Most automation stops at the hard part. We start there.
Nomagic's Physical AI Platform masters the manipulation tasks conventional robots can't - running autonomously in live 24/7 operations. Reliability at that level isn't won on average-case performance; it's won by resolving the edge cases that only show up in production. We call this mastery-first approach: and it's the foundation of everything we build.
Intelligence learned to think. Now it has to learn to act in the physical world.
Software has automated the flow of information - how we plan, communicate, and decide. What it hasn't automated is doing - the physical work of moving, sorting, handling the real, messy world. Warehouses are where that gap is most addressable and most valuable to close first. They won't be where it ends.
Physical AI makes new use cases possible - automating the varied, dynamic, messy work that conventional systems could never touch.
Have a use case in mind?
Most of the industry is betting on generality-first: train one large model on as much as possible and expect it to handle whatever comes next. It's a reasonable bet on average performance, which is not good enough to automate warehouse processes. Autonomy is decided by the last few percent of situations: the rare, awkward, genuinely hard cases. A generalist model handles the easy majority and stalls on exactly those - and because it can't clear them on its own, it can't run unattended. The long tail is what stands between "impressive demo" and "actually autonomous."
So we go the other way: mastery-first. Go deep before going broad. Master the hardest cases in a real deployment until the system clears them without help, then carry that capability to the next task, and the next. Depth first, breadth as a result - not the reverse.
That's what turns into a flywheel. Every live deployment surfaces edge cases only production can reveal; those cases sharpen the models; sharper models earn more deployments - and the loop turns again. It's a lead that widens with every turn: the longer we run, the further ahead we get, because the advantage is built from real operational data no one else has.
Tens of millions of real picks across millions of SKUs - the Nomagic Library of Chaos, our growing catalogue of real-world edge cases feeding models in production.
The Physical AI Platform is how our technology reaches the floor. It brings together intelligence, robotic skill, and reliable operations - the three things it takes to run autonomously at scale
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We have deployed VLA models in production - moving this technology out of the research lab and into real, operating environments.
Why it matters
Traditional robots rely on rigid, hand-coded rules: brittle systems that break the moment conditions change. Our VLA-powered robots are trained to generalize, adapting to new objects, environments, and tasks without being reprogrammed for each one.
In production, we use VLA to resolve edge cases: the unpredictable, messy situations that trip up conventional automation - turning moments that once required human intervention into ones the robot handles on its own.
And because the model understands rather than just executes, it unlocks entirely new use cases that weren't possible before - tasks too varied, too dynamic, or too complex to hand-code in advance. That means faster deployment, fewer limitations, and robots that get more useful over time, not less.
Built for the real world
Warehouses are where we prove it first — bringing adaptable, intelligent automation to operations that used to demand constant human oversight. They won't be where it ends.
You're not buying a robot. You're buying a system that keeps getting smarter.
Every client who started with us is still with us. Read their stories:
