Image & video
- Bounding boxes and object tracks
- Semantic and instance segmentation
- Keypoints, pose and attributes
- Events, states and action boundaries
Built for real-world systems
Specialized programs for data that moves through time and space.
For teams building automotive perception, autonomous machines and embodied AI, we turn complex sensor streams into dependable training and evaluation data—designed around the decisions the system must make.
Automotive & autonomy
Camera, LiDAR and sensor-fusion ground truth
Perception, tracking, road semantics, behavior and difficult edge cases across time.
Explore serviceRobotics
Episode-level supervision for embodied systems
Manipulation, trajectories, state, action, contact, failure, recovery and outcome.
Explore servicePhysical AI
Multimodal data that preserves context
Connect vision, depth, point clouds, state, language, action and outcome.
Explore serviceSupported work
Broad managed coverage. Specialized depth in visual and Physical AI.
LiDAR, 3D & sensor fusion
- 3D cuboids and point segmentation
- Multi-frame object tracking
- Camera–LiDAR alignment
- Radar and multimodal sensor review
Robotics & Physical AI
- Episode and trajectory annotation
- Action, state and failure labeling
- Manipulation and human–object interaction
- Teleoperation quality and task outcome review
Text, audio & GenAI
- Classification and structured extraction
- Transcription and acoustic events
- Preference and rubric-based evaluation
- Safety review and failure taxonomy
Self-service annotation · Grasp Studio
Run annotation in-house without building the workflow from scratch.
Grasp Studio gives startups, research teams and lean ML groups a fast path from raw data to review-ready labels. Start with a focused project, invite your own team and use AI-assisted workflows to move through repetitive work faster.
Bring your own team Start with one project Scale into managed delivery
Already have access? Open Studio Studio workspaceYour team · Your workflow
01
Move faster with AI assistance
Use suggestions and assisted workflows where they remove repetitive work. Your team stays in control of every accepted label.
02
Keep annotation and review together
Define the work, assign it, resolve ambiguity and review progress without stitching together a fragile toolchain.
03
Work across your data program
Organize self-service projects across image, video, text, audio, LiDAR/3D and robotics episodes, with workflows matched to the data.
How we engage
Fit the operating model to the problem.
Pilot
Clarify the ontology, annotate a representative sample, measure disagreement and expose operational risk before committing to volume.
Production program
A managed annotation line with trained teams, live quality controls, change management and delivery matched to your cadence.
Recovery & quality audit
Diagnose inconsistent labels, ambiguous instructions or review bottlenecks; repair priority data and leave a stronger specification behind.
Quality architecture
Controls selected for the failure mode.
Consensus is useful for subjective judgments; specialist review matters for domain-heavy labels; targeted sampling matters when errors cluster. We select a defensible combination rather than applying one universal score.
- Representative calibration and gold sets
- Annotator qualification and monitored ramp-up
- Independent review and specialist escalation
- Agreement analysis and edge-case adjudication
- Traceable revisions and customer acceptance
What would make your next training run more useful?
Bring a rough brief, an existing ontology or simply the model failure you need to address. We'll help define the smallest useful first step.
Request your pilot plan