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. If light doesn't scatter or friction doesn't hold, a model learns the wrong world - ours doesn't, at a fraction of the cost of manual capture.
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.
Cosmos 3 thinks before it renders
At GTC Taipei, NVIDIA shipped Cosmos 3: a world model that reasons about a scene before it generates one. It folds the generate-and-render core of a synthetic-data pipeline into a single model. The validation half, the part that decides whether the data trains a production model or quietly breaks it, did not move.
NVIDIA opened the stack. The real game just started.
At GTC 2026, NVIDIA released Cosmos and the Physical AI Data Factory Blueprint under their Open Model License. The tooling fight is over. The operational fight, validation traces, sim-to-real proof, regulatory-grade lineage, is starting. Most companies are not ready for it.
Robots Are Shipping. Training Data Is Not.
Humanoids are deploying, $6B flowed into physical AI in Q1 2026, and the bottleneck has shifted from hardware to training data. Physics-accurate synthetic data is the binding constraint.
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.