Muhammad K.

I build AI systems that survive contact with production — RAG pipelines, agent workflows, and the Next.js + Python apps around them. Most AI projects die between the demo and the deploy: retrieval that works on 10 test PDFs falls apart on 10,000 real ones, agents loop, API costs explode, latency kills the UX. My work starts where the demo ends. What I ship: • RAG systems — vector search (PostgreSQL/pgvector, Supabase), chunking and retrieval strategies tuned to your actual documents, with evaluation built in so "it answers correctly" is measured, not assumed • LLM agents & automation — multi-step agent workflows with real guardrails: error handling, retries, cost ceilings, and human-in-the-loop where it matters • AI SaaS, end to end — Next.js + TypeScript frontends, Python/FastAPI backends, streaming UX, auth, billing. The whole product, not just the AI endpoint • Cost & reliability engineering — model routing (flagship models for hard steps, fast models for the rest), caching, token tracking, and observability with Langfuse, so your API bill and latency stay predictable Stack I work in daily: Next.js, React, TypeScript, Python, FastAPI, Node.js, PostgreSQL + pgvector, Claude / Gemini / OpenAI APIs, Docker, Vercel, AWS. Why clients come back (100% Job Success, 14 completed contracts, 500+ hours): I spent 6+ years shipping production web software before AI. You get engineering discipline with your AI — clean architecture, honest scoping, and straight answers about what will and won't work. Recent work: an autonomous RAG-driven talent-intelligence engine and an AI expense & budget tracker (both in my portfolio below). If you have an AI feature that needs to work for real users, send me the messy details. That's the part I like.