Small Talk with Ulises Olave, CTO and Co-Founder of Fitia

Interviews

What is Small Talk?

Small Talk is our short Q&A with founders from the JetBrains Startup Program. They answer a handful of questions in their own words about what they’re building, why they started, and what they’ve figured out along the way. No pitch, no polish – just a two-minute read to meet the person behind the product.

This time, we’re talking to Ulises Olave, CTO and Co-Founder of Fitia, a nutrition app that has grown to 10 million+ users in Latin America and beyond. He shares how his team brought four kinds of databases together in one place, why the rapid advancement of AI made JetBrains the first company his team went to for help, and how a small engineering team achieved a pace of one major feature per month.

TL;DR

• Fitia’s AI coach processes more than 30 million food logs a month, alongside data on weight, sleep, steps, and water. As the product has evolved, this data has been spread across different database types and providers.
• DataGrip has given Fitia’s team one central place to work with all their databases, including PostgreSQL, BigQuery, Azure, and Redis. Its built-in AI features have also helped the team cut query times from hours down to mere seconds.
• GoLand has helped Fitia roughly double their development speed on its Go microservices. They now ship three times faster than before, at a rate of one major feature per month.

What is Fitia, for someone who’s never used it?

Fitia is a meal-planning and calorie-tracking app. You tell it your goal – whether that’s losing weight or gaining muscle – and it builds a personalized plan that adapts to your data, your progress, and the foods you actually like to eat. The engine behind this is our own AI coach. We’re the top nutrition app in Latin America, and this year the goal is to become a leading health and fitness app in the US, too.

What was the biggest challenge you faced as Fitia grew?

Data. We store food records for all our users. This means more than 30 million items logged every month, plus data on weight, sleep, steps, and water intake. As our data grew, it ended up spread across several different database types and providers, and keeping integrity across all of them became a challenge. Even a simple query became a real headache in that kind of fragmented setup.

How did you deal with that?

We centralized everything in one place. We pulled our PostgreSQL, BigQuery, Azure, and Redis databases into DataGrip, so instead of jumping between providers, we could work with all our data in a single tool. And after centralizing it, we’ve been able to leverage DataGrip’s built-in AI as well. The query suggestions cut work that used to take hours down to seconds, especially against those complex multi-provider setups.

Why JetBrains?

We’d been using JetBrains tools from early on, so when AI started advancing at an almost absurd pace, JetBrains was the first company we turned to. Every month one of the big providers would release a better model, which meant we could suddenly deliver more features, and faster. We needed every developer to be fast enough to ship powerful features every month without expanding the team, and we needed one tool that covered the whole path, from writing the code to deploying it. We applied to the Startup Program, got accepted in a few days, and within a week the team was using DataGrip and GoLand daily.

Was there anything that surprised you?

How well integrated the AI is. At first, we were just focused on getting more efficient with solid IDEs. We didn’t expect the AI to be built in so natively. The context-aware chat lets you ask questions right inside the IDE, using your own code as context, and that changed how we worked more than we thought it would.

What specific impact has GoLand had?

With GoLand, we roughly doubled our development speed for our Go microservices. The code suggestions and optimization tooling let us build and deploy faster, which means our mobile team can rely on clean, reliable backend services and ship features much faster. The product goal is one major feature every month. That used to take us about three times as long. This year we’ve been holding that pace consistently.

Where does Fitia go from here?

AI has opened up possibilities in nutrition that were almost unimaginable before, from identifying food in a photo to reading a user’s data and giving personalized recommendations. Our strategy isn’t to compete with the big model providers – it’s to build on top of them, using their capabilities while training on our own community data, so we can create features that actually mean something to our users. Beyond that, we’ll just keep focusing on developer efficiency, because that’s what lets a small team move like a big one.

Read the full case study here.

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