Validation tooling
Deterministic tests around probabilistic systems, so a model change shows up as a failed check instead of a customer complaint.
Request early accessThe problem
A model upgrade, a prompt edit or a provider change can shift outputs in ways nobody notices until a customer does.
Teams spot-check a few examples by hand, call it evaluated, and ship. The next release repeats the gamble.
How it works
Capture what good looks like
Turn known-good and known-bad examples into versioned test cases with explicit pass criteria.
Cover the failure modes
Add cases for accuracy, drift and the ways this system is known to fail, including adversarial inputs.
Run on every change
The suite runs whenever the model, prompt or data changes, and a regression fails the build.
Keep the evidence
Each run produces a report you can attach to a release decision or hand to a reviewer.
Who it is for
- ML and application engineers shipping model-backed features
- Data and AI leaders who need a release gate they can explain
- Reviewers who want results they can rerun themselves