01
Reproducible
Every simulation runs off a visible, pinned seed. The same seed produces the same result, every time — retype it to replay an identical Monte Carlo. No hidden randomness, no run-to-run drift you can’t control.
Trust page
Rigor you can check, not just claims.
Paid analytics is only worth trusting if you can verify it. R-MAP is built so every number is reproducible by construction, your data stays on your own device, and the model is honest about what it knows versus what it assumes. This page explains how — and how to check us.
33 / 33instruments load clean
12 / 12engine checks match Python, bit-for-bit
0of your data leaves your device
4provenance states tracked end-to-end
Four guarantees
What “trustworthy” actually means here.
Not a promise — a set of properties built into every instrument and testable by anyone who owns one.
02
Verified
The core engines are checked against an independent Python re-implementation — the random generator and the loss/VaR maths match bit-for-bit across 12 of 12 tests. All 34 instruments load and render clean.
03
Private
Each instrument is a single file that runs on your machine. Bring your own data; it never leaves your device. No accounts to feed a data lake, no telemetry harvesting your registers, no cloud custody of your numbers.
04
Honest
Where a result is assumed rather than measured, it is labeled as assumed. Sample data is fictional and marked. Instruments inform decisions — they are not financial, legal, or safety advice, and they say so.
Reproducible by construction
Determinism you can pin
- Seeded RNG → every model draws from a seed you can see and set.
- Pinned & persisted → the seed is shown on-screen; runs advance it by one so you know when a result changed.
- Replayable → retype a seed and the exact simulation returns — identical distribution, identical tail.
- No black box → assumptions, drivers, and inputs are all editable and visible.
Verified, not asserted
How the maths is checked
- Independent harness → a separate Python re-implementation re-derives the engine outputs.
- Bit-for-bit parity → the RNG, loss engine, and VaR/CVaR conventions match to the last digit (12/12).
- Clean loads → all 34 instruments load and render without errors on every release.
- Second opinion → every build passes an independent cross-functional review before it ships.
Provenance, end to end
Every number knows where it came from.
The platform layer (KEYSTONE) aggregates across models — so it stamps each measure with its origin and carries that stamp all the way to the board pack. A roll-up is always auditable back to source, and a simulated figure is never silently treated as a measured one.
Sample
Shipped, fictional
The industry-realistic demo data every instrument opens with — clearly marked as sample, never mistaken for your reality.
Imported · Edited
Your inputs
Data you bring or change carries an imported or edited stamp, so a portfolio can be discounted or trusted by origin.
Simulated
Model output
Results the model computes are marked simulated and weighted accordingly — assumptions surfaced, never hidden inside a total.
Mixed units are never summed silently, uncatalogued metrics are surfaced rather than dropped, and assumed correlations are labeled assumed, not measured.
Check us yourself
Don’t take our word for it.
The point of building it this way is that a skeptic can verify it in minutes: pin a seed and re-run — the result is identical; read the provenance ledger — every measure names its origin; edit one input — watch it flow through every linked view; export and re-import — the portfolio round-trips through the shared schema unchanged.
