About
Our project bridges the gap between startups and investors by leveraging advanced AI algorithms and cutting-edge tools to create a seamless matchmaking experience. By enriching LinkedIn profiles using Proxycurl and Large Language Models (LLMs), we generate comprehensive, high-quality profiles that offer deeper insights into startups and investors alike. LangChain is integrated to further enhance the profiles, enabling dynamic and context-aware interactions. To ensure precision and speed, we implemented Typesense for fast keyword-based searches and Pinecone for semantic search capabilities. These tools allow for robust querying, enabling users to find the most relevant matches based on nuanced and contextual information. This dual-layered search approach combines the strengths of traditional search with the power of semantic understanding, ensuring unmatched accuracy in connecting startups with investors. For efficient data handling, we utilized MongoDB to store and manage enriched profiles and search indices. This allows for scalability and streamlined data access, ensuring optimal performance even as the dataset grows. Our solution empowers startups to showcase their strengths and innovations effectively, while providing investors with curated, in-depth information to make informed decisions. By fostering meaningful connections, we create opportunities for collaboration and investment, driving growth and innovation in the startup ecosystem. Skills/Technologies Used: Large Language Models (LLMs): To enrich and summarize LinkedIn profiles. LangChain: For advanced data structuring and dynamic interactions. Proxycurl: For extracting and enriching LinkedIn data. Pinecone: For semantic similarity search. Typesense: For fast, real-time keyword-based search. MongoDB: For scalable data storage and management. Python: As the core programming language for development. This project not only simplifies the investor-startup discovery process but also enhances the quality.