Production-shape financial data. Zero real customers.
Customer records, transactions, account histories, trade flows, and risk-factor time series — at the shape of your real book, without exporting a single PII row. Every delivered dataset carries source-relative fidelity and privacy measurements inside a signed evidence bundle your regulator can verify offline.
Whether the generated schema actually covers the concepts you asked for. PASS means every extracted concept is represented.
How readable, deterministic, and policy-clean the synthetic rows are after every post-render repair has run.
Cross-field invariants the engine evaluates against every generated row (arithmetic totals, derived numerics, enum coherence).
Four workflows the biggest customer asks for first.
Risk-model backtesting
Retrain credit-risk, fraud, and AML models against 10× more data than your production cohort without moving a single real PII record out of the enclave. The sealed contract makes every backtest reproducible for internal model-risk governance.
Open-banking sandbox + partner onboarding
Hand partners and fintech integrators a realistic dataset the day an API contract is signed — no DPA negotiation, no PII exposure. The dataset's statistical shape matches your live data under the same QA harness the industry publishes against.
Core-banking migration testing
Stress-test a core migration against millions of synthetic accounts that match your real schema, distribution, and constraint graph (FK between accounts / customers / transactions intact by construction). No production cut-over surprises.
Regulatory stress testing
Generate adversarial cohorts tuned to the CCAR / EBA / PRA scenarios your risk team actually has to submit. Every run ships a signed evidence bundle with the contract hash, seed, and determinism proof your regulator's reviewer can verify offline.
The things your second-line team asks.
GDPR Art 4(1) posture
Synthetic data generated from statistical distributions — never derived from an identifiable natural person — falls outside GDPR's definition of personal data. DPIAs are simplified; cross-border transfers stop being a blocker.
PCI-DSS tokenisation-compatible
Account and card-number columns are fabricated from the real format rules (Luhn-valid, PAN-structure-correct) without ever referencing a real card. Test harnesses that route on BIN range still work; nothing ties back to a real cardholder.
FFIEC model-governance-ready
Every generation run produces a sealed evidence bundle: contract, evidence record, determinism proof, quality report, privacy report. The FFIEC SR 11-7 auditor's question — “can you reproduce this training set?” — has a YES answer by design.
Audit-ready transcripts
Every job, every parameter change, every evidence-bundle download logged to the immutable audit trail. Tenant-isolated, timestamped, queryable, and ready for internal audit-committee review.
Three products, one sealed contract.
Tabular synthesis
Measured against each customer source with per-run fidelity and privacy reports. Constraint-aware generation for primary/foreign keys, monotonic fields, and rate-limited columns.
ExploreAgentic Data Scientist
A typed tool-surface planner + executor + self-healer that drives the synthesis pipeline end to end — configure the scenario once, and the agent runs, validates, and re-plans if a gate fails.
ExploreEncrypted connectors
Direct pulls from Snowflake, Databricks, Redshift, BigQuery, Postgres, and more. Secrets live in a per-tenant Fernet-encrypted vault — never in plaintext config, never round-tripped through the API response.
ExploreGive your risk team 10× the data.
Free tier: 25 runs a month. Enterprise: custom data residency, a Hybrid SDK deployment that keeps real data inside your VPC, and a dedicated FS solutions engineer.