Dedupe Excel & CSV files
with fuzzy matching

Most tools — including Excel — only catch duplicates when two records are character-for-character identical. "Microsoft Corp" and "Microsoft Corporation" are different strings, so they survive as two separate records.

Clean compares how similar two records are, not whether they're identical. It gives every pair a score between 0 and 1, and anything above your threshold gets flagged as a likely duplicate. Common real-world messiness — differences in casing, punctuation, abbreviations, and business suffixes like Inc., LLC, and Corp. — is handled automatically before the comparison runs.

When you add a second column — matching on both company name and contact name together — Clean combines the similarity across both fields into a single decision. That's how "Jen Walsh at Acme Corp" and "Jennifer Walsh at Acme Corporation" get grouped as the same person, even though neither field is an exact match on its own. Two weak signals become one strong one.

Deduplication finds duplicate records within a single file — two rows in the same spreadsheet that represent the same contact or company. Reconciliation compares two separate files — checking which rows in your new list already exist in your reference list, and which are genuinely new. Use Clean when you have one messy file to clean up before importing to your CRM. Use the reconcile tool when you have a new list — a trade show export, an Apollo download, a vendor list — and want to check it against an existing database before importing.

Excel only catches exact character matches — "Microsoft Corp" and "Microsoft Corporation" are treated as completely different records. Clean uses fuzzy matching to score similarity between strings, so Clean catches the variants that exact matching misses. Clean also lets you match across multiple columns simultaneously, so "Jen Walsh at Acme Corp" and "Jennifer Walsh at Acme Corporation" are correctly identified as the same person.

Your file is processed in memory and deleted immediately after your session. It is never written to permanent storage, never shared, and never used for any purpose other than generating your results. You can verify this in our privacy policy.

For deduplication, 0.75–0.82 works well for most contact and company lists. Go higher (0.88+) if you want to be conservative. Go lower (0.75) if your data is clean and you want to catch more variants.

CSV, XLSX, and XLS. Maximum 10 MB per file. If your file is larger, contact us — we can run it via the API.

Unique sheet: one row per entity, duplicates removed. Clusters sheet: only the duplicate clusters with similarity scores, for manual review before merging. All rows scored sheet: your original file with a cluster ID, duplicate flag, and similarity score added to every row.

Yes. Select company name and contact name together and Clean combines both signals into a single match decision. "Jen Walsh at Acme Corp" matching "Jennifer Walsh at Acme Corporation" works because the combined name and company similarity is strong even though neither field is an exact match on its own.

Pricing is based on the number of rows in your file, excluding the header row. Clean is free for files up to 500 rows with no account required. For larger files: $1.99 up to 3,000 rows, $4.99 up to 10,000 rows, $9.99 up to 25,000 rows, $19.99 up to 50,000 rows, and $29.99 up to 100,000 rows. You can preview your results before paying — payment is only required to download.

Yes. Microsoft's Fuzzy Lookup add-in for Excel is a 2017-era download that's slow on anything beyond a few thousand rows and only runs on Windows desktop Excel. Clean is the modern fuzzy lookup for Excel: it works in your browser, handles up to 100,000 rows per file, and supports both .xlsx and .csv files. No install, no plugin, no Power Query required.

Excel's built-in Remove Duplicates only catches character-for-character matches, so "Jen Walsh" and "Jennifer Walsh" survive as two separate rows. To catch spelling variants you need fuzzy matching. Upload your file to Clean, pick the name column (and a second column like company or email if you have one), and Clean scores every pair between 0 and 1. Anything above your threshold (typically 0.78) is grouped as the same person — including casing, punctuation, abbreviations, and entity suffixes like Inc. or LLC.

Yes. Clean runs entirely in your browser — drop your .csv file into the uploader, pick the column(s) to compare, and download the deduplicated file. No spreadsheet app, no Power Query, no add-in, no install. Works on Mac, Windows, Linux, and Chromebook.

Select two or more columns when you configure the run — for example, contact name plus company name, or first name plus last name plus email domain. Clean combines the similarity across every selected column into a single match decision, so two weak signals (a partial name match and a partial company match) can become one strong duplicate flag. This is how "Jen Walsh at Acme Corp" is correctly grouped with "Jennifer Walsh at Acme Corporation".

Power Query's fuzzy merge requires Excel for Windows desktop, has limited threshold control, and slows dramatically past a few thousand rows. Clean is browser-based, works on any OS, supports up to 100,000 rows per file, lets you tune the similarity threshold, and ships three output formats (clean file, clusters for review, all rows scored) — none of which Power Query offers natively.

Clean's free tier covers files up to 500 rows. For a 50,000-row file you can preview the deduplicated results for free — see exactly which clusters Clean would flag — and only pay $19.99 to download the cleaned file. If you need to process this volume regularly or larger, the underlying Similarity API handles millions of rows for $1.99 per 10k rows on a pay-as-you-go plan.