The goal of process design is the construction of a process model that is a priori optimal w.r.t. the goal(s) of the business owning the process. Process design is therefore a major factor in determining the process performance and ultimately the success of a business. Despite this importance, the designed process is often less than optimal. This is due to two major challenges: First, since the design is an a priori ability, no actual execution data is available to provide the foundations for design decisions. Second, since modeling decision support is typically basic at best, the quality of the design largely depends on the ability of business analysts to make the ”right” design choices. To address these challenges, we present in this paper our deep Business Optimization Platform that enables (semi-) automated process optimization during process design based on actual execution data. Our platform achieves this task by matching new processes to existing processes stored in a repository based on similarity metrics and by using a set of formalized best-practice process optimization patterns.