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.”
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 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.
