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Automotive & Aftermarket
ACES/PIES CompliantYMM Fitment SearchVIN Decode

Auto Parts eCommerce avec Fitment ACES/PIES

Votre magasin de pièces auto vend les mauvaises pièces — Avant qu'ACES/PIES ne le corrige

60%
Fewer Returns
With accurate fitment data
100K+
SKU Capacity
With cross-reference support
95+
Lighthouse Score
Performance target
<200ms
Search Response
YMM + VIN queries
What ACES/PIES Fitment Actually Fixes — And What It Won't

Your buyer lands on your catalog with a 2019 Silverado 1500 and three engine options to choose from. Without ACES/PIES fitment data threaded through your product database, that buyer guesses — and 1 in 5 guesses wrong. Your return rate climbs, your margin shrinks, and Google sees the pattern in your search abandonment metrics. ACES/PIES integration connects every SKU in your catalog to specific vehicle applications with year, make, model, submodel, and engine-level precision. It powers year-make-model search, VIN decode, and the faceted filtering that takes a mechanic from 10,000 results to the right part in three clicks. Your storefront stops selling wrong parts, your structured data qualifies for rich snippets, and your team stops manually updating fitment spreadsheets every model year. That's what your business gains when fitment data is architecture, not afterthought.

Où les projets échouent

Customers buy the wrong parts because your catalog doesn't verify fitment Once return rates climb past 20%, you're eating into margins fast — and the trust damage sticks around longer than the refund does.
Your ACES/PIES files are probably sitting in spreadsheets right now with no real pipeline connecting them to your storefront That means every time a new model year drops, someone has to manually update listings — and until they do, you're missing sales.
That Year/Make/Model search? If it's running through a plugin, it's one platform update away from breaking When it goes down during peak season, customers can't filter by vehicle and they leave. Simple as that.
Product pages without structured fitment data are invisible on Google and Amazon No schema, no rich snippets, no visibility in automotive search results.
Catalogs with 50K+ SKUs and complex cross-reference relationships will choke a standard eCommerce setup Slow loads on mobile push bounce rates past 60%, and most of those visitors aren't coming back.
And without VIN decode, customers are still guessing at trim and engine variants On something like GM's lineup — where a single model year can carry three or four different engine options — that ambiguity gets expensive quickly.

Conformité

ACES XML Ingestion Pipeline

We automate the import and validation of ACES vehicle application files with full VCdb mapping. Monthly update cycles pull in new vehicle years automatically. Nobody touches a spreadsheet.

PIES Product Data Management

Product attributes — dimensions, weight, materials, images — get mapped to PIES standards and packaged into clean data feeds ready for Amazon, eBay Motors, and wherever else you sell.

Year/Make/Model/Engine Search

Cascading dropdown filters pull directly from your ACES data and return only verified compatible parts. Response times stay under 200ms even across catalogs with 100K+ SKUs. That's not a goal — it's just how the system's built.

VIN Decode & Lookup

A customer pastes their VIN and the system fills in year, make, model, trim, and engine without them lifting another finger. No guessing, no wrong part, no return.

Cross-Reference & Interchange

OEM part numbers map to aftermarket equivalents through bidirectional cross-reference tables. When someone searches by OE number, they land on your listing — not a competitor's.

My Garage / Saved Vehicles

Returning customers save their vehicles to their account and see pre-filtered results every time they come back. That's what turns a one-time buyer into someone who comes back for the next job too.

Ce que nous construisons

Stack year, make, model, engine, and brand filters without choking your database on 50K+ SKU catalogs

Wrong-part returns drop below 8% when every product page verifies fitment before the buyer hits checkout

Upload ACES XML and PIES XML from SEMA Data Co. or AutoSync with built-in validation that catches bad records before they ship

Your team stops manually updating spreadsheets every model year because ACES/PIES pipelines ingest new data automatically

Decode VIN in real time to surface exact trim and engine variants so buyers stop guessing on GM's four-engine lineups

Rich snippets appear in Google's automotive search results once your product pages carry structured fitment schema

Export marketplace-ready feeds for Amazon Automotive Part Finder, eBay Motors, and Walmart in one click

Page load stays under 1.2 seconds on mobile even when a search query hits 50,000 SKUs with cross-reference lookups

Run your storefront on decoupled Next.js while your fitment database scales independently on PostgreSQL

Your catalog syndicates to Amazon, eBay, and Walmart without reformatting feeds for each platform's spec requirements

Serve mobile-first YMM search to mechanics searching from the shop floor on a phone with spotty signal

