Company

Raw robot data in. Training-ready datasets out.

Robots now learn from data instead of code. We turn the raw data robotics teams collect into datasets they can train on, and show exactly what we checked and fixed.

Why now

Robots now learn from data

Manipulation, humanoids and autonomy have moved from hand-written behaviors to policies trained on demonstrations and rollouts. Data is now the product.

Collection outran cleaning

Teleop fleets, egocentric capture and simulation multiply the data every team produces. The tooling and people to clean, check and label it have not kept up.

Software alone isn't enough

Automated checks catch sync errors, gaps and frozen sensors. Whether a grasp succeeded, where a step starts and why an episode failed still takes trained human judgment.

What makes us different

Not a labeling vendor. A data pipeline that shows its work.

Robotics-native formats
LeRobot, HDF5, ROS 2 bags / MCAP and egocentric video in, the same format back out.
Software first, people verify
Automated checks do the first pass on every frame; trained reviewers verify every episode.
API-first
Create a job, upload, check status and download through an API, or point us at your bucket.
Guarantees in writing
Fixed turnaround, acceptance criteria agreed up front and free re-work if we miss.

Where we're going

Now
Pipeline with human verification
Cleaning, quality scoring, segmentation, language and failure labels for robot learning teams, starting with free pilots.
Next
Self-serve API
The API out of early access, with keys, webhooks and an OpenAPI reference anyone can build against.
Later
Shared quality standards
Published quality checks and reason codes, so datasets from different teams can be compared on the same terms.

Proof, in public