Train your AI without putting real data at risk
The data you most need to build with is the data you're least allowed to move. Here's how synthetic data breaks that trade-off — and how to adopt it without regret.
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Synthetic data: the complete guide
What synthetic data is, the three types (tabular, text, image), how generators work, when it beats real data, and where it still falls short.
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Gretel is gone. Here's how to choose what replaces it.
Which Gretel were you using? A vendor-by-vendor guide to the alternatives, a scorecard, and a migration checklist.
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Generative AI, ML, or statistics? Choosing the right synthetic data engine
Three families that inject three kinds of information — and a use-case guide to combining them.
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Synthetic vs. anonymized: why masking isn't enough
Why removing columns and hashing IDs still leaks — and what actually protects people.
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How to measure synthetic data
The utility and privacy metrics that separate data you can trust from data that just looks right.
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Synthetic data in your dev pipeline
From locked-down to launch-ready — making generation a step in how you provision environments.
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Teaching models the rare cases
Amplifying rare fraud, uncommon cases, and edge conditions safely — so models actually learn them.
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A buyer's guide to synthetic data platforms
The questions that matter when you're evaluating a platform — a practical checklist.
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MOSTLY AI vs. Tonic vs. Syntheticore: which one fits your data?
An honest scorecard on deployment, data types, privacy metrics, pricing, and open source — plus who each platform actually fits.
Read →Synthetic data and privacy law: GDPR, HIPAA and CCPA
What each regime says about anonymized vs. synthetic data, where it still counts as personal data, and the evidence a DPO will ask for.
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