Gretel Blog

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.

Can synthetic data really be used in machine learning? We explore the utility of synthetic data created from popular datasets and tested on popular ML algorithms.

How thoughtful, high-quality synthetic data generation, rather than 'indiscriminate' use, can prevent model collapse.