Most migrations do not break on the standard 60% of the data. They break on the other 40%. Custom attributes, content entities that do not map one-on-one, taxonomy that does not match the destination platform, and values that grew inconsistent over years.
Source attributes rarely come through clean. Hundreds of fields, duplicate values, inconsistent naming. Our datamapping engine reads them all in and lets you decide per attribute what happens next. Bundle them into a single Shopify metafield group, merge the ones the source meant three different ways, or disable the ones that add nothing. No manual cleanup after launch.
Per-field transformations you can define and re-run: stripping HTML, lowercasing, merging fields, formatting numeric values, consolidating locale variants. Every modifier is versioned and runs identically in every sync. No ad-hoc edits lost by run three.
Full remapping of source entities into a different structure on the destination platform. A built-out blog with posts, tags, topics, categories, and authors does not get flattened onto Shopify's limited native blog. It diverts into a metaobject structure that preserves the original taxonomy. Automatic redirect generation included, so internal links and SEO stay intact.
Source categories and attributes mapped to Shopify's official product taxonomy. This is what lets products land correctly in the Unified Commerce ecosystem: Shop, Google Merchant integration, and the emerging Agentic Commerce layer where AI shoppers rely on structured category data. No retrofitting taxonomy after launch.
All source attributes and specification sets are read, classified, and mapped to Shopify metafields or metaobjects. Value-cleaning tooling included: duplicate values like "Vegan", "vegan", and "VEGAN" are merged, inconsistencies surfaced, taxonomy values standardised. Before the dry-run fires, not after.
