Expert insights on synthetic data

The lastest

Training effective models without the annotation budget

Learn how to bypass costly annotation workflows by using LLM-generated labels and lightweight fine-tuning to build high-quality NER models with minimal human input.

Blog posts

Tonic Textual is now on the Databricks Marketplace: unstructured data, meet easy ingestion

Product updates
Generative AI
Product updates
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Webinar highlights: Accelerating domain-specific AI model training with private data

Data privacy
Data privacy
Generative AI
Healthcare
Data de-identification
Tonic Structural
Tonic Textual

Tonic.ai product updates: March 2025

Product updates
Tonic.ai editorial
Product updates
Tonic Structural
Tonic Textual
Tonic Validate

Optimizing modern software testing strategies with better test data

Technical deep dive
Technical deep dive
Tonic Structural
Tonic Fabricate

Tonic.ai product updates: December 2024

Product updates
Tonic.ai editorial
Product updates
Tonic Structural
Tonic Textual
Tonic Validate

The importance of high quality synthesis when creating safe training datasets

Generative AI
Generative AI
Data privacy
Data de-identification
Data synthesis
Tonic Textual

LLM RAG vs fine tuning: which method is best?

Generative AI
Generative AI
Tonic Textual
Tonic Validate

Protecting privacy without hurting RAG performance

Data de-identification
Data de-identification
Data privacy
Generative AI
Technical deep dive
Tonic Textual
Tonic Validate

We are joining forces with Google Cloud to accelerate AI and software development with privacy-first data solutions on Google Cloud Marketplace

Product updates
Product updates
Tonic Structural
Tonic Textual

Tonic.ai product updates: October 2024

Product updates
Product updates
Tonic Structural
Tonic Textual

Building a RAG system on Databricks with your unstructured data using Tonic Textual

Technical deep dive
Technical deep dive
Tonic Textual

Using synthesized data for HIPAA expert determination

Healthcare
Healthcare
Data privacy
Data synthesis
Tonic Textual