Syntho

By generating synthetic data that mirrors the structure, relationships, and distributions of production data, Syntho enables development and data science teams to work with realistic datasets without the privacy, compliance, and security risks of using real customer data.

The platform's data synthesis engine preserves the statistical relationships between variables in the source data ensuring that synthetic records reflect realistic correlations, distributions, and edge cases rather than random or obviously artificial patterns.

Quality assessment tools measure how closely the synthetic data matches the statistical properties of the real data, giving data teams confidence that models and systems trained or tested on synthetic data will behave correctly in production.

Syntho is used by financial institutions and healthcare organizations where regulatory compliance prevents broad use of real customer data in development environments, by AI teams that need large training datasets that don't exist in sufficient volume in the real world, and by software testing teams that need realistic data for QA without exposing production data to the development environment.

Its combination of synthesis quality, privacy guarantees, and compliance documentation makes it a practical solution for organizations where data privacy and AI development requirements intersect.