Connecting ERP to the Shop Floor in Manufacturing

Modern AI-powered document processing goes well beyond traditional OCR by understanding document structure, not just extracting raw characters from a scanned page. Traditional OCR extracts raw text from scanned documents, but modern AI document processing understands structure, recognising that a number in a specific position is an invoice total rather than just text.

This matters for processing invoices, contracts and forms at scale, where the goal is structured data ready for a database rather than a wall of extracted text needing manual cleanup.

We typically pair AI extraction with a human review step for low-confidence results, which keeps accuracy high without requiring every document to be manually checked.

The structural understanding is what makes this genuinely useful at scale: a modern document AI model can recognise that a table of line items belongs to a purchase order, that a specific field is a due date rather than an issue date, and that a handwritten signature block signals an approved contract, none of which plain text extraction can distinguish on its own.

Accuracy varies significantly by document type and source quality. Clean, digitally generated PDFs extract close to perfectly, while poor-quality scans, handwriting, or documents with unusual layouts push accuracy down meaningfully, which is exactly why a confidence score on every extracted field matters more than a single overall accuracy number.

We route documents based on that confidence score: high-confidence extractions flow straight into the target system automatically, while low-confidence fields are flagged for a human reviewer to quickly confirm or correct, rather than requiring someone to check every document from scratch. This keeps the manual workload proportional to actual uncertainty rather than blanket caution.

A common pitfall is expecting near-100% automation from day one. We set client expectations that accuracy improves over the first weeks of production use as the system encounters real document variations, and we track which fields most often trigger low-confidence flags, since that data usually points to specific template or vendor document formats worth handling with a dedicated extraction rule.