Unstructured PDFs such as invoices, contracts, or reports can now be automatically processed and transferred to databases in a structured manner, which is a real game changer in terms of saving time and avoiding errors. This is made possible by AI-supported data extraction from PDFs, in which Document AI recognizes and interprets content from documents and makes it usable for downstream processes. This allows companies to extract PDF data automatically, avoid manual typing, and lay the foundation for scalable process automation.
The most important facts at a glance
- PDFs are visual documents, not data sources. Information in PDFs is readable by humans but not directly usable by systems. Only AI-supported data extraction makes content from PDFs available in a structured form.
- Document AI combines OCR, layout analysis, and semantic interpretation. AI-based document processing recognizes text, tables, and relationships and assigns content such as amounts, dates, or contractual partners based on context.
- AI extracts data. Processes make this data usable. The real added value comes when extracted PDF data is automatically validated and transferred to databases or target systems.
- Processing PDF data automatically means thinking about architecture. Successful solutions make a clear distinction between recognition, verification, and transfer.
- AI-supported data extraction is particularly suitable for variable documents. Invoices, contracts, or reports with changing layouts can thus be reliably automated without maintaining rigid templates.
PDFs are not structured data
PDFs are ubiquitous. Invoices, contracts, reports, and statements are available as unstructured PDFs in almost all companies. The information they contain is easily visible and readable for humans, but cannot be used operationally by IT systems. This is already evident in the name: PDF stands for Portable Document Format, which is a format developed to display documents identically regardless of systems, programs, or end devices, but not to provide structured data or process PDF data directly.
It has long been a digital standard for PDFs to be read via OCR (Optical Character Recognition). This minimizes transfer errors in PDF processing and time consumption compared to manual transfer.
AI-supported data extraction from PDFs now additionally structures the read data in databases or target systems, i.e., where it can be checked, evaluated, or automatically processed.
Which AI can access PDFs?
What is commonly referred to as “document AI” is not a single tool, but rather a combination of several capabilities: optical character recognition (OCR), layout recognition, and semantic interpretation, often supported by large language models. Solutions such as Azure Document Intelligence, Google Cloud Document AI, AWS Textract, or AI-powered document processing with OpenAI are often cited as examples in this context.
How data extraction works with Document AI
Document AI does not work in a single “AI step,” but along a clear processing chain. Each step builds on the previous one, and each fulfills its own clearly defined task.
1. Document input: PDF, scan, or image
It starts with a document in visual form: a digital PDF, a scanned document, or a photo. For the system, this is not initially an invoice, a contract, or a data record—but a visual representation. Only the following steps turn it into usable information.
2. OCR: From image to text
The first technical processing step is optical character recognition (OCR). It translates letters, numbers, and special characters from the visual representation into machine-readable text.
3. Layout and structure recognition: Order in the document
Based on the recognized text, Document AI analyzes the visual structure of the document. Paragraphs, headings, tables, columns, rows, and field groups are identified. Only at this point does it become apparent, for example, that a number is part of a table, that certain information is located in the header area, or that several values logically belong together. At this point, structure replaces pure text sequence.
4. Semantic interpretation: Recognizing meaning
In the next step, the structured content is interpreted semantically. Document AI models assign text fragments to content categories: for example, invoice number, invoice date, contractual partner, total amount, or service period. The AI works context-based. It recognizes meanings even when field names vary or information is located in different places. Important: AI provides plausible assignments, not guaranteed truths.
5. Document classification
At the same time or afterwards, the document is classified as a whole. Is it an invoice, a contract, a delivery note, or a report? This classification is crucial because it sets the framework for further processing: Which fields are relevant, which structures are expected, which rules can be applied later?
Document AI has done its job. What happens next?
In a well-designed process, the work does not end with extraction; that is where it really begins. The key point is that the following steps are automated, controlled by clearly defined rules and workflows. No manual follow-up, no Excel, no “someone will look at it,” just a robust, repeatable process.
Let's take incoming invoices as an example.
Step 1: Automated transfer of extraction results
After Document AI processing, the result is available in a structured format, often in JSON format, for example; invoice number, date, supplier, amounts, tax portions. This result is automatically transferred to the downstream process. Not manually, but as machine-readable input.
