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

We tested 25 publicly attributed Lofty 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 900 usable answers. 0 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.

The Lofty cohort recorded two name mentions across 900 usable answers. Only one of those answers met the reviewed recommendation definition, and one answer cited an audited domain. All 25 sites had complete three-panel coverage, so the 23 sites with no name mention meet the complete-panel rule for that narrow statement.

These numbers justify asking whether a particular business is represented in relevant AI answers. They do not identify the cause of its absence. The research did not hold local competition, reputation, content, or purchased services constant. Start with the business and its evidence before prescribing a platform migration.

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.