Unique products across two Shopify storefronts.
Supporting systems proof / Shopify operationsAnonymized verified case study
Project LoomCatalogs, controlled.
Reliable product content starts with reliable product data. An AI-assisted workflow that turns inconsistent supplier inputs into reviewed Shopify catalog exports across two storefronts.
Thai Anh Tu
Performance Creative Specialist
Variant rows in the confirmed delivery scope.
Image records across the selected exports.
Final CSV and delivery batches.
Supporting production capabilityAutomate repetition.Keep judgment
Automate repetition.Keep judgment
human.
The operational problem
Supplier storefronts vary in structure, language, option logic, and gallery behavior. At catalog scale, a small defect can multiply across every color and size combination.
The system
Store-scoped configuration, extraction, normalization, variant generation, image mapping, QA gates, exception reports, and pre-import review form one repeatable release path.
The boundary
Deterministic transformations handle repeatable work. AI assists difficult-page residue and grounded drafting. Configuration, exceptions, upload, and post-import checks stay human-owned.
Why it supports creative work
Reliable product data, variants, and image relationships make product-content handoffs more usable. The same discipline supports creative production: clear inputs, repeatable steps, and review before delivery.
Complete verified case study / 8 pages
Inside the workflow.
01 / 08
Evidence scope
Verified catalog operations, presented anonymously. Figures cover selected exports across two Shopify storefronts and describe delivered output. They do not represent sales or campaign results.