TurnB Approach


Analyzing Support Requests 

  • Consolidated and analyzed twelve months of historical partner support requests.
  • Ran intent analysis to group requests into clear, well-defined categories.
  • Built step-by-step playbooks and friendly response templates for every category, housed in a governed knowledge base.

Building an AI Agent to Assess and Respond 

  • Developed an LLM-powered AI agent using Copilot Studio, capable of understanding a request, accessing the knowledge base, and executing the necessary actions.
  • The agent extracts key details from each request and classifies it against a known category set, with a confidence score determining whether it can be handled automatically.
  • Once classified, it retrieves the relevant playbook via RAG, executes the steps through connected systems, and drafts a reply in the same thread.

Human Oversight and Continuous Learning 

  • Requests below the confidence threshold were routed to human agents, along with an AI-generated draft reply and recommended next actions.
  • Weekly reviews refined the playbooks and training examples, so the system kept improving.
Approaches background
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Implications

  • 75% of partner requests were auto-classified and responded to within the first three months of production, with human agents freed up to focus on complex or novel scenarios.
  • Saved roughly 1.1 hours per day, adding up to approximately 286 hours over a fiscal year.
  • First-response times dropped from hours to minutes, enabling 24/7 global coverage.
  • Guidance became more consistent across regions and partner types.
Implications