

Train and run AI
on the right data
The data infrastructure layer for Physical AI and Enterprise teams - multimodal by design, from pre and post-training to deployment.












Manage, curate, and annotate AI data
With customizable multimodal workflows and native agent integrations.

Label and annotate
Native video annotation, LiDAR, audio, text, and sensor fusion in the same workflow. Label lineage and quality controls built in for production scale.
Explore annotation & labeling
Label and annotate
Native video annotation, LiDAR, audio, text, and sensor fusion in the same workflow. Label lineage and quality controls built in for production scale.
Explore annotation & labeling
Curate and collect
Collect data from dedicated facilities or your fleet. Use embedding-based search and model-in-the-loop curation to find rare edge cases, and close distribution gaps before they break models in production.
Explore data collection & curation
Align and evaluate
Orchestrate RLHF, rubric-based evaluation, and pairwise comparison for models running in production. Find where your AI fails and route it back into training.
Explore model alignmentOne platform.
Full data pipeline.

Data services for production-grade AI

Annotation services
Vetted domain experts matched to your task, across physical AI, multimodal systems, and LLMs.
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Collection services
Training-ready data for physical AI, collected by in-field operators and at our teleoperation facilities.
Explore collection services
Physical AI data services
The only end-to-end data infrastructure partner for physical AI, from collection to deployment feedback.
Explore Physical AI data servicesOur Customers
Innovator Spotlight

“The reason we used Encord is because of flexibility and infrastructure.”
UiPath achieved near 99% model accuracy with Encord
UiPath uses Encord to annotate and manage training data across image and text - benefitting from genuine pipeline visibility, they’ve grown their dataset 10x and improved table extraction model accuracy.
10x
dataset growth
4x
reduction in error rate

“The reason we used Encord is because of flexibility and infrastructure.”
UiPath achieved near 99% model accuracy with Encord
UiPath uses Encord to annotate and manage training data across image and text - benefitting from genuine pipeline visibility, they’ve grown their dataset 10x and improved table extraction model accuracy.
10x
dataset growth
4x
reduction in error rate
Built for Physical AI
Data infrastructure for the multimodal sensor info Physical AI runs on




Trusted by Enterprise
Quality annotation, evaluation, and RLHF for the AI you ship to customers.



Proven at scale.
Designed for reliable AI.
Designed for reliable AI.
API/SDK-first. Zero data migration. Your data stays in your cloud.
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Get the data right
300+ of the best AI teams in the world use Encord. Join them.








