Yes—our layout-aware parsing detects headers, earnings, deductions, taxes, and totals while preserving reading order in multi-column stubs. That means you can reliably find the same fields even when formats vary by payroll provider or employer, reducing manual review.

Table extraction is designed to keep line items aligned so rows, columns, and amounts don’t get scrambled. This makes it easier to verify gross-to-net math, tax withholdings, and benefit deductions with fewer downstream corrections.

Absolutely—outputs can include structured JSON plus page-level and element-level metadata like coordinates and surrounding context. You can store normalized fields (e.g., pay period, employer, net pay) and still prove exactly where each number came from.

Built-in validation and auto-correction loops catch common issues like missing decimals, misread identifiers, and mismatched totals. That improves straight-through verification rates and reduces time spent reconciling exceptions.

By preserving structure and validating totals, the extraction is more consistent and less error-prone than basic OCR. You get cleaner, more reliable fields for your rules engine, which helps prevent avoidable mismatches that trigger extra review.

You receive structured JSON that maps cleanly to the fields your verification process needs, like employer name, pay period, gross pay, deductions, and net pay. Because each value can include traceability metadata, integrations can automate decisions while keeping human-friendly evidence ready when needed.