How It Works

See what CleanSmart does to messy data, step by step.

Your data has problems you can see and problems you can't. Inconsistent names, missing fields, duplicate records hiding behind typos. Every one of them costs you time, accuracy, or both.

CleanSmart runs your data through four cleaning steps at once: AutoFormat standardizes formatting, SmartMatch finds and merges duplicates, SmartFill predicts missing values, and LogicGuard flags anything that doesn't add up. Upload a file, review the changes, export clean data. That's it.

Names cleaned up, no manual scrubbing.

Capitalization is all over the place. Some names are uppercase, some lowercase, some a mix. NULL values sit where names should be. AutoFormat standardizes casing across every name field in your dataset so your records look consistent and your outreach doesn't start with "Dear SKYLER."

A table with two columns, first and last name. Some last names are

A table listing first and last names. Names include Ari, Skyler, and Rory.

Emails and phone numbers, standardized automatically.

Phone numbers come in every format imaginable: dashes, dots, parentheses, country codes, no country codes. Sometimes a URL ends up in the email column. Sometimes an email ends up in the phone column. AutoFormat detects what each value actually is, fixes what's wrong, and standardizes everything to a consistent format. One pass, no formulas.

A spreadsheet with email addresses and corresponding phone numbers.

Company names and job titles, consistent across every record.

"APERTURE LABS," "tyrell corp," "gringotts bank." Same problem, different column. And titles are worse: "VP Marketing," "V.P. Marketing," "Vice President Marketing," and "???" all in the same dataset. AutoFormat standardizes company name casing and normalizes title variations so every version of the same role matches, no matter how it was entered.

A spreadsheet listing company names and job titles; highlighted entries include

Table listing companies and job titles, with some rows highlighted in green.

Addresses resolved, cities filled from what's already there.

States spelled out, abbreviated, uppercase, lowercase. Country shows up as "US," "USA," "United States of America," or "UNITED STATES." Postal codes are gibberish half the time. SmartFill uses the valid data points in each record, like a real zip code and state, to predict the correct city. AutoFormat handles the rest: consistent state abbreviations, standardized country names, and cleaned-up postal codes.

A spreadsheet with address information including city, state, postal code, and country.

Table of addresses with columns for street, city, state, zip code, and country.

Revenue, headcount, and dates in every format but the right one.

"$170M," "USD," "unknown," and an actual number all in the same revenue column. Employee counts show up as "fifty," "many," "3+," or just a dash. Dates are the worst: "13/40/2025," "yesterday," "26-Jul-2023," and "04/30/24" sitting side by side. AutoFormat normalizes currency values, converts text numbers to actual numbers, and standardizes every date to a single consistent format. LogicGuard flags the ones that can't be real, like a date that doesn't exist.

Table with columns: annual revenue, employee count, and created at. Data includes financial figures, employee numbers, and dates.

Table showing annual revenue, employee count, and creation dates for various companies.

That's one cleaning pass. Every fix you just saw, names, emails, phones, addresses, company data, dates, and numbers, happens automatically when you upload a file. No formulas, no scripting, no doing it field by field.

Ready to clean your data?

Start Your Free Trial