We help manufacturers automate dexterous tasks by recording how their best workers perform them — and using that data to train robots.
Our EMG sleeve captures physical information that cameras miss.
Backed by
- CombinatorY Combinator
Boost VC- Sterling Road
- Oxford Seed FundOxford Seed Fund
Cameras show what happened. EMG reveals how.
Video tells you where the hand moved. EMG gives another signal for what the human was physically doing to make that movement happen — muscle activation, inferred effort, the preparation before contact. We measure further upstream in the control signal than a glove, and the hand stays completely free.
Illustrative signal. Real data streams from the wearable sleeve on the worker's forearm.
How it works
Record the expert. Deploy the robot.
Your line keeps running. We record how the work is actually done — then train a robot to do it.
Record
Your best workers do the task wearing our EMG sleeve, on camera, with hand tracking. The sleeve leaves the hand completely unobstructed.
Build the dataset
EMG, video, and hand pose are aligned into a task-specific dataset — including the muscle activation and inferred effort cameras miss.
Train
Demonstrations aren't copied one-to-one. They're retargeted into the robot's action space to train a policy for your task.
Deploy
The robot runs on its own sensors — it never wears the sleeve. The EMG guides training, then drops away.
Every deployment produces physical-interaction data that exists nowhere else. That data makes the next deployment better.
Validation
In factories now.
A European manufacturer identified delicate, force-sensitive sewing as one of its hardest automation problems — and signed an LOI for us to support robotic implementation.
We're also on construction sites, recording electricians, plumbers, and demolition crews — completely different work, captured with the same sleeve.
We started with sewing because it's brutally hard for a robot: the fabric stretches, the grip changes constantly, and one clumsy move ruins the piece. Crack that, and easier tasks follow.
Who it's for
Built for tasks that resist automation.
If a robot integrator has already told you no, that's our starting point.
Sewing & deformable materials
Fabric, cables, gaskets — material that moves when you touch it and punishes a clumsy grip.
Delicate assembly & finishing
Insertions, fits, and surface work where inferred effort matters more than position.
Machine tending & tool use
Tasks built around how a skilled operator handles the tool, not just where it goes.
Construction & field work
Skilled manual work outside the cage — recorded where it actually happens.
For robotics and AI labs — generalised datasets of human physical interaction, recorded during real production work: EMG, video, and hand pose, aligned.
Get in touchWatch it work.
The sleeve decoding real muscle activity, as it happens.
Live demos as posted on LinkedIn.
The Team
Meet the founders.
Maxim Williams
CEO & Co-Founder
Oxford MBiol research — signal processing · EMG calibration paper in preparation (1st author)
Built with a hardware company from the ground up · Seen a unicorn scale from the early days
Arsh Patankar
CTO & Co-Founder
Oxford medic — research in machine learning and AI in cancer and stroke imaging
Published multiple papers · Repeat founder — medical LLM used at scale · Worked on Oxford's first quadruped robot
FAQ
Common questions.
Waitlist
Join the waitlist.
Manufacturers, robotics teams, researchers — or anyone who wants in early. Tell us what you're working on and we'll be in touch.
No spam. Early access and updates only.