One data infrastructure for the whole robot learning loop.

Why one platform

Most robotics teams stitch together a recorder, a labeling vendor, a spreadsheet of QA notes, and a separate evaluation harness. Each handoff loses context: the reviewer doesn't see why a label was chosen, the evaluation doesn't know which frames were disputed, and the next collection run repeats the same gaps.

Agentuor keeps that context in one place. Because the same agents observe collection, annotation, review, and evaluation, a failure discovered during evaluation can be traced back to the exact annotation guideline or collection scenario that caused it — and pushed forward as a recommendation the next time it appears.

Human oversight by design

Agents recommend; people decide. Every suggested label or workflow action ships with a confidence score and a provenance trail showing what evidence produced it. Reviewers can accept, edit, or reject in one click, and each decision feeds back into future recommendations for that dataset.

Delivery that fits your team

Run Agentuor as self-serve SaaS inside your own workspace, hand the execution to our managed services team, or combine the two — keeping sensitive or expert work in-house while bursting volume to us. Compare delivery models.