Member Spend Intelligence

Hundreds of thousands of member purchases. No scalable way to identify savings opportunities against the buying group catalog. Manual analysis measured in weeks put a hard ceiling on how many members could be helped in a year.

A semantic AI matching engine that cross-references every member purchase against the full national contract catalog regardless of how the product is described in either system. Unit of measure normalisation. ML ranking surfacing the highest-confidence match first. Delivered on the client's existing platform.

Matching works across catalogues that share no common key, at the scale of a national contract book.