Content-led shopping sites from a single, reusable template
Alongside its product-discovery storefronts, the client runs content-led brands — editorial and lifestyle publications that blend articles, trends and curated shopping. Each brand needs its own voice, layout and audience focus, but building a bespoke site per title would be slow and hard to maintain. The client needed a shared, content-first template that could be themed and configured per brand while drawing all its content from one managed source.
We built a reusable Next.js template optimised for editorial commerce, deployed across multiple brands including For Living, The Gilded Queen and Gently. Unlike the product-discovery storefronts, these sites are article-driven: they pull articles, trends, hubs and curated product cards from a central GraphQL content API (managed in the client's CMS), and present them in clean, SEO-focused layouts. Each brand is the same template with its own theme, content and routing — including support for advanced setups such as serving a brand from a sub-path of a partner's domain. The result is a consistent way to spin up content-led shopping sites without rebuilding the foundation each time.
Multiple editorial brands now run on one shared template, each with its own identity but a common, maintainable codebase and a single content pipeline. New titles can be launched by theming and configuring the template rather than starting from scratch, and content published once in the CMS flows straight to the live sites.
We separated content from presentation from the start: the template owns layout and behaviour, while all editorial content is served by a shared GraphQL API fed by the client's CMS. This let us deploy several brands — For Living, The Gilded Queen and Gently — as themed configurations of the same codebase. We handled real-world hosting needs along the way, including serving one brand from a sub-path of a partner's domain via base-path routing. Throughout, we kept the sites server-rendered and SEO-focused so each brand performs well in search and loads quickly for readers.
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