Marcello Domenis

Marcello is a CTO and founder building AI and computer graphics systems for construction and building design, focused on deploying AI in real production workflows and turning deep tech into usable products.

He previously bootstrapped an AI agent platform to hundreds of users and worked at Sanctuary AI on spatial intelligence for humanoid robots. He is now CTO and co-founder of Clev, where he leads AI-driven design automation used by architecture and engineering firms with over €40M in combined annual revenue. The company secured early backing from an international VC-backed accelerator and multiple European co-design partners.

He operates across North America and Europe and brings a combined perspective of venture capital, early stage startup execution, and hands-on engineering. His speaking topics include cloud and AI deployment, early adoption in production, and building technical startups from zero to traction.


Session

05-07
14:00
40min
Building an Early-Stage Startup: AI That Actually Works and What Investors Fund
Marcello Domenis

The zero to one phase of an AI startup is where most projects quietly fail. Not because the models are weak, but because the problem is wrong, the scope is off, the team is misbuilt, and execution drifts away from real customer value.

This talk presents a practical zero to one playbook for developers and technical leaders who want to move from builder to founder in the AI era. It connects three elements that are usually treated separately: early product execution, early team design, and what investors actually fund at pre seed and seed stage.

You will learn how to choose a problem customers will pay to solve, how to turn AI capabilities into a real product wedge instead of a demo, how to run tight design partner loops, how to structure a two to four person early team, which roles to delay, and which concrete signals make investors back early technical teams. The session also highlights common early stage failure patterns, including overengineering, premature scaling, and misaligned hires.

Main Stage