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Develop with real-world fidelity — without the regulatory exposure.

SyntheticGen

Generates statistically faithful synthetic versions of sensitive datasets (financial transactions, patient records, telecom CDRs) so ML teams can develop and test models without regulatory exposure. Enables model development to proceed in parallel with data governance approvals, cutting development cycles by weeks.

Why It Matters

SyntheticGen breaks the deadlock between data governance and ML velocity.
Instead of teams waiting weeks for approval to touch sensitive data, they develop and test against statistically faithful synthetic versions — real-world fidelity without real-world regulatory exposure.
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Development without the wait

Model building proceeds in parallel with data governance approvals, cutting weeks off development cycles that would otherwise idle expensive ML teams.

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Mathematical privacy, not wishful anonymization

Differential privacy guarantees withstand re-identification attacks that defeat basic masking and anonymization — a distinction regulators increasingly understand and demand.

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Covers the most sensitive data types

Financial transactions, patient records, and telecom CDRs — precisely the datasets that are most valuable for ML and most dangerous to expose.

The Cloudly Advantage

SyntheticGen flips Cloudly’s regulated-industry positioning from defense to offense — where our governance products help clients pass audits, SyntheticGen helps them move faster because of privacy constraints, not despite them.
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Sells speed to innovation buyers

"Unblock your stalled ML projects this quarter" targets Chief Data and Innovation Officers with budget urgency — a different, faster buyer than compliance.

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Pairs with the governance suite

SyntheticGen for development, DataLineage360 and NoteBook Sync for production — Cloudly covers the full data sensitivity lifecycle no competitor matches end-to-end.

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Enables everything else we sell

Synthetic data lets prospects pilot AutoML Accelerator, TrainForge, and FeatureVault without data approvals — removing the biggest blocker to our own POCs.

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AWS Clean Rooms differentiator

Clean Rooms integration opens partner-data collaboration deals and pulls through AWS Migration and consultancy services.

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Emerging-market banking fit

Central bank data-residency and privacy mandates make synthetic data especially valuable where regulatory approval cycles are longest.

The Final Takeaway

High-margin recurring usage — Synthetic data generation is consumed continuously across projects — usage grows with every new ML initiative the client launches.

Powered By

SDV (Synthetic Data Vault)

Core to the SyntheticGen technology stack.

CTGAN

Core to the SyntheticGen technology stack.

Gretel AI SDK

Core to the SyntheticGen technology stack.

AWS Clean Rooms

Core to the SyntheticGen technology stack.

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