Zero setup, zero training
No templates, no labeled data, no schema files. Drop the document in and Docusift returns clean structured data.
Pay Stubs extraction
Extract Income and withholding from Pay Stubs
Pull every income and withholding value out of any pay stubs — 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 income and withholding out of a pay stub by hand is slow, and it is exactly where a mistake slips through. A pay stub packs gross pay, a stack of earning and deduction lines, pre-tax and post-tax items, and year-to-date totals into a dense grid that every payroll provider formats its own way. Underwriters and HR teams need gross, net, and each withholding pulled exactly, and one misread column throws income verification off.
Docusift reads the employer and employee blocks, the pay period, every earning and deduction line, the tax withholdings, and both current and year-to-date totals from any payroll layout into clean fields ready for income verification or payroll reconciliation.
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 income and withholding route to review automatically — and the clean data pushes to Google Sheets, a webhook, QuickBooks, or Xero.
| Type | Description | Current | YTD |
|---|---|---|---|
| Earning | Regular | 3,200.00 | 38,400.00 |
| Earning | Overtime | 180.00 | 1,240.00 |
| Deduction | 401(k) | 256.00 | 3,072.00 |
| Tax | Federal withholding | 412.00 | 4,944.00 |
Example structured output
{
"document_type": "pay_stub",
"employer": "Acme Corp",
"employee": { "name": "J. Rivera", "employee_id": "E-1180" },
"pay_period": "2026-04-01 to 2026-04-15",
"gross_pay": 3380.00,
"net_pay": 2461.00,
"earnings": [
{ "description": "Regular", "current": 3200.00, "ytd": 38400.00 },
{ "description": "Overtime", "current": 180.00, "ytd": 1240.00 }
],
"deductions": [ { "description": "401(k)", "current": 256.00, "ytd": 3072.00 } ],
"taxes": [ { "description": "Federal withholding", "current": 412.00, "ytd": 4944.00 } ],
"confidence": 0.98
}Why teams choose Docusift
A confidence score on every field
Every value ships with a confidence score and a citation back to the source pixel in the original document, so low-confidence fields route to review instead of landing unchecked.
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 income and withholding from pay stubs?
Upload the pay stubs to Docusift via the dashboard, the REST API, or by emailing it to your workspace inbox. Docusift returns structured JSON with every income and withholding 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 income and withholding from pay stubs 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 pay stub from scratch, so it works on multi-page, multi-currency, and scanned variants without per-vendor configuration.
Does it work on scanned pay stubs?
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 income and withholding 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 income and withholding?
Docusift reads each pay stub visually rather than matching a template, and every extracted value ships with a per-field confidence score and a citation back to the source pixel — so low-confidence income and withholding route to human review automatically instead of landing unchecked.
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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