Heating, ventilation, and air conditioning (HVAC) systems account for more than half of commercial building energy usage. Unfortunately, bottlenecks in early-stage design, control system implementation, and building operation hinder the realization of maximally efficient buildings. First, high-fidelity simulation-crucial to an iterative, performance-driven design process-is too slow to use early in design, producing volumes of data but offering little insight. Second, creating dynamical models and implementing advanced model-based HVAC controllers are both labor-intensive jobs beyond the scope of most projects. Third, during operation, fixed occupancy schedules become outdated and undermine the energy savings of even the most advanced HVAC equipment. This work addresses these bottlenecks using a novel software platform that seamlessly translates the building information model (BIM) to alternate forms to suit various stages of the design and operation life cycle. First, in early stage design, we improve the speed of high-fidelity simulation by using a resistor-capacitor network abstraction to inform the thermal zone layout, facilitating model order reduction while retaining simulation accuracy. Second, during control system implementation, we use a Modelica abstraction to eliminate the tedious manual control-oriented model creation process and greatly speed the implementation of model predictive control (MPC). Third, in operation, we enable controllers to adapt to changing occupancy schedules using a self-tuning Markov model abstraction. This three-pronged approach removes tedious, specialized manual work to empower designers, engineers, and building operators to create and operate smarter, more energy efficient commercial buildings.