# Dual-path customer support system with Google Sheets: n8n workflow

> Started by someone opening a chat with it, and works across Google Sheets and in-memory vectors.

Source: https://millionrounds.com/workflow/dual-path-customer-support-system-with-google-sheets (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)

# Dual-path customer support system with Google Sheets

Started by someone opening a chat with it, and works across Google Sheets and in-memory vectors. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 15 steps, 1 branch: a working afternoon to understand properly.

Medium Calls a model: Gemini Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fdual-path-customer-support-system-with-google-sheets)

**15 steps** Medium: a working afternoon

**1** Branch: places the flow splits

**0** Loops or waits

**2** Services it connects to

## The flow

Starts it 2 Model 8 Logic 3 Service 2

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

- 1 **When clicking ‘Execute workflow’** Manual trigger *Starts it*
- 2 **Embeddings HuggingFace Inference** Embeddings Hugging Face Inference *Model*
- 3 **Default Data Loader** Document Default Data Loader *Model*
- 4 **When chat message received** Chat trigger *Starts it*
- 5 **Embeddings HuggingFace Inference2** Embeddings Hugging Face Inference *Model*
- 6 **Knowledge Database** Google Sheets *Service*
- 7 **Generate & Store Embeddings** Vector Store In Memory *Model*
- 8 **Retrieve & Score Embeddings** Vector Store In Memory *Model*
- 9 **Forward Chat Message** Merge *Logic*
- 10 **Extract Questions** Set *Logic*
- 11 **Determine Question Type** If *Logic*
- 12 **Chat Model** Google Gemini *Model*
- 13 **Get Respective Answers** Google Sheets *Service*
- 14 **Respond to Chat1** Chat *Model*
- 15 **Respond to Chat** Chat *Model*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 15, with 1 branch
- **Size:** Medium: a working afternoon
- **Connects to:** Google Sheets, in-memory vectors
- **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 Google Sheets and in-memory vectors accounts, 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. 15 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 15 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%2Fdual-path-customer-support-system-with-google-sheets)
- **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.

[Google Sheets](https://millionrounds.com/workflows?service=Google+Sheets#library)[in-memory vectors](https://millionrounds.com/workflows?service=in-memory+vectors#library)

## Workflows like it

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

- [**Answer customer questions on WhatsApp with OpenAI and Gmail** 15 steps · calls a model · Gmail, Google Sheets](https://millionrounds.com/workflow/answer-customer-questions-on-whatsapp-with-openai-and-gmail)
- [**Automate customer support with Gmail and Google Sheets** 15 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/automate-customer-support-with-gmail-and-google-sheets)
- [**Route WhatsApp support requests with Groq and Google Sheets** 15 steps · calls a model · Gmail, Google Sheets](https://millionrounds.com/workflow/route-whatsapp-support-requests-with-groq-and-google-sheets)
- [**Triage tenant complaints with Slack and Google Sheets** 15 steps · calls a model · Email, Google Sheets](https://millionrounds.com/workflow/triage-tenant-complaints-with-slack-and-google-sheets)
- [**Answer WhatsApp FAQs with OpenAI and Google Sheets** 14 steps · calls a model · Google Sheets](https://millionrounds.com/workflow/answer-whatsapp-faqs-with-openai-and-google-sheets)
- [**Answer code of conduct questions in Slack with OpenAI and RAG** 14 steps · calls a model · Google Drive, Slack](https://millionrounds.com/workflow/answer-code-of-conduct-questions-in-slack-with-openai-and-ra)