Automated Startup Analysis Using Large Language Models and API Integration

Identifying and analyzing startups is a crucial yet time-intensive task for investors, researchers, and venture capitalists. Traditional methods rely on manual searches, conferences, and personal networks, which often lead to inefficiencies. This paper presents an AI-driven approach that leverages Large Language Models (LLMs) in conjunction with API integrations to streamline the process of startup discovery and evaluation. By combining OpenAI’s LLM agents with Bing and Crunchbase APIs, the proposed system automates data collection, web scraping, and information synthesis. The methodology involves generating precise search queries, extracting company details, and retrieving structured insights on startups, including their founders, funding details, and industry presence. Experimental results demonstrate the effectiveness of this approach in improving the speed and accuracy of startup analysis. The findings highlight the potential of LLM-powered sourcing agents in transforming startup research, making it more scalable and data-driven for decision-makers in the investment and business ecosystem.