Qontext addresses fragmented business context as AI adoption expands across teams, a challenge that often limits outcomes more than model capability.
Berlin-based Qontext, which is developing an independent context layer for AI, has secured $2.7 million in pre-seed funding. The round was led by HV Capital, with participation from Zero Prime Ventures and a group of founders and operators from the AI infrastructure, automation, and enterprise software sectors, including Jan Oberhauser (n8n), Emil Eifrem (neo4j), Bastian Nominacher (Celonis), Philipp Heltewig (Cognigy), and Fabian Veit (make.com), among others.
Founded in 2025 by Lorenz Hieber and Nikita Kowalski, Qontext provides AI systems in production with relevant, up-to-date context. Its platform is used by fast-growing startups and larger enterprises deploying AI across functions such as marketing, sales, and customer support, helping organisations increase the number of processes that can be reliably automated.
Despite rapid advances in AI capabilities, many organisations struggle to achieve consistent outcomes and measurable returns. This is often due not to model quality, but to the absence of a reliable foundation of contextual information covering customers, products, processes, and internal policies. Such data is typically fragmented across systems and teams, frequently changing and sometimes inconsistent, which limits the scalability and reliability of AI applications.
Putting a great model into an organisation without context is like expecting a world-class hire to deliver on day one without any onboarding—the capabilities are there, but the results won’t be. With Qontext, companies can roll out new AI tools and agents that are fully context-aware from day one,
says Lorenz Hieber, co-founder and CEO of Qontext.
In many organisations, context is also rebuilt separately for each AI use case, leading to duplicated integration and maintenance efforts that slow adoption and make it difficult to scale AI broadly.
Nikita Kowalski, co-founder and CTO of Qontext, added that the company works with large volumes of continuously changing data and complex access controls across both human users and AI agents, noting that addressing this challenge is essential to enabling AI at scale.
With the new funding, Qontext plans to expand its platform and team to develop reusable context infrastructure, enabling AI processes to operate on reliable and continuously updated context across applications and use cases.