Commerce engineering for parts sellers

Shopify, Magento and Adobe Commerce, WooCommerce, and headless storefronts on Next.js, chosen by catalogue size, scale and roadmap rather than preference. Automotive parts retail places an unusual demand on the platform: catalogues are very large and every product needs to be associated with the vehicles it fits, which is a relationship most ecommerce platforms do not model natively. We establish whether you need vehicle fitment early, because it shapes the data architecture and the platform choice more than any other requirement in this sector.

Yes. We build guided parts finders, registration or VIN based lookup, semantic search and recommendation engines on current model stacks including Claude. The genuine value in automotive is reducing the wrong part problem, because most returns here are compatibility failures rather than faults. We ground every compatibility answer strictly in your fitment data rather than allowing a model to infer it, since a confidently invented fitment claim produces a return, a refund and often a customer who does not come back. Uncertainty is surfaced rather than hidden.

Yes. We connect payment providers, shipping carriers, marketplaces and tax systems so orders, fulfilment and settlements stay in step without manual work. Automotive adds practical shipping complexity worth scoping early: parts vary enormously in size and weight, some are classed as hazardous, and others carry a core charge or surcharge, all of which affect how shipping is calculated at checkout. We make operations idempotent so a retried webhook cannot duplicate an order, and surface sync discrepancies in a report rather than leaving them for a customer to find.

Yes, and speed matters here because parts buyers frequently arrive with a specific vehicle and need to confirm fitment quickly before comparing elsewhere. We build against the Core Web Vitals thresholds, Largest Contentful Paint under 2.5 seconds, Interaction to Next Paint under 200 milliseconds and Cumulative Layout Shift under 0.1 at the 75th percentile. Google and Deloitte measured a 0.1 second improvement lifting retail conversion by 8.4 percent and average order value by 9.2 percent. In automotive the fitment lookup itself is usually the slowest interaction and the first thing we optimise.

You do, completely. You receive clean, documented, well structured code and full ownership of everything we build, held in your own repositories from the first commit rather than transferred at the end. There is no proprietary framework you can only maintain through us and no arrangement where you keep paying to use what you commissioned. If you later bring the work in house or move to another agency you should be able to do so without asking our permission or unpicking a dependency on us. For a parts retailer whose fitment structure represents years of accumulated work, that ownership covers the data model we build around it, not only the storefront templates.

Most automotive stores go live in six to fourteen weeks depending on catalogue size, custom features and integrations. A focused theme build or straightforward replatform can be faster, while a headless or heavily customised store takes longer, and we share a clear timeline before starting. Where vehicle fitment data is involved, importing and validating it is usually the largest single task and frequently determines the launch date, particularly when fitment comes from several suppliers in different formats that have to be reconciled first.

Cost depends on scope, platform and the integrations and data work involved. We scope every project to your goals and budget, then give a fixed proposal with no surprises, usually within 24 hours of your enquiry. In automotive the largest variable by some distance is fitment data: licensing it, importing it and keeping it current is an ongoing cost rather than a one off build item, and we would rather set that expectation at proposal stage than have it appear as a surprise after launch.

That is the goal every time, and the method decides the outcome. We map every existing URL to its destination as a one to one redirect plan rather than pointing old pages at the homepage, preserve titles, descriptions and structured data, and migrate product, customer and order data with reconciliation rather than a raw dump. Automotive stores often have large numbers of vehicle specific landing pages, which are exactly the URLs most easily lost in a rebuild, so we inventory them first. We then monitor Search Console daily for thirty days.

Yes. We build iOS and Android applications with saved vehicles, parts lookup, order tracking and fast reordering, connected to the same backend as your store. The strongest case in automotive is usually trade rather than consumer: a workshop ordering several times a week benefits genuinely from saved vehicles and rapid reordering, while a consumer buying a part once a year will not keep an application installed. We would rather build for the segment that returns than for the larger but less frequent audience. We would rather establish which segment actually returns from your own order data than assume it, because an application built for the wrong audience is an expensive way to find out.

Yes, and it needs designing for rather than hoping. We architect for peak load with edge caching, queueing for checkout and horizontally scalable infrastructure, then load test against realistic volumes rather than average traffic. Fitment lookup is the load intensive part in automotive rather than product pages, since every visitor runs it before buying, so it gets cached and indexed carefully. Inventory logic that prevents overselling matters too, because cancelling a confirmed order for a workshop waiting on a part damages a trade relationship rather than just a sale.

Yes. Every build includes thirty days of post launch support, and beyond that we continue with monitoring, security patching, updates, new features and ongoing conversion and SEO work. In automotive the maintenance item that matters most is fitment data currency: new model years and revised part numbers arrive continuously, and a catalogue that silently falls behind starts producing wrong part returns. We monitor the freshness of those feeds specifically, because a stale fitment file looks perfectly healthy while quietly generating returns and refunds.

As a first class data relationship rather than as text in a description, because it is the single most important thing your customers need to get right. We model the association between parts and the vehicles they fit, support lookup by make, model, year and engine variant or by registration and VIN where a data provider is available, and surface fitment clearly on the product page rather than only in search. Where compatibility is conditional we say so explicitly, since an ambiguous fit note is the most common cause of a wrong part order.