How accurate is AI extraction, what affects it, and how to build a review workflow that catches errors without slowing you down.
Most vendors quote character-level accuracy — the percentage of individual characters read correctly. That number is typically 95-99% on clean documents and sounds impressive. But it's the wrong metric for real-world use.
What actually matters is field accuracy: did the tool correctly extract the invoice total, the transaction date, the vendor name? A tool can read 99% of characters correctly and still put the total in the wrong column or confuse an invoice number with a PO number. Field accuracy on well-structured digital PDFs typically runs 92-98%. On scanned documents, 85-95%. On photos of crumpled receipts, 75-90%.
Lido reports accuracy at the field level with confidence scores on every extracted value. A confidence score of 0.95 on an invoice total means the AI is very sure about that number. A score of 0.72 means it's less certain and you should verify it manually. This turns accuracy from a marketing claim into a practical workflow tool.
Document quality. A clean digital PDF generated by accounting software will extract near-perfectly. A third-generation photocopy scanned at 150 DPI will have errors. The single highest-impact thing you can do for accuracy is scan at 300+ DPI in color.
Table complexity. Simple single-page tables with clear borders extract at near-perfect rates. Multi-page tables that span page breaks, tables with merged cells, and tables without visible borders are harder. Layout-agnostic AI handles these better than template-based tools, but complexity always reduces accuracy at the margins.
Layout consistency. If all your PDFs come from the same system, accuracy will be very high regardless of tool choice. If they come from 50 different sources with 50 different layouts, you need a tool that adapts per-document. Template-based tools require a new template for each layout. AI-based tools like Lido handle layout variation automatically.
Content type. Printed text in standard fonts: 98%+ character accuracy. Handwritten annotations: 70-85%. Faded thermal prints: 80-92%. Mixed content (printed form with handwritten fill-ins): varies by field.
The goal isn't 100% automation — it's minimizing manual effort while maintaining data quality. Here's the workflow that works:
Step 1: Set a confidence threshold (e.g., 0.90). Extractions above the threshold flow through to Google Sheets automatically.
Step 2: Extractions below the threshold get flagged in a "Review" tab in the same Google Sheet. A team member spot-checks these — typically 5-15% of total documents.
Step 3: Corrected values get confirmed and moved to the main data tab. The correction also helps you understand which document types need attention.
This approach gives you 85-95% full automation with manual review only on the documents that actually need it. Most teams find that the review step takes 10-15 seconds per flagged document — a quick glance to confirm or correct a single value.
Don't trust vendor demos. Test on your actual documents. Lido offers a 50-page free trial — upload your hardest PDFs (scanned invoices, multi-page bank statements, documents with inconsistent layouts) and check the output against the originals. That's the only meaningful accuracy benchmark for your specific workflow.
Upload your actual PDFs and see field-level confidence scores. 50 free pages, no credit card required.
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