# Automate ITSM ticket classification and resolution with Gemini: n8n workflow

> Started by someone opening a chat with it, and works across Qdrant. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply...

Source: https://millionrounds.com/workflow/automate-itsm-ticket-classification-and-resolution-with-gemi (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [Customer support](https://millionrounds.com/workflows?category=Customer+support#library)

# Automate ITSM ticket classification and resolution with Gemini

Started by someone opening a chat with it, and works across Qdrant. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 17 steps: a working afternoon to understand properly.

Medium Calls a model: Gemini Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fautomate-itsm-ticket-classification-and-resolution-with-gemi)

**17 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**1** Service it connects to

## The flow

Starts it 2 Model 12 Logic 1 Service 2

The steps in the order they run, from what starts it.

- 1 **Google Gemini Chat Model** Lm Chat Google Gemini *Model*
- 2 **Simple Memory** Memory Buffer Window *Model*
- 3 **When chat message received** Chat trigger *Starts it*
- 4 **Google Gemini Chat Model1** Lm Chat Google Gemini *Model*
- 5 **Google Gemini Chat Model2** Lm Chat Google Gemini *Model*
- 6 **Embeddings Google Gemini** Embeddings Google Gemini *Model*
- 7 **When clicking ‘Execute workflow’** Manual trigger *Starts it*
- 8 **Embeddings Google Gemini1** Embeddings Google Gemini *Model*
- 9 **Default Data Loader** Document Default Data Loader *Model*
- 10 **AI Agent** Agent *Model*
- 11 **Text Classifier** Text Classifier *Model*
- 12 **Summarization Chain** Chain Summarization *Model*
- 13 **Qdrant Vector Store1** Vector Store Qdrant *Model*
- 14 **Edit Fields** Set *Logic*
- 15 **Qdrant Vector Store** Vector Store Qdrant *Model*
- 16 **Create an incident** Service Now *Service*
- 17 **HTTP Request** Http Request *Service*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 17
- **Size:** Medium: a working afternoon
- **Connects to:** Qdrant
- **Model:** Gemini
- **Retrieval:** Answers are drawn from documents you supply, not from the model alone

## Questions

What does it need to run?

An n8n account, plus your own Qdrant account, and a key for Gemini. You connect your own accounts after importing it; none are included.

What starts it?

It starts on someone opening a chat with it.

Does it use AI?

Yes. It calls Gemini. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Plan an afternoon. 17 steps is enough that you should read each one before running it.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

**Before you run it**

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 17 steps before you point it at anything that matters.

## Run it in n8n

- **Create your n8n account** Free to start. n8n is where the workflow runs. [Create an n8n account ↗](https://millionrounds.com/n8n)
- **Download the workflow** Free with a Millionrounds account. [Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fautomate-itsm-ticket-classification-and-resolution-with-gemi)
- **Paste it into n8n** Open a new workflow in n8n and paste. Then connect your own accounts.

Stored with the author's traces removed: canvas notes, instance identifiers, saved credential names and any live document they had left behind.

## Connects to

Each opens the other workflows that use it.

[Qdrant](https://millionrounds.com/workflows?service=Qdrant#library)

## Workflows like it

Also customer support, with the same services where possible, closest in size first.

- [**Answer Instagram FAQ DMs with Gemini and Qdrant** 17 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/answer-instagram-faq-dms-with-gemini-and-qdrant)
- [**Answer infrastructure questions in Mattermost with OpenRouter** 17 steps · calls a model · Mattermost, Qdrant](https://millionrounds.com/workflow/answer-infrastructure-questions-in-mattermost-with-openroute)
- [**AI voice chatbot with ElevenLabs and OpenAI** 18 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/ai-voice-chatbot-with-elevenlabs-and-openai)
- [**Answer infrastructure questions in Slack with OpenAI** 16 steps · calls a model · Qdrant, Slack](https://millionrounds.com/workflow/answer-infrastructure-questions-in-slack-with-openai)
- [**Classify and route DevOps support messages with Slack** 15 steps · calls a model · Qdrant, Slack](https://millionrounds.com/workflow/classify-and-route-devops-support-messages-with-slack)
- [**Run a WhatsApp customer support chatbot with OpenWA and OpenAI** 26 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/run-a-whatsapp-customer-support-chatbot-with-openwa-and-open)