AI Product Manager with engineering background. I build production-ready AI products and define problem-first end-to-end product strategy that delivers customer-centric features & business outcomes.
Production-ready prototypes demonstrating end-to-end product ownership, AI integration, and measurable impact
Conversational AI advisor helping fitness & health enthusiasts make confident health-tech purchase decisions through neutral, source-backed recommendations.
Production-ready RAG-based Knowledge chatbot delivering accurate answers from 250+ pages of product documentation, setup manuals & curated tests/reviews in seconds.
Beta Program validating GenAI features before production rollout. Dual goals: validate product quality through real customer feedback and create communication potential through innovation.
Insights on product strategy, AI implementation, and lessons learned
Shared learnings on using prototypes to test assumptions rather than just create mockups. Focused on user research, hypothesis-driven testing, and intentional MVP scoping for a health-tech purchase advisor.
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Spent a day immersed in AI and product talks in Köln. From GenAI fundamentals and AI muscle development to cutting customer feedback analysis from weeks to hours—here's what stuck with me from industry leaders at Google, Kameleoon, Taxfix, Scout24, SIXT, and Make...
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Wrapped up my MSc exploring the potential of integrating GenAI in agile product working teams at RWTH Aachen University. Key takeaways: AI boosts human capabilities in productivity, innovation, and decision-making through augmentation, not replacement...
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Participated in the Porsche 6-Hours-Run alongside an amazing team at Porsche Digital, supporting children and teenagers. Completed 11 rounds of 911m laps. Grateful for the experience and proud of what we achieved together...
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Visit LinkedIn Profile →Open to PM roles & opportunities, or just a conversation about product and AI.
Fitness & health enthusiastic online wearables shoppers are overwhelmed with options and experience decision paralysis while comparing from information overload, technical jargon, contradictory reviews, and commission-biased recommendations.
Built conversational Health-Tech product comparison advisor that guides users through wearable selection via smart questions, compares 3-4 devices side-by-side, provides neutral recommendations, review curation, and product specific Q&A support in one personalized flow - from needs analysis to pre and post-purchase support. 100% commission-free for unbiased recommendations.
Led discovery and user research (n=4) for AI Health-Tech shopping advisor, identifying critical pain points: 67% experience decision paralysis and lack trust in recommendations for technical products. Validated market opportunity for e-commerce smart wearables with SAM of €1.8B, SOM of 9M€ targeting 0.5% market share through 5% commission model, projecting €450K+ annual revenue potential. Defined comprehensive product strategy including Business Value Proposition, competitive analysis, Lean assumptions, technical constraints, OKRs, and North Star Metric (MAU) as foundation for MVP development alongside Go-to-Market strategy.
Designed conversational AI flow prototypes in Miro and Figma for early UX validation before development investment, reducing design risk through 4 iterative rounds of user testing and mentor feedback. Validated product assumptions via landing page demand test, achieving 8% conversion rate (16/200 visitors submitted email), confirming market interest for MVP development. Wrote and prioritized 40+ user stories with acceptance criteria in JIRA based on user value and business outcomes, structuring product backlog for efficient sprint planning across 3 sprints with clear development priorities.
Explore the conversational AI flow designed in Figma:
Online shoppers waste hours jumping between product pages, +20 Tabs to compare, review sites, and forums to make confident purchase decisions. They need unbiased guidance based on actual product data and verified curated reviews - all in one place.
Built product-specific knowledge bot retrieving answers from product specifications, user manuals, setup guides, and verified test results. Pre-purchase support helps users compare recommended products, clarify technical details, translate complex specs into understandable benefits, and make confident purchase decisions. Post-purchase support guides initial setup, configuration, feature usage, and troubleshooting - transforming product documentation into on-demand assistance.
Building production-ready RAG pipeline with multi-model LLM integration (Claude, GPT) for cost-quality optimization. Built FastAPI backend with Langfuse observability for real-time tracing. Established systematic evaluation system for model comparison, system & user prompt versioning management, golden dataset with 40 test cases (10 factual, 10 for comparison, 10 complex, 10 edge cases according to CRISP framework), defined evaluation criteria with expected outputs (labelled as good/partial/bad), custom domain scores and 4 LLM-as-a-Judge evaluators for objective quality assessment
Launching innovative GenAI features required early validation with real users. Traditional approach: iterate until 100% perfect before rollout - slow, expensive, risky. Needed structured method to validate features early, gather feedback, and de-risk investment before full production.
Beta Program Framework: Structured approach enabling real customers to provide direct feedback on pre-production features through time-limited rollouts. Dual goals: (1) Validate features to improve product quality before production, (2) Create communication potential through innovation. Designed UX journeys, onboarding flows, and feedback mechanisms. Leveraged Community of 300+ members actively participating in product development while gaining early access to exclusive GenAI features.
Coordinated cross-functional teams across Business Owners, Marketing, UX, QA, Legal, and Product. Conducted competitor analysis and created user stories with UX journey maps. Collaborated with Quality and Marketing stakeholders to define success criteria: speech quality, content accuracy, and feature availability. Built comprehensive KPI framework tracking Adoption Rate, Usage Frequency, Customer Satisfaction, Session Duration, and Abandonment Rate. Established technical quality gates including Latency, Error Rate, Accuracy, and Safety Score (99% harmful content filtered). Developed cost estimation model covering API usage, infrastructure, and development resources. Proposed multi-phase rollout (internal → power users → broader audience) with structured feedback collection and weekly iteration cycles.
Feature: Location-aware GenAI generating audio stories about nearby sights using ChatGPT + Voice.AI integration.
Community: 100 selected real customers providing direct feedback on pre-production feature across USA, DE+UK, China, Japan/South Korea. Time-limited rollout: <6 months | Budget: ~1M€
Customer Benefits: Early access to exclusive location-aware AI feature + active participation in shaping production version as enthusiastic Community members.