How To Check If Ai Models Recommend My Brand

An AI model recommends your brand when it names your company as a suggested option in response to a buying-intent question, without you naming it first. A mention is weaker. The model may reference your brand while discussing a topic, or repeat it only after the user asked about you directly. Recommendation is the stronger signal because it places your brand on the shortlist. Specifically, Arobis defines AI Recommendation Frequency as the percentage of relevant buying-intent prompts in which an engine names your brand unprompted. Track mentions and recommendations separately, since a brand can be mentioned often and still never be recommended.

Test ChatGPT, Gemini, Perplexity, Claude, Microsoft Copilot and Google AI Overviews or AI Mode, because each engine draws on different sources. According to Marketing By Consultants, across a study of 14,472 citations the websites cited by Gemini and ChatGPT overlapped by only about 8 percent, so testing a single engine gives an incomplete picture. Test at least the engines your buyers actually use in your category.

Use 10 to 20 customer-style questions, asked the way a buyer would type them. Marketing By Consultants recommends asking these questions to ChatGPT and at least one Google AI tool, then repeating the set monthly to track trends. For example, a question might ask for the best tools for a specific use case or alternatives to a named competitor. Avoid asking only for your brand name, because that measures recall, not recommendation.

Record the engine, the exact prompt, the date, whether your brand was mentioned, its position in the list, whether a source link was cited, and which competitors were named. A consistent template makes results comparable across months. However, engines reword results, so save the full answer text. You will need the original when a claim is disputed or a competitor starts appearing more often.

AI Recommendation Frequency is calculated by dividing the number of relevant buying-intent prompts in which an engine names your brand unprompted by the total number of relevant prompts, then multiplying by 100. Arobis defines this metric this way. For example, being recommended in 3 of 10 category questions equals a 30 percent rate. Calculate the rate per engine and as an overall figure, so you can see which models favor your brand.

AI answers change between runs because language models generate responses probabilistically and retrieve live sources. As a result, the same question can produce different brand lists on different days. According to RankFor.ai, across 50 brands and 3,750 answers, three models agreed on the top brand only 41.6 percent of the time. Specifically, an analysis by Kevin Indig in Search Engine Land found that cited sources turned over by 56 to 74 percent from one week to the next. A single test is therefore a snapshot, not a verdict.

Re-run the same question set monthly and compare the rates, not individual answers. According to RankFor.ai, an independent analysis by Kevin Indig (Search Engine Land, 2026) found cited sources turn over by 56 to 74% from one week to the next, so a one-week change may be noise. A rate that holds across several monthly runs is a meaningful trend. Keep the question set fixed for at least several cycles before you change it, otherwise you cannot tell whether a shift reflects the market or your own edits.

AI engines rely heavily on third-party sources rather than brand websites. According to RankFor.ai, about 85 percent of sources AI cites sit off the brand's own domain, across 167,551 citations. Similarly, according to SearchIntel, 84 percent of AI citations come from earned media, with journalism alone at 27 percent. Influence therefore comes from independent reviews, editorial coverage, analyst comparisons, and credible mentions. According to RankFor.ai, about five credible mentions per buyer question gets a brand into roughly 80 percent of the answers for that question.

Find the sources the engine cites in its answer, then correct or supplement them. Where the error comes from a third-party page, request an update from that publisher. Otherwise, add accurate, citable information to your own site, such as clear product descriptions and structured data that engines can parse. Then re-test the same prompts after the next crawl cycle, because corrections take time to appear in retrieved sources.

Yes. A manual method using a spreadsheet, a fixed question set, and the logging template above works at no cost and is tool-agnostic. Free checkers and audits can give a quick snapshot. The Agent-Ready Check from Fastlook scores a site from 0 to 100 across 15 checks. However, neither replaces repeated testing. Manual testing is slow across many prompts and engines. It also cannot show the source turnover or variance described above, so use it as a baseline before adopting automated tracking.