Exclusive: Interloom, a Startup Capturing ‘Tacit Knowledge’ to Power AI Agents, Raises $16.5 Million in Venture Funding

Why It Matters

By unlocking hidden, experience‑based knowledge, Interloom enables AI agents to act reliably in complex enterprise environments, accelerating automation adoption and mitigating the talent‑exit risk.

Key Takeaways

• Interloom raised $16.5M led by DN Capital.
• Uses context graph to map undocumented operational knowledge.
• Reduced knowledge gap at Commerzbank from 50% to 5%.
• Targets AI agent performance amid retiring workforce.
• Competes with major AI platforms on enterprise context.

Pulse Analysis

The concept of tacit knowledge—expertise that never makes it into manuals—has long hampered large‑scale automation. Interloom’s approach sidesteps this bottleneck by ingesting millions of unstructured operational records and constructing a dynamic context graph. This graph functions like a real‑time map of problem‑resolution pathways, allowing AI agents to draw on the same intuition that seasoned employees use, without requiring explicit documentation. The result is a more nuanced, organization‑specific intelligence layer that can adapt as processes evolve.

The fresh $16.5 million infusion signals investor confidence that context‑rich AI will be the next frontier of enterprise automation. DN Capital and Bek Ventures, both seasoned in vertical AI and robotic process automation, see the "corporate memory" problem as a growth engine, especially as the U.S. experiences the "Great Retirement" of 10,000 baby boomers daily. The exodus of seasoned staff threatens to erase decades of institutional know‑how, creating a market hungry for solutions that can capture and codify that expertise before it disappears.

Looking ahead, Interloom is expanding its suite with a "Chief of Staff" layer that offers managers real‑time visibility and version control over AI‑driven workflows. While tech giants like OpenAI, ServiceNow and Microsoft are building generic agent platforms, Interloom’s differentiator lies in its deep, cross‑process context graph that promises enterprise‑wide insight. If the company can scale this capability across more sectors, it could set a new standard for how AI agents are orchestrated, shifting the competitive balance from pure model performance to contextual fidelity.