  Comprehensive IT support teams in large scale organizations require more man
power for handling engagement and requests of employees from different channels
on a 24*7 basis. Automated email technical queries help desk is proposed to
have instant real-time quick solutions and email categorisation. Email topic
modelling with various machine learning, deep-learning approaches are compared
with different features for a scalable, generalised solution along with
sure-shot static rules. Email's title, body, attachment, OCR text, and some
feature engineered custom features are given as input elements. XGBoost
cascaded hierarchical models, Bi-LSTM model with word embeddings perform well
showing 77.3 overall accuracy For the real world corporate email data set. By
introducing the thresholding techniques, the overall automation system
architecture provides 85.6 percentage of accuracy for real world corporate
emails. Combination of quick fixes, static rules, ML categorization as a low
cost inference solution reduces 81 percentage of the human effort in the
process of automation and real time implementation.
