'enum' | 'number_range'
-    custom_config?: Record<string, any>
-    tags?: string[]
-    capture_rationale?: boolean
-    selection_mode?: 'single_choice' | 'multi_label' | null
-    /** Only honored on multi_label parents; backend 400s otherwise. */
-    allow_discovery?: boolean
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/metrics/${metricId}`, data)
-    return response.data
-  }
-
-  async deleteMetric(metricId: string): Promise<void> {
-    await this.client.delete(`/api/v1/metrics/${metricId}`)
-  }
-
-  async seedDefaultMetrics(): Promise<any[]> {
-    const response = await this.client.post('/api/v1/metrics/seed-defaults')
-    return response.data
-  }
-
-  async generateMetric(data: {
-    mode: 'description' | 'examples'
-    surface: 'agent' | 'voice_playground' | 'blind_test'
-    description?: string
-    examples?: Array<{ transcript: string; rating: any; notes?: string }>
-    provider?: string
-    model?: string
-    credential_id?: string
-    llm_config?: Record<string, any>
-  }): Promise<{
-    name: string
-    description: string
-    metric_type: 'rating' | 'boolean' | 'number' | 'text'
-    custom_data_type: 'boolean' | 'enum' | 'number_range' | null
-    custom_config: Record<string, any>
-    supported_surfaces: string[]
-    enabled_surfaces: string[]
-    suggested_tags: string[]
-    provider?: string
-    model?: string
-  }> {
-    const response = await this.client.post('/api/v1/metrics/generate', data)
-    return response.data
-  }
-
-  async parseBulkMetric(data: {
-    prompt: string
-    surface: 'agent' | 'voice_playground' | 'blind_test'
-    /** When set, the response includes a ``parent`` block + all labels are children. */
-    parent_name?: string
-    parent_description?: string
-    selection_mode?: 'single_choice' | 'multi_label'
-  }): Promise<{
-    metrics: Array<{
-      name: string
-      description: string
-      metric_type: 'rating' | 'boolean' | 'number' | 'text'
-      custom_data_type: 'boolean' | 'enum' | 'number_range' | null
-      custom_config: Record<string, any>
-      supported_surfaces: string[]
-      enabled_surfaces: string[]
-      capture_rationale: boolean
-      suggested_tags: string[]
-      source_label: { label_name: string; definition: string; examples: string }
-    }>
-    parent?: {
-      name: string
-      description: string | null
-      selection_mode: 'single_choice' | 'multi_label'
-      supported_surfaces: string[]
-      enabled_surfaces: string[]
-    } | null
-  }> {
-    const response = await this.client.post('/api/v1/metrics/parse-bulk', data)
-    return response.data
-  }
-
-  // Alert endpoints
-  async listAlerts(status?: string): Promise<any[]> {
-    const params: any = {}
-    if (status) {
-      params.status_filter = status
-    }
-    const response = await this.client.get('/api/v1/alerts', { params })
-    return response.data
-  }
-
-  async getAlert(alertId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/alerts/${alertId}`)
-    return response.data
-  }
-
-  async createAlert(data: {
-    name: string
-    description?: string | null
-    metric_type: string
-    aggregation: string
-    operator: string
-    threshold_value: number
-    time_window_minutes: number
-    agent_ids?: string[] | null
-    notify_frequency: string
-    notify_emails?: string[]
-    notify_webhooks?: string[]
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/alerts', data)
-    return response.data
-  }
-
-  async updateAlert(alertId: string, data: {
-    name?: string
-    description?: string | null
-    metric_type?: string
-    aggregation?: string
-    operator?: string
-    threshold_value?: number
-    time_window_minutes?: number
-    agent_ids?: string[] | null
-    notify_frequency?: string
-    notify_emails?: string[]
-    notify_webhooks?: string[]
-    status?: string
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/alerts/${alertId}`, data)
-    return response.data
-  }
-
-  async deleteAlert(alertId: string): Promise<void> {
-    await this.client.delete(`/api/v1/alerts/${alertId}`)
-  }
-
-  async toggleAlertStatus(alertId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/alerts/${alertId}/toggle`)
-    return response.data
-  }
-
-  // Alert History endpoints
-  async listAlertHistory(status?: string, alertId?: string, skip = 0, limit = 100): Promise<any[]> {
-    const params: any = { skip, limit }
-    if (status) {
-      params.status_filter = status
-    }
-    if (alertId) {
-      params.alert_id = alertId
-    }
-    const response = await this.client.get('/api/v1/alerts/history/all', { params })
-    return response.data
-  }
-
-  async getAlertHistoryItem(historyId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/alerts/history/${historyId}`)
-    return response.data
-  }
-
-  async acknowledgeAlertHistory(historyId: string): Promise<any> {
-    const response = await this.client.put(`/api/v1/alerts/history/${historyId}`, {
-      status: 'acknowledged'
-    })
-    return response.data
-  }
-
-  async resolveAlertHistory(historyId: string, resolutionNotes?: string): Promise<any> {
-    const response = await this.client.put(`/api/v1/alerts/history/${historyId}`, {
-      status: 'resolved',
-      resolution_notes: resolutionNotes
-    })
-    return response.data
-  }
-
-  // Alert Evaluation & Notification endpoints
-  async triggerAlert(alertId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/alerts/${alertId}/trigger`)
-    return response.data
-  }
-
-  async evaluateAllAlerts(): Promise<any> {
-    const response = await this.client.post('/api/v1/alerts/evaluate/all')
-    return response.data
-  }
-
-  async testAlertNotification(alertId: string, data: {
-    webhook_url?: string
-    email?: string
-  }): Promise<any> {
-    const response = await this.client.post(`/api/v1/alerts/${alertId}/test-notification`, data)
-    return response.data
-  }
-
-  // Cron Job endpoints
-  async listCronJobs(): Promise<any[]> {
-    const response = await this.client.get('/api/v1/cron-jobs')
-    return response.data
-  }
-
-  async getCronJob(cronJobId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/cron-jobs/${cronJobId}`)
-    return response.data
-  }
-
-  async createCronJob(data: {
-    name: string
-    cron_expression: string
-    timezone: string
-    max_runs: number
-    evaluator_ids: string[]
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/cron-jobs', data)
-    return response.data
-  }
-
-  async updateCronJob(cronJobId: string, data: {
-    name?: string
-    cron_expression?: string
-    timezone?: string
-    max_runs?: number
-    evaluator_ids?: string[]
-    status?: string
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/cron-jobs/${cronJobId}`, data)
-    return response.data
-  }
-
-  async deleteCronJob(cronJobId: string): Promise<void> {
-    await this.client.delete(`/api/v1/cron-jobs/${cronJobId}`)
-  }
-
-  async toggleCronJobStatus(cronJobId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/cron-jobs/${cronJobId}/toggle`)
-    return response.data
-  }
-
-  // Voice Playground (TTS Comparison) endpoints
-  async listTTSProviders(): Promise<any[]> {
-    const response = await this.client.get('/api/v1/voice-playground/tts-providers')
-    return response.data
-  }
-
-  async listCustomTTSVoices(provider?: string): Promise<Array<{
-    id: string
-    provider: string
-    voice_id: string
-    name: string
-    gender: string
-    accent: string
-    description?: string | null
-    is_custom: boolean
-    created_at?: string | null
-    updated_at?: string | null
-  }>> {
-    const response = await this.client.get('/api/v1/voice-playground/custom-voices', {
-      params: provider ? { provider } : undefined,
-    })
-    return response.data
-  }
-
-  async createCustomTTSVoice(data: {
-    provider: string
-    voice_id: string
-    name: string
-    gender?: string
-    accent?: string
-    description?: string
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/voice-playground/custom-voices', data)
-    return response.data
-  }
-
-  async updateCustomTTSVoice(customVoiceId: string, data: {
-    voice_id?: string
-    name?: string
-    gender?: string
-    accent?: string
-    description?: string
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/voice-playground/custom-voices/${customVoiceId}`, data)
-    return response.data
-  }
-
-  async deleteCustomTTSVoice(customVoiceId: string): Promise<any> {
-    const response = await this.client.delete(`/api/v1/voice-playground/custom-voices/${customVoiceId}`)
-    return response.data
-  }
-
-  async createTTSComparison(data: {
-    name?: string
-    mode?: 'benchmark' | 'blind_test_only'
-    // Legacy benchmark fields
-    provider_a?: string
-    model_a?: string
-    voices_a?: Array<{ id: string; name: string; sample_rate_hz?: number }>
-    provider_b?: string
-    model_b?: string
-    voices_b?: Array<{ id: string; name: string; sample_rate_hz?: number }>
-    // New per-side benchmark config
-    side_a?: VoicePlaygroundSideConfig
-    side_b?: VoicePlaygroundSideConfig
-    // Common
-    sample_texts?: string[]
-    num_runs?: number
-    eval_stt_provider?: string
-    eval_stt_model?: string
-    // blind_test_only
-    pairs?: Array<VoicePlaygroundBlindTestPair>
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/voice-playground/comparisons', data)
-    return response.data
-  }
-
-  async listVoicePlaygroundCallImportRows(params: {
-    call_import_id?: string
-    with_recording?: boolean
-    skip?: number
-    limit?: number
-  } = {}): Promise<{
-    items: Array<{
-      id: string
-      call_import_id: string
-      call_import_filename: string | null
-      conversation_id: string
-      transcript: string | null
-      recording_s3_key: string | null
-      has_recording: boolean
-      status: string
-      created_at: string | null
-    }>
-    total: number
-    skip: number
-    limit: number
-  }> {
-    const response = await this.client.get('/api/v1/voice-playground/call-import-rows', { params })
-    return response.data
-  }
-
-  async uploadVoicePlaygroundAudio(file: File): Promise<{
-    s3_key: string
-    presigned_url: string | null
-    filename: string
-    size_bytes: number
-    content_type: string
-  }> {
-    const form = new FormData()
-    form.append('file', file)
-    const response = await this.client.post('/api/v1/voice-playground/uploads', form, {
-      headers: { 'Content-Type': 'multipart/form-data' },
-    })
-    return response.data
-  }
-
-  async listTTSComparisons(skip = 0, limit = 50): Promise<any[]> {
-    const response = await this.client.get('/api/v1/voice-playground/comparisons', {
-      params: { skip, limit },
-    })
-    return response.data
-  }
-
-  async getTTSComparison(comparisonId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/voice-playground/comparisons/${comparisonId}`)
-    return response.data
-  }
-
-  async generateTTSComparison(comparisonId: string): Promise<{ message: string; task_id: string }> {
-    const response = await this.client.post(`/api/v1/voice-playground/comparisons/${comparisonId}/generate`)
-    return response.data
-  }
-
-  async submitBlindTest(comparisonId: string, results: Array<{
-    sample_index: number
-    preferred: 'A' | 'B'
-    voice_a_id: string
-    voice_b_id: string
-  }>): Promise<any> {
-    const response = await this.client.post(
-      `/api/v1/voice-playground/comparisons/${comparisonId}/blind-test`,
-      { results }
-    )
-    return response.data
-  }
-
-  async deleteTTSComparison(comparisonId: string): Promise<void> {
-    await this.client.delete(`/api/v1/voice-playground/comparisons/${comparisonId}`)
-  }
-
-  // Blind Test Sharing (owner side)
-  async createBlindTestShare(comparisonId: string, payload: {
-    title: string
-    description?: string
-    creator_notes?: string
-    custom_metrics: Array<{ key: string; label: string; type: 'rating' | 'comment'; scale?: number }>
-  }): Promise<any> {
-    const response = await this.client.post(
-      `/api/v1/voice-playground/comparisons/${comparisonId}/share`,
-      payload
-    )
-    return response.data
-  }
-
-  async getBlindTestShare(comparisonId: string): Promise<any> {
-    const response = await this.client.get(
-      `/api/v1/voice-playground/comparisons/${comparisonId}/share`
-    )
-    return response.data
-  }
-
-  async updateBlindTestShare(shareId: string, payload: {
-    title?: string
-    description?: string
-    creator_notes?: string
-    custom_metrics?: Array<{ key: string; label: string; type: 'rating' | 'comment'; scale?: number }>
-    status?: 'open' | 'closed'
-  }): Promise<any> {
-    const response = await this.client.patch(
-      `/api/v1/voice-playground/shares/${shareId}`,
-      payload
-    )
-    return response.data
-  }
-
-  async deleteBlindTestShare(shareId: string): Promise<void> {
-    await this.client.delete(`/api/v1/voice-playground/shares/${shareId}`)
-  }
-
-  async listBlindTestResponses(shareId: string, skip = 0, limit = 100): Promise<{
-    items: any[]
-    total: number
-  }> {
-    const response = await this.client.get(
-      `/api/v1/voice-playground/shares/${shareId}/responses`,
-      { params: { skip, limit } }
-    )
-    return response.data
-  }
-
-  async generateSampleTexts(params: {
-    voice_bundle_id?: string
-    provider?: string
-    model?: string
-    scenario?: string
-    count?: number
-    length?: string
-    temperature?: number
-    llm_config?: LLMGenerationConfig | null
-  }): Promise<{ samples: string[]; provider: string; model: string }> {
-    const response = await this.client.post('/api/v1/voice-playground/generate-samples', params)
-    return response.data
-  }
-
-  async getTTSAnalytics(): Promise<Array<{
-    provider: string
-    model: string
-    voice_id: string
-    voice_name: string
-    sample_count: number
-    avg_mos: number | null
-    avg_valence: number | null
-    avg_arousal: number | null
-    avg_prosody: number | null
-    avg_ttfb_ms: number | null
-    avg_latency_ms: number | null
-    avg_wer: number | null
-    avg_cer: number | null
-  }>> {
-    const response = await this.client.get('/api/v1/voice-playground/analytics')
-    return response.data
-  }
-
-  async downloadTTSComparisonReport(
-    comparisonId: string,
-    includeUnfinishedSamples = false,
-    reportOptions?: TTSReportOptionsPayload
-  ): Promise<Blob> {
-    const response = await this.client.get(
-      `/api/v1/voice-playground/comparisons/${comparisonId}/report.pdf`,
-      {
-        params: {
-          include_unfinished_samples: includeUnfinishedSamples,
-          ...(reportOptions ? { report_options: JSON.stringify(reportOptions) } : {}),
-        },
-        responseType: 'blob',
-      }
-    )
-    return response.data
-  }
-
-  async createTTSComparisonReportJob(
-    comparisonId: string,
-    reportOptions?: TTSReportOptionsPayload
-  ): Promise<{
-    id: string
-    comparison_id: string
-    status: string
-    format: string
-    task_id?: string
-    report_options?: TTSReportOptionsPayload
-    created_at?: string | null
-  }> {
-    const response = await this.client.post(
-      `/api/v1/voice-playground/comparisons/${comparisonId}/reports`,
-      reportOptions ? { report_options: reportOptions } : {}
-    )
-    return response.data
-  }
-
-  async getVoicePlaygroundReportThresholdDefaults(): Promise<{
-    zone_threshold_overrides: NonNullable<TTSReportOptionsPayload['zone_threshold_overrides']>
-    is_custom: boolean
-  }> {
-    const response = await this.client.get('/api/v1/voice-playground/report-threshold-defaults')
-    return response.data
-  }
-
-  async updateVoicePlaygroundReportThresholdDefaults(data: {
-    zone_threshold_overrides?: NonNullable<TTSReportOptionsPayload['zone_threshold_overrides']>
-    reset_to_system_defaults?: boolean
-  }): Promise<{
-    zone_threshold_overrides: NonNullable<TTSReportOptionsPayload['zone_threshold_overrides']>
-    is_custom: boolean
-    message: string
-  }> {
-    const response = await this.client.put('/api/v1/voice-playground/report-threshold-defaults', data)
-    return response.data
-  }
-
-  async getTTSComparisonReportJob(reportJobId: string): Promise<{
-    id: string
-    comparison_id: string
-    status: string
-    format: string
-    filename?: string | null
-    error_message?: string | null
-    task_id?: string | null
-    download_url?: string | null
-    report_options?: TTSReportOptionsPayload
-    created_at?: string | null
-    updated_at?: string | null
-  }> {
-    const response = await this.client.get(`/api/v1/voice-playground/reports/${reportJobId}`)
-    return response.data
-  }
-
-  // Prompt Partials
-  async listPromptPartials(
-    skip = 0,
-    limit = 100,
-    search?: string,
-    kind: 'all' | 'partial' | 'imported_agent' | 'metric' = 'all',
-  ): Promise<any[]> {
-    const response = await this.client.get('/api/v1/prompt-partials', {
-      params: {
-        skip,
-        limit,
-        kind,
-        ...(search ? { search } : {}),
-      },
-    })
-    return response.data
-  }
-
-  async listImportedAgents(skip = 0, limit = 100, search?: string): Promise<import('../types/api').ImportedAgent[]> {
-    return this.listPromptPartials(skip, limit, search, 'imported_agent')
-  }
-
-  async listMetricPartials(skip = 0, limit = 100, search?: string): Promise<import('../types/api').MetricPartial[]> {
-    return this.listPromptPartials(skip, limit, search, 'metric')
-  }
-
-  async createImportedAgent(data: {
-    name: string
-    description?: string | null
-    content: string
-  }): Promise<import('../types/api').ImportedAgent> {
-    const response = await this.client.post('/api/v1/prompt-partials', {
-      ...data,
-      tags: ['__imported_agent__'],
-    })
-    return response.data
-  }
-
-  async createMetricPartial(data: {
-    name: string
-    description?: string | null
-    content: string
-  }): Promise<import('../types/api').MetricPartial> {
-    const response = await this.client.post('/api/v1/prompt-partials', {
-      ...data,
-      tags: ['__metric_partial__'],
-    })
-    return response.data
-  }
-
-  async updateMetricPartial(
-    partialId: string,
-    data: {
-      name?: string
-      description?: string | null
-      content?: string
-      change_summary?: string
-    },
-  ): Promise<import('../types/api').MetricPartial> {
-    const response = await this.client.put(`/api/v1/prompt-partials/${partialId}`, {
-      ...data,
-      tags: ['__metric_partial__'],
-    })
-    return response.data
-  }
-
-  async generateAgentFlowchart(
-    partialId: string,
-    options?: {
-      provider?: string
-      model?: string
-      credential_id?: string
-      regenerate?: boolean
-    },
-  ): Promise<import('../types/api').AgentFlowGraph> {
-    const response = await this.client.post(
-      `/api/v1/prompt-partials/${partialId}/flowchart`,
-      {
-        provider: options?.provider,
-        model: options?.model,
-        credential_id: options?.credential_id,
-        regenerate: options?.regenerate ?? false,
-      },
-    )
-    return response.data
-  }
-
-  async saveAgentFlowchartLayout(
-    partialId: string,
-    nodes: Array<{ id: string; position_x: number; position_y: number }>,
-  ): Promise<import('../types/api').AgentFlowGraph> {
-    const response = await this.client.put(
-      `/api/v1/prompt-partials/${partialId}/flowchart/layout`,
-      { nodes },
-    )
-    return response.data
-  }
-
-  async mapAgentFlowchartPromptSections(
-    partialId: string,
-    options?: { provider?: string; model?: string; credential_id?: string },
-  ): Promise<import('../types/api').AgentFlowGraph> {
-    const response = await this.client.post(
-      `/api/v1/prompt-partials/${partialId}/flowchart/prompt-map`,
-      {
-        provider: options?.provider,
-        model: options?.model,
-        credential_id: options?.credential_id,
-      },
-    )
-    return response.data
-  }
-
-  async updateImportedAgent(
-    partialId: string,
-    data: {
-      name?: string
-      description?: string | null
-      content?: string
-      change_summary?: string
-    },
-  ): Promise<import('../types/api').ImportedAgent> {
-    const response = await this.client.put(`/api/v1/prompt-partials/${partialId}`, {
-      ...data,
-      tags: ['__imported_agent__'],
-    })
-    return response.data
-  }
-
-  async getPromptPartial(partialId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/prompt-partials/${partialId}`)
-    return response.data
-  }
-
-  async createPromptPartial(data: {
-    name: string
-    description?: string
-    content: string
-    tags?: string[]
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/prompt-partials', data)
-    return response.data
-  }
-
-  async updatePromptPartial(partialId: string, data: {
-    name?: string
-    description?: string
-    content?: string
-    tags?: string[]
-    change_summary?: string
-  }): Promise<any> {
-    const response = await this.client.put(`/api/v1/prompt-partials/${partialId}`, data)
-    return response.data
-  }
-
-  async deletePromptPartial(partialId: string): Promise<void> {
-    await this.client.delete(`/api/v1/prompt-partials/${partialId}`)
-  }
-
-  async listPromptPartialVersions(partialId: string): Promise<any[]> {
-    const response = await this.client.get(`/api/v1/prompt-partials/${partialId}/versions`)
-    return response.data
-  }
-
-  async getPromptPartialVersion(partialId: string, versionNumber: number): Promise<any> {
-    const response = await this.client.get(`/api/v1/prompt-partials/${partialId}/versions/${versionNumber}`)
-    return response.data
-  }
-
-  async revertPromptPartial(partialId: string, versionNumber: number): Promise<any> {
-    const response = await this.client.post(`/api/v1/prompt-partials/${partialId}/revert/${versionNumber}`)
-    return response.data
-  }
-
-  async clonePromptPartial(partialId: string): Promise<any> {
-    const response = await this.client.post(`/api/v1/prompt-partials/${partialId}/clone`)
-    return response.data
-  }
-
-  async generatePromptWithAI(data: {
-    description: string
-    tone?: string
-    format_style?: string
-    provider?: string
-    model?: string
-    credential_id?: string
-    llm_config?: LLMGenerationConfig | null
-  }): Promise<{ content: string; provider: string; model: string }> {
-    const response = await this.client.post('/api/v1/prompt-partials/generate', data)
-    return response.data
-  }
-
-  async improvePromptWithAI(data: {
-    content: string
-    instructions?: string
-    provider?: string
-    model?: string
-    credential_id?: string
-    llm_config?: LLMGenerationConfig | null
-  }): Promise<{ content: string; provider: string; model: string }> {
-    const response = await this.client.post('/api/v1/prompt-partials/improve', data)
-    return response.data
-  }
-
-  // License / Enterprise
-  async getLicenseInfo(): Promise<LicenseInfoResponse> {
-    const response = await this.client.get('/api/v1/settings/license-info')
-    return response.data
-  }
-
-  async getReportBranding(): Promise<ReportBranding> {
-    const response = await this.client.get('/api/v1/settings/report-branding')
-    return response.data
-  }
-
-  async updateReportBranding(data: { heading?: string | null }): Promise<ReportBranding> {
-    const response = await this.client.patch('/api/v1/settings/report-branding', data)
-    return response.data
-  }
-
-  async uploadReportBrandingImages(
-    files: File[],
-    role: 'internal' | 'external' | 'generic' = 'generic',
-  ): Promise<ReportBranding> {
-    const formData = new FormData()
-    for (const file of files) {
-      formData.append('files', file)
-    }
-    formData.append('role', role)
-    const response = await this.client.post('/api/v1/settings/report-branding/images', formData, {
-      headers: { 'Content-Type': 'multipart/form-data' },
-    })
-    return response.data
-  }
-
-  async deleteReportBrandingImage(imageId: string): Promise<ReportBranding> {
-    const response = await this.client.delete(`/api/v1/settings/report-branding/images/${imageId}`)
-    return response.data
-  }
-
-  // GEPA Prompt Optimization (Enterprise)
-  async createOptimizationRun(data: {
-    agent_id: string
-    evaluator_id?: string
-    voice_bundle_id?: string
-    config?: Record<string, any>
-  }): Promise<any> {
-    const response = await this.client.post('/api/v1/prompt-optimization/runs', data)
-    return response.data
-  }
-
-  async deleteOptimizationRun(runId: string): Promise<void> {
-    await this.client.delete(`/api/v1/prompt-optimization/runs/${runId}`)
-  }
-
-  async listOptimizationRuns(agentId?: string): Promise<any[]> {
-    const params = agentId ? { agent_id: agentId } : {}
-    const response = await this.client.get('/api/v1/prompt-optimization/runs', { params })
-    return response.data
-  }
-
-  async getOptimizationRun(runId: string): Promise<any> {
-    const response = await this.client.get(`/api/v1/prompt-optimization/runs/${runId}`)
-    return response.data
-  }
-
-  async listOptimizationCandidates(runId: string): Promise<any[]> {
-    const response = await this.client.get(`/api/v1/prompt-optimization/runs/${runId}/candidates`)
-    return response.data
-  }
-
-  async acceptCandidate(runId: string, candidateId: string): Promise<any> {
-    const response = await this.client.post(
-      `/api/v1/prompt-optimization/runs/${runId}/candidates/${candidateId}/accept`
-    )
-    return response.data
-  }
-
-  async pushCandidateToProvider(runId: string, candidateId: string): Promise<any> {
-    const response = await this.client.post(
-      `/api/v1/prompt-optimization/runs/${runId}/candidates/${candidateId}/push`
-    )
-    return response.data
-  }
-
-  async syncProviderPrompt(agentId: string): Promise<{
-    synced: boolean
-    provider_prompt: string | null
-    provider_prompt_synced_at: string | null
-  }> {
-    const response = await this.client.post(`/api/v1/agents/${agentId}/sync-provider-prompt`)
-    return response.data
-  }
-
-  // -------------------------------------------------------------------
-  // Judge Alignment (AlignEval-style hybrid integration)
-  //
-  // Per-org thresholds and judge model selection are sourced from the
-  // backend (no hardcoded defaults in the frontend).
-  // -------------------------------------------------------------------
-
-  async getJudgeAlignmentSettings(): Promise<JudgeAlignmentSettings> {
-    const response = await this.client.get('/api/v1/judge-alignment/settings')
-    return response.data
-  }
-
-  async updateJudgeAlignmentSettings(
-    data: { min_labels_to_evaluate: number; min_labels_to_optimize: number }
-  ): Promise<JudgeAlignmentSettings> {
-    const response = await this.client.patch('/api/v1/judge-alignment/settings', data)
-    return response.data
-  }
-
-  async listJudgeCapableModels(): Promise<JudgeModelCatalogEntry[]> {
-    const response = await this.client.get('/api/v1/judge-alignment/available-models')
-    return response.data
-  }
-
-  async listJudgeDatasets(): Promise<JudgeDataset[]> {
-    const response = await this.client.get('/api/v1/judge-alignment/datasets')
-    return response.data
-  }
-
-  async createJudgeDataset(data: {
-    name: string
-    description?: string
-    source_type: 'transcript' | 'metric_output' | 'csv'
-    source_config?: Record<string, any>
-    input_field?: string
-    output_field?: string
-  }): Promise<JudgeDataset> {
-    const response = await this.client.post('/api/v1/judge-alignment/datasets', data)
-    return response.data
-  }
-
-  async uploadJudgeDatasetCsv(
-    file: File,
-    name: string,
-    description?: string
-  ): Promise<JudgeDataset> {
-    const formData = new FormData()
-    formData.append('file', file)
-    formData.append('name', name)
-    if (description) formData.append('description', description)
-    const response = await this.client.post(
-      '/api/v1/judge-alignment/datasets/upload-csv',
-      formData,
-      { headers: { 'Content-Type': 'multipart/form-data' } }
-    )
-    return response.data
-  }
-
-  async getJudgeDataset(datasetId: string): Promise<JudgeDataset> {
-    const response = await this.client.get(`/api/v1/judge-alignment/datasets/${datasetId}`)
-    return response.data
-  }
-
-  async deleteJudgeDataset(datasetId: string): Promise<void> {
-    await this.client.delete(`/api/v1/judge-alignment/datasets/${datasetId}`)
-  }
-
-  async listJudgeSamples(
-    datasetId: string,
-    options: { only_labeled?: boolean; skip?: number; limit?: number } = {}
-  ): Promise<JudgeSample[]> {
-    const response = await this.client.get(
-      `/api/v1/judge-alignment/datasets/${datasetId}/samples`,
-      { params: options }
-    )
-    return response.data
-  }
-
-  async labelJudgeSample(
-    sampleId: string,
-    label: 'pass' | 'fail' | null
-  ): Promise<JudgeSample> {
-    const response = await this.client.patch(
-      `/api/v1/judge-alignment/samples/${sampleId}`,
-      { label }
-    )
-    return response.data
-  }
-
-  async bulkLabelJudgeSamples(
-    datasetId: string,
-    items: Array<{ sample_id: string; label: 'pass' | 'fail' | null }>
-  ): Promise<{ updated: number; requested: number }> {
-    const response = await this.client.post(
-      `/api/v1/judge-alignment/datasets/${datasetId}/samples/bulk-label`,
-      { items }
-    )
-    return response.data
-  }
-
-  async listJudgeRuns(datasetId: string): Promise<JudgeRun[]> {
-    const response = await this.client.get(
-      `/api/v1/judge-alignment/datasets/${datasetId}/runs`
-    )
-    return response.data
-  }
-
-  async triggerJudgeRun(
-    datasetId: string,
-    data: { evaluator_id: string; split?: 'all' | 'dev' | 'test'; sample_ids?: string[] }
-  ): Promise<JudgeRun> {
-    const response = await this.client.post(
-      `/api/v1/judge-alignment/datasets/${datasetId}/run`,
-      data
-    )
-    return response.data
-  }
-
-  async getJudgeRun(runId: string): Promise<JudgeRun> {
-    const response = await this.client.get(`/api/v1/judge-alignment/runs/${runId}`)
-    return response.data
-  }
-
-  async recomputeJudgeRunMetrics(runId: string): Promise<JudgeRun> {
-    const response = await this.client.post(
-      `/api/v1/judge-alignment/runs/${runId}/recompute-metrics`
-    )
-    return response.data
-  }
-
-  async deleteJudgeRun(runId: string): Promise<void> {
-    await this.client.delete(`/api/v1/judge-alignment/runs/${runId}`)
-  }
-
-  async optimizeJudge(
-    datasetId: string,
-    data: {
-      evaluator_id: string
-      dev_ratio?: number
-      seed?: number
-      max_metric_calls?: number
-      minibatch_size?: number
-      agent_id?: string
-    }
-  ): Promise<JudgeOptimizeResponse> {
-    const response = await this.client.post(
-      `/api/v1/judge-alignment/datasets/${datasetId}/optimize`,
-      data
-    )
-    return response.data
-  }
-}
-
-// -------------------------------------------------------------------
-// Judge Alignment shared types
-// -------------------------------------------------------------------
-
-export interface JudgeAlignmentSettings {
-  min_labels_to_evaluate: number
-  min_labels_to_optimize: number
-  defaults: { min_labels_to_evaluate: number; min_labels_to_optimize: number }
-}
-
-export interface JudgeModelCatalogEntry {
-  provider: string
-  provider_label: string
-  model: string
-  label: string
-}
-
-export interface JudgeDataset {
-  id: string
-  name: string
-  description?: string | null
-  source_type: 'transcript' | 'metric_output' | 'csv'
-  source_config: Record<string, any>
-  input_field: string
-  output_field: string
-  total_samples: number
-  labeled_samples: number
-  unlabeled_samples: number
-  created_at?: string
-  updated_at?: string
-}
-
-export interface JudgeSample {
-  id: string
-  dataset_id: string
-  external_id?: string | null
-  input_text: string
-  output_text: string
-  label?: 'pass' | 'fail' | null
-  labeled_by?: string | null
-  labeled_at?: string | null
-  extra?: Record<string, any> | null
-  created_at?: string
-}
-
-export interface JudgeRunMetrics {
-  precision: number
-  recall: number
-  f1: number
-  kappa: number
-  tp: number
-  fp: number
-  tn: number
-  fn: number
-  n: number
-}
-
-export interface JudgeRun {
-  id: string
-  dataset_id: string
-  evaluator_id?: string | null
-  split: 'all' | 'dev' | 'test'
-  llm_provider?: string | null
-  llm_model?: string | null
-  status: 'pending' | 'queued' | 'running' | 'completed' | 'failed'
-  metrics?: JudgeRunMetrics | null
-  predictions?: Record<string, { prediction: 'pass' | 'fail' | null; explanation?: string; raw?: string }> | null
-  error_message?: string | null
-  celery_task_id?: string | null
-  gepa_optimization_id?: string | null
-  created_at?: string
-  updated_at?: string
-}
-
-export interface JudgeOptimizeResponse {
-  optimization_run_id: string
-  dev_sample_count: number
-  test_sample_count: number
-}
-
-export const AUDIO_UPLOAD_CHUNK_SIZE = 25
-
-export interface CallImportAudioUploadProgress {
-  chunkIndex: number
-  totalChunks: number
-  uploadedFiles: number
-  totalFiles: number
-}
-
-// Factory function to create ApiClient instance
-// This ensures TypeScript correctly infers the type as ApiClient, not AxiosInstance
-function createApiClient(): ApiClient {
-  return new ApiClient()
-}
-
-// Create instance
-const apiClientInstance = createApiClient()
-
-// Export with explicit type annotation - CRITICAL for TypeScript to recognize as ApiClient
-// Without this explicit type, TypeScript may incorrectly infer AxiosInstance
-export const apiClient: ApiClient = apiClientInstance
-
-// Re-export the type for explicit typing if needed
-export type { ApiClient }
-
-
-// ----------------------------------------------------------------------
-// Public (unauthenticated) blind-test API.
-// Uses a bare axios instance so no Authorization / X-API-Key headers are
-// attached. Anyone with the share_token in the URL can call these.
-// ----------------------------------------------------------------------
-
-const publicClient = axios.create({
-  baseURL: API_BASE_URL,
-  headers: { 'Content-Type': 'application/json' },
-})
-
-export interface PublicBlindTestEntrySubmit {
-  sample_index: number
-  preferred: 'X' | 'Y'
-  ratings_x?: Record<string, number>
-  ratings_y?: Record<string, number>
-  comment?: string
-}
-
-export const publicBlindTestApi = {
-  async getForm(shareToken: string): Promise<any> {
-    const res = await publicClient.get(`/api/v1/public/blind-tests/${shareToken}`)
-    return res.data
-  },
-  async submit(shareToken: string, payload: {
-    rater_name: string
-    rater_email: string
-    client_token: string
-    responses: PublicBlindTestEntrySubmit[]
-  }): Promise<any> {
-    const res = await publicClient.post(
-      `/api/v1/public/blind-tests/${shareToken}/responses`,
-      payload
-    )
-    return res.data
-  },
-}
+import axios, { AxiosInstance } from 'axios'
+import {
+  clearAuthSession,
+  getApiErrorDetail,
+  hasRevocableUserCredentials,
+  isOrganizationAccessDenied,
+  organizationAccessDeniedMessage,
+  redirectToLoginWithMessage,
+  type UserSessionCredentials,
+} from './authSession'
+import type {
+  GenerateScenariosFromPromptParams,
+  GenerateTestPromptParams,
+  GenerateTestSetupParams,
+} from '../types/agentTestSetupGeneration'
+import type {
+  AudioFile,
+  Evaluation,
+  EvaluationCreate,
+  EvaluationResult,
+  DashboardSummary,
+  ModelConfigEntry,
+  APIKey,
+  MessageResponse,
+  EvaluationStatus,
+  OrganizationMember,
+  Invitation,
+  InvitationCreate,
+  InvitationPreview,
+  Profile,
+  UserUpdate,
+  UserPreferences,
+  UserPreferencesUpdate,
+  Role,
+  Integration,
+  IntegrationCreate,
+  S3ConnectionTestResponse,
+  S3ListFilesResponse,
+  S3BrowseResponse,
+  S3Status,
+  CallImport,
+  CallImportDetail,
+  CallImportListResponse,
+  CallImportRow,
+  ObservabilityCall,
+  CallImportSchema,
+  CallImportSchemaCreate,
+  CallImportSchemaListResponse,
+  CallImportSchemaUpdate,
+  CallImportStatus,
+  CallImportTag,
+  CallImportUploadResponse,
+  CallImportPreviewResponse,
+  CallImportEvaluation,
+  CallImportEvaluationBaselineCandidatesResponse,
+  CallImportEvaluationPdfReport,
+  CallImportEvaluationPdfReportListResponse,
+  CallImportEvaluationLLMOverride,
+  CallImportEvaluationListResponse,
+  CallImportEvaluationRow,
+  CallImportEvaluationRowListResponse,
+  CallImportEvaluationRetryResponse,
+  CallImportEvaluationBulkActionResponse,
+  CallImportEvaluationAggregateResponse,
+  CallImportInsightsResponse,
+  EvaluatorResultListResponse,
+  EvaluatorResultsOverviewResponse,
+  EvaluatorResultsAggregateResponse,
+  ListEvaluatorResultsParams,
+  CallImportTranscribeRequest,
+  CallImportTranscribeResponse,
+  CallImportRetryFailedRowsResponse,
+  Workspace,
+  LLMGenerationConfig,
+  TestAgent,
+} from '../types/api'
+
+export interface EnterpriseFeatureMeta {
+  title: string
+  description?: string
+  category?: string
+}
+
+export type EnterpriseFeatureCatalog = Record<string, EnterpriseFeatureMeta>
+
+export type VoicePlaygroundSourceType = 'tts' | 'recording' | 'upload'
+
+export interface VoicePlaygroundSideConfig {
+  source_type: VoicePlaygroundSourceType
+  provider?: string
+  model?: string
+  voices?: Array<{ id: string; name: string; sample_rate_hz?: number }>
+  call_import_row_ids?: string[]
+  upload_s3_keys?: string[]
+}
+
+export type VoicePlaygroundBlindTestRefType = 'recording' | 'upload' | 'tts_sample'
+
+export interface VoicePlaygroundBlindTestAudioRef {
+  type: VoicePlaygroundBlindTestRefType
+  call_import_row_id?: string
+  upload_s3_key?: string
+  tts_sample_id?: string
+  label?: string
+}
+
+export interface VoicePlaygroundBlindTestPair {
+  text?: string
+  x: VoicePlaygroundBlindTestAudioRef
+  y: VoicePlaygroundBlindTestAudioRef
+}
+
+export interface UsagePolicy {
+  extended_history: boolean
+  max_history_days: number | null
+}
+
+export interface LicenseInfoResponse {
+  is_enterprise: boolean
+  enabled_features: string[]
+  all_enterprise_features: string[]
+  feature_catalog?: EnterpriseFeatureCatalog
+  organization?: string
+  usage_policy?: UsagePolicy
+}
+
+export interface ReportBranding {
+  heading?: string | null
+  has_logo: boolean
+  images: Array<{
+    id: string
+    filename: string
+    content_type: string
+    size_bytes: number
+    role: 'internal' | 'external' | 'generic'
+    updated_at?: string | null
+    data_uri?: string | null
+  }>
+}
+
+export interface AuthProviderConfig {
+  name: 'api_key' | 'local_password' | 'external_oidc'
+  enabled: boolean
+  display_name: string
+  description?: string
+  supports_password?: boolean
+  supports_signup?: boolean
+  oidc_issuer?: string | null
+  oidc_client_id?: string | null
+  oidc_authorize_url?: string | null
+}
+
+export interface AuthConfigResponse {
+  providers: AuthProviderConfig[]
+  tier: 'oss' | 'enterprise'
+  gated_signup?: boolean
+}
+
+export interface AuthUserSummary {
+  id: string
+  email: string
+  name?: string | null
+  first_name?: string | null
+  last_name?: string | null
+  organization_id: string
+  role?: string | null
+  has_password?: boolean
+  email_is_placeholder?: boolean
+}
+
+export interface TokenResponse {
+  access_token: string
+  refresh_token?: string
+  token_type: string
+  expires_in: number
+  user: AuthUserSummary
+}
+
+export interface LoginOrgOption {
+  id: string
+  name: string
+  role: string
+}
+
+export interface LoginOrgSelectionResponse {
+  requires_org_selection: true
+  organizations: LoginOrgOption[]
+}
+
+export interface PlatformAdminSummary {
+  id: string
+  email: string
+}
+
+export interface PlatformTokenResponse {
+  access_token: string
+  token_type: string
+  expires_in: number
+  admin: PlatformAdminSummary
+}
+
+export interface PlatformOrganizationItem {
+  id: string
+  name: string
+  is_active: boolean
+  member_count: number
+  created_at?: string | null
+  disabled_at?: string | null
+}
+
+export interface PlatformOrganizationListResponse {
+  items: PlatformOrganizationItem[]
+  total: number
+  offset: number
+  limit: number
+}
+
+export interface PlatformOrganizationStats {
+  total: number
+  active: number
+  disabled: number
+}
+
+export interface PlatformOrgUser {
+  id: string
+  email: string
+  role: string
+  is_active: boolean
+}
+
+export interface PlatformSignupCode {
+  id: string
+  label?: string | null
+  max_uses?: number | null
+  use_count: number
+  expires_at?: string | null
+  is_active: boolean
+  created_at?: string | null
+  code?: string | null
+}
+
+export type LoginResponse = TokenResponse | LoginOrgSelectionResponse
+
+export function isLoginOrgSelectionResponse(
+  response: LoginResponse
+): response is LoginOrgSelectionResponse {
+  return 'requires_org_selection' in response && response.requires_org_selection === true
+}
+
+export interface OrganizationSummary {
+  id: string
+  name: string
+}
+
+export type LLMGatewayMode = 'inherit' | 'enabled' | 'disabled'
+export type LLMGatewayType = 'inherit' | 'bifrost' | 'litellm_proxy'
+export type LLMGatewayInterface = 'inherit' | 'litellm_shim' | 'native_openai'
+export type LLMGatewayRouting = 'direct' | 'bifrost' | 'litellm_proxy'
+export type GatewayInterfaceMode = 'inherit' | 'litellm_shim' | 'native_openai'
+
+export interface LLMGatewaySettings {
+  mode: LLMGatewayMode
+  gateway_type: LLMGatewayType
+  gateway_interface?: LLMGatewayInterface
+  base_url?: string | null
+  has_virtual_key: boolean
+  has_master_key: boolean
+  platform_enabled: boolean
+  platform_gateway_type: 'bifrost' | 'litellm_proxy'
+  platform_gateway_interface?: 'litellm_shim' | 'native_openai'
+  platform_base_url?: string | null
+  effective_routing: LLMGatewayRouting
+  effective_gateway_type?: 'bifrost' | 'litellm_proxy' | null
+  effective_gateway_interface?: 'litellm_shim' | 'native_openai' | null
+  effective_base_url?: string | null
+  effective_has_virtual_key: boolean
+  effective_has_master_key: boolean
+  gateway_managed_credentials: boolean
+}
+
+export interface LLMGatewaySettingsUpdate {
+  mode: LLMGatewayMode
+  gateway_type?: LLMGatewayType
+  gateway_interface?: LLMGatewayInterface
+  base_url?: string | null
+  virtual_key?: string | null
+  master_key?: string | null
+  clear_virtual_key?: boolean
+  clear_master_key?: boolean
+}
+
+export interface TelephonyIntegrationResponse {
+  id: string
+  organization_id: string
+  provider: string
+  name?: string | null
+  verify_app_uuid?: string | null
+  voice_app_id?: string | null
+  sip_domain?: string | null
+  masking_config?: Record<string, any> | null
+  is_active: boolean
+  /** True if this row is the default credential for (org, provider). */
+  is_default?: boolean
+  last_tested_at?: string | null
+  created_at: string
+  updated_at: string
+}
+
+export interface TelephonyIntegrationCreatePayload {
+  provider?: string
+  name?: string
+  auth_id: string
+  auth_token: string
+  verify_app_uuid?: string
+  voice_app_id?: string
+  sip_domain?: string
+  masking_config?: Record<string, any>
+  /** Mark the new credential as the default for (org, provider). */
+  is_default?: boolean
+}
+
+export interface TelephonyIntegrationUpdatePayload {
+  /** When set, update this specific credential row instead of the legacy single-row flow. */
+  id?: string
+  provider?: string
+  name?: string
+  auth_id?: string
+  auth_token?: string
+  verify_app_uuid?: string
+  voice_app_id?: string
+  sip_domain?: string
+  masking_config?: Record<string, any>
+  is_active?: boolean
+}
+
+export interface TelephonyPhoneNumberResponse {
+  id: string
+  phone_number: string
+  country_iso2?: string | null
+  region?: string | null
+  number_type?: string | null
+  capabilities?: Record<string, any> | null
+  is_masking_pool: boolean
+  inbound_enabled?: boolean
+  outbound_enabled?: boolean
+  source?: string
+  agent_id?: string | null
+  linked_agent_name?: string | null
+  provider?: string | null
+  is_active: boolean
+  created_at: string
+}
+
+export interface TelephonyAvailableNumber {
+  e164: string
+  provider_number_id?: string | null
+  country?: string | null
+  region?: string | null
+  capabilities?: Record<string, any> | null
+  status?: string | null
+  application_id?: string | null
+  already_imported: boolean
+  imported_number_id?: string | null
+}
+
+export interface TelephonyImportNumberResult {
+  number: string
+  success: boolean
+  message: string
+  answer_url: string
+  webhook_configured?: boolean | null
+  imported_number_id?: string | null
+  application_id?: string | null
+}
+
+export interface TelephonyImportNumbersResponse {
+  provider: string
+  results: TelephonyImportNumberResult[]
+  answer_url: string
+}
+
+/** @deprecated Use TelephonyAvailableNumber */
+export type VobizAvailableNumber = TelephonyAvailableNumber
+
+/** @deprecated Use TelephonyImportNumbersResponse */
+export type VobizImportNumbersResponse = TelephonyImportNumbersResponse
+
+/** @deprecated Use TelephonyImportNumberResult */
+export type VobizImportNumberResult = TelephonyImportNumberResult
+
+export interface PlatformOutboundPoolNumber {
+  phone_number: string
+  provider: string
+}
+
+export interface PlatformOutboundPoolResponse {
+  numbers: PlatformOutboundPoolNumber[]
+  max_concurrent_per_org: number
+  shared_across_orgs: boolean
+}
+
+/** @deprecated Use PlatformOutboundPoolResponse */
+export type VobizOutboundPoolResponse = PlatformOutboundPoolResponse
+
+export interface VobizOutboundCallRequest {
+  to_number: string
+  agent_id: string
+  from_number?: string
+  persona_id?: string
+  scenario_id?: string
+  evaluator_id?: string
+}
+
+export interface TelephonyDialTargetResponse {
+  id: string
+  phone_number: string
+  label?: string | null
+  created_at: string
+  updated_at: string
+}
+
+export interface VobizOutboundCallResponse {
+  provider_request_uuid: string
+  call_status: string
+  from_number: string
+  to_number: string
+  call_ref: string
+  call_short_id?: string
+  evaluator_result_id?: string
+  result_id?: string
+  otel_correlation?: {
+    otlp_endpoint: string
+    api_key_header: string
+    suggested_env_vars: Record<string, string>
+    suggested_otlp_headers: Record<string, string>
+    suggested_span_attributes: Record<string, string>
+  }
+  message: string
+}
+
+export interface EvaluatorSuiteCombination {
+  id: string
+  evaluator_id: string
+  scenario_id: string
+  scenario_name?: string | null
+  scenario_description?: string | null
+  scenario_required_info?: Record<string, unknown> | null
+}
+
+export interface EvaluatorSuite {
+  id: string
+  organization_id: string
+  name?: string | null
+  agent_id: string
+  persona_id: string
+  agent_name?: string | null
+  persona_name?: string | null
+  agent_call_type?: string | null
+  agent_call_medium?: string | null
+  metric_ids?: string[] | null
+  llm_provider?: string | null
+  llm_model?: string | null
+  tags?: string[] | null
+  default_runs_per_combination: number
+  round_robin_index: number
+  is_active: boolean
+  agent_suite_count: number
+  combination_count: number
+  combinations: EvaluatorSuiteCombination[]
+  created_at: string
+  updated_at: string
+}
+
+export interface RunEvaluatorSuiteResponse {
+  total_runs: number
+  task_ids: string[]
+  evaluator_results: any[]
+  phone_call_refs: string[]
+}
+
+export interface RunNextCombinationResponse {
+  evaluator_id: string
+  scenario_id: string
+  scenario_name: string
+  combination_index: number
+  next_index: number
+  evaluator_result_id?: string | null
+  result_id?: string | null
+  task_id?: string | null
+  phone_call_ref?: string | null
+  call_short_id?: string | null
+}
+
+export interface ChooseNextCombinationResponse {
+  evaluator_id: string
+  scenario_id: string
+  scenario_name: string
+  combination_index: number
+  next_index: number
+}
+
+type TTSReportOptionsPayload = {
+  show_runs?: boolean
+  min_runs_to_show?: number
+  include_latency?: boolean
+  include_ttfb?: boolean
+  include_endpoint?: boolean
+  include_naturalness?: boolean
+  include_hallucination?: boolean
+  include_prosody?: boolean
+  include_arousal?: boolean
+  include_valence?: boolean
+  include_cer?: boolean
+  include_wer?: boolean
+  include_hallucination_examples?: boolean
+  hallucination_examples_limit?: number
+  include_disclaimer_sections?: boolean
+  include_methodology_sections?: boolean
+  zone_threshold_overrides?: Record<string, {
+    good_min?: number
+    neutral_min?: number
+    good_max?: number
+    neutral_max?: number
+  }>
+}
+
+// When running in production (served from same origin), use relative path
+// Otherwise use environment variable or default
+export const apiBaseUrl =
+  import.meta.env.VITE_API_URL ||
+  (import.meta.env.PROD ? '' : 'http://localhost:8000')
+
+const API_BASE_URL = apiBaseUrl
+
+let refreshPromise: Promise<string | null> | null = null
+
+class ApiClient {
+  private client: AxiosInstance
+
+  constructor() {
+    this.client = axios.create({
+      baseURL: API_BASE_URL,
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    })
+
+    // Add request interceptor to add API key + active workspace to headers.
+    // Workspace selection is read directly from localStorage to avoid a
+    // circular import between this module and the workspace store.
+    this.client.interceptors.request.use((config) => {
+      const accessToken = localStorage.getItem('accessToken')
+      const apiKey = localStorage.getItem('apiKey')
+      const workspaceId = localStorage.getItem('activeWorkspaceId')
+      if (accessToken) {
+        config.headers.Authorization = `Bearer ${accessToken}`
+        // Logged-in UI sessions must not send a stale X-API-Key — the backend
+        // prefers API keys over Bearer and would return 401 on invalid keys.
+        if (config.headers['X-API-Key']) {
+          delete config.headers['X-API-Key']
+        }
+      } else if (config.headers.Authorization) {
+        delete config.headers.Authorization
+      }
+      if (!accessToken && apiKey) {
+        config.headers['X-API-Key'] = apiKey
+      } else if (config.headers['X-API-Key']) {
+        delete config.headers['X-API-Key']
+      }
+      // Send X-Workspace-Id so the backend's get_workspace_id dep scopes
+      // listings to the active workspace. When absent (e.g. a brand-new
+      // session that hasn't called listWorkspaces yet) the backend
+      // falls back to the org's Default workspace.
+      if (workspaceId) {
+        config.headers['X-Workspace-Id'] = workspaceId
+      } else if (config.headers['X-Workspace-Id']) {
+        delete config.headers['X-Workspace-Id']
+      }
+      return config
+    })
+
+    // Add response interceptor for error handling and silent token refresh
+    this.client.interceptors.response.use(
+      (response) => response,
+      async (error) => {
+        const response = error.response
+        if (response?.data instanceof Blob) {
+          try {
+            const text = await response.data.text()
+            const trimmed = text.trim()
+            if (trimmed.startsWith('{') || trimmed.startsWith('[')) {
+              response.data = JSON.parse(text)
+            }
+          } catch {
+            // Keep the original blob when it isn't JSON.
+          }
+        }
+
+        const originalRequest = error.config
+        const requestUrl = String(originalRequest?.url || '')
+        const isAuthEndpoint =
+          requestUrl.includes('/auth/login') ||
+          requestUrl.includes('/auth/signup') ||
+          requestUrl.includes('/auth/refresh') ||
+          requestUrl.includes('/auth/logout') ||
+          requestUrl.includes('/auth/config')
+
+        const detail = getApiErrorDetail(error)
+        if (
+          response?.status === 403 &&
+          isOrganizationAccessDenied(detail) &&
+          !isAuthEndpoint
+        ) {
+          redirectToLoginWithMessage(organizationAccessDeniedMessage(detail))
+          return Promise.reject(error)
+        }
+
+        if (
+          response?.status === 401 &&
+          originalRequest &&
+          !originalRequest._retry
+        ) {
+          if (!isAuthEndpoint) {
+            originalRequest._retry = true
+            const newToken = await this.tryRefreshAccessToken()
+            if (newToken) {
+              originalRequest.headers.Authorization = `Bearer ${newToken}`
+              return this.client(originalRequest)
+            }
+          }
+
+          clearAuthSession()
+          window.location.href = '/login'
+        }
+        return Promise.reject(error)
+      },
+    )
+  }
+
+  private async tryRefreshAccessToken(): Promise<string | null> {
+    const refreshToken = localStorage.getItem('refreshToken')
+    if (!refreshToken) {
+      return null
+    }
+
+    if (!refreshPromise) {
+      refreshPromise = axios
+        .post(
+          `${API_BASE_URL}/api/v1/auth/refresh`,
+          { refresh_token: refreshToken },
+          { headers: { 'Content-Type': 'application/json' } },
+        )
+        .then((res) => {
+          const { access_token, refresh_token: rotatedRefresh } = res.data as TokenResponse
+          this.setAccessToken(access_token)
+          if (rotatedRefresh) {
+            this.setRefreshToken(rotatedRefresh)
+          }
+          return access_token as string
+        })
+        .catch((err) => {
+          const refreshDetail = getApiErrorDetail(err)
+          if (
+            err?.response?.status === 403 &&
+            isOrganizationAccessDenied(refreshDetail)
+          ) {
+            redirectToLoginWithMessage(
+              organizationAccessDeniedMessage(refreshDetail),
+            )
+          }
+          return null
+        })
+        .finally(() => {
+          refreshPromise = null
+        })
+    }
+
+    return refreshPromise
+  }
+
+  setApiKey(apiKey: string) {
+    localStorage.setItem('apiKey', apiKey)
+  }
+
+  clearApiKey() {
+    localStorage.removeItem('apiKey')
+  }
+
+  setAccessToken(accessToken: string) {
+    localStorage.setItem('accessToken', accessToken)
+  }
+
+  clearAccessToken() {
+    localStorage.removeItem('accessToken')
+  }
+
+  setRefreshToken(refreshToken: string) {
+    localStorage.setItem('refreshToken', refreshToken)
+  }
+
+  clearRefreshToken() {
+    localStorage.removeItem('refreshToken')
+  }
+
+  // Workspace endpoints (in-org isolation boundary for call imports + metrics).
+  async listWorkspaces(): Promise<Workspace[]> {
+    const response = await this.client.get('/api/v1/workspaces')
+    return response.data
+  }
+
+  async createWorkspace(payload: {
+    name: string
+    slug?: string
+  }): Promise<Workspace> {
+    const response = await this.client.post('/api/v1/workspaces', payload)
+    return response.data
+  }
+
+  async updateWorkspace(
+    workspaceId: string,
+    payload: { name?: string; is_active?: boolean },
+  ): Promise<Workspace> {
+    const response = await this.client.patch(
+      `/api/v1/workspaces/${workspaceId}`,
+      payload,
+    )
+    return response.data
+  }
+
+  async deleteWorkspace(workspaceId: string): Promise<void> {
+    await this.client.delete(`/api/v1/workspaces/${workspaceId}`)
+  }
+
+  async listCapabilities(): Promise<import('../types/api').CapabilityDomain[]> {
+    const response = await this.client.get('/api/v1/capabilities')
+    return response.data
+  }
+
+  async listWorkspaceRoles(): Promise<import('../types/api').WorkspaceRole[]> {
+    const response = await this.client.get('/api/v1/workspace-roles')
+    return response.data
+  }
+
+  async createWorkspaceRole(
+    payload: import('../types/api').WorkspaceRoleCreate,
+  ): Promise<import('../types/api').WorkspaceRole> {
+    const response = await this.client.post('/api/v1/workspace-roles', payload)
+    return response.data
+  }
+
+  async updateWorkspaceRole(
+    roleId: string,
+    payload: import('../types/api').WorkspaceRoleUpdate,
+  ): Promise<import('../types/api').WorkspaceRole> {
+    const response = await this.client.patch(
+      `/api/v1/workspace-roles/${roleId}`,
+      payload,
+    )
+    return response.data
+  }
+
+  async deleteWorkspaceRole(roleId: string): Promise<void> {
+    await this.client.delete(`/api/v1/workspace-roles/${roleId}`)
+  }
+
+  async listWorkspaceMembers(
+    workspaceId: string,
+  ): Promise<import('../types/api').WorkspaceMember[]> {
+    const response = await this.client.get(
+      `/api/v1/workspaces/${workspaceId}/members`,
+    )
+    return response.data
+  }
+
+  async addWorkspaceMember(
+    workspaceId: string,
+    payload: { user_id: string; role_id: string },
+  ): Promise<import('../types/api').WorkspaceMember> {
+    const response = await this.client.post(
+      `/api/v1/workspaces/${workspaceId}/members`,
+      payload,
+    )
+    return response.data
+  }
+
+  async updateWorkspaceMember(
+    workspaceId: string,
+    userId: string,
+    payload: { role_id: string },
+  ): Promise<import('../types/api').WorkspaceMember> {
+    const response = await this.client.patch(
+      `/api/v1/workspaces/${workspaceId}/members/${userId}`,
+      payload,
+    )
+    return response.data
+  }
+
+  async removeWorkspaceMember(
+    workspaceId: string,
+    userId: string,
+  ): Promise<void> {
+    await this.client.delete(
+      `/api/v1/workspaces/${workspaceId}/members/${userId}`,
+    )
+  }
+
+  // Auth endpoints
+  async getAuthConfig(): Promise<AuthConfigResponse> {
+    const response = await this.client.get('/api/v1/auth/config')
+    return response.data
+  }
+
+  async signup(data: {
+    email: string
+    password: string
+    organization_name?: string
+    first_name?: string
+    last_name?: string
+    reference_code?: string
+    invite_token?: string
+  }): Promise<TokenResponse> {
+    const response = await this.client.post('/api/v1/auth/signup', data)
+    return response.data
+  }
+
+  async previewInvitation(token: string): Promise<InvitationPreview> {
+    const response = await axios.get(`${API_BASE_URL}/api/v1/auth/invitations/preview/${encodeURIComponent(token)}`)
+    return response.data
+  }
+
+  async acceptInvitationByToken(token: string): Promise<TokenResponse> {
+    const response = await this.client.post('/api/v1/auth/invitations/accept-by-token', { token })
+    return response.data
+  }
+
+  private platformHeaders() {
+    const token = localStorage.getItem('platformAccessToken')
+    return token ? { Authorization: `Bearer ${token}` } : {}
+  }
+
+  async platformLogin(email: string, password: string): Promise<PlatformTokenResponse> {
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/platform/auth/login`,
+      { email, password },
+      { headers: { 'Content-Type': 'application/json' } },
+    )
+    return response.data
+  }
+
+  async platformLogout(accessToken?: string | null): Promise<{ success: boolean; admin_id: string }> {
+    const token = accessToken ?? localStorage.getItem('platformAccessToken')
+    const headers: Record<string, string> = { 'Content-Type': 'application/json' }
+    if (token) {
+      headers.Authorization = `Bearer ${token}`
+    }
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/platform/auth/logout`,
+      {},
+      { headers },
+    )
+    return response.data
+  }
+
+  /**
+   * Voluntary logout: revoke JWT blacklist + refresh token on the server.
+   * Uses raw axios so credentials survive after local session is cleared.
+   */
+  revokeUserSessionBestEffort(credentials: UserSessionCredentials): void {
+    if (!hasRevocableUserCredentials(credentials)) {
+      return
+    }
+    const auth = { accessToken: credentials.accessToken, apiKey: credentials.apiKey }
+    void this.revokeUserSession(credentials.refreshToken, auth)
+      .catch(() => this.revokeUserSession(credentials.refreshToken, auth))
+      .catch(() => {})
+  }
+
+  revokePlatformSessionBestEffort(accessToken?: string | null): void {
+    if (!accessToken) {
+      return
+    }
+    void this.platformLogout(accessToken)
+      .catch(() => this.platformLogout(accessToken))
+      .catch(() => {})
+  }
+
+  async getPlatformOrganizationStats(): Promise<PlatformOrganizationStats> {
+    const response = await axios.get(`${API_BASE_URL}/api/v1/platform/organizations/stats`, {
+      headers: this.platformHeaders(),
+    })
+    return response.data
+  }
+
+  async listPlatformOrganizations(params?: {
+    offset?: number
+    limit?: number
+    search?: string
+    is_active?: boolean
+  }): Promise<PlatformOrganizationListResponse> {
+    const response = await axios.get(`${API_BASE_URL}/api/v1/platform/organizations`, {
+      headers: this.platformHeaders(),
+      params,
+    })
+    return response.data
+  }
+
+  async updatePlatformOrganization(
+    orgId: string,
+    data: { is_active: boolean },
+  ): Promise<PlatformOrganizationItem> {
+    const response = await axios.patch(
+      `${API_BASE_URL}/api/v1/platform/organizations/${orgId}`,
+      data,
+      { headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' } },
+    )
+    return response.data
+  }
+
+  async listPlatformOrganizationUsers(
+    orgId: string,
+    params?: { role?: string },
+  ): Promise<PlatformOrgUser[]> {
+    const response = await axios.get(
+      `${API_BASE_URL}/api/v1/platform/organizations/${orgId}/users`,
+      { headers: this.platformHeaders(), params },
+    )
+    return response.data
+  }
+
+  async platformResetUserPassword(
+    orgId: string,
+    userId: string,
+    newPassword: string,
+  ): Promise<{ user_id: string; email: string; message: string }> {
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/platform/organizations/${orgId}/users/${userId}/reset-password`,
+      { new_password: newPassword },
+      { headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' } },
+    )
+    return response.data
+  }
+
+  async listPlatformSignupCodes(): Promise<PlatformSignupCode[]> {
+    const response = await axios.get(`${API_BASE_URL}/api/v1/platform/signup-codes`, {
+      headers: this.platformHeaders(),
+    })
+    return response.data
+  }
+
+  async createPlatformSignupCode(data: {
+    code: string
+    label?: string
+    max_uses?: number
+    expires_at?: string
+  }): Promise<PlatformSignupCode> {
+    const response = await axios.post(`${API_BASE_URL}/api/v1/platform/signup-codes`, data, {
+      headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' },
+    })
+    return response.data
+  }
+
+  async updatePlatformSignupCode(
+    codeId: string,
+    data: { is_active?: boolean; max_uses?: number; label?: string },
+  ): Promise<PlatformSignupCode> {
+    const response = await axios.patch(
+      `${API_BASE_URL}/api/v1/platform/signup-codes/${codeId}`,
+      data,
+      { headers: { ...this.platformHeaders(), 'Content-Type': 'application/json' } },
+    )
+    return response.data
+  }
+
+  async deactivatePlatformSignupCode(codeId: string): Promise<PlatformSignupCode> {
+    const response = await axios.delete(
+      `${API_BASE_URL}/api/v1/platform/signup-codes/${codeId}`,
+      { headers: this.platformHeaders() },
+    )
+    return response.data
+  }
+
+  async loginWithPassword(
+    email: string,
+    password: string,
+    organizationId?: string,
+    inviteToken?: string,
+  ): Promise<LoginResponse> {
+    const response = await this.client.post('/api/v1/auth/login', {
+      email,
+      password,
+      ...(organizationId ? { organization_id: organizationId } : {}),
+      ...(inviteToken ? { invite_token: inviteToken } : {}),
+    })
+    return response.data
+  }
+
+  async logout(
+    refreshToken?: string | null,
+    credentials?: { accessToken?: string | null; apiKey?: string | null },
+  ): Promise<{ success: boolean; auth_method: string }> {
+    return this.revokeUserSession(refreshToken, credentials)
+  }
+
+  private async revokeUserSession(
+    refreshToken?: string | null,
+    credentials?: { accessToken?: string | null; apiKey?: string | null },
+  ): Promise<{ success: boolean; auth_method: string }> {
+    const headers: Record<string, string> = { 'Content-Type': 'application/json' }
+    const accessToken = credentials?.accessToken ?? localStorage.getItem('accessToken')
+    const apiKey = credentials?.apiKey ?? localStorage.getItem('apiKey')
+    if (accessToken) {
+      headers.Authorization = `Bearer ${accessToken}`
+    } else if (apiKey) {
+      headers['X-API-Key'] = apiKey
+    }
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/auth/logout`,
+      {
+        refresh_token: refreshToken ?? localStorage.getItem('refreshToken') ?? undefined,
+      },
+      { headers },
+    )
+    return response.data
+  }
+
+  async refreshSession(refreshToken: string): Promise<TokenResponse> {
+    const response = await axios.post(
+      `${API_BASE_URL}/api/v1/auth/refresh`,
+      { refresh_token: refreshToken },
+      { headers: { 'Content-Type': 'application/json' } },
+    )
+    return response.data
+  }
+
+  async getMe(): Promise<AuthUserSummary> {
+    const response = await this.client.get('/api/v1/auth/me')
+    return response.data
+  }
+
+  async switchOrganization(organizationId: string): Promise<TokenResponse> {
+    const response = await this.client.post('/api/v1/auth/switch-org', {
+      organization_id: organizationId,
+      refresh_token: localStorage.getItem('refreshToken') || undefined,
+    })
+    return response.data
+  }
+
+  async setPassword(data: {
+    new_password: string
+    current_password?: string
+    email?: string
+  }): Promise<AuthUserSummary> {
+    const response = await this.client.post('/api/v1/auth/password', data)
+    return response.data
+  }
+
+  async generateApiKey(name?: string): Promise<APIKey> {
+    const response = await this.client.post('/api/v1/auth/generate-key', { name })
+    return response.data
+  }
+
+  async validateApiKey(): Promise<{ valid: boolean; message: string }> {
+    const response = await this.client.post('/api/v1/auth/validate')
+    return response.data
+  }
+
+  // Settings / API Key Management endpoints
+  async listApiKeys(): Promise<any[]> {
+    const response = await this.client.get('/api/v1/settings/api-keys')
+    return response.data
+  }
+
+  async createApiKey(name?: string): Promise<any> {
+    const response = await this.client.post('/api/v1/settings/api-keys', { name })
+    return response.data
+  }
+
+  async deleteApiKey(keyId: string): Promise<void> {
+    await this.client.delete(`/api/v1/settings/api-keys/${keyId}`)
+  }
+
+  async regenerateApiKey(keyId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/settings/api-keys/${keyId}/regenerate`)
+    return response.data
+  }
+
+  // Audio endpoints
+  async uploadAudio(file: File): Promise<AudioFile> {
+    const formData = new FormData()
+    formData.append('file', file)
+    const response = await this.client.post('/api/v1/audio/upload', formData, {
+      headers: {
+        'Content-Type': 'multipart/form-data',
+      },
+    })
+    return response.data
+  }
+
+  async getAudio(audioId: string): Promise<AudioFile> {
+    const response = await this.client.get(`/api/v1/audio/${audioId}`)
+    return response.data
+  }
+
+  async listAudio(skip = 0, limit = 100): Promise<AudioFile[]> {
+    const response = await this.client.get('/api/v1/audio', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async deleteAudio(audioId: string): Promise<MessageResponse> {
+    const response = await this.client.delete(`/api/v1/audio/${audioId}`)
+    return response.data
+  }
+
+  async downloadAudio(audioId: string): Promise<Blob> {
+    const response = await this.client.get(`/api/v1/audio/${audioId}/download`, {
+      responseType: 'blob',
+    })
+    return response.data
+  }
+
+  // Evaluation endpoints
+  async createEvaluation(data: EvaluationCreate): Promise<Evaluation> {
+    const response = await this.client.post('/api/v1/evaluations/create', data)
+    return response.data
+  }
+
+  async getEvaluation(evaluationId: string): Promise<Evaluation> {
+    const response = await this.client.get(`/api/v1/evaluations/${evaluationId}`)
+    return response.data
+  }
+
+  async listEvaluations(
+    skip = 0,
+    limit = 100,
+    status?: EvaluationStatus
+  ): Promise<Evaluation[]> {
+    const response = await this.client.get('/api/v1/evaluations', {
+      params: { skip, limit, status },
+    })
+    return response.data
+  }
+
+  async cancelEvaluation(evaluationId: string): Promise<MessageResponse> {
+    const response = await this.client.post(`/api/v1/evaluations/${evaluationId}/cancel`)
+    return response.data
+  }
+
+  async deleteEvaluation(evaluationId: string): Promise<MessageResponse> {
+    const response = await this.client.delete(`/api/v1/evaluations/${evaluationId}`)
+    return response.data
+  }
+
+  // Results endpoints
+  async getEvaluationResult(evaluationId: string): Promise<EvaluationResult> {
+    const response = await this.client.get(`/api/v1/results/${evaluationId}`)
+    return response.data
+  }
+
+  async getMetrics(evaluationId: string): Promise<{
+    evaluation_id: string
+    metrics: Record<string, any>
+    processing_time?: number | null
+  }> {
+    const response = await this.client.get(`/api/v1/results/${evaluationId}/metrics`)
+    return response.data
+  }
+
+  async getTranscript(evaluationId: string): Promise<{
+    evaluation_id: string
+    transcript: string
+  }> {
+    const response = await this.client.get(`/api/v1/results/${evaluationId}/transcript`)
+    return response.data
+  }
+
+  async compareEvaluations(evaluationIds: string[]): Promise<{
+    evaluations: EvaluationResult[]
+    comparison_metrics: Record<string, any>
+  }> {
+    const response = await this.client.post('/api/v1/results/compare', {
+      evaluation_ids: evaluationIds,
+    })
+    return response.data
+  }
+
+  // Agents endpoints
+  async createAgent(data: {
+    name: string
+    phone_number?: string
+    telephony_phone_number_id?: string
+    language: string
+    description?: string | null
+    call_type: string
+    call_medium: string
+    voice_bundle_id?: string
+    ai_provider_id?: string
+    voice_ai_integration_id?: string
+    voice_ai_agent_id?: string
+    provider_prompt?: string | null
+    silence_hangup_secs?: number
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/agents', data)
+    return response.data
+  }
+
+  async listAgents(skip = 0, limit = 100): Promise<any[]> {
+    const response = await this.client.get('/api/v1/agents', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getAgent(agentId: string): Promise<TestAgent> {
+    const response = await this.client.get(`/api/v1/agents/${agentId}`)
+    return response.data
+  }
+
+  async checkAgentPhoneAssignment(params: {
+    phoneNumber?: string
+    telephonyPhoneNumberId?: string
+    excludeAgentId?: string
+  }): Promise<import('../types/api').AgentPhoneAssignmentCheckResponse> {
+    const response = await this.client.get('/api/v1/agents/check-phone-assignment', {
+      params: {
+        phone_number: params.phoneNumber,
+        telephony_phone_number_id: params.telephonyPhoneNumberId,
+        exclude_agent_id: params.excludeAgentId,
+      },
+    })
+    return response.data
+  }
+
+  async updateAgent(agentId: string, data: {
+    name?: string
+    phone_number?: string | null
+    telephony_phone_number_id?: string | null
+    language?: string
+    description?: string | null
+    call_type?: string
+    call_medium?: string
+    silence_hangup_secs?: number
+    voice_bundle_id?: string | null
+    ai_provider_id?: string
+    voice_ai_integration_id?: string | null
+    voice_ai_agent_id?: string | null
+    provider_prompt?: string | null
+    prompt_variables?: Record<string, string>
+  }): Promise<TestAgent> {
+    const response = await this.client.put(`/api/v1/agents/${agentId}`, data)
+    return response.data
+  }
+
+  async deleteAgent(agentId: string, force?: boolean): Promise<any> {
+    const response = await this.client.delete(`/api/v1/agents/${agentId}`, {
+      params: force ? { force: true } : undefined,
+    })
+    return response.data
+  }
+
+  async getAgentDeleteImpact(agentId: string): Promise<{
+    agent_id: string
+    agent_name: string
+    dependencies: Record<string, number>
+    can_delete_without_force: boolean
+  }> {
+    const response = await this.client.get(`/api/v1/agents/${agentId}/delete-impact`)
+    return response.data
+  }
+
+  async generateAgentDescription(data: {
+    description: string
+    tone?: string
+    format_style?: string
+    provider?: string
+    model?: string
+    agent_id?: string
+    include_linked_scenarios?: boolean
+    append_scenarios_to_output?: boolean
+  }): Promise<{ content: string; provider: string; model: string }> {
+    const response = await this.client.post('/api/v1/agents/generate-description', data)
+    return response.data
+  }
+
+  async generateTestPromptFromProduction(
+    data: GenerateTestPromptParams,
+  ): Promise<{
+    sections: Array<{ key: string; title: string; content: string }>
+    test_agent_prompt: string
+    provider: string
+    model: string
+  }> {
+    const response = await this.client.post('/api/v1/agents/generate-test-prompt', data)
+    return response.data
+  }
+
+  async generateScenariosFromPrompt(
+    data: GenerateScenariosFromPromptParams,
+  ): Promise<{
+    scenarios: Array<{ name: string; description: string; goal?: string | null }>
+    provider: string
+    model: string
+  }> {
+    const response = await this.client.post('/api/v1/agents/generate-scenarios-from-prompt', data)
+    return response.data
+  }
+
+  async generateTestSetupFromProduction(
+    data: GenerateTestSetupParams,
+  ): Promise<{
+    sections: Array<{ key: string; title: string; content: string }>
+    test_agent_prompt: string
+    scenarios: Array<{ name: string; description: string; goal?: string | null }>
+    provider: string
+    model: string
+  }> {
+    const response = await this.client.post('/api/v1/agents/generate-test-setup', data)
+    return response.data
+  }
+
+  // Personas endpoints
+  async listPersonas(skip = 0, limit = 100): Promise<any[]> {
+    const response = await this.client.get('/api/v1/personas', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getPersona(personaId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/personas/${personaId}`)
+    return response.data
+  }
+
+  async createPersona(data: {
+    name: string
+    gender: string
+    tts_provider?: string
+    tts_voice_id?: string
+    tts_voice_name?: string
+    is_custom?: boolean
+    description?: string
+    tts_config?: Record<string, unknown>
+    llm_temperature?: number
+    llm_max_tokens?: number
+    response_delay_ms?: number
+    max_turns?: number
+    allow_interruptions?: boolean
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/personas', data)
+    return response.data
+  }
+
+  async updatePersona(personaId: string, data: any): Promise<any> {
+    const response = await this.client.put(`/api/v1/personas/${personaId}`, data)
+    return response.data
+  }
+
+  async deletePersona(personaId: string, force?: boolean): Promise<any> {
+    const response = await this.client.delete(`/api/v1/personas/${personaId}`, {
+      params: force ? { force: true } : undefined,
+    })
+    return response.data
+  }
+
+  async clonePersona(personaId: string, name?: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/personas/${personaId}/clone`, { name })
+    return response.data
+  }
+
+  async seedDemoData(): Promise<any> {
+    const response = await this.client.post('/api/v1/personas/seed-data')
+    return response.data
+  }
+
+  async getPersonaAgentPromptSources(agentId: string): Promise<{
+    agent_id: string
+    agent_name: string
+    test_agent_prompt: string
+    agent_prompt: string
+  }> {
+    const response = await this.client.get(`/api/v1/personas/agent-prompt-sources/${agentId}`)
+    return response.data
+  }
+
+  async generatePersonaPrompt(data: {
+    agent_id: string
+    source?: 'test_agent' | 'agent' | 'auto'
+    persona_name?: string
+    persona_gender?: string
+    additional_context?: string
+    provider?: string
+    model?: string
+    credential_id?: string
+    llm_config?: LLMGenerationConfig | null
+  }): Promise<{
+    persona_prompt: string
+    source_used: string
+    provider: string
+    model: string
+  }> {
+    const response = await this.client.post('/api/v1/personas/generate-prompt', data)
+    return response.data
+  }
+
+  // Persona voice options (built-in + custom voices, ungated)
+  async getPersonaVoiceOptions(provider?: string): Promise<{
+    providers: Array<{
+      id: string
+      name: string
+      voices: Array<{
+        id: string
+        name: string
+        gender: string
+        is_custom: boolean
+        custom_voice_id?: string
+        description?: string | null
+      }>
+    }>
+  }> {
+    const response = await this.client.get('/api/v1/personas/voice-options', {
+      params: provider ? { provider } : undefined,
+    })
+    return response.data
+  }
+
+  // Custom voice CRUD (persona-scoped, ungated)
+  async listPersonaCustomVoices(provider?: string): Promise<any[]> {
+    const response = await this.client.get('/api/v1/personas/custom-voices', {
+      params: provider ? { provider } : undefined,
+    })
+    return response.data
+  }
+
+  async createPersonaCustomVoice(data: {
+    provider: string
+    voice_id: string
+    name: string
+    gender?: string
+    description?: string
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/personas/custom-voices', data)
+    return response.data
+  }
+
+  async updatePersonaCustomVoice(customVoiceId: string, data: {
+    voice_id?: string
+    name?: string
+    gender?: string
+    description?: string
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/personas/custom-voices/${customVoiceId}`, data)
+    return response.data
+  }
+
+  async deletePersonaCustomVoice(customVoiceId: string): Promise<any> {
+    const response = await this.client.delete(`/api/v1/personas/custom-voices/${customVoiceId}`)
+    return response.data
+  }
+
+  // Scenarios endpoints
+  async listScenarios(skip = 0, limit = 100, agentId?: string): Promise<any[]> {
+    const response = await this.client.get('/api/v1/scenarios', {
+      params: { skip, limit, ...(agentId ? { agent_id: agentId } : {}) },
+    })
+    return response.data
+  }
+
+  async getScenario(scenarioId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/scenarios/${scenarioId}`)
+    return response.data
+  }
+
+  async createScenario(data: {
+    name: string
+    agent_id?: string | null
+    description?: string | null
+    required_info: Record<string, string>
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/scenarios', data)
+    return response.data
+  }
+
+  async updateScenario(scenarioId: string, data: {
+    name?: string
+    agent_id?: string | null
+    description?: string | null
+    required_info?: Record<string, string>
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/scenarios/${scenarioId}`, data)
+    return response.data
+  }
+
+  async deleteScenario(scenarioId: string, force?: boolean): Promise<any> {
+    const response = await this.client.delete(`/api/v1/scenarios/${scenarioId}`, {
+      params: force ? { force: true } : undefined,
+    })
+    return response.data
+  }
+
+  // Chat/Inference endpoints
+  async chatCompletion(data: {
+    messages: Array<{ role: string; content: string }>
+    provider: string
+    model: string
+    temperature?: number
+    max_tokens?: number
+    llm_config?: LLMGenerationConfig | null
+  }): Promise<{
+    text: string
+    model: string
+    usage?: { prompt_tokens?: number; completion_tokens?: number; total_tokens?: number }
+    processing_time?: number
+  }> {
+    const response = await this.client.post('/api/v1/chat/completion', data)
+    return response.data
+  }
+
+  // VoiceBundle endpoints
+  async listVoiceBundles(skip = 0, limit = 100): Promise<any[]> {
+    const response = await this.client.get('/api/v1/voicebundles', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getVoiceBundle(voicebundleId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/voicebundles/${voicebundleId}`)
+    return response.data
+  }
+
+  async createVoiceBundle(data: any): Promise<any> {
+    const response = await this.client.post('/api/v1/voicebundles', data)
+    return response.data
+  }
+
+  async updateVoiceBundle(voicebundleId: string, data: any): Promise<any> {
+    const response = await this.client.put(`/api/v1/voicebundles/${voicebundleId}`, data)
+    return response.data
+  }
+
+  async deleteVoiceBundle(voicebundleId: string, force?: boolean): Promise<any> {
+    const response = await this.client.delete(`/api/v1/voicebundles/${voicebundleId}`, {
+      params: force ? { force: true } : undefined,
+    })
+    return response.data
+  }
+
+  // AI Provider endpoints
+  async listAIProviders(): Promise<any[]> {
+    const response = await this.client.get('/api/v1/aiproviders')
+    return response.data
+  }
+
+  async getAIProvider(aiproviderId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/aiproviders/${aiproviderId}`)
+    return response.data
+  }
+
+  async createAIProvider(data: any): Promise<any> {
+    const response = await this.client.post('/api/v1/aiproviders', data)
+    return response.data
+  }
+
+  async updateAIProvider(aiproviderId: string, data: any): Promise<any> {
+    const response = await this.client.put(`/api/v1/aiproviders/${aiproviderId}`, data)
+    return response.data
+  }
+
+  async deleteAIProvider(aiproviderId: string): Promise<void> {
+    await this.client.delete(`/api/v1/aiproviders/${aiproviderId}`)
+  }
+
+  async setDefaultAIProvider(aiproviderId: string): Promise<any> {
+    const response = await this.client.post(
+      `/api/v1/aiproviders/${aiproviderId}/set-default`,
+    )
+    return response.data
+  }
+
+  async testAIProvider(aiproviderId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/aiproviders/${aiproviderId}/test`)
+    return response.data
+  }
+
+  async getLLMGatewaySettings(): Promise<LLMGatewaySettings> {
+    const response = await this.client.get('/api/v1/organizations/llm-gateway')
+    return response.data
+  }
+
+  async updateLLMGatewaySettings(data: LLMGatewaySettingsUpdate): Promise<LLMGatewaySettings> {
+    const response = await this.client.put('/api/v1/organizations/llm-gateway', data)
+    return response.data
+  }
+
+  // IAM endpoints
+  async getOrganization(): Promise<OrganizationSummary> {
+    const response = await this.client.get('/api/v1/iam/organization')
+    return response.data
+  }
+
+  async updateOrganization(data: { name: string }): Promise<OrganizationSummary> {
+    const response = await this.client.patch('/api/v1/iam/organization', data)
+    return response.data
+  }
+
+  async listOrganizationUsers(): Promise<OrganizationMember[]> {
+    const response = await this.client.get('/api/v1/iam/users')
+    return response.data
+  }
+
+  async inviteUser(data: InvitationCreate): Promise<Invitation> {
+    const response = await this.client.post('/api/v1/iam/invitations', data)
+    return response.data
+  }
+
+  async listInvitations(): Promise<Invitation[]> {
+    const response = await this.client.get('/api/v1/iam/invitations')
+    return response.data
+  }
+
+  async updateUserRole(userId: string, role: Role): Promise<OrganizationMember> {
+    const response = await this.client.put(`/api/v1/iam/users/${userId}/role`, { role })
+    return response.data
+  }
+
+  async removeUser(userId: string): Promise<void> {
+    await this.client.delete(`/api/v1/iam/users/${userId}`)
+  }
+
+  async cancelInvitation(invitationId: string): Promise<void> {
+    await this.client.delete(`/api/v1/iam/invitations/${invitationId}`)
+  }
+
+  /**
+   * Admin-initiated password reset for another organization member.
+   *
+   * The caller must be an ADMIN of the same organization as `userId`. The
+   * backend ignores any current-password concept here (that's for the
+   * self-service `/auth/password` endpoint). Communicate the new password
+   * to the user out-of-band.
+   */
+  async adminResetUserPassword(
+    userId: string,
+    newPassword: string,
+  ): Promise<{ user_id: string; email: string; message: string }> {
+    const response = await this.client.post(
+      `/api/v1/iam/users/${userId}/reset-password`,
+      { new_password: newPassword },
+    )
+    return response.data
+  }
+
+  // Profile endpoints
+  async getProfile(): Promise<Profile> {
+    const response = await this.client.get('/api/v1/profile')
+    return response.data
+  }
+
+  async updateProfile(data: UserUpdate): Promise<Profile> {
+    const response = await this.client.put('/api/v1/profile', data)
+    return response.data
+  }
+
+  async getMyInvitations(): Promise<Invitation[]> {
+    const response = await this.client.get('/api/v1/profile/invitations')
+    return response.data
+  }
+
+  async acceptInvitation(invitationId: string): Promise<MessageResponse> {
+    const response = await this.client.post(`/api/v1/profile/invitations/${invitationId}/accept`)
+    return response.data
+  }
+
+  async declineInvitation(invitationId: string): Promise<MessageResponse> {
+    const response = await this.client.post(`/api/v1/profile/invitations/${invitationId}/decline`)
+    return response.data
+  }
+
+  // User Preferences endpoints
+  async getUserPreferences(): Promise<UserPreferences> {
+    const response = await this.client.get('/api/v1/profile/preferences')
+    return response.data
+  }
+
+  async updateUserPreferences(data: UserPreferencesUpdate): Promise<UserPreferences> {
+    const response = await this.client.put('/api/v1/profile/preferences', data)
+    return response.data
+  }
+
+  // Integration endpoints
+  async listIntegrations(): Promise<Integration[]> {
+    const response = await this.client.get('/api/v1/integrations')
+    return response.data
+  }
+
+  async getIntegration(integrationId: string): Promise<Integration> {
+    const response = await this.client.get(`/api/v1/integrations/${integrationId}`)
+    return response.data
+  }
+
+  async previewIntegrationAgentPrompt(
+    integrationId: string,
+    voiceAiAgentId: string,
+  ): Promise<{ provider_prompt: string }> {
+    const response = await this.client.post(
+      `/api/v1/integrations/${integrationId}/preview-agent-prompt`,
+      { voice_ai_agent_id: voiceAiAgentId },
+    )
+    return response.data
+  }
+
+  async createIntegration(data: IntegrationCreate): Promise<Integration> {
+    const response = await this.client.post('/api/v1/integrations', data)
+    return response.data
+  }
+
+  async updateIntegration(integrationId: string, data: Partial<IntegrationCreate>): Promise<Integration> {
+    const response = await this.client.put(`/api/v1/integrations/${integrationId}`, data)
+    return response.data
+  }
+
+  async deleteIntegration(integrationId: string, force?: boolean): Promise<any> {
+    const response = await this.client.delete(`/api/v1/integrations/${integrationId}`, {
+      params: force ? { force: true } : undefined,
+    })
+    return response.data
+  }
+
+  async setDefaultIntegration(integrationId: string): Promise<Integration> {
+    const response = await this.client.post(
+      `/api/v1/integrations/${integrationId}/set-default`,
+    )
+    return response.data
+  }
+
+  async getIntegrationApiKey(integrationId: string): Promise<{ api_key: string; public_key?: string | null }> {
+    const response = await this.client.get(`/api/v1/integrations/${integrationId}/api-key`)
+    return response.data
+  }
+
+  // Telephony endpoints (provider-agnostic)
+  async createTelephonyConfig(data: TelephonyIntegrationCreatePayload): Promise<TelephonyIntegrationResponse> {
+    const response = await this.client.post('/api/v1/telephony/config', data)
+    return response.data
+  }
+
+  async getTelephonyConfig(provider: string = 'plivo'): Promise<TelephonyIntegrationResponse> {
+    const response = await this.client.get('/api/v1/telephony/config', { params: { provider } })
+    return response.data
+  }
+
+  async listTelephonyConfigs(provider?: string): Promise<TelephonyIntegrationResponse[]> {
+    const response = await this.client.get('/api/v1/telephony/configs', {
+      params: provider ? { provider } : undefined,
+    })
+    return response.data
+  }
+
+  async updateTelephonyConfig(data: TelephonyIntegrationUpdatePayload): Promise<TelephonyIntegrationResponse> {
+    const response = await this.client.put('/api/v1/telephony/config', data)
+    return response.data
+  }
+
+  async setDefaultTelephonyConfig(integrationId: string): Promise<TelephonyIntegrationResponse> {
+    const response = await this.client.post(
+      `/api/v1/telephony/config/${integrationId}/set-default`,
+    )
+    return response.data
+  }
+
+  async deleteTelephonyConfig(integrationId: string): Promise<void> {
+    await this.client.delete(`/api/v1/telephony/config/${integrationId}`)
+  }
+
+  async listTelephonyNumbers(provider?: string): Promise<TelephonyPhoneNumberResponse[]> {
+    const response = await this.client.get('/api/v1/telephony/numbers', {
+      params: provider ? { provider } : undefined,
+    })
+    return response.data
+  }
+
+  async listAvailableTelephonyNumbers(
+    provider: string,
+    credentialId?: string,
+  ): Promise<TelephonyAvailableNumber[]> {
+    const response = await this.client.get('/api/v1/telephony/numbers/available', {
+      params: {
+        provider,
+        ...(credentialId ? { credential_id: credentialId } : {}),
+      },
+    })
+    return response.data
+  }
+
+  async importTelephonyNumbers(
+    provider: string,
+    numbers: string[],
+    agentId?: string,
+    credentialId?: string,
+  ): Promise<TelephonyImportNumbersResponse> {
+    const response = await this.client.post('/api/v1/telephony/numbers/import', {
+      provider,
+      numbers,
+      agent_id: agentId || undefined,
+      credential_id: credentialId || undefined,
+    })
+    return response.data
+  }
+
+  async listVobizAvailableNumbers(): Promise<TelephonyAvailableNumber[]> {
+    return this.listAvailableTelephonyNumbers('vobiz')
+  }
+
+  async importVobizNumbers(numbers: string[], agentId?: string): Promise<TelephonyImportNumbersResponse> {
+    return this.importTelephonyNumbers('vobiz', numbers, agentId)
+  }
+
+  async deleteTelephonyNumber(numberId: string): Promise<void> {
+    await this.client.delete(`/api/v1/telephony/numbers/${numberId}`)
+  }
+
+  async deleteImportedVobizNumber(numberId: string): Promise<void> {
+    await this.deleteTelephonyNumber(numberId)
+  }
+
+  async listTelephonyOutboundPool(): Promise<PlatformOutboundPoolResponse> {
+    const response = await this.client.get('/api/v1/telephony/outbound-pool')
+    return response.data
+  }
+
+  async listVobizOutboundPool(): Promise<PlatformOutboundPoolResponse> {
+    return this.listTelephonyOutboundPool()
+  }
+
+  async listTelephonyDialTargets(): Promise<TelephonyDialTargetResponse[]> {
+    const response = await this.client.get('/api/v1/telephony/dial-targets')
+    return response.data
+  }
+
+  async createVobizOutboundCall(data: VobizOutboundCallRequest): Promise<VobizOutboundCallResponse> {
+    const response = await this.client.post('/api/v1/telephony/vobiz/calls/outbound', data)
+    return response.data
+  }
+
+  async listDialTargets(): Promise<TelephonyDialTargetResponse[]> {
+    const response = await this.client.get('/api/v1/telephony/dial-targets')
+    return response.data
+  }
+
+  async createDialTarget(data: { phone_number: string; label?: string }): Promise<TelephonyDialTargetResponse> {
+    const response = await this.client.post('/api/v1/telephony/dial-targets', data)
+    return response.data
+  }
+
+  async updateDialTarget(
+    targetId: string,
+    data: { phone_number?: string; label?: string },
+  ): Promise<TelephonyDialTargetResponse> {
+    const response = await this.client.put(`/api/v1/telephony/dial-targets/${targetId}`, data)
+    return response.data
+  }
+
+  async deleteDialTarget(targetId: string): Promise<void> {
+    await this.client.delete(`/api/v1/telephony/dial-targets/${targetId}`)
+  }
+
+  // Data Sources endpoints
+  async testS3Connection(): Promise<S3ConnectionTestResponse> {
+    const response = await this.client.post('/api/v1/data-sources/s3/test')
+    return response.data
+  }
+
+  async listS3Files(prefix?: string, maxKeys = 1000): Promise<S3ListFilesResponse> {
+    const response = await this.client.get('/api/v1/data-sources/s3/files', {
+      params: { prefix, max_keys: maxKeys },
+    })
+    return response.data
+  }
+
+  async browseS3(path = '', maxKeys = 1000): Promise<S3BrowseResponse> {
+    const response = await this.client.get('/api/v1/data-sources/s3/browse', {
+      params: { path, max_keys: maxKeys },
+    })
+    return response.data
+  }
+
+  async uploadToS3(file: File, customFilename?: string): Promise<MessageResponse> {
+    const formData = new FormData()
+    formData.append('file', file)
+    if (customFilename) {
+      formData.append('filename', customFilename)
+    }
+    const response = await this.client.post('/api/v1/data-sources/s3/upload', formData, {
+      headers: {
+        'Content-Type': 'multipart/form-data',
+      },
+    })
+    return response.data
+  }
+
+  async getS3Status(): Promise<S3Status> {
+    const response = await this.client.get('/api/v1/data-sources/s3/status')
+    return response.data
+  }
+
+  async downloadFromS3(fileKey: string): Promise<void> {
+    const response = await this.client.get(`/api/v1/data-sources/s3/files/${encodeURIComponent(fileKey)}/download`, {
+      responseType: 'blob',
+    })
+    // Create a blob URL and trigger download
+    const url = window.URL.createObjectURL(new Blob([response.data]))
+    const link = document.createElement('a')
+    link.href = url
+    link.setAttribute('download', fileKey.split('/').pop() || 'file')
+    document.body.appendChild(link)
+    link.click()
+    link.remove()
+    window.URL.revokeObjectURL(url)
+  }
+
+  async getS3PresignedUrl(fileKey: string, expiration: number = 3600): Promise<{ url: string; expires_in: number }> {
+    const response = await this.client.get(`/api/v1/data-sources/s3/files/${encodeURIComponent(fileKey)}/presigned-url`, {
+      params: { expiration },
+    })
+    return response.data
+  }
+
+  async deleteFromS3(fileKey: string): Promise<MessageResponse> {
+    const response = await this.client.delete(`/api/v1/data-sources/s3/files/${encodeURIComponent(fileKey)}`)
+    return response.data
+  }
+
+  // Call Imports endpoints
+
+  /**
+   * Inspect an uploaded CSV / Excel file and get its sheets + headers.
+   *
+   * Drives the column-mapping UI without forcing the frontend to parse
+   * CSV / xlsx itself. CSV files come back as a single synthetic sheet;
+   * Excel workbooks come back with one entry per worksheet.
+   */
+  async previewCallImportFile(file: File): Promise<CallImportPreviewResponse> {
+    const formData = new FormData()
+    formData.append('file', file)
+    const response = await this.client.post(
+      '/api/v1/call-imports/preview',
+      formData,
+      { headers: { 'Content-Type': 'multipart/form-data' } },
+    )
+    return response.data
+  }
+
+  async uploadCallImport(
+    file: File,
+    options: {
+      provider: string
+      telephonyIntegrationId: string
+      /** Reusable Input Parameter schema this upload is mapped against. */
+      schemaId: string
+      /**
+       * Schema-driven mapping: ``{parameter_name: csv_header}``. Must
+       * include every required parameter on the schema. Pass an empty
+       * string / omit a parameter to leave it unmapped (only allowed
+       * for optional parameters).
+       */
+      parameterMapping: Record<string, string>
+      /**
+       * CSV/Excel headers the user has explicitly skipped. Every source
+       * column must either appear in ``parameterMapping`` or here, or
+       * the backend rejects the upload with 400.
+       */
+      skippedColumns?: string[]
+      dataset?: string | null
+      tagIds?: string[]
+      /**
+       * Worksheet name to import when the file is an Excel workbook.
+       * Required for .xlsx / .xlsm uploads; must be left undefined / null
+       * for CSV uploads (the backend rejects a non-empty value for CSV).
+       */
+      sheetName?: string | null
+    }
+  ): Promise<CallImportUploadResponse> {
+    const formData = new FormData()
+    formData.append('file', file)
+    formData.append('provider', options.provider)
+    formData.append('telephony_integration_id', options.telephonyIntegrationId)
+    formData.append('schema_id', options.schemaId)
+    formData.append('parameter_mapping', JSON.stringify(options.parameterMapping))
+    formData.append('skipped_columns', JSON.stringify(options.skippedColumns || []))
+    if (options.dataset !== undefined && options.dataset !== null) {
+      formData.append('dataset', options.dataset)
+    }
+    if (options.tagIds && options.tagIds.length > 0) {
+      for (const tagId of options.tagIds) {
+        formData.append('tag_ids', tagId)
+      }
+    }
+    if (options.sheetName) {
+      formData.append('sheet_name', options.sheetName)
+    }
+    const response = await this.client.post('/api/v1/call-imports/upload', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    })
+    return response.data
+  }
+
+  /**
+   * UPLOAD stage of the staged call-import flow.
+   *
+   * Persists the CSV / Excel file to S3, captures a sheets snapshot,
+   * and creates a ``CallImport`` row with ``status='uploaded'``. The
+   * returned record has no mapping / provider / rows yet — the caller
+   * follows up with {@link updateCallImportMapping} and
+   * {@link startCallImport} to advance the batch.
+   */
+  async createCallImport(
+    file: File,
+    options: {
+      dataset: string
+      tagIds?: string[]
+      /**
+       * Optional schema pre-pick. The user can still change it during
+       * the MAP stage; provided here only so the detail page can pre-
+       * select the schema dropdown.
+       */
+      schemaId?: string | null
+    },
+  ): Promise<CallImport> {
+    const formData = new FormData()
+    formData.append('file', file)
+    formData.append('dataset', options.dataset)
+    if (options.tagIds && options.tagIds.length > 0) {
+      for (const tagId of options.tagIds) {
+        formData.append('tag_ids', tagId)
+      }
+    }
+    if (options.schemaId) {
+      formData.append('schema_id', options.schemaId)
+    }
+    const response = await this.client.post('/api/v1/call-imports', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    })
+    return response.data
+  }
+
+  async uploadCallImportAudio(
+    files: File[],
+    options: {
+      dataset: string
+      tagIds?: string[]
+      batchName?: string
+    },
+  ): Promise<CallImportUploadResponse> {
+    const formData = new FormData()
+    for (const file of files) {
+      formData.append('files', file)
+    }
+    formData.append('dataset', options.dataset)
+    if (options.batchName?.trim()) {
+      formData.append('batch_name', options.batchName.trim())
+    }
+    if (options.tagIds && options.tagIds.length > 0) {
+      for (const tagId of options.tagIds) {
+        formData.append('tag_ids', tagId)
+      }
+    }
+    const response = await this.client.post('/api/v1/call-imports/audio-upload', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    })
+    return response.data
+  }
+
+  async appendCallImportAudio(
+    importId: string,
+    files: File[],
+  ): Promise<CallImportUploadResponse> {
+    const formData = new FormData()
+    for (const file of files) {
+      formData.append('files', file)
+    }
+    const response = await this.client.post(
+      `/api/v1/call-imports/${importId}/audio-append`,
+      formData,
+      {
+        headers: { 'Content-Type': 'multipart/form-data' },
+      },
+    )
+    return response.data
+  }
+
+  async uploadCallImportAudioChunked(
+    files: File[],
+    options: {
+      dataset: string
+      tagIds?: string[]
+      batchName?: string
+      onProgress?: (progress: CallImportAudioUploadProgress) => void
+    },
+  ): Promise<CallImportUploadResponse> {
+    if (files.length === 0) {
+      throw new Error('At least one audio file is required.')
+    }
+
+    const chunks: File[][] = []
+    for (let i = 0; i < files.length; i += AUDIO_UPLOAD_CHUNK_SIZE) {
+      chunks.push(files.slice(i, i + AUDIO_UPLOAD_CHUNK_SIZE))
+    }
+
+    let result: CallImportUploadResponse | null = null
+    for (let chunkIndex = 0; chunkIndex < chunks.length; chunkIndex += 1) {
+      const chunk = chunks[chunkIndex]
+      options.onProgress?.({
+        chunkIndex: chunkIndex + 1,
+        totalChunks: chunks.length,
+        uploadedFiles: chunkIndex * AUDIO_UPLOAD_CHUNK_SIZE,
+        totalFiles: files.length,
+      })
+
+      if (chunkIndex === 0) {
+        result = await this.uploadCallImportAudio(chunk, {
+          dataset: options.dataset,
+          tagIds: options.tagIds,
+          batchName: options.batchName,
+        })
+      } else {
+        result = await this.appendCallImportAudio(result!.id, chunk)
+      }
+
+      options.onProgress?.({
+        chunkIndex: chunkIndex + 1,
+        totalChunks: chunks.length,
+        uploadedFiles: Math.min(
+          (chunkIndex + 1) * AUDIO_UPLOAD_CHUNK_SIZE,
+          files.length,
+        ),
+        totalFiles: files.length,
+      })
+    }
+
+    return result!
+  }
+
+  async uploadCallImportAudioAppendChunked(
+    importId: string,
+    files: File[],
+    options?: {
+      onProgress?: (progress: CallImportAudioUploadProgress) => void
+    },
+  ): Promise<CallImportUploadResponse> {
+    if (files.length === 0) {
+      throw new Error('At least one audio file is required.')
+    }
+
+    const chunks: File[][] = []
+    for (let i = 0; i < files.length; i += AUDIO_UPLOAD_CHUNK_SIZE) {
+      chunks.push(files.slice(i, i + AUDIO_UPLOAD_CHUNK_SIZE))
+    }
+
+    let result: CallImportUploadResponse | null = null
+    for (let chunkIndex = 0; chunkIndex < chunks.length; chunkIndex += 1) {
+      const chunk = chunks[chunkIndex]
+      options?.onProgress?.({
+        chunkIndex: chunkIndex + 1,
+        totalChunks: chunks.length,
+        uploadedFiles: chunkIndex * AUDIO_UPLOAD_CHUNK_SIZE,
+        totalFiles: files.length,
+      })
+
+      result = await this.appendCallImportAudio(importId, chunk)
+
+      options?.onProgress?.({
+        chunkIndex: chunkIndex + 1,
+        totalChunks: chunks.length,
+        uploadedFiles: Math.min(
+          (chunkIndex + 1) * AUDIO_UPLOAD_CHUNK_SIZE,
+          files.length,
+        ),
+        totalFiles: files.length,
+      })
+    }
+
+    return result!
+  }
+
+  /**
+   * MAP stage of the staged call-import flow.
+   *
+   * Persists the schema + sheet + parameter mapping on the batch. The
+   * backend validates against the sheet snapshot it cached at upload
+   * time, so this is purely a metadata operation (no S3 fetch).
+   * Idempotent: safe to call repeatedly while the batch is in
+   * ``uploaded`` or ``mapped`` state.
+   */
+  async updateCallImportMapping(
+    id: string,
+    options: {
+      schemaId: string
+      sheetName?: string | null
+      parameterMapping: Record<string, string>
+      skippedColumns?: string[]
+    },
+  ): Promise<CallImport> {
+    const response = await this.client.patch(
+      `/api/v1/call-imports/${id}/mapping`,
+      {
+        schema_id: options.schemaId,
+        sheet_name: options.sheetName ?? null,
+        parameter_mapping: options.parameterMapping,
+        skipped_columns: options.skippedColumns ?? [],
+      },
+    )
+    return response.data
+  }
+
+  /**
+   * IMPORT stage of the staged call-import flow.
+   *
+   * Materialises rows from the staged source file using the persisted
+   * mapping and fans them out to the ``imports`` Celery queue. Returns
+   * the same shape as the legacy one-shot ``uploadCallImport``.
+   */
+  async startCallImport(
+    id: string,
+    options: {
+      provider: string | null
+      telephonyIntegrationId: string | null
+    },
+  ): Promise<CallImportUploadResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${id}/import`,
+      {
+        provider: options.provider,
+        telephony_integration_id: options.telephonyIntegrationId,
+      },
+    )
+    return response.data
+  }
+
+  // -------------------------------------------------------------------
+  // Call Import Schemas (reusable Input Parameter schemas)
+  // -------------------------------------------------------------------
+
+  async listCallImportSchemas(): Promise<CallImportSchemaListResponse> {
+    const response = await this.client.get('/api/v1/call-import-schemas')
+    return response.data
+  }
+
+  async getCallImportSchema(id: string): Promise<CallImportSchema> {
+    const response = await this.client.get(`/api/v1/call-import-schemas/${id}`)
+    return response.data
+  }
+
+  async createCallImportSchema(
+    payload: CallImportSchemaCreate,
+  ): Promise<CallImportSchema> {
+    const response = await this.client.post('/api/v1/call-import-schemas', payload)
+    return response.data
+  }
+
+  async updateCallImportSchema(
+    id: string,
+    payload: CallImportSchemaUpdate,
+  ): Promise<CallImportSchema> {
+    const response = await this.client.patch(
+      `/api/v1/call-import-schemas/${id}`,
+      payload,
+    )
+    return response.data
+  }
+
+  async deleteCallImportSchema(
+    id: string,
+    options?: { force?: boolean },
+  ): Promise<void> {
+    await this.client.delete(`/api/v1/call-import-schemas/${id}`, {
+      params: options?.force ? { force: true } : undefined,
+    })
+  }
+
+  async listCallImports(
+    params: {
+      page?: number
+      page_size?: number
+      status?: CallImportStatus
+      dataset?: string
+      tag_id?: string[]
+      source_format?: string
+    } = {}
+  ): Promise<CallImportListResponse> {
+    // Send tag_id repeated rather than as a JSON array.
+    const search = new URLSearchParams()
+    if (params.page !== undefined) search.set('page', String(params.page))
+    if (params.page_size !== undefined) search.set('page_size', String(params.page_size))
+    if (params.status) search.set('status', params.status)
+    if (params.dataset !== undefined) search.set('dataset', params.dataset)
+    if (params.source_format) search.set('source_format', params.source_format)
+    for (const tag of params.tag_id || []) search.append('tag_id', tag)
+    const response = await this.client.get('/api/v1/call-imports', { params: search })
+    return response.data
+  }
+
+  async listCallImportDatasets(): Promise<string[]> {
+    const response = await this.client.get('/api/v1/call-imports/datasets')
+    return response.data
+  }
+
+  async getCallImport(
+    id: string,
+    params: {
+      row_limit?: number
+      row_offset?: number
+      q?: string
+      /**
+       * Optional filter on ``CallImportRow.diarised_transcript_status``.
+       * When set, ``filtered_total_rows`` on the response reflects the
+       * post-filter row count (combined with ``q`` when both are
+       * supplied) so the UI can paginate against the same slice.
+       */
+      diarised_status?: 'pending' | 'running' | 'completed' | 'failed'
+    } = {}
+  ): Promise<CallImportDetail> {
+    const response = await this.client.get(`/api/v1/call-imports/${id}`, { params })
+    return response.data
+  }
+
+  async listCallImportRowIds(
+    id: string,
+    params: {
+      q?: string
+      diarised_status?: 'pending' | 'running' | 'completed' | 'failed'
+    } = {}
+  ): Promise<{ ids: string[]; total: number }> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${id}/row-ids`,
+      { params },
+    )
+    return response.data
+  }
+
+  async updateCallImport(
+    id: string,
+    payload: {
+      original_filename?: string | null
+      dataset?: string | null
+      tag_ids?: string[]
+      /**
+       * Reassign the Input Parameter schema. Only honoured while the
+       * batch is in ``uploaded`` / ``mapped`` state. Switching schemas
+       * resets the mapping and rewinds the batch to ``uploaded``.
+       */
+      schema_id?: string | null
+    },
+  ): Promise<CallImport> {
+    const response = await this.client.patch(`/api/v1/call-imports/${id}`, payload)
+    return response.data
+  }
+
+  async deleteCallImport(
+    id: string,
+  ): Promise<{ id: string; status: 'accepted' | 'completed' }> {
+    const response = await this.client.delete(`/api/v1/call-imports/${id}`)
+    return response.data
+  }
+
+  async deleteCallImportRow(id: string, rowId: string): Promise<void> {
+    await this.client.delete(`/api/v1/call-imports/${id}/rows/${rowId}`)
+  }
+
+  async bulkDeleteCallImportRows(
+    id: string,
+    rowIds: string[],
+  ): Promise<{ deleted: number; status?: 'completed' | 'accepted' }> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${id}/rows/bulk-delete`,
+      { row_ids: rowIds },
+    )
+    return response.data
+  }
+
+  async createCallImportEvaluation(
+    callImportId: string,
+    payload: {
+      metric_ids: string[]
+      name?: string | null
+      /**
+       * Which transcript to score against. ``'diarised'`` (default)
+       * auto-diarizes then scores; ``'production'`` scores the CSV
+       * transcript directly.
+       */
+      transcript_sources?: Array<'production' | 'diarised'>
+      /** Run-level LLM provider key. Leave undefined for legacy default. */
+      llm_provider?: string | null
+      llm_model?: string | null
+      llm_credential_id?: string | null
+      llm_config?: LLMGenerationConfig | null
+      /** Per-metric LLM override map keyed by metric UUID. */
+      metric_llm_overrides?: Record<string, CallImportEvaluationLLMOverride> | null
+      /** When true, diarize rows missing transcripts before evaluation. */
+      auto_transcribe?: boolean
+      transcribe_overwrite?: boolean
+      /**
+       * Diarisation pipeline shape for the auto-transcribe step.
+       * 'stt_llm' (default) runs STT then an LLM diariser over the
+       * resulting text. 'llm_only' skips STT and feeds the audio
+       * directly to the multimodal ``diarization_llm_*`` model — STT
+       * fields must be omitted in that case.
+       */
+      transcribe_mode?: 'stt_llm' | 'llm_only'
+      stt_provider?: string | null
+      stt_model?: string | null
+      stt_credential_id?: string | null
+      stt_language?: string | null
+      /**
+       * LLM diariser config: post-STT, an LLM splits the plain
+       * transcript into agent/user turns using ``diarization_prompt``
+       * (or the canonical default when empty). Required by the server
+       * when ``auto_transcribe`` is set. Also used in 'llm_only' mode
+       * where this same LLM receives the audio directly.
+       */
+      diarization_llm_provider?: string | null
+      diarization_llm_model?: string | null
+      diarization_llm_credential_id?: string | null
+      diarization_prompt?: string | null
+      /**
+       * Opt into LLM-driven discovery of brand-new top-level metrics
+       * for this run. Surfaces candidates in the Discovered metrics
+       * panel on the evaluation detail Flow tab.
+       */
+      discover_new_metrics?: boolean
+      /** Telephony credentials when starting from a mapped batch. */
+      provider?: string | null
+      telephony_integration_id?: string | null
+    },
+  ): Promise<CallImportEvaluation> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations`,
+      payload,
+    )
+    return response.data
+  }
+
+  /** Aggregated metric distributions for a single evaluation run. */
+  async getCallImportEvaluationAggregate(
+    callImportId: string,
+    evaluationId: string,
+    baselineEvaluationId?: string | null,
+  ): Promise<CallImportEvaluationAggregateResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/aggregate`,
+      {
+        params: baselineEvaluationId
+          ? { baseline_evaluation_id: baselineEvaluationId }
+          : undefined,
+      },
+    )
+    return response.data
+  }
+
+  /**
+   * Read the cached LLM TLDR for an evaluation. Returns ``null`` when
+   * the user has never generated one (the empty-state CTA renders
+   * this case).
+   */
+  async getCallImportEvaluationInsights(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').EvaluationTldrSummary | null> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/insights`,
+    )
+    return response.data ?? null
+  }
+
+  /**
+   * Generate (or return cached) the LLM TLDR for an evaluation run.
+   *
+   * - Pass ``regenerate: true`` to force a fresh LLM call even when a
+   *   cached summary exists at the current ``completed_rows`` watermark.
+   * - Pass ``provider`` + ``model`` to pin a specific LLM. Omit both to
+   *   let the backend auto-detect the org's first active OpenAI /
+   *   Anthropic / Google credential (mirroring Prompt Partials).
+   */
+  async generateCallImportEvaluationInsights(
+    callImportId: string,
+    evaluationId: string,
+    options?: {
+      regenerate?: boolean
+      provider?: string | null
+      model?: string | null
+      credential_id?: string | null
+      max_llm_calls?: number | null
+    },
+  ): Promise<import('../types/api').EvaluationTldrSummary> {
+    const body: Record<string, unknown> = {
+      regenerate: Boolean(options?.regenerate),
+    }
+    if (options?.provider) body.provider = options.provider
+    if (options?.model) body.model = options.model
+    if (options?.credential_id) body.credential_id = options.credential_id
+    if (options?.max_llm_calls != null) body.max_llm_calls = options.max_llm_calls
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/insights`,
+      body,
+    )
+    return response.data
+  }
+
+  async getCallImportEvaluationUserInsights(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').EvaluationUserInsightsState | null> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/user-insights`,
+    )
+    return response.data
+  }
+
+  async getCallImportEvaluationMetricClusters(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').EvaluationMetricClustersState | null> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters`,
+    )
+    return response.data
+  }
+
+  async getCallImportEvaluationPromptImprovements(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').EvaluationPromptImprovementsState | null> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/prompt-improvements`,
+    )
+    return response.data
+  }
+
+  async generateCallImportEvaluationPromptImprovements(
+    callImportId: string,
+    evaluationId: string,
+    data: {
+      imported_agent_id: string
+      regenerate?: boolean
+      force?: boolean
+      provider?: string
+      model?: string
+      credential_id?: string
+    },
+  ): Promise<import('../types/api').EvaluationPromptImprovementsState> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/prompt-improvements`,
+      data,
+    )
+    return response.data
+  }
+
+  async listCallImportEvaluationMetricClusterEligibleRows(
+    callImportId: string,
+    evaluationId: string,
+    options?: {
+      limit?: number
+      count_only?: boolean
+    },
+  ): Promise<import('../types/api').MetricClusterEligibleRowsResponse> {
+    const params = new URLSearchParams()
+    if (options?.limit != null) params.set('limit', String(options.limit))
+    if (options?.count_only) params.set('count_only', 'true')
+    const query = params.toString()
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/eligible-rows${query ? `?${query}` : ''}`,
+    )
+    return response.data
+  }
+
+  async getCallImportEvaluationMetricClusterFailurePolicies(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').MetricFailurePoliciesResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/failure-policies`,
+    )
+    return response.data
+  }
+
+  async saveCallImportEvaluationMetricClusterFailurePolicies(
+    callImportId: string,
+    evaluationId: string,
+    policies: Record<string, import('../types/api').MetricFailurePolicy>,
+  ): Promise<import('../types/api').MetricFailurePoliciesResponse> {
+    const response = await this.client.put(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/failure-policies`,
+      { policies, source: 'user' },
+    )
+    return response.data
+  }
+
+  async generateCallImportEvaluationMetricClusters(
+    callImportId: string,
+    evaluationId: string,
+    options?: {
+      regenerate?: boolean
+      force?: boolean
+      provider?: string | null
+      model?: string | null
+      credential_id?: string | null
+      max_llm_calls?: number | null
+      evaluation_row_ids?: string[] | null
+      row_limit?: number | null
+      failure_policies?: Record<
+        string,
+        import('../types/api').MetricFailurePolicy
+      > | null
+    },
+  ): Promise<import('../types/api').EvaluationMetricClustersState> {
+    const body: Record<string, unknown> = {
+      regenerate: Boolean(options?.regenerate),
+      force: Boolean(options?.force),
+    }
+    if (options?.provider) body.provider = options.provider
+    if (options?.model) body.model = options.model
+    if (options?.credential_id) body.credential_id = options.credential_id
+    if (options?.max_llm_calls != null) body.max_llm_calls = options.max_llm_calls
+    if (options?.evaluation_row_ids?.length) {
+      body.evaluation_row_ids = options.evaluation_row_ids
+    }
+    if (options?.row_limit != null) body.row_limit = options.row_limit
+    if (options?.failure_policies) {
+      body.failure_policies = options.failure_policies
+    }
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters`,
+      body,
+    )
+    return response.data
+  }
+
+  async cancelCallImportEvaluationMetricClusters(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').EvaluationMetricClustersState> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/metric-clusters/cancel`,
+    )
+    return response.data
+  }
+
+  async generateCallImportEvaluationUserInsights(
+    callImportId: string,
+    evaluationId: string,
+    options?: {
+      regenerate?: boolean
+      force?: boolean
+      provider?: string | null
+      model?: string | null
+      credential_id?: string | null
+      max_llm_calls?: number | null
+    },
+  ): Promise<import('../types/api').EvaluationUserInsightsState> {
+    const body: Record<string, unknown> = {
+      regenerate: Boolean(options?.regenerate),
+      force: Boolean(options?.force),
+    }
+    if (options?.provider) body.provider = options.provider
+    if (options?.model) body.model = options.model
+    if (options?.credential_id) body.credential_id = options.credential_id
+    if (options?.max_llm_calls != null) body.max_llm_calls = options.max_llm_calls
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/user-insights`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Aggregate flow chart for a parent metric: returns nodes/edges built
+   * from per-row LLM-inferred ``sequence`` arrays. Used by the React
+   * Flow visualisation on the evaluation overview.
+   */
+  async getCallImportEvaluationFlow(
+    callImportId: string,
+    evaluationId: string,
+    parentMetricId: string,
+  ): Promise<import('../types/api').MetricFlowResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/flow`,
+      { params: { parent_metric_id: parentMetricId } },
+    )
+    return response.data
+  }
+
+  /**
+   * List LLM-discovered candidate sub-labels for a parent metric in an
+   * evaluation. Returned items power the Discovered Labels panel where
+   * users decide which candidates to promote or merge.
+   */
+  async getCallImportEvaluationDiscoveredLabels(
+    callImportId: string,
+    evaluationId: string,
+    parentMetricId: string,
+  ): Promise<import('../types/api').DiscoveredLabelsResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-labels`,
+      { params: { parent_metric_id: parentMetricId } },
+    )
+    return response.data
+  }
+
+  /**
+   * Merge a discovered slug into another within an evaluation. Rewrites
+   * every row's discovered_labels and sequence array so the panel +
+   * flow chart converge on the surviving slug.
+   */
+  async mergeCallImportEvaluationDiscoveredLabels(
+    callImportId: string,
+    evaluationId: string,
+    body: { parent_metric_id: string; from_key: string; to_key: string },
+  ): Promise<import('../types/api').DiscoveredLabelsResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-labels/merge`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Delete (tombstone) an LLM-discovered candidate. Strips the slug
+   * from every row's discovered_labels + sequence and records a
+   * deletion alias on the evaluation so workers finishing later don't
+   * resurrect it. Use for gibberish candidates the LLM proposed.
+   */
+  async deleteCallImportEvaluationDiscoveredLabel(
+    callImportId: string,
+    evaluationId: string,
+    body: { parent_metric_id: string; key: string },
+  ): Promise<import('../types/api').DiscoveredLabelsResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-labels/delete`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Promote an LLM-discovered candidate into a real child Metric under
+   * the given parent. ``key`` must equal ``slugify(name)`` so existing
+   * rows' sequence arrays auto-resolve against the new child.
+   *
+   * ``capture_rationale`` defaults to true on the backend (matches the
+   * Discovered Labels UX: candidates are LLM-suggested with rationales,
+   * so the user almost always wants future rows that hit them to keep
+   * capturing rationales).
+   */
+  async promoteDiscoveredChild(
+    parentMetricId: string,
+    body: {
+      key: string
+      name: string
+      description?: string | null
+      capture_rationale?: boolean
+    },
+  ): Promise<import('../types/api').MetricSummary> {
+    const response = await this.client.post(
+      `/api/v1/metrics/${parentMetricId}/children/from-discovered`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * List LLM-discovered candidate TOP-LEVEL metrics for an evaluation.
+   * Mirrors :func:`getCallImportEvaluationDiscoveredLabels` but is
+   * scoped to the evaluation as a whole (no ``parent_metric_id``).
+   * Returns an empty ``items`` list when the evaluation didn't opt
+   * into top-level metric discovery, so callers can fetch
+   * unconditionally.
+   */
+  async getCallImportEvaluationDiscoveredMetrics(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<import('../types/api').DiscoveredMetricsResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-metrics`,
+    )
+    return response.data
+  }
+
+  /**
+   * Merge one discovered top-level metric slug into another. Rewrites
+   * every row's ``__discovered_metrics__`` list and records the
+   * alias on the evaluation so workers finishing later converge on
+   * the surviving slug.
+   */
+  async mergeCallImportEvaluationDiscoveredMetrics(
+    callImportId: string,
+    evaluationId: string,
+    body: { from_key: string; to_key: string },
+  ): Promise<import('../types/api').DiscoveredMetricsResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-metrics/merge`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Tombstone an LLM-discovered top-level metric candidate. Strips
+   * the slug from every row's ``__discovered_metrics__`` list and
+   * records a deletion alias on the evaluation so workers finishing
+   * later can't resurrect it.
+   */
+  async deleteCallImportEvaluationDiscoveredMetric(
+    callImportId: string,
+    evaluationId: string,
+    body: { key: string },
+  ): Promise<import('../types/api').DiscoveredMetricsResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/discovered-metrics/delete`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Promote an LLM-discovered top-level metric candidate into a real
+   * standalone :class:`Metric` row. ``key`` must equal
+   * ``slugify(name)`` so already-scored rows that referenced the
+   * candidate keep resolving against the promoted metric.
+   *
+   * ``metric_type`` selects how the new metric will be scored on
+   * future runs (``boolean`` / ``rating`` / ``category``). ``category``
+   * creates a ``multi_label`` parent with no children — the user adds
+   * children via the existing Metrics page.
+   */
+  async promoteDiscoveredMetric(body: {
+    key: string
+    name: string
+    description?: string | null
+    metric_type?: 'boolean' | 'rating' | 'category'
+    capture_rationale?: boolean
+    custom_config?: Record<string, unknown> | null
+  }): Promise<import('../types/api').MetricSummary> {
+    const response = await this.client.post(
+      `/api/v1/metrics/from-discovered`,
+      body,
+    )
+    return response.data
+  }
+
+  /** Cross-run insights for the call import detail page. */
+  async getCallImportInsights(
+    callImportId: string,
+  ): Promise<CallImportInsightsResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/insights`,
+    )
+    return response.data
+  }
+
+  /**
+   * Fetch the canonical LLM diariser prompt. Used by the Transcribe /
+   * Run Evaluation modals to pre-fill the prompt textarea so the
+   * operator sees the actual default they'd otherwise get.
+   */
+  async getCallImportDiarisationPromptDefault(): Promise<string> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/diarisation-prompt-default`,
+    )
+    return (response.data?.prompt ?? '') as string
+  }
+
+  /** Fan out diarization tasks for a batch of rows. */
+  async transcribeCallImport(
+    callImportId: string,
+    payload: CallImportTranscribeRequest,
+  ): Promise<CallImportTranscribeResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/transcribe`,
+      payload,
+    )
+    return response.data
+  }
+
+  /** Diarize a single row's recording. */
+  async transcribeCallImportRow(
+    callImportId: string,
+    rowId: string,
+    payload: CallImportTranscribeRequest,
+  ): Promise<CallImportTranscribeResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/rows/${rowId}/transcribe`,
+      payload,
+    )
+    return response.data
+  }
+
+  /**
+   * Re-enqueue every failed import row in this batch.
+   *
+   * Rows are reset to `pending` first so the UI can immediately show that
+   * the retry sweep started; rows that fail to enqueue again are returned
+   * in `enqueue_failed` and stay `failed`.
+   */
+  async retryFailedCallImportRows(
+    callImportId: string,
+    options?: {
+      provider: string | null
+      telephonyIntegrationId: string | null
+    },
+  ): Promise<CallImportRetryFailedRowsResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/retry-failed`,
+      options
+        ? {
+            provider: options.provider,
+            telephony_integration_id: options.telephonyIntegrationId,
+          }
+        : undefined,
+    )
+    return response.data
+  }
+
+  /**
+   * Abort an in-flight (or queued) diarisation for a single row.
+   *
+   * Idempotent — calling on a row whose diarisation is already in a
+   * terminal state (``completed`` / ``failed`` / ``idle``) returns the
+   * row unchanged so the UI can wire this to a "Stop" button without
+   * pre-checking state. The backend flips the row's
+   * ``diarised_transcript_status`` to ``failed`` and stamps
+   * ``"Diarisation cancelled by user"`` as the error so the existing
+   * diarisation pill in the row header renders the right state on the
+   * next poll.
+   */
+  async cancelCallImportRowDiarisation(
+    callImportId: string,
+    rowId: string,
+  ): Promise<CallImportRow> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/rows/${rowId}/cancel-diarisation`,
+    )
+    return response.data
+  }
+
+  /**
+   * Abort in-flight diarisation for many rows in one call.
+   *
+   * Pass ``rowIds`` to cancel a specific subset. Omit it (or pass
+   * ``null``) to cancel every row in the import whose
+   * ``diarised_transcript_status`` is currently ``pending`` or
+   * ``running`` — the "stop everything" affordance.
+   */
+  async cancelCallImportDiarisation(
+    callImportId: string,
+    rowIds?: string[] | null,
+  ): Promise<{ cancelled: number; skipped: number }> {
+    const body =
+      rowIds === undefined || rowIds === null
+        ? {}
+        : { row_ids: rowIds }
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/cancel-diarisation`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Flip ``diarised_speaker_swap`` on a row and re-render
+   * ``diarised_transcript`` from ``diarised_segments``. Returns the
+   * updated row so the caller can swap it into local state without an
+   * extra refetch.
+   *
+   * The backend rejects this with 409 when the row has no structured
+   * segments (e.g. legacy diarisations done before turns were
+   * persisted) — callers should surface that as "re-diarise to enable
+   * speaker swapping".
+   */
+  async toggleCallImportRowSpeakerSwap(
+    callImportId: string,
+    rowId: string,
+  ): Promise<CallImportRow> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/rows/${rowId}/diarised-speaker-swap`,
+    )
+    return response.data
+  }
+
+  async updateCallImportEvaluation(
+    callImportId: string,
+    evaluationId: string,
+    payload: { name?: string | null },
+  ): Promise<CallImportEvaluation> {
+    const response = await this.client.patch(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}`,
+      payload,
+    )
+    return response.data
+  }
+
+  async bulkDeleteCallImportEvaluations(
+    callImportId: string,
+    evaluationIds: string[],
+  ): Promise<{ deleted: number }> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/bulk-delete`,
+      { evaluation_ids: evaluationIds },
+    )
+    return response.data
+  }
+
+  async deleteCallImportEvaluationRow(
+    callImportId: string,
+    evaluationId: string,
+    evalRowId: string,
+  ): Promise<void> {
+    await this.client.delete(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows/${evalRowId}`,
+    )
+  }
+
+  /**
+   * Re-enqueue failed evaluation rows. With no body, every row in the
+   * run whose status is ``failed`` is re-run with the run's saved
+   * config. Pass ``evalRowIds`` to scope the retry to a subset (used
+   * by the per-row "Retry" button), and/or the ``llm_*`` / ``stt_*``
+   * fields to swap out the LLM / STT provider before re-enqueueing
+   * (the backend validates + persists them onto the run).
+   *
+   * Rows still in progress / already completed are silently skipped
+   * on the server and reported in the response's ``skipped`` list —
+   * the mutation never fails just because the caller's selection is
+   * stale.
+   */
+  async retryCallImportEvaluation(
+    callImportId: string,
+    evaluationId: string,
+    options?: {
+      evalRowIds?: string[]
+      llmProvider?: string | null
+      llmModel?: string | null
+      llmCredentialId?: string | null
+      llmConfig?: LLMGenerationConfig | null
+      sttProvider?: string | null
+      sttModel?: string | null
+      sttCredentialId?: string | null
+      transcribeOverwrite?: boolean
+      transcribeMode?: 'stt_llm' | 'llm_only'
+      diarizationLlmProvider?: string | null
+      diarizationLlmModel?: string | null
+      diarizationLlmCredentialId?: string | null
+      diarizationPrompt?: string | null
+      /**
+       * Override telephony credentials on the batch when failed rows must
+       * re-fetch recordings. Send both fields together; omit to keep the
+       * batch's existing pinned credentials.
+       */
+      provider?: string | null
+      telephonyIntegrationId?: string | null
+      /**
+       * Metric-subset retry: when set, only these metrics are
+       * recomputed and merged into the row's existing metric_scores
+       * (other metrics' previous values are preserved). The backend
+       * auto-flips ``include_completed`` to true in this case so
+       * already-successful rows are eligible.
+       */
+      metricIds?: string[]
+      /**
+       * When true, rows currently ``completed`` are eligible for
+       * retry too (otherwise only ``failed`` rows are picked up).
+       * Required-and-implied when ``metricIds`` is set.
+       */
+      includeCompleted?: boolean
+    },
+  ): Promise<CallImportEvaluationRetryResponse> {
+    const body: Record<string, unknown> = {}
+    if (options?.evalRowIds && options.evalRowIds.length > 0) {
+      body.eval_row_ids = options.evalRowIds
+    }
+    if (options?.llmProvider) body.llm_provider = options.llmProvider
+    if (options?.llmModel) body.llm_model = options.llmModel
+    if (options?.llmCredentialId !== undefined) {
+      body.llm_credential_id = options.llmCredentialId
+    }
+    if (options?.llmConfig !== undefined) {
+      body.llm_config = options.llmConfig
+    }
+    if (options?.sttProvider) body.stt_provider = options.sttProvider
+    if (options?.sttModel) body.stt_model = options.sttModel
+    if (options?.sttCredentialId !== undefined) {
+      body.stt_credential_id = options.sttCredentialId
+    }
+    if (options?.transcribeOverwrite) {
+      body.transcribe_overwrite = true
+    }
+    if (options?.transcribeMode) {
+      body.transcribe_mode = options.transcribeMode
+    }
+    if (options?.diarizationLlmProvider) {
+      body.diarization_llm_provider = options.diarizationLlmProvider
+    }
+    if (options?.diarizationLlmModel) {
+      body.diarization_llm_model = options.diarizationLlmModel
+    }
+    if (options?.diarizationLlmCredentialId !== undefined) {
+      body.diarization_llm_credential_id = options.diarizationLlmCredentialId
+    }
+    if (options?.diarizationPrompt !== undefined) {
+      body.diarization_prompt = options.diarizationPrompt
+    }
+    if (
+      options?.provider !== undefined ||
+      options?.telephonyIntegrationId !== undefined
+    ) {
+      body.provider = options.provider ?? null
+      body.telephony_integration_id = options.telephonyIntegrationId ?? null
+    }
+    if (options?.metricIds && options.metricIds.length > 0) {
+      body.metric_ids = options.metricIds
+    }
+    if (options?.includeCompleted) {
+      body.include_completed = true
+    }
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/retry`,
+      body,
+    )
+    return response.data
+  }
+
+  /**
+   * Re-enqueue a single failed evaluation row. Returns the refreshed
+   * row so the UI can flip the badge to ``pending`` immediately
+   * without waiting for the next polling tick. Server returns 409 if
+   * the row is still in progress or already completed.
+   */
+  async retryCallImportEvaluationRow(
+    callImportId: string,
+    evaluationId: string,
+    evalRowId: string,
+  ): Promise<CallImportEvaluationRow> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows/${evalRowId}/retry`,
+    )
+    return response.data
+  }
+
+  /**
+   * Abort an entire in-flight (or queued) evaluation run.
+   *
+   * Idempotent — calling on a run whose rows are already terminal
+   * returns the run unchanged so the UI can wire this to an "Abort
+   * run" button without pre-checking state. The backend flips every
+   * cancellable row's ``status`` to ``failed`` and stamps
+   * ``"Evaluation cancelled by user"`` as the error so the polling UI
+   * surfaces the cancel on the next tick. Pairs with the worker's
+   * cancellation guard which prevents a late-finishing task from
+   * overwriting the cancelled state.
+   */
+  async cancelCallImportEvaluation(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<CallImportEvaluationBulkActionResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/cancel`,
+    )
+    return response.data
+  }
+
+  /**
+   * Force-fail only rows currently in ``pending`` for an evaluation run.
+   *
+   * Unlike ``cancelCallImportEvaluation``, this does NOT touch rows that
+   * are already ``running``. Useful when queued rows are stuck indefinitely
+   * but active workers should keep progressing.
+   */
+  async forceFailCallImportEvaluationPending(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<CallImportEvaluationBulkActionResponse> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/force-fail-pending`,
+    )
+    return response.data
+  }
+
+  /**
+   * Abort an in-flight (or queued) evaluation for a single row.
+   *
+   * Mirrors :func:`cancelCallImportEvaluation` but scoped to one row,
+   * intended for the per-row Stop button on the evaluation detail
+   * rows table. The parent run's counters are rolled up server-side
+   * so the run-level pill updates on the same response.
+   */
+  async cancelCallImportEvaluationRow(
+    callImportId: string,
+    evaluationId: string,
+    evalRowId: string,
+  ): Promise<CallImportEvaluationRow> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows/${evalRowId}/cancel`,
+    )
+    return response.data
+  }
+
+  async listCallImportEvaluations(callImportId: string): Promise<CallImportEvaluationListResponse> {
+    const response = await this.client.get(`/api/v1/call-imports/${callImportId}/evaluations`)
+    return response.data
+  }
+
+  async getCallImportEvaluation(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<CallImportEvaluation> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}`,
+    )
+    return response.data
+  }
+
+  async listCallImportEvaluationRows(
+    callImportId: string,
+    evaluationId: string,
+    params: {
+      page?: number
+      page_size?: number
+      q?: string
+      metric_id?: string
+      metric_value?: string
+      status?: string
+      // Flow-chart drilldown: filter to rows whose sequence under the
+      // given parent contains ``flow_node`` (and optionally is
+      // immediately followed by ``flow_edge_target`` for edge clicks).
+      // Accepts a child UUID, a ``disc:<slug>`` discovered id, or a
+      // raw slug.
+      flow_parent_id?: string
+      flow_node?: string
+      flow_edge_target?: string
+      // Discovered-label filters: either pin to a specific discovered
+      // slug or surface every row that produced any candidate under
+      // ``discovered_parent_id``.
+      discovered_parent_id?: string
+      discovered_label_key?: string
+      has_discovered?: boolean
+      // Column-click sorting from the UI. ``sort_by`` is either a
+      // built-in column key (``row_index`` / ``conversation_id`` /
+      // ``status``) or ``metric:<metric_uuid>`` for per-metric value
+      // sorting; ``sort_dir`` is ``asc`` (default) or ``desc``.
+      sort_by?: string
+      sort_dir?: 'asc' | 'desc'
+    } = {},
+  ): Promise<CallImportEvaluationRowListResponse> {
+    const cleaned: Record<string, any> = {}
+    for (const [k, v] of Object.entries(params)) {
+      if (v !== undefined && v !== null && v !== '') cleaned[k] = v
+    }
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/rows`,
+      { params: cleaned },
+    )
+    return response.data
+  }
+
+  async exportCallImportEvaluation(
+    callImportId: string,
+    evaluationId: string,
+    format: 'csv' | 'xlsx' = 'csv',
+  ): Promise<Blob> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/export`,
+      { params: { format }, responseType: 'blob' },
+    )
+    return response.data
+  }
+
+  async generateCallImportEvaluationPdfReport(
+    callImportId: string,
+    evaluationId: string,
+    vendorName: string,
+    reportType: 'external' | 'internal',
+    includeWeeklyDelta = false,
+    options?: {
+      internalBrandImageId?: string | null
+      externalBrandImageId?: string | null
+      useCase?: string | null
+      baselineEvaluationId?: string | null
+      reportConfig?: Record<string, any>
+      platformBaseUrl?: string | null
+    },
+  ): Promise<CallImportEvaluationPdfReport | Blob> {
+    const response = await this.client.post(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-report`,
+      {
+        vendor_name: vendorName,
+        report_type: reportType,
+        include_weekly_delta: includeWeeklyDelta,
+        include_period_delta: includeWeeklyDelta,
+        baseline_evaluation_id: options?.baselineEvaluationId || null,
+        use_case: options?.useCase || null,
+        internal_brand_image_id: options?.internalBrandImageId || null,
+        external_brand_image_id: options?.externalBrandImageId || null,
+        report_config: options?.reportConfig || {},
+        platform_base_url: options?.platformBaseUrl || null,
+      },
+      { responseType: 'arraybuffer' },
+    )
+    const contentType = String(response.headers['content-type'] || '')
+    if (contentType.includes('application/pdf')) {
+      return new Blob([response.data], { type: 'application/pdf' })
+    }
+    const text = new TextDecoder().decode(response.data)
+    return JSON.parse(text) as CallImportEvaluationPdfReport
+  }
+
+  async listCallImportEvaluationPdfReports(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<CallImportEvaluationPdfReportListResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports`,
+    )
+    return response.data
+  }
+
+  async getCallImportEvaluationPdfReport(
+    callImportId: string,
+    evaluationId: string,
+    reportId: string,
+  ): Promise<CallImportEvaluationPdfReport> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports/${reportId}`,
+    )
+    return response.data
+  }
+
+  async downloadCallImportEvaluationPdfReport(
+    callImportId: string,
+    evaluationId: string,
+    reportId: string,
+  ): Promise<Blob> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/pdf-reports/${reportId}/download`,
+      { responseType: 'blob' },
+    )
+    return response.data
+  }
+
+  async listCallImportEvaluationBaselineCandidates(
+    callImportId: string,
+    evaluationId: string,
+  ): Promise<CallImportEvaluationBaselineCandidatesResponse> {
+    const response = await this.client.get(
+      `/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}/baseline-candidates`,
+    )
+    return response.data
+  }
+
+  async deleteCallImportEvaluation(callImportId: string, evaluationId: string): Promise<void> {
+    await this.client.delete(`/api/v1/call-imports/${callImportId}/evaluations/${evaluationId}`)
+  }
+
+  // Call Import Tags endpoints
+  async listCallImportTags(): Promise<CallImportTag[]> {
+    const response = await this.client.get('/api/v1/call-import-tags')
+    return response.data
+  }
+
+  async createCallImportTag(payload: {
+    name: string
+    color?: string | null
+  }): Promise<CallImportTag> {
+    const response = await this.client.post('/api/v1/call-import-tags', payload)
+    return response.data
+  }
+
+  async updateCallImportTag(
+    tagId: string,
+    payload: { name?: string; color?: string | null },
+  ): Promise<CallImportTag> {
+    const response = await this.client.patch(
+      `/api/v1/call-import-tags/${tagId}`,
+      payload,
+    )
+    return response.data
+  }
+
+  async deleteCallImportTag(tagId: string): Promise<void> {
+    await this.client.delete(`/api/v1/call-import-tags/${tagId}`)
+  }
+
+  // Model Config endpoints
+  async getDashboardSummary(): Promise<DashboardSummary> {
+    const response = await this.client.get('/api/v1/dashboard/summary')
+    return response.data
+  }
+
+  async getOrgUsageFxRate(): Promise<{
+    base: string
+    quote: string
+    rate: number
+    as_of: string
+    source: string
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/fx-rate')
+    return response.data
+  }
+
+  async getOrgUsageSummary(params: {
+    start?: string
+    end?: string
+    tz?: string
+    workspace_id?: string
+    product_section?: string
+    model?: string
+    resource_id?: string
+    usage_kind?: string
+    call_import_id?: string
+    evaluation_id?: string
+    dataset?: string
+    tag_id?: string
+  }): Promise<{
+    start: string
+    end: string
+    totals: {
+      prompt_tokens: number
+      completion_tokens: number
+      total_tokens: number
+      cache_read_tokens: number
+      cache_creation_tokens: number
+      reasoning_tokens: number
+      audio_seconds: number
+      tts_characters: number
+      call_count: number
+      input_cost_micro_usd?: number
+      output_cost_micro_usd?: number
+      cache_read_cost_micro_usd?: number
+      cache_creation_cost_micro_usd?: number
+      reasoning_cost_micro_usd?: number
+      audio_cost_micro_usd?: number
+      tts_cost_micro_usd?: number
+      total_cost_micro_usd?: number
+      costs?: {
+        input_cost_usd: number
+        output_cost_usd: number
+        cache_read_cost_usd: number
+        cache_write_cost_usd: number
+        reasoning_cost_usd: number
+        audio_cost_usd: number
+        tts_cost_usd: number
+        total_cost_usd: number
+        currency: string
+        has_unpriced_usage: boolean
+      }
+    }
+    last_updated_at?: string | null
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/summary', {
+      params,
+    })
+    return response.data
+  }
+
+  async getOrgUsageBreakdown(params: {
+    start?: string
+    end?: string
+    tz?: string
+    group_by?: 'workspace' | 'product_section' | 'model' | 'resource' | 'usage_kind' | 'call_import'
+    workspace_id?: string
+    product_section?: string
+    model?: string
+    resource_id?: string
+    usage_kind?: string
+    call_import_id?: string
+    evaluation_id?: string
+    dataset?: string
+    tag_id?: string
+    limit?: number
+    offset?: number
+  }): Promise<{
+    start: string
+    end: string
+    group_by: string
+    rows: Array<{
+      workspace_id?: string | null
+      workspace_name?: string | null
+      product_section?: string | null
+      product_section_label?: string | null
+      model?: string | null
+      resource_id?: string | null
+      resource_type?: string | null
+      resource_label?: string | null
+      call_import_id?: string | null
+      call_import_label?: string | null
+      usage_kind?: string | null
+      prompt_tokens: number
+      completion_tokens: number
+      total_tokens: number
+      cache_read_tokens: number
+      cache_creation_tokens: number
+      reasoning_tokens: number
+      audio_seconds: number
+      tts_characters: number
+      call_count: number
+      input_cost_micro_usd?: number
+      output_cost_micro_usd?: number
+      cache_read_cost_micro_usd?: number
+      cache_creation_cost_micro_usd?: number
+      reasoning_cost_micro_usd?: number
+      audio_cost_micro_usd?: number
+      tts_cost_micro_usd?: number
+      total_cost_micro_usd?: number
+      costs?: {
+        input_cost_usd: number
+        output_cost_usd: number
+        cache_read_cost_usd: number
+        cache_write_cost_usd: number
+        reasoning_cost_usd: number
+        audio_cost_usd: number
+        tts_cost_usd: number
+        total_cost_usd: number
+        currency: string
+        has_unpriced_usage: boolean
+      }
+    }>
+    total_count: number
+    truncated_at_limit?: boolean
+    last_updated_at?: string | null
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/breakdown', {
+      params,
+    })
+    return response.data
+  }
+
+  async listUsagePricingAvailableModels(): Promise<{ models: string[] }> {
+    const response = await this.client.get(
+      '/api/v1/organizations/usage/pricing/available-models',
+    )
+    return response.data
+  }
+
+  async getUsagePricingEffective(
+    model: string,
+    params?: { usage_kind?: string; as_of?: string },
+  ): Promise<{
+    model: string
+    usage_kind: string
+    as_of: string
+    catalog_rates?: Record<string, number | null> | null
+    effective_rates?: Record<string, number | null> | null
+    effective_source?: string | null
+    has_override: boolean
+    override?: {
+      effective_from: string
+      effective_to?: string | null
+      rates: Record<string, number | null>
+    } | null
+  }> {
+    const response = await this.client.get(
+      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
+      { params },
+    )
+    return response.data
+  }
+
+  async listUsagePricingOverrides(params?: {
+    model?: string
+    usage_kind?: string
+  }): Promise<
+    Array<{
+      id: string
+      organization_id: string
+      model: string
+      usage_kind: string
+      effective_from: string
+      effective_to?: string | null
+      rates: {
+        input_per_1m?: number | null
+        output_per_1m?: number | null
+        cache_read_per_1m?: number | null
+        cache_write_per_1m?: number | null
+        reasoning_per_1m?: number | null
+        audio_per_minute?: number | null
+        tts_per_1m_characters?: number | null
+      }
+      recompute_enqueued?: boolean
+      recompute_job_id?: string | null
+    }>
+  > {
+    const response = await this.client.get('/api/v1/organizations/usage/pricing/overrides', {
+      params,
+    })
+    return response.data
+  }
+
+  async upsertUsagePricingOverride(
+    model: string,
+    body: {
+      usage_kind?: string
+      effective_from: string
+      effective_to?: string
+      rates: {
+        input_per_1m?: number
+        output_per_1m?: number
+        cache_read_per_1m?: number
+        cache_write_per_1m?: number
+        reasoning_per_1m?: number
+        audio_per_minute?: number
+        tts_per_1m_characters?: number
+      }
+      recompute?: boolean
+    }
+  ): Promise<{
+    id: string
+    model: string
+    usage_kind: string
+    effective_from: string
+    effective_to?: string | null
+    rates: Record<string, number | null | undefined>
+    recompute_enqueued?: boolean
+    recompute_job_id?: string | null
+  }> {
+    const response = await this.client.put(
+      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
+      body
+    )
+    return response.data
+  }
+
+  async deleteUsagePricingOverride(
+    model: string,
+    params?: { usage_kind?: string; effective_from?: string; recompute?: boolean }
+  ): Promise<{
+    deleted: boolean
+    model: string
+    usage_kind: string
+    recompute_enqueued?: boolean
+    recompute_job_id?: string | null
+  }> {
+    const response = await this.client.delete(
+      `/api/v1/organizations/usage/pricing/overrides/${encodeURIComponent(model)}`,
+      { params }
+    )
+    return response.data
+  }
+
+  async triggerUsageCostRecompute(body?: {
+    start_date?: string
+    end_date?: string
+    model?: string
+    usage_kind?: string
+  }): Promise<{
+    id: string
+    organization_id: string
+    status: string
+    model?: string | null
+    usage_kind?: string | null
+    start_date?: string | null
+    end_date?: string | null
+    updated_rows: number
+    error_message?: string | null
+    celery_task_id?: string | null
+    created_at?: string | null
+    updated_at?: string | null
+    completed_at?: string | null
+  }> {
+    const response = await this.client.post(
+      '/api/v1/organizations/usage/pricing/recompute',
+      body ?? {}
+    )
+    return response.data
+  }
+
+  async getUsageCostRecomputeJob(jobId: string): Promise<{
+    id: string
+    organization_id: string
+    status: string
+    model?: string | null
+    usage_kind?: string | null
+    start_date?: string | null
+    end_date?: string | null
+    updated_rows: number
+    error_message?: string | null
+    celery_task_id?: string | null
+    created_at?: string | null
+    updated_at?: string | null
+    completed_at?: string | null
+  }> {
+    const response = await this.client.get(
+      `/api/v1/organizations/usage/pricing/recompute/${jobId}`
+    )
+    return response.data
+  }
+
+  async getOrgUsageFilters(params?: {
+    start?: string
+    end?: string
+    tz?: string
+    workspace_id?: string
+    product_section?: string
+    model?: string
+    resource_id?: string
+    evaluation_id?: string
+    usage_kind?: string
+    call_import_id?: string
+    q?: string
+    dataset?: string
+    tag_id?: string
+  }): Promise<{
+    workspaces: Array<{ id: string; name: string }>
+    product_sections: Array<{ id: string; label: string }>
+    call_imports: Array<{ id: string; label: string }>
+    evaluations: Array<{ id: string; label: string }>
+    models: string[]
+    resources: Array<{ id: string; type?: string; label: string; product_section?: string }>
+    usage_kinds: Array<{ id: string; label: string }>
+    datasets: string[]
+    tags: Array<{ id: string; label: string }>
+  }> {
+    const response = await this.client.get('/api/v1/organizations/usage/filters', {
+      params,
+    })
+    return response.data
+  }
+
+  async getAllModels(): Promise<Record<string, ModelConfigEntry>> {
+    const response = await this.client.get('/api/v1/model-config/models')
+    return response.data
+  }
+
+  async getModelConfig(modelName: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/model-config/models/${modelName}`)
+    return response.data
+  }
+
+  async getModelsByProvider(provider: string): Promise<string[]> {
+    const response = await this.client.get(`/api/v1/model-config/providers/${provider}/models`)
+    return response.data
+  }
+
+  async getModelOptions(provider: string): Promise<{ stt: string[]; llm: string[]; tts: string[]; s2s: string[]; tts_voices: Record<string, { id: string; name: string; gender?: string }[]>; tts_sample_rates?: number[] }> {
+    const response = await this.client.get(`/api/v1/model-config/providers/${provider}/options`)
+    const data = response.data
+    // Ensure s2s and tts_voices are always present (for backward compatibility)
+    return {
+      ...data,
+      s2s: data.s2s || [],
+      tts_voices: data.tts_voices || {},
+    }
+  }
+
+  async getModelsByType(provider: string, modelType: 'stt' | 'llm' | 'tts'): Promise<string[]> {
+    const response = await this.client.get(`/api/v1/model-config/providers/${provider}/types/${modelType}/models`)
+    return response.data
+  }
+
+  // Manual Evaluations endpoints
+  async listManualEvaluationAudioFiles(prefix?: string, maxKeys = 1000): Promise<S3ListFilesResponse> {
+    const response = await this.client.get('/api/v1/manual-evaluations/audio-files', {
+      params: { prefix, max_keys: maxKeys },
+    })
+    return response.data
+  }
+
+  async getAudioPresignedUrl(fileKey: string, expiration = 3600): Promise<{ url: string; expires_in: number }> {
+    const response = await this.client.get(
+      `/api/v1/manual-evaluations/audio-files/${encodeURIComponent(fileKey)}/presigned-url`,
+      {
+        params: { expiration },
+      }
+    )
+    return response.data
+  }
+
+  async transcribeAudio(data: {
+    audio_file_key: string
+    stt_provider: string
+    stt_model: string
+    name?: string
+    language?: string
+    enable_speaker_diarization?: boolean
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/manual-evaluations/transcribe', data)
+    return response.data
+  }
+
+  async listManualTranscriptions(skip = 0, limit = 100): Promise<any[]> {
+    const response = await this.client.get('/api/v1/manual-evaluations/transcriptions', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getManualTranscription(transcriptionId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/manual-evaluations/transcriptions/${transcriptionId}`)
+    return response.data
+  }
+
+  async updateManualTranscription(transcriptionId: string, name: string): Promise<any> {
+    const response = await this.client.patch(`/api/v1/manual-evaluations/transcriptions/${transcriptionId}`, { name })
+    return response.data
+  }
+
+  async deleteManualTranscription(transcriptionId: string): Promise<MessageResponse> {
+    const response = await this.client.delete(`/api/v1/manual-evaluations/transcriptions/${transcriptionId}`)
+    return response.data
+  }
+
+  // Test Agent endpoints
+  async createTestAgentConversation(data: {
+    agent_id: string
+    persona_id: string
+    scenario_id: string
+    voice_bundle_id: string
+    conversation_metadata?: Record<string, any>
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/test-agents/conversations', data)
+    return response.data
+  }
+
+  async getTestAgentConversation(conversationId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/test-agents/conversations/${conversationId}`)
+    return response.data
+  }
+
+  async listTestAgentConversations(): Promise<any[]> {
+    const response = await this.client.get('/api/v1/test-agents/conversations')
+    return response.data
+  }
+
+  async startTestAgentConversation(conversationId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/test-agents/conversations/${conversationId}/start`)
+    return response.data
+  }
+
+  async processTestAgentAudio(conversationId: string, audioFile: File, chunkTimestamp?: number): Promise<any> {
+    const formData = new FormData()
+    formData.append('audio_file', audioFile)
+    if (chunkTimestamp !== undefined) {
+      formData.append('chunk_timestamp', chunkTimestamp.toString())
+    }
+    const response = await this.client.post(
+      `/api/v1/test-agents/conversations/${conversationId}/process-audio`,
+      formData,
+      {
+        headers: {
+          'Content-Type': 'multipart/form-data',
+        },
+      }
+    )
+    return response.data
+  }
+
+  async getTestAgentResponseAudio(conversationId: string): Promise<Blob> {
+    const response = await this.client.get(
+      `/api/v1/test-agents/conversations/${conversationId}/response-audio`,
+      {
+        responseType: 'blob',
+      }
+    )
+    return response.data
+  }
+
+  async endTestAgentConversation(conversationId: string, finalAudioKey?: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/test-agents/conversations/${conversationId}/end`, {
+      final_audio_key: finalAudioKey,
+    })
+    return response.data
+  }
+
+  async deleteTestAgentConversation(conversationId: string): Promise<void> {
+    await this.client.delete(`/api/v1/test-agents/conversations/${conversationId}`)
+  }
+
+  // Voice Agent endpoints
+  async getVoiceAgentConnection(): Promise<{ ws_url: string; endpoint: string }> {
+    const response = await this.client.post('/api/v1/voice-agent/connect')
+    return response.data
+  }
+
+  // Playground endpoints
+  async createWebCall(data: {
+    agent_id: string
+    metadata?: Record<string, any>
+    retell_llm_dynamic_variables?: Record<string, any>
+    custom_sip_headers?: Record<string, string>
+    ui_surface?: 'agents_talk' | 'agent_playground'
+  }): Promise<{
+    call_type: string
+    access_token?: string
+    call_id?: string
+    agent_id: string
+    agent_version?: number
+    call_status?: string
+    agent_name?: string
+    metadata?: Record<string, any>
+    retell_llm_dynamic_variables?: Record<string, any>
+    sample_rate?: number
+    call_short_id?: string
+    signed_url?: string
+    host?: string
+    room_name?: string
+    conversation_id?: string
+  }> {
+    const response = await this.client.post('/api/v1/playground/web-call', data)
+    return response.data
+  }
+
+  async listCallRecordings(skip = 0, limit = 100): Promise<any[]> {
+    const response = await this.client.get('/api/v1/playground/call-recordings', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getCallRecording(callShortId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/playground/call-recordings/${callShortId}`)
+    return response.data
+  }
+
+  async refreshCallRecording(callShortId: string): Promise<{ message: string }> {
+    const response = await this.client.post(`/api/v1/playground/call-recordings/${callShortId}/refresh`)
+    return response.data
+  }
+
+  async updateCallRecording(callShortId: string, providerCallId: string): Promise<{ message: string; provider_call_id: string }> {
+    const response = await this.client.put(`/api/v1/playground/call-recordings/${callShortId}`, {
+      provider_call_id: providerCallId
+    })
+    return response.data
+  }
+
+  async deleteCallRecording(callShortId: string): Promise<{ message: string }> {
+    const response = await this.client.delete(`/api/v1/playground/call-recordings/${callShortId}`)
+    return response.data
+  }
+
+  async reEvaluateCallRecording(callShortId: string): Promise<{
+    message: string
+    evaluator_result_id: string
+    result_id: string
+    audio_s3_key: string
+    task_id: string
+  }> {
+    const response = await this.client.post(`/api/v1/playground/call-recordings/${callShortId}/re-evaluate`)
+    return response.data
+  }
+
+  async getCallRecordingAudioUrl(callShortId: string): Promise<string> {
+    const response = await this.client.get(
+      `/api/v1/playground/call-recordings/${callShortId}/audio`,
+      { responseType: 'blob' }
+    )
+    return URL.createObjectURL(response.data)
+  }
+
+  async createCustomWebsocketSession(data: {
+    agent_id: string
+    websocket_url: string
+    transcript_entries: Array<{ role: 'user' | 'agent'; content: string; timestamp: string }>
+    started_at?: string
+    ended_at?: string
+    call_short_id?: string
+    audio_file?: File
+  }): Promise<{
+    message: string
+    call_short_id: string
+    audio_s3_key?: string | null
+    evaluator_result_id?: string | null
+  }> {
+    const formData = new FormData()
+    formData.append('agent_id', data.agent_id)
+    formData.append('websocket_url', data.websocket_url)
+    formData.append('transcript_entries', JSON.stringify(data.transcript_entries))
+    if (data.started_at) {
+      formData.append('started_at', data.started_at)
+    }
+    if (data.ended_at) {
+      formData.append('ended_at', data.ended_at)
+    }
+    if (data.call_short_id) {
+      formData.append('call_short_id', data.call_short_id)
+    }
+    if (data.audio_file) {
+      formData.append('audio_file', data.audio_file)
+    }
+
+    const response = await this.client.post('/api/v1/playground/custom-websocket-sessions', formData, {
+      headers: {
+        'Content-Type': 'multipart/form-data',
+      },
+    })
+    return response.data
+  }
+
+  async evaluateCustomWebsocketSession(callShortId: string): Promise<{
+    message: string
+    evaluator_result_id: string
+    result_id: string
+    task_id: string
+  }> {
+    const response = await this.client.post(`/api/v1/playground/custom-websocket-sessions/${callShortId}/evaluate`)
+    return response.data
+  }
+
+  async getAgentSttConfig(agentId: string): Promise<{
+    available: boolean
+    provider?: string
+    model?: string
+    reason?: string
+  }> {
+    const response = await this.client.get(`/api/v1/playground/agents/${agentId}/stt-config`)
+    return response.data
+  }
+
+  async transcribeTurn(
+    agentId: string,
+    channel: 'user' | 'agent',
+    audioBlob: Blob,
+  ): Promise<{ transcript: string; channel: string }> {
+    const formData = new FormData()
+    formData.append('agent_id', agentId)
+    formData.append('channel', channel)
+    formData.append('audio_file', audioBlob, `turn_${channel}_${Date.now()}.wav`)
+    const response = await this.client.post('/api/v1/playground/transcribe-turn', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    })
+    return response.data
+  }
+
+  async summarizeTranscript(params: {
+    transcript?: string
+    entries?: Array<{ role: string; content: string; timestamp?: string }>
+    callShortId?: string
+    agentId?: string
+    force?: boolean
+  }): Promise<{
+    summary: string
+    provider: string
+    model: string
+    source?: 'voice_bundle' | 'org_fallback'
+    cached?: boolean
+    generated_at?: string
+    usage?: { prompt_tokens?: number; completion_tokens?: number; total_tokens?: number }
+  }> {
+    const body: Record<string, any> = {}
+    if (params.transcript) body.transcript = params.transcript
+    if (params.entries && params.entries.length > 0) body.entries = params.entries
+    if (params.callShortId) body.call_short_id = params.callShortId
+    if (params.agentId) body.agent_id = params.agentId
+    if (params.force) body.force = true
+    const response = await this.client.post('/api/v1/playground/summarize-transcript', body)
+    return response.data
+  }
+
+  // Observability endpoints
+  async listObservabilityCalls(skip = 0, limit = 100): Promise<ObservabilityCall[]> {
+    const response = await this.client.get('/api/v1/observability/calls', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  private buildAuthenticatedApiUrl(path: string): string {
+    const normalizedPath = path.startsWith('/') ? path : `/${path}`
+    const configuredBase = (this.client.defaults.baseURL || '').replace(/\/$/, '')
+    const url = configuredBase
+      ? new URL(`${configuredBase}${normalizedPath}`)
+      : new URL(normalizedPath, window.location.origin)
+
+    const accessToken = localStorage.getItem('accessToken')
+    const apiKey = localStorage.getItem('apiKey')
+    const workspaceId = localStorage.getItem('activeWorkspaceId')
+    if (accessToken) {
+      url.searchParams.set('token', accessToken)
+    } else if (apiKey) {
+      url.searchParams.set('api_key', apiKey)
+    }
+    if (workspaceId) {
+      url.searchParams.set('workspace_id', workspaceId)
+    }
+    return url.toString()
+  }
+
+  async getObservabilityCall(callShortId: string): Promise<ObservabilityCall> {
+    const response = await this.client.get(`/api/v1/observability/calls/${callShortId}`)
+    return response.data
+  }
+
+  getObservabilityCallLiveEventsUrl(callShortId: string): string {
+    return this.buildAuthenticatedApiUrl(
+      `/api/v1/observability/calls/${callShortId}/live-events`,
+    )
+  }
+
+  async getObservabilityCallAudioUrl(callShortId: string): Promise<string> {
+    const response = await this.client.get(
+      `/api/v1/observability/calls/${callShortId}/audio`,
+      { responseType: 'blob' },
+    )
+    return URL.createObjectURL(response.data)
+  }
+
+  async deleteObservabilityCall(callShortId: string): Promise<{ message: string }> {
+    const response = await this.client.delete(`/api/v1/observability/calls/${callShortId}`)
+    return response.data
+  }
+
+  async evaluateObservabilityCall(callShortId: string, evaluatorId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/observability/calls/${callShortId}/evaluate`, {
+      evaluator_id: evaluatorId,
+    })
+    return response.data
+  }
+
+  // Evaluator endpoints
+  async createEvaluator(data: {
+    name?: string
+    agent_id?: string
+    persona_id?: string
+    scenario_id?: string
+    custom_prompt?: string
+    metric_ids?: string[]
+    llm_provider?: string
+    llm_model?: string
+    tags?: string[]
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/evaluators', data)
+    return response.data
+  }
+
+  async formatCustomPrompt(prompt: string): Promise<{ formatted_prompt: string }> {
+    const response = await this.client.post('/api/v1/evaluators/format-prompt', { prompt })
+    return response.data
+  }
+
+  async createEvaluatorsBulk(data: {
+    name?: string
+    agent_id: string
+    scenario_id: string
+    persona_ids: string[]
+    tags?: string[]
+  }): Promise<any[]> {
+    const response = await this.client.post('/api/v1/evaluators/bulk', data)
+    return response.data
+  }
+
+  async listEvaluators(): Promise<any[]> {
+    const response = await this.client.get('/api/v1/evaluators')
+    return response.data
+  }
+
+  async getEvaluator(evaluatorId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/evaluators/${evaluatorId}`)
+    return response.data
+  }
+
+  async updateEvaluator(evaluatorId: string, data: {
+    agent_id?: string
+    persona_id?: string
+    scenario_id?: string
+    name?: string
+    custom_prompt?: string
+    metric_ids?: string[]
+    llm_provider?: string
+    llm_model?: string
+    tags?: string[]
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/evaluators/${evaluatorId}`, data)
+    return response.data
+  }
+
+  async deleteEvaluator(evaluatorId: string, force?: boolean): Promise<any> {
+    const response = await this.client.delete(`/api/v1/evaluators/${evaluatorId}`, {
+      params: force ? { force: true } : undefined,
+    })
+    return response.data
+  }
+
+  async runEvaluators(evaluatorIds: string[]): Promise<{ task_ids: string[]; evaluator_results: any[] }> {
+    const response = await this.client.post('/api/v1/evaluators/run', { evaluator_ids: evaluatorIds })
+    return response.data
+  }
+
+  async createEvaluatorSuite(data: {
+    name?: string
+    agent_id: string
+    persona_id: string
+    scenario_ids: string[]
+    metric_ids?: string[]
+    llm_provider?: string
+    llm_model?: string
+    tags?: string[]
+    default_runs_per_combination?: number
+  }): Promise<EvaluatorSuite> {
+    const response = await this.client.post('/api/v1/evaluator-suites', data)
+    return response.data
+  }
+
+  async listEvaluatorSuites(): Promise<EvaluatorSuite[]> {
+    const response = await this.client.get('/api/v1/evaluator-suites')
+    return response.data
+  }
+
+  async getEvaluatorSuite(suiteId: string): Promise<EvaluatorSuite> {
+    const response = await this.client.get(`/api/v1/evaluator-suites/${suiteId}`)
+    return response.data
+  }
+
+  async updateEvaluatorSuite(
+    suiteId: string,
+    data: {
+      name?: string
+      metric_ids?: string[] | null
+      llm_provider?: string | null
+      llm_model?: string | null
+      tags?: string[]
+      default_runs_per_combination?: number
+    },
+  ): Promise<EvaluatorSuite> {
+    const response = await this.client.put(`/api/v1/evaluator-suites/${suiteId}`, data)
+    return response.data
+  }
+
+  async addEvaluatorSuiteScenarios(suiteId: string, scenarioIds: string[]): Promise<EvaluatorSuite> {
+    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/scenarios`, {
+      scenario_ids: scenarioIds,
+    })
+    return response.data
+  }
+
+  async removeEvaluatorSuiteScenario(suiteId: string, scenarioId: string): Promise<EvaluatorSuite> {
+    const response = await this.client.delete(
+      `/api/v1/evaluator-suites/${suiteId}/scenarios/${scenarioId}`,
+    )
+    return response.data
+  }
+
+  async deleteEvaluatorSuite(suiteId: string): Promise<void> {
+    await this.client.delete(`/api/v1/evaluator-suites/${suiteId}`)
+  }
+
+  async runEvaluatorSuite(
+    suiteId: string,
+    data: { runs_per_combination: number; to_number?: string; from_number?: string },
+  ): Promise<RunEvaluatorSuiteResponse> {
+    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/run`, data)
+    return response.data
+  }
+
+  async runNextCombination(
+    suiteId: string,
+    data?: { from_number?: string },
+  ): Promise<RunNextCombinationResponse> {
+    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/run-next`, data || {})
+    return response.data
+  }
+
+  async chooseNextCombination(suiteId: string): Promise<ChooseNextCombinationResponse> {
+    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/choose-next`)
+    return response.data
+  }
+
+  async activateEvaluatorSuite(suiteId: string): Promise<EvaluatorSuite> {
+    const response = await this.client.post(`/api/v1/evaluator-suites/${suiteId}/activate`)
+    return response.data
+  }
+
+  // Metric endpoints
+  async createMetric(data: {
+    name: string
+    description?: string
+    /**
+     * Optional illustrative example surfaced alongside the description
+     * in the LLM judge's rubric. Mainly used by categorization label
+     * children but accepted on every metric.
+     */
+    example?: string | null
+    metric_type: 'number' | 'boolean' | 'rating' | 'text'
+    trigger?: 'always'
+    enabled?: boolean
+    metric_origin?: 'default' | 'custom'
+    supported_surfaces?: string[]
+    enabled_surfaces?: string[]
+    custom_data_type?: 'boolean' | 'enum' | 'number_range'
+    custom_config?: Record<string, any>
+    tags?: string[]
+    capture_rationale?: boolean
+    parent_metric_id?: string | null
+    selection_mode?: 'single_choice' | 'multi_label' | null
+    /**
+     * Scope of the new metric:
+     *   - "workspace" (default): stamped with the active X-Workspace-Id
+     *     header so it only appears inside that workspace.
+     *   - "organization": stored with workspace_id=NULL so it surfaces
+     *     in every workspace of the caller's org.
+     * Ignored when parent_metric_id is set — children always inherit
+     * their parent's scope.
+     */
+    scope?: 'workspace' | 'organization'
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/metrics', data)
+    return response.data
+  }
+
+  /**
+   * Atomically create a parent (category) metric + N children, used by
+   * the "Create category" flow on the Metrics page and by the
+   * /parse-bulk → save path when a hierarchy was requested.
+   */
+  async createMetricWithChildren(data: {
+    name: string
+    description?: string | null
+    selection_mode: 'single_choice' | 'multi_label'
+    enabled?: boolean
+    supported_surfaces?: string[]
+    enabled_surfaces?: string[]
+    tags?: string[] | null
+    allow_discovery?: boolean
+    /**
+     * Parent-level "Enable LLM Rationale" toggle. When true the LLM
+     * judge emits one rationale at the category level (children never
+     * carry rationales in hierarchical mode).
+     */
+    capture_rationale?: boolean
+    children: Array<{
+      name: string
+      description?: string | null
+      /**
+       * Optional illustrative example for this label, surfaced
+       * alongside the description in the LLM judge's rubric.
+       */
+      example?: string | null
+      enabled?: boolean
+      capture_rationale?: boolean | null
+      tags?: string[] | null
+    }>
+    /**
+     * Same semantics as {@link createMetric}'s ``scope``: when
+     * ``"organization"`` the parent + every child are stored with
+     * ``workspace_id=NULL`` so the whole category subtree is visible
+     * in every workspace of the org.
+     */
+    scope?: 'workspace' | 'organization'
+  }): Promise<any> {
+    const response = await this.client.post(
+      '/api/v1/metrics/with-children',
+      data,
+    )
+    return response.data
+  }
+
+  async addMetricChild(parentMetricId: string, data: {
+    name: string
+    description?: string | null
+    /**
+     * Optional illustrative example for this label, surfaced alongside
+     * the description in the LLM judge's rubric.
+     */
+    example?: string | null
+    enabled?: boolean
+    capture_rationale?: boolean | null
+    tags?: string[] | null
+  }): Promise<any> {
+    const response = await this.client.post(
+      `/api/v1/metrics/${parentMetricId}/children`,
+      data,
+    )
+    return response.data
+  }
+
+  async listMetrics(
+    surface?: string,
+    includeChildren: boolean = true,
+    options?: { includeDrafts?: boolean; draftsOnly?: boolean; enabledOnly?: boolean },
+  ): Promise<any[]> {
+    const response = await this.client.get('/api/v1/metrics', {
+      params: {
+        ...(surface ? { surface } : {}),
+        include_children: includeChildren,
+        ...(options?.includeDrafts ? { include_drafts: true } : {}),
+        ...(options?.draftsOnly ? { drafts_only: true } : {}),
+        ...(options?.enabledOnly ? { enabled_only: true } : {}),
+      },
+    })
+    return response.data
+  }
+
+  async createMetricDraft(data: {
+    name: string
+    description?: string
+    metric_type: string
+    trigger: string
+    metric_origin?: string
+    studio_notes?: string
+    parent_metric_id?: string
+    selection_mode?: string
+    custom_data_type?: string
+    custom_config?: Record<string, unknown>
+    capture_rationale?: boolean
+    compare_transcripts?: boolean
+    scope?: 'workspace' | 'organization'
+    supported_surfaces?: string[]
+    enabled_surfaces?: string[]
+    tags?: string[]
+    enabled?: boolean
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/metrics/drafts', data)
+    return response.data
+  }
+
+  async createMetricDraftWithChildren(data: {
+    name: string
+    description?: string | null
+    selection_mode: 'single_choice' | 'multi_label'
+    allow_discovery?: boolean
+    capture_rationale?: boolean
+    supported_surfaces: string[]
+    enabled_surfaces: string[]
+    children: Array<{
+      name: string
+      description?: string | null
+      example?: string | null
+      capture_rationale?: boolean
+      enabled?: boolean
+    }>
+    scope?: 'workspace' | 'organization'
+    studio_notes?: string
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/metrics/drafts/with-children', data)
+    return response.data
+  }
+
+  async promoteMetric(metricId: string): Promise<{ metric: any; promoted_at: string }> {
+    const response = await this.client.post(`/api/v1/metrics/${metricId}/promote`)
+    return response.data
+  }
+
+  async createMetricStudioRun(data: {
+    name?: string
+    metric_ids: string[]
+    sources: Array<{
+      source_kind: 'call_import_row' | 'call_recording' | 'evaluator_result'
+      source_ref: string
+      display_label?: string
+    }>
+    transcript_source?: 'production' | 'diarised'
+    llm_provider?: string
+    llm_model?: string
+    llm_credential_id?: string
+    llm_config?: LLMGenerationConfig | null
+    metric_llm_overrides?: Record<string, unknown>
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/metric-studio/runs', data)
+    return response.data
+  }
+
+  async listMetricStudioRuns(skip = 0, limit = 50): Promise<{ items: any[]; total: number }> {
+    const response = await this.client.get('/api/v1/metric-studio/runs', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getMetricStudioRun(runId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/metric-studio/runs/${runId}`)
+    return response.data
+  }
+
+  async listMetricStudioRunResults(
+    runId: string,
+    skip = 0,
+    limit = 100,
+  ): Promise<{ items: any[]; total: number }> {
+    const response = await this.client.get(`/api/v1/metric-studio/runs/${runId}/results`, {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async retryMetricStudioRun(
+    runId: string,
+    resultIds?: string[],
+  ): Promise<any> {
+    const response = await this.client.post(`/api/v1/metric-studio/runs/${runId}/retry`, {
+      result_ids: resultIds,
+    })
+    return response.data
+  }
+
+  async deleteMetricStudioRun(runId: string): Promise<void> {
+    await this.client.delete(`/api/v1/metric-studio/runs/${runId}`)
+  }
+
+  // Evaluator Results endpoints
+  async listEvaluatorResults(
+    paramsOrEvaluatorId?: ListEvaluatorResultsParams | string,
+    playground?: boolean,
+    testAgentsOnly?: boolean,
+    agentId?: string
+  ): Promise<EvaluatorResultListResponse> {
+    let params: Record<string, unknown> = {}
+    if (typeof paramsOrEvaluatorId === 'string' || paramsOrEvaluatorId === undefined) {
+      if (paramsOrEvaluatorId) {
+        params.evaluator_id = paramsOrEvaluatorId
+      }
+      if (agentId) {
+        params.agent_id = agentId
+      }
+      if (playground !== undefined) {
+        params.playground = playground
+      }
+      if (testAgentsOnly !== undefined) {
+        params.test_agents_only = testAgentsOnly
+      }
+    } else {
+      const p = paramsOrEvaluatorId
+      if (p.skip !== undefined) params.skip = p.skip
+      if (p.limit !== undefined) params.limit = p.limit
+      if (p.evaluatorId) params.evaluator_id = p.evaluatorId
+      if (p.agentId) params.agent_id = p.agentId
+      if (p.suiteId) params.suite_id = p.suiteId
+      if (p.scenarioId) params.scenario_id = p.scenarioId
+      if (p.status) params.status = p.status
+      if (p.unassignedOnly) params.unassigned_only = true
+      if (p.playground !== undefined) params.playground = p.playground
+      if (p.testAgentsOnly !== undefined) params.test_agents_only = p.testAgentsOnly
+    }
+    const response = await this.client.get('/api/v1/evaluator-results', { params })
+    return response.data
+  }
+
+  async getEvaluatorResultsOverview(params?: {
+    agentId?: string
+    suiteId?: string
+  }): Promise<EvaluatorResultsOverviewResponse> {
+    const query: Record<string, string> = {}
+    if (params?.agentId) query.agent_id = params.agentId
+    if (params?.suiteId) query.suite_id = params.suiteId
+    const response = await this.client.get('/api/v1/evaluator-results/overview', { params: query })
+    return response.data
+  }
+
+  async getEvaluatorResultsAggregate(params: {
+    suiteId?: string
+    agentId?: string
+    scenarioId?: string
+  }): Promise<EvaluatorResultsAggregateResponse> {
+    const query: Record<string, string> = {}
+    if (params.suiteId) query.suite_id = params.suiteId
+    if (params.agentId) query.agent_id = params.agentId
+    if (params.scenarioId) query.scenario_id = params.scenarioId
+    const response = await this.client.get('/api/v1/evaluator-results/aggregate', { params: query })
+    return response.data
+  }
+
+  async getEvaluatorResult(id: string, includeRelations: boolean = true): Promise<any> {
+    const params = includeRelations ? { include_relations: 'true' } : {}
+    const response = await this.client.get(`/api/v1/evaluator-results/${id}`, { params })
+    return response.data
+  }
+
+  async getEvaluatorResultMetrics(id: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/evaluator-results/${id}/metrics`)
+    return response.data
+  }
+
+  async getSyntheticCallTraceForResult(evaluatorResultId: string): Promise<any> {
+    const response = await this.client.get(
+      `/api/v1/observability/traces/results/${evaluatorResultId}`,
+    )
+    return response.data
+  }
+
+  async listSyntheticCallTraces(params?: {
+    skip?: number
+    limit?: number
+    status?: string
+  }): Promise<{ items: any[]; total: number }> {
+    const response = await this.client.get('/api/v1/observability/traces', { params })
+    return response.data
+  }
+
+  async getSyntheticCallTrace(traceId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/observability/traces/${traceId}`)
+    return response.data
+  }
+
+  async getSyntheticCallTraceByCallShortId(callShortId: string): Promise<any> {
+    const response = await this.client.get(
+      `/api/v1/observability/traces/by-call-short-id/${callShortId}`,
+    )
+    return response.data
+  }
+
+  async getSyntheticCallTraceSetup(): Promise<any> {
+    const response = await this.client.get('/api/v1/observability/traces/setup')
+    return response.data
+  }
+
+  async createSyntheticTraceSession(data: {
+    transport?: 'webrtc' | 'websocket' | 'phone' | 'custom'
+    evaluator_result_id?: string
+  }): Promise<{
+    trace_id: string
+    call_short_id: string
+    workspace_id: string
+    transport: string
+    status: string
+    otel_correlation: {
+      otlp_endpoint: string
+      api_key_header: string
+      suggested_env_vars: Record<string, string>
+      suggested_otlp_headers: Record<string, string>
+      suggested_span_attributes: Record<string, string>
+    }
+  }> {
+    const response = await this.client.post('/api/v1/observability/traces/sessions', data)
+    return response.data
+  }
+
+  async closeSyntheticTraceSession(callShortId: string): Promise<{
+    trace_id: string
+    call_short_id: string
+    status: string
+  }> {
+    const response = await this.client.post(
+      `/api/v1/observability/traces/sessions/${callShortId}/close`,
+    )
+    return response.data
+  }
+
+  async ingestSyntheticTraceJson(data: {
+    call_short_id: string
+    spans: Array<{
+      name: string
+      turn_number: number
+      ttfb_ms?: number
+      attributes?: Record<string, unknown>
+    }>
+  }): Promise<{
+    accepted_spans: number
+    synthetic_call_trace_id?: string
+    correlated: boolean
+  }> {
+    const response = await this.client.post('/api/v1/observability/traces/ingest', data)
+    return response.data
+  }
+
+  async getEvaluatorResultOtelCorrelation(id: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/evaluator-results/${id}/otel-correlation`)
+    return response.data
+  }
+
+  async getEvaluatorResultAudioUrl(resultId: string): Promise<string> {
+    const response = await this.client.get(
+      `/api/v1/evaluator-results/${resultId}/audio`,
+      { responseType: 'blob' },
+    )
+    return URL.createObjectURL(response.data)
+  }
+
+  async createEvaluatorResultManual(data: {
+    evaluator_id: string
+    audio_s3_key: string
+    duration_seconds?: number
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/evaluator-results', data)
+    return response.data
+  }
+
+  async reEvaluateResult(id: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/evaluator-results/${id}/re-evaluate`)
+    return response.data
+  }
+
+  async deleteEvaluatorResult(id: string): Promise<void> {
+    await this.client.delete(`/api/v1/evaluator-results/${id}`)
+  }
+
+  async deleteEvaluatorResultsBulk(ids: string[]): Promise<void> {
+    // FastAPI expects multiple query parameters with the same name
+    // Build query string manually: result_ids=id1&result_ids=id2
+    const params = new URLSearchParams()
+    ids.forEach(id => params.append('result_ids', id))
+    await this.client.delete(`/api/v1/evaluator-results?${params.toString()}`)
+  }
+
+  async getMetric(metricId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/metrics/${metricId}`)
+    return response.data
+  }
+
+  async updateMetric(metricId: string, data: {
+    name?: string
+    description?: string
+    /**
+     * Pass ``""`` (empty string) to clear the stored example; omit to
+     * leave the persisted value untouched.
+     */
+    example?: string | null
+    metric_type?: 'number' | 'boolean' | 'rating' | 'text'
+    trigger?: 'always'
+    enabled?: boolean
+    metric_origin?: 'default' | 'custom'
+    supported_surfaces?: string[]
+    enabled_surfaces?: string[]
+    custom_data_type?: 'boolean' | 'enum' | 'number_range'
+    custom_config?: Record<string, any>
+    tags?: string[]
+    capture_rationale?: boolean
+    selection_mode?: 'single_choice' | 'multi_label' | null
+    /** Only honored on multi_label parents; backend 400s otherwise. */
+    allow_discovery?: boolean
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/metrics/${metricId}`, data)
+    return response.data
+  }
+
+  async deleteMetric(metricId: string): Promise<void> {
+    await this.client.delete(`/api/v1/metrics/${metricId}`)
+  }
+
+  async seedDefaultMetrics(): Promise<any[]> {
+    const response = await this.client.post('/api/v1/metrics/seed-defaults')
+    return response.data
+  }
+
+  async generateMetric(data: {
+    mode: 'description' | 'examples'
+    surface: 'agent' | 'voice_playground' | 'blind_test'
+    description?: string
+    examples?: Array<{ transcript: string; rating: any; notes?: string }>
+    provider?: string
+    model?: string
+    credential_id?: string
+    llm_config?: Record<string, any>
+  }): Promise<{
+    name: string
+    description: string
+    metric_type: 'rating' | 'boolean' | 'number' | 'text'
+    custom_data_type: 'boolean' | 'enum' | 'number_range' | null
+    custom_config: Record<string, any>
+    supported_surfaces: string[]
+    enabled_surfaces: string[]
+    suggested_tags: string[]
+    provider?: string
+    model?: string
+  }> {
+    const response = await this.client.post('/api/v1/metrics/generate', data)
+    return response.data
+  }
+
+  async parseBulkMetric(data: {
+    prompt: string
+    surface: 'agent' | 'voice_playground' | 'blind_test'
+    /** When set, the response includes a ``parent`` block + all labels are children. */
+    parent_name?: string
+    parent_description?: string
+    selection_mode?: 'single_choice' | 'multi_label'
+  }): Promise<{
+    metrics: Array<{
+      name: string
+      description: string
+      metric_type: 'rating' | 'boolean' | 'number' | 'text'
+      custom_data_type: 'boolean' | 'enum' | 'number_range' | null
+      custom_config: Record<string, any>
+      supported_surfaces: string[]
+      enabled_surfaces: string[]
+      capture_rationale: boolean
+      suggested_tags: string[]
+      source_label: { label_name: string; definition: string; examples: string }
+    }>
+    parent?: {
+      name: string
+      description: string | null
+      selection_mode: 'single_choice' | 'multi_label'
+      supported_surfaces: string[]
+      enabled_surfaces: string[]
+    } | null
+  }> {
+    const response = await this.client.post('/api/v1/metrics/parse-bulk', data)
+    return response.data
+  }
+
+  // Alert endpoints
+  async listAlerts(status?: string): Promise<any[]> {
+    const params: any = {}
+    if (status) {
+      params.status_filter = status
+    }
+    const response = await this.client.get('/api/v1/alerts', { params })
+    return response.data
+  }
+
+  async getAlert(alertId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/alerts/${alertId}`)
+    return response.data
+  }
+
+  async createAlert(data: {
+    name: string
+    description?: string | null
+    metric_type: string
+    aggregation: string
+    operator: string
+    threshold_value: number
+    time_window_minutes: number
+    agent_ids?: string[] | null
+    notify_frequency: string
+    notify_emails?: string[]
+    notify_webhooks?: string[]
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/alerts', data)
+    return response.data
+  }
+
+  async updateAlert(alertId: string, data: {
+    name?: string
+    description?: string | null
+    metric_type?: string
+    aggregation?: string
+    operator?: string
+    threshold_value?: number
+    time_window_minutes?: number
+    agent_ids?: string[] | null
+    notify_frequency?: string
+    notify_emails?: string[]
+    notify_webhooks?: string[]
+    status?: string
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/alerts/${alertId}`, data)
+    return response.data
+  }
+
+  async deleteAlert(alertId: string): Promise<void> {
+    await this.client.delete(`/api/v1/alerts/${alertId}`)
+  }
+
+  async toggleAlertStatus(alertId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/alerts/${alertId}/toggle`)
+    return response.data
+  }
+
+  // Alert History endpoints
+  async listAlertHistory(status?: string, alertId?: string, skip = 0, limit = 100): Promise<any[]> {
+    const params: any = { skip, limit }
+    if (status) {
+      params.status_filter = status
+    }
+    if (alertId) {
+      params.alert_id = alertId
+    }
+    const response = await this.client.get('/api/v1/alerts/history/all', { params })
+    return response.data
+  }
+
+  async getAlertHistoryItem(historyId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/alerts/history/${historyId}`)
+    return response.data
+  }
+
+  async acknowledgeAlertHistory(historyId: string): Promise<any> {
+    const response = await this.client.put(`/api/v1/alerts/history/${historyId}`, {
+      status: 'acknowledged'
+    })
+    return response.data
+  }
+
+  async resolveAlertHistory(historyId: string, resolutionNotes?: string): Promise<any> {
+    const response = await this.client.put(`/api/v1/alerts/history/${historyId}`, {
+      status: 'resolved',
+      resolution_notes: resolutionNotes
+    })
+    return response.data
+  }
+
+  // Alert Evaluation & Notification endpoints
+  async triggerAlert(alertId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/alerts/${alertId}/trigger`)
+    return response.data
+  }
+
+  async evaluateAllAlerts(): Promise<any> {
+    const response = await this.client.post('/api/v1/alerts/evaluate/all')
+    return response.data
+  }
+
+  async testAlertNotification(alertId: string, data: {
+    webhook_url?: string
+    email?: string
+  }): Promise<any> {
+    const response = await this.client.post(`/api/v1/alerts/${alertId}/test-notification`, data)
+    return response.data
+  }
+
+  // Cron Job endpoints
+  async listCronJobs(): Promise<any[]> {
+    const response = await this.client.get('/api/v1/cron-jobs')
+    return response.data
+  }
+
+  async getCronJob(cronJobId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/cron-jobs/${cronJobId}`)
+    return response.data
+  }
+
+  async createCronJob(data: {
+    name: string
+    cron_expression: string
+    timezone: string
+    max_runs: number
+    evaluator_ids: string[]
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/cron-jobs', data)
+    return response.data
+  }
+
+  async updateCronJob(cronJobId: string, data: {
+    name?: string
+    cron_expression?: string
+    timezone?: string
+    max_runs?: number
+    evaluator_ids?: string[]
+    status?: string
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/cron-jobs/${cronJobId}`, data)
+    return response.data
+  }
+
+  async deleteCronJob(cronJobId: string): Promise<void> {
+    await this.client.delete(`/api/v1/cron-jobs/${cronJobId}`)
+  }
+
+  async toggleCronJobStatus(cronJobId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/cron-jobs/${cronJobId}/toggle`)
+    return response.data
+  }
+
+  // Voice Playground (TTS Comparison) endpoints
+  async listTTSProviders(): Promise<any[]> {
+    const response = await this.client.get('/api/v1/voice-playground/tts-providers')
+    return response.data
+  }
+
+  async listCustomTTSVoices(provider?: string): Promise<Array<{
+    id: string
+    provider: string
+    voice_id: string
+    name: string
+    gender: string
+    accent: string
+    description?: string | null
+    is_custom: boolean
+    created_at?: string | null
+    updated_at?: string | null
+  }>> {
+    const response = await this.client.get('/api/v1/voice-playground/custom-voices', {
+      params: provider ? { provider } : undefined,
+    })
+    return response.data
+  }
+
+  async createCustomTTSVoice(data: {
+    provider: string
+    voice_id: string
+    name: string
+    gender?: string
+    accent?: string
+    description?: string
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/voice-playground/custom-voices', data)
+    return response.data
+  }
+
+  async updateCustomTTSVoice(customVoiceId: string, data: {
+    voice_id?: string
+    name?: string
+    gender?: string
+    accent?: string
+    description?: string
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/voice-playground/custom-voices/${customVoiceId}`, data)
+    return response.data
+  }
+
+  async deleteCustomTTSVoice(customVoiceId: string): Promise<any> {
+    const response = await this.client.delete(`/api/v1/voice-playground/custom-voices/${customVoiceId}`)
+    return response.data
+  }
+
+  async createTTSComparison(data: {
+    name?: string
+    mode?: 'benchmark' | 'blind_test_only'
+    // Legacy benchmark fields
+    provider_a?: string
+    model_a?: string
+    voices_a?: Array<{ id: string; name: string; sample_rate_hz?: number }>
+    provider_b?: string
+    model_b?: string
+    voices_b?: Array<{ id: string; name: string; sample_rate_hz?: number }>
+    // New per-side benchmark config
+    side_a?: VoicePlaygroundSideConfig
+    side_b?: VoicePlaygroundSideConfig
+    // Common
+    sample_texts?: string[]
+    num_runs?: number
+    eval_stt_provider?: string
+    eval_stt_model?: string
+    // blind_test_only
+    pairs?: Array<VoicePlaygroundBlindTestPair>
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/voice-playground/comparisons', data)
+    return response.data
+  }
+
+  async listVoicePlaygroundCallImportRows(params: {
+    call_import_id?: string
+    with_recording?: boolean
+    skip?: number
+    limit?: number
+  } = {}): Promise<{
+    items: Array<{
+      id: string
+      call_import_id: string
+      call_import_filename: string | null
+      conversation_id: string
+      transcript: string | null
+      recording_s3_key: string | null
+      has_recording: boolean
+      status: string
+      created_at: string | null
+    }>
+    total: number
+    skip: number
+    limit: number
+  }> {
+    const response = await this.client.get('/api/v1/voice-playground/call-import-rows', { params })
+    return response.data
+  }
+
+  async uploadVoicePlaygroundAudio(file: File): Promise<{
+    s3_key: string
+    presigned_url: string | null
+    filename: string
+    size_bytes: number
+    content_type: string
+  }> {
+    const form = new FormData()
+    form.append('file', file)
+    const response = await this.client.post('/api/v1/voice-playground/uploads', form, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    })
+    return response.data
+  }
+
+  async listTTSComparisons(skip = 0, limit = 50): Promise<any[]> {
+    const response = await this.client.get('/api/v1/voice-playground/comparisons', {
+      params: { skip, limit },
+    })
+    return response.data
+  }
+
+  async getTTSComparison(comparisonId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/voice-playground/comparisons/${comparisonId}`)
+    return response.data
+  }
+
+  async generateTTSComparison(comparisonId: string): Promise<{ message: string; task_id: string }> {
+    const response = await this.client.post(`/api/v1/voice-playground/comparisons/${comparisonId}/generate`)
+    return response.data
+  }
+
+  async submitBlindTest(comparisonId: string, results: Array<{
+    sample_index: number
+    preferred: 'A' | 'B'
+    voice_a_id: string
+    voice_b_id: string
+  }>): Promise<any> {
+    const response = await this.client.post(
+      `/api/v1/voice-playground/comparisons/${comparisonId}/blind-test`,
+      { results }
+    )
+    return response.data
+  }
+
+  async deleteTTSComparison(comparisonId: string): Promise<void> {
+    await this.client.delete(`/api/v1/voice-playground/comparisons/${comparisonId}`)
+  }
+
+  // Blind Test Sharing (owner side)
+  async createBlindTestShare(comparisonId: string, payload: {
+    title: string
+    description?: string
+    creator_notes?: string
+    custom_metrics: Array<{ key: string; label: string; type: 'rating' | 'comment'; scale?: number }>
+  }): Promise<any> {
+    const response = await this.client.post(
+      `/api/v1/voice-playground/comparisons/${comparisonId}/share`,
+      payload
+    )
+    return response.data
+  }
+
+  async getBlindTestShare(comparisonId: string): Promise<any> {
+    const response = await this.client.get(
+      `/api/v1/voice-playground/comparisons/${comparisonId}/share`
+    )
+    return response.data
+  }
+
+  async updateBlindTestShare(shareId: string, payload: {
+    title?: string
+    description?: string
+    creator_notes?: string
+    custom_metrics?: Array<{ key: string; label: string; type: 'rating' | 'comment'; scale?: number }>
+    status?: 'open' | 'closed'
+  }): Promise<any> {
+    const response = await this.client.patch(
+      `/api/v1/voice-playground/shares/${shareId}`,
+      payload
+    )
+    return response.data
+  }
+
+  async deleteBlindTestShare(shareId: string): Promise<void> {
+    await this.client.delete(`/api/v1/voice-playground/shares/${shareId}`)
+  }
+
+  async listBlindTestResponses(shareId: string, skip = 0, limit = 100): Promise<{
+    items: any[]
+    total: number
+  }> {
+    const response = await this.client.get(
+      `/api/v1/voice-playground/shares/${shareId}/responses`,
+      { params: { skip, limit } }
+    )
+    return response.data
+  }
+
+  async generateSampleTexts(params: {
+    voice_bundle_id?: string
+    provider?: string
+    model?: string
+    scenario?: string
+    count?: number
+    length?: string
+    temperature?: number
+    llm_config?: LLMGenerationConfig | null
+  }): Promise<{ samples: string[]; provider: string; model: string }> {
+    const response = await this.client.post('/api/v1/voice-playground/generate-samples', params)
+    return response.data
+  }
+
+  async getTTSAnalytics(): Promise<Array<{
+    provider: string
+    model: string
+    voice_id: string
+    voice_name: string
+    sample_count: number
+    avg_mos: number | null
+    avg_valence: number | null
+    avg_arousal: number | null
+    avg_prosody: number | null
+    avg_ttfb_ms: number | null
+    avg_latency_ms: number | null
+    avg_wer: number | null
+    avg_cer: number | null
+  }>> {
+    const response = await this.client.get('/api/v1/voice-playground/analytics')
+    return response.data
+  }
+
+  async downloadTTSComparisonReport(
+    comparisonId: string,
+    includeUnfinishedSamples = false,
+    reportOptions?: TTSReportOptionsPayload
+  ): Promise<Blob> {
+    const response = await this.client.get(
+      `/api/v1/voice-playground/comparisons/${comparisonId}/report.pdf`,
+      {
+        params: {
+          include_unfinished_samples: includeUnfinishedSamples,
+          ...(reportOptions ? { report_options: JSON.stringify(reportOptions) } : {}),
+        },
+        responseType: 'blob',
+      }
+    )
+    return response.data
+  }
+
+  async createTTSComparisonReportJob(
+    comparisonId: string,
+    reportOptions?: TTSReportOptionsPayload
+  ): Promise<{
+    id: string
+    comparison_id: string
+    status: string
+    format: string
+    task_id?: string
+    report_options?: TTSReportOptionsPayload
+    created_at?: string | null
+  }> {
+    const response = await this.client.post(
+      `/api/v1/voice-playground/comparisons/${comparisonId}/reports`,
+      reportOptions ? { report_options: reportOptions } : {}
+    )
+    return response.data
+  }
+
+  async getVoicePlaygroundReportThresholdDefaults(): Promise<{
+    zone_threshold_overrides: NonNullable<TTSReportOptionsPayload['zone_threshold_overrides']>
+    is_custom: boolean
+  }> {
+    const response = await this.client.get('/api/v1/voice-playground/report-threshold-defaults')
+    return response.data
+  }
+
+  async updateVoicePlaygroundReportThresholdDefaults(data: {
+    zone_threshold_overrides?: NonNullable<TTSReportOptionsPayload['zone_threshold_overrides']>
+    reset_to_system_defaults?: boolean
+  }): Promise<{
+    zone_threshold_overrides: NonNullable<TTSReportOptionsPayload['zone_threshold_overrides']>
+    is_custom: boolean
+    message: string
+  }> {
+    const response = await this.client.put('/api/v1/voice-playground/report-threshold-defaults', data)
+    return response.data
+  }
+
+  async getTTSComparisonReportJob(reportJobId: string): Promise<{
+    id: string
+    comparison_id: string
+    status: string
+    format: string
+    filename?: string | null
+    error_message?: string | null
+    task_id?: string | null
+    download_url?: string | null
+    report_options?: TTSReportOptionsPayload
+    created_at?: string | null
+    updated_at?: string | null
+  }> {
+    const response = await this.client.get(`/api/v1/voice-playground/reports/${reportJobId}`)
+    return response.data
+  }
+
+  // Prompt Partials
+  async listPromptPartials(
+    skip = 0,
+    limit = 100,
+    search?: string,
+    kind: 'all' | 'partial' | 'imported_agent' | 'metric' = 'all',
+  ): Promise<any[]> {
+    const response = await this.client.get('/api/v1/prompt-partials', {
+      params: {
+        skip,
+        limit,
+        kind,
+        ...(search ? { search } : {}),
+      },
+    })
+    return response.data
+  }
+
+  async listImportedAgents(skip = 0, limit = 100, search?: string): Promise<import('../types/api').ImportedAgent[]> {
+    return this.listPromptPartials(skip, limit, search, 'imported_agent')
+  }
+
+  async listMetricPartials(skip = 0, limit = 100, search?: string): Promise<import('../types/api').MetricPartial[]> {
+    return this.listPromptPartials(skip, limit, search, 'metric')
+  }
+
+  async createImportedAgent(data: {
+    name: string
+    description?: string | null
+    content: string
+  }): Promise<import('../types/api').ImportedAgent> {
+    const response = await this.client.post('/api/v1/prompt-partials', {
+      ...data,
+      tags: ['__imported_agent__'],
+    })
+    return response.data
+  }
+
+  async createMetricPartial(data: {
+    name: string
+    description?: string | null
+    content: string
+  }): Promise<import('../types/api').MetricPartial> {
+    const response = await this.client.post('/api/v1/prompt-partials', {
+      ...data,
+      tags: ['__metric_partial__'],
+    })
+    return response.data
+  }
+
+  async updateMetricPartial(
+    partialId: string,
+    data: {
+      name?: string
+      description?: string | null
+      content?: string
+      change_summary?: string
+    },
+  ): Promise<import('../types/api').MetricPartial> {
+    const response = await this.client.put(`/api/v1/prompt-partials/${partialId}`, {
+      ...data,
+      tags: ['__metric_partial__'],
+    })
+    return response.data
+  }
+
+  async generateAgentFlowchart(
+    partialId: string,
+    options?: {
+      provider?: string
+      model?: string
+      credential_id?: string
+      regenerate?: boolean
+    },
+  ): Promise<import('../types/api').AgentFlowGraph> {
+    const response = await this.client.post(
+      `/api/v1/prompt-partials/${partialId}/flowchart`,
+      {
+        provider: options?.provider,
+        model: options?.model,
+        credential_id: options?.credential_id,
+        regenerate: options?.regenerate ?? false,
+      },
+    )
+    return response.data
+  }
+
+  async saveAgentFlowchartLayout(
+    partialId: string,
+    nodes: Array<{ id: string; position_x: number; position_y: number }>,
+  ): Promise<import('../types/api').AgentFlowGraph> {
+    const response = await this.client.put(
+      `/api/v1/prompt-partials/${partialId}/flowchart/layout`,
+      { nodes },
+    )
+    return response.data
+  }
+
+  async mapAgentFlowchartPromptSections(
+    partialId: string,
+    options?: { provider?: string; model?: string; credential_id?: string },
+  ): Promise<import('../types/api').AgentFlowGraph> {
+    const response = await this.client.post(
+      `/api/v1/prompt-partials/${partialId}/flowchart/prompt-map`,
+      {
+        provider: options?.provider,
+        model: options?.model,
+        credential_id: options?.credential_id,
+      },
+    )
+    return response.data
+  }
+
+  async updateImportedAgent(
+    partialId: string,
+    data: {
+      name?: string
+      description?: string | null
+      content?: string
+      change_summary?: string
+    },
+  ): Promise<import('../types/api').ImportedAgent> {
+    const response = await this.client.put(`/api/v1/prompt-partials/${partialId}`, {
+      ...data,
+      tags: ['__imported_agent__'],
+    })
+    return response.data
+  }
+
+  async getPromptPartial(partialId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/prompt-partials/${partialId}`)
+    return response.data
+  }
+
+  async createPromptPartial(data: {
+    name: string
+    description?: string
+    content: string
+    tags?: string[]
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/prompt-partials', data)
+    return response.data
+  }
+
+  async updatePromptPartial(partialId: string, data: {
+    name?: string
+    description?: string
+    content?: string
+    tags?: string[]
+    change_summary?: string
+  }): Promise<any> {
+    const response = await this.client.put(`/api/v1/prompt-partials/${partialId}`, data)
+    return response.data
+  }
+
+  async deletePromptPartial(partialId: string): Promise<void> {
+    await this.client.delete(`/api/v1/prompt-partials/${partialId}`)
+  }
+
+  async listPromptPartialVersions(partialId: string): Promise<any[]> {
+    const response = await this.client.get(`/api/v1/prompt-partials/${partialId}/versions`)
+    return response.data
+  }
+
+  async getPromptPartialVersion(partialId: string, versionNumber: number): Promise<any> {
+    const response = await this.client.get(`/api/v1/prompt-partials/${partialId}/versions/${versionNumber}`)
+    return response.data
+  }
+
+  async revertPromptPartial(partialId: string, versionNumber: number): Promise<any> {
+    const response = await this.client.post(`/api/v1/prompt-partials/${partialId}/revert/${versionNumber}`)
+    return response.data
+  }
+
+  async clonePromptPartial(partialId: string): Promise<any> {
+    const response = await this.client.post(`/api/v1/prompt-partials/${partialId}/clone`)
+    return response.data
+  }
+
+  async generatePromptWithAI(data: {
+    description: string
+    tone?: string
+    format_style?: string
+    provider?: string
+    model?: string
+    credential_id?: string
+    llm_config?: LLMGenerationConfig | null
+  }): Promise<{ content: string; provider: string; model: string }> {
+    const response = await this.client.post('/api/v1/prompt-partials/generate', data)
+    return response.data
+  }
+
+  async improvePromptWithAI(data: {
+    content: string
+    instructions?: string
+    provider?: string
+    model?: string
+    credential_id?: string
+    llm_config?: LLMGenerationConfig | null
+  }): Promise<{ content: string; provider: string; model: string }> {
+    const response = await this.client.post('/api/v1/prompt-partials/improve', data)
+    return response.data
+  }
+
+  // License / Enterprise
+  async getLicenseInfo(): Promise<LicenseInfoResponse> {
+    const response = await this.client.get('/api/v1/settings/license-info')
+    return response.data
+  }
+
+  async getReportBranding(): Promise<ReportBranding> {
+    const response = await this.client.get('/api/v1/settings/report-branding')
+    return response.data
+  }
+
+  async updateReportBranding(data: { heading?: string | null }): Promise<ReportBranding> {
+    const response = await this.client.patch('/api/v1/settings/report-branding', data)
+    return response.data
+  }
+
+  async uploadReportBrandingImages(
+    files: File[],
+    role: 'internal' | 'external' | 'generic' = 'generic',
+  ): Promise<ReportBranding> {
+    const formData = new FormData()
+    for (const file of files) {
+      formData.append('files', file)
+    }
+    formData.append('role', role)
+    const response = await this.client.post('/api/v1/settings/report-branding/images', formData, {
+      headers: { 'Content-Type': 'multipart/form-data' },
+    })
+    return response.data
+  }
+
+  async deleteReportBrandingImage(imageId: string): Promise<ReportBranding> {
+    const response = await this.client.delete(`/api/v1/settings/report-branding/images/${imageId}`)
+    return response.data
+  }
+
+  // GEPA Prompt Optimization (Enterprise)
+  async createOptimizationRun(data: {
+    agent_id: string
+    evaluator_id?: string
+    voice_bundle_id?: string
+    config?: Record<string, any>
+  }): Promise<any> {
+    const response = await this.client.post('/api/v1/prompt-optimization/runs', data)
+    return response.data
+  }
+
+  async deleteOptimizationRun(runId: string): Promise<void> {
+    await this.client.delete(`/api/v1/prompt-optimization/runs/${runId}`)
+  }
+
+  async listOptimizationRuns(agentId?: string): Promise<any[]> {
+    const params = agentId ? { agent_id: agentId } : {}
+    const response = await this.client.get('/api/v1/prompt-optimization/runs', { params })
+    return response.data
+  }
+
+  async getOptimizationRun(runId: string): Promise<any> {
+    const response = await this.client.get(`/api/v1/prompt-optimization/runs/${runId}`)
+    return response.data
+  }
+
+  async listOptimizationCandidates(runId: string): Promise<any[]> {
+    const response = await this.client.get(`/api/v1/prompt-optimization/runs/${runId}/candidates`)
+    return response.data
+  }
+
+  async acceptCandidate(runId: string, candidateId: string): Promise<any> {
+    const response = await this.client.post(
+      `/api/v1/prompt-optimization/runs/${runId}/candidates/${candidateId}/accept`
+    )
+    return response.data
+  }
+
+  async pushCandidateToProvider(runId: string, candidateId: string): Promise<any> {
+    const response = await this.client.post(
+      `/api/v1/prompt-optimization/runs/${runId}/candidates/${candidateId}/push`
+    )
+    return response.data
+  }
+
+  async syncProviderPrompt(agentId: string): Promise<{
+    synced: boolean
+    provider_prompt: string | null
+    provider_prompt_synced_at: string | null
+  }> {
+    const response = await this.client.post(`/api/v1/agents/${agentId}/sync-provider-prompt`)
+    return response.data
+  }
+
+  // -------------------------------------------------------------------
+  // Judge Alignment (AlignEval-style hybrid integration)
+  //
+  // Per-org thresholds and judge model selection are sourced from the
+  // backend (no hardcoded defaults in the frontend).
+  // -------------------------------------------------------------------
+
+  async getJudgeAlignmentSettings(): Promise<JudgeAlignmentSettings> {
+    const response = await this.client.get('/api/v1/judge-alignment/settings')
+    return response.data
+  }
+
+  async updateJudgeAlignmentSettings(
+    data: { min_labels_to_evaluate: number; min_labels_to_optimize: number }
+  ): Promise<JudgeAlignmentSettings> {
+    const response = await this.client.patch('/api/v1/judge-alignment/settings', data)
+    return response.data
+  }
+
+  async listJudgeCapableModels(): Promise<JudgeModelCatalogEntry[]> {
+    const response = await this.client.get('/api/v1/judge-alignment/available-models')
+    return response.data
+  }
+
+  async listJudgeDatasets(): Promise<JudgeDataset[]> {
+    const response = await this.client.get('/api/v1/judge-alignment/datasets')
+    return response.data
+  }
+
+  async createJudgeDataset(data: {
+    name: string
+    description?: string
+    source_type: 'transcript' | 'metric_output' | 'csv'
+    source_config?: Record<string, any>
+    input_field?: string
+    output_field?: string
+  }): Promise<JudgeDataset> {
+    const response = await this.client.post('/api/v1/judge-alignment/datasets', data)
+    return response.data
+  }
+
+  async uploadJudgeDatasetCsv(
+    file: File,
+    name: string,
+    description?: string
+  ): Promise<JudgeDataset> {
+    const formData = new FormData()
+    formData.append('file', file)
+    formData.append('name', name)
+    if (description) formData.append('description', description)
+    const response = await this.client.post(
+      '/api/v1/judge-alignment/datasets/upload-csv',
+      formData,
+      { headers: { 'Content-Type': 'multipart/form-data' } }
+    )
+    return response.data
+  }
+
+  async getJudgeDataset(datasetId: string): Promise<JudgeDataset> {
+    const response = await this.client.get(`/api/v1/judge-alignment/datasets/${datasetId}`)
+    return response.data
+  }
+
+  async deleteJudgeDataset(datasetId: string): Promise<void> {
+    await this.client.delete(`/api/v1/judge-alignment/datasets/${datasetId}`)
+  }
+
+  async listJudgeSamples(
+    datasetId: string,
+    options: { only_labeled?: boolean; skip?: number; limit?: number } = {}
+  ): Promise<JudgeSample[]> {
+    const response = await this.client.get(
+      `/api/v1/judge-alignment/datasets/${datasetId}/samples`,
+      { params: options }
+    )
+    return response.data
+  }
+
+  async labelJudgeSample(
+    sampleId: string,
+    label: 'pass' | 'fail' | null
+  ): Promise<JudgeSample> {
+    const response = await this.client.patch(
+      `/api/v1/judge-alignment/samples/${sampleId}`,
+      { label }
+    )
+    return response.data
+  }
+
+  async bulkLabelJudgeSamples(
+    datasetId: string,
+    items: Array<{ sample_id: string; label: 'pass' | 'fail' | null }>
+  ): Promise<{ updated: number; requested: number }> {
+    const response = await this.client.post(
+      `/api/v1/judge-alignment/datasets/${datasetId}/samples/bulk-label`,
+      { items }
+    )
+    return response.data
+  }
+
+  async listJudgeRuns(datasetId: string): Promise<JudgeRun[]> {
+    const response = await this.client.get(
+      `/api/v1/judge-alignment/datasets/${datasetId}/runs`
+    )
+    return response.data
+  }
+
+  async triggerJudgeRun(
+    datasetId: string,
+    data: { evaluator_id: string; split?: 'all' | 'dev' | 'test'; sample_ids?: string[] }
+  ): Promise<JudgeRun> {
+    const response = await this.client.post(
+      `/api/v1/judge-alignment/datasets/${datasetId}/run`,
+      data
+    )
+    return response.data
+  }
+
+  async getJudgeRun(runId: string): Promise<JudgeRun> {
+    const response = await this.client.get(`/api/v1/judge-alignment/runs/${runId}`)
+    return response.data
+  }
+
+  async recomputeJudgeRunMetrics(runId: string): Promise<JudgeRun> {
+    const response = await this.client.post(
+      `/api/v1/judge-alignment/runs/${runId}/recompute-metrics`
+    )
+    return response.data
+  }
+
+  async deleteJudgeRun(runId: string): Promise<void> {
+    await this.client.delete(`/api/v1/judge-alignment/runs/${runId}`)
+  }
+
+  async optimizeJudge(
+    datasetId: string,
+    data: {
+      evaluator_id: string
+      dev_ratio?: number
+      seed?: number
+      max_metric_calls?: number
+      minibatch_size?: number
+      agent_id?: string
+    }
+  ): Promise<JudgeOptimizeResponse> {
+    const response = await this.client.post(
+      `/api/v1/judge-alignment/datasets/${datasetId}/optimize`,
+      data
+    )
+    return response.data
+  }
+}
+
+// -------------------------------------------------------------------
+// Judge Alignment shared types
+// -------------------------------------------------------------------
+
+export interface JudgeAlignmentSettings {
+  min_labels_to_evaluate: number
+  min_labels_to_optimize: number
+  defaults: { min_labels_to_evaluate: number; min_labels_to_optimize: number }
+}
+
+export interface JudgeModelCatalogEntry {
+  provider: string
+  provider_label: string
+  model: string
+  label: string
+}
+
+export interface JudgeDataset {
+  id: string
+  name: string
+  description?: string | null
+  source_type: 'transcript' | 'metric_output' | 'csv'
+  source_config: Record<string, any>
+  input_field: string
+  output_field: string
+  total_samples: number
+  labeled_samples: number
+  unlabeled_samples: number
+  created_at?: string
+  updated_at?: string
+}
+
+export interface JudgeSample {
+  id: string
+  dataset_id: string
+  external_id?: string | null
+  input_text: string
+  output_text: string
+  label?: 'pass' | 'fail' | null
+  labeled_by?: string | null
+  labeled_at?: string | null
+  extra?: Record<string, any> | null
+  created_at?: string
+}
+
+export interface JudgeRunMetrics {
+  precision: number
+  recall: number
+  f1: number
+  kappa: number
+  tp: number
+  fp: number
+  tn: number
+  fn: number
+  n: number
+}
+
+export interface JudgeRun {
+  id: string
+  dataset_id: string
+  evaluator_id?: string | null
+  split: 'all' | 'dev' | 'test'
+  llm_provider?: string | null
+  llm_model?: string | null
+  status: 'pending' | 'queued' | 'running' | 'completed' | 'failed'
+  metrics?: JudgeRunMetrics | null
+  predictions?: Record<string, { prediction: 'pass' | 'fail' | null; explanation?: string; raw?: string }> | null
+  error_message?: string | null
+  celery_task_id?: string | null
+  gepa_optimization_id?: string | null
+  created_at?: string
+  updated_at?: string
+}
+
+export interface JudgeOptimizeResponse {
+  optimization_run_id: string
+  dev_sample_count: number
+  test_sample_count: number
+}
+
+export const AUDIO_UPLOAD_CHUNK_SIZE = 25
+
+export interface CallImportAudioUploadProgress {
+  chunkIndex: number
+  totalChunks: number
+  uploadedFiles: number
+  totalFiles: number
+}
+
+// Factory function to create ApiClient instance
+// This ensures TypeScript correctly infers the type as ApiClient, not AxiosInstance
+function createApiClient(): ApiClient {
+  return new ApiClient()
+}
+
+// Create instance
+const apiClientInstance = createApiClient()
+
+// Export with explicit type annotation - CRITICAL for TypeScript to recognize as ApiClient
+// Without this explicit type, TypeScript may incorrectly infer AxiosInstance
+export const apiClient: ApiClient = apiClientInstance
+
+// Re-export the type for explicit typing if needed
+export type { ApiClient }
+
+
+// ----------------------------------------------------------------------
+// Public (unauthenticated) blind-test API.
+// Uses a bare axios instance so no Authorization / X-API-Key headers are
+// attached. Anyone with the share_token in the URL can call these.
+// ----------------------------------------------------------------------
+
+const publicClient = axios.create({
+  baseURL: API_BASE_URL,
+  headers: { 'Content-Type': 'application/json' },
+})
+
+export interface PublicBlindTestEntrySubmit {
+  sample_index: number
+  preferred: 'X' | 'Y'
+  ratings_x?: Record<string, number>
+  ratings_y?: Record<string, number>
+  comment?: string
+}
+
+export const publicBlindTestApi = {
+  async getForm(shareToken: string): Promise<any> {
+    const res = await publicClient.get(`/api/v1/public/blind-tests/${shareToken}`)
+    return res.data
+  },
+  async submit(shareToken: string, payload: {
+    rater_name: string
+    rater_email: string
+    client_token: string
+    responses: PublicBlindTestEntrySubmit[]
+  }): Promise<any> {
+    const res = await publicClient.post(
+      `/api/v1/public/blind-tests/${shareToken}/responses`,
+      payload
+    )
+    return res.data
+  },
+}
diff --git a/frontend/src/pages/evaluators/components/EvaluatorPhoneOutboundForm.tsx b/frontend/src/pages/evaluators/components/EvaluatorPhoneOutboundForm.tsx
index 4586417d..0b602840 100644
--- a/frontend/src/pages/evaluators/components/EvaluatorPhoneOutboundForm.tsx
+++ b/frontend/src/pages/evaluators/components/EvaluatorPhoneOutboundForm.tsx
@@ -1,110 +1,116 @@
-import { useState } from 'react'
-import { useMutation, useQuery } from '@tanstack/react-query'
-import { apiClient } from '../../../lib/api'
-import Button from '../../../components/Button'
-import { Phone } from 'lucide-react'
-import { MODERN_INPUT_CLASS, MODERN_SELECT_CLASS } from './evaluatorUi'
-
-interface Props {
-  agentId: string
-  evaluatorId: string
-  personaId: string
-  scenarioId: string
-  personaName?: string
-  scenarioName?: string
-  showToast: (message: string, type: 'success' | 'error') => void
-}
-
-export default function EvaluatorPhoneOutboundForm({
-  agentId,
-  evaluatorId,
-  personaId,
-  scenarioId,
-  personaName,
-  scenarioName,
-  showToast,
-}: Props) {
-  const [toNumber, setToNumber] = useState('')
-  const [fromNumber, setFromNumber] = useState('')
-
-  const { data: dialTargets = [] } = useQuery({
-    queryKey: ['telephony-dial-targets'],
-    queryFn: () => apiClient.listTelephonyDialTargets(),
-  })
-
-  const callMutation = useMutation({
-    mutationFn: () =>
-      apiClient.createVobizOutboundCall({
-        agent_id: agentId,
-        evaluator_id: evaluatorId,
-        persona_id: personaId,
-        scenario_id: scenarioId,
-        to_number: toNumber,
-        from_number: fromNumber || undefined,
-      }),
-    onSuccess: () => showToast('Outbound call initiated', 'success'),
-    onError: (err: any) => {
-      const detail = err?.response?.data?.detail
-      showToast(typeof detail === 'string' ? detail : 'Call failed', 'error')
-    },
-  })
-
-  return (
-    <div className="space-y-4">
-      {(personaName || scenarioName) && (
-        <div className="flex flex-wrap gap-2">
-          {personaName && (
-            <span className="inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium bg-purple-50 text-purple-700 border border-purple-100">
-              {personaName}
-            </span>
-          )}
-          {scenarioName && (
-            <span className="inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium bg-green-50 text-green-700 border border-green-100">
-              {scenarioName}
-            </span>
-          )}
-        </div>
-      )}
-      <div>
-        <label className="block text-sm font-medium text-gray-700 mb-1.5">To number *</label>
-        <input
-          type="tel"
-          value={toNumber}
-          onChange={(e) => setToNumber(e.target.value)}
-          placeholder="+1234567890"
-          className={MODERN_INPUT_CLASS}
-        />
-        {dialTargets.length > 0 && (
-          <select
-            className={`${MODERN_SELECT_CLASS} mt-2`}
-            value=""
-            onChange={(e) => e.target.value && setToNumber(e.target.value)}
-          >
-            <option value="">Contacts…</option>
-            {dialTargets.map((t: any) => (
-              <option key={t.id} value={t.phone_number}>{t.label || t.phone_number}</option>
-            ))}
-          </select>
-        )}
-      </div>
-      <div>
-        <label className="block text-sm font-medium text-gray-700 mb-1.5">From number (optional)</label>
-        <input
-          type="tel"
-          value={fromNumber}
-          onChange={(e) => setFromNumber(e.target.value)}
-          className={MODERN_INPUT_CLASS}
-        />
-      </div>
-      <Button
-        variant="primary"
-        onClick={() => callMutation.mutate()}
-        isLoading={callMutation.isPending}
-        disabled={!toNumber.trim()}
-        leftIcon={<Phone className="h-4 w-4" />}
-      >
-        Place call
-      </Button>
-    </div>
-  )
-}
+import { useState } from 'react'
+import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
+import { apiClient } from '../../../lib/api'
+import Button from '../../../components/Button'
+import { Phone } from 'lucide-react'
+import { MODERN_INPUT_CLASS, MODERN_SELECT_CLASS } from './evaluatorUi'
+
+interface Props {
+  agentId: string
+  evaluatorId: string
+  personaId: string
+  scenarioId: string
+  personaName?: string
+  scenarioName?: string
+  showToast: (message: string, type: 'success' | 'error') => void
+}
+
+export default function EvaluatorPhoneOutboundForm({
+  agentId,
+  evaluatorId,
+  personaId,
+  scenarioId,
+  personaName,
+  scenarioName,
+  showToast,
+}: Props) {
+  const [toNumber, setToNumber] = useState('')
+  const [fromNumber, setFromNumber] = useState('')
+
+  const queryClient = useQueryClient()
+  const { data: dialTargets = [] } = useQuery({
+    queryKey: ['telephony-dial-targets'],
+    queryFn: () => apiClient.listTelephonyDialTargets(),
+  })
+
+  const callMutation = useMutation({
+    mutationFn: () =>
+      apiClient.createVobizOutboundCall({
+        agent_id: agentId,
+        evaluator_id: evaluatorId,
+        persona_id: personaId,
+        scenario_id: scenarioId,
+        to_number: toNumber,
+        from_number: fromNumber || undefined,
+      }),
+    onSuccess: (data) => {
+      queryClient.invalidateQueries({ queryKey: ['observability-traces'] })
+      const traceHint = data?.call_short_id ? ` Trace id ${data.call_short_id}.` : ''
+      const suffix = data?.result_id ? ` (result ${data.result_id})` : ''
+      showToast(`Outbound call initiated${suffix}.${traceHint} Export STT/LLM/TTS with this id.`, 'success')
+    },
+    onError: (err: any) => {
+      const detail = err?.response?.data?.detail
+      showToast(typeof detail === 'string' ? detail : 'Call failed', 'error')
+    },
+  })
+
+  return (
+    <div className="space-y-4">
+      {(personaName || scenarioName) && (
+        <div className="flex flex-wrap gap-2">
+          {personaName && (
+            <span className="inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium bg-purple-50 text-purple-700 border border-purple-100">
+              {personaName}
+            </span>
+          )}
+          {scenarioName && (
+            <span className="inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium bg-green-50 text-green-700 border border-green-100">
+              {scenarioName}
+            </span>
+          )}
+        </div>
+      )}
+      <div>
+        <label className="block text-sm font-medium text-gray-700 mb-1.5">To number *</label>
+        <input
+          type="tel"
+          value={toNumber}
+          onChange={(e) => setToNumber(e.target.value)}
+          placeholder="+1234567890"
+          className={MODERN_INPUT_CLASS}
+        />
+        {dialTargets.length > 0 && (
+          <select
+            className={`${MODERN_SELECT_CLASS} mt-2`}
+            value=""
+            onChange={(e) => e.target.value && setToNumber(e.target.value)}
+          >
+            <option value="">Contacts…</option>
+            {dialTargets.map((t: any) => (
+              <option key={t.id} value={t.phone_number}>{t.label || t.phone_number}</option>
+            ))}
+          </select>
+        )}
+      </div>
+      <div>
+        <label className="block text-sm font-medium text-gray-700 mb-1.5">From number (optional)</label>
+        <input
+          type="tel"
+          value={fromNumber}
+          onChange={(e) => setFromNumber(e.target.value)}
+          className={MODERN_INPUT_CLASS}
+        />
+      </div>
+      <Button
+        variant="primary"
+        onClick={() => callMutation.mutate()}
+        isLoading={callMutation.isPending}
+        disabled={!toNumber.trim()}
+        leftIcon={<Phone className="h-4 w-4" />}
+      >
+        Place call
+      </Button>
+    </div>
+  )
+}
diff --git a/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx b/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx
index 07a721c5..086a008a 100644
--- a/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx
+++ b/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx
@@ -1,184 +1,185 @@
-import { useState } from 'react'
-import { createPortal } from 'react-dom'
-import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
-import { apiClient, EvaluatorSuite } from '../../../lib/api'
-import Button from '../../../components/Button'
-import { X, Play } from 'lucide-react'
-import { MODERN_INPUT_CLASS, MODERN_SELECT_CLASS } from './evaluatorUi'
-
-interface Props {
-  open: boolean
-  onClose: () => void
-  suites: EvaluatorSuite[]
-  showToast: (message: string, type: 'success' | 'error') => void
-}
-
-export default function EvaluatorSmartRunModal({ open, onClose, suites, showToast }: Props) {
-  const queryClient = useQueryClient()
-  const [runsPerCombination, setRunsPerCombination] = useState(1)
-  const [toNumber, setToNumber] = useState('')
-  const [fromNumber, setFromNumber] = useState('')
-
-  const singleSuite = suites.length === 1 ? suites[0] : null
-  const isInbound = singleSuite?.agent_call_type === 'inbound'
-  const isPhoneOutbound =
-    singleSuite?.agent_call_medium === 'phone_call' && singleSuite?.agent_call_type !== 'inbound'
-  const isWeb = singleSuite?.agent_call_medium === 'web_call'
-
-  const { data: dialTargets = [] } = useQuery({
-    queryKey: ['telephony-dial-targets'],
-    queryFn: () => apiClient.listTelephonyDialTargets(),
-    enabled: open && isPhoneOutbound,
-  })
-
-  const runMutation = useMutation({
-    mutationFn: ({ suiteId, runs }: { suiteId: string; runs: number }) =>
-      apiClient.runEvaluatorSuite(suiteId, {
-        runs_per_combination: runs,
-        to_number: toNumber || undefined,
-        from_number: fromNumber || undefined,
-      }),
-    onSuccess: (data) => {
-      queryClient.invalidateQueries({ queryKey: ['evaluator-suites'] })
-      queryClient.invalidateQueries({ queryKey: ['evaluator-results'] })
-      showToast(`Queued ${data.total_runs} run${data.total_runs !== 1 ? 's' : ''}`, 'success')
-      onClose()
-    },
-    onError: (err: any) => {
-      const detail = err?.response?.data?.detail
-      showToast(typeof detail === 'string' ? detail : 'Failed to run suite', 'error')
-    },
-  })
-
-  if (!open || suites.length === 0) return null
-
-  const totalRuns = singleSuite ? singleSuite.combination_count * runsPerCombination : 0
-  const nextIdx = singleSuite
-    ? singleSuite.round_robin_index % Math.max(singleSuite.combination_count, 1)
-    : 0
-  const nextScenario = singleSuite?.combinations[nextIdx]?.scenario_name
-
-  const modal = (
-    <div className="fixed inset-0 z-[9999] overflow-y-auto">
-      <div className="flex min-h-screen items-center justify-center p-4">
-        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 transition-opacity" onClick={onClose} />
-        <div className="relative bg-white rounded-lg shadow-xl w-full max-w-lg flex flex-col">
-          <div className="flex items-center justify-between px-6 py-4 border-b border-gray-200">
-            <div>
-              <h2 className="text-lg font-semibold text-gray-900">
-                Run Evaluator Suite{suites.length > 1 ? 's' : ''}
-              </h2>
-              {singleSuite && !isInbound && (
-                <p className="text-sm text-gray-500 mt-0.5">
-                  Queue batch evaluation runs across all combinations
-                </p>
-              )}
-            </div>
-            <button type="button" onClick={onClose} className="text-gray-400 hover:text-gray-600 p-1 rounded-lg hover:bg-gray-100">
-              <X className="h-5 w-5" />
-            </button>
-          </div>
-
-          <div className="px-6 py-5 space-y-4">
-            {suites.length > 1 && (
-              <p className="text-sm text-gray-600 rounded-lg bg-gray-50 border border-gray-100 p-3">
-                {suites.length} suites selected — run each individually from the list.
-              </p>
-            )}
-
-            {singleSuite && isInbound && (
-              <div className="rounded-xl border border-amber-200 bg-amber-50 p-4 text-sm space-y-2">
-                <p className="font-medium text-amber-900">Inbound agent — manual runs disabled</p>
-                <p className="text-amber-800">
-                  Scenarios rotate automatically when callers reach the agent.
-                </p>
-                <p className="text-amber-800">
-                  Next in rotation: <strong>{nextScenario || '—'}</strong> ({nextIdx + 1} of {singleSuite.combination_count})
-                </p>
-              </div>
-            )}
-
-            {singleSuite && !isInbound && (
-              <>
-                <div>
-                  <label className="block text-sm font-medium text-gray-700 mb-1.5">Runs per combination</label>
-                  <input
-                    type="number"
-                    min={1}
-                    value={runsPerCombination}
-                    onChange={(e) => setRunsPerCombination(Math.max(1, parseInt(e.target.value, 10) || 1))}
-                    className={`${MODERN_INPUT_CLASS} w-28`}
-                  />
-                  <div className="mt-3 rounded-lg bg-indigo-50 border border-indigo-100 px-4 py-3 text-sm text-indigo-800">
-                    <strong>{singleSuite.combination_count}</strong> combinations × <strong>{runsPerCombination}</strong> ={' '}
-                    <strong>{totalRuns} total runs</strong>
-                  </div>
-                </div>
-
-                {isPhoneOutbound && (
-                  <>
-                    <div>
-                      <label className="block text-sm font-medium text-gray-700 mb-1.5">To number *</label>
-                      <input
-                        type="tel"
-                        value={toNumber}
-                        onChange={(e) => setToNumber(e.target.value)}
-                        placeholder="+1234567890"
-                        className={MODERN_INPUT_CLASS}
-                      />
-                      {dialTargets.length > 0 && (
-                        <select
-                          className={`${MODERN_SELECT_CLASS} mt-2`}
-                          value=""
-                          onChange={(e) => e.target.value && setToNumber(e.target.value)}
-                        >
-                          <option value="">Contacts…</option>
-                          {dialTargets.map((t: any) => (
-                            <option key={t.id} value={t.phone_number}>{t.label || t.phone_number}</option>
-                          ))}
-                        </select>
-                      )}
-                    </div>
-                    <div>
-                      <label className="block text-sm font-medium text-gray-700 mb-1.5">From number (optional)</label>
-                      <input
-                        type="tel"
-                        value={fromNumber}
-                        onChange={(e) => setFromNumber(e.target.value)}
-                        className={MODERN_INPUT_CLASS}
-                      />
-                    </div>
-                  </>
-                )}
-
-                {isWeb && (
-                  <p className="text-sm text-gray-600 rounded-lg bg-gray-50 border border-gray-100 p-3">
-                    Web bridge runs will be queued via Celery workers.
-                  </p>
-                )}
-              </>
-            )}
-          </div>
-
-          <div className="flex justify-end gap-2 px-6 py-4 border-t border-gray-200 bg-gray-50 rounded-b-lg">
-            <Button variant="outline" onClick={onClose}>Close</Button>
-            {singleSuite && !isInbound && (
-              <Button
-                variant="primary"
-                onClick={() => runMutation.mutate({ suiteId: singleSuite.id, runs: runsPerCombination })}
-                isLoading={runMutation.isPending}
-                disabled={isPhoneOutbound && !toNumber.trim()}
-                leftIcon={<Play className="h-4 w-4" />}
-              >
-                Queue {totalRuns} runs
-              </Button>
-            )}
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-
-  if (typeof document === 'undefined') return null
-  return createPortal(modal, document.body)
-}
+import { useState } from 'react'
+import { createPortal } from 'react-dom'
+import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
+import { apiClient, EvaluatorSuite } from '../../../lib/api'
+import Button from '../../../components/Button'
+import { X, Play } from 'lucide-react'
+import { MODERN_INPUT_CLASS, MODERN_SELECT_CLASS } from './evaluatorUi'
+
+interface Props {
+  open: boolean
+  onClose: () => void
+  suites: EvaluatorSuite[]
+  showToast: (message: string, type: 'success' | 'error') => void
+}
+
+export default function EvaluatorSmartRunModal({ open, onClose, suites, showToast }: Props) {
+  const queryClient = useQueryClient()
+  const [runsPerCombination, setRunsPerCombination] = useState(1)
+  const [toNumber, setToNumber] = useState('')
+  const [fromNumber, setFromNumber] = useState('')
+
+  const singleSuite = suites.length === 1 ? suites[0] : null
+  const isInbound = singleSuite?.agent_call_type === 'inbound'
+  const isPhoneOutbound =
+    singleSuite?.agent_call_medium === 'phone_call' && singleSuite?.agent_call_type !== 'inbound'
+  const isWeb = singleSuite?.agent_call_medium === 'web_call'
+
+  const { data: dialTargets = [] } = useQuery({
+    queryKey: ['telephony-dial-targets'],
+    queryFn: () => apiClient.listTelephonyDialTargets(),
+    enabled: open && isPhoneOutbound,
+  })
+
+  const runMutation = useMutation({
+    mutationFn: ({ suiteId, runs }: { suiteId: string; runs: number }) =>
+      apiClient.runEvaluatorSuite(suiteId, {
+        runs_per_combination: runs,
+        to_number: toNumber || undefined,
+        from_number: fromNumber || undefined,
+      }),
+    onSuccess: (data) => {
+      queryClient.invalidateQueries({ queryKey: ['observability-traces'] })
+      queryClient.invalidateQueries({ queryKey: ['evaluator-suites'] })
+      queryClient.invalidateQueries({ queryKey: ['evaluator-results'] })
+      showToast(`Queued ${data.total_runs} run${data.total_runs !== 1 ? 's' : ''}. Phone traces appear in Call Traces after each call — use Refresh.`, 'success')
+      onClose()
+    },
+    onError: (err: any) => {
+      const detail = err?.response?.data?.detail
+      showToast(typeof detail === 'string' ? detail : 'Failed to run suite', 'error')
+    },
+  })
+
+  if (!open || suites.length === 0) return null
+
+  const totalRuns = singleSuite ? singleSuite.combination_count * runsPerCombination : 0
+  const nextIdx = singleSuite
+    ? singleSuite.round_robin_index % Math.max(singleSuite.combination_count, 1)
+    : 0
+  const nextScenario = singleSuite?.combinations[nextIdx]?.scenario_name
+
+  const modal = (
+    <div className="fixed inset-0 z-[9999] overflow-y-auto">
+      <div className="flex min-h-screen items-center justify-center p-4">
+        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 transition-opacity" onClick={onClose} />
+        <div className="relative bg-white rounded-lg shadow-xl w-full max-w-lg flex flex-col">
+          <div className="flex items-center justify-between px-6 py-4 border-b border-gray-200">
+            <div>
+              <h2 className="text-lg font-semibold text-gray-900">
+                Run Evaluator Suite{suites.length > 1 ? 's' : ''}
+              </h2>
+              {singleSuite && !isInbound && (
+                <p className="text-sm text-gray-500 mt-0.5">
+                  Queue batch evaluation runs across all combinations
+                </p>
+              )}
+            </div>
+            <button type="button" onClick={onClose} className="text-gray-400 hover:text-gray-600 p-1 rounded-lg hover:bg-gray-100">
+              <X className="h-5 w-5" />
+            </button>
+          </div>
+
+          <div className="px-6 py-5 space-y-4">
+            {suites.length > 1 && (
+              <p className="text-sm text-gray-600 rounded-lg bg-gray-50 border border-gray-100 p-3">
+                {suites.length} suites selected — run each individually from the list.
+              </p>
+            )}
+
+            {singleSuite && isInbound && (
+              <div className="rounded-xl border border-amber-200 bg-amber-50 p-4 text-sm space-y-2">
+                <p className="font-medium text-amber-900">Inbound agent — manual runs disabled</p>
+                <p className="text-amber-800">
+                  Scenarios rotate automatically when callers reach the agent.
+                </p>
+                <p className="text-amber-800">
+                  Next in rotation: <strong>{nextScenario || '—'}</strong> ({nextIdx + 1} of {singleSuite.combination_count})
+                </p>
+              </div>
+            )}
+
+            {singleSuite && !isInbound && (
+              <>
+                <div>
+                  <label className="block text-sm font-medium text-gray-700 mb-1.5">Runs per combination</label>
+                  <input
+                    type="number"
+                    min={1}
+                    value={runsPerCombination}
+                    onChange={(e) => setRunsPerCombination(Math.max(1, parseInt(e.target.value, 10) || 1))}
+                    className={`${MODERN_INPUT_CLASS} w-28`}
+                  />
+                  <div className="mt-3 rounded-lg bg-indigo-50 border border-indigo-100 px-4 py-3 text-sm text-indigo-800">
+                    <strong>{singleSuite.combination_count}</strong> combinations × <strong>{runsPerCombination}</strong> ={' '}
+                    <strong>{totalRuns} total runs</strong>
+                  </div>
+                </div>
+
+                {isPhoneOutbound && (
+                  <>
+                    <div>
+                      <label className="block text-sm font-medium text-gray-700 mb-1.5">To number *</label>
+                      <input
+                        type="tel"
+                        value={toNumber}
+                        onChange={(e) => setToNumber(e.target.value)}
+                        placeholder="+1234567890"
+                        className={MODERN_INPUT_CLASS}
+                      />
+                      {dialTargets.length > 0 && (
+                        <select
+                          className={`${MODERN_SELECT_CLASS} mt-2`}
+                          value=""
+                          onChange={(e) => e.target.value && setToNumber(e.target.value)}
+                        >
+                          <option value="">Contacts…</option>
+                          {dialTargets.map((t: any) => (
+                            <option key={t.id} value={t.phone_number}>{t.label || t.phone_number}</option>
+                          ))}
+                        </select>
+                      )}
+                    </div>
+                    <div>
+                      <label className="block text-sm font-medium text-gray-700 mb-1.5">From number (optional)</label>
+                      <input
+                        type="tel"
+                        value={fromNumber}
+                        onChange={(e) => setFromNumber(e.target.value)}
+                        className={MODERN_INPUT_CLASS}
+                      />
+                    </div>
+                  </>
+                )}
+
+                {isWeb && (
+                  <p className="text-sm text-gray-600 rounded-lg bg-gray-50 border border-gray-100 p-3">
+                    Web bridge runs will be queued via Celery workers.
+                  </p>
+                )}
+              </>
+            )}
+          </div>
+
+          <div className="flex justify-end gap-2 px-6 py-4 border-t border-gray-200 bg-gray-50 rounded-b-lg">
+            <Button variant="outline" onClick={onClose}>Close</Button>
+            {singleSuite && !isInbound && (
+              <Button
+                variant="primary"
+                onClick={() => runMutation.mutate({ suiteId: singleSuite.id, runs: runsPerCombination })}
+                isLoading={runMutation.isPending}
+                disabled={isPhoneOutbound && !toNumber.trim()}
+                leftIcon={<Play className="h-4 w-4" />}
+              >
+                Queue {totalRuns} runs
+              </Button>
+            )}
+          </div>
+        </div>
+      </div>
+    </div>
+  )
+
+  if (typeof document === 'undefined') return null
+  return createPortal(modal, document.body)
+}
diff --git a/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx b/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx
index 4c61fce9..2d8b13f1 100644
--- a/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx
+++ b/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx
@@ -1,6 +1,6 @@
 import React, { useState, useRef, useEffect, useMemo } from 'react'
 import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query'
-import { useParams, useNavigate } from 'react-router-dom'
+import { useParams, useNavigate, Link } from 'react-router-dom'
 import { apiClient } from '../../../lib/api'
 import { ArrowLeft, Clock, CheckCircle, XCircle, Loader, BarChart3, Phone, Brain, HelpCircle, Sparkles, AudioWaveform, MessageSquare, Download, RotateCcw, PhoneIncoming, PhoneOutgoing, Tag, ExternalLink } from 'lucide-react'
 import { motion, AnimatePresence } from 'framer-motion'
@@ -1160,6 +1160,25 @@ export default function EvaluatorResultDetailPage({
         </div>
       )}
 
