Turn any camera into an AI-powered camera: leveraging synthetic data can improve accuracy in facial recognition under a broad range of conditions, allowing for more robust, less biased detection and classification. Applications are vast, including home security, authentication for access control, retail automation and advanced object detection for drones and autonomous vehicles.

Physical objects can be reliably modeled by synthetic data, enabling robust and accurate object classification at a fraction of the time and cost needed to acquire and label real images. Retailers are already using these models to drive new customer experiences and in inventory management solutions, achieving up to 5% revenue growth (ECR).

Deep learning models together with synthetic data can improve the detection and diagnosis of disease, including more robust cancer detection in digital pathology and more accurate lesion detection in MRI. AI-powered medical imaging solutions also remove a major bottleneck in diagnostic workflow allowing for more effective and satisfying patient care.

Fully simulated environments have proven highly effective for quickly and efficiently training industrial robots at real world tasks, including the semantic separation of real world scenes. Synthetic datasets are able to overcome shortcomings related to limited data availability and data that is unrepresentative of the full range of potential environmental complexities.

Highly accurate graphical modeling of manufacturing environments and processes can facilitate rapid and cost effective creation of computer vision quality control systems, driving process improvement, higher quality goods, and manufacturing efficiency. Synthetic data has helped some manufacturers reduce waste by 20% and increase production by as much as 50%. (McKinsey & Company)