Image
Detection, segmentation, classification, keypoints and fine-grained attributes.
Managed annotation for automotive, robotics & AI
Grasp designs and runs custom annotation programs for automotive, robotics and AI teams—across camera, video, LiDAR, 3D and multimodal sensor data. Start with a focused pilot and scale with a workflow built for your system.

Capabilities
Detection, segmentation, classification, keypoints and fine-grained attributes.
Object tracks, events, states, actions and temporally precise boundaries.
Transcription, speaker turns, acoustic events, intent and quality review.
Classification, extraction, preference data, rubric-led evaluation and red teaming.
Cuboids, point-level segmentation, sensor fusion and multi-frame tracking.
Episodes, trajectories, actions, failure states and multimodal sensor alignment.
Self-service annotation · Grasp Studio
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.
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Use suggestions and assisted workflows where they remove repetitive work. Your team stays in control of every accepted label.
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Define the work, assign it, resolve ambiguity and review progress without stitching together a fragile toolchain.
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Organize self-service projects across image, video, text, audio, LiDAR/3D and robotics episodes, with workflows matched to the data.
A controlled path to production
We translate the model objective into an annotation spec, acceptance criteria, staffing plan and delivery format.
A representative calibration set exposes ambiguity early and gives both teams a concrete quality baseline.
A managed team works to the agreed ontology with documented decisions and active production oversight.
Independent review, consensus where it matters, targeted sampling and specialist escalation catch systematic errors.
Validated data, traceable review outcomes and acceptance support arrive in the format your pipeline needs.
Quality you can inspect
We design quality controls around the consequences of error in your use case. Ambiguity is surfaced, decisions are documented, and review effort is placed where it changes the outcome.
Talk through your quality targetAlign the team and clarify edge cases before volume production starts.
Separate annotation and review responsibilities for meaningful quality control.
Focus review on difficult classes, low-confidence work and known failure modes.
Quality is evaluated against your task, not reduced to a context-free headline number.
Retain task decisions, revisions and review outcomes for investigation and improvement.
Managed teams, fair-work requirements and accountable partner oversight.
Responsible annotation
Work stays with managed in-house teams and vetted partners, with named accountability instead of anonymous handoffs. Stable teams, paid training and clear ownership preserve domain knowledge, resolve edge cases faster and reduce rework—supporting predictable cost and throughput.
Our operating commitments
Managed services
We own scoping, workforce, operations, review and delivery for custom projects across all supported modalities.
Request a pilot planGrasp Studio
Bring your own team, work across modalities and move faster with AI-assisted annotation and collaborative review. Start small in Studio, then add managed capacity when you need it.
Explore self-service Studio