Building an AI Customer Support System

The System Architecture

1. Intake & Classification

Every incoming ticket/chat is classified by: topic (billing, technical, account), urgency, and complexity. This determines handling path before AI attempts to answer.

2. Knowledge Base (RAG Layer)

Your documentation, FAQs, past resolved tickets, and product specs are embedded in a vector database. When a ticket comes in, relevant knowledge is retrieved and injected into the AI's context.

3. Response Generation

AI generates a response grounded in retrieved knowledge. If the knowledge base doesn't contain the answer, it says so — it does not hallucinate.

4. Confidence & Escalation

High confidence + low complexity → auto-send. Medium → agent review with AI draft. Low confidence or urgent/complex → immediate escalation.

5. Feedback Loop

Track: was the AI helpful? Did the customer need to follow up? Did an agent override the AI? Feed this back into knowledge base improvement.

What to Automate First

• Shipping status queries (connect to order tracking API)
• Password reset instructions
• Refund policy questions
• Feature how-to questions (covered in docs)

Build Your AI Support System

Our workshop covers AI customer support with hands-on RAG implementation and escalation design.