INTELLIGENT DOCUMENT PROCESSING

AI-powered intelligent document processing

Vertesia's AI-powered intelligent document processing (IDP) engine uses vision models and Semantic DocPrep to understand complex documents, extract the right data, preserve citations, and route low-confidence results for review. Process contracts, claims, policies, applications, and other variable documents without brittle templates or per-layout retraining.

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IDP AT A GLANCE

Why traditional IDP falls short

OCR-based IDP handles predictable forms well, but enterprise documents (contracts, claims, policies, regulatory filings) are long, variable, and packed with business meaning: clauses, tables, cross-references, annotations, exceptions. Flattening them into text or matching against brittle templates loses too much, so teams still review exceptions by hand and hesitate to automate decisions.

Just adding AI to OCR-based IDP isn't enough

Some IDP vendors have added a large language model (LLM) on top of their OCR pipeline but that doesn't work well. The page has already been flattened which means the layout, table relationships, reading order, and source location are gone before the model sees anything and this information cannot be recovered.

broken-table-v2
Tables stop being tables

Rows continue across a page break; headers don't follow. Once the grid is gone, no amount of prompting reconnects a value to the column it belonged to.

definition
Clauses lose their definitions

Terms, exclusions, and conditions depend on language elsewhere in the document. Separate them and the model reads words that look right and mean something else.

documentation
Cross-page answers disappear

Many real questions about a document are answered across several pages. Break the document into fragments first and the evidence is gone before anyone asks.

no-proof
Values arrive without proof

An extracted value that can't be traced to its exact place on the page is a claim, not a fact, and nothing built on it can be audited.

This is why AI-powered IDP has to start earlier in the pipeline than most modernization efforts do. The quality of a document processing system is set by how well the document is understood on the way in not by which model reads the output afterward.

HOW VERTESIA IDP WORKS

Vertesia's AI-powered IDP understands the whole document before extracting data

Semantic DocPrep, the technology behind Vertesia IDP, uses vision models to read pages as they appear, preserving layout, tables, headings, and reading order. From there, Vertesia extracts the data you need, validates it, links every answer back to its source, and routes exceptions for review. Unlike template-driven capture, Vertesia IDP understands the document before it automates the process.

fast-time

No templates or setup

Semantic DocPrep uses vision models rather than template-matching. It handles new document formats without any configuration. A new invoice layout, a differently formatted claim, or an inconsistently structured report are all processed with the same accuracy as a familiar format, with nothing to train and nothing to configure before you start.

accuracy

Multi-page extraction

Traditional OCR-based IDP loses context at page boundaries, splitting or dropping content that spans pages. Vertesia's semantic structure preserves document context end-to-end, so cross-page tables, continued paragraphs, and multi-section forms are extracted correctly regardless of document length.

schema

Citations and deep linking

Every value extracted by Vertesia can be traced to its source because Semantic DocPrep captures bounding box data for every element on every page. The platform generates structured citations linking directly to the page, paragraph, or table row where a value originated with location metadata included in any downstream output.

human-loop

Human-in-the-loop (HITL)

Vertesia supports configurable human review at any point in the processing pipeline. Low-confidence extractions are flagged automatically, and reviewers see the source document alongside the extracted data to approve, correct, or escalate. Every correction is logged and contributes to improving future processing.

Intelligent document ingestion

Vertesia's IDP capabilities are built into the document ingestion pipeline, not bolted on afterward. When a document enters Vertesia, it passes through Semantic DocPrep automatically. There is no separate processing step, no manual trigger, and no additional configuration required.

TECHNOLOGY COMPARISON

Vertesia IDP vs traditional IDP

Vertesia IDP

Traditional IDP

Vertesia IDP

Traditional IDP

Capability
Core reading technology
Vision models read each page as it appears on screen; OCR is applied selectively as needed
OCR converts the document to image, then matches characters against trained patterns
Setup required
No training or template configuration needed; ingest a document and receive structured output immediately
Weeks to months of template creation, model training, and validation before processing new document types
Variable templates
same document type, different layouts
No templates required; models understand document structure dynamically across any layout
Requires a separate template per layout variation; each new vendor or format needs manual configuration
Digital vs scanned PDF handling
Vision models interpret the native digital PDF directly; PDFs are not converted to images. OCR supplements where character clarity is needed.
Traditional IDP converts the native digital PDF to an image first, discarding embedded text fidelity, then applies OCR to the scanned image; quality degrades with document condition.
Multi-page extraction
long invoices, cross-page tables
Semantic structure preserves document context across pages; continued line items and cross-page tables are captured correctly
Line items that span page breaks are split or lost; accuracy degrades as document length grows
Handwriting
Vision model interprets the visual appearance of the page, including handwritten annotations and notes
OCR is trained on printed characters; fails or produces high error rates on handwritten content
Dot matrix and impact printer output
Vision models read the visual rendering of characters, not the character shape, handling non-standard print quality
OCR character-matching fails on the low-contrast, dotted patterns produced by impact printers
IDP VALIDATION

Multiple validation methods

Extracted data needs to be accurate which is why Vertesia supports both programmatic validation and agentic validation, giving your pipeline a complete safety net from format compliance all the way to business-logic accuracy.

progress

Programmatic validation

Programmatic validation applies deterministic business rules to extracted data: checking value ranges, verifying formats, confirming required fields are present.

agentic-workflow

Agentic validation

Agentic validation uses AI agents to review extracted content for semantic consistency, cross-document alignment, and business logic, catching errors that rule-based checks cannot see.

