Self-improving, self-governing multi-agent systems — with the guardrails, observability, and cost discipline that make autonomy something you can actually trust. Largely self-taught, built solo. Not prototypes — five separately-governed systems, live today.
Separately deployed, separately governed, with real users and real safety rails.
Every automation is priced against a role-matched human baseline. On well-scoped work, that's often a 1,000× cost advantage — and the system tracks it per task, so the claim is a number, not a slogan. See the systems & screens ›
Same engineering discipline, framed for what you care about.
Multi-agent systems that collect data, catch drift over long campaigns, schedule around blocked steps, and keep every autonomous action auditable and cost-bounded.
A productized build path — intake, scope, blueprint, delivery — that turns an idea into a shipped, governed application without agency overhead.
A self-hosted automation engine, a bandit model router, call-to-next-steps intelligence, an OSINT recon engine, and more — so your build starts 80% done.
I built all of this solo, largely self-taught. Whether it's a national-lab pitch, a client's SaaS, or a consumer app, the same backbone shows up: design it, govern it, ship it, repeat it.
~30 engines, 5 concurrent learning loops, a curated LLM gateway, a self-healing escalation bridge, and a nightly "Dream Cycle" that proposes its own improvements. Designed so a human works about 10 hours a week while the agents carry the load.
Full breakdown ›Every unit of work passes a contract, an expert review panel, and a hard gate before it touches production — not by reminder, by design. Behavioral discipline, ROI economics, and kill-switch rollback are built in.
How I gate production AI ›An autonomous system, a custom application, or just comparing notes — I'd welcome the conversation.