ResearchRabbit: AI Research Discovery & Mapping Tool

ResearchRabbit reimagines how researchers navigate the ever-expanding world of academic literature. By combining machine learning with intuitive visual mapping, the platform helps users uncover relevant papers, trace citation networks, and identify co-authorship connections that might otherwise stay hidden in traditional search tools. As you build collections of papers that interest you, ResearchRabbit quietly learns your research preferences, refining its suggestions over time so you spend less time searching and more time reading what matters. Beyond discovery, the platform doubles as a collaborative workspace. Teams and research groups can share collections, leave comments, and jointly explore literature maps, making it especially useful for literature reviews, grant proposals, or onboarding new team members into a research area. Alerts keep users informed of new, relevant publications without flooding their inbox with noise—ResearchRabbit only notifies when it's confident the content truly matches your interests. Whether you're a PhD student building your first literature review or a seasoned scientist tracking emerging trends in your field, ResearchRabbit's visual-first approach to research management offers a refreshing alternative to spreadsheet-based citation tracking and manual database searches.

Start by creating a collection and adding a few seed papers relevant to your research topic. ResearchRabbit will analyze these papers to generate personalized recommendations and build interactive visualizations showing citation networks and co-authorship connections. From there, explore related papers, follow citation trails to related work, and add promising results to your collections to further refine your recommendations. Invite collaborators to shared collections to comment and contribute, and enable alerts to get notified when new relevant papers are published in your area of interest.