Connecting AI to Your Own Systems – RAG, MCP, and Automation

RAG SYSTEM

Connect LLMs to knowledge bases, ticket systems, and inventory data—using RAG. Through MCP and n8n, the AI even triggers real-world actions. This turns a generic chat into an assistant that knows your company’s knowledge and works alongside you.

Answers with supporting evidence

The AI responds based on your actual documents rather than on a hunch—which significantly reduces hallucinations.

Always Up-to-Date

New content is available immediately—without having to retrain the model. Changing knowledge means updating the source.

Sources are traceable

Answers cite the source—verifiable rather than a black box, especially when it comes to critical decisions.

Data Under Control

The sources of knowledge remain in-house or within the EU—only the relevant portion is used in the model.

From Chat to Action

Through MCP and n8n, the AI triggers real-world actions—creating a ticket, retrieving data, or starting a workflow.

Model-independent

Works with both Open-Weight and Frontier models, whether self-hosted or via NWS—no lock-in.

The Problem

Off-the-shelf AI doesn’t know your company—and you can tell from its answers. This is exactly where RAG comes in.

Generic AI doesn’t know you

A standard LLM knows nothing about your products, contracts, and processes. The answers tend to be general and often unhelpful.

Hallucinations

Without a factual basis, AI comes up with answers that sound plausible but are incorrect—a risk for any decision.

Knowledge is scattered

Wiki, tickets, file repositories, inventory: Even humans have a hard time finding the right information—let alone an AI without access to it.

How we work with you

Four steps, the same for every NETWAYS solution—from selecting sources to the active assistant during operation.

Step 1

Analysis & Concept

We identify the use cases, review your data sources and systems, and determine what the AI should have access to—and what it shouldn't.

→ Clear boundaries for access and permissions right from the start.

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Step 2

Setup & Integration

We're building the RAG pipeline: connecting sources, indexing (embeddings), and setting up retrieval—plus MCP/n8n for actions.

→ Your knowledge becomes searchable without being incorporated into model training.

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Step 3

Commissioning & Grounding

The solution goes live: The AI answers questions using context from your sources and cites the source. Campaigns are run through MCP/n8n.

→ Reliable answers backed by evidence, rather than guesswork.

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Step 4

Support & Operations

Upon request, we can handle the operation, updating of sources, and maintenance (MyEngineer)—or we can train your team.

→ We'll add new sources and tools without requiring you to maintain the pipeline.

How RAG Works

RAG stands for Retrieval-Augmented Generation: Before providing an answer, the AI searches your sources. Four steps from a question to a documented answer.

Linking to Knowledge Sources

Connect sources

Knowledge bases, tickets, documents, and inventory are integrated and indexed as embeddings—making them searchable.

Effect: Your scattered knowledge becomes discoverable.

Retrieval

Find relevant information

For each question, the retrieval system pulls the relevant excerpts from your sources—rather than feeding all the information into the model.

Effect: Only the relevant context reaches the model.

Generate a response

Well-reasoned answer

The LLM answers the question within this context and cites the source—in a way that is transparent and verifiable.

Result: reliable answers instead of hallucinations.

Trading via MCP

Trigger an action

Using MCP-Tools or n8n, the AI triggers actual processes as needed—creating a ticket, retrieving data, or starting a workflow.

Effect: Responses turn into actions.

What You’ll Achieve

Answers based on company knowledge, fewer wild guesses—an assistant that takes action.

Answers to Company Knowledge

The AI is familiar with your documents, tickets, and data, and responds specifically to your context—not in general terms.

Fewer hallucinations

Grounding information in reliable sources with citations makes answers verifiable and trustworthy.

An Active Assistant

With MCP and n8n, it’s not just about providing information—the AI triggers actual processes in your systems.

What is your solution built with?

Tried-and-true open-source components—run in-house or via NWS. You decide what you’ll do yourself and what NETWAYS will handle.

vLLM

The inference backend for the language model—high-performance and OpenAI-compatible, available as a self-hosted solution or as a managed AI model via NWS.

OpenWebUI

The familiar chat interface with native RAG and tool integration—this is where your employees can ask questions about company knowledge.

n8n

Orchestrates the RAG pipeline and actions: connects sources, calls the retrieval process, and triggers actual operations in your systems via MCP.

Snipe IT

Example of a connected data source: inventory and asset data that the AI accesses via RAG and uses to provide targeted responses.

We’ll integrate what you’re already using with

RAG depends on your sources. A selection of the systems we typically integrate as a knowledge base—and the building blocks that support the pipeline.

Sources of Knowledge

Confluence
Nextcloud
SharePoint
File Storage
Wikis

Data & Inventory

Snipe IT
PostgreSQL / MySQL
CRM Systems
Icinga

Ticket Systems & ITSM

Jira
Zammad
OTRS
ServiceNow

Model & Integration

vLLM
NWS AI
OpenAI-compatible API
MCP
n8n

Vector & Retrieval

pgvector
Qdrant
Elastic / OpenSearch
bge models

Questions & Answers

Frequently Asked Questions About This Solution

What is RAG?

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RAG stands for Retrieval-Augmented Generation. Instead of simply responding based on what it has learned during training, the system first searches your own sources, extracts the relevant passages, and provides them to the language model as context. The answer will then be based on your actual documents—including citations.

How do I integrate AI with internal data?

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By connecting the sources—Wiki, tickets, file repositories, databases—and indexing them as embeddings. For each question, the retrieval system finds the relevant passages that the model uses to generate an answer. NETWAYS builds this pipeline and also manages access and permissions.

What is MCP?

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MCP (Model Context Protocol) is an open standard that allows a language model to access external tools and data sources. This allows the AI not only to read, but also to perform defined actions—such as creating a ticket or querying a database—via a clean, controllable interface.

Can AI access internal systems?

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Yes—under control. Through MCP-Tools and n8n, the AI is granted targeted access to individual systems and is only allowed to trigger clearly defined actions. We work together to define permissions and limits so that the AI only sees and does what is intended.

Does RAG reduce hallucinations?

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Clearly. Because the answer is based on specific, cited sources and provides the reference, the model makes fewer assumptions and becomes verifiable. You can never completely rule out nonsense, but sound reasoning combined with proper citations makes answers reliable enough for everyday work.

Do I need a separate model for this?

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No. RAG works with both Open Weight models and Frontier models—whether self-hosted, via NWS’s Managed AI Models, or via API. You can start small with NWS and later switch to running your own operation without having to rebuild the RAG pipeline.

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