Gretel Blog

Learn how to generate secure, high-quality synthetic tabular data using Navigator Fine Tuning and Differential Privacy tools. 🛡️ 🚀

Seed to succeed: use the sample-to-dataset workflow to create diverse, large-scale synthetic datasets tailored to your needs with nothing but a few samples.

Leveraging Gretel's privacy-preserving synthetic data generation platform to fine-tune Azure OpenAI Service models in the financial domain.

Gretel announces Data Designer for building high-quality, custom synthetic datasets with Navigator. Improve AI models on domain specific tasks and reasoning.

Gretel fine-tuned, synthetically-enhanced GLiNER models for better PII & PHI detection—datasets included.

How to safely fine-tune LLMs on sensitive medical text for healthcare AI applications using Gretel and Amazon Bedrock

What The Recent Paper on LLM Reasoning Got Right—And What It Missed.

Gretel, a leader in privacy-preserving AI and synthetic data generation, has been named one of LinkedIn’s Top U.S. Startups

Gretel's synthetic GSM8k dataset shows an 84% improvement for AI Reasoning tasks vs synthetic data generated without the Reflection technique.

Gretel's Workflow Builder streamlines multi-step synthetic data generation for financial services, boosting privacy and efficiency with no coding required.

Enhance finance chatbots with privacy-first synthetic data using Gretel and Databricks to boost performance while ensuring compliance with privacy regulations.

Generate domain-specific tabular datasets with multiple modalities.