Check out our Github for research, source code, and examples including our core synthetic data generation library.
gretelai / 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 guarantees

Read 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

Privacy, applied

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.

Start creating safe data

Sign up now to start using our public beta. Gretel is free to use during our beta period.

Connect with the Gretel community

Join our slack community to connect with the Gretel team and engage with our community of data scientists and engineers.

Join gretelgroup.slack.com