OpenClaw Customer Support Automation

Background

Support teams struggle with:

• Volume spikes and mixed priority
• FAQ answers repeated manually
• Slow escalation for refunds and complaints
• No consistent lookup for policies and scripts

This use case builds classify → lookup → suggest → escalate.

Key steps

1. Classify tickets: FAQ, policy lookup, refund, complaint, other.
2. Match to knowledge base: product docs, FAQ, internal playbooks.
3. Suggest reply for FAQ and policy cases.
4. Escalate refund, complaint, legal to human queue.
5. Log classification and escalation for audit.

Workflow sketch

Incoming ticket → Classify → Match KB → If FAQ: suggest reply; else escalate → Log

Skill recommendations

openclaw skills install @clawhub/summarize

Add RAG or document-indexing skills if you have an internal KB. Use Feishu or similar for handoff and logs.

Expected results

• FAQ tickets with ready reply suggestions
• Refund/complaint tickets routed to human queue
• Classification and escalation logged
• Clear rules for what AI can and cannot answer

Common pitfalls

• Auto-sending refund or compensation commitments
• Missing high-risk tickets because classification rules are too loose
• No audit trail for escalations

Related pages

• E-commerce / support solutions

• Internal knowledge base

• Security checklist

• Official docs → docs.openclaw.ai

• Issues → GitHub Issues