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Zarathustra – AI-powered sales research in 60 seconds

Hacker News

Zarathustra – AI-powered sales research in 60 seconds

Hi all, I'm a first-time startup founder genuinely looking for your brutal honesty and sincere feedback. I’ve been building a tool that’s designed to save salespeople time on company research. Essentially, I created it to solve a real pain point my team had internally during sales outreach—we were wasting tons of time researching companies and prospects to craft personalized messages. So, I built something that does it in under 60 seconds. After some friends encouraged me to share it publicly, I made it available for others to use because this problem seemed to resonate with a lot of people in subreddits and casual conversations. The tool works like this: you type in any company link, optionally share a little about what you do, and it generates a detailed sales report for free. The sales report includes things like key insights, target audience, products and services, engagement strategies etc. While our internal team loves it and it saves us a ton of time, something feels off since we launched it publicly. People are trying it, but engagement and retention is much lower than we expected. I’d love to get your thoughts on a few things: * Is company research actually a significant pain point for salespeople? * Are we attracting the wrong users? * What’s your brutally honest feedback? What’s good, what’s bad, and what could be better about the idea? If you have a few minutes to give me your thoughts, I’d really appreciate it. Please don’t hold back—I value the truth more than comfort right now. Thanks so much for your insights!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
93%93% 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
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, users · Missing: mobile apps, ios, entrepreneurs
28%28% 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
27%27% 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
15%15% 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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