Adversarial testing and reproducible evaluation that find the failures average accuracy hides — before your users, or a regulator, do.
A model that scores well on a benchmark can still leak data, hallucinate with total confidence, or be jailbroken in minutes by a motivated user. Aggregate accuracy hides exactly the failures that matter most in production — the rare, adversarial and high-stakes cases where a wrong answer causes real harm.
Red-teaming exists to find those failures before your users do. Instead of measuring average performance on friendly inputs, it probes the model the way an adversary would: crafting prompts to extract private data, to bypass safety rules, to trigger bias, or to make the system confidently assert something false. What it finds is often surprising, and always more useful than another accuracy number.
Beyond safety, systematic evaluation is becoming a regulatory expectation. Structured, reproducible testing turns "it seems fine" into documented evidence you can put in front of a regulator, a board or an enterprise customer running their own due diligence.
We build an evaluation harness tailored to your system: a reproducible suite that runs the same battery of tests on every model or prompt change, so quality never silently regresses. Alongside it we run structured red-team exercises using adversarial prompt libraries, jailbreak techniques and bias probes, combined with human judgement where automated scoring falls short.
Every finding is severity-ranked and comes with a concrete reproduction and a remediation suggestion — not just "the model failed" but exactly how, when and what to do about it. The output is designed to slot directly into your development process and, where relevant, into EU AI Act conformity documentation.
A full adversarial review before a model or feature reaches real users.
Systematic attempts to bypass safety rules and prompt-injection defences.
Probing for unfair or discriminatory behaviour across groups.
Measuring how often, and how confidently, the model asserts false things.
Automated tests that catch quality drops on every model update.
Structured results that feed conformity documentation.
We agree what "safe enough" means for your system and which risks matter most.
We gather representative and adversarial inputs, including known jailbreak patterns.
We design the evaluation harness and the red-team plan.
We build the reproducible test suite and run the adversarial exercises.
We score findings by severity and validate them with human review.
We deliver the report, the reusable suite and remediation guidance.
We combine automated evaluation harnesses with human-in-the-loop review, because the failures that matter most are rarely the ones a script alone will catch.
A written report with reproducible tests, severity-ranked findings and concrete remediation steps you can act on immediately.
Yes. We evaluate models you build, buy or call via an API — the harness sits at the application layer.
Our evaluations are structured to feed directly into conformity documentation and human-oversight evidence.
On every material model or prompt change, and on a scheduled cadence for anything already in production.
Compact, efficient models that run at the edge — low latency, full privacy.
Privacy-safe training data at scale — augment, balance, and unblock ML.
Cooperating agents with tool-calling, handoff and MCP/A2A protocols.
Book a 30-minute call. We will tell you honestly whether we can help.
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