AI

AI finds you the perfect investor matches across +40k investor DB

Hacker News

AI finds you the perfect investor matches across +40k investor DB

After raising +$3M for my last startup and shutting it down, I've been coding for the last 6 months a tool called EasyVC to skip most of the annoying tasks behind fundraising from investors. 1. It takes your startup's description and matches it against several data points of each investor of the database (VCs and Business Angels), such as investment thesis, portfolio companies, investment stage, etc. 2. It gives you all the contact details of the investor, including verified emails and LinkedIn profiles. 3. It suggests the founders of the portfolio companies you should reach out to along with their LinkedIn profiles, in order to get warm intros. This is for any tech founder raising from pre-seed to series A, of any industry. Hope you like it, and would love to hear any questions/feedback/comments! :D

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, tasks, coding · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released
55%55% 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 · Strong signals: month · Missing: mobile apps, ios, personal
37%37% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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