Route and analyze customer feedback with Qwen3-VL, Tally, PostgreSQL
UnknownWhat this workflow does
This AI workflow has 26 nodes and runs on Tally events. Along the way it connects OpenAI Chat Model, Discord, Postgres and Tallyforms (community).
How it runs
Trigger: On Tally events.
- Field Mapping (Edit Fields (Set)): sets 4 fields: Customer Name, Email Address, Message…
- Sentiment Analysis (Basic LLM Chain): sends a prompt to an LLM, with model: Sentiment LLM; output parser: Sentiment Parser
- If: checks a condition and branches into true / false
- Text Classification (Basic LLM Chain): sends a prompt to an LLM, with model: Classification LLM; output parser: Classification Parser
- AI Results Merge (Merge): merges the data from its input branches
- Fetch Image (HTTP Request): calls an HTTP API
- Empty Keywords Handler (Edit Fields (Set)): sets 1 field: keywords
- Data Aggregation (Aggregate): combines items into one
- Image Keyword Extraction (Code): runs custom JavaScript code
- Image Results Merge (Merge): merges the data from its input branches
…plus 9 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
We can't fully verify this one: it relies on 1 community package, and n8n 3.0 turns unverified community packages off by default (N8N_UNVERIFIED_PACKAGES_ENABLED=false).
- Check that n8n-nodes-tallyforms is verified or supports 3.0 (used in Tally Trigger).
Nodes used (13)
- Tally Trigger
n8n-nodes-tallyforms.tallyTrigger - Edit Fields (Set)
n8n-nodes-base.set - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - If
n8n-nodes-base.if - HTTP Request
n8n-nodes-base.httpRequest - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Discord
n8n-nodes-base.discord - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Code
n8n-nodes-base.code - Merge
n8n-nodes-base.merge - Aggregate
n8n-nodes-base.aggregate - Postgres
n8n-nodes-base.postgres - Switch
n8n-nodes-base.switch
From the author
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…