IT service providers differentiate themselves through offering after-sales support for hardware and software products. Thus, businesses, including large corporations, have intricate work-flows for servicing such support requests while reducing man-hours needed. These work-flows generally operate through a ticketing system for resolving customer issues. A lot of man-hours are spent in searching old tickets for correct problem and resolution for such issues. Support requests pertaining to enterprise hardware are more challenging than desktop support for end-user products. Enterprise hardware requires deeper diagnosis involving several systems and expertise of multiple agents. In this work we propose a cognitive agent, Neev, which helps in mitigating the problem in a three-fold fashion (1) retrieving a summary of relevant ticket text (2) Tagging the relevant parts as a part-of-the-problem or a part-of-the-solution (3) Focusing on the precise problem and solution. We evaluate the performance of our system using a rank-based metric where a ticket extraction is successful if the problem or solution occur in the top-n suggestions. We report the results for varying top-n values for both problem and solution on varying severity of the tickets. We find that the accuracy for problem extraction in top-1 is 62% and it reaches 86% and 94% for top-3 and top-5 cases, respectively. Furthermore, the accuracy for solution extraction reaches 62% and 88% for top-3 and top-8 cases, respectively.