Synthetic

The platform's generative models learn the statistical properties, distributions, relationships, and edge cases present in real datasets, then produce synthetic records that preserve the machine learning utility of the original data while containing no real individuals' information enabling data science work that privacy regulations would otherwise prohibit.

Synthetic's tabular data generation produces realistic records for structured datasets including financial transactions, customer records, healthcare encounters, and operational logs with configurable privacy guarantees that balance statistical fidelity against the risk of re-identification that connects synthetic records to real individuals.

Time-series generation creates realistic temporal data with appropriate seasonality, trend, and autocorrelation patterns for sensor data, transaction sequences, and behavioral streams.

Rare event injection allows teams to oversample edge cases and anomalies that appear infrequently in real data but are critical for model robustness generating adversarial examples and stress test cases that real production data doesn't provide in sufficient volume for reliable ML training.

Data science teams building ML models on sensitive data that cannot be shared outside regulated environments, software engineers needing realistic test data for QA without using production databases containing real user records, compliance teams validating analytics systems without exposing confidential business data, and organizations sharing datasets with research partners or vendors without transferring real personal information use Synthetic to overcome data access constraints that would otherwise block valuable AI and analytics work.

The platform's compliance documentation provides the evidence that data protection officers and regulators need to assess synthetic data programs against GDPR, HIPAA, CCPA, and other applicable privacy frameworks.