In the description and analyzation of a case study, the study chosen was that of the customer support company Qsupport and their inquiry into a more sophisticated expert system from the developer, Kalki. Qsupport is a customer support company that is based in Bangalore, India. Personal as well as corporate customers send queries on various software and hardware related problems by e-mail. Qsupport has a support team, who go through the e-mails and reply to all the questions the questions posed by the customers. In case the support person does not have an answer at hand to the numerous and extensive questions, they must go through the appropriate reference material and find out the answers. The primary reference material for all unknown answers is Qsupport's personal database, since all questions asked and answered are sent and stored in a database. A problem arose as Qsupport was using a simple word search program to search this database and find appropriate questions. As the database grew in size and magnitude it had the capacity to contain around 15,000 questions and their answers. This sharply brought down the accuracy of the keyword search system and

The objective of the new expert system developed by Kalki was to cut down the time it took to locate questions and answers in the database once a query was received by the Qsupport support team. The problem with a simple keyword search was that too many matches would be found requiring the user a good amount of time to sift through each of these matches received from the search. So to development a more sophisticated search process was the primary objective. Simultaneously, some automation of the answer retrieval system was desired so that cycle time in answering a query could be further reduced and maximize Qsupport's customer service. The end desired result was an expert system which when prompted with a new query, will search the existing database and find out the most accurate questions and their answers. Since the entire customer text of the e-mail was fed to the expert system, it was not required of the user to identify and type in keywords to be searched. Also, the expert system used an advanced algorithm for searching incorporating Natural Language Processing methods, the results were accurate. The results were confined to the best four matches (which was found sufficient due to the high accuracy) and this minimized the time required of user to go through them. The expert system provided an interface for the user to combine suitable responses from those retrieved and send it back so that time required in formulating the answer was reduced. Since the whole expert system was developed as an ActiveX dll, it could be easily incorporated into any other system (including a web page) without much change being required. The expert system also provided various statistical information regarding its own performance as well as on the queries asked by various users and their domains/sub

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