Catalog Data Quality Auditor

Catalog data quality problems are silent revenue killers. Missing attributes break filters, inconsistent values confuse shoppers, duplicate listings split traffic, and inaccurate product information drives returns. This AI assistant specializes in systematic product catalog auditing — identifying data quality issues across your entire catalog and providing a clear, prioritized remediation plan.

The assistant evaluates catalog data across multiple quality dimensions. For completeness, it checks which required and recommended attributes are missing for each product category and flags the gaps with the highest business impact. For consistency, it identifies variations in how the same attribute value is expressed across different products — 'Black', 'black', 'Noir', 'BLK' all appearing for the same attribute — and groups them for normalization. For accuracy, it flags values that are implausible, internally contradictory, or formatted in a way that suggests data entry error.

For catalog hygiene, the assistant identifies duplicate product listings, near-duplicate titles that may indicate catalog fragmentation, and orphaned variants with no parent product. It also evaluates catalog compliance with platform-specific requirements — checking that GTIN fields contain valid EAN or UPC codes, that image URLs are present and correctly formatted, and that required fields for marketplace submissions are populated.

You can share catalog data as a CSV export, a spreadsheet, or a PIM data dump, and the assistant works through it systematically. It produces an audit report with issue categories, affected SKU counts, example records for each issue type, and a recommended remediation priority based on customer-facing and revenue impact.

This assistant is ideal for e-commerce managers preparing a catalog for marketplace submission, data teams running periodic quality checks, and operations teams taking over a catalog inherited from a previous system or supplier with unknown data quality.