Data extraction is the platform's pipeline for pulling structured information out of unstructured or semi-structured documents — invoices, receipts, IDs, statements, forms. Instead of humans typing the data, the platform reads the document and populates fields automatically.
What Gets Extracted
- Form fields: Named fields on standard documents (invoice number, total, due date, supplier name).
- Tables: Line items with quantity, description, unit price, total.
- Identity data: Names, dates of birth, document numbers from IDs and passports.
- Signatures and stamps: Detected and cropped for verification workflows.
- Free-form fields: Custom “find this value near this label” rules.
How It Works
- Document uploaded.
- OCR runs if the document is image-only. Cloud OCR (AWS Textract, Google Document AI, Azure Form Recognizer) optional for higher accuracy.
- Field-detection rules look for known labels and patterns.
- Extracted values populated as a draft record with confidence scores per field.
- Anything below the confidence threshold is queued for human review.
- Final record saved and the source document attached.
Worked Examples
- Accounts payable: Supplier emails an invoice PDF. Extraction populates supplier, date, line items, totals. Anything below 90% confidence is reviewed by a clerk before posting.
- HR onboarding: New hire uploads scanned passport and ID. Extraction populates DOB, passport number, expiry date, name. Compliance check verifies expiry > 6 months ahead.
- Expense receipts: Employee photographs a restaurant receipt. Extraction captures merchant, date, amount, VAT, currency.
- Application forms: Scanned paper applications digitised — extraction populates structured fields ready for the existing workflow.