AI Solutions

AI Model Testing and Evaluation

Proving the model works on data it has never seen - including the cases where it must not fail.

How We Work, Step by Step
  1. 1Define metrics
  2. 2Test on held-out data
  3. 3Analyse errors
  4. 4Adversarial testing
  5. 5Client acceptance

What We Do for You

  • Test on data the model has never seen.
  • Choose metrics that match the cost of being wrong.
  • Read the failures by category, not just the score.
  • Check performance across the groups it will affect.
  • Attack it deliberately before your customers can.

How this is bought: Bought as a one-off assessment with a written report and a priced plan of action. Build an estimate for your case.

Our Approaches Explained

Held-out and cross-validation testing

Measuring performance on data kept aside, not on what the model already studied.

Metric selection

Choosing measures that match the cost of being wrong: precision, recall, F1, AUC, RMSE, calibration.

Error analysis

Reading the failures by category to find the pattern, rather than reporting one score.

Fairness and subgroup testing

Checking performance across the groups the system will touch, and recording differences.

Robustness and adversarial testing

Deliberately noisy, malformed and hostile inputs, including prompt injection for language systems.

Human evaluation

Expert review where the answer cannot be scored automatically, with a written rubric.

Acceptance testing with the client

The business confirms the model meets the criteria agreed at engineering stage.

Documentation

A model card recording intended use, limits, test results and known failure modes.

The Standards We Work To

OWASP Top 10 for LLM ApplicationsNIST AI RMF measure functionModel cardsStatistical significance testing

We follow the structure and controls these standards describe. We do not claim to be certified against them - where you need a formal certificate, we prepare the evidence and an accredited body performs the audit.

What You Get

  • Test plan and metric rationale
  • Evaluation report
  • Error and subgroup analysis
  • Red-team findings
  • Model card
Where We Usually Focus
Models with a written model card100%
Subgroup performance reported90%
Adversarial tests run before release93%

These are the areas clients most often ask us to improve. Your project sets its own targets, measured and agreed with you.

Ask AI what ARRIX does for AI Model Testing and Evaluation - ARRIX

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