Multi-line editor for the cleanup logic
IFERROR-wrapped VALUE-wrapped DATEVALUE-wrapped INDEX gets long fast. The multi-line editor renders it as readable code instead of a 250-character ribbon you have to parse character by character.
Numbers stored as text, dates the wrong way around, blanks where there should be zeros, totals that don't tie. The last mile between an ERP export and a report nobody questions is where Formula Foundry lives — the multi-line editor, the visual builder, and an AI assistant that explains why the SUMIFS keeps returning zero.
Free plan — no credit card required.
From raw export to clean dataset.
Color-coded editor + AI explain make it obvious when a column you thought was numeric is actually text — including the silent kind that looks right until SUMIFS returns zero.
Visual builder for the conditional logic (IFERROR / VALUE / DATEVALUE / TRIM cascades). Real-time preview so you know the formula works on row 1 before you fill 50,000 cells.
Same export structure next month. Same cleanup formula. Save the working version once; insert it on next month's export instead of rebuilding it.
The toolkit for the messy middle.
IFERROR-wrapped VALUE-wrapped DATEVALUE-wrapped INDEX gets long fast. The multi-line editor renders it as readable code instead of a 250-character ribbon you have to parse character by character.
Build the cleanup formula as a tree — pick the function, fill the arguments, see real-time output on row 1 before you fill the column. Stops the 'tweak and pray' loop.
When SUMIFS returns zero on what looks like a numeric column, ask the AI to explain why. It catches the 'numbers are stored as text', 'date is in the wrong locale', and 'whitespace in the lookup key' bugs that every clean-up job hits.
Same export, same cleanup. Save the working formula once, insert it next month with one click. The team library means you're not the only person who can run the cleanup.
Building the cleanup formula in the visual builder.
ERP cleanup FAQs
Clean the next export in an afternoon.
Install Formula Foundry, drop in your messy CSV, and turn it into a dataset the rest of the org can actually use.