Intelligent Document Processing (IDP)

What is Intelligent Document Processing (IDP)?

Intelligent Document Processing (IDP) turns documents into structured data a business system can use. It combines optical character recognition (OCR) to read text from scans and images, AI models to understand layout and meaning, and rules to check the result. A typical pipeline has four steps: classify the document, extract the fields that matter, validate them against business rules or other systems, and send the data on, or route it to a person when confidence is low.

For example, supplier invoices arrive by email as PDFs in many different layouts. An IDP system identifies each one as an invoice, pulls out the supplier, invoice number, line items, tax and total, checks them against the purchase order, and creates a draft entry in the accounting system.

IDP shows up wherever paperwork drives work: accounts payable, insurance claims, loan applications, customs documents, HR onboarding, contract review and customer identity checks. Modern IDP relies heavily on natural language processing and, increasingly, on large language models.

How is IDP different from OCR?

OCR converts an image of text into machine-readable characters. It tells you what the words are, but not what they mean. It cannot tell which number on an invoice is the total and which is the tax, or that a scanned page is a delivery note rather than an invoice.

IDP builds on OCR. It adds classification, field extraction based on context, checks against business rules, and a review step for uncertain results. Older template-based tools needed a template for each document layout and broke when a supplier changed theirs. AI-based IDP learns from examples and handles new layouts better, although it still needs testing on real documents.

In short: OCR gives you text, IDP gives you validated data ready to use in a process.

What are the risks of IDP?

The biggest risk is wrong data that looks right. An extraction model may misread a digit or attach a value to the wrong field, and that error then flows into payments or records. Language models can also invent values that do not appear in the document, a form of AI hallucination.

Controls that reduce this:

• Confidence scores on every field, with low-confidence fields sent to a reviewer.
• Cross-checks against known data, such as purchase orders or supplier records.
• Rules that block impossible values, such as totals that do not add up.
• A log of what was extracted, from which document, and who approved it.

Documents often contain personal or financial data, so storage, access and retention also need clear rules from the start.

How do you measure IDP accuracy?

Measure at the field level, not just the document level. A document counted as processed may still have one wrong field that matters. Build a test set of real documents with the correct values recorded by hand, covering the main layouts, poor scans and edge cases.

Useful measures include field-level accuracy, the share of documents that pass straight through with no human touch, the share sent to review, and how often reviewers correct the system. Track how long a document takes from arrival to posting, and how many errors are found later in downstream systems.

Re-run the test set whenever you change the model, the prompts or the rules, the same way you would with any AI evaluation. Accuracy that held last quarter can drift as document types change.

How do you get started with IDP?

Start with one document type that is high in volume and painful to process by hand, such as supplier invoices or onboarding forms. Collect a representative sample, including the messy ones, and agree which fields matter and what counts as correct.

Then design the full flow, not just the extraction: where documents arrive, how they are classified, which checks apply, who reviews exceptions, and which system receives the final data. Extraction alone rarely saves much if someone still has to retype the result.

Run the new process alongside the manual one for a period, compare results, and widen automatic processing only as accuracy holds. Keep the review step even after launch; with a human-in-the-loop design, reviewers' corrections also become examples for improving the system.

How does Vizio AI build IDP?

Document processing is one of the AI-powered internal workflows our DX Studio builds. We design the full flow: classification, extraction, validation against the systems you already use, a review step for uncertain cases, and integration so approved data lands in finance, CRM or operations tools without retyping. Where it helps, we connect the output to dashboards that show volumes, exceptions and processing times.

We support the system after launch, including testing it again when document types or models change. The starting point is a sample of real documents for one document type and an agreed list of the fields that matter.