The Problem of Unstructured Data in Organizations
It is estimated that over 80 percent of data in organizations is unstructured — scanned invoices, contract PDFs, hand-filled forms, emails with attachments, meeting minutes. Each of these documents contains valuable data that should flow into ERP, CRM, or database systems — but extracting it through traditional methods requires manual work or expensive OCR systems with rules for every document format.
How Do LLMs Perform Structured Extraction?
Language models approach data extraction differently from traditional rule-based systems. Instead of defining patterns for every invoice layout, the model receives a document and a target schema — a description of fields, data types, and format requirements — and independently finds and maps the information. The result is returned directly as JSON ready for processing by downstream systems.
The advantage over rule-based approaches is particularly visible with variable document formats. An invoice from a Polish supplier, a foreign debit note, and a scan of a hand-filled order can all be processed by the same model without configuring separate templates for each format.
Practical Applications in Enterprise Environments
- Invoices and financial documents — automatic extraction of document number, date, line items, amounts, contractor data, and bank account number directly into the accounting system
- Contracts and amendments — extracting parties, contract subject, validity dates, key clauses regarding penalties and termination
- Onboarding forms — processing employee or customer applications and loading data into HR or CRM systems
- Business correspondence — identifying intent, contact data, and commitments from emails and letters
- Medical and compliance documentation — extracting dates, procedures, and identifiers from documentation while maintaining personal data anonymization
Validation and Extraction Confidence
Raw model output should rarely go directly into production systems without a validation layer. A solid enterprise approach includes several quality control mechanisms. First, schema validation — checking whether the returned JSON meets type and format requirements (ISO dates, tax ID codes, IBAN numbers). Second, business logic — does the invoice line item total match the gross value? Is the issue date not later than the payment deadline? Third, confidence scoring — the model can return a confidence assessment for each field, allowing uncertain cases to be routed to manual review.
Anonymization as a Processing Prerequisite
Many documents subject to extraction contain personal data — names on invoices, employee data in forms, party information in contracts. Processing them through external models requires a legal basis compliant with GDPR. The alternative is anonymization before extraction — removing or pseudonymizing personal data, processing the document, and restoring original values on the client's server side. ESKOM.AI integrates automatic anonymization as a step preceding every processing of documents containing personal data.
Structured extraction with LLMs is one of the fastest-returning investments in automation — organizations processing several thousand documents per month report a 70-90 percent reduction in manual data entry costs while cutting processing time from hours to seconds.