Measure the true semantic distance between any two texts using real AI vector embeddings — not keyword matching. Detect definition drift, validate ontology alignment, and check semantic consistency across your data systems.
// How it works: Your texts are sent to the server, where text-embedding-3-small converts each into a 1,536-dimension float vector. Cosine similarity is computed between the two vectors and returned as a score from 0 to 1. This is the same technique used by enterprise semantic search, RAG pipelines, and ontology alignment systems. Learn more in the Vector Embeddings glossary entry.
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