Strategy & planning
What are the most important factors for AI search visibility?
The ones people underweight
Independent corroboration is the strongest and least controllable. Nothing you publish about yourself substitutes for being described by someone else, because models weight independence heavily when justifying a recommendation.
Entity clarity is the most overlooked. A brand an engine cannot resolve confidently gets hedged language or gets skipped, no matter how good the content is.
The ones people overweight
Keyword targeting, word count, and publishing frequency. All three are weakly related to being cited and absorb most of the effort in a typical programme.
Anything sold as an AI-specific file or tag deserves scepticism until there is evidence engines consume it. Effort is better spent on the HTML they demonstrably do fetch.
See where you actually stand
We ask AI engines the questions your buyers ask and show you whether you were named, who was named instead, and which sources they cited. Free.
Related questions
On a 0–100 scale, most established brands land between 40 and 65. Above 70 usually means an engine names you unprompted in open category questions; below 35 means you are found only when someone already knows your name. The number matters far less than which underlying signals are dragging it down.
Only if it adds coverage of questions you did not previously answer. Volume alone does nothing — an engine retrieves one passage per sub-query, so a second article on a question you already answer well competes with yourself. Breadth across a topic's fan-out beats depth on any single point.
Barely, on current evidence. Around 9–10% of major sites publish an llms.txt, but monitoring of AI crawler traffic shows the file is almost never requested — GPTBot, ClaudeBot, PerplexityBot and Google-Extended overwhelmingly crawl HTML directly. No major AI company has committed to reading it, and Google has said it does not support it.
More on strategy & planning
Last reviewed 2026-07-30.