Investment Thesis
How We Invest
We believe the defining software wave of this decade is AI-native workflow automation for European enterprise. Here is why — and how we evaluate it.
European mid-market companies — the hundreds of thousands of 50–500 person businesses in DACH, Benelux, and Scandinavia — run on a combination of legacy ERP systems, manual spreadsheet workflows, and a generation of SaaS tools that was never designed to be intelligent. The productivity gap between what these organisations could do and what they currently do is enormous. AI-native software — architectures built from scratch around machine intelligence rather than retrofitted — is beginning to close that gap. We are at the beginning of this wave, and we want to fund the founders building the infrastructure and vertical applications that will define it.
The European Automation Opportunity
American software investors have historically underpriced the DACH and Northern European mid-market because the sales motion is harder — longer cycles, more procurement committees, higher requirements for local language and regulatory compliance. That friction is a feature, not a bug, for us. Software that survives the German enterprise procurement process is software that actually works in production. The retention numbers that come out of these environments are structurally better than US SMB SaaS.
The opportunity is amplified by timing. Three forces have converged simultaneously: foundation models capable of reasoning over unstructured business data; a generation of European founders with real enterprise operating experience who don't need to relocate to San Francisco to build; and an enterprise buyer that is, for the first time, willing to move quickly on AI tooling because their competitors are. The window for seed-stage companies to establish durable workflow positions is open. It will not stay open forever.
What We Look For
Technical moat comes first. We look for products that are genuinely hard to replicate — not because of first-mover advantage, but because the core model, the data flywheel, or the integration depth creates a defensible position over time. The best AI-native B2B companies we've seen either own a proprietary dataset generated through usage, or occupy a position in the workflow that would require the buyer to change fundamental processes to switch them out. Vendor lock-in through genuine operational embedding is legitimate moat.
On the team, we strongly prefer operator founders — people who have run the software, managed the sales cycles, and dealt with enterprise customers at a human level. Academic AI founders can be exceptional, but they need a commercial co-founder who has earned the enterprise buyer's trust before. What we are evaluating is not just whether the technology works; it is whether this team knows how to turn technical capability into a business in this specific market.
How We Work
We write checks between $1.5M and $4M at seed and pre-seed. We take board seats or observer seats — the choice depends on what is actually useful for the founder at that stage, not what looks good in the term sheet. We have made the mistake of over-boarding early companies before and learned from it. Our preference is to be the investor that a founder calls before they call the board meeting, not the one they present to after decisions are made.
Our most active area of portfolio support is go-to-market acceleration in the German-speaking market. The network of enterprise buyers, procurement decision-makers, and commercial executives that Katrin has built over eight years at German SaaS unicorns is one of the most direct forms of value we bring. We make introductions that matter because we know which doors are worth opening. Beyond GTM, we support on subsequent fundraising with European and transatlantic funds, and on technical architecture decisions where Maximilian's background is relevant.