Data infrastructure for Physical AI development
The only end-to-end data partner for teams building robots, autonomous systems, and embodied AI. Collection, curation, annotation, and deployment feedback – all under one roof, with full traceability.
Physical AI needs a different set of tools
Complex training data for physical AI can't be downloaded. It has to be collected, frame-by-frame, in the kinds of environments an autonomous system will eventually work. Models are judged differently in the physical world, and tools built for image classification or text annotation are the wrong foundation. Encord is built from the ground up for this problem.
The four-step pipeline
End-to-end data development for physical AI
Built for physical AI data infrastructure
Bespoke data collection & QC
Dedicated in-field operators and lab facilities with reconfigurable sets, teleoperation arms and standardised hardware - so what gets collected in the real world is what you actually need.
Curate the data that matters
Your complete data layer, from capture to curation. Embedding-based search removes redundant episodes, while natural language filtering creates targeted subsets - helping you surface edge cases and trim datasets before they reach production. Supports image, video, LiDAR, PCD, audio and data from cloud buckets.
Native LiDAR & PCD annotation
Visualize and annotate LiDAR scans and point cloud data directly in one platform. Label 3D bounding boxes, segment scenes, and track objects across frames with sensor fusion across scenes, all on the native formats your sensors produce.
Deployment feedback, built in
Every model fails in the field eventually. We use human in the loop supervision and intervention to capture those failure modes through remote teleoperation - feeding them back into the pipeline and updating collection and annotation policies to make your model reliable in the real world.
Built for VLM and VLA workflows
VLA annotation requires an understanding of what a robot is doing and why - and what comes next. Working across video timelines, multiple label tracks, and schemas with complex reasoning, that’s a different problem from image classification. Encord is built for it.
Vision Language Action (VLA)
Train VLA models on structured observation-action data across diverse embodiments and tasks. Close the gap between lab demonstrations and real deployment conditions.
Vision Language Action (VLA)
Train VLA models on structured observation-action data across diverse embodiments and tasks. Close the gap between lab demonstrations and real deployment conditions.
Build datasets
From raw collection to versioned, export-ready datasets. Track ground truth against each model version, surface what's missing, and ship training data that reflects where your model is actually failing.
Action captioning
Generate structured, timestamped action labels from video demonstrations. Encode robot behavior — grasp type, motion primitives, contact states in formats models can train on directly, not just describe.
What this looks like in production
See how 300+ of the best AI teams use Encord
Enterprise-grade.
Built for scale.
Designed for reliable AI.
Built for scale.
Designed for reliable AI.
API/SDK-first. Zero data migration. Your data stays in your cloud.
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Design your
collection protocol
We start with your task definition, deployment environment, and hardware configuration. From there we design the collection protocol, pilot it at our facilities, and scale.