Work
Client work is confidential by default, so what we show is the shape of the work. References are available on request.
Bills, invoices, applications, field photos — documents a person currently reads one at a time. We build extraction pipelines with confidence scoring and human-review routing: routine documents are processed automatically, ambiguous ones reach a person with context.
What makes them trustworthy is the evaluation loop — every reviewer correction feeds a measured accuracy baseline.
Policy manuals, program rules, support histories — knowledge that exists but can't be asked a question. Our retrieval systems answer with citations, refuse when the source doesn't support an answer, and log everything for audit.
A wrong answer delivered confidently is worse than no answer — refusal behavior gets evaluated as rigorously as accuracy.
Teams arrive running the model they started with. We build an evaluation set from their production traffic, benchmark the candidates, and deliver a decision with the data behind it.
Sometimes the verdict is "switch and save," sometimes "stay put." No vendor pays us, so either answer is fine.
Workflows where the AI plans, uses tools, and acts — with hard boundaries: deterministic code owns money, permissions, and irreversible actions; the model owns understanding and language.
That division is the architecture. It's what lets an agent be useful on Monday and auditable on Friday.