Interloom, an enterprise platform focused on capturing operational knowledge for AI systems, has raised $16.5 million in a seed funding round led by DN Capital, with participation from Bek Ventures and Air Street Capital.
The company is addressing a key limitation in enterprise AI adoption by enabling systems to access and learn from real-world organizational workflows.
What The Company Does
Founded in 2024, Interloom develops a platform that transforms operational knowledge into a persistent memory layer for both employees and AI agents. Its system captures how work is actually performed across an organization, using data from past decisions, workflows, and outcomes.
At the core of the platform is a “context graph,” which continuously maps relationships between actions, decisions, and results. This allows AI agents to reference prior solutions and apply them to new tasks, improving the accuracy and relevance of automated processes.
Rather than relying on static documentation, Interloom builds a dynamic representation of operational knowledge that evolves as teams work. This enables both human users and AI systems to access contextual guidance and make more informed decisions.
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Market Context / Industry Background
As enterprises adopt AI tools, a major challenge is the lack of structured operational context. While AI systems can process large datasets, much of the knowledge required to perform tasks effectively remains informal, embedded in workflows, or undocumented.
This gap limits the ability of AI agents to operate autonomously in complex environments, particularly in areas where decisions depend on institutional knowledge or prior experience.
There is increasing demand for systems that can capture, structure, and reuse this knowledge, enabling more effective automation and reducing dependency on individual expertise. Platforms that can bridge this gap are becoming a critical component of enterprise AI infrastructure.
Founder / Investor Commentary
Founder and CEO Fabian Jakobi highlighted the limitations of current AI systems in operational settings, noting that without access to company-specific knowledge, their effectiveness remains constrained.
He explained that Interloom’s approach is to ground AI-driven decisions in historical outcomes, ensuring that both automation and human decision-making are informed by prior experience. This creates a form of organizational memory that persists even as teams change over time.
Growth Plans / Use Of Funds
The newly raised funding will be used to further develop Interloom’s platform, with a focus on expanding its capabilities in enterprise AI and workflow automation. The company aims to enhance how its system captures and structures knowledge while improving integration with existing enterprise tools.
Scaling adoption among large organizations is a key priority, as businesses look to deploy AI systems that can operate more effectively within complex operational environments.
About Interloom
Interloom is an enterprise software company building a memory layer for AI-driven workflows. Founded in 2024, the company develops platforms that capture and structure operational knowledge, enabling both employees and AI agents to learn from past actions and decisions. Its mission is to improve efficiency, knowledge retention, and automation across organizations by embedding real-world context into everyday workflows.
