Content Intelligence The following diagram illustrates the basic architecture of MaggieMe™'s content intelligence engine. From inception, Viyya designed MaggieMe™ with scalability in mind. MaggieMe™ leverages state-of-the-art technology and best practices in search engine architecture to ensure the system can scale with its success. The above diagram illustrates the basic architecture of MaggieMe™ 's content intelligence engine. Notice that user requests are handled by one of our clustered JBoss servers behind a load-balancer; there can be any number of identical clustered JBoss servers in our solution, which gives us the ability to expand capacity by scaling out the number of low-cost virtual servers. Each node in the cluster has its own read-only copy of our search index, which ensures searches are extremely fast for end-users. Additional information, such as each article's popularity score, is pre-computed and stored in the database for fast lookups.