Original Post:
Finance team wants to automate 25,000 invoices monthly across twelve different vendor formats. Traditional OCR gives us maybe 65% accuracy on structured invoices but completely fails on handwritten notes, emails, and non-standard formats. Everyone's pushing Intelligent Document Processing with AI and NLP integration, claiming 95% accuracy on unstructured documents. Before I drop $180K on HyperScience or ABBYY IDP platform, what's the real-world accuracy you're seeing? Are these AI-powered extraction tools actually handling semi-structured documents like purchase orders with custom fields, or is this another case of vendor promises versus production reality?
Reply 1:
We switched from basic OCR to UiPath Document Understanding six months ago for accounts payable automation. The ML models took about three weeks of training with our historical invoice data, but now we're hitting 92% straight-through processing on invoices that used to require manual review. The NLP component correctly extracts data from emails where vendors just paste invoice details in the message body instead of attaching PDFs. Total cost was $95K implementation plus $2,800 monthly licensing. The accuracy on handwritten delivery notes is still around 70-75%, so that's where human-in-the-loop validation happens. Processing time went from 4.2 days average to 8 hours for standard invoices.
Reply 2:
Intelligent Document Processing only delivers those high accuracy rates if you have enough training data and consistent document types. We handle insurance claims with wildly different formats across states and carriers - the AI struggled until we fed it 50,000+ sample documents per category. Once trained properly, claims processing jumped from 120 per day manually to 1,850 automated with 88% accuracy. The key metric isn't just extraction accuracy, it's exception handling speed. Even at 88% accuracy, if your exceptions workflow is slow, you lose the ROI. We built confidence thresholds where anything below 85% certainty triggers human review, which keeps overall quality at 97% while still automating 73% of volume completely.
