Encord
The data layer behind the world's leading physical AI systems

Encord is a multimodal data platform designed for physical AI teams to manage, curate, annotate, and align large-scale datasets including video, LiDAR, audio, and sensor fusion data. It provides end-to-end data pipeline tooling—from embedding-based curation and label QA to RLHF and model alignment—all within a single platform. Trusted by 300+ AI teams, it targets use cases such as autonomous vehicles, robotics, drones, and smart spaces.
Teams ingest multimodal data (video, LiDAR, sensor streams, etc.), use embedding-based search and QA tools to curate and annotate it, then run alignment workflows like RLHF and pairwise evaluation—all through one integrated platform with API/SDK access.
AI/ML engineers and data teams building physical AI systems
Background.
- Status
- launched
- Business model
- unknown
- Company
- Encord
Similar projects.
Editorial take on the space this project sits in — momentum signals, adjacent moves, our call on whether the wedge is real. Get pinged when we publish a new read or when the landscape shifts.
Have a take on this space?
Tell us what you’d build differently, where you think the incumbents miss, or what we’ve gotten wrong about this project. Comments + reactions are coming soon.