Aertrip.com is a leading travel platform offering a wide range of services, including flight, hotel, and holiday bookings. With millions of travelers seeking personalized experiences and seamless booking processes, Aertrip required advanced AI solutions to manage its large user base, optimize search results, and enhance customer interactions.
As the travel industry evolved, Aertrip faced several challenges:
Aertrip required a comprehensive AI solution that could tackle these challenges while ensuring a superior user experience.
With a massive number of flights, hotels, and destinations available, Aertrip needed to deliver personalized search results to each user. Generic results were leading to poor user satisfaction and increased drop-offs.
Users often asked highly complex travel-related questions, such as multi-city itineraries, text-white specific hotel amenities, and budget combinations. Traditional search algorithms struggled to understand and address these sophisticated queries.
Aertrip’s support team was inundated with repetitive questions about bookings, cancellations, and trip modifications. A more efficient system was needed to automate these inquiries while still offering a personalized touch.
Amplework Software developed and implemented a custom Large Language Model (LLM) and AI solution to address these key pain points. The core of the solution revolved around improving personalized travel experiences, automating customer support, and optimizing search results.
The LLM was integrated into Aertrip’s search engine to provide personalized travel recommendations based on each user's preferences, historical behavior, and data (e.g., previous searches, favorite destinations, and budget). This allowed Aertrip to deliver more relevant and tailored flight, hotel, and package suggestions.
The LLM was equipped with advanced natural language processing (NLP) capabilities to handle complex travel queries. Whether a user searched for “flights from New York to Tokyo with a layover in Paris,” or “budget hotels in Barcelona with free Wi-Fi and airport shuttle,” the AI could interpret these sophisticated requests and return the most accurate results.
Aertrip’s platform also benefited from AI-powered dynamic pricing models. By analyzing real-time data on flight prices, hotel availability, and customer demand, the AI was able to recommend the best times to book and offer personalized deals. The LLM analyzed price trends and suggested alternative dates or locations when prices were lower, improving the user experience.
We implemented an AI-based customer support chatbot that could handle common inquiries about bookings, cancellations, refunds, and travel updates. The chatbot was built on LLM technology and was able to respond to queries in natural language, providing an engaging and seamless support experience. It also handled real-time ticket modifications, cancellations, and refund requests.
The LLM-powered system provided predictive travel insights based on user behavior. For example, if a user frequently traveled to beach destinations, the AI suggested similar vacation spots, weather updates, and trending activities. It also generated detailed itineraries based on the user's interests, making planning more convenient and tailored to individual preferences.
Aertrip serves a global audience, and to cater to users from different regions, the AI- powered system included multi-language support. The LLM was trained to understand and provide responses in multiple languages, making the platform more accessible to international travelers.
To ensure the AI could offer relevant travel recommendations, we analyzed vast amounts of data from Aertrip’s platform, including user preferences, past searches, and booking trends. The LLM was trained using this data to understand user behaviors and provide the best possible travel options for each unique query.
To ensure the AI could offer relevant travel recommendations, we analyzed vast amounts of data from Aertrip’s platform, including user preferences, past searches, and booking trends. The LLM was trained using this data to understand user behaviors and provide the best possible travel options for each unique query.
The LLM was integrated into Aertrip’s customer service interface, where it handled common customer support queries via a conversational AI chatbot. This feature was developed using advanced NLP techniques to ensure that the chatbot could interpret and respond to complex travel-related inquiries in a natural, human-like manner.
The AI was connected to real-time data feeds for flight and hotel prices, allowing the LLM to dynamically recommend the best times for users to book their trips. The pricing model provided personalized discounts and price predictions, helping users find the most cost-effective options.
After deployment, the LLM continued to evolve by collecting real-time feedback from users. This feedback was used to fine-tune the model, improving the accuracy of recommendations and customer support responses over time.
The implementation of LLM and AI at Aertrip delivered transformative results:
Increase in Conversion Rates
The AI-powered personalized search results led to a significant increase in booking conversions. Users found more relevant flight, hotel, and holiday package options, which improved their overall experience.
The conversational AI chatbot reduced customer support response times dramatically, handling a wide range of booking-related queries with high accuracy. This reduced the need for human agents to step in, allowing them to focus on more complex issues.
Faster Query Resolution
The AI-driven itinerary planning tool enhanced user engagement by offering detailed, personalized travel suggestions, activities, and destinations based on each user’s interests and preferences.
Higher Engagement with Itinerary Planning
With real-time dynamic pricing and alternative recommendations, Aertrip saw a noticeable reduction in booking abandonment rates, as users were offered alternative dates, routes, or hotels when their initial preferences weren’t available or were too expensive.
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