Does AgentFire do AEO and help you get found in AI search?

We tested 25 publicly attributed AgentFire websites using 12 fixed, non-branded questions per site across three web-enabled API panels. That design creates 900 prompt units: 25 websites multiplied by 12 questions multiplied by three panels. The study asks whether a business appears when a consumer describes a local real estate need without naming the agent or website.

The published result denominator is 898 usable answers. 2 unavailable answers were excluded. All Gemini rows, including corrected-pilot rows, were excluded from this reported three-panel result. The separate expanded attempt ledger is not used to calculate these rates.

A name mention means that the business or an approved alias appears in the answer. A reviewed recommendation means the business or approved principal is presented as an option. An audited-domain citation means the answer cites or links to the specific website being audited. These outcomes overlap and should not be added together.

Engine-specific rates use each panel's own eligible-answer count. The model labels below identify the test configuration; these API runs are not measurements of the ChatGPT, Claude, or Perplexity consumer interfaces. A result on one panel should not be treated as evidence that another panel will return the same options.

AgentFire-attributed businesses appeared in 152 answers, while 17 of the 25 sampled sites had at least one name mention. Sixteen sites had a recommendation. The first measure captures repeated exposure across questions and panels; the second shows how broadly exposure was distributed within this selected cohort.

The cohort recorded 67 audited-domain citation answers and citations for 13 sampled sites. A recommendation can draw on brokerage pages, directories, or editorial sources instead of the audited website. Inspecting the cited URLs helps an agent understand whether the website supplies useful evidence or the answer relies on another source.

Begin with the questions a buyer or seller would ask before knowing your name. Record the exact market, question wording, test date, model, answer, and cited URLs. Score a name mention, a recommendation, and a citation separately. Preserve missing answers as missing rather than treating them as proof of absence.

Then inspect the evidence behind the answer. Can a reader verify who you are, where you work, what experience supports the service, and which original pages answer the question? Address crawl access, clear page headings, original local research, accurate business identity, useful internal links, and visible sources before increasing publishing volume.

This is a diagnostic sequence, not a claim that these actions caused the cohort results. Google Search Central says its existing SEO foundations remain relevant to AI Overviews and AI Mode; it does not require a special AI schema or guarantee inclusion. Apply that guidance to Google while testing other answer systems on their own terms.

SEO concerns discovery in search. AEO focuses on providing clear answers; GEO concerns visibility within generated responses. LLM visibility describes whether a business or source appears in a model-generated answer. These labels overlap, but none substitutes for a defined measurement. Here, the measured outcomes are name mentions, reviewed recommendations, and audited-domain citations, not a generic optimization score.