Retrieval-Augmented Generation with Serverless Cloud Computing for Enterprise Knowledge Management and Decision Intelligence

Abstract

Retrieval-Augmented Generation (RAG) combined with serverless cloud computing represents a transformative approach for improving enterprise knowledge management and decision intelligence by integrating large language models with dynamic organizational information repositories. Traditional enterprise knowledge systems often struggle with fragmented data sources, outdated information, and limited contextual understanding. RAG addresses these limitations by retrieving relevant information from enterprise databases, documents, and knowledge graphs before generating responses through artificial intelligence models. When deployed using serverless cloud architectures, RAG systems gain scalability, cost efficiency, and operational flexibility by dynamically allocating computational resources according to demand. This integration enables organizations to develop intelligent systems capable of providing accurate insights, supporting strategic decisions, and enhancing employee productivity. The research explores the role of serverless RAG frameworks in enterprise environments, focusing on architecture, implementation strategies, knowledge accessibility, and decision-support capabilities. The study adopts a conceptual research methodology based on analysis of existing literature, technological developments, and enterprise AI adoption practices. Findings indicate that serverless RAG solutions can improve organizational intelligence by reducing information retrieval barriers, enhancing contextual accuracy, and enabling real-time decision-making. However, challenges related to data security, model reliability, governance, and integration complexity remain important considerations. The study concludes that the combination of RAG and serverless computing provides a sustainable foundation for next-generation enterprise knowledge ecosystems