Roman Mazuryk - AI Workflow Consulting & Proof-of-Work Platform

AI Systems Consultant for operations-heavy SMEs and regulated teams. This site presents practical AI workflow offers, public proof of work, MedTech/regulatory authority, and full-stack implementation evidence.

Live: mazuryk.dev

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What This Repository Is

This repository powers mazuryk.dev: a public consulting and proof-of-work platform built around one commercial thesis:

Start with a workflow audit, then build the right AI-assisted pilot.

The site supports two audiences:

• SME and regulated-operations clients who need practical help finding, scoping, and implementing useful AI workflows.
• AI/product employers and collaborators who need evidence of product judgment, systems thinking, and hands-on implementation ability.

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Positioning

I help operations-heavy SMEs and regulated teams turn fragmented workflows into practical AI-assisted systems: audits, prototypes, internal tools, SOP systems, dashboards, and implementation roadmaps.

The portfolio hierarchy is intentional:

1. AI consulting - the primary commercial pillar.
2. MedTech and regulated operations - authority proof from real implementation-heavy environments.
3. Full-stack product delivery - implementation proof that the ideas can become working systems.

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Productized Offers Represented

Offer	Purpose	Site entry point
AI Workflow Opportunity Audit	Identify the right AI use case before building	AI Consulting
Prototype Sprint	Turn the selected workflow into a tangible pilot	AI Consulting
Knowledge & SOP System	Structure scattered knowledge, procedures, onboarding, and guidance	AI Workflow Library
Dashboard & Internal Tool	Improve visibility, ownership, reporting, and workflow state	Build Proof

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What This Repository Proves

• AI consulting positioning translated into a live public website.
• A clear offer system for SME workflow discovery, prototyping, and implementation.
• MedTech and regulated-operations background used as credibility, not as a limiting niche.
• Full-stack implementation proof through React, Vite, structured content, deployment, and documentation.
• AI-assisted delivery discipline through PRD, architecture notes, roadmap, decisions log, changelog, and Git workflow.

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Main Routes

Route	Role
/ai	Productized AI consulting offers and first CTA
/ai-workflow	Practical workflow examples and reusable AI system patterns
/proof-of-work	Regulated and MedTech domain proof
/fullstack	Implementation proof: prototypes, dashboards, internal tools, and build capability
/about	Background, operating principles, and role fit
/contact	Workflow discussion and collaboration entry point

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Tech Stack

Layer	Choice	Why
Framework	React 19 + Vite 7	Fast builds, modern React, simple deployment
Styling	CSS modules by concern + custom properties	Precise control over a portfolio/consulting UI
Icons	lucide-react	Consistent product-style iconography
Analytics	Vercel Analytics	Lightweight feedback loop
Deployment	Vercel	Automatic deploys, CDN hosting, preview URLs
Documentation	Markdown in /docs	Public evidence of product and engineering discipline

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Getting Started

Prerequisites: Node.js >= 18

git clone https://github.com/romahawk/portfolio-react.git
cd portfolio-react
npm install
npm run dev
npm run build
npm run lint

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Project Structure

src/
  components/          React page and section components
  components/case-studies/
                       Detailed project and proof components
  data/                Static content and workflow examples
  hooks/               Metadata, language, and UI hooks
  locales/             English and German copy
  assets/css/          Component-scoped CSS

docs/
  PRD.md               Product requirements and target audiences
  ARCHITECTURE.md      System design and key trade-offs
  ROADMAP.md           Consulting-first site roadmap
  DECISIONS_LOG.md     Architectural and product decisions

public/
  images/              Profile, proof, and Open Graph assets

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Documentation

Doc	Purpose
PRD	Why the site exists, who it serves, and what it must prove
Architecture	Static site architecture, content structure, and trade-offs
Roadmap	Current consulting-first improvement plan
Decisions Log	Decision history and implementation rationale
Changelog	Visible history of shipped improvements

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Workflow

All meaningful changes should follow:

Issue or task -> branch -> focused change -> build/lint -> PR -> deploy

The repository is not only a codebase. It is part of the proof: it should show how I think, document, build, and iterate.

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Next Improvements

• Add README screenshots for the homepage, AI offer section, AI workflow library, and proof-of-work pages.
• Update GitHub repository topics around AI consulting, workflow automation, internal tools, MedTech, and regulated operations.
• Keep documentation aligned with the current commercial positioning as the offers evolve.