Pseudorandom number generation · RNaaS
Mesinja is a next-generation pseudorandom number generator (PRNG) platform built for scientific integrity, scale, and verification. It delivers reproducible, audit-friendly randomness through a unique mathematical approach based on approximate solutions to transcendental equations.
The problem
Most digital systems rely on random numbers: for cryptography, for simulations, for games, and for device security. But very few developers or engineers stop to examine how those numbers are generated.
Today’s most widely used pseudorandom number generators (PRNGs) work by mixing and shuffling internal data. They rely on operations like substitution, permutation, addition, rotation, and exclusive-or (XOR). These structures were designed for speed and ease of implementation.
Their long-term resilience has gone largely unconsidered. But with the development of quantum computing, these methods are under pressure. Algorithms like Simon’s and Shor’s algorithms are already capable of analysing structure and may materially reduce the computational effort required to unmask underlying keys. What appears secure under today’s assumptions will be vulnerable with the wider availability of quantum computing.
A single weakness in a widely deployed PRNG could compromise entire systems such as banking infrastructure, authentication processes, or the fairness of regulated games.
The approach
Mesinja takes a structurally different approach. Mesinja offers a new class of PRNGs designed for statistical integrity, auditability, and scale.
Rather than evolving internal state, Mesinja generates its output by solving a series of distinct transcendental equations with each engineered to have an approximation of a transcendental number as its solution. Digit blocks are sampled from these approximations and assembled into a random stream.
The statistical properties of Mesinja RNGs are not a by-product of algorithmic complexity or iterative transformation. They reflect the typical long-run digit distribution that is highly likely to be found in most transcendental numbers. The statistical properties of Mesinja RNGs have been confirmed through large-scale testing.
Because the randomness arises from the mathematical nature of the source numbers, not the mechanics of how they’re generated, Mesinja RNGs are structurally independent of the vulnerabilities that quantum computing is expected to exploit.
Mesinja RNGs therefore avoid the risk that quantum computing based attacks can reverse-engineer the internal structure of traditional RNGs.
Standards
Mesinja does not follow the same design principles as PRNGs currently specified under standards like NIST SP 800-90A. Those standards were built for a different class of generator, being ones that rely on entropy input, and state update functions.
Scope & certification
Mesinja is not certified for cryptographic use. However, the structure of the generator lends itself to environments where auditability, repeatability, and statistical strength are more important than conformity with legacy design patterns.
We expect formal standards to evolve as quantum-resilient alternatives gain recognition.
In the meantime, Mesinja is offered for scientific and non-cryptographic applications, or for users not bound by current certification regimes.
Use cases
Mesinja RNGs are available now for scientific, technical, and operational contexts that require reproducibility and statistical robustness. Use cases include simulations, data sampling, and randomisation in regulated environments.
Meets auditability, reproducibility, and statistical fairness requirements. Suitable for certification environments with stringent standards.
See Gaming & Lottery Applications →Delivers consistent performance across high-volume, parallelised, and long-duration simulations. Suitable for use in physics, AI, finance, and other settings such as fluid flow modelling and computational biology among other applications.
See Simulation Use Cases →Mesinja supports deterministic, stream-based encryption with structural resilience.
See Quantum-Resistant Symmetric Key Encryption Use Case →Supports structured stochastic processes in large-scale and distributed AI systems. Suitable for environments where stream independence, reproducibility, and architectural clarity are engineering considerations.
See Artificial Intelligence Use Case →Pre-seeded at manufacture with a unique randomness pool, Mesinja RNGs provide a solution to entropy starvation on low power IoT devices. Mesinja RNGs permit you to maintain secure operation across devices.
See How Mesinja Addresses IoT Entropy Starvation →Works as a digital post-processor to produce stable, uniform output from physical randomness sources, while preserving the intrinsic uncertainty associated with the raw entropy from those physical randomness sources.
Learn About Debiasing and Whitening →Properties
Mesinja RNGs are:
under defined inputs
using transparent methods
even at extreme scale
with structured parameter control
with low computational overhead
They offer a high quality, defensible alternative for developers, researchers, and engineers who require clarity, not just compliance.
Contact
Have a specific use case in mind?
Whether you are developing a simulation platform, testing TRNG hardware, building for embedded or regulated environments, or have some other application in mind, we would love to hear from you.
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