I build and validate AI systems you can trust.
Most AI systems look great in the demo and quietly drift once real data hits them — nobody notices until it costs something. I catch that: eval sets, accuracy benchmarks, confidence scoring, audit logs — the evidence that a system is doing what it claims. My deepest proof of this so far is in document and financial data extraction, but the same rigor works on any AI system handling data you can't afford to get wrong.