Buyers find the exact part in three clicks instead of abandoning your search after scrolling past 200 unfiltered results

Notre processus

01

Fitment Data Audit

We start by digging into your existing ACES/PIES files, VCdb coverage, and cross-reference tables to find gaps in vehicle application data. You walk away from this phase with a data quality scorecard — a clear picture of what you're working with before we touch anything.
Week 1
02

Architecture & Data Modeling

From there, we design the fitment database schema, YMM search index, VIN decode integration, and marketplace feed pipelines. Every SKU relationship gets mapped out before we write a single line of code.
Week 2-3
03

Storefront & Search Build

Then we build — headless storefront with cascading YMM filters, VIN lookup, faceted search, fitment verification badges on product pages, and My Garage functionality for returning customers.
Week 4-7
04

Data Pipeline & Marketplace Sync

We wire up automated ACES/PIES ingestion pipelines with monthly VCdb sync and configure feed exports for Amazon, eBay Motors, and whatever other channels you're selling on.
Week 8-9
05

Launch & Performance Tuning

Finally, we load test against your full catalog, verify fitment accuracy across a sample set of vehicles, optimize Core Web Vitals, and go live — with 30 days of post-launch support included.
Week 10-11
Next.jsSupabaseVercelAlgoliaShopify HydrogenPostgreSQLACES XMLPIES XML

Questions fréquentes

Que sont les normes de données ACES et PIES ?

ACES (Aftermarket Catalog Exchange Standard) définit quelles pièces conviennent à quels véhicules — année, marque, modèle, moteur, finition. PIES (Product Information Exchange Standard) gère le côté produit : dimensions, images, prix, attributs. Ensemble, ils sont l'épine dorsale de l'échange de données des pièces de rechange automobile en Amérique du Nord, maintenu par l'Auto Care Association. Si vous vendez des pièces auto sérieusement, vous travaillez avec ces normes, que vous le sachiez ou non.

Comment fonctionne la recherche Année/Marque/Modèle sur un site de pièces auto ?

Les filtres déroulants en cascade interrogent directement votre base de données de fitment mappée ACES. Choisissez une année, et seules les marques valides s'affichent. Choisissez une marque, et elle se restreint aux modèles correspondants, puis moteur et finition. Le résultat est une liste de pièces avec fitment confirmé pour cette configuration de véhicule exact — généralement en moins de 200 millisecondes, même sur de grands catalogues.

Pouvez-vous intégrer le décodage VIN à notre catalogue de pièces ?

Oui. Nous intégrons des API de décodage VIN qui extraient l'année, la marque, le modèle, la finition, le moteur et la transmission d'un VIN à 17 caractères. Ces données se mappent sur vos applications de véhicule ACES, remplissent automatiquement le filtre de fitment et affichent uniquement les pièces compatibles. C'est vraiment utile sur les années modèles où le même véhicule a été livré avec plusieurs options de moteur ou de transmission — ce qui est plus courant que la plupart des gens ne le pensent.

Comment gérez-vous les catalogues avec 100 000+ SKU ?

Nous utilisons une architecture headless avec PostgreSQL gérant la base de données de fitment et un index de recherche dédié — Algolia ou Meilisearch — pour le filtrage facetté en temps réel. Le frontend Next.js sert des shells de produits statiques et charge les données de fitment dynamiquement. Les chargements de page restent sous 2 secondes indépendamment de la profondeur du catalogue.

Le magasin peut-il syndiquer les données de produits à Amazon et eBay Motors ?

Oui. Nous construisons des pipelines de flux automatisés qui exportent votre catalogue dans le format exact requis par le Product Finder automobile d'Amazon et la structure de fitment d'eBay Motors. Quand vous ajoutez des SKU ou des applications de véhicule, les mises à jour se poussent automatiquement vers vos listes de place de marché — aucune exportation manuelle.

Combien de temps prend une construction eCommerce de pièces auto ?

Une construction complète — intégration ACES/PIES, recherche YMM, VIN lookup et syndication sur les places de marché — prend généralement 10 à 11 semaines de l'audit des données au lancement. Les constructions plus simples sans flux de syndication sur les places de marché peuvent être livrées en 7 à 8 semaines. La plus grande variable est la taille du catalogue et la propreté de vos données de fitment existantes quand nous commençons.

Auto Parts eCommerce from $12,000
Fixed-fee. ACES/PIES integration included. 30-day post-launch support.
See all packages →
Next.js DevelopmenteCommerce DevelopmentCore Web Vitals OptimizationCore Web Vitals & Jamstack Guide

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