Headless Commerce & AI Recommendations for a Multi-Brand Retailer

Our client operates five distinct apparel brands, each acquired or launched over the past decade and each running on a different e-commerce platform two on Shopify Plus, one on Magento, one on a custom-built legacy system, and one on a platform that was being sunset by its vendor entirely.

This fragmentation meant five separate teams maintaining five separate codebases, no shared component library, inconsistent checkout experiences, and critically no shared customer data layer. A customer who shopped two of the brands was treated as two completely separate people, with no ability to build cross-brand loyalty programmes or share inventory intelligently.

The brief was ambitious: consolidate onto a single headless commerce architecture that could serve all five brand storefronts with distinct visual identities, while sharing a unified product catalogue, customer data platform, and the feature leadership was most excited about a shared AI recommendation engine that could learn from behaviour across all five brands.

Each brand had its own peak season (swimwear in spring, outerwear in fall). Migrations needed to be scheduled around each brand's calendar no single cutover date could work for all five.

Despite sharing infrastructure, each storefront needed to look and feel completely distinct shared backend, fully independent frontend design systems.

With five previously siloed data sources, building a unified recommendation model required reconciling inconsistent product taxonomies before any meaningful training could begin.

We adopted a headless commerce architecture: a single commerce backend (catalogue, inventory, customer data, checkout) decoupled from five independent Next.js storefronts one per brand each consuming the same APIs but with completely independent design systems.