"I need 10 million rows of high-entropy fraud data that obeys 50 complex business rules to train a production-grade model."
Most synthetic data tools use statistical mimicry (GANs/VAEs) to copy the look of your data. For ML models in fraud, risk, or healthcare, "looking real" isn't enough.
"Generated a transaction where the user spent more than their balance."
"Model averages out rare edge cases, exactly where you need data the most."
invariants: - ledger_balance: policy: deterministic constraint: "SUM(credits) >= SUM(debits)" state: accounts.balance_map entropy: - fuzz_pii: fields: [ssn, email, card_num] shadow: true
Transforming live production signals into high-fidelity synthetic fuel.
Intercept live database WAL logs via Change Data Capture (CDC).
Dynamically fuzz PII while preserving cross-table referential integrity.
Validate fuzzed data against the StateMap to ensure no causal violations.
Deliver sub-millisecond, secure data streams to downstream ML pipelines.
Beyond simple schema checks. Our `StateMap` keeps an in-memory tally of entity states, enforcing ledgers, medical lifecycles, and physical constraints across millions of rows.
Don't wait for your model to "find" anomalies. Explicitly instruct the engine to skew probabilities and inject long-tail fraud patterns or rare medical edge cases on demand.
The missing piece for DoWhy and EconML. Generate datasets where the "Ground Truth Effect" is known with absolute mathematical certainty, allowing for robust model validation.
High-stakes industries requiring absolute logical integrity.
Ensure synthetic transaction streams preserve anti-money laundering (AML) signals across millions of entities.
Inject rare sybil attacks or coordinated transaction rings into the stream to benchmark detection latency.
Preserve complex patient lifecycles—diagnosis, treatment, recovery—ensuring temporal and physical logic.
Model worldwide logistics chains with deterministic inventory constraints and causal delay propagation.
Why CausalFoundry is the next evolution of synthetic data.
| Feature | Legacy GANs/VAEs |
CausalFoundry
|
|---|---|---|
| Logic Discovery | Stochastic (Guessing) | Deterministic Defined Invariants |
| Cross-Row Integrity | None (Isolated) | Enforced Via stateful StateMap |
| Rare Edge Cases | Averaged Out | Injectable Scenario Injection |
| PII Removal | Anonymization | Entropy Pass Shadow Generation |
| Delivery Model | Batch File | Streaming Kafka / CDC Native |
CausalFoundry is an enterprise-grade synthetic data factory designed for ML, Data Science, and Data Engineering teams. Unlike standard generators that rely on statistical mimicry, CausalFoundry uses a Causal Invariant Engine to enforce strict business rules, ledger invariants, and physical constraints.
It is the ideal platform for generating high-entropy training data for Causal ML models (using frameworks like DoWhy and EconML), providing a deterministic ground truth that avoids the pitfalls of GAN-based hallucinations. By utilizing Change Data Capture (CDC) and a stateful StateMap, it can shadow production streams in real-time, delivering secure, fuzzed data via Kafka with sub-millisecond latency.
Exploring the frontier of causal AI, verifiable integrity, and high-fidelity synthetic training data.
Learn why statistically plausible lies are a liability in high-stakes systems and how we build ground truth using causal invariants.
OMOP CDM, OpenMRS, and RxClaims support. Built for high-integrity healthcare engineering and research.