Freeze buyer questions, see which products ChatGPT, Gemini, and Perplexity recommend, publish buyer-matched content, then run the same questions again.
Recommendations follow the language engines find.
When a shopper asks an AI engine what to buy, the engine assembles an answer from what it can read: reviews, Reddit, comparison sites, and product pages it can reach.
Those sources carry buyer language. Most product pages carry seller language. That gap shapes the answer a shopper sees.
Your own store pages are the surface you control. Publish buyer-matched content there and engines can reach it.
The AI Visibility Monitor builds six buyer-phrased questions from your Voice Map, runs each one three times across ChatGPT, Gemini, and Perplexity, and records which products the engines recommend and why. That is 54 recorded answers per check.
The measurement step, run twice.
This is the measurement step, run twice. First as a baseline before you change anything. Then as a re-check after you publish buyer-matched content and the engines have had time to read it. The re-check runs the exact same frozen questions. Same questions, same engines, dated results. That is the difference between a measurement and a screenshot.
Run a Category Scan or Product Scan to map buyer language and freeze the questions.
Run the frozen questions before publication and record the recommendation evidence.
Publish buyer-matched content to an accessible surface and record the change.
Run the exact same questions again and compare.
What we will not tell you.
AI answers change between identical runs. DecodeIQ measures that variance and only reports movement that exceeds it.
The re-check opens after a cooldown, because engines take time to index new content. Your dashboard shows the date it unlocks.
Each engine reports its own status, and when an engine does not disclose whether it searched the live web, we show that as unknown rather than guessing.
If any part of a check fails, you get no score instead of a partial one.
Shopify sellers control the pages engines can reach. Amazon listing edits are observational for blocked external engines, and the results say so.
No audited competitor pairs product-level AI visibility measurement with a buyer-voice taxonomy. We re-checked this claim against an audited competitor set on 2026-07-24.
Start your baseline.
Visibility questions are built from your Voice Map, so a scan comes first. Run a Category Scan for your category, or a Product Scan for one product. Then run the baseline check. Credit cost is shown before you run a check.
Already have a Voice Map? Sign in and start your baseline check.
Want a free directional spot-check first? The Checker runs one ChatGPT query.
Also in the loop: Category ScanProduct Scan
See it for your platform: Shopify storesAmazon sellersEtsy sellers