Early systems engineering and requirements definition requires quantitative information about potential solutions prior to having sufficient information or time to develop detailed models. This research develops and demonstrates a transparent and repeatable process for rapidly creating quantitative models that leverage existing expert knowledge. This process is built upon established modeling frameworks and current literature for low fidelity modeling and hierarchical expert-based methods. The process includes system definition using interactive morphological analysis and gathering information from subject-matter experts with computer-based interfaces in order to create a series of linear performance models. Available volunteers provided data for a relevant aerospace design to test the process as a whole and several hypotheses about specific methodological decisions made during the development. The collected data was analyzed for similarity among participants and for similarity to model parameters of an existing trusted truth model. The results of the analysis demonstrated the ability for expert-based models to accurately match the behavior of the truth models and of historical data.