Generative AI Customer Support System for Empathetic Response Routing
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Solution Overview
Problem
Conventional customer support systems face inefficiencies due to the reliance on human agents for ticket labeling, routing, and response generation, leading to high labor costs, long response times, and limited scalability, as they struggle to handle complex queries and categorize tickets effectively.
Innovation Solution
The implementation of a generative AI system that fine-tunes machine learning models to automatically generate empathetic template answers, route tickets, and identify knowledge-based articles, using natural language understanding and supervised learning to classify questions and route them to appropriate agents, thereby reducing the burden on human agents and enhancing response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human agents manually handle ticket labeling, routing, and response generation, then customer support can address complex queries with human judgment, but labor costs increase and response times lengthen
Solution Approach 1:
The system enables self-service through AI agents that automatically perform ticket labeling, routing, and response generation without human intervention. The AI model analyzes incoming tickets, assigns appropriate labels based on learned patterns, routes to correct queues, and generates draft responses, allowing the system to serve itself rather than relying on human agents for routine tasks
Solution Approach 2:
The patent replaces the mechanical system of human agents manually processing tickets with an automated AI-based system. The AI model uses machine learning algorithms to perform classification, routing, and response generation tasks that were previously done manually, substituting human mechanical operations with automated computational processes
2Productivity
If more human agents are hired to handle increased ticket volume, then customer support capacity increases, but labor costs and operational complexity increase
Solution Approach 1:
The AI agent system performs multiple functions including ticket classification, routing, response generation, and knowledge base searching within a single unified platform. This multi-functional approach replaces the need for multiple specialized human roles (triage agents, response writers, researchers) with one automated system that handles all these tasks
Solution Approach 2:
The system automatically scales to handle increased ticket volumes without requiring additional human resources. The AI model processes tickets autonomously, dynamically adjusting its capacity based on incoming workload, thereby eliminating the operational complexity of hiring, training, and managing additional human agents
3Loss of information
If manual searching of institutional knowledge is performed, then agents can find relevant information, but time is wasted in unproductive searches
Solution Approach 1:
The AI model incorporates feedback mechanisms where past successful information retrieval patterns are learned and applied to future searches. The system analyzes historical data about which knowledge base articles and resources were most effective for different ticket types, using this feedback to improve and optimize its search strategies over time
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing institutional knowledge before it is needed. The AI model pre-organizes knowledge base articles, documentation, and historical solutions into structured formats with extracted key features, making the information readily accessible and eliminating the need for time-consuming manual searches when tickets arrive
Data Source
AI summary
A computer-implemented method is disclosed for using generative AI for customer support. An AI model may be fine-tuned on the task of generating a template workflow answer given a prompt of real answers. In some implementations, an AI empathy model is trained/fine-tuned to customize template answers to be more empathic. In some implementations, the template workflow answer may include an API call step.