Digital tools offer many ways to find musical patterns with machines. But the task of formulating digital-musical queries systematically, interpreting the results, and refining our methods to yield intelligent insights about musical practice is far more difficult. In this presentation, a team of musicologists and data scientists will share our experiences in developing CRIM Intervals, a Python-Pandas toolkit designed to support Citations: The Renaissance Imitation Mass, modeling human expertise in terms that can be used by computers to analyze encoded musical scores, and presenting the results of automated score-reading in forms that scholars can interrogate and refine. This presentation explains how we developed these tools, from understanding the constraints that define a given musical event, to the development of the tools needed to model those constraints, and in turn to the stages of refinement needed to eliminate false negatives and positives.