RealisticRNG Engine Documentation

1. Overview

RealisticRNG is a deterministic randomness engine created for scenarios where being able to prove how a result was generated is as important as the result itself. Instead of using a generic “random()” helper, the engine is treated as a first-class infrastructure component for critical systems.

Rather than hiding randomness behind an implementation detail, RealisticRNG turns it into a verifiable, auditable and reproducible layer, suitable for AI, finance, cybersecurity, research, simulations, games and auditable raffles.

2. Mathematical foundations

At its core, the RealisticRNG Engine relies on mathematical models capable of generating sequences with robust statistical behavior while preserving deterministic control over the process. The same parameters produce the same sequence, enabling reproducibility without giving up realistic statistical properties.

2.1. Controlled chaos

The engine uses controlled chaotic dynamics and carefully calibrated mathematical transformations to produce values that behave as random under statistical analysis, yet can be reproduced whenever needed, from well-defined input parameters.

This balance between apparent chaos and deterministic control makes RealisticRNG a good fit for simulations, AI experiments, A/B tests, risk models and any scenario where repeating an experiment under exactly the same conditions matters.

3. Engine architecture

At a high level, the RealisticRNG Engine can be seen as a processing chain: parameter input → mathematical core → post-processing → output. Each stage is designed to be traceable and, when required, auditable.

3.1. High-level view

The engine architecture includes:

Input: seeds, mode configuration and numeric ranges.
Mathematical core: proprietary controlled-chaos methods (internal details not disclosed) responsible for delivering deterministic behavior with robust statistical properties.
Post-processing: clipping, normalization and mapping into the desired range or target domain (indexes, tickets, keys, etc.).
Output: final values, optionally accompanied by metadata that enables reconstruction or verification of the process.

4. Auditability & transparency

A key differentiator of RealisticRNG is that it was designed for auditability. Each generated sequence can be associated with parameters, seeds, hashes and metadata, making it possible to recompute or validate the result later.

This greatly simplifies the creation of audit trails for:

regulators and external oversight bodies;
internal risk, compliance and security teams;
enterprise customers requiring independent verification;
researchers who depend on scientific reproducibility.

5. Modes of operation

The same mathematical engine can operate in different modes, tuning its behavior to the needs of each application:

Scientific mode: strict reproducibility for research and experiments.
Probabilistic mode: aimed at statistical modeling, simulations and risk scenarios.
Deterministic test mode: useful for debugging, QA and controlled testing.
Application-specific profiles: tuned behavior for games, raffles, AI systems and more.

6. Industry use cases

RealisticRNG is intended to be a central engine that can be applied across multiple sectors. Below are examples of how it can be used.

Fintech & Banking

Portfolio simulations, reproducible risk models, stress tests and scenario generation for audit-ready reporting.

AI & Data Science

Controlled-seed experiments, pipeline re-execution, A/B tests and fair comparisons under the same sequences.

Research & Simulation

Scientific reproducibility for physical, epidemiologic and complex multi-agent simulations.

Cybersecurity & Compliance

Verifiable artifacts, mathematical proofs in logs and workflows that can be independently audited.

Robotics & Games

Emergent behaviors, loot tables, NPC AI and environment simulations with repeatable “controlled chaos”.

Raffles & Draws

Public-facing layer used by Sorteio (PIX) and Raffle (Stripe), with technical receipts and verifiable hashes.

7. Integration into systems

The RealisticRNG Engine can be embedded as an infrastructure component through:

local calls inside on-premise applications;
dedicated APIs;
language-specific SDKs;
containerized or worker-based deployments in public or private clouds.

The exact delivery model depends on security, compliance and scale requirements.

8. Security & governance

Security includes protecting IP, controlling versions, and governing parameters and access:

secure handling of seeds and parameters;
engine version control with a clear change history;
access and usage policies for sensitive environments;
external audits when appropriate.

9. Products built on RealisticRNG

The engine is the underlying technology. Examples of official applications include:

RealisticRNG — Sorteio: Portuguese tool for auditable draws with PIX in BRL.
https://sorteio.realisticrng.com/
RealisticRNG — Raffle: Global English version with USD billing via Stripe.
https://raffle.realisticrng.com/
Future APIs/SDKs: integration layers for banks, fintechs, AI labs, research institutions and companies.

10. FAQ

Common topics raised in technical and commercial conversations include:

How does reproducibility work in regulated environments?
What licensing options exist for internal use or as part of a product?
Can the engine be used in academic or scientific projects?
What guarantees of auditability and transparency can be offered to third parties?

Detailed answers are provided case by case, depending on each project context.

11. Contact & licensing

For companies, researchers or partners interested in using the RealisticRNG Engine:

RealisticRNG Engine — Deterministic, Auditable Randomness