As artificial intelligence becomes central to modern business operations, companies are increasingly recognising that the quality and accessibility of their internal data determine the effectiveness of AI systems. Organisations are therefore investing in infrastructure that allows them to manage and process large volumes of data while maintaining control over how it is used. In this context, Berlin based startup Tower has raised €5.5 million across pre seed and seed funding rounds to build infrastructure designed to support the next generation of data engineering in the AI era.
The company secured its pre seed investment led by DIG Ventures. The subsequent seed round was led by Speedinvest with participation from existing investors and additional venture firms including Flyer One Ventures, Roosh Ventures, Celero Ventures and Angel Invest. The funding round also attracted several angel investors from the technology and data infrastructure sectors, including Jordan Tigani, Olivier Pomel, Ben Liebald and Maik Taro Wehmeyer.
Infrastructure for the AI driven data economy
As companies deploy artificial intelligence across their operations, the need for fresh, reliable and well structured data has become more important than ever. AI models and analytical systems rely on accurate business data to generate insights and support decision making. However many organisations still struggle with fragmented data storage systems and complex pipelines that make it difficult to operationalise AI workloads.
Tower aims to address this challenge by providing infrastructure that combines analytical storage and processing capabilities within a single environment. The platform enables data engineering teams to manage large scale datasets while building analytics pipelines and AI applications that operate directly on company owned data.
By bringing storage and compute together, Tower allows organisations to simplify their data infrastructure and create systems capable of supporting advanced analytics and AI powered services.
Built by experienced data infrastructure engineers
Tower was founded by Serhii Sokolenko and Brad Heller, both former engineers at the cloud data platform Snowflake. Drawing on their experience working with large scale data infrastructure, the founders created Tower to address what they describe as the final stage of building reliable data systems.
The rapid development of AI powered coding assistants has made it easier for developers and data engineers to generate code, build pipelines and experiment with data applications. However, turning these prototypes into reliable production systems remains a significant challenge for many organisations.
Tower focuses on this so called last mile of development by providing an environment where both human engineers and AI generated code can work together effectively. The platform allows teams to transform rapidly generated ideas into stable production systems that can run on real business data.
Supporting collaboration between humans and AI agents
One of Tower’s central ideas is enabling collaboration between developers and AI agents within the data engineering process. AI coding tools can accelerate development by generating pipelines, agents and applications in minutes, but these outputs still require infrastructure capable of operating them reliably.
Tower provides a platform where these AI generated components can be tested, refined and deployed in production environments. This approach allows companies to benefit from faster development cycles while maintaining operational stability and data governance.
By supporting this collaboration, Tower aims to help organisations move from experimental AI projects to fully operational systems that deliver measurable business value.
Open architecture and data ownership
The platform is built on the Apache Iceberg open table format, a widely used standard for large scale data storage. This technology allows organisations to maintain ownership of their data while ensuring compatibility with multiple analytics tools and data processing engines.
Using open standards ensures that companies are not locked into proprietary systems and can continue using their preferred data platforms and tools. It also enables AI models to access up to date company data rather than relying on outdated public information sources.
Maintaining control over data is increasingly important as organisations seek to ensure security, regulatory compliance and transparency in how AI systems operate.
Expanding the platform and market reach
With the newly secured funding, Tower plans to expand its go to market operations while continuing to develop its platform capabilities. The company aims to enhance its infrastructure tools and support more organisations building AI powered analytics systems.
As businesses continue to integrate artificial intelligence into their operations, the demand for infrastructure that can manage reliable and secure data workflows is expected to grow significantly.
By focusing on the operational foundations of AI driven data engineering, Tower aims to help companies transform their internal data into production ready systems capable of powering the next generation of AI applications.
