Tripeur is an end-to-end corporate travel management platform that uses advanced Artificial Intelligence and Machine Learning algorithms to drive efficiency, ease of use, personalization and transparency for business travel by employees and executives alike. Like every travel platform, Tripeur does not own the inventory it sells, relying on a range of GDS providers and aggregators to source flight content for customer display. That dependency was sensible at launch, but the quality of a travel platform is bounded by the content layer beneath it.
Tripeur relies on a range of GDS providers and aggregators to source flight content for customer display, but the platform faced significant technical challenges that hindered further scalability. Five separate problems all traced to a single dependency, since the content, the pricing, the error semantics, and the carrier roster all belonged to an intermediary rather than to Tripeur itself.
Flight search results from aggregators came back slowly. In corporate travel that costs more than it appears, since a business traveller books between other tasks and abandons a slow search to finish later or elsewhere. Search is also not a single event, because a traveller compares routes, times, and fares, so the delay multiplies across every comparison attempted.
Between three and ten bookings in every hundred failed somewhere in the flow. The range matters more than the average. A steady 3 percent failure is a known cost that can be planned around, while a rate swinging between 3 and 10 percent cannot be staffed for or explained to customers, and unpredictability is what erodes confidence in a booking platform.
Aggregators provided only limited availability of the discounts and offers airlines run themselves. For corporate travel this is a direct commercial problem, since companies choose a travel management platform substantially on whether it saves them money, and fares consistently higher than booking direct weaken that argument however good the workflow is.
New airline options were absent from aggregator listings, and inadequate error response handling compounded it. Aggregators add carriers on their own timetable, driven by their priorities rather than Tripeur's customers, so a client whose employees fly a route served by a new carrier finds it missing and concludes the platform is incomplete.
Tripeur could have tuned its own layer with caching, retries, and heuristics, but that approach has a ceiling set by somebody else. No cache produces an airline the aggregator does not carry, and no retry recovers a booking whose failure was never explained. Tripeur sought technology partners experienced in travel who could build a platform serving millions of customers, starting by removing the intermediary where it mattered most.
Direct connectivity was established with low-cost carrier airlines. LCCs were the right starting point rather than an arbitrary one, since they dominate domestic corporate volume, are frequently the newest entrants missing from aggregator listings, and run the most aggressive discount programmes. One body of work therefore addressed coverage, pricing, and reliability together.
The platform integrated seamlessly with the discount and offer systems of LCC airlines. Direct connectivity reaches an airline's inventory, but discounts often live in separate systems with their own rules, so connecting to inventory alone would have delivered the same fares more reliably rather than better fares. Cost saving is the metric a procurement team actually examines.
A scalable solution was deployed using Kubernetes and a serverless architecture. Corporate travel search is extremely bursty, concentrating in working hours and falling to almost nothing overnight. Fixed capacity means paying for peak all day and still falling short at the peak, so serverless carries the spiky search load while Kubernetes scales the persistent services.
Result caching was implemented for frequently searched flights, and a proof of concept was built for flight price prediction. Corporate travel repeats far more than leisure travel, since companies have offices in fixed places and staff travel between them, which makes cache hit rates unusually high and each hit removes a round trip to a carrier.
Comprehensive coverage was achieved, encompassing nearly 99 percent of airline listings. The gap that had made the platform look incomplete to corporate clients effectively closed.
The search-to-booking ratio improved from 40 percent to 60 percent. Half again as many searches now end in a booking, showing that speed, coverage, and reliability had been suppressing conversion.
Customer satisfaction surged from 32 percent to 62 percent. A 32 percent CSAT describes a platform users tolerate rather than like, and nearly doubling it changes the renewal conversation entirely.
Eternalight rebuilt Tripeur's flight content layer on Node.js in a microservices architecture, running serverless compute for bursty search traffic under Kubernetes orchestration on AWS, with MongoDB behind it. Direct low-cost carrier connectivity replaced aggregator hops, result caching serves frequently searched routes, and an AI and ML layer delivered a flight price prediction proof of concept.
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