Encord

Encord is a multimodal data layer designed for physical AI systems, intended for AI teams managing large-scale data pipelines. It supports the curation, annotation, and alignment of petabytes of data, including sensor streams, video, text, LiDAR, RGB-D, audio, and geospatial data. The product is built for organizations deploying physical AI, from initial training runs through real-world deployment, and is used by over 300 AI teams, including Woven by Toyota, AXA, UiPath, and Zipline. Key capabilities include embedding-based curation, label quality assurance, and lineage tracking to catch data errors before they compound. The platform integrates into CI/CD workflows through an API and SDK, allowing programmatic labeling job triggers and version control. It also combines annotation with alignment tasks such as reinforcement learning from human feedback (RLHF), rubric-based evaluation, and pairwise comparison within a single platform. Additionally, Encord offers end-to-end data services for collection, annotation, and evaluation for teams that lack the workforce to handle these tasks at scale, maintaining consistent quality controls across the pipeline.