The Problem

AI shopping systems do not see your catalog the way your customers do.

Traditional ecommerce quality is not AI readiness. AI systems synthesize answers from structured data, descriptions, reviews, external mentions, and live retrieval. Weaknesses that were tolerable in human browsing become expensive when a machine has to choose what to recommend.

Catalog Signal ScanAT RISK
AI readsignored340 of 1,100 SKUsare invisible to AI shopping agents.
Where catalogs fail the machine read

Six structural reasons AI ignores, misreads, or skips a product.

Humans infer meaning from images, tolerate inconsistent terminology, and interpret ambiguous attributes. AI agents cannot. Each gap below moves a product closer to invisible.

Missing product facts

Required attributes are blank, inconsistent, or buried in unstructured copy.

Generic descriptions

Products sound interchangeable, so AI cannot explain meaningful differences.

Weak retrieval

Real shopping queries fail to surface the right SKU or category.

Unsupported claims

AI repeats claims that cannot be traced back to reliable catalog evidence.

Thin authority

The broader web does not provide enough external trust signals for the brand.

Inaccurate representation

The answer can be wrong or incomplete before the customer ever reaches your page.

If AI can't read it, it can't sell it

Find the gaps before an AI agent does.

A baseline CEI assessment shows exactly where your catalog is legible to machines and where evidence is missing, unclear, or hard to retrieve.

Request a CEI assessment