Most robotics fails between prototype and product. Our four-stage pipeline is built specifically to survive that crossing — and the lifetime that follows it.
Identify task envelope, sensors required, failure modes, regulatory surface. Translate intent into hard engineering targets.
Domain-randomized simulation. Reinforcement and imitation learning. Compress thousands of physical hours into days of compute.
CAD to chassis. Electronics on the bench, software in the loop. Reality always wins — plan for it from day one.
Field trials, fleet rollout, lifecycle support. The robot's first day in the wild is the start, not the end.
Every engagement starts with a multi-day discovery sprint. We sit with the people who will eventually use, supervise, or repair the machine. We watch them work. We measure cycle times, identify edge cases, surface the failure modes nobody put in the spec.
The output is a one-page mission brief: what the robot must do, what it must never do, and how we'll know when it's working.
Before we cut metal, we build the world. Domain-randomized simulations let us run a million failure scenarios overnight. Reinforcement learning policies are validated against adversarial scenes long before they touch a real motor.
This phase is where we burn our risk early — cheaply, in parallel, in pixels.
The first physical prototype is intentionally ugly. We bias toward learning per dollar, not finish per dollar. Custom PCBs are spun in-house, mechanical parts are CNC'd or printed within 48 hours of design freeze, firmware is iterated daily.
The bench is wired to log everything. Every failure becomes a regression test.
We don't ship and disappear. Every deployment includes telemetry, OTA updates, an anomaly detection pipeline, and human supervisors trained alongside the robot. We treat the first 90 days in the field as Phase Zero of the next iteration.
Long-term, our customers get a roadmap, not a relic.
Open a channel with the lab. We'll scope a discovery sprint within the week.