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September 10, 2025

Retrieval-Augmented Generation (RAG) for Real-Time Business Intelligence

Key Points

RAG improves real-time context-aware information extraction, enhancing decision-making in business.
The integration of live data feeds with LLMs allows for interactive and explainable intelligence generation.
Current BI systems exhibit limitations that RAG architecture addresses through innovative data source integration.
The findings indicate that RAG could redefine business intelligence by providing context-based answers to complex queries.

Abstract

In today's era, information is vast and overloaded which makes data-driven intelligence, real-time and context-aware information extraction a challenge. The Retrieval-Augmented Generation (RAG) mechanism can be a potential solution for dynamically retrieving information relevant to the domain. This mechanism interacts with Large Language Model (LLM) models using live data feeds, as well as BI Systems, and creates interactive, meaningful, and explainable intelligence. This paper illustrates the limitations of current BI systems, new architecture, source live data integration, and evaluation techniques with business impact. RAG for Business Intelligence can be a game changer for BI systems analytical landscape to context-based answers for complex business-driven questions

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Cite This Study

Gopal Rath (Tue,) studied this question.

synapsesocial.com/papers/68c1c32154b1d3bfb60f0ceb https://doi.org/https://doi.org/10.36948/ijfmr.2025.v07i04.52677

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