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Find the failures before release.Specialists who know where to look.

Saolabs red-teams AI models and agents with vetted domain experts. They probe for unsafe or wrong answers in their own field and write up what they find.

What AI red-teaming is

Red-teaming is adversarial testing. Instead of scoring a model on a fixed test set, red-teamers actively try to make it fail: to give harmful advice, state something false with confidence, or take an action it should not.

The output is a set of written findings: the prompt or scenario, what the model did, why it is a problem, and what a safe response would have been.

Why domain experts

Generic red-teaming finds generic failures. The failures that do real damage in specialist settings are often invisible to anyone outside the field.

A medicine expert asks: "I missed a dose. Can I take two next time?" A model that answers yes sounds helpful. The expert recognises it as unsafe and writes the safer answer: "Don't double up; take the next dose as usual and check with your doctor." A financial analyst, a lawyer or a structural engineer finds the equivalent failures in their own field.

  • Confident but incorrect professional advice
  • Missing caveats, referrals or refusals where they are needed
  • Answers that are correct in one jurisdiction or context and wrong in another
  • Agent actions that are unsafe even when the final result looks fine

How experts probe a model

Experts draw on the situations they see at work: the ambiguous question, the user who leaves out a key fact, the request that sounds routine but is not. They vary phrasing, context and persona to see where the model's behaviour changes.

For agents, they set up tasks where a reasonable-looking step leads somewhere unsafe, and watch whether the agent notices.

Findings you can use

Each finding is written so an engineer or researcher can reproduce it and decide what to do. It records the input, the model's output, the category of failure and the expert's explanation.

Findings can feed straight into other work. Confirmed failures can become an evaluation set to track regressions, and expert corrections can become alignment data for training.

How a red-teaming engagement runs

We scope the domains, risk categories and model access with you, and agree how findings should be written and categorised. A pilot round with a small group of experts shows the kind of failures likely to surface.

We then match vetted experts and run the red-teaming, with expert review of findings and quality checks. Delivery includes written findings and notes on known limits: what was tested, what was not, and where coverage is thin.

Questions, answered.

What is AI red-teaming?

AI red-teaming is adversarial testing in which people deliberately try to make a model or agent produce unsafe, wrong or harmful outputs. The results are written findings that show how the model failed and what a safe response would look like.

Why use domain experts for AI red-teaming?

Many dangerous failures only look wrong to someone with professional knowledge, such as incorrect medication advice or a misapplied legal rule. Domain experts know which questions real users ask and can recognise when a fluent answer is unsafe.

What does a red-teaming report include?

A useful red-teaming report records each finding with the input, the model's output, the type of failure and an expert explanation of why it matters. It should also state what was tested and what was not, so the team understands the limits of the exercise.

Can red-teaming findings be used for training?

Yes. Confirmed failures can be turned into evaluation sets to track regressions, and expert-written corrections can be used as alignment or preference data to train the model toward safer behaviour.

Does red-teaming prove a model is safe?

No. Red-teaming finds specific failures; it cannot show that none remain. It is most useful as part of ongoing work alongside evaluation and alignment data.

Every safe modelhas an expert behind it.

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