Standardize data to compare used vehicles more effectively

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Making data comparable despite the diversity of sources

In the used vehicle resale industry, access to reliable, readable, and consistent information is essential for identifying the best opportunities. Each year, between 3 and 4 million vehicles enter the market, generating a large volume of data from highly heterogeneous sources. Classification systems vary by supplier, making direct vehicle comparisons extremely difficult.
For this client, the challenge was twofold: to aggregate the data in a structured way and adapt it to their own evaluation criteria - customized to support commercial decision-making.

LLM models for large-scale information standardization

Explorai implemented a solution powered by large language models (LLMs), such as GPT-3.5, to automate the collection of vehicle data - regardless of format or source - and convert it into a standardized format aligned with the customer's internal taxonomy. These models are trained to recognize, extract, classify, and organize key attributes of each vehicle, ensuring seamless integration with the company's customized data structure.

Centralized, readable and comparable information

Data standardization has significantly enhaced the user experience on the customer's web platform. Each vehicle data sheet - regardless of its original source - is now presented in a unified format, making it easier to compare available options. This reduces analysis time and increases confidence in the data presented.

Better-informed decisions to boost sales performance

Beyond the improved interface, this transformation equips analysts and managers with structured information aligned to internal performance benchmarks. Purchase and resale decisions can now rely on cleansed, categorized, and interpretable data - strengthening the relevance of strategy trade-offs. The solution positions the company as a technological leader in the automative resale industry.