Interloom Technologies

High-Level Overview

Interloom Technologies is a Munich-based AI startup founded in 2024 that builds a "navigation system for work" to automate complex enterprise processes beyond traditional robotic process automation (RPA).[1][2][4][5] It serves enterprises in sectors like IT service management, claims processing, customer support, pricing, supplier management, and compliance, solving the problem of undocumented knowledge and fluid workflows that leave over 70% of work outside rigid software systems.[1][4][5] By pooling enterprise data to infer real processes via AI-powered task mining, knowledge graphs, and agents, Interloom enables humans and AI to collaborate, targeting 30x productivity gains while keeping humans in the decision loop; it raised $3M in seed funding led by Air Street Capital just four months ago, signaling strong early momentum.[1][2][3][6]

Origin Story

Interloom emerged from stealth in 2024, founded by serial entrepreneur Fabian Jakobi (CEO) and a small team of about ten with deep expertise in enterprise automation.[2][3] The idea stemmed from their prior venture, Boxplot, which they bootstrapped to serve Fortune 500 customers by building AI-augmented knowledge graphs; it was acquired within weeks by Hyperscience, a leader in AI-enabled document processing that had just raised a $100M Series E.[2] Frustrated by legacy RPA's failure to automate most enterprise processes—due to subtle variations, custom tools, and human judgment—the team at Interloom flipped the script: instead of imposing hardcoded workflows, their system observes actual behaviors from data, chats, and notes.[2][3][4] This Munich "Mittelstand"-focused startup quickly secured $3M day-one funding from Air Street Capital, positioning it for global expansion including the U.S.[2][3]

Core Differentiators

Interloom stands out in the $6B+ RPA market by rejecting legacy layers and building an AI-first, cloud- and model-agnostic platform that automates up to two-thirds of tasks—far beyond traditional RPA's one-third limit.[2][3]

• Real-World Process Inference: Uses task mining, proprietary knowledge graphs, and vector embeddings to map how teams *actually* work from historic data, conversations, and traces, rather than rigid rules—enabling dynamic adaptation like a GPS for messy workflows.[2][4][5]
• Human-AI Symbiosis: Deploys reliable AI agents with clear reporting, feedback loops, and expert oversight, boosting productivity 30x-100x while ensuring human decisions on high-value work.[3][4][5]
• Enterprise-Ready Deployment: Seamless integration via SAML/OpenID SSO, flexible permissions, and support for fluid teams; targets complex use cases like procurement, onboarding, and compliance across IT, support, and operations.[1][5]
• Proven Team Edge: Founders' track record at Boxplot/Hyperscience gives domain mastery in scaling AI graphs for multinationals and governments.[2]

Competitors like Factory Intelligent Solutions or Thinksense.ai focus on narrower RPA, while Interloom redefines hyperautomation for AI-era complexity.[1]

Role in the Broader Tech Landscape

Interloom rides the generative AI wave transforming enterprise automation, where LLMs and agents finally tackle the 70% of unstructured work that rigid RPA couldn't touch amid rising demands for efficiency in high-complexity sectors.[2][3][4] Timing is ideal: post-2023 AI breakthroughs enable inference from real data, aligning with global RPA spend exceeding $6B yearly and a shift to "AI-first" systems amid labor shortages and compliance pressures.[3] Market forces like Europe's "Mittelstand" digitization and U.S. enterprise AI adoption favor its global push, while it influences the ecosystem by pioneering adaptive platforms that make AI deployment reliable from day one—potentially unlocking trillions in trapped productivity as humans orchestrate agents at scale.[4][5]

Quick Take & Future Outlook

Interloom is poised to disrupt RPA incumbents by scaling its navigation-like system, starting with Germany's Mittelstand and expanding to U.S. enterprises, with rapid hiring to fuel product iterations.[3] Trends like multimodal AI agents, sovereign data graphs, and regulatory pushes for auditable automation will accelerate its trajectory, potentially automating entire workflows end-to-end. Its influence could evolve from niche innovator to ecosystem standard, as more firms seek 100x gains in a human-AI hybrid world—proving that observing real work, not dictating it, is the true path to enterprise transformation.[2][4]