ACES and PIES Data, Straight Into Your Shopify Parts Catalog where fitment is everything.
Aftermarket catalogs aren't like other catalogs: every part carries fitment data — which years, makes, and models it actually fits — and a single wrong compatibility field means a guaranteed return. Apimio is the automotive aftermarket PIM for Shopify: Catalog Hub structures fitment attributes on one canonical record per part, Quality Guard blocks parts with incomplete fitment from publishing, Supplier Bridge ingests manufacturer files, and Trade Portal serves your installer and distributor network live data instead of stale spreadsheets.
Three aftermarket teams that outgrow spreadsheets
Aftermarket product data pain shows up differently depending on where you sit in the parts chain. Pick the closest match.
The parts brand / manufacturer
Your own lines, sold D2C and through distribution
You make or private-label parts and sell them everywhere — your Shopify store, marketplaces, and a distributor network. Fitment lives in an engineering spreadsheet, product content lives somewhere else, and every channel gets a slightly different, slightly stale version. When a part supersedes another, updating every surface is a manual hunt.
The WD / distributor
Thousands of SKUs across dozens of manufacturer lines
Every manufacturer sends data in a different format — different columns, different fitment notation, different image standards. Your team normalizes it by hand before anything can list. Onboarding a new line takes weeks, and the catalog's weakest data is whatever line was onboarded in the biggest hurry.
The aftermarket retailer
Selling parts on Shopify where wrong-fit returns eat margin
Your returns aren't about quality — they're about fitment. "Didn't fit my car" is your top return reason, and every one costs you shipping both ways plus a frustrated customer. The fix isn't better parts; it's complete, correct compatibility data on every listing, enforced before the listing goes live.
Why general-purpose catalog tools break on auto parts
Aftermarket catalogs combine the hardest properties in product data: huge SKU counts, dense technical attributes, vehicle compatibility that changes by sub-model and year, and a parts chain where everyone reformats everyone else's data. Here's the operating reality — and what Apimio replaces it with.
Parts data today
Parts data on Apimio
Fitment lives in a spreadsheet nobody fully trusts
Vehicle compatibility sits in a master sheet maintained by one person, in notation only they fully understand. Listings get built from memory of it. When a customer asks "does this fit a 2019 trim level X?", support guesses — and a wrong guess becomes a return and a one-star review.
Fitment is structured data on the canonical record
Catalog Hub's flexible attribute schema models fitment the way your category needs it — year/make/model/sub-model attributes, position, engine notes — on one canonical record per part. Compatibility stops being tribal knowledge and becomes queryable, auditable catalog data every listing is built from.
Every manufacturer line arrives in a different shape
One supplier sends fitment as a column per year, another as a free-text range, a third as a separate compatibility file. Normalizing each new line by hand takes weeks, so new lines launch with minimum-viable data — and minimum-viable fitment is how wrong-part orders happen.
Supplier files map once, then flow
Supplier Bridge's AI mapping reads each manufacturer's format — whatever the column layout — and maps it to your attribute schema. The mapping is saved per supplier, so line updates become a re-import, not a re-project. New lines onboard in days with full data, not weeks with minimums.
Supersessions update one surface at a time
Part A is replaced by Part B. The Shopify listing gets updated this week, the marketplace next week, the distributor pricelist next month. In between, customers and dealers order a part that no longer exists, and your team handles the fallout order by order.
One edit updates every surface
A supersession, price change, or spec correction lands on the canonical record once. Store Sync pushes it to every connected Shopify store in real time, the Trade Portal serves it to B2B buyers on next load, and exports carry it to marketplaces. The part chain reads one source of truth.
Distributors and installers work from stale PDFs
Your B2B buyers get a pricelist PDF exported quarterly. Between exports, prices move, parts supersede, and new SKUs launch — none of which the PDF knows. Every discrepancy becomes a support email, and every support email is your team re-assembling data that was current somewhere, once.
Distributors get a live portal, not a PDF
Trade Portal gives each distributor or installer a branded portal scoped to the lines and price tiers they buy — fed live from Catalog Hub. Pricelists are never stale because they aren't copies; they're views. The quarterly PDF export ritual ends.
"Doesn't fit" is the top return reason
Wrong-fit returns run 10–30% in categories with weak compatibility data. Each one costs double shipping, restocking labor, and customer trust. The root cause is almost never the part — it's a listing that shipped with incomplete or wrong fitment data and nobody caught it before publish.
