Software that replicates reality to test and train AI systems

Ensuring public safety while accelerating autonomy for auto, drones, robotics, agriculture, and security

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Synthetic data just got real

Testing AI in the real-world is hard, risky, and expensive

Parallel Domain offers an API for machine learning, computer vision, and perception teams to generate high-fidelity synthetic camera, lidar, and radar data.

This data is used to train and test perception models by simulating scenarios in procedurally generated worlds or replicas of any location on Earth.

We support perception use cases across industries

Synthetic data, real impact

Parallel Domain Data Lab offers the highest quality synthetic data needed to train and test perception models

Rich scenario diversity

Procedurally generated scenarios to easily “collect” synthetic sensor data at scale

Industry leading content fidelity

Curated and generative high quality content library with supporting a variety of regions, agents and environmental conditions

Highly accurate custom annotations

Pixel perfect annotations that can be customized to meet your workflow needs

Accurate sensor simulation

Select from a wide variety of common camera, LiDAR, and radar sensors, or tune your own configuration

Thanks to its high degree of realism, flexibility and scalability, the Parallel Domain platform enables rapid exploration of cutting-edge machine learning ideas. Its cost effectiveness enables accelerated paths to deployments at scale. This combination makes the platform really unique and a huge advantage for us to develop the future of robot autonomy.

Senior Machine Learning Manager, Toyota Research Institute

Adrien Gaidon

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