Sistema Multiagente de IA para Suporte à Decisão em Marketing Digital

Abstract

In recent years, the significant growth in the volume of available data and the increased capacity to process this data have led companies to seek more effective alternatives for analyzing information, extracting strategic value, and maintaining competitiveness in digital marketing. In this context, traditional promotional campaign approaches have proven insufficient when compared to data-driven digital strategies, such as search engine optimization (SEO) and artificial intelligence optimization.

A common limitation for companies of different sizes is the high cost of proprietary platforms that perform analyses and diagnoses from data. AI agents, as proposed by Alavi (2024), offer transparency and responsiveness in real-time data analysis. From this perspective, in this work, a multi-agent Artificial Intelligence system was developed to automate the collection, analysis, and synthesis of digital marketing data. The system architecture utilizes Retrieval Augmented Generation (RAG) techniques on a curated knowledge base to generate actionable insights and strategies grounded in natural language. The results obtained demonstrate that the system has proven effective in automating the complete diagnostic and analysis cycle, providing strategic and personalized action plans in less than ten minutes.