Data cleaning, validation, and standardization

Messy files in. Clean data out.

CellWise cleans, validates, and standardizes your Excel, CSV, and JSON files into one consistent format — automatically. However many formats come in, one comes out.

XLSXCSVJSONclean · validate · mapYour formatevery file, same shapevalidatedColumn mappingDuplicates flagged
How it works

AI reads the mess. Deterministic rules write the output.

Four steps, and you can see what changed at every one of them.

01

Upload

Source

Excel, CSV, or JSON — mixed layouts and partial rows are fine.

02

Clean & validate

AI

Columns matched, values normalized, duplicates and gaps flagged.

03

Standardize

Rules

Every file mapped onto the one output format you defined.

04

Export

Output

Download it or hand it off, with a record of every change.

Excel
CSV
JSON
Column mapping
Duplicate detection
Date normalization
Unit conversion
Required-field checks
One output format
Excel
CSV
JSON
Column mapping
Duplicate detection
Date normalization
Unit conversion
Required-field checks
One output format
Problem / Solution

The data is all there. It just doesn't line up.

Nobody agrees on how to lay out a spreadsheet. The cost of that lands on whoever receives the file — usually as an afternoon of copy, paste, and find-and-replace.

What arrives

  • Every client and system exports data in its own layout, with its own column names.
  • Dates, currencies, units, and codes arrive written a dozen different ways.
  • Duplicates, blank required fields, and typos only surface after something has already gone wrong.
  • So somebody spends the afternoon reformatting spreadsheets by hand. Again, next month.

What leaves

One format, defined by you, that every uploaded file is mapped onto. Same columns, same types, same rules — whether the source was a client's export, a legacy system dump, or a spreadsheet somebody maintained by hand.

ConsistentValidatedTraceableReusable
Who it's for

Built for the people the messy files land on.

Two teams feel this problem every week: the firms that receive data from dozens of clients, and the consultants who have to make old systems agree with new ones.

For accounting & bookkeeping firms

Different clients. Different formats. One clean result, every time.

  • Client exports arrive in whatever shape their software produced. Upload them as-is.
  • Set the rules for a client once — every later file from them follows the same path.
  • Missing tax IDs, broken dates, and duplicate entries get caught before they reach your ledger.
  • Close the month without a manual reformatting pass.
For ERP & data consultants

Prep legacy data once. Standardize it everywhere it needs to go.

  • Pull exports from spreadsheets, legacy systems, and departmental files into one place.
  • Normalize naming, units, and codes across sources that never agreed with each other.
  • Validate completeness before load, not after the client calls.
  • Reuse the same rule set on the next project instead of rebuilding it from scratch.
How it's built

AI where the data is ambiguous. Rules where the answer must be exact.

Two different jobs, two different tools. Guessing what a column means needs a model. Producing the output needs rules that behave the same way every single time.

AI reads the mess

Detecting what a column actually contains, matching it to your target field, and spotting the value that does not belong is judgment work. That is where the model earns its place.

You approve the mapping

Before anything is transformed, you see how each source column was matched and what was flagged. Accept it, correct it, or set a rule that handles it next time.

Rules write the output

The transform itself is deterministic. The same file and the same rules produce the same output today, next quarter, and on the auditor's machine.

Where we stand

Other data tools grew into everything-apps. We didn't.

Tools in this category keep widening. Obvious — the product previously called Flatfile — now presents itself as a general-purpose AI agent that also drafts documents, builds presentations, and connects other software. That is a reasonable bet. It is not ours.

CellWise does one job: turn messy files into clean, standardized data. There is nothing else on the roadmap that changes that.

Repeatable beats clever

A general agent improvises each time you ask. Data work needs the opposite: the same input should produce the same output, every run.

Breadth costs depth

A tool that also writes documents and builds slides has to spread its attention. Ours goes into the parts of file cleanup that are genuinely hard.

You can check the work

Every mapping, correction, and rejected row is recorded. You can show a client or an auditor exactly what changed and why.

Trust

Every change is on the record.

If you hand cleaned data to a client, a colleague, or an auditor, you need to be able to say what happened to it. CellWise keeps that answer available by default.

01

Every mapping and correction is written to a change log you can export.

02

The standardization step is deterministic, so results can be reproduced and checked.

03

Your files are not used to train models — not ours, not anyone's.

04

Data is processed on EU-based servers with industry-standard encryption.

FAQ

Straight answers.

The questions that come up before a firm lets new software touch client data.

Excel, CSV, and JSON. Files do not need to be tidy first — inconsistent headers, partial rows, merged exports, and mixed layouts are the normal case, not the exception.

Book a demo

See it standardize your data.

Bring a file that normally costs you an afternoon. We will show you what comes out the other side, and what it would take to run the rest of them the same way.

You can also email us at info@cellwise.io.

We reply within one business day and set up a walkthrough on your own files.