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

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

by roman10·Feb 25, 2026·4 points·3 comments
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AI Analysis

●MidBig Brain

Thorough methodology, but it's a one-off report—no recurring product or unique insight beyond the YC analysis.

Strengths
  • •Position-adjusted scoring methodology grounded in published GEO research rather than raw mention counts.
  • •Surprising finding (pitchwise.se blog drives more visibility than accelerators' own websites) suggests genuine data digging.
  • •No-signup report with transparent methodology builds credibility.
Weaknesses
  • •No ongoing platform or tool—just a single analysis you can commission. Unclear if GeoVector has a scalable service.
  • •AI-generated answer visibility is inherently volatile; unclear how actionable these metrics are for strategy.
Category
Data
Target Audience

Marketing leaders and brand strategists monitoring AI visibility; enterprise research teams.

Similar To

SEMrush · Similarweb · Brandwatch

Post Description

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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