+      {resultData.id && (
+        <div className="mb-6 bg-white rounded-lg shadow p-4 flex flex-wrap items-center justify-between gap-3">
+          <div>
+            <h2 className="text-sm font-semibold text-gray-900">Synthetic call trace</h2>
+            <p className="text-xs text-gray-500 mt-0.5">
+              STT / LLM / TTS spans from your voice agent layer, correlated by{' '}
+              <code className="text-xs bg-gray-100 px-1 rounded">call_short_id</code>. Phone tests
+              may also include SUT response latency from EfficientAI&apos;s caller.
+            </p>
+          </div>
+          <Link
+            to={`/call-traces?result=${resultData.id}`}
+            className="text-sm font-medium text-indigo-600 hover:text-indigo-800"
+          >
+            Open Call Traces →
+          </Link>
+        </div>
+      )}
+
       {/* Unified Transcript & Call Details Section */}
       {hasCallMediaOrTranscript ? (
         <div className="bg-white shadow rounded-lg overflow-hidden">
diff --git a/frontend/src/pages/test-insights/TestInsights.tsx b/frontend/src/pages/test-insights/TestInsights.tsx
new file mode 100644
index 00000000..f60d1a12
--- /dev/null
+++ b/frontend/src/pages/test-insights/TestInsights.tsx
@@ -0,0 +1,613 @@
+import { useEffect, useRef, useState } from 'react'
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { useSearchParams } from 'react-router-dom'
+import {
+  Activity,
+  BookOpen,
+  Check,
+  Copy,
+  Eye,
+  Globe,
+  RefreshCw,
+} from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import SyntheticCallTracePanel from '../../components/call-recordings/SyntheticCallTracePanel'
+import Button from '../../components/Button'
+import { useWorkspaceStore } from '../../store/workspaceStore'
+
+type Tab = 'runs' | 'setup'
+
+const tracesQueryKey = (workspaceId: string | null) =>
+  ['observability-traces', workspaceId] as const
+
+function formatWhen(iso: string): string {
+  return new Date(iso).toLocaleString()
+}
+
+function formatRelative(iso: string): string {
+  const date = new Date(iso)
+  const diffMs = Date.now() - date.getTime()
+  const mins = Math.floor(diffMs / 60000)
+  const hours = Math.floor(diffMs / 3600000)
+  const days = Math.floor(diffMs / 86400000)
+  if (mins < 1) return 'Just now'
+  if (mins < 60) return `${mins}m ago`
+  if (hours < 24) return `${hours}h ago`
+  if (days < 7) return `${days}d ago`
+  return date.toLocaleDateString()
+}
+
+function CopyButton({ text, label }: { text: string; label?: string }) {
+  const [copied, setCopied] = useState(false)
+  return (
+    <button
+      type="button"
+      onClick={() => {
+        void navigator.clipboard.writeText(text).then(
+          () => {
+            setCopied(true)
+            setTimeout(() => setCopied(false), 2000)
+          },
+          () => {},
+        )
+      }}
+      className="inline-flex items-center gap-1 text-xs font-medium text-indigo-600 hover:text-indigo-800"
+      title={label ?? 'Copy'}
+    >
+      {copied ? <Check className="w-3.5 h-3.5" /> : <Copy className="w-3.5 h-3.5" />}
+      {copied ? 'Copied' : 'Copy'}
+    </button>
+  )
+}
+
+function StatusBadge({ status }: { status: string }) {
+  const open = status === 'open'
+  const label = status === 'finalized' ? 'closed' : status
+  return (
+    <span
+      className={`inline-flex items-center gap-1.5 text-xs font-medium rounded-full px-2.5 py-0.5 capitalize ${
+        open
+          ? 'bg-amber-50 text-amber-700 border border-amber-200'
+          : 'bg-emerald-50 text-emerald-700 border border-emerald-200'
+      }`}
+    >
+      <span className={`h-1.5 w-1.5 rounded-full ${open ? 'bg-amber-500 animate-pulse' : 'bg-emerald-500'}`} />
+      {label}
+    </span>
+  )
+}
+
+function TransportBadge({ transport }: { transport?: string }) {
+  const t = (transport || 'webrtc').toLowerCase()
+  if (t !== 'webrtc') {
+    return (
+      <span className="inline-flex items-center gap-1 text-[11px] font-medium rounded-md px-2 py-0.5 border bg-gray-50 text-gray-600 border-gray-100 capitalize">
+        {t}
+      </span>
+    )
+  }
+  return (
+    <span className="inline-flex items-center gap-1 text-[11px] font-medium rounded-md px-2 py-0.5 border bg-blue-50 text-blue-700 border-blue-100">
+      <Globe className="w-3 h-3" />
+      WebRTC
+    </span>
+  )
+}
+
+function TraceDetailShell({
+  traceId,
+  onClose,
+  layout,
+}: {
+  traceId: string
+  onClose: () => void
+  layout: 'desktop' | 'mobile'
+}) {
+  useEffect(() => {
+    if (layout !== 'mobile') return
+    const onKey = (e: KeyboardEvent) => {
+      if (e.key === 'Escape') onClose()
+    }
+    window.addEventListener('keydown', onKey)
+    return () => window.removeEventListener('keydown', onKey)
+  }, [layout, onClose])
+
+  if (layout === 'mobile') {
+    return (
+      <>
+        <button
+          type="button"
+          className="fixed inset-0 z-40 bg-gray-900/50 backdrop-blur-[1px]"
+          aria-label="Close trace detail"
+          onClick={onClose}
+        />
+        <div
+          role="dialog"
+          aria-modal="true"
+          aria-label="Call trace detail"
+          className="fixed inset-y-0 right-0 z-50 w-full max-w-lg bg-white shadow-2xl flex flex-col border-l border-gray-200"
+        >
+          <SyntheticCallTracePanel traceId={traceId} onClose={onClose} />
+        </div>
+      </>
+    )
+  }
+
+  return (
+    <div className="flex flex-1 min-w-0 min-h-[calc(100vh-11rem)] rounded-xl border border-gray-200 bg-white shadow-sm overflow-hidden">
+      <SyntheticCallTracePanel traceId={traceId} onClose={onClose} />
+    </div>
+  )
+}
+
+export default function TestInsights() {
+  const queryClient = useQueryClient()
+  const activeWorkspaceId = useWorkspaceStore((s) => s.activeWorkspaceId)
+  const [searchParams, setSearchParams] = useSearchParams()
+  const resultFromUrl = searchParams.get('result')
+  const traceFromUrl = searchParams.get('trace')
+  const [tab, setTab] = useState<Tab>('runs')
+  const [selectedTraceId, setSelectedTraceId] = useState<string | null>(traceFromUrl)
+  const prevWorkspaceRef = useRef<string | null>(null)
+  const [isMobileView, setIsMobileView] = useState(() =>
+    typeof window !== 'undefined' ? window.matchMedia('(max-width: 1023px)').matches : false,
+  )
+
+  const {
+    data: listData,
+    isLoading: loadingList,
+    isFetching: fetchingList,
+    isError: listError,
+    error: listErrorDetail,
+    refetch: refetchTraces,
+    dataUpdatedAt,
+  } = useQuery({
+    queryKey: tracesQueryKey(activeWorkspaceId),
+    queryFn: () => apiClient.listSyntheticCallTraces({ limit: 100 }),
+    enabled: tab === 'runs' && Boolean(activeWorkspaceId),
+    retry: false,
+    refetchOnWindowFocus: true,
+    staleTime: 30_000,
+    refetchInterval: (query) => {
+      const items = query.state.data?.items ?? []
+      const hasOpen = items.some((t: { status?: string }) => t.status === 'open')
+      return hasOpen ? 15_000 : false
+    },
+  })
+
+  const { data: setup, isLoading: loadingSetup } = useQuery({
+    queryKey: ['synthetic-call-trace-setup'],
+    queryFn: () => apiClient.getSyntheticCallTraceSetup(),
+    enabled: tab === 'setup',
+    retry: false,
+    staleTime: 5 * 60 * 1000,
+  })
+
+  const traces = listData?.items ?? []
+  const listErrorMessage =
+    listError && listErrorDetail instanceof Error
+      ? listErrorDetail.message
+      : listError
+        ? 'Could not load traces'
+        : null
+
+  const openTraces = traces.filter((t: any) => t.status === 'open').length
+  const withLatency = traces.filter((t: any) => t.response_latency_p50_ms != null).length
+
+  const envBlock = setup?.one_time_env_vars
+    ? Object.entries(setup.one_time_env_vars)
+        .map(([k, v]) => `${k}=${v}`)
+        .join('\n')
+    : ''
+
+  const openTrace = (traceId: string) => {
+    setSelectedTraceId(traceId)
+    const next = new URLSearchParams(searchParams)
+    next.set('trace', traceId)
+    next.delete('result')
+    setSearchParams(next, { replace: true })
+  }
+
+  const closeTrace = () => {
+    setSelectedTraceId(null)
+    const next = new URLSearchParams(searchParams)
+    next.delete('trace')
+    next.delete('result')
+    setSearchParams(next, { replace: true })
+  }
+
+  const handleRefresh = () => {
+    void refetchTraces()
+    if (selectedTraceId) {
+      void queryClient.invalidateQueries({
+        queryKey: ['synthetic-call-trace', selectedTraceId, 'by-trace'],
+      })
+    }
+  }
+
+  useEffect(() => {
+    if (prevWorkspaceRef.current !== null && prevWorkspaceRef.current !== activeWorkspaceId) {
+      setSelectedTraceId(null)
+      setSearchParams(
+        (prev) => {
+          const next = new URLSearchParams(prev)
+          next.delete('trace')
+          next.delete('result')
+          return next
+        },
+        { replace: true },
+      )
+    }
+    prevWorkspaceRef.current = activeWorkspaceId
+  }, [activeWorkspaceId, setSearchParams])
+
+  useEffect(() => {
+    if (traceFromUrl) {
+      setSelectedTraceId(traceFromUrl)
+      return
+    }
+    if (!resultFromUrl) return
+    let cancelled = false
+    apiClient
+      .getSyntheticCallTraceForResult(resultFromUrl)
+      .then((trace) => {
+        if (!cancelled && trace?.id) {
+          setSelectedTraceId(trace.id)
+          setSearchParams(
+            (prev) => {
+              const next = new URLSearchParams(prev)
+              next.set('trace', trace.id)
+              next.delete('result')
+              return next
+            },
+            { replace: true },
+          )
+        }
+      })
+      .catch(() => {})
+    return () => {
+      cancelled = true
+    }
+  }, [resultFromUrl, traceFromUrl, setSearchParams])
+
+  const drawerOpen = Boolean(selectedTraceId)
+
+  useEffect(() => {
+    const mq = window.matchMedia('(max-width: 1023px)')
+    const onChange = () => setIsMobileView(mq.matches)
+    onChange()
+    mq.addEventListener('change', onChange)
+    return () => mq.removeEventListener('change', onChange)
+  }, [])
+
+  useEffect(() => {
+    if (!drawerOpen || !isMobileView) return
+    const prev = document.body.style.overflow
+    document.body.style.overflow = 'hidden'
+    return () => {
+      document.body.style.overflow = prev
+    }
+  }, [drawerOpen, isMobileView])
+
+  const lastUpdatedLabel =
+    dataUpdatedAt > 0 ? `Updated ${new Date(dataUpdatedAt).toLocaleTimeString()}` : null
+
+  const totalCount = listData?.total ?? traces.length
+  const showingPartial = totalCount > traces.length
+
+  return (
+    <div className="space-y-6">
+      <div className="flex flex-col sm:flex-row sm:items-start sm:justify-between gap-4">
+        <div>
+          <h1 className="text-2xl font-bold text-gray-900 tracking-tight">Call Traces</h1>
+          <p className="mt-1 text-sm text-gray-600 max-w-2xl leading-relaxed">
+            See how fast your voice agent responds on each turn — speech recognition, model, and
+            text-to-speech timing from a local Pipecat WebRTC call.
+          </p>
+        </div>
+        {tab === 'runs' && (
+          <button
+            type="button"
+            onClick={handleRefresh}
+            disabled={fetchingList}
+            className="inline-flex items-center gap-2 px-3 py-2 text-sm font-medium text-gray-700 bg-white border border-gray-200 rounded-lg shadow-sm hover:bg-gray-50 disabled:opacity-50 shrink-0"
+          >
+            <RefreshCw className={`w-4 h-4 ${fetchingList ? 'animate-spin' : ''}`} />
+            Refresh
+          </button>
+        )}
+      </div>
+
+      <div className="flex gap-1 border-b border-gray-200">
+        <button
+          type="button"
+          onClick={() => setTab('runs')}
+          className={`px-4 py-2.5 text-sm font-medium border-b-2 -mb-px transition-colors ${
+            tab === 'runs'
+              ? 'border-indigo-600 text-indigo-600'
+              : 'border-transparent text-gray-500 hover:text-gray-700'
+          }`}
+        >
+          <Activity className="w-4 h-4 inline mr-1.5 -mt-0.5" />
+          Traces
+        </button>
+        <button
+          type="button"
+          onClick={() => setTab('setup')}
+          className={`px-4 py-2.5 text-sm font-medium border-b-2 -mb-px transition-colors ${
+            tab === 'setup'
+              ? 'border-indigo-600 text-indigo-600'
+              : 'border-transparent text-gray-500 hover:text-gray-700'
+          }`}
+        >
+          <BookOpen className="w-4 h-4 inline mr-1.5 -mt-0.5" />
+          Connect Pipecat
+        </button>
+      </div>
+
+      {tab === 'setup' && (
+        <div className="rounded-xl border border-gray-200 bg-white shadow-sm overflow-hidden">
+          {loadingSetup && <p className="p-6 text-sm text-gray-500">Loading setup…</p>}
+          {setup && (
+            <div className="divide-y divide-gray-100">
+              <div className="p-6 bg-gradient-to-br from-indigo-50/80 to-white">
+                <h2 className="text-lg font-semibold text-gray-900">Local Pipecat WebRTC</h2>
+                <p className="text-sm text-gray-600 mt-1 max-w-2xl">
+                  Run your Pipecat agent on your machine, talk in the browser, and traces show up
+                  here automatically. Full example:{' '}
+                  <code className="text-xs bg-white/80 px-1.5 py-0.5 rounded border border-gray-200">
+                    docs/examples/pipecat_multi_agent_webrtc_tracing.py
+                  </code>
+                </p>
+              </div>
+
+              {(setup.setup_steps ?? []).length > 0 && (
+                <div className="p-6 space-y-4">
+                  <h3 className="text-sm font-semibold text-gray-900">Quick start</h3>
+                  <ol className="space-y-4">
+                    {(setup.setup_steps as Array<{ title: string; detail: string }>).map(
+                      (step, idx) => (
+                        <li key={step.title} className="flex gap-4">
+                          <span className="flex h-7 w-7 shrink-0 items-center justify-center rounded-full bg-indigo-600 text-xs font-bold text-white">
+                            {idx + 1}
+                          </span>
+                          <div>
+                            <p className="text-sm font-medium text-gray-900">{step.title}</p>
+                            <p className="text-sm text-gray-600 mt-0.5 leading-relaxed">{step.detail}</p>
+                          </div>
+                        </li>
+                      ),
+                    )}
+                  </ol>
+                </div>
+              )}
+
+              <div className="p-6 space-y-4 bg-gray-50/50">
+                <h3 className="text-sm font-semibold text-gray-900">Your workspace settings</h3>
+                <div className="space-y-3">
+                  <div className="rounded-lg border border-gray-200 bg-white p-4">
+                    <div className="flex items-center justify-between gap-2 mb-2">
+                      <p className="text-xs font-medium text-gray-500">Trace export URL</p>
+                      <CopyButton text={setup.otlp_endpoint} label="Copy export URL" />
+                    </div>
+                    <p className="font-mono text-xs text-gray-800 break-all">{setup.otlp_endpoint}</p>
+                  </div>
+                  {envBlock && (
+                    <div className="rounded-lg border border-gray-200 bg-white p-4">
+                      <div className="flex items-center justify-between gap-2 mb-2">
+                        <p className="text-xs font-medium text-gray-500">Add to Pipecat .env</p>
+                        <CopyButton text={envBlock} label="Copy env block" />
+                      </div>
+                      <pre className="text-xs font-mono text-gray-800 whitespace-pre-wrap">{envBlock}</pre>
+                    </div>
+                  )}
+                </div>
+                {setup.per_call_correlation?.note && (
+                  <p className="text-sm text-gray-600 leading-relaxed">{setup.per_call_correlation.note}</p>
+                )}
+              </div>
+
+              {setup.pipecat_python_example && (
+                <div className="p-6 space-y-2">
+                  <div className="flex items-center justify-between gap-2">
+                    <h3 className="text-sm font-semibold text-gray-900">Tracing snippet for bot.py</h3>
+                    <CopyButton text={setup.pipecat_python_example} label="Copy code" />
+                  </div>
+                  <pre className="text-xs font-mono bg-slate-900 text-slate-100 p-4 rounded-lg overflow-x-auto max-h-80">
+                    {setup.pipecat_python_example}
+                  </pre>
+                </div>
+              )}
+            </div>
+          )}
+        </div>
+      )}
+
+      {tab === 'runs' && !activeWorkspaceId && (
+        <div className="rounded-xl border border-amber-200 bg-amber-50 px-6 py-8 text-center text-sm text-amber-900">
+          Select a workspace to view call traces.
+        </div>
+      )}
+
+      {tab === 'runs' && activeWorkspaceId && (
+        <div className="flex flex-col lg:flex-row gap-4 lg:gap-6 lg:items-start">
+          <div className={`min-w-0 ${selectedTraceId ? 'lg:w-[44%] lg:shrink-0' : 'w-full'}`}>
+            {traces.length > 0 && (
+              <div className="grid grid-cols-3 gap-3 mb-4">
+                <div className="rounded-xl border border-gray-200 bg-white px-4 py-3 shadow-sm">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">Total</p>
+                  <p className="text-2xl font-semibold text-gray-900 tabular-nums">{totalCount}</p>
+                  {showingPartial && (
+                    <p className="text-[10px] text-gray-400 mt-0.5">Showing {traces.length}</p>
+                  )}
+                </div>
+                <div className="rounded-xl border border-gray-200 bg-white px-4 py-3 shadow-sm">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">In progress</p>
+                  <p className="text-2xl font-semibold text-amber-600 tabular-nums">{openTraces}</p>
+                </div>
+                <div className="rounded-xl border border-gray-200 bg-white px-4 py-3 shadow-sm">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">With timing</p>
+                  <p className="text-2xl font-semibold text-indigo-600 tabular-nums">{withLatency}</p>
+                </div>
+              </div>
+            )}
+
+            <div className="bg-white rounded-xl border border-gray-200 shadow-sm overflow-hidden">
+              <div className="px-4 py-3 border-b border-gray-100 bg-gradient-to-r from-gray-50 to-white flex justify-between items-center gap-2">
+                <div>
+                  <h2 className="text-sm font-semibold text-gray-900">Recent calls</h2>
+                  {lastUpdatedLabel && (
+                    <p className="text-xs text-gray-500 mt-0.5">{lastUpdatedLabel}</p>
+                  )}
+                </div>
+              </div>
+
+              {loadingList && !listData && (
+                <p className="p-10 text-sm text-gray-500 text-center">Loading traces…</p>
+              )}
+
+              {listErrorMessage && (
+                <div className="p-4 text-sm text-red-800 bg-red-50 border-b border-red-100 space-y-2">
+                  <p>Could not load traces: {listErrorMessage}</p>
+                  <button
+                    type="button"
+                    onClick={() => refetchTraces()}
+                    className="text-xs font-medium text-red-900 underline"
+                  >
+                    Retry
+                  </button>
+                </div>
+              )}
+
+              {!loadingList && !listError && activeWorkspaceId && traces.length === 0 && (
+                <div className="p-12 text-center text-sm text-gray-600 space-y-4">
+                  <div className="w-12 h-12 rounded-full bg-indigo-50 flex items-center justify-center mx-auto">
+                    <Activity className="w-6 h-6 text-indigo-400" />
+                  </div>
+                  <div>
+                    <p className="font-medium text-gray-900">No calls traced yet</p>
+                    <p className="mt-1 max-w-sm mx-auto">
+                      Connect your Pipecat agent, run a short WebRTC call at{' '}
+                      <code className="text-xs bg-gray-100 px-1 rounded">localhost:7860/client</code>,
+                      then refresh this list.
+                    </p>
+                  </div>
+                  <button
+                    type="button"
+                    onClick={() => setTab('setup')}
+                    className="text-indigo-600 hover:text-indigo-800 text-sm font-medium"
+                  >
+                    Connect Pipecat →
+                  </button>
+                </div>
+              )}
+
+              {traces.length > 0 && (
+                <div className="overflow-x-auto">
+                  <table className="min-w-full divide-y divide-gray-100">
+                    <thead>
+                      <tr className="bg-gray-50/80">
+                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                          Call ID
+                        </th>
+                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                          Type
+                        </th>
+                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                          Status
+                        </th>
+                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                          Turns
+                        </th>
+                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                          Median
+                        </th>
+                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                          When
+                        </th>
+                        <th className="px-4 py-3" />
+                      </tr>
+                    </thead>
+                    <tbody className="divide-y divide-gray-100">
+                      {traces.map((trace: any) => {
+                        const selected = selectedTraceId === trace.id
+                        return (
+                          <tr
+                            key={trace.id}
+                            role="button"
+                            tabIndex={0}
+                            className={`cursor-pointer transition-colors ${
+                              selected
+                                ? 'bg-indigo-50/70 ring-1 ring-inset ring-indigo-200'
+                                : 'hover:bg-gray-50/80'
+                            }`}
+                            onClick={() => openTrace(trace.id)}
+                            onKeyDown={(e) => {
+                              if (e.key === 'Enter' || e.key === ' ') {
+                                e.preventDefault()
+                                openTrace(trace.id)
+                              }
+                            }}
+                          >
+                            <td className="px-4 py-3.5 whitespace-nowrap">
+                              <span className="font-mono font-semibold text-indigo-600">
+                                #{trace.call_short_id || trace.id.slice(0, 8)}
+                              </span>
+                            </td>
+                            <td className="px-4 py-3.5 whitespace-nowrap">
+                              <TransportBadge transport={trace.transport} />
+                            </td>
+                            <td className="px-4 py-3.5 whitespace-nowrap">
+                              <StatusBadge status={trace.status} />
+                            </td>
+                            <td className="px-4 py-3.5 whitespace-nowrap text-sm text-gray-700 tabular-nums font-medium">
+                              {trace.turn_count}
+                            </td>
+                            <td className="px-4 py-3.5 whitespace-nowrap text-sm tabular-nums">
+                              {trace.response_latency_p50_ms != null ? (
+                                <span className="font-medium text-gray-900">
+                                  {Math.round(trace.response_latency_p50_ms)}
+                                  <span className="text-gray-400 font-normal ml-0.5">ms</span>
+                                </span>
+                              ) : (
+                                <span className="text-gray-300">—</span>
+                              )}
+                            </td>
+                            <td className="px-4 py-3.5 whitespace-nowrap text-sm text-gray-500">
+                              <span title={formatWhen(trace.started_at)}>{formatRelative(trace.started_at)}</span>
+                            </td>
+                            <td
+                              className="px-4 py-3.5 whitespace-nowrap text-right"
+                              onClick={(e) => e.stopPropagation()}
+                            >
+                              <Button
+                                variant="ghost"
+                                size="sm"
+                                onClick={() => openTrace(trace.id)}
+                                leftIcon={<Eye className="w-4 h-4" />}
+                              >
+                                View
+                              </Button>
+                            </td>
+                          </tr>
+                        )
+                      })}
+                    </tbody>
+                  </table>
+                </div>
+              )}
+            </div>
+          </div>
+
+          {selectedTraceId && !isMobileView && (
+            <TraceDetailShell traceId={selectedTraceId} onClose={closeTrace} layout="desktop" />
+          )}
+        </div>
+      )}
+
+      {drawerOpen && selectedTraceId && isMobileView && (
+        <TraceDetailShell traceId={selectedTraceId} onClose={closeTrace} layout="mobile" />
+      )}
+    </div>
+  )
+}
diff --git a/pyproject.toml b/pyproject.toml
index 94e8e3c6..9d990488 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,176 +1,182 @@
-[project]
-name = "efficientai"
-version = "0.1.0"
-description = "Voice AI Evaluation Platform"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    # Cap below 0.137: FastAPI 0.137+ uses _IncludedRouter without .path, which
-    # breaks prometheus-fastapi-instrumentator on every API request when
-    # OBSERVABILITY_ENABLED is true. See prometheus-fastapi-instrumentator#370.
-    "fastapi>=0.104.0,<0.137",
-    "uvicorn[standard]>=0.24.0",
-    "sqlalchemy>=2.0.0",
-    "alembic>=1.12.0",
-    "psycopg2-binary>=2.9.9",
-    "pydantic[email]>=2.5.0",
-    "pydantic-settings>=2.1.0",
-    "python-multipart>=0.0.6",
-    "python-jose[cryptography]>=3.3.0",
-    "bcrypt>=4.2.0",
-    "cryptography>=41.0.0",
-    "celery>=5.3.0",
-    "redis>=5.0.0",
-    "librosa>=0.10.0",
-    "soundfile>=0.12.0",
-    "pydub>=0.25.1",
-    "python-dotenv>=1.0.0",
-    "httpx>=0.25.0",
-    "click>=8.1.0",
-    "pyyaml>=6.0.0",
-    "aiofiles>=23.2.0",
-    "boto3>=1.34.0",
-    "google-cloud-storage>=2.14.0",
-    "azure-storage-blob>=12.19.0",
-    "cartesia>=0.1.0",
-    "deepgram-sdk>=3.0.0,<4.0.0",
-    "sarvamai>=0.1.0",
-    "loguru>=0.7.0",
-    "retell-sdk>=0.1.0",
-    "livekit>=0.11.0",
-    "daily-python>=0.10.0",
-    "aiortc>=1.6.0",
-    "av>=10.0.0",
-    "croniter>=2.0.0",
-    "pytz>=2024.1",
-    "jiwer>=3.0.0",
-    "litellm>=1.84.0,<2.0.0",
-    "jinja2>=3.1.0",
-    "wait-for2>=0.4.0",
-    "docstring-parser>=0.15",
-    "pyloudnorm>=0.1.0",
-    "numpy>=1.24.0",
-    "aiohttp>=3.9.0",
-    "websockets>=12.0",
-    "pillow>=10.0.0",
-    "protobuf>=4.25.0",
-    "requests>=2.31.0",
-    "nltk>=3.8.0",
-    "openai>=1.0.0",
-    "anthropic>=0.18.0",
-    "huggingface-hub>=0.20.0",
-    "soxr>=0.3.0",
-    "typing-extensions>=4.9.0",
-    "onnxruntime>=1.17.0",
-    "prometheus-fastapi-instrumentator>=7.0.0",
-    "plivo>=4.47.0",
-    "openpyxl>=3.1.0",
-    "weasyprint>=62.0",
-    "flexprice>=2.1.0",
-]
-
-[project.optional-dependencies]
-dev = [
-    "pytest>=7.4.0",
-    "pytest-asyncio>=0.21.0",
-    "pytest-cov>=4.1.0",
-    "pytest-mock>=3.14.0",
-    "pytest-xdist>=3.5.0",
-    "factory-boy>=3.3.1",
-    "moto[s3]>=5.0.0",
-    "black>=23.11.0",
-    "ruff>=0.1.0",
-    "mypy>=1.7.0",
-]
-local-smart-turn-v3 = [
-    "transformers>=5.5.0,<6.0.0",
-    "onnxruntime>=1.17.0",
-    "importlib-resources>=6.4.0",
-]
-cartesia = [
-    "cartesia>=0.1.0",
-    "websockets>=12.0",
-]
-deepgram = [
-    "deepgram-sdk>=3.0.0,<4.0.0",
-]
-# Local Whisper transcription (requires PyTorch) - use OpenAI API instead for lighter install
-local-whisper = [
-    "openai-whisper>=20231117",
-]
-# Qualitative Voice AI Metrics (MOS, Emotion, Prosody) + Speaker Diarization
-qualitative-voice = [
-    "torch>=2.0.0",
-    "torchaudio>=2.1.0",
-    "transformers>=5.5.0,<6.0.0",
-    "librosa>=0.10.0",
-    "imageio-ffmpeg>=0.5.1",
-    "scipy>=1.11.0",
-    "pyannote.audio>=3.1.0",
-    "praat-parselmouth>=0.4.3",
-]
-# NVIDIA NeMo ASR for TTS hallucination detection (WER/CER via Conformer CTC)
-nemo-asr = [
-    "nemo_toolkit[asr]>=1.20.0",
-]
-# Google AI services (Gemini, Cloud TTS, Cloud Speech)
-google = [
-    "google-genai>=1.0.0",
-    "google-cloud-texttospeech>=2.16.0",
-    "google-cloud-speech>=2.26.0",
-]
-plivo = [
-    "plivo>=4.47.0",
-]
-# GEPA prompt optimization (enterprise feature)
-gepa = [
-    "gepa",
-    "dspy",
-]
-# Optional: Speaker Consistency metric (has version conflicts with torchaudio)
-# speechbrain = ["speechbrain>=1.0.0"]
-
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[tool.hatch.build]
-force-include = { "src/efficientai" = "efficientai" }
-
-[tool.hatch.build.targets.wheel]
-packages = ["app", "efficientai"]
-
-[project.scripts]
-eai = "app.cli:main"
-
-[tool.ruff]
-line-length = 100
-target-version = "py311"
-
-[tool.ruff.lint]
-select = ["E", "F", "I", "N", "W"]
-ignore = []
-
-[tool.black]
-line-length = 100
-target-version = ["py311"]
-
-[tool.mypy]
-python_version = "3.11"
-warn_return_any = true
-warn_unused_configs = true
-disallow_untyped_defs = false
-
-[tool.pytest.ini_options]
-testpaths = ["tests"]
-python_files = ["test_*.py"]
-asyncio_mode = "auto"
-markers = [
-    "unit: fast pure unit tests",
-    "integration: database or app-level integration tests",
-    "slow: expensive or long-running tests",
-]
-filterwarnings = [
-    "ignore:Error loading \\.env file, using defaults\\..*:UserWarning:app\\.database",
-    "ignore:'audioop' is deprecated and slated for removal in Python 3.13:DeprecationWarning:efficientai\\.audio\\.utils",
-]
+[project]
+name = "efficientai"
+version = "0.1.0"
+description = "Voice AI Evaluation Platform"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    # Cap below 0.137: FastAPI 0.137+ uses _IncludedRouter without .path, which
+    # breaks prometheus-fastapi-instrumentator on every API request when
+    # OBSERVABILITY_ENABLED is true. See prometheus-fastapi-instrumentator#370.
+    "fastapi>=0.104.0,<0.137",
+    "uvicorn[standard]>=0.24.0",
+    "sqlalchemy>=2.0.0",
+    "alembic>=1.12.0",
+    "psycopg2-binary>=2.9.9",
+    "pydantic[email]>=2.5.0",
+    "pydantic-settings>=2.1.0",
+    "python-multipart>=0.0.6",
+    "python-jose[cryptography]>=3.3.0",
+    "bcrypt>=4.2.0",
+    "cryptography>=41.0.0",
+    "celery>=5.3.0",
+    "redis>=5.0.0",
+    "librosa>=0.10.0",
+    "soundfile>=0.12.0",
+    "pydub>=0.25.1",
+    "python-dotenv>=1.0.0",
+    "httpx>=0.25.0",
+    "click>=8.1.0",
+    "pyyaml>=6.0.0",
+    "aiofiles>=23.2.0",
+    "boto3>=1.34.0",
+    "google-cloud-storage>=2.14.0",
+    "azure-storage-blob>=12.19.0",
+    "cartesia>=0.1.0",
+    "deepgram-sdk>=3.0.0,<4.0.0",
+    "sarvamai>=0.1.0",
+    "loguru>=0.7.0",
+    "retell-sdk>=0.1.0",
+    "livekit>=0.11.0",
+    "daily-python>=0.10.0",
+    "aiortc>=1.6.0",
+    "av>=10.0.0",
+    "croniter>=2.0.0",
+    "pytz>=2024.1",
+    "jiwer>=3.0.0",
+    "litellm>=1.84.0,<2.0.0",
+    "jinja2>=3.1.0",
+    "wait-for2>=0.4.0",
+    "docstring-parser>=0.15",
+    "pyloudnorm>=0.1.0",
+    "numpy>=1.24.0",
+    "aiohttp>=3.9.0",
+    "websockets>=12.0",
+    "pillow>=10.0.0",
+    "protobuf>=4.25.0",
+    "opentelemetry-proto>=1.27.0",
+    "requests>=2.31.0",
+    "nltk>=3.8.0",
+    "openai>=1.0.0",
+    "anthropic>=0.18.0",
+    "huggingface-hub>=0.20.0",
+    "soxr>=0.3.0",
+    "typing-extensions>=4.9.0",
+    "onnxruntime>=1.17.0",
+    "prometheus-fastapi-instrumentator>=7.0.0",
+    "plivo>=4.47.0",
+    "openpyxl>=3.1.0",
+    "weasyprint>=62.0",
+    "flexprice>=2.1.0",
+    "audioop-lts>=0.2.2; python_version >= '3.13'",
+]
+
+[project.optional-dependencies]
+otel = [
+    "opentelemetry-sdk>=1.27.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.27.0",
+]
+dev = [
+    "pytest>=7.4.0",
+    "pytest-asyncio>=0.21.0",
+    "pytest-cov>=4.1.0",
+    "pytest-mock>=3.14.0",
+    "pytest-xdist>=3.5.0",
+    "factory-boy>=3.3.1",
+    "moto[s3]>=5.0.0",
+    "black>=23.11.0",
+    "ruff>=0.1.0",
+    "mypy>=1.7.0",
+]
+local-smart-turn-v3 = [
+    "transformers>=5.5.0,<6.0.0",
+    "onnxruntime>=1.17.0",
+    "importlib-resources>=6.4.0",
+]
+cartesia = [
+    "cartesia>=0.1.0",
+    "websockets>=12.0",
+]
+deepgram = [
+    "deepgram-sdk>=3.0.0,<4.0.0",
+]
+# Local Whisper transcription (requires PyTorch) - use OpenAI API instead for lighter install
+local-whisper = [
+    "openai-whisper>=20231117",
+]
+# Qualitative Voice AI Metrics (MOS, Emotion, Prosody) + Speaker Diarization
+qualitative-voice = [
+    "torch>=2.0.0",
+    "torchaudio>=2.1.0",
+    "transformers>=5.5.0,<6.0.0",
+    "librosa>=0.10.0",
+    "imageio-ffmpeg>=0.5.1",
+    "scipy>=1.11.0",
+    "pyannote.audio>=3.1.0",
+    "praat-parselmouth>=0.4.3",
+]
+# NVIDIA NeMo ASR for TTS hallucination detection (WER/CER via Conformer CTC)
+nemo-asr = [
+    "nemo_toolkit[asr]>=1.20.0",
+]
+# Google AI services (Gemini, Cloud TTS, Cloud Speech)
+google = [
+    "google-genai>=1.0.0",
+    "google-cloud-texttospeech>=2.16.0",
+    "google-cloud-speech>=2.26.0",
+]
+plivo = [
+    "plivo>=4.47.0",
+]
+# GEPA prompt optimization (enterprise feature)
+gepa = [
+    "gepa",
+    "dspy",
+]
+# Optional: Speaker Consistency metric (has version conflicts with torchaudio)
+# speechbrain = ["speechbrain>=1.0.0"]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build]
+force-include = { "src/efficientai" = "efficientai" }
+
+[tool.hatch.build.targets.wheel]
+packages = ["app", "efficientai"]
+
+[project.scripts]
+eai = "app.cli:main"
+
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "N", "W"]
+ignore = []
+
+[tool.black]
+line-length = 100
+target-version = ["py311"]
+
+[tool.mypy]
+python_version = "3.11"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = false
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+asyncio_mode = "auto"
+markers = [
+    "unit: fast pure unit tests",
+    "integration: database or app-level integration tests",
+    "slow: expensive or long-running tests",
+]
+filterwarnings = [
+    "ignore:Error loading \\.env file, using defaults\\..*:UserWarning:app\\.database",
+    "ignore:'audioop' is deprecated and slated for removal in Python 3.13:DeprecationWarning:efficientai\\.audio\\.utils",
+]
diff --git a/scripts/pipecat_trace_dev_bot.py b/scripts/pipecat_trace_dev_bot.py
new file mode 100644
index 00000000..36bce88a
--- /dev/null
+++ b/scripts/pipecat_trace_dev_bot.py
@@ -0,0 +1,207 @@
+"""
+Dev WebSocket bot for local EfficientAI + Pipecat trace testing.
+
+Speaks the same generic JSON audio protocol as Agent Playground Custom WebSocket.
+Exports sample STT/LLM/TTS spans per detected user turn (audio activity).
+
+Usage:
+  export EFFICIENTAI_API_KEY="<workspace api key>"
+  uv run python scripts/pipecat_trace_dev_bot.py
+
+Then Agent Playground → Custom WebSocket → ws://localhost:9001 → Connect.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import os
+import sys
+import time
+import uuid
+
+import httpx
+import websockets
+
+sys.stdout.reconfigure(line_buffering=True)
+
+PORT = 9001
+ECHO_DELAY_S = 2.0
+GREETING_DELAY_S = 1.5
+# ~100ms frames from playground generic protocol → 80 frames ≈ 8s of speech
+FRAMES_PER_TURN = 80
+GREETING_TEXT = (
+    "Dev trace bot ready. Speak a few times — each speech burst exports one trace turn."
+)
+
+
+def _parse_trace_handshake(message: dict) -> dict | None:
+    if message.get("type") == "efficientai_trace_handshake" and message.get("call_short_id"):
+        return {
+            "call_short_id": str(message["call_short_id"]),
+            "agent_id": message.get("agent_id"),
+            "workspace_id": message.get("workspace_id"),
+            "otel_correlation": dict(message.get("otel_correlation") or {}),
+        }
+    legacy = message.get("efficientai_call_short_id")
+    if legacy:
+        return {
+            "call_short_id": str(legacy),
+            "agent_id": message.get("agent_id"),
+            "workspace_id": message.get("workspace_id"),
+            "otel_correlation": dict(message.get("otel_correlation") or {}),
+        }
+    return None
+
+
+async def _post_turn_spans(handshake: dict, turn_number: int) -> None:
+    api_key = os.environ.get("EFFICIENTAI_API_KEY", "").strip()
+    if not api_key:
+        print("    [trace] EFFICIENTAI_API_KEY not set — skipping span export")
+        return
+
+    otel = handshake.get("otel_correlation") or {}
+    endpoint = otel.get("otlp_endpoint") or os.environ.get(
+        "EFFICIENTAI_OTLP_ENDPOINT",
+        "http://localhost:8000/api/v1/observability/traces",
+    )
+    call_short_id = handshake["call_short_id"]
+    agent_id = handshake.get("agent_id")
+    workspace_id = handshake.get("workspace_id")
+
+    trace_id = f"dev{call_short_id}"
+    base_ttfb = 0.1 + (turn_number % 3) * 0.05
+    spans = []
+    for name, op, mult in [("stt", "stt", 1.0), ("llm", "chat", 2.5), ("tts", "tts", 1.4)]:
+        spans.append(
+            {
+                "traceId": trace_id,
+                "spanId": f"{name}-t{turn_number}-{uuid.uuid4().hex[:6]}",
+                "name": name,
+                "attributes": [
+                    {"key": "gen_ai.operation.name", "value": {"stringValue": op}},
+                    {"key": "turn.number", "value": {"intValue": str(turn_number)}},
+                    {"key": "metrics.ttfb", "value": {"doubleValue": round(base_ttfb * mult, 3)}},
+                    {"key": "efficientai.call_short_id", "value": {"stringValue": call_short_id}},
+                ],
+            }
+        )
+        if agent_id:
+            spans[-1]["attributes"].append(
+                {"key": "efficientai.agent_id", "value": {"stringValue": str(agent_id)}}
+            )
+        if workspace_id:
+            spans[-1]["attributes"].append(
+                {"key": "efficientai.workspace_id", "value": {"stringValue": str(workspace_id)}}
+            )
+
+    headers = {
+        "X-API-Key": api_key,
+        "Content-Type": "application/json",
+        "X-EfficientAI-Call-Short-Id": call_short_id,
+    }
+    if agent_id:
+        headers["X-EfficientAI-Agent-Id"] = str(agent_id)
+    if workspace_id:
+        headers["X-Workspace-Id"] = str(workspace_id)
+
+    payload = {"resourceSpans": [{"scopeSpans": [{"spans": spans}]}]}
+    async with httpx.AsyncClient(timeout=20.0) as client:
+        resp = await client.post(endpoint, headers=headers, json=payload)
+        resp.raise_for_status()
+        body = resp.json()
+        print(
+            f"    [trace] Turn {turn_number}: exported 3 spans "
+            f"(stt/llm/tts ~{int(base_ttfb*1000)}/{int(base_ttfb*mult*2500)}/{int(base_ttfb*1400)}ms) "
+            f"correlated={body.get('correlated')}"
+        )
+
+
+async def _close_trace_session(handshake: dict) -> None:
+    api_key = os.environ.get("EFFICIENTAI_API_KEY", "").strip()
+    workspace_id = handshake.get("workspace_id")
+    call_short_id = handshake.get("call_short_id")
+    if not api_key or not workspace_id or not call_short_id:
+        return
+    base = os.environ.get("EFFICIENTAI_API_BASE", "http://localhost:8000").rstrip("/")
+    async with httpx.AsyncClient(timeout=20.0) as client:
+        await client.post(
+            f"{base}/api/v1/observability/traces/sessions/{call_short_id}/close",
+            headers={"X-API-Key": api_key, "X-Workspace-Id": str(workspace_id)},
+        )
+        print(f"    [trace] Closed session {call_short_id}")
+
+
+async def echo_worker(queue: asyncio.Queue, ws):
+    while True:
+        frame_b64, scheduled_time = await queue.get()
+        wait = scheduled_time - time.time()
+        if wait > 0:
+            await asyncio.sleep(wait)
+        try:
+            await ws.send(json.dumps({"audio": frame_b64}))
+        except websockets.exceptions.ConnectionClosed:
+            break
+
+
+async def handler(ws):
+    remote = ws.remote_address
+    print(f"[+] Client connected from {remote}")
+
+    echo_queue: asyncio.Queue = asyncio.Queue()
+    worker_task = asyncio.create_task(echo_worker(echo_queue, ws))
+    audio_count = 0
+    turn_number = 0
+    last_export_at_frames = 0
+    start = time.time()
+    greeting_sent = False
+    handshake: dict | None = None
+
+    try:
+        async for raw in ws:
+            if not greeting_sent and time.time() - start > GREETING_DELAY_S:
+                greeting_sent = True
+                await ws.send(
+                    json.dumps({"type": "transcript", "role": "agent", "content": GREETING_TEXT})
+                )
+
+            try:
+                msg = json.loads(raw)
+            except (json.JSONDecodeError, TypeError):
+                continue
+
+            if handshake is None:
+                parsed = _parse_trace_handshake(msg)
+                if parsed:
+                    handshake = parsed
+                    print(f"    [trace] Handshake call_short_id={handshake['call_short_id']}")
+                    continue
+
+            audio_b64 = msg.get("audio")
+            if audio_b64 and isinstance(audio_b64, str) and len(audio_b64) > 20:
+                audio_count += 1
+                echo_queue.put_nowait((audio_b64, time.time() + ECHO_DELAY_S))
+
+                if handshake and audio_count - last_export_at_frames >= FRAMES_PER_TURN:
+                    turn_number += 1
+                    last_export_at_frames = audio_count
+                    await _post_turn_spans(handshake, turn_number)
+    finally:
+        worker_task.cancel()
+        if handshake and audio_count > last_export_at_frames:
+            turn_number += 1
+            await _post_turn_spans(handshake, turn_number)
+        if handshake:
+            await _close_trace_session(handshake)
+        print(f"[-] Session ended — {audio_count} audio frames, {turn_number} trace turns exported")
+
+
+async def main():
+    print(f"Pipecat trace dev bot on ws://localhost:{PORT}")
+    print("Set EFFICIENTAI_API_KEY once, then connect from Agent Playground Custom WebSocket.")
+    async with websockets.serve(handler, "0.0.0.0", PORT):
+        await asyncio.Future()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/scripts/test_local_pipecat_trace.py b/scripts/test_local_pipecat_trace.py
new file mode 100644
index 00000000..382d2a41
--- /dev/null
+++ b/scripts/test_local_pipecat_trace.py
@@ -0,0 +1,163 @@
+#!/usr/bin/env python3
+"""
+End-to-end local test for call trace ingest (simulates Pipecat export).
+
+  1. POST /observability/traces/sessions
+  2. POST /observability/traces (OTLP JSON)
+  3. POST /sessions/{call_short_id}/close
+  4. GET /observability/traces
+
+Usage:
+  export EFFICIENTAI_API_KEY="<from Workspace → API keys>"
+  export EFFICIENTAI_WORKSPACE_ID="<workspace-uuid>"
+  uv run python scripts/test_local_pipecat_trace.py
+
+Optional:
+  EFFICIENTAI_BASE_URL=http://localhost:8000
+  EFFICIENTAI_TRANSPORT=webrtc
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import sys
+import uuid
+
+import httpx
+
+BASE = os.environ.get("EFFICIENTAI_BASE_URL", "http://localhost:8000").rstrip("/")
+API_KEY = os.environ.get("EFFICIENTAI_API_KEY", "").strip()
+WORKSPACE_ID = os.environ.get("EFFICIENTAI_WORKSPACE_ID", "").strip()
+TRANSPORT = os.environ.get("EFFICIENTAI_TRANSPORT", "webrtc").strip()
+TRACES = "/api/v1/observability/traces"
+
+
+def _headers() -> dict[str, str]:
+    h = {"X-API-Key": API_KEY, "Content-Type": "application/json"}
+    if WORKSPACE_ID:
+        h["X-Workspace-Id"] = WORKSPACE_ID
+    return h
+
+
+def _require_env() -> None:
+    missing = [n for n, v in [
+        ("EFFICIENTAI_API_KEY", API_KEY),
+        ("EFFICIENTAI_WORKSPACE_ID", WORKSPACE_ID),
+    ] if not v]
+    if missing:
+        print("Missing required environment variables:", ", ".join(missing))
+        sys.exit(1)
+
+
+def _otlp_json_payload(call_short_id: str) -> dict:
+    trace_id = uuid.uuid4().hex
+
+    def span(name: str, span_id: str, op: str, turn: int, ttfb_s: float) -> dict:
+        return {
+            "traceId": trace_id,
+            "spanId": span_id,
+            "name": name,
+            "attributes": [
+                {"key": "gen_ai.operation.name", "value": {"stringValue": op}},
+                {"key": "turn.number", "value": {"intValue": str(turn)}},
+                {"key": "metrics.ttfb", "value": {"doubleValue": ttfb_s}},
+                {"key": "efficientai.call_short_id", "value": {"stringValue": call_short_id}},
+                {"key": "efficientai.workspace_id", "value": {"stringValue": WORKSPACE_ID}},
+            ],
+        }
+
+    return {
+        "resourceSpans": [{
+            "scopeSpans": [{
+                "spans": [
+                    span("stt", "a1", "stt", 1, 0.12),
+                    span("llm", "a2", "chat", 1, 0.38),
+                    span("tts", "a3", "tts", 1, 0.21),
+                ],
+            }],
+        }],
+    }
+
+
+def main() -> int:
+    _require_env()
+    client = httpx.Client(base_url=BASE, timeout=30.0)
+    headers = _headers()
+
+    print(f"→ Health: {BASE}/health")
+    health = client.get("/health")
+    health.raise_for_status()
+    print(f"  {health.json()}")
+
+    print(f"\n→ POST {TRACES}/sessions (transport={TRANSPORT})")
+    session_resp = client.post(
+        f"{TRACES}/sessions",
+        headers=headers,
+        json={"transport": TRANSPORT},
+    )
+    if session_resp.status_code >= 400:
+        print(f"  ERROR {session_resp.status_code}: {session_resp.text}")
+        return 1
+    session = session_resp.json()
+    call_short_id = session["call_short_id"]
+    trace_id = session["trace_id"]
+    print(f"  call_short_id={call_short_id}  trace_id={trace_id}")
+    print(f"  otlp_endpoint={session['otel_correlation']['otlp_endpoint']}")
+
+    print(f"\n→ POST {TRACES} (simulating Pipecat export)")
+    otlp_headers = {
+        "X-API-Key": API_KEY,
+        "Content-Type": "application/json",
+        "X-EfficientAI-Call-Short-Id": call_short_id,
+        "X-Workspace-Id": WORKSPACE_ID,
+    }
+    otlp_resp = client.post(
+        TRACES,
+        headers=otlp_headers,
+        content=json.dumps(_otlp_json_payload(call_short_id)),
+    )
+    if otlp_resp.status_code >= 400:
+        print(f"  ERROR {otlp_resp.status_code}: {otlp_resp.text}")
+        return 1
+    otlp = otlp_resp.json()
+    print(f"  accepted_spans={otlp['accepted_spans']} correlated={otlp['correlated']}")
+
+    print(f"\n→ POST {TRACES}/sessions/{call_short_id}/close")
+    close_resp = client.post(
+        f"{TRACES}/sessions/{call_short_id}/close",
+        headers={"X-API-Key": API_KEY, "X-Workspace-Id": WORKSPACE_ID},
+    )
+    close_resp.raise_for_status()
+    print(f"  status={close_resp.json()['status']}")
+
+    print(f"\n→ GET {TRACES}?status=closed")
+    list_resp = client.get(
+        TRACES,
+        headers={"X-API-Key": API_KEY, "X-Workspace-Id": WORKSPACE_ID},
+        params={"status": "closed", "limit": 5},
+    )
+    list_resp.raise_for_status()
+    items = list_resp.json().get("items") or []
+    match = next((t for t in items if t.get("call_short_id") == call_short_id), None)
+    if not match:
+        print("  WARNING: trace not found in list (check workspace scope)")
+        return 1
+    print(f"  found trace turn_count={match.get('turn_count')}")
+
+    print(f"\n→ GET {TRACES}/{trace_id}")
+    detail = client.get(
+        f"{TRACES}/{trace_id}",
+        headers={"X-API-Key": API_KEY, "X-Workspace-Id": WORKSPACE_ID},
+    )
+    detail.raise_for_status()
+    d = detail.json()
+    print(f"  turns={len(d.get('turns') or [])} otel_spans={len(d.get('otel_spans') or [])}")
+
+    print("\n✅ Local call trace path OK.")
+    print(f"   Open Call Traces UI and look for call_short_id {call_short_id}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/test_ws_echo.py b/scripts/test_ws_echo.py
index 2fb30d8f..443be54b 100644
--- a/scripts/test_ws_echo.py
+++ b/scripts/test_ws_echo.py
@@ -1,118 +1,128 @@
-"""
-Minimal WebSocket echo server for testing the Generic WebSocket voice client.
-
-Usage:
-    pip install websockets
-    python scripts/test_ws_echo.py
-
-Then connect from the Custom WebSocket tab using:
-    ws://localhost:8766
-
-The server:
-  - Buffers incoming audio frames for ECHO_DELAY_S seconds, then replays them
-  - Sends periodic fake agent transcript messages
-  - Logs activity to the console
-"""
-
-import asyncio
-import json
-import sys
-import time
-
-import websockets
-
-sys.stdout.reconfigure(line_buffering=True)
-
-PORT = 9876
-ECHO_DELAY_S = 3.0
-GREETING_DELAY_S = 2.0
-FRAME_INTERVAL_S = 0.1  # matches the generic protocol's 100ms sendIntervalMs
-
-GREETING_TEXT = "Hello! I'm the echo bot. Whatever you say, I'll repeat back after a 3-second delay."
-
-
-async def echo_worker(queue: asyncio.Queue, ws):
-    """Drains the queue and replays audio frames with original timing."""
-    while True:
-        frame_b64, scheduled_time = await queue.get()
-        now = time.time()
-        wait = scheduled_time - now
-        if wait > 0:
-            await asyncio.sleep(wait)
-        try:
-            await ws.send(json.dumps({"audio": frame_b64}))
-        except websockets.exceptions.ConnectionClosed:
-            break
-
-
-async def handler(ws):
-    remote = ws.remote_address
-    print(f"[+] Client connected from {remote}")
-
-    echo_queue: asyncio.Queue = asyncio.Queue()
-    worker_task = asyncio.create_task(echo_worker(echo_queue, ws))
-
-    audio_count = 0
-    start = time.time()
-    greeting_sent = False
-
-    try:
-        async for raw in ws:
-            elapsed = time.time() - start
-
-            if not greeting_sent and elapsed > GREETING_DELAY_S:
-                greeting_sent = True
-                await ws.send(json.dumps({
-                    "type": "transcript",
-                    "role": "agent",
-                    "content": GREETING_TEXT,
-                }))
-                print(f"    [transcript] Sent greeting")
-
-            try:
-                msg = json.loads(raw)
-            except (json.JSONDecodeError, TypeError):
-                print(f"    [?] Non-JSON message ({len(raw)} bytes)")
-                continue
-
-            audio_b64 = msg.get("audio")
-            if audio_b64 and isinstance(audio_b64, str) and len(audio_b64) > 20:
-                audio_count += 1
-                if audio_count % 50 == 1:
-                    print(f"    [audio] Received frame #{audio_count} ({len(audio_b64)} b64 chars)")
-
-                echo_queue.put_nowait((audio_b64, time.time() + ECHO_DELAY_S))
-
-                if audio_count % 200 == 0:
-                    await ws.send(json.dumps({
-                        "type": "transcript",
-                        "role": "user",
-                        "content": f"(echo test — {audio_count} audio frames received)",
-                    }))
-                    await ws.send(json.dumps({
-                        "type": "transcript",
-                        "role": "agent",
-                        "content": f"I've echoed back {audio_count} audio frames so far!",
-                    }))
-            else:
-                print(f"    [msg] {json.dumps(msg)[:200]}")
-
-    except websockets.exceptions.ConnectionClosed as e:
-        print(f"[-] Client {remote} disconnected: {e.code} {e.reason}")
-    except Exception as e:
-        print(f"[!] Error: {e}")
-    finally:
-        worker_task.cancel()
-        print(f"[-] Session ended — {audio_count} audio frames received in {time.time() - start:.1f}s")
-
-
-async def main():
-    print(f"Echo WebSocket server starting on ws://localhost:{PORT}")
-    print(f"Connect from the Custom WebSocket tab with: ws://localhost:{PORT}")
-    print()
-    async with websockets.serve(handler, "0.0.0.0", PORT):
-        await asyncio.Future()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
+"""
+Minimal WebSocket echo server for testing the Generic WebSocket voice client.
+
+Usage:
+    pip install websockets
+    python scripts/test_ws_echo.py
+
+Then connect from the Custom WebSocket tab using:
+    ws://localhost:8766
+
+The server:
+  - Buffers incoming audio frames for ECHO_DELAY_S seconds, then replays them
+  - Sends periodic fake agent transcript messages
+  - Logs activity to the console
+"""
+
+import asyncio
+import json
+import sys
+import time
+
+import websockets
+
+sys.stdout.reconfigure(line_buffering=True)
+
+PORT = 9876
+ECHO_DELAY_S = 3.0
+GREETING_DELAY_S = 2.0
+FRAME_INTERVAL_S = 0.1  # matches the generic protocol's 100ms sendIntervalMs
+
+GREETING_TEXT = "Hello! I'm the echo bot. Whatever you say, I'll repeat back after a 3-second delay."
+
+
+async def echo_worker(queue: asyncio.Queue, ws):
+    """Drains the queue and replays audio frames with original timing."""
+    while True:
+        frame_b64, scheduled_time = await queue.get()
+        now = time.time()
+        wait = scheduled_time - now
+        if wait > 0:
+            await asyncio.sleep(wait)
+        try:
+            await ws.send(json.dumps({"audio": frame_b64}))
+        except websockets.exceptions.ConnectionClosed:
+            break
+
+
+async def handler(ws):
+    remote = ws.remote_address
+    print(f"[+] Client connected from {remote}")
+
+    echo_queue: asyncio.Queue = asyncio.Queue()
+    worker_task = asyncio.create_task(echo_worker(echo_queue, ws))
+
+    audio_count = 0
+    start = time.time()
+    greeting_sent = False
+    efficientai_call_short_id = None
+
+    try:
+        async for raw in ws:
+            elapsed = time.time() - start
+
+            if not greeting_sent and elapsed > GREETING_DELAY_S:
+                greeting_sent = True
+                await ws.send(json.dumps({
+                    "type": "transcript",
+                    "role": "agent",
+                    "content": GREETING_TEXT,
+                }))
+                print(f"    [transcript] Sent greeting")
+
+            try:
+                msg = json.loads(raw)
+            except (json.JSONDecodeError, TypeError):
+                print(f"    [?] Non-JSON message ({len(raw)} bytes)")
+                continue
+
+            cid = msg.get("efficientai_call_short_id")
+            if cid:
+                efficientai_call_short_id = str(cid)
+                print(f"    [trace] Received efficientai_call_short_id={efficientai_call_short_id}")
+                print("    [trace] Set EFFICIENTAI_CALL_SHORT_ID in your Pipecat process to this value.")
+                continue
+
+            audio_b64 = msg.get("audio")
+            if audio_b64 and isinstance(audio_b64, str) and len(audio_b64) > 20:
+                audio_count += 1
+                if audio_count % 50 == 1:
+                    print(f"    [audio] Received frame #{audio_count} ({len(audio_b64)} b64 chars)")
+
+                echo_queue.put_nowait((audio_b64, time.time() + ECHO_DELAY_S))
+
+                if audio_count % 200 == 0:
+                    await ws.send(json.dumps({
+                        "type": "transcript",
+                        "role": "user",
+                        "content": f"(echo test — {audio_count} audio frames received)",
+                    }))
+                    await ws.send(json.dumps({
+                        "type": "transcript",
+                        "role": "agent",
+                        "content": f"I've echoed back {audio_count} audio frames so far!",
+                    }))
+            else:
+                print(f"    [msg] {json.dumps(msg)[:200]}")
+
+    except websockets.exceptions.ConnectionClosed as e:
+        print(f"[-] Client {remote} disconnected: {e.code} {e.reason}")
+    except Exception as e:
+        print(f"[!] Error: {e}")
+    finally:
+        worker_task.cancel()
+        if efficientai_call_short_id:
+            print(f"[-] Trace session id was {efficientai_call_short_id}")
+        print(f"[-] Session ended — {audio_count} audio frames received in {time.time() - start:.1f}s")
+
+
+async def main():
+    print(f"Echo WebSocket server starting on ws://localhost:{PORT}")
+    print(f"Connect from the Custom WebSocket tab with: ws://localhost:{PORT}")
+    print()
+    async with websockets.serve(handler, "0.0.0.0", PORT):
+        await asyncio.Future()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/src/efficientai/integrations/__init__.py b/src/efficientai/integrations/__init__.py
new file mode 100644
index 00000000..0b6f2c57
--- /dev/null
+++ b/src/efficientai/integrations/__init__.py
@@ -0,0 +1 @@
+# EfficientAI integrations package
diff --git a/src/efficientai/integrations/efficientai_traces/__init__.py b/src/efficientai/integrations/efficientai_traces/__init__.py
new file mode 100644
index 00000000..2c7dadf6
--- /dev/null
+++ b/src/efficientai/integrations/efficientai_traces/__init__.py
@@ -0,0 +1,49 @@
+"""EfficientAI synthetic trace helpers for self-hosted Pipecat agents."""
+
+from efficientai.integrations.efficientai_traces.correlation import (
+    build_outbound_sip_headers,
+    extract_call_short_id,
+    otlp_export_headers,
+    span_correlation_attributes,
+)
+from efficientai.integrations.efficientai_traces.handshake import (
+    HANDSHAKE_TYPE,
+    configure_pipecat_tracing_from_handshake,
+    parse_trace_handshake,
+)
+from efficientai.integrations.efficientai_traces.pipecat_upstream import (
+    close_trace_session,
+    ensure_trace_session,
+    mint_trace_session,
+    missing_deployment_trace_env,
+    require_deployment_trace_env,
+    resolve_trace_transport,
+    setup_pipecat_worker_tracing,
+)
+from efficientai.integrations.efficientai_traces.setup import (
+    configure_pipecat_tracing,
+    create_otlp_exporter,
+    flush_efficientai_tracing,
+    setup_efficientai_tracing,
+)
+
+__all__ = [
+    "HANDSHAKE_TYPE",
+    "build_outbound_sip_headers",
+    "close_trace_session",
+    "configure_pipecat_tracing",
+    "configure_pipecat_tracing_from_handshake",
+    "create_otlp_exporter",
+    "flush_efficientai_tracing",
+    "ensure_trace_session",
+    "extract_call_short_id",
+    "mint_trace_session",
+    "missing_deployment_trace_env",
+    "otlp_export_headers",
+    "parse_trace_handshake",
+    "require_deployment_trace_env",
+    "resolve_trace_transport",
+    "setup_efficientai_tracing",
+    "setup_pipecat_worker_tracing",
+    "span_correlation_attributes",
+]
diff --git a/src/efficientai/integrations/efficientai_traces/correlation.py b/src/efficientai/integrations/efficientai_traces/correlation.py
new file mode 100644
index 00000000..af278a1e
--- /dev/null
+++ b/src/efficientai/integrations/efficientai_traces/correlation.py
@@ -0,0 +1,149 @@
+"""Correlation ids shared between EfficientAI dial-out and customer Pipecat ingest."""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, Mapping, Optional
+
+# OTLP HTTP headers (customer → EfficientAI)
+OTLP_HEADER_API_KEY = "X-API-Key"
+OTLP_HEADER_CALL_SHORT_ID = "X-EfficientAI-Call-Short-Id"
+OTLP_HEADER_RUN_ID = "X-EfficientAI-Run-Id"
+
+# SIP headers on outbound INVITE (EfficientAI → customer PSTN/SIP)
+# Vobiz forwards X-VH-* ; Plivo prefixes custom keys with X-PH-
+SIP_VH_CALL_SHORT_ID = "X-VH-EfficientAI-Call-Short-Id"
+SIP_VH_RUN_ID = "X-VH-EfficientAI-Run-Id"
+SIP_PLIVO_CALL_SHORT_ID = "EfficientAI-Call-Short-Id"
+SIP_PLIVO_RUN_ID = "EfficientAI-Run-Id"
+
+# Span / resource attributes
+ATTR_CALL_SHORT_ID = "efficientai.call_short_id"
+ATTR_RUN_ID = "efficientai.evaluator_result_id"
+ATTR_AGENT_ID = "efficientai.agent_id"
+ATTR_WORKSPACE_ID = "efficientai.workspace_id"
+ATTR_ENVIRONMENT = "efficientai.environment"
+ATTR_TRANSPORT = "efficientai.transport"
+
+_CALL_SHORT_ID_RE = re.compile(r"^\d{6}$")
+
+_HEADER_ALIASES_CALL_SHORT_ID = (
+    OTLP_HEADER_CALL_SHORT_ID,
+    SIP_VH_CALL_SHORT_ID,
+    "X-PH-EfficientAI-Call-Short-Id",
+    "SipHeader_X-VH-EfficientAI-Call-Short-Id",
+    "SipHeader_X-PH-EfficientAI-Call-Short-Id",
+    "X-EfficientAI-Call-Short-Id",
+    "EfficientAI-Call-Short-Id",
+    "efficientai.call_short_id",
+    "Call-Short-Id",
+    "call_short_id",
+    "X-Custom-Call-Short-Id",
+)
+
+
+def _normalize_header_key(key: str) -> str:
+    return key.strip().lower().replace("_", "-")
+
+
+def _lookup(mapping: Mapping[str, Any], *candidates: str) -> Optional[str]:
+    if not mapping:
+        return None
+    normalized = {_normalize_header_key(str(k)): v for k, v in mapping.items()}
+    for name in candidates:
+        val = normalized.get(_normalize_header_key(name))
+        if val is not None and str(val).strip():
+            return str(val).strip()
+    return None
+
+
+def _valid_call_short_id(value: Optional[str]) -> Optional[str]:
+    if not value:
+        return None
+    cleaned = str(value).strip()
+    if _CALL_SHORT_ID_RE.match(cleaned):
+        return cleaned
+    return None
+
+
+def extract_call_short_id(
+    *,
+    sip_headers: Optional[Mapping[str, Any]] = None,
+    webhook_params: Optional[Mapping[str, Any]] = None,
+    custom_params: Optional[Mapping[str, Any]] = None,
+) -> Optional[str]:
+    """Resolve EfficientAI 6-digit call id from inbound telephony metadata."""
+    for mapping in (sip_headers, webhook_params, custom_params):
+        found = _lookup(mapping, *_HEADER_ALIASES_CALL_SHORT_ID)
+        valid = _valid_call_short_id(found)
+        if valid:
+            return valid
+    return None
+
+
+def build_outbound_sip_headers(
+    *,
+    call_short_id: str,
+    evaluator_result_id: Optional[str] = None,
+    agent_id: Optional[str] = None,
+) -> Dict[str, str]:
+    """Headers to attach on Vobiz/Plivo outbound INVITE for customer-side correlation."""
+    cid = _valid_call_short_id(call_short_id)
+    if not cid:
+        raise ValueError("call_short_id must be a 6-digit string")
+
+    headers: Dict[str, str] = {
+        SIP_VH_CALL_SHORT_ID: cid,
+        SIP_PLIVO_CALL_SHORT_ID: cid,
+    }
+    if evaluator_result_id:
+        headers[SIP_VH_RUN_ID] = str(evaluator_result_id)
+        headers[SIP_PLIVO_RUN_ID] = str(evaluator_result_id)
+    if agent_id:
+        headers["X-VH-EfficientAI-Agent-Id"] = str(agent_id)
+        headers["EfficientAI-Agent-Id"] = str(agent_id)
+    return headers
+
+
+def format_plivo_sip_headers(headers: Mapping[str, str]) -> str:
+    """Plivo/Vobiz REST API expects comma-separated key=value pairs."""
+    return ",".join(f"{k}={v}" for k, v in headers.items())
+
+
+def otlp_export_headers(
+    *,
+    api_key: str,
+    call_short_id: Optional[str] = None,
+    evaluator_result_id: Optional[str] = None,
+) -> Dict[str, str]:
+    headers = {OTLP_HEADER_API_KEY: api_key}
+    cid = _valid_call_short_id(call_short_id)
+    if cid:
+        headers[OTLP_HEADER_CALL_SHORT_ID] = cid
+    if evaluator_result_id:
+        headers[OTLP_HEADER_RUN_ID] = str(evaluator_result_id)
+    return headers
+
+
+def span_correlation_attributes(
+    *,
+    call_short_id: Optional[str] = None,
+    evaluator_result_id: Optional[str] = None,
+    agent_id: Optional[str] = None,
+    workspace_id: Optional[str] = None,
+    transport: Optional[str] = None,
+    environment: str = "pre_prod",
+) -> Dict[str, str]:
+    attrs: Dict[str, str] = {ATTR_ENVIRONMENT: environment}
+    cid = _valid_call_short_id(call_short_id)
+    if cid:
+        attrs[ATTR_CALL_SHORT_ID] = cid
+    if evaluator_result_id:
+        attrs[ATTR_RUN_ID] = str(evaluator_result_id)
+    if agent_id:
+        attrs[ATTR_AGENT_ID] = str(agent_id)
+    if workspace_id:
+        attrs[ATTR_WORKSPACE_ID] = str(workspace_id)
+    if transport:
+        attrs[ATTR_TRANSPORT] = str(transport)
+    return attrs
diff --git a/src/efficientai/integrations/efficientai_traces/handshake.py b/src/efficientai/integrations/efficientai_traces/handshake.py
new file mode 100644
index 00000000..c67955ed
--- /dev/null
+++ b/src/efficientai/integrations/efficientai_traces/handshake.py
@@ -0,0 +1,71 @@
+"""Parse EfficientAI trace handshake messages from synthetic test transports."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Mapping, Optional
+
+from efficientai.integrations.efficientai_traces.setup import configure_pipecat_tracing
+
+HANDSHAKE_TYPE = "efficientai_trace_handshake"
+LEGACY_CALL_SHORT_ID_KEY = "efficientai_call_short_id"
+
+
+def parse_trace_handshake(message: Mapping[str, Any]) -> Optional[Dict[str, Any]]:
+    """Normalize playground / session API handshake payloads."""
+    if not isinstance(message, Mapping):
+        return None
+
+    if message.get("type") == HANDSHAKE_TYPE:
+        call_short_id = message.get("call_short_id")
+        if not call_short_id:
+            return None
+        return {
+            "type": HANDSHAKE_TYPE,
+            "call_short_id": str(call_short_id),
+            "trace_id": message.get("trace_id"),
+            "agent_id": message.get("agent_id"),
+            "workspace_id": message.get("workspace_id"),
+            "transport": message.get("transport"),
+            "otel_correlation": dict(message.get("otel_correlation") or {}),
+        }
+
+    legacy_id = message.get(LEGACY_CALL_SHORT_ID_KEY)
+    if legacy_id:
+        return {
+            "type": HANDSHAKE_TYPE,
+            "call_short_id": str(legacy_id),
+            "trace_id": message.get("trace_id"),
+            "agent_id": message.get("agent_id"),
+            "workspace_id": message.get("workspace_id"),
+            "transport": message.get("transport"),
+            "otel_correlation": dict(message.get("otel_correlation") or {}),
+        }
+    return None
+
+
+def configure_pipecat_tracing_from_handshake(
+    message: Mapping[str, Any],
+    *,
+    api_key: Optional[str] = None,
+    service_name: str = "pipecat-agent",
+) -> Dict[str, Any]:
+    """
+    Configure Pipecat OTLP export from an EfficientAI handshake message.
+
+  Only ``EFFICIENTAI_API_KEY`` must be configured on the customer server (once).
+  ``call_short_id``, ``agent_id``, ``workspace_id``, and OTLP endpoint come
+  from the handshake automatically.
+    """
+    handshake = parse_trace_handshake(message)
+    if not handshake:
+        raise ValueError("Message is not an EfficientAI trace handshake")
+
+    otel = handshake.get("otel_correlation") or {}
+    return configure_pipecat_tracing(
+        call_short_id=handshake["call_short_id"],
+        agent_id=str(handshake["agent_id"]) if handshake.get("agent_id") else None,
+        workspace_id=str(handshake["workspace_id"]) if handshake.get("workspace_id") else None,
+        otlp_endpoint=otel.get("otlp_endpoint"),
+        api_key=api_key,
+        service_name=service_name,
+    )
diff --git a/src/efficientai/integrations/efficientai_traces/pipecat_upstream.py b/src/efficientai/integrations/efficientai_traces/pipecat_upstream.py
new file mode 100644
index 00000000..98d386cd
--- /dev/null
+++ b/src/efficientai/integrations/efficientai_traces/pipecat_upstream.py
@@ -0,0 +1,206 @@
+"""Bridge upstream pipecat-ai (PipelineWorker) to EfficientAI trace ingest."""
+
+from __future__ import annotations
+
+import os
+from typing import Any, Dict, Optional
+
+import httpx
+
+from efficientai.integrations.efficientai_traces.correlation import span_correlation_attributes
+from efficientai.integrations.efficientai_traces.handshake import parse_trace_handshake
+from efficientai.integrations.efficientai_traces.setup import setup_efficientai_tracing, flush_efficientai_tracing
+
+DEFAULT_API_BASE = os.environ.get("EFFICIENTAI_API_BASE", "http://localhost:8000").rstrip("/")
+DEFAULT_OTLP_PATH = "/api/v1/observability/traces"
+_VALID_TRANSPORTS = frozenset({"webrtc", "websocket", "phone", "custom"})
+
+
+def _env(name: str) -> Optional[str]:
+    val = os.environ.get(name)
+    return str(val).strip() if val and str(val).strip() else None
+
+
+def missing_deployment_trace_env() -> list[str]:
+    """Return unset deployment env vars needed when no handshake is present."""
+    required = (
+        "EFFICIENTAI_API_KEY",
+        "EFFICIENTAI_WORKSPACE_ID",
+    )
+    return [name for name in required if not _env(name)]
+
+
+def require_deployment_trace_env() -> None:
+    """Fail fast with a clear message before accepting a WebRTC/WebSocket call."""
+    missing = missing_deployment_trace_env()
+    if missing:
+        raise ValueError(
+            "Missing EfficientAI trace env in pipecat .env: "
+            + ", ".join(missing)
+            + ". Add them once (see docs/synthetic-call-traces-pipecat.md), then restart bot.py."
+        )
+
+
+def resolve_trace_transport(runner_args: Any = None, transport: Any = None) -> str:
+    """Infer the active Pipecat transport for trace session labeling."""
+    explicit = (_env("EFFICIENTAI_TRACE_TRANSPORT") or "").lower()
+    if explicit in _VALID_TRANSPORTS:
+        return explicit
+
+    if runner_args is not None:
+        runner_transport = getattr(runner_args, "transport", None)
+        if isinstance(runner_transport, str) and runner_transport.lower() in _VALID_TRANSPORTS:
+            return runner_transport.lower()
+        if getattr(runner_args, "websocket", None) is not None:
+            return "websocket"
+
+    if transport is not None:
+        type_name = type(transport).__name__.lower()
+        module_name = (type(transport).__module__ or "").lower()
+        if "webrtc" in type_name or "webrtc" in module_name:
+            return "webrtc"
+        if "websocket" in type_name or "websocket" in module_name:
+            return "websocket"
+
+    return "webrtc"
+
+
+async def mint_trace_session(
+    *,
+    workspace_id: str,
+    api_key: str,
+    api_base_url: str = DEFAULT_API_BASE,
+    transport: str = "websocket",
+    evaluator_result_id: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Open an EfficientAI trace session (call_short_id minted server-side)."""
+    payload: Dict[str, Any] = {"transport": transport}
+    if evaluator_result_id:
+        payload["evaluator_result_id"] = evaluator_result_id
+
+    async with httpx.AsyncClient(timeout=20.0) as client:
+        resp = await client.post(
+            f"{api_base_url}/api/v1/observability/traces/sessions",
+            headers={
+                "X-API-Key": api_key,
+                "X-Workspace-Id": workspace_id,
+                "Content-Type": "application/json",
+            },
+            json=payload,
+        )
+        resp.raise_for_status()
+        return resp.json()
+
+
+async def ensure_trace_session(
+    *,
+    transport: str = "websocket",
+    handshake: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    """
+    Resolve per-call trace context.
+
+    Priority:
+    1. EfficientAI WS handshake (playground custom WebSocket)
+    2. EFFICIENTAI_CALL_SHORT_ID env (manual dev fallback)
+    3. POST /observability/traces/sessions using deployment env (API key + workspace)
+    """
+    if handshake:
+        parsed = parse_trace_handshake(handshake)
+        if parsed:
+            otel = parsed.get("otel_correlation") or {}
+            return {
+                "call_short_id": parsed["call_short_id"],
+                "agent_id": parsed.get("agent_id"),
+                "workspace_id": parsed.get("workspace_id"),
+                "otlp_endpoint": otel.get("otlp_endpoint")
+                or f"{DEFAULT_API_BASE}{DEFAULT_OTLP_PATH}",
+            }
+
+    call_short_id = _env("EFFICIENTAI_CALL_SHORT_ID")
+    if call_short_id:
+        return {
+            "call_short_id": call_short_id,
+            "agent_id": _env("EFFICIENTAI_AGENT_ID"),
+            "workspace_id": _env("EFFICIENTAI_WORKSPACE_ID"),
+            "otlp_endpoint": _env("EFFICIENTAI_OTLP_ENDPOINT")
+            or f"{DEFAULT_API_BASE}{DEFAULT_OTLP_PATH}",
+        }
+
+    api_key = _env("EFFICIENTAI_API_KEY")
+    workspace_id = _env("EFFICIENTAI_WORKSPACE_ID")
+    if not api_key or not workspace_id:
+        require_deployment_trace_env()
+
+    session = await mint_trace_session(
+        workspace_id=workspace_id,
+        api_key=api_key,
+        transport=transport,
+    )
+    otel = session.get("otel_correlation") or {}
+    return {
+        "call_short_id": session["call_short_id"],
+        "trace_id": session.get("trace_id"),
+        "workspace_id": session.get("workspace_id") or workspace_id,
+        "transport": session.get("transport") or transport,
+        "otlp_endpoint": otel.get("otlp_endpoint") or f"{DEFAULT_API_BASE}{DEFAULT_OTLP_PATH}",
+    }
+
+
+def setup_pipecat_worker_tracing(trace_ctx: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Configure OTLP export and return kwargs for upstream ``PipelineWorker``.
+
+    Usage::
+
+        trace_ctx = await ensure_trace_session()
+        tracing = setup_pipecat_worker_tracing(trace_ctx)
+        worker = PipelineWorker(
+            pipeline,
+            enable_tracing=True,
+            additional_span_attributes=tracing["additional_span_attributes"],
+            ...
+        )
+    """
+    call_short_id = trace_ctx["call_short_id"]
+    agent_id = trace_ctx.get("agent_id")
+    workspace_id = trace_ctx.get("workspace_id")
+    transport = trace_ctx.get("transport")
+    otlp_endpoint = trace_ctx.get("otlp_endpoint")
+
+    setup_efficientai_tracing(
+        service_name="pipecat-agent",
+        call_short_id=call_short_id,
+        agent_id=str(agent_id) if agent_id else None,
+        workspace_id=str(workspace_id) if workspace_id else None,
+        transport=str(transport) if transport else None,
+        otlp_endpoint=otlp_endpoint,
+        api_key=_env("EFFICIENTAI_API_KEY"),
+    )
+
+    attrs = span_correlation_attributes(
+        call_short_id=call_short_id,
+        agent_id=str(agent_id) if agent_id else None,
+        workspace_id=str(workspace_id) if workspace_id else None,
+        transport=str(transport) if transport else None,
+    )
+    return {
+        "additional_span_attributes": attrs,
+        "call_short_id": call_short_id,
+        "transport": transport,
+    }
+
+
+async def close_trace_session(trace_ctx: Dict[str, Any]) -> None:
+    """Close trace row when Pipecat call ends."""
+    flush_efficientai_tracing()
+    call_short_id = trace_ctx.get("call_short_id")
+    api_key = _env("EFFICIENTAI_API_KEY")
+    workspace_id = trace_ctx.get("workspace_id") or _env("EFFICIENTAI_WORKSPACE_ID")
+    if not call_short_id or not api_key or not workspace_id:
+        return
+    async with httpx.AsyncClient(timeout=20.0) as client:
+        await client.post(
+            f"{DEFAULT_API_BASE}/api/v1/observability/traces/sessions/{call_short_id}/close",
+            headers={"X-API-Key": api_key, "X-Workspace-Id": str(workspace_id)},
+        )
diff --git a/src/efficientai/integrations/efficientai_traces/setup.py b/src/efficientai/integrations/efficientai_traces/setup.py
new file mode 100644
index 00000000..d8568194
--- /dev/null
+++ b/src/efficientai/integrations/efficientai_traces/setup.py
@@ -0,0 +1,334 @@
+"""OTLP export setup for customer Pipecat pipelines."""
+
+from __future__ import annotations
+
+import os
+import threading
+from typing import Any, Dict, Mapping, Optional, Sequence
+
+from efficientai.integrations.efficientai_traces.correlation import (
+    extract_call_short_id,
+    otlp_export_headers,
+    span_correlation_attributes,
+)
+from efficientai.utils.tracing.setup import is_tracing_available, setup_tracing, flush_tracing
+
+try:
+    from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+    from opentelemetry.sdk.trace import TracerProvider
+    from opentelemetry.sdk.trace.export import SpanExporter, SpanExportResult
+    from opentelemetry.trace import Span
+    from opentelemetry.sdk.trace import ReadableSpan
+    from opentelemetry.context import Context
+    from opentelemetry.sdk.trace import SpanProcessor
+
+    _OTEL_HTTP_AVAILABLE = True
+except ImportError:
+    _OTEL_HTTP_AVAILABLE = False
+    OTLPSpanExporter = None  # type: ignore
+    SpanExporter = object  # type: ignore
+    SpanExportResult = object  # type: ignore
+    SpanProcessor = object  # type: ignore
+    Span = object  # type: ignore
+    ReadableSpan = object  # type: ignore
+    Context = object  # type: ignore
+    TracerProvider = object  # type: ignore
+
+_efficientai_correlation_processor: Optional["_EfficientAICorrelationSpanProcessor"] = None
+_mutable_otlp_exporter: Optional["_MutableOtlpExporter"] = None
+
+
+class _MutableOtlpExporter(SpanExporter):  # type: ignore[misc]
+    """OTLP exporter whose HTTP correlation headers update per call."""
+
+    def __init__(self, *, endpoint: str, api_key: str):
+        self._endpoint = endpoint
+        self._api_key = api_key
+        self._call_short_id: Optional[str] = None
+        self._evaluator_result_id: Optional[str] = None
+        self._inner: Any = None
+        self._lock = threading.Lock()
+
+    def configure(
+        self,
+        *,
+        call_short_id: Optional[str] = None,
+        evaluator_result_id: Optional[str] = None,
+    ) -> None:
+        with self._lock:
+            self._call_short_id = call_short_id
+            self._evaluator_result_id = evaluator_result_id
+            self._inner = OTLPSpanExporter(
+                endpoint=self._endpoint,
+                headers=otlp_export_headers(
+                    api_key=self._api_key,
+                    call_short_id=call_short_id,
+                    evaluator_result_id=evaluator_result_id,
+                ),
+            )
+
+    def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult:
+        with self._lock:
+            if self._inner is None:
+                self.configure()
+            inner = self._inner
+        return inner.export(spans)
+
+    def shutdown(self) -> None:
+        with self._lock:
+            if self._inner is not None:
+                self._inner.shutdown()
+
+    def force_flush(self, timeout_millis: int = 30000) -> bool:
+        with self._lock:
+            if self._inner is None:
+                return True
+            return bool(self._inner.force_flush(timeout_millis))
+
+
+class _EfficientAICorrelationSpanProcessor(SpanProcessor):  # type: ignore[misc]
+    def __init__(self, attributes: Mapping[str, str]):
+        self._attributes = dict(attributes)
+
+    def update_attributes(self, attributes: Mapping[str, str]) -> None:
+        self._attributes = dict(attributes)
+
+    def on_start(self, span: Span, parent_context: Optional[Context] = None) -> None:
+        for key, value in self._attributes.items():
+            span.set_attribute(key, value)
+
+    def on_end(self, span: ReadableSpan) -> None:
+        return
+
+    def shutdown(self) -> None:
+        return
+
+    def force_flush(self, timeout_millis: int = 30000) -> bool:
+        return True
+
+
+def _env(name: str, default: Optional[str] = None) -> Optional[str]:
+    val = os.environ.get(name)
+    if val is None or not str(val).strip():
+        return default
+    return str(val).strip()
+
+
+def _resolve_call_short_id(
+    *,
+    call_short_id: Optional[str] = None,
+    sip_headers: Optional[Mapping[str, Any]] = None,
+    webhook_params: Optional[Mapping[str, Any]] = None,
+) -> Optional[str]:
+    return (
+        call_short_id
+        or extract_call_short_id(sip_headers=sip_headers, webhook_params=webhook_params)
+        or _env("EFFICIENTAI_CALL_SHORT_ID")
+    )
+
+
+def create_otlp_exporter(
+    *,
+    endpoint: Optional[str] = None,
+    api_key: Optional[str] = None,
+    call_short_id: Optional[str] = None,
+    evaluator_result_id: Optional[str] = None,
+    sip_headers: Optional[Mapping[str, Any]] = None,
+    webhook_params: Optional[Mapping[str, Any]] = None,
+    mutable: bool = True,
+) -> Any:
+    """Build an OTLP/HTTP exporter pointed at EfficientAI observability traces ingest."""
+    if not _OTEL_HTTP_AVAILABLE:
+        raise RuntimeError(
+            "Install opentelemetry-exporter-otlp-proto-http and opentelemetry-sdk"
+        )
+
+    resolved_endpoint = endpoint or _env("EFFICIENTAI_OTLP_ENDPOINT")
+    resolved_key = api_key or _env("EFFICIENTAI_API_KEY")
+    if not resolved_endpoint:
+        raise ValueError("EFFICIENTAI_OTLP_ENDPOINT is required")
+    if not resolved_key:
+        raise ValueError("EFFICIENTAI_API_KEY is required")
+
+    cid = _resolve_call_short_id(
+        call_short_id=call_short_id,
+        sip_headers=sip_headers,
+        webhook_params=webhook_params,
+    )
+    run_id = evaluator_result_id or _env("EFFICIENTAI_RUN_ID")
+
+    if mutable:
+        global _mutable_otlp_exporter
+        if _mutable_otlp_exporter is None:
+            _mutable_otlp_exporter = _MutableOtlpExporter(
+                endpoint=resolved_endpoint,
+                api_key=resolved_key,
+            )
+        _mutable_otlp_exporter.configure(
+            call_short_id=cid,
+            evaluator_result_id=run_id,
+        )
+        return _mutable_otlp_exporter
+
+    return OTLPSpanExporter(
+        endpoint=resolved_endpoint,
+        headers=otlp_export_headers(
+            api_key=resolved_key,
+            call_short_id=cid,
+            evaluator_result_id=run_id,
+        ),
+    )
+
+
+def _attach_correlation_processor(provider: TracerProvider, attrs: Dict[str, str]) -> None:
+    global _efficientai_correlation_processor
+    if _efficientai_correlation_processor is None:
+        _efficientai_correlation_processor = _EfficientAICorrelationSpanProcessor(attrs)
+        provider.add_span_processor(_efficientai_correlation_processor)
+    else:
+        _efficientai_correlation_processor.update_attributes(attrs)
+
+
+def setup_efficientai_tracing(
+    *,
+    service_name: str = "pipecat-agent",
+    call_short_id: Optional[str] = None,
+    evaluator_result_id: Optional[str] = None,
+    agent_id: Optional[str] = None,
+    workspace_id: Optional[str] = None,
+    transport: Optional[str] = None,
+    otlp_endpoint: Optional[str] = None,
+    api_key: Optional[str] = None,
+    sip_headers: Optional[Mapping[str, Any]] = None,
+    webhook_params: Optional[Mapping[str, Any]] = None,
+    console_export: bool = False,
+) -> bool:
+    """
+    Configure OpenTelemetry export to EfficientAI for the current process/call.
+
+    Call once per inbound call after reading SIP/webhook metadata.
+    """
+    if not is_tracing_available():
+        return False
+
+    cid = _resolve_call_short_id(
+        call_short_id=call_short_id,
+        sip_headers=sip_headers,
+        webhook_params=webhook_params,
+    )
+    run_id = evaluator_result_id or _env("EFFICIENTAI_RUN_ID")
+    agent = agent_id or _env("EFFICIENTAI_AGENT_ID")
+    workspace = workspace_id or _env("EFFICIENTAI_WORKSPACE_ID")
+    attrs = span_correlation_attributes(
+        call_short_id=cid,
+        evaluator_result_id=run_id,
+        agent_id=agent,
+        workspace_id=workspace,
+        transport=transport,
+    )
+
+    from opentelemetry import trace
+
+    provider = trace.get_tracer_provider()
+    if isinstance(provider, TracerProvider):
+        _attach_correlation_processor(provider, attrs)
+        exporter = create_otlp_exporter(
+            endpoint=otlp_endpoint,
+            api_key=api_key,
+            call_short_id=cid,
+            evaluator_result_id=run_id,
+            sip_headers=sip_headers,
+            webhook_params=webhook_params,
+            mutable=True,
+        )
+        _ = exporter
+        return True
+
+    exporter = create_otlp_exporter(
+        endpoint=otlp_endpoint,
+        api_key=api_key,
+        call_short_id=cid,
+        evaluator_result_id=run_id,
+        sip_headers=sip_headers,
+        webhook_params=webhook_params,
+    )
+    ok = setup_tracing(service_name, exporter=exporter, console_export=console_export)
+    if not ok:
+        return False
+
+    provider = trace.get_tracer_provider()
+    if isinstance(provider, TracerProvider) and attrs:
+        _attach_correlation_processor(provider, attrs)
+    return True
+
+
+def configure_pipecat_tracing(
+    *,
+    call_short_id: Optional[str] = None,
+    agent_id: Optional[str] = None,
+    workspace_id: Optional[str] = None,
+    otlp_endpoint: Optional[str] = None,
+    api_key: Optional[str] = None,
+    sip_headers: Optional[Mapping[str, Any]] = None,
+    webhook_params: Optional[Mapping[str, Any]] = None,
+    handshake: Optional[Mapping[str, Any]] = None,
+    additional_span_attributes: Optional[Dict[str, str]] = None,
+    service_name: str = "pipecat-agent",
+) -> Dict[str, Any]:
+    """
+    Configure OTLP export and return kwargs for ``PipelineTask(..., **kwargs)``.
+
+    Example::
+
+        task = PipelineTask(
+            pipeline,
+            enable_tracing=True,
+            **configure_pipecat_tracing(sip_headers=inbound_headers),
+        )
+    """
+    if handshake is not None:
+        from efficientai.integrations.efficientai_traces.handshake import (
+            configure_pipecat_tracing_from_handshake,
+        )
+
+        return configure_pipecat_tracing_from_handshake(
+            handshake,
+            api_key=api_key,
+            service_name=service_name,
+        )
+
+    setup_efficientai_tracing(
+        service_name=service_name,
+        call_short_id=call_short_id,
+        agent_id=agent_id,
+        workspace_id=workspace_id,
+        otlp_endpoint=otlp_endpoint,
+        api_key=api_key,
+        sip_headers=sip_headers,
+        webhook_params=webhook_params,
+    )
+    cid = _resolve_call_short_id(
+        call_short_id=call_short_id,
+        sip_headers=sip_headers,
+        webhook_params=webhook_params,
+    )
+    run_id = _env("EFFICIENTAI_RUN_ID")
+    agent = agent_id or _env("EFFICIENTAI_AGENT_ID")
+    workspace = workspace_id or _env("EFFICIENTAI_WORKSPACE_ID")
+    attrs = span_correlation_attributes(
+        call_short_id=cid,
+        evaluator_result_id=run_id,
+        agent_id=agent,
+        workspace_id=workspace,
+    )
+    if additional_span_attributes:
+        attrs.update(additional_span_attributes)
+    return {
+        "enable_tracing": True,
+        "additional_span_attributes": attrs,
+    }
+
+
+def flush_efficientai_tracing(timeout_millis: int = 30000) -> bool:
+    """Flush pending OTLP spans before closing an EfficientAI trace session."""
+    return flush_tracing(timeout_millis)
diff --git a/src/efficientai/utils/tracing/setup.py b/src/efficientai/utils/tracing/setup.py
index 350dd569..bd047732 100644
--- a/src/efficientai/utils/tracing/setup.py
+++ b/src/efficientai/utils/tracing/setup.py
@@ -88,3 +88,16 @@ def setup_tracing(
     except Exception as e:
         print(f"Error setting up tracing: {e}")
         return False
+
+
+def flush_tracing(timeout_millis: int = 30000) -> bool:
+    """Block until batched spans are exported (call before closing a trace session)."""
+    if not OPENTELEMETRY_AVAILABLE:
+        return False
+    try:
+        provider = trace.get_tracer_provider()
+        if isinstance(provider, TracerProvider):
+            return bool(provider.force_flush(timeout_millis))
+    except Exception:
+        return False
+    return False
diff --git a/tests/test_core/test_security_headers_middleware.py b/tests/test_core/test_security_headers_middleware.py
index 5b7f9af3..8646dcd9 100644
--- a/tests/test_core/test_security_headers_middleware.py
+++ b/tests/test_core/test_security_headers_middleware.py
@@ -1,139 +1,166 @@
-"""Tests for HTTP security response headers."""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import pytest
-from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-from fastapi.testclient import TestClient
-
-from app.config import settings
-from app.core.security_headers_middleware import SecurityHeadersMiddleware
-
-
-@pytest.fixture
-def security_client(tmp_path):
-    """Minimal app with SecurityHeadersMiddleware (avoids heavy conftest client)."""
-    app = FastAPI()
-    app.add_middleware(SecurityHeadersMiddleware)
-
-    @app.get("/health")
-    def health():
-        return {"status": "healthy"}
-
-    @app.get("/api/v1/auth/config")
-    def auth_config():
-        return {"providers": []}
-
-    assets_dir = tmp_path / "assets"
-    assets_dir.mkdir()
-    (assets_dir / "app.js").write_text("console.log('ok');", encoding="utf-8")
-    app.mount("/assets", StaticFiles(directory=str(assets_dir)), name="assets")
-
-    with TestClient(app) as client:
-        yield client
-
-
-def test_health_includes_baseline_security_headers(security_client):
-    response = security_client.get("/health")
-
-    assert response.status_code == 200
-    assert response.headers["X-Content-Type-Options"] == "nosniff"
-    assert response.headers["Referrer-Policy"] == "strict-origin-when-cross-origin"
-    assert response.headers["X-Frame-Options"] == "SAMEORIGIN"
-    assert "no-store" in response.headers["Cache-Control"]
-    assert response.headers["Pragma"] == "no-cache"
-    assert response.headers["Expires"] == "0"
-
-
-def test_api_routes_include_private_no_store_cache(security_client):
-    response = security_client.get("/api/v1/auth/config")
-
-    assert response.status_code == 200
-    assert "no-store" in response.headers["Cache-Control"]
-    assert "private" in response.headers["Cache-Control"]
-
-
-def test_csp_report_only_header_when_enabled(security_client, monkeypatch):
-    monkeypatch.setattr(settings, "CSP_ENABLED", True)
-    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", True)
-
-    response = security_client.get("/health")
-
-    assert "Content-Security-Policy-Report-Only" in response.headers
-    assert "Content-Security-Policy" not in response.headers
-    assert "default-src 'self'" in response.headers["Content-Security-Policy-Report-Only"]
-
-
-def test_csp_enforcing_header_when_report_only_disabled(security_client, monkeypatch):
-    monkeypatch.setattr(settings, "CSP_ENABLED", True)
-    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
-
-    response = security_client.get("/health")
-
-    assert "Content-Security-Policy" in response.headers
-    assert "Content-Security-Policy-Report-Only" not in response.headers
-
-
-def test_csp_allows_voice_provider_connect_src(security_client, monkeypatch):
-    monkeypatch.setattr(settings, "CSP_ENABLED", True)
-    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
-
-    response = security_client.get("/health")
-    policy = response.headers["Content-Security-Policy"]
-
-    assert "https://api.vapi.ai" in policy
-    assert "https://*.daily.co" in policy
-    assert "wss://*.livekit.cloud" in policy
-    assert "https://api.elevenlabs.io" in policy
-    assert "https://*.ingest.sentry.io" in policy
-    assert "'unsafe-eval'" in policy
-    assert "blob:" in policy
-    assert "https://c.daily.co" in policy
-    assert "worker-src 'self' blob:" in policy
-
-
-def test_csp_allows_frame_src_for_pdf_preview_and_voice(security_client, monkeypatch):
-    monkeypatch.setattr(settings, "CSP_ENABLED", True)
-    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
-
-    response = security_client.get("/health")
-    policy = response.headers["Content-Security-Policy"]
-
-    assert "frame-src 'self' blob:" in policy
-    assert "https://*.daily.co" in policy
-    assert "https://*.s3.amazonaws.com" in policy
-    assert "https://*.amazonaws.com" in policy
-    assert "https://*.cloudfront.net" in policy
-    assert "https://storage.googleapis.com" in policy
-    assert "https://*.blob.core.windows.net" in policy
-
-
-def test_asset_routes_use_long_cache(security_client):
-    response = security_client.get("/assets/app.js")
-
-    assert response.status_code == 200
-    assert response.headers["Cache-Control"] == "public, max-age=31536000, immutable"
-    assert "Pragma" not in response.headers
-
-
-@pytest.mark.skipif(
-    not (Path(settings.FRONTEND_DIR) / "assets").is_dir(),
-    reason="frontend dist assets not built in test environment",
-)
-def test_built_frontend_assets_use_long_cache():
-    """Integration check when frontend/dist exists locally."""
-    assets_dir = Path(settings.FRONTEND_DIR) / "assets"
-    asset_file = next(assets_dir.iterdir())
-
-    app = FastAPI()
-    app.add_middleware(SecurityHeadersMiddleware)
-    app.mount("/assets", StaticFiles(directory=str(assets_dir)), name="assets")
-
-    with TestClient(app) as client:
-        response = client.get(f"/assets/{asset_file.name}")
-
-    assert response.status_code == 200
-    assert response.headers["Cache-Control"] == "public, max-age=31536000, immutable"
+"""Tests for HTTP security response headers."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+from fastapi.testclient import TestClient
+
+from app.config import settings
+from app.core.security_headers_middleware import SecurityHeadersMiddleware
+
+
+@pytest.fixture
+def security_client(tmp_path):
+    """Minimal app with SecurityHeadersMiddleware (avoids heavy conftest client)."""
+    app = FastAPI()
+    app.add_middleware(SecurityHeadersMiddleware)
+
+    @app.get("/health")
+    def health():
+        return {"status": "healthy"}
+
+    @app.get("/api/v1/auth/config")
+    def auth_config():
+        return {"providers": []}
+
+    assets_dir = tmp_path / "assets"
+    assets_dir.mkdir()
+    (assets_dir / "app.js").write_text("console.log('ok');", encoding="utf-8")
+    app.mount("/assets", StaticFiles(directory=str(assets_dir)), name="assets")
+
+    with TestClient(app) as client:
+        yield client
+
+
+def test_health_includes_baseline_security_headers(security_client):
+    response = security_client.get("/health")
+
+    assert response.status_code == 200
+    assert response.headers["X-Content-Type-Options"] == "nosniff"
+    assert response.headers["Referrer-Policy"] == "strict-origin-when-cross-origin"
+    assert response.headers["X-Frame-Options"] == "SAMEORIGIN"
+    assert "no-store" in response.headers["Cache-Control"]
+    assert response.headers["Pragma"] == "no-cache"
+    assert response.headers["Expires"] == "0"
+
+
+def test_api_routes_include_private_no_store_cache(security_client):
+    response = security_client.get("/api/v1/auth/config")
+
+    assert response.status_code == 200
+    assert "no-store" in response.headers["Cache-Control"]
+    assert "private" in response.headers["Cache-Control"]
+
+
+def test_csp_report_only_header_when_enabled(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", True)
+
+    response = security_client.get("/health")
+
+    assert "Content-Security-Policy-Report-Only" in response.headers
+    assert "Content-Security-Policy" not in response.headers
+    assert "default-src 'self'" in response.headers["Content-Security-Policy-Report-Only"]
+
+
+def test_csp_enforcing_header_when_report_only_disabled(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
+
+    response = security_client.get("/health")
+
+    assert "Content-Security-Policy" in response.headers
+    assert "Content-Security-Policy-Report-Only" not in response.headers
+
+
+def test_csp_allows_voice_provider_connect_src(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
+
+    response = security_client.get("/health")
+    policy = response.headers["Content-Security-Policy"]
+
+    assert "https://api.vapi.ai" in policy
+    assert "https://*.daily.co" in policy
+    assert "wss://*.livekit.cloud" in policy
+    assert "https://api.elevenlabs.io" in policy
+    assert "https://*.ingest.sentry.io" in policy
+    assert "'unsafe-eval'" in policy
+    assert "blob:" in policy
+    assert "https://c.daily.co" in policy
+    assert "worker-src 'self' blob:" in policy
+
+
+def test_csp_allows_frame_src_for_pdf_preview_and_voice(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
+
+    response = security_client.get("/health")
+    policy = response.headers["Content-Security-Policy"]
+
+    assert "frame-src 'self' blob:" in policy
+    assert "https://*.daily.co" in policy
+    assert "https://*.s3.amazonaws.com" in policy
+    assert "https://*.amazonaws.com" in policy
+    assert "https://*.cloudfront.net" in policy
+    assert "https://storage.googleapis.com" in policy
+    assert "https://*.blob.core.windows.net" in policy
+
+
+def test_csp_skipped_for_openapi_docs_paths(security_client, monkeypatch):
+    monkeypatch.setattr(settings, "CSP_ENABLED", True)
+    monkeypatch.setattr(settings, "CSP_REPORT_ONLY", False)
+
+    @security_client.app.get("/docs")
+    def swagger_docs():
+        return {"ok": True}
+
+    @security_client.app.get("/docs/oauth2-redirect")
+    def swagger_oauth():
+        return {"ok": True}
+
+    @security_client.app.get("/redoc")
+    def redoc():
+        return {"ok": True}
+
+    @security_client.app.get("/openapi.json")
+    def openapi():
+        return {"openapi": "3.1.0"}
+
+    for path in ("/docs", "/docs/oauth2-redirect", "/redoc", "/openapi.json"):
+        response = security_client.get(path)
+        assert response.status_code == 200
+        assert "Content-Security-Policy" not in response.headers
+        assert "Content-Security-Policy-Report-Only" not in response.headers
+
+
+def test_asset_routes_use_long_cache(security_client):
+    response = security_client.get("/assets/app.js")
+
+    assert response.status_code == 200
+    assert response.headers["Cache-Control"] == "public, max-age=31536000, immutable"
+    assert "Pragma" not in response.headers
+
+
+@pytest.mark.skipif(
+    not (Path(settings.FRONTEND_DIR) / "assets").is_dir(),
+    reason="frontend dist assets not built in test environment",
+)
+def test_built_frontend_assets_use_long_cache():
+    """Integration check when frontend/dist exists locally."""
+    assets_dir = Path(settings.FRONTEND_DIR) / "assets"
+    asset_file = next(assets_dir.iterdir())
+
+    app = FastAPI()
+    app.add_middleware(SecurityHeadersMiddleware)
+    app.mount("/assets", StaticFiles(directory=str(assets_dir)), name="assets")
+
+    with TestClient(app) as client:
+        response = client.get(f"/assets/{asset_file.name}")
+
+    assert response.status_code == 200
+    assert response.headers["Cache-Control"] == "public, max-age=31536000, immutable"
diff --git a/tests/test_efficientai_traces_correlation.py b/tests/test_efficientai_traces_correlation.py
new file mode 100644
index 00000000..95365dd1
--- /dev/null
+++ b/tests/test_efficientai_traces_correlation.py
@@ -0,0 +1,79 @@
+"""Tests for EfficientAI trace correlation helpers."""
+
+from efficientai.integrations.efficientai_traces.correlation import (
+    build_outbound_sip_headers,
+    extract_call_short_id,
+    format_plivo_sip_headers,
+    otlp_export_headers,
+    span_correlation_attributes,
+)
+
+
+def test_extract_call_short_id_from_vobiz_sip_header():
+    cid = extract_call_short_id(
+        sip_headers={"X-VH-EfficientAI-Call-Short-Id": "482931"},
+    )
+    assert cid == "482931"
+
+
+def test_extract_call_short_id_from_plivo_prefixed_header():
+    cid = extract_call_short_id(
+        sip_headers={"X-PH-EfficientAI-Call-Short-Id": "123456"},
+    )
+    assert cid == "123456"
+
+
+def test_build_outbound_sip_headers_includes_vobiz_and_plivo_keys():
+    headers = build_outbound_sip_headers(
+        call_short_id="482931",
+        evaluator_result_id="f1d04e90-bae5-4e1b-bbaa-60b2fd7b692f",
+    )
+    assert headers["X-VH-EfficientAI-Call-Short-Id"] == "482931"
+    assert headers["EfficientAI-Call-Short-Id"] == "482931"
+    assert "X-VH-EfficientAI-Run-Id" in headers
+    plivo = format_plivo_sip_headers(headers)
+    assert "X-VH-EfficientAI-Call-Short-Id=482931" in plivo
+
+
+def test_otlp_export_headers_uses_call_short_id():
+    headers = otlp_export_headers(api_key="secret", call_short_id="482931")
+    assert headers["X-API-Key"] == "secret"
+    assert headers["X-EfficientAI-Call-Short-Id"] == "482931"
+
+
+def test_extract_call_short_id_from_env_style_webhook_param():
+    cid = extract_call_short_id(
+        webhook_params={"SipHeader_X-VH-EfficientAI-Call-Short-Id": "654321"},
+    )
+    assert cid == "654321"
+
+
+def test_span_correlation_attributes_includes_workspace_id():
+    attrs = span_correlation_attributes(
+        call_short_id="482931",
+        agent_id="agent-1",
+        workspace_id="ws-1",
+    )
+    assert attrs["efficientai.workspace_id"] == "ws-1"
+    assert attrs["efficientai.call_short_id"] == "482931"
+
+
+def test_parse_trace_handshake_full_and_legacy():
+    from efficientai.integrations.efficientai_traces.handshake import parse_trace_handshake
+
+    full = parse_trace_handshake(
+        {
+            "type": "efficientai_trace_handshake",
+            "call_short_id": "482931",
+            "agent_id": "a1",
+            "workspace_id": "w1",
+            "otel_correlation": {"otlp_endpoint": "http://localhost:8000/x"},
+        }
+    )
+    assert full is not None
+    assert full["call_short_id"] == "482931"
+    assert full["agent_id"] == "a1"
+
+    legacy = parse_trace_handshake({"efficientai_call_short_id": "123456"})
+    assert legacy is not None
+    assert legacy["call_short_id"] == "123456"
diff --git a/tests/test_services/test_telephony/test_vobiz.py b/tests/test_services/test_telephony/test_vobiz.py
index aa468c05..0c5edfe7 100644
--- a/tests/test_services/test_telephony/test_vobiz.py
+++ b/tests/test_services/test_telephony/test_vobiz.py
@@ -1,234 +1,237 @@
-"""Unit tests for Vobiz telephony helpers."""
-
-import json
-from unittest.mock import MagicMock, patch
-from uuid import uuid4
-
-import pytest
-
-from app.models.database import Organization, TelephonyIntegration
-from app.services.telephony.phone_routing import safe_normalize_phone
-from app.services.telephony.plivo_client import expand_phone_candidates, normalize_e164
-from app.services.telephony.vobiz_client import VobizClient, build_vobiz_client_for_org
-from app.services.telephony.vobiz_session import create_call_session, delete_call_session, get_call_session
-from app.services.telephony.vobiz_xml import reject_call, speak_and_hangup, stream_to_agent
-from efficientai.serializers.vobiz import VobizFrameSerializer
-
-
-def test_normalize_e164_adds_plus_for_digit_only_numbers():
-    assert normalize_e164("91171366938") == "+91171366938"
-    assert normalize_e164("+91171366938") == "+91171366938"
-
-
-def test_normalize_e164_handles_indian_trunk_prefix():
-    assert normalize_e164("08071579610") == "+918071579610"
-
-
-def test_expand_phone_candidates_includes_trunk_and_e164_variants():
-    candidates = expand_phone_candidates("08071579610", default_country_code="91")
-    assert "+918071579610" in candidates
-
-
-def test_safe_normalize_phone_returns_none_for_invalid():
-    assert safe_normalize_phone("") is None
-    assert safe_normalize_phone("not-a-number") is None
-
-
-def test_stream_to_agent_xml_contains_websocket_and_record():
-    xml = stream_to_agent(
-        "wss://example.com/api/v1/telephony/vobiz/ws?agent_id=abc&session=xyz",
-        record_action_url="https://example.com/api/v1/telephony/vobiz/webhooks/recording-ready",
-    )
-    assert "<Stream bidirectional=\"true\"" in xml
-    assert "audio/x-mulaw;rate=8000" in xml
-    assert "wss://example.com/api/v1/telephony/vobiz/ws" in xml
-    assert "<Record action=" in xml
-
-
-def test_speak_and_hangup_escapes_xml_entities():
-    xml = speak_and_hangup("Route <failed> & unavailable")
-    assert "&lt;failed&gt;" in xml
-    assert "&amp;" in xml
-    assert "<Hangup" in xml
-
-
-def test_reject_call_returns_spoken_message():
-    xml = reject_call("Number not configured")
-    assert "Number not configured" in xml
-    assert "<Speak>" in xml
-
-
-@patch("app.services.telephony.vobiz_client.httpx.Client")
-def test_vobiz_client_create_outbound_call(mock_client_cls):
-    mock_response = MagicMock()
-    mock_response.status_code = 200
-    mock_response.content = b'{"request_uuid": "req-123", "message": "call queued"}'
-    mock_response.json.return_value = {"request_uuid": "req-123", "message": "call queued"}
-
-    mock_client = MagicMock()
-    mock_client.__enter__.return_value = mock_client
-    mock_client.__exit__.return_value = False
-    mock_client.post.return_value = mock_response
-    mock_client.request.return_value = mock_response
-    mock_client_cls.return_value = mock_client
-
-    client = VobizClient(auth_id="MA_TEST", auth_token="token", api_base="https://api.vobiz.ai")
-    result = client.create_outbound_call(
-        from_="+14155550100",
-        to_="+14155550101",
-        answer_url="https://example.com/answer",
-        hangup_url="https://example.com/events",
-    )
-
-    assert result["request_uuid"] == "req-123"
-    mock_client.request.assert_called_once()
-    args, kwargs = mock_client.request.call_args
-    assert args[0] == "POST"
-    assert args[1] == "https://api.vobiz.ai/api/v1/Account/MA_TEST/Call/"
-    assert kwargs["json"]["from"] == "+14155550100"
-    assert kwargs["json"]["to"] == "+14155550101"
-    assert kwargs["json"]["answer_url"] == "https://example.com/answer"
-
-
-@patch("app.services.telephony.vobiz_client.httpx.Client")
-def test_vobiz_client_hangup_call(mock_client_cls):
-    mock_response = MagicMock()
-    mock_response.status_code = 204
-
-    mock_client = MagicMock()
-    mock_client.__enter__.return_value = mock_client
-    mock_client.__exit__.return_value = False
-    mock_client.request.return_value = mock_response
-    mock_client_cls.return_value = mock_client
-
-    client = VobizClient(auth_id="MA_TEST", auth_token="token")
-    assert client.hangup_call("call-1") is True
-    mock_client.request.assert_called_once()
-    args, kwargs = mock_client.request.call_args
-    assert args[0] == "DELETE"
-    assert args[1] == "https://api.vobiz.ai/api/v1/Account/MA_TEST/Call/call-1/"
-
-
-@patch("app.services.telephony.vobiz_client.httpx.Client")
-def test_vobiz_client_list_account_numbers(mock_client_cls):
-    mock_response = MagicMock()
-    mock_response.status_code = 200
-    mock_response.content = b'{"items": [{"e164": "+919876543210", "id": "num-1"}], "total": 1}'
-    mock_response.json.return_value = {"items": [{"e164": "+919876543210", "id": "num-1"}], "total": 1}
-
-    mock_client = MagicMock()
-    mock_client.__enter__.return_value = mock_client
-    mock_client.__exit__.return_value = False
-    mock_client.request.return_value = mock_response
-    mock_client_cls.return_value = mock_client
-
-    client = VobizClient(auth_id="MA_TEST", auth_token="token")
-    numbers = client.list_account_numbers()
-    assert numbers[0]["e164"] == "+919876543210"
-
-
-@patch("app.services.telephony.vobiz_client.VobizClient.attach_number_to_application")
-@patch("app.services.telephony.vobiz_client.VobizClient.create_application")
-def test_vobiz_client_set_number_answer_url_creates_app(mock_create, mock_attach):
-    mock_create.return_value = {"app_id": "app-123"}
-    mock_attach.return_value = {"message": "linked"}
-
-    client = VobizClient(auth_id="MA_TEST", auth_token="token")
-    ok, message, app_id = client.set_number_answer_url(
-        "+919876543210",
-        "https://example.com/answer",
-    )
-    assert ok is True
-    assert app_id == "app-123"
-    mock_create.assert_called_once()
-    mock_attach.assert_called_once_with("+919876543210", "app-123")
-
-
-@patch("app.services.telephony.vobiz_client.build_vobiz_client_from_settings")
-@patch("app.services.telephony.vobiz_client.resolve_telephony_integration")
-@patch("app.services.telephony.vobiz_client.decrypt_api_key")
-def test_build_vobiz_client_for_org_uses_byo_credentials(mock_decrypt, mock_resolve, mock_platform):
-    org_id = uuid4()
-    integration = TelephonyIntegration(
-        id=uuid4(),
-        organization_id=org_id,
-        provider="vobiz",
-        auth_id="enc-id",
-        auth_token="enc-token",
-        is_active=True,
-    )
-    mock_resolve.return_value = integration
-    mock_decrypt.side_effect = lambda value: "plain-" + value
-
-    client, resolved = build_vobiz_client_for_org(MagicMock(), org_id)
-    assert resolved is integration
-    assert client.auth_id == "plain-enc-id"
-    mock_platform.assert_not_called()
-
-
-@patch("app.services.telephony.vobiz_client.build_vobiz_client_from_settings")
-@patch("app.services.telephony.vobiz_client.resolve_telephony_integration")
-def test_build_vobiz_client_for_org_falls_back_to_platform(mock_resolve, mock_platform):
-    mock_resolve.return_value = None
-    platform_client = VobizClient(auth_id="MA_PLATFORM", auth_token="token")
-    mock_platform.return_value = platform_client
-
-    client, resolved = build_vobiz_client_for_org(MagicMock(), uuid4())
-    assert resolved is None
-    assert client is platform_client
-
-
-def test_vobiz_call_session_round_trip(monkeypatch):
-    store = {}
-
-    class FakeRedis:
-        def setex(self, key, ttl, value):
-            store[key] = value
-
-        def get(self, key):
-            return store.get(key)
-
-        def delete(self, key):
-            store.pop(key, None)
-
-    monkeypatch.setattr(
-        "app.services.telephony.vobiz_session._get_redis",
-        lambda: FakeRedis(),
-    )
-
-    agent_id = str(uuid4())
-    org_id = str(uuid4())
-    session = create_call_session(agent_id=agent_id, organization_id=org_id, to_number="+14155550101")
-    loaded = get_call_session(session.call_ref)
-    assert loaded is not None
-    assert loaded.agent_id == agent_id
-    assert loaded.organization_id == org_id
-    assert loaded.to_number == "+14155550101"
-
-    delete_call_session(session.call_ref)
-    assert get_call_session(session.call_ref) is None
-
-
-@pytest.mark.asyncio
-async def test_vobiz_serializer_round_trip_media_frame():
-    serializer = VobizFrameSerializer(
-        stream_id="stream-1",
-        call_id="call-1",
-        params=VobizFrameSerializer.InputParams(sample_rate=8000),
-    )
-
-    from efficientai.frames.frames import StartFrame
-
-    await serializer.setup(StartFrame(audio_in_sample_rate=8000, audio_out_sample_rate=8000))
-
-    media_message = json.dumps(
-        {
-            "event": "media",
-            "media": {
-                "payload": "f8f8f8f8",
-            },
-        }
-    )
-    frame = await serializer.deserialize(media_message)
-    assert frame is not None
-    assert len(frame.audio) > 0
+"""Unit tests for Vobiz telephony helpers."""
+
+import json
+from unittest.mock import MagicMock, patch
+from uuid import uuid4
+
+import pytest
+
+from app.models.database import Organization, TelephonyIntegration
+from app.services.telephony.phone_routing import safe_normalize_phone
+from app.services.telephony.plivo_client import expand_phone_candidates, normalize_e164
+from app.services.telephony.vobiz_client import VobizClient, build_vobiz_client_for_org
+from app.services.telephony.vobiz_session import create_call_session, delete_call_session, get_call_session
+from app.services.telephony.vobiz_xml import reject_call, speak_and_hangup, stream_to_agent
+from efficientai.serializers.vobiz import VobizFrameSerializer
+
+
+def test_normalize_e164_adds_plus_for_digit_only_numbers():
+    assert normalize_e164("91171366938") == "+91171366938"
+    assert normalize_e164("+91171366938") == "+91171366938"
+
+
+def test_normalize_e164_handles_indian_trunk_prefix():
+    assert normalize_e164("08071579610") == "+918071579610"
+
+
+def test_expand_phone_candidates_includes_trunk_and_e164_variants():
+    candidates = expand_phone_candidates("08071579610", default_country_code="91")
+    assert "+918071579610" in candidates
+
+
+def test_safe_normalize_phone_returns_none_for_invalid():
+    assert safe_normalize_phone("") is None
+    assert safe_normalize_phone("not-a-number") is None
+
+
+def test_stream_to_agent_xml_contains_websocket_and_record():
+    xml = stream_to_agent(
+        "wss://example.com/api/v1/telephony/vobiz/ws?agent_id=abc&session=xyz",
+        record_action_url="https://example.com/api/v1/telephony/vobiz/webhooks/recording-ready",
+    )
+    assert "<Stream bidirectional=\"true\"" in xml
+    assert "audio/x-mulaw;rate=8000" in xml
+    assert "wss://example.com/api/v1/telephony/vobiz/ws" in xml
+    assert "<Record action=" in xml
+
+
+def test_speak_and_hangup_escapes_xml_entities():
+    xml = speak_and_hangup("Route <failed> & unavailable")
+    assert "&lt;failed&gt;" in xml
+    assert "&amp;" in xml
+    assert "<Hangup" in xml
+
+
+def test_reject_call_returns_spoken_message():
+    xml = reject_call("Number not configured")
+    assert "Number not configured" in xml
+    assert "<Speak>" in xml
+
+
+@patch("app.services.telephony.vobiz_client.httpx.Client")
+def test_vobiz_client_create_outbound_call(mock_client_cls):
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.content = b'{"request_uuid": "req-123", "message": "call queued"}'
+    mock_response.json.return_value = {"request_uuid": "req-123", "message": "call queued"}
+
+    mock_client = MagicMock()
+    mock_client.__enter__.return_value = mock_client
+    mock_client.__exit__.return_value = False
+    mock_client.post.return_value = mock_response
+    mock_client.request.return_value = mock_response
+    mock_client_cls.return_value = mock_client
+
+    client = VobizClient(auth_id="MA_TEST", auth_token="token", api_base="https://api.vobiz.ai")
+    result = client.create_outbound_call(
+        from_="+14155550100",
+        to_="+14155550101",
+        answer_url="https://example.com/answer",
+        hangup_url="https://example.com/events",
+        sip_headers={"X-VH-EfficientAI-Call-Short-Id": "482931"},
+    )
+
+    assert result["request_uuid"] == "req-123"
+    mock_client.request.assert_called_once()
+    args, kwargs = mock_client.request.call_args
+    assert args[0] == "POST"
+    assert args[1] == "https://api.vobiz.ai/api/v1/Account/MA_TEST/Call/"
+    assert kwargs["json"]["from"] == "+14155550100"
+    assert kwargs["json"]["to"] == "+14155550101"
+    assert kwargs["json"]["answer_url"] == "https://example.com/answer"
+    assert "sipHeaders" in kwargs["json"]
+    assert "482931" in kwargs["json"]["sipHeaders"]
+
+
+@patch("app.services.telephony.vobiz_client.httpx.Client")
+def test_vobiz_client_hangup_call(mock_client_cls):
+    mock_response = MagicMock()
+    mock_response.status_code = 204
+
+    mock_client = MagicMock()
+    mock_client.__enter__.return_value = mock_client
+    mock_client.__exit__.return_value = False
+    mock_client.request.return_value = mock_response
+    mock_client_cls.return_value = mock_client
+
+    client = VobizClient(auth_id="MA_TEST", auth_token="token")
+    assert client.hangup_call("call-1") is True
+    mock_client.request.assert_called_once()
+    args, kwargs = mock_client.request.call_args
+    assert args[0] == "DELETE"
+    assert args[1] == "https://api.vobiz.ai/api/v1/Account/MA_TEST/Call/call-1/"
+
+
+@patch("app.services.telephony.vobiz_client.httpx.Client")
+def test_vobiz_client_list_account_numbers(mock_client_cls):
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.content = b'{"items": [{"e164": "+919876543210", "id": "num-1"}], "total": 1}'
+    mock_response.json.return_value = {"items": [{"e164": "+919876543210", "id": "num-1"}], "total": 1}
+
+    mock_client = MagicMock()
+    mock_client.__enter__.return_value = mock_client
+    mock_client.__exit__.return_value = False
+    mock_client.request.return_value = mock_response
+    mock_client_cls.return_value = mock_client
+
+    client = VobizClient(auth_id="MA_TEST", auth_token="token")
+    numbers = client.list_account_numbers()
+    assert numbers[0]["e164"] == "+919876543210"
+
+
+@patch("app.services.telephony.vobiz_client.VobizClient.attach_number_to_application")
+@patch("app.services.telephony.vobiz_client.VobizClient.create_application")
+def test_vobiz_client_set_number_answer_url_creates_app(mock_create, mock_attach):
+    mock_create.return_value = {"app_id": "app-123"}
+    mock_attach.return_value = {"message": "linked"}
+
+    client = VobizClient(auth_id="MA_TEST", auth_token="token")
+    ok, message, app_id = client.set_number_answer_url(
+        "+919876543210",
+        "https://example.com/answer",
+    )
+    assert ok is True
+    assert app_id == "app-123"
+    mock_create.assert_called_once()
+    mock_attach.assert_called_once_with("+919876543210", "app-123")
+
+
+@patch("app.services.telephony.vobiz_client.build_vobiz_client_from_settings")
+@patch("app.services.telephony.vobiz_client.resolve_telephony_integration")
+@patch("app.services.telephony.vobiz_client.decrypt_api_key")
+def test_build_vobiz_client_for_org_uses_byo_credentials(mock_decrypt, mock_resolve, mock_platform):
+    org_id = uuid4()
+    integration = TelephonyIntegration(
+        id=uuid4(),
+        organization_id=org_id,
+        provider="vobiz",
+        auth_id="enc-id",
+        auth_token="enc-token",
+        is_active=True,
+    )
+    mock_resolve.return_value = integration
+    mock_decrypt.side_effect = lambda value: "plain-" + value
+
+    client, resolved = build_vobiz_client_for_org(MagicMock(), org_id)
+    assert resolved is integration
+    assert client.auth_id == "plain-enc-id"
+    mock_platform.assert_not_called()
+
+
+@patch("app.services.telephony.vobiz_client.build_vobiz_client_from_settings")
+@patch("app.services.telephony.vobiz_client.resolve_telephony_integration")
+def test_build_vobiz_client_for_org_falls_back_to_platform(mock_resolve, mock_platform):
+    mock_resolve.return_value = None
+    platform_client = VobizClient(auth_id="MA_PLATFORM", auth_token="token")
+    mock_platform.return_value = platform_client
+
+    client, resolved = build_vobiz_client_for_org(MagicMock(), uuid4())
+    assert resolved is None
+    assert client is platform_client
+
+
+def test_vobiz_call_session_round_trip(monkeypatch):
+    store = {}
+
+    class FakeRedis:
+        def setex(self, key, ttl, value):
+            store[key] = value
+
+        def get(self, key):
+            return store.get(key)
+
+        def delete(self, key):
+            store.pop(key, None)
+
+    monkeypatch.setattr(
+        "app.services.telephony.vobiz_session._get_redis",
+        lambda: FakeRedis(),
+    )
+
+    agent_id = str(uuid4())
+    org_id = str(uuid4())
+    session = create_call_session(agent_id=agent_id, organization_id=org_id, to_number="+14155550101")
+    loaded = get_call_session(session.call_ref)
+    assert loaded is not None
+    assert loaded.agent_id == agent_id
+    assert loaded.organization_id == org_id
+    assert loaded.to_number == "+14155550101"
+
+    delete_call_session(session.call_ref)
+    assert get_call_session(session.call_ref) is None
+
+
+@pytest.mark.asyncio
+async def test_vobiz_serializer_round_trip_media_frame():
+    serializer = VobizFrameSerializer(
+        stream_id="stream-1",
+        call_id="call-1",
+        params=VobizFrameSerializer.InputParams(sample_rate=8000),
+    )
+
+    from efficientai.frames.frames import StartFrame
+
+    await serializer.setup(StartFrame(audio_in_sample_rate=8000, audio_out_sample_rate=8000))
+
+    media_message = json.dumps(
+        {
+            "event": "media",
+            "media": {
+                "payload": "f8f8f8f8",
+            },
+        }
+    )
+    frame = await serializer.deserialize(media_message)
+    assert frame is not None
+    assert len(frame.audio) > 0
diff --git a/tests/test_synthetic_trace_ingest_service.py b/tests/test_synthetic_trace_ingest_service.py
new file mode 100644
index 00000000..69696359
--- /dev/null
+++ b/tests/test_synthetic_trace_ingest_service.py
@@ -0,0 +1,364 @@
+"""Tests for synthetic trace OTLP ingest correlation."""
+
+from uuid import uuid4
+
+from app.models.database import (
+    CallRecording,
+    CallRecordingSource,
+    EvaluatorResult,
+    EvaluatorResultStatus,
+)
+from app.models.enums import CallRecordingStatus
+from app.services.synthetic_traces.otlp_mapper import (
+    derive_turns_from_spans,
+    extract_correlation_ids,
+    filter_spans_for_trace,
+    merge_tier1_and_otel_turns,
+)
+from app.services.synthetic_traces.trace_service import (
+    backfill_missing_traces_from_call_recordings,
+    close_trace_session,
+    ingest_json_spans,
+    ingest_otlp_spans,
+    list_traces,
+    open_trace,
+    open_trace_session,
+)
+
+
+def _stt_span(call_short_id: str, turn: int = 1) -> dict:
+    return {
+        "trace_id": "abc",
+        "span_id": f"stt-{turn}",
+        "name": "stt",
+        "attributes": {
+            "efficientai.call_short_id": call_short_id,
+            "turn.number": turn,
+            "gen_ai.operation.name": "stt",
+            "metrics.ttfb": 0.12,
+            "transcript": "hello",
+        },
+    }
+
+
+def test_extract_correlation_ids_rejects_invalid_call_short_id():
+    ids = extract_correlation_ids(
+        [{"attributes": {"efficientai.call_short_id": "not-a-id"}}]
+    )
+    assert ids.get("call_short_id") is None
+
+def test_ingest_otlp_spans_correlates_by_call_short_id(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    call_short_id = "482931"
+    result = EvaluatorResult(
+        id=uuid4(),
+        result_id="900001",
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        evaluator_id=uuid4(),
+        agent_id=uuid4(),
+        persona_id=uuid4(),
+        scenario_id=uuid4(),
+        name="Phone test",
+        status=EvaluatorResultStatus.QUEUED.value,
+    )
+    db_session.add(result)
+    db_session.commit()
+
+    open_trace(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        evaluator_result_id=result.id,
+        agent_id=result.agent_id,
+        call_short_id=call_short_id,
+        transport="phone",
+    )
+
+    trace, accepted, correlated = ingest_otlp_spans(
+        db_session,
+        organization_id=org_id,
+        spans=[_stt_span(call_short_id)],
+        header_call_short_id=call_short_id,
+    )
+
+    assert accepted == 1
+    assert correlated is True
+    assert trace is not None
+    assert trace.call_short_id == call_short_id
+    assert trace.turn_count == 1
+    assert trace.tier == "component"
+
+    detail_turns = trace.synthetic_trace_payload.turns if hasattr(trace, "synthetic_trace_payload") else None
+    if detail_turns is None:
+        from app.models.database import SyntheticTracePayload
+
+        payload = (
+            db_session.query(SyntheticTracePayload)
+            .filter(SyntheticTracePayload.synthetic_call_trace_id == trace.id)
+            .first()
+        )
+        detail_turns = payload.turns
+
+    assert detail_turns[0]["stt_ttfb_ms"] == 120.0
+    assert detail_turns[0]["transcript"] == "User: hello"
+
+
+def test_ingest_otlp_spans_merges_with_tier1_turns(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    call_short_id = "123456"
+    result = EvaluatorResult(
+        id=uuid4(),
+        result_id="900002",
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        evaluator_id=uuid4(),
+        agent_id=uuid4(),
+        persona_id=uuid4(),
+        scenario_id=uuid4(),
+        name="Phone test",
+        status=EvaluatorResultStatus.QUEUED.value,
+    )
+    db_session.add(result)
+    db_session.commit()
+
+    trace = open_trace(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        evaluator_result_id=result.id,
+        agent_id=result.agent_id,
+        call_short_id=call_short_id,
+        transport="phone",
+        tier="black_box",
+    )
+
+    from app.models.database import SyntheticTracePayload
+    from app.services.synthetic_traces.trace_service import record_tier1_turns
+
+    record_tier1_turns(
+        db_session,
+        trace,
+        [{"turn_number": 1, "sut_response_latency_ms": 850.0}],
+    )
+
+    trace, _accepted, correlated = ingest_otlp_spans(
+        db_session,
+        organization_id=org_id,
+        spans=[
+            {
+                "trace_id": "def",
+                "span_id": "llm-1",
+                "name": "llm",
+                "attributes": {
+                    "efficientai.call_short_id": call_short_id,
+                    "turn.number": 1,
+                    "gen_ai.operation.name": "chat",
+                    "metrics.ttfb": 0.4,
+                },
+            }
+        ],
+        header_call_short_id=call_short_id,
+    )
+
+    assert correlated is True
+    assert trace.tier == "mixed"
+
+    payload = (
+        db_session.query(SyntheticTracePayload)
+        .filter(SyntheticTracePayload.synthetic_call_trace_id == trace.id)
+        .first()
+    )
+    assert payload.turns[0]["sut_response_latency_ms"] == 850.0
+    assert payload.turns[0]["llm_ttfb_ms"] == 400.0
+
+
+def test_backfill_creates_trace_from_vobiz_call_recording(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    result = EvaluatorResult(
+        id=uuid4(),
+        result_id="900003",
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        evaluator_id=uuid4(),
+        agent_id=uuid4(),
+        persona_id=uuid4(),
+        scenario_id=uuid4(),
+        name="Phone test",
+        status=EvaluatorResultStatus.COMPLETED.value,
+    )
+    db_session.add(result)
+    db_session.commit()
+
+    recording = CallRecording(
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        call_short_id="555555",
+        status=CallRecordingStatus.UPDATED,
+        source=CallRecordingSource.WEBHOOK,
+        call_event="call_ended",
+        call_data={"ended_at": "2026-08-31T12:00:00Z"},
+        provider_platform="vobiz",
+        evaluator_result_id=result.id,
+    )
+    db_session.add(recording)
+    db_session.commit()
+
+    created = backfill_missing_traces_from_call_recordings(
+        db_session, organization_id=org_id
+    )
+    assert created == 1
+
+    rows, total = list_traces(
+        db_session,
+        organization_id=org_id,
+        workspace_id=uuid4(),
+    )
+    assert total == 0
+
+    rows, total = list_traces(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+    )
+    assert total == 1
+    assert rows[0].evaluator_result_id == result.id
+    assert rows[0].status == "closed"
+
+
+def test_ingest_otlp_spans_prefers_span_call_short_id_over_stale_header(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    first = open_trace_session(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        transport="webrtc",
+    )
+    second = open_trace_session(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        transport="webrtc",
+    )
+
+    trace, accepted, correlated = ingest_otlp_spans(
+        db_session,
+        organization_id=org_id,
+        spans=[_stt_span(second.call_short_id)],
+        header_call_short_id=first.call_short_id,
+        workspace_id=default_workspace.id,
+    )
+    assert accepted == 1
+    assert correlated is True
+    assert trace is not None
+    assert trace.call_short_id == second.call_short_id
+    assert trace.turn_count == 1
+    assert first.turn_count == 0
+
+
+def test_open_trace_session_mint_ingest_and_close(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    trace = open_trace_session(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        transport="websocket",
+    )
+    assert trace.call_short_id
+    assert trace.transport == "websocket"
+    assert trace.tier == "component"
+    assert trace.status == "open"
+
+    call_short_id = trace.call_short_id
+    ingested, accepted, correlated = ingest_otlp_spans(
+        db_session,
+        organization_id=org_id,
+        spans=[_stt_span(call_short_id)],
+        header_call_short_id=call_short_id,
+        workspace_id=default_workspace.id,
+    )
+    assert ingested is not None
+    assert accepted == 1
+    assert correlated is True
+
+    closed = close_trace_session(
+        db_session,
+        organization_id=org_id,
+        call_short_id=call_short_id,
+        workspace_id=default_workspace.id,
+    )
+    assert closed is not None
+    assert closed.status == "closed"
+
+
+def test_maybe_auto_close_open_trace_after_idle(db_session, org_id, default_workspace):
+    from datetime import timedelta
+
+    from app.models.database import SyntheticCallTrace
+    from app.services.synthetic_traces.trace_service import (
+        maybe_auto_close_open_trace,
+        open_trace_session,
+        record_tier1_turns,
+    )
+
+    trace = open_trace_session(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        transport="webrtc",
+    )
+    record_tier1_turns(
+        db_session,
+        trace,
+        [{"turn_number": 1, "sut_response_latency_ms": 500.0}],
+    )
+    row = db_session.get(SyntheticCallTrace, trace.id)
+    row.updated_at = row.updated_at - timedelta(seconds=130)
+    db_session.commit()
+
+    closed = maybe_auto_close_open_trace(db_session, row)
+    assert closed.status == "closed"
+    assert closed.ended_at is not None
+
+
+def test_ingest_json_spans(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    trace = open_trace_session(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        transport="custom",
+    )
+    call_short_id = trace.call_short_id
+
+    ingested, accepted, correlated = ingest_json_spans(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        call_short_id=call_short_id,
+        spans=[
+            {"name": "llm", "turn_number": 1, "ttfb_ms": 250},
+        ],
+    )
+    assert accepted == 1
+    assert correlated is True
+    assert ingested is not None
+    assert ingested.turn_count == 1
diff --git a/tests/test_synthetic_trace_otlp_ingest.py b/tests/test_synthetic_trace_otlp_ingest.py
new file mode 100644
index 00000000..45577bc5
--- /dev/null
+++ b/tests/test_synthetic_trace_otlp_ingest.py
@@ -0,0 +1,37 @@
+"""Tests for OTLP JSON parsing."""
+
+import json
+
+from app.services.synthetic_traces.otlp_ingest import parse_otlp_json
+
+
+def test_parse_otlp_json_span_attributes():
+    body = json.dumps(
+        {
+            "resourceSpans": [
+                {
+                    "scopeSpans": [
+                        {
+                            "spans": [
+                                {
+                                    "traceId": "abc123",
+                                    "spanId": "def456",
+                                    "name": "conversation",
+                                    "attributes": [
+                                        {
+                                            "key": "conversation.id",
+                                            "value": {"stringValue": "conv-1"},
+                                        }
+                                    ],
+                                }
+                            ]
+                        }
+                    ]
+                }
+            ]
+        }
+    ).encode()
+    spans = parse_otlp_json(body)
+    assert len(spans) == 1
+    assert spans[0]["name"] == "conversation"
+    assert spans[0]["attributes"]["conversation.id"] == "conv-1"
diff --git a/tests/test_synthetic_trace_otlp_mapper.py b/tests/test_synthetic_trace_otlp_mapper.py
new file mode 100644
index 00000000..1f4c3116
--- /dev/null
+++ b/tests/test_synthetic_trace_otlp_mapper.py
@@ -0,0 +1,443 @@
+"""Tests for synthetic trace OTLP mapping."""
+
+from app.services.synthetic_traces.otlp_mapper import (
+    compute_trace_latency_summary,
+    derive_turns_from_spans,
+    extract_correlation_ids,
+    filter_spans_for_trace,
+    merge_tier1_and_otel_turns,
+)
+
+
+def test_derive_turns_from_spans_extracts_component_ttfb():
+    spans = [
+        {
+            "name": "turn",
+            "span_id": "turn-1",
+            "attributes": {"turn.number": 1},
+        },
+        {
+            "name": "stt",
+            "span_id": "stt-1",
+            "attributes": {
+                "turn.number": 1,
+                "gen_ai.operation.name": "stt",
+                "metrics.ttfb": 0.12,
+                "transcript": "hello",
+            },
+        },
+        {
+            "name": "llm",
+            "span_id": "llm-1",
+            "attributes": {
+                "turn.number": 1,
+                "gen_ai.operation.name": "chat",
+                "metrics.ttfb": 0.45,
+            },
+        },
+        {
+            "name": "tts",
+            "span_id": "tts-1",
+            "attributes": {
+                "turn.number": 1,
+                "gen_ai.operation.name": "tts",
+                "metrics.ttfb": 0.08,
+            },
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert len(turns) == 1
+    assert turns[0]["turn_number"] == 1
+    assert turns[0]["stt_ttfb_ms"] == 120.0
+    assert turns[0]["llm_ttfb_ms"] == 450.0
+    assert turns[0]["tts_ttfb_ms"] == 80.0
+    assert turns[0]["transcript"] == "User: hello"
+
+
+def test_derive_turns_from_pipecat_parent_child_spans():
+    """Pipecat turn spans carry turn.number; children link via parent_span_id."""
+    spans = [
+        {
+            "name": "turn",
+            "span_id": "turn-2",
+            "attributes": {
+                "turn.number": 2,
+                "turn.duration_seconds": 10.239,
+                "turn.user_bot_latency_seconds": 4.5,
+            },
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-2",
+            "parent_span_id": "turn-2",
+            "attributes": {"metrics.ttfb": 3.958},
+        },
+        {
+            "name": "stt",
+            "span_id": "stt-2",
+            "parent_span_id": "turn-2",
+            "start_time_unix_nano": 0,
+            "end_time_unix_nano": 17_161_000_000,
+            "attributes": {"metrics.ttfb": 0},
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert len(turns) == 1
+    assert turns[0]["turn_number"] == 1
+    assert turns[0]["llm_ttfb_ms"] == 3958.0
+    assert turns[0].get("stt_ttfb_ms") is None
+    assert turns[0]["sut_response_latency_ms"] == 4500.0
+    assert turns[0]["tts_ttfb_ms"] == 542.0
+    assert turns[0]["extra"]["turn_duration_seconds"] == 10.239
+
+
+def test_compute_trace_latency_summary_uses_sut_then_llm_fallback():
+    turns = [
+        {"turn_number": 1, "sut_response_latency_ms": 1000.0, "llm_ttfb_ms": 400.0},
+        {"turn_number": 2, "sut_response_latency_ms": 2000.0, "llm_ttfb_ms": 800.0},
+        {"turn_number": 3, "sut_response_latency_ms": 3000.0, "llm_ttfb_ms": 1200.0},
+    ]
+    summary = compute_trace_latency_summary(turns)
+    assert summary["turn_count"] == 3
+    assert summary["response_latency_p50_ms"] == 2000.0
+    assert summary["response_latency_p90_ms"] == 3000.0
+    assert summary["component_aggregates"]["llm_ttfb_ms"]["p50"] == 800.0
+
+
+def test_derive_turns_renumbers_duplicate_pipecat_turn_one_spans():
+    """Pipecat may emit multiple turn spans numbered 1 (intro + user turn)."""
+    spans = [
+        {
+            "name": "turn",
+            "span_id": "turn-intro",
+            "start_time_unix_nano": 100,
+            "attributes": {"turn.number": 1},
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-intro",
+            "parent_span_id": "turn-intro",
+            "attributes": {"metrics.ttfb": 1.551},
+        },
+        {
+            "name": "turn",
+            "span_id": "turn-user",
+            "start_time_unix_nano": 200,
+            "attributes": {"turn.number": 1},
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-user",
+            "parent_span_id": "turn-user",
+            "attributes": {"metrics.ttfb": 1.396},
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert len(turns) == 2
+    assert turns[0]["turn_number"] == 1
+    assert turns[0]["llm_ttfb_ms"] == 1551.0
+    assert turns[1]["turn_number"] == 2
+    assert turns[1]["llm_ttfb_ms"] == 1396.0
+
+
+def test_finalize_turn_uses_llm_as_sut_when_user_bot_latency_missing():
+    spans = [
+        {
+            "name": "turn",
+            "span_id": "turn-1",
+            "attributes": {
+                "turn.number": 1,
+                "turn.duration_seconds": 7.14,
+            },
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-1",
+            "parent_span_id": "turn-1",
+            "attributes": {"metrics.ttfb": 1.138},
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert turns[0]["sut_response_latency_ms"] == 1138.0
+    assert turns[0]["llm_ttfb_ms"] == 1138.0
+    assert turns[0].get("tts_ttfb_ms") is None
+
+
+def test_derive_turns_llm_response_with_turn_number_on_span():
+    spans = [
+        {
+            "name": "llm_response",
+            "span_id": "llm-1",
+            "attributes": {
+                "turn.number": 1,
+                "metrics.ttfb": 1.138,
+            },
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert turns[0]["llm_ttfb_ms"] == 1138.0
+
+
+def test_extract_correlation_ids_validates_call_short_id():
+    ids = extract_correlation_ids(
+        [{"attributes": {"efficientai.call_short_id": "abc"}}]
+    )
+    assert ids.get("call_short_id") is None
+
+    ids = extract_correlation_ids(
+        [{"attributes": {"efficientai.call_short_id": "482931"}}]
+    )
+    assert ids["call_short_id"] == "482931"
+
+
+def test_merge_tier1_and_otel_turns():
+    tier1 = [{"turn_number": 1, "sut_response_latency_ms": 900.0}]
+    otel = [{"turn_number": 1, "llm_ttfb_ms": 400.0}]
+    merged = merge_tier1_and_otel_turns(tier1, otel)
+    assert merged[0]["sut_response_latency_ms"] == 900.0
+    assert merged[0]["llm_ttfb_ms"] == 400.0
+
+
+def test_filter_spans_for_trace_keeps_all_trace_ids_when_call_scoped():
+    """Multi-agent Pipecat may emit multiple OTLP trace_ids for one call_short_id."""
+    spans = [
+        {
+            "trace_id": "call-a",
+            "span_id": "turn-a",
+            "name": "turn",
+            "start_time_unix_nano": 100,
+            "attributes": {"efficientai.call_short_id": "111111"},
+        },
+        {
+            "trace_id": "call-b",
+            "span_id": "turn-b",
+            "name": "turn",
+            "start_time_unix_nano": 200,
+            "attributes": {"efficientai.call_short_id": "111111"},
+        },
+        {
+            "trace_id": "call-b",
+            "span_id": "llm-b",
+            "name": "llm_response",
+            "parent_span_id": "turn-b",
+            "attributes": {"efficientai.call_short_id": "111111", "metrics.ttfb": 1.0},
+        },
+    ]
+    filtered = filter_spans_for_trace(spans, call_short_id="111111")
+    trace_ids = {s.get("trace_id") for s in filtered}
+    assert trace_ids == {"call-a", "call-b"}
+    turns = derive_turns_from_spans(filtered)
+    assert len(turns) == 2
+
+
+def test_derive_turns_assigns_orphan_components_by_time_window():
+    """Orphan STT/TTS from another worker should land in the chronologically correct turn."""
+    spans = [
+        {
+            "name": "turn",
+            "span_id": "turn-1",
+            "start_time_unix_nano": 100,
+            "end_time_unix_nano": 8_000_000_000,
+            "attributes": {"turn.user_bot_latency_seconds": 1.5},
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-1",
+            "parent_span_id": "turn-1",
+            "start_time_unix_nano": 200,
+            "attributes": {"metrics.ttfb": 1.0, "output": "Welcome to Acme Corp!"},
+        },
+        {
+            "name": "turn",
+            "span_id": "turn-2",
+            "start_time_unix_nano": 10_000_000_000,
+            "end_time_unix_nano": 20_000_000_000,
+            "attributes": {},
+        },
+        {
+            "name": "tts",
+            "span_id": "tts-orphan",
+            "start_time_unix_nano": 500,
+            "attributes": {
+                "gen_ai.operation.name": "tts",
+                "metrics.ttfb": 0.2,
+                "text": "Welcome to Acme Corp!",
+                "turn.number": 1,
+            },
+        },
+        {
+            "name": "stt",
+            "span_id": "stt-orphan",
+            "start_time_unix_nano": 10_500_000_000,
+            "attributes": {
+                "gen_ai.operation.name": "stt",
+                "metrics.ttfb": 0.5,
+                "transcript": "rocket boots",
+                "turn.number": 1,
+            },
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert len(turns) == 2
+    assert turns[0]["tts_ttfb_ms"] == 200.0
+    assert turns[0]["llm_ttfb_ms"] == 1000.0
+    assert "Welcome to Acme Corp" in turns[0]["transcript"]
+    assert turns[1]["stt_ttfb_ms"] == 500.0
+    assert "rocket boots" in turns[1]["transcript"]
+
+
+def test_derive_turns_pairs_user_with_async_bot_response():
+    spans = [
+        {
+            "name": "llm_response",
+            "span_id": "llm-greet",
+            "start_time_unix_nano": 100,
+            "attributes": {"metrics.ttfb": 1.6, "output": "Welcome to Acme Corp!"},
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-support",
+            "start_time_unix_nano": 200,
+            "attributes": {"metrics.ttfb": 8.9, "output": "Our partners would be happy to help."},
+        },
+        {
+            "name": "tts",
+            "span_id": "tts-greet",
+            "start_time_unix_nano": 300,
+            "attributes": {
+                "gen_ai.operation.name": "tts",
+                "metrics.ttfb": 0.27,
+                "text": "Welcome to Acme Corp!",
+            },
+        },
+        {
+            "name": "stt",
+            "span_id": "stt-user",
+            "start_time_unix_nano": 10_000,
+            "attributes": {
+                "gen_ai.operation.name": "stt",
+                "metrics.ttfb": 0.54,
+                "transcript": "Invisible Bank",
+            },
+        },
+        {
+            "name": "tts",
+            "span_id": "tts-support",
+            "start_time_unix_nano": 20_000,
+            "attributes": {
+                "gen_ai.operation.name": "tts",
+                "metrics.ttfb": 0.36,
+                "text": "Our partners would be happy to help.",
+            },
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert len(turns) == 2
+    assert "Welcome" in turns[0]["extra"]["assistant_text"]
+    assert turns[0]["tts_ttfb_ms"] == 270.0
+    assert "Invisible Bank" in turns[1]["extra"]["user_text"]
+    assert "partners" in turns[1]["extra"]["assistant_text"]
+    assert turns[1]["tts_ttfb_ms"] == 360.0
+
+
+def test_derive_turns_extracts_s2s_latency():
+    spans = [
+        {
+            "name": "s2s",
+            "span_id": "s2s-1",
+            "start_time_unix_nano": 100,
+            "attributes": {
+                "gen_ai.operation.name": "s2s",
+                "gen_ai.request.model": "gpt-4o-realtime-preview",
+                "metrics.ttfb": 0.85,
+                "transcript": "book a flight",
+                "transcript.is_input": True,
+                "output": "Sure, where would you like to go?",
+            },
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert len(turns) == 1
+    assert turns[0]["s2s_ttfb_ms"] == 850.0
+    assert turns[0]["extra"]["pipeline_mode"] == "s2s"
+    assert "book a flight" in turns[0]["transcript"]
+
+
+def test_derive_turns_skips_llm_input_json_for_transcript():
+    spans = [
+        {
+            "name": "turn",
+            "span_id": "turn-1",
+            "start_time_unix_nano": 100,
+            "attributes": {"turn.number": 1},
+        },
+        {
+            "name": "llm_response",
+            "span_id": "llm-1",
+            "parent_span_id": "turn-1",
+            "attributes": {
+                "metrics.ttfb": 1.0,
+                "input": '[{"role": "user", "content": "hello"}]',
+                "output": "Hi there!",
+            },
+        },
+    ]
+    turns = derive_turns_from_spans(spans)
+    assert turns[0]["transcript"] == "Assistant: Hi there!"
+
+
+def test_extract_pipeline_models_from_spans():
+    from app.services.synthetic_traces.otlp_mapper import extract_pipeline_models
+
+    models = extract_pipeline_models(
+        [
+            {
+                "name": "stt",
+                "attributes": {
+                    "gen_ai.operation.name": "stt",
+                    "gen_ai.system": "elevenlabs",
+                    "gen_ai.request.model": "scribe_v2_realtime",
+                },
+            },
+            {
+                "name": "llm_response",
+                "attributes": {
+                    "gen_ai.operation.name": "chat",
+                    "gen_ai.provider.name": "fireworks",
+                    "gen_ai.request.model": "accounts/fireworks/models/deepseek-v4-flash-0731",
+                },
+            },
+            {
+                "name": "tts",
+                "attributes": {
+                    "gen_ai.operation.name": "tts",
+                    "gen_ai.provider.name": "elevenlabs",
+                    "settings.model": "eleven_flash_v2_5",
+                },
+            },
+        ]
+    )
+    assert models["stt"]["provider"] == "elevenlabs"
+    assert models["stt"]["model"] == "scribe_v2_realtime"
+    assert models["llm"]["model"] == "deepseek-v4-flash-0731"
+    assert models["tts"]["model"] == "eleven_flash_v2_5"
+
+
+def test_spans_indicate_session_end():
+    from app.services.synthetic_traces.otlp_mapper import spans_indicate_session_end
+
+    assert spans_indicate_session_end(
+        [
+            {
+                "name": "conversation",
+                "end_time_unix_nano": 1_000_000_000,
+                "attributes": {},
+            }
+        ]
+    )
+    assert spans_indicate_session_end(
+        [{"name": "turn", "attributes": {"turn.ended_by_conversation_end": True}}]
+    )
+    assert not spans_indicate_session_end(
+        [{"name": "turn", "attributes": {"turn.number": 1}}]
+    )

From 64bff2ed3e5d7ccc75bed64ff22b5b16483ec6a6 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Thu, 3 Sep 2026 01:41:31 +0530
Subject: [PATCH 54/55] feat: UI updates in Call Traces and some fixes

---
 app/models/synthetic_trace_schemas.py         |   1 +
 frontend/src/App.tsx                          |   2 +
 .../SyntheticCallTracePanel.tsx               | 850 +++++++++---------
 .../call-recordings/TraceCharts.tsx           | 218 +++++
 .../call-recordings/TraceDetailDrawer.tsx     |  66 ++
 .../call-recordings/TraceRecordingBar.tsx     |  75 ++
 .../call-recordings/TraceTurnBadges.tsx       |  47 +
 .../call-recordings/TraceTurnDetail.tsx       | 159 ++++
 .../call-recordings/TraceWaterfall.tsx        | 131 +++
 .../components/call-recordings/traceUtils.ts  |  96 ++
 frontend/src/lib/workspaceNavigation.ts       |   1 +
 .../pages/test-insights/CallTraceDetail.tsx   |   7 +
 .../src/pages/test-insights/TestInsights.tsx  | 645 +++++++------
 13 files changed, 1515 insertions(+), 783 deletions(-)
 create mode 100644 frontend/src/components/call-recordings/TraceCharts.tsx
 create mode 100644 frontend/src/components/call-recordings/TraceDetailDrawer.tsx
 create mode 100644 frontend/src/components/call-recordings/TraceRecordingBar.tsx
 create mode 100644 frontend/src/components/call-recordings/TraceTurnBadges.tsx
 create mode 100644 frontend/src/components/call-recordings/TraceTurnDetail.tsx
 create mode 100644 frontend/src/components/call-recordings/TraceWaterfall.tsx
 create mode 100644 frontend/src/components/call-recordings/traceUtils.ts
 create mode 100644 frontend/src/pages/test-insights/CallTraceDetail.tsx

diff --git a/app/models/synthetic_trace_schemas.py b/app/models/synthetic_trace_schemas.py
index 8400169d..ccd18005 100644
--- a/app/models/synthetic_trace_schemas.py
+++ b/app/models/synthetic_trace_schemas.py
@@ -52,6 +52,7 @@ class SyntheticCallTraceSummary(BaseModel):
     response_latency_p95_ms: Optional[float] = None
     component_aggregates: Optional[Dict[str, Any]] = None
     failure_flags: Optional[List[str]] = None
+    call_recording_id: Optional[UUID] = None
 
     model_config = ConfigDict(from_attributes=True)
 
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 6f50c227..0a621760 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -64,6 +64,7 @@ import Observability from './pages/observability/Observability'
 import ObservabilityCalls from './pages/observability/ObservabilityCalls'
 import ObservabilityCallDetail from './pages/observability/ObservabilityCallDetail'
 import TestInsights from './pages/test-insights/TestInsights'
+import CallTraceDetail from './pages/test-insights/CallTraceDetail'
 
 // Alerting
 import Alerts from './pages/alerting/Alerts'
@@ -203,6 +204,7 @@ function App() {
           <Route path="observability/calls" element={<ObservabilityCalls />} />
           <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
           <Route path="call-traces" element={<TestInsights />} />
+          <Route path="call-traces/:traceId" element={<CallTraceDetail />} />
           <Route path="test-insights" element={<PreserveSearchRedirect to="/call-traces" />} />
           <Route path="iam" element={<IAM />} />
           <Route path="usage" element={<UsagePage />} />
diff --git a/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx b/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
index 30d6a76c..684c90f4 100644
--- a/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
+++ b/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
@@ -1,20 +1,21 @@
 import { useMemo, useState } from 'react'
 import { useQuery } from '@tanstack/react-query'
 import { Link } from 'react-router-dom'
-import {
-  Activity,
-  ChevronDown,
-  ChevronRight,
-  Clock,
-  Cpu,
-  Mic,
-  MessageSquare,
-  Radio,
-  Volume2,
-  X,
-  Zap,
-} from 'lucide-react'
+import { Clock, ChevronDown, ChevronRight, ExternalLink, Radio, X } from 'lucide-react'
 import { apiClient } from '../../lib/api'
+import TraceTurnDetail from './TraceTurnDetail'
+import TraceWaterfall from './TraceWaterfall'
+import TraceRecordingBar from './TraceRecordingBar'
+import { TurnSignalBadges } from './TraceTurnBadges'
+import {
+  FAILURE_FLAG_LABELS,
+  formatCallDuration,
+  formatMs,
+  isTurnIncomplete,
+  PIPELINE_MODE_LABELS,
+  sessionPipelineMode,
+  spanHasError,
+} from './traceUtils'
 
 interface SyntheticCallTracePanelProps {
   evaluatorResultId?: string
@@ -72,69 +73,38 @@ interface OtelSpan {
   start_time_unix_nano?: number | null
   end_time_unix_nano?: number | null
   attributes?: Record<string, unknown>
-  events?: Array<Record<string, unknown>>
+  events?: Array<{ name?: string; attributes?: Record<string, unknown> }>
 }
 
-type DetailTab = 'conversation' | 'latency' | 'raw'
+type DetailTab = 'trace' | 'waterfall' | 'transcript' | 'spans'
 type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
 
-const COMPONENT_STYLES: Record<
-  ComponentKind,
-  { label: string; bar: string; text: string; bg: string; border: string; icon: typeof Mic }
-> = {
-  stt: {
-    label: 'STT',
-    bar: 'bg-sky-500',
-    text: 'text-sky-700',
-    bg: 'bg-sky-50',
-    border: 'border-sky-200',
-    icon: Mic,
-  },
-  llm: {
-    label: 'LLM',
-    bar: 'bg-violet-500',
-    text: 'text-violet-700',
-    bg: 'bg-violet-50',
-    border: 'border-violet-200',
-    icon: Cpu,
-  },
-  tts: {
-    label: 'TTS',
-    bar: 'bg-emerald-500',
-    text: 'text-emerald-700',
-    bg: 'bg-emerald-50',
-    border: 'border-emerald-200',
-    icon: Volume2,
-  },
-  s2s: {
-    label: 'S2S',
-    bar: 'bg-fuchsia-500',
-    text: 'text-fuchsia-700',
-    bg: 'bg-fuchsia-50',
-    border: 'border-fuchsia-200',
-    icon: Radio,
-  },
+const COMPONENT_LABELS: Record<ComponentKind, string> = {
+  stt: 'STT',
+  llm: 'LLM',
+  tts: 'TTS',
+  s2s: 'S2S',
 }
 
-function formatMs(value?: number | null): string {
-  if (value == null || Number.isNaN(value)) return '—'
-  return `${Math.round(value)}ms`
-}
-
-function latencyTone(ms?: number | null): string {
-  if (ms == null) return 'text-gray-900'
-  if (ms < 800) return 'text-emerald-700'
-  if (ms < 2000) return 'text-amber-700'
-  return 'text-rose-700'
+const TABS: Array<{ id: DetailTab; label: string }> = [
+  { id: 'trace', label: 'Trace' },
+  { id: 'waterfall', label: 'Waterfall' },
+  { id: 'transcript', label: 'Transcript' },
+  { id: 'spans', label: 'Spans' },
+]
+
+function formatOffset(ms: number): string {
+  const totalSec = ms / 1000
+  const mins = Math.floor(totalSec / 60)
+  const sec = totalSec % 60
+  return `(+${String(mins).padStart(2, '0')}:${sec.toFixed(2).padStart(5, '0')})`
 }
 
 function shortModel(raw: unknown): string | null {
   if (raw == null) return null
   const text = String(raw).trim()
   if (!text) return null
-  if (text.includes('/models/')) {
-    return text.split('/models/')[1]?.split('/')[0] ?? text
-  }
+  if (text.includes('/models/')) return text.split('/models/')[1]?.split('/')[0] ?? text
   if (text.includes('/')) return text.split('/').pop() ?? text
   return text
 }
@@ -164,8 +134,7 @@ function metaFromSpan(span: OtelSpan): ComponentMeta {
     shortModel(attrs['gen_ai.request.model']) ??
     shortModel(attrs['param.model']) ??
     (kind === 'tts' || kind === 'stt' ? shortModel(attrs['settings.model']) : null)
-  const provider =
-    attrStr(attrs, 'gen_ai.provider.name') ?? attrStr(attrs, 'gen_ai.system')
+  const provider = attrStr(attrs, 'gen_ai.provider.name') ?? attrStr(attrs, 'gen_ai.system')
   return { model, provider: provider?.toLowerCase() ?? null }
 }
 
@@ -239,11 +208,6 @@ function turnMetaFromSpans(spans: OtelSpan[]): Partial<TraceTurnExtra> {
   return out
 }
 
-function providerLabel(provider?: string | null, model?: string | null): string {
-  if (provider && model) return `${provider} · ${model}`
-  return provider ?? model ?? '—'
-}
-
 function mergeTurnMeta(
   turn: TraceTurn,
   spanMeta: Partial<TraceTurnExtra>,
@@ -260,9 +224,8 @@ function mergeTurnMeta(
       ;(extra as Record<string, string>)[providerKey] = sessionModels[kind]!.provider!
     }
   }
-  if (isS2sTurn(turn)) {
-    fill('s2s')
-  } else {
+  if (isS2sTurn(turn)) fill('s2s')
+  else {
     fill('stt')
     fill('llm')
     fill('tts')
@@ -270,127 +233,43 @@ function mergeTurnMeta(
   return extra
 }
 
-function PipelineModelsBar({ models }: { models: PipelineModels }) {
-  const items = (['stt', 'llm', 'tts', 's2s'] as const)
-    .map((key) => ({ key, meta: models[key], ...COMPONENT_STYLES[key] }))
-    .filter((item) => item.meta?.model || item.meta?.provider)
-
-  if (items.length === 0) return null
-
-  return (
-    <div className="px-5 py-3 border-b border-gray-100 bg-slate-50/60 shrink-0">
-      <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400 mb-2.5">
-        Voice pipeline models
-      </p>
-      <div className="grid grid-cols-2 sm:grid-cols-4 gap-2">
-        {items.map(({ key, meta, label, icon: Icon, bg, border, text }) => (
-          <div key={key} className={`rounded-lg border ${border} ${bg} px-3 py-2.5`}>
-            <div className="flex items-center gap-2 mb-1">
-              <Icon className={`w-3.5 h-3.5 ${text}`} />
-              <span className={`text-[10px] font-bold uppercase tracking-wider ${text}`}>{label}</span>
-            </div>
-            <p className="text-xs font-semibold text-gray-900 truncate" title={meta?.model ?? undefined}>
-              {meta?.model ?? '—'}
-            </p>
-            {meta?.provider && (
-              <p className="text-[11px] text-gray-500 truncate mt-0.5">{meta.provider}</p>
-            )}
-          </div>
-        ))}
-      </div>
-    </div>
-  )
-}
-
-interface WaterfallSegment {
-  kind: ComponentKind
-  ms: number
-}
-
-function buildWaterfallSegments(turn: TraceTurn): WaterfallSegment[] {
-  if (isS2sTurn(turn)) {
-    const ms = turn.s2s_ttfb_ms ?? turn.sut_response_latency_ms
-    return ms != null ? [{ kind: 's2s', ms }] : []
+function turnOffsetMs(turns: TraceTurn[], turnNumber: number): number {
+  let ms = 0
+  for (const t of turns) {
+    if (t.turn_number >= turnNumber) break
+    ms += t.sut_response_latency_ms ?? 0
   }
-  const segments: WaterfallSegment[] = []
-  if (turn.stt_ttfb_ms != null) segments.push({ kind: 'stt', ms: turn.stt_ttfb_ms })
-  if (turn.llm_ttfb_ms != null) segments.push({ kind: 'llm', ms: turn.llm_ttfb_ms })
-  if (turn.tts_ttfb_ms != null) segments.push({ kind: 'tts', ms: turn.tts_ttfb_ms })
-  return segments
+  return ms
 }
 
-function LatencyWaterfall({
-  turn,
-  extra,
-  compact,
+function MetricTile({
+  label,
+  value,
+  unit,
+  highlight,
 }: {
-  turn: TraceTurn
-  extra: TraceTurnExtra
-  compact?: boolean
+  label: string
+  value: string
+  unit?: string
+  highlight?: boolean
 }) {
-  const segments = buildWaterfallSegments(turn)
-  const total = segments.reduce((sum, s) => sum + s.ms, 0) || turn.sut_response_latency_ms || 0
-
-  if (segments.length === 0) {
-    return (
-      <div className="text-xs text-gray-400 italic">
-        No component timing{turn.sut_response_latency_ms != null ? ` · total ${formatMs(turn.sut_response_latency_ms)}` : ''}
-      </div>
-    )
-  }
-
   return (
-    <div className={compact ? 'space-y-2' : 'space-y-3'}>
-      <div className="flex h-7 rounded-md overflow-hidden bg-gray-100 ring-1 ring-gray-200/80">
-        {segments.map((seg, idx) => {
-          const pct = total > 0 ? (seg.ms / total) * 100 : 100 / segments.length
-          const style = COMPONENT_STYLES[seg.kind]
-          const model = extra[`${seg.kind}_model` as keyof TraceTurnExtra] as string | undefined
-          const provider = extra[`${seg.kind}_provider` as keyof TraceTurnExtra] as string | undefined
-          return (
-            <div
-              key={`${seg.kind}-${idx}`}
-              className={`${style.bar} relative group min-w-[2rem] flex items-center justify-center`}
-              style={{ width: `${Math.max(pct, 8)}%` }}
-              title={`${style.label}: ${formatMs(seg.ms)} · ${providerLabel(provider, model)}`}
-            >
-              <span className="text-[10px] font-bold text-white/90 drop-shadow-sm px-1 truncate">
-                {formatMs(seg.ms)}
-              </span>
-            </div>
-          )
-        })}
-      </div>
-
-      <div
-        className="grid gap-2"
-        style={{ gridTemplateColumns: `repeat(${segments.length}, minmax(0, 1fr))` }}
+    <div
+      className={`rounded-lg border px-3 py-3 ${
+        highlight ? 'border-primary-400 bg-primary-50/40' : 'border-gray-200 bg-white'
+      }`}
+    >
+      <p
+        className={`text-[10px] font-semibold uppercase tracking-wider ${
+          highlight ? 'text-primary-800/70' : 'text-gray-400'
+        }`}
       >
-        {segments.map((seg, idx) => {
-          const style = COMPONENT_STYLES[seg.kind]
-          const Icon = style.icon
-          const model = extra[`${seg.kind}_model` as keyof TraceTurnExtra] as string | undefined
-          const provider = extra[`${seg.kind}_provider` as keyof TraceTurnExtra] as string | undefined
-          return (
-            <div
-              key={`${seg.kind}-detail-${idx}`}
-              className={`rounded-md border ${style.border} ${style.bg} px-2.5 py-2`}
-            >
-              <div className="flex items-center gap-1.5 mb-0.5">
-                <Icon className={`w-3 h-3 ${style.text}`} />
-                <span className={`text-[10px] font-bold uppercase ${style.text}`}>{style.label}</span>
-                <span className="ml-auto text-xs font-semibold tabular-nums text-gray-900">
-                  {formatMs(seg.ms)}
-                </span>
-              </div>
-              <p className="text-[11px] font-medium text-gray-800 truncate" title={model}>
-                {model ?? '—'}
-              </p>
-              {provider && <p className="text-[10px] text-gray-500 truncate">{provider}</p>}
-            </div>
-          )
-        })}
-      </div>
+        {label}
+      </p>
+      <p className="mt-1 text-xl font-bold tabular-nums text-gray-900">
+        {value}
+        {unit && <span className="ml-0.5 text-sm font-medium text-gray-500">{unit}</span>}
+      </p>
     </div>
   )
 }
@@ -399,35 +278,48 @@ function RawSpanRow({ span }: { span: OtelSpan }) {
   const [open, setOpen] = useState(false)
   const kind = spanKind(span)
   const meta = metaFromSpan(span)
-  const ttfb = span.attributes?.['metrics.ttfb']
   const durationMs =
     span.start_time_unix_nano != null && span.end_time_unix_nano != null
       ? Math.round((span.end_time_unix_nano - span.start_time_unix_nano) / 1_000_000)
       : null
-  const style = kind ? COMPONENT_STYLES[kind] : null
+  const hasError = spanHasError(span)
+
+  const stageLabel = kind ? COMPONENT_LABELS[kind] : 'Other'
+  const spanLabel = span.name
+  const modelLabel =
+    meta.model && meta.model !== span.name ? meta.model : meta.provider ?? '—'
 
   return (
-    <div className="rounded-lg border border-gray-200 bg-white text-xs overflow-hidden">
+    <div className="border-b border-gray-100 last:border-0">
       <button
         type="button"
         onClick={() => setOpen((v) => !v)}
-        className="w-full flex items-center gap-2 px-3 py-2.5 text-left hover:bg-gray-50"
+        className="w-full text-left hover:bg-gray-50"
       >
-        {open ? <ChevronDown className="w-3.5 h-3.5 text-gray-400 shrink-0" /> : <ChevronRight className="w-3.5 h-3.5 text-gray-400 shrink-0" />}
-        {style && (
-          <span className={`text-[10px] font-bold uppercase px-1.5 py-0.5 rounded ${style.bg} ${style.text}`}>
-            {style.label}
+        <div className="grid grid-cols-[3.5rem_minmax(0,1fr)_minmax(0,1.2fr)_4.5rem] items-center gap-4 px-4 py-3">
+          <span className="flex items-center gap-1.5 text-xs font-medium text-gray-500">
+            {open ? <ChevronDown className="h-3.5 w-3.5 shrink-0" /> : <ChevronRight className="h-3.5 w-3.5 shrink-0" />}
+            <span
+              className={`inline-flex rounded-md border px-1.5 py-0.5 text-[10px] font-semibold ${
+                kind
+                  ? 'border-primary-300 bg-primary-50/60 text-primary-800'
+                  : 'border-gray-200 bg-gray-50 text-gray-600'
+              }`}
+            >
+              {stageLabel}
+            </span>
           </span>
-        )}
-        {!style && <span className="font-medium text-gray-600">{span.name}</span>}
-        <span className="text-gray-500 truncate hidden sm:inline">{meta.model ?? ''}</span>
-        <span className="ml-auto tabular-nums text-gray-600 shrink-0 font-medium">
-          {durationMs != null ? `${durationMs}ms` : '—'}
-          {ttfb != null && <span className="text-gray-400 font-normal"> · ttfb {Math.round(Number(ttfb) * 1000)}ms</span>}
-        </span>
+          <span className={`truncate font-mono text-xs ${hasError ? 'text-rose-700' : 'text-gray-800'}`}>
+            {spanLabel}
+          </span>
+          <span className="truncate font-mono text-xs text-gray-500">{modelLabel}</span>
+          <span className="text-right tabular-nums text-xs font-medium text-gray-900">
+            {durationMs != null ? `${durationMs}ms` : '—'}
+          </span>
+        </div>
       </button>
       {open && (
-        <pre className="text-[10px] font-mono bg-slate-50 border-t border-slate-100 px-3 py-2 overflow-x-auto max-h-48 whitespace-pre-wrap">
+        <pre className="mx-4 mb-3 max-h-48 overflow-x-auto rounded-md border border-gray-100 bg-gray-50 px-3 py-2 text-xs">
           {JSON.stringify(span.attributes ?? {}, null, 2)}
         </pre>
       )}
@@ -435,114 +327,13 @@ function RawSpanRow({ span }: { span: OtelSpan }) {
   )
 }
 
-function TurnCard({
-  turn,
-  spans,
-  sessionModels,
-}: {
-  turn: TraceTurn
-  spans: OtelSpan[]
-  sessionModels: PipelineModels
-}) {
-  const [showSpans, setShowSpans] = useState(false)
-  const spanMeta = useMemo(() => turnMetaFromSpans(spans), [spans])
-  const extra = useMemo(() => mergeTurnMeta(turn, spanMeta, sessionModels), [turn, spanMeta, sessionModels])
-  const parsed = parseTranscript(turn.transcript)
-  const userText = extra.user_text ?? parsed.user
-  const assistantText = extra.assistant_text ?? parsed.assistant
-  const agent = extra.agent_role ?? extra.agent_id
-  const interrupted = Boolean(extra.was_interrupted)
-  const componentSpans = spans
-    .filter((s) => spanKind(s) != null)
-    .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
-
-  return (
-    <article className="rounded-xl border border-gray-200 bg-white shadow-sm overflow-hidden">
-      <header className="px-4 py-3 border-b border-gray-100 flex items-center justify-between gap-3 bg-gradient-to-r from-white to-gray-50/80">
-        <div className="flex items-center gap-2 flex-wrap min-w-0">
-          <span className="inline-flex items-center justify-center h-6 min-w-[1.5rem] px-1.5 rounded-md bg-gray-900 text-white text-xs font-bold">
-            {turn.turn_number}
-          </span>
-          {agent && (
-            <span className="text-[10px] font-semibold uppercase tracking-wide px-2 py-0.5 rounded-full bg-purple-100 text-purple-800">
-              {agent}
-            </span>
-          )}
-          {interrupted && (
-            <span className="text-[10px] font-semibold uppercase tracking-wide px-2 py-0.5 rounded-full bg-rose-100 text-rose-800">
-              Interrupted
-            </span>
-          )}
-        </div>
-        <div className="flex items-center gap-1.5 shrink-0">
-          <Zap className="w-3.5 h-3.5 text-indigo-400" />
-          <span className="text-[10px] uppercase tracking-wider text-gray-400 font-medium">Round-trip</span>
-          <span className={`text-base font-bold tabular-nums ${latencyTone(turn.sut_response_latency_ms)}`}>
-            {formatMs(turn.sut_response_latency_ms)}
-          </span>
-        </div>
-      </header>
-
-      <div className="px-4 py-4 space-y-4">
-        {(userText || assistantText) && (
-          <div className="space-y-2.5">
-            {userText && (
-              <div className="flex justify-end">
-                <div className="max-w-[88%] rounded-2xl rounded-tr-sm bg-indigo-600 text-white px-3.5 py-2.5 shadow-sm">
-                  <p className="text-[10px] font-semibold uppercase tracking-wider text-indigo-200 mb-0.5">User</p>
-                  <p className="text-sm leading-relaxed">{userText}</p>
-                </div>
-              </div>
-            )}
-            {assistantText && (
-              <div className="flex justify-start">
-                <div className="max-w-[88%] rounded-2xl rounded-tl-sm bg-gray-100 border border-gray-200/80 px-3.5 py-2.5">
-                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-500 mb-0.5">Agent</p>
-                  <p className="text-sm text-gray-900 leading-relaxed">{assistantText}</p>
-                </div>
-              </div>
-            )}
-          </div>
-        )}
-
-        <div>
-          <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400 mb-2">
-            Latency waterfall
-          </p>
-          <LatencyWaterfall turn={turn} extra={extra} />
-        </div>
-
-        {componentSpans.length > 0 && (
-          <div className="pt-1 border-t border-gray-100">
-            <button
-              type="button"
-              onClick={() => setShowSpans((v) => !v)}
-              className="text-xs text-gray-500 hover:text-gray-800 inline-flex items-center gap-1 font-medium"
-            >
-              {showSpans ? <ChevronDown className="w-3.5 h-3.5" /> : <ChevronRight className="w-3.5 h-3.5" />}
-              {showSpans ? 'Hide' : 'View'} span details ({componentSpans.length})
-            </button>
-            {showSpans && (
-              <div className="mt-2 space-y-1.5">
-                {componentSpans.map((span) => (
-                  <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />
-                ))}
-              </div>
-            )}
-          </div>
-        )}
-      </div>
-    </article>
-  )
-}
-
 export default function SyntheticCallTracePanel({
   evaluatorResultId,
   traceId,
   callShortId,
   onClose,
 }: SyntheticCallTracePanelProps) {
-  const [tab, setTab] = useState<DetailTab>('conversation')
+  const [tab, setTab] = useState<DetailTab>('trace')
 
   const lookupKey = traceId ?? callShortId ?? evaluatorResultId
   const lookupMode = traceId ? 'by-trace' : callShortId ? 'by-call-short-id' : 'by-result'
@@ -562,185 +353,360 @@ export default function SyntheticCallTracePanel({
   const spansByTurn = useMemo(() => buildSpansByTurn(otelSpans), [otelSpans])
   const turns = (data?.turns ?? []) as TraceTurn[]
   const pipelineModels = (data?.pipeline_models ?? {}) as PipelineModels
+  const pipelineMode = useMemo(() => sessionPipelineMode(turns), [turns])
+
+  const { data: linkedAgent } = useQuery({
+    queryKey: ['trace-agent', data?.agent_id],
+    queryFn: () => apiClient.getAgent(data!.agent_id),
+    enabled: Boolean(data?.agent_id),
+  })
+
+  const traceTurnRows = useMemo(() => {
+    return turns.map((turn) => {
+      const turnSpans = spansByTurn.get(turn.turn_number) ?? []
+      const spanMeta = turnMetaFromSpans(turnSpans)
+      const extra = mergeTurnMeta(turn, spanMeta, pipelineModels)
+      const models: Partial<Record<ComponentKind, string>> = {}
+      for (const kind of ['stt', 'llm', 'tts', 's2s'] as const) {
+        const model = extra[`${kind}_model` as keyof TraceTurnExtra] as string | undefined
+        if (model) models[kind] = model
+      }
+      return {
+        turnNumber: turn.turn_number,
+        offsetMs: turnOffsetMs(turns, turn.turn_number),
+        sttMs: turn.stt_ttfb_ms,
+        llmMs: turn.llm_ttfb_ms,
+        ttsMs: turn.tts_ttfb_ms,
+        s2sMs: turn.s2s_ttfb_ms,
+        totalMs: turn.sut_response_latency_ms,
+        talkOver: turn.talk_over,
+        interrupted: extra.was_interrupted,
+        incomplete: isTurnIncomplete(turn, pipelineMode),
+        models,
+        spans: turnSpans.map((span) => ({
+          id: span.span_id,
+          kind: spanKind(span),
+          name: span.name,
+          model: metaFromSpan(span).model ?? undefined,
+          durationMs:
+            span.start_time_unix_nano != null && span.end_time_unix_nano != null
+              ? Math.round((span.end_time_unix_nano - span.start_time_unix_nano) / 1_000_000)
+              : null,
+        })),
+      }
+    })
+  }, [turns, spansByTurn, pipelineModels, pipelineMode])
+
+  const transcriptMessages = useMemo(() => {
+    const messages: Array<{
+      role: 'user' | 'agent'
+      text: string
+      turn: number
+      offsetMs: number
+      latencyMs?: number
+      talkOver?: boolean
+      interrupted?: boolean
+      incomplete?: boolean
+    }> = []
+    for (const turn of turns) {
+      const spanMeta = turnMetaFromSpans(spansByTurn.get(turn.turn_number) ?? [])
+      const extra = mergeTurnMeta(turn, spanMeta, pipelineModels)
+      const parsed = parseTranscript(turn.transcript)
+      const offset = turnOffsetMs(turns, turn.turn_number)
+      const userText = extra.user_text ?? parsed.user
+      const assistantText = extra.assistant_text ?? parsed.assistant
+      const incomplete = isTurnIncomplete(turn, pipelineMode)
+      if (userText) {
+        messages.push({
+          role: 'user',
+          text: userText,
+          turn: turn.turn_number,
+          offsetMs: offset,
+          talkOver: turn.talk_over,
+          incomplete,
+        })
+      }
+      if (assistantText) {
+        messages.push({
+          role: 'agent',
+          text: assistantText,
+          turn: turn.turn_number,
+          offsetMs: offset + (turn.stt_ttfb_ms ?? 0),
+          latencyMs: turn.sut_response_latency_ms ?? undefined,
+          interrupted: extra.was_interrupted,
+          incomplete,
+        })
+      }
+    }
+    return messages
+  }, [turns, spansByTurn, pipelineModels, pipelineMode])
 
   if (isLoading) {
-    return <div className="flex items-center justify-center h-40 text-sm text-gray-500">Loading trace…</div>
+    return <div className="flex items-center justify-center h-48 text-sm text-gray-500">Loading trace…</div>
   }
 
   if (isError) {
     const message = (error as Error)?.message || 'Trace not available'
     if (message.includes('404') || message.toLowerCase().includes('not found')) {
       return (
-        <div className="p-6 text-sm text-gray-600">
-          No trace yet. Run a Pipecat WebRTC session with tracing, then see{' '}
-          <Link to="/call-traces" className="text-indigo-600 hover:text-indigo-800 font-medium">
+        <div className="p-8 text-sm text-gray-600">
+          No trace yet. Run a Pipecat session with tracing enabled, then see{' '}
+          <Link to="/call-traces" className="text-primary-600 hover:text-primary-800 font-medium">
             Call Traces → Connect Pipecat
           </Link>
         </div>
       )
     }
-    return <div className="p-6 text-sm text-amber-800 bg-amber-50">Could not load trace: {message}</div>
+    return <div className="p-8 text-sm text-red-800 bg-red-50">Could not load trace: {message}</div>
   }
 
   const trace = data
   if (!trace) return null
 
   const statusLabel = trace.status === 'finalized' ? 'closed' : trace.status
-  const aggregates = trace.component_aggregates as Record<string, { p50?: number }> | undefined
-  const isEmbedded = !onClose
+  const isDrawer = Boolean(onClose)
+  const isOpen = trace.status === 'open'
+
+  const medianMs =
+    trace.response_latency_p50_ms != null ? Math.round(trace.response_latency_p50_ms) : null
+  const p90Ms = trace.response_latency_p90_ms != null ? Math.round(trace.response_latency_p90_ms) : null
+  const p95Ms = trace.response_latency_p95_ms != null ? Math.round(trace.response_latency_p95_ms) : null
+
+  const startedLabel = trace.started_at
+    ? new Date(trace.started_at).toLocaleString(undefined, {
+        day: '2-digit',
+        month: '2-digit',
+        year: 'numeric',
+        hour: '2-digit',
+        minute: '2-digit',
+        second: '2-digit',
+      })
+    : null
+
+  const callDuration = formatCallDuration(trace.started_at, trace.ended_at)
+  const failureFlags = (trace.failure_flags ?? []) as string[]
+  const agentRouteId = linkedAgent?.agent_id || linkedAgent?.id || trace.agent_id
+  const agentLabel = linkedAgent?.name || (trace.agent_id ? 'Agent' : null)
 
   return (
-    <div
-      className={`flex flex-col w-full bg-gray-50/40 ${isEmbedded ? '' : 'h-full min-h-0'}`}
-    >
-      <div className="flex items-start justify-between gap-4 px-5 py-4 border-b border-gray-200 bg-white shrink-0">
-        <div className="min-w-0">
-          <div className="flex items-center gap-2 flex-wrap">
-            <h2 className="text-lg font-semibold text-gray-900 tracking-tight">
+    <div className={isDrawer ? 'flex h-full min-h-0 flex-col bg-gray-50' : 'bg-gray-50 pb-10'}>
+      <div
+        className={
+          isDrawer
+            ? 'shrink-0 overflow-x-hidden border-b border-gray-200 bg-white'
+            : 'sticky top-0 z-10 overflow-x-hidden border-b border-gray-200 bg-white shadow-sm'
+        }
+      >
+        <div className="flex items-start justify-between gap-3 border-b border-gray-100 px-5 py-4">
+          <div className="min-w-0">
+            <h2 className="font-mono text-xl font-bold tracking-tight text-primary-600">
               {trace.call_short_id ? `#${trace.call_short_id}` : 'Call trace'}
             </h2>
-            <span
-              className={`text-xs rounded-full px-2.5 py-0.5 capitalize font-medium ${
-                trace.status === 'open' ? 'bg-amber-100 text-amber-800' : 'bg-emerald-100 text-emerald-800'
-              }`}
-            >
-              {statusLabel}
-            </span>
-            <span className="text-xs px-2 py-0.5 rounded-md bg-blue-50 text-blue-700 font-medium">
-              {trace.transport === 'webrtc' ? 'WebRTC' : trace.transport}
-            </span>
-          </div>
-          <div className="flex items-center gap-3 mt-1.5 text-xs text-gray-500">
-            <span>{trace.turn_count} turns</span>
-            {trace.started_at && (
-              <span className="inline-flex items-center gap-1">
-                <Clock className="w-3 h-3" />
-                {new Date(trace.started_at).toLocaleString()}
+            <div className="mt-2.5 flex flex-wrap items-center gap-2">
+              <span
+                className={`inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-medium capitalize ${
+                  isOpen
+                    ? 'border-primary-300 bg-primary-50/60 text-primary-800'
+                    : 'border-gray-200 bg-gray-50 text-gray-600'
+                }`}
+              >
+                {isOpen && <span className="mr-1.5 inline-block h-1.5 w-1.5 rounded-full bg-primary-500" />}
+                {statusLabel}
               </span>
-            )}
-          </div>
-        </div>
-        {onClose && (
-          <button type="button" onClick={onClose} className="p-2 rounded-lg text-gray-400 hover:bg-gray-100" aria-label="Close">
-            <X className="w-5 h-5" />
-          </button>
-        )}
-      </div>
-
-      <div className="px-5 py-3 border-b border-gray-100 bg-white shrink-0">
-        <div className="grid grid-cols-3 gap-2">
-          {(['p50', 'p90', 'p95'] as const).map((label) => {
-            const value =
-              label === 'p50'
-                ? trace.response_latency_p50_ms
-                : label === 'p90'
-                  ? trace.response_latency_p90_ms
-                  : trace.response_latency_p95_ms
-            return (
-              <div key={label} className="rounded-lg bg-gray-50 border border-gray-100 px-3 py-2 text-center">
-                <p className="text-[10px] uppercase tracking-wider text-gray-400 font-semibold">{label}</p>
-                <p className={`text-lg font-bold tabular-nums ${latencyTone(value)}`}>{formatMs(value)}</p>
-              </div>
-            )
-          })}
-        </div>
-        {aggregates && (
-          <div className="flex flex-wrap gap-1.5 mt-2.5">
-            {(['stt', 'llm', 'tts', 's2s'] as const).map((kind) => {
-              const p50 = aggregates[`${kind}_ttfb_ms`]?.p50
-              if (p50 == null) return null
-              const style = COMPONENT_STYLES[kind]
-              const sessionMeta = pipelineModels[kind]
-              return (
+              <span className="inline-flex items-center gap-1 rounded-full border border-gray-200 bg-white px-2.5 py-0.5 text-xs font-medium capitalize text-gray-700">
+                <Radio className="h-3 w-3 text-gray-400" />
+                {trace.transport ?? 'webrtc'}
+              </span>
+              {startedLabel && (
+                <span className="inline-flex items-center gap-1 rounded-full border border-gray-200 bg-white px-2.5 py-0.5 text-xs text-gray-600">
+                  <Clock className="h-3 w-3 text-gray-400" />
+                  {startedLabel}
+                </span>
+              )}
+              {callDuration && (
+                <span className="inline-flex items-center rounded-full border border-gray-200 bg-white px-2.5 py-0.5 text-xs text-gray-600">
+                  Duration {callDuration}
+                </span>
+              )}
+              {pipelineMode && (
+                <span className="inline-flex items-center rounded-full border border-primary-300 bg-primary-50/60 px-2.5 py-0.5 text-xs font-medium text-primary-800">
+                  {PIPELINE_MODE_LABELS[pipelineMode]}
+                </span>
+              )}
+              {failureFlags.map((flag) => (
                 <span
-                  key={kind}
-                  className={`text-[10px] px-2 py-0.5 rounded-full border ${style.border} ${style.bg} ${style.text} font-medium`}
-                  title={providerLabel(sessionMeta?.provider, sessionMeta?.model)}
+                  key={flag}
+                  className="inline-flex items-center rounded-full border border-rose-200 bg-rose-50 px-2.5 py-0.5 text-xs font-medium text-rose-800"
                 >
-                  {style.label} p50 {Math.round(p50)}ms
-                  {sessionMeta?.model && <span className="opacity-70"> · {sessionMeta.model}</span>}
+                  {FAILURE_FLAG_LABELS[flag] ?? flag}
                 </span>
-              )
-            })}
+              ))}
+            </div>
+            <div className="mt-3 flex flex-wrap items-center gap-3 text-xs">
+              {agentRouteId && (
+                <Link
+                  to={`/agents/${agentRouteId}`}
+                  className="inline-flex items-center gap-1 font-medium text-primary-600 hover:text-primary-800"
+                >
+                  {agentLabel}
+                  <ExternalLink className="h-3 w-3" />
+                </Link>
+              )}
+              {trace.evaluator_result_id && (
+                <Link
+                  to={`/results/${trace.evaluator_result_id}`}
+                  className="inline-flex items-center gap-1 font-medium text-primary-600 hover:text-primary-800"
+                >
+                  View eval result
+                  <ExternalLink className="h-3 w-3" />
+                </Link>
+              )}
+            </div>
           </div>
-        )}
-      </div>
+          {onClose && (
+            <button
+              type="button"
+              onClick={onClose}
+              className="rounded-lg p-2 text-gray-400 hover:bg-gray-100 hover:text-gray-700"
+              aria-label="Close"
+            >
+              <X className="h-5 w-5" />
+            </button>
+          )}
+        </div>
+
+        <div className="space-y-3 px-5 py-4">
+          <TraceRecordingBar
+            callShortId={trace.call_short_id}
+            callRecordingId={trace.call_recording_id}
+            evaluatorResultId={trace.evaluator_result_id}
+          />
+          <div className="grid grid-cols-2 gap-2 sm:grid-cols-4">
+            <MetricTile
+              label="Response p50"
+              value={medianMs != null ? String(medianMs) : '—'}
+              unit={medianMs != null ? 'ms' : undefined}
+              highlight
+            />
+            <MetricTile
+              label="P90"
+              value={p90Ms != null ? String(p90Ms) : '—'}
+              unit={p90Ms != null ? 'ms' : undefined}
+            />
+            <MetricTile
+              label="P95"
+              value={p95Ms != null ? String(p95Ms) : '—'}
+              unit={p95Ms != null ? 'ms' : undefined}
+            />
+            <MetricTile label="Turns" value={String(trace.turn_count)} />
+          </div>
+        </div>
 
