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Use Case: SaaS Company — In-App Help Desk & Support Triage - AIWU – AI Plugin for WordPress
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Use Case: SaaS Company — In-App Help Desk & Support Triage

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A B2B SaaS company cut first-response time from 4 hours to 4 minutes using AIWU as their in-app help desk and support triage system — without replacing their support team.

Before You Start

This use case combines several AIWU features. Guides you’ll want to read first:

The Challenge

The company had 2,400 paying customers and a 2-person support team. 70% of support tickets were questions already answered in their documentation. The team was bottlenecked answering repetitive questions instead of handling complex issues that actually needed human judgment. Customer satisfaction scores were suffering from slow response times.

The Solution: 4-Layer Support System

Layer 1: In-App AI Chat (Instant Answers)

AIWU chatbot embedded in the SaaS app’s help panel, trained on the entire product documentation, FAQs, and troubleshooting guides.

System prompt:

You are the support assistant for [Product Name]. 
You have access to our complete documentation.

Your job is to answer user questions accurately based 
on the documentation. If you can't find the answer, 
say "I don't have information on that — let me connect 
you with the support team" and offer the escalation option.

Always be direct and solution-focused. Users are 
technical professionals — avoid over-explaining basics.
Don't suggest workarounds that aren't documented.
If the user reports a bug or unexpected behavior, 
collect their account ID and describe the issue, 
then escalate to the team.

Knowledge base trained on:

  • Full product documentation (all 120 help articles) — via Knowledge Base training
  • API reference documentation — uploaded as a dataset
  • Changelog and known issues list — updated monthly
  • Top 80 support ticket resolutions — converted to Q&A training pairs

Result: 65% of incoming chat questions answered without escalation.

Layer 2: Smart Ticket Triage

When a user submits a ticket (via the company’s existing helpdesk tool), a webhook triggers AIWU to classify it before it reaches the support team.

Workflow: New Ticket → Classify + Route

  1. Trigger: Incoming Webhook from helpdesk (Zendesk/Freshdesk)
  2. Action: Classify with AI — 
    Classify this support ticket:
"{{webhook.ticket_body}}"

Return JSON only:
{
  "category": "billing|technical|feature_request|bug|onboarding",
  "priority": "low|medium|high|urgent",
  "suggested_response": "one-sentence suggestion for first response",
  "auto_resolvable": true/false
}
  3. Branch (auto_resolvable: true): Generate full response → send to customer via helpdesk API webhook → close ticket
  4. Branch (auto_resolvable: false): Send Slack to support team with classification, priority, and suggested response

Support agents receive pre-classified tickets with a suggested first response already drafted. They review, adjust if needed, and send — instead of reading and writing from scratch.

Layer 3: Onboarding Email Sequence

New trial users receive an AI-powered onboarding sequence. Each email is generated fresh based on the user’s profile — not a fixed template.

Workflow: New User → Personalized Welcome

  1. Trigger: New user registration (WooCommerce Memberships / custom webhook)
  2. Action: Generate Text — personalized welcome email based on their signup answers (use case, industry)
  3. Action: Send Email
💡 Multi-step sequences: For a full drip campaign (Day 1, Day 4, Day 7), create separate workflows for each email and trigger them with scheduled cron events or use a dedicated email marketing tool alongside AIWU for the sequence timing.

Users with an “e-commerce” use case get e-commerce examples; users in “education” get different examples. The AI adapts the content to each user’s context.

Layer 4: Developer Support via MCP

The company’s technical support engineer uses Claude Desktop + MCP to investigate customer issues directly in WordPress:

  • “Show me the last 10 posts created by [email protected]” — finds content linked to a specific user
  • “List all published knowledge base articles that mention ‘authentication'” — searches content to verify documentation coverage
  • “Create a knowledge base article about the new webhook retry feature” — drafts and publishes docs from conversation
⚠️ Note: MCP works with WordPress data (posts, users, products, comments, media). For app-specific data outside WordPress (e.g., SaaS usage metrics, subscription status), you’ll need custom API integrations or webhooks to bring that data into WordPress first.

This reduced time-to-resolution for complex technical issues by ~40% because engineers could investigate and update docs in one session.

Results After 6 Months

Metric Before After
First response time 4 hours 4 minutes (automated) / 45 min (escalated)
Tickets auto-resolved 0% 38%
Support team capacity (tickets/day) 25 60 (with same headcount)
Trial-to-paid conversion 18% 24% (better onboarding)
CSAT score 6.8/10 8.4/10

Key Principles from This Case Study

  • AI doesn’t replace support — it removes the repetitive 70%. Human agents handled more complex work and delivered better results because they weren’t drowning in basic questions.
  • Train on real tickets, not just docs. The 80 resolved-ticket Q&A pairs were the highest-value training data. Real user language matches how users actually ask questions.
  • Ticket classification pays for itself immediately. Even if the AI doesn’t resolve the ticket, arriving pre-classified and with a drafted response cuts agent handling time in half.

What’s Next

Last verified: AIWU v.4.9.2 · Updated: 2026-02-25

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