About Davin AI
We started Davin AI because the commercial diligence market was stuck in the 1990s — qualitative, slow, and disconnected from how modern investors actually make decisions.
Why Davin AI Exists
Commercial due diligence has historically relied on expert interviews, market reports from legacy research firms, and analysts manually stitching together data in spreadsheets. The output is often a narrative — compelling, perhaps, but untestable. When a deal team asks 'How confident are we in this market size?' the honest answer is usually 'We aren't.'
Davin AI was founded to change that equation. We combine the analytical rigor of academic research with the speed and scalability of machine learning — purpose-built for the cadence and stakes of private capital transactions.
Our team sits at the intersection of data science, strategy consulting, and investment management. We've underwritten deals, built models for Fortune 100 clients, and published peer-reviewed research on market dynamics. We bring all of that to every engagement.
“The result: diligence that produces quantifiable, defensible insights on compressed timelines — so you can move from hypothesis to conviction before the deal slips away.”
Our Mission
To give every investor access to the quantitative commercial insight that was previously reserved for the largest, most analytically sophisticated funds — delivered at the speed modern dealmaking demands.
Joseph Davin
Founder & CEO
Joseph founded Davin AI to bring data science rigor to commercial due diligence. With a PhD from Harvard Business School and a faculty appointment at Cornell Tech, he bridges academic research and real-world deal execution. Before founding Davin AI, Joseph advised PE and VC firms on market entry strategy and commercial assessment, consistently identifying gaps that qualitative-only approaches missed.
Our Values
What We Believe
Academic Rigor
Every analysis follows a structured methodology — hypotheses, data collection, statistical testing, and transparent reporting of confidence levels. We treat commercial diligence like peer-reviewed research, not storytelling.
Data-First
Opinions are cheap. We start with data — structured and unstructured, proprietary and public — and let the evidence shape the narrative. When the data is ambiguous, we say so explicitly and quantify the uncertainty.
PE/VC Native
We understand deal timelines, IC dynamics, and what it means to defend a thesis under pressure. Our deliverables are built for the audience that matters: partners, operating teams, and investment committees.
Ready to see what your data reveals?
Start a conversation about how AI-powered diligence can strengthen your next investment decision.