Product Catalog Enrichment

For sparse products, we cross-reference UPC/EAN barcodes, model numbers, and brand identifiers against manufacturer databases, GS1 product registries, comparison shopping engines, and third-party product databases like Akeneo and Salsify. Our AI also infers likely attributes from product titles and images using computer vision and NLP. In extreme cases where almost no data exists, we generate likely specifications as 'inferred' fields clearly flagged as AI-estimated rather than sourced, so you can review them before publishing.

Every description is generated uniquely per product, not from a fill-in-the-blank template. Our LLM reads the extracted specifications, product category, intended buyer, and any brand voice guidelines you provide, then generates original copy from scratch. Outputs are deduplicated across your entire catalog before delivery to prevent thin content issues. You also receive both short-form (under 100 words) and long-form (150-300 words) variants for each product.

Yes. During onboarding we ingest your category taxonomy — whether it has 50 nodes or 5,000 — and fine-tune a classification model on it. Products are then mapped to your internal structure, not a generic one. The model learns from your corrections over time through an active learning loop. You can also provide training examples of your most problematic edge cases to improve accuracy from day one.

A completeness score is a per-record metric (0–1) indicating what proportion of expected attributes for that product's category are populated. The expected attribute set is defined per category — a laptop has different required fields than a t-shirt. A score of 1.0 means every expected field is filled with a validated value. Scores below your configured threshold (typically 0.80) are flagged for review. You receive completeness scores per record and aggregate completeness reports per category.

We support extraction from foreign-language source pages (Japanese, Korean, Chinese, Arabic, German, French, and 35+ others) and delivery in any target language combination. Our translation models are fine-tuned on ecommerce domain data, so technical attribute names are translated accurately rather than literally. Brand names, trademarks, and model numbers are preserved unchanged. RTL languages including Arabic and Hebrew are supported with correct encoding.

Each enriched product is assigned a re-enrichment schedule based on its volatility category: high-velocity products (electronics, apparel) are re-checked weekly; stable products (books, fixtures) monthly. Our change detection layer also monitors source pages continuously and triggers an immediate re-enrichment when a spec, description, or image changes on any source. Updated records are delivered incrementally — only changed fields are transmitted to minimize integration load.

Yes, and this is one of the most common starting points. Upload your existing catalog via CSV, Excel, or API endpoint. We match your products to external sources via GTIN, model number, or title fuzzy matching, then fill missing attributes, standardize inconsistent values, and return an enriched version of your full catalog. A typical 100,000-SKU catalog enrichment is completed within 3-5 business days. After that, we set up ongoing monitoring so your catalog stays current.

Google Shopping uses structured product attributes heavily for matching search queries to products. Missing required attributes cause listing suppression. Incomplete optional attributes reduce your Quality Score, which affects both organic Shopping ranking and paid CPC costs. Our enrichment maps directly to Google's required and recommended attribute specifications — including product_type, google_product_category, color, size, material, age_group, and gender — ensuring your feed is fully compliant and competitive.

A GTIN (Global Trade Item Number) is a standardized barcode identifier that uniquely identifies a product worldwide. Common formats include UPC (12 digits, North America), EAN (13 digits, international), and ISBN (books). GTINs are essential for catalog management because they enable accurate product matching across different retailers, prevent duplicate listings, and are required by marketplaces like Google Shopping and Amazon for most product categories.

Product data refers to structured, factual attributes like dimensions, weight, materials, and technical specifications — fields that are objective and measurable. Product content refers to marketing-oriented elements like descriptions, bullet points, lifestyle images, and brand storytelling designed to persuade shoppers. Effective catalog enrichment addresses both: accurate data enables filtering and comparison, while compelling content drives conversion and reduces returns.

Schema.org is a standardized vocabulary for structuring data on web pages so search engines can understand the content. For products, Schema.org markup defines fields like name, price, availability, brand, and review rating in a machine-readable format. When properly implemented, it enables rich search results with star ratings, prices, and stock status displayed directly in Google results — increasing click-through rates by 20-30% compared to plain text listings.

Listing suppression occurs when a marketplace hides your product from search results because it fails to meet data quality requirements. Common causes include missing required attributes (like color or size for apparel), invalid GTIN codes, mismatched category-to-attribute mappings, and low-quality images. Comprehensive catalog enrichment prevents suppression by ensuring every required field is populated correctly and validated against each marketplace's specific compliance rules.

Inaccurate or incomplete product data is a leading cause of ecommerce returns, typically accounting for 20-25% of all returns. Common issues include wrong dimensions leading to fit problems, missing compatibility information causing wrong purchases, and misleading images that do not match the actual product. Enriching catalogs with precise measurements, clear compatibility data, and multiple accurate images directly reduces return rates, which typically cost 2-3x the original product margin to process.

A Product Information Management (PIM) system is a centralized platform for storing, managing, and distributing product data across all sales channels. Popular PIM tools include Akeneo, Salsify, and inRiver. Catalog enrichment feeds into PIM systems by providing clean, structured, and complete product records that serve as the authoritative data source. The PIM then distributes this enriched data to your website, marketplaces, print catalogs, and marketing channels while maintaining consistency.