Frequently asked questions

What problem does InsightMesh solve?

AI can't reason over siloed data. InsightMesh solves this by:

• Unifying search across tools (Slack, GDrive, Jira, etc.)

• Storing semantic and structural relationships in Neo4j

• Injecting high-fidelity, access-controlled context into LLM workflows

• Enabling agents to operate with a real-world map of your org’s knowledge

How does it work?

InsightMesh is a modular stack:

• Ingestion pipelines: Pull structured and unstructured data from Slack, Google Drive, Jira, custom files, and more

• Neo4j: Stores all documents, relationships, and inferred links as a knowledge graph

• Elasticsearch + Vector DB (optional): For fast keyword + similarity search

• MCP-Server: Prepares and delivers context to LLMs or agents

• UI layer (optional): Browse, chat, or run agents via OpenWebUI or custom frontends

No black boxes. Just clean, modular infrastructure.

Who is this for?

InsightMesh is for:

• Engineering teams building internal copilots and agents

• Knowledge-heavy orgs navigating fragmented systems

• Security-conscious orgs needing access-aware context injection

• Open-source builders looking to contribute to next-gen infrastructure

What is MCP and why does it matter?

Models Context Protocol (MCP) is the bridge between raw data and AI comprehension. It defines how to:

• Format and filter context for injection into LLMs

• Preserve permissions, traceability, and structure

• Enable explainable, reproducible AI behaviors

InsightMesh uses MCP to make sure that every AI interaction is grounded in relevant, structured, secure knowledge.

What’s the long-term vision?

• 📡 Live, evolving organizational memory

• 🧠 Agentic work assistants grounded in graph context

• 🕸️ MCP-based LLM integration with private knowledge

• 🔁 Composable automation rooted in what your org actually knows

We’re building the connective layer between human knowledge, systems, and AI

Can InsightMesh help reduce hallucinations in LLMs?

Yes. By injecting grounded, structured, access-controlled data into the LLM via MCP, InsightMesh provides relevant context that keeps generations factual and traceable.

How customizable is InsightMesh for specific use cases?

Highly. You can bring your own UI, swap components, extend the Neo4j schema, or plug in custom ingestion logic. InsightMesh is designed to flex with your needs—not lock you into rigid patterns.