CatalogSignalCatalogSignal Field Notes
What AI assistants get wrong about real catalogs
Use the benchmark patterns to spot where your products may be missed or misrepresented, then focus your team on the data improvements that make the biggest practical difference.
The CEI Benchmark measures how AI shopping assistants actually read, trust, and recommend real product catalogs. Here is some of what it is showing, and it is not flattering to the current state of catalogs.
The average catalog is only about half-ready. The mean Commerce Eligibility Index score across the panel lands near 50 on a 0 to 100 scale. In plain terms, the typical brand is leaving a lot on the table before AI-assisted discovery reliably works in its favor.
Accuracy is a separate challenge. Mean funnel accuracy across the panel was 47.7%, rounded to 48% in the chart below. This is separate from the CEI readiness score and the hallucination rate. It helps teams focus on whether assistants represent their products accurately, including details such as fit, materials, and compatibility.
About one in ten responses carries a commercial-harm risk. Roughly 9% of responses contained an outright hallucination, and around 11% carried what we classify as commercial harm, a confident answer that could cost the brand a sale or mislead the shopper. At the scale AI discovery is reaching, that is not a rounding error.
Swipe or use the arrow keys to explore the chart.
Being well known does not make you safe. Familiarity and accuracy are not the same thing. Established brands tended to draw fewer outright hallucinations, but plenty of recognizable names still landed in risky territory, while some smaller, cleaner catalogs were described more accurately than far bigger ones. Brand equity does not travel into the model on its own. The data does.
Category matters. Readiness varied widely by vertical. Some categories were markedly readier than others, and the gap was large enough that a brand's number only makes sense in the context of its own category, not the market as a whole.
These aggregate patterns show the shape of the shelf while individual brand scores stay private. A lot of catalogs that look fine to a human read as half-finished to a machine. The encouraging part is that almost everything driving a low score is fixable, because it comes down to the same practical issues in the data: missing attributes, descriptions written for style instead of meaning, and claims that need stronger reconciliation with reviews.
The benchmark refreshes over time, so we can watch how the shelf moves. For now the takeaway is simple. AI is already building the shortlist, it is getting a meaningful share of catalogs wrong, and the brands that measure and close those gaps first will be the ones it learns to recommend.
See where your catalog can lead. A Commerce Eligibility Index™ assessment gives you the score, product-level evidence, and prioritized improvements that strengthen AI discoverability. Get a preliminary catalog check.
About these figures
Figures are drawn from the CatalogSignal CEI Benchmark and reflect aggregate patterns across the panel. Individual brand scores stay private.