Interloom, a Munich-based startup focused on capturing the unwritten operational knowledge that AI agents need to function in large enterprises, has raised $16.5 million in venture funding led by DN Capital, with participation from Bek Ventures and existing investor Air Street Capital, according to a Fortune exclusive. The company previously raised a $3 million seed round in March 2024.
The pitch: AI agents keep failing in enterprise deployments not because the models are bad, but because they lack the institutional knowledge that experienced employees carry in their heads. About 70% of operational decisions have never been formally documented, according to Interloom founder and CEO Fabian Jakobi.
“The most important person at the bank is the person who knows whether the documentation is right or not,” Jakobi told Fortune. “They’re often the lowest paid. But they determine quality.”
How It Works
Interloom ingests millions of operational records — support emails, service tickets, call transcripts, work orders — and builds what it calls a “context graph,” a continuously updated map of how problems actually get resolved within a specific organization. Jakobi compared the concept to Google Maps: just as Google learns optimal routes from real-time traffic data, Interloom maps the paths that operational experts take to solve problems, then uses those maps to guide AI agents and new employees, per Fortune.
The approach contrasts with the previous wave of robotic process automation (RPA), where agents followed hard-coded workflows identically every time. Bek Ventures investor Mehmet Atici, who previously backed RPA leader UiPath, said in the Fortune report that “AI is now unlocking a new wave of rapid adoption in the enterprise.”
Live Deployments
Interloom is already deployed at several large European enterprises. At Commerzbank, the company analyzed millions of customer support emails against internal documentation and found that much of it was either conflicting or incomplete. Interloom says it reduced the gap between documented and actual operational knowledge from roughly 50% to 5%, according to Fortune.
At Volkswagen, Interloom is processing customer support tickets. At Zurich Insurance, it won a company-wide AI competition — beating what Jakobi said were 2,000 other AI-native startups — for an underwriting use case. Underwriting decisions reflect a company’s particular risk appetite, its accumulated experience with specific brokers and products, and institutional knowledge that no general-purpose model possesses.
The Timing
The “Great Retirement” provides a demographic tailwind: roughly 10,000 Baby Boomers retire daily in the U.S., taking decades of institutional knowledge with them at the same moment companies are trying to deploy AI agents at scale, per Fortune.
DN Capital partner Guy Ward Thomas said that “an agent is only as good as the expert decisions it can rely on,” and that DN Capital has seen with other AI agent startups that agents without proper enterprise context rarely work well.
Interloom’s next product is what it calls internally a “Chief of Staff” — a layer giving managers real-time visibility into how their AI agents are performing, with version control for agent-driven processes. Jakobi told Fortune his biggest competitor remains inertia: the assumption within large enterprises that operations will keep functioning as they have for the past decade.