Overview
Overview This workflow transforms raw support tickets into actionable developer insights using AI and data processing. It automatically detects recurring issues, identifies root causes, ranks severity, and generates a structured engineering report. By combining embeddings, clustering, and AI analysis, it helps teams prioritize bugs, understand user pain points, and take data-driven product decisions. How It Works Scheduled Trigger Runs automatically at a defined time (e.g., daily). Workflow Configuration Defines time window, similarity threshold, scoring weights, and delivery options. Fetch Feedback Data Retrieves recent support tickets (bugs and feature requests) from Postgres. Preprocessing Cleans, normalizes, and removes duplicate messages. Embedding & Clustering Generates embeddings using OpenAI. Groups similar tickets using cosine similarity. Cluster Aggregation Combines related tickets into structured clusters. Root Cause Analysis AI agent analyzes clusters to identify: Root cause Impacted module Severity Debug steps Fix direction Severity Scoring Calculates weighted score based on: Frequency Sentiment Churn risk Enterprise impact Report Generation Generates a developer-focused report including: Executive summary Ranked bugs Feature requests Risk analysis Sprint priorities Delivery Sends report to Slack Optionally creates Jira issues Optional email delivery Setup Instructions Database Setup Configure Postgres credentials Ensure support_tickets table exists with required fields OpenAI Configuration Add API key for: Embeddings (text-embedding-3-small) AI analysis agents Slack Integration Add Slack credentials Set channel ID Email Setup (Optional) Configure SMTP or email service Jira Integration (Optional) Add Jira credentials Set project key and issue type Customize Parameters Adjust: Similarity threshold Scoring weights Time window Schedule Configuration Modify trigger timing as needed Use Cases Product teams analyzing user feedback at scale Engineering teams prioritizing bug fixes SaaS companies tracking churn-related issues Customer support insights automation AI-driven product intelligence dashboards Requirements OpenAI API key Postgres database with support ticket data Slack (optional) Email service (optional) Jira account (optional) n8n instance Key Features Automated feedback clustering using embeddings AI-driven root cause analysis Weighted severity scoring system Developer-ready intelligence reports Multi-channel delivery (Slack, Email, Jira) Fully customizable scoring and thresholds Summary A powerful AI-driven workflow that converts raw support tickets into structured developer intelligence. It automates clustering, root cause detection, prioritization, and reporting helping teams fix the right problems faster and build better products. An n8n automation workflow template by ResilNext.
ResilNext
web
free
Others in the same category, ranked by how often they are opened.
