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Rubrix

Independent validation of your AI systems, before and after go-live.

Discuss your AI systemSee the approach

01Why AI validation

AI fails differently from ordinary software.

That is why ordinary testing is not enough.

QuestionAnswers

02What we test

Four axes. One evidence-based conclusion.

  • A1CorrectnessDoes it do what it should?
  • A2RobustnessDoes it hold up against misleading input?
  • A3Data qualityIs the data complete, clean and representative?
  • A4Behaviour over timeWill it still be reliable tomorrow?
  • +Red teamingFor chatbots and language models.
More about AI validationTesting chatbots and language models

03Approach

Five steps. No guesswork.

01/ 05

Goals & risks

What must the system deliver?

02/ 05

Test scenarios

Repeatable, including edge cases.

03/ 05

Test the data

Quality, coverage, leakage, edge cases and representativeness.

04/ 05

Test the model

Quantify consistency, quality and failure patterns.

05/ 05

Conclusion & follow-up

Evidence-based, also after go-live.

04Result

No gut feeling. Evidence.

You get a repeatable dossier, not a one-off check.

01Test set
02Measurements
03Findings
04Risks
05Improvement actions

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05About

QA expertise, applied to AI.

20+

years of combined experience in analysis, testing and software quality.

06Contact

Make the reliability
of your AI measurable.

Book an exploratory call. Before or after go-live, even if your AI was built elsewhere.

contact@rubrix.be
Contact details and message