Tower

Generative artificial intelligence has triggered a global race to build models, but it has also created a problem that often goes unnoticed: training a quality model requires quality data, and building well-curated, labeled, and governed datasets is a slow, expensive, and hard-to-scale process. Tower, a Berlin-based AI startup, has chosen to work precisely on this foundational layer. Tower's platform specializes in the curation and enrichment of datasets for training generative models, with a particular focus on data quality, source traceability, and governance of the entire pipeline. In a market where anyone can train a model on data scraped without rigor, Tower positions itself as infrastructure for those who want to build reliable, controllable AI systems. Over time, the platform has evolved into a broader offering: tools for creating, testing, and deploying AI agents, with a SaaS and usage-based model that allows technical teams to access the platform with costs scaled to the number of active agents and compute resources consumed. Differentiated plans are available for early-stage startups and large enterprises, with professional services for customization and support. The positioning is clearly B2B: Tower's customers are research, data science, and AI engineering teams that need dedicated infrastructure rather than building their own tooling from scratch. Berlin, with its established tech ecosystem and proximity to major university research centers, is a natural home for this kind of startup. Tower's bet is that value in the AI economy accumulates not only in models — which are becoming increasingly commoditized — but in training data and the tools to manage it. Whoever controls the data infrastructure controls model quality, and that holds true today as much as it will tomorrow.