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Trace every number
Every number traces to a primary source or a stated method.
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The AI-visibility
research desk.
Shareof.ai research desk · Vol. 01 · Jul—Aug 2026The AI-Visibility
The AI-Visibility
Content Engine.
Original frameworks, technical field notes, and reproducible methods for understanding how AI systems retrieve, cite, and recommend brands.
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Connect every framework
Every framework links to the evidence and the next practical step.
03
Separate the evidence
Every study separates observations, inference, and limitations.
Research archive
5 matching papers
19Technical playbooks
Reverse-Engineering Co-Citation: Find the Pages That Decide Your Category
If you want on the lists an AI hands people, first find the handful of pages it reads before it answers. Here is how to rank them.
21Technical playbooks
A/B Testing Content for AI Citation: A Methodology That Survives the Noise
Ask an AI the same question twice and you may get two different sets of sources. Any before-and-after test that ignores this is measuring noise and calling it a result.
18Technical playbooks
Build a Brand-Mention Monitor Across Four LLMs in an Afternoon
A working script that runs your prompt set against ChatGPT, Claude, Gemini, and Perplexity on a schedule, then logs where your brand shows up and who gets cited.
17Technical playbooks
Measure Your Own Share of Retrieval With the Model APIs
Most people test whether AI cites their site by typing their brand into ChatGPT once. Here is how to measure it properly, with code you can run today.
20Technical playbooks
Reading Your AI Crawler Logs: GPTBot, ClaudeBot, PerplexityBot and Friends
Your access log already knows which AI engines can see you. Here is how to read it, and why the live-fetch agents matter more than the training crawlers.
See which part of the visibility funnel is losing your brand.
Run a free scan across real buyer questions, then use the field library to decide what to fix first.
Run the free scan ↗