Neo4j's Intelligent Recommendation Framework is a data model agnostic tool designed to help organizations design and manage their graph-based recommender systems. It includes an admin console for building out recommendation pipelines and exposes a GraphQL API for accessing recommendations, aiming to minimize development efforts while maximizing value from graphs. Various industries are leveraging the framework for interesting use cases such as material management, predicting flight risk, building corporate hierarchy, empowering makers to meet audience demands, and analyzing consumer behavior. The tool uses a score-based approach combining multiple techniques like collaborative filtering, content filtering, business rules, and knowledge-based filtering to build the best-fit recommendations. By utilizing graph technology, organizations can connect all their data and enable powerful suggestions to increase revenues, optimize margins, and delight customers.