AI Lab
Experiments in AI-native product engineering
Every experiment here starts from a domain I've actually shipped — ERPs, marketplaces, field tools — because AI features are only as good as the systems underneath them.
RAG chatbot over this portfolio
The chatbot on this site is a real retrieval pipeline: content chunked with citation metadata, hybrid retrieval (BM25 + Voyage embeddings) fused with Reciprocal Rank Fusion, top-k passages injected into Claude with inline citations you can click.
RAG over ERP audit trails
Retrieval-augmented answers over procurement and payment history — embeddings on audit rows, hybrid search, and citations back to source records. Built on my Construction ERP domain.
Semantic service search
'Someone to fix a leaking tap this weekend' → ranked, available professionals. Embedding search over service descriptions with availability and geo re-ranking.
LLM-drafted purchase orders
Unstructured supplier quotes → structured PO drafts, validated by Zod schemas, approved by humans inside the existing procurement state machine. LLM output as a proposal, never a mutation.
Prompt-eval harness
Small harness for versioning prompts and scoring outputs against golden sets — treating prompts like code: reviewed, tested, and regression-checked.
Attendance anomaly detection
Flagging geofence spoofing and duplicate selfie patterns in field attendance data — classical features first, model second.
// principle: LLM output is a proposal, never a mutation. Every AI feature routes through the same validation, RBAC, and audit layers as a human action.