Claude Experts in Germany

• Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
• Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
• Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
• Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
• Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
• Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
• AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
• Creation of a historical data layer as a basis for trend and time-series analyses
• AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
• Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
• Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
• Breaking down overall requirements into clearly defined work packages and tasks
• Definition, prioritization, and management of milestones throughout the entire project lifecycle

Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code

• 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

The client operates a SaaS platform for managing rail and intermodal transport (Rail-Flow Transport Management Platform).

For two of its customers, it was looking for a 3-month project management replacement. Both customers offer rail and intermodal transport services. Implementation progress differs: one customer is already live and wanted its Phase 2 enhancements implemented; the other customer is implementing its MVP and entering an intensive testing phase.

As part of the assignment, project management was taken over, including management of the internal development team (offshore Turkey).

• Took over complete project management for 2 customer projects, including the roles of Product Owner and Co-Scrum Master
• Managed agile implementation teams consisting of business analysts, implementation consultants, developers (offshore) and QA staff
• Ensured compliance with and further development of project governance
• Planning and rollout management: creation and maintenance of project and rollout plans as well as coordination of role assignments with the client’s management
• Requirements and change management: management of contractual requirements, bugs and changes
• Customer and project communication: organization and moderation of status meetings and steering committees
• Identification of project risks, initiation of countermeasures and escalation
• Ensured correct and timely invoicing for project services together with Finance

Methods: Scheduling (forward- and backward-based), including milestone planning, structured interviews, multi-project management (in English)

Tools / Technologies: Jira, Confluence / Atlas, Claude (Skills, Artifacts), MS Outlook

• Introduction of a new digital product to the Chinese market as part of a global growth strategy.

• Point of contact and coordinating interface between central product management, local Chinese teams and international stakeholders.

• Management of the entire program to coordinate complex digital projects.

• Successful market launch of a digital product in China (including successful certification for the Chinese market).

• Balancing local requirements with global priorities while taking tax and regulatory requirements into account.

• Introduction of a new way of working to improve collaboration and coordination across locations.

• Development and implementation of a scaling strategy for the digital business model in China.

• Use of Jira, Confluence, Miro, Scrum, agile working, SAFe.

• Project language: English.

Stabilization of a critical logistics outsourcing setup in the context of an SAP S/4HANA transformation. The focus was on restoring a reliable operational fact base between the client, logistics service provider, international supplier plants and the SAP/IT organization.

• Coordination between the client, global logistics service provider, international supplier plants, SAP/IT teams, Quality Management and the operational shop floor.
• Management, monitoring and operational correction of Inbound Deliveries; integration of Outbound Delivery processes.
• Analysis of interface, posting, batch and master data errors between SAP S/4HANA, logistics service provider, supplier plants and internal business functions.
• Significant reduction of critical inventory differences between the client and logistics service provider.
• Improved operational management capability in the logistics environment and creation of a common fact base.
• Reduction of recurring error patterns in the areas of Inbound Deliveries, inventory, interfaces, batch/posting logic and service provider processes.

• Support for a data, analytics and AI initiative in a regulated enterprise environment by structuring, evaluating and prioritizing several data-driven use cases based on business impact, feasibility, scalability, data maturity and governance requirements.
• Translation of complex business and analytics requirements into clear product, data and implementation logic, as well as preparation of decision-ready documents, target visions and roadmap inputs for stakeholder and management discussions.

• Claude Code subagents, MCP, Pydantic V2, pytest, bandit

• Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.

• Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.

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

• Built a scalable nearshore IT developer hub (Croatia, Czech Republic, Poland) as an independent company through a BOT model (Build – Operate – Transfer)
• Identified, selected, and managed full-service agencies; introduced management and control mechanisms, including KPIs, SLAs, and regular service reviews
• Prepared and reviewed data processing agreements and framework contracts in coordination with Legal & Compliance; integrated regulatory requirements (including KRITIS) into process design
• Advised on cloud vs. on-premise strategies, data storage, and authorization concepts; supported Procurement with tendering and service provider evaluations
• Managed change and process harmonization between internal teams and nearshore partners; reported to executive management, CFO, and CIO

Result: Scalable IT developer hub with an audit-ready governance model, reduced operating costs, and accelerated product development.

Reporting and controlling using Power BI for a production ERP system

• Analysis of ERP data and interfaces for use in Power BI dashboards
• Evaluation and migration of existing reports (e.g. Excel) to Power BI
• Development of an authorization concept for selective data access
• Documentation and training on the use and customization of Power BI dashboards

Label: Power BI, Excel, SelectLine ERP, Microsoft SQL, SQL Server Management Studio

• Development of a digital audit application for inspections in medical facilities. The goal is to connect on-site data collection, voice recording, documentation and downstream processes in one end-to-end, AI-supported workflow.

• Design and development of a Flutter audit app with Claude Code for the structured execution and documentation of inspections.

• Processing of voice recordings captured in the app through automatic transcription and AI-supported creation of structured inspection reports, followed by an approval process

• Connecting various data sources such as email, Odoo 19, attendance records and Google Drive via n8n to automate billing and follow-up processes

• Automatic provision of required documents and email delivery through n8n-controlled workflows, including the use of LLMs for text creation

• Skills: Flutter, Claude Code, LLM Integration, n8n, Odoo 19, Google Drive, Process Automation

• Repaired a broken “ScrumBan” process, then established a pure Kanban system; increased output in the Kanban flow by 22% within four weeks

• Increased team autonomy and decision-making ability by implementing new decision strategies; resulting in up to 25% better outcomes

• Redesigned retrospectives (including one-to-one coaching and workshops), which led to consistent implementation of the resulting action items

• Intensive coaching of Product Owners (POs) to develop and support “Empowered Teams”, alongside leadership development to place agile frameworks and methods in a realistic context (“de-illusioning”)

• Supported change management processes to promote an agile company culture among management and teams, improving internal communication to increase transparency and effectiveness in agile processes

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.

• Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.

• Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.

• Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.

• Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.

• Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.

• Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.