Track objects across frames, segment scenes, and classify footage frame-by-frame to create AI ready video data with high accelration rate
From multi-object tracking to temporal event boundaries, the data labelling layer follows objects across frames, segments scenes, and routes uncertain calls to right video propagation modalities, auto-labelling engines provide results with confidence, while those above threeshold are passed automatically others are directly sent to a human reviewer so nothing gets shipped unverified
Object tracking, video classification, instance segmentation, temporal segmentation, frame annotation, and pose tracking, all routed through the same Label layer.
Objects are detected and tracked across frames automatically. Identity switches and low-confidence frames are queued for review instead of shipping unverified.
Yes. Ontologies are configured per project, from flat tag lists to nested, multi-level classification schemas.
Every delivery includes annotator-level and task-level quality metrics, plus a full audit trail of who labeled or reviewed what, and when.
Labeled video data flows directly into Version for lineage tracking and Observability for production drift monitoring with no extra export intervention.