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Automated data cleaning

Messy spreadsheets,
made clean.

Upload a file full of inconsistent formatting, duplicates, and missing values. Get back data you can actually use - no code, no formulas, no cleanup on your end.

No account needed for the free tier · up to 500 rows · no credit card required

fx =CLEAN(customer_list.csv)

Before

NameEmail
JOHN DOEJOHN@GMAIL.COM
jane smithjane@gmail.com
JANE SMITHJANE@GMAIL.COM

After

NameEmail
John Doejohn@gmail.com
Jane Smithjane@gmail.com
duplicate - removed
Names Duplicate rows Dates Emails Phone numbers Postcode checks + more

Pricing

Simple, transparent pricing

Pay for only what you need. Nothing more.

Free

A$0

forever

  • Up to 500 rows
  • No account required
  • Every issue type detected
  • Free-tier cleaning rules
Try free

Pro

from A$5.99

one-time payment

  • Up to 1,000 rows, 5,000 rows, 25,000 rows
  • No account required
  • Pay once per file
  • Advanced cleaning rules applied
See tiers

Prices shown in AUD for your region. See full pricing and FAQ

The process

Three steps. No spreadsheet gymnastics.

01 · Upload

Drop in your file

A CSV or XLSX export from wherever your data already lives - no setup, no integrations to configure first.

02 · Review

See exactly what changed

Every fix is logged and reviewable - accept our automatic corrections, or make your own call on anything ambiguous.

03 · Download

Get a clean file back

Ready to drop straight into whatever tool you already use, plus a report of exactly what changed and why.

What we clean

The problems that quietly break your data

01

Duplicate rows

Same record entered twice, with slightly different spelling or formatting each time.

02

Inconsistent capitalization

"john smith", "JOHN SMITH", and "John Smith" all treated as the same person.

03

Extra whitespace

Invisible leading and trailing spaces that make identical values look different to other software.

04

Phone numbers

Matched to whichever format your file already mostly uses, with stray entries brought into line with it.

05

Dates

Standardized into one consistent format, matching whichever style your file already mostly uses.

06

Categories & states

Inconsistent labels like "Active"/"active"/"ACTIVE" and state names collapsed to one standard form.

07

Addresses & geography

Your postcode and state are checked for valid formatting. Full existence checking against an official address database is AU-only today - other regions get format validation.

08

Missing values

Filled in from patterns already in your data where possible, not guessed - otherwise left for you to review.