Tower provides a platform where engineers and AI agents collaborate to turn AI-generated code into reliable production systems.
Berlin-based Tower has raised €5.5 million across pre-seed and seed funding rounds to develop its approach to data engineering in the AI era. DIG Ventures led the pre-seed round, while Speedinvest led the seed round alongside existing investors. Additional participants include Flyer One Ventures, Roosh Ventures, Celero Ventures, and Angel Invest, as well as angel investors such as Jordan Tigani, Olivier Pomel, Ben Liebald, and Maik Taro Wehmeyer.
As AI reshapes the competitive landscape around data ownership, companies increasingly need access to fresh, reliable business data to power trustworthy AI systems. Open data storage architectures play a key role in enabling this shift, allowing organisations to retain control of their data while supporting modern analytics and AI workloads.
Tower provides infrastructure that helps companies manage analytical storage and processing while maintaining full ownership of their data. Its platform brings storage and compute together in a single environment, giving data engineering teams the tools needed to build and operate advanced analytics systems.
Founded by former Snowflake engineers Serhii Sokolenko (CEO) and Brad Heller (CTO), the company focuses on what it describes as the “last mile” of development. AI-powered coding assistants enable data teams to generate applications and pipelines faster than ever, Tower provides an environment where humans and AI agents can collaborate to transform AI-generated code into reliable, production-ready systems.
According to Sokolenko, AI coding assistants have significantly accelerated the development process, shifting the primary challenge toward deploying systems in production. While builders can quickly generate pipelines and agents, they still need infrastructure capable of running them reliably using real company data.
Tower exists to turn those ideas into production systems, powered by information unique to each company instead of public and very dated internet archives.
he said.
The platform uses the Apache Iceberg open table format, ensuring compatibility with major data platforms and leading data engine vendors. This approach allows organisations to retain control of their data while ensuring AI systems can access up-to-date, company-specific information needed for accurate analysis and decision-making.
The company plans to use the new funding to expand its go-to-market team and further develop the capabilities of its platform.