Tower has raised USD $6.4 million across pre-seed and seed rounds, targeting data engineering workflows reshaped by AI code generation.
Founded by former Snowflake engineers Serhii Sokolenko and Brad Heller, Tower positions its product as a way to move AI-generated code and data pipelines into reliable production systems. Its platform provides a shared environment where human engineers and AI agents collaborate on the work between writing code and operating it in production.
The funding included a pre-seed round led by DIG Ventures and a seed round led by Speedinvest. Other investors include Flyer One Ventures, Roosh Ventures, Celero Ventures and Angel Invest. Tower also named angel investors, including Motherduck Chief Executive Jordan Tigani, Datadog Chief Executive Olivier Pomel, Harvey.ai Vice President of Engineering Ben Liebald, and Taktile Chief Executive Maik Taro Wehmeyer.
Tower describes the problem it addresses as the "last mile" for data engineering teams, particularly those using AI assistants. AI tools have made it faster to produce working code, while operational tasks such as testing, fixing issues, deployment and ongoing operation remain difficult. Those challenges can be even harder when AI agents are involved in writing and managing code.
Heller, now Chief Technology Officer, linked the product's direction to his experience building data systems at Snowflake.
"During our time at Snowflake, we saw how the next generation of engineers wanted a platform that truly combined data processing with AI. Today, it's amazing to see how much more productive developers have become with AI coding agents; however, they still struggle with the same old operational problems. It's easier than ever to write functional code, but it's still difficult for humans - and even more difficult for AI agents - to test it, fix issues, deliver it to production, and operate it. That's what we're here to fix with Tower," said Brad Heller, CTO, Tower.
Platform approach
Tower brings storage and processing compute into a single product and targets both analytics use cases and pipeline operations for data engineering teams. The storage layer uses the Apache Iceberg open table format, widely used in data lakehouse architectures.
Iceberg compatibility matters for customers who work across multiple data engines and vendors. Tower lists Snowflake and Databricks among systems that can work with Iceberg tables, and argues that open formats improve data ownership by letting customers keep control of their data rather than being locked into a single platform format.
Sokolenko, Tower's chief executive, framed the product around a shift in focus from writing code to running it in production. He also emphasised the value of company-specific information for AI systems.
"With AI coding assistants accelerating development, the real challenge has shifted to production. Builders can now create pipelines and agents in minutes - but they still need a platform that can run them reliably on 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," said Serhii Sokolenko, CEO, Tower.
Iceberg interest
Interest in Iceberg has grown as organisations seek more flexibility in analytics architectures and as AI projects demand access to governed datasets. Tower is entering a market where cloud data platforms, lakehouse vendors and data tooling start-ups compete for engineering teams' attention. Competition has intensified as vendors add AI features and position products around agent-driven development.
Some adopters cite operational complexity as a barrier to rolling out Iceberg at scale. A Ford Motor Company engineering leader pointed to the staffing burden of operating Iceberg-based systems.
"As a data leader in the automotive industry, we see tremendous strategic value in Apache Iceberg, but operating it effectively demands skills and ongoing maintenance that many data teams aren't staffed for. What's compelling about platforms like Tower is their ability to remove that operational overhead, making it much easier to adopt Iceberg without building a specialised in-house team," said Gaurav Saxena, Director of Engineering, Ford Motor Company.
Investors backing the company describe the production gap as a growing issue as more teams rely on AI-generated code. DIG Ventures partner Melissa Klinger said data pipelines remain difficult to run reliably once they leave the development environment.
"AI has made it easier to write data pipelines, but getting them to run properly in production is still hard - and only getting harder. Serhii and Brad have lived this problem first-hand, and we're excited to be backing this talented team as they build Tower to tackle such a huge problem," said Melissa Klinger, Partner, DIG Ventures.
Speedinvest principal Florian Obst said Tower is targeting teams building vertical AI services and SaaS products, and described the platform as multi-tenant. He added that teams want analytics products that fit into existing technology stacks without large infrastructure projects.
"Teams building vertical AI services and SaaS products need an analytics platform that integrates seamlessly into their stack without standing up expensive legacy systems or stitching together complex cloud infrastructure. Serhii and Brad have built a multi-tenant platform purpose-built for fast integration and rapid iteration. That's exactly the kind of foundational infrastructure we're excited to back," said Florian Obst, Principal, Speedinvest.
Tower will use the new funding to expand its go-to-market team and deepen work on its platform.