Originally published on tamiz.pro.
You feed two names into a matching engine and get a similarity score. Easy enough. But what happens when two strings look nothing alike yet mean the same thing — or look nearly identical but mean completely different things?
This isn't a hypothetical. When we ran a vector similarity detector over a dataset of 2.9 million charity pair combinations, the results exposed a gulf between spelling-based matching and meaning-based matching that no edit-distance algorithm could bridge.
The Intuition Gap
Traditional fuzzy matching relies on character-level overlap. Levenshtein distance, Jaro-Winkler, trigram hashing — these all answer the same question: how many characters differ between string A and string B?
