I bring over 14 years of software development expertise and a decade of startup experience to help teams build AI products that actually work. After founding my first company six years ago, I run a consultancy specializing in helping startups build MVPs, solve complex technical challenges, and integrate AI effectively.

I've seen firsthand how AI projects fail due to lack of systematic experimentation—teams treat AI like traditional software and struggle with inconsistent results. That's why I co-created Lovelace, a platform designed for non-technical professionals to experiment with AI agents systematically.

I'm a product manager with over 10 years of experience building and leading products across diverse industries. Most recently, I've been leading product development for an AI-backed FinTech, navigating the unique challenge of bringing AI innovation to one of the most regulated environments.

I'm not here as someone who figured out AI from day one—I'm here as a PM who learned the hard way that building AI products is fundamentally different from traditional software development. I've watched my own teams make the same critical mistakes that plague 80% of AI projects: picking one model, writing simple prompts, getting promising early results, then struggling with inconsistency in production.

Through these challenges, I discovered that successful AI product development requires systematic experimentation, explicit domain knowledge integration, and continuous evaluation—not just once, but as an ongoing practice. The goal isn't to check the "AI box" but to deliver AI that genuinely improves users' lives.