A Novel Agentic LLM Framework for Startup Intelligence through Web and API Integration

Abstract

This paper presents an automated pipeline that uses large language models (LLMs) to simplify startup discovery and intelligence gathering. The system generates targeted search queries, retrieves relevant web data, and extracts structured information from third-party APIs, reducing the manual effort needed for research. Supporting tools handle tasks like entity resolution, data parsing, and ranking, ensuring accurate and reliable results. The pipeline does not require custom model training. Instead, it uses prompt-based agents to manage all stages, including searching, scraping, enriching, and summarizing data. By producing concise, decision-ready insights, the framework helps venture capital firms, startup scouts, and innovation teams make faster, better-informed decisions. Its modular design makes it easy to replicate and scale, offering an efficient and practical solution for startup intelligence workflows.