The case studies below show how I approach distinct kinds of problems in AI engineering. They are not redacted client engagements, they are demonstration work that exists publicly so the methodology can be evaluated before a conversation starts. Each card links to the full case study with the problem, approach, and current status. Source repositories are linked from there.
AIOrchBuilder
Open sourceMulti-agent orchestration framework that builds applications by routing specialist tasks to the language model best suited for each kind of work.
Seven specialist agents with defined contracts coordinated by an orchestrator that dynamically routes each task to the right model. Includes Ollama support for self-hosted local models, RBAC wired in from Day 1, and an auditable retrospective path for every agent action.
- Next.js
- TypeScript
- React
- Python
- Supabase
- PostgreSQL + RLS
- +4 more
ClaudeMCP
Open sourceLocal multi-backend LLM gateway. One process speaks four backends through three API protocols, so existing client code works unchanged.
Single local gateway process serving Claude, Gemini, LM Studio, and Ollama through Anthropic, Gemini, and OpenAI API protocols. CLI auth reuse eliminates double billing for Claude Max and Gemini subscribers. Multi-instance support for local backends.
- Node.js 20+
- TypeScript
- Express
- Zod
- Vitest
- SQLite
- +4 more
FieldForce Tool Tracker
CommercialReal-time asset intelligence platform for utility and construction operations.
Unified asset management platform combining sub-second GPS tracking, PostGIS geofencing, QR/barcode custody management, fleet health, and bi-directional ERP integration. Three apps in a pnpm + turborepo monorepo: NestJS API, React 18 web dashboard, Expo mobile app with SQLite offline. Code preserved for commercial deployment.
- Node.js 20
- NestJS
- TypeORM
- React 18
- Vite
- MUI v6
- +12 more
Also building
Three additional public repositories that round out the work. Research papers and archives rather than full case studies. Each links to its own README.
Engineered Prompt Library
Versioned prompt and context engineering archive. Includes the Reasoning Strategies for AI Decision Making paper that informed the AIOrchBuilder agent contracts.
Photonic Computing
Research paper on light-based architectural responses to the end of electronic scaling, with companion video, infographics, and supporting documents.
Panopticon Paradigm
Research paper examining the structural mechanisms behind digital surveillance and why consumer-grade privacy controls are illusory by design.
Want similar work for your organization?
The architectural patterns shown here apply directly to client engagements in AI Adoption and Digital Transformation. If you are evaluating whether this approach fits your situation, the fastest path is a short conversation. I take on a small number of engagements per quarter so each one gets the attention it deserves.