Validate product data before it reaches your customers
Product data can be complete and still be wrong. Docxi.ai helps catalog and product-data teams validate product records against business rules, category requirements, technical constraints, and channel-specific conditions before those records are published or exported.
Why product data validation is essential
Product data errors enter through supplier catalogs, datasheets, spreadsheets, ERP exports, manual retyping, and PIM imports. A field may be technically populated with "N/A", but still fail business validation.
Missing Mandatory Fields
Key attributes required for customer filtering or marketplace publishing are omitted.
Duplicate MPNs & SKUs
Identical manufacturer part numbers creating conflicting catalog entries.
Invalid Units & Formats
Inconsistent units (0.4 kV vs 400 V) causing search failures and erroneous filters.
Out-of-Bound Values
Unrealistic numerical data, negative pressure ratings, or inverted temperature ranges.
Validation rules Docxi.ai supports
Automate structural, technical, category-specific, and cross-field dependency validation rules across your entire catalog.
Required-Field Rules
Verify mandatory fields exist for specific categories (e.g., Electric Motors require voltage, power, speed, mounting).
Format & Pattern Rules
Validate MPN patterns, GTIN/barcode structure, IP ratings, currency codes, dates, and product URLs.
Unit & Measurement Rules
Check canonical unit usage and convert acceptable variations (e.g. 0.4 kV -> 400 V).
Range & Boundary Rules
Flag invalid ranges (e.g. min temp > max temp, negative pressure, zero weight).
Allowed-Value Vocabularies
Restrict categorical attributes to approved controlled values (e.g. Finish: Matte, Gloss, Brushed).
Uniqueness & SKU Rules
Ensure product identifiers (SKU, MPN, GTIN) are unique across supplier catalogs.
Dependency Rules
Enforce conditional field requirements (e.g., if rechargeable battery -> battery chemistry required).
Referential Integrity
Confirm related records, manufacturer references, category taxonomy links, and media assets exist.
Not every issue should block publication
Classify validation issues by business risk to prevent critical errors without holding back valid products.
| Severity Level | Meaning | Action Taken |
|---|---|---|
| Blocker | Product cannot be published safely | Stop export immediately |
| Major | Important information is missing or inconsistent | Review required before publishing |
| Minor | Quality improvement recommended | Warning flag for catalog team |
| Informational | Non-critical formatting notice | Recorded for audit history |
Frequently Asked Questions
What is product data validation?
It is the process of checking product records against predefined structural, business, category, and channel rules before publication or export.
Can validation check technical attributes?
Yes. Rules can check units, ranges, formats, dependencies, and category-specific technical requirements.
Can different categories have different rules?
Yes. Validation should be organized by product category, schema, supplier, or sales channel where necessary.
Can validation stop bad data from being exported?
Yes. Blocker-level issues can prevent export until they are corrected or explicitly approved as exceptions.
Can users override a validation result?
Authorized reviewers can accept or override a result, with the decision recorded in the audit trail.
Does validation replace human review?
No. It reduces routine checking and directs human attention to failures, conflicts, and business exceptions.
Related Solutions & Guides
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