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Many data mining techniques have been applied to technical support call center interactions between customers and employees. There remains a vast, largely untapped source of data related to intraemployee interactions. In typical call centers, there are chat rooms for more experienced first-level support technicians to help newer, less experienced technicians. In addition, when a first level technician needs more assistance, they will contact a second-level employee by phone or chat.Mining these records of inter-employee interaction could lead to improved interaction and eventually to a machine learning system that can substitute for the second-level employee in the initial stage of assisting the less trained first-level employees. One of the authors has worked in several technical support call centers and therefore has detailed knowledge of their operations and areas that can be improved. This paper was inspired by this author noticing much of the communication between various levels of employees is not retained or analyzed, leading to a phenomenon of repeating and recreating the same efforts day after day, training class after training class. This paper proposes a system of studying the interactions between first-and second-level technical support employees using data mining and machine learning techniques. The proposed system would be inserted into the normal flow of information between first-level tech support employees and the second-level co-workers they consult with when they need help. The overall goal of this proposal is to intercept solutions that are provided on an ad-hoc basis and capture them into a corporate database that will present these solutions in the future, as they are learned. This type of system would be flexible enough to adapt to new products and new questions as new questions are posed to second-level experts.