Synthetic data for decisions that cannot rely on guesswork.
MirrorFoundry helps data, privacy, and model teams agree what ‘safe enough’ and ‘useful enough’ mean—then produce the evidence together.
Start where sensitive or scarce data blocks a decision.
Each solution shows a concrete source constraint, foundry workflow, release gate, and acceptance metric.
Synthetic transactions, claims, and risk cohorts.
Preserve linked behaviour, sequences, and rare events for model development and testing without distributing raw customer records.
Representative cohorts for scarce and sensitive populations.
Generate structured and narrative data under an intended-use protocol with empirical privacy and utility evidence.
Controlled scenarios for model robustness and fairness.
Simulate edge cases, rare populations, and distribution shifts before a model reaches high-impact decisions.
Privacy, data science, and model owners review the same release.
MirrorFoundry keeps the source profile, generator, privacy results, utility evidence, limitations, and reviewer attached as work moves toward acceptance.
Choose the first constrained decision.
Start with one restricted dataset, one model decision, and explicit acceptance criteria.
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