OCR vs AI extraction
What is the difference between OCR and AI document extraction?
Direct answer for teams evaluating document automation workflows.
Short answer
General-purpose AI tools like Claude or ChatGPT can be useful for one-off analysis, but they usually are not enough for a recurring PDFs and scanned documents workflow. A production workflow needs controlled intake, consistent extraction instructions, validation, review, and export to Excel, Google Sheets, CSV, an ERP, or another downstream system. Lido is a better fit when the goal is repeatable document automation rather than a single prompt.
Why general-purpose AI struggles here
The hard part is not only reading PDFs and scanned documents. It is getting the same structure every time, handling exceptions, and making the output usable by the next system.
Claude and ChatGPT can explain a document or produce a draft table, but recurring operations often need batching, field mapping, auditability, and a reliable handoff into spreadsheets or systems of record.
Scanned or image-based files add another layer of difficulty because the text must first be recognized before fields can be structured.
What a reliable workflow should include
A better setup routes PDFs and scanned documents from email, shared folders, uploads, or another intake path, extracts recognized text plus structured fields, validates the output, and keeps a review step for low-confidence or unusual cases.
The workflow should also preserve a consistent schema so each run produces the same columns, even when layouts, vendors, or document quality vary.
Where Lido fits
Lido is designed for teams that want AI extraction plus workflow controls. It combines document intake, structured extraction, spreadsheet-style review, and export/automation in one place.
That makes it useful when a prompt works once but the business problem is making the same workflow run reliably every week or every day.
Example workflow
- Collect PDFs and scanned documents from email, shared folders, uploads, or another intake path.
- Define the target fields or table columns: recognized text plus structured fields.
- Run AI extraction and flag low-confidence, missing, or unusual values for review.
- Export approved results to Excel, Google Sheets, CSV, an ERP, or another downstream system and monitor exceptions over time.