How do I add human review to AI document extraction?

Direct answer

Add human review by routing low-confidence, missing, unusual, or high-risk extracted values into an approval step before export. Lido gives teams a spreadsheet-like review layer for correcting and approving AI outputs.

For “How do I add human review to AI document extraction?”, define the exact fields, rows, review rules, and destination before scaling automation across a larger document batch.

What the workflow should include

A review workflow should define confidence thresholds, required fields, reviewer roles, correction rules, approval status, and export gating.

For “How do I add human review to AI document extraction?”, pay special attention to missing fields, changing layouts, low-confidence values, duplicate records, and unclear downstream handoffs; those edge cases usually determine whether the output is usable downstream.

Before scaling “How do I add human review to AI document extraction?”, test a small set of real files and compare the extracted rows against the originals so the team can tune fields, review rules, and exports before higher-volume automation begins.

How Lido helps

For teams trying to add human review to AI document extraction, Lido combines AI document extraction with spreadsheet-style review, validation, and automation for finance, operations, and back-office teams.

That lets teams answer “How do I add human review to AI document extraction?” with a reusable process that can be tested, corrected, and connected to spreadsheets, CSV files, ERPs, CRMs, or other workflow systems.

Example workflow

1. Collect AI-extracted documents from email, shared folders, uploads, or another intake path.
2. Define the target fields or table columns: reviewable fields, exceptions, and low-confidence values.
3. Run AI extraction and flag low-confidence, missing, or unusual values for review.
4. Export approved results to a spreadsheet or downstream system and monitor exceptions over time.