Text and name matching solutions have many applications.  For example, it’s common for organizations to have multiple versions of customer names in their databases.   From financial services companies who have loan exposure to the same company listed with different names (e.g. GM, General Motors, etc.) to marketing teams trying to match customer names, the challenge is to identify duplicates and understand exposure and opportunity.

We offer a number of standard text models such as Levenshtein  and Jaro-Winkler and also a have proprietary models to ensure that our text and name matching models are highly accurate.

Our solutions run in-database or using NVIDIA GPUs and can be called by standard programing languages and reporting tools.