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” ofdevelopment. 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.