1. Context and Problem
Acme Corp was facing a 30% month-over-month increase in support tickets due to rapid product growth. The support team (5 agents) was overwhelmed, leading to a 48-hour first response time.
Pain Points:
- Agents spent 30% of their time just categorizing and routing tickets.
- Repetitive "how-to" questions consumed valuable senior agent time.
- Inconsistent tone and quality in responses during rush periods.
2. Solution Overview
They implemented a middleware automation that intercepts new tickets from Zendesk, sends the content to OpenAI's GPT-4 for analysis, and then updates the ticket with internal notes (category, sentiment, priority) and a draft response for the agent to review.
3. The Workflow
// ENTITIES
human:Customer
system:Zendesk
auto:Middleware "Python/AWS Lambda"
ai:Classifier "GPT-4"
human:Agent
// FLOWS
human:Customer -> system:Zendesk: Submits ticket
system:Zendesk --> auto:Middleware: Webhook (New Ticket)
auto:Middleware -> ai:Classifier: Send ticket body
ai:Classifier -> auto:Middleware: Return JSON {category, sentiment, draft}
auto:Middleware -> system:Zendesk: Post Internal Note & Draft
human:Agent -> system:Zendesk: Review & Send
4. Implementation Details
Complexity: Medium (Requires custom code)
Stack:
- Orchestration: AWS Lambda (Python)
- AI Model: OpenAI GPT-4 (via API)
- Business System: Zendesk Support API
Key Challenge: Ensuring the AI didn't hallucinate policies. They solved this by including a condensed version of their policy FAQ in the system prompt (RAG-lite).
5. Outcomes
60%
Faster First Response
40%
Reduction in Handle Time
Qualitatively, agents reported higher job satisfaction as they spent less time on "robot work" (tagging) and more time solving complex issues.