Extraction is the easy part
Modern AI models can read almost any business document and pull out the vendor, the totals and the line items. Many off-the-shelf tools do this well, and for simple, low-volume cases one of them may be all you need. We will say so.
Where projects succeed or fail is everything around extraction. Is this vendor already in the ERP under a different name? Does the invoice match an open purchase order and a receipt? Is the unit price within tolerance? Is this a duplicate of something paid last month? What should happen when a field is missing?
Intelligent document processing, done properly, answers those questions automatically for the documents that are clean, and hands a person a short, clear list of the ones that are not. That is what we build.
How the pipeline works
Every document follows the same five stages. Each stage is logged, so you can see exactly what happened to any document and why.
Capture
Documents are collected from inboxes, portals, shared drives, scanners or EDI, and classified by type.
Extract
Header fields and line items are read using a combination of AI models and document-specific rules.
Validate
Every field is checked against your records: vendors, customers, items, POs, prices, tax and duplicates.
Review
Anything below the confidence threshold, or failing a rule, goes to a review screen with the reason highlighted.
Post
Approved data is written to your ERP, accounting system or database, and the original is filed with a link.
Documents we process
Supplier invoices
Header and line-level capture, GL coding suggestions, two- and three-way matching. See invoice processing automation.
Customer purchase orders
Orders from email and PDF converted to sales orders with price and stock checks. See PO automation.
Bills of lading and PODs
Shipment details, signatures and exceptions captured and matched to loads. See shipping documents.
Carrier and freight invoices
Audited against rate confirmations and accessorial rules before approval.
Statements and remittances
Payment details matched to open invoices to speed up cash application.
Applications and forms
Onboarding forms, credit applications and certificates checked for completeness.
Contracts and agreements
Key dates, parties, renewal terms and obligations pulled into a register.
Receipts and expense documents
Captured and coded for bookkeeping, with tax amounts separated.
Quality and compliance documents
Certificates of analysis, inspection reports and safety data sheets indexed and checked.
Build on a platform, or build custom?
We are not tied to one product. We recommend the approach that fits your volumes, document variety and budget.
| Approach | Best when | Trade-offs |
|---|---|---|
| Off-the-shelf capture tool | One document type, consistent layouts, a supported accounting system | Lowest cost; limited validation and integration options |
| Document AI platform plus integration | Several document types, moderate variety, higher volumes | Strong extraction; integration and exception logic still need building |
| Custom pipeline using AI models | Unusual documents, complex validation, or a legacy system to post into | Most flexible; needs monitoring and support, which we provide |
What you get at handover
- A working pipeline in your accountsRunning on infrastructure and subscriptions you own and control.
- A review screen for exceptionsIn the tool your team already uses where possible, such as Outlook, Teams, Slack or your ERP.
- Measured accuracyField-level accuracy and straight-through rates from the pilot, not estimates.
- Audit trailEvery document, extracted value, change and approval logged with a timestamp.
- Documentation and trainingHow it works, how to handle exceptions, and how to add a new vendor or format.
- Monitoring and alertsNotifications if volumes drop, errors rise or a connection fails.
Common questions
How accurate is AI document extraction?
On clean, digital PDFs, field accuracy is typically very high. On scans, handwriting, or documents with unusual layouts it drops. The more useful number is the straight-through rate: the share of documents that pass every validation rule with no human touch. We measure it on your documents during the pilot, and design the review step for the rest.
Do we need templates for every vendor?
No. Current AI models handle new layouts without templates. We add vendor-specific rules only where a particular sender has a quirk that causes errors.
Which systems can you post data into?
Any system with an API or import function, including NetSuite, QuickBooks Online, Xero, Sage Intacct, Dynamics 365 Business Central, SAP Business One and Acumatica, plus custom databases. See integration services.
How many documents do we need to justify this?
As a rough guide, a few hundred documents a month of one type, or a process that takes more than 20 hours of staff time a week. Below that, an off-the-shelf tool is usually more economical, and we will tell you which one.
Where are our documents stored and processed?
In your own cloud storage and in AI services configured not to retain or train on your data. We document every location data passes through. See security and data handling.
Related
- How AI document processing actually worksA plain-language guide to the pipeline and where it goes wrong.
- Automation for logistics companiesDocument-heavy workflows in freight, 3PL and brokerage.
- Automation for accounting firmsClient documents, invoice capture and month-end prep.
- Automation ROI calculatorEstimate the time and cost a document workflow is taking.
Send us ten sample documents
Share a handful of real documents and tell us where the data needs to go. We will tell you how much of the process can be automated and what it would take, before you commit to anything.