Technical support services get several thousand voice calls every year. These calls vary across a range of technical issues or maintenance requests for a suite of hardware and software products. On receiving the call, a support agent creates a service request artifact that contains her interpretation of the customer's problem. This service request goes through the life cycle of the problem remediation process with the resolution also being recorded as part of the service request. It has been empirically observed that the actual complaint voiced by the customer is often different from the recorded interpretation in the service request. The service request created by support agents runs the risk of missing key information elements present in the customer voice records. In this paper, we build a framework that taps into voice calls and uses unsupervised and supervised learning methods to enrich the service requests with additional information. The enriched data is then used for automated problem resolution.