OptiVis Labs

Product

Fitment Search Engine

A white-label vehicle fitment search and catalog engine for Shopify auto-parts stores. A shopper searches or picks a vehicle and sees every part that fits, grouped by part type, with live stock.

Live

Running in production on an auto-parts retailer's Shopify store. Ask for a walkthrough on a call.

How it works

Two data files built from your catalog, one engine in your theme

  1. The catalog is the source. Product titles carry the years and the make. Tags carry the model and its year range, one tag per vehicle the part fits.
  2. Two generators build the index. fitment-index.json maps year, make and model to the products that fit. search-meta.json records each product's type, make, part group and stock flag. Re-run them when the catalog structure changes.
  3. The engine reads the shopper's words. It parses year, make, model and part keywords, tolerates typos and two-digit years, and infers the make from a model name. If the vehicle is unclear, it asks the shopper to pick one.
  4. Results come back grouped, with live stock. Search results, the year, make and model finder, and each make's collection page all show parts grouped by type, with sorting and filters. Stock is read live on every page load, so nothing needs scheduling.
How the Fitment Search Engine worksProduct titles and tags in the Shopify catalog are turned by two generators into fitment-index.json and search-meta.json. The fitment engine in the theme reads both files, parses the shopper's year, make, model and part words, pulls live stock, and renders three experiences: grouped search results, a year make model finder, and grouped make pages.Shopify catalogTitles carry the years and the makeTags carry model and years: Camry 2018-2024Product type sets the part grouptwo generatorsfitment-index.jsonYear > Make > Model> the products that fitsearch-meta.jsonPer product: type, make,part group, stock flagFitment engine in the store themeReads year, make, model and part wordsTolerates typos and two-digit yearsInfers the make from a model namePulls live stock on every page loadNo clear vehicle: asks the shopper to pick oneSearch barResults groupedby part typeFinderYear, make andmodel menusMake pagesGrouped catalogfor one make
The system as installed on a Shopify theme.

What a match means

A match is what your catalog says fits. Nothing more.

A match guarantees

  • The product's title and tags place it on that year, make and model.
  • When a shopper names a make, parts from other makes are hidden.
  • The stock shown is read from the store when the page loads.

Where exceptions exist

  • Fitment is only as accurate as the titles and tags. A wrong tag produces a wrong match.
  • Products without a model tag in the expected form are not in the fitment index.
  • Details beyond year, make and model, such as trim or options, are only as precise as the product title. The engine does not check them separately.
  • A part-number search falls back to standard Shopify search.
  • New or retagged products appear in fitment results after the generators are re-run.

Prerequisites

What your store needs first

  • PlatformA Shopify store, with access to edit the theme code.
  • TitlesEach product title includes the years and the make it fits.
  • TagsOne tag per vehicle in the form Model YYYY-YYYY, for example RAV4 2019-2024. Multi-fit parts get one tag per vehicle.
  • Product typesClean, consistent product types. They decide how results are grouped.
  • Catalog exportA product export (a Shopify bulk operation works) to build the two data files.

The engine reads what the catalog says, so title and tag quality decides how good the results are. That is the first thing to look at for any store.

Pricing

One-time setup, then a monthly fee for updates and support.

Quoted after a short call.

Talk to OptiVis about your store