Automating Vehicle Data Mapping to Slash Parts Lookup Time
A B2B automotive parts platform mapped an external vehicle data source to their internal catalogue, unlocking higher-quality coverage without pulling senior engineers off core work.
Client Feedback
β...excellent to work with - fast, professional, and highly collaborative with our engineering team. They deliver quality work quickly and communicate clearly throughout the process. Their technical skills and reliable approach have made them a valuable partner for our data projects.β
Carpata
Automotive / B2BThe Challenge
The client operated a proprietary vehicle catalogue but relied on a paid third-party vehicle data source that used a different naming and coding structure. This mismatch limited how effectively they could use external data for parts matching and availability.
Key Pain Points:
- Two vehicle datasets with incompatible nomenclature and coding
- Manual mapping work pulling senior engineers away from core platform development
- Risk of incorrect vehicle-to-parts matches if mappings were inaccurate
- Need to validate whether the mapping process could be delegated safely
Why EndSpec
The client needed a partner who could quickly understand complex datasets, apply intelligent matching logic, and produce high-accuracy results with minimal oversight . We demonstrated deep experience in data normalisation, rule-based matching, and structured NLP β proving this work could be handled externally without compromising data quality.
The Solution
We designed and executed a custom vehicle data mapping workflow, fully automating the mapping process to accurately align the external data source with the internal catalogue. Our approach accounted for both vehicle technical specifications and naming trims, combined with a tolerance for error to ensure high-quality results.
Implementation Phases:
Dataset & Problem Analysis
Reviewed the internal catalogue and external data source to understand structure, identify gaps, and define the mapping challenges and priorities.
5 daysTechnical Specs Matching
Mapped and verified key technical attributes such as body types, transmissions, and engine specifications between datasets to ensure accurate alignment.
12 daysVehicle Name Notation & Trim Mapping
Standardized vehicle naming conventions and mapped trims and variants to reconcile differences between internal and external nomenclature.
20 daysMapping Tolerance Enhancements
Introduced error tolerance to handle minor discrepancies and edge cases, improving overall mapping accuracy and reducing mismatches.
3 daysQuality Assurance & Finalization
Performed detailed QA, corrected inconsistencies, and produced final verified mappings ready for deployment.
8 daysTechnology Stack:
Results
The project delivered a fully automated, high-accuracy vehicle data mapping workflow that eliminated manual effort, reduced lookup time from hours to seconds, and ensured complete alignment with the clientβs internal catalogue.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Parts lookup time | Up to 2h per query | Seconds per query | Immediate efficiency gain |
| Engineering overhead | Senior engineers heavily involved | close to 0 involvement | Freed core engineering resources |
| Vehicle makes covered | Limited coverage due to manual constraints | most important 12 makes fully mapped | Expanded, predictable coverage |
| Process reliability | Manual, error-prone mapping | 100% adherence to workflow | Consistent, repeatable results |
What's Next
Following the successful project, the client is positioned to extend the same mapping approach to additional manufacturers and potentially automate more of the parts-matching process.
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