The Warehouse World Model.
Pre-trained across real warehouse operations. Fine-tuned to yours.
Trained on camera footage from the warehouse, not from videos on the internet.
Today, many world models are learned from internet videos, data captured in a lab, or synthetic data with the claim that they can represent any world.
Claryo's World Model is trained on footage and data from live warehouse operations (CCTV footage fused with WMS transactions) grounded in your facility. It doesn't just know what a warehouse looks like. It knows how yours runs.
Similar to how language models learn by predicting the next word, our world model learns by forecasting how your floor evolves zone by zone, shift by shift.
Three steps from footage to foresight.
One model. Three tenses.
Replay the past
It preserves your facility's history, enabling you to query any moment, search for any pallet, and audit operational incidents with precision.
Mirror the present
Real-time data updates the model to keep your digital facility aligned with your physical operations: predicting backlogs, bottlenecks and work stoppages so you can execute with confidence.
Simulate the future
Plan every shift, run any what-if scenario, including volume spikes and floor layout changes, within a spatially accurate 3D model so you can rehearse and validate changes before they touch your production floor.
Beyond digital twins.
A digital twin is authored: it renders what someone modeled and scripts what it was told. The World Model is grounded: it picks up your floor's dynamics from the camera footage to answers what is there, how it behaves, what happens next.
How it's built
Pre-trained across real warehouse operations, fine-tuned to your facility.
Data capture
The CCTV is already on the ceiling. No rigs, no downtime.
Staying current
Continuously realigned by live footage: localized updates, no retraining.
Dynamics
Learned from live observations: people, pallets, and automation in motion.
Generativity
Fills blind spots, renders unseen views, generates plausible futures.
Robot training
Robots can be trained and evaluated inside it with a small sim-to-real gap.
How it's built
Authored by hand: CAD imports, asset libraries, expert modeling.
Data capture
CAD files and survey scans; live feeds wired in separately.
Staying current
Engineered sync pipelines; batch updates run hours behind.
Dynamics
Scripted on top of static assets. It can't learn what it wasn't told.
Generativity
Renders only what was modeled.
Robot training
Synthetic data with a large sim-to-real gap.
Where warehouse AI agents learn.
Claryo's AI agents practice inside the World Model: planning shifts, resolving bottlenecks, and allocating labor, equipment, and robots in simulation before they act in your facility. They grow more intelligent, from watching the operation to running it, at the level of autonomy you choose.