LLM-Powered Knowledge Graph for Enterprise Intelligence and Analytics

Abstract

Disconnected data silos within enterprises obstruct the extraction of actionable insights, diminishing efficiency in areas such as product development, client engagement, meeting preparation, and analytics-driven decision-making. This paper introduces a system-agnostic framework leveraging large language models (LLMs) to unify diverse data sources into a comprehensive, activity-centric knowledge graph. The framework automates tasks such as entity extraction, relationship inference, and semantic enrichment, enabling advanced querying,reasoning, and analytics across data types like emails, calendars,chats, documents, and logs. Designed for enterprise flexibility,it supports applications such as contextual search, task prioritization, expertise discovery, personalized recommendations,and advanced analytics for identifying trends and actionable insights. Experimental results demonstrate its success in expertisediscovery, task management and data-driven decision-making. By integrating LLMs with knowledge graphs, this solution bridges disconnected systems and delivers intelligent, analytics-poweredenterprise tools.