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

Description

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.