Our layout-aware table parsing reads page structure so headers, rows, and footnotes stay aligned—even in dense, multi-column statements. You get cleaner balance sheets and income statements that are ready for reconciliation and analytics with far less manual cleanup.

Yes—agentic visual understanding lets us interpret scanned tables, image-based footnotes, and even chart callouts, not just plain text. This helps preserve important context (like trends and references) that traditional OCR workflows often miss.

Validation correction loops run iterative self-checks to detect issues such as shifted columns, dropped minus signs, and totals that don’t tie out. When something looks off, the system corrects it when possible and flags it when not—reducing review time and increasing straight-through processing.

We output structured JSON designed for downstream use in ETL, reconciliation, and reporting pipelines. It’s consistent and machine-readable, so you can map line items to your chart of accounts and automate ingestion faster.

Every extracted field can include citations like page references and element-level positioning, making it easy to verify values against the original document. This creates an audit-friendly trail and supports confidence-based workflows where low-confidence fields can be routed for human review.

It’s built for variability—multiple templates, dense disclosures, and uneven scan quality—by combining layout awareness, visual understanding, and validation checks. The result is more consistent extraction across issuers, fewer exceptions, and a faster path to production-grade automation.