Together they close the loop: deterministic rules catch what must never be wrong, and agentic review catches what only makes sense in context. Anything either flags routes to human review with the source document attached.

DOCUMENT TYPES

Which document types can Vertesia IDP process?

Vertesia handles hundreds of document types across industries and departments, not because each one needs a pre-built template, but because AI-driven IDP reads the document itself. These examples are a starting point, not the limit of what the system can process.

financial-report

Contracts and legal agreements

invoice

Invoices, purchase orders, and financial statements

claim-document

Insurance claims and policy documents

medical-records

Medical records and clinical documents

regulatory-compliance

Regulatory filings and compliance documents

engineering-drawing

Engineering drawings, technical specifications, and forms

FROM DOCUMENTS TO DECISIONS

Extraction is only the start.
The business process is the real end.

Most IDP systems stop at export but Vertesia keeps going. Extracted values stay connected to the source document, citation, confidence score, validation result, and the downstream workflow. A claim routes to review, a contract moves to legal, a loan package triggers an exception, a compliance document gets checked against policy all with the evidence attached. Vertesia doesn't just capture data. It turns documents into real work.

GOVERNANCE & SECURITY

IDP with built-in governance

Documents are the source of truth in most regulated processes: the signed policy beats the database row, and the executed contract is what an auditor will ask to see.

Everything processed through Vertesia's IDP engine is governed accordingly, from the moment it is ingested. Permissions, audit trails, and retention policies apply from the moment a document is ingested. Your processed data is never shared with public AI models, and access is always controlled by your organization's rules.

Vertesia is SOC2 Type II-certified, HIPAA compliant, GDPR compliant, and CCPA compliant. Our IDP capabilities are built for industries where accuracy, auditability, and data privacy are not optional.

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FAQs

Frequently asked questions about intelligent document processing

What does IDP mean?

IDP is the acronym for intelligent document processing. It is the use of AI to automatically read, classify, extract, and organize information from documents. IDP replaces manual document handling with automated processes that can operate at scale, across any document type, in any format.

What is a vision model?

A vision model is a type of AI model that reads a document by interpreting it as a fully rendered image, perceiving layout, tables, headings, handwriting, and embedded visuals all at once, rather than extracting characters one by one through traditional OCR pattern matching. Where OCR strips a page down to raw text and loses structural context, a vision model understands that a number lives inside a table, that a heading belongs to a section, and that an image carries meaning worth describing.

Vision models are a core part of Vertesia's document preparation pipeline, converting pages into structured, context-aware output (such as Markdown or XML) with full layout fidelity and per-element confidence scores that drive automated routing decisions. The result is that complex, real-world documents can be processed with the kind of understanding that previously required a human reader.

What is OCR?

OCR stands for optical character recognition, a technology long used for intelligent document processing (IDP). Modern IDP uses natural language understanding, computer vision, and machine learning to interpret documents that are complex, unstructured, or inconsistently formatted, including contracts, invoices, medical records, insurance claims, legal filings, and more.

Does Vertesia IDP use OCR?

Vertesia's IDP pipeline is built on vision models as the primary reading technology, with OCR demoted to a conditional, supplementary role rather than the mandatory first step it occupies in traditional document processing.

OCR is applied selectively on scanned pages where character clarity is degraded enough to warrant additional character-level support. For native digital PDFs, handwritten content, and non-standard print types such as dot-matrix output, OCR either adds no value or actively fails, so the vision model handles those cases without it. The result is a pipeline where OCR is one possible input among several, not the foundation of the entire process.

Can Vertesia automatically classify documents?

Yes. Vertesia automatically identifies what type of document it is processing, even when documents arrive mixed together in a batch. No manual sorting required.

Does Vertesia IDP support entity extraction and normalization?

Yes! Our platform extracts key entities, including names, dates, amounts, locations, and organizations, and normalizes them to a consistent format for downstream use.

Can Vertesia extract table and form data?

Yes. Vertesia accurately extracts structured data from tables, grids, and forms, even when the layout varies across documents from the same source.

What is a confidence score in IDP?

Confidence scores quantify the certainty in a specific output. Depending on the IDP workflow, that output could be:

  • An extracted field value (e.g., a document title, version number, or vendor name)
  • A document classification decision (e.g., "this is a contract" vs. "this is an invoice")
  • A compliance violation detection (e.g., a logo size exceeds the style guide limit)
  • A translation memory match (e.g., exact match vs. partially generated content)

The score is typically expressed as a percentage (e.g., 94%) or a categorical label (High / Medium / Low), depending on how it is surfaced in the user interface or workflow rule.

How are confidence scores used in automation routing?

Confidence scores directly determine which path a document or field takes through the processing pipeline:

  • High confidence results flow into automated downstream systems without human intervention.
  • Medium confidence results are flagged and routed to a human reviewer for confirmation or correction.
  • Low confidence results are blocked from automation entirely and require manual input.

This routing logic is the core of Vertesia's human-in-the-loop governance model: AI handles what it knows well; humans handle what requires judgment.

Does Vertesia offer multilingual document processing?

Yes, Vertesia processes documents in multiple languages without requiring separate configurations for each language.