Yes—layout-aware parsing keeps reading order and structure intact, so multi-column pages don’t get scrambled and headings stay attached to the right content. That means cleaner downstream automation and fewer manual fixes when processing real-world PDFs and scans.

It’s built for messy, real tables—handling merged headers, nested cells, and footnotes more reliably than basic OCR-to-text approaches. Tables can be returned in clean formats like Markdown or JSON, so your data is immediately usable for search, analytics, or ingestion.

Yes—multimodal parsing captures visuals and math alongside text, converting them into usable representations like Markdown tables or LaTeX when appropriate. This helps you preserve meaning in technical, financial, and scientific documents where key information isn’t purely textual.

You can export verifiable JSON with rich metadata like page numbers, element types, and spatial coordinates. That structure makes results auditable, easier to validate, and far simpler to route into downstream workflows and systems.

Because every extracted element can include page-level references and coordinates, reviewers can quickly trace content back to the source. This improves confidence during QA, supports compliance requirements, and reduces time spent hunting through documents.

Most teams integrate quickly by sending documents in and receiving structured outputs (Markdown/JSON) that slot into existing pipelines. With consistent structure and metadata, you’ll spend less time on brittle post-processing and more time building user-facing features.