Overview

In North America and globally, VIN is a crucial information key used in government vehicle registers, police information systems, auto insurance companies, auto leasing companies, auto repair shops, and roadside assistance companies. Ensuring the quality of VIN data is vital for both governmental administrative and security purposes, as well as for commercial applications. Most VIN errors occur during manual transcription or copying of VINs from documents to information systems.

Our VINfix API 2.0 offers a robust and reliable technology designed to help clients correct typographical errors in problematic VINs. It employs the state-of-the-art machine learning algorithms, which use unsupervised learning and are trained on our extensive in-house VIN database to identify hidden clusters and grouping/patterns. These clusters are modeled by measuring similarity, defined by various distance metrics.

With our VINfix technology, clients can easily resolve issues when a VIN is flagged as "invalid" by VIN decoders. They no longer need to guess what is wrong with the VIN or how to correct it, as our technology can accurately determine the issue in a split second.