How Does Our API Interact With GDSs and Data Sources?

Trip Ninja is a Flight Search product that utilizes content to help OTAs provide optimized multi-city itineraries for their travelers. But how does this work exactly?

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When travelers make a search on your OTA, you will use Trip Ninja's search products to send a POST request via an endpoint on the API that contains all the necessary information: flight origins and destinations, dates of travel, cabin class, and more.

Trip Ninja's API then builds a list of content queries that you must make to the APIs of your content sources, such as GDSs like Travelport and LCC aggregators like Atlas. Before FareStructure can start building out optimized multi-city itineraries, it needs access to the content with which to build those itineraries. This is where the pre-processing step comes in.

Trip Ninja uses machine learning capabilities to recognize which content queries you should make, based on the likelihood that the content returned by a search will make the final FareStructure constructed itineraries cheaper or more likely to sell. This helps to minimize any waste and ensures that searches for content are only made for content that is likely to be effectively used.

Once Trip Ninja determines the most effective content queries for you to make, it returns a list of these queries in a response payload. You then make queries to the respective providers in the list, and when you receive all of the query payloads from your content providers, you initiate the second call in the FareStructure sequence: compressing and sending the content to Trip Ninja for itinerary creation.

Upon successfully generating the optimal itineraries, a response payload containing these itineraries is returned for parsing and rendering on your platform within seconds.

It's important to note that at no time does Trip Ninja inject its own content into the mix or make the content queries to the content providers. However, there is an alternative approach for getting content to Trip Ninja that some may use, which is the legacy or emulation-based integration. This approach involves making the content queries to content providers on your behalf.

While this method cuts down on the number of API calls, it presents a different set of complications. For example, emulation presents an opportunity for significant delays in the emulation process, and significant complications may result from account mismanagement.

Trip Ninja prefers to integrate customers in an emulation-less manner. While this setup is not devoid of issues, they are more directly controllable by the Trip Ninja team and less reliant on the actions of third parties.

In conclusion, Trip Ninja's search products utilize content to provide optimized multi-city itineraries. By using machine learning capabilities to recognize which content queries you should make, Trip Ninja helps to minimize waste and ensures that searches for content are only made for content that is likely to be effectively used. While there is an alternative approach for getting content to Trip Ninja, it presents a different set of complications. Therefore, Trip Ninja prefers to integrate customers in an emulation-less manner to provide better service to you and your travelers.

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