AI evaluation. Before you ship.
We do not put probabilistic systems into production on faith. We build the eval suites, run the red-team protocols, and track regressions, so you launch on evidence, not on a good demo.
Six things we prove before launch.
Eval suites built for your task
A graded test set for the exact job the model does for you, accuracy, faithfulness, format, refusals, tone. Every change is scored against it, so "it feels better" becomes a number.
Red-team protocols
We attack the system the way a motivated user or attacker would: jailbreaks, prompt injection, data exfiltration, unsafe tool calls. Findings come with severity and a fix, not just a scary screenshot.
Regression tracking
Models, prompts and data drift. We track every release against the baseline so a quiet regression on edge cases is caught before your users find it.
Hallucination + faithfulness scoring
For RAG and agent systems we measure whether answers are grounded in the evidence, and flag the ones that are confidently wrong.
Safety + policy checks
Toxicity, bias, PII leakage and policy violations measured on every candidate build, wired into CI so an unsafe model cannot ship by accident.
Model + prompt selection
Sonnet, Opus, Haiku or a fine-tune, decided on evidence per workload, not on hype. We show you the accuracy-cost-latency tradeoff with real numbers.
From “it feels better” to a number.
Define what "good" means
We turn your task into measurable criteria and build the graded eval set with your domain experts.
Build the harness
Automated eval suite + red-team battery, wired to run on every model, prompt or data change.
Baseline + report
A scored baseline across candidates, the failure modes that matter, and a clear ship / do-not-ship call.
Guard the release
Evals run in CI. Regressions block the deploy. You ship probabilistic systems with the same confidence as code.
Evidence you can defend.
Not a slide that says “tested.” A harness you own, a baseline you can point to, and a release gate that holds the line on every future change.
The questions you're actually asking.
How is this different from AI governance?+
Evaluation is the measurement: does this system actually work and is it safe, proven before launch. Governance is the standing program around it: policy, oversight, audit, lifecycle. Many customers do both, evaluation first.
Do you only evaluate models built on Claude?+
No. We evaluate any LLM or ML system, including OpenAI, open-weight models and your own fine-tunes. We are an Anthropic Build Partner, so we know Claude deeply, but the eval harness is model-agnostic.
We already have a model in production. Can you still help?+
Yes. We baseline what you have, surface the failure modes, and put a regression and red-team net under it so the next change does not break it quietly.
What do you need from us?+
The task definition, a few domain experts to help grade, and access to the system or its API. We build the rest.
Don't ship AI on faith.
Book a 30-minute call. We'll scope the eval that proves your system works, and tell you straight where it's most likely to break.
Talk to us