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Deploying an AI Support Agent That Handles 70% of Queries

#ai #rag #case-study #e-commerce

An e-commerce platform was scaling fast but their support team couldn’t keep up. Ticket volume was growing 30% quarter-over-quarter, and hiring wasn’t keeping pace.

The Approach

I built an AI-powered support agent that handles the most common query types autonomously:

  • Order tracking — integrates with shipping APIs for real-time status
  • Returns & exchanges — guides customers through the process, creates return labels
  • Product questions — RAG over the product catalog and FAQ knowledge base
  • Account issues — password resets, order history, payment updates

How It Works

The system uses RAG over the company’s knowledge base combined with tool-calling:

  1. Customer message comes in via Zendesk
  2. Intent classification determines the query type
  3. RAG retrieves relevant knowledge base articles
  4. GPT-4 generates a response with access to order APIs
  5. Confidence scoring decides whether to auto-respond or escalate

Tech Stack

  • GPT-4 for response generation
  • Pinecone for vector search over knowledge base
  • Python for the backend agent
  • React for the admin dashboard
  • Zendesk API for ticket management

Results

  • 70% of queries auto-resolved without human intervention
  • $150K annual savings in support costs
  • CSAT scores improved from 3.8 to 4.6 stars
  • Average response time dropped from 4 hours to under 30 seconds

The key: don’t try to automate everything. Focus on the high-volume, well-defined query types first, and always provide a clear escalation path to humans.