The best AI document classification software in 2026: 14 tools for sorting and splitting documents

Document classification software works out what type each incoming document is, such as a bank statement or a claim form. It also splits files that hold several documents. Then each document goes to the right extraction model, queue or person. The right tool depends on who will run it. Most operations teams want one tool that sorts and reads each document. Docsumo, ABBYY Vantage and Hyperscience do both. Engineers can build on a cloud classifier from Google, Microsoft or AWS. They can also use an API such as Reducto.

We compared 14 tools. Every fact comes from the vendor's own website or documentation, checked on September 29, 2026, and the links are in the sources at the end. We checked the facts in the industry section on October 21, 2026. We didn't test the 14 tools against each other, so the testing section explains how to run your own test.

Document classification software compared

The table shows how each tool splits files, learns a type and handles uncertain documents. It follows each vendor's own site, checked September 29, 2026. Each tool's full entry, with what to watch for, comes further down.

Side-by-side pages with Docsumo are on our compare hub, including Google Document AI, Azure Document Intelligence, Grooper and Reducto.

What document classification software does

Document classification is the first step in document processing. A file arrives by email, upload, scanner or API, often as one PDF holding several documents. The software finds where each document starts and ends, and decides what each one is. Then it hands each document to an extraction model for that type, a work queue, or a person when it isn't sure.

Splitting and labeling are separate skills, and tools differ on both. Take a loan file scanned as one 60-page PDF. It has to be cut at the right pages before anything can be labeled. If the tool cuts in the wrong place, the last page of a pay stub becomes part of the bank statement after it.

One naming note before the list. Security and records teams also use the word "classification" for labels such as public or confidential. Those labels decide who can open a file and how long it's kept. That's a different kind of software, and it isn't covered here.

How tools learn your document types

Rules are the oldest method. A page with a form's title or a known header gets that form's label. Anything else goes to a person. Rules are cheap to run and easy to explain. But they break quietly when a layout changes or a new form arrives. So most tools now offer rules as an extra layer on top of a model.

Most platforms and the older cloud classifiers learn from labeled examples instead. Azure's custom classifier needs at least two document types, which Azure calls classes, and five samples of each. UiPath's trained classifier has the same minimum, but UiPath recommends 150 documents per type. Hyperscience wants at least 10 pages per layout, with 120 recommended. Accuracy on your own layouts is usually good, but every new type means collecting and labeling more samples, then training again.

The newest classifiers use a large language model (LLM) that reads each type's name and a short description. So adding a type can mean writing one sentence instead of labeling a folder of samples. Reducto and LlamaParse work only this way. Google's classifier runs on its Gemini AI models. It works from just your label names and descriptions, or after fine-tuning, which means extra training on your own samples. Microsoft's Content Understanding takes up to 200 described categories. AWS's Bedrock Data Automation matches documents to blueprints, which are AWS's name for document types you describe. UiPath and Grooper have added the same option to their older methods, and Tungsten has added an LLM-based classification copilot. Docsumo sorts files into the document types you name, with no training set. Its AI Split takes a plain-English definition of each type.

Description-based classifiers are quick to set up. Their weak point is look-alike document types, which they don't always label the same way. Put look-alike types in your pilot. Whatever the method, ask what the tool does when it isn't sure. Most tools give each label a confidence score, which says how sure the tool is. Ask what happens to a page when that score falls below the level you set. Reducto always returns its best match and advises adding an "other" category, and Microsoft gives the same advice for Azure. Docsumo holds documents it can't classify as Unclassified until a person assigns them. A classifier that files every page under its best guess makes mistakes nobody sees until later. A classifier that holds uncertain pages for a person makes mistakes you can count.

The 14 best document classification tools

Docsumo is our product, so we've put it first and said plainly what it doesn't do. The others are grouped by what they're built for.

IDP platforms

• 99%99%classification accuracy
• 99%99%field-level accuracy across 250+ document types
• 95%+95%+of documents processed straight through, without manual review
• <5 min<5 minper document, down from 2+ hours

Capture and content platforms

AP software

Cloud classifiers

LLM document APIs

What classification costs per page

Where vendors publish prices, classification alone is the cheap part. Per 1,000 pages, LlamaParse's Classify costs $1.25 in its fast mode, and Azure's custom classifier costs $3. Google's classifier or splitter costs $5 for the first million pages a month. Reducto's Classify costs $7.50, or $15 for its Deep Classify. Services that classify and extract in one step cost more, such as AWS's Analyze Lending at $0.07 a page, or $70 per 1,000 pages. Most platforms, Docsumo included, price their plans on request instead, and Rossum's Starter plan begins at $18,000 a year.

The per-page price rarely decides the budget. A misfiled or badly split document costs a person minutes to find and fix. Those minutes are worth far more than a fraction of a cent. Put your own volumes into the calculator, with the tool's price per page and the share of documents your team still touches.

