Abstract Large Language Models (LLMs) hold immense potential for advanced data retrieval and insight generation, yet integrating these capabilities into real-time industrial analytics poses a significant challenge. Traditional business intelligence (BI) systems relying on static dashboards and complex data transformations, often struggle to meet the dynamic demands of modern drilling operations. This paper introduces an Agentic Retrieval-Augmented Generation (RAG) framework that unifies structured data via knowledge graphs and domain-specific reasoning into a single conversational interface. By orchestrating multi-step queries and generating interactive visualizations, the framework delivers timely, context-aware insights, reducing user overhead and enhancing decision-making. Demonstrations in drilling workflows highlight marked improvements in safety, efficiency, and scalability, underscoring the transformative potential of LLM-driven RAG systems for next-generation industrial analytics.
Arabi et al. (Mon,) studied this question.
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