Transparency & Mathematical Proof
RealisticRNG is an auditable deterministic mathematical engine. This page explains, at a high level, how verification works in practice (technical trail, hashes, and re-execution) without exposing proprietary details.
What “auditable” means here
Traceability: recording execution parameters/identifiers to link a result to the process that produced it.
Reproducibility: under the same conditions and rules, the same result can be re-executed for validation.
Integrity: using “fingerprints” (hashes) that reveal any tampering with inputs or outputs.
Transparency is not “opening IP”. It enables independent verification of what should be verifiable: inputs, rules, and the technical receipt.
Typical elements of a technical receipt
Input/list hash: fingerprint of the processed dataset.
Execution parameters: mode, number of outputs, uniqueness rules, etc.
Execution identifier: reference for traceability and logs.
Result hash: fingerprint of the final output plus context.
In public applications, this appears as an “auditable receipt”. In enterprise integrations, it may be a technical log/report.
How someone verifies in practice
A typical verification flow is: (1) obtain the original input used (list/data), (2) recompute the hash and compare it to the recorded one, (3) apply the same rules/parameters, (4) recompute the output and confirm it matches the receipt.
This shifts trust from the “operator” to a verifiable process.
Note: legal requirements vary by jurisdiction. The technology provides a technical trail; compliance depends on the campaign context.