Berlin-Based INXM Raises €5.7 Million Pre-Seed To Automate Enterprise Processes With AI

The round was led by Cherry Ventures and Redstone, with participation from Angel Invest, Linden Capital, and other angel investors. The funding will support the company’s first enterprise deployments and further product development.

Founded in 2025 by Oelling, Matthias Kainer, Jesper Bylund, and Kamil Klüber, INXM is building what it describes as the first enterprise process execution engine powered by Compiled AI. The platform uses AI to design and optimise business processes, while executing them through deterministic workflows that ensure reliability, consistency, and auditability.

At the core of the platform is the INXM Orchestrator, which converts user intent into executable plans and coordinates tasks across systems, people, and processes. This approach enables organisations to automate complex workflows while maintaining compliance, transparency and operational control.

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Unlike traditional AI tools that rely on large language models to interpret tasks at runtime INXM separates process design from execution. AI helps create and improve workflows while the execution engine ensures predictable outcomes and full audit trails.

The company positions itself as a European-built solution with local deployment options and full data ownership, designed to meet the governance, security, and compliance requirements of enterprise customers across Europe.

“We founded INXM because we’ve seen first-hand how enterprise AI projects fail: years of implementation, armies of engineers, and AI systems that break more than they fix. Knowledge workers still copy-paste between ERP, PLM, Excel, email, and approval workflows to close a month. We have set out to build AI that finishes the work for you. We’re building the system that turns AI from a productivity tool into the operational backbone of European industry and its processes,” said Alex Oelling, CEO of INXM.

“At its core, Compiled AI means you use LLMs to generate deterministic, enterprise-ready code. You then run the code to achieve your outcome. This gives you the flexibility of natural language from AI models, but the testability of deterministic code,” said Matthias Kainer, CTO of INXM.