Evalyze-AI investor matches from your pitch deck (free signup)

Hacker News

I built Evalyze because I was frustrated by how much time founders lose sending hundreds of cold emails to the wrong investors. The tool takes a pitch deck, reads it, and suggests a ranked list of investors with short notes on why each one might be a fit. It looks at things like stage, sector, check size, geography, and portfolio patterns, then compares that to a dataset of over ten thousand investors. The goal is not to give you a giant spreadsheet, but to narrow things down to the people who are most likely to care about what you are building. Signup is free, just an email, no payment. I know some people dislike logins, but it helps me prevent spam and lets you save your runs. You can also play with a sample deck if you want to see it work before adding your own. What I would love from HN is feedback. Are the matches useful? Are the explanations clear enough? Do you think it needs more transparency about how the ranking works, or less? Where does it break? Thin decks and biotech are still tricky, and sometimes investor data gets stale. If you notice bad fits, I would be grateful if you point them out so I can fix the signals. I will be here in the comments to answer technical questions and to hear your thoughts. If signup is a blocker, let me know and I can share a sample run so you can still take a look.

Visit product View on Hacker NewsSep 24, 2025

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Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, notes · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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