Synthesize data with Gretel Synthetics
Get started creating synthetic data
Not sure how to start creating synthetic data? We will walk you through training a model on a source dataset and create a synthetic version with differential privacy guarantees using Gretel.ai.
Synthetics use cases
Improve limited datasets with synthetic data
Use synthetic data to augment data sources, improve accuracy, and reduce bias in machine learning models.Read the case study
Create synthetic data with privacy guarantees
Create and share realistic synthetic data freely across teams and organizations with differential privacy guaranteesRead the case study
Train machine learning models
Generate synthetic data to augment your datasets. This can help you create AI and ML models that perform and generalize better, while reducing algorithmic bias.
Seamlessly share data
No need to snapshot production databases to share with your team. Define transformations to your data with software, and invite team members to subscribe to data feeds in real-time
Apply state of the art NLP processing to label personal data and PII in your data streams. Stay compliant by encrypting records containing unexpected PII in real-time.