-      <PipelineModelsBar models={pipelineModels} />
-
-      <div className="flex gap-0.5 px-4 py-2 border-b border-gray-200 bg-white shrink-0">
-        {(
-          [
-            ['conversation', 'Conversation', MessageSquare],
-            ['latency', 'Waterfall', Activity],
-            ['raw', 'Raw spans', Zap],
-          ] as const
-        ).map(([id, label, Icon]) => (
-          <button
-            key={id}
-            type="button"
-            onClick={() => setTab(id)}
-            className={`px-3 py-2 text-sm font-medium rounded-lg inline-flex items-center gap-1.5 transition-colors ${
-              tab === id ? 'bg-gray-900 text-white shadow-sm' : 'text-gray-500 hover:text-gray-800 hover:bg-gray-100'
-            }`}
-          >
-            <Icon className="w-4 h-4" />
-            {label}
-          </button>
-        ))}
+        <div className="flex border-t border-gray-100 bg-white px-5">
+          {TABS.map(({ id, label }) => (
+            <button
+              key={id}
+              type="button"
+              onClick={() => setTab(id)}
+              className={`shrink-0 whitespace-nowrap border-b-2 px-4 py-3 text-sm font-medium transition-colors ${
+                tab === id
+                  ? 'border-primary-600 text-gray-900'
+                  : 'border-transparent text-gray-500 hover:text-gray-800'
+              }`}
+            >
+              {label}
+            </button>
+          ))}
+        </div>
       </div>
 
-      <div className={`${isEmbedded ? '' : 'flex-1 min-h-0'} overflow-y-auto px-4 py-4 space-y-3`}>
-        {tab === 'conversation' && (
-          <>
-            {turns.length === 0 ? (
-              <p className="text-center py-16 text-sm text-gray-500">No conversation turns yet</p>
+      <div className={isDrawer ? 'min-h-0 flex-1 overflow-y-auto overscroll-contain' : undefined}>
+        {tab === 'trace' && (
+          <div className="p-5 pb-16">
+            <TraceTurnDetail
+              rows={traceTurnRows}
+              pipelineModels={pipelineModels}
+              componentAggregates={
+                trace.component_aggregates as Record<string, { p50?: number; p90?: number; p95?: number }> | undefined
+              }
+            />
+          </div>
+        )}
+
+        {tab === 'waterfall' && (
+          <div className="p-5 pb-16">
+            {traceTurnRows.length === 0 ? (
+              <p className="py-12 text-center text-sm text-gray-500">No waterfall data</p>
             ) : (
-              turns.map((turn) => (
-                <TurnCard
-                  key={turn.turn_number}
-                  turn={turn}
-                  spans={spansByTurn.get(turn.turn_number) ?? []}
-                  sessionModels={pipelineModels}
-                />
-              ))
+              <TraceWaterfall rows={traceTurnRows} />
             )}
-          </>
+          </div>
         )}
 
-        {tab === 'latency' && (
-          <div className="space-y-3">
-            {turns.length === 0 ? (
-              <p className="text-center py-16 text-sm text-gray-500">No latency data</p>
+        {tab === 'transcript' && (
+          <div className="bg-gray-50/50 p-5 pb-16">
+            {transcriptMessages.length === 0 ? (
+              <p className="text-center py-16 text-sm text-gray-500">No transcript</p>
             ) : (
-              turns.map((turn) => {
-                const spanMeta = turnMetaFromSpans(spansByTurn.get(turn.turn_number) ?? [])
-                const extra = mergeTurnMeta(turn, spanMeta, pipelineModels)
-                return (
-                  <div key={turn.turn_number} className="rounded-xl border border-gray-200 bg-white p-4 shadow-sm">
-                    <div className="flex items-center justify-between mb-3">
-                      <span className="text-sm font-semibold text-gray-900">Turn {turn.turn_number}</span>
-                      <span className={`text-sm font-bold tabular-nums ${latencyTone(turn.sut_response_latency_ms)}`}>
-                        {formatMs(turn.sut_response_latency_ms)} total
-                      </span>
+              <div className="space-y-4 max-w-2xl mx-auto">
+                {transcriptMessages.map((msg, idx) => {
+                  const isUser = msg.role === 'user'
+                  return (
+                    <div key={`${msg.turn}-${msg.role}-${idx}`} className={isUser ? 'flex justify-end' : 'flex justify-start'}>
+                      <div className="max-w-[85%]">
+                        <div
+                          className={`rounded-xl px-4 py-3 ${
+                            isUser
+                              ? 'bg-white border border-gray-200 text-gray-900'
+                              : 'bg-gray-100 border border-gray-200/80 text-gray-900'
+                          }`}
+                        >
+                          <div className="mb-1 flex flex-wrap items-center gap-2">
+                            <p className={`text-xs font-semibold ${isUser ? 'text-gray-700' : 'text-gray-600'}`}>
+                              {isUser ? 'User' : 'Agent'}
+                            </p>
+                            <TurnSignalBadges
+                              talkOver={isUser ? msg.talkOver : undefined}
+                              interrupted={!isUser ? msg.interrupted : undefined}
+                              incomplete={msg.incomplete}
+                            />
+                          </div>
+                          <p className="text-sm leading-relaxed whitespace-pre-wrap">{msg.text}</p>
+                        </div>
+                        <p className={`text-[11px] mt-1 tabular-nums text-gray-500 ${isUser ? 'text-right' : ''}`}>
+                          Turn {msg.turn} {formatOffset(msg.offsetMs)}
+                          {msg.latencyMs != null && (
+                            <span className="ml-2">· {formatMs(msg.latencyMs)}</span>
+                          )}
+                        </p>
+                      </div>
                     </div>
-                    <LatencyWaterfall turn={turn} extra={extra} compact />
-                  </div>
-                )
-              })
+                  )
+                })}
+              </div>
             )}
           </div>
         )}
 
-        {tab === 'raw' && (
-          <div className="space-y-1.5">
+        {tab === 'spans' && (
+          <div className="bg-white pb-16">
             {otelSpans.length === 0 ? (
-              <p className="text-center py-16 text-sm text-gray-500">No spans</p>
+              <p className="py-16 text-center text-sm text-gray-500">No spans</p>
             ) : (
-              otelSpans
-                .slice()
-                .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
-                .map((span) => <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />)
+              <>
+                <div className="grid grid-cols-[3.5rem_minmax(0,1fr)_minmax(0,1.2fr)_4.5rem] items-center gap-4 border-b border-gray-200 bg-gray-50/80 px-4 py-2.5 text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+                  <span>Stage</span>
+                  <span>Span</span>
+                  <span>Model</span>
+                  <span className="text-right">Duration</span>
+                </div>
+                {otelSpans
+                  .slice()
+                  .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
+                  .map((span) => (
+                    <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />
+                  ))}
+              </>
             )}
           </div>
         )}
diff --git a/frontend/src/components/call-recordings/TraceCharts.tsx b/frontend/src/components/call-recordings/TraceCharts.tsx
new file mode 100644
index 00000000..aea42465
--- /dev/null
+++ b/frontend/src/components/call-recordings/TraceCharts.tsx
@@ -0,0 +1,218 @@
+import type { ReactNode } from 'react'
+import {
+  Bar,
+  BarChart,
+  CartesianGrid,
+  Cell,
+  Pie,
+  PieChart,
+  ResponsiveContainer,
+  Tooltip,
+  XAxis,
+  YAxis,
+} from 'recharts'
+
+type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
+
+const SLICE_COLORS: Record<ComponentKind, string> = {
+  stt: '#d4d4d8',
+  llm: '#a16207',
+  tts: '#a1a1aa',
+  s2s: '#71717a',
+}
+
+const BAR_FILL_DEFAULT = '#a1a1aa'
+const BAR_FILL_PEAK = '#854d0e'
+
+const STAGE_LABELS: Record<ComponentKind, string> = {
+  stt: 'STT',
+  llm: 'LLM',
+  tts: 'TTS',
+  s2s: 'S2S',
+}
+
+const CHART_GRID = '#f3f4f6'
+const CHART_AXIS = '#9ca3af'
+
+export interface LayerTurnInput {
+  sttMs?: number | null
+  llmMs?: number | null
+  ttsMs?: number | null
+  s2sMs?: number | null
+}
+
+function accumulateStageSums(rows: LayerTurnInput[]): Record<ComponentKind, number> {
+  const sums: Record<ComponentKind, number> = { stt: 0, llm: 0, tts: 0, s2s: 0 }
+  for (const row of rows) {
+    if (row.s2sMs != null && row.s2sMs > 0) {
+      sums.s2s += row.s2sMs
+      continue
+    }
+    if (row.sttMs != null && row.sttMs > 0) sums.stt += row.sttMs
+    if (row.llmMs != null && row.llmMs > 0) sums.llm += row.llmMs
+    if (row.ttsMs != null && row.ttsMs > 0) sums.tts += row.ttsMs
+  }
+  return sums
+}
+
+export function LayerResponseDonut({ rows }: { rows: LayerTurnInput[] }) {
+  const sums = accumulateStageSums(rows)
+
+  const slices = (['stt', 'llm', 'tts', 's2s'] as const)
+    .map((kind) => (sums[kind] > 0 ? { name: STAGE_LABELS[kind], kind, value: sums[kind] } : null))
+    .filter(Boolean) as Array<{ name: string; kind: ComponentKind; value: number }>
+
+  const total = slices.reduce((s, x) => s + x.value, 0)
+  if (total <= 0) return null
+
+  return (
+    <div className="rounded-lg border border-primary-300 bg-white p-4">
+      <p className="text-xs font-semibold uppercase tracking-wider text-gray-500">Layer response</p>
+      <p className="mt-0.5 text-[11px] text-gray-400">Cumulative stage latency across all turns</p>
+      <div className="relative mt-3 h-44">
+        <ResponsiveContainer width="100%" height="100%">
+          <PieChart>
+            <Pie
+              data={slices}
+              dataKey="value"
+              nameKey="name"
+              cx="50%"
+              cy="50%"
+              innerRadius={52}
+              outerRadius={72}
+              paddingAngle={2}
+              stroke="#fff"
+              strokeWidth={2}
+            >
+              {slices.map((slice) => (
+                <Cell key={slice.kind} fill={SLICE_COLORS[slice.kind]} />
+              ))}
+            </Pie>
+            <Tooltip
+              formatter={(value: number, name: string) => [
+                `${Math.round(value)}ms (${Math.round((value / total) * 100)}%)`,
+                name,
+              ]}
+              contentStyle={{ fontSize: 12, borderRadius: 8, border: '1px solid #e5e7eb' }}
+            />
+          </PieChart>
+        </ResponsiveContainer>
+        <div className="pointer-events-none absolute inset-0 flex flex-col items-center justify-center">
+          <span className="text-lg font-bold tabular-nums text-gray-900">
+            {total >= 1000 ? `${(total / 1000).toFixed(1)}s` : Math.round(total)}
+          </span>
+          <span className="text-[10px] text-gray-400">{total >= 1000 ? 'stage time' : 'ms stage time'}</span>
+        </div>
+      </div>
+      <div className="mt-3 flex flex-wrap justify-center gap-2 text-[11px] text-gray-600">
+        {slices.map((slice) => (
+          <span
+            key={slice.kind}
+            className={`inline-flex items-center gap-1.5 rounded-md border px-2 py-1 ${
+              slice.kind === 'llm'
+                ? 'border-primary-300 bg-primary-50/60'
+                : 'border-gray-200 bg-gray-50/80'
+            }`}
+          >
+            <span className="h-2 w-2 rounded-full" style={{ backgroundColor: SLICE_COLORS[slice.kind] }} />
+            {slice.name}{' '}
+            <span className="font-semibold tabular-nums text-gray-800">{Math.round(slice.value)}ms</span>
+          </span>
+        ))}
+      </div>
+    </div>
+  )
+}
+
+export interface TurnBarRow {
+  turn: string
+  total: number
+  talkOver?: boolean
+  interrupted?: boolean
+  incomplete?: boolean
+}
+
+function turnSignalLines(row: TurnBarRow): string[] {
+  const lines: string[] = []
+  if (row.talkOver) lines.push('Talk-over')
+  if (row.interrupted) lines.push('Interrupted')
+  if (row.incomplete) lines.push('Incomplete data')
+  return lines
+}
+
+function TurnBarTooltip({
+  active,
+  payload,
+}: {
+  active?: boolean
+  payload?: Array<{ payload?: TurnBarRow }>
+}) {
+  if (!active || !payload?.[0]?.payload) return null
+  const row = payload[0].payload
+  const signals = turnSignalLines(row)
+  return (
+    <div className="rounded-lg border border-gray-200 bg-white px-3 py-2 text-xs shadow-sm">
+      <p className="font-semibold text-gray-900">
+        {row.turn}: {Math.round(row.total)}ms
+      </p>
+      {signals.length > 0 && (
+        <ul className="mt-1 space-y-0.5 text-gray-600">
+          {signals.map((s) => (
+            <li key={s}>{s}</li>
+          ))}
+        </ul>
+      )}
+    </div>
+  )
+}
+
+export function TurnTotalsBar({ rows }: { rows: TurnBarRow[] }) {
+  if (rows.length === 0) return null
+
+  const peak = Math.max(...rows.map((r) => r.total))
+
+  return (
+    <div className="rounded-lg border border-gray-200 bg-white p-4">
+      <p className="text-xs font-semibold uppercase tracking-wider text-gray-500">Response by turn</p>
+      <p className="mt-0.5 text-[11px] text-gray-400">End-to-end agent response (sut) per turn</p>
+      <div className="mt-3 h-44">
+        <ResponsiveContainer width="100%" height="100%">
+          <BarChart data={rows} margin={{ top: 4, right: 8, left: 0, bottom: 0 }}>
+            <CartesianGrid strokeDasharray="3 3" stroke={CHART_GRID} vertical={false} />
+            <XAxis dataKey="turn" tick={{ fontSize: 11, fill: CHART_AXIS }} axisLine={false} tickLine={false} />
+            <YAxis
+              tick={{ fontSize: 11, fill: CHART_AXIS }}
+              axisLine={false}
+              tickLine={false}
+              width={44}
+              tickFormatter={(v) => `${v}`}
+            />
+            <Tooltip content={<TurnBarTooltip />} />
+            <Bar dataKey="total" radius={[3, 3, 0, 0]} maxBarSize={40}>
+              {rows.map((entry) => (
+                <Cell
+                  key={entry.turn}
+                  fill={entry.total === peak && peak > 0 ? BAR_FILL_PEAK : BAR_FILL_DEFAULT}
+                />
+              ))}
+            </Bar>
+          </BarChart>
+        </ResponsiveContainer>
+      </div>
+    </div>
+  )
+}
+
+function Section({ title, subtitle, children }: { title: string; subtitle?: string; children: ReactNode }) {
+  return (
+    <section className="space-y-3">
+      <div>
+        <h3 className="text-sm font-semibold text-gray-900">{title}</h3>
+        {subtitle && <p className="mt-0.5 text-xs text-gray-500">{subtitle}</p>}
+      </div>
+      {children}
+    </section>
+  )
+}
+
+export { Section as TraceSection }
diff --git a/frontend/src/components/call-recordings/TraceDetailDrawer.tsx b/frontend/src/components/call-recordings/TraceDetailDrawer.tsx
new file mode 100644
index 00000000..3b894c7b
--- /dev/null
+++ b/frontend/src/components/call-recordings/TraceDetailDrawer.tsx
@@ -0,0 +1,66 @@
+import { useEffect } from 'react'

+import { createPortal } from 'react-dom'

+import { AnimatePresence, motion } from 'framer-motion'

+import SyntheticCallTracePanel from './SyntheticCallTracePanel'

+

+export default function TraceDetailDrawer({

+  traceId,

+  open,

+  onClose,

+}: {

+  traceId: string | null

+  open: boolean

+  onClose: () => void

+}) {

+  useEffect(() => {

+    if (!open) return

+    const onKey = (e: KeyboardEvent) => {

+      if (e.key === 'Escape') onClose()

+    }

+    window.addEventListener('keydown', onKey)

+    return () => window.removeEventListener('keydown', onKey)

+  }, [open, onClose])

+

+  useEffect(() => {

+    if (!open) return

+    const prev = document.body.style.overflow

+    document.body.style.overflow = 'hidden'

+    return () => {

+      document.body.style.overflow = prev

+    }

+  }, [open])

+

+  if (typeof document === 'undefined') return null

+

+  return createPortal(

+    <AnimatePresence>

+      {open && traceId && (

+        <>

+          <motion.button

+            type="button"

+            aria-label="Close trace detail"

+            initial={{ opacity: 0 }}

+            animate={{ opacity: 1 }}

+            exit={{ opacity: 0 }}

+            transition={{ duration: 0.2 }}

+            className="fixed inset-0 z-[100] bg-gray-900/45 backdrop-blur-[1px]"

+            onClick={onClose}

+          />

+          <motion.aside

+            role="dialog"

+            aria-modal="true"

+            aria-label="Call trace detail"

+            initial={{ x: '100%' }}

+            animate={{ x: 0 }}

+            exit={{ x: '100%' }}

+            transition={{ type: 'spring', damping: 32, stiffness: 320 }}

+            className="fixed top-0 right-0 bottom-0 z-[101] flex w-full max-w-4xl flex-col overflow-hidden border-l border-gray-200 bg-white shadow-2xl"

+          >

+            <SyntheticCallTracePanel traceId={traceId} onClose={onClose} />

+          </motion.aside>

+        </>

+      )}

+    </AnimatePresence>,

+    document.body,

+  )

+}

diff --git a/frontend/src/components/call-recordings/TraceRecordingBar.tsx b/frontend/src/components/call-recordings/TraceRecordingBar.tsx
new file mode 100644
index 00000000..6f4453dd
--- /dev/null
+++ b/frontend/src/components/call-recordings/TraceRecordingBar.tsx
@@ -0,0 +1,75 @@
+import { useEffect, useRef, useState } from 'react'
+import { Loader } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { hasEvaluatorResultRecording } from '../../lib/recordingUrls'
+
+export default function TraceRecordingBar({
+  callShortId,
+  callRecordingId,
+  evaluatorResultId,
+}: {
+  callShortId?: string | null
+  callRecordingId?: string | null
+  evaluatorResultId?: string | null
+}) {
+  const [audioUrl, setAudioUrl] = useState<string | null>(null)
+  const [loading, setLoading] = useState(false)
+  const [failed, setFailed] = useState(false)
+  const blobRef = useRef<string | null>(null)
+
+  useEffect(() => {
+    let cancelled = false
+
+    async function load() {
+      if (!callRecordingId && !evaluatorResultId) return
+      setLoading(true)
+      setFailed(false)
+      try {
+        if (callRecordingId && callShortId) {
+          const url = await apiClient.getCallRecordingAudioUrl(callShortId)
+          if (!cancelled) {
+            blobRef.current = url
+            setAudioUrl(url)
+          }
+          return
+        }
+        if (evaluatorResultId) {
+          const result = await apiClient.getEvaluatorResult(evaluatorResultId, false)
+          if (!hasEvaluatorResultRecording(result)) return
+          const url = await apiClient.getEvaluatorResultAudioUrl(evaluatorResultId)
+          if (!cancelled) {
+            blobRef.current = url
+            setAudioUrl(url)
+          }
+        }
+      } catch {
+        if (!cancelled) setFailed(true)
+      } finally {
+        if (!cancelled) setLoading(false)
+      }
+    }
+
+    void load()
+    return () => {
+      cancelled = true
+      if (blobRef.current) URL.revokeObjectURL(blobRef.current)
+    }
+  }, [callRecordingId, callShortId, evaluatorResultId])
+
+  if (!callRecordingId && !evaluatorResultId) return null
+  if (failed) return null
+
+  return (
+    <div className="rounded-lg border border-gray-200 bg-gray-50/80 px-4 py-3">
+      <p className="mb-2 text-[10px] font-semibold uppercase tracking-wider text-gray-400">Recording</p>
+      {loading ? (
+        <div className="flex items-center gap-2 text-xs text-gray-500">
+          <Loader className="h-4 w-4 animate-spin" />
+          Loading audio…
+        </div>
+      ) : audioUrl ? (
+        <audio controls src={audioUrl} className="h-9 w-full max-w-md" preload="metadata" />
+      ) : null}
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/TraceTurnBadges.tsx b/frontend/src/components/call-recordings/TraceTurnBadges.tsx
new file mode 100644
index 00000000..1d5ff7de
--- /dev/null
+++ b/frontend/src/components/call-recordings/TraceTurnBadges.tsx
@@ -0,0 +1,47 @@
+import { AlertTriangle, MessageSquareOff, Mic } from 'lucide-react'
+
+export function TalkOverBadge() {
+  return (
+    <span className="inline-flex items-center gap-1 rounded-md border border-amber-200 bg-amber-50 px-1.5 py-0.5 text-[10px] font-medium text-amber-800">
+      <Mic className="h-3 w-3" />
+      Talk-over
+    </span>
+  )
+}
+
+export function InterruptedBadge() {
+  return (
+    <span className="inline-flex items-center gap-1 rounded-md border border-rose-200 bg-rose-50 px-1.5 py-0.5 text-[10px] font-medium text-rose-800">
+      <MessageSquareOff className="h-3 w-3" />
+      Interrupted
+    </span>
+  )
+}
+
+export function IncompleteTurnBadge() {
+  return (
+    <span className="inline-flex items-center gap-1 rounded-md border border-gray-300 bg-gray-50 px-1.5 py-0.5 text-[10px] font-medium text-gray-600">
+      <AlertTriangle className="h-3 w-3" />
+      Incomplete
+    </span>
+  )
+}
+
+export function TurnSignalBadges({
+  talkOver,
+  interrupted,
+  incomplete,
+}: {
+  talkOver?: boolean
+  interrupted?: boolean
+  incomplete?: boolean
+}) {
+  if (!talkOver && !interrupted && !incomplete) return null
+  return (
+    <span className="inline-flex flex-wrap items-center gap-1">
+      {talkOver && <TalkOverBadge />}
+      {interrupted && <InterruptedBadge />}
+      {incomplete && <IncompleteTurnBadge />}
+    </span>
+  )
+}
diff --git a/frontend/src/components/call-recordings/TraceTurnDetail.tsx b/frontend/src/components/call-recordings/TraceTurnDetail.tsx
new file mode 100644
index 00000000..79363bf6
--- /dev/null
+++ b/frontend/src/components/call-recordings/TraceTurnDetail.tsx
@@ -0,0 +1,159 @@
+import { LayerResponseDonut, TraceSection, TurnTotalsBar } from './TraceCharts'
+import { formatMs } from './traceUtils'
+
+type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
+
+const STAGE_LABELS: Record<ComponentKind, string> = {
+  stt: 'STT',
+  llm: 'LLM',
+  tts: 'TTS',
+  s2s: 'S2S',
+}
+
+export interface TraceTurnRow {
+  turnNumber: number
+  offsetMs: number
+  sttMs?: number | null
+  llmMs?: number | null
+  ttsMs?: number | null
+  s2sMs?: number | null
+  totalMs?: number | null
+  talkOver?: boolean
+  interrupted?: boolean
+  incomplete?: boolean
+  models: Partial<Record<ComponentKind, string>>
+  spans: Array<{
+    id: string
+    kind: ComponentKind | null
+    name: string
+    model?: string
+    durationMs: number | null
+  }>
+}
+
+interface ComponentMeta {
+  model?: string | null
+  provider?: string | null
+}
+
+interface PipelineModels {
+  stt?: ComponentMeta
+  llm?: ComponentMeta
+  tts?: ComponentMeta
+  s2s?: ComponentMeta
+}
+
+interface ComponentAggregates {
+  [key: string]: { p50?: number; p90?: number; p95?: number } | undefined
+}
+
+function formatMsLocal(value?: number | null): string {
+  return formatMs(value)
+}
+
+function StageChip({ label }: { label: string }) {
+  return (
+    <span className="inline-flex rounded-md border border-primary-300 bg-primary-50/60 px-2 py-0.5 text-xs font-semibold text-primary-800">
+      {label}
+    </span>
+  )
+}
+
+function PipelineStageTable({
+  models,
+  aggregates,
+}: {
+  models: PipelineModels
+  aggregates?: ComponentAggregates | null
+}) {
+  const rows = (['stt', 'llm', 'tts', 's2s'] as const)
+    .map((kind) => {
+      const meta = models[kind]
+      const stats = aggregates?.[`${kind}_ttfb_ms`]
+      if (!meta?.model && !meta?.provider && !stats) return null
+      return { kind, meta, stats }
+    })
+    .filter(Boolean) as Array<{
+    kind: ComponentKind
+    meta?: ComponentMeta
+    stats?: { p50?: number; p90?: number; p95?: number }
+  }>
+
+  if (rows.length === 0) return null
+
+  return (
+    <div className="overflow-hidden rounded-lg border border-primary-200 bg-white">
+      <table className="min-w-full text-sm">
+        <thead>
+          <tr className="border-b border-gray-100 bg-gray-50/80 text-left text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+            <th className="px-4 py-2.5">Stage</th>
+            <th className="px-4 py-2.5">Provider</th>
+            <th className="px-4 py-2.5">Model</th>
+            <th className="px-4 py-2.5 text-right">p50</th>
+            <th className="px-4 py-2.5 text-right">p90</th>
+            <th className="px-4 py-2.5 text-right">p95</th>
+          </tr>
+        </thead>
+        <tbody className="divide-y divide-gray-100">
+          {rows.map(({ kind, meta, stats }) => (
+            <tr key={kind}>
+              <td className="px-4 py-2.5">
+                <StageChip label={STAGE_LABELS[kind]} />
+              </td>
+              <td className="px-4 py-2.5 capitalize text-gray-700">{meta?.provider ?? '—'}</td>
+              <td className="px-4 py-2.5 font-mono text-xs text-gray-600">{meta?.model ?? '—'}</td>
+              <td className="px-4 py-2.5 text-right tabular-nums font-semibold text-primary-800">{formatMsLocal(stats?.p50)}</td>
+              <td className="px-4 py-2.5 text-right tabular-nums text-gray-800">{formatMsLocal(stats?.p90)}</td>
+              <td className="px-4 py-2.5 text-right tabular-nums text-gray-800">{formatMsLocal(stats?.p95)}</td>
+            </tr>
+          ))}
+        </tbody>
+      </table>
+    </div>
+  )
+}
+
+export default function TraceTurnDetail({
+  rows,
+  pipelineModels,
+  componentAggregates,
+}: {
+  rows: TraceTurnRow[]
+  pipelineModels: PipelineModels
+  componentAggregates?: ComponentAggregates | null
+}) {
+  if (rows.length === 0) {
+    return <p className="py-12 text-center text-sm text-gray-500">No turn data</p>
+  }
+
+  const barRows = rows
+    .filter((r) => r.totalMs != null && r.totalMs > 0)
+    .map((r) => ({
+      turn: `T${r.turnNumber}`,
+      total: r.totalMs!,
+      talkOver: r.talkOver,
+      interrupted: r.interrupted,
+      incomplete: r.incomplete,
+    }))
+
+  return (
+    <div className="space-y-8">
+      <TraceSection title="Session overview" subtitle="Stage breakdown and per-turn response latency">
+        <div className="grid gap-4 md:grid-cols-2">
+          <LayerResponseDonut rows={rows} />
+          {barRows.length > 0 ? (
+            <TurnTotalsBar rows={barRows} />
+          ) : (
+            <div className="flex items-center justify-center rounded-lg border border-gray-200 bg-white p-4 text-sm text-gray-500">
+              No per-turn response data
+            </div>
+          )}
+        </div>
+      </TraceSection>
+
+      <TraceSection title="Pipeline" subtitle="Models and TTFB percentiles per stage (p50 / p90 / p95)">
+        <PipelineStageTable models={pipelineModels} aggregates={componentAggregates} />
+      </TraceSection>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/TraceWaterfall.tsx b/frontend/src/components/call-recordings/TraceWaterfall.tsx
new file mode 100644
index 00000000..94004336
--- /dev/null
+++ b/frontend/src/components/call-recordings/TraceWaterfall.tsx
@@ -0,0 +1,131 @@
+import { ArrowRight } from 'lucide-react'
+import { TurnSignalBadges } from './TraceTurnBadges'
+import { formatMs } from './traceUtils'
+
+type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
+
+export interface WaterfallRow {
+  turnNumber: number
+  sttMs?: number | null
+  llmMs?: number | null
+  ttsMs?: number | null
+  s2sMs?: number | null
+  totalMs?: number | null
+  talkOver?: boolean
+  interrupted?: boolean
+  incomplete?: boolean
+}
+
+const STAGE_LABELS: Record<ComponentKind, string> = {
+  stt: 'STT',
+  llm: 'LLM',
+  tts: 'TTS',
+  s2s: 'S2S',
+}
+
+function formatMsLocal(value?: number | null): string {
+  return formatMs(value)
+}
+
+function turnSegments(row: WaterfallRow): Array<{ kind: ComponentKind; ms: number }> {
+  const parts: Array<{ kind: ComponentKind; ms: number }> = []
+  if (row.s2sMs != null && row.s2sMs > 0) parts.push({ kind: 's2s', ms: row.s2sMs })
+  else {
+    if (row.sttMs != null && row.sttMs > 0) parts.push({ kind: 'stt', ms: row.sttMs })
+    if (row.llmMs != null && row.llmMs > 0) parts.push({ kind: 'llm', ms: row.llmMs })
+    if (row.ttsMs != null && row.ttsMs > 0) parts.push({ kind: 'tts', ms: row.ttsMs })
+  }
+  return parts
+}
+
+function StageNode({ kind, ms }: { kind: ComponentKind; ms: number }) {
+  const highlight = kind === 'llm'
+  return (
+    <div
+      className={`flex min-w-[4.5rem] flex-col rounded-lg border px-2.5 py-2 ${
+        highlight
+          ? 'border-primary-300 bg-primary-50/50'
+          : 'border-gray-200 bg-white'
+      }`}
+      title={`${STAGE_LABELS[kind]} ${formatMsLocal(ms)}`}
+    >
+      <span className={`text-[10px] font-semibold uppercase tracking-wide ${highlight ? 'text-primary-800' : 'text-gray-400'}`}>
+        {STAGE_LABELS[kind]}
+      </span>
+      <span className={`mt-0.5 text-sm font-semibold tabular-nums ${highlight ? 'text-primary-900' : 'text-gray-800'}`}>
+        {formatMsLocal(ms)}
+      </span>
+    </div>
+  )
+}
+
+export default function TraceWaterfall({ rows }: { rows: WaterfallRow[] }) {
+  if (rows.length === 0) return null
+
+  const turnTotal = (row: WaterfallRow) => {
+    const segments = turnSegments(row)
+    return row.totalMs ?? (segments.length > 0 ? segments.reduce((s, p) => s + p.ms, 0) : 0)
+  }
+
+  const maxTotal = Math.max(...rows.map(turnTotal), 1)
+
+  return (
+    <div className="overflow-hidden rounded-lg border border-primary-200 bg-white">
+      <div className="border-b border-gray-100 bg-gray-50/60 px-4 py-2.5">
+        <p className="text-xs text-gray-500">
+          Per-turn pipeline flow — stages run left to right in order
+        </p>
+      </div>
+
+      <div className="divide-y divide-gray-100">
+        {rows.map((row) => {
+          const segments = turnSegments(row)
+          const total = turnTotal(row)
+          const widthPct = total > 0 ? Math.max((total / maxTotal) * 100, 12) : 12
+
+          return (
+            <div key={row.turnNumber} className="px-4 py-4">
+              <div className="mb-3 flex flex-wrap items-center justify-between gap-3">
+                <div className="flex flex-wrap items-center gap-2">
+                  <span className="text-xs font-semibold uppercase tracking-wide text-gray-400">
+                    Turn {row.turnNumber}
+                  </span>
+                  <TurnSignalBadges
+                    talkOver={row.talkOver}
+                    interrupted={row.interrupted}
+                    incomplete={row.incomplete}
+                  />
+                </div>
+                <span className="text-sm font-semibold tabular-nums text-gray-900">
+                  {formatMsLocal(total)}
+                  <span className="ml-1 text-xs font-normal text-gray-400">total</span>
+                </span>
+              </div>
+
+              {segments.length === 0 ? (
+                <p className="text-xs text-gray-400">No stage timing</p>
+              ) : (
+                <div className="flex flex-wrap items-center gap-1.5 sm:gap-2">
+                  {segments.map((seg, idx) => (
+                    <div key={seg.kind} className="flex items-center gap-1.5 sm:gap-2">
+                      {idx > 0 && <ArrowRight className="h-3.5 w-3.5 shrink-0 text-gray-300" aria-hidden />}
+                      <StageNode kind={seg.kind} ms={seg.ms} />
+                    </div>
+                  ))}
+                </div>
+              )}
+
+              <div className="mt-3 h-1 overflow-hidden rounded-full bg-gray-100">
+                <div
+                  className="h-full rounded-full bg-gray-300"
+                  style={{ width: `${widthPct}%` }}
+                  title={`${formatMsLocal(total)} of ${formatMsLocal(maxTotal)} max`}
+                />
+              </div>
+            </div>
+          )
+        })}
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/traceUtils.ts b/frontend/src/components/call-recordings/traceUtils.ts
new file mode 100644
index 00000000..2aa57bff
--- /dev/null
+++ b/frontend/src/components/call-recordings/traceUtils.ts
@@ -0,0 +1,96 @@
+export type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
+export type PipelineMode = 's2s' | 'stt_llm_tts'
+
+export interface TraceTurnLike {
+  turn_number: number
+  sut_response_latency_ms?: number | null
+  talk_over?: boolean
+  stt_ttfb_ms?: number | null
+  llm_ttfb_ms?: number | null
+  tts_ttfb_ms?: number | null
+  s2s_ttfb_ms?: number | null
+  caller_stream_complete_at?: number | null
+  sut_speech_start_at?: number | null
+  sut_speech_stop_at?: number | null
+  extra?: {
+    was_interrupted?: boolean
+    pipeline_mode?: PipelineMode
+  }
+}
+
+export interface OtelSpanLike {
+  trace_id: string
+  span_id: string
+  parent_span_id?: string | null
+  name: string
+  start_time_unix_nano?: number | null
+  end_time_unix_nano?: number | null
+  attributes?: Record<string, unknown>
+  events?: Array<{ name?: string; attributes?: Record<string, unknown> }>
+}
+
+export const FAILURE_FLAG_LABELS: Record<string, string> = {
+  no_turns: 'No turns',
+  high_latency: 'High latency',
+}
+
+export const PIPELINE_MODE_LABELS: Record<PipelineMode, string> = {
+  s2s: 'S2S',
+  stt_llm_tts: 'STT · LLM · TTS',
+}
+
+export function formatMs(value?: number | null): string {
+  if (value == null || Number.isNaN(value)) return '—'
+  return `${Math.round(value)}ms`
+}
+
+export function formatCallDuration(startedAt?: string | null, endedAt?: string | null): string | null {
+  if (!startedAt || !endedAt) return null
+  const ms = new Date(endedAt).getTime() - new Date(startedAt).getTime()
+  if (ms < 0) return null
+  const sec = Math.round(ms / 1000)
+  if (sec < 60) return `${sec}s`
+  const m = Math.floor(sec / 60)
+  const s = sec % 60
+  return `${m}m ${s}s`
+}
+
+export function sessionPipelineMode(turns: TraceTurnLike[]): PipelineMode | null {
+  if (turns.some((t) => t.extra?.pipeline_mode === 's2s' || (t.s2s_ttfb_ms != null && t.s2s_ttfb_ms > 0))) {
+    return 's2s'
+  }
+  if (turns.some((t) => t.stt_ttfb_ms || t.llm_ttfb_ms || t.tts_ttfb_ms)) return 'stt_llm_tts'
+  return null
+}
+
+export function isTurnIncomplete(turn: TraceTurnLike, mode: PipelineMode | null): boolean {
+  const hasData =
+    turn.sut_response_latency_ms != null ||
+    turn.stt_ttfb_ms != null ||
+    turn.llm_ttfb_ms != null ||
+    turn.tts_ttfb_ms != null ||
+    turn.s2s_ttfb_ms != null
+  if (!hasData) return false
+
+  if (mode === 's2s') {
+    return turn.sut_response_latency_ms != null && (turn.s2s_ttfb_ms == null || turn.s2s_ttfb_ms <= 0)
+  }
+  if (turn.sut_response_latency_ms == null) return false
+  const missingLlm = turn.llm_ttfb_ms == null || turn.llm_ttfb_ms <= 0
+  const missingStages =
+    (turn.stt_ttfb_ms == null || turn.stt_ttfb_ms <= 0) &&
+    (turn.tts_ttfb_ms == null || turn.tts_ttfb_ms <= 0)
+  return missingLlm || missingStages
+}
+
+export function spanHasError(span: OtelSpanLike): boolean {
+  const attrs = span.attributes ?? {}
+  const status = String(attrs['otel.status_code'] ?? attrs['status'] ?? '').toUpperCase()
+  if (status === 'ERROR') return true
+  if (attrs['error'] === true) return true
+  if (attrs['exception.type'] || attrs['exception.message']) return true
+  return (span.events ?? []).some((e) => {
+    const n = String(e.name ?? '').toLowerCase()
+    return n.includes('exception') || n.includes('error')
+  })
+}
diff --git a/frontend/src/lib/workspaceNavigation.ts b/frontend/src/lib/workspaceNavigation.ts
index 27f6fb84..04d09dd2 100644
--- a/frontend/src/lib/workspaceNavigation.ts
+++ b/frontend/src/lib/workspaceNavigation.ts
@@ -19,6 +19,7 @@ const WORKSPACE_DETAIL_REDIRECTS: Array<{ pattern: RegExp; parent: string }> = [
   { pattern: /^\/playground\/call-recordings\/[^/]+$/, parent: '/playground' },
   { pattern: /^\/playground\/test-agent-results\/[^/]+$/, parent: '/playground' },
   { pattern: /^\/observability\/calls\/[^/]+$/, parent: '/observability/calls' },
+  { pattern: /^\/call-traces\/[^/]+$/, parent: '/call-traces' },
   { pattern: /^\/judge-alignment\/datasets\/[^/]+$/, parent: '/judge-alignment' },
   { pattern: /^\/prompt-partials\/[^/]+$/, parent: '/prompt-partials' },
 ]
diff --git a/frontend/src/pages/test-insights/CallTraceDetail.tsx b/frontend/src/pages/test-insights/CallTraceDetail.tsx
new file mode 100644
index 00000000..31d86cc5
--- /dev/null
+++ b/frontend/src/pages/test-insights/CallTraceDetail.tsx
@@ -0,0 +1,7 @@
+import { Navigate, useParams } from 'react-router-dom'
+
+export default function CallTraceDetail() {
+  const { traceId } = useParams<{ traceId: string }>()
+  if (!traceId) return <Navigate to="/call-traces" replace />
+  return <Navigate to={`/call-traces?trace=${traceId}`} replace />
+}
diff --git a/frontend/src/pages/test-insights/TestInsights.tsx b/frontend/src/pages/test-insights/TestInsights.tsx
index f60d1a12..9ff8b91c 100644
--- a/frontend/src/pages/test-insights/TestInsights.tsx
+++ b/frontend/src/pages/test-insights/TestInsights.tsx
@@ -1,4 +1,4 @@
-import { useEffect, useRef, useState } from 'react'
+import { useEffect, useMemo, useRef, useState } from 'react'
 import { useQuery, useQueryClient } from '@tanstack/react-query'
 import { useSearchParams } from 'react-router-dom'
 import {
@@ -7,18 +7,26 @@ import {
   Check,
   Copy,
   Eye,
-  Globe,
+  Loader,
   RefreshCw,
+  Route,
 } from 'lucide-react'
 import { apiClient } from '../../lib/api'
-import SyntheticCallTracePanel from '../../components/call-recordings/SyntheticCallTracePanel'
 import Button from '../../components/Button'
+import TraceDetailDrawer from '../../components/call-recordings/TraceDetailDrawer'
+import { FAILURE_FLAG_LABELS } from '../../components/call-recordings/traceUtils'
 import { useWorkspaceStore } from '../../store/workspaceStore'
 
 type Tab = 'runs' | 'setup'
+type StatusFilter = 'all' | 'open' | 'closed'
 
-const tracesQueryKey = (workspaceId: string | null) =>
-  ['observability-traces', workspaceId] as const
+const PAGE_SIZE = 25
+
+const tracesQueryKey = (
+  workspaceId: string | null,
+  page: number,
+  status: StatusFilter,
+) => ['observability-traces', workspaceId, page, status] as const
 
 function formatWhen(iso: string): string {
   return new Date(iso).toLocaleString()
@@ -51,7 +59,7 @@ function CopyButton({ text, label }: { text: string; label?: string }) {
           () => {},
         )
       }}
-      className="inline-flex items-center gap-1 text-xs font-medium text-indigo-600 hover:text-indigo-800"
+      className="inline-flex items-center gap-1 text-xs font-medium text-primary-600 hover:text-primary-800"
       title={label ?? 'Copy'}
     >
       {copied ? <Check className="w-3.5 h-3.5" /> : <Copy className="w-3.5 h-3.5" />}
@@ -60,86 +68,17 @@ function CopyButton({ text, label }: { text: string; label?: string }) {
   )
 }
 
-function StatusBadge({ status }: { status: string }) {
+function StatusLabel({ status }: { status: string }) {
   const open = status === 'open'
   const label = status === 'finalized' ? 'closed' : status
   return (
-    <span
-      className={`inline-flex items-center gap-1.5 text-xs font-medium rounded-full px-2.5 py-0.5 capitalize ${
-        open
-          ? 'bg-amber-50 text-amber-700 border border-amber-200'
-          : 'bg-emerald-50 text-emerald-700 border border-emerald-200'
-      }`}
-    >
-      <span className={`h-1.5 w-1.5 rounded-full ${open ? 'bg-amber-500 animate-pulse' : 'bg-emerald-500'}`} />
+    <span className={`text-sm capitalize ${open ? 'text-amber-700 font-medium' : 'text-gray-600'}`}>
+      {open && <span className="inline-block w-1.5 h-1.5 rounded-full bg-amber-500 mr-1.5 align-middle" />}
       {label}
     </span>
   )
 }
 
-function TransportBadge({ transport }: { transport?: string }) {
-  const t = (transport || 'webrtc').toLowerCase()
-  if (t !== 'webrtc') {
-    return (
-      <span className="inline-flex items-center gap-1 text-[11px] font-medium rounded-md px-2 py-0.5 border bg-gray-50 text-gray-600 border-gray-100 capitalize">
-        {t}
-      </span>
-    )
-  }
-  return (
-    <span className="inline-flex items-center gap-1 text-[11px] font-medium rounded-md px-2 py-0.5 border bg-blue-50 text-blue-700 border-blue-100">
-      <Globe className="w-3 h-3" />
-      WebRTC
-    </span>
-  )
-}
-
-function TraceDetailShell({
-  traceId,
-  onClose,
-  layout,
-}: {
-  traceId: string
-  onClose: () => void
-  layout: 'desktop' | 'mobile'
-}) {
-  useEffect(() => {
-    if (layout !== 'mobile') return
-    const onKey = (e: KeyboardEvent) => {
-      if (e.key === 'Escape') onClose()
-    }
-    window.addEventListener('keydown', onKey)
-    return () => window.removeEventListener('keydown', onKey)
-  }, [layout, onClose])
-
-  if (layout === 'mobile') {
-    return (
-      <>
-        <button
-          type="button"
-          className="fixed inset-0 z-40 bg-gray-900/50 backdrop-blur-[1px]"
-          aria-label="Close trace detail"
-          onClick={onClose}
-        />
-        <div
-          role="dialog"
-          aria-modal="true"
-          aria-label="Call trace detail"
-          className="fixed inset-y-0 right-0 z-50 w-full max-w-lg bg-white shadow-2xl flex flex-col border-l border-gray-200"
-        >
-          <SyntheticCallTracePanel traceId={traceId} onClose={onClose} />
-        </div>
-      </>
-    )
-  }
-
-  return (
-    <div className="flex flex-1 min-w-0 min-h-[calc(100vh-11rem)] rounded-xl border border-gray-200 bg-white shadow-sm overflow-hidden">
-      <SyntheticCallTracePanel traceId={traceId} onClose={onClose} />
-    </div>
-  )
-}
-
 export default function TestInsights() {
   const queryClient = useQueryClient()
   const activeWorkspaceId = useWorkspaceStore((s) => s.activeWorkspaceId)
@@ -148,10 +87,12 @@ export default function TestInsights() {
   const traceFromUrl = searchParams.get('trace')
   const [tab, setTab] = useState<Tab>('runs')
   const [selectedTraceId, setSelectedTraceId] = useState<string | null>(traceFromUrl)
+  const [page, setPage] = useState(0)
+  const [statusFilter, setStatusFilter] = useState<StatusFilter>('all')
+  const [searchQuery, setSearchQuery] = useState('')
   const prevWorkspaceRef = useRef<string | null>(null)
-  const [isMobileView, setIsMobileView] = useState(() =>
-    typeof window !== 'undefined' ? window.matchMedia('(max-width: 1023px)').matches : false,
-  )
+
+  const apiStatus = statusFilter === 'all' ? undefined : statusFilter
 
   const {
     data: listData,
@@ -162,17 +103,19 @@ export default function TestInsights() {
     refetch: refetchTraces,
     dataUpdatedAt,
   } = useQuery({
-    queryKey: tracesQueryKey(activeWorkspaceId),
-    queryFn: () => apiClient.listSyntheticCallTraces({ limit: 100 }),
+    queryKey: tracesQueryKey(activeWorkspaceId, page, statusFilter),
+    queryFn: () =>
+      apiClient.listSyntheticCallTraces({
+        skip: page * PAGE_SIZE,
+        limit: PAGE_SIZE,
+        status: apiStatus,
+      }),
     enabled: tab === 'runs' && Boolean(activeWorkspaceId),
     retry: false,
-    refetchOnWindowFocus: true,
-    staleTime: 30_000,
-    refetchInterval: (query) => {
-      const items = query.state.data?.items ?? []
-      const hasOpen = items.some((t: { status?: string }) => t.status === 'open')
-      return hasOpen ? 15_000 : false
-    },
+    staleTime: 0,
+    refetchOnMount: 'always',
+    refetchOnWindowFocus: false,
+    refetchOnReconnect: false,
   })
 
   const { data: setup, isLoading: loadingSetup } = useQuery({
@@ -184,6 +127,30 @@ export default function TestInsights() {
   })
 
   const traces = listData?.items ?? []
+  const totalCount = listData?.total ?? 0
+  const totalPages = Math.max(1, Math.ceil(totalCount / PAGE_SIZE))
+
+  const filteredTraces = useMemo(() => {
+    const q = searchQuery.trim().toLowerCase()
+    if (!q) return traces
+    return traces.filter((t: { call_short_id?: string; id?: string; transport?: string }) => {
+      const id = (t.call_short_id ?? t.id ?? '').toLowerCase()
+      const transport = (t.transport ?? '').toLowerCase()
+      return id.includes(q) || transport.includes(q)
+    })
+  }, [traces, searchQuery])
+
+  const summaryStats = useMemo(() => {
+    const open = traces.filter((t: { status?: string }) => t.status === 'open').length
+    const closed = traces.filter(
+      (t: { status?: string }) => t.status === 'closed' || t.status === 'finalized',
+    ).length
+    const withLatency = traces.filter(
+      (t: { response_latency_p50_ms?: number | null }) => t.response_latency_p50_ms != null,
+    ).length
+    return { open, closed, withLatency }
+  }, [traces])
+
   const listErrorMessage =
     listError && listErrorDetail instanceof Error
       ? listErrorDetail.message
@@ -191,9 +158,6 @@ export default function TestInsights() {
         ? 'Could not load traces'
         : null
 
-  const openTraces = traces.filter((t: any) => t.status === 'open').length
-  const withLatency = traces.filter((t: any) => t.response_latency_p50_ms != null).length
-
   const envBlock = setup?.one_time_env_vars
     ? Object.entries(setup.one_time_env_vars)
         .map(([k, v]) => `${k}=${v}`)
@@ -218,15 +182,12 @@ export default function TestInsights() {
 
   const handleRefresh = () => {
     void refetchTraces()
-    if (selectedTraceId) {
-      void queryClient.invalidateQueries({
-        queryKey: ['synthetic-call-trace', selectedTraceId, 'by-trace'],
-      })
-    }
+    void queryClient.invalidateQueries({ queryKey: ['observability-traces'] })
   }
 
   useEffect(() => {
     if (prevWorkspaceRef.current !== null && prevWorkspaceRef.current !== activeWorkspaceId) {
+      setPage(0)
       setSelectedTraceId(null)
       setSearchParams(
         (prev) => {
@@ -252,69 +213,32 @@ export default function TestInsights() {
       .getSyntheticCallTraceForResult(resultFromUrl)
       .then((trace) => {
         if (!cancelled && trace?.id) {
-          setSelectedTraceId(trace.id)
-          setSearchParams(
-            (prev) => {
-              const next = new URLSearchParams(prev)
-              next.set('trace', trace.id)
-              next.delete('result')
-              return next
-            },
-            { replace: true },
-          )
+          openTrace(trace.id)
         }
       })
       .catch(() => {})
     return () => {
       cancelled = true
     }
-  }, [resultFromUrl, traceFromUrl, setSearchParams])
-
-  const drawerOpen = Boolean(selectedTraceId)
-
-  useEffect(() => {
-    const mq = window.matchMedia('(max-width: 1023px)')
-    const onChange = () => setIsMobileView(mq.matches)
-    onChange()
-    mq.addEventListener('change', onChange)
-    return () => mq.removeEventListener('change', onChange)
-  }, [])
-
-  useEffect(() => {
-    if (!drawerOpen || !isMobileView) return
-    const prev = document.body.style.overflow
-    document.body.style.overflow = 'hidden'
-    return () => {
-      document.body.style.overflow = prev
-    }
-  }, [drawerOpen, isMobileView])
+  }, [resultFromUrl, traceFromUrl])
 
   const lastUpdatedLabel =
     dataUpdatedAt > 0 ? `Updated ${new Date(dataUpdatedAt).toLocaleTimeString()}` : null
 
-  const totalCount = listData?.total ?? traces.length
-  const showingPartial = totalCount > traces.length
-
   return (
     <div className="space-y-6">
-      <div className="flex flex-col sm:flex-row sm:items-start sm:justify-between gap-4">
+      <div className="flex flex-col sm:flex-row sm:items-center sm:justify-between gap-4">
         <div>
-          <h1 className="text-2xl font-bold text-gray-900 tracking-tight">Call Traces</h1>
-          <p className="mt-1 text-sm text-gray-600 max-w-2xl leading-relaxed">
-            See how fast your voice agent responds on each turn — speech recognition, model, and
-            text-to-speech timing from a local Pipecat WebRTC call.
+          <h1 className="text-3xl font-bold text-gray-900">Call Traces</h1>
+          <p className="mt-2 text-sm text-gray-600">
+            Per-turn STT, LLM, and TTS latency from Pipecat voice agents
           </p>
         </div>
         {tab === 'runs' && (
-          <button
-            type="button"
-            onClick={handleRefresh}
-            disabled={fetchingList}
-            className="inline-flex items-center gap-2 px-3 py-2 text-sm font-medium text-gray-700 bg-white border border-gray-200 rounded-lg shadow-sm hover:bg-gray-50 disabled:opacity-50 shrink-0"
-          >
-            <RefreshCw className={`w-4 h-4 ${fetchingList ? 'animate-spin' : ''}`} />
+          <Button variant="outline" onClick={handleRefresh} disabled={fetchingList}>
+            <RefreshCw className={`w-4 h-4 mr-2 ${fetchingList ? 'animate-spin' : ''}`} />
             Refresh
-          </button>
+          </Button>
         )}
       </div>
 
@@ -324,7 +248,7 @@ export default function TestInsights() {
           onClick={() => setTab('runs')}
           className={`px-4 py-2.5 text-sm font-medium border-b-2 -mb-px transition-colors ${
             tab === 'runs'
-              ? 'border-indigo-600 text-indigo-600'
+              ? 'border-primary-600 text-primary-700'
               : 'border-transparent text-gray-500 hover:text-gray-700'
           }`}
         >
@@ -336,7 +260,7 @@ export default function TestInsights() {
           onClick={() => setTab('setup')}
           className={`px-4 py-2.5 text-sm font-medium border-b-2 -mb-px transition-colors ${
             tab === 'setup'
-              ? 'border-indigo-600 text-indigo-600'
+              ? 'border-primary-600 text-primary-700'
               : 'border-transparent text-gray-500 hover:text-gray-700'
           }`}
         >
@@ -346,71 +270,56 @@ export default function TestInsights() {
       </div>
 
       {tab === 'setup' && (
-        <div className="rounded-xl border border-gray-200 bg-white shadow-sm overflow-hidden">
+        <div className="bg-white shadow rounded-lg border border-gray-200 overflow-hidden">
           {loadingSetup && <p className="p-6 text-sm text-gray-500">Loading setup…</p>}
           {setup && (
-            <div className="divide-y divide-gray-100">
-              <div className="p-6 bg-gradient-to-br from-indigo-50/80 to-white">
-                <h2 className="text-lg font-semibold text-gray-900">Local Pipecat WebRTC</h2>
+            <div className="divide-y divide-gray-200">
+              <div className="px-6 py-5">
+                <h2 className="text-lg font-semibold text-gray-900">Pipecat WebRTC integration</h2>
                 <p className="text-sm text-gray-600 mt-1 max-w-2xl">
-                  Run your Pipecat agent on your machine, talk in the browser, and traces show up
-                  here automatically. Full example:{' '}
-                  <code className="text-xs bg-white/80 px-1.5 py-0.5 rounded border border-gray-200">
-                    docs/examples/pipecat_multi_agent_webrtc_tracing.py
-                  </code>
+                  Run your Pipecat agent locally, connect via WebRTC, and traces appear here automatically.
                 </p>
               </div>
 
               {(setup.setup_steps ?? []).length > 0 && (
-                <div className="p-6 space-y-4">
+                <div className="px-6 py-5 space-y-3">
                   <h3 className="text-sm font-semibold text-gray-900">Quick start</h3>
-                  <ol className="space-y-4">
-                    {(setup.setup_steps as Array<{ title: string; detail: string }>).map(
-                      (step, idx) => (
-                        <li key={step.title} className="flex gap-4">
-                          <span className="flex h-7 w-7 shrink-0 items-center justify-center rounded-full bg-indigo-600 text-xs font-bold text-white">
-                            {idx + 1}
-                          </span>
-                          <div>
-                            <p className="text-sm font-medium text-gray-900">{step.title}</p>
-                            <p className="text-sm text-gray-600 mt-0.5 leading-relaxed">{step.detail}</p>
-                          </div>
-                        </li>
-                      ),
-                    )}
+                  <ol className="list-decimal list-inside space-y-2 text-sm text-gray-700">
+                    {(setup.setup_steps as Array<{ title: string; detail: string }>).map((step) => (
+                      <li key={step.title}>
+                        <span className="font-medium text-gray-900">{step.title}</span>
+                        {' — '}
+                        {step.detail}
+                      </li>
+                    ))}
                   </ol>
                 </div>
               )}
 
-              <div className="p-6 space-y-4 bg-gray-50/50">
-                <h3 className="text-sm font-semibold text-gray-900">Your workspace settings</h3>
-                <div className="space-y-3">
+              <div className="px-6 py-5 bg-gray-50 space-y-3">
+                <h3 className="text-sm font-semibold text-gray-900">Workspace settings</h3>
+                <div className="rounded-lg border border-gray-200 bg-white p-4">
+                  <div className="flex items-center justify-between gap-2 mb-2">
+                    <p className="text-xs font-medium text-gray-500">OTLP export URL</p>
+                    <CopyButton text={setup.otlp_endpoint} label="Copy export URL" />
+                  </div>
+                  <p className="font-mono text-xs text-gray-800 break-all">{setup.otlp_endpoint}</p>
+                </div>
+                {envBlock && (
                   <div className="rounded-lg border border-gray-200 bg-white p-4">
                     <div className="flex items-center justify-between gap-2 mb-2">
-                      <p className="text-xs font-medium text-gray-500">Trace export URL</p>
-                      <CopyButton text={setup.otlp_endpoint} label="Copy export URL" />
+                      <p className="text-xs font-medium text-gray-500">Environment variables</p>
+                      <CopyButton text={envBlock} label="Copy env block" />
                     </div>
-                    <p className="font-mono text-xs text-gray-800 break-all">{setup.otlp_endpoint}</p>
+                    <pre className="text-xs font-mono text-gray-800 whitespace-pre-wrap">{envBlock}</pre>
                   </div>
-                  {envBlock && (
-                    <div className="rounded-lg border border-gray-200 bg-white p-4">
-                      <div className="flex items-center justify-between gap-2 mb-2">
-                        <p className="text-xs font-medium text-gray-500">Add to Pipecat .env</p>
-                        <CopyButton text={envBlock} label="Copy env block" />
-                      </div>
-                      <pre className="text-xs font-mono text-gray-800 whitespace-pre-wrap">{envBlock}</pre>
-                    </div>
-                  )}
-                </div>
-                {setup.per_call_correlation?.note && (
-                  <p className="text-sm text-gray-600 leading-relaxed">{setup.per_call_correlation.note}</p>
                 )}
               </div>
 
               {setup.pipecat_python_example && (
-                <div className="p-6 space-y-2">
+                <div className="px-6 py-5 space-y-2">
                   <div className="flex items-center justify-between gap-2">
-                    <h3 className="text-sm font-semibold text-gray-900">Tracing snippet for bot.py</h3>
+                    <h3 className="text-sm font-semibold text-gray-900">Tracing snippet</h3>
                     <CopyButton text={setup.pipecat_python_example} label="Copy code" />
                   </div>
                   <pre className="text-xs font-mono bg-slate-900 text-slate-100 p-4 rounded-lg overflow-x-auto max-h-80">
@@ -424,190 +333,244 @@ export default function TestInsights() {
       )}
 
       {tab === 'runs' && !activeWorkspaceId && (
-        <div className="rounded-xl border border-amber-200 bg-amber-50 px-6 py-8 text-center text-sm text-amber-900">
+        <div className="bg-amber-50 border border-amber-200 rounded-lg px-6 py-8 text-center text-sm text-amber-900">
           Select a workspace to view call traces.
         </div>
       )}
 
       {tab === 'runs' && activeWorkspaceId && (
-        <div className="flex flex-col lg:flex-row gap-4 lg:gap-6 lg:items-start">
-          <div className={`min-w-0 ${selectedTraceId ? 'lg:w-[44%] lg:shrink-0' : 'w-full'}`}>
-            {traces.length > 0 && (
-              <div className="grid grid-cols-3 gap-3 mb-4">
-                <div className="rounded-xl border border-gray-200 bg-white px-4 py-3 shadow-sm">
-                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">Total</p>
-                  <p className="text-2xl font-semibold text-gray-900 tabular-nums">{totalCount}</p>
-                  {showingPartial && (
-                    <p className="text-[10px] text-gray-400 mt-0.5">Showing {traces.length}</p>
-                  )}
-                </div>
-                <div className="rounded-xl border border-gray-200 bg-white px-4 py-3 shadow-sm">
-                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">In progress</p>
-                  <p className="text-2xl font-semibold text-amber-600 tabular-nums">{openTraces}</p>
-                </div>
-                <div className="rounded-xl border border-gray-200 bg-white px-4 py-3 shadow-sm">
-                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">With timing</p>
-                  <p className="text-2xl font-semibold text-indigo-600 tabular-nums">{withLatency}</p>
-                </div>
+        <>
+          {!loadingList && traces.length > 0 && (
+            <div className="grid grid-cols-2 md:grid-cols-4 gap-3">
+              <div className="rounded-lg border border-primary-400 bg-primary-50/40 px-4 py-3 shadow-sm">
+                <p className="text-[10px] font-semibold uppercase tracking-wider text-primary-800/70">Total</p>
+                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{totalCount}</p>
               </div>
-            )}
+              <div className="rounded-lg border border-gray-200 bg-white px-4 py-3 shadow-sm">
+                <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">Open</p>
+                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{summaryStats.open}</p>
+              </div>
+              <div className="rounded-lg border border-gray-200 bg-white px-4 py-3 shadow-sm">
+                <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">Closed</p>
+                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{summaryStats.closed}</p>
+              </div>
+              <div className="rounded-lg border border-gray-200 bg-white px-4 py-3 shadow-sm">
+                <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">With latency</p>
+                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{summaryStats.withLatency}</p>
+              </div>
+            </div>
+          )}
 
-            <div className="bg-white rounded-xl border border-gray-200 shadow-sm overflow-hidden">
-              <div className="px-4 py-3 border-b border-gray-100 bg-gradient-to-r from-gray-50 to-white flex justify-between items-center gap-2">
-                <div>
-                  <h2 className="text-sm font-semibold text-gray-900">Recent calls</h2>
-                  {lastUpdatedLabel && (
-                    <p className="text-xs text-gray-500 mt-0.5">{lastUpdatedLabel}</p>
-                  )}
+          <div className="bg-white shadow rounded-lg overflow-hidden border border-gray-200">
+            <div className="px-6 py-4 border-b border-gray-200 flex flex-col sm:flex-row sm:items-center sm:justify-between gap-4">
+              <div className="flex items-center gap-3 flex-wrap">
+                <div className="flex items-center gap-2">
+                  <Route className="h-5 w-5 text-gray-500" />
+                  <h2 className="text-lg font-semibold text-gray-900">Trace sessions</h2>
                 </div>
+                <div className="flex items-center gap-1">
+                  {(
+                    [
+                      { key: 'all' as const, label: 'All' },
+                      { key: 'open' as const, label: 'Open' },
+                      { key: 'closed' as const, label: 'Closed' },
+                    ] as const
+                  ).map(({ key, label }) => (
+                    <button
+                      key={key}
+                      type="button"
+                      onClick={() => {
+                        setStatusFilter(key)
+                        setPage(0)
+                      }}
+                      className={`px-3 py-1.5 text-xs font-medium rounded-lg transition-colors ${
+                        statusFilter === key
+                          ? 'bg-primary-100 text-primary-800 border border-primary-300'
+                          : 'text-gray-600 hover:bg-gray-100 border border-transparent'
+                      }`}
+                    >
+                      {label}
+                    </button>
+                  ))}
+                </div>
+              </div>
+              <div className="flex items-center gap-3">
+                <input
+                  type="search"
+                  placeholder="Search call ID…"
+                  value={searchQuery}
+                  onChange={(e) => setSearchQuery(e.target.value)}
+                  className="text-sm border border-gray-300 rounded-lg px-3 py-1.5 w-40 sm:w-48 focus:ring-primary-500 focus:border-primary-500"
+                />
+                {lastUpdatedLabel && (
+                  <span className="text-xs text-gray-500 hidden sm:inline">{lastUpdatedLabel}</span>
+                )}
               </div>
+            </div>
 
-              {loadingList && !listData && (
-                <p className="p-10 text-sm text-gray-500 text-center">Loading traces…</p>
-              )}
+            {loadingList && !listData && (
+              <div className="p-12 text-center">
+                <Loader className="w-6 h-6 text-primary-500 animate-spin mx-auto mb-3" />
+                <p className="text-sm text-gray-500">Loading traces…</p>
+              </div>
+            )}
 
-              {listErrorMessage && (
-                <div className="p-4 text-sm text-red-800 bg-red-50 border-b border-red-100 space-y-2">
-                  <p>Could not load traces: {listErrorMessage}</p>
-                  <button
-                    type="button"
-                    onClick={() => refetchTraces()}
-                    className="text-xs font-medium text-red-900 underline"
-                  >
-                    Retry
-                  </button>
-                </div>
-              )}
+            {listErrorMessage && (
+              <div className="p-4 text-sm text-red-800 bg-red-50 border-b border-red-100">
+                Could not load traces: {listErrorMessage}
+              </div>
+            )}
 
-              {!loadingList && !listError && activeWorkspaceId && traces.length === 0 && (
-                <div className="p-12 text-center text-sm text-gray-600 space-y-4">
-                  <div className="w-12 h-12 rounded-full bg-indigo-50 flex items-center justify-center mx-auto">
-                    <Activity className="w-6 h-6 text-indigo-400" />
-                  </div>
-                  <div>
-                    <p className="font-medium text-gray-900">No calls traced yet</p>
-                    <p className="mt-1 max-w-sm mx-auto">
-                      Connect your Pipecat agent, run a short WebRTC call at{' '}
-                      <code className="text-xs bg-gray-100 px-1 rounded">localhost:7860/client</code>,
-                      then refresh this list.
-                    </p>
-                  </div>
-                  <button
-                    type="button"
-                    onClick={() => setTab('setup')}
-                    className="text-indigo-600 hover:text-indigo-800 text-sm font-medium"
-                  >
-                    Connect Pipecat →
-                  </button>
-                </div>
-              )}
+            {!loadingList && !listError && traces.length === 0 && (
+              <div className="p-12 text-center text-sm text-gray-600">
+                <p className="font-medium text-gray-900 mb-1">No traces yet</p>
+                <p className="mb-4">Connect Pipecat and run a WebRTC call to see traces here.</p>
+                <button
+                  type="button"
+                  onClick={() => setTab('setup')}
+                  className="text-primary-600 hover:text-primary-800 font-medium"
+                >
+                  Connect Pipecat →
+                </button>
+              </div>
+            )}
 
-              {traces.length > 0 && (
+            {filteredTraces.length > 0 && (
+              <>
                 <div className="overflow-x-auto">
-                  <table className="min-w-full divide-y divide-gray-100">
-                    <thead>
-                      <tr className="bg-gray-50/80">
-                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                  <table className="min-w-full divide-y divide-gray-200">
+                    <thead className="bg-gray-50">
+                      <tr>
+                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
                           Call ID
                         </th>
-                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
-                          Type
+                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                          Transport
                         </th>
-                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
                           Status
                         </th>
-                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
+                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
                           Turns
                         </th>
-                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
-                          Median
+                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                          Median (p50)
                         </th>
-                        <th className="px-4 py-3 text-left text-[10px] font-semibold text-gray-500 uppercase tracking-wider">
-                          When
+                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                          Started
+                        </th>
+                        <th className="px-6 py-3 text-right text-xs font-medium text-gray-500 uppercase tracking-wider">
+                          Actions
                         </th>
-                        <th className="px-4 py-3" />
                       </tr>
                     </thead>
-                    <tbody className="divide-y divide-gray-100">
-                      {traces.map((trace: any) => {
-                        const selected = selectedTraceId === trace.id
-                        return (
-                          <tr
-                            key={trace.id}
-                            role="button"
-                            tabIndex={0}
-                            className={`cursor-pointer transition-colors ${
-                              selected
-                                ? 'bg-indigo-50/70 ring-1 ring-inset ring-indigo-200'
-                                : 'hover:bg-gray-50/80'
-                            }`}
-                            onClick={() => openTrace(trace.id)}
-                            onKeyDown={(e) => {
-                              if (e.key === 'Enter' || e.key === ' ') {
-                                e.preventDefault()
-                                openTrace(trace.id)
-                              }
-                            }}
-                          >
-                            <td className="px-4 py-3.5 whitespace-nowrap">
-                              <span className="font-mono font-semibold text-indigo-600">
-                                #{trace.call_short_id || trace.id.slice(0, 8)}
-                              </span>
-                            </td>
-                            <td className="px-4 py-3.5 whitespace-nowrap">
-                              <TransportBadge transport={trace.transport} />
-                            </td>
-                            <td className="px-4 py-3.5 whitespace-nowrap">
-                              <StatusBadge status={trace.status} />
-                            </td>
-                            <td className="px-4 py-3.5 whitespace-nowrap text-sm text-gray-700 tabular-nums font-medium">
-                              {trace.turn_count}
-                            </td>
-                            <td className="px-4 py-3.5 whitespace-nowrap text-sm tabular-nums">
-                              {trace.response_latency_p50_ms != null ? (
-                                <span className="font-medium text-gray-900">
-                                  {Math.round(trace.response_latency_p50_ms)}
-                                  <span className="text-gray-400 font-normal ml-0.5">ms</span>
+                    <tbody className="bg-white divide-y divide-gray-200">
+                      {filteredTraces.map((trace: any) => (
+                        <tr
+                          key={trace.id}
+                          className={`transition-colors cursor-pointer ${
+                            selectedTraceId === trace.id
+                              ? 'bg-primary-50/60 hover:bg-primary-50/80'
+                              : 'hover:bg-gray-50'
+                          }`}
+                          onClick={() => openTrace(trace.id)}
+                        >
+                          <td className="px-6 py-4 whitespace-nowrap">
+                            <span className="font-mono font-semibold text-primary-600">
+                              #{trace.call_short_id || trace.id.slice(0, 8)}
+                            </span>
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 capitalize">
+                            {trace.transport ?? 'webrtc'}
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap">
+                            <div className="flex flex-col gap-1">
+                              <StatusLabel status={trace.status} />
+                              {(trace.failure_flags as string[] | undefined)?.map((flag) => (
+                                <span
+                                  key={flag}
+                                  className="inline-flex w-fit rounded-md border border-rose-200 bg-rose-50 px-1.5 py-0.5 text-[10px] font-medium text-rose-800"
+                                >
+                                  {FAILURE_FLAG_LABELS[flag] ?? flag}
                                 </span>
-                              ) : (
-                                <span className="text-gray-300">—</span>
-                              )}
-                            </td>
-                            <td className="px-4 py-3.5 whitespace-nowrap text-sm text-gray-500">
-                              <span title={formatWhen(trace.started_at)}>{formatRelative(trace.started_at)}</span>
-                            </td>
-                            <td
-                              className="px-4 py-3.5 whitespace-nowrap text-right"
-                              onClick={(e) => e.stopPropagation()}
+                              ))}
+                            </div>
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 tabular-nums">
+                            {trace.turn_count}
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap text-sm tabular-nums font-medium text-primary-800">
+                            {trace.response_latency_p50_ms != null
+                              ? `${Math.round(trace.response_latency_p50_ms)} ms`
+                              : '—'}
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-500">
+                            <span title={formatWhen(trace.started_at)}>{formatRelative(trace.started_at)}</span>
+                          </td>
+                          <td
+                            className="px-6 py-4 whitespace-nowrap text-right"
+                            onClick={(e) => e.stopPropagation()}
+                          >
+                            <Button
+                              variant="ghost"
+                              size="sm"
+                              onClick={() => openTrace(trace.id)}
+                              leftIcon={<Eye className="w-4 h-4" />}
                             >
-                              <Button
-                                variant="ghost"
-                                size="sm"
-                                onClick={() => openTrace(trace.id)}
-                                leftIcon={<Eye className="w-4 h-4" />}
-                              >
-                                View
-                              </Button>
-                            </td>
-                          </tr>
-                        )
-                      })}
+                              View
+                            </Button>
+                          </td>
+                        </tr>
+                      ))}
                     </tbody>
                   </table>
                 </div>
-              )}
-            </div>
-          </div>
 
-          {selectedTraceId && !isMobileView && (
-            <TraceDetailShell traceId={selectedTraceId} onClose={closeTrace} layout="desktop" />
-          )}
-        </div>
-      )}
+                {totalPages > 1 && (
+                  <div className="px-6 py-4 border-t border-gray-200 flex items-center justify-between text-sm">
+                    <p className="text-gray-500">
+                      Page {page + 1} of {totalPages} · {totalCount} traces
+                    </p>
+                    <div className="flex gap-2">
+                      <Button
+                        variant="outline"
+                        size="sm"
+                        disabled={page === 0}
+                        onClick={() => setPage((p) => Math.max(0, p - 1))}
+                      >
+                        Previous
+                      </Button>
+                      <Button
+                        variant="outline"
+                        size="sm"
+                        disabled={page >= totalPages - 1}
+                        onClick={() => setPage((p) => p + 1)}
+                      >
+                        Next
+                      </Button>
+                    </div>
+                  </div>
+                )}
+              </>
+            )}
 
-      {drawerOpen && selectedTraceId && isMobileView && (
-        <TraceDetailShell traceId={selectedTraceId} onClose={closeTrace} layout="mobile" />
+            {traces.length > 0 && filteredTraces.length === 0 && (
+              <div className="p-12 text-center text-sm text-gray-500">
+                No traces match your search.{' '}
+                <button type="button" onClick={() => setSearchQuery('')} className="text-primary-600 font-medium">
+                  Clear search
+                </button>
+              </div>
+            )}
+          </div>
+        </>
       )}
+
+      <TraceDetailDrawer
+        traceId={selectedTraceId}
+        open={Boolean(selectedTraceId)}
+        onClose={closeTrace}
+      />
     </div>
   )
 }

From 4bf951aba68cfa4382261304008a392a6e0eac78 Mon Sep 17 00:00:00 2001
From: M Sami <m.sami.bhat.dev@gmail.com>
Date: Fri, 4 Sep 2026 00:30:05 +0530
Subject: [PATCH 55/55] feat(api): enhance Agent trace ingestion with pipecat
 otel integration.

---
 app/api/v1/routes/integrations.py             | 1007 ++---
 app/api/v1/routes/playground.py               |  295 +-
 app/api/v1/routes/synthetic_traces.py         |    1 +
 app/api/v1/routes/voice_agent.py              | 2184 +++++------
 app/app_factory.py                            |  512 +--
 app/cli.py                                    | 3254 +++++++++--------
 app/models/schemas.py                         |   14 +
 app/models/synthetic_trace_schemas.py         |    1 +
 app/services/media_urls.py                    |  189 +-
 .../playground/post_call_processing.py        |  336 +-
 app/services/playground/provider_call_logs.py |  213 ++
 .../internal_otlp_exporter.py                 |  126 +
 .../synthetic_traces/trace_service.py         |   37 +
 app/services/voice_agent/bot_fast_api.py      |  880 ++---
 .../voice_agent/playground_tracing.py         |  146 +
 app/services/voice_agent/voice_bundle.py      |    3 +
 app/services/voice_providers/base.py          |  244 +-
 app/services/voice_providers/elevenlabs.py    |  670 ++--
 app/services/voice_providers/retell.py        |  858 ++---
 app/services/voice_providers/vapi.py          |   36 +-
 .../voice_providers/vapi_recording.py         |  116 +-
 .../voice_providers/voice_agent_catalog.py    |   81 +
 docker/Dockerfile.api                         |  154 +-
 frontend/.gitignore                           |   52 +-
 frontend/src/App.tsx                          |    9 +-
 frontend/src/components/Layout.tsx            |    3 +-
 frontend/src/components/VoiceAgent.tsx        | 1507 ++++----
 .../call-recordings/CallEventTimeline.tsx     |  158 +
 .../call-recordings/CallWaveformPlayer.tsx    |  767 ++++
 .../EvaluatorCallDetailPanel.tsx              |  251 ++
 .../ObservabilityCallDetailPanel.tsx          |  604 +++
 .../ProviderCallTracePanel.tsx                |  112 +
 .../call-recordings/ProviderMetricsPanels.tsx |  364 ++
 .../call-recordings/RetellCallDetails.tsx     |  777 ++--
 .../SyntheticCallTracePanel.tsx               |  292 +-
 .../TestVoiceAgentResultDetails.tsx           | 1686 ++++-----
 .../call-recordings/TraceCharts.tsx           |   13 +-
 .../call-recordings/TraceDetailDrawer.tsx     |  196 +-
 .../call-recordings/VapiCallDetails.tsx       |  126 +-
 .../VoiceAiCallDetailPanel.tsx                |  216 ++
 .../call-recordings/callTimelineUtils.test.ts |   71 +
 .../call-recordings/callTimelineUtils.ts      |  519 +++
 frontend/src/lib/api.ts                       |   68 +-
 frontend/src/lib/callDetailTheme.ts           |   34 +
 frontend/src/lib/callRecordingQuery.ts        |   83 +
 frontend/src/lib/evaluatorResultQuery.ts      |   46 +
 frontend/src/lib/observabilityCallQuery.ts    |   20 +
 frontend/src/lib/recordingUrls.ts             |  153 +-
 frontend/src/lib/waveformAudioCache.ts        |  165 +
 frontend/src/lib/waveformPeaksFast.ts         |   88 +
 frontend/src/lib/workspaceNavigation.ts       |   77 +-
 .../pages/agents/components/AgentEditForm.tsx | 1877 +++++-----
 .../agents/components/CreateAgentModal.tsx    | 1142 +++---
 .../components/create/PlatformConnectStep.tsx |   17 +-
 .../components/create/VoiceAgentPicker.tsx    |  151 +
 .../components/create/createAgentTypes.ts     |    2 +-
 .../components/EvaluatorSmartRunModal.tsx     |    2 +-
 .../results/EvaluatorResultDetail.tsx         |  581 +--
 .../observability/ObservabilityCallDetail.tsx |  768 +---
 .../observability/observabilityCallUi.tsx     |  113 +
 .../playground/agent/AgentPlayground.tsx      | 3244 ++++++++--------
 .../playground/agent/CallRecordingDetail.tsx  | 1746 ++++-----
 .../agent/TestAgentResultDetail.tsx           |  218 +-
 .../pages/test-insights/CallTraceDetail.tsx   |    4 +-
 .../src/pages/test-insights/TestInsights.tsx  |  393 +-
 frontend/src/types/api.ts                     |   14 +
 frontend/vite.config.ts                       |   73 +-
 pyproject.toml                                |    2 +
 tests/test_api/test_integrations_routes.py    |  302 +-
 .../test_playground_provider_metrics.py       |   24 +
 .../test_services/test_playground_tracing.py  |   76 +
 .../test_services/test_provider_call_logs.py  |   37 +
 72 files changed, 17651 insertions(+), 12949 deletions(-)
 create mode 100644 app/services/playground/provider_call_logs.py
 create mode 100644 app/services/synthetic_traces/internal_otlp_exporter.py
 create mode 100644 app/services/voice_agent/playground_tracing.py
 create mode 100644 app/services/voice_providers/voice_agent_catalog.py
 create mode 100644 frontend/src/components/call-recordings/CallEventTimeline.tsx
 create mode 100644 frontend/src/components/call-recordings/CallWaveformPlayer.tsx
 create mode 100644 frontend/src/components/call-recordings/EvaluatorCallDetailPanel.tsx
 create mode 100644 frontend/src/components/call-recordings/ObservabilityCallDetailPanel.tsx
 create mode 100644 frontend/src/components/call-recordings/ProviderCallTracePanel.tsx
 create mode 100644 frontend/src/components/call-recordings/ProviderMetricsPanels.tsx
 create mode 100644 frontend/src/components/call-recordings/VoiceAiCallDetailPanel.tsx
 create mode 100644 frontend/src/components/call-recordings/callTimelineUtils.test.ts
 create mode 100644 frontend/src/components/call-recordings/callTimelineUtils.ts
 create mode 100644 frontend/src/lib/callDetailTheme.ts
 create mode 100644 frontend/src/lib/callRecordingQuery.ts
 create mode 100644 frontend/src/lib/evaluatorResultQuery.ts
 create mode 100644 frontend/src/lib/observabilityCallQuery.ts
 create mode 100644 frontend/src/lib/waveformAudioCache.ts
 create mode 100644 frontend/src/lib/waveformPeaksFast.ts
 create mode 100644 frontend/src/pages/agents/components/create/VoiceAgentPicker.tsx
 create mode 100644 frontend/src/pages/observability/observabilityCallUi.tsx
 create mode 100644 tests/test_services/test_playground_provider_metrics.py
 create mode 100644 tests/test_services/test_playground_tracing.py
 create mode 100644 tests/test_services/test_provider_call_logs.py

diff --git a/app/api/v1/routes/integrations.py b/app/api/v1/routes/integrations.py
index 338f295b..0972c411 100644
--- a/app/api/v1/routes/integrations.py
+++ b/app/api/v1/routes/integrations.py
@@ -1,473 +1,534 @@
-"""
-Integrations API Routes
-Manage integrations with external voice AI platforms (Retell, Vapi, etc.)
-"""
-from fastapi import APIRouter, Depends, HTTPException, status, Query
-from fastapi.responses import JSONResponse
-from sqlalchemy.orm import Session
-from datetime import datetime, timezone
-from typing import List
-from uuid import UUID
-from loguru import logger
-
-from app.dependencies import get_db, get_organization_id, get_api_key
-from app.models.database import Integration, IntegrationPlatform, Agent
-from app.models.schemas import (
-    IntegrationCreate, IntegrationUpdate, IntegrationResponse,
-    PreviewIntegrationAgentPromptRequest, PreviewIntegrationAgentPromptResponse,
-)
-from app.core.encryption import encrypt_api_key, decrypt_api_key
-from app.services.credentials.resolver import clear_other_defaults
-from app.services.voice_providers import get_voice_provider
-from app.services.ai.llm_gateway import get_credential_effective_routing_label
-
-router = APIRouter(prefix="/integrations", tags=["Integrations"])
-
-
-def _integration_response(
-    integration: Integration,
-    organization_id: UUID,
-    db: Session,
-) -> IntegrationResponse:
-    effective_routing = get_credential_effective_routing_label(
-        organization_id,
-        db,
-        integration,
-    )
-    response = IntegrationResponse.model_validate(integration)
-    return response.model_copy(update={"effective_routing": effective_routing})
-
-
-def _validate_smallest_connection(raw_api_key: str):
-    """Validate a Smallest key via GET /atoms/v1/user."""
-    try:
-        provider_class = get_voice_provider(IntegrationPlatform.SMALLEST.value)
-        provider = provider_class(api_key=raw_api_key)
-        provider.test_connection()
-        if hasattr(provider, "get_user_details"):
-            return provider.get_user_details()
-        return None
-    except Exception as e:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Smallest API key validation failed: {str(e)}"
-        )
-
-
-@router.post("", response_model=IntegrationResponse, status_code=status.HTTP_201_CREATED, operation_id="createIntegration")
-async def create_integration(
-    integration_data: IntegrationCreate,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """Create a new credential row for a voice AI platform.
-
-    Multiple credentials per platform are now supported. The first row
-    created for a given (org, platform) automatically becomes the
-    default; subsequent rows can be promoted via
-    ``POST /integrations/{id}/set-default``. ``integration_data.is_default``
-    can also be set explicitly to mark the new row as the default at
-    creation time.
-    Requires at least WRITER role.
-    """
-    from sqlalchemy import func
-    platform_value = integration_data.platform.value if hasattr(integration_data.platform, 'value') else integration_data.platform
-
-    user_details = None
-    if platform_value.lower() == IntegrationPlatform.SMALLEST.value:
-        user_details = _validate_smallest_connection(integration_data.api_key)
-
-    encrypted_api_key = encrypt_api_key(integration_data.api_key)
-    integration_name = integration_data.name
-    if not integration_name and isinstance(user_details, dict):
-        email = user_details.get("email") or user_details.get("userEmail")
-        if email:
-            integration_name = f"Smallest ({email})"
-
-    existing_default = db.query(Integration).filter(
-        Integration.organization_id == organization_id,
-        func.lower(Integration.platform) == platform_value.lower(),
-        Integration.is_default.is_(True),
-        Integration.is_active.is_(True),
-    ).first()
-
-    requested_default = bool(integration_data.is_default)
-    will_be_default = requested_default or existing_default is None
-    insert_as_default = will_be_default and existing_default is None
-
-    integration = Integration(
-        organization_id=organization_id,
-        platform=platform_value,
-        name=integration_name,
-        api_key=encrypted_api_key,
-        public_key=integration_data.public_key,
-        is_active=True,
-        is_default=insert_as_default,
-        routing_mode=integration_data.routing_mode.value,
-        last_tested_at=datetime.now(timezone.utc) if user_details is not None else None,
-    )
-
-    db.add(integration)
-    db.flush()
-
-    if will_be_default:
-        clear_other_defaults(
-            Integration,
-            db,
-            organization_id,
-            keep_id=integration.id,
-            provider_field="platform",
-            provider_value=platform_value,
-        )
-        integration.is_default = True
-
-    db.commit()
-    db.refresh(integration)
-
-    return _integration_response(integration, organization_id, db)
-
-
-@router.post(
-    "/{integration_id}/set-default",
-    response_model=IntegrationResponse,
-    operation_id="setDefaultIntegration",
-)
-async def set_default_integration(
-    integration_id: UUID,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """Mark this integration as the default for its (org, platform).
-
-    Atomically clears the default flag on every other row for the same
-    (org, platform) so the partial unique index in migration 028 holds.
-    """
-    integration = db.query(Integration).filter(
-        Integration.id == integration_id,
-        Integration.organization_id == organization_id,
-    ).first()
-
-    if not integration:
-        raise HTTPException(status_code=404, detail="Integration not found")
-
-    if not integration.is_active:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Cannot mark an inactive integration as default",
-        )
-
-    platform_value = (
-        integration.platform.value
-        if hasattr(integration.platform, "value")
-        else integration.platform
-    )
-
-    clear_other_defaults(
-        Integration,
-        db,
-        organization_id,
-        keep_id=integration.id,
-        provider_field="platform",
-        provider_value=platform_value,
-    )
-    integration.is_default = True
-    db.commit()
-    db.refresh(integration)
-    return _integration_response(integration, organization_id, db)
-
-
-@router.get("", response_model=List[IntegrationResponse], operation_id="listIntegrations")
-async def list_integrations(
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    List all integrations for the organization.
-    Requires at least READER role.
-    """
-    integrations = db.query(Integration).filter(
-        Integration.organization_id == organization_id
-    ).order_by(Integration.created_at.desc()).all()
-
-    valid_platforms = {p.value for p in IntegrationPlatform}
-    filtered_integrations: List[Integration] = []
-    for integration in integrations:
-        raw_platform = (
-            integration.platform.value
-            if hasattr(integration.platform, "value")
-            else str(integration.platform).lower()
-        )
-        if raw_platform in valid_platforms:
-            filtered_integrations.append(integration)
-        else:
-            logger.warning(
-                "Skipping integration {} with invalid platform '{}'",
-                integration.id,
-                integration.platform,
-            )
-
-    return [
-        _integration_response(integration, organization_id, db)
-        for integration in filtered_integrations
-    ]
-
-
-@router.get("/{integration_id}", response_model=IntegrationResponse)
-async def get_integration(
-    integration_id: UUID,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Get a specific integration.
-    Requires at least READER role.
-    """
-    integration = db.query(Integration).filter(
-        Integration.id == integration_id,
-        Integration.organization_id == organization_id
-    ).first()
-    
-    if not integration:
-        raise HTTPException(status_code=404, detail="Integration not found")
-
-    raw_platform = (
-        integration.platform.value
-        if hasattr(integration.platform, "value")
-        else str(integration.platform).lower()
-    )
-    if raw_platform not in {p.value for p in IntegrationPlatform}:
-        raise HTTPException(status_code=404, detail="Integration not found")
-    
-    return _integration_response(integration, organization_id, db)
-
-
-@router.put("/{integration_id}", response_model=IntegrationResponse, operation_id="updateIntegration")
-async def update_integration(
-    integration_id: UUID,
-    integration_update: IntegrationUpdate,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Update an integration.
-    Requires at least WRITER role.
-    """
-    integration = db.query(Integration).filter(
-        Integration.id == integration_id,
-        Integration.organization_id == organization_id
-    ).first()
-    
-    if not integration:
-        raise HTTPException(status_code=404, detail="Integration not found")
-    
-    if integration_update.name is not None:
-        integration.name = integration_update.name
-    
-    if integration_update.api_key is not None:
-        platform_value = integration.platform.value if hasattr(integration.platform, 'value') else integration.platform
-        if platform_value.lower() == IntegrationPlatform.SMALLEST.value:
-            _validate_smallest_connection(integration_update.api_key)
-            integration.last_tested_at = datetime.now(timezone.utc)
-        integration.api_key = encrypt_api_key(integration_update.api_key)
-    
-    if integration_update.public_key is not None:
-        integration.public_key = integration_update.public_key
-    
-    if integration_update.is_active is not None:
-        integration.is_active = integration_update.is_active
-
-    if integration_update.routing_mode is not None:
-        integration.routing_mode = integration_update.routing_mode.value
-    
-    db.commit()
-    db.refresh(integration)
-    
-    return _integration_response(integration, organization_id, db)
-
-
-@router.delete("/{integration_id}", operation_id="deleteIntegration")
-async def delete_integration(
-    integration_id: UUID,
-    force: bool = Query(False, description="Force delete and unlink all agents using this integration"),
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Delete an integration. Returns 409 if agents are using it unless force=true.
-    Requires at least WRITER role.
-    """
-    integration = db.query(Integration).filter(
-        Integration.id == integration_id,
-        Integration.organization_id == organization_id
-    ).first()
-
-    if not integration:
-        raise HTTPException(status_code=404, detail="Integration not found")
-
-    agents_count = db.query(Agent).filter(
-        Agent.voice_ai_integration_id == integration_id,
-        Agent.organization_id == organization_id,
-    ).count()
-
-    dependencies = {}
-    if agents_count > 0:
-        dependencies["agents"] = agents_count
-
-    if dependencies and not force:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail={
-                "message": f"Cannot delete integration. It is used by {agents_count} agent(s).",
-                "dependencies": dependencies,
-                "hint": "Use force=true to delete this integration and unlink all agents.",
-            },
-        )
-
-    if dependencies:
-        db.query(Agent).filter(
-            Agent.voice_ai_integration_id == integration_id,
-            Agent.organization_id == organization_id,
-        ).update(
-            {Agent.voice_ai_integration_id: None, Agent.voice_ai_agent_id: None},
-            synchronize_session=False,
-        )
-
-    was_default = bool(integration.is_default)
-    platform_value = (
-        integration.platform.value
-        if hasattr(integration.platform, "value")
-        else integration.platform
-    )
-    db.delete(integration)
-    db.flush()
-
-    # If we just removed the default credential, promote the next active
-    # row (most recently updated) so resolution-by-default keeps working.
-    if was_default:
-        from sqlalchemy import func, desc
-        replacement = (
-            db.query(Integration)
-            .filter(
-                Integration.organization_id == organization_id,
-                func.lower(Integration.platform) == platform_value.lower(),
-                Integration.is_active.is_(True),
-            )
-            .order_by(desc(Integration.updated_at), desc(Integration.created_at))
-            .first()
-        )
-        if replacement:
-            replacement.is_default = True
-
-    db.commit()
-
-    if dependencies:
-        return JSONResponse(
-            status_code=200,
-            content={
-                "message": "Integration deleted and agents unlinked successfully.",
-                "deleted": dependencies,
-            },
-        )
-
-    return JSONResponse(status_code=204, content=None)
-
-@router.get("/{integration_id}/api-key")
-async def get_integration_api_key(
-    integration_id: UUID,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db)
-):
-    """
-    Get the decrypted API key for an integration.
-    This endpoint is used for client-side operations like web calls.
-    Requires at least READER role.
-    """
-    integration = db.query(Integration).filter(
-        Integration.id == integration_id,
-        Integration.organization_id == organization_id,
-        Integration.is_active == True
-    ).first()
-    
-    if not integration:
-        raise HTTPException(status_code=404, detail="Integration not found or inactive")
-    
-    # Only allow for Retell and Vapi platforms (for web calls)
-    if integration.platform not in [IntegrationPlatform.RETELL, IntegrationPlatform.VAPI]:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="API key retrieval is only available for Retell and Vapi integrations"
-        )
-    
-    try:
-        decrypted_api_key = decrypt_api_key(integration.api_key)
-        return {
-            "api_key": decrypted_api_key,
-            "public_key": integration.public_key
-        }
-    except Exception as e:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to decrypt API key: {str(e)}"
-        )
-
-
-@router.post(
-    "/{integration_id}/preview-agent-prompt",
-    response_model=PreviewIntegrationAgentPromptResponse,
-    operation_id="previewIntegrationAgentPrompt",
-)
-async def preview_integration_agent_prompt(
-    integration_id: UUID,
-    body: PreviewIntegrationAgentPromptRequest,
-    organization_id: UUID = Depends(get_organization_id),
-    api_key: str = Depends(get_api_key),
-    db: Session = Depends(get_db),
-):
-    """Fetch a provider agent prompt before an EfficientAI agent exists."""
-    integration = db.query(Integration).filter(
-        Integration.id == integration_id,
-        Integration.organization_id == organization_id,
-        Integration.is_active == True,
-    ).first()
-
-    if not integration:
-        raise HTTPException(status_code=404, detail="Integration not found or inactive")
-
-    if integration.platform not in [
-        IntegrationPlatform.RETELL,
-        IntegrationPlatform.VAPI,
-        IntegrationPlatform.ELEVENLABS,
-        IntegrationPlatform.SMALLEST,
-    ]:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"Integration platform {integration.platform.value} is not supported for prompt preview. "
-                "Only Retell, Vapi, ElevenLabs, and Smallest are supported."
-            ),
-        )
-
-    try:
-        from app.services.voice_providers.prompt_sync import fetch_provider_prompt
-
-        prompt = fetch_provider_prompt(integration, body.voice_ai_agent_id)
-    except Exception as e:
-        raise HTTPException(
-            status_code=502,
-            detail=f"Failed to fetch prompt from provider: {str(e)}",
-        )
-
-    if not isinstance(prompt, str) or not prompt.strip():
-        raise HTTPException(
-            status_code=422,
-            detail="Provider returned no prompt. Verify the external agent has a system prompt configured.",
-        )
-
-    return PreviewIntegrationAgentPromptResponse(provider_prompt=prompt)
+"""
+Integrations API Routes
+Manage integrations with external voice AI platforms (Retell, Vapi, etc.)
+"""
+from fastapi import APIRouter, Depends, HTTPException, status, Query
+from fastapi.responses import JSONResponse
+from sqlalchemy.orm import Session
+from datetime import datetime, timezone
+from typing import List, Optional
+from uuid import UUID
+from loguru import logger
+
+from app.dependencies import get_db, get_organization_id, get_api_key
+from app.models.database import Integration, IntegrationPlatform, Agent
+from app.models.schemas import (
+    IntegrationCreate, IntegrationUpdate, IntegrationResponse,
+    PreviewIntegrationAgentPromptRequest, PreviewIntegrationAgentPromptResponse,
+    ListIntegrationVoiceAgentsResponse, IntegrationVoiceAgentListItem,
+)
+from app.core.encryption import encrypt_api_key, decrypt_api_key
+from app.services.credentials.resolver import clear_other_defaults
+from app.services.voice_providers import get_voice_provider
+from app.services.ai.llm_gateway import get_credential_effective_routing_label
+
+router = APIRouter(prefix="/integrations", tags=["Integrations"])
+
+
+def _integration_response(
+    integration: Integration,
+    organization_id: UUID,
+    db: Session,
+) -> IntegrationResponse:
+    effective_routing = get_credential_effective_routing_label(
+        organization_id,
+        db,
+        integration,
+    )
+    response = IntegrationResponse.model_validate(integration)
+    return response.model_copy(update={"effective_routing": effective_routing})
+
+
+def _validate_smallest_connection(raw_api_key: str):
+    """Validate a Smallest key via GET /atoms/v1/user."""
+    try:
+        provider_class = get_voice_provider(IntegrationPlatform.SMALLEST.value)
+        provider = provider_class(api_key=raw_api_key)
+        provider.test_connection()
+        if hasattr(provider, "get_user_details"):
+            return provider.get_user_details()
+        return None
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Smallest API key validation failed: {str(e)}"
+        )
+
+
+@router.post("", response_model=IntegrationResponse, status_code=status.HTTP_201_CREATED, operation_id="createIntegration")
+async def create_integration(
+    integration_data: IntegrationCreate,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """Create a new credential row for a voice AI platform.
+
+    Multiple credentials per platform are now supported. The first row
+    created for a given (org, platform) automatically becomes the
+    default; subsequent rows can be promoted via
+    ``POST /integrations/{id}/set-default``. ``integration_data.is_default``
+    can also be set explicitly to mark the new row as the default at
+    creation time.
+    Requires at least WRITER role.
+    """
+    from sqlalchemy import func
+    platform_value = integration_data.platform.value if hasattr(integration_data.platform, 'value') else integration_data.platform
+
+    user_details = None
+    if platform_value.lower() == IntegrationPlatform.SMALLEST.value:
+        user_details = _validate_smallest_connection(integration_data.api_key)
+
+    encrypted_api_key = encrypt_api_key(integration_data.api_key)
+    integration_name = integration_data.name
+    if not integration_name and isinstance(user_details, dict):
+        email = user_details.get("email") or user_details.get("userEmail")
+        if email:
+            integration_name = f"Smallest ({email})"
+
+    existing_default = db.query(Integration).filter(
+        Integration.organization_id == organization_id,
+        func.lower(Integration.platform) == platform_value.lower(),
+        Integration.is_default.is_(True),
+        Integration.is_active.is_(True),
+    ).first()
+
+    requested_default = bool(integration_data.is_default)
+    will_be_default = requested_default or existing_default is None
+    insert_as_default = will_be_default and existing_default is None
+
+    integration = Integration(
+        organization_id=organization_id,
+        platform=platform_value,
+        name=integration_name,
+        api_key=encrypted_api_key,
+        public_key=integration_data.public_key,
+        is_active=True,
+        is_default=insert_as_default,
+        routing_mode=integration_data.routing_mode.value,
+        last_tested_at=datetime.now(timezone.utc) if user_details is not None else None,
+    )
+
+    db.add(integration)
+    db.flush()
+
+    if will_be_default:
+        clear_other_defaults(
+            Integration,
+            db,
+            organization_id,
+            keep_id=integration.id,
+            provider_field="platform",
+            provider_value=platform_value,
+        )
+        integration.is_default = True
+
+    db.commit()
+    db.refresh(integration)
+
+    return _integration_response(integration, organization_id, db)
+
+
+@router.post(
+    "/{integration_id}/set-default",
+    response_model=IntegrationResponse,
+    operation_id="setDefaultIntegration",
+)
+async def set_default_integration(
+    integration_id: UUID,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """Mark this integration as the default for its (org, platform).
+
+    Atomically clears the default flag on every other row for the same
+    (org, platform) so the partial unique index in migration 028 holds.
+    """
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id,
+    ).first()
+
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found")
+
+    if not integration.is_active:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Cannot mark an inactive integration as default",
+        )
+
+    platform_value = (
+        integration.platform.value
+        if hasattr(integration.platform, "value")
+        else integration.platform
+    )
+
+    clear_other_defaults(
+        Integration,
+        db,
+        organization_id,
+        keep_id=integration.id,
+        provider_field="platform",
+        provider_value=platform_value,
+    )
+    integration.is_default = True
+    db.commit()
+    db.refresh(integration)
+    return _integration_response(integration, organization_id, db)
+
+
+@router.get("", response_model=List[IntegrationResponse], operation_id="listIntegrations")
+async def list_integrations(
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    List all integrations for the organization.
+    Requires at least READER role.
+    """
+    integrations = db.query(Integration).filter(
+        Integration.organization_id == organization_id
+    ).order_by(Integration.created_at.desc()).all()
+
+    valid_platforms = {p.value for p in IntegrationPlatform}
+    filtered_integrations: List[Integration] = []
+    for integration in integrations:
+        raw_platform = (
+            integration.platform.value
+            if hasattr(integration.platform, "value")
+            else str(integration.platform).lower()
+        )
+        if raw_platform in valid_platforms:
+            filtered_integrations.append(integration)
+        else:
+            logger.warning(
+                "Skipping integration {} with invalid platform '{}'",
+                integration.id,
+                integration.platform,
+            )
+
+    return [
+        _integration_response(integration, organization_id, db)
+        for integration in filtered_integrations
+    ]
+
+
+@router.get("/{integration_id}", response_model=IntegrationResponse)
+async def get_integration(
+    integration_id: UUID,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Get a specific integration.
+    Requires at least READER role.
+    """
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id
+    ).first()
+    
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found")
+
+    raw_platform = (
+        integration.platform.value
+        if hasattr(integration.platform, "value")
+        else str(integration.platform).lower()
+    )
+    if raw_platform not in {p.value for p in IntegrationPlatform}:
+        raise HTTPException(status_code=404, detail="Integration not found")
+    
+    return _integration_response(integration, organization_id, db)
+
+
+@router.put("/{integration_id}", response_model=IntegrationResponse, operation_id="updateIntegration")
+async def update_integration(
+    integration_id: UUID,
+    integration_update: IntegrationUpdate,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Update an integration.
+    Requires at least WRITER role.
+    """
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id
+    ).first()
+    
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found")
+    
+    if integration_update.name is not None:
+        integration.name = integration_update.name
+    
+    if integration_update.api_key is not None:
+        platform_value = integration.platform.value if hasattr(integration.platform, 'value') else integration.platform
+        if platform_value.lower() == IntegrationPlatform.SMALLEST.value:
+            _validate_smallest_connection(integration_update.api_key)
+            integration.last_tested_at = datetime.now(timezone.utc)
+        integration.api_key = encrypt_api_key(integration_update.api_key)
+    
+    if integration_update.public_key is not None:
+        integration.public_key = integration_update.public_key
+    
+    if integration_update.is_active is not None:
+        integration.is_active = integration_update.is_active
+
+    if integration_update.routing_mode is not None:
+        integration.routing_mode = integration_update.routing_mode.value
+    
+    db.commit()
+    db.refresh(integration)
+    
+    return _integration_response(integration, organization_id, db)
+
+
+@router.delete("/{integration_id}", operation_id="deleteIntegration")
+async def delete_integration(
+    integration_id: UUID,
+    force: bool = Query(False, description="Force delete and unlink all agents using this integration"),
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Delete an integration. Returns 409 if agents are using it unless force=true.
+    Requires at least WRITER role.
+    """
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id
+    ).first()
+
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found")
+
+    agents_count = db.query(Agent).filter(
+        Agent.voice_ai_integration_id == integration_id,
+        Agent.organization_id == organization_id,
+    ).count()
+
+    dependencies = {}
+    if agents_count > 0:
+        dependencies["agents"] = agents_count
+
+    if dependencies and not force:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail={
+                "message": f"Cannot delete integration. It is used by {agents_count} agent(s).",
+                "dependencies": dependencies,
+                "hint": "Use force=true to delete this integration and unlink all agents.",
+            },
+        )
+
+    if dependencies:
+        db.query(Agent).filter(
+            Agent.voice_ai_integration_id == integration_id,
+            Agent.organization_id == organization_id,
+        ).update(
+            {Agent.voice_ai_integration_id: None, Agent.voice_ai_agent_id: None},
+            synchronize_session=False,
+        )
+
+    was_default = bool(integration.is_default)
+    platform_value = (
+        integration.platform.value
+        if hasattr(integration.platform, "value")
+        else integration.platform
+    )
+    db.delete(integration)
+    db.flush()
+
+    # If we just removed the default credential, promote the next active
+    # row (most recently updated) so resolution-by-default keeps working.
+    if was_default:
+        from sqlalchemy import func, desc
+        replacement = (
+            db.query(Integration)
+            .filter(
+                Integration.organization_id == organization_id,
+                func.lower(Integration.platform) == platform_value.lower(),
+                Integration.is_active.is_(True),
+            )
+            .order_by(desc(Integration.updated_at), desc(Integration.created_at))
+            .first()
+        )
+        if replacement:
+            replacement.is_default = True
+
+    db.commit()
+
+    if dependencies:
+        return JSONResponse(
+            status_code=200,
+            content={
+                "message": "Integration deleted and agents unlinked successfully.",
+                "deleted": dependencies,
+            },
+        )
+
+    return JSONResponse(status_code=204, content=None)
+
+@router.get("/{integration_id}/api-key")
+async def get_integration_api_key(
+    integration_id: UUID,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db)
+):
+    """
+    Get the decrypted API key for an integration.
+    This endpoint is used for client-side operations like web calls.
+    Requires at least READER role.
+    """
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id,
+        Integration.is_active == True
+    ).first()
+    
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found or inactive")
+    
+    # Only allow for Retell and Vapi platforms (for web calls)
+    if integration.platform not in [IntegrationPlatform.RETELL, IntegrationPlatform.VAPI]:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="API key retrieval is only available for Retell and Vapi integrations"
+        )
+    
+    try:
+        decrypted_api_key = decrypt_api_key(integration.api_key)
+        return {
+            "api_key": decrypted_api_key,
+            "public_key": integration.public_key
+        }
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to decrypt API key: {str(e)}"
+        )
+
+
+@router.post(
+    "/{integration_id}/preview-agent-prompt",
+    response_model=PreviewIntegrationAgentPromptResponse,
+    operation_id="previewIntegrationAgentPrompt",
+)
+async def preview_integration_agent_prompt(
+    integration_id: UUID,
+    body: PreviewIntegrationAgentPromptRequest,
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """Fetch a provider agent prompt before an EfficientAI agent exists."""
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id,
+        Integration.is_active == True,
+    ).first()
+
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found or inactive")
+
+    if integration.platform not in [
+        IntegrationPlatform.RETELL,
+        IntegrationPlatform.VAPI,
+        IntegrationPlatform.ELEVENLABS,
+        IntegrationPlatform.SMALLEST,
+    ]:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                f"Integration platform {integration.platform.value} is not supported for prompt preview. "
+                "Only Retell, Vapi, ElevenLabs, and Smallest are supported."
+            ),
+        )
+
+    try:
+        from app.services.voice_providers.prompt_sync import fetch_provider_prompt
+
+        prompt = fetch_provider_prompt(integration, body.voice_ai_agent_id)
+    except Exception as e:
+        raise HTTPException(
+            status_code=502,
+            detail=f"Failed to fetch prompt from provider: {str(e)}",
+        )
+
+    if not isinstance(prompt, str) or not prompt.strip():
+        raise HTTPException(
+            status_code=422,
+            detail="Provider returned no prompt. Verify the external agent has a system prompt configured.",
+        )
+
+    return PreviewIntegrationAgentPromptResponse(provider_prompt=prompt)
+
+
+@router.get(
+    "/{integration_id}/voice-agents",
+    response_model=ListIntegrationVoiceAgentsResponse,
+    operation_id="listIntegrationVoiceAgents",
+)
+async def list_integration_voice_agents_route(
+    integration_id: UUID,
+    refresh: bool = Query(False),
+    search: Optional[str] = Query(None),
+    organization_id: UUID = Depends(get_organization_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """List remote voice agents for an org integration (cached ~60s)."""
+    integration = db.query(Integration).filter(
+        Integration.id == integration_id,
+        Integration.organization_id == organization_id,
+        Integration.is_active == True,
+    ).first()
+
+    if not integration:
+        raise HTTPException(status_code=404, detail="Integration not found or inactive")
+
+    if integration.platform not in [
+        IntegrationPlatform.RETELL,
+        IntegrationPlatform.VAPI,
+        IntegrationPlatform.ELEVENLABS,
+        IntegrationPlatform.SMALLEST,
+    ]:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(
+                f"Integration platform {integration.platform.value} is not supported for agent listing."
+            ),
+        )
+
+    try:
+        from app.services.voice_providers.voice_agent_catalog import list_integration_voice_agents
+
+        result = list_integration_voice_agents(
+            integration,
+            refresh=refresh,
+            search=search,
+        )
+    except Exception as e:
+        raise HTTPException(
+            status_code=502,
+            detail=f"Failed to list agents from provider: {str(e)}",
+        )
+
+    return ListIntegrationVoiceAgentsResponse(
+        agents=[IntegrationVoiceAgentListItem(**row) for row in result.agents],
+        platform=result.platform,
+        cached=result.cached,
+        truncated=result.truncated,
+        list_supported=result.list_supported,
+        message=result.message,
+    )
diff --git a/app/api/v1/routes/playground.py b/app/api/v1/routes/playground.py
index b598898e..9204b296 100644
--- a/app/api/v1/routes/playground.py
+++ b/app/api/v1/routes/playground.py
@@ -2,7 +2,7 @@
 Playground API Routes
 API endpoints for testing voice agents in the playground
 """
-from fastapi import APIRouter, Depends, HTTPException, status, Body, BackgroundTasks, Form, File, UploadFile
+from fastapi import APIRouter, Depends, HTTPException, status, Body, BackgroundTasks, Form, File, UploadFile, Query
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 from typing import Dict, Any, Optional, List
@@ -41,6 +41,80 @@
 from app.services.evaluators.call_data_transcript import extract_transcript_from_call_data
 
 router = APIRouter(prefix="/playground", tags=["playground"])
+
+
+def _resolve_playground_audio_url(
+    call_data: dict[str, Any],
+    platform: str,
+    *,
+    stereo: bool = False,
+) -> Optional[str]:
+    artifact = call_data.get("artifact", {}) if isinstance(call_data.get("artifact"), dict) else {}
+    recording = artifact.get("recording", {}) if isinstance(artifact.get("recording"), dict) else {}
+    mono_recording = recording.get("mono", {}) if isinstance(recording.get("mono"), dict) else {}
+    recording_urls = call_data.get("recording_urls", {}) if isinstance(call_data.get("recording_urls"), dict) else {}
+
+    plat = (platform or "").lower()
+    if plat == "vapi":
+        from app.services.voice_providers.vapi_recording import extract_vapi_recording_url
+
+        return extract_vapi_recording_url(call_data, stereo=stereo)
+
+    return (
+        call_data.get("recordingUrl")
+        or call_data.get("stereoRecordingUrl")
+        or artifact.get("recordingUrl")
+        or artifact.get("stereoRecordingUrl")
+        or mono_recording.get("combinedUrl")
+        or recording_urls.get("combined_url")
+        or recording_urls.get("stereo_url")
+        or call_data.get("recording_url")
+        or recording_urls.get("conversation_audio")
+    )
+
+
+def _refresh_call_recording_from_provider_sync(
+    db: Session,
+    call_recording: CallRecording,
+    organization_id: UUID,
+) -> bool:
+    """Re-fetch provider call payload to refresh expiring presigned recording URLs."""
+    from app.services.playground.post_call_processing import persist_provider_call_metrics
+
+    if not call_recording.provider_call_id or not call_recording.provider_platform:
+        return False
+
+    agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+    if not agent or not agent.voice_ai_integration_id:
+        return False
+
+    integration = db.query(Integration).filter(
+        Integration.id == agent.voice_ai_integration_id,
+        Integration.organization_id == organization_id,
+    ).first()
+    if not integration:
+        return False
+
+    try:
+        provider_class = get_voice_provider(call_recording.provider_platform)
+        provider = provider_class(api_key=decrypt_api_key(integration.api_key))
+        if not hasattr(provider, "retrieve_call_metrics"):
+            return False
+        metrics = provider.retrieve_call_metrics(call_recording.provider_call_id)
+        if not isinstance(metrics, dict):
+            return False
+        persist_provider_call_metrics(db, call_recording.id, metrics)
+        db.refresh(call_recording)
+        return True
+    except Exception as exc:
+        logger.warning(
+            "[Audio Proxy] Failed to refresh provider call data for %s: %s",
+            call_recording.call_short_id,
+            exc,
+        )
+        return False
+
+
 def generate_unique_result_id(db: Session) -> str:
     """Generate a unique 6-digit result ID for EvaluatorResult."""
     max_attempts = 100
@@ -52,6 +126,58 @@ def generate_unique_result_id(db: Session) -> str:
     raise ValueError("Failed to generate unique result ID")
 
 
+def sync_provider_call_metrics(
+    call_recording_id: UUID,
+    provider_call_id: str,
+    provider_platform: str,
+    integration_api_key: str,
+    max_attempts: int = 12,
+    poll_interval: int = 5,
+):
+    """Re-fetch provider metrics and update stored call_data (no evaluator creation)."""
+    import time
+
+    from app.database import SessionLocal
+    from app.services.playground.post_call_processing import (
+        call_metrics_indicate_ended,
+        persist_provider_call_metrics,
+        provider_metrics_enriched,
+    )
+
+    db = SessionLocal()
+    try:
+        call_recording = db.query(CallRecording).filter(CallRecording.id == call_recording_id).first()
+        if not call_recording or not provider_call_id:
+            return
+
+        try:
+            provider_class = get_voice_provider(provider_platform)
+            provider = provider_class(api_key=integration_api_key)
+        except ValueError:
+            return
+
+        if not hasattr(provider, "retrieve_call_metrics"):
+            return
+
+        call_metrics: dict[str, Any] | None = None
+        for attempt in range(max_attempts):
+            if attempt > 0:
+                time.sleep(poll_interval)
+            try:
+                call_metrics = provider.retrieve_call_metrics(provider_call_id)
+                if not isinstance(call_metrics, dict):
+                    continue
+                persist_provider_call_metrics(db, call_recording_id, call_metrics)
+                if provider_metrics_enriched(provider_platform, call_metrics):
+                    break
+                if call_metrics_indicate_ended(call_metrics) and attempt >= 3:
+                    break
+            except Exception as exc:
+                logger.warning(f"[Sync Provider Metrics] attempt {attempt + 1} failed: {exc}")
+    finally:
+        db.close()
+
+
 def poll_call_metrics(
     call_recording_id: UUID,
     provider_call_id: str,
@@ -74,7 +200,11 @@ def poll_call_metrics(
     """
     import time
     from app.database import SessionLocal
-    from app.services.playground.post_call_processing import merge_playground_call_data
+    from app.services.playground.post_call_processing import (
+        call_metrics_indicate_ended,
+        merge_playground_call_data,
+        provider_metrics_enriched,
+    )
     from app.services.voice_providers import get_voice_provider
     
     db = SessionLocal()
@@ -123,19 +253,11 @@ def poll_call_metrics(
                 db.commit()
                 db.refresh(call_recording)
                 
-                # Check if call is complete (supports raw + normalized payloads)
-                call_status = (
-                    call_metrics.get("call_status")
-                    or call_metrics.get("status")
-                    or ""
-                )
-                call_status = str(call_status).lower()
-                end_timestamp = call_metrics.get("end_timestamp") or call_metrics.get("endedAt")
-                
-                # If call is complete, stop polling
-                if end_timestamp or call_status in ["ended", "completed", "failed", "end-of-call-report", "done"]:
+                # Keep polling after hangup until provider enriches analysis/latency payloads.
+                if call_metrics_indicate_ended(call_metrics):
                     call_complete = True
-                    break
+                    if provider_metrics_enriched(provider_platform, call_metrics):
+                        break
                     
             except Exception as e:
                 # Log error but continue polling
@@ -937,12 +1059,14 @@ async def get_call_recording(
             EvaluatorResult.id == call_recording.evaluator_result_id
         ).first()
         if evaluator_result:
+            eval_call_data = evaluator_result.call_data if isinstance(evaluator_result.call_data, dict) else {}
             evaluation_info = {
                 "id": str(evaluator_result.id),
                 "result_id": evaluator_result.result_id,
                 "status": evaluator_result.status,
                 "metric_scores": evaluator_result.metric_scores,
                 "transcription": evaluator_result.transcription,
+                "call_analysis": eval_call_data.get("call_analysis"),
             }
     
     return {
@@ -1010,9 +1134,6 @@ async def refresh_call_recording(
             detail="Integration not found"
         )
 
-    if call_recording.evaluator_result_id:
-        return {"message": "Call recording already processed"}
-
     try:
         decrypted_api_key = decrypt_api_key(integration.api_key)
     except Exception as e:
@@ -1020,6 +1141,16 @@ async def refresh_call_recording(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail=f"Failed to decrypt API key: {str(e)}"
         )
+
+    if call_recording.evaluator_result_id:
+        background_tasks.add_task(
+            sync_provider_call_metrics,
+            call_recording.id,
+            call_recording.provider_call_id,
+            call_recording.provider_platform,
+            decrypted_api_key,
+        )
+        return {"message": "Provider metrics sync initiated"}
     
     # Start background task to poll for call metrics
     background_tasks.add_task(
@@ -1284,9 +1415,84 @@ def _download_audio_from_payload(payload: Dict[str, Any]):
     }
 
 
+@router.get("/call-recordings/{call_short_id}/logs", response_model=Dict[str, Any])
+async def get_call_recording_logs(
+    call_short_id: str,
+    organization_id: UUID = Depends(get_organization_id),
+    workspace_id: UUID = Depends(get_workspace_id),
+    api_key: str = Depends(get_api_key),
+    db: Session = Depends(get_db),
+):
+    """Return normalized provider logs for Vapi and Retell playground calls."""
+    del api_key  # Dependency enforcement only
+    from app.services.playground.provider_call_logs import (
+        fetch_retell_call_logs,
+        fetch_vapi_call_logs,
+    )
+
+    call_recording = db.query(CallRecording).filter(
+        CallRecording.call_short_id == call_short_id,
+        CallRecording.organization_id == organization_id,
+        CallRecording.workspace_id == workspace_id,
+        CallRecording.source == CallRecordingSource.PLAYGROUND,
+    ).first()
+
+    if not call_recording:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Call recording not found")
+
+    platform = (call_recording.provider_platform or "").lower()
+    call_data = call_recording.call_data if isinstance(call_recording.call_data, dict) else {}
+
+    if platform not in ("vapi", "retell"):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Provider logs are only available for Vapi and Retell calls",
+        )
+
+    try:
+        if platform == "retell":
+            entries = fetch_retell_call_logs(call_data)
+            return {"platform": platform, "entries": entries, "count": len(entries)}
+
+        if not call_recording.provider_call_id:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="Call recording does not have a provider call id",
+            )
+
+        agent = db.query(Agent).filter(Agent.id == call_recording.agent_id).first()
+        if not agent or not agent.voice_ai_integration_id:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Agent or integration not found")
+
+        integration = db.query(Integration).filter(
+            Integration.id == agent.voice_ai_integration_id,
+            Integration.organization_id == organization_id,
+        ).first()
+        if not integration:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Integration not found")
+
+        decrypted_api_key = decrypt_api_key(integration.api_key)
+        entries = fetch_vapi_call_logs(
+            api_key=decrypted_api_key,
+            provider_call_id=call_recording.provider_call_id,
+            call_data=call_data,
+        )
+        return {"platform": platform, "entries": entries, "count": len(entries)}
+    except HTTPException:
+        raise
+    except Exception as exc:
+        logger.warning("[CallLogs] Failed to fetch logs for %s: %s", call_short_id, exc)
+        raise HTTPException(
+            status_code=status.HTTP_502_BAD_GATEWAY,
+            detail="Failed to fetch provider logs. Try Refresh on the call first.",
+        )
+
+
 @router.get("/call-recordings/{call_short_id}/audio")
 async def stream_call_audio(
     call_short_id: str,
+    proxy: bool = Query(False, description="Stream audio through API (CORS-safe for waveform)"),
+    stereo: bool = Query(False, description="Prefer stereo recording when available"),
     organization_id: UUID = Depends(get_organization_id),
     workspace_id: UUID = Depends(get_workspace_id),
     api_key: str = Depends(get_api_key),
@@ -1309,31 +1515,52 @@ async def stream_call_audio(
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Call recording not found")
 
     call_data = call_recording.call_data or {}
-    recording_urls = call_data.get("recording_urls", {})
     platform = (call_recording.provider_platform or "").lower()
 
-    # For Retell / Vapi / Smallest the URL is public – redirect directly
     if platform in ("retell", "vapi", "smallest"):
-        artifact = call_data.get("artifact", {})
-        recording = artifact.get("recording", {}) if isinstance(artifact, dict) else {}
-        mono_recording = recording.get("mono", {}) if isinstance(recording, dict) else {}
-        url = (
-            call_data.get("recordingUrl")
-            or call_data.get("stereoRecordingUrl")
-            or artifact.get("recordingUrl")
-            or artifact.get("stereoRecordingUrl")
-            or mono_recording.get("combinedUrl")
-            or recording_urls.get("combined_url")
-            or recording_urls.get("stereo_url")
-            or call_data.get("recording_url")
-            or recording_urls.get("conversation_audio")
-        )
+        url = _resolve_playground_audio_url(call_data, platform, stereo=stereo and platform == "vapi")
         if not url:
             raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No recording URL available")
+        if proxy:
+            import requests as http_requests
+
+            upstream = http_requests.get(url, stream=True, timeout=90)
+            if upstream.status_code in (401, 403) and platform == "vapi":
+                upstream.close()
+                if not _refresh_call_recording_from_provider_sync(db, call_recording, organization_id):
+                    raise HTTPException(
+                        status_code=status.HTTP_502_BAD_GATEWAY,
+                        detail=(
+                            "Provider recording URL expired. "
+                            "Use Sync provider data on the call, then try again."
+                        ),
+                    )
+                call_data = call_recording.call_data or {}
+                url = _resolve_playground_audio_url(call_data, platform, stereo=stereo)
+                if not url:
+                    raise HTTPException(
+                        status_code=status.HTTP_404_NOT_FOUND,
+                        detail="No recording URL available after refresh",
+                    )
+                upstream = http_requests.get(url, stream=True, timeout=90)
+            if upstream.status_code != 200:
+                raise HTTPException(
+                    status_code=status.HTTP_502_BAD_GATEWAY,
+                    detail=(
+                        "Provider recording URL expired or unavailable. "
+                        "Use Sync provider data on the call, then try again."
+                    ),
+                )
+            content_type = upstream.headers.get("content-type", "audio/wav")
+            return StreamingResponse(
+                upstream.iter_content(chunk_size=8192),
+                media_type=content_type,
+                headers={"Content-Disposition": f'inline; filename="call_{call_short_id}.wav"'},
+            )
         from fastapi.responses import RedirectResponse
         return RedirectResponse(url)
 
