Anna works at the intersection of venture capital, data, and product—focused on reducing randomness in fundraising. She has worked with and structured datasets covering 10,000+ VC funds, CVCs, and family offices, building a practical understanding of how investment behavior actually manifests in data: what signals matter, which ones are noise, and how investor intent can (and cannot) be inferred from deals, portfolios, and public information.

Her expertise lies in turning fragmented, unreliable VC data into usable systems. Her work focuses on identifying real patterns in investor activity and translating them into AI-native tools that help founders understand who is likely to invest—especially within the opaque and inefficient European VC landscape.