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Route and analyze customer feedback with Qwen3-VL, Tally, PostgreSQL and Discord

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Created by: Neloy Barman || neloy-barman

Neloy Barman

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Last update 4 hours ago

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Self-Hosted

This workflow provides a complete end-to-end system for capturing, analyzing, and routing customer feedback. By combining local multimodal AI processing with structured data storage, it allows teams to respond to customer needs in real-time without compromising data privacy.

Who is this for?

This is designed for Customer Success Managers, Product Teams, and Community Leads who need to automate the triage of high-volume feedback. It is particularly useful for organizations that handle sensitive customer data and prefer local AI processing over cloud-based API calls.

🛠️ Tech Stack

  • Tally.so: For front-end feedback collection.
  • LM Studio: To host the local AI models (Qwen3-VL).
  • PostgreSQL: For persistent data storage and reporting.
  • Discord: For real-time team notifications.

✨ How it works

  1. Form Submission: The workflow triggers when a new submission is received from Tally.so.
  2. Multimodal Analysis: The OpenAI node (pointing to LM Studio) processes the input using the Qwen3-VL model across three specific layers:
    • Sentiment Analysis: Evaluates the text to determine if the customer is Positive, Negative, or Neutral.
    • Zero-Shot Classification: Categorizes the feedback into pre-defined labels based on instructions in the prompt.
    • Vision Processing: Analyzes any attached images to extract descriptive keywords or identify UI elements mentioned in the feedback.
  3. Data Storage: The PostgreSQL node logs the user's details, the original message, and all AI-generated insights.
  4. AI-Driven Routing: The same Qwen3-VL model makes the routing decision by evaluating the classification results and determining the appropriate path for the data to follow.
  5. Discord Notification: The Discord node sends a formatted message to the corresponding channel, ensuring the support team sees urgent issues while the marketing team sees positive testimonials.

📋 Requirements

  • LM Studio running a local server on port 1234.
  • Qwen3-VL-4B (GGUF) model loaded in LM Studio.
  • PostgreSQL instance with a table configured for feedback data.
  • Discord Bot Token and specific Channel IDs.

🚀 How to set up

  1. Prepare your Local AI:
    • Open LM Studio and download the Qwen3-VL-4B model.
    • Start the Local Server on port 1234 and ensure CORS is enabled.
    • Disable the Require Authentication setting in the Local Server tab.
  2. Configure PostgreSQL:
    • Ensure your database is running. Create a table named customer_feedback with columns for name, email_address, feedback_message, image_url, sentiment, category, and img_keywords.
  3. Import the Workflow:
    • Import the JSON file into your n8n instance.
  4. Link Services:
    • Update the Webhook node with your Tally.so URL.
    • In the Discord nodes, paste the relevant Channel IDs for your #support, #feedback, and #general channels.
  5. Test and Activate:
    • Toggle the workflow to Active.
    • Send a test submission through your Tally form and verify the data appears in PostgreSQL and Discord.

🔑 Credential Setup

To run this workflow, you must configure the following credentials in n8n:

  • OpenAI API (Local):
    • Create a new OpenAI API credential.
    • API Key: Enter any placeholder text (e.g., lm-studio).
    • Base URL: Set this to your machine's local IP address (e.g., http://192.168.1.10:1234/v1) to ensure n8n can connect to the local AI server, especially if running within a Docker container.
  • PostgreSQL:
    • Create a new PostgreSQL credential.
    • Enter your database Host, Database Name, User, and Password. If using the provided Docker setup, the host is usually db.
  • Discord Bot:
  • Tally:
    • Create a new Tally API credential.
    • Enter your API Key, which you can find in your Tally.so account settings.

⚙️ How to customize

  • Refine AI Logic: Update the System Message in the AI node to change classification categories or sentiment sensitivity.
  • Switch to Cloud AI: If you prefer not to use a local model, you can swap the local LM Studio connection for any 3rd party API, such as OpenAI (GPT-4o), Anthropic (Claude), or Google Gemini, by updating the node credentials and Base URL.
  • Expand Destinations: Add more Discord nodes or integrate Slack to notify different departments based on the AI's routing decision.
  • Custom Triggers: Replace the Tally webhook with a Typeform, Google Forms, or a custom Webhook trigger if your collection stack differs.