Service
We build AI capabilities into existing operations — document processing, retrieval-augmented assistants, classification, and workflow automation — with evaluation and guardrails from day one.
The question is never “where can we add AI”. It is “which repetitive, expensive, judgement-light work is currently consuming the team’s week”. Once that is named, the technology choice is usually obvious and often boring.
Every AI feature we ship comes with an evaluation set drawn from real historical cases, structured output validation, cost and latency monitoring, and a clear boundary marking where a human must sign off.
Case studies
Problem, constraint, decision, consequence. No vanity metrics, no logos we are not allowed to name.
All case studiesSample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.
Document intake and support triage moved to a draft-first AI workflow with human approval, measured against an evaluation set built from two years of historical cases.
A nightly batch pipeline that had outgrown its window, re-architected into an incremental streaming system with correctness checks that alert before customers notice.
FAQ
We look for high-volume, judgement-light, text- or data-heavy work with a tolerant error budget and an existing human review step. If the task fails those tests, deterministic software is usually cheaper and safer.
Retrieval grounded in your own data, structured outputs validated against a schema, an evaluation set built from real historical cases, and explicit human review on anything with financial or legal consequence.
Start a conversation
Tell us what you are trying to build, fix or decide. If we are not the right team for it, we will say so and point you somewhere better.