Intelligent document processing (IDP) is the use of AI to automatically extract, classify, and structure data from documents—regardless of format, layout, or quality. Unlike basic OCR, which simply converts images to text, IDP combines optical character recognition, natural language processing, and machine learning to understand what a document is, what data matters, and how to organize it for downstream systems. It is the technology category that turns unstructured documents into structured, usable data without manual intervention.
Lido takes an AI-first approach to document processing that delivers IDP capabilities without the traditional complexity. There are no templates to build, no models to train, and no months-long implementation cycles. Finance teams upload documents in any format—invoices, purchase orders, medical claims, bills of lading—and Lido extracts structured data on the first pass. Companies like ACS Industries have replaced enterprise IDP workflows with Lido, processing over 400 purchase orders per week across every document format without a single template.
IDP follows a five-stage pipeline, and each stage uses AI differently. Understanding this pipeline is the fastest way to evaluate whether an IDP tool is genuinely intelligent or just OCR with a marketing upgrade.
Traditional IDP platforms—ABBYY FlexiCapture, Kofax, UiPath Document Understanding—were built for a world where AI couldn’t understand documents on its own. They compensated with template engines, classification taxonomies, and supervised training pipelines that required hundreds of sample documents per type. The result: powerful extraction capability locked behind months of implementation and six-figure budgets.
That tradeoff made sense when it was the only option. It no longer does. Modern large language models can read a document, understand its structure, and extract data accurately on the first attempt—no templates, no training samples, no dedicated IT team managing a rules engine. The “intelligent” part of intelligent document processing has moved from the platform to the AI model itself.
This shift matters most for mid-market companies and growing finance teams. A 50-person accounts payable department at a Fortune 500 company can justify a 12-month ABBYY implementation. A 5-person AP team processing invoices from 200 vendors cannot. AI-first IDP tools like Lido close that gap—delivering enterprise-grade extraction accuracy with a time-to-value measured in hours, not quarters. For teams evaluating alternatives to template-based platforms like Nanonets, the question isn’t whether AI-first approaches work. It’s why you’d still invest in building templates at all.
These three technologies overlap enough to cause confusion, but they solve fundamentally different problems.
The practical test is format variability. If your documents come in one or two consistent formats, OCR plus scripting may be enough. If you’re handling dozens or hundreds of formats—which is the reality for most AP teams, CPA firms, and logistics companies—you need genuine IDP capability.
IDP creates the largest ROI in industries where document volumes are high, formats are inconsistent, and manual data entry is a bottleneck that directly impacts cash flow or compliance.
Not every tool marketed as IDP actually delivers on the promise. These are the criteria that separate genuine intelligent document processing from rebranded OCR.
Lido delivers IDP outcomes—accurate extraction, structured output, downstream integration—through an AI-first architecture that skips the traditional IDP complexity entirely. There are no templates to configure, no training sets to assemble, and no classification taxonomies to maintain. You upload a document, define the fields you need, and Lido extracts.
This isn’t a theoretical advantage. ACS Industries replaced a UiPath-based document processing workflow with Lido and now processes over 400 purchase orders per week. Every document format is handled automatically—no templates built, no exceptions manually coded. The formats that broke their previous RPA pipeline work on the first pass with Lido.
Relay uses Lido to process over 16,000 medical claims—healthcare documentation with complex multi-column layouts, variable fields, and inconsistent formatting across payers. This is exactly the kind of document variability that traditional IDP tools require extensive template libraries to handle. Lido handles it natively.
A CPA firm processing 3,500 audits per year came to Lido because their documents arrive in “thousands of formats.” That’s not an exaggeration—audit documentation includes bank statements, invoices, receipts, tax forms, and supporting documents from every client, vendor, and institution their clients work with. Template-based extraction is mathematically impractical at that scale. AI-first extraction is the only approach that works.
For finance and operations teams evaluating IDP platforms, the question has shifted. It’s no longer “do we need intelligent document processing?” It’s “do we need the traditional enterprise version of it, or can we get the same results without the implementation overhead?” Lido exists because the answer, increasingly, is the latter.
OCR converts images of text into machine-readable characters—it sees the text but doesn’t understand it. IDP adds classification, contextual extraction, and validation on top of OCR, turning raw text into structured data fields like invoice numbers, totals, and vendor names. Lido uses an AI-first IDP approach that handles both the recognition and the structuring in a single step, without requiring templates or training.
Modern IDP platforms handle invoices, purchase orders, receipts, bank statements, medical claims, bills of lading, contracts, tax forms, and virtually any structured or semi-structured document. The key differentiator is whether a platform needs templates for each new type. Lido handles new document types automatically—its AI reads and understands documents without pre-built templates, which is why customers use it for documents in thousands of formats.
Traditional IDP platforms like ABBYY and Kofax require templates—you map extraction zones for each document layout, which can take weeks per document type. AI-first IDP tools eliminate this entirely. Lido requires zero template setup. You define the fields you want extracted, and the AI locates them regardless of where they appear on the page or how the layout is structured.
Accuracy depends on scan quality and the IDP platform’s preprocessing capabilities. High-quality scans (300 DPI or above) typically yield extraction accuracy above 95%. Lower-quality scans, faxes, and photos introduce more variability. Lido combines advanced OCR preprocessing with AI-powered contextual extraction, which means it can often infer correct values even when individual characters are ambiguous—because it understands what the field should contain based on document context.
Accounts payable, healthcare, logistics, financial services, and legal see the highest ROI from IDP because they process high volumes of documents in inconsistent formats. Any industry where manual data entry is a bottleneck—and where errors have financial or compliance consequences—benefits from IDP. Lido serves customers across all of these verticals, from AP teams processing hundreds of vendor invoices to healthcare companies handling thousands of medical claims.
Template-based IDP tools require you to build and maintain extraction templates for each document layout—a process that takes weeks per type and breaks when vendors change their formats. Lido uses an AI-first approach where the model understands documents contextually, extracting data accurately from any layout on the first attempt. This means zero setup time, no template maintenance, and consistent accuracy across documents the system has never seen before.
%20(1).svg)
