Capture, OCR & indexing
How this is scored
How documents get in and become findable: scanning and email intake, OCR, classification and metadata extraction, and whether incoming e-invoices (XRechnung, ZUGFeRD) are read as structured data rather than as pictures.
0 — Manual upload only; no OCR, no metadata beyond file name and folder.
3 — Upload, drag-and-drop and scan import with OCR mentioned, but indexing is manual and no word on email intake or e-invoice formats.
5 — Scanner, email and folder intake, full-text OCR, document types with metadata fields, and automatic extraction of common fields (date, sender, amount) documented.
8 — AI- or rule-based classification and field extraction with confidence and a validation step, XRechnung and ZUGFeRD read as structured data, mailbox and Microsoft 365 intake, and duplicate detection documented.
10 — Capture is engineered and shown: classification and extraction trainable per document type, validation queues, e-invoice validation against the standard, intake channels listed with their limits, and where AI processing runs (location, model provider) stated.
The Data Protection Officer
The captured pages describe document libraries, real-time co-authoring and a connectors pipeline that ingests external items into a registered schema with semantic labels, but say nothing about scanning, OCR, classification with validation, or reading XRechnung and ZUGFeRD as structured data. For a system expected to hold personnel files and health certificates, I found no public information on how paper documents or incoming invoices actually get in. 2 4