Building memory infrastructure for the agentic era
One graph. Many humans. Many agents.
Our Mission
AI systems are improving quickly, but most of them still operate with shallow memory. Teams keep replaying context in tickets, docs, standups, and chat prompts, while agents re-learn the same project history over and over.
ScientiaMesh solves this by turning everyday captures into a living knowledge graph that can be queried by people and by AI agents through MCP.
The Problem We're Solving
Modern teams face three recurring constraints:
- •Context Fragmentation: Relevant knowledge is split across tools and owners, so decisions slow down.
- •Agent Amnesia: Most assistants start from zero every time, unless humans manually rehydrate context.
- •Low Traceability: Outputs lose credibility when teams cannot trace claims back to source material.
Our Approach
ScientiaMesh is designed around three principles:
Scoped by Design
Memory should be shareable without being exposed. Mesh-level permissions keep context scoped to the right people and agents.
Assistant Agnostic
We do not lock teams into one assistant. Use Claude, ChatGPT, OpenClaw, Clawdbot workflows, or custom agents via MCP.
Graph-Native Memory
Instead of static documents, we maintain connected memory with lineage so retrieval stays useful as your context volume grows.
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