AI Product Manager in Germany

• 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.

• Hands-on development of AI-native product prototypes with specialized AI agents for research, requirements, business logic, UX/flow design, test case generation and quality assurance.
• Structured use and orchestration of AI agents through clearly defined roles, inputs/outputs and handover points; breaking down complex product tasks into verifiable work packages and iterative prototyping cycles.
• Establishment of human-in-the-loop quality gates to validate AI-generated results for functional correctness, consistency, completeness and feasibility; targeted rework cycles in case of deviations.
• Development of a regulatory GenAI/rules prototype for CRD VI with a structured decision flow, web UI, rule-based validation and automated test cases; iteration of the business logic through to a pilot-ready POC.
• Design of an AI-to-Action banking prototype: AI intent → consent → bank/product logic → conversion including admin console; translating the product idea into MVP scope, role model, user flows and clickable prototypes.

• 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

AI-supported document processing and inventory management integration

Design and development of an AI-supported application for the automated processing of delivery and invoice documents, connected to SelectLine ERP. Documents are analyzed using AI, matched with orders and line items, and prepared for posting goods receipts.

IMPACT:

• Automated extraction of structured order, delivery and invoice data from PDF and image documents
• Automatic and manual mapping of documents to orders and order line items
• Integration with SelectLine ERP for order import, status synchronization and goods receipt postings
• Traceable processing through separate analysis, mapping and posting processes as well as technical logging
• Development of a containerized end-to-end architecture with AI analysis, workflow automation and relational data storage

KEYWORDS: AI, document analysis, OpenAI, n8n, SelectLine ERP, FastAPI, Python, JavaScript, MariaDB, Docker, REST API, PDF, OCR, mapping, inventory management, goods receipt, workflow automation

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.

Re-engaged to own two product streams end to end, from concept and UX design through to AI-assisted implementation.

• Redesigned the offer and follow-up journey across 38 insurance plans, 16 providers, and four age segments, spanning web, WhatsApp, email, and SMS.
• Launched the rebuilt conversion flow in a live A/B test against the legacy version - evaluation still in progress.

• Built and validated SupportPiloten, an AI-powered content operations service; won the first paying pilot customer
• Tested agentic workflows with Claude Code and Codex for analysis, research, documentation, and prototyping
• Continued developing my own AI product; training in AI governance, AI compliance, and the EU AI Act (ongoing)

CRM and revenue platform managing billing and subscription workflows.

• Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
• Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
• Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
• Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.

• Clarification of project objectives, scope and the target business vision for building the AI-based knowledge database.
• Derivation of the initial architecture and implementation strategy for the Zasterbot chatbot, including separation of the MVP and further development stages.
• Development of a target business vision for building a structured knowledge database and integrating a future chatbot.
• Derivation and prioritization of use cases for information retrieval and provision by the chatbot.
• Modeling of data structures and flows for the effective organization of the knowledge database on the Base44 platform.
• Design and implementation of data models for storing and linking relevant information.
• Development of processes for extracting, analyzing and preparing raw data for the knowledge database.
• Ensuring data consistency and quality as the basis for the future chatbot.
• Planning the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for precise and context-specific answers.
• Implementation of functions for analyzing and displaying data from the knowledge database.
• Use of the Base44 platform with JSON Schema-based entities and a flexible permission concept.
• Implementation of Deno functions for backend logic, event processing and connecting external APIs.
• Integration of OpenAI services for initial data analysis.

• Orchestrate cross-functional delivery across organisational units in the Connectivity domain, aligning teams around integrated end-to-end, customer-testable value rather than isolated component delivery.
• Drive a shift from local component optimisation towards shared outcomes and a common delivery goal, increasing focus and enabling significantly faster integrated delivery.
• Coordinate across 15 cross-functional organisations in a highly complex OEM environment; bring Product, Engineering, Programme Management and specialist functions together to resolve dependencies and improve decision-making.
• Coach Product Managers, Product Owners and stakeholders on product responsibility, prioritisation, outcome orientation and aligned backlogs.
• Use Claude through an AWS Bedrock integration to analyse Jira and Confluence content, identify patterns, dependencies and quality gaps, and support structured product and delivery decisions.
• Establish AI-native requirements excellence with LLM-supported quality gates for epics, features, stories, acceptance criteria, roadmaps and task breakdowns; scale adoption through templates and prompt playbooks.

• Own the 12–18 month roadmap and key outcomes for Elli's enterprise customer platform, covering onboarding, pricing, billing, analytics and broader platform modernization; redesigned the Fleet onboarding funnel to double conversion, supporting a projected €20.7M revenue uplift by 2028.
• Lead the broader Energy Intelligence product and directly own its AI/ML, asset and portfolio-optimization capabilities, including MLOps and safe strategy deployment, strategy lifecycle management and backtesting; delegated data and V2G integration roadmap ownership to a new PO as the platform scope expanded.
• Built a Human-in-the-loop GenAI/RAG support workflow, increasing L1 resolution by 24%, routing accuracy to 91%, and reducing L2 workload by 30%.
• Introduced standardized data contracts and a self-service Python toolkit for traders and Data Scientists, increasing platform adoption by 15% and reducing support effort by 50%.
• Built and scaled a real-time orchestration product from 32 to 3,000+ endpoints across four markets, growing recurring revenue from €1.4k to €56.3k MRR.
• Developed product and AI capability across the organization, training 20 PMs on RAG, agents and prototyping; mentoring a junior PM and coaching an Enterprise Platform Tech Lead toward Product Management ownership.

• Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
• Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
• Designed and implemented evals and observability through Mastra studio.
• Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
• Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.

• Defined the vision, strategy, and roadmap of AI-powered analytics products, ensuring they met the business needs of Sales, Marketing, Finance, and executive teams across Europe.
• Collected business requirements, prioritized AI product features, and led Agile development of forecasting and analytics solutions. Defined product specifications, user stories, and acceptance criteria to ensure successful delivery.
• Collaborated with business stakeholders, Product Owners, data scientists, ML engineers and software engineers to transform AI models into scalable business products and integrate AI insights into operational workflows.
• Designed and implemented Generative AI solutions leveraging Large Language Models (LLMs) to automate reporting and enable natural-language querying of enterprise data, reducing manual effort by approximately 30%.
• Defined product goals and success metrics, tracked product performance and user adoption, and continuously improved the product based on user feedback and business results.
• Established data governance, master data quality and reporting standards across SQL, BigQuery and Power BI environments to ensure reliable, secure and scalable analytics.

• Independently managed the POS tender process for food service operations in department stores.

• Structured requirements gathering, both functional and technical.

• Created a management-ready specification as a basis for decision-making by management and IT.

• Market overview of relevant POS providers.

• Close coordination with Galeria's IT and business departments.