At Turismocity, we started in 2014 in Buenos Aires with a clear mission: to create the most efficient travel search engine for Latin America. We didn't just want to "list" flights; we wanted to predict opportunities using technology.
Today, serving millions of unique monthly users and operating across the Americas, the technical challenges have evolved from "how do we get data?" to "how do we scale globally?".
Here is a look under the hood of our journey from a bootstrapped startup to acquiring US-based Farecompare and Brazil's Quanto Custa Viajar.
The Metasearch Challenge
Unlike a standard e-commerce site where you control your inventory, a metasearch engine relies on real-time connections with hundreds of external providers (Airlines, OTAs, and GDS).
Our core engine has to perform three complex tasks in milliseconds:
- Ingestion: Querying multiple provider APIs simultaneously without timing out.
- Normalization: Taking messy data from different sources and standardizing it into a clean, comparable format.
- Intelligence: Filtering thousands of results to highlight the "smartest" option for the user, not just the cheapest.
Scaling Through Acquisitions
In December 2020, we took a major leap by acquiring Farecompare, a key player in the US / Global market. Shortly after, in 2021, following our Series A funding round, we acquired Quanto Custa Viajar in Brazil.
From an engineering perspective, M&A (Mergers and Acquisitions) is not just a business deal; it's a Tech Integration Challenge.
We had to answer critical architectural questions:
- Do we merge the backends or keep them separate?
- How do we unify data lakes to get global insights?
- How do we leverage Farecompare's global reach with Turismocity's localized efficient algorithms?
Leveraging AI for Price Detection
One of our biggest differentiators has always been our ability to "detect" rather than just "search." We use historical data and algorithms to identify when a flight price drops significantly below its average. This isn't magic; it's data science applied to user intent.
By analyzing millions of search patterns, our stack can alert a user about a deal to Miami or Madrid before they even know they want to travel.
What's Next?
We are continuing to expand our footprint in Latin America and beyond. Our engineering team is focused on optimizing high-concurrency performance and refining our machine learning models to make travel more accessible for everyone.
We are building the technology that moves millions.
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