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DATA INFRASTRUCTURE FOR PHYSICAL AI

Data that teaches
machines to act.

We capture the real world in first person—hands, motion, intent and context—then turn it into training data your embodied models can use.

Start with a sample ↘
First-person data capture during robotic assembly
LIVE CAPTURE / 04BENGALURU · INDIA
CAPTURE SPEC03:28:12egocentric session
SENSORSRGB + IMU + AUDIOtime-synced at source
EGOCENTRIC VIDEO ✳ ACTION TRAJECTORIES ✳ MULTIMODAL ANNOTATION ✳ CONSENTED AT SCALE ✳

Every signal
behind the action.

Purpose-built collection programs that fill the gap between what your model can see and what it needs to do next.

01
✦

First-person capture

Natural human demonstrations captured across diverse settings, tasks, tools and environments.

Explore capture ↗
02
⌁

Multimodal sensing

Time-aligned RGB, stereo, IMU, audio and spatial signals—structured from the very first frame.

Explore sensors ↗
03
◫

Annotation layers

Action labels, language, objects, poses and trajectories, verified by people who understand the task.

Explore labels ↗

Built in the
messy real world.

Controlled labs give you clean scenes. We give you the long tail: unfamiliar objects, changing lighting, cluttered worktops, and the small decisions that make action intelligent.

12M+annotated frames
38task categories
7sensor streams
100%consent-cleared
[ 001 ]

Precision
assembly

Tools, fasteners, hand-offs

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[ 002 ]

Food &
kitchen work

Manipulation, sequencing, mess

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[ 003 ]

Retail
operations

Picking, scanning, sorting

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From brief to
benchmark.

03–06 WEEKS
TO FIRST DELIVERY
01

Map the missing

We translate model behavior into a precise capture and annotation specification.

02

Capture with intent

Our trained field teams record diverse, consented demonstrations in India.

03

Deliver ready to learn

Quality-controlled data lands in your stack with schemas, docs and provenance.

✦

Your next model
needs better experience.

Tell us where your agent gets stuck. We’ll show you what the right data could look like.

Let's talk ↗
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