What Is Intelligent Document Processing?

What is intelligent document processing?

Intelligent document processing (IDP) helps software read files and pull out useful data. It uses OCR to read scans and AI to find fields such as names, dates, and totals. Rules then check the data and send it to the next tool. IDP often handles bills, claims, forms, contracts, and reports.

Intelligent document processing shows up in searches for at least three jobs.

Some buyers need to move fields from forms into finance software. Others want a list of IDP vendors to compare. A third group has a set of files and wants sourced answers about what those files say.

This guide separates those jobs and names tools for each one. It also shows how Atlas helps once you have the files and need to check their claims.

If your search is broader than IDP, start with best document AI tools. If the document job is narrower, compare PDF analyzer, AI contract reader, contract analyzer, and report AI patterns before choosing a tool category.

Quick verdict

IDP turns PDFs, forms, emails, and scans into data that other software can use. It identifies the file type, pulls out fields, checks them, and sends the result to the next step.

If you need that job, test each tool on your own files before you buy it.

Atlas is not IDP software. It does not run OCR or send fields to an ERP or CRM. Use Atlas to ask sourced questions about files you already have. You can open the exact passage behind an answer before you use it.

What to look for in document processing

IDP vendors use different names for a similar flow. AWS focuses on reducing manual data entry. Microsoft focuses on reading files and sending the data through a workflow. IBM joins file reading with its wider automation tools. Most products still follow the same steps.

• Ingestion. The system accepts scans, PDFs, emails, and other file types, often across structured, semi-structured, and unstructured formats.
• Classification. The system decides what kind of document it is looking at, such as an invoice, a claim form, or a contract.
• Extraction. OCR and machine learning pull specific fields or text out of the document. OCR converts image or scanned text into machine-readable text. The extraction stage in IDP builds on that base step by pulling structured fields and layout cues on top of the raw OCR output.
• Validation. The system checks extracted values against rules, reference data, or confidence thresholds and flags edge cases.
• Human review. A person resolves weak-confidence extractions, unusual layouts, or documents the model has not seen before.
• System handoff. Validated data moves into a downstream system: an ERP, a CRM, a data warehouse, or a workflow like Salesforce automation.

Test each vendor on your own file types before you trust a demo. Users often report the same problem: a pilot works on clean samples but fails on odd layouts or blurred scans. Ask for a trial with your files instead of the vendor's samples.

IDP examples in practice

For an invoice, IDP can find the vendor, line items, total, tax, and due date. It can send unclear fields to a person for review. Once checked, the data goes to finance software.

A claims flow can split a packet into forms, letters, scans, and records. It pulls out the fields the claim system needs. Hard cases go to a specialist.

File review is a different job. Here, a person needs to know what a report or contract says. The key need is an answer linked to its source passage.

Intelligent document processing examples

These examples show several ways to use IDP. The right proof depends on the result you need: extracted fields, routed work, governed data, vendor research, or sourced review.

These are examples, not a ranking. Choose based on the job your team needs to do.

AWS

AWS uses machine learning to reduce manual data entry. Textract reads fields, while Comprehend handles language tasks.

AWS fits teams that already use its cloud and want IDP in the same stack. Check the latest Textract and Comprehend pages because names and limits can change.

Microsoft Power Automate

Microsoft puts IDP inside Power Automate. It reads files, pulls out data, and sends that data through a flow.

It suits teams that use Microsoft 365 and Power Platform. Test whether its flow builder handles your file types without much custom code.

ABBYY

ABBYY is an IDP vendor. Its tools read files, pull out data, sort the files, and check the results.

ABBYY has sold file-reading tools for many years. Check its site for current claims about accuracy and language support.

Databricks

Databricks handles IDP inside its Lakehouse data platform. It can parse files, pull out fields, govern the data, and analyze it.

It fits teams that want file data in the same system as their other data. Ask how the new fields join your tables and who will own the live flow.

Gartner Peer Insights

Gartner Peer Insights functions as a review and category-research surface rather than a single vendor. It defines the IDP solutions market and lists the capabilities buyers expect.

