Extract Exactly the Invoice Fields You Need — Nothing More, Nothing Less
Most invoice extraction tools dump everything they can find into Excel — 40 columns of noise. You spend the next 15 minutes deleting irrelevant columns, reordering the rest, and renaming headers to match your AP system. Column-name extraction gives you ONLY the fields you asked for, in the order you typed them. You type "Vendor Name | PO Number | Due Date | Total" and that IS your output.
5–10s per page · Up to 99% accuracy on printed text · No template setup
You Name the Fields — You Get Exactly Those Fields
This approach is called Column-Name Extraction. Instead of the tool deciding which fields to pull from an invoice, you specify every column you want by name — and the AI locates each one anywhere on the page, mapping it to the right output column regardless of how the source document labels it.
Why does this matter? Most invoice tools default to extracting everything they can find — 30, 40, sometimes 50+ columns. You then manually delete what you don't need, reorder what's left, and rename headers. Column-name extraction eliminates that post-processing step entirely: the columns you name are the only columns you get, in the order you typed them.
The Value Isn't Speed — It's Never Cleaning Up Output Again
Most invoice extraction tools compete on how fast they process a page. But the real time sink in AP workflows isn't the extraction itself — it's what happens after. Column deletion, reordering, header renaming, and format reconciliation can take longer than the original manual entry. Selectivity solves that.
What Most Tools Give You: Everything, Unfiltered
40+ columns dumped into Excel. The tool extracts every data point it detects — Vendor Name, Invoice Number, 8 date variations, 5 different tax breakdowns, remittance address, bank details, shipping info, line-level metadata, and fields you have never needed. You open the spreadsheet and spend the first 10 minutes hunting for the 6 columns you actually want.
Column order is unpredictable. One invoice produces headers ordered alphabetically. Another orders them by position on the page — top-left to bottom-right. A third groups them by data type. Every batch produces a slightly different column layout, so you cannot paste the output directly into your AP system or pivot table without manual reordering first.
Header names don't match your system. Your AP tool expects "Vendor Name" but the output says "Supplier." Your ERP expects "PO Number" but the output says "Purchase Order Ref." Every column header needs to be renamed before the data flows into your downstream process. Multiply by every batch and the rework adds up.
What Column-Name Extraction Gives You: Exactly What You Named
You type the columns — those ARE the only columns you get. Type Vendor Name | Invoice Number | PO Number | Due Date | Total and your output has exactly five columns. No extra fields, no hidden metadata, no column you have to delete. The columns you don't name are columns you never see. This is called Custom Column Extraction — the column names you enter become the extraction instructions, and the AI ignores everything else.
Columns appear in the exact order you typed them. Need "Vendor Name | PO Number | Invoice Number | Due Date | Total" in that specific sequence because your approval workflow reads them left-to-right? Type them in that order. The output matches your intent — no reordering required. Whether you process 10 invoices or 500, the column sequence is deterministic: it is the sequence you specified.
Headers ARE your column names — rename-free by design. The column header in your Excel output is the exact text you typed. No mapping step, no "output field → system field" translation, no renaming ritual before import. Your AP system expects "PO Number"? Type "PO Number" as the column name and that is the header you get. Ideal for workflows where the output feeds directly into an ERP, accounting software, or shared spreadsheet.
From a Stack of Invoices to a Clean, AP-Ready Spreadsheet
Here is the three-step workflow that turns a folder of mixed-format invoices into exactly the columns your AP process needs — no more, no less.
Upload your invoices — any format, any vendor
Drop in PDFs, scanned paper invoices, or phone photos — all in one batch. Each page processes in 5–10 seconds. Formats can be mixed freely: a machine-generated PDF from one vendor's ERP and a photo of a paper invoice from another go into the same upload. For recurring collection from external parties, use a Collection Link — a shareable upload URL where vendors can submit invoice files directly to your account's processing queue without creating their own account.
Type the column names you need — exactly those, in order
Enter your field names separated by the pipe character: Vendor Name | Invoice Number | PO Number | Due Date | Subtotal | Tax Amount | Total. Add line-level fields for itemized extraction: Description | Qty | Unit Price | Line Total. For automatic classification, use an Inferred Column like GL Code (options: 5100-Supplies / 5200-Software / 5300-Services) — the AI reads each invoice and assigns the appropriate code, even though the document itself has no GL Code field.
