Lido Blog

Matching invoices to purchase orders is one of the most straightforward concepts in accounts payable, and one of the most painful to execute at scale.

Most invoice extraction tools work fine until you need more than the header. Pulling vendor name, invoice date, and total amount is table stakes. The real challenge starts when you need line-item descriptions, per-item quantities, tax breakdowns applied to specific items, or custom fields that don't exist in the tool's default schema.

Invoice data extraction works until you add your twentieth vendor. The first few templates are manageable. But when you're processing invoices from 200 or 300 different suppliers — each with its own layout, line item structure, and date format — you're no longer doing automation. You're maintaining a system that requires constant attention just to keep up with the documents flowing in.

Every extraction tool works on clean digital PDFs. The real test for scanned invoice data extraction is what happens when you feed it a faxed copy with dark edges, a phone photo taken under fluorescent lighting, or a dot matrix printout from a system that should have been retired in 1998.

Invoice processing has a scaling problem that most companies discover the hard way. At 50 invoices a month, someone handles it between their other responsibilities. At 500, it's a full-time job. At 5,000, you're staffing an entire team to do work that follows predictable rules, and the error rate is climbing anyway.

Docparser does exactly what it promises for companies with a stable set of document formats. You set up a template, map your fields, connect a Zapier integration, and it runs. The problem shows up when the number of formats grows.

Nanonets works well enough when your documents are clean, digital, and consistent. But if you're processing invoices from 200+ vendors, scanned documents, or anything with handwriting, you've probably already discovered the limits. Model retraining takes weeks. Accuracy drops on layouts the system hasn't seen. And you're still paying for every extraction attempt, including the ones that come back wrong.

Everyone's had the same idea. You have a stack of invoices to process, ChatGPT is right there, and it can read documents. Upload a PDF, ask it to extract the invoice number, vendor name, line items, and total, and it gives you a clean answer. It works. You think you've just saved yourself $10,000 a year in software costs.Then you try it on your actual workload.

CPA firms face a document problem that most extraction tools can't solve. A firm doing 3,500 compliance audits a year doesn't process the same payroll format over and over. They process hundreds — probably thousands — of different formats, and they can't predict what's coming next.

Trucking runs on paper. Every load generates a stack of documents that someone has to process: driver tickets, bills of lading, carrier invoices, fuel receipts, lumper fees. It's why so many of them end up with entire teams doing nothing but data entry.