We

We compiled a database of 45k+ investors to raise a seed round

Hacker News

We compiled a database of 45k+ investors to raise a seed round

Hey guys! Investor Scout ( https://investorscout.co ) is a categorized database of over 45,000 angel investors and venture capitalists. Finding the right investors to pitch your startup to––then finding the best way to reach out to them directly––is a nightmare. The data is spread out all over the internet, down the google rabbit hole, Twitter, LinkedIn, and AngelList. Research can take hundreds of hours on its own, not even counting the time it takes to actually reach out to investors, go through initial screens, get meetings, and finally close a round. Last year we launched Investor Hunt on Product Hunt and it was obvious to us that we hit on a strong need, so we’ve been working hard on improving everything about the product. Investor Scout is Investor Hunt 2.0 (we’ve re-branded from “Hunt” to “Scout”). Since our 1.0 almost 200 companies like Rip Van Wafels and The Family have used Investor Hunt. For our 2.0 we’ve completely revamped the UI, updated our dataset (we’re also halfway through another massive dataset update that we’ll be launching this year), and built out a more sophisticated system for saving investors & exporting their contact info. Like our 1.0, investors are categorized by what they’re likely to invest in next (based on their interests and what they’ve already invested in). The dataset includes emails (almost 30k), phone numbers, investor tags, past investments, the company they work for, and social media profiles. We’d love feedback––thanks for reading this - Matt & Aaron PS: We just launched on Product Hunt: https://producthunt.com/posts/investor-scout

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, email · Missing: mac, agents, macos
90%90% 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
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
63%63% 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: google, way · Missing: mobile apps, ios, personal
49%49% 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
41%41% 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
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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