SVGS APP

TrustMRR

Stop hunting for illustrations that almost fit. SVGs.app uses AI to generate unique, production-ready vector graphics from simple text prompts in seconds. Choose from curated styles like “Notion Style” or “Minimal Outline,” get perfectly consistent SVGs, and download them instantly. Publish to the community and earn credits when your designs get likes. Turn ideas into professional, scalable graphics for websites, apps, and presentations. Fast, easy, and on-brand.

Visit product View on TrustMRROct 28, 2025Design Tools Software stripe

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Actual performance

5customers

$100MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating1

MRR growth 30d-25.0%

Launch Intel predictions

Analyze your own launch →

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

82%82% 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.

TrustMRRFits verified-revenue profile · Strong signals: apps · 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, presentations · Missing: mac, agents, macos

68%68% 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

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

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

41%41% 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

18%18% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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