Encord Raises $60M for AI Data Infrastructure
Encord, a company specializing in AI-native data infrastructure, has secured $60 million in a Series C funding round led by Wellington Management. The funding, which brings the company's total to $110 million, is intended to scale its platform as physical AI applications reach an inflection point.
Founded in 2020 by Ulrik Stig Hansen and Eric Landau, Encord is headquartered in London with a significant presence in San Francisco. The company's platform is designed to manage, curate, and annotate the complex multimodal data—including images, video, and sensor data—that "physical AI" systems depend on. This focus on the data layer addresses a primary bottleneck in deploying AI, where the model's performance is fundamentally limited by the quality of its training data. The term "physical AI" refers to systems that perceive, reason about, and interact with the real world, powering applications like robotics, autonomous vehicles, and smart factory equipment. Encord's infrastructure is built to handle the petabyte-scale, varied data formats these systems generate, a task for which legacy data platforms were not designed. The company has seen the data volume on its platform grow to over 5 petabytes, more than triple the data used to train GPT-4. In the biopharmaceutical sector, this technology is critical for lab automation and the development of digital twins for bioprocess optimization. By creating high-quality, annotated datasets from instruments and manufacturing processes, companies can train AI models to identify process deviations, optimize yields, and ensure GMP compliance. This directly addresses major challenges in cell and gene therapy manufacturing, such as the lack of data standardization and the need for robust data management to support process analytics. Encord's platform finds application in scaling computer vision for robotic microsurgery and improving diagnostic processes in medical imaging. For instance, it allows surgical robotics firms to efficiently annotate large volumes of video data to train models that enhance surgical precision. In diagnostics, it helps create the high-quality datasets needed to train models for detecting abnormalities in MRIs, CT scans, and X-rays. The Series C funding was supported by existing investors including Y Combinator and CRV, alongside new investors Bright Pixel Capital and Isomer Capital. Lead investor Wellington Management has a history of backing AI leaders and focuses on late-stage companies poised to emerge as market leaders. This investment signals confidence that Encord is transitioning from a developer-focused tool to a scaled infrastructure partner essential for the broader adoption of physical AI.
