Turn support tickets into developer insights with OpenAI, Postgres, Slack and Jira

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.