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RecruitDoor: ATS for Agencies

Hacker News

RecruitDoor: ATS for Agencies

I'm the co-founder of RecruitDoor [1], an applicant tracking system (ATS) for recruitment agencies. We're a self-funded startup, and we've been targeting South African recruitment agencies over the past few months, with some great feedback, and most of our customers loving the product that we've developed. We're focusing our efforts internationally next month, and I would love some feedback from the international community. We built RecruitDoor on the idea of ease and simplicity, and even though we think we've built a great product, constructive feedback is always appreciated! Richard. [1] https://www.recruitdoor.com

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88%88% 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: using · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, 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
37%37% 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: month, way · Missing: mobile apps, ios, personal
36%36% 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
32%32% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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