Building the Data Infrastructure for Physical AI

Large language models trained on internet text. Image models trained on web-scraped photos. But robots that need to pick up a coffee mug, navigate a warehouse, or fold laundry cannot learn from internet data. They need egocentric video of real humans performing real tasks in real environments — captured with depth, pose, and action labels at millisecond precision.

This data does not exist on the internet. It cannot be synthesized in simulation without a crippling domain gap. Benchmarks like Ego4D and Open X-Embodiment have demonstrated the value of large-scale real-world datasets, but every frontier robotics lab we have spoken to cites the same bottleneck: not compute, not algorithms — data.

Claru closes that gap. We operate the capture infrastructure, enrichment pipelines, and annotation workforce to deliver training-ready datasets for robotics and physical AI — from brief to first delivery in days, not months.

Our core engineering and research team includes ex-FAANG engineers and researchers with deep expertise in computer vision, robotics, and AI infrastructure. We have built large-scale data pipelines, trained production ML models, and shipped AI products used by millions. Our enrichment pipeline leverages foundation models including Depth Anything V2, ViTPose, and SAM3 to produce embodied AI datasets with six enrichment layers on every clip.

Beyond the core team, Claru operates a global data collection network: 10,000+ trained collectors across 100+ cities on 6 continents. Every collector is equipped with wearable cameras and follows structured capture protocols designed for each project. This network gives us the geographic and environmental diversity that physical AI models need to generalize beyond controlled lab settings.