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AI-Powered Hotel Recommendation System Enhancement for Business Travel Platform

neoteric.eu

Hospitality & leisure

Information technology

eCommerce

Current Challenges in Hotel Recommendations

Existing system suffers from slow response times causing user wait delays and increased customer support costs. Users spend excessive time filtering through hundreds of hotel options to find compliant, personalized choices aligned with company travel policies.

About the Client

Enterprise SaaS platform for business travel management

Key Project Goals

Validate AI's ability to improve hotel recommendation accuracy
Reduce hotel selection time through instant search results
Ensure top 20% placement of user-preferred hotels in search listings
Create scalable model architecture for future data expansion

Core System Capabilities

AI-driven personalized hotel ranking algorithm
Real-time search result generation (<2s response time)
Feature engineering for hotel attributes (amenities, compliance, historical performance)
Adaptive model architecture for dynamic hotel inventory scaling

Technology Stack

Python

Google Cloud Function

Flask

AI Predictive Models

System Integrations

Existing SaaS travel management platform
Third-party hotel inventory APIs
User behavior analytics tools
Company travel policy compliance engine

Operational Requirements

Support 10,000+ concurrent user sessions
Maintain <2 second response time under peak load
Ensure GDPR-compliant data processing
Achieve 99.9% system availability

Expected Business Impact

Improved user experience through instant, personalized recommendations; 40% reduction in customer support inquiries related to booking delays; 25% increase in policy-compliant bookings; scalable AI infrastructure enabling continuous model improvement with new data ingestion.