A conglomerate that designs and sells ready-to-assemble furniture, kitchen appliances, and home accessories, among other goods and home services. To generate substantial cost savings, the client wanted to handle a large quantity of unstructured data in its technology landscape smartly and efficiently through AI technology and machine learning models. Thus, it was looking for an incident resolution workflow to assign a ticket to the most relevant team immediately once an incident is reported and improve the overall IT service management.

HCLTech came on board to implement an end-to-end solution using Natural Language Processing (NLP) and Deep Learning to automate the ticket assignation system in a real-time production environment. It resulted in improved efficiency of the incident resolution workflow. We built machine learning models to predict automatically the User Group, Priority, and Service of the tickets created in the system. It resulted in the automated classification of 30,000 e-mails per month and cost savings of $300,000 per month per ticket.

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