Calibrated synthetic patients, delivered your way.
Stop faking test data. Generate US patient records whose fields move together like a real population — blood pressure with age, A1c with BMI — as one-time snapshots or year-by-year histories. Call it from a REST API or from your AI agent over MCP.
Free: 5,000 rows/day. No credit card.
$ curl -s -X POST …/v1/datasets/person \ -b 'sidgen_session=…' \ -d '{"clientId":"…","count":5000,"seed":418293}' → 202 · dataset job queued · poll /v1/datasets/{id}
Person data with API + MCP access.
Synthetic people and Type-2 diabetes patients, with multiple progressive stages.
Person Profile
LiveSynthetic person records with 69 jointly-distributed attributes — demographics, health, behavioral, financial — calibrated against public US reference data.
Open generatorT2DM Cohort
LiveSynthetic type-2-diabetes patient records — biologically staged, with calibrated A1c, complications (CKD, neuropathy, retinopathy, nephropathy), insulin use and mortality. A severity dial from newly-diagnosed to advanced.
Open generator↳ More record types on the roadmap. Want one prioritized? Tell us.
No patient data ever
touched these models.
The generators are fit to published aggregate statistics — national survey distributions, not individual records. No PHI is ingested, stored, or modeled at any stage, so the output is synthetic by construction: nothing to re-identify, nothing to leak.
↳ No BAA required — synthetic output calibrated to public statistics contains no protected health information.
Every column is fit to a published distribution — then tested against it.
Faker-style libraries sample uniformly. We fit marginals and joint distributions to national survey data, so a draw reproduces the real shape — skew, tails, and conditional structure included.
Marginals are easy. Joints are the test.
Any library can match one column at a time. The hard part is the relationships between them — blood pressure rises with age, A1c rises with BMI, income tracks insurance. Sample those independently and your data falls apart the moment a model looks at two fields together.
- joint distributions across attribute pairs
- conditional-on-age and on-sex slices hold their shape
- rare cohorts stay balanced at scale
↳ Full per-attribute + joint report ships with every dataset. See the Person fidelity analysis →
The same four guarantees on every generator.
Distributions, not dice rolls
Marginals and joints fit to NHANES, ACS, and CDC. Validated with Kolmogorov–Smirnov against the published reference data on every build.
No source dataset required
Other tools train on your real records first. We generate from public statistics — bring nothing, get production-grade test data with zero PHI exposure.
Deterministic by seed
Same seed, same data, byte-identical, every time. Reproducible test runs, bug reports, and benchmarks across your whole team.
Bulk export to 1M rows
Up to one million records as CSV, JSONL, or FHIR R4 per dataset. Free tier covers 5,000 rows/day; larger one-off datasets are free by request.
Built for demos, CI fixtures, and model training.
Ship a demo that holds up to scrutiny.
Fill a demo or prototype with records whose distributions match real US health statistics. No BAA, no pilot site, no waiting.
Reproducible fixtures that never drift.
Seed-deterministic datasets for CI: byte-identical every run, so a failing test means a real regression — not reshuffled mock data. Validate pipelines without exposing PHI.
Train and tune on a dataset built for accuracy.
Augment thin training sets and balance rare cohorts against a source calibrated end-to-end, not just per column. Joint and conditional slices match, so models generalize.