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Nat.app, personal CRM that knows who you're losing touch with

Hacker News

Nat.app, personal CRM that knows who you're losing touch with

Hi HN! founder of nat.app here. I've seen so many people building personal CRMs over the past years. Somehow, they all seemed to fail and die a year or two in. The main reason for this in my opinion is that the personal CRM business is a bad business. People sort of want this and will tweet about in from time to time, but only very few people are willing to pay and put in the effort (aka. reaching back out to people), to make it work. Those very few people are our market and because it's a small market, there is no way to sustain such a business unless you're bootstrapped and working on this part time (which we are). Our approach is very specific: We focus on Google users (which is why there is only a Google login) who communicate mainly through email and put every IRL meeting in their calendar. For those people, we are able to capture 80% of their social interactions with Google's APIs. Which means that our if-else rules are accurately able to identify contacts our customers are losing touch with. nat.app then acts as a safety net and displays those contacts to our customers. This product really has been a labor of love over the past 3 years and I'm very happy to be sharing it with you today.

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Actual performance

53points
39comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, email · Missing: mac, agents, macos
89%89% 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
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, google, users · Missing: mobile apps, ios, entrepreneurs
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · Missing: arr, mrr, revenue
34%34% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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.

Correct prediction on native model

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