Trip Planning AI
Project Overview
Industry: Hospitality & Travel (Hotels, Resorts, Destination Management)
Property Size: 20+ hotels across 5 destinations
Project Duration: 8 months
Team Size: 2 AI Engineers, 1 UX Designer, 1 Travel Concierge, 1 Product Manager
Wanderlust Journeys' agents were spending hours manually researching and building custom itineraries, creating a significant bottleneck that led to lost leads. They partnered with Deepiom to deploy an intelligent Trip Planning AI that transformed their sales workflow. The platform leverages machine learning to:
- Analyze Client Preferences: Instantly captures traveler profiles, from budget and pace to specific interests like "culinary tours" or "adventure sports."
- Generate Dynamic Itineraries: Constructs complete, day-by-day travel plans with optimized routes, booking links, and activity suggestions in seconds.
- Offer Smart Recommendations: Suggests unique, off-the-beaten-path experiences based on the traveler's profile, adding significant value beyond generic tourist spots.
This powerful tool turned agents into expert advisors, slashing itinerary creation time by 95% and allowing them to focus on client relationships, ultimately boosting conversion rates and customer satisfaction.
Client Feedback
The AI trip planner has transformed how guests experience their stays. They no longer spend hours researching — instead, they get curated itineraries instantly, and we capture more revenue from in-destination services.
Implementation Timeline
Before Implementation
- Manual concierge itineraries (slow, inconsistent)
- Generic recommendations with low personalization
- Limited revenue from local experiences
- Low engagement with hotel apps
After Implementation
- Instant AI-generated itineraries
- 87% guest satisfaction with recommendations
- 25% uplift in activity booking revenue
- Strong engagement with hotel’s digital ecosystem
Quality Control Process
- Ongoing evaluation of itinerary satisfaction via surveys
- Monitoring of partner reliability and availability accuracy
- Regular retraining of AI models with new guest data
- Continuous concierge input to refine system recommendations
Implementation Challenges
- Onboarding and integrating diverse local partners
- Handling cultural nuances in recommendations for international guests
- Training concierges to enhance, not duplicate, AI itineraries
- Ensuring real-time updates across multiple destinations and providers
Continuous Improvement
- Machine learning retraining using guest behavior and booking data
- Seasonal content curation for festivals, events, and promotions
- Expansion into AI-powered group itinerary planning
- Ongoing UI/UX enhancements for mobile itinerary visualization
Future Enhancements
- Voice-activated itinerary assistance via in-room devices
- AR-based guided tours through the hotel app
- Predictive travel suggestions for repeat guests
- AI-powered social itinerary sharing for group travelers
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Risk Management
Customer Matching
