Rule Learning and Formal Concept Analysis (FCA) are two fields of science that study similar topic yet speak in a very different terms. This paper describes rule-based machine learning models with FCA-based terminology which results in decision quiver model. A decision quiver, discussed in the paper, is a supervised machine learning model that is based on intents, generators of intents, and predictions for each intent (or generator). We show that the finding of the optimal set of intents is a cornerstone task in constructing a decision quiver (and thus, any rule-based model). The paper finishes with the baseline algorithm to construct decision quivers. The algorithm produces machine learning models that are much smaller than the state-of-the-art ensembles of decision trees, yet that offer the similar quality of predictions.