Adaptive Document Classification
Document Intelligence & Segmentation
Automatically recognise document types, split mixed document batches, and organise content with AI-powered intelligence, delivering fast, accurate
processing across high-volume business operations.
Accelerate Intelligent Document Processing with Document-Native AI
Accelerate your Intelligent Document Processing (IDP) workflows with Document-Native AI that automatically identifies document types across large document batches, intelligently separates mixed files into individual documents, and routes each one to the optimal extraction model. Process high-volume document batches with exceptional accuracy, speed, and enterprise-scale automation.
From Intelligent Automation to Autonomous Intelligence
Traditional IDP platforms automate documents
Regoxa transforms how enterprises learn from them
By combining Document-Native AI, Adaptive AI Skills, and Autonomous Feedback, Regoxa creates a living intelligence layer across your document ecosystem. Every document processed expands knowledge. Every user validation enhances precision. Every workflow execution strengthens automation. The result is an Intelligent Document Processing (IDP) platform that continuously evolves—delivering greater accuracy, higher straight-through processing, faster deployment, and automation that improves over time.
AI That Understands. Executes. Evolves.
Regoxa is more than an Intelligent Document Processing (IDP) platform—it's an adaptive intelligence platform engineered for enterprise automation. Powered by Document-Native AI, Adaptive AI Skills, and Autonomous Feedback, Regoxa continuously learns from every document, every decision, and every workflow. The result is a platform that doesn't just automate work—it becomes more intelligent with every interaction, delivering higher accuracy, faster deployment, greater straight-through processing, and continuously improving business outcomes.
How document classification works
Ingest Capture documents from any source or enterprise system
Prepare
Understand Document-Native AI identifies document types, context, and boundaries.
Classify
Classify & Route Adaptive AI Skills classify, segment, and direct each document to the right extraction workflow.
Improve
Learn & Improve Autonomous Feedback continuously enhances accuracy and optimises IDP performance
What is document classification?
Document classification is the automated process of identifying what type of document has entered your system, such as an invoice, a claim form, a contract, or an ID, and sorting it accordingly without a person having to open and read each file. Regoxa's classification engine analyzes a document's layout, text content, and visual structure to assign it a category, then routes it to the right extraction and validation workflow for that document type, cutting the manual triage step out of document-heavy processes entirely.
How does AI document classification differ from manual, rule-based sorting?
Rule-based sorting depends on fixed keywords or template matching, which breaks down the moment a document's layout changes. AI document classification in Regoxa instead learns from examples, recognizing a document type through a combination of its visual layout, language, and content patterns. This allows it to continue classifying correctly even when formats vary across vendors, regions, or document versions.
Can Regoxa's document classification be customized for our company's specific document types?
Yes. Regoxa lets you define classification categories around your own business documents, whether that's purchase orders, loan applications, delivery notes, or internal correspondence, by training the model on a set of representative examples for each category. Once trained, the model applies your organization's specific taxonomy to every incoming document, rather than forcing your documents into generic, pre-set categories.
What is document splitting, and how is it different from document classification?
Document splitting addresses a different problem. When a single scanned file or email attachment contains multiple distinct documents, such as three invoices batched into one PDF, splitting separates them into individual files before classification and extraction occur. Regoxa handles this automatically by detecting document boundaries within a batch, ensuring each document is processed independently rather than treated as one oversized, mismatched record.
How does document splitting improve data extraction accuracy?
When multiple documents are treated as a single file, extraction models can pull data from the wrong section or miss fields entirely because the document no longer matches an expected structure. By splitting batched files into individual documents first, Regoxa ensures each document is classified and extracted using the correct model for its type, which meaningfully reduces mismatched or incomplete data downstream.
How much technical expertise is required to build a machine learning document classification model in Regoxa?
Very little. Regoxa's classification training workflow is designed for business and operations users, not just data scientists. You provide a handful of labeled example documents per category, and the platform's machine learning models learn the distinguishing patterns automatically. There is no need to write classification rules or code a model from scratch to get a working, production-ready classifier.
How does document classification fit into a broader Intelligent Document Processing workflow?
Classification is the routing layer of Intelligent Document Processing. Once Regoxa's AI-powered OCR captures a document's text and structure, classification determines what the document is, which then decides which extraction model, validation rules, and downstream workflow it should follow. Without accurate classification, even the best OCR and extraction models are applied to the wrong document type, making it the step that keeps the rest of the IDP pipeline accurate.
