Encord
The multimodal data layer for physical AI. Manage, curate, annotate, and align petabytes of data - from sensor streams to video to text.
About Encord
The multimodal data layer for physical AI. Manage, curate, annotate, and align petabytes of data - from sensor streams to video to text.
Products
Multimodal data infrastructure layer for Physical AI and Enterprise teams — manage, curate, annotate, and align AI data across video, image, audio, LiDAR, text, document, geospa...
Native video annotation, LiDAR, audio, text, and sensor fusion in the same workflow.
Orchestrate RLHF, rubric-based evaluation, and pairwise comparison for models running in production.
Collect data from dedicated facilities or your fleet.
Vetted domain experts matched to your task, across physical AI, multimodal systems, and LLMs.
Platform product listed within Encord's platform menu for AI data management workflows.
Training-ready data for physical AI, collected by in-field operators and at our teleoperation facilities.
The only end-to-end data infrastructure partner for physical AI, from collection to deployment feedback.
One platform. Full data pipeline. The data infrastructure layer for Physical AI and Enterprise teams - multimodal by design, from pre and post-training to deployment.
What customers say
“With other labeling tools, we needed to integrate another tool for data management and exploration capabilities, but Encord combined the two needs and provided a single comprehensive solution, along with excellent customer care and support,”
“We connect Encord Active with our large dataset and then use metrics to prioritize building a collection of images. This collection is then sent to Encord Annotate for labeling images in preparation for training. And all this without the data leaving our server.”
“…as we started growing and adding more customers and more people using the tool there were certainly a bunch of challenges that came in, like our previous OS labeling tool kept running out of disk, so we had to start doing maintenance ourselves. We had to start editing the code and diverging from the main branch, which we really didn’t want to do…because we wanted to focus on our product.”
- Annotation and Labeling
Archetype AI uses Encord's video annotation tools, tracking, bulk operations and SDK-based data pipelines to develop its Physical AI model. Encord reports a 70% increase in overall productivity and doubled annotation…
Case study - Annotation and Labeling
Hudl uses Encord for sports-video annotation and data management, integrating its own pre-labeling models and a Validation Task Agent. Encord reports tenfold faster tracking annotation and a 40% reduction in ice hockey…
Case study - Encord Platform
King's College London researchers used Encord's tracking and model-assisted annotation for endoscopy videos. Encord reports annotation 6.4 times faster than CVAT and a sixteenfold labeling-efficiency improvement for the…
Case study
Security and compliance
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- API/SDK-first architecture
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