Human-verified annotation for robotics perception systems. Built for teams deploying autonomous vehicles, industrial robots, and physical AI systems that operate in unstructured environments.
LiDAR, radar, camera data annotated in synchronized 3D space with timestamp alignment across all sensor modalities.
Edge-case testing, occlusion handling, and failure mode detection to ensure robust real-world performance.
Domain experts review annotations with automated consistency checks and audit trails for every label.
Export directly into training pipelines with standard formats (KITTI, nuScenes) and full lineage tracking.
Autonomy systems fail when training data doesn't reflect real-world edge cases
Synthetic data misses critical real-world scenarios: lighting variations, sensor noise, dynamic occlusions, and environmental unpredictability.
Perception stacks fuse LiDAR, camera, and radar. Misaligned timestamps or inconsistent labels break downstream planning and control modules.
Models deployed to robots fail silently. Without versioned labels and audit trails, you can't iterate on edge-case failures or retrain systematically.
From raw sensor logs to production-ready datasets
3D bounding boxes, semantic segmentation, object tracking across frames.
Synchronized annotation across LiDAR, camera, and radar streams with timestamp alignment.
Occlusions, adverse weather, sensor artifacts, truncated objects, and failure modes.
Frame-by-frame temporal consistency checks for moving objects and dynamic scenes.
Domain-specific labels: pick & place, grasping poses, obstacle trajectories, and more.
Multi-stage review by robotics engineers and perception specialists with audit trails.
From raw sensor logs to production-ready training data
Send sensor logs via API or S3 bucket. LiDAR .pcd files, ROS bags, camera streams — we handle standard formats.
Domain experts label in 3D space. Multi-stage QA, automated consistency checks, and feedback loops ensure precision.
Pull versioned datasets via API. Export directly to your training pipeline. Full lineage tracking included.
All data is versioned, audit-trailed, and exportable in standard formats (KITTI, nuScenes, custom JSON schemas).
Physical AI systems interact with the real world. Poor labels lead to unsafe behaviors.
We train annotators on your system's real-world failure modes. As your robot encounters edge cases in deployment, we refine labeling criteria and retrain the team.
We label in 3D point cloud space, not 2D image projections. Bounding boxes, semantic maps, and object tracks are natively aligned across all sensor modalities.
All annotations performed by domain experts trained on robotics perception requirements and edge-case scenarios specific to autonomous systems.
Every dataset undergoes peer review by robotics engineers plus automated consistency checks to ensure production-ready quality.
All annotators operate under strict NDAs with full audit trails and compliance frameworks suitable for enterprise deployment requirements.
GDPR-compliant workflows with data residency controls and industry-standard security protocols for sensitive robotics deployments.
Built for robotics teams that deploy in production, not research labs
Track every dataset version deployed to production. Roll back labels, A/B test model variants, and debug failures with full provenance.
Automate ingestion from your data collection pipelines. Export labeled batches directly into training workflows. No manual file transfers.
Custom labels for manipulation (grasp poses, contact points), navigation (traversability, obstacle trajectories), and industrial automation tasks.
Partner with us to build production-ready datasets for autonomous systems operating in the physical world.
Onboarding robotics and autonomy teams. API access rolling out Q2 2026.