What Fabric does
Measure how your catalog performs inside AI assistants, then improve the content that decides it.
Shoppers increasingly find products by asking an AI assistant rather than by searching a storefront. When someone asks ChatGPT, Perplexity or Gemini for a recommendation, the assistant answers from whatever product content it can read and understand.
Fabric measures how your brand performs in those answers, and improves the catalog content that decides them.
The two halves
Measure
An AI Visibility Assessment asks the AI channels real shopper questions in your categories and counts how often your domain comes back, and how it is characterized when it does.
Improve
Enrichment fills in the attributes AI assistants read to match a product to a shopper's question — the material, the fit, the use case, the answers to the questions buyers ask.
Why content depth is the lever
An assistant can only recommend a product it can describe. Two catalogs selling the same jacket perform differently when one records the fill weight, the temperature range and the cut, and the other records a title and a price.
That gap is content depth, and it is the thing Fabric is built to close. Visibility is the outcome; depth is the input you control.
How your catalog gets measured
Bringing a catalog into Fabric produces a Catalog Quality Scorecard — a graded read of the content you have today, computed once per import and rolled up into two scores:
| Score | What it covers |
|---|---|
| Structural Catalog Score | Whether the foundations are present and well formed: titles, descriptions, categories, identifiers, pricing, imagery, variants. |
| AI Answerability Score | Whether the depth an assistant needs is there: attributes, FAQ and knowledge content, and how well the data reads to a model. |
The two are deliberately separate. A catalog can be structurally clean and still answer nothing — that is the common case, and it is invisible until you measure both.
Vocabulary
A few terms are used precisely throughout these docs.