AI-Driven Startup Blueprint Generator using Retrieval-Augmented Generation and Large Language Models

Abstract

The rapid growth of the start-up ecosystem has created a need for intelligent systems that can assist entrepreneurs in transforming ideas into structured business plans. However, developing a comprehensive start-up blueprint requires domain expertise, market research, and strategic analysis, which is challenging for early-stage founders. This paper presents Start Genie AI, an AI-driven start-up blueprint generator that utilizes Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to automate business plan creation. The system integrates external data sources, including government portals and market insights, to enhance the accuracy and relevance of generated outputs. The proposed system implements a RAG pipeline consisting of data processing, embedding generation, vector-based retrieval, and context-aware content generation. It is deployed as a full-stack web application with a React-based frontend and a FastAPI backend. The results indicate that the system can generate structured, investor-ready business blueprints within seconds, significantly reducing manual effort while maintaining high contextual accuracy. The proposed approach demonstrates the effectiveness of combining retrieval mechanisms with generative AI for real-world entrepreneurial applications.