Spark · Data & standards
Fitment Data Engine
The four-wheel aftermarket has had a shared fitment language for years. Powersports never got one — so the same part is described a thousand different ways and search engines stop trying. We're building the missing layer, and wiring it straight onto your pages.
What it is
Make it machine
readable. Then
make it findable.
A curated, contemporary powersports fitment dataset — and the structured-data standards that carry it onto real dealer pages, tied to real price and availability.
The dataset on its own would sit in a database nobody crawls. The markup on its own would describe listings that may be wrong. Together they make your catalogue something a search engine, or a language model, can answer a customer's question with.
Google and Bing
Vehicle, Product, Offer and LocalBusiness markup tied to live price and availability, so a unit on the floor is a unit in the index.
LLMs and AI overviews
Entity clarity, machine-readable fitment answers and clean feed endpoints — so assistants cite your stock rather than guessing around it.
Maintained, not shipped once
An update cadence and QA standard, because model years keep arriving and supersessions keep breaking catalogues that stand still.
Why it matters now
A standard
with a hole in it.
ACES and PIES organise fitment for cars and trucks. Powersports fitment stayed fragmented, inconsistent and trapped inside individual dealer systems — an aftermarket worth billions with no shared way to say what fits what.
That gap is why a customer searching for a specific job on a specific bike lands on a forum thread instead of the dealer twenty minutes away who has the part in a drawer. We build to line up with the conventions the wider aftermarket already uses, so nobody has to learn a private dialect to work with us.
What's in it
Three workstreams.
One clean layer.
| Workstream | What we do | What you get |
|---|---|---|
| Fitment curation | Normalise make, model, year and trim across OEM and aftermarket part numbers, reconcile supersessions, and prioritise current-decade models where existing datasets are weakest. | A master fitment database and a part-number map that makes one part one part. |
| Structured data layer | Publish and deploy schema standards for inventory pages — Vehicle, Product, Offer and LocalBusiness markup wired to live price and availability. | Markup templates for your website platform, validation tooling, and a listing spec your vendor can follow. |
| AI discoverability | Format data and content for retrieval: entity clarity, machine-readable fitment answers, feed endpoints and llms.txt-style surfaces. | An AI-readiness standard and a visibility score per store, monitored over time rather than guessed at. |
Nobody walks in asking for part 51423-KZL-901. They ask if it fits their bike.
The whole argument for fitment data
What it gives you
Two audiences,
one dataset.
Dealers need their floor to be findable. Brands need their catalogue to survive the trip through a thousand dealer websites intact.
Findable by the bike
- Fitment search that worksCustomers find parts by what they ride, on your site, without knowing a part number.
- Fewer wrong-part returnsCorrect fitment at the point of sale costs less than correcting it at the counter.
- Pages that qualify for rich resultsPrice and availability read straight off the listing.
Catalogue integrity downstream
- One canonical part numberYour SKUs reconciled across the variations and supersessions that fragment them in the field.
- Consistent description everywhereDealer sites describe your product the way you do, because they're working from templates.
- Channel visibilityHow your products actually surface across dealer sites, search results and AI answers.
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