-    # ElevenLabs requires API key header – proxy the stream
+    recording_urls = call_data.get("recording_urls", {})
     if platform == "elevenlabs":
         audio_url = recording_urls.get("conversation_audio")
         if not audio_url:
diff --git a/app/api/v1/routes/synthetic_traces.py b/app/api/v1/routes/synthetic_traces.py
index 694833b0..ac7524d7 100644
--- a/app/api/v1/routes/synthetic_traces.py
+++ b/app/api/v1/routes/synthetic_traces.py
@@ -193,6 +193,7 @@ def create_trace_session(
             organization_id=organization_id,
             workspace_id=workspace_id,
             evaluator_result_id=payload.evaluator_result_id,
+            agent_id=payload.agent_id,
             transport=payload.transport,
         )
     except ValueError as exc:
diff --git a/app/api/v1/routes/voice_agent.py b/app/api/v1/routes/voice_agent.py
index 9657c763..ef3de23b 100644
--- a/app/api/v1/routes/voice_agent.py
+++ b/app/api/v1/routes/voice_agent.py
@@ -1,1064 +1,1120 @@
-"""
-Voice Agent API Routes
-API endpoints for managing voice agent WebSocket connections.
-"""
-
-from fastapi import APIRouter, Depends, HTTPException, WebSocket, WebSocketDisconnect, Request, status
-from sqlalchemy.orm import Session
-from uuid import UUID, uuid4
-from typing import Dict, Any, Optional, List
-from loguru import logger
-
-from app.database import get_db
-from app.dependencies import get_organization_id, get_api_key
-from app.models.database import AIProvider, ModelProvider, Integration, IntegrationPlatform, Workspace
-from app.core.encryption import decrypt_api_key
-from app.services.voice_agent.bot_fast_api import run_bot
-from app.services.ai.llm_service import _resolve_azure_endpoint_from_provider
-from app.services.voice_agent.voice_bundle import run_voice_bundle_fastapi
-from app.services.storage.s3_service import s3_service
-
-router = APIRouter(prefix="/voice-agent", tags=["voice-agent"])
-ws_router = APIRouter(prefix="/voice-agent", tags=["voice-agent-media"])
-
-
-@ws_router.websocket("/ws")
-async def websocket_endpoint(
-    websocket: WebSocket,
-):
-    """
-    WebSocket endpoint for voice agent connection.
-
-    Authentication precedence (since browsers can't set custom headers on
-    WebSockets reliably, everything goes on query params):
-
-        1. ?token=<bearer>      -> local-password / SSO session token
-        2. ?X-API-Key=<key>     -> API key (legacy header name)
-        3. ?api_key=<key>       -> API key
-
-    Under the hood we reuse the same pluggable auth registry used by the
-    HTTP routes so the authorization rules stay consistent.
-    """
-    from app.core.auth.providers import AuthError, RawCredential, get_provider_registry
-
-    bearer_token = (
-        websocket.query_params.get("token")
-        or websocket.query_params.get("access_token")
-    )
-    api_key = (
-        websocket.query_params.get("X-API-Key")
-        or websocket.query_params.get("api_key")
-    )
-
-    if not bearer_token and not api_key:
-        from urllib.parse import parse_qs
-
-        raw_qs = websocket.scope.get("query_string", b"")
-        if isinstance(raw_qs, bytes):
-            raw_qs = raw_qs.decode("utf-8", errors="replace")
-        parsed = parse_qs(raw_qs)
-        bearer_token = bearer_token or (parsed.get("token") or parsed.get("access_token") or [None])[0]
-        api_key = api_key or (parsed.get("X-API-Key") or parsed.get("api_key") or [None])[0]
-
-    if not bearer_token and not api_key:
-        print(
-            f"[MEDIA-WS] rejected: no credentials (query_string_len="
-            f"{len(websocket.scope.get('query_string') or b'')})",
-            flush=True,
-        )
-        await websocket.close(code=1008, reason="Authentication required")
-        return
-
-    await websocket.accept()
-    print("[MEDIA-WS] WebSocket connection accepted", flush=True)
-
-    try:
-        db = next(get_db())
-
-        cred = RawCredential(bearer_token=bearer_token, api_key=api_key)
-        registry = get_provider_registry()
-        provider = registry.find(cred)
-        if provider is None:
-            print("WebSocket connection rejected: No provider accepts the credential")
-            await websocket.close(code=1008, reason="Invalid credentials")
-            db.close()
-            return
-
-        try:
-            principal = provider.authenticate(cred, db)
-        except AuthError as e:
-            print(f"WebSocket connection rejected: {e}")
-            await websocket.close(code=1008, reason=str(e))
-            db.close()
-            return
-
-        organization_id = principal.organization_id
-        if not organization_id:
-            print("WebSocket connection rejected: Principal has no organization")
-            await websocket.close(code=1008, reason="Invalid credentials")
-            db.close()
-            return
-        
-        # Get agent_id, persona_id and scenario_id from query params
-        agent_id = websocket.query_params.get("agent_id")
-        persona_id = websocket.query_params.get("persona_id")
-        scenario_id = websocket.query_params.get("scenario_id")
-        ui_surface = websocket.query_params.get("ui_surface")
-        
-        # Fetch agent and voice bundle once for routing and instructions
-        agent = None
-        voice_bundle = None
-        try:
-            if agent_id:
-                from app.models.database import Agent, VoiceBundle
-                agent_uuid = UUID(agent_id)
-                agent = db.query(Agent).filter(
-                    Agent.id == agent_uuid,
-                    Agent.organization_id == organization_id
-                ).first()
-                if agent and agent.voice_bundle_id:
-                    voice_bundle = db.query(VoiceBundle).filter(
-                        VoiceBundle.id == agent.voice_bundle_id,
-                        VoiceBundle.organization_id == organization_id
-                    ).first()
-        except ValueError:
-            pass
-
-        # Resolve workspace_id: derive from agent when available, otherwise fall
-        # back to the organization's default workspace so created records remain
-        # workspace-scoped.
-        workspace_id: Optional[UUID] = None
-        if agent and getattr(agent, "workspace_id", None):
-            workspace_id = agent.workspace_id
-        else:
-            default_ws = db.query(Workspace).filter(
-                Workspace.organization_id == organization_id,
-                Workspace.is_default == True,  # noqa: E712
-            ).first()
-            if default_ws:
-                workspace_id = default_ws.id
-
-        use_voice_bundle_pipeline = bool(voice_bundle and voice_bundle.bundle_type == "stt_llm_tts")
-
-        def resolve_api_key_for_provider(provider: ModelProvider) -> str | None:
-            """Resolve API key from AIProvider (preferred) or Integration for given provider."""
-            from sqlalchemy import func
-            # 1) AIProvider (handle both string and enum comparisons)
-            provider_value = provider.value if hasattr(provider, 'value') else provider
-            
-            ai_provider_rec = db.query(AIProvider).filter(
-                AIProvider.organization_id == organization_id,
-                AIProvider.provider == provider_value,
-                AIProvider.is_active == True,
-            ).first()
-            
-            # If not found, try case-insensitive match
-            if not ai_provider_rec:
-                ai_provider_rec = db.query(AIProvider).filter(
-                    AIProvider.organization_id == organization_id,
-                    func.lower(AIProvider.provider) == provider_value.lower(),
-                    AIProvider.is_active == True,
-                ).first()
-            if ai_provider_rec:
-                try:
-                    key = decrypt_api_key(ai_provider_rec.api_key)
-                    logger.debug(
-                        f"[resolve_api_key] Found AIProvider key for '{provider_value}': "
-                        f"starts={key[:6]}... ends=...{key[-4:]}, len={len(key)}"
-                    )
-                    return key
-                except Exception as e:
-                    logger.error(f"Failed to decrypt AIProvider key for {provider}: {e}", exc_info=True)
-            else:
-                logger.debug(f"[resolve_api_key] No AIProvider record found for '{provider_value}'")
-
-            # 2) Integration mapping (only for platforms that exist in IntegrationPlatform)
-            platform_map = {
-                ModelProvider.DEEPGRAM: IntegrationPlatform.DEEPGRAM,
-                ModelProvider.CARTESIA: IntegrationPlatform.CARTESIA,
-                ModelProvider.ELEVENLABS: IntegrationPlatform.ELEVENLABS,
-                ModelProvider.MURF: IntegrationPlatform.MURF,
-                ModelProvider.SARVAM: IntegrationPlatform.SARVAM,
-                ModelProvider.VOICEMAKER: IntegrationPlatform.VOICEMAKER,
-                ModelProvider.SMALLEST: IntegrationPlatform.SMALLEST,
-            }
-            plat = platform_map.get(provider)
-            if plat:
-                # Handle both string and enum comparisons for platform
-                plat_value = plat.value if hasattr(plat, 'value') else plat
-                integ = db.query(Integration).filter(
-                    Integration.organization_id == organization_id,
-                    Integration.platform == plat_value,
-                    Integration.is_active == True,
-                ).first()
-                
-                # If not found, try case-insensitive match
-                if not integ:
-                    integ = db.query(Integration).filter(
-                        Integration.organization_id == organization_id,
-                        func.lower(Integration.platform) == plat_value.lower(),
-                        Integration.is_active == True,
-                    ).first()
-                
-                if integ:
-                    try:
-                        key = decrypt_api_key(integ.api_key)
-                        logger.debug(
-                            f"[resolve_api_key] Found Integration key for '{provider_value}' (platform={plat_value}): "
-                            f"starts={key[:6]}... ends=...{key[-4:]}, len={len(key)}"
-                        )
-                        return key
-                    except Exception as e:
-                        logger.error(f"Failed to decrypt Integration key for {provider}: {e}", exc_info=True)
-                else:
-                    logger.debug(f"[resolve_api_key] No Integration record found for platform '{plat_value}'")
-            else:
-                logger.debug(f"[resolve_api_key] No platform mapping for provider '{provider_value}'")
-
-            logger.warning(f"[resolve_api_key] Could not resolve any API key for provider '{provider_value}'")
-            return None
-
-        def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
-            """Resolve Azure OpenAI endpoint URL from the org's AIProvider credential."""
-            from sqlalchemy import func
-
-            provider_value = provider.value if hasattr(provider, "value") else provider
-            ai_provider_rec = db.query(AIProvider).filter(
-                AIProvider.organization_id == organization_id,
-                AIProvider.provider == provider_value,
-                AIProvider.is_active == True,
-            ).first()
-            if not ai_provider_rec:
-                ai_provider_rec = db.query(AIProvider).filter(
-                    AIProvider.organization_id == organization_id,
-                    func.lower(AIProvider.provider) == provider_value.lower(),
-                    AIProvider.is_active == True,
-                ).first()
-            if not ai_provider_rec:
-                return None
-            return _resolve_azure_endpoint_from_provider(ai_provider_rec, None)
-
-        # Determine which AI Provider to use (only needed for S2S/Gemini path)
-        # Priority: 1) Agent's ai_provider_id, 2) Default Google
-        ai_provider = None
-        google_api_key = None
-        if not use_voice_bundle_pipeline:
-            if agent and agent.ai_provider_id:
-                ai_provider = db.query(AIProvider).filter(
-                    AIProvider.id == agent.ai_provider_id,
-                    AIProvider.organization_id == organization_id,
-                    AIProvider.is_active == True
-                ).first()
-
-            # Fall back to Google AI Provider if no agent-specific provider found
-            if not ai_provider:
-                from sqlalchemy import func
-                google_value = ModelProvider.GOOGLE.value
-                ai_provider = db.query(AIProvider).filter(
-                    AIProvider.organization_id == organization_id,
-                    AIProvider.provider == google_value,
-                    AIProvider.is_active == True
-                ).first()
-                # If not found, try case-insensitive match
-                if not ai_provider:
-                    ai_provider = db.query(AIProvider).filter(
-                        AIProvider.organization_id == organization_id,
-                        func.lower(AIProvider.provider) == google_value.lower(),
-                        AIProvider.is_active == True
-                    ).first()
-
-            if not ai_provider:
-                await websocket.close(
-                    code=1008,
-                    reason="AI Provider not configured. Please configure an AI Provider in AI Providers settings or select one when creating the agent."
-                )
-                return
-
-            # Decrypt API key
-            try:
-                google_api_key = decrypt_api_key(ai_provider.api_key)
-                if not google_api_key or not google_api_key.strip():
-                    logger.error("Decrypted API key is empty")
-                    await websocket.close(
-                        code=1008,
-                        reason="API key is empty. Please configure a valid API key in AI Providers settings."
-                    )
-                    return
-            except Exception as e:
-                logger.error(f"Failed to decrypt API key: {e}", exc_info=True)
-                await websocket.close(
-                    code=1008,
-                    reason=f"Failed to decrypt API key: {str(e)}"
-                )
-                return
-        
-        system_instruction = None
-        instruction_parts = []
-        
-        # Build system instruction as a bundle: Agent + Persona + Scenario
-        from app.models.database import Agent, Persona, Scenario
-        
-        # 1. Add Agent description (base instruction) and get voice bundle for model
-        model_name = None
-        if agent:
-            if agent.description:
-                instruction_parts.append(agent.description)
-            if voice_bundle and voice_bundle.bundle_type == "s2s" and voice_bundle.s2s_model:
-                model_name = voice_bundle.s2s_model
-        
-        # 2. Add Persona information (characteristics)
-        persona = None
-        if persona_id:
-            try:
-                persona_uuid = UUID(persona_id)
-                persona_query = db.query(Persona).filter(
-                    Persona.id == persona_uuid,
-                    Persona.organization_id == organization_id
-                )
-                if workspace_id is not None:
-                    persona_query = persona_query.filter(Persona.workspace_id == workspace_id)
-                persona = persona_query.first()
-                if persona:
-                    persona_parts = []
-                    persona_parts.append(f"\n\nPersona: {persona.name}")
-                    if persona.gender:
-                        gender_val = persona.gender.value if hasattr(persona.gender, "value") else persona.gender
-                        persona_parts.append(f"Gender: {gender_val}")
-                    if getattr(persona, "tts_provider", None):
-                        persona_parts.append(f"Voice provider: {persona.tts_provider}")
-                    if getattr(persona, "tts_voice_name", None):
-                        persona_parts.append(f"Voice: {persona.tts_voice_name}")
-
-                    if persona_parts:
-                        instruction_parts.append("\n".join(persona_parts))
-            except ValueError:
-                pass
-        
-        # 3. Add Scenario information (context and goals)
-        if scenario_id:
-            try:
-                scenario_uuid = UUID(scenario_id)
-                scenario_query = db.query(Scenario).filter(
-                    Scenario.id == scenario_uuid,
-                    Scenario.organization_id == organization_id
-                )
-                if workspace_id is not None:
-                    scenario_query = scenario_query.filter(Scenario.workspace_id == workspace_id)
-                scenario = scenario_query.first()
-                if scenario:
-                    scenario_parts = []
-                    scenario_parts.append(f"\n\nScenario: {scenario.name}")
-                    if scenario.description:
-                        scenario_parts.append(f"Description: {scenario.description}")
-                    if scenario.required_info:
-                        required_info_str = ", ".join([f"{k}: {v}" for k, v in scenario.required_info.items()]) if isinstance(scenario.required_info, dict) else str(scenario.required_info)
-                        if required_info_str:
-                            scenario_parts.append(f"Required information to collect: {required_info_str}")
-                    
-                    if scenario_parts:
-                        instruction_parts.append("\n".join(scenario_parts))
-            except ValueError:
-                pass
-        
-        # Combine all parts into final system instruction
-        if instruction_parts:
-            system_instruction = "\n".join(instruction_parts)
-        
-        # Generate result_id BEFORE running bot (for meaningful S3 path)
-        # Evaluator is only created if persona_id and scenario_id are provided
-        # But evaluator results can be created even without persona/scenario
-        evaluator = None
-        result_id = None
-        scenario_name = "Test Call"  # Default name for calls without scenario
-        
-        # Generate result_id for all test calls (with or without persona/scenario)
-        if agent_id:
-            try:
-                from app.models.database import EvaluatorResult
-                import random
-                
-                # Generate unique 6-digit result ID
-                max_attempts = 100
-                for _ in range(max_attempts):
-                    candidate_id = f"{random.randint(100000, 999999)}"
-                    existing = db.query(EvaluatorResult).filter(EvaluatorResult.result_id == candidate_id).first()
-                    if not existing:
-                        result_id = candidate_id
-                        break
-                
-                if not result_id:
-                    logger.warning("Failed to generate unique result ID, will use UUID in S3 path")
-            except Exception as e:
-                logger.warning(f"Error generating result_id: {e}")
-        
-        # Find or create evaluator only if persona_id and scenario_id are provided
-        if agent_id and persona_id and scenario_id:
-            try:
-                from app.models.database import Evaluator, EvaluatorResult, EvaluatorResultStatus, Scenario
-                from app.api.v1.routes.evaluators import generate_unique_evaluator_id
-                import random
-                
-                # Find evaluator by agent, persona, scenario
-                evaluator_query = db.query(Evaluator).filter(
-                    Evaluator.agent_id == UUID(agent_id),
-                    Evaluator.persona_id == UUID(persona_id),
-                    Evaluator.scenario_id == UUID(scenario_id),
-                    Evaluator.organization_id == organization_id
-                )
-                if workspace_id is not None:
-                    evaluator_query = evaluator_query.filter(Evaluator.workspace_id == workspace_id)
-                evaluator = evaluator_query.first()
-                
-                # If no evaluator exists, create one automatically for test voice agent calls
-                if not evaluator:
-                    logger.info(f"Creating evaluator automatically for test voice agent: agent={agent_id}, persona={persona_id}, scenario={scenario_id}")
-                    evaluator_id = generate_unique_evaluator_id(db)
-                    evaluator = Evaluator(
-                        evaluator_id=evaluator_id,
-                        organization_id=organization_id,
-                        workspace_id=workspace_id,
-                        agent_id=UUID(agent_id),
-                        persona_id=UUID(persona_id),
-                        scenario_id=UUID(scenario_id),
-                        tags=["auto-created", "test-voice-agent"]
-                    )
-                    db.add(evaluator)
-                    db.commit()
-                    db.refresh(evaluator)
-                    logger.info(f"✅ Created evaluator {evaluator_id} for test voice agent")
-                
-                if evaluator:
-                    # Get scenario name
-                    scenario_name_query = db.query(Scenario).filter(Scenario.id == UUID(scenario_id))
-                    if workspace_id is not None:
-                        scenario_name_query = scenario_name_query.filter(Scenario.workspace_id == workspace_id)
-                    scenario = scenario_name_query.first()
-                    scenario_name = scenario.name if scenario else "Unknown Scenario"
-            except Exception as e:
-                logger.warning(f"Error finding/creating evaluator or generating result_id: {e}")
-        
-        # Check if this is a test agent that should bridge to Voice AI agent
-        # This happens when evaluator is run via the run_evaluator_task
-        test_agent_bridge_mode = False
-        retell_call_id = None
-        retell_access_token = None
-        retell_sample_rate = None
-        
-        if evaluator and agent and agent.voice_bundle_id and agent.voice_ai_integration_id:
-            # Check if there's an active call for this evaluator
-            # The call info is stored in the EvaluatorResult's error_message field temporarily
-            try:
-                from app.models.database import EvaluatorResult
-                # Find the most recent result for this evaluator that has call info
-                active_result = db.query(EvaluatorResult).filter(
-                    EvaluatorResult.evaluator_id == evaluator.id,
-                    EvaluatorResult.status == EvaluatorResultStatus.QUEUED.value,
-                    EvaluatorResult.error_message.isnot(None),
-                    EvaluatorResult.error_message.like("call_id:%")
-                ).order_by(EvaluatorResult.timestamp.desc()).first()
-                
-                if active_result and active_result.error_message:
-                    # Parse call info from error_message: "call_id:xxx|access_token:yyy|sample_rate:zzz"
-                    call_info_parts = active_result.error_message.split("|")
-                    for part in call_info_parts:
-                        if part.startswith("call_id:"):
-                            retell_call_id = part.split(":", 1)[1]
-                        elif part.startswith("access_token:"):
-                            retell_access_token = part.split(":", 1)[1]
-                        elif part.startswith("sample_rate:"):
-                            retell_sample_rate = int(part.split(":", 1)[1])
-                    
-                    if retell_call_id and retell_access_token:
-                        test_agent_bridge_mode = True
-                        logger.info(
-                            f"[VoiceAgent] Test agent bridge mode detected: "
-                            f"evaluator={evaluator.evaluator_id}, call_id={retell_call_id}"
-                        )
-            except Exception as e:
-                logger.warning(f"[VoiceAgent] Error checking for bridge mode: {e}", exc_info=True)
-        
-        # Run the bot with the appropriate pipeline
-        call_metadata = None
-        session_uuid: Optional[UUID] = None
-        from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
-
-        if agent_id and workspace_id and not test_agent_bridge_mode:
-            session_uuid = uuid4()
-
-        agent_silence_hangup_secs = resolve_agent_silence_hangup_secs(agent)
-        try:
-            if use_voice_bundle_pipeline:
-                # Resolve per-provider keys for voice bundle
-                stt_provider = voice_bundle.stt_provider if voice_bundle else None
-                tts_provider = voice_bundle.tts_provider if voice_bundle else None
-                llm_provider = voice_bundle.llm_provider if voice_bundle else None
-
-                stt_api_key = resolve_api_key_for_provider(stt_provider) if stt_provider else None
-                tts_api_key = resolve_api_key_for_provider(tts_provider) if tts_provider else None
-                llm_api_key = resolve_api_key_for_provider(llm_provider) if llm_provider else None
-                llm_endpoint_url = (
-                    resolve_azure_endpoint_for_provider(llm_provider)
-                    if llm_provider and (
-                        llm_provider.value if hasattr(llm_provider, "value") else str(llm_provider)
-                    ).lower() == "azure"
-                    else None
-                )
-
-                # If in bridge mode, we need to bridge test agent to Retell call
-                # For now, we'll run the voice bundle normally and note that bridging
-                # requires WebRTC implementation on the backend
-                if test_agent_bridge_mode:
-                    logger.info(
-                        f"[VoiceAgent] Bridge mode: Test agent should connect to Retell call {retell_call_id}. "
-                        f"Full WebRTC bridging requires additional implementation."
-                    )
-                    # TODO: Implement WebRTC bridging between test agent WebSocket and Retell call
-                    # This would require:
-                    # 1. Joining Retell call using WebRTC (aiortc or similar)
-                    # 2. Bridging audio streams bidirectionally
-                    # 3. Recording the bridged conversation
-
-                call_metadata = await run_voice_bundle_fastapi(
-                    websocket,
-                    system_instruction,
-                    str(organization_id),
-                    str(workspace_id) if workspace_id else None,
-                    agent_id,
-                    persona_id,
-                    scenario_id,
-                    evaluator_id=str(evaluator.id) if evaluator else None,
-                    result_id=result_id,
-                    voice_bundle=voice_bundle,
-                    persona=persona,
-                    stt_api_key=stt_api_key,
-                    tts_api_key=tts_api_key,
-                    llm_api_key=llm_api_key,
-                    llm_endpoint_url=llm_endpoint_url,
-                    silence_hangup_secs=agent_silence_hangup_secs,
-                )
-            else:
-                call_metadata = await run_bot(
-                    websocket,
-                    google_api_key,
-                    system_instruction,
-                    str(organization_id),
-                    agent_id,
-                    persona_id,
-                    scenario_id,
-                    evaluator_id=str(evaluator.id) if evaluator else None,
-                    result_id=result_id,
-                    model_name=model_name,  # Pass model name from voice bundle
-                    silence_hangup_secs=agent_silence_hangup_secs,
-                )
-        except Exception as bot_error:
-            logger.error(f"Error in run_bot: {bot_error}", exc_info=True)
-            # Continue to try creating evaluator result if we have metadata
-        
-        # Create evaluator result if we have the required data (only if no error)
-        # Also create if we have a live transcript even without S3 audio
-        has_audio = call_metadata and call_metadata.get("s3_key")
-        has_transcript = call_metadata and call_metadata.get("transcription")
-        has_usable_data = has_audio or has_transcript
-        if call_metadata and has_usable_data and not call_metadata.get("error") and agent_id and result_id:
-            # If we don't have evaluator but have persona/scenario, try to create one
-            if not evaluator and agent_id and persona_id and scenario_id:
-                try:
-                    from app.models.database import Evaluator, Scenario
-                    from app.api.v1.routes.evaluators import generate_unique_evaluator_id
-                    import random
-                    
-                    fallback_eval_query = db.query(Evaluator).filter(
-                        Evaluator.agent_id == UUID(agent_id),
-                        Evaluator.persona_id == UUID(persona_id),
-                        Evaluator.scenario_id == UUID(scenario_id),
-                        Evaluator.organization_id == organization_id
-                    )
-                    if workspace_id is not None:
-                        fallback_eval_query = fallback_eval_query.filter(Evaluator.workspace_id == workspace_id)
-                    evaluator = fallback_eval_query.first()
-                    
-                    if not evaluator:
-                        evaluator_id = generate_unique_evaluator_id(db)
-                        evaluator = Evaluator(
-                            evaluator_id=evaluator_id,
-                            organization_id=organization_id,
-                            workspace_id=workspace_id,
-                            agent_id=UUID(agent_id),
-                            persona_id=UUID(persona_id),
-                            scenario_id=UUID(scenario_id),
-                            tags=["auto-created", "test-voice-agent"]
-                        )
-                        db.add(evaluator)
-                        db.commit()
-                        db.refresh(evaluator)
-                    
-                    fallback_scenario_query = db.query(Scenario).filter(Scenario.id == UUID(scenario_id))
-                    if workspace_id is not None:
-                        fallback_scenario_query = fallback_scenario_query.filter(Scenario.workspace_id == workspace_id)
-                    scenario = fallback_scenario_query.first()
-                    scenario_name = scenario.name if scenario else "Unknown Scenario"
-                    
-                    # Generate result_id
-                    max_attempts = 100
-                    for _ in range(max_attempts):
-                        candidate_id = f"{random.randint(100000, 999999)}"
-                        existing = db.query(EvaluatorResult).filter(EvaluatorResult.result_id == candidate_id).first()
-                        if not existing:
-                            result_id = candidate_id
-                            break
-                except Exception as e:
-                    logger.error(f"Error creating evaluator/result_id after bot run: {e}")
-            
-            # Create evaluator result for all test calls (with or without persona/scenario)
-            # evaluator_id is optional - can be None if no persona/scenario
-            if result_id and agent_id:
-                try:
-                    from app.models.database import (
-                        CallRecording,
-                        CallRecordingSource,
-                        EvaluatorResult,
-                        EvaluatorResultStatus,
-                    )
-                    from app.models.enums import CallRecordingStatus
-                    from app.utils.call_recordings import generate_unique_call_short_id
-                    from app.workers.celery_app import process_evaluator_result_task
-
-                    # Determine name for the result
-                    if scenario_name and scenario_name != "Test Call":
-                        result_name = scenario_name
-                    elif agent:
-                        result_name = f"Test Call - {agent.name}"
-                    else:
-                        result_name = "Test Call"
-
-                    logger.info(f"Creating evaluator result: result_id={result_id}, agent_id={agent_id}, persona_id={persona_id}, scenario_id={scenario_id}, s3_key={call_metadata.get('s3_key')}")
-
-                    call_short_id = generate_unique_call_short_id(db)
-                    speaker_segments = call_metadata.get("speaker_segments") or []
-                    if not isinstance(speaker_segments, list):
-                        speaker_segments = []
-                    playground_call_data = {
-                        "source": "voice_bundle",
-                        "result_id": result_id,
-                        "transcript": call_metadata.get("transcription"),
-                        "speaker_segments": speaker_segments,
-                        "recording_s3_key": call_metadata.get("s3_key"),
-                        "duration_seconds": call_metadata.get("duration"),
-                    }
-                    if ui_surface:
-                        playground_call_data["ui_surface"] = ui_surface
-
-                    # Create evaluator result with QUEUED status
-                    # persona_id and scenario_id can be None for test calls without persona/scenario
-                    evaluator_result = EvaluatorResult(
-                        result_id=result_id,
-                        organization_id=organization_id,
-                        workspace_id=workspace_id,
-                        evaluator_id=evaluator.id if evaluator else None,  # Optional
-                        agent_id=UUID(agent_id),
-                        persona_id=UUID(persona_id) if persona_id else None,  # Optional
-                        scenario_id=UUID(scenario_id) if scenario_id else None,  # Optional
-                        name=result_name,
-                        duration_seconds=call_metadata.get("duration"),
-                        status=EvaluatorResultStatus.QUEUED.value,  # Use .value to get the string
-                        audio_s3_key=call_metadata.get("s3_key"),
-                        transcription=call_metadata.get("transcription"),
-                        speaker_segments=speaker_segments or None,
-                    )
-                    db.add(evaluator_result)
-                    db.flush()
-
-                    call_recording = CallRecording(
-                        organization_id=organization_id,
-                        workspace_id=workspace_id,
-                        call_short_id=call_short_id,
-                        status=CallRecordingStatus.UPDATED,
-                        source=CallRecordingSource.PLAYGROUND,
-                        call_data=playground_call_data,
-                        provider_call_id=f"voice_bundle_{result_id}",
-                        provider_platform="voice_bundle",
-                        agent_id=UUID(agent_id),
-                        evaluator_result_id=evaluator_result.id,
-                    )
-                    db.add(call_recording)
-                    db.commit()
-                    db.refresh(evaluator_result)
-
-                    # Playground scoring bills via playground.evaluation_completed.
-                    
-                    logger.info(f"✅ Evaluator result created in database: id={evaluator_result.id}, result_id={result_id}")
-                    
-                    # Trigger Celery task
-                    try:
-                        logger.info(f"Triggering Celery task for evaluator result: {evaluator_result.id}")
-                        
-                        # Check if Celery app is properly configured
-                        from app.workers.celery_app import celery_app
-                        logger.info(f"Celery broker URL: {celery_app.conf.broker_url}")
-                        logger.info(f"Celery result backend: {celery_app.conf.result_backend}")
-                        
-                        # Verify task is registered
-                        if 'process_evaluator_result' not in celery_app.tasks:
-                            logger.error("❌ Task 'process_evaluator_result' is not registered in Celery app!")
-                            logger.error(f"Available tasks: {list(celery_app.tasks.keys())}")
-                        else:
-                            logger.info("✅ Task 'process_evaluator_result' is registered")
-                        
-                        task = process_evaluator_result_task.delay(str(evaluator_result.id))
-                        logger.info(f"✅ Celery task triggered: task_id={task.id}, task_state={task.state}")
-                        
-                        # Try to get task info to verify it was queued
-                        try:
-                            task_info = task.info
-                            logger.info(f"Task info: {task_info}")
-                        except Exception as info_error:
-                            logger.warning(f"Could not get task info (this is normal for async tasks): {info_error}")
-                        
-                        evaluator_result.celery_task_id = task.id
-                        db.commit()
-                        logger.info(f"✅ Updated evaluator result with celery_task_id: {task.id}")
-                    except Exception as task_error:
-                        logger.error(f"❌ Failed to trigger Celery task: {task_error}", exc_info=True)
-                        # Still log that we created the result even if task trigger failed
-                        logger.warning(f"Evaluator result {result_id} created but Celery task was not triggered. Task may need to be triggered manually.")
-                        logger.warning(f"Please ensure Celery worker is running: celery -A app.workers.celery_app worker --loglevel=info")
-                    
-                    logger.info(f"✅ Created evaluator result {result_id} and triggered processing task")
-                except Exception as e:
-                    logger.error(f"❌ Error creating evaluator result: {e}", exc_info=True)
-        
-    except WebSocketDisconnect:
-        print("WebSocket disconnected by client")
-    except Exception as e:
-        print(f"Exception in voice agent WebSocket: {e}")
-        import traceback
-        traceback.print_exc()
-        try:
-            await websocket.close(code=1011, reason=f"Server error: {str(e)}")
-        except:
-            pass
-    finally:
-        try:
-            if 'db' in locals():
-                db.close()
-        except:
-            pass
-
-
-@router.options("/connect")
-async def bot_connect_options():
-    """Handle CORS preflight requests."""
-    from fastapi.responses import Response
-    return Response(
-        status_code=200,
-        headers={
-            "Access-Control-Allow-Origin": "*",
-            "Access-Control-Allow-Methods": "GET, POST, OPTIONS",
-            "Access-Control-Allow-Headers": "*",
-            "Access-Control-Allow-Credentials": "true",
-        }
-    )
-
-@router.post("/connect", response_model=Dict[str, Any])
-@router.get("/connect", response_model=Dict[str, Any])
-async def bot_connect(
-    request: Request,
-    db: Session = Depends(get_db),
-) -> Dict[str, Any]:
-    """
-    Get WebSocket connection URL for voice agent.
-    Returns the WebSocket URL that the client should connect to.
-
-    Accepts either a Bearer access token (email/password / SSO login) or an
-    API key (legacy / machine access). Credentials may be supplied via the
-    `Authorization` header, `X-API-Key` header, cookies (`access_token` /
-    `api_key`), or query parameters (`token` / `X-API-Key` / `api_key`).
-
-    Supports both GET and POST requests for compatibility with different client
-    implementations. Pipecat's `startBotAndConnect` issues an HTTP request to
-    this endpoint; the WebSocket URL returned here embeds the same credential
-    so the subsequent /ws connection can authenticate without re-prompting.
-    """
-    from app.core.auth.providers import (
-        AuthError,
-        RawCredential,
-        get_provider_registry,
-    )
-
-    print("=" * 80)
-    print(f"[BACKEND] /connect endpoint called at {__import__('datetime').datetime.now()}")
-    print(f"[BACKEND] Request method: {request.method}")
-    print(f"[BACKEND] Request URL: {request.url}")
-    print(f"[BACKEND] Request cookies present: {list(request.cookies.keys())}")
-    print(f"[BACKEND] Query params: {dict(request.query_params)}")
-
-    def _extract_bearer(value: Optional[str]) -> Optional[str]:
-        if not value:
-            return None
-        scheme, _, token = value.partition(" ")
-        if scheme.lower() != "bearer" or not token.strip():
-            return None
-        return token.strip()
-
-    # Bearer / access token: header, query param, then cookie.
-    bearer_token = (
-        _extract_bearer(request.headers.get("Authorization"))
-        or request.query_params.get("token")
-        or request.query_params.get("access_token")
-        or request.cookies.get("access_token")
-    )
-
-    # API key: header, query param, then cookie.
-    api_key = (
-        request.headers.get("X-API-Key")
-        or request.headers.get("X-EFFICIENTAI-API-KEY")
-        or request.query_params.get("X-API-Key")
-        or request.query_params.get("api_key")
-        or request.cookies.get("api_key")
-    )
-
-    print(
-        f"[BACKEND] Bearer token: {'found' if bearer_token else 'not found'}, "
-        f"API key: {'found' if api_key else 'not found'}"
-    )
-
-    from app.config import settings
-
-    if not bearer_token and not api_key:
-        print("[BACKEND] ❌ No credentials found, returning 401")
-        raise HTTPException(
-            status_code=401,
-            detail=(
-                "Authentication required. Send an Authorization: Bearer "
-                "header, X-API-Key header, or matching cookie/query param."
-            ),
-        )
-
-    cred = RawCredential(bearer_token=bearer_token, api_key=api_key)
-    registry = get_provider_registry()
-    provider = registry.find(cred)
-    if provider is None:
-        print("[BACKEND] ❌ No auth provider accepted the credential")
-        raise HTTPException(status_code=401, detail="Invalid credentials")
-
-    try:
-        principal = provider.authenticate(cred, db)
-    except AuthError as e:
-        print(f"[BACKEND] ❌ Auth provider rejected credential: {e}")
-        raise HTTPException(status_code=e.status_code, detail=str(e))
-
-    organization_id = principal.organization_id
-    if not organization_id:
-        print("[BACKEND] ❌ Principal has no organization")
-        raise HTTPException(status_code=401, detail="Invalid credentials")
-
-    print(f"[BACKEND] ✅ Authenticated via {provider.name} (org={organization_id})")
-    
-    # Get agent_id, persona_id and scenario_id from query params first
-    agent_id = request.query_params.get("agent_id")
-    persona_id = request.query_params.get("persona_id")
-    scenario_id = request.query_params.get("scenario_id")
-    ui_surface = request.query_params.get("ui_surface")
-    
-    # Determine which AI Provider to use based on agent configuration
-    ai_provider = None
-    agent = None
-    voice_bundle = None
-    use_voice_bundle_pipeline = False
-    
-    if agent_id:
-        try:
-            from app.models.database import Agent, VoiceBundle
-            agent_uuid = UUID(agent_id)
-            agent = db.query(Agent).filter(
-                Agent.id == agent_uuid,
-                Agent.organization_id == organization_id
-            ).first()
-            
-            # Check if agent has a voice bundle (stt_llm_tts type)
-            if agent and agent.voice_bundle_id:
-                voice_bundle = db.query(VoiceBundle).filter(
-                    VoiceBundle.id == agent.voice_bundle_id,
-                    VoiceBundle.organization_id == organization_id
-                ).first()
-                if voice_bundle and voice_bundle.bundle_type == "stt_llm_tts":
-                    use_voice_bundle_pipeline = True
-                    print(f"[BACKEND] ✅ Using custom voice bundle: {voice_bundle.name}")
-            
-            if agent and agent.ai_provider_id:
-                ai_provider = db.query(AIProvider).filter(
-                    AIProvider.id == agent.ai_provider_id,
-                    AIProvider.organization_id == organization_id,
-                    AIProvider.is_active == True
-                ).first()
-        except ValueError:
-            pass
-    
-    # For custom voice bundles (stt_llm_tts), we don't need a Google provider
-    # The voice bundle has its own STT/LLM/TTS providers configured
-    if not use_voice_bundle_pipeline:
-        # Fall back to Google AI Provider if no agent-specific provider found (needed for S2S/Gemini path)
-        if not ai_provider:
-            from sqlalchemy import func
-            google_value = ModelProvider.GOOGLE.value
-            ai_provider = db.query(AIProvider).filter(
-                AIProvider.organization_id == organization_id,
-                AIProvider.provider == google_value,
-                AIProvider.is_active == True
-            ).first()
-            # If not found, try case-insensitive match
-            if not ai_provider:
-                ai_provider = db.query(AIProvider).filter(
-                    AIProvider.organization_id == organization_id,
-                    func.lower(AIProvider.provider) == google_value.lower(),
-                    AIProvider.is_active == True
-                ).first()
-        
-        if not ai_provider:
-            print("[BACKEND] ❌ AI Provider not configured")
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail="AI Provider not configured. Please configure an AI Provider in AI Providers settings or select one when creating the agent."
-            )
-        
-        provider_val = ai_provider.provider.value if hasattr(ai_provider.provider, 'value') else ai_provider.provider
-        print(f"[BACKEND] ✅ AI Provider found: {provider_val}")
-    else:
-        # For voice bundle pipeline, verify the required providers are configured
-        from sqlalchemy import func
-        missing_providers = []
-        
-        def check_provider(provider_enum):
-            """Check if a provider is configured (either as AIProvider or Integration)."""
-            if not provider_enum:
-                return True  # Not required
-            
-            provider_value = provider_enum.value if hasattr(provider_enum, 'value') else str(provider_enum)
-            
-            # Check AIProvider
-            found = db.query(AIProvider).filter(
-                AIProvider.organization_id == organization_id,
-                AIProvider.is_active == True,
-            ).filter(
-                (AIProvider.provider == provider_value) | 
-                (func.lower(AIProvider.provider) == provider_value.lower())
-            ).first()
-            
-            if found:
-                return True
-            
-            # Check Integration for Deepgram, Cartesia, and ElevenLabs
-            platform_map = {
-                'deepgram': IntegrationPlatform.DEEPGRAM,
-                'cartesia': IntegrationPlatform.CARTESIA,
-                'elevenlabs': IntegrationPlatform.ELEVENLABS,
-                'murf': IntegrationPlatform.MURF,
-                'sarvam': IntegrationPlatform.SARVAM,
-                'voicemaker': IntegrationPlatform.VOICEMAKER,
-                'smallest': IntegrationPlatform.SMALLEST,
-            }
-            plat = platform_map.get(provider_value.lower())
-            if plat:
-                plat_value = plat.value if hasattr(plat, 'value') else plat
-                found = db.query(Integration).filter(
-                    Integration.organization_id == organization_id,
-                    Integration.is_active == True,
-                ).filter(
-                    (Integration.platform == plat_value) | 
-                    (func.lower(Integration.platform) == plat_value.lower())
-                ).first()
-                if found:
-                    return True
-            
-            return False
-        
-        # Check required providers for the voice bundle
-        if voice_bundle.stt_provider and not check_provider(voice_bundle.stt_provider):
-            missing_providers.append(f"STT: {voice_bundle.stt_provider}")
-        if voice_bundle.llm_provider and not check_provider(voice_bundle.llm_provider):
-            missing_providers.append(f"LLM: {voice_bundle.llm_provider}")
-        if voice_bundle.tts_provider and not check_provider(voice_bundle.tts_provider):
-            missing_providers.append(f"TTS: {voice_bundle.tts_provider}")
-        
-        if missing_providers:
-            print(f"[BACKEND] ❌ Missing providers for voice bundle: {missing_providers}")
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Missing AI providers for voice bundle: {', '.join(missing_providers)}. Please configure them in Integrations settings."
-            )
-        
-        print(f"[BACKEND] ✅ Voice bundle providers configured")
-    
-    # Determine WebSocket URL — prefer dedicated media server when configured.
-    from urllib.parse import quote
-
-    from app.services.media_urls import build_voice_agent_ws_url
-
-    if bearer_token:
-        ws_auth_query = f"token={quote(bearer_token, safe='')}"
-    else:
-        ws_auth_query = f"X-API-Key={quote(api_key or '', safe='')}"
-
-    ws_url = build_voice_agent_ws_url(
-        auth_query=ws_auth_query,
-        agent_id=agent_id,
-        persona_id=persona_id,
-        scenario_id=scenario_id,
-        ui_surface=ui_surface,
-        fallback_host=request.headers.get("host", f"localhost:{settings.PORT}"),
-        fallback_scheme=(
-            request.headers.get("x-forwarded-proto")
-            or getattr(request.url, "scheme", "http")
-            or "http"
-        ),
-    )
-    
-    # Return the response in the format Pipecat expects
-    # Pipecat expects a JSON response with ws_url field
-    # The response should be simple and match exactly what Pipecat expects
-    from fastapi.responses import JSONResponse
-    response_data = {
-        "ws_url": ws_url
-    }
-    print(f"[BACKEND] ✅ Returning WebSocket URL: {ws_url}")
-    print(f"[BACKEND] Response data: {response_data}")
-    print("=" * 80)
-    
-    # Return JSON response - CORS is handled by middleware
-    return JSONResponse(
-        content=response_data,
-        status_code=200,
-        headers={
-            "Content-Type": "application/json",
-        }
-    )
-
-
-@router.get("/audio", response_model=List[Dict[str, Any]])
-async def list_voice_agent_audio_files(
-    organization_id: UUID = Depends(get_organization_id),
-    max_keys: int = 1000,
-):
-    """
-    List audio files for voice agent conversations for the current organization.
-    
-    Args:
-        organization_id: Organization ID from API key
-        max_keys: Maximum number of files to return
-        
-    Returns:
-        List of audio file metadata with keys: key, size, last_modified, filename
-    """
-    try:
-        files = s3_service.list_audio_files(
-            organization_id=str(organization_id),
-            max_keys=max_keys
-        )
-        return files
-    except Exception as e:
-        logger.error(f"Error listing voice agent audio files: {e}")
-        raise HTTPException(
-            status_code=500,
-            detail=f"Failed to list audio files: {str(e)}"
-        )
-
+"""
+Voice Agent API Routes
+API endpoints for managing voice agent WebSocket connections.
+"""
+
+from fastapi import APIRouter, Depends, HTTPException, WebSocket, WebSocketDisconnect, Request, status
+from sqlalchemy.orm import Session
+from uuid import UUID, uuid4
+from typing import Dict, Any, Optional, List
+from loguru import logger
+
+from app.database import get_db
+from app.dependencies import get_organization_id, get_api_key
+from app.models.database import AIProvider, ModelProvider, Integration, IntegrationPlatform, Workspace
+from app.core.encryption import decrypt_api_key
+from app.services.voice_agent.bot_fast_api import run_bot
+from app.services.ai.llm_service import _resolve_azure_endpoint_from_provider
+from app.services.voice_agent.voice_bundle import run_voice_bundle_fastapi
+from app.services.storage.s3_service import s3_service
+
+router = APIRouter(prefix="/voice-agent", tags=["voice-agent"])
+ws_router = APIRouter(prefix="/voice-agent", tags=["voice-agent-media"])
+
+
+@ws_router.websocket("/ws")
+async def websocket_endpoint(
+    websocket: WebSocket,
+):
+    """
+    WebSocket endpoint for voice agent connection.
+
+    Authentication precedence (since browsers can't set custom headers on
+    WebSockets reliably, everything goes on query params):
+
+        1. ?token=<bearer>      -> local-password / SSO session token
+        2. ?X-API-Key=<key>     -> API key (legacy header name)
+        3. ?api_key=<key>       -> API key
+
+    Under the hood we reuse the same pluggable auth registry used by the
+    HTTP routes so the authorization rules stay consistent.
+    """
+    from app.core.auth.providers import AuthError, RawCredential, get_provider_registry
+
+    bearer_token = (
+        websocket.query_params.get("token")
+        or websocket.query_params.get("access_token")
+    )
+    api_key = (
+        websocket.query_params.get("X-API-Key")
+        or websocket.query_params.get("api_key")
+    )
+
+    if not bearer_token and not api_key:
+        from urllib.parse import parse_qs
+
+        raw_qs = websocket.scope.get("query_string", b"")
+        if isinstance(raw_qs, bytes):
+            raw_qs = raw_qs.decode("utf-8", errors="replace")
+        parsed = parse_qs(raw_qs)
+        bearer_token = bearer_token or (parsed.get("token") or parsed.get("access_token") or [None])[0]
+        api_key = api_key or (parsed.get("X-API-Key") or parsed.get("api_key") or [None])[0]
+
+    if not bearer_token and not api_key:
+        print(
+            f"[MEDIA-WS] rejected: no credentials (query_string_len="
+            f"{len(websocket.scope.get('query_string') or b'')})",
+            flush=True,
+        )
+        await websocket.close(code=1008, reason="Authentication required")
+        return
+
+    await websocket.accept()
+    print("[MEDIA-WS] WebSocket connection accepted", flush=True)
+
+    trace_call_short_id: Optional[str] = None
+    organization_id: Optional[UUID] = None
+    workspace_id: Optional[UUID] = None
+
+    try:
+        db = next(get_db())
+
+        cred = RawCredential(bearer_token=bearer_token, api_key=api_key)
+        registry = get_provider_registry()
+        provider = registry.find(cred)
+        if provider is None:
+            print("WebSocket connection rejected: No provider accepts the credential")
+            await websocket.close(code=1008, reason="Invalid credentials")
+            db.close()
+            return
+
+        try:
+            principal = provider.authenticate(cred, db)
+        except AuthError as e:
+            print(f"WebSocket connection rejected: {e}")
+            await websocket.close(code=1008, reason=str(e))
+            db.close()
+            return
+
+        organization_id = principal.organization_id
+        if not organization_id:
+            print("WebSocket connection rejected: Principal has no organization")
+            await websocket.close(code=1008, reason="Invalid credentials")
+            db.close()
+            return
+        
+        # Get agent_id, persona_id and scenario_id from query params
+        agent_id = websocket.query_params.get("agent_id")
+        persona_id = websocket.query_params.get("persona_id")
+        scenario_id = websocket.query_params.get("scenario_id")
+        ui_surface = websocket.query_params.get("ui_surface")
+        trace_call_short_id = (websocket.query_params.get("call_short_id") or "").strip() or None
+        trace_api_key = api_key
+        
+        # Fetch agent and voice bundle once for routing and instructions
+        agent = None
+        voice_bundle = None
+        try:
+            if agent_id:
+                from app.models.database import Agent, VoiceBundle
+                agent_uuid = UUID(agent_id)
+                agent = db.query(Agent).filter(
+                    Agent.id == agent_uuid,
+                    Agent.organization_id == organization_id
+                ).first()
+                if agent and agent.voice_bundle_id:
+                    voice_bundle = db.query(VoiceBundle).filter(
+                        VoiceBundle.id == agent.voice_bundle_id,
+                        VoiceBundle.organization_id == organization_id
+                    ).first()
+        except ValueError:
+            pass
+
+        # Resolve workspace_id: derive from agent when available, otherwise fall
+        # back to the organization's default workspace so created records remain
+        # workspace-scoped.
+        workspace_id = None
+        if agent and getattr(agent, "workspace_id", None):
+            workspace_id = agent.workspace_id
+        else:
+            default_ws = db.query(Workspace).filter(
+                Workspace.organization_id == organization_id,
+                Workspace.is_default == True,  # noqa: E712
+            ).first()
+            if default_ws:
+                workspace_id = default_ws.id
+
+        use_voice_bundle_pipeline = bool(voice_bundle and voice_bundle.bundle_type == "stt_llm_tts")
+
+        def resolve_api_key_for_provider(provider: ModelProvider) -> str | None:
+            """Resolve API key from AIProvider (preferred) or Integration for given provider."""
+            from sqlalchemy import func
+            # 1) AIProvider (handle both string and enum comparisons)
+            provider_value = provider.value if hasattr(provider, 'value') else provider
+            
+            ai_provider_rec = db.query(AIProvider).filter(
+                AIProvider.organization_id == organization_id,
+                AIProvider.provider == provider_value,
+                AIProvider.is_active == True,
+            ).first()
+            
+            # If not found, try case-insensitive match
+            if not ai_provider_rec:
+                ai_provider_rec = db.query(AIProvider).filter(
+                    AIProvider.organization_id == organization_id,
+                    func.lower(AIProvider.provider) == provider_value.lower(),
+                    AIProvider.is_active == True,
+                ).first()
+            if ai_provider_rec:
+                try:
+                    key = decrypt_api_key(ai_provider_rec.api_key)
+                    logger.debug(
+                        f"[resolve_api_key] Found AIProvider key for '{provider_value}': "
+                        f"starts={key[:6]}... ends=...{key[-4:]}, len={len(key)}"
+                    )
+                    return key
+                except Exception as e:
+                    logger.error(f"Failed to decrypt AIProvider key for {provider}: {e}", exc_info=True)
+            else:
+                logger.debug(f"[resolve_api_key] No AIProvider record found for '{provider_value}'")
+
+            # 2) Integration mapping (only for platforms that exist in IntegrationPlatform)
+            platform_map = {
+                ModelProvider.DEEPGRAM: IntegrationPlatform.DEEPGRAM,
+                ModelProvider.CARTESIA: IntegrationPlatform.CARTESIA,
+                ModelProvider.ELEVENLABS: IntegrationPlatform.ELEVENLABS,
+                ModelProvider.MURF: IntegrationPlatform.MURF,
+                ModelProvider.SARVAM: IntegrationPlatform.SARVAM,
+                ModelProvider.VOICEMAKER: IntegrationPlatform.VOICEMAKER,
+                ModelProvider.SMALLEST: IntegrationPlatform.SMALLEST,
+            }
+            plat = platform_map.get(provider)
+            if plat:
+                # Handle both string and enum comparisons for platform
+                plat_value = plat.value if hasattr(plat, 'value') else plat
+                integ = db.query(Integration).filter(
+                    Integration.organization_id == organization_id,
+                    Integration.platform == plat_value,
+                    Integration.is_active == True,
+                ).first()
+                
+                # If not found, try case-insensitive match
+                if not integ:
+                    integ = db.query(Integration).filter(
+                        Integration.organization_id == organization_id,
+                        func.lower(Integration.platform) == plat_value.lower(),
+                        Integration.is_active == True,
+                    ).first()
+                
+                if integ:
+                    try:
+                        key = decrypt_api_key(integ.api_key)
+                        logger.debug(
+                            f"[resolve_api_key] Found Integration key for '{provider_value}' (platform={plat_value}): "
+                            f"starts={key[:6]}... ends=...{key[-4:]}, len={len(key)}"
+                        )
+                        return key
+                    except Exception as e:
+                        logger.error(f"Failed to decrypt Integration key for {provider}: {e}", exc_info=True)
+                else:
+                    logger.debug(f"[resolve_api_key] No Integration record found for platform '{plat_value}'")
+            else:
+                logger.debug(f"[resolve_api_key] No platform mapping for provider '{provider_value}'")
+
+            logger.warning(f"[resolve_api_key] Could not resolve any API key for provider '{provider_value}'")
+            return None
+
+        def resolve_azure_endpoint_for_provider(provider: ModelProvider) -> str | None:
+            """Resolve Azure OpenAI endpoint URL from the org's AIProvider credential."""
+            from sqlalchemy import func
+
+            provider_value = provider.value if hasattr(provider, "value") else provider
+            ai_provider_rec = db.query(AIProvider).filter(
+                AIProvider.organization_id == organization_id,
+                AIProvider.provider == provider_value,
+                AIProvider.is_active == True,
+            ).first()
+            if not ai_provider_rec:
+                ai_provider_rec = db.query(AIProvider).filter(
+                    AIProvider.organization_id == organization_id,
+                    func.lower(AIProvider.provider) == provider_value.lower(),
+                    AIProvider.is_active == True,
+                ).first()
+            if not ai_provider_rec:
+                return None
+            return _resolve_azure_endpoint_from_provider(ai_provider_rec, None)
+
+        # Determine which AI Provider to use (only needed for S2S/Gemini path)
+        # Priority: 1) Agent's ai_provider_id, 2) Default Google
+        ai_provider = None
+        google_api_key = None
+        if not use_voice_bundle_pipeline:
+            if agent and agent.ai_provider_id:
+                ai_provider = db.query(AIProvider).filter(
+                    AIProvider.id == agent.ai_provider_id,
+                    AIProvider.organization_id == organization_id,
+                    AIProvider.is_active == True
+                ).first()
+
+            # Fall back to Google AI Provider if no agent-specific provider found
+            if not ai_provider:
+                from sqlalchemy import func
+                google_value = ModelProvider.GOOGLE.value
+                ai_provider = db.query(AIProvider).filter(
+                    AIProvider.organization_id == organization_id,
+                    AIProvider.provider == google_value,
+                    AIProvider.is_active == True
+                ).first()
+                # If not found, try case-insensitive match
+                if not ai_provider:
+                    ai_provider = db.query(AIProvider).filter(
+                        AIProvider.organization_id == organization_id,
+                        func.lower(AIProvider.provider) == google_value.lower(),
+                        AIProvider.is_active == True
+                    ).first()
+
+            if not ai_provider:
+                await websocket.close(
+                    code=1008,
+                    reason="AI Provider not configured. Please configure an AI Provider in AI Providers settings or select one when creating the agent."
+                )
+                return
+
+            # Decrypt API key
+            try:
+                google_api_key = decrypt_api_key(ai_provider.api_key)
+                if not google_api_key or not google_api_key.strip():
+                    logger.error("Decrypted API key is empty")
+                    await websocket.close(
+                        code=1008,
+                        reason="API key is empty. Please configure a valid API key in AI Providers settings."
+                    )
+                    return
+            except Exception as e:
+                logger.error(f"Failed to decrypt API key: {e}", exc_info=True)
+                await websocket.close(
+                    code=1008,
+                    reason=f"Failed to decrypt API key: {str(e)}"
+                )
+                return
+        
+        system_instruction = None
+        instruction_parts = []
+        
+        # Build system instruction as a bundle: Agent + Persona + Scenario
+        from app.models.database import Agent, Persona, Scenario
+        
+        # 1. Add Agent description (base instruction) and get voice bundle for model
+        model_name = None
+        if agent:
+            if agent.description:
+                instruction_parts.append(agent.description)
+            if voice_bundle and voice_bundle.bundle_type == "s2s" and voice_bundle.s2s_model:
+                model_name = voice_bundle.s2s_model
+        
+        # 2. Add Persona information (characteristics)
+        persona = None
+        if persona_id:
+            try:
+                persona_uuid = UUID(persona_id)
+                persona_query = db.query(Persona).filter(
+                    Persona.id == persona_uuid,
+                    Persona.organization_id == organization_id
+                )
+                if workspace_id is not None:
+                    persona_query = persona_query.filter(Persona.workspace_id == workspace_id)
+                persona = persona_query.first()
+                if persona:
+                    persona_parts = []
+                    persona_parts.append(f"\n\nPersona: {persona.name}")
+                    if persona.gender:
+                        gender_val = persona.gender.value if hasattr(persona.gender, "value") else persona.gender
+                        persona_parts.append(f"Gender: {gender_val}")
+                    if getattr(persona, "tts_provider", None):
+                        persona_parts.append(f"Voice provider: {persona.tts_provider}")
+                    if getattr(persona, "tts_voice_name", None):
+                        persona_parts.append(f"Voice: {persona.tts_voice_name}")
+
+                    if persona_parts:
+                        instruction_parts.append("\n".join(persona_parts))
+            except ValueError:
+                pass
+        
+        # 3. Add Scenario information (context and goals)
+        if scenario_id:
+            try:
+                scenario_uuid = UUID(scenario_id)
+                scenario_query = db.query(Scenario).filter(
+                    Scenario.id == scenario_uuid,
+                    Scenario.organization_id == organization_id
+                )
+                if workspace_id is not None:
+                    scenario_query = scenario_query.filter(Scenario.workspace_id == workspace_id)
+                scenario = scenario_query.first()
+                if scenario:
+                    scenario_parts = []
+                    scenario_parts.append(f"\n\nScenario: {scenario.name}")
+                    if scenario.description:
+                        scenario_parts.append(f"Description: {scenario.description}")
+                    if scenario.required_info:
+                        required_info_str = ", ".join([f"{k}: {v}" for k, v in scenario.required_info.items()]) if isinstance(scenario.required_info, dict) else str(scenario.required_info)
+                        if required_info_str:
+                            scenario_parts.append(f"Required information to collect: {required_info_str}")
+                    
+                    if scenario_parts:
+                        instruction_parts.append("\n".join(scenario_parts))
+            except ValueError:
+                pass
+        
+        # Combine all parts into final system instruction
+        if instruction_parts:
+            system_instruction = "\n".join(instruction_parts)
+        
+        # Generate result_id BEFORE running bot (for meaningful S3 path)
+        # Evaluator is only created if persona_id and scenario_id are provided
+        # But evaluator results can be created even without persona/scenario
+        evaluator = None
+        result_id = None
+        scenario_name = "Test Call"  # Default name for calls without scenario
+        
+        # Generate result_id for all test calls (with or without persona/scenario)
+        if agent_id:
+            try:
+                from app.models.database import EvaluatorResult
+                import random
+                
+                # Generate unique 6-digit result ID
+                max_attempts = 100
+                for _ in range(max_attempts):
+                    candidate_id = f"{random.randint(100000, 999999)}"
+                    existing = db.query(EvaluatorResult).filter(EvaluatorResult.result_id == candidate_id).first()
+                    if not existing:
+                        result_id = candidate_id
+                        break
+                
+                if not result_id:
+                    logger.warning("Failed to generate unique result ID, will use UUID in S3 path")
+            except Exception as e:
+                logger.warning(f"Error generating result_id: {e}")
+        
+        # Find or create evaluator only if persona_id and scenario_id are provided
+        if agent_id and persona_id and scenario_id:
+            try:
+                from app.models.database import Evaluator, EvaluatorResult, EvaluatorResultStatus, Scenario
+                from app.api.v1.routes.evaluators import generate_unique_evaluator_id
+                import random
+                
+                # Find evaluator by agent, persona, scenario
+                evaluator_query = db.query(Evaluator).filter(
+                    Evaluator.agent_id == UUID(agent_id),
+                    Evaluator.persona_id == UUID(persona_id),
+                    Evaluator.scenario_id == UUID(scenario_id),
+                    Evaluator.organization_id == organization_id
+                )
+                if workspace_id is not None:
+                    evaluator_query = evaluator_query.filter(Evaluator.workspace_id == workspace_id)
+                evaluator = evaluator_query.first()
+                
+                # If no evaluator exists, create one automatically for test voice agent calls
+                if not evaluator:
+                    logger.info(f"Creating evaluator automatically for test voice agent: agent={agent_id}, persona={persona_id}, scenario={scenario_id}")
+                    evaluator_id = generate_unique_evaluator_id(db)
+                    evaluator = Evaluator(
+                        evaluator_id=evaluator_id,
+                        organization_id=organization_id,
+                        workspace_id=workspace_id,
+                        agent_id=UUID(agent_id),
+                        persona_id=UUID(persona_id),
+                        scenario_id=UUID(scenario_id),
+                        tags=["auto-created", "test-voice-agent"]
+                    )
+                    db.add(evaluator)
+                    db.commit()
+                    db.refresh(evaluator)
+                    logger.info(f"✅ Created evaluator {evaluator_id} for test voice agent")
+                
+                if evaluator:
+                    # Get scenario name
+                    scenario_name_query = db.query(Scenario).filter(Scenario.id == UUID(scenario_id))
+                    if workspace_id is not None:
+                        scenario_name_query = scenario_name_query.filter(Scenario.workspace_id == workspace_id)
+                    scenario = scenario_name_query.first()
+                    scenario_name = scenario.name if scenario else "Unknown Scenario"
+            except Exception as e:
+                logger.warning(f"Error finding/creating evaluator or generating result_id: {e}")
+        
+        # Check if this is a test agent that should bridge to Voice AI agent
+        # This happens when evaluator is run via the run_evaluator_task
+        test_agent_bridge_mode = False
+        retell_call_id = None
+        retell_access_token = None
+        retell_sample_rate = None
+        
+        if evaluator and agent and agent.voice_bundle_id and agent.voice_ai_integration_id:
+            # Check if there's an active call for this evaluator
+            # The call info is stored in the EvaluatorResult's error_message field temporarily
+            try:
+                from app.models.database import EvaluatorResult
+                # Find the most recent result for this evaluator that has call info
+                active_result = db.query(EvaluatorResult).filter(
+                    EvaluatorResult.evaluator_id == evaluator.id,
+                    EvaluatorResult.status == EvaluatorResultStatus.QUEUED.value,
+                    EvaluatorResult.error_message.isnot(None),
+                    EvaluatorResult.error_message.like("call_id:%")
+                ).order_by(EvaluatorResult.timestamp.desc()).first()
+                
+                if active_result and active_result.error_message:
+                    # Parse call info from error_message: "call_id:xxx|access_token:yyy|sample_rate:zzz"
+                    call_info_parts = active_result.error_message.split("|")
+                    for part in call_info_parts:
+                        if part.startswith("call_id:"):
+                            retell_call_id = part.split(":", 1)[1]
+                        elif part.startswith("access_token:"):
+                            retell_access_token = part.split(":", 1)[1]
+                        elif part.startswith("sample_rate:"):
+                            retell_sample_rate = int(part.split(":", 1)[1])
+                    
+                    if retell_call_id and retell_access_token:
+                        test_agent_bridge_mode = True
+                        logger.info(
+                            f"[VoiceAgent] Test agent bridge mode detected: "
+                            f"evaluator={evaluator.evaluator_id}, call_id={retell_call_id}"
+                        )
+            except Exception as e:
+                logger.warning(f"[VoiceAgent] Error checking for bridge mode: {e}", exc_info=True)
+        
+        # Run the bot with the appropriate pipeline
+        call_metadata = None
+        session_uuid: Optional[UUID] = None
+        from app.services.voice_agent.call_silence_hangup import resolve_agent_silence_hangup_secs
+
+        if agent_id and workspace_id and not test_agent_bridge_mode:
+            session_uuid = uuid4()
+
+        agent_silence_hangup_secs = resolve_agent_silence_hangup_secs(agent)
+        tracing_task_kwargs: Dict[str, Any] = {}
+        if trace_call_short_id and workspace_id and organization_id:
+            from app.services.voice_agent.playground_tracing import build_pipeline_tracing_kwargs
+
+            tracing_task_kwargs = build_pipeline_tracing_kwargs(
+                call_short_id=trace_call_short_id,
+                workspace_id=str(workspace_id),
+                organization_id=str(organization_id),
+                agent_id=agent_id,
+                api_key=trace_api_key,
+            )
+            if not tracing_task_kwargs:
+                logger.warning(
+                    "Playground OTLP tracing disabled for call_short_id={} "
+                    "(install efficientai[otel] on the media/API process)",
+                    trace_call_short_id,
+                )
+        try:
+            if use_voice_bundle_pipeline:
+                # Resolve per-provider keys for voice bundle
+                stt_provider = voice_bundle.stt_provider if voice_bundle else None
+                tts_provider = voice_bundle.tts_provider if voice_bundle else None
+                llm_provider = voice_bundle.llm_provider if voice_bundle else None
+
+                stt_api_key = resolve_api_key_for_provider(stt_provider) if stt_provider else None
+                tts_api_key = resolve_api_key_for_provider(tts_provider) if tts_provider else None
+                llm_api_key = resolve_api_key_for_provider(llm_provider) if llm_provider else None
+                llm_endpoint_url = (
+                    resolve_azure_endpoint_for_provider(llm_provider)
+                    if llm_provider and (
+                        llm_provider.value if hasattr(llm_provider, "value") else str(llm_provider)
+                    ).lower() == "azure"
+                    else None
+                )
+
+                # If in bridge mode, we need to bridge test agent to Retell call
+                # For now, we'll run the voice bundle normally and note that bridging
+                # requires WebRTC implementation on the backend
+                if test_agent_bridge_mode:
+                    logger.info(
+                        f"[VoiceAgent] Bridge mode: Test agent should connect to Retell call {retell_call_id}. "
+                        f"Full WebRTC bridging requires additional implementation."
+                    )
+                    # TODO: Implement WebRTC bridging between test agent WebSocket and Retell call
+                    # This would require:
+                    # 1. Joining Retell call using WebRTC (aiortc or similar)
+                    # 2. Bridging audio streams bidirectionally
+                    # 3. Recording the bridged conversation
+
+                call_metadata = await run_voice_bundle_fastapi(
+                    websocket,
+                    system_instruction,
+                    str(organization_id),
+                    str(workspace_id) if workspace_id else None,
+                    agent_id,
+                    persona_id,
+                    scenario_id,
+                    evaluator_id=str(evaluator.id) if evaluator else None,
+                    result_id=result_id,
+                    voice_bundle=voice_bundle,
+                    persona=persona,
+                    stt_api_key=stt_api_key,
+                    tts_api_key=tts_api_key,
+                    llm_api_key=llm_api_key,
+                    llm_endpoint_url=llm_endpoint_url,
+                    silence_hangup_secs=agent_silence_hangup_secs,
+                    tracing_task_kwargs=tracing_task_kwargs,
+                )
+            else:
+                call_metadata = await run_bot(
+                    websocket,
+                    google_api_key,
+                    system_instruction,
+                    str(organization_id),
+                    agent_id,
+                    persona_id,
+                    scenario_id,
+                    evaluator_id=str(evaluator.id) if evaluator else None,
+                    result_id=result_id,
+                    model_name=model_name,  # Pass model name from voice bundle
+                    silence_hangup_secs=agent_silence_hangup_secs,
+                    workspace_id=str(workspace_id) if workspace_id else None,
+                    tracing_task_kwargs=tracing_task_kwargs,
+                )
+        except Exception as bot_error:
+            logger.error(f"Error in run_bot: {bot_error}", exc_info=True)
+            # Continue to try creating evaluator result if we have metadata
+        
+        # Create evaluator result if we have the required data (only if no error)
+        # Also create if we have a live transcript even without S3 audio
+        has_audio = call_metadata and call_metadata.get("s3_key")
+        has_transcript = call_metadata and call_metadata.get("transcription")
+        has_usable_data = has_audio or has_transcript
+        if call_metadata and has_usable_data and not call_metadata.get("error") and agent_id and result_id:
+            # If we don't have evaluator but have persona/scenario, try to create one
+            if not evaluator and agent_id and persona_id and scenario_id:
+                try:
+                    from app.models.database import Evaluator, Scenario
+                    from app.api.v1.routes.evaluators import generate_unique_evaluator_id
+                    import random
+                    
+                    fallback_eval_query = db.query(Evaluator).filter(
+                        Evaluator.agent_id == UUID(agent_id),
+                        Evaluator.persona_id == UUID(persona_id),
+                        Evaluator.scenario_id == UUID(scenario_id),
+                        Evaluator.organization_id == organization_id
+                    )
+                    if workspace_id is not None:
+                        fallback_eval_query = fallback_eval_query.filter(Evaluator.workspace_id == workspace_id)
+                    evaluator = fallback_eval_query.first()
+                    
+                    if not evaluator:
+                        evaluator_id = generate_unique_evaluator_id(db)
+                        evaluator = Evaluator(
+                            evaluator_id=evaluator_id,
+                            organization_id=organization_id,
+                            workspace_id=workspace_id,
+                            agent_id=UUID(agent_id),
+                            persona_id=UUID(persona_id),
+                            scenario_id=UUID(scenario_id),
+                            tags=["auto-created", "test-voice-agent"]
+                        )
+                        db.add(evaluator)
+                        db.commit()
+                        db.refresh(evaluator)
+                    
+                    fallback_scenario_query = db.query(Scenario).filter(Scenario.id == UUID(scenario_id))
+                    if workspace_id is not None:
+                        fallback_scenario_query = fallback_scenario_query.filter(Scenario.workspace_id == workspace_id)
+                    scenario = fallback_scenario_query.first()
+                    scenario_name = scenario.name if scenario else "Unknown Scenario"
+                    
+                    # Generate result_id
+                    max_attempts = 100
+                    for _ in range(max_attempts):
+                        candidate_id = f"{random.randint(100000, 999999)}"
+                        existing = db.query(EvaluatorResult).filter(EvaluatorResult.result_id == candidate_id).first()
+                        if not existing:
+                            result_id = candidate_id
+                            break
+                except Exception as e:
+                    logger.error(f"Error creating evaluator/result_id after bot run: {e}")
+            
+            # Create evaluator result for all test calls (with or without persona/scenario)
+            # evaluator_id is optional - can be None if no persona/scenario
+            if result_id and agent_id:
+                try:
+                    from app.models.database import (
+                        CallRecording,
+                        CallRecordingSource,
+                        EvaluatorResult,
+                        EvaluatorResultStatus,
+                    )
+                    from app.models.enums import CallRecordingStatus
+                    from app.utils.call_recordings import generate_unique_call_short_id
+                    from app.workers.celery_app import process_evaluator_result_task
+
+                    # Determine name for the result
+                    if scenario_name and scenario_name != "Test Call":
+                        result_name = scenario_name
+                    elif agent:
+                        result_name = f"Test Call - {agent.name}"
+                    else:
+                        result_name = "Test Call"
+
+                    logger.info(f"Creating evaluator result: result_id={result_id}, agent_id={agent_id}, persona_id={persona_id}, scenario_id={scenario_id}, s3_key={call_metadata.get('s3_key')}")
+
+                    call_short_id = trace_call_short_id or generate_unique_call_short_id(db)
+                    speaker_segments = call_metadata.get("speaker_segments") or []
+                    if not isinstance(speaker_segments, list):
+                        speaker_segments = []
+                    playground_call_data = {
+                        "source": "voice_bundle",
+                        "result_id": result_id,
+                        "transcript": call_metadata.get("transcription"),
+                        "speaker_segments": speaker_segments,
+                        "recording_s3_key": call_metadata.get("s3_key"),
+                        "duration_seconds": call_metadata.get("duration"),
+                    }
+                    if ui_surface:
+                        playground_call_data["ui_surface"] = ui_surface
+
+                    # Create evaluator result with QUEUED status
+                    # persona_id and scenario_id can be None for test calls without persona/scenario
+                    evaluator_result = EvaluatorResult(
+                        result_id=result_id,
+                        organization_id=organization_id,
+                        workspace_id=workspace_id,
+                        evaluator_id=evaluator.id if evaluator else None,  # Optional
+                        agent_id=UUID(agent_id),
+                        persona_id=UUID(persona_id) if persona_id else None,  # Optional
+                        scenario_id=UUID(scenario_id) if scenario_id else None,  # Optional
+                        name=result_name,
+                        duration_seconds=call_metadata.get("duration"),
+                        status=EvaluatorResultStatus.QUEUED.value,  # Use .value to get the string
+                        audio_s3_key=call_metadata.get("s3_key"),
+                        transcription=call_metadata.get("transcription"),
+                        speaker_segments=speaker_segments or None,
+                    )
+                    db.add(evaluator_result)
+                    db.flush()
+
+                    call_recording = CallRecording(
+                        organization_id=organization_id,
+                        workspace_id=workspace_id,
+                        call_short_id=call_short_id,
+                        status=CallRecordingStatus.UPDATED,
+                        source=CallRecordingSource.PLAYGROUND,
+                        call_data=playground_call_data,
+                        provider_call_id=f"voice_bundle_{result_id}",
+                        provider_platform="voice_bundle",
+                        agent_id=UUID(agent_id),
+                        evaluator_result_id=evaluator_result.id,
+                    )
+                    db.add(call_recording)
+                    db.flush()
+
+                    if trace_call_short_id:
+                        from app.services.synthetic_traces.trace_service import link_trace_to_evaluator_result
+
+                        link_trace_to_evaluator_result(
+                            db,
+                            organization_id=organization_id,
+                            call_short_id=trace_call_short_id,
+                            evaluator_result_id=evaluator_result.id,
+                            call_recording_id=call_recording.id,
+                        )
+
+                    db.commit()
+                    db.refresh(evaluator_result)
+
+                    # Playground scoring bills via playground.evaluation_completed.
+                    
+                    logger.info(f"✅ Evaluator result created in database: id={evaluator_result.id}, result_id={result_id}")
+                    
+                    # Trigger Celery task
+                    try:
+                        logger.info(f"Triggering Celery task for evaluator result: {evaluator_result.id}")
+                        
+                        # Check if Celery app is properly configured
+                        from app.workers.celery_app import celery_app
+                        logger.info(f"Celery broker URL: {celery_app.conf.broker_url}")
+                        logger.info(f"Celery result backend: {celery_app.conf.result_backend}")
+                        
+                        # Verify task is registered
+                        if 'process_evaluator_result' not in celery_app.tasks:
+                            logger.error("❌ Task 'process_evaluator_result' is not registered in Celery app!")
+                            logger.error(f"Available tasks: {list(celery_app.tasks.keys())}")
+                        else:
+                            logger.info("✅ Task 'process_evaluator_result' is registered")
+                        
+                        task = process_evaluator_result_task.delay(str(evaluator_result.id))
+                        logger.info(f"✅ Celery task triggered: task_id={task.id}, task_state={task.state}")
+                        
+                        # Try to get task info to verify it was queued
+                        try:
+                            task_info = task.info
+                            logger.info(f"Task info: {task_info}")
+                        except Exception as info_error:
+                            logger.warning(f"Could not get task info (this is normal for async tasks): {info_error}")
+                        
+                        evaluator_result.celery_task_id = task.id
+                        db.commit()
+                        logger.info(f"✅ Updated evaluator result with celery_task_id: {task.id}")
+                    except Exception as task_error:
+                        logger.error(f"❌ Failed to trigger Celery task: {task_error}", exc_info=True)
+                        # Still log that we created the result even if task trigger failed
+                        logger.warning(f"Evaluator result {result_id} created but Celery task was not triggered. Task may need to be triggered manually.")
+                        logger.warning(f"Please ensure Celery worker is running: celery -A app.workers.celery_app worker --loglevel=info")
+                    
+                    logger.info(f"✅ Created evaluator result {result_id} and triggered processing task")
+                except Exception as e:
+                    logger.error(f"❌ Error creating evaluator result: {e}", exc_info=True)
+        
+    except WebSocketDisconnect:
+        print("WebSocket disconnected by client")
+    except Exception as e:
+        print(f"Exception in voice agent WebSocket: {e}")
+        import traceback
+        traceback.print_exc()
+        try:
+            await websocket.close(code=1011, reason=f"Server error: {str(e)}")
+        except:
+            pass
+    finally:
+        try:
+            if trace_call_short_id and organization_id:
+                from app.services.voice_agent.playground_tracing import flush_playground_tracing
+                from app.services.synthetic_traces.trace_service import close_trace_session
+
+                flush_playground_tracing()
+                if 'db' in locals():
+                    close_trace_session(
+                        db,
+                        organization_id=organization_id,
+                        call_short_id=trace_call_short_id,
+                        workspace_id=workspace_id,
+                    )
+        except Exception as trace_close_error:
+            logger.warning(f"Failed to close playground trace session: {trace_close_error}")
+        try:
+            if 'db' in locals():
+                db.close()
+        except:
+            pass
+
+
+@router.options("/connect")
+async def bot_connect_options():
+    """Handle CORS preflight requests."""
+    from fastapi.responses import Response
+    return Response(
+        status_code=200,
+        headers={
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Methods": "GET, POST, OPTIONS",
+            "Access-Control-Allow-Headers": "*",
+            "Access-Control-Allow-Credentials": "true",
+        }
+    )
+
+@router.post("/connect", response_model=Dict[str, Any])
+@router.get("/connect", response_model=Dict[str, Any])
+async def bot_connect(
+    request: Request,
+    db: Session = Depends(get_db),
+) -> Dict[str, Any]:
+    """
+    Get WebSocket connection URL for voice agent.
+    Returns the WebSocket URL that the client should connect to.
+
+    Accepts either a Bearer access token (email/password / SSO login) or an
+    API key (legacy / machine access). Credentials may be supplied via the
+    `Authorization` header, `X-API-Key` header, cookies (`access_token` /
+    `api_key`), or query parameters (`token` / `X-API-Key` / `api_key`).
+
+    Supports both GET and POST requests for compatibility with different client
+    implementations. Pipecat's `startBotAndConnect` issues an HTTP request to
+    this endpoint; the WebSocket URL returned here embeds the same credential
+    so the subsequent /ws connection can authenticate without re-prompting.
+    """
+    from app.core.auth.providers import (
+        AuthError,
+        RawCredential,
+        get_provider_registry,
+    )
+
+    print("=" * 80)
+    print(f"[BACKEND] /connect endpoint called at {__import__('datetime').datetime.now()}")
+    print(f"[BACKEND] Request method: {request.method}")
+    print(f"[BACKEND] Request URL: {request.url}")
+    print(f"[BACKEND] Request cookies present: {list(request.cookies.keys())}")
+    print(f"[BACKEND] Query params: {dict(request.query_params)}")
+
+    def _extract_bearer(value: Optional[str]) -> Optional[str]:
+        if not value:
+            return None
+        scheme, _, token = value.partition(" ")
+        if scheme.lower() != "bearer" or not token.strip():
+            return None
+        return token.strip()
+
+    # Bearer / access token: header, query param, then cookie.
+    bearer_token = (
+        _extract_bearer(request.headers.get("Authorization"))
+        or request.query_params.get("token")
+        or request.query_params.get("access_token")
+        or request.cookies.get("access_token")
+    )
+
+    # API key: header, query param, then cookie.
+    api_key = (
+        request.headers.get("X-API-Key")
+        or request.headers.get("X-EFFICIENTAI-API-KEY")
+        or request.query_params.get("X-API-Key")
+        or request.query_params.get("api_key")
+        or request.cookies.get("api_key")
+    )
+
+    print(
+        f"[BACKEND] Bearer token: {'found' if bearer_token else 'not found'}, "
+        f"API key: {'found' if api_key else 'not found'}"
+    )
+
+    from app.config import settings
+
+    if not bearer_token and not api_key:
+        print("[BACKEND] ❌ No credentials found, returning 401")
+        raise HTTPException(
+            status_code=401,
+            detail=(
+                "Authentication required. Send an Authorization: Bearer "
+                "header, X-API-Key header, or matching cookie/query param."
+            ),
+        )
+
+    cred = RawCredential(bearer_token=bearer_token, api_key=api_key)
+    registry = get_provider_registry()
+    provider = registry.find(cred)
+    if provider is None:
+        print("[BACKEND] ❌ No auth provider accepted the credential")
+        raise HTTPException(status_code=401, detail="Invalid credentials")
+
+    try:
+        principal = provider.authenticate(cred, db)
+    except AuthError as e:
+        print(f"[BACKEND] ❌ Auth provider rejected credential: {e}")
+        raise HTTPException(status_code=e.status_code, detail=str(e))
+
+    organization_id = principal.organization_id
+    if not organization_id:
+        print("[BACKEND] ❌ Principal has no organization")
+        raise HTTPException(status_code=401, detail="Invalid credentials")
+
+    print(f"[BACKEND] ✅ Authenticated via {provider.name} (org={organization_id})")
+    
+    # Get agent_id, persona_id and scenario_id from query params first
+    agent_id = request.query_params.get("agent_id")
+    persona_id = request.query_params.get("persona_id")
+    scenario_id = request.query_params.get("scenario_id")
+    ui_surface = request.query_params.get("ui_surface")
+    call_short_id = request.query_params.get("call_short_id")
+    
+    # Determine which AI Provider to use based on agent configuration
+    ai_provider = None
+    agent = None
+    voice_bundle = None
+    use_voice_bundle_pipeline = False
+    
+    if agent_id:
+        try:
+            from app.models.database import Agent, VoiceBundle
+            agent_uuid = UUID(agent_id)
+            agent = db.query(Agent).filter(
+                Agent.id == agent_uuid,
+                Agent.organization_id == organization_id
+            ).first()
+            
+            # Check if agent has a voice bundle (stt_llm_tts type)
+            if agent and agent.voice_bundle_id:
+                voice_bundle = db.query(VoiceBundle).filter(
+                    VoiceBundle.id == agent.voice_bundle_id,
+                    VoiceBundle.organization_id == organization_id
+                ).first()
+                if voice_bundle and voice_bundle.bundle_type == "stt_llm_tts":
+                    use_voice_bundle_pipeline = True
+                    print(f"[BACKEND] ✅ Using custom voice bundle: {voice_bundle.name}")
+            
+            if agent and agent.ai_provider_id:
+                ai_provider = db.query(AIProvider).filter(
+                    AIProvider.id == agent.ai_provider_id,
+                    AIProvider.organization_id == organization_id,
+                    AIProvider.is_active == True
+                ).first()
+        except ValueError:
+            pass
+    
+    # For custom voice bundles (stt_llm_tts), we don't need a Google provider
+    # The voice bundle has its own STT/LLM/TTS providers configured
+    if not use_voice_bundle_pipeline:
+        # Fall back to Google AI Provider if no agent-specific provider found (needed for S2S/Gemini path)
+        if not ai_provider:
+            from sqlalchemy import func
+            google_value = ModelProvider.GOOGLE.value
+            ai_provider = db.query(AIProvider).filter(
+                AIProvider.organization_id == organization_id,
+                AIProvider.provider == google_value,
+                AIProvider.is_active == True
+            ).first()
+            # If not found, try case-insensitive match
+            if not ai_provider:
+                ai_provider = db.query(AIProvider).filter(
+                    AIProvider.organization_id == organization_id,
+                    func.lower(AIProvider.provider) == google_value.lower(),
+                    AIProvider.is_active == True
+                ).first()
+        
+        if not ai_provider:
+            print("[BACKEND] ❌ AI Provider not configured")
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="AI Provider not configured. Please configure an AI Provider in AI Providers settings or select one when creating the agent."
+            )
+        
+        provider_val = ai_provider.provider.value if hasattr(ai_provider.provider, 'value') else ai_provider.provider
+        print(f"[BACKEND] ✅ AI Provider found: {provider_val}")
+    else:
+        # For voice bundle pipeline, verify the required providers are configured
+        from sqlalchemy import func
+        missing_providers = []
+        
+        def check_provider(provider_enum):
+            """Check if a provider is configured (either as AIProvider or Integration)."""
+            if not provider_enum:
+                return True  # Not required
+            
+            provider_value = provider_enum.value if hasattr(provider_enum, 'value') else str(provider_enum)
+            
+            # Check AIProvider
+            found = db.query(AIProvider).filter(
+                AIProvider.organization_id == organization_id,
+                AIProvider.is_active == True,
+            ).filter(
+                (AIProvider.provider == provider_value) | 
+                (func.lower(AIProvider.provider) == provider_value.lower())
+            ).first()
+            
+            if found:
+                return True
+            
+            # Check Integration for Deepgram, Cartesia, and ElevenLabs
+            platform_map = {
+                'deepgram': IntegrationPlatform.DEEPGRAM,
+                'cartesia': IntegrationPlatform.CARTESIA,
+                'elevenlabs': IntegrationPlatform.ELEVENLABS,
+                'murf': IntegrationPlatform.MURF,
+                'sarvam': IntegrationPlatform.SARVAM,
+                'voicemaker': IntegrationPlatform.VOICEMAKER,
+                'smallest': IntegrationPlatform.SMALLEST,
+            }
+            plat = platform_map.get(provider_value.lower())
+            if plat:
+                plat_value = plat.value if hasattr(plat, 'value') else plat
+                found = db.query(Integration).filter(
+                    Integration.organization_id == organization_id,
+                    Integration.is_active == True,
+                ).filter(
+                    (Integration.platform == plat_value) | 
+                    (func.lower(Integration.platform) == plat_value.lower())
+                ).first()
+                if found:
+                    return True
+            
+            return False
+        
+        # Check required providers for the voice bundle
+        if voice_bundle.stt_provider and not check_provider(voice_bundle.stt_provider):
+            missing_providers.append(f"STT: {voice_bundle.stt_provider}")
+        if voice_bundle.llm_provider and not check_provider(voice_bundle.llm_provider):
+            missing_providers.append(f"LLM: {voice_bundle.llm_provider}")
+        if voice_bundle.tts_provider and not check_provider(voice_bundle.tts_provider):
+            missing_providers.append(f"TTS: {voice_bundle.tts_provider}")
+        
+        if missing_providers:
+            print(f"[BACKEND] ❌ Missing providers for voice bundle: {missing_providers}")
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Missing AI providers for voice bundle: {', '.join(missing_providers)}. Please configure them in Integrations settings."
+            )
+        
+        print(f"[BACKEND] ✅ Voice bundle providers configured")
+    
+    # Determine WebSocket URL — prefer dedicated media server when configured.
+    from urllib.parse import quote
+
+    from app.services.media_urls import build_voice_agent_ws_url
+
+    if bearer_token:
+        ws_auth_query = f"token={quote(bearer_token, safe='')}"
+    else:
+        ws_auth_query = f"X-API-Key={quote(api_key or '', safe='')}"
+
+    ws_url = build_voice_agent_ws_url(
+        auth_query=ws_auth_query,
+        agent_id=agent_id,
+        persona_id=persona_id,
+        scenario_id=scenario_id,
+        ui_surface=ui_surface,
+        call_short_id=call_short_id,
+        fallback_host=request.headers.get("host", f"localhost:{settings.PORT}"),
+        fallback_scheme=(
+            request.headers.get("x-forwarded-proto")
+            or getattr(request.url, "scheme", "http")
+            or "http"
+        ),
+    )
+    
+    # Return the response in the format Pipecat expects
+    # Pipecat expects a JSON response with ws_url field
+    # The response should be simple and match exactly what Pipecat expects
+    from fastapi.responses import JSONResponse
+    response_data = {
+        "ws_url": ws_url
+    }
+    print(f"[BACKEND] ✅ Returning WebSocket URL: {ws_url}")
+    print(f"[BACKEND] Response data: {response_data}")
+    print("=" * 80)
+    
+    # Return JSON response - CORS is handled by middleware
+    return JSONResponse(
+        content=response_data,
+        status_code=200,
+        headers={
+            "Content-Type": "application/json",
+        }
+    )
+
+
+@router.get("/audio", response_model=List[Dict[str, Any]])
+async def list_voice_agent_audio_files(
+    organization_id: UUID = Depends(get_organization_id),
+    max_keys: int = 1000,
+):
+    """
+    List audio files for voice agent conversations for the current organization.
+    
+    Args:
+        organization_id: Organization ID from API key
+        max_keys: Maximum number of files to return
+        
+    Returns:
+        List of audio file metadata with keys: key, size, last_modified, filename
+    """
+    try:
+        files = s3_service.list_audio_files(
+            organization_id=str(organization_id),
+            max_keys=max_keys
+        )
+        return files
+    except Exception as e:
+        logger.error(f"Error listing voice agent audio files: {e}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to list audio files: {str(e)}"
+        )
+
diff --git a/app/app_factory.py b/app/app_factory.py
index c2af3e09..2b4e6ff2 100644
--- a/app/app_factory.py
+++ b/app/app_factory.py
@@ -1,249 +1,263 @@
-"""Application factory for API and media service modes."""
-
-from __future__ import annotations
-
-import logging
-from contextlib import asynccontextmanager
-from pathlib import Path
-
-from fastapi import Depends, FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import FileResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
-
-from app.config import settings, validate_auth_configuration
-from app.core.auth.rbac import require_admin
-from app.core.health import build_health_status
-from app.core.migration_middleware import MigrationCheckMiddleware
-from app.core.migrations import check_migrations_status, ensure_migrations_directory, run_migrations
-from app.core.operational_access_middleware import OperationalAccessMiddleware
-from app.core.rbac_middleware import ReaderReadOnlyMiddleware
-from app.core.security_headers_middleware import SecurityHeadersMiddleware
-from app.core.usage_context_middleware import LLMUsageContextMiddleware
-from app.database import init_db
-
-logger = logging.getLogger(__name__)
-
-
-def _service_mode() -> str:
-    """Prefer os.environ so subprocess / reload cannot leave stale settings."""
-    import os
-
-    return (os.environ.get("SERVICE_MODE") or settings.SERVICE_MODE or "api").strip().lower()
-
-
-def _includes_http_routes() -> bool:
-    return _service_mode() in ("api", "all")
-
-
-def _includes_media_routes() -> bool:
-    mode = _service_mode()
-    if mode in ("media", "all"):
-        return True
-    if mode == "api":
-        # Single-process dev: mount voice-agent / Vobiz WS on API when no
-        # dedicated media URL is configured (e.g. bare ``eai start``).
-        from app.services.media_urls import separate_media_server_configured
-
-        return not separate_media_server_configured()
-    return False
-
-
-@asynccontextmanager
-async def _api_lifespan(app: FastAPI):
-    logger.info("=" * 60)
-    logger.info("Starting EfficientAI Application (mode=%s)", settings.SERVICE_MODE)
-    logger.info("=" * 60)
-
-    ensure_migrations_directory()
-
-    if _includes_http_routes():
-        try:
-            validate_auth_configuration()
-            logger.info("Authentication configuration validated")
-        except Exception as e:
-            logger.error("CRITICAL: Authentication configuration is invalid: %s", e)
-            raise
-
-    try:
-        init_db()
-        logger.info("Database tables initialized")
-    except Exception as e:
-        logger.error("Error initializing database: %s", e)
-        raise
-
-    if _includes_http_routes():
-        try:
-            run_migrations()
-        except Exception as e:
-            logger.error("CRITICAL: Database migrations failed: %s", e)
-            raise
-
-        is_up_to_date, pending = check_migrations_status()
-        if not is_up_to_date:
-            logger.warning("Warning: %d migration(s) still pending: %s", len(pending), ", ".join(pending))
-        else:
-            logger.info("All migrations are up to date")
-
-        from app.services.billing.flexprice_service import log_startup_status
-
-        log_startup_status(component="api")
-
-    logger.info("Application startup complete - Ready to serve requests")
-    logger.info("=" * 60)
-    yield
-    logger.info("Shutting down EfficientAI Application...")
-
-
-def _add_common_middleware(app: FastAPI) -> None:
-    if _includes_http_routes():
-        app.add_middleware(MigrationCheckMiddleware)
-        app.add_middleware(ReaderReadOnlyMiddleware)
-        app.add_middleware(LLMUsageContextMiddleware)
-
-    if settings.OBSERVABILITY_ENABLED and settings.LOKI_ENABLED and settings.LOKI_MULTI_TENANT:
-        from app.core.observability_middleware import OrgLoggingMiddleware
-
-        app.add_middleware(OrgLoggingMiddleware)
-
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=settings.CORS_ORIGINS,
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-    app.add_middleware(SecurityHeadersMiddleware)
-    if _includes_http_routes():
-        app.add_middleware(OperationalAccessMiddleware)
-
-    if settings.OBSERVABILITY_ENABLED and _includes_http_routes():
-        from prometheus_fastapi_instrumentator import Instrumentator
-
-        Instrumentator(
-            should_group_status_codes=True,
-            should_ignore_untemplated=True,
-            should_group_untemplated=True,
-            excluded_handlers=["/health", "/metrics"],
-        ).instrument(app).expose(app, endpoint="/metrics", include_in_schema=False)
-
-
-def _mount_frontend(app: FastAPI) -> None:
-    if not _includes_http_routes():
-        return
-
-    frontend_dist = Path(settings.FRONTEND_DIR)
-    if not frontend_dist.exists() or not frontend_dist.is_dir():
-        return
-
-    static_dir = frontend_dist / "assets"
-    if static_dir.exists():
-        app.mount("/assets", StaticFiles(directory=str(static_dir)), name="assets")
-
-    @app.get("/{full_path:path}")
-    async def serve_frontend(full_path: str):
-        if (
-            full_path.startswith("api/")
-            or full_path.startswith("docs")
-            or full_path.startswith("redoc")
-            or full_path.startswith("assets/")
-            or full_path == "health"
-            or full_path == "health/detail"
-            or full_path == "metrics"
-        ):
-            return {"detail": "Not found"}
-
-        file_path = frontend_dist / full_path
-        if file_path.exists() and file_path.is_file() and file_path.parent == frontend_dist:
-            return FileResponse(str(file_path))
-
-        index_path = frontend_dist / "index.html"
-        if index_path.exists():
-            return FileResponse(str(index_path))
-        return {"detail": "Frontend not found"}
-
-
-def create_app() -> FastAPI:
-    """Create API, media, or combined app based on SERVICE_MODE."""
-    import os
-
-    from app.config import apply_service_mode
-
-    env_mode = os.environ.get("SERVICE_MODE")
-    if env_mode:
-        apply_service_mode(env_mode)
-
-    mode = _service_mode()
-    print(
-        f"[{mode.upper()}] create_app: http_routes={_includes_http_routes()} "
-        f"media_routes={_includes_media_routes()} "
-        f"MEDIA_WS_BASE_URL={settings.MEDIA_WS_BASE_URL!r}",
-        flush=True,
-    )
-
-    title_suffix = {
-        "api": " API",
-        "media": " Media",
-        "all": "",
-    }.get(settings.SERVICE_MODE, "")
-
-    app = FastAPI(
-        title=f"{settings.APP_NAME}{title_suffix}",
-        version=settings.APP_VERSION,
-        description="EfficientAI Voice AI Evaluation Platform",
-        docs_url="/docs" if settings.DEBUG and _includes_http_routes() else None,
-        redoc_url="/redoc" if settings.DEBUG and _includes_http_routes() else None,
-        openapi_url="/openapi.json" if settings.DEBUG and _includes_http_routes() else None,
-        lifespan=_api_lifespan,
-    )
-
-    _add_common_middleware(app)
-
-    if _includes_http_routes():
-        from app.api.v1.api import api_router
-
-        app.include_router(api_router, prefix=settings.API_V1_PREFIX)
-
-    if _includes_media_routes():
-        from app.api.v1.media import media_router
-
-        app.include_router(media_router, prefix=settings.API_V1_PREFIX)
-        logger.info(
-            "Live voice WebSockets mounted (SERVICE_MODE=%s, MEDIA_WS_BASE_URL=%r)",
-            settings.SERVICE_MODE,
-            settings.MEDIA_WS_BASE_URL,
-        )
-        if _service_mode() == "media":
-            logger.info(
-                "Vobiz telephony edge: /telephony/vobiz/webhooks/* and /telephony/vobiz/ws"
-            )
-        if settings.SERVICE_MODE == "api":
-            logger.info(
-                "Voice WebSockets co-located on API (unset MEDIA_WS_BASE_URL to "
-                "keep this mode; set it to ws://host:8001 when running eai telephony-worker)"
-            )
-
-    @app.get("/health")
-    async def health_check():
-        payload, status_code = build_health_status(detailed=False)
-        return JSONResponse(content=payload, status_code=status_code)
-
-    if _includes_http_routes():
-
-        @app.get("/health/detail")
-        async def health_detail(_admin=Depends(require_admin)):
-            from app.database import SessionLocal
-            from app.services.observability.catalog_storage_stats import collect_catalog_storage_stats
-
-            payload, status_code = build_health_status(detailed=True)
-            db = SessionLocal()
-            try:
-                payload["catalog_storage"] = collect_catalog_storage_stats(db)
-            except Exception as exc:
-                payload["catalog_storage"] = {"error": str(exc)}
-            finally:
-                db.close()
-            return JSONResponse(content=payload, status_code=status_code)
-
-    _mount_frontend(app)
-    return app
+"""Application factory for API and media service modes."""
+
+from __future__ import annotations
+
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+
+from fastapi import Depends, FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, JSONResponse
+
+from app.config import settings, validate_auth_configuration
+from app.core.auth.rbac import require_admin
+from app.core.health import build_health_status
+from app.core.migration_middleware import MigrationCheckMiddleware
+from app.core.migrations import check_migrations_status, ensure_migrations_directory, run_migrations
+from app.core.operational_access_middleware import OperationalAccessMiddleware
+from app.core.rbac_middleware import ReaderReadOnlyMiddleware
+from app.core.security_headers_middleware import SecurityHeadersMiddleware
+from app.core.usage_context_middleware import LLMUsageContextMiddleware
+from app.database import init_db
+
+logger = logging.getLogger(__name__)
+
+
+def _service_mode() -> str:
+    """Prefer os.environ so subprocess / reload cannot leave stale settings."""
+    import os
+
+    return (os.environ.get("SERVICE_MODE") or settings.SERVICE_MODE or "api").strip().lower()
+
+
+def _includes_http_routes() -> bool:
+    return _service_mode() in ("api", "all")
+
+
+def _includes_media_routes() -> bool:
+    mode = _service_mode()
+    if mode in ("media", "all"):
+        return True
+    if mode == "api":
+        # Single-process dev: mount voice-agent / Vobiz WS on API when no
+        # dedicated media URL is configured (e.g. bare ``eai start``).
+        from app.services.media_urls import separate_media_server_configured
+
+        return not separate_media_server_configured()
+    return False
+
+
+@asynccontextmanager
+async def _api_lifespan(app: FastAPI):
+    logger.info("=" * 60)
+    logger.info("Starting EfficientAI Application (mode=%s)", settings.SERVICE_MODE)
+    logger.info("=" * 60)
+
+    ensure_migrations_directory()
+
+    if _includes_http_routes():
+        try:
+            validate_auth_configuration()
+            logger.info("Authentication configuration validated")
+        except Exception as e:
+            logger.error("CRITICAL: Authentication configuration is invalid: %s", e)
+            raise
+
+    try:
+        init_db()
+        logger.info("Database tables initialized")
+    except Exception as e:
+        logger.error("Error initializing database: %s", e)
+        raise
+
+    if _includes_http_routes():
+        try:
+            run_migrations()
+        except Exception as e:
+            logger.error("CRITICAL: Database migrations failed: %s", e)
+            raise
+
+        is_up_to_date, pending = check_migrations_status()
+        if not is_up_to_date:
+            logger.warning("Warning: %d migration(s) still pending: %s", len(pending), ", ".join(pending))
+        else:
+            logger.info("All migrations are up to date")
+
+        from app.services.billing.flexprice_service import log_startup_status
+
+        log_startup_status(component="api")
+
+    logger.info("Application startup complete - Ready to serve requests")
+    logger.info("=" * 60)
+    yield
+    logger.info("Shutting down EfficientAI Application...")
+
+
+def _add_common_middleware(app: FastAPI) -> None:
+    if _includes_http_routes():
+        app.add_middleware(MigrationCheckMiddleware)
+        app.add_middleware(ReaderReadOnlyMiddleware)
+        app.add_middleware(LLMUsageContextMiddleware)
+
+    if settings.OBSERVABILITY_ENABLED and settings.LOKI_ENABLED and settings.LOKI_MULTI_TENANT:
+        from app.core.observability_middleware import OrgLoggingMiddleware
+
+        app.add_middleware(OrgLoggingMiddleware)
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=settings.CORS_ORIGINS,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    app.add_middleware(SecurityHeadersMiddleware)
+    if _includes_http_routes():
+        app.add_middleware(OperationalAccessMiddleware)
+
+    if settings.OBSERVABILITY_ENABLED and _includes_http_routes():
+        from prometheus_fastapi_instrumentator import Instrumentator
+
+        Instrumentator(
+            should_group_status_codes=True,
+            should_ignore_untemplated=True,
+            should_group_untemplated=True,
+            excluded_handlers=["/health", "/metrics"],
+        ).instrument(app).expose(app, endpoint="/metrics", include_in_schema=False)
+
+
+def _mount_frontend(app: FastAPI) -> None:
+    if not _includes_http_routes():
+        return
+
+    frontend_dist = Path(settings.FRONTEND_DIR).resolve()
+    if not frontend_dist.exists() or not frontend_dist.is_dir():
+        return
+
+    assets_dir = frontend_dist / "assets"
+
+    @app.get("/assets/{asset_path:path}", include_in_schema=False)
+    async def serve_frontend_asset(asset_path: str):
+        from fastapi import HTTPException
+
+        if not assets_dir.is_dir():
+            raise HTTPException(status_code=404, detail="Asset not found")
+        candidate = (assets_dir / asset_path).resolve()
+        try:
+            candidate.relative_to(assets_dir.resolve())
+        except ValueError:
+            raise HTTPException(status_code=404, detail="Asset not found")
+        if not candidate.is_file():
+            raise HTTPException(status_code=404, detail="Asset not found")
+        return FileResponse(str(candidate))
+
+    @app.get("/{full_path:path}", include_in_schema=False)
+    async def serve_frontend(full_path: str):
+        from fastapi import HTTPException
+
+        if (
+            full_path.startswith("api/")
+            or full_path.startswith("docs")
+            or full_path.startswith("redoc")
+            or full_path.startswith("assets/")
+            or full_path == "health"
+            or full_path == "health/detail"
+            or full_path == "metrics"
+        ):
+            raise HTTPException(status_code=404, detail="Not found")
+
+        file_path = frontend_dist / full_path
+        if file_path.exists() and file_path.is_file() and file_path.parent == frontend_dist:
+            return FileResponse(str(file_path))
+
+        index_path = frontend_dist / "index.html"
+        if index_path.exists():
+            return FileResponse(str(index_path))
+        raise HTTPException(status_code=404, detail="Frontend not found")
+
+
+def create_app() -> FastAPI:
+    """Create API, media, or combined app based on SERVICE_MODE."""
+    import os
+
+    from app.config import apply_service_mode
+
+    env_mode = os.environ.get("SERVICE_MODE")
+    if env_mode:
+        apply_service_mode(env_mode)
+
+    mode = _service_mode()
+    print(
+        f"[{mode.upper()}] create_app: http_routes={_includes_http_routes()} "
+        f"media_routes={_includes_media_routes()} "
+        f"MEDIA_WS_BASE_URL={settings.MEDIA_WS_BASE_URL!r}",
+        flush=True,
+    )
+
+    title_suffix = {
+        "api": " API",
+        "media": " Media",
+        "all": "",
+    }.get(settings.SERVICE_MODE, "")
+
+    app = FastAPI(
+        title=f"{settings.APP_NAME}{title_suffix}",
+        version=settings.APP_VERSION,
+        description="EfficientAI Voice AI Evaluation Platform",
+        docs_url="/docs" if settings.DEBUG and _includes_http_routes() else None,
+        redoc_url="/redoc" if settings.DEBUG and _includes_http_routes() else None,
+        openapi_url="/openapi.json" if settings.DEBUG and _includes_http_routes() else None,
+        lifespan=_api_lifespan,
+    )
+
+    _add_common_middleware(app)
+
+    if _includes_http_routes():
+        from app.api.v1.api import api_router
+
+        app.include_router(api_router, prefix=settings.API_V1_PREFIX)
+
+    if _includes_media_routes():
+        from app.api.v1.media import media_router
+
+        app.include_router(media_router, prefix=settings.API_V1_PREFIX)
+        logger.info(
+            "Live voice WebSockets mounted (SERVICE_MODE=%s, MEDIA_WS_BASE_URL=%r)",
+            settings.SERVICE_MODE,
+            settings.MEDIA_WS_BASE_URL,
+        )
+        if _service_mode() == "media":
+            logger.info(
+                "Vobiz telephony edge: /telephony/vobiz/webhooks/* and /telephony/vobiz/ws"
+            )
+        if settings.SERVICE_MODE == "api":
+            logger.info(
+                "Voice WebSockets co-located on API (unset MEDIA_WS_BASE_URL to "
+                "keep this mode; set it to ws://host:8001 when running eai telephony-worker)"
+            )
+
+    @app.get("/health")
+    async def health_check():
+        payload, status_code = build_health_status(detailed=False)
+        return JSONResponse(content=payload, status_code=status_code)
+
+    if _includes_http_routes():
+
+        @app.get("/health/detail")
+        async def health_detail(_admin=Depends(require_admin)):
+            from app.database import SessionLocal
+            from app.services.observability.catalog_storage_stats import collect_catalog_storage_stats
+
+            payload, status_code = build_health_status(detailed=True)
+            db = SessionLocal()
+            try:
+                payload["catalog_storage"] = collect_catalog_storage_stats(db)
+            except Exception as exc:
+                payload["catalog_storage"] = {"error": str(exc)}
+            finally:
+                db.close()
+            return JSONResponse(content=payload, status_code=status_code)
+
+    _mount_frontend(app)
+    return app
diff --git a/app/cli.py b/app/cli.py
index 7fbefbff..2e07fbd8 100644
--- a/app/cli.py
+++ b/app/cli.py
@@ -1,1621 +1,1633 @@
-"""CLI for EfficientAI platform."""
-
-import click
-import yaml
-import os
-import sys
-import subprocess
-import threading
-import time
-from pathlib import Path
-from typing import Optional
-
-
-@click.group()
-def main():
-    """EfficientAI - Voice AI Evaluation Platform CLI."""
-    pass
-
-
-class FrontendWatcher:
-    """Watch frontend files and rebuild on changes."""
-    
-    def __init__(self, frontend_dir: Path):
-        self.frontend_dir = frontend_dir
-        self.watching = False
-        self.thread = None
-        self.last_build_time = 0
-        self.build_lock = threading.Lock()
-        
-    def should_rebuild(self) -> bool:
-        """Check if frontend files have changed."""
-        src_dir = self.frontend_dir / "src"
-        if not src_dir.exists():
-            return False
-        
-        # Check modification time of source files
-        max_mtime = 0
-        for ext in [".tsx", ".ts", ".css", ".jsx", ".js"]:
-            for file_path in src_dir.rglob(f"*{ext}"):
-                if file_path.is_file():
-                    max_mtime = max(max_mtime, file_path.stat().st_mtime)
-        
-        # Also check config files
-        config_files = [
-            self.frontend_dir / "vite.config.ts",
-            self.frontend_dir / "tailwind.config.js",
-            self.frontend_dir / "tsconfig.json",
-            self.frontend_dir / "package.json",
-        ]
-        for config_file in config_files:
-            if config_file.exists():
-                max_mtime = max(max_mtime, config_file.stat().st_mtime)
-        
-        if max_mtime > self.last_build_time:
-            self.last_build_time = max_mtime
-            return True
-        return False
-    
-    def build_frontend(self):
-        """Rebuild the frontend."""
-        with self.build_lock:
-            try:
-                click.echo("\n🔄 Frontend files changed, rebuilding...")
-                result = subprocess.run(
-                    ["npm", "run", "build"],
-                    cwd=self.frontend_dir,
-                    check=False,
-                    capture_output=True,
-                    text=True,
-                )
-                if result.returncode == 0:
-                    click.echo("✅ Frontend rebuilt successfully")
-                else:
-                    click.echo(f"⚠️  Frontend build had warnings (check logs)", err=True)
-                    if result.stderr:
-                        click.echo(result.stderr[:500], err=True)  # Show first 500 chars
-            except Exception as e:
-                click.echo(f"❌ Frontend build error: {e}", err=True)
-    
-    def watch_loop(self):
-        """Watch loop that runs in background thread."""
-        while self.watching:
-            try:
-                if self.should_rebuild():
-                    self.build_frontend()
-                time.sleep(1)  # Check every second
-            except Exception as e:
-                click.echo(f"❌ Watcher error: {e}", err=True)
-                time.sleep(5)  # Wait longer on error
-    
-    def start(self):
-        """Start the watcher in a background thread."""
-        if self.watching:
-            return
-        self.watching = True
-        # Set initial build time to avoid rebuilding immediately
-        self.last_build_time = time.time()
-        self.thread = threading.Thread(target=self.watch_loop, daemon=True)
-        self.thread.start()
-    
-    def stop(self):
-        """Stop the watcher."""
-        self.watching = False
-        if self.thread:
-            self.thread.join(timeout=1)
-
-
-def start_frontend_watcher(frontend_dir: Path) -> FrontendWatcher:
-    """Start a frontend file watcher."""
-    watcher = FrontendWatcher(frontend_dir)
-    watcher.start()
-    return watcher
-
-
-def _read_config_media_ws_base_url(config_path: Path) -> Optional[str]:
-    """Return vobiz.media_ws_base_url from YAML when explicitly configured."""
-    try:
-        with open(config_path, encoding="utf-8") as handle:
-            data = yaml.safe_load(handle) or {}
-        value = ((data.get("vobiz") or {}).get("media_ws_base_url") or "").strip()
-        return value or None
-    except Exception:
-        return None
-
-
-@main.command()
-@click.option(
-    "--verbose",
-    "-v",
-    is_flag=True,
-    help="Show detailed migration output",
-)
-def migrate(verbose: bool):
-    """Run pending database migrations."""
-    import logging
-    from app.core.migrations import run_migrations, ensure_migrations_directory
-    
-    if verbose:
-        logging.basicConfig(level=logging.INFO)
-    
-    click.echo("🔄 Running database migrations...")
-    ensure_migrations_directory()
-    
-    try:
-        run_migrations()
-        click.echo("✅ All migrations completed successfully!")
-    except Exception as e:
-        click.echo(f"❌ Migration failed: {e}", err=True)
-        sys.exit(1)
-
-
-@main.command()
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option(
-    "--host",
-    default=None,
-    help="Host to bind to (overrides config)",
-)
-@click.option(
-    "--port",
-    default=None,
-    type=int,
-    help="Port to bind to (overrides config)",
-)
-@click.option(
-    "--build-frontend/--no-build-frontend",
-    default=True,
-    help="Build frontend before starting (default: True)",
-)
-@click.option(
-    "--reload/--no-reload",
-    default=True,
-    help="Enable auto-reload for development (default: True)",
-)
-@click.option(
-    "--watch-frontend/--no-watch-frontend",
-    default=False,
-    help="Watch frontend files and rebuild automatically (default: False)",
-)
-@click.option(
-    "--force-rebuild",
-    is_flag=True,
-    default=False,
-    help="Force rebuild of frontend without prompting",
-)
-@click.option(
-    "--skip-migrations",
-    is_flag=True,
-    default=False,
-    help="Skip running migrations before starting (not recommended)",
-)
-def start(config: str, host: Optional[str], port: Optional[int], build_frontend: bool, reload: bool, watch_frontend: bool, force_rebuild: bool, skip_migrations: bool):
-    """Start the EfficientAI application server."""
-    from app.config import apply_service_mode, load_config_from_file, settings
-
-    # Load configuration from YAML file
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        click.echo(f"💡 Create a config.yml file or use --config to specify a different path.", err=True)
-        sys.exit(1)
-
-    explicit_media_ws = _read_config_media_ws_base_url(config_path)
-    os.environ["SERVICE_MODE"] = "api"
-    if not explicit_media_ws:
-        # Single-process dev: co-locate voice WebSockets on the API port unless
-        # config.yml sets vobiz.media_ws_base_url (used with eai telephony-worker).
-        os.environ.pop("MEDIA_WS_BASE_URL", None)
-
-    try:
-        load_config_from_file(str(config_path))
-        apply_service_mode("api")
-        if not explicit_media_ws:
-            settings.MEDIA_WS_BASE_URL = ""
-        click.echo(f"✅ Loaded configuration from {config_path}")
-    except Exception as e:
-        click.echo(f"❌ Error loading config: {e}", err=True)
-        sys.exit(1)
-    
-    # Override with CLI options if provided
-    if host:
-        settings.HOST = host
-    if port:
-        settings.PORT = port
-    
-    # Build frontend if requested
-    # Check if frontend is already built
-    frontend_dist = Path(__file__).parent.parent / "frontend" / "dist"
-    if build_frontend and frontend_dist.exists() and any(frontend_dist.iterdir()):
-        # If watching, we always want to rebuild to catch latest changes at start
-        if not force_rebuild and not watch_frontend and not click.confirm("Frontend dist directory already exists. Rebuild anyway?"):
-            build_frontend = False
-    
-    if build_frontend:
-        click.echo("🔨 Building frontend...")
-        frontend_dir = Path(__file__).parent.parent / "frontend"
-        if not frontend_dir.exists():
-            click.echo(f"❌ Frontend directory not found: {frontend_dir}", err=True)
-            sys.exit(1)
-        
-        try:
-            # Check if node_modules exists, if not, install dependencies
-            if not (frontend_dir / "node_modules").exists():
-                click.echo("📦 Installing frontend dependencies...")
-                subprocess.run(
-                    ["npm", "install", "--legacy-peer-deps"],
-                    cwd=frontend_dir,
-                    check=True,
-                    capture_output=True,
-                )
-            
-            # Build frontend (show output in real-time for better debugging)
-            click.echo("   Running TypeScript check and Vite build...")
-            result = subprocess.run(
-                ["npm", "run", "build"],
-                cwd=frontend_dir,
-                check=False,  # Don't fail immediately, we'll check return code
-                capture_output=True,
-                text=True,
-            )
-            if result.returncode != 0:
-                click.echo(f"❌ Frontend build failed with return code {result.returncode}", err=True)
-                if result.stdout:
-                    click.echo(f"\nSTDOUT:\n{result.stdout}", err=True)
-                if result.stderr:
-                    click.echo(f"\nSTDERR:\n{result.stderr}", err=True)
-                click.echo("\n💡 Try running 'npm run build' manually in the frontend directory to see full error details.", err=True)
-                sys.exit(1)
-            
-            click.echo("✅ Frontend built successfully")
-        except subprocess.CalledProcessError as e:
-            click.echo(f"❌ Error building frontend:", err=True)
-            if e.stdout:
-                click.echo(f"STDOUT:\n{e.stdout}", err=True)
-            if e.stderr:
-                click.echo(f"STDERR:\n{e.stderr}", err=True)
-            click.echo(f"\nReturn code: {e.returncode}", err=True)
-            sys.exit(1)
-        except FileNotFoundError:
-            click.echo("❌ npm not found. Please install Node.js and npm.", err=True)
-            sys.exit(1)
-    
-    # Initialize DB tables and run migrations before starting (unless explicitly skipped)
-    if not skip_migrations:
-        click.echo("🔄 Initializing database and running migrations...")
-        from app.database import init_db
-        from app.core.migrations import run_migrations, ensure_migrations_directory
-        try:
-            init_db()
-            ensure_migrations_directory()
-            run_migrations()
-            click.echo("✅ Database initialized and migrations completed")
-        except Exception as e:
-            click.echo(f"❌ Migration failed: {e}", err=True)
-            click.echo("💡 You can skip migrations with --skip-migrations (not recommended)", err=True)
-            sys.exit(1)
-    else:
-        click.echo("⚠️  Skipping migrations (not recommended - migrations will run on startup)")
-    
-    # Start frontend watcher if requested
-    frontend_watcher = None
-    if watch_frontend:
-        click.echo("👀 Starting frontend file watcher...")
-        frontend_dir = Path(__file__).parent.parent / "frontend"
-        frontend_watcher = start_frontend_watcher(frontend_dir)
-    
-    # Start the server
-    import uvicorn
-    
-    click.echo(f"🚀 Starting EfficientAI server...")
-    click.echo(f"   Host: {settings.HOST}")
-    click.echo(f"   Port: {settings.PORT}")
-    click.echo(f"   API: http://{settings.HOST}:{settings.PORT}{settings.API_V1_PREFIX}")
-    click.echo(f"   Frontend: http://{settings.HOST}:{settings.PORT}/")
-    click.echo(f"   Docs: http://{settings.HOST}:{settings.PORT}/docs")
-    if watch_frontend:
-        click.echo(f"   Frontend watcher: Active (rebuilding on file changes)")
-    
-    # Use import string for reload to work properly
-    try:
-        uvicorn.run(
-            "app.main:app",
-            host=settings.HOST,
-            port=settings.PORT,
-            reload=reload,
-        )
-    finally:
-        # Clean up watcher on exit
-        if frontend_watcher:
-            frontend_watcher.stop()
-
-
-@main.command()
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option(
-    "--loglevel",
-    "-l",
-    default="info",
-    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
-    help="Log level for Celery worker",
-)
-@click.option(
-    "--queues",
-    "-Q",
-    "queues",
-    default=None,
-    help=(
-        "Comma-separated list of Celery queues this worker should consume "
-        "(forwarded to celery's -Q flag). Defaults to the default queue."
-    ),
-)
-@click.option(
-    "--concurrency",
-    default=None,
-    type=int,
-    help="Number of concurrent worker processes/threads (Celery --concurrency).",
-)
-@click.option(
-    "--pool",
-    "-P",
-    "pool",
-    default=None,
-    type=click.Choice(["prefork", "threads", "solo", "eventlet", "gevent"], case_sensitive=False),
-    help="Celery worker pool implementation (forwarded to celery's -P flag).",
-)
-def worker(config: str, loglevel: str, queues: Optional[str], concurrency: Optional[int], pool: Optional[str]):
-    """Start the Celery worker for background task processing."""
-    from app.config import load_config_from_file
-    
-    # Load configuration from YAML file
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        click.echo(f"💡 Create a config.yml file or use --config to specify a different path.", err=True)
-        sys.exit(1)
-    
-    try:
-        load_config_from_file(str(config_path))
-        click.echo(f"✅ Loaded configuration from {config_path}")
-    except Exception as e:
-        click.echo(f"❌ Error loading config: {e}", err=True)
-        sys.exit(1)
-    
-    click.echo(f"🚀 Starting Celery worker...")
-    click.echo(f"   Log level: {loglevel}")
-    if queues:
-        click.echo(f"   Queues: {queues}")
-    if concurrency is not None:
-        click.echo(f"   Concurrency: {concurrency}")
-    if pool:
-        click.echo(f"   Pool: {pool}")
-    
-    # Start Celery worker
-    try:
-        import subprocess
-        cmd = ["celery", "-A", "app.workers.celery_app", "worker", f"--loglevel={loglevel}"]
-        if queues:
-            cmd.append(f"--queues={queues}")
-        if concurrency is not None:
-            cmd.append(f"--concurrency={concurrency}")
-        if pool:
-            cmd.append(f"--pool={pool}")
-        subprocess.run(cmd, check=True)
-    except KeyboardInterrupt:
-        click.echo("\n👋 Celery worker stopped")
-    except subprocess.CalledProcessError as e:
-        click.echo(f"❌ Celery worker failed: {e}", err=True)
-        sys.exit(1)
-    except FileNotFoundError:
-        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
-        sys.exit(1)
-
-
-@main.command("beat")
-@click.option(
-    "--config",
-    "-c",
-    default="config.yml",
-    help="Path to configuration file",
-)
-@click.option(
-    "--loglevel",
-    "-l",
-    default="info",
-    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
-    help="Log level for Celery beat",
-)
-@click.option(
-    "--platform-worker-concurrency",
-    default=2,
-    type=int,
-    help="Concurrency for the co-located platform task worker (default: 2; thread pool).",
-)
-def beat(config: str, loglevel: str, platform_worker_concurrency: int):
-    """Start Celery Beat + platform task worker (alerts, FX, prune) — single replica only."""
-    from app.config import load_config_from_file
-
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        sys.exit(1)
-
-    try:
-        load_config_from_file(str(config_path))
-        click.echo(f"✅ Loaded configuration from {config_path}")
-    except Exception as e:
-        click.echo(f"❌ Error loading config: {e}", err=True)
-        sys.exit(1)
-
-    from app.workers.config import PLATFORM_WORKER_QUEUE
-
-    click.echo(
-        "🚀 Starting Celery Beat + platform worker "
-        f"(queue={PLATFORM_WORKER_QUEUE}, concurrency={platform_worker_concurrency})"
-    )
-    platform_proc = None
-    try:
-        import subprocess
-
-        platform_proc = subprocess.Popen(
-            [
-                "celery",
-                "-A",
-                "app.workers.celery_app",
-                "worker",
-                f"--queues={PLATFORM_WORKER_QUEUE}",
-                "--pool=threads",
-                f"--concurrency={platform_worker_concurrency}",
-                f"--loglevel={loglevel}",
-            ],
-        )
-        subprocess.run(
-            [
-                "celery",
-                "-A",
-                "app.workers.celery_app",
-                "beat",
-                f"--loglevel={loglevel}",
-            ],
-            check=True,
-        )
-    except KeyboardInterrupt:
-        click.echo("\n👋 Celery Beat stopped")
-    except subprocess.CalledProcessError as e:
-        click.echo(f"❌ Celery Beat failed: {e}", err=True)
-        sys.exit(1)
-    except FileNotFoundError:
-        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
-        sys.exit(1)
-    finally:
-        if platform_proc is not None and platform_proc.poll() is None:
-            platform_proc.terminate()
-            try:
-                platform_proc.wait(timeout=5)
-            except subprocess.TimeoutExpired:
-                platform_proc.kill()
-
-
-@main.command("telephony-worker")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option("--host", default=None, help="Host to bind (default from config)")
-@click.option("--port", default=None, type=int, help="Media server port (default 8001)")
-def telephony_worker(config: str, host: Optional[str], port: Optional[int]):
-    """Start the media server for live voice WebSocket connections."""
-    os.environ["SERVICE_MODE"] = "media"
-    from app.config import apply_service_mode, load_config_from_file, settings
-
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        sys.exit(1)
-
-    try:
-        load_config_from_file(str(config_path))
-        apply_service_mode("media")
-    except Exception as e:
-        click.echo(f"❌ Error loading config: {e}", err=True)
-        sys.exit(1)
-    bind_host = host or settings.HOST
-    bind_port = port or settings.MEDIA_PORT
-
-    from app.app_factory import create_app
-
-    app = create_app()
-    print(
-        f"[MEDIA] telephony-worker ready on {bind_host}:{bind_port} "
-        f"(SERVICE_MODE={settings.SERVICE_MODE}, media_routes mounted)",
-        flush=True,
-    )
-
-    import uvicorn
-
-    click.echo(f"🎙️  Starting EfficientAI media server on {bind_host}:{bind_port}")
-    uvicorn.run(
-        app,
-        host=bind_host,
-        port=bind_port,
-        reload=False,
-    )
-
-
-@main.command("start-worker-all")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option("--loglevel", "-l", default="info", help="Celery log level")
-@click.option("--media-port", default=None, type=int, help="Media server port (default 8001)")
-def start_worker_all(config: str, loglevel: str, media_port: Optional[int]):
-    """Start Celery worker and media server together.
-
-    Deprecated: prefer separate ``eai telephony-worker`` and ``eai worker`` services
-    (see docker-compose ``media`` + ``worker``).
-    """
-    import signal
-    import atexit
-
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        sys.exit(1)
-
-    from app.config import load_config_from_file, settings
-
-    load_config_from_file(str(config_path))
-
-    media_proc = None
-    celery_proc = None
-
-    def cleanup():
-        nonlocal media_proc, celery_proc
-        for proc, label in ((media_proc, "media server"), (celery_proc, "Celery worker")):
-            if proc is None or proc.poll() is not None:
-                continue
-            try:
-                proc.terminate()
-                proc.wait(timeout=5)
-                click.echo(f"✅ {label} stopped")
-            except Exception:
-                proc.kill()
-
-    atexit.register(cleanup)
-
-    def _handle_signal(sig, frame):
-        cleanup()
-        sys.exit(0)
-
-    signal.signal(signal.SIGINT, _handle_signal)
-    signal.signal(signal.SIGTERM, _handle_signal)
-
-    port = media_port or settings.MEDIA_PORT
-    telephony_env = os.environ.copy()
-    telephony_env["SERVICE_MODE"] = "media"
-    media_proc = subprocess.Popen(
-        [sys.executable, "-m", "app.cli", "telephony-worker", "--config", str(config_path), "--port", str(port)],
-        env=telephony_env,
-    )
-    celery_proc = subprocess.Popen(
-        ["celery", "-A", "app.workers.celery_app", "worker", f"--loglevel={loglevel}"],
-    )
-    click.echo(f"🚀 Started media server (pid={media_proc.pid}) and Celery worker (pid={celery_proc.pid})")
-
-    try:
-        while True:
-            if media_proc.poll() is not None:
-                click.echo("❌ Media server exited", err=True)
-                break
-            if celery_proc.poll() is not None:
-                click.echo("❌ Celery worker exited", err=True)
-                break
-            time.sleep(1)
-    except KeyboardInterrupt:
-        pass
-    finally:
-        cleanup()
-
-
-@main.command()
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option(
-    "--host",
-    default=None,
-    help="Host to bind to (overrides config)",
-)
-@click.option(
-    "--port",
-    default=None,
-    type=int,
-    help="Port to bind to (overrides config)",
-)
-@click.option(
-    "--build-frontend/--no-build-frontend",
-    default=True,
-    help="Build frontend before starting (default: True)",
-)
-@click.option(
-    "--reload/--no-reload",
-    default=True,
-    help="Enable auto-reload for development (default: True)",
-)
-@click.option(
-    "--watch-frontend/--no-watch-frontend",
-    default=False,
-    help="Watch frontend files and rebuild automatically (default: False)",
-)
-@click.option(
-    "--force-rebuild",
-    is_flag=True,
-    default=False,
-    help="Force rebuild of frontend without prompting",
-)
-@click.option(
-    "--skip-migrations",
-    is_flag=True,
-    default=False,
-    help="Skip running migrations before starting (not recommended)",
-)
-@click.option(
-    "--worker-loglevel",
-    "-l",
-    default="info",
-    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
-    help="Log level for Celery worker",
-)
-@click.option(
-    "--imports-worker/--no-imports-worker",
-    default=True,
-    help=(
-        "Also start a dedicated worker for the `imports` queue used by call "
-        "import CSV processing (default: True). Disable to keep the previous "
-        "single-worker behavior."
-    ),
-)
-@click.option(
-    "--imports-worker-concurrency",
-    default=12,
-    type=int,
-    help=(
-        "Concurrency for the imports+diarization+evaluations worker "
-        "(default: 12; thread pool). Use lower values (8–12) when DB sharding "
-        "is enabled; 32 threads can exhaust per-shard SQLAlchemy pools."
-    ),
-)
-@click.option(
-    "--usage-worker/--no-usage-worker",
-    default=True,
-    help=(
-        "Also start a dedicated worker for the `usage` queue (flush + cost recompute; "
-        "default: True)."
-    ),
-)
-@click.option(
-    "--usage-worker-concurrency",
-    default=4,
-    type=int,
-    help="Concurrency for the usage worker (default: 4; thread pool).",
-)
-@click.option(
-    "--beat/--no-beat",
-    default=True,
-    help=(
-        "Start Celery Beat for platform periodic tasks (usage flush, alerts, etc.; "
-        "default: True — single replica only in production)."
-    ),
-)
-@click.option(
-    "--beat-loglevel",
-    default=None,
-    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
-    help="Log level for Celery Beat (defaults to --worker-loglevel).",
-)
-@click.option(
-    "--telephony-worker/--no-telephony-worker",
-    default=True,
-    help="Spawn telephony media server for live voice WebSockets (default: on).",
-)
-@click.option(
-    "--media-port",
-    default=None,
-    type=int,
-    help="Telephony media server port (default: MEDIA_PORT / 8001).",
-)
-def start_all(
-    config: str,
-    host: Optional[str],
-    port: Optional[int],
-    build_frontend: bool,
-    reload: bool,
-    watch_frontend: bool,
-    force_rebuild: bool,
-    skip_migrations: bool,
-    worker_loglevel: str,
-    imports_worker: bool,
-    imports_worker_concurrency: int,
-    usage_worker: bool,
-    usage_worker_concurrency: int,
-    beat: bool,
-    beat_loglevel: Optional[str],
-    telephony_worker: bool,
-    media_port: Optional[int],
-):
-    """Start the application server and Celery worker(s) together.
-
-    By default this also spawns a telephony media server (``eai telephony-worker``)
-    and a second Celery worker that consumes the ``imports`` queue (call-import CSV
-    fan-out). Use --no-telephony-worker or --no-imports-worker to skip either.
-    """
-    import signal
-    import atexit
-
-    click.echo("🚀 Starting EfficientAI (App + Worker)...")
-    if telephony_worker:
-        click.echo("   Telephony media server will run on a separate port (SERVICE_MODE=media).")
-    if imports_worker:
-        click.echo(
-            "   This will start the API server, the default Celery worker, "
-            "and a dedicated worker for the `imports` queue."
-        )
-    else:
-        click.echo("   This will start both the API server and Celery worker")
-    click.echo("   Press Ctrl+C to stop all services\n")
-    
-    # Load configuration
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        click.echo(f"💡 Create a config.yml file or use --config to specify a different path.", err=True)
-        sys.exit(1)
-    
-    try:
-        from app.config import load_config_from_file, settings
-        load_config_from_file(str(config_path))
-        click.echo(f"✅ Loaded configuration from {config_path}")
-    except Exception as e:
-        click.echo(f"❌ Error loading config: {e}", err=True)
-        sys.exit(1)
-
-    bind_media_port = media_port or settings.MEDIA_PORT
-
-    os.environ["SERVICE_MODE"] = "api"
-    
-    # Store worker processes for cleanup.
-    worker_process = None
-    worker_imports_process = None
-    worker_usage_process = None
-    beat_process = None
-    platform_worker_process = None
-    telephony_process = None
-
-    def _terminate(proc, label: str):
-        """Best-effort terminate -> wait -> kill for a worker subprocess."""
-        if proc is None or proc.poll() is not None:
-            return
-        try:
-            click.echo(f"\n👋 Stopping {label}...")
-            proc.terminate()
-            proc.wait(timeout=5)
-            click.echo(f"✅ {label} stopped")
-        except subprocess.TimeoutExpired:
-            proc.kill()
-            proc.wait()
-        except Exception:
-            pass
-
-    def cleanup_processes():
-        """Clean up spawned processes."""
-        nonlocal worker_process, worker_imports_process, worker_usage_process, beat_process, platform_worker_process, telephony_process
-        _terminate(telephony_process, "Telephony media server")
-        _terminate(beat_process, "Celery Beat")
-        _terminate(platform_worker_process, "Celery platform worker")
-        _terminate(worker_process, "Celery worker (default)")
-        _terminate(worker_imports_process, "Celery worker (imports)")
-        _terminate(worker_usage_process, "Celery worker (usage)")
-    
-    # Register cleanup on exit
-    atexit.register(cleanup_processes)
-    
-    def signal_handler(sig, frame):
-        """Handle Ctrl+C gracefully."""
-        cleanup_processes()
-        sys.exit(0)
-    
-    signal.signal(signal.SIGINT, signal_handler)
-    signal.signal(signal.SIGTERM, signal_handler)
-    
-    # Initialize DB tables and run migrations before starting (unless explicitly skipped)
-    if not skip_migrations:
-        click.echo("🔄 Initializing database and running migrations...")
-        from app.database import init_db
-        from app.core.migrations import run_migrations, ensure_migrations_directory
-        try:
-            init_db()
-            ensure_migrations_directory()
-            run_migrations()
-            click.echo("✅ Database initialized and migrations completed")
-        except Exception as e:
-            click.echo(f"❌ Migration failed: {e}", err=True)
-            click.echo("💡 You can skip migrations with --skip-migrations (not recommended)", err=True)
-            sys.exit(1)
-    
-    # Build frontend if needed
-    if build_frontend:
-        frontend_dir = Path(__file__).parent.parent / "frontend"
-        if frontend_dir.exists():
-            click.echo("🔨 Building frontend...")
-            try:
-                if not (frontend_dir / "node_modules").exists():
-                    click.echo("   Installing frontend dependencies...")
-                    subprocess.run(["npm", "install", "--legacy-peer-deps"], cwd=frontend_dir, check=True, capture_output=True)
-                subprocess.run(["npm", "run", "build"], cwd=frontend_dir, check=True, capture_output=True)
-                click.echo("✅ Frontend built successfully")
-            except subprocess.CalledProcessError as e:
-                click.echo(f"❌ Frontend build failed: {e}", err=True)
-                sys.exit(1)
-    
-    # Start frontend watcher if requested
-    frontend_watcher = None
-    if watch_frontend:
-        click.echo("👀 Starting frontend file watcher...")
-        frontend_dir = Path(__file__).parent.parent / "frontend"
-        frontend_watcher = start_frontend_watcher(frontend_dir)
-    
-    def _spawn_worker(args: list[str], label: str, prefix: str) -> subprocess.Popen:
-        """Spawn a Celery worker subprocess and stream its stdout with a prefix."""
-        proc = subprocess.Popen(
-            args,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT,
-            text=True,
-            bufsize=1,
-        )
-        click.echo(f"✅ {label} started")
-
-        def _stream():
-            if proc.stdout:
-                for line in iter(proc.stdout.readline, ""):
-                    if line:
-                        click.echo(f"{prefix} {line.rstrip()}", err=False)
-                proc.stdout.close()
-
-        threading.Thread(target=_stream, daemon=True).start()
-        return proc
-
-    if telephony_worker:
-        try:
-            telephony_env = os.environ.copy()
-            telephony_env["SERVICE_MODE"] = "media"
-            telephony_process = subprocess.Popen(
-                [
-                    sys.executable,
-                    "-m",
-                    "app.cli",
-                    "telephony-worker",
-                    "--config",
-                    str(config_path),
-                    "--port",
-                    str(bind_media_port),
-                ],
-                env=telephony_env,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.STDOUT,
-                text=True,
-                bufsize=1,
-            )
-            click.echo(
-                f"✅ Telephony media server started on port {bind_media_port} "
-                f"(pid={telephony_process.pid})"
-            )
-
-            def _stream_telephony():
-                if telephony_process.stdout:
-                    for line in iter(telephony_process.stdout.readline, ""):
-                        if line:
-                            click.echo(f"[MEDIA] {line.rstrip()}", err=False)
-                    telephony_process.stdout.close()
-
-            threading.Thread(target=_stream_telephony, daemon=True).start()
-        except FileNotFoundError:
-            click.echo("❌ Python interpreter not found for telephony-worker subprocess", err=True)
-            sys.exit(1)
-        except Exception as e:
-            click.echo(f"❌ Failed to start telephony media server: {e}", err=True)
-            sys.exit(1)
-
-    # Start Celery workers as subprocess(es) with output streaming
-    try:
-        worker_process = _spawn_worker(
-            [
-                "celery",
-                "-A",
-                "app.workers.celery_app",
-                "worker",
-                f"--loglevel={worker_loglevel}",
-                "-Q",
-                "celery,audio-metrics",
-                "-c",
-                "8",
-            ],
-            label="Celery worker (celery + audio-metrics queues, concurrency=8)",
-            prefix="[WORKER]",
-        )
-
-        if imports_worker:
-            from app.workers.config import IMPORTS_WORKER_QUEUES
-
-            worker_imports_process = _spawn_worker(
-                [
-                    "celery",
-                    "-A",
-                    "app.workers.celery_app",
-                    "worker",
-                    f"--loglevel={worker_loglevel}",
-                    "-Q",
-                    IMPORTS_WORKER_QUEUES,
-                    "-P",
-                    "threads",
-                    "-c",
-                    str(imports_worker_concurrency),
-                ],
-                label=(
-                    f"Celery worker ({IMPORTS_WORKER_QUEUES} queues, "
-                    f"pool=threads, concurrency={imports_worker_concurrency})"
-                ),
-                prefix="[WORKER-IMPORTS]",
-            )
-
-        if usage_worker:
-            from app.workers.config import USAGE_WORKER_QUEUE
-
-            worker_usage_process = _spawn_worker(
-                [
-                    "celery",
-                    "-A",
-                    "app.workers.celery_app",
-                    "worker",
-                    f"--loglevel={worker_loglevel}",
-                    "-Q",
-                    USAGE_WORKER_QUEUE,
-                    "-P",
-                    "threads",
-                    "-c",
-                    str(usage_worker_concurrency),
-                ],
-                label=(
-                    f"Celery worker ({USAGE_WORKER_QUEUE} queue, "
-                    f"pool=threads, concurrency={usage_worker_concurrency})"
-                ),
-                prefix="[WORKER-USAGE]",
-            )
-
-        if beat:
-            from app.workers.config import PLATFORM_WORKER_QUEUE
-
-            beat_level = beat_loglevel or worker_loglevel
-            platform_worker_process = _spawn_worker(
-                [
-                    "celery",
-                    "-A",
-                    "app.workers.celery_app",
-                    "worker",
-                    f"--loglevel={beat_level}",
-                    "-Q",
-                    PLATFORM_WORKER_QUEUE,
-                    "-P",
-                    "threads",
-                    "-c",
-                    "2",
-                ],
-                label=f"Celery platform worker ({PLATFORM_WORKER_QUEUE} queue)",
-                prefix="[BEAT-WORKER]",
-            )
-            beat_process = _spawn_worker(
-                [
-                    "celery",
-                    "-A",
-                    "app.workers.celery_app",
-                    "beat",
-                    f"--loglevel={beat_level}",
-                ],
-                label=f"Celery Beat (scheduler, loglevel={beat_level})",
-                prefix="[BEAT]",
-            )
-
-    except FileNotFoundError:
-        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
-        sys.exit(1)
-    except Exception as e:
-        click.echo(f"❌ Failed to start worker: {e}", err=True)
-        sys.exit(1)
-    
-    # Small delay to let worker start
-    time.sleep(1)
-    
-    # Start the application server in the main process
-    # This allows uvicorn's reload to work properly (it needs to spawn child processes)
-    try:
-        from app.config import settings
-        import uvicorn
-        
-        # Override with CLI options if provided
-        if host:
-            settings.HOST = host
-        if port:
-            settings.PORT = port
-        
-        click.echo("✅ Application server starting...")
-        click.echo(f"   Host: {settings.HOST}")
-        click.echo(f"   Port: {settings.PORT}")
-        click.echo(f"   API: http://{settings.HOST}:{settings.PORT}{settings.API_V1_PREFIX}")
-        click.echo(f"   Frontend: http://{settings.HOST}:{settings.PORT}/")
-        click.echo(f"   Docs: http://{settings.HOST}:{settings.PORT}/docs")
-        if watch_frontend:
-            click.echo(f"   Frontend watcher: Active (rebuilding on file changes)")
-        if imports_worker:
-            from app.workers.config import IMPORTS_WORKER_QUEUES
-
-            click.echo(
-                f"   Workers: default queue + {IMPORTS_WORKER_QUEUES} "
-                f"(concurrency={imports_worker_concurrency}; imports preferred)"
-            )
-        else:
-            click.echo("   Workers: default queue only (--no-imports-worker)")
-        if usage_worker:
-            from app.workers.config import USAGE_WORKER_QUEUE
-
-            click.echo(
-                f"   Usage worker: {USAGE_WORKER_QUEUE} queue "
-                f"(concurrency={usage_worker_concurrency})"
-            )
-        else:
-            click.echo("   Usage worker: disabled (--no-usage-worker)")
-        if beat:
-            click.echo("   Celery Beat: scheduler + platform worker (alerts, FX, prune); flush on worker-usage")
-        else:
-            click.echo("   Celery Beat: disabled (--no-beat)")
-        if telephony_worker:
-            telephony_public = (settings.VOBIZ_WEBHOOK_BASE_URL or "").strip()
-            click.echo(f"   Telephony edge: http://localhost:{bind_media_port} (local)")
-            if telephony_public:
-                click.echo(f"   Vobiz webhook_base_url: {telephony_public}")
-            else:
-                click.echo(
-                    "   Set vobiz.webhook_base_url to your telephony public URL "
-                    f"(e.g. ngrok http {bind_media_port}) for inbound PSTN"
-                )
-            if (settings.MEDIA_WS_BASE_URL or "").strip():
-                click.echo(f"   Browser voice-agent WS: {settings.MEDIA_WS_BASE_URL}")
-        click.echo("\n📝 All services are running. Press Ctrl+C to stop.\n")
-        
-        # Run uvicorn in the main process (allows reload to work)
-        uvicorn.run(
-            "app.main:app",
-            host=settings.HOST,
-            port=settings.PORT,
-            reload=reload,
-        )
-    except KeyboardInterrupt:
-        pass
-    except Exception as e:
-        click.echo(f"❌ App error: {e}", err=True)
-    finally:
-        cleanup_processes()
-        if frontend_watcher:
-            frontend_watcher.stop()
-
-
-@main.command()
-@click.option(
-    "--output",
-    "-o",
-    type=click.Path(),
-    default="config.yml",
-    help="Output file path for example config",
-)
-def init_config(output: str):
-    """Generate an example configuration file."""
-    example_config = """# EfficientAI Configuration File
-
-# Application Settings
-app:
-  name: "Voice AI Evaluation Platform"
-  version: "0.1.0"
-  debug: true
-  secret_key: "your-secret-key-here-change-in-production"
-
-# Server Settings
-server:
-  host: "0.0.0.0"
-  port: 8000
-
-# Database Configuration
-database:
-  url: "postgresql://efficientai:password@localhost:5432/efficientai"
-  # Alternative: specify individual components
-  # user: "efficientai"
-  # password: "password"
-  # host: "localhost"
-  # port: 5432
-  # db: "efficientai"
-
-# Redis Configuration
-redis:
-  url: "redis://localhost:6379/0"
-  # Alternative: specify individual components
-  # host: "localhost"
-  # port: 6379
-  # db: 0
-
-# Celery Configuration
-celery:
-  broker_url: "redis://localhost:6379/0"
-  result_backend: "redis://localhost:6379/0"
-
-# File Storage
-storage:
-  upload_dir: "./uploads"
-  max_file_size_mb: 500
-  allowed_audio_formats:
-    - "wav"
-    - "mp3"
-    - "flac"
-    - "m4a"
-
-# CORS Settings
-cors:
-  origins:
-    - "http://localhost:3000"
-    - "http://localhost:8000"
-
-# API Settings
-api:
-  prefix: "/api/v1"
-  key_header: "X-API-Key"
-  rate_limit_per_minute: 60
-"""
-    
-    output_path = Path(output)
-    if output_path.exists():
-        if not click.confirm(f"File {output} already exists. Overwrite?"):
-            click.echo("Cancelled.")
-            return
-    
-    try:
-        output_path.write_text(example_config)
-        click.echo(f"✅ Created example configuration file: {output}")
-        click.echo(f"💡 Edit {output} with your settings, then run: eai start --config {output}")
-    except Exception as e:
-        click.echo(f"❌ Error creating config file: {e}", err=True)
-        sys.exit(1)
-
-
-def _bootstrap_sharding_config(config_path: str) -> None:
-    from app.config import load_config_from_file
-    from app.db_sharding.pool_manager import db_pool_manager
-
-    load_config_from_file(config_path)
-    db_pool_manager.reset()
-
-
-@click.group()
-def sharding():
-    """Call-import data-plane sharding admin (rebalance / registry)."""
-    pass
-
-
-main.add_command(sharding)
-
-
-@sharding.command("list-slices")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option(
-    "--call-import-id",
-    required=True,
-    help="Call import UUID whose slice registry to inspect",
-)
-def sharding_list_slices(config: str, call_import_id: str):
-    """Show catalog slice registry rows for a call import."""
-    from uuid import UUID
-
-    from app.db_sharding.pool_manager import open_catalog_session
-    from app.db_sharding.rebalance import RebalanceError, list_shard_slices, require_sharding_enabled
-
-    try:
-        _bootstrap_sharding_config(config)
-        require_sharding_enabled()
-        cid = UUID(call_import_id)
-    except (ValueError, RebalanceError) as exc:
-        click.echo(f"❌ {exc}", err=True)
-        sys.exit(1)
-
-    catalog = open_catalog_session()
-    try:
-        slices = list_shard_slices(catalog, cid)
-        if not slices:
-            click.echo(f"No registry slices for call_import {cid}")
-            return
-        click.echo(f"call_import_id: {cid}")
-        click.echo(f"slices: {len(slices)}")
-        for item in slices:
-            click.echo(
-                f"  slice {item.slice_id}: shard={item.shard_id} "
-                f"rows [{item.row_index_min}, {item.row_index_max}] "
-                f"(count={item.row_count})"
-            )
-    finally:
-        catalog.close()
-
-
-@sharding.command("rebalance-slices")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option("--call-import-id", required=True, help="Call import UUID to rebalance")
-@click.option("--from-shard", required=True, help="Source shard id (e.g. data-shard-01)")
-@click.option("--to-shard", required=True, help="Target shard id (e.g. data-shard-02)")
-@click.option(
-    "--slice-id",
-    "slice_ids",
-    multiple=True,
-    type=int,
-    help="Move only these slice ids (default: all slices on --from-shard)",
-)
-@click.option(
-    "--dry-run",
-    is_flag=True,
-    help="Plan only: print row counts without copying or updating registry",
-)
-@click.option(
-    "--force",
-    is_flag=True,
-    help="Skip quiescence checks (use only after pausing workers)",
-)
-def sharding_rebalance_slices(
-    config: str,
-    call_import_id: str,
-    from_shard: str,
-    to_shard: str,
-    slice_ids: tuple[int, ...],
-    dry_run: bool,
-    force: bool,
-):
-    """Copy call-import slice rows (and eval rows) from one shard to another."""
-    from uuid import UUID
-
-    from app.db_sharding.pool_manager import open_catalog_session
-    from app.db_sharding.rebalance import (
-        RebalanceError,
-        require_sharding_enabled,
-        build_rebalance_plan,
-        execute_rebalance_slices,
-    )
-
-    try:
-        _bootstrap_sharding_config(config)
-        require_sharding_enabled()
-        cid = UUID(call_import_id)
-    except (ValueError, RebalanceError) as exc:
-        click.echo(f"❌ {exc}", err=True)
-        sys.exit(1)
-
-    catalog = open_catalog_session()
-    try:
-        plan = build_rebalance_plan(
-            catalog,
-            cid,
-            from_shard_id=from_shard,
-            to_shard_id=to_shard,
-            slice_ids=slice_ids or None,
-        )
-        click.echo("Rebalance plan:")
-        click.echo(f"  call_import_id: {plan.call_import_id}")
-        click.echo(f"  from_shard:     {plan.from_shard_id}")
-        click.echo(f"  to_shard:       {plan.to_shard_id}")
-        click.echo(f"  slices:         {len(plan.slices)}")
-        for item in plan.slices:
-            click.echo(
-                f"    slice {item.slice_id}: rows [{item.row_index_min}, {item.row_index_max}]"
-            )
-        click.echo(f"  import_rows:    {plan.import_row_count}")
-        click.echo(f"  eval_rows:      {plan.eval_row_count}")
-
-        result = execute_rebalance_slices(
-            catalog,
-            plan,
-            dry_run=dry_run,
-            force=force,
-        )
-        if result.dry_run:
-            click.echo("✅ Dry run complete (no changes made)")
-        else:
-            click.echo(
-                "✅ Rebalance complete: "
-                f"slices={result.slices_moved}, "
-                f"import_rows={result.import_rows_moved}, "
-                f"eval_rows={result.eval_rows_moved}"
-            )
-    except RebalanceError as exc:
-        click.echo(f"❌ {exc}", err=True)
-        sys.exit(1)
-    finally:
-        catalog.close()
-
-
-@click.group()
-def usage():
-    """Usage pricing ops (seed rates, diff catalog, recompute costs)."""
-    pass
-
-
-main.add_command(usage)
-
-
-def _load_cli_config(config: str) -> Path:
-    config_path = Path(config)
-    if not config_path.exists():
-        click.echo(f"❌ Config file not found: {config}", err=True)
-        sys.exit(1)
-    from app.config import load_config_from_file
-
-    load_config_from_file(str(config_path))
-    return config_path
-
-
-@usage.command("seed-rates")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option(
-    "--effective-from",
-    type=click.DateTime(formats=["%Y-%m-%d"]),
-    default=None,
-    help="Effective date for seeded rates (default: 2020-01-01)",
-)
-def usage_seed_rates(config: str, effective_from):
-    """Upsert model_pricing_rates from models.json pricing blocks."""
-    _load_cli_config(config)
-    from app.database import SessionLocal
-    from app.services.usage.pricing_ops import seed_rates_from_models_json
-
-    day = effective_from.date() if effective_from else None
-    db = SessionLocal()
-    try:
-        count = seed_rates_from_models_json(db, effective_from=day)
-        db.commit()
-        click.echo(f"✅ Seeded/updated {count} pricing rate row(s)")
-    finally:
-        db.close()
-
-
-@usage.command("diff-rates")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option(
-    "--effective-from",
-    type=click.DateTime(formats=["%Y-%m-%d"]),
-    default=None,
-    help="Compare rates at this effective_from date (default: 2020-01-01)",
-)
-@click.option("--json", "as_json", is_flag=True, help="Print machine-readable JSON")
-def usage_diff_rates(config: str, effective_from, as_json: bool):
-    """Diff models.json pricing blocks vs model_pricing_rates in Postgres."""
-    import json as json_module
-
-    _load_cli_config(config)
-    from app.database import SessionLocal
-    from app.services.usage.pricing_ops import diff_models_json_vs_db
-
-    day = effective_from.date() if effective_from else None
-    db = SessionLocal()
-    try:
-        report = diff_models_json_vs_db(db, effective_from=day)
-    finally:
-        db.close()
-
-    if as_json:
-        click.echo(json_module.dumps(report, indent=2, default=str))
-        return
-
-    click.echo(f"effective_from: {report['effective_from']}")
-    click.echo(
-        f"models.json priced: {report['models_json_count']} | "
-        f"database rows: {report['database_count']} | "
-        f"in_sync: {report['in_sync']}"
-    )
-    if report["only_in_models_json"]:
-        click.echo(f"\nOnly in models.json ({len(report['only_in_models_json'])}):")
-        for item in report["only_in_models_json"][:20]:
-            click.echo(f"  - {item['model']} ({item['usage_kind']})")
-    if report["only_in_database"]:
-        click.echo(f"\nOnly in database ({len(report['only_in_database'])}):")
-        for item in report["only_in_database"][:20]:
-            click.echo(f"  - {item['model']} ({item['usage_kind']})")
-    if report["mismatches"]:
-        click.echo(f"\nMismatched rates ({len(report['mismatches'])}):")
-        for item in report["mismatches"][:20]:
-            click.echo(f"  - {item['model']} ({item['usage_kind']})")
-            for field, values in item["fields"].items():
-                click.echo(
-                    f"      {field}: json={values['models_json']} db={values['database']}"
-                )
-    missing = report["missing_pricing_blocks"]
-    if missing:
-        click.echo(f"\nmodels.json entries missing pricing blocks ({len(missing)}):")
-        for model in missing[:20]:
-            click.echo(f"  - {model}")
-    unresolved = report["litellm_unresolved"]
-    if unresolved:
-        click.echo(f"\nLiteLLM unresolved ({len(unresolved)}):")
-        for item in unresolved[:20]:
-            click.echo(f"  - {item.get('model')} ({item.get('reason', 'unresolved')})")
-
-
-@usage.command("recompute")
-@click.option(
-    "--config",
-    "-c",
-    type=click.Path(exists=True, readable=True),
-    default="config.yml",
-    help="Path to configuration YAML file",
-)
-@click.option("--organization-id", default=None, help="Scope recompute to one org UUID")
-@click.option("--model", default=None, help="Scope recompute to one model")
-@click.option("--usage-kind", default=None, help="Scope recompute to llm/stt/tts")
-@click.option("--start-date", type=click.DateTime(formats=["%Y-%m-%d"]), default=None)
-@click.option("--end-date", type=click.DateTime(formats=["%Y-%m-%d"]), default=None)
-@click.option(
-    "--async/--sync",
-    "run_async",
-    default=True,
-    help="Enqueue Celery task (default) or run synchronously in this process",
-)
-def usage_recompute(
-    config: str,
-    organization_id: Optional[str],
-    model: Optional[str],
-    usage_kind: Optional[str],
-    start_date,
-    end_date,
-    run_async: bool,
-):
-    """Backfill or recompute stored usage costs on llm_usage_daily rollups."""
-    from uuid import UUID
-
-    _load_cli_config(config)
-    start = start_date.date() if start_date else None
-    end = end_date.date() if end_date else None
-    org_uuid = UUID(organization_id) if organization_id else None
-
-    if run_async:
-        if org_uuid is None:
-            click.echo(
-                "❌ --organization-id is required for async recompute (creates a tracked job).",
-                err=True,
-            )
-            click.echo(
-                "💡 Use --sync to recompute in this process without an org scope, or pass --organization-id.",
-                err=True,
-            )
-            sys.exit(1)
-
-        from app.database import SessionLocal
-        from app.services.usage.pricing_jobs import (
-            create_recompute_job,
-            enqueue_recompute_job,
-            job_to_dict,
-        )
-
-        db = SessionLocal()
-        try:
-            job = create_recompute_job(
-                db,
-                organization_id=org_uuid,
-                model=model,
-                usage_kind=usage_kind,
-                start_date=start,
-                end_date=end,
-            )
-            enqueue_recompute_job(db, job)
-            db.refresh(job)
-            payload = job_to_dict(job)
-            click.echo(f"✅ Enqueued recompute job {payload['id']} (status={payload['status']})")
-            if payload.get("celery_task_id"):
-                click.echo(f"   Celery task: {payload['celery_task_id']}")
-        except Exception as exc:
-            click.echo(f"❌ Failed to enqueue recompute job: {exc}", err=True)
-            sys.exit(1)
-        finally:
-            db.close()
-        return
-
-    from app.database import SessionLocal
-    from app.services.usage.pricing import recompute_usage_costs
-
-    db = SessionLocal()
-    try:
-        updated = recompute_usage_costs(
-            db,
-            organization_id=org_uuid,
-            model=model,
-            usage_kind=usage_kind,
-            start_date=start,
-            end_date=end,
-        )
-        click.echo(f"✅ Recomputed costs for {updated} rollup row(s)")
-    finally:
-        db.close()
-
-
-@usage.command("sync-litellm")
-@click.option("--local", is_flag=True, help="Use bundled LiteLLM model_cost JSON")
-@click.option(
-    "--write-models",
-    is_flag=True,
-    help="Merge generated pricing into app/config/models.json",
-)
-@click.option("--stdout", is_flag=True, help="Print pricing_catalog.json to stdout")
-def usage_sync_litellm(local: bool, write_models: bool, stdout: bool):
-    """Fetch LiteLLM prices and regenerate pricing_catalog.json."""
-    import subprocess
-    import sys as sys_module
-
-    script = Path(__file__).resolve().parent.parent / "scripts" / "sync_pricing_catalog_from_litellm.py"
-    cmd = [sys_module.executable, str(script)]
-    if local:
-        cmd.append("--local")
-    if write_models:
-        cmd.append("--write-models")
-    if stdout:
-        cmd.append("--stdout")
-    subprocess.run(cmd, check=True)
-
-
-if __name__ == "__main__":
-    main()
-
+"""CLI for EfficientAI platform."""
+
+import click
+import yaml
+import os
+import sys
+import subprocess
+import threading
+import time
+from pathlib import Path
+from typing import Optional
+
+
+@click.group()
+def main():
+    """EfficientAI - Voice AI Evaluation Platform CLI."""
+    pass
+
+
+class FrontendWatcher:
+    """Watch frontend files and rebuild on changes."""
+    
+    def __init__(self, frontend_dir: Path):
+        self.frontend_dir = frontend_dir
+        self.watching = False
+        self.thread = None
+        self.last_build_time = 0
+        self.build_lock = threading.Lock()
+        
+    def should_rebuild(self) -> bool:
+        """Check if frontend files have changed."""
+        src_dir = self.frontend_dir / "src"
+        if not src_dir.exists():
+            return False
+        
+        # Check modification time of source files
+        max_mtime = 0
+        for ext in [".tsx", ".ts", ".css", ".jsx", ".js"]:
+            for file_path in src_dir.rglob(f"*{ext}"):
+                if file_path.is_file():
+                    max_mtime = max(max_mtime, file_path.stat().st_mtime)
+        
+        # Also check config files
+        config_files = [
+            self.frontend_dir / "vite.config.ts",
+            self.frontend_dir / "tailwind.config.js",
+            self.frontend_dir / "tsconfig.json",
+            self.frontend_dir / "package.json",
+        ]
+        for config_file in config_files:
+            if config_file.exists():
+                max_mtime = max(max_mtime, config_file.stat().st_mtime)
+        
+        if max_mtime > self.last_build_time:
+            self.last_build_time = max_mtime
+            return True
+        return False
+    
+    def build_frontend(self):
+        """Rebuild the frontend atomically to avoid serving half-written dist."""
+        import shutil
+
+        with self.build_lock:
+            try:
+                click.echo("\n🔄 Frontend files changed, rebuilding...")
+                staging_dir = self.frontend_dir / "dist.staging"
+                dist_dir = self.frontend_dir / "dist"
+                shutil.rmtree(staging_dir, ignore_errors=True)
+                result = subprocess.run(
+                    ["npm", "run", "build", "--", "--outDir", "dist.staging"],
+                    cwd=self.frontend_dir,
+                    check=False,
+                    capture_output=True,
+                    text=True,
+                )
+                if result.returncode == 0:
+                    backup_dir = self.frontend_dir / "dist.prev"
+                    shutil.rmtree(backup_dir, ignore_errors=True)
+                    if dist_dir.exists():
+                        dist_dir.rename(backup_dir)
+                    staging_dir.rename(dist_dir)
+                    shutil.rmtree(backup_dir, ignore_errors=True)
+                    click.echo("✅ Frontend rebuilt successfully")
+                else:
+                    shutil.rmtree(staging_dir, ignore_errors=True)
+                    click.echo("⚠️  Frontend build had warnings (check logs)", err=True)
+                    if result.stderr:
+                        click.echo(result.stderr[:500], err=True)
+            except Exception as e:
+                click.echo(f"❌ Frontend build error: {e}", err=True)
+    
+    def watch_loop(self):
+        """Watch loop that runs in background thread."""
+        while self.watching:
+            try:
+                if self.should_rebuild():
+                    self.build_frontend()
+                time.sleep(1)  # Check every second
+            except Exception as e:
+                click.echo(f"❌ Watcher error: {e}", err=True)
+                time.sleep(5)  # Wait longer on error
+    
+    def start(self):
+        """Start the watcher in a background thread."""
+        if self.watching:
+            return
+        self.watching = True
+        # Set initial build time to avoid rebuilding immediately
+        self.last_build_time = time.time()
+        self.thread = threading.Thread(target=self.watch_loop, daemon=True)
+        self.thread.start()
+    
+    def stop(self):
+        """Stop the watcher."""
+        self.watching = False
+        if self.thread:
+            self.thread.join(timeout=1)
+
+
+def start_frontend_watcher(frontend_dir: Path) -> FrontendWatcher:
+    """Start a frontend file watcher."""
+    watcher = FrontendWatcher(frontend_dir)
+    watcher.start()
+    return watcher
+
+
+def _read_config_media_ws_base_url(config_path: Path) -> Optional[str]:
+    """Return vobiz.media_ws_base_url from YAML when explicitly configured."""
+    try:
+        with open(config_path, encoding="utf-8") as handle:
+            data = yaml.safe_load(handle) or {}
+        value = ((data.get("vobiz") or {}).get("media_ws_base_url") or "").strip()
+        return value or None
+    except Exception:
+        return None
+
+
+@main.command()
+@click.option(
+    "--verbose",
+    "-v",
+    is_flag=True,
+    help="Show detailed migration output",
+)
+def migrate(verbose: bool):
+    """Run pending database migrations."""
+    import logging
+    from app.core.migrations import run_migrations, ensure_migrations_directory
+    
+    if verbose:
+        logging.basicConfig(level=logging.INFO)
+    
+    click.echo("🔄 Running database migrations...")
+    ensure_migrations_directory()
+    
+    try:
+        run_migrations()
+        click.echo("✅ All migrations completed successfully!")
+    except Exception as e:
+        click.echo(f"❌ Migration failed: {e}", err=True)
+        sys.exit(1)
+
+
+@main.command()
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--host",
+    default=None,
+    help="Host to bind to (overrides config)",
+)
+@click.option(
+    "--port",
+    default=None,
+    type=int,
+    help="Port to bind to (overrides config)",
+)
+@click.option(
+    "--build-frontend/--no-build-frontend",
+    default=True,
+    help="Build frontend before starting (default: True)",
+)
+@click.option(
+    "--reload/--no-reload",
+    default=True,
+    help="Enable auto-reload for development (default: True)",
+)
+@click.option(
+    "--watch-frontend/--no-watch-frontend",
+    default=False,
+    help="Watch frontend files and rebuild automatically (default: False)",
+)
+@click.option(
+    "--force-rebuild",
+    is_flag=True,
+    default=False,
+    help="Force rebuild of frontend without prompting",
+)
+@click.option(
+    "--skip-migrations",
+    is_flag=True,
+    default=False,
+    help="Skip running migrations before starting (not recommended)",
+)
+def start(config: str, host: Optional[str], port: Optional[int], build_frontend: bool, reload: bool, watch_frontend: bool, force_rebuild: bool, skip_migrations: bool):
+    """Start the EfficientAI application server."""
+    from app.config import apply_service_mode, load_config_from_file, settings
+
+    # Load configuration from YAML file
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        click.echo(f"💡 Create a config.yml file or use --config to specify a different path.", err=True)
+        sys.exit(1)
+
+    explicit_media_ws = _read_config_media_ws_base_url(config_path)
+    os.environ["SERVICE_MODE"] = "api"
+    if not explicit_media_ws:
+        # Single-process dev: co-locate voice WebSockets on the API port unless
+        # config.yml sets vobiz.media_ws_base_url (used with eai telephony-worker).
+        os.environ.pop("MEDIA_WS_BASE_URL", None)
+
+    try:
+        load_config_from_file(str(config_path))
+        apply_service_mode("api")
+        if not explicit_media_ws:
+            settings.MEDIA_WS_BASE_URL = ""
+        click.echo(f"✅ Loaded configuration from {config_path}")
+    except Exception as e:
+        click.echo(f"❌ Error loading config: {e}", err=True)
+        sys.exit(1)
+    
+    # Override with CLI options if provided
+    if host:
+        settings.HOST = host
+    if port:
+        settings.PORT = port
+    
+    # Build frontend if requested
+    # Check if frontend is already built
+    frontend_dist = Path(__file__).parent.parent / "frontend" / "dist"
+    if build_frontend and frontend_dist.exists() and any(frontend_dist.iterdir()):
+        # If watching, we always want to rebuild to catch latest changes at start
+        if not force_rebuild and not watch_frontend and not click.confirm("Frontend dist directory already exists. Rebuild anyway?"):
+            build_frontend = False
+    
+    if build_frontend:
+        click.echo("🔨 Building frontend...")
+        frontend_dir = Path(__file__).parent.parent / "frontend"
+        if not frontend_dir.exists():
+            click.echo(f"❌ Frontend directory not found: {frontend_dir}", err=True)
+            sys.exit(1)
+        
+        try:
+            # Check if node_modules exists, if not, install dependencies
+            if not (frontend_dir / "node_modules").exists():
+                click.echo("📦 Installing frontend dependencies...")
+                subprocess.run(
+                    ["npm", "install", "--legacy-peer-deps"],
+                    cwd=frontend_dir,
+                    check=True,
+                    capture_output=True,
+                )
+            
+            # Build frontend (show output in real-time for better debugging)
+            click.echo("   Running TypeScript check and Vite build...")
+            result = subprocess.run(
+                ["npm", "run", "build"],
+                cwd=frontend_dir,
+                check=False,  # Don't fail immediately, we'll check return code
+                capture_output=True,
+                text=True,
+            )
+            if result.returncode != 0:
+                click.echo(f"❌ Frontend build failed with return code {result.returncode}", err=True)
+                if result.stdout:
+                    click.echo(f"\nSTDOUT:\n{result.stdout}", err=True)
+                if result.stderr:
+                    click.echo(f"\nSTDERR:\n{result.stderr}", err=True)
+                click.echo("\n💡 Try running 'npm run build' manually in the frontend directory to see full error details.", err=True)
+                sys.exit(1)
+            
+            click.echo("✅ Frontend built successfully")
+        except subprocess.CalledProcessError as e:
+            click.echo(f"❌ Error building frontend:", err=True)
+            if e.stdout:
+                click.echo(f"STDOUT:\n{e.stdout}", err=True)
+            if e.stderr:
+                click.echo(f"STDERR:\n{e.stderr}", err=True)
+            click.echo(f"\nReturn code: {e.returncode}", err=True)
+            sys.exit(1)
+        except FileNotFoundError:
+            click.echo("❌ npm not found. Please install Node.js and npm.", err=True)
+            sys.exit(1)
+    
+    # Initialize DB tables and run migrations before starting (unless explicitly skipped)
+    if not skip_migrations:
+        click.echo("🔄 Initializing database and running migrations...")
+        from app.database import init_db
+        from app.core.migrations import run_migrations, ensure_migrations_directory
+        try:
+            init_db()
+            ensure_migrations_directory()
+            run_migrations()
+            click.echo("✅ Database initialized and migrations completed")
+        except Exception as e:
+            click.echo(f"❌ Migration failed: {e}", err=True)
+            click.echo("💡 You can skip migrations with --skip-migrations (not recommended)", err=True)
+            sys.exit(1)
+    else:
+        click.echo("⚠️  Skipping migrations (not recommended - migrations will run on startup)")
+    
+    # Start frontend watcher if requested
+    frontend_watcher = None
+    if watch_frontend:
+        click.echo("👀 Starting frontend file watcher...")
+        frontend_dir = Path(__file__).parent.parent / "frontend"
+        frontend_watcher = start_frontend_watcher(frontend_dir)
+    
+    # Start the server
+    import uvicorn
+    
+    click.echo(f"🚀 Starting EfficientAI server...")
+    click.echo(f"   Host: {settings.HOST}")
+    click.echo(f"   Port: {settings.PORT}")
+    click.echo(f"   API: http://{settings.HOST}:{settings.PORT}{settings.API_V1_PREFIX}")
+    click.echo(f"   Frontend: http://{settings.HOST}:{settings.PORT}/")
+    click.echo(f"   Docs: http://{settings.HOST}:{settings.PORT}/docs")
+    if watch_frontend:
+        click.echo(f"   Frontend watcher: Active (rebuilding on file changes)")
+    
+    # Use import string for reload to work properly
+    try:
+        uvicorn.run(
+            "app.main:app",
+            host=settings.HOST,
+            port=settings.PORT,
+            reload=reload,
+        )
+    finally:
+        # Clean up watcher on exit
+        if frontend_watcher:
+            frontend_watcher.stop()
+
+
+@main.command()
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--loglevel",
+    "-l",
+    default="info",
+    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
+    help="Log level for Celery worker",
+)
+@click.option(
+    "--queues",
+    "-Q",
+    "queues",
+    default=None,
+    help=(
+        "Comma-separated list of Celery queues this worker should consume "
+        "(forwarded to celery's -Q flag). Defaults to the default queue."
+    ),
+)
+@click.option(
+    "--concurrency",
+    default=None,
+    type=int,
+    help="Number of concurrent worker processes/threads (Celery --concurrency).",
+)
+@click.option(
+    "--pool",
+    "-P",
+    "pool",
+    default=None,
+    type=click.Choice(["prefork", "threads", "solo", "eventlet", "gevent"], case_sensitive=False),
+    help="Celery worker pool implementation (forwarded to celery's -P flag).",
+)
+def worker(config: str, loglevel: str, queues: Optional[str], concurrency: Optional[int], pool: Optional[str]):
+    """Start the Celery worker for background task processing."""
+    from app.config import load_config_from_file
+    
+    # Load configuration from YAML file
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        click.echo(f"💡 Create a config.yml file or use --config to specify a different path.", err=True)
+        sys.exit(1)
+    
+    try:
+        load_config_from_file(str(config_path))
+        click.echo(f"✅ Loaded configuration from {config_path}")
+    except Exception as e:
+        click.echo(f"❌ Error loading config: {e}", err=True)
+        sys.exit(1)
+    
+    click.echo(f"🚀 Starting Celery worker...")
+    click.echo(f"   Log level: {loglevel}")
+    if queues:
+        click.echo(f"   Queues: {queues}")
+    if concurrency is not None:
+        click.echo(f"   Concurrency: {concurrency}")
+    if pool:
+        click.echo(f"   Pool: {pool}")
+    
+    # Start Celery worker
+    try:
+        import subprocess
+        cmd = ["celery", "-A", "app.workers.celery_app", "worker", f"--loglevel={loglevel}"]
+        if queues:
+            cmd.append(f"--queues={queues}")
+        if concurrency is not None:
+            cmd.append(f"--concurrency={concurrency}")
+        if pool:
+            cmd.append(f"--pool={pool}")
+        subprocess.run(cmd, check=True)
+    except KeyboardInterrupt:
+        click.echo("\n👋 Celery worker stopped")
+    except subprocess.CalledProcessError as e:
+        click.echo(f"❌ Celery worker failed: {e}", err=True)
+        sys.exit(1)
+    except FileNotFoundError:
+        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
+        sys.exit(1)
+
+
+@main.command("beat")
+@click.option(
+    "--config",
+    "-c",
+    default="config.yml",
+    help="Path to configuration file",
+)
+@click.option(
+    "--loglevel",
+    "-l",
+    default="info",
+    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
+    help="Log level for Celery beat",
+)
+@click.option(
+    "--platform-worker-concurrency",
+    default=2,
+    type=int,
+    help="Concurrency for the co-located platform task worker (default: 2; thread pool).",
+)
+def beat(config: str, loglevel: str, platform_worker_concurrency: int):
+    """Start Celery Beat + platform task worker (alerts, FX, prune) — single replica only."""
+    from app.config import load_config_from_file
+
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        sys.exit(1)
+
+    try:
+        load_config_from_file(str(config_path))
+        click.echo(f"✅ Loaded configuration from {config_path}")
+    except Exception as e:
+        click.echo(f"❌ Error loading config: {e}", err=True)
+        sys.exit(1)
+
+    from app.workers.config import PLATFORM_WORKER_QUEUE
+
+    click.echo(
+        "🚀 Starting Celery Beat + platform worker "
+        f"(queue={PLATFORM_WORKER_QUEUE}, concurrency={platform_worker_concurrency})"
+    )
+    platform_proc = None
+    try:
+        import subprocess
+
+        platform_proc = subprocess.Popen(
+            [
+                "celery",
+                "-A",
+                "app.workers.celery_app",
+                "worker",
+                f"--queues={PLATFORM_WORKER_QUEUE}",
+                "--pool=threads",
+                f"--concurrency={platform_worker_concurrency}",
+                f"--loglevel={loglevel}",
+            ],
+        )
+        subprocess.run(
+            [
+                "celery",
+                "-A",
+                "app.workers.celery_app",
+                "beat",
+                f"--loglevel={loglevel}",
+            ],
+            check=True,
+        )
+    except KeyboardInterrupt:
+        click.echo("\n👋 Celery Beat stopped")
+    except subprocess.CalledProcessError as e:
+        click.echo(f"❌ Celery Beat failed: {e}", err=True)
+        sys.exit(1)
+    except FileNotFoundError:
+        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
+        sys.exit(1)
+    finally:
+        if platform_proc is not None and platform_proc.poll() is None:
+            platform_proc.terminate()
+            try:
+                platform_proc.wait(timeout=5)
+            except subprocess.TimeoutExpired:
+                platform_proc.kill()
+
+
+@main.command("telephony-worker")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option("--host", default=None, help="Host to bind (default from config)")
+@click.option("--port", default=None, type=int, help="Media server port (default 8001)")
+def telephony_worker(config: str, host: Optional[str], port: Optional[int]):
+    """Start the media server for live voice WebSocket connections."""
+    os.environ["SERVICE_MODE"] = "media"
+    from app.config import apply_service_mode, load_config_from_file, settings
+
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        sys.exit(1)
+
+    try:
+        load_config_from_file(str(config_path))
+        apply_service_mode("media")
+    except Exception as e:
+        click.echo(f"❌ Error loading config: {e}", err=True)
+        sys.exit(1)
+    bind_host = host or settings.HOST
+    bind_port = port or settings.MEDIA_PORT
+
+    from app.app_factory import create_app
+
+    app = create_app()
+    print(
+        f"[MEDIA] telephony-worker ready on {bind_host}:{bind_port} "
+        f"(SERVICE_MODE={settings.SERVICE_MODE}, media_routes mounted)",
+        flush=True,
+    )
+
+    import uvicorn
+
+    click.echo(f"🎙️  Starting EfficientAI media server on {bind_host}:{bind_port}")
+    uvicorn.run(
+        app,
+        host=bind_host,
+        port=bind_port,
+        reload=False,
+    )
+
+
+@main.command("start-worker-all")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option("--loglevel", "-l", default="info", help="Celery log level")
+@click.option("--media-port", default=None, type=int, help="Media server port (default 8001)")
+def start_worker_all(config: str, loglevel: str, media_port: Optional[int]):
+    """Start Celery worker and media server together.
+
+    Deprecated: prefer separate ``eai telephony-worker`` and ``eai worker`` services
+    (see docker-compose ``media`` + ``worker``).
+    """
+    import signal
+    import atexit
+
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        sys.exit(1)
+
+    from app.config import load_config_from_file, settings
+
+    load_config_from_file(str(config_path))
+
+    media_proc = None
+    celery_proc = None
+
+    def cleanup():
+        nonlocal media_proc, celery_proc
+        for proc, label in ((media_proc, "media server"), (celery_proc, "Celery worker")):
+            if proc is None or proc.poll() is not None:
+                continue
+            try:
+                proc.terminate()
+                proc.wait(timeout=5)
+                click.echo(f"✅ {label} stopped")
+            except Exception:
+                proc.kill()
+
+    atexit.register(cleanup)
+
+    def _handle_signal(sig, frame):
+        cleanup()
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, _handle_signal)
+    signal.signal(signal.SIGTERM, _handle_signal)
+
+    port = media_port or settings.MEDIA_PORT
+    telephony_env = os.environ.copy()
+    telephony_env["SERVICE_MODE"] = "media"
+    media_proc = subprocess.Popen(
+        [sys.executable, "-m", "app.cli", "telephony-worker", "--config", str(config_path), "--port", str(port)],
+        env=telephony_env,
+    )
+    celery_proc = subprocess.Popen(
+        ["celery", "-A", "app.workers.celery_app", "worker", f"--loglevel={loglevel}"],
+    )
+    click.echo(f"🚀 Started media server (pid={media_proc.pid}) and Celery worker (pid={celery_proc.pid})")
+
+    try:
+        while True:
+            if media_proc.poll() is not None:
+                click.echo("❌ Media server exited", err=True)
+                break
+            if celery_proc.poll() is not None:
+                click.echo("❌ Celery worker exited", err=True)
+                break
+            time.sleep(1)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        cleanup()
+
+
+@main.command()
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--host",
+    default=None,
+    help="Host to bind to (overrides config)",
+)
+@click.option(
+    "--port",
+    default=None,
+    type=int,
+    help="Port to bind to (overrides config)",
+)
+@click.option(
+    "--build-frontend/--no-build-frontend",
+    default=True,
+    help="Build frontend before starting (default: True)",
+)
+@click.option(
+    "--reload/--no-reload",
+    default=True,
+    help="Enable auto-reload for development (default: True)",
+)
+@click.option(
+    "--watch-frontend/--no-watch-frontend",
+    default=False,
+    help="Watch frontend files and rebuild automatically (default: False)",
+)
+@click.option(
+    "--force-rebuild",
+    is_flag=True,
+    default=False,
+    help="Force rebuild of frontend without prompting",
+)
+@click.option(
+    "--skip-migrations",
+    is_flag=True,
+    default=False,
+    help="Skip running migrations before starting (not recommended)",
+)
+@click.option(
+    "--worker-loglevel",
+    "-l",
+    default="info",
+    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
+    help="Log level for Celery worker",
+)
+@click.option(
+    "--imports-worker/--no-imports-worker",
+    default=True,
+    help=(
+        "Also start a dedicated worker for the `imports` queue used by call "
+        "import CSV processing (default: True). Disable to keep the previous "
+        "single-worker behavior."
+    ),
+)
+@click.option(
+    "--imports-worker-concurrency",
+    default=12,
+    type=int,
+    help=(
+        "Concurrency for the imports+diarization+evaluations worker "
+        "(default: 12; thread pool). Use lower values (8–12) when DB sharding "
+        "is enabled; 32 threads can exhaust per-shard SQLAlchemy pools."
+    ),
+)
+@click.option(
+    "--usage-worker/--no-usage-worker",
+    default=True,
+    help=(
+        "Also start a dedicated worker for the `usage` queue (flush + cost recompute; "
+        "default: True)."
+    ),
+)
+@click.option(
+    "--usage-worker-concurrency",
+    default=4,
+    type=int,
+    help="Concurrency for the usage worker (default: 4; thread pool).",
+)
+@click.option(
+    "--beat/--no-beat",
+    default=True,
+    help=(
+        "Start Celery Beat for platform periodic tasks (usage flush, alerts, etc.; "
+        "default: True — single replica only in production)."
+    ),
+)
+@click.option(
+    "--beat-loglevel",
+    default=None,
+    type=click.Choice(["debug", "info", "warning", "error", "critical"], case_sensitive=False),
+    help="Log level for Celery Beat (defaults to --worker-loglevel).",
+)
+@click.option(
+    "--telephony-worker/--no-telephony-worker",
+    default=True,
+    help="Spawn telephony media server for live voice WebSockets (default: on).",
+)
+@click.option(
+    "--media-port",
+    default=None,
+    type=int,
+    help="Telephony media server port (default: MEDIA_PORT / 8001).",
+)
+def start_all(
+    config: str,
+    host: Optional[str],
+    port: Optional[int],
+    build_frontend: bool,
+    reload: bool,
+    watch_frontend: bool,
+    force_rebuild: bool,
+    skip_migrations: bool,
+    worker_loglevel: str,
+    imports_worker: bool,
+    imports_worker_concurrency: int,
+    usage_worker: bool,
+    usage_worker_concurrency: int,
+    beat: bool,
+    beat_loglevel: Optional[str],
+    telephony_worker: bool,
+    media_port: Optional[int],
+):
+    """Start the application server and Celery worker(s) together.
+
+    By default this also spawns a telephony media server (``eai telephony-worker``)
+    and a second Celery worker that consumes the ``imports`` queue (call-import CSV
+    fan-out). Use --no-telephony-worker or --no-imports-worker to skip either.
+    """
+    import signal
+    import atexit
+
+    click.echo("🚀 Starting EfficientAI (App + Worker)...")
+    if telephony_worker:
+        click.echo("   Telephony media server will run on a separate port (SERVICE_MODE=media).")
+    if imports_worker:
+        click.echo(
+            "   This will start the API server, the default Celery worker, "
+            "and a dedicated worker for the `imports` queue."
+        )
+    else:
+        click.echo("   This will start both the API server and Celery worker")
+    click.echo("   Press Ctrl+C to stop all services\n")
+    
+    # Load configuration
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        click.echo(f"💡 Create a config.yml file or use --config to specify a different path.", err=True)
+        sys.exit(1)
+    
+    try:
+        from app.config import load_config_from_file, settings
+        load_config_from_file(str(config_path))
+        click.echo(f"✅ Loaded configuration from {config_path}")
+    except Exception as e:
+        click.echo(f"❌ Error loading config: {e}", err=True)
+        sys.exit(1)
+
+    bind_media_port = media_port or settings.MEDIA_PORT
+
+    os.environ["SERVICE_MODE"] = "api"
+    
+    # Store worker processes for cleanup.
+    worker_process = None
+    worker_imports_process = None
+    worker_usage_process = None
+    beat_process = None
+    platform_worker_process = None
+    telephony_process = None
+
+    def _terminate(proc, label: str):
+        """Best-effort terminate -> wait -> kill for a worker subprocess."""
+        if proc is None or proc.poll() is not None:
+            return
+        try:
+            click.echo(f"\n👋 Stopping {label}...")
+            proc.terminate()
+            proc.wait(timeout=5)
+            click.echo(f"✅ {label} stopped")
+        except subprocess.TimeoutExpired:
+            proc.kill()
+            proc.wait()
+        except Exception:
+            pass
+
+    def cleanup_processes():
+        """Clean up spawned processes."""
+        nonlocal worker_process, worker_imports_process, worker_usage_process, beat_process, platform_worker_process, telephony_process
+        _terminate(telephony_process, "Telephony media server")
+        _terminate(beat_process, "Celery Beat")
+        _terminate(platform_worker_process, "Celery platform worker")
+        _terminate(worker_process, "Celery worker (default)")
+        _terminate(worker_imports_process, "Celery worker (imports)")
+        _terminate(worker_usage_process, "Celery worker (usage)")
+    
+    # Register cleanup on exit
+    atexit.register(cleanup_processes)
+    
+    def signal_handler(sig, frame):
+        """Handle Ctrl+C gracefully."""
+        cleanup_processes()
+        sys.exit(0)
+    
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+    
+    # Initialize DB tables and run migrations before starting (unless explicitly skipped)
+    if not skip_migrations:
+        click.echo("🔄 Initializing database and running migrations...")
+        from app.database import init_db
+        from app.core.migrations import run_migrations, ensure_migrations_directory
+        try:
+            init_db()
+            ensure_migrations_directory()
+            run_migrations()
+            click.echo("✅ Database initialized and migrations completed")
+        except Exception as e:
+            click.echo(f"❌ Migration failed: {e}", err=True)
+            click.echo("💡 You can skip migrations with --skip-migrations (not recommended)", err=True)
+            sys.exit(1)
+    
+    # Build frontend if needed
+    if build_frontend:
+        frontend_dir = Path(__file__).parent.parent / "frontend"
+        if frontend_dir.exists():
+            click.echo("🔨 Building frontend...")
+            try:
+                if not (frontend_dir / "node_modules").exists():
+                    click.echo("   Installing frontend dependencies...")
+                    subprocess.run(["npm", "install", "--legacy-peer-deps"], cwd=frontend_dir, check=True, capture_output=True)
+                subprocess.run(["npm", "run", "build"], cwd=frontend_dir, check=True, capture_output=True)
+                click.echo("✅ Frontend built successfully")
+            except subprocess.CalledProcessError as e:
+                click.echo(f"❌ Frontend build failed: {e}", err=True)
+                sys.exit(1)
+    
+    # Start frontend watcher if requested
+    frontend_watcher = None
+    if watch_frontend:
+        click.echo("👀 Starting frontend file watcher...")
+        frontend_dir = Path(__file__).parent.parent / "frontend"
+        frontend_watcher = start_frontend_watcher(frontend_dir)
+    
+    def _spawn_worker(args: list[str], label: str, prefix: str) -> subprocess.Popen:
+        """Spawn a Celery worker subprocess and stream its stdout with a prefix."""
+        proc = subprocess.Popen(
+            args,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+            bufsize=1,
+        )
+        click.echo(f"✅ {label} started")
+
+        def _stream():
+            if proc.stdout:
+                for line in iter(proc.stdout.readline, ""):
+                    if line:
+                        click.echo(f"{prefix} {line.rstrip()}", err=False)
+                proc.stdout.close()
+
+        threading.Thread(target=_stream, daemon=True).start()
+        return proc
+
+    if telephony_worker:
+        try:
+            telephony_env = os.environ.copy()
+            telephony_env["SERVICE_MODE"] = "media"
+            telephony_process = subprocess.Popen(
+                [
+                    sys.executable,
+                    "-m",
+                    "app.cli",
+                    "telephony-worker",
+                    "--config",
+                    str(config_path),
+                    "--port",
+                    str(bind_media_port),
+                ],
+                env=telephony_env,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+                bufsize=1,
+            )
+            click.echo(
+                f"✅ Telephony media server started on port {bind_media_port} "
+                f"(pid={telephony_process.pid})"
+            )
+
+            def _stream_telephony():
+                if telephony_process.stdout:
+                    for line in iter(telephony_process.stdout.readline, ""):
+                        if line:
+                            click.echo(f"[MEDIA] {line.rstrip()}", err=False)
+                    telephony_process.stdout.close()
+
+            threading.Thread(target=_stream_telephony, daemon=True).start()
+        except FileNotFoundError:
+            click.echo("❌ Python interpreter not found for telephony-worker subprocess", err=True)
+            sys.exit(1)
+        except Exception as e:
+            click.echo(f"❌ Failed to start telephony media server: {e}", err=True)
+            sys.exit(1)
+
+    # Start Celery workers as subprocess(es) with output streaming
+    try:
+        worker_process = _spawn_worker(
+            [
+                "celery",
+                "-A",
+                "app.workers.celery_app",
+                "worker",
+                f"--loglevel={worker_loglevel}",
+                "-Q",
+                "celery,audio-metrics",
+                "-c",
+                "8",
+            ],
+            label="Celery worker (celery + audio-metrics queues, concurrency=8)",
+            prefix="[WORKER]",
+        )
+
+        if imports_worker:
+            from app.workers.config import IMPORTS_WORKER_QUEUES
+
+            worker_imports_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "worker",
+                    f"--loglevel={worker_loglevel}",
+                    "-Q",
+                    IMPORTS_WORKER_QUEUES,
+                    "-P",
+                    "threads",
+                    "-c",
+                    str(imports_worker_concurrency),
+                ],
+                label=(
+                    f"Celery worker ({IMPORTS_WORKER_QUEUES} queues, "
+                    f"pool=threads, concurrency={imports_worker_concurrency})"
+                ),
+                prefix="[WORKER-IMPORTS]",
+            )
+
+        if usage_worker:
+            from app.workers.config import USAGE_WORKER_QUEUE
+
+            worker_usage_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "worker",
+                    f"--loglevel={worker_loglevel}",
+                    "-Q",
+                    USAGE_WORKER_QUEUE,
+                    "-P",
+                    "threads",
+                    "-c",
+                    str(usage_worker_concurrency),
+                ],
+                label=(
+                    f"Celery worker ({USAGE_WORKER_QUEUE} queue, "
+                    f"pool=threads, concurrency={usage_worker_concurrency})"
+                ),
+                prefix="[WORKER-USAGE]",
+            )
+
+        if beat:
+            from app.workers.config import PLATFORM_WORKER_QUEUE
+
+            beat_level = beat_loglevel or worker_loglevel
+            platform_worker_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "worker",
+                    f"--loglevel={beat_level}",
+                    "-Q",
+                    PLATFORM_WORKER_QUEUE,
+                    "-P",
+                    "threads",
+                    "-c",
+                    "2",
+                ],
+                label=f"Celery platform worker ({PLATFORM_WORKER_QUEUE} queue)",
+                prefix="[BEAT-WORKER]",
+            )
+            beat_process = _spawn_worker(
+                [
+                    "celery",
+                    "-A",
+                    "app.workers.celery_app",
+                    "beat",
+                    f"--loglevel={beat_level}",
+                ],
+                label=f"Celery Beat (scheduler, loglevel={beat_level})",
+                prefix="[BEAT]",
+            )
+
+    except FileNotFoundError:
+        click.echo("❌ Celery not found. Please install it: pip install celery", err=True)
+        sys.exit(1)
+    except Exception as e:
+        click.echo(f"❌ Failed to start worker: {e}", err=True)
+        sys.exit(1)
+    
+    # Small delay to let worker start
+    time.sleep(1)
+    
+    # Start the application server in the main process
+    # This allows uvicorn's reload to work properly (it needs to spawn child processes)
+    try:
+        from app.config import settings
+        import uvicorn
+        
+        # Override with CLI options if provided
+        if host:
+            settings.HOST = host
+        if port:
+            settings.PORT = port
+        
+        click.echo("✅ Application server starting...")
+        click.echo(f"   Host: {settings.HOST}")
+        click.echo(f"   Port: {settings.PORT}")
+        click.echo(f"   API: http://{settings.HOST}:{settings.PORT}{settings.API_V1_PREFIX}")
+        click.echo(f"   Frontend: http://{settings.HOST}:{settings.PORT}/")
+        click.echo(f"   Docs: http://{settings.HOST}:{settings.PORT}/docs")
+        if watch_frontend:
+            click.echo(f"   Frontend watcher: Active (rebuilding on file changes)")
+        if imports_worker:
+            from app.workers.config import IMPORTS_WORKER_QUEUES
+
+            click.echo(
+                f"   Workers: default queue + {IMPORTS_WORKER_QUEUES} "
+                f"(concurrency={imports_worker_concurrency}; imports preferred)"
+            )
+        else:
+            click.echo("   Workers: default queue only (--no-imports-worker)")
+        if usage_worker:
+            from app.workers.config import USAGE_WORKER_QUEUE
+
+            click.echo(
+                f"   Usage worker: {USAGE_WORKER_QUEUE} queue "
+                f"(concurrency={usage_worker_concurrency})"
+            )
+        else:
+            click.echo("   Usage worker: disabled (--no-usage-worker)")
+        if beat:
+            click.echo("   Celery Beat: scheduler + platform worker (alerts, FX, prune); flush on worker-usage")
+        else:
+            click.echo("   Celery Beat: disabled (--no-beat)")
+        if telephony_worker:
+            telephony_public = (settings.VOBIZ_WEBHOOK_BASE_URL or "").strip()
+            click.echo(f"   Telephony edge: http://localhost:{bind_media_port} (local)")
+            if telephony_public:
+                click.echo(f"   Vobiz webhook_base_url: {telephony_public}")
+            else:
+                click.echo(
+                    "   Set vobiz.webhook_base_url to your telephony public URL "
+                    f"(e.g. ngrok http {bind_media_port}) for inbound PSTN"
+                )
+            if (settings.MEDIA_WS_BASE_URL or "").strip():
+                click.echo(f"   Browser voice-agent WS: {settings.MEDIA_WS_BASE_URL}")
+        click.echo("\n📝 All services are running. Press Ctrl+C to stop.\n")
+        
+        # Run uvicorn in the main process (allows reload to work)
+        uvicorn.run(
+            "app.main:app",
+            host=settings.HOST,
+            port=settings.PORT,
+            reload=reload,
+        )
+    except KeyboardInterrupt:
+        pass
+    except Exception as e:
+        click.echo(f"❌ App error: {e}", err=True)
+    finally:
+        cleanup_processes()
+        if frontend_watcher:
+            frontend_watcher.stop()
+
+
+@main.command()
+@click.option(
+    "--output",
+    "-o",
+    type=click.Path(),
+    default="config.yml",
+    help="Output file path for example config",
+)
+def init_config(output: str):
+    """Generate an example configuration file."""
+    example_config = """# EfficientAI Configuration File
+
+# Application Settings
+app:
+  name: "Voice AI Evaluation Platform"
+  version: "0.1.0"
+  debug: true
+  secret_key: "your-secret-key-here-change-in-production"
+
+# Server Settings
+server:
+  host: "0.0.0.0"
+  port: 8000
+
+# Database Configuration
+database:
+  url: "postgresql://efficientai:password@localhost:5432/efficientai"
+  # Alternative: specify individual components
+  # user: "efficientai"
+  # password: "password"
+  # host: "localhost"
+  # port: 5432
+  # db: "efficientai"
+
+# Redis Configuration
+redis:
+  url: "redis://localhost:6379/0"
+  # Alternative: specify individual components
+  # host: "localhost"
+  # port: 6379
+  # db: 0
+
+# Celery Configuration
+celery:
+  broker_url: "redis://localhost:6379/0"
+  result_backend: "redis://localhost:6379/0"
+
+# File Storage
+storage:
+  upload_dir: "./uploads"
+  max_file_size_mb: 500
+  allowed_audio_formats:
+    - "wav"
+    - "mp3"
+    - "flac"
+    - "m4a"
+
+# CORS Settings
+cors:
+  origins:
+    - "http://localhost:3000"
+    - "http://localhost:8000"
+
+# API Settings
+api:
+  prefix: "/api/v1"
+  key_header: "X-API-Key"
+  rate_limit_per_minute: 60
+"""
+    
+    output_path = Path(output)
+    if output_path.exists():
+        if not click.confirm(f"File {output} already exists. Overwrite?"):
+            click.echo("Cancelled.")
+            return
+    
+    try:
+        output_path.write_text(example_config)
+        click.echo(f"✅ Created example configuration file: {output}")
+        click.echo(f"💡 Edit {output} with your settings, then run: eai start --config {output}")
+    except Exception as e:
+        click.echo(f"❌ Error creating config file: {e}", err=True)
+        sys.exit(1)
+
+
+def _bootstrap_sharding_config(config_path: str) -> None:
+    from app.config import load_config_from_file
+    from app.db_sharding.pool_manager import db_pool_manager
+
+    load_config_from_file(config_path)
+    db_pool_manager.reset()
+
+
+@click.group()
+def sharding():
+    """Call-import data-plane sharding admin (rebalance / registry)."""
+    pass
+
+
+main.add_command(sharding)
+
+
+@sharding.command("list-slices")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--call-import-id",
+    required=True,
+    help="Call import UUID whose slice registry to inspect",
+)
+def sharding_list_slices(config: str, call_import_id: str):
+    """Show catalog slice registry rows for a call import."""
+    from uuid import UUID
+
+    from app.db_sharding.pool_manager import open_catalog_session
+    from app.db_sharding.rebalance import RebalanceError, list_shard_slices, require_sharding_enabled
+
+    try:
+        _bootstrap_sharding_config(config)
+        require_sharding_enabled()
+        cid = UUID(call_import_id)
+    except (ValueError, RebalanceError) as exc:
+        click.echo(f"❌ {exc}", err=True)
+        sys.exit(1)
+
+    catalog = open_catalog_session()
+    try:
+        slices = list_shard_slices(catalog, cid)
+        if not slices:
+            click.echo(f"No registry slices for call_import {cid}")
+            return
+        click.echo(f"call_import_id: {cid}")
+        click.echo(f"slices: {len(slices)}")
+        for item in slices:
+            click.echo(
+                f"  slice {item.slice_id}: shard={item.shard_id} "
+                f"rows [{item.row_index_min}, {item.row_index_max}] "
+                f"(count={item.row_count})"
+            )
+    finally:
+        catalog.close()
+
+
+@sharding.command("rebalance-slices")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option("--call-import-id", required=True, help="Call import UUID to rebalance")
+@click.option("--from-shard", required=True, help="Source shard id (e.g. data-shard-01)")
+@click.option("--to-shard", required=True, help="Target shard id (e.g. data-shard-02)")
+@click.option(
+    "--slice-id",
+    "slice_ids",
+    multiple=True,
+    type=int,
+    help="Move only these slice ids (default: all slices on --from-shard)",
+)
+@click.option(
+    "--dry-run",
+    is_flag=True,
+    help="Plan only: print row counts without copying or updating registry",
+)
+@click.option(
+    "--force",
+    is_flag=True,
+    help="Skip quiescence checks (use only after pausing workers)",
+)
+def sharding_rebalance_slices(
+    config: str,
+    call_import_id: str,
+    from_shard: str,
+    to_shard: str,
+    slice_ids: tuple[int, ...],
+    dry_run: bool,
+    force: bool,
+):
+    """Copy call-import slice rows (and eval rows) from one shard to another."""
+    from uuid import UUID
+
+    from app.db_sharding.pool_manager import open_catalog_session
+    from app.db_sharding.rebalance import (
+        RebalanceError,
+        require_sharding_enabled,
+        build_rebalance_plan,
+        execute_rebalance_slices,
+    )
+
+    try:
+        _bootstrap_sharding_config(config)
+        require_sharding_enabled()
+        cid = UUID(call_import_id)
+    except (ValueError, RebalanceError) as exc:
+        click.echo(f"❌ {exc}", err=True)
+        sys.exit(1)
+
+    catalog = open_catalog_session()
+    try:
+        plan = build_rebalance_plan(
+            catalog,
+            cid,
+            from_shard_id=from_shard,
+            to_shard_id=to_shard,
+            slice_ids=slice_ids or None,
+        )
+        click.echo("Rebalance plan:")
+        click.echo(f"  call_import_id: {plan.call_import_id}")
+        click.echo(f"  from_shard:     {plan.from_shard_id}")
+        click.echo(f"  to_shard:       {plan.to_shard_id}")
+        click.echo(f"  slices:         {len(plan.slices)}")
+        for item in plan.slices:
+            click.echo(
+                f"    slice {item.slice_id}: rows [{item.row_index_min}, {item.row_index_max}]"
+            )
+        click.echo(f"  import_rows:    {plan.import_row_count}")
+        click.echo(f"  eval_rows:      {plan.eval_row_count}")
+
+        result = execute_rebalance_slices(
+            catalog,
+            plan,
+            dry_run=dry_run,
+            force=force,
+        )
+        if result.dry_run:
+            click.echo("✅ Dry run complete (no changes made)")
+        else:
+            click.echo(
+                "✅ Rebalance complete: "
+                f"slices={result.slices_moved}, "
+                f"import_rows={result.import_rows_moved}, "
+                f"eval_rows={result.eval_rows_moved}"
+            )
+    except RebalanceError as exc:
+        click.echo(f"❌ {exc}", err=True)
+        sys.exit(1)
+    finally:
+        catalog.close()
+
+
+@click.group()
+def usage():
+    """Usage pricing ops (seed rates, diff catalog, recompute costs)."""
+    pass
+
+
+main.add_command(usage)
+
+
+def _load_cli_config(config: str) -> Path:
+    config_path = Path(config)
+    if not config_path.exists():
+        click.echo(f"❌ Config file not found: {config}", err=True)
+        sys.exit(1)
+    from app.config import load_config_from_file
+
+    load_config_from_file(str(config_path))
+    return config_path
+
+
+@usage.command("seed-rates")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--effective-from",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    default=None,
+    help="Effective date for seeded rates (default: 2020-01-01)",
+)
+def usage_seed_rates(config: str, effective_from):
+    """Upsert model_pricing_rates from models.json pricing blocks."""
+    _load_cli_config(config)
+    from app.database import SessionLocal
+    from app.services.usage.pricing_ops import seed_rates_from_models_json
+
+    day = effective_from.date() if effective_from else None
+    db = SessionLocal()
+    try:
+        count = seed_rates_from_models_json(db, effective_from=day)
+        db.commit()
+        click.echo(f"✅ Seeded/updated {count} pricing rate row(s)")
+    finally:
+        db.close()
+
+
+@usage.command("diff-rates")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option(
+    "--effective-from",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    default=None,
+    help="Compare rates at this effective_from date (default: 2020-01-01)",
+)
+@click.option("--json", "as_json", is_flag=True, help="Print machine-readable JSON")
+def usage_diff_rates(config: str, effective_from, as_json: bool):
+    """Diff models.json pricing blocks vs model_pricing_rates in Postgres."""
+    import json as json_module
+
+    _load_cli_config(config)
+    from app.database import SessionLocal
+    from app.services.usage.pricing_ops import diff_models_json_vs_db
+
+    day = effective_from.date() if effective_from else None
+    db = SessionLocal()
+    try:
+        report = diff_models_json_vs_db(db, effective_from=day)
+    finally:
+        db.close()
+
+    if as_json:
+        click.echo(json_module.dumps(report, indent=2, default=str))
+        return
+
+    click.echo(f"effective_from: {report['effective_from']}")
+    click.echo(
+        f"models.json priced: {report['models_json_count']} | "
+        f"database rows: {report['database_count']} | "
+        f"in_sync: {report['in_sync']}"
+    )
+    if report["only_in_models_json"]:
+        click.echo(f"\nOnly in models.json ({len(report['only_in_models_json'])}):")
+        for item in report["only_in_models_json"][:20]:
+            click.echo(f"  - {item['model']} ({item['usage_kind']})")
+    if report["only_in_database"]:
+        click.echo(f"\nOnly in database ({len(report['only_in_database'])}):")
+        for item in report["only_in_database"][:20]:
+            click.echo(f"  - {item['model']} ({item['usage_kind']})")
+    if report["mismatches"]:
+        click.echo(f"\nMismatched rates ({len(report['mismatches'])}):")
+        for item in report["mismatches"][:20]:
+            click.echo(f"  - {item['model']} ({item['usage_kind']})")
+            for field, values in item["fields"].items():
+                click.echo(
+                    f"      {field}: json={values['models_json']} db={values['database']}"
+                )
+    missing = report["missing_pricing_blocks"]
+    if missing:
+        click.echo(f"\nmodels.json entries missing pricing blocks ({len(missing)}):")
+        for model in missing[:20]:
+            click.echo(f"  - {model}")
+    unresolved = report["litellm_unresolved"]
+    if unresolved:
+        click.echo(f"\nLiteLLM unresolved ({len(unresolved)}):")
+        for item in unresolved[:20]:
+            click.echo(f"  - {item.get('model')} ({item.get('reason', 'unresolved')})")
+
+
+@usage.command("recompute")
+@click.option(
+    "--config",
+    "-c",
+    type=click.Path(exists=True, readable=True),
+    default="config.yml",
+    help="Path to configuration YAML file",
+)
+@click.option("--organization-id", default=None, help="Scope recompute to one org UUID")
+@click.option("--model", default=None, help="Scope recompute to one model")
+@click.option("--usage-kind", default=None, help="Scope recompute to llm/stt/tts")
+@click.option("--start-date", type=click.DateTime(formats=["%Y-%m-%d"]), default=None)
+@click.option("--end-date", type=click.DateTime(formats=["%Y-%m-%d"]), default=None)
+@click.option(
+    "--async/--sync",
+    "run_async",
+    default=True,
+    help="Enqueue Celery task (default) or run synchronously in this process",
+)
+def usage_recompute(
+    config: str,
+    organization_id: Optional[str],
+    model: Optional[str],
+    usage_kind: Optional[str],
+    start_date,
+    end_date,
+    run_async: bool,
+):
+    """Backfill or recompute stored usage costs on llm_usage_daily rollups."""
+    from uuid import UUID
+
+    _load_cli_config(config)
+    start = start_date.date() if start_date else None
+    end = end_date.date() if end_date else None
+    org_uuid = UUID(organization_id) if organization_id else None
+
+    if run_async:
+        if org_uuid is None:
+            click.echo(
+                "❌ --organization-id is required for async recompute (creates a tracked job).",
+                err=True,
+            )
+            click.echo(
+                "💡 Use --sync to recompute in this process without an org scope, or pass --organization-id.",
+                err=True,
+            )
+            sys.exit(1)
+
+        from app.database import SessionLocal
+        from app.services.usage.pricing_jobs import (
+            create_recompute_job,
+            enqueue_recompute_job,
+            job_to_dict,
+        )
+
+        db = SessionLocal()
+        try:
+            job = create_recompute_job(
+                db,
+                organization_id=org_uuid,
+                model=model,
+                usage_kind=usage_kind,
+                start_date=start,
+                end_date=end,
+            )
+            enqueue_recompute_job(db, job)
+            db.refresh(job)
+            payload = job_to_dict(job)
+            click.echo(f"✅ Enqueued recompute job {payload['id']} (status={payload['status']})")
+            if payload.get("celery_task_id"):
+                click.echo(f"   Celery task: {payload['celery_task_id']}")
+        except Exception as exc:
+            click.echo(f"❌ Failed to enqueue recompute job: {exc}", err=True)
+            sys.exit(1)
+        finally:
+            db.close()
+        return
+
+    from app.database import SessionLocal
+    from app.services.usage.pricing import recompute_usage_costs
+
+    db = SessionLocal()
+    try:
+        updated = recompute_usage_costs(
+            db,
+            organization_id=org_uuid,
+            model=model,
+            usage_kind=usage_kind,
+            start_date=start,
+            end_date=end,
+        )
+        click.echo(f"✅ Recomputed costs for {updated} rollup row(s)")
+    finally:
+        db.close()
+
+
+@usage.command("sync-litellm")
+@click.option("--local", is_flag=True, help="Use bundled LiteLLM model_cost JSON")
+@click.option(
+    "--write-models",
+    is_flag=True,
+    help="Merge generated pricing into app/config/models.json",
+)
+@click.option("--stdout", is_flag=True, help="Print pricing_catalog.json to stdout")
+def usage_sync_litellm(local: bool, write_models: bool, stdout: bool):
+    """Fetch LiteLLM prices and regenerate pricing_catalog.json."""
+    import subprocess
+    import sys as sys_module
+
+    script = Path(__file__).resolve().parent.parent / "scripts" / "sync_pricing_catalog_from_litellm.py"
+    cmd = [sys_module.executable, str(script)]
+    if local:
+        cmd.append("--local")
+    if write_models:
+        cmd.append("--write-models")
+    if stdout:
+        cmd.append("--stdout")
+    subprocess.run(cmd, check=True)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/app/models/schemas.py b/app/models/schemas.py
index 9a26c892..32d66a03 100644
--- a/app/models/schemas.py
+++ b/app/models/schemas.py
@@ -283,6 +283,20 @@ class PreviewIntegrationAgentPromptResponse(BaseModel):
     provider_prompt: str
 
 
+class IntegrationVoiceAgentListItem(BaseModel):
+    id: str
+    name: str
+
+
+class ListIntegrationVoiceAgentsResponse(BaseModel):
+    agents: List[IntegrationVoiceAgentListItem]
+    platform: str
+    cached: bool = False
+    truncated: bool = False
+    list_supported: bool = True
+    message: Optional[str] = None
+
+
 
 
 class AgentPhoneAssignmentConflict(BaseModel):
diff --git a/app/models/synthetic_trace_schemas.py b/app/models/synthetic_trace_schemas.py
index ccd18005..beddff14 100644
--- a/app/models/synthetic_trace_schemas.py
+++ b/app/models/synthetic_trace_schemas.py
@@ -105,6 +105,7 @@ class OtlpSetupInfo(BaseModel):
 
 class TraceSessionCreateRequest(BaseModel):
     evaluator_result_id: Optional[UUID] = None
+    agent_id: Optional[UUID] = None
     transport: str = "websocket"
 
 
diff --git a/app/services/media_urls.py b/app/services/media_urls.py
index 80cc674b..29117086 100644
--- a/app/services/media_urls.py
+++ b/app/services/media_urls.py
@@ -1,93 +1,96 @@
-"""URL helpers for routing live voice WebSockets to the media server."""
-
-from __future__ import annotations
-
-from typing import Optional
-from urllib.parse import quote
-
-from app.config import settings
-
-
-def _normalize_ws_base(base: str) -> str:
-    base = base.rstrip("/")
-    if base.startswith("https://"):
-        return "wss://" + base[len("https://") :]
-    if base.startswith("http://"):
-        return "ws://" + base[len("http://") :]
-    if base.startswith("wss://") or base.startswith("ws://"):
-        return base
-    return f"wss://{base}"
-
-
-def media_ws_base_url() -> Optional[str]:
-    """Explicit dedicated media server base (``MEDIA_WS_BASE_URL`` / config only)."""
-    base = (settings.MEDIA_WS_BASE_URL or "").strip()
-    if not base:
-        return None
-    return _normalize_ws_base(base)
-
-
-def carrier_media_ws_base_url() -> Optional[str]:
-    """
-    WebSocket base for carrier (Vobiz) answer XML.
-
-    Uses ``MEDIA_WS_BASE_URL`` when set; otherwise reuses the telephony edge
-    webhook base (``vobiz_webhook_base_url`` / config) so a single public host
-    serves Vobiz webhooks and live audio.
-    """
-    explicit = media_ws_base_url()
-    if explicit:
-        return explicit
-    try:
-        from app.services.telephony.vobiz_agent_context import vobiz_webhook_base_url
-
-        return _normalize_ws_base(vobiz_webhook_base_url())
-    except ValueError:
-        pass
-    webhook_base = (settings.VOBIZ_WEBHOOK_BASE_URL or settings.PLIVO_WEBHOOK_BASE_URL or "").strip()
-    if not webhook_base:
-        return None
-    return _normalize_ws_base(webhook_base)
-
-
-def separate_media_server_configured() -> bool:
-    """True when live voice WebSockets should run on a dedicated media process."""
-    return bool((settings.MEDIA_WS_BASE_URL or "").strip())
-
-
-def ws_base_from_http_host(host: str, *, scheme: str = "http") -> str:
-    """Build ws/wss base from an HTTP Host header (browser / reverse-proxy)."""
-    ws_scheme = "wss" if scheme == "https" else "ws"
-    return f"{ws_scheme}://{host.rstrip('/')}"
-
-
-def build_voice_agent_ws_url(
-    *,
-    auth_query: str,
-    agent_id: Optional[str] = None,
-    persona_id: Optional[str] = None,
-    scenario_id: Optional[str] = None,
-    ui_surface: Optional[str] = None,
-    fallback_host: Optional[str] = None,
-    fallback_scheme: str = "http",
-) -> str:
-    """Build the browser voice-agent WebSocket URL."""
-    ws_base = media_ws_base_url()
-    if ws_base:
-        base = ws_base
-    elif fallback_host:
-        base = ws_base_from_http_host(fallback_host, scheme=fallback_scheme)
-    else:
-        base = f"ws://localhost:{settings.PORT}"
-
-    query = auth_query
-    if agent_id:
-        query += f"&agent_id={quote(agent_id)}"
-    if persona_id:
-        query += f"&persona_id={quote(persona_id)}"
-    if scenario_id:
-        query += f"&scenario_id={quote(scenario_id)}"
-    if ui_surface:
-        query += f"&ui_surface={quote(ui_surface, safe='')}"
-
-    return f"{base}{settings.API_V1_PREFIX}/voice-agent/ws?{query}"
+"""URL helpers for routing live voice WebSockets to the media server."""
+
+from __future__ import annotations
+
+from typing import Optional
+from urllib.parse import quote
+
+from app.config import settings
+
+
+def _normalize_ws_base(base: str) -> str:
+    base = base.rstrip("/")
+    if base.startswith("https://"):
+        return "wss://" + base[len("https://") :]
+    if base.startswith("http://"):
+        return "ws://" + base[len("http://") :]
+    if base.startswith("wss://") or base.startswith("ws://"):
+        return base
+    return f"wss://{base}"
+
+
+def media_ws_base_url() -> Optional[str]:
+    """Explicit dedicated media server base (``MEDIA_WS_BASE_URL`` / config only)."""
+    base = (settings.MEDIA_WS_BASE_URL or "").strip()
+    if not base:
+        return None
+    return _normalize_ws_base(base)
+
+
+def carrier_media_ws_base_url() -> Optional[str]:
+    """
+    WebSocket base for carrier (Vobiz) answer XML.
+
+    Uses ``MEDIA_WS_BASE_URL`` when set; otherwise reuses the telephony edge
+    webhook base (``vobiz_webhook_base_url`` / config) so a single public host
+    serves Vobiz webhooks and live audio.
+    """
+    explicit = media_ws_base_url()
+    if explicit:
+        return explicit
+    try:
+        from app.services.telephony.vobiz_agent_context import vobiz_webhook_base_url
+
+        return _normalize_ws_base(vobiz_webhook_base_url())
+    except ValueError:
+        pass
+    webhook_base = (settings.VOBIZ_WEBHOOK_BASE_URL or settings.PLIVO_WEBHOOK_BASE_URL or "").strip()
+    if not webhook_base:
+        return None
+    return _normalize_ws_base(webhook_base)
+
+
+def separate_media_server_configured() -> bool:
+    """True when live voice WebSockets should run on a dedicated media process."""
+    return bool((settings.MEDIA_WS_BASE_URL or "").strip())
+
+
+def ws_base_from_http_host(host: str, *, scheme: str = "http") -> str:
+    """Build ws/wss base from an HTTP Host header (browser / reverse-proxy)."""
+    ws_scheme = "wss" if scheme == "https" else "ws"
+    return f"{ws_scheme}://{host.rstrip('/')}"
+
+
+def build_voice_agent_ws_url(
+    *,
+    auth_query: str,
+    agent_id: Optional[str] = None,
+    persona_id: Optional[str] = None,
+    scenario_id: Optional[str] = None,
+    call_short_id: Optional[str] = None,
+    ui_surface: Optional[str] = None,
+    fallback_host: Optional[str] = None,
+    fallback_scheme: str = "http",
+) -> str:
+    """Build the browser voice-agent WebSocket URL."""
+    ws_base = media_ws_base_url()
+    if ws_base:
+        base = ws_base
+    elif fallback_host:
+        base = ws_base_from_http_host(fallback_host, scheme=fallback_scheme)
+    else:
+        base = f"ws://localhost:{settings.PORT}"
+
+    query = auth_query
+    if agent_id:
+        query += f"&agent_id={quote(agent_id)}"
+    if persona_id:
+        query += f"&persona_id={quote(persona_id)}"
+    if scenario_id:
+        query += f"&scenario_id={quote(scenario_id)}"
+    if ui_surface:
+        query += f"&ui_surface={quote(ui_surface, safe='')}"
+    if call_short_id:
+        query += f"&call_short_id={quote(call_short_id, safe='')}"
+
+    return f"{base}{settings.API_V1_PREFIX}/voice-agent/ws?{query}"
diff --git a/app/services/playground/post_call_processing.py b/app/services/playground/post_call_processing.py
index 05a7f11f..1d319f26 100644
--- a/app/services/playground/post_call_processing.py
+++ b/app/services/playground/post_call_processing.py
@@ -1,133 +1,203 @@
-"""Atomic post-call processing for playground Voice AI poll tasks."""
-from __future__ import annotations
-
-from typing import Any, Optional
-from uuid import UUID
-
-from loguru import logger
-from sqlalchemy.orm import Session
-
-from app.models.database import CallRecording
-
-PLAYGROUND_CALL_DATA_PRESERVE_KEYS = ("ui_surface", "external_usage_recorded")
-
-
-def merge_playground_call_data(
-    prev: Optional[dict[str, Any]],
-    new: dict[str, Any],
-) -> dict[str, Any]:
-    """Keep internal audit fields when provider metrics replace call_data."""
-    merged = dict(new)
-    if isinstance(prev, dict):
-        for key in PLAYGROUND_CALL_DATA_PRESERVE_KEYS:
-            if prev.get(key) is not None:
-                merged[key] = prev[key]
-    return merged
-
-
-def _lock_call_recording(db: Session, call_recording_id: UUID) -> Optional[CallRecording]:
-    return (
-        db.query(CallRecording)
-        .filter(CallRecording.id == call_recording_id)
-        .with_for_update()
-        .first()
-    )
-
-
-def record_playground_post_call_usage_once(
-    db: Session,
-    call_recording_id: UUID,
-    *,
-    provider_platform: str,
-    call_metrics: dict[str, Any],
-) -> tuple[bool, dict[str, Any]]:
-    """
-    Record external provider usage at most once per call recording.
-
-    Returns (should_create_evaluator, updated_call_metrics).
-    """
-    from app.services.usage.external_agent_usage import (
-        apply_playground_provider_usage_from_call_data,
-    )
-
-    locked = _lock_call_recording(db, call_recording_id)
-    if not locked:
-        db.rollback()
-        return False, call_metrics
-
-    if locked.evaluator_result_id:
-        db.rollback()
-        logger.info(
-            "[Poll Call Metrics] Skipping post-call processing — "
-            "evaluator result already exists"
-        )
-        return False, call_metrics
-
-    stored_data = locked.call_data if isinstance(locked.call_data, dict) else {}
-    metrics = dict(call_metrics) if isinstance(call_metrics, dict) else call_metrics
-    platform_key = str(provider_platform or "").lower()
-
-    if stored_data.get("external_usage_recorded") and isinstance(metrics, dict):
-        metrics = merge_playground_call_data(stored_data, metrics)
-        locked.call_data = metrics
-        db.commit()
-    elif isinstance(metrics, dict):
-        try:
-            apply_playground_provider_usage_from_call_data(
-                organization_id=locked.organization_id,
-                workspace_id=locked.workspace_id,
-                agent_id=locked.agent_id,
-                provider_platform=platform_key,
-                call_short_id=locked.call_short_id,
-                call_data=metrics,
-            )
-        except Exception:
-            db.rollback()
-            logger.exception(
-                "[Poll Call Metrics] Usage counters failed for "
-                f"call recording {call_recording_id}"
-            )
-            return False, call_metrics
-
-        metrics["external_usage_recorded"] = True
-        metrics = merge_playground_call_data(stored_data, metrics)
-        locked.call_data = metrics
-        try:
-            db.commit()
-        except Exception:
-            db.rollback()
-            logger.exception(
-                "[Poll Call Metrics] Failed to persist external_usage_recorded "
-                f"for call recording {call_recording_id}"
-            )
-            return False, call_metrics
-
-    return True, metrics
-
-
-def claim_playground_evaluator_result_slot(
-    db: Session,
-    call_recording_id: UUID,
-    *,
-    provider_call_id: str | None = None,
-) -> Optional[CallRecording]:
-    """
-    Lock the call recording row and return it when evaluator creation may proceed.
-
-    Caller must create/link EvaluatorResult and commit before the lock is released.
-    """
-    locked = _lock_call_recording(db, call_recording_id)
-    if not locked:
-        db.rollback()
-        return None
-
-    if locked.evaluator_result_id:
-        db.rollback()
-        call_ref = provider_call_id or locked.provider_call_id or str(call_recording_id)
-        logger.info(
-            f"[Poll Call Metrics] Skipping evaluator creation — "
-            f"another poll already created result for call {call_ref}"
-        )
-        return None
-
-    return locked
+"""Atomic post-call processing for playground Voice AI poll tasks."""
+from __future__ import annotations
+
+from typing import Any, Optional
+from uuid import UUID
+
+from loguru import logger
+from sqlalchemy.orm import Session
+
+from app.models.database import CallRecording
+
+PLAYGROUND_CALL_DATA_PRESERVE_KEYS = ("ui_surface", "external_usage_recorded")
+
+_ENDED_STATUSES = frozenset(
+    {"ended", "completed", "failed", "end-of-call-report", "done"}
+)
+
+
+def call_metrics_indicate_ended(metrics: dict[str, Any]) -> bool:
+    status = str(metrics.get("call_status") or metrics.get("status") or "").lower()
+    end_timestamp = metrics.get("end_timestamp") or metrics.get("endedAt")
+    return bool(end_timestamp) or status in _ENDED_STATUSES
+
+
+def provider_metrics_enriched(provider_platform: str, metrics: dict[str, Any]) -> bool:
+    """True when the provider payload has analysis and/or pipeline latency details."""
+    plat = str(provider_platform or "").lower()
+    if plat == "vapi":
+        analysis = metrics.get("analysis") if isinstance(metrics.get("analysis"), dict) else {}
+        perf = (metrics.get("artifact") or {}).get("performanceMetrics") or {}
+        return bool(analysis.get("summary")) or bool(perf.get("turnLatencies"))
+    if plat == "retell":
+        return bool(metrics.get("call_analysis")) or bool(metrics.get("latency"))
+    if plat == "elevenlabs":
+        status = str(metrics.get("status") or "").lower()
+        return status in {"done", "completed"} or bool(metrics.get("conversation_turn_metrics"))
+    if plat == "smallest":
+        raw = metrics.get("raw_data") if isinstance(metrics.get("raw_data"), dict) else {}
+        return bool(raw.get("latencyStats")) or bool(metrics.get("transcript"))
+    return call_metrics_indicate_ended(metrics)
+
+
+def merge_playground_call_data(
+    prev: Optional[dict[str, Any]],
+    new: dict[str, Any],
+) -> dict[str, Any]:
+    """Keep internal audit fields when provider metrics replace call_data."""
+    merged = dict(new)
+    if isinstance(prev, dict):
+        for key in PLAYGROUND_CALL_DATA_PRESERVE_KEYS:
+            if prev.get(key) is not None:
+                merged[key] = prev[key]
+    return merged
+
+
+def _lock_call_recording(db: Session, call_recording_id: UUID) -> Optional[CallRecording]:
+    return (
+        db.query(CallRecording)
+        .filter(CallRecording.id == call_recording_id)
+        .with_for_update()
+        .first()
+    )
+
+
+def record_playground_post_call_usage_once(
+    db: Session,
+    call_recording_id: UUID,
+    *,
+    provider_platform: str,
+    call_metrics: dict[str, Any],
+) -> tuple[bool, dict[str, Any]]:
+    """
+    Record external provider usage at most once per call recording.
+
+    Returns (should_create_evaluator, updated_call_metrics).
+    """
+    from app.services.usage.external_agent_usage import (
+        apply_playground_provider_usage_from_call_data,
+    )
+
+    locked = _lock_call_recording(db, call_recording_id)
+    if not locked:
+        db.rollback()
+        return False, call_metrics
+
+    if locked.evaluator_result_id:
+        db.rollback()
+        logger.info(
+            "[Poll Call Metrics] Skipping post-call processing — "
+            "evaluator result already exists"
+        )
+        return False, call_metrics
+
+    stored_data = locked.call_data if isinstance(locked.call_data, dict) else {}
+    metrics = dict(call_metrics) if isinstance(call_metrics, dict) else call_metrics
+    platform_key = str(provider_platform or "").lower()
+
+    if stored_data.get("external_usage_recorded") and isinstance(metrics, dict):
+        metrics = merge_playground_call_data(stored_data, metrics)
+        locked.call_data = metrics
+        db.commit()
+    elif isinstance(metrics, dict):
+        try:
+            apply_playground_provider_usage_from_call_data(
+                organization_id=locked.organization_id,
+                workspace_id=locked.workspace_id,
+                agent_id=locked.agent_id,
+                provider_platform=platform_key,
+                call_short_id=locked.call_short_id,
+                call_data=metrics,
+            )
+        except Exception:
+            db.rollback()
+            logger.exception(
+                "[Poll Call Metrics] Usage counters failed for "
+                f"call recording {call_recording_id}"
+            )
+            return False, call_metrics
+
+        metrics["external_usage_recorded"] = True
+        metrics = merge_playground_call_data(stored_data, metrics)
+        locked.call_data = metrics
+        try:
+            db.commit()
+        except Exception:
+            db.rollback()
+            logger.exception(
+                "[Poll Call Metrics] Failed to persist external_usage_recorded "
+                f"for call recording {call_recording_id}"
+            )
+            return False, call_metrics
+
+    return True, metrics
+
+
+def claim_playground_evaluator_result_slot(
+    db: Session,
+    call_recording_id: UUID,
+    *,
+    provider_call_id: str | None = None,
+) -> Optional[CallRecording]:
+    """
+    Lock the call recording row and return it when evaluator creation may proceed.
+
+    Caller must create/link EvaluatorResult and commit before the lock is released.
+    """
+    locked = _lock_call_recording(db, call_recording_id)
+    if not locked:
+        db.rollback()
+        return None
+
+    if locked.evaluator_result_id:
+        db.rollback()
+        call_ref = provider_call_id or locked.provider_call_id or str(call_recording_id)
+        logger.info(
+            f"[Poll Call Metrics] Skipping evaluator creation — "
+            f"another poll already created result for call {call_ref}"
+        )
+        return None
+
+    return locked
+
+
+def persist_provider_call_metrics(
+    db: Session,
+    call_recording_id: UUID,
+    call_metrics: dict[str, Any],
+) -> bool:
+    """Update stored provider call_data without creating an evaluator result."""
+    from app.models.enums import CallRecordingStatus
+
+    locked = _lock_call_recording(db, call_recording_id)
+    if not locked:
+        db.rollback()
+        return False
+
+    prev_data = locked.call_data if isinstance(locked.call_data, dict) else {}
+    merged = merge_playground_call_data(prev_data, call_metrics)
+    locked.call_data = merged
+    locked.status = CallRecordingStatus.UPDATED
+    db.commit()
+    return True
+
+
+def persist_provider_call_metrics(
+    db: Session,
+    call_recording_id: UUID,
+    call_metrics: dict[str, Any],
+) -> bool:
+    """Update stored provider call_data without creating an evaluator result."""
+    from app.models.enums import CallRecordingStatus
+
+    locked = _lock_call_recording(db, call_recording_id)
+    if not locked:
+        db.rollback()
+        return False
+
+    prev_data = locked.call_data if isinstance(locked.call_data, dict) else {}
+    merged = merge_playground_call_data(prev_data, call_metrics)
+    locked.call_data = merged
+    locked.status = CallRecordingStatus.UPDATED
+    db.commit()
+    return True
diff --git a/app/services/playground/provider_call_logs.py b/app/services/playground/provider_call_logs.py
new file mode 100644
index 00000000..ada52f81
--- /dev/null
+++ b/app/services/playground/provider_call_logs.py
@@ -0,0 +1,213 @@
+"""Fetch and normalize provider call logs for Vapi and Retell."""
+
+from __future__ import annotations
+
+import gzip
+import json
+import re
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+import requests
+from loguru import logger
+
+VAPI_API_URL = "https://api.vapi.ai"
+
+
+def _fetch_bytes(url: str, *, headers: Optional[dict[str, str]] = None, timeout: int = 60) -> bytes:
+    response = requests.get(url, headers=headers or {}, timeout=timeout, allow_redirects=True)
+    response.raise_for_status()
+    return response.content
+
+
+def _decode_log_payload(content: bytes) -> str:
+    if not content:
+        return ""
+    if content[:2] == b"\x1f\x8b":
+        try:
+            return gzip.decompress(content).decode("utf-8")
+        except OSError:
+            pass
+    for encoding in ("utf-8", "utf-8-sig", "latin-1"):
+        try:
+            return content.decode(encoding)
+        except UnicodeDecodeError:
+            continue
+    return content.decode("utf-8", errors="replace")
+
+
+def _parse_json_lines(text: str) -> List[Dict[str, Any]]:
+    entries: List[Dict[str, Any]] = []
+    for line in text.splitlines():
+        stripped = line.strip()
+        if not stripped:
+            continue
+        try:
+            parsed = json.loads(stripped)
+        except json.JSONDecodeError:
+            entries.append({"message": stripped})
+            continue
+        if isinstance(parsed, dict):
+            entries.append(parsed)
+        else:
+            entries.append({"message": str(parsed)})
+    return entries
+
+
+def _coerce_timestamp(value: Any) -> Optional[str]:
+    if value is None:
+        return None
+    if isinstance(value, (int, float)):
+        ms = float(value)
+        if ms > 1_000_000_000_000:
+            ms /= 1000.0
+        return datetime.fromtimestamp(ms, tz=timezone.utc).isoformat()
+    if isinstance(value, str):
+        text = value.strip()
+        if not text:
+            return None
+        if text.isdigit():
+            return _coerce_timestamp(int(text))
+        try:
+            return datetime.fromisoformat(text.replace("Z", "+00:00")).astimezone(timezone.utc).isoformat()
+        except ValueError:
+            return text
+    return str(value)
+
+
+def _normalize_category(value: Any) -> str:
+    text = str(value or "call").strip().lower()
+    if text in {"transcriber", "stt", "speech-to-text", "asr"}:
+        return "transcriber"
+    if text in {"voice", "tts", "speech", "synthesizer"}:
+        return "voice"
+    if text in {"llm", "model", "assistant"}:
+        return "llm"
+    if text in {"call", "system", "pipeline"}:
+        return "call"
+    return text or "call"
+
+
+def _normalize_level(value: Any) -> str:
+    text = str(value or "info").strip().lower()
+    if text in {"warn", "warning"}:
+        return "warning"
+    if text in {"err", "error", "fatal"}:
+        return "error"
+    if text in {"debug", "trace"}:
+        return "debug"
+    return "info"
+
+
+def _summary_from_raw(raw: Dict[str, Any]) -> str:
+    for key in ("message", "event", "name", "title", "type", "action", "status"):
+        value = raw.get(key)
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+    category = raw.get("category") or raw.get("component")
+    if isinstance(category, str) and category.strip():
+        return category.strip()
+    return "log event"
+
+
+def normalize_provider_log_entry(raw: Dict[str, Any]) -> Dict[str, Any]:
+    time_value = (
+        raw.get("time")
+        or raw.get("timestamp")
+        or raw.get("createdAt")
+        or raw.get("created_at")
+        or raw.get("ts")
+        or raw.get("date")
+    )
+    category = _normalize_category(
+        raw.get("category")
+        or raw.get("component")
+        or raw.get("service")
+        or raw.get("source")
+        or raw.get("type")
+    )
+    return {
+        "time": _coerce_timestamp(time_value),
+        "level": _normalize_level(raw.get("level") or raw.get("severity") or raw.get("logLevel")),
+        "category": category,
+        "summary": _summary_from_raw(raw),
+        "raw": raw,
+    }
+
+
+def fetch_vapi_call_logs(
+    *,
+    api_key: str,
+    provider_call_id: str,
+    call_data: Optional[Dict[str, Any]] = None,
+) -> List[Dict[str, Any]]:
+    content: Optional[bytes] = None
+    call_data = call_data or {}
+    artifact = call_data.get("artifact") if isinstance(call_data.get("artifact"), dict) else {}
+    log_url = artifact.get("presignedLogUrl") or artifact.get("logUrl")
+    if log_url:
+        try:
+            content = _fetch_bytes(str(log_url))
+        except Exception as exc:
+            logger.warning("[VapiLogs] Failed to fetch artifact log URL for %s: %s", provider_call_id, exc)
+
+    if content is None:
+        response = requests.get(
+            f"{VAPI_API_URL}/call/{provider_call_id}/call-logs",
+            headers={"Authorization": f"Bearer {api_key}"},
+            timeout=60,
+            allow_redirects=True,
+        )
+        response.raise_for_status()
+        content = response.content
+
+    text = _decode_log_payload(content or b"")
+    return [normalize_provider_log_entry(entry) for entry in _parse_json_lines(text)]
+
+
+_RETELL_LINE_RE = re.compile(
+    r"^(?P<time>\d{4}-\d{2}-\d{2}[ T]\d{2}:\d{2}:\d{2}(?:\.\d+)?(?:Z|[+-]\d{2}:?\d{2})?)\s+(?P<rest>.+)$"
+)
+
+
+def _parse_retell_log_text(text: str) -> List[Dict[str, Any]]:
+    if not text.strip():
+        return []
+    try:
+        parsed = json.loads(text)
+    except json.JSONDecodeError:
+        parsed = None
+    if isinstance(parsed, list):
+        return [item if isinstance(item, dict) else {"message": str(item)} for item in parsed]
+    if isinstance(parsed, dict):
+        nested = parsed.get("logs") or parsed.get("entries")
+        if isinstance(nested, list):
+            return [item if isinstance(item, dict) else {"message": str(item)} for item in nested]
+        return [parsed]
+
+    entries: List[Dict[str, Any]] = []
+    for line in text.splitlines():
+        stripped = line.strip()
+        if not stripped:
+            continue
+        try:
+            item = json.loads(stripped)
+            entries.append(item if isinstance(item, dict) else {"message": str(item)})
+            continue
+        except json.JSONDecodeError:
+            pass
+        match = _RETELL_LINE_RE.match(stripped)
+        if match:
+            entries.append({"time": match.group("time"), "message": match.group("rest")})
+        else:
+            entries.append({"message": stripped})
+    return entries
+
+
+def fetch_retell_call_logs(call_data: Dict[str, Any]) -> List[Dict[str, Any]]:
+    log_url = call_data.get("public_log_url")
+    if not log_url:
+        return []
+    content = _fetch_bytes(str(log_url))
+    text = _decode_log_payload(content)
+    return [normalize_provider_log_entry(entry) for entry in _parse_retell_log_text(text)]
diff --git a/app/services/synthetic_traces/internal_otlp_exporter.py b/app/services/synthetic_traces/internal_otlp_exporter.py
new file mode 100644
index 00000000..219da9af
--- /dev/null
+++ b/app/services/synthetic_traces/internal_otlp_exporter.py
@@ -0,0 +1,126 @@
+"""In-process OTLP span exporter for playground voice pipelines."""
+
+from __future__ import annotations
+
+import threading
+from typing import Any, Dict, Optional, Sequence
+from uuid import UUID
+
+from loguru import logger
+
+try:
+    from opentelemetry.sdk.trace import ReadableSpan
+    from opentelemetry.sdk.trace.export import SpanExporter, SpanExportResult
+
+    _OTEL_AVAILABLE = True
+except ImportError:
+    _OTEL_AVAILABLE = False
+    SpanExporter = object  # type: ignore[misc,assignment]
+    ReadableSpan = object  # type: ignore[misc,assignment]
+    SpanExportResult = object  # type: ignore[misc,assignment]
+
+
+def _attr_value(value: Any) -> Any:
+    if isinstance(value, (str, int, float, bool)) or value is None:
+        return value
+    if isinstance(value, (list, tuple)):
+        return [_attr_value(v) for v in value]
+    return str(value)
+
+
+def readable_span_to_dict(span: ReadableSpan) -> Dict[str, Any]:
+    attributes = {
+        str(key): _attr_value(val) for key, val in (span.attributes or {}).items()
+    }
+    events = []
+    for event in span.events or []:
+        events.append(
+            {
+                "name": event.name,
+                "time_unix_nano": event.timestamp,
+                "attributes": {
+                    str(key): _attr_value(val)
+                    for key, val in (event.attributes or {}).items()
+                },
+            }
+        )
+    ctx = span.get_span_context()
+    parent_span_id = None
+    if span.parent is not None:
+        parent_span_id = format(span.parent.span_id, "016x")
+    return {
+        "trace_id": format(ctx.trace_id, "032x"),
+        "span_id": format(ctx.span_id, "016x"),
+        "parent_span_id": parent_span_id,
+        "name": span.name or "",
+        "start_time_unix_nano": span.start_time,
+        "end_time_unix_nano": span.end_time,
+        "attributes": attributes,
+        "events": events,
+    }
+
+
+class InternalOtlpSpanExporter(SpanExporter):  # type: ignore[misc]
+    """Export Pipecat spans directly into synthetic trace storage (no HTTP hop)."""
+
+    def __init__(self) -> None:
+        self._lock = threading.Lock()
+        self._organization_id: Optional[UUID] = None
+        self._workspace_id: Optional[UUID] = None
+        self._call_short_id: Optional[str] = None
+        self._agent_id: Optional[str] = None
+
+    def configure(
+        self,
+        *,
+        organization_id: UUID,
+        workspace_id: UUID,
+        call_short_id: str,
+        agent_id: Optional[str] = None,
+    ) -> None:
+        with self._lock:
+            self._organization_id = organization_id
+            self._workspace_id = workspace_id
+            self._call_short_id = call_short_id
+            self._agent_id = agent_id
+
+    def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult:
+        if not spans or not _OTEL_AVAILABLE:
+            return SpanExportResult.SUCCESS
+
+        with self._lock:
+            organization_id = self._organization_id
+            workspace_id = self._workspace_id
+            call_short_id = self._call_short_id
+            agent_id = self._agent_id
+
+        if organization_id is None or workspace_id is None or not call_short_id:
+            logger.warning("Internal OTLP export skipped: exporter not configured")
+            return SpanExportResult.FAILURE
+
+        from app.database import SessionLocal
+        from app.services.synthetic_traces.trace_service import ingest_otlp_spans
+
+        payload = [readable_span_to_dict(span) for span in spans]
+        db = SessionLocal()
+        try:
+            ingest_otlp_spans(
+                db,
+                organization_id=organization_id,
+                spans=payload,
+                header_call_short_id=call_short_id,
+                header_agent_id=agent_id,
+                workspace_id=workspace_id,
+            )
+        except Exception as exc:
+            logger.warning("Internal OTLP ingest failed: {}", exc)
+            return SpanExportResult.FAILURE
+        finally:
+            db.close()
+        return SpanExportResult.SUCCESS
+
+    def shutdown(self) -> None:
+        return
+
+    def force_flush(self, timeout_millis: int = 30000) -> bool:
+        return True
diff --git a/app/services/synthetic_traces/trace_service.py b/app/services/synthetic_traces/trace_service.py
index d1f5b45d..b70e4e95 100644
--- a/app/services/synthetic_traces/trace_service.py
+++ b/app/services/synthetic_traces/trace_service.py
@@ -363,6 +363,43 @@ def link_trace_to_call_recording(
     return trace
 
 
+def link_trace_to_evaluator_result(
+    db: Session,
+    *,
+    organization_id: UUID,
+    call_short_id: str,
+    evaluator_result_id: UUID,
+    call_recording_id: Optional[UUID] = None,
+) -> Optional[SyntheticCallTrace]:
+    trace = (
+        db.query(SyntheticCallTrace)
+        .filter(
+            SyntheticCallTrace.organization_id == organization_id,
+            SyntheticCallTrace.call_short_id == call_short_id,
+        )
+        .order_by(SyntheticCallTrace.created_at.desc())
+        .first()
+    )
+    if not trace:
+        return None
+    trace.evaluator_result_id = evaluator_result_id
+    if call_recording_id:
+        trace.call_recording_id = call_recording_id
+    result = (
+        db.query(EvaluatorResult)
+        .filter(
+            EvaluatorResult.id == evaluator_result_id,
+            EvaluatorResult.organization_id == organization_id,
+        )
+        .first()
+    )
+    if result:
+        result.synthetic_call_trace_id = trace.id
+    db.commit()
+    db.refresh(trace)
+    return trace
+
+
 OBSERVABILITY_TRACES_API_PATH = "/api/v1/observability/traces"
 
 
diff --git a/app/services/voice_agent/bot_fast_api.py b/app/services/voice_agent/bot_fast_api.py
index 3bcd92a7..7e22f9a0 100644
--- a/app/services/voice_agent/bot_fast_api.py
+++ b/app/services/voice_agent/bot_fast_api.py
@@ -1,439 +1,441 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Bot FastAPI module - uses lazy imports to avoid loading heavy dependencies at module import.
-
-This module defers importing efficientai services until run_bot() is actually called,
-allowing the API/worker to start without loading Google AI SDKs at import time.
-"""
-
-import os
-import sys
-import tempfile
-import time
-
-from loguru import logger
-
-from app.services.voice_agent.audio_recorder import get_audio_recorder_class
-from app.services.voice_agent.utils.audio_merge import merge_and_upload_audio
-
-
-def _get_lazy_imports():
-    """Lazy import all efficientai dependencies when needed.
-    
-    Returns a dict with all required classes/functions for the bot.
-    This avoids loading heavy AI SDKs at module import time.
-    """
-    from efficientai.audio.vad.silero import SileroVADAnalyzer
-    from efficientai.frames.frames import (
-        LLMRunFrame, Frame, AudioRawFrame, OutputAudioRawFrame, 
-        TTSAudioRawFrame, EndFrame, StartFrame, CancelFrame
-    )
-    from efficientai.pipeline.pipeline import Pipeline
-    from efficientai.pipeline.runner import PipelineRunner
-    from efficientai.pipeline.task import PipelineParams, PipelineTask
-    from efficientai.processors.aggregators.llm_context import LLMContext
-    from efficientai.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    from efficientai.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-    from efficientai.serializers.protobuf import ProtobufFrameSerializer
-    from efficientai.services.google.gemini_live.llm import GeminiLiveLLMService
-    from efficientai.transports.websocket.fastapi import (
-        FastAPIWebsocketParams,
-        FastAPIWebsocketTransport,
-    )
-    from efficientai.processors.frame_processor import FrameProcessor
-    
-    return {
-        "SileroVADAnalyzer": SileroVADAnalyzer,
-        "LLMRunFrame": LLMRunFrame,
-        "Frame": Frame,
-        "AudioRawFrame": AudioRawFrame,
-        "OutputAudioRawFrame": OutputAudioRawFrame,
-        "TTSAudioRawFrame": TTSAudioRawFrame,
-        "EndFrame": EndFrame,
-        "StartFrame": StartFrame,
-        "CancelFrame": CancelFrame,
-        "Pipeline": Pipeline,
-        "PipelineRunner": PipelineRunner,
-        "PipelineParams": PipelineParams,
-        "PipelineTask": PipelineTask,
-        "LLMContext": LLMContext,
-        "LLMContextAggregatorPair": LLMContextAggregatorPair,
-        "RTVIConfig": RTVIConfig,
-        "RTVIObserver": RTVIObserver,
-        "RTVIProcessor": RTVIProcessor,
-        "ProtobufFrameSerializer": ProtobufFrameSerializer,
-        "GeminiLiveLLMService": GeminiLiveLLMService,
-        "FastAPIWebsocketParams": FastAPIWebsocketParams,
-        "FastAPIWebsocketTransport": FastAPIWebsocketTransport,
-        "FrameProcessor": FrameProcessor,
-    }
-
-
-# Cache for lazy imports (loaded once, reused)
-_imports_cache = None
-
-
-def _get_imports():
-    """Get cached lazy imports."""
-    global _imports_cache
-    if _imports_cache is None:
-        _imports_cache = _get_lazy_imports()
-    return _imports_cache
-
-
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-# Default system instruction (used as fallback if no agent description is provided)
-DEFAULT_SYSTEM_INSTRUCTION = """
-You are Gemini Chatbot, a friendly, helpful robot.
-Your goal is to demonstrate your capabilities in a succinct way.
-Your output will be converted to audio so don't include special characters in your answers.
-Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most.
-"""
-
-
-async def run_bot(websocket_client, google_api_key: str, system_instruction: str = None, organization_id: str = None, agent_id: str = None, persona_id: str = None, scenario_id: str = None, evaluator_id: str = None, result_id: str = None, model_name: str = None, serializer=None, telephony_mode: bool = False, call_short_id: str = None, silence_hangup_secs: float | None = None, workspace_id: str = None):
-    """
-    Run the voice agent bot with the provided Google API key.
-    
-    Args:
-        websocket_client: WebSocket client connection
-        google_api_key: Decrypted Google API key for Gemini
-        system_instruction: Optional system instruction (overrides default)
-        organization_id: Organization ID for organizing S3 uploads
-    """
-    # Lazy load all efficientai dependencies
-    imports = _get_imports()
-    AudioRecorder = get_audio_recorder_class()
-    
-    # Initialize variables for return values
-    call_start_time = time.time()
-    s3_key_result = None
-    duration_result = None
-    transcript_text = None
-    conversation_turns = []
-    
-    try:
-        transport_serializer = serializer or imports["ProtobufFrameSerializer"]()
-        from app.services.voice_agent.tts_sample_rate import (
-            resolve_websocket_audio_in_sample_rate_hz,
-            resolve_websocket_audio_out_sample_rate_hz,
-        )
-
-        transport_in_sample_rate = resolve_websocket_audio_in_sample_rate_hz(
-            telephony_mode=telephony_mode,
-        )
-        transport_out_sample_rate = resolve_websocket_audio_out_sample_rate_hz(
-            telephony_mode=telephony_mode,
-        )
-        ws_transport = imports["FastAPIWebsocketTransport"](
-            websocket=websocket_client,
-            params=imports["FastAPIWebsocketParams"](
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                add_wav_header=False,
-                audio_in_sample_rate=transport_in_sample_rate if telephony_mode else None,
-                audio_out_sample_rate=transport_out_sample_rate if telephony_mode else None,
-                vad_analyzer=imports["SileroVADAnalyzer"](),
-                serializer=transport_serializer,
-            ),
-        )
-
-        # Use provided system instruction or fallback to default
-        if system_instruction and system_instruction.strip():
-            instruction = system_instruction.strip()
-        else:
-            instruction = DEFAULT_SYSTEM_INSTRUCTION.strip()
-
-        # Validate API key before passing to Gemini
-        if not google_api_key or not google_api_key.strip():
-            raise ValueError("Google API key is empty or invalid")
-        
-        # Determine model name
-        if model_name:
-            if not model_name.startswith("models/"):
-                formatted_model_name = f"models/{model_name}"
-            else:
-                formatted_model_name = model_name
-        else:
-            formatted_model_name = "models/gemini-2.5-flash-native-audio-preview-12-2025"
-        logger.info(f"Using Gemini Live model: {formatted_model_name}")
-        
-        # Gemini S2S uses native voices (e.g. Puck), not provider voice IDs from Persona/VoiceBundle.
-        llm = imports["GeminiLiveLLMService"](
-            api_key=google_api_key,
-            voice_id="Puck",
-            transcribe_model_audio=True,
-            system_instruction=instruction,
-            model=formatted_model_name,
-        )
-        context = imports["LLMContext"](
-            [
-                {
-                    "role": "user",
-                    "content": "Start by greeting the user warmly and introducing yourself based on the system instruction.",
-                }
-            ],
-        )
-
-        context_aggregator = imports["LLMContextAggregatorPair"](context)
-
-        recorder_sample_rate = 8000 if telephony_mode else 24000
-
-        if telephony_mode:
-            from app.services.voice_agent.telephony_recording_paths import telephony_recording_temp_path
-
-            user_audio_path = telephony_recording_temp_path(suffix=".wav")
-            bot_audio_path = telephony_recording_temp_path(suffix=".wav")
-        else:
-            user_audio_fd, user_audio_path = tempfile.mkstemp(suffix=".wav")
-            os.close(user_audio_fd)
-            bot_audio_fd, bot_audio_path = tempfile.mkstemp(suffix=".wav")
-            os.close(bot_audio_fd)
-        
-        # Use a common start time for synchronization
-        start_time = time.time()
-        recorder_alignment = "stream" if telephony_mode else "wall_clock"
-        user_recorder = AudioRecorder(
-            user_audio_path,
-            start_time,
-            target_sample_rate=recorder_sample_rate,
-            recorder_name="UserAudioRecorder",
-            alignment_mode=recorder_alignment,
-        )
-        bot_recorder = AudioRecorder(
-            bot_audio_path,
-            start_time,
-            target_sample_rate=recorder_sample_rate,
-            recorder_name="BotAudioRecorder",
-            alignment_mode=recorder_alignment,
-        )
-
-        from app.services.voice_agent.live_transcript_processor import create_live_transcript_processor
-
-        live_transcript_processor = create_live_transcript_processor(call_short_id) if telephony_mode else None
-        user_transcript_processor = live_transcript_processor
-        agent_transcript_processor = (
-            create_live_transcript_processor(call_short_id) if telephony_mode and call_short_id else None
-        )
-
-        pipeline_task_ref: list = []
-
-        async def on_silence_hangup():
-            if pipeline_task_ref:
-                await pipeline_task_ref[0].cancel()
-
-        silence_hangup_processor = None
-        if silence_hangup_secs is not None and silence_hangup_secs > 0:
-            from app.services.voice_agent.call_silence_hangup import CallSilenceHangupProcessor
-
-            silence_hangup_processor = CallSilenceHangupProcessor(
-                timeout_secs=silence_hangup_secs,
-                on_hangup=on_silence_hangup,
-            )
-
-        if telephony_mode:
-            pipeline_processors = [ws_transport.input()]
-            if silence_hangup_processor:
-                pipeline_processors.append(silence_hangup_processor)
-            pipeline_processors.extend([user_recorder, context_aggregator.user()])
-            if user_transcript_processor:
-                pipeline_processors.append(user_transcript_processor)
-            pipeline_processors.append(llm)
-            from app.services.usage.voice_usage_processor import create_llm_usage_recorder
-
-            usage_recorder = create_llm_usage_recorder(
-                organization_id=organization_id,
-                workspace_id=workspace_id,
-                product_section="agents" if agent_id else ("telephony" if telephony_mode else "playground"),
-                resource_id=agent_id,
-                resource_type="agent" if agent_id else None,
-            )
-            if usage_recorder:
-                pipeline_processors.append(usage_recorder)
-            if agent_transcript_processor:
-                pipeline_processors.append(agent_transcript_processor)
-            pipeline_processors.extend([
-                bot_recorder,
-                ws_transport.output(),
-                context_aggregator.assistant(),
-            ])
-            pipeline = imports["Pipeline"](pipeline_processors)
-            task = imports["PipelineTask"](
-                pipeline,
-                params=imports["PipelineParams"](
-                    enable_metrics=True,
-                    enable_usage_metrics=True,
-                    audio_in_sample_rate=transport_in_sample_rate,
-                    audio_out_sample_rate=transport_out_sample_rate,
-                ),
-            )
-            pipeline_task_ref.append(task)
-
-            @ws_transport.event_handler("on_client_connected")
-            async def on_client_connected(transport, client):
-                logger.info("Vobiz telephony client connected via WebSocket")
-                await task.queue_frames([imports["LLMRunFrame"]()])
-
-            @ws_transport.event_handler("on_client_disconnected")
-            async def on_client_disconnected(transport, client):
-                logger.info("Vobiz telephony client disconnected")
-                await task.cancel()
-        else:
-            # RTVI events for efficientai client UI
-            rtvi = imports["RTVIProcessor"](config=imports["RTVIConfig"](config=[]))
-
-            from app.services.usage.voice_usage_processor import create_llm_usage_recorder
-
-            usage_recorder = create_llm_usage_recorder(
-                organization_id=organization_id,
-                workspace_id=workspace_id,
-                product_section="agents" if agent_id else "playground",
-                resource_id=agent_id,
-                resource_type="agent" if agent_id else None,
-            )
-            pipeline_steps = [
-                ws_transport.input(),
-                user_recorder,
-                context_aggregator.user(),
-                rtvi,
-                llm,
-            ]
-            if usage_recorder:
-                pipeline_steps.append(usage_recorder)
-            pipeline_steps.extend(
-                [
-                    bot_recorder,
-                    ws_transport.output(),
-                    context_aggregator.assistant(),
-                ]
-            )
-            pipeline = imports["Pipeline"](pipeline_steps)
-
-            task = imports["PipelineTask"](
-                pipeline,
-                params=imports["PipelineParams"](
-                    enable_metrics=True,
-                    enable_usage_metrics=True,
-                ),
-                observers=[imports["RTVIObserver"](rtvi)],
-            )
-
-            @rtvi.event_handler("on_client_ready")
-            async def on_client_ready(rtvi):
-                await rtvi.set_bot_ready()
-                await task.queue_frames([imports["LLMRunFrame"]()])
-
-            @ws_transport.event_handler("on_client_connected")
-            async def on_client_connected(transport, client):
-                logger.info("efficientai client connected via WebSocket")
-
-            @ws_transport.event_handler("on_client_disconnected")
-            async def on_client_disconnected(transport, client):
-                logger.info("efficientai Client disconnected")
-                await task.cancel()
-
-        # Verify WebSocket is still open before starting
-        if websocket_client.client_state.name != "CONNECTED":
-            raise Exception(f"WebSocket is not in CONNECTED state: {websocket_client.client_state.name}")
-        runner = imports["PipelineRunner"](handle_sigint=False)
-        
-        try:
-            await runner.run(task)
-        except Exception as run_error:
-            logger.error(f"Error in runner.run(): {run_error}", exc_info=True)
-            raise
-        finally:
-            # Close recorders explicitly to ensure files are flushed
-            await user_recorder.cleanup()
-            await bot_recorder.cleanup()
-            s3_key_result, duration_result = merge_and_upload_audio(
-                user_audio_path=user_audio_path,
-                bot_audio_path=bot_audio_path,
-                call_start_time=call_start_time,
-                organization_id=organization_id,
-                evaluator_id=evaluator_id,
-                result_id=result_id,
-            )
-            
-            # Extract conversation transcript from the LLM context
-            try:
-                raw_messages = context.messages if hasattr(context, 'messages') else []
-                conversation_turns = []
-                transcript_parts = []
-                elapsed = 0.0
-                for msg in raw_messages:
-                    role = msg.get("role", "")
-                    content = msg.get("content", "")
-                    if not content or role == "system":
-                        continue
-                    from app.services.telephony.call_recording_lifecycle import is_bootstrap_user_message
-
-                    if role == "user" and is_bootstrap_user_message(content):
-                        continue
-                    speaker = "user" if role == "user" else "assistant"
-                    turn_duration = max(1.0, len(content.split()) * 0.4)
-                    conversation_turns.append({
-                        "speaker": speaker,
-                        "text": content,
-                        "start": round(elapsed, 2),
-                        "end": round(elapsed + turn_duration, 2),
-                    })
-                    transcript_parts.append(f"{speaker}: {content}")
-                    elapsed += turn_duration
-                transcript_text = "\n".join(transcript_parts) if transcript_parts else None
-                logger.info(f"Captured {len(conversation_turns)} conversation turns from live Gemini pipeline")
-            except Exception as ctx_err:
-                logger.warning(f"Failed to extract conversation context: {ctx_err}")
-                conversation_turns = []
-                transcript_text = None
-
-            if telephony_mode and call_short_id:
-                from app.database import SessionLocal
-                from app.services.telephony.call_recording_lifecycle import persist_telephony_call_artifacts
-
-                db = SessionLocal()
-                try:
-                    persist_telephony_call_artifacts(
-                        db,
-                        call_short_id=call_short_id,
-                        conversation_turns=conversation_turns,
-                        transcript_text=transcript_text,
-                        s3_key=s3_key_result,
-                        duration=duration_result,
-                    )
-                finally:
-                    db.close()
-
-    except Exception as e:
-        logger.error(f"Error in run_bot: {e}", exc_info=True)
-        return {
-            "s3_key": s3_key_result,
-            "duration": duration_result,
-            "agent_id": agent_id,
-            "persona_id": persona_id,
-            "scenario_id": scenario_id,
-            "transcription": transcript_text,
-            "speaker_segments": conversation_turns if conversation_turns else None,
-            "error": str(e)
-        }
-    
-    metadata = {
-        "s3_key": s3_key_result,
-        "duration": duration_result,
-        "agent_id": agent_id,
-        "persona_id": persona_id,
-        "scenario_id": scenario_id,
-        "transcription": transcript_text,
-        "speaker_segments": conversation_turns if conversation_turns else None,
-    }
-    if not s3_key_result and not transcript_text:
-        metadata["error"] = "No audio file was uploaded and no transcript captured"
-    return metadata
-
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Bot FastAPI module - uses lazy imports to avoid loading heavy dependencies at module import.
+
+This module defers importing efficientai services until run_bot() is actually called,
+allowing the API/worker to start without loading Google AI SDKs at import time.
+"""
+
+import os
+import sys
+import tempfile
+import time
+
+from loguru import logger
+
+from app.services.voice_agent.audio_recorder import get_audio_recorder_class
+from app.services.voice_agent.utils.audio_merge import merge_and_upload_audio
+
+
+def _get_lazy_imports():
+    """Lazy import all efficientai dependencies when needed.
+    
+    Returns a dict with all required classes/functions for the bot.
+    This avoids loading heavy AI SDKs at module import time.
+    """
+    from efficientai.audio.vad.silero import SileroVADAnalyzer
+    from efficientai.frames.frames import (
+        LLMRunFrame, Frame, AudioRawFrame, OutputAudioRawFrame, 
+        TTSAudioRawFrame, EndFrame, StartFrame, CancelFrame
+    )
+    from efficientai.pipeline.pipeline import Pipeline
+    from efficientai.pipeline.runner import PipelineRunner
+    from efficientai.pipeline.task import PipelineParams, PipelineTask
+    from efficientai.processors.aggregators.llm_context import LLMContext
+    from efficientai.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+    from efficientai.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+    from efficientai.serializers.protobuf import ProtobufFrameSerializer
+    from efficientai.services.google.gemini_live.llm import GeminiLiveLLMService
+    from efficientai.transports.websocket.fastapi import (
+        FastAPIWebsocketParams,
+        FastAPIWebsocketTransport,
+    )
+    from efficientai.processors.frame_processor import FrameProcessor
+    
+    return {
+        "SileroVADAnalyzer": SileroVADAnalyzer,
+        "LLMRunFrame": LLMRunFrame,
+        "Frame": Frame,
+        "AudioRawFrame": AudioRawFrame,
+        "OutputAudioRawFrame": OutputAudioRawFrame,
+        "TTSAudioRawFrame": TTSAudioRawFrame,
+        "EndFrame": EndFrame,
+        "StartFrame": StartFrame,
+        "CancelFrame": CancelFrame,
+        "Pipeline": Pipeline,
+        "PipelineRunner": PipelineRunner,
+        "PipelineParams": PipelineParams,
+        "PipelineTask": PipelineTask,
+        "LLMContext": LLMContext,
+        "LLMContextAggregatorPair": LLMContextAggregatorPair,
+        "RTVIConfig": RTVIConfig,
+        "RTVIObserver": RTVIObserver,
+        "RTVIProcessor": RTVIProcessor,
+        "ProtobufFrameSerializer": ProtobufFrameSerializer,
+        "GeminiLiveLLMService": GeminiLiveLLMService,
+        "FastAPIWebsocketParams": FastAPIWebsocketParams,
+        "FastAPIWebsocketTransport": FastAPIWebsocketTransport,
+        "FrameProcessor": FrameProcessor,
+    }
+
+
+# Cache for lazy imports (loaded once, reused)
+_imports_cache = None
+
+
+def _get_imports():
+    """Get cached lazy imports."""
+    global _imports_cache
+    if _imports_cache is None:
+        _imports_cache = _get_lazy_imports()
+    return _imports_cache
+
+
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+# Default system instruction (used as fallback if no agent description is provided)
+DEFAULT_SYSTEM_INSTRUCTION = """
+You are Gemini Chatbot, a friendly, helpful robot.
+Your goal is to demonstrate your capabilities in a succinct way.
+Your output will be converted to audio so don't include special characters in your answers.
+Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most.
+"""
+
+
+async def run_bot(websocket_client, google_api_key: str, system_instruction: str = None, organization_id: str = None, agent_id: str = None, persona_id: str = None, scenario_id: str = None, evaluator_id: str = None, result_id: str = None, model_name: str = None, serializer=None, telephony_mode: bool = False, call_short_id: str = None, silence_hangup_secs: float | None = None, workspace_id: str = None, tracing_task_kwargs: dict | None = None):
+    """
+    Run the voice agent bot with the provided Google API key.
+    
+    Args:
+        websocket_client: WebSocket client connection
+        google_api_key: Decrypted Google API key for Gemini
+        system_instruction: Optional system instruction (overrides default)
+        organization_id: Organization ID for organizing S3 uploads
+    """
+    # Lazy load all efficientai dependencies
+    imports = _get_imports()
+    AudioRecorder = get_audio_recorder_class()
+    
+    # Initialize variables for return values
+    call_start_time = time.time()
+    s3_key_result = None
+    duration_result = None
+    transcript_text = None
+    conversation_turns = []
+    
+    try:
+        transport_serializer = serializer or imports["ProtobufFrameSerializer"]()
+        from app.services.voice_agent.tts_sample_rate import (
+            resolve_websocket_audio_in_sample_rate_hz,
+            resolve_websocket_audio_out_sample_rate_hz,
+        )
+
+        transport_in_sample_rate = resolve_websocket_audio_in_sample_rate_hz(
+            telephony_mode=telephony_mode,
+        )
+        transport_out_sample_rate = resolve_websocket_audio_out_sample_rate_hz(
+            telephony_mode=telephony_mode,
+        )
+        ws_transport = imports["FastAPIWebsocketTransport"](
+            websocket=websocket_client,
+            params=imports["FastAPIWebsocketParams"](
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                add_wav_header=False,
+                audio_in_sample_rate=transport_in_sample_rate if telephony_mode else None,
+                audio_out_sample_rate=transport_out_sample_rate if telephony_mode else None,
+                vad_analyzer=imports["SileroVADAnalyzer"](),
+                serializer=transport_serializer,
+            ),
+        )
+
+        # Use provided system instruction or fallback to default
+        if system_instruction and system_instruction.strip():
+            instruction = system_instruction.strip()
+        else:
+            instruction = DEFAULT_SYSTEM_INSTRUCTION.strip()
+
+        # Validate API key before passing to Gemini
+        if not google_api_key or not google_api_key.strip():
+            raise ValueError("Google API key is empty or invalid")
+        
+        # Determine model name
+        if model_name:
+            if not model_name.startswith("models/"):
+                formatted_model_name = f"models/{model_name}"
+            else:
+                formatted_model_name = model_name
+        else:
+            formatted_model_name = "models/gemini-2.5-flash-native-audio-preview-12-2025"
+        logger.info(f"Using Gemini Live model: {formatted_model_name}")
+        
+        # Gemini S2S uses native voices (e.g. Puck), not provider voice IDs from Persona/VoiceBundle.
+        llm = imports["GeminiLiveLLMService"](
+            api_key=google_api_key,
+            voice_id="Puck",
+            transcribe_model_audio=True,
+            system_instruction=instruction,
+            model=formatted_model_name,
+        )
+        context = imports["LLMContext"](
+            [
+                {
+                    "role": "user",
+                    "content": "Start by greeting the user warmly and introducing yourself based on the system instruction.",
+                }
+            ],
+        )
+
+        context_aggregator = imports["LLMContextAggregatorPair"](context)
+
+        recorder_sample_rate = 8000 if telephony_mode else 24000
+
+        if telephony_mode:
+            from app.services.voice_agent.telephony_recording_paths import telephony_recording_temp_path
+
+            user_audio_path = telephony_recording_temp_path(suffix=".wav")
+            bot_audio_path = telephony_recording_temp_path(suffix=".wav")
+        else:
+            user_audio_fd, user_audio_path = tempfile.mkstemp(suffix=".wav")
+            os.close(user_audio_fd)
+            bot_audio_fd, bot_audio_path = tempfile.mkstemp(suffix=".wav")
+            os.close(bot_audio_fd)
+        
+        # Use a common start time for synchronization
+        start_time = time.time()
+        recorder_alignment = "stream" if telephony_mode else "wall_clock"
+        user_recorder = AudioRecorder(
+            user_audio_path,
+            start_time,
+            target_sample_rate=recorder_sample_rate,
+            recorder_name="UserAudioRecorder",
+            alignment_mode=recorder_alignment,
+        )
+        bot_recorder = AudioRecorder(
+            bot_audio_path,
+            start_time,
+            target_sample_rate=recorder_sample_rate,
+            recorder_name="BotAudioRecorder",
+            alignment_mode=recorder_alignment,
+        )
+
+        from app.services.voice_agent.live_transcript_processor import create_live_transcript_processor
+
+        live_transcript_processor = create_live_transcript_processor(call_short_id) if telephony_mode else None
+        user_transcript_processor = live_transcript_processor
+        agent_transcript_processor = (
+            create_live_transcript_processor(call_short_id) if telephony_mode and call_short_id else None
+        )
+
+        pipeline_task_ref: list = []
+
+        async def on_silence_hangup():
+            if pipeline_task_ref:
+                await pipeline_task_ref[0].cancel()
+
+        silence_hangup_processor = None
+        if silence_hangup_secs is not None and silence_hangup_secs > 0:
+            from app.services.voice_agent.call_silence_hangup import CallSilenceHangupProcessor
+
+            silence_hangup_processor = CallSilenceHangupProcessor(
+                timeout_secs=silence_hangup_secs,
+                on_hangup=on_silence_hangup,
+            )
+
+        if telephony_mode:
+            pipeline_processors = [ws_transport.input()]
+            if silence_hangup_processor:
+                pipeline_processors.append(silence_hangup_processor)
+            pipeline_processors.extend([user_recorder, context_aggregator.user()])
+            if user_transcript_processor:
+                pipeline_processors.append(user_transcript_processor)
+            pipeline_processors.append(llm)
+            from app.services.usage.voice_usage_processor import create_llm_usage_recorder
+
+            usage_recorder = create_llm_usage_recorder(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section="agents" if agent_id else ("telephony" if telephony_mode else "playground"),
+                resource_id=agent_id,
+                resource_type="agent" if agent_id else None,
+            )
+            if usage_recorder:
+                pipeline_processors.append(usage_recorder)
+            if agent_transcript_processor:
+                pipeline_processors.append(agent_transcript_processor)
+            pipeline_processors.extend([
+                bot_recorder,
+                ws_transport.output(),
+                context_aggregator.assistant(),
+            ])
+            pipeline = imports["Pipeline"](pipeline_processors)
+            task = imports["PipelineTask"](
+                pipeline,
+                params=imports["PipelineParams"](
+                    enable_metrics=True,
+                    enable_usage_metrics=True,
+                    audio_in_sample_rate=transport_in_sample_rate,
+                    audio_out_sample_rate=transport_out_sample_rate,
+                ),
+                **(tracing_task_kwargs or {}),
+            )
+            pipeline_task_ref.append(task)
+
+            @ws_transport.event_handler("on_client_connected")
+            async def on_client_connected(transport, client):
+                logger.info("Vobiz telephony client connected via WebSocket")
+                await task.queue_frames([imports["LLMRunFrame"]()])
+
+            @ws_transport.event_handler("on_client_disconnected")
+            async def on_client_disconnected(transport, client):
+                logger.info("Vobiz telephony client disconnected")
+                await task.cancel()
+        else:
+            # RTVI events for efficientai client UI
+            rtvi = imports["RTVIProcessor"](config=imports["RTVIConfig"](config=[]))
+
+            from app.services.usage.voice_usage_processor import create_llm_usage_recorder
+
+            usage_recorder = create_llm_usage_recorder(
+                organization_id=organization_id,
+                workspace_id=workspace_id,
+                product_section="agents" if agent_id else "playground",
+                resource_id=agent_id,
+                resource_type="agent" if agent_id else None,
+            )
+            pipeline_steps = [
+                ws_transport.input(),
+                user_recorder,
+                context_aggregator.user(),
+                rtvi,
+                llm,
+            ]
+            if usage_recorder:
+                pipeline_steps.append(usage_recorder)
+            pipeline_steps.extend(
+                [
+                    bot_recorder,
+                    ws_transport.output(),
+                    context_aggregator.assistant(),
+                ]
+            )
+            pipeline = imports["Pipeline"](pipeline_steps)
+
+            task = imports["PipelineTask"](
+                pipeline,
+                params=imports["PipelineParams"](
+                    enable_metrics=True,
+                    enable_usage_metrics=True,
+                ),
+                observers=[imports["RTVIObserver"](rtvi)],
+                **(tracing_task_kwargs or {}),
+            )
+
+            @rtvi.event_handler("on_client_ready")
+            async def on_client_ready(rtvi):
+                await rtvi.set_bot_ready()
+                await task.queue_frames([imports["LLMRunFrame"]()])
+
+            @ws_transport.event_handler("on_client_connected")
+            async def on_client_connected(transport, client):
+                logger.info("efficientai client connected via WebSocket")
+
+            @ws_transport.event_handler("on_client_disconnected")
+            async def on_client_disconnected(transport, client):
+                logger.info("efficientai Client disconnected")
+                await task.cancel()
+
+        # Verify WebSocket is still open before starting
+        if websocket_client.client_state.name != "CONNECTED":
+            raise Exception(f"WebSocket is not in CONNECTED state: {websocket_client.client_state.name}")
+        runner = imports["PipelineRunner"](handle_sigint=False)
+        
+        try:
+            await runner.run(task)
+        except Exception as run_error:
+            logger.error(f"Error in runner.run(): {run_error}", exc_info=True)
+            raise
+        finally:
+            # Close recorders explicitly to ensure files are flushed
+            await user_recorder.cleanup()
+            await bot_recorder.cleanup()
+            s3_key_result, duration_result = merge_and_upload_audio(
+                user_audio_path=user_audio_path,
+                bot_audio_path=bot_audio_path,
+                call_start_time=call_start_time,
+                organization_id=organization_id,
+                evaluator_id=evaluator_id,
+                result_id=result_id,
+            )
+            
+            # Extract conversation transcript from the LLM context
+            try:
+                raw_messages = context.messages if hasattr(context, 'messages') else []
+                conversation_turns = []
+                transcript_parts = []
+                elapsed = 0.0
+                for msg in raw_messages:
+                    role = msg.get("role", "")
+                    content = msg.get("content", "")
+                    if not content or role == "system":
+                        continue
+                    from app.services.telephony.call_recording_lifecycle import is_bootstrap_user_message
+
+                    if role == "user" and is_bootstrap_user_message(content):
+                        continue
+                    speaker = "user" if role == "user" else "assistant"
+                    turn_duration = max(1.0, len(content.split()) * 0.4)
+                    conversation_turns.append({
+                        "speaker": speaker,
+                        "text": content,
+                        "start": round(elapsed, 2),
+                        "end": round(elapsed + turn_duration, 2),
+                    })
+                    transcript_parts.append(f"{speaker}: {content}")
+                    elapsed += turn_duration
+                transcript_text = "\n".join(transcript_parts) if transcript_parts else None
+                logger.info(f"Captured {len(conversation_turns)} conversation turns from live Gemini pipeline")
+            except Exception as ctx_err:
+                logger.warning(f"Failed to extract conversation context: {ctx_err}")
+                conversation_turns = []
+                transcript_text = None
+
+            if telephony_mode and call_short_id:
+                from app.database import SessionLocal
+                from app.services.telephony.call_recording_lifecycle import persist_telephony_call_artifacts
+
+                db = SessionLocal()
+                try:
+                    persist_telephony_call_artifacts(
+                        db,
+                        call_short_id=call_short_id,
+                        conversation_turns=conversation_turns,
+                        transcript_text=transcript_text,
+                        s3_key=s3_key_result,
+                        duration=duration_result,
+                    )
+                finally:
+                    db.close()
+
+    except Exception as e:
+        logger.error(f"Error in run_bot: {e}", exc_info=True)
+        return {
+            "s3_key": s3_key_result,
+            "duration": duration_result,
+            "agent_id": agent_id,
+            "persona_id": persona_id,
+            "scenario_id": scenario_id,
+            "transcription": transcript_text,
+            "speaker_segments": conversation_turns if conversation_turns else None,
+            "error": str(e)
+        }
+    
+    metadata = {
+        "s3_key": s3_key_result,
+        "duration": duration_result,
+        "agent_id": agent_id,
+        "persona_id": persona_id,
+        "scenario_id": scenario_id,
+        "transcription": transcript_text,
+        "speaker_segments": conversation_turns if conversation_turns else None,
+    }
+    if not s3_key_result and not transcript_text:
+        metadata["error"] = "No audio file was uploaded and no transcript captured"
+    return metadata
+
diff --git a/app/services/voice_agent/playground_tracing.py b/app/services/voice_agent/playground_tracing.py
new file mode 100644
index 00000000..ec8a6325
--- /dev/null
+++ b/app/services/voice_agent/playground_tracing.py
@@ -0,0 +1,146 @@
+"""OTLP tracing helpers for in-process playground voice-agent pipelines."""
+
+from __future__ import annotations
+
+import threading
+from typing import Any, Dict, Optional
+
+from loguru import logger
+
+from app.services.synthetic_traces.internal_otlp_exporter import InternalOtlpSpanExporter
+
+
+_mutable_internal_exporter: Optional[InternalOtlpSpanExporter] = None
+_correlation_processor: Any = None
+_setup_lock = threading.Lock()
+
+
+def _correlation_span_processor_class():
+    from opentelemetry.context import Context
+    from opentelemetry.sdk.trace import ReadableSpan, Span, SpanProcessor
+    from opentelemetry.sdk.trace import TracerProvider
+
+    class _CorrelationSpanProcessor(SpanProcessor):
+        def __init__(self, attributes: Dict[str, str]):
+            self._attributes = dict(attributes)
+
+        def update_attributes(self, attributes: Dict[str, str]) -> None:
+            self._attributes = dict(attributes)
+
+        def on_start(self, span: Span, parent_context: Optional[Context] = None) -> None:
+            for key, value in self._attributes.items():
+                span.set_attribute(key, value)
+
+        def on_end(self, span: ReadableSpan) -> None:
+            return
+
+        def shutdown(self) -> None:
+            return
+
+        def force_flush(self, timeout_millis: int = 30000) -> bool:
+            return True
+
+    return _CorrelationSpanProcessor, TracerProvider
+
+
+def build_pipeline_tracing_kwargs(
+    *,
+    call_short_id: str,
+    workspace_id: str,
+    organization_id: str,
+    agent_id: Optional[str] = None,
+    api_key: Optional[str] = None,
+    otlp_endpoint: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Return PipelineTask kwargs for OTLP export correlated to a trace session."""
+    del api_key, otlp_endpoint  # HTTP export superseded by in-process ingest for playground
+
+    if not call_short_id or not workspace_id or not organization_id:
+        logger.debug(
+            "playground tracing skipped: call_short_id={} workspace_id={} organization_id={}",
+            bool(call_short_id),
+            bool(workspace_id),
+            bool(organization_id),
+        )
+        return {}
+
+    try:
+        from uuid import UUID
+
+        from efficientai.integrations.efficientai_traces.correlation import (
+            span_correlation_attributes,
+        )
+        from efficientai.utils.tracing.setup import is_tracing_available, setup_tracing
+        from opentelemetry import trace
+    except ImportError:
+        logger.warning(
+            "OpenTelemetry SDK not installed (pip install efficientai[otel]); "
+            "playground pipeline tracing disabled"
+        )
+        return {}
+
+    if not is_tracing_available():
+        logger.warning("OpenTelemetry SDK not installed; playground pipeline tracing disabled")
+        return {}
+
+    CorrelationSpanProcessor, TracerProvider = _correlation_span_processor_class()
+
+    org_uuid = UUID(organization_id)
+    ws_uuid = UUID(workspace_id)
+    attrs = span_correlation_attributes(
+        call_short_id=call_short_id,
+        agent_id=agent_id,
+        workspace_id=workspace_id,
+        transport="websocket",
+    )
+
+    global _mutable_internal_exporter, _correlation_processor
+
+    with _setup_lock:
+        if _mutable_internal_exporter is None:
+            _mutable_internal_exporter = InternalOtlpSpanExporter()
+            if not setup_tracing("pipecat-agent", exporter=_mutable_internal_exporter):
+                logger.warning("Failed to initialize playground tracing exporter")
+                _mutable_internal_exporter = None
+                return {}
+
+        provider = trace.get_tracer_provider()
+        if isinstance(provider, TracerProvider):
+            if _correlation_processor is None:
+                _correlation_processor = CorrelationSpanProcessor(attrs)
+                provider.add_span_processor(_correlation_processor)
+            else:
+                _correlation_processor.update_attributes(attrs)
+
+        _mutable_internal_exporter.configure(
+            organization_id=org_uuid,
+            workspace_id=ws_uuid,
+            call_short_id=call_short_id,
+            agent_id=agent_id,
+        )
+
+    logger.info(
+        "Playground pipeline tracing enabled for call_short_id={} workspace_id={}",
+        call_short_id,
+        workspace_id,
+    )
+    return {
+        "enable_tracing": True,
+        "additional_span_attributes": attrs,
+    }
+
+
+def flush_playground_tracing() -> None:
+    try:
+        from efficientai.integrations.efficientai_traces import flush_efficientai_tracing
+
+        flush_efficientai_tracing()
+    except ImportError:
+        try:
+            from efficientai.utils.tracing.setup import flush_tracing
+
+            flush_tracing()
+        except ImportError:
+            return
+    except Exception as exc:
+        logger.warning("Failed to flush playground OTLP spans: {}", exc)
diff --git a/app/services/voice_agent/voice_bundle.py b/app/services/voice_agent/voice_bundle.py
index ca3787bd..a131ed82 100644
--- a/app/services/voice_agent/voice_bundle.py
+++ b/app/services/voice_agent/voice_bundle.py
@@ -526,6 +526,7 @@ async def run_voice_bundle_fastapi(
     telephony_mode: bool = False,
     call_short_id: str | None = None,
     silence_hangup_secs: float | None = None,
+    tracing_task_kwargs: dict | None = None,
 ):
     """
     Run the STT+LLM+TTS voice bundle pipeline over a FastAPI WebSocket.
@@ -873,6 +874,7 @@ async def on_silence_hangup():
                 pipeline,
                 params=pipeline_task_params,
                 observers=[sut_latency_observer],
+                **(tracing_task_kwargs or {}),
             )
             pipeline_task_ref.append(task)
 