Step 2: Rule-based validation
Now automation rules come into play: Is there an invoice number? Is the amount numerically correct? Does the supplier match a known creditor? Do the net, tax, and gross totals add up? These checks are fully automated.
Step 3: Mapping to the accounting model
The checked data is then mapped to a defined invoice and accounting model. This mapping determines which extracted field corresponds to which target field, such as cost center, G/L account, or company code. Here, a “recognized invoice” becomes an invoice ready for posting.
Step 4: Transfer to the target system – via SQL or API
Now the technical transfer takes place: If the invoice data is written directly to a relational database, this is done using SQL statements, for example to create an accounting record or an invoice record. If the transfer is made to an ERP (enterprise resource planning) system, it usually runs via API calls so that the system can apply its own business logic, e.g., for approvals, company codes, or tax logic.
Both methods are fully automated. The key point is that from this point on, there is no more room for interpretation. A data record either complies with the schema or is rejected and forwarded for clarification.
Step 5: Document storage and linking
At the same time, the original document is automatically stored in a DMS (document management system) and linked to the posting or transaction. The invoice is thus archived in an audit-proof manner and can be retrieved at any time without manual filing.
Infobox: SQL & API – why both play a role
SQL (Structured Query Language)
SQL is used when structured data is written or updated directly in databases. SQL enforces fixed structures, mandatory fields, and consistency, thus ensuring reliability at the data level.
API (Application Programming Interface)
APIs are used to transfer data to applications such as ERP, CRM, or DMS systems. The target system processes the data using its own business logic before it is stored internally.
Technology as a means, not an end
Those who want to automatically transfer PDFs to databases often look for the “right” technology. In practice, however, it is not the individual tool that determines success, but the underlying architecture. Technologies change – processes, data models, and responsibilities remain. A viable solution for AI-supported data extraction therefore does not consist of a single product, but of clearly separated, interacting architectural building blocks.
Conclusion: Don't “introduce AI,” build processes
AI does not solve organizational problems. It can recognize, structure, and make information available, but it does not replace clean processes or clear responsibilities.
The real value comes from:
- the clear separation of recognition and transfer
- robust, rule-based automation
- clearly defined responsibilities and interfaces
If you really want to automatically transfer PDFs to databases, you don't need miracle AI. You need a well-thought-out process in which AI plays a clearly defined but effective role.
FAQ: AI-supported data extraction
Which AI can access PDFs?
Many AI systems can read PDFs and recognize content, for example, via OCR, layout analysis, and semantic interpretation. What is often referred to as Document AI (e.g., Azure Document Intelligence, Google Cloud Document AI, AWS Textract, or OpenAI-based solutions) interprets PDFs and does not access a database.
Can AI create a database?
AI can create simple databases or tables, for example for tests, small applications, or prototypes. However, this is not sufficient for productive systems: structure, rules, consistency, and responsibility must be clearly defined and implemented via fixed processes and interfaces, not “automatically by AI.”
Which AI can convert PDFs to Excel?
Many AI solutions can recognize tables from PDFs and output them as Excel files. However, Excel is not a target system, but at most a temporary check or test format.
Is AI-supported data extraction reliable enough for productive processes?
Yes, if AI is embedded in a rule-based, automated process. The necessary reliability is achieved through validations, plausibility checks, and clear transfers to target systems.
For which documents is AI-supported data extraction particularly suitable?
Unstructured or varying documents such as invoices, contracts, or reports are particularly suitable. AI-supported data extraction is less useful where data is already strictly structured.
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Jens ist Mitgründer von bakedwith, einer Boutique-Beratung für smarte Automatisierung und KI. Er unterstützt mittelständische Unternehmen und Konzerne dabei, Prozesse zu optimieren, manuelle Arbeit zu reduzieren und durch effiziente Workflows Wachstum zu erzielen. Zuvor war Jens Growth Lead bei OMR und begleitete als Freelancer zahlreiche Unternehmen bei der Optimierung ihrer CRM- und Automationssysteme. Seine Leidenschaft gilt der Verbindung von Wachstum und Effizienz – damit Teams sich auf das Wesentliche konzentrieren können.
