Interloom Raised $16.5M to Tackle AI Agents' Tacit Knowledge Problem with Context Graph

Munich startup Interloom raised $16.5M

The Munich startup is building what it calls a 'context graph', a continuously updated map of how operational decisions actually get made inside an enterprise, drawn from millions of real cases rather than documentation that may never have been written. There is a particular friction point in every enterprise AI deployment, and anyone who has tried to roll out an AI agent in a large organisation tends to run into it early. The agent might be technically capable. It can read documentation, follow instructions, and execute steps. What it cannot do is replicate the judgment of the person who has been doing the job for fifteen years and knows, from experience, exactly why the standard playbook does not work on Tuesdays in the logistics department. That knowledge has never been written down, because nobody needed to write it down, until now. This is the problem Interloom is building towards. The Munich-based startup, which operates across Munich, Berlin, and London, announced on 19 March that it has raised $16.5 million in a seed round led by DN Capital, with participation from Bek Ventures and existing backer Air Street Capital. The round represents a significant step up from the company's initial $3 million seed, which Air Street led in March 2024 when the company emerged from stealth. Interloom's core product is what it calls a Context Graph: a continuously evolving model of how operational decisions actually get made inside a given organisation, constructed by ingesting millions of real cases, support emails, service tickets, call transcripts, work orders, and extracting the patterns of how expert workers resolve problems. Founder and CEO Fabian Jakobi describes the challenge in terms of tacit knowledge, the concept coined by British-Hungarian philosopher Michael Polanyi whose central observation was that most expertise cannot be fully articulated by the expert who holds it. Jakobi estimates that around 70% of operational decisions are never formally documented. The analogy Jakobi uses is Google Maps: just as the navigation tool learns optimal routes from real-time traffic, Interloom builds a map of the paths operational experts actually take to solve problems, then uses that map to guide AI agents and new employees facing similar situations. The system updates continuously, so that every resolved case adds to the institutional memory rather than disappearing when the person who handled it leaves or retires. That retirement risk is part of the pitch. The press release cites the figure of 10,000 baby boomers leaving the US workforce daily, a demographic statistic widely documented by Pew Research. The argument is that enterprises face a compounding problem: institutional knowledge built up over decades is being lost at precisely the moment AI is expected to step in and automate complex operational work. Without capturing that knowledge first, the AI has nothing useful to draw on. Interloom's early customer base includes Zurich Insurance, JLL, and logistics group Fiege, as well as Commerzbank and Volkswagen, the latter two confirmed independently by Fortune in its exclusive on the funding. At Commerzbank, Interloom analysed millions of customer support emails against internal documentation and reportedly reduced the gap between what was documented and how work actually happened from around 50% to 5%. At Zurich Insurance, the company won an internal AI competition against what Jakobi described to Fortune as 2,000 competing AI-native startups for an underwriting use case. The investor lineup carries its own thesis-confirming logic. Guy Ward Thomas, the DN Capital partner leading the investment, was previously the first institutional backer of Cognigy, the German enterprise conversational AI platform, which DN Capital backed from its Series A in 2019 and which was acquired by NICE in August 2025 for $955 million -- described at the time as Europe's largest AI exit. Ward Thomas has noted that the fundamental lesson from that investment was how critical organisation-specific context is to making AI agents work in practice. Mehmet Atici, who leads the Bek Ventures participation, was an early backer of UiPath, the robotic process automation pioneer that listed in New York in 2021. His argument is that the current wave of AI agent adoption represents the next major inflection in enterprise automation after RPA.

