# DATAMIMIC: Deterministic test data for regulated enterprises

> Open-source synthetic data engine with AI agent integration, deterministic output, and enterprise governance for banking and healthcare.
- Title: DATAMIMIC: Deterministic test data for regulated enterprises
- Summary: Open-source synthetic data engine with AI agent integration, deterministic output, and enterprise governance for banking and healthcare. Same seed plus same…
- Keywords: datamimic, synthetic data, test data generation, deterministic generation, pii pseudonymization, gdpr compliance, ai agents, mcp integration, rapiddweller, healthcare data, finance data, ci/cd pipelines
- Source: GitHub — https://github.com/rapiddweller/datamimic
- Read time: 3 min
- Topics: datamimic, synthetic data, test data generation, deterministic generation, pii pseudonymization, gdpr compliance, ai agents, mcp integration, rapiddweller, healthcare data, finance data, ci/cd pipelines
## Two editions, one determinism contract
Community Edition is MIT-licensed Python for synthetic data and pseudonymization. Enterprise Platform adds PII scanning, multi-system execution, governance, and audit trails.
## Built for AI agents from day one
CLI exposes machine-readable capabilities, progressive reference queries, and a single scaffold transaction. Optional MCP adapter maps the same operations to tool calls.
## Determinism means byte-identical output
Same engine version, same model, same seed produces identical output every run on every machine. Verified per-service on every CI run.

> Same engine version + same model + same seed = byte-identical output, every run, every machine.
## Domain services know real constraints
Healthcare generates age-appropriate conditions. Finance produces balance-consistent accounts. Faker gives you a 25-year-old with Alzheimer's.
## Seeded pseudonymization for regression tests
Set rngSeed and the same source record maps to the same synthetic value every run. Drop the seed for privacy-maximized one-time delivery.
## PII field detection is manual in CE
You identify and model PII fields in the XML pipeline. Enterprise Platform automates this with a probability-scored scanner and DataWorkbench.
## Time-series generation with ISO 8601
Any generate block becomes a time-series loop with start, end, and interval attributes. Output is prefix-stable by construction.
## Three reasons teams adopt CE standalone
Reproducible test data for CI/CD. Deterministic backend for AI agents with verifiable content hashes. Pseudonymization of staging exports without a scanner license.
## Enterprise Platform adds operational scale
Rust fastpath, ML engine, keyset building from live schemas, and billion-record workloads. Three runtime profiles: Performance, Balanced, Flexibility.
## Multi-system execution with referential integrity
Enterprise Platform coordinates Oracle, MongoDB, and Kafka in one run. Deterministic end-to-end, with per-stage execution logging.
## Template engine for industry messages
Spec-aware form editors for EDIFACT, SWIFT MT, HL7 v2.x, and HL7 FHIR. Upload your own specs, validate in real time, generate deterministic test messages.
## SWIFT messages are test-only artifacts
Generated messages are syntactically valid but not network-validated. They must not be transmitted on production SWIFTNet or from a CSP-attested secure zone.
## Governance layer for regulated enterprises
Role-based access, audit dashboards, approval flows, reusable templates, and scheduler. Deployed in EU banking for Oracle, MongoDB, and Kafka pipelines.
## Compliance evidence, not attestation
DATAMIMIC produces reproducible artifacts for GDPR, DORA, PCI DSS, and HIPAA programs. It does not replace your DPO, CISO, or auditor.
## CE and EE are separate engines
They share the DSL and determinism contract. CE is Python with multiprocessing. EE is independently optimised with Rust, ML, and distributed execution.
## Seven domain packs in Community Edition
Healthcare, Finance, Insurance, E-commerce, Public sector, Demographics, and Common. All services are versioned, seeded, and emit provenance hashes.
## CLI is the baseline agent contract
Capabilities returns the structural surface. Reference queries load typed fragments. Scaffold compiles intent to XML. Lint and dry-run verify before real execution.
## MIT licensed, contributions welcome
Community Edition is open source. If you are building integrations, domain extensions, or MCP tooling on top of DATAMIMIC, they want to hear from you.
## Key takeaway

Same seed plus same model always produces byte-identical output, making test data reproducible across runs and machines.