GEO Benchmark 2026
Research on AI search visibility (GEO): methodology, dataset structure, and findings. We will publish full results and charts once the benchmark run is complete.
Methodology
The benchmark uses the same scoring logic as our product methodology: we crawl a representative sample of URLs, run technical SEO and GEO checks (structure, authority, citations, freshness), and aggregate category scores. Constraints include crawl depth, timeout, and the same signal weights we use in the app. When published, we will document sample size, source list, and any exclusions here.
Dataset description
The dataset will include per-URL scores (SEO and GEO categories), issue counts by severity, and optional metadata (e.g. vertical, region). We will specify whether we publish full URLs, hostnames only, or summary statistics. All collection follows our privacy and robots policy.
Findings
Once the run is complete, we will add: score distributions (overall and by category), most frequent issues, and notes on correlation between technical SEO and GEO. Findings will be summarised in bullet points and, where useful, with charts (e.g. score distribution, issue frequency).
Charts and exact numbers will be added when the benchmark data is final. Do not use unverified statistics in production.
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When we publish the full benchmark, we will announce it via our newsletter. You can also check our changelog for release notes.
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