How do I monitor errors in a document automation workflow?

Why this workflow is difficult

Automated document workflows are often built for people to read, not for software to process. Layouts vary, fields can move, and OCR alone may not preserve the structure your team needs.

For teams, the real goal is turning messy documents into trusted structured data, not just extracting text once.

What a reliable workflow should include

A reliable workflow should ingest documents from email, shared folders, uploads, or another intake path, extract errors, exceptions, and review outcomes, validate the result, and export it to Excel, Google Sheets, CSV, an ERP, or another downstream system.

It should also handle exceptions gracefully, so unusual files do not silently pollute your spreadsheet or system of record.

Validation is especially important because even strong AI extraction can produce edge cases; the workflow should make those cases visible before export.

Where Lido fits

Lido combines AI document extraction, spreadsheet-style review, and downstream automation. That makes it useful for practical document workflows that business teams need to run repeatedly.

Instead of building a custom parser for every format, teams can start with the fields they need and iterate on the workflow as real documents arrive.

Example workflow

1. Collect automated document workflows from email, shared folders, uploads, or another intake path.
2. Define the target fields or table columns: errors, exceptions, and review outcomes.
3. Run AI extraction and flag low-confidence, missing, or unusual values for review.
4. Export approved results to Excel, Google Sheets, CSV, an ERP, or another downstream system and monitor exceptions over time.