DealProof

a screenshot of the interface

Investors often get buried in pitch decks, data rooms, and founder claims. The hard part is not summarizing everything. It is knowing which claims actually matter, whether the evidence supports them, and what still needs diligence.

DealProof was inspired by the idea of turning AI from a “summary machine” into a red-team analyst for investment decisions. DealProof ingests deal materials, extracts the most decision-critical claims, checks them against source evidence, and produces an investor-ready diligence memo. It helps answer:

What can we trust?
What is weak or unsupported?
What is contradicted?
What follow-up evidence should we request before IC?

We built DealProof with a Next.js frontend and a FastAPI backend. The backend runs an agentic diligence workflow that parses materials, chunks evidence, extracts claims, ranks them by materiality, retrieves supporting or contradicting evidence, scores risk, and generates a structured report. The UI presents a concise claim review workspace and memo so investors are not overwhelmed by unnecessary claims.

The biggest challenge was keeping the output useful instead of noisy. Early versions extracted too many claims, which made the report harder to use. We refined the pipeline to focus on a smaller set of high-impact claims and improved the report layout so the full analysis is easier to read.

I learned that diligence AI needs precision more than volume. A good investor tool should not just generate more text. It should reduce uncertainty, surface risk, and make the next decision clearer.