01PROFILE
Understand the data-generating process.
Map entities, relationships, sequences, missingness, constraints, sensitive fields, and rare conditions before choosing a generator.
Synthetic data · model simulation · release evidence
Make data useful.
Prove it safe.
MirrorFoundry gives regulated teams one private workflow to generate high-fidelity synthetic data, test disclosure risk, and simulate model behaviour before release.
No public registrationPrivate deploymentEvidence-led acceptance
MF / RELEASE 0247
Live evidenceDatasetEU claims cohort18 linked tables · 4.2M generated rows
Fidelity report94.2Threshold met
Univariate0.96
Correlations0.93
Task utility0.91
Multi-table relationships, event timing, and downstream task utility remain inside the agreed tolerance.
Release ownerModel validation
Built for complex enterprise data
RelationalTime seriesTransactionsFree textEvent streams
A data foundry, not a data shortcutFrom restricted source
From restricted source
to reviewable release.
Generation alone is not the product. MirrorFoundry keeps the source profile, synthesis configuration, privacy tests, task utility, simulations, limitations, and approval in one evidence chain.
02GENERATE
Forge new data—not masked copies.
Preserve useful statistical and relational behaviour while producing artificial records under explicit rules and constraints.
03VALIDATE
Replace “looks realistic” with evidence.
Measure distributions, task performance, similarity, membership risk, memorisation, and rare-record exposure against agreed gates.
04SIMULATE
Test the world beyond your holdout set.
Create controlled populations, edge cases, and distribution shifts to expose robustness, fairness, and policy failure modes.
One system for the data team, privacy owner, model validator, and accountable approver.
See every platform layerEvidence built into every run
A release report your reviewers can actually interrogate.
Each accepted dataset carries the assumptions, configuration, holdout design, thresholds, observed results, exceptions, intended use, and named decision owner.
Review the trust modelRELEASE / 0247Decision ledgerREADY FOR REVIEW
01Source boundary
18 tables · 37 protected fields
Approved02Fidelity threshold
All priority tasks within tolerance
Passed03Privacy evaluation
8 checks · 2 documented limitations
Passed04Model scenarios
37 scenarios · 1 policy exception
ReviewNext decisionModel owner sign-offNamed reviewer required →
Designed for consequential dataOne platform.
One platform.
Different proof obligations.
The workflow adapts to the decisions, threat model, data structure, and acceptance measures of each regulated use case.
Financial services
Reproduce risk behaviour, not customer records.
Generate linked transactions, claims, credit histories, and fraud scenarios while retaining the sequences and rare events your models depend on.
Explore the solutionApproved inputs
Transactions
Claims
Entity networks
MirrorFoundry release gateAcceptance measureRisk-behaviour coverageEnterprise deployment
Your data stays inside the boundary you approve.
MirrorFoundry is provisioned only after qualification and a signed SOW. Deployment, integrations, residency, retention, access, and exit requirements are defined before sensitive sources are connected.
See how delivery worksYOUR APPROVED ENVIRONMENT
MirrorFoundryPrivate workspace
Source systemsModel endpointsEvidence storeAdmin accessDedicated managed tenant · private cloud · customer-controlled environment*
*Final topology depends on diligence and the signed agreement.Sales-led by design
Six gates from blocked data to approved access.
No credit card, no public tenant, and no production data before scope. Existing customer users sign in after their administrator invites them.
01Qualify
Use case & owner
02Design
Privacy & validation
03Agree
Compliant SOW
04Provision
Private environment
05Accept
Data & model evidence
06Invite
Admin-led access
Before qualificationUseful answers.
Useful answers.
No synthetic certainty.
Need a specific requirement covered?
itops@mirrorfoundry.aiNo. A generator can overfit or reproduce sensitive patterns. MirrorFoundry treats every release as an intended-use decision with empirical privacy tests, utility thresholds, limitations, and a named reviewer.
Start with one blocked decisionForge the data.
Forge the data.
Keep the proof.
Bring the restricted source, intended use, model task, and review threshold. We will shape the privacy, validation, deployment, and commercial plan.
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