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.