Recent research has shown both a desire for and a lack of theoretical underpinning to systems engineeringSystems engineering (SE). With the rise of the digital paradigm, advanced data analytics (e.g., AIArtificial Intelligence (AI)/ML) are becoming the norm. Algorithms based on rigorously developed mathematical theory are slated to enhance the transformation along with the maturation of the discipline and tools of SE. For example, mathematical theory is being used in SE for problem formulation and verificationVerification, which are also enhanced when paired with generative AIGenerative AI, such as large language modelsLarge Language Models (LLM) (LLMsLarge Language Models (LLM)). Our research leverages the rich mathematical theory from the lineage of A. Wayne Wymore paired with modern digital means to advance the state of the art, practice, and discipline of SE. Our past research defined a metamodel for sufficient conditions for defining verificationVerification models, which we have converted from manual, tabular analysis to a software-enabled workflow and platform. In this article, we reveal the minimum viable product (MVP) by describing the architecture and a simple exemplar of its use to facilitate understanding of degrees of equivalence between system models through a mathematical determination of homomorphism.