How does AI invoice data extraction differ from OCR?

Template-based OCR requires someone to map field locations for each vendor's layout - invoice number top-right, total bottom-left. It works until a vendor redesigns their invoice, adds a logo, or you onboard fifty new suppliers. Template maintenance becomes a permanent project, and accuracy collapses on any document the template library hasn't seen. Template-free capture uses models trained on large volumes of real invoices to locate fields by meaning rather than position, so a brand-new vendor's first invoice extracts as well as the thousandth.

Human-in-the-loop means extracted data is presented as suggestions a person confirms or corrects before anything posts to the books. It's not a temporary crutch - it's the correct permanent architecture for financial data. The goal of good AI is not removing the human; it's changing the human's job from typing to validating, and concentrating attention on low-confidence fields and exceptions instead of every field on every invoice.

OCR (optical character recognition) scans an invoice image and converts printed characters into machine-readable text. Classic OCR stops there - it produces text, not understanding, which is why OCR-only tools still require humans to interpret and key the results into fields.

IDP layers machine learning on top of OCR to classify documents, locate fields regardless of layout, extract line items, and assign meaning - turning a document into structured data. Modern AP platforms go further by validating that data against ERP records before anyone acts on it.

Templates are positional rules per vendor layout (brittle, high maintenance). ML extraction learns field patterns from training data (layout-independent, improves with volume). LLM-based extraction adds language understanding for messy or unusual documents. Mature platforms blend approaches and route each document or field to whichever model performs best.

Template-free AI extraction. Modern systems trained on millions of invoices need no per-vendor setup: new vendors extract on day one, and corrections improve the model rather than triggering template rework. If your team maintains a template library, you're doing the machine's job.

Ask what's actually measured: field coverage or accuracy, header or line level, on whose invoices, over what period and sample. Ask whether the number reflects diverse live customer environments or a curated test set, and whether human review is assumed. Then test with your own messy invoices - the demo set is always clean.

Many do, typically as one engine among several. It's reliable for accounting when governed correctly: suggestions validated against ERP rules and confirmed by humans before posting. The risk isn't the model - it's any system that posts unvalidated machine output directly to the GL.

Confidence scoring is the model's own estimate of how likely each extracted value is correct. Low-confidence fields should be visually flagged and routed for human confirmation rather than silently accepted - confidence-based routing is what lets teams trust high-confidence data without reviewing everything equally.

Bundled, in almost all cases. Standalone capture hands you structured text and leaves coding, validation, matching, and workflow as your problem; platform-embedded extraction feeds directly into ERP-aligned coding and approvals. Standalone engines make sense mainly at very high volume feeding a custom pipeline.

Stampli's extraction is not standalone OCR - it combines OCR, AI extraction, vendor auto-detection, PO number detection, and GL prediction, orchestrating multiple models to choose the best prediction per field. Stampli AI performs on average 87% of finance work across 2,700+ unique fields, with every suggestion subject to human review and approval before posting to the ERP. Because suggestions are aligned to ERP structure - not just read off the page - AP starts from an ERP-valid draft, not raw text. The system improves with every correction.