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AI-powered customer feedback routing with Gmail, Slack, Pipedrive, Zendesk & Notion

PollupAIPollupAI
25 views
2/3/2026
AWSS3Cost ManagementData ProcessingBilling
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Who’s it for

This workflow is built for B2B SaaS and CX teams that are drowning in unstructured customer feedback across tools. It’s ideal for Customer Success, Product and Support leaders who want a light “voice of customer engine” without rebuilding their stack: Gmail for interactions, Slack for conversations, Pipedrive for notes and Zendesk for tickets, plus Notion for follow-up tasks. 

How it works / What it does

The workflow runs on a schedule or manual trigger and first sets the CSM’s email address. It then uses an AI “Data agent” to pull recent customer signals from multiple sources: Gmail messages, Slack messages, Pipedrive notes and Zendesk tickets. A “Signals agent” compresses each piece of feedback into a concise, neutral summary, which is then grouped by topic via a “Clustering agent”. Each cluster gets a label, count and examples. Finally, an “Action agent” routes clusters based on their label:

  • Create Zendesk tickets for product/performance issues
  • Post to a dedicated Slack channel for billing / contract topics
  • Create Notion tasks for sales-related feedback
  • Send targeted Gmail messages to the CSM for high-risk or engagement-related items

How to set up

  1. Import the workflow into n8n.
  2. Connect credentials for Gmail, Slack, Pipedrive, Zendesk, Notion and OpenAI.
  3. Update the CSM email in the “Set CSM email” node.
  4. Adjust date filters, send-to addresses and Slack channel IDs as needed.
  5. Enable the schedule trigger for weekly or daily digests.

Requirements

  • Active accounts & credentials for: Gmail, Slack, Pipedrive, Zendesk and Notion
  • OpenAI (or compatible) API key for the LLM node
  • At least one Slack channel for posting feedback (e.g. #billing-feedback)

How to customize the workflow

  • Change the time window or filters (sender, channel, query) for each data source.
  • Edit the clustering and routing prompts to match your own categories and teams.
  • Add new destinations (e.g. Jira, HubSpot) by connecting more tools to the Action agent.
  • Modify thresholds (e.g. minimum count) before a cluster triggers an action.
  • Localize labels and email copy to your team’s language and tone.

n8n AI-Powered Workflow Template

This n8n workflow provides a robust template for building AI-powered automation solutions using LangChain components. It serves as a foundational structure, demonstrating how to integrate various AI building blocks like agents, LLM chains, memory, and output parsers.

What it does

This workflow is a template that includes the following core AI components, ready to be configured and connected:

  1. Manual Trigger: Allows for manual execution of the workflow, useful for testing and development.
  2. Schedule Trigger: Enables the workflow to run automatically at specified intervals.
  3. Edit Fields (Set): A utility node for manipulating and transforming data within the workflow.
  4. Sticky Note: For adding comments and documentation directly within the workflow canvas.
  5. AI Agent: A LangChain agent node, capable of complex reasoning and tool usage.
  6. Basic LLM Chain: A LangChain node for creating sequential chains of operations involving a Large Language Model.
  7. OpenAI Chat Model: A LangChain node for integrating with OpenAI's chat models, serving as the core language model.
  8. Simple Memory: A LangChain memory node (Buffer Window Memory) to maintain conversational context.
  9. Structured Output Parser: A LangChain node to parse and structure the output from LLMs, often into JSON or other defined formats.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance (cloud or self-hosted).
  • OpenAI API Key: Required for the "OpenAI Chat Model" node to function.
  • LangChain Credentials: While LangChain nodes are built-in, specific configurations might require API keys or access tokens for external services if you extend the agent's capabilities with custom tools.

Setup/Usage

  1. Import the workflow: Download the JSON file and import it into your n8n instance.
  2. Configure Credentials:
    • For the "OpenAI Chat Model" node, ensure your OpenAI API key is configured as a credential.
  3. Customize AI Components:
    • AI Agent: Define your agent's capabilities, tools, and prompts.
    • Basic LLM Chain: Configure the prompt templates and sequence of operations for your LLM chain.
    • Simple Memory: Adjust memory parameters if needed (e.g., window size).
    • Structured Output Parser: Define the desired output schema (e.g., JSON schema) for parsing LLM responses.
  4. Connect and Extend: Connect the various AI components to build your desired automation logic. For example, you might feed output from the "Structured Output Parser" into a service node (like Gmail, Slack, Pipedrive, Zendesk, or Notion) to take action based on the AI's analysis.
  5. Test: Use the "Manual Trigger" to test the workflow and ensure all components are functioning as expected.
  6. Activate: Once configured and tested, activate the workflow, optionally using the "Schedule Trigger" for automated runs.

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