Physical AI · Autonomous systems & perception
Perception data, labelled by people who check their work.
LiDAR, bounding boxes and segmentation masks, labelled with human-in-the-loop quality checks at 98% accuracy.
Perception fails on what it was never shown.
Autonomous systems stumble on the cases their training data missed. Labels have to be accurate and consistent, and checked by people, before they reach a training run.
Data samples
Check our data samples.
Bounding boxes
First-person video
First-person video
Action labelsSupported data types
What we capture and label.
- LiDAR
- Camera frames
- Bounding boxes
- Segmentation masks
- Video
Proven in production
The standard every hour is held to.
- 1.8M
- Frames annotated in six months for one robotics lab
- 100%
- Of recorded hours reviewed by a person
- 98%
- Labelling accuracy with human-in-the-loop checks
- 4
- Consent languages: Arabic, English, Filipino and Urdu
Other physical AI verticals
What kind of data do you capture?
First-person video of real work: picking, packing and shelf stocking in warehouses, kitchen work, housekeeping, laundry and cleaning, and hospital portering. We also capture photographs of handwritten Arabic pages.
How is it recorded?
Hands-free, with a phone sideways on a head strap, the Roboband headband (up to eight synced cameras) or the wristband camera. Phone recording works on iPhone 11 to 17, Pixel 6 to 9, Galaxy S21 to S24 and more.
How do you handle consent and privacy?
Every worker agrees before recording, in Arabic, English, Filipino or Urdu. We never process content to identify anyone.
How do you check quality?
A person reviews every recorded hour. Only accepted hours are paid for and delivered, and labelling is checked with a human in the loop.
Bring real-world data to your autonomous systems & perception work.
Tell us what your robot needs to learn. We will scope the environments, tasks and labels with you.
