Schema in. Sealed dataset out.
Describe a dataset in one English sentence, get back a sealed synthetic CSV in under a minute.
With the contract, the seed, the cryptographic hash chain, and the same offline verifier as every other engine here. Same sealed contract, same seed: byte-for-byte equal across clusters, regions and years.
- Product
- Fabricate
- Workflow
- Reviewed generation
- Rows requested
- 100
- Seed
- 42
- Contract
- Sealed
- Rows generated
- 100
- Artifacts
- 3
- Has output
- Yes
- Started
- 10:17:45 PM
- Completed
- 10:17:52 PM
- Duration
- Under a minute
- Metered at
- 10:17:52 PM
| invoice_id | currency | grand_total | invoice_date | subtotal | tax | customer_id |
|---|---|---|---|---|---|---|
| 196 | USD | 108.00 | 2026-06-09 | 100.00 | 8.00 | 167 |
| 1288 | USD | 54.00 | 2026-06-11 | 50.00 | 4.00 | 432 |
| 1197 | USD | 405.00 | 2026-07-02 | 375.00 | 30.00 | 167 |
| 926 | USD | 216.00 | 2026-06-07 | 200.00 | 16.00 | 918 |
| 1559 | USD | 232.20 | 2026-06-11 | 215.00 | 17.20 | 922 |
Six promises Mock Data keeps every time.
These aren't configuration toggles — they're how the engine was built. The engine simply behaves this way for every prompt, every tenant, every run.
Built for latency
Built for latency, not training. Generation is deterministic and fast — a 100k-row run generates and seals in tens of seconds — so it fits inside an interactive UX or a CI step. Contract authoring adds one LLM round before the preview.
Deterministic by construction
Same sealed contract + same seed + same engine version = byte-equal output. Every delivery records a SHA-256 per artifact, and replay verification re-runs the sealed contract and proves the digests match — byte for byte.
Any industry, on demand
Mock is LLM-driven schema fabrication, not a fixed template list. Describe a bank, an EHR cohort, a smart-meter feed, a retail basket — an LLM drafts a domain-aware sealed contract, and you can override any field per prompt.
Same evidence chain as the rest
Mock outputs join the same cryptographic hash-chained ledger as Synthesis, Virtual SCADA and ICS Security. The bundle is structurally identical — your downstream tooling treats them the same.
Two surfaces, one engine
Use the rady CLI for shell pipelines and one-off ops, or drive the same reviewed chain from the typed Python SDK — create_fabricate_preview(...) then approve_fabricate_preview(...). Same compiler, same contract; the audit log records who called it and from where.
Quality fail-closed
Even on the fast path we check distribution sanity, range bounds, enum compliance and nullability against the contract before sealing. A regression aborts — you never receive a quietly-broken dataset.
From a sentence to a sealed bundle in six stages.
Each stage writes a typed artefact into the chain. Even the prompt joins the audit trail — so an auditor can trace any byte back to the operator who asked for it.
One English sentence, an inline JSON schema, or a referenced YAML template. The compiler accepts all three and normalises to a sealed contract.
Schema, ranges, enums, constraints, seed, engine version and operator intent are sealed into one artefact before any row exists. Its hash is the dataset's identity.
The fabricator emits rows deterministically from the sealed contract — sampling rules, range/enum constraints and correlations. Row generation makes no external model call.
Distribution sanity, range bounds, enum compliance, nullability and constraint satisfaction are checked. Fail-closed — a mismatch aborts before sealing.
Per-step I/O hashed and chained; the final bundle is signed and ready to verify offline with the verifier CLI.
CSV artifacts plus a sealed evidence bundle land in your tenant's artifact store — download from the UI, the SDK, or the CLI. Re-run the sealed contract anywhere and the dataset hash matches.
$ PV=$(rady fabricate preview \
"200 SaaS customers, MRR 50-5000, plan
Starter/Growth/Enterprise, region AMER/EMEA/APAC" \
--rows 200 --seed 42 --quiet)
$ rady fabricate approve $PV \
--rows 200 --output ./customers.csv
Approve · 200 rows
✓ Fabricate job e2f91b37 submitted
✓ Wrote 23,487 bytes to ./customers.csv (mime=text/csv)
→ verify offline: rady jobs evidence e2f91b37Same contract from the CLI or the typed Python SDK — create_fabricate_preview(...) → approve_fabricate_preview(...).
Not just another faker with a UI.
RadMah binds every generated dataset to its approved contract, verification record and audit trail—so teams can review what was requested, what was delivered and whether it passed before use.
Four patterns teams use every week.
None require touching production data; all produce a bundle you can hand to anyone in the business.
Sandbox / demo data
A realistic demo environment for sales, CS or training without touching production. Re-seed in 30 seconds for a fresh run.
Load & soak testing
Millions of rows that pass your validation but exercise edge ranges your real data doesn't carry. Seed-pinned, so failures reproduce.
Test fixtures in CI
Script the rady fabricate chain in CI to generate fixtures per branch. The sealed contract is in git; the bundle isn't — it rebuilds byte-equal on demand.
Rapid prototyping
Prototype a dashboard, an ML model or an API contract against data shaped like the real thing — before legal even joins the call.
This dataset was generated from the approved Contract K. The version chain below records every refinement the user applied.
Sealed, and yours to verify.
Every Mock run ships the same audit trail: a hash match proving the dataset is reproducible, the constraint checks, and the contract fingerprint. Four guarantees apply by default — none are paid add-ons.
- Tenant-isolated
Per-tenant keys at rest, per-tenant artefact prefix, ORM-level row filtering.
- Deterministic generation
Rows are generated deterministically from the sealed contract — no external model call at generation time.
- Tamper-evident
The offline verifier flags any in-place mutation of the bundle, however it travelled.
- Audit-ready
Caller, scope, contract hash and bundle root recorded on every call.
Bring a sentence. We’ll ship a sealed bundle.
A 30-minute working session: tell us the dataset shape you need and one downstream task you want to drive with it. We cut the contract, generate the bundle, and walk you through verifying it offline.
Mock Data is the fastest path from “I need a realistic-looking dataset for a demo, an integration test, or a pitch deck” to a sealed file you can hand a teammate. No database, no ETL, no warehouse ticket. A PM writes the prompt in English; the fabricator returns a deterministic CSV plus a bundle anyone can verify offline.
The prompt is parsed into a contract (columns, dtypes, cardinalities, business rules), the contract is sealed on approval, and a domain-aware fabricator emits rows under a fixed seed. Re-run the sealed contract with the same seed a year later and the output is byte-equal. CLI path:rady fabricate preview → approve.