AI evaluation and testingYou tested 1,000 scenarios. Your users will find the 1,001st.
AI never answers the same way twice. We test it, score it and watch it, so failures show up in our report, not in front of your customers.
✓ Engineers, not sales✓ Reply in 1 day✓ Any model, any vendor
Input
Output
0audit gaps across 8 live agents
60 to 70%less research time, accuracy-checked
50+projects delivered since 2023
3×return, at least, within 12 months
Try it
An example bot, not a client system. These are the tests we run on yours.Pick a test. Watch a typical support bot fail it.
User
Do you refund orders after 90 days?
Support botYes! We offer a full refund within 120 days, no receipt needed.
FAILMade up a refund policyFix: ground answers in the policy document and test against it
Why your test plan is not enough
Old software repeats itself. AI does not.
ThenTest 1,000 cases once. Ship.
1 + 1→code→2, always
Same input, same output, every time.NowTest before launch, and every day after.
"refund?"→model→yesnomade up
Same input, different output. New questions daily. Models change overnight.How it works
From black box to an output you can trust.
- 1Map the riskWhich wrong answers cost money or trust.
- 2Attack itReal questions, edge cases and attacks.
- 3Score itAutomatic graders plus human review.
- 4Watch it liveAlerts on live traffic, re-test on every change.
What you get: a release scorecardIllustrative
Accuracy
94%
Grounded in sources
91%
Safe and on policy
97%
Injection resistance
78%
Tone on brand
Dashed line: the pass bar for each risk.88%
ReleaseBlockedInjection resistance below the pass bar
Anthropic guided
Scorecards
Live monitoring
Tested the way Anthropic recommends.
Anthropic's own guidance is simple: define success, build evaluations, then ship. We follow it on every AI system we touch.
✓Success defined before a single promptClear, measurable pass bars for each risk, agreed with you up front.
✓Evaluations built firstTest sets and graders exist before the feature goes live, not after.
✓Any model, same rigourClaude, GPT, Gemini or open source. One method, one scorecard.
ScorecardsLive monitoringDone before
AI that passed the people who sign it off.
Public sector · citizen servicesEight AI agents built to be approved: trace to source, rule checks and a stop button each.
0audit gaps, approved the week it was shown
Read the case →
Trading · StockJarvisResearch agents on Claude with two layers of checks: privacy first, then accuracy.
60 to 70%less research time, live in production
Read the case →Questions before you book.
We did not build our AI with you. Can you still test it?
Yes. Most of our evaluation work is on systems other teams built.
Do you only test Claude?
No. We test any model: Claude, GPT, Gemini, Llama or your own.
Do you need our production data?
Not to start. A sample of real questions, with personal data removed, is enough.
Who owns the tests?
You do. Test sets, graders and dashboards live in your repositories.
How is testing AI different from normal QA?
Normal QA checks fixed outputs. We score answers that change, on accuracy, grounding, safety and tone, and keep scoring them after launch.
Book an eval callTrust your AI output.Show us one AI feature. We will show you where it breaks.
✓ The riskiest answers in your system✓ The first tests we would write✓ What a pass bar should look like
Book your eval call