Download — zero cleanup needed
Export to XLSX, CSV, or JSON. The output has exactly the columns you named, in the order you named them, with the headers you typed. Paste directly into your AP system, ERP, or shared spreadsheet — no deletion, reordering, or renaming. If you use Google Sheets, the Google Sheets add-on lets you extract results into an active sheet through a sidebar without leaving your spreadsheet. For calculations during extraction, use a Computed Column — write the formula directly in the column name, like Line Total (Qty × Unit Price), and the AI performs the math as it extracts, so you get calculated values alongside raw data in one pass.
When It Works Best — and When to Be Specific
Column-name extraction produces reliable results across standard invoice formats. A few edge cases are worth knowing before processing a large AP batch.
When it works best
Machine-generated PDFs from accounting and ERP software. Invoices from QuickBooks, Xero, SAP, NetSuite, and similar platforms extract with near-perfect accuracy — all fields have clean, digitally embedded text.
Standard printed invoice fields across any label variation. The AI matches fields by meaning, so "Invoice #," "Bill No.," "Ref. Number," and "Document ID" all map to your Invoice Number column without per-vendor configuration.
Niche and custom fields you define precisely. Ask for Payment Terms, IBAN, Remittance Address, or Early Payment Discount — if the information is printed on the document, a specific column name finds it.
Clean office scans at standard quality (200 dpi or above). Scanned paper invoices from a desktop scanner or all-in-one office printer extract reliably, including vendor stamps, rubber-stamp dates, and printed line item tables.
Worth a spot-check
Ambiguous field names on invoices with multiple amount values. A column named Amount on an invoice that shows Subtotal, Tax, Discount, and Total may return the wrong value. Use specific names: Total Due or Grand Total.
Line items that span page breaks. If a product row continues across a page boundary, extraction may split or partially capture it. Spot-check multi-page invoices with long line item tables by verifying the last row on each page.
Handwritten corrections over printed figures. When a vendor writes a corrected value by hand over a printed number, the AI may pick either value depending on visibility and contrast. Flag invoices with visible manual amendments for human review.
The tool extracts what is on the page — it does not validate accounting accuracy. If a vendor prints an incorrect subtotal or tax calculation, the tool extracts the printed values. Arithmetic verification and tax compliance checks remain a human step before payment.
Frequently Asked Questions
Can I choose exactly which fields to extract and skip the rest?
Yes — that is what column-name extraction is designed for. You type the column names you want and the AI extracts only those fields. Type Vendor Name | Invoice Number | Due Date | Total and your output has exactly four columns. Anything you do not name is not extracted. The column names you enter become the exact headers in your Excel file. No extra columns to delete, no fields you did not ask for.
What if the field label on the invoice is different from my column name?
The AI matches by meaning, not by exact label text. If you type Invoice Date, the AI finds the corresponding value whether the original document says "Date," "Bill Date," "Issue Date," or "Date of Invoice." This is semantic matching — the AI understands what each field represents, so label variations across vendors do not break extraction. The same column definition works for a vendor whose invoices are in English, another in German ("Rechnungsdatum"), and a third in French ("Date de facture").
Can I extract line item details alongside invoice-level fields?
Yes. Define columns for both invoice-level fields (Vendor Name | Invoice Number | Invoice Date) and line-level fields (Description | Qty | Unit Price | Line Total). The AI extracts each line item as its own row and repeats the invoice-level fields on every row. A 10-line invoice produces 10 output rows, each carrying the full invoice context — filterable by vendor, date, or amount without losing traceability to the source document.
What happens when a field I asked for doesn't exist on a particular invoice?
That cell is left blank or marked N/A. The AI does not invent, guess, or fabricate data. This makes it safe to process a mixed batch — invoices with PO numbers alongside invoices without — in a single run. The column stays in your output table with clearly empty cells for the invoices where it does not apply, so you can handle those cases downstream without calculation errors from forged values.
Does this work with scanned invoices and phone photos, not just digital PDFs?
Yes. The tool is built on a Vision Large Model that reads the visual layout of the document, not just embedded text layers. Scanned paper invoices, phone photos, slightly skewed or low-contrast images all work. Recognition accuracy for printed text reaches up to 99% on clean scans at standard office quality (200 dpi or higher). Unlike traditional OCR that struggles with non-digital inputs, the vision-based approach handles the full range of real-world invoice formats.