Data-Driven AI Solutions for Accelerating Enterprise Knowledge Retrieval: A Multi-Agent Framework to Reduce Preliminary Bottlenecks in Software Development and Infrastructure Deployment

• Het Rachhadiya

  Author

This paper presents a comprehensive overview of a data-driven AI framework for enterprise knowledge retrieval, laying out the context and influences that make such solutions highly relevant in current enterprise settings. Business knowledge is both integral and proprietary for any organization, as the contents of private documents are constantly consumed by internal employees to accomplish critical workstreams. Modern software development and network infrastructure deployment, among many other fields, are often based on exploring exhaustive documentation and lengthy research papers. Paradoxically, this upfront research and learning introduce a significant bottleneck, requiring substantial time before a new project can be initiated, with frequent needs to re-review previously examined information. Accelerating delivery remains a key aspiration for organizations seeking a competitive edge. This paper first describes the background and sequence of developments that brought much of the employed technologies into existence. It then introduces the hosting company and discusses its necessity for such a novel solution. Finally, it lists the pursued objectives and innovations that address the insufficiencies of existing solutions. The proposed framework leverages data-driven AI solutions, including retrieval-augmented generation and multi-agent architectures, to intelligently index, query, and synthesize proprietary business knowledge, thereby reducing preliminary research requirements and enabling faster delivery cycles while preserving informational accuracy and security.

• PDF