Automated Ticket Urgency Classification Workflow

This n8n workflow automates the process of classifying the urgency of support tickets received via a webhook. Designed for customer support or IT helpdesk teams, it ensures timely and organized ticket management through natural language processing and vector similarity search. When a new ticket is submitted, the workflow extracts relevant content, generates embeddings, stores and queries these embeddings in Pinecone, uses a language model to analyze and classify ticket urgency, and logs the results to Google Sheets. An alert system is in place to notify support teams of any errors during processing.

The process begins with a webhook trigger that receives ticket data. The text content of the ticket is split into manageable chunks for analysis. These chunks are converted into embeddings using Cohere, facilitating semantic understanding. The embeddings are inserted into Pinecone, a vector database, which also supports querying for similar tickets or information. The workflow then utilizes a language model (Anthropic) to interpret the ticket content in the context of urgency classification. Results are stored in a Google Sheet for record-keeping, and if any errors occur, a Slack notification is sent to alert staff.

This automation improves response times, standardizes ticket prioritization, and helps support teams focus on high-urgency issues quickly and efficiently.