Synthetic Data

Synthetic data is artificially generated data that mimics the statistical properties of real-world data without containing actual personal information, enabling AI model training while preserving privacy, augmenting limited datasets, and creating training data for rare scenarios. The sector addresses one of AI's fundamental bottlenecks: the availability of high-quality, diverse, labeled training data. Real-world data is expensive to collect, difficult to label accurately, and increasingly constrained by privacy regulations (GDPR, CCPA). Companies like Gretel.ai, Mostly AI, Tonic.ai, and Synthesis AI generate synthetic data for tabular datasets, images, video, and text that enables AI development without the privacy, cost, and availability constraints of real data. The autonomous vehicle industry has been a major adopter, using synthetic driving scenarios (NVIDIA Omniverse, Cognata) to train perception systems on billions of edge cases that real-world testing would take decades to encounter.