About

Most organizations already run AI in production. Few can prove it is governed, reliable, or worth its cost.

That gap is where the practice works. AI touches business processes and decisions, and that creates exposure against the EU AI Act, NIST AI RMF, and ISO/IEC 42001. We help organizations see where that exposure sits, then close it: governance readiness, ISO/IEC 42001 gap analysis, AIMS implementation, and audit readiness.

But governed AI is not the same as AI that delivers. Models drift, hallucinate, and degrade under context load, a compliance risk and a business failure at once. We build the evaluation, monitoring, and traceability that keep AI dependable and worth running, not just compliant on paper. Value also runs the other way: finding where AI can deliver more and weighing that opportunity against its risk, so the AI estate is managed for value, not only for safety.

What separates this from policy-led governance is the architecture underneath. Guardrails that live only in documents do not survive contact with production. We design the provenance, lineage, traceability, human oversight, and logging that turn an organization's existing enterprise platforms into governed landscapes where AI and agentic workloads run within defined boundaries.

Our approach is execution oriented. We start with evidence, not assumptions, and prefer first principles over defaulting to frameworks. We support enterprises, startups, and AI-native organizations across the DACH region and North America — in English and German.

Reinhardt Mühlhäusser combines current expertise in AI risk management and AI governance frameworks — ISO/IEC 42001, NIST AI RMF, and the EU AI Act — with two decades of designing enterprise architectures and delivering technology programs for Fortune 500 organizations across Europe, North America, and Asia.

His work has always sat at the intersection of architecture, delivery, and risk. He has built risk management applications for product development processes, led complex implementation programs, and worked in environments where governance was not theory — it had to work in real projects, with real deadlines, real dependencies, and real business impact. He started at SAP in Germany, co-founded and ran a 30-person consultancy serving Fortune 500 clients including BMW Group, Volvo, and Mettler-Toledo, and has led global transformation programs across three continents.

Today he helps enterprises, startups, and AI-native organizations assess AI risk, understand where AI systems create exposure, and build a practical path toward governed AI — including AI risk assessments, EU AI Act and NIST AI RMF alignment, ISO/IEC 42001 gap analysis, AIMS implementation, and audit readiness.

He holds master's degrees in Information Technology and Business Administration from TU Berlin. On AI governance he is a PECB Certified ISO/IEC 42001 Provisional Implementer and Provisional Auditor, and holds the OCEG GRCA and GRCP credentials; he works to the NIST AI RMF, ISO/IEC 23894, ISO/IEC 38507, and the EU AI Act as standards rather than certifications. On AI specifically he is a Databricks Certified Generative AI Engineer and Machine Learning Engineer, and holds IBM's Data Science and Machine Learning Professional credentials. His architecture and delivery credentials span TOGAF, Databricks Certified Data Engineer, and SAP (LeanIX, Signavio, BTP, Business Data Cloud).