Encord native multimodal data platform

The key wedge here is workflow compression, because physical AI teams break when sensor capture, synchronization, labeling, and recollection sit in different systems. A robotics team is not just labeling images. It is aligning video, depth, LiDAR, force, joint state, and actions into one training example, then sending operators back out to recollect the exact edge cases where the model failed. Encord is positioning around that closed loop, not around annotation alone.1 2 3 4

• For robotics and AV, native multimodal support matters because the training unit is a synchronized sequence, not a single file. Encord centers its product on multimodal sensor streams and action paired data, with support for video, LiDAR, audio, documents, and sensor fusion in one system.1 3 4
• Physical collection is the second half of the stack. Encord now markets in field operators, teleoperation facilities, and configurable lab environments, while Scale has also moved into robotics collection with contractor run demonstrations and a Universal Robots integration that captures force, robot, and vision data directly on deployed hardware.2 4 5 6
• This is why the comparison with Scale is concrete. Scale is much larger, with estimated $1.5B revenue in 2024 and a broad labeling business, but its robotics push arrived later as an expansion from general data operations. Encord is narrower, with an identity built around physical AI data infrastructure from the start.2 5 7

The market is moving toward vendors that own the full data loop for robots, from capture to curation to retraining. As physical AI deployments spread from labs into factories, warehouses, and vehicles, the winning platforms will look less like generic labeling tools and more like operating systems for collecting and fixing real world behavior data.2 4 5 6