GENAI-BASED ENTERPRISE KNOWLEDGE MANAGEMENT ASSISTANT

Abstract

Organizations generate and maintain large volumes of knowledge through documents, reports, policies, manuals, emails, project records, meeting notes, and internal communications. As the amount of information increases, employees may find it difficult to locate accurate and relevant knowledge quickly. Traditional enterprise knowledge-management systems often depend on keyword-based search and manually organized repositories, which can make information discovery timeconsuming and inefficient. The proposed GenAI-Based Enterprise Knowledge Management Assistant is an intelligent platform designed to help employees access organizational knowledge through natural-language interaction. The system uses Generative Artificial Intelligence to understand user questions and retrieve relevant information from authorized enterprise knowledge sources. Instead of requiring users to search through multiple documents or systems, the assistant can provide contextual answers based on available organizational information. The platform can use document processing, semantic search, embeddings, retrievalaugmented generation, and large language models to understand and respond to enterprise queries. Documents can be collected from approved knowledge repositories, processed into searchable representations, and indexed for efficient retrieval. When a user submits a question, the system retrieves relevant information and uses the language model to generate a concise and context-aware response. Security and access control are important components of the proposed system. Users should only receive information that they are authorized to access according to organizational roles and permissions. The system can maintain source references for generated answers, helping users verify information against the original enterprise documents. Conversation history, feedback, and usage analytics can also be used to improve the quality and usefulness of the knowledge assistant.