Stop retyping documents your systems could read.
Invoices, contracts, applications and delivery notes still arrive as PDFs, scans and photos that someone keys in by hand. Modern AI models read varied layouts far better than template-based OCR. We build document processing pipelines that extract, validate and pass exceptions to people, with AI-native engineers doing the build.
AI document processing no longer needs a template per supplier.
Older OCR tools needed a template for every layout: one supplier puts the total bottom right, another spreads it over two pages with a different date format. Each new sender meant more setup, and anything unusual fell out as an exception. Vision-capable language models read a page more like a person does, finding the invoice number whether it is labelled "Inv #", "Reference" or not labelled at all.
That does not make them infallible. Models can misread a smudged digit, merge two line items or, worse, supply a plausible value that is not on the page. The engineering that matters is everything around the model: checking extracted values against each other and against your systems, scoring confidence per field, and sending doubtful cases to a person.
To be candid, if your documents come from a handful of senders in stable formats, the capture feature inside your accounting software may be enough. Custom pipelines pay off with varied layouts, document types no product handles well, or data that has to flow into bespoke systems.
What AI data extraction involves for each document type
The model is similar across document types. What changes is what counts as correct, and how we prove it.
| What gets extracted | Hardest part | How it is checked | |
|---|---|---|---|
| Invoices and receipts | Supplier, dates, totals, tax, line items | Line items spanning pages | Totals reconcile, supplier and PO match |
| Contracts | Parties, term, renewal, key clauses | Obligations phrased many ways | Page citation per field, legal sign-off |
| Application forms | Applicant details, answers, signatures | Handwriting and tick boxes | Format rules, cross-checks with records |
| Delivery and customs documents | References, quantities, weights, codes | Poor scans and phone photos | Match against orders and shipments |
| Financial statements | Figures by line and period | Inconsistent table layouts | Sums and balances must add up |
How we roll out AI document processing
The build phase stays short partly because we are AI-native: every new document type needs a batch of similar extraction and validation code, which coding agents draft and senior engineers check against the ground truth before it ships.
Collect a real sample
Week 1A few hundred representative documents, including the ugly ones, with correct values recorded by your team. This ground truth drives every later decision.
Measure field by field
Weeks 2 to 3We test candidate models and approaches and report accuracy per field, because a wrong bank account number matters far more than a wrong reference note.
Build the review queue
Weeks 3 to 6Validation rules, confidence thresholds and a simple screen where staff confirm or correct flagged fields, with the source region highlighted beside each value.
Integrate and tune
Weeks 6 to 10Approved data flows into your accounting, ERP or case system. Corrections feed back into prompts and rules, and thresholds relax only as measured accuracy allows.
Signs of a document pipeline you can trust
Whether we build it or you buy it, ask these questions before extracted data reaches financial or legal systems.
Every value links back to its source
Reviewers should see the highlighted region of the page, not just a number in a table.
It knows when it is unsure
Low-confidence fields and failed validation rules go to a person instead of passing silently.
Accuracy is reported per field and document type
A single overall accuracy figure hides the specific fields that cause real problems.
The straight-through rate is tracked
The share of documents needing no human touch is the business metric that matters, and it should rise as corrections accumulate.
Sensitive data is handled deliberately
Know where documents are processed, how long copies are kept, and whether personal data is redacted before reaching a model provider.
A new supplier does not need a developer
Adding a layout or field should be configuration and testing, not a code change.
Questions we often hear
How accurate is AI document processing?
It varies with document quality, layout variety and the field in question, so we measure it on a sample of your own documents before committing. Clean digital invoices usually extract reliably; handwritten forms and poor scans less so. Validation rules and human review of low-confidence fields close the gap.
What is the difference between AI document processing and OCR?
OCR turns an image into text but does not understand what the text means. AI document processing reads layout and context to find specific values, such as which of three dates is the due date, and returns structured data. OCR often remains one step inside an AI pipeline.
Can AI read scanned and handwritten documents?
Printed scans are generally handled well, and modern vision models cope with phone photos better than older tools. Handwriting is less reliable and depends on legibility, so handwritten fields usually get stricter confidence thresholds and more human review.
Is it safe to process confidential contracts with AI?
It can be, with the right setup. Options include enterprise API terms that exclude model training and limit retention, processing in a chosen region, redacting personal data first, or running an open-weight model in your own environment when documents must not leave it.
How much does AI document processing cost per document?
Model costs per page are usually small next to the staff time spent keying data, though they vary with page count, image quality and model choice. The larger costs are the initial build, integration and review effort. Our automation ROI calculator helps weigh that against your current processing time.
Related reading and tools
Tell us which documents are piling up.
Document types, rough monthly volumes and the system the data should land in are all we need to begin. Expect a reply within 24 hours on whether custom AI, an existing product or a simpler fix suits you best.
Working with companies globally · Response within 24 hours