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

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 customerfeedback with columns for name, emailaddress, feedbackmessage, imageurl, sentiment, category, and imgkeywords. 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 St