Adam Szymański. Image: Oxla
This Warsaw-based start-up was founded in 2020, and aims to address the ‘performance bottleneck’ of high-volume data processing.
Adam Szymański has been programming professionally for more than 20 years, with his early-career pursuits beginning in game development, including a stint at renowned Polish developer CD Projekt Red. During his time working in game development, he learned essential code optimisation skills.
After leaving CD Projekt Red, Szymański worked as a software engineer at Google, where he became “deeply involved” with big data and machine learning.
A few years later, Szymański met Kacper Szcześniak. With vital skills in code optimisation and experience working with big data, Szymański was inspired to join forces with Szcześniak in 2020 and co-found our Start-up of the Week, Oxla.
Oxla has developed a high-performance analytical database designed for large-scale workloads. According to Szymański, who is also CTO, the Warsaw-based start-up’s database is purpose-built to “maximise data processing efficiency with modern hardware capabilities”, while reducing compute costs.
Big data, big spend
Speaking to SiliconRepublic.com, Szymański explained that while data production has grown exponentially in the last decade, leading data warehouses – which are enterprise systems used for the analysis and reporting of structured and semi-structured data from multiple sources – are “prohibitively expensive for processing terabytes of data due to performance inefficiencies”.
“This is largely because they rely on outdated technology that fails to leverage modern hardware capabilities,” says Szymański. “The main challenge stems from a disparity: while core counts have increased more than 20-fold in the past decade, data transfer between RAM and the CPU has slowed by three-fold per CPU clock tick, creating a performance bottleneck.”
Oxla intends on addressing this bottleneck by deploying a parallelised query engine that optimises data transfer between CPU and RAM, resulting in a “substantial performance boost” that, according to Szymański, yields up to a 90pc reduction in compute costs.
“By making big data analytics affordable, we aim to empower companies facing rising data processing costs, unlock previously cost-prohibitive use cases, and democratise data science.”
How it’s going
As for current progress, Oxla bagged significant investment just last month, raising more than €10m in a seed round backed by TQ Ventures, Lead Ventures, Warsaw Equity Group and 4growth VC. Szymański says this funding enabled the company to build a 40-person engineering team.
Earlier this year, the start-up signed its first customers and recently began “heavily investing” in go-to-market efforts. Meanwhile, Szymański is in the middle of what he describes as an “intense” event tour, which he says has been fascinating as he has met people dealing with the very problem that he and Szcześniak set out to solve.
Going forward, Szymański says the start-up has plans to raise a Series A in the second half of 2025, and ultimately wants to grow into “the go-to solution for data analytics” while committing to “democratising access to data science”.
“What I mean by this is that, thanks to substantially reducing compute costs, Oxla can unlock new R&D efforts that previously weren’t economically viable, while also lowering the entry threshold to data science for many new organisations.”
Oxla has had quite a journey building its product, which Szymański says is the result of the hard work and passion of the team along with the luck of having partners that believe in the company’s mission.
“This is especially true given that our project was unusual – we built in stealth for three years,” he says. “The reality of VC funding right now is that many founders are bootstrapping and only attract investment after reaching profitability.
“While I understand this cautious approach, I believe breakthrough innovations require significant R&D efforts, and I hope our example will prove that deep-tech infrastructure companies are an excellent investment.”
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