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

⚡ 46 views · 🎫 Ticket Management & Triage

Description

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:
  • Create a new Discord Bot API credential.
  • Paste your Bot Token obtained from the Discord Developer Portal.
• 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.

🔗 Nodes Used

HTTP Request, Postgres, Discord, Basic LLM Chain, OpenAI Chat Model, Structured Output Parser

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup