Thesis

Discover with conviction. Manage with clarity. Invest with insight.

As a VC scout and participant in a student-led venture fund, I experienced how fragmented investing can be. Sourcing startups, checking portfolio progress, and managing LP relationships meant switching between databases, spreadsheets, and conversations. I built Thesis to bring these workflows together—a platform shaped by problems I faced myself.

Three workspaces. One investment journey.

Discovery: Source startups through connected databases, extend access through the Model Context Protocol (MCP), and rank companies against a defined investment thesis. Inspect alignment signals and evidence before progressing to diligence and partner review.
Observability: Track portfolio performance, founder needs, and fund activity. Give investment teams actionable follow-ups and LPs a clear snapshot of their capital and the fund’s progress.
Intelligence: Explore potential outcomes through analytics, scenario modeling, and Monte Carlo simulations, with validated machine learning predictions as the next step.

Built from experience

I connected sourcing, structured evaluation, portfolio monitoring, and investment analysis around my own workflow. The biggest challenge was creating useful rankings without hiding uncertainty. I learned that a score only matters when its evidence, assumptions, and gaps are visible.