Our Story

For the past few years, we've been building and securing AI systems in enterprise environments. We watched the industry pour billions into making AI faster, smarter, and more capable. We built some of those systems ourselves. But somewhere in the rush for capability, something critical was forgotten: the brakes.

There's a fundamental architecture gap in AI security. We have no airbags, no seatbelts, no crash tests for the most powerful technology being deployed in production systems right now.

The Real Problem

As regulations grow around AI, companies need to adapt. But it's not just compliance. The issue is deeper: as more freedom is given to AI—like access to documents and tools—the stochastic nature of AI makes it incredibly difficult to control, align to your interests, and keep secure.

We started testing this in the wild. We jailbroke the LLMs of banks and enterprises. We showed how prompt-injected documents could manipulate their systems. We exposed vulnerabilities in every state-of-the-art model.

But here's what struck us: when we looked at why some companies seemed more secure, we found something unsettling. They weren't actually secure—they were just incapable. They had such a limited set of functions their AI could perform that it was practically useless. The only way they achieved security was by removing all capability.

That's not a solution. That's a trap.

A Different Approach

What we're working on is different. Instead of choosing between capability and security, we're building both. Our goal is simple: an agent with the maximum amount of capability and the minimum amount of risk. We're building runtime defense architectures that verify every action, ensure provenance of every decision, and enforce least-privilege at the cryptographic level.

This isn't detection. This isn't a band-aid. This is prevention.