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Automotive & Aftermarket
ACES/PIES CompliantYMM Fitment SearchVIN Decode

Tu tienda de repuestos envía piezas incorrectas. Corregimos tu stack ACES/PIES.

Si eres un operador de autopartes viendo cómo se acumulan los contracargos por datos de compatibilidad incorrectos, ya llegaste al límite de los catálogos basados en hojas de cálculo.

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.

Dónde fallan los proyectos

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.

Cumplimiento

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.

Qué construimos

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

Nuestro proceso

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

Preguntas frecuentes

¿Qué son los estándares de datos ACES y PIES?

ACES (Aftermarket Catalog Exchange Standard) define qué piezas corresponden a qué vehículos -- año, marca, modelo, motor, versión. PIES (Product Information Exchange Standard) gestiona el lado del producto: dimensiones, imágenes, precios y atributos. Juntos constituyen la columna vertebral del intercambio de datos en el mercado de autopartes de posventa en Norteamérica, mantenidos por la Auto Care Association. Si vendes autopartes en serio, estás trabajando con estos estándares lo sepas o no.

¿Cómo funciona la búsqueda por Año/Marca/Modelo en una tienda de autopartes?

Los filtros en cascada consultan directamente tu base de datos de compatibilidad mapeada en ACES. Selecciona un año, y solo aparecen las marcas válidas. Selecciona una marca, y se filtran los modelos correspondientes, luego motor y versión. El resultado es una lista de piezas con compatibilidad confirmada para esa configuración de vehículo exacta -- típicamente en menos de 200 milisegundos, incluso en catálogos extensos.

¿Pueden integrar la decodificación de VIN con nuestro catálogo de piezas?

Sí. Integramos APIs de decodificación de VIN que extraen año, marca, modelo, versión, motor y transmisión a partir de un VIN de 17 caracteres. Esos datos se mapean a las aplicaciones de vehículo en tu base ACES, rellenan automáticamente el filtro de compatibilidad y muestran solo las piezas compatibles. Es genuinamente útil en años modelo donde el mismo vehículo se comercializó con múltiples opciones de motor o transmisión -- lo cual es más común de lo que la mayoría de la gente cree.

¿Cómo gestionan catálogos con más de 100,000 SKUs?

Usamos una arquitectura headless con PostgreSQL gestionando la base de datos de compatibilidad y un índice de búsqueda dedicado -- Algolia o Meilisearch -- para filtrado facetado en tiempo real. El frontend en Next.js sirve shells de producto estáticos y carga los datos de compatibilidad de forma dinámica. Los tiempos de carga se mantienen por debajo de 2 segundos sin importar la profundidad del catálogo.

¿Puede la tienda sindicalizar datos de producto a Amazon y eBay Motors?

Sí. Desarrollamos pipelines de feeds automatizados que exportan tu catálogo en el formato exacto que requieren el Automotive Part Finder de Amazon y la estructura de compatibilidad de eBay Motors. Cuando agregas SKUs o aplicaciones de vehículo, las actualizaciones se envían automáticamente a tus listados en marketplace -- sin exportaciones manuales.

¿Cuánto tiempo toma desarrollar una tienda de autopartes en eCommerce?

Una implementación completa -- integración ACES/PIES, búsqueda YMM, decodificación de VIN y sindicación a marketplaces -- típicamente toma entre 10 y 11 semanas desde la auditoría de datos hasta el lanzamiento. Las implementaciones más simples sin feeds de marketplace pueden estar listas en 7 u 8 semanas. La mayor variable es el tamaño del catálogo y qué tan limpios están tus datos de compatibilidad existentes cuando comenzamos.

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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