Interloom tackles a core blocker for enterprise AI agents: most of the knowledge required to do real work is never written down. The company estimates that around 70% of operational decisions have never been formally documented, leaving generic agents without the context they need to act correctly.
To solve this, Interloom builds a 'context graph' that captures tacit knowledge by observing and mapping how problems are actually resolved within an organization. The graph encodes the implicit steps, exceptions and judgment calls that experienced employees make, turning them into structured context that AI agents can use.
Grounded in this graph, Interloom's agents can execute multi-step workflows the way an experienced team member would, rather than hallucinating or stalling when faced with undocumented edge cases. This positions the product in the knowledge-management and agent-infrastructure layer for enterprises.
Founded by CEO Fabian Jakobi, Interloom raised a $16.5M Series A led by DN Capital in March 2026, with participation from Bek Ventures and Air Street Capital. Combined with a $3M seed from March 2024, total funding stands at about $19.5M.