What is the difference between OCR and intelligent document processing?

OCR turns an image of text into text. Intelligent document processing works out what the document is, finds the values you care about (a date, an amount, a name), returns them as typed data with a confidence, and hands the uncertain ones to a person. OCR is often one step inside IDP.

How accurate is intelligent document processing?

It depends on the documents, the fields, and the model, so measure it on your own documents. What matters as much as accuracy is that every value comes back with a confidence, so confident results flow through and uncertain ones are reviewed instead of silently accepted.

Which AI models are used for intelligent document processing?

Current IDP uses large language models and multimodal models that can read a PDF or image directly, such as Anthropic Claude, Google Gemini, and OpenAI models. A good platform lets you choose the model per task and change it by configuration.

What happens to the data after it's extracted?

It should land where your operation can use it: on the record the document belongs to, as data that rules, workflows, and reports use like any other field, with the source document one click away.

Guides

What is intelligent document processing?

Q: Does intelligent document processing need templates for every document layout?

Modern IDP doesn't. Instead of drawing zones on each layout, you describe each document type and each field in plain language, and the model finds them, including in layouts it hasn't seen before.

Reviewed October 23, 2026

The short answer

Intelligent document processing (IDP) uses AI models to turn incoming documents into usable data: it splits packages into individual documents, classifies each one, extracts the fields you need with a confidence for every value, and sends anything uncertain to a person for review.

Every document-heavy operation has the same bottleneck: documents arrive as PDFs, scans, emails, and packages of dozens of pages, and someone has to work out what each one is, file it in the right place, and key the important values into a system before the real work can start.

Intelligent document processing (IDP) automates that front end. It uses AI models to read documents the way a trained person would, and turns them into data your systems and people can act on.

How intelligent document processing works

A typical IDP pipeline has six steps:

Ingest. Documents arrive by upload, email, sFTP, watch folders, or API, and are stored with the record they belong to.
Split. A combined package, such as a scanned file or a first-notice packet, is split into its individual documents.
Classify. Each document is matched to one of the document types you define, such as a pay stub, a policy declarations page, or a recorded deed.
Extract. The fields you need are read as typed values (dates, amounts, yes/no, text), each with its own confidence.
Validate and route. Business rules check the values. Confident results flow straight on; uncertain ones go to a person, with the source document beside the value.
Deliver. The data lands on the record, where workflows, rules, and reports use it.

IDP vs. OCR vs. template-based capture

Approach	What it does	Limitation
OCR	Turns images of text into text	Doesn’t know what the document is or which values matter
Template-based capture	Reads fixed zones on known layouts	Breaks when a layout changes or a new form arrives
Intelligent document processing	Classifies documents and extracts fields from descriptions, with a confidence for each	Needs good review routing for low-confidence results

Modern IDP uses large language models and multimodal models. Instead of drawing zones on every layout, you describe each document type and each field in plain language. Multimodal models can also read the original PDF or image when tables, handwriting, or layout matter, rather than relying on OCR text alone.

Where intelligent document processing is used

Lending and servicing: splitting loan files into notes, deeds, statements, and disclosures; reading income and asset documents.
Insurance: first-notice packets, repair estimates and invoices, declarations pages and endorsements.
Title and real estate: recorded documents, final policies, leases, and due-diligence packages.
Onboarding and compliance: identity and supporting documents, checked for completeness before review.
Contact centers: transcribing recorded calls and voicemail into searchable text.

What to look for

Package splitting. Real documents arrive bundled. The system should split packages and link each document back to the original.
Document types you define in plain language, with a fallback for documents that don’t match.
A confidence for every value, not just for the document as a whole.
Human review built in. Uncertain results should land on a work queue with a priority and a due date, showing the value next to its source.
Data that lands on the record, not in a separate silo you then have to integrate.
Choice of AI model, per task and by configuration, so you’re not locked into one provider as models improve.
Security and audit. Documents should stay in storage you control, AI should run with the calling user’s permissions, and every result should be part of your audit trail.
Cost control at volume, such as batch processing and per-record cost tracking, so AI cost is a line item rather than a surprise.

How Quandis approaches intelligent document processing

QDocs is Quandis’s intelligent document processing, built on the QBO platform. It splits mixed packages, classifies each document against the types you describe, and extracts the fields you need as typed values, each with a confidence. Confident results flow on; uncertain ones go to a person on a worklist, with the source document beside the value. You choose the model for each task (Claude, Gemini, OpenAI, or Amazon Bedrock) by configuration. Documents stay in the storage you configure, and every step runs inside your security and your audit trail. Because QBO orchestrates the AI, the extracted data is immediately available to the case, its workflow, and its rules.