Why AI Synthesis
Build effective ML models
Gain confidence in your ML model training with more accurate synthetic data
Answer nuanced scientific questions
Perform analysis on data that provides proven degrees of accuracy and privacy.
Optimize your product and your business
Future-proof complex features and forecast scenarios to drive business decisions.
Achieve compliance and data governance
Meet the requirements of today and tomorrow’s data privacy laws with the most advanced protection processes available.
How we synthesize
Deep neural network generative models
Capture and reproduce complex relationships throughout your data with a high degree of statistical integrity thanks to Variational Autoencoders. Create rich synthetic data by approximating the multivariate joint probability distribution of real-world information.
Work across continuous, categorical, and location data
One generator to rule all the variables: meet AI Synthesizer. Preserve the distributions of continuous variables, the category frequencies of categorical variables, the unique realism required by longitude and latitude pairings, and the correlations across them all.
Time series data over interdependent rows
Synthesize row-to-row relationships to mimic your most nuanced datasets. Unlock analysis to extract meaningful conclusions and perform forecasting to predict future values and support business decisions.
Extensive database coverage
Connect natively to all the leading SQL and NoSQL databases for realistic, privacy-preserving synthesis no matter where your data lives. Our algorithms are database agnostic, and their output feeds directly into your Jupyter notebook.