Claude Code Experts

• 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

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.

• 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.

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

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

Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.

• Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
• Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
• Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
• Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
• Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
• Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.

Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.

• Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
• Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
• Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
• Integrated PostgreSQL for persistent storage and management of application data.
• Containerized the application and its services using Docker Compose.
• Automated infrastructure provisioning and deployment using Terraform on AWS.
• Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
• Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
• Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
• Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
• Designed and implemented the frontend delivery architecture using AWS CloudFront.

Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS

• Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
• Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
• Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
• Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.

• Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
• GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
• AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner

• 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.

• 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