AI OCR system classifies business documents.
Document data analysis in a business dashboard.
Data from PDF files, scans and photos is read by AI OCR.
AI analyzes a document and recognizes important fields.
Document library after automatic classification.
OCR reads data from a label, invoice or form.

Capture, classify and route data from invoices, forms, reports, scans and other repeatable documents.

First we check which documents reach the company, which data is needed and where the process stops after extraction.

Describe the problem See how we work

OCR for invoices, forms, reports and scans

This service is for companies where documents, attachments or PDF files trigger further work: invoicing, approval, ticket handling, reporting, system updates or customer contact.

  • employees manually retype data from documents,
  • invoice PDFs, scans, forms or email attachments arrive and need handling,
  • document data must be entered into Excel, CRM, ERP or an accounting system,
  • documents must be classified by case type,

Typical document problems

Manual data retyping

Data from invoices in PDF or scans, forms or photos is manually moved into Excel, CRM, ERP or a report.

Documents arrive through different channels

Files arrive by email, form, as a scan, photo, PDF or customer attachment.

Missing classification

The team must manually recognize whether a document is an invoice, order, protocol, complaint, ticket or another case type.

Hard to check completeness

It is not immediately clear whether the document contains all required data, attachments or signatures.

When is KSeF, XML or API a better option than OCR?

  • data extraction from invoice PDFs, scans, forms, email attachments and photos,
  • document and attachment classification,
  • missing information detection,
  • preparing data for Excel, CRM, ERP or an accounting system,
  • exception handling and handoff to a human,

OCR is only one part of the process. For invoices, it is worth checking first whether the data is available in a structured format such as XML, e-invoices, KSeF or a system export. OCR makes the most sense when the document arrives as a PDF, scan, photo or attachment without structured data. The biggest value appears when the extracted data reaches the right place: approval, report, system, export or further handling.

What do you get at the end?

  • less manual data retyping,
  • a clear way to review exceptions and missing fields,
  • data handed over to a system, report or approval step,
  • a small scope that can be tested safely.

Which documents can be analyzed?

  • invoices in PDF or scans when no structured file is available,
  • orders,
  • forms,
  • protocols,
  • service tickets,
  • work sheets,
  • warranty documents,
  • transport documents,
  • PDF files from customers,
  • scans,
  • photos,
  • email attachments,
  • summaries and reports,
  • documents requiring approval.

Not every document should be fully automated right away. Sometimes the best first step is extracting a few key fields, classifying the document type or preparing the data for manual approval.

Starting at PLN 6,900 net

The simplest version may extract agreed data from one document type and prepare the result for review or export.

The exact scope, timeline and price are agreed after the conversation.

Ask about the first scope

How we work on document automation

  • 1. You describe the documents and process — We check which documents reach the company, where they come from, who handles them and what happens to the data after extraction.
  • 2. We choose the first document type — We do not automate everything at once. We choose the document that is repetitive and gives real value after automation.
  • 3. We check data and exceptions — We analyze file quality, document layout, missing fields, unusual cases and risks of incorrect extraction.
  • 4. We choose the solution — It can be OCR, AI, validation rules, integration, export, a dashboard or a simple approval panel.
  • 5. We test a pilot — We test the solution on a limited document set and decide whether it is worth expanding.
  • 6. We decide what comes next — After the pilot we can extend the scope, add integrations, more document types or automatic reporting.

See exactly how we work

What we do not promise with OCR and AI

  • We do not promise 100 percent extraction accuracy for every document.
  • We do not assume every document can be automated immediately.
  • We do not remove people from the process where review or decisions are needed.
  • We do not deploy OCR without checking what should happen to the data after extraction.
  • We do not recommend a large system if a small pilot, export or approval panel is enough.

In practice, the best document implementations combine automation with human control. The system should read, organize and suggest, but exceptions must go to the responsible person.

Examples of similar work

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System for documents, invoices, payments, statuses, exports and operational communication.

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AI Quality & Cost Control

A panel for controlled use of AI in a process: input data, output, cost, quality, approval and history.

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KPI & Performance Control

A solution example showing how KPI, results, process statuses, exceptions and management decisions can be gathered in one dashboard.

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Related areas

Well-structured documents can also feed a private company knowledge assistant. See the solution

Do you have documents that someone still handles manually?

Describe which documents reach the company, which data needs to be extracted and what happens next. We will check whether the better first step in your case is OCR, XML or e-invoice data, an accounting system integration or a simple approval panel.

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