Case study – AI Model for Revenue Life Cycle Management

Business Problem: 

A travel recommendation engine wants to provide Travel Personalization and Review Intelligence solutions that enable hotels, hospitality consultants and OTAs to measure and enhance their products & services, improve their reputation and promote guest satisfaction. 


  • Aggregate and analyze online customer reviews to recommend meaningful and actionable insights for the hospitality industry.
  • Constantly rank reviews from different sources with varying weightage.



  • Natural Language processing in different languages.
  • Building hospitality taxonomy and constantly train models.



  • Designed, built and operated core Machine Learning and Lexical Analytics platform using Stanford NLP and custom algorithms.
  • Build region specific overrides and multi-tier taxonomy for better accuracy​.
  • The underlying ML-Ops platform provides all the necessary tools for data scientists to put their best model into production. 
  • Implemented ML models and Deep learning models with experimentation.


Business Outcomes:

  • 13 %: Increase in Hotel Occupancy.
  • 30 %: More efficient improved revenue per room.


Tech Stack: 

  • Custom Algorithms 
  • Stanford NLP
  • SciKit 
  • PyTorch 
  • Keras 
  • TensorFlow
  • OpenCV
  • Python
  • Docker
  • Data Visualization custom charts
  • Casandra
  • AWS Native