KCS Cognitive Analyzer: Leveraging Large Language Models for Contextual, Explainable, and Profit-Optimized Customer Churn Reduction
Author
Ms. Dimple Chavan, Rohan Borse, Shreyash Ilhe, Nihal Tadavi, Anurag Bhagat
Abstract
In today’s fast-paced digital world, customer satisfaction and efficient technical support are crucial for organizational success. Knowledge-Centered Service (KCS) frameworks aim to enhance support quality by creating, managing, and reusing knowledge base (KB) articles. However, maintaining the accuracy and relevance of such KBs is a significant, manual challenge. The Cognitive KCS Analyzer is designed as an AI-powered system to automate the evaluation, scoring, and improvement of KB articles using advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. The system automatically identifies content gaps, assesses article quality based on parameters such as clarity, completeness, and searchability, and generates missing or updated content suggestions. Implemented using Python, external APIs (OpenAI, Google Vertex AI), and a Stream-lit interface, this project aims to reduce manual review efforts, ensure knowledge consistency, and significantly improve response accuracy and resolution time in customer support operations. The Cognitive KCS Analyzer represents a critical step toward AI-driven automation in enterprise knowledge management.
Keywords
Knowledge-Centered Support (KCS), Knowledge Base (KB) Quality, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs).
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References
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