Catalog enrichment

The problem

Product data is messy: inconsistent supplier feeds, thin descriptions, missing attributes, no SEO copy or alt text. It hurts search, filtering and conversion, and nobody can fix thousands of SKUs by hand.

The approach

Run every SKU through an enrichment pipeline: normalize units and names, extract attributes from text and images, generate descriptions in your brand voice, write alt text and translations, and route low-confidence items to a human reviewer.

How it works

Before: a supplier feed with a vague description, no material and empty alt text. After: a structured record with waterproof rating, material, weight, alt text and bilingual readiness.

Watch-outs

• Hallucinated specs are the risk. Generate copy from verified attributes, never the other way round.
• Brand voice drifts without a style guide and examples. Write them down first.
• This is becoming table stakes. Treat it as the quick win that opens the door to bigger work.

Accelerators and stack

• Large language models with structured output
• Vision models for attribute extraction from images
• PIM and commerce platform import tooling

What it takes to start

A catalog export, a sample supplier feed and your style guide. A 500-SKU pilot runs in two to three weeks.

Hours per SKU reduced to minutes of review. Search and filter coverage is the metric to watch.

In short

Questions