Call centers serve as a critical point of contact between businesses and customers. The communication between customers and agents in such calls typically involves asking questions and responding to them. On average, a 10-minute call includes 2-3 customer questions. Such questions provide insights into customer's asks as well as identify areas of improvement for the questions where agents are taking longer time to respond. This motivates a need to peek into each call flowing through the contact center and derive business insights over such questions. To facilitate such deeper analysis and business intelligence at scale, there is a need to efficiently identify and rank the group of questions being asked over millions of calls flowing through a contact center. In this paper, we present a system for question monitoring via question extraction, rewriting and grouping, which enables contact centers to discover questions from calls at scale. Our in-house system leverages natural language processing techniques to transform customer questions into a format that is easily understandable, facilitating streamlined analysis of the data.