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This thesis presents a multi-agent AI system for automated market trend analysis by processing emails, newsletters, and PDFs. The system employs a structured workflow of autonomous agents for content extraction, summarization, subtopic identification, and topic clustering, leveraging OpenAI’s GPT-4 within an Autogen-based framework.
Key features include multi-tenant adaptability, customer-specific trend analysis, and Azure SQL integration for structured data storage. The system demonstrates good accuracy in trend extraction and classification, addressing challenges like varying LLMs output and multischema architecture.
This research underscores the potential of AI-powered automation in business intelligence, optimizing decision-making and strategic market analysis.