We screen and quality-check egocentric data used to train foundation models in embodied AI.
We translate project guidelines into clear review rubrics, examples, and escalation paths. Reviewers inspect first-person videos for camera placement, field of view, required environments, objects and interactions, task completion, duration, and technical usability. Every submission receives a consistent accept, reject, or escalation decision before it moves downstream.
Reviewer onboarding, calibration rounds, and layered audits keep decisions aligned. Recurring failure patterns are surfaced early so project guidelines and incoming data can improve.
Our expandable reviewer team has screened more than 25,000 hours in a month and achieved 3,000+ reviewed hours in a day. We can add trained reviewers as volumes grow while preserving the same project-specific workflow, calibration, and QA standard.
