Article
How to improve product data quality for AI discovery
Prioritize complete titles, attributes, identifiers, descriptions and variant fields before scaling feed work.
Recommended workflow
Start with public evidence, identify unknowns, prioritize product types and only request optional Deep Access when the decision needs SKU-level fields.
- Run a public scan
- Review structured data and product URL discovery
- Mark private fields as unknown
- Upgrade when PDF or Deep Access diagnostics are needed
Common mistakes
Most teams over-focus on visible copy while ignoring identifiers, attributes, schema parity, feed consistency and policy visibility.
- Thin product descriptions
- Missing variant identifiers
- Conflicting feed and PDP facts
- FAQ content hidden from parsing
How CatalogWise helps
CatalogWise creates a traceable readiness preview, locks premium report value and prepares optional Deep Access diagnostics without claiming third-party outcomes.
FAQ
Does this guarantee AI placement?
No. It improves readiness and machine-readable signal quality only.
When should I request Shopify Deep Access?
Request Shopify Deep Access when public evidence is not enough and you need SKU-level diagnostics, metafields, variants or feed evidence.
Related resources
Turn this into a catalog readiness result.
Run a public scan, review visible gaps and unlock the premium report when you need SKU-level diagnostics and a remediation roadmap.
Run free public preview