Enterprise RAG knowledge layer
Turn private company data into a usable enterprise RAG knowledge layer
Meratic helps enterprise teams turn private company data from Slack, SharePoint, Confluence, Notion, and internal docs into a structured Agent RAG Layer for enterprise RAG, AI agents, L1 specialist copilots, and internal AI workflows. Instead of forcing every team to hand-build retrieval logic, we organize the context those systems need to answer with company-specific accuracy.
Private
Your enterprise RAG stack stays grounded in private company data.
Structured
We normalize fragmented knowledge into retrieval-ready context for AI agents.
RAG-ready
Your existing RAG tools get better context without rebuilding the stack.
Ideal for
Enterprise AI teams, platform owners, knowledge ops leads, internal tooling groups, and L1 specialist teams.
Primary CTA
Share your source systems and we map the enterprise RAG knowledge layer.
Where private company data blocks enterprise RAG today
Support data is in one system. Installation guides are in OneNote. Deploy commands are buried in Slack. Process documentation sits in SharePoint, PDFs, and wikis. The information exists, but it is not organized as a usable knowledge layer for enterprise RAG or AI agents.
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Important operating knowledge is distributed across team-specific tools.
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The latest answer often lives in chat history rather than an official system.
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Enterprise RAG and agent workflows fail when retrieval depends on scattered, ungoverned sources.
How the enterprise RAG architecture and Agent RAG Layer fit into your stack
Meratic sits between scattered private company data and the AI systems already in use across the business. We do not replace your enterprise RAG tools. We make them more useful by organizing the knowledge layer, Agent RAG Layer, and retrieval context they depend on.
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Connects to the systems where operational knowledge is already stored.
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Normalizes content into a governed enterprise knowledge layer with cleaner retrieval structure.
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Feeds better context into enterprise RAG pipelines, Agent RAG Layer deployments, copilots, and AI agent workflows.
Why enterprise RAG fails without a knowledge layer
Most enterprise AI stacks can orchestrate prompts and agents, but they still fail when private company data is not organized into a consistent retrieval layer for enterprise RAG.
Scattered internal knowledge
Critical private company data lives in Slack threads, SharePoint folders, PDFs, docs, tickets, and wikis.
General models lack company context
Even strong foundation models cannot answer well when the required private company data was never part of their training data.
Enterprise RAG needs a knowledge layer
Teams should not need to handcraft retrieval logic and context plumbing for every workflow, app, assistant, or AI agent.
Source systems in your enterprise knowledge layer
We focus on the places where private company data usually lives, then shape that content into a cleaner retrieval surface for enterprise RAG, AI agents, and copilots.
Slack
Conversations and decisions
SharePoint
Files, folders, and internal portals
Confluence
Process and team documentation
Notion
Operating docs and project knowledge
Google Drive
Docs, sheets, and shared artifacts
Internal docs
PDFs, SOPs, wikis, and knowledge bases
We turn private company data into structured retrieval context
Once you give us access to the right internal sources, we organize, clean, and structure that data so your enterprise RAG tools, AI agents, copilots, internal AI apps, and low-code workflows can retrieve the right company context at the moment a model needs it.
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Source ingestion across collaboration tools, file systems, docs, and internal knowledge bases.
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Knowledge normalization so the same concept is not fragmented across disconnected artifacts and retrieval indexes.
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Structured output designed to plug into the enterprise RAG systems, Agent RAG Layer deployments, copilots, AI agents, and applications you already use.
Example applications
How the enterprise RAG delivery model works
We sit between messy private company data and your AI tooling. The goal is not to replace your current stack. The goal is to make that stack more useful by giving enterprise RAG systems and AI agents access to cleaner company context when they would otherwise have none.
Delivery flow
- 1. You share the systems where proprietary knowledge currently lives.
- 2. We organize and structure the content into a governed enterprise knowledge layer.
- 3. Your enterprise RAG tools and AI workflows query better context with less manual setup.
Need an enterprise RAG knowledge layer for private company data?
Share your details and a short note about your stack, and we will show you how to turn fragmented private company data into a governed Agent RAG Layer for enterprise RAG, L1 specialist copilots, and AI agent workflows.
Good fit checklist
- You already use enterprise RAG pipelines, copilots, internal AI apps, or low-code tools.
- Your important private company data is spread across internal systems.
- You need a governed knowledge layer without rebuilding your whole stack.