Incomplete fitment cannot publish
Quality Guard's category-aware rules make fitment fields required for parts categories — a part missing compatibility data scores below threshold and is blocked from publishing until it's complete. Wrong-fit returns get attacked at the only point that scales: before the listing exists.
ACES and PIES solved the data problem. They did not solve the Shopify problem.
Two standards from the Auto Care Association: ACES carries vehicle fitment, PIES carries the product attributes. Both are well specified. Neither knows what a Shopify variant is.
ACES — which vehicles a part fits
Applications tie a part to a base vehicle, with qualifiers narrowing it further: engine, drive type, bed length, trim. One part can carry hundreds of application rows.
Shopify has no concept of fitment
There is no native place for an application row. It ends up in a metafield, a tag, a separate app, or a spreadsheet somebody maintains by hand.
PIES — what the part actually is
Descriptions by code (short, extended, features and benefits, application comment), price types like MAP and jobber, package dimensions, digital assets and attributes.
One price field, several PIES price types
A product has a price and a compare-at price. PIES carries MAP, jobber and others, so an import has to decide what to keep and what to drop.
Usually shipped as an SDC package
Most sellers receive this through a data provider as a segments workbook rather than raw XML, which is the practical shape the data arrives in.
One body field, several description codes
Short, extended, features and benefits and application comments are distinct in PIES and usually get flattened into a single description on the way in.
Where SEMA Data fits
SEMA Data — the SEMA Data Co-op — is how a large share of aftermarket sellers receive ACES and PIES in the first place: manufacturers publish once, receivers pull standardized data rather than chasing each supplier for a spreadsheet. It solves distribution. What it does not do is decide how any of it should look in your storefront. That mapping — which PIES description code becomes your product copy, whether MAP or jobber price is the one you publish, how an application row attaches to a variant — is still a decision somebody has to make, and it is the part Apimio handles with you rather than leaving in a workbook.
Further reading
If you are working out what the standards actually carry and where they collide with Shopify, start with what ACES and PIES actually are. It covers applications and qualifiers, the PIES price and description codes, and the four places parts data gets flattened on the way in. If your data arrives through the co-op, SEMA Data Co-op: what you get and what you still decide covers the segments workbook, the decisions the feed cannot make for you, and how supersessions and interchange get handled.
Four surfaces, fitment-first
The same Apimio platform, configured around what aftermarket catalogs actually need. Click any card for the full product page.
Catalog Hub
One canonical record per part number — flexible attributes for fitment (year/make/model, position, engine), cross-references, and supersession notes; Variant Manager for finish/size/side variants; per-store overrides for channel pricing. Automotive catalog management on a structured foundation instead of a spreadsheet.
Read the Catalog Hub product pageSupplier Bridge
AI-mapped ingestion for manufacturer and supplier files — every line's format maps to your schema once, then re-imports flow automatically. The normalize-by-hand step that makes onboarding a new manufacturer line take weeks simply disappears.
Read the Supplier Bridge product pageQuality Guard
Category-aware completeness rules with fitment fields set as required — parts with missing or incomplete compatibility data are blocked from publishing. The publish gate is the structural fix for "doesn't fit my car" returns.
Read the Quality Guard product pageTrade Portal
Branded, scoped portals for your distributor and installer network — each B2B buyer sees the lines and price tiers they carry, live from the canonical catalog. Replaces the quarterly pricelist PDF with data that's current every time they look.
Read the Trade Portal product pageWhat changes — milestone by milestone
The aftermarket rollout follows the same arc most verticals see on Apimio, with fitment completeness as the lead indicator.
Common questions from aftermarket teams
The questions parts brands, distributors, and retailers evaluating an automotive PIM actually ask.
An automotive aftermarket PIM is product information management built around the data shape auto parts require: fitment (vehicle compatibility by year, make, model, and sub-model), dense technical attributes, cross-references and supersessions, and very large SKU counts flowing between manufacturers, distributors, and retailers. A general-purpose catalog tool treats fitment as a description field; an aftermarket PIM treats it as structured, validated, required data — which is what makes complete listings and low wrong-fit returns possible at scale.
