Research & whitepapers
The paper library.
How AI assistants choose what to recommend, written down: frameworks you can grade any vendor with, the Index methodology in full, and data papers from the frozen monthly editions. All free, no card.
How AI Actually Chooses What to Recommend
The mechanism behind every AI recommendation: two memories, chunk-level retrieval, and the answer-versus-citation split.
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The AI Visibility Audit: The Complete Framework
The published six-layer framework for auditing AI visibility. Use it to grade any vendor, including us.
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The Crawler Question
Which AI bots visit your site, the five jobs they do, and whether you should let them in.
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Is Your GEO Tool Measuring Anything Real?
Seven questions to ask any GEO tool before you pay for it, with a scorecard. We volunteer to be graded.
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How CiteHawk Measures AI Recommendations
Methodology v1.1: the evergreen reference for how the AI Index collects, scores, and freezes its monthly records.
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The AIs Don't Agree
Three pages on where the eight assistants split, from the frozen July 2026 edition of the AI Index.
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Who AI Recommends: Category Spotlight
A two-page spotlight on who AI recommends in a category, generated from the public Index edition. Sample: digital marketing agencies.
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Vertical Intelligence Reports
Deep reports on what AI recommends in 11 service verticals, from law firms to roofing companies. July 2026 edition.
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The AI Recommendation Report
The flagship report on what AI assistants recommend and why, built on the August 2026 edition. Coming soon.
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