E-commerce Product Data Enrichment - Use Cases | Entity Enricher

E-commerce Product Data Enrichment

Supplier feeds arrive half-empty, in the wrong language, with the same product spelled three different ways. Entity Enricher turns raw product rows and spec-sheet PDFs into complete, deduplicated catalog records -- attributes, categories, compliance data, and descriptions in every language your channels sell in.

Why Product Catalogs Need AI Enrichment

A product catalog is only as good as its worst supplier feed. Missing attributes break faceted search, inconsistent brand spellings split one product into five listings, and every new market multiplies the translation workload. PIM systems store product data well -- but they do not fill the gaps, and manual data entry does not scale past a few hundred SKUs.

With Entity Enricher, you define the exact record your catalog needs and enrich from two sources at once: what the models already know about the product and brand, and attached supplier documents -- spec-sheet PDFs, packaging photos, datasheets. Semantic IDs collapse duplicate products to one record even when suppliers spell names differently, and fields marked multilingual come back in up to 40 languages from a single call.

Example: Retail Product Schema

Below is a sample schema for enriching retail products. You can generate schemas like this automatically from a sample product JSON using AI schema generation, or build them visually in the schema editor. Fields flagged multilingual are returned in every language you select; the semantic_id field keeps one identity per real-world product.

RetailProduct.json
{
  "name": "RetailProduct",
  "properties": {
    "product_name": { "type": "string", "is_key": true, "multilingual": true },
    "gtin": { "type": "string" },
    "brand": { "type": "string" },
    "semantic_id": { "type": "string" },
    "category_path": { "type": "array", "items": { "type": "string" } },
    "description": { "type": "string", "multilingual": true },
    "materials": { "type": "array", "items": { "type": "string" } },
    "dimensions": {
      "type": "object",
      "properties": {
        "width_cm": { "type": "number" },
        "height_cm": { "type": "number" },
        "depth_cm": { "type": "number" },
        "weight_g": { "type": "number" }
      }
    },
    "care_instructions": { "type": "string", "multilingual": true },
    "compliance": {
      "type": "object",
      "properties": {
        "ce_marking": { "type": "boolean" },
        "age_restriction": { "type": "string" },
        "safety_warnings": { "type": "array" }
      }
    },
    "seo_keywords": { "type": "array", "items": { "type": "string" } }
  }
}

Enrichment Fields by Expertise Domain

Entity Enricher splits your schema into expertise domains, running parallel LLM calls for each domain. Merchandising, logistics, and compliance fields each get a specialist prompt instead of one monolithic request.

FieldExpertiseDescription
product_nameGeneralNormalized product title, localized per sales channel
gtinGeneralGTIN / EAN / UPC barcode identifiers
brandMerchandisingCanonical brand name across supplier spellings
category_pathMerchandisingTaxonomy placement for navigation and faceted search
materialsProduct SpecsComposition and material breakdown from spec sheets
dimensionsLogisticsPackaged dimensions and weight for shipping and storage
complianceComplianceCE marking, age restrictions, and mandatory safety warnings
seo_keywordsMarketingSearch terms and synonyms shoppers actually use

Product Enrichment Workflow

1

Define Your Product Schema

Paste one product row from your PIM or feed. AI generates a typed schema with expertise domains for merchandising, logistics, compliance, and marketing -- mark names and descriptions multilingual.

2

Feed Products and Documents

Send SKUs, partial rows, or barcodes -- and attach supplier spec sheets, datasheets, or packaging photos so extracted attributes come from the source document, not guesswork.

3

Multi-Model Enrichment & Deduplication

Multiple LLMs fill each product in parallel and cross-check every field. Semantic IDs resolve duplicate products across supplier spellings, languages, and repeat imports.

4

Fusion & Export to Your Catalog

Conflicts between models are detected and resolved with full reasoning. Export enriched products as JSON or Excel, or push them into your PIM and shop via the API or n8n.

Common E-commerce Use Cases

Supplier Feed Onboarding

Turn heterogeneous supplier feeds into your catalog format in one pass -- normalized brands, mapped categories, and attributes extracted from attached spec sheets.

Multilingual Catalog Expansion

Launch new markets without a translation pipeline: names, descriptions, and care instructions come back in every channel language from the same enrichment call.

Attribute Completion for Faceted Search

Fill the missing materials, dimensions, and category fields that break filters and comparison pages -- validated against your schema types, not free text.

Marketplace & Compliance Readiness

Meet marketplace listing requirements with complete GTINs, safety warnings, age restrictions, and CE-marking flags before your products go live.

Start Enriching Your Product Catalog Today

Define your product schema, attach a supplier spec sheet, and get complete, multilingual, deduplicated catalog records in minutes -- no per-SKU data entry.

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