How accurate is AI extraction, what affects it, and how to build a review workflow that catches errors without slowing you down.
Here's the thing — most vendors quote character-level accuracy. It's the percentage of individual characters read correctly, and it usually lands somewhere between 95-99% on clean documents. Sounds great, right?
It's the wrong number. What actually matters is field accuracy: did the tool get the invoice total right, put the date in the right column, tell the difference between an invoice number and a PO number? A tool can read 99% of characters perfectly and still scramble your data completely.
In my experience, field accuracy on clean digital PDFs runs 92-98%. Scanned docs, 85-95%. Photos of crumpled receipts from someone's jacket pocket? You're looking at 75-90%, and honestly that's impressive given what you're feeding it.
Lido reports accuracy at the field level with a confidence score on every extracted value. A 0.95 on an invoice total means the vision model is very confident. A 0.72 means it's uncertain — check that one yourself. That's the difference between a marketing claim and something you can actually act on.
Document quality. A clean digital PDF from your accounting software will extract nearly perfectly. A third-generation photocopy scanned at 150 DPI won't. If there's one thing you can do to improve accuracy overnight, it's scanning at 300+ DPI in color.
Table complexity. Simple tables with clear borders? Near-perfect. Multi-page tables that break across page boundaries, merged cells, no visible borders — those are genuinely hard. The AI behind this handles them better than template-based tools do, but complexity always costs you something at the margins.
Layout consistency. I worked with a firm processing invoices from over 40 different vendors. Every layout was different. Template-based tools would've required 40 templates and someone to maintain them. Lido's extraction engine adapted per-document automatically — no templates, no setup per vendor.
Content type. Printed text in a standard font hits 98%+ character accuracy. Handwritten annotations drop to 70-85%. Faded thermal receipts, 80-92%. Mixed content — a printed form someone filled in by hand — varies wildly by field. Don't expect miracles on handwriting, but printed content is very reliable.
The goal isn't full automation. It's minimizing the manual work while keeping your data clean. Here's the workflow I'd actually recommend.
Step 1: Pick a confidence threshold — say, 0.90. Anything above it flows straight into your Google Sheet. No human needed.
Step 2: Anything below the threshold lands in a "Review" tab in the same sheet. In practice, that's usually 5-15% of documents. A quick human look is all it takes.
Step 3: You confirm or correct the flagged values and move them to the main tab. Most teams find this takes about 10-15 seconds per document — it's a glance, not a deep dive.
Last month we tested this with a team processing ~300 invoices a week. They went from reviewing everything manually to reviewing about 30 documents. Same data quality, a fraction of the time.
Don't trust demos. Vendor demos use vendor-friendly documents. Upload your actual PDFs — your messiest scanned invoices, your multi-page bank statements, the ones you'd normally dread touching.
Lido gives you 50 pages free. Use them on your hardest documents and check the output against the originals. That's the only benchmark that means anything for your workflow.
Upload your actual PDFs and see field-level confidence scores. 50 free pages, no credit card required.
50 free pages. All features included. No credit card required.