AI Model Validation

Structured human evaluation of what your model actually produces — scored against a rubric, with the disagreements visible rather than averaged away.

What is included

AI Model Validation, in detail

Rubric design

Clear, testable criteria so two reviewers reach the same score for the same reason.

Blind evaluation

Reviewers scoring without knowing which model or version produced the output.

Side-by-side comparison

Head-to-head evaluation between model versions or vendors.

Failure taxonomy

Errors categorised so you know what kind of wrong the model is, not just how often.

Reproducible reporting

Scores, agreement statistics and the raw judgements handed over with the report.

How we work

Four steps, no surprises

  1. ConsultationWe learn the business and what success looks like.
  2. Audit & scopeA written plan: what we build, in what order, at what cost.
  3. BuildDelivered in stages you review as we go.
  4. SupportMonitoring and iteration once it is live.

Questions

Frequently asked

Can you evaluate safety behaviour?

Yes — refusal quality, harmful-content handling and jailbreak resistance, against a rubric you approve.

How large should an eval set be?

Large enough to separate the models you are comparing. We size it from the effect you need to detect.

Let us look at what you are trying to build

Tell us the problem and we will tell you honestly whether we are the right people to solve it — and what it would take.

Book appointment