• Qontext has raised a $2.7m pre-seed to build the independent context layer that enables companies to run all their AI processes on the same reusable, up-to-date context base.

• As AI spreads across teams, the bottleneck is rarely model capability but fragmented context – Qontext turns scattered business knowledge into governed AI context.

• The round was led by HV Capital with participation from Zero Prime Ventures, plus a group of leading AI and enterprise software founders.

• Angels include Jan Oberhauser (n8n), Emil Eifrem (neo4j), Bastian Nominacher (Celonis), Philipp Heltewig (Cognigy), Fabian Veit (make.com), among others.

Berlin, February 5, 2026 — Qontext, the company building the independent context layer for AI, today announced a $2.7m pre-seed round led by HV Capital, with participation from Zero Prime Ventures and a group of founders and operators across AI infrastructure, automation, and enterprise software. The angel group includes Jan Oberhauser (n8n), Emil Eifrem (neo4j), Bastian Nominacher (Celonis), Philipp Heltewig (Cognigy), and Fabian Veit (make.com), among others.

Qontext, which was founded by Lorenz Hieber and Nikita Kowalski in 2025, is already powering AI in production with relevant context for customers ranging from fast-growing startups to larger enterprises rolling out AI across marketing, sales, and support. With Qontext, companies can more than double the number of processes that can be reliably automated with AI.

Context is fragmented across tools

Although AI capabilities are accelerating fast, many organisations struggle to show consistent outcomes and ROI. The reason is rarely model quality. It’s the missing foundation underneath: reliable, up-to-date context about customers, products, processes, and policies that define how work actually happens. That context is scattered across systems and teams, changes daily, and often exists in conflicting versions. Without a strong context foundation, AI outputs stay generic, inconsistent, and hard to scale beyond isolated pilots, leading to a limited automation rate.

In addition, context is currently rebuilt separately for each AI application, which does not scale with more and more AI adoption. Each new agent or workflow becomes a standalone project – reconnecting tools, re-curating information, and maintaining pipelines over time. This creates duplicated effort, locks context inside individual tools, and makes it impossible to broadly roll out AI.

Context should be its own layer

Qontext is built around a fundamental shift: context should be its own layer – independent from models and applications. By centralizing and maintaining context once, Qontext delivers the right information at the moment an AI process runs – whether that’s a chat experience, an autonomous agent, or an automation workflow. This turns context from a recurring implementation burden into a compounding infrastructure asset. Each new AI process benefits from the same foundation, with consistent governance and control.

“Putting a great model into an organization 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,” says Lorenz Hieber, Co-founder & CEO of Qontext. “With Qontext, companies can roll out new AI tools and agents that are fully context-aware from day one.”

Backing by HV Capital and AI leaders

The need for a dedicated context layer is increasingly recognized across the AI ecosystem. “Context fragmentation is one of the toughest infrastructure problems in AI today, and Qontext is solving it at scale,” says Jan Oberhauser, Founder & CEO of n8n and angel investor. Ann-Christin Stiehl, Investor at HV Capital, adds: “What convinced us is that Qontext is not another AI feature, but a foundational layer every serious AI stack will need.”

Building this layer is a significant technical challenge. “We’re dealing with millions of data points, constantly changing information, and complex access controls across humans and agents,” says Nikita Kowalski, Co-founder & CTO of Qontext. “But solving this is also the biggest lever for making AI work at scale.”

With the new funding, Qontext will expand its platform and team to build reusable context infrastructure, allowing AI processes to run on trusted, continuously updated context across applications and use cases.