We design autonomous systems — aerial and ground robots, and the embedded stack that runs them — using agentic AI across the whole design cycle, with formal verification as the evidence that the result is safe.
Building a new robot normally takes a large team — mechanical, electrical, RF, software, controls, safety, supply chain. We think a small team using agentic AI can do it, provided the design is verified rather than merely tested.
Speed only counts if the result holds up. We treat verification as the inner loop of design rather than a gate at the end, so each iteration produces evidence alongside the artifact.
Requirements and safety properties are written down formally at the start — the operating envelope, the failure conditions, what the system must never do.
Agentic AI works the full stack: board layout, mechanical CAD, firmware, control laws, and the integration between them — the disciplines that usually need separate teams.
Formal verification, reachability analysis, and model-based simulation check the candidate against the specification. What fails comes straight back into the next iteration.
Board layout, CAD, firmware, and control design are usually separate tools owned by separate specialists, and much of the schedule goes into moving work between them. We drive them as a single automated pipeline, so a design change propagates through the whole stack in one pass.
Modern autonomy runs on learned components, and a learned component cannot be assured by testing alone. Our foundation is neural network verification and the analysis of neural network control systems — establishing what a system can and cannot do across a whole range of conditions, not only the ones that were tried.
We are designing cyber-physical platforms — quadcopters among them — to be produced in North America, with the supply chain treated as a design constraint from the first iteration rather than a problem discovered at the end.
Platforms that are affordable to buy, safe to operate, and open enough to teach with.
Instrumented, reproducible testbeds for autonomy and controls work.
Trusted, domestically produced systems with assurance evidence built in.
Inspection, survey, and logistics platforms adapted to a specific job.