We begin with catalogue sources, compatibility rules, warehouses, suppliers, and the selection workflow. Then we design search and part pages, create the interface, develop imports and integrations, and test the journey from query to dispatch.
We assess the range, TecDoc or other sources, vehicle coverage, suppliers, warehouses, pricing, fitment, returns, CRM, and staff workflows.
We design categories, garage, vehicle selection, VIN request, OEM search, alternatives, part page, basket, and checkout using online store development principles.
We create an interface for both retail customers and specialists, covering mobile search, offer tables, compatibility, and uncertain-fitment states.
We implement search, filters, part pages, alternatives, basket, account area, content management, and reliable operation with large datasets.
We connect feeds, warehouses, CRM or ERP, payment, and shipping and validate conversions and product data for future paid search advertising.
We configure indexation, analytics, import and order monitoring, train the team, and verify critical workflows after launch.
Tell us about catalogue sources, vehicle coverage, OEM and cross-reference numbers, suppliers, warehouses, CRM, payment, shipping, and returns. We will propose a data model, search journeys, and integrations aligned with your sales operation.
After consultation, you will receive a budget, schedule, and data-risk estimate. The store will be prepared for SEO, advertising, analytics, and catalogue growth.
| Quick Start | Optimal | Advanced |
|---|---|---|
| Price: from $2,000 | Price: from $4,500 | Price: from $6,000 |
| Development — 10–14 days | Development — 25–50 days | Development — 50+ days |
| Adapted design | Custom UX/UI design | Fully custom design system |
| Catalogue up to 150 products | Full auto parts catalogue | Large catalogue with complex architecture |
| Basic categories and part numbers | Vehicle fitment, OEM search, and filters | VIN, cross-references, and complex compatibility |
| Basket and checkout | Fast checkout and account area | Trade account and custom scenarios |
| 1–2 payment methods | Several payment systems | Multiple payments and contract terms |
| One delivery service | Popular couriers and collection | Routing by warehouse and supplier |
| Manual price management | One supplier feed or API | Several feeds, ERP, CRM, and automation |
| Mobile adaptation | Full responsiveness | Mobile-first and high-load readiness |
| Basic SEO and Analytics | SEO architecture and commerce events | SEO-ready platform and end-to-end analytics |
| Simple admin panel | CMS for catalogue and orders | Roles, imports, monitoring, and support |
Prices match the current online store packages. Final budget depends on catalogue sources, SKU volume, compatibility rules, VIN and OEM search, suppliers, warehouses, margins, CRM, ERP, trade functions, and source data quality.
A product name is not enough. One part may fit only a specific generation, engine, year, or trim, carry several OEM numbers, and have many alternatives. A mistake creates a return, vehicle downtime, and loss of trust.
Consequently, online store development for auto parts begins with data modelling and validation rules. The interface must distinguish confirmed compatibility, a possible substitute, and an item requiring staff verification.
Vehicle selection usually covers make, model, generation, year, body, engine, and modification. A chosen vehicle remains in the customer garage and filters categories. Different part groups require different attributes, so a universal flat table quickly becomes impractical.
Categories follow repair logic and demand: brakes, suspension, engine, filters, electrical, body, and other systems. Names and attributes are normalised between sources to avoid duplicate part types.
Search should tolerate spaces, hyphens, and case without losing precision. An OEM relationship does not always guarantee interchangeability, so its type and source are stored. Supplier crosses, manual relationships, and manufacturer data may carry different confidence.
VIN helps identify a vehicle, but decoding depends on the service, market, and available data. A sufficiently accurate modification can filter the catalogue; otherwise the website creates a staff request instead of promising a correct part automatically.
The request contains VIN, required system, contact, notes, and optionally an old-part image. CRM receives source and page context. After verification, the customer can continue to a prepared basket or selected offer.
A part page explains manufacturer, number, specifications, application, warranty, and status. Several supplier offers compare price, location, lead time, returns, and source reliability. The cheapest option should not automatically outrank every other condition.
Images must match the part or be labelled illustrative. Dimensions, material, contents, and technical attributes remain structured for filtering, comparison, and commerce feeds.
Every source uses its own part numbers, brands, pricing, locations, and lead times. Import rules cover normalisation, currency, margin, minimum profit, priority, reservation, and failure behaviour. A malformed file must not zero the catalogue or publish unrealistic prices.
Synchronisation logs show time, record counts, omissions, and errors. Large feeds use queues and partial updates. When a supplier is unavailable, the platform follows an agreed fallback and alerts the responsible team.
The basket preserves exact part number, manufacturer, vehicle, supply source, lead time, and price. Shipping can depend on warehouse and dimensions, while one order may split across suppliers. Checkout explains this before payment.
Returns depend on category, packaging, installation marks, supplier policy, and applicable requirements. Customers see rules before purchase, while staff see the source conditions. Return reasons are recorded to improve fitment quality.
Regular buyers need quick lists of part numbers, contract pricing, credit limits, invoices, repeat orders, and dispatch status. Account permissions may differ for an owner, purchaser, and technician.
Trade pricing should originate in CRM or ERP or follow an agreed customer group. The website should not create parallel debt and inventory records when an internal system is the source of truth.
Search architecture covers categories, manufacturers, and only useful vehicle landing pages. Generating URLs for every model, year, engine, and filter combination creates empty or duplicate pages.
Before migration, an SEO audit protects categories, parts, links, and traffic. Product, Offer, and BreadcrumbList use factual data, while unavailable items receive an appropriate status or relevant alternatives.
Millions of fitment relationships cannot run as an expensive query on every page view. Indexes, search infrastructure, cache, and background imports are designed for real data volume and tested beyond a small demonstration dataset.
Measurement covers vehicle selection, OEM search, VIN requests, alternatives, baskets, payments, cancellations, and returns. Empty search results reveal catalogue gaps and supplier demand.
Budget depends not only on pages and design but on catalogue licensing, relationship quality, source count, SKU volume, updates, and business rules. Estimation requires sample feeds, APIs, margins, warehouses, and real fitment journeys.
A Basic store starts at $2,000, an Optimal project with fitment, imports, and integrations starts at $4,500, and an Advanced platform with a large catalogue and several suppliers starts at $6,000.
Quick Start takes 10–14 days, Optimal takes 25–50 days, and an Advanced project with complex data and integrations starts at around 50 days.
Yes, with lawful access, appropriate licensing, and a suitable data format or API. Scope depends on catalogue version, markets, languages, imagery, relationships, and update frequency.
A VIN can be decoded through an agreed service or sent to staff for manual validation. An automatic answer is shown only when the source provides sufficient confidence.
Yes. We normalise part numbers, brands, prices, stock, and lead times and define priority and margin rules. Every source needs a stable format and failure policy.
We store OEM and cross-reference relationships while distinguishing exact replacements from possible alternatives. Critical compatibility is not confirmed without reliable data.
Yes. It may include contract pricing, part-number quick order, history, invoices, saved vehicles, and dispatch status.
Yes. We implement URLs, metadata, canonical, sitemap, structured data, performance, and indexation controls. Mass pages are created only when they provide useful stock and content.
Yes. We assess part numbers, brands, categories, URLs, fitment, images, and traffic, define migration and redirect rules, and test a sample before the full import.
We need catalogue sources, feed samples, margin rules, warehouses, suppliers, shipping and return rules, CRM or ERP details, brand assets, and compatibility owners.
