RAG KI Solution Using Your Own Data
Integrate the AI Knowledge Base, Tickets, and More
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 include a reference to 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: for example, creating a ticket, retrieving data, or starting a process.
Model-independent
Works with Open Weight, such as Frontier models, whether self-hosted or via NETWAYS Managed Services—no vendor lock-in.
The Problem Behind Generic AI
Off-the-shelf AI doesn’t know your knowledge base, your tickets, or your inventory. This is exactly where a RAG AI solution using its own data comes into play.
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 people 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: the result is a fully functional assistant that operates seamlessly during normal operations.
Analysis & Concept
We'll identify the use cases, review your data sources and systems, and determine what the AI should and shouldn't have access to.
→ Clear boundaries for access and permissions right from the start.
Setup & Integration
→ Your knowledge becomes searchable without being incorporated into model training.
Commissioning & Grounding
→ Reliable answers backed by evidence, rather than guesswork.
Support & Operations
→ We'll add new sources and tools without requiring you to maintain the pipeline.
How a RAG AI solution works with your own data
RAG stands for Retrieval Augmented Generation: before providing an answer, the AI searches your sources—whether it’s a knowledge base or a ticket system. Four steps to a substantiated answer.
Connect sources
Effect: Your scattered knowledge becomes discoverable.
Find relevant information
Effect: Only the relevant context reaches the model.
Well-reasoned answer
Result: reliable answers instead of hallucinations.
Trigger an action
Effect: Responses turn into actions.
What You’ll Achieve
Answers to Company Knowledge · Fewer Hallucinations · A Hands-On Assistant
Answers to Company Knowledge
The AI is familiar with your documents, tickets, and data, and provides specific responses based on your context.
Fewer hallucinations
Grounding information in reliable sources with citations makes answers verifiable and trustworthy.
An assistant who takes action
With MCP and n8n, it’s not just about providing information—the AI actually triggers real processes in your systems.
What is your solution built with?
Proven open-source components, operated in-house or through NETWAYS Managed Services. You decide what you’ll do yourself and what NETWAYS will handle.
vLLM
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, initiates retrieval, and triggers actual processes in your systems via MCP.
Snipe-IT
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 components that make up 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 a RAG AI solution using your own data?
A RAG AI solution connects a language model to your own sources, such as a knowledge base, tickets, or inventory. Instead of simply responding based on what it learned during training, the system first searches these sources and provides the model with the relevant excerpts as context. The answer will then be based on your actual data, including the source.
What is RAG?
What is an AI knowledge base?
An AI knowledge base is a collection of your internal content—such as a wiki, documents, or Confluence—that is made searchable by a language model. It is often the entry point for a RAG AI solution, but it is not the only connected source.
How do I integrate AI with internal data?
By linking sources, wikis, tickets, file repositories, and 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?
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?
Yes, under control. Through MCP Tools and n8n, the AI is granted targeted access to individual systems and is permitted to trigger only 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?
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?
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 NETWAYS Managed Services and later switch to running your own operations without having to rebuild the RAG pipeline.
