Krilup

TrustMRR

Krilup is a mobile-first LinkedIn copilot (PWA) for creators, marketers and sales pros. It turns LinkedIn into a daily routine: custom feeds of the profiles that matter, AI comments in 8 tones trained on your own writing style, a smarter inbox with labels, notes, AI replies and scheduled messages, automations triggered by likes, comments, profile visits and invitations, plus streaks, stats and 52 free SEO tools. Django 5 + PostgreSQL, Stripe billing, FR/EN UI, single plan at 29€/month.

Visit product View on TrustMRRApr 18, 2024Sales Software B2C

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Actual performance

143customers

$9,348MRR/mo

Made the leaderboard

Traction signals

Domain Rating6

MRR growth 30d-38.9%

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: stripe, inbox, single · Missing: mac, agents, macos

75%75% 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.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

70%70% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, 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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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