AI Lab
Where I learn AI by building it
No black boxes. Each experiment here builds a layer of the modern AI stack from first principles — embeddings, retrieval, agent loops, and the data engineering underneath.
Agents & tool calling
Reasoning loops that retrieve context, decide when to invoke tools, execute them, and synthesize answers — built by hand to understand every step of the agentic pattern.
RAG & vector search
PDF ingestion → chunking → Sentence Transformer embeddings → ChromaDB semantic retrieval. The full retrieval-augmented generation pipeline, no framework magic.
Local-first LLMs
Running llama3 on Ollama for private, zero-cost inference — because understanding deployment constraints is part of AI engineering, not an afterthought.
AI-assisted engineering
Spec-driven development with AI pair programming in production since 2022: write the contract, direct the model, verify rigorously. Applied daily at enterprise scale.
Experiments
marketingManager
PrivateAdvertising for people who do not run advertising. Say what you want in one sentence — in English, Tamil or Hindi — and it writes the Meta campaign, runs it, then proposes the single change worth making, which you approve or skip. Spend limits enforced in code, not asked of a model.
Automated print-readiness checking for custom print orders: measures artwork against the ordered product on CPU in ~1s, clears clean files, explains the fix, escalates the rest. 78.7% auto-approved, 0 wrong clearances in 500. Demo video + live app.
Uplift targeting plus an evidence-grounded local LLM agent and always-valid experiments — a growth system built to say "not you" and "not worth it". Runs offline on a laptop, synthetic data. 9-min demo: https://youtu.be/RgPZ0DGvoOw
webjson
Privateai-resume-builder
PrivateGenerative UI SDK for Civo relaxAI — schema-enforced, streaming, sovereign-by-default. Runs on local open-weight models too.
Why this matters
Production AI systems fail in the plumbing: retrieval quality, context management, latency, cost, observability. 9+ years of building enterprise systems taught me to engineer for those failure modes — the AI Lab is where I apply that discipline to LLM-based products. The goal isn't demos; it's understanding deep enough to ship AI features that survive real users.