Why confidence scores matter more than accuracy claims

When evaluating document extraction tools, the first number everyone looks at is accuracy. "99.2% field-level accuracy" sounds impressive on a landing page. But in practice, that number tells you very little about whether you can actually trust the output.

The problem with aggregate accuracy

An accuracy metric is averaged across thousands of fields. It doesn't tell you which values are wrong. If your extraction hits 98% accuracy on a 500-row dataset, that's still 10 incorrect values — and you have no idea which ones. So you end up reviewing everything anyway.

Confidence scores change the workflow

With per-value confidence scores, the review process becomes targeted. Instead of scanning every row, you sort by confidence and only review values below your threshold. A team processing 200 invoices a day can cut review time by 70% — not because accuracy improved, but because trust is granular.

• High confidence (95%+): auto-validated, no human review needed
• Medium confidence (80–95%): flagged for quick review
• Low confidence (<80%): requires manual verification

Source tracing closes the loop

Confidence alone isn't enough. When a value is flagged, you need to see where it came from. Orkom links every extracted value back to its exact position in the source document — so verification takes seconds, not minutes.

The goal isn't to eliminate human review. It's to make human review fast, targeted, and meaningful.

What this looks like in practice

Here's a simplified example of what the extraction output looks like when every value carries its own confidence score:

{
  "vendor": { "value": "Greenfield Supplies", "confidence": 0.98 },
  "amount": { "value": 2450.00, "confidence": 0.95 },
  "date":   { "value": "2026-01-15", "confidence": 0.72 }
}

The date field has a low confidence score — maybe the document had a smudged date or ambiguous formatting. Instead of discovering this error downstream (in your accounting system, or worse, in an audit), you catch it at extraction time.

Bottom line

Accuracy is table stakes. What separates a production-grade extraction system from a demo is whether it tells you where it's uncertain — and gives you the tools to verify quickly. That's what confidence scores and source tracing are for.