AI Solutions
Document AI & OCR: Turning Invoices, Forms and PDFs Into Validated Structured Data
Build document-processing workflows that combine capture, OCR, structured extraction, validation, human review and API integration for invoices, forms and PDFs.
Many business workflows still begin with a document: an invoice, employee form, statement, scanned contract or PDF attachment. OCR can make the text searchable, but automation usually needs more: structured fields, validation, exception handling and an integration into the destination workflow. Start with the target schema Define the fields the downstream process actually needs before selecting OCR or extraction technology. For an invoice that may include supplier, invoice number/date, currency, line items, tax and totals. For HR documents it may include employee identifiers, country-specific fields and banking values. Separate OCR from extraction OCR converts images into text. Extraction decides which text belongs to which business field. Validation decides whether the extracted value is acceptable. Keeping these stages explicit makes errors easier to diagnose. Normalize input before extraction Check file type, page orientation, image quality, duplicate pages and whether the PDF already contains usable text. Poor capture quality should be detected early rather than hidden behind a low-confidence model result. Use deterministic validation where rules are known Dates, amounts, required fields, supported currencies, account formats and RIB/IBAN rules can often be validated deterministically after extraction. AI should not replace a reliable business rule when the rule is known. Keep confidence and review in the workflow Not every document should be auto-approved. Define thresholds and exception conditions that send uncertain or high-impact values to human review. The review screen should show enough source context to correct a value without reopening the entire process manually. Preserve traceability When a value matters operationally, retain the relationship between the structured field and the page/region/source from which it came. This helps reviewers explain corrections and supports later troubleshooting. Protect document data Documents may contain personal, financial or confidential information. Define who can upload, view, process and retain each document. Keep privileged extraction/integration credentials on trusted server infrastructure, not in browser code. Integrate only validated data Do not let a plausible extraction silently become an authoritative ERP, HRIS or CRM value. The pipeline should distinguish raw extraction, normalized/validated values, review status and final downstream write. Measure the right quality signals Evaluate field-level accuracy, validation failures, review rates, document types that frequently fail, and downstream correction rates. A single “OCR accuracy” number is usually too broad to explain whether the business workflow is reliable. A practical document-AI sequence define the target fields and downstream API; collect representative document samples; normalize capture and OCR; extract into a controlled schema; apply deterministic validation; route exceptions to review; write approved data to the destination; measure corrections and failure patterns; iterate by document type. IT-Keys applies these patterns in Document AI, OCR and intelligent extraction and broader business process automation . Our TeroPDF work provides first-hand document-processing product context where appropriate.