Train frontier robot models inside Claryo's Warehouse World Model.
Robot models are only as good as the data they are trained on.
The data wall is real
There's no web-scale corpus of physical work. Teleoperation hours are expensive, lab data is narrow, and the industrial long tail never shows up on the internet.
Generated video hallucination
Generative video models invent geometry the building doesn't have, and a robot trained on hallucinated physics learns the wrong world.
Simulators reality gap
Hand-built sims are fast or faithful, never both. Every layout change means weeks of scene work before training can resume.
How it works
FAQs
Claryo turns the cameras and systems already installed into a live model of the warehouse, with no new sensors. On top of it sit AI agents for operators, simulation and fleet monitoring for robotics vendors, and training data for frontier labs, always on the facility's terms.
The Claryo warehouse world model learns how a facility evolves and how actions change it: pretrained across real warehouse operations and fine-tuned to each building, so it can reconstruct the present, replay the past, and predict the future. The datasets, sensor streams generation models, and simulator all inherit that grounding.
Petabyte-scale: millions of square feet of live operations, on the order of 50 TB per facility per month. Video with depth, geometry, and object masks, fused with warehouse-system transactions and the real trajectories of people, pallets, and forklifts. Collection and use are governed by explicit consent agreements with each facility.
Real footage only covers shifts that happened, and the rare events that matter (congestion, spills, odd lighting, near-misses) are exactly what's scarce. Generation models built on the world model produce endless labeled variations of that long tail, conditioned on real geometry, for any layout and configuration.
It is learned: reconstructed from real buildings and driven by behavior learned from real traffic, so the long tail that breaks policies in CAD-clean worlds is present by default. It is also fast; policies train and evaluate against photorealistic sensor streams from any viewpoint at market-leading speed.
Pretrain on the grounded industrial datasets, multiply the long tail with generated scenarios, then benchmark against replayed real operations: the same floor, the same day, scored against what really happened. It runs inside your existing training loop, compatible with any robot stack.