How to test a classifier on your own files

1. Pull a real month of filesTake whole packets as they arrived, not hand-picked single documents, and keep the rare types and the pages that fit no type.
2. Write the answer key firstMark where each document starts and what it is before any tool sees the files, so every vendor is scored against the same truth.
3. Score splitting and labeling apartCount wrong page boundaries separately from wrong labels, because one bad cut can spoil two documents.
4. Read accuracy per typeAn overall average can hide a type the tool keeps confusing with its look-alike, such as a bank statement and a printed transaction history.
5. Check what happens below the thresholdSee whether uncertain documents go to a person with a reason, or get filed under the tool's best guess.
6. Add one new type during the pilotCount the samples, rules or descriptions it took, and how long before the tool labeled it reliably.

Our take. Buy the classifier that admits doubt. In a pilot, a tool that files every page somewhere looks better on day one than one that sets aside the pages it can't place. But a misfiled pay stub comes back weeks later as a wrong income figure, while a page set aside costs a reviewer a few seconds. We'd rather see a short pile of exceptions, each with the reason it was held, than trust a queue that is quietly wrong.

Classification for lending, insurance and healthcare files

Each industry sends a different mix of documents, so the best fit changes with the file. Below are the tools from this list that fit lending, financial compliance, insurance and healthcare work, with the reason for each. Each fact comes from the vendor's own site, checked on October 21, 2026.

Lending and financial services files

Lenders work on the whole loan file, not one document at a time. One borrower upload can hold bank statements, pay stubs, W-2s, tax returns and an ID. The classifier decides which checks run on which pages, so a wrong label can become a wrong income figure later.

When you test a tool, include pages that look alike. A bank statement looks like a printed transaction history, and one kind of 1099 looks much like another. A tax packet mixes the 1040 with its schedules. A 12-page statement also has to stay one document, even when its pages arrive out of order.

Lenders that want one platform for the whole file can start with Docsumo. It classifies documents with 99% accuracy. Its lending platform splits the file and reads each document. AI Split takes a plain-English definition of each document type, for packets such as loan application binders. Splitting and classification come with the Business plan. Docsumo sorts tax packets by form. When a statement's transaction table runs over several pages, Docsumo joins it into one table. On the Enterprise plan, Docsumo also checks the documents against each other.

Docsumo works with Encompass, MeridianLink and nCino, and with ERPs such as NetSuite and SAP, through the API and webhooks. Any other system with an API connects the same way. On the Enterprise plan, a workflow step called External API can also post the data to the other system. Docsumo runs in the cloud only.

Ocrolus isn't one of the 14 tools here, but it's built for lenders. Ocrolus says it handles more than 2,100 document types. Its Classify step splits mixed PDFs into separate forms and marks pages it doesn't support as UNKNOWN. Inside Encompass, Ocrolus offers its own screen, called a custom form. Ocrolus's documentation also covers a MeridianLink Mortgage integration. Ocrolus's own staff resolve unusual or unclear cases. Docsumo sends those cases to your team instead. We compare the two in Docsumo vs Ocrolus.

Banks that must keep customer files on their own servers can look at ABBYY Vantage, Hyperscience or Tungsten TotalAgility, which all run on-premises. ABBYY's pre-trained classifier covers more than 70 types, from bank statements and pay stubs to loan applications and IRS forms. It labels anything else Unknown. Hyperscience lists loan applications, mortgage applications and KYC forms among its financial documents.

Banks that already store files in Doxis can use Doxis AI.dp. Doxis says it classifies and routes documents from application intake to loan closing. Grooper also runs on-premises and handles difficult paper such as auto loan deal jackets. ParaScript offers mortgage document classification, and its site highlights handwriting.

Teams already on UiPath can use UiPath IXP. UiPath's pre-trained extraction models include bank statements, checks, pay slips, W-2s and the 1040. Its list has no 1099, so test your 1099s before you rely on it.

Engineering teams on a tight budget pay the least per page with a cloud classifier or an LLM document API. Azure's custom classifier costs $3 per 1,000 pages. Azure's prebuilt models for US bank statements, pay stubs, checks, mortgage forms and tax forms cost $10 per 1,000 pages. The classifier labels each document and can split the file. Your code then sends each one to the right model. Google's custom classifier costs $5 per 1,000 pages. Its bank statement parser, which reads the data on a statement, costs $0.75 per document, not per page. Google's W2 parser lists only the 2018, 2019 and 2020 versions of the form. Google discontinued its lending splitter, which split loan files, on June 30, 2026.

AWS's Analyze Lending splits a loan package and labels each page with one of 35 types, or as UNCLASSIFIED. Its types include the 1003, the W-2, pay slips and bank statements. It costs $0.07 a page. In AWS's own pricing example, only the pages of supported types are billed.

With all three clouds, your engineers build the review screen and the connection to your loan system. Our guide to Textract, Azure and Google Document AI for classification compares the three. Fintech teams that build their own system can also send documents to Reducto or LlamaParse through their APIs. Both classify from a short description of each type, and LlamaParse's Classify costs $1.25 per 1,000 pages in its fast mode. Docsumo prices its plans on request, and its review queue is part of the platform.

Test any vendor's accuracy figure, ours included, on a month of your own loan files, and score it by document type. For bank statements alone, see our list of the best bank statement extraction software.

Classification for financial compliance

Compliance teams ask a different question of a classifier. They need to show what happened to each document, and who saw it. Check these points before you automate classification in a regulated workflow.

