Bridge: An AI-Driven Skill and Investment Matchmaking Platform for Early-Stage Entrepreneurs

Abstract

Access to funding and skilled collaborators remains one of the most significant barriers faced by first-time entrepreneurs, limiting innovation and economic opportunity at the grassroots level. Existing platforms such as AngelList/Wellfound, Gust, and OpenVC address investor discovery and team-building as separate, disconnected functions, leaving founders without a unified, trustworthy space to demonstrate credibility. This paper presents Bridge, an AI-driven platform that unifies investor discovery, skill-based team matching, and transparent milestone tracking within a single system. Unlike existing platforms, Bridge mandates pitch deck submission at profile creation and automatically scores it using a Large Language Model (LLM), producing a standardized credibility signal absent from current solutions. The core matching engine uses cosine similarity over profile and skill-tag vectors, evaluated against real historical founder-investor data and a keyword-matching baseline. Experimental comparison demonstrates that the proposed similarity-based approach outperforms naive keyword filtering in ranking relevant matches, while the mandatory AI-scored pitch deck provides measurable credibility standardization not found in existing investor- or team-matching platforms.