Document AI
Document AI — paperwork that processes itself
Invoices, delivery notes, contracts and inbound mail are read, validated and posted to your systems automatically — with human review exactly where it belongs.
The most underestimated time sink in the back office
Count what arrives in a normal week: supplier invoices, delivery notes, order confirmations, contracts, forms, scans of all of the above in every imaginable quality. Someone opens each one, reads it, types the relevant fields into a system, files the document and moves on. Multiply by hundreds of documents and you get the quiet full-time job hidden inside most back offices — along with the typos, the mis-filed PDFs and the invoice that sat in a mailbox until the discount deadline had passed.
Document AI turns this into a pipeline: documents come in, structured data goes out, and people only look at the cases that genuinely need judgement.
How the pipeline works
1. Capture and classify
Documents arrive however they arrive — email attachments, scans, uploads, photographed paper. The system classifies each one: invoice, delivery note, contract, complaint. No pre-sorting, no "please use this form" discipline required from senders.
2. Extract and validate
Modern AI models read the fields you need — amounts, dates, positions, references — even from layouts they've never seen. Extraction alone is not enough, though: every value is validated against your business rules and master data. Does the supplier exist? Does the total match the line items? Does the delivery note match an open order? Only data that passes these checks moves on automatically.
3. Review where it matters
Every extraction carries a confidence score. High-confidence documents flow straight through; anything ambiguous lands in a review queue where a person confirms or corrects it in seconds — side by side with the original document. Corrections feed back into the system, so the review queue gets shorter over time, not longer.
4. Hand off and log
Validated data is posted where it belongs: ERP, accounting, DMS, industry software. The original is archived in a compliant, searchable way, and every step — what was read, what was checked, who approved what — is written to an audit log.
What this changes in practice
- Hours become minutes: the manual typing disappears; what remains is a short daily review of flagged cases.
- Errors stop propagating: validation catches the transposed digits and duplicate invoices that manual entry lets through.
- Documents stop getting lost: every inbound document is tracked from arrival to posting — nothing ages silently in a personal mailbox.
- Deadlines are met: early-payment discounts, response windows and contract dates surface automatically instead of depending on whoever opened the mail.
- Audits get boring: a complete, searchable trail replaces the annual folder hunt.
GDPR and data residency
Business documents are full of personal data, so the pipeline is built GDPR-first: processing on EU infrastructure (or your own servers) on request, data minimisation by design, a data processing agreement as standard, and retention rules that your compliance requirements define — not the tool's defaults. As a German studio, we treat this as the baseline, not a premium tier.
Which document type is the right place to begin differs by sector — invoices in property management, delivery notes and CMR papers in logistics, intake forms in healthcare. The industry overview shows where the volume usually sits. Once extraction runs reliably, the next step is normally to connect it to the surrounding process automation so the data lands where decisions are made.
Starting point: one document type
The proven way to introduce document AI is to start with your highest-volume document type — usually supplier invoices or delivery notes — and automate it end to end. That first pipeline typically goes live within weeks, delivers a measurable time saving immediately, and creates the pattern every further document type follows. As always: after a short discovery you get a binding fixed price, so the business case is on paper before the project starts.
Frequently asked questions
Can the system read document layouts it has never seen before?
Yes. Modern AI models extract the fields you need — amounts, dates, positions, references — even from layouts they were not trained on, so senders do not have to use a fixed template. Extraction alone is never trusted, though: every value is validated against your business rules and master data before it moves on automatically.
What happens when the AI is unsure about a document?
Every extraction carries a confidence score. High-confidence documents flow straight through, while anything ambiguous lands in a review queue where a person confirms or corrects it in seconds, side by side with the original. Those corrections feed back into the system, so the review queue gets shorter over time rather than longer.
Which systems can the extracted data be posted to?
Validated data is handed off to wherever it belongs — ERP, accounting, a document management system or your industry software. Where modern APIs exist we use them; where they do not, we build robust bridges, and the original document is archived in a compliant, searchable way with a full audit log.
Where is our document data processed?
On EU infrastructure by default, or on your own servers on request. The pipeline is built GDPR-first: data minimisation by design, a data processing agreement as standard and retention rules defined by your compliance requirements, not by a tool's defaults.
Where is the best place to start with document AI?
With your highest-volume document type — usually supplier invoices or delivery notes — automated end to end. That first pipeline typically goes live within weeks and delivers a measurable time saving immediately. After a short discovery you get a binding fixed price, so the business case is on paper before the project starts.
How many documents does your team type in by hand?
Send us a rough number and a sample workflow — in a free intro call we'll tell you what automated processing would save, at a binding fixed price.