Automatically enrich and categorize support tickets

WORKFLOW AUTOMATION

Every new ticket reaches the team with all the relevant context: customer data, contract status, and relevant solutions from previous cases—all automatically compiled before anyone even looks at it. Built with n8n, compliant with data protection regulations and hosted in-house.

New ticket arrives
via email, portal, or API
Attach data
Existing customer? Support contract? SLA?
U
LLM enhances the content

summarizes, identifies gaps, translates

RAG reviews old cases
finds similar tickets that have already been resolved
Organize & Prioritize
Category, Urgency, Proposed Solution
User takes over

with the full context—people decide

The Problem

In support, the same steps are repeated for every case: gathering context, reviewing the history, and assessing the urgency. It takes time before an answer even emerges.

Manual Research

Agents have to click through CRM systems, contract data, and old tickets just to be able to figure out the case at all.

Incorrect Prioritization

Without an SLA or contractual context, urgent cases end up in the same queue as non-critical requests.

Data must not be leaked

U.S. cloud services often exclude customer data from tickets—a solution must be hosted in-house.

How we work with you

Four steps, the same for every NETWAYS solution—from analyzing your support process to fully automated processes in live operation.

Step 1

Analysis & Concept

We'll take a look at your support process and ticket system and determine which steps are worth automating.

→ We only automate what actually saves time—it’s not an end in itself.

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Step 2

Setup & Integration

n8n is deployed within your organization and connected via APIs to your ticketing system, CRM, and knowledge bases.

→ A seamless integration instead of a standalone solution that breaks down later.

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Step 3

Commissioning & RAG

The workflows go live: Tickets are enriched, classified, and provided with solutions from past cases using RAG.

→ Humans remain in the loop—AI makes suggestions but does not make decisions.

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Step 4

Support & Operations

If you'd like, we can take full responsibility for operating and maintaining the workflows—or train your team to manage them on their own.

→ Updates and availability won't take up any of your time.

What’s Happening Behind the Scenes

Four building blocks that can be implemented individually or in combination—depending on where you have the greatest leverage.

Attach data

Collect ticket context

As soon as a ticket is received, the workflow retrieves the customer status, support contract, and SLA from the connected systems and attaches them all to the ticket.

Result: fewer follow-up inquiries, proper prioritization according to the SLA.

Enrich the content

LLM processes the content

In the background, a language model summarizes long threads, extracts error messages and system data from the body of the text, identifies missing required information, and translates foreign-language inquiries—even before an agent opens the ticket.

Result: Agents read a clear summary instead of wading through raw text.

Classify

Classify & Prioritize

The model determines the category, urgency, and sentiment, and routes the ticket to the right team—instead of having someone sort each request manually.

Result: shorter processing time, higher first-contact resolution.

Suggest

Solutions from Past Cases

Using RAG, the system semantically searches previous tickets and the knowledge base and presents the agent with appropriate response templates.

Effect: Knowledge gained from solved cases becomes immediately usable again.

What You’ll Achieve

Support starts with the full context, saves time, and improves quality

Processed faster

Searching, processing, and sorting are done automatically. Agents read a finished synopsis and spend their time solving problems, not searching.

Prioritized correctly

Urgent and contractually guaranteed cases are identified and given priority—without manual triage.

Data remains in-house

Workflows and models run in your data center or at NWS—no sensitive tickets leave the EU.

What is your solution built with?

Tried-and-true open-source components. You decide which components you’ll manage yourself and where you’ll rely on NETWAYS services.

n8n

Open-source platform for workflow and process automation. It connects the ticketing system, CRM, and knowledge bases via visual nodes—without the need for in-depth programming. It is managed entirely in-house, so no ticket data is shared with a third-party SaaS provider.

Open WebUI

A familiar chat interface for the connected models. Agents use it to search through existing knowledge and receive suggested answers—similar to ChatGPT, but internally. It’s easy to use right away, with no training required.

Snipe IT

Central database for hardware and software inventory. It provides the ticket workflow with the asset context—which device, which license, and which contract are associated with the case. Scattered lists are transformed into a reliable source that can be integrated with automation.

vLLM

High-performance inference backend for running your own language models. It provides the computing power needed to aggregate, classify, and enrich tickets. This way, AI processing stays on your hardware or at NWS—not in an external cloud.

We’ll integrate what you’re already using with

n8n comes with native integrations for over 400 systems—and everything else can be added via API. A selection of the tools that our workflows typically integrate with

Ticket Systems & Help Desk

  • Jira Service Management
  • Zendesk
  • Freshdesk
  • Zammad
  • OTRS
  • ServiceNow

Communication

  • Slack
  • Microsoft Teams
  • Rocket.Chat
  • Mattermost
  • Telegram

Data & Office

  • Microsoft 365
  • Google Workspace
  • PostgreSQL / MySQL
  • Excel / Google Sheets

CRM

  • Salesforce
  • HubSpot
  • Microsoft Dynamics

Knowledge & Documentation

  • Confluence
  • BookStack
  • Notion
  • SharePoint

Questions & Answers

Frequently Asked Questions About This Solution

How do I automate support tickets?

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A workflow engine like n8n connects your ticketing system to other sources. Every time a new ticket is created, a defined process runs automatically: enrich the context, classify it, search for similar cases, and attach the results to the ticket. NETWAYS replicates your existing process in these workflows.

What is n8n?

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n8n is an open-source platform for workflow and process automation. Visual nodes allow you to connect systems and map workflows without in-depth programming. Unlike pure SaaS services, n8n can be operated in-house, so no data is transferred to an external provider.

Can AI categorize tickets?

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Yes. A language model categorizes tickets by category, urgency, and sentiment, and can route them to the appropriate team. This classification is intended as a suggestion—the final decision and response remain with the human (human-in-the-loop).

What does “content enrichment” of a ticket mean?

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Beyond simply appending master data, a language model processes the ticket content itself: It summarizes long histories, extracts error messages and system information from the body text, identifies missing required fields, and translates foreign-language inquiries. The agent then opens the ticket with a clear summary instead of the raw text—this all happens in the background.

What is RAG?

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RAG stands for "Retrieval-Augmented Generation." Instead of simply responding based on its training, the model first searches your own data—such as past tickets and the knowledge base—and uses the content it finds as a basis for its response. This is how suggested answers based on the company's own knowledge are generated.

Is this GDPR-compliant?

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Yes. All components—the workflow engine, RAG, and model—can be run in your own data center or in the German, ISO-certified NWS cloud. This means that customer data from tickets never leaves the company or the EU.

We look forward to your message






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