How do you extract data from invoices with an LLM?
You extract invoice data by sending the document to a vision model with a schema for the header fields — vendor, date, total — and a line-items array. LLMs handle varied layouts well, but totals, tax, and table rows are error-prone, and a general model gives you no confidence signal, so a wrong number flows straight into your ledger. A model that returns per-field confidence lets you gate the risky fields.
Last updated 2026-07-20
The approach
Define a schema with header fields plus a line_items array and let the vision model fill it. Unlike rigid template OCR, this flexes across vendor formats without per-vendor setup.
Where errors hide
The risk concentrates in numeric fields — total, tax — and multi-row tables. A single misread digit in a total is expensive and invisible until reconciliation.
Gate the numbers
With per-field confidence (Interfaze's precontext), auto-approve high-confidence fields and route low-confidence totals and rows to review — the same pattern KYC teams use, applied to accounts payable.
FAQ
Are LLMs better than template OCR for invoices?
They're more flexible across formats, but you still need verification on numeric fields — per-field confidence scores provide it.