How to check if your products show up in AI recommendations

Step 1: build the question matrix

A serious diagnostic is not one question asked once. It is a matrix with three axes: real buyer questions, your main categories and each AI engine. The cartesian product of the three gives you a test set you can repeat identically next month.

Axis 1: real buyer questions. Not questions containing your brand name (those are too easy and do not reflect how someone who does not know you yet searches). Use the language of a buyer with a need but no solution: "what should I buy for oily acne-prone skin", "best vacuum for pet hair under 200 euros", "gift idea for someone who hikes". Pull these phrasings from real emails, reviews and Google Ads search terms.

Axis 2: your main categories. Take the top 5 categories by profit contribution, not by product count. A high-margin, low-volume category matters more in this test than a high-volume, thin-margin one.

Axis 3: the engines. At least three: ChatGPT (with web search on and off, they are different answers), Perplexity and Google AI Overviews. Add Gemini if your audience uses it.

Five questions per category, five categories, three engines is 75 tests. It sounds like a lot, but it runs in about ninety minutes and becomes your benchmark month after month.

Step 2: score every answer

For each combination, ask the question, read the answer and give it one of three scores. Use the exact same definitions every time, otherwise the comparison over time has no value.

• Present. Your brand or a specific product of yours is named explicitly in the shortlist, with a link or a citable name.
• Partial. You appear indirectly: mentioned in a source cited at the bottom, or your category appears without your name, or you only show up if you reformulate the question with a hint.
• Absent. You do not appear at all, even though directly comparable competitors do.

Also note who appears in your place. The competitors dominating AI answers are your work map: they have the signals you do not have yet.

The golden rule of the diagnostic: if you ask with the brand name, you measure awareness. If you ask with the buyer need, you measure real visibility. Only the second one counts.

Step 3: read the pattern, not the single test

An isolated test says nothing. The pattern across the 75 cells says everything. Here is how you translate patterns into causes.

Result pattern	What it means	Probable cause
Absent on all engines, all categories	Structural foundation problem	Unconfirmable brand entity: missing Organization schema, no profile on authority sources, site invisible to AI crawlers
Present on Perplexity, absent on ChatGPT without web search	Retrieval vs training-data gap	You are findable in real time (clean, crawlable site), but you do not exist in the model training data (missing older external mentions, press, top X lists)
Present in one category, absent in the rest	Uneven content coverage	The visible category has pages with complete schema and Q&A content; the others have thin pages, no feed attributes or structured FAQ
Partial everywhere (cited as a source, not recommended)	Indexable but not citable as a brand	The AI pulls your text as a reference but does not treat you as a buyable entity: missing product attributes, price, availability, structured reviews
Present everywhere, but with wrong info (old price, no stock)	Correct data, but stale	Unsynced Merchant feed, or Product schema with static values that do not match the actual store

Each pattern points to a different kind of fix. Structural absence needs foundation (schema, brand authority, crawler access). The Perplexity vs ChatGPT gap needs external authority work (mentions, press, presence on sources the models have learned). Uneven coverage needs structured content extended to the missing categories.

Step 4: set the re-check rhythm

AI answers are not stable. They change with every re-crawl and every model iteration. That is why the diagnostic is a recurring process, not a one-off event.

• Monthly for real-time retrieval engines (Perplexity, ChatGPT with search, Google AI Overviews). Here your site changes show up within weeks.
• Quarterly for training data (models without browsing). There the cycle is months, tied to model retraining.
• After any major change: site relaunch, platform migration, category restructuring. These moments can break schema or crawler access without you noticing.

Keep the notes in the same spreadsheet, month after month. The trend matters more than the absolute score: if the count of "present" cells rises, the fixes are working.

The honest limits of checking it yourself

This process tells you whether you are visible and gives you a hypothesis about why. It does not confirm the cause at a technical level and does not give you the repair order. Here is what a manual test cannot do:

• Schema validation. You can see that you do not appear, but you cannot confirm from the AI answer whether the Product schema is incomplete, has syntax errors or is missing a required field. That needs code inspection and dedicated validators.
• Feed attributes. AI shopping leans increasingly on the product feed. Attribute quality (gtin, brand, availability, categorisation) is invisible from the outside, only visible inside the feed.
• Brand authority signals. You can suspect an authority problem, but real evaluation (confirmable entity, sameAs, presence on sources the models read) needs systematic analysis, not impressions.

In other words, the DIY diagnostic is the thermometer. The audit is the X-ray. The thermometer tells you that you have a fever, the X-ray tells you where it comes from.

What you can do today

Open a spreadsheet. Write the first 5 real buyer questions for your highest-margin category. Ask each one in Perplexity and in ChatGPT with search on. Score present, partial or absent, plus who appears in your place. In 15 minutes you will have your first baseline and you will know whether the problem is isolated to one category or structural across the whole store.

When you want to move from thermometer to X-ray, the AI-Ready Catalog Audit takes the diagnostic further: it validates the schema, checks feed attributes and evaluates brand authority on the categories that bring you profit. Details on the AI shopping optimization page. See also how products appear in AI recommendations and the 7 technical reasons you do not appear.