Artificial intelligence data infrastructure startup Encord said it has raised $60 million in a Series C funding round led by Wellington Management, as investors bet on growing demand for tools that help companies manage complex data for real-world AI applications.
The round included participation from existing investors Y Combinator, CRV, N47, and Crane Venture Partners, along with new backers Bright Pixel and Isomer Capital. The latest financing brings Encord’s total funding to $110 million.
The company said the new capital will be used to accelerate the development of what it describes as a universal, AI-native data layer designed to support the full lifecycle of production AI systems, from data preparation and curation to model evaluation and alignment.
The investment comes as AI applications increasingly move beyond text-based systems into physical environments such as autonomous vehicles, robotics, and drones, where performance depends heavily on the quality, scale, and management of multimodal data, including video, images, and sensor inputs.
Industry analysts say the shift from experimental AI models to large-scale commercial deployment is exposing weaknesses in traditional enterprise data infrastructure, which was largely built for general cloud storage and analytics rather than the specialized requirements of AI training and operations.
Encord’s platform focuses on helping companies organize, filter, label and monitor large volumes of data while integrating human feedback into model development.
The company said its infrastructure is designed to address operational risks such as data quality failures, model drift and the growing computational burden of processing multimodal datasets.
The startup said usage of its platform expanded rapidly over the past year, with managed data volumes increasing from about one petabyte to more than five petabytes.
Revenue from customers developing physical AI applications also grew tenfold over the same period.
Encord said its clients include companies working in autonomous systems, computer vision and synthetic media, including projects linked to automotive, robotics and simulation use cases.
The funding comes amid strong investor interest in the broader AI infrastructure stack, as companies building large-scale models and real-world AI systems seek specialized tools to manage increasingly complex data pipelines and governance requirements.
The round reflects a growing shift in AI investment toward the infrastructure layer, particularly tools that enable reliable deployment rather than model development alone.
As AI moves into safety-critical and operational environments, data quality, traceability and governance are becoming key bottlenecks.