Industry
Automotive & Aftermarket
Aftermarket parts, tyres, batteries, and lubricants live and die on fitment: make, model, year, and OEM cross-reference. As buyers and workshops turn to AI to find the right part or a valid alternative, fitment data that isn't structured for AI simply doesn't get found.
Context
Industry challenges
Fitment is a massive, constantly changing dataset
Vehicle make, model, year, and trim combinations multiply fast, and OEM part numbers, alternatives, and supersessions change regularly. Keeping this structured and current is a genuine data problem, not a content problem.
The same part is called different things by different sources
OEM references, brand part numbers, and generic descriptions for the same physical part rarely match across your catalog, supplier feeds, and manufacturer data, making AI matching unreliable even when the part is correct.
Alternatives and equivalents are where the real expertise is
Knowing which battery, filter, or brake part is a valid equivalent when the exact OEM part isn't available usually lives with experienced counter staff, not structured data.
High SKU volume makes manual structuring a non-starter
With tens of thousands of SKUs across multiple brands, manually verifying and structuring fitment data isn't realistic without transforming it at scale.
Opportunity
Transformation opportunities
Get matched correctly when AI checks fitment
Normalize OEM references, part numbers, and fitment data across brands and sources so make/model/year matching is accurate and AI-usable.
Automate the alternatives question your counter staff answer all day
Automate product matching and equivalent/alternative suggestions on top of structured fitment and compatibility data.
Be the business AI surfaces for a specific fitment search
Test whether AI correctly identifies your parts and alternatives when buyers search by vehicle make, model, and year.
In practice
Use cases
Fitment-based discovery
A buyer asks AI for a compatible replacement battery for a specific vehicle year and trim. Your structured fitment data is what makes an accurate answer possible.
Automated alternative matching
A workshop looks for an alternative to an out-of-stock OEM part. Automated matching surfaces your verified equivalent instead of a generic keyword result.
Consistent multi-channel fitment data
Your product feed stays accurate across multiple sales channels because fitment and OEM cross-reference data is structured once and reused everywhere.
Services
How we help
See how accurately AI matches your parts to real vehicles.
We'll test fitment accuracy for your catalog against real make/model/year searches.
Request a fitment accuracy check