Through Catalog Hub's flexible attribute schema: you define fitment attributes the way your categories need them — year ranges, make, model, sub-model, position, engine or chassis notes — and they live as structured fields on the canonical record per part. Because fitment is real attribute data (not free text), Quality Guard can require it per category, your team can audit it, and every channel — Shopify stores, the distributor portal, exports — publishes from the same validated compatibility data.
Apimio is not an ACES/PIES certification or validation service. What it does is hold the same information in your own attribute schema: if your fitment and product data originates in ACES/PIES-style structures, Supplier Bridge maps those files into Catalog Hub's attributes, where the data becomes governed, gated, and publishable to your channels. Teams that must deliver certified ACES/PIES output to specific trading partners typically keep their standards tooling for that handoff and use Apimio as the operational catalog feeding their own stores, portal, and exports.
Structurally, at publish time. Wrong-fit returns almost always trace back to a listing that went live with incomplete or incorrect compatibility data. Quality Guard makes fitment fields required for parts categories and blocks below-threshold listings from publishing — so the listing that causes a "doesn't fit my car" return doesn't get created in the first place. Combined with one canonical record (so corrected fitment propagates everywhere at once), teams typically see wrong-fit returns trend down within the first quarter.
This is exactly what Supplier Bridge exists for. Each manufacturer's file format — column layout, fitment notation, image conventions — is AI-mapped to your attribute schema once, and the mapping is saved. From then on, line updates are re-imports, not re-projects: drop the new file, Quality Guard validates every row, below-threshold parts queue for review instead of silently listing incomplete. Most teams get their top lines mapped in the first two weeks.
The canonical record is the control point. When Part A supersedes to Part B, you update the record once — cross-reference attributes carry the old part number so customers and dealers searching the superseded number still find the right part. Store Sync propagates the change to every connected Shopify store in real time, and the Trade Portal reflects it immediately because it reads live from the catalog. No more surface-by-surface supersession hunts.
Yes. Trade Portal is the live, self-serve channel — each distributor sees their scoped lines and pricing, always current. For partners who want files, multi-format exports generate from the same canonical record, so an exported sheet is a snapshot of validated data rather than a separately-maintained document. Either way, your team stops hand-assembling per-distributor spreadsheets.
The workflow on this page applies to any automotive seller whose products are vehicle-specific — aftermarket parts is simply the most demanding case. OEM accessory programs selling online, dealerships running parts ecommerce, tools-and-equipment brands with model-specific compatibility, and powersports or commercial-vehicle parts sellers all share the same data shape: a canonical record per part number, fitment attributes that must be complete before publish, supplier or manufacturer files arriving in mixed formats, and a B2B channel that needs current data. If your automotive catalog has compatibility data on it, the fitment-first structure here is the right PIM setup — whether the parts are aftermarket, OEM, or accessories.
Two standards from the Auto Care Association. ACES carries vehicle fitment — which vehicles a part fits, narrowed by qualifiers like engine or trim. PIES carries the product itself: descriptions by code, price types such as MAP and jobber, package dimensions and digital assets. ACES answers "does it fit", PIES answers "what is it".
Yes. SEMA Data is how many aftermarket sellers receive ACES and PIES, usually as an SDC segments workbook. Bring the package your data provider already sends and we map a real segment with you on a call, rather than asking you to reshape it first.
Shopify has no native place for an ACES application row, which is why fitment usually ends up in a spreadsheet or a separate app. Apimio maps applications and qualifiers against the products they belong to in Catalog Hub, so fitment travels with the part instead of alongside it.
They map to separate fields rather than collapsing into one number. You decide which is published to the storefront and which is held for reference, instead of losing the distinction on import.
Automotive catalog management software holds a parts catalog with the data an aftermarket seller actually needs — part numbers, brand, MAP and jobber pricing, PIES attributes and ACES year/make/model fitment — and publishes it to the storefront and to dealers without hand-editing spreadsheets. Apimio does this for Shopify: fitment and PIES attributes land in their own fields, supersessions are tracked, and the catalog stays in sync across stores. Bring your SDC package to the demo and we map it with you.
Make fitment your moat, not your return reason
Import your parts catalog, see Quality Guard surface the fitment gaps, and switch on the publish gate. The 14-day trial includes the full stack — Catalog Hub, Supplier Bridge, Quality Guard, Trade Portal.