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.