Product taxonomy for AI commerce
Category presence and category correctness are different checks. Product Feed Scan detects missing category context; selecting the right category still requires a catalog and destination-taxonomy review.
Find where category information is lost
Inspect the exporter mapping from store category or product type to the destination field. Compare a representative product before export and after parsing. If every row is empty, investigate a mapping problem before editing products individually.
Review meaning and specificity
Use the actual product function, material and audience to inspect a proposed category. Review ambiguous items separately. Do not choose a popular category for visibility or blindly replace internal categories with a destination-specific taxonomy. Keep a documented mapping that can be reviewed when the external taxonomy changes.
Validate the correction in two stages
Rescan to confirm that expected category values survive export. Then manually check a sample of assignments against the destination's current taxonomy. A disappearance of the missing-category finding proves coverage improved, not that every assignment became correct.
Practical example
Synthetic mapping: a cooking utensil exported as an empty category needs a mapping fix. The same utensil exported under an unrelated clothing category needs a semantic correction that a presence check will not detect.
What Product Feed Scan checks
General and AI readiness checks inspect category coverage. The scanner does not infer the correct taxonomy node, validate every taxonomy ID or automatically rewrite category mappings.
Open the relevant checker See a sample report