Berlin based Qontext has raised 2.7 million dollars in pre seed funding to build what it describes as an independent context layer for AI systems in production, addressing one of the most persistent bottlenecks in enterprise AI adoption.
The round was led by HV Capital, joined by Zero Prime Ventures and a group of prominent founders and operators from the AI infrastructure and enterprise software ecosystem. Participants include Jan Oberhauser of n8n, Emil Eifrem of neo4j, Bastian Nominacher of Celonis, Philipp Heltewig of Cognigy, Fabian Veit of make.com, and other experienced builders who have scaled complex software platforms.
Building the missing layer for production AI
Founded in 2025 by Lorenz Hieber and Nikita Kowalski, Qontext focuses on a problem many organisations encounter once AI moves from experimentation into real business workflows. While models themselves have improved rapidly, companies often struggle to get reliable, repeatable results from AI systems operating in production environments.
According to Qontext, the issue is rarely the intelligence of the model. Instead, it lies in the lack of consistent, up to date context. Information about customers, products, internal processes, permissions, and policies is usually scattered across multiple tools and teams. It changes frequently and is often inconsistent, which limits how effectively AI agents can operate at scale.
Context as a foundation, not an afterthought
Qontext positions its platform as a shared and reusable context layer that sits independently of individual AI models or applications. Rather than rebuilding context separately for every new AI use case, companies can use Qontext to provide a single source of truth that AI agents can access securely and continuously.
Lorenz Hieber, co founder and CEO, compares deploying AI without context to hiring a highly skilled employee and expecting immediate results without onboarding. The capability exists, but without understanding how the organisation works, performance will fall short. Qontext aims to ensure AI tools and agents are context aware from the moment they go live.
Solving duplication and scaling challenges
In many organisations, each AI project involves custom integrations and manual work to assemble the required data. This duplication slows down adoption, increases maintenance costs, and makes it difficult to expand AI beyond isolated pilots.
Qontext’s approach is to decouple context from individual workflows and make it reusable across teams and applications. This allows AI systems in areas such as marketing, sales, and customer support to operate on the same consistent understanding of the business, while respecting access controls and data governance rules.
Designed for dynamic, real world environments
Co founder and CTO Nikita Kowalski highlights that context is not static. Enterprise data is constantly changing, and permissions differ between users, teams, and AI agents. Qontext is built to handle large volumes of continuously evolving data while maintaining strict control over who or what can access specific information.
This capability is particularly important for organisations that want to automate more complex and sensitive processes without compromising security or accuracy.
Backing from experienced operators
The investor group reflects Qontext’s ambition to become core infrastructure for AI driven organisations. Many of the angels involved have built platforms that power automation, data infrastructure, and enterprise workflows at scale. Their participation signals confidence that context will become a critical layer as AI adoption matures.
Expanding the platform and team
With the new funding, Qontext plans to grow its team and further develop its platform. The focus will be on building robust, reusable context infrastructure that can support multiple AI processes across an organisation, reducing friction and enabling more reliable automation.
As companies move beyond isolated AI experiments and seek measurable returns, Qontext is betting that context, not just models, will define the next phase of enterprise AI adoption.
