Generative AI Experts in Munich

CXO AI OS is an agent system made up of six building blocks. Instead of using AI as a chat window, it creates a system that understands a company’s context, makes decisions according to its rules, and acts on its behalf.

• For mid-sized companies: a guided sprint followed by operation for a team, department, or prioritized cluster, based on an AI assessment
• For self-employed professionals: a program in which participants build their own agent system
• Sequence in the company: assessment, prioritization, sprint, operation
• Implementation in Claude Cowork or ChatGPT Work, without coding
• Architecture: Chief of Staff, Goals, Advisors, Agents, Context, Catalog

• Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
• Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
• Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

AI telephone assistant platform

Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL

• Conception and hands-on development of an AI-supported telephone assistant platform (voice AI / LLM) – from idea and architecture to MVP/product.
• Built agentic workflows and full automations with Claude Code and Google AI Studio.
• Also delivered AI-supported work in client engagements: used Claude Code for governance documentation, requirement drafts, and automations.

• Led enterprise reporting modernization by migrating legacy SSRS reporting solutions to Power BI, supporting 500+ business users while ensuring full GDPR/DSGVO compliance.
• Designed and optimized Power BI and Microsoft Fabric semantic models using star schema, dimensional modeling, advanced DAX, and performance optimization techniques, reducing query latency by 25%.
• Delivered 20+ executive and operational dashboards featuring KPI scorecards, drill-through, bookmarks, and row-level security, improving reporting efficiency by 20%.
• Enabled self-service analytics through governed Power BI datasets, dataflows, and gateway architecture, increasing business-led reporting adoption by 35%.
• Configured an incremental refresh policy and query folding for a 50+ million row sales dataset, reducing daily report refresh times by 85%.
• Deployed automated ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, and Snowflake, reducing reporting delivery timelines by 40% through workflow automation.
• Spearheaded Microsoft Fabric analytics modernization initiatives including lakehouse architecture, OneLake integration, and centralized data platform development, reducing data latency from 2 hours to 20 minutes.
• Translated business requirements from 15+ stakeholders into scalable Power BI semantic models and dashboards, improving reporting consistency and reducing ad-hoc reporting requests by 25%.
• Applied Microsoft Copilot and generative AI tools to accelerate SQL development, DAX authoring, technical documentation, and testing activities, reducing development effort by approximately 15 hours per week.

• Defined the vision, strategy, and roadmap of AI-powered analytics products, ensuring they met the business needs of Sales, Marketing, Finance, and executive teams across Europe.
• Collected business requirements, prioritized AI product features, and led Agile development of forecasting and analytics solutions. Defined product specifications, user stories, and acceptance criteria to ensure successful delivery.
• Collaborated with business stakeholders, Product Owners, data scientists, ML engineers and software engineers to transform AI models into scalable business products and integrate AI insights into operational workflows.
• Designed and implemented Generative AI solutions leveraging Large Language Models (LLMs) to automate reporting and enable natural-language querying of enterprise data, reducing manual effort by approximately 30%.
• Defined product goals and success metrics, tracked product performance and user adoption, and continuously improved the product based on user feedback and business results.
• Established data governance, master data quality and reporting standards across SQL, BigQuery and Power BI environments to ensure reliable, secure and scalable analytics.

• Overall responsibility for the project lifecycle from scope definition to completion
• Close collaboration with platform teams, IT leaders, and external service providers
• Application of SAFe principles and structured sprint work
• Creation of a migration roadmap with clear milestones
• Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
• Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
• Regular status reports and running knowledge transfer sessions

BMW AG · Mercedes-Benz AG · Munich Re · CARIAD / VW Group

• Leading UX and product direction of PromptM, an enterprise AI prompt management platform at BMW, designing human-agent interaction models, role-based governance flows, and a Playground testing environment for prompt validation and iteration
• Redesigned insurance policy workflows at Munich Re, restructuring form layouts, improving field prioritisation based on user needs, and introducing new design components into the existing product system
• Led end-to-end UX at BMW shopfloor: user flows, wireframes, usability testing, and Figma design system adopted across 5+ agile squads, reducing order processing time by 35%
• UX Coach at Mercedes-Benz, upskilling 50+ team members in user research, usability testing, and agile UX methods; reduced design-to-dev handoff by 30%
• Facilitated 10+ design sprints and discovery workshops; enforced WCAG 2.1 AA accessibility across all deliverables

• Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
• Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
• Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
• Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
• Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).

Consulting and prototype work on AI voice and multilingual agent systems, using AI-assisted delivery across realtime translation prototypes, call-centre automation, and voice-to-voice consultation workflows.

• Built and advised on AI voice / agentic conversation prototypes, including realtime translation and consumer-facing consultation experiences.
• Worked across call-centre automation, voice UX, product architecture, implementation tradeoffs, and prototype development.

Co-founded an AI-native language learning startup, defining the product vision, AI architecture and technical roadmap. Designed and built the AI and backend stack, including LLM fine-tuning pipelines, custom agentic workflows, and scalable inference infrastructure. First product currently in private beta.

I help leadership teams turn the potential of AI into measurable business results — fast, pragmatic, and with people at the core.

As Co-Founder of Infinite Mind, I work with CEOs and innovation leaders to identify high-impact AI opportunities, design actionable solutions, and support adoption across the organization. Our focus: driving productivity gains, smarter workflows, and scalable value.

Over the past ten years, I've worked at the intersection of Digital Transformation, Data, and Machine Learning, advising companies in software, high-tech, media, and insurance. I’ve led large-scale initiatives, including the group-wide adoption of Generative AI, and understand the strategic and human challenges of driving change at scale.

I combine a technical background in machine learning (M.Sc. Electrical & Computer Engineering, TUM) with a broader perspective shaped by degrees in Physics and Philosophy (LMU Munich). In addition to my consulting work, I’ve co-founded a tech-enabled charity and supported early-stage founders as a business coach.

If you're looking to go beyond the AI hype and make it actually work in your business — let’s talk.

• Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
• Migration of all applications; avoiding high contractual penalties for the client.
• Onboarding and coordination o...

• Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
• Enhance chatbots with RAG, in-context learning
• Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
• Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
• High-throughput serving with vLLM
• Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
• Generate and filter synthetic data, clustering
• Detect hallucinations
• Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
• Visualization of experiments (matplotlib)