We build AI systems that hold up in production — agentic workflows, evaluation infrastructure, and the data foundations underneath. No demos that die in the boardroom. No hand-waving about what the model "probably" does.
Where AI earns its place in your business — and where it doesn't. Concrete sequencing, cost models, and a build plan your engineers can actually execute against.
Tool-using agents and retrieval pipelines wired into real systems of record. Scoped, observable, and evaluated — not wrappers around a chat window.
Test suites for non-deterministic systems. Golden datasets, regression evals, and gating in CI so model behaviour is measured, not assumed.
The unglamorous work that decides whether any of it works: entity models, pipelines, governance, and the deploy path from laptop to production.
The scholarly record on automating science — passage-backed close readings of 58 sources on whether discovery can be mechanized, what has been claimed, and what actually holds up under scrutiny.
Read →Most AI pilots fail in production for a reason that was visible on day one — nobody defined what "correct" meant. The fix is unglamorous and cheap if you do it first.
Read →Working on something where AI has to be more than a prototype? Tell us what you're building and what's getting in the way.