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AI Support Governance

Prevent AI hallucinations and ensure accurate, helpful customer support responses through intelligent human oversight.

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Risk Scenarios

Technical Issues

Complex technical problems requiring expert knowledge

Policy Questions

Billing, refunds, and policy interpretation questions

Sensitive Topics

Complaints, legal issues, or emotional situations

High-Value Customers

VIP customers requiring special attention

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Approval Triggers

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Automatic Human Review

  • Customer mentions legal action or complaints
  • Requests involving refunds over threshold amount
  • Technical issues AI confidence < 80%
  • VIP customer tier interactions
  • Escalated or repeat issues

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Conditional Review

  • First-time customer interactions
  • Complex multi-step solutions
  • Policy edge cases or exceptions
  • Integration or API support questions

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Implementation Examples

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Support Response Review

{
  "functionName": "send_support_response",
  "args": {
    "customer_id": "cust_12345",
    "ticket_id": "tick_67890",
    "issue_type": "billing_dispute",
    "proposed_response": "Based on our records...",
    "customer_tier": "premium",
    "ai_confidence": 0.75
  },
  "assessment_result": {
    "risk_level": "high",
    "reason": "Billing dispute with premium customer",
    "tags": ["#Billing", "#PremiumCustomer", "#Dispute"]
  }
}

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Knowledge Base Updates

{
  "functionName": "update_knowledge_base",
  "args": {
    "article_title": "How to reset your password",
    "content_changes": "Added new mobile app instructions",
    "affected_categories": ["account_management", "mobile"]
  },
  "flow_config": {
    "approvers": ["support_lead", "technical_writer"],
    "channel": "#support-kb-updates"
  }
}

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Quality Assurance

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Response Quality Checks

  • Accuracy: Technical information verification
  • Tone: Appropriate empathy and professionalism
  • Completeness: All customer questions addressed
  • Policy Compliance: Adherence to company policies

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Continuous Improvement

  • Feedback Loop: Track human override patterns
  • AI Training: Use approved responses for model improvement
  • Policy Updates: Refine triggers based on review patterns
  • Performance Metrics: Monitor resolution times and satisfaction