The data enginefor Physical AI
Physically grounded synthetic data from digital twins - photoreal, auto-labeled, and validated to transfer. Robots, vehicles, and vision systems learn the real world from our data before they ever touch it.
Generated, labeled, and validated. Ready to train.
Presented at & technology partners · NVIDIA Inception member
Train better models with data that doesn't exist yet
Photoreal synthetic imagery, auto-labeled and privacy-safe, delivered through a single API.
Data on demand
Generate thousands of labeled images in hours, not months - covering edge cases that real-world capture can't reach. Powered by physics-accurate simulation for training data that transfers to production.
Learn moreBuilt 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.
Learn morePrivacy-safe by default
No real people, no PII, no consent issues. Iterate freely on sensitive use cases without compliance bottlenecks slowing your release cycle.
Learn moreFaster iteration
Generate a new training set in minutes, not weeks. Remove data bottlenecks from your ML pipeline so you can test hypotheses and retrain the same day.
Learn moreFrom 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 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 pixel-perfect labels, on demand. Cover long-tail edge cases without a single real-world capture.
Learn moreValidate
Every dataset ships with evidence: realism, coverage, privacy, and distribution scores. Audit-ready lineage for regulated deployments, tracked across every iteration.
Learn moreSynthetic 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.
Autonomous Vehicles
Train perception models on every road condition, weather, and edge case imaginable.
Medical Imaging
Privacy-safe medical training data. No patient consent required, full regulatory compliance.
Object Detection
High-quality bounding boxes and segmentation masks across millions of synthetic objects.
Synthetic Imagery
Photoreal synthetic images with pixel-perfect annotations for any scenario.
Dataset Augmentation
Fill gaps in existing datasets. Boost underrepresented classes and edge cases.
From the datadoo team
Insights, research, and engineering deep-dives.
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.
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.
What SIGGRAPH 2026 means for synthetic training data
NVIDIA's SIGGRAPH 2026 program, read as a synthetic data release: a 4B world model with open weights running on workstation GPUs, and papers that automate scene reconstruction, materials, and motion. Generating data keeps getting cheaper. Knowing whether it transfers is still the expensive part.
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.