Beasley Labs: Governed AI agents that run inside your own AWS account

I'm David Beasley, founder of Beasley Labs. I spent 14+ years in Army intelligence and government-grade security and compliance. The whole time, I watched the most capable AI tools arrive — and watched the teams who'd benefit most get locked out of them. Not because they couldn't afford them, but because using them meant shipping sensitive data into someone else's cloud. The people guarding the most important data were the ones who couldn't touch the best tools. That gap stuck with me until I had to build for it. "We'd love to use AI, but compliance said no." For a bank, a hospital, a defense team — that's not caution, it's the law. The blocker is almost never the model; it's the data leaving the boundary it's required to stay inside. So I flipped it: with RAI (Regulated Agent Infrastructure), the agents run inside your own AWS account. Your data never leaves your control, every agent action is governed and logged, and promotion to production is a deliberate, reviewable act — never a silent deploy. One stamp, one agent, one boundary. Two big shifts. First, technically: I stopped thinking about "an AI app" and started thinking about infrastructure — the agent isn't the product, the governed boundary around it is. Second, in how I build: I'm solo, and I run the company on its own agents — built with AI, dogfooding the same governance I'm selling. That forced an honest test: if I won't trust an agent inside my own boundary, why would a regulated buyer? Living inside the constraint is what shaped the product. It's early and I'm building in the open. If "compliance said no" is a sentence you've heard at work — what would you need to see to trust an agent with your regulated data? Genuinely here for the feedback.