Original investigations into how brands appear in AI search. We audit websites, test recommendations, and publish the evidence so marketers can make better decisions.
200
pricing and feature claims checked
50 products, 100 answers. 18 contradictions; unsupported and unanswered claims counted separately.
18 of 200 pricing and feature claims contradicted official sources. We checked the answers against named plans, billing terms and documented limits.
71
answers in the expanded language comparison
Matched English/Japanese questions across two API configurations. Reported separately from the original 36-answer pilot.
We asked matched English and Japanese buying questions through two API configurations. Name overlap was 0–11.1% in the eight groups we could compare.
500
corporate crawler policies checked
Eight search, training and user-directed agents. Ambiguous and inaccessible cases stay separate.
We inspected robots.txt on 500 corporate domains. Only 302 files could be classified without uncertainty; search and training agents often had different rules.
1,000
buyer answers audited
100 questions × 10 answers. An audit of the websites linked in AI recommendations.
521 hostnames appeared in our Gemini API sample. Vendor sites led the count, sometimes linked beneath a competitor’s recommendation.
180
answers across two dated waves
Three questions, 30 answers each, repeated one week later. Two collection dates, not continuous monitoring.
Teamwork.com appeared in 28 of 30 answers, then 2 of 30 a week later. We repeated three fixed software-buying questions and kept every answer.
500
corporate websites audited
Every company in the S&P 500. 54 llms.txt files had both a title and links.
We checked all 500 companies in the S&P 500. Seventy-one had a detected llms.txt. Only 54 had both a title and a Markdown link. Here is what the responses revealed.
Our reports publish the sample, collection dates, methods, limitations, and underlying data. Inspect the results, cite the work, or use the methods to ask your own questions.
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