Clean and structure messy data into spreadsheets
Raw exports and copy-pasted tables are not data. They are work waiting to happen. Give Kuse the mess and the structure you need. It produces a clean, usable spreadsheet.
No signup needed · 1,800 free credits
Data cleaning is the work before the work
- Every data project starts with hours of cleanup. Inconsistent formats, merged cells, duplicate rows, missing values — before any analysis can happen.
- Manual cleaning is error-prone and untraceable. Corrections get made, then made again differently next time. No audit trail, no consistency.
- The same messy exports arrive every month. The same person spends the same hours cleaning the same format each cycle.
Messy data in, clean spreadsheet out
Describe the work in plain language
Tell Kuse what the messy data looks like, what the clean output should look like, and any specific rules to apply.
Connect your apps
Connect Google Sheets, Airtable, or a CSV source. Kuse processes new data files on a schedule or on demand.
Set a schedule or run it anytime
Run monthly on recurring exports or on demand when new data arrives that needs cleaning before analysis.
Get finished results in your workspace
A clean, structured file lands in your workspace alongside the source data — ready for analysis or import.
Kuse Workflows
Clean data in your workspace, without the spreadsheet gymnastics.
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50,000+ professionals use Kuse every day
A practical guide to AI data cleaning
01
What is AI data cleaning?
AI data cleaning uses AI to automatically identify and fix inconsistencies, duplicates, missing values, and format errors in raw datasets. Instead of writing cleaning scripts or manually fixing spreadsheets, you describe the output structure you need and Kuse applies the transformations — reusably, every time new data arrives.
02
Who is AI data cleaning for?
- Operations teams processing recurring CRM or billing exports
- Marketing teams standardizing lead data before CRM import
- Finance teams normalizing expense or transaction data
- Analysts who spend too much time cleaning before they can analyze
- Anyone who receives the same messy format on a recurring schedule
03
What types of data issues can AI fix?
- Inconsistent date formats (DD/MM/YYYY vs MM-DD-YY)
- Mixed case name fields (john DOE, JOHN Doe)
- Duplicate records with slight variations
- Missing values that can be inferred from other fields
- Non-standard category labels that should be normalized
- Phone number and address format inconsistencies
04
How to set up recurring AI data cleaning
Share an example of the raw data and an example of what clean looks like. Be explicit about your rules — "standardize dates to YYYY-MM-DD", "merge first and last name fields", "flag but keep duplicates, do not delete." Connect your data source and output destination. For recurring exports, set the workflow to run automatically when new data arrives.
05
Common mistakes to avoid
- Not preserving raw data: Always save the source alongside the clean version
- Deleting duplicates automatically: Flag them first, delete after human review
- Vague cleaning instructions: "Make it clean" does not work — define the rules explicitly
- No validation step: Spot-check the output against the source before using it in analysis
06
Why AI data cleaning works better in Kuse
Data cleaning scripts apply fixed rules. Kuse applies judgment. Tell it "this column should be a date in YYYY-MM-DD format" and it normalizes every variation it finds, including formats you did not anticipate. Because your schema templates and cleaning rules live in your workspace, recurring cleanups get faster and more accurate each time they run.
07
Frequently asked questions
Can Kuse clean data directly from Google Sheets?
Yes. Connect your Google Sheets as a source. Kuse reads the current data, applies your cleaning rules, and outputs a clean version.
Will Kuse delete duplicates automatically?
By default, Kuse flags duplicates and keeps both records. You can configure it to remove them automatically if you prefer.
Can I reuse the same cleaning rules for recurring exports?
Yes. Define your rules once in the workflow prompt. Every time the workflow runs, it applies the same rules to new data.