Know when your AI got worse, before users do.
An eval suite is a set of tests for AI behaviour. We build eval suites that run on every pull request, catch regressions across prompt changes and model upgrades, and let you compare options with numbers.
When you need it.
Without evals, every prompt change and model upgrade is a guess. This is often the missing piece in an AI roadmap.
- Prompt changes are tested by eyeSomeone tries a few questions and says it looks fine.
- Model upgrades are scaryA new model version might be better, or might break something nobody checks.
- Arguments instead of numbersTeams debate which prompt is better without data.
- Users find the problems firstQuality issues arrive as complaints.
How it works.
The shape of what we build, from your data to the people who use it.
- Golden datasetReal cases + expected results
- ChecksExact, rubric and safety
- Run on every PRIn your CI
- CompareAgainst the last release
- Gate the releaseBlock when quality drops
What we build.
The parts of a production system, each one tested and handed over.
Golden datasets
Real cases from your product, with the answers or behaviour you would accept.
Several kinds of checks
Exact checks where possible, rubric-based grading where needed, and safety checks for things that must never happen.
CI integration
The suite runs on every pull request and reports the change against the last release.
Regression gates
A release is blocked when key scores drop below an agreed line.
A/B harness
Compare prompts, models or settings side by side on the same cases.
Red-team prompts
Attempts to make the system misbehave, kept in the suite so fixes stay fixed.
What you get.
- An eval suite built from your own real cases
- Checks running in your CI on every pull request
- A release gate with agreed thresholds
- Side-by-side comparisons for prompt and model changes
- A process for adding new cases as issues are found
How we run it.
Start with a short discovery sprint and decide with real evidence.
- Step 1
Discovery sprint
2 weeks
We map the problem, look at your data and systems, and agree what “good” means as a real eval set.
- Step 2
Build
Working version early
We build the first working version in your environment, with tests and monitoring from the start.
- Step 3
Harden
Before real users
Latency, cost, failure handling and security, then a runbook your team can follow.
- Step 4
Pilot and run
Real users
A pilot with real users and weekly reviews, then ongoing improvement if you want us to stay.
What we build with.
Model- and provider-agnostic: we pick what fits your latency, cost and compliance needs.
- Evaluation
- Golden datasetsRubric gradingRegression gatesA/B harnessesRed-team prompts
- Where it runs
- Your CI pipelinePull request checksRelease dashboards
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyWhat is an eval suite?
A set of tests for AI behaviour: real inputs with the results you’d accept, scored automatically, so you can tell if a change made things better or worse.
Can you test something as fuzzy as an answer?
Yes. Some checks are exact; others grade answers against a rubric; safety checks look for things that must never happen. Together they give a usable score.
Do we need this if we use a top model?
Yes. Models change, prompts change and your data changes. Evals are how you know the system still works after each change.
Can you add evals to a system that’s already live?
Yes. We usually start from real conversations and support tickets to build the first dataset.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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