Geneo – Track your brand visibility across AI search

Hacker News

Hi HN, I built Geneo to solve a problem I was facing: as AI search (ChatGPT, Google AI Overviews, Perplexity) becomes more common, it's increasingly unclear whether and how your brand is showing up in these new search experiences. Geneo helps you: Track brand mentions across AI-generated search results Analyze if your domain is being cited (or ignored) Benchmark your visibility against competitors Identify high-potential queries and topics to create content that’s more likely to be referenced by AI Get AI-driven content recommendations to improve your brand’s presence This is especially useful for marketers, content teams, and SEO professionals trying to adapt to the generative search era. I just launched it on Product Hunt today: https://www.producthunt.com/products/geneo Would love your feedback on the idea, execution, and direction. AMA! Tony

Visit product View on Hacker NewsJun 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, perplexity · Missing: mac, agents, macos

91%91% 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.

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

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

32%32% 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

29%29% predicted probability of success on AppSumo, 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

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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