Encord Raises $60M for 'Physical AI'
London-based Encord just raised $60 million in a Series C round to build data infrastructure for "physical AI." The platform helps companies manage complex, real-world data from sensors and cameras, positioning itself as a key player for AI models tied to spatial data in fields like robotics and logistics.
The latest $60 million funding round was led by Wellington Management, with notable participation from existing investors like Y Combinator and CRV, as well as new investors Bright Pixel Capital and Isomer Capital. This Series C investment brings Encord's total funding to $110 million and boosts its valuation to $550 million. Founded in 2020 by co-CEOs Ulrik Stig Hansen and Eric Landau, Encord aims to solve a critical bottleneck in AI development: the time-consuming process of preparing and managing high-quality data. Hansen and Landau observed that machine learning teams were spending upwards of 80% of their time on data-related tasks rather than on model development itself. Encord's platform is designed for the complexities of "physical AI," which involves models that interact with the real world through sensors, cameras, and robotics. This requires managing diverse and complex datasets, including video, audio, LiDAR, and telemetry, which traditional data platforms often struggle to handle. The company has seen revenue from its physical AI customers grow tenfold in the past year. Key clients already leveraging Encord's data infrastructure include Woven by Toyota, aerial logistics company Zipline, and drone manufacturer Skydio. The platform is also used by over 300 AI teams in various sectors, including healthcare, with clients like Philips and Cedars-Sinai. In the sports technology sector, a key area of interest for location-based applications, Encord's platform is used to analyze game footage. It helps computer vision models detect key events like goals or free kicks and track player positions, allowing coaches and players to review critical moments and improve performance. The company faces competition from other data-labeling and infrastructure providers like Scale AI, Labelbox, and SuperAnnotate. However, Encord differentiates itself by focusing specifically on the multimodal, sensor-heavy data characteristic of physical AI and by offering a unified platform that covers the entire data lifecycle.
