The trap
Most IDP vendors quote 99%+ accuracy. The number is real. The problem is what happens to the 1%.
If you process 10,000 documents/month and 1% fail, that's 100 documents/month requiring human attention. If those 100 documents are silently buried in your queue, you've built a system you can't actually trust.
The fix
A production-grade IDP layer does more than extract data. It routes uncertainty.
1. Confidence scoring
The model returns a confidence score per field. High-confidence fields go through. Low-confidence fields are flagged.
2. Human-in-the-loop
Flagged documents land in a review queue with the relevant context attached: the original document, the extracted fields, the ambiguous parts highlighted. A reviewer corrects in 30 seconds.
3. Learning loop
Corrections feed back into the model. Over weeks, the confidence threshold drops naturally because the model learns the edge cases.
What this looks like in practice
Take a contract-review IDP layer. The model handles 88-92% of clauses with high confidence. The remaining 8-12%, the actual deviations from firm standards, go to a partner. The design target: partner review time down by roughly three-quarters.
Arora