@@ -921,6 +923,7 @@ async def on_client_disconnected(transport, client):
                 pipeline,
                 params=pipeline_task_params,
                 observers=[imports["RTVIObserver"](rtvi)],
+                **(tracing_task_kwargs or {}),
             )
             if silence_hangup_processor:
                 pipeline_task_ref.append(task)
diff --git a/app/services/voice_providers/base.py b/app/services/voice_providers/base.py
index 76248ade..eba82408 100644
--- a/app/services/voice_providers/base.py
+++ b/app/services/voice_providers/base.py
@@ -1,116 +1,128 @@
-"""
-Base Voice Provider Interface
-All voice providers should inherit from this class
-"""
-from abc import ABC, abstractmethod
-from typing import Dict, Any, Optional
-
-
-class BaseVoiceProvider(ABC): 
-    """Base class for voice AI provider integrations."""
-    
-    def __init__(self, api_key: str):
-        """
-        Initialize the voice provider with an API key.
-        
-        Args:
-            api_key: The API key for the voice provider
-        """
-        self.api_key = api_key
-    
-    @abstractmethod
-    def create_web_call(self, agent_id: str, **kwargs) -> Dict[str, Any]:
-        """
-        Create a web call with the voice agent.
-        
-        Args:
-            agent_id: The agent ID from the voice provider
-            **kwargs: Additional provider-specific parameters
-            
-        Returns:
-            Dictionary containing call information (access_token, call_id, etc.)
-        """
-        pass
-    
-    @abstractmethod
-    def create_agent(
-        self,
-        response_engine: Dict[str, Any],
-        voice_id: str,
-        **kwargs
-    ) -> Dict[str, Any]:
-        """
-        Create a new agent in the voice provider platform.
-        
-        Args:
-            response_engine: Configuration for the response engine (LLM)
-            voice_id: Voice ID to use for the agent
-            **kwargs: Additional provider-specific parameters
-            
-        Returns:
-            Dictionary containing agent information (agent_id, etc.)
-        """
-        pass
-    
-    @abstractmethod
-    def get_agent(self, agent_id: str) -> Dict[str, Any]:
-        """
-        Get agent details from the voice provider.
-        
-        Args:
-            agent_id: The agent ID from the voice provider
-            
-        Returns:
-            Dictionary containing agent information
-        """
-        pass
-
-    @abstractmethod
-    def retrieve_call_metrics(self, call_id: str) -> Dict[str, Any]:
-        """
-        Retrieve call metrics and details from the voice provider.
-        
-        Args:
-            call_id: The call ID from the voice provider
-            
-        Returns:
-            Dictionary containing call information (transcript, metrics, etc.)
-        """
-        pass
-
-    @abstractmethod
-    def extract_agent_prompt(self, agent_id: str) -> Optional[str]:
-        """
-        Fetch the current system prompt / instructions from the provider
-        for the given agent.
-
-        Returns:
-            The system prompt string, or None if it cannot be determined.
-        """
-        pass
-
-    @abstractmethod
-    def update_agent_prompt(self, agent_id: str, system_prompt: str, **kwargs) -> Dict[str, Any]:
-        """
-        Update the agent's system prompt / instructions in the provider platform.
-
-        Args:
-            agent_id: The agent ID from the voice provider
-            system_prompt: The new system prompt text
-            **kwargs: Additional provider-specific parameters
-
-        Returns:
-            Dictionary containing the updated agent information
-        """
-        pass
-
-    @abstractmethod
-    def test_connection(self) -> bool:
-        """
-        Test the connection to the voice provider API.
-        
-        Returns:
-            True if connection is successful, raises an exception otherwise.
-        """
-        pass
-
+"""
+Base Voice Provider Interface
+All voice providers should inherit from this class
+"""
+from abc import ABC, abstractmethod
+from typing import Dict, Any, List, Optional
+
+
+class BaseVoiceProvider(ABC): 
+    """Base class for voice AI provider integrations."""
+    
+    def __init__(self, api_key: str):
+        """
+        Initialize the voice provider with an API key.
+        
+        Args:
+            api_key: The API key for the voice provider
+        """
+        self.api_key = api_key
+    
+    @abstractmethod
+    def create_web_call(self, agent_id: str, **kwargs) -> Dict[str, Any]:
+        """
+        Create a web call with the voice agent.
+        
+        Args:
+            agent_id: The agent ID from the voice provider
+            **kwargs: Additional provider-specific parameters
+            
+        Returns:
+            Dictionary containing call information (access_token, call_id, etc.)
+        """
+        pass
+    
+    @abstractmethod
+    def create_agent(
+        self,
+        response_engine: Dict[str, Any],
+        voice_id: str,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Create a new agent in the voice provider platform.
+        
+        Args:
+            response_engine: Configuration for the response engine (LLM)
+            voice_id: Voice ID to use for the agent
+            **kwargs: Additional provider-specific parameters
+            
+        Returns:
+            Dictionary containing agent information (agent_id, etc.)
+        """
+        pass
+    
+    @abstractmethod
+    def get_agent(self, agent_id: str) -> Dict[str, Any]:
+        """
+        Get agent details from the voice provider.
+        
+        Args:
+            agent_id: The agent ID from the voice provider
+            
+        Returns:
+            Dictionary containing agent information
+        """
+        pass
+
+    @abstractmethod
+    def retrieve_call_metrics(self, call_id: str) -> Dict[str, Any]:
+        """
+        Retrieve call metrics and details from the voice provider.
+        
+        Args:
+            call_id: The call ID from the voice provider
+            
+        Returns:
+            Dictionary containing call information (transcript, metrics, etc.)
+        """
+        pass
+
+    @abstractmethod
+    def extract_agent_prompt(self, agent_id: str) -> Optional[str]:
+        """
+        Fetch the current system prompt / instructions from the provider
+        for the given agent.
+
+        Returns:
+            The system prompt string, or None if it cannot be determined.
+        """
+        pass
+
+    @abstractmethod
+    def update_agent_prompt(self, agent_id: str, system_prompt: str, **kwargs) -> Dict[str, Any]:
+        """
+        Update the agent's system prompt / instructions in the provider platform.
+
+        Args:
+            agent_id: The agent ID from the voice provider
+            system_prompt: The new system prompt text
+            **kwargs: Additional provider-specific parameters
+
+        Returns:
+            Dictionary containing the updated agent information
+        """
+        pass
+
+    @abstractmethod
+    def test_connection(self) -> bool:
+        """
+        Test the connection to the voice provider API.
+        
+        Returns:
+            True if connection is successful, raises an exception otherwise.
+        """
+        pass
+
+    def list_agents(self, *, search: Optional[str] = None) -> List[Dict[str, str]]:
+        """
+        List remote voice agents for UI selection.
+
+        Returns:
+            List of {"id": str, "name": str} sorted by name.
+
+        Raises:
+            NotImplementedError: Provider does not support listing agents.
+        """
+        raise NotImplementedError(f"{self.__class__.__name__} does not support listing agents")
+
diff --git a/app/services/voice_providers/elevenlabs.py b/app/services/voice_providers/elevenlabs.py
index 362110fc..f6492d8f 100644
--- a/app/services/voice_providers/elevenlabs.py
+++ b/app/services/voice_providers/elevenlabs.py
@@ -1,306 +1,364 @@
-"""
-ElevenLabs Voice Provider Implementation
-Handles integration with ElevenLabs Conversational AI agents
-"""
-from typing import Dict, Any, Optional
-import requests
-from loguru import logger
-
-from app.services.voice_providers.base import BaseVoiceProvider
-
-ELEVENLABS_API_URL = "https://api.elevenlabs.io/v1"
-
-
-class ElevenLabsVoiceProvider(BaseVoiceProvider):
-    """ElevenLabs voice provider implementation for Conversational AI."""
-
-    def __init__(self, api_key: str):
-        super().__init__(api_key)
-        self.api_url = ELEVENLABS_API_URL
-
-    def create_web_call(
-        self,
-        agent_id: str,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs
-    ) -> Dict[str, Any]:
-        """
-        Create a web call session with an ElevenLabs Conversational AI agent.
-
-        Requests a signed URL from the ElevenLabs API that the frontend SDK
-        can use to establish a WebSocket connection with the agent.
-
-        Args:
-            agent_id: ElevenLabs agent ID
-            metadata: Optional metadata (unused by ElevenLabs, kept for interface compat)
-
-        Returns:
-            Dictionary containing signed_url and agent_id for the frontend SDK.
-        """
-        try:
-            url = f"{self.api_url}/convai/conversation/get-signed-url"
-            headers = {
-                "xi-api-key": self.api_key,
-            }
-            params = {"agent_id": agent_id}
-
-            logger.info(f"[ElevenLabsProvider] Requesting signed URL for agent_id={agent_id}")
-
-            response = requests.get(url, headers=headers, params=params, timeout=30)
-
-            if response.status_code == 200:
-                data = response.json()
-                signed_url = data.get("signed_url")
-
-                if not signed_url:
-                    logger.error(f"[ElevenLabsProvider] Response missing 'signed_url': {data}")
-                    raise ValueError("ElevenLabs API returned success but no signed_url")
-
-                logger.info(f"[ElevenLabsProvider] Signed URL obtained for agent {agent_id}")
-
-                return {
-                    "signed_url": signed_url,
-                    "agent_id": agent_id,
-                    "call_type": "web_call",
-                    "call_id": None,
-                }
-            else:
-                try:
-                    error_data = response.json()
-                    error_msg = error_data.get("detail", {}).get("message", "") or str(error_data)
-                except Exception:
-                    error_msg = response.text[:500]
-
-                logger.error(
-                    f"[ElevenLabsProvider] Failed to get signed URL "
-                    f"(status {response.status_code}): {error_msg}"
-                )
-                raise ValueError(f"ElevenLabs API error ({response.status_code}): {error_msg}")
-
-        except requests.exceptions.RequestException as e:
-            logger.error(f"[ElevenLabsProvider] Request error: {e}")
-            raise ValueError(f"Failed to create ElevenLabs web call: {str(e)}")
-
-    def create_agent(self, response_engine: Dict[str, Any], voice_id: str, **kwargs) -> Dict[str, Any]:
-        """Not implemented - agents are created via the ElevenLabs dashboard."""
-        raise NotImplementedError("ElevenLabs agents are managed through the ElevenLabs dashboard")
-
-    def get_agent(self, agent_id: str) -> Dict[str, Any]:
-        """Get agent details from ElevenLabs."""
-        try:
-            url = f"{self.api_url}/convai/agents/{agent_id}"
-            headers = {"xi-api-key": self.api_key}
-
-            response = requests.get(url, headers=headers, timeout=15)
-            response.raise_for_status()
-            return response.json()
-        except Exception as e:
-            raise ValueError(f"Failed to get ElevenLabs agent: {str(e)}")
-
-    def retrieve_call_metrics(self, call_id: str) -> Dict[str, Any]:
-        """
-        Retrieve conversation details from ElevenLabs.
-
-        Calls GET /v1/convai/conversations/{conversation_id}
-
-        ElevenLabs status values: initiated, in-progress, processing, done, failed
-        Response includes transcript (list), metadata (start_time_unix_secs,
-        call_duration_secs), analysis, and audio flags.
-
-        Args:
-            call_id: ElevenLabs conversation ID
-        """
-        try:
-            url = f"{self.api_url}/convai/conversations/{call_id}"
-            headers = {"xi-api-key": self.api_key}
-
-            response = requests.get(url, headers=headers, timeout=30)
-            response.raise_for_status()
-            data = response.json()
-
-            el_status = data.get("status", "unknown")
-            logger.info(
-                f"[ElevenLabsProvider] Retrieved conversation {call_id}, "
-                f"status: {el_status}"
-            )
-
-            # Map ElevenLabs status to the normalised values the poller recognises
-            STATUS_MAP = {
-                "done": "ended",
-                "failed": "failed",
-                "in-progress": "in-progress",
-                "processing": "processing",
-                "initiated": "initiated",
-            }
-            normalised_status = STATUS_MAP.get(el_status, el_status)
-
-            # --- Transcript --------------------------------------------------
-            transcript_text = ""
-            speaker_segments = []
-            transcript_entries = data.get("transcript", [])
-            if isinstance(transcript_entries, list):
-                for entry in transcript_entries:
-                    role = entry.get("role", "unknown")
-                    message = entry.get("message", "")
-                    if not message:
-                        continue
-                    speaker = "Agent" if role in ("agent", "ai") else "User"
-                    time_secs = entry.get("time_in_call_secs", 0)
-                    speaker_segments.append({
-                        "speaker": speaker,
-                        "text": message,
-                        "start": time_secs,
-                        "end": time_secs,
-                    })
-                transcript_text = "\n".join(
-                    f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
-                )
-
-            # --- Metadata / timing -------------------------------------------
-            metadata = data.get("metadata") or {}
-            duration_seconds = metadata.get("call_duration_secs", 0)
-            start_time_unix = metadata.get("start_time_unix_secs")
-
-            start_timestamp = None
-            end_timestamp = None
-            if start_time_unix:
-                from datetime import datetime, timezone
-                start_dt = datetime.fromtimestamp(start_time_unix, tz=timezone.utc)
-                start_timestamp = start_dt.isoformat()
-                if duration_seconds and normalised_status == "ended":
-                    from datetime import timedelta
-                    end_dt = start_dt + timedelta(seconds=duration_seconds)
-                    end_timestamp = end_dt.isoformat()
-
-            # --- Analysis (if returned by ElevenLabs) ------------------------
-            el_analysis = data.get("analysis") or {}
-            analysis = {
-                "summary": el_analysis.get("transcript_summary", ""),
-                "evaluation": el_analysis.get("evaluation_criteria_results"),
-                "data_collection": el_analysis.get("data_collection_results"),
-                "latency_stats": {},
-                "interruption_count": 0,
-                "cost": metadata.get("cost"),
-            }
-
-            # --- Audio URLs --------------------------------------------------
-            recording_urls = {}
-            if data.get("has_audio"):
-                recording_urls["conversation_audio"] = (
-                    f"{self.api_url}/convai/conversations/{call_id}/audio"
-                )
-            recording_url = recording_urls.get("conversation_audio")
-
-            return {
-                "call_id": data.get("conversation_id") or call_id,
-                "call_status": normalised_status,
-                "start_timestamp": start_timestamp,
-                "end_timestamp": end_timestamp,
-                "duration_seconds": duration_seconds,
-                "transcript": transcript_text,
-                "transcript_object": speaker_segments,
-                "analysis": analysis,
-                "cost": metadata.get("cost"),
-                "ended_reason": metadata.get("termination_reason"),
-                "recording_url": recording_url,
-                "recording_urls": recording_urls,
-                "agent_id": data.get("agent_id"),
-                "raw_data": data,
-            }
-        except Exception as e:
-            logger.error(f"[ElevenLabsProvider] Error getting conversation: {e}", exc_info=True)
-            raise ValueError(f"Failed to retrieve ElevenLabs conversation: {str(e)}")
-
-    def extract_agent_prompt(self, agent_id: str) -> Optional[str]:
-        """Extract the system prompt from an ElevenLabs Conversational AI agent."""
-        try:
-            data = self.get_agent(agent_id)
-            conv_config = data.get("conversation_config") or {}
-            agent_config = conv_config.get("agent") or {}
-            prompt_config = agent_config.get("prompt") or {}
-            prompt = prompt_config.get("prompt")
-            if prompt:
-                prompt = self._strip_code_fences(prompt)
-            return prompt
-        except Exception as e:
-            logger.warning(f"[ElevenLabsProvider] Failed to extract agent prompt: {e}")
-            return None
-
-    @staticmethod
-    def _strip_code_fences(text: str) -> str:
-        """Remove wrapping triple-backtick code fences that some providers add.
-
-        Handles both complete fences (opening + closing) and prompts that
-        only start with an opening fence (e.g. truncated or provider quirk).
-        """
-        import re
-        trimmed = text.strip()
-        # Try complete fence first (opening + closing)
-        m = re.match(r'^```[\w]*\n?([\s\S]*?)```\s*$', trimmed)
-        if m:
-            return m.group(1).strip()
-        # Opening fence only (no closing)
-        m = re.match(r'^```[\w]*\n?([\s\S]*)$', trimmed)
-        if m:
-            return m.group(1).strip()
-        return trimmed
-
-    def update_agent_prompt(self, agent_id: str, system_prompt: str, **kwargs) -> Dict[str, Any]:
-        """
-        Update an ElevenLabs Conversational AI agent's system prompt.
-
-        Args:
-            agent_id: ElevenLabs agent ID
-            system_prompt: New system prompt text
-
-        Returns:
-            Updated agent data from ElevenLabs
-        """
-        try:
-            url = f"{self.api_url}/convai/agents/{agent_id}"
-            headers = {
-                "xi-api-key": self.api_key,
-                "Content-Type": "application/json",
-            }
-            payload = {
-                "conversation_config": {
-                    "agent": {
-                        "prompt": {
-                            "prompt": system_prompt,
-                        },
-                    },
-                },
-            }
-            logger.info(f"[ElevenLabsProvider] Updating agent prompt: PATCH {url}")
-            response = requests.patch(url, headers=headers, json=payload, timeout=30)
-
-            if not response.ok:
-                try:
-                    error_body = response.json()
-                except Exception:
-                    error_body = response.text[:500]
-                raise ValueError(
-                    f"ElevenLabs API error ({response.status_code}): {error_body}"
-                )
-
-            data = response.json()
-            logger.info(f"[ElevenLabsProvider] Agent {agent_id} prompt updated")
-            return data
-        except requests.exceptions.RequestException as e:
-            raise ValueError(f"Failed to update ElevenLabs agent prompt: {str(e)}")
-
-    def test_connection(self) -> bool:
-        """Test the ElevenLabs API connection."""
-        try:
-            url = f"{self.api_url}/user"
-            headers = {"xi-api-key": self.api_key}
-
-            response = requests.get(url, headers=headers, timeout=10)
-            if response.status_code == 200:
-                return True
-            elif response.status_code == 401:
-                raise ValueError("Invalid API key")
-            else:
-                raise ValueError(f"API error (status {response.status_code})")
-        except requests.exceptions.RequestException as e:
-            raise ValueError(f"ElevenLabs connection test failed: {str(e)}")
+"""
+ElevenLabs Voice Provider Implementation
+Handles integration with ElevenLabs Conversational AI agents
+"""
+from typing import Dict, Any, Optional, List
+import requests
+from loguru import logger
+
+from app.services.voice_providers.base import BaseVoiceProvider
+
+ELEVENLABS_API_URL = "https://api.elevenlabs.io/v1"
+
+
+class ElevenLabsVoiceProvider(BaseVoiceProvider):
+    """ElevenLabs voice provider implementation for Conversational AI."""
+
+    def __init__(self, api_key: str):
+        super().__init__(api_key)
+        self.api_url = ELEVENLABS_API_URL
+
+    def create_web_call(
+        self,
+        agent_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Create a web call session with an ElevenLabs Conversational AI agent.
+
+        Requests a signed URL from the ElevenLabs API that the frontend SDK
+        can use to establish a WebSocket connection with the agent.
+
+        Args:
+            agent_id: ElevenLabs agent ID
+            metadata: Optional metadata (unused by ElevenLabs, kept for interface compat)
+
+        Returns:
+            Dictionary containing signed_url and agent_id for the frontend SDK.
+        """
+        try:
+            url = f"{self.api_url}/convai/conversation/get-signed-url"
+            headers = {
+                "xi-api-key": self.api_key,
+            }
+            params = {"agent_id": agent_id}
+
+            logger.info(f"[ElevenLabsProvider] Requesting signed URL for agent_id={agent_id}")
+
+            response = requests.get(url, headers=headers, params=params, timeout=30)
+
+            if response.status_code == 200:
+                data = response.json()
+                signed_url = data.get("signed_url")
+
+                if not signed_url:
+                    logger.error(f"[ElevenLabsProvider] Response missing 'signed_url': {data}")
+                    raise ValueError("ElevenLabs API returned success but no signed_url")
+
+                logger.info(f"[ElevenLabsProvider] Signed URL obtained for agent {agent_id}")
+
+                return {
+                    "signed_url": signed_url,
+                    "agent_id": agent_id,
+                    "call_type": "web_call",
+                    "call_id": None,
+                }
+            else:
+                try:
+                    error_data = response.json()
+                    error_msg = error_data.get("detail", {}).get("message", "") or str(error_data)
+                except Exception:
+                    error_msg = response.text[:500]
+
+                logger.error(
+                    f"[ElevenLabsProvider] Failed to get signed URL "
+                    f"(status {response.status_code}): {error_msg}"
+                )
+                raise ValueError(f"ElevenLabs API error ({response.status_code}): {error_msg}")
+
+        except requests.exceptions.RequestException as e:
+            logger.error(f"[ElevenLabsProvider] Request error: {e}")
+            raise ValueError(f"Failed to create ElevenLabs web call: {str(e)}")
+
+    def create_agent(self, response_engine: Dict[str, Any], voice_id: str, **kwargs) -> Dict[str, Any]:
+        """Not implemented - agents are created via the ElevenLabs dashboard."""
+        raise NotImplementedError("ElevenLabs agents are managed through the ElevenLabs dashboard")
+
+    def get_agent(self, agent_id: str) -> Dict[str, Any]:
+        """Get agent details from ElevenLabs."""
+        try:
+            url = f"{self.api_url}/convai/agents/{agent_id}"
+            headers = {"xi-api-key": self.api_key}
+
+            response = requests.get(url, headers=headers, timeout=15)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            raise ValueError(f"Failed to get ElevenLabs agent: {str(e)}")
+
+    def retrieve_call_metrics(self, call_id: str) -> Dict[str, Any]:
+        """
+        Retrieve conversation details from ElevenLabs.
+
+        Calls GET /v1/convai/conversations/{conversation_id}
+
+        ElevenLabs status values: initiated, in-progress, processing, done, failed
+        Response includes transcript (list), metadata (start_time_unix_secs,
+        call_duration_secs), analysis, and audio flags.
+
+        Args:
+            call_id: ElevenLabs conversation ID
+        """
+        try:
+            url = f"{self.api_url}/convai/conversations/{call_id}"
+            headers = {"xi-api-key": self.api_key}
+
+            response = requests.get(url, headers=headers, timeout=30)
+            response.raise_for_status()
+            data = response.json()
+
+            el_status = data.get("status", "unknown")
+            logger.info(
+                f"[ElevenLabsProvider] Retrieved conversation {call_id}, "
+                f"status: {el_status}"
+            )
+
+            # Map ElevenLabs status to the normalised values the poller recognises
+            STATUS_MAP = {
+                "done": "ended",
+                "failed": "failed",
+                "in-progress": "in-progress",
+                "processing": "processing",
+                "initiated": "initiated",
+            }
+            normalised_status = STATUS_MAP.get(el_status, el_status)
+
+            # --- Transcript --------------------------------------------------
+            transcript_text = ""
+            speaker_segments = []
+            transcript_entries = data.get("transcript", [])
+            if isinstance(transcript_entries, list):
+                for entry in transcript_entries:
+                    role = entry.get("role", "unknown")
+                    message = entry.get("message", "")
+                    if not message:
+                        continue
+                    speaker = "Agent" if role in ("agent", "ai") else "User"
+                    time_secs = entry.get("time_in_call_secs", 0)
+                    speaker_segments.append({
+                        "speaker": speaker,
+                        "text": message,
+                        "start": time_secs,
+                        "end": time_secs,
+                    })
+                transcript_text = "\n".join(
+                    f"{seg['speaker']}: {seg['text']}" for seg in speaker_segments
+                )
+
+            # --- Metadata / timing -------------------------------------------
+            metadata = data.get("metadata") or {}
+            duration_seconds = metadata.get("call_duration_secs", 0)
+            start_time_unix = metadata.get("start_time_unix_secs")
+
+            start_timestamp = None
+            end_timestamp = None
+            if start_time_unix:
+                from datetime import datetime, timezone
+                start_dt = datetime.fromtimestamp(start_time_unix, tz=timezone.utc)
+                start_timestamp = start_dt.isoformat()
+                if duration_seconds and normalised_status == "ended":
+                    from datetime import timedelta
+                    end_dt = start_dt + timedelta(seconds=duration_seconds)
+                    end_timestamp = end_dt.isoformat()
+
+            # --- Analysis (if returned by ElevenLabs) ------------------------
+            el_analysis = data.get("analysis") or {}
+            analysis = {
+                "summary": el_analysis.get("transcript_summary", ""),
+                "evaluation": el_analysis.get("evaluation_criteria_results"),
+                "data_collection": el_analysis.get("data_collection_results"),
+                "latency_stats": {},
+                "interruption_count": 0,
+                "cost": metadata.get("cost"),
+            }
+
+            # --- Audio URLs --------------------------------------------------
+            recording_urls = {}
+            if data.get("has_audio"):
+                recording_urls["conversation_audio"] = (
+                    f"{self.api_url}/convai/conversations/{call_id}/audio"
+                )
+            recording_url = recording_urls.get("conversation_audio")
+
+            return {
+                "call_id": data.get("conversation_id") or call_id,
+                "call_status": normalised_status,
+                "start_timestamp": start_timestamp,
+                "end_timestamp": end_timestamp,
+                "duration_seconds": duration_seconds,
+                "transcript": transcript_text,
+                "transcript_object": speaker_segments,
+                "analysis": analysis,
+                "cost": metadata.get("cost"),
+                "ended_reason": metadata.get("termination_reason"),
+                "recording_url": recording_url,
+                "recording_urls": recording_urls,
+                "agent_id": data.get("agent_id"),
+                "raw_data": data,
+            }
+        except Exception as e:
+            logger.error(f"[ElevenLabsProvider] Error getting conversation: {e}", exc_info=True)
+            raise ValueError(f"Failed to retrieve ElevenLabs conversation: {str(e)}")
+
+    def extract_agent_prompt(self, agent_id: str) -> Optional[str]:
+        """Extract the system prompt from an ElevenLabs Conversational AI agent."""
+        try:
+            data = self.get_agent(agent_id)
+            conv_config = data.get("conversation_config") or {}
+            agent_config = conv_config.get("agent") or {}
+            prompt_config = agent_config.get("prompt") or {}
+            prompt = prompt_config.get("prompt")
+            if prompt:
+                prompt = self._strip_code_fences(prompt)
+            return prompt
+        except Exception as e:
+            logger.warning(f"[ElevenLabsProvider] Failed to extract agent prompt: {e}")
+            return None
+
+    @staticmethod
+    def _strip_code_fences(text: str) -> str:
+        """Remove wrapping triple-backtick code fences that some providers add.
+
+        Handles both complete fences (opening + closing) and prompts that
+        only start with an opening fence (e.g. truncated or provider quirk).
+        """
+        import re
+        trimmed = text.strip()
+        # Try complete fence first (opening + closing)
+        m = re.match(r'^```[\w]*\n?([\s\S]*?)```\s*$', trimmed)
+        if m:
+            return m.group(1).strip()
+        # Opening fence only (no closing)
+        m = re.match(r'^```[\w]*\n?([\s\S]*)$', trimmed)
+        if m:
+            return m.group(1).strip()
+        return trimmed
+
+    def update_agent_prompt(self, agent_id: str, system_prompt: str, **kwargs) -> Dict[str, Any]:
+        """
+        Update an ElevenLabs Conversational AI agent's system prompt.
+
+        Args:
+            agent_id: ElevenLabs agent ID
+            system_prompt: New system prompt text
+
+        Returns:
+            Updated agent data from ElevenLabs
+        """
+        try:
+            url = f"{self.api_url}/convai/agents/{agent_id}"
+            headers = {
+                "xi-api-key": self.api_key,
+                "Content-Type": "application/json",
+            }
+            payload = {
+                "conversation_config": {
+                    "agent": {
+                        "prompt": {
+                            "prompt": system_prompt,
+                        },
+                    },
+                },
+            }
+            logger.info(f"[ElevenLabsProvider] Updating agent prompt: PATCH {url}")
+            response = requests.patch(url, headers=headers, json=payload, timeout=30)
+
+            if not response.ok:
+                try:
+                    error_body = response.json()
+                except Exception:
+                    error_body = response.text[:500]
+                raise ValueError(
+                    f"ElevenLabs API error ({response.status_code}): {error_body}"
+                )
+
+            data = response.json()
+            logger.info(f"[ElevenLabsProvider] Agent {agent_id} prompt updated")
+            return data
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"Failed to update ElevenLabs agent prompt: {str(e)}")
+
+    def test_connection(self) -> bool:
+        """Test the ElevenLabs API connection."""
+        try:
+            url = f"{self.api_url}/user"
+            headers = {"xi-api-key": self.api_key}
+
+            response = requests.get(url, headers=headers, timeout=10)
+            if response.status_code == 200:
+                return True
+            elif response.status_code == 401:
+                raise ValueError("Invalid API key")
+            else:
+                raise ValueError(f"API error (status {response.status_code})")
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"ElevenLabs connection test failed: {str(e)}")
+
+    def list_agents(self, *, search: Optional[str] = None) -> List[Dict[str, str]]:
+        """List ElevenLabs ConvAI agents (paginated, capped)."""
+        max_pages = 10
+        page_size = 100
+        agents: List[Dict[str, str]] = []
+        cursor: Optional[str] = None
+        headers = {"xi-api-key": self.api_key}
+        needle = (search or "").strip().lower()
+        self.last_list_truncated = False
+
+        for page_idx in range(max_pages):
+            params: Dict[str, Any] = {
+                "page_size": page_size,
+                "archived": False,
+            }
+            if cursor:
+                params["cursor"] = cursor
+            if search and search.strip():
+                params["search"] = search.strip()
+
+            response = requests.get(
+                f"{self.api_url}/convai/agents",
+                headers=headers,
+                params=params,
+                timeout=25,
+            )
+            if response.status_code == 401:
+                raise ValueError("Invalid API key")
+            if response.status_code != 200:
+                try:
+                    detail = response.json()
+                except Exception:
+                    detail = response.text[:200]
+                raise ValueError(f"ElevenLabs API error ({response.status_code}): {detail}")
+
+            data = response.json()
+            for item in data.get("agents") or []:
+                if not isinstance(item, dict):
+                    continue
+                agent_id = str(item.get("agent_id") or "").strip()
+                if not agent_id:
+                    continue
+                name = str(item.get("name") or agent_id).strip()
+                if needle and not (search and search.strip()) and needle not in name.lower() and needle not in agent_id.lower():
+                    continue
+                agents.append({"id": agent_id, "name": name})
+
+            if not data.get("has_more"):
+                break
+            cursor = data.get("next_cursor")
+            if not cursor:
+                break
+            if page_idx == max_pages - 1 and data.get("has_more"):
+                self.last_list_truncated = True
+
+        agents.sort(key=lambda row: row["name"].lower())
+        return agents
diff --git a/app/services/voice_providers/retell.py b/app/services/voice_providers/retell.py
index aa394d43..28f1fd57 100644
--- a/app/services/voice_providers/retell.py
+++ b/app/services/voice_providers/retell.py
@@ -1,416 +1,442 @@
-"""
-Retell Voice Provider Implementation
-Handles integration with Retell AI voice agents
-"""
-from typing import Dict, Any, Optional
-from retell import Retell
-from loguru import logger
-
-from app.services.voice_providers.base import BaseVoiceProvider
-
-
-class RetellVoiceProvider(BaseVoiceProvider):
-    """Retell AI voice provider implementation."""
-    
-    def __init__(self, api_key: str):
-        """
-        Initialize Retell client.
-        
-        Args:
-            api_key: Retell API key
-        """
-        super().__init__(api_key)
-        self.client = Retell(api_key=api_key)
-    
-    def create_web_call(
-        self,
-        agent_id: str,
-        metadata: Optional[Dict[str, Any]] = None,
-        retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None,
-        custom_sip_headers: Optional[Dict[str, str]] = None,
-        **kwargs
-    ) -> Dict[str, Any]:
-        """
-        Create a web call with Retell agent.
-        
-        This method uses create_web_call which returns both access_token and call_id.
-        The call_id can be used with the frontend SDK's startConversation method.
-        
-        Args:
-            agent_id: Retell agent ID
-            metadata: Optional metadata to attach to the call
-            retell_llm_dynamic_variables: Optional dynamic variables for the LLM
-            custom_sip_headers: Optional custom SIP headers (not supported by Retell SDK)
-            **kwargs: Additional parameters
-            
-        Returns:
-            Dictionary containing call information including access_token, call_id, etc.
-        """
-        try:
-            # Build parameters dict, only including supported parameters
-            call_params = {
-                "agent_id": agent_id,
-            }
-            
-            # Only add optional parameters if they are provided and not empty
-            if metadata:
-                call_params["metadata"] = metadata
-            if retell_llm_dynamic_variables:
-                call_params["retell_llm_dynamic_variables"] = retell_llm_dynamic_variables
-            
-            # Note: custom_sip_headers is not supported by Retell SDK's create_web_call
-            # If needed in the future, it may be added to the SDK
-            
-            # Add any additional kwargs that are supported
-            call_params.update(kwargs)
-            
-            # Log the call parameters for debugging (without sensitive data)
-            print(f"[Retell] Creating web call with agent_id: {agent_id}")
-            
-            web_call_response = self.client.call.create_web_call(**call_params)
-            
-            # Convert the response to a dictionary
-            # Handle both Pydantic models and dict responses
-            if isinstance(web_call_response, dict):
-                return web_call_response
-            elif hasattr(web_call_response, "model_dump"):
-                # Pydantic v2
-                return web_call_response.model_dump()
-            elif hasattr(web_call_response, "dict"):
-                # Pydantic v1
-                return web_call_response.dict()
-            else:
-                # Fallback to attribute access
-                return {
-                    "call_type": getattr(web_call_response, "call_type", "web_call"),
-                    "access_token": getattr(web_call_response, "access_token", None),
-                    "call_id": getattr(web_call_response, "call_id", None),
-                    "agent_id": getattr(web_call_response, "agent_id", agent_id),
-                    "agent_version": getattr(web_call_response, "agent_version", None),
-                    "call_status": getattr(web_call_response, "call_status", "registered"),
-                    "agent_name": getattr(web_call_response, "agent_name", None),
-                    "metadata": getattr(web_call_response, "metadata", metadata or {}),
-                    "retell_llm_dynamic_variables": getattr(
-                        web_call_response, "retell_llm_dynamic_variables", retell_llm_dynamic_variables or {}
-                    ),
-                }
-        except Exception as e:
-            error_message = str(e)
-
-            # Try to extract the human-readable message from Retell's API error body
-            upstream_msg = None
-            if hasattr(e, 'body') and isinstance(e.body, dict):
-                upstream_msg = e.body.get('message')
-            elif hasattr(e, 'response') and isinstance(e.response, dict):
-                upstream_msg = e.response.get('message')
-
-            if upstream_msg:
-                error_message = upstream_msg
-            elif hasattr(e, 'status_code'):
-                error_message = f"Retell API error (status {e.status_code}): {error_message}"
-
-            raise ValueError(
-                f"Retell: {error_message}"
-            )
-    
-    def create_agent(
-        self,
-        response_engine: Dict[str, Any],
-        voice_id: str,
-        **kwargs
-    ) -> Dict[str, Any]:
-        """
-        Create a new Retell agent.
-        
-        Args:
-            response_engine: Configuration for the response engine
-                Example: {"llm_id": "llm_234sdertfsdsfsdf", "type": "retell-llm"}
-            voice_id: Voice ID to use (e.g., "11labs-Adrian")
-            **kwargs: Additional agent configuration parameters
-            
-        Returns:
-            Dictionary containing agent information including agent_id
-        """
-        try:
-            agent_response = self.client.agent.create(
-                response_engine=response_engine,
-                voice_id=voice_id,
-                **kwargs
-            )
-            
-            # Convert the response to a dictionary
-            if isinstance(agent_response, dict):
-                return agent_response
-            elif hasattr(agent_response, "model_dump"):
-                return agent_response.model_dump()
-            elif hasattr(agent_response, "dict"):
-                return agent_response.dict()
-            else:
-                return {
-                    "agent_id": getattr(agent_response, "agent_id", None),
-                    "agent_name": getattr(agent_response, "agent_name", None),
-                    "voice_id": getattr(agent_response, "voice_id", voice_id),
-                    "response_engine": getattr(agent_response, "response_engine", response_engine),
-                }
-        except Exception as e:
-            raise ValueError(f"Failed to create Retell agent: {str(e)}")
-    
-    def register_call(
-        self,
-        agent_id: str,
-        metadata: Optional[Dict[str, Any]] = None,
-        retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None,
-        **kwargs
-    ) -> Dict[str, Any]:
-        """
-        Register a call with Retell agent (alternative to create_web_call).
-        This is the method recommended by the Retell SDK README.
-        
-        Args:
-            agent_id: Retell agent ID
-            metadata: Optional metadata to attach to the call
-            retell_llm_dynamic_variables: Optional dynamic variables for the LLM
-            **kwargs: Additional parameters
-            
-        Returns:
-            Dictionary containing call information including call_id, sample_rate, etc.
-        """
-        try:
-            # Build parameters dict
-            call_params = {
-                "agent_id": agent_id,
-            }
-            
-            if metadata:
-                call_params["metadata"] = metadata
-            if retell_llm_dynamic_variables:
-                call_params["retell_llm_dynamic_variables"] = retell_llm_dynamic_variables
-            
-            call_params.update(kwargs)
-            
-            print(f"[Retell] Registering call with agent_id: {agent_id}")
-            
-            # Try register_call if it exists, otherwise fall back to create_web_call
-            if hasattr(self.client.call, 'register'):
-                register_response = self.client.call.register(**call_params)
-            elif hasattr(self.client.call, 'register_call'):
-                register_response = self.client.call.register_call(**call_params)
-            else:
-                # Fall back to create_web_call
-                print("[Retell] register_call not available, using create_web_call")
-                return self.create_web_call(agent_id, metadata, retell_llm_dynamic_variables, None, **kwargs)
-            
-            # Convert response
-            if isinstance(register_response, dict):
-                return register_response
-            elif hasattr(register_response, "model_dump"):
-                return register_response.model_dump()
-            elif hasattr(register_response, "dict"):
-                return register_response.dict()
-            else:
-                return {
-                    "call_id": getattr(register_response, "call_id", None),
-                    "sample_rate": getattr(register_response, "sample_rate", 24000),
-                }
-        except Exception as e:
-            raise ValueError(f"Failed to register Retell call: {str(e)}")
-    
-    def get_agent(self, agent_id: str) -> Dict[str, Any]:
-        """
-        Get Retell agent details.
-        
-        Args:
-            agent_id: Retell agent ID
-            
-        Returns:
-            Dictionary containing agent information
-        """
-        try:
-            agent_response = self.client.agent.retrieve(agent_id=agent_id)
-            
-            # Convert the response to a dictionary
-            if isinstance(agent_response, dict):
-                return agent_response
-            elif hasattr(agent_response, "model_dump"):
-                return agent_response.model_dump()
-            elif hasattr(agent_response, "dict"):
-                return agent_response.dict()
-            else:
-                return {
-                    "agent_id": getattr(agent_response, "agent_id", agent_id),
-                    "agent_name": getattr(agent_response, "agent_name", None),
-                    "voice_id": getattr(agent_response, "voice_id", None),
-                    "response_engine": getattr(agent_response, "response_engine", None),
-                }
-        except Exception as e:
-            raise ValueError(f"Failed to get Retell agent: {str(e)}")
-    
-    def retrieve_call_metrics(self, call_id: str) -> Dict[str, Any]:
-        """
-        Retrieve call metrics and details from Retell.
-        
-        Args:
-            call_id: Retell call ID
-            
-        Returns:
-            Dictionary containing call information including metrics, transcript, etc.
-        """
-        try:
-            call_response = self.client.call.retrieve(call_id)
-            
-            # Convert the response to a dictionary
-            if isinstance(call_response, dict):
-                return call_response
-            elif hasattr(call_response, "model_dump"):
-                return call_response.model_dump()
-            elif hasattr(call_response, "dict"):
-                return call_response.dict()
-            else:
-                # Fallback to attribute access
-                return {
-                    "call_id": getattr(call_response, "call_id", call_id),
-                    "call_type": getattr(call_response, "call_type", None),
-                    "call_status": getattr(call_response, "call_status", None),
-                    "transcript": getattr(call_response, "transcript", None),
-                    "duration_ms": getattr(call_response, "duration_ms", None),
-                    "latency": getattr(call_response, "latency", None),
-                    "call_cost": getattr(call_response, "call_cost", None),
-                    "call_analysis": getattr(call_response, "call_analysis", None),
-                }
-        except Exception as e:
-            raise ValueError(f"Failed to retrieve Retell call metrics: {str(e)}")
-
-    def extract_agent_prompt(self, agent_id: str) -> Optional[str]:
-        """Extract the system prompt from a Retell agent.
-
-        Handles all three response engine types:
-        - retell-llm: fetch LLM by llm_id, read general_prompt
-        - conversation-flow: fetch flow by conversation_flow_id, read global_prompt
-        - custom-llm: no extractable prompt (websocket-based)
-        """
-        try:
-            agent_response = self.client.agent.retrieve(agent_id=agent_id)
-
-            response_engine = getattr(agent_response, "response_engine", None)
-            if response_engine is None:
-                logger.warning("[RetellProvider] No response_engine on agent")
-                return None
-
-            engine_type = getattr(response_engine, "type", None)
-            if isinstance(response_engine, dict):
-                engine_type = response_engine.get("type")
-
-            logger.debug(f"[RetellProvider] response_engine type={engine_type}")
-
-            # --- retell-llm: fetch the LLM and read general_prompt ---
-            llm_id = getattr(response_engine, "llm_id", None)
-            if isinstance(response_engine, dict):
-                llm_id = response_engine.get("llm_id", llm_id)  
-
-            if llm_id:
-                logger.debug(f"[RetellProvider] Fetching LLM {llm_id}")
-                llm_response = self.client.llm.retrieve(llm_id=llm_id)
-
-                prompt = getattr(llm_response, "general_prompt", None)
-                if isinstance(llm_response, dict):
-                    prompt = llm_response.get("general_prompt", prompt)
-                if prompt:
-                    return prompt
-
-                if hasattr(llm_response, "model_dump"):
-                    prompt = llm_response.model_dump().get("general_prompt")
-                    if prompt:
-                        return prompt
-
-                logger.warning(f"[RetellProvider] LLM {llm_id} returned no general_prompt")
-                return None
-
-            # --- conversation-flow: fetch the flow and read global_prompt ---
-            flow_id = getattr(response_engine, "conversation_flow_id", None)
-            if isinstance(response_engine, dict):
-                flow_id = response_engine.get("conversation_flow_id", flow_id)
-
-            if flow_id:
-                logger.debug(f"[RetellProvider] Fetching conversation flow {flow_id}")
-                flow_response = self.client.conversation_flow.retrieve(
-                    conversation_flow_id=flow_id
-                )
-
-                prompt = getattr(flow_response, "global_prompt", None)
-                if isinstance(flow_response, dict):
-                    prompt = flow_response.get("global_prompt", prompt)
-                if prompt:
-                    return prompt
-
-                if hasattr(flow_response, "model_dump"):
-                    prompt = flow_response.model_dump().get("global_prompt")
-                    if prompt:
-                        return prompt
-
-                logger.warning(f"[RetellProvider] Conversation flow {flow_id} returned no global_prompt")
-                return None
-
-            # --- custom-llm or unknown: try system_prompt fallback ---
-            if isinstance(response_engine, dict):
-                return response_engine.get("system_prompt")
-            return getattr(response_engine, "system_prompt", None)
-
-        except Exception as e:
-            logger.warning(f"[RetellProvider] Failed to extract agent prompt: {e}")
-            return None
-
-    def update_agent_prompt(self, agent_id: str, system_prompt: str, **kwargs) -> Dict[str, Any]:
-        """
-        Update a Retell agent's system prompt.
-
-        Retrieves the current agent to find its LLM configuration, then updates
-        the prompt via the agent update endpoint.
-
-        Args:
-            agent_id: Retell agent ID
-            system_prompt: New system prompt text
-
-        Returns:
-            Updated agent data from Retell
-        """
-        try:
-            current = self.get_agent(agent_id)
-            response_engine = current.get("response_engine") or {}
-
-            llm_id = response_engine.get("llm_id")
-            if llm_id:
-                llm_response = self.client.llm.update(
-                    llm_id=llm_id,
-                    general_prompt=system_prompt,
-                )
-                if isinstance(llm_response, dict):
-                    return llm_response
-                elif hasattr(llm_response, "model_dump"):
-                    return llm_response.model_dump()
-                return {"llm_id": llm_id, "updated": True}
-
-            update_response = self.client.agent.update(
-                agent_id=agent_id,
-                response_engine={
-                    **response_engine,
-                    "system_prompt": system_prompt,
-                },
-            )
-            if isinstance(update_response, dict):
-                return update_response
-            elif hasattr(update_response, "model_dump"):
-                return update_response.model_dump()
-            return {"agent_id": agent_id, "updated": True}
-        except Exception as e:
-            raise ValueError(f"Failed to update Retell agent prompt: {str(e)}")
-
-    def test_connection(self) -> bool:
-        """
-        Test Retell connection by attempting to list agents.
-        """
-        try:
-            self.client.agent.list()
-            return True
-        except Exception as e:
-            raise ValueError(f"Retell connection test failed: {str(e)}")
-
+"""
+Retell Voice Provider Implementation
+Handles integration with Retell AI voice agents
+"""
+from typing import Dict, Any, Optional, List
+from retell import Retell
+from loguru import logger
+
+from app.services.voice_providers.base import BaseVoiceProvider
+
+
+class RetellVoiceProvider(BaseVoiceProvider):
+    """Retell AI voice provider implementation."""
+    
+    def __init__(self, api_key: str):
+        """
+        Initialize Retell client.
+        
+        Args:
+            api_key: Retell API key
+        """
+        super().__init__(api_key)
+        self.client = Retell(api_key=api_key)
+    
+    def create_web_call(
+        self,
+        agent_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+        retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None,
+        custom_sip_headers: Optional[Dict[str, str]] = None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Create a web call with Retell agent.
+        
+        This method uses create_web_call which returns both access_token and call_id.
+        The call_id can be used with the frontend SDK's startConversation method.
+        
+        Args:
+            agent_id: Retell agent ID
+            metadata: Optional metadata to attach to the call
+            retell_llm_dynamic_variables: Optional dynamic variables for the LLM
+            custom_sip_headers: Optional custom SIP headers (not supported by Retell SDK)
+            **kwargs: Additional parameters
+            
+        Returns:
+            Dictionary containing call information including access_token, call_id, etc.
+        """
+        try:
+            # Build parameters dict, only including supported parameters
+            call_params = {
+                "agent_id": agent_id,
+            }
+            
+            # Only add optional parameters if they are provided and not empty
+            if metadata:
+                call_params["metadata"] = metadata
+            if retell_llm_dynamic_variables:
+                call_params["retell_llm_dynamic_variables"] = retell_llm_dynamic_variables
+            
+            # Note: custom_sip_headers is not supported by Retell SDK's create_web_call
+            # If needed in the future, it may be added to the SDK
+            
+            # Add any additional kwargs that are supported
+            call_params.update(kwargs)
+            
+            # Log the call parameters for debugging (without sensitive data)
+            print(f"[Retell] Creating web call with agent_id: {agent_id}")
+            
+            web_call_response = self.client.call.create_web_call(**call_params)
+            
+            # Convert the response to a dictionary
+            # Handle both Pydantic models and dict responses
+            if isinstance(web_call_response, dict):
+                return web_call_response
+            elif hasattr(web_call_response, "model_dump"):
+                # Pydantic v2
+                return web_call_response.model_dump()
+            elif hasattr(web_call_response, "dict"):
+                # Pydantic v1
+                return web_call_response.dict()
+            else:
+                # Fallback to attribute access
+                return {
+                    "call_type": getattr(web_call_response, "call_type", "web_call"),
+                    "access_token": getattr(web_call_response, "access_token", None),
+                    "call_id": getattr(web_call_response, "call_id", None),
+                    "agent_id": getattr(web_call_response, "agent_id", agent_id),
+                    "agent_version": getattr(web_call_response, "agent_version", None),
+                    "call_status": getattr(web_call_response, "call_status", "registered"),
+                    "agent_name": getattr(web_call_response, "agent_name", None),
+                    "metadata": getattr(web_call_response, "metadata", metadata or {}),
+                    "retell_llm_dynamic_variables": getattr(
+                        web_call_response, "retell_llm_dynamic_variables", retell_llm_dynamic_variables or {}
+                    ),
+                }
+        except Exception as e:
+            error_message = str(e)
+
+            # Try to extract the human-readable message from Retell's API error body
+            upstream_msg = None
+            if hasattr(e, 'body') and isinstance(e.body, dict):
+                upstream_msg = e.body.get('message')
+            elif hasattr(e, 'response') and isinstance(e.response, dict):
+                upstream_msg = e.response.get('message')
+
+            if upstream_msg:
+                error_message = upstream_msg
+            elif hasattr(e, 'status_code'):
+                error_message = f"Retell API error (status {e.status_code}): {error_message}"
+
+            raise ValueError(
+                f"Retell: {error_message}"
+            )
+    
+    def create_agent(
+        self,
+        response_engine: Dict[str, Any],
+        voice_id: str,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Create a new Retell agent.
+        
+        Args:
+            response_engine: Configuration for the response engine
+                Example: {"llm_id": "llm_234sdertfsdsfsdf", "type": "retell-llm"}
+            voice_id: Voice ID to use (e.g., "11labs-Adrian")
+            **kwargs: Additional agent configuration parameters
+            
+        Returns:
+            Dictionary containing agent information including agent_id
+        """
+        try:
+            agent_response = self.client.agent.create(
+                response_engine=response_engine,
+                voice_id=voice_id,
+                **kwargs
+            )
+            
+            # Convert the response to a dictionary
+            if isinstance(agent_response, dict):
+                return agent_response
+            elif hasattr(agent_response, "model_dump"):
+                return agent_response.model_dump()
+            elif hasattr(agent_response, "dict"):
+                return agent_response.dict()
+            else:
+                return {
+                    "agent_id": getattr(agent_response, "agent_id", None),
+                    "agent_name": getattr(agent_response, "agent_name", None),
+                    "voice_id": getattr(agent_response, "voice_id", voice_id),
+                    "response_engine": getattr(agent_response, "response_engine", response_engine),
+                }
+        except Exception as e:
+            raise ValueError(f"Failed to create Retell agent: {str(e)}")
+    
+    def register_call(
+        self,
+        agent_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+        retell_llm_dynamic_variables: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Register a call with Retell agent (alternative to create_web_call).
+        This is the method recommended by the Retell SDK README.
+        
+        Args:
+            agent_id: Retell agent ID
+            metadata: Optional metadata to attach to the call
+            retell_llm_dynamic_variables: Optional dynamic variables for the LLM
+            **kwargs: Additional parameters
+            
+        Returns:
+            Dictionary containing call information including call_id, sample_rate, etc.
+        """
+        try:
+            # Build parameters dict
+            call_params = {
+                "agent_id": agent_id,
+            }
+            
+            if metadata:
+                call_params["metadata"] = metadata
+            if retell_llm_dynamic_variables:
+                call_params["retell_llm_dynamic_variables"] = retell_llm_dynamic_variables
+            
+            call_params.update(kwargs)
+            
+            print(f"[Retell] Registering call with agent_id: {agent_id}")
+            
+            # Try register_call if it exists, otherwise fall back to create_web_call
+            if hasattr(self.client.call, 'register'):
+                register_response = self.client.call.register(**call_params)
+            elif hasattr(self.client.call, 'register_call'):
+                register_response = self.client.call.register_call(**call_params)
+            else:
+                # Fall back to create_web_call
+                print("[Retell] register_call not available, using create_web_call")
+                return self.create_web_call(agent_id, metadata, retell_llm_dynamic_variables, None, **kwargs)
+            
+            # Convert response
+            if isinstance(register_response, dict):
+                return register_response
+            elif hasattr(register_response, "model_dump"):
+                return register_response.model_dump()
+            elif hasattr(register_response, "dict"):
+                return register_response.dict()
+            else:
+                return {
+                    "call_id": getattr(register_response, "call_id", None),
+                    "sample_rate": getattr(register_response, "sample_rate", 24000),
+                }
+        except Exception as e:
+            raise ValueError(f"Failed to register Retell call: {str(e)}")
+    
+    def get_agent(self, agent_id: str) -> Dict[str, Any]:
+        """
+        Get Retell agent details.
+        
+        Args:
+            agent_id: Retell agent ID
+            
+        Returns:
+            Dictionary containing agent information
+        """
+        try:
+            agent_response = self.client.agent.retrieve(agent_id=agent_id)
+            
+            # Convert the response to a dictionary
+            if isinstance(agent_response, dict):
+                return agent_response
+            elif hasattr(agent_response, "model_dump"):
+                return agent_response.model_dump()
+            elif hasattr(agent_response, "dict"):
+                return agent_response.dict()
+            else:
+                return {
+                    "agent_id": getattr(agent_response, "agent_id", agent_id),
+                    "agent_name": getattr(agent_response, "agent_name", None),
+                    "voice_id": getattr(agent_response, "voice_id", None),
+                    "response_engine": getattr(agent_response, "response_engine", None),
+                }
+        except Exception as e:
+            raise ValueError(f"Failed to get Retell agent: {str(e)}")
+    
+    def retrieve_call_metrics(self, call_id: str) -> Dict[str, Any]:
+        """
+        Retrieve call metrics and details from Retell.
+        
+        Args:
+            call_id: Retell call ID
+            
+        Returns:
+            Dictionary containing call information including metrics, transcript, etc.
+        """
+        try:
+            call_response = self.client.call.retrieve(call_id)
+            
+            # Convert the response to a dictionary
+            if isinstance(call_response, dict):
+                return call_response
+            elif hasattr(call_response, "model_dump"):
+                return call_response.model_dump()
+            elif hasattr(call_response, "dict"):
+                return call_response.dict()
+            else:
+                # Fallback to attribute access
+                return {
+                    "call_id": getattr(call_response, "call_id", call_id),
+                    "call_type": getattr(call_response, "call_type", None),
+                    "call_status": getattr(call_response, "call_status", None),
+                    "transcript": getattr(call_response, "transcript", None),
+                    "duration_ms": getattr(call_response, "duration_ms", None),
+                    "latency": getattr(call_response, "latency", None),
+                    "call_cost": getattr(call_response, "call_cost", None),
+                    "call_analysis": getattr(call_response, "call_analysis", None),
+                }
+        except Exception as e:
+            raise ValueError(f"Failed to retrieve Retell call metrics: {str(e)}")
+
+    def extract_agent_prompt(self, agent_id: str) -> Optional[str]:
+        """Extract the system prompt from a Retell agent.
+
+        Handles all three response engine types:
+        - retell-llm: fetch LLM by llm_id, read general_prompt
+        - conversation-flow: fetch flow by conversation_flow_id, read global_prompt
+        - custom-llm: no extractable prompt (websocket-based)
+        """
+        try:
+            agent_response = self.client.agent.retrieve(agent_id=agent_id)
+
+            response_engine = getattr(agent_response, "response_engine", None)
+            if response_engine is None:
+                logger.warning("[RetellProvider] No response_engine on agent")
+                return None
+
+            engine_type = getattr(response_engine, "type", None)
+            if isinstance(response_engine, dict):
+                engine_type = response_engine.get("type")
+
+            logger.debug(f"[RetellProvider] response_engine type={engine_type}")
+
+            # --- retell-llm: fetch the LLM and read general_prompt ---
+            llm_id = getattr(response_engine, "llm_id", None)
+            if isinstance(response_engine, dict):
+                llm_id = response_engine.get("llm_id", llm_id)  
+
+            if llm_id:
+                logger.debug(f"[RetellProvider] Fetching LLM {llm_id}")
+                llm_response = self.client.llm.retrieve(llm_id=llm_id)
+
+                prompt = getattr(llm_response, "general_prompt", None)
+                if isinstance(llm_response, dict):
+                    prompt = llm_response.get("general_prompt", prompt)
+                if prompt:
+                    return prompt
+
+                if hasattr(llm_response, "model_dump"):
+                    prompt = llm_response.model_dump().get("general_prompt")
+                    if prompt:
+                        return prompt
+
+                logger.warning(f"[RetellProvider] LLM {llm_id} returned no general_prompt")
+                return None
+
+            # --- conversation-flow: fetch the flow and read global_prompt ---
+            flow_id = getattr(response_engine, "conversation_flow_id", None)
+            if isinstance(response_engine, dict):
+                flow_id = response_engine.get("conversation_flow_id", flow_id)
+
+            if flow_id:
+                logger.debug(f"[RetellProvider] Fetching conversation flow {flow_id}")
+                flow_response = self.client.conversation_flow.retrieve(
+                    conversation_flow_id=flow_id
+                )
+
+                prompt = getattr(flow_response, "global_prompt", None)
+                if isinstance(flow_response, dict):
+                    prompt = flow_response.get("global_prompt", prompt)
+                if prompt:
+                    return prompt
+
+                if hasattr(flow_response, "model_dump"):
+                    prompt = flow_response.model_dump().get("global_prompt")
+                    if prompt:
+                        return prompt
+
+                logger.warning(f"[RetellProvider] Conversation flow {flow_id} returned no global_prompt")
+                return None
+
+            # --- custom-llm or unknown: try system_prompt fallback ---
+            if isinstance(response_engine, dict):
+                return response_engine.get("system_prompt")
+            return getattr(response_engine, "system_prompt", None)
+
+        except Exception as e:
+            logger.warning(f"[RetellProvider] Failed to extract agent prompt: {e}")
+            return None
+
+    def update_agent_prompt(self, agent_id: str, system_prompt: str, **kwargs) -> Dict[str, Any]:
+        """
+        Update a Retell agent's system prompt.
+
+        Retrieves the current agent to find its LLM configuration, then updates
+        the prompt via the agent update endpoint.
+
+        Args:
+            agent_id: Retell agent ID
+            system_prompt: New system prompt text
+
+        Returns:
+            Updated agent data from Retell
+        """
+        try:
+            current = self.get_agent(agent_id)
+            response_engine = current.get("response_engine") or {}
+
+            llm_id = response_engine.get("llm_id")
+            if llm_id:
+                llm_response = self.client.llm.update(
+                    llm_id=llm_id,
+                    general_prompt=system_prompt,
+                )
+                if isinstance(llm_response, dict):
+                    return llm_response
+                elif hasattr(llm_response, "model_dump"):
+                    return llm_response.model_dump()
+                return {"llm_id": llm_id, "updated": True}
+
+            update_response = self.client.agent.update(
+                agent_id=agent_id,
+                response_engine={
+                    **response_engine,
+                    "system_prompt": system_prompt,
+                },
+            )
+            if isinstance(update_response, dict):
+                return update_response
+            elif hasattr(update_response, "model_dump"):
+                return update_response.model_dump()
+            return {"agent_id": agent_id, "updated": True}
+        except Exception as e:
+            raise ValueError(f"Failed to update Retell agent prompt: {str(e)}")
+
+    def test_connection(self) -> bool:
+        """
+        Test Retell connection by attempting to list agents.
+        """
+        try:
+            self.client.agent.list()
+            return True
+        except Exception as e:
+            raise ValueError(f"Retell connection test failed: {str(e)}")
+
+    def list_agents(self, *, search: Optional[str] = None) -> List[Dict[str, str]]:
+        """List Retell agents."""
+        try:
+            raw = self.client.agent.list()
+            items = raw if isinstance(raw, list) else getattr(raw, "agents", None) or []
+            agents: List[Dict[str, str]] = []
+            needle = (search or "").strip().lower()
+            for item in items:
+                if hasattr(item, "model_dump"):
+                    item = item.model_dump()
+                elif hasattr(item, "dict"):
+                    item = item.dict()
+                if not isinstance(item, dict):
+                    continue
+                agent_id = str(item.get("agent_id") or item.get("id") or "").strip()
+                if not agent_id:
+                    continue
+                name = str(item.get("agent_name") or item.get("name") or agent_id).strip()
+                if needle and needle not in name.lower() and needle not in agent_id.lower():
+                    continue
+                agents.append({"id": agent_id, "name": name})
+            agents.sort(key=lambda row: row["name"].lower())
+            return agents
+        except Exception as e:
+            raise ValueError(f"Failed to list Retell agents: {str(e)}")
+
diff --git a/app/services/voice_providers/vapi.py b/app/services/voice_providers/vapi.py
index abbc2f2f..da480478 100644
--- a/app/services/voice_providers/vapi.py
+++ b/app/services/voice_providers/vapi.py
@@ -2,7 +2,7 @@
 Vapi Voice Provider Implementation
 Handles integration with Vapi voice AI agents
 """
-from typing import Dict, Any, Optional
+from typing import Dict, Any, Optional, List
 import requests
 from loguru import logger
 
@@ -379,3 +379,37 @@ def test_connection(self) -> bool:
             raise ValueError(f"Vapi connection test failed: {str(e)}")
         except Exception as e:
             raise ValueError(f"Vapi connection test failed: {str(e)}")
+
+    def list_agents(self, *, search: Optional[str] = None) -> List[Dict[str, str]]:
+        """List Vapi assistants."""
+        try:
+            url = f"{self.api_url}/assistant"
+            headers = {
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json",
+            }
+            response = requests.get(url, headers=headers, timeout=20)
+            if response.status_code == 401:
+                raise ValueError("Invalid API key")
+            if response.status_code != 200:
+                data = response.json() if response.content else {}
+                raise ValueError(data.get("message", f"API error (status {response.status_code})"))
+
+            raw = response.json()
+            items = raw if isinstance(raw, list) else raw.get("data") or raw.get("assistants") or []
+            agents: List[Dict[str, str]] = []
+            needle = (search or "").strip().lower()
+            for item in items:
+                if not isinstance(item, dict):
+                    continue
+                agent_id = str(item.get("id") or item.get("assistantId") or "").strip()
+                if not agent_id:
+                    continue
+                name = str(item.get("name") or item.get("assistantName") or agent_id).strip()
+                if needle and needle not in name.lower() and needle not in agent_id.lower():
+                    continue
+                agents.append({"id": agent_id, "name": name})
+            agents.sort(key=lambda row: row["name"].lower())
+            return agents
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"Failed to list Vapi agents: {str(e)}")
diff --git a/app/services/voice_providers/vapi_recording.py b/app/services/voice_providers/vapi_recording.py
index 6565ff0d..a3c23603 100644
--- a/app/services/voice_providers/vapi_recording.py
+++ b/app/services/voice_providers/vapi_recording.py
@@ -1,47 +1,69 @@
-"""Helpers for resolving Vapi call recording URLs."""
-
-from __future__ import annotations
-
-from typing import Any, Dict, Optional
-
-
-def is_presigned_storage_url(url: Optional[str]) -> bool:
-    """Return True when the URL includes S3/R2 presigned query parameters."""
-    if not url:
-        return False
-    return "X-Amz-Signature=" in url or "X-Amz-Algorithm=" in url
-
-
-def extract_vapi_recording_url(call_data: Any) -> Optional[str]:
-    """
-    Resolve the best Vapi recording URL from provider call_data.
-
-    HIPAA deployments expose time-limited presigned URLs on the artifact;
-    those must be preferred over raw R2 object URLs that reject browser playback.
-    """
-    if not isinstance(call_data, dict):
-        return None
-
-    artifact = call_data.get("artifact") if isinstance(call_data.get("artifact"), dict) else {}
-    recording = artifact.get("recording") if isinstance(artifact.get("recording"), dict) else {}
-    mono = recording.get("mono") if isinstance(recording.get("mono"), dict) else {}
-    recording_urls = call_data.get("recording_urls") if isinstance(call_data.get("recording_urls"), dict) else {}
-    provider_payload = (
-        call_data.get("provider_payload") if isinstance(call_data.get("provider_payload"), dict) else {}
-    )
-
-    return (
-        artifact.get("presignedMonoUrl")
-        or artifact.get("presignedStereoUrl")
-        or call_data.get("presignedMonoUrl")
-        or call_data.get("presignedStereoUrl")
-        or call_data.get("recordingUrl")
-        or call_data.get("stereoRecordingUrl")
-        or artifact.get("recordingUrl")
-        or artifact.get("stereoRecordingUrl")
-        or mono.get("combinedUrl")
-        or recording_urls.get("combined_url")
-        or recording_urls.get("stereo_url")
-        or provider_payload.get("recordingUrl")
-        or provider_payload.get("stereoRecordingUrl")
-    )
+"""Helpers for resolving Vapi call recording URLs."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+
+def is_presigned_storage_url(url: Optional[str]) -> bool:
+    """Return True when the URL includes S3/R2 presigned query parameters."""
+    if not url:
+        return False
+    return "X-Amz-Signature=" in url or "X-Amz-Algorithm=" in url
+
+
+def extract_vapi_stereo_url(call_data: Any) -> Optional[str]:
+    """Prefer presigned stereo URL for dual-channel waveform."""
+    if not isinstance(call_data, dict):
+        return None
+
+    artifact = call_data.get("artifact") if isinstance(call_data.get("artifact"), dict) else {}
+    recording_urls = call_data.get("recording_urls") if isinstance(call_data.get("recording_urls"), dict) else {}
+
+    return (
+        artifact.get("presignedStereoUrl")
+        or call_data.get("presignedStereoUrl")
+        or call_data.get("stereoRecordingUrl")
+        or artifact.get("stereoRecordingUrl")
+        or recording_urls.get("stereo_url")
+    )
+
+
+def extract_vapi_recording_url(call_data: Any, *, stereo: bool = False) -> Optional[str]:
+    """
+    Resolve the best Vapi recording URL from provider call_data.
+
+    HIPAA deployments expose time-limited presigned URLs on the artifact;
+    those must be preferred over raw R2 object URLs that reject browser playback.
+    """
+    if not isinstance(call_data, dict):
+        return None
+
+    if stereo:
+        stereo_url = extract_vapi_stereo_url(call_data)
+        if stereo_url:
+            return stereo_url
+
+    artifact = call_data.get("artifact") if isinstance(call_data.get("artifact"), dict) else {}
+    recording = artifact.get("recording") if isinstance(artifact.get("recording"), dict) else {}
+    mono = recording.get("mono") if isinstance(recording.get("mono"), dict) else {}
+    recording_urls = call_data.get("recording_urls") if isinstance(call_data.get("recording_urls"), dict) else {}
+    provider_payload = (
+        call_data.get("provider_payload") if isinstance(call_data.get("provider_payload"), dict) else {}
+    )
+
+    return (
+        artifact.get("presignedMonoUrl")
+        or artifact.get("presignedStereoUrl")
+        or call_data.get("presignedMonoUrl")
+        or call_data.get("presignedStereoUrl")
+        or call_data.get("recordingUrl")
+        or call_data.get("stereoRecordingUrl")
+        or artifact.get("recordingUrl")
+        or artifact.get("stereoRecordingUrl")
+        or mono.get("combinedUrl")
+        or recording_urls.get("combined_url")
+        or recording_urls.get("stereo_url")
+        or provider_payload.get("recordingUrl")
+        or provider_payload.get("stereoRecordingUrl")
+    )
diff --git a/app/services/voice_providers/voice_agent_catalog.py b/app/services/voice_providers/voice_agent_catalog.py
new file mode 100644
index 00000000..0c1da63f
--- /dev/null
+++ b/app/services/voice_providers/voice_agent_catalog.py
@@ -0,0 +1,81 @@
+"""List remote voice agents for integration picker UIs."""
+
+from __future__ import annotations
+
+import threading
+import time
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from app.models.database import Integration
+from app.services.voice_providers.prompt_sync import _build_voice_provider
+
+CACHE_TTL_SECONDS = 60
+_cache: Dict[str, tuple[float, Dict[str, Any]]] = {}
+_cache_lock = threading.Lock()
+
+
+@dataclass
+class VoiceAgentListResult:
+    agents: List[Dict[str, str]]
+    platform: str
+    cached: bool
+    truncated: bool
+    list_supported: bool
+    message: Optional[str] = None
+
+
+def _cache_key(integration_id: str, search: Optional[str]) -> str:
+    return f"{integration_id}:{(search or '').strip().lower()}"
+
+
+def list_integration_voice_agents(
+    integration: Integration,
+    *,
+    refresh: bool = False,
+    search: Optional[str] = None,
+) -> VoiceAgentListResult:
+    platform_val = (
+        integration.platform.value
+        if hasattr(integration.platform, "value")
+        else str(integration.platform)
+    )
+    key = _cache_key(str(integration.id), search)
+
+    if not refresh:
+        with _cache_lock:
+            cached = _cache.get(key)
+            if cached and (time.time() - cached[0]) < CACHE_TTL_SECONDS:
+                return VoiceAgentListResult(**cached[1], cached=True)
+
+    provider = _build_voice_provider(integration)
+    truncated = False
+    message: Optional[str] = None
+    list_supported = True
+
+    try:
+        agents = provider.list_agents(search=search)
+        if platform_val.lower() == "elevenlabs":
+            truncated = bool(getattr(provider, "last_list_truncated", False))
+            if truncated:
+                message = "Showing the first 1,000 ElevenLabs agents. Use search to narrow results."
+    except NotImplementedError:
+        agents = []
+        list_supported = False
+        message = "This platform does not support listing agents. Enter the agent ID manually."
+    except Exception as exc:
+        raise ValueError(str(exc)) from exc
+
+    payload = {
+        "agents": agents,
+        "platform": platform_val,
+        "cached": False,
+        "truncated": truncated,
+        "list_supported": list_supported,
+        "message": message,
+    }
+
+    with _cache_lock:
+        _cache[key] = (time.time(), payload)
+
+    return VoiceAgentListResult(**payload)
diff --git a/docker/Dockerfile.api b/docker/Dockerfile.api
index 22845648..1313eb84 100644
--- a/docker/Dockerfile.api
+++ b/docker/Dockerfile.api
@@ -1,77 +1,77 @@
-# ============================================================
-# Stage 1: Frontend build (Node only — not shipped to runtime)
-# ============================================================
-FROM node:18-bookworm-slim AS frontend-builder
-
-WORKDIR /app/frontend
-
-COPY frontend/package.json frontend/package-lock.json* ./
-RUN --mount=type=cache,target=/root/.npm \
-    npm ci --legacy-peer-deps || npm install --legacy-peer-deps
-
-COPY frontend/ ./
-RUN npm run build
-
-# ============================================================
-# Stage 2: API runtime (Python only — no node_modules / esbuild)
-# ============================================================
-FROM python:3.11-slim
-
-# Install build tools and WeasyPrint system libraries.
-# build-essential + cmake: packages like praat-parselmouth on arm64/Apple Silicon
-# libgobject/libpango/libcairo/libgdk-pixbuf/libffi/shared-mime-info: PDF rendering
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    cmake \
-    libgobject-2.0-0 \
-    libpango-1.0-0 \
-    libpangocairo-1.0-0 \
-    libcairo2 \
-    libgdk-pixbuf-2.0-0 \
-    libffi-dev \
-    shared-mime-info \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install uv via pip to get the glibc-linked binary (the musl-linked binary
-# from ghcr.io/astral-sh/uv has DNS resolution issues inside Docker)
-RUN pip install --no-cache-dir uv
-
-WORKDIR /app
-
-# ============================================================
-# LAYER 1: Python dependencies (cached unless pyproject.toml changes)
-# ============================================================
-COPY pyproject.toml README.md ./
-
-# Create minimal src structure for editable install
-RUN mkdir -p src/efficientai && touch src/efficientai/__init__.py
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -e .
-
-# Security: fail build if compromised litellm .pth file is present (CVE: supply chain attack on litellm 1.82.8)
-RUN if find /usr/local/lib/ -name "litellm_init.pth" 2>/dev/null | grep -q .; then \
-        echo "SECURITY: Malicious litellm_init.pth detected! Aborting build." && exit 1; \
-    fi
-
-# ============================================================
-# LAYER 2: Frontend static assets (built in stage 1, dist only)
-# ============================================================
-COPY --from=frontend-builder /app/frontend/dist /app/frontend/dist
-
-# ============================================================
-# LAYER 3: Backend code (rebuilds on backend code changes)
-# ============================================================
-COPY src/ ./src/
-COPY app/ ./app/
-COPY scripts/ ./scripts/
-
-RUN uv pip install --system -e .
-
-# Create uploads directory
-RUN mkdir -p /app/uploads
-
-EXPOSE 8000
-
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
+# ============================================================
+# Stage 1: Frontend build (Node only — not shipped to runtime)
+# ============================================================
+FROM node:18-bookworm-slim AS frontend-builder
+
+WORKDIR /app/frontend
+
+COPY frontend/package.json frontend/package-lock.json* ./
+RUN --mount=type=cache,target=/root/.npm \
+    npm ci --legacy-peer-deps || npm install --legacy-peer-deps
+
+COPY frontend/ ./
+RUN npm run build
+
+# ============================================================
+# Stage 2: API runtime (Python only — no node_modules / esbuild)
+# ============================================================
+FROM python:3.11-slim
+
+# Install build tools and WeasyPrint system libraries.
+# build-essential + cmake: packages like praat-parselmouth on arm64/Apple Silicon
+# libgobject/libpango/libcairo/libgdk-pixbuf/libffi/shared-mime-info: PDF rendering
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    cmake \
+    libgobject-2.0-0 \
+    libpango-1.0-0 \
+    libpangocairo-1.0-0 \
+    libcairo2 \
+    libgdk-pixbuf-2.0-0 \
+    libffi-dev \
+    shared-mime-info \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv via pip to get the glibc-linked binary (the musl-linked binary
+# from ghcr.io/astral-sh/uv has DNS resolution issues inside Docker)
+RUN pip install --no-cache-dir uv
+
+WORKDIR /app
+
+# ============================================================
+# LAYER 1: Python dependencies (cached unless pyproject.toml changes)
+# ============================================================
+COPY pyproject.toml README.md ./
+
+# Create minimal src structure for editable install
+RUN mkdir -p src/efficientai && touch src/efficientai/__init__.py
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=cache,target=/root/.cache/pip \
+    uv pip install --system -e ".[otel]"
+
+# Security: fail build if compromised litellm .pth file is present (CVE: supply chain attack on litellm 1.82.8)
+RUN if find /usr/local/lib/ -name "litellm_init.pth" 2>/dev/null | grep -q .; then \
+        echo "SECURITY: Malicious litellm_init.pth detected! Aborting build." && exit 1; \
+    fi
+
+# ============================================================
+# LAYER 2: Frontend static assets (built in stage 1, dist only)
+# ============================================================
+COPY --from=frontend-builder /app/frontend/dist /app/frontend/dist
+
+# ============================================================
+# LAYER 3: Backend code (rebuilds on backend code changes)
+# ============================================================
+COPY src/ ./src/
+COPY app/ ./app/
+COPY scripts/ ./scripts/
+
+RUN uv pip install --system -e ".[otel]"
+
+# Create uploads directory
+RUN mkdir -p /app/uploads
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/frontend/.gitignore b/frontend/.gitignore
index 11182f75..993b1cf9 100644
--- a/frontend/.gitignore
+++ b/frontend/.gitignore
@@ -1,25 +1,27 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
-
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+dist.staging
+dist.prev
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 0a621760..e60272b8 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -61,7 +61,6 @@ import EvaluationsList from './pages/evaluators/results/EvaluationsList'
 
 // Observability
 import Observability from './pages/observability/Observability'
-import ObservabilityCalls from './pages/observability/ObservabilityCalls'
 import ObservabilityCallDetail from './pages/observability/ObservabilityCallDetail'
 import TestInsights from './pages/test-insights/TestInsights'
 import CallTraceDetail from './pages/test-insights/CallTraceDetail'
@@ -201,11 +200,13 @@ function App() {
           />
           <Route path="results/:id" element={<EvaluatorResultDetail />} />
           <Route path="observability" element={<Observability />} />
-          <Route path="observability/calls" element={<ObservabilityCalls />} />
+          <Route path="observability/calls" element={<Navigate to="/calls" replace />} />
           <Route path="observability/calls/:callShortId" element={<ObservabilityCallDetail />} />
-          <Route path="call-traces" element={<TestInsights />} />
+          <Route path="calls" element={<TestInsights />} />
+          <Route path="calls/:traceId" element={<CallTraceDetail />} />
+          <Route path="call-traces" element={<Navigate to="/calls" replace />} />
           <Route path="call-traces/:traceId" element={<CallTraceDetail />} />
-          <Route path="test-insights" element={<PreserveSearchRedirect to="/call-traces" />} />
+          <Route path="test-insights" element={<PreserveSearchRedirect to="/calls" />} />
           <Route path="iam" element={<IAM />} />
           <Route path="usage" element={<UsagePage />} />
           <Route path="usage/pricing" element={<UsagePricingRedirect />} />
diff --git a/frontend/src/components/Layout.tsx b/frontend/src/components/Layout.tsx
index 22d029d4..109fa72b 100644
--- a/frontend/src/components/Layout.tsx
+++ b/frontend/src/components/Layout.tsx
@@ -98,8 +98,7 @@ const navigationSections: NavSection[] = [
     icon: BarChart3,
     items: [
       { name: 'Overview', href: '/observability', icon: Activity },
-      { name: 'Calls', href: '/observability/calls', icon: Phone },
-      { name: 'Call Traces', href: '/call-traces', icon: Route },
+      { name: 'Calls', href: '/calls', icon: Route },
     ],
   },
   {
diff --git a/frontend/src/components/VoiceAgent.tsx b/frontend/src/components/VoiceAgent.tsx
index 07650b79..1720496c 100644
--- a/frontend/src/components/VoiceAgent.tsx
+++ b/frontend/src/components/VoiceAgent.tsx
@@ -1,741 +1,766 @@
-/**
- * Voice Agent Component
- * 
- * This component connects to a Pipecat voice agent using WebSocket.
- * It uses the Pipecat client library to handle real-time voice interactions.
- */
-
-import { useState, useRef, useEffect } from 'react'
-import { PipecatClient, PipecatClientOptions, RTVIEvent } from '@pipecat-ai/client-js'
-import { WebSocketTransport } from '@pipecat-ai/websocket-transport'
-import ReactMarkdown from 'react-markdown'
-import { Mic, MicOff, Loader, MessageSquare, AlertTriangle } from 'lucide-react'
-import { useQuery } from '@tanstack/react-query'
-import Button from './Button'
-import VoiceOrb, { VoiceOrbSpeaker } from './VoiceOrb'
-import { useAgentStore } from '../store/agentStore'
-import { apiClient, apiBaseUrl } from '../lib/api'
-
-interface VoiceAgentProps {
-  personaId?: string
-  scenarioId?: string
-  agentId?: string
-  customEndpoint?: string
-  customEndpointLabel?: string
-  billingSurface?: 'agents_talk' | 'agent_playground'
-  onSessionSaved?: () => void
-  compact?: boolean
-  sidebarLayout?: boolean
-  agentDisplayName?: string
-}
-
-type TranscriptEntry = { role: 'user' | 'agent'; content: string; timestamp: string }
-
-export default function VoiceAgent({ personaId, scenarioId, agentId, customEndpoint, customEndpointLabel, billingSurface, onSessionSaved, compact = false, sidebarLayout = false, agentDisplayName }: VoiceAgentProps) {
-  const { selectedAgent } = useAgentStore()
-  
-  // Use agentId prop if provided, otherwise fall back to selectedAgent from store
-  const effectiveAgentId = agentId || selectedAgent?.id
-  const [isConnected, setIsConnected] = useState(false)
-  const [isConnecting, setIsConnecting] = useState(false)
-  const [status, setStatus] = useState<string>('Disconnected')
-  const [error, setError] = useState<string | null>(null)
-  const [logs, setLogs] = useState<Array<{ timestamp: string; message: string; type: 'user' | 'bot' | 'system' }>>([])
-  const [transcriptEntries, setTranscriptEntries] = useState<TranscriptEntry[]>([])
-  const [sessionStartedAt, setSessionStartedAt] = useState<string | null>(null)
-  const [sessionEndedAt, setSessionEndedAt] = useState<string | null>(null)
-  const [recordedBlob, setRecordedBlob] = useState<Blob | null>(null)
-  const [isSavingSession, setIsSavingSession] = useState(false)
-  const [savedCallShortId, setSavedCallShortId] = useState<string | null>(null)
-  const [isEvaluatingSavedSession, setIsEvaluatingSavedSession] = useState(false)
-  const [activeSpeaker, setActiveSpeaker] = useState<VoiceOrbSpeaker>(null)
-  const formatErrorMessage = (rawError: any) => {
-    if (!rawError) return 'Unknown error'
-    if (typeof rawError === 'string') return rawError
-    if (typeof rawError?.message === 'string') return rawError.message
-    if (typeof rawError?.data?.message === 'string') return rawError.data.message
-    if (typeof rawError?.data?.detail === 'string') return rawError.data.detail
-    return JSON.stringify(rawError)
-  }
-
-  const getCustomEndpointCompatibilityError = (endpoint: string) => {
-    try {
-      const parsed = new URL(endpoint)
-      const host = parsed.hostname.toLowerCase()
-      const path = parsed.pathname.toLowerCase()
-
-      // This component uses Pipecat's RTVI websocket protocol.
-      // Raw provider websocket URLs (e.g. ElevenLabs ConvAI) are not RTVI-compatible.
-      if (host.includes('api.elevenlabs.io') && path.includes('/v1/convai/conversation')) {
-        return 'This is a native ElevenLabs websocket URL, not an RTVI websocket endpoint. Use the Voice AI Agent flow for ElevenLabs, or connect through an RTVI-compatible bridge endpoint.'
-      }
-    } catch {
-      return null
-    }
-
-    return null
-  }
-
-
-  const { data: s3Status } = useQuery({
-    queryKey: ['s3-status'],
-    queryFn: () => apiClient.getS3Status(),
-    staleTime: 60_000,
-  })
-
-  const pcClientRef = useRef<PipecatClient | null>(null)
-  const botAudioRef = useRef<HTMLAudioElement | null>(null)
-  const isConnectingRef = useRef(false) // Guard to prevent multiple simultaneous connections
-  const micStreamRef = useRef<MediaStream | null>(null)
-  const mediaRecorderRef = useRef<MediaRecorder | null>(null)
-  const recordedChunksRef = useRef<Blob[]>([])
-
-  const appendTranscript = (entry: TranscriptEntry) => {
-    setTranscriptEntries((prev) => [...prev, entry])
-  }
-
-  const startRecording = (botTrack?: MediaStreamTrack) => {
-    if (mediaRecorderRef.current || typeof MediaRecorder === 'undefined') return
-
-    const tracks: MediaStreamTrack[] = []
-    if (botTrack) {
-      tracks.push(botTrack)
-    } else if (botAudioRef.current?.srcObject && 'getAudioTracks' in botAudioRef.current.srcObject) {
-      tracks.push(...botAudioRef.current.srcObject.getAudioTracks())
-    }
-
-    if (micStreamRef.current) {
-      tracks.push(...micStreamRef.current.getAudioTracks())
-    }
-
-    if (tracks.length === 0) return
-
-    try {
-      const stream = new MediaStream(tracks)
-      const mimeType = MediaRecorder.isTypeSupported('audio/webm;codecs=opus')
-        ? 'audio/webm;codecs=opus'
-        : 'audio/webm'
-      const recorder = new MediaRecorder(stream, { mimeType })
-      recordedChunksRef.current = []
-      recorder.ondataavailable = (event: BlobEvent) => {
-        if (event.data.size > 0) {
-          recordedChunksRef.current.push(event.data)
-        }
-      }
-      recorder.onstop = () => {
-        if (recordedChunksRef.current.length > 0) {
-          setRecordedBlob(new Blob(recordedChunksRef.current, { type: recorder.mimeType || 'audio/webm' }))
-        }
-      }
-      recorder.start(1000)
-      mediaRecorderRef.current = recorder
-      log('Recording started', 'system')
-    } catch (recorderError: any) {
-      log(`Failed to start recording: ${recorderError?.message || String(recorderError)}`, 'system')
-    }
-  }
-
-  const stopRecording = () => {
-    if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {
-      mediaRecorderRef.current.stop()
-    }
-    mediaRecorderRef.current = null
-  }
-
-  const handleSaveSession = async () => {
-    if (!effectiveAgentId || !customEndpoint || transcriptEntries.length === 0) return
-
-    try {
-      setIsSavingSession(true)
-      setError(null)
-      const audioFile = recordedBlob
-        ? new File([recordedBlob], `custom-websocket-${Date.now()}.webm`, { type: recordedBlob.type || 'audio/webm' })
-        : undefined
-      const response = await apiClient.createCustomWebsocketSession({
-        agent_id: effectiveAgentId,
-        websocket_url: customEndpoint,
-        transcript_entries: transcriptEntries,
-        started_at: sessionStartedAt || undefined,
-        ended_at: sessionEndedAt || new Date().toISOString(),
-        audio_file: audioFile,
-      })
-      setSavedCallShortId(response.call_short_id)
-      log(`Session saved as call ${response.call_short_id}`, 'system')
-      onSessionSaved?.()
-    } catch (saveError: any) {
-      const errorMessage = saveError?.response?.data?.detail || saveError?.message || 'Failed to save session'
-      setError(errorMessage)
-      log(`Save failed: ${errorMessage}`, 'system')
-    } finally {
-      setIsSavingSession(false)
-    }
-  }
-
-  const handleEvaluateSavedSession = async () => {
-    if (!savedCallShortId) return
-    try {
-      setIsEvaluatingSavedSession(true)
-      await apiClient.evaluateCustomWebsocketSession(savedCallShortId)
-      log(`Evaluation queued for ${savedCallShortId}`, 'system')
-      onSessionSaved?.()
-    } catch (evalError: any) {
-      const errorMessage = evalError?.response?.data?.detail || evalError?.message || 'Failed to queue evaluation'
-      setError(errorMessage)
-      log(`Evaluation failed: ${errorMessage}`, 'system')
-    } finally {
-      setIsEvaluatingSavedSession(false)
-    }
-  }
-
-  useEffect(() => {
-    // Create audio element for bot audio playback
-    const audio = document.createElement('audio')
-    audio.autoplay = true
-    botAudioRef.current = audio
-    document.body.appendChild(audio)
-
-    return () => {
-      // Cleanup on unmount
-      if (pcClientRef.current) {
-        stopRecording()
-        pcClientRef.current.disconnect().catch(console.error)
-      }
-      if (botAudioRef.current) {
-        botAudioRef.current.remove()
-      }
-      if (micStreamRef.current) {
-        micStreamRef.current.getTracks().forEach((track) => track.stop())
-        micStreamRef.current = null
-      }
-    }
-  }, [])
-
-  const log = (message: string, type: 'user' | 'bot' | 'system' = 'system') => {
-    const entry = {
-      timestamp: new Date().toISOString(),
-      message,
-      type,
-    }
-    setLogs((prev) => [...prev, entry])
-    console.log(`[${type.toUpperCase()}] ${message}`)
-  }
-
-  const updateStatus = (newStatus: string) => {
-    setStatus(newStatus)
-    log(`Status: ${newStatus}`, 'system')
-  }
-
-  const setupMediaTracks = () => {
-    if (!pcClientRef.current) return
-
-    const tracks = pcClientRef.current.tracks()
-    if (tracks.bot?.audio && botAudioRef.current) {
-      setupAudioTrack(tracks.bot.audio)
-    }
-  }
-
-  const setupTrackListeners = () => {
-    if (!pcClientRef.current) return
-
-    // Listen for new tracks starting
-    pcClientRef.current.on(RTVIEvent.TrackStarted, (track, participant) => {
-      // Only handle non-local (bot) tracks
-      if (!participant?.local && track.kind === 'audio' && botAudioRef.current) {
-        setupAudioTrack(track)
-      }
-    })
-
-    // Listen for tracks stopping
-    pcClientRef.current.on(RTVIEvent.TrackStopped, (track, participant) => {
-      log(
-        `Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`,
-        'system'
-      )
-    })
-  }
-
-  const setupAudioTrack = (track: MediaStreamTrack) => {
-    if (!botAudioRef.current) return
-
-    log('Setting up audio track', 'system')
-
-    if (
-      botAudioRef.current.srcObject &&
-      'getAudioTracks' in botAudioRef.current.srcObject
-    ) {
-      const oldTrack = botAudioRef.current.srcObject.getAudioTracks()[0]
-      if (oldTrack?.id === track.id) return
-    }
-
-    botAudioRef.current.srcObject = new MediaStream([track])
-    startRecording(track)
-  }
-
-  const connect = async () => {
-    // Prevent multiple simultaneous connection attempts
-    if (isConnectingRef.current) {
-      log('Connection already in progress, ignoring duplicate request', 'system')
-      return
-    }
-
-    if (pcClientRef.current) {
-      log('Client already exists, disconnecting first...', 'system')
-      try {
-        await pcClientRef.current.disconnect()
-      } catch (e) {
-        // Ignore disconnect errors
-      }
-      pcClientRef.current = null
-    }
-
-    try {
-      isConnectingRef.current = true
-      setIsConnecting(true)
-      setError(null)
-      updateStatus('Connecting...')
-
-      if (customEndpoint) {
-        const compatibilityError = getCustomEndpointCompatibilityError(customEndpoint)
-        if (compatibilityError) {
-          setError(compatibilityError)
-          log(`Error: ${compatibilityError}`, 'system')
-          updateStatus('Error')
-          setIsConnecting(false)
-          setIsConnected(false)
-          return
-        }
-      }
-
-      // Resolve credentials. The backend `/connect` and websocket endpoints
-      // accept either a Bearer access token (email/password / SSO login) or
-      // an API key (legacy / machine access). We pass whichever the user has.
-      const accessToken = localStorage.getItem('accessToken')
-      const apiKey = localStorage.getItem('apiKey')
-      if (!accessToken && !apiKey) {
-        throw new Error('Not authenticated. Please log in first.')
-      }
-
-      if (!customEndpoint) {
-        // Cookies are set as a fallback - we also pass the credential as a
-        // header below, which is more reliable in cross-origin dev setups.
-        if (accessToken) {
-          document.cookie = `access_token=${accessToken}; path=/; SameSite=Lax`
-        }
-        if (apiKey) {
-          document.cookie = `api_key=${apiKey}; path=/; SameSite=Lax`
-        }
-        log('Auth credentials set for /connect', 'system')
-      } else {
-        log('Using custom endpoint, skipping backend API cookie flow', 'system')
-      }
-      setSavedCallShortId(null)
-      setRecordedBlob(null)
-      setTranscriptEntries([])
-      setSessionStartedAt(new Date().toISOString())
-      setSessionEndedAt(null)
-
-      try {
-        micStreamRef.current = await navigator.mediaDevices.getUserMedia({ audio: true })
-      } catch {
-        micStreamRef.current = null
-        log('Microphone stream unavailable for local recording', 'system')
-      }
-
-      // Create WebSocketTransport
-      // Note: WebSocketTransport might need endpoint URL for RTVI protocol initialization
-      // But the constructor doesn't accept parameters, so we'll set it after creation
-      const transport = new WebSocketTransport()
-
-      const PipecatConfig: PipecatClientOptions = {
-        transport: transport,
-        enableMic: true,
-        enableCam: false,
-        callbacks: {
-          onConnected: () => {
-            updateStatus('Connected')
-            setIsConnected(true)
-            setIsConnecting(false)
-            log('✅ Successfully connected to WebSocket', 'system')
-          },
-          onDisconnected: () => {
-            updateStatus('Disconnected')
-            setIsConnected(false)
-            setIsConnecting(false)
-            setActiveSpeaker(null)
-            setSessionEndedAt(new Date().toISOString())
-            stopRecording()
-            log('Client disconnected', 'system')
-          },
-          onBotReady: (data) => {
-            log(`Bot ready: ${JSON.stringify(data)}`, 'system')
-            setupMediaTracks()
-          },
-          onUserTranscript: (data) => {
-            if (data.final) {
-              log(`User: ${data.text}`, 'user')
-              appendTranscript({ role: 'user', content: data.text, timestamp: new Date().toISOString() })
-            }
-          },
-          onBotTranscript: (data) => {
-            log(`Bot: ${data.text}`, 'bot')
-            appendTranscript({ role: 'agent', content: data.text, timestamp: new Date().toISOString() })
-          },
-          onUserStartedSpeaking: () => setActiveSpeaker('user'),
-          onUserStoppedSpeaking: () => setActiveSpeaker((prev) => (prev === 'user' ? null : prev)),
-          onBotStartedSpeaking: () => setActiveSpeaker('agent'),
-          onBotStoppedSpeaking: () => setActiveSpeaker((prev) => (prev === 'agent' ? null : prev)),
-          onMessageError: (error: any) => {
-            console.error('Message error:', error)
-            const errorMessage = formatErrorMessage(error)
-            log(`Error: ${errorMessage}`, 'system')
-          },
-          onError: (error: any) => {
-            console.error('Error:', error)
-            const errorMessage = formatErrorMessage(error)
-            setError(errorMessage)
-            log(`Error: ${errorMessage}`, 'system')
-          },
-        },
-      }
-
-      const pcClient = new PipecatClient(PipecatConfig)
-      pcClientRef.current = pcClient
-
-        // Expose for debugging
-        ; (window as any).pcClient = pcClient
-
-      setupTrackListeners()
-
-      log('Initializing devices...', 'system')
-      await pcClient.initDevices()
-
-      // Use startBotAndConnect exactly like the Pipecat example
-      // This handles RTVI protocol handshake automatically
-      let endpointUrl = customEndpoint || `${apiBaseUrl}/api/v1/voice-agent/connect`
-
-      // Append agent_id, persona_id and scenario_id if present
-      const params = new URLSearchParams()
-      if (!customEndpoint && effectiveAgentId) {
-        params.append('agent_id', effectiveAgentId)
-      }
-      if (!customEndpoint && personaId) params.append('persona_id', personaId)
-      if (!customEndpoint && scenarioId) params.append('scenario_id', scenarioId)
-      if (!customEndpoint && billingSurface) params.append('ui_surface', billingSurface)
-
-      if (!customEndpoint && params.toString()) {
-        endpointUrl += `?${params.toString()}`
-      }
-
-      log(`Connecting to bot endpoint: ${endpointUrl}`, 'system')
-      const endpointProtocol = endpointUrl.startsWith('wss://') || endpointUrl.startsWith('ws://')
-        ? 'websocket'
-        : 'http'
-
-      if (endpointProtocol === 'websocket') {
-        log('Using direct connect() for websocket URL...', 'system')
-        await pcClient.connect({ wsUrl: endpointUrl })
-        log('✅ WebSocket transport connected', 'system')
-      } else {
-        log('Using startBotAndConnect() - this will handle RTVI protocol handshake...', 'system')
-        // Pass credentials via headers in addition to cookies so /connect
-        // works even when the API is on a different origin (e.g. dev mode
-        // with frontend on :3000 and API on :8000 where cookies aren't shared).
-        const authHeaders = new Headers()
-        if (!customEndpoint) {
-          if (accessToken) {
-            authHeaders.set('Authorization', `Bearer ${accessToken}`)
-          }
-          if (apiKey) {
-            authHeaders.set('X-API-Key', apiKey)
-          }
-        }
-        await pcClient.startBotAndConnect({
-          endpoint: endpointUrl,
-          headers: authHeaders,
-        })
-        log('✅ Connection established and RTVI handshake complete!', 'system')
-      }
-    } catch (error: any) {
-      const errorMessage = formatErrorMessage(error) || 'Failed to connect'
-      setError(errorMessage)
-      log(`Error connecting: ${errorMessage}`, 'system')
-      updateStatus('Error')
-      setIsConnecting(false)
-      setIsConnected(false)
-      setActiveSpeaker(null)
-
-      // Clean up if there's an error
-      if (pcClientRef.current) {
-        try {
-          await pcClientRef.current.disconnect()
-        } catch (disconnectError: any) {
-          // Ignore disconnect errors - they're expected if connection failed
-          log(`Error during disconnect (expected if connection failed): ${disconnectError?.message || String(disconnectError)}`, 'system')
-        } finally {
-          pcClientRef.current = null
-        }
-      }
-    } finally {
-      // Always reset the connection guard
-      isConnectingRef.current = false
-    }
-  }
-
-  const disconnect = async () => {
-    if (pcClientRef.current) {
-      try {
-        stopRecording()
-        await pcClientRef.current.disconnect()
-        pcClientRef.current = null
-
-        if (
-          botAudioRef.current?.srcObject &&
-          'getAudioTracks' in botAudioRef.current.srcObject
-        ) {
-          botAudioRef.current.srcObject
-            .getAudioTracks()
-            .forEach((track) => track.stop())
-          botAudioRef.current.srcObject = null
-        }
-
-        updateStatus('Disconnected')
-        setIsConnected(false)
-        setActiveSpeaker(null)
-        setSessionEndedAt(new Date().toISOString())
-        log('Disconnected successfully', 'system')
-      } catch (error: any) {
-        const errorMessage = error?.message || 'Failed to disconnect'
-        setError(errorMessage)
-        log(`Error disconnecting: ${errorMessage}`, 'system')
-      } finally {
-        // Ensure state is reset even if disconnect fails
-        pcClientRef.current = null
-        stopRecording()
-        setIsConnected(false)
-        setIsConnecting(false)
-      }
-    }
-  }
-
-  // Get agent name for display - use selectedAgent if available, otherwise use generic
-  const agentName = agentDisplayName || selectedAgent?.name || 'Voice Agent'
-  const agentDescription = selectedAgent?.description || 'Interact with a real-time voice AI agent'
-
-  if (compact) {
-    const transcriptPanel = (
-      <div className={`${sidebarLayout ? 'flex-1 min-h-0' : 'max-h-48'} overflow-y-auto space-y-2 ${sidebarLayout ? 'pb-2' : 'border-t border-gray-200 pt-3'}`}>
-        {transcriptEntries.length === 0 ? (
-          <p className="text-xs text-gray-400 text-center py-4">
-            {isConnected ? 'Listening… transcript will appear here.' : 'Start a call to see the conversation.'}
-          </p>
-        ) : (
-          transcriptEntries.map((entry, idx) => (
-            <div
-              key={idx}
-              className={`text-xs rounded-lg px-2 py-1.5 ${
-                entry.role === 'user' ? 'bg-blue-50 text-blue-900 ml-2' : 'bg-gray-100 text-gray-800 mr-2'
-              }`}
-            >
-              <span className="font-semibold opacity-60">{entry.role === 'user' ? 'You' : 'Agent'}: </span>
-              {entry.content}
-            </div>
-          ))
-        )}
-      </div>
-    )
-
-    const controlsPanel = (
-      <div className={`flex flex-col items-center ${sidebarLayout ? 'shrink-0 py-4 border-t border-gray-200 bg-gray-50' : 'py-6'}`}>
-        <VoiceOrb
-          variant="green"
-          size={sidebarLayout ? 'sm' : 'md'}
-          connected={isConnected}
-          connecting={isConnecting}
-          activeSpeaker={activeSpeaker}
-          className={sidebarLayout ? 'mb-3' : 'mb-5'}
-        />
-        <p className="text-sm font-medium text-gray-900 mb-0.5">{agentName}</p>
-        <p className="text-xs text-gray-500 mb-3">
-          {activeSpeaker === 'user'
-            ? 'You are speaking…'
-            : activeSpeaker === 'agent'
-              ? 'Agent is speaking…'
-              : isConnected
-                ? 'Connected'
-                : status}
-        </p>
-        {!isConnected ? (
-          <Button
-            onClick={connect}
-            isLoading={isConnecting}
-            leftIcon={isConnecting ? <Loader className="h-4 w-4 animate-spin" /> : <Mic className="h-4 w-4" />}
-            disabled={isConnecting}
-          >
-            Start call
-          </Button>
-        ) : (
-          <Button onClick={disconnect} variant="danger" leftIcon={<MicOff className="h-4 w-4" />}>
-            End call
-          </Button>
-        )}
-      </div>
-    )
-
-    return (
-      <div className={`flex flex-col ${sidebarLayout ? 'h-full min-h-0' : 'min-h-[420px]'}`}>
-        {error && (
-          <div className="bg-red-50 border border-red-200 rounded-lg p-3 mb-3 text-sm text-red-800 shrink-0">{error}</div>
-        )}
-        {sidebarLayout ? (
-          <>
-            <div className="px-1 pt-1 pb-2 text-xs font-medium text-gray-500 uppercase tracking-wide shrink-0">Transcript</div>
-            {transcriptPanel}
-            {controlsPanel}
-          </>
-        ) : (
-          <>
-            {controlsPanel}
-            {transcriptEntries.length > 0 && transcriptPanel}
-          </>
-        )}
-      </div>
-    )
-  }
-
-  return (
-    <div className="space-y-6">
-      <div className="bg-white shadow rounded-lg p-6">
-        <div className="flex items-center justify-between mb-4">
-          <div>
-            <h2 className="text-lg font-semibold text-gray-900">{agentName}</h2>
-            {customEndpoint && (
-              <p className="text-xs text-gray-500 mt-1">
-                {customEndpointLabel || 'WebSocket URL'}: <span className="font-mono">{customEndpoint}</span>
-              </p>
-            )}
-            {selectedAgent?.description ? (
-              <div className="prose prose-sm max-w-none prose-headings:text-gray-900 prose-p:text-gray-700 prose-code:text-gray-800 prose-code:bg-gray-100 prose-code:px-1 prose-code:py-0.5 prose-code:rounded prose-pre:bg-gray-900 prose-pre:text-gray-100 prose-ul:text-gray-700 prose-ol:text-gray-700 mt-2 max-h-48 overflow-y-auto pr-1">
-                <ReactMarkdown>{agentDescription}</ReactMarkdown>
-              </div>
-            ) : (
-              <p className="text-sm text-gray-600 mt-1">{agentDescription}</p>
-            )}
-          </div>
-        </div>
-
-        {s3Status && !s3Status.enabled && (
-          <div className="flex items-start gap-3 bg-amber-50 border border-amber-200 rounded-lg p-4 mb-4">
-            <AlertTriangle className="h-5 w-5 text-amber-500 flex-shrink-0 mt-0.5" />
-            <div>
-              <p className="text-sm font-medium text-amber-800">Storage not configured</p>
-              <p className="text-xs text-amber-700 mt-0.5">
-                Audio recordings will not be saved. Transcripts and analysis will still be captured from the live conversation.
-              </p>
-            </div>
-          </div>
-        )}
-
-        {error && (
-          <div className="bg-red-50 border border-red-200 rounded-lg p-4 mb-4">
-            <p className="text-sm text-red-800">{error}</p>
-          </div>
-        )}
-
-        <div className="flex items-center justify-between mb-4">
-          <div className="flex items-center gap-4">
-            <div className="flex items-center gap-2">
-              {isConnected ? (
-                <div className="flex items-center gap-2">
-                  <div className="w-3 h-3 bg-green-500 rounded-full animate-pulse"></div>
-                  <span className="text-sm font-medium text-gray-700">Connected</span>
-                </div>
-              ) : (
-                <span className="text-sm text-gray-500">{status}</span>
-              )}
-            </div>
-          </div>
-          <div className="flex gap-2">
-            {!isConnected ? (
-              <Button
-                onClick={connect}
-                isLoading={isConnecting}
-                leftIcon={isConnecting ? <Loader className="h-4 w-4 animate-spin" /> : <Mic className="h-4 w-4" />}
-                disabled={isConnecting}
-              >
-                Connect
-              </Button>
-            ) : (
-              <Button
-                onClick={disconnect}
-                variant="danger"
-                leftIcon={<MicOff className="h-4 w-4" />}
-              >
-                Disconnect
-              </Button>
-            )}
-          </div>
-        </div>
-
-        {/* Debug Log */}
-        <div className="bg-gray-50 rounded-lg p-4">
-          <div className="flex items-center gap-2 mb-2">
-            <MessageSquare className="h-4 w-4 text-gray-500" />
-            <h3 className="text-sm font-semibold text-gray-900">Debug Log</h3>
-          </div>
-          <div className="h-64 overflow-y-auto bg-white rounded border border-gray-200 p-3 font-mono text-xs">
-            {logs.length === 0 ? (
-              <p className="text-gray-500 text-center py-8">No logs yet. Connect to start.</p>
-            ) : (
-              logs.map((log, idx) => (
-                <div
-                  key={idx}
-                  className={`mb-1 ${log.type === 'user'
-                      ? 'text-blue-600'
-                      : log.type === 'bot'
-                        ? 'text-green-600'
-                        : 'text-gray-600'
-                    }`}
-                >
-                  <span className="text-gray-400">
-                    {new Date(log.timestamp).toLocaleTimeString()}
-                  </span>{' '}
-                  - {log.message}
-                </div>
-              ))
-            )}
-          </div>
-        </div>
-
-        {customEndpoint && !isConnected && transcriptEntries.length > 0 && (
-          <div className="mt-4 rounded-lg border border-gray-200 p-4">
-            <h3 className="text-sm font-semibold text-gray-900">Save Test Session</h3>
-            <p className="mt-1 text-xs text-gray-600">
-              Save transcript{recordedBlob ? ' + recording' : ''} so you can evaluate this call later.
-            </p>
-            <div className="mt-3 flex flex-wrap gap-2">
-              <Button onClick={handleSaveSession} isLoading={isSavingSession} disabled={isSavingSession}>
-                {savedCallShortId ? 'Saved' : 'Save Session'}
-              </Button>
-              {savedCallShortId && (
-                <Button
-                  variant="outline"
-                  onClick={handleEvaluateSavedSession}
-                  isLoading={isEvaluatingSavedSession}
-                  disabled={isEvaluatingSavedSession}
-                >
-                  Run Evaluation
-                </Button>
-              )}
-            </div>
-            {savedCallShortId && (
-              <p className="mt-2 text-xs text-green-700">
-                Saved as call ID <span className="font-mono font-semibold">{savedCallShortId}</span>
-              </p>
-            )}
-          </div>
-        )}
-      </div>
-    </div>
-  )
-}
-
+/**
+ * Voice Agent Component
+ * 
+ * This component connects to a Pipecat voice agent using WebSocket.
+ * It uses the Pipecat client library to handle real-time voice interactions.
+ */
+
+import { useState, useRef, useEffect } from 'react'
+import { PipecatClient, PipecatClientOptions, RTVIEvent } from '@pipecat-ai/client-js'
+import { WebSocketTransport } from '@pipecat-ai/websocket-transport'
+import ReactMarkdown from 'react-markdown'
+import { Mic, MicOff, Loader, MessageSquare, AlertTriangle } from 'lucide-react'
+import { useQuery } from '@tanstack/react-query'
+import Button from './Button'
+import VoiceOrb, { VoiceOrbSpeaker } from './VoiceOrb'
+import { useAgentStore } from '../store/agentStore'
+import { apiClient, apiBaseUrl } from '../lib/api'
+
+interface VoiceAgentProps {
+  personaId?: string
+  scenarioId?: string
+  agentId?: string
+  customEndpoint?: string
+  customEndpointLabel?: string
+  billingSurface?: 'agents_talk' | 'agent_playground'
+  onSessionSaved?: () => void
+  compact?: boolean
+  sidebarLayout?: boolean
+  agentDisplayName?: string
+}
+
+type TranscriptEntry = { role: 'user' | 'agent'; content: string; timestamp: string }
+
+export default function VoiceAgent({ personaId, scenarioId, agentId, customEndpoint, customEndpointLabel, billingSurface, onSessionSaved, compact = false, sidebarLayout = false, agentDisplayName }: VoiceAgentProps) {
+  const { selectedAgent } = useAgentStore()
+  
+  // Use agentId prop if provided, otherwise fall back to selectedAgent from store
+  const effectiveAgentId = agentId || selectedAgent?.id
+  const [isConnected, setIsConnected] = useState(false)
+  const [isConnecting, setIsConnecting] = useState(false)
+  const [status, setStatus] = useState<string>('Disconnected')
+  const [error, setError] = useState<string | null>(null)
+  const [logs, setLogs] = useState<Array<{ timestamp: string; message: string; type: 'user' | 'bot' | 'system' }>>([])
+  const [transcriptEntries, setTranscriptEntries] = useState<TranscriptEntry[]>([])
+  const [sessionStartedAt, setSessionStartedAt] = useState<string | null>(null)
+  const [sessionEndedAt, setSessionEndedAt] = useState<string | null>(null)
+  const [recordedBlob, setRecordedBlob] = useState<Blob | null>(null)
+  const [isSavingSession, setIsSavingSession] = useState(false)
+  const [savedCallShortId, setSavedCallShortId] = useState<string | null>(null)
+  const [isEvaluatingSavedSession, setIsEvaluatingSavedSession] = useState(false)
+  const [activeSpeaker, setActiveSpeaker] = useState<VoiceOrbSpeaker>(null)
+  const formatErrorMessage = (rawError: any) => {
+    if (!rawError) return 'Unknown error'
+    if (typeof rawError === 'string') return rawError
+    if (typeof rawError?.message === 'string') return rawError.message
+    if (typeof rawError?.data?.message === 'string') return rawError.data.message
+    if (typeof rawError?.data?.detail === 'string') return rawError.data.detail
+    return JSON.stringify(rawError)
+  }
+
+  const getCustomEndpointCompatibilityError = (endpoint: string) => {
+    try {
+      const parsed = new URL(endpoint)
+      const host = parsed.hostname.toLowerCase()
+      const path = parsed.pathname.toLowerCase()
+
+      // This component uses Pipecat's RTVI websocket protocol.
+      // Raw provider websocket URLs (e.g. ElevenLabs ConvAI) are not RTVI-compatible.
+      if (host.includes('api.elevenlabs.io') && path.includes('/v1/convai/conversation')) {
+        return 'This is a native ElevenLabs websocket URL, not an RTVI websocket endpoint. Use the Voice AI Agent flow for ElevenLabs, or connect through an RTVI-compatible bridge endpoint.'
+      }
+    } catch {
+      return null
+    }
+
+    return null
+  }
+
+
+  const { data: s3Status } = useQuery({
+    queryKey: ['s3-status'],
+    queryFn: () => apiClient.getS3Status(),
+    staleTime: 60_000,
+  })
+
+  const pcClientRef = useRef<PipecatClient | null>(null)
+  const botAudioRef = useRef<HTMLAudioElement | null>(null)
+  const isConnectingRef = useRef(false) // Guard to prevent multiple simultaneous connections
+  const micStreamRef = useRef<MediaStream | null>(null)
+  const mediaRecorderRef = useRef<MediaRecorder | null>(null)
+  const recordedChunksRef = useRef<Blob[]>([])
+  const traceSessionRef = useRef<string | null>(null)
+
+  const appendTranscript = (entry: TranscriptEntry) => {
+    setTranscriptEntries((prev) => [...prev, entry])
+  }
+
+  const startRecording = (botTrack?: MediaStreamTrack) => {
+    if (mediaRecorderRef.current || typeof MediaRecorder === 'undefined') return
+
+    const tracks: MediaStreamTrack[] = []
+    if (botTrack) {
+      tracks.push(botTrack)
+    } else if (botAudioRef.current?.srcObject && 'getAudioTracks' in botAudioRef.current.srcObject) {
+      tracks.push(...botAudioRef.current.srcObject.getAudioTracks())
+    }
+
+    if (micStreamRef.current) {
+      tracks.push(...micStreamRef.current.getAudioTracks())
+    }
+
+    if (tracks.length === 0) return
+
+    try {
+      const stream = new MediaStream(tracks)
+      const mimeType = MediaRecorder.isTypeSupported('audio/webm;codecs=opus')
+        ? 'audio/webm;codecs=opus'
+        : 'audio/webm'
+      const recorder = new MediaRecorder(stream, { mimeType })
+      recordedChunksRef.current = []
+      recorder.ondataavailable = (event: BlobEvent) => {
+        if (event.data.size > 0) {
+          recordedChunksRef.current.push(event.data)
+        }
+      }
+      recorder.onstop = () => {
+        if (recordedChunksRef.current.length > 0) {
+          setRecordedBlob(new Blob(recordedChunksRef.current, { type: recorder.mimeType || 'audio/webm' }))
+        }
+      }
+      recorder.start(1000)
+      mediaRecorderRef.current = recorder
+      log('Recording started', 'system')
+    } catch (recorderError: any) {
+      log(`Failed to start recording: ${recorderError?.message || String(recorderError)}`, 'system')
+    }
+  }
+
+  const stopRecording = () => {
+    if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {
+      mediaRecorderRef.current.stop()
+    }
+    mediaRecorderRef.current = null
+  }
+
+  const handleSaveSession = async () => {
+    if (!effectiveAgentId || !customEndpoint || transcriptEntries.length === 0) return
+
+    try {
+      setIsSavingSession(true)
+      setError(null)
+      const audioFile = recordedBlob
+        ? new File([recordedBlob], `custom-websocket-${Date.now()}.webm`, { type: recordedBlob.type || 'audio/webm' })
+        : undefined
+      const response = await apiClient.createCustomWebsocketSession({
+        agent_id: effectiveAgentId,
+        websocket_url: customEndpoint,
+        transcript_entries: transcriptEntries,
+        started_at: sessionStartedAt || undefined,
+        ended_at: sessionEndedAt || new Date().toISOString(),
+        audio_file: audioFile,
+      })
+      setSavedCallShortId(response.call_short_id)
+      log(`Session saved as call ${response.call_short_id}`, 'system')
+      onSessionSaved?.()
+    } catch (saveError: any) {
+      const errorMessage = saveError?.response?.data?.detail || saveError?.message || 'Failed to save session'
+      setError(errorMessage)
+      log(`Save failed: ${errorMessage}`, 'system')
+    } finally {
+      setIsSavingSession(false)
+    }
+  }
+
+  const handleEvaluateSavedSession = async () => {
+    if (!savedCallShortId) return
+    try {
+      setIsEvaluatingSavedSession(true)
+      await apiClient.evaluateCustomWebsocketSession(savedCallShortId)
+      log(`Evaluation queued for ${savedCallShortId}`, 'system')
+      onSessionSaved?.()
+    } catch (evalError: any) {
+      const errorMessage = evalError?.response?.data?.detail || evalError?.message || 'Failed to queue evaluation'
+      setError(errorMessage)
+      log(`Evaluation failed: ${errorMessage}`, 'system')
+    } finally {
+      setIsEvaluatingSavedSession(false)
+    }
+  }
+
+  useEffect(() => {
+    // Create audio element for bot audio playback
+    const audio = document.createElement('audio')
+    audio.autoplay = true
+    botAudioRef.current = audio
+    document.body.appendChild(audio)
+
+    return () => {
+      // Cleanup on unmount
+      if (pcClientRef.current) {
+        stopRecording()
+        pcClientRef.current.disconnect().catch(console.error)
+      }
+      if (botAudioRef.current) {
+        botAudioRef.current.remove()
+      }
+      if (micStreamRef.current) {
+        micStreamRef.current.getTracks().forEach((track) => track.stop())
+        micStreamRef.current = null
+      }
+    }
+  }, [])
+
+  const log = (message: string, type: 'user' | 'bot' | 'system' = 'system') => {
+    const entry = {
+      timestamp: new Date().toISOString(),
+      message,
+      type,
+    }
+    setLogs((prev) => [...prev, entry])
+    console.log(`[${type.toUpperCase()}] ${message}`)
+  }
+
+  const updateStatus = (newStatus: string) => {
+    setStatus(newStatus)
+    log(`Status: ${newStatus}`, 'system')
+  }
+
+  const setupMediaTracks = () => {
+    if (!pcClientRef.current) return
+
+    const tracks = pcClientRef.current.tracks()
+    if (tracks.bot?.audio && botAudioRef.current) {
+      setupAudioTrack(tracks.bot.audio)
+    }
+  }
+
+  const setupTrackListeners = () => {
+    if (!pcClientRef.current) return
+
+    // Listen for new tracks starting
+    pcClientRef.current.on(RTVIEvent.TrackStarted, (track, participant) => {
+      // Only handle non-local (bot) tracks
+      if (!participant?.local && track.kind === 'audio' && botAudioRef.current) {
+        setupAudioTrack(track)
+      }
+    })
+
+    // Listen for tracks stopping
+    pcClientRef.current.on(RTVIEvent.TrackStopped, (track, participant) => {
+      log(
+        `Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`,
+        'system'
+      )
+    })
+  }
+
+  const setupAudioTrack = (track: MediaStreamTrack) => {
+    if (!botAudioRef.current) return
+
+    log('Setting up audio track', 'system')
+
+    if (
+      botAudioRef.current.srcObject &&
+      'getAudioTracks' in botAudioRef.current.srcObject
+    ) {
+      const oldTrack = botAudioRef.current.srcObject.getAudioTracks()[0]
+      if (oldTrack?.id === track.id) return
+    }
+
+    botAudioRef.current.srcObject = new MediaStream([track])
+    startRecording(track)
+  }
+
+  const connect = async () => {
+    // Prevent multiple simultaneous connection attempts
+    if (isConnectingRef.current) {
+      log('Connection already in progress, ignoring duplicate request', 'system')
+      return
+    }
+
+    if (pcClientRef.current) {
+      log('Client already exists, disconnecting first...', 'system')
+      try {
+        await pcClientRef.current.disconnect()
+      } catch (e) {
+        // Ignore disconnect errors
+      }
+      pcClientRef.current = null
+    }
+
+    try {
+      isConnectingRef.current = true
+      setIsConnecting(true)
+      setError(null)
+      updateStatus('Connecting...')
+
+      if (customEndpoint) {
+        const compatibilityError = getCustomEndpointCompatibilityError(customEndpoint)
+        if (compatibilityError) {
+          setError(compatibilityError)
+          log(`Error: ${compatibilityError}`, 'system')
+          updateStatus('Error')
+          setIsConnecting(false)
+          setIsConnected(false)
+          return
+        }
+      }
+
+      // Resolve credentials. The backend `/connect` and websocket endpoints
+      // accept either a Bearer access token (email/password / SSO login) or
+      // an API key (legacy / machine access). We pass whichever the user has.
+      const accessToken = localStorage.getItem('accessToken')
+      const apiKey = localStorage.getItem('apiKey')
+      if (!accessToken && !apiKey) {
+        throw new Error('Not authenticated. Please log in first.')
+      }
+
+      if (!customEndpoint) {
+        // Cookies are set as a fallback - we also pass the credential as a
+        // header below, which is more reliable in cross-origin dev setups.
+        if (accessToken) {
+          document.cookie = `access_token=${accessToken}; path=/; SameSite=Lax`
+        }
+        if (apiKey) {
+          document.cookie = `api_key=${apiKey}; path=/; SameSite=Lax`
+        }
+        log('Auth credentials set for /connect', 'system')
+      } else {
+        log('Using custom endpoint, skipping backend API cookie flow', 'system')
+      }
+      setSavedCallShortId(null)
+      setRecordedBlob(null)
+      setTranscriptEntries([])
+      setSessionStartedAt(new Date().toISOString())
+      setSessionEndedAt(null)
+      traceSessionRef.current = null
+
+      let traceCallShortId: string | null = null
+      if (!customEndpoint && (accessToken || apiKey)) {
+        try {
+          const session = await apiClient.createSyntheticTraceSession({
+            transport: 'websocket',
+            ...(effectiveAgentId ? { agent_id: effectiveAgentId } : {}),
+          })
+          traceCallShortId = session.call_short_id
+          traceSessionRef.current = session.call_short_id
+          log(`Trace session ${session.call_short_id}`, 'system')
+        } catch (sessionErr: any) {
+          const msg =
+            sessionErr?.response?.data?.detail || sessionErr?.message || 'trace session failed'
+          log(`Trace session skipped: ${msg}`, 'system')
+        }
+      }
+
+      try {
+        micStreamRef.current = await navigator.mediaDevices.getUserMedia({ audio: true })
+      } catch {
+        micStreamRef.current = null
+        log('Microphone stream unavailable for local recording', 'system')
+      }
+
+      // Create WebSocketTransport
+      // Note: WebSocketTransport might need endpoint URL for RTVI protocol initialization
+      // But the constructor doesn't accept parameters, so we'll set it after creation
+      const transport = new WebSocketTransport()
+
+      const PipecatConfig: PipecatClientOptions = {
+        transport: transport,
+        enableMic: true,
+        enableCam: false,
+        callbacks: {
+          onConnected: () => {
+            updateStatus('Connected')
+            setIsConnected(true)
+            setIsConnecting(false)
+            log('✅ Successfully connected to WebSocket', 'system')
+          },
+          onDisconnected: () => {
+            updateStatus('Disconnected')
+            setIsConnected(false)
+            setIsConnecting(false)
+            setActiveSpeaker(null)
+            setSessionEndedAt(new Date().toISOString())
+            stopRecording()
+            log('Client disconnected', 'system')
+            const traceId = traceSessionRef.current
+            if (traceId) {
+              traceSessionRef.current = null
+              apiClient.closeSyntheticTraceSession(traceId).catch(() => undefined)
+            }
+          },
+          onBotReady: (data) => {
+            log(`Bot ready: ${JSON.stringify(data)}`, 'system')
+            setupMediaTracks()
+          },
+          onUserTranscript: (data) => {
+            if (data.final) {
+              log(`User: ${data.text}`, 'user')
+              appendTranscript({ role: 'user', content: data.text, timestamp: new Date().toISOString() })
+            }
+          },
+          onBotTranscript: (data) => {
+            log(`Bot: ${data.text}`, 'bot')
+            appendTranscript({ role: 'agent', content: data.text, timestamp: new Date().toISOString() })
+          },
+          onUserStartedSpeaking: () => setActiveSpeaker('user'),
+          onUserStoppedSpeaking: () => setActiveSpeaker((prev) => (prev === 'user' ? null : prev)),
+          onBotStartedSpeaking: () => setActiveSpeaker('agent'),
+          onBotStoppedSpeaking: () => setActiveSpeaker((prev) => (prev === 'agent' ? null : prev)),
+          onMessageError: (error: any) => {
+            console.error('Message error:', error)
+            const errorMessage = formatErrorMessage(error)
+            log(`Error: ${errorMessage}`, 'system')
+          },
+          onError: (error: any) => {
+            console.error('Error:', error)
+            const errorMessage = formatErrorMessage(error)
+            setError(errorMessage)
+            log(`Error: ${errorMessage}`, 'system')
+          },
+        },
+      }
+
+      const pcClient = new PipecatClient(PipecatConfig)
+      pcClientRef.current = pcClient
+
+        // Expose for debugging
+        ; (window as any).pcClient = pcClient
+
+      setupTrackListeners()
+
+      log('Initializing devices...', 'system')
+      await pcClient.initDevices()
+
+      // Use startBotAndConnect exactly like the Pipecat example
+      // This handles RTVI protocol handshake automatically
+      let endpointUrl = customEndpoint || `${apiBaseUrl}/api/v1/voice-agent/connect`
+
+      // Append agent_id, persona_id and scenario_id if present
+      const params = new URLSearchParams()
+      if (!customEndpoint && effectiveAgentId) {
+        params.append('agent_id', effectiveAgentId)
+      }
+      if (!customEndpoint && personaId) params.append('persona_id', personaId)
+      if (!customEndpoint && scenarioId) params.append('scenario_id', scenarioId)
+      if (!customEndpoint && billingSurface) params.append('ui_surface', billingSurface)
+      if (!customEndpoint && traceCallShortId) params.append('call_short_id', traceCallShortId)
+
+      if (!customEndpoint && params.toString()) {
+        endpointUrl += `?${params.toString()}`
+      }
+
+      log(`Connecting to bot endpoint: ${endpointUrl}`, 'system')
+      const endpointProtocol = endpointUrl.startsWith('wss://') || endpointUrl.startsWith('ws://')
+        ? 'websocket'
+        : 'http'
+
+      if (endpointProtocol === 'websocket') {
+        log('Using direct connect() for websocket URL...', 'system')
+        await pcClient.connect({ wsUrl: endpointUrl })
+        log('✅ WebSocket transport connected', 'system')
+      } else {
+        log('Using startBotAndConnect() - this will handle RTVI protocol handshake...', 'system')
+        // Pass credentials via headers in addition to cookies so /connect
+        // works even when the API is on a different origin (e.g. dev mode
+        // with frontend on :3000 and API on :8000 where cookies aren't shared).
+        const authHeaders = new Headers()
+        if (!customEndpoint) {
+          if (accessToken) {
+            authHeaders.set('Authorization', `Bearer ${accessToken}`)
+          }
+          if (apiKey) {
+            authHeaders.set('X-API-Key', apiKey)
+          }
+        }
+        await pcClient.startBotAndConnect({
+          endpoint: endpointUrl,
+          headers: authHeaders,
+        })
+        log('✅ Connection established and RTVI handshake complete!', 'system')
+      }
+    } catch (error: any) {
+      const errorMessage = formatErrorMessage(error) || 'Failed to connect'
+      setError(errorMessage)
+      log(`Error connecting: ${errorMessage}`, 'system')
+      updateStatus('Error')
+      setIsConnecting(false)
+      setIsConnected(false)
+      setActiveSpeaker(null)
+
+      // Clean up if there's an error
+      if (pcClientRef.current) {
+        try {
+          await pcClientRef.current.disconnect()
+        } catch (disconnectError: any) {
+          // Ignore disconnect errors - they're expected if connection failed
+          log(`Error during disconnect (expected if connection failed): ${disconnectError?.message || String(disconnectError)}`, 'system')
+        } finally {
+          pcClientRef.current = null
+        }
+      }
+    } finally {
+      // Always reset the connection guard
+      isConnectingRef.current = false
+    }
+  }
+
+  const disconnect = async () => {
+    if (pcClientRef.current) {
+      try {
+        stopRecording()
+        await pcClientRef.current.disconnect()
+        pcClientRef.current = null
+
+        if (
+          botAudioRef.current?.srcObject &&
+          'getAudioTracks' in botAudioRef.current.srcObject
+        ) {
+          botAudioRef.current.srcObject
+            .getAudioTracks()
+            .forEach((track) => track.stop())
+          botAudioRef.current.srcObject = null
+        }
+
+        updateStatus('Disconnected')
+        setIsConnected(false)
+        setActiveSpeaker(null)
+        setSessionEndedAt(new Date().toISOString())
+        log('Disconnected successfully', 'system')
+      } catch (error: any) {
+        const errorMessage = error?.message || 'Failed to disconnect'
+        setError(errorMessage)
+        log(`Error disconnecting: ${errorMessage}`, 'system')
+      } finally {
+        // Ensure state is reset even if disconnect fails
+        pcClientRef.current = null
+        stopRecording()
+        setIsConnected(false)
+        setIsConnecting(false)
+      }
+    }
+  }
+
+  // Get agent name for display - use selectedAgent if available, otherwise use generic
+  const agentName = agentDisplayName || selectedAgent?.name || 'Voice Agent'
+  const agentDescription = selectedAgent?.description || 'Interact with a real-time voice AI agent'
+
+  if (compact) {
+    const transcriptPanel = (
+      <div className={`${sidebarLayout ? 'flex-1 min-h-0' : 'max-h-48'} overflow-y-auto space-y-2 ${sidebarLayout ? 'pb-2' : 'border-t border-gray-200 pt-3'}`}>
+        {transcriptEntries.length === 0 ? (
+          <p className="text-xs text-gray-400 text-center py-4">
+            {isConnected ? 'Listening… transcript will appear here.' : 'Start a call to see the conversation.'}
+          </p>
+        ) : (
+          transcriptEntries.map((entry, idx) => (
+            <div
+              key={idx}
+              className={`text-xs rounded-lg px-2 py-1.5 ${
+                entry.role === 'user' ? 'bg-blue-50 text-blue-900 ml-2' : 'bg-gray-100 text-gray-800 mr-2'
+              }`}
+            >
+              <span className="font-semibold opacity-60">{entry.role === 'user' ? 'You' : 'Agent'}: </span>
+              {entry.content}
+            </div>
+          ))
+        )}
+      </div>
+    )
+
+    const controlsPanel = (
+      <div className={`flex flex-col items-center ${sidebarLayout ? 'shrink-0 py-4 border-t border-gray-200 bg-gray-50' : 'py-6'}`}>
+        <VoiceOrb
+          variant="green"
+          size={sidebarLayout ? 'sm' : 'md'}
+          connected={isConnected}
+          connecting={isConnecting}
+          activeSpeaker={activeSpeaker}
+          className={sidebarLayout ? 'mb-3' : 'mb-5'}
+        />
+        <p className="text-sm font-medium text-gray-900 mb-0.5">{agentName}</p>
+        <p className="text-xs text-gray-500 mb-3">
+          {activeSpeaker === 'user'
+            ? 'You are speaking…'
+            : activeSpeaker === 'agent'
+              ? 'Agent is speaking…'
+              : isConnected
+                ? 'Connected'
+                : status}
+        </p>
+        {!isConnected ? (
+          <Button
+            onClick={connect}
+            isLoading={isConnecting}
+            leftIcon={isConnecting ? <Loader className="h-4 w-4 animate-spin" /> : <Mic className="h-4 w-4" />}
+            disabled={isConnecting}
+          >
+            Start call
+          </Button>
+        ) : (
+          <Button onClick={disconnect} variant="danger" leftIcon={<MicOff className="h-4 w-4" />}>
+            End call
+          </Button>
+        )}
+      </div>
+    )
+
+    return (
+      <div className={`flex flex-col ${sidebarLayout ? 'h-full min-h-0' : 'min-h-[420px]'}`}>
+        {error && (
+          <div className="bg-red-50 border border-red-200 rounded-lg p-3 mb-3 text-sm text-red-800 shrink-0">{error}</div>
+        )}
+        {sidebarLayout ? (
+          <>
+            <div className="px-1 pt-1 pb-2 text-xs font-medium text-gray-500 uppercase tracking-wide shrink-0">Transcript</div>
+            {transcriptPanel}
+            {controlsPanel}
+          </>
+        ) : (
+          <>
+            {controlsPanel}
+            {transcriptEntries.length > 0 && transcriptPanel}
+          </>
+        )}
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-6">
+      <div className="bg-white shadow rounded-lg p-6">
+        <div className="flex items-center justify-between mb-4">
+          <div>
+            <h2 className="text-lg font-semibold text-gray-900">{agentName}</h2>
+            {customEndpoint && (
+              <p className="text-xs text-gray-500 mt-1">
+                {customEndpointLabel || 'WebSocket URL'}: <span className="font-mono">{customEndpoint}</span>
+              </p>
+            )}
+            {selectedAgent?.description ? (
+              <div className="prose prose-sm max-w-none prose-headings:text-gray-900 prose-p:text-gray-700 prose-code:text-gray-800 prose-code:bg-gray-100 prose-code:px-1 prose-code:py-0.5 prose-code:rounded prose-pre:bg-gray-900 prose-pre:text-gray-100 prose-ul:text-gray-700 prose-ol:text-gray-700 mt-2 max-h-48 overflow-y-auto pr-1">
+                <ReactMarkdown>{agentDescription}</ReactMarkdown>
+              </div>
+            ) : (
+              <p className="text-sm text-gray-600 mt-1">{agentDescription}</p>
+            )}
+          </div>
+        </div>
+
+        {s3Status && !s3Status.enabled && (
+          <div className="flex items-start gap-3 bg-amber-50 border border-amber-200 rounded-lg p-4 mb-4">
+            <AlertTriangle className="h-5 w-5 text-amber-500 flex-shrink-0 mt-0.5" />
+            <div>
+              <p className="text-sm font-medium text-amber-800">Storage not configured</p>
+              <p className="text-xs text-amber-700 mt-0.5">
+                Audio recordings will not be saved. Transcripts and analysis will still be captured from the live conversation.
+              </p>
+            </div>
+          </div>
+        )}
+
+        {error && (
+          <div className="bg-red-50 border border-red-200 rounded-lg p-4 mb-4">
+            <p className="text-sm text-red-800">{error}</p>
+          </div>
+        )}
+
+        <div className="flex items-center justify-between mb-4">
+          <div className="flex items-center gap-4">
+            <div className="flex items-center gap-2">
+              {isConnected ? (
+                <div className="flex items-center gap-2">
+                  <div className="w-3 h-3 bg-green-500 rounded-full animate-pulse"></div>
+                  <span className="text-sm font-medium text-gray-700">Connected</span>
+                </div>
+              ) : (
+                <span className="text-sm text-gray-500">{status}</span>
+              )}
+            </div>
+          </div>
+          <div className="flex gap-2">
+            {!isConnected ? (
+              <Button
+                onClick={connect}
+                isLoading={isConnecting}
+                leftIcon={isConnecting ? <Loader className="h-4 w-4 animate-spin" /> : <Mic className="h-4 w-4" />}
+                disabled={isConnecting}
+              >
+                Connect
+              </Button>
+            ) : (
+              <Button
+                onClick={disconnect}
+                variant="danger"
+                leftIcon={<MicOff className="h-4 w-4" />}
+              >
+                Disconnect
+              </Button>
+            )}
+          </div>
+        </div>
+
+        {/* Debug Log */}
+        <div className="bg-gray-50 rounded-lg p-4">
+          <div className="flex items-center gap-2 mb-2">
+            <MessageSquare className="h-4 w-4 text-gray-500" />
+            <h3 className="text-sm font-semibold text-gray-900">Debug Log</h3>
+          </div>
+          <div className="h-64 overflow-y-auto bg-white rounded border border-gray-200 p-3 font-mono text-xs">
+            {logs.length === 0 ? (
+              <p className="text-gray-500 text-center py-8">No logs yet. Connect to start.</p>
+            ) : (
+              logs.map((log, idx) => (
+                <div
+                  key={idx}
+                  className={`mb-1 ${log.type === 'user'
+                      ? 'text-blue-600'
+                      : log.type === 'bot'
+                        ? 'text-green-600'
+                        : 'text-gray-600'
+                    }`}
+                >
+                  <span className="text-gray-400">
+                    {new Date(log.timestamp).toLocaleTimeString()}
+                  </span>{' '}
+                  - {log.message}
+                </div>
+              ))
+            )}
+          </div>
+        </div>
+
+        {customEndpoint && !isConnected && transcriptEntries.length > 0 && (
+          <div className="mt-4 rounded-lg border border-gray-200 p-4">
+            <h3 className="text-sm font-semibold text-gray-900">Save Test Session</h3>
+            <p className="mt-1 text-xs text-gray-600">
+              Save transcript{recordedBlob ? ' + recording' : ''} so you can evaluate this call later.
+            </p>
+            <div className="mt-3 flex flex-wrap gap-2">
+              <Button onClick={handleSaveSession} isLoading={isSavingSession} disabled={isSavingSession}>
+                {savedCallShortId ? 'Saved' : 'Save Session'}
+              </Button>
+              {savedCallShortId && (
+                <Button
+                  variant="outline"
+                  onClick={handleEvaluateSavedSession}
+                  isLoading={isEvaluatingSavedSession}
+                  disabled={isEvaluatingSavedSession}
+                >
+                  Run Evaluation
+                </Button>
+              )}
+            </div>
+            {savedCallShortId && (
+              <p className="mt-2 text-xs text-green-700">
+                Saved as call ID <span className="font-mono font-semibold">{savedCallShortId}</span>
+              </p>
+            )}
+          </div>
+        )}
+      </div>
+    </div>
+  )
+}
+
diff --git a/frontend/src/components/call-recordings/CallEventTimeline.tsx b/frontend/src/components/call-recordings/CallEventTimeline.tsx
new file mode 100644
index 00000000..b69b47e8
--- /dev/null
+++ b/frontend/src/components/call-recordings/CallEventTimeline.tsx
@@ -0,0 +1,158 @@
+import { useMemo, useState } from 'react'
+import { ExternalLink, Search } from 'lucide-react'
+import {
+  CallTimelineEvent,
+  timelineCategoryLabel,
+  TimelineCategory,
+} from './callTimelineUtils'
+
+const CATEGORY_STYLES: Record<TimelineCategory, string> = {
+  call: 'bg-gray-100 text-gray-800 border-gray-200',
+  stt: 'bg-purple-50 text-purple-800 border-purple-200',
+  llm: 'bg-primary-50 text-primary-900 border-primary-200',
+  tts: 'bg-sky-50 text-sky-800 border-sky-200',
+  transport: 'bg-orange-50 text-orange-800 border-orange-200',
+  message: 'bg-emerald-50 text-emerald-800 border-emerald-200',
+  analysis: 'bg-violet-50 text-violet-800 border-violet-200',
+  pipeline: 'bg-indigo-50 text-indigo-800 border-indigo-200',
+  s2s: 'bg-teal-50 text-teal-800 border-teal-200',
+}
+
+function formatOffset(ms: number): string {
+  const totalSec = ms / 1000
+  const mins = Math.floor(totalSec / 60)
+  const sec = totalSec % 60
+  return `${String(mins).padStart(2, '0')}:${sec.toFixed(2).padStart(5, '0')}`
+}
+
+function isUrl(text: string): boolean {
+  return /^https?:\/\//i.test(text.trim())
+}
+
+export default function CallEventTimeline({
+  events,
+  externalLogUrl,
+  emptyMessage = 'No timeline events yet.',
+}: {
+  events: CallTimelineEvent[]
+  externalLogUrl?: string | null
+  emptyMessage?: string
+}) {
+  const [query, setQuery] = useState('')
+  const [categoryFilter, setCategoryFilter] = useState<TimelineCategory | 'all'>('all')
+
+  const filtered = useMemo(() => {
+    const q = query.trim().toLowerCase()
+    return events.filter((ev) => {
+      if (categoryFilter !== 'all' && ev.category !== categoryFilter) return false
+      if (!q) return true
+      return (
+        ev.title.toLowerCase().includes(q) ||
+        (ev.detail || '').toLowerCase().includes(q) ||
+        timelineCategoryLabel(ev.category).toLowerCase().includes(q)
+      )
+    })
+  }, [events, query, categoryFilter])
+
+  const categories = useMemo(() => {
+    const set = new Set(events.map((e) => e.category))
+    return Array.from(set)
+  }, [events])
+
+  if (!events.length) {
+    return (
+      <div className="rounded-lg border border-gray-200 bg-white px-4 py-10 text-center text-sm text-gray-500">
+        {emptyMessage}
+        {externalLogUrl ? (
+          <div className="mt-3">
+            <a
+              href={externalLogUrl}
+              target="_blank"
+              rel="noopener noreferrer"
+              className="inline-flex items-center gap-1 text-primary-600 hover:text-primary-800"
+            >
+              Open provider log
+              <ExternalLink className="h-3.5 w-3.5" />
+            </a>
+          </div>
+        ) : null}
+      </div>
+    )
+  }
+
+  return (
+    <div className="overflow-hidden rounded-lg border border-gray-200 bg-white">
+      <div className="flex flex-wrap items-center gap-2 border-b border-gray-200 bg-gray-50 px-4 py-3">
+        <div className="relative min-w-[200px] flex-1">
+          <Search className="pointer-events-none absolute left-2.5 top-2 h-4 w-4 text-gray-400" />
+          <input
+            type="search"
+            value={query}
+            onChange={(e) => setQuery(e.target.value)}
+            placeholder="Search timeline"
+            className="w-full rounded-md border border-gray-200 bg-white py-1.5 pl-8 pr-3 text-sm"
+          />
+        </div>
+        <select
+          value={categoryFilter}
+          onChange={(e) => setCategoryFilter(e.target.value as TimelineCategory | 'all')}
+          className="rounded-md border border-gray-200 bg-white px-2 py-1.5 text-sm text-gray-700"
+        >
+          <option value="all">All categories</option>
+          {categories.map((cat) => (
+            <option key={cat} value={cat}>
+              {timelineCategoryLabel(cat)}
+            </option>
+          ))}
+        </select>
+        {externalLogUrl ? (
+          <a
+            href={externalLogUrl}
+            target="_blank"
+            rel="noopener noreferrer"
+            className="inline-flex items-center gap-1 rounded-md border border-gray-200 bg-white px-2.5 py-1.5 text-xs font-medium text-gray-700 hover:bg-gray-100"
+          >
+            Provider log
+            <ExternalLink className="h-3.5 w-3.5" />
+          </a>
+        ) : null}
+        <span className="text-xs text-gray-500">{filtered.length} events</span>
+      </div>
+
+      <div className="max-h-[min(70vh,560px)] overflow-y-auto">
+        <div className="grid grid-cols-[5.5rem_5.5rem_1fr] gap-3 border-b border-gray-100 bg-white px-4 py-2 text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+          <span>Offset</span>
+          <span>Category</span>
+          <span>Event</span>
+        </div>
+        {filtered.map((ev) => (
+          <div
+            key={ev.id}
+            className="grid grid-cols-[5.5rem_5.5rem_1fr] gap-3 border-b border-gray-50 px-4 py-3 text-sm hover:bg-gray-50/80"
+          >
+            <span className="font-mono text-xs tabular-nums text-gray-600">
+              +{formatOffset(ev.offsetMs)}
+            </span>
+            <span>
+              <span
+                className={`inline-flex rounded border px-1.5 py-0.5 text-[10px] font-semibold ${CATEGORY_STYLES[ev.category]}`}
+              >
+                {timelineCategoryLabel(ev.category)}
+              </span>
+            </span>
+            <div className="min-w-0">
+              <p className={`font-medium ${ev.level === 'error' ? 'text-rose-700' : 'text-gray-900'}`}>
+                {ev.title}
+              </p>
+              {ev.detail ? (
+                <p className="mt-0.5 break-words text-xs text-gray-600">
+                  {isUrl(ev.detail) ? 'See provider log link above' : ev.detail}
+                </p>
+              ) : null}
+            </div>
+          </div>
+        ))}
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/CallWaveformPlayer.tsx b/frontend/src/components/call-recordings/CallWaveformPlayer.tsx
new file mode 100644
index 00000000..ab8da218
--- /dev/null
+++ b/frontend/src/components/call-recordings/CallWaveformPlayer.tsx
@@ -0,0 +1,767 @@
+import { useCallback, useEffect, useMemo, useRef, useState } from 'react'
+import { Download, Loader, Pause, Play } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { WAVEFORM_COLORS } from '../../lib/callDetailTheme'
+import { extractWavPeaks, isWavBuffer } from '../../lib/waveformPeaksFast'
+import {
+  fetchCallRecordingAudio,
+  fetchEvaluatorRecordingAudio,
+  getCachedEvaluatorWaveform,
+  getCachedWaveform,
+  preferStereoWaveform,
+  setCachedEvaluatorWaveform,
+  setCachedWaveform,
+  type CachedWaveformTrack,
+} from '../../lib/waveformAudioCache'
+
+interface WaveformTrack {
+  label: string
+  color: string
+  mutedColor: string
+  peaks: Float32Array
+}
+
+interface PaintState {
+  tracks: WaveformTrack[]
+  progressRatio: number
+  duration: number
+  showSkeleton: boolean
+  showStereoLayout: boolean
+}
+
+const TRACK_GAP = 6
+const TRACK_HEIGHT = 52
+const PEAK_WIDTH = 512
+
+function formatClock(seconds: number): string {
+  if (!Number.isFinite(seconds) || seconds < 0) return '00:00'
+  const mins = Math.floor(seconds / 60)
+  const sec = Math.floor(seconds % 60)
+  return `${String(mins).padStart(2, '0')}:${String(sec).padStart(2, '0')}`
+}
+
+function shouldFetchStereo(callData: Record<string, unknown> | null | undefined, platform?: string | null): boolean {
+  const platformName = (platform || '').toLowerCase()
+  return preferStereoWaveform(callData, platform) || platformName === 'vapi'
+}
+
+function tracksFromPeaks(peaks: ArrayBuffer[], stereo: boolean): WaveformTrack[] {
+  if (stereo && peaks.length >= 2) {
+    return [
+      {
+        label: 'User',
+        color: WAVEFORM_COLORS.user,
+        mutedColor: 'rgba(251, 146, 60, 0.28)',
+        peaks: new Float32Array(peaks[0]),
+      },
+      {
+        label: 'Assistant',
+        color: WAVEFORM_COLORS.assistant,
+        mutedColor: 'rgba(45, 212, 191, 0.28)',
+        peaks: new Float32Array(peaks[1]),
+      },
+    ]
+  }
+  return [
+    {
+      label: 'Call audio',
+      color: WAVEFORM_COLORS.mono,
+      mutedColor: 'rgba(196, 181, 253, 0.35)',
+      peaks: new Float32Array(peaks[0]),
+    },
+  ]
+}
+
+function decodePeaksOnMainThread(
+  arrayBuffer: ArrayBuffer,
+  stereo: boolean,
+): Promise<{ duration: number; tracks: WaveformTrack[] }> {
+  return new Promise((resolve, reject) => {
+    const ctx = new AudioContext()
+    void ctx
+      .decodeAudioData(arrayBuffer.slice(0))
+      .then((audioBuffer) => {
+        if (!audioBuffer) {
+          reject(new Error('decode_failed'))
+          return
+        }
+        const width = PEAK_WIDTH
+        const useStereo = stereo && audioBuffer.numberOfChannels >= 2
+        const downsample = (channel: Float32Array) => {
+          const peaks = new Float32Array(width * 2)
+          const block = Math.max(1, Math.floor(channel.length / width))
+          for (let i = 0; i < width; i++) {
+            const start = i * block
+            const end = Math.min(channel.length, start + block)
+            let min = 0
+            let max = 0
+            for (let j = start; j < end; j++) {
+              const v = channel[j]
+              if (v < min) min = v
+              if (v > max) max = v
+            }
+            peaks[i * 2] = min
+            peaks[i * 2 + 1] = max
+          }
+          return peaks
+        }
+
+        const peakBuffers: ArrayBuffer[] = []
+        if (useStereo) {
+          peakBuffers.push(downsample(audioBuffer.getChannelData(0)).buffer)
+          peakBuffers.push(downsample(audioBuffer.getChannelData(1)).buffer)
+        } else {
+          peakBuffers.push(downsample(audioBuffer.getChannelData(0)).buffer)
+        }
+
+        resolve({
+          duration: audioBuffer.duration,
+          tracks: tracksFromPeaks(peakBuffers, useStereo),
+        })
+      })
+      .catch(() => reject(new Error('decode_failed')))
+      .finally(() => {
+        void ctx.close()
+      })
+  })
+}
+
+async function buildWaveformTracks(
+  arrayBuffer: ArrayBuffer,
+  stereo: boolean,
+): Promise<{ duration: number; tracks: WaveformTrack[] }> {
+  if (isWavBuffer(arrayBuffer)) {
+    const wav = extractWavPeaks(arrayBuffer, stereo, PEAK_WIDTH)
+    if (wav) {
+      return {
+        duration: wav.duration,
+        tracks: tracksFromPeaks(wav.peaks, wav.stereo),
+      }
+    }
+  }
+  return decodePeaksOnMainThread(arrayBuffer, stereo)
+}
+
+function guessAudioMimeType(arrayBuffer: ArrayBuffer): string {
+  const bytes = new Uint8Array(arrayBuffer, 0, Math.min(12, arrayBuffer.byteLength))
+  if (bytes.length >= 4 && bytes[0] === 0x52 && bytes[1] === 0x49 && bytes[2] === 0x46 && bytes[3] === 0x46) {
+    return 'audio/wav'
+  }
+  if (bytes.length >= 3 && bytes[0] === 0x49 && bytes[1] === 0x44 && bytes[2] === 0x33) {
+    return 'audio/mpeg'
+  }
+  if (bytes.length >= 2 && bytes[0] === 0xff && (bytes[1] & 0xe0) === 0xe0) {
+    return 'audio/mpeg'
+  }
+  return 'audio/wav'
+}
+
+function createBlobUrl(arrayBuffer: ArrayBuffer): string {
+  return URL.createObjectURL(new Blob([arrayBuffer], { type: guessAudioMimeType(arrayBuffer) }))
+}
+
+function drawMirroredTrack(
+  ctx: CanvasRenderingContext2D,
+  peaks: Float32Array,
+  width: number,
+  height: number,
+  yOffset: number,
+  activeColor: string,
+  mutedColor: string,
+  progressRatio: number,
+) {
+  const barCount = peaks.length / 2
+  const midY = yOffset + height / 2
+  const amp = height * 0.46
+  const barW = Math.max(1.5, width / barCount - 0.5)
+  const playedBars = Math.floor(barCount * progressRatio)
+
+  for (let i = 0; i < barCount; i++) {
+    const min = peaks[i * 2]
+    const max = peaks[i * 2 + 1]
+    const x = (i / barCount) * width
+    const magnitude = Math.max(Math.abs(min), Math.abs(max))
+    const barH = Math.max(2, magnitude * amp * 2)
+    const top = midY - barH / 2
+    ctx.fillStyle = i <= playedBars ? activeColor : mutedColor
+    ctx.fillRect(x, top, barW, barH)
+  }
+
+  ctx.strokeStyle = 'rgba(20, 184, 166, 0.35)'
+  ctx.lineWidth = 1
+  ctx.beginPath()
+  ctx.moveTo(0, midY)
+  ctx.lineTo(width, midY)
+  ctx.stroke()
+}
+
+function drawSkeletonTrack(
+  ctx: CanvasRenderingContext2D,
+  width: number,
+  height: number,
+  yOffset: number,
+  color: string,
+) {
+  const midY = yOffset + height / 2
+  const barCount = 80
+  const barW = Math.max(1.5, width / barCount - 0.5)
+  for (let i = 0; i < barCount; i++) {
+    const x = (i / barCount) * width
+    const barH = 6 + ((i * 17) % 11)
+    ctx.fillStyle = color
+    ctx.fillRect(x, midY - barH / 2, barW, barH)
+  }
+}
+
+function drawTimeRuler(
+  ctx: CanvasRenderingContext2D,
+  width: number,
+  y: number,
+  duration: number,
+) {
+  if (duration <= 0) return
+  const step = duration <= 30 ? 5 : duration <= 120 ? 10 : 30
+  ctx.fillStyle = 'rgba(148, 163, 184, 0.9)'
+  ctx.font = '10px ui-monospace, monospace'
+  for (let t = 0; t <= duration; t += step) {
+    const x = (t / duration) * width
+    ctx.fillRect(x, y, 1, 6)
+    if (t > 0) ctx.fillText(String(t), x + 3, y + 16)
+  }
+}
+
+function paintCanvas(canvas: HTMLCanvasElement, container: HTMLDivElement, state: PaintState): void {
+  const width = container.clientWidth
+  if (width <= 0) return
+
+  const trackCount = state.showStereoLayout ? 2 : Math.max(state.tracks.length, 1)
+  const waveformHeight = trackCount * TRACK_HEIGHT + (trackCount > 1 ? TRACK_GAP : 0)
+  const rulerHeight = 22
+  const height = waveformHeight + rulerHeight
+  const dpr = window.devicePixelRatio || 1
+
+  canvas.width = width * dpr
+  canvas.height = height * dpr
+  canvas.style.width = `${width}px`
+  canvas.style.height = `${height}px`
+
+  const ctx = canvas.getContext('2d')
+  if (!ctx) return
+  ctx.setTransform(dpr, 0, 0, dpr, 0, 0)
+  ctx.fillStyle = WAVEFORM_COLORS.canvasBg
+  ctx.fillRect(0, 0, width, height)
+
+  if (state.showSkeleton || state.tracks.length === 0) {
+    drawSkeletonTrack(ctx, width, TRACK_HEIGHT, 0, WAVEFORM_COLORS.skeleton)
+    if (trackCount > 1) {
+      drawSkeletonTrack(ctx, width, TRACK_HEIGHT, TRACK_HEIGHT + TRACK_GAP, 'rgba(148, 163, 184, 0.18)')
+    }
+  } else {
+    state.tracks.forEach((track, index) => {
+      const y = index * (TRACK_HEIGHT + TRACK_GAP)
+      drawMirroredTrack(
+        ctx,
+        track.peaks,
+        width,
+        TRACK_HEIGHT,
+        y,
+        track.color,
+        track.mutedColor,
+        state.progressRatio,
+      )
+    })
+  }
+
+  const playX = state.progressRatio * width
+  ctx.strokeStyle = WAVEFORM_COLORS.playhead
+  ctx.lineWidth = 2
+  ctx.beginPath()
+  ctx.moveTo(playX, 0)
+  ctx.lineTo(playX, waveformHeight)
+  ctx.stroke()
+  drawTimeRuler(ctx, width, waveformHeight + 4, state.duration)
+}
+
+export default function CallWaveformPlayer({
+  callShortId,
+  callRecordingId,
+  evaluatorResultId,
+  callData,
+  platform,
+}: {
+  callShortId?: string
+  callRecordingId?: string | null
+  evaluatorResultId?: string
+  callData?: Record<string, unknown> | null
+  platform?: string | null
+}) {
+  const canvasRef = useRef<HTMLCanvasElement>(null)
+  const containerRef = useRef<HTMLDivElement>(null)
+  const audioRef = useRef<HTMLAudioElement>(null)
+  const blobRef = useRef<string | null>(null)
+  const ownsBlobRef = useRef(false)
+  const draggingRef = useRef(false)
+  const loadedStereoRef = useRef(false)
+  const decodeTaskRef = useRef(0)
+  const paintStateRef = useRef<PaintState>({
+    tracks: [],
+    progressRatio: 0,
+    duration: 0,
+    showSkeleton: false,
+    showStereoLayout: false,
+  })
+  const callDataRef = useRef(callData)
+  const platformRef = useRef(platform)
+  callDataRef.current = callData
+  platformRef.current = platform
+
+  const [tracks, setTracks] = useState<WaveformTrack[]>([])
+  const [audioUrl, setAudioUrl] = useState<string | null>(null)
+  const [duration, setDuration] = useState(0)
+  const [currentTime, setCurrentTime] = useState(0)
+  const [playing, setPlaying] = useState(false)
+  const [fetching, setFetching] = useState(false)
+  const [decoding, setDecoding] = useState(false)
+  const [loadError, setLoadError] = useState<string | null>(null)
+  const [playbackRate, setPlaybackRate] = useState(1)
+
+  const wantStereo = useMemo(() => shouldFetchStereo(callData, platform), [callData, platform])
+  const showStereoLayout = tracks.length > 1 || (wantStereo && decoding)
+  const progressRatio = duration > 0 ? currentTime / duration : 0
+  const showSkeleton = decoding && tracks.length === 0
+
+  paintStateRef.current = {
+    tracks,
+    progressRatio,
+    duration,
+    showSkeleton,
+    showStereoLayout,
+  }
+
+  const repaint = useCallback(() => {
+    const canvas = canvasRef.current
+    const container = containerRef.current
+    if (!canvas || !container) return
+    paintCanvas(canvas, container, paintStateRef.current)
+  }, [])
+
+  const setBlobFromBuffer = useCallback((arrayBuffer: ArrayBuffer) => {
+    if (blobRef.current && ownsBlobRef.current) URL.revokeObjectURL(blobRef.current)
+    const url = createBlobUrl(arrayBuffer)
+    blobRef.current = url
+    ownsBlobRef.current = true
+    setAudioUrl(url)
+    return url
+  }, [])
+
+  const setStreamAudioUrl = useCallback((url: string) => {
+    if (blobRef.current && ownsBlobRef.current) URL.revokeObjectURL(blobRef.current)
+    blobRef.current = url
+    ownsBlobRef.current = false
+    setAudioUrl(url)
+    return url
+  }, [])
+
+  const decodeAndSetTracks = useCallback(
+    async (
+      arrayBuffer: ArrayBuffer,
+      stereo: boolean,
+      options?: {
+        cacheCallShortId?: string
+        cacheEvaluatorId?: string
+        usedStereo?: boolean
+        blobUrl?: string
+        cancelled?: () => boolean
+      },
+    ) => {
+      const taskId = ++decodeTaskRef.current
+      setDecoding(true)
+      try {
+        const { duration: decodedDuration, tracks: decodedTracks } = await buildWaveformTracks(
+          arrayBuffer,
+          stereo,
+        )
+        if (taskId !== decodeTaskRef.current || options?.cancelled?.()) return
+
+        setTracks(decodedTracks)
+        setDuration((prev) => (prev > 0 ? prev : decodedDuration))
+        setDecoding(false)
+        setLoadError(null)
+        if (stereo && decodedTracks.length > 1) loadedStereoRef.current = true
+
+        const blobUrl = options?.blobUrl ?? blobRef.current
+        if (!blobUrl || options?.cancelled?.()) return
+
+        if (options?.cacheEvaluatorId) {
+          setCachedEvaluatorWaveform(options.cacheEvaluatorId, {
+            tracks: decodedTracks as CachedWaveformTrack[],
+            duration: decodedDuration,
+            blobUrl,
+          })
+          ownsBlobRef.current = false
+        } else if (options?.cacheCallShortId) {
+          setCachedWaveform(options.cacheCallShortId, options.usedStereo ?? stereo, {
+            tracks: decodedTracks as CachedWaveformTrack[],
+            duration: decodedDuration,
+            blobUrl,
+          })
+          ownsBlobRef.current = false
+        }
+      } catch {
+        if (taskId === decodeTaskRef.current) {
+          setDecoding(false)
+          setLoadError('Could not render waveform for this recording.')
+        }
+      }
+    },
+    [],
+  )
+
+  useEffect(() => {
+    let cancelled = false
+    const isCancelled = () => cancelled
+    loadedStereoRef.current = false
+    decodeTaskRef.current += 1
+    setPlaying(false)
+    setCurrentTime(0)
+
+    function applyCached(cached: ReturnType<typeof getCachedWaveform>) {
+      if (!cached || cancelled) return false
+      if (blobRef.current && ownsBlobRef.current) URL.revokeObjectURL(blobRef.current)
+      blobRef.current = cached.blobUrl
+      ownsBlobRef.current = false
+      setAudioUrl(cached.blobUrl)
+      setTracks(Array.isArray(cached.tracks) ? cached.tracks : [])
+      setDuration(
+        typeof cached.duration === 'number' && Number.isFinite(cached.duration) ? cached.duration : 0,
+      )
+      setFetching(false)
+      setDecoding(false)
+      setLoadError(null)
+      loadedStereoRef.current = cached.tracks.length > 1
+      return true
+    }
+
+    async function loadWaveformFromBuffer(
+      arrayBuffer: ArrayBuffer,
+      stereo: boolean,
+      cacheCallShortId?: string,
+      cacheEvaluatorId?: string,
+      usedStereo?: boolean,
+      blobUrl?: string,
+    ) {
+      if (!arrayBuffer?.byteLength || cancelled) return
+      const playbackUrl = blobUrl ?? blobRef.current
+      if (!playbackUrl) return
+      loadedStereoRef.current = usedStereo ?? stereo
+      void decodeAndSetTracks(arrayBuffer, stereo, {
+        cacheCallShortId,
+        cacheEvaluatorId,
+        usedStereo: usedStereo ?? stereo,
+        blobUrl: playbackUrl,
+        cancelled: isCancelled,
+      })
+    }
+
+    async function load() {
+      if (!callRecordingId && !callShortId && !evaluatorResultId) return
+
+      const stereo = shouldFetchStereo(callDataRef.current, platformRef.current)
+
+      if (callShortId && applyCached(getCachedWaveform(callShortId, stereo))) return
+      if (callShortId && !stereo && applyCached(getCachedWaveform(callShortId, false))) return
+      if (evaluatorResultId && applyCached(getCachedEvaluatorWaveform(evaluatorResultId))) return
+
+      setDecoding(false)
+      setLoadError(null)
+      setTracks([])
+      setDuration(0)
+      setCurrentTime(0)
+
+      if (callShortId) {
+        setStreamAudioUrl(apiClient.getCallRecordingAudioStreamUrl(callShortId, { stereo: false }))
+        setFetching(false)
+
+        void (async () => {
+          try {
+            const arrayBuffer = await fetchCallRecordingAudio(callShortId, false)
+            if (!arrayBuffer?.byteLength || cancelled) {
+              if (!cancelled) {
+                setLoadError('Recording unavailable. Refresh the call to renew provider audio URLs.')
+              }
+              return
+            }
+            await loadWaveformFromBuffer(arrayBuffer, false, callShortId, undefined, false)
+          } catch {
+            if (!cancelled) setLoadError('Could not load recording waveform.')
+          }
+        })()
+        return
+      }
+
+      setFetching(true)
+      setAudioUrl(null)
+
+      try {
+        let arrayBuffer: ArrayBuffer | null = null
+        let usedStereo = false
+        let cacheEvaluatorId: string | undefined
+
+        if (evaluatorResultId) {
+          arrayBuffer = await fetchEvaluatorRecordingAudio(evaluatorResultId)
+          cacheEvaluatorId = evaluatorResultId
+        }
+
+        if (!arrayBuffer?.byteLength || cancelled) {
+          if (!cancelled) {
+            setLoadError('Recording unavailable for this evaluation run.')
+          }
+          return
+        }
+
+        const blobUrl = setBlobFromBuffer(arrayBuffer)
+        setFetching(false)
+        loadedStereoRef.current = usedStereo
+
+        void decodeAndSetTracks(arrayBuffer, usedStereo, {
+          cacheEvaluatorId,
+          usedStereo,
+          blobUrl,
+          cancelled: isCancelled,
+        })
+      } catch {
+        if (!cancelled) setLoadError('Could not load recording audio.')
+      } finally {
+        if (!cancelled) setFetching(false)
+      }
+    }
+
+    void load()
+    return () => {
+      cancelled = true
+      decodeTaskRef.current += 1
+      if (blobRef.current && ownsBlobRef.current) URL.revokeObjectURL(blobRef.current)
+    }
+  }, [callRecordingId, callShortId, evaluatorResultId, decodeAndSetTracks, setBlobFromBuffer, setStreamAudioUrl])
+
+  useEffect(() => {
+    if (!callShortId || !wantStereo || loadedStereoRef.current || tracks.length > 1) return
+
+    let cancelled = false
+    void (async () => {
+      const arrayBuffer = await fetchCallRecordingAudio(callShortId, true)
+      if (!arrayBuffer?.byteLength || cancelled) return
+      const playbackUrl = blobRef.current
+      if (!playbackUrl) return
+      await decodeAndSetTracks(arrayBuffer, true, {
+        cacheCallShortId: callShortId,
+        usedStereo: true,
+        blobUrl: playbackUrl,
+        cancelled: () => cancelled,
+      })
+    })()
+
+    return () => {
+      cancelled = true
+    }
+  }, [callShortId, wantStereo, tracks.length, decodeAndSetTracks])
+
+  useEffect(() => {
+    repaint()
+  }, [tracks, duration, showSkeleton, showStereoLayout, repaint])
+
+  useEffect(() => {
+    const onResize = () => repaint()
+    window.addEventListener('resize', onResize)
+    const container = containerRef.current
+    let observer: ResizeObserver | undefined
+    if (container && typeof ResizeObserver !== 'undefined') {
+      observer = new ResizeObserver(() => repaint())
+      observer.observe(container)
+    }
+    return () => {
+      window.removeEventListener('resize', onResize)
+      observer?.disconnect()
+    }
+  }, [repaint])
+
+  useEffect(() => {
+    if (!playing) return
+    let frame = 0
+    let lastUiUpdate = 0
+    const tick = (now: number) => {
+      const audio = audioRef.current
+      if (audio && duration > 0) {
+        paintStateRef.current.progressRatio = audio.currentTime / duration
+        repaint()
+        if (now - lastUiUpdate > 250) {
+          setCurrentTime(audio.currentTime)
+          lastUiUpdate = now
+        }
+      }
+      frame = requestAnimationFrame(tick)
+    }
+    frame = requestAnimationFrame(tick)
+    return () => cancelAnimationFrame(frame)
+  }, [playing, duration, repaint])
+
+  const seekFromClientX = (clientX: number) => {
+    const container = containerRef.current
+    const audio = audioRef.current
+    if (!container || !audio || !duration) return
+    const rect = container.getBoundingClientRect()
+    const ratio = Math.max(0, Math.min(1, (clientX - rect.left) / rect.width))
+    audio.currentTime = ratio * duration
+    setCurrentTime(audio.currentTime)
+    paintStateRef.current.progressRatio = ratio
+    repaint()
+  }
+
+  const togglePlay = async () => {
+    const audio = audioRef.current
+    if (!audio) return
+    if (playing) {
+      audio.pause()
+      return
+    }
+    audio.playbackRate = playbackRate
+    try {
+      await audio.play()
+    } catch {
+      // ignore autoplay / interruption errors
+    }
+  }
+
+  useEffect(() => {
+    const audio = audioRef.current
+    if (audio) audio.playbackRate = playbackRate
+  }, [playbackRate])
+
+  const handleDownload = async () => {
+    if (!callShortId) return
+    try {
+      const blobUrl = await apiClient.getCallRecordingAudioUrl(callShortId, {
+        stereo: loadedStereoRef.current,
+      })
+      const anchor = document.createElement('a')
+      anchor.href = blobUrl
+      anchor.download = `call-${callShortId}.wav`
+      anchor.click()
+      window.setTimeout(() => URL.revokeObjectURL(blobUrl), 1000)
+    } catch {
+      setLoadError('Download failed. Try Refresh on the call first.')
+    }
+  }
+
+  if (!callRecordingId && !callShortId && !evaluatorResultId) return null
+
+  const canPlay = Boolean(audioUrl) && !fetching
+  const canSeek = canPlay && duration > 0
+
+  return (
+    <div className="overflow-hidden rounded-xl border border-gray-200 bg-white shadow-sm">
+      <div className="flex items-center justify-between border-b border-gray-100 px-4 py-2">
+        <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-500">Recording</p>
+        <span className="font-mono text-xs tabular-nums text-gray-600">
+          {formatClock(currentTime)} / {formatClock(duration)}
+        </span>
+      </div>
+
+      <div className="px-3 pt-3">
+        {showStereoLayout ? (
+          <div className="mb-2 flex gap-5 px-1 text-[10px] font-semibold uppercase tracking-wider">
+            <span className="text-orange-500">User</span>
+            <span className="text-teal-500">Assistant</span>
+          </div>
+        ) : null}
+
+        {fetching && !audioUrl ? (
+          <div className="flex h-36 items-center justify-center gap-2 text-sm text-gray-500">
+            <Loader className="h-4 w-4 animate-spin" />
+            Loading audio…
+          </div>
+        ) : loadError && !audioUrl ? (
+          <div className="flex h-36 items-center justify-center px-4 text-center text-sm text-amber-700">
+            {loadError}
+          </div>
+        ) : (
+          <div className="relative">
+            <div
+              ref={containerRef}
+              className={`relative select-none rounded-lg ${canSeek ? 'cursor-pointer' : 'cursor-default'}`}
+              onMouseDown={(e) => {
+                if (!canSeek) return
+                draggingRef.current = true
+                seekFromClientX(e.clientX)
+              }}
+              onMouseMove={(e) => {
+                if (draggingRef.current) seekFromClientX(e.clientX)
+              }}
+              onMouseUp={() => {
+                draggingRef.current = false
+              }}
+              onMouseLeave={() => {
+                draggingRef.current = false
+              }}
+            >
+              <canvas ref={canvasRef} className="block w-full rounded-lg" />
+            </div>
+          </div>
+        )}
+      </div>
+
+      <div className="flex flex-wrap items-center gap-3 border-t border-gray-100 px-4 py-3">
+        <button
+          type="button"
+          onClick={() => void togglePlay()}
+          disabled={!canPlay}
+          className="inline-flex h-10 w-10 items-center justify-center rounded-full bg-primary-400 text-primary-900 shadow-md shadow-primary-200/80 hover:bg-primary-300 disabled:opacity-40"
+          aria-label={playing ? 'Pause' : 'Play'}
+        >
+          {playing ? <Pause className="h-4 w-4" /> : <Play className="ml-0.5 h-4 w-4" />}
+        </button>
+
+        <select
+          value={playbackRate}
+          onChange={(e) => setPlaybackRate(Number(e.target.value))}
+          className="rounded-md border border-gray-200 bg-white px-2.5 py-1.5 text-xs text-gray-700"
+        >
+          {[0.75, 1, 1.25, 1.5, 2].map((rate) => (
+            <option key={rate} value={rate}>
+              {rate}x
+            </option>
+          ))}
+        </select>
+
+        {callShortId ? (
+          <button
+            type="button"
+            onClick={() => void handleDownload()}
+            className="ml-auto inline-flex items-center gap-1.5 rounded-md border border-gray-200 px-2.5 py-1.5 text-xs font-medium text-gray-600 hover:bg-gray-50"
+          >
+            <Download className="h-3.5 w-3.5" />
+            Audio
+          </button>
+        ) : null}
+      </div>
+
+      {audioUrl ? (
+        <audio
+          ref={audioRef}
+          src={audioUrl}
+          preload="auto"
+          className="hidden"
+          onLoadedMetadata={(e) => {
+            const nextDuration = e.currentTarget?.duration
+            if (typeof nextDuration === 'number' && Number.isFinite(nextDuration) && nextDuration > 0) {
+              setDuration((prev) => (prev > 0 ? prev : nextDuration))
+            }
+          }}
+          onEnded={() => setPlaying(false)}
+          onPlay={() => setPlaying(true)}
+          onPause={() => setPlaying(false)}
+        />
+      ) : null}
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/EvaluatorCallDetailPanel.tsx b/frontend/src/components/call-recordings/EvaluatorCallDetailPanel.tsx
new file mode 100644
index 00000000..23b499fc
--- /dev/null
+++ b/frontend/src/components/call-recordings/EvaluatorCallDetailPanel.tsx
@@ -0,0 +1,251 @@
+import { useEffect, useMemo, useState } from 'react'
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { MessageSquare, Sparkles, Activity, X } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { getEvaluatorResultPlaceholder } from '../../lib/evaluatorResultQuery'
+import { prefetchCallRecordingAudio, prefetchEvaluatorRecordingAudio } from '../../lib/waveformAudioCache'
+import CallWaveformPlayer from './CallWaveformPlayer'
+import SyntheticCallTracePanel from './SyntheticCallTracePanel'
+
+type DrawerTab = 'transcript' | 'analysis' | 'pipeline'
+
+const TABS: Array<{ id: DrawerTab; label: string; icon: typeof MessageSquare }> = [
+  { id: 'transcript', label: 'Transcript', icon: MessageSquare },
+  { id: 'analysis', label: 'Analysis', icon: Sparkles },
+  { id: 'pipeline', label: 'Pipeline', icon: Activity },
+]
+
+function formatTime(seconds: number): string {
+  const mins = Math.floor(seconds / 60)
+  const secs = Math.floor(seconds % 60)
+  return `${mins}:${secs.toString().padStart(2, '0')}`
+}
+
+function getSpeakerLabel(speaker: string, agentName?: string): string {
+  if (speaker === 'Speaker 1' || speaker === 'user' || speaker === 'caller') return 'Caller'
+  if (speaker === 'assistant' || speaker === 'Speaker 2' || speaker === 'bot') return agentName || 'Agent'
+  return agentName || 'Agent'
+}
+
+function isUserSpeaker(speaker: string): boolean {
+  return speaker === 'Speaker 1' || speaker === 'user' || speaker === 'caller'
+}
+
+export default function EvaluatorCallDetailPanel({
+  evaluatorResultId,
+  onClose,
+}: {
+  evaluatorResultId: string
+  onClose?: () => void
+}) {
+  const [tab, setTab] = useState<DrawerTab>('transcript')
+  const queryClient = useQueryClient()
+
+  useEffect(() => {
+    if (!evaluatorResultId) return
+    prefetchEvaluatorRecordingAudio(evaluatorResultId)
+  }, [evaluatorResultId])
+
+  const { data: result, isFetching, isError, error } = useQuery({
+    queryKey: ['evaluator-result', evaluatorResultId],
+    queryFn: () => apiClient.getEvaluatorResult(evaluatorResultId, true),
+    enabled: Boolean(evaluatorResultId),
+    placeholderData: () => getEvaluatorResultPlaceholder(queryClient, evaluatorResultId),
+    staleTime: 30_000,
+  })
+
+  const detailsReady = Boolean(result?.speaker_segments || result?.transcription || result?.call_data)
+
+  const callAnalysis = useMemo(() => {
+    const fromCallData = (result?.call_data?.call_analysis || {}) as Record<string, unknown>
+    const fromMetrics = result?.metric_scores || {}
+    return {
+      call_summary:
+        (fromCallData.call_summary as string | undefined) ||
+        (fromMetrics.summary?.value as string | undefined) ||
+        null,
+      user_sentiment:
+        (fromCallData.user_sentiment as string | undefined) ||
+        (fromMetrics.sentiment?.value as string | undefined) ||
+        'Neutral',
+      call_successful:
+        fromCallData.call_successful !== undefined
+          ? Boolean(fromCallData.call_successful)
+          : fromMetrics.successful?.value !== undefined
+            ? Boolean(fromMetrics.successful.value)
+            : null,
+    }
+  }, [result])
+
+  const callShortId =
+    typeof result?.call_data?.call_short_id === 'string' ? result.call_data.call_short_id : undefined
+
+  useEffect(() => {
+    if (!callShortId) return
+    prefetchCallRecordingAudio(callShortId, false)
+  }, [callShortId])
+
+  if (!result && !isFetching) {
+    return <div className="p-8 text-sm text-gray-600">Call details not found.</div>
+  }
+
+  const fetchError =
+    isError && error
+      ? (error as { response?: { data?: { detail?: string } }; message?: string }).response?.data
+          ?.detail ||
+        (error as { message?: string }).message ||
+        'Failed to load call details'
+      : null
+
+  return (
+    <div className="flex h-full min-h-0 flex-col bg-gray-50">
+      <div className="shrink-0 border-b border-gray-200 bg-white px-5 py-4">
+        <div className="flex items-start justify-between gap-3">
+          <div className="min-w-0">
+            <h2 className="font-mono text-xl font-bold tracking-tight text-primary-600">
+              #{result?.result_id ?? evaluatorResultId}
+            </h2>
+            <p className="mt-1 text-sm text-gray-600">
+              {result?.agent?.name ? `${result.agent.name} · ` : ''}
+              Transcript, analysis, and pipeline trace
+            </p>
+          </div>
+          {onClose ? (
+            <button
+              type="button"
+              onClick={onClose}
+              aria-label="Close"
+              className="rounded-lg p-2 text-gray-500 hover:bg-gray-100 hover:text-gray-800"
+            >
+              <X className="h-5 w-5" />
+            </button>
+          ) : null}
+        </div>
+      </div>
+
+      <div className="min-h-0 flex-1 overflow-y-auto p-5">
+        <div className="space-y-4">
+          {fetchError ? (
+            <div className="rounded-lg border border-red-200 bg-red-50 px-4 py-3 text-sm text-red-800">
+              {fetchError}
+            </div>
+          ) : null}
+          <CallWaveformPlayer
+            evaluatorResultId={callShortId ? undefined : evaluatorResultId}
+            callShortId={callShortId}
+            callRecordingId={callShortId}
+            callData={result?.call_data}
+            platform={result?.provider_platform}
+          />
+
+          <div className="flex flex-wrap gap-1 border-b border-gray-200 bg-white px-1">
+            {TABS.map(({ id, label, icon: Icon }) => (
+              <button
+                key={id}
+                type="button"
+                onClick={() => setTab(id)}
+                className={`inline-flex items-center gap-1.5 border-b-2 px-3 py-2 text-sm font-medium transition-colors ${
+                  tab === id
+                    ? 'border-primary-500 text-primary-800'
+                    : 'border-transparent text-gray-500 hover:border-gray-300 hover:text-gray-700'
+                }`}
+              >
+                <Icon className="h-4 w-4" />
+                {label}
+              </button>
+            ))}
+          </div>
+
+          {!detailsReady && tab !== 'pipeline' ? (
+            <div className="space-y-3">
+              <div className="h-48 animate-pulse rounded-xl bg-gray-100" />
+            </div>
+          ) : null}
+
+          {detailsReady && tab === 'transcript' ? (
+            <div className="rounded-xl border border-gray-200 bg-white p-4">
+              <div className="max-h-[min(60vh,520px)] space-y-3 overflow-y-auto pr-1">
+                {result?.speaker_segments && result.speaker_segments.length > 0 ? (
+                  result.speaker_segments.map(
+                    (segment: { speaker: string; text: string; start: number }, idx: number) => (
+                    <div
+                      key={idx}
+                      className={`flex ${isUserSpeaker(segment.speaker) ? 'justify-end' : 'justify-start'}`}
+                    >
+                      <div
+                        className={`max-w-[85%] rounded-2xl px-4 py-2.5 ${
+                          isUserSpeaker(segment.speaker)
+                            ? 'bg-indigo-600 text-white rounded-br-sm'
+                            : 'bg-gray-100 text-gray-800 rounded-bl-sm'
+                        }`}
+                      >
+                        <div
+                          className={`mb-0.5 flex items-center gap-2 text-[10px] font-semibold uppercase tracking-wider ${
+                            isUserSpeaker(segment.speaker) ? 'text-indigo-200' : 'text-gray-400'
+                          }`}
+                        >
+                          <span>{getSpeakerLabel(segment.speaker, result.agent?.name)}</span>
+                          <span className="tabular-nums">{formatTime(segment.start)}</span>
+                        </div>
+                        <p className="text-sm leading-relaxed">{segment.text}</p>
+                      </div>
+                    </div>
+                  ),
+                  )
+                ) : result?.transcription ? (
+                  <p className="whitespace-pre-wrap text-sm leading-relaxed text-gray-700">
+                    {result.transcription}
+                  </p>
+                ) : (
+                  <p className="py-8 text-center text-sm text-gray-500">No transcript available.</p>
+                )}
+              </div>
+            </div>
+          ) : null}
+
+          {detailsReady && tab === 'analysis' ? (
+            <div className="rounded-xl border border-gray-200 bg-white p-5">
+              {callAnalysis.call_summary ? (
+                <div className="mb-4 rounded-lg border border-indigo-100 bg-indigo-50 p-4">
+                  <p className="mb-1 text-xs font-semibold uppercase tracking-wider text-indigo-900">
+                    Summary
+                  </p>
+                  <p className="text-sm leading-relaxed text-indigo-800">{callAnalysis.call_summary}</p>
+                </div>
+              ) : null}
+              <div className="grid grid-cols-2 gap-3">
+                <div className="rounded-lg bg-gray-50 p-3">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-500">
+                    Sentiment
+                  </p>
+                  <p className="mt-1 text-sm font-medium text-gray-900">{callAnalysis.user_sentiment}</p>
+                </div>
+                <div className="rounded-lg bg-gray-50 p-3">
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-500">
+                    Success
+                  </p>
+                  <p className="mt-1 text-sm font-medium text-gray-900">
+                    {callAnalysis.call_successful === true
+                      ? 'Successful'
+                      : callAnalysis.call_successful === false
+                        ? 'Unsuccessful'
+                        : 'N/A'}
+                  </p>
+                </div>
+              </div>
+              {!callAnalysis.call_summary &&
+              callAnalysis.call_successful == null &&
+              callAnalysis.user_sentiment === 'Neutral' ? (
+                <p className="mt-4 text-center text-sm text-gray-500">No call analysis available.</p>
+              ) : null}
+            </div>
+          ) : null}
+
+          {tab === 'pipeline' ? (
+            <SyntheticCallTracePanel evaluatorResultId={evaluatorResultId} embedded />
+          ) : null}
+        </div>
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/ObservabilityCallDetailPanel.tsx b/frontend/src/components/call-recordings/ObservabilityCallDetailPanel.tsx
new file mode 100644
index 00000000..796c0117
--- /dev/null
+++ b/frontend/src/components/call-recordings/ObservabilityCallDetailPanel.tsx
@@ -0,0 +1,604 @@
+import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
+import { useEffect, useState } from 'react'
+import { useNavigate } from 'react-router-dom'
+import {
+  Phone,
+  Clock,
+  PhoneIncoming,
+  PhoneOutgoing,
+  MessageSquare,
+  Trash2,
+  Download,
+  Tag,
+  Loader,
+  Sparkles,
+  X,
+} from 'lucide-react'
+import { motion, AnimatePresence } from 'framer-motion'
+
+import Button from '../Button'
+import ConfirmModal from '../ConfirmModal'
+import { apiClient } from '../../lib/api'
+import { getObservabilityCallPlaceholder } from '../../lib/observabilityCallQuery'
+import RetellCallDetails from './RetellCallDetails'
+import VapiCallDetails from './VapiCallDetails'
+import VobizCallDetails from './VobizCallDetails'
+import { ObservabilityCall } from '../../types/api'
+import { useRecordingPresignedUrl } from '../../hooks/useRecordingPresignedUrl'
+import { CallAgentLink } from '../../pages/observability/CallAgentLink'
+import { EndReasonBadge, EventBadge, PlatformBadge } from '../../pages/observability/observabilityCallUi'
+
+const LIVE_EVENTS = new Set([
+  'outbound_initiated',
+  'ringing',
+  'call_started',
+  'call_in_progress',
+  'in-progress',
+  'answered',
+])
+
+export default function ObservabilityCallDetailPanel({
+  callShortId,
+  onClose,
+}: {
+  callShortId: string
+  onClose?: () => void
+}) {
+  const navigate = useNavigate()
+  const queryClient = useQueryClient()
+  const [showDelete, setShowDelete] = useState(false)
+  const [showEvalModal, setShowEvalModal] = useState(false)
+  const [selectedEvaluator, setSelectedEvaluator] = useState('')
+  const [liveTranscript, setLiveTranscript] = useState<
+    Array<{ role: string; content: string; timestamp?: string }>
+  >([])
+
+  const { data: callRecording, isFetching, isError, error } = useQuery<ObservabilityCall>({
+    queryKey: ['observability-call', callShortId],
+    queryFn: () => apiClient.getObservabilityCall(callShortId),
+    enabled: Boolean(callShortId),
+    placeholderData: () => getObservabilityCallPlaceholder(queryClient, callShortId),
+    refetchInterval: (query) => {
+      const data = query.state.data
+      if (!data) return false
+      const isLive = data.is_live || LIVE_EVENTS.has((data.call_event || '').toLowerCase())
+      return isLive ? 3000 : false
+    },
+  })
+
+  useEffect(() => {
+    const existing = callRecording?.call_data?.live_transcript
+    if (!Array.isArray(existing) || existing.length === 0) return
+    setLiveTranscript((prev) => (existing.length >= prev.length ? existing : prev))
+  }, [callRecording?.call_data?.live_transcript])
+
+  useEffect(() => {
+    if (callRecording?.call_event === 'call_ended') {
+      queryClient.invalidateQueries({ queryKey: ['observability-call', callShortId] })
+    }
+  }, [callRecording?.call_event, callShortId, queryClient])
+
+  useEffect(() => {
+    if (!callShortId || !callRecording) return
+    const isLive =
+      callRecording.is_live || LIVE_EVENTS.has((callRecording.call_event || '').toLowerCase())
+    if (!isLive) return
+
+    let eventSource: EventSource | null = null
+    try {
+      eventSource = new EventSource(apiClient.getObservabilityCallLiveEventsUrl(callShortId))
+      eventSource.onmessage = (event) => {
+        try {
+          const entry = JSON.parse(event.data)
+          setLiveTranscript((prev) => [...prev, entry])
+        } catch {
+          // ignore malformed events
+        }
+      }
+    } catch {
+      // polling still updates live_transcript from call_data
+    }
+
+    return () => {
+      eventSource?.close()
+    }
+  }, [callShortId, callRecording?.call_event, callRecording?.is_live])
+
+  const storageKey = callRecording?.call_data?.recording_s3_key ?? undefined
+  const providerRecordingUrl = callRecording?.call_data?.recording_url ?? null
+  const hasStorageRecording = !!storageKey
+
+  const { data: presignedRecording, isLoading: presignedLoading } =
+    useRecordingPresignedUrl(storageKey)
+
+  const { data: evaluators = [] } = useQuery({
+    queryKey: ['evaluators'],
+    queryFn: () => apiClient.listEvaluators(),
+    enabled: showEvalModal,
+  })
+
+  const deleteMutation = useMutation({
+    mutationFn: () => apiClient.deleteObservabilityCall(callShortId),
+    onSuccess: () => {
+      queryClient.invalidateQueries({ queryKey: ['observability-calls'] })
+      onClose?.()
+    },
+  })
+
+  const evaluateMutation = useMutation({
+    mutationFn: (evaluatorId: string) =>
+      apiClient.evaluateObservabilityCall(callShortId, evaluatorId),
+    onSuccess: (data) => {
+      setShowEvalModal(false)
+      setSelectedEvaluator('')
+      navigate(`/results/${data.result_id}`)
+    },
+  })
+
+  if (!callRecording && !isFetching) {
+    return (
+      <div className="flex h-full flex-col items-center justify-center gap-3 p-6 text-center">
+        <p className="text-sm font-medium text-rose-800">Call not found</p>
+        {onClose ? (
+          <Button variant="ghost" size="sm" onClick={onClose}>
+            Close
+          </Button>
+        ) : null}
+      </div>
+    )
+  }
+
+  if (!callRecording) return null
+
+  const detailsReady = Boolean(callRecording.call_data)
+  const fetchError =
+    isError && error
+      ? (error as { response?: { data?: { detail?: string } }; message?: string }).response?.data
+          ?.detail ||
+        (error as { message?: string }).message ||
+        'Failed to load call'
+      : null
+
+  const callData = callRecording.call_data
+  const liveTranscriptEntries: Array<{
+    role: string
+    content: string
+    timestamp?: string
+    start_time?: number
+  }> = Array.isArray(callData?.live_transcript) ? callData.live_transcript : []
+  const messagesFromLive = liveTranscriptEntries
+    .filter((entry) => entry?.content)
+    .map((entry) => ({
+      role: entry.role === 'user' ? 'user' : 'assistant',
+      content: entry.content,
+      start_time:
+        entry.start_time ?? (entry.timestamp ? new Date(entry.timestamp).getTime() : undefined),
+    }))
+  const messages: Array<{ role: string; content: string; start_time?: number }> | undefined =
+    Array.isArray(callData?.messages) && callData.messages.length > 0
+      ? callData.messages
+      : messagesFromLive.length > 0
+        ? messagesFromLive
+        : undefined
+  const playbackUrl = presignedRecording?.url || providerRecordingUrl
+  const audioLoading = hasStorageRecording && presignedLoading && !playbackUrl
+  const isLiveCall =
+    callRecording.is_live || LIVE_EVENTS.has((callRecording.call_event || '').toLowerCase())
+
+  const toTranscriptTurn = (entry: { role: string; content: string; start_time?: number }) => ({
+    role: entry.role === 'user' ? ('user' as const) : ('agent' as const),
+    content: entry.content,
+    start_time: entry.start_time,
+  })
+
+  const persistedTurns = (messages || messagesFromLive).map(toTranscriptTurn)
+  const liveTurns = liveTranscript
+    .filter((entry) => entry?.content)
+    .map((entry) =>
+      toTranscriptTurn({
+        role: entry.role,
+        content: entry.content,
+        start_time: entry.timestamp ? new Date(entry.timestamp).getTime() : undefined,
+      }),
+    )
+
+  const transcriptTurns = isLiveCall && liveTurns.length > 0 ? liveTurns : persistedTurns
+  const hasTranscript = transcriptTurns.length > 0
+
+  const computeDuration = (): string | null => {
+    const started = callData?.startedAt || callData?.started_at
+    const ended = callData?.endedAt || callData?.ended_at
+    if (started && ended) {
+      const start = new Date(started).getTime()
+      const end = new Date(ended).getTime()
+      if (!Number.isNaN(start) && !Number.isNaN(end)) {
+        const diffSec = Math.floor((end - start) / 1000)
+        return `${Math.floor(diffSec / 60)}m ${diffSec % 60}s`
+      }
+    }
+    if (typeof callData?.duration_seconds === 'number') {
+      const diffSec = Math.floor(callData.duration_seconds)
+      return `${Math.floor(diffSec / 60)}m ${diffSec % 60}s`
+    }
+    return null
+  }
+
+  const formatMessageTime = (timestamp: number): string =>
+    new Date(timestamp).toLocaleTimeString([], {
+      hour: '2-digit',
+      minute: '2-digit',
+      second: '2-digit',
+    })
+
+  const duration = computeDuration()
+
+  return (
+    <div className="flex h-full min-h-0 flex-col bg-gray-50">
+      <div className="shrink-0 border-b border-gray-200 bg-white px-5 py-4">
+        <div className="flex items-start justify-between gap-3">
+          <div className="min-w-0">
+            <h2 className="font-mono text-xl font-bold tracking-tight text-primary-600">
+              #{callRecording.call_short_id}
+            </h2>
+            <p className="mt-1 text-sm text-gray-600">Provider call from observability ingest</p>
+            <div className="mt-2.5 flex flex-wrap items-center gap-2">
+              <EventBadge event={callRecording.call_event ?? undefined} />
+              <PlatformBadge platform={callRecording.provider_platform ?? undefined} />
+              {duration ? (
+                <span className="inline-flex items-center gap-1 rounded-full border border-gray-200 bg-white px-2.5 py-0.5 text-xs text-gray-600">
+                  <Clock className="h-3 w-3 text-gray-400" />
+                  {duration}
+                </span>
+              ) : null}
+            </div>
+          </div>
+          <div className="flex shrink-0 items-center gap-2">
+            {hasTranscript ? (
+              <Button
+                variant="primary"
+                size="sm"
+                onClick={() => setShowEvalModal(true)}
+                leftIcon={<Sparkles className="h-4 w-4" />}
+              >
+                Evaluate
+              </Button>
+            ) : null}
+            <button
+              type="button"
+              onClick={() => setShowDelete(true)}
+              className="rounded-lg p-2 text-gray-400 hover:bg-rose-50 hover:text-rose-600"
+              aria-label="Delete call"
+            >
+              <Trash2 className="h-4 w-4" />
+            </button>
+            {onClose ? (
+              <button
+                type="button"
+                onClick={onClose}
+                aria-label="Close"
+                className="rounded-lg p-2 text-gray-500 hover:bg-gray-100 hover:text-gray-800"
+              >
+                <X className="h-5 w-5" />
+              </button>
+            ) : null}
+          </div>
+        </div>
+
+        <div className="mt-4 grid grid-cols-2 gap-3 text-xs sm:grid-cols-4">
+          <div>
+            <p className="font-medium text-gray-500">Agent</p>
+            <div className="mt-1">
+              <CallAgentLink agent={callRecording.agent} callData={callData} />
+            </div>
+          </div>
+          <div>
+            <p className="font-medium text-gray-500">Provider call ID</p>
+            <p className="mt-1 truncate font-mono text-gray-900" title={callRecording.provider_call_id ?? undefined}>
+              {callRecording.provider_call_id || 'N/A'}
+            </p>
+          </div>
+          <div>
+            <p className="font-medium text-gray-500">Status</p>
+            <p className="mt-1 capitalize text-gray-900">{callRecording.status || '—'}</p>
+          </div>
+          <div>
+            <p className="font-medium text-gray-500">Created</p>
+            <p className="mt-1 text-gray-900">
+              {callRecording.created_at
+                ? new Date(callRecording.created_at).toLocaleString()
+                : 'N/A'}
+            </p>
+          </div>
+        </div>
+      </div>
+
+      <div className="min-h-0 flex-1 overflow-y-auto p-5 space-y-5">
+        {fetchError ? (
+          <div className="rounded-lg border border-red-200 bg-red-50 px-4 py-3 text-sm text-red-800">
+            {fetchError}
+          </div>
+        ) : null}
+        {!detailsReady ? (
+          <div className="space-y-3">
+            <div className="h-40 animate-pulse rounded-xl bg-gray-100" />
+            <div className="h-56 animate-pulse rounded-xl bg-gray-100" />
+          </div>
+        ) : (
+          <>
+        {callRecording.provider_platform === 'retell' && callData ? (
+          <section className="rounded-xl border border-gray-200 bg-white p-5">
+            <RetellCallDetails callData={callData} hideTranscript={hasTranscript} />
+          </section>
+        ) : null}
+
+        {callRecording.provider_platform === 'vapi' && callData ? (
+          <section className="rounded-xl border border-gray-200 bg-white p-5">
+            <VapiCallDetails callData={callData} hideTranscript={hasTranscript} />
+          </section>
+        ) : null}
+
+        {callRecording.provider_platform === 'vobiz' && callData ? (
+          <section className="rounded-xl border border-gray-200 bg-white p-5">
+            <VobizCallDetails callData={callData} />
+          </section>
+        ) : null}
+
+        {(hasStorageRecording || providerRecordingUrl) && !isLiveCall ? (
+          <section className="rounded-xl border border-gray-200 bg-white p-5">
+            <h3 className="mb-3 text-sm font-semibold text-gray-900">Call recording</h3>
+            {audioLoading ? (
+              <div className="flex items-center gap-2 text-sm text-gray-500">
+                <Loader className="h-4 w-4 animate-spin" />
+                Loading recording…
+              </div>
+            ) : playbackUrl ? (
+              <div className="flex flex-col gap-3 sm:flex-row sm:items-center">
+                <audio controls src={playbackUrl} preload="metadata" className="w-full max-w-xl" />
+                <a
+                  href={playbackUrl}
+                  download={`call_${callRecording.call_short_id}.wav`}
+                  className="inline-flex items-center gap-2 text-sm text-primary-600 hover:text-primary-800"
+                >
+                  <Download className="h-4 w-4" />
+                  Download
+                </a>
+              </div>
+            ) : null}
+          </section>
+        ) : null}
+
+        {hasTranscript || isLiveCall ? (
+          <div className="grid grid-cols-1 gap-5 lg:grid-cols-3">
+            <div className="lg:col-span-2">
+              <div className="flex h-[480px] flex-col rounded-xl border border-gray-200 bg-white">
+                <div className="flex shrink-0 items-center justify-between border-b border-gray-100 px-4 py-3">
+                  <div className="flex items-center gap-2">
+                    <MessageSquare className="h-4 w-4 text-primary-500" />
+                    <span className="text-sm font-medium text-gray-900">
+                      {isLiveCall ? 'Live transcript' : 'Transcript'}
+                    </span>
+                    {isLiveCall ? (
+                      <span className="rounded-full bg-sky-100 px-2 py-0.5 text-xs text-sky-800 animate-pulse">
+                        Live
+                      </span>
+                    ) : null}
+                    <span className="rounded-full bg-gray-100 px-2 py-0.5 text-xs text-gray-600">
+                      {transcriptTurns.length} turns
+                    </span>
+                  </div>
+                </div>
+                <div className="flex-1 space-y-3 overflow-y-auto p-4">
+                  {transcriptTurns.length === 0 ? (
+                    <p className="py-8 text-center text-sm text-gray-500">Waiting for speech…</p>
+                  ) : (
+                    transcriptTurns.map((turn, index) => {
+                      const isUser = turn.role === 'user'
+                      return (
+                        <motion.div
+                          key={index}
+                          className={`flex ${isUser ? 'justify-end' : 'justify-start'}`}
+                          initial={{ opacity: 0, y: 8 }}
+                          animate={{ opacity: 1, y: 0 }}
+                        >
+                          <div
+                            className={`max-w-[80%] rounded-2xl px-4 py-2.5 ${
+                              isUser
+                                ? 'rounded-br-sm bg-primary-600 text-white'
+                                : 'rounded-bl-sm border border-gray-200 bg-gray-50 text-gray-800'
+                            }`}
+                          >
+                            <div
+                              className={`mb-0.5 flex items-center gap-2 ${
+                                isUser ? 'text-primary-200' : 'text-gray-400'
+                              }`}
+                            >
+                              <span className="text-[10px] font-semibold uppercase tracking-wider">
+                                {isUser ? 'Caller' : 'Agent'}
+                              </span>
+                              {turn.start_time ? (
+                                <span className="text-[10px] tabular-nums">
+                                  {formatMessageTime(turn.start_time)}
+                                </span>
+                              ) : null}
+                            </div>
+                            <p className="text-sm leading-relaxed">{turn.content}</p>
+                          </div>
+                        </motion.div>
+                      )
+                    })
+                  )}
+                </div>
+              </div>
+            </div>
+
+            <div className="space-y-3 rounded-xl border border-gray-200 bg-white p-5">
+              <h3 className="flex items-center gap-2 text-sm font-semibold text-gray-900">
+                <Phone className="h-4 w-4 text-primary-500" />
+                Call summary
+              </h3>
+              {callData?.startedAt || callData?.started_at ? (
+                <div>
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+                    Started
+                  </p>
+                  <p className="text-sm text-gray-900">
+                    {new Date(callData.startedAt || callData.started_at!).toLocaleString()}
+                  </p>
+                </div>
+              ) : null}
+              {callData?.endedAt || callData?.ended_at ? (
+                <div>
+                  <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+                    Ended
+                  </p>
+                  <p className="text-sm text-gray-900">
+                    {new Date(callData.endedAt || callData.ended_at!).toLocaleString()}
+                  </p>
+                </div>
+              ) : null}
+              {callData?.from_phone_number || callData?.to_phone_number ? (
+                <div className="space-y-2">
+                  {callData.from_phone_number ? (
+                    <div className="flex items-center gap-2 text-sm text-gray-700">
+                      <PhoneOutgoing className="h-3.5 w-3.5 text-gray-400" />
+                      <span className="font-mono">{callData.from_phone_number}</span>
+                    </div>
+                  ) : null}
+                  {callData.to_phone_number ? (
+                    <div className="flex items-center gap-2 text-sm text-gray-700">
+                      <PhoneIncoming className="h-3.5 w-3.5 text-gray-400" />
+                      <span className="font-mono">{callData.to_phone_number}</span>
+                    </div>
+                  ) : null}
+                </div>
+              ) : null}
+              {callData?.endedReason ? (
+                <div>
+                  <p className="mb-1 text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+                    End reason
+                  </p>
+                  <EndReasonBadge reason={callData.endedReason} />
+                </div>
+              ) : null}
+              {callData?.metadata && Object.keys(callData.metadata).length > 0 ? (
+                <div className="space-y-1.5">
+                  {Object.entries(callData.metadata).map(([key, value]) => (
+                    <div key={key} className="flex items-start gap-2">
+                      <Tag className="mt-0.5 h-3 w-3 shrink-0 text-gray-300" />
+                      <div className="min-w-0 text-xs">
+                        <span className="text-gray-500">{key}:</span>{' '}
+                        <span className="font-medium text-gray-800">{String(value)}</span>
+                      </div>
+                    </div>
+                  ))}
+                </div>
+              ) : null}
+            </div>
+          </div>
+        ) : null}
+          </>
+        )}
+      </div>
+
+      <ConfirmModal
+        title="Delete call"
+        description="This will permanently remove this call record."
+        isOpen={showDelete}
+        isLoading={deleteMutation.isPending}
+        onCancel={() => setShowDelete(false)}
+        onConfirm={() => deleteMutation.mutate()}
+      />
+
+      <AnimatePresence>
+        {showEvalModal ? (
+          <motion.div
+            className="fixed inset-0 z-[110] flex items-center justify-center"
+            initial={{ opacity: 0 }}
+            animate={{ opacity: 1 }}
+            exit={{ opacity: 0 }}
+          >
+            <div
+              className="absolute inset-0 bg-gray-500/75"
+              onClick={() => {
+                setShowEvalModal(false)
+                setSelectedEvaluator('')
+              }}
+            />
+            <motion.div
+              className="relative mx-4 w-full max-w-lg overflow-hidden rounded-2xl bg-white shadow-2xl"
+              initial={{ opacity: 0, scale: 0.95, y: 20 }}
+              animate={{ opacity: 1, scale: 1, y: 0 }}
+              exit={{ opacity: 0, scale: 0.95, y: 20 }}
+            >
+              <div className="border-b border-gray-100 px-6 py-5">
+                <div className="flex items-center justify-between">
+                  <div>
+                    <h2 className="text-lg font-semibold text-gray-900">Run evaluation</h2>
+                    <p className="mt-0.5 text-sm text-gray-500">
+                      Evaluate call{' '}
+                      <span className="font-mono font-semibold">{callRecording.call_short_id}</span>
+                    </p>
+                  </div>
+                  <button
+                    type="button"
+                    onClick={() => {
+                      setShowEvalModal(false)
+                      setSelectedEvaluator('')
+                    }}
+                    className="flex h-8 w-8 items-center justify-center rounded-lg text-gray-400 hover:bg-gray-100 hover:text-gray-600"
+                  >
+                    <X className="h-4 w-4" />
+                  </button>
+                </div>
+              </div>
+              <div className="space-y-4 p-6">
+                <select
+                  value={selectedEvaluator}
+                  onChange={(e) => setSelectedEvaluator(e.target.value)}
+                  className="w-full rounded-xl border border-gray-200 px-3 py-2.5 text-sm focus:border-primary-500 focus:ring-2 focus:ring-primary-500"
+                >
+                  <option value="">Choose an evaluator…</option>
+                  {evaluators.map((evaluator: { id: string; evaluator_id: string; custom_prompt?: string; name?: string; agent_id?: string }) => (
+                    <option key={evaluator.id} value={evaluator.id}>
+                      {evaluator.evaluator_id} —{' '}
+                      {evaluator.custom_prompt
+                        ? `Custom: ${evaluator.name || 'Unnamed'}`
+                        : `Agent: ${evaluator.agent_id?.substring(0, 8) || '?'}…`}
+                    </option>
+                  ))}
+                </select>
+                {evaluateMutation.isError ? (
+                  <div className="rounded-lg border border-rose-200 bg-rose-50 p-3 text-sm text-rose-700">
+                    {(evaluateMutation.error as { response?: { data?: { detail?: string } } })?.response
+                      ?.data?.detail || 'Failed to start evaluation'}
+                  </div>
+                ) : null}
+              </div>
+              <div className="flex justify-end gap-3 border-t border-gray-100 bg-gray-50/50 px-6 py-4">
+                <Button
+                  variant="ghost"
+                  size="sm"
+                  onClick={() => {
+                    setShowEvalModal(false)
+                    setSelectedEvaluator('')
+                  }}
+                >
+                  Cancel
+                </Button>
+                <Button
+                  size="sm"
+                  onClick={() => evaluateMutation.mutate(selectedEvaluator)}
+                  disabled={!selectedEvaluator || evaluateMutation.isPending}
+                  isLoading={evaluateMutation.isPending}
+                  leftIcon={!evaluateMutation.isPending ? <Sparkles className="h-4 w-4" /> : undefined}
+                >
+                  Run evaluation
+                </Button>
+              </div>
+            </motion.div>
+          </motion.div>
+        ) : null}
+      </AnimatePresence>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/ProviderCallTracePanel.tsx b/frontend/src/components/call-recordings/ProviderCallTracePanel.tsx
new file mode 100644
index 00000000..324d0729
--- /dev/null
+++ b/frontend/src/components/call-recordings/ProviderCallTracePanel.tsx
@@ -0,0 +1,112 @@
+import { useEffect } from 'react'
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { RefreshCw, X } from 'lucide-react'
+import { apiClient } from '../../lib/api'
+import { getCallRecordingPlaceholder, hasCallRecordingDetails } from '../../lib/callRecordingQuery'
+import { preferStereoWaveform, prefetchCallRecordingAudio } from '../../lib/waveformAudioCache'
+import VoiceAiCallDetailPanel from './VoiceAiCallDetailPanel'
+
+interface ProviderCallTracePanelProps {
+  callShortId: string
+  onClose?: () => void
+}
+
+export default function ProviderCallTracePanel({ callShortId, onClose }: ProviderCallTracePanelProps) {
+  const queryClient = useQueryClient()
+
+  const { data: recording, isError, error, isFetching } = useQuery({
+    queryKey: ['call-recording', callShortId],
+    queryFn: () => apiClient.getCallRecording(callShortId),
+    enabled: Boolean(callShortId),
+    placeholderData: () => getCallRecordingPlaceholder(queryClient, callShortId),
+    staleTime: 30_000,
+    refetchOnMount: (query) =>
+      hasCallRecordingDetails(query.state.data as Record<string, unknown> | undefined)
+        ? true
+        : 'always',
+  })
+
+  useEffect(() => {
+    if (!callShortId) return
+    const stereo = preferStereoWaveform(recording?.call_data, recording?.provider_platform)
+    prefetchCallRecordingAudio(callShortId, stereo)
+    if (!stereo) prefetchCallRecordingAudio(callShortId, false)
+  }, [callShortId, recording?.call_data, recording?.provider_platform])
+
+  const handleRefresh = async () => {
+    await apiClient.refreshCallRecording(callShortId)
+    await queryClient.invalidateQueries({ queryKey: ['call-recording', callShortId] })
+  }
+
+  const errorMessage =
+    isError && error
+      ? (error as { response?: { data?: { detail?: string } }; message?: string }).response?.data
+          ?.detail ||
+        (error as { message?: string }).message ||
+        'Failed to load call'
+      : null
+
+  const platform = recording?.provider_platform
+  const platformLabel =
+    platform === 'retell'
+      ? 'Retell'
+      : platform === 'vapi'
+        ? 'Vapi'
+        : platform === 'elevenlabs'
+          ? 'ElevenLabs'
+          : platform === 'smallest'
+            ? 'Smallest'
+            : platform || 'Provider'
+
+  return (
+    <div className="flex h-full min-h-0 flex-col bg-gray-50">
+      <div className="shrink-0 border-b border-gray-200 bg-white px-5 py-4">
+        <div className="flex items-start justify-between gap-3">
+          <div className="min-w-0">
+            <h2 className="font-mono text-xl font-bold tracking-tight text-primary-600">
+              #{callShortId}
+            </h2>
+            <p className="mt-1 text-sm text-gray-600">
+              {platformLabel} provider metrics and transcript
+            </p>
+          </div>
+          <div className="flex shrink-0 items-center gap-2">
+            <button
+              type="button"
+              onClick={handleRefresh}
+              disabled={isFetching}
+              className="inline-flex items-center gap-1 rounded-lg border border-gray-200 px-2.5 py-1.5 text-xs font-medium text-gray-700 hover:bg-gray-50 disabled:opacity-50"
+            >
+              <RefreshCw className={`h-3.5 w-3.5 ${isFetching ? 'animate-spin' : ''}`} />
+              Refresh
+            </button>
+            {onClose ? (
+              <button
+                type="button"
+                onClick={onClose}
+                aria-label="Close"
+                className="rounded-lg p-2 text-gray-500 hover:bg-gray-100 hover:text-gray-800"
+              >
+                <X className="h-5 w-5" />
+              </button>
+            ) : null}
+          </div>
+        </div>
+      </div>
+
+      <div className="min-h-0 flex-1 overflow-y-auto p-5">
+        {errorMessage ? (
+          <div className="rounded-lg border border-red-200 bg-red-50 px-4 py-3 text-sm text-red-800">
+            {errorMessage}
+          </div>
+        ) : recording ? (
+          <VoiceAiCallDetailPanel
+            recording={recording as Parameters<typeof VoiceAiCallDetailPanel>[0]['recording']}
+            callShortId={callShortId}
+            detailsLoading={isFetching && !hasCallRecordingDetails(recording as Record<string, unknown>)}
+          />
+        ) : null}
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/ProviderMetricsPanels.tsx b/frontend/src/components/call-recordings/ProviderMetricsPanels.tsx
new file mode 100644
index 00000000..45aa8edd
--- /dev/null
+++ b/frontend/src/components/call-recordings/ProviderMetricsPanels.tsx
@@ -0,0 +1,364 @@
+import { useMemo } from 'react'
+import {
+  Activity,
+  ArrowLeftRight,
+  Brain,
+  Gauge,
+  Mic,
+  Speaker,
+} from 'lucide-react'
+import { Cell, Pie, PieChart, ResponsiveContainer, Tooltip } from 'recharts'
+import { METRIC_COST_COLORS, STAGE_COLORS } from '../../lib/callDetailTheme'
+
+export interface CostSlice {
+  key: string
+  label: string
+  value: number
+  color: string
+}
+
+export interface PipelineStage {
+  key: string
+  label: string
+  ms: number
+  color: string
+  icon: typeof Mic
+}
+
+export interface TurnLatencyRow {
+  turn: number
+  stt?: number
+  endpointing?: number
+  llm?: number
+  tts?: number
+  total?: number
+}
+
+const COST_COLORS: Record<string, string> = METRIC_COST_COLORS
+
+function parseVapiCostSlices(callData: Record<string, unknown>): CostSlice[] {
+  const raw = callData as Record<string, any>
+  const breakdown = raw.costBreakdown || raw.cost_breakdown || {}
+  const fromBreakdown: CostSlice[] = [
+    { key: 'stt', label: 'STT', value: breakdown.stt ?? 0, color: COST_COLORS.stt },
+    { key: 'llm', label: 'LLM', value: breakdown.llm ?? 0, color: COST_COLORS.llm },
+    { key: 'tts', label: 'TTS', value: breakdown.tts ?? 0, color: COST_COLORS.tts },
+    { key: 'transport', label: 'Transport', value: breakdown.transport ?? 0, color: COST_COLORS.transport },
+    { key: 'vapi', label: 'Vapi Platform', value: breakdown.vapi ?? 0, color: COST_COLORS.vapi },
+  ]
+  const analysisTotal =
+    (breakdown.analysis?.summary ?? 0) +
+    (breakdown.analysis?.success_evaluation ?? 0) +
+    (breakdown.analysis?.structured_data ?? 0)
+  if (analysisTotal > 0) {
+    fromBreakdown.push({ key: 'analysis', label: 'Analysis', value: analysisTotal, color: COST_COLORS.analysis })
+  }
+
+  const slices = fromBreakdown.filter((s) => s.value > 0)
+  if (slices.length) return slices
+
+  for (const item of raw.costs || []) {
+    if (!item || typeof item !== 'object') continue
+    const type = String(item.type || 'other').toLowerCase()
+    const key = type.includes('transcriber') ? 'stt' : type.includes('model') ? 'llm' : type.includes('voice') ? 'tts' : type.includes('vapi') ? 'vapi' : type
+    const label = String(item.type || 'Cost')
+    const value = Number(item.cost ?? 0)
+    if (value <= 0) continue
+    const existing = slices.find((s) => s.key === key)
+    if (existing) existing.value += value
+    else slices.push({ key, label, value, color: COST_COLORS[key] || COST_COLORS.other })
+  }
+  return slices
+}
+
+function parseVapiPipeline(callData: Record<string, unknown>): {
+  stages: PipelineStage[]
+  turns: TurnLatencyRow[]
+  avgTurnMs: number | null
+} {
+  const raw = callData as Record<string, any>
+  const perf = raw.artifact?.performanceMetrics || {}
+  const stats = raw.analysis?.latency_stats || {}
+
+  const stages: PipelineStage[] = [
+    { key: 'transport_in', label: 'From Transport', ms: perf.fromTransportLatencyAverage ?? stats.from_transport_latency_avg ?? 0, color: STAGE_COLORS.s2s, icon: ArrowLeftRight },
+    { key: 'stt', label: 'Transcriber', ms: perf.transcriberLatencyAverage ?? stats.transcriber_latency_avg ?? 0, color: STAGE_COLORS.stt, icon: Mic },
+    { key: 'endpointing', label: 'Endpointing', ms: perf.endpointingLatencyAverage ?? stats.endpointing_latency_avg ?? 0, color: STAGE_COLORS.endpointing, icon: Gauge },
+    { key: 'llm', label: 'LLM', ms: perf.modelLatencyAverage ?? stats.model_latency_avg ?? 0, color: STAGE_COLORS.llm, icon: Brain },
+    { key: 'tts', label: 'Voice', ms: perf.voiceLatencyAverage ?? stats.voice_latency_avg ?? 0, color: STAGE_COLORS.tts, icon: Speaker },
+    { key: 'transport_out', label: 'To Transport', ms: perf.toTransportLatencyAverage ?? stats.to_transport_latency_avg ?? 0, color: STAGE_COLORS.transport, icon: ArrowLeftRight },
+  ].filter((s) => s.ms > 0)
+
+  const turnRows: TurnLatencyRow[] = (perf.turnLatencies || []).map((turn: any, idx: number) => ({
+    turn: Number(turn.turnNumber ?? turn.turn ?? idx + 1),
+    stt: turn.transcriberLatency,
+    endpointing: turn.endpointingLatency,
+    llm: turn.modelLatency,
+    tts: turn.voiceLatency,
+    total: turn.turnLatency,
+  }))
+
+  const avgTurnMs =
+    perf.turnLatencyAverage ??
+    stats.turn_latency_avg ??
+    (turnRows.length
+      ? Math.round(turnRows.reduce((sum, t) => sum + (t.total ?? 0), 0) / turnRows.length)
+      : null)
+
+  return { stages, turns: turnRows, avgTurnMs }
+}
+
+function parseRetellCostSlices(callData: Record<string, unknown>): CostSlice[] {
+  const raw = callData as Record<string, any>
+  const colors = [STAGE_COLORS.stt, STAGE_COLORS.tts, STAGE_COLORS.llm, STAGE_COLORS.transport, STAGE_COLORS.s2s]
+  return (raw.call_cost?.product_costs || []).map((item: any, idx: number) => ({
+    key: String(item.product || idx),
+    label: String(item.product || 'Product').replace(/_/g, ' '),
+    value: Number(item.cost ?? 0),
+    color: colors[idx % colors.length],
+  })).filter((s: CostSlice) => s.value > 0)
+}
+
+export function ProviderCostPanel({
+  callData,
+  platform,
+  totalCost,
+  durationSec,
+}: {
+  callData: Record<string, unknown>
+  platform?: string | null
+  totalCost?: number | null
+  durationSec?: number | null
+}) {
+  const slices = useMemo(() => {
+    if (platform === 'retell') return parseRetellCostSlices(callData)
+    return parseVapiCostSlices(callData)
+  }, [callData, platform])
+
+  const total =
+    totalCost ??
+    (slices.reduce((sum, s) => sum + s.value, 0) ||
+      Number((callData as any).cost ?? (callData as any).call_cost?.combined_cost ?? 0))
+
+  const topDriver = slices.length
+    ? [...slices].sort((a, b) => b.value - a.value)[0]
+    : null
+
+  if (!slices.length && !total) {
+    return <p className="py-12 text-center text-sm text-gray-500">No cost data available yet.</p>
+  }
+
+  return (
+    <div className="space-y-5">
+      <div className="grid grid-cols-3 gap-3">
+        <div className="rounded-xl border border-gray-200 bg-white p-4">
+          <p className="text-xs font-medium text-gray-500">Total cost</p>
+          <p className="mt-1 text-2xl font-bold text-gray-900">${total.toFixed(4)}</p>
+        </div>
+        <div className="rounded-xl border border-gray-200 bg-white p-4">
+          <p className="text-xs font-medium text-gray-500">Duration</p>
+          <p className="mt-1 text-2xl font-bold text-gray-900">
+            {durationSec != null ? `${Math.round(durationSec)}s` : '—'}
+          </p>
+        </div>
+        <div className="rounded-xl border border-gray-200 bg-white p-4">
+          <p className="text-xs font-medium text-gray-500">Categories</p>
+          <p className="mt-1 text-2xl font-bold text-gray-900">{slices.length}</p>
+        </div>
+      </div>
+
+      <div className="grid gap-4 lg:grid-cols-[220px_1fr]">
+        <div className="relative flex items-center justify-center rounded-xl border border-gray-200 bg-white p-4">
+          <ResponsiveContainer width="100%" height={200}>
+            <PieChart>
+              <Pie
+                data={slices}
+                dataKey="value"
+                nameKey="label"
+                cx="50%"
+                cy="50%"
+                innerRadius={52}
+                outerRadius={78}
+                paddingAngle={2}
+                stroke="none"
+              >
+                {slices.map((slice) => (
+                  <Cell key={slice.key} fill={slice.color} />
+                ))}
+              </Pie>
+              <Tooltip formatter={(v: number) => `$${v.toFixed(4)}`} />
+            </PieChart>
+          </ResponsiveContainer>
+          <div className="pointer-events-none absolute inset-0 flex flex-col items-center justify-center">
+            <p className="text-lg font-bold text-gray-900">${total.toFixed(2)}</p>
+            <p className="text-[10px] uppercase tracking-wider text-gray-400">Total</p>
+          </div>
+        </div>
+
+        <div className="space-y-2 rounded-xl border border-gray-200 bg-white p-4">
+          {slices.map((slice) => {
+            const pct = total > 0 ? (slice.value / total) * 100 : 0
+            return (
+              <div key={slice.key} className="flex items-center gap-3">
+                <span className="h-2.5 w-2.5 shrink-0 rounded-full" style={{ backgroundColor: slice.color }} />
+                <div className="min-w-0 flex-1">
+                  <div className="flex items-center justify-between gap-2 text-sm">
+                    <span className="truncate text-gray-700">{slice.label}</span>
+                    <span className="shrink-0 font-medium text-gray-900">${slice.value.toFixed(4)}</span>
+                  </div>
+                  <div className="mt-1 h-1.5 overflow-hidden rounded-full bg-gray-100">
+                    <div className="h-full rounded-full" style={{ width: `${pct}%`, backgroundColor: slice.color }} />
+                  </div>
+                  <p className="mt-0.5 text-[10px] text-gray-400">{pct.toFixed(1)}% of total</p>
+                </div>
+              </div>
+            )
+          })}
+        </div>
+      </div>
+
+      {topDriver ? (
+        <div className="rounded-xl border border-violet-100 bg-violet-50/60 px-4 py-3 text-sm text-violet-900">
+          <span className="font-medium">Top driver:</span> {topDriver.label} accounts for{' '}
+          {total > 0 ? ((topDriver.value / total) * 100).toFixed(1) : '0'}% (${topDriver.value.toFixed(4)})
+        </div>
+      ) : null}
+
+      <div className="grid gap-3 sm:grid-cols-3">
+        {slices
+          .filter((s) => ['stt', 'llm', 'tts'].includes(s.key))
+          .map((slice) => {
+            const breakdown = (callData as any).costBreakdown || (callData as any).cost_breakdown || {}
+            const promptTokens = breakdown.llmPromptTokens ?? breakdown.llm_prompt_tokens
+            const ttsChars = breakdown.ttsCharacters ?? breakdown.tts_characters
+            const detail =
+              slice.key === 'llm'
+                ? promptTokens != null
+                  ? `Prompt ${Number(promptTokens).toLocaleString()} tokens`
+                  : 'Language model usage'
+                : slice.key === 'tts'
+                  ? ttsChars != null
+                    ? `${Number(ttsChars).toLocaleString()} characters`
+                    : 'Text-to-speech synthesis'
+                  : 'Speech-to-text processing'
+            return (
+              <div key={slice.key} className="rounded-xl border border-gray-200 bg-white p-4">
+                <p className="text-xs font-semibold uppercase tracking-wider text-gray-500">{slice.label}</p>
+                <p className="mt-1 text-xl font-bold text-gray-900">${slice.value.toFixed(4)}</p>
+                <p className="mt-2 text-xs text-gray-500">{detail}</p>
+              </div>
+            )
+          })}
+      </div>
+    </div>
+  )
+}
+
+export function ProviderLatencyPanel({
+  callData,
+  platform,
+}: {
+  callData: Record<string, unknown>
+  platform?: string | null
+}) {
+  const { stages, turns, avgTurnMs } = useMemo(() => {
+    if (platform === 'vapi') return parseVapiPipeline(callData)
+    const raw = callData as Record<string, any>
+    const latency = raw.latency || {}
+    const stages: PipelineStage[] = [
+      { key: 'asr', label: 'ASR', ms: latency.asr?.p50 ?? 0, color: STAGE_COLORS.stt, icon: Mic },
+      { key: 'llm', label: 'LLM', ms: latency.llm?.p50 ?? 0, color: STAGE_COLORS.llm, icon: Brain },
+      { key: 'tts', label: 'TTS', ms: latency.tts?.p50 ?? 0, color: STAGE_COLORS.tts, icon: Speaker },
+      { key: 'e2e', label: 'E2E', ms: latency.e2e?.p50 ?? 0, color: STAGE_COLORS.endpointing, icon: Activity },
+    ].filter((s) => s.ms > 0)
+    return { stages, turns: [] as TurnLatencyRow[], avgTurnMs: latency.e2e?.p50 ?? null }
+  }, [callData, platform])
+
+  const pipelineTotal = stages.reduce((sum, s) => sum + s.ms, 0) || 1
+
+  if (!stages.length && !turns.length) {
+    return <p className="py-12 text-center text-sm text-gray-500">Latency data not available yet.</p>
+  }
+
+  return (
+    <div className="space-y-5">
+      <div className="grid grid-cols-2 gap-3">
+        <div className="rounded-xl border border-gray-200 bg-white p-4">
+          <p className="text-xs font-medium text-gray-500">Turns</p>
+          <p className="mt-1 text-2xl font-bold text-gray-900">{turns.length || '—'}</p>
+        </div>
+        <div className="rounded-xl border border-gray-200 bg-white p-4">
+          <p className="text-xs font-medium text-gray-500">Avg turn</p>
+          <p className="mt-1 text-2xl font-bold text-gray-900">
+            {avgTurnMs != null ? `${Math.round(avgTurnMs)}ms` : '—'}
+          </p>
+        </div>
+      </div>
+
+      {stages.length > 0 ? (
+        <div className="rounded-xl border border-gray-200 bg-white p-4">
+          <p className="mb-3 text-sm font-semibold text-gray-900">Pipeline breakdown</p>
+          <div className="flex h-3 overflow-hidden rounded-full bg-gray-100">
+            {stages.map((stage) => (
+              <div
+                key={stage.key}
+                title={`${stage.label}: ${Math.round(stage.ms)}ms`}
+                style={{ width: `${(stage.ms / pipelineTotal) * 100}%`, backgroundColor: stage.color }}
+              />
+            ))}
+          </div>
+          <div className="mt-4 grid gap-3 sm:grid-cols-2 lg:grid-cols-3">
+            {stages.map((stage) => {
+              const Icon = stage.icon
+              return (
+                <div key={stage.key} className="rounded-lg border border-gray-100 bg-gray-50/80 p-3">
+                  <div className="flex items-center gap-2">
+                    <span className="h-2 w-2 rounded-full" style={{ backgroundColor: stage.color }} />
+                    <Icon className="h-3.5 w-3.5 text-gray-500" />
+                    <span className="text-xs font-medium text-gray-600">{stage.label}</span>
+                  </div>
+                  <p className="mt-2 text-lg font-bold text-gray-900">{Math.round(stage.ms)}ms</p>
+                </div>
+              )
+            })}
+          </div>
+        </div>
+      ) : null}
+
+      {turns.length > 0 ? (
+        <div className="overflow-hidden rounded-xl border border-gray-200 bg-white">
+          <div className="border-b border-gray-100 px-4 py-3">
+            <p className="text-sm font-semibold text-gray-900">Latency per turn</p>
+            <p className="text-xs text-gray-500">All values in milliseconds</p>
+          </div>
+          <div className="overflow-x-auto">
+            <table className="min-w-full text-sm">
+              <thead className="bg-gray-50 text-left text-[10px] font-semibold uppercase tracking-wider text-gray-500">
+                <tr>
+                  <th className="px-4 py-2">Turn</th>
+                  <th className="px-4 py-2">Total</th>
+                  <th className="px-4 py-2">STT</th>
+                  <th className="px-4 py-2">Endpointing</th>
+                  <th className="px-4 py-2">LLM</th>
+                  <th className="px-4 py-2">Voice</th>
+                </tr>
+              </thead>
+              <tbody>
+                {turns.map((row) => (
+                  <tr key={row.turn} className="border-t border-gray-100">
+                    <td className="px-4 py-2.5 font-medium text-gray-900">#{row.turn}</td>
+                    <td className="px-4 py-2.5 font-semibold text-gray-900">{row.total ?? '—'}ms</td>
+                    <td className="px-4 py-2.5 text-gray-600">{row.stt ?? '—'}ms</td>
+                    <td className="px-4 py-2.5 text-gray-600">{row.endpointing ?? '—'}ms</td>
+                    <td className="px-4 py-2.5 text-gray-600">{row.llm ?? '—'}ms</td>
+                    <td className="px-4 py-2.5 text-gray-600">{row.tts ?? '—'}ms</td>
+                  </tr>
+                ))}
+              </tbody>
+            </table>
+          </div>
+        </div>
+      ) : null}
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/RetellCallDetails.tsx b/frontend/src/components/call-recordings/RetellCallDetails.tsx
index 16bb4dcc..f9eeeb4b 100644
--- a/frontend/src/components/call-recordings/RetellCallDetails.tsx
+++ b/frontend/src/components/call-recordings/RetellCallDetails.tsx
@@ -1,361 +1,416 @@
-import { useState } from 'react'
-import {
-  DollarSign, MessageSquare, TrendingUp, Download, Activity, Server
-} from 'lucide-react'
-import {
-  BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer,
-  PieChart, Pie, Cell
-} from 'recharts'
-
-interface RetellCallData {
-  call_type?: string
-  call_id?: string
-  agent_id?: string
-  agent_name?: string
-  call_status?: string
-  start_timestamp?: number
-  end_timestamp?: number
-  duration_ms?: number
-  transcript?: string
-  transcript_object?: Array<{
-    role: string
-    content: string
-    words?: Array<{
-      word: string
-      start: number
-      end: number
-    }>
-  }>
-  recording_url?: string
-  recording_multi_channel_url?: string
-  latency?: {
-    e2e?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
-    asr?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
-    llm?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
-    tts?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
-  }
-  call_cost?: {
-    total_duration_seconds?: number
-    combined_cost?: number
-    product_costs?: Array<{
-      product: string
-      cost: number
-      unit_price: number
-    }>
-  }
-  call_analysis?: {
-    call_summary?: string
-    in_voicemail?: boolean
-    user_sentiment?: string
-    call_successful?: boolean
-  }
-  disconnection_reason?: string
-  transfer_destination?: string
-  metadata?: Record<string, any>
-  retell_llm_dynamic_variables?: Record<string, any>
-  collected_dynamic_variables?: Record<string, any>
-}
-
-interface RetellCallDetailsProps {
-  callData: RetellCallData
-  hideTranscript?: boolean
-}
-
-const COLORS = ['#0088FE', '#00C49F', '#FFBB28', '#FF8042', '#8884d8'];
-
-export default function RetellCallDetails({ callData, hideTranscript = false }: RetellCallDetailsProps) {
-  const [activeTab, setActiveTab] = useState<'overview' | 'transcript'>('overview')
-
-  const formatDuration = (ms?: number) => {
-    if (!ms) return 'N/A'
-    const seconds = Math.floor(ms / 1000)
-    const minutes = Math.floor(seconds / 60)
-    const remainingSeconds = seconds % 60
-    return `${minutes}m ${remainingSeconds}s`
-  }
-
-  const formatTimestamp = (timestamp?: number) => {
-    if (!timestamp) return 'N/A'
-    return new Date(timestamp).toLocaleString()
-  }
-
-  const getSentimentColor = (sentiment?: string) => {
-    if (!sentiment) return 'text-gray-500 bg-gray-100'
-    const s = sentiment.toLowerCase()
-    if (s.includes('positive') || s.includes('happy')) return 'text-green-700 bg-green-100'
-    if (s.includes('negative') || s.includes('angry')) return 'text-red-700 bg-red-100'
-    return 'text-blue-700 bg-blue-100'
-  }
-
-  // Prepare Chart Data
-  const latencyData = [
-    { name: 'E2E', p50: callData.latency?.e2e?.p50, p90: callData.latency?.e2e?.p90, max: callData.latency?.e2e?.max },
-    { name: 'ASR', p50: callData.latency?.asr?.p50, p90: callData.latency?.asr?.p90, max: callData.latency?.asr?.max },
-    { name: 'LLM', p50: callData.latency?.llm?.p50, p90: callData.latency?.llm?.p90, max: callData.latency?.llm?.max },
-    { name: 'TTS', p50: callData.latency?.tts?.p50, p90: callData.latency?.tts?.p90, max: callData.latency?.tts?.max },
-  ].filter(item => item.p50 !== undefined)
-
-  const costData = callData.call_cost?.product_costs?.map(item => ({
-    name: item.product,
-    value: item.cost
-  })) || []
-
-  const SummaryCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-      <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-        <TrendingUp className="h-5 w-5 text-indigo-600" />
-        Call Analysis
-      </h3>
-
-      {callData.call_analysis ? (
-        <div className="space-y-6">
-          <div className="p-4 bg-indigo-50 rounded-lg border border-indigo-100">
-            <p className="text-sm font-medium text-indigo-900 mb-2">Summary</p>
-            <p className="text-sm text-indigo-800 leading-relaxed">
-              {callData.call_analysis.call_summary || "No summary available."}
-            </p>
-          </div>
-
-          <div className="grid grid-cols-2 gap-4">
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Sentiment</p>
-              <div className="flex items-center gap-2">
-                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${getSentimentColor(callData.call_analysis.user_sentiment)}`}>
-                  {callData.call_analysis.user_sentiment || 'Neutral'}
-                </span>
-              </div>
-            </div>
-
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Success Status</p>
-              <div className="flex items-center gap-2">
-                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${callData.call_analysis.call_successful
-                  ? 'text-green-700 bg-green-100'
-                  : 'text-yellow-700 bg-yellow-100'
-                  }`}>
-                  {callData.call_analysis.call_successful ? 'Successful' : 'Unsuccessful'}
-                </span>
-              </div>
-            </div>
-
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Voicemail</p>
-              <span className="text-sm font-medium text-gray-900">
-                {callData.call_analysis.in_voicemail ? 'Yes' : 'No'}
-              </span>
-            </div>
-
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Ended Reason</p>
-              <span className="text-sm font-medium text-gray-900 capitalize">
-                {callData.disconnection_reason?.replace(/_/g, ' ') || 'Normal'}
-              </span>
-            </div>
-          </div>
-        </div>
-      ) : (
-        <div className="text-center py-8 text-gray-500">Analysis not available</div>
-      )}
-    </div>
-  )
-
-  const TranscriptCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col h-[600px]">
-      <div className="flex items-center justify-between mb-4 flex-shrink-0">
-        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
-          <MessageSquare className="h-5 w-5 text-indigo-600" />
-          Transcript
-        </h3>
-        {callData.recording_url && (
-          <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
-            <audio controls src={callData.recording_url} className="h-8 w-64" />
-            <a href={callData.recording_url} download className="text-gray-500 hover:text-indigo-600 p-1">
-              <Download className="h-4 w-4" />
-            </a>
-          </div>
-        )}
-      </div>
-
-      <div className="flex-1 overflow-y-auto space-y-4 pr-2 custom-scrollbar">
-        {callData.transcript_object?.map((msg, idx) => (
-          <div key={idx} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
-            <div className={`max-w-[80%] rounded-2xl px-4 py-3 ${msg.role === 'user'
-              ? 'bg-indigo-600 text-white rounded-br-none'
-              : 'bg-gray-100 text-gray-800 rounded-bl-none'
-              }`}>
-              <div className="flex items-center gap-2 mb-1 opacity-80">
-                <span className="text-xs font-semibold uppercase tracking-wider">
-                  {msg.role === 'user' ? 'User' : (callData.agent_name || 'Agent')}
-                </span>
-                {msg.words && msg.words.length > 0 && (
-                  <span className="text-[10px]">
-                    {msg.words[0].start.toFixed(1)}s
-                  </span>
-                )}
-              </div>
-              <p className="text-sm leading-relaxed whitespace-pre-wrap">{msg.content}</p>
-            </div>
-          </div>
-        ))}
-      </div>
-    </div>
-  )
-
-  const StatsParams = () => (
-    <div className="space-y-6">
-      {/* Cost Card */}
-      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-          <DollarSign className="h-5 w-5 text-indigo-600" />
-          Cost Breakdown
-        </h3>
-        <div className="flex items-center justify-between">
-          <div className="w-1/2">
-            <div className="mb-4">
-              <p className="text-sm text-gray-500">Total Cost</p>
-              <p className="text-3xl font-bold text-gray-900">
-                ${callData.call_cost?.combined_cost?.toFixed(3) || '0.000'}
-              </p>
-              <p className="text-xs text-gray-500 mt-1">
-                Duration: {callData.call_cost?.total_duration_seconds}s
-              </p>
-            </div>
-            <div className="space-y-2">
-              {callData.call_cost?.product_costs?.map((prod, i) => (
-                <div key={i} className="flex justify-between items-center text-sm">
-                  <span className="text-gray-600 capitalize">{prod.product.replace(/_/g, ' ')}</span>
-                  <span className="font-medium text-gray-900">${prod.cost.toFixed(3)}</span>
-                </div>
-              ))}
-            </div>
-          </div>
-          <div className="w-1/2 h-40">
-            <ResponsiveContainer width="100%" height="100%">
-              <PieChart>
-                <Pie
-                  data={costData}
-                  cx="50%"
-                  cy="50%"
-                  innerRadius={0}
-                  outerRadius={60}
-                  paddingAngle={5}
-                  dataKey="value"
-                >
-                  {costData.map((_entry, index) => (
-                    <Cell key={`cell-${index}`} fill={COLORS[index % COLORS.length]} />
-                  ))}
-                </Pie>
-                <Tooltip formatter={(value: number) => `$${value.toFixed(3)}`} />
-              </PieChart>
-            </ResponsiveContainer>
-          </div>
-        </div>
-      </div>
-
-      {/* Latency Chart */}
-      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-          <Activity className="h-5 w-5 text-indigo-600" />
-          Latency Performance
-        </h3>
-        <div className="h-64">
-          <ResponsiveContainer width="100%" height="100%">
-            <BarChart data={latencyData}>
-              <CartesianGrid strokeDasharray="3 3" vertical={false} />
-              <XAxis dataKey="name" />
-              <YAxis unit="ms" />
-              <Tooltip
-                contentStyle={{ borderRadius: '8px', border: 'none', boxShadow: '0 4px 6px -1px rgb(0 0 0 / 0.1)' }}
-                cursor={{ fill: '#F3F4F6' }}
-              />
-              <Legend />
-              <Bar dataKey="p50" name="P50 (ms)" fill="#818cf8" radius={[4, 4, 0, 0]} />
-              <Bar dataKey="p90" name="P90 (ms)" fill="#4f46e5" radius={[4, 4, 0, 0]} />
-              <Bar dataKey="max" name="Max (ms)" fill="#312e81" radius={[4, 4, 0, 0]} />
-            </BarChart>
-          </ResponsiveContainer>
-        </div>
-      </div>
-
-      {/* System Info */}
-      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-          <Server className="h-5 w-5 text-indigo-600" />
-          System Details
-        </h3>
-        <div className="grid grid-cols-2 gap-4 text-sm">
-          <div>
-            <p className="text-gray-500 mb-1">Agent ID</p>
-            <p className="font-mono bg-gray-50 p-1 rounded text-gray-700 truncate">{callData.agent_id}</p>
-          </div>
-          <div>
-            <p className="text-gray-500 mb-1">Call ID</p>
-            <p className="font-mono bg-gray-50 p-1 rounded text-gray-700 truncate">{callData.call_id}</p>
-          </div>
-          <div>
-            <p className="text-gray-500 mb-1">Start Time</p>
-            <p className="text-gray-700">{formatTimestamp(callData.start_timestamp)}</p>
-          </div>
-          <div>
-            <p className="text-gray-500 mb-1">Duration</p>
-            <p className="text-gray-700">{formatDuration(callData.duration_ms)}</p>
-          </div>
-        </div>
-      </div>
-
-    </div>
-  )
-
-  if (hideTranscript) {
-    return (
-      <div className="space-y-6">
-        <SummaryCard />
-        <StatsParams />
-      </div>
-    )
-  }
-
-  return (
-    <div className="space-y-6">
-      {/* Navigation Tabs */}
-      <div className="flex border-b border-gray-200">
-        <button
-          onClick={() => setActiveTab('overview')}
-          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'overview'
-            ? 'border-indigo-600 text-indigo-600'
-            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-            }`}
-        >
-          Overview
-        </button>
-        <button
-          onClick={() => setActiveTab('transcript')}
-          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'transcript'
-            ? 'border-indigo-600 text-indigo-600'
-            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-            }`}
-        >
-          Transcript
-        </button>
-      </div>
-
-      {activeTab === 'overview' && (
-        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
-          <div className="lg:col-span-2 space-y-6">
-            <SummaryCard />
-            <TranscriptCard />
-          </div>
-          <div className="lg:col-span-1">
-            <StatsParams />
-          </div>
-        </div>
-      )}
-
-      {activeTab === 'transcript' && (
-        <div className="space-y-6">
-          <TranscriptCard />
-        </div>
-      )}
-    </div>
-  )
-}
+import { useState } from 'react'
+import {
+  DollarSign, MessageSquare, TrendingUp, Download, Activity, Server
+} from 'lucide-react'
+import {
+  BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer,
+  PieChart, Pie, Cell
+} from 'recharts'
+
+interface RetellCallData {
+  call_type?: string
+  call_id?: string
+  agent_id?: string
+  agent_name?: string
+  call_status?: string
+  start_timestamp?: number
+  end_timestamp?: number
+  duration_ms?: number
+  transcript?: string
+  transcript_object?: Array<{
+    role: string
+    content: string
+    words?: Array<{
+      word: string
+      start: number
+      end: number
+    }>
+  }>
+  recording_url?: string
+  recording_multi_channel_url?: string
+  latency?: {
+    e2e?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
+    asr?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
+    llm?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
+    tts?: { p50?: number; p90?: number; p95?: number; p99?: number; max?: number; min?: number }
+  }
+  call_cost?: {
+    total_duration_seconds?: number
+    combined_cost?: number
+    product_costs?: Array<{
+      product: string
+      cost: number
+      unit_price: number
+    }>
+  }
+  call_analysis?: {
+    call_summary?: string
+    in_voicemail?: boolean
+    user_sentiment?: string
+    call_successful?: boolean
+  }
+  disconnection_reason?: string
+  transfer_destination?: string
+  metadata?: Record<string, any>
+  retell_llm_dynamic_variables?: Record<string, any>
+  collected_dynamic_variables?: Record<string, any>
+}
+
+export type RetellDetailSection = 'full' | 'transcript' | 'cost' | 'latency' | 'analysis' | 'system'
+
+interface RetellCallDetailsProps {
+  callData: RetellCallData
+  hideTranscript?: boolean
+  section?: RetellDetailSection
+  compact?: boolean
+  evaluatorAnalysis?: {
+    call_summary?: string
+    user_sentiment?: string
+    call_successful?: boolean
+  } | null
+}
+
+const COLORS = ['#0088FE', '#00C49F', '#FFBB28', '#FF8042', '#8884d8'];
+
+export default function RetellCallDetails({
+  callData,
+  hideTranscript = false,
+  section = 'full',
+  compact = false,
+  evaluatorAnalysis = null,
+}: RetellCallDetailsProps) {
+  const [activeTab, setActiveTab] = useState<'overview' | 'transcript'>('overview')
+
+  const formatDuration = (ms?: number) => {
+    if (!ms) return 'N/A'
+    const seconds = Math.floor(ms / 1000)
+    const minutes = Math.floor(seconds / 60)
+    const remainingSeconds = seconds % 60
+    return `${minutes}m ${remainingSeconds}s`
+  }
+
+  const formatTimestamp = (timestamp?: number) => {
+    if (!timestamp) return 'N/A'
+    return new Date(timestamp).toLocaleString()
+  }
+
+  const getSentimentColor = (sentiment?: string) => {
+    if (!sentiment) return 'text-gray-500 bg-gray-100'
+    const s = sentiment.toLowerCase()
+    if (s.includes('positive') || s.includes('happy')) return 'text-green-700 bg-green-100'
+    if (s.includes('negative') || s.includes('angry')) return 'text-red-700 bg-red-100'
+    return 'text-blue-700 bg-blue-100'
+  }
+
+  // Prepare Chart Data
+  const latencyData = [
+    { name: 'E2E', p50: callData.latency?.e2e?.p50, p90: callData.latency?.e2e?.p90, max: callData.latency?.e2e?.max },
+    { name: 'ASR', p50: callData.latency?.asr?.p50, p90: callData.latency?.asr?.p90, max: callData.latency?.asr?.max },
+    { name: 'LLM', p50: callData.latency?.llm?.p50, p90: callData.latency?.llm?.p90, max: callData.latency?.llm?.max },
+    { name: 'TTS', p50: callData.latency?.tts?.p50, p90: callData.latency?.tts?.p90, max: callData.latency?.tts?.max },
+  ].filter(item => item.p50 !== undefined)
+
+  const costData = callData.call_cost?.product_costs?.map(item => ({
+    name: item.product,
+    value: item.cost
+  })) || []
+
+  const mergedAnalysis = callData.call_analysis || (evaluatorAnalysis
+    ? {
+        call_summary: evaluatorAnalysis.call_summary,
+        user_sentiment: evaluatorAnalysis.user_sentiment,
+        call_successful: evaluatorAnalysis.call_successful,
+      }
+    : undefined)
+  const usingEvaluatorSummary = !callData.call_analysis && !!evaluatorAnalysis?.call_summary
+  const publicLogUrl = (callData as any).public_log_url as string | undefined
+
+  const SummaryCard = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+      <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+        <TrendingUp className="h-5 w-5 text-indigo-600" />
+        Call Analysis
+      </h3>
+
+      {mergedAnalysis ? (
+        <div className="space-y-6">
+          {usingEvaluatorSummary && (
+            <p className="text-xs text-gray-500">
+              Retell post-call analysis is still pending — showing EfficientAI-generated summary from the evaluation.
+            </p>
+          )}
+          <div className="p-4 bg-indigo-50 rounded-lg border border-indigo-100">
+            <p className="text-sm font-medium text-indigo-900 mb-2">Summary</p>
+            <p className="text-sm text-indigo-800 leading-relaxed">
+              {mergedAnalysis.call_summary || "No summary available."}
+            </p>
+          </div>
+
+          <div className="grid grid-cols-2 gap-4">
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Sentiment</p>
+              <div className="flex items-center gap-2">
+                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${getSentimentColor(mergedAnalysis.user_sentiment)}`}>
+                  {mergedAnalysis.user_sentiment || 'Neutral'}
+                </span>
+              </div>
+            </div>
+
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Success Status</p>
+              <div className="flex items-center gap-2">
+                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${mergedAnalysis.call_successful
+                  ? 'text-green-700 bg-green-100'
+                  : 'text-yellow-700 bg-yellow-100'
+                  }`}>
+                  {mergedAnalysis.call_successful ? 'Successful' : 'Unsuccessful'}
+                </span>
+              </div>
+            </div>
+
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Voicemail</p>
+              <span className="text-sm font-medium text-gray-900">
+                {callData.call_analysis?.in_voicemail ? 'Yes' : 'No'}
+              </span>
+            </div>
+
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Ended Reason</p>
+              <span className="text-sm font-medium text-gray-900 capitalize">
+                {callData.disconnection_reason?.replace(/_/g, ' ') || 'Normal'}
+              </span>
+            </div>
+          </div>
+
+          {publicLogUrl && (
+            <a
+              href={publicLogUrl}
+              target="_blank"
+              rel="noopener noreferrer"
+              className="inline-flex text-sm text-indigo-600 hover:text-indigo-800"
+            >
+              Open Retell call log
+            </a>
+          )}
+        </div>
+      ) : (
+        <div className="text-center py-8 text-gray-500">
+          Analysis not available yet. Use Sync provider data after the call ends.
+        </div>
+      )}
+    </div>
+  )
+
+  const TranscriptCard = () => (
+    <div className={`bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col ${compact ? 'max-h-[min(60vh,520px)]' : 'h-[600px]'}`}>
+      <div className="flex items-center justify-between mb-4 flex-shrink-0">
+        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
+          <MessageSquare className="h-5 w-5 text-indigo-600" />
+          Transcript
+        </h3>
+        {!compact && callData.recording_url && (
+          <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
+            <audio controls src={callData.recording_url} className="h-8 w-64" />
+            <a href={callData.recording_url} download className="text-gray-500 hover:text-indigo-600 p-1">
+              <Download className="h-4 w-4" />
+            </a>
+          </div>
+        )}
+      </div>
+
+      <div className="flex-1 overflow-y-auto space-y-4 pr-2 custom-scrollbar">
+        {callData.transcript_object?.map((msg, idx) => (
+          <div key={idx} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
+            <div className={`max-w-[80%] rounded-2xl px-4 py-3 ${msg.role === 'user'
+              ? 'bg-indigo-600 text-white rounded-br-none'
+              : 'bg-gray-100 text-gray-800 rounded-bl-none'
+              }`}>
+              <div className="flex items-center gap-2 mb-1 opacity-80">
+                <span className="text-xs font-semibold uppercase tracking-wider">
+                  {msg.role === 'user' ? 'User' : (callData.agent_name || 'Agent')}
+                </span>
+                {msg.words && msg.words.length > 0 && (
+                  <span className="text-[10px]">
+                    {msg.words[0].start.toFixed(1)}s
+                  </span>
+                )}
+              </div>
+              <p className="text-sm leading-relaxed whitespace-pre-wrap">{msg.content}</p>
+            </div>
+          </div>
+        ))}
+      </div>
+    </div>
+  )
+
+  const CostSection = () => (
+      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+          <DollarSign className="h-5 w-5 text-indigo-600" />
+          Cost Breakdown
+        </h3>
+        <div className="flex items-center justify-between">
+          <div className="w-1/2">
+            <div className="mb-4">
+              <p className="text-sm text-gray-500">Total Cost</p>
+              <p className="text-3xl font-bold text-gray-900">
+                ${callData.call_cost?.combined_cost?.toFixed(3) || '0.000'}
+              </p>
+              <p className="text-xs text-gray-500 mt-1">
+                Duration: {callData.call_cost?.total_duration_seconds}s
+              </p>
+            </div>
+            <div className="space-y-2">
+              {callData.call_cost?.product_costs?.map((prod, i) => (
+                <div key={i} className="flex justify-between items-center text-sm">
+                  <span className="text-gray-600 capitalize">{prod.product.replace(/_/g, ' ')}</span>
+                  <span className="font-medium text-gray-900">${prod.cost.toFixed(3)}</span>
+                </div>
+              ))}
+            </div>
+          </div>
+          <div className="w-1/2 h-40">
+            <ResponsiveContainer width="100%" height="100%">
+              <PieChart>
+                <Pie
+                  data={costData}
+                  cx="50%"
+                  cy="50%"
+                  innerRadius={0}
+                  outerRadius={60}
+                  paddingAngle={5}
+                  dataKey="value"
+                >
+                  {costData.map((_entry, index) => (
+                    <Cell key={`cell-${index}`} fill={COLORS[index % COLORS.length]} />
+                  ))}
+                </Pie>
+                <Tooltip formatter={(value: number) => `$${value.toFixed(3)}`} />
+              </PieChart>
+            </ResponsiveContainer>
+          </div>
+        </div>
+      </div>
+  )
+
+  const LatencySection = () => (
+      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+          <Activity className="h-5 w-5 text-indigo-600" />
+          Latency Performance
+        </h3>
+        <div className="h-64">
+          <ResponsiveContainer width="100%" height="100%">
+            <BarChart data={latencyData}>
+              <CartesianGrid strokeDasharray="3 3" vertical={false} />
+              <XAxis dataKey="name" />
+              <YAxis unit="ms" />
+              <Tooltip
+                contentStyle={{ borderRadius: '8px', border: 'none', boxShadow: '0 4px 6px -1px rgb(0 0 0 / 0.1)' }}
+                cursor={{ fill: '#F3F4F6' }}
+              />
+              <Legend />
+              <Bar dataKey="p50" name="P50 (ms)" fill="#818cf8" radius={[4, 4, 0, 0]} />
+              <Bar dataKey="p90" name="P90 (ms)" fill="#4f46e5" radius={[4, 4, 0, 0]} />
+              <Bar dataKey="max" name="Max (ms)" fill="#312e81" radius={[4, 4, 0, 0]} />
+            </BarChart>
+          </ResponsiveContainer>
+        </div>
+      </div>
+  )
+
+  const SystemSection = () => (
+      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+          <Server className="h-5 w-5 text-indigo-600" />
+          System Details
+        </h3>
+        <div className="grid grid-cols-2 gap-4 text-sm">
+          <div>
+            <p className="text-gray-500 mb-1">Agent ID</p>
+            <p className="font-mono bg-gray-50 p-1 rounded text-gray-700 truncate">{callData.agent_id}</p>
+          </div>
+          <div>
+            <p className="text-gray-500 mb-1">Call ID</p>
+            <p className="font-mono bg-gray-50 p-1 rounded text-gray-700 truncate">{callData.call_id}</p>
+          </div>
+          <div>
+            <p className="text-gray-500 mb-1">Start Time</p>
+            <p className="text-gray-700">{formatTimestamp(callData.start_timestamp)}</p>
+          </div>
+          <div>
+            <p className="text-gray-500 mb-1">Duration</p>
+            <p className="text-gray-700">{formatDuration(callData.duration_ms)}</p>
+          </div>
+        </div>
+      </div>
+  )
+
+  const StatsParams = () => (
+    <div className="space-y-6">
+      <CostSection />
+      <LatencySection />
+      <SystemSection />
+    </div>
+  )
+
+  if (section === 'transcript') return <TranscriptCard />
+  if (section === 'cost') return <CostSection />
+  if (section === 'latency') return <LatencySection />
+  if (section === 'analysis') return <SummaryCard />
+  if (section === 'system') return <SystemSection />
+
+  if (hideTranscript) {
+    return (
+      <div className="space-y-6">
+        <SummaryCard />
+        <StatsParams />
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-6">
+      {/* Navigation Tabs */}
+      <div className="flex border-b border-gray-200">
+        <button
+          onClick={() => setActiveTab('overview')}
+          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'overview'
+            ? 'border-indigo-600 text-indigo-600'
+            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+            }`}
+        >
+          Overview
+        </button>
+        <button
+          onClick={() => setActiveTab('transcript')}
+          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'transcript'
+            ? 'border-indigo-600 text-indigo-600'
+            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+            }`}
+        >
+          Transcript
+        </button>
+      </div>
+
+      {activeTab === 'overview' && (
+        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
+          <div className="lg:col-span-2 space-y-6">
+            <SummaryCard />
+            <TranscriptCard />
+          </div>
+          <div className="lg:col-span-1">
+            <StatsParams />
+          </div>
+        </div>
+      )}
+
+      {activeTab === 'transcript' && (
+        <div className="space-y-6">
+          <TranscriptCard />
+        </div>
+      )}
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx b/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
index 684c90f4..9150be11 100644
--- a/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
+++ b/frontend/src/components/call-recordings/SyntheticCallTracePanel.tsx
@@ -5,7 +5,9 @@ import { Clock, ChevronDown, ChevronRight, ExternalLink, Radio, X } from 'lucide
 import { apiClient } from '../../lib/api'
 import TraceTurnDetail from './TraceTurnDetail'
 import TraceWaterfall from './TraceWaterfall'
-import TraceRecordingBar from './TraceRecordingBar'
+import CallWaveformPlayer from './CallWaveformPlayer'
+import CallEventTimeline from './CallEventTimeline'
+import { buildOtelCallTimeline } from './callTimelineUtils'
 import { TurnSignalBadges } from './TraceTurnBadges'
 import {
   FAILURE_FLAG_LABELS,
@@ -22,6 +24,8 @@ interface SyntheticCallTracePanelProps {
   traceId?: string
   callShortId?: string
   onClose?: () => void
+  embedded?: boolean
+  hideRecording?: boolean
 }
 
