Two exports, matched every month

TidyRecon cleans your shop and warehouse exports, matches them row by row, and saves the steps so next month is one run.

Open the app Compare two CSVs free

9 of 13 matchedclosed 2024-01-31

SO-1007	Values differ: 22.40 against 22.41	16days
SO-1009	Only in orders	4days

A handwritten merchant's ledger page from September 1833, with customer names on the left and columns of amounts on the right. — A merchant's ledger, Pittsboro, North Carolina, 1833. Each line was once ticked against another book by hand.

The same two files, every month

Once a month someone exports orders from the shop and shipments from the warehouse, pastes both into a spreadsheet, and hunts for the rows that disagree.

Most of it is last month's work again: trim the spaces, fix the dates, line up the order numbers. Then the real question. Which orders never shipped, and which shipments match no order?

TidyRecon keeps that work. You clean the files once, the steps become a recipe, and every month after you drop in the new exports and press Run.

Nothing is uploaded. The browser tab you have open reads the files. See how to check that yourself.

A painted scene of Jakob Fugger standing beside his seated bookkeeper, who writes in an open ledger in front of drawers labelled with cities. — Jakob Fugger and his bookkeeper, Augsburg, about 1517. Each drawer holds the accounts of one branch.

One recipe, every month

January: clean the files once.

Trim, rename and fix dates. Each step is written into the recipe, never into your file.

Save it, and it runs the same way.

The steps and match keys get a fingerprint, so February runs exactly like January did.

February: drop the new exports, press Run.

What matched closes. What did not is still open, and now 29 days older.

Cleanorders.csv · 12 rows

Name	Total
SO-1002	45.00
SO-1003	12.50
SO-1004	30.00

Steps2 steps

Trim spaces in Name
Upper-case Name

RecipeVersion 1 of 1

Name	Orders to shipments
Steps	2
Match on	Name = Order Number
Fingerprint	a3f09c1e…

Run

11 of 13 matchedclosed 2024-02-29

Open item	Since	Age
SO-0998	2024-01-02	58days
SO-1007	2024-01-15	45days

Try the match

Twelve sample orders against the warehouse's shipment report. It runs here, in this tab.

7 of 15 matchedClosed 2024-01-31

Matched7

Values differ1

Only in orders4

Only in shipments3

Open item	Class	Age
SO-0998	Only in shipments	29days
SO-1003	Only in orders	25days
SO-1003	Only in shipments	23days
SO-1007	Values differ	16days
SO-1009	Only in orders	4days

3 more open.

Close the month

Trim spaces Ignore case

Amount tolerance

Turn on Trim spaces and Ignore case, then close February: two late shipments arrive and their orders close.

Run it on your own files

When the export changes

Shops rename columns without warning. A recipe that kept going would match the wrong thing, so it stops and says why.

Stops on a renamed column

No result table until the shape matches the recipe again.

Names the column

Order became OrderNo in this month's file.

Keeps what is open

Last month's open items wait, with their age.

RerunVersion 2 of 2

The left file changed shape, so the recipe stopped before matching. Missing:

Order now OrderNo

Open item	Age
SO-0998	32days
SO-1007	19days

Previous version

Download CSV exceptions only

Files stay on this computer

Keeps every version

Step back to last month's recipe in one click.

Exports the exceptions

A CSV of only the rows that need a look.

Never uploads

Recipes and runs live in this browser.

Ten templates to start from

Each comes with sample files and a guide.

Shop and warehouse

Shopify orders and 3PL shipments on Name Orders and the payout report on Name Payouts and the orders inside them sum minus fee Amazon settlement and orders total compared Stock count and system inventory qty compared

Money

PayPal and the bank statement dates within 2 days Timesheets and payroll hours compared

Customers and data

CRM customers and subscriptions on email Data before and after a migration status compared Supplier prices against last period price compared

Postal workers lined up beside Royal Mail parcel post vans outside a stone post office building in 1919. — Parcel post begins at the General Post Office, 1919. Every parcel still has to meet its order.

Plans by rows

Prices are set at launch. The limits are fixed.

Questions

Is my data uploaded? No. The file is read by this browser tab and stays on your computer. Recipes and runs are kept in the browser's own storage.

What files can it read? CSV, Excel (.xlsx and .xls) and JSON, with the encoding, delimiter and header row found for you.

Does it use AI to match? No. Rows match exactly, by a fixed similarity score, within an amount tolerance or within a date window. The same files and recipe always give the same result.

What if a column is renamed? The run stops before matching and names the column that is missing, so nothing is matched on the wrong field.

Can one payout match many orders? Yes, on Pro. A payout matches the sum of its orders minus the fee.

Does it work offline? Yes. After the first visit the app opens without a connection.

When should I not use it? When you need a live connection to a shop or a bank. TidyRecon works on files you have already exported. See the comparison.

Where do I start? Pick the template closest to your files, download its samples, and press Run once before you try your own.