PaperSphere

Our project aims to bridge the gap between researchers worldwide, enabling collaboration and reducing the time spent on literature reviews. Using Perplexity, Streamlit, Groq, Neo4j, and Python, we built a platform that connects researchers through a knowledge graph, providing AI-powered summarizations and visualizing the relationships between research papers. With the integration of CrewaI and LLM, we enhanced the platform’s intelligence, offering accurate recommendations and insights. Despite challenges in optimizing data retrieval and fine-tuning AI algorithms, we successfully created an interactive platform that helps researchers discover, connect, and collaborate more efficiently. Through this process, we gained valuable experience in handling large-scale data, working with AI models, and developing user-friendly interfaces. Moving forward, we plan to enhance the platform with more advanced AI-driven features, expand the knowledge graph, and improve real-time collaboration tools to make research even more accessible and interconnected.