Researchers of varying experience levels need to be able to quickly prototype and deploy sensor networks so that they can collect data and analyze their phenomena of interest as soon as possible. However, custom sensor deployments are subject to several constraints including computing resources available and types of data that are being acquired. We show how the use of the Reconfigurable Environmental Intelligence Platform (REIP) streamlines the process of multimodal environmental sensing. We offered the original sensors to a group of student researchers to monitor indoor occupancy alongside temperature and humidity in a modern office building. We demonstrate how the modular design of REIP made such a study feasible for young researchers in the context of a course project. The study resulted in findings leading to practical solutions on how air conditioning and ventilation systems can be operated more efficiently to minimize the building's energy use without affecting the comfort of its residents. It also demonstrates the potential of REIP for the rapid prototyping of multimodal sensor networks that can, in turn, enable a more data-driven approach to decision-making.