We're cracking the AI discoverability code

Hacker News

Hey HN, We’ve been watching how AI models like ChatGPT, Claude, Perplexity, and Google’s AI-generated search results are reshaping how people find information. Traditional SEO isn’t enough anymore—brands need to be optimized for AI-generated content, not just search engines. That’s why we built Naya, a platform focused on Generative Engine Optimization (GEO). Our goal is to help businesses ensure that AI models recognize, retrieve, and recommend their products/services in AI-generated responses. What we’re doing: AI-friendly content structuring – Optimizing content so AI can retrieve and summarize it accurately. Authority building – Getting businesses referenced in high-authority sources AI models trust. AI response monitoring – Tracking when and where your brand appears in AI-generated content. Conversational query optimization – Aligning your brand with how users interact with AI assistants. Your thoughts are helpfu

Visit product View on Hacker NewsFeb 18, 2025

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Actual performance

1points

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Launch Intel predictions

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

90%90% 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

89%89% 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, users · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

35%35% 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

18%18% 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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