Automated validation, expert judgement, one queue.
Agents catch what rules can catch. Experts see only the cases that need a human call — with every piece of context attached. Every decision improves the next recommendation.
Quality in robotics datasets fails quietly: a slightly inconsistent boundary, a missed occlusion, an outcome label applied to the wrong episode. Agentuor runs continuous validation across schema, geometry, temporal consistency, and inter-annotator agreement, then uses agents to prioritize what a reviewer should look at first. Domain experts work from a ranked queue rather than a random sample, and their decisions are recorded as provenance the whole team can inspect.
Automated validation
Schema, geometry, temporal continuity, and agreement checks run on every submitted task.
Smart routing
Agents rank items by disagreement, ambiguity, and downstream impact so expert time goes where it matters.
Expert review workflows
Structured adjudication with side-by-side comparison, comments, and decision reasons.
Full audit trail
Who labeled, who reviewed, what changed, and why — exportable for compliance and model cards.
Failure-mode tagging
Reviewers tag recurring problems; agents cluster them into fixable classes.
Quality SLAs
Agreement, accuracy against gold sets, and turnaround tracked per batch and per contributor.
Step by step
Validate automatically
Every submission is checked and scored on arrival.
Rank for review
Agents order the queue by risk and ambiguity.
Adjudicate
Experts decide with context; decisions carry reasons.
Feed back
Decisions update recommendations and guideline clarifications.
Common questions
Can our own domain experts review?
Yes — in SaaS you bring your reviewers; in managed services we supply them; hybrid mixes both.
How is inter-annotator agreement measured?
Per label type and per contributor, with thresholds you configure and trends over time.
Can we export the audit trail?
Yes, in machine-readable form alongside the dataset.