Explore documented SEO, AEO, and GEO experiments from real websites, with referral evidence, crawler analysis, percentage charts, and the workflow behind every result.
These are not invented customer stories or estimated outcomes. Each case study separates observed data from interpretation and shows when its evidence snapshot was published.
Search, referral, crawler, and answer-engine evidence—kept separate so every chart and conclusion is easier to understand and verify.
Search Console
ChatGPT
Claude
Bot activity
Crawler breakdownThree focused comparisons from the latest Admin-published evidence snapshot. Every value is shown as a percentage, so the signal is easy to understand at a glance.
Share of GPT, Claude, Perplexity, spider, and remaining AI activity in the published evidence snapshot.
Relative share of AI-classified and search-classified crawler events.
Separates AI page retrieval associated with a user action from automated AI crawler activity.
Percentages describe the published activity mix only. They do not prove rankings, citations, training use, or recommendations.
24,972 attributed visits from AI platforms, including 5,869 from ChatGPT, plus a 97,446-event crawler evidence set with transparent confidence labels.
791 attributed visits from AI platforms, with ChatGPT ranking sixth among all referral sources in the supplied export.
A transparent look at how we use our own product data to prioritize pages, improve documentation, and test AI visibility workflows.
This library is built for people looking for documented AI visibility results—not generic advice. Every study connects real website evidence with a clear SEO, AEO, or GEO workflow.
Review attributed visits and pageviews from AI platforms such as ChatGPT, Claude, Perplexity, and Gemini. Referral traffic is reported separately from crawler requests, so a bot visit is never presented as a human visit or an AI citation.
Analyze how GPT, Claude, Perplexity, spider bots, AI bots, and search bots appear in the latest published activity snapshot. These analytics show access and retrieval patterns while keeping their limitations visible.
Use live percentage charts to compare platform activity, AI bots versus search bots, and user-triggered retrieval versus automated AI crawling. Each chart can be exported as a branded 1000 × 1000 image for reports or social posts.
Follow the starting point, the action taken, the evidence collected, and the next decision. The studies show how technical SEO, answer-ready content, generative search clarity, and crawler monitoring fit into one measurable workflow.
A useful case study should help you understand what happened, what the numbers mean, and what can be tested next on your own website.
Start with the publication date and evidence type. Search Console data, referral exports, crawler logs, and saved answer-engine observations answer different questions and should not be combined into one visibility claim.
Use percentages and trend context to see which AI platforms or crawler groups account for the largest share. A change in bot activity can guide investigation, but it does not by itself prove ranking or citation impact.
Connect the observed signal to a practical action: improve crawl access, clarify a page answer, strengthen entity context, build an intent page, or monitor whether the next published snapshot changes.
Explore the Plinkod live AI visibility data, the eFootballLab search opportunity analysis, or the TruboRankAI SEO, AEO, and GEO workflow.
Across the two owner-operated properties in the supplied referrer exports, AI answer platforms generated 25,763 attributed visits and 2,696 pageviews. These are observed referrals, not estimated visibility.
ChatGPT, Perplexity, Claude, and Gemini combined.
Attributed across Plinkod and eFootballLab.
Measured in the two aggregate referrer exports.
Evidence snapshot manually published 17 September 2026.
AI visibility is still evolving. Search engines, crawlers, and answer engines do not always expose complete information about why a website appears or disappears.
That is why these studies focus on what can be documented: page improvements, crawler activity, Search Console context, observed AI answer appearances, and the actions taken next.
Short answers about the evidence, charts, and update process used across this case study library.
It is a documented analysis of a real website using published referral, crawler, search, and answer-engine evidence. Each study separates observed data from assumptions and explains the workflow behind the result.
The charts compare published activity as percentages, including GPT, Claude, Perplexity, spider bots, AI bots versus search bots, and user-triggered versus automated AI activity.
The statistics update when a reviewed evidence snapshot is published from the TruboRankAI Admin dashboard. The latest publication date is shown beside the charts.
We separate what was changed, what was observed, and what cannot yet be attributed with certainty. This keeps each study useful without turning incomplete signals into exaggerated claims.
