The Myth of 100% Automation
In the real world, documents arrive damaged, skewed, handwritten, low-resolution, or with ambiguous layouts. Vendors promising 100% autonomous document processing often leave operations teams with silent data corruption that surfaces months later in regulatory audits.
The mark of mature engineering is designing a human-in-the-loop (HITL) system where automation handles the repetitive 85% of clean volume, while human specialists seamlessly review and resolve the complex 15%.
Calibrating Confidence Thresholds
Not all fields are created equal. A low confidence score on an optional invoice memo can be safely accepted, whereas uncertainty on a vendor bank account number or total payable amount must trigger mandatory human review.
Confidence scoring must be calculated at the field level, taking into account OCR optical clarity, spatial layout consistency, and cross-field mathematical reconciliation (e.g., verifying that subtotal + tax = total).
Creating the Feedback Flywheel
When a reviewer corrects an extracted value, that action shouldn't just patch a database record. The original document snippet, the model's prediction, and the reviewer's correction should be anonymized and stored in a regression evaluation set. Over time, recurring errors pinpoint specific extraction rules or model prompts that need recalibration.