Expert insights on synthetic data

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Our core tenets for de-identification in AI data licensing

No framework governs how people are protected when a company sells or licenses its operating data for AI training. These are the seven rules we work by, and the rules we believe any such transfer should meet.

Blog posts

How to create realistic, safe, document-based test data for MongoDB

Technical deep dive
Technical deep dive
Tonic Structural

Generating high quality test data for MySQL through de-identification and synthesis

Data de-identification
Data de-identification
Test data management
Tonic Structural

Why I joined Tonic.ai: A business grad's perspective amid the pandemic

Tonic.ai editorial
Tonic.ai editorial
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June ‘21 product update: support for all the databases

Product updates
Product updates
Tonic Structural

Data anonymization techniques defined: Transforming real data into realistic test data

Technical deep dive
Technical deep dive
Tonic Structural

How to generate safe, useful test data for Amazon Redshift

Technical deep dive
Technical deep dive
Tonic Structural

Why I joined Tonic.ai: A software engineer's perspective

Tonic.ai editorial
Tonic.ai editorial
Tonic Structural

Creating realistic, secure test data for Databricks

Technical deep dive
Technical deep dive
Tonic Structural

Spring 2021 product update

Product updates
Product updates
Tonic Structural

How to create realistic test data for PostgreSQL

Technical deep dive
Technical deep dive
Tonic Structural
Tonic Fabricate

Using Docker to manage your test database(s)

Technical deep dive
Technical deep dive
Tonic Structural

Mockaroo and Tonic.ai: Partners in mock data generation

Tonic.ai editorial
Tonic.ai editorial
Tonic Structural