Founded by two former Snowflake engineers, the Berlin startup wants to be the platform where AI-generated data pipelines actually work.
The hard part of building with AI is no longer getting the code. Gets the code to work. The gap between what an AI coding assistant can produce in minutes and what a production system needs to stay viable is the problem Tower is trying to close.
TRaised by a startup based in Berlin 5.5 million euros (about 6.4 million dollars) Backed by investors including Speedinvest and DIG Ventures, in a pre-seed and seed round with angel participation from some of the best-known names in the world of data infrastructure.
the castle Both were founded by ex-Snowflake engineers Serhii Sokolenko and Brad Heller, who watched engineers struggle to manage data pipelines rather than write them. CEO Sokolenko previously worked in product management at Databricks and Snowflake in Berlin and Google Cloud, AWS and Microsoft in Seattle. CTO Heller worked on Snowflake’s control plane. Tower is the third startup for both.
Their platform is designed to handle what the press release calls the “last mile” of AI-powered development: testing, debugging, delivery to production, and continuous operation of AI-generated code. As Brad Heller says, the tool problem has changed.
“It’s easier than ever to write functional code, but it’s still hard for humans, and harder for AI agents to test, troubleshoot, deliver and manage. With Tower, we’re here to fix that.”
Tower brings storage and computing to a platform built around the Apache Iceberg open-desktop format. Iceberg has become the de facto open standard for analytics storage, compatible with Snowflake, Databricks and most major data engine vendors, meaning Tower customers retain ownership of their data and are not locked into a single stack.
The platform also supports AI agents that are fed fresh, company-owned data rather than the outdated public internet archives that most foundational models train on.
The pre-seed round was led by DIG Ventures alongside existing investors, while the seed was led by Speedinvest. Additional backers include Flyer One Ventures, Roosh Ventures, Celero Ventures and Angel Invest.
The angel syndicate is notable: Jordan Tigani, CEO of MotherDuck and founding engineer of Google BigQuery; Olivier Pomel, CEO and co-founder of Datadog; Ben Liebald, vice president of engineering at Harvey; and Mike Taro Wehmeyer, co-founder and CEO of Taktile.
The list reads less like a random collection of investors and more like a who’s who of the generation of data infrastructure that Tower is building to succeed, or at least boost. Tigani, in particular, spent years arguing that the data industry had overengineered itself for scale it never needed; Tower’s thesis that AI coding assistants create a new problem of operational complexity is entirely in line with this tradition.
Gaurav Saxena, director of engineering at Ford Motor Company, offered a customer-side view. According to him, Apache Iceberg represents real strategic value for enterprises, but the operational requirements to manage it are a real limitation.
“Running it effectively requires skills and ongoing maintenance that many data teams don’t have. What’s attractive about platforms like Tower is that they can eliminate these operational costs, making it much easier to adopt Iceberg without having to build a specialized in-house team.”
The traction numbers in the press release suggest real usage, albeit early. As of February, just months after launch, the platform surpassed 200,000 downloads across more than 30,000 unique apps, and its Python SDK reached 70,000 monthly downloads. These figures are self-reported and have not been verified.
Sokolenko attributes the company’s ambition to where AI-powered products currently fail: reasoning, not generation.
“Builders can now create pipelines and agents in minutes, but they still need a platform that can reliably run them on real company data. Tower exists to turn these ideas into production systems powered by unique data for each company, rather than public and outdated web archives.”
Speedinvest’s Florian Obst, who led the firm’s investment, pointed to multi-tenant architecture as a key differentiator: a platform designed for rapid integration and rapid iteration, rather than being retrofitted from scratch from an enterprise monolith.
Tower will use the new capital to grow its go-to-market team and deepen the platform’s capabilities. The market it enters is competitive, with Snowflake, Databricks and newer data infrastructure startups all investing heavily in the same AI-era data engineering story.
Tower is betting that none of them pay much attention to the problem that arises after the AI finishes writing the code. THat betting can prove a good time. The sooner AI coding tools become available, the greater the gap between what’s created and what’s ready for production. Tower wants to be the one to fill it.
