Service 04
LLM evals and reliability
We build the eval suites and honesty gates that make an AI system trustworthy in production. Every change to an answer path is graded against a battery of checks, and a regression is reverted, not patched over. This is the layer most teams skip and then regret.
What it includes
- An eval battery per agent, plus golden cases and determinism checks, so the same question keeps getting the same answer
- A hard gate: precision, leak, and wrong-value thresholds a change must clear
- A multi-stage verifier that fails closed to an honest abstain
What you get
- A repeatable eval suite wired into your change process
- A gate that blocks a regression from reaching users
- A measured honesty baseline you can hold the system to
Common questions
Why do evals matter for an AI product?
Without them you cannot tell whether a change made the system better or quietly worse. Evals turn that from a guess into a number you can gate on.
What does a hard gate check?
On one production system the gate holds abstain precision at 1.000, zero cross-tenant leaks, zero golden wrong-values, and zero determinism violations. A change that drops any of these is reverted.
Can you add evals to a system we already have?
Yes. We can wrap an existing agent or RAG pipeline with an eval battery and a gate without rebuilding it, then tighten from there.
Start a project
Name the workflow that eats your team's week
Name the workflow that eats your team's time. We will tell you honestly whether AI fits, and if it does, scope a first piece with a known price.