The people who build AI are the people who know how to test AI.

Traditional security knows how to test an application. It knows how to look for injection, broken authentication, privilege escalation, vulnerable libraries, exposed credentials. We do all of that, with evidence and step-by-step reproduction. The difference shows up in the next layer.

Companies everywhere have wired language models into their own systems, their own data and their own customers. That created a class of exposure that was on nobody's checklist: prompts that can be manipulated, context that can leak, models that start hallucinating after a vendor update, inference costs that grow with nobody measuring them. Testing that requires having built it.

That is where our origin stops being a detail. We are not a security company that learned AI later. We are an applied AI company that also does offensive security. Our teams build systems, integrations and agents that run in production every day, and that is why we know where they tend to break.

Our work does not end in a report. Leadership gets risk translated into business language, with priority and impact. Engineering gets reproduction, severity and actionable remediation. And the company gets a maturity path, so it stops reacting to audits and starts operating with predictability.