Deterministic, Auditable and Realistic Randomness.
The technical foundation for trustworthy decisions in AI, Compliance and Scientific Research.
The technical foundation for trustworthy decisions in AI, Compliance and Scientific Research.
Models, experimentation, A/B testing, model tuning and auditability of Machine Learning pipelines.
Traceability, audit trails, technical logs and integration into regulated, high-risk environments.
Draws, campaigns, promotions, benefits and financial mechanisms that demand mathematical transparency.
Physical, epidemiological and multi-agent simulations with controlled, reproducible stochastic models.
Controlled stochastic behavior, game mechanics, balancing and realistic random event distributions.
Executive overview of RealisticRNG as a deterministic, auditable and realistic randomness engine for multiple industries.
Deep-dive technical document with mathematical foundations, architecture, sector use cases and integration best practices.
Practical integration guide via SDK, API, containers and cloud, with example flows and technical recommendations.
Complete manual with operational view, usage modes, configuration parameters and guidance for high-criticality environments.
Portuguese-first tool for transparent draws with mathematical proof, technical receipt and independent auditing.
International, English-first version for global raffles with dollar payments, technical validation and third-party verification.
Instead of relying on a generic “random()”, RealisticRNG was designed as a deterministic randomness engine: you can reproduce sequences when needed, prove how a specific outcome was reached and, at the same time, preserve statistically robust behavior.
The goal is straightforward: provide a mathematical foundation strong enough for critical applications — from AI and Scientific Research to regulated environments, banking, fintech and cybersecurity — without sacrificing clarity, transparency and auditability.