AI evaluation
Model evaluation
Accuracy, robustness and regression evaluation against agreed benchmarks, run on every release. For an enterprise this is the control that turns AI adoption from a leap of faith into a governed release process.
Discuss your AI initiativeScope of work
Process
How an engagement runs
01
Scope
We agree what to test, against which benchmarks and thresholds.
02
Harness
A reproducible test or evaluation harness is built in your repositories.
03
Execute
Suites run on every release; failures are triaged with your team.
04
Report
Findings, evidence and a remediation backlog, written for review.
Questions
Common questions
What is AI model evaluation?
Measurement of a model's accuracy, robustness and regression behaviour against agreed benchmarks, run on every release.
Which benchmarks do you use?
Public benchmarks where they fit, domain benchmarks built for you where they do not. Selection is agreed before measurement starts.
How often should models be evaluated?
On every release and every prompt or retrieval change. Evaluation is a release gate, not an annual audit.