Thousands of labeled images
Data on demand
Generate thousands of labeled images covering edge cases that real-world capture can't reach. Powered by physics-accurate simulation for training data that transfers to production.
GTC Berlin · Oct 20–22 · Meet usThe data engine
The data engine
for Physical AI
We create the training data that doesn't exist yet: the cases you can't record, labeled by the renderer.
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In a pilot
We build the set around what your model gets wrong.You test it on real data you held back.
Presented at & technology partners · NVIDIA Inception member
Platform
Train better models with data that doesn't exist yet
Photoreal synthetic imagery, auto-labeled and privacy-safe, validated before it ships.
Physics-first
Built to transfer
Physics-first rendering and domain control close the sim-to-real gap. Light scatters, materials respond to force, and friction holds - so a model trained on our data learns the world it will actually be deployed into.
No real people in the data
Privacy-safe by default
No real people, no PII, no consent issues. Iterate freely on sensitive use cases without compliance bottlenecks slowing your release cycle.
1.4x faster iteration
Faster iteration
Generate a new training set without a new capture campaign. Remove data bottlenecks from your ML pipeline so you can test hypotheses and retrain the same day.
Physical AI
From synthetic data
to Physical AI
Synthetic data is our foundation. Digital Twins and Physical AI are where that expertise leads.
Synthetic Data
Photoreal, auto-labeled, privacy-safe training data generated at scale. This is what our team has been building for over a decade.
See the product→Digital Twins
Physics-accurate replicas of real-world environments, built in NVIDIA Omniverse. The foundation for every dataset we generate.
Learn more→Physical AI
Robots, autonomous vehicles, and industrial systems trained on data that obeys the laws of physics. The end goal of everything we build.
View solutions→Generate & Validate
Generate the world. Prove the transfer.
Generate the rare cases real data can't. Real-world edge cases are expensive, slow, and sometimes impossible to capture. Synthetic data removes that constraint.
Generate
Configure scenes as code. Produce high-fidelity synthetic imagery with exact labels from the renderer, on demand. Cover long-tail edge cases without staging them in the real world.
Learn moreValidate
Every dataset ships with evidence: realism, coverage, privacy, and distribution scores. Audit-ready lineage for regulated deployments, tracked across every iteration.
Learn moreSolutions
Synthetic data for production AI systems
From autonomous vehicles to medical imaging, datadoo generates the training data your models need.
Robotics & Physical AI
Synthetic environments for robot training. Physics-accurate scenes for sim-to-real transfer.
Learn more
Autonomous Vehicles
Train perception models on simulated road conditions, weather, and edge cases.
Learn more
Medical Imaging
Privacy-safe medical training data, generated without patient images.
Learn more
Object Detection
High-quality bounding boxes and segmentation masks, read from the renderer.
Learn more
Synthetic Imagery
Photoreal synthetic images with exact renderer annotations for the scenarios you define.
Learn more
Dataset Augmentation
Fill gaps in existing datasets. Boost underrepresented classes and edge cases.
Learn more
Blog
From the datadoo team
Insights, research, and engineering deep-dives.
CompanyOct 9, 20264 min read
We'll be at GTC Berlin, October 20–22
Part of our team will be at NVIDIA GTC in Berlin from October 20 to 22, attending sessions and taking meetings. The sessions on our list, sorted by stage of the synthetic data pipeline, and how to set up a meeting.
Read article
ResearchSep 17, 20267 min read
Notes on Skild S1 and NVIDIA's September data releases
Skild AI's S1 learns an unseen task from one video, roughly 380 teleoperated demonstrations saved by Skild's count. Notes on that release, NVIDIA's NuRec and robotaxi posts from the same weeks, and the quality-control ratio Skild published alongside.
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ResearchAug 17, 20265 min read
Cosmos, GR00T, Nemotron: the open Physical AI stack, read from the data side
NVIDIA now ships an open model for each layer of Physical AI: Cosmos 3 generates worlds, GR00T acts in them, Nemotron 3 runs the agents around them. A plain map of the stack as it stands in August 2026, and the data work all three layers still assume you bring.
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Now taking design partners
Building Physical AI?
We're taking a small number of design partners. Bring your hardest data problem - we'll scope a digital twin and prove transfer on your metric.