Me

Measuring brand share in AI answers – a Y Combinator case study

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Measuring brand share in AI answers – a Y Combinator case study

After working in data science at Google, I built GeoVector to systematically measure how brands appear in AI-generated answers. Our approach is research-based, using position-adjusted scoring grounded in published GEO literature. The Y Combinator report is one example of the analysis we run. We ran 150 prompts across ChatGPT and Gemini, tracking 21 brands. Three things that surprised us: 1. Techstars outranks YC on ChatGPT despite YC's far stronger Google presence 2. YC's own site accounts for just 8 of 940 AI source references 3. The single most-cited source driving competitor visibility is a blog post on pitchwise.se — not any accelerator's own website Full report at the link, no signup. GeoVector runs this analysis for any brand or vertical.

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Actual performance

4points
3comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, chatgpt, single · Missing: mac, agents, macos
84%84% 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: gemini · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, 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
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google, answers · Missing: mobile apps, ios, personal
46%46% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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