Automated Dispatch of Helpdesk Email Tickets
Date:
Tools: Python, REST APIs, Docker, Mongo DB
Methods:Several ML,NLP techniques such as
- SVM , Siamese LSTM Networks , Automated rule engine using Gini impurity & Association rule mining , Model Selection , Continuous retraining
Description: Framework for end-to-end automated help desk email ticket assignment system driven by high accuracy, coverage, business continuity, scalability, and optimal usage of computational resources. The primary objective of the system is to determine the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group with high accuracy. While meeting this objective, it should also meet the objective of being able to operate at desired accuracy levels in the face of changing business needs by automatically adapting to the changes. The proposed system uses a system of classifiers with separate strategies for handling frequent and sparse resolver groups augmented with a semiautomatic rule engine and retraining strategies to ensure that it is accurate, robust, and adaptive to changing business needs. Our system has been deployed in the production of six major service providers in diverse service domains and currently assigns 100,000 emails per month, on an average, with an accuracy close to ninety percent and covering at least ninety percent of email tickets. This translates to achieving human-level accuracy and results in a net savings of more than 50,000 man-hours of effort per annum. To date, our deployed system has already served more than two million tickets in production.
Outcomes: - Deployed for 10+ Telco and Retail clients , served 2 Million tickets till date
- AI magazine article and Best deployment paper award at IAAI
Related papers: - IAAI paper : https://doi.org/10.1609/aaai.v33i01.33019381
- AI Magazine : https://doi.org/10.1609/aimag.v41i3.5321
Realted Patents : - Automated rule extraction for ticket assignment