Why fuzzy matching wins
Excel's Remove Duplicates only catches exact matches. Real duplicates differ by case, punctuation, spacing, nicknames, and typos — fuzzy matching catches those.
This is the core of the whole thing. Excel and Google Sheets can only remove exact duplicates: two rows are "the same" only if every character matches. Real-world lists are never that tidy, so exact-match dedupe leaves most of the duplicates behind.
The problem with exact match
Consider three rows that any human would call the same company:
| Row | Company | |
|---|---|---|
| 1 | Acme Inc | info@acme.com |
| 2 | Acme, Inc. | Info@Acme.com |
| 3 | ACME | info@acme.com |
Excel's "Remove Duplicates" sees three distinct values in the Company column and keeps all three. The differences are trivial to a person — a comma, a period, casing, a trailing space — but they're enough to defeat character-for-character matching.
The same thing happens with people: Bob versus Robert, bob@x.com versus
Bob@X.com, a transposed letter in a street name, a missing middle initial.
What fuzzy matching does instead
Fuzzy matching scores how similar two records are across several fields, instead
of demanding an exact string match. It normalizes obvious noise (lowercasing,
trimming whitespace) and uses similarity scorers that treat near-identical text as
a strong signal. So Acme Inc and Acme, Inc. score as a near-certain match, and
the three rows above collapse into one.
Crucially, it weighs the whole record, not one column. A shared email or phone can confirm a match even when the names differ (a nickname, a maiden name), and a strong identifier like a national id counts for more than a weak one like a city. That's how it catches real duplicates without merging two different people who happen to share a last name.
Take any list you currently hand-dedupe and search it for one company you know appears twice with different spelling. Excel's Remove Duplicates won't touch it. That single case is the gap this tool closes.
Fuzzy, not reckless
Matching more loosely than exact-match raises an obvious worry: won't it merge things that aren't the same? Two safeguards keep that in check:
- The engine uses a tuned acceptance threshold and excludes misleading columns (provenance ids, a source label) from the match, so it doesn't link rows just because they came from the same export.
- Every proposed merge is then reviewed by the AI agent, which rejects over-merges and flags anything genuinely ambiguous for you to check.
You get the recall of fuzzy matching with a precision backstop on top.