Zero setup, zero training
No templates, no labeled data, no schema files. Drop the document in and Docusift returns clean structured data.
Invoices extraction
Extract Totals from Invoices
Pull every totals value out of any invoices — including scans and multi-page variants — into clean structured JSON. Each value carries a confidence score and a citation back to its source pixel.
Pulling totals out of an invoice by hand is slow, and it is exactly where a mistake slips through. No two vendors lay out an invoice the same way. The total sits top-right on one, bottom-left on the next; tax is a line on some and baked into unit prices on others; line items wrap, span pages, or hide inside a nested table. Template tools break the moment a supplier changes their layout.
Docusift reads each invoice visually, so it finds the vendor, invoice number, dates, every line item, tax, and total regardless of where they sit — and it does it on a layout it has never seen before. Line quantities and unit prices come back tied to their descriptions, and every field carries a confidence score.
Each extracted value ships with a per-field confidence score and a citation back to the exact spot on the page it was read from, so low-confidence totals route to review automatically — and the clean data pushes to Google Sheets, a webhook, QuickBooks, or Xero.
| Line | Description | Qty | Unit price | Amount |
|---|---|---|---|---|
| 1 | Steel bracket, 40mm | 120 | 2.15 | 258.00 |
| 2 | Powder coat finish | 120 | 0.60 | 72.00 |
| 3 | Freight — LTL | 1 | 86.50 | 86.50 |
| Tax (8.25%) | 34.03 |
Example structured output
{
"document_type": "invoice",
"vendor": "Ironline Fabrication",
"invoice_number": "INV-20418",
"invoice_date": "2026-04-11",
"due_date": "2026-05-11",
"currency": "USD",
"line_items": [
{ "description": "Steel bracket, 40mm", "quantity": 120, "unit_price": 2.15, "amount": 258.00 },
{ "description": "Powder coat finish", "quantity": 120, "unit_price": 0.60, "amount": 72.00 }
],
"subtotal": 416.50,
"tax": 34.03,
"total": 450.53,
"confidence": 0.99
}Why teams choose Docusift
98%+ field accuracy
Every value ships with a confidence score and a citation back to the source pixel in the original document.
Fast turnaround
Seconds per document, not minutes. Built for production pipelines, not batch jobs.
Privacy first
Workspace-isolated processing, encrypted in transit and at rest, and one-click data deletion.
Frequently asked questions
How do I extract totals from invoices?
Upload the invoices to Docusift via the dashboard, the REST API, or by emailing it to your workspace inbox. Docusift returns structured JSON with every totals value, its confidence score, and a citation back to the source pixel — typically in under a second per page.
What is the best way to pull totals from invoices automatically?
The best way is a tool that reads layouts visually rather than matching templates. Template tools break when a vendor changes their format; Docusift parses each invoice from scratch, so it works on multi-page, multi-currency, and scanned variants without per-vendor configuration.
Does it work on scanned invoices?
Yes. OCR, layout analysis, and field extraction run in a single pass, so scans, mobile photos, and native PDFs use the same endpoint and reach the same accuracy bar.
Can I push the extracted totals into my own system?
Yes. Receive structured JSON via REST, hit a webhook, or sync directly into Google Sheets, your data warehouse, or your accounting tool.
How accurate is Docusift at extracting totals?
98% or higher on production invoices across thousands of layouts. Every value ships with a per-field confidence score so low-confidence extractions can be routed to human review automatically.
Do I need to train a model first?
No. Docusift recognizes hundreds of document types out of the box. Custom fields are configured with a single sentence — no labeled training data required.
How is pricing calculated?
Pay per page processed. There is a free tier for evaluation and volume discounts for production workloads. No seat fees.
Can Docusift handle scanned or photographed documents?
Yes. The pipeline runs OCR + layout analysis + extraction in one pass, so scans, mobile photos, and native PDFs all flow through the same endpoint.
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