Enrich
Turn inconsistent product pages into structured product data you can compare across stores.
What Enrich produces
From inconsistent pages to comparable products
Enrich turns each successful Capture into one normalized, page-grain Item. It then uses those Items to build the Product, Variant, Listing, Offer, Review, Store, and Change relationships Explore reads.
- Input
- Source-backed Captures
- Paid output
- One normalized Item per successful Capture
- Final model
- Products, Variants, Listings, Offers, and history
Why Enrich?
Raw scraped data is messy. Product titles are in different languages, categories vary between sites, and options and identifiers use source-specific formats.
Normalization makes those records consistent, but comparison also requires knowing which Listings represent the same exact Variant, what each seller offers, and how those observations change over time.
Enrich owns both jobs in one lifecycle. It produces the normalized Items you pay for, then resolves the complete Item set into source-backed relationships before the Enrichment is ready for Explore.
How it works
One Enrichment moves through two connected phases: normalize each successful Capture, then publish the complete Item set for Explore.
Normalize product content
Enrich uses the captured text and product images to translate content when needed, classify it against an industry-standard taxonomy, fill category-specific attributes, and add signals such as price tier, style, use context, and seasonality.
Assemble one complete Item
Source options are mapped to normalized attributes. Each successful Capture becomes one page-grain Item containing the normalized product content, option matrix, identifiers, SKUs, offers, seller data, review aggregates, media, and lineage needed downstream.
Resolve Products and exact Variants
Across that Item set, Enrich uses identifier evidence, normalized product content, selected options, and similarity signals to connect product families and equivalent configurations. Clear brand, category, identifier, and option conflicts rule out candidate matches.
Publish the model Explore reads
Enrich publishes Products, Variants, Listings, Stores, Offers, Reviews, and Changes. Listings stay connected to their exact Variants, while price, availability, and review history remain tied to the source observations behind them.
Use cases
Catalog standardization
Use one taxonomy, attribute model, and option structure across the sources in your dataset.
Price and availability comparison
Compare source-backed Offers connected to the same exact Variant across stores and sellers, then follow their observed history.
Assortment overlap
Compare exact observed Variants across stores while keeping unmatched Listings and matching coverage visible.
Product data integration
Reuse normalized Items and their final product relationships in the data products, monitoring, and analytical workflows your team controls.
See Enrich in action
Pricing
One credit per Capture. Final preparation included.
Each Capture enriched by Enrich costs 1 credit. Enrich then resolves the resulting Items and publishes the final cross-store model for Explore without consuming additional credits.