Gretel.ai

Generates realistic synthetic datasets from original tabular data while preserving statistical properties, distributions, and relationships between columns. The synthetic data maintains the utility of the original dataset for model training and testing without exposing sensitive information.

Applies differential privacy guarantees to synthetic data generation, allowing users to control the privacy-utility tradeoff through epsilon values. This ensures mathematically provable privacy protection against membership inference and other attacks.

Processes large volumes of data in batch mode to generate synthetic datasets at scale. Optimized for enterprise-scale data generation with support for distributed processing and scheduled generation jobs.

Automatically identifies and appropriately handles sensitive columns (PII, PHI, financial data) during synthetic data generation. Applies targeted privacy protections to sensitive fields while preserving utility in non-sensitive columns.

Provides REST API endpoints for programmatic access to synthetic data generation, enabling integration with data pipelines, applications, and workflows. Supports on-demand generation and streaming of synthetic records.

Provides a free tier with generous limits allowing teams to experiment with synthetic data generation, validate the approach, and prove ROI before committing to enterprise plans. Includes full feature access at limited scale.

Automatically tests synthetic datasets against membership inference attacks to verify that the presence or absence of specific individuals cannot be determined from the synthetic data. Provides quantitative metrics on privacy robustness.

Enables secure sharing of datasets across teams, departments, and external vendors by providing privacy-certified synthetic data that meets regulatory requirements. Includes audit trails and compliance documentation.

Maintains statistical distributions, correlations, and relationships from the original dataset in the synthetic data. Ensures that ML models trained on synthetic data perform similarly to models trained on real data.

Provides controls to adjust the balance between data privacy and utility through parameters like epsilon values and generation settings. Allows users to find the optimal point for their specific use case.

Automatically generates compliance documentation and certificates proving that synthetic data meets regulatory standards like HIPAA, GDPR, CCPA, and other privacy regulations. Includes audit trails and evidence for regulatory review.

Generates synthetic datasets specifically optimized for ML model training and testing workflows. Ensures datasets are large enough, balanced appropriately, and maintain the statistical properties needed for effective model development.

Evaluates and reports on the quality of generated synthetic data through multiple metrics including statistical fidelity, correlation preservation, and utility scores. Provides detailed reports comparing synthetic and original data characteristics.

Generates synthetic data for multiple related tables while preserving foreign key relationships, referential integrity, and cross-table correlations. Maintains the structure and relationships of relational databases.