What Is Inside a Document AI Workflow
Extraction is only useful if it can be trusted at a glance. Here is how layouts are handled, how confidence is scored per field, and how a confirmed value reaches your ERP.
From a scanned document to a confirmed record
Built to handle documents that do not match each other
The same field, an invoice number or a delivery date, sits in a different place on every vendor's template. The extraction is trained to find the field by what it is, not by its fixed position, so it holds up across the layout variety your inbox actually receives.
- Fields located by meaning, not fixed position.
- Multiple vendor and format layouts handled together.
- New layouts added as they appear, without a rebuild.
Every field graded, not just extracted
Each extracted value carries a confidence score based on how clearly it was read from the source. High-confidence fields move straight to posting, while anything below the agreed threshold is set aside for a person, so review effort concentrates where it is actually needed.
- Per-field confidence, not one score for the document.
- Threshold for automatic posting agreed with your team.
- Review effort concentrated on the fields that need it.
The same rules your ERP already enforces
Extracted values are checked against your existing validation rules, such as a valid vendor code or a matching purchase order, before posting. A confirmed record enters your ERP exactly as if a person had typed it, following the same checks and approvals.
- Extracted data checked against your existing rules.
- Posting follows your normal approval workflow.
- No bypass of controls your ERP already applies.
Everything the document AI build includes
Source highlighting
Each extracted field shown against its exact location on the original document.
Review queue
Flagged fields gathered in one place for quick confirmation before posting.
Duplicate detection
Checking a new document against records already posted to catch duplicate submissions.
Three-way matching
Cross-checking an invoice against its purchase order and receipt before it is approved.
Processing analytics
Volume, accuracy and turnaround tracked over time, by document type and source.
Model-agnostic build
Built so the underlying extraction approach can be upgraded without rebuilding the pipeline.
See What Extraction Looks Like on Your Documents
Book a free consultation. Bring a sample batch, and we will show you what fields could be extracted and how confidently.
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