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Encord

AI · London, United Kingdom · Founded 2020

Encord is an AI-native data infrastructure platform for Physical AI, used by enterprises building robotics, autonomous vehicles, ADAS, humanoid robots, drones, and warehouse automation systems. The platform enables teams to curate, manage, annotate, and evaluate the multimodal data required to train and deploy real-world AI models, including video, 3D point clouds, sensor streams, LIDAR, telemetry, DICOM, and document data. Encord supports the full data lifecycle for Vision-Language-Action (VLA) models, foundation models, and embodied AI systems. The platform spans three core products: Encord Annotate for collaborative multimodal annotation, Encord Active for data curation and quality evaluation, and Encord Index for petabyte-scale dataset search and management. The company positions itself as the universal data layer for production-grade Physical AI, addressing the infrastructure gap left by cloud-era data tools designed for text and tabular workloads. Customers include enterprises across robotics, autonomous systems, healthcare, life sciences, and defense. Encord reports managing over five petabytes of multimodal data on behalf of customers, with tenfold revenue growth from Physical AI customers over the past year. The company operates a B2B enterprise sales model. Encord was founded in 2020 by Eric Landau (CEO) and Ulrik Stig Hansen (President), both of Imperial College London, and is headquartered in London with an office in San Francisco. The company has raised $110 million in total funding across Seed, Series A, Series B ($30M, August 2024, led by Next47), and Series C ($60M, February 2026, led by Wellington Management, with participation from Y Combinator, CRV, Next47, Crane Venture Partners, Harpoon Ventures, Bright Pixel Capital, and Isomer Capital). Keywords: AI data infrastructure, Physical AI, multimodal data, data curation, training data platform, computer vision, Vision-Language-Action models, VLA, 3D point cloud annotation, video annotation, sensor fusion, active learning, model evaluation, MLOps, robotics, humanoid robots, autonomous vehicles, ADAS, drones, warehouse automation, foundation models, embodied AI, data-centric AI, enterprise AI, multimodal AI.