Choose the Right Plan for You

Generate values for primary and foreign keys using the Kernel Density Estimation (KDE) method while preserving the statistical distributions.

This option accelerates machine learning model training by distributing computations across multiple GPUs, reducing training time and enabling the use of larger models and datasets for improved accuracy.

Accuracy reports include median accuracy, univariate/bi-variate distributions, correlation, clustering, and utility metrics to evaluate model performance and relationships between variables.

Train models using advanced ML algorithms for increased accuracy in single and multi-table configurations.

By utilizing state-of-the-art privacy-protecting methods as part of models training, we can observe a tangible increase in differential privacy (DP), minimizing the possibility of sensitive data disclosure.

Synthetic records are linked by similarities in related tables, preserving logical relationships between records such as customer and orders.

Privacy reports include neighbor distance metrics, like Distance to the closest records (DCR) and Nearest neighbor distance ratio (NNDR).

Out of the box integration with AWS SageMaker, Azure Machine Learning, and GCP VertexAI.

The majority of service request types now require a shorter appointment time, with the appointment time for TDspore:SynGen installation as an example, being decreased from 3 days to 1 day.