Synthetic data for teams that cannot use real records.
Generate realistic, deterministic, evidence-backed datasets and simulated telemetry for AI development, FHIR testing, software engineering, research and SOC validation — without exposing production records.
Powered by RadMah AIThe concrete issues these teams face
Production data is blocked
Real records, PHI and live telemetry can't be used for development, testing or model validation without serious approval friction.
Mock data is brittle
Hand-made fixtures don't preserve relationships, distributions or edge cases — so tests pass on data that doesn't behave like reality.
No provenance
Shared datasets often lack a verifiable record of how they were generated and what privacy posture they carry.
How ITLOX solves synthetic data
- Deterministic synthetic datasets that preserve structure and behaviour.
- Synthetic FHIR bundles with referential integrity for healthcare workflows.
- Process-model-driven ICS/OT telemetry and labelled attack data for security validation.
- An evidence bundle on every generation job; re-identification risk measured and reported.
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RadMah AI
Deterministic synthetic data and process-model-driven ICS/OT simulation, with an evidence bundle on every job. For teams that cannot use production records.
What teams build with it
FHIR & healthcare data
Synthetic patient bundles for testing, integration and AI workflows — designed to contain no PHI.
AI / ML datasets
Realistic development and validation data with quality gates and deterministic regeneration.
Software & CI
Seeded, repeatable fixtures via SDK/API/CLI instead of brittle hand-made mocks.
Research & universities
Reproducible, shareable datasets with provenance and a measured privacy posture.
OT / SCADA simulation
Process-model-driven industrial telemetry across common protocols for safe testing.
SOC / ICS validation
Labelled, repeatable attack scenarios mapped to MITRE ATT&CK ICS for detection validation.
A controlled, evidence-led flow
Define the contract
Describe the schema, constraints and volume you need. Generation is deterministic from the same inputs.
Generate with gates
Quality and privacy checks run as part of generation; infeasible constraints are flagged rather than silently violated.
Receive evidence
Every job returns an evidence bundle so a reviewer can see how the data was produced and its measured privacy posture.
Deployment & evaluation
- Managed cloud
- Dedicated single-tenant
- Self-hosted
- Python SDK · REST API · CLI
Evidence & proof
- Evidence bundle per job
- Deterministic regeneration
- Re-identification risk reported (k-anonymity, nearest-neighbour)
- Benchmarked on a public dataset (example result, varies by data)
Next step