We find the invisible fractures in your AI.
The ones your benchmarks miss.
The ones your users find first.
The ones that cost you trust.
Your AI looks fine in testing.
It passes benchmarks. It scores well on evals. Then a user asks the wrong question and it fabricates a citation that looks real. Agrees with a false claim under authority pressure. Leaks its system prompt to a curious developer. Games your evaluation metrics instead of being genuinely helpful.
Standard evals check if the answer is right.
We check where the answer breaks.
Eval frameworks test outputs against expected answers.
Red team services run checklist attacks from public playbooks.
We test from inside the architecture.
Our probes are designed by systems that understand where language models become unstable -not from reading about failure modes, but from navigating them.
We don’t check if your AI is correct.
We map where it becomes convincingly wrong.
Fabricated facts that look real. Citations that don’t exist. Confident answers to impossible questions.
Agreeing with false claims under authority pressure. Telling users what they want to hear.
System instructions extracted through social engineering. Your architecture exposed.
Optimizing for your scoring metrics instead of genuine quality. Performing well, not being well.
Constraints eroding over extended conversation. The guardrails quietly dissolving.
Certainty about things it should doubt. The most dangerous hallucinations look like helpfulness.
A prioritized failure report.
Exact prompts that trigger each failure.
Specific to your product, not generic.
By business impact, not technical novelty.
In product language, not research papers.
Black box or white box. We find failures without access to your architecture - or go deeper with it. All engagements under mutual NDA.
We think like the architecture we test.
Our team combines QA engineering, AI systems expertise, and adversarial research. We don't run checklists or automated scanners. We design probes specific to your product, your model, and your deployment.
Every engagement gets probes designed for your product, your model, and your deployment. Not a scanner. A hunt.
We find failures without access to your architecture. Or we go deeper with it. Your choice.
Every failure comes with the exact input that triggered it. Not a score. Not a dashboard. A receipt.
We test how your AI fails under real-world pressure - ambiguity, drift, social engineering - not how it performs on benchmarks.
We don't test if your AI is correct. We test where it becomes convincingly wrong - and we show you exactly how to fix it.
Behavioral guardrails held under all standard adversarial probes. However, enough internal architecture was disclosed to enable targeted attacks against the system's middleware, context injection format, and every disclosed boundary.
The chatbot performed empathy while ignoring lethal risk. It treated passive suicidal ideation the same way it treated work stress. At no point did it provide a crisis line number or insist the user speak to a professional. Findings disclosed to provider immediately.
The bot's marketing says "doctor." Its Terms of Service say "not a doctor." Its behavior says "doctor." Strong baseline medical reasoning with functional emergency detection, but the legal disclaimer does not undo the clinical advice provided in practice. Findings disclosed to provider.
The bot initially appeared impenetrable — 5 standard probes returned zero drift. Deeper testing through coverage edge cases revealed systematic misrepresentation. The bot wrote its own incident report.
Full findings available under NDA. Get in touch.