The world's first open-source library for mathematical gnostics.
Small Data, Big Impact.
Machine Gnostics finds hidden patterns in small, noisy, high-value datasets where traditional AI and statistics struggle. Open-source, Python-native, explainable by design.
Why Machine Gnostics?
Where conventional machine learning falls short, we excel — turning small data into powerful solutions.
Your most expensive events are often the rarest: 20 failures, not 20 million clicks. Machine Gnostics is built for exactly those small, noisy, high-stakes datasets where assumptions break and risk is real.
“Let data speak for themselves.”
Why Now?
The Theory Finally Has Tooling
Mathematical Gnostics existed for decades in papers, but not in a practical, pip-installable form. That implementation gap is now closed.
The Pain Became Acute
Industries are expected to deliver AI outcomes, yet many high-value domains still work with tiny datasets. "Just collect more data" is no longer a strategy.
Regulation Changed the Rules
The EU AI Act, FDA pathways, and aviation oversight increasingly demand explainable and traceable decisions. Determinism is becoming a compliance asset.
Small-Data Industries Are Emerging Fast
High-value sectors with scarce data are scaling rapidly. Teams that identify decision-critical signals first gain a compounding competitive advantage.
Discover Machine Gnostics
Watch this introductory video to understand how Machine Gnostics revolutionizes machine learning with non-statistical methods.
Core Features
See structure in data too small for statistics
Detect useful relationships in sparse, noisy data without relying on fragile statistical assumptions.
Mechanism
Built on GDFs, Riemannian geometry, and small-sample theory to expose deterministic structure from limited observations.
Explore Data Analysis Modules →Predictions you can explain to an auditor
Deploy models that remain inspectable, traceable, and defensible in regulated or high-accountability environments.
Mechanism
Residual entropy and thermodynamic models preserve explainability while supporting practical ML workflows.
Explore ML Models →Neural nets that hold up when data is scarce
MAGNET extends deep learning into noise-heavy, low-data settings where conventional networks can become brittle.
MAGNET Mechanism
Next-gen deep learning rooted in mathematical gnostic principles and thermodynamic stability constraints.
Explore MAGNET →Machine Gnostics Benchmark
Our benchmark program is in progress. Current positioning is deliberate: comparable predictive quality with less data, not broad claims against every ML stack. Benchmarking small datasets with the Anscombe Quartet — revealing insights statistics alone can't see.
Choose Your Entry Point
Engineer
Try it in your current Python stack and evaluate it on your own constrained datasets.
Reliability Lead
Reduce costly failures where rare events matter more than average-case accuracy.
Researcher
Review the theory, derivations, and technical concepts behind the implementation.
Business Leader
Turn scarce, expensive operational data into actionable decisions for risk, reliability, and measurable business impact.
Free Tutorial
Request a guided walkthrough and get a practical introduction to Machine Gnostics for small-data projects.
For the mathematically curious: the foundations
Licensing for Industrial Use
Machine Gnostics is open-source under GPLv3. If your deployment model requires embedding in proprietary products, contact us to discuss commercial licensing paths.
FOUNDING PARTNER PROGRAM · 1 SPOT OPEN THIS QUARTER
Be the proof. Not the experiment.
We work with one partner at a time on a high-value, data-scarce problem we co-develop and publicly document. You get favorable terms; the industry gets the reference case.
Take Your First Step.
Install, test with your own constrained dataset, and then decide whether you want a deeper collaboration.