Temporal identity
Actors disappear behind vehicles, re-enter the scene and change state across long sequences.
Automotive & autonomy
Annotation for perception systems that cannot afford vague ground truth.
Grasp turns camera, video, LiDAR and multimodal road data into model-ready supervision for automotive perception, ADAS and autonomous systems. We design the ontology, operate the team and review the edge cases with you.
Where programs get difficult
The hard part is rarely drawing the first label.
Sensor disagreement
Camera, LiDAR and other signals do not always align cleanly in time, space or confidence.
Ambiguous road behavior
Intent, right-of-way and unusual maneuvers need observable rules rather than intuition.
Rare critical cases
Low-frequency events need targeted discovery and review, not only uniform random sampling.
What we deliver
Ground truth built for the system around it.
- 2D and 3D object annotation
- Multi-frame tracks and identity continuity
- Lane, road and drivable-area segmentation
- Actor states, events and behavior attributes
- Camera–LiDAR alignment and sensor review
- Edge-case taxonomies and evaluation sets
Quality controls
- Calibration sets drawn from the real operating distribution
- Independent review for safety-relevant classes and boundaries
- Occlusion, truncation and identity-switch checks across time
- Specialist adjudication for ambiguous road interactions
- Traceable revisions and customer acceptance criteria
From ambiguity to production
Prove the workflow before adding volume.
Define
Tie the ontology and acceptance rules to the operating domain, model behavior and failure taxonomy.
Calibrate
Annotate a representative set containing ordinary scenes, ambiguous boundaries and difficult actors.
Operate
Run trained teams with temporal context, documented decisions and specialist escalation.
Accept
Review targeted samples, analyze disagreements and deliver traceable outputs in your pipeline format.
Start with the scenes your current pipeline finds hardest.
Bring a sample sequence, an existing ontology or a recurring perception failure. We’ll define a focused pilot that produces useful evidence before production volume begins.
Request a pilot plan