Investigation type 06 · AI Answer-Engine Audit · verdict: cited, misdescribed, or absent
Researchers now ask ChatGPT, Claude, and Perplexity which journals and papers to trust before they open a search engine. If the models don't name your journal, you lose readers and authors at the recommendation stage — invisibly, before a single click. This audit measures where your journal stands, and why.
LLM visibility for a scholarly journal is the degree to which large language models cite the journal or its articles when a researcher asks which sources are authoritative in a field.
A journal has high LLM visibility when answer engines name it accurately and often; low visibility when the models cite competing journals or omit it entirely. It differs from Google Scholar indexing: indexing governs whether articles are findable in a search index; LLM visibility governs whether they are recommended inside a generated answer — frequently with no click ever reaching the journal.
The problem, stated plainly
Absence from AI answers is a discoverability failure that standard analytics cannot see.
A journal can be fully indexed in Google Scholar and still be invisible in ChatGPT, because the two systems select sources by different mechanisms. When an answer engine shapes a researcher's shortlist without sending a click, the loss never appears in your traffic reports. It looks like nothing is wrong — while prospective authors are quietly routed to the journals the models do cite. This audit makes that invisible loss measurable.
What the audit delivers · six phases
30–50 real research prompts in your journal's subject area — not generic brand queries. The questions researchers actually ask: who wrote the seminal work on a topic, where to publish on it, which journals lead the field.
Every prompt run across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. For each: is the journal named, which articles are cited, and how accurately is it described. Citation behavior differs by engine, so all are tested.
The same prompt set run against 3–5 competing journals. The result is a clear ranking of who dominates the answers in your field — and by how much.
Where the models draw their answers from — which domains, databases, and pages they cite for your field — and why your site is or isn't among them.
Crawler access for the AI user-agents, full-text and metadata machine-readability, structured data, and llms.txt — the site-side signals that determine whether your content is even eligible to be cited.
A dated report: the finding, the evidence behind it, a stated confidence level, and a prioritized, DOI-safe plan for what to change. The prompt set ships with it, so you can re-run and verify yourself.
Questions publishers ask
Deliverable & engagement
Start an audit
You'll get a first read on where you stand across the answer engines — with evidence, not a guess. Any platform: WordPress, Joomla, OJS, or custom.
Request an LLM visibility audit Paid diagnostic · written findings · confidence level stated · no guaranteed-citation claims