EU AI Act Experts in Munich

Carve-out and post-merger integration of two automotive engineering companies under Accursia Capital, 6 locations in Germany and Spain, 250+ employees after the carve-out. Reporting directly to the investor, with overall IT responsibility for both companies.

• Consolidation of the ERP landscape into one instance (replacement of SAP legacy)
• TISAX certification
• M365, identity and data center migration
• Replacement of end-of-life infrastructure
• Weekly investor reporting

Results:

• Critical infrastructure risk eliminated, backup and platform stabilized
• One IT organization established for both companies
• Weekly investor reporting successfully established

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.

PMO & Project Organization Blueprint for Restructuring

Renewable Energy / Solar Equipment

Technologies / Methods: PMO setup, KPI tracking, project organization, Jira, Confluence

• Developed measures to improve management control during a restruct...

• Consulting across two integrated areas: Commercial Strategy (pricing, sales steering, marketing strategy, market expansion, margin management) and Operational Efficiency (process automation, AI integration, workflow design, last-mile automation).
• Development of custom SaaS solutions, explicitly tailored to the specific requirements and processes of each company.
• Delivery of AI training and change management workshops for managing directors and specialist departments, including AI competence training with a certificate of attendance under Art. 4 of the EU AI Act.

• Developing a model-based AI where natural language is interpreted as formal relations.
• Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
• Develops all kinds of model solutions.
• Backed by natural language and data annotation.
• Requirements to code and other solutions.

GenAI – Chat & Voicebot

Setup of a GenAI solution with a knowledge base as well as chat and voicebots.

• Definition of use cases and chat/voicebot functionalities
• Project setup for cloud, knowledge base, LLMs and RAG
• AI guardrails, IAM concept
• Governance, Risk & Compliance (GRC)

Result:

Setup of an enterprise GenAI solution with knowledge base, chat/voicebots, IAM and governance.

• 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, complete signed audit trail; graduated autonomy model („internal → autonomous per skill“).
• Verified knowledge graph („Company Brain“) with source evidence for each answer; proprietary 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.
• Development of the governance-focused multi-agent platform TeamIntel (see AI reference projects).

Conducted an independent technical assessment of an AI-enabled software platform, combining AI risk auditing, cybersecurity analysis and software architecture review. Reviewed application components, APIs, data flows, LLM integrations, retrieval-augmented generation, multi-agent orchestration, authentication, tenant isolation, dependency vulnerabilities, AI governance and model lifecycle controls. Authored a technical audit report with a structured risk register, source-code evidence, regulatory references, corrective actions, AI system inventory, risk classification, remediation roadmap and Key Risk Indicators.

Technologies: TypeScript, JavaScript, React, Next.js, Node.js, REST APIs, MongoDB, Redis, AWS serverless services, OAuth 2.0 / OpenID Connect, LLM APIs, RAG, vector search, multi-agent orchestration, Model Context Protocol, C4 architecture modelling, threat modelling, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP guidance, MITRE ATLAS

• Lead AI readiness and process assessments across HR and Finance, identify automation opportunities, develop ROI business cases and translate findings into prioritized recommendations for executive decision-making.
• Built CloudMcFly's internal AI operating system in self-hosted n8n, with a 22-strong digital workforce across seven business functions and an estimated delivery capacity equivalent to three to five people. A separate document-classification workflow reduced manual HR ticket routing by 60%.
• Translate EU AI Act requirements into practical governance roadmaps and deliver "AI Driving License" workshops, strengthening leadership understanding of AI adoption and governance responsibilities.

