LLM-Driven Multi-Agent System for Market Trend Analysis and Capability Mapping
- 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,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 multi� schema architecture.
This research underscores the potential of AI-powered automation in business intelligence, optimizing decision-making and strategic market analysis.…
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| Document Type: | Master's Thesis |
|---|---|
| Zitierlink: | https://opus.hs-offenburg.de/10548 | Bibliografische Angaben |
| Title (English): | LLM-Driven Multi-Agent System for Market Trend Analysis and Capability Mapping |
| Author: | Anusha Shivaraju |
| Advisor: | Daniela Oelke, Immo Brueggemann |
| Year of Publication: | 2025 |
| Publishing Institution: | Hochschule Offenburg |
| Granting Institution: | Hochschule Offenburg |
| Contributing Corporation: | Creatum GmbH |
| Place of publication: | Offenburg |
| Publisher: | Hochschule Offenburg |
| Page Number: | 47 |
| Language: | English | Inhaltliche Informationen |
| Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
| Collections of the Offenburg University: | Abschlussarbeiten / Master-Studiengänge / CME |
| DDC classes: | 600 Technik, Medizin, angewandte Wissenschaften |
| Tag: | AI-Driven Solutions; Autogen Framework; Azure SQL; Business Intelligence; Market Trends Analysis; Multi-Agent System; Multi-Tenant Environment; Newsletters and Email Processing; OpenAI GPT-4.0; Technological Insights; Topic Clustering | Formale Angaben |
| Open Access: | Closed Access |
| Licence (German): |  Creative Commons - CC0 1.0 - Universell - Public Domain Dedication |