Fashion brand where the e-commerce platform can't handle size-run stock levels accurately -- so a size that's sold out still appears available until a manual inventory update?
Returns process that runs through a separate tool, so refunds and restocking happen days after the item is back in the warehouse because no system connects the two?
Fashion E-commerce Platform Development
Fashion e-commerce has specific requirements that generic platforms handle poorly. Size run inventory that shows a sold-out size as available until someone manually updates it. A returns process that runs through a separate tool, so refunds and restocking happen days after the item is back in the warehouse. Personalisation that does not account for the fact that a customer's size varies by brand and fit type.
We build custom fashion e-commerce platforms that treat size, variant, and returns as first-class requirements. Product catalogues with accurate size run stock levels, size recommendations that draw on purchase history and measurements, lookbook content integrated with product data, loyalty tied to both purchases and returns, and a returns flow that automates refunds and restock triggers from the moment the item is scanned back in.
Product catalogue with size run and colour variant management
Personalised size recommendations based on purchase history and measurements
Loyalty programme integrated with purchase and returns
Returns management with automated refund and restock triggers
RaftLabs builds custom fashion e-commerce platforms with full size run and colour variant management, personalised size recommendations based on purchase history and measurements, lookbook and editorial content integrated with shoppable products, loyalty programmes tied to purchase and returns, and a returns management flow that triggers automated refunds and restock updates. Most fashion e-commerce projects ship in 10--14 weeks at a fixed cost.
100+Software products shipped
·FixedCost delivery
·10-14Week delivery cycles
·24+Industries served
When the platform becomes the problem
Most fashion brands start on Shopify or a similar platform. It works until it doesn't. The turning point usually comes when the size run is large enough that Shopify's variant limits create SKU management problems, or when the brand wants personalisation that requires more than a plugin can deliver without conflicting with the rest of the stack, or when the returns volume is high enough that the manual process of connecting returns data to inventory and refunds is taking a full day of someone's time every week.
A custom fashion e-commerce platform is built around the actual data model for fashion. Size runs are tracked at the SKU level per location. Returns are part of the buying flow, not a separate process handled in a different tool. Personalisation draws on real purchase history and brand-specific size chart data. The result is a platform that reflects how fashion actually works rather than forcing fashion operations to fit a platform built for simpler catalogues.
What we build
Product catalogue and size run management
Product catalogue built for fashion: size and colour variant management with a full size run matrix per style. Stock tracked at the variant level per warehouse location so the website shows real-time availability per size. Sold-out sizes are marked immediately when the last unit sells -- no manual inventory update required between the sale and the website. Variant combinations modelled as a matrix so buyers can select colour first and see which sizes are available in that colour, or select size first and see available colours. Category and attribute management for filtering by size, colour, fit type, material, and style so buyers can browse the catalogue the way they think about clothing, not the way the data is structured.
Size recommendation and personalisation
Size recommendation engine built on top of real data: the customer's stated measurements, their purchase history with return and exchange signals, and the brand's size chart per style and fit type. A recommendation is returned with a confidence level and a note if the customer is between sizes in that style. Recommendations update when the customer returns or exchanges an item so the system learns their actual fit preferences over time. Style personalisation based on browsing and purchase history surfaced on the homepage and category pages. Outfit suggestions using items the customer has bought combined with new arrivals in their size. These features connect to your existing catalogue and customer data -- no separate tool required.
Lookbook and editorial content integration
Lookbook and editorial content integrated with the product catalogue so every editorial image links directly to the items featured in it. Shoppable lookbooks where the buyer clicks a hotspot on a look image and sees the product, size availability, and an add-to-bag option without leaving the editorial context. Seasonal campaign pages built in a CMS so the marketing team can publish new lookbooks and link products without a developer. Blog and editorial content managed alongside product data so the brand's editorial voice and its commerce catalogue work as a single system rather than two separate tools that link to each other.