How does document categorization improve document workflow automation?
Document workflow automation depends on immediately knowing what kind of document has arrived and where it needs to go. Categorization is what makes that decision automatic. Once Regoxa categorizes a document, it can be routed directly into the correct approval chain, extraction pipeline, or business system without a person manually deciding whether "this is an invoice" or "this is a claim." This removes one of the most common manual bottlenecks in document-heavy processes.
Can document classification improve accuracy over time, or does it stay static after training?
It improves continuously. Regoxa supports a human-in-the-loop review step where corrections made to misclassified documents are fed back into the model, allowing accuracy to increase as the system processes more of your specific document mix. This means classification performance is not fixed at initial training and can adapt as new document variants, formats, or edge cases appear in production.
What types of documents can Regoxa classify, structured, semi-structured, or unstructured?
All three. Regoxa's classification models are built to handle structured forms, such as standardized IDs or applications, semi-structured documents, such as invoices or utility bills where layouts vary by vendor, and unstructured content, such as contracts or free-form correspondence. A single classification workflow can cover the full range of document types most enterprises handle rather than requiring separate tools for each.
Frequently asked questions
Document classification is the automated process of identifying what type of document has entered your system, such as an invoice, a claim form, a contract, or an ID, and sorting it accordingly without a person having to open and read each file. Regoxa's classification engine analyzes a document's layout, text content, and visual structure to assign it a category, then routes it to the right extraction and validation workflow for that document type, cutting the manual triage step out of document-heavy processes entirely.
Rule-based sorting depends on fixed keywords or template matching, which breaks down the moment a document's layout changes. AI document classification in Regoxa instead learns from examples, recognizing a document type through a combination of its visual layout, language, and content patterns. This allows it to continue classifying correctly even when formats vary across vendors, regions, or document versions.
Yes. Regoxa lets you define classification categories around your own business documents, whether that's purchase orders, loan applications, delivery notes, or internal correspondence, by training the model on a set of representative examples for each category. Once trained, the model applies your organization's specific taxonomy to every incoming document, rather than forcing your documents into generic, pre-set categories.
Document splitting addresses a different problem. When a single scanned file or email attachment contains multiple distinct documents, such as three invoices batched into one PDF, splitting separates them into individual files before classification and extraction occur. Regoxa handles this automatically by detecting document boundaries within a batch, ensuring each document is processed independently rather than treated as one oversized, mismatched record.
When multiple documents are treated as a single file, extraction models can pull data from the wrong section or miss fields entirely because the document no longer matches an expected structure. By splitting batched files into individual documents first, Regoxa ensures each document is classified and extracted using the correct model for its type, which meaningfully reduces mismatched or incomplete data downstream.
Very little. Regoxa's classification training workflow is designed for business and operations users, not just data scientists. You provide a handful of labeled example documents per category, and the platform's machine learning models learn the distinguishing patterns automatically. There is no need to write classification rules or code a model from scratch to get a working, production-ready classifier.
Classification is the routing layer of Intelligent Document Processing. Once Regoxa's AI-powered OCR captures a document's text and structure, classification determines what the document is, which then decides which extraction model, validation rules, and downstream workflow it should follow. Without accurate classification, even the best OCR and extraction models are applied to the wrong document type, making it the step that keeps the rest of the IDP pipeline accurate.
Document workflow automation depends on immediately knowing what kind of document has arrived and where it needs to go. Categorization is what makes that decision automatic. Once Regoxa categorizes a document, it can be routed directly into the correct approval chain, extraction pipeline, or business system without a person manually deciding whether "this is an invoice" or "this is a claim." This removes one of the most common manual bottlenecks in document-heavy processes.
It improves continuously. Regoxa supports a human-in-the-loop review step where corrections made to misclassified documents are fed back into the model, allowing accuracy to increase as the system processes more of your specific document mix. This means classification performance is not fixed at initial training and can adapt as new document variants, formats, or edge cases appear in production.
All three. Regoxa's classification models are built to handle structured forms, such as standardized IDs or applications, semi-structured documents, such as invoices or utility bills where layouts vary by vendor, and unstructured content, such as contracts or free-form correspondence. A single classification workflow can cover the full range of document types most enterprises handle rather than requiring separate tools for each.