INTRODUCTION OF AI OPERATING SYSTEM & DEVELOPMENT OF DIGITAL INFRASTRUCTURE

Technology / Start-up – Munich, 3 employees

Objective:

Selection and implementation of an AI operating system, development of a structured knowledge base, and creation of AI agents and skills to increase efficiency across the entire company

• Requirements analysis and evaluation of suitable AI operating systems based on the company’s specific needs
• Design and development of a central knowledge base as the foundation for AI-supported processes
• Development and configuration of AI agents and skills for recurring business processes
• Prompt engineering for precise, context-based outputs from the AI agents
• Personal coaching for the founders and employees on using AI independently and effectively
• Overall project management, including planning, prioritisation and progress tracking
• Documentation of the implemented solutions and creation of usage concepts for sustainable operation

Tools: Langdock, SharePoint, Prompt Engineering, AI agents, AI skills

• Strategic consulting and implementation of AI-supported automation solutions for service processes (email communication, document handling, review processing, voice agents)
• Technical orchestration using n8n (workflow automation), Claude and ChatGPT (LLMs), as well as custom API integrations into existing enterprise tools
• Measurable customer results: response times reduced from hours to minutes, 8 fewer hours of invoice processing per week, 75% fewer routine calls
• Assessment of regulatory requirements (GDPR, compliance) as part of customer consulting
• Technical Product Ownership (backlog, roadmap, prioritization)
• Formal contracting vehicle for freelance assignments, including 1&1 Telecommunications
• Building & scaling enterprise AI platforms

• Agile development of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
• Extraction of context-relevant content based on user profiles & competency dimensions
• Personalized delivery of learning content to improve sales performance
• Close coordination with sales teams & stakeholders to validate features
• Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
• Integration into existing tools & CRM systems for smooth adoption
• Technologies used: Python, OpenAI, DB tech such as PostgreSQL, CI/CD for AirFlow DAGs, FastAPI

• Whitepapers, case studies, landing pages, specialist articles and press releases for B2B clients from the IT and cybersecurity sectors, including TrendAI, SoftwareOne, All for One, SVA, Axians, NetApp, Indevis, Aconso

Key topics: Cybersecurity, AI, Cloud Computing, Digitalization, Big Data Analytics, Virtualization, Data Management, IoT, New Work, Data Center Modernization, SAP S/4HANA Migration

• Topics: Business Analysis, Data Privacy, Data Management, Stakeholder Management, Conceptualization
• This project focuses on developing and implementing a strategic approach for data deletion in SAP systems. The goal is to identify the relevant data and structures during system migration to ensure both data privacy and IT system efficiency. At the same time, downtime should be minimized and regulatory requirements met.
• Development of a comprehensive approach for data deletion in SAP systems, considering data privacy and business requirements.
• Ensuring efficient and structured data transfer to the new system.
• Optimizing system efficiency and reducing downtimes during migration.
• Creating functional and technical concepts to ensure compliant and sustainable data management.
• Topic preparation: Detailed study of the "data deletion" area to lay the foundation for a structured data migration.
• Definition of project structure: Setting roles, interfaces and the project's organizational structure.
• Regulatory requirements: Analysis of data privacy regulations and business requirements to define deletion criteria.
• Approach: Developing possible scenarios and methods for data cleansing and deletion.
• Deletion concepts: Creating functional and technical deletion concepts that structure the implementation and provide clear guidelines.
• Setting deletion criteria: Defining which data and structures to delete or transfer.
• Responsibilities: Clarifying responsibilities within the project team and among stakeholders.
• Analysis of ongoing activities: Identifying and collecting existing activities in the "data deletion" area.
• Effort, cost and timeline planning: Creating estimates for resources, effort and budget.
• Implementation initiatives: Developing and executing concrete measures to apply the defined deletion strategies.
• IT system efficiency: Analyzing the existing IT infrastructure to identify optimization potential for data deletion and transfer.
• Technology trends: Evaluating new technologies and tools that can support the data cleansing process.
• Cost-benefit analysis: Assessing the financial impact of data cleansing and the introduction of new solution approaches.
• Risk management: Identifying potential risks during implementation and developing appropriate mitigation measures.
• This project lays the foundation for a sustainable and compliant data transfer to a new SAP system. With a clear approach to data deletion, it meets data privacy requirements, reduces downtimes and increases the efficiency of the new system. The results and recommendations will help companies develop a future-proof data strategy that meets legal and business needs.