capturGO delivers high-quality, consented egocentric manipulation datasets for training and evaluating embodied AI systems. Mobile-first capture, standardized protocols, human-in-the-loop QA.
Collect, QA, annotate, deliver. Every cycle widens task coverage and sharpens the protocol behind the next one.
Contributors capture egocentric video against a versioned task taxonomy, following a standardized protocol for camera placement, lighting and task setup.
Every submission is scored against measurable acceptance criteria. Footage that misses the threshold never reaches a dataset.
Action labels, task context and environment metadata applied frame-accurately across the clip by trained annotators.
Structured, embodiment-agnostic datasets shipped in your format, ready for post-training and evaluation.
Datasets are built against a structured catalog of manipulation tasks, then deliberately varied until the long tail is represented.
Restock or return goods to their designated display or storage location.
Select and categorize items by order, category or destination.
Packaging, boxing, sealing, unsealing and dismantling.
Ingredient prep, cutting, cooking, beverage making and plating.
Table setting, serving, clearing and tableware circulation.
Cleaning commercial environments, equipment and facilities.
Combining raw materials, parts and components into finished products.
Troubleshooting, repairing, adjusting and maintaining equipment.
Documents, vouchers, data entry, scanning and office affairs.
New categories go live as customer campaigns come online.

Low-cost, standardized egocentric collection without specialized robotics hardware. The device a contributor already owns becomes the rig.
We propose a scoped pilot to validate collection quality, task coverage, annotation requirements and unit economics before scaling volume. Our distributed model gives cost-efficient access to diverse real-world environments while holding one standardized protocol and QA bar.
Every clip carries a record of who collected it, under what agreement, and for what permitted use.
Contributors accept terms before recording. Bystanders and identifying details are blurred automatically at the edge.
Datasets ship with manifests your legal and compliance teams can review before a single frame is used.