AI Harness & Evals
Proof your AI actually works
Teams stall at the same place: the demo is good, nobody can prove the next change did not break it. An eval harness fixes that. We build the dataset from your real cases, write graders for the things you actually care about, and put a quality gate in CI so a prompt, model or retrieval change is a measurable decision instead of a gamble.
blocked 3 releases this quarter · p95 latency 1.8s · $0.004/run
- Golden dataset assembled from real traffic and expert review
- Automated graders: exactness, groundedness, citation validity, tone, safety
- Regression suite in CI with a pass threshold per release
- Red-team set for prompt injection, data leakage and jailbreaks
- Hallucination controls: grounding requirements, refusal paths, citation enforcement
- Model comparison so you can switch vendors on evidence
- 01
Define correct
We agree what a good answer is, case by case, with the people who own the outcome.
- 02
Build the set
Real cases, including the ugly ones that break systems in production.
- 03
Grade automatically
Deterministic checks where possible, model graders where not, calibrated against human review.
- 04
Gate releases
No prompt or model change ships without passing the suite.
Constrain what the model can say: require retrieval with citations, verify claims against the retrieved span, and refuse when support is missing. Then measure it — an ungrounded-answer rate you track per release is what actually keeps it low.
Yes, and it is one of the most common ways we start. We instrument the existing system, build the dataset from its real traffic, and hand back a suite your team runs in CI.