A Comparative Review of Large Language Model Approaches for Startup Success Prediction and Market Research

The evaluation and planning of new business ven- tures remain a complex and high-risk endeavor, traditionally dependent on human expertise. The recent proliferation of Large Language Models (LLMs) has catalyzed a paradigm shift, introducing data-driven methodologies for startup analysis, market research, and strategic planning. This paper presents a comparative review of the state-of-the-art in LLM applications for the entrepreneurial domain. We synthesize findings from recent studies, categorizing them into three primary areas: (1) predictive modeling for startup success and funding, (2) simu- lation of market dynamics and customer behavior, and (3) the development of advanced architectures like Retrieval-Augmented Generation (RAG) and multi-agent systems to enhance reliability and capability. Our review identifies a clear trajectory from single-model analytical tools toward sophisticated, generative, and agentic frameworks. While significant progress has been made in prediction and guidance, we identify a critical research gap: the absence of systems capable of autonomously generating comprehensive, actionable business plans from a nascent idea. We conclude by positioning the development of such generative, multi-agent systems as the next logical frontier in applying agentic AI to the real-world challenge of business creation.