Our expertise lies in developing advanced methods for synthetic data generation, including techniques based on generative models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). These methods ensure that the generated data maintains statistical properties and complexity like real-world datasets while free from sensitive information.
Synthetic data is instrumental in training AI models, testing systems, and enabling research in fields like healthcare, finance, aviation and energy, where access to real data is limited. By bridging the gap between data availability and data privacy, our work facilitates innovation while upholding ethical and legal standards.