Intelligent Service Quality Monitoring

Implementation Time:
9 months
Solution Provider: AI Singapore

How might IRAS use AI to automate baseline quality monitoring of live chats, so that there is comprehensive review of all live chat interactions and supervisors can divert efforts to coach exception cases?

IRAS defined a set of service quality metrics that were of interest and engaged data labelers to annotate the live chats

    2 AI models were built:
    – Text Classification model to classify each chat turn/utterance according to the service quality metrics, using contextual information from preceding and subsequent turns
    – Named Entity Recognition model to pick out granular instances of each service quality metric within each chat turn The models were deployed to automate the scoring of chat dialogues based on the metrics, and IRAS could evaluate the performance of each agent by aggregating the predicted scores

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Implementation Time

9 months

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