Encord

Encord is a multimodal data infrastructure platform designed for Physical AI and enterprise teams, enabling them to manage, curate, annotate, and align massive datasets across diverse modalities—including video, images, LiDAR, audio, text, documents, geospatial data, and HTML. The platform serves as a central data layer that spans the entire AI lifecycle, from pre-training data preparation to post-training validation and deployment. Users can ingest petabytes of sensor streams, raw video footage, or unstructured text, then apply customizable workflows to clean, label, and structure the data for model training. The annotation tools support native video annotation with frame-by-frame precision, LiDAR point cloud labeling for autonomous driving and robotics, as well as audio transcription, text classification, and document parsing. Beyond manual annotation, Encord integrates with existing data pipelines and AI agents to automate parts of the curation process, reducing manual effort while maintaining quality control. For enterprise deployment, the platform provides data alignment capabilities to synchronize multi-sensor recordings (e.g., camera, radar, and GPS streams) so that models learn from coherent, timestamped inputs. It also includes versioning and collaboration features for teams to review annotations, resolve disagreements, and track changes over time. The infrastructure is built to handle high‑volume, real‑time data flows from production environments, allowing teams to continuously feed new data back into the training loop for iterative improvement. Visual analytics dashboards help users monitor annotation progress, dataset statistics, and model performance drift. Encord is used by over 300 AI teams, including those working on autonomous vehicles, robotics, medica