AI-native robots for coffee shops and dark kitchens
No complex installation. Our robots adapt to your existing environment.
When I was building a robot coffee shop in SF, I stood next to the people working every single day in restaurants and dark kitchens. It's grueling, repetitive work — eight hours on your feet, the same motion a thousand times over. I always thought: there has to be a better way.
The problem was never the will to automate. It was that the technology simply wasn't there. Robots couldn't handle soft packaging, unfamiliar items, and a constantly changing environment. New menu item, different cup, unexpected angle — and the whole system breaks. Brittle, expensive to reprogram, collecting dust.
Then foundational models came to robotics — and everything changed.
We're Oli Robotics. We build AI-native bimanual robots that actually work in production — in coffee shops and dark kitchens, on the equipment you already own.
Policy reliability limits how fast you can scale. Our deployment-first approach closes the gap between 90% in the lab and 99% in production.
90%
Lab
99%
Production
At Oli, we teach robots to learn from human demonstration. A worker shows the task once. The robot learns. Every deployment gets faster, every motion captured compounds into a better model.
We're not trying to replace people. We're trying to free them from the work nobody wants to do — so they can focus on the problems that actually need a human mind.
How it works
Demonstrate
Collect dexterous demonstrations on your actual equipment. Our HW-agnostic force-feedback gloves capture grip pressure, timing, and contact dynamics — no expert operator required.
Deploy
Deploy in your existing environment. No facility redesign. Oli runs on commercial equipment from day one, collecting distribution-native training data while generating economic value.
Improve
RL from real-world edge cases. Every failure in production becomes a training signal. Each deployment makes the entire network more reliable.
More robots deployed = more data = smarter models
Each real-world failure becomes a training signal. Each correction compounds. Your robot on day 30 is fundamentally better than on day 1 — and so is every other robot in the network.
Human demo
Worker shows the task
Robot deploys
Runs on real equipment
Edge case
Failure in production
Human correction
Operator intervenes
RL update
Model learns from signal
Robot improves
Better next deployment
Built for contact-rich food service tasks
If your operation runs a fixed menu in a constrained environment, we can automate it.