Research first.
Product second.
Snath AI is an independent research initiative with one thesis: the science of how AI systems learn continuously, without forgetting, and without human annotation — published openly before it becomes a product.
DAS · UCR · LTL · EIM · PAV · PERSIST
10 abstract contracts · UCR
from 0.45 baseline · LTL
Six papers. One architecture.
Each one opens the gap the next one closes.
The same architecture that runs the science runs the product.
Lár is the deterministic graph execution engine the whole programme is built on. Every compliance primitive, every routing invariant, every adapter in the papers — it all runs on Lár. Pure Python. No magic loops. Every decision path is explicit code.
Aadithya Vishnu Sajeev
Founder · Snath AI Open Source Research Initiative
Self-taught architect. Six papers in the Lár series, all authored independently. Background in computer science and cognitive systems. Currently based between Kerala and Ireland.
Vinay S
Co-founder · Product & Design
Product architect and designer behind the Snath AI identity, developer experience, and the compliance showcase. Shaped how the engine becomes a product users can reason about.
Open by default
Code under Apache 2.0. Papers under CC BY 4.0. Every result deposited on Zenodo with a permanent DOI before it becomes a product claim.
Reproducible
Runs are HMAC-SHA256 audited. Seeds are published. The numbers on the research page are the numbers in the repo.
Independent
No institutional funding. No lab affiliation. All compute is personal. That constraint forces efficiency.
Falsifiable
Experiments are pre-registered. We publish honest negatives. There is a standing list of what we have not proven on the research page.
Start with the science.
Six published papers with DOIs, runnable code, and honest accounts of what isn't proven. The product is built on top of it.