Recommender systems are becoming increasingly important for businesses because they can help companies offer personalized product recommendations to customers. There have been many acknowledged recognized successes of consumer-oriented recommender systems, particularly in e-commerce. In this work, we describe our experiences building a business-to-business (B2B) recommendation engine that matches company clients to internal company products. The underlying pairing of clients and products is based on co-clustering principles and helps reveal potential future purchases. Unlike most consumer-oriented recommendation systems, our approach takes into account the need for interpretability. We do not only provide a cursory explanation, as offered in most traditional recommender systems. In our approach, the recipient of the generated recommendations are sales and marketing teams; thus, we offer a detailed reasoning in straightforward English that considers multiple aspects regarding why a client may be a suitable match for the particular offering. Finally, we analyze the outcome of a country-wide deployment of the proposed methodology for selected IBM sales teams.