We build the opposite of a generator: an AI that proves its own work — and, in time, builds the next AI.
The wedge
AI can generate anything now — but it can't prove any of it right, and in science and engineering an answer that only looks right costs a tape-out, a launch window, or a life. Ours proves its work against the governing science and signs it — evidence you can replay and hand to a reviewer. Correct by construction, and the proof is yours to keep.
The engine
Give it a real problem and it does what an expert team would — in days, at a fraction of the cost and compute. It searches the whole landscape of prior work — even the knowledge that doesn't live in a repository — and stands on the best of it rather than starting from a blank page, verifies every step, and keeps a memory that compounds — sharper with every problem it solves, for everyone who uses it.
The colleague
You don't have to become an AI expert to use it. It carries the domain knowledge itself, so a scientist or engineer in any field reaches a proven result without a team of their own — and it works around the clock, while you're offline, keeping what it built alive as the libraries and the science around it change. You bring the problem; the expertise is built in.
On your terms
Real R&D runs for weeks, not one prompt — so it holds a project's memory across weeks, puts provenance on every result to keep it traceable and repeatable, and runs many specialists at once on a multidisciplinary problem. It works on your machines and your infrastructure, and what it learns and proves stays yours to keep — never locked inside a vendor.
The horizon
A specialist model for every field and every skill, software made to be run by agents, and autonomous systems that stand up other systems — in any domain. Frontier research, in any field, without a frontier lab of your own.
The bet
Every one of these is impossible unless the same system is both provably correct and radically economical — not one or the other, but both, together. That is the whole idea. It's also why this compounds instead of plateauing: each proven result makes the next one cheaper, faster, and more certain.
What we're building
Team
Co-Founder & CEO
PhD in engineering and a repeat founder — he has commercialized AI and computer vision for real-world engineering, and leads an AI infrastructure-monitoring company.
LinkedIn
Co-Founder & CTO
PhD in Computer Science and an AI Applied Scientist at Amazon, with published, patented work in robotics perception and computer vision.
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If your work can't afford to be wrong, we'd like to hear from you.