Fitment search · Auto parts
Year, make, and model fitment search for auto parts
Most auto parts shoppers do not know a part number. They know their car. Atomz writes year, make, model, engine, and trim fitment into structured fields so a shopper can find every part that fits their vehicle and skip the ones that do not.
Score your store free first →brake pads for a 2018 Honda Civic EX 1.5L
Before Atomz, an agent reads
A title and a price, with none of the fields this search needs to match.
Result, passed over
After Atomz, an agent reads
Result, your product is the answer
Every word in the question maps to a structured attribute, so the right product is the answer.
The problem
Why keyword search misses this.
Fitment is a relationship between a part and a range of vehicles, and keyword search has no concept of it. It cannot tell that a part fitting 2016 to 2021 covers a 2018, or that the 1.5L and the 2.0L take different parts. A shopper either gets parts that do not fit their car or gives up and calls a competitor who shows fitment.
What goes in, what matches
The fields that answer the search.
The left column is what a shopper types. The right is the structured field Atomz writes to your metafields, the thing search and an agent match on.
fits a 2018 Honda Civic
Civic 1.5 turbo brake pads
does this fit my 2020 Civic
How Atomz does it
Written once, to fields you own.
Atomz writes the fitment range, make, model, engine, and trim into your Shopify metafields as structured fields, so search and the assistant return only the parts that fit the exact vehicle a shopper describes, in plain language.
Everything lives in your Shopify metafields, so the same data powers your on-store search, your AI Assistant, and the off-site agents that read Shopify Catalog.
Auto parts fitment search: questions merchants ask
How does fitment search handle a year range?+
Atomz writes the start and end year as a range, so a query for any year inside it resolves correctly. A part listed for 2016 to 2021 returns for a 2018 without anyone listing every year by hand.
Can it tell engine and trim variants apart?+
Yes. Engine and trim are their own fields, so a 1.5L car and a 2.0L car see the parts that fit each, not a mixed list.
Does this work inside an AI assistant too?+
Yes. The same fitment fields ground the on-store assistant, so a shopper can ask in plain language whether a part fits their car and get a grounded yes or no.
Get started
See what an agent reads when it hits your store.
Drop your store URL for a free readability score, or add the app and Atomz starts making the catalog readable today.