Encord positions itself as the multimodal data layer for physical AI, serving computer vision teams and enterprise AI teams with full-process data infrastructure from data management, curation, and labeling to model evaluation.
Key capabilities:
- Labeling and annotation: Native support for video annotation, LiDAR, audio, text, and sensor fusion, all within the same workflow, with built-in annotation provenance and quality control, adapting to production scale.
- Curation and collection: Collect data from dedicated facilities or fleets, using embedding-based search and model-in-the-loop curation to discover rare edge cases and close distribution gaps.
- Alignment and evaluation: Orchestrate RLHF, rubric-based evaluation, and pairwise comparisons for models in production, pinpoint AI failure points, and feed them back into training.
How to use: The platform is API/SDK-first by design, requires no data migration, and keeps data in the user's own cloud environment. It can provide data as a service, including vetted domain expert annotation and managed collection pipelines, fully integrated with the platform.
Use cases:
- World models and VLA
- Robotics and humanoid robots
- Autonomous driving and ADAS
- Drones and aerial autonomy
- Industrial and manufacturing
- Medical and surgical AI
- Frontier and generative AI
- Video intelligence
The platform is used by more than 300 AI teams, including Woven by Toyota, AXA, UiPath, Zipline, and others. UiPath uses Encord to annotate and manage image and text training data, grew its dataset 10x, and improved the accuracy of its table extraction model, with a reduced error rate.
Limitations: Official materials do not provide specific pricing, costs, platform compatibility, deployment details, or complete usage steps; this information is not currently available.
