The Data Engine for Physical AI
MOVAS AI builds the data layer that embodied intelligence runs on — from first-person capture in live working environments through annotation, retargeting and evaluation.
Why We Exist
Model architecture in embodied AI has largely converged. What has not converged is data. The corpora that trained language models — scraped, abundant, effectively free — have no equivalent in the physical world. Contact dynamics, deformable materials, tool use and long-horizon procedure are not represented on the open web in any usable form.
MOVAS closes that gap by collecting where the work actually happens: kitchens, shop floors, storerooms, workshops. Every hour is captured head-mounted, with the sensor configuration downstream training requires rather than the one that looks best on a screen.
What Makes It Defensible
The corpus is built on employer-signed contributor agreements. That single structural choice is what allows commercial training rights on workplace footage — and it is not something a consumer crowdsourcing app can replicate, because it has no counterparty to sign with.
The second advantage is refinement. Raw footage is commodity; the value sits in what happens between capture and a trainable trajectory — hand pose, contact state, retargeting, physics validation. That pipeline is where MOVAS invests.
Why MOVAS
Employer-signed access
We contract with the operating entity of each site, then with each individual filmed. It is slower than a consumer app, and it is the only structure that makes workplace footage licensable for commercial training.
Captured for training, not for viewing
Stabilisation disabled at firmware level, IMU hardware-timestamped, quality gates enforced at ingest with thresholds published per delivery.
Refinement is the product
Raw footage is a commodity. Hand pose, per-joint contact, object 6-DoF, retargeting and physics validation are what turn an hour of video into something a policy can learn from.
We do not train models or build robots
We have no plans to build an embodiment or to compete with our customers’ models. We supply the data your policies train on, and the evidence that it worked.
By the Numbers
Operating Principles
Capture for training, not for viewing
Electronic stabilisation and distortion correction are disabled. Footage that looks smoother is footage that has had its ego-motion signal destroyed.
Publish the gates
Quality thresholds, inter-rater agreement and sync drift are published per delivery. Quality claims that cannot be checked are not claims.
Curation over volume
A targeted hour aimed at a known failure mode is worth more than a hundred generic ones. Scale matters, but only after relevance.