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BrianChase515/README.md

Hi, I'm Brian Chase

I build practical AI and analytics systems that turn fragmented customer and operational signals into clearer decisions.

My work focuses on the layer between raw data and business action:

  • Predictive customer experience and decision intelligence
  • Voice-of-customer and conversation analytics
  • Synthetic customer research and simulation
  • Knowledge-grounded workforce enablement
  • Deterministic AI orchestration and validation
  • Operational friction and value-realization analysis

Featured Projects

Predictive CX Decision Intelligence

A forecasting and decision-support system spanning pre-period planning outlooks, in-period forecasts, operational pressure detection, recovery sensitivity, and model-learning controls.

Synthetic Customer Intelligence Lab

Transforms synthetic feedback patterns into privacy-safe customer composites for policy and process hypothesis testing, then reuses those profiles in stateful service-training simulations.

Business Unit Persona System

Combines reusable AI personas with business-unit-specific knowledge, procedures, specialist roles, training behaviors, and escalation hierarchies.

Customer Value Leakage Engine

Identifies where expected customer value breaks down across onboarding, digital activation, service handoffs, early product use, and expectation alignment.

Transcript Intelligence Pipeline

Converts noisy service conversations into normalized speaker turns, privacy-safe evidence, operational classifications, customer-effort signals, and recommended actions.

Feedback Taxonomy and Action Engine

Turns unstructured feedback into a governed taxonomy, classification-quality audit, friction scoreboard, and measurable 30/90-day action plan.

How I Build

I separate probabilistic language generation from deterministic controls whenever the work requires reliability.

My projects commonly include:

  • Explicit data and evidence boundaries
  • Transparent confidence or priority logic
  • Reproducible synthetic demonstrations
  • Structured validation and failure handling
  • Human-review checkpoints
  • Executive-ready outputs
  • Clear distinctions between evidence, inference, and scenario modeling

Portfolio Safety

The public repositories on this profile use fictional organizations, synthetic datasets, independently created business rules, and clean-room implementations. They contain no employer, customer, or confidential information.

Current Focus

I am interested in senior and lead opportunities across:

  • AI Product Management
  • AI Enablement and Adoption
  • Customer Success and CX Analytics
  • Decision Intelligence
  • Operational Analytics
  • Applied AI Solutions

Pinned Loading

  1. predictive-cx-decision-intelligence predictive-cx-decision-intelligence Public

    Synthetic predictive CX framework for next-month planning, in-month forecasting, operational pressure detection, and recovery sensitivity.

    Python

  2. synthetic-customer-intelligence-lab synthetic-customer-intelligence-lab Public

    Dual-purpose customer intelligence lab that builds privacy-safe composites for policy testing and stateful service-training simulations.

    Python

  3. business-unit-persona-system business-unit-persona-system Public

    Configurable AI assistants grounded in business-unit knowledge, local roles, procedures, and escalation paths.

    Python

  4. customer-value-leakage-engine customer-value-leakage-engine Public

    Detects front-end customer value leakage and turns onboarding, activation, handoff, and expectation failures into prioritized operational actions.

    Python

  5. transcript-intelligence-pipeline transcript-intelligence-pipeline Public

    Python pipeline that converts noisy conversations into privacy-safe transcripts, issue fingerprints, evidence excerpts, and operational actions.

    Python

  6. feedback-taxonomy-action-engine feedback-taxonomy-action-engine Public

    Four-pass Python engine that converts unstructured feedback into a governed taxonomy, quality audit, friction scoreboard, and 30/90-day action plan.

    Python