Mostly

Generates statistically representative synthetic datasets that eliminate personally identifiable information while preserving underlying data patterns, correlations, and distributions. Uses machine learning to learn from original data and create new records that are realistic but entirely synthetic.

Analyzes and reports on how well synthetic data preserves statistical properties, distributions, and correlations from the original dataset. Provides metrics and visualizations to assess whether synthetic data is suitable for analytical workflows.

Generates synthetic versions of interconnected database tables while preserving relationships, foreign keys, and referential integrity. Handles complex data structures beyond flat files, maintaining the logical connections between tables.

Generates synthetic time-series data that preserves temporal patterns, trends, seasonality, and autocorrelations from original sequences. Maintains the temporal structure and dependencies that make time-series data useful for forecasting and trend analysis.

Transforms datasets to meet regulatory requirements (GDPR, HIPAA, CCPA) by generating synthetic data that eliminates PII and sensitive attributes. Provides compliance documentation and audit trails for regulatory submissions.

Allows users to test synthetic data generation on real datasets without upfront commitment through a freemium model. Provides transparent, volume-based pricing that scales with data size rather than surprise enterprise fees.

Evaluates and reports on whether synthetic data maintains sufficient analytical utility for intended use cases. Assesses whether statistical properties, patterns, and relationships needed for analysis are preserved in the synthetic version.

Facilitates secure sharing of production datasets with external vendors, partners, and service providers by generating synthetic versions that eliminate sensitive information while maintaining analytical value. Enables collaboration without exposing real customer or operational data.