Choose document AI by the evidence it returns.

Accuracy matters, but it is not enough for agentic and regulated workflows. Compare extraction approaches by whether they return verifiable source evidence: bounding boxes, citation URLs, cropped source artifacts, and audit-ready JSON.

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Auditability matrix

Capability	Ninjadoc	Traditional OCR	Document parser	Generic LLM
Extract structured answers from documents
Return bounding boxes for each answer
Return citation URLs for source review
Provide cropped source evidence
MCP-ready for agent workflows
No document templates required
Audit-ready JSON in the extraction response

This is a category-level comparison. Validate vendor-specific capabilities before making a procurement decision.

First principles

The question is not just "can it extract?"

A document system used by an agent, reviewer, or regulated workflow needs to answer a second question: "Can we prove where this came from?"

The extraction layer should return source evidence at the same time it returns the answer. Otherwise your team has to build a separate verification layer after the fact.

What to evaluate

Does every answer include page and location evidence?

Can reviewers open a source URL or cropped source region?

Can agents pass citations downstream without rewriting the payload?

Can your product draw highlights, crops, and review queues from the response?

Does pricing make evidence practical across all pages, not only exceptions?

Build on source-grounded extraction.

Give your agents and reviewers answers they can trace back to the original document.

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