The success of deep learning, a way to approach machine learning, has brought an insatiable hunger for data. However, data at scale is often proprietary, expensive, and laborious for people to manually prepare.  The best way to deal with these challenges is with synthetic data — data created in virtual worlds rather than collected from the real world.  

Our Services

We partner with businesses across industries to support their unique training data needs.

DIVERSITY

We offer diverse images and scenarios to help algorithms generalize well and reduce bias.  

Photorealistic objects, difficult to reach places such as underwater locations, hard to replicate scenarios like extreme weather, varying perspectives from top down to bottoms up, costly real world tests like crashes all become easily simulated in virtual worlds.

ACCURACY

We provide fully annotated synthetic data that can be used to immediately train machine learning algorithms.  

Annotations such as 2D and 3D bounding boxes, semantic and instance level segmentations, surface normals, depth masks, edge masks, velocity annotations are all automatically generated in real time.