Tired of manually creating FAQs from support tickets? This tutorial shows how to build an automated FAQ generator workflow in Needle that analyzes support conversations and creates FAQ content.

Overview

The workflow scans your support tickets, identifies common questions, and generates polished FAQ entries—transforming customer conversations into self-service knowledge base content.

Key Actions

1. Support Ticket Trigger – Monitors Zendesk, Intercom, or HelpScout tickets
2. Question Detection – Identifies frequently asked questions in conversations
3. Answer Extraction – Pulls best answers from agent responses
4. AI Polishing – Rewrites into professional FAQ format
5. Publication – Exports to Notion, Google Docs, or knowledge base

What You'll Need

• Support Platform – Zendesk, Intercom, HelpScout, or Freshdesk Tip: Start with resolved tickets from the past 30 days to find common patterns
• Knowledge Base – Notion, Confluence, or help center platform
• Review Process – Optional: Send FAQs to Slack for team review before publishing

How It Works

The workflow can run in two modes:

Mode 1: Batch Analysis (Weekly/Monthly)

1. Retrieves all resolved tickets from the past week
2. AI analyzes conversations to find:
   • Recurring questions (asked 3+ times)
   • Questions that took longest to resolve
   • High-impact questions from enterprise customers
3. For each common question:
   • Extracts the best agent answer
   • Rewrites in clear, customer-friendly language
   • Adds context and related info
4. Generates FAQ entries with:
   • Question (clear, searchable)
   • Answer (step-by-step if applicable)
   • Related questions
   • Tags/categories
5. Sends to Slack for review or directly publishes to knowledge base

Mode 2: Real-Time (Triggered per ticket)

1. Triggers when a ticket is resolved
2. If question is novel and high-quality:
   • Drafts FAQ entry
   • Sends to #support-team for review
   • Publishes when approved

Wrap-up

With this Needle workflow, your support team continuously builds a comprehensive FAQ library from actual customer questions—reducing ticket volume, improving customer self-service, and scaling support knowledge. Perfect for support teams with high ticket volumes and growing knowledge bases.