Logisticsexample use case
Extracting data from documents — without retyping
Contracts, delivery notes, CMRs, material certificates, order forms, applications and partner spreadsheets arrive as PDFs, scans and photos, and someone retypes them field by field into the system. AI reads the document, pulls out exactly the data you need, checks it and writes it into your ERP or CRM. Anything unclear goes to a person.
Sound familiar?
- People spend hours a week copying data from documents into the ERP, CRM or a spreadsheet — dates, batch numbers, amounts, addresses, deadlines.
- Every partner sends a different format: one a PDF from their system, another a stamped scan, a third a spreadsheet with their own columns.
- A typo in a contract number, a batch or the weight on a CMR is found only at a complaint, an audit or invoicing.
- Your existing document capture tool handles one template, and everything else still needs a person.
How it works
1. Reads the document in any format
Native and scanned PDFs, phone photos, Excel, Word, email attachments. Multi-page documents and tables that run across pages are processed as a whole, not page by page.
2. Extracts the fields you define
Together we decide which data you need from which document type — for example the counterparty, effective date and notice period from a contract, or the batch, standard and measured values from a certificate.
3. Checks that the data makes sense
It verifies totals, formats (company IDs, dates, batch numbers) and consistency with your ERP or CRM: does this customer, order or material exist? Conflicts are flagged.
4. Writes it into the system — or hands it to a person
Documents where everything checks out go straight into the system. Unclear ones go to a queue where a person sees the document and the extracted data side by side and corrects them with one click.
What the system handles
- Contracts and amendments, delivery notes, CMRs, material certificates, order forms, applications, timesheets, partner spreadsheets and reports.
- Native and scanned PDFs, phone photos, Excel, Word; printed documents with handwritten notes.
- Slovak, Czech, English, German and other languages — even within one document.
- Writing into your ERP, CRM, DMS, SharePoint or a spreadsheet — via API, database, file import or MCP.
- Intake from a mailbox, a shared folder or a scanner — documents enter processing on their own.
Human oversight and safety
- You set the validation rules: which fields are mandatory, what must match the system and what a person always reviews.
- Every value is stored with the page and location it came from — at audit time its origin can be traced.
- Accuracy is measured on a sample of your real documents before rollout and after every change, not on demo examples.
- Automatic posting is released gradually, only for document types with stable accuracy. The rest stays in “propose, a person confirms” mode.
What result is realistic
~75%
of documents with no manual retyping
For a typical company with hundreds of documents a month across a few types, we plan for roughly 75% of documents written into the system with no manual retyping; the rest is reviewed in seconds rather than minutes. The more varied and less legible the documents, the larger the share that goes to review — we find the real number on your sample before the pilot. At that volume, production rollout typically pays back within about a year.
The figures are indicative for typical volumes. We give a precise estimate for your company after analysing the process.
Indicative scope
- Analysis of document types and sample
- €900 – €1,800
- Pilot on your real documents (2–4 weeks)
- €3,500 – €6,500
- Production rollout with ERP / CRM integration
- €6,000 – €14,000
- Operations, monitoring and accuracy tracking
- from €290 / month
Prices are indicative and exclude VAT. We give an exact figure only after analysing your process — not off the cuff on the first call.
Frequently asked questions
How is this different from automated invoice processing?
Invoices are a specialised case with a fixed set of fields and a link to accounting — we have a separate solution for them, Automated invoice processing. Document data extraction covers everything else: contracts, delivery notes, CMRs, certificates, forms and spreadsheets, where you define the fields and validation rules yourself.
How accurate is extracting data from PDFs?
It depends on the documents. On clean system-generated PDFs field-level accuracy is typically very high; on poor scans and handwriting it is noticeably lower. That is why we measure it on a sample of your documents and attach a confidence to every field — anything that fails validation goes to a person.
Do we need a template for every document type?
No. You define what data you want, not where it sits on the page. A document from a new partner with a different layout is usually handled without configuration. A new document type means adding a field definition and testing it on a sample.
When is it not worth it?
If you have a few dozen documents a month, or they all come in one fixed template, an existing OCR tool with templates or an import from the partner’s system is often enough. We work it out together on the first call — and if it does not pay off, we tell you.
Where do our documents go?
We process them in the EU, with providers contractually barred from training on your data, or with a model running on your premises if documents must not leave the company. The original stays in your system.
Related service
AI agents & automation
We build this solution as part of our service „AI agents & process automation“.
An agent that processes emails, orders and invoices instead of your teamRead more
- How to get data from PDFs into your system without retyping: OCR, templates or AI?How to get data from PDFs, scans and spreadsheets into your system without retyping: OCR, templates or AI, which documents are hard and how to measure accuracy.
- Automated invoice processing: OCR, RPA or AI? What to choose and whyThree technologies that get mixed up when companies talk about extracting data from invoices and delivery notes. What each one actually does, where it breaks, what a process that keeps errors out of the books looks like, and what it costs.
- From PoC to production: why most AI projects never shipAI projects do not die because the model fails. They die in the space between “it looked good in the demo” and “it runs reliably and we know what it costs”.
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You do not need to know whether you need AI, automation or a new system. Show us the process that slows you down — we will tell you what can be automated and whether it pays off.