Finance & Documents
AI Data Extraction & Review
Fields extracted against your template, with confidence, held for a human to approve.
Available now · Human approval · Complete audit trail
The operational problem
Reading a document is not the same as producing trusted data
A convincing extraction demo can still fail in operations. Pages arrive in bundles, layouts change, required fields differ by process and a plausible-looking value can still be wrong.
The service therefore treats extraction as a controlled data pipeline: the schema is agreed first, confidence is recorded per field, business checks run before approval and uncertain values wait for a person instead of being completed by guesswork.
Control points
What the extraction pipeline controls
Document boundary
Bundles and multi-document files are separated before fields are extracted from the wrong page or document.
Field schema
Names, formats and required fields come from your process and downstream system—not from a generic prompt.
Confidence
Every extracted field carries its own confidence so review can focus on uncertainty, not retype the whole document.
Validation
Formats, totals, required values and reference data can be checked independently of the extraction model.
Approval history
Original value, suggested value, reviewer correction and final approval remain traceable for each document.
Method
How a document becomes approved data
- Define the data contractWe agree the document type, field schema, required formats, validation rules and review threshold.
- Separate and classifyIncoming pages are assigned to a document and type before extraction begins. Unknown types are held.
- Extract with field-level confidenceThe service reads every requested field and keeps the evidence and confidence next to the value.
- Validate independentlyDeterministic rules check totals, formats, required fields and known reference values where available.
- Route only exceptionsFields below the agreed threshold or failing a rule wait in a human review queue.
- Release approved dataOnly the approved record moves to export or a downstream delivery step, with the complete history attached.
Example output
The reviewer sees values and confidence together
Illustrative review record — no customer data
- Supplier
- Nordic Components AB
- Invoice
- 2026-0917-118
- Total
- 12,480.00 EUR
- Due
- 2026-10-17
- Confidence
- 0.97
Data quality
The rules behind a trustworthy extraction
The schema comes before the model
A named field, expected format and destination make the result testable. “Read this invoice” does not.
Confidence routes work; it does not prove truth
A high score is one signal. Business rules and human review remain separate controls.
Unknown values stay unknown
A blank, ambiguous or unsupported value is held visibly instead of being completed with a plausible answer.
Corrections become evidence
Reviewer changes are logged at field level so recurring errors can be measured and the template improved.
Fixed start
What the 14-day pilot delivers
- Agreed extraction schemaThe exact fields, formats, required values, thresholds and validation rules for one document process.
- One real document batchYour representative documents run through separation, extraction, validation and review.
- Working review queueReviewers see the source, proposed value, confidence and failed checks before they approve or correct it.
- Measured exceptionsA results summary shows which fields passed automatically, which needed review and why—without inventing an accuracy claim in advance.
- Delivery recommendationWe document what is ready to automate next and what should remain under human control.
Fit
When extraction automation is—and is not—ready
A good fit
- The same document types arrive repeatedly and the required output fields are known.
- People retype data or review every field even though only a minority are uncertain.
- The downstream ERP, accounting system or export has a defined schema.
Not the right fit
- Nobody can define which values are required or what a valid record looks like.
- The source documents are not representative enough to set a meaningful pilot scope.
- You expect every new document type and layout to run unattended from day one.
Where the human approves
You approve anything under your threshold. The AI works through a fixed list of allowed operations; every approval and result is logged.
Implementation questions
Questions finance and data teams should ask
Do you promise one accuracy percentage for every field?
No. Accuracy depends on document type, field, layout and source quality. The pilot measures the agreed fields separately and records which values required review.
What happens when the model is unsure?
The field is routed to review according to the agreed threshold or validation rule. The service preserves the proposed value and evidence but does not release it as approved data.
Can reviewers correct extracted values?
Yes. The original suggestion, correction, reviewer and approval time remain in the audit history.
Can one PDF contain several documents?
Yes. Document separation is handled before extraction. Unclear boundaries are held for review rather than silently assigning pages to the wrong record.
Which languages and document types are supported?
The platform currently handles twenty document types across five languages. The pilot still validates the exact types, fields and layouts in your batch before production scope is agreed.
Does the pilot write directly to our ERP?
The extraction pilot produces approved structured data. Delivery into an ERP is scoped separately so its validation rules, permissions and confirmation behaviour can be tested explicitly.
Experience behind the service
Built from a production document platform
The service is based on SmartDocto, TechOne’s production document platform. The platform handles twenty document types in five languages, accepts documents through four intake channels and supports four delivery channels. We show the control model and platform capabilities without publishing customer documents or unmeasured accuracy claims.
Technical stewardship: David Máj, Founder & Technology Consultant. Last reviewed 24 September 2026.