Why I built AEVRIS — the problem I kept running into

Every AI company I looked at was focused on making LLMs more capable. Almost nobody was focused on making them secure in production.

The more I dug into it, the more obvious the gap became. Input scanning existed — but if an attack got through, there was nothing on the other side. No output verification. No behavioral monitoring. No way to know if your LLM had been compromised until a user caught it.

I kept asking: who is watching what the LLM sends back? Who is worried about adversarial AI? The answer was nobody.

So I built AEVRIS. Five agents scanning every prompt before it reaches your LLM. Output alignment verification before the response reaches your user. AGI Guard monitoring behavioral drift at runtime. MCP tool inspection before any tool description enters your agent context.

Three industry firsts. One API. Patent pending.

If you're building with LLMs — what security questions keep you up at night? I built this for you and I want to know what matters most.