Qontext Secures $2.7M to Solve AI Context Problem
The Qontext founders: Lorenz Hieber and Nikita Kowalski - © Qontext
Berlin-based Qontext has secured $2.7 million in pre-seed funding to build what it calls an independent context layer for artificial intelligence. The round was led by HV Capital, with participation from Zero Prime Ventures and notable angel investors including Jan Oberhauser (n8n), Emil Eifrem (neo4j), and Bastian Nominacher (Celonis), according to the company's press release.
Founded in 2025 by Lorenz Hieber and Nikita Kowalski, Qontext addresses a growing challenge in AI adoption: fragmented business knowledge. While AI models are becoming increasingly powerful, many organizations struggle to achieve consistent results because critical information about customers, products, and processes is scattered across different systems and teams.
The company's approach is straightforward. Instead of rebuilding context separately for each AI application, Qontext creates a centralized layer that delivers relevant, up-to-date information whenever an AI process runs. This could be a chatbot, an autonomous agent, or an automation workflow. The platform already serves customers ranging from startups to larger enterprises using AI across marketing, sales, and support functions.
"Putting a great model into an organization without context is like expecting a world-class hire to deliver on day one without any onboarding," explains Lorenz Hieber, Co-founder and CEO. The company claims its customers can more than double the number of processes that can be reliably automated with AI.
The technical challenge is significant. "We're dealing with millions of data points, constantly changing information, and complex access controls," notes Nikita Kowalski, Co-founder and CTO. The fresh funding will support platform expansion and team growth as Qontext builds out its reusable context infrastructure.
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