Prompt Engineering 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

• Set up backend assistants, prompt engineering, customer communication, growth strategy

• Successful implementation of AI shopping assistants e.g. Thalgo.de

• Agile Transformation Coach within an SAP-driven End-to-End Lead-to-Cash program, supporting leadership and management teams in implementing and evolving the Sherpa Way of Working, strengthening Agile practices, role definitions and responsibility clarity (RACI), and delivery effectiveness
• Member of the leadership core team for the Way of Working, shaping and evolving agile operating models, challenging existing practices, and driving pragmatic, system-level improvements
• Conceptualized a Polarion-based Scrum Master dashboard as a single, role-based entry point for sprint status, dependencies, risks, and governance artefacts, reducing reporting overhead and improving transparency
• Provided targeted 1:1 coaching to the Master Scrum Master and Scrum Masters, strengthening leadership capability, role effectiveness, and support for team-specific challenges, including the redesign of Scrum Master syncs and collaboration formats
• Worked with teams and leadership on End-to-End Lead-to-Cash process analysis and documentation in SAP Signavio, supporting alignment, transparency, and a shared understanding of process expectations across teams

• 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

• Led a 16-person finance and accounting organization during a restructuring phase; strengthened closing discipline, financial control, cash transparency and accountability.
• Supported cost programs with approximately €2 Mio. in annual savings; established regular KPI reviews for P&L, working capital, POC and cash topics.
• Responsible for monthly, quarterly and annual closing under HGB/IFRS, management/group reporting, audit, tax and GoBD compliance.
• Finance automation with clear business benefits: 13-week liquidity planning, cash flow reporting, P&L versus plan, variance analysis and bank reconciliation; reduced manual work by 40–60 %.
• Improved accounts payable processes through OCR-supported invoice processing and three-way matching with exception handling.
• Sparring partner to management on restructuring, liquidity, working capital, risks and process simplification.

• 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.

• Concept & IT implementation of inbound B2C campaigns, BPM process documentation
• Cross-departmental implementation of multiple whitepapers / guides
• Setup of the customer journey with MS Dyn365 / CRM: forms, landing pages, campaigns
• Project controlling, process documentation, testing and training / coaching, analytics reports
• Hands-on implementation & on-time launch of all campaigns for digital new customer acquisition

• Advise organizations on integrating AI into project and program management practices, delivering measurable productivity improvements
• Designed and implemented an AI agent to improve project communication, transparency, and reporting quality

• 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

• Delivered end-to-end product & service design (discovery to delivery) — combining Product discovery, UX/UI Design and Prototyping within an agile delivery framework.
• Applied AI-assisted workflows across research and prototyping to accelerate discovery and validation cycles.

• Established a new venture focusing on AI-powered UX and rapid prototyping

• Leading the development of an AI-based prototyping framework and prompt optimization for product design

• Managing stakeholder relationships and strategizing go-to-market approaches

• Planning and creating coaching programs (to be launched in 2025) on AI-driven product discovery

• Developed internal processes enabling prototype creation in minutes

• Published best practices and insights on LinkedIn, driving industry interest and awareness

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

AI Assistance and Target Vision for Partially Autonomous Energy Portfolio Management

Building an AI control layer directly on the up-to-date daily live portfolio of a large energy provider — not as an isolated pilot, but as an operational extension of the existing DB1 and portfolio management. The goal is the gradual development from assistance through monitoring/alerting to analysis agents with Human-in-the-Loop approvals, supplemented by a role-specific System of Engagement alongside the BI System of Record. At the same time, the business case, target vision and management pitch compared with static monthly reporting are being developed.

• AI control layer: Design and build on the existing live portfolio data product (several million contracts) — development stages assistance → monitoring/alerting → agents with Human-in-the-Loop approvals.
• LLM-supported data analysis: Semantic queries, SQL/tool integration and additional RAG components based on portfolio, plan-versus-actual and data quality data (Azure OpenAI, Snowflake), with drill-downs to individual contract level.
• Analysis agents: Multi-stage agents for variance and driver analyses of churn, price adjustments, volumes and procurement costs.
• Views concept: Role-specific interfaces for business units, management and C-level as a System of Engagement alongside the BI System of Record.
• Business case & pitch: Target vision and cost-benefit argumentation compared with static monthly reporting.
• LLM setup (privacy & security): Coordination with IT Security and Data Protection — EU region, data separation and approval processes.
• Agent architecture: Multi-stage agent pipelines (analysis → validation → summary) with documented data sources, tool calls, review steps and source references for each statement.
• Data foundation: Built on the up-to-date daily DB1 data product (Snowflake, dbt) — portfolio, plan-versus-actual and data quality metrics as the common basis for all AI analyses.
• Guardrails & evaluation: Evaluation and approval processes for LLM responses relating to management-relevant statements — test sets, metrics and human review.
• Prototyping & validation: Iterative validation of agent responses with the business unit — test question catalogue, feedback loops and response quality for each release.
• Roadmap & development stages: Detailed stages from assistance → monitoring/alerting → partially autonomous management, including transition criteria and governance for each stage.
• Integration: Integration into the existing BI and data landscape — BI remains the System of Record, while the AI layer provides interactive drill-down paths as the System of Engagement.
• Enablement: Enablement of business users — prompting guides, training and an operating model for ongoing use.
• Management: Coordination of business units, Data Engineering, IT Security and Data Protection.
• Change Management: Communication and expectation management with business units and management throughout the development stages.

Results:

• Built on an existing up-to-date daily data product with several million contracts
• Established an LLM setup coordinated with Data Protection and IT Security in the EU region, including data separation and approvals
• Defined three development stages through to partially autonomous management
• Designed role-specific views for business units, management and C-level
• Developed the business case and management pitch for the development stages
• Established an iterative response-quality validation process with the business unit
• Designed the operating model for assistance operations and initiated validation

Stack: Azure OpenAI, Azure AI Foundry, Snowflake, dbt, React, TypeScript, Entra ID, RAG, Agentic AI, analysis agents, Human-in-the-Loop, Prompt Engineering, LLM Evaluation, LLMOps, GDPR / EU region, Azure DevOps, Python, SQL, Change Management