I turn financial operations into autonomous systems.

When I joined, the company was managing €2B in receivables across 10 countries through fragmented ERPs and 20+ Google Sheets. Cash forecasts moved 50% from one week to the next. I dealt with 2am liquidity crises, securitisation advance rates threatening to drop below covenant thresholds, and the very real possibility that our lenders would convert debt into equity if cash positions deteriorated further. I was fast-tracked from junior analyst to product manager, eventually owning treasury operations across all European markets. I reduced overdue debt by 85%, built the unit economics model (LTV-CAC, cohort analysis) that BlackRock, SoftBank, and the board used to make investment decisions, and helped build the financial infrastructure behind €1.3B in fundraising. Before that, I built analytics models for Coca-Cola, IKEA, and Mahou at MRM Worldwide, and cut my teeth on fintech at Fintonic.

That experience taught me where real value is created in financial operations, and where it's wasted. Treasury is repetitive, rule-based, and high-stakes: the perfect environment for AI. So I started building. I designed AI agents using Claude for cash application and invoice matching, achieved a 30% improvement in forecast accuracy, and built the product vision for autonomous treasury systems that reduce manual work by half. I know exactly where AI delivers ROI in finance, and where it's still hype.

I know exactly where AI delivers ROI in finance, and where it's still hype.

I complement that operational depth with formal AI and ML training: Stanford's Machine Learning Specialisation, Anthropic's certification programme, and Reforge's AI Product Management curriculum. Not to collect credentials, but to build the technical intuition that separates a product manager who uses AI buzzwords from one who can actually ship autonomous systems.

I also invest. I've been on both sides of the table: building the financial models and data rooms behind €1.3B in fundraising rounds, and deploying my own capital as an angel investor in early-stage startups and as an LP in a venture fund. That dual perspective, operator and investor, shapes how I evaluate AI products: not just "does it work?" but "does it create defensible value at scale?"

I believe the best AI products will be built by people who've operated the systems they're replacing. Not by technologists guessing at business problems, and not by operators afraid of the technology. The intersection is where I live.