Scholtz – Find customers and users that want your product

Hacker News

Hello HN! I’m Daniel, and I’m building Scholtz ( https://scholtz.ai ) – an AI tool that helps founders and teams find the people who truly care about their product: customers, users, early adopters, partners, investors, and more. Why I built this: At my previous startup, the worst job I disliked was sifting through endless lead-gen lists and CRM spam. It was always impersonal and artificial – like blind guessing. I wanted something that actually understands what your startup is about and then suggests real and genuine people who may be interested in your product or service. What it does: - You add your website – that's it. - Scholtz scans it and identifies what your business is. - Next, it finds people who actually care about your product or service – the sort of people who might indeed be interested in what you're producing. - And finally, it shows you how to contact them directly – no spam, no filler. Still in beta: It's a one-man project, and I appreciate your input – what's great, what's unclear, what's lacking? Here's the link if you'd like to give it a go: https://scholtz.ai/welcome I'm providing a trial that allows you to search for up to 10 individuals to try it out. Scholtz is currently focused on LinkedIn profiles in the U.S. tech industry. Thank you for reading, and I look forward to hearing your thoughts! – Daniel

Visit product View on Hacker NewsMay 26, 2025

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

90%90% 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: user · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, users, way · Missing: mobile apps, ios, entrepreneurs

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% 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

19%19% 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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