As the world generates more data than ever before, the infrastructure required to search, analyse and store it is placing growing pressure on energy systems and corporate budgets alike. Gothenburg based Vesiro believes a major part of that problem can be solved at the software layer. The Chalmers University spinout has now raised €1.6 million in seed funding to scale its technology and bring more efficient search to energy hungry data environments.
The funding round was backed by a strong group of institutional and private investors, including Chalmers Ventures, Industrifonden, Länsförsäkringar Göteborg & Bohuslän, Yuncture, First Gate Invest, E14 Invest, Mach One, and a group of experienced angel investors.
Tackling the rising energy cost of data
Global data generation is accelerating at an unprecedented pace. Data centres already account for around one per cent of global electricity consumption, and that share is expected to rise sharply as AI, real time analytics and digital services expand. For many organisations, search infrastructure built on platforms like Elasticsearch has become both mission critical and increasingly expensive to operate.
Vesiro was founded to address this challenge by improving how search workloads are handled at scale. Rather than replacing existing systems, the company has developed a performance focused plug in for Elasticsearch that enables organisations to process large volumes of data more efficiently using fewer servers.
This approach allows companies to extract the same insights while reducing compute demand, energy consumption and operational costs.
Software optimisation instead of hardware expansion
At the core of Vesiro’s offering is the idea that performance gains should come from smarter software rather than constant hardware expansion. Many organisations respond to growing data loads by adding servers, which increases costs and energy use without addressing underlying inefficiencies.
Vesiro’s plug in is designed to optimise how Elasticsearch handles large scale queries, improving response times and throughput. According to internal benchmarks and early pilot deployments, customers can achieve significant performance improvements that allow them to downsize infrastructure while maintaining service quality.
“Data volumes are growing faster than today’s infrastructure can handle, and our technology makes it possible to analyse large datasets using far fewer servers. This lowers costs while also reducing energy consumption,” said Oskar Hagman, co founder and CEO of Vesiro.
Built for demanding enterprise use cases
The Vesiro solution is aimed at organisations running extensive Elasticsearch clusters, particularly in sectors where search performance is tightly linked to revenue and operations. These include business intelligence platforms, ecommerce systems, AI driven analytics, and large scale log and event processing.
One of the key advantages of the plug in is that it does not require companies to redesign their existing architecture. Instead, it integrates directly into current Elasticsearch deployments, allowing teams to improve efficiency without major system changes or operational disruption.
This lowers the barrier to adoption and makes the technology relevant for both growing scaleups and large enterprises managing complex data environments.
From university research to commercial scale
Vesiro emerged from research conducted at Chalmers University of Technology, with a focus on high performance computing and data intensive systems. The company has since transitioned from academic research into a commercial product with real world deployments.
The new funding will be used primarily to expand Vesiro’s technical team, deepen product development, and accelerate market rollout. The company plans to refine its technology further while increasing engagement with customers facing rising infrastructure and energy costs.
As demand for data continues to grow, Vesiro is positioning itself as a software layer that allows organisations to scale insight without scaling energy use at the same pace.