Use it to discover vendors and read buyer language about implementation and support. Treat ratings as peer opinion. They reflect what individual users experienced rather than a Gartner endorsement or a guarantee of product performance. Verify any specific capability claim on the vendor's own page before relying on it.

MuleSoft

MuleSoft turns files into checked data for Salesforce and other systems. A person can review weak results inside the flow.

It fits teams that run much of their work in Salesforce. Ask how staff review weak results before you treat the flow as fully automatic.

IBM

IBM offers no-code tools to sort files, pull out data, and link the results with its content tools.

It suits large firms that want IDP in a wider set of workflow tools. Check IBM's current plans and setup choices because product lines change.

Atlas

Atlas fits when you have files and need to ask what they say. Add a PDF or report to a project and ask a clear question. Atlas answers with citation badges that open the source passage it used.

See Document AI tools, AI document reader, chat with PDFs, and AI that cites sources for adjacent workflows.

Atlas is not a ranked alternative to the extraction vendors above. It does not classify a document type, extract structured fields into a schema, or hand data to an ERP or CRM.

Use it for checking what a document says rather than for automating what happens to it next.

Intelligent document processing options table

Table 1: The categories in this table are not competing for the same budget line. A team can run AWS or ABBYY for extraction automation.

That same team can still use a source-grounded workflow like Atlas whenever someone needs to read a document and check a specific claim before writing a report.

Where Atlas fits after documents are collected

The IDP tools above solve another problem than evidence review. Once documents exist as project sources, the recurring question shifts from what fields a document contains to whether the document supports the claim someone wants to make.

Atlas's grounded chat and citation system are built for that second question.

The screenshot above is a cited response next to the source document. A reader can open the citation badge to check the exact passage before using the claim.

A practical continuation looks like this:

1. Add the PDF, report, or other document to an Atlas project as a source.
2. Wait for processing to finish, then confirm the source is usable: pages render, search finds a phrase, and a simple question returns a relevant answer.
3. Ask a specific, narrow question. For example, "What evidence does this contract give for the termination clause?" works better than "What does this say?"
4. Read the answer and check whether the important claims carry citation badges.
5. Open a citation badge to see the exact passage Atlas used, and confirm the passage supports the sentence.
6. If a citation is missing or weak, ask Atlas to revise the claim or narrow the question, rather than reusing an unverified answer.

A citation badge means Atlas found source evidence tied to the claim. It does not mean the claim is correct or complete, and that judgment still belongs to the person reading the passage.

This path is slower than accepting a summary at face value. It catches a failure mode IDP buyers already worry about: an extraction output can look structured and confident while missing the caveat in the source document.

How to pick a document processing workflow

Start by defining what the output needs to be. Do not start from the category label "intelligent document processing."

Production extraction

• Choose an extraction platform (AWS, Microsoft Power Automate, ABBYY, Databricks, MuleSoft, or IBM) when the job is production extraction: turning documents into structured fields that a workflow, ERP, or CRM consumes at scale. Test the vendor on your own documents before buying, and confirm how edge cases reach a human reviewer queue.

Vendor research

• Use Gartner Peer Insights for vendor discovery and category research before you narrow a shortlist, and validate any specific capability claim against the vendor's own page.
• Use a data-platform approach like Databricks when extracted document data needs to live alongside the rest of your governed data instead of sitting in a separate silo.

Source-grounded review

• Use Atlas when the job is reading and questioning documents you already have: verifying a claim in a contract, checking what a report supports, or comparing evidence across a handful of sources before writing something that depends on getting it right.

Most document-heavy teams end up needing both lanes at separate points. They need an extraction flow for the documents that feed a system of record, and a source-grounded reading workflow for the documents someone needs to understand.

Next step

Start by naming the document outcome: extracted fields, checked workflow data, vendor shortlist, governed data path, or cited evidence review. Then test that outcome on your own documents before treating any intelligent document processing system as production-ready.

For adjacent source-checking workflows, compare Best Legal Document Organizer Software and Tools, Articles AI Guide to Work and Science, and Best AI Tools to Extract Data from PDFs before choosing where this article fits in the larger Atlas research workflow.