From paper to data: automating document processing in logistics, insurance and healthcare

Many teams still spend hours each day copying information from documents into systems. A logistics coordinator re-types bills of lading and commercial invoices. A claims handler keys in policy numbers and repair estimates. A clinic administrator transcribes lab reports. The work is repetitive, and every manual step adds delay and the risk of error.

Why older automation often fell short

Traditional optical character recognition (OCR) turns an image into text, but it does not know which number is the gross weight and which is the container number. Template-based tools solve that by mapping positions on a page, but every new supplier or form layout needs a new template, so maintenance grows with every change.

What LLM-based extraction changes

A large language model reads a document much as a person would. You describe the fields you need, such as consignee, claim number or test result, and the model finds them whatever the layout. A complete pipeline then:

1. collects documents from email, shared folders, scanners or an API,
2. converts scans to text with OCR and layout detection,
3. extracts the fields you defined,
4. checks them against rules, such as totals adding up or dates being valid,
5. sends only uncertain fields to a person for review, and
6. delivers clean data to your ERP, database or spreadsheet.

Where it helps most

• Logistics and supply chain: bills of lading, commercial invoices, packing lists and customs declarations.
• Insurance: claim forms, policy schedules, medical bills and survey reports.
• Healthcare: intake forms, lab reports, referral letters and prior-approval requests.

In healthcare in particular, UAE rules restrict processing health data outside the country, so running extraction on a locally hosted model avoids sending documents to outside services.

How to start

Pick one document type with high volume and clear fields. Collect a sample of real documents, define the fields and rules, and run a pilot. Measure accuracy for each field, the share of documents that need review and the time saved per document. Move to the next document type once the first is running smoothly.

See a sample bill of lading, claim form and lab report processed in our Structured Data Extraction demo.