We build eval suites from your real cases, score every change on accuracy, tone and refusal behaviour, and trace each prompt, retrieval and tool call so failures are diagnosable rather than mysterious.
Any AI feature that customers or staff depend on needs this layer. It is the difference between an assistant you can keep improving and one nobody trusts after the first bad answer.
The control that decides whether AI survives production
An evaluation suite is a fixed set of real cases with known-good outcomes, scored automatically whenever anything changes. It is the least glamorous part of AI engineering and the most predictive of success: reported rollback rates fall from roughly 47% for agents with no automated evaluation coverage to about 9% with full coverage.
The reason is simple. AI systems degrade invisibly. A prompt tweak, a retrieval change or a provider model update can shift behaviour in ways no one notices until a customer receives a bad answer. Without evals, your quality assurance is complaints.
How we build them
From your material, not generic benchmarks: the awkward questions, the ambiguous ones, the edge cases, and crucially the queries where the correct response is to refuse or escalate. We score accuracy, tone, policy adherence and refusal behaviour, then run the suite inside the deployment pipeline so a regression blocks a release rather than reaching users.
Tracing sits alongside it. Every prompt, retrieval and tool call is recorded, so an incident is reconstructed from evidence instead of debated. Logs are written to be legible to your privacy officer and auditors, not only to engineers — governance evidence nobody can interpret is not evidence.
What it enables
Deliberate model upgrades, backed by data rather than optimism. Faster iteration, because you can change something and know within minutes whether it helped. And a defensible answer when someone asks how you know the system is working — which, in regulated sectors, is a question that arrives eventually.