What is document AI? How it works, what it's used for, and how to choose

Document AI is software that uses AI to read documents the way a person would. It finds the text and layout of a PDF, scan or photo, pulls out the fields and tables, works out what kind of document it is and returns structured data your systems can use. The term names a category as well as a product. Google Cloud sells one called Document AI, Microsoft's is Azure Document Intelligence, and Docsumo, which publishes this guide, is a document AI platform.

Below, we follow one pay stub through the process, then cover where businesses use document AI, how it differs from OCR and chatbots, and what to test before you buy.

What is document AI?#

Most business documents are laid out for a person, not a system. Document AI closes that gap without anyone retyping the data. Hand it the pay stub below and you get back the employer, the pay period, gross pay of $2,580.00 and net pay of $1,903.51 as named fields, each with a confidence score.

The same idea goes by several names, depending on who's talking. Analysts call the software category intelligent document processing (IDP). Researchers use the other names interchangeably, and a Microsoft Research survey of the field opens with "Document AI, or Document Intelligence". These are the three you'll run into most.

• Document AI

  The name cloud providers and researchers use. Google Cloud sells a product called Document AI.

• Document intelligence

  The name of Microsoft's cloud service, Azure Document Intelligence, and of the category for many software buyers. More in what is document intelligence.

• Document understanding

  The research name for the problem, and a UiPath product name. See what is document understanding.

How document AI works#

Take the pay stub above. OCR reads the characters first, printed or handwritten. A layout model then works out which figures sit under "Current" and which under "YTD", so $2,580.00 stays this period's gross pay instead of becoming a stray number. The classifier recognizes a pay stub rather than a W-2, which tells the model what to look for. In a 38-page loan upload, the same step finds where each document starts and ends.

Extraction maps what's on the page to the fields you asked for. Most tools now use large language models here, so a stub from a payroll provider they've never seen needs no template. Then come the checks. Gross pay minus deductions should equal net pay, and the net should match a payroll deposit on the applicant's bank statement. Every value carries a confidence score, and anything under your threshold waits for a person.

What document AI is used for#

It pays off where documents drive a decision and arrive in volume.

• Lending

  Income and cash flow pulled from bank statements, pay stubs and tax returns, then checked against the application. See bank statement processing.

• Insurance

  ACORD forms and certificates of insurance read field by field, so nobody rekeys a submission. More on ACORD form processing.

• Accounts payable

  Invoice lines checked against the purchase order before anything is posted. How it works in accounts payable automation.

• Healthcare

  Eligibility and claims paperwork read before a caseworker or claims team decides. See document processing for healthcare.

• Logistics

  Bills of lading and delivery receipts matched to the freight invoice. The logistics page has examples.

Document AI vs OCR vs a general chatbot#

All three read documents. They differ in what comes back, and in how much is left for a person to do.

A general model is a strong reader, and many document AI tools run large language models underneath. The difference is control. A business process needs the same schema every time and a score on every value, so the doubtful ones reach a person and every correction is on record. The trade-offs are covered in LLMs in document processing.

Where document AI still needs a person#

Faint scans, cursive handwriting and a supplier's redesigned invoice still trip models up, and so do values that disagree across documents. A model can also be confident and wrong. So ask a vendor what happens when the model misses. The better tools score every value, send the doubtful ones to a reviewer with the source line highlighted, and learn from the corrections. We list the usual failure modes, and how to catch each one, in where document intelligence fails.

Kinds of document AI tools#

Three kinds of product sell document AI. They differ in how much of the process they cover and who runs them.

• Document AI platforms

  Tools such as Docsumo, our product, cover the workflow from sorting documents to a review queue and delivery to your systems. An operations team runs them, and they're usually priced by plan and volume.

• Cloud APIs

  Google Document AI, Azure Document Intelligence and Amazon Textract read documents and return JSON, priced per page. Your engineers build the review screen and the integrations.

• Enterprise IDP suites

  Products such as ABBYY Vantage and Hyperscience, built for large automation programs with their own teams. Pricing usually comes after a demo.

Our take. Don't shortlist by name. Vendors picked "document AI", "document intelligence" or "IDP" for branding, and the products under each label overlap. Most tools now read a clean page well. The hours go into the file: a loan application is several documents that have to agree with each other, plus the few values someone still has to check. Pick the tool built for that part.

For a vendor-by-vendor comparison with prices, see document AI platforms compared.

How to choose a document AI tool#

A demo on the vendor's samples tells you little. Run a pilot on your own files and check these.

• Your own documentsSend a batch of recent files, including the phone photos and long statements nobody enjoys keying.
• Fields a person fixedCount corrected fields and documents that went straight through. That's your real accuracy.
• Confidence that means somethingLow scores should line up with wrong values. Set thresholds per field once you've seen your results.
• The review screenYour team will work in it every day. A reviewer should see the source line for each value and fix it in seconds.
• Checks across documentsDoes the name on the ID match the application? Does the invoice match the purchase order?
• Security and data useAsk for SOC 2 Type 2, HIPAA if you handle health data, and whether your documents train shared models.
• The full costA per-page API price leaves out the review screen and integrations you'd build yourself.

Where Docsumo fits#

Docsumo is a document AI platform built for lending, insurance and accounts payable teams. It sorts every document in a loan, claim or AP file, extracts each field and table with a confidence score, and sends your team only the exceptions. On the Enterprise plan it also checks documents against each other. Data reaches your systems through an API and webhooks. One limit to know: it runs in the cloud only, so if documents can't leave your own data center, a cloud API that ships containers fits better.

• 99%99%field-level accuracy across 250+ document types
• 95%+95%+of documents processed straight through, without manual review

More on the product: the intelligent document processing platform, the Document AI capability and security.

Where to start#

Start with who will run it. Engineers building a pipeline can begin with a cloud API, while an operations team replacing data entry needs review built in. Either way, test on your own hardest files and count what a person had to fix.

Book a demo with a few of your own documents, or start a free trial.

Frequently asked questions#