Data Cleaner

Paste messy text. Get structured columns in seconds, instead of an afternoon of find-and-replace. The output is designed to be consistent across every record, so a field that says "256GB" in one row and "256 GB" in another comes out the same way. That consistency matters most when you're preparing data for analysis or reporting, where a single mismatched value can break a pivot table you spent an hour building.

The AI detects patterns in your input and works out the shape of the data on its own, so you don't have to hand-configure column rules for common cases. Smart column recognition handles most standard records. You can still define output columns yourself when you want tighter control. Big difference. This is the gap between a blank regex playground and data cleaning software a non-technical user can actually run.

You can name the exact output columns you need before running a clean, which tells the tool how to lay out the result. This helps with structured formats like product databases, where fields should always appear in the same order. It keeps you from getting a table that's clean but shaped wrong. Think of it as a structured spreadsheet tool you steer, not one that guesses and runs. If you've spent time reordering columns by hand after an automated tool shuffled everything, you know how much difference a small bit of upfront configuration can make to your daily workflow.

Cleaned data comes back as Excel, CSV, or through an API, so it can slot into whatever you already use. Analysts can drop a CSV into their reporting stack. Developers can wire the API into an existing workflow. When you export clean data straight into the tool you already trust, the cleanup step stops being a dead end.

Processing runs on Microsoft Azure infrastructure, and the vendor says sensitive information isn't permanently stored. For teams nervous about pasting real customer or catalog data into a web tool, that's the claim worth checking against your own policy before you commit. Security posture here is a vendor statement, not a certification you can inspect on the site. Read it as a starting point, not a guarantee. If your data is regulated, ask them directly.

You can paste data and run a clean before creating an account, which lowers the barrier to testing whether the output actually fits your data. The free tier covers 30,000 characters per month, enough for small datasets and a few trial runs. You only pay once your volume grows past that. No card, no setup call.