Insights on AI, Document Processing & Automation

Every OCR API vendor's homepage looks the same: "industry-leading accuracy," "seamless integration," "enterprise-ready." None of that tells you whether the API will actually work on your documents, at your volume, for a price that makes sense in six months.

Every AP team already knows manual invoice processing is slow. Fewer have actually put a number on it. This post does the math — labor, errors, approval delays, and the per-invoice cost of invoice OCR versus keying invoices by hand — so you can see exactly where the savings come from before you evaluate AP automation for your own team.

Most OCR and IDP vendors quote a single accuracy number and leave it there. We wanted to know what actually happens field by field, template by template, when SoceTonAI DoxTract processes real invoices at scale — so we ran a benchmark and published the full methodology and results.

Ask five vendors how they extract data from documents and you'll get two very different answers. Some point to structured data extraction rules — fixed zones, templates, regex patterns. Others point to an LLM — send the page in, get JSON out, no configuration required. Both are legitimate approaches, and both fail in predictable, opposite ways.

"99% accurate" is probably the most repeated — and least useful — number in OCR marketing. It sounds close to perfect until you realize a 99% character accuracy rate still puts roughly 30 wrong characters on a single 500-word page. For a purchase order or invoice, one of those characters can be a digit in the total amount.

If you're building or buying Document AI for invoices, receipts, or forms, you've almost certainly landed on the "big three" cloud OCR APIs: AWS Textract, Google Document AI, and Azure Document Intelligence. All three promise the same thing — turn a scanned document into structured data — but they price, package, and perform very differently once you're past the demo.

"OCR" and "Document AI" get used almost interchangeably — search for either term and you'll land on the same handful of products. But they're not the same thing, and knowing the difference matters if you're choosing a tool to extract data from invoices, receipts, contracts, or forms.

Invoices get most of the attention in finance automation conversations, but they're only half the story. Before an invoice ever shows up, there's usually a purchase order — the document that says what was ordered, from whom, at what price, and under what terms. Getting PO data into a system accurately is just as important as extracting the invoice itself, because it's what makes automated matching and approval possible in the first place.

Every document-processing product claims to "extract data." But there's a big gap between pulling raw text off a page and pulling out data you can actually use in a database, spreadsheet, or workflow. That gap is exactly what structured data extraction is meant to close.

Not every document you need to process looks like every other document. A vendor's invoice format, a specific customs form, an internal expense form your company built five years ago — each has its own layout, its own labels, its own quirks. General-purpose extraction can get you most of the way there, but for documents you receive over and over in the exact same format, there's a more precise tool: a template.