Findr

AppSumo

Customers appreciate Findr for its efficient search functionality, seamless integration with internal apps, and user-friendly interface. Some users have reported issues with the search feature post-update, impacting productivity. Despite this, with a 3.9 rating and a 60-day money-back guarantee, it's worth giving Findr a try for those in need of an AI-powered search engine.

Visit product View on AppSumoNov 12, 2024

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Actual performance

44reviews

Did not reach leaderboard

Traction signals

Rating3.8 / 5

Purchases69

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: friendly, interface, efficient · Missing: plus, platform, intuitive

88%88% predicted probability of success on AppSumo, 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, user · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, 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

35%35% 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

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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