Building the data backbone for Physical AI.
We build large-scale egocentric, multimodal datasets that power robotics, wearable AI, and embodied intelligence — fast, secure, and at scale.
The Problem
AI is blind to the physical world
Current AI models understand text and images but fail at real-world task execution. Contextual reasoning in physical environments remains largely unsolved.
No structured data pipeline exists
Long-horizon physical manipulation lacks training data at scale. There is no structured egocentric data pipeline for embodied AI research and deployment.
The missing data layer
Just as internet-scale text enabled LLMs, real-world egocentric data will unlock general-purpose robotics, autonomous systems, and intelligent wearables.
Our Approach
From real world to AI-ready data.
Capture
Egocentric Data Collection
First-person video, audio & sensor capture at petabyte scale. 4K/60fps, 360° FOV, multi-camera, long-duration recording sessions.
Process
Multimodal Alignment
Synchronized video, audio, depth & sensor data unified in one pipeline. Spatial audio, LiDAR, IMU, and environmental sensors.
Annotate
Structured Task Labels
Hierarchical labels, long-horizon tasks, action segmentation, and intent classification. Hand-object interaction tracking and grasp analysis.
Deliver
Secure Infrastructure
End-to-end encryption, GDPR compliance, granular access controls, and audit logging. Petabyte-scale storage with distributed processing.
Our Solutions
Data infrastructure for the physical world.
Egocentric Video Capture
First-person video and sensor data collection at scale, designed for robotics research, wearable AI, and embodied intelligence applications.
Multimodal Datasets
Synchronized multi-stream datasets combining video, audio, depth, IMU, and environmental sensors in unified, research-ready formats.
Precision Annotation
Structured task-level annotations for hand-object interactions, action recognition, scene segmentation, and spatial understanding.
Careers
Build the future with us.
ML / Computer Vision Engineer
EngineeringIndia · Remote
Work on perception systems, annotation pipelines, and data quality infrastructure for Physical AI.
Data Infrastructure Engineer
EngineeringIndia · Remote
Build petabyte-scale data pipelines, storage systems, and delivery infrastructure for egocentric datasets.
Robotics Data Specialist
ResearchIndia · Remote
Design and execute egocentric data collection protocols for manipulation, navigation, and human-robot interaction.
Annotation Systems Lead
OperationsIndia · Remote
Lead annotation quality, build tooling, and scale our global annotation workforce for structured task labeling.
Don't see your role? Send us an open application →
Partnerships
Become an early partner.
The ChatGPT moment for robotics is near. Join us in building the data infrastructure that will power the next wave of intelligent machines.
From the Blog
Latest insights.
Can an Off-the-Shelf VLM Read First-Person Video? We Tried
We pointed Qwen2.5-VL at egocentric worker footage to see how far an off-the-shelf VLM gets you before you need anything custom. Here's the honest answer.
What It's Actually Like to Label Video Data All Day
A first-person account of what precision video annotation looks like at scale — and why it matters for Physical AI.
Embodied AI: Teaching Machines to Act in the Physical World
Embodied AI represents a fundamental shift — intelligence that isn't just computed, but experienced through real physical interaction with the world.