I’m building a fuzzy matching flow and need a consistent similarity measure to drive decisions.

Provides a consistent similarity measure you can use throughout a fuzzy matching flow to compare records reliably. This helps you make repeatable, explainable decisions when selecting matches or routing uncertain cases.

This feature offers a single, consistent similarity measure to support decision-making in a fuzzy matching flow. It is designed to produce comparable similarity values across the comparisons you run, so thresholds and rules behave predictably. You can use the similarity output to determine whether two records should be treated as a match, a non-match, or sent for additional review. The measure supports building clear decision logic, such as accept/reject bands or multi-step matching strategies. Consistency makes it easier to calibrate thresholds and evaluate changes without introducing unintended shifts in behavior. It also improves explainability by allowing you to reference a stable score when auditing why a decision was made. Typical use cases include deduplicating customer profiles, linking entities across datasets, reconciling supplier or product catalogs, and matching names/addresses with minor variations. It can be incorporated into automated workflows to prioritize candidates, rank potential matches, or trigger downstream actions based on confidence. By relying on one coherent similarity measure, you reduce ambiguity and make your fuzzy matching pipeline easier to maintain and govern.