Description
Encord provides the essential data infrastructure layer for Physical AI and enterprise teams, designed to handle multimodal data from pre-training through to deployment. The platform empowers teams to manage, curate, and annotate AI data with customizable workflows and native agent integrations, supporting a wide range of data types including video, image, audio, LiDAR, text, documents, geospatial, and HTML.
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Key capabilities include native video annotation, LiDAR, audio, text, and sensor fusion within a single workflow, all built with label lineage and quality controls for production-scale operations. Encord facilitates data collection from dedicated facilities or fleets, utilizing embedding-based search and model-in-the-loop curation to identify rare edge cases and close distribution gaps before they impact model performance in production.
Furthermore, Encord enables the orchestration of Reinforcement Learning from Human Feedback (RLHF), rubric-based evaluation, and pairwise comparison for models already in production. This allows teams to pinpoint AI failures and efficiently route them back into the training process. The platform offers a comprehensive, one-platform solution for the full data pipeline, including Data-as-a-Service for production-grade AI.
Encord also provides specialized services, such as annotation services with vetted domain experts for physical AI, multimodal systems, and LLMs, as well as collection services for training-ready data for physical AI, gathered by in-field operators and at teleoperation facilities. It positions itself as the sole end-to-end data infrastructure partner for physical AI, covering the entire lifecycle from collection to deployment feedback.
The platform is trusted by over 300 AI teams, including notable companies like Woven by Toyota, AXA, UiPath, and Zipline. UiPath, for instance, achieved near 99% model accuracy and a 4x reduction in error rate by using Encord for annotating and managing image and text training data, leading to a 10x dataset growth and enhanced pipeline visibility.
Encord is specifically built for Physical AI, providing data infrastructure for multimodal sensor information crucial for systems like World Models & VLA, Robotics & Humanoids, Autonomous Vehicles & ADAS, Drones & Aerial Autonomy, and Industrial & Manufacturing applications. It supports diverse data inputs such as RGB, depth, LiDAR, and force/torque, enabling the training of perception, manipulation, and embodied AI systems.
- CategoryMachine Learning Platforms
- Pricing ModelContact sales
- DeploymentData stays in your cloud
- PlatformsWeb
Encord's Core Features
Multimodal data management for physical AI
Data curation and annotation tools
Support for video, image, audio, LiDAR, text, and sensor fusion
Label lineage and quality controls
Embedding-based search for data collection
Model-in-the-loop curation
RLHF and rubric-based model evaluation
Pairwise comparison for production models
Data-as-a-Service offerings
Annotation and collection services
API/SDK-first approach
Zero data migration
Data stays in your cloud
How to use Encord?
Explore Product: Discover Encord's capabilities for your AI data needs.
Speak to an AI Expert: Consult with specialists to tailor solutions.
Configure Workflows: Set up customizable multimodal annotation and curation processes.
Collect and Curate Data: Utilize tools to gather and refine datasets.
Annotate and Label: Apply labels to various data types within integrated workflows.
Align and Evaluate Models: Orchestrate feedback loops and performance assessments.
Deploy and Iterate: Integrate Encord's data infrastructure into your AI lifecycle.
Encord's Use Cases
- Physical AI Data Management
- Autonomous Vehicle Perception
- Robotics and Embodied AI
- Drone Autonomy
- Industrial AI Training
- LLM Data Alignment
- Data Curation for Edge Cases
- Production Model Evaluation
