Practical perspective for catalog and merchandising teams navigating the shift to AI-driven product discovery.
ChatGPT, Perplexity, and Google AI Mode use a fundamentally different evaluation model than keyword search. Here is what your catalog is missing.
Spoiler: it is not one or the other. AI shopping agents need a specific combination that most catalog tools were never designed to produce.
Before any agent writes a recommendation, it scans for these five data points. Missing one can drop your listing from consideration entirely.
Perplexity Shopping is becoming a significant product discovery channel. Here is how its recommendation logic differs from Google Shopping.
Keyword-optimized product titles built the last decade of ecommerce. That playbook actively hurts you when AI agents try to evaluate your listings.
A practical checklist for catalog and merchandising teams who want to audit their listings before AI agents do it for them.
Google rich results are table stakes. AI shopping agents parse product schema differently and look for fields most schema guides never mention.
Most product descriptions are written for human shoppers who skim. AI shopping assistants do the opposite. They parse every sentence for specific evaluative signals.
We break down a before and after of a real product listing rewrite, showing each change and which agent evaluation criterion it addresses.
Inside our 12-point scoring system: what we measure, why those dimensions matter to AI agents, and how we prioritize which listings to fix first.
We ran our rewriting engine on three early-access retailer catalogs. Here is what changed in their AI agent recommendation rates over 60 days.
The same conversation kept happening with retailers: beautiful catalogs, strong Google rankings, but AI shopping agents could not recommend a single product. We built ReFiBuy to solve that.
ReFiBuy audits and rewrites product listings that AI shopping agents can actually evaluate.
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