LEVERAGING GENERATIVE AI FOR ENHANCED ENTERPRISE KNOWLEDGE RETRIEVAL: A TECHNICAL DEEP DIVE

Abstract

As organizations grapple with fragmented data across Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM) systems, the need for efficient knowledge retrieval becomes increasingly critical. This article presents an innovative approach leveraging Generative AI technologies, specifically focusing on Retrieval-Augmented Generation (RAG) architectures integrated with Knowledge Graphs and vector databases. Using Amazon Bedrock as the foundational platform, it demonstrates how this integration significantly enhances enterprise knowledge management through improved query processing, contextual understanding, and cross-system data integration. The implementation addresses key challenges in data harmonization, security compliance, and user experience while providing measurable improvements in operational efficiency. The article also explores the system's impact on decision-making processes, cross-functional collaboration, and resource utilization. Through comprehensive testing and real-world deployment, it validates the effectiveness of this approach in creating a unified, intelligent knowledge retrieval system that bridges traditional data silos and enhances enterprise-wide information accessibility.