Extract table data from images into Excel.
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Three workflows a generic PDF converter does not cover.
Extract table data from images into Excel.
Turn statement transactions into structured Excel data.
Pull structured fields out of a PDF.
Compress, convert, edit and organise files in the formats people use most.
Extraction, checking and question-answering for when converting a file is not enough.
A bank statement is not a general table. It has a shape every bank shares: transactions in date order, money moving in one direction or the other, and a running balance that ties each row to the one before it. That structure is what makes a statement checkable in a way an arbitrary table is not. Each row can be tested against the balance column: previous balance, plus credit, minus debit, should equal this balance. When it does not, something was misread — and the row can be flagged rather than handed over as if it were right. So the useful question is not whether extraction is perfect. It is whether you can tell which rows to look at.
Yes. A PDF with a real text layer is read directly; a scan or photo goes through OCR first. The balance check runs either way, which matters more on a scan because that is where misreads happen.
It is flagged, not guessed. The row appears in the Review Required sheet with the reason, so a number you should check never arrives looking like a number you should trust.
Often, yes — a missing row usually breaks the running balance, and that break is what gets flagged. It is not a guarantee: a missing row can be masked if other errors happen to offset it.
No. Documents are not used to train models, and deleting a document removes it from the server. Files are encrypted in transit.
A generic converter gives you the table as it appeared. A statement converter knows what the columns mean, so it can check them. That difference is explained in full on the comparison page linked below.
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