Exclusive: Interloom, which wants to solve AI agents' 'tacit knowledge' problem, raises $16.5 million in VC funding | Fortune

Michael Polyani, the British-Hungarian philosopher, economist, and scientist, is perhaps best known today for coining the term "tacit knowledge." His great observation was that a large part of what constitutes expertise in any given field is never written down. In some cases, it exists only as a kind of professional intuition that even the expert can't fully articulate. "We know more than we can tell," was Polyani's famous catch phrase. Today, tacit knowledge presents a challenge to companies that want to automate workflows with AI agents. Much -- perhaps even most -- of the knowledge these agents need is not written down. Interloom, a Munich-based startup that is aiming to transform traditional business process automation for the AI age, thinks it can crack the problem of tacit knowledge. And it has just raised a new $16.5 million venture capital round to help it achieve that mission. The funding is being led by DN Capital, with participation from Bek Ventures and existing investor Air Street Capital. The company previously announced a $3 million seed round in March 2024. Interloom did not disclose its valuation after the new funding. Fabian Jakobi, Interloom's founder and CEO, argues that the current wave of excitement about AI agents overlooks the tacit knowledge bottleneck. About 70% of operational decisions have never been formally documented, he said. When a complex support ticket lands on a veteran staffer's desk, they know the workaround, the right internal team to escalate to, and the resolution -- not because it's in a manual, but because they've seen it before. "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." Interloom's approach is to ingest millions of operational records -- support emails, service tickets, call transcripts, work orders -- and use them to build what it calls a "context graph," a continuously updated map of how problems actually get resolved within a given organization. Jakobi likens 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, and uses those maps to guide AI agents and new employees alike. Jakobi is a serial entrepreneur. He previously founded Boxplot, which in 2021 he sold to Hyperscience, a New York-based AI software company that specializes in extracting data from unstructured documents. Interloom's software is already live with several large European enterprises. At Commerzbank, Interloom analyzed millions of customer support emails and checked them against existing internal documentation -- finding that much of it was either conflicting or incomplete. The company says it reduced the gap between documented and actual operational knowledge from roughly 50% to 5%. At Volkswagen, it is processing customer support tickets. And at Zurich Insurance, Interloom won a company-wide AI competition -- beating out what Jakobi says were 2,000 other AI-native startups -- for an underwriting use case. An underwriting decision at an insurance firm, Jakobi said, reflects that company's particular risk appetite, its accumulated experience with certain brokers and products, and institutional knowledge that no general-purpose model possesses. "The Zurich underwriter knows how their broker chat underwriting works much better than Accenture does," Jakobi said, taking aim at the large consulting firms that have traditionally dominated enterprise process work. The broader argument is that AI agents, no matter how capable, are useless in large enterprises without organization-specific context. Jakobi frames this as the "corporate memory" problem. "In software, the compiler tells you if the code works," Jakobi said. "We don't have that luxury [in other domains.] The evaluation has to come from a human expert." Interloom's new backers agree with that thesis. Guy Ward Thomas, a partner at DN Capital, said that "an agent is only as good as the expert decisions it can rely on." And Thomas said that DN Capital has seen with other AI agent startups that when these agents don't have the right context about the enterprise in which they are being deployed, they rarely work well. "Our experience with vertical AI agents and voice platforms showed us how important context is," he said. Mehmet Atici of Bek Ventures previously backed UiPath, which had been the leader in the previous wave of RPA, or robotic process automation. But RPA relied on agents that were, for the most part, hard-coded to follow the same exact workflow in the same exact way every time. "We've seen automation's transformative potential firsthand and we believe AI is now unlocking a new wave of rapid adoption in the enterprise," Atici said. Interloom's timing may be propitious. The so-called "Great Retirement" is seeing roughly 10,000 Baby Boomers retiring daily in the U.S. Walking out the door with them is decades of institutional knowledge -- just as companies are trying to deploy AI at scale. Jakobi sees the competitive landscape in characteristically blunt terms. His biggest rival, he says, is inertia -- the assumption within large enterprises that operations will continue to function as they have for the past decade. Interloom's next product push is what it is calling internally a "Chief of Staff" -- a layer designed to give managers real-time visibility into how their AI agents are performing, complete with version control for agent-driven processes. But Interloom is hardly the only company trying to create an AI agent management and orchestration layer. Almost every company marketing AI agents, from OpenAI to ServiceNow to Microsoft, has been working on similar kinds of products. Jakobi, however, said that he thinks Interlooms "context graph" gives it a distinct advantage over these larger players, which he says rarely have insight across an entire complex process.