Synthetic data with cryptographic evidence.
From a natural-language prompt to audit-ready datasets — with privacy guarantees, statistical fidelity, and industrial-control simulation built in. Pick your interface, mint a key, and ship in minutes.
Built on
High-fidelity
the flagship engine, parametric and diffusion engines — trained on your data, benchmarked against utility scores you can reproduce.
Auditable
Every run ships a multi-file evidence bundle: distribution reports, correlation matrices, privacy metrics, Merkle-rooted BLAKE3 proofs.
Private by default
Tenant isolation, least-privilege access control, SSO/SCIM, bring-your-own CA, offline signing, and an on-prem/air-gapped deployment path.
Pick your path
Every interface reaches the same backend and produces the same evidence bundle. Start with whichever matches your workflow — you can switch later.
GUI
→Click-through the whole platform in a browser.
open https://app.radmah.aiBest for Domain experts who want the visual dashboard, approval gates, and live evidence previews.
REST API
→Every endpoint, schema, and error code.
curl -X POST https://api.radmah.ai/v1/client/fabricate/preview \
-H "X-API-Key: $RADMAH_API_KEY" \
-d '{ "description": "customer support tickets",
"requested_records": 10000 }'Best for Anything that speaks HTTP — Go, Rust, Ruby, .NET, curl, Postman, custom Lambdas.
Python SDK
→Typed, retry-aware, async-first client.
from radmah_sdk import RadMahClient
client = RadMahClient(api_key="sl_live_…")
pv = client.create_fabricate_preview(
"customer support tickets",
requested_records=10_000,
)
client.wait_fabricate_preview(pv["preview_id"])
ap = client.approve_fabricate_preview(pv["preview_id"])
client.get_job(ap["job_id"]).wait()
bundle = client.download_evidence_bundle(ap["job_id"])Best for Data scientists, ML engineers, and data pipelines that already live in Python.
CLI (rady)
→Terminal-first — everything the SDK does, from your shell.
rady auth login
PV=$(rady fabricate preview \
"customer support tickets" --rows 10000 -q)
rady fabricate approve $PV --rows 10000
rady evidence verify ./bundle.jsonBest for Shell-native workflows, CI jobs, one-off investigations, and ops playbooks.
Popular guides
Generate mock data
Describe a dataset in plain English and receive structured CSV. No upload, no training.
Synthesis from your data
Upload a CSV, train the flagship engine, and produce high-fidelity synthetic data that preserves statistical properties.
Verify an evidence bundle
Offline verifier, Merkle root reconstruction, BLAKE3 proofs, Ed25519 signatures when configured.
Build a cyber-range dataset
Blend Virtual SCADA benign traffic with labelled attacks for IDS/SOC training.
Delegate to the agent
Hand a multi-step analysis to the Agentic Data Scientist — review the plan before execution.
Deploy on-premises
SSO/SCIM, bring-your-own CA, air-gapped install, tenant isolation, compliance-ready infrastructure.
Every run ships an evidence bundle
Manifest, provenance, seal, deterministic replay metadata, quality reports, and generated data artifacts — Merkle-rooted, verifiable offline. This is what auditors, compliance reviewers, and ML validation teams actually need.
evidence_bundle.json # one sealed JSON document per run
├── contract # sealed generation contract + contract_hash
├── determinism_report # seed, RNG streams, recomputed hashes, byte-match verdict
├── constraint_report # hard/soft constraint evaluation, per-violation detail
├── artifact_index # every delivered artifact with its SHA-256
├── engine_manifest # engine name + version, library versions, model hash
├── privacy_report # membership / attribute-inference + linkage risk
├── utility_report # statistical metrics + task scores
├── value_safety_scan # scanner verdicts, repairs, blocked cells
├── run_telemetry # rows generated, throughput, elapsed seconds
└── hsfg_seal # release seal hash — notarizable to a signed Merkle logPlatform status
Live dashboard for API availability, worker health, and scheduled maintenance.
radmah.ai/status →