Go Viral is a mobile application that provides an AI-generated Virality Score for short-form videos intended for TikTok, Instagram Reels, and YouTube Shorts. It addresses low viewer retention and underperforming content by scanning videos for specific retention killers and delivering concrete editing recommendations before posting.
The application evaluates videos up to 90 seconds in length. Users upload a draft from TikTok, Reels, their camera roll, or YouTube Shorts. The AI then analyzes the opening hook both visually and verbally, pacing, storytelling structure, lighting, visual appeal, and presence of a call-to-action. It returns a Virality Score from 0 to 100 along with a detailed breakdown that includes second-by-second retention predictions, hook strength rating, pacing recommendations, B-roll suggestions, and a step-by-step checklist of fixes. The process is presented in three steps: upload, AI audit, and follow the checklist to re-edit.
Go Viral is delivered as a native app for Android and iOS devices. It is intended for video creators who want data-driven decisions instead of guessing about hooks, retention, or editing choices. The FAQ notes that many perceived shadowban issues are actually weak hooks or poor pacing that the tool identifies.
A free tier supplies the overall Virality Score and up to three video analyses per day. Paid Pro plans begin at $4.99 per month and add full detailed breakdowns, hook optimization tips, AI-generated captions, and unlimited analyses. The site states that more than 10,000 creators have used the application.
Go Viral is an Analytics (BI, web, product) project. It focuses on improving the chances of short-form videos going viral by providing actionable AI-driven feedback. It is built as a consumer product for content creators on TikTok, Reels, and Shorts. It runs on the web, iOS, and Android.
Among its 5 catalogued features are video analysis, hook detection, and retention prediction. The interface is available in 6 languages, including German, English, and Spanish.
Summary written by a language model from the project’s public pages.
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