• Every document in the file is accounted forThe tool should list each document it found, so a missing one stands out, such as the second month of statements.
• Every label has a confidence scoreUncertain documents should go to a person with the reason, not into a folder under the tool's best guess.
• Label changes are loggedKeep a record of each label a reviewer changed, and who changed it, for your next audit.
• Access is set by roleStaff should see only the documents their job needs. Account numbers and SSNs should be redacted where nobody needs them.
• Data stays where your policy saysCheck the hosting regions on offer, or whether the tool runs on your own servers.
• Document type isn't data classificationLabels such as confidential come from data classification software. So do retention periods, which set how long to keep each file. Ask whether your policy means data classification or document type.

Docsumo runs in the cloud only, and you choose the region where your data is hosted, such as the US, the EU or the UK. It's SOC 2 Type 2 audited and ISO 27001 certified, with role-based access, single sign-on and multi-factor authentication. It finds and redacts sensitive data such as SSNs and account numbers. Audit logging comes with the Business plan, and your documents aren't used to train shared or third-party models. The details are on our security page.

If your policy keeps customer files on your own servers, ABBYY Vantage, Hyperscience, Tungsten TotalAgility, Grooper and Doxis can run there. Reducto can too, on its Enterprise plan, which also offers a VPC, a private network in the cloud. Reducto has completed SOC 2 Type 2. Hyperscience's cloud service is authorized at the FedRAMP High level, for US government work. LlamaParse and Reducto return the reasoning behind each label, which helps when an auditor asks why a document was filed where it was. For compliance checks beyond classification, see our guide to document compliance automation.

Insurance claims and submissions

Insurance teams classify two kinds of mail. Underwriting gets broker emails with an application, supplements, loss runs and a schedule of values attached. Claims teams start with a first notice of loss. It can arrive on an ACORD loss notice, such as the ACORD 1 for property or the ACORD 2 for auto. Police reports, repair estimates, medical bills and photos follow. They may all come in one PDF, or arrive one email at a time over several weeks.

Each document needs a label before anyone can read it, because an estimate and a police report need different fields. To scale data entry, classify each document as it arrives and send each type to its own queue. Pages the classifier can't place should wait for a person, not go to the closest guess.

Docsumo splits a submission or claim PDF into its documents and labels each one. It classifies documents with 99% accuracy. You name the types you need, such as ACORD 125, loss run or repair estimate, and it sorts files into them with no training set. Splitting and classification come with the Business plan. Pages it can't place wait as Unclassified for a person to assign. On the Enterprise plan, Case Routing files a document that arrives later into the open claim by its claim number. Also on the Enterprise plan, Docsumo checks the loss runs against the losses reported on the ACORD 125. It works with Guidewire, Duck Creek and Applied Epic through the API and webhooks, and any other system with an API connects the same way. See Docsumo for insurance.

Insurers that must run software on their own servers can look at ABBYY Vantage and Hyperscience. ABBYY's pre-trained classifier covers the ACORD 2 auto loss notice and the ACORD 25 and 125. Hyperscience says its pre-trained models cover ACORD forms, and its insurance page lists claim forms and policy applications. Doxis says it automates claims intake, classification and routing from the first notice of loss to settlement. Insurance teams that already use UiPath can start from its pre-trained extraction models. They cover the ACORD 25 certificate and the ACORD 125, 126, 131 and 140 application forms. ParaScript says it identifies and separates each document type in a single PDF submission. Engineering teams at insurance technology companies can build on a cloud classifier, or send documents to Reducto or LlamaParse through their APIs. Either way, they add their own review step.

Our insurance OCR guide compares insurance tools side by side, including Indico Data and Affinda, which aren't in this list. For the steps after the label, see our guides to FNOL automation and insurance submission intake. Our list of claims data extraction tools compares eight tools on loss notices, estimates and medical bills.

Healthcare back offices

Healthcare back offices get claim forms, EOBs, referrals and records, often faxed as one long file. Docsumo for healthcare splits and reads them, and it signs a BAA with customers who process protected health information. To sort and index medical records, see our list of healthcare document classification software, which compares 14 tools. Our list of healthcare data extraction software compares 13 tools for these documents. If your volume is mostly scanned mail rather than case files, our guide to the digital mailroom covers intake and routing.

Classify only, or classify and extract?

Pick by what happens after the label. Some documents only need to reach the right folder or person, as in a mailroom or an archive. For those, a capture tool such as Grooper or ParaScript can be enough, or the classifier on your own cloud. Other documents have data that must be read and checked before anyone decides. For those, choose a platform that classifies and extracts in one place. If it has to run on your own servers, look at ABBYY Vantage, Hyperscience or Tungsten TotalAgility. Engineers building their own pipeline can start with Reducto, LlamaParse or their cloud's classifier.

If your team processes loan, claim or healthcare files and wants only the uncertain fields in front of its reviewers, that's where Docsumo fits. For extraction tools, see our comparison of IDP software. For how classifiers work, see our guide to document classification.

Book a demo and bring one of your messiest mixed files, or start a free trial to test extraction on your own documents.

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