Find AI – Perplexity Meets LinkedIn

Hacker News

As a founder, finding early customers is always a challenge. I'd come up with specific guesses for people to talk to - such "VCs that used to be startup founders" or "Former lawyers who are now CTOs." Running those types of searches typically involves opening dozens of LinkedIn profiles in tabs, and looking at them one-by-one. And, it turns out that going through LinkedIn profiles one-by-one is a daily job for many people. I started building Find AI to make it easier to search for people. I initially started just having GPT review people's LinkedIn profiles and websites, but it cost thousands of dollars per search (!). The product we're launching today can now run the same searches in seconds for pennies. Find AI is Perplexity-style search over LinkedIn-type data. Ask vague questions, and the AI will go find and analyze people to get you matches. The results are really impressive - here are some questions I've used: - Find potential future founders by looking for tech company PMs who previously started a company - Find potential chief science officers by looking for PhDs with industry experience who now work at a startup but have never founded a company before - Find other founders who have a dog and might want my vet app product The database currently consists of tech companies and people, but we're working to scale up to more people. The data is all first-party and retrieved from public sources. Our first customers have been VCs, who are using Find AI to keep track of new AI companies. We just launched email alerts on searches, so you can get updates as new companies match your criteria. Try it out and let me know what you think.

Visit product View on Hacker NewsJun 26, 2024

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

96%96% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, 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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

18%18% predicted probability of success on AppSumo, 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.

Incorrect prediction on native model

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