Artificial Intelligence Experts in Munich

• Generative AI products for corporate clients, owned from strategy through specification to production.
• Central strategy, local configuration: multi-tenant AI assistant for occupational pension schemes (bAV), delivered as an interactive avatar with text and voice path. Three tenants run on one codebase, each with its own conversation guide, while the knowledge base, guardrails and escalation paths stay central
• Versioned, AI-ready knowledge base composed into a tenant-agnostic voice context and tenant-specific text prompts — the configuration layer that keeps local adaptation from forking the product
• Conversational design: answer limits, scope and off-topic handling, anti-hallucination rules, escalation and lead handover to human advisors
• Five eval suites as a quality gate before any prompt or model change (anti-hallucination, LLM-as-judge failure modes, multi-turn consistency, voice KPIs, action vocabulary with confusion matrix); user test with 10 testers (Hamburg, 07/2026) drove the rework from alpha to beta
• Coordinated external developers, compliance and client stakeholders; GDPR-compliant EU stack, IDD-compliant, EU AI Act classification documented
• Second product line: white-label social media generator for consultancy chilli mind (CH/DE) — one codebase, per-client branding and configuration
• Results: 239+ deployments and a pilot with corporate customers · 108+ deployments for the white-label product · repeatable pattern for multi-tenant AI products in a regulated environment

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

The program supports the comprehensive realignment of the German Consumer Panel Services business. It combines a significant panel boost with the reprocessing of historical data and the integration of new receipt data. By significantly expanding and stabilizing the panel with the involvement of external partners, the aim is to improve the validity of the data base and create a reliable foundation for methodology, weighting and customer reporting. At the same time, historically grown processes for data delivery, OCR, matching, item QC, methodology and reporting are being harmonized, further developed technologically and reorganized. The goal is a scalable end-to-end landscape with higher data quality, clear responsibilities, reliable governance and sustainably manageable operational processes.

• Overall management of the restatement program, including the integrated roadmap as well as milestones, dependencies, risks and management decisions.
• Coordination of the panel boost and alignment of the required data deliveries, quality requirements and prerequisites for methodology, weighting and reporting.
• Alignment of business, product, data science, technology, operations and external partners around a shared target picture, aligned priorities and an integrated approach.
• Design of the organizational change triggered by the fundamental realignment of the data base, methodology and management logic, which has a lasting impact on established decision-making and collaboration patterns.
• Establishment and further development of governance, reporting and escalation structures as well as program-wide monitoring and operational processes for reliable management and sustainable handover.
• Management of critical data, technology and provider dependencies, including reprocessing, OCR transition and the timely synchronization of delivery, testing, methodology and reporting.
• Orchestration of international collaboration with teams and stakeholders in Germany, the United Kingdom, Portugal and Romania, as well as with external suppliers in Germany and Austria.

Impact Areas and Expertise: Program & Delivery Leadership, Business & Technology Alignment, Organization & Transformation, Governance & Sustainable Operations, Strategy & Target, Methodic Leadership, Transformation & Change Leadership, Executive Advisory

• 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

Product:

• Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
• Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
• Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
• Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
• Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.

Agent Orchestration & RAG Systems

• Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
• Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.

• Recurring problem in my own AI-assisted projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but remain difficult to follow and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions form a consistently linked knowledge graph, traceable from requirement to architecture decision – queryable by both people and AI agents. Technically based on RDF/OWL and a custom MCP server.

• Result: Working MCP daemon, Docker image published automatically to GHCR, nine hexagonal modules, eleven ADRs (including an Open-Core licensing model). Requirements engineering and Ubiquitous Language hexagons are active. Public as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and GHCR image; Open-Core model.

• Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ, Interface Development, Software Architecture, Continuous Integration, Knowledge Management

• Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
• Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
• Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
• Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
• Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
• Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
• Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.

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.

• Modernization of a large-scale Java enterprise application*

The project is modernizing a complex enterprise application that has grown over many years. The existing Spring-based legacy system runs on Java 8, OSGi, and Eclipse RCP and is being gradually migrated to a modern, maintainable architecture with Java 25 and Quarkus.

Marcus works on analysis, architecture, refactoring, and implementation. One focus is on untangling historically grown structures and dependencies and on building a clean, sustainable Java and Quarkus technology stack.

Tools & technologies: Java 8, Java 25, Quarkus, Hibernate ORM with Panache, EclipseLink, OSGi, Eclipse RCP, Maven, JUnit, Mockito, REST, JSON, Git, Eclipse IDE, IntelliJ IDEA Ultimate, Jira, Confluence

• Business analyst/project manager/trainer/entrepreneur
• Consulting companies in the areas of business analysis, process management, agile project management, and organizational development.
• Leading digitalization and transformation projects (LichtBlick SE, E.ON, Co.met, Stromnetz Hamburg)
• Introducing agile ways of working and building efficient process and PMO structures
• Successfully implementing a product launch (idea-to-market, > 100 epics over 3 years)
• Creating requirements specifications, process models, and IT concepts
• Coaching and training specialists and managers (Jira/Confluence, procurement)
• Developing my own product “TriggaBuddy” – concept, CAD design, 3D printing, registered design
• Consulting assignments: E.ON, LichtBlick SE, Co.Met, Stromnetz Hamburg GmbH

Training wave for education of first transformation agents. Training of candidates in: Lean management, Six Sigma & data analysis, change management, rationalization meetings, conflict & crisis management, stakeholder management international, project management, cultural development, AI for OpEx. Strong response to training & certification concept.

• 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.