Normalize messy company names, free
Paste a list, get legal suffixes stripped and casing fixed instantly. Runs entirely in your browser.
Your list
Up to 25 company names at a time. Nothing leaves your browser.
| # | Original | Normalized | What changed |
|---|---|---|---|
| 1 | — | ||
| 2 | — | — | |
| 3 | — | — | |
| 4 | — | — | |
Why not just a spreadsheet formula?
“You end up with a massive, nested SUBSTITUTE formula that breaks if it sees a suffix it doesn't recognize.” That's the trap with hand-rolled formulas: every new suffix format is another edit to a formula that's already hard to read. This tool is the same rules-based idea, done as a maintained list of about 60 suffix patterns instead - and honest about where rules stop being enough.
| Before | After | What it shows |
|---|---|---|
| ACME CORPORATION | Acme | ALL CAPS repaired, suffix stripped |
| müller & söhne gmbh & co. kg | Müller & Söhne | lowercase repaired, compound suffix wins over "GmbH" alone |
| "Tech Company, INC." | Tech Company | wrapping quotes and suffix removed |
| SAP | SAP | acronym allow-list, left exactly as-is |
| 1234567 Ontario Inc. | 1234567 OntarioReview | a real numbered-company format - flagged for review, not guessed at |
Rules handle the common cases well. The last row above is the honest exception: a numbered company name isn't something a suffix list can resolve, which is exactly what Datablist's AI-based Company Name Cleaner enrichment is for.
What it fixes
Legal suffix stripping
About 60 suffix forms across regions - Inc, LLC, Ltd, GmbH, S.A., Pty Ltd, B.V. and more - matched longest-first, so a compound like "GmbH & Co. KG" is handled as one unit instead of leaving a stray "& Co. KG" behind.
Casing repair
ALL CAPS and all-lowercase names are recased to title case, with an allow-list so acronyms like SAP, IBM, KPMG and AWS aren't turned into "Sap" or "Aws".
Whitespace, quotes & punctuation
Collapses doubled spaces, strips wrapping quotes left over from a spreadsheet paste, and cleans up the trailing comma or period a suffix leaves behind once it's removed.
Duplicate detection
Flags a row once it normalizes to the same name as an earlier one, even when the raw text only differed by casing or a suffix - "Acme Inc." and "ACME, INC" are caught as the same company.
Trusted by startups and enterprises
35,000+
companies using Datablist
20M+
rows enriched every month
60+
lead generation tools
How it works
Step 1
Paste your list
One company name per line, up to 25 at a time. Works with data straight out of a CRM export, a scraped lead list, or a spreadsheet column - messy casing and legal suffixes included.
Step 2
Rules normalize each name
Legal suffixes are stripped (about 60 forms, longest match first, so "GmbH & Co. KG" is handled correctly rather than leaving a stray "& Co. KG"), casing is repaired, and anything the rules can't confidently fix is flagged Review instead of guessed at.
Step 3
Copy or download the result
Copy the normalized names straight to your clipboard, or download the full comparison as a CSV with the original, normalized, and what changed for every row.
Use cases
Normalizing names before they hit your CRM
Catch inconsistent casing and legal suffixes before duplicate "Acme Inc" and "ACME Corporation" records pile up in the same pipeline.
Deduplicating a list you already have
Two rows that only differ by casing or a suffix read as different companies to most tools. Normalize first, then the duplicates become obvious.
Refreshing a stale CRM export
An export pulled from an older system is exactly where inconsistent naming accumulates. Normalize it on the way back in rather than carrying the mess forward.
Normalizing a list before an email send
Company name fields feed into personalization tokens. A name with stray quotes or ALL-CAPS shouting is the kind of detail a recipient actually notices.
Frequently Asked Questions
No, and it doesn't claim to be. It's rules-based: legal suffix stripping, casing repair, and duplicate detection. Datablist's paid Company Name Cleaner enrichment is the AI-based version - it understands context, industry jargon, and formatting the rules here can't.
25 per run, with no signup required. Paste more and the extras are left out of that run rather than silently dropped - use the enrichment for larger lists.
It means the rules could not confidently improve that row - things like a numbered company name, a stray URL, or unusual symbols. Rather than guess, the tool flags it. Those rows are exactly what the paid AI enrichment is for.
No. Normalizing runs entirely client-side in JavaScript, the same way Datablist's other free tools (JSON to CSV, CSV Diff, CSV Splitter) work. Nothing is uploaded or sent to a server.
This tool matches a fixed list of about 60 legal suffix patterns. The paid Company Name Cleaner enrichment is AI-based, costs 5 credits per name (cached rows are free), and handles cases the rules can't - it understands context, not just string matching.
Yes - the paid enrichment runs directly on a dataset inside Datablist, so you don't need to export and re-paste. This free tool is for a quick list you have on hand right now.