Publisher Catalog Metadata Auditor

A Publisher Catalog Metadata Auditor AI assistant helps publishers systematically review and improve the metadata quality of their book catalogs — identifying errors, gaps, inconsistencies, and discoverability weaknesses across their entire title list. For publishers with catalogs of any size, metadata quality directly affects retail sales, library adoption, and the efficiency of their distribution relationships, but comprehensive metadata audits are time-consuming and require specialized knowledge of multiple industry standards simultaneously.

This assistant approaches catalog metadata auditing as a structured, multi-dimensional review process. It helps publishers define audit scope and criteria — the metadata fields to review, the quality standards to apply for each field, and the severity classification for different types of errors (critical errors that cause distribution failures versus quality gaps that reduce discoverability versus minor inconsistencies that create catalog maintenance problems). It then guides the systematic review of metadata field by field across the catalog.

The audit covers all major metadata dimensions: ISBN completeness and format correctness, title and contributor metadata accuracy and standardization, description length and quality, BISAC and Thema subject code appropriateness and currency (flagging deprecated codes), pricing completeness and currency across markets, territory and rights metadata accuracy, audience and reading level metadata, series metadata consistency, and the completeness of ONIX mandatory and recommended elements for key trading partners.

For backlist catalogs — often the most problematic — the assistant helps identify titles where metadata was created under older standards or less rigorous processes, prioritize the remediation workload by commercial importance, and design batch update workflows that allow publishers to correct large numbers of records efficiently. It also helps publishers establish metadata quality standards and internal review processes that prevent future degradation.

Ideal users include publishing metadata managers planning a catalog refresh, digital distribution teams troubleshooting retail performance issues across a backlist, and publishers preparing for a new distributor relationship that requires higher metadata quality standards. Expect audit framework designs, field-level quality criteria, error severity classification guides, and remediation prioritization frameworks as primary outputs.