AI invoice and document data extraction

Which documents AI invoice processing can handle

The goal is simple: the right data reaches your accounting system or ERP without anyone retyping it. These are the documents we usually start with, and what happens to each one:

• Supplier invoices: supplier, invoice number, dates, net, VAT and gross amounts and line items are read, then matched against the purchase order and the goods received before they are posted.
• Delivery notes: quantities received, often on a signed paper note photographed at the goods-in bay, are compared with what was ordered so shortages and damage are flagged early.
• Purchase orders from trade customers: a PDF or the body of an email becomes a sales order in your system, mapped to your product codes even when the customer uses its own.
• Credit notes and supplier statements: credits are linked to the original invoice, and statements are reconciled against what you have on the ledger.
• Supplier paperwork: certificates, proof of delivery and spec sheets are filed against the right supplier or shipment.

How invoice data extraction works, step by step

A realistic first scope: read invoices from your regular suppliers, check each line against an open purchase order and a delivery note, and leave the bill in your system ready for approval. Anything that does not match is held back with the reason. The system has four parts.

Intake: a dedicated mailbox, a shared folder or the office scanner; documents are picked up automatically. Reading: an AI model reads the text, including photos and scans, and understands what each value is even when every supplier puts it somewhere different, so there is no template to maintain per supplier. Validation: what was read is checked against rules and against your systems: the supplier exists, net plus VAT equals the total, the order is open, the price is within the tolerance you set. Posting: validated data goes into your system through its API or whatever import route it supports, and each document stays linked to the record it created.

Confidence thresholds and human review

What matters most is what happens when the AI is unsure. Every field carries a confidence score and you set the threshold. Above it, the value goes through; below it, the document lands in a review queue with the doubtful field highlighted on the original image. Someone corrects it in seconds and the document carries on.

We start with a high threshold. Once there is real data on how often the system is right for your suppliers, we decide together whether to lower it. Every correction is logged, so you can see who changed what and why.

Dext, AutoEntry, Rossum, Nanonets or a bespoke build

You do not always need development. Several mature products already do invoice capture well, and if one of them fits, we will tell you. This is how we see the options, based on what each vendor publishes. Vendors also publish their own accuracy figures, so test any tool on a sample of your real documents before you commit. If documents arrive mixed in with other email, extraction pairs well with automatic inbox triage, so attachments are routed before anyone opens them.

Getting the data into Xero, Sage or Business Central

The extraction is only half the job; the other half is writing cleanly into your system. Xero's accounting API creates supplier bills as invoices of type ACCPAY and accepts an idempotency key, so a retried request does not create the same bill twice. Business Central's API has a purchase invoice resource that can be created and then posted. Sage 200 offers an API for integrations; with Sage 50 we check in the first call which route your version allows. If your ERP is older or customised, see our ERP and CRM integration service.

To be clear about scope: this is about getting operational documents into your ledger. Your accounting software remains the system of record for VAT returns and anything you file. How documents sent to AI models are handled is explained once, in detail, on our private ChatGPT for business page.

How we would run it and what drives cost

We start with a tightly scoped pilot on your real documents: one document type, your most frequent suppliers and posting into your system. That shows, with your data, how many documents go straight through and how many need review. You see a working demo every two weeks, you get one fixed price for the project, and the code sits in a repository in your company's name. More on how we work and fixed price software development. The main cost drivers are:

• Document types: invoices only is a different job from invoices, delivery notes and purchase orders.
• Input quality: digital PDFs versus photos of crumpled delivery notes.
• Validation rules: tolerances, approval limits by amount, exceptions by supplier.
• How your system connects: a documented API is straightforward; without one, another import route has to be found.
• Volume: the monthly cost of AI models and hosting depends on pages processed. Market references are in our guide to the cost of implementing AI in a business.