I need a consistent numeric measure to track and explain why two strings were considered a match or not.

Provide a consistent numeric similarity score for every string comparison so results can be tracked over time and compared across datasets. Pair the score with clear, human-readable reasons to explain why two strings were treated as a match or not.

This feature adds a consistent numeric similarity measure for comparing two strings, enabling you to quantify how close two values are in a repeatable way. Each comparison returns a score that can be stored, trended, and used to set match thresholds for different workflows. Alongside the numeric score, the feature produces an explanation describing the main factors that increased or decreased similarity, helping users understand match decisions. This makes it easier to justify automated matching outcomes during reviews, audits, or stakeholder discussions. The score can be used to group results (high-confidence matches, possible matches, and non-matches) and prioritize manual verification where needed. It also supports monitoring quality over time by tracking average scores, score distributions, and changes after rule updates. In practice, this is useful for deduplication, entity resolution, record linkage, and cleaning user-entered text such as names or addresses. The explanation component supports debugging by showing why a surprising match happened or why an expected match failed. Overall, the feature improves transparency, consistency, and operational control for string matching decisions.