Enhancing Response Generation Systems: Knowledge Graph & Generative AI Synergy for Business Communication and Strategic Decision Making

In the contemporary business environment, it is crucial to have an efficient and precise response generation system to build client trust, optimize operations, and provide customized solutions. Current response management systems often face challenges such as insufficient depth, scalability issues, and inconsistencies. There is an urgent requirement for a comprehensive system that can retrieve information for generating high-quality insights and convert it to a reply. To address these needs, an approach has been explored that integrates a domain-oriented Knowledge Graph (KG), vector embeddings, Large Language Model (LLM) and utilize the pathway parsing technique that allows for in-depth multi-hop analysis within the KG, resulting in detailed and contextually rich data retrieval. This combination enhances the performance and precision of handling inquiries, streamlines entity extraction and step identification, leverages KG for Standard Operating Procedure (SOP) guidance, and offers superior recommendations or strategies for informed decision making. The concept will be illustrated through a business study focusing on the collections department use case, which involves customer correspondence. This approach ensures a more efficient, and accurate responses, leading to reduced human intervention and latency, along with that customer satisfaction is improved and business processes are streamlined. By adopting this method, businesses can enhance their communication, make data-driven decisions, and ultimately achieve better results in the competitive market. The efficacy is evident in the practical instances, owing to its profound grasp of context.