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.
Physical AI · Synthetic Data
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.
No new capture campaign
Faster iteration
Generate a new training set without a new capture campaign. Change the scene, the conditions or the labels, and render it again.
Physical AI
From synthetic data
to Physical AI
A digital twin of your environment produces the data, and the data trains the system that works in it.
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
Check each dataset for realism, coverage and privacy before it reaches your pipeline, and keep the lineage of what was generated, from which scene, with which settings.
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.
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Autonomous Vehicles
Train perception models on simulated road conditions, weather, and edge cases.
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Medical Imaging
Privacy-safe medical training data, generated without patient images.
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Object Detection
High-quality bounding boxes and segmentation masks, read from the renderer.
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Synthetic Imagery
Photoreal synthetic images with exact renderer annotations for the scenarios you define.
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Dataset Augmentation
Fill gaps in existing datasets. Boost underrepresented classes and edge cases.
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Blog
From the datadoo team
Notes on Physical AI and synthetic data.
CompanyOct 23, 20269 min read
Notes from GTC Berlin 2026
We are back from GTC Berlin, and NVIDIA has started on the half of the pipeline it used to leave to everyone else: checking the result. Data Factory blueprints with glass defects, simulation checkpoints, Halos SAFE, and what they leave to the team that ships the model.
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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.
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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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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.