Gretel Blog

What if we could ensure that personal data was protected, benefiting not just the individual but also giving developers faster, worry-free access to data?

Generate artificial records to balance biased datasets and improve overall model accuracy.

In this post, we walk through building a data pipeline that will automatically transform datasets so they can be safely used in development environments.

Create a fair, balanced, privacy preserving version of the 1994 US Census dataset using gretel-synthetics.

A practical guide to creating differentially private, synthetic data with Python and TensorFlow.

We are pleased to share that Gretel raised $12M in Series A funding. We're picking up strong momentum in our mission to help developers create safe data.

Create a simple workflow to perform Named Entity Recognition (NER) on sample data using Gretel and load the records into Elasticsearch.

We are releasing new features that make working with data easier by helping you deep dive into records, use blueprints to auto-anonymize data, and more.

We are launching Gretel Blueprints, making it easy to anonymize and balance datasets with just a few clicks.

Gretel's Premium SDK now includes detailed reporting that shows you how accurate your synthetic data's statistical distributions and correlations are.

Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.

Learn how we are improving our product by adding new features that make connecting to Gretel easier, faster and more streamlined.