Encord Announces $60 Million in Series C Funding

Encord, the data layer behind the world's leading AI, led by Eric Landau and Ulrik Stig Hansen, has raised $60 million in Series C funding led by Wellington Management to scale its AI-native data infrastructure as physical AI hits an inflection point. The round also included participation from existing investors Y Combinator, CRV, N47, and Crane Venture Partners, and new investors Bright Pixel and Isomer Capital, bringing the total funding to $110 Million.

In the coming years, AI will shape nearly every aspect of daily life, placing model reliability under increasing scrutiny. Encord’s technology ensures models are continuously trained and operated on high-quality data at scale, improving performance and dependability over time.

AI Is Entering the Physical World

AI is moving beyond chat interfaces into real-world environments, where system reliability depends entirely on the quality of underlying data. While recent progress has been driven by text-based models trained on massive internet datasets, physical AI, such as autonomous vehicles, drones, and robotics, demands far more than scale alone.

These systems rely on vast, multimodal data that must be captured, structured, and continuously refined across the model lifecycle, often in real time. As companies transition from prototype to production, new challenges emerge: data governance gaps, infrastructure strain, the complexity of multimodal processing, and the need to integrate human oversight into workflows.

As a result, many teams encounter critical data quality issues just as they approach deployment, making robust data pipelines and validation essential for real-world AI performance.

Legacy Data Infrastructure Can’t Keep Up

Traditional cloud-based data systems were not designed for AI workflows, leaving companies unable to effectively manage, curate, and align the data required for modern models. While these systems may suffice during prototyping, they often break down at production scale.

As AI, especially physical AI, moves toward real-world deployment, a new data infrastructure is required to support reliable performance at scale.

Encord addresses this with an AI-native universal data layer that supports the full data lifecycle, from pre-training to post-deployment alignment.

The platform enables precise data curation, unified data management, and robust annotation and alignment, ensuring models are trained on the right data and continuously improved through feedback.

Building the Missing Data Infrastructure for the Future

As AI moves into the physical world, the scale and complexity of real-world conditions, from outages to unpredictable environments, will demand far more robust data systems. At the same time, AI adoption is becoming essential across industries, not just within tech-native companies.

Work with leaders like Woven by Toyota, Skydio, and Synthesia highlights a clear trend: exponential growth in data and compute requirements, driving the need for purpose-built, AI-native infrastructure. This is already reflected in rapid platform growth, with data volumes scaling from 1 to over 5 petabytes and strong expansion among physical AI customers.

In this new landscape, a universal data layer becomes foundational, enabling companies to manage, curate, annotate, and align data at scale. The goal is to provide that missing layer and unlock AI capabilities that were previously out of reach.