ENRIQ: Enterprise Neural Retrieval and Intelligent Querying

Abstract

Enterprise Content Management Systems face growing challenges in efficiently retrieving and utilizing vast amounts of unstructured organizational knowledge. This paper introduces ENRIQ (Enterprise Neural Retrieval and Intelligent Querying), a novel approach that leverages Retrieval-Augmented Generation (RAG) to enhance document search and interaction capabilities in enterprise environments. Our system combines advanced neural retrieval mechanisms with large language models to provide contextually aware, accurate responses while maintaining data privacy and provenance. Through comprehensive evaluation on real enterprise datasets, we demonstrate that ENRIQ achieves a 47% improvement in response accuracy and a 35% reduction in query resolution time compared to traditional keyword-based systems. The proposed architecture ensures scalability, maintains document versioning, and provides explainable results with source attribution.