FLAGSHIP WEDGE
Wearable AI Data Network
Harbor leads in wearable training data because first-person capture delivers signal phone uploads cannot—egocentric POV, real failure modes, and rich metadata scored at source.
Why this data is different
- • Natural occlusion, reflection, and lighting variation
- • Egocentric hand-object interaction
- • Gaze, head pose, and spatial context
- • Real failure modes that matter for robotics and embodied AI
What you receive
- • Structured metadata + quality scoring
- • Self-annotated context from the person on site
- • Evaluation-ready packaging
- • Edge cases almost impossible to capture any other way
Who this is for
- • Robotics and embodied AI teams
- • Multimodal model trainers
- • Industrial inspection and field AI
- • Smart glasses and wearable AI product teams
- • Teams tired of generic, low-context training data