Summary: OCR turns images into text; an LLM understands structure and outputs clean data (JSON). Combined with validation and human review, the result is fast and dependable.

Why Older Approaches Fall Short

Template-based OCR is rigid: every vendor has a different format, and every layout change breaks the rules. LLMs read context, so they tolerate format variation better.

Pipeline Flow

  1. Ingest: email, folder, upload, WhatsApp.
  2. Pre-process: clean up the image (rotation, contrast).
  3. OCR / vision model: extract text or read the image directly.
  4. Structured LLM: output JSON with a fixed schema (invoice number, date, tax ID, items, total, tax).
  5. Rule validation: total = subtotal + tax, date format, tax ID format, duplicates.
  6. Confidence score: below threshold → human review queue.
  7. Post to ERP/accounting (RPA or API).
  8. Archive + audit log.

Why Validation Is the Key

An LLM can misread numbers. A deterministic validation layer (math, format, matching to the PO) catches most errors before they enter the system.

Suitable Documents

Invoices, tax invoices, transfer receipts, delivery notes, ID documents (mind personal data compliance), contracts (clause extraction), candidate CVs.

Measuring Accuracy

Prepare 100-200 sample documents with correct answers. Measure accuracy per field, not just per document. Aim for straight-through processing of high-confidence documents and review for the rest.

Security and Privacy

  • Financial and identity documents are sensitive: apply access control, encryption, and limited retention.
  • Understand the model provider's data policy; consider controlled processing or self-hosted models where needed.
  • Avoid storing raw data in logs.

Results to Expect

A significant reduction in manual entry time and errors on patterned documents. The size depends heavily on document quality; measure on your data and do not trust generic claims (calculate ROI).

FAQ

Can it read handwriting?

To a degree, but accuracy is lower; require human review.

How often does the LLM get it wrong?

It depends on document quality and the prompt/schema, which is why validation and confidence scores are needed.

Next steps

A pile of invoices or documents? Send 10 samples and we will show you the extraction results.