 interface ComponentMeta {
@@ -76,7 +80,7 @@ interface OtelSpan {
   events?: Array<{ name?: string; attributes?: Record<string, unknown> }>
 }
 
-type DetailTab = 'trace' | 'waterfall' | 'transcript' | 'spans'
+type DetailTab = 'trace' | 'waterfall' | 'transcript' | 'timeline' | 'spans'
 type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
 
 const COMPONENT_LABELS: Record<ComponentKind, string> = {
@@ -90,6 +94,7 @@ const TABS: Array<{ id: DetailTab; label: string }> = [
   { id: 'trace', label: 'Trace' },
   { id: 'waterfall', label: 'Waterfall' },
   { id: 'transcript', label: 'Transcript' },
+  { id: 'timeline', label: 'Timeline' },
   { id: 'spans', label: 'Spans' },
 ]
 
@@ -332,8 +337,11 @@ export default function SyntheticCallTracePanel({
   traceId,
   callShortId,
   onClose,
+  embedded = false,
+  hideRecording = false,
 }: SyntheticCallTracePanelProps) {
   const [tab, setTab] = useState<DetailTab>('trace')
+  const visibleTabs = embedded ? TABS.filter((item) => item.id !== 'transcript') : TABS
 
   const lookupKey = traceId ?? callShortId ?? evaluatorResultId
   const lookupMode = traceId ? 'by-trace' : callShortId ? 'by-call-short-id' : 'by-result'
@@ -441,23 +449,54 @@ export default function SyntheticCallTracePanel({
     return messages
   }, [turns, spansByTurn, pipelineModels, pipelineMode])
 
-  if (isLoading) {
-    return <div className="flex items-center justify-center h-48 text-sm text-gray-500">Loading trace…</div>
+  const timelineEvents = useMemo(() => {
+    const spanEvents = buildOtelCallTimeline(otelSpans)
+    const messageEvents = transcriptMessages.map((msg, idx) => ({
+      id: `turn-msg-${idx}`,
+      offsetMs: msg.offsetMs,
+      category: 'message' as const,
+      level: 'info' as const,
+      title: msg.role === 'user' ? `User spoke (turn ${msg.turn})` : `Agent spoke (turn ${msg.turn})`,
+      detail: msg.text,
+    }))
+    return [...spanEvents, ...messageEvents].sort(
+      (a, b) => a.offsetMs - b.offsetMs || a.title.localeCompare(b.title),
+    )
+  }, [otelSpans, transcriptMessages])
+
+  if (isLoading && !data) {
+    return (
+      <div className={`space-y-3 ${embedded ? 'p-1' : 'p-5'}`}>
+        <div className="h-8 w-48 animate-pulse rounded-lg bg-gray-100" />
+        <div className={`animate-pulse rounded-xl bg-gray-100 ${embedded ? 'h-32' : 'h-48'}`} />
+        <div className="h-64 animate-pulse rounded-xl bg-gray-100" />
+      </div>
+    )
   }
 
   if (isError) {
     const message = (error as Error)?.message || 'Trace not available'
     if (message.includes('404') || message.toLowerCase().includes('not found')) {
       return (
-        <div className="p-8 text-sm text-gray-600">
-          No trace yet. Run a Pipecat session with tracing enabled, then see{' '}
-          <Link to="/call-traces" className="text-primary-600 hover:text-primary-800 font-medium">
-            Call Traces → Connect Pipecat
-          </Link>
+        <div className={`text-sm text-gray-600 ${embedded ? 'rounded-xl border border-gray-200 bg-white p-6' : 'p-8'}`}>
+          {embedded ? (
+            <p>No pipeline trace for this run yet. OTLP tracing is available for internal Test Agent web sessions.</p>
+          ) : (
+            <>
+              No trace yet. Run a Pipecat session with tracing enabled, then see{' '}
+              <Link to="/calls" className="text-primary-600 hover:text-primary-800 font-medium">
+                Connect Pipecat →
+              </Link>
+            </>
+          )}
         </div>
       )
     }
-    return <div className="p-8 text-sm text-red-800 bg-red-50">Could not load trace: {message}</div>
+    return (
+      <div className={`text-sm text-red-800 bg-red-50 ${embedded ? 'rounded-xl border border-red-200 p-4' : 'p-8'}`}>
+        Could not load trace: {message}
+      </div>
+    )
   }
 
   const trace = data
@@ -488,6 +527,133 @@ export default function SyntheticCallTracePanel({
   const agentRouteId = linkedAgent?.agent_id || linkedAgent?.id || trace.agent_id
   const agentLabel = linkedAgent?.name || (trace.agent_id ? 'Agent' : null)
 
+  const tabContent = (
+    <>
+      {tab === 'trace' && (
+        <div className={embedded ? 'pb-4' : 'p-5 pb-16'}>
+          <TraceTurnDetail
+            rows={traceTurnRows}
+            pipelineModels={pipelineModels}
+            componentAggregates={
+              trace.component_aggregates as Record<string, { p50?: number; p90?: number; p95?: number }> | undefined
+            }
+          />
+        </div>
+      )}
+
+      {tab === 'waterfall' && (
+        <div className={embedded ? 'pb-4' : 'p-5 pb-16'}>
+          {traceTurnRows.length === 0 ? (
+            <p className="py-12 text-center text-sm text-gray-500">No waterfall data</p>
+          ) : (
+            <TraceWaterfall rows={traceTurnRows} />
+          )}
+        </div>
+      )}
+
+      {tab === 'transcript' && !embedded && (
+        <div className="bg-gray-50/50 p-5 pb-16">
+          {transcriptMessages.length === 0 ? (
+            <p className="text-center py-16 text-sm text-gray-500">No transcript</p>
+          ) : (
+            <div className="space-y-4 max-w-2xl mx-auto">
+              {transcriptMessages.map((msg, idx) => {
+                const isUser = msg.role === 'user'
+                return (
+                  <div key={`${msg.turn}-${msg.role}-${idx}`} className={isUser ? 'flex justify-end' : 'flex justify-start'}>
+                    <div className="max-w-[85%]">
+                      <div
+                        className={`rounded-xl px-4 py-3 ${
+                          isUser
+                            ? 'bg-white border border-gray-200 text-gray-900'
+                            : 'bg-gray-100 border border-gray-200/80 text-gray-900'
+                        }`}
+                      >
+                        <div className="mb-1 flex flex-wrap items-center gap-2">
+                          <p className={`text-xs font-semibold ${isUser ? 'text-gray-700' : 'text-gray-600'}`}>
+                            {isUser ? 'User' : 'Agent'}
+                          </p>
+                          <TurnSignalBadges
+                            talkOver={isUser ? msg.talkOver : undefined}
+                            interrupted={!isUser ? msg.interrupted : undefined}
+                            incomplete={msg.incomplete}
+                          />
+                        </div>
+                        <p className="text-sm leading-relaxed whitespace-pre-wrap">{msg.text}</p>
+                      </div>
+                      <p className={`text-[11px] mt-1 tabular-nums text-gray-500 ${isUser ? 'text-right' : ''}`}>
+                        Turn {msg.turn} {formatOffset(msg.offsetMs)}
+                        {msg.latencyMs != null && (
+                          <span className="ml-2">· {formatMs(msg.latencyMs)}</span>
+                        )}
+                      </p>
+                    </div>
+                  </div>
+                )
+              })}
+            </div>
+          )}
+        </div>
+      )}
+
+      {tab === 'timeline' && (
+        <div className={embedded ? 'pb-4' : 'p-5 pb-16'}>
+          <CallEventTimeline
+            events={timelineEvents}
+            emptyMessage="No pipeline events captured for this session."
+          />
+        </div>
+      )}
+
+      {tab === 'spans' && (
+        <div className={embedded ? 'overflow-hidden rounded-xl border border-gray-200 bg-white pb-4' : 'bg-white pb-16'}>
+          {otelSpans.length === 0 ? (
+            <p className="py-16 text-center text-sm text-gray-500">No spans</p>
+          ) : (
+            <>
+              <div className="grid grid-cols-[3.5rem_minmax(0,1fr)_minmax(0,1.2fr)_4.5rem] items-center gap-4 border-b border-gray-200 bg-gray-50/80 px-4 py-2.5 text-[10px] font-semibold uppercase tracking-wider text-gray-400">
+                <span>Stage</span>
+                <span>Span</span>
+                <span>Model</span>
+                <span className="text-right">Duration</span>
+              </div>
+              {otelSpans
+                .slice()
+                .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
+                .map((span) => (
+                  <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />
+                ))}
+            </>
+          )}
+        </div>
+      )}
+    </>
+  )
+
+  if (embedded) {
+    return (
+      <div className="rounded-xl border border-gray-200 bg-white">
+        <div className="flex flex-wrap gap-1 border-b border-gray-100 px-2">
+          {visibleTabs.map(({ id, label }) => (
+            <button
+              key={id}
+              type="button"
+              onClick={() => setTab(id)}
+              className={`shrink-0 whitespace-nowrap border-b-2 px-3 py-2 text-sm font-medium transition-colors ${
+                tab === id
+                  ? 'border-primary-600 text-gray-900'
+                  : 'border-transparent text-gray-500 hover:text-gray-800'
+              }`}
+            >
+              {label}
+            </button>
+          ))}
+        </div>
+        <div className="p-4">{tabContent}</div>
+      </div>
+    )
+  }
+
   return (
     <div className={isDrawer ? 'flex h-full min-h-0 flex-col bg-gray-50' : 'bg-gray-50 pb-10'}>
       <div
@@ -576,11 +742,15 @@ export default function SyntheticCallTracePanel({
         </div>
 
         <div className="space-y-3 px-5 py-4">
-          <TraceRecordingBar
-            callShortId={trace.call_short_id}
-            callRecordingId={trace.call_recording_id}
-            evaluatorResultId={trace.evaluator_result_id}
-          />
+          {!hideRecording && (
+            <CallWaveformPlayer
+              callShortId={trace.call_short_id}
+              callRecordingId={trace.call_recording_id ?? trace.call_short_id}
+              evaluatorResultId={
+                trace.call_short_id ? undefined : (trace.evaluator_result_id ?? evaluatorResultId)
+              }
+            />
+          )}
           <div className="grid grid-cols-2 gap-2 sm:grid-cols-4">
             <MetricTile
               label="Response p50"
@@ -603,7 +773,7 @@ export default function SyntheticCallTracePanel({
         </div>
 
         <div className="flex border-t border-gray-100 bg-white px-5">
-          {TABS.map(({ id, label }) => (
+          {visibleTabs.map(({ id, label }) => (
             <button
               key={id}
               type="button"
@@ -621,95 +791,7 @@ export default function SyntheticCallTracePanel({
       </div>
 
       <div className={isDrawer ? 'min-h-0 flex-1 overflow-y-auto overscroll-contain' : undefined}>
-        {tab === 'trace' && (
-          <div className="p-5 pb-16">
-            <TraceTurnDetail
-              rows={traceTurnRows}
-              pipelineModels={pipelineModels}
-              componentAggregates={
-                trace.component_aggregates as Record<string, { p50?: number; p90?: number; p95?: number }> | undefined
-              }
-            />
-          </div>
-        )}
-
-        {tab === 'waterfall' && (
-          <div className="p-5 pb-16">
-            {traceTurnRows.length === 0 ? (
-              <p className="py-12 text-center text-sm text-gray-500">No waterfall data</p>
-            ) : (
-              <TraceWaterfall rows={traceTurnRows} />
-            )}
-          </div>
-        )}
-
-        {tab === 'transcript' && (
-          <div className="bg-gray-50/50 p-5 pb-16">
-            {transcriptMessages.length === 0 ? (
-              <p className="text-center py-16 text-sm text-gray-500">No transcript</p>
-            ) : (
-              <div className="space-y-4 max-w-2xl mx-auto">
-                {transcriptMessages.map((msg, idx) => {
-                  const isUser = msg.role === 'user'
-                  return (
-                    <div key={`${msg.turn}-${msg.role}-${idx}`} className={isUser ? 'flex justify-end' : 'flex justify-start'}>
-                      <div className="max-w-[85%]">
-                        <div
-                          className={`rounded-xl px-4 py-3 ${
-                            isUser
-                              ? 'bg-white border border-gray-200 text-gray-900'
-                              : 'bg-gray-100 border border-gray-200/80 text-gray-900'
-                          }`}
-                        >
-                          <div className="mb-1 flex flex-wrap items-center gap-2">
-                            <p className={`text-xs font-semibold ${isUser ? 'text-gray-700' : 'text-gray-600'}`}>
-                              {isUser ? 'User' : 'Agent'}
-                            </p>
-                            <TurnSignalBadges
-                              talkOver={isUser ? msg.talkOver : undefined}
-                              interrupted={!isUser ? msg.interrupted : undefined}
-                              incomplete={msg.incomplete}
-                            />
-                          </div>
-                          <p className="text-sm leading-relaxed whitespace-pre-wrap">{msg.text}</p>
-                        </div>
-                        <p className={`text-[11px] mt-1 tabular-nums text-gray-500 ${isUser ? 'text-right' : ''}`}>
-                          Turn {msg.turn} {formatOffset(msg.offsetMs)}
-                          {msg.latencyMs != null && (
-                            <span className="ml-2">· {formatMs(msg.latencyMs)}</span>
-                          )}
-                        </p>
-                      </div>
-                    </div>
-                  )
-                })}
-              </div>
-            )}
-          </div>
-        )}
-
-        {tab === 'spans' && (
-          <div className="bg-white pb-16">
-            {otelSpans.length === 0 ? (
-              <p className="py-16 text-center text-sm text-gray-500">No spans</p>
-            ) : (
-              <>
-                <div className="grid grid-cols-[3.5rem_minmax(0,1fr)_minmax(0,1.2fr)_4.5rem] items-center gap-4 border-b border-gray-200 bg-gray-50/80 px-4 py-2.5 text-[10px] font-semibold uppercase tracking-wider text-gray-400">
-                  <span>Stage</span>
-                  <span>Span</span>
-                  <span>Model</span>
-                  <span className="text-right">Duration</span>
-                </div>
-                {otelSpans
-                  .slice()
-                  .sort((a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0))
-                  .map((span) => (
-                    <RawSpanRow key={`${span.trace_id}-${span.span_id}`} span={span} />
-                  ))}
-              </>
-            )}
-          </div>
-        )}
+        {tabContent}
       </div>
     </div>
   )
diff --git a/frontend/src/components/call-recordings/TestVoiceAgentResultDetails.tsx b/frontend/src/components/call-recordings/TestVoiceAgentResultDetails.tsx
index 7107a677..8b36a9b0 100644
--- a/frontend/src/components/call-recordings/TestVoiceAgentResultDetails.tsx
+++ b/frontend/src/components/call-recordings/TestVoiceAgentResultDetails.tsx
@@ -1,838 +1,848 @@
-import { useState, type ReactNode } from 'react'
-import {
-  Clock, MessageSquare, TrendingUp, Download, Server, BarChart3, HelpCircle, Brain, Sparkles, AudioWaveform
-} from 'lucide-react'
-
-const LEGACY_CATEGORY_LABEL_METRIC_NAMES = new Set([
-  'yes',
-  'no',
-  'true',
-  'false',
-  'same',
-  'different',
-])
-
-function isLegacyCategoryLabelMetric(metric: {
-  type?: string | null
-  metric_name?: string | null
-}): boolean {
-  if ((metric.type || '').toLowerCase() !== 'boolean') return false
-  const name = (metric.metric_name || '').trim().toLowerCase()
-  return LEGACY_CATEGORY_LABEL_METRIC_NAMES.has(name)
-}
-// Metric information for tooltips and categorization
-const METRIC_INFO: Record<string, { 
-  description: string
-  ideal: string
-  unit?: string
-  category: 'acoustic' | 'ai_voice' | 'llm'
-}> = {
-  'Pitch Variance': { 
-    description: 'F0 variation measuring prosodic expressiveness. Higher values indicate more expressive speech.',
-    ideal: '20-50 Hz (natural speech)',
-    unit: 'Hz',
-    category: 'acoustic'
-  },
-  'Jitter': { 
-    description: 'Cycle-to-cycle pitch period variation indicating vocal stability. Lower is better.',
-    ideal: '< 1% (healthy voice)',
-    unit: '%',
-    category: 'acoustic'
-  },
-  'Shimmer': { 
-    description: 'Amplitude perturbation measuring voice quality consistency. Lower is better.',
-    ideal: '< 3% (clear voice)',
-    unit: '%',
-    category: 'acoustic'
-  },
-  'HNR': { 
-    description: 'Harmonics-to-Noise Ratio measuring signal clarity. Higher indicates cleaner voice.',
-    ideal: '> 20 dB (clear, non-breathy)',
-    unit: 'dB',
-    category: 'acoustic'
-  },
-  'MOS Score': { 
-    description: 'Mean Opinion Score predicting human perception of audio quality (1-5 scale).',
-    ideal: '4.0+ (studio quality), 3.0 (phone quality), <2.0 (poor/robotic)',
-    category: 'ai_voice'
-  },
-  'Emotion Category': {
-    description: 'Dominant emotion detected in the voice (angry, happy, sad, neutral, fearful, etc.).',
-    ideal: 'Depends on context - should match expected tone',
-    category: 'ai_voice'
-  },
-  'Emotion Confidence': { 
-    description: 'Confidence score for the detected emotion category.',
-    ideal: '> 0.7 (high confidence)',
-    category: 'ai_voice'
-  },
-  'Valence': { 
-    description: 'Emotional positivity/negativity scale. Negative = sad/angry, Positive = happy/excited.',
-    ideal: '-1.0 to +1.0 (context dependent)',
-    category: 'ai_voice'
-  },
-  'Arousal': { 
-    description: 'Emotional intensity/energy level. Low = calm/sleepy, High = excited/energetic.',
-    ideal: '0.3-0.6 (engaged but not agitated)',
-    category: 'ai_voice'
-  },
-  'Speaker Consistency': {
-    description: 'Voice identity stability throughout the call. Detects if voice changed mid-call (glitch).',
-    ideal: '> 0.8 (same voice), < 0.5 indicates voice glitch',
-    category: 'ai_voice'
-  },
-  'Prosody Score': { 
-    description: 'Expressiveness/drama score. Low = monotone/flat, High = expressive/dynamic.',
-    ideal: '0.4-0.7 (natural expressiveness)',
-    category: 'ai_voice'
-  },
-  'Follow Instructions': { 
-    description: 'How well the agent followed the given instructions and guidelines.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-  'Problem Resolution': { 
-    description: 'Whether the agent successfully resolved the customer\'s problem or query.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-  'Professionalism': { 
-    description: 'Professional demeanor, appropriate language, and courteous behavior.',
-    ideal: '> 0.85 (85%+)',
-    category: 'llm'
-  },
-  'Clarity and Empathy': { 
-    description: 'Clear communication combined with understanding and acknowledgment of customer feelings.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-  'Objective Achieved': {
-    description: 'Whether the conversation\'s primary objective was successfully achieved.',
-    ideal: 'Yes/True',
-    category: 'llm'
-  },
-  'Overall Quality': {
-    description: 'Holistic assessment of the entire conversation quality.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-}
-
-const getMetricInfo = (metricName: string) => METRIC_INFO[metricName] || null
-
-const SECTION_INFO: Record<'conversation' | 'ai_voice' | 'acoustic', string> = {
-  conversation: 'LLM-based evaluation of how well the agent handled intent, instructions, and resolution quality.',
-  ai_voice: 'ML-based voice quality metrics on naturalness, affect, consistency, and expressiveness.',
-  acoustic: 'Signal-level acoustic measurements from the recording (pitch stability, perturbation, noise ratio).',
-}
-
-const MetricTooltip = ({ metricName }: { metricName: string }) => {
-  const [isVisible, setIsVisible] = useState(false)
-  const info = getMetricInfo(metricName)
-
-  if (!info) return null
-
-  return (
-    <div className="relative inline-flex flex-shrink-0 ml-1 mt-0.5">
-      <button
-        type="button"
-        className="text-gray-400 hover:text-gray-600 focus:outline-none transition-colors"
-        onMouseEnter={() => setIsVisible(true)}
-        onMouseLeave={() => setIsVisible(false)}
-        onClick={() => setIsVisible(!isVisible)}
-        aria-label={`Info about ${metricName}`}
-      >
-        <HelpCircle className="w-3.5 h-3.5" />
-      </button>
-      {isVisible && (
-        <div className="absolute z-50 left-1/2 -translate-x-1/2 bottom-full mb-2 w-64 p-3 text-xs bg-gray-900 text-white rounded-lg shadow-xl pointer-events-none">
-          <div className="font-semibold text-gray-100 mb-1.5">{metricName}</div>
-          <p className="text-gray-300 mb-2 leading-relaxed">{info.description}</p>
-          <div className="flex items-center gap-1 pt-1.5 border-t border-gray-700">
-            <span className="text-emerald-400 font-medium">Ideal:</span>
-            <span className="text-gray-200">{info.ideal}</span>
-          </div>
-          <div className="absolute left-1/2 -translate-x-1/2 top-full w-0 h-0 border-l-[6px] border-l-transparent border-r-[6px] border-r-transparent border-t-[6px] border-t-gray-900" />
-        </div>
-      )}
-    </div>
-  )
-}
-
-const SectionTooltip = ({ section }: { section: 'conversation' | 'ai_voice' | 'acoustic' }) => {
-  const [isVisible, setIsVisible] = useState(false)
-
-  return (
-    <div className="relative inline-block">
-      <button
-        type="button"
-        className="text-gray-400 hover:text-gray-600 focus:outline-none transition-colors"
-        onMouseEnter={() => setIsVisible(true)}
-        onMouseLeave={() => setIsVisible(false)}
-        onClick={() => setIsVisible(!isVisible)}
-        aria-label={`Info about ${section} metrics`}
-      >
-        <HelpCircle className="w-3.5 h-3.5" />
-      </button>
-      {isVisible && (
-        <div className="absolute z-50 left-1/2 -translate-x-1/2 bottom-full mb-2 w-72 p-3 text-xs bg-gray-900 text-white rounded-lg shadow-xl pointer-events-none">
-          <p className="text-gray-200 leading-relaxed">{SECTION_INFO[section]}</p>
-          <div className="absolute left-1/2 -translate-x-1/2 top-full w-0 h-0 border-l-[6px] border-l-transparent border-r-[6px] border-r-transparent border-t-[6px] border-t-gray-900" />
-        </div>
-      )}
-    </div>
-  )
-}
-
-interface TestVoiceAgentResultData {
-  id?: string
-  result_id?: string
-  name?: string
-  timestamp?: string
-  duration_seconds?: number | null
-  status?: 'queued' | 'transcribing' | 'evaluating' | 'completed' | 'failed'
-  transcription?: string | null
-  speaker_segments?: Array<{
-    speaker: string
-    text: string
-    start: number
-    end: number
-  }> | null
-  metric_scores?: Record<
-    string,
-    {
-      value: any
-      type: string
-      metric_name: string
-      parent_metric_id?: string | null
-      rationale?: string | null
-      skipped?: string
-      error?: string | null
-    }
-  > | null
-  call_analysis?: {
-    call_summary?: string
-    user_sentiment?: string
-    call_successful?: boolean
-  }
-  audio_s3_key?: string | null
-  audioUrl?: string
-  agent?: {
-    id?: string
-    name?: string
-    description?: string
-  }
-  persona?: {
-    name?: string
-  }
-  scenario?: {
-    name?: string
-  }
-}
-
-interface TestVoiceAgentResultDetailsProps {
-  resultData: TestVoiceAgentResultData
-}
-
-export default function TestVoiceAgentResultDetails({ resultData }: TestVoiceAgentResultDetailsProps) {
-  const [activeTab, setActiveTab] = useState<'overview' | 'transcript' | 'debug'>('overview')
-
-  const formatDuration = (seconds?: number | null) => {
-    if (!seconds) return 'N/A'
-    const mins = Math.floor(seconds / 60)
-    const secs = Math.floor(seconds % 60)
-    return `${mins}m ${secs}s`
-  }
-
-  const formatTimestamp = (timestamp?: string) => {
-    if (!timestamp) return 'N/A'
-    return new Date(timestamp).toLocaleString()
-  }
-
-  const formatTime = (seconds: number) => {
-    const mins = Math.floor(seconds / 60)
-    const secs = Math.floor(seconds % 60)
-    return `${mins}:${secs.toString().padStart(2, '0')}`
-  }
-
-  const getSentimentColor = (sentiment?: string) => {
-    if (!sentiment) return 'text-gray-500 bg-gray-100'
-    const s = sentiment.toLowerCase()
-    if (s.includes('positive') || s.includes('happy')) return 'text-green-700 bg-green-100'
-    if (s.includes('negative') || s.includes('angry')) return 'text-red-700 bg-red-100'
-    return 'text-blue-700 bg-blue-100'
-  }
-
-  // Extract summary, sentiment, and successful from metric_scores or call_analysis
-  const summary = resultData.call_analysis?.call_summary || 
-    (resultData.metric_scores?.summary?.value) || 
-    null
-  
-  const sentiment = resultData.call_analysis?.user_sentiment || 
-    (resultData.metric_scores?.sentiment?.value) || 
-    'Neutral'
-  
-  const successful = resultData.call_analysis?.call_successful !== undefined 
-    ? resultData.call_analysis.call_successful 
-    : (resultData.metric_scores?.successful?.value !== undefined 
-        ? resultData.metric_scores.successful.value 
-        : null)
-
-  const getSpeakerLabel = (speaker: string) => {
-    if (speaker === 'Speaker 1' || speaker === 'user' || speaker === 'caller') {
-      return 'You'
-    }
-    if (speaker === 'assistant' || speaker === 'Speaker 2' || speaker === 'bot') {
-      return resultData.agent?.name || 'Agent'
-    }
-    return resultData.agent?.name || 'Agent'
-  }
-
-  const isUserSpeaker = (speaker: string) => {
-    return speaker === 'Speaker 1' || speaker === 'user' || speaker === 'caller'
-  }
-
-  const SummaryCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-      <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-        <TrendingUp className="h-5 w-5 text-indigo-600" />
-        Call Analysis
-      </h3>
-
-      {summary || resultData.call_analysis ? (
-        <div className="space-y-6">
-          {summary && (
-            <div className="p-4 bg-indigo-50 rounded-lg border border-indigo-100">
-              <p className="text-sm font-medium text-indigo-900 mb-2">Summary</p>
-              <p className="text-sm text-indigo-800 leading-relaxed">{summary}</p>
-            </div>
-          )}
-
-          <div className="grid grid-cols-2 gap-4">
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Sentiment</p>
-              <div className="flex items-center gap-2">
-                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${getSentimentColor(sentiment)}`}>
-                  {sentiment}
-                </span>
-              </div>
-            </div>
-
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Success Status</p>
-              <div className="flex items-center gap-2">
-                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${
-                  successful === true
-                    ? 'text-green-700 bg-green-100'
-                    : successful === false
-                    ? 'text-red-700 bg-red-100'
-                    : 'text-gray-500 bg-gray-100'
-                }`}>
-                  {successful === true ? 'Successful' : successful === false ? 'Unsuccessful' : 'N/A'}
-                </span>
-              </div>
-            </div>
-
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Status</p>
-              <span className={`px-2 py-1 rounded-full text-xs font-semibold capitalize ${
-                resultData.status === 'completed'
-                  ? 'text-green-700 bg-green-100'
-                  : resultData.status === 'failed'
-                  ? 'text-red-700 bg-red-100'
-                  : 'text-yellow-700 bg-yellow-100'
-              }`}>
-                {resultData.status || 'Unknown'}
-              </span>
-            </div>
-
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Duration</p>
-              <span className="text-sm font-medium text-gray-900">
-                {formatDuration(resultData.duration_seconds)}
-              </span>
-            </div>
-          </div>
-        </div>
-      ) : (
-        <div className="text-center py-8 text-gray-500">Analysis not available</div>
-      )}
-    </div>
-  )
-
-  // Helper function to format metric values
-  const formatMetricValue = (value: any, type: string, metricName: string): ReactNode => {
-    if (value === null || value === undefined) return <span className="text-gray-400">N/A</span>
-
-    const normalizedType = type?.toLowerCase()
-
-    if (normalizedType === 'category') {
-      if (value === '') return <span className="text-gray-400">N/A</span>
-      return (
-        <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-lg bg-indigo-50 text-indigo-700 text-sm font-semibold whitespace-normal break-words leading-snug">
-          {String(value)}
-        </span>
-      )
-    }
-
-    if (metricName === 'Emotion Category') {
-      const emotion = String(value).toLowerCase()
-      const emotionConfig: Record<string, { emoji: string; color: string; bg: string }> = {
-        neutral: { emoji: '😐', color: 'text-gray-700', bg: 'bg-gray-100' },
-        happy: { emoji: '😊', color: 'text-green-700', bg: 'bg-green-100' },
-        sad: { emoji: '😢', color: 'text-blue-700', bg: 'bg-blue-100' },
-        angry: { emoji: '😠', color: 'text-red-700', bg: 'bg-red-100' },
-        fearful: { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
-        fear: { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
-        surprised: { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
-        surprise: { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
-        disgusted: { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
-        disgust: { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
-        calm: { emoji: '😌', color: 'text-teal-700', bg: 'bg-teal-100' },
-      }
-      const config = emotionConfig[emotion] || { emoji: '🎭', color: 'text-gray-700', bg: 'bg-gray-100' }
-
-      return (
-        <div className={`inline-flex items-center gap-2 px-3 py-1.5 rounded-full ${config.bg}`}>
-          <span className="text-xl">{config.emoji}</span>
-          <span className={`font-semibold capitalize ${config.color}`}>{value}</span>
-        </div>
-      )
-    }
-
-    if (normalizedType === 'boolean') {
-      const boolValue = value === true || value === 1 || value === '1' || value === 'true'
-      return <span className="block text-base font-semibold text-gray-900">{boolValue ? 'Yes' : 'No'}</span>
-    }
-
-    if (normalizedType === 'rating') {
-      if (typeof value === 'string' && isNaN(parseFloat(value))) {
-        return (
-          <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-full bg-purple-100 text-purple-700 font-semibold capitalize whitespace-normal break-words text-left leading-snug">
-            {value}
-          </span>
-        )
-      }
-
-      const numValue = typeof value === 'number' ? value : parseFloat(value)
-      if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
-
-      const normalizedValue = Math.max(0, Math.min(1, numValue))
-      const percentage = Math.round(normalizedValue * 100)
-      const getBarColor = (pct: number): string => {
-        if (pct >= 70) return 'bg-green-500'
-        if (pct >= 50) return 'bg-yellow-500'
-        return 'bg-red-500'
-      }
-
-      return (
-        <div className="flex flex-col gap-2">
-          <span className="text-2xl font-bold text-gray-900">{percentage}%</span>
-          <div className="w-full h-3 bg-gray-200 rounded-full overflow-hidden">
-            <div className={`h-full rounded-full transition-all ${getBarColor(percentage)}`} style={{ width: `${percentage}%` }} />
-          </div>
-        </div>
-      )
-    }
-
-    if (normalizedType === 'number') {
-      const numValue = typeof value === 'number' ? value : parseFloat(value)
-      if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
-
-      const info = getMetricInfo(metricName)
-      if (info?.category === 'acoustic' || info?.category === 'ai_voice') {
-        return (
-          <div className="flex items-baseline gap-1">
-            <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(2)}</span>
-            <span className="text-sm font-medium text-violet-600">{info.unit || ''}</span>
-          </div>
-        )
-      }
-      return <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(1)}</span>
-    }
-
-    return <span className="block max-w-full text-base font-semibold leading-snug text-gray-900 whitespace-normal break-words">{String(value)}</span>
-  }
-
-  const renderMetricRationale = (metric: { rationale?: string | null }) => {
-    const rationale = typeof metric.rationale === 'string' ? metric.rationale.trim() : ''
-    if (!rationale) return null
-    return (
-      <p className="mt-2 text-xs text-gray-600 leading-relaxed border-t border-gray-100 pt-2">
-        {rationale}
-      </p>
-    )
-  }
-
-  // Helper to check if metric has a valid value
-  const hasValidValue = (metric: any): boolean => {
-    const val = metric?.value
-    if (val === null || val === undefined) return false
-    if (val === '') return false
-    if (typeof val === 'string' && val.toLowerCase() === 'n/a') return false
-    if (typeof val === 'string' && val.toLowerCase() === 'na') return false
-    if (typeof val === 'string' && val.trim() === '') return false
-    return true
-  }
-
-  const isAudioCategoryMetric = (metricName?: string): boolean => {
-    if (!metricName) return false
-    const info = getMetricInfo(metricName)
-    return info?.category === 'acoustic' || info?.category === 'ai_voice'
-  }
-
-  const MetricsCard = () => {
-    if (!resultData.metric_scores || Object.keys(resultData.metric_scores).length === 0) {
-      return null
-    }
-
-    // Categorize metrics
-    const metrics = Object.entries(resultData.metric_scores)
-    const llmMetrics = metrics.filter(([, m]) => {
-      if (!hasValidValue(m)) return false
-      if (m.parent_metric_id || isLegacyCategoryLabelMetric(m)) return false
-      const info = getMetricInfo(m.metric_name)
-      return !info || info.category === 'llm'
-    })
-    const acousticMetrics = metrics.filter(([, m]) => {
-      if (!hasValidValue(m)) return false
-      if (m.parent_metric_id || isLegacyCategoryLabelMetric(m)) return false
-      const info = getMetricInfo(m.metric_name)
-      return info?.category === 'acoustic'
-    })
-    const aiVoiceMetrics = metrics.filter(([, m]) => {
-      if (!hasValidValue(m)) return false
-      if (m.parent_metric_id || isLegacyCategoryLabelMetric(m)) return false
-      const info = getMetricInfo(m.metric_name)
-      return info?.category === 'ai_voice'
-    })
-    const unavailableAudioMetrics = metrics.filter(([, m]) => {
-      if (!isAudioCategoryMetric(m?.metric_name)) return false
-      if (m?.skipped === 'audio_required') return true
-      if (typeof m?.error === 'string' && m.error.trim().length > 0) return true
-      return false
-    })
-    const hasAnyAudioMetric = metrics.some(([, m]) => isAudioCategoryMetric(m?.metric_name))
-    const hasAudioUnavailableNotice = hasAnyAudioMetric && unavailableAudioMetrics.length > 0
-
-    return (
-      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-          <BarChart3 className="h-5 w-5 text-indigo-600" />
-          Evaluation Metrics
-        </h3>
-        
-        <div className="space-y-6">
-          {hasAudioUnavailableNotice && (
-            <div className="rounded-md border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-800">
-              {unavailableAudioMetrics.length === 1
-                ? 'An audio metric is unavailable for this run. Audio analysis could not complete for that metric.'
-                : 'Some audio metrics are unavailable for this run. Audio analysis could not fully complete for one or more metrics.'}
-            </div>
-          )}
-
-          {/* LLM Conversation Metrics */}
-          {llmMetrics.length > 0 && (
-            <div>
-              <div className="flex items-center gap-2 mb-3">
-                <Brain className="w-4 h-4 text-emerald-600" />
-                <h4 className="text-sm font-semibold text-emerald-800 uppercase tracking-wide">Conversation Quality</h4>
-                <SectionTooltip section="conversation" />
-                <span className="px-2 py-0.5 text-xs bg-emerald-100 text-emerald-700 rounded-full">LLM Evaluation</span>
-              </div>
-              <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 lg:grid-cols-4 xl:grid-cols-5 gap-4">
-                {llmMetrics.map(([id, metric]) => (
-                  <div key={id} className="min-w-0 border border-gray-200 rounded-lg p-4">
-                    <div className="text-xs font-bold uppercase text-gray-700 mb-2 flex min-w-0 items-start gap-1.5">
-                      <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name}</span>
-                      <MetricTooltip metricName={metric.metric_name} />
-                    </div>
-                    <div>
-                      {formatMetricValue(metric.value, metric.type, metric.metric_name)}
-                      {renderMetricRationale(metric)}
-                    </div>
-                  </div>
-                ))}
-              </div>
-            </div>
-          )}
-
-          {/* AI Voice Quality Metrics */}
-          {aiVoiceMetrics.length > 0 && (
-            <div>
-              <div className="flex items-center gap-2 mb-3">
-                <Sparkles className="w-4 h-4 text-purple-600" />
-                <h4 className="text-sm font-semibold text-purple-800 uppercase tracking-wide">AI Voice Quality</h4>
-                <SectionTooltip section="ai_voice" />
-                <span className="px-2 py-0.5 text-xs bg-purple-100 text-purple-700 rounded-full">ML Analysis</span>
-              </div>
-              <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
-                {aiVoiceMetrics.map(([id, metric]) => (
-                  <div key={id} className="min-w-0 border border-purple-200 bg-purple-50/50 rounded-lg p-4">
-                    <div className="text-xs font-bold uppercase text-purple-800 mb-2 flex min-w-0 items-start gap-1.5">
-                      <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name}</span>
-                      <MetricTooltip metricName={metric.metric_name} />
-                    </div>
-                    <div>
-                      {formatMetricValue(metric.value, metric.type, metric.metric_name)}
-                      {renderMetricRationale(metric)}
-                    </div>
-                  </div>
-                ))}
-              </div>
-            </div>
-          )}
-
-          {/* Acoustic Metrics */}
-          {acousticMetrics.length > 0 && (
-            <div>
-              <div className="flex items-center gap-2 mb-3">
-                <AudioWaveform className="w-4 h-4 text-violet-600" />
-                <h4 className="text-sm font-semibold text-violet-800 uppercase tracking-wide">Acoustic Analysis</h4>
-                <SectionTooltip section="acoustic" />
-                <span className="px-2 py-0.5 text-xs bg-violet-100 text-violet-700 rounded-full">Signal Analysis</span>
-              </div>
-              <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
-                {acousticMetrics.map(([id, metric]) => (
-                  <div key={id} className="min-w-0 border border-violet-200 bg-violet-50/50 rounded-lg p-4">
-                    <div className="text-xs font-bold uppercase text-violet-800 mb-2 flex min-w-0 items-start gap-1.5">
-                      <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name}</span>
-                      <MetricTooltip metricName={metric.metric_name} />
-                    </div>
-                    <div>
-                      {formatMetricValue(metric.value, metric.type, metric.metric_name)}
-                      {renderMetricRationale(metric)}
-                    </div>
-                  </div>
-                ))}
-              </div>
-            </div>
-          )}
-        </div>
-      </div>
-    )
-  }
-
-  const TranscriptCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col h-[600px]">
-      <div className="flex items-center justify-between mb-4 flex-shrink-0">
-        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
-          <MessageSquare className="h-5 w-5 text-indigo-600" />
-          Transcript
-        </h3>
-        {resultData.audioUrl && (
-          <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
-            <audio controls src={resultData.audioUrl} className="h-8 w-64" />
-            <a href={resultData.audioUrl} download className="text-gray-500 hover:text-indigo-600 p-1">
-              <Download className="h-4 w-4" />
-            </a>
-          </div>
-        )}
-      </div>
-
-      <div className="flex-1 overflow-y-auto space-y-4 pr-2 custom-scrollbar">
-        {resultData.speaker_segments && resultData.speaker_segments.length > 0 ? (
-          resultData.speaker_segments.map((segment, idx) => (
-            <div key={idx} className={`flex ${isUserSpeaker(segment.speaker) ? 'justify-end' : 'justify-start'}`}>
-              <div className={`max-w-[80%] rounded-2xl px-4 py-3 ${
-                isUserSpeaker(segment.speaker)
-                  ? 'bg-indigo-600 text-white rounded-br-none'
-                  : 'bg-gray-100 text-gray-800 rounded-bl-none'
-              }`}>
-                <div className="flex items-center gap-2 mb-1 opacity-80">
-                  <span className="text-xs font-semibold uppercase tracking-wider">
-                    {getSpeakerLabel(segment.speaker)}
-                  </span>
-                  <span className="text-[10px]">
-                    {formatTime(segment.start)}
-                  </span>
-                </div>
-                <p className="text-sm leading-relaxed whitespace-pre-wrap">{segment.text}</p>
-              </div>
-            </div>
-          ))
-        ) : resultData.transcription ? (
-          <div className="prose max-w-none">
-            <p className="text-gray-700 whitespace-pre-wrap text-sm">{resultData.transcription}</p>
-          </div>
-        ) : (
-          <div className="text-center py-8 text-gray-500">
-            {resultData.status === 'transcribing' ? 'Transcription in progress...' : 'No transcript available'}
-          </div>
-        )}
-      </div>
-    </div>
-  )
-
-  const StatsParams = () => (
-    <div className="space-y-6">
-      {/* Call Info Card */}
-      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-          <Clock className="h-5 w-5 text-indigo-600" />
-          Call Information
-        </h3>
-        <div className="space-y-4">
-          <div className="p-4 bg-gray-50 rounded-lg">
-            <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Duration</p>
-            <p className="text-2xl font-bold text-gray-900">
-              {formatDuration(resultData.duration_seconds)}
-            </p>
-          </div>
-          
-          <div className="p-4 bg-gray-50 rounded-lg">
-            <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Timestamp</p>
-            <p className="text-sm font-medium text-gray-900">
-              {formatTimestamp(resultData.timestamp)}
-            </p>
-          </div>
-
-          {resultData.agent && (
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Agent</p>
-              <p className="text-sm font-medium text-gray-900">{resultData.agent.name}</p>
-              {resultData.agent.description && (
-                <p className="text-xs text-gray-500 mt-1 line-clamp-2">{resultData.agent.description}</p>
-              )}
-            </div>
-          )}
-
-          {resultData.persona && (
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Persona</p>
-              <p className="text-sm font-medium text-gray-900">{resultData.persona.name}</p>
-            </div>
-          )}
-
-          {resultData.scenario && (
-            <div className="p-4 bg-gray-50 rounded-lg">
-              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Scenario</p>
-              <p className="text-sm font-medium text-gray-900">{resultData.scenario.name}</p>
-            </div>
-          )}
-        </div>
-      </div>
-
-      {/* System Info */}
-      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
-          <Server className="h-5 w-5 text-indigo-600" />
-          System Details
-        </h3>
-        <div className="grid grid-cols-1 gap-4 text-sm">
-          <div>
-            <p className="text-gray-500 mb-1">Result ID</p>
-            <p className="font-mono bg-gray-50 p-2 rounded text-gray-700 truncate">{resultData.result_id || resultData.id}</p>
-          </div>
-          <div>
-            <p className="text-gray-500 mb-1">Status</p>
-            <p className={`inline-flex px-2 py-1 rounded-full text-xs font-semibold capitalize ${
-              resultData.status === 'completed'
-                ? 'text-green-700 bg-green-100'
-                : resultData.status === 'failed'
-                ? 'text-red-700 bg-red-100'
-                : 'text-yellow-700 bg-yellow-100'
-            }`}>
-              {resultData.status || 'Unknown'}
-            </p>
-          </div>
-          {resultData.audio_s3_key && (
-            <div>
-              <p className="text-gray-500 mb-1">Audio Storage</p>
-              <p className="font-mono bg-gray-50 p-2 rounded text-gray-700 text-xs truncate">{resultData.audio_s3_key}</p>
-            </div>
-          )}
-        </div>
-      </div>
-    </div>
-  )
-
-  const DebugView = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
-      <div className="grid grid-cols-1 md:grid-cols-2 gap-6">
-        <div>
-          <h4 className="font-semibold text-gray-900 mb-2">Metric Scores</h4>
-          <pre className="text-xs bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto h-64 custom-scrollbar">
-            {JSON.stringify(resultData.metric_scores || {}, null, 2)}
-          </pre>
-        </div>
-        <div>
-          <h4 className="font-semibold text-gray-900 mb-2">Full Result Data</h4>
-          <pre className="text-xs bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto h-64 custom-scrollbar">
-            {JSON.stringify({
-              id: resultData.id,
-              result_id: resultData.result_id,
-              status: resultData.status,
-              duration_seconds: resultData.duration_seconds,
-              audio_s3_key: resultData.audio_s3_key,
-              agent: resultData.agent,
-              persona: resultData.persona,
-              scenario: resultData.scenario,
-            }, null, 2)}
-          </pre>
-        </div>
-      </div>
-    </div>
-  )
-
-  return (
-    <div className="space-y-6">
-      {/* Navigation Tabs */}
-      <div className="flex border-b border-gray-200">
-        <button
-          onClick={() => setActiveTab('overview')}
-          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'overview'
-            ? 'border-indigo-600 text-indigo-600'
-            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-          }`}
-        >
-          Overview
-        </button>
-        <button
-          onClick={() => setActiveTab('transcript')}
-          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'transcript'
-            ? 'border-indigo-600 text-indigo-600'
-            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-          }`}
-        >
-          Transcript
-        </button>
-        <button
-          onClick={() => setActiveTab('debug')}
-          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'debug'
-            ? 'border-indigo-600 text-indigo-600'
-            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-          }`}
-        >
-          Debug Data
-        </button>
-      </div>
-
-      {activeTab === 'overview' && (
-        <div className="space-y-6">
-          {/* Evaluation Metrics - Full Width at Top */}
-          <MetricsCard />
-          
-          {/* Main Content Grid */}
-          <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
-            <div className="lg:col-span-2 space-y-6">
-              <SummaryCard />
-              <TranscriptCard />
-            </div>
-            <div className="lg:col-span-1">
-              <StatsParams />
-            </div>
-          </div>
-        </div>
-      )}
-
-      {activeTab === 'transcript' && (
-        <div className="space-y-6">
-          <TranscriptCard />
-        </div>
-      )}
-
-      {activeTab === 'debug' && <DebugView />}
-    </div>
-  )
-}
+import { useState, type ReactNode } from 'react'
+import {
+  Clock, MessageSquare, TrendingUp, Download, Server, BarChart3, HelpCircle, Brain, Sparkles, AudioWaveform
+} from 'lucide-react'
+
+const LEGACY_CATEGORY_LABEL_METRIC_NAMES = new Set([
+  'yes',
+  'no',
+  'true',
+  'false',
+  'same',
+  'different',
+])
+
+function isLegacyCategoryLabelMetric(metric: {
+  type?: string | null
+  metric_name?: string | null
+}): boolean {
+  if ((metric.type || '').toLowerCase() !== 'boolean') return false
+  const name = (metric.metric_name || '').trim().toLowerCase()
+  return LEGACY_CATEGORY_LABEL_METRIC_NAMES.has(name)
+}
+// Metric information for tooltips and categorization
+const METRIC_INFO: Record<string, { 
+  description: string
+  ideal: string
+  unit?: string
+  category: 'acoustic' | 'ai_voice' | 'llm'
+}> = {
+  'Pitch Variance': { 
+    description: 'F0 variation measuring prosodic expressiveness. Higher values indicate more expressive speech.',
+    ideal: '20-50 Hz (natural speech)',
+    unit: 'Hz',
+    category: 'acoustic'
+  },
+  'Jitter': { 
+    description: 'Cycle-to-cycle pitch period variation indicating vocal stability. Lower is better.',
+    ideal: '< 1% (healthy voice)',
+    unit: '%',
+    category: 'acoustic'
+  },
+  'Shimmer': { 
+    description: 'Amplitude perturbation measuring voice quality consistency. Lower is better.',
+    ideal: '< 3% (clear voice)',
+    unit: '%',
+    category: 'acoustic'
+  },
+  'HNR': { 
+    description: 'Harmonics-to-Noise Ratio measuring signal clarity. Higher indicates cleaner voice.',
+    ideal: '> 20 dB (clear, non-breathy)',
+    unit: 'dB',
+    category: 'acoustic'
+  },
+  'MOS Score': { 
+    description: 'Mean Opinion Score predicting human perception of audio quality (1-5 scale).',
+    ideal: '4.0+ (studio quality), 3.0 (phone quality), <2.0 (poor/robotic)',
+    category: 'ai_voice'
+  },
+  'Emotion Category': {
+    description: 'Dominant emotion detected in the voice (angry, happy, sad, neutral, fearful, etc.).',
+    ideal: 'Depends on context - should match expected tone',
+    category: 'ai_voice'
+  },
+  'Emotion Confidence': { 
+    description: 'Confidence score for the detected emotion category.',
+    ideal: '> 0.7 (high confidence)',
+    category: 'ai_voice'
+  },
+  'Valence': { 
+    description: 'Emotional positivity/negativity scale. Negative = sad/angry, Positive = happy/excited.',
+    ideal: '-1.0 to +1.0 (context dependent)',
+    category: 'ai_voice'
+  },
+  'Arousal': { 
+    description: 'Emotional intensity/energy level. Low = calm/sleepy, High = excited/energetic.',
+    ideal: '0.3-0.6 (engaged but not agitated)',
+    category: 'ai_voice'
+  },
+  'Speaker Consistency': {
+    description: 'Voice identity stability throughout the call. Detects if voice changed mid-call (glitch).',
+    ideal: '> 0.8 (same voice), < 0.5 indicates voice glitch',
+    category: 'ai_voice'
+  },
+  'Prosody Score': { 
+    description: 'Expressiveness/drama score. Low = monotone/flat, High = expressive/dynamic.',
+    ideal: '0.4-0.7 (natural expressiveness)',
+    category: 'ai_voice'
+  },
+  'Follow Instructions': { 
+    description: 'How well the agent followed the given instructions and guidelines.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+  'Problem Resolution': { 
+    description: 'Whether the agent successfully resolved the customer\'s problem or query.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+  'Professionalism': { 
+    description: 'Professional demeanor, appropriate language, and courteous behavior.',
+    ideal: '> 0.85 (85%+)',
+    category: 'llm'
+  },
+  'Clarity and Empathy': { 
+    description: 'Clear communication combined with understanding and acknowledgment of customer feelings.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+  'Objective Achieved': {
+    description: 'Whether the conversation\'s primary objective was successfully achieved.',
+    ideal: 'Yes/True',
+    category: 'llm'
+  },
+  'Overall Quality': {
+    description: 'Holistic assessment of the entire conversation quality.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+}
+
+const getMetricInfo = (metricName: string) => METRIC_INFO[metricName] || null
+
+const SECTION_INFO: Record<'conversation' | 'ai_voice' | 'acoustic', string> = {
+  conversation: 'LLM-based evaluation of how well the agent handled intent, instructions, and resolution quality.',
+  ai_voice: 'ML-based voice quality metrics on naturalness, affect, consistency, and expressiveness.',
+  acoustic: 'Signal-level acoustic measurements from the recording (pitch stability, perturbation, noise ratio).',
+}
+
+const MetricTooltip = ({ metricName }: { metricName: string }) => {
+  const [isVisible, setIsVisible] = useState(false)
+  const info = getMetricInfo(metricName)
+
+  if (!info) return null
+
+  return (
+    <div className="relative inline-flex flex-shrink-0 ml-1 mt-0.5">
+      <button
+        type="button"
+        className="text-gray-400 hover:text-gray-600 focus:outline-none transition-colors"
+        onMouseEnter={() => setIsVisible(true)}
+        onMouseLeave={() => setIsVisible(false)}
+        onClick={() => setIsVisible(!isVisible)}
+        aria-label={`Info about ${metricName}`}
+      >
+        <HelpCircle className="w-3.5 h-3.5" />
+      </button>
+      {isVisible && (
+        <div className="absolute z-50 left-1/2 -translate-x-1/2 bottom-full mb-2 w-64 p-3 text-xs bg-gray-900 text-white rounded-lg shadow-xl pointer-events-none">
+          <div className="font-semibold text-gray-100 mb-1.5">{metricName}</div>
+          <p className="text-gray-300 mb-2 leading-relaxed">{info.description}</p>
+          <div className="flex items-center gap-1 pt-1.5 border-t border-gray-700">
+            <span className="text-emerald-400 font-medium">Ideal:</span>
+            <span className="text-gray-200">{info.ideal}</span>
+          </div>
+          <div className="absolute left-1/2 -translate-x-1/2 top-full w-0 h-0 border-l-[6px] border-l-transparent border-r-[6px] border-r-transparent border-t-[6px] border-t-gray-900" />
+        </div>
+      )}
+    </div>
+  )
+}
+
+const SectionTooltip = ({ section }: { section: 'conversation' | 'ai_voice' | 'acoustic' }) => {
+  const [isVisible, setIsVisible] = useState(false)
+
+  return (
+    <div className="relative inline-block">
+      <button
+        type="button"
+        className="text-gray-400 hover:text-gray-600 focus:outline-none transition-colors"
+        onMouseEnter={() => setIsVisible(true)}
+        onMouseLeave={() => setIsVisible(false)}
+        onClick={() => setIsVisible(!isVisible)}
+        aria-label={`Info about ${section} metrics`}
+      >
+        <HelpCircle className="w-3.5 h-3.5" />
+      </button>
+      {isVisible && (
+        <div className="absolute z-50 left-1/2 -translate-x-1/2 bottom-full mb-2 w-72 p-3 text-xs bg-gray-900 text-white rounded-lg shadow-xl pointer-events-none">
+          <p className="text-gray-200 leading-relaxed">{SECTION_INFO[section]}</p>
+          <div className="absolute left-1/2 -translate-x-1/2 top-full w-0 h-0 border-l-[6px] border-l-transparent border-r-[6px] border-r-transparent border-t-[6px] border-t-gray-900" />
+        </div>
+      )}
+    </div>
+  )
+}
+
+interface TestVoiceAgentResultData {
+  id?: string
+  result_id?: string
+  name?: string
+  timestamp?: string
+  duration_seconds?: number | null
+  status?: 'queued' | 'transcribing' | 'evaluating' | 'completed' | 'failed'
+  transcription?: string | null
+  speaker_segments?: Array<{
+    speaker: string
+    text: string
+    start: number
+    end: number
+  }> | null
+  metric_scores?: Record<
+    string,
+    {
+      value: any
+      type: string
+      metric_name: string
+      parent_metric_id?: string | null
+      rationale?: string | null
+      skipped?: string
+      error?: string | null
+    }
+  > | null
+  call_analysis?: {
+    call_summary?: string
+    user_sentiment?: string
+    call_successful?: boolean
+  }
+  audio_s3_key?: string | null
+  audioUrl?: string
+  agent?: {
+    id?: string
+    name?: string
+    description?: string
+  }
+  persona?: {
+    name?: string
+  }
+  scenario?: {
+    name?: string
+  }
+}
+
+interface TestVoiceAgentResultDetailsProps {
+  resultData: TestVoiceAgentResultData
+  metricsOnly?: boolean
+}
+
+export default function TestVoiceAgentResultDetails({
+  resultData,
+  metricsOnly = false,
+}: TestVoiceAgentResultDetailsProps) {
+  const [activeTab, setActiveTab] = useState<'overview' | 'transcript' | 'debug'>('overview')
+
+  const formatDuration = (seconds?: number | null) => {
+    if (!seconds) return 'N/A'
+    const mins = Math.floor(seconds / 60)
+    const secs = Math.floor(seconds % 60)
+    return `${mins}m ${secs}s`
+  }
+
+  const formatTimestamp = (timestamp?: string) => {
+    if (!timestamp) return 'N/A'
+    return new Date(timestamp).toLocaleString()
+  }
+
+  const formatTime = (seconds: number) => {
+    const mins = Math.floor(seconds / 60)
+    const secs = Math.floor(seconds % 60)
+    return `${mins}:${secs.toString().padStart(2, '0')}`
+  }
+
+  const getSentimentColor = (sentiment?: string) => {
+    if (!sentiment) return 'text-gray-500 bg-gray-100'
+    const s = sentiment.toLowerCase()
+    if (s.includes('positive') || s.includes('happy')) return 'text-green-700 bg-green-100'
+    if (s.includes('negative') || s.includes('angry')) return 'text-red-700 bg-red-100'
+    return 'text-blue-700 bg-blue-100'
+  }
+
+  // Extract summary, sentiment, and successful from metric_scores or call_analysis
+  const summary = resultData.call_analysis?.call_summary || 
+    (resultData.metric_scores?.summary?.value) || 
+    null
+  
+  const sentiment = resultData.call_analysis?.user_sentiment || 
+    (resultData.metric_scores?.sentiment?.value) || 
+    'Neutral'
+  
+  const successful = resultData.call_analysis?.call_successful !== undefined 
+    ? resultData.call_analysis.call_successful 
+    : (resultData.metric_scores?.successful?.value !== undefined 
+        ? resultData.metric_scores.successful.value 
+        : null)
+
+  const getSpeakerLabel = (speaker: string) => {
+    if (speaker === 'Speaker 1' || speaker === 'user' || speaker === 'caller') {
+      return 'You'
+    }
+    if (speaker === 'assistant' || speaker === 'Speaker 2' || speaker === 'bot') {
+      return resultData.agent?.name || 'Agent'
+    }
+    return resultData.agent?.name || 'Agent'
+  }
+
+  const isUserSpeaker = (speaker: string) => {
+    return speaker === 'Speaker 1' || speaker === 'user' || speaker === 'caller'
+  }
+
+  const SummaryCard = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+      <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+        <TrendingUp className="h-5 w-5 text-indigo-600" />
+        Call Analysis
+      </h3>
+
+      {summary || resultData.call_analysis ? (
+        <div className="space-y-6">
+          {summary && (
+            <div className="p-4 bg-indigo-50 rounded-lg border border-indigo-100">
+              <p className="text-sm font-medium text-indigo-900 mb-2">Summary</p>
+              <p className="text-sm text-indigo-800 leading-relaxed">{summary}</p>
+            </div>
+          )}
+
+          <div className="grid grid-cols-2 gap-4">
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Sentiment</p>
+              <div className="flex items-center gap-2">
+                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${getSentimentColor(sentiment)}`}>
+                  {sentiment}
+                </span>
+              </div>
+            </div>
+
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Success Status</p>
+              <div className="flex items-center gap-2">
+                <span className={`px-2 py-1 rounded-full text-xs font-semibold ${
+                  successful === true
+                    ? 'text-green-700 bg-green-100'
+                    : successful === false
+                    ? 'text-red-700 bg-red-100'
+                    : 'text-gray-500 bg-gray-100'
+                }`}>
+                  {successful === true ? 'Successful' : successful === false ? 'Unsuccessful' : 'N/A'}
+                </span>
+              </div>
+            </div>
+
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Status</p>
+              <span className={`px-2 py-1 rounded-full text-xs font-semibold capitalize ${
+                resultData.status === 'completed'
+                  ? 'text-green-700 bg-green-100'
+                  : resultData.status === 'failed'
+                  ? 'text-red-700 bg-red-100'
+                  : 'text-yellow-700 bg-yellow-100'
+              }`}>
+                {resultData.status || 'Unknown'}
+              </span>
+            </div>
+
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Duration</p>
+              <span className="text-sm font-medium text-gray-900">
+                {formatDuration(resultData.duration_seconds)}
+              </span>
+            </div>
+          </div>
+        </div>
+      ) : (
+        <div className="text-center py-8 text-gray-500">Analysis not available</div>
+      )}
+    </div>
+  )
+
+  // Helper function to format metric values
+  const formatMetricValue = (value: any, type: string, metricName: string): ReactNode => {
+    if (value === null || value === undefined) return <span className="text-gray-400">N/A</span>
+
+    const normalizedType = type?.toLowerCase()
+
+    if (normalizedType === 'category') {
+      if (value === '') return <span className="text-gray-400">N/A</span>
+      return (
+        <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-lg bg-indigo-50 text-indigo-700 text-sm font-semibold whitespace-normal break-words leading-snug">
+          {String(value)}
+        </span>
+      )
+    }
+
+    if (metricName === 'Emotion Category') {
+      const emotion = String(value).toLowerCase()
+      const emotionConfig: Record<string, { emoji: string; color: string; bg: string }> = {
+        neutral: { emoji: '😐', color: 'text-gray-700', bg: 'bg-gray-100' },
+        happy: { emoji: '😊', color: 'text-green-700', bg: 'bg-green-100' },
+        sad: { emoji: '😢', color: 'text-blue-700', bg: 'bg-blue-100' },
+        angry: { emoji: '😠', color: 'text-red-700', bg: 'bg-red-100' },
+        fearful: { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
+        fear: { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
+        surprised: { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
+        surprise: { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
+        disgusted: { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
+        disgust: { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
+        calm: { emoji: '😌', color: 'text-teal-700', bg: 'bg-teal-100' },
+      }
+      const config = emotionConfig[emotion] || { emoji: '🎭', color: 'text-gray-700', bg: 'bg-gray-100' }
+
+      return (
+        <div className={`inline-flex items-center gap-2 px-3 py-1.5 rounded-full ${config.bg}`}>
+          <span className="text-xl">{config.emoji}</span>
+          <span className={`font-semibold capitalize ${config.color}`}>{value}</span>
+        </div>
+      )
+    }
+
+    if (normalizedType === 'boolean') {
+      const boolValue = value === true || value === 1 || value === '1' || value === 'true'
+      return <span className="block text-base font-semibold text-gray-900">{boolValue ? 'Yes' : 'No'}</span>
+    }
+
+    if (normalizedType === 'rating') {
+      if (typeof value === 'string' && isNaN(parseFloat(value))) {
+        return (
+          <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-full bg-purple-100 text-purple-700 font-semibold capitalize whitespace-normal break-words text-left leading-snug">
+            {value}
+          </span>
+        )
+      }
+
+      const numValue = typeof value === 'number' ? value : parseFloat(value)
+      if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
+
+      const normalizedValue = Math.max(0, Math.min(1, numValue))
+      const percentage = Math.round(normalizedValue * 100)
+      const getBarColor = (pct: number): string => {
+        if (pct >= 70) return 'bg-green-500'
+        if (pct >= 50) return 'bg-yellow-500'
+        return 'bg-red-500'
+      }
+
+      return (
+        <div className="flex flex-col gap-2">
+          <span className="text-2xl font-bold text-gray-900">{percentage}%</span>
+          <div className="w-full h-3 bg-gray-200 rounded-full overflow-hidden">
+            <div className={`h-full rounded-full transition-all ${getBarColor(percentage)}`} style={{ width: `${percentage}%` }} />
+          </div>
+        </div>
+      )
+    }
+
+    if (normalizedType === 'number') {
+      const numValue = typeof value === 'number' ? value : parseFloat(value)
+      if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
+
+      const info = getMetricInfo(metricName)
+      if (info?.category === 'acoustic' || info?.category === 'ai_voice') {
+        return (
+          <div className="flex items-baseline gap-1">
+            <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(2)}</span>
+            <span className="text-sm font-medium text-violet-600">{info.unit || ''}</span>
+          </div>
+        )
+      }
+      return <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(1)}</span>
+    }
+
+    return <span className="block max-w-full text-base font-semibold leading-snug text-gray-900 whitespace-normal break-words">{String(value)}</span>
+  }
+
+  const renderMetricRationale = (metric: { rationale?: string | null }) => {
+    const rationale = typeof metric.rationale === 'string' ? metric.rationale.trim() : ''
+    if (!rationale) return null
+    return (
+      <p className="mt-2 text-xs text-gray-600 leading-relaxed border-t border-gray-100 pt-2">
+        {rationale}
+      </p>
+    )
+  }
+
+  // Helper to check if metric has a valid value
+  const hasValidValue = (metric: any): boolean => {
+    const val = metric?.value
+    if (val === null || val === undefined) return false
+    if (val === '') return false
+    if (typeof val === 'string' && val.toLowerCase() === 'n/a') return false
+    if (typeof val === 'string' && val.toLowerCase() === 'na') return false
+    if (typeof val === 'string' && val.trim() === '') return false
+    return true
+  }
+
+  const isAudioCategoryMetric = (metricName?: string): boolean => {
+    if (!metricName) return false
+    const info = getMetricInfo(metricName)
+    return info?.category === 'acoustic' || info?.category === 'ai_voice'
+  }
+
+  const MetricsCard = () => {
+    if (!resultData.metric_scores || Object.keys(resultData.metric_scores).length === 0) {
+      return null
+    }
+
+    // Categorize metrics
+    const metrics = Object.entries(resultData.metric_scores)
+    const llmMetrics = metrics.filter(([, m]) => {
+      if (!hasValidValue(m)) return false
+      if (m.parent_metric_id || isLegacyCategoryLabelMetric(m)) return false
+      const info = getMetricInfo(m.metric_name)
+      return !info || info.category === 'llm'
+    })
+    const acousticMetrics = metrics.filter(([, m]) => {
+      if (!hasValidValue(m)) return false
+      if (m.parent_metric_id || isLegacyCategoryLabelMetric(m)) return false
+      const info = getMetricInfo(m.metric_name)
+      return info?.category === 'acoustic'
+    })
+    const aiVoiceMetrics = metrics.filter(([, m]) => {
+      if (!hasValidValue(m)) return false
+      if (m.parent_metric_id || isLegacyCategoryLabelMetric(m)) return false
+      const info = getMetricInfo(m.metric_name)
+      return info?.category === 'ai_voice'
+    })
+    const unavailableAudioMetrics = metrics.filter(([, m]) => {
+      if (!isAudioCategoryMetric(m?.metric_name)) return false
+      if (m?.skipped === 'audio_required') return true
+      if (typeof m?.error === 'string' && m.error.trim().length > 0) return true
+      return false
+    })
+    const hasAnyAudioMetric = metrics.some(([, m]) => isAudioCategoryMetric(m?.metric_name))
+    const hasAudioUnavailableNotice = hasAnyAudioMetric && unavailableAudioMetrics.length > 0
+
+    return (
+      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+          <BarChart3 className="h-5 w-5 text-indigo-600" />
+          Evaluation Metrics
+        </h3>
+        
+        <div className="space-y-6">
+          {hasAudioUnavailableNotice && (
+            <div className="rounded-md border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-800">
+              {unavailableAudioMetrics.length === 1
+                ? 'An audio metric is unavailable for this run. Audio analysis could not complete for that metric.'
+                : 'Some audio metrics are unavailable for this run. Audio analysis could not fully complete for one or more metrics.'}
+            </div>
+          )}
+
+          {/* LLM Conversation Metrics */}
+          {llmMetrics.length > 0 && (
+            <div>
+              <div className="flex items-center gap-2 mb-3">
+                <Brain className="w-4 h-4 text-emerald-600" />
+                <h4 className="text-sm font-semibold text-emerald-800 uppercase tracking-wide">Conversation Quality</h4>
+                <SectionTooltip section="conversation" />
+                <span className="px-2 py-0.5 text-xs bg-emerald-100 text-emerald-700 rounded-full">LLM Evaluation</span>
+              </div>
+              <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 lg:grid-cols-4 xl:grid-cols-5 gap-4">
+                {llmMetrics.map(([id, metric]) => (
+                  <div key={id} className="min-w-0 border border-gray-200 rounded-lg p-4">
+                    <div className="text-xs font-bold uppercase text-gray-700 mb-2 flex min-w-0 items-start gap-1.5">
+                      <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name}</span>
+                      <MetricTooltip metricName={metric.metric_name} />
+                    </div>
+                    <div>
+                      {formatMetricValue(metric.value, metric.type, metric.metric_name)}
+                      {renderMetricRationale(metric)}
+                    </div>
+                  </div>
+                ))}
+              </div>
+            </div>
+          )}
+
+          {/* AI Voice Quality Metrics */}
+          {aiVoiceMetrics.length > 0 && (
+            <div>
+              <div className="flex items-center gap-2 mb-3">
+                <Sparkles className="w-4 h-4 text-purple-600" />
+                <h4 className="text-sm font-semibold text-purple-800 uppercase tracking-wide">AI Voice Quality</h4>
+                <SectionTooltip section="ai_voice" />
+                <span className="px-2 py-0.5 text-xs bg-purple-100 text-purple-700 rounded-full">ML Analysis</span>
+              </div>
+              <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
+                {aiVoiceMetrics.map(([id, metric]) => (
+                  <div key={id} className="min-w-0 border border-purple-200 bg-purple-50/50 rounded-lg p-4">
+                    <div className="text-xs font-bold uppercase text-purple-800 mb-2 flex min-w-0 items-start gap-1.5">
+                      <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name}</span>
+                      <MetricTooltip metricName={metric.metric_name} />
+                    </div>
+                    <div>
+                      {formatMetricValue(metric.value, metric.type, metric.metric_name)}
+                      {renderMetricRationale(metric)}
+                    </div>
+                  </div>
+                ))}
+              </div>
+            </div>
+          )}
+
+          {/* Acoustic Metrics */}
+          {acousticMetrics.length > 0 && (
+            <div>
+              <div className="flex items-center gap-2 mb-3">
+                <AudioWaveform className="w-4 h-4 text-violet-600" />
+                <h4 className="text-sm font-semibold text-violet-800 uppercase tracking-wide">Acoustic Analysis</h4>
+                <SectionTooltip section="acoustic" />
+                <span className="px-2 py-0.5 text-xs bg-violet-100 text-violet-700 rounded-full">Signal Analysis</span>
+              </div>
+              <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
+                {acousticMetrics.map(([id, metric]) => (
+                  <div key={id} className="min-w-0 border border-violet-200 bg-violet-50/50 rounded-lg p-4">
+                    <div className="text-xs font-bold uppercase text-violet-800 mb-2 flex min-w-0 items-start gap-1.5">
+                      <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name}</span>
+                      <MetricTooltip metricName={metric.metric_name} />
+                    </div>
+                    <div>
+                      {formatMetricValue(metric.value, metric.type, metric.metric_name)}
+                      {renderMetricRationale(metric)}
+                    </div>
+                  </div>
+                ))}
+              </div>
+            </div>
+          )}
+        </div>
+      </div>
+    )
+  }
+
+  const TranscriptCard = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col h-[600px]">
+      <div className="flex items-center justify-between mb-4 flex-shrink-0">
+        <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
+          <MessageSquare className="h-5 w-5 text-indigo-600" />
+          Transcript
+        </h3>
+        {resultData.audioUrl && (
+          <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
+            <audio controls src={resultData.audioUrl} className="h-8 w-64" />
+            <a href={resultData.audioUrl} download className="text-gray-500 hover:text-indigo-600 p-1">
+              <Download className="h-4 w-4" />
+            </a>
+          </div>
+        )}
+      </div>
+
+      <div className="flex-1 overflow-y-auto space-y-4 pr-2 custom-scrollbar">
+        {resultData.speaker_segments && resultData.speaker_segments.length > 0 ? (
+          resultData.speaker_segments.map((segment, idx) => (
+            <div key={idx} className={`flex ${isUserSpeaker(segment.speaker) ? 'justify-end' : 'justify-start'}`}>
+              <div className={`max-w-[80%] rounded-2xl px-4 py-3 ${
+                isUserSpeaker(segment.speaker)
+                  ? 'bg-indigo-600 text-white rounded-br-none'
+                  : 'bg-gray-100 text-gray-800 rounded-bl-none'
+              }`}>
+                <div className="flex items-center gap-2 mb-1 opacity-80">
+                  <span className="text-xs font-semibold uppercase tracking-wider">
+                    {getSpeakerLabel(segment.speaker)}
+                  </span>
+                  <span className="text-[10px]">
+                    {formatTime(segment.start)}
+                  </span>
+                </div>
+                <p className="text-sm leading-relaxed whitespace-pre-wrap">{segment.text}</p>
+              </div>
+            </div>
+          ))
+        ) : resultData.transcription ? (
+          <div className="prose max-w-none">
+            <p className="text-gray-700 whitespace-pre-wrap text-sm">{resultData.transcription}</p>
+          </div>
+        ) : (
+          <div className="text-center py-8 text-gray-500">
+            {resultData.status === 'transcribing' ? 'Transcription in progress...' : 'No transcript available'}
+          </div>
+        )}
+      </div>
+    </div>
+  )
+
+  const StatsParams = () => (
+    <div className="space-y-6">
+      {/* Call Info Card */}
+      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+          <Clock className="h-5 w-5 text-indigo-600" />
+          Call Information
+        </h3>
+        <div className="space-y-4">
+          <div className="p-4 bg-gray-50 rounded-lg">
+            <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Duration</p>
+            <p className="text-2xl font-bold text-gray-900">
+              {formatDuration(resultData.duration_seconds)}
+            </p>
+          </div>
+          
+          <div className="p-4 bg-gray-50 rounded-lg">
+            <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Timestamp</p>
+            <p className="text-sm font-medium text-gray-900">
+              {formatTimestamp(resultData.timestamp)}
+            </p>
+          </div>
+
+          {resultData.agent && (
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Agent</p>
+              <p className="text-sm font-medium text-gray-900">{resultData.agent.name}</p>
+              {resultData.agent.description && (
+                <p className="text-xs text-gray-500 mt-1 line-clamp-2">{resultData.agent.description}</p>
+              )}
+            </div>
+          )}
+
+          {resultData.persona && (
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Persona</p>
+              <p className="text-sm font-medium text-gray-900">{resultData.persona.name}</p>
+            </div>
+          )}
+
+          {resultData.scenario && (
+            <div className="p-4 bg-gray-50 rounded-lg">
+              <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Scenario</p>
+              <p className="text-sm font-medium text-gray-900">{resultData.scenario.name}</p>
+            </div>
+          )}
+        </div>
+      </div>
+
+      {/* System Info */}
+      <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+        <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
+          <Server className="h-5 w-5 text-indigo-600" />
+          System Details
+        </h3>
+        <div className="grid grid-cols-1 gap-4 text-sm">
+          <div>
+            <p className="text-gray-500 mb-1">Result ID</p>
+            <p className="font-mono bg-gray-50 p-2 rounded text-gray-700 truncate">{resultData.result_id || resultData.id}</p>
+          </div>
+          <div>
+            <p className="text-gray-500 mb-1">Status</p>
+            <p className={`inline-flex px-2 py-1 rounded-full text-xs font-semibold capitalize ${
+              resultData.status === 'completed'
+                ? 'text-green-700 bg-green-100'
+                : resultData.status === 'failed'
+                ? 'text-red-700 bg-red-100'
+                : 'text-yellow-700 bg-yellow-100'
+            }`}>
+              {resultData.status || 'Unknown'}
+            </p>
+          </div>
+          {resultData.audio_s3_key && (
+            <div>
+              <p className="text-gray-500 mb-1">Audio Storage</p>
+              <p className="font-mono bg-gray-50 p-2 rounded text-gray-700 text-xs truncate">{resultData.audio_s3_key}</p>
+            </div>
+          )}
+        </div>
+      </div>
+    </div>
+  )
+
+  const DebugView = () => (
+    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
+      <div className="grid grid-cols-1 md:grid-cols-2 gap-6">
+        <div>
+          <h4 className="font-semibold text-gray-900 mb-2">Metric Scores</h4>
+          <pre className="text-xs bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto h-64 custom-scrollbar">
+            {JSON.stringify(resultData.metric_scores || {}, null, 2)}
+          </pre>
+        </div>
+        <div>
+          <h4 className="font-semibold text-gray-900 mb-2">Full Result Data</h4>
+          <pre className="text-xs bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto h-64 custom-scrollbar">
+            {JSON.stringify({
+              id: resultData.id,
+              result_id: resultData.result_id,
+              status: resultData.status,
+              duration_seconds: resultData.duration_seconds,
+              audio_s3_key: resultData.audio_s3_key,
+              agent: resultData.agent,
+              persona: resultData.persona,
+              scenario: resultData.scenario,
+            }, null, 2)}
+          </pre>
+        </div>
+      </div>
+    </div>
+  )
+
+  return (
+    <div className="space-y-6">
+      {metricsOnly ? (
+        <MetricsCard />
+      ) : (
+        <>
+      {/* Navigation Tabs */}
+      <div className="flex border-b border-gray-200">
+        <button
+          onClick={() => setActiveTab('overview')}
+          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'overview'
+            ? 'border-indigo-600 text-indigo-600'
+            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+          }`}
+        >
+          Overview
+        </button>
+        <button
+          onClick={() => setActiveTab('transcript')}
+          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'transcript'
+            ? 'border-indigo-600 text-indigo-600'
+            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+          }`}
+        >
+          Transcript
+        </button>
+        <button
+          onClick={() => setActiveTab('debug')}
+          className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${activeTab === 'debug'
+            ? 'border-indigo-600 text-indigo-600'
+            : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+          }`}
+        >
+          Debug Data
+        </button>
+      </div>
+
+      {activeTab === 'overview' && (
+        <div className="space-y-6">
+          {/* Evaluation Metrics - Full Width at Top */}
+          <MetricsCard />
+          
+          {/* Main Content Grid */}
+          <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
+            <div className="lg:col-span-2 space-y-6">
+              <SummaryCard />
+              <TranscriptCard />
+            </div>
+            <div className="lg:col-span-1">
+              <StatsParams />
+            </div>
+          </div>
+        </div>
+      )}
+
+      {activeTab === 'transcript' && (
+        <div className="space-y-6">
+          <TranscriptCard />
+        </div>
+      )}
+
+      {activeTab === 'debug' && <DebugView />}
+        </>
+      )}
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/TraceCharts.tsx b/frontend/src/components/call-recordings/TraceCharts.tsx
index aea42465..9bda8772 100644
--- a/frontend/src/components/call-recordings/TraceCharts.tsx
+++ b/frontend/src/components/call-recordings/TraceCharts.tsx
@@ -11,18 +11,19 @@ import {
   XAxis,
   YAxis,
 } from 'recharts'
+import { CHART_BAR, STAGE_COLORS } from '../../lib/callDetailTheme'
 
 type ComponentKind = 'stt' | 'llm' | 'tts' | 's2s'
 
 const SLICE_COLORS: Record<ComponentKind, string> = {
-  stt: '#d4d4d8',
-  llm: '#a16207',
-  tts: '#a1a1aa',
-  s2s: '#71717a',
+  stt: STAGE_COLORS.stt,
+  llm: STAGE_COLORS.llm,
+  tts: STAGE_COLORS.tts,
+  s2s: STAGE_COLORS.s2s,
 }
 
-const BAR_FILL_DEFAULT = '#a1a1aa'
-const BAR_FILL_PEAK = '#854d0e'
+const BAR_FILL_DEFAULT = CHART_BAR.default
+const BAR_FILL_PEAK = CHART_BAR.peak
 
 const STAGE_LABELS: Record<ComponentKind, string> = {
   stt: 'STT',
diff --git a/frontend/src/components/call-recordings/TraceDetailDrawer.tsx b/frontend/src/components/call-recordings/TraceDetailDrawer.tsx
index 3b894c7b..0a48f53f 100644
--- a/frontend/src/components/call-recordings/TraceDetailDrawer.tsx
+++ b/frontend/src/components/call-recordings/TraceDetailDrawer.tsx
@@ -1,66 +1,130 @@
-import { useEffect } from 'react'

-import { createPortal } from 'react-dom'

-import { AnimatePresence, motion } from 'framer-motion'

-import SyntheticCallTracePanel from './SyntheticCallTracePanel'

-

-export default function TraceDetailDrawer({

-  traceId,

-  open,

-  onClose,

-}: {

-  traceId: string | null

-  open: boolean

-  onClose: () => void

-}) {

-  useEffect(() => {

-    if (!open) return

-    const onKey = (e: KeyboardEvent) => {

-      if (e.key === 'Escape') onClose()

-    }

-    window.addEventListener('keydown', onKey)

-    return () => window.removeEventListener('keydown', onKey)

-  }, [open, onClose])

-

-  useEffect(() => {

-    if (!open) return

-    const prev = document.body.style.overflow

-    document.body.style.overflow = 'hidden'

-    return () => {

-      document.body.style.overflow = prev

-    }

-  }, [open])

-

-  if (typeof document === 'undefined') return null

-

-  return createPortal(

-    <AnimatePresence>

-      {open && traceId && (

-        <>

-          <motion.button

-            type="button"

-            aria-label="Close trace detail"

-            initial={{ opacity: 0 }}

-            animate={{ opacity: 1 }}

-            exit={{ opacity: 0 }}

-            transition={{ duration: 0.2 }}

-            className="fixed inset-0 z-[100] bg-gray-900/45 backdrop-blur-[1px]"

-            onClick={onClose}

-          />

-          <motion.aside

-            role="dialog"

-            aria-modal="true"

-            aria-label="Call trace detail"

-            initial={{ x: '100%' }}

-            animate={{ x: 0 }}

-            exit={{ x: '100%' }}

-            transition={{ type: 'spring', damping: 32, stiffness: 320 }}

-            className="fixed top-0 right-0 bottom-0 z-[101] flex w-full max-w-4xl flex-col overflow-hidden border-l border-gray-200 bg-white shadow-2xl"

-          >

-            <SyntheticCallTracePanel traceId={traceId} onClose={onClose} />

-          </motion.aside>

-        </>

-      )}

-    </AnimatePresence>,

-    document.body,

-  )

-}

+import { useEffect } from 'react'
+import { createPortal } from 'react-dom'
+import { AnimatePresence, motion } from 'framer-motion'
+import { useQueryClient } from '@tanstack/react-query'
+import { apiClient } from '../../lib/api'
+import { prefetchCallRecordingQuery } from '../../lib/callRecordingQuery'
+import {
+  prefetchCallRecordingAudio,
+  prefetchEvaluatorRecordingAudio,
+} from '../../lib/waveformAudioCache'
+import SyntheticCallTracePanel from './SyntheticCallTracePanel'
+import ProviderCallTracePanel from './ProviderCallTracePanel'
+import EvaluatorCallDetailPanel from './EvaluatorCallDetailPanel'
+import ObservabilityCallDetailPanel from './ObservabilityCallDetailPanel'
+
+export default function TraceDetailDrawer({
+  open,
+  onClose,
+  traceId = null,
+  evaluatorResultId = null,
+  callShortId = null,
+  observabilityCallShortId = null,
+}: {
+  open: boolean
+  onClose: () => void
+  traceId?: string | null
+  evaluatorResultId?: string | null
+  callShortId?: string | null
+  observabilityCallShortId?: string | null
+}) {
+  const queryClient = useQueryClient()
+  const hasContent = Boolean(traceId || evaluatorResultId || callShortId || observabilityCallShortId)
+  const ariaLabel = callShortId
+    ? 'Provider call detail'
+    : observabilityCallShortId
+      ? 'Observability call detail'
+      : evaluatorResultId
+        ? 'Test agent call trace'
+        : 'Call trace detail'
+
+  useEffect(() => {
+    if (!open) return
+    const onKey = (e: KeyboardEvent) => {
+      if (e.key === 'Escape') onClose()
+    }
+    window.addEventListener('keydown', onKey)
+    return () => window.removeEventListener('keydown', onKey)
+  }, [open, onClose])
+
+  useEffect(() => {
+    if (!open) return
+    const prev = document.body.style.overflow
+    document.body.style.overflow = 'hidden'
+    return () => {
+      document.body.style.overflow = prev
+    }
+  }, [open])
+
+  useEffect(() => {
+    if (!open) return
+    if (callShortId) {
+      prefetchCallRecordingAudio(callShortId, false)
+      void prefetchCallRecordingQuery(queryClient, callShortId)
+    }
+    if (observabilityCallShortId) {
+      void queryClient.prefetchQuery({
+        queryKey: ['observability-call', observabilityCallShortId],
+        queryFn: () => apiClient.getObservabilityCall(observabilityCallShortId),
+        staleTime: 30_000,
+      })
+    }
+    if (evaluatorResultId) {
+      prefetchEvaluatorRecordingAudio(evaluatorResultId)
+      void queryClient.prefetchQuery({
+        queryKey: ['evaluator-result', evaluatorResultId],
+        queryFn: () => apiClient.getEvaluatorResult(evaluatorResultId, true),
+        staleTime: 30_000,
+      })
+    }
+  }, [open, callShortId, observabilityCallShortId, evaluatorResultId, queryClient])
+
+  if (typeof document === 'undefined') return null
+
+  return createPortal(
+    <AnimatePresence>
+      {open && hasContent && (
+        <>
+          <motion.button
+            type="button"
+            aria-label="Close detail drawer"
+            initial={{ opacity: 0 }}
+            animate={{ opacity: 1 }}
+            exit={{ opacity: 0 }}
+            transition={{ duration: 0.2 }}
+            className="fixed inset-0 z-[100] bg-gray-900/45 backdrop-blur-[1px]"
+            onClick={onClose}
+          />
+          <motion.aside
+            role="dialog"
+            aria-modal="true"
+            aria-label={ariaLabel}
+            initial={{ x: '100%' }}
+            animate={{ x: 0 }}
+            exit={{ x: '100%' }}
+            transition={{ type: 'spring', damping: 32, stiffness: 320 }}
+            className="fixed top-0 right-0 bottom-0 z-[101] flex w-full max-w-4xl flex-col overflow-hidden border-l border-gray-200 bg-white shadow-2xl"
+          >
+            {callShortId ? (
+              <ProviderCallTracePanel callShortId={callShortId} onClose={onClose} />
+            ) : observabilityCallShortId ? (
+              <ObservabilityCallDetailPanel
+                callShortId={observabilityCallShortId}
+                onClose={onClose}
+              />
+            ) : evaluatorResultId ? (
+              <EvaluatorCallDetailPanel evaluatorResultId={evaluatorResultId} onClose={onClose} />
+            ) : (
+              <SyntheticCallTracePanel
+                traceId={traceId ?? undefined}
+                onClose={onClose}
+                hideRecording
+              />
+            )}
+          </motion.aside>
+        </>
+      )}
+    </AnimatePresence>,
+    document.body,
+  )
+}
diff --git a/frontend/src/components/call-recordings/VapiCallDetails.tsx b/frontend/src/components/call-recordings/VapiCallDetails.tsx
index 09ae62a8..1eba230a 100644
--- a/frontend/src/components/call-recordings/VapiCallDetails.tsx
+++ b/frontend/src/components/call-recordings/VapiCallDetails.tsx
@@ -101,15 +101,30 @@ interface VapiCallData {
   raw_data?: any
 }
 
+export type VapiDetailSection = 'full' | 'transcript' | 'cost' | 'latency' | 'analysis' | 'system'
+
 interface VapiCallDetailsProps {
   callData: VapiCallData
   hideTranscript?: boolean
+  section?: VapiDetailSection
+  compact?: boolean
+  evaluatorAnalysis?: {
+    call_summary?: string
+    user_sentiment?: string
+    call_successful?: boolean
+  } | null
 }
 
 const COLORS = ['#8b5cf6', '#06b6d4', '#f59e0b', '#ef4444', '#10b981'];
 type LatencyStats = NonNullable<NonNullable<VapiCallData['analysis']>['latency_stats']>
 
-export default function VapiCallDetails({ callData, hideTranscript = false }: VapiCallDetailsProps) {
+export default function VapiCallDetails({
+  callData,
+  hideTranscript = false,
+  section = 'full',
+  compact = false,
+  evaluatorAnalysis = null,
+}: VapiCallDetailsProps) {
   const [activeTab, setActiveTab] = useState<'overview' | 'transcript'>('overview')
 
   const formatDuration = (seconds?: number) => {
@@ -227,10 +242,22 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
   ].filter(item => item.value > 0)
 
   const SummaryCard = () => {
-    const summary = rawAnalysis.summary || raw.summary || callData.analysis?.summary
-    const successEval = rawAnalysis.successEvaluation ?? callData.analysis?.success_evaluation
+    const providerSummary = rawAnalysis.summary || raw.summary || callData.analysis?.summary
+    const summary = providerSummary || evaluatorAnalysis?.call_summary
+    const successEval =
+      rawAnalysis.successEvaluation ??
+      callData.analysis?.success_evaluation ??
+      (evaluatorAnalysis?.call_successful !== undefined ? evaluatorAnalysis.call_successful : undefined)
     const interruptionCount = perf.numAssistantInterrupted ?? callData.analysis?.interruption_count ?? 0
     const isSuccessful = successEval === true || successEval === 'true'
+    const usingEvaluatorSummary = !providerSummary && !!evaluatorAnalysis?.call_summary
+    const hasPipelineMetrics = latencyData.length > 0
+    const hasContent =
+      !!summary ||
+      successEval !== undefined ||
+      !!evaluatorAnalysis?.user_sentiment ||
+      !!endedReason ||
+      hasPipelineMetrics
     
     return (
       <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
@@ -239,8 +266,13 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
           Call Analysis
         </h3>
 
-        {summary || successEval !== undefined ? (
+        {hasContent ? (
           <div className="space-y-6">
+            {usingEvaluatorSummary && (
+              <p className="text-xs text-gray-500">
+                Vapi post-call analysis is still pending — showing EfficientAI-generated summary from the evaluation.
+              </p>
+            )}
             {summary && (
               <div className="p-4 bg-violet-50 rounded-lg border border-violet-100">
                 <p className="text-sm font-medium text-violet-900 mb-2">Summary</p>
@@ -251,6 +283,15 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
             )}
 
             <div className="grid grid-cols-2 gap-4">
+              {evaluatorAnalysis?.user_sentiment && (
+                <div className="p-4 bg-gray-50 rounded-lg">
+                  <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">User Sentiment</p>
+                  <span className="text-sm font-medium text-gray-900 capitalize">
+                    {evaluatorAnalysis.user_sentiment}
+                  </span>
+                </div>
+              )}
+
               <div className="p-4 bg-gray-50 rounded-lg">
                 <p className="text-xs text-gray-500 uppercase tracking-wider font-semibold mb-1">Success</p>
                 <div className="flex items-center gap-2">
@@ -295,20 +336,22 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
             </div>
           </div>
         ) : (
-          <div className="text-center py-8 text-gray-500">Analysis not available</div>
+          <div className="text-center py-8 text-gray-500">
+            Analysis not available yet. Use Sync provider data after the call ends.
+          </div>
         )}
       </div>
     )
   }
 
   const TranscriptCard = () => (
-    <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col h-[600px]">
+    <div className={`bg-white rounded-xl shadow-sm border border-gray-200 p-6 flex flex-col ${compact ? 'max-h-[min(60vh,520px)]' : 'h-[600px]'}`}>
       <div className="flex items-center justify-between mb-4 flex-shrink-0">
         <h3 className="text-lg font-semibold text-gray-900 flex items-center gap-2">
           <MessageSquare className="h-5 w-5 text-violet-600" />
           Transcript
         </h3>
-        {recordingUrl && (
+        {!compact && recordingUrl && (
           <div className="flex items-center gap-2 bg-gray-100 rounded-full px-3 py-1">
             <audio controls src={recordingUrl} className="h-8 w-64" />
             <a href={recordingUrl} download className="text-gray-500 hover:text-violet-600 p-1">
@@ -361,9 +404,7 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
     </div>
   )
 
-  const StatsParams = () => (
-    <div className="space-y-6">
-      {/* Cost Card */}
+  const CostSection = () => (
       <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
         <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
           <DollarSign className="h-5 w-5 text-violet-600" />
@@ -467,8 +508,9 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
           </div>
         </div>
       </div>
+  )
 
-      {/* Latency Chart */}
+  const LatencySection = () => (
       <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
         <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
           <Activity className="h-5 w-5 text-violet-600" />
@@ -536,9 +578,42 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
             )}
           </div>
         )}
+
+        {(latencyStats?.turn_latencies?.length || perf.turnLatencies?.length) ? (
+          <div className="mt-6 pt-4 border-t border-gray-200">
+            <p className="text-sm font-semibold text-gray-900 mb-3">Per-turn pipeline latency (Vapi)</p>
+            <div className="overflow-x-auto">
+              <table className="min-w-full text-xs">
+                <thead>
+                  <tr className="text-left text-gray-500 border-b">
+                    <th className="py-2 pr-4">Turn</th>
+                    <th className="py-2 pr-4">STT</th>
+                    <th className="py-2 pr-4">LLM</th>
+                    <th className="py-2 pr-4">TTS</th>
+                    <th className="py-2 pr-4">Endpointing</th>
+                    <th className="py-2">Total</th>
+                  </tr>
+                </thead>
+                <tbody>
+                  {(latencyStats.turn_latencies || perf.turnLatencies || []).map((turn: any, idx: number) => (
+                    <tr key={idx} className="border-b border-gray-100">
+                      <td className="py-2 pr-4 font-medium">{idx + 1}</td>
+                      <td className="py-2 pr-4">{turn.transcriberLatency ?? '—'}ms</td>
+                      <td className="py-2 pr-4">{turn.modelLatency ?? '—'}ms</td>
+                      <td className="py-2 pr-4">{turn.voiceLatency ?? '—'}ms</td>
+                      <td className="py-2 pr-4">{turn.endpointingLatency ?? '—'}ms</td>
+                      <td className="py-2">{turn.turnLatency ?? '—'}ms</td>
+                    </tr>
+                  ))}
+                </tbody>
+              </table>
+            </div>
+          </div>
+        ) : null}
       </div>
+  )
 
-      {/* System Info */}
+  const SystemSection = () => (
       <div className="bg-white rounded-xl shadow-sm border border-gray-200 p-6">
         <h3 className="text-lg font-semibold text-gray-900 mb-4 flex items-center gap-2">
           <Server className="h-5 w-5 text-violet-600" />
@@ -572,6 +647,20 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
         </div>
 
         {/* Recording URLs */}
+        {(artifact.presignedLogUrl || artifact.logUrl) && (
+          <div className="mt-4 pt-4 border-t border-gray-200">
+            <p className="text-xs text-gray-500 uppercase font-semibold mb-2">Provider logs</p>
+            <a
+              href={artifact.presignedLogUrl || artifact.logUrl}
+              target="_blank"
+              rel="noopener noreferrer"
+              className="text-xs text-violet-600 hover:text-violet-800"
+            >
+              Open Vapi call log
+            </a>
+          </div>
+        )}
+
         {Object.values(recordingLinks).some(Boolean) && (
           <div className="mt-4 pt-4 border-t border-gray-200">
             <p className="text-xs text-gray-500 uppercase font-semibold mb-2">Recordings</p>
@@ -624,9 +713,22 @@ export default function VapiCallDetails({ callData, hideTranscript = false }: Va
           </div>
         )}
       </div>
+  )
+
+  const StatsParams = () => (
+    <div className="space-y-6">
+      <CostSection />
+      <LatencySection />
+      <SystemSection />
     </div>
   )
 
+  if (section === 'transcript') return <TranscriptCard />
+  if (section === 'cost') return <CostSection />
+  if (section === 'latency') return <LatencySection />
+  if (section === 'analysis') return <SummaryCard />
+  if (section === 'system') return <SystemSection />
+
   if (hideTranscript) {
     return (
       <div className="space-y-6">
diff --git a/frontend/src/components/call-recordings/VoiceAiCallDetailPanel.tsx b/frontend/src/components/call-recordings/VoiceAiCallDetailPanel.tsx
new file mode 100644
index 00000000..78226722
--- /dev/null
+++ b/frontend/src/components/call-recordings/VoiceAiCallDetailPanel.tsx
@@ -0,0 +1,216 @@
+import { useMemo, useState, useEffect } from 'react'
+import { Clock, DollarSign, FileText, ListTree, Loader, MessageSquare, Sparkles } from 'lucide-react'
+import CallWaveformPlayer from './CallWaveformPlayer'
+import { ProviderCostPanel, ProviderLatencyPanel } from './ProviderMetricsPanels'
+import VapiCallDetails, { type VapiDetailSection } from './VapiCallDetails'
+import RetellCallDetails, { type RetellDetailSection } from './RetellCallDetails'
+import ElevenLabsCallDetails from './ElevenLabsCallDetails'
+import SmallestCallDetails from './SmallestCallDetails'
+import CallEventTimeline from './CallEventTimeline'
+import {
+  buildRetellCallTimeline,
+  buildVapiCallTimeline,
+} from './callTimelineUtils'
+
+type DrawerTab = 'transcript' | 'cost' | 'latency' | 'logs' | 'analysis'
+
+const TABS: Array<{ id: DrawerTab; label: string; icon: typeof FileText }> = [
+  { id: 'transcript', label: 'Transcript', icon: MessageSquare },
+  { id: 'cost', label: 'Cost', icon: DollarSign },
+  { id: 'latency', label: 'Latency', icon: Clock },
+  { id: 'logs', label: 'Logs', icon: ListTree },
+  { id: 'analysis', label: 'Analysis', icon: Sparkles },
+]
+
+function formatDuration(seconds?: number | null): string {
+  if (seconds == null || Number.isNaN(seconds)) return '—'
+  const mins = Math.floor(seconds / 60)
+  const sec = Math.floor(seconds % 60)
+  if (mins > 0) return `${mins}m ${sec}s`
+  return `${sec}s`
+}
+
+function extractCost(recording: { call_data?: Record<string, unknown> }): number | null {
+  const data = recording.call_data as Record<string, unknown> | undefined
+  if (!data) return null
+  if (typeof data.cost === 'number') return data.cost
+  const breakdown = (data.cost_breakdown || data.costBreakdown) as { total?: number } | undefined
+  if (typeof breakdown?.total === 'number') return breakdown.total
+  return null
+}
+
+function extractDurationSeconds(recording: { call_data?: Record<string, unknown> }): number | null {
+  const data = recording.call_data as Record<string, unknown> | undefined
+  if (!data) return null
+  if (typeof data.duration_seconds === 'number') return data.duration_seconds
+  if (typeof data.duration_ms === 'number') return data.duration_ms / 1000
+  const started = data.startedAt || data.start_timestamp
+  const ended = data.endedAt || data.end_timestamp
+  if (typeof started === 'string' && typeof ended === 'string') {
+    const startMs = Date.parse(started)
+    const endMs = Date.parse(ended)
+    if (!Number.isNaN(startMs) && !Number.isNaN(endMs) && endMs >= startMs) {
+      return (endMs - startMs) / 1000
+    }
+  }
+  return null
+}
+
+function externalLogUrl(platform: string | undefined, callData: Record<string, unknown>): string | null {
+  if (platform === 'vapi') {
+    const artifact = (callData.artifact || {}) as Record<string, unknown>
+    const url = artifact.presignedLogUrl || artifact.logUrl
+    return url ? String(url) : null
+  }
+  if (platform === 'retell' && callData.public_log_url) {
+    return String(callData.public_log_url)
+  }
+  return null
+}
+
+export default function VoiceAiCallDetailPanel({
+  recording,
+  callShortId,
+  hideWaveform = false,
+  detailsLoading = false,
+}: {
+  recording: {
+    id?: string | null
+    provider_platform?: string
+    call_data?: Record<string, unknown>
+    evaluation?: { call_analysis?: Record<string, unknown> | null } | null
+  }
+  callShortId: string
+  hideWaveform?: boolean
+  detailsLoading?: boolean
+}) {
+  const [tab, setTab] = useState<DrawerTab>('transcript')
+  const platform = recording.provider_platform
+  const callData = (recording.call_data || {}) as Record<string, unknown>
+
+  useEffect(() => {
+    setTab('transcript')
+  }, [callShortId])
+  const evaluatorAnalysis = recording.evaluation?.call_analysis ?? null
+
+  const cost = extractCost(recording)
+  const durationSec = extractDurationSeconds(recording)
+  const logUrl = externalLogUrl(platform, callData)
+
+  const logEvents = useMemo(() => {
+    if (tab !== 'logs') return []
+    if (platform === 'vapi') return buildVapiCallTimeline(callData)
+    if (platform === 'retell') return buildRetellCallTimeline(callData)
+    return []
+  }, [platform, callData, tab])
+
+  const renderPlatformSection = (section: VapiDetailSection | RetellDetailSection) => {
+    if (platform === 'retell') {
+      return (
+        <RetellCallDetails
+          callData={callData as any}
+          section={section as RetellDetailSection}
+          compact
+          evaluatorAnalysis={evaluatorAnalysis}
+        />
+      )
+    }
+    if (platform === 'vapi') {
+      return (
+        <VapiCallDetails
+          callData={callData as any}
+          section={section as VapiDetailSection}
+          compact
+          evaluatorAnalysis={evaluatorAnalysis}
+        />
+      )
+    }
+    if (platform === 'elevenlabs') {
+      return <ElevenLabsCallDetails callData={callData as any} callShortId={callShortId} hideTranscript={section !== 'transcript'} />
+    }
+    if (platform === 'smallest') {
+      return <SmallestCallDetails callData={callData as any} hideTranscript={section !== 'transcript'} />
+    }
+    return (
+      <pre className="overflow-x-auto rounded-lg bg-gray-900 p-4 text-xs text-gray-100">
+        {JSON.stringify(callData, null, 2)}
+      </pre>
+    )
+  }
+
+  return (
+    <div className="space-y-4">
+      <div className="flex flex-wrap gap-2">
+        <span className="inline-flex items-center gap-1.5 rounded-full border border-gray-200 bg-white px-3 py-1 text-xs font-medium text-gray-700">
+          <DollarSign className="h-3.5 w-3.5 text-primary-600" />
+          {cost != null ? `$${cost.toFixed(4)}` : 'Cost —'}
+        </span>
+        <span className="inline-flex items-center gap-1.5 rounded-full border border-gray-200 bg-white px-3 py-1 text-xs font-medium text-gray-700">
+          <Clock className="h-3.5 w-3.5 text-primary-600" />
+          {formatDuration(durationSec)}
+        </span>
+        {platform ? (
+          <span className="inline-flex items-center rounded-full border border-gray-200 bg-white px-3 py-1 text-xs font-medium capitalize text-gray-700">
+            {platform}
+          </span>
+        ) : null}
+      </div>
+
+      {!hideWaveform ? (
+        <CallWaveformPlayer
+          callShortId={callShortId}
+          callRecordingId={recording.id}
+          callData={callData}
+          platform={platform}
+        />
+      ) : null}
+
+      <div className="flex flex-wrap gap-1 border-b border-gray-200">
+        {TABS.map(({ id, label, icon: Icon }) => (
+          <button
+            key={id}
+            type="button"
+            onClick={() => setTab(id)}
+            className={`inline-flex items-center gap-1.5 border-b-2 px-3 py-2 text-sm font-medium transition-colors ${
+              tab === id
+                ? 'border-primary-500 text-primary-800'
+                : 'border-transparent text-gray-500 hover:border-gray-300 hover:text-gray-700'
+            }`}
+          >
+            <Icon className="h-4 w-4" />
+            {label}
+          </button>
+        ))}
+      </div>
+
+      <div>
+        {detailsLoading ? (
+          <div className="flex items-center justify-center gap-2 py-12 text-sm text-gray-500">
+            <Loader className="h-4 w-4 animate-spin" />
+            Loading call details…
+          </div>
+        ) : null}
+        {!detailsLoading && tab === 'transcript' ? renderPlatformSection('transcript') : null}
+        {!detailsLoading && tab === 'cost' ? (
+          <ProviderCostPanel
+            callData={callData}
+            platform={platform}
+            totalCost={cost}
+            durationSec={durationSec}
+          />
+        ) : null}
+        {!detailsLoading && tab === 'latency' ? (
+          <ProviderLatencyPanel callData={callData} platform={platform} />
+        ) : null}
+        {!detailsLoading && tab === 'analysis' ? renderPlatformSection('analysis') : null}
+        {!detailsLoading && tab === 'logs' ? (
+          <CallEventTimeline
+            events={logEvents}
+            externalLogUrl={logUrl}
+            emptyMessage="No structured logs from the provider yet. Try Refresh after the call ends."
+          />
+        ) : null}
+      </div>
+    </div>
+  )
+}
diff --git a/frontend/src/components/call-recordings/callTimelineUtils.test.ts b/frontend/src/components/call-recordings/callTimelineUtils.test.ts
new file mode 100644
index 00000000..aac22a05
--- /dev/null
+++ b/frontend/src/components/call-recordings/callTimelineUtils.test.ts
@@ -0,0 +1,71 @@
+import { describe, expect, it } from 'vitest'
+import { buildVapiCallTimeline } from './callTimelineUtils'
+
+describe('buildVapiCallTimeline', () => {
+  it('orders call ended after pipeline events and uses real timestamps', () => {
+    const events = buildVapiCallTimeline({
+      status: 'ended',
+      type: 'webCall',
+      createdAt: '2026-09-02T18:24:30.000Z',
+      startedAt: '2026-09-02T18:24:31.000Z',
+      endedAt: '2026-09-02T18:25:03.000Z',
+      endedReason: 'customer-ended-call',
+      artifact: {
+        messages: [
+          { role: 'system', message: 'System prompt', secondsFromStart: 0 },
+          { role: 'bot', message: 'Hello.', secondsFromStart: 1.62 },
+          { role: 'user', message: 'Hi', secondsFromStart: 8.46, duration: 1200 },
+        ],
+        performanceMetrics: {
+          turnLatencies: [
+            {
+              turnNumber: 1,
+              transcriberLatency: 571,
+              endpointingLatency: 304,
+              modelLatency: 359,
+              voiceLatency: 350,
+              turnLatency: 1287,
+            },
+            {
+              turnNumber: 2,
+              transcriberLatency: 516,
+              endpointingLatency: 300,
+              modelLatency: 509,
+              voiceLatency: 354,
+              turnLatency: 1397,
+            },
+          ],
+        },
+      },
+    })
+
+    const endedIdx = events.findIndex((e) => e.title === 'Call ended')
+    const lastPipelineIdx = events.map((e) => e.title).lastIndexOf('Turn 2 pipeline complete')
+    expect(endedIdx).toBeGreaterThan(lastPipelineIdx)
+
+    const ended = events[endedIdx]
+    expect(ended.offsetMs).toBeGreaterThanOrEqual(32000)
+
+    expect(events.some((e) => e.title === 'Turn 1 — transcriber')).toBe(true)
+    expect(events.some((e) => e.detail?.includes('$'))).toBe(false)
+    expect(events.some((e) => e.detail?.startsWith('https://'))).toBe(false)
+  })
+
+  it('ignores null entries in artifact messages', () => {
+    const events = buildVapiCallTimeline({
+      status: 'ended',
+      startedAt: '2026-09-02T18:24:31.000Z',
+      endedAt: '2026-09-02T18:25:03.000Z',
+      artifact: {
+        messages: [
+          null,
+          { role: 'user', message: 'Hi', secondsFromStart: 1 },
+          { role: 'bot', message: 'Hello', secondsFromStart: 2 },
+        ],
+      },
+    })
+
+    expect(events.some((e) => e.title === 'User spoke')).toBe(true)
+    expect(events.some((e) => e.title === 'Call ended')).toBe(true)
+  })
+})
diff --git a/frontend/src/components/call-recordings/callTimelineUtils.ts b/frontend/src/components/call-recordings/callTimelineUtils.ts
new file mode 100644
index 00000000..e18d74cf
--- /dev/null
+++ b/frontend/src/components/call-recordings/callTimelineUtils.ts
@@ -0,0 +1,519 @@
+export type TimelineCategory =
+  | 'call'
+  | 'stt'
+  | 'llm'
+  | 'tts'
+  | 'transport'
+  | 'message'
+  | 'analysis'
+  | 'pipeline'
+  | 's2s'
+
+export type TimelineLevel = 'info' | 'warn' | 'error'
+
+export interface CallTimelineEvent {
+  id: string
+  offsetMs: number
+  category: TimelineCategory
+  level: TimelineLevel
+  title: string
+  detail?: string
+  raw?: Record<string, unknown>
+  sortOrder?: number
+}
+
+const CATEGORY_LABELS: Record<TimelineCategory, string> = {
+  call: 'Call',
+  stt: 'Transcriber',
+  llm: 'LLM',
+  tts: 'Voice',
+  transport: 'Transport',
+  message: 'Message',
+  analysis: 'Analysis',
+  pipeline: 'Pipeline',
+  s2s: 'S2S',
+}
+
+export function timelineCategoryLabel(category: TimelineCategory): string {
+  return CATEGORY_LABELS[category] ?? category
+}
+
+function truncate(text: string, max = 120): string {
+  const t = text.trim()
+  if (t.length <= max) return t
+  return `${t.slice(0, max)}…`
+}
+
+function parseIsoMs(value: unknown): number | null {
+  if (typeof value !== 'string' || !value) return null
+  const ms = Date.parse(value)
+  return Number.isNaN(ms) ? null : ms
+}
+
+function isTimelineMessage(msg: unknown): msg is Record<string, unknown> {
+  return msg != null && typeof msg === 'object'
+}
+
+function messageOffsetMs(msg: Record<string, unknown>, callStartMs: number | null): number {
+  if (typeof msg.secondsFromStart === 'number') {
+    return Math.max(0, Math.round(msg.secondsFromStart * 1000))
+  }
+  if (typeof msg.time === 'number') {
+    if (msg.time > 1e12 && callStartMs) return Math.max(0, Math.round(msg.time - callStartMs))
+    return Math.max(0, Math.round(msg.time))
+  }
+  return 0
+}
+
+function messageEndOffsetMs(msg: Record<string, unknown>, callStartMs: number | null): number {
+  const start = messageOffsetMs(msg, callStartMs)
+  const duration =
+    typeof msg.duration === 'number'
+      ? msg.duration
+      : typeof msg.durationMs === 'number'
+        ? msg.durationMs
+        : typeof msg.endTime === 'number' && typeof msg.time === 'number' && msg.time <= 1e12
+          ? msg.endTime - msg.time
+          : 0
+  return start + Math.max(0, Math.round(duration))
+}
+
+function sortTimeline(events: CallTimelineEvent[]): CallTimelineEvent[] {
+  return [...events].sort((a, b) => {
+    if (a.offsetMs !== b.offsetMs) return a.offsetMs - b.offsetMs
+    return (a.sortOrder ?? 0) - (b.sortOrder ?? 0)
+  })
+}
+
+function resolveVapiTurnNumber(turn: Record<string, unknown>, index: number): number {
+  const candidates = [turn.turnNumber, turn.turn, turn.turn_number]
+  for (const value of candidates) {
+    const n = Number(value)
+    if (!Number.isNaN(n) && n > 0) return n
+  }
+  return index + 1
+}
+
+export function buildVapiCallTimeline(callData: Record<string, unknown>): CallTimelineEvent[] {
+  const events: CallTimelineEvent[] = []
+  const raw = callData as Record<string, any>
+  const artifact = raw.artifact || {}
+  const perf = artifact.performanceMetrics || {}
+  let seq = 0
+
+  const callStartMs = parseIsoMs(raw.startedAt) ?? parseIsoMs(raw.createdAt)
+  const callEndMs = parseIsoMs(raw.endedAt)
+
+  const push = (partial: Omit<CallTimelineEvent, 'id'>) => {
+    events.push({ ...partial, id: `vapi-${seq++}` })
+  }
+
+  const createdMs = parseIsoMs(raw.createdAt)
+  if (createdMs != null && callStartMs != null && createdMs < callStartMs) {
+    push({
+      offsetMs: callStartMs - createdMs,
+      category: 'call',
+      level: 'info',
+      title: 'Call queued',
+      detail: raw.type ? String(raw.type) : undefined,
+      sortOrder: 10,
+    })
+  } else if (raw.createdAt || raw.startedAt) {
+    push({
+      offsetMs: 0,
+      category: 'call',
+      level: 'info',
+      title: 'Call queued',
+      detail: raw.type ? String(raw.type) : undefined,
+      sortOrder: 10,
+    })
+  }
+
+  if (raw.startedAt) {
+    push({
+      offsetMs: 0,
+      category: 'call',
+      level: 'info',
+      title: 'Call started',
+      detail: raw.assistantId ? `Assistant ${String(raw.assistantId).slice(0, 8)}…` : undefined,
+      sortOrder: 20,
+    })
+  }
+
+  const messages = [...(artifact.messages || raw.messages || [])]
+    .filter(isTimelineMessage)
+    .sort(
+    (a, b) => messageOffsetMs(a, callStartMs) - messageOffsetMs(b, callStartMs),
+  )
+
+  for (const msg of messages) {
+    const role = String(msg.role || 'unknown')
+    const offsetMs = messageOffsetMs(msg, callStartMs)
+    const text = msg.message || msg.content || ''
+    if (role === 'system') {
+      push({
+        offsetMs,
+        category: 'call',
+        level: 'info',
+        title: 'System prompt configured',
+        detail: truncate(String(text), 160),
+        sortOrder: 30,
+      })
+      continue
+    }
+    if (role === 'user') {
+      push({
+        offsetMs,
+        category: 'message',
+        level: 'info',
+        title: 'User spoke',
+        detail: truncate(String(text)),
+        sortOrder: 40,
+      })
+      continue
+    }
+    if (role === 'bot' || role === 'assistant') {
+      push({
+        offsetMs,
+        category: 'message',
+        level: 'info',
+        title: 'Agent spoke',
+        detail: truncate(String(text)),
+        sortOrder: 50,
+      })
+      continue
+    }
+    if (role === 'tool') {
+      push({
+        offsetMs,
+        category: 'pipeline',
+        level: 'info',
+        title: 'Tool result',
+        detail: truncate(String(text || msg.result || JSON.stringify(msg))),
+        sortOrder: 55,
+      })
+      continue
+    }
+    const toolCalls = msg.toolCalls || msg.tool_calls
+    if (Array.isArray(toolCalls) && toolCalls.length > 0) {
+      push({
+        offsetMs,
+        category: 'pipeline',
+        level: 'info',
+        title: 'Tool call',
+        detail: truncate(
+          toolCalls
+            .map((tc) => {
+              const item = tc as Record<string, unknown>
+              const fn = item.function as Record<string, unknown> | undefined
+              return String(fn?.name || item.name || 'tool')
+            })
+            .join(', '),
+        ),
+        sortOrder: 54,
+      })
+    }
+  }
+
+  const userMessages = messages.filter((m) => m.role === 'user')
+  const turnLatencies: Record<string, unknown>[] = (perf.turnLatencies || []).filter(
+    (turn: unknown): turn is Record<string, unknown> =>
+      turn != null && typeof turn === 'object',
+  )
+
+  for (let index = 0; index < turnLatencies.length; index++) {
+    const turn = turnLatencies[index]
+    const turnNum = resolveVapiTurnNumber(turn, index)
+    const userMsg = userMessages[index] as Record<string, unknown> | undefined
+    const turnStartMs = userMsg ? messageEndOffsetMs(userMsg, callStartMs) : null
+    const prevComplete = events.filter((e) => e.title.includes('pipeline complete')).at(-1)?.offsetMs
+    const fallbackStart = turnStartMs ?? (index === 0 ? 0 : (prevComplete ?? 0) + 200)
+
+    const stt = Number(turn.transcriberLatency ?? 0)
+    const endpointing = Number(turn.endpointingLatency ?? 0)
+    const llm = Number(turn.modelLatency ?? 0)
+    const voice = Number(turn.voiceLatency ?? 0)
+    const total = Number(turn.turnLatency ?? stt + endpointing + llm + voice)
+
+    if (stt > 0) {
+      push({
+        offsetMs: fallbackStart + stt,
+        category: 'stt',
+        level: 'info',
+        title: `Turn ${turnNum} — transcriber`,
+        detail: `${stt}ms`,
+        sortOrder: 60,
+      })
+    }
+    if (endpointing > 0) {
+      push({
+        offsetMs: fallbackStart + stt + endpointing,
+        category: 'pipeline',
+        level: 'info',
+        title: `Turn ${turnNum} — endpointing`,
+        detail: `${endpointing}ms`,
+        sortOrder: 61,
+      })
+    }
+    if (llm > 0) {
+      push({
+        offsetMs: fallbackStart + stt + endpointing + llm,
+        category: 'llm',
+        level: 'info',
+        title: `Turn ${turnNum} — LLM`,
+        detail: `${llm}ms`,
+        sortOrder: 62,
+      })
+    }
+    if (voice > 0) {
+      push({
+        offsetMs: fallbackStart + stt + endpointing + llm + voice,
+        category: 'tts',
+        level: 'info',
+        title: `Turn ${turnNum} — voice`,
+        detail: `${voice}ms`,
+        sortOrder: 63,
+      })
+    }
+    push({
+      offsetMs: fallbackStart + total,
+      category: 'pipeline',
+      level: 'info',
+      title: `Turn ${turnNum} pipeline complete`,
+      detail: `STT ${stt || '—'}ms · LLM ${llm || '—'}ms · TTS ${voice || '—'}ms · total ${total || '—'}ms`,
+      raw: turn as Record<string, unknown>,
+      sortOrder: 64,
+    })
+  }
+
+  let callEndOffsetMs = 0
+  if (callStartMs != null && callEndMs != null) {
+    callEndOffsetMs = Math.max(0, callEndMs - callStartMs)
+  }
+  for (const msg of messages) {
+    callEndOffsetMs = Math.max(callEndOffsetMs, messageEndOffsetMs(msg, callStartMs))
+  }
+  for (const turn of turnLatencies) {
+    const idx = turnLatencies.indexOf(turn)
+    const userMsg = userMessages[idx] as Record<string, unknown> | undefined
+    const turnStart = userMsg ? messageEndOffsetMs(userMsg, callStartMs) : 0
+    const total = Number(turn.turnLatency ?? 0)
+    if (total > 0) callEndOffsetMs = Math.max(callEndOffsetMs, turnStart + total)
+  }
+
+  if (raw.endedAt || raw.status === 'ended') {
+    push({
+      offsetMs: callEndOffsetMs,
+      category: 'call',
+      level: 'info',
+      title: 'Call ended',
+      detail: raw.endedReason ? String(raw.endedReason).replace(/-/g, ' ') : undefined,
+      sortOrder: 1000,
+    })
+  }
+
+  const analysis = raw.analysis as Record<string, unknown> | undefined
+  if (analysis?.summary) {
+    push({
+      offsetMs: callEndOffsetMs,
+      category: 'analysis',
+      level: 'info',
+      title: 'Call summary',
+      detail: truncate(String(analysis.summary)),
+      sortOrder: 1010,
+    })
+  }
+  if (analysis?.successEvaluation != null) {
+    push({
+      offsetMs: callEndOffsetMs,
+      category: 'analysis',
+      level: analysis.successEvaluation === false || analysis.successEvaluation === 'false' ? 'warn' : 'info',
+      title: 'Success evaluation',
+      detail: String(analysis.successEvaluation),
+      sortOrder: 1011,
+    })
+  }
+
+  if (typeof raw.cost === 'number') {
+    push({
+      offsetMs: callEndOffsetMs,
+      category: 'call',
+      level: 'info',
+      title: 'Call cost',
+      detail: `$${raw.cost.toFixed(4)}`,
+      sortOrder: 1020,
+    })
+  }
+
+  return sortTimeline(events)
+}
+
+export function buildRetellCallTimeline(callData: Record<string, unknown>): CallTimelineEvent[] {
+  const events: CallTimelineEvent[] = []
+  const raw = callData as Record<string, any>
+  let seq = 0
+  const push = (partial: Omit<CallTimelineEvent, 'id'>) => {
+    events.push({ ...partial, id: `retell-${seq++}` })
+  }
+
+  const callStartMs = typeof raw.start_timestamp === 'number' ? raw.start_timestamp : null
+
+  if (raw.start_timestamp) {
+    push({ offsetMs: 0, category: 'call', level: 'info', title: 'Call started', sortOrder: 10 })
+  }
+
+  const latency = raw.latency as Record<string, { p50?: number; p90?: number; max?: number }> | undefined
+  if (latency) {
+    const parts = ['e2e', 'asr', 'llm', 'tts']
+      .map((key) => {
+        const bucket = latency[key]
+        if (!bucket?.p50) return null
+        return `${key.toUpperCase()} p50 ${bucket.p50}ms`
+      })
+      .filter(Boolean)
+    if (parts.length > 0) {
+      push({
+        offsetMs: 0,
+        category: 'pipeline',
+        level: 'info',
+        title: 'Latency overview',
+        detail: parts.join(' · '),
+        sortOrder: 15,
+      })
+    }
+  }
+
+  const transcript = raw.transcript_object || []
+  for (const entry of transcript) {
+    const role = String(entry.role || 'unknown')
+    let offsetMs = 0
+    if (entry.words?.length) {
+      offsetMs = Math.round((entry.words[0].start ?? 0) * 1000)
+    } else if (callStartMs && typeof entry.start === 'number') {
+      offsetMs = Math.max(0, entry.start - callStartMs)
+    }
+    push({
+      offsetMs,
+      category: 'message',
+      level: 'info',
+      title: role === 'user' ? 'User spoke' : 'Agent spoke',
+      detail: truncate(String(entry.content || '')),
+      sortOrder: role === 'user' ? 40 : 50,
+    })
+  }
+
+  if (raw.disconnection_reason) {
+    const durationMs = typeof raw.duration_ms === 'number' ? raw.duration_ms : 0
+    push({
+      offsetMs: durationMs,
+      category: 'call',
+      level: 'info',
+      title: 'Call disconnected',
+      detail: String(raw.disconnection_reason).replace(/_/g, ' '),
+      sortOrder: 1000,
+    })
+  }
+
+  const callAnalysis = raw.call_analysis as Record<string, unknown> | undefined
+  if (callAnalysis?.call_summary) {
+    const durationMs = typeof raw.duration_ms === 'number' ? raw.duration_ms : 0
+    push({
+      offsetMs: durationMs,
+      category: 'analysis',
+      level: 'info',
+      title: 'Call summary',
+      detail: truncate(String(callAnalysis.call_summary)),
+      sortOrder: 1010,
+    })
+  }
+
+  if (typeof raw.call_cost === 'number' || typeof raw.cost === 'number') {
+    const durationMs = typeof raw.duration_ms === 'number' ? raw.duration_ms : 0
+    const cost = typeof raw.call_cost === 'number' ? raw.call_cost : raw.cost
+    push({
+      offsetMs: durationMs,
+      category: 'call',
+      level: 'info',
+      title: 'Call cost',
+      detail: `$${Number(cost).toFixed(4)}`,
+      sortOrder: 1020,
+    })
+  }
+
+  return sortTimeline(events)
+}
+
+export interface OtelSpanLike {
+  span_id: string
+  name: string
+  start_time_unix_nano?: number | null
+  end_time_unix_nano?: number | null
+  attributes?: Record<string, unknown>
+  events?: Array<{ name?: string; attributes?: Record<string, unknown> }>
+}
+
+function spanKindFromName(span: OtelSpanLike): TimelineCategory {
+  const op = String(span.attributes?.['gen_ai.operation.name'] || '').toLowerCase()
+  const name = span.name.toLowerCase()
+  if (op.includes('stt') || name.includes('stt')) return 'stt'
+  if (op.includes('tts') || name.includes('tts')) return 'tts'
+  if (op.includes('llm') || name.includes('llm')) return 'llm'
+  if (op.includes('s2s') || name.includes('s2s')) return 's2s'
+  if (name.includes('turn')) return 'pipeline'
+  return 'pipeline'
+}
+
+export function buildOtelCallTimeline(spans: OtelSpanLike[]): CallTimelineEvent[] {
+  if (!spans.length) return []
+  const starts = spans
+    .map((s) => s.start_time_unix_nano)
+    .filter((v): v is number => v != null)
+  const traceStart = starts.length ? Math.min(...starts) : 0
+  let seq = 0
+
+  const events: CallTimelineEvent[] = []
+  for (const span of [...spans].sort(
+    (a, b) => (a.start_time_unix_nano ?? 0) - (b.start_time_unix_nano ?? 0),
+  )) {
+    const offsetMs =
+      span.start_time_unix_nano != null
+        ? Math.round((span.start_time_unix_nano - traceStart) / 1_000_000)
+        : 0
+    const durationMs =
+      span.start_time_unix_nano != null && span.end_time_unix_nano != null
+        ? Math.round((span.end_time_unix_nano - span.start_time_unix_nano) / 1_000_000)
+        : undefined
+    const kind = spanKindFromName(span)
+    const model =
+      span.attributes?.['gen_ai.request.model'] ||
+      span.attributes?.['settings.model'] ||
+      span.attributes?.['model']
+    const hasError = span.attributes?.['error.type'] != null
+    events.push({
+      id: `otel-${seq++}`,
+      offsetMs,
+      category: kind,
+      level: hasError ? 'error' : 'info',
+      title: span.name || kind,
+      detail: [
+        durationMs != null ? `${durationMs}ms` : null,
+        model ? String(model) : null,
+        span.attributes?.transcript ? truncate(String(span.attributes.transcript)) : null,
+      ]
+        .filter(Boolean)
+        .join(' · '),
+      raw: span.attributes,
+    })
+    for (const ev of span.events || []) {
+      events.push({
+        id: `otel-ev-${seq++}`,
+        offsetMs,
+        category: kind,
+        level: 'info',
+        title: ev.name || 'span event',
+        detail: ev.attributes ? truncate(JSON.stringify(ev.attributes)) : undefined,
+      })
+    }
+  }
+  return sortTimeline(events)
+}
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index aeca1097..1a16bcf5 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -72,6 +72,7 @@ import type {
   CallImportRetryFailedRowsResponse,
   Workspace,
   LLMGenerationConfig,
+  ListIntegrationVoiceAgentsResponse,
   TestAgent,
 } from '../types/api'
 
@@ -1710,6 +1711,22 @@ class ApiClient {
     return response.data
   }
 
+  async listIntegrationVoiceAgents(
+    integrationId: string,
+    options?: { refresh?: boolean; search?: string },
+  ): Promise<ListIntegrationVoiceAgentsResponse> {
+    const response = await this.client.get(
+      `/api/v1/integrations/${integrationId}/voice-agents`,
+      {
+        params: {
+          ...(options?.refresh ? { refresh: true } : {}),
+          ...(options?.search ? { search: options.search } : {}),
+        },
+      },
+    )
+    return response.data
+  }
+
   async createIntegration(data: IntegrationCreate): Promise<Integration> {
     const response = await this.client.post('/api/v1/integrations', data)
     return response.data
@@ -3918,6 +3935,29 @@ class ApiClient {
     return response.data
   }
 
+  async getCallRecordingLogs(callShortId: string): Promise<{
+    platform: string
+    entries: Array<{
+      time?: string | null
+      level?: string | null
+      category?: string | null
+      summary?: string | null
+      raw?: Record<string, unknown>
+    }>
+    count: number
+  }> {
+    const response = await this.client.get(`/api/v1/playground/call-recordings/${callShortId}/logs`)
+    return response.data
+  }
+
+  getCallRecordingAudioStreamUrl(callShortId: string, options?: { stereo?: boolean }): string {
+    const params = new URLSearchParams({ proxy: 'true' })
+    if (options?.stereo) params.set('stereo', 'true')
+    return this.buildAuthenticatedApiUrl(
+      `/api/v1/playground/call-recordings/${callShortId}/audio?${params.toString()}`,
+    )
+  }
+
   async refreshCallRecording(callShortId: string): Promise<{ message: string }> {
     const response = await this.client.post(`/api/v1/playground/call-recordings/${callShortId}/refresh`)
     return response.data
@@ -3946,14 +3986,29 @@ class ApiClient {
     return response.data
   }
 
-  async getCallRecordingAudioUrl(callShortId: string): Promise<string> {
+  async getCallRecordingAudioUrl(callShortId: string, options?: { stereo?: boolean }): Promise<string> {
+    const params = new URLSearchParams({ proxy: 'true' })
+    if (options?.stereo) params.set('stereo', 'true')
     const response = await this.client.get(
-      `/api/v1/playground/call-recordings/${callShortId}/audio`,
+      `/api/v1/playground/call-recordings/${callShortId}/audio?${params.toString()}`,
       { responseType: 'blob' }
     )
     return URL.createObjectURL(response.data)
   }
 
+  async getCallRecordingAudioBuffer(
+    callShortId: string,
+    options?: { stereo?: boolean },
+  ): Promise<ArrayBuffer> {
+    const params = new URLSearchParams({ proxy: 'true' })
+    if (options?.stereo) params.set('stereo', 'true')
+    const response = await this.client.get(
+      `/api/v1/playground/call-recordings/${callShortId}/audio?${params.toString()}`,
+      { responseType: 'arraybuffer' },
+    )
+    return response.data as ArrayBuffer
+  }
+
   async createCustomWebsocketSession(data: {
     agent_id: string
     websocket_url: string
@@ -4601,6 +4656,7 @@ class ApiClient {
   async createSyntheticTraceSession(data: {
     transport?: 'webrtc' | 'websocket' | 'phone' | 'custom'
     evaluator_result_id?: string
+    agent_id?: string
   }): Promise<{
     trace_id: string
     call_short_id: string
@@ -4660,6 +4716,14 @@ class ApiClient {
     return URL.createObjectURL(response.data)
   }
 
