Announcing Granular Bounding Boxes in LlamaParse

Agentic Document AI has introduced Granular Bounding Boxes for LlamaParse, addressing the need for high precision in extracting and attributing data from complex documents like financial reports and audit records. Traditional document parsers often provide only broad layout-level bounding boxes, which are inadequate for enterprise fintech applications and compliance reviews that require exact verification. The new feature offers three levels of granularity—line, word, and cell-level tracking—allowing users to pinpoint the exact location of extracted data within a document. This enhancement enables audit-grade citations and high-precision redaction by allowing precise targeting of specific text, including personal identifiable information (PII), without manual page-blocking. The feature is available in beta across all paid tiers on LlamaParse, with additional verification provided by Agentic Plus for workflows where attribution accuracy is crucial.

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