Top 5 data annotation providers powering physical AI and embodied systems

The race to build physical AI is reshaping the entire AI infrastructure landscape. While generative AI was built primarily on text and language data, robotics and embodied systems require a fundamentally different type of data: a continuous stream of multimodal, real-world interaction data. From humanoid robots and autonomous vehicles to industrial automation systems, the next generation of AI depends on the ability to perceive, interpret, and act within physical environments — reliably and in real time.

This shift is creating a new suite of services focused on one of the most critical layers of the robotics stack: AI data operations. Annotation, sensor fusion validation, simulation workflows, egocentric datasets, and multimodal quality assurance are becoming essential components of training embodied systems. As a result, data infrastructure providers are increasingly positioning themselves as foundational partners for the future of physical AI.

Scale AI

Among the companies trying to become the foundation of the new physical AI cycle, Scale AI occupies a special place. Today, Scale AI is actively positioning itself as an infrastructure layer for robotics and autonomous systems.

The company works with multimodal data pipelines, which include:

• LiDAR and 3D point clouds;
• sensor fusion workflows;
• video annotation;
• robotics perception data;
• evaluation pipelines for autonomous systems.

That is, Scale AI is trying to take the position of “AWS for AI data pipelines”. If cloud platforms are once standardized on compute infrastructure, then Scale AI positions its data engine as an infrastructure layer for collecting, curating, annotating, training, and evaluating AI models, with dedicated expansion into Physical AI and robotics.

Humanoid AI models require a constant cycle:

• collection of real-world interaction data;
• annotation and validation;
• simulation training;
• model retraining;
• deployment in a physical environment.

Without a scalable data infrastructure, this process becomes too expensive and slow.

Keymakr

Keymakr is betting on a segment that is becoming increasingly important for physical AI - high-precision data annotation and complex QA workflows. For early CV models, annotation errors were often acceptable, whereas in robotics, small inaccuracies can affect system behavior in a physical environment.

The company is actively working with:

• 3D point cloud segmentation;
• robotics perception datasets;
• multimodal annotation;
• video sequence labeling;
• VLM and VLA workflows.

A separate role in Keymakr is played by the Keylabs platform, which provides its own platform for working with complex multimodal datasets and validation pipelines. In physical AI, the problem is no longer just to mark up data, but to provide a stable quality layer for models that work in the real world.

Keymakr pays close attention to QA processes, manual validation, and working with complex multimodal datasets. The company looks like a partner for teams that need consistent quality AI data, rather than just an annotation service. With the development of VLA models and physical AI, the quality layer is gradually becoming a key element in the entire robotics stack.

TELUS Digital

The company is actively developing the data operations direction for robotics, autonomous systems, and multimodal AI workflows. TELUS Digital's key bet is enterprise-scale infrastructure for the full cycle of working with AI data. The company focuses not only on annotation, but also on more complex tasks:

• pre-training and post-training datasets;
• simulation training workflows;
• human feedback pipelines;
• synthetic data operations;
• evaluation and validation systems.

For robotics companies, collecting real data is often too expensive or limited by physical risks, so more and more training is being conducted in synthetic environments and digital twins. TELUS Digital positions itself as an end-to-end AI training data provider for frontier AI, including multimodal, multi-agent, physical AI, and AGI use cases.

Another strength of the company is its scalable workforce operations. Physical AI requires much more complex human-in-the-loop processes than traditional NLP labeling. These include multimodal annotation, robotics interaction validation, quality assurance, and evaluation for VLA models.

Appen

Appen has long been one of the most well-known players in the machine learning data labeling market. The company built its business around datasets for NLP, search relevance, and computer vision.

Today, Appen explicitly lists Physical AI as one of its data product areas, including LiDAR annotation, robotics trajectories, sensor fusion, egocentric video annotation, and robot performance evaluation. These include complex workflows:

• RLHF-style evaluation;
• multimodal validation;
• egocentric video annotation;
• human interaction data;
• robotics training pipelines.

Physical AI requires huge amounts of human feedback and real-world interaction data, and therefore, distributed workforce models can become critical for scaling robotics training.

In addition, embodied AI is changing the very nature of data operations. Dynamic environments, video streams, and sensor-rich datasets are becoming increasingly important. This creates new requirements for quality control and continuous validation.

Encord

Encord is one of the new companies that are building infrastructure specifically for multimodal AI and robotics workflows. Encord relies more on the software layer for managing complex datasets than on workforce operations.

The company's main focus is on video-centric AI pipelines, namely:

• multimodal dataset management;
• active learning workflows;
• data orchestration;
• QA and validation systems;
• automation for annotation pipelines.

The company places a special emphasis on active learning - an approach in which the model itself determines which data requires additional verification or annotation. Robots and autonomous systems (for example, drones or humanoids) generate terabytes of video every day. However, much of this data can be repetitive or low-value, so Encord emphasizes data curation, intelligent filtering, active learning, and prioritizing the most informative samples for annotation.