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BetterAEO – Measure AI search readiness and get AI recommendations

Hacker News

BetterAEO – Measure AI search readiness and get AI recommendations

Hi HN, I’m launching betterAEO, a tool we’ve been building to help websites understand and improve how they appear in AI-generated answers from tools like ChatGPT, Google AI Overviews, Perplexity, and Claude. The motivation came from noticing that traditional SEO metrics don’t capture how AI engines select and summarize content. Sites with strong SEO can still be invisible in AI summaries, meaning users get answers that favor competitors even when your content is high quality. betterAEO lets you: - Measure your site’s AI-readiness with a single AEO Score - Identify gaps in schema, snippets, trust signals, topical authority, and answer formatting - Get AI-powered recommendations to fix or improve issues and boost visibility - Track progress over time with historical scoring and reporting You can run single-page scans or full-site crawls. No signup is required for testing, and you can see results instantly: https://betteraeo.com Background & Approach: betterAEO was created based on extensive research, articles, and publicly available guidelines on Answer Engine Optimization (AEO) as of today. It is constantly evolving, adapting to new AI signals, trends, and best practices as they emerge. The system evaluates multiple signals, including structured data, content alignment with search intent, technical crawlability, answer presentation, and more, and provides actionable recommendations powered by AI. We built this while working with SEO teams and content marketers frustrated that AI engines increasingly control visibility, but there was no structured framework to measure or optimize for it. I’d love feedback from the HN community, especially on the usefulness of the AEO score, the AI recommendations, and any additional signals you’d want to see measured.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, google, user · Missing: mac, agents, macos
92%92% 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 · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting
85%85% 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, answers, 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: users · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
36%36% 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
11%11% 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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