Custom evals for models, retrieval systems, and agents, built from your production tasks and failure history.
Run candidate models, prompts, retrieval pipelines, and agents on the same production tasks. See the quality, cost, latency, and failure tradeoffs.
Measure every change against the current system. Catch regressions by workflow, user segment, and failure mode before deployment.
A working evaluation suite in your stack. Not a benchmark report.
01/04
Choose the release or selection decision, target workflows, costly failures, and pass criteria.
02/04
Create representative cases, rubrics, graders, and expert review. Calibrate automated scores against human judgment.
03/04
Connect models, traces, data, and CI. Your team owns the code and can reproduce every result.
04/04
Compare candidates, isolate regressions, and set thresholds for ship, hold, or rollback.
Evaluation systems used by teams at
LLM Stats is backed by Y Combinator.
Scope, delivery, privacy, and how to start.
A runnable evaluation suite: representative cases, rubrics, graders, human review where needed, runners, and release thresholds. It runs in your stack and belongs to your team.
We start with one decision and one workflow. We scope, build, calibrate, and integrate the first suite before expanding coverage.
Yes. We work with private data, internal workflows, and pre-release models, including inside your infrastructure.
Yes. We design expert review, preference studies, and annotation workflows when automated grading is not enough. Learn about data labeling.
Send the decision, workflow, known failures, and any existing eval. We return a proposed scope, required access, and timeline.
Bring us a release decision.
We will tell you what to test, how to score it, and what the first suite should include.
Share an existing eval, a workflow, or sample failures.
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