Semantic memory and structured reasoning for AI agents.
chatur.ai builds infrastructure for AI systems that think differently. Starting with persistent memory, ontology authoring, and RDF-backed knowledge graphs.
While the field converges on narrow recipes, we're rebuilding on first principles. Symbolic reasoning. Structured memory. Semantic web standards. The infrastructure that makes agents actually learn.
Products
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Our thesis
Rethinking AI infrastructure
Problem
LLMs forget. Every session starts from zero. No continuity. No learning. No long-term reasoning. A system that can't remember isn't intelligent—it's just stateless.
Our answer
You author schemas—OWL ontologies declaring what's worth remembering. Cortex extracts facts, stores them in an RDF graph, and injects relevant context on every prompt. Memory you can reason about.
Why it matters
Symbolic reasoning isn't dead—it's misunderstood. Combine it with LLMs and you get agents that actually learn. Systems that carry knowledge forward. Continuity.
Principles
- • Local first — your data stays on your machine
- • Explicit — only facts you declare, never hallucinations
- • Standards — W3C RDF, SPARQL, SHACL, OWL
- • Queryable — reason over your memory programmatically
Partnership & collaboration
Work with us
We're building something new. If you're working on knowledge graphs, agentic systems, semantic infrastructure, or applied AI—let's talk. Consulting, collaborations, research partnerships. Every inquiry gets a reply.