Raphael Danilo is a French-born venture capital investor at Evening Fund who maintains his role as CEO of Yobs, an AI-powered interview intelligence company, while actively investing as a GP rather than being a former or retired founder. His dual perspective as both an active operator building AI products at companies like Yobs and Gong and an early-stage investor gives him hands-on expertise in hiring optimization and AI/ML that he applies when evaluating startups and advising founders on talent and fundraising strategies.

• Deep conviction in the French technology ecosystem as a major hub for future artificial intelligence and machine learning breakthroughs. This thesis is driven by the belief that strict European privacy regulations actually serve as an advantage for building enterprise grade products, with the recent massive seed funding of Mistral serving as prime evidence of the region producing top tier technical talent.
• Strong emphasis on bringing rigorous scientific methods to startup recruiting rather than relying on intuition. A key recommendation is treating hiring exactly like a sales process by creating structured interview pipelines that assign specific competencies to interviewers and utilizing standardized high quality questions to maintain a strict talent bar as a company scales.
• Focus on strategic leadership and empathetic management, especially as a startup grows past its initial core team and operates remotely. Exceptional management requires understanding the unique strengths of individual team members and coordinating them effectively, heavily relying on high transparency, frequent individual meetings, and an intentional shift from a traditional boss mentality to a coaching mindset.
• Dedication to operator led and distributed venture capital models. There is a firm belief that founders derive the most value from early stage investors who have firsthand experience building companies themselves, and that embracing distributed work allows investors to back top talent anywhere without being constrained by geographic boundaries.