LLM evaluation: the instrument panel for every model decision.
You can’t improve what you can’t measure, and you can’t ship what you can’t defend. We build evaluation suites that tell you — and your risk committee — exactly how good a model is at your task.
Public benchmarks tell you how a model does on someone else’s problem. An enterprise needs to know how it does on claims from last quarter, on the contract types that cause disputes, on the support tickets that escalate. That requires a custom evaluation suite: representative data, clear scoring rules, and a way to run it every time anything changes — a prompt, a model version, a retrieval index.
Our evaluation engagements produce a harness you own and run in CI. It combines deterministic checks (schemas, exact matches, executable tests), LLM-as-judge scoring calibrated against your experts, and a structured human review protocol for the cases automation can’t settle. Every metric comes with a confidence interval, and every judge comes with an agreement score against humans.
Signs you need evaluation.
The right technique, chosen by the eval.
- Golden sets. Stratified, adjudicated examples covering common cases and the long tail.
- Calibrated LLM-as-judge. Rubric-based judges measured against human ratings, with agreement reported.
- Red-teaming. Adversarial probing for jailbreaks, data leakage, bias and unsafe advice.
- Online evaluation. Sampling production traffic for continuous quality and drift monitoring.
- Evaluation harness in your repo, wired into CI
- Golden datasets with documentation
- Judge prompts with human-agreement scores
- Baseline report across candidate models
All assets transfer to you at handover. No licence fees.
Questions buyers ask us
How do you know an LLM judge is reliable?
We have your experts rate a sample of outputs, then measure the judge’s agreement with them (Cohen’s kappa, correlation and confusion analysis). Judges that don’t clear the bar are revised or replaced by human review.
How big should an evaluation set be?
Large enough for the confidence interval you need. For detecting a 3-point difference at typical accuracies, 800–1,500 examples per slice is a reasonable start. We size it explicitly.
Can we buy evaluation without fine-tuning?
Yes. About a third of our engagements start with evaluation only, often to choose between vendors or to satisfy a governance requirement.
Bring us the task. We'll bring the model.
A 45-minute technical briefing with a senior research engineer. You leave with a feasibility read, a data plan and a fixed quote — whether or not you hire us.
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