The data layer for AI that moves through the physical world.

Physical AI breaks the data assumptions that text-only models were built on. Teams training robots, humanoids and autonomous systems hit a different wall:

• Real-world capture is hard to scale. You need synchronized multi-sensor rigs (RGB, depth, LIDAR, IMU, audio) operating across diverse, uncontrolled environments — not a clean lab.
• Spatial annotation is specialist work. 3D bounding boxes, point-cloud segmentation, 6-DoF pose, and frame-accurate event labeling demand trained annotators and calibrated tooling, not a generic crowd.
• Embodied context is cultural too. A delivery robot in Bengaluru, a humanoid in a Tamil-speaking home, or a teleoperated arm on an Indian factory floor encounters objects, gestures, signage and speech that Western datasets simply don't contain.
• Long-tail safety events are rare and precious. Edge cases — a cow on a highway, an unmarked speed bump, an ambiguous hand signal — are where models fail, and where careful human capture and labeling pays off most.

Physical-world capture raises consent, privacy and safety obligations beyond typical AI data work:

• DPDP Act 2023 — faces, voices, vehicle plates and location traces are personal data; we capture with consent, apply de-identification, and maintain processing records.
• Public-space capture protocols — documented field-ops SOPs, signage, and opt-out handling for bystanders.
• Sensor & site safety — risk assessments for teleoperation and on-site robotics capture; trained field crews.
• Provenance & licensing — every clip and scan is tagged with capture metadata, consent status, and usage license for clean downstream training.