P. R. Manjee
Selected Work
Open-source demonstrations of methodology. Real client and commercial work is kept private.

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 source

Multi-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
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ClaudeMCP

Open source

Local 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
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FieldForce Tool Tracker

Commercial

Real-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
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PDF (2.3 MB)

Research

Three published papers on surveillance architecture, machine reasoning, and post-electronic computing. Each link opens the paper to read in full.

The Panopticon Paradigm

Structural subversion and the controlled illusion of digital privacy. Examines the mechanisms behind digital surveillance and why consumer-grade privacy controls are illusory by design.

Reasoning Strategies for AI Decision Making

A practical guide to cognitive templates for machine reasoning. The research that produced the Engineered Prompt Library below.

Photonic Computing

A light-based architectural response to the end of electronic scaling. Covers the physics, the benchmarks, the honest risks, and a hybrid deployment recommendation.

Also building

Applied archives that came out of the research above.

Engineered Prompt Library

Versioned prompt and context engineering archive: master templates, plugins, and four generations of prompt libraries. These files exist as a direct result of the Reasoning Strategies for AI Decision Making research: the paper defined the cognitive templates, and the library is those templates built out and versioned for production use.