The approach
From pilot to proof
Most organizations don't lack AI ambition — they lack evidence. Pilots that never graduate. Demos that impress in the room but fail under load. Evaluations that measure the wrong thing.
I work with teams who want the opposite: systems built to survive scrutiny. Benchmarks with clear pass/fail criteria. Retrieval pipelines measured on real queries. Agentic workflows that produce artifacts, not anecdotes.
What transformation looks like
| Phase | What you get |
|---|---|
| Diagnose | Honest assessment of model behavior, retrieval quality, and failure modes — before capital is committed |
| Design | Architecture grounded in your constraints: latency, cost, compliance, and the humans who will operate it |
| Deliver | Working systems with reproducible evals, not slide decks with architecture diagrams |
| Prove | Live demos, published benchmarks, and metrics that outlast the engagement |
Capabilities
LLM evaluation & safety — custom benchmarks (ECG-Bench), cross-provider eval suites, intervention studies
RAG & retrieval — embedding/reranker sweeps, hybrid retrieval, production retrieval architecture
Agentic systems — discovery pipelines, knowledge graphs, multi-agent workflows with measurable outputs
NLP & search — semantic search, translation systems, subtitle and scene memory at scale
Document AI — extraction arbitration, table pipelines, PDF intelligence
How I work
I embed as a builder, not an advisor who disappears after the workshop. The goal is always the same: leave you with capability you own — systems that run, metrics that matter, and proof that compounds.
Success isn't measured by what ships during the engagement. It's measured by what keeps working after.
Live systems
- Quote Memory — production fuzzy subtitle search
- Hindi Jinnie — deployed EN→HI translation
- PaperLens — PDF highlight, annotate, and export
Want to discuss a project? Contact me.