Introduction

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 training 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

 Technological Framework

Why this setup?

A multi-framework ML stack enables experimentation across classical NLP, deep learning, and custom lexical models — ensuring the highest accuracy for hospitality-specific review intelligence across multiple languages.

Why this tools?

Cassandra supports rapid reads/writes across geographies, while AWS services provide horizontal scaling and operational reliability. Custom charts helped translate sentiment signals into hotel-ready dashboards. 

Why this Setup?

Multi-source review aggregation with weighted ranking ensures that insights reflect true sentiment across all channels, enabling hoteliers to act on the most impactful feedback.

Takeaway

Zapcom built a production-grade NLP and ML platform that aggregates, ranks, and analyses hospitality reviews across multiple languages delivering 13% improvement in revenue per room and 30% increase in recommendation relevancy.