What logic are you going to use? Do you use fuzzy logic?

When matching records across systems, we typically start with deterministic logic: exact matches on key fields such as email, phone, or customer ID. If those fields are missing or inconsistent, we fall back to fuzzy logic. Fuzzy logic uses similarity metrics (Levenshtein distance, Jaro‑Winkler, cosine similarity) to score how close two strings are. We can also use probabilistic record linkage, where each field contributes a weight to an overall match score. For example, a 90% match on email and 80% on name might be enough to flag a potential duplicate. The choice of thresholds depends on the business tolerance for false positives versus false negatives. In practice, we combine deterministic rules for high‑confidence matches with fuzzy scoring for ambiguous cases, and then review borderline cases manually or with a machine learning model.