Applications targeting Smart Cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of city ecosystems. A major obstacle involves the differences in the levels of available information. In this demonstration we present REMI, a reusable elements framework to handle varying degrees of information availability by design from two complementary angles, namely graceful degradation (GRADE) and data enrichment (DARE). In a nutshell, we develop reusable machine learning black boxes for mining and aggregating streaming data, either to infer missing data from available data, or to adapt expected accuracy based on data availability. We illustrate the proposed approach using tram data from the city of Warsaw.