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Data layer for physical intelligence

Adapt models to new embodiments

Capture embodiment-specific demonstrations and edge cases so finetuning reflects the hardware, sensors, tasks, and constraints you are targeting.

Increase model accuracy with annotations

Use AI-assisted labels with human review to turn raw clips into trusted signals for actions, objects, contacts, failures, and task stages.

Scale up data collection

Coordinate collection across real and simulated environments, varied embodiments, and repeated scenarios while keeping review rules consistent.

Data collection and evaluation for physical AI

Collect, label, evaluate, and deliver the right data for your embodiment.

VideoImageTrajectoriesTeleopEgocentricSimulationEvaluation

Accelerate your robotics rollouts with curated data.

Quality

Robotics video labeling with review paths for actions, contacts, object states, task phases, and failure cases.

Cost Effective

Start with a focused pilot, validate the schema on a small set of episodes, then expand only once the labels are useful.

Scalable

Move from experiments into larger runs with human review, remote teleop, simulated teleop, and export pipelines.

Diverse Datasets

Build datasets from real robotics video, egocentric capture, teleop trajectories, simulated scenes, and asset variants.

Accelerate your data collection.

Get a tailor-made data pipeline for your AI embodiment.

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