The traditional process of software development has revolved around the development, testing, and deployment of monolithic applications. The data-driven nature of Machine Learning and Cognitive Computing applications have disrupted this paradigm. Instead, the development of applications revolves around the processing of training data and experimentation with the learning and inference algorithms that power the application. This in turn has changed the primary activities of software developers and promoted the decomposition of monolithic applications into pipelines of independently developed micro-applications with simpler scope and objectives. We present Document Introspection System for CognitiVe Research (DISCVR), a prototype of an extensible open application that analyzes user-provided research papers and delivers recommendations for related content and papers. The DISCVR application is designed to support research into the description and construction of future scale-out cognitive computing applications by defining cognitive component pipelines with a flexible interface that supports many programming languages for easy development of current and future capabilities. DISCVR also provides a platform for individual learning and inference micro-services through the implementation and integration of individual components, each of which can power other cognitive applications. Lastly, by using the same tools and techniques as modern distributed applications, DISCVR provides system architects with a realistic cognitive computing application that can be used to evaluate the performance and energy-efficiency implications of novel computer systems.