Building an AI-Powered Ticket Insight Engine
Priyanshu Chaurasiya
About 6 hours ago
The world has changed a lot since AI started becoming part of our daily work. It has changed not only the way we work, but also the way we think about work. Almost every time I do something repetitive, a question automatically comes to my mind: should I do this manually? Can I take help from AI? Or can AI do this entire thing for me while I enjoy a cup of tea?
So hello, I am Priyanshu Chaurasiya. I hope you also get these kinds of questions whenever you find yourself doing repetitive tasks with a fixed procedure. I had the same thought, and that thought eventually led me to build my first AI application.
The application automates monthly ticket analysis in our project. The picture is not clear yet, right? Don’t worry. Grab your cup of tea, have some patience, and let me walk you through the journey of building the Ticket Insight Engine.
Manual Ticket Analysis Pain
Before talking about the solution, let me first introduce the problem.
I work in the Managed Services team on the Adecco project at Coforge. Like any support team, we deal with tickets every day and resolve issues reported by users. At the end of every month, one of the team members receives a sheet containing all tickets from the previous month. Their job is to manually analyze every ticket and answer questions like:
- What were the major issues this month?
- How many tickets belonged to the same category?
- What resolution was provided?
- Is there any permanent fix that can reduce such tickets in the future?
Honestly, it looks horrible, and it actually is.
The process is repetitive, time-consuming, and completely manual. Looking at this workflow, I kept asking myself, “Why are we doing this manually? Why can’t an application do this for us?” Those “why’s” eventually became the motivation behind building the Ticket Insight Engine.
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