AI document processing, steps and cost

How does AI document processing work, and what does it cost?

Written by Miroslav Jugović. Published October 5, 2026.

One document workflow

$10,000 to $25,000, 2 to 4 weeks

Model usage

usually tens of dollars a month for a few thousand documents

Who checks

a person reviews every document the system is unsure about

Where it runs

your cloud account, in the region you choose

The five steps

Every document workflow we build follows the same path. The model does the reading. Rules and people do the checking.

Step	What happens	Who checks it
Intake	Documents arrive from an inbox, an upload form or a folder	The system logs every file
Extraction	The model reads the document and returns the fields you need	Each field gets a confidence score
Checks	Your rules run on the fields: totals add up, the supplier exists, the date is valid	Failures are flagged
Review	Unclear or failed documents go to a queue	A person approves or fixes them
Export	Approved data goes to your ERP, accounting tool or database	The export is logged

Why the review queue matters

A model that reads documents is right most of the time, and that is not enough for money or contracts. The review queue is where the remaining cases go. Your team stops typing every field and starts checking the few the system is unsure about.

What it costs

One document workflow, from intake to export, is a Feature Sprint: $10,000 to $25,000 at a fixed price, 2 to 4 weeks, in production on your cloud account with monitoring and 30 days of fixes. The price moves up with more document types, more target systems and more rules. Model usage is billed to your own account and is usually tens of dollars a month for a few thousand documents.

A sample build

The invoice intake agent is a sample build, a practice project with no client. It reads invoice PDFs from a shared inbox, extracts the fields, checks them against rules and puts each invoice in a queue where a person approves or fixes it. It shows the shape of a Feature-tier build.

How this applies to us

Send us a few real documents with the fields you need, and describe where the data should end up. That is enough for a written quote in three business days.

Follow-up questions.

Which documents work well?

Documents that arrive often and carry the same kinds of fields, even when the layout changes. Invoices, purchase orders, delivery notes, claims, applications and standard contracts are good fits. A document type you see ten times a year is usually not worth automating.

Do we need training data?

You need real documents, not a labelled training set. We collect a sample of your recent documents, including the messy ones, and record the correct fields for each. That sample becomes the evaluation set that every change is measured against.

What happens when a field is read wrong?

The system scores how sure it is about each field. Low scores and rule failures go to a review queue, where a person fixes the value. Each fix is logged, and recurring mistakes go into the evaluation set.