The Neo4j Intelligent Recommendations Framework is built on a graph database that connects customers, products, services, vendors, and suppliers. The framework uses advanced technologies such as graph analytics and machine learning algorithms to produce highly relevant recommendations. A CPG company uses the framework to fill open positions with internal talent by analyzing employee job histories, skills, reviews, locations, and salaries in an extensive graph database. Each recommendation engine has a pipeline of Cypher queries called phases that build recommendation lists, including Discover Phase, Boost Phase, Exclude Phase, Diversify Phase, and others. These phases provide flexibility and power to deliver real-time recommendations, reduce query complexity, create easy-to-maintain engines quickly, score and weight phases depending on context, and enable building powerful engines for virtually any business case. The framework uses hybrid scoring, which permits the simultaneous use of multiple recommendation methods to select the best fit for each user and situation in real time.