+  async getEvaluatorResultAudioBuffer(resultId: string): Promise<ArrayBuffer> {
+    const response = await this.client.get(
+      `/api/v1/evaluator-results/${resultId}/audio`,
+      { responseType: 'arraybuffer' },
+    )
+    return response.data as ArrayBuffer
+  }
+
   async createEvaluatorResultManual(data: {
     evaluator_id: string
     audio_s3_key: string
diff --git a/frontend/src/lib/callDetailTheme.ts b/frontend/src/lib/callDetailTheme.ts
new file mode 100644
index 00000000..aa721acb
--- /dev/null
+++ b/frontend/src/lib/callDetailTheme.ts
@@ -0,0 +1,34 @@
+export const STAGE_COLORS = {
+  stt: '#60a5fa',
+  llm: '#fde047',
+  tts: '#2dd4bf',
+  s2s: '#c084fc',
+  transport: '#fb923c',
+  endpointing: '#4ade80',
+  analysis: '#a78bfa',
+} as const
+
+export const CHART_BAR = {
+  default: '#e5e7eb',
+  peak: '#fde047',
+} as const
+
+export const WAVEFORM_COLORS = {
+  user: '#fb923c',
+  assistant: '#2dd4bf',
+  mono: '#c4b5fd',
+  playhead: '#eab308',
+  canvasBg: '#f9fafb',
+  skeleton: 'rgba(148, 163, 184, 0.28)',
+} as const
+
+export const METRIC_COST_COLORS: Record<string, string> = {
+  transport: STAGE_COLORS.transport,
+  stt: STAGE_COLORS.stt,
+  llm: '#eab308',
+  tts: STAGE_COLORS.tts,
+  vapi: '#f87171',
+  analysis: STAGE_COLORS.analysis,
+  knowledge: '#818cf8',
+  other: '#94a3b8',
+}
diff --git a/frontend/src/lib/callRecordingQuery.ts b/frontend/src/lib/callRecordingQuery.ts
new file mode 100644
index 00000000..7fc530ef
--- /dev/null
+++ b/frontend/src/lib/callRecordingQuery.ts
@@ -0,0 +1,83 @@
+import type { QueryClient } from '@tanstack/react-query'
+import { apiClient } from './api'
+
+function findCallRecordingRow(
+  queryClient: QueryClient,
+  callShortId: string,
+): Record<string, unknown> | undefined {
+  const queries = queryClient.getQueriesData<Array<Record<string, unknown>> | Record<string, unknown>>({
+    queryKey: ['call-recordings'],
+  })
+  for (const [, data] of queries) {
+    const list = Array.isArray(data) ? data : null
+    if (!list) continue
+    const row = list.find((item) => item.call_short_id === callShortId)
+    if (row) return row
+  }
+  return undefined
+}
+
+export function getCallRecordingPlaceholder(
+  queryClient: QueryClient,
+  callShortId: string,
+): Record<string, unknown> | undefined {
+  const cached = queryClient.getQueryData<Record<string, unknown>>(['call-recording', callShortId])
+  if (cached && hasCallRecordingDetails(cached)) return cached
+
+  const row = findCallRecordingRow(queryClient, callShortId)
+  if (!row) return undefined
+
+  return placeholderFromRow(row)
+}
+
+export function hasCallRecordingDetails(
+  recording: Record<string, unknown> | null | undefined,
+): boolean {
+  const callData = recording?.call_data
+  return Boolean(callData && typeof callData === 'object' && Object.keys(callData as object).length > 0)
+}
+
+function placeholderFromRow(row: Record<string, unknown>): Record<string, unknown> {
+  return {
+    id: row.id,
+    call_short_id: row.call_short_id,
+    provider_platform: row.provider_platform,
+    provider_call_id: row.provider_call_id,
+    agent_id: row.agent_id,
+    status: row.status,
+    created_at: row.created_at,
+    updated_at: row.updated_at,
+    call_data: row.call_data ?? null,
+  }
+}
+
+export function warmCallRecordingQueryFromList(
+  queryClient: QueryClient,
+  rows: Array<Record<string, unknown>>,
+): void {
+  for (const row of rows) {
+    const callShortId = row.call_short_id
+    if (typeof callShortId !== 'string' || !callShortId) continue
+    const existing = queryClient.getQueryData<Record<string, unknown>>(['call-recording', callShortId])
+    if (existing && hasCallRecordingDetails(existing)) continue
+    if (hasCallRecordingDetails(placeholderFromRow(row))) {
+      queryClient.setQueryData(['call-recording', callShortId], placeholderFromRow(row))
+    }
+  }
+}
+
+export function prefetchCallRecordingQuery(
+  queryClient: QueryClient,
+  callShortId: string,
+): Promise<void> {
+  const existing = queryClient.getQueryData<Record<string, unknown>>(['call-recording', callShortId])
+  if (hasCallRecordingDetails(existing)) return Promise.resolve()
+
+  return queryClient
+    .fetchQuery({
+      queryKey: ['call-recording', callShortId],
+      queryFn: () => apiClient.getCallRecording(callShortId),
+      staleTime: 30_000,
+    })
+    .then(() => undefined)
+}
diff --git a/frontend/src/lib/evaluatorResultQuery.ts b/frontend/src/lib/evaluatorResultQuery.ts
new file mode 100644
index 00000000..e4842d9f
--- /dev/null
+++ b/frontend/src/lib/evaluatorResultQuery.ts
@@ -0,0 +1,46 @@
+import type { QueryClient } from '@tanstack/react-query'
+
+type ResultRow = Record<string, unknown>
+
+function rowFromQueries(
+  queryClient: QueryClient,
+  id: string,
+  queryKeyPrefix: string,
+): ResultRow | undefined {
+  const queries = queryClient.getQueriesData<{ items?: ResultRow[] } | ResultRow[]>({
+    queryKey: [queryKeyPrefix],
+  })
+  for (const [, data] of queries) {
+    const items = Array.isArray(data) ? data : data?.items
+    if (!items?.length) continue
+    const row = items.find((item) => item.id === id || item.result_id === id)
+    if (row) return row
+  }
+  return undefined
+}
+
+export function getEvaluatorResultPlaceholder(
+  queryClient: QueryClient,
+  id: string,
+): Record<string, unknown> | undefined {
+  const cached = queryClient.getQueryData<Record<string, unknown>>(['evaluator-result', id])
+  if (cached) return cached
+
+  const row =
+    rowFromQueries(queryClient, id, 'test-voice-agent-results') ||
+    rowFromQueries(queryClient, id, 'evaluator-results')
+  if (!row) return undefined
+
+  return {
+    id: row.id,
+    result_id: row.result_id,
+    name: row.name,
+    status: row.status,
+    metric_scores: row.metric_scores ?? null,
+    agent: row.agent ?? null,
+    scenario: row.scenario ?? null,
+    timestamp: row.timestamp,
+    duration_seconds: row.duration_seconds,
+    error_message: row.error_message ?? null,
+  }
+}
diff --git a/frontend/src/lib/observabilityCallQuery.ts b/frontend/src/lib/observabilityCallQuery.ts
new file mode 100644
index 00000000..e03e5f27
--- /dev/null
+++ b/frontend/src/lib/observabilityCallQuery.ts
@@ -0,0 +1,20 @@
+import type { QueryClient } from '@tanstack/react-query'
+import type { ObservabilityCall } from '../types/api'
+
+export function getObservabilityCallPlaceholder(
+  queryClient: QueryClient,
+  callShortId: string,
+): ObservabilityCall | undefined {
+  const cached = queryClient.getQueryData<ObservabilityCall>(['observability-call', callShortId])
+  if (cached) return cached
+
+  const queries = queryClient.getQueriesData<ObservabilityCall[]>({
+    queryKey: ['observability-calls'],
+  })
+  for (const [, data] of queries) {
+    if (!Array.isArray(data)) continue
+    const row = data.find((call) => call.call_short_id === callShortId)
+    if (row) return row
+  }
+  return undefined
+}
diff --git a/frontend/src/lib/recordingUrls.ts b/frontend/src/lib/recordingUrls.ts
index 030e7cbb..1621297a 100644
--- a/frontend/src/lib/recordingUrls.ts
+++ b/frontend/src/lib/recordingUrls.ts
@@ -1,59 +1,94 @@
-/** Resolve a browser-playable recording URL from provider call_data. */
-
-export function getProviderRecordingUrl(
-  callData: Record<string, unknown> | null | undefined,
-  platform?: string | null,
-): string | null {
-  if (!callData) return null
-
-  const plat = (platform || '').toLowerCase()
-
-  if (plat === 'vapi') {
-    const artifact = (callData.artifact || {}) as Record<string, unknown>
-    const recording = (artifact.recording || {}) as Record<string, unknown>
-    const mono = (recording.mono || {}) as Record<string, unknown>
-    const recordingUrls = (callData.recording_urls || {}) as Record<string, unknown>
-
-    return (
-      pickString(artifact.presignedMonoUrl) ||
-      pickString(artifact.presignedStereoUrl) ||
-      pickString(callData.presignedMonoUrl) ||
-      pickString(callData.presignedStereoUrl) ||
-      pickString(callData.recordingUrl) ||
-      pickString(callData.stereoRecordingUrl) ||
-      pickString(artifact.recordingUrl) ||
-      pickString(artifact.stereoRecordingUrl) ||
-      pickString(mono.combinedUrl) ||
-      pickString(recordingUrls.combined_url) ||
-      pickString(recordingUrls.stereo_url) ||
-      null
-    )
-  }
-
-  if (plat === 'elevenlabs') {
-    const recordingUrls = (callData.recording_urls || {}) as Record<string, unknown>
-    return pickString(callData.recording_url) || pickString(recordingUrls.conversation_audio)
-  }
-
-  return (
-    pickString(callData.recording_url) ||
-    pickString(callData.recordingUrl) ||
-    null
-  )
-}
-
-export function hasEvaluatorResultRecording(
-  result: {
-    audio_s3_key?: string | null
-    provider_platform?: string | null
-    call_data?: Record<string, unknown> | null
-  } | null | undefined,
-): boolean {
-  if (!result) return false
-  const audioS3Key = result.audio_s3_key || pickString(result.call_data?.recording_s3_key)
-  return Boolean(audioS3Key || getProviderRecordingUrl(result.call_data, result.provider_platform))
-}
-
-function pickString(value: unknown): string | null {
-  return typeof value === 'string' && value.trim() ? value.trim() : null
-}
+/** Resolve a browser-playable recording URL from provider call_data. */
+
+export function getProviderRecordingUrl(
+  callData: Record<string, unknown> | null | undefined,
+  platform?: string | null,
+): string | null {
+  if (!callData) return null
+
+  const plat = (platform || '').toLowerCase()
+
+  if (plat === 'vapi') {
+    const artifact = (callData.artifact || {}) as Record<string, unknown>
+    const recording = (artifact.recording || {}) as Record<string, unknown>
+    const mono = (recording.mono || {}) as Record<string, unknown>
+    const recordingUrls = (callData.recording_urls || {}) as Record<string, unknown>
+
+    return (
+      pickString(artifact.presignedMonoUrl) ||
+      pickString(artifact.presignedStereoUrl) ||
+      pickString(callData.presignedMonoUrl) ||
+      pickString(callData.presignedStereoUrl) ||
+      pickString(callData.recordingUrl) ||
+      pickString(callData.stereoRecordingUrl) ||
+      pickString(artifact.recordingUrl) ||
+      pickString(artifact.stereoRecordingUrl) ||
+      pickString(mono.combinedUrl) ||
+      pickString(recordingUrls.combined_url) ||
+      pickString(recordingUrls.stereo_url) ||
+      null
+    )
+  }
+
+  if (plat === 'elevenlabs') {
+    const recordingUrls = (callData.recording_urls || {}) as Record<string, unknown>
+    return pickString(callData.recording_url) || pickString(recordingUrls.conversation_audio)
+  }
+
+  return (
+    pickString(callData.recording_url) ||
+    pickString(callData.recordingUrl) ||
+    null
+  )
+}
+
+export function hasEvaluatorResultRecording(
+  result: {
+    audio_s3_key?: string | null
+    provider_platform?: string | null
+    call_data?: Record<string, unknown> | null
+  } | null | undefined,
+): boolean {
+  if (!result) return false
+  const audioS3Key = result.audio_s3_key || pickString(result.call_data?.recording_s3_key)
+  return Boolean(audioS3Key || getProviderRecordingUrl(result.call_data, result.provider_platform))
+}
+
+function pickString(value: unknown): string | null {
+  return typeof value === 'string' && value.trim() ? value.trim() : null
+}
+
+export interface VapiRecordingTracks {
+  stereo: string | null
+  combined: string | null
+  customer: string | null
+  assistant: string | null
+}
+
+export function getVapiRecordingTracks(callData: Record<string, unknown> | null | undefined): VapiRecordingTracks {
+  if (!callData) {
+    return { stereo: null, combined: null, customer: null, assistant: null }
+  }
+  const artifact = (callData.artifact || {}) as Record<string, unknown>
+  const recording = (artifact.recording || {}) as Record<string, unknown>
+  const mono = (recording.mono || {}) as Record<string, unknown>
+  const recordingUrls = (callData.recording_urls || {}) as Record<string, unknown>
+
+  return {
+    stereo:
+      pickString(artifact.presignedStereoUrl) ||
+      pickString(callData.presignedStereoUrl) ||
+      pickString(callData.stereoRecordingUrl) ||
+      pickString(artifact.stereoRecordingUrl) ||
+      pickString(recordingUrls.stereo_url),
+    combined:
+      pickString(artifact.presignedMonoUrl) ||
+      pickString(callData.presignedMonoUrl) ||
+      pickString(callData.recordingUrl) ||
+      pickString(artifact.recordingUrl) ||
+      pickString(mono.combinedUrl) ||
+      pickString(recordingUrls.combined_url),
+    customer: pickString(mono.customerUrl) || pickString(recordingUrls.customer_url),
+    assistant: pickString(mono.assistantUrl) || pickString(recordingUrls.assistant_url),
+  }
+}
diff --git a/frontend/src/lib/waveformAudioCache.ts b/frontend/src/lib/waveformAudioCache.ts
new file mode 100644
index 00000000..cc227bbc
--- /dev/null
+++ b/frontend/src/lib/waveformAudioCache.ts
@@ -0,0 +1,165 @@
+import { apiClient } from './api'
+import { getVapiRecordingTracks } from './recordingUrls'
+
+export interface CachedWaveformTrack {
+  label: string
+  color: string
+  mutedColor: string
+  peaks: Float32Array
+}
+
+export interface CachedWaveform {
+  tracks: CachedWaveformTrack[]
+  duration: number
+  blobUrl: string
+}
+
+const MAX_CACHE_ENTRIES = 10
+const MAX_RAW_AUDIO_ENTRIES = 12
+const waveformCache = new Map<string, CachedWaveform>()
+const rawAudioCache = new Map<string, ArrayBuffer>()
+const inflightFetches = new Map<string, Promise<ArrayBuffer | null>>()
+
+function cacheKey(callShortId: string, stereo: boolean): string {
+  return `${callShortId}:${stereo ? 'stereo' : 'mono'}`
+}
+
+function evictOldestMap<T>(map: Map<string, T>, max: number, onEvict?: (value: T) => void): void {
+  while (map.size > max) {
+    const oldestKey = map.keys().next().value
+    if (!oldestKey) break
+    const entry = map.get(oldestKey)
+    if (entry !== undefined) onEvict?.(entry)
+    map.delete(oldestKey)
+  }
+}
+
+function evictOldestIfNeeded(): void {
+  evictOldestMap(waveformCache, MAX_CACHE_ENTRIES)
+}
+
+function evictRawAudioIfNeeded(): void {
+  evictOldestMap(rawAudioCache, MAX_RAW_AUDIO_ENTRIES)
+}
+
+export function preferStereoWaveform(
+  callData: Record<string, unknown> | null | undefined,
+  platform?: string | null,
+): boolean {
+  if ((platform || '').toLowerCase() !== 'vapi') return false
+  return Boolean(getVapiRecordingTracks(callData).stereo)
+}
+
+export function getCachedWaveform(callShortId: string, stereo: boolean): CachedWaveform | null {
+  const key = cacheKey(callShortId, stereo)
+  const entry = waveformCache.get(key)
+  if (!entry) return null
+  waveformCache.delete(key)
+  waveformCache.set(key, entry)
+  return entry
+}
+
+export function setCachedWaveform(
+  callShortId: string,
+  stereo: boolean,
+  entry: CachedWaveform,
+): void {
+  const key = cacheKey(callShortId, stereo)
+  waveformCache.set(key, entry)
+  evictOldestIfNeeded()
+}
+
+function getRawAudio(key: string): ArrayBuffer | null {
+  const entry = rawAudioCache.get(key)
+  if (!entry) return null
+  rawAudioCache.delete(key)
+  rawAudioCache.set(key, entry)
+  return entry
+}
+
+function setRawAudio(key: string, buffer: ArrayBuffer): void {
+  rawAudioCache.set(key, buffer)
+  evictRawAudioIfNeeded()
+}
+
+export async function fetchCallRecordingAudio(
+  callShortId: string,
+  stereo: boolean,
+): Promise<ArrayBuffer | null> {
+  const key = cacheKey(callShortId, stereo)
+  const cached = getRawAudio(key)
+  if (cached) return cached
+
+  const inflight = inflightFetches.get(key)
+  if (inflight) return inflight
+
+  const promise = apiClient
+    .getCallRecordingAudioBuffer(callShortId, { stereo })
+    .then((buffer) => {
+      if (buffer?.byteLength) setRawAudio(key, buffer)
+      return buffer
+    })
+    .catch(() => null)
+    .finally(() => {
+      inflightFetches.delete(key)
+    })
+
+  inflightFetches.set(key, promise)
+  return promise
+}
+
+export function prefetchCallRecordingAudio(callShortId: string, stereo = false): void {
+  const key = cacheKey(callShortId, stereo)
+  if (waveformCache.has(key) || rawAudioCache.has(key) || inflightFetches.has(key)) return
+  void fetchCallRecordingAudio(callShortId, stereo)
+}
+
+function evaluatorCacheKey(evaluatorResultId: string): string {
+  return `eval:${evaluatorResultId}`
+}
+
+export function getCachedEvaluatorWaveform(evaluatorResultId: string): CachedWaveform | null {
+  const key = evaluatorCacheKey(evaluatorResultId)
+  const entry = waveformCache.get(key)
+  if (!entry) return null
+  waveformCache.delete(key)
+  waveformCache.set(key, entry)
+  return entry
+}
+
+export function setCachedEvaluatorWaveform(evaluatorResultId: string, entry: CachedWaveform): void {
+  const key = evaluatorCacheKey(evaluatorResultId)
+  waveformCache.set(key, entry)
+  evictOldestIfNeeded()
+}
+
+export async function fetchEvaluatorRecordingAudio(
+  evaluatorResultId: string,
+): Promise<ArrayBuffer | null> {
+  const key = evaluatorCacheKey(evaluatorResultId)
+  const cached = getRawAudio(key)
+  if (cached) return cached
+
+  const inflight = inflightFetches.get(key)
+  if (inflight) return inflight
+
+  const promise = apiClient
+    .getEvaluatorResultAudioBuffer(evaluatorResultId)
+    .then((buffer) => {
+      if (buffer?.byteLength) setRawAudio(key, buffer)
+      return buffer
+    })
+    .catch(() => null)
+    .finally(() => {
+      inflightFetches.delete(key)
+    })
+
+  inflightFetches.set(key, promise)
+  return promise
+}
+
+export function prefetchEvaluatorRecordingAudio(evaluatorResultId: string): void {
+  const key = evaluatorCacheKey(evaluatorResultId)
+  if (waveformCache.has(key) || rawAudioCache.has(key) || inflightFetches.has(key)) return
+  void fetchEvaluatorRecordingAudio(evaluatorResultId)
+}
diff --git a/frontend/src/lib/waveformPeaksFast.ts b/frontend/src/lib/waveformPeaksFast.ts
new file mode 100644
index 00000000..4f6709d5
--- /dev/null
+++ b/frontend/src/lib/waveformPeaksFast.ts
@@ -0,0 +1,88 @@
+const PEAK_WIDTH = 512
+
+function readFourCC(view: DataView, offset: number): string {
+  return String.fromCharCode(
+    view.getUint8(offset),
+    view.getUint8(offset + 1),
+    view.getUint8(offset + 2),
+    view.getUint8(offset + 3),
+  )
+}
+
+export function extractWavPeaks(
+  arrayBuffer: ArrayBuffer,
+  stereo: boolean,
+  width = PEAK_WIDTH,
+): { duration: number; peaks: ArrayBuffer[]; stereo: boolean } | null {
+  try {
+    if (arrayBuffer.byteLength < 44) return null
+    const view = new DataView(arrayBuffer)
+    if (readFourCC(view, 0) !== 'RIFF' || readFourCC(view, 8) !== 'WAVE') return null
+
+    let offset = 12
+    let numChannels = 0
+    let sampleRate = 0
+    let bitsPerSample = 16
+    let dataOffset = 0
+    let dataSize = 0
+
+    while (offset + 8 <= arrayBuffer.byteLength) {
+      const chunkId = readFourCC(view, offset)
+      const chunkSize = view.getUint32(offset + 4, true)
+      const chunkDataOffset = offset + 8
+      if (chunkId === 'fmt ') {
+        numChannels = view.getUint16(chunkDataOffset + 2, true)
+        sampleRate = view.getUint32(chunkDataOffset + 4, true)
+        bitsPerSample = view.getUint16(chunkDataOffset + 14, true)
+      } else if (chunkId === 'data') {
+        dataOffset = chunkDataOffset
+        dataSize = chunkSize
+      }
+      offset = chunkDataOffset + chunkSize + (chunkSize % 2)
+    }
+
+    if (!dataOffset || !sampleRate || bitsPerSample !== 16 || numChannels < 1) return null
+    if (dataOffset % 2 !== 0) return null
+    if (dataOffset + dataSize > arrayBuffer.byteLength) return null
+
+    const frameCount = Math.floor(dataSize / (bitsPerSample / 8) / numChannels)
+    if (frameCount <= 0) return null
+
+    const samples = new Int16Array(arrayBuffer, dataOffset, frameCount * numChannels)
+    const useStereo = stereo && numChannels >= 2
+    const duration = frameCount / sampleRate
+
+    const downsampleChannel = (channelIndex: number): ArrayBuffer => {
+      const peaks = new Float32Array(width * 2)
+      const block = Math.max(1, Math.floor(frameCount / width))
+      for (let i = 0; i < width; i++) {
+        const start = i * block
+        const end = Math.min(frameCount, start + block)
+        let min = 0
+        let max = 0
+        for (let j = start; j < end; j++) {
+          const v = samples[j * numChannels + channelIndex] / 32768
+          if (v < min) min = v
+          if (v > max) max = v
+        }
+        peaks[i * 2] = min
+        peaks[i * 2 + 1] = max
+      }
+      return peaks.buffer
+    }
+
+    const peaks: ArrayBuffer[] = useStereo
+      ? [downsampleChannel(0), downsampleChannel(1)]
+      : [downsampleChannel(0)]
+
+    return { duration, peaks, stereo: useStereo }
+  } catch {
+    return null
+  }
+}
+
+export function isWavBuffer(arrayBuffer: ArrayBuffer): boolean {
+  if (arrayBuffer.byteLength < 12) return false
+  const view = new DataView(arrayBuffer)
+  return readFourCC(view, 0) === 'RIFF' && readFourCC(view, 8) === 'WAVE'
+}
diff --git a/frontend/src/lib/workspaceNavigation.ts b/frontend/src/lib/workspaceNavigation.ts
index 04d09dd2..370658ed 100644
--- a/frontend/src/lib/workspaceNavigation.ts
+++ b/frontend/src/lib/workspaceNavigation.ts
@@ -1,38 +1,39 @@
-/**
- * Maps workspace-scoped detail URLs to their parent list route when the
- * active workspace changes. More-specific patterns must appear first.
- */
-const WORKSPACE_DETAIL_REDIRECTS: Array<{ pattern: RegExp; parent: string }> = [
-  { pattern: /^\/call-imports\/[^/]+\/evaluations\/[^/]+$/, parent: '/call-imports' },
-  { pattern: /^\/call-imports\/[^/]+$/, parent: '/call-imports' },
-  { pattern: /^\/evaluations\/[^/]+$/, parent: '/evaluations' },
-  {
-    pattern: /^\/results\/agents\/[^/]+\/suites\/[^/]+\/scenarios\/[^/]+$/,
-    parent: '/results',
-  },
-  { pattern: /^\/results\/agents\/[^/]+\/suites\/[^/]+$/, parent: '/results' },
-  { pattern: /^\/results\/agents\/[^/]+$/, parent: '/results' },
-  { pattern: /^\/results\/unassigned$/, parent: '/results' },
-  { pattern: /^\/results\/[^/]+$/, parent: '/results' },
-  { pattern: /^\/evaluate-test-agents\/[^/]+$/, parent: '/evaluate-test-agents' },
-  { pattern: /^\/agents\/[^/]+$/, parent: '/agents' },
-  { pattern: /^\/playground\/call-recordings\/[^/]+$/, parent: '/playground' },
-  { pattern: /^\/playground\/test-agent-results\/[^/]+$/, parent: '/playground' },
-  { pattern: /^\/observability\/calls\/[^/]+$/, parent: '/observability/calls' },
-  { pattern: /^\/call-traces\/[^/]+$/, parent: '/call-traces' },
-  { pattern: /^\/judge-alignment\/datasets\/[^/]+$/, parent: '/judge-alignment' },
-  { pattern: /^\/prompt-partials\/[^/]+$/, parent: '/prompt-partials' },
-]
-
-/**
- * Returns the list-route to navigate to when switching workspaces from a
- * detail page, or null if the current path is already a list/sub-page.
- */
-export function resolveWorkspaceSwitchPath(pathname: string): string | null {
-  for (const { pattern, parent } of WORKSPACE_DETAIL_REDIRECTS) {
-    if (pattern.test(pathname)) {
-      return parent
-    }
-  }
-  return null
-}
+/**
+ * Maps workspace-scoped detail URLs to their parent list route when the
+ * active workspace changes. More-specific patterns must appear first.
+ */
+const WORKSPACE_DETAIL_REDIRECTS: Array<{ pattern: RegExp; parent: string }> = [
+  { pattern: /^\/call-imports\/[^/]+\/evaluations\/[^/]+$/, parent: '/call-imports' },
+  { pattern: /^\/call-imports\/[^/]+$/, parent: '/call-imports' },
+  { pattern: /^\/evaluations\/[^/]+$/, parent: '/evaluations' },
+  {
+    pattern: /^\/results\/agents\/[^/]+\/suites\/[^/]+\/scenarios\/[^/]+$/,
+    parent: '/results',
+  },
+  { pattern: /^\/results\/agents\/[^/]+\/suites\/[^/]+$/, parent: '/results' },
+  { pattern: /^\/results\/agents\/[^/]+$/, parent: '/results' },
+  { pattern: /^\/results\/unassigned$/, parent: '/results' },
+  { pattern: /^\/results\/[^/]+$/, parent: '/results' },
+  { pattern: /^\/evaluate-test-agents\/[^/]+$/, parent: '/evaluate-test-agents' },
+  { pattern: /^\/agents\/[^/]+$/, parent: '/agents' },
+  { pattern: /^\/playground\/call-recordings\/[^/]+$/, parent: '/playground' },
+  { pattern: /^\/playground\/test-agent-results\/[^/]+$/, parent: '/playground' },
+  { pattern: /^\/observability\/calls\/[^/]+$/, parent: '/calls' },
+  { pattern: /^\/call-traces\/[^/]+$/, parent: '/calls' },
+  { pattern: /^\/calls\/[^/]+$/, parent: '/calls' },
+  { pattern: /^\/judge-alignment\/datasets\/[^/]+$/, parent: '/judge-alignment' },
+  { pattern: /^\/prompt-partials\/[^/]+$/, parent: '/prompt-partials' },
+]
+
+/**
+ * Returns the list-route to navigate to when switching workspaces from a
+ * detail page, or null if the current path is already a list/sub-page.
+ */
+export function resolveWorkspaceSwitchPath(pathname: string): string | null {
+  for (const { pattern, parent } of WORKSPACE_DETAIL_REDIRECTS) {
+    if (pattern.test(pathname)) {
+      return parent
+    }
+  }
+  return null
+}
diff --git a/frontend/src/pages/agents/components/AgentEditForm.tsx b/frontend/src/pages/agents/components/AgentEditForm.tsx
index d734ff8b..9e636385 100644
--- a/frontend/src/pages/agents/components/AgentEditForm.tsx
+++ b/frontend/src/pages/agents/components/AgentEditForm.tsx
@@ -1,932 +1,945 @@
-import { useState, useEffect } from 'react'
-import { useNavigate } from 'react-router-dom'
-import { useQuery, useMutation } from '@tanstack/react-query'
-import { Sparkles, Loader2, Bot, Eye, Code, Trash2, Save, PhoneOutgoing, PhoneIncoming } from 'lucide-react'
-import ParamSlider from './ParamSlider'
-import { OverviewSection, formatSilenceHangupLabel } from './AgentOverviewLayout'
-import ReactMarkdown from 'react-markdown'
-import Button from '../../../components/Button'
-import { apiClient } from '../../../lib/api'
-import { VoiceBundle, Integration, AIProvider, IntegrationPlatform } from '../../../types/api'
-import { getIntegrationPlatformLabel, getIntegrationPlatformLogo, getTelephonyProviderLabel } from '../../../config/providers'
-import { useOrgTelephony } from '../../../hooks/useOrgTelephony'
-import { TelephonyProvider } from '../../../types/api'
-import type { AgentDetailTab } from './AgentInfoView'
-import TestAgentSubTabNav, { type TestAgentSubTab } from './TestAgentSubTabNav'
-import VoiceBundleDetailCard from './VoiceBundleDetailCard'
-import AgentPromptComposer from './AgentPromptComposer'
-import {
-  formatGatewayCredentialLabel,
-  resolveLLMModelsForCredential,
-} from '../../../lib/llmModelOptions'
-import { useAgentPhoneAssignmentCheck } from './useAgentPhoneAssignmentCheck'
-import { formatAgentPhoneConflictMessage } from './agentPhoneValidation'
-
-interface FormData {
-  name: string
-  phone_number: string
-  language: string
-  description: string
-  prompt_variables: Record<string, string>
-  silence_hangup_secs: number
-  call_type: string
-  call_medium: 'phone_call' | 'web_call'
-  telephony_phone_number_id: string
-  voice_bundle_id: string
-  voice_ai_integration_id: string
-  voice_ai_agent_id: string
-  provider_prompt: string
-}
-
-interface AgentEditFormProps {
-  formData: FormData
-  onChange: (data: FormData) => void
-  onSubmit: (e: React.FormEvent) => void
-  onDelete: () => void
-  voiceBundles: VoiceBundle[]
-  integrations: Integration[]
-  showToast: (message: string, type: 'success' | 'error') => void
-  activeTab: AgentDetailTab
-  onSaveSystemPrompt: () => void
-  agentId?: string
-}
-
-const SUPPORTED_VOICE_AI_PLATFORMS: IntegrationPlatform[] = [
-  IntegrationPlatform.RETELL,
-  IntegrationPlatform.VAPI,
-  IntegrationPlatform.ELEVENLABS,
-  IntegrationPlatform.SMALLEST,
-]
-
-export default function AgentEditForm({
-  formData,
-  onChange,
-  onSubmit,
-  onDelete,
-  voiceBundles,
-  integrations,
-  showToast,
-  activeTab,
-  onSaveSystemPrompt,
-  agentId,
-}: AgentEditFormProps) {
-  const navigate = useNavigate()
-  const [descriptionEditorMode, setDescriptionEditorMode] = useState<'write' | 'preview'>('write')
-  const [providerPromptEditorMode, setProviderPromptEditorMode] = useState<'write' | 'preview'>('write')
-  const [showAIGeneratePanel, setShowAIGeneratePanel] = useState(false)
-  const [includeLinkedScenarios, setIncludeLinkedScenarios] = useState(true)
-  const [aiDescription, setAiDescription] = useState('')
-  const [aiTone, setAiTone] = useState('professional')
-  const [aiFormat, setAiFormat] = useState('structured')
-  const [aiCredentialId, setAiCredentialId] = useState('')
-  const [aiModel, setAiModel] = useState('')
-  const [phoneNumberInputMode, setPhoneNumberInputMode] = useState<'provider' | 'custom'>('provider')
-  const [testAgentSubTab, setTestAgentSubTab] = useState<TestAgentSubTab>('prompt')
-
-  const { data: aiProviders = [] } = useQuery<AIProvider[]>({
-    queryKey: ['ai-providers'],
-    queryFn: () => apiClient.listAIProviders(),
-  })
-
-  const selectedAiProviderRow = aiCredentialId
-    ? aiProviders.find((p) => p.id === aiCredentialId)
-    : undefined
-  const aiProvider = selectedAiProviderRow?.provider ?? ''
-
-  const { data: modelOptions } = useQuery({
-    queryKey: ['model-options', aiProvider],
-    queryFn: () => apiClient.getModelOptions(aiProvider),
-    enabled: !!aiProvider,
-  })
-  const {
-    canUseProviderNumbers,
-    numbersForCallType,
-  } = useOrgTelephony(formData.call_medium === 'phone_call')
-  const telephonyNumbers = numbersForCallType(formData.call_type)
-  const isTelephonyConfigError = !canUseProviderNumbers
-  const { conflict: phoneConflict, isChecking: isCheckingPhoneAssignment, hasConflict: hasPhoneConflict } =
-    useAgentPhoneAssignmentCheck({
-      enabled: formData.call_medium === 'phone_call',
-      callMedium: formData.call_medium,
-      phoneNumber: formData.phone_number,
-      telephonyPhoneNumberId:
-        phoneNumberInputMode === 'provider' ? formData.telephony_phone_number_id : undefined,
-      excludeAgentId: agentId,
-    })
-
-  const handleFormSubmit = (e: React.FormEvent) => {
-    e.preventDefault()
-    if (formData.call_medium === 'phone_call') {
-      if (isCheckingPhoneAssignment) {
-        showToast('Checking phone number availability…', 'error')
-        return
-      }
-      if (hasPhoneConflict && phoneConflict) {
-        showToast(formatAgentPhoneConflictMessage(phoneConflict), 'error')
-        return
-      }
-    }
-    onSubmit(e)
-  }
-
-  const llmModels = modelOptions?.llm || []
-  const modelResolution = selectedAiProviderRow
-    ? resolveLLMModelsForCredential(selectedAiProviderRow, llmModels)
-    : { mode: 'catalog' as const, models: llmModels }
-  const gatewayDirectModel =
-    modelResolution.mode === 'gateway_direct' ? modelResolution.model : null
-  const selectableModels =
-    modelResolution.mode === 'catalog' ? modelResolution.models : []
-
-  useEffect(() => {
-    if (gatewayDirectModel) {
-      if (aiModel) setAiModel('')
-      return
-    }
-    if (aiProvider && selectableModels.length > 0 && !selectableModels.includes(aiModel)) {
-      setAiModel(selectableModels[0])
-    }
-  }, [aiProvider, selectableModels, aiModel, gatewayDirectModel])
-
-  useEffect(() => {
-    if (formData.call_medium !== 'phone_call') {
-      return
-    }
-
-    if (formData.telephony_phone_number_id) {
-      setPhoneNumberInputMode('provider')
-      return
-    }
-
-    if (!canUseProviderNumbers || telephonyNumbers.length === 0) {
-      setPhoneNumberInputMode('custom')
-      return
-    }
-
-    const selectedExists = telephonyNumbers.some((n) => n.id === formData.telephony_phone_number_id)
-    if (!selectedExists && phoneNumberInputMode === 'provider') {
-      onChange({ ...formData, telephony_phone_number_id: '', phone_number: '' })
-    }
-  }, [
-    formData,
-    onChange,
-    phoneNumberInputMode,
-    canUseProviderNumbers,
-    telephonyNumbers,
-  ])
-
-  const generateDescriptionMutation = useMutation({
-    mutationFn: (data: {
-      description: string
-      tone?: string
-      format_style?: string
-      provider?: string
-      model?: string
-      agent_id?: string
-      include_linked_scenarios?: boolean
-      append_scenarios_to_output?: boolean
-    }) => apiClient.generateAgentDescription(data),
-    onSuccess: (data) => {
-      onChange({ ...formData, description: data.content })
-      setShowAIGeneratePanel(false)
-      setAiDescription('')
-      setDescriptionEditorMode('preview')
-      showToast('Description generated successfully!', 'success')
-    },
-    onError: (err: any) => {
-      showToast(err?.response?.data?.detail || 'Failed to generate description with AI', 'error')
-    },
-  })
-
-  const voiceAgentIntegrations = integrations.filter(
-    (integration) =>
-      integration.is_active &&
-      SUPPORTED_VOICE_AI_PLATFORMS.includes(integration.platform as IntegrationPlatform),
-  )
-
-  const selectedVoiceIntegration = voiceAgentIntegrations.find(
-    (integration) => integration.id === formData.voice_ai_integration_id,
-  )
-
-  const linkedVoiceBundle = formData.voice_bundle_id
-    ? voiceBundles.find((vb) => vb.id === formData.voice_bundle_id)
-    : undefined
-
-  const hasPlatformLink = Boolean(formData.voice_ai_integration_id || formData.voice_ai_agent_id)
-
-  const productionPromptProse =
-    'prose prose-sm max-w-none prose-headings:text-gray-900 prose-p:text-gray-700 prose-code:text-gray-800 prose-code:bg-gray-100 prose-code:px-1 prose-code:py-0.5 prose-code:rounded prose-pre:bg-gray-900 prose-pre:text-gray-100 prose-ul:text-gray-700 prose-ol:text-gray-700'
-
-  return (
-    <form onSubmit={handleFormSubmit}>
-      {activeTab === 'overview' && (
-        <div className="space-y-5 max-w-3xl">
-          <OverviewSection title="Agent profile" description="Name and language shown to evaluators and logs.">
-            <div className="space-y-4">
-              <div>
-                <label htmlFor="agent-name" className="block text-sm font-medium text-gray-700 mb-1.5">
-                  Name *
-                </label>
-                <input
-                  id="agent-name"
-                  type="text"
-                  required
-                  value={formData.name}
-                  onChange={(e) => onChange({ ...formData, name: e.target.value })}
-                  className="w-full px-3 py-2.5 border border-gray-200 rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400"
-                  placeholder="Customer Support Bot"
-                />
-              </div>
-
-              <div>
-                <label htmlFor="agent-language" className="block text-sm font-medium text-gray-700 mb-1.5">
-                  Language
-                </label>
-                <select
-                  id="agent-language"
-                  value={formData.language}
-                  onChange={(e) => onChange({ ...formData, language: e.target.value })}
-                  className="w-full px-3 py-2.5 border border-gray-200 rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400"
-                >
-                  <option value="en">English</option>
-                  <option value="es">Spanish</option>
-                  <option value="fr">French</option>
-                  <option value="de">German</option>
-                  <option value="zh">Chinese</option>
-                  <option value="hi">Hindi</option>
-                </select>
-              </div>
-            </div>
-          </OverviewSection>
-
-          <OverviewSection title="Call setup" description="How sessions are placed and routed.">
-            <div className="space-y-5">
-              <div>
-                <span className="block text-sm font-medium text-gray-700 mb-2">Call medium *</span>
-                <div className="inline-flex rounded-xl border border-gray-200 bg-gray-50/80 p-1 shadow-sm">
-                  {(['web_call', 'phone_call'] as const).map((medium) => (
-                    <button
-                      key={medium}
-                      type="button"
-                      onClick={() =>
-                        onChange({
-                          ...formData,
-                          call_medium: medium,
-                          phone_number: medium === 'web_call' ? '' : formData.phone_number,
-                        })
-                      }
-                      className={`rounded-lg px-4 py-2 text-sm font-medium transition-all focus:outline-none focus-visible:ring-2 focus-visible:ring-primary-500 ${
-                        formData.call_medium === medium
-                          ? 'bg-white text-gray-900 shadow-sm ring-1 ring-gray-200/80'
-                          : 'text-gray-600 hover:text-gray-900'
-                      }`}
-                    >
-                      {medium === 'web_call' ? 'Web call' : 'Phone call'}
-                    </button>
-                  ))}
-                </div>
-              </div>
-
-              <div>
-                <span className="block text-sm font-medium text-gray-700 mb-2">Call type</span>
-                <div className="inline-flex rounded-xl border border-gray-200 bg-gray-50/80 p-1 shadow-sm">
-                  {(['outbound', 'inbound'] as const).map((type) => (
-                    <button
-                      key={type}
-                      type="button"
-                      onClick={() => {
-                        const nextType = type
-                        const nextNumbers = numbersForCallType(nextType)
-                        const stillValid = nextNumbers.some((n) => n.id === formData.telephony_phone_number_id)
-                        onChange({
-                          ...formData,
-                          call_type: nextType,
-                          ...(stillValid
-                            ? {}
-                            : { telephony_phone_number_id: '', phone_number: '' }),
-                        })
-                      }}
-                      className={`inline-flex items-center gap-1.5 rounded-lg px-4 py-2 text-sm font-medium transition-all focus:outline-none focus-visible:ring-2 focus-visible:ring-primary-500 ${
-                        formData.call_type === type
-                          ? 'bg-white text-gray-900 shadow-sm ring-1 ring-gray-200/80'
-                          : 'text-gray-600 hover:text-gray-900'
-                      }`}
-                    >
-                      {type === 'outbound' ? (
-                        <PhoneOutgoing className="h-3.5 w-3.5" />
-                      ) : (
-                        <PhoneIncoming className="h-3.5 w-3.5" />
-                      )}
-                      {type === 'outbound' ? 'Outbound' : 'Inbound'}
-                    </button>
-                  ))}
-                </div>
-              </div>
-
-              {formData.call_medium === 'phone_call' && (
-                <div className="space-y-3 rounded-lg border border-gray-100 bg-gray-50/50 p-4">
-                  <div className="flex flex-wrap items-center justify-between gap-2">
-                    <label className="text-sm font-medium text-gray-700">Phone number *</label>
-                    <div className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs">
-                      <button
-                        type="button"
-                        onClick={() => {
-                          setPhoneNumberInputMode('provider')
-                          onChange({ ...formData, phone_number: '' })
-                        }}
-                        disabled={!canUseProviderNumbers || telephonyNumbers.length === 0}
-                        className={`rounded-md px-2.5 py-1 font-medium ${
-                          phoneNumberInputMode === 'provider'
-                            ? 'bg-primary-600 text-white'
-                            : 'text-gray-600 hover:bg-gray-50'
-                        } disabled:opacity-40`}
-                      >
-                        From provider
-                      </button>
-                      <button
-                        type="button"
-                        onClick={() => {
-                          setPhoneNumberInputMode('custom')
-                          onChange({ ...formData, telephony_phone_number_id: '' })
-                        }}
-                        className={`rounded-md px-2.5 py-1 font-medium ${
-                          phoneNumberInputMode === 'custom'
-                            ? 'bg-primary-600 text-white'
-                            : 'text-gray-600 hover:bg-gray-50'
-                        }`}
-                      >
-                        Custom
-                      </button>
-                    </div>
-                  </div>
-
-                  {isTelephonyConfigError && (
-                    <p className="text-xs text-amber-800 bg-amber-50 border border-amber-200/80 rounded-lg px-3 py-2">
-                      No synced telephony numbers found yet. Import numbers on the Telephony Numbers page,
-                      configure a provider in Integrations, or enter a custom number below.
-                    </p>
-                  )}
-
-                  {phoneNumberInputMode === 'provider' ? (
-                    <select
-                      required
-                      value={formData.telephony_phone_number_id}
-                      onChange={(e) => {
-                        const selected = telephonyNumbers.find((n) => n.id === e.target.value)
-                        onChange({
-                          ...formData,
-                          telephony_phone_number_id: e.target.value,
-                          phone_number: selected?.phone_number || '',
-                        })
-                      }}
-                      className="w-full px-3 py-2.5 border border-gray-200 rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400"
-                      disabled={!canUseProviderNumbers || telephonyNumbers.length === 0}
-                    >
-                      <option value="">Select a synced telephony number</option>
-                      {telephonyNumbers.map((number) => (
-                        <option
-                          key={number.id}
-                          value={number.id}
-                          disabled={!!number.agent_id && number.id !== formData.telephony_phone_number_id}
-                        >
-                          {number.phone_number}
-                          {number.provider
-                            ? ` [${getTelephonyProviderLabel(number.provider as TelephonyProvider)}]`
-                            : ''}
-                          {number.region ? ` - ${number.region}` : ''}
-                          {number.country_iso2 ? ` (${number.country_iso2})` : ''}
-                          {number.agent_id && number.id !== formData.telephony_phone_number_id
-                            ? ` [Assigned to ${number.linked_agent_name || 'another agent'}]`
-                            : ''}
-                        </option>
-                      ))}
-                    </select>
-                  ) : (
-                    <input
-                      type="text"
-                      required
-                      value={formData.phone_number}
-                      onChange={(e) =>
-                        onChange({
-                          ...formData,
-                          phone_number: e.target.value.replace(/[^\d+]/g, ''),
-                        })
-                      }
-                      className={`w-full px-3 py-2.5 border rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400 ${
-                        hasPhoneConflict ? 'border-red-300' : 'border-gray-200'
-                      }`}
-                      placeholder="+1234567890"
-                    />
-                  )}
-                  {formData.call_medium === 'phone_call' && hasPhoneConflict && phoneConflict && (
-                    <p className="text-xs text-red-600 mt-1">
-                      {formatAgentPhoneConflictMessage(phoneConflict)}
-                    </p>
-                  )}
-                  {formData.call_medium === 'phone_call' && isCheckingPhoneAssignment && (
-                    <p className="text-xs text-gray-500 mt-1">Checking number availability…</p>
-                  )}
-                </div>
-              )}
-            </div>
-          </OverviewSection>
-
-          <OverviewSection title="Live session" description="Hang up when neither side speaks for too long.">
-            <ParamSlider
-              label="End call after silence"
-              helpText={`${formatSilenceHangupLabel(formData.silence_hangup_secs)} · Resets on voice activity. Default 15. Set to 0 to disable.`}
-              min={0}
-              max={600}
-              step={1}
-              integer
-              value={formData.silence_hangup_secs}
-              onChange={(next) =>
-                onChange({
-                  ...formData,
-                  silence_hangup_secs: next ?? 0,
-                })
-              }
-            />
-          </OverviewSection>
-
-          <div className="flex gap-3 pt-2">
-            <Button
-              type="button"
-              variant="outline"
-              onClick={onDelete}
-              leftIcon={<Trash2 className="w-4 h-4" />}
-              className="border-red-200 text-red-700 hover:bg-red-50 hover:border-red-300"
-            >
-              Delete agent
-            </Button>
-          </div>
-        </div>
-      )}
-
-      {activeTab === 'test_agent' && (
-        <div className="max-w-4xl space-y-4">
-          <TestAgentSubTabNav value={testAgentSubTab} onChange={setTestAgentSubTab} />
-
-          {testAgentSubTab === 'configuration' && (
-            <div className="space-y-4">
-              <div>
-                <label className="block text-sm font-medium text-gray-700 mb-2">Voice Bundle</label>
-                <select
-                  value={formData.voice_bundle_id}
-                  onChange={(e) => onChange({ ...formData, voice_bundle_id: e.target.value })}
-                  className="w-full px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent bg-white"
-                >
-                  <option value="">Select a Voice Bundle</option>
-                  {voiceBundles
-                    .filter((vb) => vb.is_active)
-                    .map((vb) => (
-                      <option key={vb.id} value={vb.id}>
-                        {vb.name}
-                      </option>
-                    ))}
-                </select>
-                {voiceBundles.filter((vb) => vb.is_active).length === 0 && (
-                  <p className="mt-1 text-xs text-gray-500">
-                    No active voice bundles available. Create one in Voice Bundles.
-                  </p>
-                )}
-              </div>
-              {linkedVoiceBundle && (
-                <VoiceBundleDetailCard
-                  bundle={linkedVoiceBundle}
-                  allowParamTuning
-                  onEdit={() => {
-                    const returnPath = agentId ? `/agents/${agentId}?tab=test_agent` : '/agents'
-                    navigate(
-                      `/voicebundles?edit=${linkedVoiceBundle.id}&return=${encodeURIComponent(returnPath)}`
-                    )
-                  }}
-                  onManageInVoiceBundles={() => navigate('/voicebundles')}
-                />
-              )}
-            </div>
-          )}
-
-          {testAgentSubTab === 'prompt' && (
-            <div className="border border-gray-200 rounded-lg p-4 bg-gray-50">
-              <div className="flex items-center justify-between mb-3">
-                <label className="block text-sm font-medium text-gray-700">EfficientAI Test Agent Prompt</label>
-                <div className="flex items-center gap-2">
-                  <button
-                    type="button"
-                    onClick={() => setShowAIGeneratePanel(!showAIGeneratePanel)}
-                    disabled={generateDescriptionMutation.isPending}
-                    className={`inline-flex items-center gap-1.5 px-3 py-1 text-xs font-medium rounded-lg border transition-colors ${
-                      showAIGeneratePanel
-                        ? 'bg-amber-100 text-amber-800 border-amber-300'
-                        : 'bg-amber-50 text-amber-700 border-amber-200 hover:bg-amber-100'
-                    }`}
-                  >
-                    {generateDescriptionMutation.isPending ? (
-                      <Loader2 className="h-3 w-3 animate-spin" />
-                    ) : (
-                      <Sparkles className="h-3 w-3" />
-                    )}
-                    {generateDescriptionMutation.isPending ? 'Generating...' : 'AI Generate'}
-                  </button>
-                  <button
-                    type="button"
-                    onClick={onSaveSystemPrompt}
-                    disabled={!formData.description?.trim()}
-                    className="inline-flex items-center gap-1.5 px-3 py-1 text-xs font-medium rounded-lg border border-emerald-200 bg-emerald-50 text-emerald-700 hover:bg-emerald-100 disabled:opacity-50"
-                  >
-                    <Save className="h-3 w-3" />
-                    Save Prompt
-                  </button>
-                  <div className="flex items-center bg-gray-100 rounded-lg p-0.5">
-                    <button
-                      type="button"
-                      onClick={() => setDescriptionEditorMode('write')}
-                      className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
-                        descriptionEditorMode === 'write'
-                          ? 'bg-white text-gray-900 shadow-sm'
-                          : 'text-gray-500 hover:text-gray-700'
-                      }`}
-                    >
-                      <Code className="h-3 w-3" />
-                      Write
-                    </button>
-                    <button
-                      type="button"
-                      onClick={() => setDescriptionEditorMode('preview')}
-                      className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
-                        descriptionEditorMode === 'preview'
-                          ? 'bg-white text-gray-900 shadow-sm'
-                          : 'text-gray-500 hover:text-gray-700'
-                      }`}
-                    >
-                      <Eye className="h-3 w-3" />
-                      Preview
-                    </button>
-                  </div>
-                </div>
-              </div>
-
-              {/* AI Generate Panel */}
-              {showAIGeneratePanel && (
-                <div className="mb-3 p-3 bg-amber-50 rounded-lg border border-amber-200">
-                  <div className="flex items-center gap-2 mb-2">
-                    <Sparkles className="h-4 w-4 text-amber-600" />
-                    <span className="text-sm font-medium text-amber-900">Generate Description with AI</span>
-                  </div>
-                  <p className="text-xs text-amber-700 mb-3">
-                    Describe what this agent should do and AI will generate a rich markdown description.
-                  </p>
-                  <textarea
-                    value={aiDescription}
-                    onChange={(e) => setAiDescription(e.target.value)}
-                    placeholder="e.g., A customer support agent that handles refund requests, tracks orders, and escalates complex issues..."
-                    rows={3}
-                    className="w-full px-3 py-2 text-sm border border-amber-200 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white mb-2"
-                  />
-                  {agentId ? (
-                    <label className="flex items-center gap-2 text-xs text-gray-700 mb-2">
-                      <input
-                        type="checkbox"
-                        checked={includeLinkedScenarios}
-                        onChange={(e) => setIncludeLinkedScenarios(e.target.checked)}
-                        className="rounded border-gray-300 text-amber-600 focus:ring-amber-500"
-                      />
-                      Include linked scenarios as AI context (does not append to prompt)
-                    </label>
-                  ) : null}
-                  <div className="grid grid-cols-2 gap-3 mb-2">
-                    <div>
-                      <label className="block text-xs font-medium text-gray-600 mb-1">Tone</label>
-                      <select
-                        value={aiTone}
-                        onChange={(e) => setAiTone(e.target.value)}
-                        className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white"
-                      >
-                        <option value="professional">Professional</option>
-                        <option value="casual">Casual / Friendly</option>
-                        <option value="technical">Technical</option>
-                        <option value="concise">Concise / Direct</option>
-                        <option value="detailed">Detailed / Thorough</option>
-                      </select>
-                    </div>
-                    <div>
-                      <label className="block text-xs font-medium text-gray-600 mb-1">Format Style</label>
-                      <select
-                        value={aiFormat}
-                        onChange={(e) => setAiFormat(e.target.value)}
-                        className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white"
-                      >
-                        <option value="structured">Structured (sections & bullet points)</option>
-                        <option value="narrative">Narrative (flowing text)</option>
-                        <option value="template">Template (with placeholders)</option>
-                        <option value="step-by-step">Step-by-step Instructions</option>
-                      </select>
-                    </div>
-                  </div>
-                  <div className="flex gap-3 mb-2">
-                    <div className="flex-1">
-                      <label className="block text-xs font-medium text-gray-600 mb-1">
-                        <Bot className="w-3 h-3 inline mr-1" />
-                        LLM Provider
-                      </label>
-                      <select
-                        value={aiCredentialId}
-                        onChange={(e) => {
-                          setAiCredentialId(e.target.value)
-                          setAiModel('')
-                        }}
-                        className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white"
-                      >
-                        <option value="">Auto-detect (use first available)</option>
-                        {aiProviders
-                          .filter((p) => p.is_active)
-                          .map((p) => (
-                            <option key={p.id} value={p.id}>
-                              {formatGatewayCredentialLabel(p, {
-                                custom: 'Custom',
-                                openai: 'OpenAI',
-                                anthropic: 'Anthropic',
-                                google: 'Google',
-                              })}
-                            </option>
-                          ))}
-                      </select>
-                    </div>
-                    <div className="flex-1">
-                      <label className="block text-xs font-medium text-gray-600 mb-1">Model</label>
-                      {gatewayDirectModel ? (
-                        <div
-                          className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg bg-gray-50 text-gray-700 truncate"
-                          title={gatewayDirectModel}
-                        >
-                          {gatewayDirectModel}
-                        </div>
-                      ) : (
-                        <select
-                          value={aiModel}
-                          onChange={(e) => setAiModel(e.target.value)}
-                          disabled={!aiCredentialId}
-                          className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white disabled:bg-gray-50 disabled:text-gray-400"
-                        >
-                          {!aiCredentialId ? (
-                            <option value="">Select a provider first</option>
-                          ) : selectableModels.length === 0 ? (
-                            <option value="">Loading models...</option>
-                          ) : (
-                            selectableModels.map((m: string) => (
-                              <option key={m} value={m}>
-                                {m}
-                              </option>
-                            ))
-                          )}
-                        </select>
-                      )}
-                    </div>
-                  </div>
-                  <div className="flex items-center gap-2 justify-end">
-                    <button
-                      type="button"
-                      onClick={() => setShowAIGeneratePanel(false)}
-                      className="px-3 py-1.5 text-xs font-medium text-gray-600 hover:text-gray-800"
-                    >
-                      Cancel
-                    </button>
-                    <button
-                      type="button"
-                      onClick={() =>
-                        generateDescriptionMutation.mutate({
-                          description: aiDescription,
-                          tone: aiTone,
-                          format_style: aiFormat,
-                          ...(aiProvider ? { provider: aiProvider } : {}),
-                          ...(aiModel ? { model: aiModel } : {}),
-                          ...(agentId
-                            ? {
-                                agent_id: agentId,
-                                include_linked_scenarios: includeLinkedScenarios,
-                                append_scenarios_to_output: false,
-                              }
-                            : {}),
-                        })
-                      }
-                      disabled={generateDescriptionMutation.isPending || !aiDescription.trim()}
-                      className="inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium bg-amber-600 text-white rounded-lg hover:bg-amber-700 disabled:opacity-50"
-                    >
-                      {generateDescriptionMutation.isPending ? (
-                        <>
-                          <Loader2 className="h-3 w-3 animate-spin" /> Generating...
-                        </>
-                      ) : (
-                        <>
-                          <Sparkles className="h-3 w-3" /> Generate
-                        </>
-                      )}
-                    </button>
-                  </div>
-                </div>
-              )}
-
-              <div className="mb-4 rounded-lg border border-gray-200 bg-white p-3">
-                <div className="flex items-center justify-between gap-2 mb-2">
-                  <label className="text-sm font-medium text-gray-700">Custom prompt variables</label>
-                  <button
-                    type="button"
-                    className="text-xs font-medium text-primary-600 hover:text-primary-800"
-                    onClick={() => {
-                      const base = { ...(formData.prompt_variables || {}) }
-                      let n = 1
-                      let key = 'custom_var'
-                      while (base[key]) {
-                        n += 1
-                        key = `custom_var_${n}`
-                      }
-                      base[key] = ''
-                      onChange({ ...formData, prompt_variables: base })
-                    }}
-                  >
-                    + Add variable
-                  </button>
-                </div>
-                <p className="text-xs text-gray-500 mb-2">
-                  Define keys you can insert with <code className="text-gray-700">{'{'}</code> or{' '}
-                  <code className="text-gray-700">@</code>. Values are optional descriptions for your team.
-                </p>
-                {Object.keys(formData.prompt_variables || {}).length === 0 ? (
-                  <p className="text-xs text-gray-400 italic">No custom variables yet.</p>
-                ) : (
-                  <div className="space-y-2">
-                    {Object.entries(formData.prompt_variables || {}).map(([key, desc]) => (
-                      <div key={key} className="flex flex-wrap items-center gap-2">
-                        <input
-                          type="text"
-                          value={key}
-                          onChange={(e) => {
-                            const nextKey = e.target.value.replace(/\s+/g, '_')
-                            const vars = { ...(formData.prompt_variables || {}) }
-                            delete vars[key]
-                            if (nextKey) vars[nextKey] = desc
-                            onChange({ ...formData, prompt_variables: vars })
-                          }}
-                          className="w-36 px-2 py-1.5 text-xs font-mono border border-gray-300 rounded-md"
-                          placeholder="variable_key"
-                        />
-                        <input
-                          type="text"
-                          value={desc}
-                          onChange={(e) =>
-                            onChange({
-                              ...formData,
-                              prompt_variables: {
-                                ...(formData.prompt_variables || {}),
-                                [key]: e.target.value,
-                              },
-                            })
-                          }
-                          className="flex-1 min-w-[120px] px-2 py-1.5 text-xs border border-gray-300 rounded-md"
-                          placeholder="Description (optional)"
-                        />
-                        <button
-                          type="button"
-                          className="text-xs text-red-600 hover:text-red-800"
-                          onClick={() => {
-                            const vars = { ...(formData.prompt_variables || {}) }
-                            delete vars[key]
-                            onChange({ ...formData, prompt_variables: vars })
-                          }}
-                        >
-                          Remove
-                        </button>
-                      </div>
-                    ))}
-                  </div>
-                )}
-              </div>
-
-              {descriptionEditorMode === 'write' ? (
-                <AgentPromptComposer
-                  value={formData.description}
-                  onChange={(description) => onChange({ ...formData, description })}
-                  customVariables={formData.prompt_variables}
-                />
-              ) : (
-                <div className="min-h-[380px] max-h-[70vh] overflow-y-auto border border-gray-300 rounded-lg p-4 prose prose-sm max-w-none prose-headings:text-gray-900 prose-p:text-gray-700 prose-code:text-gray-800 prose-code:bg-gray-100 prose-code:px-1 prose-code:py-0.5 prose-code:rounded prose-pre:bg-gray-900 prose-pre:text-gray-100">
-                  {formData.description ? (
-                    <ReactMarkdown>{formData.description}</ReactMarkdown>
-                  ) : (
-                    <p className="text-gray-400 italic">Nothing to preview yet...</p>
-                  )}
-                </div>
-              )}
-            </div>
-          )}
-        </div>
-      )}
-
-      {activeTab === 'voice_ai_agent' && hasPlatformLink && (
-        <div className="max-w-2xl space-y-4">
-          <div>
-            <label className="block text-sm font-medium text-gray-700 mb-2">Integration Provider</label>
-            <div className="flex items-center gap-3">
-              <select
-                value={formData.voice_ai_integration_id}
-                onChange={(e) => onChange({ ...formData, voice_ai_integration_id: e.target.value })}
-                className="w-full px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent bg-white"
-              >
-                <option value="">Select an Integration</option>
-                {voiceAgentIntegrations.map((integration) => {
-                  const platformLabel = getIntegrationPlatformLabel(
-                    integration.platform as IntegrationPlatform,
-                  )
-                  return (
-                    <option key={integration.id} value={integration.id}>
-                      {integration.name || platformLabel} ({platformLabel})
-                    </option>
-                  )
-                })}
-              </select>
-              {selectedVoiceIntegration && (
-                <div className="flex-shrink-0">
-                  {(() => {
-                    const logo = getIntegrationPlatformLogo(
-                      selectedVoiceIntegration.platform as IntegrationPlatform,
-                    )
-                    const label = getIntegrationPlatformLabel(
-                      selectedVoiceIntegration.platform as IntegrationPlatform,
-                    )
-                    return logo ? (
-                      <img src={logo} alt={label} className="h-8 w-8 rounded-full object-contain" />
-                    ) : null
-                  })()}
-                </div>
-              )}
-            </div>
-          </div>
-          <div>
-            <label className="block text-sm font-medium text-gray-700 mb-2">Agent ID</label>
-            <input
-              type="text"
-              value={formData.voice_ai_agent_id}
-              onChange={(e) => onChange({ ...formData, voice_ai_agent_id: e.target.value })}
-              className="w-full px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent bg-white"
-              placeholder="Enter agent ID from Retell/Vapi/ElevenLabs/Smallest"
-            />
-          </div>
-        </div>
-      )}
-
-      {activeTab === 'voice_ai_agent' && !hasPlatformLink && (
-        <div className="border border-gray-200 rounded-lg p-4 bg-gray-50">
-          <div className="flex items-center justify-between mb-3">
-            <label className="block text-sm font-medium text-gray-700">Production Agent Prompt</label>
-            <div className="flex items-center bg-gray-100 rounded-lg p-0.5">
-              <button
-                type="button"
-                onClick={() => setProviderPromptEditorMode('write')}
-                className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
-                  providerPromptEditorMode === 'write'
-                    ? 'bg-white text-gray-900 shadow-sm'
-                    : 'text-gray-500 hover:text-gray-700'
-                }`}
-              >
-                <Code className="h-3 w-3" />
-                Write
-              </button>
-              <button
-                type="button"
-                onClick={() => setProviderPromptEditorMode('preview')}
-                className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
-                  providerPromptEditorMode === 'preview'
-                    ? 'bg-white text-gray-900 shadow-sm'
-                    : 'text-gray-500 hover:text-gray-700'
-                }`}
-              >
-                <Eye className="h-3 w-3" />
-                Preview
-              </button>
-            </div>
-          </div>
-          {providerPromptEditorMode === 'write' ? (
-            <textarea
-              value={formData.provider_prompt}
-              onChange={(e) => onChange({ ...formData, provider_prompt: e.target.value })}
-              className="w-full min-h-[380px] px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent font-mono text-sm resize-y"
-              rows={16}
-              placeholder="Paste or edit the production system prompt…"
-            />
-          ) : (
-            <div className="min-h-[380px] max-h-[70vh] overflow-y-auto border border-gray-300 rounded-lg p-4 bg-white">
-              {formData.provider_prompt?.trim() ? (
-                <div className={productionPromptProse}>
-                  <ReactMarkdown>{formData.provider_prompt}</ReactMarkdown>
-                </div>
-              ) : (
-                <p className="text-gray-400 italic">Nothing to preview yet…</p>
-              )}
-            </div>
-          )}
-        </div>
-      )}
-    </form>
-  )
-}
+import { useState, useEffect } from 'react'
+import { useNavigate } from 'react-router-dom'
+import { useQuery, useMutation } from '@tanstack/react-query'
+import { Sparkles, Loader2, Bot, Eye, Code, Trash2, Save, PhoneOutgoing, PhoneIncoming } from 'lucide-react'
+import ParamSlider from './ParamSlider'
+import { OverviewSection, formatSilenceHangupLabel } from './AgentOverviewLayout'
+import ReactMarkdown from 'react-markdown'
+import Button from '../../../components/Button'
+import { apiClient } from '../../../lib/api'
+import { VoiceBundle, Integration, AIProvider, IntegrationPlatform } from '../../../types/api'
+import { getIntegrationPlatformLabel, getIntegrationPlatformLogo, getTelephonyProviderLabel } from '../../../config/providers'
+import { useOrgTelephony } from '../../../hooks/useOrgTelephony'
+import { TelephonyProvider } from '../../../types/api'
+import type { AgentDetailTab } from './AgentInfoView'
+import TestAgentSubTabNav, { type TestAgentSubTab } from './TestAgentSubTabNav'
+import VoiceBundleDetailCard from './VoiceBundleDetailCard'
+import AgentPromptComposer from './AgentPromptComposer'
+import {
+  formatGatewayCredentialLabel,
+  resolveLLMModelsForCredential,
+} from '../../../lib/llmModelOptions'
+import { useAgentPhoneAssignmentCheck } from './useAgentPhoneAssignmentCheck'
+import { formatAgentPhoneConflictMessage } from './agentPhoneValidation'
+import VoiceAgentPicker from './create/VoiceAgentPicker'
+
+interface FormData {
+  name: string
+  phone_number: string
+  language: string
+  description: string
+  prompt_variables: Record<string, string>
+  silence_hangup_secs: number
+  call_type: string
+  call_medium: 'phone_call' | 'web_call'
+  telephony_phone_number_id: string
+  voice_bundle_id: string
+  voice_ai_integration_id: string
+  voice_ai_agent_id: string
+  provider_prompt: string
+}
+
+interface AgentEditFormProps {
+  formData: FormData
+  onChange: (data: FormData) => void
+  onSubmit: (e: React.FormEvent) => void
+  onDelete: () => void
+  voiceBundles: VoiceBundle[]
+  integrations: Integration[]
+  showToast: (message: string, type: 'success' | 'error') => void
+  activeTab: AgentDetailTab
+  onSaveSystemPrompt: () => void
+  agentId?: string
+}
+
+const SUPPORTED_VOICE_AI_PLATFORMS: IntegrationPlatform[] = [
+  IntegrationPlatform.RETELL,
+  IntegrationPlatform.VAPI,
+  IntegrationPlatform.ELEVENLABS,
+  IntegrationPlatform.SMALLEST,
+]
+
+export default function AgentEditForm({
+  formData,
+  onChange,
+  onSubmit,
+  onDelete,
+  voiceBundles,
+  integrations,
+  showToast,
+  activeTab,
+  onSaveSystemPrompt,
+  agentId,
+}: AgentEditFormProps) {
+  const navigate = useNavigate()
+  const [descriptionEditorMode, setDescriptionEditorMode] = useState<'write' | 'preview'>('write')
+  const [providerPromptEditorMode, setProviderPromptEditorMode] = useState<'write' | 'preview'>('write')
+  const [showAIGeneratePanel, setShowAIGeneratePanel] = useState(false)
+  const [includeLinkedScenarios, setIncludeLinkedScenarios] = useState(true)
+  const [aiDescription, setAiDescription] = useState('')
+  const [aiTone, setAiTone] = useState('professional')
+  const [aiFormat, setAiFormat] = useState('structured')
+  const [aiCredentialId, setAiCredentialId] = useState('')
+  const [aiModel, setAiModel] = useState('')
+  const [phoneNumberInputMode, setPhoneNumberInputMode] = useState<'provider' | 'custom'>('provider')
+  const [testAgentSubTab, setTestAgentSubTab] = useState<TestAgentSubTab>('prompt')
+
+  const { data: aiProviders = [] } = useQuery<AIProvider[]>({
+    queryKey: ['ai-providers'],
+    queryFn: () => apiClient.listAIProviders(),
+  })
+
+  const selectedAiProviderRow = aiCredentialId
+    ? aiProviders.find((p) => p.id === aiCredentialId)
+    : undefined
+  const aiProvider = selectedAiProviderRow?.provider ?? ''
+
+  const { data: modelOptions } = useQuery({
+    queryKey: ['model-options', aiProvider],
+    queryFn: () => apiClient.getModelOptions(aiProvider),
+    enabled: !!aiProvider,
+  })
+  const {
+    canUseProviderNumbers,
+    numbersForCallType,
+  } = useOrgTelephony(formData.call_medium === 'phone_call')
+  const telephonyNumbers = numbersForCallType(formData.call_type)
+  const isTelephonyConfigError = !canUseProviderNumbers
+  const { conflict: phoneConflict, isChecking: isCheckingPhoneAssignment, hasConflict: hasPhoneConflict } =
+    useAgentPhoneAssignmentCheck({
+      enabled: formData.call_medium === 'phone_call',
+      callMedium: formData.call_medium,
+      phoneNumber: formData.phone_number,
+      telephonyPhoneNumberId:
+        phoneNumberInputMode === 'provider' ? formData.telephony_phone_number_id : undefined,
+      excludeAgentId: agentId,
+    })
+
+  const handleFormSubmit = (e: React.FormEvent) => {
+    e.preventDefault()
+    if (formData.call_medium === 'phone_call') {
+      if (isCheckingPhoneAssignment) {
+        showToast('Checking phone number availability…', 'error')
+        return
+      }
+      if (hasPhoneConflict && phoneConflict) {
+        showToast(formatAgentPhoneConflictMessage(phoneConflict), 'error')
+        return
+      }
+    }
+    onSubmit(e)
+  }
+
+  const llmModels = modelOptions?.llm || []
+  const modelResolution = selectedAiProviderRow
+    ? resolveLLMModelsForCredential(selectedAiProviderRow, llmModels)
+    : { mode: 'catalog' as const, models: llmModels }
+  const gatewayDirectModel =
+    modelResolution.mode === 'gateway_direct' ? modelResolution.model : null
+  const selectableModels =
+    modelResolution.mode === 'catalog' ? modelResolution.models : []
+
+  useEffect(() => {
+    if (gatewayDirectModel) {
+      if (aiModel) setAiModel('')
+      return
+    }
+    if (aiProvider && selectableModels.length > 0 && !selectableModels.includes(aiModel)) {
+      setAiModel(selectableModels[0])
+    }
+  }, [aiProvider, selectableModels, aiModel, gatewayDirectModel])
+
+  useEffect(() => {
+    if (formData.call_medium !== 'phone_call') {
+      return
+    }
+
+    if (formData.telephony_phone_number_id) {
+      setPhoneNumberInputMode('provider')
+      return
+    }
+
+    if (!canUseProviderNumbers || telephonyNumbers.length === 0) {
+      setPhoneNumberInputMode('custom')
+      return
+    }
+
+    const selectedExists = telephonyNumbers.some((n) => n.id === formData.telephony_phone_number_id)
+    if (!selectedExists && phoneNumberInputMode === 'provider') {
+      onChange({ ...formData, telephony_phone_number_id: '', phone_number: '' })
+    }
+  }, [
+    formData,
+    onChange,
+    phoneNumberInputMode,
+    canUseProviderNumbers,
+    telephonyNumbers,
+  ])
+
+  const generateDescriptionMutation = useMutation({
+    mutationFn: (data: {
+      description: string
+      tone?: string
+      format_style?: string
+      provider?: string
+      model?: string
+      agent_id?: string
+      include_linked_scenarios?: boolean
+      append_scenarios_to_output?: boolean
+    }) => apiClient.generateAgentDescription(data),
+    onSuccess: (data) => {
+      onChange({ ...formData, description: data.content })
+      setShowAIGeneratePanel(false)
+      setAiDescription('')
+      setDescriptionEditorMode('preview')
+      showToast('Description generated successfully!', 'success')
+    },
+    onError: (err: any) => {
+      showToast(err?.response?.data?.detail || 'Failed to generate description with AI', 'error')
+    },
+  })
+
+  const voiceAgentIntegrations = integrations.filter(
+    (integration) =>
+      integration.is_active &&
+      SUPPORTED_VOICE_AI_PLATFORMS.includes(integration.platform as IntegrationPlatform),
+  )
+
+  const selectedVoiceIntegration = voiceAgentIntegrations.find(
+    (integration) => integration.id === formData.voice_ai_integration_id,
+  )
+
+  const linkedVoiceBundle = formData.voice_bundle_id
+    ? voiceBundles.find((vb) => vb.id === formData.voice_bundle_id)
+    : undefined
+
+  const hasPlatformLink = Boolean(formData.voice_ai_integration_id || formData.voice_ai_agent_id)
+
+  const productionPromptProse =
+    'prose prose-sm max-w-none prose-headings:text-gray-900 prose-p:text-gray-700 prose-code:text-gray-800 prose-code:bg-gray-100 prose-code:px-1 prose-code:py-0.5 prose-code:rounded prose-pre:bg-gray-900 prose-pre:text-gray-100 prose-ul:text-gray-700 prose-ol:text-gray-700'
+
+  return (
+    <form onSubmit={handleFormSubmit}>
+      {activeTab === 'overview' && (
+        <div className="space-y-5 max-w-3xl">
+          <OverviewSection title="Agent profile" description="Name and language shown to evaluators and logs.">
+            <div className="space-y-4">
+              <div>
+                <label htmlFor="agent-name" className="block text-sm font-medium text-gray-700 mb-1.5">
+                  Name *
+                </label>
+                <input
+                  id="agent-name"
+                  type="text"
+                  required
+                  value={formData.name}
+                  onChange={(e) => onChange({ ...formData, name: e.target.value })}
+                  className="w-full px-3 py-2.5 border border-gray-200 rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400"
+                  placeholder="Customer Support Bot"
+                />
+              </div>
+
+              <div>
+                <label htmlFor="agent-language" className="block text-sm font-medium text-gray-700 mb-1.5">
+                  Language
+                </label>
+                <select
+                  id="agent-language"
+                  value={formData.language}
+                  onChange={(e) => onChange({ ...formData, language: e.target.value })}
+                  className="w-full px-3 py-2.5 border border-gray-200 rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400"
+                >
+                  <option value="en">English</option>
+                  <option value="es">Spanish</option>
+                  <option value="fr">French</option>
+                  <option value="de">German</option>
+                  <option value="zh">Chinese</option>
+                  <option value="hi">Hindi</option>
+                </select>
+              </div>
+            </div>
+          </OverviewSection>
+
+          <OverviewSection title="Call setup" description="How sessions are placed and routed.">
+            <div className="space-y-5">
+              <div>
+                <span className="block text-sm font-medium text-gray-700 mb-2">Call medium *</span>
+                <div className="inline-flex rounded-xl border border-gray-200 bg-gray-50/80 p-1 shadow-sm">
+                  {(['web_call', 'phone_call'] as const).map((medium) => (
+                    <button
+                      key={medium}
+                      type="button"
+                      onClick={() =>
+                        onChange({
+                          ...formData,
+                          call_medium: medium,
+                          phone_number: medium === 'web_call' ? '' : formData.phone_number,
+                        })
+                      }
+                      className={`rounded-lg px-4 py-2 text-sm font-medium transition-all focus:outline-none focus-visible:ring-2 focus-visible:ring-primary-500 ${
+                        formData.call_medium === medium
+                          ? 'bg-white text-gray-900 shadow-sm ring-1 ring-gray-200/80'
+                          : 'text-gray-600 hover:text-gray-900'
+                      }`}
+                    >
+                      {medium === 'web_call' ? 'Web call' : 'Phone call'}
+                    </button>
+                  ))}
+                </div>
+              </div>
+
+              <div>
+                <span className="block text-sm font-medium text-gray-700 mb-2">Call type</span>
+                <div className="inline-flex rounded-xl border border-gray-200 bg-gray-50/80 p-1 shadow-sm">
+                  {(['outbound', 'inbound'] as const).map((type) => (
+                    <button
+                      key={type}
+                      type="button"
+                      onClick={() => {
+                        const nextType = type
+                        const nextNumbers = numbersForCallType(nextType)
+                        const stillValid = nextNumbers.some((n) => n.id === formData.telephony_phone_number_id)
+                        onChange({
+                          ...formData,
+                          call_type: nextType,
+                          ...(stillValid
+                            ? {}
+                            : { telephony_phone_number_id: '', phone_number: '' }),
+                        })
+                      }}
+                      className={`inline-flex items-center gap-1.5 rounded-lg px-4 py-2 text-sm font-medium transition-all focus:outline-none focus-visible:ring-2 focus-visible:ring-primary-500 ${
+                        formData.call_type === type
+                          ? 'bg-white text-gray-900 shadow-sm ring-1 ring-gray-200/80'
+                          : 'text-gray-600 hover:text-gray-900'
+                      }`}
+                    >
+                      {type === 'outbound' ? (
+                        <PhoneOutgoing className="h-3.5 w-3.5" />
+                      ) : (
+                        <PhoneIncoming className="h-3.5 w-3.5" />
+                      )}
+                      {type === 'outbound' ? 'Outbound' : 'Inbound'}
+                    </button>
+                  ))}
+                </div>
+              </div>
+
+              {formData.call_medium === 'phone_call' && (
+                <div className="space-y-3 rounded-lg border border-gray-100 bg-gray-50/50 p-4">
+                  <div className="flex flex-wrap items-center justify-between gap-2">
+                    <label className="text-sm font-medium text-gray-700">Phone number *</label>
+                    <div className="inline-flex rounded-lg border border-gray-200 bg-white p-0.5 text-xs">
+                      <button
+                        type="button"
+                        onClick={() => {
+                          setPhoneNumberInputMode('provider')
+                          onChange({ ...formData, phone_number: '' })
+                        }}
+                        disabled={!canUseProviderNumbers || telephonyNumbers.length === 0}
+                        className={`rounded-md px-2.5 py-1 font-medium ${
+                          phoneNumberInputMode === 'provider'
+                            ? 'bg-primary-600 text-white'
+                            : 'text-gray-600 hover:bg-gray-50'
+                        } disabled:opacity-40`}
+                      >
+                        From provider
+                      </button>
+                      <button
+                        type="button"
+                        onClick={() => {
+                          setPhoneNumberInputMode('custom')
+                          onChange({ ...formData, telephony_phone_number_id: '' })
+                        }}
+                        className={`rounded-md px-2.5 py-1 font-medium ${
+                          phoneNumberInputMode === 'custom'
+                            ? 'bg-primary-600 text-white'
+                            : 'text-gray-600 hover:bg-gray-50'
+                        }`}
+                      >
+                        Custom
+                      </button>
+                    </div>
+                  </div>
+
+                  {isTelephonyConfigError && (
+                    <p className="text-xs text-amber-800 bg-amber-50 border border-amber-200/80 rounded-lg px-3 py-2">
+                      No synced telephony numbers found yet. Import numbers on the Telephony Numbers page,
+                      configure a provider in Integrations, or enter a custom number below.
+                    </p>
+                  )}
+
+                  {phoneNumberInputMode === 'provider' ? (
+                    <select
+                      required
+                      value={formData.telephony_phone_number_id}
+                      onChange={(e) => {
+                        const selected = telephonyNumbers.find((n) => n.id === e.target.value)
+                        onChange({
+                          ...formData,
+                          telephony_phone_number_id: e.target.value,
+                          phone_number: selected?.phone_number || '',
+                        })
+                      }}
+                      className="w-full px-3 py-2.5 border border-gray-200 rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400"
+                      disabled={!canUseProviderNumbers || telephonyNumbers.length === 0}
+                    >
+                      <option value="">Select a synced telephony number</option>
+                      {telephonyNumbers.map((number) => (
+                        <option
+                          key={number.id}
+                          value={number.id}
+                          disabled={!!number.agent_id && number.id !== formData.telephony_phone_number_id}
+                        >
+                          {number.phone_number}
+                          {number.provider
+                            ? ` [${getTelephonyProviderLabel(number.provider as TelephonyProvider)}]`
+                            : ''}
+                          {number.region ? ` - ${number.region}` : ''}
+                          {number.country_iso2 ? ` (${number.country_iso2})` : ''}
+                          {number.agent_id && number.id !== formData.telephony_phone_number_id
+                            ? ` [Assigned to ${number.linked_agent_name || 'another agent'}]`
+                            : ''}
+                        </option>
+                      ))}
+                    </select>
+                  ) : (
+                    <input
+                      type="text"
+                      required
+                      value={formData.phone_number}
+                      onChange={(e) =>
+                        onChange({
+                          ...formData,
+                          phone_number: e.target.value.replace(/[^\d+]/g, ''),
+                        })
+                      }
+                      className={`w-full px-3 py-2.5 border rounded-lg bg-white shadow-sm focus:ring-2 focus:ring-primary-500/30 focus:border-primary-400 ${
+                        hasPhoneConflict ? 'border-red-300' : 'border-gray-200'
+                      }`}
+                      placeholder="+1234567890"
+                    />
+                  )}
+                  {formData.call_medium === 'phone_call' && hasPhoneConflict && phoneConflict && (
+                    <p className="text-xs text-red-600 mt-1">
+                      {formatAgentPhoneConflictMessage(phoneConflict)}
+                    </p>
+                  )}
+                  {formData.call_medium === 'phone_call' && isCheckingPhoneAssignment && (
+                    <p className="text-xs text-gray-500 mt-1">Checking number availability…</p>
+                  )}
+                </div>
+              )}
+            </div>
+          </OverviewSection>
+
+          <OverviewSection title="Live session" description="Hang up when neither side speaks for too long.">
+            <ParamSlider
+              label="End call after silence"
+              helpText={`${formatSilenceHangupLabel(formData.silence_hangup_secs)} · Resets on voice activity. Default 15. Set to 0 to disable.`}
+              min={0}
+              max={600}
+              step={1}
+              integer
+              value={formData.silence_hangup_secs}
+              onChange={(next) =>
+                onChange({
+                  ...formData,
+                  silence_hangup_secs: next ?? 0,
+                })
+              }
+            />
+          </OverviewSection>
+
+          <div className="flex gap-3 pt-2">
+            <Button
+              type="button"
+              variant="outline"
+              onClick={onDelete}
+              leftIcon={<Trash2 className="w-4 h-4" />}
+              className="border-red-200 text-red-700 hover:bg-red-50 hover:border-red-300"
+            >
+              Delete agent
+            </Button>
+          </div>
+        </div>
+      )}
+
+      {activeTab === 'test_agent' && (
+        <div className="max-w-4xl space-y-4">
+          <TestAgentSubTabNav value={testAgentSubTab} onChange={setTestAgentSubTab} />
+
+          {testAgentSubTab === 'configuration' && (
+            <div className="space-y-4">
+              <div>
+                <label className="block text-sm font-medium text-gray-700 mb-2">Voice Bundle</label>
+                <select
+                  value={formData.voice_bundle_id}
+                  onChange={(e) => onChange({ ...formData, voice_bundle_id: e.target.value })}
+                  className="w-full px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent bg-white"
+                >
+                  <option value="">Select a Voice Bundle</option>
+                  {voiceBundles
+                    .filter((vb) => vb.is_active)
+                    .map((vb) => (
+                      <option key={vb.id} value={vb.id}>
+                        {vb.name}
+                      </option>
+                    ))}
+                </select>
+                {voiceBundles.filter((vb) => vb.is_active).length === 0 && (
+                  <p className="mt-1 text-xs text-gray-500">
+                    No active voice bundles available. Create one in Voice Bundles.
+                  </p>
+                )}
+              </div>
+              {linkedVoiceBundle && (
+                <VoiceBundleDetailCard
+                  bundle={linkedVoiceBundle}
+                  allowParamTuning
+                  onEdit={() => {
+                    const returnPath = agentId ? `/agents/${agentId}?tab=test_agent` : '/agents'
+                    navigate(
+                      `/voicebundles?edit=${linkedVoiceBundle.id}&return=${encodeURIComponent(returnPath)}`
+                    )
+                  }}
+                  onManageInVoiceBundles={() => navigate('/voicebundles')}
+                />
+              )}
+            </div>
+          )}
+
+          {testAgentSubTab === 'prompt' && (
+            <div className="border border-gray-200 rounded-lg p-4 bg-gray-50">
+              <div className="flex items-center justify-between mb-3">
+                <label className="block text-sm font-medium text-gray-700">EfficientAI Test Agent Prompt</label>
+                <div className="flex items-center gap-2">
+                  <button
+                    type="button"
+                    onClick={() => setShowAIGeneratePanel(!showAIGeneratePanel)}
+                    disabled={generateDescriptionMutation.isPending}
+                    className={`inline-flex items-center gap-1.5 px-3 py-1 text-xs font-medium rounded-lg border transition-colors ${
+                      showAIGeneratePanel
+                        ? 'bg-amber-100 text-amber-800 border-amber-300'
+                        : 'bg-amber-50 text-amber-700 border-amber-200 hover:bg-amber-100'
+                    }`}
+                  >
+                    {generateDescriptionMutation.isPending ? (
+                      <Loader2 className="h-3 w-3 animate-spin" />
+                    ) : (
+                      <Sparkles className="h-3 w-3" />
+                    )}
+                    {generateDescriptionMutation.isPending ? 'Generating...' : 'AI Generate'}
+                  </button>
+                  <button
+                    type="button"
+                    onClick={onSaveSystemPrompt}
+                    disabled={!formData.description?.trim()}
+                    className="inline-flex items-center gap-1.5 px-3 py-1 text-xs font-medium rounded-lg border border-emerald-200 bg-emerald-50 text-emerald-700 hover:bg-emerald-100 disabled:opacity-50"
+                  >
+                    <Save className="h-3 w-3" />
+                    Save Prompt
+                  </button>
+                  <div className="flex items-center bg-gray-100 rounded-lg p-0.5">
+                    <button
+                      type="button"
+                      onClick={() => setDescriptionEditorMode('write')}
+                      className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
+                        descriptionEditorMode === 'write'
+                          ? 'bg-white text-gray-900 shadow-sm'
+                          : 'text-gray-500 hover:text-gray-700'
+                      }`}
+                    >
+                      <Code className="h-3 w-3" />
+                      Write
+                    </button>
+                    <button
+                      type="button"
+                      onClick={() => setDescriptionEditorMode('preview')}
+                      className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
+                        descriptionEditorMode === 'preview'
+                          ? 'bg-white text-gray-900 shadow-sm'
+                          : 'text-gray-500 hover:text-gray-700'
+                      }`}
+                    >
+                      <Eye className="h-3 w-3" />
+                      Preview
+                    </button>
+                  </div>
+                </div>
+              </div>
+
+              {/* AI Generate Panel */}
+              {showAIGeneratePanel && (
+                <div className="mb-3 p-3 bg-amber-50 rounded-lg border border-amber-200">
+                  <div className="flex items-center gap-2 mb-2">
+                    <Sparkles className="h-4 w-4 text-amber-600" />
+                    <span className="text-sm font-medium text-amber-900">Generate Description with AI</span>
+                  </div>
+                  <p className="text-xs text-amber-700 mb-3">
+                    Describe what this agent should do and AI will generate a rich markdown description.
+                  </p>
+                  <textarea
+                    value={aiDescription}
+                    onChange={(e) => setAiDescription(e.target.value)}
+                    placeholder="e.g., A customer support agent that handles refund requests, tracks orders, and escalates complex issues..."
+                    rows={3}
+                    className="w-full px-3 py-2 text-sm border border-amber-200 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white mb-2"
+                  />
+                  {agentId ? (
+                    <label className="flex items-center gap-2 text-xs text-gray-700 mb-2">
+                      <input
+                        type="checkbox"
+                        checked={includeLinkedScenarios}
+                        onChange={(e) => setIncludeLinkedScenarios(e.target.checked)}
+                        className="rounded border-gray-300 text-amber-600 focus:ring-amber-500"
+                      />
+                      Include linked scenarios as AI context (does not append to prompt)
+                    </label>
+                  ) : null}
+                  <div className="grid grid-cols-2 gap-3 mb-2">
+                    <div>
+                      <label className="block text-xs font-medium text-gray-600 mb-1">Tone</label>
+                      <select
+                        value={aiTone}
+                        onChange={(e) => setAiTone(e.target.value)}
+                        className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white"
+                      >
+                        <option value="professional">Professional</option>
+                        <option value="casual">Casual / Friendly</option>
+                        <option value="technical">Technical</option>
+                        <option value="concise">Concise / Direct</option>
+                        <option value="detailed">Detailed / Thorough</option>
+                      </select>
+                    </div>
+                    <div>
+                      <label className="block text-xs font-medium text-gray-600 mb-1">Format Style</label>
+                      <select
+                        value={aiFormat}
+                        onChange={(e) => setAiFormat(e.target.value)}
+                        className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white"
+                      >
+                        <option value="structured">Structured (sections & bullet points)</option>
+                        <option value="narrative">Narrative (flowing text)</option>
+                        <option value="template">Template (with placeholders)</option>
+                        <option value="step-by-step">Step-by-step Instructions</option>
+                      </select>
+                    </div>
+                  </div>
+                  <div className="flex gap-3 mb-2">
+                    <div className="flex-1">
+                      <label className="block text-xs font-medium text-gray-600 mb-1">
+                        <Bot className="w-3 h-3 inline mr-1" />
+                        LLM Provider
+                      </label>
+                      <select
+                        value={aiCredentialId}
+                        onChange={(e) => {
+                          setAiCredentialId(e.target.value)
+                          setAiModel('')
+                        }}
+                        className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white"
+                      >
+                        <option value="">Auto-detect (use first available)</option>
+                        {aiProviders
+                          .filter((p) => p.is_active)
+                          .map((p) => (
+                            <option key={p.id} value={p.id}>
+                              {formatGatewayCredentialLabel(p, {
+                                custom: 'Custom',
+                                openai: 'OpenAI',
+                                anthropic: 'Anthropic',
+                                google: 'Google',
+                              })}
+                            </option>
+                          ))}
+                      </select>
+                    </div>
+                    <div className="flex-1">
+                      <label className="block text-xs font-medium text-gray-600 mb-1">Model</label>
+                      {gatewayDirectModel ? (
+                        <div
+                          className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg bg-gray-50 text-gray-700 truncate"
+                          title={gatewayDirectModel}
+                        >
+                          {gatewayDirectModel}
+                        </div>
+                      ) : (
+                        <select
+                          value={aiModel}
+                          onChange={(e) => setAiModel(e.target.value)}
+                          disabled={!aiCredentialId}
+                          className="w-full px-3 py-1.5 text-sm border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-500 bg-white disabled:bg-gray-50 disabled:text-gray-400"
+                        >
+                          {!aiCredentialId ? (
+                            <option value="">Select a provider first</option>
+                          ) : selectableModels.length === 0 ? (
+                            <option value="">Loading models...</option>
+                          ) : (
+                            selectableModels.map((m: string) => (
+                              <option key={m} value={m}>
+                                {m}
+                              </option>
+                            ))
+                          )}
+                        </select>
+                      )}
+                    </div>
+                  </div>
+                  <div className="flex items-center gap-2 justify-end">
+                    <button
+                      type="button"
+                      onClick={() => setShowAIGeneratePanel(false)}
+                      className="px-3 py-1.5 text-xs font-medium text-gray-600 hover:text-gray-800"
+                    >
+                      Cancel
+                    </button>
+                    <button
+                      type="button"
+                      onClick={() =>
+                        generateDescriptionMutation.mutate({
+                          description: aiDescription,
+                          tone: aiTone,
+                          format_style: aiFormat,
+                          ...(aiProvider ? { provider: aiProvider } : {}),
+                          ...(aiModel ? { model: aiModel } : {}),
+                          ...(agentId
+                            ? {
+                                agent_id: agentId,
+                                include_linked_scenarios: includeLinkedScenarios,
+                                append_scenarios_to_output: false,
+                              }
+                            : {}),
+                        })
+                      }
+                      disabled={generateDescriptionMutation.isPending || !aiDescription.trim()}
+                      className="inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium bg-amber-600 text-white rounded-lg hover:bg-amber-700 disabled:opacity-50"
+                    >
+                      {generateDescriptionMutation.isPending ? (
+                        <>
+                          <Loader2 className="h-3 w-3 animate-spin" /> Generating...
+                        </>
+                      ) : (
+                        <>
+                          <Sparkles className="h-3 w-3" /> Generate
+                        </>
+                      )}
+                    </button>
+                  </div>
+                </div>
+              )}
+
+              <div className="mb-4 rounded-lg border border-gray-200 bg-white p-3">
+                <div className="flex items-center justify-between gap-2 mb-2">
+                  <label className="text-sm font-medium text-gray-700">Custom prompt variables</label>
+                  <button
+                    type="button"
+                    className="text-xs font-medium text-primary-600 hover:text-primary-800"
+                    onClick={() => {
+                      const base = { ...(formData.prompt_variables || {}) }
+                      let n = 1
+                      let key = 'custom_var'
+                      while (base[key]) {
+                        n += 1
+                        key = `custom_var_${n}`
+                      }
+                      base[key] = ''
+                      onChange({ ...formData, prompt_variables: base })
+                    }}
+                  >
+                    + Add variable
+                  </button>
+                </div>
+                <p className="text-xs text-gray-500 mb-2">
+                  Define keys you can insert with <code className="text-gray-700">{'{'}</code> or{' '}
+                  <code className="text-gray-700">@</code>. Values are optional descriptions for your team.
+                </p>
+                {Object.keys(formData.prompt_variables || {}).length === 0 ? (
+                  <p className="text-xs text-gray-400 italic">No custom variables yet.</p>
+                ) : (
+                  <div className="space-y-2">
+                    {Object.entries(formData.prompt_variables || {}).map(([key, desc]) => (
+                      <div key={key} className="flex flex-wrap items-center gap-2">
+                        <input
+                          type="text"
+                          value={key}
+                          onChange={(e) => {
+                            const nextKey = e.target.value.replace(/\s+/g, '_')
+                            const vars = { ...(formData.prompt_variables || {}) }
+                            delete vars[key]
+                            if (nextKey) vars[nextKey] = desc
+                            onChange({ ...formData, prompt_variables: vars })
+                          }}
+                          className="w-36 px-2 py-1.5 text-xs font-mono border border-gray-300 rounded-md"
+                          placeholder="variable_key"
+                        />
+                        <input
+                          type="text"
+                          value={desc}
+                          onChange={(e) =>
+                            onChange({
+                              ...formData,
+                              prompt_variables: {
+                                ...(formData.prompt_variables || {}),
+                                [key]: e.target.value,
+                              },
+                            })
+                          }
+                          className="flex-1 min-w-[120px] px-2 py-1.5 text-xs border border-gray-300 rounded-md"
+                          placeholder="Description (optional)"
+                        />
+                        <button
+                          type="button"
+                          className="text-xs text-red-600 hover:text-red-800"
+                          onClick={() => {
+                            const vars = { ...(formData.prompt_variables || {}) }
+                            delete vars[key]
+                            onChange({ ...formData, prompt_variables: vars })
+                          }}
+                        >
+                          Remove
+                        </button>
+                      </div>
+                    ))}
+                  </div>
+                )}
+              </div>
+
+              {descriptionEditorMode === 'write' ? (
+                <AgentPromptComposer
+                  value={formData.description}
+                  onChange={(description) => onChange({ ...formData, description })}
+                  customVariables={formData.prompt_variables}
+                />
+              ) : (
+                <div className="min-h-[380px] max-h-[70vh] overflow-y-auto border border-gray-300 rounded-lg p-4 prose prose-sm max-w-none prose-headings:text-gray-900 prose-p:text-gray-700 prose-code:text-gray-800 prose-code:bg-gray-100 prose-code:px-1 prose-code:py-0.5 prose-code:rounded prose-pre:bg-gray-900 prose-pre:text-gray-100">
+                  {formData.description ? (
+                    <ReactMarkdown>{formData.description}</ReactMarkdown>
+                  ) : (
+                    <p className="text-gray-400 italic">Nothing to preview yet...</p>
+                  )}
+                </div>
+              )}
+            </div>
+          )}
+        </div>
+      )}
+
+      {activeTab === 'voice_ai_agent' && hasPlatformLink && (
+        <div className="max-w-2xl space-y-4">
+          <div>
+            <label className="block text-sm font-medium text-gray-700 mb-2">Integration Provider</label>
+            <div className="flex items-center gap-3">
+              <select
+                value={formData.voice_ai_integration_id}
+                onChange={(e) =>
+                  onChange({
+                    ...formData,
+                    voice_ai_integration_id: e.target.value,
+                    voice_ai_agent_id: '',
+                  })
+                }
+                className="w-full px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent bg-white"
+              >
+                <option value="">Select an Integration</option>
+                {voiceAgentIntegrations.map((integration) => {
+                  const platformLabel = getIntegrationPlatformLabel(
+                    integration.platform as IntegrationPlatform,
+                  )
+                  return (
+                    <option key={integration.id} value={integration.id}>
+                      {integration.name || platformLabel} ({platformLabel})
+                    </option>
+                  )
+                })}
+              </select>
+              {selectedVoiceIntegration && (
+                <div className="flex-shrink-0">
+                  {(() => {
+                    const logo = getIntegrationPlatformLogo(
+                      selectedVoiceIntegration.platform as IntegrationPlatform,
+                    )
+                    const label = getIntegrationPlatformLabel(
+                      selectedVoiceIntegration.platform as IntegrationPlatform,
+                    )
+                    return logo ? (
+                      <img src={logo} alt={label} className="h-8 w-8 rounded-full object-contain" />
+                    ) : null
+                  })()}
+                </div>
+              )}
+            </div>
+          </div>
+          {formData.voice_ai_integration_id ? (
+            <VoiceAgentPicker
+              integrationId={formData.voice_ai_integration_id}
+              platformLabel={
+                selectedVoiceIntegration
+                  ? getIntegrationPlatformLabel(
+                      selectedVoiceIntegration.platform as IntegrationPlatform,
+                    )
+                  : 'provider'
+              }
+              value={formData.voice_ai_agent_id}
+              onChange={(agentId) => onChange({ ...formData, voice_ai_agent_id: agentId })}
+            />
+          ) : (
+            <p className="text-sm text-gray-500">Select an integration to choose an agent.</p>
+          )}
+        </div>
+      )}
+
+      {activeTab === 'voice_ai_agent' && !hasPlatformLink && (
+        <div className="border border-gray-200 rounded-lg p-4 bg-gray-50">
+          <div className="flex items-center justify-between mb-3">
+            <label className="block text-sm font-medium text-gray-700">Production Agent Prompt</label>
+            <div className="flex items-center bg-gray-100 rounded-lg p-0.5">
+              <button
+                type="button"
+                onClick={() => setProviderPromptEditorMode('write')}
+                className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
+                  providerPromptEditorMode === 'write'
+                    ? 'bg-white text-gray-900 shadow-sm'
+                    : 'text-gray-500 hover:text-gray-700'
+                }`}
+              >
+                <Code className="h-3 w-3" />
+                Write
+              </button>
+              <button
+                type="button"
+                onClick={() => setProviderPromptEditorMode('preview')}
+                className={`inline-flex items-center gap-1 px-3 py-1 text-xs font-medium rounded-md transition-colors ${
+                  providerPromptEditorMode === 'preview'
+                    ? 'bg-white text-gray-900 shadow-sm'
+                    : 'text-gray-500 hover:text-gray-700'
+                }`}
+              >
+                <Eye className="h-3 w-3" />
+                Preview
+              </button>
+            </div>
+          </div>
+          {providerPromptEditorMode === 'write' ? (
+            <textarea
+              value={formData.provider_prompt}
+              onChange={(e) => onChange({ ...formData, provider_prompt: e.target.value })}
+              className="w-full min-h-[380px] px-3 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-primary-500 focus:border-transparent font-mono text-sm resize-y"
+              rows={16}
+              placeholder="Paste or edit the production system prompt…"
+            />
+          ) : (
+            <div className="min-h-[380px] max-h-[70vh] overflow-y-auto border border-gray-300 rounded-lg p-4 bg-white">
+              {formData.provider_prompt?.trim() ? (
+                <div className={productionPromptProse}>
+                  <ReactMarkdown>{formData.provider_prompt}</ReactMarkdown>
+                </div>
+              ) : (
+                <p className="text-gray-400 italic">Nothing to preview yet…</p>
+              )}
+            </div>
+          )}
+        </div>
+      )}
+    </form>
+  )
+}
diff --git a/frontend/src/pages/agents/components/CreateAgentModal.tsx b/frontend/src/pages/agents/components/CreateAgentModal.tsx
index 9c558a52..777994a7 100644
--- a/frontend/src/pages/agents/components/CreateAgentModal.tsx
+++ b/frontend/src/pages/agents/components/CreateAgentModal.tsx
@@ -1,569 +1,573 @@
-import { useState, useEffect } from 'react'
-import { createPortal } from 'react-dom'
-import { useQuery, useMutation } from '@tanstack/react-query'
-import { X } from 'lucide-react'
-import Button from '../../../components/Button'
-import { apiClient } from '../../../lib/api'
-import { AIProvider, VoiceBundle, Integration, IntegrationPlatform } from '../../../types/api'
-import { resolveLLMModelsForCredential } from '../../../lib/llmModelOptions'
-import { useAgentPhoneAssignmentCheck } from './useAgentPhoneAssignmentCheck'
-import { extractPhoneConflictDetail } from './agentPhoneValidation'
-import CreateAgentPathSelector from './create/CreateAgentPathSelector'
-import TelephonyBasicsStep, { validateTelephonyBasics } from './create/TelephonyBasicsStep'
-import PlatformConnectStep, { isPlatformConnectValid } from './create/PlatformConnectStep'
-import VoiceBundleStep from './create/VoiceBundleStep'
-import ProductionPromptStep, { isPromptStepValid } from './create/ProductionPromptStep'
-import {
-  type CreateAgentPath,
-  type CreateAgentFormData,
-  type CreateStepId,
-  DEFAULT_CREATE_AGENT_FORM,
-  TELEPHONY_STEPS,
-  PLATFORM_STEPS,
-} from './create/createAgentTypes'
-
-interface CreateAgentModalProps {
-  isOpen: boolean
-  onClose: () => void
-  onSuccess: () => void
-  showToast: (message: string, type: 'success' | 'error') => void
-}
-
-export default function CreateAgentModal({
-  isOpen,
-  onClose,
-  onSuccess,
-  showToast,
-}: CreateAgentModalProps) {
-  const [createPath, setCreatePath] = useState<CreateAgentPath>('telephony')
-  const [currentStep, setCurrentStep] = useState<CreateStepId>(1)
-  const [formData, setFormData] = useState<CreateAgentFormData>(DEFAULT_CREATE_AGENT_FORM)
-  const [productionPrompt, setProductionPrompt] = useState('')
-  const [setupAdditionalContext, setSetupAdditionalContext] = useState('')
-  const [phoneNumberInputMode, setPhoneNumberInputMode] = useState<'provider' | 'custom'>('provider')
-  const [selectedPlatform, setSelectedPlatform] = useState<IntegrationPlatform | null>(null)
-  const [aiCredentialId, setAiCredentialId] = useState('')
-  const [aiModel, setAiModel] = useState('')
-  const [promptFetchError, setPromptFetchError] = useState<string | null>(null)
-  const [hasFetchedPlatformPrompt, setHasFetchedPlatformPrompt] = useState(false)
-
-  const steps = createPath === 'telephony' ? TELEPHONY_STEPS : PLATFORM_STEPS
-
-  const { data: voiceBundles = [] } = useQuery<VoiceBundle[]>({
-    queryKey: ['voicebundles'],
-    queryFn: () => apiClient.listVoiceBundles(),
-  })
-
-  const { data: integrations = [] } = useQuery<Integration[]>({
-    queryKey: ['integrations'],
-    queryFn: () => apiClient.listIntegrations(),
-  })
-
-  const { data: aiProviders = [] } = useQuery<AIProvider[]>({
-    queryKey: ['ai-providers'],
-    queryFn: () => apiClient.listAIProviders(),
-  })
-
-  const { isChecking: isCheckingPhoneAssignment, hasConflict: hasPhoneConflict } =
-    useAgentPhoneAssignmentCheck({
-      enabled: isOpen && createPath === 'telephony',
-      callMedium: 'phone_call',
-      phoneNumber: formData.phone_number,
-      telephonyPhoneNumberId:
-        phoneNumberInputMode === 'provider' ? formData.telephony_phone_number_id : undefined,
-    })
-
-  const selectedAiProviderRow = aiCredentialId
-    ? aiProviders.find((p) => p.id === aiCredentialId)
-    : undefined
-  const aiProvider = selectedAiProviderRow?.provider ?? ''
-
-  const { data: modelOptions } = useQuery({
-    queryKey: ['model-options', aiProvider],
-    queryFn: () => apiClient.getModelOptions(aiProvider),
-    enabled: !!aiProvider,
-  })
-
-  const llmModels = modelOptions?.llm || []
-  const modelResolution = selectedAiProviderRow
-    ? resolveLLMModelsForCredential(selectedAiProviderRow, llmModels)
-    : { mode: 'catalog' as const, models: llmModels }
-  const gatewayDirectModel =
-    modelResolution.mode === 'gateway_direct' ? modelResolution.model : null
-  const selectableModels =
-    modelResolution.mode === 'catalog' ? modelResolution.models : []
-
-  useEffect(() => {
-    if (gatewayDirectModel) {
-      if (aiModel) setAiModel('')
-      return
-    }
-    if (aiProvider && selectableModels.length > 0 && !selectableModels.includes(aiModel)) {
-      setAiModel(selectableModels[0])
-    }
-  }, [aiProvider, selectableModels, aiModel, gatewayDirectModel])
-
-  useEffect(() => {
-    if (!isOpen) return
-    const prev = document.body.style.overflow
-    document.body.style.overflow = 'hidden'
-    return () => {
-      document.body.style.overflow = prev
-    }
-  }, [isOpen])
-
-  const buildGenerationParams = () => ({
-    ...(aiProvider ? { provider: aiProvider } : {}),
-    ...(aiCredentialId ? { credential_id: aiCredentialId } : {}),
-    ...(aiModel ? { model: aiModel } : {}),
-  })
-
-  const generateTestPromptMutation = useMutation({
-    mutationFn: () => {
-      if (!formData.name.trim()) {
-        throw new Error('Agent name is required before generating a test prompt')
-      }
-      if (!productionPrompt.trim()) {
-        throw new Error('Production prompt is required')
-      }
-      return apiClient.generateTestPromptFromProduction({
-        production_prompt: productionPrompt,
-        agent_name: formData.name.trim(),
-        language: formData.language,
-        call_type: formData.call_type,
-        additional_context: setupAdditionalContext.trim() || undefined,
-        ...buildGenerationParams(),
-      })
-    },
-    onSuccess: (data) => {
-      setFormData((prev) => ({ ...prev, description: data.test_agent_prompt }))
-      showToast('Test agent prompt generated from production prompt', 'success')
-    },
-    onError: (err: any) => {
-      showToast(err?.message || err?.response?.data?.detail || 'Failed to generate test prompt', 'error')
-    },
-  })
-
-  const fetchPlatformPromptMutation = useMutation({
-    mutationFn: () => {
-      if (!formData.voice_ai_integration_id || !formData.voice_ai_agent_id) {
-        throw new Error('Integration and Agent ID are required')
-      }
-      return apiClient.previewIntegrationAgentPrompt(
-        formData.voice_ai_integration_id,
-        formData.voice_ai_agent_id.trim(),
-      )
-    },
-    onSuccess: (data) => {
-      setProductionPrompt(data.provider_prompt)
-      setPromptFetchError(null)
-      setHasFetchedPlatformPrompt(true)
-    },
-    onError: (err: any) => {
-      const message = err?.response?.data?.detail || err?.message || 'Failed to fetch production prompt'
-      setPromptFetchError(typeof message === 'string' ? message : 'Failed to fetch production prompt')
-      setProductionPrompt('')
-    },
-  })
-
-  const createMutation = useMutation({
-    mutationFn: async (data: CreateAgentFormData) => {
-      const payload: Record<string, unknown> = {
-        name: data.name,
-        language: data.language,
-        description: data.description || null,
-        call_type: data.call_type,
-        call_medium: createPath === 'platform' ? 'web_call' : 'phone_call',
-        voice_bundle_id: data.voice_bundle_id.trim(),
-        silence_hangup_secs: data.silence_hangup_secs ?? 15,
-      }
-
-      if (createPath === 'telephony') {
-        if (data.phone_number) payload.phone_number = data.phone_number
-        if (data.telephony_phone_number_id) {
-          payload.telephony_phone_number_id = data.telephony_phone_number_id
-        }
-        if (productionPrompt.trim()) {
-          payload.provider_prompt = productionPrompt.trim()
-        }
-      }
-
-      if (createPath === 'platform') {
-        payload.voice_ai_integration_id = data.voice_ai_integration_id.trim()
-        payload.voice_ai_agent_id = data.voice_ai_agent_id.trim()
-        if (productionPrompt.trim()) {
-          payload.provider_prompt = productionPrompt.trim()
-        }
-      }
-
-      return apiClient.createAgent(payload as Parameters<typeof apiClient.createAgent>[0])
-    },
-    onSuccess: () => {
-      onSuccess()
-      resetForm()
-      showToast('Agent created successfully!', 'success')
-    },
-    onError: (error: any) => {
-      const conflictMessage = extractPhoneConflictDetail(error.response?.data?.detail)
-      showToast(conflictMessage || `Failed to create agent: ${error.message}`, 'error')
-    },
-  })
-
-  const resetForm = () => {
-    setFormData(DEFAULT_CREATE_AGENT_FORM)
-    setCreatePath('telephony')
-    setCurrentStep(1)
-    setProductionPrompt('')
-    setSetupAdditionalContext('')
-    setPhoneNumberInputMode('provider')
-    setSelectedPlatform(null)
-    setAiCredentialId('')
-    setAiModel('')
-    setPromptFetchError(null)
-    setHasFetchedPlatformPrompt(false)
-  }
-
-  const handlePathChange = (path: CreateAgentPath) => {
-    setCreatePath(path)
-    setCurrentStep(1)
-    setFormData(DEFAULT_CREATE_AGENT_FORM)
-    setProductionPrompt('')
-    setSetupAdditionalContext('')
-    setSelectedPlatform(null)
-    setPromptFetchError(null)
-    setHasFetchedPlatformPrompt(false)
-  }
-
-  const validateCurrentStep = (): boolean => {
-    if (createPath === 'telephony') {
-      if (currentStep === 1) {
-        if (!validateTelephonyBasics(formData, phoneNumberInputMode, isCheckingPhoneAssignment, hasPhoneConflict)) {
-          if (!formData.name.trim()) showToast('Name is required.', 'error')
-          else if (phoneNumberInputMode === 'provider' && !formData.telephony_phone_number_id) {
-            showToast('Please select a telephony number from your provider.', 'error')
-          } else if (phoneNumberInputMode === 'custom' && !formData.phone_number?.trim()) {
-            showToast('Phone number is required for phone calls.', 'error')
-          } else if (isCheckingPhoneAssignment) {
-            showToast('Checking phone number availability…', 'error')
-          } else if (hasPhoneConflict) {
-            showToast('This phone number is already assigned to another agent.', 'error')
-          }
-          return false
-        }
-        return true
-      }
-      if (currentStep === 2) {
-        if (!isPromptStepValid(productionPrompt, formData.description)) {
-          if (!productionPrompt.trim()) showToast('Production prompt is required.', 'error')
-          else showToast('Test agent prompt must be at least 10 words.', 'error')
-          return false
-        }
-        return true
-      }
-      if (currentStep === 3) {
-        if (!formData.voice_bundle_id?.trim()) {
-          showToast('Voice bundle is required.', 'error')
-          return false
-        }
-        return true
-      }
-    }
-
-    if (createPath === 'platform') {
-      if (currentStep === 1) {
-        if (!isPlatformConnectValid(formData.name, selectedPlatform, formData.voice_ai_integration_id, formData.voice_ai_agent_id)) {
-          if (!formData.name.trim()) showToast('Name is required.', 'error')
-          else if (!selectedPlatform) showToast('Please connect a platform.', 'error')
-          else showToast('Integration and Agent ID are required.', 'error')
-          return false
-        }
-        return true
-      }
-      if (currentStep === 2) {
-        if (!formData.voice_bundle_id?.trim()) {
-          showToast('Voice bundle is required.', 'error')
-          return false
-        }
-        return true
-      }
-      if (currentStep === 3) {
-        if (!isPromptStepValid(productionPrompt, formData.description)) {
-          if (!productionPrompt.trim()) showToast('Production prompt must be fetched from the provider.', 'error')
-          else showToast('Test agent prompt must be at least 10 words.', 'error')
-          return false
-        }
-        return true
-      }
-    }
-
-    return true
-  }
-
-  const handleNext = () => {
-    if (!validateCurrentStep()) return
-
-    if (createPath === 'platform' && currentStep === 2) {
-      setCurrentStep(3)
-      if (!hasFetchedPlatformPrompt) {
-        fetchPlatformPromptMutation.mutate()
-      }
-      return
-    }
-
-    setCurrentStep((step) => Math.min(step + 1, 3) as CreateStepId)
-  }
-
-  const handleBack = () => {
-    setCurrentStep((step) => Math.max(step - 1, 1) as CreateStepId)
-  }
-
-  const handleCreate = () => {
-    if (currentStep !== 3 || !validateCurrentStep()) return
-    if (createPath === 'telephony') {
-      if (!validateTelephonyBasics(formData, phoneNumberInputMode, isCheckingPhoneAssignment, hasPhoneConflict)) {
-        showToast('Please fix telephony settings.', 'error')
-        return
-      }
-    }
-    if (!isPromptStepValid(productionPrompt, formData.description)) {
-      showToast('Production and test agent prompts are required.', 'error')
-      return
-    }
-    if (!formData.voice_bundle_id?.trim()) {
-      showToast('Voice bundle is required.', 'error')
-      return
-    }
-    createMutation.mutate(formData)
-  }
-
-  const handlePlatformSelect = (platform: IntegrationPlatform | null) => {
-    setSelectedPlatform(platform)
-    setFormData((prev) => ({
-      ...prev,
-      voice_ai_integration_id: '',
-      voice_ai_agent_id: '',
-    }))
-    setProductionPrompt('')
-    setPromptFetchError(null)
-    setHasFetchedPlatformPrompt(false)
-  }
-
-  const handlePlatformIntegrationChange = (integrationId: string) => {
-    setFormData((prev) => ({ ...prev, voice_ai_integration_id: integrationId }))
-    setProductionPrompt('')
-    setPromptFetchError(null)
-    setHasFetchedPlatformPrompt(false)
-  }
-
-  const handlePlatformAgentIdChange = (agentId: string) => {
-    setFormData((prev) => ({ ...prev, voice_ai_agent_id: agentId }))
-    setProductionPrompt('')
-    setPromptFetchError(null)
-    setHasFetchedPlatformPrompt(false)
-  }
-
-  if (!isOpen) return null
-
-  const renderStepContent = () => {
-    if (createPath === 'telephony') {
-      if (currentStep === 1) {
-        return (
-          <TelephonyBasicsStep
-            formData={formData}
-            onChange={setFormData}
-            isOpen={isOpen}
-            phoneNumberInputMode={phoneNumberInputMode}
-            onPhoneNumberInputModeChange={setPhoneNumberInputMode}
-          />
-        )
-      }
-      if (currentStep === 2) {
-        return (
-          <ProductionPromptStep
-            agentName={formData.name}
-            language={formData.language}
-            callType={formData.call_type}
-            productionPrompt={productionPrompt}
-            onProductionPromptChange={setProductionPrompt}
-            testAgentPrompt={formData.description}
-            onTestAgentPromptChange={(description) => setFormData((prev) => ({ ...prev, description }))}
-            additionalContext={setupAdditionalContext}
-            onAdditionalContextChange={setSetupAdditionalContext}
-            aiProviders={aiProviders}
-            aiCredentialId={aiCredentialId}
-            onAiCredentialIdChange={setAiCredentialId}
-            aiModel={aiModel}
-            onAiModelChange={setAiModel}
-            selectableModels={selectableModels}
-            gatewayDirectModel={gatewayDirectModel}
-            aiProvider={aiProvider}
-            onGenerateTestPrompt={() => generateTestPromptMutation.mutate()}
-            isGenerating={generateTestPromptMutation.isPending}
-            canGenerate={Boolean(formData.name.trim() && productionPrompt.trim())}
-          />
-        )
-      }
-      return (
-        <VoiceBundleStep
-          voiceBundles={voiceBundles}
-          value={formData.voice_bundle_id}
-          onChange={(voiceBundleId) => setFormData((prev) => ({ ...prev, voice_bundle_id: voiceBundleId }))}
-        />
-      )
-    }
-
-    if (currentStep === 1) {
-      return (
-        <PlatformConnectStep
-          integrations={integrations}
-          agentName={formData.name}
-          onAgentNameChange={(name) => setFormData((prev) => ({ ...prev, name }))}
-          selectedPlatform={selectedPlatform}
-          onSelectPlatform={handlePlatformSelect}
-          voiceAiIntegrationId={formData.voice_ai_integration_id}
-          voiceAiAgentId={formData.voice_ai_agent_id}
-          onIntegrationChange={handlePlatformIntegrationChange}
-          onAgentIdChange={handlePlatformAgentIdChange}
-        />
-      )
-    }
-    if (currentStep === 2) {
-      return (
-        <VoiceBundleStep
-          voiceBundles={voiceBundles}
-          value={formData.voice_bundle_id}
-          onChange={(voiceBundleId) => setFormData((prev) => ({ ...prev, voice_bundle_id: voiceBundleId }))}
-        />
-      )
-    }
-    return (
-      <ProductionPromptStep
-        agentName={formData.name}
-        language={formData.language}
-        callType={formData.call_type}
-        productionPrompt={productionPrompt}
-        onProductionPromptChange={setProductionPrompt}
-        productionPromptReadOnly
-        isFetchingProductionPrompt={fetchPlatformPromptMutation.isPending}
-        fetchError={promptFetchError}
-        testAgentPrompt={formData.description}
-        onTestAgentPromptChange={(description) => setFormData((prev) => ({ ...prev, description }))}
-        additionalContext={setupAdditionalContext}
-        onAdditionalContextChange={setSetupAdditionalContext}
-        aiProviders={aiProviders}
-        aiCredentialId={aiCredentialId}
-        onAiCredentialIdChange={setAiCredentialId}
-        aiModel={aiModel}
-        onAiModelChange={setAiModel}
-        selectableModels={selectableModels}
-        gatewayDirectModel={gatewayDirectModel}
-        aiProvider={aiProvider}
-        onGenerateTestPrompt={() => generateTestPromptMutation.mutate()}
-        isGenerating={generateTestPromptMutation.isPending}
-        canGenerate={Boolean(formData.name.trim() && productionPrompt.trim())}
-      />
-    )
-  }
-
-  return createPortal(
-    <div
-      className="fixed inset-0 z-[9999] flex items-center justify-center p-4 sm:p-6 bg-gray-900/50 backdrop-blur-sm"
-      onClick={onClose}
-      role="presentation"
-    >
-      <div
-        className="bg-white rounded-2xl shadow-2xl ring-1 ring-gray-200/80 w-[min(96vw,88rem)] h-[min(92vh,920px)] flex flex-col overflow-hidden"
-        onClick={(e) => e.stopPropagation()}
-        role="dialog"
-        aria-modal="true"
-        aria-labelledby="create-agent-modal-title"
-      >
-        <div className="flex items-center justify-between shrink-0 px-6 py-5 border-b border-gray-100">
-          <div>
-            <h2 id="create-agent-modal-title" className="text-2xl font-bold text-gray-900 tracking-tight">
-              Create Test Agent
-            </h2>
-            <p className="text-sm text-gray-500 mt-1">
-              Step {currentStep} of {steps.length} · {steps[currentStep - 1]?.title}
-            </p>
-          </div>
-          <button
-            type="button"
-            onClick={onClose}
-            className="rounded-lg p-2 text-gray-400 hover:text-gray-600 hover:bg-gray-100 transition-colors"
-            aria-label="Close"
-          >
-            <X className="w-5 h-5" />
-          </button>
-        </div>
-
-        <div className="shrink-0 px-6 py-4 border-b border-gray-100 bg-gray-50/50 space-y-4">
-          <CreateAgentPathSelector value={createPath} onChange={handlePathChange} />
-          <div className="flex items-center">
-            {steps.map((step, index) => {
-              const isComplete = currentStep > step.id
-              const isCurrent = currentStep === step.id
-              return (
-                <div key={step.id} className={`flex items-center ${index < steps.length - 1 ? 'flex-1' : ''}`}>
-                  <div className="flex items-center gap-2 min-w-0">
-                    <div
-                      className={`flex h-8 w-8 shrink-0 items-center justify-center rounded-full text-sm font-semibold ${
-                        isComplete
-                          ? 'bg-primary-600 text-white'
-                          : isCurrent
-                            ? 'border-2 border-primary-600 text-primary-600 bg-white'
-                            : 'border-2 border-gray-300 text-gray-400 bg-white'
-                      }`}
-                    >
-                      {step.id}
-                    </div>
-                    <div className="hidden sm:block min-w-0">
-                      <p className={`text-sm font-medium ${isCurrent ? 'text-gray-900' : 'text-gray-500'}`}>
-                        {step.title}
-                      </p>
-                      <p className="text-xs text-gray-400 truncate">{step.description}</p>
-                    </div>
-                  </div>
-                  {index < steps.length - 1 && (
-                    <div className={`mx-3 h-0.5 flex-1 ${isComplete ? 'bg-primary-600' : 'bg-gray-200'}`} />
-                  )}
-                </div>
-              )
-            })}
-          </div>
-        </div>
-
-        <div className="flex-1 min-h-0 overflow-y-auto px-6 py-5">
-          {renderStepContent()}
-        </div>
-
-        <div className="shrink-0 px-6 py-4 border-t border-gray-100 bg-gray-50/80 flex gap-3">
-          <Button type="button" variant="outline" onClick={onClose}>
-            Cancel
-          </Button>
-          {currentStep > 1 && (
-            <Button type="button" variant="outline" onClick={handleBack}>
-              Back
-            </Button>
-          )}
-          <div className="flex-1" />
-          {currentStep < 3 ? (
-            <Button type="button" variant="primary" onClick={handleNext}>
-              Next
-            </Button>
-          ) : (
-            <Button
-              type="button"
-              variant="primary"
-              onClick={handleCreate}
-              isLoading={createMutation.isPending}
-            >
-              Create Agent
-            </Button>
-          )}
-        </div>
-      </div>
-    </div>,
-    document.body,
-  )
-}
+import { useState, useEffect } from 'react'
+import { createPortal } from 'react-dom'
+import { useQuery, useMutation } from '@tanstack/react-query'
+import { X } from 'lucide-react'
+import Button from '../../../components/Button'
+import { apiClient } from '../../../lib/api'
+import { AIProvider, VoiceBundle, Integration, IntegrationPlatform } from '../../../types/api'
+import { resolveLLMModelsForCredential } from '../../../lib/llmModelOptions'
+import { useAgentPhoneAssignmentCheck } from './useAgentPhoneAssignmentCheck'
+import { extractPhoneConflictDetail } from './agentPhoneValidation'
+import CreateAgentPathSelector from './create/CreateAgentPathSelector'
+import TelephonyBasicsStep, { validateTelephonyBasics } from './create/TelephonyBasicsStep'
+import PlatformConnectStep, { isPlatformConnectValid } from './create/PlatformConnectStep'
+import VoiceBundleStep from './create/VoiceBundleStep'
+import ProductionPromptStep, { isPromptStepValid } from './create/ProductionPromptStep'
+import {
+  type CreateAgentPath,
+  type CreateAgentFormData,
+  type CreateStepId,
+  DEFAULT_CREATE_AGENT_FORM,
+  TELEPHONY_STEPS,
+  PLATFORM_STEPS,
+} from './create/createAgentTypes'
+
+interface CreateAgentModalProps {
+  isOpen: boolean
+  onClose: () => void
+  onSuccess: () => void
+  showToast: (message: string, type: 'success' | 'error') => void
+}
+
+export default function CreateAgentModal({
+  isOpen,
+  onClose,
+  onSuccess,
+  showToast,
+}: CreateAgentModalProps) {
+  const [createPath, setCreatePath] = useState<CreateAgentPath>('telephony')
+  const [currentStep, setCurrentStep] = useState<CreateStepId>(1)
+  const [formData, setFormData] = useState<CreateAgentFormData>(DEFAULT_CREATE_AGENT_FORM)
+  const [productionPrompt, setProductionPrompt] = useState('')
+  const [setupAdditionalContext, setSetupAdditionalContext] = useState('')
+  const [phoneNumberInputMode, setPhoneNumberInputMode] = useState<'provider' | 'custom'>('provider')
+  const [selectedPlatform, setSelectedPlatform] = useState<IntegrationPlatform | null>(null)
+  const [aiCredentialId, setAiCredentialId] = useState('')
+  const [aiModel, setAiModel] = useState('')
+  const [promptFetchError, setPromptFetchError] = useState<string | null>(null)
+  const [hasFetchedPlatformPrompt, setHasFetchedPlatformPrompt] = useState(false)
+
+  const steps = createPath === 'telephony' ? TELEPHONY_STEPS : PLATFORM_STEPS
+
+  const { data: voiceBundles = [] } = useQuery<VoiceBundle[]>({
+    queryKey: ['voicebundles'],
+    queryFn: () => apiClient.listVoiceBundles(),
+  })
+
+  const { data: integrations = [] } = useQuery<Integration[]>({
+    queryKey: ['integrations'],
+    queryFn: () => apiClient.listIntegrations(),
+  })
+
+  const { data: aiProviders = [] } = useQuery<AIProvider[]>({
+    queryKey: ['ai-providers'],
+    queryFn: () => apiClient.listAIProviders(),
+  })
+
+  const { isChecking: isCheckingPhoneAssignment, hasConflict: hasPhoneConflict } =
+    useAgentPhoneAssignmentCheck({
+      enabled: isOpen && createPath === 'telephony',
+      callMedium: 'phone_call',
+      phoneNumber: formData.phone_number,
+      telephonyPhoneNumberId:
+        phoneNumberInputMode === 'provider' ? formData.telephony_phone_number_id : undefined,
+    })
+
+  const selectedAiProviderRow = aiCredentialId
+    ? aiProviders.find((p) => p.id === aiCredentialId)
+    : undefined
+  const aiProvider = selectedAiProviderRow?.provider ?? ''
+
+  const { data: modelOptions } = useQuery({
+    queryKey: ['model-options', aiProvider],
+    queryFn: () => apiClient.getModelOptions(aiProvider),
+    enabled: !!aiProvider,
+  })
+
+  const llmModels = modelOptions?.llm || []
+  const modelResolution = selectedAiProviderRow
+    ? resolveLLMModelsForCredential(selectedAiProviderRow, llmModels)
+    : { mode: 'catalog' as const, models: llmModels }
+  const gatewayDirectModel =
+    modelResolution.mode === 'gateway_direct' ? modelResolution.model : null
+  const selectableModels =
+    modelResolution.mode === 'catalog' ? modelResolution.models : []
+
+  useEffect(() => {
+    if (gatewayDirectModel) {
+      if (aiModel) setAiModel('')
+      return
+    }
+    if (aiProvider && selectableModels.length > 0 && !selectableModels.includes(aiModel)) {
+      setAiModel(selectableModels[0])
+    }
+  }, [aiProvider, selectableModels, aiModel, gatewayDirectModel])
+
+  useEffect(() => {
+    if (!isOpen) return
+    const prev = document.body.style.overflow
+    document.body.style.overflow = 'hidden'
+    return () => {
+      document.body.style.overflow = prev
+    }
+  }, [isOpen])
+
+  const buildGenerationParams = () => ({
+    ...(aiProvider ? { provider: aiProvider } : {}),
+    ...(aiCredentialId ? { credential_id: aiCredentialId } : {}),
+    ...(aiModel ? { model: aiModel } : {}),
+  })
+
+  const generateTestPromptMutation = useMutation({
+    mutationFn: () => {
+      if (!formData.name.trim()) {
+        throw new Error('Agent name is required before generating a test prompt')
+      }
+      if (!productionPrompt.trim()) {
+        throw new Error('Production prompt is required')
+      }
+      return apiClient.generateTestPromptFromProduction({
+        production_prompt: productionPrompt,
+        agent_name: formData.name.trim(),
+        language: formData.language,
+        call_type: formData.call_type,
+        additional_context: setupAdditionalContext.trim() || undefined,
+        ...buildGenerationParams(),
+      })
+    },
+    onSuccess: (data) => {
+      setFormData((prev) => ({ ...prev, description: data.test_agent_prompt }))
+      showToast('Test agent prompt generated from production prompt', 'success')
+    },
+    onError: (err: any) => {
+      showToast(err?.message || err?.response?.data?.detail || 'Failed to generate test prompt', 'error')
+    },
+  })
+
+  const fetchPlatformPromptMutation = useMutation({
+    mutationFn: () => {
+      if (!formData.voice_ai_integration_id || !formData.voice_ai_agent_id) {
+        throw new Error('Integration and Agent ID are required')
+      }
+      return apiClient.previewIntegrationAgentPrompt(
+        formData.voice_ai_integration_id,
+        formData.voice_ai_agent_id.trim(),
+      )
+    },
+    onSuccess: (data) => {
+      setProductionPrompt(data.provider_prompt)
+      setPromptFetchError(null)
+      setHasFetchedPlatformPrompt(true)
+    },
+    onError: (err: any) => {
+      const message = err?.response?.data?.detail || err?.message || 'Failed to fetch production prompt'
+      setPromptFetchError(typeof message === 'string' ? message : 'Failed to fetch production prompt')
+      setProductionPrompt('')
+    },
+  })
+
+  const createMutation = useMutation({
+    mutationFn: async (data: CreateAgentFormData) => {
+      const payload: Record<string, unknown> = {
+        name: data.name,
+        language: data.language,
+        description: data.description || null,
+        call_type: data.call_type,
+        call_medium: createPath === 'platform' ? 'web_call' : 'phone_call',
+        voice_bundle_id: data.voice_bundle_id.trim(),
+        silence_hangup_secs: data.silence_hangup_secs ?? 15,
+      }
+
+      if (createPath === 'telephony') {
+        if (data.phone_number) payload.phone_number = data.phone_number
+        if (data.telephony_phone_number_id) {
+          payload.telephony_phone_number_id = data.telephony_phone_number_id
+        }
+        if (productionPrompt.trim()) {
+          payload.provider_prompt = productionPrompt.trim()
+        }
+      }
+
+      if (createPath === 'platform') {
+        payload.voice_ai_integration_id = data.voice_ai_integration_id.trim()
+        payload.voice_ai_agent_id = data.voice_ai_agent_id.trim()
+        if (productionPrompt.trim()) {
+          payload.provider_prompt = productionPrompt.trim()
+        }
+      }
+
+      return apiClient.createAgent(payload as Parameters<typeof apiClient.createAgent>[0])
+    },
+    onSuccess: () => {
+      onSuccess()
+      resetForm()
+      showToast('Agent created successfully!', 'success')
+    },
+    onError: (error: any) => {
+      const conflictMessage = extractPhoneConflictDetail(error.response?.data?.detail)
+      showToast(conflictMessage || `Failed to create agent: ${error.message}`, 'error')
+    },
+  })
+
+  const resetForm = () => {
+    setFormData(DEFAULT_CREATE_AGENT_FORM)
+    setCreatePath('telephony')
+    setCurrentStep(1)
+    setProductionPrompt('')
+    setSetupAdditionalContext('')
+    setPhoneNumberInputMode('provider')
+    setSelectedPlatform(null)
+    setAiCredentialId('')
+    setAiModel('')
+    setPromptFetchError(null)
+    setHasFetchedPlatformPrompt(false)
+  }
+
+  const handlePathChange = (path: CreateAgentPath) => {
+    setCreatePath(path)
+    setCurrentStep(1)
+    setFormData(DEFAULT_CREATE_AGENT_FORM)
+    setProductionPrompt('')
+    setSetupAdditionalContext('')
+    setSelectedPlatform(null)
+    setPromptFetchError(null)
+    setHasFetchedPlatformPrompt(false)
+  }
+
+  const validateCurrentStep = (): boolean => {
+    if (createPath === 'telephony') {
+      if (currentStep === 1) {
+        if (!validateTelephonyBasics(formData, phoneNumberInputMode, isCheckingPhoneAssignment, hasPhoneConflict)) {
+          if (!formData.name.trim()) showToast('Name is required.', 'error')
+          else if (phoneNumberInputMode === 'provider' && !formData.telephony_phone_number_id) {
+            showToast('Please select a telephony number from your provider.', 'error')
+          } else if (phoneNumberInputMode === 'custom' && !formData.phone_number?.trim()) {
+            showToast('Phone number is required for phone calls.', 'error')
+          } else if (isCheckingPhoneAssignment) {
+            showToast('Checking phone number availability…', 'error')
+          } else if (hasPhoneConflict) {
+            showToast('This phone number is already assigned to another agent.', 'error')
+          }
+          return false
+        }
+        return true
+      }
+      if (currentStep === 2) {
+        if (!isPromptStepValid(productionPrompt, formData.description)) {
+          if (!productionPrompt.trim()) showToast('Production prompt is required.', 'error')
+          else showToast('Test agent prompt must be at least 10 words.', 'error')
+          return false
+        }
+        return true
+      }
+      if (currentStep === 3) {
+        if (!formData.voice_bundle_id?.trim()) {
+          showToast('Voice bundle is required.', 'error')
+          return false
+        }
+        return true
+      }
+    }
+
+    if (createPath === 'platform') {
+      if (currentStep === 1) {
+        if (!isPlatformConnectValid(formData.name, selectedPlatform, formData.voice_ai_integration_id, formData.voice_ai_agent_id)) {
+          if (!formData.name.trim()) showToast('Name is required.', 'error')
+          else if (!selectedPlatform) showToast('Please connect a platform.', 'error')
+          else showToast('Integration and Agent ID are required.', 'error')
+          return false
+        }
+        return true
+      }
+      if (currentStep === 2) {
+        if (!formData.voice_bundle_id?.trim()) {
+          showToast('Voice bundle is required.', 'error')
+          return false
+        }
+        return true
+      }
+      if (currentStep === 3) {
+        if (!isPromptStepValid(productionPrompt, formData.description)) {
+          if (!productionPrompt.trim()) showToast('Production prompt must be fetched from the provider.', 'error')
+          else showToast('Test agent prompt must be at least 10 words.', 'error')
+          return false
+        }
+        return true
+      }
+    }
+
+    return true
+  }
+
+  const handleNext = () => {
+    if (!validateCurrentStep()) return
+
+    if (createPath === 'platform' && currentStep === 2) {
+      setCurrentStep(3)
+      if (!hasFetchedPlatformPrompt) {
+        fetchPlatformPromptMutation.mutate()
+      }
+      return
+    }
+
+    setCurrentStep((step) => Math.min(step + 1, 3) as CreateStepId)
+  }
+
+  const handleBack = () => {
+    setCurrentStep((step) => Math.max(step - 1, 1) as CreateStepId)
+  }
+
+  const handleCreate = () => {
+    if (currentStep !== 3 || !validateCurrentStep()) return
+    if (createPath === 'telephony') {
+      if (!validateTelephonyBasics(formData, phoneNumberInputMode, isCheckingPhoneAssignment, hasPhoneConflict)) {
+        showToast('Please fix telephony settings.', 'error')
+        return
+      }
+    }
+    if (!isPromptStepValid(productionPrompt, formData.description)) {
+      showToast('Production and test agent prompts are required.', 'error')
+      return
+    }
+    if (!formData.voice_bundle_id?.trim()) {
+      showToast('Voice bundle is required.', 'error')
+      return
+    }
+    createMutation.mutate(formData)
+  }
+
+  const handlePlatformSelect = (platform: IntegrationPlatform | null) => {
+    setSelectedPlatform(platform)
+    setFormData((prev) => ({
+      ...prev,
+      voice_ai_integration_id: '',
+      voice_ai_agent_id: '',
+    }))
+    setProductionPrompt('')
+    setPromptFetchError(null)
+    setHasFetchedPlatformPrompt(false)
+  }
+
+  const handlePlatformIntegrationChange = (integrationId: string) => {
+    setFormData((prev) => ({
+      ...prev,
+      voice_ai_integration_id: integrationId,
+      voice_ai_agent_id: '',
+    }))
+    setProductionPrompt('')
+    setPromptFetchError(null)
+    setHasFetchedPlatformPrompt(false)
+  }
+
+  const handlePlatformAgentIdChange = (agentId: string) => {
+    setFormData((prev) => ({ ...prev, voice_ai_agent_id: agentId }))
+    setProductionPrompt('')
+    setPromptFetchError(null)
+    setHasFetchedPlatformPrompt(false)
+  }
+
+  if (!isOpen) return null
+
+  const renderStepContent = () => {
+    if (createPath === 'telephony') {
+      if (currentStep === 1) {
+        return (
+          <TelephonyBasicsStep
+            formData={formData}
+            onChange={setFormData}
+            isOpen={isOpen}
+            phoneNumberInputMode={phoneNumberInputMode}
+            onPhoneNumberInputModeChange={setPhoneNumberInputMode}
+          />
+        )
+      }
+      if (currentStep === 2) {
+        return (
+          <ProductionPromptStep
+            agentName={formData.name}
+            language={formData.language}
+            callType={formData.call_type}
+            productionPrompt={productionPrompt}
+            onProductionPromptChange={setProductionPrompt}
+            testAgentPrompt={formData.description}
+            onTestAgentPromptChange={(description) => setFormData((prev) => ({ ...prev, description }))}
+            additionalContext={setupAdditionalContext}
+            onAdditionalContextChange={setSetupAdditionalContext}
+            aiProviders={aiProviders}
+            aiCredentialId={aiCredentialId}
+            onAiCredentialIdChange={setAiCredentialId}
+            aiModel={aiModel}
+            onAiModelChange={setAiModel}
+            selectableModels={selectableModels}
+            gatewayDirectModel={gatewayDirectModel}
+            aiProvider={aiProvider}
+            onGenerateTestPrompt={() => generateTestPromptMutation.mutate()}
+            isGenerating={generateTestPromptMutation.isPending}
+            canGenerate={Boolean(formData.name.trim() && productionPrompt.trim())}
+          />
+        )
+      }
+      return (
+        <VoiceBundleStep
+          voiceBundles={voiceBundles}
+          value={formData.voice_bundle_id}
+          onChange={(voiceBundleId) => setFormData((prev) => ({ ...prev, voice_bundle_id: voiceBundleId }))}
+        />
+      )
+    }
+
+    if (currentStep === 1) {
+      return (
+        <PlatformConnectStep
+          integrations={integrations}
+          agentName={formData.name}
+          onAgentNameChange={(name) => setFormData((prev) => ({ ...prev, name }))}
+          selectedPlatform={selectedPlatform}
+          onSelectPlatform={handlePlatformSelect}
+          voiceAiIntegrationId={formData.voice_ai_integration_id}
+          voiceAiAgentId={formData.voice_ai_agent_id}
+          onIntegrationChange={handlePlatformIntegrationChange}
+          onAgentIdChange={handlePlatformAgentIdChange}
+        />
+      )
+    }
+    if (currentStep === 2) {
+      return (
+        <VoiceBundleStep
+          voiceBundles={voiceBundles}
+          value={formData.voice_bundle_id}
+          onChange={(voiceBundleId) => setFormData((prev) => ({ ...prev, voice_bundle_id: voiceBundleId }))}
+        />
+      )
+    }
+    return (
+      <ProductionPromptStep
+        agentName={formData.name}
+        language={formData.language}
+        callType={formData.call_type}
+        productionPrompt={productionPrompt}
+        onProductionPromptChange={setProductionPrompt}
+        productionPromptReadOnly
+        isFetchingProductionPrompt={fetchPlatformPromptMutation.isPending}
+        fetchError={promptFetchError}
+        testAgentPrompt={formData.description}
+        onTestAgentPromptChange={(description) => setFormData((prev) => ({ ...prev, description }))}
+        additionalContext={setupAdditionalContext}
+        onAdditionalContextChange={setSetupAdditionalContext}
+        aiProviders={aiProviders}
+        aiCredentialId={aiCredentialId}
+        onAiCredentialIdChange={setAiCredentialId}
+        aiModel={aiModel}
+        onAiModelChange={setAiModel}
+        selectableModels={selectableModels}
+        gatewayDirectModel={gatewayDirectModel}
+        aiProvider={aiProvider}
+        onGenerateTestPrompt={() => generateTestPromptMutation.mutate()}
+        isGenerating={generateTestPromptMutation.isPending}
+        canGenerate={Boolean(formData.name.trim() && productionPrompt.trim())}
+      />
+    )
+  }
+
+  return createPortal(
+    <div
+      className="fixed inset-0 z-[9999] flex items-center justify-center p-4 sm:p-6 bg-gray-900/50 backdrop-blur-sm"
+      onClick={onClose}
+      role="presentation"
+    >
+      <div
+        className="bg-white rounded-2xl shadow-2xl ring-1 ring-gray-200/80 w-[min(96vw,88rem)] h-[min(92vh,920px)] flex flex-col overflow-hidden"
+        onClick={(e) => e.stopPropagation()}
+        role="dialog"
+        aria-modal="true"
+        aria-labelledby="create-agent-modal-title"
+      >
+        <div className="flex items-center justify-between shrink-0 px-6 py-5 border-b border-gray-100">
+          <div>
+            <h2 id="create-agent-modal-title" className="text-2xl font-bold text-gray-900 tracking-tight">
+              Create Test Agent
+            </h2>
+            <p className="text-sm text-gray-500 mt-1">
+              Step {currentStep} of {steps.length} · {steps[currentStep - 1]?.title}
+            </p>
+          </div>
+          <button
+            type="button"
+            onClick={onClose}
+            className="rounded-lg p-2 text-gray-400 hover:text-gray-600 hover:bg-gray-100 transition-colors"
+            aria-label="Close"
+          >
+            <X className="w-5 h-5" />
+          </button>
+        </div>
+
+        <div className="shrink-0 px-6 py-4 border-b border-gray-100 bg-gray-50/50 space-y-4">
+          <CreateAgentPathSelector value={createPath} onChange={handlePathChange} />
+          <div className="flex items-center">
+            {steps.map((step, index) => {
+              const isComplete = currentStep > step.id
+              const isCurrent = currentStep === step.id
+              return (
+                <div key={step.id} className={`flex items-center ${index < steps.length - 1 ? 'flex-1' : ''}`}>
+                  <div className="flex items-center gap-2 min-w-0">
+                    <div
+                      className={`flex h-8 w-8 shrink-0 items-center justify-center rounded-full text-sm font-semibold ${
+                        isComplete
+                          ? 'bg-primary-600 text-white'
+                          : isCurrent
+                            ? 'border-2 border-primary-600 text-primary-600 bg-white'
+                            : 'border-2 border-gray-300 text-gray-400 bg-white'
+                      }`}
+                    >
+                      {step.id}
+                    </div>
+                    <div className="hidden sm:block min-w-0">
+                      <p className={`text-sm font-medium ${isCurrent ? 'text-gray-900' : 'text-gray-500'}`}>
+                        {step.title}
+                      </p>
+                      <p className="text-xs text-gray-400 truncate">{step.description}</p>
+                    </div>
+                  </div>
+                  {index < steps.length - 1 && (
+                    <div className={`mx-3 h-0.5 flex-1 ${isComplete ? 'bg-primary-600' : 'bg-gray-200'}`} />
+                  )}
+                </div>
+              )
+            })}
+          </div>
+        </div>
+
+        <div className="flex-1 min-h-0 overflow-y-auto px-6 py-5">
+          {renderStepContent()}
+        </div>
+
+        <div className="shrink-0 px-6 py-4 border-t border-gray-100 bg-gray-50/80 flex gap-3">
+          <Button type="button" variant="outline" onClick={onClose}>
+            Cancel
+          </Button>
+          {currentStep > 1 && (
+            <Button type="button" variant="outline" onClick={handleBack}>
+              Back
+            </Button>
+          )}
+          <div className="flex-1" />
+          {currentStep < 3 ? (
+            <Button type="button" variant="primary" onClick={handleNext}>
+              Next
+            </Button>
+          ) : (
+            <Button
+              type="button"
+              variant="primary"
+              onClick={handleCreate}
+              isLoading={createMutation.isPending}
+            >
+              Create Agent
+            </Button>
+          )}
+        </div>
+      </div>
+    </div>,
+    document.body,
+  )
+}
diff --git a/frontend/src/pages/agents/components/create/PlatformConnectStep.tsx b/frontend/src/pages/agents/components/create/PlatformConnectStep.tsx
index 36a0af82..1146cccc 100644
--- a/frontend/src/pages/agents/components/create/PlatformConnectStep.tsx
+++ b/frontend/src/pages/agents/components/create/PlatformConnectStep.tsx
@@ -1,6 +1,7 @@
 import { Link } from 'react-router-dom'
 import { Integration, IntegrationPlatform } from '../../../../types/api'
 import { getIntegrationPlatformLabel, getIntegrationPlatformLogo } from '../../../../config/providers'
+import VoiceAgentPicker from './VoiceAgentPicker'
 
 const PLATFORM_OPTIONS: IntegrationPlatform[] = [
   IntegrationPlatform.VAPI,
@@ -52,7 +53,7 @@ export default function PlatformConnectStep({
       </div>
 
       <p className="text-sm text-gray-600">
-        Choose a voice AI platform and connect it with your integration credentials and external Agent ID.
+        Choose a voice AI platform, pick an integration, and select the external agent to test.
       </p>
 
       <div className="grid grid-cols-1 sm:grid-cols-2 gap-3">
@@ -115,16 +116,14 @@ export default function PlatformConnectStep({
                           ))}
                         </select>
                       </div>
-                      <div>
-                        <label className="block text-xs font-medium text-gray-600 mb-1">Agent ID *</label>
-                        <input
-                          type="text"
+                      {voiceAiIntegrationId ? (
+                        <VoiceAgentPicker
+                          integrationId={voiceAiIntegrationId}
+                          platformLabel={label}
                           value={voiceAiAgentId}
-                          onChange={(e) => onAgentIdChange(e.target.value)}
-                          placeholder={`Enter ${label} agent ID`}
-                          className="w-full px-3 py-2 text-sm border border-gray-300 rounded-lg bg-white focus:ring-2 focus:ring-primary-500 font-mono"
+                          onChange={onAgentIdChange}
                         />
-                      </div>
+                      ) : null}
                     </>
                   )}
                 </div>
diff --git a/frontend/src/pages/agents/components/create/VoiceAgentPicker.tsx b/frontend/src/pages/agents/components/create/VoiceAgentPicker.tsx
new file mode 100644
index 00000000..28c78a40
--- /dev/null
+++ b/frontend/src/pages/agents/components/create/VoiceAgentPicker.tsx
@@ -0,0 +1,151 @@
+import { useEffect, useState } from 'react'
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { RefreshCw } from 'lucide-react'
+import { apiClient } from '../../../../lib/api'
+
+interface VoiceAgentPickerProps {
+  integrationId: string
+  platformLabel: string
+  value: string
+  onChange: (agentId: string) => void
+}
+
+export default function VoiceAgentPicker({
+  integrationId,
+  platformLabel,
+  value,
+  onChange,
+}: VoiceAgentPickerProps) {
+  const queryClient = useQueryClient()
+  const [manualEntry, setManualEntry] = useState(false)
+  const [isRefreshing, setIsRefreshing] = useState(false)
+
+  const { data, isLoading, isError, error, isFetching } = useQuery({
+    queryKey: ['integration-voice-agents', integrationId],
+    queryFn: () => apiClient.listIntegrationVoiceAgents(integrationId),
+    enabled: Boolean(integrationId) && !manualEntry,
+    staleTime: 60_000,
+  })
+
+  useEffect(() => {
+    setManualEntry(false)
+  }, [integrationId])
+
+  useEffect(() => {
+    if (data && !data.list_supported) {
+      setManualEntry(true)
+    }
+  }, [data?.list_supported, integrationId])
+
+  const handleRefresh = async () => {
+    if (!integrationId) return
+    setIsRefreshing(true)
+    try {
+      const fresh = await apiClient.listIntegrationVoiceAgents(integrationId, { refresh: true })
+      queryClient.setQueryData(['integration-voice-agents', integrationId], fresh)
+    } finally {
+      setIsRefreshing(false)
+    }
+  }
+
+  const errorMessage =
+    isError && error
+      ? (error as { response?: { data?: { detail?: string } }; message?: string }).response?.data
+          ?.detail ||
+        (error as { message?: string }).message ||
+        'Failed to load agents'
+      : null
+
+  const showPicker = Boolean(integrationId) && !manualEntry && data?.list_supported !== false
+  const agents = data?.agents ?? []
+  const busy = isLoading || isFetching || isRefreshing
+
+  return (
+    <div className="space-y-2">
+      <div className="flex items-center justify-between gap-2">
+        <label className="block text-xs font-medium text-gray-600">Agent *</label>
+        {showPicker ? (
+          <button
+            type="button"
+            onClick={handleRefresh}
+            disabled={busy}
+            className="inline-flex items-center gap-1 text-xs text-gray-600 hover:text-gray-900 disabled:opacity-50"
+          >
+            <RefreshCw className={`h-3.5 w-3.5 ${busy ? 'animate-spin' : ''}`} />
+            Refresh
+          </button>
+        ) : null}
+      </div>
+
+      {showPicker ? (
+        <>
+          {errorMessage ? (
+            <p className="text-xs text-red-700 bg-red-50 border border-red-200 rounded-md px-3 py-2">
+              {errorMessage}
+            </p>
+          ) : null}
+          {data?.message ? (
+            <p className="text-xs text-amber-700 bg-amber-50 border border-amber-200 rounded-md px-3 py-2">
+              {data.message}
+            </p>
+          ) : null}
+          {data?.truncated ? (
+            <p className="text-xs text-gray-600">
+              Large account — not all agents may be listed. Use manual entry if yours is missing.
+            </p>
+          ) : null}
+          <select
+            value={value}
+            onChange={(e) => onChange(e.target.value)}
+            disabled={busy || Boolean(errorMessage)}
+            className="w-full px-3 py-2 text-sm border border-gray-300 rounded-lg bg-white focus:ring-2 focus:ring-primary-500 disabled:bg-gray-50"
+          >
+            <option value="">{busy ? 'Loading agents…' : 'Select agent'}</option>
+            {agents.map((agent) => (
+              <option key={agent.id} value={agent.id}>
+                {agent.name} ({agent.id})
+              </option>
+            ))}
+          </select>
+          {!busy && agents.length === 0 && !errorMessage ? (
+            <p className="text-xs text-gray-500">No agents returned for this integration.</p>
+          ) : null}
+          <button
+            type="button"
+            onClick={() => {
+              setManualEntry(true)
+              onChange('')
+            }}
+            className="text-xs text-primary-700 hover:text-primary-900 underline"
+          >
+            Enter agent ID manually
+          </button>
+        </>
+      ) : (
+        <>
+          {data?.message ? (
+            <p className="text-xs text-amber-700 bg-amber-50 border border-amber-200 rounded-md px-3 py-2">
+              {data.message}
+            </p>
+          ) : null}
+          <input
+            type="text"
+            value={value}
+            onChange={(e) => onChange(e.target.value)}
+            placeholder={`Enter ${platformLabel} agent ID`}
+            className="w-full px-3 py-2 text-sm border border-gray-300 rounded-lg bg-white focus:ring-2 focus:ring-primary-500 font-mono"
+          />
+          {data?.list_supported !== false ? (
+            <button
+              type="button"
+              onClick={() => setManualEntry(false)}
+              className="text-xs text-primary-700 hover:text-primary-900 underline"
+            >
+              Choose from list
+            </button>
+          ) : null}
+        </>
+      )}
+    </div>
+  )
+}
diff --git a/frontend/src/pages/agents/components/create/createAgentTypes.ts b/frontend/src/pages/agents/components/create/createAgentTypes.ts
index bccbed52..ce21787e 100644
--- a/frontend/src/pages/agents/components/create/createAgentTypes.ts
+++ b/frontend/src/pages/agents/components/create/createAgentTypes.ts
@@ -35,7 +35,7 @@ export const TELEPHONY_STEPS = [
 ] as const
 
 export const PLATFORM_STEPS = [
-  { id: 1, title: 'Connect', description: 'Choose platform, integration, Agent ID' },
+  { id: 1, title: 'Connect', description: 'Choose platform, integration, agent' },
   { id: 2, title: 'Voice', description: 'Select voice bundle' },
   { id: 3, title: 'Prompts', description: 'Imported prompt → test prompt' },
 ] as const
diff --git a/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx b/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx
index 086a008a..5925cb35 100644
--- a/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx
+++ b/frontend/src/pages/evaluators/components/EvaluatorSmartRunModal.tsx
@@ -42,7 +42,7 @@ export default function EvaluatorSmartRunModal({ open, onClose, suites, showToas
       queryClient.invalidateQueries({ queryKey: ['observability-traces'] })
       queryClient.invalidateQueries({ queryKey: ['evaluator-suites'] })
       queryClient.invalidateQueries({ queryKey: ['evaluator-results'] })
-      showToast(`Queued ${data.total_runs} run${data.total_runs !== 1 ? 's' : ''}. Phone traces appear in Call Traces after each call — use Refresh.`, 'success')
+      showToast(`Queued ${data.total_runs} run${data.total_runs !== 1 ? 's' : ''}. Phone calls appear in Calls after each call — use Refresh.`, 'success')
       onClose()
     },
     onError: (err: any) => {
diff --git a/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx b/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx
index 2d8b13f1..e0dcb418 100644
--- a/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx
+++ b/frontend/src/pages/evaluators/results/EvaluatorResultDetail.tsx
@@ -1,19 +1,15 @@
-import React, { useState, useRef, useEffect, useMemo } from 'react'
+import React, { useState, useEffect, useMemo } from 'react'
 import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query'
-import { useParams, useNavigate, Link } from 'react-router-dom'
+import { useParams, useNavigate } from 'react-router-dom'
+import { getEvaluatorResultPlaceholder } from '../../../lib/evaluatorResultQuery'
 import { apiClient } from '../../../lib/api'
-import { ArrowLeft, Clock, CheckCircle, XCircle, Loader, BarChart3, Phone, Brain, HelpCircle, Sparkles, AudioWaveform, MessageSquare, Download, RotateCcw, PhoneIncoming, PhoneOutgoing, Tag, ExternalLink } from 'lucide-react'
+import { ArrowLeft, Clock, CheckCircle, XCircle, Loader, BarChart3, Brain, HelpCircle, Sparkles, AudioWaveform, RotateCcw, Activity } from 'lucide-react'
 import { motion, AnimatePresence } from 'framer-motion'
 import Button from '../../../components/Button'
-import RetellCallDetails from '../../../components/call-recordings/RetellCallDetails'
-import VapiCallDetails from '../../../components/call-recordings/VapiCallDetails'
-import ElevenLabsCallDetails from '../../../components/call-recordings/ElevenLabsCallDetails'
-import VobizCallDetails from '../../../components/call-recordings/VobizCallDetails'
 import { useToast } from '../../../hooks/useToast'
-import { useRecordingPresignedUrl } from '../../../hooks/useRecordingPresignedUrl'
 import { displayEvaluatorResultStatus } from './evaluatorResultStatus'
 import ResultsHierarchyNav from './ResultsHierarchyNav'
-import { getProviderRecordingUrl, hasEvaluatorResultRecording } from '../../../lib/recordingUrls'
+import TraceDetailDrawer from '../../../components/call-recordings/TraceDetailDrawer'
 
 const LEGACY_CATEGORY_LABEL_METRIC_NAMES = new Set([
   'yes',
@@ -33,18 +29,6 @@ function isLegacyCategoryLabelMetric(metric: {
   return LEGACY_CATEGORY_LABEL_METRIC_NAMES.has(name)
 }
 
-function getVobizPhoneNumbers(callData: any): { from: string | null; to: string | null } {
-  return {
-    from: callData?.from_phone_number || callData?.from_number || callData?.From || null,
-    to: callData?.to_phone_number || callData?.to_number || callData?.To || null,
-  }
-}
-
-function getResultAudioS3Key(result: any): string | null {
-  return result?.audio_s3_key || result?.call_data?.recording_s3_key || null
-}
-
-const AUTH_GATED_PROVIDERS = new Set(['elevenlabs'])
 // Comprehensive metric information with descriptions and ideal values
 const METRIC_INFO: Record<string, { 
   description: string
@@ -357,17 +341,15 @@ export default function EvaluatorResultDetailPage({
   const location = window.location.pathname
   const isFromPlayground = location.includes('/playground/test-agent-results')
   const { showToast, ToastContainer } = useToast()
-  const [audioUrl, setAudioUrl] = useState<string | null>(null)
-  const [audioDuration, setAudioDuration] = useState(0)
-  const [activeSegmentIndex, setActiveSegmentIndex] = useState<number | null>(null)
-  const [activeTab, setActiveTab] = useState<'overview' | 'transcript'>('overview')
   const [reEvalInProgress, setReEvalInProgress] = useState(false)
-  const audioRef = useRef<HTMLAudioElement>(null)
+  const [detailDrawerOpen, setDetailDrawerOpen] = useState(false)
 
   const { data: result, isLoading, error, refetch: refetchResult } = useQuery({
     queryKey: ['evaluator-result', id],
     queryFn: () => apiClient.getEvaluatorResult(id!),
     enabled: !!id,
+    placeholderData: () => (id ? getEvaluatorResultPlaceholder(queryClient, id) : undefined),
+    staleTime: 30_000,
     refetchInterval: (query) => {
       const data = query.state.data as any
       const inProgress = reEvalInProgress || (data && ['queued', 'call_initiating', 'call_connecting', 'call_in_progress', 'call_ended', 'transcribing', 'evaluating', 'fetching_details'].includes(data?.status))
@@ -440,10 +422,6 @@ export default function EvaluatorResultDetailPage({
     )
   }
 
-  const audioS3Key = getResultAudioS3Key(result)
-
-  const { data: presignedUrl } = useRecordingPresignedUrl(audioS3Key)
-
   const reEvaluateMutation = useMutation({
     mutationFn: (resultId: string) => apiClient.reEvaluateResult(resultId),
     onMutate: () => {
@@ -473,97 +451,6 @@ export default function EvaluatorResultDetailPage({
     }
   }, [result?.status, queryClient])
 
-  useEffect(() => {
-    let cancelled = false
-    let objectUrl: string | null = null
-
-    async function resolveAudioUrl() {
-      if (presignedUrl?.url) {
-        if (!cancelled) setAudioUrl(presignedUrl.url)
-        return
-      }
-
-      const provider = (result?.provider_platform || '').toLowerCase()
-      const providerRecordingUrl = getProviderRecordingUrl(result?.call_data, provider)
-      const resultId = result?.result_id || id
-
-      if (!audioS3Key && providerRecordingUrl && provider !== 'elevenlabs') {
-        if (!cancelled) setAudioUrl(providerRecordingUrl)
-        return
-      }
-
-      if (!audioS3Key && resultId && (AUTH_GATED_PROVIDERS.has(provider) || provider === 'vapi')) {
-        try {
-          objectUrl = await apiClient.getEvaluatorResultAudioUrl(resultId)
-          if (!cancelled) setAudioUrl(objectUrl)
-          return
-        } catch {
-          // fall through
-        }
-      }
-
-      if (!cancelled) setAudioUrl(null)
-    }
-
-    void resolveAudioUrl()
-
-    return () => {
-      cancelled = true
-      if (objectUrl) URL.revokeObjectURL(objectUrl)
-    }
-  }, [
-    presignedUrl,
-    audioS3Key,
-    result?.call_data,
-    result?.provider_platform,
-    result?.result_id,
-    id,
-  ])
-
-  useEffect(() => {
-    const audio = audioRef.current
-    if (!audio || !audioUrl) return
-
-    const handleLoadedMetadata = () => {
-      if (audio.duration && audio.duration !== Infinity) {
-        setAudioDuration(audio.duration)
-      }
-    }
-
-    const handleTimeUpdate = () => {
-      if (result && 'speaker_segments' in result) {
-        const resultData = result as EvaluatorResultDetail
-        if (resultData.speaker_segments) {
-          const activeIndex = resultData.speaker_segments.findIndex(
-            (seg) => audio.currentTime >= seg.start && audio.currentTime <= seg.end
-          )
-          setActiveSegmentIndex(activeIndex >= 0 ? activeIndex : null)
-        }
-      }
-    }
-
-    audio.addEventListener('loadedmetadata', handleLoadedMetadata)
-    audio.addEventListener('timeupdate', handleTimeUpdate)
-    
-    return () => {
-      audio.removeEventListener('loadedmetadata', handleLoadedMetadata)
-      audio.removeEventListener('timeupdate', handleTimeUpdate)
-    }
-  }, [result, audioUrl])
-
-  const formatTime = (seconds: number): string => {
-    const mins = Math.floor(seconds / 60)
-    const secs = Math.floor(seconds % 60)
-    return `${mins}:${secs.toString().padStart(2, '0')}`
-  }
-
-  const handleSegmentClick = (startTime: number) => {
-    if (audioRef.current) {
-      audioRef.current.currentTime = startTime
-      audioRef.current.play()
-    }
-  }
-
   const formatDuration = (seconds: number | null): string => {
     if (!seconds) return '--'
     const mins = Math.floor(seconds / 60)
@@ -587,9 +474,6 @@ export default function EvaluatorResultDetailPage({
         return (endMs - startMs) / 1000
       }
     }
-    if (audioDuration && audioDuration > 0) {
-      return audioDuration
-    }
     return result?.duration_seconds ?? null
   }
 
@@ -608,7 +492,7 @@ export default function EvaluatorResultDetailPage({
       case 'call_in_progress':
         return { dot: 'bg-blue-500', bg: 'bg-blue-50', text: 'text-blue-700', border: 'border-blue-200', label: 'In Call', icon: <Loader className="w-4 h-4 text-blue-500 animate-spin" /> }
       case 'call_ended':
-        return { dot: 'bg-indigo-500', bg: 'bg-indigo-50', text: 'text-indigo-700', border: 'border-indigo-200', label: 'Call Ended', icon: <Phone className="w-4 h-4 text-indigo-500" /> }
+        return { dot: 'bg-indigo-500', bg: 'bg-indigo-50', text: 'text-indigo-700', border: 'border-indigo-200', label: 'Call Ended', icon: <Loader className="w-4 h-4 text-indigo-500 animate-spin" /> }
       case 'transcribing':
         return { dot: 'bg-cyan-500', bg: 'bg-cyan-50', text: 'text-cyan-700', border: 'border-cyan-200', label: 'Transcribing', icon: <Loader className="w-4 h-4 text-cyan-500 animate-spin" /> }
       case 'evaluating':
@@ -741,7 +625,7 @@ export default function EvaluatorResultDetailPage({
     )
   }
 
-  if (isLoading) {
+  if (isLoading && !result) {
     return (
       <div className={embedded ? 'flex items-center justify-center py-16' : 'flex items-center justify-center min-h-[60vh]'}>
         <div className="text-center">
@@ -752,7 +636,7 @@ export default function EvaluatorResultDetailPage({
     )
   }
 
-  if (error || !result) {
+  if ((error && !result) || !result) {
     return (
       <div className="max-w-2xl mx-auto px-4 py-12">
         <div className="bg-rose-50 border border-rose-200 rounded-xl p-6 text-center">
@@ -777,14 +661,11 @@ export default function EvaluatorResultDetailPage({
   const resultData = result as EvaluatorResultDetail
   const displayStatus = displayEvaluatorResultStatus(resultData)
   const statusConfig = getStatusConfig(displayStatus)
-  const vobizPhoneNumbers = getVobizPhoneNumbers(resultData.call_data)
-  const providerRecordingUrl = getProviderRecordingUrl(resultData.call_data, resultData.provider_platform)
-  const hasCallMediaOrTranscript = Boolean(
-    hasEvaluatorResultRecording(resultData) ||
-    resultData.transcription ||
-    resultData.speaker_segments?.length ||
-    (Array.isArray(resultData.call_data?.messages) && resultData.call_data.messages.length > 0)
-  )
+  const callShortId =
+    typeof resultData.call_data?.call_short_id === 'string' ? resultData.call_data.call_short_id : null
+  const platform = (resultData.provider_platform || '').toLowerCase()
+  const useProviderDrawer =
+    Boolean(callShortId) && ['vapi', 'retell', 'elevenlabs', 'smallest'].includes(platform)
 
   return (
     <div
@@ -827,6 +708,14 @@ export default function EvaluatorResultDetailPage({
               </p>
             </div>
             <div className="flex items-center gap-3">
+              <Button
+                variant="outline"
+                size="sm"
+                onClick={() => setDetailDrawerOpen(true)}
+                leftIcon={<Activity className="w-4 h-4" />}
+              >
+                Call details
+              </Button>
               {!reEvalInProgress && (resultData.status === 'completed' || resultData.status === 'failed') && resultData.evaluator_id && (
                 <Button
                   variant="outline"
@@ -1091,422 +980,12 @@ export default function EvaluatorResultDetailPage({
         </div>
       </div>
 
