The Annotation Layer Behind Physical AI

Physical AI refers to AI systems like robots, autonomous vehicles, drones, and other embodied agents,  that perceive, reason, and act in the real world. Training these systems requires multimodal, sensor-synchronized datasets (image, video, LiDAR, radar, IMU, GPS, audio) rather than the single-modality data most annotation platforms are built for.

Image, video, LiDAR, radar, IMU, GPS, and audio, annotated across 2D and 3D data in one platform, with support for bounding boxes, polygons, point clouds, segmentation, keypoint and pose estimation, and object tracking.

Through synchronized multi-sensor annotation via cameras, LiDAR, radar, and other sensors are annotated in alignment, not in isolation. This includes time-series synchronization, calibration visualization, and multi-view annotation tools.

Yes,  SLAM and localization support, trajectory and robot pose labeling, manipulation and grasp-point annotation, obstacle and navigation-map annotation, and human-robot interaction labeling for systems that share space with people.

Manufacturing, warehousing and logistics, supply chain, surgical assistance, hospital operations, defense, precision farming, autonomous harvesting, construction, autonomous vehicles, autonomous mining, and urban maintenance.

Every project runs through a structured QC framework: Maker-Checker and Maker-Editor review stages, Consensus scoring, sample generation, and error-proofing, before data reaches your training pipeline.

The core annotation types overlap (bounding boxes, segmentation, keypoints), but Physical AI adds robotics-specific tooling like  SLAM, grasp-point, navigation-map, and human-robot interaction annotation plus multi-sensor synchronization, which standard CV workflows don't need.

Data connects directly from your own cloud storage rather than being transferred, with dedicated VPC options. Hosting is SOC 2, ISO 27001, and HITRUST compliant.