Modern language models can generate convincing but incorrect data. For critical business use, AI systems must move beyond simple extraction to verification, providing auditable evidence and confidence scores for every data point, linking it directly back to the source document.
A monolithic model struggles with complex documents. A better approach is a staged pipeline that separates tasks like image correction, text recognition, layout analysis, and final extraction. This isolates failure points, making errors easier to identify, test, and fix.
Effective "human-in-the-loop" systems don't require people to re-read every AI-processed document. Instead, the system flags low-confidence or ambiguous results for human review. This shifts the human role from transcriber to verifier, focusing expertise on exceptions and creating a valuable feedback loop.