-      {/* Provider-specific call details (Call Analysis, Cost, Latency, System Details) */}
-      {resultData.call_data && (resultData.provider_platform === 'retell' || (resultData.call_data.call_id?.startsWith('call_') && resultData.provider_platform !== 'vobiz')) && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Provider Call Details
-            <span className="ml-2 px-2 py-0.5 text-xs bg-blue-100 text-blue-800 rounded-full">retell</span>
-            {(resultData.provider_call_id || resultData.call_data.call_id) && (
-              <span className="ml-2 text-xs text-gray-500 font-mono">
-                {resultData.provider_call_id || resultData.call_data.call_id}
-              </span>
-            )}
-          </h2>
-          <RetellCallDetails callData={resultData.call_data} hideTranscript />
-        </div>
-      )}
-
-      {resultData.call_data && resultData.provider_platform === 'vapi' && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Provider Call Details
-            <span className="ml-2 px-2 py-0.5 text-xs bg-violet-100 text-violet-800 rounded-full">vapi</span>
-            {(resultData.provider_call_id || resultData.call_data.call_id) && (
-              <span className="ml-2 text-xs text-gray-500 font-mono">
-                {resultData.provider_call_id || resultData.call_data.call_id}
-              </span>
-            )}
-          </h2>
-          <VapiCallDetails callData={resultData.call_data} hideTranscript />
-        </div>
-      )}
-
-      {resultData.call_data && resultData.provider_platform === 'elevenlabs' && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Provider Call Details
-            <span className="ml-2 px-2 py-0.5 text-xs bg-emerald-100 text-emerald-800 rounded-full">elevenlabs</span>
-            {(resultData.provider_call_id || resultData.call_data.call_id) && (
-              <span className="ml-2 text-xs text-gray-500 font-mono">
-                {resultData.provider_call_id || resultData.call_data.call_id}
-              </span>
-            )}
-          </h2>
-          <ElevenLabsCallDetails
-            callData={resultData.call_data}
-            callShortId={resultData.call_data?.call_short_id}
-            hideTranscript
-          />
-        </div>
-      )}
-
-      {resultData.call_data && resultData.provider_platform === 'vobiz' && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Provider Call Details
-            <span className="ml-2 px-2 py-0.5 text-xs bg-teal-100 text-teal-800 rounded-full">vobiz</span>
-            {(resultData.provider_call_id || resultData.call_data.call_ref) && (
-              <span className="ml-2 text-xs text-gray-500 font-mono">
-                {resultData.provider_call_id || resultData.call_data.call_ref}
-              </span>
-            )}
-          </h2>
-          <VobizCallDetails callData={resultData.call_data} />
-        </div>
-      )}
-
-      {resultData.id && (
-        <div className="mb-6 bg-white rounded-lg shadow p-4 flex flex-wrap items-center justify-between gap-3">
-          <div>
-            <h2 className="text-sm font-semibold text-gray-900">Synthetic call trace</h2>
-            <p className="text-xs text-gray-500 mt-0.5">
-              STT / LLM / TTS spans from your voice agent layer, correlated by{' '}
-              <code className="text-xs bg-gray-100 px-1 rounded">call_short_id</code>. Phone tests
-              may also include SUT response latency from EfficientAI&apos;s caller.
-            </p>
-          </div>
-          <Link
-            to={`/call-traces?result=${resultData.id}`}
-            className="text-sm font-medium text-indigo-600 hover:text-indigo-800"
-          >
-            Open Call Traces →
-          </Link>
-        </div>
-      )}
-
-      {/* Unified Transcript & Call Details Section */}
-      {hasCallMediaOrTranscript ? (
-        <div className="bg-white shadow rounded-lg overflow-hidden">
-          {/* Tab Navigation */}
-          <div className="px-6 border-b border-gray-100 flex items-center gap-0">
-            <button
-              onClick={() => setActiveTab('overview')}
-              className={`px-4 py-3 text-sm font-medium border-b-2 transition-colors -mb-px ${
-                activeTab === 'overview'
-                  ? 'border-indigo-600 text-indigo-600'
-                  : 'border-transparent text-gray-500 hover:text-gray-700'
-              }`}
-            >
-              Overview
-            </button>
-            <button
-              onClick={() => setActiveTab('transcript')}
-              className={`px-4 py-3 text-sm font-medium border-b-2 transition-colors -mb-px ${
-                activeTab === 'transcript'
-                  ? 'border-indigo-600 text-indigo-600'
-                  : 'border-transparent text-gray-500 hover:text-gray-700'
-              }`}
-            >
-              Transcript
-            </button>
-          </div>
-
-          <div className="p-6">
-            {/* Overview Tab */}
-            {activeTab === 'overview' && (
-              <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
-                {/* Transcript Preview (2 cols) */}
-                <div className="lg:col-span-2">
-                  <div className="rounded-xl border border-gray-100 bg-gray-50/30 flex flex-col h-[560px]">
-                    <div className="px-4 py-3 border-b border-gray-100 flex items-center justify-between flex-shrink-0">
-                      <div className="flex items-center gap-2">
-                        <MessageSquare className="h-4 w-4 text-indigo-500" />
-                        <span className="text-sm font-medium text-gray-900">Transcript</span>
-                        {resultData.speaker_segments && resultData.speaker_segments.length > 0 && (
-                          <span className="px-2 py-0.5 text-xs bg-gray-100 text-gray-600 rounded-full">
-                            {resultData.speaker_segments.length} messages
-                          </span>
-                        )}
-                      </div>
-                      {audioUrl && (
-                        <div className="flex items-center gap-2">
-                          <audio controls src={audioUrl} className="h-8 w-56" />
-                          <a 
-                            href={audioUrl} 
-                            download 
-                            className="p-1.5 text-gray-400 hover:text-indigo-600 hover:bg-indigo-50 rounded-lg transition-colors"
-                          >
-                            <Download className="h-4 w-4" />
-                          </a>
-                        </div>
-                      )}
-                    </div>
-
-                    <div className="flex-1 overflow-y-auto p-4 space-y-3">
-                      {resultData.speaker_segments && resultData.speaker_segments.length > 0 ? (
-                        resultData.speaker_segments.map((segment, index) => {
-                          const isUser = segment.speaker === 'Speaker 1'
-                          const isActive = activeSegmentIndex === index
-                          
-                          return (
-                            <div 
-                              key={index} 
-                              className={`flex ${isUser ? 'justify-end' : 'justify-start'}`}
-                              onClick={() => handleSegmentClick(segment.start)}
-                            >
-                              <div className={`max-w-[80%] rounded-2xl px-4 py-2.5 cursor-pointer transition-all ${
-                                isUser
-                                  ? 'bg-indigo-600 text-white rounded-br-sm'
-                                  : 'bg-white border border-gray-200 text-gray-800 rounded-bl-sm'
-                              } ${isActive ? 'ring-2 ring-indigo-300 shadow-md' : ''}`}>
-                                <div className={`flex items-center gap-2 mb-0.5 ${isUser ? 'text-indigo-200' : 'text-gray-400'}`}>
-                                  <span className="text-[10px] font-semibold uppercase tracking-wider">
-                                    {isUser ? 'Caller' : (resultData.agent?.name || 'Agent')}
-                                  </span>
-                                  <span className="text-[10px] tabular-nums">{formatTime(segment.start)}</span>
-                                </div>
-                                <p className="text-sm leading-relaxed">{segment.text}</p>
-                              </div>
-                            </div>
-                          )
-                        })
-                      ) : resultData.transcription ? (
-                        <div className="p-4 bg-white rounded-lg border border-gray-100">
-                          <p className="text-sm text-gray-700 whitespace-pre-wrap leading-relaxed">{resultData.transcription}</p>
-                        </div>
-                      ) : (
-                        <div className="text-center py-12 text-gray-500">
-                          <MessageSquare className="w-8 h-8 text-gray-300 mx-auto mb-2" />
-                          <p className="text-sm">
-                            {resultData.status === 'transcribing' ? 'Transcription in progress...' : 'No transcript available'}
-                          </p>
-                        </div>
-                      )}
-                    </div>
-                  </div>
-                </div>
-
-                {/* Call Info Sidebar (1 col) */}
-                <div className="lg:col-span-1 space-y-4">
-                  <div className="rounded-xl border border-gray-100 bg-gray-50/30 p-5">
-                    <h3 className="text-sm font-semibold text-gray-900 mb-4 flex items-center gap-2">
-                      <Phone className="h-4 w-4 text-indigo-500" />
-                      Call Summary
-                    </h3>
-                    <div className="space-y-3">
-                      <div className="p-3 bg-white rounded-lg border border-gray-100">
-                        <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Duration</p>
-                        <p className="text-base font-semibold text-gray-900 tabular-nums">{formatDuration(getEffectiveDuration())}</p>
-                      </div>
-                      
-                      <div className="p-3 bg-white rounded-lg border border-gray-100">
-                        <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Status</p>
-                        <span className={`inline-flex items-center gap-1.5 px-2 py-0.5 rounded-full text-[11px] font-medium border ${statusConfig.bg} ${statusConfig.text} ${statusConfig.border}`}>
-                          <span className={`w-1.5 h-1.5 rounded-full ${statusConfig.dot}`} />
-                          {statusConfig.label}
-                        </span>
-                      </div>
-
-                      {resultData.agent && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Agent</p>
-                          <p className="text-sm font-medium text-gray-900">{resultData.agent.name}</p>
-                          {resultData.agent.description && (
-                            <p className="text-xs text-gray-500 mt-1 line-clamp-2">{resultData.agent.description}</p>
-                          )}
-                        </div>
-                      )}
-
-                      {resultData.persona && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Persona</p>
-                          <p className="text-sm font-medium text-gray-900">{resultData.persona.name}</p>
-                          <div className="flex flex-wrap gap-1.5 mt-1.5">
-                            {resultData.persona.tts_provider && (
-                              <span className="text-[10px] px-1.5 py-0.5 bg-gray-100 text-gray-600 rounded">{resultData.persona.tts_provider}</span>
-                            )}
-                            {resultData.persona.tts_voice_name && (
-                              <span className="text-[10px] px-1.5 py-0.5 bg-gray-100 text-gray-600 rounded">{resultData.persona.tts_voice_name}</span>
-                            )}
-                            <span className="text-[10px] px-1.5 py-0.5 bg-gray-100 text-gray-600 rounded">{resultData.persona.gender}</span>
-                          </div>
-                        </div>
-                      )}
-
-                      {resultData.scenario && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Scenario</p>
-                          <p className="text-sm font-medium text-gray-900">{resultData.scenario.name}</p>
-                          {resultData.scenario.description && (
-                            <p className="text-xs text-gray-500 mt-1 line-clamp-2">{resultData.scenario.description}</p>
-                          )}
-                        </div>
-                      )}
-
-                      {/* Call metadata from call_data (live calls, provider calls) */}
-                      {(resultData.call_data?.from_phone_number ||
-                        resultData.call_data?.to_phone_number ||
-                        vobizPhoneNumbers.from ||
-                        vobizPhoneNumbers.to) && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Phone Numbers</p>
-                          <div className="space-y-2 mt-1.5">
-                            {(resultData.call_data.from_phone_number || vobizPhoneNumbers.from) && (
-                              <div className="flex items-center gap-2">
-                                <PhoneOutgoing className="w-3.5 h-3.5 text-gray-400" />
-                                <span className="text-sm text-gray-700 font-mono">
-                                  {resultData.call_data.from_phone_number || vobizPhoneNumbers.from}
-                                </span>
-                              </div>
-                            )}
-                            {(resultData.call_data.to_phone_number || vobizPhoneNumbers.to) && (
-                              <div className="flex items-center gap-2">
-                                <PhoneIncoming className="w-3.5 h-3.5 text-gray-400" />
-                                <span className="text-sm text-gray-700 font-mono">
-                                  {resultData.call_data.to_phone_number || vobizPhoneNumbers.to}
-                                </span>
-                              </div>
-                            )}
-                          </div>
-                        </div>
-                      )}
-
-                      {resultData.call_data?.endedReason && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">End Reason</p>
-                          <span className="inline-flex items-center px-2.5 py-1 rounded-full text-xs font-medium border bg-gray-50 text-gray-700 border-gray-200">
-                            {resultData.call_data.endedReason.replace(/-/g, ' ').replace(/\b\w/g, (l: string) => l.toUpperCase())}
-                          </span>
-                        </div>
-                      )}
-
-                      {resultData.call_data?.metadata && Object.keys(resultData.call_data.metadata).length > 0 && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-2">Metadata</p>
-                          <div className="space-y-1.5">
-                            {Object.entries(resultData.call_data.metadata).map(([key, value]) => (
-                              <div key={key} className="flex items-start gap-2">
-                                <Tag className="w-3 h-3 text-gray-300 mt-0.5 flex-shrink-0" />
-                                <div className="min-w-0">
-                                  <span className="text-xs text-gray-500">{key}:</span>
-                                  <span className="text-xs text-gray-800 ml-1 font-medium">{String(value)}</span>
-                                </div>
-                              </div>
-                            ))}
-                          </div>
-                        </div>
-                      )}
-
-                      {(providerRecordingUrl || audioS3Key) && (
-                        <div className="p-3 bg-white rounded-lg border border-gray-100">
-                          <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">Recording</p>
-                          {providerRecordingUrl ? (
-                            <a
-                              href={providerRecordingUrl}
-                              target="_blank"
-                              rel="noopener noreferrer"
-                              className="inline-flex items-center gap-1.5 text-sm font-medium text-primary-600 hover:text-primary-800"
-                            >
-                              <ExternalLink className="w-3.5 h-3.5" />
-                              Open provider recording
-                            </a>
-                          ) : (
-                            <p className="text-sm text-gray-600">Recording available in player above</p>
-                          )}
-                          {resultData.call_data?.call_short_id && (
-                            <a
-                              href={`/observability/calls/${resultData.call_data.call_short_id}`}
-                              className="mt-2 inline-flex items-center gap-1.5 text-xs font-medium text-gray-500 hover:text-gray-700"
-                            >
-                              <ExternalLink className="w-3 h-3" />
-                              View in Observability
-                            </a>
-                          )}
-                        </div>
-                      )}
-                    </div>
-                  </div>
-                </div>
-              </div>
-            )}
-
-            {/* Full Transcript Tab */}
-            {activeTab === 'transcript' && (
-              <div className="rounded-xl border border-gray-100 bg-gray-50/30 flex flex-col h-[650px]">
-                <div className="px-4 py-3 border-b border-gray-100 flex items-center justify-between flex-shrink-0">
-                  <div className="flex items-center gap-2">
-                    <MessageSquare className="h-4 w-4 text-indigo-500" />
-                    <span className="text-sm font-medium text-gray-900">Full Transcript</span>
-                  </div>
-                  {audioUrl && (
-                    <div className="flex items-center gap-2">
-                      <audio controls src={audioUrl} className="h-8 w-56" />
-                      <a 
-                        href={audioUrl} 
-                        download 
-                        className="p-1.5 text-gray-400 hover:text-indigo-600 hover:bg-indigo-50 rounded-lg transition-colors"
-                      >
-                        <Download className="h-4 w-4" />
-                      </a>
-                    </div>
-                  )}
-                </div>
-
-                <div className="flex-1 overflow-y-auto p-4 space-y-3">
-                  {resultData.speaker_segments && resultData.speaker_segments.length > 0 ? (
-                    resultData.speaker_segments.map((segment, index) => {
-                      const isUser = segment.speaker === 'Speaker 1'
-                      const isActive = activeSegmentIndex === index
-                      
-                      return (
-                        <div 
-                          key={index} 
-                          className={`flex ${isUser ? 'justify-end' : 'justify-start'}`}
-                          onClick={() => handleSegmentClick(segment.start)}
-                        >
-                          <div className={`max-w-[70%] rounded-2xl px-4 py-2.5 cursor-pointer transition-all ${
-                            isUser
-                              ? 'bg-indigo-600 text-white rounded-br-sm'
-                              : 'bg-white border border-gray-200 text-gray-800 rounded-bl-sm'
-                          } ${isActive ? 'ring-2 ring-indigo-300 shadow-md' : ''}`}>
-                            <div className={`flex items-center gap-2 mb-0.5 ${isUser ? 'text-indigo-200' : 'text-gray-400'}`}>
-                              <span className="text-[10px] font-semibold uppercase tracking-wider">
-                                {isUser ? 'Caller' : (resultData.agent?.name || 'Agent')}
-                              </span>
-                              <span className="text-[10px] tabular-nums">{formatTime(segment.start)}</span>
-                            </div>
-                            <p className="text-sm leading-relaxed">{segment.text}</p>
-                          </div>
-                        </div>
-                      )
-                    })
-                  ) : resultData.transcription ? (
-                    <div className="p-4 bg-white rounded-lg border border-gray-100">
-                      <p className="text-sm text-gray-700 whitespace-pre-wrap leading-relaxed">{resultData.transcription}</p>
-                    </div>
-                  ) : (
-                    <div className="text-center py-12 text-gray-500">
-                      <MessageSquare className="w-8 h-8 text-gray-300 mx-auto mb-2" />
-                      <p className="text-sm">
-                        {resultData.status === 'transcribing' ? 'Transcription in progress...' : 'No transcript available'}
-                      </p>
-                    </div>
-                  )}
-                </div>
-              </div>
-            )}
-
-            {/* Hidden audio element */}
-            <audio
-              ref={audioRef}
-              src={audioUrl || ''}
-              preload="metadata"
-              onEnded={() => setActiveSegmentIndex(null)}
-              onLoadedMetadata={() => {
-                if (audioRef.current?.duration && audioRef.current.duration !== Infinity) {
-                  setAudioDuration(audioRef.current.duration)
-                }
-              }}
-              className="hidden"
-            />
-          </div>
-        </div>
-      ) : null}
+      <TraceDetailDrawer
+        open={detailDrawerOpen}
+        callShortId={useProviderDrawer ? callShortId : null}
+        evaluatorResultId={useProviderDrawer ? null : resultData.id}
+        onClose={() => setDetailDrawerOpen(false)}
+      />
       <ToastContainer />
     </div>
   )
diff --git a/frontend/src/pages/observability/ObservabilityCallDetail.tsx b/frontend/src/pages/observability/ObservabilityCallDetail.tsx
index 9b1e7e00..fa7509e5 100644
--- a/frontend/src/pages/observability/ObservabilityCallDetail.tsx
+++ b/frontend/src/pages/observability/ObservabilityCallDetail.tsx
@@ -1,761 +1,7 @@
-import { useNavigate, useParams } from 'react-router-dom'
-import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
-import { useEffect, useState } from 'react'
-import {
-  ArrowLeft, Phone, Clock, PhoneIncoming, PhoneOutgoing,
-  MessageSquare, Trash2, Download, Tag,
-  Loader, XCircle, Sparkles, X,
-} from 'lucide-react'
-import { motion, AnimatePresence } from 'framer-motion'
-
-import Button from '../../components/Button'
-import ConfirmModal from '../../components/ConfirmModal'
-import { apiClient } from '../../lib/api'
-import RetellCallDetails from '../../components/call-recordings/RetellCallDetails'
-import VapiCallDetails from '../../components/call-recordings/VapiCallDetails'
-import VobizCallDetails from '../../components/call-recordings/VobizCallDetails'
-import { getIntegrationPlatformLabel, getIntegrationPlatformLogo } from '../../config/providers'
-import { IntegrationPlatform, ObservabilityCall } from '../../types/api'
-import { useRecordingPresignedUrl } from '../../hooks/useRecordingPresignedUrl'
-import { CallAgentLink } from './CallAgentLink'
-
-export default function ObservabilityCallDetail() {
-  const navigate = useNavigate()
-  const { callShortId } = useParams<{ callShortId: string }>()
-  const queryClient = useQueryClient()
-  const [showDelete, setShowDelete] = useState(false)
-  const [showEvalModal, setShowEvalModal] = useState(false)
-  const [selectedEvaluator, setSelectedEvaluator] = useState('')
-  const [liveTranscript, setLiveTranscript] = useState<Array<{ role: string; content: string; timestamp?: string }>>([])
-
-  const liveEvents = new Set([
-    'outbound_initiated',
-    'ringing',
-    'call_started',
-    'call_in_progress',
-    'in-progress',
-    'answered',
-  ])
-
-  const {
-    data: callRecording,
-    isLoading,
-  } = useQuery<ObservabilityCall>({
-    queryKey: ['observability-call', callShortId],
-    queryFn: () => apiClient.getObservabilityCall(callShortId!),
-    enabled: !!callShortId,
-    refetchInterval: (query) => {
-      const data = query.state.data as any
-      if (!data) return false
-      const isLive = data.is_live || liveEvents.has((data.call_event || '').toLowerCase())
-      return isLive ? 3000 : false
-    },
-  })
-
-  useEffect(() => {
-    const existing = callRecording?.call_data?.live_transcript
-    if (!Array.isArray(existing) || existing.length === 0) return
-    setLiveTranscript((prev) => (existing.length >= prev.length ? existing : prev))
-  }, [callRecording?.call_data?.live_transcript])
-
-  useEffect(() => {
-    if (callRecording?.call_event === 'call_ended') {
-      queryClient.invalidateQueries({ queryKey: ['observability-call', callShortId] })
-    }
-  }, [callRecording?.call_event, callShortId, queryClient])
-
-  useEffect(() => {
-    if (!callShortId || !callRecording) return
-    const isLive = callRecording.is_live || liveEvents.has((callRecording.call_event || '').toLowerCase())
-    if (!isLive) return
-
-    let eventSource: EventSource | null = null
-    try {
-      const url = apiClient.getObservabilityCallLiveEventsUrl(callShortId)
-      eventSource = new EventSource(url)
-
-      eventSource.onmessage = (event) => {
-        try {
-          const entry = JSON.parse(event.data)
-          setLiveTranscript((prev) => [...prev, entry])
-        } catch {
-          // ignore malformed events
-        }
-      }
-    } catch {
-      // Polling via react-query still updates live_transcript from call_data
-    }
-
-    return () => {
-      eventSource?.close()
-    }
-  }, [callShortId, callRecording?.call_event, callRecording?.is_live])
-
-  const storageKey = callRecording?.call_data?.recording_s3_key ?? undefined
-  const providerRecordingUrl = callRecording?.call_data?.recording_url ?? null
-  const hasStorageRecording = !!storageKey
-
-  const {
-    data: presignedRecording,
-    isLoading: presignedLoading,
-  } = useRecordingPresignedUrl(storageKey)
-
-  const { data: evaluators = [] } = useQuery({
-    queryKey: ['evaluators'],
-    queryFn: () => apiClient.listEvaluators(),
-    enabled: showEvalModal,
-  })
-
-  const deleteMutation = useMutation({
-    mutationFn: () => apiClient.deleteObservabilityCall(callShortId!),
-    onSuccess: () => {
-      queryClient.invalidateQueries({ queryKey: ['observability-calls'] })
-      navigate('/observability/calls')
-    },
-  })
-
-  const evaluateMutation = useMutation({
-    mutationFn: (evaluatorId: string) =>
-      apiClient.evaluateObservabilityCall(callShortId!, evaluatorId),
-    onSuccess: (data) => {
-      setShowEvalModal(false)
-      setSelectedEvaluator('')
-      navigate(`/results/${data.result_id}`)
-    },
-  })
-
-  if (isLoading) {
-    return (
-      <div className="flex items-center justify-center min-h-[60vh]">
-        <div className="text-center">
-          <Loader className="w-8 h-8 text-indigo-500 animate-spin mx-auto" />
-          <p className="text-sm text-gray-500 mt-3">Loading call details...</p>
-        </div>
-      </div>
-    )
-  }
-
-  if (!callRecording) {
-    return (
-      <div className="max-w-2xl mx-auto px-4 py-12">
-        <div className="bg-rose-50 border border-rose-200 rounded-xl p-6 text-center">
-          <XCircle className="w-10 h-10 text-rose-400 mx-auto mb-3" />
-          <p className="text-sm font-medium text-rose-800">Call not found</p>
-          <Button
-            onClick={() => navigate('/observability/calls')}
-            variant="ghost"
-            size="sm"
-            className="mt-4"
-          >
-            <ArrowLeft className="w-4 h-4 mr-1.5" />
-            Back to Calls
-          </Button>
-        </div>
-      </div>
-    )
-  }
-
-  const callData = callRecording.call_data
-  const liveTranscriptEntries: Array<{ role: string; content: string; timestamp?: string; start_time?: number }> =
-    Array.isArray(callData?.live_transcript) ? callData.live_transcript : []
-  const messagesFromLive = liveTranscriptEntries
-    .filter((entry) => entry?.content)
-    .map((entry) => ({
-      role: entry.role === 'user' ? 'user' : 'assistant',
-      content: entry.content,
-      start_time: entry.start_time
-        ?? (entry.timestamp ? new Date(entry.timestamp).getTime() : undefined),
-    }))
-  const messages: any[] | undefined = Array.isArray(callData?.messages) && callData.messages.length > 0
-    ? callData.messages
-    : messagesFromLive.length > 0
-      ? messagesFromLive
-      : undefined
-  const playbackUrl = presignedRecording?.url || providerRecordingUrl
-  const audioLoading = hasStorageRecording && presignedLoading && !playbackUrl
-
-  const isLiveCall = callRecording.is_live || liveEvents.has((callRecording.call_event || '').toLowerCase())
-
-  const toTranscriptTurn = (entry: { role: string; content: string; start_time?: number }) => ({
-    role: entry.role === 'user' ? 'user' as const : 'agent' as const,
-    content: entry.content,
-    start_time: entry.start_time,
-  })
-
-  const persistedTurns = (messages || messagesFromLive).map(toTranscriptTurn)
-  const liveTurns = liveTranscript
-    .filter((entry) => entry?.content)
-    .map((entry) => toTranscriptTurn({
-      role: entry.role,
-      content: entry.content,
-      start_time: entry.timestamp ? new Date(entry.timestamp).getTime() : undefined,
-    }))
-
-  const transcriptTurns = isLiveCall && liveTurns.length > 0 ? liveTurns : persistedTurns
-  const hasTranscript = transcriptTurns.length > 0
-
-  const computeDuration = (): string | null => {
-    const started = callData?.startedAt || callData?.started_at
-    const ended = callData?.endedAt || callData?.ended_at
-    if (started && ended) {
-      const start = new Date(started).getTime()
-      const end = new Date(ended).getTime()
-      if (!isNaN(start) && !isNaN(end)) {
-        const diffSec = Math.floor((end - start) / 1000)
-        const mins = Math.floor(diffSec / 60)
-        const secs = diffSec % 60
-        return `${mins}m ${secs}s`
-      }
-    }
-    if (typeof callData?.duration_seconds === 'number') {
-      const diffSec = Math.floor(callData.duration_seconds)
-      const mins = Math.floor(diffSec / 60)
-      const secs = diffSec % 60
-      return `${mins}m ${secs}s`
-    }
-    return null
-  }
-
-  const formatMessageTime = (timestamp: number): string => {
-    const date = new Date(timestamp)
-    return date.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit', second: '2-digit' })
-  }
-
-  const duration = computeDuration()
-
-  return (
-    <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
-      {/* Header */}
-      <div className="mb-6">
-        <Button
-          variant="outline"
-          onClick={() => navigate('/observability/calls')}
-          leftIcon={<ArrowLeft className="h-4 w-4" />}
-          className="mb-4"
-        >
-          Back to Calls
-        </Button>
-
-        <div className="bg-white shadow rounded-lg p-6">
-          <div className="flex items-center justify-between">
-            <div>
-              <h1 className="text-2xl font-bold text-gray-900">Call Details</h1>
-              <p className="text-sm text-gray-500 mt-1">
-                Call ID:{' '}
-                <span className="font-mono font-semibold text-primary-600">
-                  {callRecording.call_short_id}
-                </span>
-              </p>
-            </div>
-            <div className="flex items-center gap-3">
-              {hasTranscript && (
-                <Button
-                  variant="primary"
-                  size="sm"
-                  onClick={() => setShowEvalModal(true)}
-                  leftIcon={<Sparkles className="h-4 w-4" />}
-                >
-                  Run Evaluation
-                </Button>
-              )}
-              <Button
-                variant="danger"
-                size="sm"
-                onClick={() => setShowDelete(true)}
-                isLoading={deleteMutation.isPending}
-                leftIcon={!deleteMutation.isPending ? <Trash2 className="h-4 w-4" /> : undefined}
-              >
-                Delete
-              </Button>
-              <EventBadge event={callRecording.call_event ?? undefined} />
-            </div>
-          </div>
-
-          {/* Metadata grid */}
-          <div className="mt-6 grid grid-cols-2 md:grid-cols-6 gap-4">
-            <div>
-              <p className="text-xs text-gray-500 font-medium mb-1">Status</p>
-              <span
-                className={`inline-flex items-center gap-1.5 px-2.5 py-1 rounded-full text-xs font-medium border ${
-                  callRecording.status === 'updated'
-                    ? 'bg-emerald-50 text-emerald-700 border-emerald-200'
-                    : 'bg-yellow-50 text-yellow-700 border-yellow-200'
-                }`}
-              >
-                <span
-                  className={`w-1.5 h-1.5 rounded-full ${
-                    callRecording.status === 'updated' ? 'bg-emerald-500' : 'bg-yellow-500'
-                  }`}
-                />
-                {callRecording.status === 'updated' ? 'Received' : callRecording.status}
-              </span>
-            </div>
-            <div>
-              <p className="text-xs text-gray-500 font-medium mb-1">Platform</p>
-              <PlatformBadge platform={callRecording.provider_platform ?? undefined} />
-            </div>
-            <div>
-              <p className="text-xs text-gray-500 font-medium mb-1">Agent</p>
-              <CallAgentLink
-                agent={callRecording.agent}
-                callData={callData}
-              />
-            </div>
-            <div>
-              <p className="text-xs text-gray-500 font-medium mb-1">Provider Call ID</p>
-              <p
-                className="text-sm font-mono text-gray-900 text-xs truncate max-w-[180px]"
-                title={callRecording.provider_call_id ?? undefined}
-              >
-                {callRecording.provider_call_id || 'N/A'}
-              </p>
-            </div>
-            {duration && (
-              <div>
-                <p className="text-xs text-gray-500 font-medium mb-1">Duration</p>
-                <p className="text-sm text-gray-900 flex items-center">
-                  <Clock className="w-4 h-4 mr-1" />
-                  {duration}
-                </p>
-              </div>
-            )}
-            <div>
-              <p className="text-xs text-gray-500 font-medium mb-1">Created</p>
-              <p className="text-sm text-gray-900">
-                {callRecording.created_at
-                  ? new Date(callRecording.created_at).toLocaleString()
-                  : 'N/A'}
-              </p>
-            </div>
-          </div>
-        </div>
-      </div>
-
-      {/* Provider-specific call details (Call Analysis, Cost, Latency, System Details) */}
-      {callRecording.provider_platform === 'retell' && callData && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Provider Call Details
-            <span className="ml-2 px-2 py-0.5 text-xs bg-blue-100 text-blue-800 rounded-full">
-              retell
-            </span>
-          </h2>
-          <RetellCallDetails callData={callData} hideTranscript={hasTranscript} />
-        </div>
-      )}
-
-      {callRecording.provider_platform === 'vapi' && callData && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Provider Call Details
-            <span className="ml-2 px-2 py-0.5 text-xs bg-violet-100 text-violet-800 rounded-full">
-              vapi
-            </span>
-          </h2>
-          <VapiCallDetails callData={callData} hideTranscript={hasTranscript} />
-        </div>
-      )}
-
-      {callRecording.provider_platform === 'vobiz' && callData && (
-        <div className="mb-6">
-          <VobizCallDetails callData={callData} />
-        </div>
-      )}
-
-      {(hasStorageRecording || providerRecordingUrl) && !isLiveCall && (
-        <div className="mb-6 bg-white rounded-lg shadow p-6">
-          <h2 className="text-lg font-semibold text-gray-900 flex items-center mb-4">
-            <Phone className="w-5 h-5 mr-2" />
-            Call Recording
-          </h2>
-          {audioLoading ? (
-            <div className="flex items-center gap-2 text-sm text-gray-500">
-              <Loader className="w-4 h-4 animate-spin" />
-              Loading recording...
-            </div>
-          ) : playbackUrl ? (
-            <div className="flex flex-col sm:flex-row sm:items-center gap-4">
-              <audio controls src={playbackUrl} preload="metadata" className="w-full max-w-xl" />
-              <a
-                href={playbackUrl}
-                download={`call_${callRecording.call_short_id}.wav`}
-                className="inline-flex items-center gap-2 text-sm text-indigo-600 hover:text-indigo-800"
-              >
-                <Download className="w-4 h-4" />
-                Download recording
-              </a>
-            </div>
-          ) : null}
-        </div>
-      )}
-
-      {(hasTranscript || isLiveCall) && (
-        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6 mb-6">
-          {/* Left: Transcript */}
-          <div className="lg:col-span-2">
-            <div className="rounded-xl border border-gray-100 bg-gray-50/30 flex flex-col h-[560px]">
-              <div className="px-4 py-3 border-b border-gray-100 flex items-center justify-between flex-shrink-0">
-                <div className="flex items-center gap-2">
-                  <MessageSquare className="h-4 w-4 text-indigo-500" />
-                  <span className="text-sm font-medium text-gray-900">
-                    {isLiveCall ? 'Live Transcript' : 'Transcript'}
-                  </span>
-                  {isLiveCall && (
-                    <span className="px-2 py-0.5 text-xs bg-sky-100 text-sky-800 rounded-full animate-pulse">
-                      Live
-                    </span>
-                  )}
-                  <span className="px-2 py-0.5 text-xs bg-gray-100 text-gray-600 rounded-full">
-                    {transcriptTurns.length} turns
-                  </span>
-                </div>
-                {(hasStorageRecording || providerRecordingUrl) && isLiveCall && playbackUrl && (
-                  <div className="flex items-center gap-2">
-                    <audio controls src={playbackUrl} preload="metadata" className="h-8 w-56" />
-                  </div>
-                )}
-              </div>
-
-              <div className="flex-1 overflow-y-auto p-4 space-y-3">
-                {transcriptTurns.length === 0 ? (
-                  <p className="text-sm text-gray-500 text-center py-8">Waiting for speech…</p>
-                ) : (
-                  transcriptTurns.map((turn, index) => {
-                  const isUser = turn.role === 'user'
-
-                  return (
-                    <motion.div
-                      key={index}
-                      className={`flex ${isUser ? 'justify-end' : 'justify-start'}`}
-                      initial={{ opacity: 0, y: 8 }}
-                      animate={{ opacity: 1, y: 0 }}
-                      transition={{ duration: 0.2, delay: index * 0.03 }}
-                    >
-                      <div
-                        className={`max-w-[80%] rounded-2xl px-4 py-2.5 ${
-                          isUser
-                            ? 'bg-indigo-600 text-white rounded-br-sm'
-                            : 'bg-white border border-gray-200 text-gray-800 rounded-bl-sm'
-                        }`}
-                      >
-                        <div
-                          className={`flex items-center gap-2 mb-0.5 ${
-                            isUser ? 'text-indigo-200' : 'text-gray-400'
-                          }`}
-                        >
-                          <span className="text-[10px] font-semibold uppercase tracking-wider">
-                            {isUser ? 'Caller' : 'Agent'}
-                          </span>
-                          {turn.start_time && (
-                            <span className="text-[10px] tabular-nums">
-                              {formatMessageTime(turn.start_time)}
-                            </span>
-                          )}
-                        </div>
-                        <p className="text-sm leading-relaxed">{turn.content}</p>
-                      </div>
-                    </motion.div>
-                  )
-                })
-                )}
-              </div>
-            </div>
-          </div>
-
-          {/* Right: Call Summary */}
-          <div className="lg:col-span-1 space-y-4">
-            <div className="rounded-xl border border-gray-100 bg-gray-50/30 p-5">
-              <h3 className="text-sm font-semibold text-gray-900 mb-4 flex items-center gap-2">
-                <Phone className="h-4 w-4 text-indigo-500" />
-                Call Summary
-              </h3>
-              <div className="space-y-3">
-                {duration && (
-                  <div className="p-3 bg-white rounded-lg border border-gray-100">
-                    <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">
-                      Duration
-                    </p>
-                    <p className="text-base font-semibold text-gray-900 tabular-nums">{duration}</p>
-                  </div>
-                )}
-
-                {callData?.startedAt && (
-                  <div className="p-3 bg-white rounded-lg border border-gray-100">
-                    <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">
-                      Started At
-                    </p>
-                    <p className="text-sm text-gray-900">
-                      {new Date(callData.startedAt).toLocaleString()}
-                    </p>
-                  </div>
-                )}
-
-                {callData?.endedAt && (
-                  <div className="p-3 bg-white rounded-lg border border-gray-100">
-                    <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">
-                      Ended At
-                    </p>
-                    <p className="text-sm text-gray-900">
-                      {new Date(callData.endedAt).toLocaleString()}
-                    </p>
-                  </div>
-                )}
-
-                {(callData?.from_phone_number || callData?.to_phone_number) && (
-                  <div className="p-3 bg-white rounded-lg border border-gray-100">
-                    <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">
-                      Phone Numbers
-                    </p>
-                    <div className="space-y-2 mt-1.5">
-                      {callData.from_phone_number && (
-                        <div className="flex items-center gap-2">
-                          <PhoneOutgoing className="w-3.5 h-3.5 text-gray-400" />
-                          <span className="text-sm text-gray-700 font-mono">
-                            {callData.from_phone_number}
-                          </span>
-                        </div>
-                      )}
-                      {callData.to_phone_number && (
-                        <div className="flex items-center gap-2">
-                          <PhoneIncoming className="w-3.5 h-3.5 text-gray-400" />
-                          <span className="text-sm text-gray-700 font-mono">
-                            {callData.to_phone_number}
-                          </span>
-                        </div>
-                      )}
-                    </div>
-                  </div>
-                )}
-
-                {callData?.endedReason && (
-                  <div className="p-3 bg-white rounded-lg border border-gray-100">
-                    <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-1">
-                      End Reason
-                    </p>
-                    <EndReasonBadge reason={callData.endedReason} />
-                  </div>
-                )}
-
-                {callData?.metadata &&
-                  Object.keys(callData.metadata).length > 0 && (
-                    <div className="p-3 bg-white rounded-lg border border-gray-100">
-                      <p className="text-[10px] text-gray-400 uppercase tracking-wider font-semibold mb-2">
-                        Metadata
-                      </p>
-                      <div className="space-y-1.5">
-                        {Object.entries(callData.metadata).map(([key, value]) => (
-                          <div key={key} className="flex items-start gap-2">
-                            <Tag className="w-3 h-3 text-gray-300 mt-0.5 flex-shrink-0" />
-                            <div className="min-w-0">
-                              <span className="text-xs text-gray-500">{key}:</span>
-                              <span className="text-xs text-gray-800 ml-1 font-medium">
-                                {String(value)}
-                              </span>
-                            </div>
-                          </div>
-                        ))}
-                      </div>
-                    </div>
-                  )}
-
-              </div>
-            </div>
-          </div>
-        </div>
-      )}
-
-      <ConfirmModal
-        title="Delete call"
-        description="This will permanently remove this call record."
-        isOpen={showDelete}
-        isLoading={deleteMutation.isPending}
-        onCancel={() => setShowDelete(false)}
-        onConfirm={() => deleteMutation.mutate()}
-      />
-
-      {/* Evaluate Modal */}
-      <AnimatePresence>
-        {showEvalModal && (
-          <motion.div
-            className="fixed inset-0 z-50 flex items-center justify-center"
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            exit={{ opacity: 0 }}
-          >
-            <div
-              className="absolute inset-0 bg-gray-500 bg-opacity-75"
-              onClick={() => {
-                setShowEvalModal(false)
-                setSelectedEvaluator('')
-              }}
-            />
-            <motion.div
-              className="relative bg-white rounded-2xl shadow-2xl max-w-lg w-full mx-4 overflow-hidden"
-              initial={{ opacity: 0, scale: 0.95, y: 20 }}
-              animate={{ opacity: 1, scale: 1, y: 0 }}
-              exit={{ opacity: 0, scale: 0.95, y: 20 }}
-              transition={{ duration: 0.2 }}
-            >
-              <div className="px-6 py-5 border-b border-gray-100">
-                <div className="flex items-center justify-between">
-                  <div>
-                    <h2 className="text-lg font-semibold text-gray-900">Run Evaluation</h2>
-                    <p className="text-sm text-gray-500 mt-0.5">
-                      Evaluate call <span className="font-mono font-semibold">{callRecording.call_short_id}</span> against an evaluator
-                    </p>
-                  </div>
-                  <button
-                    onClick={() => {
-                      setShowEvalModal(false)
-                      setSelectedEvaluator('')
-                    }}
-                    className="w-8 h-8 flex items-center justify-center rounded-lg text-gray-400 hover:text-gray-600 hover:bg-gray-100 transition-colors"
-                  >
-                    <X className="w-4 h-4" />
-                  </button>
-                </div>
-              </div>
-
-              <div className="p-6 space-y-4">
-                <div>
-                  <label className="block text-sm font-medium text-gray-700 mb-2">
-                    Select Evaluator
-                  </label>
-                  <select
-                    value={selectedEvaluator}
-                    onChange={(e) => setSelectedEvaluator(e.target.value)}
-                    className="w-full px-3 py-2.5 text-sm border border-gray-200 rounded-xl focus:ring-2 focus:ring-indigo-500 focus:border-indigo-500 bg-white"
-                  >
-                    <option value="">Choose an evaluator...</option>
-                    {evaluators.map((evaluator: any) => (
-                      <option key={evaluator.id} value={evaluator.id}>
-                        {evaluator.evaluator_id} &mdash;{' '}
-                        {evaluator.custom_prompt
-                          ? `Custom: ${evaluator.name || 'Unnamed'}`
-                          : `Agent: ${evaluator.agent_id?.substring(0, 8) || '?'}...`}
-                      </option>
-                    ))}
-                  </select>
-                </div>
-
-                {evaluateMutation.isError && (
-                  <div className="p-3 bg-rose-50 border border-rose-200 rounded-lg text-sm text-rose-700">
-                    {(evaluateMutation.error as any)?.response?.data?.detail || 'Failed to start evaluation'}
-                  </div>
-                )}
-              </div>
-
-              <div className="px-6 py-4 border-t border-gray-100 bg-gray-50/50 flex justify-end gap-3">
-                <Button
-                  variant="ghost"
-                  size="sm"
-                  onClick={() => {
-                    setShowEvalModal(false)
-                    setSelectedEvaluator('')
-                  }}
-                >
-                  Cancel
-                </Button>
-                <Button
-                  size="sm"
-                  onClick={() => evaluateMutation.mutate(selectedEvaluator)}
-                  disabled={!selectedEvaluator || evaluateMutation.isPending}
-                  isLoading={evaluateMutation.isPending}
-                  leftIcon={!evaluateMutation.isPending ? <Sparkles className="h-4 w-4" /> : undefined}
-                >
-                  {evaluateMutation.isPending ? 'Starting...' : 'Run Evaluation'}
-                </Button>
-              </div>
-            </motion.div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  )
-}
-
-function EventBadge({ event }: { event?: string }) {
-  if (!event) return <span className="text-gray-400">&mdash;</span>
-
-  const variants: Record<
-    string,
-    { label: string; bg: string; text: string; border: string; dot: string }
-  > = {
-    call_started: {
-      label: 'Call Started',
-      bg: 'bg-blue-50',
-      text: 'text-blue-700',
-      border: 'border-blue-200',
-      dot: 'bg-blue-500',
-    },
-    call_ended: {
-      label: 'Call Ended',
-      bg: 'bg-emerald-50',
-      text: 'text-emerald-700',
-      border: 'border-emerald-200',
-      dot: 'bg-emerald-500',
-    },
-    call_analyzed: {
-      label: 'Call Analyzed',
-      bg: 'bg-purple-50',
-      text: 'text-purple-700',
-      border: 'border-purple-200',
-      dot: 'bg-purple-500',
-    },
-  }
-
-  const variant = variants[event.toLowerCase()] || {
-    label: event,
-    bg: 'bg-gray-50',
-    text: 'text-gray-600',
-    border: 'border-gray-200',
-    dot: 'bg-gray-400',
-  }
-
-  return (
-    <span
-      className={`inline-flex items-center gap-1.5 px-3 py-1 rounded-full text-xs font-semibold border ${variant.bg} ${variant.text} ${variant.border}`}
-    >
-      <span className={`w-1.5 h-1.5 rounded-full ${variant.dot}`} />
-      {variant.label}
-    </span>
-  )
-}
-
-function EndReasonBadge({ reason }: { reason: string }) {
-  const colors: Record<string, string> = {
-    'customer-hungup': 'bg-amber-50 text-amber-700 border-amber-200',
-    'assistant-ended-call': 'bg-blue-50 text-blue-700 border-blue-200',
-    voicemail: 'bg-purple-50 text-purple-700 border-purple-200',
-    error: 'bg-rose-50 text-rose-700 border-rose-200',
-  }
-
-  const colorClass = colors[reason.toLowerCase()] || 'bg-gray-50 text-gray-700 border-gray-200'
-  const label = reason
-    .replace(/-/g, ' ')
-    .replace(/\b\w/g, (l) => l.toUpperCase())
-
-  return (
-    <span
-      className={`inline-flex items-center px-2.5 py-1 rounded-full text-xs font-medium border ${colorClass}`}
-    >
-      {label}
-    </span>
-  )
-}
-
-function PlatformBadge({ platform }: { platform?: string }) {
-  if (!platform) return <span className="text-gray-400">N/A</span>
-  const normalized = platform.toLowerCase() as IntegrationPlatform
-  const label = getIntegrationPlatformLabel(normalized)
-  const logo = getIntegrationPlatformLogo(normalized)
-
-  return (
-    <span className="inline-flex items-center gap-2 text-sm text-gray-700">
-      {logo && <img src={logo} alt={label} className="h-5 w-5 object-contain" />}
-      <span>{label}</span>
-    </span>
-  )
-}
+import { Navigate, useParams } from 'react-router-dom'
+
+export default function ObservabilityCallDetail() {
+  const { callShortId } = useParams<{ callShortId: string }>()
+  if (!callShortId) return <Navigate to="/calls" replace />
+  return <Navigate to={`/calls?obs=${callShortId}`} replace />
+}
diff --git a/frontend/src/pages/observability/observabilityCallUi.tsx b/frontend/src/pages/observability/observabilityCallUi.tsx
new file mode 100644
index 00000000..648b5670
--- /dev/null
+++ b/frontend/src/pages/observability/observabilityCallUi.tsx
@@ -0,0 +1,113 @@
+import { getIntegrationPlatformLabel, getIntegrationPlatformLogo } from '../../config/providers'
+import { IntegrationPlatform } from '../../types/api'
+
+export function EventBadge({ event }: { event?: string }) {
+  if (!event) return <span className="text-gray-400">&mdash;</span>
+
+  const variants: Record<
+    string,
+    { label: string; bg: string; text: string; border: string; dot: string; pulse?: boolean }
+  > = {
+    outbound_initiated: {
+      label: 'Ringing',
+      bg: 'bg-amber-50',
+      text: 'text-amber-700',
+      border: 'border-amber-200',
+      dot: 'bg-amber-500',
+      pulse: true,
+    },
+    ringing: {
+      label: 'Ringing',
+      bg: 'bg-amber-50',
+      text: 'text-amber-700',
+      border: 'border-amber-200',
+      dot: 'bg-amber-500',
+      pulse: true,
+    },
+    call_in_progress: {
+      label: 'In Progress',
+      bg: 'bg-sky-50',
+      text: 'text-sky-700',
+      border: 'border-sky-200',
+      dot: 'bg-sky-500',
+      pulse: true,
+    },
+    call_started: {
+      label: 'Call Started',
+      bg: 'bg-blue-50',
+      text: 'text-blue-700',
+      border: 'border-blue-200',
+      dot: 'bg-blue-500',
+    },
+    call_ended: {
+      label: 'Call Ended',
+      bg: 'bg-emerald-50',
+      text: 'text-emerald-700',
+      border: 'border-emerald-200',
+      dot: 'bg-emerald-500',
+    },
+    failed: {
+      label: 'Failed',
+      bg: 'bg-rose-50',
+      text: 'text-rose-700',
+      border: 'border-rose-200',
+      dot: 'bg-rose-500',
+    },
+    call_analyzed: {
+      label: 'Call Analyzed',
+      bg: 'bg-purple-50',
+      text: 'text-purple-700',
+      border: 'border-purple-200',
+      dot: 'bg-purple-500',
+    },
+  }
+
+  const variant = variants[event.toLowerCase()] || {
+    label: event,
+    bg: 'bg-gray-50',
+    text: 'text-gray-600',
+    border: 'border-gray-200',
+    dot: 'bg-gray-400',
+  }
+
+  return (
+    <span
+      className={`inline-flex items-center gap-1.5 px-2.5 py-1 rounded-full text-xs font-medium border ${variant.bg} ${variant.text} ${variant.border}`}
+    >
+      <span className={`w-1.5 h-1.5 rounded-full ${variant.dot} ${variant.pulse ? 'animate-pulse' : ''}`} />
+      {variant.label}
+    </span>
+  )
+}
+
+export function EndReasonBadge({ reason }: { reason: string }) {
+  const colors: Record<string, string> = {
+    'customer-hungup': 'bg-amber-50 text-amber-700 border-amber-200',
+    'assistant-ended-call': 'bg-blue-50 text-blue-700 border-blue-200',
+    voicemail: 'bg-purple-50 text-purple-700 border-purple-200',
+    error: 'bg-rose-50 text-rose-700 border-rose-200',
+  }
+
+  const colorClass = colors[reason.toLowerCase()] || 'bg-gray-50 text-gray-700 border-gray-200'
+  const label = reason.replace(/-/g, ' ').replace(/\b\w/g, (l) => l.toUpperCase())
+
+  return (
+    <span className={`inline-flex items-center px-2.5 py-1 rounded-full text-xs font-medium border ${colorClass}`}>
+      {label}
+    </span>
+  )
+}
+
+export function PlatformBadge({ platform }: { platform?: string }) {
+  if (!platform) return <span className="text-gray-400">N/A</span>
+  const normalized = platform.toLowerCase() as IntegrationPlatform
+  const label = getIntegrationPlatformLabel(normalized)
+  const logo = getIntegrationPlatformLogo(normalized)
+
+  return (
+    <span className="inline-flex items-center gap-2 text-sm text-gray-700">
+      {logo && <img src={logo} alt={label} className="h-5 w-5 object-contain" />}
+      <span>{label}</span>
+    </span>
+  )
+}
diff --git a/frontend/src/pages/playground/agent/AgentPlayground.tsx b/frontend/src/pages/playground/agent/AgentPlayground.tsx
index 66d195dc..43b24bc0 100644
--- a/frontend/src/pages/playground/agent/AgentPlayground.tsx
+++ b/frontend/src/pages/playground/agent/AgentPlayground.tsx
@@ -1,1589 +1,1655 @@
-import { useState, useEffect, useRef } from 'react'
-import { useNavigate } from 'react-router-dom'
-import { useAgentStore } from '../../../store/agentStore'
-import { useQuery, useQueryClient } from '@tanstack/react-query'
-import { apiClient } from '../../../lib/api'
-import { Play, X, Phone, PhoneOff, RefreshCw, Mic, Bot, PhoneCall, Trash2, AlertTriangle, CheckSquare, Square, Bookmark, BookmarkCheck } from 'lucide-react'
-import Button from '../../../components/Button'
-import { useToast } from '../../../hooks/useToast'
-import { RetellWebClient } from 'retell-client-js-sdk'
-import Vapi from '@vapi-ai/web'
-import { Conversation } from '@elevenlabs/client'
-import VoiceAgent from '../../../components/VoiceAgent'
-import GenericVoiceWSClient from '../../../components/GenericVoiceWSClient'
-import { getProtocolById } from '../../../lib/wsProtocols'
-import { getIntegrationPlatformLogo } from '../../../config/providers'
-import { IntegrationPlatform } from '../../../types/api'
-
-// Type for RetellWebClient - using the actual SDK methods
-type RetellWebClientWithMethods = RetellWebClient & {
-  startCall: (config: {
-    accessToken: string;
-    sampleRate?: number;
-    captureDeviceId?: string;
-    playbackDeviceId?: string;
-    emitRawAudioSamples?: boolean;
-    callId?: string;
-  }) => Promise<void>
-  stopCall: () => void
-}
-
-export default function AgentPlayground() {
-  const { selectedAgent } = useAgentStore()
-  const { showToast, ToastContainer } = useToast()
-  const navigate = useNavigate()
-  const queryClient = useQueryClient()
-  const [showModal, setShowModal] = useState(false)
-  const [showTestModal, setShowTestModal] = useState(false)
-  const [selectedTestType, setSelectedTestType] = useState<'test_agent' | 'voice_ai_agent' | null>(null)
-  const [isConnecting, setIsConnecting] = useState(false)
-  const [isConnected, setIsConnected] = useState(false)
-  const [isRefreshingStatus, setIsRefreshingStatus] = useState(false)
-  const [transcripts, setTranscripts] = useState<Array<{ role: 'user' | 'agent', content: string }>>([])
-
-  const retellClientRef = useRef<RetellWebClientWithMethods | null>(null)
-  const vapiClientRef = useRef<any>(null)
-  const elevenLabsConversationRef = useRef<any>(null)
-  const smallestClientRef = useRef<any>(null)
-  const currentCallShortIdRef = useRef<string | null>(null)
-
-  const userInitiatedDisconnectRef = useRef(false)
-
-  // Fetch integrations to check if agent has Retell integration
-  const { data: integrations = [] } = useQuery({
-    queryKey: ['integrations'],
-    queryFn: () => apiClient.listIntegrations(),
-  })
-
-  // Fetch full agent details including integration info
-  const { data: fullAgent } = useQuery({
-    queryKey: ['agent', selectedAgent?.id],
-    queryFn: () => apiClient.getAgent(selectedAgent?.id || ''),
-    enabled: !!selectedAgent?.id,
-  })
-
-
-  // Fetch test voice agent evaluation results (playground results only, excluding Voice AI agent results)
-  const { data: testVoiceAgentList, refetch: refetchTestResults } = useQuery({
-    queryKey: ['test-voice-agent-results'],
-    queryFn: async () => {
-      return await apiClient.listEvaluatorResults(undefined, true, true)
-    },
-  })
-  const testVoiceAgentResults = testVoiceAgentList?.items ?? []
-
-  // Fetch call recordings (for Voice AI Agents tab)
-  const { data: callRecordings = [], refetch: refetchCallRecordings } = useQuery({
-    queryKey: ['call-recordings'],
-    queryFn: () => apiClient.listCallRecordings(),
-    // Refetch every 5 seconds if there are any evaluations in progress
-    refetchInterval: (query) => {
-      const data = query.state.data as any[]
-      if (data && Array.isArray(data)) {
-        const hasInProgress = data.some((recording: any) => 
-          recording.evaluation_status && 
-          ['queued', 'transcribing', 'evaluating'].includes(recording.evaluation_status)
-        )
-        return hasInProgress ? 5000 : false
-      }
-      return false
-    },
-  })
-
-  // Check S3 storage status for warning
-  const { data: s3Status } = useQuery({
-    queryKey: ['s3-status'],
-    queryFn: () => apiClient.getS3Status(),
-    staleTime: 60_000,
-  })
-
-  const [activeTab, setActiveTab] = useState<'test_agents' | 'voice_ai_agents' | 'custom_websocket'>('voice_ai_agents')
-  const [customWebsocketUrl, setCustomWebsocketUrl] = useState('')
-
-  // Saved WebSocket URLs (persisted in localStorage)
-  type SavedWsUrl = { id: string; label: string; url: string; createdAt: string }
-  const SAVED_WS_KEY = 'efficientai-saved-ws-urls'
-  const loadSavedUrls = (): SavedWsUrl[] => {
-    try { return JSON.parse(localStorage.getItem(SAVED_WS_KEY) || '[]') } catch { return [] }
-  }
-  const [savedWsUrls, setSavedWsUrls] = useState<SavedWsUrl[]>(loadSavedUrls)
-  const [showSaveForm, setShowSaveForm] = useState(false)
-  const [saveLabel, setSaveLabel] = useState('')
-
-  const persistSavedUrls = (urls: SavedWsUrl[]) => {
-    setSavedWsUrls(urls)
-    localStorage.setItem(SAVED_WS_KEY, JSON.stringify(urls))
-  }
-  const handleSaveUrl = () => {
-    const url = customWebsocketUrl.trim()
-    if (!url) return
-    const label = saveLabel.trim() || url
-    const entry: SavedWsUrl = { id: crypto.randomUUID(), label, url, createdAt: new Date().toISOString() }
-    persistSavedUrls([entry, ...savedWsUrls])
-    setSaveLabel('')
-    setShowSaveForm(false)
-  }
-  const handleDeleteSavedUrl = (id: string) => {
-    persistSavedUrls(savedWsUrls.filter((s) => s.id !== id))
-  }
-  const isCurrentUrlSaved = savedWsUrls.some((s) => s.url === customWebsocketUrl.trim())
-  const [selectedCallIds, setSelectedCallIds] = useState<Set<string>>(new Set())
-  const [isDeletingSelected, setIsDeletingSelected] = useState(false)
-  const [selectedTestResultIds, setSelectedTestResultIds] = useState<Set<string>>(new Set())
-  const [isDeletingSelectedTests, setIsDeletingSelectedTests] = useState(false)
-
-
-  // Find the integration for the agent
-  const agentIntegration = fullAgent?.voice_ai_integration_id
-    ? integrations.find((int: any) => int.id === fullAgent.voice_ai_integration_id)
-    : null
-
-  const isRetellAgent = agentIntegration?.platform === 'retell'
-  const isVapiAgent = agentIntegration?.platform === 'vapi'
-  const isElevenLabsAgent = agentIntegration?.platform === 'elevenlabs'
-  const isSmallestAgent = agentIntegration?.platform === 'smallest'
-
-  const hasVoiceAIIntegration = Boolean(
-    fullAgent?.voice_ai_integration_id &&
-    fullAgent?.voice_ai_agent_id &&
-    (isRetellAgent || isVapiAgent || isElevenLabsAgent || isSmallestAgent)
-  )
-  // Playground web-call testing via Retell/Vapi/ElevenLabs/Smallest is independent
-  // of the agent's production call_medium (phone_call vs web_call).
-  const canMakeCall = hasVoiceAIIntegration
-
-  // Check if agent has Test Agent capabilities (voice bundle with STT/TTS/LLM)
-  const hasTestAgent = fullAgent?.voice_bundle_id != null
-  const hasVoiceAIAgent = hasVoiceAIIntegration
-
-  // Initialize Clients when modal opens
-  useEffect(() => {
-    if (showModal && canMakeCall) {
-      if (isRetellAgent && !retellClientRef.current) {
-        const client = new RetellWebClient() as unknown as RetellWebClientWithMethods
-        console.log('RetellWebClient initialized')
-        retellClientRef.current = client
-      } else if (isVapiAgent && !vapiClientRef.current && agentIntegration?.public_key) {
-        const client = new Vapi(agentIntegration.public_key)
-        console.log('Vapi client initialized')
-        vapiClientRef.current = client
-      }
-      // ElevenLabs: no persistent client — sessions are created on connect
-    }
-
-    return () => {
-      if (retellClientRef.current) {
-        try {
-          retellClientRef.current.stopCall()
-        } catch (e) {
-          console.error('Error stopping Retell call on cleanup:', e)
-        }
-      }
-      if (vapiClientRef.current) {
-        try {
-          vapiClientRef.current.stop()
-        } catch (e) {
-          console.error('Error stopping Vapi call on cleanup:', e)
-        }
-      }
-      if (elevenLabsConversationRef.current) {
-        try {
-          elevenLabsConversationRef.current.endSession()
-        } catch (e) {
-          console.error('Error ending ElevenLabs session on cleanup:', e)
-        }
-        elevenLabsConversationRef.current = null
-      }
-      if (smallestClientRef.current) {
-        try {
-          smallestClientRef.current.stopSession()
-        } catch (e) {
-          console.error('Error stopping Smallest session on cleanup:', e)
-        }
-        smallestClientRef.current = null
-      }
-    }
-  }, [showModal, canMakeCall, isRetellAgent, isVapiAgent, isElevenLabsAgent, isSmallestAgent, agentIntegration?.public_key])
-
-  const handleConnect = async () => {
-    if (!canMakeCall || !fullAgent?.id) {
-      showToast('Agent is not configured for web calls', 'error')
-      return
-    }
-
-    setIsConnecting(true)
-    setTranscripts([])
-
-    if (isRetellAgent) {
-      if (!retellClientRef.current) {
-        retellClientRef.current = new RetellWebClient() as unknown as RetellWebClientWithMethods
-      }
-
-      const client = retellClientRef.current
-
-      try {
-        // IMPORTANT: Request microphone permission FIRST, before creating the web call
-        try {
-          const testStream = await navigator.mediaDevices.getUserMedia({ audio: true })
-          console.log('Microphone access granted')
-          testStream.getTracks().forEach(track => track.stop())
-        } catch (micError: any) {
-          console.error('Microphone permission denied:', micError)
-          setIsConnecting(false)
-          showToast('Microphone permission is required for voice calls', 'error')
-          return
-        }
-
-        // Set up event handlers
-        client.on('call_started', () => {
-          console.log('Retell call started')
-          setIsConnected(true)
-          setIsConnecting(false)
-          showToast('Connected to agent', 'success')
-        })
-
-        // Handle updates (transcripts)
-        client.on('update', (update: any) => {
-          if (update.transcript) {
-            // Retell sends transcript as an array of {role, content, words} objects
-            // We need to convert this to our simpler format
-            const transcriptArray = Array.isArray(update.transcript) 
-              ? update.transcript 
-              : [{ role: update.role || 'agent', content: update.transcript }]
-            
-            setTranscripts(
-              transcriptArray
-                .filter((msg: any) => msg && msg.content && typeof msg.content === 'string')
-                .map((msg: any) => ({
-                  role: msg.role === 'user' ? 'user' as const : 'agent' as const,
-                  content: msg.content
-                }))
-            )
-          }
-        })
-
-        client.on('call_ended', (data?: any) => {
-          console.log('Retell call ended', data)
-          setIsConnected(false)
-          setIsConnecting(false)
-          if (!userInitiatedDisconnectRef.current) {
-            const reason = data?.reason || data?.code || 'Unknown reason'
-            showToast(`Call ended: ${reason}`)
-          }
-          userInitiatedDisconnectRef.current = false
-
-          // Trigger refresh of metrics
-          if (currentCallShortIdRef.current) {
-            apiClient.refreshCallRecording(currentCallShortIdRef.current)
-              .then(() => refetchCallRecordings())
-              .catch(err => console.error('Failed to refresh metrics', err))
-          }
-        })
-
-        client.on('error', (error: any) => {
-          console.error('Retell error:', error)
-          setIsConnecting(false)
-          setIsConnected(false)
-          showToast(`Error: ${error?.message || 'Unknown error'}`, 'error')
-        })
-
-        // Create web call
-        console.log('Creating web call...')
-        const webCallResponse = await apiClient.createWebCall({
-          agent_id: fullAgent.id,
-          metadata: {},
-          retell_llm_dynamic_variables: {},
-          custom_sip_headers: {},
-          ui_surface: 'agent_playground',
-        })
-
-        if (!webCallResponse.call_id || !webCallResponse.access_token) {
-          throw new Error('No call_id or access_token received')
-        }
-
-        if (webCallResponse.call_short_id) {
-          currentCallShortIdRef.current = webCallResponse.call_short_id
-        }
-
-        // Start call
-        await client.startCall({
-          accessToken: webCallResponse.access_token,
-          callId: webCallResponse.call_id,
-          sampleRate: webCallResponse.sample_rate || 24000,
-        })
-      } catch (error: any) {
-        console.error('Failed to connect Retell:', error)
-        setIsConnecting(false)
-        setIsConnected(false)
-        const detail = error?.response?.data?.detail || error?.message || 'Unknown error'
-        showToast(`Failed to connect: ${detail}`, 'error')
-      }
-    } else if (isVapiAgent) {
-      const client = vapiClientRef.current
-      if (!client) {
-        setIsConnecting(false)
-        showToast('Vapi client not initialized', 'error')
-        return
-      }
-
-      try {
-        // Create backend record for Vapi call
-        console.log('Creating Vapi web call record...')
-        const webCallResponse = await apiClient.createWebCall({
-          agent_id: fullAgent.id,
-          metadata: {},
-          ui_surface: 'agent_playground',
-        })
-
-        if (webCallResponse.call_short_id) {
-          currentCallShortIdRef.current = webCallResponse.call_short_id
-        }
-
-        // Vapi Event Handlers
-        client.on('call-start', async (call: any) => {
-          console.log('Vapi call started', call)
-          setIsConnected(true)
-          setIsConnecting(false)
-          showToast('Connected to agent', 'success')
-
-          // Update backend with Vapi Call ID
-          if (currentCallShortIdRef.current && call?.id) {
-            try {
-              await apiClient.updateCallRecording(currentCallShortIdRef.current, call.id)
-              console.log('Updated call recording with Vapi ID:', call.id)
-            } catch (err) {
-              console.error('Failed to update call recording provider ID', err)
-            }
-          }
-        })
-
-        // Vapi Transcription Handling
-        client.on('message', (message: any) => {
-          if (message.type === 'transcript' && message.transcriptType === 'final') {
-            const role = message.role === 'user' ? 'user' : 'agent'
-            setTranscripts(prev => [...prev, { role, content: message.transcript }])
-          }
-        })
-
-        client.on('call-end', async (call: any) => {
-          console.log('Vapi call ended', call)
-          setIsConnected(false)
-          setIsConnecting(false)
-          if (!userInitiatedDisconnectRef.current) {
-            showToast('Call ended')
-          }
-          userInitiatedDisconnectRef.current = false
-
-          // Ensure we have provider ID before refreshing
-          if (currentCallShortIdRef.current) {
-            if (call?.id) {
-              try {
-                await apiClient.updateCallRecording(currentCallShortIdRef.current, call.id)
-              } catch (e) {
-                console.error('Failed to update call recording on end', e)
-              }
-            }
-
-            apiClient.refreshCallRecording(currentCallShortIdRef.current)
-              .then(() => refetchCallRecordings())
-              .catch(err => console.error('Failed to refresh metrics', err))
-          }
-        })
-
-        client.on('error', (error: any) => {
-          console.error('Vapi error:', error)
-          setIsConnecting(false)
-          setIsConnected(false)
-          showToast(`Error: ${error?.message || 'Unknown error'}`, 'error')
-        })
-
-        // Start Call
-        // Note: For Vapi, we just need the assistant ID (voice_ai_agent_id)
-        console.log('Starting Vapi call...')
-        const vapiCall = await client.start(fullAgent.voice_ai_agent_id)
-        console.log('Vapi start returned:', vapiCall)
-
-        // Try to get ID from return value immediately
-        if (currentCallShortIdRef.current && vapiCall?.id) {
-          try {
-            await apiClient.updateCallRecording(currentCallShortIdRef.current, vapiCall.id)
-            console.log('Updated call recording with Vapi ID (from start):', vapiCall.id)
-          } catch (err) {
-            console.error('Failed to update call recording provider ID', err)
-          }
-        }
-      } catch (error: any) {
-        console.error('Failed to connect Vapi:', error)
-        setIsConnecting(false)
-        setIsConnected(false)
-        const detail = error?.response?.data?.detail || error?.message || 'Unknown error'
-        showToast(`Failed to connect: ${detail}`, 'error')
-      }
-    } else if (isElevenLabsAgent) {
-      try {
-        // Request microphone permission first
-        try {
-          const testStream = await navigator.mediaDevices.getUserMedia({ audio: true })
-          testStream.getTracks().forEach(track => track.stop())
-        } catch (micError: any) {
-          console.error('Microphone permission denied:', micError)
-          setIsConnecting(false)
-          showToast('Microphone permission is required for voice calls', 'error')
-          return
-        }
-
-        // Create backend record and get signed URL
-        console.log('Creating ElevenLabs web call...')
-        const webCallResponse = await apiClient.createWebCall({
-          agent_id: fullAgent.id,
-          metadata: {},
-          ui_surface: 'agent_playground',
-        })
-
-        if (webCallResponse.call_short_id) {
-          currentCallShortIdRef.current = webCallResponse.call_short_id
-        }
-
-        const signedUrl = webCallResponse.signed_url
-        if (!signedUrl) {
-          throw new Error('No signed_url received from backend')
-        }
-
-        console.log('Starting ElevenLabs conversation session with signed URL...')
-
-        let elevenLabsConversationIdStored = false
-
-        const conversationInstance = await Conversation.startSession({
-          signedUrl,
-          onConnect: () => {
-            console.log('ElevenLabs conversation connected')
-            setIsConnected(true)
-            setIsConnecting(false)
-            showToast('Connected to agent', 'success')
-          },
-          onDisconnect: () => {
-            console.log('ElevenLabs conversation disconnected')
-            setIsConnected(false)
-            setIsConnecting(false)
-            elevenLabsConversationRef.current = null
-            if (!userInitiatedDisconnectRef.current) {
-              showToast('Call ended')
-            }
-            userInitiatedDisconnectRef.current = false
-
-            // Only refresh if we successfully stored the conversation ID,
-            // otherwise the backend has no provider_call_id to fetch metrics for
-            if (currentCallShortIdRef.current && elevenLabsConversationIdStored) {
-              const callShortId = currentCallShortIdRef.current
-              // ElevenLabs transitions through "processing" before "done",
-              // so wait a few seconds before requesting metrics
-              setTimeout(() => {
-                apiClient.refreshCallRecording(callShortId)
-                  .then(() => refetchCallRecordings())
-                  .catch(err => console.error('Failed to refresh metrics', err))
-              }, 5000)
-            }
-          },
-          onMessage: (message: any) => {
-            console.log('ElevenLabs message:', message)
-            if (message?.source && message?.message) {
-              const role = message.source === 'user' ? 'user' as const : 'agent' as const
-              setTranscripts(prev => [...prev, { role, content: message.message }])
-            }
-          },
-          onError: (error: any) => {
-            console.error('ElevenLabs onError:', error)
-            setIsConnecting(false)
-            setIsConnected(false)
-            const msg = typeof error === 'string' ? error : error?.message || JSON.stringify(error)
-            showToast(`ElevenLabs error: ${msg}`, 'error')
-          },
-          onStatusChange: (status: any) => {
-            console.log('ElevenLabs status change:', status)
-          },
-        })
-
-        elevenLabsConversationRef.current = conversationInstance
-
-        // Get conversation ID AFTER startSession resolves (the instance is now available)
-        try {
-          const conversationId = conversationInstance?.getId()
-          console.log('ElevenLabs conversation ID:', conversationId)
-          if (currentCallShortIdRef.current && conversationId) {
-            await apiClient.updateCallRecording(currentCallShortIdRef.current, conversationId)
-            elevenLabsConversationIdStored = true
-            console.log('Updated call recording with ElevenLabs conversation ID:', conversationId)
-          } else {
-            console.warn('Missing callShortId or conversationId for ElevenLabs update', {
-              callShortId: currentCallShortIdRef.current,
-              conversationId,
-            })
-          }
-        } catch (e) {
-          console.error('Failed to update call recording with ElevenLabs conversation ID:', e)
-        }
-      } catch (error: any) {
-        console.error('Failed to connect ElevenLabs:', error)
-        setIsConnecting(false)
-        setIsConnected(false)
-        const detail = error?.response?.data?.detail
-          || (typeof error === 'string' ? error : error?.message || JSON.stringify(error))
-        showToast(`Failed to connect: ${detail}`, 'error')
-      }
-    } else if (isSmallestAgent) {
-      try {
-        const { AtomsClient } = await import('atoms-client-sdk')
-
-        // Request microphone permission first
-        try {
-          const testStream = await navigator.mediaDevices.getUserMedia({ audio: true })
-          testStream.getTracks().forEach(track => track.stop())
-        } catch (micError: any) {
-          console.error('Microphone permission denied:', micError)
-          setIsConnecting(false)
-          showToast('Microphone permission is required for voice calls', 'error')
-          return
-        }
-
-        const webCallResponse = await apiClient.createWebCall({
-          agent_id: fullAgent.id,
-          metadata: {},
-          ui_surface: 'agent_playground',
-        })
-
-        if (webCallResponse.call_short_id) {
-          currentCallShortIdRef.current = webCallResponse.call_short_id
-        }
-
-        if (!webCallResponse.access_token || !webCallResponse.host) {
-          throw new Error('Smallest webcall response missing access token or host')
-        }
-
-        const client = new AtomsClient()
-        smallestClientRef.current = client
-
-        client.on('session_started', () => {
-          console.log('Smallest webcall connected')
-          setIsConnected(true)
-          setIsConnecting(false)
-          showToast('Connected to agent', 'success')
-        })
-        client.on('session_ended', () => {
-          console.log('Smallest webcall disconnected')
-          setIsConnected(false)
-          setIsConnecting(false)
-          if (!userInitiatedDisconnectRef.current) {
-            showToast('Call ended')
-          }
-          userInitiatedDisconnectRef.current = false
-
-          if (currentCallShortIdRef.current) {
-            const callShortId = currentCallShortIdRef.current
-            setTimeout(() => {
-              apiClient.refreshCallRecording(callShortId)
-                .then(() => refetchCallRecordings())
-                .catch(err => console.error('Failed to refresh metrics', err))
-            }, 3000)
-          }
-        })
-        client.on('transcript', (data: any) => {
-          const content = data?.text
-          if (content && typeof content === 'string') {
-            setTranscripts(prev => [...prev, { role: 'agent', content }])
-          }
-        })
-        client.on('error', (errorMessage: string) => {
-          console.error('Smallest SDK error:', errorMessage)
-        })
-
-        await client.startSession({
-          accessToken: webCallResponse.access_token,
-          host: webCallResponse.host,
-          mode: 'webcall',
-        })
-        await client.startAudioPlayback()
-      } catch (error: any) {
-        console.error('Failed to connect Smallest:', error)
-        if (smallestClientRef.current) {
-          try {
-            smallestClientRef.current.stopSession()
-          } catch {
-            // ignore cleanup errors
-          }
-          smallestClientRef.current = null
-        }
-        setIsConnecting(false)
-        setIsConnected(false)
-        const detail = error?.response?.data?.detail
-          || (typeof error === 'string' ? error : error?.message || JSON.stringify(error))
-        showToast(`Failed to connect: ${detail}`, 'error')
-      }
-    }
-  }
-
-  const handleDisconnect = async () => {
-    userInitiatedDisconnectRef.current = true
-
-    if (isRetellAgent && retellClientRef.current) {
-      try {
-        retellClientRef.current.stopCall()
-      } catch (error: any) {
-        console.error('Failed to disconnect Retell:', error)
-      }
-    } else if (isVapiAgent && vapiClientRef.current) {
-      try {
-        vapiClientRef.current.stop()
-      } catch (error: any) {
-        console.error('Failed to disconnect Vapi:', error)
-      }
-    } else if (isElevenLabsAgent && elevenLabsConversationRef.current) {
-      try {
-        await elevenLabsConversationRef.current.endSession()
-        elevenLabsConversationRef.current = null
-      } catch (error: any) {
-        console.error('Failed to disconnect ElevenLabs:', error)
-      }
-    } else if (isSmallestAgent && smallestClientRef.current) {
-      try {
-        smallestClientRef.current.stopSession()
-        smallestClientRef.current = null
-      } catch (error: any) {
-        console.error('Failed to disconnect Smallest:', error)
-      }
-    }
-
-    setIsConnected(false)
-    showToast('Disconnected', 'success')
-  }
-
-  const handleCloseModal = () => {
-    if (isConnected) {
-      handleDisconnect()
-    }
-    setShowModal(false)
-    setShowTestModal(false)
-    setSelectedTestType(null)
-    setIsConnecting(false)
-    setIsConnected(false)
-  }
-
-  const handleTestTypeSelection = (type: 'test_agent' | 'voice_ai_agent') => {
-    setSelectedTestType(type)
-    if (type === 'voice_ai_agent') {
-      setShowModal(true)
-      setShowTestModal(false)
-    } else {
-      setShowTestModal(false)
-    }
-  }
-
-
-  const handleViewTestResult = (resultId: string) => {
-    navigate(`/playground/test-agent-results/${resultId}`)
-  }
-
-  const toggleTestResultSelection = (resultId: string) => {
-    setSelectedTestResultIds(prev => {
-      const next = new Set(prev)
-      if (next.has(resultId)) {
-        next.delete(resultId)
-      } else {
-        next.add(resultId)
-      }
-      return next
-    })
-  }
-
-  const toggleSelectAllTestResults = () => {
-    const allIds = testVoiceAgentResults.map((r: any) => r.id)
-    const allSelected = allIds.length > 0 && allIds.every((id: string) => selectedTestResultIds.has(id))
-    setSelectedTestResultIds(allSelected ? new Set() : new Set(allIds))
-  }
-
-  const handleDeleteSelectedTestResults = async () => {
-    if (selectedTestResultIds.size === 0) return
-    if (!window.confirm(`Delete ${selectedTestResultIds.size} test result${selectedTestResultIds.size > 1 ? 's' : ''}? This cannot be undone.`)) return
-
-    setIsDeletingSelectedTests(true)
-    const ids = Array.from(selectedTestResultIds)
-    try {
-      const results = await Promise.allSettled(ids.map(id => apiClient.deleteEvaluatorResult(id)))
-      const successCount = results.filter(r => r.status === 'fulfilled').length
-      const failCount = results.filter(r => r.status === 'rejected').length
-
-      if (successCount > 0) {
-        queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
-        showToast(`Deleted ${successCount} test result${successCount > 1 ? 's' : ''}`, 'success')
-      }
-      if (failCount > 0) {
-        showToast(`Failed to delete ${failCount} result${failCount > 1 ? 's' : ''}`, 'error')
-      }
-
-      setSelectedTestResultIds(prev => {
-        const next = new Set(prev)
-        ids.filter((_, i) => results[i].status === 'fulfilled').forEach(id => next.delete(id))
-        return next
-      })
-    } finally {
-      setIsDeletingSelectedTests(false)
-    }
-  }
-
-
-  const handleViewCallRecording = (callShortId: string) => {
-    navigate(`/playground/call-recordings/${callShortId}`)
-  }
-
-  const handleEvaluateCustomSession = async (callShortId: string) => {
-    try {
-      await apiClient.evaluateCustomWebsocketSession(callShortId)
-      showToast('Evaluation queued for custom websocket session', 'success')
-      queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
-      queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
-    } catch (error: any) {
-      const detail = error?.response?.data?.detail || error?.message || 'Failed to queue evaluation'
-      showToast(detail, 'error')
-    }
-  }
-
-  const voiceAICallRecordings = callRecordings.filter((recording: any) => recording.provider_platform !== 'custom_websocket')
-  const customWebsocketSessions = callRecordings.filter((recording: any) => recording.provider_platform === 'custom_websocket')
-
-
-  const toggleCallSelection = (callShortId: string) => {
-    setSelectedCallIds(prev => {
-      const next = new Set(prev)
-      if (next.has(callShortId)) {
-        next.delete(callShortId)
-      } else {
-        next.add(callShortId)
-      }
-      return next
-    })
-  }
-
-  const toggleSelectAllCalls = () => {
-    const allIds = voiceAICallRecordings.map((r: any) => r.call_short_id)
-    const allSelected = allIds.length > 0 && allIds.every((id: string) => selectedCallIds.has(id))
-    setSelectedCallIds(allSelected ? new Set() : new Set(allIds))
-  }
-
-  const handleDeleteSelectedCalls = async () => {
-    if (selectedCallIds.size === 0) return
-    if (!window.confirm(`Delete ${selectedCallIds.size} call recording${selectedCallIds.size > 1 ? 's' : ''}? This cannot be undone.`)) return
-
-    setIsDeletingSelected(true)
-    const ids = Array.from(selectedCallIds)
-    try {
-      const results = await Promise.allSettled(ids.map(id => apiClient.deleteCallRecording(id)))
-      const successCount = results.filter(r => r.status === 'fulfilled').length
-      const failCount = results.filter(r => r.status === 'rejected').length
-
-      if (successCount > 0) {
-        queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
-        showToast(`Deleted ${successCount} recording${successCount > 1 ? 's' : ''}`, 'success')
-      }
-      if (failCount > 0) {
-        showToast(`Failed to delete ${failCount} recording${failCount > 1 ? 's' : ''}`, 'error')
-      }
-
-      setSelectedCallIds(prev => {
-        const next = new Set(prev)
-        ids.filter((_, i) => results[i].status === 'fulfilled').forEach(id => next.delete(id))
-        return next
-      })
-    } finally {
-      setIsDeletingSelected(false)
-    }
-  }
-
-  const handleRefreshStatus = async () => {
-    setIsRefreshingStatus(true)
-    try {
-      await Promise.all([refetchTestResults(), refetchCallRecordings()])
-      showToast('Latest evaluation status refreshed', 'success')
-    } catch {
-      showToast('Failed to refresh status', 'error')
-    } finally {
-      setIsRefreshingStatus(false)
-    }
-  }
-
-
-  return (
-    <>
-      <ToastContainer />
-      <div className="bg-white shadow rounded-lg">
-        <div className="px-6 py-4 border-b border-gray-200 flex items-center justify-between">
-          <div>
-            <h2 className="text-lg font-semibold text-gray-900">Agent Playground</h2>
-            <p className="mt-1 text-sm text-gray-600">
-              Test your voice AI agent with real-time web calls
-            </p>
-          </div>
-          <div className="flex gap-2">
-            <Button
-              variant="outline"
-              onClick={handleRefreshStatus}
-              leftIcon={<RefreshCw className="h-4 w-4" />}
-              isLoading={isRefreshingStatus}
-              disabled={!selectedAgent}
-            >
-              Refresh
-            </Button>
-            <Button
-              variant="primary"
-              onClick={() => setShowTestModal(true)}
-              leftIcon={<Play className="h-5 w-5" />}
-              disabled={!selectedAgent || (!hasTestAgent && !hasVoiceAIAgent)}
-            >
-              Test
-            </Button>
-          </div>
-        </div>
-
-        <div className="p-6">
-          {!selectedAgent ? (
-            <div className="bg-yellow-50 border border-yellow-200 rounded-lg p-4">
-              <p className="text-sm text-yellow-800">
-                Please select an agent from the top bar to use the Agent Playground.
-              </p>
-            </div>
-          ) : !hasTestAgent && !hasVoiceAIAgent ? (
-            <div className="bg-blue-50 border border-blue-200 rounded-lg p-4">
-              <p className="text-sm text-blue-800">
-                <strong>Selected Agent:</strong> {selectedAgent.name}
-              </p>
-              <p className="text-sm text-blue-700 mt-2">
-                {!hasVoiceAIIntegration && 'Link a Voice AI integration (Retell, Vapi, ElevenLabs, or Smallest) and set the Agent ID. '}
-                {!hasTestAgent && 'Attach a Voice Bundle for Test Agent mode. '}
-                Please check your agent configuration.
-              </p>
-            </div>
-          ) : (
-            <div className="bg-green-50 border border-green-200 rounded-lg p-4">
-              <p className="text-sm text-green-800">
-                <strong>Ready to test:</strong> {selectedAgent.name}
-              </p>
-              <p className="text-sm text-green-700 mt-1">
-                Click the Play button to start a web call with your Voice Agent.
-              </p>
-            </div>
-          )}
-
-          {/* Tabs Section */}
-          <div className="mt-6">
-            <div className="border-b border-gray-200">
-              <nav className="-mb-px flex space-x-8" aria-label="Tabs">
-                <button
-                  onClick={() => setActiveTab('voice_ai_agents')}
-                  className={`
-                    flex items-center gap-2 py-4 px-1 border-b-2 font-medium text-sm
-                    ${
-                      activeTab === 'voice_ai_agents'
-                        ? 'border-blue-500 text-blue-600'
-                        : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-                    }
-                  `}
-                >
-                  <PhoneCall className="h-4 w-4" />
-                  Voice AI Agents
-                </button>
-                <button
-                  onClick={() => setActiveTab('test_agents')}
-                  className={`
-                    flex items-center gap-2 py-4 px-1 border-b-2 font-medium text-sm
-                    ${
-                      activeTab === 'test_agents'
-                        ? 'border-blue-500 text-blue-600'
-                        : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-                    }
-                  `}
-                >
-                  <Bot className="h-4 w-4" />
-                  Test Agents
-                </button>
-                <button
-                  onClick={() => setActiveTab('custom_websocket')}
-                  className={`
-                    flex items-center gap-2 py-4 px-1 border-b-2 font-medium text-sm
-                    ${
-                      activeTab === 'custom_websocket'
-                        ? 'border-blue-500 text-blue-600'
-                        : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
-                    }
-                  `}
-                >
-                  <Mic className="h-4 w-4" />
-                  Custom WebSocket Testing
-                </button>
-              </nav>
-            </div>
-            {/* Test Agents Tab Content */}
-            {activeTab === 'test_agents' && (
-              <div className="mt-4">
-                {selectedTestResultIds.size > 0 && (
-                  <div className="flex items-center justify-between mb-3">
-                    <span className="text-sm text-gray-600">{selectedTestResultIds.size} selected</span>
-                    <Button
-                      variant="danger"
-                      onClick={handleDeleteSelectedTestResults}
-                      disabled={isDeletingSelectedTests}
-                      isLoading={isDeletingSelectedTests}
-                      leftIcon={!isDeletingSelectedTests ? <Trash2 className="h-4 w-4" /> : undefined}
-                    >
-                      Delete ({selectedTestResultIds.size})
-                    </Button>
-                  </div>
-                )}
-                {testVoiceAgentResults.length === 0 ? (
-                  <div className="bg-gray-50 border border-gray-200 rounded-lg p-4 text-center">
-                    <p className="text-sm text-gray-600">No test agent results found</p>
-                  </div>
-                ) : (
-                  <div className="overflow-x-auto">
-                    <table className="min-w-full divide-y divide-gray-200">
-                      <thead className="bg-gray-50">
-                        <tr>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-10">
-                            <button
-                              type="button"
-                              onClick={toggleSelectAllTestResults}
-                              className="flex-shrink-0"
-                              aria-label="Select all test results"
-                            >
-                              {testVoiceAgentResults.length > 0 && testVoiceAgentResults.every((r: any) => selectedTestResultIds.has(r.id)) ? (
-                                <CheckSquare className="w-5 h-5 text-primary-600" />
-                              ) : (
-                                <Square className="w-5 h-5 text-gray-400" />
-                              )}
-                            </button>
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Call ID
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Status
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Agent
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Created
-                          </th>
-                        </tr>
-                      </thead>
-                      <tbody className="bg-white divide-y divide-gray-200">
-                        {testVoiceAgentResults.map((result: any) => {
-                          const isSelected = selectedTestResultIds.has(result.id)
-                          return (
-                            <tr
-                              key={result.id}
-                              className={`hover:bg-gray-50 cursor-pointer transition-colors ${isSelected ? 'bg-blue-50' : ''}`}
-                              onClick={() => handleViewTestResult(result.id)}
-                            >
-                              <td className="px-4 py-3 whitespace-nowrap" onClick={(e) => e.stopPropagation()}>
-                                <button
-                                  type="button"
-                                  onClick={() => toggleTestResultSelection(result.id)}
-                                  className="flex-shrink-0"
-                                >
-                                  {isSelected ? (
-                                    <CheckSquare className="w-5 h-5 text-primary-600" />
-                                  ) : (
-                                    <Square className="w-5 h-5 text-gray-400" />
-                                  )}
-                                </button>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap">
-                                <span className="font-mono text-sm font-semibold text-primary-600">
-                                  {result.result_id || result.id.substring(0, 8)}
-                                </span>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap">
-                                <span
-                                  className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${
-                                    result.status === 'completed'
-                                      ? 'bg-green-100 text-green-800'
-                                      : result.status === 'failed'
-                                      ? 'bg-red-100 text-red-800'
-                                      : 'bg-yellow-100 text-yellow-800'
-                                  }`}
-                                >
-                                  {result.status}
-                                </span>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap text-sm text-gray-500">
-                                {result.agent?.name || 'N/A'}
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap text-sm text-gray-500">
-                                {result.created_at
-                                  ? new Date(result.created_at).toLocaleString()
-                                  : 'N/A'}
-                              </td>
-                            </tr>
-                          )
-                        })}
-                      </tbody>
-                    </table>
-                  </div>
-                )}
-              </div>
-            )}
-
-            {/* Voice AI Agents Tab Content */}
-            {activeTab === 'voice_ai_agents' && (
-              <div className="mt-4">
-                {selectedCallIds.size > 0 && (
-                  <div className="flex items-center justify-between mb-3">
-                    <span className="text-sm text-gray-600">{selectedCallIds.size} selected</span>
-                    <Button
-                      variant="danger"
-                      onClick={handleDeleteSelectedCalls}
-                      disabled={isDeletingSelected}
-                      isLoading={isDeletingSelected}
-                      leftIcon={!isDeletingSelected ? <Trash2 className="h-4 w-4" /> : undefined}
-                    >
-                      Delete ({selectedCallIds.size})
-                    </Button>
-                  </div>
-                )}
-                {voiceAICallRecordings.length === 0 ? (
-                  <div className="bg-gray-50 border border-gray-200 rounded-lg p-4 text-center">
-                    <p className="text-sm text-gray-600">No call recordings found</p>
-                  </div>
-                ) : (
-                  <div className="overflow-x-auto">
-                    <table className="min-w-full divide-y divide-gray-200">
-                      <thead className="bg-gray-50">
-                        <tr>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-10">
-                            <button
-                              type="button"
-                              onClick={toggleSelectAllCalls}
-                              className="flex-shrink-0"
-                              aria-label="Select all call recordings"
-                            >
-                              {voiceAICallRecordings.length > 0 && voiceAICallRecordings.every((r: any) => selectedCallIds.has(r.call_short_id)) ? (
-                                <CheckSquare className="w-5 h-5 text-primary-600" />
-                              ) : (
-                                <Square className="w-5 h-5 text-gray-400" />
-                              )}
-                            </button>
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Call ID
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Status
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Evaluation
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Platform
-                          </th>
-                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                            Created
-                          </th>
-                        </tr>
-                      </thead>
-                      <tbody className="bg-white divide-y divide-gray-200">
-                        {voiceAICallRecordings.map((recording: any) => {
-                          const isSelected = selectedCallIds.has(recording.call_short_id)
-                          return (
-                            <tr
-                              key={recording.id}
-                              className={`hover:bg-gray-50 cursor-pointer transition-colors ${isSelected ? 'bg-blue-50' : ''}`}
-                              onClick={() => handleViewCallRecording(recording.call_short_id)}
-                            >
-                              <td className="px-4 py-3 whitespace-nowrap" onClick={(e) => e.stopPropagation()}>
-                                <button
-                                  type="button"
-                                  onClick={() => toggleCallSelection(recording.call_short_id)}
-                                  className="flex-shrink-0"
-                                >
-                                  {isSelected ? (
-                                    <CheckSquare className="w-5 h-5 text-primary-600" />
-                                  ) : (
-                                    <Square className="w-5 h-5 text-gray-400" />
-                                  )}
-                                </button>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap">
-                                <span className="font-mono text-sm font-semibold text-primary-600">
-                                  {recording.call_short_id}
-                                </span>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap">
-                                <span
-                                  className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${recording.status === 'UPDATED'
-                                    ? 'bg-green-100 text-green-800'
-                                    : 'bg-yellow-100 text-yellow-800'
-                                    }`}
-                                >
-                                  {recording.status}
-                                </span>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap">
-                                {recording.evaluator_result_id ? (
-                                  <span
-                                    className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${
-                                      recording.evaluation_status === 'completed'
-                                        ? 'bg-green-100 text-green-800'
-                                        : recording.evaluation_status === 'failed'
-                                        ? 'bg-red-100 text-red-800'
-                                        : recording.evaluation_status === 'evaluating'
-                                        ? 'bg-blue-100 text-blue-800'
-                                        : 'bg-yellow-100 text-yellow-800'
-                                    }`}
-                                  >
-                                    {recording.evaluation_status || 'queued'}
-                                  </span>
-                                ) : recording.status === 'UPDATED' ? (
-                                  <span className="inline-flex px-2 py-1 text-xs font-semibold rounded-full bg-gray-100 text-gray-600">
-                                    Pending
-                                  </span>
-                                ) : (
-                                  <span className="text-xs text-gray-400">—</span>
-                                )}
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap">
-                                <div className="flex items-center gap-2">
-                                  {recording.provider_platform ? (() => {
-                                    const logo = getIntegrationPlatformLogo(
-                                      recording.provider_platform as IntegrationPlatform,
-                                    )
-                                    return logo ? (
-                                      <img
-                                        src={logo}
-                                        alt={recording.provider_platform}
-                                        className="h-5 w-5 object-contain"
-                                      />
-                                    ) : null
-                                  })() : null}
-                                  <span className="text-sm text-gray-500 capitalize">
-                                    {recording.provider_platform || 'N/A'}
-                                  </span>
-                                </div>
-                              </td>
-                              <td className="px-4 py-3 whitespace-nowrap text-sm text-gray-500">
-                                {recording.created_at
-                                  ? new Date(recording.created_at).toLocaleString()
-                                  : 'N/A'}
-                              </td>
-                            </tr>
-                          )
-                        })}
-                      </tbody>
-                    </table>
-                  </div>
-                )}
-              </div>
-            )}
-
-            {activeTab === 'custom_websocket' && (
-              <div className="mt-4 space-y-4">
-                <div className="rounded-lg border border-gray-200 p-4">
-                  <h3 className="text-sm font-semibold text-gray-900">Test a Custom WebSocket Voice Agent</h3>
-                  <p className="mt-1 text-xs text-gray-600">
-                    Paste or pick a saved WebSocket endpoint and start a live call. After disconnect you can save the transcript and evaluate.
-                  </p>
-
-                  {/* URL input + save button */}
-                  <div className="mt-3 flex gap-2">
-                    <input
-                      value={customWebsocketUrl}
-                      onChange={(e) => setCustomWebsocketUrl(e.target.value)}
-                      placeholder="wss://your-voice-agent.example.com/ws"
-                      className="flex-1 rounded-lg border border-gray-300 px-3 py-2 text-sm focus:border-primary-500 focus:outline-none focus:ring-2 focus:ring-primary-200"
-                    />
-                    {customWebsocketUrl.trim() && (
-                      isCurrentUrlSaved ? (
-                        <button
-                          className="flex items-center gap-1 px-3 py-2 rounded-lg text-xs font-medium text-primary-600 bg-primary-50 border border-primary-200"
-                          title="Already saved"
-                          disabled
-                        >
-                          <BookmarkCheck className="h-4 w-4" />
-                          Saved
-                        </button>
-                      ) : (
-                        <button
-                          className="flex items-center gap-1 px-3 py-2 rounded-lg text-xs font-medium text-gray-700 bg-white border border-gray-300 hover:bg-gray-50 transition-colors"
-                          onClick={() => setShowSaveForm((v) => !v)}
-                          title="Save this URL for later"
-                        >
-                          <Bookmark className="h-4 w-4" />
-                          Save
-                        </button>
-                      )
-                    )}
-                  </div>
-
-                  {/* Inline save form */}
-                  {showSaveForm && (
-                    <div className="mt-2 flex gap-2 items-center">
-                      <input
-                        value={saveLabel}
-                        onChange={(e) => setSaveLabel(e.target.value)}
-                        placeholder="Label (e.g. My Voice Bot)"
-                        className="flex-1 rounded-lg border border-gray-300 px-3 py-1.5 text-sm focus:border-primary-500 focus:outline-none focus:ring-2 focus:ring-primary-200"
-                        onKeyDown={(e) => { if (e.key === 'Enter') handleSaveUrl() }}
-                        autoFocus
-                      />
-                      <Button size="sm" onClick={handleSaveUrl}>Save</Button>
-                      <button className="text-gray-400 hover:text-gray-600" onClick={() => setShowSaveForm(false)}>
-                        <X className="h-4 w-4" />
-                      </button>
-                    </div>
-                  )}
-
-                  {/* Saved URLs list */}
-                  {savedWsUrls.length > 0 && (
-                    <div className="mt-3">
-                      <p className="text-[10px] font-medium text-gray-400 uppercase tracking-wider mb-1.5">Saved Endpoints</p>
-                      <div className="flex flex-wrap gap-2">
-                        {savedWsUrls.map((s) => (
-                          <button
-                            key={s.id}
-                            className={`group flex items-center gap-1.5 px-3 py-1.5 rounded-lg border text-xs transition-colors ${
-                              customWebsocketUrl.trim() === s.url
-                                ? 'border-primary-400 bg-primary-50 text-primary-700 font-medium'
-                                : 'border-gray-200 bg-white text-gray-700 hover:bg-gray-50 hover:border-gray-300'
-                            }`}
-                            onClick={() => setCustomWebsocketUrl(s.url)}
-                            title={s.url}
-                          >
-                            <Bookmark className="h-3 w-3 flex-shrink-0" />
-                            <span className="truncate max-w-[200px]">{s.label}</span>
-                            <span
-                              className="hidden group-hover:inline-flex ml-1 text-gray-400 hover:text-red-500"
-                              onClick={(e) => { e.stopPropagation(); handleDeleteSavedUrl(s.id) }}
-                              title="Remove"
-                            >
-                              <X className="h-3 w-3" />
-                            </span>
-                          </button>
-                        ))}
-                      </div>
-                    </div>
-                  )}
-                </div>
-
-                {customWebsocketUrl.trim().startsWith('ws://') || customWebsocketUrl.trim().startsWith('wss://') ? (
-                  <GenericVoiceWSClient
-                    websocketUrl={customWebsocketUrl.trim()}
-                    protocol={getProtocolById('generic')}
-                    agentId={selectedAgent?.id}
-                    onSessionSaved={() => {
-                      queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
-                      queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
-                    }}
-                  />
-                ) : (
-                  <div className="rounded-lg border border-amber-200 bg-amber-50 p-4 text-sm text-amber-800">
-                    Enter a valid WebSocket URL (<code className="bg-amber-100 px-1 rounded">ws://</code> or <code className="bg-amber-100 px-1 rounded">wss://</code>) to start testing.
-                  </div>
-                )}
-
-                <div className="rounded-lg border border-gray-200">
-                  <div className="border-b border-gray-200 px-4 py-3">
-                    <h4 className="text-sm font-semibold text-gray-900">Saved Custom Sessions</h4>
-                  </div>
-                  <div className="p-4">
-                    {customWebsocketSessions.length === 0 ? (
-                      <p className="text-sm text-gray-600">No saved custom websocket sessions yet.</p>
-                    ) : (
-                      <div className="overflow-x-auto">
-                        <table className="min-w-full divide-y divide-gray-200">
-                          <thead className="bg-gray-50">
-                            <tr>
-                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Call ID</th>
-                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Status</th>
-                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Evaluation</th>
-                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Created</th>
-                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Actions</th>
-                            </tr>
-                          </thead>
-                          <tbody className="divide-y divide-gray-200 bg-white">
-                            {customWebsocketSessions.map((session: any) => (
-                              <tr
-                                key={session.id}
-                                className="hover:bg-gray-50 cursor-pointer transition-colors"
-                                onClick={() => handleViewCallRecording(session.call_short_id)}
-                              >
-                                <td className="px-4 py-3 text-sm font-mono font-semibold text-primary-600">
-                                  {session.call_short_id}
-                                </td>
-                                <td className="px-4 py-3 text-sm text-gray-600">{session.status}</td>
-                                <td className="px-4 py-3">
-                                  {session.evaluator_result_id ? (
-                                    <span className="inline-flex rounded-full bg-blue-100 px-2 py-1 text-xs font-semibold text-blue-800">
-                                      {session.evaluation_status || 'queued'}
-                                    </span>
-                                  ) : (
-                                    <span className="inline-flex rounded-full bg-gray-100 px-2 py-1 text-xs font-semibold text-gray-700">
-                                      pending
-                                    </span>
-                                  )}
-                                </td>
-                                <td className="px-4 py-3 text-sm text-gray-600">
-                                  {session.created_at ? new Date(session.created_at).toLocaleString() : 'N/A'}
-                                </td>
-                                <td className="px-4 py-3" onClick={(e) => e.stopPropagation()}>
-                                  <div className="flex flex-wrap gap-2">
-                                    {!session.evaluator_result_id && (
-                                      <Button
-                                        variant="outline"
-                                        onClick={() => handleEvaluateCustomSession(session.call_short_id)}
-                                      >
-                                        Run Evaluation
-                                      </Button>
-                                    )}
-                                    {session.evaluator_result_id && (
-                                      <Button
-                                        variant="outline"
-                                        onClick={() => handleViewTestResult(session.evaluator_result_id)}
-                                      >
-                                        Open Result
-                                      </Button>
-                                    )}
-                                  </div>
-                                </td>
-                              </tr>
-                            ))}
-                          </tbody>
-                        </table>
-                      </div>
-                    )}
-                  </div>
-                </div>
-              </div>
-            )}
-          </div>
-        </div>
-      </div>
-
-
-      {/* Test Type Selection Modal */}
-      {showTestModal && (
-        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-50">
-          <div className="bg-white rounded-lg shadow-xl max-w-md w-full mx-4">
-            <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
-              <h3 className="text-lg font-semibold">Select Test Type</h3>
-              <button
-                onClick={handleCloseModal}
-                className="text-gray-400 hover:text-gray-600"
-              >
-                <X className="h-5 w-5" />
-              </button>
-            </div>
-
-            <div className="p-6">
-              <div className="text-center mb-4">
-                <p className="text-sm text-gray-600 mb-2">
-                  Agent: <strong>{selectedAgent?.name}</strong>
-                </p>
-                <p className="text-xs text-gray-500">
-                  Choose how you want to test this agent
-                </p>
-              </div>
-
-              {s3Status && !s3Status.enabled && (
-                <div className="flex items-start gap-3 p-3 bg-amber-50 border border-amber-200 rounded-lg mb-3">
-                  <AlertTriangle className="h-5 w-5 text-amber-500 flex-shrink-0 mt-0.5" />
-                  <div>
-                    <p className="text-sm font-medium text-amber-800">Storage not configured</p>
-                    <p className="text-xs text-amber-700 mt-0.5">
-                      S3 storage is not configured. Audio recordings will not be saved. Configure storage in Settings &gt; Data Sources to enable audio playback.
-                    </p>
-                  </div>
-                </div>
-              )}
-
-              <div className="flex items-start gap-2.5 p-3 bg-blue-50 border border-blue-200 rounded-lg mb-1">
-                <AlertTriangle className="h-4 w-4 text-blue-500 flex-shrink-0 mt-0.5" />
-                <p className="text-xs text-blue-700">
-                  First-time runs may take longer as ML models required for audio evaluation metrics are downloaded and cached locally. Subsequent runs will be significantly faster.
-                </p>
-              </div>
-
-              <div className="space-y-3">
-                {hasTestAgent && (
-                  <button
-                    onClick={() => handleTestTypeSelection('test_agent')}
-                    className="w-full p-4 border-2 border-gray-200 rounded-lg hover:border-blue-500 hover:bg-blue-50 transition-all text-left"
-                  >
-                    <div className="flex items-center gap-3">
-                      <div className="p-2 bg-blue-100 rounded-lg">
-                        <Mic className="h-5 w-5 text-blue-600" />
-                      </div>
-                      <div>
-                        <h4 className="font-semibold text-gray-900">Test Agent</h4>
-                        <p className="text-sm text-gray-600">Test with Voice Bundle (STT/TTS/LLM)</p>
-                      </div>
-                    </div>
-                  </button>
-                )}
-
-                {hasVoiceAIAgent && (
-                  <button
-                    onClick={() => handleTestTypeSelection('voice_ai_agent')}
-                    className="w-full p-4 border-2 border-gray-200 rounded-lg hover:border-blue-500 hover:bg-blue-50 transition-all text-left"
-                  >
-                    <div className="flex items-center gap-3">
-                      <div className="p-2 bg-green-100 rounded-lg">
-                        <Phone className="h-5 w-5 text-green-600" />
-                      </div>
-                      <div>
-                        <h4 className="font-semibold text-gray-900">Voice AI Agent</h4>
-                        <p className="text-sm text-gray-600">Test with Voice AI Integration (Retell, Vapi, ElevenLabs, Smallest)</p>
-                      </div>
-                    </div>
-                  </button>
-                )}
-
-                {!hasTestAgent && !hasVoiceAIAgent && (
-                  <div className="text-center py-4">
-                    <p className="text-sm text-gray-600 mb-4">
-                      This agent is not configured for testing.
-                    </p>
-                    <Button
-                      variant="outline"
-                      onClick={handleCloseModal}
-                    >
-                      Close
-                    </Button>
-                  </div>
-                )}
-              </div>
-            </div>
-          </div>
-        </div>
-      )}
-
-      {/* Voice AI Agent Connect Modal (Retell) */}
-      {showModal && selectedTestType === 'voice_ai_agent' && (
-        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-50">
-          <div className="bg-white rounded-lg shadow-xl max-w-md w-full mx-4">
-            <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
-              <h3 className="text-lg font-semibold">Connect to Voice AI Agent</h3>
-              <button
-                onClick={handleCloseModal}
-                className="text-gray-400 hover:text-gray-600"
-                disabled={isConnecting}
-              >
-                <X className="h-5 w-5" />
-              </button>
-            </div>
-
-            <div className="p-6">
-              {!canMakeCall ? (
-                <div className="text-center py-4">
-                  <p className="text-sm text-gray-600 mb-4">
-                    This agent is not configured for web calls.
-                  </p>
-                  <Button
-                    variant="outline"
-                    onClick={handleCloseModal}
-                  >
-                    Close
-                  </Button>
-                </div>
-              ) : (
-                <div className="space-y-4">
-                  <div className="text-center">
-                    <p className="text-sm text-gray-600 mb-2">
-                      Agent: <strong>{selectedAgent?.name}</strong>
-                    </p>
-                    <p className="text-xs text-gray-500">
-                      Provider: {agentIntegration?.platform || 'Unknown'}
-                    </p>
-                    <p className="text-xs text-gray-500">
-                      Agent ID: <span className="font-mono font-semibold text-primary-600">{fullAgent?.voice_ai_agent_id}</span>
-                    </p>
-                  </div>
-
-                  {isConnected && (
-                    <div className="h-48 overflow-y-auto bg-gray-50 rounded p-4 mb-4 space-y-3 border border-gray-200">
-                      {transcripts.length === 0 ? (
-                        <p className="text-gray-400 text-xs text-center italic">Waiting for connection...</p>
-                      ) : (
-                        transcripts.map((msg, idx) => (
-                          <div key={idx} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
-                            <div className={`max-w-[80%] rounded-2xl px-4 py-2.5 ${msg.role === 'user'
-                              ? 'bg-indigo-600 text-white rounded-br-none'
-                              : 'bg-white border border-gray-200 text-gray-800 rounded-bl-none'
-                              }`}>
-                              <p className={`text-[10px] font-semibold uppercase tracking-wider mb-0.5 ${
-                                msg.role === 'user' ? 'opacity-70' : 'text-gray-400'
-                              }`}>
-                                {msg.role === 'user' ? 'You' : 'Agent'}
-                              </p>
-                              <p className="text-sm leading-relaxed">{msg.content}</p>
-                            </div>
-                          </div>
-                        ))
-                      )}
-                    </div>
-                  )}
-
-                  {!isConnected ? (
-                    <Button
-                      variant="primary"
-                      onClick={handleConnect}
-                      isLoading={isConnecting}
-                      leftIcon={!isConnecting ? <Phone className="h-5 w-5" /> : undefined}
-                      className="w-full"
-                      disabled={isConnecting}
-                    >
-                      {isConnecting ? 'Connecting...' : 'Connect'}
-                    </Button>
-                  ) : (
-                    <div className="space-y-3">
-                      <div className="bg-green-50 border border-green-200 rounded-lg p-3 text-center">
-                        <p className="text-sm text-green-800 font-medium">Connected</p>
-                        <p className="text-xs text-green-700 mt-1">Call is active</p>
-                      </div>
-                      <Button
-                        variant="danger"
-                        onClick={handleDisconnect}
-                        leftIcon={<PhoneOff className="h-5 w-5" />}
-                        className="w-full"
-                      >
-                        Disconnect
-                      </Button>
-                    </div>
-                  )}
-                </div>
-              )}
-            </div>
-          </div>
-        </div>
-      )}
-
-      {/* Test Agent Modal */}
-      {selectedTestType === 'test_agent' && !showTestModal && (
-        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-50">
-          <div className="bg-white rounded-lg shadow-xl max-w-4xl w-full mx-4 max-h-[90vh] overflow-y-auto">
-            <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
-              <h3 className="text-lg font-semibold">Test Agent - {selectedAgent?.name}</h3>
-              <button
-                onClick={handleCloseModal}
-                className="text-gray-400 hover:text-gray-600"
-              >
-                <X className="h-5 w-5" />
-              </button>
-            </div>
-
-            <div className="p-6">
-              <VoiceAgent agentId={selectedAgent?.id} billingSurface="agent_playground" />
-            </div>
-          </div>
-        </div>
-      )}
-    </>
-  )
-}
-
+import { useState, useEffect, useRef } from 'react'
+import { useNavigate } from 'react-router-dom'
+import { useAgentStore } from '../../../store/agentStore'
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { apiClient } from '../../../lib/api'
+import { Play, X, Phone, PhoneOff, RefreshCw, Mic, Bot, PhoneCall, Trash2, AlertTriangle, CheckSquare, Square, Bookmark, BookmarkCheck, Activity } from 'lucide-react'
+import Button from '../../../components/Button'
+import { useToast } from '../../../hooks/useToast'
+import { RetellWebClient } from 'retell-client-js-sdk'
+import Vapi from '@vapi-ai/web'
+import { Conversation } from '@elevenlabs/client'
+import VoiceAgent from '../../../components/VoiceAgent'
+import GenericVoiceWSClient from '../../../components/GenericVoiceWSClient'
+import TraceDetailDrawer from '../../../components/call-recordings/TraceDetailDrawer'
+import { getProtocolById } from '../../../lib/wsProtocols'
+import { prefetchCallRecordingQuery, warmCallRecordingQueryFromList } from '../../../lib/callRecordingQuery'
+import { prefetchCallRecordingAudio } from '../../../lib/waveformAudioCache'
+import { getIntegrationPlatformLogo } from '../../../config/providers'
+import { IntegrationPlatform } from '../../../types/api'
+
+// Type for RetellWebClient - using the actual SDK methods
+type RetellWebClientWithMethods = RetellWebClient & {
+  startCall: (config: {
+    accessToken: string;
+    sampleRate?: number;
+    captureDeviceId?: string;
+    playbackDeviceId?: string;
+    emitRawAudioSamples?: boolean;
+    callId?: string;
+  }) => Promise<void>
+  stopCall: () => void
+}
+
+export default function AgentPlayground() {
+  const { selectedAgent } = useAgentStore()
+  const { showToast, ToastContainer } = useToast()
+  const navigate = useNavigate()
+  const queryClient = useQueryClient()
+  const [showModal, setShowModal] = useState(false)
+  const [showTestModal, setShowTestModal] = useState(false)
+  const [selectedTestType, setSelectedTestType] = useState<'test_agent' | 'voice_ai_agent' | null>(null)
+  const [isConnecting, setIsConnecting] = useState(false)
+  const [isConnected, setIsConnected] = useState(false)
+  const [isRefreshingStatus, setIsRefreshingStatus] = useState(false)
+  const [transcripts, setTranscripts] = useState<Array<{ role: 'user' | 'agent', content: string }>>([])
+
+  const retellClientRef = useRef<RetellWebClientWithMethods | null>(null)
+  const vapiClientRef = useRef<any>(null)
+  const elevenLabsConversationRef = useRef<any>(null)
+  const smallestClientRef = useRef<any>(null)
+  const currentCallShortIdRef = useRef<string | null>(null)
+
+  const userInitiatedDisconnectRef = useRef(false)
+
+  // Fetch integrations to check if agent has Retell integration
+  const { data: integrations = [] } = useQuery({
+    queryKey: ['integrations'],
+    queryFn: () => apiClient.listIntegrations(),
+  })
+
+  // Fetch full agent details including integration info
+  const { data: fullAgent } = useQuery({
+    queryKey: ['agent', selectedAgent?.id],
+    queryFn: () => apiClient.getAgent(selectedAgent?.id || ''),
+    enabled: !!selectedAgent?.id,
+  })
+
+
+  // Fetch test voice agent evaluation results (playground results only, excluding Voice AI agent results)
+  const { data: testVoiceAgentList, refetch: refetchTestResults } = useQuery({
+    queryKey: ['test-voice-agent-results'],
+    queryFn: async () => {
+      return await apiClient.listEvaluatorResults(undefined, true, true)
+    },
+  })
+  const testVoiceAgentResults = testVoiceAgentList?.items ?? []
+
+  // Fetch call recordings (for Voice AI Agents tab)
+  const { data: callRecordings = [], refetch: refetchCallRecordings } = useQuery({
+    queryKey: ['call-recordings'],
+    queryFn: () => apiClient.listCallRecordings(),
+    // Refetch every 5 seconds if there are any evaluations in progress
+    refetchInterval: (query) => {
+      const data = query.state.data as any[]
+      if (data && Array.isArray(data)) {
+        const hasInProgress = data.some((recording: any) => 
+          recording.evaluation_status && 
+          ['queued', 'transcribing', 'evaluating'].includes(recording.evaluation_status)
+        )
+        return hasInProgress ? 5000 : false
+      }
+      return false
+    },
+  })
+
+  // Check S3 storage status for warning
+  const { data: s3Status } = useQuery({
+    queryKey: ['s3-status'],
+    queryFn: () => apiClient.getS3Status(),
+    staleTime: 60_000,
+  })
+
+  const [activeTab, setActiveTab] = useState<'test_agents' | 'voice_ai_agents' | 'custom_websocket'>('voice_ai_agents')
+  const [customWebsocketUrl, setCustomWebsocketUrl] = useState('')
+
+  // Saved WebSocket URLs (persisted in localStorage)
+  type SavedWsUrl = { id: string; label: string; url: string; createdAt: string }
+  const SAVED_WS_KEY = 'efficientai-saved-ws-urls'
+  const loadSavedUrls = (): SavedWsUrl[] => {
+    try { return JSON.parse(localStorage.getItem(SAVED_WS_KEY) || '[]') } catch { return [] }
+  }
+  const [savedWsUrls, setSavedWsUrls] = useState<SavedWsUrl[]>(loadSavedUrls)
+  const [showSaveForm, setShowSaveForm] = useState(false)
+  const [saveLabel, setSaveLabel] = useState('')
+
+  const persistSavedUrls = (urls: SavedWsUrl[]) => {
+    setSavedWsUrls(urls)
+    localStorage.setItem(SAVED_WS_KEY, JSON.stringify(urls))
+  }
+  const handleSaveUrl = () => {
+    const url = customWebsocketUrl.trim()
+    if (!url) return
+    const label = saveLabel.trim() || url
+    const entry: SavedWsUrl = { id: crypto.randomUUID(), label, url, createdAt: new Date().toISOString() }
+    persistSavedUrls([entry, ...savedWsUrls])
+    setSaveLabel('')
+    setShowSaveForm(false)
+  }
+  const handleDeleteSavedUrl = (id: string) => {
+    persistSavedUrls(savedWsUrls.filter((s) => s.id !== id))
+  }
+  const isCurrentUrlSaved = savedWsUrls.some((s) => s.url === customWebsocketUrl.trim())
+  const [selectedCallIds, setSelectedCallIds] = useState<Set<string>>(new Set())
+  const [isDeletingSelected, setIsDeletingSelected] = useState(false)
+  const [selectedTestResultIds, setSelectedTestResultIds] = useState<Set<string>>(new Set())
+  const [otlpTraceResultId, setOtlpTraceResultId] = useState<string | null>(null)
+  const [voiceAiCallShortId, setVoiceAiCallShortId] = useState<string | null>(null)
+
+  const isVoiceAiProviderRecording = (recording: { provider_platform?: string | null }) => {
+    const platform = (recording.provider_platform || '').toLowerCase()
+    return (
+      platform === IntegrationPlatform.RETELL ||
+      platform === IntegrationPlatform.VAPI ||
+      platform === IntegrationPlatform.ELEVENLABS ||
+      platform === IntegrationPlatform.SMALLEST ||
+      platform === 'retell' ||
+      platform === 'vapi' ||
+      platform === 'elevenlabs' ||
+      platform === 'smallest'
+    )
+  }
+  const [isDeletingSelectedTests, setIsDeletingSelectedTests] = useState(false)
+
+
+  // Find the integration for the agent
+  const agentIntegration = fullAgent?.voice_ai_integration_id
+    ? integrations.find((int: any) => int.id === fullAgent.voice_ai_integration_id)
+    : null
+
+  const isRetellAgent = agentIntegration?.platform === 'retell'
+  const isVapiAgent = agentIntegration?.platform === 'vapi'
+  const isElevenLabsAgent = agentIntegration?.platform === 'elevenlabs'
+  const isSmallestAgent = agentIntegration?.platform === 'smallest'
+
+  const hasVoiceAIIntegration = Boolean(
+    fullAgent?.voice_ai_integration_id &&
+    fullAgent?.voice_ai_agent_id &&
+    (isRetellAgent || isVapiAgent || isElevenLabsAgent || isSmallestAgent)
+  )
+  // Playground web-call testing via Retell/Vapi/ElevenLabs/Smallest is independent
+  // of the agent's production call_medium (phone_call vs web_call).
+  const canMakeCall = hasVoiceAIIntegration
+
+  // Check if agent has Test Agent capabilities (voice bundle with STT/TTS/LLM)
+  const hasTestAgent = fullAgent?.voice_bundle_id != null
+  const hasVoiceAIAgent = hasVoiceAIIntegration
+
+  // Initialize Clients when modal opens
+  useEffect(() => {
+    if (showModal && canMakeCall) {
+      if (isRetellAgent && !retellClientRef.current) {
+        const client = new RetellWebClient() as unknown as RetellWebClientWithMethods
+        console.log('RetellWebClient initialized')
+        retellClientRef.current = client
+      } else if (isVapiAgent && !vapiClientRef.current && agentIntegration?.public_key) {
+        const client = new Vapi(agentIntegration.public_key)
+        console.log('Vapi client initialized')
+        vapiClientRef.current = client
+      }
+      // ElevenLabs: no persistent client — sessions are created on connect
+    }
+
+    return () => {
+      if (retellClientRef.current) {
+        try {
+          retellClientRef.current.stopCall()
+        } catch (e) {
+          console.error('Error stopping Retell call on cleanup:', e)
+        }
+      }
+      if (vapiClientRef.current) {
+        try {
+          vapiClientRef.current.stop()
+        } catch (e) {
+          console.error('Error stopping Vapi call on cleanup:', e)
+        }
+      }
+      if (elevenLabsConversationRef.current) {
+        try {
+          elevenLabsConversationRef.current.endSession()
+        } catch (e) {
+          console.error('Error ending ElevenLabs session on cleanup:', e)
+        }
+        elevenLabsConversationRef.current = null
+      }
+      if (smallestClientRef.current) {
+        try {
+          smallestClientRef.current.stopSession()
+        } catch (e) {
+          console.error('Error stopping Smallest session on cleanup:', e)
+        }
+        smallestClientRef.current = null
+      }
+    }
+  }, [showModal, canMakeCall, isRetellAgent, isVapiAgent, isElevenLabsAgent, isSmallestAgent, agentIntegration?.public_key])
+
+  const handleConnect = async () => {
+    if (!canMakeCall || !fullAgent?.id) {
+      showToast('Agent is not configured for web calls', 'error')
+      return
+    }
+
+    setIsConnecting(true)
+    setTranscripts([])
+
+    if (isRetellAgent) {
+      if (!retellClientRef.current) {
+        retellClientRef.current = new RetellWebClient() as unknown as RetellWebClientWithMethods
+      }
+
+      const client = retellClientRef.current
+
+      try {
+        // IMPORTANT: Request microphone permission FIRST, before creating the web call
+        try {
+          const testStream = await navigator.mediaDevices.getUserMedia({ audio: true })
+          console.log('Microphone access granted')
+          testStream.getTracks().forEach(track => track.stop())
+        } catch (micError: any) {
+          console.error('Microphone permission denied:', micError)
+          setIsConnecting(false)
+          showToast('Microphone permission is required for voice calls', 'error')
+          return
+        }
+
+        // Set up event handlers
+        client.on('call_started', () => {
+          console.log('Retell call started')
+          setIsConnected(true)
+          setIsConnecting(false)
+          showToast('Connected to agent', 'success')
+        })
+
+        // Handle updates (transcripts)
+        client.on('update', (update: any) => {
+          if (update.transcript) {
+            // Retell sends transcript as an array of {role, content, words} objects
+            // We need to convert this to our simpler format
+            const transcriptArray = Array.isArray(update.transcript) 
+              ? update.transcript 
+              : [{ role: update.role || 'agent', content: update.transcript }]
+            
+            setTranscripts(
+              transcriptArray
+                .filter((msg: any) => msg && msg.content && typeof msg.content === 'string')
+                .map((msg: any) => ({
+                  role: msg.role === 'user' ? 'user' as const : 'agent' as const,
+                  content: msg.content
+                }))
+            )
+          }
+        })
+
+        client.on('call_ended', (data?: any) => {
+          console.log('Retell call ended', data)
+          setIsConnected(false)
+          setIsConnecting(false)
+          if (!userInitiatedDisconnectRef.current) {
+            const reason = data?.reason || data?.code || 'Unknown reason'
+            showToast(`Call ended: ${reason}`)
+          }
+          userInitiatedDisconnectRef.current = false
+
+          // Trigger refresh of metrics
+          if (currentCallShortIdRef.current) {
+            apiClient.refreshCallRecording(currentCallShortIdRef.current)
+              .then(() => refetchCallRecordings())
+              .catch(err => console.error('Failed to refresh metrics', err))
+          }
+        })
+
+        client.on('error', (error: any) => {
+          console.error('Retell error:', error)
+          setIsConnecting(false)
+          setIsConnected(false)
+          showToast(`Error: ${error?.message || 'Unknown error'}`, 'error')
+        })
+
+        // Create web call
+        console.log('Creating web call...')
+        const webCallResponse = await apiClient.createWebCall({
+          agent_id: fullAgent.id,
+          metadata: {},
+          retell_llm_dynamic_variables: {},
+          custom_sip_headers: {},
+          ui_surface: 'agent_playground',
+        })
+
+        if (!webCallResponse.call_id || !webCallResponse.access_token) {
+          throw new Error('No call_id or access_token received')
+        }
+
+        if (webCallResponse.call_short_id) {
+          currentCallShortIdRef.current = webCallResponse.call_short_id
+        }
+
+        // Start call
+        await client.startCall({
+          accessToken: webCallResponse.access_token,
+          callId: webCallResponse.call_id,
+          sampleRate: webCallResponse.sample_rate || 24000,
+        })
+      } catch (error: any) {
+        console.error('Failed to connect Retell:', error)
+        setIsConnecting(false)
+        setIsConnected(false)
+        const detail = error?.response?.data?.detail || error?.message || 'Unknown error'
+        showToast(`Failed to connect: ${detail}`, 'error')
+      }
+    } else if (isVapiAgent) {
+      const client = vapiClientRef.current
+      if (!client) {
+        setIsConnecting(false)
+        showToast('Vapi client not initialized', 'error')
+        return
+      }
+
+      try {
+        // Create backend record for Vapi call
+        console.log('Creating Vapi web call record...')
+        const webCallResponse = await apiClient.createWebCall({
+          agent_id: fullAgent.id,
+          metadata: {},
+          ui_surface: 'agent_playground',
+        })
+
+        if (webCallResponse.call_short_id) {
+          currentCallShortIdRef.current = webCallResponse.call_short_id
+        }
+
+        // Vapi Event Handlers
+        client.on('call-start', async (call: any) => {
+          console.log('Vapi call started', call)
+          setIsConnected(true)
+          setIsConnecting(false)
+          showToast('Connected to agent', 'success')
+
+          // Update backend with Vapi Call ID
+          if (currentCallShortIdRef.current && call?.id) {
+            try {
+              await apiClient.updateCallRecording(currentCallShortIdRef.current, call.id)
+              console.log('Updated call recording with Vapi ID:', call.id)
+            } catch (err) {
+              console.error('Failed to update call recording provider ID', err)
+            }
+          }
+        })
+
+        // Vapi Transcription Handling
+        client.on('message', (message: any) => {
+          if (message.type === 'transcript' && message.transcriptType === 'final') {
+            const role = message.role === 'user' ? 'user' : 'agent'
+            setTranscripts(prev => [...prev, { role, content: message.transcript }])
+          }
+        })
+
+        client.on('call-end', async (call: any) => {
+          console.log('Vapi call ended', call)
+          setIsConnected(false)
+          setIsConnecting(false)
+          if (!userInitiatedDisconnectRef.current) {
+            showToast('Call ended')
+          }
+          userInitiatedDisconnectRef.current = false
+
+          // Ensure we have provider ID before refreshing
+          if (currentCallShortIdRef.current) {
+            if (call?.id) {
+              try {
+                await apiClient.updateCallRecording(currentCallShortIdRef.current, call.id)
+              } catch (e) {
+                console.error('Failed to update call recording on end', e)
+              }
+            }
+
+            apiClient.refreshCallRecording(currentCallShortIdRef.current)
+              .then(() => refetchCallRecordings())
+              .catch(err => console.error('Failed to refresh metrics', err))
+          }
+        })
+
+        client.on('error', (error: any) => {
+          console.error('Vapi error:', error)
+          setIsConnecting(false)
+          setIsConnected(false)
+          showToast(`Error: ${error?.message || 'Unknown error'}`, 'error')
+        })
+
+        // Start Call
+        // Note: For Vapi, we just need the assistant ID (voice_ai_agent_id)
+        console.log('Starting Vapi call...')
+        const vapiCall = await client.start(fullAgent.voice_ai_agent_id)
+        console.log('Vapi start returned:', vapiCall)
+
+        // Try to get ID from return value immediately
+        if (currentCallShortIdRef.current && vapiCall?.id) {
+          try {
+            await apiClient.updateCallRecording(currentCallShortIdRef.current, vapiCall.id)
+            console.log('Updated call recording with Vapi ID (from start):', vapiCall.id)
+          } catch (err) {
+            console.error('Failed to update call recording provider ID', err)
+          }
+        }
+      } catch (error: any) {
+        console.error('Failed to connect Vapi:', error)
+        setIsConnecting(false)
+        setIsConnected(false)
+        const detail = error?.response?.data?.detail || error?.message || 'Unknown error'
+        showToast(`Failed to connect: ${detail}`, 'error')
+      }
+    } else if (isElevenLabsAgent) {
+      try {
+        // Request microphone permission first
+        try {
+          const testStream = await navigator.mediaDevices.getUserMedia({ audio: true })
+          testStream.getTracks().forEach(track => track.stop())
+        } catch (micError: any) {
+          console.error('Microphone permission denied:', micError)
+          setIsConnecting(false)
+          showToast('Microphone permission is required for voice calls', 'error')
+          return
+        }
+
+        // Create backend record and get signed URL
+        console.log('Creating ElevenLabs web call...')
+        const webCallResponse = await apiClient.createWebCall({
+          agent_id: fullAgent.id,
+          metadata: {},
+          ui_surface: 'agent_playground',
+        })
+
+        if (webCallResponse.call_short_id) {
+          currentCallShortIdRef.current = webCallResponse.call_short_id
+        }
+
+        const signedUrl = webCallResponse.signed_url
+        if (!signedUrl) {
+          throw new Error('No signed_url received from backend')
+        }
+
+        console.log('Starting ElevenLabs conversation session with signed URL...')
+
+        let elevenLabsConversationIdStored = false
+
+        const conversationInstance = await Conversation.startSession({
+          signedUrl,
+          onConnect: () => {
+            console.log('ElevenLabs conversation connected')
+            setIsConnected(true)
+            setIsConnecting(false)
+            showToast('Connected to agent', 'success')
+          },
+          onDisconnect: () => {
+            console.log('ElevenLabs conversation disconnected')
+            setIsConnected(false)
+            setIsConnecting(false)
+            elevenLabsConversationRef.current = null
+            if (!userInitiatedDisconnectRef.current) {
+              showToast('Call ended')
+            }
+            userInitiatedDisconnectRef.current = false
+
+            // Only refresh if we successfully stored the conversation ID,
+            // otherwise the backend has no provider_call_id to fetch metrics for
+            if (currentCallShortIdRef.current && elevenLabsConversationIdStored) {
+              const callShortId = currentCallShortIdRef.current
+              // ElevenLabs transitions through "processing" before "done",
+              // so wait a few seconds before requesting metrics
+              setTimeout(() => {
+                apiClient.refreshCallRecording(callShortId)
+                  .then(() => refetchCallRecordings())
+                  .catch(err => console.error('Failed to refresh metrics', err))
+              }, 5000)
+            }
+          },
+          onMessage: (message: any) => {
+            console.log('ElevenLabs message:', message)
+            if (message?.source && message?.message) {
+              const role = message.source === 'user' ? 'user' as const : 'agent' as const
+              setTranscripts(prev => [...prev, { role, content: message.message }])
+            }
+          },
+          onError: (error: any) => {
+            console.error('ElevenLabs onError:', error)
+            setIsConnecting(false)
+            setIsConnected(false)
+            const msg = typeof error === 'string' ? error : error?.message || JSON.stringify(error)
+            showToast(`ElevenLabs error: ${msg}`, 'error')
+          },
+          onStatusChange: (status: any) => {
+            console.log('ElevenLabs status change:', status)
+          },
+        })
+
+        elevenLabsConversationRef.current = conversationInstance
+
+        // Get conversation ID AFTER startSession resolves (the instance is now available)
+        try {
+          const conversationId = conversationInstance?.getId()
+          console.log('ElevenLabs conversation ID:', conversationId)
+          if (currentCallShortIdRef.current && conversationId) {
+            await apiClient.updateCallRecording(currentCallShortIdRef.current, conversationId)
+            elevenLabsConversationIdStored = true
+            console.log('Updated call recording with ElevenLabs conversation ID:', conversationId)
+          } else {
+            console.warn('Missing callShortId or conversationId for ElevenLabs update', {
+              callShortId: currentCallShortIdRef.current,
+              conversationId,
+            })
+          }
+        } catch (e) {
+          console.error('Failed to update call recording with ElevenLabs conversation ID:', e)
+        }
+      } catch (error: any) {
+        console.error('Failed to connect ElevenLabs:', error)
+        setIsConnecting(false)
+        setIsConnected(false)
+        const detail = error?.response?.data?.detail
+          || (typeof error === 'string' ? error : error?.message || JSON.stringify(error))
+        showToast(`Failed to connect: ${detail}`, 'error')
+      }
+    } else if (isSmallestAgent) {
+      try {
+        const { AtomsClient } = await import('atoms-client-sdk')
+
+        // Request microphone permission first
+        try {
+          const testStream = await navigator.mediaDevices.getUserMedia({ audio: true })
+          testStream.getTracks().forEach(track => track.stop())
+        } catch (micError: any) {
+          console.error('Microphone permission denied:', micError)
+          setIsConnecting(false)
+          showToast('Microphone permission is required for voice calls', 'error')
+          return
+        }
+
+        const webCallResponse = await apiClient.createWebCall({
+          agent_id: fullAgent.id,
+          metadata: {},
+          ui_surface: 'agent_playground',
+        })
+
+        if (webCallResponse.call_short_id) {
+          currentCallShortIdRef.current = webCallResponse.call_short_id
+        }
+
+        if (!webCallResponse.access_token || !webCallResponse.host) {
+          throw new Error('Smallest webcall response missing access token or host')
+        }
+
+        const client = new AtomsClient()
+        smallestClientRef.current = client
+
+        client.on('session_started', () => {
+          console.log('Smallest webcall connected')
+          setIsConnected(true)
+          setIsConnecting(false)
+          showToast('Connected to agent', 'success')
+        })
+        client.on('session_ended', () => {
+          console.log('Smallest webcall disconnected')
+          setIsConnected(false)
+          setIsConnecting(false)
+          if (!userInitiatedDisconnectRef.current) {
+            showToast('Call ended')
+          }
+          userInitiatedDisconnectRef.current = false
+
+          if (currentCallShortIdRef.current) {
+            const callShortId = currentCallShortIdRef.current
+            setTimeout(() => {
+              apiClient.refreshCallRecording(callShortId)
+                .then(() => refetchCallRecordings())
+                .catch(err => console.error('Failed to refresh metrics', err))
+            }, 3000)
+          }
+        })
+        client.on('transcript', (data: any) => {
+          const content = data?.text
+          if (content && typeof content === 'string') {
+            setTranscripts(prev => [...prev, { role: 'agent', content }])
+          }
+        })
+        client.on('error', (errorMessage: string) => {
+          console.error('Smallest SDK error:', errorMessage)
+        })
+
+        await client.startSession({
+          accessToken: webCallResponse.access_token,
+          host: webCallResponse.host,
+          mode: 'webcall',
+        })
+        await client.startAudioPlayback()
+      } catch (error: any) {
+        console.error('Failed to connect Smallest:', error)
+        if (smallestClientRef.current) {
+          try {
+            smallestClientRef.current.stopSession()
+          } catch {
+            // ignore cleanup errors
+          }
+          smallestClientRef.current = null
+        }
+        setIsConnecting(false)
+        setIsConnected(false)
+        const detail = error?.response?.data?.detail
+          || (typeof error === 'string' ? error : error?.message || JSON.stringify(error))
+        showToast(`Failed to connect: ${detail}`, 'error')
+      }
+    }
+  }
+
+  const handleDisconnect = async () => {
+    userInitiatedDisconnectRef.current = true
+
+    if (isRetellAgent && retellClientRef.current) {
+      try {
+        retellClientRef.current.stopCall()
+      } catch (error: any) {
+        console.error('Failed to disconnect Retell:', error)
+      }
+    } else if (isVapiAgent && vapiClientRef.current) {
+      try {
+        vapiClientRef.current.stop()
+      } catch (error: any) {
+        console.error('Failed to disconnect Vapi:', error)
+      }
+    } else if (isElevenLabsAgent && elevenLabsConversationRef.current) {
+      try {
+        await elevenLabsConversationRef.current.endSession()
+        elevenLabsConversationRef.current = null
+      } catch (error: any) {
+        console.error('Failed to disconnect ElevenLabs:', error)
+      }
+    } else if (isSmallestAgent && smallestClientRef.current) {
+      try {
+        smallestClientRef.current.stopSession()
+        smallestClientRef.current = null
+      } catch (error: any) {
+        console.error('Failed to disconnect Smallest:', error)
+      }
+    }
+
+    setIsConnected(false)
+    showToast('Disconnected', 'success')
+  }
+
+  const handleCloseModal = () => {
+    if (isConnected) {
+      handleDisconnect()
+    }
+    setShowModal(false)
+    setShowTestModal(false)
+    setSelectedTestType(null)
+    setIsConnecting(false)
+    setIsConnected(false)
+  }
+
+  const handleTestTypeSelection = (type: 'test_agent' | 'voice_ai_agent') => {
+    setSelectedTestType(type)
+    if (type === 'voice_ai_agent') {
+      setShowModal(true)
+      setShowTestModal(false)
+    } else {
+      setShowTestModal(false)
+    }
+  }
+
+
+  const handleViewTestResult = (resultId: string) => {
+    navigate(`/playground/test-agent-results/${resultId}`)
+  }
+
+  const toggleTestResultSelection = (resultId: string) => {
+    setSelectedTestResultIds(prev => {
+      const next = new Set(prev)
+      if (next.has(resultId)) {
+        next.delete(resultId)
+      } else {
+        next.add(resultId)
+      }
+      return next
+    })
+  }
+
+  const toggleSelectAllTestResults = () => {
+    const allIds = testVoiceAgentResults.map((r: any) => r.id)
+    const allSelected = allIds.length > 0 && allIds.every((id: string) => selectedTestResultIds.has(id))
+    setSelectedTestResultIds(allSelected ? new Set() : new Set(allIds))
+  }
+
+  const handleDeleteSelectedTestResults = async () => {
+    if (selectedTestResultIds.size === 0) return
+    if (!window.confirm(`Delete ${selectedTestResultIds.size} test result${selectedTestResultIds.size > 1 ? 's' : ''}? This cannot be undone.`)) return
+
+    setIsDeletingSelectedTests(true)
+    const ids = Array.from(selectedTestResultIds)
+    try {
+      const results = await Promise.allSettled(ids.map(id => apiClient.deleteEvaluatorResult(id)))
+      const successCount = results.filter(r => r.status === 'fulfilled').length
+      const failCount = results.filter(r => r.status === 'rejected').length
+
+      if (successCount > 0) {
+        queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
+        showToast(`Deleted ${successCount} test result${successCount > 1 ? 's' : ''}`, 'success')
+      }
+      if (failCount > 0) {
+        showToast(`Failed to delete ${failCount} result${failCount > 1 ? 's' : ''}`, 'error')
+      }
+
+      setSelectedTestResultIds(prev => {
+        const next = new Set(prev)
+        ids.filter((_, i) => results[i].status === 'fulfilled').forEach(id => next.delete(id))
+        return next
+      })
+    } finally {
+      setIsDeletingSelectedTests(false)
+    }
+  }
+
+
+  const handleOpenVoiceAiDrawer = (callShortId: string) => {
+    prefetchCallRecordingAudio(callShortId, false)
+    void prefetchCallRecordingQuery(queryClient, callShortId)
+    setVoiceAiCallShortId(callShortId)
+  }
+
+  const handleViewCallRecording = (callShortId: string) => {
+    navigate(`/playground/call-recordings/${callShortId}`)
+  }
+
+  const handleEvaluateCustomSession = async (callShortId: string) => {
+    try {
+      await apiClient.evaluateCustomWebsocketSession(callShortId)
+      showToast('Evaluation queued for custom websocket session', 'success')
+      queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
+      queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
+    } catch (error: any) {
+      const detail = error?.response?.data?.detail || error?.message || 'Failed to queue evaluation'
+      showToast(detail, 'error')
+    }
+  }
+
+  const voiceAICallRecordings = callRecordings.filter(isVoiceAiProviderRecording)
+  const customWebsocketSessions = callRecordings.filter((recording: any) => recording.provider_platform === 'custom_websocket')
+
+  useEffect(() => {
+    warmCallRecordingQueryFromList(queryClient, callRecordings)
+  }, [callRecordings, queryClient])
+
+
+  const toggleCallSelection = (callShortId: string) => {
+    setSelectedCallIds(prev => {
+      const next = new Set(prev)
+      if (next.has(callShortId)) {
+        next.delete(callShortId)
+      } else {
+        next.add(callShortId)
+      }
+      return next
+    })
+  }
+
+  const toggleSelectAllCalls = () => {
+    const allIds = voiceAICallRecordings.map((r: any) => r.call_short_id)
+    const allSelected = allIds.length > 0 && allIds.every((id: string) => selectedCallIds.has(id))
+    setSelectedCallIds(allSelected ? new Set() : new Set(allIds))
+  }
+
+  const handleDeleteSelectedCalls = async () => {
+    if (selectedCallIds.size === 0) return
+    if (!window.confirm(`Delete ${selectedCallIds.size} call recording${selectedCallIds.size > 1 ? 's' : ''}? This cannot be undone.`)) return
+
+    setIsDeletingSelected(true)
+    const ids = Array.from(selectedCallIds)
+    try {
+      const results = await Promise.allSettled(ids.map(id => apiClient.deleteCallRecording(id)))
+      const successCount = results.filter(r => r.status === 'fulfilled').length
+      const failCount = results.filter(r => r.status === 'rejected').length
+
+      if (successCount > 0) {
+        queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
+        showToast(`Deleted ${successCount} recording${successCount > 1 ? 's' : ''}`, 'success')
+      }
+      if (failCount > 0) {
+        showToast(`Failed to delete ${failCount} recording${failCount > 1 ? 's' : ''}`, 'error')
+      }
+
+      setSelectedCallIds(prev => {
+        const next = new Set(prev)
+        ids.filter((_, i) => results[i].status === 'fulfilled').forEach(id => next.delete(id))
+        return next
+      })
+    } finally {
+      setIsDeletingSelected(false)
+    }
+  }
+
+  const handleRefreshStatus = async () => {
+    setIsRefreshingStatus(true)
+    try {
+      await Promise.all([refetchTestResults(), refetchCallRecordings()])
+      showToast('Latest evaluation status refreshed', 'success')
+    } catch {
+      showToast('Failed to refresh status', 'error')
+    } finally {
+      setIsRefreshingStatus(false)
+    }
+  }
+
+
+  return (
+    <>
+      <ToastContainer />
+      <div className="bg-white shadow rounded-lg">
+        <div className="px-6 py-4 border-b border-gray-200 flex items-center justify-between">
+          <div>
+            <h2 className="text-lg font-semibold text-gray-900">Agent Playground</h2>
+            <p className="mt-1 text-sm text-gray-600">
+              Test your voice AI agent with real-time web calls
+            </p>
+          </div>
+          <div className="flex gap-2">
+            <Button
+              variant="outline"
+              onClick={handleRefreshStatus}
+              leftIcon={<RefreshCw className="h-4 w-4" />}
+              isLoading={isRefreshingStatus}
+              disabled={!selectedAgent}
+            >
+              Refresh
+            </Button>
+            <Button
+              variant="primary"
+              onClick={() => setShowTestModal(true)}
+              leftIcon={<Play className="h-5 w-5" />}
+              disabled={!selectedAgent || (!hasTestAgent && !hasVoiceAIAgent)}
+            >
+              Test
+            </Button>
+          </div>
+        </div>
+
+        <div className="p-6">
+          {!selectedAgent ? (
+            <div className="bg-yellow-50 border border-yellow-200 rounded-lg p-4">
+              <p className="text-sm text-yellow-800">
+                Please select an agent from the top bar to use the Agent Playground.
+              </p>
+            </div>
+          ) : !hasTestAgent && !hasVoiceAIAgent ? (
+            <div className="bg-blue-50 border border-blue-200 rounded-lg p-4">
+              <p className="text-sm text-blue-800">
+                <strong>Selected Agent:</strong> {selectedAgent.name}
+              </p>
+              <p className="text-sm text-blue-700 mt-2">
+                {!hasVoiceAIIntegration && 'Link a Voice AI integration (Retell, Vapi, ElevenLabs, or Smallest) and set the Agent ID. '}
+                {!hasTestAgent && 'Attach a Voice Bundle for Test Agent mode. '}
+                Please check your agent configuration.
+              </p>
+            </div>
+          ) : (
+            <div className="bg-green-50 border border-green-200 rounded-lg p-4">
+              <p className="text-sm text-green-800">
+                <strong>Ready to test:</strong> {selectedAgent.name}
+              </p>
+              <p className="text-sm text-green-700 mt-1">
+                Click the Play button to start a web call with your Voice Agent.
+              </p>
+            </div>
+          )}
+
+          {/* Tabs Section */}
+          <div className="mt-6">
+            <div className="border-b border-gray-200">
+              <nav className="-mb-px flex space-x-8" aria-label="Tabs">
+                <button
+                  onClick={() => setActiveTab('voice_ai_agents')}
+                  className={`
+                    flex items-center gap-2 py-4 px-1 border-b-2 font-medium text-sm
+                    ${
+                      activeTab === 'voice_ai_agents'
+                        ? 'border-blue-500 text-blue-600'
+                        : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+                    }
+                  `}
+                >
+                  <PhoneCall className="h-4 w-4" />
+                  Voice AI Agents
+                </button>
+                <button
+                  onClick={() => setActiveTab('test_agents')}
+                  className={`
+                    flex items-center gap-2 py-4 px-1 border-b-2 font-medium text-sm
+                    ${
+                      activeTab === 'test_agents'
+                        ? 'border-blue-500 text-blue-600'
+                        : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+                    }
+                  `}
+                >
+                  <Bot className="h-4 w-4" />
+                  Test Agents
+                </button>
+                <button
+                  onClick={() => setActiveTab('custom_websocket')}
+                  className={`
+                    flex items-center gap-2 py-4 px-1 border-b-2 font-medium text-sm
+                    ${
+                      activeTab === 'custom_websocket'
+                        ? 'border-blue-500 text-blue-600'
+                        : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'
+                    }
+                  `}
+                >
+                  <Mic className="h-4 w-4" />
+                  Custom WebSocket Testing
+                </button>
+              </nav>
+            </div>
+            {/* Test Agents Tab Content */}
+            {activeTab === 'test_agents' && (
+              <div className="mt-4">
+                {selectedTestResultIds.size > 0 && (
+                  <div className="flex items-center justify-between mb-3">
+                    <span className="text-sm text-gray-600">{selectedTestResultIds.size} selected</span>
+                    <Button
+                      variant="danger"
+                      onClick={handleDeleteSelectedTestResults}
+                      disabled={isDeletingSelectedTests}
+                      isLoading={isDeletingSelectedTests}
+                      leftIcon={!isDeletingSelectedTests ? <Trash2 className="h-4 w-4" /> : undefined}
+                    >
+                      Delete ({selectedTestResultIds.size})
+                    </Button>
+                  </div>
+                )}
+                {testVoiceAgentResults.length === 0 ? (
+                  <div className="bg-gray-50 border border-gray-200 rounded-lg p-4 text-center">
+                    <p className="text-sm text-gray-600">No test agent results found</p>
+                  </div>
+                ) : (
+                  <div className="overflow-x-auto">
+                    <table className="min-w-full divide-y divide-gray-200">
+                      <thead className="bg-gray-50">
+                        <tr>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-10">
+                            <button
+                              type="button"
+                              onClick={toggleSelectAllTestResults}
+                              className="flex-shrink-0"
+                              aria-label="Select all test results"
+                            >
+                              {testVoiceAgentResults.length > 0 && testVoiceAgentResults.every((r: any) => selectedTestResultIds.has(r.id)) ? (
+                                <CheckSquare className="w-5 h-5 text-primary-600" />
+                              ) : (
+                                <Square className="w-5 h-5 text-gray-400" />
+                              )}
+                            </button>
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Call ID
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Status
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Agent
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Created
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-24">
+                            Trace
+                          </th>
+                        </tr>
+                      </thead>
+                      <tbody className="bg-white divide-y divide-gray-200">
+                        {testVoiceAgentResults.map((result: any) => {
+                          const isSelected = selectedTestResultIds.has(result.id)
+                          return (
+                            <tr
+                              key={result.id}
+                              className={`hover:bg-gray-50 cursor-pointer transition-colors ${isSelected ? 'bg-blue-50' : ''}`}
+                              onClick={() => handleViewTestResult(result.id)}
+                            >
+                              <td className="px-4 py-3 whitespace-nowrap" onClick={(e) => e.stopPropagation()}>
+                                <button
+                                  type="button"
+                                  onClick={() => toggleTestResultSelection(result.id)}
+                                  className="flex-shrink-0"
+                                >
+                                  {isSelected ? (
+                                    <CheckSquare className="w-5 h-5 text-primary-600" />
+                                  ) : (
+                                    <Square className="w-5 h-5 text-gray-400" />
+                                  )}
+                                </button>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap">
+                                <span className="font-mono text-sm font-semibold text-primary-600">
+                                  {result.result_id || result.id.substring(0, 8)}
+                                </span>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap">
+                                <span
+                                  className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${
+                                    result.status === 'completed'
+                                      ? 'bg-green-100 text-green-800'
+                                      : result.status === 'failed'
+                                      ? 'bg-red-100 text-red-800'
+                                      : 'bg-yellow-100 text-yellow-800'
+                                  }`}
+                                >
+                                  {result.status}
+                                </span>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap text-sm text-gray-500">
+                                {result.agent?.name || 'N/A'}
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap text-sm text-gray-500">
+                                {result.created_at
+                                  ? new Date(result.created_at).toLocaleString()
+                                  : 'N/A'}
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap" onClick={(e) => e.stopPropagation()}>
+                                <button
+                                  type="button"
+                                  onClick={() => setOtlpTraceResultId(result.id)}
+                                  className="inline-flex items-center gap-1 rounded-md border border-gray-200 px-2 py-1 text-xs font-medium text-gray-700 hover:bg-gray-50"
+                                  title="View OTLP call trace"
+                                >
+                                  <Activity className="h-3.5 w-3.5" />
+                                  Trace
+                                </button>
+                              </td>
+                            </tr>
+                          )
+                        })}
+                      </tbody>
+                    </table>
+                  </div>
+                )}
+              </div>
+            )}
+
+            {/* Voice AI Agents Tab Content */}
+            {activeTab === 'voice_ai_agents' && (
+              <div className="mt-4">
+                {selectedCallIds.size > 0 && (
+                  <div className="flex items-center justify-between mb-3">
+                    <span className="text-sm text-gray-600">{selectedCallIds.size} selected</span>
+                    <Button
+                      variant="danger"
+                      onClick={handleDeleteSelectedCalls}
+                      disabled={isDeletingSelected}
+                      isLoading={isDeletingSelected}
+                      leftIcon={!isDeletingSelected ? <Trash2 className="h-4 w-4" /> : undefined}
+                    >
+                      Delete ({selectedCallIds.size})
+                    </Button>
+                  </div>
+                )}
+                {voiceAICallRecordings.length === 0 ? (
+                  <div className="bg-gray-50 border border-gray-200 rounded-lg p-4 text-center">
+                    <p className="text-sm text-gray-600">
+                      No Retell, Vapi, ElevenLabs, or Smallest calls yet. Start a Voice AI Agent test from the agent sidebar.
+                    </p>
+                  </div>
+                ) : (
+                  <div className="overflow-x-auto">
+                    <table className="min-w-full divide-y divide-gray-200">
+                      <thead className="bg-gray-50">
+                        <tr>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider w-10">
+                            <button
+                              type="button"
+                              onClick={toggleSelectAllCalls}
+                              className="flex-shrink-0"
+                              aria-label="Select all call recordings"
+                            >
+                              {voiceAICallRecordings.length > 0 && voiceAICallRecordings.every((r: any) => selectedCallIds.has(r.call_short_id)) ? (
+                                <CheckSquare className="w-5 h-5 text-primary-600" />
+                              ) : (
+                                <Square className="w-5 h-5 text-gray-400" />
+                              )}
+                            </button>
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Call ID
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Status
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Evaluation
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Platform
+                          </th>
+                          <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                            Created
+                          </th>
+                        </tr>
+                      </thead>
+                      <tbody className="bg-white divide-y divide-gray-200">
+                        {voiceAICallRecordings.map((recording: any) => {
+                          const isSelected = selectedCallIds.has(recording.call_short_id)
+                          return (
+                            <tr
+                              key={recording.id}
+                              className={`hover:bg-gray-50 cursor-pointer transition-colors ${isSelected ? 'bg-blue-50' : ''}`}
+                              onClick={() => handleOpenVoiceAiDrawer(recording.call_short_id)}
+                              onMouseEnter={() => {
+                                prefetchCallRecordingAudio(recording.call_short_id, false)
+                                void prefetchCallRecordingQuery(queryClient, recording.call_short_id)
+                              }}
+                            >
+                              <td className="px-4 py-3 whitespace-nowrap" onClick={(e) => e.stopPropagation()}>
+                                <button
+                                  type="button"
+                                  onClick={() => toggleCallSelection(recording.call_short_id)}
+                                  className="flex-shrink-0"
+                                >
+                                  {isSelected ? (
+                                    <CheckSquare className="w-5 h-5 text-primary-600" />
+                                  ) : (
+                                    <Square className="w-5 h-5 text-gray-400" />
+                                  )}
+                                </button>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap">
+                                <span className="font-mono text-sm font-semibold text-primary-600">
+                                  {recording.call_short_id}
+                                </span>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap">
+                                <span
+                                  className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${recording.status === 'UPDATED'
+                                    ? 'bg-green-100 text-green-800'
+                                    : 'bg-yellow-100 text-yellow-800'
+                                    }`}
+                                >
+                                  {recording.status}
+                                </span>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap">
+                                {recording.evaluator_result_id ? (
+                                  <span
+                                    className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${
+                                      recording.evaluation_status === 'completed'
+                                        ? 'bg-green-100 text-green-800'
+                                        : recording.evaluation_status === 'failed'
+                                        ? 'bg-red-100 text-red-800'
+                                        : recording.evaluation_status === 'evaluating'
+                                        ? 'bg-blue-100 text-blue-800'
+                                        : 'bg-yellow-100 text-yellow-800'
+                                    }`}
+                                  >
+                                    {recording.evaluation_status || 'queued'}
+                                  </span>
+                                ) : recording.status === 'UPDATED' ? (
+                                  <span className="inline-flex px-2 py-1 text-xs font-semibold rounded-full bg-gray-100 text-gray-600">
+                                    Pending
+                                  </span>
+                                ) : (
+                                  <span className="text-xs text-gray-400">—</span>
+                                )}
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap">
+                                <div className="flex items-center gap-2">
+                                  {recording.provider_platform ? (() => {
+                                    const logo = getIntegrationPlatformLogo(
+                                      recording.provider_platform as IntegrationPlatform,
+                                    )
+                                    return logo ? (
+                                      <img
+                                        src={logo}
+                                        alt={recording.provider_platform}
+                                        className="h-5 w-5 object-contain"
+                                      />
+                                    ) : null
+                                  })() : null}
+                                  <span className="text-sm text-gray-500 capitalize">
+                                    {recording.provider_platform || 'N/A'}
+                                  </span>
+                                </div>
+                              </td>
+                              <td className="px-4 py-3 whitespace-nowrap text-sm text-gray-500">
+                                {recording.created_at
+                                  ? new Date(recording.created_at).toLocaleString()
+                                  : 'N/A'}
+                              </td>
+                            </tr>
+                          )
+                        })}
+                      </tbody>
+                    </table>
+                  </div>
+                )}
+              </div>
+            )}
+
+            {activeTab === 'custom_websocket' && (
+              <div className="mt-4 space-y-4">
+                <div className="rounded-lg border border-gray-200 p-4">
+                  <h3 className="text-sm font-semibold text-gray-900">Test a Custom WebSocket Voice Agent</h3>
+                  <p className="mt-1 text-xs text-gray-600">
+                    Paste or pick a saved WebSocket endpoint and start a live call. After disconnect you can save the transcript and evaluate.
+                  </p>
+
+                  {/* URL input + save button */}
+                  <div className="mt-3 flex gap-2">
+                    <input
+                      value={customWebsocketUrl}
+                      onChange={(e) => setCustomWebsocketUrl(e.target.value)}
+                      placeholder="wss://your-voice-agent.example.com/ws"
+                      className="flex-1 rounded-lg border border-gray-300 px-3 py-2 text-sm focus:border-primary-500 focus:outline-none focus:ring-2 focus:ring-primary-200"
+                    />
+                    {customWebsocketUrl.trim() && (
+                      isCurrentUrlSaved ? (
+                        <button
+                          className="flex items-center gap-1 px-3 py-2 rounded-lg text-xs font-medium text-primary-600 bg-primary-50 border border-primary-200"
+                          title="Already saved"
+                          disabled
+                        >
+                          <BookmarkCheck className="h-4 w-4" />
+                          Saved
+                        </button>
+                      ) : (
+                        <button
+                          className="flex items-center gap-1 px-3 py-2 rounded-lg text-xs font-medium text-gray-700 bg-white border border-gray-300 hover:bg-gray-50 transition-colors"
+                          onClick={() => setShowSaveForm((v) => !v)}
+                          title="Save this URL for later"
+                        >
+                          <Bookmark className="h-4 w-4" />
+                          Save
+                        </button>
+                      )
+                    )}
+                  </div>
+
+                  {/* Inline save form */}
+                  {showSaveForm && (
+                    <div className="mt-2 flex gap-2 items-center">
+                      <input
+                        value={saveLabel}
+                        onChange={(e) => setSaveLabel(e.target.value)}
+                        placeholder="Label (e.g. My Voice Bot)"
+                        className="flex-1 rounded-lg border border-gray-300 px-3 py-1.5 text-sm focus:border-primary-500 focus:outline-none focus:ring-2 focus:ring-primary-200"
+                        onKeyDown={(e) => { if (e.key === 'Enter') handleSaveUrl() }}
+                        autoFocus
+                      />
+                      <Button size="sm" onClick={handleSaveUrl}>Save</Button>
+                      <button className="text-gray-400 hover:text-gray-600" onClick={() => setShowSaveForm(false)}>
+                        <X className="h-4 w-4" />
+                      </button>
+                    </div>
+                  )}
+
+                  {/* Saved URLs list */}
+                  {savedWsUrls.length > 0 && (
+                    <div className="mt-3">
+                      <p className="text-[10px] font-medium text-gray-400 uppercase tracking-wider mb-1.5">Saved Endpoints</p>
+                      <div className="flex flex-wrap gap-2">
+                        {savedWsUrls.map((s) => (
+                          <button
+                            key={s.id}
+                            className={`group flex items-center gap-1.5 px-3 py-1.5 rounded-lg border text-xs transition-colors ${
+                              customWebsocketUrl.trim() === s.url
+                                ? 'border-primary-400 bg-primary-50 text-primary-700 font-medium'
+                                : 'border-gray-200 bg-white text-gray-700 hover:bg-gray-50 hover:border-gray-300'
+                            }`}
+                            onClick={() => setCustomWebsocketUrl(s.url)}
+                            title={s.url}
+                          >
+                            <Bookmark className="h-3 w-3 flex-shrink-0" />
+                            <span className="truncate max-w-[200px]">{s.label}</span>
+                            <span
+                              className="hidden group-hover:inline-flex ml-1 text-gray-400 hover:text-red-500"
+                              onClick={(e) => { e.stopPropagation(); handleDeleteSavedUrl(s.id) }}
+                              title="Remove"
+                            >
+                              <X className="h-3 w-3" />
+                            </span>
+                          </button>
+                        ))}
+                      </div>
+                    </div>
+                  )}
+                </div>
+
+                {customWebsocketUrl.trim().startsWith('ws://') || customWebsocketUrl.trim().startsWith('wss://') ? (
+                  <GenericVoiceWSClient
+                    websocketUrl={customWebsocketUrl.trim()}
+                    protocol={getProtocolById('generic')}
+                    agentId={selectedAgent?.id}
+                    onSessionSaved={() => {
+                      queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
+                      queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
+                    }}
+                  />
+                ) : (
+                  <div className="rounded-lg border border-amber-200 bg-amber-50 p-4 text-sm text-amber-800">
+                    Enter a valid WebSocket URL (<code className="bg-amber-100 px-1 rounded">ws://</code> or <code className="bg-amber-100 px-1 rounded">wss://</code>) to start testing.
+                  </div>
+                )}
+
+                <div className="rounded-lg border border-gray-200">
+                  <div className="border-b border-gray-200 px-4 py-3">
+                    <h4 className="text-sm font-semibold text-gray-900">Saved Custom Sessions</h4>
+                  </div>
+                  <div className="p-4">
+                    {customWebsocketSessions.length === 0 ? (
+                      <p className="text-sm text-gray-600">No saved custom websocket sessions yet.</p>
+                    ) : (
+                      <div className="overflow-x-auto">
+                        <table className="min-w-full divide-y divide-gray-200">
+                          <thead className="bg-gray-50">
+                            <tr>
+                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Call ID</th>
+                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Status</th>
+                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Evaluation</th>
+                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Created</th>
+                              <th className="px-4 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Actions</th>
+                            </tr>
+                          </thead>
+                          <tbody className="divide-y divide-gray-200 bg-white">
+                            {customWebsocketSessions.map((session: any) => (
+                              <tr
+                                key={session.id}
+                                className="hover:bg-gray-50 cursor-pointer transition-colors"
+                                onClick={() => handleViewCallRecording(session.call_short_id)}
+                              >
+                                <td className="px-4 py-3 text-sm font-mono font-semibold text-primary-600">
+                                  {session.call_short_id}
+                                </td>
+                                <td className="px-4 py-3 text-sm text-gray-600">{session.status}</td>
+                                <td className="px-4 py-3">
+                                  {session.evaluator_result_id ? (
+                                    <span className="inline-flex rounded-full bg-blue-100 px-2 py-1 text-xs font-semibold text-blue-800">
+                                      {session.evaluation_status || 'queued'}
+                                    </span>
+                                  ) : (
+                                    <span className="inline-flex rounded-full bg-gray-100 px-2 py-1 text-xs font-semibold text-gray-700">
+                                      pending
+                                    </span>
+                                  )}
+                                </td>
+                                <td className="px-4 py-3 text-sm text-gray-600">
+                                  {session.created_at ? new Date(session.created_at).toLocaleString() : 'N/A'}
+                                </td>
+                                <td className="px-4 py-3" onClick={(e) => e.stopPropagation()}>
+                                  <div className="flex flex-wrap gap-2">
+                                    {!session.evaluator_result_id && (
+                                      <Button
+                                        variant="outline"
+                                        onClick={() => handleEvaluateCustomSession(session.call_short_id)}
+                                      >
+                                        Run Evaluation
+                                      </Button>
+                                    )}
+                                    {session.evaluator_result_id && (
+                                      <Button
+                                        variant="outline"
+                                        onClick={() => handleViewTestResult(session.evaluator_result_id)}
+                                      >
+                                        Open Result
+                                      </Button>
+                                    )}
+                                  </div>
+                                </td>
+                              </tr>
+                            ))}
+                          </tbody>
+                        </table>
+                      </div>
+                    )}
+                  </div>
+                </div>
+              </div>
+            )}
+          </div>
+        </div>
+      </div>
+
+
+      {/* Test Type Selection Modal */}
+      {showTestModal && (
+        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-50">
+          <div className="bg-white rounded-lg shadow-xl max-w-md w-full mx-4">
+            <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
+              <h3 className="text-lg font-semibold">Select Test Type</h3>
+              <button
+                onClick={handleCloseModal}
+                className="text-gray-400 hover:text-gray-600"
+              >
+                <X className="h-5 w-5" />
+              </button>
+            </div>
+
+            <div className="p-6">
+              <div className="text-center mb-4">
+                <p className="text-sm text-gray-600 mb-2">
+                  Agent: <strong>{selectedAgent?.name}</strong>
+                </p>
+                <p className="text-xs text-gray-500">
+                  Choose how you want to test this agent
+                </p>
+              </div>
+
+              {s3Status && !s3Status.enabled && (
+                <div className="flex items-start gap-3 p-3 bg-amber-50 border border-amber-200 rounded-lg mb-3">
+                  <AlertTriangle className="h-5 w-5 text-amber-500 flex-shrink-0 mt-0.5" />
+                  <div>
+                    <p className="text-sm font-medium text-amber-800">Storage not configured</p>
+                    <p className="text-xs text-amber-700 mt-0.5">
+                      S3 storage is not configured. Audio recordings will not be saved. Configure storage in Settings &gt; Data Sources to enable audio playback.
+                    </p>
+                  </div>
+                </div>
+              )}
+
+              <div className="flex items-start gap-2.5 p-3 bg-blue-50 border border-blue-200 rounded-lg mb-1">
+                <AlertTriangle className="h-4 w-4 text-blue-500 flex-shrink-0 mt-0.5" />
+                <p className="text-xs text-blue-700">
+                  First-time runs may take longer as ML models required for audio evaluation metrics are downloaded and cached locally. Subsequent runs will be significantly faster.
+                </p>
+              </div>
+
+              <div className="space-y-3">
+                {hasTestAgent && (
+                  <button
+                    onClick={() => handleTestTypeSelection('test_agent')}
+                    className="w-full p-4 border-2 border-gray-200 rounded-lg hover:border-blue-500 hover:bg-blue-50 transition-all text-left"
+                  >
+                    <div className="flex items-center gap-3">
+                      <div className="p-2 bg-blue-100 rounded-lg">
+                        <Mic className="h-5 w-5 text-blue-600" />
+                      </div>
+                      <div>
+                        <h4 className="font-semibold text-gray-900">Test Agent</h4>
+                        <p className="text-sm text-gray-600">Test with Voice Bundle (STT/TTS/LLM)</p>
+                      </div>
+                    </div>
+                  </button>
+                )}
+
+                {hasVoiceAIAgent && (
+                  <button
+                    onClick={() => handleTestTypeSelection('voice_ai_agent')}
+                    className="w-full p-4 border-2 border-gray-200 rounded-lg hover:border-blue-500 hover:bg-blue-50 transition-all text-left"
+                  >
+                    <div className="flex items-center gap-3">
+                      <div className="p-2 bg-green-100 rounded-lg">
+                        <Phone className="h-5 w-5 text-green-600" />
+                      </div>
+                      <div>
+                        <h4 className="font-semibold text-gray-900">Voice AI Agent</h4>
+                        <p className="text-sm text-gray-600">Test with Voice AI Integration (Retell, Vapi, ElevenLabs, Smallest)</p>
+                      </div>
+                    </div>
+                  </button>
+                )}
+
+                {!hasTestAgent && !hasVoiceAIAgent && (
+                  <div className="text-center py-4">
+                    <p className="text-sm text-gray-600 mb-4">
+                      This agent is not configured for testing.
+                    </p>
+                    <Button
+                      variant="outline"
+                      onClick={handleCloseModal}
+                    >
+                      Close
+                    </Button>
+                  </div>
+                )}
+              </div>
+            </div>
+          </div>
+        </div>
+      )}
+
+      {/* Voice AI Agent Connect Modal (Retell) */}
+      {showModal && selectedTestType === 'voice_ai_agent' && (
+        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-50">
+          <div className="bg-white rounded-lg shadow-xl max-w-md w-full mx-4">
+            <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
+              <h3 className="text-lg font-semibold">Connect to Voice AI Agent</h3>
+              <button
+                onClick={handleCloseModal}
+                className="text-gray-400 hover:text-gray-600"
+                disabled={isConnecting}
+              >
+                <X className="h-5 w-5" />
+              </button>
+            </div>
+
+            <div className="p-6">
+              {!canMakeCall ? (
+                <div className="text-center py-4">
+                  <p className="text-sm text-gray-600 mb-4">
+                    This agent is not configured for web calls.
+                  </p>
+                  <Button
+                    variant="outline"
+                    onClick={handleCloseModal}
+                  >
+                    Close
+                  </Button>
+                </div>
+              ) : (
+                <div className="space-y-4">
+                  <div className="text-center">
+                    <p className="text-sm text-gray-600 mb-2">
+                      Agent: <strong>{selectedAgent?.name}</strong>
+                    </p>
+                    <p className="text-xs text-gray-500">
+                      Provider: {agentIntegration?.platform || 'Unknown'}
+                    </p>
+                    <p className="text-xs text-gray-500">
+                      Agent ID: <span className="font-mono font-semibold text-primary-600">{fullAgent?.voice_ai_agent_id}</span>
+                    </p>
+                  </div>
+
+                  {isConnected && (
+                    <div className="h-48 overflow-y-auto bg-gray-50 rounded p-4 mb-4 space-y-3 border border-gray-200">
+                      {transcripts.length === 0 ? (
+                        <p className="text-gray-400 text-xs text-center italic">Waiting for connection...</p>
+                      ) : (
+                        transcripts.map((msg, idx) => (
+                          <div key={idx} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
+                            <div className={`max-w-[80%] rounded-2xl px-4 py-2.5 ${msg.role === 'user'
+                              ? 'bg-indigo-600 text-white rounded-br-none'
+                              : 'bg-white border border-gray-200 text-gray-800 rounded-bl-none'
+                              }`}>
+                              <p className={`text-[10px] font-semibold uppercase tracking-wider mb-0.5 ${
+                                msg.role === 'user' ? 'opacity-70' : 'text-gray-400'
+                              }`}>
+                                {msg.role === 'user' ? 'You' : 'Agent'}
+                              </p>
+                              <p className="text-sm leading-relaxed">{msg.content}</p>
+                            </div>
+                          </div>
+                        ))
+                      )}
+                    </div>
+                  )}
+
+                  {!isConnected ? (
+                    <Button
+                      variant="primary"
+                      onClick={handleConnect}
+                      isLoading={isConnecting}
+                      leftIcon={!isConnecting ? <Phone className="h-5 w-5" /> : undefined}
+                      className="w-full"
+                      disabled={isConnecting}
+                    >
+                      {isConnecting ? 'Connecting...' : 'Connect'}
+                    </Button>
+                  ) : (
+                    <div className="space-y-3">
+                      <div className="bg-green-50 border border-green-200 rounded-lg p-3 text-center">
+                        <p className="text-sm text-green-800 font-medium">Connected</p>
+                        <p className="text-xs text-green-700 mt-1">Call is active</p>
+                      </div>
+                      <Button
+                        variant="danger"
+                        onClick={handleDisconnect}
+                        leftIcon={<PhoneOff className="h-5 w-5" />}
+                        className="w-full"
+                      >
+                        Disconnect
+                      </Button>
+                    </div>
+                  )}
+                </div>
+              )}
+            </div>
+          </div>
+        </div>
+      )}
+
+      {/* Test Agent Modal */}
+      {selectedTestType === 'test_agent' && !showTestModal && (
+        <div className="fixed inset-0 bg-gray-500 bg-opacity-75 flex items-center justify-center z-50">
+          <div className="bg-white rounded-lg shadow-xl max-w-4xl w-full mx-4 max-h-[90vh] overflow-y-auto">
+            <div className="px-6 py-4 border-b border-gray-200 flex justify-between items-center">
+              <h3 className="text-lg font-semibold">Test Agent - {selectedAgent?.name}</h3>
+              <button
+                onClick={handleCloseModal}
+                className="text-gray-400 hover:text-gray-600"
+              >
+                <X className="h-5 w-5" />
+              </button>
+            </div>
+
+            <div className="p-6">
+              <VoiceAgent
+                agentId={selectedAgent?.id}
+                billingSurface="agent_playground"
+                onSessionSaved={() => {
+                  queryClient.invalidateQueries({ queryKey: ['test-voice-agent-results'] })
+                }}
+              />
+            </div>
+          </div>
+        </div>
+      )}
+
+      <TraceDetailDrawer
+        open={Boolean(otlpTraceResultId)}
+        evaluatorResultId={otlpTraceResultId}
+        onClose={() => setOtlpTraceResultId(null)}
+      />
+      <TraceDetailDrawer
+        open={Boolean(voiceAiCallShortId)}
+        callShortId={voiceAiCallShortId}
+        onClose={() => setVoiceAiCallShortId(null)}
+      />
+    </>
+  )
+}
+
diff --git a/frontend/src/pages/playground/agent/CallRecordingDetail.tsx b/frontend/src/pages/playground/agent/CallRecordingDetail.tsx
index bd4110d8..e8162aba 100644
--- a/frontend/src/pages/playground/agent/CallRecordingDetail.tsx
+++ b/frontend/src/pages/playground/agent/CallRecordingDetail.tsx
@@ -1,838 +1,908 @@
-import React, { useState, useEffect, useMemo } from 'react'
-import { useParams, useNavigate } from 'react-router-dom'
-import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query'
-import { apiClient } from '../../../lib/api'
-import ConfirmModal from '../../../components/ConfirmModal'
-import { ArrowLeft, RefreshCw, Trash2, BarChart3, CheckCircle, XCircle, HelpCircle, Brain, Sparkles, AudioWaveform, Loader, RotateCcw } from 'lucide-react'
-import Button from '../../../components/Button'
-import { useToast } from '../../../hooks/useToast'
-import RetellCallDetails from '../../../components/call-recordings/RetellCallDetails'
-import VapiCallDetails from '../../../components/call-recordings/VapiCallDetails'
-import ElevenLabsCallDetails from '../../../components/call-recordings/ElevenLabsCallDetails'
-import CustomWebSocketCallDetails from '../../../components/call-recordings/CustomWebSocketCallDetails'
-import SmallestCallDetails from '../../../components/call-recordings/SmallestCallDetails'
-
-const LEGACY_CATEGORY_LABEL_METRIC_NAMES = new Set([
-  'yes',
-  'no',
-  'true',
-  'false',
-  'same',
-  'different',
-])
-
-function isLegacyCategoryLabelMetric(metric: {
-  type?: string | null
-  metric_name?: string | null
-}): boolean {
-  if ((metric.type || '').toLowerCase() !== 'boolean') return false
-  const name = (metric.metric_name || '').trim().toLowerCase()
-  return LEGACY_CATEGORY_LABEL_METRIC_NAMES.has(name)
-}
-// Comprehensive metric information with descriptions and ideal values
-const METRIC_INFO: Record<string, { 
-  description: string
-  ideal: string
-  unit?: string
-  category: 'acoustic' | 'ai_voice' | 'llm'
-}> = {
-  // Acoustic Metrics (Parselmouth)
-  'Pitch Variance': { 
-    description: 'F0 variation measuring prosodic expressiveness. Higher values indicate more expressive speech.',
-    ideal: '20-50 Hz (natural speech)',
-    unit: 'Hz',
-    category: 'acoustic'
-  },
-  'Jitter': { 
-    description: 'Cycle-to-cycle pitch period variation indicating vocal stability. Lower is better.',
-    ideal: '< 1% (healthy voice)',
-    unit: '%',
-    category: 'acoustic'
-  },
-  'Shimmer': { 
-    description: 'Amplitude perturbation measuring voice quality consistency. Lower is better.',
-    ideal: '< 3% (clear voice)',
-    unit: '%',
-    category: 'acoustic'
-  },
-  'HNR': { 
-    description: 'Harmonics-to-Noise Ratio measuring signal clarity. Higher indicates cleaner voice.',
-    ideal: '> 20 dB (clear, non-breathy)',
-    unit: 'dB',
-    category: 'acoustic'
-  },
-  
-  // AI Voice Quality Metrics
-  'MOS Score': { 
-    description: 'Mean Opinion Score predicting human perception of audio quality (1-5 scale).',
-    ideal: '4.0+ (studio quality), 3.0 (phone quality), <2.0 (poor/robotic)',
-    category: 'ai_voice'
-  },
-  'Emotion Category': { 
-    description: 'Dominant emotion detected in the voice (angry, happy, sad, neutral, fearful, etc.).',
-    ideal: 'Depends on context - should match expected tone',
-    category: 'ai_voice'
-  },
-  'Emotion Confidence': { 
-    description: 'Confidence score for the detected emotion category.',
-    ideal: '> 0.7 (high confidence)',
-    category: 'ai_voice'
-  },
-  'Valence': { 
-    description: 'Emotional positivity/negativity scale. Negative = sad/angry, Positive = happy/excited.',
-    ideal: '-1.0 to +1.0 (context dependent)',
-    category: 'ai_voice'
-  },
-  'Arousal': { 
-    description: 'Emotional intensity/energy level. Low = calm/sleepy, High = excited/energetic.',
-    ideal: '0.3-0.6 (engaged but not agitated)',
-    category: 'ai_voice'
-  },
-  'Speaker Consistency': { 
-    description: 'Voice identity stability throughout the call. Detects if voice changed mid-call (glitch).',
-    ideal: '> 0.8 (same voice), < 0.5 indicates voice glitch',
-    category: 'ai_voice'
-  },
-  'Prosody Score': { 
-    description: 'Expressiveness/drama score. Low = monotone/flat, High = expressive/dynamic.',
-    ideal: '0.4-0.7 (natural expressiveness)',
-    category: 'ai_voice'
-  },
-  
-  // LLM Conversation Metrics
-  'Follow Instructions': { 
-    description: 'How well the agent followed the given instructions and guidelines.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-  'Problem Resolution': { 
-    description: 'Whether the agent successfully resolved the customer\'s problem or query.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-  'Professionalism': { 
-    description: 'Professional demeanor, appropriate language, and courteous behavior.',
-    ideal: '> 0.85 (85%+)',
-    category: 'llm'
-  },
-  'Clarity and Empathy': { 
-    description: 'Clear communication combined with understanding and acknowledgment of customer feelings.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-  'Objective Achieved': { 
-    description: 'Whether the conversation\'s primary objective was successfully achieved.',
-    ideal: 'Yes/True',
-    category: 'llm'
-  },
-  'Overall Quality': { 
-    description: 'Holistic assessment of the entire conversation quality.',
-    ideal: '> 0.8 (80%+)',
-    category: 'llm'
-  },
-}
-
-const getMetricInfo = (metricName: string) => METRIC_INFO[metricName]
-
-const isAudioMetric = (metricName: string): boolean => {
-  const info = METRIC_INFO[metricName]
-  return info?.category === 'acoustic' || info?.category === 'ai_voice'
-}
-
-const getAudioMetricInfo = (metricName: string) => {
-  const info = METRIC_INFO[metricName]
-  if (!info) return undefined
-  return { unit: info.unit || '', description: info.description }
-}
-
-// Tooltip component for metrics
-const MetricTooltip = ({ metricName }: { metricName: string }) => {
-  const [isVisible, setIsVisible] = useState(false)
-  const info = getMetricInfo(metricName)
-  
-  if (!info) return null
-  
-  return (
-    <div className="relative inline-flex flex-shrink-0 ml-1 mt-0.5">
-      <button
-        type="button"
-        className="text-gray-400 hover:text-gray-600 focus:outline-none transition-colors"
-        onMouseEnter={() => setIsVisible(true)}
-        onMouseLeave={() => setIsVisible(false)}
-        onClick={() => setIsVisible(!isVisible)}
-        aria-label={`Info about ${metricName}`}
-      >
-        <HelpCircle className="w-3.5 h-3.5" />
-      </button>
-      
-      {isVisible && (
-        <div className="absolute z-50 left-1/2 -translate-x-1/2 bottom-full mb-2 w-64 p-3 text-xs bg-gray-900 text-white rounded-lg shadow-xl pointer-events-none">
-          <div className="font-semibold text-gray-100 mb-1.5">{metricName}</div>
-          <p className="text-gray-300 mb-2 leading-relaxed">{info.description}</p>
-          <div className="flex items-center gap-1 pt-1.5 border-t border-gray-700">
-            <span className="text-emerald-400 font-medium">Ideal:</span>
-            <span className="text-gray-200">{info.ideal}</span>
-          </div>
-          {/* Arrow pointing down */}
-          <div className="absolute left-1/2 -translate-x-1/2 top-full w-0 h-0 border-l-[6px] border-l-transparent border-r-[6px] border-r-transparent border-t-[6px] border-t-gray-900" />
-        </div>
-      )}
-    </div>
-  )
-}
-
-// Format metric value for display
-const formatMetricValue = (value: any, type: string, metricName?: string): React.ReactNode => {
-  if (value === null || value === undefined) return <span className="text-gray-400">N/A</span>
-  
-  const normalizedType = type?.toLowerCase()
-
-  if (normalizedType === 'category') {
-    if (value === '') return <span className="text-gray-400">N/A</span>
-    return (
-      <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-lg bg-indigo-50 text-indigo-700 text-sm font-semibold whitespace-normal break-words leading-snug">
-        {String(value)}
-      </span>
-    )
-  }
-  
-  // Handle Emotion Category - categorical text values with styling
-  if (metricName === 'Emotion Category') {
-    const emotion = String(value).toLowerCase()
-    const emotionConfig: Record<string, { emoji: string; color: string; bg: string }> = {
-      'neutral': { emoji: '😐', color: 'text-gray-700', bg: 'bg-gray-100' },
-      'happy': { emoji: '😊', color: 'text-green-700', bg: 'bg-green-100' },
-      'sad': { emoji: '😢', color: 'text-blue-700', bg: 'bg-blue-100' },
-      'angry': { emoji: '😠', color: 'text-red-700', bg: 'bg-red-100' },
-      'fearful': { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
-      'fear': { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
-      'surprised': { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
-      'surprise': { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
-      'disgusted': { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
-      'disgust': { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
-      'calm': { emoji: '😌', color: 'text-teal-700', bg: 'bg-teal-100' },
-    }
-    const config = emotionConfig[emotion] || { emoji: '🎭', color: 'text-gray-700', bg: 'bg-gray-100' }
-    
-    return (
-      <div className={`inline-flex items-center gap-2 px-3 py-1.5 rounded-full ${config.bg}`}>
-        <span className="text-xl">{config.emoji}</span>
-        <span className={`font-semibold capitalize ${config.color}`}>{value}</span>
-      </div>
-    )
-  }
-  
-  // Handle boolean metrics
-  if (normalizedType === 'boolean') {
-    const boolValue = value === true || value === 1 || value === '1' || value === 'true'
-    return <span className="block text-base font-semibold text-gray-900">{boolValue ? 'Yes' : 'No'}</span>
-  }
-  
-  // Handle rating metrics with progress bar
-  if (normalizedType === 'rating') {
-    if (typeof value === 'string' && isNaN(parseFloat(value))) {
-      return (
-        <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-full bg-purple-100 text-purple-700 font-semibold capitalize whitespace-normal break-words text-left leading-snug">
-          {value}
-        </span>
-      )
-    }
-    
-    const numValue = typeof value === 'number' ? value : parseFloat(value)
-    if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
-    
-    const normalizedValue = Math.max(0, Math.min(1, numValue))
-    const percentage = Math.round(normalizedValue * 100)
-    
-    const getBarColor = (pct: number): string => {
-      if (pct >= 70) return 'bg-green-500'
-      if (pct >= 50) return 'bg-yellow-500'
-      return 'bg-red-500'
-    }
-    
-    return (
-      <div className="flex flex-col gap-2">
-        <span className="text-2xl font-bold text-gray-900">{percentage}%</span>
-        <div className="w-full h-3 bg-gray-200 rounded-full overflow-hidden">
-          <div 
-            className={`h-full rounded-full transition-all ${getBarColor(percentage)}`}
-            style={{ width: `${percentage}%` }}
-          />
-        </div>
-      </div>
-    )
-  }
-  
-  // Handle number metrics (including audio metrics)
-  if (normalizedType === 'number') {
-    const numValue = typeof value === 'number' ? value : parseFloat(value)
-    if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
-    
-    if (metricName && isAudioMetric(metricName)) {
-      const audioInfo = getAudioMetricInfo(metricName)
-      return (
-        <div className="flex items-baseline gap-1">
-          <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(2)}</span>
-          <span className="text-sm font-medium text-violet-600">{audioInfo?.unit}</span>
-        </div>
-      )
-    }
-    
-    return <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(1)}</span>
-  }
-  
-  return <span className="block max-w-full text-base font-semibold leading-snug text-gray-900 whitespace-normal break-words">{String(value)}</span>
-}
-
-const renderMetricRationale = (metric: { rationale?: string | null }) => {
-  const rationale = typeof metric.rationale === 'string' ? metric.rationale.trim() : ''
-  if (!rationale) return null
-  return (
-    <p className="mt-2 text-xs text-gray-600 leading-relaxed border-t border-gray-100 pt-2">
-      {rationale}
-    </p>
-  )
-}
-
-// Helper to check if a metric has a valid value
-const hasValidValue = (metric: { value: any }) => {
-  const val = metric.value
-  if (val === null || val === undefined) return false
-  if (val === '') return false
-  if (typeof val === 'string' && val.toLowerCase() === 'n/a') return false
-  if (typeof val === 'string' && val.toLowerCase() === 'na') return false
-  if (typeof val === 'string' && val.trim() === '') return false
-  return true
-}
-
-const isAudioCategoryMetric = (metricName?: string): boolean => {
-  if (!metricName) return false
-  const info = getMetricInfo(metricName)
-  return info?.category === 'acoustic' || info?.category === 'ai_voice'
-}
-
-function EvaluationStepper({ status }: { status: string }) {
-  const [dots, setDots] = useState('')
-
-  useEffect(() => {
-    const id = setInterval(() => setDots(prev => prev.length >= 3 ? '' : prev + '.'), 500)
-    return () => clearInterval(id)
-  }, [])
-
-  const steps = [
-    { key: 'queued', label: 'Queued', sublabel: 'Preparing worker' },
-    { key: 'transcribing', label: 'Analyzing Audio', sublabel: 'Acoustic & voice quality' },
-    { key: 'evaluating', label: 'Evaluating', sublabel: 'LLM conversation metrics' },
-  ]
-
-  const activeIdx = Math.max(0, steps.findIndex(s => s.key === status))
-
-  const statusMessages: Record<string, string> = {
-    queued: 'Downloading audio & preparing evaluation',
-    transcribing: 'Running acoustic analysis & voice quality metrics',
-    evaluating: 'Evaluating conversation quality with LLM',
-  }
-
-  return (
-    <div className="py-8 px-4">
-      {/* Steps */}
-      <div className="flex items-start justify-center max-w-md mx-auto">
-        {steps.map((step, idx) => {
-          const isActive = idx === activeIdx
-          const isDone = idx < activeIdx
-
-          return (
-            <React.Fragment key={step.key}>
-              <div className="flex flex-col items-center" style={{ minWidth: 90 }}>
-                <div
-                  className={[
-                    'w-11 h-11 rounded-full flex items-center justify-center transition-all duration-500 relative',
-                    isDone ? 'bg-emerald-500 text-white shadow-md' : '',
-                    isActive ? 'bg-blue-600 text-white shadow-lg shadow-blue-200' : '',
-                    !isDone && !isActive ? 'bg-gray-100 text-gray-400 border-2 border-gray-200' : '',
-                  ].join(' ')}
-                >
-                  {isActive && (
-                    <span className="absolute inset-0 rounded-full animate-ping bg-blue-400 opacity-30" />
-                  )}
-                  {isDone ? (
-                    <CheckCircle className="w-5 h-5" />
-                  ) : isActive ? (
-                    <Loader className="w-5 h-5 animate-spin" />
-                  ) : (
-                    <span className="text-sm font-semibold">{idx + 1}</span>
-                  )}
-                </div>
-                <p className={`mt-2.5 text-xs font-semibold text-center leading-tight ${isActive ? 'text-blue-700' : isDone ? 'text-emerald-600' : 'text-gray-400'}`}>
-                  {step.label}
-                </p>
-                <p className={`text-[10px] text-center leading-tight mt-0.5 ${isActive ? 'text-blue-500' : isDone ? 'text-emerald-500' : 'text-gray-300'}`}>
-                  {step.sublabel}
-                </p>
-              </div>
-              {idx < steps.length - 1 && (
-                <div className="flex-1 flex items-center pt-1" style={{ minWidth: 40 }}>
-                  <div className="w-full relative h-1 rounded-full bg-gray-200 overflow-hidden" style={{ marginTop: 18 }}>
-                    <div
-                      className={`absolute inset-y-0 left-0 rounded-full transition-all duration-700 ${isDone ? 'w-full bg-emerald-400' : isActive ? 'bg-blue-400 animate-[indeterminate_1.5s_ease-in-out_infinite]' : 'w-0'}`}
-                      style={isActive ? { width: '60%' } : undefined}
-                    />
-                  </div>
-                </div>
-              )}
-            </React.Fragment>
-          )
-        })}
-      </div>
-
-      {/* Animated status message */}
-      <div className="mt-6 text-center">
-        <span className="inline-flex items-center gap-2 px-4 py-2 bg-blue-50 border border-blue-100 rounded-full text-sm text-blue-700">
-          <Loader className="w-3.5 h-3.5 animate-spin" />
-          {statusMessages[status] || 'Processing'}{dots}
-        </span>
-      </div>
-    </div>
-  )
-}
-
-
-export default function CallRecordingDetail() {
-  const { callShortId } = useParams<{ callShortId: string }>()
-  const navigate = useNavigate()
-  const queryClient = useQueryClient()
-  const { showToast, ToastContainer } = useToast()
-  const [showDelete, setShowDelete] = useState(false)
-  const [reEvalInProgress, setReEvalInProgress] = useState(false)
-
-  const { data: callRecording, refetch: refetchCallDetails, isLoading } = useQuery({
-    queryKey: ['call-recording', callShortId],
-    queryFn: () => apiClient.getCallRecording(callShortId!),
-    enabled: !!callShortId,
-    refetchInterval: (query) => {
-      const data = query.state.data as any
-      const inProgress = reEvalInProgress || (data?.evaluation?.status && ['queued', 'transcribing', 'evaluating'].includes(data.evaluation.status))
-      return inProgress ? 2000 : false
-    },
-  })
-
-  const { data: metrics = [] } = useQuery({
-    queryKey: ['metrics'],
-    queryFn: () => apiClient.listMetrics(),
-  })
-
-  const childMetricIds = useMemo(() => {
-    const ids = new Set<string>()
-    const visit = (metric: { id?: string; parent_metric_id?: string | null; children?: any[] }) => {
-      if (metric.parent_metric_id && metric.id) ids.add(metric.id)
-      for (const child of metric.children || []) {
-        if (child?.id) ids.add(child.id)
-        visit(child)
-      }
-    }
-    for (const metric of metrics as Array<{ id?: string; parent_metric_id?: string | null; children?: any[] }>) {
-      visit(metric)
-    }
-    return ids
-  }, [metrics])
-
-  const shouldHideMetricScore = (
-    metricId: string,
-    metric: { parent_metric_id?: string | null; type?: string | null; metric_name?: string | null },
-  ) => {
-    return Boolean(
-      metric.parent_metric_id ||
-        childMetricIds.has(metricId) ||
-        isLegacyCategoryLabelMetric(metric),
-    )
-  }
-  // Clear optimistic re-eval state once the backend confirms a terminal status
-  useEffect(() => {
-    if (reEvalInProgress && callRecording?.evaluation?.status && ['completed', 'failed'].includes(callRecording.evaluation.status)) {
-      setReEvalInProgress(false)
-    }
-  }, [callRecording?.evaluation?.status, reEvalInProgress])
-
-  const refreshMutation = useMutation({
-    mutationFn: () => apiClient.refreshCallRecording(callShortId!),
-    onSuccess: () => {
-      showToast('Call recording refresh initiated', 'success')
-      setTimeout(() => {
-        refetchCallDetails()
-      }, 2000)
-    },
-    onError: (error: any) => {
-      showToast(`Failed to refresh: ${error.response?.data?.detail || error.message}`, 'error')
-    },
-  })
-
-  const reEvaluateMutation = useMutation({
-    mutationFn: () => apiClient.reEvaluateCallRecording(callShortId!),
-    onMutate: () => {
-      setReEvalInProgress(true)
-    },
-    onSuccess: () => {
-      showToast('Re-evaluation started — audio will be analyzed for voice quality metrics', 'success')
-      refetchCallDetails()
-    },
-    onError: (error: any) => {
-      setReEvalInProgress(false)
-      showToast(`Re-evaluation failed: ${error.response?.data?.detail || error.message}`, 'error')
-    },
-  })
-
-  const deleteMutation = useMutation({
-    mutationFn: () => apiClient.deleteCallRecording(callShortId!),
-    onSuccess: () => {
-      showToast('Call recording deleted successfully', 'success')
-      queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
-      navigate('/playground')
-    },
-    onError: (error: any) => {
-      showToast(`Failed to delete: ${error.response?.data?.detail || error.message}`, 'error')
-    },
-  })
-
-  const handleDelete = () => {
-    setShowDelete(true)
-  }
-
-  if (isLoading) {
-    return (
-      <div className="flex items-center justify-center min-h-screen">
-        <div className="text-gray-600">Loading call recording...</div>
-      </div>
-    )
-  }
-
-  if (!callRecording) {
-    return (
-      <div className="flex items-center justify-center min-h-screen">
-        <div className="text-center">
-          <p className="text-gray-600 mb-4">Call recording not found</p>
-          <Button variant="outline" onClick={() => navigate('/playground')}>
-            <ArrowLeft className="h-4 w-4 mr-2" />
-            Back to Playground
-          </Button>
-        </div>
-      </div>
-    )
-  }
-
-  return (
-    <>
-      <ToastContainer />
-      <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
-        {/* Header */}
-        <div className="mb-6">
-          <Button
-            variant="outline"
-            onClick={() => navigate('/playground')}
-            leftIcon={<ArrowLeft className="h-4 w-4" />}
-            className="mb-4"
-          >
-            Back to Playground
-          </Button>
-          <div className="bg-white shadow rounded-lg p-6">
-            <div className="flex items-center justify-between">
-              <div>
-                <h1 className="text-2xl font-bold text-gray-900">Call Recording Details</h1>
-                <p className="text-sm text-gray-500 mt-1">
-                  Call ID: <span className="font-mono font-semibold text-primary-600">{callRecording.call_short_id}</span>
-                </p>
-              </div>
-              <div className="flex gap-2">
-                {callRecording.status === 'PENDING' && (
-                  <Button
-                    variant="outline"
-                    onClick={() => refreshMutation.mutate()}
-                    leftIcon={<RefreshCw className="h-4 w-4" />}
-                    isLoading={refreshMutation.isPending}
-                  >
-                    Refresh
-                  </Button>
-                )}
-                {callRecording.call_data && callRecording.provider_call_id && (
-                  <Button
-                    variant="outline"
-                    onClick={() => reEvaluateMutation.mutate()}
-                    leftIcon={<RotateCcw className="h-4 w-4" />}
-                    isLoading={reEvaluateMutation.isPending}
-                  >
-                    Re-evaluate
-                  </Button>
-                )}
-                <Button
-                  variant="danger"
-                  onClick={handleDelete}
-                  leftIcon={<Trash2 className="h-4 w-4" />}
-                  isLoading={deleteMutation.isPending}
-                >
-                  Delete
-                </Button>
-              </div>
-            </div>
-
-            {/* Metadata */}
-            <div className="mt-6 grid grid-cols-2 md:grid-cols-5 gap-4">
-              <div>
-                <p className="text-xs text-gray-500 font-medium mb-1">Status</p>
-                <span
-                  className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${callRecording.status === 'UPDATED'
-                    ? 'bg-green-100 text-green-800'
-                    : 'bg-yellow-100 text-yellow-800'
-                    }`}
-                >
-                  {callRecording.status}
-                </span>
-              </div>
-              <div>
-                <p className="text-xs text-gray-500 font-medium mb-1">Evaluation</p>
-                {callRecording.evaluation ? (
-                  <span
-                    className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${
-                      callRecording.evaluation.status === 'completed'
-                        ? 'bg-green-100 text-green-800'
-                        : callRecording.evaluation.status === 'failed'
-                        ? 'bg-red-100 text-red-800'
-                        : callRecording.evaluation.status === 'evaluating'
-                        ? 'bg-blue-100 text-blue-800'
-                        : 'bg-yellow-100 text-yellow-800'
-                    }`}
-                  >
-                    {callRecording.evaluation.status || 'queued'}
-                  </span>
-                ) : (
-                  <span className="text-sm text-gray-500">Pending</span>
-                )}
-              </div>
-              <div>
-                <p className="text-xs text-gray-500 font-medium mb-1">Platform</p>
-                <p className="text-sm text-gray-900">
-                  {callRecording.provider_platform === 'custom_websocket'
-                    ? 'Custom WebSocket'
-                    : callRecording.provider_platform || 'N/A'}
-                </p>
-              </div>
-              <div>
-                <p className="text-xs text-gray-500 font-medium mb-1">Provider Call ID</p>
-                <p className="text-sm font-mono font-semibold text-primary-600 text-xs">
-                  {callRecording.provider_call_id || 'N/A'}
-                </p>
-              </div>
-              <div>
-                <p className="text-xs text-gray-500 font-medium mb-1">Created</p>
-                <p className="text-sm text-gray-900">
-                  {callRecording.created_at
-                    ? new Date(callRecording.created_at).toLocaleString()
-                    : 'N/A'}
-                </p>
-              </div>
-            </div>
-          </div>
-        </div>
-
-        {/* Evaluation Metrics Section */}
-        {(callRecording.evaluation || reEvalInProgress) && (
-          <div className="mb-6 bg-white rounded-lg shadow p-6">
-            <h2 className="text-xl font-semibold text-gray-900 flex items-center mb-4">
-              <BarChart3 className="w-5 h-5 mr-2" />
-              Evaluation Metrics
-              {callRecording.evaluation?.result_id && (
-                <span className="ml-2 text-sm font-mono font-semibold text-primary-600">
-                  #{callRecording.evaluation.result_id}
-                </span>
-              )}
-            </h2>
-            
-            {!reEvalInProgress && callRecording.evaluation?.status === 'completed' && callRecording.evaluation.metric_scores && Object.keys(callRecording.evaluation.metric_scores).length > 0 ? (
-              (() => {
-                const metricScores = callRecording.evaluation.metric_scores
-                
-                // Categorize metrics - only include those with valid values
-                const acousticMetrics = Object.entries(metricScores).filter(
-                  ([metricId, metric]: [string, any]) => {
-                    if (!hasValidValue(metric)) return false
-                    if (shouldHideMetricScore(metricId, metric)) return false
-                    const info = getMetricInfo(metric.metric_name || '')
-                    return info?.category === 'acoustic'
-                  }
-                )
-                const aiVoiceMetrics = Object.entries(metricScores).filter(
-                  ([metricId, metric]: [string, any]) => {
-                    if (!hasValidValue(metric)) return false
-                    if (shouldHideMetricScore(metricId, metric)) return false
-                    const info = getMetricInfo(metric.metric_name || '')
-                    return info?.category === 'ai_voice'
-                  }
-                )
-                const llmMetrics = Object.entries(metricScores).filter(
-                  ([metricId, metric]: [string, any]) => {
-                    if (!hasValidValue(metric)) return false
-                    if (shouldHideMetricScore(metricId, metric)) return false
-                    const info = getMetricInfo(metric.metric_name || '')
-                    return !info || info.category === 'llm'
-                  }
-                )
-                const unavailableAudioMetrics = Object.entries(metricScores).filter(
-                  ([, metric]: [string, any]) => {
-                    const metricName = metric.metric_name || ''
-                    if (!isAudioCategoryMetric(metricName)) return false
-                    if (metric.skipped === 'audio_required') return true
-                    if (typeof metric.error === 'string' && metric.error.trim().length > 0) return true
-                    return false
-                  }
-                )
-                const hasAnyAudioMetric = Object.entries(metricScores).some(
-                  ([, metric]: [string, any]) => isAudioCategoryMetric(metric.metric_name || '')
-                )
-                const hasAudioUnavailableNotice = hasAnyAudioMetric && unavailableAudioMetrics.length > 0
-                
-                return (
-                  <div className="space-y-6">
-                    {hasAudioUnavailableNotice && (
-                      <div className="rounded-md border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-800">
-                        {unavailableAudioMetrics.length === 1
-                          ? 'An audio metric is unavailable for this run. We retried fetching call audio from the provider, but audio analysis could not complete.'
-                          : 'Some audio metrics are unavailable for this run. We retried fetching call audio from the provider, but audio analysis could not fully complete.'}
-                      </div>
-                    )}
-
-                    {/* AI Voice Quality Metrics */}
-                    {aiVoiceMetrics.length > 0 && (
-                      <div>
-                        <div className="flex items-center gap-2 mb-3">
-                          <Sparkles className="w-4 h-4 text-purple-600" />
-                          <h3 className="text-sm font-semibold text-purple-800 uppercase tracking-wide">AI Voice Quality</h3>
-                          <span className="px-2 py-0.5 text-xs bg-purple-100 text-purple-700 rounded-full">ML Analysis</span>
-                        </div>
-                        <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
-                          {aiVoiceMetrics.map(([metricId, metric]: [string, any]) => (
-                            <div key={metricId} className="min-w-0 border border-purple-200 bg-purple-50/50 rounded-lg p-4">
-                              <div className="text-xs font-bold uppercase text-purple-800 mb-2 flex min-w-0 items-start gap-1.5">
-                                <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name || metricId}</span>
-                                <MetricTooltip metricName={metric.metric_name || metricId} />
-                              </div>
-                              <div>
-                                {formatMetricValue(metric.value, metric.type, metric.metric_name)}
-                                {renderMetricRationale(metric)}
-                              </div>
-                            </div>
-                          ))}
-                        </div>
-                      </div>
-                    )}
-                    
-                    {/* Acoustic Metrics (Parselmouth) */}
-                    {acousticMetrics.length > 0 && (
-                      <div>
-                        <div className="flex items-center gap-2 mb-3">
-                          <AudioWaveform className="w-4 h-4 text-violet-600" />
-                          <h3 className="text-sm font-semibold text-violet-800 uppercase tracking-wide">Acoustic Metrics</h3>
-                          <span className="px-2 py-0.5 text-xs bg-violet-100 text-violet-700 rounded-full">Signal Analysis</span>
-                        </div>
-                        <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
-                          {acousticMetrics.map(([metricId, metric]: [string, any]) => (
-                            <div key={metricId} className="min-w-0 border border-violet-200 bg-violet-50/50 rounded-lg p-4">
-                              <div className="text-xs font-bold uppercase text-violet-800 mb-2 flex min-w-0 items-start gap-1.5">
-                                <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name || metricId}</span>
-                                <MetricTooltip metricName={metric.metric_name || metricId} />
-                              </div>
-                              <div>
-                                {formatMetricValue(metric.value, metric.type, metric.metric_name)}
-                                {renderMetricRationale(metric)}
-                              </div>
-                            </div>
-                          ))}
-                        </div>
-                      </div>
-                    )}
-                    
-                    {/* LLM-based Metrics */}
-                    {llmMetrics.length > 0 && (
-                      <div>
-                        <div className="flex items-center gap-2 mb-3">
-                          <Brain className="w-4 h-4 text-emerald-600" />
-                          <h3 className="text-sm font-semibold text-emerald-800 uppercase tracking-wide">Conversation Metrics</h3>
-                          <span className="px-2 py-0.5 text-xs bg-emerald-100 text-emerald-700 rounded-full">LLM Evaluation</span>
-                        </div>
-                        <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 lg:grid-cols-4 xl:grid-cols-5 gap-4">
-                          {llmMetrics.map(([metricId, metric]: [string, any]) => (
-                            <div key={metricId} className="min-w-0 border border-gray-200 rounded-lg p-4">
-                              <div className="text-xs font-bold uppercase text-gray-700 mb-2 flex min-w-0 items-start gap-1.5">
-                                <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name || metricId}</span>
-                                <MetricTooltip metricName={metric.metric_name || metricId} />
-                              </div>
-                              <div>
-                                {formatMetricValue(metric.value, metric.type, metric.metric_name)}
-                                {renderMetricRationale(metric)}
-                              </div>
-                            </div>
-                          ))}
-                        </div>
-                      </div>
-                    )}
-                  </div>
-                )
-              })()
-            ) : reEvalInProgress || ['evaluating', 'transcribing', 'queued'].includes(callRecording.evaluation?.status || '') ? (
-              <EvaluationStepper status={
-                reEvalInProgress && !['queued', 'transcribing', 'evaluating'].includes(callRecording.evaluation?.status || '')
-                  ? 'queued'
-                  : (callRecording.evaluation?.status || 'queued')
-              } />
-            ) : callRecording.evaluation?.status === 'failed' ? (
-              <div className="bg-red-50 border border-red-200 rounded-lg p-4 text-center">
-                <XCircle className="w-6 h-6 text-red-500 mx-auto mb-2" />
-                <p className="text-red-700">Evaluation failed. Please try again.</p>
-              </div>
-            ) : (
-              <div className="text-center py-8 text-gray-500">
-                No evaluation metrics available yet.
-              </div>
-            )}
-          </div>
-        )}
-
-        {/* Call Data */}
-        <div className="bg-white shadow rounded-lg p-6">
-          {callRecording.call_data ? (
-            <>
-              {callRecording.provider_platform === 'retell' ? (
-                <RetellCallDetails callData={callRecording.call_data} />
-              ) : callRecording.provider_platform === 'vapi' ? (
-                <VapiCallDetails callData={callRecording.call_data} />
-              ) : callRecording.provider_platform === 'elevenlabs' ? (
-                <ElevenLabsCallDetails callData={callRecording.call_data} callShortId={callShortId} />
-              ) : callRecording.provider_platform === 'custom_websocket' ? (
-                <CustomWebSocketCallDetails callData={callRecording.call_data} callShortId={callShortId} />
-              ) : callRecording.provider_platform === 'smallest' ? (
-                <SmallestCallDetails callData={callRecording.call_data} />
-              ) : (
-                <>
-                  <h2 className="text-lg font-semibold text-gray-900 mb-4">Call Data (JSON)</h2>
-                  <pre className="bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto text-xs max-h-[600px]">
-                    {JSON.stringify(callRecording.call_data, null, 2)}
-                  </pre>
-                </>
-              )}
-            </>
-          ) : (
-            <div className="text-center py-8 text-gray-500">
-              No call data available. Try refreshing if the call just ended.
-            </div>
-          )}
-        </div>
-      </div>
-
-      <ConfirmModal
-        title="Delete call recording"
-        description="This will permanently remove this playground call recording."
-        isOpen={showDelete}
-        isLoading={deleteMutation.isPending}
-        onCancel={() => setShowDelete(false)}
-        onConfirm={() => deleteMutation.mutate()}
-      />
-    </>
-  )
-}
+import React, { useState, useEffect, useMemo } from 'react'
+import { useParams, useNavigate } from 'react-router-dom'
+import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query'
+import { apiClient } from '../../../lib/api'
+import { getCallRecordingPlaceholder, hasCallRecordingDetails } from '../../../lib/callRecordingQuery'
+import ConfirmModal from '../../../components/ConfirmModal'
+import { ArrowLeft, RefreshCw, Trash2, BarChart3, CheckCircle, XCircle, HelpCircle, Brain, Sparkles, AudioWaveform, Loader, RotateCcw, Activity } from 'lucide-react'
+import Button from '../../../components/Button'
+import { useToast } from '../../../hooks/useToast'
+import TraceDetailDrawer from '../../../components/call-recordings/TraceDetailDrawer'
+import { prefetchCallRecordingAudio } from '../../../lib/waveformAudioCache'
+import CustomWebSocketCallDetails from '../../../components/call-recordings/CustomWebSocketCallDetails'
+import { getIntegrationPlatformLabel } from '../../../config/providers'
+import { IntegrationPlatform } from '../../../types/api'
+
+const LEGACY_CATEGORY_LABEL_METRIC_NAMES = new Set([
+  'yes',
+  'no',
+  'true',
+  'false',
+  'same',
+  'different',
+])
+
+function isLegacyCategoryLabelMetric(metric: {
+  type?: string | null
+  metric_name?: string | null
+}): boolean {
+  if ((metric.type || '').toLowerCase() !== 'boolean') return false
+  const name = (metric.metric_name || '').trim().toLowerCase()
+  return LEGACY_CATEGORY_LABEL_METRIC_NAMES.has(name)
+}
+// Comprehensive metric information with descriptions and ideal values
+const METRIC_INFO: Record<string, { 
+  description: string
+  ideal: string
+  unit?: string
+  category: 'acoustic' | 'ai_voice' | 'llm'
+}> = {
+  // Acoustic Metrics (Parselmouth)
+  'Pitch Variance': { 
+    description: 'F0 variation measuring prosodic expressiveness. Higher values indicate more expressive speech.',
+    ideal: '20-50 Hz (natural speech)',
+    unit: 'Hz',
+    category: 'acoustic'
+  },
+  'Jitter': { 
+    description: 'Cycle-to-cycle pitch period variation indicating vocal stability. Lower is better.',
+    ideal: '< 1% (healthy voice)',
+    unit: '%',
+    category: 'acoustic'
+  },
+  'Shimmer': { 
+    description: 'Amplitude perturbation measuring voice quality consistency. Lower is better.',
+    ideal: '< 3% (clear voice)',
+    unit: '%',
+    category: 'acoustic'
+  },
+  'HNR': { 
+    description: 'Harmonics-to-Noise Ratio measuring signal clarity. Higher indicates cleaner voice.',
+    ideal: '> 20 dB (clear, non-breathy)',
+    unit: 'dB',
+    category: 'acoustic'
+  },
+  
+  // AI Voice Quality Metrics
+  'MOS Score': { 
+    description: 'Mean Opinion Score predicting human perception of audio quality (1-5 scale).',
+    ideal: '4.0+ (studio quality), 3.0 (phone quality), <2.0 (poor/robotic)',
+    category: 'ai_voice'
+  },
+  'Emotion Category': { 
+    description: 'Dominant emotion detected in the voice (angry, happy, sad, neutral, fearful, etc.).',
+    ideal: 'Depends on context - should match expected tone',
+    category: 'ai_voice'
+  },
+  'Emotion Confidence': { 
+    description: 'Confidence score for the detected emotion category.',
+    ideal: '> 0.7 (high confidence)',
+    category: 'ai_voice'
+  },
+  'Valence': { 
+    description: 'Emotional positivity/negativity scale. Negative = sad/angry, Positive = happy/excited.',
+    ideal: '-1.0 to +1.0 (context dependent)',
+    category: 'ai_voice'
+  },
+  'Arousal': { 
+    description: 'Emotional intensity/energy level. Low = calm/sleepy, High = excited/energetic.',
+    ideal: '0.3-0.6 (engaged but not agitated)',
+    category: 'ai_voice'
+  },
+  'Speaker Consistency': { 
+    description: 'Voice identity stability throughout the call. Detects if voice changed mid-call (glitch).',
+    ideal: '> 0.8 (same voice), < 0.5 indicates voice glitch',
+    category: 'ai_voice'
+  },
+  'Prosody Score': { 
+    description: 'Expressiveness/drama score. Low = monotone/flat, High = expressive/dynamic.',
+    ideal: '0.4-0.7 (natural expressiveness)',
+    category: 'ai_voice'
+  },
+  
+  // LLM Conversation Metrics
+  'Follow Instructions': { 
+    description: 'How well the agent followed the given instructions and guidelines.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+  'Problem Resolution': { 
+    description: 'Whether the agent successfully resolved the customer\'s problem or query.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+  'Professionalism': { 
+    description: 'Professional demeanor, appropriate language, and courteous behavior.',
+    ideal: '> 0.85 (85%+)',
+    category: 'llm'
+  },
+  'Clarity and Empathy': { 
+    description: 'Clear communication combined with understanding and acknowledgment of customer feelings.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+  'Objective Achieved': { 
+    description: 'Whether the conversation\'s primary objective was successfully achieved.',
+    ideal: 'Yes/True',
+    category: 'llm'
+  },
+  'Overall Quality': { 
+    description: 'Holistic assessment of the entire conversation quality.',
+    ideal: '> 0.8 (80%+)',
+    category: 'llm'
+  },
+}
+
+const getMetricInfo = (metricName: string) => METRIC_INFO[metricName]
+
+const isAudioMetric = (metricName: string): boolean => {
+  const info = METRIC_INFO[metricName]
+  return info?.category === 'acoustic' || info?.category === 'ai_voice'
+}
+
+const getAudioMetricInfo = (metricName: string) => {
+  const info = METRIC_INFO[metricName]
+  if (!info) return undefined
+  return { unit: info.unit || '', description: info.description }
+}
+
+// Tooltip component for metrics
+const MetricTooltip = ({ metricName }: { metricName: string }) => {
+  const [isVisible, setIsVisible] = useState(false)
+  const info = getMetricInfo(metricName)
+  
+  if (!info) return null
+  
+  return (
+    <div className="relative inline-flex flex-shrink-0 ml-1 mt-0.5">
+      <button
+        type="button"
+        className="text-gray-400 hover:text-gray-600 focus:outline-none transition-colors"
+        onMouseEnter={() => setIsVisible(true)}
+        onMouseLeave={() => setIsVisible(false)}
+        onClick={() => setIsVisible(!isVisible)}
+        aria-label={`Info about ${metricName}`}
+      >
+        <HelpCircle className="w-3.5 h-3.5" />
+      </button>
+      
+      {isVisible && (
+        <div className="absolute z-50 left-1/2 -translate-x-1/2 bottom-full mb-2 w-64 p-3 text-xs bg-gray-900 text-white rounded-lg shadow-xl pointer-events-none">
+          <div className="font-semibold text-gray-100 mb-1.5">{metricName}</div>
+          <p className="text-gray-300 mb-2 leading-relaxed">{info.description}</p>
+          <div className="flex items-center gap-1 pt-1.5 border-t border-gray-700">
+            <span className="text-emerald-400 font-medium">Ideal:</span>
+            <span className="text-gray-200">{info.ideal}</span>
+          </div>
+          {/* Arrow pointing down */}
+          <div className="absolute left-1/2 -translate-x-1/2 top-full w-0 h-0 border-l-[6px] border-l-transparent border-r-[6px] border-r-transparent border-t-[6px] border-t-gray-900" />
+        </div>
+      )}
+    </div>
+  )
+}
+
+// Format metric value for display
+const formatMetricValue = (value: any, type: string, metricName?: string): React.ReactNode => {
+  if (value === null || value === undefined) return <span className="text-gray-400">N/A</span>
+  
+  const normalizedType = type?.toLowerCase()
+
+  if (normalizedType === 'category') {
+    if (value === '') return <span className="text-gray-400">N/A</span>
+    return (
+      <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-lg bg-indigo-50 text-indigo-700 text-sm font-semibold whitespace-normal break-words leading-snug">
+        {String(value)}
+      </span>
+    )
+  }
+  
+  // Handle Emotion Category - categorical text values with styling
+  if (metricName === 'Emotion Category') {
+    const emotion = String(value).toLowerCase()
+    const emotionConfig: Record<string, { emoji: string; color: string; bg: string }> = {
+      'neutral': { emoji: '😐', color: 'text-gray-700', bg: 'bg-gray-100' },
+      'happy': { emoji: '😊', color: 'text-green-700', bg: 'bg-green-100' },
+      'sad': { emoji: '😢', color: 'text-blue-700', bg: 'bg-blue-100' },
+      'angry': { emoji: '😠', color: 'text-red-700', bg: 'bg-red-100' },
+      'fearful': { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
+      'fear': { emoji: '😨', color: 'text-purple-700', bg: 'bg-purple-100' },
+      'surprised': { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
+      'surprise': { emoji: '😲', color: 'text-yellow-700', bg: 'bg-yellow-100' },
+      'disgusted': { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
+      'disgust': { emoji: '🤢', color: 'text-green-800', bg: 'bg-green-200' },
+      'calm': { emoji: '😌', color: 'text-teal-700', bg: 'bg-teal-100' },
+    }
+    const config = emotionConfig[emotion] || { emoji: '🎭', color: 'text-gray-700', bg: 'bg-gray-100' }
+    
+    return (
+      <div className={`inline-flex items-center gap-2 px-3 py-1.5 rounded-full ${config.bg}`}>
+        <span className="text-xl">{config.emoji}</span>
+        <span className={`font-semibold capitalize ${config.color}`}>{value}</span>
+      </div>
+    )
+  }
+  
+  // Handle boolean metrics
+  if (normalizedType === 'boolean') {
+    const boolValue = value === true || value === 1 || value === '1' || value === 'true'
+    return <span className="block text-base font-semibold text-gray-900">{boolValue ? 'Yes' : 'No'}</span>
+  }
+  
+  // Handle rating metrics with progress bar
+  if (normalizedType === 'rating') {
+    if (typeof value === 'string' && isNaN(parseFloat(value))) {
+      return (
+        <span className="inline-flex max-w-full items-center px-3 py-1.5 rounded-full bg-purple-100 text-purple-700 font-semibold capitalize whitespace-normal break-words text-left leading-snug">
+          {value}
+        </span>
+      )
+    }
+    
+    const numValue = typeof value === 'number' ? value : parseFloat(value)
+    if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
+    
+    const normalizedValue = Math.max(0, Math.min(1, numValue))
+    const percentage = Math.round(normalizedValue * 100)
+    
+    const getBarColor = (pct: number): string => {
+      if (pct >= 70) return 'bg-green-500'
+      if (pct >= 50) return 'bg-yellow-500'
+      return 'bg-red-500'
+    }
+    
+    return (
+      <div className="flex flex-col gap-2">
+        <span className="text-2xl font-bold text-gray-900">{percentage}%</span>
+        <div className="w-full h-3 bg-gray-200 rounded-full overflow-hidden">
+          <div 
+            className={`h-full rounded-full transition-all ${getBarColor(percentage)}`}
+            style={{ width: `${percentage}%` }}
+          />
+        </div>
+      </div>
+    )
+  }
+  
+  // Handle number metrics (including audio metrics)
+  if (normalizedType === 'number') {
+    const numValue = typeof value === 'number' ? value : parseFloat(value)
+    if (isNaN(numValue)) return <span className="text-gray-400">N/A</span>
+    
+    if (metricName && isAudioMetric(metricName)) {
+      const audioInfo = getAudioMetricInfo(metricName)
+      return (
+        <div className="flex items-baseline gap-1">
+          <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(2)}</span>
+          <span className="text-sm font-medium text-violet-600">{audioInfo?.unit}</span>
+        </div>
+      )
+    }
+    
+    return <span className="text-2xl font-bold text-gray-900">{numValue.toFixed(1)}</span>
+  }
+  
+  return <span className="block max-w-full text-base font-semibold leading-snug text-gray-900 whitespace-normal break-words">{String(value)}</span>
+}
+
+const renderMetricRationale = (metric: { rationale?: string | null }) => {
+  const rationale = typeof metric.rationale === 'string' ? metric.rationale.trim() : ''
+  if (!rationale) return null
+  return (
+    <p className="mt-2 text-xs text-gray-600 leading-relaxed border-t border-gray-100 pt-2">
+      {rationale}
+    </p>
+  )
+}
+
+// Helper to check if a metric has a valid value
+const hasValidValue = (metric: { value: any }) => {
+  const val = metric.value
+  if (val === null || val === undefined) return false
+  if (val === '') return false
+  if (typeof val === 'string' && val.toLowerCase() === 'n/a') return false
+  if (typeof val === 'string' && val.toLowerCase() === 'na') return false
+  if (typeof val === 'string' && val.trim() === '') return false
+  return true
+}
+
+const isAudioCategoryMetric = (metricName?: string): boolean => {
+  if (!metricName) return false
+  const info = getMetricInfo(metricName)
+  return info?.category === 'acoustic' || info?.category === 'ai_voice'
+}
+
+function EvaluationStepper({ status }: { status: string }) {
+  const [dots, setDots] = useState('')
+
+  useEffect(() => {
+    const id = setInterval(() => setDots(prev => prev.length >= 3 ? '' : prev + '.'), 500)
+    return () => clearInterval(id)
+  }, [])
+
+  const steps = [
+    { key: 'queued', label: 'Queued', sublabel: 'Preparing worker' },
+    { key: 'transcribing', label: 'Analyzing Audio', sublabel: 'Acoustic & voice quality' },
+    { key: 'evaluating', label: 'Evaluating', sublabel: 'LLM conversation metrics' },
+  ]
+
+  const activeIdx = Math.max(0, steps.findIndex(s => s.key === status))
+
+  const statusMessages: Record<string, string> = {
+    queued: 'Downloading audio & preparing evaluation',
+    transcribing: 'Running acoustic analysis & voice quality metrics',
+    evaluating: 'Evaluating conversation quality with LLM',
+  }
+
+  return (
+    <div className="py-8 px-4">
+      {/* Steps */}
+      <div className="flex items-start justify-center max-w-md mx-auto">
+        {steps.map((step, idx) => {
+          const isActive = idx === activeIdx
+          const isDone = idx < activeIdx
+
+          return (
+            <React.Fragment key={step.key}>
+              <div className="flex flex-col items-center" style={{ minWidth: 90 }}>
+                <div
+                  className={[
+                    'w-11 h-11 rounded-full flex items-center justify-center transition-all duration-500 relative',
+                    isDone ? 'bg-emerald-500 text-white shadow-md' : '',
+                    isActive ? 'bg-blue-600 text-white shadow-lg shadow-blue-200' : '',
+                    !isDone && !isActive ? 'bg-gray-100 text-gray-400 border-2 border-gray-200' : '',
+                  ].join(' ')}
+                >
+                  {isActive && (
+                    <span className="absolute inset-0 rounded-full animate-ping bg-blue-400 opacity-30" />
+                  )}
+                  {isDone ? (
+                    <CheckCircle className="w-5 h-5" />
+                  ) : isActive ? (
+                    <Loader className="w-5 h-5 animate-spin" />
+                  ) : (
+                    <span className="text-sm font-semibold">{idx + 1}</span>
+                  )}
+                </div>
+                <p className={`mt-2.5 text-xs font-semibold text-center leading-tight ${isActive ? 'text-blue-700' : isDone ? 'text-emerald-600' : 'text-gray-400'}`}>
+                  {step.label}
+                </p>
+                <p className={`text-[10px] text-center leading-tight mt-0.5 ${isActive ? 'text-blue-500' : isDone ? 'text-emerald-500' : 'text-gray-300'}`}>
+                  {step.sublabel}
+                </p>
+              </div>
+              {idx < steps.length - 1 && (
+                <div className="flex-1 flex items-center pt-1" style={{ minWidth: 40 }}>
+                  <div className="w-full relative h-1 rounded-full bg-gray-200 overflow-hidden" style={{ marginTop: 18 }}>
+                    <div
+                      className={`absolute inset-y-0 left-0 rounded-full transition-all duration-700 ${isDone ? 'w-full bg-emerald-400' : isActive ? 'bg-blue-400 animate-[indeterminate_1.5s_ease-in-out_infinite]' : 'w-0'}`}
+                      style={isActive ? { width: '60%' } : undefined}
+                    />
+                  </div>
+                </div>
+              )}
+            </React.Fragment>
+          )
+        })}
+      </div>
+
+      {/* Animated status message */}
+      <div className="mt-6 text-center">
+        <span className="inline-flex items-center gap-2 px-4 py-2 bg-blue-50 border border-blue-100 rounded-full text-sm text-blue-700">
+          <Loader className="w-3.5 h-3.5 animate-spin" />
+          {statusMessages[status] || 'Processing'}{dots}
+        </span>
+      </div>
+    </div>
+  )
+}
+
+
+export default function CallRecordingDetail() {
+  const { callShortId } = useParams<{ callShortId: string }>()
+  const navigate = useNavigate()
+  const queryClient = useQueryClient()
+  const { showToast, ToastContainer } = useToast()
+  const [showDelete, setShowDelete] = useState(false)
+  const [reEvalInProgress, setReEvalInProgress] = useState(false)
+  const [callDrawerOpen, setCallDrawerOpen] = useState(false)
+
+  const { data: callRecording, refetch: refetchCallDetails, isLoading } = useQuery({
+    queryKey: ['call-recording', callShortId],
+    queryFn: () => apiClient.getCallRecording(callShortId!),
+    enabled: !!callShortId,
+    placeholderData: () =>
+      callShortId ? getCallRecordingPlaceholder(queryClient, callShortId) : undefined,
+    refetchOnMount: (query) =>
+      hasCallRecordingDetails(query.state.data as Record<string, unknown> | undefined)
+        ? true
+        : 'always',
+    refetchInterval: (query) => {
+      const data = query.state.data as any
+      const inProgress = reEvalInProgress || (data?.evaluation?.status && ['queued', 'transcribing', 'evaluating'].includes(data.evaluation.status))
+      return inProgress ? 2000 : false
+    },
+  })
+
+  const { data: metrics = [] } = useQuery({
+    queryKey: ['metrics'],
+    queryFn: () => apiClient.listMetrics(),
+  })
+
+  const childMetricIds = useMemo(() => {
+    const ids = new Set<string>()
+    const visit = (metric: { id?: string; parent_metric_id?: string | null; children?: any[] }) => {
+      if (metric.parent_metric_id && metric.id) ids.add(metric.id)
+      for (const child of metric.children || []) {
+        if (child?.id) ids.add(child.id)
+        visit(child)
+      }
+    }
+    for (const metric of metrics as Array<{ id?: string; parent_metric_id?: string | null; children?: any[] }>) {
+      visit(metric)
+    }
+    return ids
+  }, [metrics])
+
+  const formatPlatformLabel = (platform: string | null | undefined): string => {
+    if (!platform) return 'N/A'
+    if (platform === 'custom_websocket') return 'Custom WebSocket'
+    if (platform === 'voice_bundle') return 'Test Agent (internal)'
+    const normalized = platform.toLowerCase()
+    if (
+      normalized === IntegrationPlatform.RETELL ||
+      normalized === IntegrationPlatform.VAPI ||
+      normalized === IntegrationPlatform.ELEVENLABS ||
+      normalized === IntegrationPlatform.SMALLEST
+    ) {
+      return getIntegrationPlatformLabel(normalized as IntegrationPlatform)
+    }
+    return platform
+  }
+
+  const isVoiceAiPlatform = (platform: string | null | undefined): boolean => {
+    const normalized = (platform || '').toLowerCase()
+    return (
+      normalized === IntegrationPlatform.RETELL ||
+      normalized === IntegrationPlatform.VAPI ||
+      normalized === IntegrationPlatform.ELEVENLABS ||
+      normalized === IntegrationPlatform.SMALLEST
+    )
+  }
+
+  const openCallDrawer = () => {
+    if (callShortId) prefetchCallRecordingAudio(callShortId, false)
+    setCallDrawerOpen(true)
+  }
+
+  const shouldHideMetricScore = (
+    metricId: string,
+    metric: { parent_metric_id?: string | null; type?: string | null; metric_name?: string | null },
+  ) => {
+    return Boolean(
+      metric.parent_metric_id ||
+        childMetricIds.has(metricId) ||
+        isLegacyCategoryLabelMetric(metric),
+    )
+  }
+  // Clear optimistic re-eval state once the backend confirms a terminal status
+  useEffect(() => {
+    if (reEvalInProgress && callRecording?.evaluation?.status && ['completed', 'failed'].includes(callRecording.evaluation.status)) {
+      setReEvalInProgress(false)
+    }
+  }, [callRecording?.evaluation?.status, reEvalInProgress])
+
+  useEffect(() => {
+    if (!callRecording) return
+    if (callRecording.provider_platform === 'voice_bundle' && callRecording.evaluator_result_id) {
+      navigate(`/playground/test-agent-results/${callRecording.evaluator_result_id}`, { replace: true })
+    }
+  }, [callRecording, navigate])
+
+  const refreshMutation = useMutation({
+    mutationFn: () => apiClient.refreshCallRecording(callShortId!),
+    onSuccess: () => {
+      showToast('Call recording refresh initiated', 'success')
+      setTimeout(() => {
+        refetchCallDetails()
+      }, 2000)
+    },
+    onError: (error: any) => {
+      showToast(`Failed to refresh: ${error.response?.data?.detail || error.message}`, 'error')
+    },
+  })
+
+  const reEvaluateMutation = useMutation({
+    mutationFn: () => apiClient.reEvaluateCallRecording(callShortId!),
+    onMutate: () => {
+      setReEvalInProgress(true)
+    },
+    onSuccess: () => {
+      showToast('Re-evaluation started — audio will be analyzed for voice quality metrics', 'success')
+      refetchCallDetails()
+    },
+    onError: (error: any) => {
+      setReEvalInProgress(false)
+      showToast(`Re-evaluation failed: ${error.response?.data?.detail || error.message}`, 'error')
+    },
+  })
+
+  const deleteMutation = useMutation({
+    mutationFn: () => apiClient.deleteCallRecording(callShortId!),
+    onSuccess: () => {
+      showToast('Call recording deleted successfully', 'success')
+      queryClient.invalidateQueries({ queryKey: ['call-recordings'] })
+      navigate('/playground')
+    },
+    onError: (error: any) => {
+      showToast(`Failed to delete: ${error.response?.data?.detail || error.message}`, 'error')
+    },
+  })
+
+  const handleDelete = () => {
+    setShowDelete(true)
+  }
+
+  if (isLoading && !callRecording) {
+    return (
+      <div className="flex items-center justify-center min-h-screen">
+        <div className="text-gray-600">Loading call recording...</div>
+      </div>
+    )
+  }
+
+  if (!callRecording) {
+    return (
+      <div className="flex items-center justify-center min-h-screen">
+        <div className="text-center">
+          <p className="text-gray-600 mb-4">Call recording not found</p>
+          <Button variant="outline" onClick={() => navigate('/playground')}>
+            <ArrowLeft className="h-4 w-4 mr-2" />
+            Back to Playground
+          </Button>
+        </div>
+      </div>
+    )
+  }
+
+  return (
+    <>
+      <ToastContainer />
+      <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
+        {/* Header */}
+        <div className="mb-6">
+          <Button
+            variant="outline"
+            onClick={() => navigate('/playground')}
+            leftIcon={<ArrowLeft className="h-4 w-4" />}
+            className="mb-4"
+          >
+            Back to Playground
+          </Button>
+          <div className="bg-white shadow rounded-lg p-6">
+            <div className="flex items-center justify-between">
+              <div>
+                <h1 className="text-2xl font-bold text-gray-900">Call Recording Details</h1>
+                <p className="text-sm text-gray-500 mt-1">
+                  Call ID: <span className="font-mono font-semibold text-primary-600">{callRecording.call_short_id}</span>
+                </p>
+              </div>
+              <div className="flex gap-2">
+                {isVoiceAiPlatform(callRecording.provider_platform) && callShortId ? (
+                  <Button
+                    variant="outline"
+                    onClick={openCallDrawer}
+                    leftIcon={<Activity className="h-4 w-4" />}
+                  >
+                    Call details
+                  </Button>
+                ) : null}
+                {(callRecording.status === 'PENDING' || isVoiceAiPlatform(callRecording.provider_platform)) && (
+                  <Button
+                    variant="outline"
+                    onClick={() => refreshMutation.mutate()}
+                    leftIcon={<RefreshCw className="h-4 w-4" />}
+                    isLoading={refreshMutation.isPending}
+                  >
+                    {isVoiceAiPlatform(callRecording.provider_platform) ? 'Sync provider data' : 'Refresh'}
+                  </Button>
+                )}
+                {callRecording.call_data && callRecording.provider_call_id && (
+                  <Button
+                    variant="outline"
+                    onClick={() => reEvaluateMutation.mutate()}
+                    leftIcon={<RotateCcw className="h-4 w-4" />}
+                    isLoading={reEvaluateMutation.isPending}
+                  >
+                    Re-evaluate
+                  </Button>
+                )}
+                <Button
+                  variant="danger"
+                  onClick={handleDelete}
+                  leftIcon={<Trash2 className="h-4 w-4" />}
+                  isLoading={deleteMutation.isPending}
+                >
+                  Delete
+                </Button>
+              </div>
+            </div>
+
+            {/* Metadata */}
+            <div className="mt-6 grid grid-cols-2 md:grid-cols-5 gap-4">
+              <div>
+                <p className="text-xs text-gray-500 font-medium mb-1">Status</p>
+                <span
+                  className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${callRecording.status === 'UPDATED'
+                    ? 'bg-green-100 text-green-800'
+                    : 'bg-yellow-100 text-yellow-800'
+                    }`}
+                >
+                  {callRecording.status}
+                </span>
+              </div>
+              <div>
+                <p className="text-xs text-gray-500 font-medium mb-1">Evaluation</p>
+                {callRecording.evaluation ? (
+                  <span
+                    className={`inline-flex px-2 py-1 text-xs font-semibold rounded-full ${
+                      callRecording.evaluation.status === 'completed'
+                        ? 'bg-green-100 text-green-800'
+                        : callRecording.evaluation.status === 'failed'
+                        ? 'bg-red-100 text-red-800'
+                        : callRecording.evaluation.status === 'evaluating'
+                        ? 'bg-blue-100 text-blue-800'
+                        : 'bg-yellow-100 text-yellow-800'
+                    }`}
+                  >
+                    {callRecording.evaluation.status || 'queued'}
+                  </span>
+                ) : (
+                  <span className="text-sm text-gray-500">Pending</span>
+                )}
+              </div>
+              <div>
+                <p className="text-xs text-gray-500 font-medium mb-1">Platform</p>
+                <p className="text-sm text-gray-900">
+                  {formatPlatformLabel(callRecording.provider_platform)}
+                </p>
+              </div>
+              <div>
+                <p className="text-xs text-gray-500 font-medium mb-1">Provider Call ID</p>
+                <p className="text-sm font-mono font-semibold text-primary-600 text-xs">
+                  {callRecording.provider_call_id || 'N/A'}
+                </p>
+              </div>
+              <div>
+                <p className="text-xs text-gray-500 font-medium mb-1">Created</p>
+                <p className="text-sm text-gray-900">
+                  {callRecording.created_at
+                    ? new Date(callRecording.created_at).toLocaleString()
+                    : 'N/A'}
+                </p>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        {/* Evaluation Metrics Section */}
+        {(callRecording.evaluation || reEvalInProgress) && (
+          <div className="mb-6 bg-white rounded-lg shadow p-6">
+            <h2 className="text-xl font-semibold text-gray-900 flex items-center mb-4">
+              <BarChart3 className="w-5 h-5 mr-2" />
+              Evaluation Metrics
+              {callRecording.evaluation?.result_id && (
+                <span className="ml-2 text-sm font-mono font-semibold text-primary-600">
+                  #{callRecording.evaluation.result_id}
+                </span>
+              )}
+            </h2>
+            
+            {!reEvalInProgress && callRecording.evaluation?.status === 'completed' && callRecording.evaluation.metric_scores && Object.keys(callRecording.evaluation.metric_scores).length > 0 ? (
+              (() => {
+                const metricScores = callRecording.evaluation.metric_scores
+                
+                // Categorize metrics - only include those with valid values
+                const acousticMetrics = Object.entries(metricScores).filter(
+                  ([metricId, metric]: [string, any]) => {
+                    if (!hasValidValue(metric)) return false
+                    if (shouldHideMetricScore(metricId, metric)) return false
+                    const info = getMetricInfo(metric.metric_name || '')
+                    return info?.category === 'acoustic'
+                  }
+                )
+                const aiVoiceMetrics = Object.entries(metricScores).filter(
+                  ([metricId, metric]: [string, any]) => {
+                    if (!hasValidValue(metric)) return false
+                    if (shouldHideMetricScore(metricId, metric)) return false
+                    const info = getMetricInfo(metric.metric_name || '')
+                    return info?.category === 'ai_voice'
+                  }
+                )
+                const llmMetrics = Object.entries(metricScores).filter(
+                  ([metricId, metric]: [string, any]) => {
+                    if (!hasValidValue(metric)) return false
+                    if (shouldHideMetricScore(metricId, metric)) return false
+                    const info = getMetricInfo(metric.metric_name || '')
+                    return !info || info.category === 'llm'
+                  }
+                )
+                const unavailableAudioMetrics = Object.entries(metricScores).filter(
+                  ([, metric]: [string, any]) => {
+                    const metricName = metric.metric_name || ''
+                    if (!isAudioCategoryMetric(metricName)) return false
+                    if (metric.skipped === 'audio_required') return true
+                    if (typeof metric.error === 'string' && metric.error.trim().length > 0) return true
+                    return false
+                  }
+                )
+                const hasAnyAudioMetric = Object.entries(metricScores).some(
+                  ([, metric]: [string, any]) => isAudioCategoryMetric(metric.metric_name || '')
+                )
+                const hasAudioUnavailableNotice = hasAnyAudioMetric && unavailableAudioMetrics.length > 0
+                
+                return (
+                  <div className="space-y-6">
+                    {hasAudioUnavailableNotice && (
+                      <div className="rounded-md border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-800">
+                        {unavailableAudioMetrics.length === 1
+                          ? 'An audio metric is unavailable for this run. We retried fetching call audio from the provider, but audio analysis could not complete.'
+                          : 'Some audio metrics are unavailable for this run. We retried fetching call audio from the provider, but audio analysis could not fully complete.'}
+                      </div>
+                    )}
+
+                    {/* AI Voice Quality Metrics */}
+                    {aiVoiceMetrics.length > 0 && (
+                      <div>
+                        <div className="flex items-center gap-2 mb-3">
+                          <Sparkles className="w-4 h-4 text-purple-600" />
+                          <h3 className="text-sm font-semibold text-purple-800 uppercase tracking-wide">AI Voice Quality</h3>
+                          <span className="px-2 py-0.5 text-xs bg-purple-100 text-purple-700 rounded-full">ML Analysis</span>
+                        </div>
+                        <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
+                          {aiVoiceMetrics.map(([metricId, metric]: [string, any]) => (
+                            <div key={metricId} className="min-w-0 border border-purple-200 bg-purple-50/50 rounded-lg p-4">
+                              <div className="text-xs font-bold uppercase text-purple-800 mb-2 flex min-w-0 items-start gap-1.5">
+                                <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name || metricId}</span>
+                                <MetricTooltip metricName={metric.metric_name || metricId} />
+                              </div>
+                              <div>
+                                {formatMetricValue(metric.value, metric.type, metric.metric_name)}
+                                {renderMetricRationale(metric)}
+                              </div>
+                            </div>
+                          ))}
+                        </div>
+                      </div>
+                    )}
+                    
+                    {/* Acoustic Metrics (Parselmouth) */}
+                    {acousticMetrics.length > 0 && (
+                      <div>
+                        <div className="flex items-center gap-2 mb-3">
+                          <AudioWaveform className="w-4 h-4 text-violet-600" />
+                          <h3 className="text-sm font-semibold text-violet-800 uppercase tracking-wide">Acoustic Metrics</h3>
+                          <span className="px-2 py-0.5 text-xs bg-violet-100 text-violet-700 rounded-full">Signal Analysis</span>
+                        </div>
+                        <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-4 gap-4">
+                          {acousticMetrics.map(([metricId, metric]: [string, any]) => (
+                            <div key={metricId} className="min-w-0 border border-violet-200 bg-violet-50/50 rounded-lg p-4">
+                              <div className="text-xs font-bold uppercase text-violet-800 mb-2 flex min-w-0 items-start gap-1.5">
+                                <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name || metricId}</span>
+                                <MetricTooltip metricName={metric.metric_name || metricId} />
+                              </div>
+                              <div>
+                                {formatMetricValue(metric.value, metric.type, metric.metric_name)}
+                                {renderMetricRationale(metric)}
+                              </div>
+                            </div>
+                          ))}
+                        </div>
+                      </div>
+                    )}
+                    
+                    {/* LLM-based Metrics */}
+                    {llmMetrics.length > 0 && (
+                      <div>
+                        <div className="flex items-center gap-2 mb-3">
+                          <Brain className="w-4 h-4 text-emerald-600" />
+                          <h3 className="text-sm font-semibold text-emerald-800 uppercase tracking-wide">Conversation Metrics</h3>
+                          <span className="px-2 py-0.5 text-xs bg-emerald-100 text-emerald-700 rounded-full">LLM Evaluation</span>
+                        </div>
+                        <div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 lg:grid-cols-4 xl:grid-cols-5 gap-4">
+                          {llmMetrics.map(([metricId, metric]: [string, any]) => (
+                            <div key={metricId} className="min-w-0 border border-gray-200 rounded-lg p-4">
+                              <div className="text-xs font-bold uppercase text-gray-700 mb-2 flex min-w-0 items-start gap-1.5">
+                                <span className="min-w-0 flex-1 whitespace-normal break-words leading-snug">{metric.metric_name || metricId}</span>
+                                <MetricTooltip metricName={metric.metric_name || metricId} />
+                              </div>
+                              <div>
+                                {formatMetricValue(metric.value, metric.type, metric.metric_name)}
+                                {renderMetricRationale(metric)}
+                              </div>
+                            </div>
+                          ))}
+                        </div>
+                      </div>
+                    )}
+                  </div>
+                )
+              })()
+            ) : reEvalInProgress || ['evaluating', 'transcribing', 'queued'].includes(callRecording.evaluation?.status || '') ? (
+              <EvaluationStepper status={
+                reEvalInProgress && !['queued', 'transcribing', 'evaluating'].includes(callRecording.evaluation?.status || '')
+                  ? 'queued'
+                  : (callRecording.evaluation?.status || 'queued')
+              } />
+            ) : callRecording.evaluation?.status === 'failed' ? (
+              <div className="bg-red-50 border border-red-200 rounded-lg p-4 text-center">
+                <XCircle className="w-6 h-6 text-red-500 mx-auto mb-2" />
+                <p className="text-red-700">Evaluation failed. Please try again.</p>
+              </div>
+            ) : (
+              <div className="text-center py-8 text-gray-500">
+                No evaluation metrics available yet.
+              </div>
+            )}
+          </div>
+        )}
+
+        {/* Call Data — non–Voice AI only; Voice AI uses the Call details drawer */}
+        {!isVoiceAiPlatform(callRecording.provider_platform) ? (
+        <div className="bg-white shadow rounded-lg p-6">
+          {callRecording.call_data ? (
+            <>
+              {(() => {
+                const platform = (callRecording.provider_platform || '').toLowerCase()
+                if (platform === 'custom_websocket') {
+                  return (
+                    <CustomWebSocketCallDetails
+                      callData={callRecording.call_data}
+                      callShortId={callShortId}
+                    />
+                  )
+                }
+                if (platform === 'voice_bundle') {
+                  return (
+                    <div className="text-sm text-gray-600">
+                      This is an internal test-agent call. Open it from the Test Agents tab for full details.
+                    </div>
+                  )
+                }
+                return (
+                  <>
+                    <h2 className="text-lg font-semibold text-gray-900 mb-4">Call Data (JSON)</h2>
+                    <pre className="bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto text-xs max-h-[600px]">
+                      {JSON.stringify(callRecording.call_data, null, 2)}
+                    </pre>
+                  </>
+                )
+              })()}
+            </>
+          ) : (
+            <div className="text-center py-8 text-gray-500">
+              No call data available. Try refreshing if the call just ended.
+            </div>
+          )}
+        </div>
+        ) : null}
+
+      </div>
+
+      <TraceDetailDrawer
+        open={callDrawerOpen}
+        callShortId={callShortId ?? null}
+        onClose={() => setCallDrawerOpen(false)}
+      />
+
+      <ConfirmModal
+        title="Delete call recording"
+        description="This will permanently remove this playground call recording."
+        isOpen={showDelete}
+        isLoading={deleteMutation.isPending}
+        onCancel={() => setShowDelete(false)}
+        onConfirm={() => deleteMutation.mutate()}
+      />
+    </>
+  )
+}
diff --git a/frontend/src/pages/playground/agent/TestAgentResultDetail.tsx b/frontend/src/pages/playground/agent/TestAgentResultDetail.tsx
index 39060559..2e427ba3 100644
--- a/frontend/src/pages/playground/agent/TestAgentResultDetail.tsx
+++ b/frontend/src/pages/playground/agent/TestAgentResultDetail.tsx
@@ -1,102 +1,116 @@
-import { useParams, useNavigate } from 'react-router-dom'
-import { useQuery } from '@tanstack/react-query'
-import { apiClient } from '../../../lib/api'
-import { ArrowLeft } from 'lucide-react'
-import Button from '../../../components/Button'
-import { useToast } from '../../../hooks/useToast'
-import TestVoiceAgentResultDetails from '../../../components/call-recordings/TestVoiceAgentResultDetails'
-
-export default function TestAgentResultDetail() {
-  const { id } = useParams<{ id: string }>()
-  const navigate = useNavigate()
-  const { ToastContainer } = useToast()
-
-  const { data: result, isLoading } = useQuery({
-    queryKey: ['test-agent-result', id],
-    queryFn: () => apiClient.getEvaluatorResult(id!, true),
-    enabled: !!id,
-  })
-
-  const { data: presignedUrl } = useQuery({
-    queryKey: ['audio-presigned-url', result?.audio_s3_key],
-    queryFn: () => {
-      if (!result?.audio_s3_key) return null
-      return apiClient.getAudioPresignedUrl(result.audio_s3_key)
-    },
-    enabled: !!result?.audio_s3_key,
-  })
-
-  if (isLoading) {
-    return (
-      <div className="flex items-center justify-center min-h-screen">
-        <div className="text-gray-600">Loading result...</div>
-      </div>
-    )
-  }
-
-  if (!result) {
-    return (
-      <div className="flex items-center justify-center min-h-screen">
-        <div className="text-center">
-          <p className="text-gray-600 mb-4">Result not found</p>
-          <Button variant="outline" onClick={() => navigate('/playground')}>
-            <ArrowLeft className="h-4 w-4 mr-2" />
-            Back to Playground
-          </Button>
-        </div>
-      </div>
-    )
-  }
-
-  // Prepare the result data with audio URL and extract call_analysis from call_data
-  const resultData = {
-    ...result,
-    call_analysis: result.call_data?.call_analysis || undefined,
-    audioUrl: presignedUrl?.url || undefined,
-  }
-
-  return (
-    <>
-      <ToastContainer />
-      <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
-        {/* Header */}
-        <div className="mb-6">
-          <Button
-            variant="outline"
-            onClick={() => navigate('/playground')}
-            leftIcon={<ArrowLeft className="h-4 w-4" />}
-            className="mb-4"
-          >
-            Back to Playground
-          </Button>
-          <div className="bg-white shadow rounded-lg p-6">
-            <div className="flex items-center justify-between">
-              <div>
-                <h1 className="text-2xl font-bold text-gray-900">Test Agent Call Details</h1>
-                <p className="text-sm text-gray-500 mt-1">
-                  Call ID: <span className="font-mono font-semibold text-primary-600">{result.result_id}</span>
-                </p>
-              </div>
-              <div className="flex items-center gap-2">
-                <span className={`px-3 py-1 rounded-full text-sm font-semibold ${
-                  result.status === 'completed'
-                    ? 'bg-green-100 text-green-800'
-                    : result.status === 'failed'
-                    ? 'bg-red-100 text-red-800'
-                    : 'bg-yellow-100 text-yellow-800'
-                }`}>
-                  {result.status}
-                </span>
-              </div>
-            </div>
-          </div>
-        </div>
-
-        {/* Call Details - Using the same component structure as RetellCallDetails */}
-        <div className="bg-white shadow rounded-lg p-6">
-          <TestVoiceAgentResultDetails resultData={resultData} />
-        </div>
-      </div>
-    </>
-  )
-}
+import { useState } from 'react'
+import { useParams, useNavigate } from 'react-router-dom'
+import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { apiClient } from '../../../lib/api'
+import { getEvaluatorResultPlaceholder } from '../../../lib/evaluatorResultQuery'
+import { Activity, ArrowLeft } from 'lucide-react'
+import Button from '../../../components/Button'
+import { useToast } from '../../../hooks/useToast'
+import TestVoiceAgentResultDetails from '../../../components/call-recordings/TestVoiceAgentResultDetails'
+import TraceDetailDrawer from '../../../components/call-recordings/TraceDetailDrawer'
+
+export default function TestAgentResultDetail() {
+  const { id } = useParams<{ id: string }>()
+  const navigate = useNavigate()
+  const { ToastContainer } = useToast()
+  const queryClient = useQueryClient()
+  const [traceDrawerOpen, setTraceDrawerOpen] = useState(false)
+
+  const { data: result, isLoading } = useQuery({
+    queryKey: ['evaluator-result', id],
+    queryFn: () => apiClient.getEvaluatorResult(id!, true),
+    enabled: !!id,
+    placeholderData: () => (id ? getEvaluatorResultPlaceholder(queryClient, id) : undefined),
+    staleTime: 30_000,
+  })
+
+  if (isLoading && !result) {
+    return (
+      <div className="flex items-center justify-center min-h-screen">
+        <div className="text-gray-600">Loading result...</div>
+      </div>
+    )
+  }
+
+  if (!result) {
+    return (
+      <div className="flex items-center justify-center min-h-screen">
+        <div className="text-center">
+          <p className="text-gray-600 mb-4">Result not found</p>
+          <Button variant="outline" onClick={() => navigate('/playground')}>
+            <ArrowLeft className="h-4 w-4 mr-2" />
+            Back to Playground
+          </Button>
+        </div>
+      </div>
+    )
+  }
+
+  const resultData = {
+    ...result,
+    call_analysis: result.call_data?.call_analysis || undefined,
+  }
+
+  const callShortId =
+    typeof result.call_data?.call_short_id === 'string' ? result.call_data.call_short_id : null
+  const platform = (result.provider_platform || '').toLowerCase()
+  const useProviderDrawer =
+    Boolean(callShortId) && ['vapi', 'retell', 'elevenlabs', 'smallest'].includes(platform)
+
+  return (
+    <>
+      <ToastContainer />
+      <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
+        <div className="mb-6">
+          <Button
+            variant="outline"
+            onClick={() => navigate('/playground')}
+            leftIcon={<ArrowLeft className="h-4 w-4" />}
+            className="mb-4"
+          >
+            Back to Playground
+          </Button>
+          <div className="bg-white shadow rounded-lg p-6">
+            <div className="flex items-center justify-between">
+              <div>
+                <h1 className="text-2xl font-bold text-gray-900">Test Agent Call Details</h1>
+                <p className="text-sm text-gray-500 mt-1">
+                  Call ID: <span className="font-mono font-semibold text-primary-600">{result.result_id}</span>
+                </p>
+              </div>
+              <div className="flex items-center gap-2">
+                <Button
+                  variant="outline"
+                  onClick={() => setTraceDrawerOpen(true)}
+                  leftIcon={<Activity className="h-4 w-4" />}
+                >
+                  Call details
+                </Button>
+                <span className={`px-3 py-1 rounded-full text-sm font-semibold ${
+                  result.status === 'completed'
+                    ? 'bg-green-100 text-green-800'
+                    : result.status === 'failed'
+                    ? 'bg-red-100 text-red-800'
+                    : 'bg-yellow-100 text-yellow-800'
+                }`}>
+                  {result.status}
+                </span>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        <div className="bg-white shadow rounded-lg p-6">
+          <TestVoiceAgentResultDetails resultData={resultData} metricsOnly />
+        </div>
+      </div>
+
+      <TraceDetailDrawer
+        open={traceDrawerOpen}
+        callShortId={useProviderDrawer ? callShortId : null}
+        evaluatorResultId={useProviderDrawer ? null : result.id}
+        onClose={() => setTraceDrawerOpen(false)}
+      />
+    </>
+  )
+}
diff --git a/frontend/src/pages/test-insights/CallTraceDetail.tsx b/frontend/src/pages/test-insights/CallTraceDetail.tsx
index 31d86cc5..17f94ef3 100644
--- a/frontend/src/pages/test-insights/CallTraceDetail.tsx
+++ b/frontend/src/pages/test-insights/CallTraceDetail.tsx
@@ -2,6 +2,6 @@ import { Navigate, useParams } from 'react-router-dom'
 
 export default function CallTraceDetail() {
   const { traceId } = useParams<{ traceId: string }>()
-  if (!traceId) return <Navigate to="/call-traces" replace />
-  return <Navigate to={`/call-traces?trace=${traceId}`} replace />
+  if (!traceId) return <Navigate to="/calls" replace />
+  return <Navigate to={`/calls?trace=${traceId}`} replace />
 }
diff --git a/frontend/src/pages/test-insights/TestInsights.tsx b/frontend/src/pages/test-insights/TestInsights.tsx
index 9ff8b91c..846999b1 100644
--- a/frontend/src/pages/test-insights/TestInsights.tsx
+++ b/frontend/src/pages/test-insights/TestInsights.tsx
@@ -1,5 +1,5 @@
 import { useEffect, useMemo, useRef, useState } from 'react'
-import { useQuery, useQueryClient } from '@tanstack/react-query'
+import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query'
 import { useSearchParams } from 'react-router-dom'
 import {
   Activity,
@@ -8,13 +8,18 @@ import {
   Copy,
   Eye,
   Loader,
+  PhoneCall,
   RefreshCw,
-  Route,
+  Trash2,
 } from 'lucide-react'
 import { apiClient } from '../../lib/api'
 import Button from '../../components/Button'
+import ConfirmModal from '../../components/ConfirmModal'
 import TraceDetailDrawer from '../../components/call-recordings/TraceDetailDrawer'
 import { FAILURE_FLAG_LABELS } from '../../components/call-recordings/traceUtils'
+import { CallAgentLink } from '../observability/CallAgentLink'
+import { EventBadge, PlatformBadge } from '../observability/observabilityCallUi'
+import { ObservabilityCall } from '../../types/api'
 import { useWorkspaceStore } from '../../store/workspaceStore'
 
 type Tab = 'runs' | 'setup'
@@ -85,8 +90,11 @@ export default function TestInsights() {
   const [searchParams, setSearchParams] = useSearchParams()
   const resultFromUrl = searchParams.get('result')
   const traceFromUrl = searchParams.get('trace')
+  const obsFromUrl = searchParams.get('obs')
   const [tab, setTab] = useState<Tab>('runs')
   const [selectedTraceId, setSelectedTraceId] = useState<string | null>(traceFromUrl)
+  const [selectedObsCallId, setSelectedObsCallId] = useState<string | null>(obsFromUrl)
+  const [deleteObsCallId, setDeleteObsCallId] = useState<string | null>(null)
   const [page, setPage] = useState(0)
   const [statusFilter, setStatusFilter] = useState<StatusFilter>('all')
   const [searchQuery, setSearchQuery] = useState('')
@@ -126,6 +134,36 @@ export default function TestInsights() {
     staleTime: 5 * 60 * 1000,
   })
 
+  const {
+    data: observabilityCalls = [],
+    isLoading: loadingObsCalls,
+    isFetching: fetchingObsCalls,
+    refetch: refetchObsCalls,
+  } = useQuery<ObservabilityCall[]>({
+    queryKey: ['observability-calls', activeWorkspaceId],
+    queryFn: () => apiClient.listObservabilityCalls(),
+    enabled: tab === 'runs' && Boolean(activeWorkspaceId),
+    refetchInterval: (query) => {
+      const data = query.state.data
+      if (!data || !Array.isArray(data)) return false
+      return data.some((call) => call.is_live) ? 3000 : false
+    },
+  })
+
+  const deleteObsMutation = useMutation({
+    mutationFn: (callShortId: string) => apiClient.deleteObservabilityCall(callShortId),
+    onSuccess: (_data, callShortId) => {
+      setDeleteObsCallId(null)
+      setSelectedObsCallId((current) => (current === callShortId ? null : current))
+      const next = new URLSearchParams(searchParams)
+      if (next.get('obs') === callShortId) {
+        next.delete('obs')
+        setSearchParams(next, { replace: true })
+      }
+      void queryClient.invalidateQueries({ queryKey: ['observability-calls'] })
+    },
+  })
+
   const traces = listData?.items ?? []
   const totalCount = listData?.total ?? 0
   const totalPages = Math.max(1, Math.ceil(totalCount / PAGE_SIZE))
@@ -140,6 +178,23 @@ export default function TestInsights() {
     })
   }, [traces, searchQuery])
 
+  const filteredObsCalls = useMemo(() => {
+    const q = searchQuery.trim().toLowerCase()
+    if (!q) return observabilityCalls
+    return observabilityCalls.filter((call) => {
+      const id = (call.call_short_id ?? '').toLowerCase()
+      const providerId = (call.provider_call_id ?? '').toLowerCase()
+      const agentName = (call.agent?.name ?? '').toLowerCase()
+      return id.includes(q) || providerId.includes(q) || agentName.includes(q)
+    })
+  }, [observabilityCalls, searchQuery])
+
+  const showPipelineRows = true
+  const showProviderRows = true
+  const hasListRows =
+    (showPipelineRows && filteredTraces.length > 0) ||
+    (showProviderRows && filteredObsCalls.length > 0)
+
   const summaryStats = useMemo(() => {
     const open = traces.filter((t: { status?: string }) => t.status === 'open').length
     const closed = traces.filter(
@@ -151,6 +206,12 @@ export default function TestInsights() {
     return { open, closed, withLatency }
   }, [traces])
 
+  const providerSummary = useMemo(() => {
+    const ended = observabilityCalls.filter((c) => c.call_event === 'call_ended').length
+    const live = observabilityCalls.filter((c) => c.is_live).length
+    return { total: observabilityCalls.length, ended, live }
+  }, [observabilityCalls])
+
   const listErrorMessage =
     listError && listErrorDetail instanceof Error
       ? listErrorDetail.message
@@ -166,9 +227,21 @@ export default function TestInsights() {
 
   const openTrace = (traceId: string) => {
     setSelectedTraceId(traceId)
+    setSelectedObsCallId(null)
     const next = new URLSearchParams(searchParams)
     next.set('trace', traceId)
     next.delete('result')
+    next.delete('obs')
+    setSearchParams(next, { replace: true })
+  }
+
+  const openObsCall = (callShortId: string) => {
+    setSelectedObsCallId(callShortId)
+    setSelectedTraceId(null)
+    const next = new URLSearchParams(searchParams)
+    next.set('obs', callShortId)
+    next.delete('trace')
+    next.delete('result')
     setSearchParams(next, { replace: true })
   }
 
@@ -180,20 +253,31 @@ export default function TestInsights() {
     setSearchParams(next, { replace: true })
   }
 
+  const closeObsCall = () => {
+    setSelectedObsCallId(null)
+    const next = new URLSearchParams(searchParams)
+    next.delete('obs')
+    setSearchParams(next, { replace: true })
+  }
+
   const handleRefresh = () => {
     void refetchTraces()
+    void refetchObsCalls()
     void queryClient.invalidateQueries({ queryKey: ['observability-traces'] })
+    void queryClient.invalidateQueries({ queryKey: ['observability-calls'] })
   }
 
   useEffect(() => {
     if (prevWorkspaceRef.current !== null && prevWorkspaceRef.current !== activeWorkspaceId) {
       setPage(0)
       setSelectedTraceId(null)
+      setSelectedObsCallId(null)
       setSearchParams(
         (prev) => {
           const next = new URLSearchParams(prev)
           next.delete('trace')
           next.delete('result')
+          next.delete('obs')
           return next
         },
         { replace: true },
@@ -205,6 +289,12 @@ export default function TestInsights() {
   useEffect(() => {
     if (traceFromUrl) {
       setSelectedTraceId(traceFromUrl)
+      setSelectedObsCallId(null)
+      return
+    }
+    if (obsFromUrl) {
+      setSelectedObsCallId(obsFromUrl)
+      setSelectedTraceId(null)
       return
     }
     if (!resultFromUrl) return
@@ -220,7 +310,11 @@ export default function TestInsights() {
     return () => {
       cancelled = true
     }
-  }, [resultFromUrl, traceFromUrl])
+  }, [resultFromUrl, traceFromUrl, obsFromUrl])
+
+  const isListLoading =
+    (showPipelineRows && loadingList && !listData) ||
+    (showProviderRows && loadingObsCalls && observabilityCalls.length === 0)
 
   const lastUpdatedLabel =
     dataUpdatedAt > 0 ? `Updated ${new Date(dataUpdatedAt).toLocaleTimeString()}` : null
@@ -229,14 +323,20 @@ export default function TestInsights() {
     <div className="space-y-6">
       <div className="flex flex-col sm:flex-row sm:items-center sm:justify-between gap-4">
         <div>
-          <h1 className="text-3xl font-bold text-gray-900">Call Traces</h1>
+          <h1 className="text-3xl font-bold text-gray-900">Calls</h1>
           <p className="mt-2 text-sm text-gray-600">
-            Per-turn STT, LLM, and TTS latency from Pipecat voice agents
+            Voice agent calls and session traces for your workspace
           </p>
         </div>
         {tab === 'runs' && (
-          <Button variant="outline" onClick={handleRefresh} disabled={fetchingList}>
-            <RefreshCw className={`w-4 h-4 mr-2 ${fetchingList ? 'animate-spin' : ''}`} />
+          <Button
+            variant="outline"
+            onClick={handleRefresh}
+            disabled={fetchingList || fetchingObsCalls}
+          >
+            <RefreshCw
+              className={`w-4 h-4 mr-2 ${fetchingList || fetchingObsCalls ? 'animate-spin' : ''}`}
+            />
             Refresh
           </Button>
         )}
@@ -253,7 +353,7 @@ export default function TestInsights() {
           }`}
         >
           <Activity className="w-4 h-4 inline mr-1.5 -mt-0.5" />
-          Traces
+          Calls
         </button>
         <button
           type="button"
@@ -334,17 +434,19 @@ export default function TestInsights() {
 
       {tab === 'runs' && !activeWorkspaceId && (
         <div className="bg-amber-50 border border-amber-200 rounded-lg px-6 py-8 text-center text-sm text-amber-900">
-          Select a workspace to view call traces.
+          Select a workspace to view calls.
         </div>
       )}
 
       {tab === 'runs' && activeWorkspaceId && (
         <>
-          {!loadingList && traces.length > 0 && (
+          {(traces.length > 0 || observabilityCalls.length > 0) && (
             <div className="grid grid-cols-2 md:grid-cols-4 gap-3">
               <div className="rounded-lg border border-primary-400 bg-primary-50/40 px-4 py-3 shadow-sm">
                 <p className="text-[10px] font-semibold uppercase tracking-wider text-primary-800/70">Total</p>
-                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{totalCount}</p>
+                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">
+                  {totalCount + providerSummary.total}
+                </p>
               </div>
               <div className="rounded-lg border border-gray-200 bg-white px-4 py-3 shadow-sm">
                 <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">Open</p>
@@ -355,8 +457,10 @@ export default function TestInsights() {
                 <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{summaryStats.closed}</p>
               </div>
               <div className="rounded-lg border border-gray-200 bg-white px-4 py-3 shadow-sm">
-                <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">With latency</p>
-                <p className="text-2xl font-semibold text-gray-900 tabular-nums mt-0.5">{summaryStats.withLatency}</p>
+                <p className="text-[10px] font-semibold uppercase tracking-wider text-gray-400">Live</p>
+                <p className="text-2xl font-semibold text-sky-600 tabular-nums mt-0.5">
+                  {providerSummary.live}
+                </p>
               </div>
             </div>
           )}
@@ -365,13 +469,13 @@ export default function TestInsights() {
             <div className="px-6 py-4 border-b border-gray-200 flex flex-col sm:flex-row sm:items-center sm:justify-between gap-4">
               <div className="flex items-center gap-3 flex-wrap">
                 <div className="flex items-center gap-2">
-                  <Route className="h-5 w-5 text-gray-500" />
-                  <h2 className="text-lg font-semibold text-gray-900">Trace sessions</h2>
+                  <PhoneCall className="h-5 w-5 text-gray-500" />
+                  <h2 className="text-lg font-semibold text-gray-900">All calls</h2>
                 </div>
                 <div className="flex items-center gap-1">
                   {(
                     [
-                      { key: 'all' as const, label: 'All' },
+                      { key: 'all' as const, label: 'Any status' },
                       { key: 'open' as const, label: 'Open' },
                       { key: 'closed' as const, label: 'Closed' },
                     ] as const
@@ -385,7 +489,7 @@ export default function TestInsights() {
                       }}
                       className={`px-3 py-1.5 text-xs font-medium rounded-lg transition-colors ${
                         statusFilter === key
-                          ? 'bg-primary-100 text-primary-800 border border-primary-300'
+                          ? 'bg-gray-200 text-gray-800 border border-gray-300'
                           : 'text-gray-600 hover:bg-gray-100 border border-transparent'
                       }`}
                     >
@@ -408,23 +512,23 @@ export default function TestInsights() {
               </div>
             </div>
 
-            {loadingList && !listData && (
+            {isListLoading && (
               <div className="p-12 text-center">
                 <Loader className="w-6 h-6 text-primary-500 animate-spin mx-auto mb-3" />
-                <p className="text-sm text-gray-500">Loading traces…</p>
+                <p className="text-sm text-gray-500">Loading calls…</p>
               </div>
             )}
 
-            {listErrorMessage && (
+            {listErrorMessage && showPipelineRows && (
               <div className="p-4 text-sm text-red-800 bg-red-50 border-b border-red-100">
                 Could not load traces: {listErrorMessage}
               </div>
             )}
 
-            {!loadingList && !listError && traces.length === 0 && (
+            {!isListLoading && !hasListRows && !listError && (
               <div className="p-12 text-center text-sm text-gray-600">
-                <p className="font-medium text-gray-900 mb-1">No traces yet</p>
-                <p className="mb-4">Connect Pipecat and run a WebRTC call to see traces here.</p>
+                <p className="font-medium text-gray-900 mb-1">No calls yet</p>
+                <p className="mb-4">Run a voice agent session to see calls here.</p>
                 <button
                   type="button"
                   onClick={() => setTab('setup')}
@@ -435,39 +539,101 @@ export default function TestInsights() {
               </div>
             )}
 
-            {filteredTraces.length > 0 && (
-              <>
-                <div className="overflow-x-auto">
-                  <table className="min-w-full divide-y divide-gray-200">
-                    <thead className="bg-gray-50">
-                      <tr>
-                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Call ID
-                        </th>
-                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Transport
-                        </th>
-                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Status
-                        </th>
-                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Turns
-                        </th>
-                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Median (p50)
-                        </th>
-                        <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Started
-                        </th>
-                        <th className="px-6 py-3 text-right text-xs font-medium text-gray-500 uppercase tracking-wider">
-                          Actions
-                        </th>
-                      </tr>
-                    </thead>
-                    <tbody className="bg-white divide-y divide-gray-200">
-                      {filteredTraces.map((trace: any) => (
+            {hasListRows && (
+              <div className="overflow-x-auto">
+                <table className="min-w-full divide-y divide-gray-200">
+                  <thead className="bg-gray-50">
+                    <tr>
+                      <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                        Call ID
+                      </th>
+                      <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                        Status
+                      </th>
+                      <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                        Platform
+                      </th>
+                      <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                        Details
+                      </th>
+                      <th className="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">
+                        Started
+                      </th>
+                      <th className="px-6 py-3 text-right text-xs font-medium text-gray-500 uppercase tracking-wider">
+                        Actions
+                      </th>
+                    </tr>
+                  </thead>
+                  <tbody className="bg-white divide-y divide-gray-200">
+                    {showProviderRows &&
+                      filteredObsCalls.map((call) => (
+                        <tr
+                          key={`obs-${call.id}`}
+                          className={`transition-colors cursor-pointer ${
+                            selectedObsCallId === call.call_short_id
+                              ? 'bg-primary-50/60 hover:bg-primary-50/80'
+                              : 'hover:bg-gray-50'
+                          }`}
+                          onClick={() => openObsCall(call.call_short_id)}
+                        >
+                          <td className="px-6 py-4 whitespace-nowrap">
+                            <span className="font-mono font-semibold text-primary-600">
+                              #{call.call_short_id}
+                            </span>
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap">
+                            <EventBadge event={call.call_event ?? undefined} />
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap">
+                            <PlatformBadge platform={call.provider_platform ?? undefined} />
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap" onClick={(e) => e.stopPropagation()}>
+                            <CallAgentLink agent={call.agent} />
+                          </td>
+                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-500">
+                            <span title={call.created_at ? formatWhen(call.created_at) : undefined}>
+                              {call.created_at ? formatRelative(call.created_at) : '—'}
+                            </span>
+                          </td>
+                          <td
+                            className="px-6 py-4 whitespace-nowrap text-right"
+                            onClick={(e) => e.stopPropagation()}
+                          >
+                            <div className="flex items-center justify-end gap-1">
+                              <Button
+                                variant="ghost"
+                                size="sm"
+                                onClick={() => openObsCall(call.call_short_id)}
+                                leftIcon={<Eye className="w-4 h-4" />}
+                              >
+                                View
+                              </Button>
+                              <button
+                                type="button"
+                                onClick={() => setDeleteObsCallId(call.call_short_id)}
+                                className="rounded-lg p-1.5 text-gray-400 hover:bg-rose-50 hover:text-rose-600"
+                                aria-label="Delete call"
+                              >
+                                <Trash2 className="h-4 w-4" />
+                              </button>
+                            </div>
+                          </td>
+                        </tr>
+                      ))}
+
+                    {showPipelineRows &&
+                      filteredTraces.map((trace: {
+                        id: string
+                        call_short_id?: string
+                        transport?: string
+                        status: string
+                        failure_flags?: string[]
+                        turn_count: number
+                        response_latency_p50_ms?: number | null
+                        started_at: string
+                      }) => (
                         <tr
-                          key={trace.id}
+                          key={`trace-${trace.id}`}
                           className={`transition-colors cursor-pointer ${
                             selectedTraceId === trace.id
                               ? 'bg-primary-50/60 hover:bg-primary-50/80'
@@ -480,13 +646,10 @@ export default function TestInsights() {
                               #{trace.call_short_id || trace.id.slice(0, 8)}
                             </span>
                           </td>
-                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 capitalize">
-                            {trace.transport ?? 'webrtc'}
-                          </td>
                           <td className="px-6 py-4 whitespace-nowrap">
                             <div className="flex flex-col gap-1">
                               <StatusLabel status={trace.status} />
-                              {(trace.failure_flags as string[] | undefined)?.map((flag) => (
+                              {trace.failure_flags?.map((flag) => (
                                 <span
                                   key={flag}
                                   className="inline-flex w-fit rounded-md border border-rose-200 bg-rose-50 px-1.5 py-0.5 text-[10px] font-medium text-rose-800"
@@ -496,16 +659,19 @@ export default function TestInsights() {
                               ))}
                             </div>
                           </td>
-                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 tabular-nums">
-                            {trace.turn_count}
+                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 capitalize">
+                            {trace.transport ?? 'webrtc'}
                           </td>
-                          <td className="px-6 py-4 whitespace-nowrap text-sm tabular-nums font-medium text-primary-800">
+                          <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-700 tabular-nums">
+                            {trace.turn_count} turns
                             {trace.response_latency_p50_ms != null
-                              ? `${Math.round(trace.response_latency_p50_ms)} ms`
-                              : '—'}
+                              ? ` · ${Math.round(trace.response_latency_p50_ms)} ms p50`
+                              : ''}
                           </td>
                           <td className="px-6 py-4 whitespace-nowrap text-sm text-gray-500">
-                            <span title={formatWhen(trace.started_at)}>{formatRelative(trace.started_at)}</span>
+                            <span title={formatWhen(trace.started_at)}>
+                              {formatRelative(trace.started_at)}
+                            </span>
                           </td>
                           <td
                             className="px-6 py-4 whitespace-nowrap text-right"
@@ -522,54 +688,75 @@ export default function TestInsights() {
                           </td>
                         </tr>
                       ))}
-                    </tbody>
-                  </table>
-                </div>
-
-                {totalPages > 1 && (
-                  <div className="px-6 py-4 border-t border-gray-200 flex items-center justify-between text-sm">
-                    <p className="text-gray-500">
-                      Page {page + 1} of {totalPages} · {totalCount} traces
-                    </p>
-                    <div className="flex gap-2">
-                      <Button
-                        variant="outline"
-                        size="sm"
-                        disabled={page === 0}
-                        onClick={() => setPage((p) => Math.max(0, p - 1))}
-                      >
-                        Previous
-                      </Button>
-                      <Button
-                        variant="outline"
-                        size="sm"
-                        disabled={page >= totalPages - 1}
-                        onClick={() => setPage((p) => p + 1)}
-                      >
-                        Next
-                      </Button>
-                    </div>
-                  </div>
-                )}
-              </>
+                  </tbody>
+                </table>
+              </div>
             )}
 
-            {traces.length > 0 && filteredTraces.length === 0 && (
-              <div className="p-12 text-center text-sm text-gray-500">
-                No traces match your search.{' '}
-                <button type="button" onClick={() => setSearchQuery('')} className="text-primary-600 font-medium">
-                  Clear search
-                </button>
+            {showPipelineRows && totalPages > 1 && filteredTraces.length > 0 && (
+              <div className="px-6 py-4 border-t border-gray-200 flex items-center justify-between text-sm">
+                <p className="text-gray-500">
+                  Page {page + 1} of {totalPages} · {totalCount} sessions
+                </p>
+                <div className="flex gap-2">
+                  <Button
+                    variant="outline"
+                    size="sm"
+                    disabled={page === 0}
+                    onClick={() => setPage((p) => Math.max(0, p - 1))}
+                  >
+                    Previous
+                  </Button>
+                  <Button
+                    variant="outline"
+                    size="sm"
+                    disabled={page >= totalPages - 1}
+                    onClick={() => setPage((p) => p + 1)}
+                  >
+                    Next
+                  </Button>
+                </div>
               </div>
             )}
+
+            {hasListRows &&
+              showPipelineRows &&
+              traces.length > 0 &&
+              filteredTraces.length === 0 &&
+              showProviderRows &&
+              filteredObsCalls.length === 0 && (
+                <div className="p-12 text-center text-sm text-gray-500">
+                  No rows match your search.{' '}
+                  <button
+                    type="button"
+                    onClick={() => setSearchQuery('')}
+                    className="text-primary-600 font-medium"
+                  >
+                    Clear search
+                  </button>
+                </div>
+              )}
           </div>
+
+          <ConfirmModal
+            title="Delete call"
+            description="This will permanently remove this provider call record."
+            isOpen={Boolean(deleteObsCallId)}
+            isLoading={deleteObsMutation.isPending}
+            onCancel={() => setDeleteObsCallId(null)}
+            onConfirm={() => deleteObsCallId && deleteObsMutation.mutate(deleteObsCallId)}
+          />
         </>
       )}
 
       <TraceDetailDrawer
         traceId={selectedTraceId}
-        open={Boolean(selectedTraceId)}
-        onClose={closeTrace}
+        observabilityCallShortId={selectedObsCallId}
+        open={Boolean(selectedTraceId || selectedObsCallId)}
+        onClose={() => {
+          if (selectedTraceId) closeTrace()
+          if (selectedObsCallId) closeObsCall()
+        }}
       />
     </div>
   )
diff --git a/frontend/src/types/api.ts b/frontend/src/types/api.ts
index cbe252ea..e23578af 100644
--- a/frontend/src/types/api.ts
+++ b/frontend/src/types/api.ts
@@ -376,6 +376,20 @@ export interface IntegrationCreate {
   is_default?: boolean
 }
 
+export interface IntegrationVoiceAgentListItem {
+  id: string
+  name: string
+}
+
+export interface ListIntegrationVoiceAgentsResponse {
+  agents: IntegrationVoiceAgentListItem[]
+  platform: string
+  cached: boolean
+  truncated: boolean
+  list_supported: boolean
+  message?: string | null
+}
+
 // VoiceBundle Types
 export enum ModelProvider {
   OPENAI = 'openai',
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
index ed3e3ff3..c5606ee6 100644
--- a/frontend/vite.config.ts
+++ b/frontend/vite.config.ts
@@ -1,37 +1,36 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-import path from 'path'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-  resolve: {
-    alias: {
-      '@': path.resolve(__dirname, './src'),
-    },
-    extensions: ['.js', '.jsx', '.ts', '.tsx', '.json'],
-    conditions: ['import', 'module', 'browser', 'default'],
-  },
-  optimizeDeps: {
-    include: ['country-flag-icons/react/3x2'],
-  },
-  server: {
-    port: 3000,
-    proxy: {
-      '/api': {
-        target: 'http://localhost:8000',
-        changeOrigin: true,
-      },
-    },
-  },
-  build: {
-    outDir: 'dist',
-    assetsDir: 'assets',
-    // Use relative paths for assets when building for production
-    base: './',
-  },
-  test: {
-    environment: 'node',
-  },
-})
-
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import path from 'path'
+
+// https://vitejs.dev/config/
+export default defineConfig({
+  plugins: [react()],
+  resolve: {
+    alias: {
+      '@': path.resolve(__dirname, './src'),
+    },
+    extensions: ['.js', '.jsx', '.ts', '.tsx', '.json'],
+    conditions: ['import', 'module', 'browser', 'default'],
+  },
+  optimizeDeps: {
+    include: ['country-flag-icons/react/3x2'],
+  },
+  server: {
+    port: 3000,
+    proxy: {
+      '/api': {
+        target: 'http://localhost:8000',
+        changeOrigin: true,
+      },
+    },
+  },
+  build: {
+    outDir: 'dist',
+    assetsDir: 'assets',
+    base: '/',
+  },
+  test: {
+    environment: 'node',
+  },
+})
+
diff --git a/pyproject.toml b/pyproject.toml
index 9d990488..c38f7a32 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,8 @@ dependencies = [
     "pillow>=10.0.0",
     "protobuf>=4.25.0",
     "opentelemetry-proto>=1.27.0",
+    "opentelemetry-sdk>=1.27.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.27.0",
     "requests>=2.31.0",
     "nltk>=3.8.0",
     "openai>=1.0.0",
diff --git a/tests/test_api/test_integrations_routes.py b/tests/test_api/test_integrations_routes.py
index e2b433bd..2fd64ffe 100644
--- a/tests/test_api/test_integrations_routes.py
+++ b/tests/test_api/test_integrations_routes.py
@@ -1,126 +1,176 @@
-"""API tests for integrations routes."""
-
-import importlib
-
-from app.api.v1.routes import integrations as integrations_route
-
-prompt_sync_module = importlib.import_module("app.services.voice_providers.prompt_sync")
-
-
-def test_create_and_list_integrations(authenticated_client):
-    payload = {"platform": "retell", "api_key": "secret-key", "name": "Retell Main"}
-    create_response = authenticated_client.post("/api/v1/integrations", json=payload)
-
-    assert create_response.status_code == 201
-    body = create_response.json()
-    assert body["platform"] == "retell"
-    assert body["name"] == "Retell Main"
-
-    list_response = authenticated_client.get("/api/v1/integrations")
-    assert list_response.status_code == 200
-    assert len(list_response.json()) == 1
-
-
-def test_update_integration(authenticated_client, make_integration):
-    integration = make_integration(platform="retell")
-    response = authenticated_client.put(
-        f"/api/v1/integrations/{integration.id}",
-        json={"name": "Updated Integration", "public_key": "pub-key"},
-    )
-
-    assert response.status_code == 200
-    assert response.json()["name"] == "Updated Integration"
-    assert response.json()["public_key"] == "pub-key"
-
-
-def test_get_integration_api_key(authenticated_client, monkeypatch, make_integration):
-    integration = make_integration(platform="retell", api_key="encrypted")
-    monkeypatch.setattr(integrations_route, "decrypt_api_key", lambda _v: "decrypted-key")
-
-    response = authenticated_client.get(f"/api/v1/integrations/{integration.id}/api-key")
-
-    assert response.status_code == 200
-    assert response.json()["api_key"] == "decrypted-key"
-
-
-def test_create_smallest_integration_validates_key_and_sets_default_name(authenticated_client, monkeypatch):
-    calls = {"count": 0}
-
-    def _validate(api_key: str):
-        calls["count"] += 1
-        assert api_key == "smallest-secret"
-        return {"email": "owner@smallest.ai"}
-
-    monkeypatch.setattr(integrations_route, "_validate_smallest_connection", _validate)
-
-    response = authenticated_client.post(
-        "/api/v1/integrations",
-        json={"platform": "smallest", "api_key": "smallest-secret"},
-    )
-
-    assert response.status_code == 201
-    assert response.json()["platform"] == "smallest"
-    assert response.json()["name"] == "Smallest (owner@smallest.ai)"
-    assert calls["count"] == 1
-
-
-def test_update_smallest_integration_validates_updated_key(authenticated_client, monkeypatch, make_integration):
-    integration = make_integration(platform="smallest", api_key="encrypted")
-    calls = {"count": 0}
-
-    def _validate(api_key: str):
-        calls["count"] += 1
-        assert api_key == "next-smallest-key"
-        return {"email": "owner@smallest.ai"}
-
-    monkeypatch.setattr(integrations_route, "_validate_smallest_connection", _validate)
-
-    response = authenticated_client.put(
-        f"/api/v1/integrations/{integration.id}",
-        json={"api_key": "next-smallest-key"},
-    )
-
-    assert response.status_code == 200
-    assert calls["count"] == 1
-
-
-def test_preview_integration_agent_prompt_success(authenticated_client, monkeypatch, make_integration):
-    integration = make_integration(platform="retell")
-    monkeypatch.setattr(
-        prompt_sync_module,
-        "fetch_provider_prompt",
-        lambda _integration, agent_id: f"Prompt for {agent_id}",
-    )
-
-    response = authenticated_client.post(
-        f"/api/v1/integrations/{integration.id}/preview-agent-prompt",
-        json={"voice_ai_agent_id": "external-agent-123"},
-    )
-
-    assert response.status_code == 200
-    assert response.json()["provider_prompt"] == "Prompt for external-agent-123"
-
-
-def test_preview_integration_agent_prompt_not_found(authenticated_client):
-    response = authenticated_client.post(
-        "/api/v1/integrations/11111111-1111-1111-1111-111111111111/preview-agent-prompt",
-        json={"voice_ai_agent_id": "external-agent-123"},
-    )
-
-    assert response.status_code == 404
-
-
-def test_preview_integration_agent_prompt_empty(authenticated_client, monkeypatch, make_integration):
-    integration = make_integration(platform="vapi")
-    monkeypatch.setattr(
-        prompt_sync_module,
-        "fetch_provider_prompt",
-        lambda _integration, _agent_id: None,
-    )
-
-    response = authenticated_client.post(
-        f"/api/v1/integrations/{integration.id}/preview-agent-prompt",
-        json={"voice_ai_agent_id": "external-agent-123"},
-    )
-
-    assert response.status_code == 422
+"""API tests for integrations routes."""
+
+import importlib
+
+from app.api.v1.routes import integrations as integrations_route
+
+prompt_sync_module = importlib.import_module("app.services.voice_providers.prompt_sync")
+
+
+def test_create_and_list_integrations(authenticated_client):
+    payload = {"platform": "retell", "api_key": "secret-key", "name": "Retell Main"}
+    create_response = authenticated_client.post("/api/v1/integrations", json=payload)
+
+    assert create_response.status_code == 201
+    body = create_response.json()
+    assert body["platform"] == "retell"
+    assert body["name"] == "Retell Main"
+
+    list_response = authenticated_client.get("/api/v1/integrations")
+    assert list_response.status_code == 200
+    assert len(list_response.json()) == 1
+
+
+def test_update_integration(authenticated_client, make_integration):
+    integration = make_integration(platform="retell")
+    response = authenticated_client.put(
+        f"/api/v1/integrations/{integration.id}",
+        json={"name": "Updated Integration", "public_key": "pub-key"},
+    )
+
+    assert response.status_code == 200
+    assert response.json()["name"] == "Updated Integration"
+    assert response.json()["public_key"] == "pub-key"
+
+
+def test_get_integration_api_key(authenticated_client, monkeypatch, make_integration):
+    integration = make_integration(platform="retell", api_key="encrypted")
+    monkeypatch.setattr(integrations_route, "decrypt_api_key", lambda _v: "decrypted-key")
+
+    response = authenticated_client.get(f"/api/v1/integrations/{integration.id}/api-key")
+
+    assert response.status_code == 200
+    assert response.json()["api_key"] == "decrypted-key"
+
+
+def test_create_smallest_integration_validates_key_and_sets_default_name(authenticated_client, monkeypatch):
+    calls = {"count": 0}
+
+    def _validate(api_key: str):
+        calls["count"] += 1
+        assert api_key == "smallest-secret"
+        return {"email": "owner@smallest.ai"}
+
+    monkeypatch.setattr(integrations_route, "_validate_smallest_connection", _validate)
+
+    response = authenticated_client.post(
+        "/api/v1/integrations",
+        json={"platform": "smallest", "api_key": "smallest-secret"},
+    )
+
+    assert response.status_code == 201
+    assert response.json()["platform"] == "smallest"
+    assert response.json()["name"] == "Smallest (owner@smallest.ai)"
+    assert calls["count"] == 1
+
+
+def test_update_smallest_integration_validates_updated_key(authenticated_client, monkeypatch, make_integration):
+    integration = make_integration(platform="smallest", api_key="encrypted")
+    calls = {"count": 0}
+
+    def _validate(api_key: str):
+        calls["count"] += 1
+        assert api_key == "next-smallest-key"
+        return {"email": "owner@smallest.ai"}
+
+    monkeypatch.setattr(integrations_route, "_validate_smallest_connection", _validate)
+
+    response = authenticated_client.put(
+        f"/api/v1/integrations/{integration.id}",
+        json={"api_key": "next-smallest-key"},
+    )
+
+    assert response.status_code == 200
+    assert calls["count"] == 1
+
+
+def test_preview_integration_agent_prompt_success(authenticated_client, monkeypatch, make_integration):
+    integration = make_integration(platform="retell")
+    monkeypatch.setattr(
+        prompt_sync_module,
+        "fetch_provider_prompt",
+        lambda _integration, agent_id: f"Prompt for {agent_id}",
+    )
+
+    response = authenticated_client.post(
+        f"/api/v1/integrations/{integration.id}/preview-agent-prompt",
+        json={"voice_ai_agent_id": "external-agent-123"},
+    )
+
+    assert response.status_code == 200
+    assert response.json()["provider_prompt"] == "Prompt for external-agent-123"
+
+
+def test_preview_integration_agent_prompt_not_found(authenticated_client):
+    response = authenticated_client.post(
+        "/api/v1/integrations/11111111-1111-1111-1111-111111111111/preview-agent-prompt",
+        json={"voice_ai_agent_id": "external-agent-123"},
+    )
+
+    assert response.status_code == 404
+
+
+def test_preview_integration_agent_prompt_empty(authenticated_client, monkeypatch, make_integration):
+    integration = make_integration(platform="vapi")
+    monkeypatch.setattr(
+        prompt_sync_module,
+        "fetch_provider_prompt",
+        lambda _integration, _agent_id: None,
+    )
+
+    response = authenticated_client.post(
+        f"/api/v1/integrations/{integration.id}/preview-agent-prompt",
+        json={"voice_ai_agent_id": "external-agent-123"},
+    )
+
+    assert response.status_code == 422
+
+
+def test_list_integration_voice_agents_success(authenticated_client, monkeypatch, make_integration):
+    integration = make_integration(platform="elevenlabs")
+    catalog_module = importlib.import_module("app.services.voice_providers.voice_agent_catalog")
+
+    class _Result:
+        agents = [{"id": "agent-1", "name": "Support Bot"}]
+        platform = "elevenlabs"
+        cached = False
+        truncated = False
+        list_supported = True
+        message = None
+
+    monkeypatch.setattr(
+        catalog_module,
+        "list_integration_voice_agents",
+        lambda _integration, refresh=False, search=None: _Result(),
+    )
+
+    response = authenticated_client.get(f"/api/v1/integrations/{integration.id}/voice-agents")
+
+    assert response.status_code == 200
+    body = response.json()
+    assert body["platform"] == "elevenlabs"
+    assert body["agents"] == [{"id": "agent-1", "name": "Support Bot"}]
+    assert body["list_supported"] is True
+
+
+def test_list_integration_voice_agents_not_found(authenticated_client):
+    response = authenticated_client.get(
+        "/api/v1/integrations/11111111-1111-1111-1111-111111111111/voice-agents",
+    )
+
+    assert response.status_code == 404
+
+
+def test_list_integration_voice_agents_provider_error(authenticated_client, monkeypatch, make_integration):
+    integration = make_integration(platform="vapi")
+    catalog_module = importlib.import_module("app.services.voice_providers.voice_agent_catalog")
+
+    def _raise(_integration, refresh=False, search=None):
+        raise ValueError("Provider unavailable")
+
+    monkeypatch.setattr(catalog_module, "list_integration_voice_agents", _raise)
+
+    response = authenticated_client.get(f"/api/v1/integrations/{integration.id}/voice-agents")
+
+    assert response.status_code == 502
+    assert "Provider unavailable" in response.json()["detail"]
diff --git a/tests/test_services/test_playground_provider_metrics.py b/tests/test_services/test_playground_provider_metrics.py
new file mode 100644
index 00000000..e8ccf130
--- /dev/null
+++ b/tests/test_services/test_playground_provider_metrics.py
@@ -0,0 +1,24 @@
+"""Tests for Voice AI provider metric enrichment helpers."""
+
+from app.services.playground.post_call_processing import (
+    call_metrics_indicate_ended,
+    provider_metrics_enriched,
+)
+
+
+def test_call_metrics_indicate_ended_vapi():
+    assert call_metrics_indicate_ended({"status": "ended", "endedAt": "2026-01-01T00:00:00Z"})
+
+
+def test_provider_metrics_enriched_vapi_performance():
+    metrics = {
+        "status": "ended",
+        "analysis": {},
+        "artifact": {"performanceMetrics": {"turnLatencies": [{"turnLatency": 100}]}},
+    }
+    assert provider_metrics_enriched("vapi", metrics)
+
+
+def test_provider_metrics_enriched_retell_analysis():
+    metrics = {"call_status": "ended", "call_analysis": {"call_summary": "ok"}}
+    assert provider_metrics_enriched("retell", metrics)
diff --git a/tests/test_services/test_playground_tracing.py b/tests/test_services/test_playground_tracing.py
new file mode 100644
index 00000000..e98d9a26
--- /dev/null
+++ b/tests/test_services/test_playground_tracing.py
@@ -0,0 +1,76 @@
+"""Tests for in-process playground OTLP export."""
+
+from app.services.synthetic_traces.internal_otlp_exporter import readable_span_to_dict
+from app.services.synthetic_traces.trace_service import (
+    get_trace_by_call_short_id,
+    ingest_otlp_spans,
+    open_trace_session,
+)
+
+
+class _FakeSpanContext:
+    def __init__(self, trace_id: int, span_id: int):
+        self.trace_id = trace_id
+        self.span_id = span_id
+
+
+class _FakeSpan:
+    def __init__(self):
+        self.name = "stt"
+        self.attributes = {
+            "efficientai.call_short_id": "482931",
+            "turn.number": 1,
+            "gen_ai.operation.name": "stt",
+            "metrics.ttfb": 0.12,
+            "transcript": "hello",
+        }
+        self.events = []
+        self.parent = None
+        self.start_time = 1_000_000_000
+        self.end_time = 2_000_000_000
+        self._context = _FakeSpanContext(0xABC, 0x1)
+
+    def get_span_context(self):
+        return self._context
+
+
+def test_readable_span_to_dict_normalizes_attributes():
+    payload = readable_span_to_dict(_FakeSpan())
+    assert payload["name"] == "stt"
+    assert payload["attributes"]["efficientai.call_short_id"] == "482931"
+    assert payload["trace_id"] == format(0xABC, "032x")
+
+
+def test_internal_exporter_ingests_via_readable_span_dict(
+    db_session,
+    org_id,
+    default_workspace,
+):
+    call_short_id = "482931"
+    open_trace_session(
+        db_session,
+        organization_id=org_id,
+        workspace_id=default_workspace.id,
+        call_short_id=call_short_id,
+        transport="websocket",
+    )
+
+    trace, accepted, correlated = ingest_otlp_spans(
+        db_session,
+        organization_id=org_id,
+        spans=[readable_span_to_dict(_FakeSpan())],
+        header_call_short_id=call_short_id,
+        workspace_id=default_workspace.id,
+    )
+
+    assert accepted == 1
+    assert correlated is True
+    assert trace is not None
+    stored = get_trace_by_call_short_id(
+        db_session,
+        organization_id=org_id,
+        call_short_id=call_short_id,
+        workspace_id=default_workspace.id,
+    )
+    assert stored is not None
+    assert stored.turn_count >= 1
diff --git a/tests/test_services/test_provider_call_logs.py b/tests/test_services/test_provider_call_logs.py
new file mode 100644
index 00000000..beb49f43
--- /dev/null
+++ b/tests/test_services/test_provider_call_logs.py
@@ -0,0 +1,37 @@
+from app.services.playground.provider_call_logs import (
+    _parse_json_lines,
+    _parse_retell_log_text,
+    normalize_provider_log_entry,
+)
+
+
+def test_normalize_vapi_log_entry():
+    entry = normalize_provider_log_entry(
+        {
+            "time": "2025-09-02T18:26:45.070Z",
+            "level": "info",
+            "category": "transcriber",
+            "message": "Soniox WebSocket request",
+            "service": "deepgram",
+        }
+    )
+    assert entry["level"] == "info"
+    assert entry["category"] == "transcriber"
+    assert entry["summary"] == "Soniox WebSocket request"
+    assert entry["raw"]["service"] == "deepgram"
+
+
+def test_parse_json_lines_handles_plain_text():
+    entries = _parse_json_lines('{"message":"call.queued"}\nnot json')
+    assert len(entries) == 2
+    assert entries[0]["message"] == "call.queued"
+    assert entries[1]["message"] == "not json"
+
+
+def test_parse_retell_log_text_json_lines():
+    text = '{"time":"2025-09-02T18:26:45.070Z","level":"info","category":"voice","message":"TTS chunk"}\n'
+    entries = _parse_retell_log_text(text)
+    assert len(entries) == 1
+    normalized = normalize_provider_log_entry(entries[0])
+    assert normalized["category"] == "voice"
+    assert normalized["summary"] == "TTS chunk"