Product Data Enrichment at Scale
Normalize variants, fill missing attributes, and enforce data standards to reduce customer confusion and returns.
The problem
Your product catalog is a mess. Some products have detailed descriptions, others just have a SKU. Variant names are inconsistent: Red vs red vs RED. Critical attributes like dimensions or materials are missing on 40% of your catalog. Customer support gets questions about product details that should be on the page. Returns are higher than industry average because customers do not know what they are buying. You have tried cleanup projects, but with 10,000+ SKUs and new products added weekly, manual maintenance is impossible.
Why it happens
Catalog quality degrades because data entry happens in multiple places with different standards. Your supplier sends CSVs with inconsistent schemas. Bulk imports do not validate completeness. Staff add products manually with varying levels of detail. Shopify flexible metafield system is powerful but requires enforcement - nothing stops someone from leaving critical fields empty. The real problem is the lack of a data quality layer: validation rules, enrichment workflows, and ongoing monitoring. Without automated quality gates, entropy wins.
Your options
We believe in honest recommendations. If native Shopify or an app will work, we'll tell you. Custom builds are for when they won't.
Shopify Product Editor
Manual editing in Shopify admin with metafields for custom attributes. Bulk edit via CSV imports.
When to use
Small catalog (under 500 products), infrequent changes, and staff time available for manual data entry and maintenance.
PIM Apps
Product Information Management apps like Plytix or Akeneo that provide structured data entry and validation.
When to use
Medium-sized catalog (500-5,000 products) with consistent product types. Need basic validation and bulk editing.
AI-Powered Data Enrichment Pipeline
Automated quality scoring, AI-generated descriptions and attributes, validation rules, and scheduled enrichment jobs with human review.
When to use
Large catalog (5,000+ products), multiple data sources, complex variant structures, or preparing for AI shopping channels (UCP compliance).
Our recommended architecture
A multi-stage pipeline: quality scoring identifies gaps, AI enrichment fills missing data, human review approves changes, and validation rules prevent future degradation. Continuous monitoring ensures quality stays high.
Quality scoring: analyze completeness, consistency, and AI-readiness for each product
Flag high-impact gaps (bestsellers with missing critical attributes)
AI enrichment: generate descriptions, normalize variants, extract attributes from images
Human review queue for AI-generated content before publishing
Validation rules on product create/update to enforce standards
Scheduled enrichment jobs for bulk catalog improvements
Quality dashboard tracking progress and highlighting regressions
Example outcomes
94% catalog completeness (up from 61%) across all critical attributes
28% reduction in product-related customer service inquiries
15% decrease in return rate attributed to better product information
UCP compliance achieved: 100% of products have required metadata for AI shopping agents
Related Services
Ready to solve this problem?
Book a call and we'll walk through your specific situation, share relevant examples, and give you a clear path forward.