Agentic AI for Intelligent Customer Support Escalation

Overview

Smart Support: Balancing Automation and Human Care in Retail Customer Service

A well known Retail chain’s customer support teams struggle to balance efficiency and quality. Simple queries can be resolved quickly with chatbots, but more complex issues often get stuck in back-and-forth conversations. 

This creates frustration for customers and extra workload for human agents. The challenge was to build a system that knows when to handle an issue on its own and when to pass it to a human—without disrupting the customer experience. 

Services

- Chatbots - Conversational AI 

Industry Type

- Intelligent Customer Support

Challenges

  • - Ambiguity in Queries: Customers often explain issues in vague or emotional language and it may take time to understand the exact issue.

  • - Escalation Accuracy: Avoiding under-escalation (frustrated customers) and over-escalation (overloaded agents)

  • - Knowledge Gaps: Ensuring the AI had access to the latest product and policy updates.

  • - Trust & Oversight: Giving agents confidence that the AI’s handoffs were accurate and complete.

Outcomes

  • - 40% reduction in manual agent workload – AI resolved routine issues without human intervention

  • - Faster resolutions – average response time improved by 35%

  • - Improved customer satisfaction – fewer transfers and repeated explanations.

  • - Smarter escalations – 90% accuracy in deciding when to involve a human agent.

  • - Continuous learning – feedback loops helped the system improve over time.

Technology Stack

LangChain
LangGraph
Large Language Models

Project Solutions

    • Classify queries based on complexity, sensitivity, and customer sentiment.
    • Resolve simple issues using the knowledge base and automation workflows.
    • Escalate complex cases with a detailed context summary (previous chat, attempted fixes, detected sentiment).
    • Learn from agent feedback to continuously refine escalation accuracy.
    • Provide transparency with detailed analysis on why a case was escalated or resolved autonomously.
Project Solutions