Supplier invoices, contracts, purchase orders, delivery notes, scanned forms, emails with orders: much of a company’s information arrives as documents, not tables. Turning it into useful data used to require manual entry. With artificial intelligence it can be almost fully automated.
Why OCR alone is not enough
OCR turns an image or PDF into text, but it does not know what that text means. Traditional solutions rely on templates: "the total is in this corner". When a supplier changes its layout, the template breaks. Language models, on the other hand, understand the document and find the total, the date or the tax ID even when they move.
The recommended flow
- Capture: documents arrive by email, a shared folder or a form.
- Classification: the AI identifies the document type (invoice, contract, purchase order…).
- Extraction: OCR plus a language model extract the fields into a defined schema, for example JSON.
- Validation: business rules check formats, totals, dates and matches with your records.
- Human review only for doubtful or low-confidence cases.
- Integration with your ERP, database or ETL flow, keeping the link to the original document.
What data is usually extracted
- Invoices: supplier, tax ID, number, dates, subtotal, taxes, total and line items.
- Contracts: parties, purpose, term, amounts, penalties and renewals.
- Orders and delivery notes: products, quantities, addresses and delivery dates.
- Emails: request type, customer, urgency and order details.
How to measure whether it works
Start with a pilot on the most frequent document types and measure with your own files: percentage of fields extracted correctly, percentage of documents that pass without review and total process time compared with manual entry. Those numbers tell you where to scale.
More details in our AI data structuring service and in the case of a Peruvian services company.