Data2Dialogue: Structured Enterprise Knowledge Grounding in LLM Agents for Personalized Wellness Sales

Large language models (LLMs) have the potential to revolutionize various domains, including personalized wellness sales, by integrating structured enterprise knowledge into their frameworks. In this context, we introduce Data2Dialogue, a framework designed specifically for enhancing LLMs’ conversational abilities through structured data integration. By leveraging diverse sources of enterprise knowledge, Data2Dialogue enables LLMs to provide tailored product recommendations that cater to individual wellness needs. The approach incorporates advanced knowledge extraction and contextualization techniques to ensure the information delivered is both relevant and responsive to users’ inquiries in real-time. A multi-step inferencing process enhances the LLM’s grasp of enterprise-specific knowledge while improving dialogic engagement. Experimental results indicate that Data2Dialogue leads to higher customer satisfaction and increased sales conversions compared to conventional methods. By grounding LLMs in structured knowledge, the framework fosters more accurate and context-rich interactions, effectively aligning user inquiries with product offerings.