Use this guide to move from OCR-led document processing into a broader document intelligence model with citations, reviewability, and mixed-document support.
Summary
The right migration path does not discard OCR; it repositions OCR as one step in a larger document-intelligence workflow that includes understanding, review, and evidence traceability.
Sections
3
Questions Covered
3
Executive Summary
Move from OCR to document intelligence by preserving the useful parts of capture, replacing the brittle parts of template dependence, and adding a review-ready evidence layer on top.
Section 1
OCR remains useful for turning scans and images into machine-readable text. The migration is about refusing to stop there. The new system should add understanding, evidence handling, and review support on top of text capture.
Section 2
The best first migration target is the workflow where templates break often, layouts vary, or reviewers keep re-reading documents to validate extraction output. That is where document intelligence creates immediate value.
Section 3
If the new platform is faster but reviewers still cannot trust or verify the output, the migration has not solved the right problem. Measure the time to a trusted answer, not only the time to a parsed field.
It is for teams with existing OCR, capture, or template-based extraction tools that want to modernize into cited document intelligence without breaking production workflows.
Migrate the workflow with the highest pain from document variation, template maintenance, or repeated manual review. That creates the clearest value signal.
Avoid treating the migration as a full rip-and-replace before you know where OCR is still useful and where understanding, review, and evidence handling create the real lift.
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