Fuzzy matching
The definition as the field writes it comes first, weaknesses and all. What Entity Bind does about it comes last, in its own paragraph, so you can take one without the other. Filed under matching methods.
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
every figure, with its population →What fuzzy matching means
Fuzzy matching is the everyday name for approximate string comparison: Levenshtein distance, Jaro–Winkler, trigram overlap, token-set ratios and the fuzzy join in a warehouse. It answers 'how similar are these two strings' and it answers it fast, which is why almost every organisation's first duplicate hunt is a fuzzy match and why so many stop there.
The limitation is not that similarity scores are inaccurate; it is that they are uncalibrated and unidimensional. A 0.92 similarity does not mean a 92% chance of being the same company, and the same 0.92 means something different for a two-token name than for an eight-token one. Similarity also cannot represent conflicting evidence: two records with a 0.97 name similarity and two different registration numbers are not the same company, and no string metric can express that.
The failure mode is systematic rather than random. Fuzzy matching over-merges same-stem groups — a holding company and its eleven subsidiaries, all sharing a name and a registered office — and under-merges rebrandings and trading names, which are the duplicates most worth catching.
Entity Bind uses string similarity as one feature among many rather than as the decision, weighs it against identifier agreement and conflict, and refuses to bind anything below its published confidence floor. Where the evidence will not carry a decision, the record comes back as an abstention with a ranked shortlist rather than as the best-scoring row.
For an illustrative company review, Tutorial Willow Ltd and Tutorial Willow Holdings Ltd can share a group domain while quoting different company numbers from the same register. Preserve the separate companies and verify the identifiers. Conversely, Tutorial Willow Ltd and TUTORIAL WILLOW LIMITED quoting the same register-qualified number provide agreement that spelling alone cannot establish. A name-only row can remain unresolved. These are fictional teaching examples, not measured product outcomes.
Where this shows up in Entity Bind
Three pages where this term stops being vocabulary and starts changing an answer, a payload or a price.
- Compared with fuzzy matchingA score for how alike two names look, against a decision about which company they name — and the rows where the fuzzy matching you already run wins.Open
- What is entity resolution?Entity resolution decides which records in one or more datasets refer to the same real-world entity, and joins them under a single stable identifier.Open
- How it worksThe seven-stage cascade in order, what each stage can settle on its own, and what it hands to the next.Open
The fastest way past a definition is a file: which of your rows are the same company, which of your existing matches we would not have made, and what we could not decide.
Start freeThe terms next to this one
Each with its own sentence, because the next question is usually about the word beside this one.
Edit distance
Edit distance counts the minimum number of single-character insertions, deletions or substitutions needed to turn one string into another; Levenshtein distance is the standard form.
Jaro–Winkler
Jaro–Winkler is a string similarity measure that scores character agreement within a sliding window and then boosts pairs sharing a common prefix, which makes it well suited to names and poorly suited to truncation.
Probabilistic matching
Probabilistic matching scores a candidate pair by weighing the agreement and disagreement of several fields into a single number, then applies thresholds to that number rather than to any one field.
Confidence floor
A confidence floor is the score below which a system refuses to return a match at all, expressed as an acceptance contract rather than as a promise about accuracy.
Over-merge
An over-merge is a cluster containing records from more than one real entity — the failure that turns two customers, two suppliers or two counterparties into one.
Calibration
A confidence score is calibrated when it matches observed reality — of the decisions returned at 0.95, about 95% should be correct — which is a different property from being accurate, and a much rarer one.
The other 12 terms on this shelf
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
Internal fixtures only. The historical 2026-08-29 per-tier recording covers 317 GB companies with identifier-rich arrivals seeding the catalogue first; the context tier includes country and registered-town prose. Its legacy replay has no full-prompt-verified calls and cannot certify current calibration. The September 5 CI reports cover 1,176 labelled pairs and 705 records over 250 companies using offline stubs. These are not independent customer holdouts or live production validation; the 99% minimum-input product target remains unmet. Scope is company and organisation identity only — not people, vessels, bank-account ownership, sanctions or emissions. Results may not be used for eligibility decisions about individuals, including employment, credit and tenancy.
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