Embedding-Powered Data Reconciliation: Where Rules Fall Short

Massive-scale data reconciliation breaks under traditional rule-based approaches—generating thousands of false positives that drown out real issues. This article demonstrates how embedding similarity and LLM classification can layer onto rule engines to handle semantic equivalence and edge cases, dramatically reducing operational noise while maintaining deterministic correctness where it matters most.