You have the vision. You have the market. You don't have the technical co-founder. Or the one you have is drowning in architecture decisions they've never faced at scale. I've been on both sides — building AI startups and advising the investors who fund them. 30+ startups mentored from idea through Series B. €200M+ total funding raised. I know exactly what it takes because I've built it and I've evaluated it from the investor side at Berkeley SkyDeck.
You're a domain expert with an AI idea, but every architecture decision you make now constrains your next 2 years. Choose wrong and you rebuild from scratch before Series A — burning time and capital you don't have.
Your MVP was hacked together by contractors. Investors keep asking about your 'technical moat.' Your answer — 'we're faster' — doesn't survive 30 seconds of scrutiny from anyone who has built production AI.
You raised a Seed round but your burn rate is unsustainable. The architecture doesn't scale without rebuilding because nobody designed for 10x growth. You're spending investor money on technical debt, not product development.
Every VC asks the same question: 'What happens when OpenAI builds this?' You need an answer backed by genuine defensibility — proprietary data, deep workflow integration, or architectural innovation — not speed alone.
A stage-appropriate engagement model that grows with you—from first architecture decision to Series B readiness. Not a one-size-fits-all program, but targeted intervention at each critical inflection point.
Validate the AI opportunity: technical feasibility, data requirements, competitive landscape, and build a credible technical narrative for investor conversations
Design a production-path architecture—not a throwaway prototype. Choose the right AI stack, define data strategy, and build the foundation that scales to Series A metrics
Prepare for fundraising: technical due diligence readiness, scalability proof points, team structure for growth, and an AI moat narrative that survives investor scrutiny
Post-funding execution: hire the technical team, implement MLOps, establish governance, and build the infrastructure for 10x growth
A startup-native methodology built from founding, advising, and evaluating AI startups. Designed for speed and capital efficiency—every technical decision maps to fundraising milestones and business metrics.
AI-native startups from pre-seed to Series A+. Domain experts building AI products who need technical co-pilot expertise. Founded teams that need fractional CTO guidance before they can afford a full-time hire. You're building something real — not another ChatGPT wrapper — and you need someone who knows what 'production-grade' actually means.
Any stage from idea to post-Series A. The engagement model adapts: Idea Sprint for pre-seed validation, MVP Architecture for seed-stage building, Series-Ready for fundraising prep, or Fractional CTO for ongoing technical leadership. Each is scoped individually — book a call for a quote.
I help with the technical side of fundraising: building the technical narrative, preparing for due diligence, creating architecture documentation that impresses investors, and coaching founders on technical Q&A. I don't do financial modeling or investor introductions directly, but my network includes active VCs and angels across Europe.
Both, depending on stage and need. At the Idea Sprint stage, I'm primarily advising and architecting. For MVP builds, I can lead development with your team or contractors. For Series-Ready and beyond, I'm primarily strategic—designing architecture, reviewing code, and mentoring your technical team.
Accelerators give you 3 months of generalist mentorship across a cohort. I provide deep, 1-on-1 AI technical expertise tailored to your specific product. No equity requirement, no cohort schedule, no one-size-fits-all curriculum. You get the AI expertise of a senior CTO who has built at scale—when you need it, for as long as you need it.
That's exactly what the Idea Sprint discovers—before you burn months and significant capital. I'll give you an honest assessment: technically feasible, feasible with modifications, or not feasible with current technology. If it's not feasible, I'll help you pivot to a viable alternative. Better to know early and affordably than after a massive investment.
This is one of the most critical things I help with. An AI moat isn't just about models—it's about data, workflows, and switching costs. I help you identify your genuine defensible advantage, build the technical foundation that reinforces it, and articulate it in a way that survives investor scrutiny and competitive pressure.
My primary model is cash-based to keep incentives clean. For early-stage startups with limited cash, I can discuss hybrid arrangements. But I prefer cash compensation because it keeps the advisory relationship objective—you want someone who tells you the hard truth, not someone financially incentivized to be optimistic.
Absolutely—that's a core use case. Many of my best engagements are with domain experts (healthcare, finance, logistics) who have deep industry knowledge and an AI product vision but need technical co-pilot expertise to build it right. I translate between business vision and technical execution.
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