Our layout-aware table extraction reconstructs transaction tables as they appear on the page, preserving column order, multi-line descriptions, and running balances. This helps you reliably capture date, description, debit/credit, and balance even when banks update templates or use multi-column formats.

Yes. Agentic Parsing Auto Mode adapts per page, using the right combination of vision and language models and escalating only when a page is visually complex. You get high accuracy on tough pages without paying maximum cost for every clean page.

You’ll receive structured JSON plus page-level metadata like coordinates and document structure details. That means you can audit extractions, reconcile totals, and quickly route exceptions to review with clear references to the exact source location.

Self-correction validation loops automatically check for inconsistencies such as impossible balances, split rows, or likely digit errors and attempt fixes before results are returned. This reduces manual cleanup and improves straight-through processing for reconciliation and underwriting workflows.

The parser understands page structure and can separate transaction content from repeated elements like headers, footers, and page numbers. It also keeps continuity across pages so tables don’t restart incorrectly or merge into the wrong rows.

You can start by consuming the JSON output in your existing ETL or underwriting systems, then use the included metadata to power a simple review flow for low-confidence fields. Most teams get to a reliable first deployment fast, then iterate on validation rules and exception handling as volume grows.