Copastur embeds personalized LLM fors mart, cost-effective flight recommendations​​

01
The client​

Copastur is a consulting services and resource management company for corporate trips, leisure, and events with 50 years of history.​

02
The challenge​

The complexity in evaluating different flight options led to overvaluation of small cost differences, and Copastur faced limitations with airline fares, unable to influence customers to opt for potentially more profitable ones even when they are more suitable for the user and have reduced cost variation.​

03
The solution​

Developing a personalized LLM-powered model to determine the ideal fares in airline ticket purchase requests.​


AI/R Compass UOL, in partnership with AWS, developed a customized model to determine optimal fares for airline ticket purchase requests using airline information, historical transactional data, and personalized LLM to contextualize recommendations and enable Copastur to offer personalized and informative suggestions according to the specific needs of each client.​

 

We leveraged Amazon SageMaker to create the customized model and Amazon Bedrock to restrict recommendations via prompt, ensuring a more contextualized and humanized approach.​


Data integration was carried out using AWS Glue with storage in Amazon S3, and the resulting recommendations were stored in Amazon S3 and consumed via AWS Lambda Functions for the Amazon API Gateway. ​

 

The complete infrastructure was created via AWS CloudFormation, providing access to Amazon SageMaker Studio for the data scientists involved, delivering a flexible structure that can be adapted to different projects.​

04
Main results​

Recommendation for the most advantageous rate, improving experience and optimizing profits even in situations where the tariff is not the lowest​

Improved ​conversion rate and user satisfaction with intelligent, personalized recommendations​​

Increased average fare with higher costs but greater convenience for users​

Reduced search abandonment with greater efficiency in users' ability to find what they are looking for​

05
Our Impact

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