Loyalty and rewards programme
Loyalty programme integrated into the buying and returns flow. Points earned on purchases, with multipliers for full-price purchases and new arrivals. Points adjusted on returns so the programme does not reward customers for buying and returning repeatedly. Reward redemption at checkout with configurable minimum point thresholds and partial redemption options. VIP tier structure with different earn rates and benefits by tier -- early access to new arrivals, free returns, extended return windows. Referral rewards tied to the first purchase of the referred customer. All loyalty data stored in the same system as purchase history so personalisation and loyalty are built on the same customer record rather than synced between separate platforms.
Returns and refund management
Returns management built into the buying flow. The customer initiates a return from their order history, selects items and reason codes, and receives a return label or drop-off instructions. When the item is scanned at the warehouse, the restock trigger fires automatically -- the item is returned to the correct size bin in inventory without a manual update. The refund is triggered at the same time and processed within the configured window. Return reason data is aggregated so the brand can see which styles, sizes, and descriptions are generating the most returns and why. Exchange flow built into the returns process so the customer can select a different size and have the exchange order created before the return is even received.
Multi-channel inventory and order management
Order management that covers all sales channels from a single backend. Orders from the website, from wholesale accounts, and from marketplace channels all flow into the same order management system with the same inventory pool. Multi-location stock management so the system knows which warehouse has which sizes and can route fulfilment to the closest location. Purchase order management for restocking: the system flags when size-level stock drops below the reorder point and creates a draft purchase order for the buyer's review. Inventory sync across channels so a sale on one channel immediately reduces available stock across all others -- no overselling because two channels showed the same unit as available.
Frequently asked questions
Shopify handles straightforward fashion e-commerce well. A custom platform is the right choice when Shopify's constraints are causing real operational problems. The most common triggers are size run complexity -- Shopify's 100-variant limit means large size-colour matrices require workarounds that create inventory management errors. Personalisation requirements that go beyond what a plugin can deliver without conflicting with checkout or speed. A loyalty programme that needs to adjust points on returns, which most Shopify loyalty apps do not support. Or a returns process that needs to connect directly to warehouse stock management and trigger restocks automatically, which requires a custom integration that becomes fragile at scale. If Shopify plus a small number of reliable plugins would solve the problem, we'll say so. A custom build has ongoing maintenance cost that you need to weigh against the operational cost of the platform constraints.
Size recommendation accuracy depends on the quality of the data feeding the engine. A recommendation built only on stated measurements is less accurate than one built on a combination of measurements, purchase history, and return or exchange signals. The recommendation improves over time as the customer buys and returns items because each transaction teaches the system more about their actual fit preferences across brands and styles. For a brand launching size recommendations for the first time, we build the engine to use whatever data is available -- measurements if the customer provides them, purchase history if they have one -- and improve as more data accumulates. We also connect to the brand's size chart data at the style level because fit varies significantly between styles even within a brand, and a generic size chart across all styles produces less useful recommendations.
Yes. We build lookbook and editorial content management into the same platform as the product catalogue so the marketing team can link products to editorial content directly without needing a developer. The CMS layer lets the team create a new lookbook, drop in the campaign images, tag the products featured in each image, and publish -- with the product data, size availability, and add-to-bag functionality pulled in automatically from the catalogue. This removes the manual step of building a lookbook page in one tool and then manually linking to product pages in another. When a product sells out, the shoppable hotspot on the lookbook updates to reflect availability without a content update.
A full fashion e-commerce platform -- product catalogue with size run management, size recommendations, lookbook integration, loyalty programme, and returns management -- typically runs $30,000--$80,000. A more focused build covering the catalogue, size run management, and checkout with returns integration typically runs $15,000--$40,000. The range is driven by the size of the product catalogue, the complexity of the personalisation requirements, the number of integrations with warehouse and fulfilment systems, and whether a mobile app is included alongside the web platform. We scope and price every project before development starts so you know what you are getting and what it costs before you commit.
